STUDY OF THE GENETIC DIVERSITY OF KILLER CELL IMMUNOGLOBULIN LIKE RECEPTORS (KIRs) IN SOME HUMAN POPULATIONS OF SUB-HIMALAYAN REGION.
A THESIS SUBMITTED TO THE UNIVERSITY OF NORTH BENGAL FOR THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D.) IN ZOOLOGY
BY POKHRAJ GUHA
SUPERVISOR Prof. Tapas K. Chaudhuri
CO-SUPERVISOR
Dr. Soumen Bhattacharjee Dr. Biswajit Haldar
DEPARTMENT OF ZOOLOGY UNIVERSITY OF NORTH BENGAL JANUARY 2017 DEDICATED
TO
MY PARENTS
AND
MY MENTOR
PROF. T.K. CHAUDHURI. DECLARATION
I declare that the thesis entitled “STUDY OF THE GENETIC DIVERSITY OF KILLER CELL
IMMUNOGLOBULIN LIKE RECEPTORS (KIRs) IN SOME HUMAN POPULATIONS OF
SUB-HIMALAYAN REGION”, has been prepared by me under the guidance of Prof. T.K.
Chaudhuri and Dr. Soumen Bhattacharjee, Department of Zoology, University of North Bengal and Dr. Biswajit Halder, Department of Pathology, North Bengal Medical College and Hospital.
No part of the thesis has formed the basis for the award of any degree or fellowship previously.
(Pokhraj Guha) Department of Zoology University of North Bengal Raja Rammohunpur, Dist. Darjeeling
Dated: 19.01.2017
PREFACE
I started my research work in 2009 under the supervision and expert guidance of Prof. T. K. Chaudhuri and co-supervision of Dr. Soumen Bhattacharjee, Department of Zoology, University of North Bengal, and Dr. Biswajit Halder, Department of Pathology, North Bengal Medical College and Hospital, Sushrutnagar, Dist. Darjeeling. My entire work has been documented in this dissertation entitled “STUDY OF THE GENETIC DIVERSITY OF KILLER CELL IMMUNOGLOBULIN LIKE RECEPTORS (KIRS) IN SOME HUMAN POPULATIONS OF SUB-HIMALAYAN REGION”.
Killer-cell immunoglobulin-like receptors (KIR) constitute a family of receptor molecules that are present on both natural killer (NK) cells as well as on a subset of T- lymphocytes. The nomenclature of these receptors is based on the number of extracellular immunoglobulin-like domains (2D or 3D) and the character of the cytoplasmic tail (L or S). KIRs with long cytoplasmic tails are involved in inhibitory signaling while those with short cytoplasmic tails transduce activation signals. The members of the KIR family interact with human leukocyte antigen (HLA) expressed on the surface of all nucleated cells. Normal KIR/HLA interaction protect the cell against cytotoxic effects of NK cells, failure of which may result in positive intracellular signals. These resulting signals affect NK cell activity causing the release of cytotoxic enzymes. Thus, KIR-HLA interaction plays a major role in innate immunity. The KIR genes are highly polymorphic and are located on the long arm of chromosome 19, within the Leukocyte Receptor Complex (LRC). KIR gene content and haplotypes vary between ethnic cohorts and individuals within a population. Based on all these unique features, KIR has gained importance parallel to other established markers in the fields of human genetic diversity, in tracing the routes of human migration in different parts of the world and also in disease researches.
The sub-Himalayan part of India is inhabited by many tribes and castes each having their own cultural and linguistic ethnicity. In one hand, some populations in this region arose
Page | i due to blending of gene pool of different population groups while; on the other hand, some other populations of this region are strictly endogamous with a highly conserved gene pool of their own. In other words, it can be said that the sub-Himalayan part of West Bengal, India, is a melting pot of different ethnicity and is therefore extensively rich in genetic diversity. The present study was designed with the broad objective to study the KIR gene profile in different ethnic populations inhabiting the northern part of West Bengal. In addition, we also aimed to study association of KIR genes with Rheumatoid arthritis, which is one of the most common autoimmune debilitating diseases, affecting nearly 1% of the total world population. This disease is characterized by progressive articular damage leading to joint deformities and disability. Blood samples were collected from the volunteers with their prior informed consent. Individuals with three generations of unrelatedness were selected for the study. RA was diagnosed by the physician and the subjects were recruited from the Department of Pathology, North Bengal Medical College & Hospital, Sushrutnagar, Siliguri and 3gen diagnostic Pvt. Ltd, Siliguri. Blood samples were collected from the participants under appropriate conditions and brought to the Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, where the further experiments were performed. The findings of the study are published in various research journals and are presented and discussed in details in the Results and Discussion part of this dissertation.
Page | ii ABSTRACT
Killer cells Immunoglobulin-like Receptors (KIR) constitute a family of regulatory receptor molecules found on the surface of NK cells and T cell subsets. They can be either activating or inhibitory in function. These receptors interact with polymorphic Human Leukocyte Antigen (HLA) class I molecules and influence the innate and adaptive immune responses to infection. Variations in gene content and sequence polymorphism are noticeable in the KIR family and these features make the family suitable for distinguishing individuals and populations around the world. The KIR multigene family consists of 14 genes and 2 pseudogenes. These genes are clustered together on chromosome 19q13.4, within the 1 Mb Leukocyte Receptor Complex (LRC). Two distinct KIR haplotypes can be distinguished based on gene contents, namely A and B. The A haplotypes have seven KIR loci, with only one gene coding for an activating receptor i.e. KIR2DS4 gene. On the other hand, the B haplotypes are characterized by presence of loci most of which encode activating receptors. Inspite of variations in gene content, all KIR haplotypes contain the ‘framework genes, namely KIR3DP1, KIR3DL2, KIR3DL3 and KIR2DL4 that are well conserved in virtually all individuals. KIR genotyping has documented substantial variation in the relative frequencies of A and B haplotypes in different populations. These unique characteristics of KIR genes have generated immense interest among the researchers all around the world. As a result, KIR genes are progressively gaining applications in the field of human genetic diversity and for tracing the routes of migration in different parts of the world in a way parallel to other established markers in this field such as HLA, mitochondrial DNA (mtDNA) and Y- chromosome haplogroups.
The present study was aimed to estimate the frequencies of 16 KIR genes among five ethnic populations inhabiting northern part of Bengal, namely Rajbanshi, Rabha, Bengali, Gurkha and the Muslims, and to study their phylogenetic relationship among themselves and with other populations around the World. Additionally, the frequencies of the KIR
Page | iii genes in patients with Rheumatoid Arthritis were also estimated to study the association of the KIR genes with occurrence of the disease if any.
Blood Samples were collected from the volunteers with their prior informed consent. In both the population-based and disease related section of the study, three generation pedigree of the volunteers were taken into account and only unrelated individuals were included in the study. DNA was extracted from each blood sample using the phenol- chloroform extraction procedure. Sixteen KIR genes were analyzed in the sample individuals using PCR-SSP typing. Different freely available statistical software were used to analyze the data.
It is evident from the population-based section of our study that the KIR genes demonstrated the highest range of frequencies among the Gurkhas in comparison to other studied populations. In contrast, the lowest range was demonstrated in case of the Muslims. Interestingly, all the five studied populations demonstrated the lowest frequency value for KIR2DL3 gene while the highest frequency value was seen to shuffle between the A haplotype associated genes except that for the activating receptor (i.e. KIR2DL4). Based on the distribution of KIR genes, 76 KIR genotypes were listed from our studied populations, out of which, 3 were AA genotypes while the remaining 73 were Bx genotypes.
When compared with published data from other populations around the World, it was found that the Rajbanshis are phylogenetically closer to the East Asians, especially the Tibetan population than that with the Dravidian populations, which are considered native to India. Besides having some genotypes in common with that of the East Asians, the Rajbanshis also share some genotypes with the Dravidian settlers of India, such as the Paravars and Kanikars. There are many theories regarding the origin and migration of the Rajbanshis in the northern part of Bengal. Nevertheless, all of them confirmed the presence of mongoloid elements in the Rajbanshis.
Page | iv The Rabha tribe is a small endogamous population belonging to the Indo-mongoloid group of Northeastern region of India. Their KIR gene frequencies ranged between 0.26 and 0.96 corresponding to KIR2DS3 and KIR2DL1 respectively. Phylogenetic assessment revealed that the Rabhas shared the same clade with the East Asians owing to their tribal ethnicity and strict endogamous nature, which helped them to retain their ancestral genotypic pattern. Thus, it is evident from our study that although Indian by origin, the gene pool of the Rabha population has been considerably influenced by mongoloid lineage.
The frequencies of most of the KIR genes (except the framework genes) in case of the Bengali population varied between 0.34 and 0.88. It is evident from the measures of Nei genetic distances that the Bengalis have the least distance from North Indians (0.012) followed by the Rajbanshis (0.014) and the Maharashtrians (0.016) , thereby revealing their proximity with the populations of Indian origin. The Bengalis clustered with the population of Indian origin namely Trinidad Asian, North Indians, Paravar and the Kanikars, the clade supported by 95% bootstrap value. Thus, it is evident from our study that the Indo-European-speaking Bengalis share both Dravidian and Indo-Aryan gene pool with considerable influences of Mongoloid and European descents. Evidences from previously published data on Human Leukocyte Antigen (HLA) and Y-chromosome haplogroup diversity supported the view.
It is evident from the phylogenetic analyses that the Gurkhas have shown prominent tendency to remain in close proximity to the North East and the South East Asians. This suggests the strong connections of the populations with East Asian lineage. However, sub-continental influence on their gene pool cannot be ignored. Furthermore, this study also predicts the continuous gene flow between Nepali speaking Gurkha population of India with the people of Nepal, whose gene pool has received considerable influence from both the South Asian and East Asian lineages.
The KIR gene frequencies among the Muslim ranged between 0.41 (KIR2DL3) and 0.90 (KIR3DL1 and KIR2DL1). It is observed from the measures of Nei genetic distances that
Page | v the Bengali Muslims inhabiting the northern part of Bengal were most distantly related to two population groups of China from Zhejiang (0.0874) and Jiansu (0.0638) provinces, while the least distance was observed with the heterogenous Bengali population (0.0053). A similar observation was also made from the Principal Component Analyses, wherein the Muslims occupied a position within the Indian cluster, very close to that of the Bengali community, which was indeed a population with mixed religious background. Similarly, in the phylogenetic tree based on KIR genotypes, the Muslims huddled with the Bengali population sharing the same cluster with other populations of Indian origin, the clade supported by 92% bootstrap value. Thus, it can be said that the impressions of Indian gene pool can be noticed prominently in the KIR gene distribution of the Muslim populations of West Bengal.
In the second part of the study, the association of KIR genes was dealt with Rheumatoid arthritis (RA), if any. RA is the most common type of inflammatory arthritis worldwide affecting joints as well as extra-articular structures. RA mostly affects the small joints of hands and feet resulting in pain, stiffness and loss of function. Recent researches have demonstrated the autoimmune origin of the disease.
The comparative analyses of RF titre and anti-CCP, the two principal parameters of RA diagnosis, among the RA patient and the control groups were performed. In case of RA patients, the median anti-CCP and RF titre value were found to be 182.7 IU/ml and 142.3 IU/ml respectively. It was also found that the sensitivity and specificity of RF titre and anti-CCP antibodies in case of RA patients of this region was more or less consistent with previously published data. In addition, it was observed from the ROC curve that the Area under the Curve (AUC) for anti-CCP was slightly greater than that of RF titre. However, there were several crossovers, suggesting that neither of the serological markers is superior to the other in all circumstances of the study.
In a small subsection of the disease-based study, other clinical parameters of RA diagnosis in anti-CCP positive RA patients were estimated. These parameters include C- reactive Protein (CRP), Anti-Streptolysin O (ASO) titre, Erythrocyte Sedimentation Rate
Page | vi (ESR). The mean CRP value in the RA patients and the control samples were found to be 46.4±42.9 mg/dl and 2.2±1.6 mg/dl respectively. The mean ASO titre in the patients and the control were 147.5±107.8 IU/ml and 83.1±49.7 IU/ml respectively. On the other hand, the estimated mean ESR values were reported to be 37.6±23.0 and 12.7±4.1 mm/hr in the RA and the control group respectively.
Molecular typing of KIR genes were also performed in the RA patient group and the control group. Significant associations of KIR2DL3, KIR2DS2 and KIR2DL2 genes were evident with RA patient when compared with control samples. Thus, decreased frequency of KIR2DL3 among the RA patient group may indicate the protective role of the gene against the incidence of RA. On the other hand, increased frequencies of KIR2DS2 and KIR2DL2 may indicate their function in enhancing the odds of the disease occurrence. Analyses of the KIR genotypes revealed that the genotype IDs 3 and 81are significantly associated with RA. No significant differences were found in the proportions of AA and Bx genotypes between the patient and the control group. However, it was interesting to observe that when the Bx genotypes was divided into two groups namely AB and BB, it was found that the frequency of the BB genotypes was significantly higher than the AB genotypes among the patients compared to the control.
LD analyses revealed that the two important parameters that have biological interpretations namely, Dˊ and r2, reported the highest values for the two locus haplotype KIR3DL1-KIR2DS4 followed by KIR2DL2-KIR2DS2. Thus, it can be said that both KIR2DS2 and KIR2DL2 are significantly associated with RA and are good predictors of each other. They are predicted to co-inherit in roughly 87% of the time.
In conclusion, these results will not only help to understand the genetic background of the studied populations, but also in tracing the population migration events in the eastern part of the country. This study may also help in illustrating the extensive genetic admixture amongst the different population groups of the country. Furthermore, our study will help to decipher the degree of association between the output of the clinical parameters and the role of KIR genetic predisposition in the occurrence of RA.
Page | vii ACKNOWLEDGEMENTS
This thesis owes its successful existence to the help, support and inspiration of several people. Although I realize that a mere expression of gratitude is not enough to acknowledge their actual contribution, however, I take this opportunity to appreciate and thank each one of these wonderful people for their kind help and support.
First of all, I would like to express my deepest gratitude to my respected guide, Prof. Tapas Kumar Chaudhuri, Cellular Immunology Laboratory, Department of Zoology, University of North Bengal for giving me the opportunity to work under his supervision. I am grateful to him for his excellent guidance and scientific advices during the course of the study. It was a great priviledge and honor to work and study under his guidance. His experienced suggestions made my work, a lot easier. Thank you Sir! For all your kind help.
I am very much thankful to my respected Co-Supervisor Dr. Soumen Bhattacharjee, Department of Zoology, University of North Bengal, for his immense support in framing the work of the thesis. His valuable suggestion and inputs really helped a lot in giving a complete shape to my work . His dynamism, vision, sincerity and motivation have deeply inspired me. He has taught me the methodology to carry out the research and to present the research works as clearly as possible. I am indebted to him for his kind help, support and encouragement.
I am also thankful to my respected Co-supervisor Dr. Biswajit Haldar, Department of Pathology, North Bengal Medical College and Hospital, for his help and support, particularly in providing adequate number of Rheumatoid Arthritic and control subjects for the study. I am grateful to him for providing me access to the instrumentation facilities of his laboratory, 3gen Diagnostics Pvt. Ltd., for performing experiments during the course of my study. I also take this opportunity to thank all the staff of the above mentioned diagnostic laboratory for extending their help and support during my experiments, especially Mr. Bimol Baidya and Mr. Mithun Chakraborty.
Page | viii I express my sincere gratitude to the Head, Department of Zoology, University of North Bengal for allowing me to use the central instrumentation facility as and when needed during the course of my study. I also express my deep sense of gratitude to all my teachers of the Department, Prof. Ananda Mukhopadhyay, Prof. Sudip Barat, Dr. Min Bahadur, Dr. Dhiraj Saha and Mr. Tilak Saha for their valuable suggestion and kind help.
I appreciate the company of my fellow scholars with whom I worked in the Laboratory. My special thanks to Mr. Avishek Das, who stood beside me like a younger brother as and when required during the course of my work. I acknowledge the help and support of Dr. Priyankar Dey and Mr. Somit Dutta. I also appreciate the inspirations of Dr. Manoj Lama and Mr. Subhrajyoti Roy, Asst. Professors, Department of Zoology, University of Gour Banga.
I would also like to thank Dr. Partha Pratim Paan, for his skilled and experienced suggestions. I also thank him for providing me patients and contral subjects during the course of my study.
I would also like to thank Dr. Chittaranjan Nayak, Former HOD, Computer Centre, University of North Bengal, for helping me with the statistical analyses during the initial days of my work.
Thanks are also extended to all the participants for their patience and cooperation. Without them, the work would have been impossible.
I express my sincere gratitude to te staffs of the Central Library, University of North Bengal for their services and kind help.
I like to sincerely acknowledge the University Grants Commission (UGC) and Department of Biotechnology (DBT), New Delhi, for providing the financial assistance to carry out my research work.
Page | ix It is a good opportunity to express my gratefulness to my parents who are my constant source of encouragement and inspirations. Today, I stand here because of their never ending support and encouragement. I remain ever grateful to them. Last, but certainly not the least, my dear wife, Saswati Dey Guha, deserves a special word of appreciation for her moral support, patience and love. She supported me in every possible way to see the completion of this work.
Dated: 19.01.2017 Pokhraj Guha
Page | x Table of Contents
CHAPTER 1. INTRODUCTION 1
1.1. NEW GENES: SOURCES AND EVOLUTION 2
1.1.1. EXON SHUFFLING 2
1.1.2. GENE DUPLICATION 2
1.1.3. RETROPOSITION OF GENES. 3
1.1.4. MOBILE ELEMENTS 3
1.1.5. LATERAL GENE TRANSFER 3
1.1.6. GENE FUSION/FISSION 3
1.2. THE FOUR FORCES OF EVOLUTION 4
1.3. EVOLUTION OF MODERN HUMAN 5
1.3.1. MULTIREGIONAL MODEL 6
1.3.2. MODEL OF RECENT AFRICAN ORIGIN (RAO) 6
1.3.3. ASSIMILATION MODEL 7
1.4. GENETIC TOOLS FOR STUDYING HISTORY 8
1.5. INDIAN SUBCONTINENT 10
1.6. UTILITY OF POPULATION STUDIES 12
1.7. OBJECTIVES OF THE STUDY 13
CHAPTER 2. REVIEW OF LITERATURE 14
2.1. NK CELLS AND KIR 14
2.1.1. KIR EXPRESSION AND FUNCTION IN NK CELLS 18
2.2. T CELLS AND KIR 19
2.2.1. KIR EXPRESSION AND FUNCTIONS IN T CELLS 20
2.3. KILLER CELL IMMUNOGLOBULIN LIKE RECEPTOR (KIR) 22
2.3.1. KIR NOMENCLATURE 22
2.3.2. KIR STRUCTURE 23
2.3.3. KIR GENE ORGANIZATION 26
Page | xi 2.3.4. KIR EXON/INTRON ARRANGEMENT 27
2.3.5. KIR MOLECULAR CHARACTERISTICS 30 2.3.5.1. Two domain KIRs 30 2.3.5.1.1. KIR2DL1 30 2.3.5.1.2. KIR2DL2/3 30 2.3.5.1.3. KIR2DL4 30 2.3.5.1.4. KIR2DL5 31 2.3.5.1.5. KIR2DS1 31 2.3.5.1.6. KIR2DS2 32 2.3.5.1.7. KIR2DS3 and KIR2DS5 32 2.3.5.1.8. KIR2DS4 32 2.3.5.2. Three domain KIRs 33 2.3.5.2.1. KIR3DL1/3DS1 33 2.3.5.2.2. KIR3DL2 34 2.3.5.2.3. KIR3DL3 34
2.3.6. KIR HAPLOTYPES 34
2.3.7. KIR DIVERSIFICATION MECHANISMS 40 2.3.7.1. Non-reciprocal crossing over 40 2.3.7.2. Reciprocal crossing over 42
2.3.8. KIR ALLELIC VARIABILITY 44
2.3.9. KIR-LIGAND INTERACTION 44
2.3.10. KIR SIGNALLING 49 2.3.10.1. NK receptors associated with ITAM-bearing transmembrane adaptor proteins 50 2.3.10.2. Inhibitory NK receptor signaling 51 2.3.10.3. KIR2DL4 signaling 52
2.3.11. KIR AND DISEASES 52 2.3.11.1. Infection 53 2.3.11.2. Autoimmune diseases 55 2.3.11.3. Tumor immunity 56 2.3.11.4. Pregnancy 57
Page | xii 2.3.11.5. Hematopoietic Stem Cell Transplantation (HCT) 57
2.3.12. EVOLUTIONARY ASPECT OF KIR 58
2.3.13. KIR GENOTYPING 59
2.4. RHEUMATOID ARTHRITIS 60
2.4.1. EPIDEMIOLOGY 62
2.4.2. DISEASE ETIOLOGY 63 2.4.2.1. Genetic factors 63 2.4.2.2. Environmental factors 64
2.4.3. PATHOPHYSIOLOGY OF RA 65
2.4.4. CELL POPULATIONS IN RA 69 2.4.4.1. Monocytes/Macrophages 69 2.4.4.2. Fibroblast-like synoviocytes 70 2.4.4.3. T cells 71 2.4.4.4. B cells 71 2.4.4.5. Neutrophils 71 2.4.4.6. Dendritic cells [DCs] 72 2.4.4.7. NK cells 72
2.4.5. DIAGNOSIS 73 2.4.5.1. Laboratory diagnosis 73 2.4.5.1.1. Antibodies 74 2.4.5.1.1.1. RF antibodies 74 2.4.5.1.1.2. Anti-cyclic citrullinated peptide (CCP) antibodies 75 2.4.5.1.2. Inflammatory markers in RA 76 2.4.5.1.2.1. Erythrocyte Sedimentation Rate (ESR) 77 2.4.5.1.2.2. C-Reactive Protein (CRP) 77
2.4.6. ASSESSMENT OF DISEASE ACTIVITY 77 2.4.6.1. Assessment of clinical disease activity 78 2.4.6.2. Swollen and tender joint counts 78 2.4.6.3. Pain 78 2.4.6.4. Global assessment of disease activity 79 2.4.6.5. Acute phase reactants 79
Page | xiii 2.4.6.6. Disease activity scores and indices 79 2.4.6.7. Criteria to assess disease activity including remission 80 2.4.6.8. Assessment of radiological progression 81 2.4.6.8.1. Included Joints 82 2.4.6.8.2. Scoring methods 82 2.4.6.8.3. Radiographic remission and healing 83 2.4.6.9. Assessment of function 84
2.4.7. TREATMENTS 85
2.4.8. KIR IN RA 86
CHAPTER 3. MATERIALS AND METHODS 87
3.1. SAMPLE COLLECTION 87
3.1.1. POPULATION BASED STUDY 87 3.1.1.1. Study Populations 87 3.1.1.1.1. Rajbanshi 88 3.1.1.1.2. Bengali 89 3.1.1.1.3. Rabha 89 3.1.1.1.4. Gurkha 90 3.1.1.1.5. Muslim 90 3.1.1.2. Sample collection strategy 90
3.1.2. RA BASED STUDY 91 3.1.2.1. Study population 91 3.1.2.2. Sample collection strategy 93
3.2. EXPERIMENTAL PROCEDURES: 94
3.2.1. POPULATION BASED STUDY 94 3.2.1.1. Extraction of genomic DNA 94 3.2.1.2. Characterization of DNA 95 3.2.1.2.1. The integrity: 95 3.2.1.2.2. The Purity and concentration: 95 3.2.1.3. Storage 96 3.2.1.4. PCR-SSP Typing 96
Page | xiv 3.2.1.4.1. PCR Primers 96 3.2.1.4.2. Preparation of reaction mixture: 98 3.2.1.4.3. PCR typing protocol 98 3.2.1.4.4. Gel checking 99
3.2.2. RA BASED STUDY 101 3.2.2.1. Molecular Typing of KIR genes 101 3.2.2.2. RF titre assay 101 3.2.2.3. Anti-CCP estimation 101 3.2.2.4. C-reactive protein (CRP) estimation 102 3.2.2.5. Anti-streptolysin O (ASO) titre assay 102 3.2.2.6. Erythrocyte Sedimentation Rate (ESR) Estimation 102 3.2.2.6.1. Material 103 3.2.2.6.2. Methods 103 3.2.2.7. Serum Ceruloplasmin Assay 103 3.2.2.7.1. Materials 104 3.2.2.7.2. Methodology 104 3.2.2.8. Serum Creatinine Assay 105 3.2.2.8.1. Materials 105 3.2.2.8.2. Methodology 105
3.3. STATISTICAL ANALYSES 106
3.3.1. POPULATION BASED STUDY 106 3.3.1.1. Estimation of observed phenotypic frequency, KIR locus frequency and genotypic frequency. 106 3.3.1.2. KIR gene frequencies of the referral populations 106 3.3.1.3. Chi-Square (χ2) test and estimation of significant differences 107 3.3.1.4. Distance analyses 107 3.3.1.5. Principle Component analyses 108 3.3.1.6. Restricted Maximum Likelihood (REML) analysis 108 3.3.1.7. Estimation of Linkage Disequilibrium (LD) and Haplogroup analyses 109
3.3.2. RA BASED STUDY 110 3.3.2.1. Estimation of clinical parameters between RA patients and controls. 110
Page | xv 3.3.2.2. Diagnostic Test Evaluation 110 3.3.2.2.1. Odds Ratio and relative Risk. 110 3.3.2.3. Regression Analysis and Graphical Representations 111 3.3.2.4. KIR-RA association based analyses 111
CHAPTER 4. RESULTS AND DISCUSSION 112
4.1. FREQUENCY AND DISTRIBUTION OF KIR GENES WITHIN THE HUMAN POPULATION
OF SUB-HIMALAYAN REGION OF INDIA. 112
4.1.1. KIR GENE FREQUENCIES 112
4.1.2. KIR GENOTYPIC FREQUENCIES 113
4.2. STUDY OF HETEROGENEITY AMONG THE LOCAL POPULATION(S). 118
4.2.1. CHI-SQUARE TEST 118
4.2.2. PRINCIPAL COMPONENT ANALYSES 122
4.2.3. ESTIMATION OF GENETIC DISTANCES 124
4.2.4. COMPARISON OF BX SUBSETS 126
4.2.5. ESTIMATION OF HAPLOTYPE FREQUENCIES 129
4.2.6. LINKAGE DISEQUILIBRIUM 132
4.3. TO TRACE THE PHYLOGENETIC RELATIONSHIPS OF THE STUDIED POPULATIONS
WITH THAT OF THE DIFFERENT WORLD POPULATIONS BASED ON KIR GENE
PROFILES. 138
4.3.1. NEIGHBOUR JOINING TREE: 138
4.3.2. RESTRICTED MAXIMUM LIKELIHOOD (REML). 139
4.4. ASSOCIATION OF KIR GENES WITH RHEUMATOID ARTHRITIS. 141
4.4.1. COMPARATIVE ESTIMATION OF CLINICAL PARAMETERS BETWEEN RA PATIENTS AND
CONTROLS. 141
4.4.2. PCR-SSP TYPING OF KIR GENES IN RA PATIENTS 151
CHAPTER 5. COMPREHENSIVE DISCUSSION 157
5.1. STUDY OF THE KIR GENE PROFILES IN DIFFERENT POPULATIONS OF THE SUB-
HIMALAYAN REGION 157
5.1.1. RAJBANSHI 157
Page | xvi 5.1.2. RABHA 159
5.1.3. BENGALI 160
5.1.4. GURKHA 164
5.1.5. MUSLIM 166
5.2. STUDY OF THE ASSOCIATION OF KIR GENES WITH RHEUMATOID ARTHRITIS. 168
CHAPTER 6. SUMMARY AND CONCLUSION 180
6.1. POPULATION BASED STUDY 180
6.2. RA-BASED STUDY 183
BIBLIOGRAPHY 187
INDICES 239
APPENDIX 1
GENERAL QUESTIONNAIRE 244
APPENDIX 2
HEALTH ASSESSMENT QUESTIONNAIRE HAQ (D1) 247
APPENDIX 3
CHEMICALS, REAGENTS AND KITS 248
APPENDIX 4
LIST OF PUBLICATIONS 253
Page | xvii List of Figures
Figure 1: The three models of Human Origin and Evolution 7
Figure 2: NK cell activation using “missing self” and “induced self” hypothesis. 15
Figure 3: Nomenclature of KIR genes and alleles. 23
Figure 4: The structural characteristics of two and three Ig-like domain KIR proteins. 25
Figure 5: Approximate KIR protein domain and region lengths. 26
Figure 6: Location of KIR genes within the Leukocyte Receptor Complex (LRC). 27
Figure 7: Organization of KIR genes. 29
Figure 8: Gene content of KIR haplotypes. Haplotype 1 represents group- A KIR haplotype and the remaining ones exemplify group-B haplotypes (haplotypes 2-7). 36
Figure 9: Centromeric and telomeric halves of KIR haplotypes. 38
Figure 10: The genomic organization of common haplotypes in the Caucasoid population. 39
Figure 11: Mechanism of diversification of KIR-HLA combinations. 42
Figure 12: Reciprocal versus non-reciprocal recombination 43
Figure 13: The complex of KIR2DL (red) and HLA-Cw4-1 (blue) molecules along with the peptide (purple) 45
Figure 14: NK cell interaction with target cells is modulated by KIRs (a) When inhibitory KIR receptors are bound by ligand (class I MHC) activating processes are inhibited (b) When the ligand is absent, activation continues resulting in target cell kill. 49
Page | xviii Figure 15: Structural differences between a normal joint and a joint affected by RA. 60
Figure 16: Pathogenesis of RA. [A] Normal knee joint [B] Early RA and [C] Late RA. 67
Figure 17: Cytokine signaling pathways involved in inflammatory arthritis. 68
Figure 18: Schematic presentation of IgM RF. 74
Figure 19: Citrullination (deimination) of peptidylarginine by peptidyl arginine deiminase (PAD) 76
Figure 20: Geographical Location of the different population group in the map of west Bengal. 91
Figure 21: 1% agarose gel electrophoresis to demonstrate the extracted DNA in fresh bloods. 95
Figure 22: 2% agarose gel electrophoresis to show the presence of different KIR genes. 99
Figure 23: Principal Component Analysis (PCA) based on carrier frequencies of nine variable KIR genes (2DL1-3, 2DS1-4, 3DL1, and 3DS1) in the studied populations along with other world populations developed by MINITAB v16. 123
Figure 24: Stacked bar diagram to show the comparative proportions of AA genotypes and different subsets of Bx genotypes in our studied populations along with some other populations around the World. 128
Figure 25: PCAscore plot based on the frequencies of the KIR AA and Bx subsets in our studied populations and other referral populations. 129
Figure 26: Neighbor Joining Dendrogram based on the genetic distances of the studied populations with other neighboring populations from Asia. 139
Page | xix Figure 27: Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles in the Bengali and other previously studied World populations using Phylip package.Clusters. 140
Figure 28: (A) Dot density graph showing the distribution of anti CCP in RA patients as well as different subgroups of control samples. (B) Box plot demonstrating the distribution of anti CCP and RF titre in RA patients as well as control samples. 143
Figure 29: Scatter diagram for comparative assessment of anti CCP and RF titre in (A) RA patient and (B) control group respectively. 145
Figure 30: ROC curve comparing the diagnostic performance of anti CCP and RF titres. The Area under the Curve (AUC) for anti CCP and RF are found to be 0.890 and 0.875 respectively. 145
Figure 31: [A] and [B] 3D scatterplot and contour diagram demonstrating the variations of DAS28 based on ESR and anti CCP. [C] and [D] 3D scatterplot and contour diagram demonstrating the variations of DAS28 based on ESR and anti CCP. 147
Figure 32: (a) Box plot analyses show the differences in the distribution of the different clinical parameters in the Rheumatoid Arthritis patients and the control samples. (b) Pyramid charts compared the distribution of the levels of different clinical parameter 149
Figure 33: Scatterplot analyses to show RA factor Vs Ceruloplasmin and RA factor vs creatinine levels respectively in RA patients. 151
Page | xx List of Tables
Table 1: Brief Description of the NK cell receptors 17
Table 2: Ligands and function of the 14 structural KIR genes 48
Table 3: ACR 1987 criteria (left panel) were intended to classify established rheumatoid arthritis. ACR/EULAR criteria 2010 (right panel) are designed to classify both early and established disease. 61
Table 4: Data on RA prevalence and incidence for several areas of the world and several countries.. 62
Table 5: Primer sequences and the amplicon sizes of the 16 KIR genes. 97
Table 6: Observed carrier frequencies (OFs) and the estimated gene frequencies (KLFs) of the 16 KIR genes in the five study populations. 113
Table 7: KIR genotypes in the studied populations. 116
Table 8: Comparison of the frequencies of KIR genes in our studied populations with that of some of the other Asian populations using χ2 test (Kyplot beta 2.0). 120
Table 9: Measures of Nei Genetic distances between studied populations and other geographically neighboring populations. 125
Table 10: Frequencies of subsets of Bx genotypes in the studied population and some other comparative populations around the World. 126
Page | xxi Table 11: Frequencies of some selected Haplotypes in the studied populations. 131
Table 12: Linkage disequilibrium parameters for pairs of KIR genes in the studied populations. 133
Table 13: Demographic data and clinical characteristics of the RA patients and control groups. 142
Table 14: Diagnostic performance of anti-CCP antibodies and Rheumatoid Factor in RA diagnosis. 144
Table 15: Multiple Regression models for prediction of DAS28 using ESR in combination with anti CCP and RF titre respectively. 146
Table 16: Statistical analyses of the different clinical parameters of the Rheumatoid Arthritic patients and control subjects. 148
Table 17: Correlation coefficient of the different clinical parameters in the RA patients. 150
Table 18: Comparison of KIR gene profiles in the RA patients and control groups. 153
Table 19: KIR genotypic profiles in the patients and the Control groups. 154
Table 20: Linkage disequilibrium parameters for pairs of KIR genes in the RA Patients. 155 List of Appendices
Appendix1: General Questionnaire 244 Appendix 2: Health Assessment Questionnaire HAQ (D1) 247 Appendix 3: Chemicals, Reagents and Kits 248 Appendix 4: List of Publications 253
Page | xxii ABBREVIATIONS
ACCP2A Anti Cyclic Citrullinated 2 Peptide Antibody
ACR American College Of Rheumatology
AGT Angiotensin Gene
AICD Activation-Induced Cell Death
AIMS Arthritis Impact Measurement Scale
APC Antigen Presenting Cell
APRs Acute Phase Reactants
CD Crohn’s Disease
CDAI Clinical Disease Activity Index
CMV Cytomegalovirus
CRP C-Reactive Protein
CTL Cytotoxic T Lymphocytes
DAF Decay-accelerating Factor
DAP 12 DNAX Activation Protein Of 12 Kda
DC Dendritic Cells
DMARDs Disease Modifying Anti Rheumatic Drugs
EBV Epstein-Barr Virus
EGA Evaluator’s Global Assessment
ELISA Enzyme linked immunosorbent assay
ERK Extracellular Signal-Regulated Kinase
ESR Erythrocyte Sedimentation Rate
EULAR European League Against Rheumatism
Page | xxiii FLS Fibroblast-like synoviocytes
Grb-2 Growth Factor Receptor Bound 2
GVHD Graft-Versus-Host Disease
HAQ Health Assessmemnt Questionnaire
HGNC Hugo Genome Nomenclature Committee
HLA Human Leukocyte Antigen
HPV Human Papilloma Virus
HSCT Hematopoietic Stem Cell Transplantation
HSV Herpes Simplex Virus
IBD Inflammatory Bowel Disease
ICAM Intercellular adhesion molecule
IFN-γ Interferon Γ
Ig Immunoglobulin Superfamily
IP Interphalangeal
ITAMs Immunoreceptor Tyrosine-Based Activating Motifs
ITIMs Immuno-Receptor Tyrosine-Based Inhibitory Motifs
JSN Joint Space Narrowing
KARAP Killer Cell activating Receptor-associated Protein
KIR Killer Cell Immunoglobulin-Like Receptors Or Killer-Cell Inhibitory Receptor
LAT Linker For The Activation Of T Cells
LD Linkage Disequilibrium
LRC Leukocyte Receptor Complex
Page | xxiv MAPK P38 Mitogen Activated Protein Kinase
MCP Metacarpophalangeal
MCP-1 Monocyte Chemotactic Protein-1
MHC Major Histocompatibity Complex mtDNA Mitochondrial DNA
MΦ Macrophage
NK Natural Killer
NKC Natural Killer Complex
NSAIDs Non Steroidal Anti Inflammatory Drugs
ODF osteoclast differentiation factor
PAD Peptidyl Arginine Deiminase
PCR Polymerase Chain Reaction
PIP Proximal Interphalangeal
PLC-γ Phospholipase C-γ
PsA Psoriatic Arthritis
RA Rheumatoid Arthritis
RANK Receptor Activator of Nuclear Factor κβ
RAO Recent African Origin
RF Rheumatoid Factor
RFLP Restriction Fragment Length Polymorphism
RRP Recurrent Respiratory Papillomatosis
RRP Recurrent Respiratory Papillomatosis
SCID Severe Combined Immunodeficiency
SDAI Simplified Disease Activity Index
Page | xxv SH2 Src Homology 2 Domain
SHIP SH-Containing Inositol 5-Phosphatase
SJC Swollen Joint Count
SNP Single Nucleotide Polymorphism
SSOP-PCR Sequence Specific Oligonucleotide Probe-Based PCR
STR Short Tandem Repeats
Syk Spleen Tyrosine Kinase
TCR T Cell Receptor
TJC Tender Joint Count
TNF-α Tumor Necrosis Factor Α
TRANCE TNF-related activation induced cytokine
UC Ulcerative Colitis
VAS Visual Analogue Scale
VCAM-1 Vascular Cell Adhesion Molecule-1
VEGF vascular endothelial growth factor
ZAP-70 Zeta Chain-Associated Protein Of 70 Kilo Dalton
Page | xxvi
Chapter 1:
Introduction
1. Introduction
Researches on human genetics over decades have played a major role in exploring intra and inter population genetic differences, which have not only strengthened our base for understanding human migration patterns on global scale but also enhanced our knowledge regarding the etiology of different diseases. (Bamshad, et al., 2001;Cavalli- Sforza and Piazza, 1975).
Although anthropometric traits such as cephalic index were once considered a favorite tool for reconstructing evolutionary relationships, but later on, it was realized that such traits might not be under complete control of biological inheritance. Furthermore, variations in such traits may have arisen due to short-term responses to environmental changes. On the other hand, certain human traits such as height, weight, skin colorations, facial features and many more, which can be easily perceived by a non-professional, are also genetically determined to some extent, but have little contributions to the understanding of the patterns of genetic variations.
Genetic studies of ABO blood group system defined by Landsteiner (Landsteiner, 1961), provided a precise definition of genetic variation for the first time. However, the full extent of individual genetic variation began to emerge only when genetic analysis could be carried out at the level of hereditary material itself. This technique became widely available only in the 1980s and are gaining importance in present day researches since greater variations exist at the DNA level compared to proteins and blood groups.
Populations with clearly different evolutionary histories demonstrated similar frequencies of a single gene owing to random genetic variation. Therefore, cumulative information on variations of more than one gene is necessary to overcome such confusion. Informations from many loci may be combined through multivariate analyses, which are useful for extracting informations of genetic and evolutionary interest.
Page | 1 It is indeed a very difficult and challenging task to interpret the role of evolutionary forces in the reconstruction of major human migration events. This is executed by comparing genetic information with relevant knowledge from other fields, which include ecogeographical, historical, paleo-anthropological, cultural, and linguistic evidences.
It is a well-established fact that the number of genes vary among organisms. Therefore, molecular mechanisms must exist that generate new gene structures and govern their evolution and sustenance in the population. A brief description of the sources of new genes and their evolution has been given below:
1.1. New genes: Sources and Evolution
The creation of new gene structures involves several molecular mechanisms, which are as follows:
1.1.1. Exon shuffling
A new exon–intron structure can be created by bringing two or more exons from different genes together, or by duplicating the same exon (Gilbert, 1978). Genomic evidences suggest that exon shuffling, also known as domain shuffling, often recombines sequences that encode various protein domains to create mosaic proteins (de Souza, et al., 1996;Kaessmann, et al., 2002). Numerous genes created by exon shuffling were identified using direct sequence comparison (Patthy, 1996).
1.1.2. Gene duplication
Duplicate genes created by this process may show different function(s) compared to that of its ancestral copy (Kimura, 1983;Ohno, 1970). In fact, gene duplication not only contributed to the evolution of new genes with different functions but also contributed substantially to the developmental evolution of an organism (Prince and Pickett, 2002).
Page | 2 1.1.3. Retroposition of genes.
Through this mechanism, duplicate genes are created by reverse transcription of expressed parental genes (Long, et al., 2003). A functional retroposed gene has a chimeric structure consisting of a retroposed coding region with a new regulatory sequence from another gene, thereby functioning differently from that of the parent gene. In mammals, the L1 retro-element is responsible for retroposing nuclear genes (Esnault, et al., 2000;Long, et al., 2003;Moran, et al., 1999).
1.1.4. Mobile elements
Analyses of human genome sequences (Nekrutenko and Li, 2001) and vertebrate genes (Lorenc and Makalowski, 2003) have shown that the integration of mobile elements into nuclear genes generate new functions as was seen in case of the human decay- accelerating factor (DAF) gene (Makalowski, et al., 1994).
1.1.5. Lateral gene transfer
This molecular mechanism may result in the exchange of genes between prokaryotic organisms. Additionally, this process may also cause the recruitment of new genes in an organism resulting in new phenotypes (Ochman, 2001). It is also evident that lateral gene transfer might be important in the evolution of eukaryotic genes (Bergthorsson, et al., 2003).
1.1.6. Gene fusion/fission
A single gene can be produced as a result of fusion of two adjacent genes by read through transcription. This may occur due to the deletion or mutation of the translation stop codon and the transcription termination signal in the upstream gene (Long, et al., 2003). Several cases of gene fusion events were reported in higher eukaryotes by Thomson and colleagues (Thomson, et al., 2000). In contrast to the above mentioned phenomenon, a single gene may get split to form two new genes by a still unclear mechanism (Long, et al., 2003).
Page | 3 A classical model describing the fate of a new gene was put forwarded by Haldane (Haldane, 1933) and Fisher (Fisher, 1935), and was later extended by Kimura (Kimura, 1983). This model described the mechanism through which duplicated gene could acquire new functions and ultimately be preserved in a lineage. However, the impact of the evolutionary forces in the sustenance or loss of a newly generated gene from a population cannot be denied.
1.2. The four forces of evolution
A gene is a DNA segment encoding a functional RNA or protein product and is considered as a molecular unit of heredity. The extensive diversity of genes on earth is based on four fundamental forces of evolution: mutation, gene flow, natural selection and random genetic drift (Long, et al., 2003).
A mutation is considered as a permanent change in the nucleotide sequences of genetic elements. Mutations of a single gene produce its alternative forms called alleles, which are the main reasons behind variations in protein products.
Random sampling of alleles when passing from generation to generation alters the frequency of an allele in a population. Such a change is referred to as the Genetic drift (Masel, 2011). Fluctuations in the allele frequencies occur due to the randomness of the transmission process until, by chance, one of the alleles is lost and the other is fixed. After few generations, the newly emerged population may show extreme variations in gene frequencies from those of parental population. Such a phenomenon of extremely random genetic drift is referred to as “founder effect,” (Templeton, 1980) as the population started with few founding members with restricted gene pool.
The transfer of genes from one population to another through interbreeding is referred to as Gene flow, which results in the increase of similarity between those populations. If two genetically different populations mate, offspring having genes from both the populations may be produced. Further, additions of new genetic variants to the established gene pool of a particular species or population may also result due to immigration (Cavalli-Sforza, et al., 1993).
Page | 4 Natural selection is a constantly shifting process by which organisms with specific gene patterns that are adaptive to a particular environment become more prevalent over time. It is influenced not only by an organism’s biology, but also by the interaction of that biology with environmental conditions (Sternberg, 2004).
Allelic distribution in context to geographical locations may give information on the evolutionary history of the allele, on the place of origin of the genetic change (mutation) that generated it and the influence of selection pressure on it (Roychoudhury and Nei, 1988). Furthermore, frequencies of alleles in a particular region may also be correlated with the environmental parameters to interpret any specific genetic adaptations (Nei, 2005). In general, degree of allelic variations between two regions is directly proportional to their geographic distances. Thus, geographic maps corresponding to allele or gene frequencies of a number of populations may be constructed. Furthermore, geographic distribution of a gene may also be correlated with disease prevalence and adaptation to environmental factor. For example, a variant of Angiotensin gene (AGT) which has been found to be 90% frequent in some African populations and 30% in European populations (Nakajima, et al., 2004) ), has been found to increase the risk of developing hypertension by 10-20% (Kunz, et al., 1997).
Much of the genetic variations seen among the humans resulted indirectly from the pattern of expansion and migrations accompanied by random genetic drift. However, the surrounding environment is the most important factor, which influences the heredity of a given attribute. In fact, environment affects the same genetic structure differentially, thereby generating a range of phenotypes for a particular genotype. Moreover, heritability will also differ based on interactions of different genotypes with varying environments (Lewontin, 1974).
1.3. Evolution of Modern Human
According to fossil evidences, modern humans evolved in Africa about 200 KYA (Klein, 1999). The physical characteristics of anatomical modern man consist of a high rounded skull, facial retraction, and a light skeleton (Lahr and Foley, 1998;Lahr, 1996). Fossils
Page | 5 with such characteristics of anatomically modern man have been found in eastern Africa dated to approximately 160–200 KYA (McDougall, et al., 2005;White, et al., 2003).
Informations gathered from human genetic variations express a similar view on human variation as that of the fossil records. Greater genetic diversity in populations from Africa imply the first appearance of human in Africa and subsequent colonization in Eurasia and the Americas (Tishkoff and Williams, 2002;Yu, et al., 2002) (Tishkoff and Verrelli, 2003). Interestingly the dates calculated for human expansion based on genetic variations and archaeological record generally coincide (Jorde, et al., 1998). There are three main theories for the evolution of modern humans:
1.3.1. Multiregional model
This theory does not support a single geographic origin for the modern Homo sapiens (Wolpoff, et al., 2002). This theory suggests the spread of Homo erectus out of Africa into the different regions of the World, who later gradually evolved into archaic Homo sapiens. Later these archaic populations are modified simultaneously to modern H. sapiens in various parts of Europe, Asia and Africa (Figure 1). This model has received considerable support from the fossil records. According to archaeological reports, there is a continuum of certain morphological traits from the first H. erectus to those of modern populations from a particular geographic area. For example, skeletal remains of early H. sapiens from various location of China, North Africa, and Europe resemble modern populations in those areas in some aspects (Thorne and Wolpoff, 1992).
1.3.2. Model of Recent African origin (RAO)
This theory suggest the evolution of archaic humans to anatomically modern humans solely in Africa (Hetherington and Reid, 2010). It further extends that individuals belonging to one such branch of Homo sapiens left Africa and populated other regions of the World by replacing other populations of the genus Homo such as Neanderthals and Homo erectus (Figure 1). Genetic and fossil evidences strongly supported this concept and made this theory the most accepted one within the scientific community (Stringer, 2003). Evidences from researches on mtDNA (Ingman, et al.,
Page | 6
2000), the Y chromosome (Underhill, et al., 2000), the X chromosome (Kaessmann, et al., 1999), and many autosomal regions (Harpending and Rogers, 2000) extended support to this RAO model of human origin.
1.3.3. Assimilation model
According to this model, gene flow occurred between modern and earlier human populations in varying degrees (Smith, et al., 1989) (Figure 1). This model suggested that modern human emerged in Africa and migrated into other regions of the World where they hybridized with earlier settled archaic populations and replaced them gradually. Therefore, modern human evolution in different parts of the World may result due to the blending of modern characters from the recent African populations with the characters of the local archaic populations. As evident from fossil evidences, this model may more correctly represent the complex and gradual nature of the processes represented.
Figure 1: The three models of Human Origin and Evolution. Adapted from Gibbons, 2011 (Gibbons, 2011).
Page | 7
1.4. Genetic tools for studying history
The word ‘population’ refers to a group of organisms of the same species inhabitating a restricted geographical area where members of opposite sex can potentially interbreed. The set of genetic information carried by a population is known as its gene pool and a population sharing a common gene pool is referred to as the Mendelian population. Although evolutionary forces cause minor variations in the gene pool of an organism through a passage from generation to generation, quite interestingly, it has been observed that, the individuals belonging to a local group are related more closely to each other than with those occupying other geographical areas. Such evolutionary characteristics cause change in the genetic constitution of a population followed by genotypic and phenotypic alterations (Guerra, et al., 1999). Thus, it can be said that gene frequencies have gained enough importance in evolutionary studies especially in human. However, selection of the right marker should be accompanied by the application of robust statistical tools in order to avoid sampling fluctuations and erroneous clarification of the data. Researches on human evolution and on anthropogenetics have been greatly benefitted by the advent of various molecular markers along with more robust DNA typing technology. Apart from the functional genes, many other markers have gained wide applications in the recent times. These DNA markers are highly polymorphic making them more informative in studying genetic variations between and amongst human populations.
Short Tandem Repeats (STR) are short fragments of highly polymorphic DNA which are very useful in studying human genetic diversity (Butler, 2007). Single Nucleotide Polymorphism (SNP) DNA markers (autosomal SNPs and Y chromosome SNPs) are characterized by single nucleotide changes in DNA. SNPs are preserved across populations and generations thereby allowing phylogenetic analyses across time and geography (Butler, 2007).
Another essential phylogenetic marker based on variations in mitochondrial DNA sequences is the Mitochondrial DNA (mtDNA) markers (Wallace, 1994). Similar to STRs and SNPs, mtDNA are highly effective in analyzing degraded samples making them suitable for analyses of anthropological samples (human remains, ancient DNA). Y
Page | 8 chromosome markers (STRs and SNPs) are paternally inherited allowing genetic analysis of the paternal line (Jobling and Tyler-Smith, 2003) while, mtDNA show maternal inheritance allowing genetic analysis of the female line.
Human Leukocyte Antigen / Major Histocompatibity Complex is another group of highly polymorphic markers (Shiina, et al., 2009) which are widely used in population genetic study nowadays. This system not only exhibit high degree of polymorphism but also show tight linkage among the loci and non-random association of alleles (Carrington, 1999). These characters have made the HLA system interesting from perspective of population genetics. The number of HLA alleles increased with the advent of different DNA based molecular typing techniques. HLA polymorphism not only has an inestimable role for population genetic studies but also play an important role in transplantation and disease associations. Earlier studies have reported associations of both susceptible and protective HLA alleles with infectious and autoimmune diseases in populations of different ethnicity (Bowness, 2002;Carrington, et al., 1999;Hill, et al., 1991).
Killer Cell Immunoglobulin-like Receptors (KIRs) are the members of a receptor family that are found on the surface of NK cells and cytotoxic T cells (Lanier, 1997). They interact with HLA class I molecules, regulate NK cell activity, and protect healthy cells from spontaneous destruction or killing by NK cell mediated cytolysis. These receptor molecules exhibit either inhibitory or activating functions or both. The structure of the KIR molecules determine their activating or inhibitory function (Middleton and Gonzelez, 2010). The KIR gene family exhibit great diversity of genes and alleles, all of which are immunoglobulin superfamily members. This gene cluster is present on chromosome 19 within the Leukocyte Receptor Complex (LRC) (Uhrberg, et al., 1997;Vilches and Parham, 2002;Wilson, et al., 2000). Fourteen genes are present in the KIR family along with two pseudogenes. KIR haplotypes are of two types depending on gene content, namely A and B, (Wilson, et al., 1997). The A haplotypes have seven KIR loci of which only one encode NK cell-activating receptor - KIR2DS4. The B haplotypes contain KIR loci most of which encode activating receptors with only two loci encoding
Page | 9 inhibitory receptors. Inspite of their differences in gene content (Uhrberg, et al., 1997;Vilches and Parham, 2002), all KIR haplotypes contain the ‘framework genes, namely KIR3DP1, KIR3DL2, KIR3DL3 and KIR2DL4. These framework genes are well conserved in virtually all individuals (Martin, et al., 2000;Vilches and Parham, 2002;Wilson, et al., 2000). Based on the analysis of KIR genotypes, variations have been observed in the frequencies of both the haplotype groups between populations. Recent studies on KIR interaction with HLA ligands have suggested its role in immunopathology. Thus, it has become important to characterize the genotypes based on KIR genes in different endemic populations. The study may also help to decipher the relatedness of ethnic populations among themselves and with the other Indian and world populations. Furthermore, the study may illuminate the possible origin of these populations and probable human migratory patterns in this part of the world. Consequently, the study may help in analyzing gene-disease co-relatedness and disease susceptibility within the local/endemic populations.
1.5. Indian Subcontinent
India has a unique geographical location between 8° N to 37° N latitude and 68°E to 97° E longitude, which has immense effects on its resident populations. The Indian population has an extensive history of frequent migrations and invasions from both the east as well as the west of the subcontinent and constant amalgamation of populations. The Dravidians are generally considered as the original inhabitants of the country who were driven southwards following Aryan invasions from north-west around 1500- 100 BC. They introduced highly elaborated caste system in India with divisions into priests (Brahmin),warriors (Kshatriya), Traders (Vaishya) and the inferior craftsman (Sudra) (Mehra, et al., 1986). These broader four groups have been subdivided into smaller groups who marry within themselves. As a result, of this the entire population has been divided into a large number of groups.
Further historical invasions have caused admixture of Negrito, Negroid, Proto-australoid, Mongoloid and European elements in the Indian populations (Rao, 1986), followed by subdivision into four linguistic families i.e. i) Austro-Asiatic ii) Dravidian iii) Indo-
Page | 10 European and iv) Tibeto-Burman respectively. Several waves of human invasions and immigrations occurred in the Indian sub-continent. In the late 16th century BC, Indo- European speakers invaded the Indian subcontinent and started imposing religious beliefs and hierarchical caste systems (Kurien, 2002). In 325-327 BC Alexander’s Macedonian army invaded from both west and east with incursions into Punjab followed by Muslims in 1200-1500 AD (McCrindle, et al., 1896). At that time, the existing religion was Hinduism, with numerous modified forms, e.g. Buddhism, Jainism and the animistic religions practiced by the tribal populations. During the Muslim rule, that spanned three centuries, a large section of the population was converted to Islam. This led to another religious division within the same caste population. Around 500 years ago, the birth of Sikhism took place, but was confined largely to the North -Western parts of India, among the Punjabi speaking population. Later empires of Muslims declined and the whole kingdom got fragmented into smaller kingdoms which were then first occupied by the Portuguese and Dutch and then it was occupied by the British from 1600-1947. This was the period when Christianity arrived. (Mayhew, 1929). Thus, invasions and massive population movements in India subdivided its population structure into caste and tribes. Such historical and racial admixture has made the Indian population a ‘melting pot of various races’ exhibiting extensive cultural, religious and linguistic diversity. The Indian caste system does not permit large-scale inter-caste, inter-religious and inter-ethnic marriages, whereby the gene pool of each caste has evolved over the times and might have been fixed (Jensen, 1991). These groups follow strict endogamy, which has resulted in a great deal of variation in the mating patterns, all of which invariably resulted in a wide genetic diversity (Naipaul, 2010). Another important dimension of the Indian populations, especially among small populations, is that it offers potential opportunities for the operation of micro-evolutionary forces, which bring rapid changes in gene frequency of certain genetic traits
Thus, it can be said that geographical location and extensive genetic diversity are the two primary reasons behind the growing interests of researchers worldwide to explore the genetic diversity among Indian populations. The primary requirement for such
Page | 11 exploration is the screening of some essential genetic markers among different population groups of India and then analyzes their relationships.
.
1.6. Utility of population studies
Genetic knowledge of population sub-structuring and stratification is a prerequisite for proper selection of controls and for identification of disease pre-disposing genes among different ethnic population groups. Furthermore, genetic profile also facilitates molecular sub-classification of the diseases. The genetic ancestry of an individual helps in better determination of the presence of a disease marker gene. In addition, differences in the genetic structure of different ethnic groups help to explain differences in drug responses among the groups. Therefore, information about the genetic ancestry of an individual may also prove beneficial to improve medical diagnosis and treatment.
Overall knowledge of the genetic ancestry of population sub-group, and information on population diversity, sub-structuring, stratification and phylogenetic relationship are the essential requisites in the biomedical research arena. However, reliability of the genetic markers stand as major hurdle in the path of correct inference of the genetic origin of a sub population and the resolving power of a genetic marker by which it can differentiate between two close population subgroups.
However, in India, the population structure is very much complex and therefore better planning and approach are required to conduct such studies in India. More defined genetic studies on the Tribal and Dravidian populations are required to reveal the exact composition of Indian gene pool. Populations with well-defined geographical and cultural identities should be chosen. Similarly, studies on more endogamous groups can reveal the structure of castes in genetic context and analyze the effect of endogamy on genetic composition of the Indian population.
Over all, these studies will define the pattern and distribution of genetic variation in Indian population and will aid in assessing the level of genetic sub-structuring and correct
Page | 12 genetic ancestry in different endogamous and tribal groups. Furthermore, such studies will also help in tracing the missing block of ancestral human settlers that will form the connecting link of standard model of human evolution.
The present study is aimed to analyze frequency and distribution of KIR genes in five ethnic population groups of India namely Rajbanshi, Rabha, Bengali, Gurkha and Muslims. This study also attempts to explore the association of KIR genes with Rheumatoid arthritis if any. Thus, this study may be able to explain numerous unanswered questions raised on the genetic structure of these populations compared to other Indian and World populations. Furthermore, studies of KIR genes in Rheumatoid Arthritis patients may reveal associations, if any. Thus, researches on differential distribution of KIR genes in different populations may be used to assess the role of each KIR gene in granting advantage to the survival of human populations in conditions of varying environment.
1.7. Objectives of the Study
1) To analyse the frequency and distribution of KIR genes within the human population of Sub-Himalayan region of India.
2) To study the heterogeneity among the local population(s).
3) To trace the phylogenetic relationships of the studied populations with that of the different World populations based on KIR gene profiles.
4) To correlate the disease association (if any) with KIR genes.
Page | 13
Chapter 2:
Review of Literature
2. Review of Literature
KIR molecules play active role in the regulation of immune responses owing to their expression on the surface of NK cells and cytotoxic T cell (Vilches, et al., 2000). However, these cells bear several other receptors on their surface apart from KIRs, whose signals integrate with each other including KIRs and thereby modify the outcome of the signal. Thus, a brief insight into these cells and their receptors is crucial to our understanding of KIR genes and their function in immune responses.
2.1. NK cells and KIR
NK cells, derived from lymphoid progenitor cells in the bone marrow, constitute 10 to 15% of circulating leukocytes. These cells are known to participate actively in innate immune responses which is demonstrated by their unique ability to destroy tumor and virally infected cells (Ljunggren and Karre, 1990). Thus, these cells serve to bridge the gap between the onset of illness and the humoral and cell specific immune responses (Bancroft, 1993;Trinchieri, 1989). Partially activated NK cells, after getting released from bone marrow, circulate throughout the body developing self-tolerance and afterwards establish themselves in the lymph nodes, spleen and in the peripheral blood circulation (Pobezinskii, et al., 2005). These activated NK cells get up-regulated nearly 100 folds in the presence of cytokines such as IL-12, IL-18, and IFN-γ produced by dendritic and stromal cells (Ferlazzo, et al., 2004;Gerosa, et al., 2002;Perussia, 1996). During infection, chemokines released from neutrophils and macrophages interact with surface receptors on NK cells thereby attracting these cells to the infection site (Loetscher, et al., 1996).
Two hypothesis have been proposed for NK cell activation, namely the ‘missing self’ and ‘induced self’ hypothesis (Elliott and Yokoyama, 2011;Lanier, et al., 1997;Ljunggren and Karre, 1990) (Figure 2). The ‘missing self’ hypothesis suggested that NK cells attack target cells having reduced or aberrant HLA class I self-molecules. NK cell activation is normally kept in check by the inhibitory receptor interacting with its ligand HLA molecule since the inhibitory signal dominates over the activating signal.
Page | 14 However, further studies have shown that the activation of th NK cells may be determined not only by the lack of HLA class I expression but also by the expression of ligands for activating the receptors (Cerwenka, et al., 2001;Diefenbach, et al., 2001;Karre, 2008;Lanier, et al., 1997). On the other hand, the ‘induced self’ model suggested the recognition of cellular stress ligands by NK cells which are induced upon malignant transformation or viral invasion (Groh, et al., 2001).
Figure 2: NK cell activation using “missing self” and “induced self” hypothesis. (Adapted from Elliott & Yokoyama, 2011) (Elliott and Yokoyama, 2011).
Unlike T and B cells, NK cells lack antigen specificity as they do not express the specialized genes like those present in case of T and B cells for rearrangement of the T- and B-cell antigen receptor genes (Lanier, 2005;Moretta, et al., 2001;Murphy, et al., 1987). However, a series of activating and inhibitory receptors are expressed on their surfaces whose signals combine together to control the activation of the NK cell (Pegram, et al., 2011;Raulet, et al., 2001). These receptors provide opposing signals which balances to either activate or inhibit the activation of NK cells. Intracellular signal transduction in inhibitory receptors occurs through immuno-receptor tyrosine-based inhibitory motifs (ITIMs) which are conserved sequences of amino acids that are found in the cytoplasmic tail of these receptors. In contrast, some activating receptors signal through immunoreceptor tyrosine-based activating motifs (ITAMs) which contained
Page | 15 associated molecules rather than the receptor itself. However, stimulation of only one activating receptor may not be sufficient to stimulate cytotoxicity and cytokine secretion, and therefore, stimulation of multiple receptor is required (Bryceson, et al., 2006). Other activating receptors like NKG2D use either DAP-10 or DAP-12 for generating alternate signaling mechanism. Stimulation of CD244 receptor constitute the third signaling pathway in NK cells, wherein the cytoplasmic tail can recruit SH2 domain containing adapter proteins SAP or ERT (Veillette, 2006), having inhibitory and activating function respectively.
Two main types of HLA Class I specific receptors are generally expressed on NK cells, namely- the Immunoglobulin superfamily (Ig) receptors and the C-type lectin Receptors. Apart from these, about 50% of the human NK cell population express CD8 molecules, a co-receptor for HLA Class -I association (Seaman, 2000). The Ig receptors include KIR and leukocyte immunoglobulin-like receptor family (LILR) which mediates the killing of viruses and tumor cells (Biron, et al., 1999). These are located on chromosome 19 as part of the 1 Mb Leukocyte Receptor Complex (LRC) encompassing over 25 genes. On the other hand, the C-type lectin receptors, which include the CD94/NKG2 heterodimers are located centromeric to the natural killer complex (NKC) on chromosome 12 (Barten, et al., 2001). These receptors are involved in the regulation of the adaptive and innate immune responses through the release of chemokines and cytokines (Biron, C. A., et al., 1999). Several receptors on NK cells such as DNAM-1, the NKRP1 receptors and the PILR receptor are designated as co-stimulatory since they are not sufficient alone to trigger NK cell activation (Pegram, et al., 2011). Thus, they ensure that the NK cells may not get activated against normal or healthy tissue. Brief descriptions of the NK cell receptors have been enlisted in Table 1 below.
Page | 16 Table 1: Brief Description of the NK cell receptors
Receptor Description
1. Leukocyte 1. Inhibitory receptors expressed on NK cells that bind MHC Immunoglobuli class I molecules . n-Like Receptors 2. They are considered as ancestors to KIR (LILR) 2. (also known as 3. They are located centromeric to KIR LIR, ILT or CD85) 4. Their structural and functional properties bear high degree of resemblance to that of KIR
3. CD94/NKG2 1. C-type lectin receptor family and are found on the surface of NK cells CD8+ T-lymphocyte subsets.
2. Disulfide linked heterodimers consisting of the protein products of a single non-polymorphic CD94 gene as well as NKG2 genes 1-5, located on chromosome 12 Similar signaling mechanism to that of KIR receptors.
3. The ligand for CD94/NKG2A/C/E is the non-classical HLA-E. Furthermore, the affinity of inhibitory NKG2A towards HLA- E is stronger than that of activating NKG2C receptor.
4. The 2B4 1. Present on all human as well as mouse NK cells CD48 receptors also expressed on hematopoietic cells act as ligands known as CD244 2. Signal outcome may depend on the stage of NK cell maturation.
5. Killer cell 1. Inhibitory receptor that signals through an ITIM and causes lectin-like inhibition of NK cell function. receptor G1 (KLRG1) 2. Classical cadherins, (E-, N- and R-cadherins) expressed in healthy, solid tissues act as ligands. Thus this receptor may have a role in the prevention of lysis of healthy tissues. 1. Iso-form of the neural-cell adhesion molecules (N-CAM). 6. CD56 2. Divides NK cells into two subtypes. a. CD56dim (low levels of CD56) NK cells (90 %), associated with active cytotoxic activity due to the relative high level of KIR and other surface
Page | 17 markers. b. (CD56bright (high levels of CD56) NK cells (10%), associated with cytokine production including IFN- γ, TNF-α/β) and IL-10 and 13 respectively. 1. Initially known as “killer inhibitory receptors 7. Killer-cell 2. Glycoprotein molecules belonging to the Immunoglobulin immunoglobuli superfamily n-like receptors (KIRs) 3. Considered to participate in innate immune responses by stimulating target cell apoptosis through cytotoxic effects. 4. Being the genetic markers of choice for this study, this family of receptors has been described in details below:
5. DNAM-1 1. Member of the Ig-superfamily constitutively expressed upon receptor (also approximately 50% of NK cells. known as CD226) 2. CD155 act as their ligand which gets unregulated on some tumor cells, implicating the involvement of DNAM-1 in some NK cell-mediated anti-tumor responses.
3. May help in stable interactions of NK cells with target cells.
4. Shown to be involved in co-stimulation of T cells.
6. The paired Ig- Type 1 glycoprotein receptor having an inhibitory isoform like 2 receptor (PILRα). (PILR) Shown to be involved in NK cell-mediated recognition of carbohydrate chains on target cells, thereby widening the range of target cells that can be recognized by NK cells.
2.1.1. KIR expression and function in NK cells
Random expression of KIRs at NK cell surface generate NK cell repertoires with different combinations of KIR genes encoded in an individual’s genome (Valiante, et al., 1997). Thus, variations can be observed between individuals in the frequencies of NK cells having a particular KIR protein (Gumperz, et al., 1996).. NK cells can also express different amounts of a particular KIR on the cell surface (Gardiner, et al., 2001;Yawata, et al., 2006). The number of copies of a given KIR gene in an individual also influences KIR expression. In other words, frequency of NK cells expressing a given KIR is higher in those individuals having two copies of the gene than those having only one copy
Page | 18 (O'Connor, et al., 2007;Yawata, et al., 2006). Methylation of the KIR gene promoters is also known to affect KIR gene expression (Chan, et al., 2003;Santourlidis, et al., 2002). Some studies have concluded that the frequencies of NK cell subsets expressing particular KIRs are influenced by the possession of HLA class I ligands (Schonberg, et al., 2011;Shilling, et al., 2002;Yawata, et al., 2006;Yawata, et al., 2008), while others have found that the KIR repertoires of NK cells are expressed independently of HLA- class I ligands (Andersson, et al., 2009;Bjorkstrom, et al., 2012).
Along with other types of receptors that are expressed on NK cells, KIRs control the NK cell activation by binding HLA class I ligands following by signal transduction (Caligiuri, 2008). The classical mechanism describing the activation of NK cell is the “missing-self” hypothesis (Raulet, 2006) (discussed above). NK cells are “licensed” during education in the bone marrow, following the ligation of their receptors during development. Licensing renders NK cells functionally competent in respect to their activating receptors (Anfossi, et al., 2006;Kim, et al., 2008).
2.2. T cells and KIR
The T-cell repertoire of an individual includes a collection of T-cell clones, each bearing unique antigenic receptors and can respond to a plethora of antigens. However, such enormous diversity may instigate autoimmunity by recognizing self-antigens. Therefore, regulation of immune response of T cell is crucial not only for maintaining protective immunity but also for averting autoimmune responses. The two mechanisms controlling the above-mentioned immune responses are construction of the T-cell repertoire in the thymus and regulation of peripheral T cells.
Each T cell bears a unique T cell receptor (TCR) on its surface as an outcome of selection and maturation in the Thymus. Mature T cells without antigenic exposure are inactive and are known as naive T cells. These naive cells enter the circulatory system, and reside in secondary lymphoid organs and are presented with antigens by dendritic cells via HLA molecules. Furthermore, recombination results in high variability of TCR, ensuring that at least a few naive T cells will have high-affinity antigenic recognition from virtually
Page | 19 any pathogen. A cascade of intracellular signals is triggered following the engagement of the TCR, finally causing the activation of the naive cells. These activated T cells, then, undergo rapid proliferation and migration to the sites of Ag invasion, followed by production of various cytokines and cell-mediated cytotoxicity. Helper T cells play a major role in antibody production by B cells and in stimulating other T cell effector functions. Additionally, they can also produce cytokines having direct toxic effect on the target. In contrast, cytotoxic T cells are responsible for the direct lysis of infected or malignant cells. After completion of their action, most effector T cells disappear except few, which form the memory T cells. These cells can mount secondary immune responses on finding the same antigen in the future.
During thymic selection, two types of TCR appear namely the αβTCR and γδTCR. Majority of the T cells contain αβTCR and have a very diverse repertoire of Ag recognition receptors. Thus, these cells develop adaptive immune responses. There are several groups of αβTCR containing T cells based on lineage markers and functions. In contrast, γδTCR containing T cells are less numerous and heterogenic than αβTCR T cells. They may have an important role to play in the initial response to microbial invasion
Furthermore, two major branches of the T cell lineage can be distinguished based on the presence of two co-receptor molecules, namely CD4 and CD8. CD4+ cells are activated by antigenic peptides presented by MHC class-II molecules while CD8+ lymphocytes recognize Ag in the context of MHC class-I molecules. Based on the production of signature cytokines Activated CD4+ T helper cells can be subdivided into Th1, Th2, Th17 and Treg subsets i.e. IFN-γ (Th1) versus IL-4, IL-5 (Th2) (Mosmann, et al., 2005). CD8+ lymphocytes also can be assigned to Tc1 or Tc2 subsets according to their cytokine profile (Croft, et al., 1994).
2.2.1. KIR expression and functions in T cells
KIRs can be expressed by both CD8+ and CD4+ T cells and may have a role in modulating their functions (van Bergen, et al., 2004). Furthermore, both activating and
Page | 20 inhibitory KIRs are expressed on subsets of CD8+ T cells (Anfossi, et al., 2001;Mingari, et al., 1996) and CD4+ T cells (van Bergen, et al., 2004). KIR expression on T cells is acquired after thymic development and TCR rearrangement, since T cells with the same TCR β chains can vary significantly in their KIR expression patterns (Bjorkstrom, et al., 2012;Mingari, et al., 1996;Uhrberg, et al., 2001;Vely, et al., 2001). A number of studies have shown that KIR+ T cells have an effector memory (EM) phenotype, (Anfossi, et al., 2004;Anfossi, et al., 2001;Mingari, et al., 1996;van Bergen, et al., 2004) (Anfossi et al., 2004; Anfossi et al., 2001; Mingari et al., 1996; (Speiser, et al., 1999;Young, et al., 2001). The most recent and comprehensive analysis showed that the major KIR- expressing CD8+ T cell subset is CD45RA+ CD57+ CCR7− CD27− CD28− CD127− (Bjorkstrom, et al., 2012) . The frequency of KIR+ CD8+ and CD4+ T cells have been shown to increase with age, consistent with their EM phenotype (Anfossi, et al., 2001;van Bergen, et al., 2004) (Abedin, et al., 2005;Li, et al., 2009). Acquisition of KIR by T cells with a memory phenotype or with increasing age may indicate their role in dampening the T cell mediated response.
The repertoires of KIRs expressed on T cells compared to NK cells have been shown to vary significantly in the same individual (Abedin, et al., 2005;Bjorkstrom, et al., 2012;Uhrberg, et al., 2001) at both the level of epigenetic modifications and the recruitment of transcription factors to the KIR gene promoters (Chan, et al., 2003;Li, et al., 2009;Santourlidis, et al., 2002).
Several studies have examined the effects of KIR expression on T cell function. Inhibitory KIR expression on CD8+ and CD4+ T cells have been demonstrated to inhibit cytokine production (especially TNFα and IFNγ) and effector cytotoxic function, while activating KIRs co-stimulates these functions (van Bergen and Koning, 2010). Inhibitory KIR expression on T cells has also been shown to promote cell survival. In 2DL3/HLA- C1-transgenic mice, there was an accumulation of 2DL3+ CD8+ T cells due to a reduction in activation-induced cell death (AICD) (Ugolini, et al., 2001). Similarly, human KIR+ CD8+ T cell clones expressed higher levels of the anti-apoptotic molecule Bcl-2 and are more resistant to AICD than KIR− clones (Young, et al., 2001).Some
Page | 21 studies have shown that engagement of KIRs with HLA class-I molecules were required for the disruption of TCR signalling ((Fourmentraux-Neves, et al., 2008;Guerra, et al., 2002;Henel, et al., 2006) , the reduction in AICD (Gati, et al., 2003;Ugolini, et al., 2001) and the decrease in T cell effector function (Bonorino, et al., 2007;van Bergen, et al., 2009;van der Veken, et al., 2009). Conversely, others have demonstrated that KIR engagement was not required for inhibition of apoptotic pathways (Chwae, et al., 2002) and KIR-mediated reduction in CD8+ T cell effector function (Alter, et al., 2008;Anfossi, et al., 2004;Bjorkstrom, et al., 2012) .
2.3. Killer Cell Immunoglobulin Like Receptor (KIR)
2.3.1. KIR Nomenclature
A subcommittee of the World Health Organization Nomenclature Committee for the HLA system in association with HUGO Genome Nomenclature Committee (HGNC) successfully completed the task of naming the KIR genes (Marsh, et al., 2003). According to this system, the KIR genes were named based on two important structural features of their corresponding protein molecules, which include the number and type (2D or 3D) of the extracellular Ig domains and the characteristics of the cytoplasmic tails (short or long) (Figure 3). The first digit immediately after the KIR acronym signifies the number of Ig-like domains that are present in the protein molecule, followed by the letter ‘D’ for ‘domain’. The D is then followed by either an ‘L’ or ‘S’ corresponding to ‘Long’ and ‘Short’ cytoplasmic tail respectively. However, in case of pseudogenes, D is followed by the letter ‘P’ instead of ‘L’ or ‘S’. The final digit corresponds to the ranking number of the gene that is coding for a protein molecule of this structure. If multiple genes have similar sequences and structures, then the same number is given except for a final letter to distinguish, as can be seen in case of the KIR2DL5A and KIR2DL5B genes (Gomez-Lozano, et al., 2002).
The alleles of the KIR genes are named in a fashion similar to that of HLA alleles (Marsh, et al., 2003;Middleton and Gonzelez, 2010) (Figure 3). The first three digits after the separator are used to distinguish alleles that differ in exon sequences leading to non-
Page | 22 synonymous changes. The next two digits distinguish alleles differing in exon sequences that cause synonymous changes while the last two digits represent differences only in an intron or other non-coding regions including the promoters.
.
Figure 3: Nomenclature of KIR genes and alleles (Adapted from Middleton & Gonzelez, 2010) (Middleton and Gonzelez, 2010).
2.3.2. KIR structure
KIR receptors consist of an extracellular region followed by a transmembrane and a cytoplasmic region respectively. The extracellular region consists of either two or three immunoglobulin like domains designated as D0, D1and D2 (Figure 4). Thus, KIR proteins are classified as 2D and 3D receptors depending on the number of these extracellular domains (Andre, et al., 2001). Among the three domains, the D2 domain is proximal to the membrane while D0 is the most distally placed. KIR2D receptors have two possible variants, namely type I KIR2D and type II KIR2D receptors. Type I 2D receptors, include KIR2DL1-3, KIR2DS1-5 as well as the pseudo-gene KIR2DP1 (Vilches and Parham, 2002). These receptors are characterized by the presence of D1 and D2 domains but they lack a D0 domain. On the contrary, type II KIR2D receptors, which include KIR2DL4 and KIR2DL5, are equipped with a D2 domain and a membrane-distal D0 domain having similar sequence to that of KIR3D proteins. However, Type II KIR2D receptors lack a D1 domain (Boyton and Altmann, 2007). A stem region enriched with
Page | 23 proline and serine residues helps in linking the D2 domain to the transmembrane region of the KIR molecule. The transmembrane and the cytoplasmic regions have functional relevance since they determine the type of signal transduced by a NK cell (Colonna and Samaridis, 1995;D'Andrea, et al., 1995;Wagtmann, et al., 1995). The cytoplasmic domains of KIR is either Long (L) or Short (S). KIR molecules can have either long or short cytoplasmic domains irrespective of the number of extracellular Ig-like domains. Therefore, KIR2D and 3D receptors are further subdivided based on the length of their cytoplasmic tail (L or S) (Andre, et al., 2001). KIR molecules with long cytoplasmic domains consist of immunoreceptor tyrosine-based inhibitory motifs or ITIMs in the cytoplasmic region rendering them inhibitory in function. In general, long cytoplasmic tails have two ITIM motifs, through which inhibitory signals are transmitted to the NK cell. Exceptions to this are KIR2DL4, KIR3DL2 and KIR3DL3 which contain only one N-terminus ITIM motif.
Short cytoplasmic tailed KIR molecules transmit activation signals but they follow a passive pathway. The presence of a positively charged amino acid residue in the transmembrane domain of these receptors help them to associate with DAP12 signaling molecule (Sigalov, 2010) (Figure 4). DAP-12 is a DNAX activation protein of 12kD molecular weight; this molecule is also known as killer cell activating receptor-associated protein or KARAP, which contains immunoreceptor tyrosine-based activation motifs (ITAMs), responsible for the activation signals (Vilches and Parham, 2002). In addition to a single ITIM, KIR2DL4 receptor also contain a charged Arginine residue in its transmembrane region, allowing this receptor to elicit both inhibitory and activating signals (Khakoo, et al., 2000;Maxwell, et al., 2002).
Page | 24 Figure 4: The structural characteristics of two and three Ig-like domain KIR proteins. The association of activating KIR to adaptor molecules are shown in green, whereas the ITIM of inhibitory KIRs are represented as red boxes. Adapted from IPD KIR database (Robinson, et al., 2010).
The lengths of KIR proteins can vary within a range of 306 to 456 residues (Figure 5). In most of the KIR proteins, the leader peptide has 21 amino acid residues. However, a longer leader peptide is present in KIR2DL4 which may have resulted due to a different initiation codon (Selvakumar, et al., 1996). The D0 Ig-like domain present in Type II KIR2D proteins and KIR3D proteins is approximately 96 amino acid residues in length (Colonna and Samaridis, 1995;Wagtmann, et al., 1995). The D1 domain comprises of 102 amino acid residues in case of Type I KIR2D and KIR3D proteins; while the length of the D2 domain is 98 residues in case of all KIR proteins (Colonna and Samaridis, 1995) (Figure 5). The stem region is 24 residues long in most KIR proteins. However, its length is restricted to only seven residues in the divergent KIR3DL3 protein (Torkar, et al., 1998). In general, 20 amino acid residues are present in the transmembrane region of most of the KIR proteins, with the exception in case of KIR2DL1 and KIR2DL2, wherein the region is one residue shorter. This may have resulted due to a three base pair deletion in the exon 7 (Colonna and Samaridis, 1995;Wagtmann, et al., 1995). Finally, the number
Page | 25 of the amino acids in the cytoplasmic region of KIR proteins vary widely, ranging from 23 residues in some KIR3DS1 alleles to 116 residues long in KIR2DL4 proteins (Dohring, et al., 1996;Long, et al., 1997;Selvakumar, et al., 1997).
Figure 5: Approximate KIR protein domain and region lengths. The length of each domain or region is shown in digits above their corresponding boxes. Adapted from IPD KIR database (Robinson, et al., 2010).
The structural folding of three KIR proteins, KIR2DL1-3 revealed that D1 and D2 domains contain 40% sequence similarity, suggesting the domain duplication (Boyington, et al., 2000). It was further noted that the region flanked by the two Ig-like domains act as ligand binding region which interacts with HLA molecules (Boyington, et al., 2000). Chapman et al. 2003 showed that this hinge region is stabilized by a highly conserved inter-domain hydrophobic core (Chapman, et al., 2000).
2.3.3. KIR gene organization
The KIR locus representing a family of highly polymorphic genes maps to chromosome19q13.4 and is located within the broad region of Leukocyte Receptor Complex (LRC; 1 Mb) (Trowsdale, et al., 2001). These genes are encoded in a head to tail fashion within a 150 kb stretch of DNA of the LRC with the length of each gene varying approximately between 4 to16 kb as shown in (Figure 6) (Uhrberg, et al., 1997).
Page | 26 The loci are separated from one another by an approximately 2 kb DNA sequence, except for a 14 kb sequence situated upstream from KIR2DS4 (Wilson, et al., 2000).
Figure 6: Location of KIR genes within the Leukocyte Receptor Complex (LRC). Adapted from IPD KIR database(Robinson, et al., 2010).
There is little probability of two individuals inheriting the same KIR genotype. Once a NK cell is committed in expressing a particular combination of KIR genes, that pattern remains stable through time and cell divisions (Farag, et al., 2003). Different combinations of expressed receptors as well as different clonal number variations combine to form the heterogeneous repertoire (Kubota, et al., 1999). Expression of receptors does not seem to be random, with the entire KIR genotype being expressed selectively on all NK cells (Shilling, et al., 2002). Diversity at the locus may be the result of selection pressure and as such has been proposed to mimic HLA loci drift.
2.3.4. KIR Exon/Intron arrangement
All the KIR genes have a fairly consistent organization of the exon–intron structures and follow a basic pattern of arrangement. KIR proteins are encoded by eight exons in 2D
Page | 27 receptors and nine exons in 3D receptors which illustrates the similarities between these two receptors (Wilson, et al., 1997) (Figure 7). The first two exons, starting from the N- terminus, encode the signal sequence. These are followed by the next three exons (exons 3-5), each of which corresponds to a single Ig domain starting from the N-terminus i.e. D0, D1, and D2, respectively. The linker and transmembrane regions are each encoded by a single exon (6 and 7). The cytoplasmic domain is encoded by two final exons with the number of amino acids varying from 23 amino acids in 3DS1 receptors to 116 amino acids in 2LD4 receptors (Trowsdale, et al., 2001;Wilson, et al., 2000;Wilson, et al., 1997).
Two domain KIR genes are further categorized as Type I and Type II based on their exon content and the Ig-like domains they express (Figure 7). Type I 2DKIR genes include KIR2DL1, 2DL2 /3, and all 2DS genes (Vilches and Parham, 2002). All NK cells, at least express a single type 1 KIR2D molecule (Watzl, et al., 2000). They have identical exon- intron arrangement to those genes encoding three domain KIR molecules except that their exon 3 is a pseudoexon,- which although remains in-frame but is eventually spliced out, possibly due to a 3 bp deletion (Vilches and Parham, 2002). This results in the absence of D0 domain in the protein products of Type I KIR2D genes (Vilches, et al., 2000). KIR2DP1, has the same exon content to that type I KIR2D genes but a single base pair deletion in exon 4, results in a frame shift and consequently producing a stop codon (Vilches and Parham, 2002) (Figure 7).
The Type 2 two-domain KIR genes include 2DL4, 2DL5A, and 2DL5B (Vilches and Parham, 2002). Exon 4 is completely absent in these genes (Selvakumar, et al., 1997). and therefore, their protein products lack D1 domains (Selvakumar, et al., 1997).
Three domain KIR genes are characterized by the complete presence of exons 3, 4 and 5 resulting in the expression of all the Ig-like domains. Unlike other KIR3D genes, exon 6 is missing in case of KIR3DL3 gene. Pseudogene KIR3DP1 contain only one leader sequence (other receptors contain two) due to a 1.5 kb deletion of exon 2 which results in its subsequent removal (Figure 7). Molecular characteristics of all the KIR genes have been described in the next section.
Page | 28 Figure 7: Organization of KIR genes. The coding regions of the exons are represented as blue boxes; their size in base pairs is shown in digits above them. The pseudoexon 3 and the deleted KIR3DP1 exon 2 are shown in red. Adapted from IPD KIR database (Robinson, et al., 2010).
Page | 29 2.3.5. KIR Molecular Characteristics
2.3.5.1. Two domain KIRs
2.3.5.1.1. KIR2DL1
It is recognized by the monoclonal antibodies EB6 or HP-3E4 both of which also react with 2DS1 (Melero, et al., 1994;Moretta, et al., 1993). KIR2DL1 gene is a component of the ‘A’ haplotype, the most common haplotype in nearly all populations around the World (Witt, et al., 1999). At present, sequences of 15 alleles are publicly available. Recombination between 2DL1 and 2DS1 may have resulted in the formation 2DL1*004 (Shilling, et al., 1998). Ligands for 2DL1 are HLA-Cw molecules that have Asn77 and Lys80 (Winter, et al., 1998). The crystal structure of KIR2DL1 in complex with HLACw4 has been elucidated (Fan, et al., 2001). According to a recent report, the phosphorylation of individual residues within the peptide can influence the binding of KIR2DL1 with MHC (Betser-Cohen, et al., 2006).
2.3.5.1.2. KIR2DL2/3
KIR2DL2 and KIR2DL3 are members of Type 1 KIR2D receptor family (Ferrini, et al., 1994;Moretta, et al., 1990). Ligands for 2DL2 and 2DL3 are HLA-Cw molecules that have Ser77 and Asn80. Monoclonal antibodies GL183 (CD158b) and CH-L recognize 2DL2 as well as 2DL3 and 2DS2 (Ferrini, et al., 1994;Moretta, et al., 1990). The crystal structure of KIR2DL2 and HLA-Cw3 complex has been clarified (Boyington, et al., 2000).
2.3.5.1.3. KIR2DL4
KIR2DL4 is a member of the Type 2 subfamily of KIR2D receptors. 2DL4 may transmit inhibitory or stimulatory, or both types of signals. 2DL4 probably binds to HLA-G (Rajagopalan and Long, 1999) which awaits further supportive evidences. Being a framework locus, KIR2DL4 is present on most haplotypes. 2DL4 is the only KIR gene having homologues in all primate species (Grendell, et al., 2001;Guethlein, et al., 2002). Evidences suggested that despite being a long-tailed receptor, the structure of KIR2DL4
Page | 30 is such that it also has an activating function (Kikuchi-Maki, et al., 2003). Unlike other KIRs with inhibitory function (who have two cytoplasmic inhibitory motifs), KIR2DL4 contains only one inhibitory motif which has been shown to retain inhibitory potential. Furthermore, 2DL4 has a charged arginine residue in the transmembrane region, which is a feature of activating KIR molecules and therefore, this receptor has an active role in target cell lysis. Evidences also suggested the association of FcRI-γ with KIR2DL4 to promote cell surface expression and signal transduction function (Kikuchi-Maki, et al., 2005).
2.3.5.1.4. KIR2DL5
KIR2DL5 is the most recently KIR to be described (Vilches, et al., 2000). KIR2DL5 is a member of Type 2 subfamily of KIR2D receptors, transmitting inhibitory signals. While the ligand for KIR2DL5 is unknown, 2DL5 is a common constituent of the ‘B’ haplotype along with KIR2DL2 and KIR2DS2 (Uhrberg, et al., 2002).
2DL5 is the locus that formed the extra RFLP fragment, which was originally reported by Uhrberg and coworkers while defining the KIR `B' haplotype (Uhrberg, et al., 1997;Vilches and Parham, 2002). KIR2DL5 comprises of two paralogous loci, namely 2DL5A and 2DL5B. KIR2DL5A is located in the telomeric region and has one known allele, 2DL5A*001. On the other hand, KIR2DL5B is found in the centromeric KIR region. This locus has three alleles out of which 2DL5B*002 remain unexpressed.
2.3.5.1.5. KIR2DS1
KIR2DS1 is a type 1 KIR2D receptor which transmit signal owing to its non-covalent association with the DAP-12 adaptor molecule. HLA-C molecules having Asn77 and Lys80 act as ligand for KIR2DS1 molecules. Monoclonal Antibodies EB6 (CD158a) and HP3E4 react with 2DS1, both of which also recognize 2DL1 (Melero, et al., 1994;Moretta, et al., 1993).
Page | 31 2.3.5.1.6. KIR2DS2
KIR2DS2 is another member of the Type 1 subfamily of KIR2D receptors. They bind with HLA-C molecules that have Ser77 and Asn80 residues. Like other activating KIR molecules, they signal via ITAM-bearing adaptor molecule, DAP-12. Similar to 2DL2 and 2DL3, they are also recognized by monoclonal antibodies GL183 (CD158b) and CH- L (Ferrini, et al., 1994;Moretta, et al., 1990).
2.3.5.1.7. KIR2DS3 and KIR2DS5
Both KIR2DS3 and KIR2DS5 are Type 1 KIR2D receptors. No ligands have been reported for either of these receptors. Its ligand is unknown and both the receptors signal via DAP-12 adaptor protein.
2.3.5.1.8. KIR2DS4
KIR2DS4 is a member of Type 1 KIR2D receptor. Like other activating receptors, they also signal via DAP-12 molecule. KIR2DS4 has been reported to interact with HLA-Cw3 and Cw4 (Campbell, et al., 1998), wherein most of the interactions are weaker compared to those seen between inhibitory receptors and HLA-C. Most of the interactions described have been weaker than the interaction seen between inhibitory receptors and HLA-C. A recent report demonstrated the binding of KIR2DL4 to ligands found on melanoma cell- lines (Katz, et al., 2004).
An allelic variant of KIR2DS4 has a 22 nucleotide deletion in the coding sequence that leads to a truncated protein due to a premature termination codon following the first amino acid of the putative transmembrane domain (Maxwell, et al., 2002). This variant, initially termed as KIR1D, has a homologue in rhesus monkey. KIR1D is now known as KIR2DS4*003 (Hsu, et al., 2002;Maxwell, et al., 2002).
Page | 32 2.3.5.2. Three domain KIRs
2.3.5.2.1. KIR3DL1/3DS1
KIR3DL1 contains three Ig domains. It triggers inhibitory signal via ITIM motifs. 3DL1 interacts with HLA-B molecules that contain a Bw4 motif. Moreover, HLA-A allotypes carrying the Bw4 motif (HLA-A23, A24, A25 and A32), also act as ligands. Monoclonal antibodies DX9 and Z27 bind to variants of 3DL1with varying degrees of affinity. Based on phenotype detection by the antibody DX9, the eight available allotypes of KIR3DL1 can be divided into three groups corresponding to low, bimodal and high binding affinities respectively (Gardiner, et al., 2001). Furthermore, it was found that 3DL1*004 does not bind DX9. Therefore, it was postulated that binding patterns are predictably determined by the alleles of KIR3DL1 an individual possesses (Gardiner, et al., 2001).
In case of individuals heterozygous for the two 3DL1 alleles, four distinct populations of NK cells are raised due to differential expression: those positive for either allele alone, those positive for both alleles and those negative for both alleles. Furthermore, different binding patterns were also observed between individuals of the same genotype, thereby suggesting the influence of variations in the promoter regions.
In addition to the variable cell surface expression, different alleles also bind Bw4 with varying strength. As a result, different alleles display inhibitory functions differently as is found in the case of 3DL1*002, which is a much stronger inhibitory receptor than 3DL1*007 (Carr, et al., 2005).
KIR3DS1 was originally defined separately from KIR3DL1, but is now considered an allele of 3DL1, wherein long cytoplasmic tail was replaced by the shorter counterpart and associated DAP-12 molecule. No reports of 3DS1 binding Bw4 allotypes were found till date. This may result due to 6-12 amino acid difference in the structures of the Ig-like domains of 3DS1 and 3DL1, which may affect the binding affinities. It was also found that neither of the monoclonal antibodies DX9 and Z27 binds to 3DS1. 3DL1 and 3DS1 can be found occasionally on the same haplotype (Williams, et al., 2003).
Page | 33 2.3.5.2.2. KIR3DL2
KIR3DL2 is also a three-Ig-domain inhibitory receptor. Monoclonal antibody DX31 interacts with 3DL2. Being a framework locus, KIR3DL2 is present on most of the KIR. HLA-A alleles act as their ligands. Hansasuta and co-workers have demonstrated that HLA-A3 and HLAA11 tetramers undergo peptide specific interaction with KIR3DL2 (Hansasuta, et al., 2004).
2.3.5.2.3. KIR3DL3
KIR3DL3 is a three-Ig-domain receptor with unknown ligand. The 3DL3 gene has close resemblance with other 3D genes, except that exon 6 (stalk region) is missing in case of KIR3DL3. Being a framework locus, 3DL3 is present on most of the KIR haplotypes. The KIR3DL3 gene is the most centromeric of KIR genes identified (Hsu, et al., 2002). Originally thought to be a pseudogene, there is confirmed evidence that the mRNA is
detectable in CD56bright cells (Trundley, et al., 2006).
2.3.6. KIR haplotypes
Being diploid, each autosomal gene is generally present in two copies in humans, one per chromosome. However, the KIR gene family violates this basic rule due to deletion or duplication. The number of KIR genes may differ substantially between the two chromosomes and so also their types (Rajalingam, 2011).
Furthermore, KIR gene constitutions vary greatly between the individuals. The organized arrangements of inhibitory and activating KIR genes constitute a particular haplotype (Shilling, et al., 2002). More than 100 haplotype profiles have been described till date and this number is expected to grow with the discovery of new haplotypes (Gomez- Lozano, et al., 2002;Hsu, et al., 2002;Uhrberg, et al., 2002).
Two major groups of KIR haplotypes were defined based on their KIR gene content, namely A and B (Wende, et al., 1999). Originally, Restriction Fragment Length Polymorphism (RFLP) was used to distinguish between these two haplotype groups, wherein the presence of a ~24 Kb Hind III fragment defined the group B haplotype. This
Page | 34 fragment was later correlated to the presence of the KIR2DL5 gene (Uhrberg, et al., 1997;Vilches and Parham, 2002). However, recently these haplotype groups are distinguished by the number of activating and inhibitory KIR genes they possess. Group B haplotypes possess different combinations of KIR2DL5, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS5 and KIR3DS1 genes, whereas group A haplotypes possess a single activating gene, KIR2DS4, as well as four inhibitory genes namely KIR2DL1, KIR2DL3, KIR3DL1 and KIR3DL2, which encode proteins having HLA class-I specificities (Marsh, et al., 2003) The A-haplotype is relatively simple and conserved, whereas group B haplotypes are expansive and their gene content vary greatly from haplotype to haplotype (Shilling, et al., 2002). Although, group A haplotypes show little variation in gene content but they show extensive variability on the allelic level (Shilling, et al., 2002). There are numerous people around the globe with no activating receptors in their haplotypes. However, no individual has been identified for which no inhibitory receptors are expressed (Thananchai, et al., 2007). This is due to the fact that inhibitory receptors are indispensable compared to the activating receptors, wherein lack of inhibitory receptors would result in limited or no inhibition of cytotoxic effects (Burshtyn, et al., 2000).
The centromeric and telomeric ends of all the haplotypes are flanked by KIR3DL3 and KIR3DL2 respectively (Wilson, et al., 1997). The centromeric half is delimited by 3DL3 at the 5´-end and 3DP1 at the 3´-end, while the telomeric half is delimited by 2DL4 at the 5´-end and 3DL2 at the 3´-end. These four genes together constitute the framework loci that are present in most if not all of the haplotypes (Martin, et al., 2000;Vilches and Parham, 2002;Wilson, et al., 2000). Each KIR haplotype is divided into two halves: the centromeric and the telomeric halves respectively (Wilson, et al., 2000) by a stretch of 14 kb DNA enriched with L1 repeats placed between 3DP1 and 2DL4 divides the KIR haplotype into two halves: (Figure 8)
While assigning genes to a specific haplotype, following assumptions were made: (i) the framework genes (KIR3DL3, 2DL4, 3DL2 and 3DP1) are present in all the haplotypes; (ii) KIR3DL1 and 3DS1 are likely equivalent to alleles of the same locus at the telomeric
Page | 35 half and (iii) KIR2DL2 or KIR2DL3 segregate as alleles of a single locus of the centromeric half (Middleton, et al., 2007;Norman, et al., 2002;Norman, et al., 2013;Norman, et al., 2001). These two loci are present in almost all haplotypes. In other words, every individual KIR genome possesses either 2DL2 or 2DL3, and 3DL1 or 3DS1 (Figure 8).
Figure 8: Gene content of KIR haplotypes. Haplotype 1 represents group-A KIR haplotype and the remaining ones exemplify group-B haplotypes (haplotypes 2-7). The framework genes are shown in red boxes; activating KIR genes in yellow boxes; and those encoding inhibitory receptors are shown in blue boxes. The pseudogenes KIR2DP1 and 3DP1 do not express a receptor. This figure has been adapted from Rajalingam, 2011 (Rajalingam, 2011).
Immunogenetic analyses revealed that both group A and group B haplotypes show significantly different distribution among different ethnic populations. All human populations have both group-A and -B haplotypes, but their distribution varies considerably among different populations. Almost equal distribution of A and B haplotypes were found in Caucasians and Africans population (Uhrberg, et al., 1997;Yawata, et al., 2002). Homozygotes for A haplotypes (AA genotypes) are frequently found in the Northeast Asians, which include Chinese, Japanese, and Koreans (Jiang, et al., 2005;Whang, et al., 2005;Yawata, et al., 2002). Conversely, Bx genotypes
Page | 36 (either AB or BB) are found to be dominant in the American natives (Ewerton, et al., 2007;Gendzekhadze, et al., 2006), Australia (Toneva, et al., 2001), and India (Kulkarni, et al., 2008;Rajalingam, et al., 2008;Rajalingam, et al., 2002).
Linkage disequilibrium (LD) patterns of KIR loci have been studied for both the A and B-haplotypes (Witt, et al., 1999). The LD analyses of the centromeric and telomeric regions clearly indicate the evolutionary histories of these regions, which may have undergone different gene assortment and have been inherited separately during evolution. Members of group A haplotype needs to display both the centromeric (Cen A) and telomeric (Tel A) genotype organization A/A (Uhrberg, et al., 1997). Considering the inheritance pattern of KIR genes (which is the result of distinct diploid combinations of genotypes), it can be visualized that the haplotype A results only in case of Cen A/A and Tel A/A combinations, i.e., both parents pass Cen A and Tel A to their offspring who then possess CenAA/TelAA genotype (Cooley, et al., 2010). In case of A haplotype, it was found that KIR2DL3, KIR2DP1, KIR2DL1 loci are typically present in the centromeric portion while a single activating gene (KIR2DS4) could be found in its telomeric region together with KIR3DL1 (Figure 9). On the contrary, different B haplotypes may have mixed “B/x” genotypes (CenAA/TelAB, CenAB/TelAA, CenAA/TelBB, CenAB/TelAB, CenBB/TelAA, CenAB/TelBB, or CenBB/TelAB), which display all genes typical of group B, plus at least an additional KIR group A gene, or may have a pure B/B genotype without any A genes (CenBB/TelBB) (Cooley, et al., 2010;Gourraud, et al., 2010;Hsu, et al., 2002;Middleton and Gonzelez, 2010;Pyo, et al., 2010). In case of B haplotype associated KIR genes, it was noted that the unexpressed KIR2DL5B variant is usually present together with the KIR2DS3 while KIR2DL5A is frequently present with KIR2DS5 (Figure 9).
Page | 37 Figure 9: Centromeric and telomeric halves of KIR haplotypes. A 14 kb DNA stretch with numerous L1 repeats interconnects KIR3DP1 and KIR2DL4, thereby dividing the KIR haplotypes into two halves. The centromeric half is flanked by 3DL3 and 3DP1, while 2DL4 and 3DL2 delimit the telomeric half. The framework genes are shown in red boxes; activating KIR genes in yellow boxes; and those encoding inhibitory receptors are shown in blue boxes. The pseudogenes KIR2DP1 and 3DP1 do not express a receptor. This figure has been adapted from Rajalingam, 2011 (Rajalingam, 2011).
It is interesting to note that in group A, the haplotype diversity is primarily associated at the allelic polymorphism. In particular, an analysis based on the genotype of only the four KIR2DL1, 2DL3, 3DL1 and 3DL2 loci showed that at least 22 different haplotype A members with only 0.24% of unrelated individuals shared an identical genotype (Shilling, et al., 2002). No LD has been noted for the A-haplotype at the gene level but patterns have been noted between different alleles (Shilling, et al., 2002). On the contrary, the group B haplotypes have greater diversity in gene content exhibiting only a moderate allelic polymorphism. These haplotypes are characterized by strong LD between many KIR genes demonstrating continual drift within this haplotype (Witt, et al., 1999). All B haplotypes except B1, have more KIR genes than group A and the gene content is biased towards stimulatory KIR genes, whose number may vary between two to five (Figure 10).
Page | 38 Figure 10: The genomic organization of common haplotypes in the Caucasoid population. The centromeric half is flanked by 3DL3 and 3DP1, while 2DL4 and 3DL2 delimit the telomeric half. The framework genes are shown in red boxes; activating KIR genes in yellow boxes; and those encoding inhibitory receptors are shown in blue boxes. The pseudogenes KIR2DP1 and 3DP1 do not express a receptor. The recombination model is based on published data (Hsu, et al., 2002;Yawata, et al., 2002). This figure has been adapted from Uhrberg, 2005 (Uhrberg, 2005).
Inheritance of haplotypes with different gene content from mother and father (A+A, A+B, or B+B) generates extraordinary KIR diversity in humans (Figure 11). As for example, inheritance of two A haplotypes, one from each parent, result in individuals with seven functional KIR genes. In contrast, individuals who received both A and B haplotypes from their parents, may possess all the 14 KIR genes. More than 300 KIR genotypes have been reported from different studies conducted throughout the World, each having their unique KIR gene content. Furthermore, each population carries a distinct gene content profile (Ashouri, et al., 2009;Yawata, et al., 2002).
Page | 39 All human populations possess both group-A and -B haplotypes, but vary considerably in their distribution. Independent segregation of KIR and HLA gene families may result in individuals who expresses KIR receptors but lack cognate HLA class- I ligands, and vice versa. This phenomenon contributes enormously towards diversifying human genome by altering the number and type of KIR-HLA gene combinations, which in turn can also modulate disease outcomes (Du, et al., 2007) (Figure 11).
2.3.7. KIR diversification mechanisms
2.3.7.1. Non-reciprocal crossing over
New genes evolve frequently by duplication of an ancestral founder gene and sometimes remain together as a cluster of structurally and functionally related, but evolutionarily diverged genes, thereby forming a gene family (Nei, et al., 1997). Such a phenomenon of gene expansion is thought to be initiated by unequal crossing-over events, leading to non- reciprocal recombination between non-allelic genes. A similar scenario was observed in the case of KIR gene family, wherein non-reciprocal recombination led to contractions and expansions of the family (Figure 12). However, most of the gene families might either keep a novel haplotype or go back to the ancestral one, whichever has greater selective advantage. In contrast, the KIR locus has a tendency to conserve divergent recombination products along with the already established ancestral ones, thereby propagating the novel haplotypes. The selective advantage of this feature of the KIR haplotypes can at best be speculative at present. A recent study postulated non-reciprocal recombination while describing a novel extended haplotype containing a region of three duplicated KIR genes (Martin, et al., 2003), wherein a hybrid gene was generated at recombining the promoter from KIR2DL5A genes and the coding region from the KIR3DP1 pseudogene. Later, it was shown that the resulting new allele, named KIR3DP1*004, is in fact transcribed and might be properly expressed as soluble KIR in NK cells (Gomez-Lozano, et al., 2005). Inspite of having the dissimilar gene content, both the haplotypes were maintained in a population (Martin, et al., 2003;Norman, et al., 2002).
Page | 40 Page | 41 Figure 11: Mechanism of diversification of KIR-HLA combinations. The 4X4 table illustrates all four possible types of gametes from each parent assorted with different combination of one KIR haplotype and one HLA haplotype. Random associations of gametes result in zygotes having one of the 16 possible KIR and HLA combinations, producing substantial diversity between offspring in the number and type of inhibitory KIR-HLA combinations and activating KIR genes inherited. This figure was adapted from Rajalingam, 2011 (Rajalingam, 2011).
In case of the KIR locus, alignment of truly homologous sequences during prophase of the first meiotic division is frequently restricted to the four framework genes only. However, pairing of nonallelic KIR genes is likely to happen when degree of similarity between the KIR genes (>90%) approaches that of allelic variants in other genes, for example HLA class- I (Parham, et al., 1995). Thus, homologous recombination of KIR loci during meiosis likely involves the pairing of nonallelic genes. Moreover, pairing of non allelic genes lead to the exclusion of KIR genes (usually from the longer haplotype) by looping out, which could not find any opposite sequence for alignment. When compared to the non-reciprocal recombination event involving the same two haplotypes (Gomez-Lozano, et al., 2005), it was seen that there is no principal distinction between the molecular events leading to the reciprocal or non-reciprocal recombination in the KIR locus. In both the cases, nonallelic genes undergo pairings if the involved haplotypes have dissimilar gene contents.
2.3.7.2. Reciprocal crossing over
The KIR locus is characterized by a remarkable modular architecture, wherein each KIR gene represents a highly similar building block arranged in a head-to tail fashion and separated by only 1–2 kb from the next KIR gene with an exception of 14-kb sequence largely made up of DNA repeat elements. This is found in the central framework region between KIR3DP1 and KIR2DL4 (Wilson, et al., 2000). The divergent structure of this region lowers the probability of mispairing with a non-homologous sequence region in the KIR locus. Instead, this region may provide a central anchor, which facilitates and
Page | 42 also stabilizes the proper alignment of divergent KIR haplotypes during meiosis. Although the central framework region represses non-reciprocal recombination, but it might be involved in the reciprocal crossing-over events (Figure 12). On careful comparison of the most frequent haplotypes of the most frequent haplotypes in the Caucasoid population, the KIR locus can be distinguished into two separate gene clusters – a centromeric and a telomeric one – which are separated by the very same unique region of 14 Kb upstream of KIR2DL4 (Hsu, et al., 2002;Yawata, et al., 2002). LD values were much stronger between the KIR genes within a cluster than those located in different clusters. Three different centromeric (C1–C3) and three different telomeric (T1– T3) clusters generate nine possible combinations, out of which eight combinations are frequently found and are sufficient to explain the structural origin of about 90% of all the haplotypes found in the Caucasoid population (Hsu, et al., 2002;Uhrberg, et al., 2002). Furthermore, it is also possible to integrate other less frequent haplotypes in this model, if additional centromeric and telomeric clusters are postulated (Yawata, et al., 2002). Therefore it can be said that homologous recombination contribute substantially towards the KIR haplotype diversity by recombining the two halves of the KIR locus in a cut and- paste-like process.
Figure 12: Reciprocal versus non-reciprocal recombination. (A) Hypothetical model representing alignment of KIR B1 and B6 haplotypes during meiotic
Page | 43 recombination in the prophase stage. In this model, it has been assumed that the chromatids are first aligned over the full length of the shorter haplotype. This is followed by the pairing of all homologous genes that are present on both haplotypes following which, pairing of the most similar nonallelic genes occur. (B) A hypothetical model representing nonreciprocal crossing- over event between KIR2DL5A and KIR3DP1v (Gomez-Lozano, et al., 2005). This figure has been adapted from Uhrberg, 2005 (Uhrberg, 2005).
2.3.8. KIR allelic variability
Variability at the KIR locus on chromosome 19 does not remain restricted only to the substantial variation in gene content across the haplotypes. Indeed, each KIR gene adds to the variability of the locus by exhibiting considerable nucleotide sequence polymorphism (Garcia, et al., 2003;Middleton, et al., 2007;Shilling, et al., 2002). Presently, 614 polymorphic nucleotide sequences corresponding to 321 distinct protein molecules have been deposited in a centralized repository called IPD-KIR database (http://www.ebi.ac.uk/ipd/kir/) (Release 2.4.0, 15 April 2011). Several workers on KIR gene suggested the influence of sequence polymorphism not only on KIR expression and ligand binding but also on the functional capacity of the molecule (Carr, et al., 2005;Gardiner, et al., 2001;Parham, et al., 2011;Winter, et al., 1998;Yawata, et al., 2006). It has been found that the allelic variants of each inhibitory KIR gene display amino acid substitutions, mostly at sites, which have no direct implications on HLA class- I ligand binding (Norman, et al., 2007;Vivian, et al., 2011). A number of these substitutions are shown to be the subject of positive selection, and thus the evolutionary pressure that drives KIR sequence polymorphism is presumably more than polymorphic HLA class- I recognition and possibly involves rapidly evolving pathogen recognition.
2.3.9. KIR-Ligand Interaction
KIR receptors present on human NK cells recognize specific HLA class- I molecules on target cells, which are the products of highly polymorphic MHC genes located on chromosome 6, thereby participating in the complex regulation of NK cell responses
Page | 44 (Figure 13). Therefore, expression of not only the receptor but also its ligand molecule is required in order to regulate NK cell activity.
Figure 13: The complex of KIR2DL (red) and HLA-Cw4-1 (blue) molecules along with the peptide (purple). The figure was adapted from Fan et al. 2001. (Fan, et al., 2001).
Each KIR molecule interacts directly with the distinct group of HLA alleles. In fact, NK- mediated responses are dependent on the avidity of KIR-HLA interaction around the amino acid residue 80 of the α1-helix of the HLA molecule. Thus, this α1-helix region is considered to be directly responsible for defining the different NK alloreactivities. Many KIR receptors have protein variants of HLA-C as their ligands. In particular, inhibitory KIR2DL1, KIR2DL2 and KIR2DL3 receptors and to a lesser extent, the activating KIR2DS1 and probably KIR2DS2 are able to discriminate between HLA-C1 and HLA- C2 alleles, which are essentially non-overlapping (Table 2). KIR2DL1 and KIR2DS1 (weaker) are specific for HLA-C alleles (C2-group) sharing V76, N77 and K80 residues that include the majority of HLA-Cw2, 4, 5, 6 and some other alleles. In contrast, KIR2DL2 and to a lesser extent KIR2DL3 recognize HLA-C alleles (C1- group)
Page | 45 characterized by V76, S77 and N80 amino acids which are mainly defined by HLA-Cw1, 3, 7, 8 and some other alleles. In addition, some geographically localized rare HLA-B allotypes, namely B73 and B46, possess a functional C1 epitope, which have originated by recombination events and share amino acids 66–77 with HLA-Cw3 alleles. These rare HLA-B allotypes interact with KIR2DL2 and KIR2DL3 (Abi-Rached, et al., 2010;Biassoni, et al., 1995). Additionally, both KIR2DL2 and KIR2DL3 have also been found to show weak alloreactivities against some C2 allotypes, notably C*0501 and C*0202 (Moesta, et al., 2008;Pende, et al., 2009), probably due to the allelic differences within the C2 subgroup (Table 2) (Moesta, et al., 2008). Intriguingly, in humans at least 3 inhibitory and 2 activating receptors are able to sense HLA dimorphisms encompassing all the known HLA-C alleles.
It is a known fact that HLA-C evolved recently in humans and great apes like the orangutans. Apparently, interactions between KIR and HLA-C1 loci evolved before the KIR-C2 interactions, since neither HLA-C2 alleles nor C2-specific KIRs could be detected in the Orangutans (Older Aguilar, et al., 2010). KIR3DL1 loci encode specific receptors for HLA-B alleles having the Bw4 epitope corresponding to amino acids 77-83 on the HLA class- I α1-helix with the exception of HLA-B*13:01 and HLA-B*13:02 (Foley, et al., 2008). In addition, KIR3DL1 loci also encodes receptors for some HLA-A alleles characterized by Bw4-supertypic specificity like A23, A24, and A32 (Stern, et al., 2008;Thananchai, et al., 2007). It has also been suggested that KIR2DL1 may recognize HLA-A*25 alleles (Foley, et al., 2008) although these data have not been confirmed by others (Stern, et al., 2008). KIR3DL1 interacts strongly with the target cells expressing homozygous Bw4 alleles having Isoleucine-80, while weak interaction was seen in case of homozygous Bw4 alleles sharing Threonine 80 (Table 2). Although KIR3DL2 showed specificity for HLA-A3 and -A11 allotypes, but such interactions have a limited ability to inhibit NK-mediated lysis (Dohring, et al., 1996;Pende, et al., 1996).
Among the activating KIR receptors, only KIR2DS1 demonstrated the ability to bind directly to HLA ligands. However, the strength of interaction was found to be dependent on the dimorphism of amino acid residue 70 (Biassoni, et al., 1997). Although the role of
Page | 46 KIR3DS1 in HLA-B recognition has been hypothesized due to its ability to delay AIDS progression, but direct binding could not be demonstrated (Gillespie, et al., 2007;Martin, et al., 2002). However, recently a single KIR3DS1 allele (KIR3DS1*014), carrying glycine-138 instead than tryptophan, was found to have direct HLA-Bw4 binding capability (Norman, et al., 2007;O'Connor, et al., 2011) (Table 2). It was seen that non- synonymous mutations in the extracellular domain of the activating KIR molecules linked with the HLA-specific interactions are crucial to binding the affinity (Biassoni, et al., 1997;O'Connor, et al., 2011). It was found that the affinity of the KIR2DS2 for HLA- C1 ligand gets enhanced on experimental shuffling of residue 45 from tyrosine to phenylalanine, typical of 2DL2 receptor (Winter, et al., 1998). Such an outcome may be the result of the evolution followed by the selection pressure, since activating KIR may have evolved from the ancestral inhibitory receptors (Abi-Rached and Parham, 2005). Furthermore, it is also likely that the activating ones may have evolved to decrease the affinity of HLA recognition probably to prevent autoimmunity, but with time, they have acquired the potential to recognize the HLA class- I molecules presenting peptides of viral origin (Abi-Rached and Parham, 2005;Khakoo, et al., 2000;Vilches and Parham, 2002).
Class- I MHC tends to present peptides of ~9 amino acids in length which is positioned in their binding groove bounded by the α1- and α2- helices. The amino acid residue at position 8 of the peptide may be responsible for governing the KIR/HLA class-I interactions. Interestingly, this amino acid at position 8 is localized near the residue 80 amino acid of the α1-helix. In particular, the KIR2DL/HLA-C and KIR3DL1/HLA-Bw4 interactions are affected by the presence of residue 8 of the HLA peptide either having strong negative or positive charges (Malnati, et al., 1995;Peruzzi, et al., 1996;Rajagopalan and Long, 1997). The relevance of pathogen derived-peptide is known to be associated with the positive association of both the KIR3DS1 and Bw4 gene loci in HIV-infected subjects and thus suggesting a possible role for HIV-associated peptides (Stewart, et al., 2005). Pathogens present in the environment may have participated in the shaping of genetic loci of activating KIR, thus explaining the hypothesis of recurrent acquisition and loss of activating KIR loci during evolution. The KIR2DL4 receptor
Page | 47 binds to HLA-G molecules expressed on extravillous trophoblasts cells. Their interaction induces rapid production of IFN-γ, which in turn vascularize the maternal decidua, thereby providing blood to the placenta and respiratory gases and nutrients to the growing fetus (Kikuchi-Maki, et al., 2003;Moffett-King, 2002;Rajagopalan, et al., 2006;Rajagopalan and Long, 1999). Besides having activation function, KIR2DL4 also exhibits inhibitory function owing to the presence of a single ITIM motif in the cytoplasmic domain (Faure and Long, 2002;Ponte, et al., 1999).
Previously published reports suggested that KIR molecules interact with their ligands to influence the outcome of several human diseases. It is therefore necessary to define the KIR-ligand associations either in the positive or negative responses to several diseases and pathologic states by the advanced molecular techniques.
Table 2: Ligands and function of the 14 structural KIR genes
KIR Ligand Function 2DL1 HLA-C2Lys80 Inhibition 2DS1 HLA-C2Lys80(Weak) Activation 2DL2 HLA-C1Asn80, HLA-B*73, -B*46,some HLA-C2 Inhibition 2DL3 HLA-C1Asn80, HLA-B*73, -B*46 Inhibition 2DS2 HLA-C1Asn80 (weak) Activation Activation 2DL4 HLA-G Inhibition? 2DL5A/B Unknown Inhibition 2DS3 Unknown Activation 2DS4 Various HLA-C1 and HLA-C2 alleles, HLA-A*11 Activation 2DS5 Unknown Activation HLA-Bw4(I80>T80) except HLA-B*13:01/02 3DL1 HLA-A*23, -A*24, A*32 Inhibition HLA-A*25 3DS1 HLA-Bw4? Activation 3DL2 HLA-A*3, -A*11 (weak) Inhibition 3DL3 Unknown Inhibition
I80>T80: indicates the affinity of interaction with KIR3DL1.
Page | 48 2.3.10. KIR signaling
Interaction of KIR with HLA molecules may result in the following two outcomes: the first being “auto tolerant”, while the second is known as “auto aggression”, whereby target cells are programmed for cell death (Hsu, et al., 2002). Furthermore, there are two possible reasons for the induced “auto aggression”, the first being a consequence of “missing self” due to the reduced HLA expression. The second is attributed to the expression of abnormal or alternate HLA ligands, also known as “missing ligand” hypothesis (Ljunggren and Karre, 1990;Shimizu and DeMars, 1989) (Figure 14).
A single KIR/HLA interaction does not determine the cell destruction, but rather a balanced of the inhibitory and activating KIR responses result in either destruction or protection of the target (Figure 14). In certain circumstances, it can be seen that the inhibitory signals can override activatory signals as a result of higher binding affinity of the inhibitory receptors, thereby providing increased protection against “auto-aggression” (Biassoni, et al., 1997;Vales-Gomez, et al., 1998).
.
Figure 14: NK cell interaction with target cells is modulated by KIRs(a) When inhibitory KIR receptors are bound by ligand (class I MHC) activating processes are inhibited (b) When the ligand is absent, activation continues resulting in target cell kill.
Page | 49 2.3.10.1. NK receptors associated with ITAM-bearing transmembrane adaptor proteins
The signaling pathways used by the receptors on NK cells share many common features with the immune receptors that are expressed on B and T lymphocytes. Numerous receptors that are found on NK cells, transmit signal via few biochemical pathways that have been hired by most of the leukocytes (Cerwenka and Lanier, 2001;Colucci, et al., 2002;Diefenbach and Raulet, 2002;Moretta, et al., 2001). The activating NK cell receptors are characterized by the division of labor, wherein independent protein subunits are responsible for ligand recognition and signal transduction. These subunits are then assembled into the functional receptor complex. The NK cell receptors transmit signals by small transmembrane adaptor proteins that possess ITAMs in their cytoplasmic tails. NK cells express CD3ζ, FcεRIγ and DAP12 adaptor proteins. CD3ζ and FcεRIγ can be expressed either as homodimers or as heterodimers while DAP12 is exclusively expressed as disulfide-bonded homodimers. Activating receptors interact with these ITAM-containing adaptor proteins by using their transmembrane regions, often stabilized by salt bridges that are formed by pairs of of oppositely charged amino acids. Numerous human NK cell receptors pair with DAP12 (e.g. in humans several activating KIRs, CD94/NKG2C) and NKp44, CD3ζ or FcεRIγ (e.g. NKp30, NKp46) and CD16 in humans. is CD16. The biochemical events involved in the stimulation of most of the ITAM-based NK receptors are not well characterized, but are predicted to have similarity to the activation pathway of the most extensively studied activating NK cell receptor, CD16.
ITAM-bearing receptor complexes interacts with ligands, to recruit and activate tyrosine kinases Syk and ZAP70, both of which are found on all the NK cells. The ITAM domains contain a (Y x x [L/V] x [6-8] Y x x L/V) repeat, valine (V) or luecine (L) is sub-repeated 6-8 times within one ITAM. X stands for any amino acid, which has no functional role within the signalling process (Futterer, et al., 1998). ITAMs encoded within DAP10 or DAP12 proteins allow for the transphosphorylation of the tyrosine through the signalling process, once receptor clustering has occurred (Maxwell, et al., 1999). Altogether the
Page | 50 outcomes differ in case of DAP-10 and DAP-12 signaling, wherein signaling via DAP-10 results only in cytotoxicity and signaling via DAP-12 results in cytokine secretion and cytotoxicity (Billadeau, et al., 2003;Zompi, et al., 2003). ITAMs are probably phosphorylated by a src family kinase. Phosphorylated ITAMs provide docking sites for zeta chain-associated protein 70 kilo Dalton (ZAP70) and spleen tyrosine kinase (Syk)- family tyrosine kinases via the Src homology 2 (SH2) domain, both of which are expressed by all NK cells (Brumbaugh, et al., 1997;Leibson, 1997). Kinases activated by ITAMs lead to the recruitment and activation of downstream elements such as phospholipase C-γ (PLC- γ) and growth factor receptor bound 2 (Grb2). Both these cascade proteins have key role to play in the release of stored calcium, ultimately resulting in polarization and extocytosis of granules containg lytic enzymes (Maxwell, et al., 1999). Other downstream elements that undergo phosphorylation include SLP-76, 3BP2, Shc, p85 PI3-kinase, c-Cbl, linker for the activation of T cells (LAT), Vav-1 and Vav-2. This is also followed by the elevation of intracellular Ca2+ levels, and the activation of Rho, Ras, p38 mitogen activated protein kinase (MAPK) and extracellular signal-regulated kinase (ERK). Ortaldo and colleagues (Ortaldo, et al., 2001) explored gene transcription which has been induced by activation through murine NK cell Ly49D– DAP12 receptor complex using microarray analysis. Of interest, the most potently induced genes were certain chemokines, such as macrophage-inflammatory protein-1a or –1b (MIP-1a, MIP-1b), that might be involved in recruitment of other leukocytes to sites of infection.
2.3.10.2. Inhibitory NK receptor signaling
Phosphorylated tyrosine then binds and activates SHP-1 and SHP-2 through their SH2 domains. Indeed, the functional mechanism of the inhibitory NK cell receptors were explored, understood and analyzed much earlier than that of the activation receptors (Veillette, et al., 2002). All of the inhibitory KIRs possess ITIMs in their cytoplasmic domains. Most inhibitory KIR possesses two ITIM domains, although one or more than two have been reported (Bruhns, et al., 1999). ITIMs are made up of the protein sequence (I/V x Y x x L) where hydrophobic isoleucine/ valine (I/V) is positioned one residue upstream of a tyrosine (Y) (Butcher, et al., 1998). A further leucine (L)
Page | 51 is located two residues downstream of the tyrosine where x represents any amino acid. This pattern is then repeated within the cytoplasmic domain depending on the number of ITIMs present (Tomasello, et al., 1998).
Upon engaging a MHC class- I ligand, these ITIMs are phosphorylated at the tyrosine residue which then binds and activates phosphatases to counteract cellular activation (Burshtyn, et al., 1996). The predominant phosphatases associated with the inhibitory KIRs are Src homology-2 (SH2) domain-containing phosphatases 1 or 2 (SHP-1 and SHP-2 respectively). The recruitment of phosphatases prevents the phosphorylation cascade via the removal of any phosphate added during activation within the receptor cluster, ultimately inhibiting NK cell activation (Campbell, et al., 1996). Substrate- trapping experiments documented Vav as a central substrate of SHP-1. An alternative to SHP-1 is SH-containing inositol 5-phosphatase (SHIP) which also functions to inhibit activated tyrosine ITAMs, depending on the method of activation used within the cascade (Wang, et al., 2002).
2.3.10.3. KIR2DL4 signaling
One exception is the KIR2DL4 receptor, which contains one ITIM within its cytoplasmic tail, and as such has an inhibitory effect (Cantoni, et al., 1998;Rajagopalan and Long, 1999). This receptor also contains a positively charged arginine residue in its transmembrane region which facilitates its interaction with DAP10 adaptor protein (Faure and Long, 2002;Yusa, et al., 2002). Thus, it has been reported that 2DL4 carries both inhibitory as well as activating functions depending onto which HLA it binds (Faure and Long, 2002;Yusa, et al., 2002).
2.3.11. KIR and Diseases
Gene families at KIR and HLA loci tend to segregate independent of one another. As a result, many individuals express KIR receptors for which they lack HLA class- I ligands, and vice versa and, thereby creating human diversity in KIR-HLA gene combinations that can modulate disease outcomes (Du, et al., 2007). However, evidences for coevolution of interacting KIR-HLA pairs exist at the population level (Gendzekhadze, et
Page | 52 al., 2009;Single, et al., 2007). NK cells lacking inhibitory receptors for self-MHC class- I molecules were hypo-responsive when compared to the NK cells that had a self-MHC class- I ligand (Kim, et al., 2005). The signals triggered upon interaction of inhibitory receptors to the specific self-MHC class-I ligand results in ‘licensing’ (Kim, et al., 2005;Yokoyama and Kim, 2006), ‘arming’ (Raulet and Vance, 2006), or ‘educating’ the NK cells, (Anfossi, et al., 2006), altogether adding to the functional competence of the NK cells. Therefore, it would not be a over statement to say that at least one inhibitory KIR+HLA interaction is necessary for NK cell functional development and for controlling NK cell responses in different disease conditions.
2.3.11.1. Infection
Viral infection downregulates HLA Class- I expression on the surface of infected cells to escape the attacks from cytotoxic T lymphocytes. Consequently, these class-I negative cells become potential targets for lysis by NK cells following the principle of “missing self” mechanism. The contribution of NK cells towards anti-viral immune responses can be justified by the fact that NK cell deficiencies or impaired activities in patients cause them to suffer from recurrent viral infections.
The HLA class-I alleles, B*27 and B*57, are widely known for providing protection against HIV infection. Both of them carry Bw4 epitope, which is a known ligand for KIR3DL1 and possibly for KIR3DS1. It has been proposed that HLA-Bw4I80 on HIV-1 infected target cells act as ligand for KIR3DS1 on NK cells. Their interaction allows NK cell to kill the infected target (Martin, et al., 2002). Furthermore, the KIR3DS1-HLA- Bw4I80 combination was also shown to provide protection from opportunistic infections, such as pneumocystis carinii pneumonia, cytomegalovirus (CMV) retinitis, and mycobacterium avium complex, and also correlate with lower viral load (Qi, et al., 2006). Surprisingly, it was also found that an interaction between the HLA-Bw4I80 and a subset of KIR3DL1 alleles with high inhibitory capacity demonstrated significant protection from AIDS progression and was associated with the lower mean viral loads (Martin, et al., 2002). It has been found to be more protective than the KIR3DS1/HLA-Bw4I80
Page | 53 interaction (Martin, et al., 2002). Both the activating and inhibitory KIRs should be efficiently engaged for protecting the host against viral infections (Rajalingam, 2011).
Presently, hepatitis C virus (HCV) is believed to have infected 130-170 million people around the World. Their infection may either lead to acute infections or progress to chronic infection which in turn may lead to end-stage liver diseases (Alter and Seeff, 2000). It has been reported that the combination of homozygous KIR2DL3 and HLAAsp80 confer protection against chronic HCV infection (Khakoo, et al., 2004;Romero, et al., 2008). Surprisingly, this protection is effective only in case of low inoculums of virus which include infections by intravenous drug use or accidental needle-stick injury, but no effect is observed in cases of high viral loads infected by transfusion of blood products. Such an outcome may be the result of low avidity binding of KIR2DL3 with HLA-C compared to other inhibitory KIRs (Winter, et al., 1998) resulting in weak inhibitory signals which being presumably are overridden by activating signals of activating receptors. KIR2DS4 is also considered to have a protective role in case of chronic HCV infection. Similarly, the B haplotypes having KIR2DL5, but marked with the absence of both KIR2DL3 and KIR2DS4, have been found to be associated with a poor response to treatment for HCV (Carneiro, et al., 2010).
Combination of homozygous KIR2DL3 and HLA-C1/C1 group genotype has also been shown to provide the protection from hepatitis B (HBV) viral infection. In contrast, stronger interaction between KIR2DL1 and HLA-C2 group ligand has been shown to increase susceptibility to chronic hepatitis B infections (Gao, et al., 2010). Another study showed that KIR2DS2 and KIR2DS3 act as HBV susceptible genes able by inducing a persistent weak inflammatory reaction that results in continuous injury of live tissues, and ultimately to chronic hepatitis; whereas, KIR2DS1, KIR3DS1, and KIR2DL5 may provide protection by facilitating the clearance of HBV (Zhi-ming, et al., 2007).
Reduced Human Cytomegalovirus (HCMV) infection after hematopoietic stem cell transplantation (HSCT) has been found to be associated with KIR2DS2 along with KIR2DS4, or at least 5 activating KIR loci (Zaia, et al., 2009). Again, activating KIR3DS1 and KIR2DS1 receptors have been found to trigger an effective early immune
Page | 54 response against Human Papilloma virus (HPV) infected targets to establish resistance to the development of recurrent respiratory papillomatosis (RRP) (Bonagura, et al., 2010). Furthermore, KIR2DL2 and KIR2DS2 genes could be associated in all asymptomatic cases of infections by Herpes Simplex virus (HSV) (Estefania, et al., 2007).
Associations of KIR genes with the onset of psoriasis are still unresolved, since some researchers have found KIR2DS1 associated in the development of the disease (Holm, et al., 2005);(Luszczek, et al., 2003;Luszczek, et al., 2004;Suzuki, et al., 2004), while others could not find any association (Chang, et al., 2006;Williams, et al., 2005). However, a possible association between KIR2DS1 and psoriasis is intriguing, since this receptor may recognize the HLA-Cw6 alleles (C2-group) as ligands, which appear to be one of the most, associated loci with psoriasis (Nair, et al., 2006;Tiilikainen, et al., 1980). Another study revealed the association of KIR2DL5 with the development of psoriasis (Suzuki, et al., 2004). KIR2DS1 and KIR2DS2 have been found to be associated with the psoriatic arthritis (PsA) (Williams, et al., 2005).
The frequencies of KIR2DL1 and KIR2DL3 were found to be at lower frequency in patients affected by Inflammatory Bowel disease (IBD), Crohn’s disease (CD) and Ulcerative Colitis (UC) compared to the healthy donors. Furthermore, the combination of KIR2DL1/HLA-C2 group is less frequent in IBD patients than in controls (Zhang, et al., 2008).
2.3.11.2. Autoimmune diseases
NK cells not only have crucial role to play in the suppression of inflammatory responses and autoimmune disease development but also participate in their promotion. NK cells have been reported to have a protective role in animal models of diabetes (Lee, et al., 2004), colitis (Fort, et al., 1998), and experimental autoimmune encephalomyelitis (Xu, et al., 2005). In contrast, autoimmunity was promoted by NK cells in models of neonatal autoimmune ovarian disease (Setiady, et al., 2004) and autoimmune myasthenia gravis (Shi, et al., 2000).
Page | 55 Epidemiological studies have revealed the association of distinct activating KIR in the pathogenesis of autoimmune diseases, wherein the activation signals are proposed to win over the HLA-dependent inhibition and, thereby aggravating NK cell response (Lanier, 2005). Psoriasis vulgaris is a common inflammatory skin disorder having strong association with KIR2DS1 alone (Holm, et al., 2005;Suzuki, et al., 2004) or in combination with HLA-Cw6 (Luszczek, et al., 2004). In case of diabetes mellitus, a weak association of KIR2DS2:HLA-CAsn80 is observed (van der Slik, et al., 2003). Additionally, KIR2DS2 has been reported to be present in 12 % of the patients with scleroderma (12%) as compared to only 2% of the controls (Momot, et al., 2004). Dominant activating KIR genotypes, with more activating KIR than inhibitory KIR-HLA combinations, was found to be associated with certain uveitis conditions such as Birdshot Chorioretinopathy (BCR) (Levinson, et al., 2008), Vogt-Koyanagi-Harada (VKH) disease (Levinson, et al., 2010), and HLA-B27-associated Acute Anterior Uveitis (AAU) and axial spondyloarthropathy (Levinson, et al., 2010).
2.3.11.3. Tumor immunity
Evidences suggest the influences of KIR receptors on malignant tumors, especially in those that are associated with the viral infections. Cervical neoplasia involving HPV- 16/18 infection, has been shown to be associated with the presence of KIR3DS1 (Carrington, et al., 2005). In contrast, KIR3DS1 and KIR2DS1 genes confer protection against the development of severe recurrent respiratory papillomatosis (RRP), caused by HPV strain, HPV-6/11 n the larynx and upper airway (Bonagura, et al., 2010;Hiby, et al., 2008). Such conflicting roles of KIR3DS1 against different strains of HPV may result due to the interaction of 3DS1 with putative HPV strain-specific ligands. These interactions may either lead to tissue-specific inappropriate hyper-responsiveness causing development of cervical cancer or kill HPV-6/11-infected cells, thereby protecting the host from developing RRP.
Page | 56 2.3.11.4. Pregnancy
An increased frequency of HLA-C2 group alleles in both mother and father, associated with the lack of KIR2DS1 in the mother, seems to increase the risk of abortion. These data are the first evidence of a male factor that increases the risk in spontaneous abortions (Hiby, et al., 2008). Another study has shown that mothers with recurrent miscarriages showed an increase of KIR2DS1 frequency, together with a decrease in frequencies of HLA-C2 group alleles, while the expression of KIR2DL1, the inhibitory receptor for HLA-C2 group, was unchanged (Wang, et al., 2007). In contrast, interactions between the KIR2DL4 and HLA-G on human trophoblasts are known to provide the protection against maternal NK cell mediated rejection of hemiallogeneic fetal cells (Witt, et al., 2000). 2DL4 alleles may have varying ability to control the rejection of fetal cells as observed in transplantation (Witt, et al., 2000).
2.3.11.5. Hematopoietic Stem Cell Transplantation (HSCT)
Hematopoietic stem cells transplantation (HSCT) has gained wide application as a therapy for a number of malignant and nonmalignant hematologic diseases, including leukemia, lymphoma, aplastic anemia, thalassemia major, immunodeficiency diseases, and severe combined immunodeficiency (SCID) (Appelbaum, 2003;Armitage, 1994;Thomas, 1995). HSCT is performed with HLA-matched donors to minimize T cell alloreactivities, which may cause adverse reactions such as graft rejection and graft- versus-host disease (GVHD). The KIR repertoire of an HLA-matched donor may not match since HLA and KIR gene families are not linked to each other. The initial lymphocytes population to appear in the blood is the donor type NK cells (Shilling, et al., 2002), which may become alloreactive on not finding their cognate HLA ligand in the recipient (Davies, et al., 2002;Giebel, et al., 2003;Ruggeri, et al., 2002). Such alloreactive cells are greatly beneficial in decreasing the rates of GVHD and graft rejection by mediating lysis of host T cells by NK cells. Furthermore, these NK cells release hematopoietic cytokines, thereby improving engraftment and decreasing relapse (Davies, et al., 2002;Giebel, et al., 2003;Ruggeri, et al., 2002). In addition, NK cell antiviral activity may enhance immune reconstitution and decrease complications due to
Page | 57 infections (Davies, et al., 2002;Giebel, et al., 2003;Ruggeri, et al., 2002). Expression of activating KIR receptors on donor NK cells also influences HSCT outcome. Donors with Bx genotypes provided superior protection to relapse and improved disease-free survival for Acute Myeloid Leukemia (AML) patients compared to donors with AA (Cooley, et al., 2010).
Furthermore, it has also been revealed that the centromeric B homozygous (Cen-B/B) motif has the strongest protective effect on survivality (Cooley, et al., 2010). Recent studies reveal the strongest impact of activating KIR genes specific to telomeric B haplotype (Sivori, et al., 2011;Venstrom, et al., 2010). Previously published reports revealed the crucial role of activating KIR2DS1 in killing dendritic cells and T cell blasts and in mediating alloreactivities (Sivori, et al., 2011). Additionally, association between lower-grade GVHD and donor with KIR3DS1 gene has also been elucidated (Venstrom, et al., 2010). Furthermore, increase in the number of copies of donor KIR3DS1was accompanied by lowering of grade II-IV acute GVHD and transplantation related mortality. Furthermore, the lowest failure rate was evident among patients homozygous for donor KIR3DS1 (Venstrom, et al., 2010). Thus, it can be said that understanding the KIR genotype of the donor along with knowledge of the HLA types of both the donor and recipient will help to predict the degree of KIR-HLA interactions that may confer protection to limit GVHD and improve survival of grafts.
2.3.12. Evolutionary aspect of KIR
Rapid evolution of the KIR gene family is evident from comparative evidences of KIR sequences and haplotypes among different species which may have occurred probably in response to species-specific pathogenic organisms (Grendell, et al., 2001;Guethlein, et al., 2002;Khakoo, et al., 2000;Rajalingam, et al., 2001). Sequences similar to KIR have also been found recently in lower primates (Hershberger, et al., 2001;Mager, et al., 2001), ungulates (McQueen, et al., 2002) and other mammals (Volz, et al., 2001). In chimpanzee, 10 KIR genes have been identified of which only three are direct orthologs of human KIR (Khakoo, et al., 2000). Non-orthologous KIR genes are also identified in pygmy chimpanzee (Rajalingam, et al., 2001), orangutan (Guethlein, et al., 2002), rhesus
Page | 58 monkeys (Hershberger, et al., 2001), and baboon (Mager, et al., 2001). Primate species show variations in ratios of long and short tailed KIR genes. Although comparisons of KIR gene sequences have revealed the instability of the KIR genes in primate species, but the maintenance of 2DL4 in all primate species suggest the similarities. The KIR locus has been exposed to rapid evolutionary forces resulting in species-specific characteristics in different order of the mammals (McQueen and Parham, 2002). Ly49 receptors, found in rodents, are the members of the C-type lectin domain family. Although structurally unrelated to KIR, these receptors appear to perform similar function (Anderson, et al., 2001). Furthermore, during early infections, KIR loci experiences additional selective pressures while mediating enhanced innate immune responsiveness. This phenomenon potentially increases the rate of evolution at the KIR loci at a speed greater than that of HLA class-I loci (Khakoo, et al., 2000;Martin, et al., 2000).
2.3.13. KIR genotyping
Locus specific KIR genotyping detects presence or absence of a particular KIR gene within a given individual, thus producing a KIR haplotype profile. The polymerase chain reaction (PCR) acts as a powerful tool for KIR genotyping in population cohorts. The most applicable method for KIR genotyping was SSP-PCR, first applied by Uhrberg et al. (Uhrberg, et al., 1997) whereby the primers are human specific as well as sequence specific for a particular KIR which only reacts to human KIR genes (Uhrberg, et al., 1997). An alternative genotyping method used for KIR genotyping is sequence specific oligonuleotide probe-based PCR (SSOP-PCR) (Uhrberg, et al., 1997). In this method, an initial reaction amplifies the entire region of KIR DNA, flanking all KIR genes, followed by the addition of radioactive/fluorescent labelled “probes” targeting each KIR gene (Crum, et al., 2000). SSP-PCR makes the use of normal gel-electrophoresis as detection method, while SSOP-PCR detection methods include X-ray photography and densitometric readings. Still more complex methods, such as sequencing or real time PCR provide increased specificity and sensitivity; however, simplicity and cost are compromised.
Page | 59 A website named http://www.allelefrequencies.net has been designed to act as a repertoire of KIR frequency data in different populations across the whole World, and also in disease related association studies. This website offers free access and presently enriched with KIR data from 172 populations (19640 individuals) in two formats: KIR gene or allele frequencies and KIR genotype frequencies respectively (Middleton, et al., 2003).
More recently, KIR haplotypes have been completely sequenced using Next Generation Sequencer (NGS) (De Re, et al., 2011), and some of sequenced haplotypes are present on the IPD-KIR database (http://www.ebi.ac.uk/ipd/kir/sequenced_haplotypes.html) (Robinson, et al., 2013).
2.4. Rheumatoid Arthritis
Rheumatoid arthritis (RA) is a widely reported chronic inflammatory disease in different populations throughout the World. It affects joints as well as extra-articular structures including the small joints of hands and feet in a symmetrical manner and causes pain, stiffness and loss of function. RA was first recognized in 1859 by Dr A. Garrod (Storey, 2001), when he described widespread joint pain and stiffness. However, almost another century passed before it was hypothesized that RA might have an autoimmune origin. RA has a wide clinical spectrum ranging from mild joint symptoms to severe inflammation and damage to joints (Figure 15). RA is diagnosed on clinical, serological and radiological grounds.
Figure 15: Structural differences between a normal joint and a joint affected by RA. (Adapted from Choy and Panayi, 2001) (Choy and Panayi, 2001).
Page | 60 A set of criteria, revised in 1987 by the American College of Rheumatology (ACR) (Table 3), was designed for helping to help differentiating RA from other inflammatory arthritis (Arnett, et al., 1988). These criteria were developed to define established diseases and to provide a standard for recruitment into clinical trials. However, in order to solve the purpose of earlier diagnosis and implementation of disease-suppressing therapy, a new classification system was developed by the ACR and European League Against Rheumatism (EULAR) in 2010 (Aletaha, et al., 2010) (Table 2). This new system redefined the pattern of RA diagnosis by focusing on the early-stage features of the disease rather than focusing on its late-stage features like rheumatoid nodules and existent erosions (Table 3).
Table 3: ACR 1987 criteria (left panel) were intended to classify established rheumatoid arthritis. ACR/EULAR criteria 2010 (right panel) were designed to classify both early and established disease. The table has been adapted from Scott et al., 2000 (Scott, et al., 2010).
ACR 1987 Criteria ACR/EULAR 2010 Criteria 1. Morning Stiffness (at least 1 1) Joint involvement (0-5) hour) • One medium to large joint (0) 2. Arthritis of three or more joint • 2-10 medium to large joints (1) areas • 1-3 small joints (large joints not counted)(2) 3. Arthritis of hand joints ( 1 • 4-10 small joints (large joints not counted (4) swollen joints) • >10 joints (at least 1 small joint)(5) 4. Symmetrical arthritis ≥ 2) Serology (0-3) 5. Rheumatoid nodules • Negative RF and negative ACPA (0) 6. Serum Rheumatoid factor • Low positive RF or ACPA (1) 7. Radiographic Changes • High positive RF or ACPA (3) (erosions) 3) Acute Phase Reactants (0-1) • (4 of the 7 criteria must be Normal CRP and ESR (0) • Abnormal CRP or ESR (1) present 4) Duration of symptoms (0-1) • < 6 weeks (0) Criteria 1-4 must remain for at • 6 weeks (1) least 6 weeks) (Points ≥are shown in parenthesis. Rheumatoid Arthritis present if 6 points or more)
Page | 61 2.4.1. Epidemiology
RA is one of the most common inflammatory joint disease, with prevalence between 0.5% and 1% worldwide (Dejaco, et al., 2006). Epidemiological data have shown that native American populations, such as Pima Indians have a considerably high RA prevalence and it is low in countries like China, Japan and Africa compared to the Caucasians (Wolfe, 1968) (Table 4). Although the occurrence of RA is possible in any age groups, its incidence increases with age and may vary depending upon the type of classification criteria used and population demography studied (Symmons, et al., 1994). The peak age of onset has risen to 50 years or more and is more common in women than men with a ratio of 3:1 (James, et al., 2004;Symmons, 2002;Young, et al., 2000). The occurrence of the disease show considerable variations among different populations worldwide (Alamanos, Y. and Drosos, A. A., 2005).
Table 4: Data on RA prevalence and incidence for several areas of the world and several countries(Alamanos and Drosos, 2005).
Populations Country Prevalence Incidence North American USA (General) 0.9-1.1 0.02-0.07 USA (Native) 5.3-6.0 0.09-0.89 South American Argentina 0.2 Brazil 0.5 Columbia 0.1 European England 0.8-1.1 0.02-0.04 Finland 0.8 0.03-0.04 Sweden 0.5-0.9 Norway 0.4-0.5 0.02-0.03 Netherlands 0.9 0.05 Denmark 0.9 Ireland 0.9 Spain 0.5 France 0.6 0.01 Italy 0.3 Greece 0.3-0.7 0.02 Yugoslovia 0.2
Contd…..
Page | 62 Populations Country Prevalence Incidence Asian Japan 0.3 0.04-0.09 China 0.2-0.3 Taiwan 0.3 Indonesia 0.2-0.3 Philippines 0.2 Pakistan 0.1 Middle East Egypt 0.2 Israel 0.3 Oman 0.4 Turkey 0.5 African 0-0.3
2.4.2. Disease etiology
The underlying causes of RA pathogenesis are still undetermined (Rindfleisch and Muller, 2005). However, interaction between genetic and environmental factors possibly has an important role to play in the development of disease in susceptible individuals (Jawaheer, et al., 2004).
2.4.2.1. Genetic factors
Family and twin studies indicate that the individuals with first degree relation with RA patients have an increased frequency of developing this disease, particularly if the patients had severe disease or were seropositive for rheumatoid factor (Lawrence, 1970). Identical twins have higher concordance rates of the disease compared to non-identical ones, supporting the genetic susceptibility (Lawrence, 1970;Silman, et al., 1993). However, RA is a polygenic and genetically heterogeneous disease and non-inherited factors are highly important in the development of the disease. In RA, the causative role of different genes may vary between the individual patients and various combinations of polymorphisms in a selection of different genes (genotype) may predispose to the clinical picture (phenotype). Some genes are responsible for the severity of the disease rather than the occurrence. Only few genes have been found to have consistent association with RA.
The HLA region on chromosome 6 has been consistently linked to RA. The HLA genes encode individual’s tissue type and are divided into class- I (HLA-A, HLA-B, HLA-C)
Page | 63 and class- II (HLA-DR, HLA-DQ, HLA-DP) genes. The encoded proteins are crucial in determining the individual’s immune response to antigenic stimuli. HLA class- II genes are strongly linked to RA, especially HLA-DR4 and HLA-DRβ1. Particular HLA-DRβ1 molecules in RA share a sequence that influences the peptides that are bound and viewed by the immune system. This core amino acid sequence is named the “shared epitope” and these epitopes have been linked with both predisposition to, and severity of RA (Del Rincon, et al., 2003;Gorman, et al., 2004;Gregersen, et al., 1987).
A number of studies have shown the associations of non-MHC genes and RA. Among non-MHC genes, PTPN22 has been found to be consistently associated with RA (Reveille, 2006). The R620W polymorphism of PTPN22 is associated with RA in patients from the U.S. and the U.K. and has also been confirmed in other Caucasoid groups (Reveille, 2006). Iwamoto and colleagues demonstrated significantly positive association between SNP PAD 4 gene and RA in both Japanese and European populations. This gene is expressed in synovial tissues and is associated with the levels of CCPs (Iwamoto, et al., 2006). Other studies included TNF α gene polymorphisms, IL- 1 and IL-10 genes polymorphisms (Turesson, et al., 2004;van Aken, et al., 2003).
2.4.2.2. Environmental factors
Population studies have shown that non-inherited factors such as environmental triggers, particularly smoking and infections actively participate in the etiology of RA. Infectious agents, such as Mycobacterium tuberculosis, Escherichia coli, Proteus mirabilis, Epstein- Barr virus (EBV) and Parvovirus B19, all been implicated as possible triggering factors for RA, but the results have been inconsistent (Albani, et al., 1995;Ebringer, et al., 1989;Holoshitz, et al., 1986;Rashid, et al., 2007;van Eden, et al., 1988;Venables, 1988). Environmental agents are considered as triggers rather than directly being involved in the disease process and complex interplay between the genetic and environmental factors are probably important for the initiation of the disease process in the susceptible hosts. Certain viral and bacterial agents that contain identical peptide sequence to auto antigen and infection with these microbial agents, can induce an immune response that cross- reacts with the auto antigen, termed “antigenic mimicry”. Antigenic mimicry is one of the
Page | 64 hypothesis to explain the induction of autoimmunity by environmental triggers. Another concept proposes that a local immune response to any environmental agents may release pro-inflammatory cytokines to up regulate the antigen-presenting capacity resulting in an immune mediated inflammatory cascade (Feldmann, 1989). Hormonal factors may also play a possible role in the etiology of the disease as suggested by increased female preponderance, high incidence during the premenopausal or post-partum period and protective effect of oral contraceptive pills presumably due to its progesterone content (Lahita, 1990). Diet and stress have also been considered to play a possible role in the disease expression (Buchanan, et al., 1991). Vitamin D and its metabolic derivatives may have inverse relationships with the disease activity in inflammatory polyarthritis or RA, due to their immunomodulatory effects (Patel, et al., 2007). Studies have shown that higher consumption of oil-rich fishes, fruit, vegetables, olive oil and beta-cryptoxanthin have protective effect on the development of RA, whereas lower consumption of food rich in antioxidants, could increase risk of RA, but the results were inconclusive (Pattison, et al., 2004). In addition, high intake of red meat and low intake of vitamin C might have an essential role to play in the development and pathophysiology of inflammatory polyarthritis (Pattison, et al., 2004;Pattison, et al., 2004).
2.4.3. Pathophysiology of RA
The most pronounced and fundamental pathophysiological feature in RA is the destruction of articular cartilage and subchondral bone by ectopic and hyperplastic synovium. T lymphocytes and macrophages are also seen in large numbers along with the dendritic cells, plasma cells and B-lymphocytes in the synovial fluid and membrane. The lining layer of the synovial membrane, which is normally two-cell thick, becomes much thickened with increased numbers of both macrophage-like and fibroblast-like cells (Isaacs and Moreland, 2011). In RA, the synovium becomes highly vascular with increased number of new blood vessel formation termed “angiogenesis”. The junction between synovial tissue, cartilage, and the bare area of bone within the joint capsule is prone to develop erosions early in RA. The synoviocytes proliferate as the disease progresses and invade the adjoining articular cartilage, where the secretion of cytokines,
Page | 65 and cartilage and bone-degrading enzymes result in characteristic destructive changes of RA. The invading, hyperplasic synovium is called “pannus” and the zone of invasion is called “cartilage-pannus junction”. The proliferative “pannus” is locally invasive and destroy articular cartilage and subchondral bone. Synovial membrane that lines the tendons and bursae also develop similar proliferative changes leading to destruction and deformity (Isaacs and Moreland, 2011;Szekanecz and Koch, 2001;Yamanishi and Firestein, 2001).
Although the pathogenesis of RA is still unresolved (Hayer, et al., 2005), genetic studies have pointed towards the association of RA with the HLA–DRB1 alleles. The HLA– DRB1 alleles encode for the shared epitope (SE). The SE motif is a conserved sequence of amino acids in the peptide-binding pocket of the DRB1 molecule and allows the presentation of an antigenic peptide to T cells. The Antigens could be either an exogenous Ag, (viral protein) or an endogenous protein which include citrullinated protein, human cartilage glycoprotein, and heavy chain binding protein, (Blass, et al., 1999). Ag-activated CD4+ T cells use soluble mediators (IFN-γ and IL-17) and CD69 and CD11 cell-surface signaling molecules to stimulate immune cells such as monocytes, macrophages and also synovial fibroblasts. These cells produce the key cytokines that drive inflammation in RA namely IL -1, IL-6, and TNF α. TNF α and IL-1 stimulate the release of matrix metalloproteinases especially stromelysin and collagenases, from synovial fibroblasts, osteoclasts and chondrocytes. These enzymes are thought to be the main mediators of joint damage in RA as they play essential role in degrading connective-tissue matrix. Furthermore, TNF α and IL-1 inhibit the production of tissue metalloproteinase inhibitors by synovial fibroblasts. These activated cells can also stimulate vascular angiogenesis as found in the synovium of RA patients. In the synovium, adhesion molecules expressed on the surface of endothelial cells promote the recruitment of inflammatory cells such as neutrophils into joints. Neutrophils release elastase and protease, which degrade proteoglycan in the superficial layer of cartilage (Figure 16).
Page | 66 Figure 16: Pathogenesis of RA. [A] Normal knee joint [B] Early RA and [C] Late RA. This figure has been adapted from Choy and Panayi, 2001 (Choy and Panayi, 2001).
Rheumatoid synovium contains a number of pro and anti-inflammatory cytokines, which are mainly produced by T cells and macrophages. A loss of balance between the pro and anti-inflammatory cytokines may be the main pathogenic mechanism in RA. Pro- inflammatory mediators, particularly TNF-α and IL-1, have essential role to play in the inflammatory cascade, which lead to various articular and systemic manifestations during RA (Feldmann, et al., 1996;Fox, 1997;Isaacs and Moreland, 2011;Zhang and Bridges, 2001). Other pro-inflammatory factors present within the RA synovium include nitric oxide, prostaglandins, leukotrienes, and free oxygen radicals. Rheumatoid synovium is highly vascular with angiogenesis. This is stimulated by various soluble factors such as vascular endothelial growth factor (VEGF) and vascular cell adhesion molecule-1 (VCAM-1), which stimulate endothelial cell growth. There are other adhesion molecules that are abundantly present on the vascular endothelium such as E-selectin and intercellular adhesion molecules (ICAMs). Their expression is stimulated by proinflammatory cytokines, particularly TNF-α and IL-1, resulting in the recruitment of inflammatory cells via specific receptors. Chemokines such as monocyte chemotactic protein-1 and 2 (MCP-1 and MCP-2) and IL-8 are highly expressed in RA synovium and they stimulate progression of inflammatory cells into the joint (Isaacs and Moreland, 2011;Liao and Haynes, 1995;Panayi, et al., 2001) The role of different cytokines in RA pathogenesis has been summarized in Figure 17.
Page | 67 Figure 17: Cytokine signaling pathways involved in inflammatory arthritis. Th2 denotes type 2 helper T cell, Th0 precursor of type 1 and type 2 helper T cells, and OPGL osteoprotegerin ligand. This figure has been adapted from Choy and Panayi, 2001 (Choy and Panayi, 2001).
Tissue hyperplasia and lymphocyte proliferation as a result of immune response is normally counteracted by programmed cell death or apoptosis to prevent over accumulation of cells. In rheumatoid joints, apoptosis is actively inhibited despite the presence of pro-apoptotic stimulants such as hypoxia and TNF-α in rheumatoid synovium. Impaired synoviocyte apoptosis may contribute to the pathogenesis of RA (Isaacs and Moreland, 2011).
The exact mechanism behind destruction of cartilages and bones in RA is not understood, but may be related to a variety of destructive enzymes secreted by the “pannus”. The important ones are MMPs, which include collagenases, stromelysins and gelatinases, and serine and cysteine proteases such as cathepsins. These enzymes destroy the articular cartilage by acting upon collagen and proteoglycan matrix but are normally controlled by
Page | 68 physiological inhibitors such as TIMPs. An impaired regulatory mechanism between these destructive enzymes and their inhibitors may partly be responsible for the destructive nature of the disease (Goldring and Gravallese, 2000;Gravallese and Goldring, 2000;Isaacs and Moreland, 2011;Tak and Bresnihan, 2000). Other destructive factors include the cytokines TNF-α and IL-1, which activate osteoclasts leading to bone resorption. Bone destruction may also be mediated by factors such as osteoclast differentiation factor (ODF) or TNF-related activation induced cytokine (TRANCE) and receptor activator of nuclear factor κB (RANK). ODF interacts with membrane RANK that is present on osteoclast precursors, resulting in their differentiation and activation and subsequent bone destruction. The combination of TNF-α, IL-1 and ODF probably contributes to periarticular as well as systemic osteoporosis in RA. Activated CD4+ T cells express osteoprotegerin ligands, which then leads to bone degradation by stimulating osteoclastogenesis. Activated CD4+ T cells also activate B cells through surface contact and binding of αLβ2 integrin, CD154 (CD40 ligand), and CD28, and stimulate them to produce immunoglobulins, including Anti Cyclic Citrullinated 2 Peptide Antibody (ACCP2A) and Rheumatoid Factor (RF) . The pathogenic role of RF may involve complement activation by forming immune complexes (Anderson, 2004;Choy and Panayi, 2001). The comparative changes in the normal knee joint during early and late RA pathogenesis have been summarized in Figure 16.
2.4.4. Cell populations in RA
2.4.4.1. Monocytes/Macrophages
Macrophages (MΦ) have phagocytic capacity and are central effectors of synovitis (Haringman, et al., 2005). They are found both in the synovial tissue and synovial fluid. There are two types of macrophages in the RA synovial tissue: the macrophage-like type A synoviocytes in the lining and the sublining macrophages which migrated as monocytes from the circulation and are distributed into the synovium. Both types have multiple functions which include clearance of immune complexes, antigen presentation (MHC class-II are over expressed on MΦ), regulation of systemic inflammatory responses through the release of different cytokines and growth factors (TNFα, IL-
Page | 69 1,6,10,13,15 and 18 and GM-CSF), regulation of migration of monocytes (Kinne, R. W., et al., 2007). Besides, they are also involved in stimulation of angiogenesis by chemokines and chemoattractants, tissue degradation and post-injury tissue remodelling by matrix metalloproteinases (MMPs) (Kinne, et al., 2007). They express several markers of the resident macrophage population including CD68, CD163 and CD14 (Bartok and Firestein, 2010).
In addition to the monocytes/MΦ’s central role in inflammation they are also involved in bone erosions due to their ability to differentiate into osteoclasts. Upon stimulation with TNF-α, IL-1, IL-6 and IL-17, the synovial fibroblasts and activated T cells can upregulate RANKL expression on their surface which can engage with receptors on monocytes and drive them into osteoclastogenesis (Davignon, et al., 2013).
2.4.4.2. Fibroblast-like synoviocytes
Fibroblast-like synoviocytes (FLS) are non-phagocytic mesenchymal-derived cells. The FLSs found in the lining layer of synovial tissue are highly activated and exhibit features with aggressive invasive properties. They have essential roles to play in the initiation of RA and maintenance of the chronic inflammation through cell–cell contact and through the elaboration of soluble products. FLS get stimulated not only in presence of environmental stimuli but also on interactions with different cells of the inflamed synovium and secrete several mediators like cytokines, chemokines, growth factors and several other pro-inflammatory molecules like prostaglandins and leukotrienes. It has recently been shown in a SCID mouse model of arthritis that FLSs can migrate to a distant unaffected joint and invade and degrade the cartilage and thereby promote articular involvement (Lefevre, et al., 2009). In a very recent study, citrullinated fibronectin (cFn) was shown to inhibit apoptosis and increase pro-inflammatory cytokine secretion of RA FLSs (Fan, et al., 2012). This could be one possible explanation for the increased number of FLSs that contribute to the hyperplasia in RA synovial membrane.
Page | 70 2.4.4.3. T cells
The T cells constitute around 30-50% of all cell types in the sublining synovial tissue and the majority is CD4+ with T helper (Th) 1 phenotype (Bartok and Firestein, 2010). T cells are identified as CD3+ cells in the synovial tissue and are either CD4+ Th cells, CD8+ cytotoxic T cells or CD4+ regulatory T cells (Wagner, et al., 1998). The Th1 subset mediates cellular immunity and causes IFNγ secretion. The Th2 is involved in humoral immunity and forms mainly IL-13 and IL-4, while Th17, the newest member of the T cell family, which is identified through its signature cytokine, IL-17. Th17 cells are important promoters of autoimmunity in RA (Gaffen, 2009). Although synovial T cells demonstrate chronic immune activation but express low levels of cytokines and show signs of anergy (Cope, 2002).
2.4.4.4. B cells
B cells are mainly found in the sublining layer of synovial membrane. Around 5% of sublining synovial cells are B cells. The role of B cells in autoimmune responses has been attributed to autoantibody production that would drive the inflammation locally either in soluble form or as immune complexes (Marston, et al., 2010). B cells contribute to RA through both antibody-dependent and antibody -independent mechanisms. Examples of antibody-independent functions are antigen presentation, T cell activation and modulation of dendritic cells. Furthermore, extensive diversity in B cell phenotypes also contribute to its role in autoimmune responses (Anolik, et al., 2009).
2.4.4.5. Neutrophils
Both in number and function, the phagocytic neutrophils are the most important cells of the innate immune responses. Neutrophils are the first cells to be recruited at the sites of inflammation in the RA joint and accumulate mainly in the inflamed SF and to a lesser extent in synovial membrane at the site of active destruction, where they phagocytose immune complexes and release degrading proteases (Cascao, et al., 2010). Resting peripheral neutrophils have short life span, while activated neutrophils within the tissues have extended their life span and altered molecular properties. Such molecular changes
Page | 71 allow them to carry out many functions. Delayed apoptosis, together with synthesis of inflammatory mediators like IL-8, TNF-α, IL-1, IL-6, IL-12, TGF-β and BLyS, and ability to present antigen to T cells via MHC-II, makes tissue neutrophils capable of driving inflammatory processes. Several recent reports have suggested a possible direct contribution of neutrophils in early RA pathophysiology and bone remodeling (Poubelle, et al., 2007) by mediating Th17-responses (Cua and Tato, 2010), expressing PRRs (Greenblatt, et al., 2010;Hayashi, et al., 2003;Kerrigan, et al., 2009), and mediating bone resorption via activating osteoclastogenesis (Chakravarti, et al., 2009). The neutrophils use selectins, integrins and adhesion molecules to adhere to the endothelial wall while passing from the peripheral blood to the site of inflammation. This is followed by transmigration of the neutrophils through the blood vessel lining, and chemotaxis to the sites of inflammation.
2.4.4.6. Dendritic cells [DCs]
Dendritic cells have important role to play in the initiation of inflammatory arthritis as well as its perpetuation by presentation of antigens to auto reactive T cells. Through their potent antigen-presentation ability, they stimulate naive T cells, direct their effector cells function and polarize the T cell repertoire towards the Th1, Th2, or Th17 phenotypes. Myeloid DCs (mDCs) are considered especially important in promoting synovial inflammation. Plasmacytoid DCs (pDCs) are recruited in RA synovial tissue (Lebre and Tak, 2009) and constitute a population of antigen presenting cells (APC). These cells help in forming the local inflammatory environment by producing cytokines and by stimulating allogenic T cells. The number of synovial pDCs has been reported to be increased in ACPA positive RA patients (Lebre and Tak, 2009).
2.4.4.7. NK cells
NK cells affect RA pathogenesis either directly or indirectly (Ahern and Brennan, 2011).
Dalbeth and Callan reported that a subset of NK cells (CD56bright) is greatly expanded within the inflamed (synovium) joints (Dalbeth and Callan, 2002). These cells can produce more IFN-γ than those NK cells that were taken from the blood of the same
Page | 72 patient (Aramaki, et al., 2009). Moreover, this subset of NK cells has the capability to differentiate monocytes into DCs thereby increasing the chance of initiation of autoimmune condition. Other example of this phenomenon is the “DC editing”, which is a crosstalk between the NK cells and the myeloid DCs and may cause activation of NK cells as well as maturation of DCs. This process may also lead to the clearance of immature DCs by NK cells (Moretta, et al., 2006). Furthermore, NK cells can also function as antigen presenting cells in some instances (Hanna, et al., 2004).
Yen et al. reported that a unique population of CD4+CD28− T cells is uncommon in the healthy individuals but is abundantly present in the patients with RA complications (Yen, et al., 2001). Interestingly, CD4+CD28− T cells are functionally distinct from classical
CD4+ TH cells. Instead, they have certain similarities with NK cells, such as expression of CD57 instead of CD40 and bulk production of IFN-γ, granzyme B and perforin (Yen, et al., 2001).
TNF-α is a very potent osteoclastogenic cytokines and has a very crucial role to play in the pathogenesis of RA (Di Santo, 2006). TNF-α in combination with IL-15 can both induce and enhance receptor acquisition by NK cells (Lee, et al., 2010). TNF-α plus IL- 15 also activate NF-κB (Lee, et al., 2010), which has an important role in the growth and differentiation of NK cells.
Therefore, NK cells are considered to have the essential role in RA pathogenesis and researches are being carried out worldwide to elucidate the role of these molecules and their receptors, especially KIR in RA development.
2.4.5. Diagnosis
2.4.5.1. Laboratory diagnosis
Bony erosions and deformities seen in the case of RA are largely irreversible. Immediately after the diagnosis of RA, medications should be introduced within three months, since delay in therapeutic approach may result in substantially more radiographic damage at five years. Therefore, early diagnosis, although challenging, is critical.
Page | 73 Laboratory quantifications of antibodies and inflammatory markers in case of RA provide a way for early diagnosis.
2.4.5.1.1. Antibodies
The serum of RA patients contains a variety of autoantibodies against self-antigens among which the most widely known are the RF and anti-CCP antibodies.
2.4.5.1.1.1. RF antibodies
Rheumatoid factors (RF) are the auto-antibodies against the Fc portion of the Immunoglobulin G molecule and found in every immunoglobulin subclass (IgM, IgA and IgG) with the IgM class being the most common (Halldorsdottir, et al., 2000) (Figure 18).This assay is a well-established test for diagnosing RA prognosis.
Figure 18: Schematic presentation of IgM RF. (Adapted from Halldorsdottir, et al., 2000) (Halldorsdottir, et al., 2000).
Lymphoid tissue of a normal human commonly possesses B lymphocytes with RF expression on the cell surface. However, in the absence of an antigenic stimulus, RF is not found in the circulation. A modified IgG may act as a stimulus to RF production, thereby serving as an important component of RA pathogenesis (Das, et al.,
Page | 74 2004;Newkirk, et al., 2003). Co-stimulation of B cells, perhaps mediated by Toll-like receptors (TLRs), may allow B cells with low affinity receptors for IgG to become activated. Being the components of the innate immune system, TLRs provide signals after engaging various bacterial and viral products (Rifkin, et al., 2005;Shlomchik, et al., 1993). B cells are stimulated to RFs CD14-positive monocytes derived from the bone marrow (Hirohata, et al., 1995). CD20(-)CD38(+) plasma cells derived from the synovium produce RF in the synovial fluid (Van Esch, et al., 2003). Circulating B cells require IL-10 for RF production (Perez, et al., 1995). Production of RF is also associated with the shared epitope of HLA DRB1*0401 (Mattey, et al., 2002). Cigarette smoking, a risk factor for more severe RA, is associated with an increased prevalence of RF (Padyukov, et al., 2004). Mutations within the heavy and light chain genes added significant heterogeneity to the RFs (Youngblood, et al., 1994). Thus, IgM RFs from patients with RA have the capability to react with different antigenic sites on IgG molecule (Carson, 1993).
The potential physiological role of IgM RF includes the following:
1. Binding and processing of antigens embedded in immune complexes. 2. Presentation of antigens to T lymphocytes in combination with HLA molecules. 3. Immune tolerance. 4. Amplification of the humoral response to bacterial or parasitic infection. 5. Immune complex clearance.
Although reasonably sensitive (60-80%) (Palosuo, et al., 2003), the specificity of RF in RA is low (70-80%) (Suzuki, et al., 2003). Therefore, other antibodies should be taken into consideration for RA diagnosis.
2.4.5.1.1.2. Anti-cyclic citrullinated peptide (CCP) antibodies
Anti-CCP antibodies show high specificity for RA (Masson-Bessiere, et al., 2001). Citrulline, a non-standard amino acid, is the antigenic determinant for these antibodies. Citrulline is generated by enzymatic citrullination (deimination) of arginine residues (Figure 19) in a post-translational event. This reaction is catalyzed by peptidyl arginine
Page | 75 deiminase (PAD). Within rheumatoid joints, arginine residues on fibrin and fibrinogen may be most favored sites for deimination (van Boekel, et al., 2002;Vossenaar, et al., 2004;Vossenaar and Robinson, 2005;Vossenaar and van Venrooij, 2004). The RA- associated HLA-DRB1*0404 allele has also shown association with antibody production to citrullinated fibrinogen protein (Goldbach-Mansky, et al., 2000).
Figure 19:Citrullination (deimination) of peptidylarginine by peptidyl arginine deiminase
(PAD). ( Adapted from van Boekel, et al., 2002) (van Boekel, et al., 2002).
Enzyme linked immunosorbent assay (ELISA) for detection of RA specific antigens gained progressive application in RA diagnosis. An ELISA was developed for detecting anti- Filaggrin antibodies. This test has high specificity and sensitivity for the diagnosis of RA (Palosuo, et al., 1998). Citrulline is the target amino acid in Filaggrin (Schellekens, et al., 1998). Subsequently, an ELISA assay for the detection of antibodies to a cyclic peptide containing citrulline was made commercially available, which was easier to standardize, and also had high sensitivity and specificity for the diagnosis of RA. This became the assay for the detection of anti-CCP antibodies.
2.4.5.1.2. Inflammatory markers in RA
Erythrocyte Sedimentation Rate (ESR) and C-Reactive Protein (CRP) are two of the most commonly studied blood markers that have been identified and shown to be useful in the evaluation and treatment of RA. Elevation of ESR and CRP are the strongest predictors
Page | 76 of persistent, progressive disease in RA. If they are elevated in the early disease and do not improve with therapy, may lead to joint damage and other worse outcomes (James, et al., 2004;Lindqvist, et al., 2005). ESR and CRP are very sensitive to change in disease activity.
2.4.5.1.2.1. Erythrocyte Sedimentation Rate (ESR)
The ESR, an indirect assessment of inflammation, measures the distance that RBCs fall in a capillary tube over the course of an hour. The presence of inflammation causes the cells to fall more quickly due to the action of inflammatory proteins, such as fibrinogen or immunoglobulins, blocking the normal charge inhibition on RBCs. In many RA studies, an ESR level greater than 20 to 30 mm/h has been considered abnormal; however, considerable individual variability between normal and abnormal tests exists.
2.4.5.1.2.2. C-Reactive Protein (CRP)
CRP is a pentameric protein released in response to inflammatory stimuli. Because testing directly measures this protein, CRP levels are a more accurate measure of inflammation than the ESR. Measuring CRP in inflammatory conditions is preferred over the ESR as CRP responds much more quickly to inflammatory stimuli and can therefore, be used as a timely marker of active inflammation. The CRP level that has been determined to be abnormal in RA studies is generally greater than 1.0 mg/dL. But caution must be used when interpreting this value as many clinical labs report CRP in mg/L, resulting in a 10-fold higher value that may still be a normal result.
2.4.6. Assessment of Disease Activity
In RA, measurement of disease activity at specific time points or at regular intervals helps to evaluate disease progression and it is vital to assess treatment response, outcomes and prognostic factors. Various methods have been introduced and validated to measure disease activity in RA over the last few decades. These methods have been designed and modified to evaluate three different but interrelated aspects of the disease progression: clinical, radiological and functional.
Page | 77 2.4.6.1. Assessment of clinical disease activity
In the early 1990s, core sets of disease activity measures have been proposed by the ACR, EULAR and WHO/International League of Associations for Rheumatology (ILR), to standardize disease activity assessments in the clinical trials involving RA patients (Boers, et al., 1994;Felson, et al., 1993;Tugwell and Bombardier, 1982). These measures included swollen joint count (SJC), tender joint count (TJC); and patient assessment of pain and global assessment of disease activity by the patient (PGA) and by the evaluator (EGA) and acute phase reactants, which include erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). The measures further included structural damages on radiographs and functional status (Aletaha and Smolen, 2006;Tugwell and Bombardier, 1982). These measures are also very useful and crucial to assess the disease activity and treatment response in day-to-day clinical practice.
2.4.6.2. Swollen and tender joint counts
Joint involvement or inflammation in RA has traditionally been assessed using swollen (soft tissue swelling and effusion) and TJC (tenderness on pressure or motion). Over the years, different indices and counts have been developed which differ by the number of joints assessed or by the way several joints are clustered into regions (Aletaha and Smolen, 2006). Ritchie and coworkers introduced a graded TJC, assessing 26 joint areas with grades ranging between 0 to 3, depending upon the severity of joint tenderness (Ritchie, et al., 1968). Further modifications of the joint indices and simplifications of the extensive joint counts were carried out by other groups over the years, reducing the number of joints assessed (Egger, et al., 1985;Fuchs, et al., 1989;van der Heijde, et al., 1992). These simplified joint counts have been validated and gained easy and reliable application in clinical practices (Prevoo, et al., 1993;Smolen, et al., 1995).
2.4.6.3. Pain
Pain is the primary symptom for majority of the RA patients and is usually measured on a visual analogue scale (VAS) of 100 mm, evaluating the symptom for one week before the study point. Horizontal VAS is more commonly used than vertical scales and there are
Page | 78 also other reliable methods of pain assessment such as, arthritis impact measurement scale (AIMS) and McGill pain questionnaire (Aletaha and Smolen, 2006).
2.4.6.4. Global assessment of disease activity
The overall assessment of the disease activity is done on a 100 mm VAS by both the evaluator and the patients. Patient’s global assessment (PGA) of disease activity is a subjective measure and it is different from the patient assessment of global health (GH) as the latter include all possible domains of health outcomes, including those that are directly or indirectly related to the disease process. On the other hand, evaluator’s global assessment (EGA) of disease activity is comprised of subjective and objective measures that are available to the evaluator (Aletaha and Smolen, 2006).
2.4.6.5. Acute phase reactants
ESR and CRP are the most commonly used acute phase reactants (APRs) in RA to assess disease activity and progression. These inflammatory markers usually rise in direct proportion to the severity of disease activity and they correlate well with the clinical and radiographic disease progression and outcomes (Dawes, et al., 1986;Plant, et al., 2000;van Leeuwen, et al., 1993). There are also other biomarkers of disease activity such as ILs, TNF-α, MMPs and RANKL, which are expensive and complex and so are mainly used as research tools.
2.4.6.6. Disease activity scores and indices
Composite disease activity scores have been developed over the years to give reliable identification of disease activity and to overcome methodological problems. These scores use special formulas integrating SJC, TJC, ESR or CRP and GH to measure overall disease activity (Aletaha and Smolen, 2006). In 1990, Van der Heijde and coworkers introduced the disease activity score (DAS) based on 44 SJCs (van der Heijde, et al., 1993;van der Heijde, et al., 1990). Later, this score was modified as DAS28 as only 28 joints were included. This score showed similar validity and reliability as that of DAS and has gained wide application (Prevoo, et al., 1995;Prevoo, et al., 1993;Smolen, et al.,
Page | 79 1995). However, both these scores were modified in several ways to exclude the GH assessment (DAS-3 and DAS28-3) and to replace ESR with CRP (DAS-CRP and DAS28-CRP) (Aletaha and Smolen, 2006).
Formulae to calculate DAS28 with 4 or 3 variables and with ESR or CRP. DAS28 = 0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.70 x log(ESR) + 0.014 x GH DAS28-CRP = 0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.36 x log(CRP+1) + 0.014 x GH + 0.96 DAS28 -3 = [0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.70 x log (ESR)] x 1.08 + 0.16 49 DAS28 -3 CRP = [0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.36 x log(CRP+1) x 1.10 + 1.15 In order to overcome the complexities of the above formulae, simpler joint indices have been developed namely, clinical disease activity index (CDAI) and simplified disease activity index (SDAI) (Aletaha and Smolen, 2006).
CDAI = TJC28 + SJC28 + PGA + EGA
SDAI = CDAI + CRP
2.4.6.7. Criteria to assess disease activity including remission
After the introduction of the composite disease activity indices, a number of criteria have been validated, based on DAS, DAS28, SDAI and CDAI, to assess different levels of disease activity including remission. EULAR has adapted disease activity criteria based on DAS and DAS28, which have been widely used in several studies (Aletaha, et al., 2005;Balsa, et al., 2004;Fransen, et al., 2004;Fransen and van Riel, 2005;Paulus, 2004;Smolen, et al., 2003;van Gestel, et al., 1996).
EULAR criteria based on DAS
• DAS < 1.60 – remission • DAS ≥1.60 and ≤ 2.40 - low disease activity • DAS >2.40 and ≤ 3.70 - moderate disease activity • DAS> 3.70 - high disease activity
Page | 80 EULAR criteria based on DAS 28 • DAS 28 < 2.6 – remission • DAS 28 ≥ 2.6 and ≤ 3.2 - low disease activity • DAS 28 >3.2 and ≤ 5.1 - moderate disease activity • DAS 28 > 5.1 - high disease activity SDAI criteria for disease activity • SDAI ≤ 3.3 – remission • SDAI ≤ 11 - low disease activity • SDAI ≤ 26 - moderate disease activity • SDAI > 26 - high disease activity CDAI criteria for disease activity • CDAI ≤ 2.8 – remission • CDAI ≤ 10 - low disease activity • CDAI ≤ 22 - moderate disease activity • CDAI > 22 - high disease activity The United States Food and Drug Administration (FDA) has also proposed remission criteria, which are based on ACR remission criteria, but also takes into account structural damage on x-rays and the status of the treatment at the time of assessment. According to this, 5 out of 6 ACR remission criteria have to be fulfilled plus radiographic arrest for ≥ 6 months with no drug therapy (Paulus, 2004).
2.4.6.8. Assessment of radiological progression
Conventional radiography has been traditionally used to assess the structural damage in RA. X-rays of hands and feet and/or large joints have been used to define radiological damage at a given time point as well as the progression of structural damage over a period of time. The advantage of radiographic assessment of disease progression over other methods is that the damage seen on x-rays is largely irreversible and it represents the cumulative measure of disease activity and destructive process over time. Another major advantage is that, apart from providing permanent records, radiographs can also be randomized and blinded for clinical investigations of new therapeutic agents in clinical
Page | 81 trials (van der Heijde, 2000;Wollheim, et al., 1988). It has been widely recognized that radiological damage as seen on x-rays has to be quantified to define the disease status of the patient and more importantly to assess disease progression, treatment response and outcomes (Rau and Wassenberg, 2005;van der Heijde, 2000;Weisman, 1987). As there are no truly quantitative methods, semi-quantitative methods have been developed to translate the amount of structural damage on x-rays into a score value (Rau and Wassenberg, 2005). There are lots of abnormalities that can be seen on radiographs in patients with RA among which erosions and Joint space narrowing (JSN) are widely accepted and included in the scoring methods since they give reliable and additive information on radiological progression (Fries, et al., 1986;Sharp, et al., 1985).
2.4.6.8.1. Included Joints
Although any synovial joint can be affected in RA, it is not feasible to include all the joints in scoring the radiological damage. Hands (including wrists) and feet have been chosen to represent the overall radiological status of the disease as they are the most commonly involved joints in majority of patients with RA, wherein erosions and JSN can be seen very early (Drossaers-Bakker, et al., 2000;Scott, et al., 1986). The joints that are usually evaluated in the scoring methods include Proximal Interphalangeal (PIP) joints, metacarpophalangeal (MCP) joints, interphalangeal (IP) joints of thumbs, wrist joint as a st whole or as individual joints, metatarsophalangeal (MTP) joints and IP joints of the 1 toe (van der Heijde, et al., 1999). Although RA is typically a symmetrical polyarthritis, radiological changes can appear asymmetrically and so both hands and feet should be included in the radiographic evaluation (van der Heijde, et al., 1999). Postero-anterior (PA) view of the hands and feet x-rays is the most commonly used technique for radiographic assessment (Mewa, et al., 1983).
2.4.6.8.2. Scoring methods
Several scoring methods have been developed and subsequently modified over the last few decades to quantify the radiographic damage in RA (Kellgren, 1956;Steinbrocker, et al., 1949). The global method of scoring have been designed to score erosions and JSN together with one overall score, while the composite method separately scores erosions
Page | 82 and JSN that are added together at the end to give an overall score. Although there are several scoring methods available to measure radiographic damage, Larsen and Sharp and their modifications, mainly SvdH, have been the most commonly used. Each of these scoring methods has their own advantages and disadvantages. The advantage of Larsen’s score is that an experienced reader can perform it quickly, whereas the SvdH method is more time consuming (Rau and Wassenberg, 2005;Sharp, et al., 2004). However, inclusion of soft tissue swelling in the Larsen’s score may lead to a relatively high baseline score, decreasing with response to treatment. This may reduce the total possible increased score due to progressive damage, contributing to low sensitivity to change (Rau and Wassenberg, 2005). The SvdH method is better than other methods owing to its sensitivity to detect a real change in x-ray progression over time (sensitivity to change) and in detecting the changes that are clinically meaningful, termed as minimal clinically important difference (MCID). MCID is the smallest radiographic change that necessitates the physicians to alter their treatment (Bruynesteyn, et al., 2002;Bruynesteyn, et al., 2004;Drossaers-Bakker, et al., 2000;Guillemin, et al., 2005;Lassere, et al., 1999;Paimela, et al., 1998;Pincus, et al., 1997;Sharp, et al., 2004).
2.4.6.8.3. Radiographic remission and healing
The FDA has included radiographic status of the disease in their strict remission criteria. Accordingly, 5 out of 6 ACR remission criteria have to be fulfilled along with radiographic arrest (Larsen or SvdH method) for ≥ 6 months with no drug therapy (Paulus, 2004). However, different criteria are used in other studies for reporting radiographic remission. These criteria include the assessment of extension of existing erosions and development of new erosions between two time points (Makinen, et al., 2005) or no increase in radiographic score (Larsen) by > 1 point between the study points (Jantti, et al., 2001).
Interestingly, it has been reported that healing of radiographic damage or erosions can occur during sustained disease inactivity or remission in RA, as a result of treatment with DMARDS and/or biological agents (Rau, 2006). Radiographic healing has been described as a reparative process, which may be represented by various features, such as
Page | 83 recortication, sclerosis, filling in, remodelling and restoration (Rau, 2006;van der Heijde and Landewe, 2003) ultimately resulting in decreased or negative radiographic scores.
2.4.6.9. Assessment of function
Functioning is an important aspect of overall health status and it strongly influences Quality of Life (QoL). Functional assessment in patients with RA is a vital component in the evaluation of disease progression as it significantly correlates with disease activity, structural damage and long-term outcomes (Barrett, et al., 2000;Scott, et al., 2000;Wolfe and Hawley, 1998;Young, et al., 2000).
The Health Assessment Questionnaire (HAQ) or HAQ-disability index (HAQ-DI) is a 20-question fomat, which assesses the degree of difficulty a patient has in accomplishing his or her tasks in eight functional categories, such as dressing, rising, eating, walking, hygiene, reaching, gripping and usual day to day activities. For each question there is a four-level difficulty scale ranging from 0 to 3. The final score ranging from 0 to 3 is the mean value of the scores of the eight categories, with higher levels indicating more disability (Fries, et al., 1980;Ramey, et al., 1992). The HAQ has been modified several times subsequently to simplify it and to make it user friendly and also to include other domains such as depression and anxiety (Pincus, et al., 1983;Pincus, et al., 1999;Wolfe, et al., 2004). However, an improvement in HAQ depends upon the duration of disease, as it assesses both reversible and irreversible components of functional impairment. It has been shown that during the early stages of the disease (<5 years duration), It has been shown that during the early stages of the disease (<5 years duration), the HAQ score is mainly influenced by joint pain and swelling due to inflammation, which can improve with treatment (reversible), whereas in the late stages, the HAQ scores strongly correlate with structural damage (irreversible) and so the reversibility of HAQ in patients with established RA may not be as significant as in early RA (Aletaha, et al., 2006).
The Arthritis Impact Measurement Scale (AIMS) is another form of patient self -reported functional questionnaire, which includes assessment of depression and anxiety (Meenan,
Page | 84 et al., 1980). There are longer and shorter versions of the AIMS, which have been used to evaluate function in patients with arthritis including RA (Aletaha and Smolen, 2006).
Objective quantitative instruments have also been used to assess function and these include measures of grip strength and locomotion (Pincus and Callahan, 1992) wherein a vigorimeter or a dynamometer is used to indicate the pressure attained by squeezing a compressible rubber bulb (Jones, et al., 1991;Pincus, et al., 1991).
2.4.7. Treatments
The goals of treatment in RA are to control inflammation, prevent progressive joint destruction, preserve and improve activities of daily living, and alleviate pain. In addition to drug therapy, non-pharmacological treatment, including patient education, physiotherapy, occupational therapy, orthotics, and surgery are important.
A major transformation in the pharmacological treatment of RA occurred in the 1990s. The earlier therapeutic approach involved initial disease management using Non Steroidal Anti Inflammatory Drugs (NSAIDs) for few consecutive years and Disease Modifying Anti Rheumatic Drugs (DMARD) based treatment was withheld until clear evidence of disease progression was seen. DMARD mono-therapies were used in slow succession only if the disease show signs of progression even after the initial treatment with NSAIDs. This approach was generally termed as the therapeutic pyramid. This form of therapeutic approach has been replaced by early administration of DMARDs either individually or in combination. Apart from Methotrexate, other commonly utilized DMARDs include leflunomide, sulphasalazine, and hydroxychloroquine. Also cyclosporine, azathioprine, D-penicillamine, and gold salts remain in use. DMARD mono-therapy is partly effective, because of which, DMARD combination therapy has been used to maximize therapeutic effect and maintaining a tolerable toxicity alongside. Initial randomized controlled trials of mono-therapy versus combination therapy yielded conflicting results. However, more recently it has been shown that combination therapy has clear benefits and tolerable toxic effects in both early and chronic RA. In general, the
Page | 85 beneficial combinations have included methotrexate and 1 to 3 other DMARDs (O'Dell, et al., 2002;Suresh and Lambert, 2005).
Corticosteroids, and biological response modifiers are also applied for treatment of RA. In recent years, new biological drugs, especially TNFα blockers have been introduced into clinical use which include infliximab and etanercept. In combination with methotrexate, they have been able to retard considerably or even stop the radiographically detected destruction of joints in RA (Feldmann and Maini, 2001).
2.4.8. KIR in RA
Associations of KIR genes: with RA have been demonstrated in Caucasian (Yen, et al., 2001), Polish (Majorczyk, et al., 2007), Taiwanese (Yen, et al., 2006) and Western Mexico populations (Ramirez-De los Santos, et al., 2012), wherein KIR2DS2 and KIR2DL2 were reported to be associated with RA. In a study based on North Indian population Prakash et al. 2014, showed that KIR3DS1 and KIR2DS2 have risk association with RA (Prakash, et al., 2014). In contrast, Middleton et al 2007 (Middleton, et al., 2007) did not found such associations in RA patients from Northern Ireland. Yen et al., 2006 (Yen, et al., 2006) found association of KIR2DS4 gene with RA patients in Taiwanese population. McGeough et al. (2012), demonstrated the association of a positive clinical outcome in response to therapy with activating KIR2DS2+HLA-C homozygous genotype, while the relatively inhibitory KIR2DS2–HLA-C heterozygous genotype showed association with non-response to therapy (McGeough, et al., 2012). Thus, the KIR and HLA-C genotype of RA patients may provide information regarding the response to anti-TNF-α therapy. Thus, it can be seen that conflicting results make it difficult to predict real associations. Thus, there is a need to increase the sample size and to carry out such studies in different ethnic groups. Therefore, this study may prove helpful in extending the database of gene disease association in this region of the country. Furthermore, this study will also help us to predict the genetic factors of protection or susceptibility to the disease.
Page | 86
Chapter 3:
Materials and Methods
3. Materials and Methods
The details about the techniques, analytical parameters and the statistical tools that were used for collection, analyses and interpretation of the data have been discussed in this chapter. It has been subdivided into three sections. In the first section, the criteria and the procedures that were followed for the collection of the samples have been discussed. In the second section, the experimental procedures that were carried out in the laboratory have been elaborated. Finally, in the third section, the applications of the statistical tools for our data analyses have been discussed. In order to facilitate better understanding, each of these three sections has been subdivided into two subcategories: Population based study and Rheumatoid Arthritis (RA) based study.
3.1. Sample Collection
This section has been categorized into two parts. In the first part, sample collecting procedure from different populations, have been discussed while the second part contains the description of sample collection in RA patients and control groups.
3.1.1. Population Based Study
3.1.1.1. Study Populations
In this part of the study, five populations were selected from Northern part of West Bengal with the aim of assessing and quantifying the KIR genetic variation existing within and between the populations. Populations that were included in the study were not only selected from scheduled caste and scheduled tribe ethnicity but also included individuals from community based sampling. Randomly selected normal healthy individuals from each population have been included in the present study. Three major selection criteria were considered for sample collection, which includes:
Page | 87 [1] Only individuals unrelated from each other for the last three generations were selected for the study.
[2] Geographical strictness was maintained while collecting samples from a population. It was made sure to collect all the samples of a particular population from the geographical locations as mentioned in our study.
[3] Individuals having inter-caste or inter-religion marriages, in any of the last three generations, were excluded from the analyses.
The five populations that were selected for the study include:
1. Rajbanshi: an ethnic caste population from the Terai and Dooars region of West Bengal, 2. Bengali: a heterogenous population group from Siliguri and adjoining regions of West Bengal, 3. Rabha, a primitive tribal population group from the Northern part of West Bengal, 4. Gurkha, a Nepali speaking ethnic population from Siliguri and adjoining areas of West Bengal, and 5. Muslim, from Northern part of the state
Detailed description about the possible origin, socio-cultural practices and the position of these population groups in the social hierarchy network of Indian society have been discussed in detail below.
3.1.1.1.1. Rajbanshi
The Rajbanshis represent a highly diversified ethnic community of India. They have rich cultural, linguistic and social background. They account for 18.4% of the total Scheduled Caste population of West Bengal (Census of India, 2001). Although distributed dispersedly throughout the state, the Rajbanshis mainly inhabit the Terai and Dooars of the Northern region of West Bengal, especially the districts of Jalpaiguri and Coochbehar. They can also be found adequately in the bordering state of Assam. They have an Indo-European linguistic background. Besides their own dialect, they also speak
Page | 88 Bengali, Assamese and some other minor languages. They practice Hinduism and have their own rich cultural identity and heritage. Earlier reports suggested that the Koch Rajbanshis are represented by many sub-castes in North Bengal, India (Risley, 1891). Thus, it can be said that the ethnicity and genetic richness of the Rajbanshis may enhance their importance in genetic diversity studies and interpretation of the migratory routes in this part of the country.
3.1.1.1.2. Bengali
Many communities in India have emerged because of the blending of different ethnic populations. Such a community is the Bengali community. They are the major inhabitants of the state of West Bengal and are the third largest ethnic group in the world. In West Bengal, the Bengalis mainly follow Hinduism (Census of India, 2001). Many of them also follow Islam, with considerable traces of Christianity and Buddhism (Census of India, 2001). They speak the Bengali language or the ‘Bangla’ dialect, which is thought to belong to Indo-European group. They are culturally very rich and this made them as one of the unique population groups of India. Since the Bengali community is considered to have a mixed descendant, there is an urgent need to unravel the genetic profile of this extremely heterogenous population, beside exploring the genetic diversity of indigenous tribes and castes.
3.1.1.1.3. Rabha
Rabha is a very little known small endogamous Scheduled Tribe community of India (Census, 2001;Chakraborty, 2013;Sarkar, 2011) with a conserved gene pool of their own. In West Bengal, they mainly dwell in the forest villages of the Dooars region of Coochbehar and Jalpaiguri districts. Historically, they are considered as the primitive inhabitants of the region who remained isolated from other neighbouring populations due to strict endogamy. According to H.H. Risley, the Rabhas belong to the Indo-mongoloid stock (Risley, 1891), having a unique genetic constitution. Thus, there is an urgency to undertake genetic diversity studies in this group.
Page | 89 3.1.1.1.4. Gurkha
The Indian Gurkhas constitute a community of Nepali speaking people, populating the Eastern and North-Eastern states of the country, mainly in West Bengal and Sikkim (Chatterjee, 1974) with sizeable populations in the states of Meghalaya, Nagaland, Manipur, Tripura, Mizoram, Arunachal Pradesh as well as in Assam. In West Bengal, they are distributed in the Terai and Dooars, as well as in the hilly terrains of the Northern part of the state. Apart from India, they are found in Nepal and to some extent in Bhutan. They are a mixture of various clans and ethnic groups which include Bahun (Brahmins), Chhetri, Thakuri, Gurung, Magar, Newar, Tamang, Rai (Khambu), Limbu (Subba), Sherpa, Yolmo and many more (Roy, 2012). Each of them has their own languages, but Nepali language has become the common binding thread for all the Gurkha castes and clans.
3.1.1.1.5. Muslim
The Bengali Muslims inhabiting West Bengal represent the second-largest ethnic Muslim community in the world, after the Arabs (Eaton, 1993), native to modern-day Bangladesh and the Eastern states of India, including West Bengal and Assam. They speak Bengali dialects and have strong cultural similarities with the Bengali Hindus, thereby increasing the cultural richness of West Bengal. Being the second-largest community and also the largest minority group of the state, the Bengali Muslims comprise 27.01% of the total population of West Bengal (Census of India, 2011).
3.1.1.2. Sample collection strategy
Prior to sample collection, geographical locations/sites for collecting the samples were determined based on the demographic history of the place. On the day of sample collection, volunteers were questioned about their ethnicity, caste and tribal affiliations and surnames and the birthplaces of their parents. The demographic profile and other ethnical and familial information were filled in a detailed questionnaire (Appendix 1). Based on the questionnaire, only unrelated subjects were considered eligible to participate in the study. Three- generation pedigree charts were prepared to ensure un-
Page | 90 relatedness in all the samples. Two (2) ml of whole blood was obtained from the volunteers with their informed consent by venipuncture method and was collected in EDTA vacutainer tube. The blood samples were stored at -20°C until use. The investigation was approved by the Human Ethics Committee of the Department of Zoology, University of North Bengal, India. The different sample collecting sites in case of the population-based study are shown in Figure 20.
Figure 20: Geographical Location of the different population group in the map of west Bengal.
3.1.2. RA Based Study
3.1.2.1. Study population
A total number of 110 Rheumatoid Arthritis patients were included in this study. The samples were collected from an authorized diagnostic laboratory of Siliguri and from North Bengal Medical College and Hospital (NBMCH, Sushrutnagar, Siliguri, West Bengal, India) under the guidance of medical practitioner. The diagnosis of RA was made by the physician based on the medical and clinical history, physical examinations and symptoms of the disease and most importantly, their fulfilment of the American College
Page | 91 of Rheumatology criteria 2010 (Aletaha, et al., 2010). The inclusion criteria for the subjects in the patient group for the study included the following:
1. Resident of Siliguri and adjoining areas of the sub-Himalayan region of West Bengal, India.
2. Subjects fulfilling the ACR criteria 2010.
3. Subjects having the symptoms for ≥1 year duration.
4. Patient should be above 18 years of age.
5. Patients capable of giving informed consent for becoming a part of the study.
According to ACR criteria 2010 (Aletaha, et al., 2010), patient is considered to have definite RA, if he/she has 1 or more swollen joint(s) and having ≥6 score on the classification criteria which include joint distribution, serology reports, disease duration and measures of acute phase reactants. Furthermore, evaluation of the disease was also done based on DAS28 score system for confirmation. Most of the patients belonged to stage II and III as was also evident from the clinical evaluations and other relevant data.
However, there are certain exclusion criteria that were followed in the study. These are listed below:
1. Patients having a history of or currently suffering from primary inflammatory joint diseases or primary autoimmune diseases other than RA.
2. Patient suffering from known HIV or hepatitis B/C infection.
3. Individuals having latent TB infection unless they have completed adequate antibiotic prophylaxis.
4. Reported cases of malignancy (other than basal cell carcinoma) within the last 10 years
5. Patients with demyelinating disease.
Page | 92 6. Known history of recent drug or alcohol abuse
7. Patients with poor tolerability of venipuncture or patients with lack of adequate venous access for required blood sampling during the study period.
8. Patients currently involved in other clinical trial(s) involving an investigational medicinal product.
9. Other severe acute or chronic medical or psychiatric condition, or laboratory abnormality, which according to the investigator, may impart excess risk associated with study participation, or which would render the patient inappropriate for becoming a part of the study.
3.1.2.2. Sample collection strategy
The demographic profile and other ethnical and familial information of individual volunteer were filled in a detailed questionnaire (Appendix-1). Based on the questionnaire, only unrelated subjects were considered as eligible to participate in the study. Three-generation pedigree charts were prepared to ensure the un-relatedness in all the samples. The patients were also asked to complete the Health Assessment Questinnaire (HAQ) (Appendix 2) for their inclusion in the study. Five (5) ml of whole blood was then obtained from the volunteers with their informed consent by venipuncture method. Three (3) ml of the sample was allowed to stand for serum preparation while the remaining 2 ml was stored in EDTA vacutainer tubes for genotyping study, at -20°C, until use. Two (2) ml blood was separately collected for ESR test.
Both patients and control subjects have provided their written consent after knowing the purpose of the study. The non-RA or the control group included patients with spondyloarthropathy (n=12), osteoarthritis (n=2), gout (n=7), undifferentiated arthritis (n=13), mechanical pain (n=22) and healthy subjects (n=46). The investigation was approved by the Human Ethics Committee of the Department of Zoology, University of North Bengal, India.
Page | 93 3.2. Experimental Procedures:
This section is further divided into two subcategories, population-based study and RA based study, based on the methodologies that were followed. Detailed description of all the chemicals, reagents and kits (marked in bold) used for the study are given in Appendix 3.
3.2.1. Population based study
Below are the protocols that were employed for KIR genotyping of the populations that have been selected for our study.
3.2.1.1. Extraction of genomic DNA
Genomic DNA was extracted from frozen peripheral blood samples that was collected from the individual volunteers, using phenol-chloroform extraction method (Sambrook, et al., 1989) with certain modifications.
Blood samples initially kept at -20 °C, were thawed first for about 20 minutes prior to use. 500 µl of the thawed blood sample was taken into a 2 ml microfuge tube and washed with 1 ml of 1X SSC, mixed gently and centrifuges at 5000 rpm for 5 minutes. The supernatant was discarded and 1.2 ml of 1X SSC was added. The content was mixed gently and centrifuged at 5000 rpm for 3 minutes. The supernatant was discarded again. The pellet was resuspended with 1.2 ml of 50 mM KCl and centrifuged as above. After centrifugation, the supernatant was discarded again. 375 µl of High Salt Buffer was added to the pellet, followed by the addition of 25 µl of 10% SDS and 12.5 µl of 8 mg/ml Proteinase K. The content was mixed gently and incubated at 56 °C for 1 hour. To the Proteinase K digested suspension, equal volume of Phenol-Chloroform was added and centrifuged at 12000 rpm for 20 minutes at 4°C . The DNA was precipitated in chilled absolute alcohol.
Page | 94 Then, the DNA was rinsed twice with 500 µl of 70% ethanol. The DNA was then, dried and dissolved in 50 µl TE buffer.
3.2.1.2. Characterization of DNA
3.2.1.2.1. The integrity:
The integrity of the extracted DNA is an important factor to consider during extraction steps. Integrity was checked by electrophoresis on 1% agarose gel prepared in 1X TBE buffer (Appendix 4), containing Ethidium bromide (3l of 10mg/ml stock for every 50 ml of 1% agarose). The high molecular weight genomic DNA appeared as a single band near the well (Figure 21).
Figure 21:1% agarose gel electrophoresis to demonstrate the extracted DNA in fresh bloods. Lane M: M stands for Marker (λ-DNA/Hind III Marker).Lane 1-8: DNA from Rabha population (sample 1-8).
3.2.1.2.2. The Purity and concentration:
Five (5) µl of stock genomic DNA was diluted to 1.0 ml using deionized water (dilution factor = 200), mixed well and optical density was measured at 260 nm and 280 nm in a UV spectrophotometer (Rayleigh UV- 2100, China). DNA samples with 260/280 ratio greater than 1.7 but less than 2.0 were considered to be free from protein contaminations.
Page | 95 In the cases, where the ratio was less than 1.2, DNA was extracted again. Each DNA sample was checked on ethidium bromide prestained agarose gel by comparing with known quantity of phage-Lambda DNA as estimation by absorbance may be inaccurate due to RNA contamination. Samples, with large amount of RNA contamination were treated with RNAase A (Sigma Aldrich, final concentration of 100g /ml) at 37 °C for 2 hrs, followed by re-extraction with phenol-chloroform iso-amyl alcohol mixture.
DNA concentration of the sample was calculated using the formula:
OD at 260 nm X DF X 50 (1 OD = 50µg / ml of ds DNA )
The original DNA solution was diluted by a factor, DF = 200
E.g. if OD of the diluted DNA sample = 0.016
DNA concentration = 0.016 X 200 X 50 µg / ml = 160 µg / ml
3.2.1.3. Storage
Samples which were in regular use were kept at 4 °C. Rest of the samples were stored at - 80°C.
3.2.1.4. PCR-SSP Typing
Molecular typing of the genomic DNA was performed for detecting the presence of the following KIR genes : 2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1 and 3DP1 using 16 PCR–sequence-specific priming (PCR-SSP) reactions.
3.2.1.4.1. PCR Primers
The primer sequences were generously provided by R. Rajalingam (Immunogenetics and Transplantation Laboratory, University of California) on query (Rajalingam, 2011;Rajalingam, et al., 2008). Each primer carries a 3′ residue matching a unique position conserved on all known sequences of a given KIR gene (Garcia, et al., 2003).
Page | 96 Primer lengths were adjusted in such a way so that the annealing temperatures are maintained between 59°C and 67°C, which enables amplification of all KIR genes under the same PCR condition. In addition to gene-specific primers, internal positive control primers specific to the conserved adenomatous polyposis gene were included to confirm each PCR reaction. The oligonucleotide primers used in the KIR genotyping system are provided in Table 5.
Table 5: Primer sequences and the amplicon sizes of the 16 KIR genes.
Forward primer Reverse primer Genomic Target Name Sequence (5'-3') Name Sequence (5'-3') amplicon (bp) KIR genes 2DL1R1 CCACTCGTATGGAGAGTCAT 2DL1 2DL1F CCATCAGTCGCATGACG 1903 & 1818 2DL1R2 AATGTTCCGTTGACCTTGGT 2DL2 2DL2F2 ACTTCCTTCTGCACA(C/G)AGAA 2DL2R1 CCCTGCAGAGAACCTACA 1877 2DL3 2DL3F3 CTTCATCGCTGGTGCTG 2DL3R1 CAGGAGACAACTTTGGATCA 816 2DL4 2DL4F1 CTGCATGCTGTGATTAGGTA 2DL4R1 CTGTTGAGGGTCTCTTGCT 695 2DL5 2DL5F TGCCTCGAGGAGGACAT 2DL5R1 TCATAGGGTGAGTCATGGAG 1151 3DL1 3DL1F1 AT(C/T)GGTCCCATGATGCT 3DL1R1 CTGAGAGAGAAGGTTTCTCATATG 1661 3DL2 3DL2F1 TGCAGGAACCTACAGATGTTAT 3DL2R1 CTTGAGTTTGACCACACGC 1882 3DL3 3DL3F1 CACTGTGGTGTCTGAAGGAC 3DL3R1 TCTCTGTGCAGAAGGAAGC 1905 3DS1 3DS1F GGCAGAATATTCCAGGAGG 3DS1R1 GGCACGCATCATGGA 1847 2DS1F1 CTCCATCAGTCGCATGAG 2DS1 2DS1R AGGGCCCAGAGGAAAGTT 1922 & 1897 2DS1F2 CTCCATCAGTCGCATGAA 2DS2 2DS2F TGCACAGAGAGGGGAAGTA 2DS2R1 CGCTCTCTCCTGCCAA 1781 2DS3 2DS3F TCACTCCCCCTATCAGTTT 2DS3R GCATCTGTAGGTTCCTCCT 1812 2DS4 2DS4F1 TCCTGCAATGTTGGTCG 2DS4R1 ACGGAAACAAGCAGTGGA 2050 2DS4-full 2DS4F1 TCCTGCAATGTTGGTCG 2DS4fullR1 CCCTCCCTGGATAGATGGTAC 1956 2DS4-del 2DS4F1 TCCTGCAATGTTGGTCG 2DS4delR TTCCCTGGATAGATGGAGCTG 1933 2DS5F AGAGAGGGGACGTTTAACC 2DS5R GGAAAGAGCCGAAGCATC 1952 2DS5 2DS5F AGAGAGGGGACGTTTAACC 2DS5RD CAGAGGGTCACTGGGC 180 2DP1 2DP1F TCTGTTACTCACTCCCCCA 2DP1R GGAAAGAGCCGAAGCATC 1825 3DP1 3DP1F1 AGAGTATTCCGAAACACCG 3DP1R1 CTGACAACTGATAGGGGGAA 1900 Internal positive controls IC256 PIC-F ATGATGTTGACCTTTCCAGGG PIC-R ATTGTGTAACTTTTCATCAGTTGC 256 GH1 GH1F CTTCCCAACCATTCCCTTA GH1R CGGATTTCTGTTGTGTTTC 450
If the primer is marked as xx.xx nmol, then the amount of MilliQ (MQ) water that is to be added to the primer solution to make its concentration 100 μM is 10 X (xx.xx) µl. For example, if the primer is marked in the tube or product sheet as 59.68 nmol, one needs to add 596.8 µl MQ water to make this primer as 100 μM. This becomes the stock solution which can then be stored at -20 °C until use. In case of an ideal PCR, the working primer
Page | 97 concentration is 10 μM. In order to prepare this, 50 µl of 100 μM primer stock was added to 450 µl water. This can be stored at 4 °C for 3-4 months.
3.2.1.4.2. Preparation of reaction mixture:
Reaction mixtures of 25 µl volume were used to carry out the PCRs using a thermal cycler (MiniTM Gradient Thermal Cycler, PTC-1148, Bio-Rad, Singapore). The reaction mix of 25 µl was prepared in the following proportions:
PCR mixture initial vol(µl)/25u final concentration. concentration l MilliQ H2O 1X 6.38 1X 10x PCR buffer 100 mM 2.5 µl 10 mM Tris, 50 mM KCl dNTP mix 2.5 mM 2 µl 200 µM each KIRF (KIR-specific) 10 µM 1.5 µl 0.6 µM KIRR (KIR-specific) 10 µM 1.5 µl 0.6 µM Internal Control (Forward) 10 µM 1 µl 0.4 µM Internal Control (Reverse) 10 µM 1 µl 0.4 µM AmpliTaq polymerase 5 U/ul 0.2 µl 1U/25 µl DNA 100 ng/µl 2.0 µl 8 ng/µl Total volume 25 µl
3.2.1.4.3. PCR typing protocol
PCR protocol included an initial denaturation step at 94°C for 3 min, then five cycles of 94°C for 30s, 62°C for 50s, and 72°C for 1 minute. This was followed by 30 cycles of 95°C for 30 s, 60°C for 50s, and 72°C for 1 minute with small modifications of the annealing temperature for different primer sets (shown below). The numbers of amplification cycles were modified to 35 cycles for KIR2DS1 and 2DS4 and 32 cycles
Page | 98 for KIR2DS2 and 2DS5. The number of cycles for the second segment were adjusted between 30-35cycles for activating KIR genes, if needed.
72 ° C 72°C 95° C 94°C 72°C 94°C 5 cy cles 32 cy cles
3 m i n 20 s 1:30 m in 20 s 1:30m in 10 min
65°C 61°C 4°C
20 s ∝ 20 s
3.2.1.4.4. Gel checking
Appropriate aliquotes of PCR products were analyzed on ethidium bromide (0.5 µg/ml) prestained 2% agarose gels. TBE buffer was used as tank buffer, Bromophenol blue as the tracking dye and 1Kb plus DNA ladder (0.1- 12 Kb) (Catalog number: 10787018: Thermo Fisher Scientific, Massachusetts, U.S.A) was used as DNA size marker. After electrophoresis, a photograph of the agarose gel was taken over a UV- transilluminator. This was followed by careful examination of the gel pictures to determine positive amplifications against appropriate molecular weight markers (Figure 22). Each lane of the gel except for a negative control lane, are loaded with PCR product and showed a control band. Sixteen KIR gene were typed on a selected DNA sample. In case of false reactions where no control bands are found, the reactions were repeated.
Page | 99 Figure 22: 2% agarose gel electrophoresis to show the presence of different KIR genes. [a] KIR2DL5 (1151 bp) in the Rajbanshi population [RAJ10-21 in lane R1-12]. [b] KIR2DL3 (816 bp) in the Rajbanshi population [RAJ10-21 in lane R1-12]. [c] & [d] KIR2DS3 (1812 bp) and KIR3DL1 (1661 bp) respectively in the Bengali population [BEN25-32 in lane B1-B8]. [e] KIR3DS1 (1847 bp) in the Rabha population [R6-10 in lane V1-5]. [f] KIR2DL4 (695 bp) in Muslim population [M1-8 in lane M1-8]. [g] KIR2DS1 (1922 bp) in Gurkha population [G13-20 in lane G1-8]. [h] KIR2DL2 (1877 bp) in RA patients (RA25-32 in labe P1-8]. Internal control for [a,b,c,d,e,g &h] is GH1 (450 bp) and for [f] is IC256 (256bp). The marker used in Lane M is 1Kb Plus DNA Ladder.
Page | 100 3.2.2. RA based study
3.2.2.1. Molecular Typing of KIR genes
Molecular typing of 16 KIR genes were carried out using PCR-SSP in both the patient and the control group in order to study the association of KIR genes with Rheumatoid Arthritis, if any. We followed the same procedure as has been described under section 3.2.1.4.
3.2.2.2. RF titre assay
The assay is based on agglutination reaction with latex enhancement. In this immunoturbidimetric assay, heat inactivated IgG (antigen) with bound latex reacts with the RF antibodies in the sample and form antigen antibody complexes leading to agglutination reactions which are then, measured turbidimetrically. The Assay was performed with COBAS INTEGRA Rheumatoid factors II assay kit (Cat. No. 20764574 322) using COBAS INTEGRA 400 plus analyzer (Roche, Germany). Manufacturers’ instructions were followed for quantitative estimation of RA. The lower detection limit of the test was 10.00 IU/ml. The measuring range of the assay is 10-130 IU/ml. However, samples with higher values of RA were measured using post dilution rerun. The normal reference cut-off value for RF titre in the serum was considered to be up to 20 IU/ml as per the laboratory standardizations and guidelines.
3.2.2.3. Anti-CCP estimation
The estimation of anti-CCP in the serum samples were carried out using the commercially available Elecsys anti-CCP assay kit. The assay was performed on the Cobas e 411 Analyzer (Roche Diagnostics, Mannheim, Germany). We acknowledge our association with 3gen Diagnostics Pvt. Ltd., Siliguri, India, for providing the access to the instrument and in sharing their expertise in executing the assay. The Elecsys Anti-CCP immunoassay is a two-step IgG-capture test principle immunoassay with streptavidin- coated microparticles and electrochemiluminescence detection. The procedure was
Page | 101 conducted according to the manufacturer’s recommendations. The unit for measurement is U/mL and the results were considered positive at a cut off value of ≥17 U/ml.
3.2.2.4. C-reactive protein (CRP) estimation
Quantitative estimation of C-reactive protein (CRP) in the serum samples using particle enhanced turbidimetric assay was also performed. The test was performed with COBAS INTEGRA Cardiac C-Reactive protein (Latex) High Sensitive (CRPHS) Cobas C pack using COBAS INTEGRA 400 plus analyzer. Human CRP agglutinates with monoclonal anti-CRP antibody precoated latex particle. The precipitate is determined at 552 nm using turbidimetric analyses. The assay was performed as per the manufacturer’s instruction.
3.2.2.5. Anti-streptolysin O (ASO) titre assay
In vitro quantitative immunological determinations of Anti-streptolysin in the serum samples of the patient and the control groups were done by immunoturbidimetric assay using COBAS INTEGRA Antistreptolysin O pack in COBAS INTEGRA 400 Plus analyzer. In this assay, human ASO antibodies agglutinate with latex particle coated with streptolysin O antigens. The precipitate is determined spectrophotometrically at 552 nm. The assay was performed following manufacturer’s instruction. The measuring range of the assay is 20-800 IU/ml. However, higher concentrations were determined using 1:10 post dilution rerun. The normal cut off value is 200 IU/ml in adults and 150 IU/ml in children.
3.2.2.6. Erythrocyte Sedimentation Rate (ESR) Estimation
Erythrocyte Sedimentation Rate (ESR) is an indirect measure of the acute phase reaction. Furthermore, it is a simple and inexpensive measure of inflammation, estimated by Westergrens method (Westergren, 1957;Westergren, 1926) by measuring the rate at which anticoagulated erythrocytes settle down from a fixed point in an upright calibrated tube in 1 hour. Its upper limit for males and females under normal conditions are 15 mm/hr and 20 mm/hr respectively. The basic principle underlying the estimation of ESR is that the erythrocytes normally repel each other due to the net negative charges.
Page | 102 However, during acute phase reactions, fibrinogen and other positively charged proteins that are present in the blood increase in amount and promote rouleaux formation, which further increases the ESR.
3.2.2.6.1. Material
Sterile syringe (2 ml)
Sterile injection needles for drawing of blood samples.
3.8 % solution of sodium citrate solution as anticoagulant,
Westergrens pipettes with a stand. Westergrens pipette is a graded glass tube divided in millimeters. It is marked from 0 (top) to 200 (bottom). The pipette should be upright with its lower end placed in the plastic container (bowl) in a vertical position and fixed with the stand using a screw.
3.2.2.6.2. Methods
Under sterile conditions, 1 part of 3.8 % sodium citrate (anticoagulant) was mixed with 4 parts of freshly drawn venous blood in a 2 ml syringe. The mixture was then drawn into the Westergrens pipette upto zero (0) mark by rotating the screw. The tube was then set upright in a stand with a spring clip on top and rubber at bottom. The apparatus was left undisturbed and the reading of the lower value of the plasma column was taken at the end of each hour. This value corresponds to the ESR value of the individual.
3.2.2.7. Serum Ceruloplasmin Assay
Ceruloplasmin was estimated spectrophotometrically by using p-phenylenediamine (PPD) oxidase activity (Sunderman and Nomoto, 1970). Ceruloplasmin oxidizes PPD at pH 5.4 and yield a colored product, whose rate of formation is therefore, proportional to the concentration of serum ceruloplasmin.
Page | 103 3.2.2.7.1. Materials
Sodium acetate solution, (0.2 mol/L).
Acetic acid solution, (0.2 mol/L).
Acetate buffer solution,(0.1 mol/L, pH 5.45 at 37 °C).
Sodium azide solution, (1.5 mol/L).
Buffered p-phenylenediamine dihydrochloride (PPD) solution (27.6’ mmol/L).
3.2.2.7.2. Methodology
2 ml of acetate buffer solutionwas taken into two test tubes, labelled R (reaction) and B (blank).
0.1 ml Serum, was added to each tube.
Tubes R and B were placed at 37 °C in a water bath in order to reach thermal equilibrium.
A flask containing buffered PPD solution was also placed in the water bath.
1 ml of warmed, buffered PPD solution was added to both the tubes. The contents of the tubes are mixed and kept unstoppered in the water bath. Care should be taken to cover the water bath for avoiding exposure of the tubes to light.
After 5 min, 50 µl of sodium azide solution was pipetted into tube B, and the contents were mixed. The tube was replaced in the water bath.
Exactly after 30 min, 50 µl of sodium azide solution was also added to tube R, and the contents were mixed.
The contents of both the tubes R and B were transferred to spectrophotometer cuvets and absorbance was measured at 530 nm with a spectrophotometer. The color of the samples has stability for minimum 6 hours.
Page | 104 3.2.2.8. Serum Creatinine Assay
This assay is based on the direct relationship between serum creatinine and the change in absorbance over a fixed time interval, when creatinine is reacted with alkaline picrate (Lustgarten and Wenk, 1972).
3.2.2.8.1. Materials
Working reagent prepared by mixing 0.5 mol/L NaOH and saturated aqueous picric acid (prepared at room temperature) in equal volumes. Creatinine standards were prepared by dissolving National Bureau of Standards creatinine (SRM No. 914) in dilute HC1 (20 mmol/L). In case of the reference method, the calibration curve was prepared using creatinine standards.
3.2.2.8.2. Methodology
100 µl samples was added to 2.0 ml of working reagent, mixed immediately and a stopwatch was started.
At 20 s, the absorbance (A0) of the clear solution was read and recorded.
At 80 s a second reading (At) was recorded.
The change in absorbance (ΔA) was obtained by A0 from At. The reaction temperature was held constant at 30°C absorbance was measured at 515 nm. A standard curve was prepared reference information
The unknown concentration of Creatinine in a serum sample was calculated using the following formula:
= A0 At
∆𝐀𝐀 −
A (Unknown) = × Concentration of Standard A (Standard) ∆ 𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔𝐔 𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐭𝐭𝐭𝐭𝐭𝐭𝐭𝐭 ∆ Page | 105 3.3. Statistical Analyses
3.3.1. Population based study
3.3.1.1. Estimation of observed phenotypic frequency, KIR locus frequency and genotypic frequency. The observed phenotypic frequency (OF) of each KIR gene is expressed as the ratio of the total number of individuals carrying that gene in the population to the total population size. The formula is as follows: n(No. of individuals having the gene) = N(Total individuals in the population) Estimation of KIR locus𝐎𝐎𝐎𝐎 frequency (KLF) was made by using the formula: = 1 1 f
Where f = OF of a particular KIR gene𝐊𝐊𝐊𝐊𝐊𝐊 in a population.− √ − Genotypic frequency was estimated by using the following formula n(No. of individuals having a particular genotype = N(Total individuals in the population) 𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆 𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟𝐟 3.3.1.2. KIR gene frequencies of the referral populations
The KIR gene frequencies of the referral populations were taken from the following publications and ‘http://www.allelefrequencies.net’ database (Gonzalez-Galarza, et al., 2011) as follows: South Indian populations namely Kanikars, Paravars and the Mollukurumba (Rajalingam, et al., 2008), Finnish, French, Guadeloupe Caribbean, Senegal African, and Reunion having Indian Ocean origin (Denis, et al., 2005), North Indian (Rajalingam, et al., 2002), Samoan, Cook Island,, Tokelau, Tongan (Velickovic, et al., 2006), Huichol, Mestizo, Tarahumara, Purepecha (Gutierrez-Rodriguez, et al., 2006), Amazonian Amerindian (Ewerton, et al., 2007), Wichis and Chiriguanos (Flores, et al., 2007), Northern Irish (Middleton, et al., 2007), Basque population (Santin, et al., 2006), Eastern Mainland Chinese (Wu, et al., 2009), Chinese Han (Jiang, et al., 2005), Korean (Whang, et al., 2005), Japanese (Yawata, et al., 2002), Warao, Bari, Yucpa
Page | 106 (Gendzekhadze, et al., 2006), Vietnamese and Australian Aborigine (Toneva, et al., 2001), American Caucasian, Hispanic, Afro-American (Du, et al., 2007), Thai, British Caucasian, Palestinian (Norman, et al., 2001), Australian Caucasian (Witt, et al., 1999), New York Caucasian (Hsu, et al., 2002), Greeks (Niokou, et al., 2003), Afro-Caribbean, Pakistani, Trinidad Asian (Norman, et al., 2002), Chinese, Malay and Indian in Singapore (Lee, et al., 2008), Indian Parsis and Maharashtrian (Kulkarni, et al., 2008), Tibetans (Zhu, et al., 2010)and Iranian Arabs and Persians (Ashouri, et al., 2009).
3.3.1.3. Chi-Square (χ2) test and estimation of significant differences
The standard χ2 test was executed using Kyplot 2.0 beta 15 software to compare frequency differences of KIR genes between our study populations and some other geographically neighbouring populations (Kulkarni, et al., 2008;Lee, et al., 2008;Middleton, et al., 2003;Norman, et al., 2002;Rajalingam, et al., 2008;Rajalingam, et al., 2002;Wu, et al., 2009;Zhu, et al., 2010). The input file was prepared in the form of a 2X2 contingency table on which the χ2 analysis was run. The level of significance is expressed in terms of probability (p) values wherein two-tailed p values ≥ 0.05 were considered to be statistically significant. However, differences at even 1% and 0.1% were also mentioned in our study.
3.3.1.4. Distance analyses
The frequencies of KIR genes in our populations along with that of few other geographically neighbouring populations were bootstrapped for 1000 replicates followed by calculation of Nei’s genetic distances (Nei, 1972) using PHYLIP software version 3.69 (Felsenstein, 1989). Nei’s genetic distance was measured by the formula:
i n i p1ni p2ni D = ln 1 1 [ p2 ] 2 [ p2 ] 2 in ∑1ni ∑ in 2ni − � � where n is summed over loci, i over∑ alleles ∑ at the n-th∑ locus, ∑ and where
p1ni= the frequency of i-th allele of n-th locus in population 1.
Page | 107 These genetic distance data were then used to construct a consensus Neighbor-Joining (NJ) tree using the Neighbor subprogram of the above-mentioned software (Felsenstein, 1989).
3.3.1.5. Principle Component analyses
Principal components analysis (PCA) identifies a smaller number of uncorrelated variables using a large set of data. These variables are known as "principal components". The number of variables that are used in the PCA are either equal to or more than the number of derived components. The first principal component measures the largest possible variance followed by the succeeding components, which in turn measures the highest possible variance under the available constraint that is not related to the preceding components. Thus, PCA explain the maximum variance using the fewest possible number of principal components..
The PCA was carried out using the MINITAB software version 16, based on the frequencies of KIR genes in our study populations and other referral populations (Ashouri, et al., 2009;Denis, et al., 2005;Du, et al., 2007;Ewerton, et al., 2007;Flores, et al., 2007;Gendzekhadze, et al., 2006;Gonzalez-Galarza, et al., 2011;Gutierrez-Rodriguez, et al., 2006;Hsu, et al., 2002;Jiang, et al., 2005;Kulkarni, et al., 2008;Lee, et al., 2008;Middleton, et al., 2007;Niokou, et al., 2003;Norman, et al., 2002;Norman, et al., 2001;Rajalingam, et al., 2008;Rajalingam, et al., 2002;Santin, et al., 2006;Toneva, et al., 2001;Velickovic, et al., 2006;Whang, et al., 2005;Witt, et al., 1999;Wu, et al., 2009;Yawata, et al., 2002;Zhu, et al., 2010).. The first two components were selected for preparing the score plot.
3.3.1.6. Restricted Maximum Likelihood (REML) analysis
The frequency data sets of KIR genes in our study populations and other reference populations were bootstrapped for 100 replicates and then subjected to Restricted Maximum Likelihood (REML) analyses using PHYLIP, version 3.69 (Felsenstein, 1989). This was followed by the construction of a consensus tree using FigTree version 1.3.1
Page | 108 (http://tree.bio.ed.ac.uk) software. The REML analyses assumed the independent evolution of each locus by pure genetic drift.
3.3.1.7. Estimation of Linkage Disequilibrium (LD) and Haplogroup analyses
The classical linkage disequilibrium coefficient (D) between pairs of KIR loci, the standardized coefficient (D’), the conventional measure of linkage disequilibrium (r2), the two locus haplotype frequencies and the respective P values between pairs of KIR gene loci for each of the studied populations were measured. The LD values are measured using Arlequin software ver. 3.5.1.2 (Excoffier and Lischer, 2010).
Based on genotypic profile of the KIR genes in the Rajbanshis, group A and B haplotype frequencies were estimated using the following formula: group-A=2nAA+nAB/2n and
group-B=2nBB+nAB/2n. Herein, nAA represents the numbers of AA genotypes and so are
nAB and nBB for AB and BB genotypes respectively, and n represents the total size of the population.
Fourteen (14) KIR haplotypes were considered in our study which included six (6) gene content haplotypes that have been identified through segregation (Khakoo and Carrington, 2006) and analysis of sequences (Pyo, et al., 2010) and were commonly found in several major ethnic groups. The other eight (8) haplotypes that were included in this study were selected from IPD-KIR database based on their uniqueness in KIR gene content. The pseudogenes namely KIR2DP1 and KIR3DP1 were not included in the haplotypes since they have no functional relevance. Moreover, KIR2DL5A and B were also excluded to avoid ambiguity since we have estimated the frequencies of KIR2DL5 as a whole and not separately for KIR2DL5A and 2DL5B respectively. The frequencies of the haplotypes were estimated using HaploIHP software generously provided by Zhang et al (Yoo, et al., 2007).
Page | 109 3.3.2. RA based study
3.3.2.1. Estimation of clinical parameters between RA patients and controls.
The mean, standard deviation of the mean, median and Interquartile ranges of the clinical parameters were estimated using MS-Excel program of the Microsoft Office Package 2007, and the values were confirmed using IBM SPSS statistics version 19.
3.3.2.2. Diagnostic Test Evaluation
Diagnostic test evaluation criteria, which include sensitivity, specificity, positive predictive value and negative predictive value, were calculated using MedCalc, version 15.6 for Windows (MedCalc Software, Ostend, Belgium). Positive and negative cases in both the patient and the control groups were tabulated in a 2X2 table as follows:
Test Disease (n) Control (n) Total Positive a (True Positive) c (False Positive) a+c Negative b (False negative) d (True Negative) b+d Total a+b c+d a+b+c+d
The values for a particular test from the above mentioned table were used to calculate the above mentioned criteria using the following formulae:
a = a+b d 𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒 = c+d a 𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒 = a+c d 𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏 𝐏𝐏 𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏 𝐕𝐕 𝐕𝐕𝐕𝐕𝐕𝐕𝐕𝐕 = b+d 𝐍𝐍3.3.2.2.1.𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍 𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏Odds𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏 Ratio𝐕𝐕𝐕𝐕𝐕𝐕 𝐕𝐕𝐕𝐕and relative Risk.
The odds ratio (OR) and confidence interval at 95% were measured according to Altman, 1991 (Altman, 1991). The data of the 2X2 table under section 3.3.2.2 were used to calculate the above-mentioned values using the following formulae:
Page | 110 a b a× d ( ) = c = b×c d 𝐎𝐎𝐎𝐎𝐎𝐎𝐎𝐎 𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑 𝐎𝐎𝐎𝐎 % = exp(ln(OR) 1.96 × SE{ln(OR)}) to exp(ln(OR) + 1.96 × SE{ln(OR)})
𝟗𝟗𝟗𝟗 𝐂𝐂𝐂𝐂 − Where zeros caused problems with computation of the odds ratio (OR), 0.5 was added to each cell (Pagano and Gauvreau, 2000).
The Reactive Risk or risk Ratio was calculated according to Altman 1991 (Altman, 1991) using the following formula:
a a+b ( ) = c �c+d 𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑 𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑 𝐑𝐑𝐑𝐑 � Where zeros cause problems with computation of OR or RR, 0.5 is added to each cell (Pagano and Gauvreau, 2000).
3.3.2.3. Regression Analysis and Graphical Representations
Multiple regression models were computed for predicting DAS28 score in case of RA patients using ESR in combination with Anti-CCP and RF titre values. The computation was performed using IBM SPSS statistics version 19.0 software. Several softwares were used to construct the graphs and plots that were used in this part of the study, which included Sigmaplot 12.5 software, IBM SPSS Statistics version 19, Minitab version 16.1.1 and Ms-Excel program of Microsoft office package 2007. The applied software for the construction of a specific graph/plot has been mentioned in the legend of the figure representing the plot in the result section.
3.3.2.4. KIR-RA association based analyses
In this part of the RA based association study KIR gene based analyses was done wherein we followed similar statistical approaches to that of the population based study (Section 3.3.1). Herein, we have estimated the gene frequencies, genotypic frequencies, Odds ratio and linkage disequilibrium among the KIR genes in the RA patients.
Page | 111
Chapter 4:
Results and Discussion
4. Results and Discussion
This section has been divided according to the objectives of my thesis as follows.
4.1. Frequency and distribution of KIR genes within the human population of Sub-Himalayan region of India.
4.1.1. KIR gene frequencies
The carrier or observed frequencies (OFs) and the estimated gene frequencies (KLFs) of the 16 KIR genes in all the studied populations are shown in Table 6. All the 16 KIR loci were detected in each population, wherein the framework and the pseudogenes had observed frequencies of 1.00 except KIR2DP1. In all the populations, A haplotype associated KIR genes occurred more frequently than those associated with the B haplotypes. Apart from the framework genes, the carrier frequencies of the other KIR genes ranged between 0.28 (KIR2DS3) and 0.92 (KIR2DL1) among the Rajbanshis, between 0.35 (KIR2DS3) and 0.88 (KIR3DL1) among the Bengalis, between 0.26 (KIR2DS3) and 0.96 (KIR2DL1) among the Rabhas, between 0.20 (KIR2DS3) and 0.96 (KIR2DL1 and KIR2DL3) among the Gurkhas, while those among the Muslim ranged between 0.41 (KIR2DS3) and 0.90 (KIR3DL1 and KIR2DL1) respectively. As expected, the KLFs also followed the same trend as seen in case of OFs (Table 6). Thus, it became evident from Table 6 that the KIR genes demonstrated the highest range of frequencies among the Gurkhas in comparison to other studied populations, while the lowest range was demonstrated in case of the Muslims. Overall, the frequency of inhibitory KIR genes were higher than that of the activating KIR genes, with the exception of KIR2DS4, which was the most frequent gene encoding an activating receptor in all the studied populations. Another interesting observation from the table was that all the five studied populations reported the lowest frequency value for KIR2DL3 gene while the highest value shuffled between the A haplotype associated genes except that for the activating receptor (i.e. KIR2DL4).
Page | 112 Table 6: Observed carrier frequencies (OFs) and the estimated gene frequencies (KLFs) of the 16 KIR genes in the five study populations. (The highest values are marked with # and the lowest values are marked with * respectively).
KIR Rajbanshi Bengali Rabha Gurkha Muslim genes
N=75 OF KLF N=162 OF KLF N=50 OF KLF N=125 OF KLF N=140 OF KLF 3DL1 63 0.84 0.60 143 0.88# 0.66# 42 0.84 0.60 110 0.88 0.65 126 0.90# 0.68# 2DL1 69 0.92# 0.72# 133 0.82 0.58 48 0.96# 0.80# 120 0.96# 0.80# 126 0.90# 0.68# 2DL3 64 0.85 0.62 104 0.64 0.40 46 0.92 0.72 120 0.96# 0.80# 105 0.75 0.50 2DS4 60 0.80 0.55 137 0.85 0.61 38 0.76 0.51 110 0.88 0.65 115 0.82 0.58 2DL2 39 0.52 0.31 96 0.59 0.36 23 0.46 0.27 40 0.32 0.18 87 0.62 0.38 2DL5 41 0.55 0.33 107 0.66 0.42 22 0.44 0.25 60 0.48 0.28 91 0.65 0.41 3DS1 31 0.41 0.23 77 0.48 0.28 20 0.40 0.23 46 0.37 0.21 67 0.48 0.28 2DS1 34 0.45 0.26 78 0.48 0.28 23 0.46 0.27 60 0.48 0.28 67 0.48 0.28 2DS2 43 0.57 0.35 103 0.64 0.40 22 0.44 0.25 52 0.42 0.24 81 0.58 0.35 2DS3 21 0.28* 0.15* 56 0.35* 0.19* 13 0.26* 0.14* 25 0.20* 0.11* 57 0.41* 0.23* 2DS5 27 0.36 0.20 77 0.48 0.28 19 0.38 0.21 53 0.42 0.24 58 0.41 0.23 2DL4 75 1.00 1.00 162 1.00 1.00 50 1.00 1.00 125 1.00 1.00 140 1.00 1.00 3DL2 75 1.00 1.00 162 1.00 1.00 50 1.00 1.00 125 1.00 1.00 140 1.00 1.00 3DL3 75 1.00 1.00 162 1.00 1.00 50 1.00 1.00 125 1.00 1.00 140 1.00 1.00 2DP1 73 0.97 0.84 154 0.95 0.78 49 0.98 0.86 124 0.99 0.91 138 0.99 0.88 3DP1 75 1.00 1.00 162 1.00 1.00 50 1.00 1.00 125 1.00 1.00 140 1.00 1.00
4.1.2. KIR genotypic frequencies
Taken altogether, 76 KIR genotypes are listed from our studied populations, of which 3 were AA genotypes while the remaining 73 were Bx genotypes. The Bx genotypes can again be subdivided in to 33 AB and 40 BB genotypes. Table 7 showed the KIR genotype IDs according to AFND (Gonzalez-Galarza, et al., 2015), the KIR gene content of each genotype, the genotype frequencies and the number of individuals from each population having a particular genotype.
In the Rajbanshis, 30 different KIR genotypes are found, which suggest substansive richness in KIR variation (Table 7). Among 75 Rajbanshi samples, 15 samples were AA genotypes (20%), 21 were BB genotypes (28%) and the remaining 39 were AB genotypes
Page | 113 (52%). Thus, the AA and BB genotypes are present in more or less equal proportion. It was also seen that out of 15 AA genotypes in the Rajbanshis, 14 individuals have genotype ID 1 (18.67%) which is dominantly present in the North East Asians (NEAs). The most frequent BB genotype is Genotype ID 70 (5.33%), which has also been reported from 54 populations around the World (http://www.allelefrequencies.net) (Gonzalez-Galarza, et al., 2015) including South Korean, Hongkong Chinese, North and South Indian populations and also in Mumbai Parsis. In Rajbanshis, among AB genotypes the most frequent are the Genotype ID 2 and 4, which have also been observed in North East Asians, Tibetans and the Asian Indian populations (AIs).
A total of 43 different KIR genotypes have been observed in the Bengalis (N=162) which undoubtedly suggests extensive KIR variations in the studied population (Table 7). Among 162 individuals, 77 have AB genotypes (45.06%) followed by 68 BB genotypes (41.98%) and 21 AA genotypes (12.96%). This proportion clearly indicates the predominance of the BB genotypes over the AA genotypes in this population of the study area. In case of AA genotype, it was found that 19 out of 21 individuals have genotype ID 1 (11.73%). Genotype ID 71 (6.17%) was the most frequent among the BB genotypes, which has also been reported in 144 individuals from 54 populations around the world (http://www.allelefrequencies.net) that included the Hongkong Chinese, South Korean, North Indian, and Mumbai Parsis and also in South Indian Paravars and Kanikars. In contrast, the genotype ID 2 followed by IDs 4 and 10 were the most frequent ones to occur among the Bengali AB genotypes which have also been observed in North East Asians, Tibetans and the Asian Indian populations (AIs).
18 genotypes are detected in the Rabha population (Table 7). Among 50 Rabha samples, 18 samples were AA genotypes (36%), 12 were BB genotypes (24%) and the remaining 20 were AB genotypes (40%). Thus, the AA genotypes were present in greater proportion in comparison to the BB genotypes in the Rabha population. One significant observation is that the most dominant NEA genotype, ID 1, has been found in 34% (17 out of 50) of the studied Rabha population. Furthermore, Genotype ID 486, has been detected in the Rabha population, which was earlier recorded to be present in the Chinese Shaanxi Han
Page | 114 population (Wang, et al., 2011). On the other hand, it was quite interesting to find genotype IDs 161 and 300 in the Rabha population, that were only reported in the North Indians till date (Rajalingam, et al., 2002).
Finally, 32 and 41 genotypes were reported in the Gurkha and the Muslim population respectively, suggesting extensive variation in KIR genes in the studied populations (Table 7). Among the Gurkhas (N= 125), 36 individuals had AA genotypes (28.8%) while remaining 89 individuals had Bx genotypes (71.2%) which can be further subdivided into 56.8% AB and 14.4% BB genotypes. In contrast, only 19 out of 140 individuals (13.57%) among the Muslims had AA genotypes while the remaining individuals belonged to Bx genotypes (86.43%) which again can be further subcategorized into 52.14% AB and 34.29 % BB genotypes. In case of AA genotype it was found that 32 out of 36 Gurkha individuals had genotype ID 1, which is the most common and widely distributed member of the AA genotypes and is dominantly represented in the North East Asians and the Europeans. In contrast 17 out of 19 Muslim individuals had genotype ID 1. It was also seen that genotype ID 2 was the most frequent among the Bx genotypes both in the Gurkha and the Muslim population followed by genotype IDs 3 and 4 respectively which were also observed in North East and South East Asians, Tibetans and the Asian Indian populations (AIs). Interestingly, it was found that genotype ID 488 that was recorded earlier in the Han population of China Shaanxi Province, was also found in the Gurkha population. Furthermore, genotype ID 140 that was reported earlier only in the inhabitants of Tokelau, New Zealand, had also been identified in the Gurkhas. It was also observed that genotype ID 172 found in the Gurkhas has been reported earlier only in the populations having Indian origin. Genotype ID 19, which was reported in the Muslims population at a high frequency was also found at a considerable high frequency in the Iranian population and Singapore Indians. Genotype ID 51 found at high frequency in the Muslims was also reported in the Singapore Indians. Furthermore, genotype ID 275 reported earlier South Asian and Middle East populations, has also been identified in the Muslim population.
Page | 115 Table 7: KIR genotypes in the studied populations.
Frequency(%) ID 3DL1 2DL1 2DL3 2DL2 2DL5 2DL4 3DL2 3DL3 2DS4 3DS1 2DS1 2DS2 2DS3 2DS5 2DP1 3DP1 Rajbanshi Bengali Rabha Gurkha Muslim Genotype
AA 1 14 14 14 14 14 14 14 14 14 18.67 (14) 11.73 (19) 34 (17) 25.60 (32) 12.14 (17) AA AA 180 1 1 1 1 1 1 1 1 1.33 (1) 0.62 (1) AA 195 0.62 (1) 1.60 (2) 1.43 (2) AB 2 5 5 5 5 5 5 5 5 5 5 5 5 5 6.67 (5) 6.17 (10) 8.00 (4) 12.00 (15) 3.57 (5) AB 3 333333333 333333 4.00 (3) 3.70 (6) 4.00 (2) 7.20 (9) 4.29 (6) AB 4 5 5 5 5 5 5 5 5 5 5 5 6.67 (5) 4.32 (7) 8.0 (4) 5.60 (7) 2.86 (4) AB 5 3 3 3 3 3 3 3 3 3 3 3 3 3 4.00 (3) 1.23 (2) 2.14 (3) AB 6 3.70 (6) 6.00 (3) 2.40 (3) 5.00 (7) AB 7 1.60 (2) AB 8 0.62 (1) 4.00 (2) 4.80 (6) 2.14 (3) AB 9 111111 11 111111 1.33 (1) 1.23 (2) 3.20 (4) 1.43 (2) AB 10 2 2 2 2 2 2 2 2 2 2 2.67 (2) 4.32 (7) 4.80 (6) 3.57 (5) AB 11 333333 333 33333 4.00 (3) 1.23 (2) 0.80 (1) 1.43 (2) AB 12 1111 1111 111111 1.33 (1) 1.85 (3) 2.14 (3) AB 13 3333333 33 33333 4.00 (3) 2.47 (4) 3.57 (5) AB 14 1 1 1 1 1 1 1 1 1 1 1.33 (1) AB 15 3 3 3 3 3 3 3 3 3 3 4.00 (3) 0.62 (1) 4.00 (2) 4.80 (6) 0.71 (1) AB 19 2 2 2 2 2 2 2 2 2 2 2.67 (2) 4.29 (6) AB 20 3.70 (6) 2.14 (3) AB 22 2.00 (1) 0.80 (1) Bx AB 23 1.85 (3) 2.86 (4) AB 32 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) 1.23 (2) 2.40 (3) AB 33 2 2 2 2 2 2 2 2 2 2 2 2 2.67 (2) 4.94 (8) 3.57 (5) AB 36 0.62 (1) 0.71 (1) AB 37 0.80(1) AB 41 22222 2 22 22222 2.67 (2) AB 44 3.20 (4) AB 51 3.57 (5) AB 62 1.43 (2) AB 63 11111 111 111111 1.33 (1) AB 191 0.62 (1) AB 192 4.00 (2) AB 200 0.80(1) AB 260 0.62 (1) 1.60 (2) AB 275 0.71 (1) AB 370 1 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) BB 68 1 1 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) 1.85 (3) 4.00 (2) 2.40 (3) 0.71 (1) BB 69 2 2 2 2 2 2 2 2 2 2 2 2.67 (2) 1.85 (3) 1.60 (2)
continued…….
Page | 116 continued……
Frequency(%) ID 3DL1 2DL1 2DL3 2DL2 2DL5 2DL4 3DL2 3DL3 2DS4 3DS1 2DS1 2DS2 2DS3 2DS5 2DP1 3DP1 Rajbanshi Bengali Rabha Gurkha Muslim Genotype
BB 71 6.17 (10) 2.14 (3) BB 72 2 2 2 2 2 2 2 2 2.67 (2) 1.85 (3) BB 73 3.70 (6) 2.86 (4) BB 74 1.85 (3) 0.80 (1) 1.43 (2) BB 75 1.43 (2) BB 78 1 1 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) 1.85 (3) 2.14 (3) BB 79 1 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) 0.62 (1) 1.43 (2) BB 81 1 111111111111 1.33 (1) 1.23 (2) 2.14 (3) BB 82 6.06 (8) 3.57 (5)
BB 93 1.43 (2) BB 101 333333333333 4.00 (3) 2.47 (4) 2.00 (1) 1.60 (2) 1.43 (2) BB 104 2.47 (4) 1.43 (2)
BB 110 2 2 2 2 2 2 2 2 2 2 2 2 2.67 (2) 1.43 (2) BB 120 2.14 (3) BB 121 1.43 (2) BB 133 1.23 (2)
BB 140 0.80(1) BB 156 0.80(1) Bx BB 161 2.00 (1)
BB 164 1 1 1 1 1 1 1 1 1 1 1 1 1.33 (1) BB 171 1.23 (2) 2.14 (3) BB 172 1.23 (2) 1.43 (2)
BB 176 3 3 3 3 3 3 3 3 3 3 4.00 (3) 1.85 (3) 0.80 (1) 2.14 (3) BB 208 0.62 (1)
BB 265 0.80(1) BB 277 2.00 (1) 0.80 (1) BB 300 2.00 (1)
BB 312 1.60 (2) BB 330 0.62 (1)
BB 332 0.80(1) BB 341 0.62 (1) BB 342 1.23 (2) BB 486 2.00 (1)
BB 488 0.80(1) BB 530 2.00 (1) BB N1 2.00 (1)
BB N2 0.80(1)
Page | 117 4.2. Study of heterogeneity among the local population(s).
4.2.1. Chi-Square Test
Chi-square (χ2) analyses were performed to compare the differences in KIR gene frequencies of our study populations with the available published data of few other Asian populations which include populations from Eastern Mainland and Jiangsu province of China, North Indians, South Indian Paravar, Kanikar and Mollukurumba populations, Mumbai Parsis and Maharashtrians, Tibetans, Indians from Singapore and also Pakistani population. P value<0.05 was considered the limit for statistical significance. The framework and the pseudogenes were not included in the analysis. It was evident from Table 8 that none of the inhibitory KIRs differed significantly in frequencies except KIR2DL5 between the Rajbanshis and North East Asians (NEAs), mainly the Chinese from Eastern Mainland and from Jiangsu Province. In contrast, significant differences were observed for KIR3DL1 only between the Rajbanshis and the NEAs but not with other World populations. The reverse relationship is evident for KIR2DL5 where significant differences exist for all the populations except the NEAs, Tibetans and Mollukurumba. Rajbanshi and Mollukurumbas have significant difference only for KIR2DS4. The North Indians showed no significant differences from the Rajbanshis in the frequencies of activating KIR genes (Table 8).
In case of Bengali population, KIR3DL1 showed no significant differences when compared with other populations (Table 8). In contrast, the Bengali population exhibited significant differences with the North East Asians (NEAs) for all the KIR genes except KIR2DS1, KIR3DL1 and KIR3DS1. Significant differences exist in case of KIR2DS2 between the Bengalis and the NEAs and Tibetans, but interestingly, the differences were found to be insignificant when compared with other comparator populations. The Bengalis appeared to differ significantly from the North Indians based on KIR2DL2 frequency and from the Mollukurumbas and Singapore Indians when KIR2DL3 is concerned. Another interesting revelation was that the Bengalis showed no significant differences in the frequencies of the activating KIR genes when compared to the North Indians, Mollukurumba, Singapore Indians and the Maharastrian populations (Table 8).
Page | 118 Another striking observation from the χ2 analyses was that the Rabha population showed no significant difference for any of the KIR loci with that of the Rajbanshi population. All the referral populations of the Indian subcontinent, except from the Mollukurumbas and the Rajbanshis differed significantly from the Rabhas in the frequency of KIR2DL5 gene. Moreover, the Rabhas did not differ significantly from the Tibetan population except for KIR2DS3 locus (Table 8).
It was interesting to find that the Gurkhas showed no significant differences for any of the KIR loci with the Rabha and the Tibetan populations (Table 8). Surprisingly, a similar scenario was also found in case of Muslims who showed no significant differences either with the Rajbanshis or that with the Bengalis for any of the KIR loci (Table 8). Consistent significant differences were evident between the Gurkhas and the North East Asians (NEAs) for KIR2DS2 and KIR2DS5. Furthermore, the Gurkhas showed significant differences with other Indian populations for more than one locus. On the other hand, the Muslim population showed significant differences for only one or two loci with most of the Indian populations except South Indian Kanikars and Paravars. It was also evident from Table 8 that the Tibetans and NEAs showed significant differences with the Muslim population included in our study for majority of the KIR loci. Another interesting observation was that neither the Gurkhas nor the Muslims showed any significant differences for KIR3DL1 with any of the comparative populations (Table 8).
Page | 119 Table 8: Comparison of the frequencies of KIR genes in our studied populations with that of some of the other Asian populations using χ2 test (Kyplot beta 2.0). Their level of significance are shown with * (i.e. *p<0.05, **p<0.01, ***p<0.001).
KIR Genes 2DL1 2DL2 2DL3 2DL5 2DS 2DS2 2DS3 2DS4 2DS5 3DL1 3DS 1 1 Maharashtrian 97.8* 62 85.8 71.7* 47.8 62.3 44.2* 90.7* 46.4 91.4 37 (N=139) Kanikar (N=35) 97 74* 77 86** 60 74 73*** 80 60* 80 69** Paravar (N=77) 97 69* 79 83*** 65* 75* 49** 84 66*** 84 60* Pakistan (N=78) 90 67 91 78** 60 69 45* 72 48 81 56 North Indians 79.2** 87.5 65.3** 79.2** 54.2 62.5 43.1 80.6 47.2 87.5 38.9 (N=72) * Mollukurumba 95 59 85 56 41 59 20 95* 39 95 37 (N=41) Singapore Indian 81.3 68.8* 85 78.8** 53.8 60 40 77.5 55* 91.3 50 (N=80) 62.2* Parsis (N=145) 99.3** 62.8 81.9 71* 62.8* 62.8 53.8*** 86.1 51.0* 87.4 Rajbanshi * Tibetan (N=102) 100* 30** 98** 50 51 29*** 12** 88 37 88 36 Chinese (N=106) 99* 25*** 99*** 45 42 27*** 22 95** 28 94* 41 21.3** Jiangsu (N=150) 100** 100*** 44.7 46.7 24.7*** 21.3 92.7** 30.7 93.3* 40 * Maharashtrian 97.8** 62 85.8*** 71.7 47.8 62.3 44.2 90.7 46.4 91.4 37 (N=139) * Kanikar (N=35) 97* 74 77 86* 60 74 73*** 80 60 80 69* Paravar (N=77) 97** 69 79* 83** 65* 75 49* 84 66* 84 60 Pakistan (N=78) 90 67 91*** 78 60 69 45 72 48 81 56 North Indians 87.5 79.2** 65.3 79.2 54.2 62.5 43.1 80.6 47.2 87.5 38.9 (N=72) Mollukurumba 95 59 85* 56 41 59 20 95 39 95 37 (N=41) Singapore Indian 81.3 68.8 85** 78.8 53.8 60 40 77.5 55 91.3 50 (N=80) 99.3** Parsis (N=145) 62.8 81.9*** 71 62.8* 62.8 53.8** 86.1 51 87.4 62.2* Bengali * 100** Tibetan (N=102) 30*** 98*** 50* 51 29*** 12*** 88 37 88 36 * Chinese (N=106) 99*** 25*** 99*** 45** 42 27*** 22* 95* 28** 94 41 100** 21.3** 40** Jiangsu (N=150) 100*** 44.7*** 46.7 24.7*** 21.3* 92.7* 30.7** 93.3 * * * Rajbanshi (N=75) 92 52 85.3** 54.7 45.3 57.3 28 80 36 84 41.3
Maharashtrian 97.8 62 85.8 71.7*** 47.8 62.3* 44.2* 90.7* 46.4 91.4 37 (N=139)
Kanikar (N=35) 97 74* 77 86*** 60 74* 73*** 80 60 80 69* Paravar (N=77) 97 69* 79 83*** 65 75*** 49* 84 66* 84 60* Pakistan 90 67* 91 78*** 60 69** 45* 72 48 81 56 (N=78) North Indians 79.2** 87.5 65.3** 79.2*** 54.2 62.5 43.1 80.6 47.2 87.5 38.9 (N=72) * Mollukurumba 95 59 85 56 41 59 20 95* 39 95 37 (N=41) Rabha Singapore 81.3* 68.8* 85 78.8*** 53.8 60 40 77.5 55 91.3 50 Indian (N=80) 62.2 Parsis (N=145) 99.3 62.8 81.9 71*** 62.8 62.8* 53.8** 86.1 51 87.4 * Tibetan 100 30 98 50 51 29 12* 88 37 88 36 (N=102)
Page | 120 KIR Genes 2DL1 2DL2 2DL3 2DL5 2DS1 2DS2 2DS3 2DS4 2DS5 3DL1 3DS1 Chinese (N=106) 99 25* 99 45 42 27 22 95*** 28 94 41 21.3* 92.7* Jiangsu (N=150) 100 * 100* 44.7 46.7 24.7* 21.3 * 30.7* 93.3 40 Rajbanshi (N=75) 92 52 85.3 54.7 45.3 57.3 28 80 36 84 41.3 82.10 64.20 66.05 63.58 Bengali (N=162) * 59.26 *** ** 48.15 * 34.57 84.57 47.53 88.27 47.53
KIR Genes 2DL1 2DL2 2DL3 2DL5 2DS1 2DS2 2DS3 2DS4 2DS5 3DL1 3DS1 Maharashtrian 85.8* 71.7* 62.3* 44.2* 97.8 62*** 47.8 90.7 46.4 91.4 37 (N=139) * ** * ** Kanikar (N=35) 97 74*** 77*** 86*** 60 74*** 73*** 80 60 80 69*** Paravar (N=77) 97 69*** 79*** 83*** 65* 75*** 49*** 84 66** 84 60** Pakistan (N=78) 90 67*** 91 78*** 60 69*** 45*** 72** 48 81 56** 79.2* 65.3* 79.2* 62.5* 43.1* North Indians (N=72) 87.5 54.2 80.6 47.2 87.5 38.9 ** ** ** * ** Mollukurumba 95 59** 85* 56 41 59 20 95 39 95 37 (N=41) Singapore Indian 81.3* 68.8* 78.8* 85* 53.8 60* 40** 77.5 55 91.3 50 (N=80) * ** ** 62.8* 81.9* 62.8* 53.8* 62.2* Parsis (N=145) 99.3 71*** 62.8* 86.1 51 87.4 ** ** ** ** ** Tibetan (N=102) 100 30 98 50 51 29 12 88 37 88 36
Gurkha Chinese (N=106) 99 25 99 45 42 27* 22 95* 28* 94 41 24.7* Jiangsu (N=150) 100 21.3* 100 44.7 46.7 21.3 92.7 30.7* 93.3 40 * 52.0* Rajbanshi (N=75) 92 85.3* 54.7 45.3 57.3* 28 80 36 84 41.3 * 82.10 59.26 64.20 66.05 63.58 34.57 Bengali (N=162) 48.15 84.57 47.53 88.27 47.53 *** *** *** ** *** ** Rabha (N=50) 96 46 92 44 46 44 26 76 38 84 40
Maharashtrian 97.8* 62 85.8* 71.7 47.8 62.3 44.2 90.7 46.4 91.4 37 (N=139) Kanikar (N=35) 97 74 77 86* 60 74 73*** 80 60 80 69* Paravar (N=77) 97 69 79 83** 65* 75* 49 84 66*** 84 60 Pakistan (N=78) 90 67 91** 78 60 69 45 72 48 81 56 North Indians (N=72) 87.5 79.2* 65.3 79.2* 54.2 62.5 43.1 80.6 47.2 87.5 38.9 Mollukurumba 95 59 85 56 41 59 20* 95 39 95 37 (N=41) Singapore Indian 81.3 68.8 85 78.8* 53.8 60 40 77.5 55 91.3 50 (N=80) 99.3* Parsis (N=145) 62.8 81.9 71 62.8* 62.8 53.8* 86.1 51 87.4 62.2* * Tibetan (N=102) 100** 30*** 98*** 50* 51 29*** 12*** 88 37 88 36 Muslim Chinese (N=106) 99** 25*** 99*** 45** 42 27*** 22** 95** 28* 94 41 100** 21.3* 100** 44.7* 24.7* 21.3* Jiangsu (N=150) 46.7 92.7* 30.7 93.3 40 * ** * ** ** ** Rajbanshi (N=75) 92 52 85.3 54.7 45.3 57.3 28 80 36 84 41.3 Bengali (N=162) 82.1 59.26 64.2 66.05 48.15 63.58 34.57 84.57 47.53 88.27 47.53 Rabha (N=50) 96 46 92* 44* 46 44 26 76 38 84 40 Gurkha (N=125) 96 32*** 96*** 48** 48 42* 20*** 88 42 88 37
Page | 121 4.2.2. Principal Component Analyses
Principal Component Analyses (PCA) was performed based on the OFs of the KIR genes to investigate the genetic affinities of our sample populations with that of the other comparator populations (Figure 23). Frequencies of nine KIR genes were considered in the analyses and the remaining other seven genes (2DL5, 2DS5, 2DL4, 3DL2, 3DL3, 2DP1, and 3DP1) were not included since they were either invariably present in all the populations or not typed in some of the comparator populations. The score plot shown in Figure 23 was computed based on the first two components wherein the first and the second component accounted for 57.8% and 22.7% variability respectively. It was evident from Figure 23 that the positions occupied by different populations based on their KIR gene frequencies were in accordance with their geographical proximities except few outliers. Altogether, eight distinct geographical clusters were observed and are marked with different symbols in the score plot, namely Africans, Northeast Asians, North American Natives, South American Natives, Asian Indians, Europeans, South East Asians and Pacific Islanders. As per expectation, the Rajbanshis huddled with the Asian Indian Cluster, but surprisingly showed proximity to the European Cluster. Geographical data and anthropological evidences suggest the influence of the Asian Indians, the Northeast Asians and Southeast Asians on the gene pool of the Rajbanshis (Figure 23). Expectedly the Bengalis clustered with the Asian Indians and was placed in between the North Indians and the Singapore Indians in the score plot. This result is in accordance to their geographical location and anthropological background, which suggest the influence of the North Indians, South (Dravidian) Indians, the Europeans and the Northeast Asians on the Bengali population. The Rabhas have shown the tendency to move away from the Indian cluster towards the lower left half of the score plot, which have been mostly occupied by the North East and South East Asians (Figure 23).
As expected, the Muslims occupied a position within the Indian cluster very close to that of the Bengali community, which was indeed a population with mixed religious background. Interestingly, this result is in agreement with their geographical location and anthropological background which suggest the influence of the North Indians, South
Page | 122 (Dravidian) Indians, the Europeans and the Northeast Asians as well as from the Middle East lineage. On the other hand, the Gurkhas occupied a position in the lower left quadrant of the score plot in close proximity to the North East Asian, South East Asian and the European Cluster (Figure 23). Such result demonstrated the influence of the East Asian lineage on the Gurkhas.
Figure 23: Principal Component Analysis (PCA) based on carrier frequencies of nine variable KIR genes (2DL1-3, 2DS1-4, 3DL1, and 3DS1) in the studied populations along with other world populations developed by MINITAB v16. In the plot, our studied populations have been marked in red. (Jap, Japanese; Chi, Eastern Mainland Chinese; Kor, Korean; SpC, Singapore Chinese; Hkc; Hongkong Chinese; Hui, Huichol; Pur, Purepecha; Tar, Tarahumara; Zhe, Zhejiang Chinese; Tib, Tibetans; Viet, Vietnamese; Fin, Finnish; Chg, Chiriguanos; Thai, Thailand; War, Warao; Bar, Bari; Jian, Jiansu Chinese; AmA, Amazonian Amerindian; Gre, Greek; Yuc, Yucpa; CkI, Cook Island; Sam, Samoan; Tok, Tokelau; Mez, Mestizo; Ton, Tongan; Bri, British Caucasian; ReU, Reunion; NIr, Northern Irish; Fre, French Caucasian; AmC, American Caucasian; His, Hispanic; SpM, Singapore Malay; Sen, Senegal African; GuC, MK, Mollukurumba; Guadeloupe Caribbean; AfC, Afro- Caribbean; AfA, African American; Arb, Arabian; Ben, Bengalis; Pal, Palestinian; Mah, Maharashtrian; Pak, Pakistani; Tri, Trinidad Asian;
Page | 123 NIn, North Indian; Kan, Kanikar; Par, Paravar; Bas, Basque population; SpI, Singapore Indians; MuP, Indian Mumbai Parsi and AuA, Australian Aborigine).
4.2.3. Estimation of Genetic distances
Table 9 represented the measures of Nei genetic distances of the studied populations with that of the other geographically neighboring populations whose KIR gene frequencies have already been published. It was evident from Table 9 that the Rajbanshis have the least distance from Mollukurumbas (0.225) and Maharashtrians (0.248). Among the populations of the North East Asian Cluster, the one having the least distance with the Rajbanshis is the Tibetan population (0.429). It was also observed from Table 9 that the Bengalis have the least distance from North Indians (0.012) followed by the Rajbanshis (0.014) and the Maharashtrians (0.016), thereby revealing their proximity with the populations of Indian origin. On the other hand, the Rabha population showed the least genetic distance with the Rajbanshis in comparison to other Indian populations (Table 9). However, when compared to geographically neighboring populations outside India, the Rabhas showed the least genetic distances with the Thai population (0.00407) followed by the Vietnamese (0.00667) (Table 9). The Gurkhas showed the highest genetic distance with the South Indian Kanikar population (0.1281) followed by the Paravars (another South Indian population; 0.0858) and North Indian population (0.07449) respectively, while least distances was observed with the Thais (0.0039) followed by Tibetan population (0.0047) respectively. One interesting observation from Table 9 was that the Gurkhas showed the lesser genetic distance with the Rabhas compared to the Rajbanshis. In contrast, the Bengali Muslims were most distantly related to two population groups of China from Zhejiang (0.0874) and Jiangsu (0.0638) provinces, while least distances was observed with the Bengalis (0.0053) and the Maharashtrian (0.0062) population respectively. It was also evident from Table 9 that the Muslims showed lesser genetic distances with the Rajbanshis compared to the Rabha and the Gurkha populations.
Page | 124 Table 9: Measures of Nei Genetic distances between studied populations and other geographically neighboring populations. Page |Page 125 4.2.4. Comparison of Bx subsets
The comparative proportions of AA genotypes and Bx genotype subsets (namely C4T4, C4Tx, CxT4 and CxTx) among the studied populations along with that of some other populations around the World were represented by stacked bar diagram in Figure 24. Neighbouring population like Pakistan were excluded from the analyses since KIR2DL5 was not detected in that population. It was evident from Figure 24 that all the four subsets of the Bx genotypes (CxT4, C4Tx, C4T4, and CxTx) were present in all the studied populations and thereby presenting a balancing condition. The frequencies of the four different Bx subsets along with that of AA genotypes were represented in Table 10 below.
Table 10: Frequencies of subsets of Bx genotypes in the studied population and some other comparative populations around the World.
Population C4Tx C4T4 CxT4 CxTx AA Rajbanshi 14.7 10.7 18.7 36 19.9 Bengali 13.6 18.5 19.8 35.2 12.9 Rabha 6 16 16 26 36 Gurkha 3.2 5.6 24 38.4 28.8 Muslim 12.9 17.9 17.1 38.6 13.5 Paravar 19.5 28.6 31.2 16.9 3.8 Kanikar 20 34.2 25.7 16.8 3.3 North Indian 23.7 8.4 21 41.9 5 Maharashtrian 22.8 16.1 16.4 21 23.7 Parsis 17.8 29.5 16.5 11.2 25 Chinese Han 1.2 2.5 23.6 17.5 55.2 Japanese 2.9 1 21 16.2 58.9 Zhejiang 2.9 1 21.2 16.3 58.7 Jiangsu 3.33 0 23.3 31.7 41.7 Tibet 2.9 7.8 24.5 26.5 38.2 Arab 31.6 13.2 11.8 27.6 15.8 Mollukurumba 14.6 2.4 31.7 22 29.3 British 13.9 4.4 19.9 31.7 30.1 Northern Irish 16.3 8.3 21.2 18.1 36.1 Afro-American 20.7 3.4 6.9 39.6 29.4 Afro-Caribbean 19.6 0 9.9 34.8 35.7
Page | 126 Among the studied populations, the Gurkha population has the lowest frequency of the C4Tx subset followed by the Rabhas while the highest frequency was observed in the Rajbanshi population in comparison to other studied populations. It was also observed that the studied populations had comparatively lower frequencies of C4Tx subsets than that of other Indian populations. Furthermore, the estimation of C4Tx subsets in the Rabha population was comparable to that of the North Eastern populations, which include populations from China, Japan and Tibet respectively.
In case of C4T4 subset, a similar scenario was observed as was seen in case of C4Tx subset, wherein the Gurkha population has the lowest frequency in comparison to other studied populations. However, the Gurkhas are followed by the Rajbanshis in having the second lowest frequency among the studied populations. When compared with the other Indian populations, it was seen that most of the Indian populations had considerable higher frequencies of C4T4 subsets except the North Indians. Furthermore, as seen in the case of the C4Tx subsets, the NEAs returned the lowest frequencies for C4T4 subsets in context to the World populations. In fact, no C4T4 subset was recorded in case of Chinese populations from Jiangsu Province (Figure 24).
In case of CxT4 subset, minor fluctuations have been observed among the studied populations except for a slightly higher frequency in case of the Gurkhas. When compared with the other Indian and World populations, it was observed that most of the populations returned values within a definable range except for very low frequencies seen in the African populations that were considered in this analysis namely the Afro- Caribbeans and the Afro- Americans (Table 10). However, considerable higher frequencies for CxT4 subset have been seen in case of two south Indian populations namely the Paravars and the Mollukurumbas.
It was evident from Figure 24 that the frequencies of CxTx subsets in the studied populations were almost similar except the Rabha population where the value is considerably lower. Surprisingly, it can be seen that the frequencies of the CxTx subset in most of the comparator populations were less than that of the studied populations with exceptions of the North Indian and the Afro-American populations Table 10. Among the
Page | 127 Indian populations considered for the analysis, the North Indians have the highest representation (41.9%) followed by the Muslims (38.6%) and the Gurkhas (38.4%).
Figure 24:Stacked bar diagram to show the comparative proportions of AA genotypes and different subsets of Bx genotypes in our studied populations along with some other populations around the World.
Furthermore, we also performed PCA based on the frequencies of the AA and the Bx subsets of the KIR genotypes in the studied populations. Published data from neighboring populations were considered for comparison. It can be clearly seen from the score plot that the Bengalis and the Muslims remain in close proximity and share the same quadrant of the plot with the North Indians and the Arab population. Interestingly, it has been also observed that the other three populations namely the Rajbanshis, the Rabhas and the Gurkhas have shown the tendency to share the same quadrant with the East Asian populations.
Page | 128 Figure 25: PCAscore plot based on the frequencies of the KIR AA and Bx subsets in our studied populations and other referr com populations.
4.2.5. Estimation of Haplotype frequencies
In human, the KIR haplotypes are categorized into two groups, A and B (Martin, et al., 2004;Parham, 2005). The A haplotype consist of a single configuration of inhibitory genes except KIR2DS4, while B haplotypes have numerous configurations, each typified by varying numbers of activating genes along with inhibitory genes (Hsu, et al., 2002;Middleton, et al., 2007;Uhrberg, et al., 2002). Fourteen (14) KIR haplotypes were considered in our study which included six gene content haplotypes that have been identified through segregation (Khakoo and Carrington, 2006) and analyses of sequences (Pyo, et al., 2010) and were commonly found in several major ethnic groups. The other 8 haplotypes that were included in this study were selected from IPD-KIR database based on their uniqueness in KIR gene content. The pseudogenes namely KIR2DP1 and
Page | 129 KIR3DP1 were not included in the haplotypes since they have no functional relevance. Moreover KIR2DL5A and B were also excluded to avoid ambiguity since we have estimated the frequencies of KIR2DL5 as a whole and not separately as KIR2DL5A and 2DL5B. The frequencies of the haplotypes were estimated using HaploIHP software generously provided by K. Zhang (2007) on query (Yoo, et al., 2007). The gene content of the 14 selected haplotypes and their respective frequencies in our study populations were mentioned in Table 11. It was observed from this table that Haplotype 1, 3 and 8 are present in all the studied populations with the highest frequency observed for haplotype 1 in each of the populations. When compared across all the studied populations, it is seen that Haplotype 1 has the highest frequency among the Rabhas followed by the Gurkhas and the Muslims.
Interestingly, it is also observed that the frequency of Haplotype 12 is considerably high among these three populations wherein the Rajbanshi population has got the highest frequency followed by the Gurkhas and the Rabhas respectively. In case of the Bengalis, the second highest frequency was observed for Haplotype 2 while the same position was occupied by haplotype 3 in case of the Muslims (Table 11). Apart from the supplied haplotypes, many other haplotypes are found, out of which only those having frequencies greater than 5% have been included in Table 11 and named as N1-N6. However, in case of the Bengali population, no such haplotype has been reported, which has crossed our threshold value.
Page | 130 Table 11:Frequencies of some selected Haplotypes in the studied populations. Haplotypes N1 to N6 were those haplotypes that were primarily not included in our haplotype list while doing the analyses but were later included in the table as they have frequencies greater than 5 % in our studied populations.
Frequency
Haplotype 3DL3 2DS2 2DL2 2DL3 2DL1 2DL4 3DS1 3DL1 2DS3 2DS5 2DS1 3DL2 2DS4 Rajbanshi Rabha Bengali Gurkha Muslim 1 1 0 0 1 1 1 0 1 0 0 0 1 1 0.383 0.552 0.306 0.415 0.296 2 1 1 1 0 0 1 0 1 0 0 0 1 1 0.076 0.135 0.061 3 1 1 1 0 0 1 1 0 0 1 1 1 0 0.055 0.063 0.113 0.065 0.08 4 1 1 1 0 1 1 0 1 1 0 0 1 1 0.082 0.066 0.064 5 1 0 0 1 1 1 1 0 1 0 1 1 0 0.004 0.033 0.026 6 1 1 1 0 1 1 0 1 0 1 0 1 1 0.038 7 1 1 1 0 0 1 1 0 1 0 1 1 0 0.043 0.014 8 1 1 1 0 1 1 1 0 1 1 1 1 0 0.016 0.049 0.041 0.011 0.028 9 1 1 1 0 1 1 1 0 1 0 1 1 0 0.019 10 1 1 1 0 1 1 0 1 0 0 0 1 0 0.024 0.087 0.027 0.032 11 1 1 1 0 1 1 0 1 0 0 0 1 1 0.054 12 1 0 0 1 1 1 1 0 0 1 1 1 0 0.106 0.088 0.089 0.105 13 1 0 0 1 1 1 1 0 0 1 1 1 1 14 1 1 1 0 1 1 1 0 1 1 1 1 1 N1 1 0 0 1 1 1 0 1 0 0 1 1 1 0.056 0.06 N2 1 0 0 1 1 1 0 1 0 0 1 1 0 0.060 N3 1 0 0 1 1 1 1 0 0 0 0 1 1 0.057 N4 1 0 0 1 1 1 0 1 0 0 0 1 0 0.07 N5 1 1 1 0 0 1 1 0 1 1 1 1 0 0.067 N6 1 0 0 1 0 1 0 1 0 0 0 1 1 0.06
Page | 131 4.2.6. Linkage Disequilibrium
The classical linkage disequilibrium coefficient (D) between pairs of KIR loci, the standardized coefficient (D’), the conventional measure of linkage disequilibrium (r2), the two locus haplotype frequencies and the respective P values between pairs of KIR gene loci for each of the studied populations have been presented in Table 12. Statistical significance was considered at P value <0.05. The framework and the pseudogenes were excluded from this analysis. The LD analysis revealed 31 and 37 significant associations respectively in case of Rajbanshi and Bengali populations. In case of the Rabhas, 39 significant associations were reported from the LD analyses (Table 12). In Gurkha population, 38 significant associations are found while 44 significant associations have been reported in case of the Muslim population.
It is evident from Table 12 that the B haplotype associated KIR genes namely KIR2DL2, 2DL5, 3DS1, 2DS1, 2DS2, 2DS3 and 2DS5 are found to be in positive LD. Similarly, positive LD measures have also been observed between A haplotype genes namely KIR3DL1, 2DL1, 2DL3 and 2DS4. In the studied populations, negative LD values have been observed between A and B haplotype genes except between KIR2DL1 and KIR2DL5 in case of the Bengali population and between KIR2DL1 and KIR2DL3 in the Gurkhas. Negative LD values are also observed in the Rabha population in case of KIR2DL3 with KIR3DS1 and KIR2DS1 respectively.
Page | 132 Table 12: Linkage disequilibrium parameters for KIR genes pairs in the studied populations.
[A] Rajbanshi:
Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.027 ∆' 0.040 r2 0.075 P 0.020 h 0.800 2DL3 ∆ 0.030 0.068 ∆' 0.240 1.000 r2 0.053 0.506 P 0.050 0.000 h 0.747 0.853 Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DS4 ∆ 0.128 0.024 0.024 ∆' 1.000 0.370 0.200 r2 0.762 0.049 0.029 Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 P 0.000 0.060 0.140 h 0.800 0.760 0.707 2DL2 ∆ -0.050 -0.038 -0.017 -0.056 ∆' -0.650 -1.000 -0.240 -0.580 r2 0.075 0.080 0.009 0.079 P 0.020 0.010 0.400 0.020 h 0.387 0.440 0.427 0.360 2DL5 ∆ -0.073 -0.010 -0.026 -0.091 0.062 ∆' -1.000 -0.260 -0.400 -1.000 0.260 r2 0.158 0.005 0.023 0.207 0.063 P 0.000 0.540 0.190 0.000 0.030 h 0.387 0.493 0.440 0.347 0.347 3DS1 ∆ -0.081 -0.020 -0.006 -0.077 0.038 0.134 ∆' -0.860 -0.430 -0.070 -0.660 0.190 0.720 r2 0.199 0.023 0.001 0.154 0.024 0.299 P 0.000 0.190 0.760 0.000 0.180 0.000 h 0.267 0.360 0.347 0.253 0.253 0.360 2DS1 ∆ -0.087 -0.017 -0.004 -0.083 0.031 0.139 0.146 ∆' -1.000 -0.390 -0.002 -0.760 0.140 0.680 0.650 r2 0.230 0.016 0.001 0.172 0.015 0.314 0.354 P 0.000 0.270 0.990 0.000 0.280 0.000 0.000 h 0.293 0.400 0.387 0.280 0.267 0.387 0.333 2DS2 ∆ -0.042 -0.034 -0.049 -0.045 0.195 0.087 0.030 0.020 ∆' -0.610 -1.000 -0.790 -0.530 0.880 0.370 0.170 0.100 r2 0.053 0.065 0.079 0.053 0.624 0.124 0.015 0.007 P 0.050 0.030 0.010 0.050 0.000 0.000 0.290 0.480 h 0.440 0.493 0.440 0.413 0.493 0.400 0.267 0.280 2DS3 ∆ -0.062 -0.018 -0.012 -0.077 0.134 0.100 0.058 0.020 0.119 ∆' -0.540 -0.310 -0.120 -0.540 1.000 0.790 0.350 0.130 1.000 r2 0.141 0.021 0.006 0.185 0.359 0.201 0.068 0.008 0.289 P 0.000 0.210 0.500 0.000 0.000 0.000 0.020 0.440 0.000 h 0.173 0.240 0.227 0.147 0.280 0.253 0.173 0.147 0.280 2DS5 ∆ -0.102 -0.025 -0.014 -0.101 0.039 0.137 0.158 0.183 0.034 0.006 ∆' -1.000 -0.480 -0.150 -0.790 0.230 0.840 0.750 0.930 0.220 0.030 r2 0.339 0.035 0.007 0.279 0.027 0.326 0.446 0.590 0.020 0.001 P 0.000 0.100 0.480 0.000 0.150 0.000 0.000 0.000 0.220 0.810 h 0.200 0.307 0.293 0.187 0.227 0.333 0.307 0.347 0.240 0.107
Page | 133 [B] Bengali:
Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.02 ∆' 0.23 r2 0.03 P 0.02 h 0.747 2DL3 ∆ 0.01 0.11 ∆' 0.18 1.00 r2 0.01 0.39 Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 P 0.26 0.00 h 0.580 0.642 2DS4 ∆ 0.10 0.02 0.02 ∆' 1.00 0.17 0.25 r2 0.73 0.02 0.02 P 0.00 0.05 0.07 h 0.846 0.716 0.568 2DL2 ∆ -0.03 -0.07 -0.12 -0.03 ∆' -0.61 -1.00 -0.83 -0.51 r2 0.03 0.15 0.26 0.03 P 0.02 0.00 0.00 0.02 h 0.494 0.414 0.259 0.469 2DL5 ∆ -0.04 0.00 -0.03 -0.05 0.07 ∆' -1.00 0.01 -0.24 -0.88 0.36 r2 0.07 0.00 0.02 0.07 0.09 P 0.00 0.95 0.10 0.00 0.00 h 0.543 0.543 0.395 0.512 0.463 3DS1 ∆ -0.06 -0.03 -0.00 -0.06 0.03 0.16 ∆' -1.00 -0.34 -0.01 -0.77 0.17 1.00 r2 0.15 0.03 0.00 0.12 0.02 0.47 P 0.00 0.03 0.89 0.00 0.09 0.00 h 0.358 0.358 0.303 0.339 0.315 0.475 2DS1 ∆ -0.06 -0.04 -0.00 -0.06 0.03 0.15 0.22 ∆' -1.00 -0.40 -0.00 -0.69 0.15 0.92 0.87 r2 0.14 0.04 0.00 0.09 0.01 0.41 0.75 P 0.00 0.01 0.98 0.00 0.13 0.00 0.00 h 0.364 0.358 0.309 0.352 0.315 0.469 0.444 2DS2 ∆ -0.02 -0.07 -0.11 -0.03 0.17 0.05 0.03 0.03 ∆' -0.57 -1.00 -0.86 -0.45 0.77 0.23 0.18 0.16 r2 0.02 0.12 0.24 0.02 0.50 0.05 0.02 0.01 P 0.05 0.00 0.00 0.06 0.00 0.01 0.10 0.15 h 0.537 0.457 0.297 0.512 0.543 0.469 0.333 0.333 2DS3 ∆ -0.02 -0.02 -0.09 -0.03 0.12 0.11 0.06 0.04 0.11 ∆' -0.28 -0.16 -0.39 -0.33 0.82 0.89 0.35 0.24 0.90 r2 0.02 0.01 0.14 0.04 0.25 0.22 0.07 0.03 0.25 P 0.08 0.20 0.00 0.01 0.00 0.00 0.00 0.02 0.00 h 0.284 0.256 0.136 0.259 0.321 0.33 0.228 0.209 0.333 2DS5 ∆ -0.06 -0.04 -0.00 -0.05 0.06 0.13 0.16 0.17 0.02 0.02 ∆' -1.00 -0.41 -0.01 -0.62 0.30 0.81 0.63 0.67 0.11 0.12 r2 0.15 0.04 0.00 0.08 0.06 0.30 0.40 0.44 0.01 0.01 P 0.00 0.01 0.89 0.00 0.00 0.00 0.00 0.00 0.32 0.26 h 0.358 0.352 0.303 0.352 0.339 0.444 0.383 0.395 0.321 0.185
Page | 134 [C] Rabha
Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.014 Δ' 0.405 r2 0.036 h 0.820 P 0.18 2DL3 ∆ 0.007 0.037 Δ' 0.107 1.000 r2 0.005 0.479 h 0.780 0.920 P 0.61 0.00 2DS4 ∆ 0.122 0.030 0.041 Δ' 1.000 1.000 0.671 r2 0.603 0.132 0.124 h 0.760 0.760 0.740 p 0.00 0.01 0.01 2DL2 ∆ -0.086 -0.002 -0.003 -0.110 Δ' -1.000 -0.074 -0.074 -0.846 r2 0.224 0.000 0.001 0.265 h 0.300 0.440 0.420 0.240 p 0.00 0.91 0.87 0.00 2DL5 ∆ -0.070 -0.002 -0.005 -0.094 0.118 Δ' -0.777 -0.107 -0.107 -0.702 0.495 r2 0.146 0.001 0.001 0.198 0.226 h 0.300 0.420 0.400 0.240 0.320 p 0.01 0.86 0.80 0.00 0.00 3DS1 ∆ -0.076 -0.004 0.012 -0.064 0.096 0.224 Δ' -0.792 -0.167 0.375 -0.444 0.444 1.000 r2 0.179 0.002 0.008 0.094 0.155 0.848 h 0.260 0.380 0.380 0.240 0.280 0.400 p 0.00 0.77 0.52 0.03 0.01 0.00 2DS1 ∆ -0.066 -0.002 0.017 -0.050 0.088 0.178 0.196 Δ' -0.769 -0.074 0.457 -0.383 0.356 0.747 0.907 r2 0.132 0.000 0.015 0.054 0.127 0.515 0.644 h 0.320 0.440 0.440 0.300 0.300 0.380 0.380 p 0.01 0.91 0.38 0.10 0.01 0.00 0.00 2DS2 ∆ -0.070 -0.002 -0.025 -0.074 0.218 0.106 0.084 0.078 Δ' -0.777 -0.107 -0.554 -0.554 0.916 0.432 0.375 0.327 r2 0.146 0.001 0.034 0.123 0.774 0.186 0.119 0.098 h 0.300 0.420 0.380 0.260 0.420 0.300 0.260 0.280 p 0.01 0.86 0.19 0.01 0.00 0.00 0.01 0.03 2DS3 ∆ -0.058 -0.010 -0.019 -0.078 0.100 0.146 0.116 0.080 0.106 Δ' -0.493 -0.324 -0.324 -0.437 0.715 1.000 0.744 0.573 0.725 r2 -5.505 0.012 0.026 0.172 0.211 0.447 0.291 0.135 0.235 h 0.160 0.240 0.220 0.120 0.220 0.260 0.220 0.200 0.220 p 0.01 0.43 0.25 0.00 0.00 0.00 0.00 0.01 0.00 2DS5 ∆ -0.079 -0.005 -0.010 -0.089 0.125 0.213 0.188 0.145 0.133 0.121 Δ' -0.798 -0.194 -0.194 -0.597 0.610 1.000 0.825 0.708 0.624 0.752 r2 0.198 0.003 0.005 0.183 0.268 0.780 0.625 0.360 0.304 0.324 h 0.240 0.360 0.340 0.200 0.300 0.380 0.340 0.320 0.300 0.220 p 0.00 0.72 0.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Page | 135 [D] Gurkha
Locus Parameters 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.027 Δ' 0.773 r2 0.182 h 0.872 P 0.00 2DL3 ∆ 0.019 0.022 Δ' 0.545 0.583 r2 0.091 0.340 h 0.864 0.944 P 0.00 0.00 2DS4 ∆ 0.074 0.019 0.019 Δ' 0.697 0.545 0.545 r2 0.486 0.091 0.091 h 0.848 0.864 0.864 p 0.00 0.00 0.00 2DL2 ∆ -0.026 -0.011 -0.019 -0.026 Δ' -0.314 -0.412 -0.706 -0.314 r2 0.029 0.015 0.044 0.029 h 0.256 0.296 0.288 0.256 p 0.06 0.17 0.02 0.06 2DL5 ∆ -0.038 -0.013 -0.013 -0.038 0.078 Δ' -0.615 -0.615 -0.615 -0.615 0.471 r2 0.056 0.017 0.017 0.056 0.113 h 0.384 0.448 0.448 0.384 0.232 p 0.01 0.14 0.14 0.01 0.00 3DS1 ∆ -0.036 -0.009 -0.009 -0.036 0.066 0.191 Δ' -0.473 -0.367 -0.367 -0.473 0.328 1.000 r2 0.052 0.010 0.010 0.052 0.087 0.631 h 0.288 0.344 0.344 0.288 0.184 0.368 p 0.01 0.27 0.27 0.01 0.00 0.00 2DS1 ∆ -0.038 -0.013 0.074 -0.038 0.062 0.202 0.183 Δ' -0.615 -0.615 3.828 -0.615 0.375 0.808 0.958 r2 0.056 0.017 0.564 0.056 0.072 0.652 0.579 h 0.384 0.448 0.456 0.384 0.216 0.432 0.360 p 0.01 0.14 0.00 0.01 0.00 0.00 0.00 2DS2 ∆ -0.038 -0.015 -0.023 -0.030 0.187 0.048 0.031 0.032 Δ' -0.543 -0.658 -1.000 -0.429 1.000 0.223 0.144 0.149 r2 0.057 0.025 0.058 0.035 0.661 0.039 0.017 0.017 h 0.328 0.384 0.376 0.336 0.320 0.248 0.184 0.232 p 0.01 0.08 0.01 0.04 0.00 0.03 0.15 0.14 2DS3 ∆ -0.032 -0.008 -0.008 -0.032 0.024 0.064 0.054 0.056 0.037 Δ' -0.333 -0.250 -0.250 -0.333 0.176 0.615 0.430 0.538 0.315 r2 -4.756 0.010 0.010 0.061 0.017 0.103 0.080 0.079 0.035 h 0.144 0.184 0.184 0.144 0.088 0.160 0.128 0.152 0.120 p 0.01 0.25 0.25 0.01 0.15 0.00 0.00 0.00 0.04 2DS5 ∆ -0.061 -0.015 -0.007 -0.037 0.096 0.172 0.148 0.140 0.080 0.003 Δ' -0.884 -0.653 -0.306 -0.537 0.523 0.782 0.698 0.637 0.332 0.028 r2 0.145 0.024 0.005 0.053 0.175 0.488 0.385 0.324 0.107 0.000 h 0.312 0.392 0.400 0.336 0.232 0.376 0.304 0.344 0.256 0.088 p 0.00 0.08 0.42 0.01 0.00 0.00 0.00 0.00 0.00 0.86
Page | 136 [E] Muslim
Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.019 Δ' 0.206 r2 0.043 h 0.829 P 0.01 2DL3 ∆ 0.025 0.075 Δ' 0.333 1.000 r2 0.037 0.333 h 0.700 0.750 P 0.02 0.00 2DS4 ∆ 0.082 0.011 0.027 Δ' 1.000 0.130 0.200 r2 0.511 0.009 0.026 h 0.821 0.750 0.643 p 0.00 0.27 0.06 2DL2 ∆ -0.024 -0.038 -0.073 -0.025 Δ' -0.623 -1.000 -0.774 -0.366 r2 0.026 0.068 0.122 0.018 h 0.536 0.521 0.393 0.486 p 0.06 0.00 0.00 0.11 2DL5 ∆ -0.035 -0.021 -0.038 -0.048 0.075 Δ' -1.000 -0.592 -0.429 -0.771 0.343 r2 0.060 0.021 0.033 0.070 0.104 h 0.550 0.564 0.450 0.486 0.479 p 0.00 0.09 0.03 0.00 0.00 3DS1 ∆ -0.052 -0.038 -0.038 -0.065 0.038 0.168 Δ' -1.000 -0.726 -0.288 -0.693 0.211 1.000 r2 0.121 0.064 0.030 0.114 0.025 0.494 h 0.379 0.393 0.321 0.329 0.336 0.479 p 0.00 0.00 0.04 0.00 0.06 0.00 2DS1 ∆ -0.052 -0.038 -0.061 -0.065 0.031 0.160 0.214 Δ' -1.000 -0.726 -0.468 -0.693 0.172 0.957 0.857 r2 0.121 0.064 0.080 0.114 0.017 0.453 0.734 h 0.379 0.393 0.321 0.329 0.329 0.471 0.443 p 0.00 0.00 0.00 0.00 0.13 0.00 0.00 2DS2 ∆ -0.028 -0.042 -0.084 -0.032 0.133 0.095 0.080 0.073 Δ' -0.661 -1.000 -0.797 -0.431 0.609 0.471 0.398 0.363 r2 0.035 0.081 0.154 0.029 0.310 0.164 0.106 0.088 h 0.493 0.479 0.350 0.443 0.493 0.471 0.357 0.350 p 0.03 0.00 0.00 0.04 0.00 0.00 0.00 0.00 2DS3 ∆ -0.024 -0.009 -0.041 -0.042 0.111 0.121 0.077 0.055 0.093 Δ' -0.398 -0.157 -0.277 -0.393 0.722 0.850 0.361 0.260 0.542 r2 -9.434 0.004 0.037 0.049 0.218 0.267 0.097 0.051 0.147 h 0.343 0.357 0.264 0.293 0.364 0.386 0.271 0.250 0.329 p 0.06 0.46 0.02 0.01 0.00 0.00 0.00 0.01 0.00 2DS5 ∆ -0.044 -0.044 -0.032 -0.040 0.043 0.116 0.152 0.166 0.060 0.024 Δ' -0.756 -0.756 -0.220 -0.385 0.271 0.803 0.702 0.769 0.345 0.101 r2 0.090 0.090 0.023 0.046 0.032 0.246 0.380 0.455 0.061 0.010 h 0.329 0.329 0.279 0.300 0.300 0.386 0.350 0.364 0.300 0.193 p 0.00 0.00 0.07 0.01 0.04 0.00 0.00 0.00 0.00 0.24
Page | 137 4.3. To trace the phylogenetic relationships of the studied Populations with that of the different World populations based on KIR gene profiles.
4.3.1. Neighbour Joining Tree:
Based on 1000 bootstrapped replicates of the Nei genetic distance measures, a consensus Neighbour-Joining (NJ) dendrogram has been constructed (Figure 26). The numbers that are written on the branches of the dendrogram indicate the number of times that branch caused the partition of the species into the two sets. Numbers were mentioned in the tree only when they are found to be greater than 50%.
In the NJ tree, prominent cluster containing the South Asian populations can be observed, while the other populations namely, the East Asians also huddled according to their genetic proximities i.e. the NEAs and the SEAs (Figure 3). It is evident from the NJ tree that both the Rabha and the Rajbanshi populations branched away early from the South Asian cluster occupying a position somewhat intermediate between the Indian sub-cluster and other Asian populations namely the North East and South East Asians. The Neighbour-Joining (NJ) tree also revealed that the Bengalis huddled with the Indian sub- cluster, which also included other sub-continental populations, namely, the South Indian Paravar and Kanikars, Mumbai Parsis, populations of Pakistan and also from North India. It is also evident from the NJ tree that the Gurkhas had a tendency to occupy an intermediate position between the Tibetan and the Vietnamese population both of which belonged to the East Asian lineages while, the Muslim population huddled with the Bengalis within the Indian sub-cluster (Figure 26).
Page | 138 Figure 26: Neighbor Joining Dendrogram based on the genetic distances of the studied populations with other neighboring populations from Asia.
4.3.2. Restricted Maximum Likelihood (REML).
Phylogenetic tree has been constructed to represent the outcome of the Restricted Maximum Likelihood (REML) estimation based on the frequencies of KIR genotypes in our studied populations as well as other populations having previously published data (Figure 27). Both the Rajbanshis and the Bengalis clustered with the population of Indian origin, namely Trinidad Asian, North Indians, Paravar and Kanikars. The Europeans, the
Page | 139 Africans, the North East Asians and the American Natives formed related separate clusters (Figure 27). Tibetans are clustered with the North East Asians (NEAs) and thus they constitute a sister clade with the Indian group. It is also evident from REML based phylogenetic tree that the Rabhas have branched out from the Tibetan-Japanese-Korean- Chinese clade and have the tendency to occupy a position in between the Indian and the NEA Cluster. The Muslim population huddled with the Bengalis sharing the same cluster with other populations of Indian origin. The clade is supported by 92% bootstrap value (Figure 27). On the other hand, the Gurkhas branched out early from the Indian Clade and intended to occupy a position, which is somewhat intermediate between the Indian and the NEA Cluster.
Figure 27:Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles in the Bengali and other previously studied World populations using Phylip package. Clusters relating to the Asian Indians, North East Asians, Africans, Europeans, Mexicans and American Natives can be distinguished separately. Populations having incomplete genotypes were excluded from the analyses.
Page | 140 4.4. Association of KIR genes with Rheumatoid arthritis.
4.4.1. Comparative estimation of clinical parameters between RA patients and controls.
The demographic data have been summarized in Table 13. In case of RA patients the median anti-CCP value is estimated to be 182.7 IU/ml (IQR= 20.25 to 504.93 IU/ml), among which 84 patients had anti-CCP values >17 IU/ml and are recorded as anti-CCP positive while 26 patients responded negatively to the test (Table 13). In contrast, the median anti-CCP value is found to be 10.8 IU/ml (IQR= 8.2 to 13.1 IU/ml) among the members of the control group, out of which only 5 individuals are found to be positive for anti-CCP while the remaining 146 showed negative response to the test. Thus, the sensitivity and specificity of the test are estimated to be 76.36% and 96.69% respectively. Among the members of the control groups, none of the healthy subjects were found to be positive for Anti-CCP; while on the other hand, 5 non-RA patients responded positively to the test. The positive predictive value (PPV) and negative predictive value (NPV) are found to be 94.38% and 84.88% respectively (Table 13). The dot density graph (Figure 28A) prepared using Sigmaplot version 12.3, depicted the distribution pattern of anti- CCP in RA patients as well as in the control groups. It is evident from Figure 28A that the anti-CCP values in RA patients are distributed over a wide range rather than being clumped as seen in different subcategories of the control groups. However, few points are observed in the graph in case of PsA and patients with mechanical pain, whereby the anti CCP value crossed the cut off level.
In case of RF titres, the median value is found to be 142.3 IU/ml (IQR= 41.20 to 198 IU/ml) and 16.5 IU/ml (IQR= 12.0 to 20.5 IU/ml) in case of RA patients and control groups respectively. Among the RA patients, 86 samples were positive while the remaining 24 samples responded negatively to the test. In contrast, only 42 individuals were positive among the control group, while the remaining 109 responded negatively to the test. Thus, the sensitivity and specificity of the assay have been found to be 78.18 and 72.19 respectively (Table 13). The PPV and NPV have been calculated to be 67.19% and
Page | 141 81.95 respectively. From the box plot (Figure 28B), it is evident that the both the anti- CCP and RF titres in the patient group varied over a wide range in comparison to that of the control group, whereby clumped distribution is observed inspite of having some outlier points. It is also evident from Figure 28 that anti CCP has got wider distribution compared to RF titre values in the RA patients.
Table 13: Demographic data and clinical characteristics of the RA patients and control groups.
RA group (n=110) Control group (n=151)
Gender Male 28 (25.5) 76 (49.67) n (%) Female 82 (74.5) 75(50.33) Age (Years) Median (Range) 47 (39-54.5) 42(34-50)
Disease duration (Years) Mean (SD) 5.6± 2.4 -----
Anti CCP (IU/ml) {Median (IQR)} 182.7 (16.5 to 10.8(8.2 to 13.1) 504.93) Positive cases N (%) 84 (76.4 %) 5 (3.3%) Negative N (%) 26 (23.6%) 142 (96.7%) cases {Median (IQR)} 142.3 (41.20 to 16.8 (12 to 20.5) RF titre (IU/ml) 198.0) Positive N (%) 86 (78.2) 42 (27.8%) cases Negative N (%) 24 (21.8) 109 (72.2%) cases ESR Mean (SD) 42.3 (24.6) ------CRP Mean (SD) 35.7 (35.9) ------DAS28 (ESR) Mean (SD) 4.71 (4.7) ------TJC Mean (SD) 5.8 (3.7) ------SJC Mean (SD) 3.0 (2.5) ------pHG Mean (SD) 37.8 (31.1) ------Pain VAS Mean (SD) 39.9 (33.1) ------HAQ Score Mean (SD) 1.5 (0.7) ------
Page | 142 Figure 28: (A) Dot density graph showing the distribution of anti CCP in RA patients as well as different subgroups of control samples. (B) Box plot demonstrating the distribution of anti CCP and RF titre in RA patients as well as control samples.
Page | 143 Diagnostic values of both the anti CCP and RF titres in combinations have been represented in Table 14. Interestingly, it is observed that the sensitivity increased to 93.64% when positivity of either of the diagnostic tests are considered as a criterion; while on the other hand, when both Anti CCP and RF titres are considered together, then the specificity showed no change to that of anti-CCP alone. While comparing the data of the patients separately with the two subgroups of the control samples, it is seen that the specificity for both anti-CCP and RF titres increased in case of the healthy individuals while decreased values are observed in both the assays in case of the non-RA patients (Table 14).
The coefficient of correlation between anti-CCP and RF titres in the RA and non-RA groups are estimated to be 0.716 and 0.356 respectively. From the scatter diagrams (Figure 29), it is evident that in case of RA patients, anti-CCP values tended upward with the rise of RF titre (Figure 29A). In contrast, the anti-CCP values in case of the non-RA patients showed a rather clumped distribution except 5 outliers (Figure 29B). Receiver operating characteristic (ROC) curve has also been computed for both the assays (Figure 30) and it is found that the area under the curve (AUC) for anti-CCP and RF are 0.890 and 0.875 respectively, which suggests the accuracy of both the tests.
Table 14: Diagnostic performance of anti-CCP antibodies and Rheumatoid Factor in RA diagnosis.
Anti CCP RF titre Anti CCP and RF titre Anti CCP or RF titre Sensitivity 76.36% 78.18% 60.91% 93.64%
Specificity 96.69% 72.19% 96.69% 72.19% PPV* 94.38% 67.19% 93.06% 71.03% Control Control
Patients Vs. Vs. Patients NPV# 84.88% 81.95% 77.25% 93.97% Sensitivity 76.36% 78.18% 60.91% 93.64% Specificity 100.00% 86.27% 100.00% 86.27% PPV* 100.00% 86.00% 100.00% 88.03% Healthy Healthy
Patients Vs. Vs. Patients NPV# 79.69% 78.57% 70.34% 92.63% Sensitivity 76.36% 78.18% 60.91% 93.64%
RA Specificity 89.80% 42.86% 89.80% 42.86% - PPV* 94.38% 75.44% 90.06% 78.63% Non
Patient Vs. Vs. Patient NPV# 62.86% 46.67% 50.57% 75.00%
Page | 144 Figure 29:Scatter diagram for comparative assessment of anti CCP and RF titre in (A) RA patient and (B) control group respectively.
Figure 30: ROC curve comparing the diagnostic performance of anti CCP and RF titres. The Area under the Curve (AUC) for anti CCP and RF are found to be 0.890 and 0.875 respectively.
Page | 145 Multiple regression analyses have also been performed by SPSS version 19.0 (Table 15). Here, two contesting models have been considered. Model 1 consisted of ESR and anti- CCP values as independent variable for predicting DAS28 score while in case of model 2, anti-CCP was replaced by RF titre keeping all the parameters same. The coefficients of determination (adjusted R2) have been estimated to be 0.799 and 0.802 for model 1 and 2 respectively. In other words, 79.9% and 80.2 % of the variance in DAS28 score can be predicted by ESR values in combination with the anti-CCP and RF titres respectively. Changes in R2 for model 1 and 2 with respect to reference model (ESR alone) are estimated to be 0.024 and 0.027 respectively. 3D scatterplots and contour diagrams have also been constructed using Minitab ver. 16.0 for analyzing the efficiency of anti-CCP and RF titres in combination with the ESR for predicting DAS28 scores (Figure 31). It is evident that high DAS28 scores are found at combinations of high ESR values with more or less high values of anti-CCP and RF titres respectively, thereby suggesting the contribution of these two diagnostic markers in predicting DAS28 score in combination with ESR.
Table 15: Multiple Regression models for prediction of DAS28 using ESR in combination with anti CCP and RF titre respectively.
Model Summary Model R R Adjuste Std. Change Statistics Squar d R Error of R F df df2 Sig. F e Square the Square Change 1 Chang Estimat Chang e e e Standar 0.882a 0.779 0.777 0.66752 0.779 379.93 1 10 0.000 d 0 8 1 0.896 0.803 0.799 0.63323 0.024 13.013 1 10 0.000 b 7 2 0.897c 0.805 0.802 0.62885 0.027 14.691 1 10 0.000 7
a Predictor: ESR; b Predictors: ESR, anti CCP; c Predictors: ESR, RF titre
Page | 146 Figure 31: [A] and [B] 3D scatterplot and contour diagram demonstrating the variations of DAS28 based on ESR and anti CCP. [C] and [D] 3D scatterplot and contour diagram demonstrating the variations of DAS28 based on ESR and anti CCP.
In another section of the study, 50 RA patients having positive anti-CCP values are selected for comparative analyses of different clinical parameters against 50 healthy control individuals with negative Anti-CCP values. Besides RF titre, CRP and ESR estimations, the other clinical parameters selected for this comparative study are ASO titre, Ceruloplasmin and creatinine assay.
Interestingly, 92 % of the 50 patients were found to be RF positive. The RF titre fluctuated between 11IU/ml and 380 IU/ml with a mean ± SD value of 135.9± 91.4 IU/ml
Page | 147 (Table 16). In contrast, RF titre negativity has been reported in 88% of the control patients having a mean value of 14.0±5.5 IU/ml. The calculated t value of patients versus control for the RA factor is found to be 9.45 (p≤ 0.001) which indicates significant difference in RF titre distribution between the patients and the control samples. The mean CRP level in the patients group is 46.4±42.9 mg/dl while it is estimated to be 2.2±1.6 mg/dl in the control samples, which is well within the normal clinical range. Thus, considerable differences in CRP levels can be seen in between the patients and the control group, which has also been supported by the t-test. Furthermore, considerable differences can also be observed in the level of ASO titre between the patients and the controls. The mean ±SD values in the patient and the control group are estimated to be 147.5± 107.8 IU/ml and 83.1± 49.7 IU/ml respectively (Table 16). The ESR level has been estimated to be 37.6±23.0 and 12.7±4.1 mm/hr in the patient and the control subjects respectively.
Table 16: Statistical analyses of the different clinical parameters of the Rheumatoid Arthritic patients and control subjects.
Control Normal RA patients Odds Relative Sensitivity Specificity Parameters Subjects T-value Range (n=50) Ratio Risk (%) (%) (n=50) Age 47.2±14.0 35.1±11.4
RA factor 0-20.0 135.9±91.4 14.0±5.5 9.45*** 84.33 7.67 92 88 (IU/ml) IU/ml CRP (mg/dl) 0-6 mg/dl 46.4±42.9 2.2±1.6 7.32*** 44.59 12.33 74 94
ASO (IU/ml) 0-200 147.5±107.8 83.1±49.7 3.66*** 29.41 23.00 22 100 IU/ml ESR 0-20 37.6±23.0 12.7±4.5 8.01*** 31.91 7.80 78 90 (mm/hour) mm/hour Ceruloplasmin 20-35 42.1± 11 23.8±6.1 8.96*** 34.62 12.50 65.79 94.74 (mg/dl) mg/dl Creatinine 0.6-1.2 1.5±0.6 0.9±0.2 5.79*** 11.69 5.50 57.89 89.47 (mg/dl) mg/dl
Page | 148 Figure 32: (a) Box plot analyses show the differences in the distribution of the different
clinical parameters in the Rheumatoid Arthritis patients and the control samples. (b) Pyramid charts compared the distribution of the levels of different clinical parameter.
Page | 149 The box plot analyses (Figure 32) revealed the existence of considerable differences in the distributions of each of the four clinical parameters among the patients and the control samples. It can be observed that all the four parameters have shown considerable fluctuations in 50% of the RA patients. In contrast, it is noticeable that except ASO titre none of the parameters demonstrated wide range distribution in the control samples (Figure 32a). It can be seen from Figure 32b that the distribution of the above-mentioned four parameters varied over a very wide range of values in the patient group compared to the control samples. However, it is evident from the Figure 32b, frequency distribution of ESR and ASO titres have minor variations in case of the control group.
The two other non-conventional parameters for RA diagnosis i.e. plasma ceruloplasmin and serum creatinine, are compared with RF titre level in the patients and the control group using scatterplot (Figure 33). Surprisingly, in both the cases, positive correlation is noticeable from the scatterplots with a linear trendline in upward direction.
Correlation analyses are also performed among the different parameters for the RA patients (Table 17). It is evident from the table that the correlation coefficients are greater than 0.5 between Rheumatoid factor and other five parameters with RF versus CRP value leading the chart. One interesting observation is that no negative correlation is evident among the six clinical parameters. However, has also been found that among the RA patients, the lowest correlation coefficient is estimated to be between ASO titre and creatinine level.
Table 17: Correlation coefficient of the different clinical parameters in the RA patients.
RA CRP ASO ESR Cerulo Creatinine plasmin RA 1.00 CRP 0.73 1.00 ASO 0.65 0.48 1.00 ESR 0.71 0.72 0.63 1.00 Ceruloplasmin 0.57 0.52 0.38 0.40 1.00 Creatinine 0.60 0.65 0.22 0.48 0.62 1.00
Page | 150 Figure 33: Scatterplot analyses to show RA factor Vs Ceruloplasmin and RA factor vs creatinine levels respectively in RA patients.
4.4.2. PCR-SSP typing of KIR genes in RA patients
The total KIR gene profiles among RA patients and controls are presented in Table 18. On comparison with control samples, decreased incidence of, KIR2DL3 ((p = 0.0068, OR = 0.48, 95%, CI = 0.29–0.80) gene is observed among the RA patients compared to the controls, thereby conferring the protective effects against the incidence of RA. On the other hand, odds of occurrence of RA in patient group are linearly related to the increased incidence of KIR2DS2 (p = 0.0439, OR = 1.76, 95%CI = 1.02–3.04) and KIR2DL2 (p = 0.0155, OR = 2.01, 95%CI = 1.16–3.49) genes respectively. Other KIR (both inhibitory and activating) genes revealed no statistically significant difference among the patient and the control groups.
Page | 151 Genotypic data are further classified into two subgroups based on gene content, namely A and Bx (Table 18). The A subgroup is an inhibitory haplogroup carrying a fixed set of nine genes including a single activating KIR gene i.e KIR2DS4, and various inhibitory genes. In contrast, the Bx haplogroup containing activating genes is also characterized by the absence of group-A specific inhibitory KIR genes (KIR2DL2, 2DL3 and 3DL1). No significant differences are observed for these two haplogroups among the patients and the control groups. However, KIR BB genotypes are found at significantly higher frequencies among the RA patients and the control groups (47.3% vs 33.8 %, p= 0.0299, OR= 1.76, 95%CI= 1.06-2.91). On the other hand, both AA and AB genotypes are found at higher frequencies among the controls compared to the RA patients. Among the four KIR Bx Subgroups namely C4T4, C4Tx, CxT4, and CxTx; none of them showed significant differences among the patient and the control groups.
The KIR genotypes that are observed in both the patients and the control groups are presented in Table 19. In the RA patients, genotype ID 1 (11.8%) has the highest frequency followed by genotype 3 (10%) and 4 (6.4%) respectively. However, in the control group, genotype ID 1 (15.9%) has the highest frequency followed by genotype 2 (6.6%) and 13 (5.3%) respectively. Fisher Exact Test is performed in order to compare the frequencies of the genotypes among the patients and the control groups (Table 19). It is found that the frequencies of genotype ID 3 (p=0.0354, OR=3.244, 95% CI= 1.09- 9.63) and 81 (p= 0.0306, OR=12.802, 95%CI= 0.68-240.32) show significant differences between the patient and the control groups.
The classical linkage disequilibrium coefficient (D) between pairs of KIR loci in the RA patients, the standardized coefficient (D`), the conventional measure of linkage disequilibrium (r2), the two locus haplotype frequencies and respective P values between pairs of KIR gene loci for each of the studied populations have been presented in Table 20. P value <0.05 is considered to be statistically significant. The framework and the pseudogenes are excluded from this analysis. The LD analyses revealed 41 significant associations in case of the RA patients. It is evident from Table 20 that the B haplotype genes are in positive LD and so are the genes that constitute the A haplotypes. However,
Page | 152 negative LD values are observed between A and B haplotype genes except between KIR2DS3 and KIR2DL1. It is observed that among the A haplotype genes KIR2DS4 and KIR3DL1, both Dˊ and r2 values are quite high. Significantly strong LD associations are also seen between KIR2DL2-KIR2DS2 (D’=0.874, r2=0.723), KIR3DL1-KIR2DS4 (D’=0.962, r2=0.853), KIR2DS1-KIR2DL3 (D’=0.686, r2=0.434), KIR3DS1- KIR2DS1(D’=0.867, r2=0.693), and KIR2DS1-KIR2DS5 (D’=0.792, r2=0.627).
Table 18: Comparison of KIR gene profiles in the RA patients and control groups.
Sl.No Gene Patients % (N=110) Control % (N=151) p-value ( fisher test, two sided) OR 95% CI
KIR gene carrier frequencies
1 3DL1 0.86(95) 0.91(138) 0.226 NS 0.60 0.27-1.31 2 2DL1 0.82(90) 0.83(126) 0.743 NS 0.89 0.47-1.71 3 2DL3 0.51(56) 0.68(103) 0.007 0.0068** 0.48 0.29-0.80 4 2DS4 0.85(93) 0.91(137) 0.174 NS 0.56 0.26-1.19 5 2DL2 0.76(84) 0.62(93) 0.016 0.0155* 2.01 1.16-3.49 6 2DL5 0.63(69) 0.66(99) 0.695 NS 0.88 0.53-1.47 7 3DS1 0.45(50) 0.46(70) 0.901 NS 0.96 0.59-1.58 8 2DS1 0.47(52) 0.46(69) 0.803 NS 1.07 0.65-1.74 9 2DS2 0.75(83) 0.64(96) 0.044 0.0439* 1.76 1.02-3.04 10 2DS3 0.31(34) 0.33(50) 0.789 NS 0.90 0.53-1.53 11 2DS5 0.47(52) 0.44(67) 0.706 NS 1.12 0.69-1.84 KIR genotype and haplogroup frequencies 1 AA 14.5(16) 17.2(26) 0.612 NS 0.82 0.42-1.61 2 AB 38.2(42) 49(74) 0.101 NS 0.64 0.39-1.06 3 BB 47.3(52) 33.8(51) 0.030 0.0299* 1.76 1.06-2.91 4 A 14.5(16) 17.2(26) 0.612 NS 0.82 0.42-1.61 5 Bx 85.5(94) 82.8(125) 0.612 NS 1.22 0.62-2.41 KIR Bx subsets frequencies 1 C4T4 10.9(12) 11.3(17) 1.000 NS 0.97 0.44-2.11 2 C4Tx 15.5(17) 15.2(23) 1.000 NS 1.02 0.51-2.01 3 CxT4 25.5(28) 22.5(34) 0.659 NS 1.18 0.66-2.09 4 CxTx 33.6(37) 33.8(51) 1.000 NS 0.99 0.59-1.67 5 C4 26.4(29) 26.5(40) 1.000 NS 0.99 0.57-1.73 6 T4 36.4(40) 33.8(51) 0.694 NS 1.12 0.67-1.87
Page | 153 Table 19: KIR genotypic profiles in the patients and the Control groups. ID OR 3DL1 2DL1 2DL3 2DL2 2DL5 2DL4 3DL2 3DL3 2DS4 3DS1 2DS1 2DS2 2DS3 2DS5 Sl.No 2DP1 3DP1 95%CI p-Value Genotype Bx subsets Haplogroup Patient % (N) % Patient Control % (N) % Control 1 AA AA 1 13 13 13 13 13 13 13 13 13 11.8(13) 15.9(24) 2 AA AA 180 2 2 2 2 2 2 2 2 1.8(2) 0(0) 3 AA AA 195 1 1 1 1 1 1 1 1 0.9(1) 1.3(2) 4 Bx AB CxT4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 3.6(4) 6.6(10) 5 Bx AB CxT4 3 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10(11) 3.3(5) 0.0354* 3.244 1.09-9.63 6 Bx AB CxTx 4 7 7 7 7 7 7 7 7 7 7 7 6.4(7) 4.6(7) 7 Bx AB C4Tx 5 3 3 3 3 3 3 3 3 3 3 3 3 3 2.7(3) 2(3) 8 Bx AB C4T4 6 3333333333333333 2.7(3) 3.3(5) 9 Bx AB CxTx 8 3333 333 3 33333 0(0) 2(3) 10 Bx AB CxTx 9 333333 33 333333 2.7(3) 4(6) 11 Bx AB C4Tx 11 111111 111 11111 0.9(1) 0(0) 12 Bx AB CxT4 12 2222 2222 222222 0(0) 1.3(2) 13 Bx AB C4Tx 13 3333333 33 33333 2.7(3) 5.3(8) 14 Bx AB CxTx 15 1 1 1 1 1 1 1 1 1 1 0(0) 0.7(1) 15 Bx AB CxT4 18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0(0) 0.7(1) 16 Bx AB CxTx 20 2 2 2 2 2 2 2 2 2 2 2 2 1.8(2) 0(0) 17 Bx AB CxTx 23 1 1 1 1 1 1 1 1 1 1 0.9(1) 2.6(4) 18 Bx AB CxT4 28 2222 222 2222222 0(0) 1.3(2) 19 Bx AB CxTx 31 4 4 4 4 4 4 4 4 4 4 4 4 0(0) 2.6(4) 20 Bx AB CxTx 33 2 2 2 2 2 2 2 2 2 2 2 2 1.8(2) 3.3(5) 21 Bx AB CxTx 36 2 2 2 2 2 2 2 2 2 2 2 2 1.8(2) 1.3(2) 22 Bx AB CxTx 44 2 2 2 2 2 2 2 2 2 2 2 2 0(0) 1.3(2) 23 Bx AB CxTx 51 1 1 1 1 1 1 1 1 1 1 1 1 0(0) 0.7(1) 24 Bx AB CxT4 56 2222 22222222222 0(0) 1.3(2) 25 Bx AB C4Tx 382 111111 111111111 0(0) 0.7(1) 26 Bx BB CxT4 68 33 33333 333333 0(0) 2(3) 27 Bx BB C4Tx 71 6 6 6 6 6 6 6 6 6 6 6 6 5.5(6) 4(6) 28 Bx BB CxTx 72 4 4 4 4 4 4 4 4 3.6(4) 2.6(4) 29 Bx BB C4T4 73 11 1111111111111 0.9(1) 1.3(2) 30 Bx BB CxT4 74 6 6 6 6 6 6 6 6 6 6 4.5(5) 2.6(4) 31 Bx BB CxT4 78 4 444444 444444 2.7(3) 3.3(5) 32 Bx BB C4T4 81 3 333333333333 3.6(4) 0(0) 0.0306* 12.802 0.68-240.32 33 Bx BB C4T4 82 2 2222222222222 1.8(2) 2.6(4) 34 Bx BB C4Tx 90 22 2222222 22222 1.8(2) 1.3(2) 35 Bx BB C4T4 101 333333333333 2.7(3) 3.3(5) 36 Bx BB CxTx 104 2 2 2 2 2 2 2 2 2 1.8(2) 1.3(2) 37 Bx BB C4Tx 112 2 2 2 2 2 2 2 2 2 2 2 2 2 0.9(1) 1.3(2) 38 Bx BB CxT4 118 22 222222 222222 1.8(2) 0(0) 39 Bx BB CxTx 171 2 2 2 2 2 2 2 2 2 1.8(2) 1.3(2) 40 Bx BB CxTx 172 3 3 3 3 3 3 3 3 3 3 2.7(3) 2(3) 41 Bx BB CxTx 176 3 3 3 3 3 3 3 3 3 3 3 2.7(3) 2.6(4) 42 Bx BB CxT4 243 2 22222 222222 1.8(2) 0(0) 43 Bx BB CxTx 277 1 1 1 1 1 1 1 1 1 0.9(1) 0.7(1) 44 Bx BB C4T4 297 1 1111111111111 0(0) 0.7(1) 45 Bx BB CxTx 388 1 1 1 1 1 1 1 1 1 1 1 0.9(1) 0(0) 37 Bx BB C4Tx 401 22 2222 22222222 0.9(1) 0(0) 46 Bx BB C4Tx 549 1 1111 11111111 0(0) 0.7(1) 47 Bx BB CxTx 559 1 1 1 1 1 1 1 1 1 1 1 1 0.9(1) 0(0) 48 Bx BB CxTx 562 1 1 1 1 1 1 1 1 1 1 1 1 1 0.9(1) 0(0) 49 Bx BB CxTx n 1 1 1 1 1 1 1 1 1 1 1 1 0.9(1) 0(0) 50 Bx BB CxTx n1 1 1 1 1 1 1 1 1 1 1 0.9(1) 0(0)
Page | 154 Table 20: Linkage disequilibrium parameters for pairs of KIR genes in the RA Patients.
Locus Locus Parameters 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DL1 ∆ 0.058 Δ' 0.509 r2 0.192 h 0.764 P 0.00 2DL3 ∆ 0.070 0.091 Δ' 0.987 0.990 r2 0.165 0.224 h 0.509 0.509 P 0.00 0.00 2DS4 ∆ 0.114 0.048 0.067 Δ' 0.962 0.394 0.869 r2 0.853 0.125 0.139 h 0.845 0.745 0.500 p 0.00 0.00 0.00 2DL2 ∆ -0.026 -0.041 -0.088 -0.028 Δ' 0.784 0.958 0.745 0.773 r2 0.032 0.064 0.168 0.033 h 0.627 0.582 0.300 0.618 p 0.06 0.01 0.00 0.06 2DL5 ∆ -0.042 -0.008 -0.012 -0.036 0.076 Δ' 0.807 0.113 0.067 0.640 0.501 r2 0.062 0.002 0.003 0.042 0.135 h 0.500 0.509 0.309 0.500 0.555 p 0.01 0.67 0.60 0.03 0.00 3DS1 ∆ -0.069 -0.042 -0.020 -0.073 0.058 0.153 Δ' 0.894 0.421 0.089 0.890 0.537 0.918 r2 0.159 0.048 0.007 0.171 0.075 0.405 h 0.318 0.327 0.209 0.309 0.400 0.436 p 0.00 0.02 0.39 0.00 0.00 0.00 2DS1 ∆ -0.068 -0.040 -0.164 -0.072 0.061 0.140 0.207 Δ' -0.914 -0.419 -0.686 -0.909 0.541 0.807 0.867 r2 0.153 0.043 0.434 0.164 0.082 0.339 0.693
Page | 155 2DS2 2DS5 2DS3
Locus
p h r2 Δ' ∆ p h r2 Δ' ∆ p h r2 Δ' ∆ p h
Parameters 0.00 0.336 0.153 - - 0.17 0.245 0.020 - - 0.06 0.618 0.032 - - 0.00 0.336 0.914 0.068 0.219 0.021 0.766 0.027
3DL1
0.03 0.345 0.043 - - 0.58 0.264 0.003 0.169 0.009 0.01 0.573 0.065 - - 0.03 0.345 0.419 0.040 0.939 0.042
2DL1
- 0.03 0.109 0.045 - - 0.00 0.300 0.145 - - 0.00 0.218 0.37 0.218 0.007 - 0.022 0.310 0.049 0.673 0.083 0.090
2DL3
- 0.25 0.245 0.012 - - 0.05 0.609 0.034 - - 0.00 0.327 0.00 0.327 0.164 - 0.072 0.174 0.018 0.758 0.028 0.909
2DS4
0.070 0.00 0.291 0.078 0.743 0.055 0.00 0.727 0.723 0.874 0.157 0.00 0.418 0.00 0.427 0.108 0.621
2DL2
0.131 0.00 0.291 0.183 0.834 0.096 0.00 0.555 0.154 0.521 0.082 0.00 0.436 0.00 0.427 0.296 0.754
2DL5
0.179 0.13 0.173 0.021 0.195 0.033 0.00 0.400 0.084 0.556 0.063 0.00 0.418 0.00 0.391 0.522 0.752
3DS1
0.197 0.69 0.155 0.001 0.054 0.009 0.00 0.418 0.092 0.559 0.066 0.00 0.418 0.627 0.792
2DS1
0.057 0.00 0.309 0.146 0.988 0.077 0.01 0.409 0.069 0.482
Page |Page 2DS2
0.69 0.155 0.001 0.054 0.009 156
2DS3
Chapter 5:
Comprehensive Discussion
5. Comprehensive Discussion
This section has been divided into two parts for better understanding: [i] the study of the KIR gene profiles in different populations of the sub-Himalayan region and [ii]the study of the association of KIR genes with Rheumatoid Arthritis. Both these subsections are elaborated below.
5.1. Study of the KIR gene profiles in different populations of the sub-Himalayan region
This section is again divided into five sub categories, each corresponding to a particular population group. This has been done purposefully for facilitating in-depth and elaborative discussion regarding the KIR profile of each of the studied populations.
5.1.1. Rajbanshi
The geographical position of a region is the most important factor in shaping the its population structure. Similarly, in case of India too, its geographical location played the most important role in diversifying its population by exposing it to the several waves of human migrations. These included occupation by modern human during the Paleolithic period, Neolithic migration of the proto-Dravidian speakers from Zagros Mountain, Indo- European migration from Central Asia and the lastly, but not the least, Austro-Asiatic and Tibeto-Burman migration from East/Southeast Asia (Rajalingam, et al., 2008). These migratory events resulted in extensive admixture of populations and genetic heterogeneity among the Indian populations.
Rajbanshi people, having Indo-European linguistic background (Shrestha, 2009), are abundantly present in the Terai and Dooars region (outermost sub-Himalayan zones) of India. Many opinions have been put forwarded regarding their origin. However, the most accepted theory stated that the Bodo people entered India during the initial period of the Bikram Sambat and got settled along the bank of the Brahmaputra River. Later, they gradually migrated to Assam and to North and East Bengal. According to the historical
Page | 157 evidences, the Rajbanshis or the "dynasty of King" descended originally from the ‘Koches’ who emerged as a very powerful political entity in the Kamata region during early 16th Century. During this period (1515-1540), Koch Bishwa Singha established the Koch Kingdom, which was later renamed as Kochbihar during the colonial period. Inspite of having mongoloid connections, the mighty king adopted Hindu religion and culture (Das, 2009). Thus, the historical evidences clearly point towards the influence of Sino-Tibetan lineage on the Rajbanshis. However, according to Sir H. H. Risley, the Rajbanshis are derived from the Dravidian stock with considerable influence from the mongoloid lineage (Risley, 1891).
In the neighbor joining (NJ) tree (Figure 26), we can easily see that the Rajbanshis huddled with the Indian cluster close to the Mollukurumba and Maharashtrians. It is evident from the genetic distance measures that the Rajbanshis have the closest distance with the Tibetans among the members of the North East Asian cluster (Table 9). The PCA analysis (Figure 23) based on frequencies of the KIR genes also simulate the similar result and placed the Rajbanshis in the Indian Cluster. In the NJ tree, the Rajbanshis are placed in the same cluster with the Paravar and Kanikar populations of Tamil Nadu. This pattern of clustering suggest the relationship of the Rajbanshis with the populations of Indian origin (Figure 23). In Rajbanshis, the predominance of B haplotype over A haplotypes can be observed which is a characteristic feature of the Indian populations. On the contrary, it was quite interesting to find AA genotype ID 1 with considerably high frequency in the Rajbanshis, which is the characteristic of the Northeast and East Asian populations. After genotype ID 1 the most frequent genotypes in the Rajbanshis are genotype IDs 2 and 4, both of which are present among Indians as well as East/Northeast populations.
The REML analysis showed that the Rajbanshis are closer to the Tibetans amongst the North East / East Asian populations. Furthermore, the REML based unrooted tree also indicated that both the East-Asian/Tibetan and the Rajbanshi/Indian clades have common ancestral connection (Figure 23 and Figure 27). Thus, significant Tibeto-Burman (TB) Influence is noticeable in the KIR gene repertoire of the Rajbanshis inspite of belonging
Page | 158 to the Indian cluster. This opinion received considerable support from the outcome of a Y-Chromosome haplogroup diversity based study, wherein it has been shown that O3 haplogroup, which is totally absent in Indo European castes from Eastern part of India, is considerably shared between Rajbanshis and other TB groups, thereby supporting the TB connection of the Rajbanshi population (Debnath, et al., 2011). Moreover, the O3 haplogroup has an estimated age of 8.1±2.9 X 103 years in the TB linguistic groups of the Himalayan Region. This is much earlier in comparison to the Indo European (IE) arrival roughly 3500 years ago. This observation suggested that the gene pool of the Rajbanshis received Tibeto-Burman influence much earlier than the Indo-Europeans (Debnath, et al., 2011).
Furthermore, the high frequency of a Y chromosome haplotype H8, in the Sino-Tibetan populations of Tibet and north-east India suggested the occurrence of a bottleneck effect during the migration events of the Tibeto-Burman founders (Su, et al., 2000). Su et. al (2000) suggested that the Proto-Tibeto-Burman people, who are actually a subgroup of the Proto-Sino-Tibetans, vacated their homeland in the Yellow River basin about 5-6 x 103 years ago, marched probably across the Zang (Tibet)-Mien Corridor and inhabited the Himalayan range. Furthermore, he also suggested that the Baric branch crossed the Himalayas and populated the country of Bhutan, Nepal, northeastern India and northern Yunnan, while the Bodic branch eventually expanded across Tibet (Su, et al., 2000). Thus, connection of the Rajbanshi population with the Tibeto-Burman Lineage can be established based on the analyses of KIR genotypes from our study and from previously published Y-chromosome haplotype analyses.
5.1.2. Rabha
Historically, the Koch Rabhas are thought to have close relation with the Rajbanshis and are considered to have a common ancestral connection (Shrestha, 2009). This fact is noticeable from the χ2 analyses (Table 8) wherein, none of the KIR loci showed any significant differences between the Rabhas and the Rajbanshis. Moreover, Table 9 showed that the Rabhas are genetically least distant from the Rajbanshis among other Indian populations. An earlier study by Guha et al. (2013) (Guha, et al., 2013) suggested
Page | 159 that the Indo-European speaking Rajbanshis from northern part of Bengal has Tibeto- Burman influence inspite of having Indian origin. Thus, prominent inclination of the Rabhas toward the mongoloid ethnicity is evident from the genetic closeness between the Rabhas and the Rajbanshis.
Furthermore, both PCA score plot (Figure 23) and NJ tree (Figure 26) in this study showed the diversion of the Rabha population from the Indian cluster and their proximity to the mongoloid ethnicity. This view was further supported by the outcome of the REML analysis (Figure 27). Herein, the Rabhas shared the same clade with the Tibetan population. Thus, it can be easily ascertained from the results of the above analyses that the mongoloid lineage has strongly influenced the gene pool of the Rabha population.
Our findings received considerable support from the Y chromosome haplogroup diversity study, which have also demonstrated clustering tendency of the Rabhas with the North East Asians (Debnath, et al., 2011). Another study on Y chromosome haplogroup diversity by Su et al. in 2000 (Su, et al., 2000) also predicted the prehistoric Tibeto- Burman migration to the Himalayas. An earlier study by Chakraborty et al. in 1986 (Chakraborty, et al., 1986) have also shown the presence of mongoloid element in the Rabha gene pool.
5.1.3. Bengali
The Indian Bengali population is probably one of the most suitable examples of a population in India, which resulted from admixture of several populations due to human migration events. They constitute the major populating community of West Bengal region of Eastern part of India. In addition, they are dispersedly distributed all over the country, especially in the neighboring states.
A large number of KIR genotypes have been detected in the Bengali population, suggesting a high KIR diversity (Table 7). Another striking observation is the predominance of B haplotypes on A haplotypes in the Bengali population, which is also a characteristic feature of the Indian populations. This suggests a subcontinental type of KIR distribution in the Bengalis. Interestingly, both the NJ tree (Figure 26) and the PCA
Page | 160 score plot (Figure 23) reflected similar results and placed the Bengali population in the Indian cluster, suggesting its strong connection with the other populations of Indian origin. It is noteworthy that the genetic distance between the Bengalis and the North Indians is least, suggesting the proximity of the Bengalis to the North Indian lineage. Our findings receive considerable support from Y-chromosome haplogroup diversity study of Debnath et al. in 2011 (Debnath, et al., 2011). Herein, it was shown that the West Eurasian or Indian specific haplogroups, such as H, J and R are present in the Indo- European speaking Bengali population. More specifically, an Indian specific upper caste Y-haplogroup marker R1a1*-M17 has much higher incidence (40.7%) in the Bengalis, thereby implying the probable association of the Bengalis with the other ethnic Indian populations (Debnath, et al., 2011). In addition, another study by Singh et al. in 2009 (Singh, et al., 2009) based on HLA markers on the Bengali population of Siliguri revealed similar details, where it was mentioned that the frequencies of HLA-A*02 and A*11 are high in the Bengalis, as well as in the Sikhs, Gujratis Maharashtrians, and some caste groups of Western and Northern India. Furthermore, Singh et al. (2009) also documented the high frequency of A*24 in the Bengalis (Singh, et al., 2009). This feature was also prominently observed in the South Indian, North Indian and the Bangladeshi Indian populations. Singh et al. (2009) further observed that the B*07 and B*08 alleles that are found in the Bengalis are also found in the Sikhs, while the B*07 allele is highly frequent among the North Indians. Moreover HLA-B*37 reported in the Bengalis, was also found in a South Indian population (Singh, et al., 2009). On comparing the HLA frequencies with other World populations, Singh et al. (2009) reported certain interesting observations that are relevant in this discussion. HLA-A*02 and A*11 reported in the Bengalis are also present in higher frequency in the Greeks and additionally HLA-B*07, present in the Bengali population, was also present at highest frequency in the Western European populations (Singh, et al., 2009). These genetic evidences not only suggest Dravidian and Aryan association of the Bengalis but also suggest their possible European connections. Our findings, based on KIR genotypes, largely approximated these previous observations based on HLA and Y haplogroup markers, thereby further confirming the heterogeneous nature of the Bengali population and the influence on their gene pool from
Page | 161 other groups such as Dravidians, North Indians along with some putative European admixture.
It is evident from our study that most of the genotypes that are documented in the Bengali population are also found in other previously reported Indian populations. In the present study, it has been found that the KIR AA genotype ID 1 is the most frequent genotype in Bengalis, which is a characteristic of the North East Asian and European populations. After genotype AA1, the most frequent genotypes are IDs 2 and 71, both of which are present in high frequency within the Indian populations as well as in the Europeans and East/Northeast Asian populations. Genotype IDs 6 and 69 are found in the South Indian Dravidian populations but not in the North Indian populations. This shows putative Dravidian influences on the Bengali gene pool. Genotype IDs such as 23, 208 and 260, reported in the Bengalis are not found in other previously studied Indian populations but are present in many other Middle East and European populations. Such genotypes may be present because of the admixture with populations from Middle East and Caucasians. On the other hand, genotype IDs 195 and 341, present in the Bengalis are predominantly found in the Oriental populations including the North East Asians, possibly suggesting mongoloid influences on the Bengali gene pool.
The REML analysis (Figure 27) have produced a similar clustering pattern as found in the PCA score plot (Figure 23) showing that the Bengalis are clustered with the Indian populations. A similar result was also evident from NJ tree (Figure 26). Therefore, based on KIR genotypic profiling, it can be said that, although the Bengalis of Northern part of West Bengal, India share the same lineage with other Indian populations, significant influences of European and mongoloid lineages on the Bengali gene pool cannot be denied.
The linguistic background of the Bengalis provides strong evidence in favour of such an extensive admixture and genetic heterogeneity among them. The Bengali community has a strong linguistic connection with the Aryans, although their language has received considerable influences from other dialects.
Page | 162 In the subcontinental history, the most important invasion was by the West Asian semi- nomadic tribes around 2000 BC, who migrated East towards Persia and over the Hindukush mountains into the Indian subcontinent. They began conquering the regions in North India by subjugating the dark skinned Dravidian inhabitants of the Indus Valley civilization (Wolpert, 2000) and later came to be known as the Aryans. Moreover, repeated invasions over the centuries by the Persian, Greeks, Turks, Arabs and Mongols have added to the genetic and cultural diversity of the Indo-Pak subcontinent and have a large amount of genetic admixture resulting in shared ancestry (Mohyuddin, et al., 2002).
Furthermore, Calcutta (now known as Kolkata), the present metropolitan city of West Bengal, was the capital of India during the British colonial period. Thus, it was the centre of all foreign trades. Aa a result, people from not only other parts of the Indian subcontinent, but also from other countries used to visit the place for purpose of trade and commerce. Eventually many people belonging to different ethnicities settled in Calcutta This fact may justify to some extent, the presence of European and Middle East specific KIR genotypes in our studied population.
Moreover, West Bengal is geographically located at the neck of the North Eastern region of India. This is a well-known corridor for Tibeto-Burman migration. According to Debnath and coworkers (Debnath, et al., 2011), the settlement of the Tibeto-Burman (TB) speaking groups in the sub-Himalayan regions of India occurred much before the arrival of the Indo-Europeans. These findings may support the presence of East Asian specific KIR genotypes in the Bengali population as has been reported in this study. Furthermore, Guha et al. (2013) (Guha, et al., 2013) documented the presence of mongoloid component in the Rajbanshi population which is one of the most dominant ethnic caste populations of West Bengal. It may so happen that frequent marital relations between the Bengalis and the Rajbanshis during their early settlement in the region may be responsible for the contribution of the mongoloid elements into the genetic wealth of the Bengalis.
Page | 163 5.1.4. Gurkha
A considerable number of genotypes have been observed in the Gurkha population (Table 7). Careful observation revealed that both NJ tree (Figure 26) and the PCA score plot (Figure 23) are in agreement regarding the genetic relationship of the Gurkhas with other populations of the World. It has been shown in these analyses that The Gurkhas have prominent tendency to remain in close proximity to the NEAs and SEAs, suggesting strong connection with the populations of East Asian lineage. Measures of genetic distances and χ2 analyses also revealed the similar scenario.
Several interesting facts are revealed from the study of distribution in the KIR genotypes. It is found that the majority of the genotypes, reported in the Gurkhas are seen in Indians as well as NEAs and SEAs. However, genotype ID 488 found in the Gurkhas was reported earlier only in China Shaanxi Province Han population (Wang, et al., 2011).
In case of the Gurkhas, the REML phylogenetic tree (Figure 27) showed certain contradictions with the results of the NJ tree and the PCA score plot, wherein it is found that the Gurkhas branched away early from the Indian clade. Thus, it can be said that although the genetic structure of the Gurkha population has been influenced by Tibeto- Burman lineage, the effect of Indian gene pool cannot be ignored which may have occurred due to admixture with other neighboring populations.
Interestingly, it is observed that the frequency of the AB genotypes (56.8%) is far greater in the Gurkha population than that of BB genotypes (14.4%). Furthermore, the genotypic frequencies were tested for Hardy Weinberg Equilibrium and it is observed that the measured genotype frequencies are not significantly different from the expectations of Hardy-Weinberg equilibrium. It is found that group A KIR haplotypes (57.2%) occurred more frequently than group B KIR haplotypes (42.8%) in the Gurkhas. Reports from previous studies showed that the group A haplotypes are dominant over group B haplotypes among the North East Asians.
Our observations on the genetic affinities of the Gurkha population based on KIR gene frequencies, received considerable support from previously published reports on other
Page | 164 genetic markers. Y-haplogroup analyses based on 103 binary markers revealed a high percentage of O3a5-M134 in the Tamang (86.6%) (Gayden, et al., 2009) which is comparable to the frequencies observed in Tibeto-Burman speaking groups from North East India (Gayden, et al., 2007;Gayden, et al., 2011). Furthermore, the presence of O3a5-M134 in Tibet indicated a common ancestral connection for this language family, as suggested by Su et al. (Su, et al., 2000;Su, et al., 1999). According to this study, the Tamang population have close genetic relationship with the populations. The study further concluded that descendance of the Tamangs from the Tibetans who underwent migration and settled in the southern region of the Himalayan range (Qian, et al., 2000;Su, et al., 2000;Su, et al., 1999). On the other hand, the same study reported low frequency of O3a5-M134 haplogroup among the Newars, another caste group included in the Gurkhas of India, therefore revealing considerable influences from the Indian gene pool (Su, et al., 2000;Su, et al., 1999). These results along with elevated levels of Y- haplogroup R in the Newars are consistent with a recent mtDNA study, wherein reciprocal maternal gene flow was reported between North India and Nepal, given the geographic vicinity as well as the historical and socio-cultural affinities shared between these two neighboring regions (Thangaraj, et al., 2008). Furthermore, the overall age estimation for haplogroup O3a5-M134 was more recent among the Tamangs and the Newars in comparison to their Southeast Asian counterparts. This implies the recent immigration of this haplogroup from south-east Asia into the Himalayan region. Such an observation extended further support to the proposal of Su et al. (2000) (Gayden, et al., 2007;Gayden, et al., 2011;Gayden, et al., 2009;Su, et al., 2000), which stated that the people belonging to the Baric branch of the Tibeto-Burman language family were the first inhabitants of the Himalayan region, whereas the Tibetans, belonging to the Bodic branch, immigrated comparatively recently, subsequent to admixture with Central Asians gene pool (Su, et al., 2000;Su, et al., 1999).
Moreover, recent studies by Debnath et al. (2006) (Debnath and Chaudhuri, 2006) on the distribution of HLA genes also demonstrated the genetic connection of the Gurkhas with the Tibeto-Burman stock. It is a well-known fact that continuous passage across an Indo- Nepal border near Siliguri, West Bengal, facilitates immigration of the Nepali speaking
Page | 165 Gurkha people to India in search of livelihood. This might have resulted into the continuous gene flow between Nepali speaking Gurkha population of India with the people of Nepal, whose gene pool has received considerable influence from both the South Asian and East Asian lineages (Debnath and Chaudhuri, 2006).
5.1.5. Muslim
The Muslim population from Northern part of Bengal witnessed considerable diversity in their KIR gene content as was evident from their KIR genotypes (Figure 24). Both the NJ tree and the PCA score plot placed the Muslim population within the Indian cluster in close proximity to the Bengalis. Both the results of Chi-Square analyses and genetic distance measures are in agreement with the above outcome, regarding the genetic relationship of Muslims with other populations around the World.
The genotypes, reported in the Muslim population from our study,are also found in Asian Indians, Singapore Indians, East Asians (NEAs and SEAs) and in populations from Middle East (Guha, et al., 2013;Jiang, et al., 2005;Kulkarni, et al., 2008;Lee, et al., 2008;Rajalingam, et al., 2008;Rajalingam, et al., 2002;Whang, et al., 2005;Wu, et al., 2009;Yawata, et al., 2002), (Ashouri, et al., 2009;Zhu, et al., 2010). . In case of the Muslims, the BB genotypes are found at considerable high frequencies (34.39%). Furthermore, group B haplotypes (60.36%) also occurred more frequently among the Muslims than group A haplotypes (39.64%). Previous studies reported the predominance of group B KIR haplotypes in populations of Indian subcontinent (Rajalingam, et al., 2008;Rajalingam, et al., 2002). Phylogenetic tree based on KIR genotypes also showed compliance with that of the NJ tree (Figure 26) and the PCA score plot (Figure 23) and placed the Muslim population of Bengal closer to the Bengalis within the Indian cluster. Therefore, it can be said that the influence of the Indian KIR gene pool can be noticed prominently in the KIR gene distribution of the Muslim population of northern part of West Bengal. However, chances of admixture from East Asian and Middle East lineages cannot be overlooked as a whole.
Page | 166 Historical evidences unfolded certain interesting facts regarding the genetic affinities of the Muslims of West Bengal. The Partition of Bengal in 1947 divided the erstwhile British Indian province of Bengal between India and Pakistan (Barman, 2012;Chakravartty, 2005;Fraser and Sengupta, 2006;Ghosh and Indian Academy of Social Sciences., 2002;Palit and Ray, 2004;Roy and Oxford University Press., 2013;Saxena, 1987). The Western part of Bengal with predominantly Hindu population came to be known as ‘West Bengal’ and became a province of India by the same name, while the Eastern part with predominantly Muslim population became a part of Pakistan and later became an independent country as ‘Bangladesh’ after the 1971 Bangladesh Liberation War (Palit and Ray, 2004). Thus, after partition, considerable minorities of muslim were left in West Bengal, which inflated, to 27% of the state population at the present time (Palit and Ray, 2004). Furthermore, infiltrations by refugees from Bangladesh in search of food and shelter also added to the present population of Muslims in Bengal (Chari, et al., 2003;Datta, 2013;Rai, 1993;Singh, 2010). Therefore, it can be mentioned without any doubt that the present Muslim population of West Bengal share ancestral links to present Bangladeshi Muslims.
Evidences from mitochondrial DNA researches suggested that one of the oldest mtDNA haplogroups found in the Indian sub-continent, M2a, is the third most common in Bangladeshi populations (Metspalu, et al., 2004;Sultana, et al., 2014). Furthermore, admixture of India-specific branches and gene flow from East Asia in context to mtDNA, has also been reported in the Bangladeshi population (Metspalu, et al., 2004).. Another study on 15 autosomal STR loci in the present day Bangladeshi population showed the close genetic relationship with Palia and Rajbanshi population from West Bengal, India compared to East Asian or Middle Eastern populations (Eaaswarkhanth, et al., 2009;Hossain, et al., 2014). This observation supported the outcome of χ2 analyses (Table 8) in our study, wherein no significant differences were seen between the Muslims and the Rajbanshis for any of the KIR gene loci.
According to a recent study, a shared Y-chromosomal pattern was observed between the Muslims and the Hindus in India, suggesting influence of prehistorical factors and
Page | 167 geographical distribution of populations on Y-chromosomal variation rather than the practices of Hinduism or Islam (Gutala, et al., 2006). Based on Y-chromosomal distribution, the study further demonstrated that the Muslim isolates in South India predominantly originated from local populations rather than from Muslims belonging to other parts of India, or outside the country, thereby supporting the diverse origin of the Muslims in India (Eaaswarkhanth, et al., 2009;Eaaswarkhanth, et al., 2010;Gutala, et al., 2006). Thus, it can be said that the Muslim population from the Northern part of Bengal may exhibit differences in their genetic make-up form other Muslim populations of India. However, influences of Middle East lineages on the Muslim gene pool in this part of the country cannot be denied as a whole (Eaaswarkhanth, et al., 2010). Calcutta (now known as Kolkata), the present metropolitan city of West Bengal, was the capital of India during the British colonial period and was the centre of all foreign trades. As a result, Muslim people from different parts of the country come and settle in Bengal and eventually contributed to the diversity of the Muslim gene pool of the state.
Moreover, West Bengal is a well-known corridor for Tibeto-Burman migration since it is located at the neck of the Northeastern region of India (Gazi, et al., 2013). Debnath and coworkers (2008) suggested that earlier settlement of the Tibeto-Burman (TB) speaking groups in the sub-Himalayan regions of India occurred much before the Indo-Europeans influx (Debnath, et al., 2011). Such close proximity with the Tibeto-Burman may support their admixture, ultimately resulting in the presence of East Asian specific KIR genotypes in the Muslim population of Bengal.
5.2. Study of the association of KIR genes with Rheumatoid Arthritis.
Recent studies suggested the importance of the diagnosis of a disease in its early stages for implementation of proper treatment strategies (Klareskog, et al., 2009). In present time, different serological markers are available which are responsible for bringing revolutionary changes in the diagnostic approach to Rheumatoid Arthritis (RA). Two
Page | 168 such efficient markers, which have progressively gained importance in the diagnosis and therapy of RA, are the anti-CCP antibodies and RF titre. A recent study has revealed the prognostic importance of RF titres in RA disease activity (Geng, et al., 2012). Furthermore, RF titre constitutes one of the classification criteria of ACR 2010 (Binesh, et al., 2014). Moreover, anti-CCP has also been included into newly proposed diagnostic criteria for RA owing to its strong association with erosive arthritis (Visser, et al., 2002). Therefore, in this study, we have evaluated and compared the diagnostic performance of anti-CCP antibodies and Rheumatoid factor in RA patients and control groups wherein the subjects of both the groups were selected from Siliguri and its adjoining areas of Northern part of West Bengal, India. In our study, the patients were selected based on the ACR criteria 2010 as this system focuses on features at earlier stages of disease rather than late stage features.
In our study, the sensitivity and specificity of the RF titre were found to be 78.18% and 72.19% respectively, which are more or less consistent with the previously published reports (Lin, et al., 2008;Schellekens, et al., 2000;Weinblatt and Schur, 1980), with the specificity being slightly higher. An earlier reported literature have shown that the sensitivity of RF titre in RA patients varied within a range from 26 to 90% (Shmerling and Delbanco, 1991). Such fluctuations may have resulted due to the influences of several factors which include, racial and geographical differences (Binesh, et al., 2014), genetic influences (Silman, et al., 1988) and even environmental factors (such as cigarette smoking) (Enzer, et al., 2002). In the current study, we found that RF titre was positive in 27.8% of the members of the control group. Similar results were also reported from the previously published works (Binesh, et al., 2014;Westwood, et al., 2006) where RF seropositivity was demonstrated in many non-rheumatic diseases and even in normal healthy subjects, particularly in older individuals (Westwood, et al., 2006). Thus, it can be said that lower to moderate specificity of RF titre may be one of the underlying reasons behind selecting anti-CCP antibodies as another important serological marker in RA diagnosis.
The sensitivity and specificity of anti-CCP antibodies were estimated to be 76.36%and 96.69% respectively, which are consistent with the outcomes of some of the previous
Page | 169 studies (Lee and Schur, 2003;Lin, et al., 2008;Zeng, et al., 2003), but were contrary to others (Maraina, et al., 2010). Such differences in the diagnostic evaluation of anti-CCP assay may occur due to the following reasons: • heterogeneous nature of anti-CCP antibodies directed against different citrulline epitopes (van Jaarsveld, et al., 1999), • differences in the patient population (especially based on disease duration) (Binesh, et al., 2014) (Kamali, et al., 2005), and • the race of the patient group (Mimori, 2005). In our study, in spite of high specificity of anti-CCP assay, 5 false positive cases were found. According to Gaalen (van Gaalen, et al., 2004), future onset of RA can be predicted by anti-CCP. This may support the fact that patients having anti-CCP reactivity may develop RA at a later stage, although in our study, no signs and symptoms of RA progression were found in these patients.
In our study, both Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of anti-CCP assay are equivalent to previous reports (Lin, et al., 2008). It is evident from our study, that PPV of anti-CCP (94.38%) is higher than that of the corresponding RF titre (67.19 %) while the NPV of the both the assays are more or less similar (Table 14). Since the predictive value of any diagnostic test is related to its disease prediction ability, higher PPV of anti-CCP assay may increase the accuracy of RA diagnosis. Dot density graph (Figure 28a) illustrated that anti-CCP values showed a rather clumped distribution below 17 U/ml in case of the non-RA patients. This result can well be correlated with the high specificity of the anti-CCP assays wherein we found the assay to be positive in only 5 cases of the control group (n=151). Furthermore, the Pearson correlation coefficient between anti-CCP and RF titres were found to be 0.716 and 0.356 respectively, among the patient and the control groups, thereby signifying that the presence of both the factors i.e. anti-CCP and RF are relevant as far as the diagnosis of the disease is concerned (Figure 29).
Box plot (Figure 28b) analyses revealed that the boxes representing anti-CCP and RF titre in the control samples are rather small, thereby demonstrating high level of
Page | 170 agreement among the values. However, in case of RA patients, tall boxes with unequal quartiles suggest wider distributions of both the assay values.
It is observed from the ROC curve (Figure 30) that the Area Under the Curve (AUC) for anti-CCP is slightly greater than that of RF titre. However, there are several crossovers between the curves suggesting that neither of the serological markers is superior to the other in all circumstances of the study. Therefore, it may be suggested that performing both the tests in combination may be helpful in proper diagnosis of RA.
In this study, we have also aimed to analyze the role of anti-CCP and RF tire in the prediction of DAS28 score (Table 15). Since ESR is a criterion for DAS28 measurement, all the multiple regression models consisted of ESR as the first predictor, followed by anti-CCP and RF titre as second predictors respectively. It is evident from the models that 79.9% of the variance in DAS28 score can be predicted by ESR values in combination with anti-CCP while 80.2% can be predicted by ESR in combination with RF titre. Similar values of R2 change were observed in both the cases (Table 15). Furthermore, significant F changes in both the models suggest that both anti-CCP and RF titre have significant contribution in combination with ESR for the prediction of the DAS28 score. However, small changes in R2 values in both the models suggest that both anti-CCP and RF titre in combination with ESR predict shared variance in DAS28. Similar results are also obtained from the corresponding 3D scatter plots and contour diagrams (Figure 31), whereby it can be seen that higher DAS28 scores correspond to high ESR values with significant predictions from both anti-CCP and RF titre respectively.
Several observations were made from the estimated results of the different clinical parameters that we have conducted on the blood samples of 50 RA patients and 50 control subjects. Interestingly, it is observed that none of the four clinical parameters were 100% positive (above the threshold value) in all the disease individuals as are also evident from their sensitivity and specificity measures. These discrepancies have been explained below. In case of RF titre, it is found that 92% of the patients have values above the threshold level while 8 % have lower values. When compared with published data reported from other populations, it has been found that 80.56% of the RA patients
Page | 171 were RF positive in case of the Korean population, while 19.44 were found to be RF negative (Francois, 1965). In a similar study 66.5% of the Iranian RA patients were RF positive and the remaining 33.5% were RF negative (Scudder, et al., 1978). In another study, 53.33% of Turkish patients were positive for RF while the remaining were negative (Al-Timimi, 1977). Such variations in the percentage of positive and negative cases in different populations across the World may be explained based on some
fundamental points. RA factors are antibodies that are directed against the FC region of the IgG molecule. They belong to different isotypes e.g. IgM, IgE, IgG, IgA and IgD. Thus, their detection may vary based on the proportion of the different isotypes that are present in the serum. Furthermore, RA titre may give seronegative outcomes in cases of patients suffering for not more than 6 months, which may later become positive along with the progression of the disease. In fact, three patients in our study population with seronegative RF titre have the history of suffering for only 4 months. However, the sensitivity and the specificity of the RA factor test as calculated in our study are 92% and 88% respectively. Moreover, from Table 16 it is seen that the odds of exposure to RF are greater (84.33%) among the RA patient group compared to the control group. Therefore, it can be said that the RF titre assay may prove to be an essential clinical parameter for evaluating RA disease prognosis. C-reactive protein (CRP) and Erythrocyte sedimentation rate (ESR) are the two primary parameters that are frequently used for the clinical detection of acute phase reactions like inflammation. Their concentrations become relatively high during inflammation compared to normal level. Furthermore, they have relatively short lag time from the moment of stimulus, and are cost-effective too. Therefore, we have included these two parameters in our study. In a study conducted in Pakistan it was found that the CRP value ranged between 11.2 mg/dl and 108 mg/dl with a mean value of 39.1 mg/dl in severe RA patients, while the ESR estimation resulted in a mean value of 62.5 mm/hr [12 mm/hr to 146 mm/hr] (Al-Timimi and Dormandy, 1977). In another study conducted on a population group from Chandigarh, the mean CRP and ESR values were found to be 22.8 mg/dl and 51.3 mm/hr respectively (Stocks, et al., 1974). Thus, these published results are very much in agreement with our reports.
Page | 172 CRP is produced in the liver under the inductive influences of proinflammatory responses derived from monocytes and macrophages. At first, these proinflammatory responses trigger the increased secretion of interleukin –1 β (IL-1β) and tumor necrosis factor – α (TNF-α), which then through the release of interleukin 6 (IL-6), stimulate the liver to secrete CRP. Recent studies have suggested direct involvement of CRP in inflammatory activities, wherein it stimulates the secretion of inflammatory cytokines such as IL-1β, IL-6 and TNF-α from the monocytes and directly provides proinflammatory stimulus to phagocytic cells (Bharadwaj, et al., 1999). CRP has a crucial role to play in the determination of microvascular endothelial dysfunction in RA patients (Galarraga, et al., 2008). On the other hand, estimation of ESR is based on the principle that normally, erythrocytes tend to repel each other owing to the presence of net negative charges on their surface. However, during acute phase reactions, positively charged proteins such as fibrinogens promote rouleaux formation in blood. This results in further increase in the serum ESR level. These two parameters are crucial measures of RA prognosis since they are the measures of inflammatory processes, which play the central role in RA pathogenesis. However, sometimes, clinicians have to depend on additional diagnostic features for determining prognosis of RA since severe inflammation does not always authenticate development of RA. As evident from our results, both CRP and ESR are not always present above the threshold value in case of the RA patients. This may happen either due to the lack of prominent acute phase reactions in some of the patients or due to the tendency of these parameters to give lower values during early phases of the disease. However, the sensitivities and specificities of these two tests are quite high, establishing their crucial role for RA diagnostic purposes.
Anti Streptolysin-O (ASO) titer is another essential clinical parameter for RA diagnosis and is included in our study to detect rheumatic fever caused by environmental triggers like streptococcus infection. This parameter measures the plasma levels of ASO antibodies produced against streptolysin by different streptococcal strains. It has been found that out of 50 samples, only 11 samples had higher ASO titer values than 200 IU/ml. However, it is clearly evident that the mean ASO titre value is higher in case of the RA patients compared to the control samples (Table 16) since the ASO positive
Page | 173 patients have the titre values higher than threshold (200 IU/ml). Francois (1965) (Francois, 1965) showed higher prevalence of 3 groups of Streptococci in the RA patients compared to control samples. This may occur due to higher acquisition rate but reduced elimination rate resulting in prolonged infection followed by higher antibody response. This observation may justify the facts that although raised ASO titres (>150 IU/ml) are found in both the groups but their numbers in RA patients are higher than the control group. Furthermore, significantly higher ASO titres (> 300 U) are found in the RA patients compared to the controls (see Figure 32b). One interesting observation was that the sensitivity of the test was as low as 22% which may have resulted due to the presence of only 11 patients having positive titre result i.e. ASO>200 IU/ml (Table 16). On the contrary, the specificity was 100%, which suggest that the test is always negative in the absence of acute phase reactions, which in turn, is a well-established pathophysiological condition in case of rheumatoid arthritis. Thus, ASO titre may also prove useful in the diagnosis of RA.
Plasma ceruloplasmin and creatinine are generally not considered as essential diagnostic tool for RA diagnosis. However, serum ceruloplasmin has been included in the study as it has high correlation with serum antioxidant property (Scudder, et al., 1978) and may have considerable protective significance (Al-Timimi, 1977;Al-Timimi and Dormandy, 1977) especially in presence of tissue damage or destruction. Serum creatinine estimation has also been carried out in this study, as it is an important metabolic by-product of creatine, which in turn is a very crucial amino acid for building and repairing of muscular tissues.
It is interesting to observe that their levels showed significant differences among the blood samples of the RA patient and the control groups. During inflammation in RA patients, production of IL-1 and IL-6 is increased stimulating hepatocytes to release and increase the amount of ceruloplasmin in the blood plasma. Such increased level of ceruloplasmin has also been reported previously from other studies (Scudder, et al., 1978). In a study from Poland, mean ceruloplasmin level in RA patients was estimated to be 0.3 g/L, which is very much comparable to our data (Strecker, et al., 2013).
Page | 174 Furthermore, earlier studies predicted the active role of ceruloplasmin in governing the serum antioxidant activity (Scudder, et al., 1978);(Stocks, et al., 1974), which when increased may have an important role to play in the systemic inflammatory response.
On the other hand, the creatinine level showed moderate differences in between the RA and the control blood samples. Creatinine level in the blood generally rises only after marked damage of nephrons that can be observed in patients having long term RA and inflammatory responses. Thus, it may not serve as a very potent tool for early diagnosis of RA but can be useful for the detection and treatment of nephropathy during late Rheumatoid Arthritis. Moreover, these two tests have considerable sensitivity and specificity indicating that their results may prove affirmative with other clinical parameters that have been considered in our study for RA diagnosis.
KIR has become a promising target for disease association studies owing to the polygenic and multi-allelic diversity of the KIR gene locus. The present study attempted to figure out the relevance of KIR gene polymorphism in RA. Our results revealed significant associations of KIR2DL3, KIR2DS2 and KIR2DL2 with RA when compared with control samples (Table 18). In our study, both the patient and the control blood samples were drawn from a homogeneous population of Siliguri and adjoining areas of West Bengal, India. Decreased frequency of KIR2DL3 among the RA patient group compared to the control group may imply the protective role of the gene against the incidence of RA. On the other hand, the frequencies of KIR2DS2 and KIR2DL2 among the patients are significantly increased in comparison to the control group (Table 18). This may indicate their role in amplifying the odds of the disease occurrence. Other KIR (both inhibitory and activating) genes showed no statistically significant differences among the patient and the control groups. Another study reported strong association of KIR2DL2 and KIR2DS2 with extra-articular manifestations in case of RA, wherein vasculitis has been considered as the major factor responsible for such associations.
In our study, it has been observed that the frequency of the KIR2DL3 gene is decreased in RA patient group compared to control group. Earlier studies have shown that the receptor molecule encoded by the KIR2DL3 gene modulates the intracellular signaling pathways,
Page | 175 which in turn results in the blockage of cytotoxic secretion of IFN-γ like cytokines (Rajagopalan and Long, 2005). Interestingly, previous reports have shown that IFN-γ plays a pivotal role in RA auto-reactivity (Flodstrom-Tullberg, et al., 2009). These facts suggest the protective role of KIR2DL3 in case of RA.
In our study, KIR2DS2 gene is more frequent among the RA patients compared to the controls with almost two fold increased risk (OR = 1.76) which indicate that KIR2DS2 may act as a susceptible factor for RA (Table 18). In a previously published report, Yen et al. (2001) (Yen, et al., 2001) demonstrated the association of KIR2DS2 in Western European and Polish RA populations. This report is in agreement with our findings. However, our study contradicted with the outcomes of another study, conducted on Caucasian population with RA from Northern Ireland (Middleton, et al., 2007), wherein no associations of these genes were seen. This may be due to racial differences, which contributed to the differences in KIR gene frequencies. According to previously published reports, KIR2DS2 prominently increases IFN-γ secretion, which in turn results in enhanced production of perforin and granzymes, ultimately leading to the destruction of joints in RA patients (Yen, et al., 2001).There are two distinct mechanisms by which KIR2DS2 increases IFN-γ secretion. Firstly, non-covalent association of KIR2DS2 with DAP-12 adapter molecule lead to the intracellular signalling pathways in NK cells. Secondly, in case of CD28 null T-cells, KIR2DS2 has been reported to act as a co- stimulatory molecule, causing increased T-cell receptor activation (Namekawa, et al., 2000)
KIR2DL2 gene has also been found at higher frequency in the RA patients, suggesting its role as a susceptible factor for the disease. Interestingly, our result contradicted with another study from North India, where low incidence for KIR2DL2 was found among the RA patients. Such contradictions may occur due to two distinct reasons: firstly, both these populations are genetically distant from one another, thereby differing in both KIR haplotype composition and frequencies. Secondly, environmental conditions vary between the two regions, which in turn, may enforce differential selection pressure on the functional activity of the KIR genes. Altogether, it can be said that the role of KIR2DL2
Page | 176 in RA incidence in a particular population may vary depending on the environmental selection pressure and the KIR repertoire of the population.
Surprisingly, the association of inhibitory KIR2DL2 gene with RA may result from the presence of the corresponding KIR molecule on the surface of regulatory cells rather than effector cells, thereby contributing to the disease prognosis. Although known to be involved in RA pathology, the role of T and NK cells in different manifestations of the disease is not clear. Furthermore, NK cells and T cells show differences in the mechanisms of KIR inhibitory and activating functions. Thus, distinct KIR repertoire may contribute to the different forms of RA. Previous studies have shown that CD8+ T cells expressing inhibitory KIR have elevated levels of Bcl-2, making them less susceptible to activation induced cell death (AICD). Thus, they may have a survival advantage followed by their accumulation in vitro and in vivo. KIR2DL2 expressed on CD8+ T cells binds its HLA-C ligands so that the cells are less likely to undergo AICD when it is activated upon engagement of its TCR by the cognate HLA-peptide complex. If the CD8+ T cell is restricted by a detrimental HLA molecule, that binds the TCR and not KIR2D L2it will also survive for longer and its detrimental effects will be enhanced. However, its role as inhibitor of intracellular signaling could be blocked in RA patients by two hypothetical mechanisms as mentioned below. Firstly, as reported by Fadda et al. 2010 (Fadda, et al., 2010), where differences in the peptide sequences of the HLA class I molecules can regulate the activity of NK cell by antagonizing inhibition mediated by KIR2DL2 and favoring activation by its counterpart KIR2DS2. Secondly, Matsui et al. 2001 mentioned that 30% of RA patients have antibodies to KIR2DL2, which are responsible for the breakdown of self-tolerance (Matsui, et al., 2001).
In our study, no significant differences are seen in the proportions of AA and Bx genotypes between the patient and the control group (Table 18). However, it is interesting to observe that when we divided the Bx genotypes into two groups namely AB and BB, it is seen that the frequency of the BB genotypes are significantly higher among the patients compared to the control. It is already a known fact that the BB genotypes are rich in activating KIR genes which may contribute significantly to the intracellular signaling
Page | 177 (Nelson, et al., 2004;Rajalingam, 2011) and activation of cytotoxic function. This may result in secretion of cytokines (Rajagopalan and Long, 2005;Williams, et al., 2005), thereby favoring the pathology of the disease
Analyses of the KIR genotypes in the RA patients (Table 19) revealed that, genotype ID 3 and 81 (BX) showed association with RA, suggesting their role as susceptible factors of the disease. Interestingly, both the genotypes contain KIR2DL2 and KIR2DS2 genes, which have been shown to be associated with the disease, but KIR2DL3 is present only in genotype ID 3 but not in ID 81. It is noteworthy that, both KIR2DL2 and KIR2DS2 genes are present in 28 out of the reported 38 KIR genotypes among the patient group, altogether comprising of 80 RA patients. Such an outcome clearly indicates the association of KIR2DL2 and KIR2DS2 with the odds of occurrence of RA. Interestingly, the number of genotypes slashed down to only 7 when KIR2DL3 has been included along with the two above mentioned genes, thereby demonstrating the possible protective effect of KIR2DL3 against RA incidence. In addition, KIR2DL3 has been found in 23 genotypes of the control group in comparison to only 14 genotypes among the patient group.
In an earlier study by Ramirez-De los Santos (2012) (Ramirez-De los Santos, et al., 2012), the researchers found two genotypes (G2 and G3) corresponding to 9 individuals in the RA group, which did not contain any of the genes that showed significant differences namely KIR2DL3, KIR2DL2 and KIR2DS2. They further analyzed that only 8 genotypes with this feature corresponding to 27 individuals were found in different populations around the world (Gonzalez-Galarza, et al., 2011), but none of these individuals were RA patients. Interestingly, only one genotype with such feature corresponding to only two individuals was found among the RA patients in our study.
In case of the Linkage Disequilibrium (LD) analyses (Table 20), both Dˊ and r2 values are taken into consideration since they have different biological interpretations (Devlin and Risch, 1995) (Jorde, 2000). Dˊ detects only recombinational history. In other words, means that the loci are in complete disequilibrium and are always co-inherited together, while indicates that the loci are completely independent. On the other hand, r2 tells us whether one locus is a good predictor of the other in a two-locus
Page | 178 haplotype. This means that r2=1 signifies that one locus is the complete predictor of the other. Based on these two LD parameters (Table 20), it is found that five such significant combinations are reported having both the values high namely, KIR2DL2-KIR2DS2, KIR3DL1-KIR2DS4, KIR2DS1-KIR2DL3, KIR2DS1-KIR3DS1 and KIR2DS1-KIR2DS5. Among these combinations, highest values are reported for KIR3DL1-KIR2DS4 followed by KIR2DL2-KIR2DS2.Thus it can be seen that both KIR2DL2 and KIR2DS2 are good predictors of each other and are co-inherited roughly 87% of the time.
Similar conclusions can be drawn in case of KIR3DL1-KIR2DS4 haplotype wherein co- inheritance can occur in almost 96% of the cases. However, in our study, both these genes are found at low frequencies among the RA patients compared to the control group. Prakash et al., 2014, reported protective association of KIR3DL1 with RA in patients from North India (Prakash, et al., 2014), while Yen et al 2006 (Yen, et al., 2006) reported the risk association of KIR2DS4 with RA in patients from Taiwan.
In a study by Nazari et al. (2015) on Iranian patients with RA (Nazari, et al., 2015), two activating KIR genes namely KIR2DS5 and KIR3DS1 were reported to have protective role against incidence of RA. Interestingly, in our study, both these genes showed non- significant differences among the case and control groups. However, from the high r2 values that they share with KIR2DS1, it can be said that they have good predictive association with KIR2DS1, which in turn co-inherited with KIR2DL3 in roughly 69% of the cases. Previous studies in different populations around the World suggested the protective association of KIR2DL3 with RA, which is in agreement with our report.
,
Page | 179
Chapter 6:
Summary and Conclusion
6. Summary and Conclusion
This section has been divided into two subcategories for better understanding, namely, population based study and RA based study..
6.1. Population Based Study
Analyses of the genotypic and haplotypic organization of the KIR loci among the five populations have deciphered vital information about the amount, pattern and distribution of genetic variation in different studied populations of India. The present study provided valuable information to comprehend the effect of socio-cultural barriers on the genetic makeup of Indian populations. Furthermore, the perspective correlation of the KIR genetic profile of studied populations with the past human movements and other historical records facilitated the determination of their genetic ancestry/origin. The results also provided an inference about the pattern and causes of genetic similarity and differences between different Indian and world populations.
The analyses of genome wide distributed KIR loci in all the populations have divulged surfeit of facts about the genetic sketch of studied populations. The similarities and differences in the KIR gene frequencies, measures of genetic distances, haplotype distribution and several other parameters, including LD analysis, have demonstrated that the contemporary studied populations are highly diverse population groups. The reason of such high diversity include both accumulation of large number of mutations during long term survival in a particular environment and the colossus gene flow from all over the world. Furthermore, genetic variations within and between the populations revealed a definite pattern of diversity distribution, wherein populations structured into socio- religious groups have more genetic similarity within their own group and are genetically more distant from populations of other groups.
Our study is a pioneering population-based analysis of KIR genes in the five populations, which we have selected from the sub-Himalayan region of West Bengal, India. It was evident from our KIR genotyping data, that among the studied populations, the
Page | 180 Rajbanshis showed the lowest genetic distance with the Rabha population. This could be due to the fact that Rajbanshis adopted the process of transformation of a tribe into a caste resulting in a Tribe-Caste continuum, and in between this process population like Rajbanshi who were once tribal, gradually adopted the attributes of caste system (Kumar, et al., 2004). Interestingly, the Muslims are the second closest population to the Rajbanshis. Another interesting fact was that Muslims showed the lowest genetic distance with the heterogenous Bengali population. This may be because of the fact that although Muslims practiced consanguinity, they married outside their religion. Previously published reports also documented that a section of the present day Bengali Muslims are converts. They were once low cast Bengali Hindus before Muslim invasions, who were forcefully converted to Islam (Esposito, 2003). On the other hand, the Gurkhas showed lesser genetic distance with the Rabhas, compared to the Rajbanshis. These findings provoked us to hypothesize gene flow among the studied groups owing to the recent massive gene flow between the populations or possible identical recent common ancestor.
Finally, the phylogenetic assessment based on KIR loci revealed that four of the studied populations except the Rabhas, clustered with other Indian populations. In contrast, the Rabhas shared the same clade with the East Asians owing to their tribal ethnicity and strict endogamous nature, which helped them to retain their ancestral genotypic pattern. This may demonstrate substantial gene flow from the geographically neighboring populations owing to their settlements along the human migratory routes. Furthermore, proximity of the Muslims with the Bengali population may indicate the role of environmental selection on the KIR repertoire of a population in addition to influences from migratory and gene flow events. Thus, it can be said that the phylogeny of KIR polymorphisms studied in the five Indian populations generated a molecular narration of the population demography, relatedness, and genetic substrata reflecting possible signatures of ancestors and novel micro-evolutionary genetic differentiations.
In summary, our study based on KIR genotypic profiles, suggests the following statements:
Page | 181 Inspite of having Indian origin, the Indo-European speaking Rajbanshis from the Northern part of Bengal has Tibeto-Burman influence. However, this population is highly diversified with considerable admixtures from neighboring population. Furthermore, detailed analysis is required to characterize all the available KIR haplotypes. In the little known Rabha tribe, the influence of mongoloid element in Rabha gene pool was very much prominent, which was well maintained due to their strict endogamous character. The Indo-European speaking Bengalis from Northern part of Bengal share both Dravidian and Indo-Aryan gene pool with some Mongoloid and European influences, thus becoming one of the most heterogenetically diverse community of the Indian Sub-continent. The Gurkhas have shown prominent tendency cluster with the NEAs and the SEAs. This suggests strong connection with populations of East Asian lineage. However, sub-continental influence on their gene pool cannot be ignored. Furthermore, this study also suggests the continuous gene flow between Nepali speaking Gurkha population of India with the people of Nepal, whose gene pool has received considerable influence from both the South Asian and East Asian lineages. Genetic architecture of the Muslims from the Northern part of Bengal has received considerable influence not only from the neighboring populations of Bangladesh but also from the historical invasions of the Middle-Eastern populations. This study has also revealed the presence of Tibeto Burman element in the Muslim population of this region, which may differentiate them from other Muslim populations of India.
Conclusively, the combined picture represented by the empirical results of the analyses of KIR Loci infers that:
The studied five populations are genetically highly diverse people with most of the variation scattered between individuals.
Page | 182 The genetic differentiation does exist between numerous of endogamous groups across Indian mainland, but the differentiation is mainly configured geographically and linguistically. The influence of the Sino-Tibetan migration across the North Eastern Himalayan corridor is prominent on the gene pool of the populations in the Sub-Himalayan regions of the Eastern and North Eastern part of the country.
Overall, the genetic configuration of Indians is extremely complex, interwoven in numerous threads of unknown facts. More genetic data from the North Eastern part of the country is highly necessitated in tracing the missing blocks of the causes and consequences of human genetic variation. The present study has provided some clues that might help in unwinding the complex interwoven threads of Indian genetic composition. However, further studies on other available genetic markers may fine-tune the present knowledge of the genetic background of both the populations.
6.2. RA-Based Study
Rheumatoid Arthritis (RA) is one of the most common autoimmune diseases affecting nearly 1% of the population worldwide and is characterized by the progressive articular damage leading to joint deformities and disability. The multi-factorial nature of RA provides high disease heterogeneity with the specific combinations of a genetic background and environmental factors that influence the susceptibility, severity and outcome of the disease. RA heterogeneity is marked by the presence of distinct autoantibodies, such as rheumatoid factor and anti-CCP antibodies. In addition, genetic contribution has been estimated to be 50–60% towards the etiology of RA. In the present study, the aim was to study the association of the KIR genes with the prevalence of RA and to estimate the levels of clinical parameters like RF and anti CCP in the RA patients in comparison to the control group.
The finding from this study can be summarized as follows:
The sensitivity and specificity of RF titre and anti-CCP antibodies in case of RA patients of this region was more or less consistent with the previously published data.
Page | 183 Lower to moderate specificity of RF titre assay may be one of the underlying reasons behind selecting anti-CCP antibodies as another important serological marker in RA diagnosis. The Pearson correlation coefficient between anti-CCP and RF titres were found to be 0.716 and 0.356 respectively, among the patient and the control groups, thereby signifying that the presence of both the factors i.e. anti-CCP and RF are relevant as far as the diagnosis of the disease is concerned. Although, it was observed from the ROC curve that the AUC for anti-CCP was slightly greater than that of RF titre, but there were several crossovers, suggesting that neither of the serological markers is superior to the other in all circumstances of the study and performing both the tests in combination may be helpful in proper diagnosis of RA. Both anti-CCP and RF titre in combination with ESR can predict shared variance in Das28, which is an important diagnostic criteria for RA. In a small section of the study, it was shown that CRP and ESR are the two crucial measures of RA prognosis as they are the determinant of inflammation, which in turn play the central role in the pathogenesis of RA. This study also reported ASO titre as another essential clinical parameter of RA pathogenesis especially, during the occurrence of acute phase reactions and rheumatic fever. This study further demonstrated that serum ceruloplasmin has shown significant differences between the RA patients and the control groups. Thus, the study predicts the active role of ceruloplasmin in governing the serum antioxidant activity which when increased may be an important component of the systemic inflammatory response during RA. This study further documented the moderate differences in serum creatinine level between RA patients and control blood samples. Hence, it is predicted that although, serum creatine is not a very potent tool for early diagnosis of RA but can be useful for detection and treatment of nephropathy during late Rheumatoid Arthritis. Molecular typing of KIR genes was also performed in the RA patient group and the control group. The results demonstrated the significant associations of KIR2DL3,
Page | 184 KIR2DS2 and KIR2DL2 with RA when compared with control samples. Thus, it can be said that, decreased frequency of KIR2DL3 among the RA patient group may indicate the protective role of the gene against the incidence of RA. On the other hand, increased frequencies of KIR2DS2 and KIR2DL2 may imply their role in enhancing the odds of the disease occurrence. No significant differences were observed in the proportions of AA and Bx genotypes between the patient and the control group, but when the Bx genotypes was divided into two groups namely AB and BB, it was found that the frequency of the BB genotypes were significantly higher among the patients compared to the control. Further detailed analyses of the KIR genotypes have revealed that genotype ID 3 and 81 showed significant association with RA, suggesting their role as susceptibility factors of the disease. Based on the two LD parameters, which have biological interpretations, namely Dˊ and r2, it was found that five such two-locus KIR haplotypic combinations were reported having both the values high namely KIR2DL2-KIR2DS2, KIR3DL1- KIR2DS4, KIR2DS1-KIR2DL3, KIR2DS1-KIR3DS1 and KIR2DS1-KIR2DS5. Among them, highest values for these two parameters were reported for KIR3DL1-KIR2DS4 followed by KIR2DL2-KIR2DS2. Since, both KIR2DS2 and KIR2DL2 were found to be significantly associated with Rheumatoid Arthritis, hence it can be inferred that both KIR2DL2 and KIR2DS2 are good predictors of each other and are co-inherited roughly 87% of the time.
In this work, the main goal was to determine the genetic predisposition to RA and the role of KIR genes in the disease pathogenesis in a multi-ethnic population residing in Siliguri and adjoining areas of North Bengal, India. Taken together, the current findings on RA risk based on KIR genetic profile may provide a bird-eye picture of the genetic basis of the disease. In addition, further detailed studies are required on allelic polymorphism of the KIR genes, which may illuminate the role of KIR molecules in RA pathogenesis in these important communities of the sub-Himalayan region of Eastern India.
Page | 185 In conclusion, the present work has demonstrated the genotyping of KIR genes and made an effort to construct a database for five population groups in the sub-Himalayan region of West Bengal, India. Our investigation highlighted the polymorphic nature of KIR genes and their role in constructing the genetic structure of a population. This work further demonstrated the role of KIR genes in the pathogenesis of RA and added to the current understanding and comprehension of the central role of NK cells in immunity.
Page | 186
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Page | 238 Indices
A
A haplotypes, 9 Agarose, 95 AIMS, 79 Allelic variability, 44 Angiogenesis, 65 Anthropometric traits, 1 Cephalic index, 1 Anti-CCP, 74-76, 101, 111, 141, 142, 144, 146, 147, 169-171, 183, 184 Antigen presenting cells, 72 Antigenic mimicry, 64 Anti-streptolysin O, 102 Apoptosis, 18,68 Autoimmune diseases, 9,55
B
B haplotypes, 9 Biomarkers, 79 Blood group, 1 Box plot analyses, 150, Bx subsets, 126, 128
C
C Reactive Protein, 76-78, 102, 172 Cathepsins, 68 Ceruloplasmin, 103 Chemotaxis, 72 Citrulline, 75, 76, 170 Clinical disease activity index, 80, Crossing over, 40, 42 Non-reciprocal, 40 Reciprocal, 42 C Reactive Protein, 76-78, 102, 172 Cytomegalovirus, 53
D
DAP-12, 24 DAS28, 79, 80 Dendritic cells, 19, 58, 65, 71, 72 Dendrogram, 138 Disease activity score, see DAS28 DMARD Methotrexate, 85
Page | 239 Domain shuffling See Exon shuffling
E
EDTA, 91, 93 Electrophoresis, 59, 95 ELISA, 76 ERK, 51 ESR, 76-80, 93, 102, 103, 111, 142, 146-148, 150, 171-173, 184 Evaluator’s global assessment, 79 Evolution, 2, 4, 5 Assimilation model, 7 Model of Recent African origin, 6 Multiregional model, 6 Exon shuffling, 2
F
Fibroblasts, 66, 70 Filaggrin, 76 Forces of evolution, 4 Gene flow, 4 Genetic drift, 4 Mutation, 4 Natural selection,4 Fossils, 5 Founder effect, 4 Framework locus, 35
G
Gene duplication, 2 Gene fusion, 3 Genetic distances, 107,124, 125, 139, 164 Graft-versus-host disease, See GVHD Grb2, 51 GVHD, 57
H
HAQ, 84, 142, 241 Health Assessment Questionnaire, see HAQ Hepatitis B, 192 Herpes Simplex virus, 55 HLA, 9, 10, 14-16, 19, 21, 22, 26, 27, 30-33 HSCT, 57, 58 Human Leukocyte Antigen, see HLA
I
Immunoglobulin, 9, 74
Page | 240 Immunoreceptor tyrosine-based activating motifs, see ITIM Immunoreceptor tyrosine-based inhibitory motifs, see ITAM Immunoturbidimetric assay, 102 Independent segregation, 40 Induced self, 14, 15 Infection, 14, 53 Inflammatory cytokines, 65, 67, 173 Inflammatory markers, 74, 76, 79 IPD-KIR database, 25, 26, 29 ITIM, 15, 17, 24, 33, 48, 51, 52 ITAM, 15, 24, 32, 50-52
J
Joint space narrowing, 82
K
KARAP, 24 Killer Cell Immunoglobulin-like Receptors, 9 KIR Signaling, 49 KIR haplotypes, 34, 38, 40, 43 KIR-Ligand Interaction, 44 KIRs See Killer Cell Immunoglobulin-like Receptors
L
Larsen’s score, 83 LAT, 51 Leukocyte Receptor Complex (LRC), 26 Linkage disequilibrium, 37, 109, 132
M
Macrophages, 69 MAPK, 51 MIP, 51 Missing self, 17 Mobile elements, 3 Monoclonal antibodies, 30-33 Multivariate analyses, 1
N
Neolithic migration, 157 Neutrophils, 14, 66, 71, 72 NK cell activation, 14, 16, 19, 52 NK cells, 14;19, 24, 27, 44, 45, 49, 50, 51, 52, 53, 56, 57, 177
Page | 241 O
Orthologs, 58 Osteoclastogenesis, 70, 72
P
Pannus, 66 Paralogous loci, 31 Patient’s global assessment, see PGA Pedigree, 90, 93 PGA, 78 Phylogenetic tree, 139, 140, 164, 166 Pregnancy, 57 Pseudoexon, 29 Psoriasis, 55, 56
R
Retroposition, 3 Reverse transcription, 3 Rheumatoid arthritis, 13, 60 Rheumatoid factors, 61, 63, 69, 144, 150, 169, 183 RF titre, see Rheumatoid factor
S
Scheduled Caste, 87, 88 Scheduled Tribe, 87, 89 Scleroderma, 56 Serum Creatinine, 105 SH2 domain, 16 Shared epitope, 66, 75 SHI P, 52 Short Tandem Repeats, 8 Simplified disease activity index, 80 SSP-PCR, 59 SSOP-PCR, 59 swollen joint count, 78 Syk, 50 synoviocytes, 65, 69, 70 Synovium, 65-72, 75
T
T cell receptor, 19, 176, αβTCR, 20 γδTCR, 20 Tender joint count, 78 Tibeto-Burman migration, 157, 160, 163, 168 Toll-like receptors, 75
Page | 242 Transplantation, 9, 54, 57, 58, 96 Treatments, 85 Corticosteroids, 86 DMARD, 83, 85, 86 NSAIDs, 85 Tribal, 11, 12, 13, 88, 90, 181 Trophoblasts, 48, 57 Tumor immunity, 56 Twin studies, 63
V
Viral load, 53, 54 Visual analogue scale, 78
Z
ZAP70, 50, 51
Page | 243 Appendix 1
General Questionnaire
Date: …./……/………..
Cellular Immunology Laboratory Department of Zoology University of North Bengal Siliguri-734013 General Information:
• Serial No. • Name • Father’s/ Mother’s/Husband’s Name • Address
• Phone • Date of Birth • Marital Status • Sex
• Pedigree status/ relatedness:
• Diseases, if any • Duration of Disease • Significant Past History of Disease and Infection
• Treatments/Medicines taken
Any Comments:
Lab-in-Charge Cellular Immunology Laboratory Dept. of Zoology Date-
Page | 244 Page | 245 Page | 246 Appendix 2
Health Assessment Questionnaire HAQ (D1) Name: Age: Sex: Date: Telephone: Address: Height(cm): Weight (kg): Disease Duration (yrs):
Without With some With much Unable any difficulty difficulty (2) to HAQ Score= difficulty (1) do (3) (0) 1. Dress yourself, including tying sari/salwar/dhoti/ pyjama and doing buttons? 2. Get in and out of bed? 3. Lift a full cup or glass to your mouth? 4. Walk outdoors on flat ground? 5. Wash and dry your entire body? 6. Squat in the toilet or sit cross-legged on the floor? 7. Bend down to pick up clothing from the floor? 8. Turn a tap on and off? 9. Get in and out of autorickshaw/manual rickshaw/car? 10. Walk three kilometres? 11. Shop in a vegetable market? 12. Climb a flight of stairs?
Physicians global assessment (0-100) Subject Global Assessment VAS (0-100) Subject Pain Assessment VAS (0-100) Tender Joint Count Swollen Joint Count DAS28 (ESR)(0-3) CRP (mg/dl) ESR (mm/h) RF titre (+/-) MTX dosage and usage duration: other drugs dosage other drugs usage duration
Page | 247 Appendix 3
Chemicals, reagents and kits 1. 20 X SSC (pH 8.0) Nacl - 175.3 g Na-Citrate - 88.2 g Dissolve in 1000 ml distilled water and adjust pH to 8.0 with NaOH.
2. HSB (High Salt Buffer) pH 7.6 10 mM Tris-HCl - 605.7 mg 10 mM KCl - 372.8 mg 10 mM MgCl2 - 1.02 g 0.4 M NaCl - 11.69 g 2mM EDTA - 404.7 g Dissolve in 1000 ml distilled water and adjust pH to 7.6.
3. 10 % SDS Dissolve 1 g SDS in 10 ml of distilled water.
4. 50 mM KCl Dissolve 3.728 g of KCl in 1000 ml of distilled water.
5. 4 M NaCl Dissolve 116.9 g of NaCl in 500 ml of distilled water.
6. 4 M NaOH Dissolve 50 g of NaOH in 500 ml of distilled water. Filter the solution through Whatmann No. 3 filter paper.
7. Tris hydrochloride (Tris-HCl), Mol. Wt. 157.6 (Himedia)
8. Tris NH4Cl TRIS - 20.6 g/L dH2O NH4Cl - 0.83 g/100ml dH2O
9. RCLB (Red Cell Lysis Buffer) NH4Cl - 4.15 g 0.1 M Tris HCl - 50 ml Make up to 500 ml with distilled water and adjust pH to 7.5 + 0.2.
10. Proteinase K solution ( 8 mg/ml) Dissolve 8 mg Proteinase-K in 1 ml of distilled water.
11. Phenol- Chloroform (4:1)
Page | 248 4 parts Phenol + 1 part chloroform. The pH of phenol should be adjusted to 8.5-9.0 adding Tris-HCl
12. Deoxyribonucleotide Triphosphate (dNTPs) set: Bangalore Genei, India. The deoxyribonucleotide triphosphates are the monomers of DNA polymer consisting of dATP, dCTP, dGTP and dTTP. The dNTPs are used at saturation concentration in PCR amplification of DNA.
13. PCR Buffer with MgCl2 (Bangalore Genei, India) The PCR buffer is optimized for use in PCR experiments. Generally the PCR buffer is supplied along with Taq Polymerase by the commercial companies.
14. Ethidium Bromide (Gibco BRL, USA) Dissolve 0.5 µg in 1 ml of TBE buffer
15. Gel Loading Dye/Solution 0.05%Bromophenol Blue - 50 mg 4.0 % Sucrose - 20 g 0.1 M EDTA - 1.46 g 0.5 % SDS - 250 mg Dissolve EDTA in 25 ml of distilled water by adjusting the pH to 8.0 with 5 N NaOH and add bromophenol blue. Once dissolved, add sucrose and finally SDS. Adjust the final volume to 50 ml and stir at 80 °C to make the solution viscous. 1 volume of gel loading solution is optimal to 1-4 volumes of sample. Bromophenol bleu serves as the tracking dye while sucrose adds density and facilitates sample loading. EDTA is included to terminate the action of intrinsic DNAase activity. SDS helps to dissociate DNA-Protein complexes, which can otherwise interfere the electrophoresis.
16. Taq DNA Polymerase (Bangalore Genei India)
17. 10X TBE Buffer 0.9M TRIS - 109.06 g 0.02M EDTA - 7.44 g 0.9M Boric Acid - 55.647 g Dissolve in 1000 ml of distilled water and store at 4 °C. Prepare 1X as working buffer.
18. TE Buffer/Solution 1mM TRIS - 121.16 g 0.1 mM EDTA - 37.224 g Dissolve in 950 ml distilled water and adjust pH to 7.5. Adjust the final volume to 1000 ml adding distilled water.
19. Phosphate Buffered Saline (PBS), pH 7.2 (Himedia, India).
Page | 249 Page | 250 Creatinine
Page | 251 Page | 252 Appendix 4
List of Publications 1) Guha, P. & Chaudhuri, T. K. (2010) HLA and Juvenile Idiopathic Arthritis: An Overview. NBU Journal of Animal Sciences 4, 6-15.
2) Guha, P., Bhattacharjee, S., Nayak, C. & Chaudhuri, T. K. (2013) Study of the KIR gene profiles and analysis of the phylogenetic relationships of rajbanshi population of west bengal, india. Human Immunology 74, 673-80.
3) Guha, P., Srivastava, S.K., Bhattacharjee, S. & Chaudhuri, T. K. (2013) Human migration, diversity and disease association: a convergent role of established and emerging DNA markers. Frontiers in Genetics 4, 1-8.
4) Guha, P., Srivastava, S.K., Das, A., Bhattacharjee, S., Haldar, B. & Chaudhuri, T. K. (2013) Comparative analyses of the ABO, KIR, and HLA loci among the Rajbanshis of North Bengal Region, India. Annals of Pharma Research 01, 18-24.
5) Guha, P., Bhattacharjee, S. & Chaudhuri, T. K. (2014) Diversity of Killer Cell Immunoglobulin-Like Receptor Genes in the Bengali Population of Northern West Bengal, India. Scandinavian Journal of Immunology 80, 441–51.
6) Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Research 10(2), 1-7.
7) Guha, P., Das, A., Dutta, S., Bhattacharjee & Chaudhuri, T. K. (2015) Study of genetic diversity of KIR and TLR in the Rabhas, an endogamous primitive tribe of India. Human Immunology 76, 789-94. 8) Chaudhuri TK, Guha P (2015) HLA: Facts and Findings. Transcriptomics 3: e112.
Page | 253 Mini Review NBU J. Anim. Sc. 4: 6 – 15 (2010)
HLA AND JUVENILE IDIOPATHIC ARTHRITIS: AN OVERVIEW
Pokhraj Guha and Tapas Kumar Chaudhuri*
Cellular Immunology laboratory, Department of Zoology, University of North Bengal, Siliguri, West Bengal, India, 734013.
Received: November 25, 2010 Accepted: December 11, 2010
ABSTRACT Juvenile Idiopathic Arthritis (JIA) is an umbrella term referring to a group of disorders characterized by chronic arthritis and is the most common chronic rheumatic illness . Although the exact etiology of JIA remains unclear, it seems to be a complex genetic trait involving the effects of multiple genes related to immunity and inflammation. This review aims to throw light on the association between several HLA loci with JIA. HLA-B27 has consistently been found to contribute risk for pauciarticular JIA. HLA-DR1 and –DR4 are known to increase the risk for polyarticular JIA . Interestingly, HLA- DR4 might be protective in patients with Early Onset Pauciarticular Arthritis (EOPA). One of the unique features of JIA is that there appears to be a window of susceptibility during which children with predisposing HLA alleles or combination of alleles are maximally susceptible to the development of JIA, suggesting gene-gene and gene-environment interaction. To date no specific mechanisms have been identified as to how these genes are involved in disease induction. An alternative hypothesis is that the HLA molecules themselves undergo antigen processing and become immunogenic when presented by the other HLA molecule. Despite of complex traits underlying the different forms of JIA, it is assumed that the immune responses directed towards the self-antigens are the main clinical manifestations. However, genome wide studies are suggested for the identification of ‘novel’ transcripts of relevance to the disease and to enable the development of more appropriate intervention methods.
Key words: Juvenile Idiopathic arthritis, Rheumatoid arthritis, Etiology, HLA.
INTRODUCTION illness and is a significant cause of short and long term disability (Weiss and Ilowite, 2005). It may be Rheumatologists have a long term defined as persistent arthritis in one or more joints knowledge of the similarities and differences that the for at least 6 weeks if certain exclutionary conditions common chronic arthropathies of childhood share are eliminated; the disease onset subtype is defined with the adult onset of Rheumatoid arthritis (Prahalad by clinical symptoms in the first 6 months of the and Glass, 2002). Arthritis in children is more disease . Thus, classification is made at 6 months after heterogenous than RA. The pathophysiological diagnosis into one of the eight disease categories, features including the genetic markers are different each of which has it’s own specific characteristics, in many aspects from the adult counterparts, although exclusions and descriptors (Thomson and Donn, the overall gross similarities are documented 2002). JIA affects between 8 and 150 of every (Prahalad and Glass, 2002). 100,000 children, depending on the analysis. Of these JIA is the most common chronic rheumatic children, 50 percent are pauciarticular JIA, 40
*Author for Correspondence: Prof. Tapas Kr. Chaudhuri, Tel: +91- 9434377127 (M), +91-353-2776353 (O); Fax: +91-353- 2699001, E. mail: [email protected]; [email protected].
6 Pokhraj Guha and Tapas Kumar Chaudhuri percent are polyarticular and 10 percent are systemic The classification of inflammatory arthritis JIA. provide us some clues regarding the nature of these Symptoms of JIA are often non-specific diseases (Prahalad and Glass, 2002). The American initially, and include lethargy, reduced physical college of Rheumatology (ACR) uses the term activity, and poor appetite. The first manifestation, ‘Juvenile Rheumatoid Arthritis’ (JRA) as the criteria particularly in young children, may be limping. of the classification which includes JRA of Children may also become quite ill, presenting with pauciarticular, polyarticular and systemic onset. On flu-like symptoms that persist (Schanberg et al., the other hand, the Europian League against 2003). The cardinal clinical feature is persistent Rheumatism (EULAR) uses the term ‘Juvenile swelling of the affected joint(s), which commonly Chronic Arthritis’ (JCA) which include not only the include the knee, ankle, wrist and small joints of the other three subtypes but also juvenile ankylosing hands and feet. Swelling may be difficult to detect spondylitis, psoriatic arthritis and arthritis associated clinically, especially for joints such as those of the with inflammatory bowel disease. The more recent spine, sacroiliac joints, shoulder, hip and jaw, where International League of Associations for imaging techniques such as ultrasound or MRI are Rheumatology (ILAR) classified ‘Juvenile Idiopathic very useful. Arthritis’ (JIA) as seven subsets, including the Pain is an important feature of JIA, but young spondyloarthropathies. The differences between the children may have difficulty in communicating this schemata are shown in Table I (Weiss and Ilowite, symptom. Morning stiffness that improves later in 2005). This article uses the ILAR system when the day is a common feature. Late effects of arthritis applicable. include joint contracture (stiff, bent joint) and joint The very fact of the presence of subtypes damage. Children with JIA vary in the degree to in each and every classification system is sufficient which they are affected by particular symptoms to infer that juvenile arthritis is not a single entity (Schanberg et al., 2003). with uniform clinical, laboratory and immunogenetic
Table I:Summary of the differences among the schemata (Weiss and Ilowite, 2005).
Abbreviations: IBD-Inflammatory bowel disease; JAS-juvenile ankyslosing sponylitis; JpsA- juvenile psoriatic arthritis
7 HLA and Juvenile Idiopathic Arthritis features, whereas RA in adults appear to be a single JRA/JIA and adult RA (Brewer et al., 1977). The disease with different manifestations. On the similarities and the differences between JIA and contrary, patients with one subtype of JIA clearly RA are listed in Table II (Prahalad and Glass, differ from patients with the other subtypes. There 2002). are however similarities between some types of
Table II:-Similarities and differences between RA and JRA/JIA ( Prahalad and Glass, 2002).
EOPA, Early onset pauciarticular arthritis; HLA, human leukocyte antigen; JIA, juvenile idiopathic arthritis; JRA, juvenile rheumatoid arthritis; RA, rheumatoid arthritis; RF, rheumatoid factor; Th, T helper cells.
8 Pokhraj Guha and Tapas Kumar Chaudhuri
POSSIBLE ETIOLOGICAL FACTORS near 20 (Saila et al., 2000). Within this set of families The exact etiology of JIA remains unclear. there were eight sets of monozygotic twins, two of However JIA seems to be a complex genetic trait which were concordant for JIA. Both sets of twins involving the effects of multiple genes related to were concordant for disease course but were immunity and inflammation. Some hypothesize that unexpectedly different for disease onset (Savolainen arthritis may be triggered in a genetically predisposed et al., 2000). A concordance rate of 25% for a disease individual by psychologic stress, abnormal hormone with a population prevalence of 1 per 1000 implies a levels, trauma to a joint, even bacterial or viral relative risk of 250 for a monozygotic twin. These infection. Several studies have implicated rubella and data taken together provide convincing evidence that parvovirus B19 as possible causes of JIA because there is a substantial genetic component to JIA. rubella virus persists in lymphocytes and establishes a focus of persistent infection in the synovium THE ROLE OF HLA resulting in chronic inflammation (Lang and Shore, The most extensively studied susceptibility 1990). However these data remained difficult to locus for autoimmune disorders is the Major replicate in other laboratories. Highly conserved Histocompatibility complex (MHC) located on bacterial heat shock proteins (HSK) may also act as chromosome 6p (Figure 1) (Mehra and Kaur, 2003). potential disease triggers (Tucker, 1993). It is evident This region is densely packed with more than 200 that JIA is more common in young girls and also genes most of which are highly essential to the common in Caucasians. immune system, including the human leukocyte Various sources such as twin studies, family antigen (HLA) genes. The genes of the MHC in studies or association studies may provide evidences general, and the HLA genes in particular,are highly for a genetic component to the disease. Recent data polymorphic. from USA and Finland, however, suggest that the Much of the genetic works undertaken in genetic contribution to JIA may be quite considerable. the past three decades centered round HLA genes. In the National Institute of Arthritis and These earlier studies of HLA and JIA included Musculoskeletal and Skin Diseases, U.S.A. initial children classified according to either the EULAR analysis of 71 affected sibling pairs showed that 63% or the ACR criteria. Studies have consistently shown were concordant for gender and 76% for onset type associations between HLA and several autoimmune (Moroldo et al., 1997). This study was also the first diseases, including both RA and JIA. HLA to provide an estimate of the sibling recurrence risk associations are distinct for RA and JIA, (ës) for JIA of 15, although this is likely to vary demonstrating that the immunogenetic factors between subgroups. Such a high ës is indicative of a involved in susceptibility to these two diseases are factor shared between siblings – genetic or indeed different. Numerous studies of associations environmental. In another study of 118 affected of JIA with both HLA class I and class II genes sibling pairs, 14 pairs of twins were identified in which have been described, with the class I associations both twins have arthritis. One pair comprises a girl being consistently more limited than those for class with polyarthritis and a boy with persistent II. oligoarthritis. The other 13 pairs (11 monozygotic, 2 The earliest reported association was with dizygotic and 2 of unknown zygosity) were concordant an MHC class I gene, HLA-B27 in older children for gender (nine female, four male), disease onset with pauciarticular disease (Haas et al., 1994).This and disease course (Prahalad et al., 2000). have been followed by associations of HLA with most A study conducted in Finland on 41 JIA types of JIA, although in varying degrees of strength, multicase families with 88 affected siblings over a some reflecting similarities to adult arthropathies and period of 15 years estimated the ës of JIA to be others are unique to childhood.
9 HLA and Juvenile Idiopathic Arthritis
Figure 1. Gene map of the human leukocyte antigen (HLA) region. The HLA region spans 4 × 106 nucleotides on chromosome 6p21.1 to p21.3, with class II, class III and class I genes located from the centromeric (Cen) to the telomeric (Tel) end. HLA class I molecules restrict CD8+ cytotoxic T lymphocyte function and mediate immune responses against ‘endogenous’ antigens and virally infected targets, whereas HLA class II molecules are involved in the presentation of ‘exogenous’ antigens to T helper cells. The HLA class III region contains many genes encoding proteins that are unrelated to cell-mediated immunity but that nevertheless modulate or regulate immune responses in some way, including tumour necrosis factor (TNF), heat shock proteins (Hsps) and complement proteins (C2, C4) . (Adapted from Mehra and Kaur, 2003).
However the different clinical subtypes of (Brunner et al., 1993; Nepom and Glass, 1992; Paul JIA themselves differ in their HLA associations. For et al., 1993; Paul et al., 1995; Ploski et al., 1995; instance, the class I gene HLA-B27 has consistently Van Kerckhove et al., 1990). It is possible that in been found to contribute risk for pauciarticular JIA, this type of JIA, four individual genes (one HLA-A especially among older males. HLA-DR1 and -DR4, gene, two HLA-DR or DQ genes and an HLA-DP class II genes, have been reported to increase the gene) may be involved. Confirming the associations risk for polyarticular JIA. Much as in adults with that have been reported, linkage between RA, HLA-DR4 is associated with RF-positive pauciarticular JIA and the HLA region have been polyarticular disease in older children. Interestingly, shown both by using transmission disequilibrium this gene might be protective in patients with EOPA. testing in simplex families (Moroldo et al., 1997) and In this subtype, combined class I and II MHC by allele sharing among affected sibling pairs associations are seen. Other MHC-encoded genes (Prahalad et al., 2000). Similarly, linkage between such as LMP7 have also been shown to be polyarticular JIA and the HLA region has also been associated with early-onset JIA (Prahalad et al., shown by allele sharing among affected sibling pairs 2001). HLA-A2, HLA-DR5, HLADR8 and HLA- (Prahalad et al., 2000). DPB1*0201 have all been shown to be associated One of the unique features of JIA is that with JIA by several investigators, and interactions there appears to be a window of susceptibility, during between these alleles yield high odds ratios for EOPA which children with predisposing HLA alleles or
10 Pokhraj Guha and Tapas Kumar Chaudhuri combination of alleles are maximally susceptible to influence the age of onset of JIA. the development of JIA, suggesting gene–gene and In a study with 521 caucasians JIA patients gene–environment interactions (Murray et al., 1997). and 537 controls, it has been found that 2 HLA- In a study of 680 patients with JIA and 254 DRB1 alleles ( DRB*08 and *11) were associated ethnically matched, unrelated controls, survival with increased risk and 2 with decreased risk analysis was performed to calculate the age by which (DRB1*04 and *07) of JIA. Their phenotypic 50% and 80% of children with particular HLA alleles frequencies also differed between subgroups. HLA- and combinations of alleles develop disease. Certain DRB1*04 although negatively associated for other alleles were strongly associated with early subgroups was found to be associated with an susceptibility to pauciarticular JIA, including HLA- increased risk for RF-positive polyarthritis. Some A2, -DR8, -DR5 and -DPB1*0201. Of the children alleles at the HLA-DQA1 locus were associated carrying at least one of these alleles, 50% had disease witih increased risk (DQA1*0103, *04 and *05) and onset before their third birthday. Among children who others decreased risk ( DQA1*02 and *03) of JIA. carry HLA-A2 and any two HLA-DR alleles The associations at individual loci are reflected in (HLADR3, -DR5, -DR6, -DR8), the median age at the haplotypic associations seen. Three haplotypes onset of pauciarticular disease was only 2.7 years. namely DRB1*08-DQA1*0401-DQB1*0402; Combinations of HLA-A2 and -DPB1*0201 and one DRB1*11-DQA1*05-DQB1*03; and DRB1*1301- DR allele narrowed the window further to a median DQA1*01-DQB1*06 were associated with the age at onset of 2.4 years. Gender strongly influenced increased risk and one, DRB1*04-DQA1*03- the age at which many of the alleles have their effect. DQB1*03, with the decreased risk of JIA as a whole These results demonstrated the complex interactions as shown in Table III. between different HLA susceptibility alleles that
Table III: Odds ratios associated with the possession of HLA haplotypes in JIA subjects versus controls (Thomson et al., 2002).
Results are only shown for haplotypes where there are evidence for a difference between JIA subgroups or overall association. Odds ratios significantly different from 1 at the 5% level are shown in bold
11 HLA and Juvenile Idiopathic Arthritis
To date, no specific mechanisms have been At a nearby locus, also within the MHC region, a identified as to how these genes are involved in polymorphism in the TAP2 gene which encodes for disease induction. In addition there is some debate a transporter associated with antigen processing as to the relative importance of the closely linked (TAP2B), is associated with early onset pauciarticular HLA-DR and HLA-DQ loci (Haas et al., 1995). In JIA (Giannini et al., 1991). Both of these findings support of this, one study has demonstrated a may implicate antigen processing either at a polymorphism in the Y box of the DQ promoter region qualitative or quantitative level in disease associated with pauciarticular onset JIA (Haas et al., susceptibility. 1994). Another group has demonstrated limited In conclusion, it is evident that complex traits linkage disequilibrium between the early onset underlie the different forms of JIA, each one probably pauciarticular JCA associated locus HLA- unique. Despite such differences, immune responses DPB1*0201 and certain DQ/DR alleles, further directed towards self antigens are likely to be central highlighting the difficulties of identifying the true to the pathogenesis of each type of disease. This disease related gene (Davies et al., 1994). Given the responsiveness is determined by the particular array normal function of HLA molecules in presenting of genes present in an individual, Specific HLA allele processed antigenic peptides (both foreign and self) may favour the presentation of one peptide antigen to effector T lymphocytes, as part of an immune or over another to a specific T cell repertoire inflammatory reaction, it has been speculated that predetermined genetically at the thymic level and particular HLA risk alleles present potentially modified by the presence of null alleles in particular arthritogenic peptides to such T cells. An alternative TCR families. HLA association in JIA may help to hypothesis is that the HLA molecules themselves define more homogenous groups of patients, but undergo antigen processing and become identifying other genetic risk factors may lead to immunogenic when presented by other HLA better definition of JIA subgroups and hence molecule. This may explain in part how multiple clarification of the HLA associations. independent HLA disease related alleles commonly Although there are similarities between the exist in one patient. The nature of the peptides inflammatory arthritis occurring in adults and presented by the HLA molecules on antigen children, RA and JIA appear to be distinct presenting cells in JIA patients draw great interest. phenotypically, except for the older child with RF- This may provide clues as to the pathogens involved positive polyarticular arthritis. Furthermore, the especially if extrinsic antigens can be identified which various subtypes of JIA appear to be distinct entities may mimic the self antigens (Albani, 1994). Although and could represent different diseases with distinct their specific role in immunopathogenesis has yet to etiological and genetic factors. Paradoxically, be elucidated, it is clear that the ‘at risk’ HLA clinically distinct autoimmune disorders do appear to haplotypes influence disease outcome. For example, share common genetic susceptibility factors. In this an HLA DR1 haplotype predicts a poor outcome with context, it is plausible that there are common genetic respect to arthritis in the pauciarticular onset group, susceptibility factors that predispose an individual to but protects from eye disease, whereas the presence an autoimmune disorder, with other genetic (especially of HLA DR5 predicts more eye disease (Giannini et HLA) and/or environmental factors that determine al., 1991). In a similar manner a genetic the type of specific disorder and its manifestations. polymorphism of the proteasomal subunit LMP2 (a The idea of identifying ‘novel’ transcripts of non-HLA gene encoded in the MHC region on relevance to disease would become attractive. chromosome 6) has been associated with late onset Microarray based expression technology would be pauciarticular JCA (B27 associated), especially those useful if the correct sample materials are available, with more severe disease (Pryhuber et al., 1996). such as paired blood and synovial fluids. This would
12 Pokhraj Guha and Tapas Kumar Chaudhuri allow to look at alterations in mRNA levels as an studies to be carried out. Coupled with the rapid indication of gene activation or regulation. This type advances in genomic and proteomic technologies, of work, however, requires samples before and after these studies may pave the way forward to a better the treatment which may not always be ethically understanding of this complex genetic disease. acceptable when studying disease in children. The Accurate outcome data are also essential to achieve majority of the genetic research conducted for JIA the ultimate aims of being able to predict outcome so far were retrospective in nature. This may identify and establish new therapies for children with JIA. genes involved in disease susceptibility. Perhaps the Thus the advent of more comprehensive more useful and clinically relevant genetic approach approaches allowing genome-wide studies have is to define genetic predictors of outcome or disease become essential. The use of peptide libraries to look severity. This can be attempted by prospectively for relevant antigens and the further development of following a cohort of clinically well-defined patients. transgenic and gene knockout animal models are likely Again the issue of multiple samples becomes to herald greater understanding of the complex important and possibly limiting. Identifying,at initial genetic traits and environmental triggers underlying presentation, patients who are most likely to have the different forms of JIA. This, in turn, will enhance the more aggressive course of disease would have the prospect of developing more specific, effective substantial implications for treatment interventions. and potentially curative treatments for these diseases. For the understanding of JIA genetics to progress, However expertise should be the baseline of care of international collaborations that maximize the resource children with JIA, as early appropriate treatment is potential would appear to be the way forward. This essential to ensure best possible short and long term would allow larger-scale association and linkage outcome for children with JIA (Sawhney, 2002.).
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Haas, J.P., Kimura, A., Truckenbrodt, H., Suschke, J., M.P., Brunnler, G., Keller, E., Nevinny-Stickel, Sasazuki, T., Volgger, A. & Albert, E.D. 1995. C., Yao, Z. & Albert, E.D. 1993. HLA-DP/DR Early-onset pauciarticular juvenile chronic interaction in early onset pauciarticular juvenile arthritis is associated with a mutation in the Y chronic arthritis. Immunogenetics 37:442-448. box of the HLA-DQA1 promoter. Tissue Antigens Paul, C., Yao, Z., Nevinny-Stickel, C., Keller, E., 45:317-321. Schoenwald, U., Truckenbrodt, H., Hoza, J., Haas, J.P., Nevinny-Stickel, C., Schoenwald, U., Suschke, H.J. & Albert, E.D. 1995. Truckenbrodt, H., Suschke, J. & Albert, E.D. Immunogenetics of juvenile chronic arthritis. I. 1994. Susceptible and protective major HLA interaction between A2, DR5/8-DR/DQ, and histocompatibility complex class II alleles in DPB1*0201 is a general feature of all subsets of early-onset pauciarticular juvenile chronic early onset pauciarticular juvenile chronic arthritis. Hum. Immunol. 41:225-233. arthritis II. DPB1 polymorphism plays a role in Lang, B.A. & Shore, A. 1990. A review of current concepts systemic juvenile chronic arthritis. Tissue on the pathogenesis of juvenile rheumatoid Antigens 45:280-283. arthritis. J. Rheumatol. 17(Suppl 21):1–15. Ploski, R., McDowell, T.L., Symons, J.A., Flato, B., Duff, Mehra, N.K. & Kaur, G. 2003. MHC-based vaccination G.W., Thorsby, E. & Forre, O. 1995. Interaction approaches: progress and perspectives. Exp. Rev. between HLA-DR and HLA-DP, and between Mol. Med. 5: 1-17 HLA and interleukin 1 alpha in juvenile rheumatoid arthritis indicates heterogeneity of Moroldo, M.B., Donnelly, P., Saunders, J., Glass, D.N. & pathogenic mechanisms of the disease. Hum. Giannini, E.H. 1998. Transmission disequilibrium Immunol. 42:343-347. as a test of linkage and association between HLA alleles and pauciarticular-onset juvenile Prahalad, S. & Glass, D.N. 2002. Is juvenile rheumatoid rheumatoid arthritis. Arthritis Rheum. 41:1620- arthritis/juvenile idiopathic arthritis different 1624. from rheumatoid arthritis? Arthritis. Res. 4(suppl 3):303-310. Moroldo, M.B., Tague, B.L., Shear, E.S., Glass, D.N. & Giannini, E.H. 1997. Juvenile rheumatoid arthritis Prahalad, S., Kingsbury, D.J., Griffin, T.A., Cooper, B.L., in affected sibpairs. Arthritis Rheum. 40:1962- Glass, D.N., Maksymowych, W.P. & Colbert, 1966. R.A. 2001. Polymorphism in the MHC encoded LMP7 gene: association with JRA without Murray, K., Thomson, S.D. & Glass, D.N. 1997. functional significance for immunoproteasome Pathogenesis of juvenile chronic arthritis: genetic assembly. J. Rheumatol. 28:2320-2325. and environmental factors. Arch. Dis. Child. 77: 530-534. Prahalad, S., Ryan, M.H., Shear, E.S., Thompson, S.D., Giannini, E.H. & Glass, D.N. 2000. Juvenile Murray, K.J., Moroldo, M.B., Donnelly, P., Prahalad, S., rheumatoid arthritis: linkage to HLA Passo, M.H., Giannini, E.H. & Glass, D.N. 1999. demonstrated by allele sharing in affected Age-specific effects of juvenile rheumatoid sibpairs. Arthritis Rheum. 43:2335-2338. arthritis-associated HLA alleles. Arthritis Rheum. 42: 1843-1853. Prahalad, S., Ryan, M.H., Shear, E.S., Thompson, S.D., Glass, D.N. & Giannini, E.H. 2000. Twins Murray, K.J., Szer, W., Grom, A.A., Giannini, E.H., Glass, concordant for juvenile rheumatoid arthritis. D.N. & Szer, I. 1997. Antibodies to the 45kDa Arthritis Rheum. 43:2611-2612. nuclear DEK antigen in pauciarticular onset JRA and iridocyclitis: selective association with an Pryhuber, K.G., Murray, K.J., Donnelly, P., Passo, M.H., MHC gene. J. Rheumatol. 24:560-567. Maksymowych, W.P., Glass, D.N., Giannini, E.H. & Colbert, R.A. 1996. Polymorphism in the LMP2 Nepom, B.S. & Glass, D.N. 1992. Juvenile rheumatoid gene influences disease susceptibility and arthritis and HLA: report of the Park City III severity in HLA-B27 associated juvenile workshop. J. Rheumatol. Suppl. 33:70-74. rheumatoid arthritis. J. Rheumatol. 23:747-752. Paul, C., Schoenwald, U., Truckenbrodt, H., Bettinotti, Saila, H., Savolainen, A., Tuomilehto-Wolf, E., Tuomilehto,
14 Pokhraj Guha and Tapas Kumar Chaudhuri
J. & Leirisalo- Repo, M. 2000. Type of onset of Arthritis. Indian J. Pediatr. 69: 893-897 juvenile idiopathic arthritis in monozygotic twins Thomson, W. & Donn, R. 2002. Juvenile idiopathic arthritis can be phenotypically different. J. Rheumatol. genetics – What’s new? What’s next? Arthritis 27:2289-2290. Res. 4:302-306. Savolainen, A., Saila, H., Kotaniemi, K., Kaipianen- Tucker, L.B. 1993. Juvenile rheumatoid arthritis. Curr. Seppanen, O., Leirisalo-Repo, M. & Aho, K. Opin. Rheumatol. 5: 619–628. 2000. Magnitude of the genetic component in Van Kerckhove, C., Luyrink, L., Elma, M.S., juvenile idiopathic arthritis (letter). Ann. Rheum. Maksymowych, W.P., Levinson, J.E., Larson, Dis. 59:1001. M.G., Choi, E. & Glass, D.N. 1990. HLA-DP/DR Schanberg, L.E., Anthony, K.K., Gil, K.M. & Maurin, E.C. interaction in children with juvenile rheumatoid 2003. Daily pain and symptoms in children with arthritis. Immunogenetics 32: 364-368. polyarticular arthritis. Arthritis Rheum. 48: 1390– Weiss, J.E. & Ilowite, N.T. 2005. Juvenile Idiopathic 1397. Arthritis. Pediatr. Clin. N. Am. 52: 413-442. Sawhney, S. 2002. Management of Juvenile Idiopathic
15 Human Immunology 74 (2013) 673–680
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Study of the KIR gene profiles and analysis of the phylogenetic relationships of Rajbanshi population of West Bengal, India ⇑ Pokhraj Guha a, Soumen Bhattacharjee b, Chittaranjan Nayak c, Tapas Kumar Chaudhuri a, a Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal 734013, India b Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal 7340133, India c Department of Computer Center, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal 734013, India article info abstract
Article history: The natural killer (NK) cells have distinct receptors called killer cell immunoglobulin-like receptors (KIR) Received 3 August 2012 which are responsible for regulating NK cell responses to infections and malignancy. The extensive vari- Accepted 14 January 2013 ations in the number and type of KIR genes can be used as a tool to understand the differentiation of pop- Available online 24 January 2013 ulations and also for tracing genetic background. In this study, we have aimed to analyze the KIR gene polymorphism in the Rajbanshi population of West Bengal, India. To our knowledge this is the first report on the KIR gene polymorphism in the Rajbanshis, a population widely distributed in the Terai and Dooars region of West Bengal, India. Herein, we have studied the gene distribution of 14 KIR genes (KIR3DL1– 3DL3, KIR 2DL1–2DL5, 2DS1–2DS5 and 3DS1) and two pseudogenes (KIR3DP1 and 2DP1). The gene fre- quencies and genotypic frequencies were calculated, based on which statistical analyses were performed. The presence of a considerable number of genotypic profiles suggests substantial diversity in the KIR gene pool of the Rajbanshis in the region studied. Apart from the framework genes (KIR2DL4, 3DL2, 3DL3 and 3DP1) present in all the individuals, the gene frequencies of other KIR genes varied between 0.84 and 0.15. Moreover the KIR polymorphisms of the Rajbanshis were also compared with that of available pub- lished data of the populations of other ethnicities. Though the Rajbanshi population showed a tendency to cluster with other Indian population based on KIR gene frequencies, the influence of Tibeto–Burman Lineage on their KIR genotypic profiles cannot be overlooked. Furthermore, evidences from previously published data on Y chromosome haplogroup diversity study on Rajbanshis support the view. Our results will not only help to understand the genetic background of the Rajbanshi population, but also in tracing the population migration events in the North–Eastern part of India and in illustrating the extensive genetic admixture amongst the different linguistic groups of the country and also in KIR-related disease researches. Ó 2013 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.
1. Introduction try constitute 16.2% of the total population and in West Bengal their presence is to the tune of 11.07% of the total caste popula- Indian subcontinent, owing to its unique geographical loca- tion of the country [3]. The Rajbanshis constitute as high as 18.4% tion, has experienced successive waves of human immigrations of the total Scheduled Caste population of West Bengal [3]. and invasions from the Middle-East, Central Asia and Mongolia Although distributed dispersedly throughout the state, the Raj- in the historical past [1] which not only contributed to her enor- banshis are mainly concentrated in the Terai and Dooars of mous cultural and linguistic diversity but also added to the ge- Northern region of West Bengal especially in the Jalpaiguri and netic resource of the country. The sub-Himalayan part of the Coochbehar Districts. The Rajbanshis also have adequate popula- country represents a hotspot of socio-cultural and ethnic hetero- tion in the neighboring state of Assam. They have a rich cultural geneity and hosts a number of caste and tribal populations [2]. heritage and their language belongs to the Indo–European group. As per the Indian Census 2001, the Scheduled Castes in the coun- According to Risley (1892) there are many sub-castes of the Koch Rajbanshis in North Bengal, India [4]. They practice Hinduism but they have their own rich cultural identity and heritage. Beside their own dialect they also speak Bengali, Assamese and some ⇑ Corresponding author. Fax: +91 353 2699001. other minor languages. Thus, Rajbanshis are a highly diversified E-mail addresses: [email protected] (P. Guha), soumenb123@rediff- mail.com, [email protected] (S. Bhattacharjee), [email protected] (C. ethnic community with rich cultural, linguistic and social Nayak), [email protected] (T.K. Chaudhuri). background.
0198-8859/$36.00 - see front matter Ó 2013 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.humimm.2013.01.007 674 P. Guha et al. / Human Immunology 74 (2013) 673–680
Killer cell immunoglobulin-like receptors (KIRs) are members of tometry, and the ratios of absorbance at 260/280 nm were be- a group of regulatory molecules found on natural killer cells (NK) tween 1.7 and 1.9. and a subset of T lymphocytes. In humans KIRs recognize Human Genomic DNA was amplified to detect the presence or absence Leukocyte Antigen (HLA) class I proteins leading to the inhibition of the following genes: 2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, or activation of cytotoxic cell activity and cytokine production by 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1 and 3DP1 using T and NK cells, thus focusing on the role of these receptors in 16 PCR–sequence specific priming reactions. DNA primer se- immunological responses of NK cells [5]. KIR–HLA interactions quences were generously provided by R. Rajalingam on enquiry have been implicated in several diseases including leukemia [6], [11,14]. All PCR reactions were performed in 25 ll volume reaction and also in alloreactivity in transplant cases [7,8]. The KIR family containing 2.5 ll10Â PCR buffer (Bangalore Genei, Bangalore, In- consists of 16 homologous genes clustered together on chromo- dia), 0.2 mM of deoxynucleoside triphosphate (dATP, dCTP, dGTP, some 19q13.4 within the 1 Mb Leukocyte receptor complex (LRC) dTTP; (Bangalore Genei, Bangalore, India), 1.5 lM each of forward [9–10]. In this family 14 genes encode for either activating or and reverse KIR-specific primers (Imperial LifeSciences, UK), inhibitory receptors and 2 pseudogenes which do not encode 100 ng of template DNA and 2.5 U of Taq polymerase (Bangalore receptor proteins. Among the fourteen genes, seven receptors Genei, Bangalore, India). PCR protocol included an initial denatur- (KIR3DL1–3DL3, KIR2DL1–2DL3 and 2DL5) trigger inhibition reac- ation step at 94 °C for 3 min, then five cycles of 94 °C for 30 s, tion, five (3DS1, 2DS1–2DS5) are activation receptors and one 62 °C for 50 s, and 72 °C for 1 min. This was followed by 30 cycles (KIR2DL4) provides both inhibitory and activation signals, whereas of 95 °C for 30 s, 60 °C for 50 s, and 72 °C for 1 min with small mod- the pseudogenes encode no receptors [11]. Epidemiological studies ifications of the annealing temperature for different primer sets. suggested distinct role of the activating KIRs in cancer, autoim- The numbers of amplification cycles were modified to 35 cycles mune diseases and antiviral immunity [9]. The KIR gene family for KIR2DS1 and 2DS4 and 32 cycles for KIR2DS2 and 2DS5. PCR shows high degree of variability and exhibit variable gene content products were analyzed on ethidium bromide (0.5 lg/ml) pre- between individuals of different ethnic groups. The genes of the stained 2% agarose gels. After electrophoresis, a photograph of KIR family shows sequence polymorphism and based on gene con- the agarose gel was taken over a UV-transilluminator. Subse- tent two basic groups of KIR haplotypes are present, namely A and quently, gel pictures were carefully examined to determine the po- B, both of which are found at different frequencies in most of the sitive amplifications. Each lane of the gel, containing a loaded PCR populations of the World [11–14,16–38]. sample product, showed a control band, except for a negative con- The Rajbanshi population is of considerable interest for KIR trol well. Sixteen KIR genes were typed on a selected DNA sample. genotyping studies as many questions arise with regard to its ori- In case of false reactions where no control bands are found, the gin and genetic background. In this study we have investigated the reactions were repeated. diversity of the 16 KIR genes namely KIR3DL1–3, KIR2DL1–5, KIR2DS1–5, KIR3DS1, KIR3DP1 and KIR2DP1 using Polymerase Chain 2.3. 3KIR genotyping and haplogroup analysis Reaction (PCR)-sequence specific primer (SSP) method and also have estimated the gene frequencies and analyzed the KIR geno- The genotypes were defined and named in accordance with the typic profiles of the Rajbanshi population. Furthermore we have genotype numbers provided in the Allele Frequencies website also conducted phylogenetic tree analysis, Linkage Disequilibrium (http://www.allelefrequencies.net). Groups A and B haplotypes (LD), and Principle Component Analysis (PCA) based on KIR gene and frequencies were predicted on the basis of a previous study data to trace out the relationship of the Rajbanshi population with [14]. Briefly, individuals who carried only KIR3DL3–2DL3–2DL1– other populations of different ethnicity and geographic locations. 2DP1–3DP1–2DL4–3DL1–2DS4–3DL2, a fixed gene content that is To our knowledge this is the first study of KIR gene polymorphism characteristic of group A haplotypes, were considered to carry in the Rajbanshi population. two copies of a group A KIR haplotype (AA genotype). Conversely, individuals who lacked any of the four variable genes (KIR2DL1, 2DL3, 3DL1, and 2DS4) were regarded as carriers of two copies of 2. Subjects and methods a group B KIR haplotype (BB genotype). The remainder of the indi- viduals were regarded as heterozygous for A and B haplotypes and 2.1. Sample collection assigned as the AB genotype. The frequency data of KIR genes and genotypes in populations to be compared to the Rajbanshis were Blood samples were collected from 75 healthy Rajbanshi indi- extracted from the following publications and from the ‘http:// viduals who were randomly chosen from Darjeeling, Jalpaiguri www.allelefrequencies.net’ database [16] as follows: Indian Para- and Coochbehar districts of West Bengal (Supplementary Fig. 1) var, Kanikar and Mollukurumba [11], Finnish, French Caucasian, with their prior informed consent. All the donors were interviewed Senegal African, Guadeloupe Caribbean, and Reunion, a population and they completed a questionnaire concerning their health condi- from Indian Ocean origin [13], North Indian [14], Cook Island, tions and pedigree status for sample selection with the assistance Samoan, Tokelau, Tongan [17], Mestizo, Huichol, Purepecha, Tara- of the medical staff. It was ensured that no individuals had com- humara [18], Amazonian Amerindian [19], Wichis and Chiriguanos mon ancestry for earlier three generations. All the donors provided [20], Northern Irish [21], Basque population [22], Eastern Mainland their prior consent after being informed about the purpose of the Chinese [23], Chinese Han [24], Korean [25], Japanese [26], Warao, sample collection. The investigation was approved by the Human Bari, Yucpa [27], Vietnamese and Australian Aborigine [28], Amer- Ethics Committee of the Department of Zoology, University of ican Caucasian, Hispanic, African American [29], Thai, British Cau- North Bengal, India. The whole blood samples were stored with casian, Palestinian [30], Australian Caucasian [31], New York EDTA as anticoagulant at À20 °C until use. Caucasian [32], Greeks [33], Afro-Caribbean, Trinidad Asian, Paki- stani [34], Chinese, Malay and Indian in Singapore [35], Indian Par- sis and Maharashtrian [36], Tibetans [37] and Arab [38]. For 2.2. DNA Extraction and KIR specific PCR-SSP typing haplotype studies, the KIR haplotypes were resolved from the genotypes and in assigning genes to specific haplotypes following Whole DNA was extracted from the blood samples using the assumptions were made: (i) all haplotypes contained KIR3DL3, phenol–chloroform extraction procedure [15]. The concentration 2DL4, 3DL2 and 3DP1; (ii) KIR3DL1 and 3DS1 are likely equivalent and purity of DNA was measured by ultraviolet (UV) Spectropho- to alleles and (iii) KIR2DL2 segregate as an allele of KIR2DL3. [35]. P. Guha et al. / Human Immunology 74 (2013) 673–680 675
2.4. Statistical analyses Mainland, Chinese Jiangsu province, North Indians, Paravar, Kani- kar and Mollukurumba populations from South India, Maharashtri- The observed phenotypic frequency for each KIR gene was ans and Parsis from Mumbai in India, Tibetans, Singapore Indians determined by direct counting of the ratio of the number of indi- and also from the populations of Pakistan. The framework and viduals in the population carrying the gene to the total number the pseudogenes were excluded from the analysis. P value < 0.05 of individuals in the population. KIR locus genep frequenciesffiffiffiffiffiffiffiffiffiffiffi (KLFs) was the limit for statistical significance. The results shown in Ta- were estimated by using the formula: KLF = 1À 1 À f , where f is ble 2 revealed no significant differences in the gene frequencies the observed frequency of a particular KIR gene in a population. of inhibitory KIRs except KIR2DL5 between the Rajbanshis and Differences of the Rajbanshi population KIR gene content with the North East Asians (NEAs) mainly the Chinese from Eastern some other neighboring populations [11,14,23,34,36,37] were Mainland and from Jiangsu Province. In the case of KIR3DL1 no sig- compared by the standard v2 Test using Kyplot 2.0 beta 15 statis- nificant differences were observed between Rajbanshis and the tical software. A hierarchial cluster analysis was also based on al- other populations except the Chinese and the Jiangsu populations. lele frequencies of 11 KIR genes (except framework and The reverse relationship is evident for KIR2DL5 where significant pseudogenes) of Rajbanshis with the previously mentioned popu- differences exist for all the populations except the NEAs, Tibetans lations and a neighbor joining tree was constructed using SPSS and Mollukurumba. Rajbanshi and Mollukurumbas have signifi- 15.0 software. The KIR gene frequencies were also bootstrapped cant difference only for KIR2DS4. The North Indians showed no sig- for 1000 replicates and then Nei genetic distance was calculated nificant differences from the Rajbanshis in the frequencies of to construct a consensus neighbor joining tree using Phylip pack- activating KIR genes. age version 3.69 [39] (Supplementary Fig. 2). The Principal Compo- From the observed frequency data, a Euclidean distance based nents Analysis (PCA) based on KIR gene frequencies of the hierarchial cluster analysis was performed of the above mentioned Rajbanshi population and other reference populations [11,13– populations and a neighbor joining tree has been constructed as 14,16–38] was carried out using the MINITAB version 16.0 statisti- shown in Fig. 1. In the tree three main clusters are prominent: cal software. Based on genotypic profile of the KIR genes in the Raj- South Asian, South East Asians (Thai and Vietnamese) and North banshis, the frequencies of groups A and B haplotypes were East Asian including Tibet (although Tibet is a part of South Asia calculated using the following formula: group-A = 2nAA + nAB/2n by extended definition, it is the autonomous part of China). The and group-B = 2nBB + nAB/2n, where nAA, nAB, and nBB were the Rajbanshis huddled within the Indian Cluster. The genetic distance numbers of AA, AB, and BB genotypes, and n was the total number was also measured between Rajbanshis and the other populations of individuals. and it can be seen that Rajbanshis have the least distance from Restricted maximum likelihood (REML) analysis was carried out Mollukurumbas (0.225) and Maharashtrians (0.248). Among the based on the KIR genotypic frequencies by PHYLIP, version 3.69 populations of the North East Asian Cluster, the one having the [39] and the phylogenetic trees was developed using tree-drawing least distance with the Rajbanshis is the Tibetan population software FigTree Version 1.3.1 (http://tree.bio.ed.ac.uk/). The REML (0.429). The KIR gene frequencies of the Rajbanshis and other pre- analyses assumed by default that each locus evolves independently viously recorded populations were also bootstrapped for 1000 rep- by pure genetic drift. licates and then Nei genetic distance was calculated to construct a Linkage disequilibrium (LD) between the pairs of gene loci was consensus neighbor joining tree using Phylip package version 3.69 estimated by the classical LD coefficient (D), Standardized LD coef- [39] (Supplementary Fig. 2). In a Consensus tree the numbers on ficient (D’), another conventional measure of LD (r2) and statistical the branches indicate the number of times the partition of the spe- significance (P). The LD measures were computed using the Arle- cies into the two sets which are separated by that branch occurred quin software version 3.5.1.2. among the trees, out of 1000.00 trees.
3. Results 3.1.1. Principle component analysis Principle component analysis (PCA) was performed to investi- 3.1. KIR gene frequencies gate the affinity of the Rajbanshis with other already reported pop- ulations around the World in terms of the KIR gene frequencies. The observed phenotypic frequencies (OF) and the estimated KIR loci frequencies of the Rajbanshis from this study and 45 other gene frequencies (KLF) for the 16 KIR genes in our study population populations already studied around the world were used for the of 75 healthy Rajbanshi individuals are shown in Table 1. All the 16 analysis. The PC plot for the first two components is shown in loci were detected in the population among which the four frame- Fig. 2. The first component accounted for 58.3% variability and work genes namely KIR2DL4, 3DL2, 3DL3 and 3DP1 were observed the second accounted for 19.0% variability. It can be observed from in all the individuals. The A haplotype associated KIR genes oc- the PC plot that except few outliers the distribution of the KIR curred more frequently than the B haplotype KIR genes. Apart from genes in the different population is in accordance with their geo- the framework genes the carrier frequencies of the other KIR genes graphical proximities. Six distinct geographical clusters were no- ranged between 0.15 and 0.84. ticed in the PC plot which include Africans, Northeast Asians, The Chi-square (v2) analysis was performed to compare the dif- American Natives, Mexicans, Asian Indians, and the European (in- ferences in carrier frequency (OF) of KIR genes in our population cludes Caucasians, Polynesians, Thai and Vietnamese) clusters. with that of the available published data of few other Asian popu- The Rajbanshi population huddles with the Asian Indian cluster lations for the same set of KIR gens namely from China Eastern as expected, but surprisingly have proximity to the European clus-
Table 1 Observed and estimated KIR gene frequencies in the Rajbanshi population.
Frequency 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DS5 2DL4 3DL2 3DL3 2DP1 3DP1 OF 0.84 0.92 0.85 0.80 0.52 0.55 0.41 0.45 0.57 0.28 0.36 1.00 1.00 1.00 0.97 1.00 KLF 0.60 0.72 0.62 0.55 0.31 0.33 0.23 0.26 0.35 0.15 0.20 1.00 1.00 1.00 0.84 1.00
⁄OF, observed KIR frequencies; KLF, KIR locus frequencies. 676 P. Guha et al. / Human Immunology 74 (2013) 673–680
Table 2 Frequencies of the KIR genes in the Rajbanshis were compared with some of the Asian populations using v2 test. The significant values are provided in parenthesis.
KIR Genes Rajbanshi Chinese Jiangsu North Indians Kanikar Paravar N = 75 N = 106 N = 150 N = 72 N = 35 N = 77 2DL1 92.0 99(5.88) * 100(8.98)** 87.5 97 97 2DL2 52.0 25(13.34)*** 21.3(21.77)*** 79.2(11.97)*** 74(4.903)* 69(4.504)* 2DL3 85.3 99(13.36)*** 100(19.55)*** 65.3(7.99)** 77 79 2DL5 54.7 45 44.7 79.2(9.925)** 86(10.052)** 83(14.39)*** 2DS1 45.3 42 46.7 54.2 60 65(5.905)* 2DS2 57.3 27(16.47)*** 24.7(23.28)*** 62.5 74 75(5.516)* 2DS3 28.0 22 21.3 43.1 73(20.89)*** 49(7.293)** 2DS4 80.0 95(10.44)** 92.7(7.84)** 80.6 80 84 2DS5 36.0 28 30.7 47.2 60(5.588)* 66(13.901)*** 3DL1 84.0 94(5.24)* 93.3(4.937)* 87.5 80 84 3DS1 41.3 41 40 38.9 69(7.082)** 60(5.149)* KIR Genes Rajbanshi Maharashtrian Parsis Mollukurumba Tibetan Pakistan N = 75 N = 139 N = 145 N = 41 N = 102 N = 78 2DL1 92.0 97.8(4.126)* 99.3(8.575)** 95 100(5.675)* 90 2DL2 52.0 62 62.8 59 30(8.440)** 67 2DL3 85.3 85.8 81.9 85 98(10.253)** 91 2DL5 54.7 71.7(6.47)* 71(5.857)* 56 50 78(9.533)** 2DS1 45.3 47.8 62.8(6.117)* 41 51 60 2DS2 57.3 62.3 62.8 59 29(13.904)*** 69 2DS3 28.0 44.2(5.201)* 53.8(13.287)*** 20 12(7.510)** 45(4.690)* 2DS4 80.0 90.7(4.856)* 86.1 95(4.847)* 88 72 2DS5 36.0 46.4 51.0(4.499)* 39 37 48 3DL1 84.0 91.4 87.4 95 88 81 3DS1 41.3 37 62.2(8.587)** 37 36 56
* p < 0.05. ** p < 0.01. *** p < 0.001.
Fig. 1. Phylogentic tree based on neighbour-joining method (constructed using SPSS 15.0 software) showing the relationships between the Rajbanshis and other 12 other neighboring Asian populations based on KIR gene carrier frequencies. (Chi, Eastern Mainland Chinese; Jian, Jiansu Chinese; NIn, North Indians; Tib, Tibetans; Kan, Kanikars; Par, Paravars; Pak, Pakistanis, Mah, Mumbai Maharashtrians; MuP, Mumbai Parsis; Raj, Rajbanshis; Mk, Mollukurumba; Viet, Vietnamese; Thai, Thais).
ter, although the geographical location and anthropological back- (20%), 21 were BB genotypes (28%) and the remaining 39 were ground suggest the influence of the Asian Indians, the Northeast AB genotypes (52%). Thus the AA and BB genotypes are present Asians and Southeast Asians. in more or less equal proportion. It was also seen that 14 individ- uals with genotype AA have genotype ID 1 (18.67%) which is dom- 3.2. KIR genotypes inantly present in the North East Asians. Among BB genotypes the most frequent was Genotype ID 70 (5.33%) which has been re- A total of 30 different KIR genotypes have been observed in the ported in 144 individuals from 54 populations around the globe Rajbanshi population based on the presence and absence of 16 KIR (http://www.allelefrequencies.net) including the Hongkong Chi- genes suggesting the richness in KIR variation in this population. nese, South Korean, North Indian, Mumbai Parsis and also in South Fig. 3 shows the KIR genotype ID, the KIR gene content, the number Indian populations like Paravar (6.49%) and Kanikar (5.71%). The of individuals having the genotype and the percentage of each same has also been reported from Tibetan population. In Rajbans- genotype. Among 75 samples 15 samples were AA genotypes his, among AB genotypes the most frequent are the Genotype ID 2 P. Guha et al. / Human Immunology 74 (2013) 673–680 677
Fig. 2. Principle component (PCA) of carrier frequency of nine variable KIR genes (2DL1–3, 2DS1–4, 3DL1, and 3DS1) in the Rajbanshis and other world populations developed by MINITAB v16. The PCA graph shows a global relationship between Rajbanshis and other previously reported world populations. The other seven genes (2DL5, 2DS5, 2DL4, 3DL2, 3DL3, 2DP1, and 3DP1) were excluded from the analysis as they were either invariably present in all individuals or not typed in some of the populations considered for the anlaysis. (Jap, Japanese; Chi, Eastern Mainland Chinese; Zhe, Zhejiang Chinese; Kor, Korean; SpC, Singapore Chinese; Jian, Jiansu Chinese; Hkc; Hongkong Chinese; Hui, Huichol; Pur, Purepecha; Tar, Tarahumara; Tib, Tibetans; Viet, Vietnamese; Thai, Thailand; Fin, Finnish; Chg, Chiriguanos; War, Warao; Bar, Bari; AmA, Amazonian Amerindian; Yuc, Yucpa; ReU, Reunion; Gre, Greek; CkI, Cook Island; Sam, Samoan; Tok, Tokelau; Ton, Tongan; Mez, Mestizo; Bri, British Caucasian; NIr, Northern Irish; Fre, French Caucasian; AmC, American Caucasian; SpM, Singapore Malay; His, Hispanic; MK, Mollukurumba; Sen, Senegal African; GuC, Guadeloupe Caribbean; AfA, African American; AfC, Afro-Caribbean; Per, Persian; Arb, Arabian; Pal, Palestinian; Raj, Rajbanshis; Mah, Maharashtrian; Tri, Trinidad Asian; Pak, Pakistani; NIn, North Indian; Par, Paravar; Kan, Kanikar; MuP, Indian Mumbai Parsi; Bas, Basque population; SpI, Singapore Indians and AuA, Australian Aborigine). The Rajbanshi population has been marked with a red spot. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 3. KIR genotypic profiles observed and the number of individuals displaying each profile. The list presents the percentage frequency of each genotype. Black boxes indicate presence and white boxes absence of KIR genes. Genotype IDs are considered according to www.allelefrequencies.net. 678 P. Guha et al. / Human Immunology 74 (2013) 673–680 and 4 which have also been observed in North East Asians, Tibetans These observations are consistent with the characteristic negative and the Asian Indian populations (AIs). KIR genotype ID 370 was association between genes of A and B haplotypes. recorded in one individual, which has also been reported in the Tibetans. The group B KIR haplotypes occurred more frequently than the group A KIR haplotypes. The similar scenario was also re- 4. Discussion ported in natives of Indian subcontinent. One of the most important factors which shape the population structure of a region is its position in the World map. In case of In- 3.2.1. Phylogenetic analysis dia also, it’s geographical location played a prime role in diversify- Restricted maximum likelihood (REML) estimation was per- ing its population by exposing it to the several waves of human formed based on the KIR genotypic frequencies to investigate in migrations which included the Paleolithic occupation by modern a broader way the relationships of the Rajbanshis with populations human, Neolithic migration of the proto-Dravidian speakers from having previously published data. Fig. 4 shows the phylogenetic Zagros mountain, Indo–European migration from Central Asia dendrogram based on REML analysis. The Rajbanshis clustered and also the migration of the Austro–Asiatic and Tibeto–Burman with the population of Indian origin, namely Trinidad Asian, North speakers from East/Southeast Asia [11]. These resulted in extensive Indians, Paravar and Kanikars. The Africans and the North East population admixture and genetic heterogeneity. Asians form closely related separate clusters. Tibetans are clus- Rajbanshis are abundant in the Terai and Dooars (outermost tered with the North East Asians (NEAs) and thus they constitute sub-Himalayan zones) of India. They have Indo-European linguistic a sister clade with the Rajbanshis and Indian group. The similar background [40]. Many opinions have been expressed regarding scenario is also evident from the NJ tree based on KIR gene fre- their origin but the widely accepted one is that Bodo people had quencies (Fig. 1). entered India during the period of the commencement of the Bik- ram Sambat and they got settled along the bank of the Brahmapu- 3.3. Linkage Disequilibrium between the KIR Loci tra River and gradually migrated to Assam and also to North and East Bengal [41]. Historical evidences suggest that the Rajbanshis The classical linkage disequilibrium coefficient (D), the stan- were the original descendents of the ‘Koches’. The meaning of dardized coefficient (D’) and the conventional measure of linkage the word ‘Rajbanshi’ is the ‘‘dynasty of king’’. The Koches emerged disequilibrium (r2) and P value for estimating Linkage disequilib- as a very strong political power in the early 16th Century in the Ka- rium (LD) between pairs of KIR gene loci are also documented mata region with the establishment of the Koch Kingdom by the (Supplementary Table 1). P value < 0.05 was considered to be sta- Koch king Bishwa Singha (1515–1540) who had significant contri- tistically significant. The LD analysis revealed 31 significant associ- bution to the establishment of Koch Kingdom, later came to be ations in the population studied. If a significantly positive P value is known as the kingdom of Kochbihar during the colonial period. observed between a pair of KIR loci, then they are likely to be sep- The mighty king is said to have adopted Hindu religion and culture arate genes, whereas a negative P value may indicate an allelic [42]. The Koches belong to the mongoloid stock [42]. Thus histor- relationship between these two KIR genes loci. The framework ical evidences suggest the influence of Sino-Tibetan lineage on and the pseudogenes were excluded from this analysis. From the the Rajbanshis stock. According to Sir H. H. Risley, the Rajbanshis table it is evident that the genes namely KIR2DL2, 2DL5, 3DS1, are of Dravidian origin with considerable mongoloid admixture [4]. 2DS1, 2DS2, 2DS3 and 2DS5 that constitute the B haplotype are In the neighbor-joining tree (Fig. 1) it is evident that the Raj- found to be in positive LD. Additionally, positive LD were also ob- banshis huddled with the Indian cluster close to the Mollukurumba served between KIR3DL1, 2DL1, 2DL3 and 2DS4 which is consistent and Maharashtrians. The Genetic distances also suggested that with the associations between the constituents of A haplotypes. among the North–East Asian cluster, the Tibetans are closer to Raj- Negative LD values were evident between A and B haplotype genes. banshis. The PCA analysis (Fig. 2) based on frequencies of the KIR
Fig. 4. Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles in the Rajbanshis and other previously studied World populations using Phylip package [39]. Clusters relating to the Asian Indians, North east Asians, Africans, Europeans, Mexicans and American Natives can be distinguished separately. (Jap, Japanese; Chi, Chinese; Kor, Korean; Hui, Huichol; Pur, Purepecha; Tar, Tarahumara; Tib, Tibetans; Fin, Finnish; Chg, Chiriguanos; War, Warao; Wic, Wichis; Bar, Bari; AmA, Amazonian Amerindian; Yuc, Yucpa; CkI, Cook Island; Sam, Samoan; Tok, Tokelau; Ton, Tonga; Mez, Mestizo; NIr, Northern Irish; Fre, French Caucasian; AmC, American Caucasian; His, Hispanic; MK, Mollukurumba; Sen, Senegal African; GuC, Guadeloupe Caribbean; AfA, African American; AfC, Afro-Caribbean; Raj, Rajbanshis; Tri, Trinidad Asian; NIn, North Indian; Par, Paravar; Kan, Kanikar; Bas, Basque population). The Rajbanshi population is within the Asian Indian cluster. P. Guha et al. / Human Immunology 74 (2013) 673–680 679 genes also simulate the similar result and placed the Rajbanshis in dian origin, the Indo–European speaking Rajbanshis from Northern the Indian Cluster. The Rajbanshis share the common cluster with part of Bengal has Tibeto–Burman influence. However this popula- the Tamilnadu Paravar and Kanikar populations (Fig. 1), suggesting tion is highly diversified with considerable admixtures and de- a relationship with the populations of the Indian origin. In the Raj- tailed analysis is required to characterize all the available KIR banshis, the B haplotype and A haplotype frequencies are 54% and haplotypes. However, additional studies based on other available 46% respectively showing more predominance of B haplotype on A genetic markers should clarify the genetic background of the haplotypes, which is a characteristic feature of the Indian popula- Rajbanshis. tions. The AA genotype ID 1 is more frequent in the Rajbanshis which is a characteristic of the Northeast and East Asian popula- tions. After genotype AA1 the most frequent genotypes are IDs 2 Acknowledgments and 4. Both the genotypes ID 2 and 4 are present among Indians as well as East/Northeast populations. The REML analysis based We gratefully acknowledge Dr. R. Rajalingam for his generosity on genotypic profiles showed that amongst the North–East/East– to provide us with the primer sequences and valuable suggestions Asian populations, the Tibetans are closer to the Rajbanshis. Geno- during initiation of the work. This study is also partly supported by typic frequency-based REML unrooted tree also indicate a common a project grant from ICMR (Project Sanction No. 54/2/2012-HUM- ancestor for the East-Asian/Tibetan and the Rajbanshi/Indian BMS). clades (Figs. 1 and 4). Moreover, the genotype ID 370 reported in the Tibetan population has also been found in the Rajbanshis. Thus it can be said that although clustered with the Indian population, Appendix A. Supplementary data KIR gene pool of the Rajbanshis has received a significant Tibeto– Burman Influence. This view is also supported by the Y-Chromo- Supplementary data associated with this article can be found, in some Haplogroup diversity study, which has shown that the O3 the online version, at http://dx.doi.org/10.1016/j.humimm.2013. haplogroup is not only absent in Indo–European speaking castes 01.007. from East India but also is considerably shared between Rajbanshis and other TB groups, thus supporting the TB connection of the Raj- References banshi population [40]. Moreover the age of the O3 haplogroup in the TB speaking populations of the Himalayan region is 8.1 ± 2.9 [1] Bhasin MK, Walter H, Danker-Hopfe H. People of India: An investigation of KYA whereas the IE language arrived roughly 3500 years ago. 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Pokhraj Guha1, Sanjeev K. Srivastava1,2 *, Soumen Bhattacharjee3 andTapas K. Chaudhuri1* 1 Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, Siliguri, West Bengal, India 2 Department of Medical genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India 3 Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Siliguri, West Bengal, India
Edited by: With the gradual development of intelligence, human got curious to know his origin Ashok Sharma, Georgia Regents and evolutionary background. Historical statements and anthropological findings were University, USA his primary tool for solving the puzzles of his own origin, until came the golden era of Reviewed by: molecular markers which took no time to prove it’s excellence in unveiling answers to the Arun Bhardwaj, University of South Alabama Mitchell Cancer Institute, questions regarding the migration pattern of human across different geographical regions. USA As a bonus these markers proved very much beneficial in solving criminal offenses and in Vivek Pratap Singh, Baylor College of understanding the etiology of many dreaded diseases and to design their prevention. In Medicine, USA this review, we have aimed to throw light on some of the promising molecular markers *Correspondence: which are very much in application now-a-days for not only understanding the evolutionary Tapas K. Chaudhuri, Cellular Immunology Laboratory, Department background and ancient migratory routes of humans but also in the field of forensics and of Zoology, University of North human health. Bengal, Raja Rammohunpur, Siliguri, West Bengal 734013, India Keywords: SNP,mitochondrial markers,Y-haplogroup, HLA (MHC), KIR, population diversity, polymorphism e-mail: [email protected]; Sanjeev K. Srivastava, Department of Medical genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh 226014, India e-mail: [email protected]
INTRODUCTION emerging out from the analyses of these markers stirred a debate Since the origin, spread of mankind across the world has always on the validity of two distinct models of human dispersals since been an emerging area of interest for modern biologists. Humans their inception more than 25 years ago (Stringer and McKie, 1996; migrated“out of Africa”to other geographical locations around the Wolpoff and Caspari, 1997). world and eventually diversified into distinct human races popu- Multiregional continuity hypothesis (Thorne and Wolpoff, lating distinct geographical regions. Human diversity did not only 1992) proposes that humans began to migrate out of Africa about remain restricted to their socio-cultural and linguistic domains 1.5 million years ago as a single evolving species Homo sapiens, dis- but also have penetrated deep inside their genetic root. The wealth tributed throughout the Old World and all regional populations of genetic/allelic diversity is not only an excellent resource for were connected by gene flow as they are today. Some skeletal fea- human diversity studies but also is highly informative for the tures developed and persisted for varying periods in the different study of human genetic predisposition of various diseases (Sri- geographical regions justifying the development of recognizable vastava et al., 2011; Pál et al., 2012). Thus there is enough reason regional morphologies in the continents of Africa, Europe, and for growing interests in the field of genetic diversity researches. Asia. On the other side, the “recent out of Africa” model (Wilson Since human genome varies from individual to individual, no and Cann, 1992; Stringer and McKie, 1996) proposed that since two individuals are alike genetically or phenotypically. With the humans began to radiate out from Africa there have been emer- development of various molecular techniques the application of gence of several species under the genus Homo. This model also genetics to the study of human evolution gave rise to the fields of argues that H. sapiens emerged in Africa approximately 100,000 molecular evolution and molecular anthropology. Various infor- kilo years ago and began to spread globally, replacing other species mative and polymorphic genetic markers were discovered and of Homo that were encountered during its expansion, thereby the gene frequency data emerging out from their analyses largely proposing the development of all current regional morpholo- contributed to the successful study of evolution and diversity of gies outside Africa, within the last 100,000 kilo years ago. These human races worldwide. The use of a good number of uniparental alternative models of human origin arose from morphological and biparental markers for genetic diversity studies is a recent interpretations (Wolpoff and Caspari, 1997). Over the last one trend in which Y-haplogroup, mitochondrial DNA (mtDNA), and a half decade, molecular evidences from populations of dif- human leukocyte antigen (HLA) and killer-cell immunoglobulin- ferent ethnic regions around the world contributed remarkably to like receptor (KIR) are the promising ones. The inheritance pattern the debate (Hawks et al., 2000).
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Herein, we have aimed to throw some light on some of the high rate of mutation events many different mtDNA variants are established and emerging DNA markers which have important found in an individual. implications in studying the population diversity as well as pre- The application of mtDNA to trace the evolutionary pattern dicting human migration pattern and evolutionary relationships. and the migration events in human is based on the fact that certain haplotypes are observed in peoples of certain geograph- MITOCHONDRIAL DNA ical regions of the World (Figure 1) which according to Witas Although mtDNA represents a small fraction of the total genome and Zawicki (2004), might have occurred due to accumulation size of an organism, it has emerged over the last three decades of mutations in different maternal lineages as people migrated as one of the most popular markers of molecular diversity in and started populating new regions. Cann et al. (1987) has also animals (Galtier et al., 2009). Human mtDNA is acquired almost showed that the highest variation of mtDNA sequences occurs exclusively maternally, appears in multiple copies in each cell and in the African populations. The first human mtDNA lineages possess few important conserved coding sequences thus strength- described in Africa were L1, L2, and L3, with the L1a subclus- ening the reason for its selection as a marker of choice (Wallace ter being the oldest (Watson et al., 1997). All of them are still et al., 1999; Galtier et al., 2009). frequent in sub-Saharan Africa, the region having the highest Human mtDNA is a 16,569 kb circular, double-stranded diversity of mtDNA across the world and considered to be the molecule containing 2 rRNA genes, 22 tRNA genes, and 13 struc- place of origin of all mtDNA sequences (Jorde et al., 1998). Based tural genes encoding subunits of the mitochondrial respiratory on mtDNA sequences there are two major migratory routes from chain (DiMauro and Schon, 2003). All human mtDNA is inherited Africa (Maca-Meyer et al., 2001). The southern route representing maternally because almost always ovum contributes its mitochon- the haplogroup M expansion can be traced from Ethiopia through dria to the developing embryo with only rare exceptions (Giles the Arabian Peninsula to India and Eastern Asia. However, the M et al.,1980; Cummins,2000). In mitochondrial genomes the muta- haplogroup diversity is greater in India (Kivisild et al., 1999) than tion rate is several times higher than that of nuclear sequences in Ethiopia (Quintarna-Murci et al., 1999). The northern route (Brown et al., 1979, 1982; Saccone et al., 2000). As a result of such split into three main clusters. The first cluster comprising of the
FIGURE 1 | Geographical distribution of the human mtDNA haplotypes (letters in map) deducing the origin of anatomically modern humans and presumable routes of human migrations “out of Africa” (adopted and modified from Witas and Zawicki, 2004).
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haplogroups W, I and N1b are found in Europe, The Middle East and Caucasia and also in Egypt and Arabian Peninsula. The next group divided into haplogroup X and A, common in Europe and Asia respectively. The third cluster subdivided into four lineages of which the first one gave rise to haplotype B found in Japan, East Asia, and Southern Pacific Archipelago, the second formed haplogroups J and T, whereas H and V, belong to the third cluster, their derivatives being found in Europe, North Africa and Cen- tral Asia. The fourth lineage is U and the highest frequencies of its sub-haplogroups are found in India (U2, U7), North Africa (U6, U3) and in Europe (U5; Witas and Zawicki, 2004). Accord- ing to Richards et al. (1998), the major European mtDNA lineages are U5, H, I, J, K, T, V, W, and X. Haplogroup J encompassing about 16% of European mtDNA content, is probably the only one imported to Europe by the neolithic farmers. Recent studies indicate an early invasion of a single, ancestral lineage of Asian FIGURE 2 | Current phylogenetic relationships of the 20 major origin in America (Bonatto and Salzano, 1997; Silva et al., 2002). haplogroups of the globalY chromosome gene tree (adopted from The four most common American haplogroups – A, B, C, and D, Chiaroni et al., 2009). although old, have similar nucleotide polymorphism, suggesting their common origin (Silva et al., 2002). The variability pattern of particular mtDNA sequences such as the ATP6 gene from different (adopted from Chiaroni et al., 2009). It is evident that the hap- temperature zones confirm the involvement of selection factors as logroups A and B constitute the deepest branches in the phylogeny for, e.g., climate in shaping regional mtDNA variants (Mishmar and are restricted to Africa, thereby strengthening the evidence et al., 2003). that modern humans first arose there (Stringer, 2002; Goebel, Wallace et al. (1999) suggested that different mtDNA haplo- 2007). The third predominantly African haplogroup E, diver- types may be involved in modulating oxidative phosphorylation, sified some time afterward, probably descending from the East thereby influencing the physiology of individuals predisposing African population that generated the “out of Africa” expansion. or protecting them from certain diseases. Associations with spe- Haplogroups G–J, T, and L are more prevalent in regions con- cific mtDNA haplotypes have been observed for several diseases stituting Europe, Middle East, and certain regions of western such as cardiomyopathy (Shin et al., 2000), Alzheimer disease and Asia with extensions to Arabia and India. Haplogroup R is con- dementia with Lewy bodies (Chinnery et al., 2000), and multi- strained to central and western Asia and to a large portion of ple sclerosis (Kalman et al., 1999). A recent trend in functional Europe. Haplogroup N is frequent across boreal and north-west studies of mtDNA mutations has been the use cybrid cells in Asia and O in south-east Asia including the islands and part which established human cell lines are first depleted of their of New Guinea. Haplogroups C and Q display Asian ancestry own mtDNAs and then repopulated with various proportions and hold the unique privilege of having settled America. Not of mutated mtDNA genomes (King and Attardi, 1989). Thus surprisingly their origin seems to have been in north-east Asia. mtDNA haplogroup diversity are eligible enough to answer many The absence of haplogroup N in the Americas indicates that of our questions regarding our evolutionary history and also for its spread across Asia happened after the submergence of the finding the underlying causes of certain diseases of the human Bering land bridge. It is likely that haplogroup C entered America race. after Q, even though C originated phylogenetically earlier than Q (Chiaroni et al., 2009). Y HAPLOGROUP DIVERSITY Y haplogroup diversity has been carried out by Debnath et al. Genetic markers on the non-recombining portion of the Y (2011) in the Sub-Himalayan Terai and Dooars population of chromosome have gradually emerged as an important tool for ana- north-eastern India (Debnath et al., 2011). The study showed lyzing human phylogenetic relationships. These markers represent that sub-Himalayan paternal gene pool is extremely heteroge- human genetic diversity based on single nucleotide polymor- neous. Three major haplogroups, namely H, O, and R, are phisms (SNPs) on the Y chromosome. There is now extensive shared across the four linguistic groups that inhabit the area knowledge regarding the geographic origins of Y-SNPs based on namely, the Tibeto-Burman, Austro-Asiatic, Indo-European and studies of global populations (Hammer et al., 2001; Jobling and Dravidian. Tyler-Smith,2003). Because of the high geographic specificity of Y- Earlier studies indicated that Y-chromosome polymorphisms SNPs (Hammer and Zegura, 1996; Jobling and Tyler-Smith, 2003), were geographically restricted and that FST values for the NRY SNP haplogroups can be used directly to measure admixture were higher than those for mtDNA (Jobling and Tyler-Smith, among diverse populations without resorting to more complex 1995; Cavalli-Sforza and Minch, 1997; Underhill et al., 1997; models of admixture (Paetkau et al.,1995; Bertorelle and Excoffier, Hammer et al., 1998; Perez-Lezaun et al., 1999). Hammer et al. 1998). The present nomenclature system of Y chromosome geno- (2001) constructed the hierarchical tree for Y chromosome diver- types has defined 20 main haplogroups, designated A through T sity and showed that the root of the tree occurs between two haplo- (Karafet et al., 2008). The 20 haplogroups are shown in Figure 2 type sets (H1–H4 and H5–H10) which are entirely restricted to the
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African continent, thereby supporting the hypothesis of an African were the predominant alleles within the HLA DRB1 locus while origin of contemporary NRY lineages (Hammer et al., 1998; DQA1 × 0102, DQA1 × 0201, DQA1 × 0301 and DQA1 × 0501 Underhill et al., 2000). In general, the within-populations vari- were the most frequent alleles of the DQA1 locus. At the DQB1 ance component for Y-chromosome data is much smaller than the locus, DQB1 × 0201, DQB1 × 0301, DQB1 × 0501, and values reported for mtDNA (Excoffier et al., 1992; Seielstad et al., DQB1 × 0601 were seen to be in high frequency. Among all 1998; Kittles et al., 1999; Jorde et al., 2000). Hammer et al. (2001) the three loci haplotypes studied, DRB1 × 0701-DQA1 × 0201- made a major conclusion that global human NRY variation is DQB1 × 0501, DRB1 × 1101-DQA1 × 0201-DQB1 × 0301, structured, with a significant amount of intergroup variation par- and DRB1 × 1501-DQA1 × 0601-DQB1 × 0201 are unique titioned among African, Native American, and Eurasian/Oceanian in the Rajbanshis. Such HLA profile suggest proximity with the populations. Based on polymorphism of 43 biallelic markers mongoloids (Agrawal et al., 2008). nested cladistic analysis (NCA) was performed by Hammer et al. Apart from being an invaluable tool for population genetic (2001), wherein two important findings of the NCA were that studies, MHC polymorphism has important role in organ trans- Europe was a “receiver” of intercontinental signals primarily from plantation and human disease associations. HLA associations have Asia, and there existed a large number of intracontinental sig- also helped in defining syndromes of disease categories having nals within Africa. AMOVA analyses of Hammer et al. (2001) also common/shared pathogenic mechanism like ankylosing spondyli- supported the hypothesis that patrilocality effects are evident at tis and related spondylo-arthropathies that are presumed to be local and regional scales, rather than at intercontinental and global associated with HLA-B27. HLA association studies in infectious levels. and autoimmune diseases show the presence of susceptibility and Additionally, Y-chromosome microsatellites find extensive protective alleles in populations of different ethinic origins (Hill application in forensic researches whereby databases of pop- et al., 1991; Carrington et al., 1999; Bowness, 2002). HLA associ- ulation haplotype frequencies are established for Europe, the ation for psychotic disorders was documented by Debnath et al. United States and for Asia. Y microsatellite analysis provides (2005, 2006). assailant specific profile during diagnosis of the rape case when Human leukocyte antigen associations with diseases vary in the rapist is mainly azoospermic (Sibille et al., 2002). Paternity different populations. Disease predisposing genes and their molec- testing by Y microsatellite profiling finds application in cases of ular subtypes could help to determine and predict the incidence of male child to trace the actual identity of father (Rolf et al., 2001). the diseases in some populations. It is therefore important to have Thus it can be said that Y chromosome SNPs are very efficient a population based database of HLA alleles and their frequencies markers for not only evolutionary studies but also for forensic of prevalence in healthy individuals so that disease predisposing researches. influence of a particular phenotype could effectively be assessed in the populations. HUMAN LEUKOCYTE ANTIGEN Being a functionally a polymorphic system, investigations into The major histocompatibility complex (MHC)/HLA is unique in the distribution of MHC alleles in world populations are very that it is the most polymorphic genetic system in the human important in this regard since the MHC genetic makeup of genome and the only system to display functional polymorphism each of these populations would reflect interplay of both the (Marsh et al., 2000; Spinola et al., 2005). Due to its high polymor- basic genetic origin and effects of natural phenomenon such phism, tight linkage among the loci and non-random association as founder effect and environmental selection. Differences in of alleles this system has become interesting from perspective of the prevalence of HLA alleles in different populations in varied population genetics (Bharadwaj et al., 2007; Agrawal et al., 2008). environmental conditions could be utilized to assess the role of All the regions of HLA are known to be highly polymorphic, con- each of these alleles in conferring survival advantage to human stituting several closely linked loci each with large number of populations. genes that can be further split into many allelic types differing only in their nucleotide sequences. Therefore the importance of KILLER-CELL IMMUNOGLOBULIN-LIKE RECEPTORS this system in the study of polymorphism and their significance in Killer-cell immunoglobulin-like receptors were first described population selection and survival and in providing clues to mecha- by Harel-Bellan et al. (1986) and was initially known as “killer nism of generation as well as maintenance of this variability within inhibitory receptor.” The KIR family of polymorphic and highly the populations is immense. homologous genes is located on chromosome 19q13.4, within the Human leukocyte antigen polymorphism study has been car- 1 Mb leukocyte receptor complex. A total of 17 genes have been ried out in many of the ethnic populations of India including identified in the family in agreement with the HUGO Genome the primitive tribal group Toto (Debnath and Chaudhuri, 2006; Nomenclature Committee (HGNC), of which 15 are functional Agrawal et al., 2007). Debnath and Chaudhuri (2006) had shown and 2 are pseudogenes (Marsh et al., 2003). Arrayed in a head-to- earlier that HLA-B14 has the highest known frequency in the tail fashion, KIR genes stretch over a 150 kb domain of DNA with Toto population of India (32.5%) when compared to that of each gene being approximately 10–16 kb in length (Uhrberg et al., the other world populations. HLA polymorphism-based affini- 1997). Separation between all loci approximatesa2kbstretchof ties of the north and north-eastern populations of India has DNA with the exception of a 14 kb sequence upstream from 2DS4 also been carried out by Agrawal et al. (2008), where they have (Wilson et al., 2000). The probability of two individuals inherit- shown that among the Rajbanshi caste population of India, ing the same KIR genotype is slim, with their expression varying DRB1 × 1101, DRB1 × 1201, DRB1 × 1501 and DRB1 × 080× clonally, adding yet another layer of complexity. Although KIR
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haplotypes differ from each other in the number and type of genes Sub-himalayan part of north-eastern India (Guha et al., 2013). (Uhrberg et al., 1997; Witt et al., 1999; Crum et al., 2000; Norman It was shown that although clustered with the Indian popula- et al., 2001) the genes 2DL4, 3DP1, 3DL2, and 3DL3 are present in tion, KIR gene pool of the Rajbanshis has received a significant virtually all haplotypes and have therefore been termed framework Tibeto-Burman influence. This view is also supported by the Y- loci (Wilson et al., 2000). Based on KIR gene content two groups chromosome haplogroup diversity study which have shown that of haplotypes (A and B) can be distinguished in human (Uhrberg although totally absent in the Indo-European speaking castes from et al., 1997). Group A KIR haplotypes contain the inhibitory KIR east India, the O3 haplogroup is considerably shared between genes along with KIR2DS4 as the only activating receptor. Group Rajbanshis and other such Tibeto-Burman groups (Debnath et al., B haplotypes contain various combinations of 2DS1, 2DS2, 2DS3, 2011). 2DS5, 3DS1, and 2DS4. Group A haplotypes do not vary in gene The frequencies of the inhibitory KIR genes in most of the content, however, extensive variation at the allelic level can be world population groups are very high except those on the B noted (Shilling et al., 2002). In contrast, the group B haplotypes haplotypes, i.e., KIR2DL2, KIR2DL5A, and KIR2DL5B. Detailed show substantial variations in gene content but only moderate analysis revealed that indigenous populations such as aborigines allelic polymorphism can be noticed (Yawata et al., 2002a,b). and Amerindians have outlying frequencies of the KIR genes. Immunogenetic studies based on KIR genes in different ethnic Obviously there is a close inverted correspondence between the populations around the world show significant differences in the frequencies of KIR3DL1 and KIR3DS1 genes in an individual distribution of group A and B haplotypes. Whereas in the Japanese population. Based on KIR haplotype B genes Middleton and population group A genotypes (i.e., individuals homozygous for Gonzelez (2009) concluded that the population distribution of two group A haplotypes) were found at frequencies well above KIR genes was related to geography like a good anthropologi- 50%, only a single individual out of 67 exhibited a group A geno- cal marker such as HLA or Y chromosome. Unlike many other type in a survey among Australian Aborigines (Toneva et al., 2001; populations (Middleton et al., 2007), the Japanese population Yawata et al., 2002a,b). showed that the frequency of one allele of each of the KIR genes The KIR frequencies of many of the ethnic populations were KIR2DL1, KIR2DL2/2DL3, KIR2DL4, KIR3DL1/S1, KIR3DL2, analyzed worldwide. In one such work, KIR gene profile was stud- and KIR2DS4 have higher frequency compared to the next ied for the Rajbanshi population, an essential caste population of frequent allele (Yawata et al., 2006). This made Parham (2005) to
FIGURE 3 | Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles in the Rajbanshis and other previously studied World populations (adopted from Guha et al., 2013).
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predict a skewed distribution of KIR variants in the Japanese CONCLUSION population, reflecting a distinct history of directional and bal- Human have developed their interest in unveiling the mysteries ancing selection. A phylogenetic dendrogram based on KIR of human migratory pattern and evolutionary trends since his genotype frequencies has been shown in Figure 3 (adopted origin. These above mentioned markers are serving the scientific from Guha et al., 2013) to depict the relation of different World world to trail back through time to understand the dispersal pat- populations. tern of humans. Toadd to their importance, these markers are also In humans, KIRs recognize HLA class I proteins leading to responsible for understanding the underlying etiology of certain the inhibition or activation of cytotoxic cell activity and cytokine disease pathogenesis. Application of these markers especially Y- production by T and NK cells thus focusing on the role of SNPs in forensics has been an interesting achievement in the past these receptors in immunological responses of NK cells (Lanier, decade. Apart from these markers, a group of recently emerging 1998). The interaction of KIR3DS1 with HLA-B alleles that markers which are gaining the attention of the researchers all over encode molecules with isoleucine at position 80 (HLA-B Bw4- the world are the toll-like receptors (TLRs; Schwartz and Cook, 80Ile) resulted in delayed progression of HIV infection to AIDS 2005). In addition to their broad effect on the immunity, they (Martin et al.,2002). KIR 2DL4 binds to HLA-G (Ponte et al.,1999; have immense importance in the pathogenesis of human diseases. Rajagopalan and Long, 1999), a non-classical class I molecule Further knowledge on the effect of TLR polymorphisms in dis- that is expressed on the human trophoblast, and the resulting ease progression may help in the assessment of disease risk and receptor-ligand interaction may confer some protection against in developing newer therapies accordingly (Schwartz and Cook, maternal NK or T cell-mediated rejection of the hemi-allogeneic 2005). Needless to say further researches and careful investigation fetus. These are just a few to mention the roles of KIR-HLA inter- is still on demand to decode the full potential of these markers for actions in disease pathogenesis. Moreover, the degree of KIR-HLA the benefit of mankind. interactions may determine the success rate of haematopoietic cell replacement therapy in certain leukemias. Thus this fam- ACKNOWLEDGMENT ily of receptor on NK cells is turning out to be a hotcake for We are indebted to Dr. Ranjan Ghosh, Department of English, researchers throughout the World in human evolutionary and University of North Bengal for going through the manuscript and disease association studies. suggesting syntactical modifications.
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New York: Henry Holt Watson, E., Forster, P., Richards, M., Yawata, M., Yawata, N., Abi-Rached, the terms of the Creative Commons (Polish edition: “Afrykanski ´ exodus”, and Bundelt, H. J. (1997). Mitochon- L., and Parham, P. (2002a). Vari- Attribution License (CC BY). The use, Prószynski ´ i S-ka 1999). drial footprints of human expansions ation within the human killer distribution or reproduction in other Thorne, A. G., and Wolpoff, M. H. in Africa. Am. J. Hum. Genet. 61, cell immunoglobulin-like receptor forums is permitted, provided the origi- (1992). The multiregional evolution 691–704. doi: 10.1086/515503 (KIR) gene family. Crit. Rev. nal author(s) or licensor are credited and of humans. Sci. Am. 266, 76–83. doi: Wilson, A. C., and Cann, R. L. Immunol. 22, 463–482. doi: 10.1615/ that the original publication in this jour- 10.1038/scientificamerican0492-76 (1992). 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“fgene-04-00155” — 2013/8/7 — 19:30 — page8—#8 Annals of Pharma Research ISSN: 2347- 1956 Research Article Annals of Pharma Research is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
COMPARATIVE ANALYSES OF THE ABO, KIR AND HLA LOCI AMONG THE RAJBANSHIS OF NORTH BENGAL REGION, INDIA. Guha P1, Srivastava SK1&2*, Das A1, Bhattacharjee S3, Haldar B4 and Chaudhuri TK1* 1Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India, 734013. 2 Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India, 226014. 3Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India, 734013. 4Department of Pathology, North Bengal Medical College and Hospital, Sushrutangar, Darjeeling, West Bengal, India, 734012.
ABSTRACT The sub-Himalayan Terai and Dooars region of North One of the population groups, comprising 18.5% of the Bengal, India, is a hotspot of socio-cultural and ethnic total Scheduled Caste population of the state of West diversity. One of the major caste populations present Bengal as per the 2001 census of Government of India is dominantly in this region is the Rajbanshi caste. Although the Rajbanshi population. It comprises the major caste a good numzber of opinions were forwarded regarding population of North Bengal region, a part of East India. In their origin, yet their genetic identity is still an unsolved addition to this, a sizeable population of Rajbanshis also question. In the present study, we have aimed to lives in the sub-Himalayan regions of Nepal as well as analyze the ABO blood group system in the Rajbanshis of North-east India including Assam. The Rajbanshis Terai and Dooars region of North Bengal and also have represent one of the oldest and indigenous populations compared our observations with that of the predictions of Terai and Dooars regions with rich cultural heritage, from already published studies on KIR and HLA diversity unique linguistic and social background. Linguistically of the Rajbanshis. The frequencies of the A, B and O Rajbanshis belong to IE-speaking group; however, alleles in the studied population were found to be 0.1751 historic evidences indicate their tribal connection [4]. 0.2313 and 0.5937 respectively. From our study based on Thus the Rajbanshi population is of considerable interest the ABO, KIR and HLA loci, we came to the conclusion for genetic studies as many questions arise with regard that although the Rajbanshis have genetic background to its origin and genetic background. similar to the other Indian populations, yet the effect of mongoloid influence on their genetic structure can be Blood groups beside being a valuable tool in blood well documented. transfusion, forensics and paternity determination, is also one of the most simple and important genetic Keywords: Rajbanshi population, genetic diversity, HLA, markers in studies of human population variations [5]. KIR, Blood Group The ABO blood grouping system in human, discovered by Landsteiner in 1901 [6] and the Rh system defined by INTRODUCTION Landsteniner and Wiener in 1941 [7], together proved The present genetic structure of India has resulted from very useful for blood transfusion purposes. Although all contributions by several waves of human migrations and the human populations share the same blood group gene flow [1] forming a hierarchial society stratified into systems, their frequencies differ markedly in different tribes and caste populations. The sub-Himalayan Terai ethnicities across the world enhancing its importance in and Dooars regions of East India constitute an important genetic research and in tracing anthropological and geographic location due to their close proximity with the ancestral relations of human. Thus it makes a strong eastern territories of the Himalaya. Ethnically these sense to evaluate the frequencies of ABO and Rh blood regions are extremely diverse, inhabited by the groups in different ethnic populations of our country populations belonging to different linguistic groups such including the Rajbanshis. as the Tibeto-Burman (TB), Austro-Asiatic (AA), Indo- European (IE) and Dravidian (DR). On one side of East In the present study, we have investigated the ABO blood India is the north-eastern part of the country which has group frequencies in the Rajbanshi population and to been an important corridor of human dispersals while understand the genetic relationships of Rajbanshis with Nepal Himalayas on the other side acted as a barrier for other Indian populations, we have performed a bidirectional gene flow [2-3]. comparative analysis based on genetic variations of Human Leukocyte Antigen (HLA), Killer cell Ig-like Receptors (KIR) and ABO blood group system in the Rajbanshis. Corresponding Authors: Dr. Tapas Kumar Chaudhuri, Professor, Cellular Immunology laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India, 734013. Dr. Sanjeev K Srivastava, PhD, Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, UP, India. Email: [email protected], [email protected] and [email protected] [18] Guha et al., Annals of Pharma Research, 2013, 01 (01), 18-24
MATERIALS AND METHODS Blood samples were collected from 78 unrelated population and that of the other comparator population volunteers belonging to Rajbanshi caste population with were compared using Kyplot 2.0 beta 15 software. prior informed consents from the donors. All the required information regarding each subjects were The HLA data of the Rajbanshis and other comparator collected using a brief questionnaire. The Rajbanshi populations (i.e. Kayastha, Rastogi, Vaish, Shia, Sunni, samples were screened from the Terai and Dooars region Lachungpa and Mech) were extracted from Agarwal et o o of Coochbehar (26 20’N and 89 29’E) and Jalpaiguri al., 2008 [29]. Genetic Distances (Nei’s DA) were (26o32’N and 88o46’E) districts which are located in the calculated in between the Rajbanshi and the comparator northern region of the state of West Bengal. The blood populations in order to explore the genetic relatedness samples were collected from the subjects under aseptic of the Rajbanshis with the other populations. condition ABO and Rh blood groups testing were done by open slide method following the instruction of the RESULTS manufacturer using anti-A, B, and D sera (Tulip The distributions of the ABO and Rh blood group systems Diagnostics, Goa, India). All samples were tested for the in the Rajbanshi population of the Terai and Dooars ABO blood groups. The allele frequencies for both the regions of North Bengal are shown in Table 1. In the 78 systems were calculated according to the method of samples analyzed from the Rajbanshi population, O Mourant et al., (1976) [8]. blood type has the highest number of representatives (28) followed by B (25), A (18), and AB group (7) The ABO blood group frequency data of populations to respectively (Table.1). The overall phenotypic be compared with that of the Rajbanshi population were frequencies of ABO blood groups were O>B>A>AB in the extracted from previously published reports as follows: Rajbanshis (Fig. 1). The allelic frequencies of A, B and O Garo and Rabha [9], Oraon, Munda and Kharia [10], alleles were 0.1751 0.2313 and 0.5937 respectively. The Manipur Brahmins and Meltei [11], Koch population of gene frequencies of Rh D and Rh d were found to be Assam [12], OBC population of Uttarpradesh [13], 0.886 and 0.114, respectively (Table 1). The comparison Rajasthani Nomadic Tribe [14], Vishwakarma population of the ABO blood group distribution in the Rajbanshis of Mysore [15] and Kunbis population [16]. The with that of other Indian populations has been shown in phenotypic frequencies of the ABO blood groups in the Figure 2. It can be clearly understood from Figure 2a that Rajbanshis were compared with that of the reference the Rajbanshis have the lowest phenotypic frequency of populations using Kyplot 2.0 beta 15 software. Principal A blood type (23.06%, Table 2) compared to the other Component Analysis was also computed based on the populations. Moreover the frequency of the AB allele frequencies of the ABO blood grouping system in phenotype (8.97%) was also low among the Rajbanshis the Rajbanshis and other comparator populations using just being greater than the Maratha Kunbis (8.33%) and Minitab 16.0 statistical software. Hierarchial cluster Rajasthani Nomadic tribe (8.60%) respectively. From analysis was also performed based on allele frequencies figure 2b it can be concluded that the ‘O’ allele frequency of the ABO system of the Rajbanshis and previously (r) tends to remain higher than the other blood group mentioned populations using SPSS version 15.0. alleles (A and B) in the Indian populations. The Rajbanshis also have higher values of ‘r’ (0.594, Table 2) The KIR genotypic data of the Rajbanshi was extracted like that of other Indian populations and they have the from Guha et al., 2013 [17] and that of other comparator second highest frequency value next to the Koches populations were obtained from: South Indian Paravar (0.629) in case of ‘O’ alleles. and Kanikar populations [18], North Indian [19], Maharashtrian and Mumbai parsis [20], Han Chinese Table.1: Distribution of the ABO and Rh blood group and [21], Japanese [22], British [23], Northern Irish [24], Afro– their allele frequencies in the Rajbanshi populations. American [25], Afro-Caribbean [26], Chiriguanos and Blood Observed Phenotypic Allele Wichis [27]. Based on the linkage disequilibrium values, Group Number Frequency frequencies O 28 35.897 r[O]=0.594 two frequently occurring gene clusters were recognized A 18 23.077 P[A]=0.175 within the KIR gene complex [28]. The centromeric or B 25 32.051 q[B]=0.231 ‘C4’ cluster of the KIR gene complex consists of KIR2DS2- AB 07 8.974 2DL2-2DS3-2DL5 genes and is located at the centromeric Rh D +ve 77 98.717 0.886 Rh d 01 1.282 0.114 half, while the telomeric or ‘T4’ cluster located at the telomeric half of the KIR complex consists of KIR3DS1- 2DL5-2DS1- 2DS5 genes. The Bx genotypes were further subdivided into four subsets based on the arrangement of the C4 and T4 clusters: C4Tx (C4 present but T4 absent), CxT4 (c4 absent but T4 present), C4T4 (both C4 and T4 present), and CxTx (both C4 and T4 absent). The frequencies of these four subsets in the Rajbanshi
[19] Guha et al., Annals of Pharma Research, 2013, 01 (01), 18-24
populations of India. (b) Comparison of the frequencies of the ABO blood group alleles between the Rajbanshis and other Indian populations.
Figure.1: Phenotypic frequencies of the different ABO blood groups in the Rajbanshis.
Table.2: Comparison of the ABO blood group distribution in the Rajbanshis and other comparator populations of India. Figure.3: Principal Component Analyses (PCA) to Populati A B A O p q r compare the ABO blood group allele frequencies in the Rajbanson 23.0 32.0 8.9B 35.9 0.17 0.23 0.59 Rajbanshis with other comparator populations from Garhi 29.88 32.65 11.87 25.60 0.235 0.251 0.504 India. The Rajbanshis has been marked with ( ) in the Kunbo 27.06 33.04 8.31 31.09 0.196 0.235 0.567 score plot of the PCA. Viswakaris 26.52 23.76 11.83 37.74 0.218 0.195 0.597 OBma 23.67 36.88 6.89 32.676 0.163 0.255 0.582 RajasthC 25.6 40.1 8.5 25.6 0.207 0.281 0.502 Principal Component Analysis (PCA) based on ABO allele Nomaan 5 4 6 6 9 6 5 frequencies was performed to compare the Rajbanshi kocds 26.3 31.5 4.3 37.7 0.16 0.20 0.62 populations with some of the previously reported Maniph 35.2 18.58 11.58 34.72 0.269 0.161 0.579 Brahmimur 1 4 9 7 7 2 1 populations from different parts of the country. The PC Meltns 31.5 25.3 11.9 31. 0.24 0.20 0.54 plot for the first two components has been shown in Oraoei 28.8 36.6 12.6 22.1 1 0.247 0.297 0.466 Figure.3. The first component accounted for 74.5% Munn 32.9 35.5 10.5 21. 0.262 0.283 0.457 variability and the second accounted for 25.5% Kharida 3 33.4 9.4 20.9 0.305 0.275 0.420 Rabha 32.27 30.13 12.23 25.34 0.250 0.245 0.505 variability. It was observed in the score plot of the PCA a 3 6 6 4 5 1 3 that the Rajbanshis appeared in proximity to the Koches of Assam, North east India. In the plot the Rajbanshis shared the lower left quadrant with the Koches whereas other comparator populations are distributed in other quadrants throughout the graph. From the graph it was also evident that on one side the Rabhas and the Garos are in proximity while on the other the Oraons and the Mundas are closer to each other.
Figure.2: (a) Comparison of the blood group phenotypic frequencies of the Rajbanshis with that of other
Table.3: Measures of Nei’s genetic distances between the Rajbanshis and other neighbouring Indian populations. Rajbanshi Garo Rajasthani Koch Manipur Meltei Oraon Munda Kharia Rabha Rajbanshi .000 Garo .109 .000 Rajasthani .110 .041 .000 Koch .047 .149 .156 .000 Manipur .117 .117 .152 .120 .000 Meltei .089 .063 .097 .114 .055 .000 Oraon .156 .055 .051 .200 .169 .117 .000 Munda .178 .071 .078 .220 .172 .125 .030 .000 Kharia .214 .106 .122 .253 .187 .149 .074 .044 .000 Rabha .121 .024 .064 .158 .105 .056 .065 .070 .096 .000
[20] Guha et al., Annals of Pharma Research, 2013, 01 (01), 18-24
Table.4: Distribution of the four gene clusters of the Bx subsets in the Rajbanshis and other neighbouring Indian populations. Bx subsets Population C4Tx C4T4 CxT4 CxTx Rajbanshi 14.7 10.7 18.7 36 Paravar 19.5 28.6 31.2 16.9 Kanikar 20 34.2 25.7 16.8 North Indian 23.7 8.4 21 41.9 Maharashtrian 22.8 16.1 16.4 21 Parsis 17.8 29.5 16.5 11.2 Chinese Han 1.2 2.5 23.6 17.5 Japanese 2.9 1 21 16.2 Figure.4: Hierarchial Cluster Analysis (HCA) of the British 13.9 4.4 19.9 31.7 Rajbanshis and other indian populations based on ABO Northern Irish 16.3 8.3 21.2 18.1 Afro-American 20.7 3.4 6.9 39.6 allele frequencies Afro-Caribbean 19.6 0 9.9 34.8 Chiriguanos 0 1.9 50.3 16.9 Hierarchial Cluster Analysis (HCA) was also performed Wichis 0 1 51.6 17.1 based on the ABO allele frequencies and the resulting dendrogram (Figure 4) depicted similar result as that of PCA. The Rajbanshis formed a separate cluster with the Koches of Assam. The Oraon and the Mundas clustered with the Kharia while interestingly the Rajasthani nomadic tribe shared the same cluster with the Garos and the Rabhas. The Euclidean distances between the Rajbanshi and other comparator populations have been shown in Table 3 where it is clear that the Rajbanshis have the least distance with the Koches (0.047) followed by the Meltei group from Manipur (0.089). Interestingly the Rabha population which is generally considered to be genetically related to the Koches has greater distances from both the Koches and the Rajbanshis respectively. Figure.5: Comparison of the frequencies of the subsets of the Bx genotypes in the Rajbanshis with the other Indian KIR analyses: populations. All the four subsets of the Bx genotypes (CxT4, C4Tx, C4T4, and CxTx) were present in the Rajbanshi HLA Analyses: population (Table 4). The Rajbanshis have the lowest The frequencies of the HLA class II alleles in the frequency of the Bx genotypes with C4Tx configuration Rajbanshis were shown in Table 5, from the table it was (14.7%) compared to any other Asian Indian population. evident that in Rajbanshis within the DRB1 locus, In contrast the Rajbanshis were also second lowest to the DRB1*0701 have the highest frequency (13.90%) North Indians in the frequency of the C4T4 configuration followed by DRB1*1501 (13.70%) and DRB1*1201 of the Bx genotypes among the Indian populations. In the (10.20%) respectively. From the DQA1 locus, case of the CxTx subset, the North Indians have the DQA1*0201 (26.50%) was the most frequent followed by highest representation (41.9%) among the Indian the DQA1*0501 (14.20%) and DQA1*0102 (13.30%) populations followed by the Rajbanshis (36%). Overall it respectively. Similarly within the DQB1 locus DQB1*0501 was seen that the Rajbanshis along with the other Asian (29.08%) and DQB1*0301 (23.40%) are the most Indian populations have all the four subsets of the Bx KIR frequent alleles in the Rajbanshis. The genetic distance of genotypes, thereby presenting a balancing condition in the Rajbanshis (Table 6) when compared to other the Asian Indians (Figure 5). In total the Bx genotypic populations have shown that they have the least configuration of the Rajbanshis was more proximal to the distance with that of the Mech (0.048) followed by the Asian Indians compared to any other population clusters. Lachung (0.068) which are the two tribals of the Tibeto Moreover the Rajbanshis have higher frequencies of Bx Burman group, thereby showing the Tibeto-Burman genotypes (87%) compared to AA genotypes (13%) which connection of the Rajbanshis. is also a characteristic feature of the Indian populations [17].
[21] Guha et al., Annals of Pharma Research, 2013, 01 (01), 18-24
Table.5: Distribution of the HLA class-II allele frequencies in Rajbanshis. Allele % freq Allele % freq Allele % freq DRB1*0101 3.06 DQA1*0101 6.12 DQB1*0201 16.30 DRB1*0301 5.60 DQA1*0102 13.30 DQB1*0301 23.40 DRB1*0401 3.06 DQA1*0103 8.16 DQB1*0302 1.50 DRB1*0402 0.50 DQA1*0104 7.70 DQB1*0303 2.04 DRB1*0701 13.90 DQA1*0201 26.50 DQB1*0401 1.03 DRB1*080X 9.20 DQA1*0301 12.20 DQB1*0402 1.03 DRB1*090X 3.06 DQA1*0302 1.53 DQB1*0501 29.08 DRB1*1001 5.70 DQA1*0401 1.53 DQB1*0502 2.55 DRB1*1101 9.70 DQA1*0501 14.20 DQB1*0503 1.50 DRB1*1103 1.03 DQA1*0601 8.70 DQB1*0601 19.00 DRB1*1201 10.20 DQB1*0602 2.55 DRB1*1202 1.00 DRB1*1301 2.04 DRB1*1302 3.60 DRB1*1401 7.14 DRB1*1404 2.04 DRB1*1405 2.04 DRB1*1501 13.70 DRB1*1502 3.60
Table.6: Nei’s Genetic Distances in the Rajbanshis compared to other Indian populations based on HLA allele frequencies. Kayastha Rastogi Vaish Shia Sunni Lachung Mech Rajbanshi Kayastha 0 Rastogi 0.085 0 Vaish 0.051 0.017 0 Shia 0.148 0.119 0.118 0 Sunni 0.096 0.050 0.068 0.049 0 Lachung 0.131 0.144 0.153 0.212 0.158 0 Mech 0.128 0.144 0.157 0.199 0.160 0.029 0 Rajbanshi 0.128 0.096 0.132 0.169 0.109 0.068 0.048 0
DISCUSSION Many opinions have been expressed regarding the origin specific KIR genotypes that were found in the Tibetan and genetic background of the Rajbanshis. Historically population and has not been reported in any Indian the Rajbanshis are considered to be the descendants of population group. Thus it suggests that the Rajbanshis of the Koches who are mongoloid in their origin. Sir H. H. the Terai and Dooars region of North Bengal have Indian Risley commented that the Rajbanshis are of Dravidian specific genetic structure with mongoloid influence. On origin with considerable mongoloid admixture [30]. From comparing the HLA frequencies in the Rajbanshis with both the PCA and the HCA analyses, it was evident that that of other populations it was seen that the Rajbanshis the Rajbanshis have a tendency to cluster with that of have the least distance from two tribal populations of the Koches of Assam. The Koches significantly differed Tibeto-Burman inheritance i.e. the Mech and the from the Rabhas in their blood group distribution [12]. Lachung. However the HLA allele frequencies in the Similar observation was also made by Das et al., 1962 Rajbanshis suggest that although influenced by the [31] who opined that the Rajbanshis (Koch) of Assam mongoloid element, their genetic background were more similar to lower caste group of Assam, than experienced extensive admixture [29]. Our view is also the tribes like the Garo, Kachari and the Rabhas. Our supported by the Y haplogroup diversity of the study also simulated the similar result and grouped the Rajbanshis [32] which suggests that the O3 haplogroup Rajbanshis with the Koches but have significant which is absent in Indo-European speaking caste differences in their ABO allele frequencies from the populations from Eastern India is considerably present in Garos and the Rabhas. Thus the ABO blood group system the Rajbanshis and other Tibeto-Burman groups. in the Rajbanshis suggests their mongoloid connection. Moreover, this O3 haplogroup in the Tibeto Burman From the KIR genotypic analyses it was evident that the populations dated older than the arrival of the Indo- Rajbanshis have the KIR distribution similar to the Asian European language thereby suggesting earlier Tibeto- Indian populations and have a balancing condition of all Burman influence on the Rajbanshis. the clusters of the Bx genotypes. Moreover based on gene frequencies of all the KIR genes the Rajbanshis have Based on all these evidences from a number of genetic a tendency to huddle with the Asian Indian cluster [17]. markers, our study suggests that although the Indo- However it has also been mentioned by Guha et al., 2013 European speaking Rajbanshis from Terai and Dooars [17] that the Rajbanshis of North Bengal region have region of North Bengal have Indian origin, the Tibeto-
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Burman influence on their genetic background cannot be Mémoires de la Société d'anthropologie de Paris, Nouvelle ignored. However this population has extensive genetic Série, tome 2 fascicule 2, 1990, 203-212. diversity and considerable admixtures and therefore further studies based on other available genetic markers 13. Rai V, Kumar P. Genetic Analysis of ABO and Rh Blood Groups in Backward Caste Population of Uttar Pradesh, [33] are required for detailed exploration of the genetic IndiaNot Sci Biol, 2011, 3(3), 07-14. wealth of the Rajbanshis. 14. Sachdev B. Distribution of the ABO blood groups and Rh REFERENCES (d) factor among nomad tribal populations of Rajasthan, India. I.J.S.N., 2012, 3(1), 37-40. 1. Ratnagar S. Archaeological perspectives of early Indian societies. In: Thapar R (ed) Recent perspectives of early 15. DoreRaj ML, Reddy KR. The Study of ABO and Rh (D) Blood Indian history, 1995, Popular Prakashan, Mumbai, India, 1– Groups among the Vishwakarma Population of Mysore 52. District in Karnataka, India. Anthropologist, 2010, 12(3), 227-228. 2. Gayden T, Cadenas AM, Regueiro M, Singh NB, Zhivotovsky LA, Underhill PA. et al.,, The Himalayas as a directional 16. Warghat NE, Sharma NR, Baig MM. ABO and Rh Blood barrier to gene flow, Am. J. Hum. Genet., 2007, 80, 884– Group distribution among Kunbis (Maratha) population of 894. Amravati District, Maharashtra-India. Asiatic J. Biotech Res., 2011, 2 (04), 479-483. 3. Reddy BM, Langstieh BT, Kumar V, Nagaraja T, Reddy ANS et al., Austro-Asiatic tribes of Northeast India provide 17. Guha P, Bhattarcharjee S, Nayak CR, Chaudhuri TK. Study hitherto missing genetic link between South and Southeast of the KIR gene profiles and analysis of the phylogenetic Asia. PLoS One, 2007, 2, e1141. relationships of Rajbanshi population of West Bengal, India, Hum Immunol 2013, 74, 673-680. 4. Hunter WW. A Statistical Account of Bengal, 1974, Vol-X: District of Darjeeling, Jalpaiguri and State of Kuch Behar, D. 18. Rajalingam R, Du Z, Meenagh A et al., Distinct diversity of K. Publishing House, New Delhi. KIR genes in three southern Indian populations: comparison with world populations revealed a link 5. Bhasin MK, Walter H, Danker-Hopfe H. People of India: An between KIR gene content and pre-historic human Investigation of Biological Variability in Ecological, Ethno- migrations. Immunogenetics 2008, 60, 207–17. economic and Linguistic Groups, Kamal Raj Enterprises, Delhi, 1994. 19. Rajalingam R, Krausa P, Shilling HG et al., Distinctive KIR and HLA diversity in a panel of north Indian Hindu, 6. Garraty G, Dzik W, Issitt PD, Lubin DM, Reid ME, Zelinski T. Immunogenetics, 2002, 53, 1009–19. Terminology for blood group antigens and genes-historical origins and guideline in the new millennium. Transfusion 20. Kulkarni S, Single RM, Martin MP et al., Comparison of the 2000, 40, 477–89. rapidly evolving KIR locus in Parsis and Natives of India, Immunogenetics 2008, 60, 121–9. 7. Rahman M, Lodhi Y. Frequency of ABO and Rhesus blood groups in blood donors in Punjab. Pak J Med Sci 2004, 20, 21. Jiang K, Zhu FM, Lv QF, Yan LX. Distribution of killer cell 315–8. immunoglobulin-like receptor genes in the Chinese Han population. Tissue Antigens 2005, 65, 556-63. 8. Mourant AE, Kopec ADA, Domanieswska-Sobezek K. The ABO Blood Groups- Comprehensive Tables and Maps of 22. Yawata M, Yawata N, McQueen KL et al., Predominance of World Distribution, Blackwell Scientific Publication, group A KIR haplotypes in Japanese associated with Oxford, London, 1976. diverse NK cell repertoires of KIR expression, Immunogenetics, 2002, 54, 543–50. 9. Das BM. Micro Evolution, Concept Publishing House, New Delhi, 1981. 23. Norman PJ, Stephens HA, Verity DH, Chandanayingyong D, Vaughan RW. Distribution of natural killer cell 10. Balgir RS, Dash BP, Murmu B. Blood Groups, immunoglobulin-like receptor sequences in three ethnic Hemoglobinopathy and G-6-PD Deficiency Investigations groups. Immunogenetics, 2001, 52, 195–205. Among Fifteen Major Scheduled Tribes of Orissa, India, Anthropologist, 2004, 6(1), 69-75. 24. Middleton D, Meenagh A, Gourraud PA. KIR haplotype content at the allele level in 77 Northern Irish families, 11. Meitei SY, Asghar M, Achoubi ND, Murry B, Saraswathy Immunogenetics, 2007, 59, 145–58. KN, Sachdeva MP. Distribution of ABO and Rh(D) blood groups among four populations in Manipur, North East 25. Du Z, Gjertson DW, Reed EF, Rajalingam R. Receptor-ligand India. Anthropological notebooks, 2010, 16(2), 19–28. analyses defineminimal killer cell Ig-like receptor (KIR) in humans, Immunogenetics, 2007, 59, 1–15. 12. Sengupta S. Anthropological studies among the Koch population of Goalpara district, Assam. In: Bulletins et 26. Norman PJ, Carrington CV, Byng M et al., Natural killer cell immunoglobulin-like receptor (KIR) locus profiles in African
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[24] HUMAN IMMUNOLOGY doi: 10.1111/sji.12239 ...... Diversity of Killer Cell Immunoglobulin-Like Receptor Genes in the Bengali Population of Northern West Bengal, India
P. Guha*, S. Bhattacharjee† & T. K. Chaudhuri*
Abstract *Cellular Immunology Laboratory, Department The Indian Subcontinent exhibits extensive diversity in its culture, religion, of Zoology, University of North Bengal, Siliguri, ethnicity and linguistic heritage, which symbolizes extensive genetic variations † India; and Cell and Molecular Biology within the populations. The highly polymorphic Killer cell Immunoglobulin-like Laboratory, Department of Zoology, University of North Bengal, Siliguri, India Receptor (KIR) family plays an important role in tracing genetic differentiation in human population. In this study, we aimed to analyse the KIR gene polymorphism in the Bengali population of northern West Bengal, India. To Received 21 February 2014; Accepted in revised our knowledge, this is the first report on the KIR gene polymorphism in the form 6 August 2014 Bengalis of West Bengal, India. Herein, we have studied the distribution of 14 Correspondence to: T. K. Chaudhuri, Cellular KIR genes (KIR3DL1-3DL3, KIR2DL1-2DL5, KIR2DS1-2DS5 AND KIR3DS1) Immunology Laboratory, Department of Zoology, and two pseudogenes (KIR3DP1 and 2DP1) in the Bengalis. Apart from the University of North Bengal, Raja Rammohunpur, framework genes (KIR2DL4, 3DL2, 3DL3 and 3DP1), which are present in all the Siliguri, West Bengal 734013, India. E-mail: individuals, the gene frequencies of other KIR genes varied between 0.34 and [email protected] 0.88. Moreover, upon comparing the KIR polymorphism of the Bengalis with the available published data of other world populations, it has been found that the Indo-European-speaking Bengalis from the region share both Dravidian and Indo- Aryan gene pool with considerable influences of mongoloid and European descents. Furthermore, evidences from previously published data on human leucocyte antigen and Y-chromosome haplogroup diversity support the view. Our results will help to understand the genetic background of the Bengali population, in illustrating the population migration events in the eastern and north-eastern part of India, in explaining the extensive genetic admixture amongst the different linguistic groups of the region and also in KIR-related disease researches.
marker used in this study to execute the task is the Killer Introduction Cell Immunoglobulin-Like Receptors (KIR). Human immigrations over the years from different parts of Killer Cell Immunoglobulin-Like Receptors are the the world resulted not only in extensive cultural and members of a family of regulatory molecules present on the linguistic diversity of the Indian populations but also surface of the Natural Killer (NK) cells. Their direct diversified their genetic structure. Thus, India in the interaction with human leucocyte antigen (HLA) class I present day represents a hot spot of cultural and ethnic molecules lead to the inhibition or activation of cytotoxic heterogeneity [1]. In fact, many Indian communities are cell activities and cytokine production by the NK cells and the results of the blending of different ethnic populations. thereby plays significant role in modulation of immune Such a community is the Bengali community, the major responses [2]. KIR-HLA interaction(s) also play important linguistic group of West Bengal. The major religions of role in several disease progression including leucemia [3], the Bengalis in West Bengal are Hinduism and Islam with AIDS [4], infections of hepatitis C virus (HCV) [5], human considerable traces of Christianity and Buddhism. cytomegalovirus (CMV) [6], human papilloma virus (HPV) The Bengali people speak ‘Bangla’ dialect or the Bengali [7] and even in pregnancy [8] and alloreactive transplan- language. They are culturally very rich, which made them tions [9]. The KIR gene family consists of 16 homologous one of the unique population groups of India. Thus, there genes clustered together on the long arm of chromosome is an urgent need to unravel the genetic profile of this 19q13.4 within the 1 Mb leucocyte receptor complex heterogeneous population group of India and the DNA (LRC) [10, 11]. Among the family members, 14 genes code
Ó 2014 John Wiley & Sons Ltd 441 442 KIR Gene Profile of the Bengalis P. Guha et al...... for either activating or inhibitory receptors and the two others are pseudogenes that do not encode any receptor Sample Area proteins. The KIR family, besides showing sequence 1. Darjeeling polymorphism also exhibits variable gene contents among 2. Jalpaiguri 3. CoochBehar and between individuals of different ethnic groups. INDIA Accordingly, two basic groups of KIR haplotypes are present depending on gene content, namely A and B, both of which are found at different frequencies in most of the populations of the World. 1 2 In this study, we have investigated the diversity of the 3 16 KIR genes namely KIR3DL1-3, KIR2DL1-5, KIR2DS1-5, KIR3DS1, KIR3DP1 and KIR2DP1 using sequence-specific primer-polymerase chain reaction (SSP- PCR) method to delineate and analyse the KIR genotypic profiles within the Bengali population of northern West Bengal state of India. To our knowledge, this is the first study of KIR gene polymorphism in the ethnic Bengali community of West Bengal, India. WEST BENGAL Materials and methods Collections of samples and KIR genotyping. Blood samples were collected with prior informed consent from 162 healthy Bengali individuals randomly chosen from the Figure 1 Geographical map of India indicating approximate locations Terai and Dooars region of the northern part of West from where Bengali samples were collected. Bengal, India (85°500–89°500E and 21°380–27°100N) (Fig. 1) along the border line of Assam, Sikkim and the genotypes in populations to be compared to the Bengalis country of Nepal and Bhutan. Their health conditions were were extracted from the following publications and from medically examined. It was also ensured based on the the ‘http://www.allelefrequencies.net’ database [15] as questionnaire completed by the volunteers that related follows: South Indian Paravar, Kanikar and Mollukurumba individuals having common ancestry for earlier three [13], Finnish, French Caucasian, Senegal African, Guade- generations were excluded from the analyses. Only Indi- loupe Caribbean and Reunion, a population from Indian viduals having three generations of Bengali ancestry were Ocean origin [16], North Indian [14], Cook Island, selected for the study. The investigation was approved by Samoan, Tokelau, Tongan [17], Mestizo, Huichol, Purep- the Human Ethics Committee of the Department of echa, Tarahumara [18], Amazonian Amerindian [19], Zoology, University of North Bengal, India. Wichis and Chiriguanos [20], Northern Irish [21], Basque The collected blood samples were stored with EDTA population [22], Eastern Mainland Chinese [23], Chinese anticoagulant at À20 °C until use. Whole genomic DNA Han [24], Korean [25], Japanese [26], Warao, Bari, Yucpa was extracted from 500 ll of the frozen blood samples [27], Vietnamese and Australian Aborigine [28], American using the phenol–chloroform extraction procedure [12]. Caucasian, Hispanic, African American [29], Thai, British The purity and concentration of the extracted DNA were Caucasian, Palestinian [30], Australian Caucasian [31], measured by UV spectrophotometry, and ratios of absor- New York Caucasian [32], Greeks [33], Afro-Caribbean, bance at 260/280 nm were between 1.7 and 1.9. Trinidad Asian, Pakistani [34], Chinese, Malay and Indian Sequence-specific priming-based polymerase chain reac- in Singapore [35], Indian Parsis and Maharashtrian [36], tions (SSP-PCR) were used to amplify the genomic DNA Tibetans [37], Indian Rajbanshis [38] and Iranian Arabs for testing the presence or absence of the following genes: and Persians [39]. 2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, 2DS3, KIR genotyping and haplogroup analysis. The numbers 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1 and 3DP1 were assigned to the genotypes as provided in the Allele The primer sequences (Imperial Life Sciences, Gurgaon, Frequencies website (http://www.allelefrequencies.net). Haryana), were generously provided by Rajalingam [13, Group A and B haplotypes and frequencies were predicted 14]. Each set of PCR amplification included human growth based on a previous study [13] whereby individuals having hormone gene-specific primer pair as a positive control. In only KIR3DL3-2DL3-2DL1-2DP1-3DP1-2DL4-3DL1- case of false reactions (where no control bands are found) or 2DS4-3DL2, a gene content characteristic of group A in case of unique and unusual KIR genotypes, the reactions haplotypes, were considered to have two copies of a group were repeated. The frequency data of KIR genes and A KIR haplotypes (AA genotypes). Conversely, individuals
Scandinavian Journal of Immunology, 2014, 80, 441–451 P. Guha et al. KIR Gene Profile of the Bengalis 443 ...... lacking any of the four variable genes (KIR2DL1, 2DL3, Bengali individuals are shown in Table 1. It is evident that 3DL1 and 2DS4) were regarded as carriers of two copies of no significant differences are present between OF and KLF a group B KIR haplotypes (BB genotypes). The remainder of the 16 KIR genes in the studied population (data not of the individuals were regarded as heterozygous for A and shown) from the estimated v2 value. Apart from the B haplotypes and assigned as the AB genotypes. framework and the pseudogenes, the observed frequencies The KIR haplotypes were resolved from the genotypes. of the other functional KIR genes ranged between 0.34 While assigning genes to a specific haplotype the following (KIR2DS3) and 0.88 (KIR3DL1). The framework and the assumptions were made: (i) the framework genes namely pseudogenes have observed frequencies of 1.00 except KIR3DL3, 2DL4, 3DL2 and 3DP1 are present in all the KIR2DP1 (0.95). The observed frequencies of the A haplotypes; (ii) KIR3DL1 and 3DS1 are likely equivalent haplotype associated KIR genes are slightly higher than the to alleles and (iii) KIR2DL2 segregate as an allele of B haplotype associated KIR genes. Among the B haplotype KIR2DL3. associated KIR genes, KIR2DL5 and 2DS2 have slightly Statistical analysis. The observed phenotypic frequency higher frequencies. Similarly, KLF, in case of the Bengalis, (OF) for each KIR gene was calculated as the ratio of the ranged from 0.66 (KIR3DL1) to 0.19 (KIR2DS3), whereas number of individuals in the population carrying the gene the framework genes and pseudogenes have frequencies of to the total number of individuals in the sample popula- 1.00 except KIR2DP1 (0.78). v2 analysis was performed to tion. KIR locus frequencies (KLFs) were estimated using compare the differences in observed frequencies (OF) of the the formula: KLF = 1À√(1Àf), where f is the OF of a same set of KIR in our population study with that of particular KIR gene in a population. Differences of the available published reports on few other Asian populations, Bengali population KIR gene content with that of some which include China Eastern Mainland and Jiangsu other neighbouring populations were compared by the province, North Indians, South Indian Paravar, Kanikar 2 standard v test using KYPLOT 2.0 beta 15. The KIR and Mollukurumba populations, Maharashtrians and Parsis frequency data from the Bengalis and few other Asian from Mumbai (erstwhile Bombay) in India, Tibetans, populations were bootstrapped for 1000 replicates and then Singapore Indians and also from Pakistan (Table 2). The P the Nei’s genetic distances were calculated from the value <0.05 was the limit for statistical significance. The bootstrapped frequency data to construct the consensus results shown in Table 2 revealed no significant difference NJ tree using PHYLIP software version 3.69 [40].The in case of KIR3DL1 between Bengalis and other compar- principal components analysis (PCA) was computed using ative populations. In contrast, the Bengali population the MINITAB version 16.0 based on KIR gene frequencies of exhibited significant differences with the north-east Asians the Bengali population and other reference populations. (NEAs) in all the KIR genes except KIR2DS1, KIR3DL1 The frequencies of groups A and B haplotypes were and KIR3DS1. Significant differences exist in case of calculated using the following formula: group- KIR2DS2 between the Bengalis and the NEAs and A = 2nAA + nAB/2n and group-B = 2nBB + nAB/2n, Tibetans, but interestingly, the differences were found to where nAA, nAB and nBB are the numbers of AA, AB be insignificant when compared with other comparator and BB genotypes, and n is the total number of individuals. populations. The Bengalis appear to differ significantly Restricted maximum likelihood (REML) analysis was from the North Indians based on KIR2DL2 frequency and carried out based on the KIR genotypic frequencies and that from the Mollukurumbas and Singapore Indians when phylogenetic analyses were carried out using PHYLIP, version KIR2DL3 frequency is concerned. Another interesting 3.69 and the phylogenetic trees were developed using revelation was that the Bengalis showed no significant FIGTREE version 1.3.1 (http://tree.bio.ed.ac.uk). The geno- differences in the frequencies of the activating KIR genes type frequency data were bootstrapped for 100 replicates when compared to the North Indians, Mollukurumba, and then all the 100 data sets were used for REML analysis, Singapore Indians and the Maharastrian populations (see the output of which was then used to construct a consensus Table 2). tree, which was then developed for representation using In the neighbour-joining (NJ) tree (Fig. 2), it is quite FIGTREE software. The REML analyses assumed that each clear that the Bengalis formed a cluster with other reference locus evolved independently by pure genetic drift. The LD populations, which include South Indian Paravars and measures between the pairs of gene loci were computed Kanikar, Mumbai Parsis, Pakistani population and also the using the ARLEQUIN software version 3.5.1.2. North Indian population. Predictably, the Bengalis hud- dled with the Indian cluster. From the measures of Nei’s genetic distance (Table 3), it was evident that the Bengalis Results have the least distance from North Indians (0.012) followed by the Rajbanshis (0.014) and the Maharashtrians KIR gene frequencies (0.016), respectively, thereby revealing their proximity The observed phenotypic frequencies (OF) and the esti- with the populations of Indian origin. Principle component mated gene frequencies (KLF) for the 16 KIR genes in the analyses (PCA) was performed based on KIR gene
Ó 2014 John Wiley & Sons Ltd 444 ...... I eePol fteBengalis the of Profile Gene KIR
Table 1 Observed KIR frequencies and estimated KIR gene frequencies in the Bengali population.
Frequency 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DS5 2DL4 3DL2 3DL3 2DP1 3DP1
OF 0.8827 0.8210 0.6420 0.8457 0.5926 0.6605 0.4753 0.4815 0.6358 0.3457 0.4753 1.0000 1.0000 1.0000 0.9506 1.0000 KLF 0.66 0.58 0.40 0.61 0.36 0.42 0.28 0.28 0.40 0.19 0.28 1.00 1.00 1.00 0.78 1.00
OF, Observed KIR frequencies; KLF, KIR locus frequencies.
2 Table 2 Frequencies of the KIR genes in the Bengalis were compared with some of the Asian populations using v test (KYPLOT BETA 2.0) and their level of significance are shown with * (i.e. *P < 0.05, **P < 0.01, ***P < 0.001). Population references are provided in parenthesis in the second row below the population names.
Bengali Maharashtrian Kanikar Paravar Pakistan North Indians Molluk-urumba Singapore Indian Parsis Jiangsu cniainJunlo Immunology of Journal Scandinavian KIR (N = 162) (N = 139) (N = 35) (N = 77) (N = 78) (N = 72) (N = 41) (N = 80) (N = 145) Tibetan (N = 102) Chinese (N = 106) (N = 150) genes This study [36] [13] [13] [34] [14] [13] [35] [36] [37] [23] [15]
2DL1 82.10 97.8*** 97* 97** 90 87.5 95 81.3 99.3*** 100*** 99*** 100*** 2DL2 59.26 62 74 69 67 79.2** 59 68.8 62.8 30*** 25*** 21.3*** 2DL3 64.20 85.8*** 77 79* 91*** 65.3 85* 85** 81.9*** 98*** 99*** 100*** 2DL5 66.05 71.7 86* 83** 78 79.2 56 78.8 71 50* 45** 44.7***
2DS1 48.15 47.8 60 65* 60 54.2 41 53.8 62.8* 51 42 46.7 ...... 2DS2 63.58 62.3 74 75 69 62.5 59 60 62.8 29*** 27*** 24.7*** 2DS3 34.57 44.2 73*** 49* 45 43.1 20 40 53.8** 12*** 22* 21.3* 2DS4 84.57 90.7 80 84 72* 80.6 95 77.5 86.1 88 95* 92.7* 04 0 441–451 80, 2014, , 2DS5 47.53 46.4 60 66* 48 47.2 39 55 51 37 28** 30.7**
3DL1 88.27 91.4 80 84 81 87.5 95 91.3 87.4 88 94 93.3 Guha P. 3DS1 47.53 37 69* 60 56 38.9 37 50 62.2* 36 41 40
*P<0.05, ** P<0.01, ***P<0.001. tal. et P. Guha et al. KIR Gene Profile of the Bengalis 445 ......
Ben
Per 771.0 728.0 653.0 Kan
795.0 MuP
910.0 495.0 Pak
NIn 513.0 Mah 928.0 Raj 870.0 Mk 925.0 Tha 698.0 Vie
Tib
Chi
Jian
Figure 2 Neighbour-joining tree [constructed using PHYLIP package version 3.69 (39) software] showing the relationships between the Bengalis and other 12 other neighbouring Asian populations based on KIR gene carrier frequencies. (Chi, Eastern Mainland Chinese; Jian, Jiansu Chinese; NIn, North Indians; Tib, Tibetans; Kan, Kanikars; Par, Paravars; Pak, Pakistanis, Mah, Mumbai Maharashtrians; MuP, Mumbai Parsis; Ben, Bengalis; Mk, Mollukurumba; Vie, Vietnamese; Tha, Thais; Raj, Rajbanhi).The KIR gene carrier frequencies were bootstrapped for 1000 replicates and then Nei’s genetic distance was calculated to construct a consensus neighbour-joining tree. The Bengali population has been marked in bold. In a Consensus tree the numbers on the branches indicate the number of times the partition of the species into the two sets, which are separated by that branch occurred among the trees, of 1000 trees. frequencies to investigate the affinity of the Bengalis with KIR variations in the studied population (Fig. 4). Among that of other comparator world populations (Fig. 3). 162 individuals, 77 have AB genotypes (45.06%) followed Carrier frequencies of nine variable KIR genes of the by 68 BB genotypes (41.98%) and 21 AA genotypes Bengalis and 50 other previously studied world population (12.96%). This proportion clearly indicates the predomi- were used for the analysis. The other seven genes (2DL5, nance of the BB genotypes over the AA genotypes. In case 2DS5, 2DL4, 3DL2, 3DL3, 2DP1 and 3DP1) were either of AA genotype, it was found that 19 of 21 individuals invariably present in all the populations or not typed in have genotype ID 1 (11.73%), which is otherwise dom- some of the populations considered for the analyses and inantly represented in the north-east Asians and the therefore were excluded from the analysis. The PC score Europeans. Genotype ID 71 (6.17%) was the most frequent plot for the first two components is shown in Fig. 3 among the BB genotypes, which has also been reported in wherein the first and the second component accounted for 144 individuals from 54 populations around the world 57.1% and 18.1% variability, respectively. From the PC (http://www.allelefrequencies.net) that included the Hong- plot, it is evident that except few outlier populations, the kong Chinese, South Korean, North Indian and Mumbai distributions of the KIR genes in different populations are Parsis and also in South Indian Paravars and Kanikars. In in accordance with their geographical proximities. In the contrast, the genotype ID 2 followed by IDs 4 and 10 were score plot, eight distinct geographical population clusters the most frequent ones to occur among the Bengali AB were mentioned with different symbols namely Africans, genotypes, which have also been observed in north-east north-east Asians, North American natives, South Amer- Asians, Tibetans and the Asian Indian populations (AIs). ican natives, Asian Indians, Europeans, South-East Asians The group B KIR haplotypes (64.51%) occurred more and Pacific Islanders. The Pacific Island cluster coalesced frequently than the group A KIR haplotypes (35.49%), with the European cluster. Expectedly, the Bengalis which is a characteristic feature of other native populations clustered with the Asian Indians and was placed in of Indian subcontinent. The genotypic frequencies were between the North Indians and the Singapore Indians in tested for Hardy–Weinberg equilibrium [41], and it was the score plot. This result is in accordance to their observed that the measured genotype frequencies were not geographical location and anthropological background, significantly different from the expectations of Hardy– which suggest influence of the North Indians, South Weinberg equilibrium. (Dravidian) Indians, the Europeans and the north-east Furthermore, REML estimation was performed based on Asians on the Bengali population. the KIR genotypic frequencies to investigate the relation- ships of the Bengalis with populations having previously published data in a broader way (Fig. 5). The Bengalis KIR genotypes clustered with the population of Indian origin namely Based on the presence and absence of 16 KIR genes, a total Trinidad Asian, North Indians, Paravar and the Kanikars; of 43 different genotypes have been observed in the the clade supported by 95% bootstrap value. The Europeans, Bengalis (N = 162), which undoubtedly suggests extensive the Africans, the north-east Asians and the American natives
Ó 2014 John Wiley & Sons Ltd 446 KIR Gene Profile of the Bengalis P. Guha et al......
formed related separate clusters. This scenario is also evident from the NJ tree based on KIR gene frequencies (see Fig. 2).
Linkage disequilibrium between the KIR loci The classical linkage disequilibrium coefficient (D) and respective P values between pairs of KIR gene loci have been presented in Table 4. The standardized coefficient (D’), the conventional measure of linkage disequilibrium (r2) and haplotype frequencies have been presented in the Table S1. The P value <0.05 was considered to be statistically significant. The LD table revealed 37 signif- icant associations between the KIR genes in the population h, Mumbai Maharashtrians; NIn, North Indians; MuP, studied. If a significantly positive P value is observed between a pair of KIR loci, then they are likely to be separate genes, whereas a negative P value may indicate an allelic relationship between these two KIR genes loci. The framework and the pseudogenes were excluded from this analysis. From the data presented in Table 4, it is evident that the B haplotype genes were found to be in positive LD. Additionally, positive LD values were also observed between the A haplotype genes namely KIR3DL1, 2DL1, 2DL3 and 2DS4. Characteristic negative LD values were evident between A and B haplotype genes except between KIR2DL1 and KIR2DL5.
Discussion Population admixture and genetic heterogeneity among the Indian populations may be the result of extensive human immigrations and invasions, which have occurred through ages owing to the geographical position of the country. The Bengali population is probably one of the most suitable examples of population admixture in India due to human migration. They not only constitute the major populating community of West Bengal region of eastern part of India but are also dispersedly distributed all over the country. A large number of KIR genotypes are seen in the sample population, suggesting high KIR diversity in the Bengalis. The predominance of B haplotypes on A haplotypes in the Bengali population, which is also a characteristic feature of the Indian populations, suggests a subcontinental type of KIR distribution in the Bengalis. Both the NJ tree (Fig. 2) and the PCA (Fig. 3) that were performed based on the
Chi Jian Tib Mah NIn MuP Kanobserved Ben frequencies Raj Par of the Pak KIR genes Viet reflect Thai similar Mk results and placed the Bengali population in the Indian cluster, suggesting its strong connection with the other popula- tions of Indian origin. The genetic distance between the Bengalis and the North Indians was least suggesting proximity of the Bengalis to the Aryan lineage. This view Nei’s genetic distances in the Bengalis and other neighbouring population. (Chi, Eastern Mainland Chinese; Jian, Jiansu Chinese; Tib, Tibetans; Ma Population ref. no. is also supported from Y-chromosome haplogroup diversity study by Debnath et al. [42] where it was shown that the west Table 3 Mumbai Parsis; Kan, Kanikars; Ben, Bengalis; Raj, Rajbanhi; Par, Paravars; Pak, Pakistanis; Viet, Vietnamese; Thai, Thais;Chi Mk, Mollukurumba). Jian [23]Tib [15]Mah [37]NIn [36]MuP [14]Kan [36]Ben 0 [13]Raj 0.000745 This studyPar 0.007027 0 [38]Pak 0.053078 0.005628 [13]Viet 0.104886 0.060330 [34] 0 0.081418Thai 0.113942 [28] 0.056307 0.072404Mk 0.087228 0.157129 [30] 0.098210 0 0.080645 0.085069 0.166424 [13] 0.016477 0.071720 0.033915 0.017098 0.163989 0 0.016029 0.120268 0.038935 0.025286 0.044876Eurasian 0.089206 0.011635 0.126953 0.029011 0 0.040274 0.007517 0.094860 0.024900 0.114242 0.010002 0.009769 0.013636 0.010788 0.080993 0.050876 0.030912 0.027234 0.013258or 0 0.037569 0.013413 0.019076 0 0.029115Indian 0.028927 0.011822 0.045100 0.026206 0.023656 0.012002 0.068973 0.021915 0.037602 0.069078 0.010078 0.015972 0.014128 0.058069specific 0.011210 0.051684 0.023288 0.032313 0 0.044501 0.030085 0.106435 0.025316 0.039169 0.100624 0.040892 0.045904haplogroups 0.021180 0 0.036785 0.088786 0.014043 0.011763 0.009067 0.017773 0.078971 0 0.070934 0.007488 0.052691 0.043344 0.056100 0 such 0.002525 0.038255 0 0.021278as H, 0.013050 J 0 and R are
Scandinavian Journal of Immunology, 2014, 80, 441–451 P. Guha et al. KIR Gene Profile of the Bengalis 447 ......
3 Sen Afm Africans Afc Asian indians GuC 2 NIn Europeans Ben Arb Mh Pal North east asians 1 Mk AmC ReU Kan NIr Fre SpI Pacific islanders Ton Tri SpM Tok Gre Par Sam Bri CkL Raj MuP His South american natives 0 Pak Vie AuA SpC Tha South east asians Jap Hkc Fin Zhe Bas Chi Nort american natives –1 Kor Jia Tib Others
Second component Second Chg Hui War Yuc
–2 Pur Bar Tar AmA –3 –5.0 –2.5 0.0 2.5 5.0 7.5 First component
Figure 3 Principle component analysis (PCA) of carrier frequency of nine variable KIR genes (2DL1-3, 2DS1-4, 3DL1 and 3DS1) in the Bengalis and other world populations developed by MINITAB v16. The PCA graph shows a global relationship between Bengalis and other previously reported world populations. (Jap, Japanese; Chi, Eastern Mainland Chinese; Zhe, Zhejiang Chinese; Kor, Korean; SpC, Singapore Chinese; Jian, Jiansu Chinese; Hkc; Hongkong Chinese; Hui, Huichol; Pur, Purepecha; Tar, Tarahumara; Tib, Tibetans; Viet, Vietnamese; Thai, Thailand; Fin, Finnish; Chg, Chiriguanos; War, Warao; Bar, Bari; AmA, Amazonian Amerindian; Yuc, Yucpa; ReU, Reunion; Gre, Greek; CkI, Cook Island; Sam, Samoan; Tok, Tokelau; Ton, Tongan; Mez, Mestizo; Bri, British Caucasian; NIr, Northern Irish; Fre, French Caucasian; AmC, American Caucasian; SpM, Singapore Malay; His, Hispanic; MK, Mollukurumba; Sen, Senegal African; GuC, Guadeloupe Caribbean; AfA, African American; AfC, Afro-Caribbean; Arb, Arabian; Pal, Palestinian; Ben, Bengalis; Mah, Maharashtrian; Tri, Trinidad Asian; Pak, Pakistani; NIn, North Indian; Par, Paravar; Kan, Kanikar; MuP, Indian Mumbai Parsi; Bas, Basque population; SpI, Singapore Indians and AuA, Australian Aborigine). The Bengali population has been marked in bold (●).
GENOTYPE ID 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DS5 2DL4 3DL2 3DL3 2DP1 3DP1 No. FREQUENCY AA 1 19 11.73 AA 180 1 0.62 AA 195 1 0.62 AB 2 10 6.17 AB 3 6 3.70 AB 4 7 4.32 AB 5 2 1.23 AB 6 6 3.70 AB 8 1 0.62 AB 9 2 1.23 AB 10 7 4.32 AB 11 2 1.23 AB 12 3 1.85 AB 13 4 2.47 AB 15 1 0.62 AB 20 6 3.70 AB 23 3 1.85 AB 32 2 1.23 AB 33 8 4.94 AB 36 1 0.62 AB 191 1 0.62 AB 260 1 0.62 BB 68 3 1.85 BB 69 3 1.85 BB 70 4 2.47 BB 71 10 6.17 BB 72 3 1.85 BB 73 6 3.70 BB 74 3 1.85 BB 78 3 1.85 BB 79 1 0.62 BB 81 2 1.23 BB 82 8 6.06 BB 101 4 2.47 BB 104 4 2.47 BB 133 2 1.23 BB 171 2 1.23 BB 172 2 1.23 BB 176 3 1.85 BB 208 1 0.62 BB 330 1 0.62 BB 341 1 0.62 BB 342 2 1.23 143 133 104 137 96 107 77 78 103 56 77 162 162 162154 162 162 100.00
Figure 4 KIR genotypic profiles observed and the number of individuals displaying each profile. The list presents the percentage frequency of each genotype. Black boxes (■) indicate presence and white boxes (□), absence of KIR genes. Genotype IDs are considered according to www.allelefrequencies.net.
Ó 2014 John Wiley & Sons Ltd 448 KIR Gene Profile of the Bengalis P. Guha et al......
Tribidad-Asian
Afro-Caribbean
Afro-American
North-Indian Guandelope Senegal Bengali Kanikar Paravar
85 76 88 95 Tibetan Cookisland Japanese Korean 84 Chinese Tonga 82
92 Finnish French 96 84 American 76 84 87 76 Amerindian Northern Irish Yucpa ChiriguanWichi 78 Mollukurumba 77 Bari Warao
Tokelau Basque Samoa Hispanic Figure 5 Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles
in the Bengali and other previously studied Huichol Tarahumar Purepecha World populations using Phylip package. Clusters relating to the Asian Indians, north- east Asians, Africans, Europeans, Mexicans and American natives can be distinguished 1.0 separately.
Table 4 Linkage disequilibrium (LD) (∆, P) parameters for pairs of KIR genes in the Bengali population..
Locus Parameter 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3
2DL1 ∆ 0.02 P 0.02 2DL3 ∆ 0.01 0.11 P 0.26 0.00 2DS4 ∆ 0.10 0.02 0.02 P 0.00 0.05 0.07 2DL2 ∆ À0.03 À0.07 À0.12 À0.03 P 0.02 0.00 0.00 0.02 2DL5 ∆ À0.04 0.00 À0.03 À0.05 0.07 P 0.00 0.95 0.10 0.00 0.00 3DS1 ∆ À0.06 À0.03 À0.00 À0.06 0.03 0.16 P 0.00 0.03 0.89 0.00 0.09 0.00 2DS1 ∆ À0.06 À0.04 À0.00 À0.06 0.03 0.15 0.22 P 0.00 0.01 0.98 0.00 0.13 0.00 0.00 2DS2 ∆ À0.02 À0.07 À0.11 À0.03 0.17 0.05 0.03 0.03 P 0.05 0.00 0.00 0.06 0.00 0.01 0.10 0.15 2DS3 ∆ À0.02 À0.02 À0.09 À0.03 0.12 0.11 0.06 0.04 0.11 P 0.08 0.20 0.00 0.01 0.00 0.00 0.00 0.02 0.00 2DS5 ∆ À0.06 À0.04 À0.00 À0.05 0.06 0.13 0.16 0.17 0.02 0.02 P 0.00 0.01 0.89 0.00 0.00 0.00 0.00 0.00 0.32 0.26
P<0.05 are marked in bold.
Scandinavian Journal of Immunology, 2014, 80, 441–451 P. Guha et al. KIR Gene Profile of the Bengalis 449 ...... present in the Indo-European speaking Bengali population hand, genotype IDs 195 and 341 that are present in the and more specifically, an Indian specific upper caste Y- Bengalis are predominantly found in the Oriental popula- haplogroup marker R1a1*-M17 has much higher incidence tions including the north-east Asians, thereby possibly (40.7%) in the Bengalis, thus confirming the association of suggesting mongoloid influences. The REML analysis the Bengalis with the other ethnic Indian populations [42]. (Fig. 5) based on overall KIR genotypic profiles of the Moreover, a previous study on the Bengali population of Bengalis have produced a similar clustering pattern as found Siliguri by Singh et al. [43] based on HLA markers revealed in PCA analysis (Fig. 3), showing that the Bengalis are similar details, whereby it was mentioned that the frequen- clustered with the Indian populations a fact also evident cies of HLA-A*02 and A*11 are high in the Bengalis as well from NJ tree (Fig. 2). Thus, on the basis of KIR genotypic as in the Sikhs, Gujratis Maharashtrians and some caste profiling, it can be said that the Bengalis of northern part of groups of western and northern India. Singh et al. also West Bengal, India although share the same lineage with the documented high frequency of A*24 in the Bengalis, which other Indian populations, significant European and mon- was also prominently observed in the South Indian, North goloid influences cannot be denied. Indian and the Bangladeshi Indian populations. Among The linguistic background of the Bengalis provides a HLA-B alleles, Singh et al. observed that the B*07 and B*08 strong evidence in favour of such an extensive admixture alleles that are found in the Bengalis are also found in the and genetic heterogeneity among the Bengalis where from Sikhs, while the B*07 is highly frequent allele in the North it can be observed that the Bengali community has a strong Indians. Moreover, HLA-B*37 that was reported in the linguistic connection with the Aryans, although their Bengalis was also found in a South Indian population [43]. language had been considerably invaded by other dialects. When Singh et al. [43] compared the frequencies of the HLA In the subcontinental history, the most important markers of the Bengalis with the world populations, invasion was by the west Asian semi-nomadic tribes around interesting observations that are relevant in this discussion 2000 BC that migrated east towards Persia and over the are that HLA-A*02 and A*11 that were reported in the Hindukush mountains into the Indian subcontinent where Bengalis were also present in higher frequency in the Greeks, they began conquering the regions in North India by and additionally, HLA-B*07, which was present in the subjugating the dark skinned Dravidian inhabitants of the Bengali population, was also present at highest frequency in Indus Valley civilization [44] and they later came to be the Western European populations [43]. These genetic known as the Aryans. Moreover, repeated invasions over the evidences in one hand suggest Dravidian and Aryan centuries by the Persian, Greeks, Turks, Arabs and Mongols association of the Bengalis and on the other hand also have added to the genetic and cultural diversity of the Indo- suggest possible European connection. Our findings based on Pak subcontinent and have a large amount of genetic KIR genotypes largely approximated these previous obser- admixture resulting in certain level of shared ancestry [45]. vations based on HLA and Y-haplogroup markers further Furthermore, Calcutta (now known as Kolkata), the confirming that the Bengalis are a heterogeneous group present metropolitan city of West Bengal, was the capital having genetic influences from other groups such as of India during the British colonial period and also was the Dravidians, North Indians along with some putative centre of all foreign trades. This fact may justify to some European admixture. extent, the presence of European and Middle East specific Most of the genotypes that are documented in the Bengali KIR genotypes in the Bengalis of West Bengal. population are also found in other previously reported Indian Moreover, West Bengal is located at the neck of the populations. In the present study, it has been found that the north-eastern region of India, which is a well-known KIR AA genotype ID 1 is most frequent in the Bengalis, corridor for Tibeto-Burman migration. Debnath and which is a characteristic of the north-east Asian and coworkers [41] suggested that the settlement of the European populations that suggests admixture in the Tibeto-Burman (TB) speaking groups in the sub-Himala- Bengalis. After genotype AA1, the most frequent genotypes yan regions of India occurred much before the arrival of the are IDs 2 and 71, both of which are present in high frequency Indo-Europeans. These facts and findings may support the within Indian population as well as in the Europeans and presence of East Asian specific KIR genotypes in the East/north-east Asian populations. Genotype IDs 6 and 69 Bengali population as reported in this study. Furthermore, are found in the South Indian Dravidian populations but not Guha et al. [38] concluded the presence of mongoloid in the North Indian populations, thereby showing putative component in the Rajbanshi population, which is one of Dravidian influences in the Bengali gene pool. Genotype IDs the most dominant ethnic caste populations of West such as 23, 208 and 260 that are found to be present in the Bengal. It may be possible that frequent marital relations Bengalis are not found in other previously studied Indian between the Bengalis and the Rajbanshis during their early populations but are present in many other Middle East and settlements in the region may have contributed mongoloid European populations. Such genotypes may be present as a elements into the genetic wealth of the Bengalis. result of admixture of Indian populations with that of In summary, our study provides estimation of the KIR populations from Middle East and Caucasians. On the other gene frequencies in the Bengali population, which is suitable
Ó 2014 John Wiley & Sons Ltd 450 KIR Gene Profile of the Bengalis P. Guha et al...... for immunological and disease researches and also for developing the severe form of recurrent respiratory papillomatosis. anthropological studies. Based on KIR genotypic profiles, Hum Immunol 2010;71:212–9. our study suggests that the Indo-European speaking 8 Ponte M, Cantoni C, Biassoni R et al. Inhibitory receptors sensing HLA-G1 molecules in pregnancy: decidua associated natural killer Bengalis from northern part of West Bengal, India share cells express LIR-1 and CD94/NKG2A and acquire p49, an HLA-G1- both Dravidian and Indo-Aryan gene pool with some specific receptor. Proc Natl Acad Sci U S A 1999;96:5674–9. possible Mongoloid and European influences on their KIR 9 Farag SS, Fehniger TA, Ruggeri L, Velardi A, Caligiuri MA. 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Ó 2014 John Wiley & Sons Ltd Guha, et al. Immunome Res 2014, 10:2 Immunome Research http://dx.doi.org/10.4172/1745-7580.1000081
ResearchResearch Article Article OpenOpen Access Access Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration Pokhraj Guha1, Subhashis Paul2, Avishek Das1, Biswajit Halder3, Soumen Bhattacharjee2 and Tapas Kumar Chaudhuri1* 1Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India 2Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India 3Department of Pathology, North Bengal Medical College and Hospital, Shusrutnagar, Siliguri, West Bengal, India
Abstract Rheumatoid Arthritis (RA) is a chronic inflammatory condition affecting the joints causing swelling, stiffness and pain which finally leads to substantial loss of functioning and mobility in its advanced stages. In the present study we have monitored important serological parameters of fifty RA patients and also have discussed the justification of using rat as a model for human RA researches by comparing their respective serological parameters. We have also evaluated the anti-arthritic roles of raw Aloe vera gel and its effects in rat model where arthritis was induced by using Freund’s Complete Adjuvant (FCA). Three essential conclusive statements were derived from the study. Firstly, the six clinical parameters that we have selected for the study namely, RA factor, CRP, ASO, ESR, ceruloplasmin and serum creatinine were all essential for the differential diagnosis of Rheumatoid Arthritis during its early and later stages, RA factor being the most sensitive of all parameters (92% sensitivity). Secondly, this study has supported the use of the rat as a model for designing therapeutic strategies against RA. Lastly, as evident from our study, Aloe vera extracts can be beneficial for the reduction of inflammatory edema and also for the reduction of ceruloplasmin in RA condition in rat model. However, further investigations are necessary for more refined therapeutic usage ofAloe vera for the treatment of RA in human.
Keywords: Rheumatoid arthritis; RA factor; North Bengal; Aloe vera In the present study we have estimated the levels of certain clinical parameters in Rheumatoid arthritic patients of northern West North Introduction Bengal, India compared to the control subjects and also have discussed Rheumatoid arthritis (RA) is a chronic inflammatory condition the possibility of using Aloe vera crude gel in the treatment of RA affecting the bone joints causing swelling, stiffness and pain that by evaluating the effects of crude leaf gel in experimental arthritic gradually leads to the substantial loss of functioning and mobility in conditions in rat groups induced by Freund’s complete adjuvant. the advanced stages [1]. Effects of RA are not only limited to extreme Materials and Methods physical distress but also cause mental distress. As the etiology behind RA prognosis is not well understood confirmed curative measures are This section has been compartmentalized into separate categories not discovered till date. All treatment regimes and therapies presently for better understanding as follows: applied, such as cytokine therapy (anti-TNFα therapy) or Disease Human based studies Modifying Anti-Rheumatic Drugs (DMARDs) are only limited to the reduction of symptoms of the disease and delay of pathogenesis [2]. Sample collection: Blood samples of 50 RA patients and 50 controls Thus RA has become an undeniable threat to human life and therefore subjects were collected from an authorized diagnostic laboratory of preventive measures must be developed to cure this disorder. Siliguri and also from North Bengal Medical College and Hospital (NBMCH, Sushrutnagar, West Bengal, India) under the guidance of a In the recent times, a large body of research has been directed medical practitioner. Both patients and control subjects have provided towards finding herbal solutions to the treatment of the diseases. In their written consent after knowing the purpose of the study. The Indian Ayurveda, one such promising herbal candidate having anti- patients were diagnosed on the basis of physical examinations, clinical arthritic effect isAloe vera (Family Xanthorrhoeaceae), a perennial symptoms,, disease progression studies and were confirmed of having succulent xerophytic plant. In this plant water is held in the form of RA based on the reports of Anti-CCP assays. viscous mucilage within the thin walled parenchymatous cells in the innermost part of the leaves. Aloe vera has been used for many centuries Estimation of RA clinical parameters: Each blood sample was for its curative and therapeutic properties [3]. It has been traditionally used in various skin ailments [4] and has well known wound healing and *Corresponding author: Tapas Kumar Chaudhuri, Professor, Cellular anti-inflammatory activities [5,6]. Aloe vera gel has been traditionally Immunology Laboratory, Department of Zoology, University of North Bengal, Raja consumed or applied dermally to reduce joint pains. Phytochemical Rammohunpur, Siliguri, West Bengal, India, Tel: +91-9434377127; Fax: +91-353- screening of Aloe vera confirms the presence of flavonoids, alkaloids, 2699001; E-mail: [email protected] resins, tannins, steroids and other chemical substances [7]. The anti- Received September 26, 2014; Accepted November 20, 2014; Published inflammatory activity of the crude unprocessed gel has also been November 22, 2014 documented [8]. It has been documented that Aloe vera contains Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) anthroquinone that may play a key role in anti-arthritic activity [9]. Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Many of the medicinal effects of Aloe leaf extracts have been attributed Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res to the polysaccharides found in the inner leaf parenchymatous tissue 10: 081. doi: 10.4172/17457580.1000081 [5,6], but it is believed that these biological activities should be assigned Copyright: © 2014 Guha P, et al. This is an open-access article distributed under to a synergistic action of the compounds contained therein rather than the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and a single chemical substance [7]. source are credited.
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
Page 2 of 7 divided into two parts, the anti-coagulated (EDTA added) part for ESR booster dose of 0.1 ml was given on the 15th day of the experiments. The estimation and the clotted part for serum extraction for the estimation animals of three other groups were also induced with FCA following of RA factor, CRP assay, ASO titre estimation, Ceruloplasmin and the methods as mentioned above. These three groups were designated creatinine assays. as the Experimental Groups (EGs) and were fed raw Aloe vera gel as mentioned below. Erythrocyte Sedimentation Rate (ESR) of each blood sample was immediately estimated by Westergren method by measuring the rate of Wild Aloe vera plants collected from the sub-Himalayan Terai gravitational settling of anti-coagulated erythrocytes in 1 hour from a regions of Northern West Bengal were used for the experiments. fixed point in an upright calibrated tube of predefined dimensions [10]. The plants were identified by the Department of Botany, University Its normal upper limit for males is 15 mm/hr, and for females is 20 mm/ of North Bengal [accession no. 09884 (NBU)]. They belong to class hr [11]. ESR is an indirect measure of the acute phase reaction, it being Magnoliopsida under order Asparagales and family Xanthorrhoeaceae. a simple and inexpensive laboratory test for assessing inflammation. The crude gel was obtained by peeling out the outer cuticle layer and cutting the gel aseptically into small pieces. The sample homogenate Rheumatoid (RA) factor, C-Reactive Protein (CRP) and Anti- was freshly prepared with distilled water (1:5 w/v) every time before streptolysin O (ASO) were estimated by quantitative turbidometric use. Each piece of gel was weighed and then dried separately in an air assay and their normal range reference values in the serum were oven at 37°C for 48 hours to know the dry weight of the gel doses. considered to be up to 20 IU/ml, 6 mg/dl, and 200 IU/ml respectively. RA factor is a very potent marker of RA as majority of the patients have Three experimental groups (EGs) were treated with different doses this abnormal antibody in their serum at a range higher than normal. of Aloe vera doses viz., 125 µl (EG-125), 250 µl (EG-250) and 500 µl (EG-500) respectively corresponding to 25 gm wet gel/kg body weight Furthermore, we analyzed ceruloplasmin and creatinine (20 mg dry weight/kg body weight), 50 gm wet gel/kg body weight (40 concentration in the serum samples of the RA patients and the control mg dry weight/kg body weight), and 100 gm wet gel/kg body weight subjects. Ceruloplasmin estimation was done spectrophotometrically (80 mg dry weight/kg body weight). The above mentioned doses were by using p-phenylenediamine oxidase activity [12]. The levels of selected on the basis of the quantity of Aloe vera gel that should be serum creatinine were measured spectrophotometrically by studying taken by a person per day for therapeutic use. However, the absorption reactions between creatinine and alkaline picrate [13]. rate may be different in case of rat and human systems. Rat model based study The sixth group was considered as the Protective group (PG) where Experimental setup: Swiss Albino male rats weighing 60 ± 10 animals were fed with 250 μl Aloe vera (50 gm wet gel/kg body weight gm each were used for all the experiments under this section and or 40 mg dry weight/kg body weight) 7 days prior to the FCA injection were procured from an authorized animal dealer (Ghosh Enterprise, (arthritis induction). Kolkata, India). Animals were maintained under standard laboratory Parametric studies: Two rats from each of the six groups were conditions. The study was approved by the Institutional Animal Ethical sacrificed on 21st day of the experiment and the rest were sacrificed Committee (IAEC) of CPCSEA (Committee for the Purpose of Control on the 28th day to know the levels of modulatory activities of crude and Supervision of Experiments on Animals) of the University of Aloe vera after the booster doses of FCA. The left hind paws of all the North Bengal, West Bengal, India. six rat groups were amputated from the body after their sacrifice and The animals were divided into 6 groups of 4 male rats in each. The were subjected to radiological analysis. The measurements of paw first group was considered as Non-treated or control group (NT) as circumference was done at regular intervals of 2 or 3 days with the help arthritis was not induced in the rats of this group. The second group of a vernier caliper following methods of Paquet et al. and Rathore et was considered as Arthritic group or Negative Control (AG) as arthritis al. [15,16]. Body weight was also recorded at regular intervals. Blood was induced in the members of this group as per the method proposed samples were collected using insulin syringes from the tail vein for by Bendele et al. [14]. All the animals of AG were administered a dose of serum isolation for biochemical tests. Serum ceruloplasmin and 0.1 ml of Freund’s Complete Adjuvant (FCA) in the left hind paw and a creatinine estimation were done by applying the procedures followed
Figure 1: (a) Box plot analyses show the differences in the distribution of the different clinical parameters in the Rheumatoid Arthritic patients and the control samples. (b) Pyramid charts compared the distribution of the levels of different clinical parameters in the RA patients and the control samples.
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
Page 3 of 7 in case of the human samples for reducing complications during further analyses. Statistical analysis All the statistical calculations were performed using the softwares MS-Excel and Kyplot ver. 2.0 β and SPSS ver. 16.0. In the Kyplot analysis the data represented mean ± SD which was analyzed by T-test for finding the significance. The results were considered significant when p>0.05. Box plots and the frequency plots were constructed using SPSS ver. 16.0. Results Out of 50 selected anti-CCP positive RA patients 92 % were found to be positive for the Rheumatoid (RA) factor test having RA factor values above the normal range of 0-20 IU/ml. The RA factor values fluctuated within a very wide range of 11-380 IU/ml having mean ± SD value of 135.9 ± 91.4 IU/ml (Table 1). In contrast, 88% of the control Figure 2: Scatterplot analyses to show RA factor vs Ceruloplasmin and RA factor patients (anti-CCP negative) were found to be RA factor negative with a vs. creatinine levels respectively in RA patients. mean value of 14.0 ± 5.5 IU/ml. The calculated t value of patients versus control for the RA factor was found to be 9.45 (p ≤ 0.001) indicating significant differences in the distribution of the RA factors in the RA patients and control samples. The sensitivity and specificity of the RA factor estimation were found to be 92% and 88% respectively. The mean CRP value in the RA patients was found to be 46.4 ± 42.9 mg/dl whereas in the control samples it was found to be 2.2 ± 1.6 mg/dl which is well within the normal clinical range (Table 1). Thus considerable differences also exist in case of CRP levels among the patients and the control samples, which is supported by the t-test. The estimated ASO titre also showed considerable differences among the RA patients and the controls with mean ± SD values of 147.5± 107.8 IU/ml and 83.1 ± 49.7 IU/ml respectively (Table 1). The estimated ESR values in the RA and the control subjects were found to be 37.6 ± 23.0 and 12.7 ± 4.1 mm/hr respectively. Figure 3: X ray photographs of the rat paw on the 21st and the 28th days of the The box plot analyses were also performed based on the quantitative experiment in different rat groups. estimation of the four essential clinical parameters of Rheumatoid Arthritis namely RA factor, CRP, ASO titre and ESR (Figure 1a). The box plots revealed that there exist considerable differences in the distribution of each of the four clinical parameters among the patients and the control samples. It can also be observed that each of the four clinical parameters have shown considerable fluctuations in 50% of the RA patients, whereas such wide range distribution could not be observed for the control samples except for the moderate fluctuations in the ASO titre. From Figure 1b it can be seen that the frequencies of the distribution of the above mentioned four parameters varied over a very wide range of values compared to that in the control samples. However, as evident from the Figure 1b, minor variations in the frequency distribution of two parameters namely ASO titre and ESR were also observed in case of the controls. The two other parameters i.e. plasma ceruloplasmin and serum creatinine, which are generally not considered as conventional parameters for RA diagnosis, were then compared with RA factor level in the RA patients and the control samples with the help of scatterplot (Figure 2). Surprisingly in both the cases the scatterplots showed positive correlation with an upwardly directed linear trendline. Correlation analyses were also performed among the different parameters for the RA patients (Table 2). From the correlation table st th it was evident that the correlation coefficients between RA factor and Figure 4: Line diagram shows (a) the ceruloplasmin level on the 21 and 28 day respectively in the different rat groups and also (b) creatinine level on the 28th day. other five parameters were all greater than 0.5 with RA versus CRP value
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
Page 4 of 7 leading the chart. One interesting observation was that no negative portion of the IgG and it may belong to different isotypes e.g. IgM, correlation was evident among the six clinical parameters. However, IgE, IgG, IgA and IgD and thus their detection may vary based on the it was also found that among the RA patients, the lowest correlation proportion of the different isotypes present in the serum. Moreover, coefficient was evident between ASO titre and creatinine level. diagnosis of the RA factors in the patients suffering for not more than 6 months may result as seronegative, who may become positive In rat model based analyses, it was observed that with the during the progression of the disease. In fact three patients in our study exception of non-arthritic positive control (NT) group, there was population, who were diagnosed as seronegative, had a history of only significant increase in paw circumferences in the FCA-induced 4 month suffering. However the sensitivity and specificity of the RA arthritic group. The paw circumference in all the groups was presented factor test as calculated in our study are 92% and 88% respectively. in Table 3. Interestingly the paw circumference in treatment groups Therefore it could be said that the test of RA factor may prove beneficial showed significant reduction after treatment with Aloe vera crude and is an essential clinical parameter for evaluating the disease gel homogenate. Among the three experimental groups, 125 µl prognosis provided it is accompanied by other parametric evaluations dose, corresponding to 20 mg dry gel/kg body weight, showed the for confirmation of the results. Moreover from Table 1 it is observed maximum reduction rate of paw edema. Whereas 250 µl and 500 that the odds of exposure to RA factor were greater by 84.33% among µl doses, corresponding to 50 gm and 100 gm wet weight/kg body the RA patients compared to the control. The CRP and ESR are the two weights respectively, showed greater protection rates after the booster primary parameters which are frequently used for the clinical detection dose of FCA. However in the protective group (PG), 250 µl doses of acute phase reactions like inflammations. The concentration of these showed very minor paw swelling even after FCA injection in both two parameters become relatively high during inflammation compared the initial and booster doses. Similar results were also evident from to normal level. Moreover they have a relatively short time lag from the x-ray photographs where it was observed that in the protection the moment of stimulus, and are cost-effective. CRP is a very sensitive group, the paw swelling was significantly lower compared to the other parameter in detection of inflammation and therefore was included experimental groups (Figure 3). in our study. In a study conducted in a Pakistani population it was The levels of ceruloplasmin and serum creatinine in the control found that the CRP values ranged between 11.2 to 108 mg/dl with a and the experimental rat groups have also been presented in Figure mean value of 39.1mg/dl in severe RA condition, while the ESR values 4. It was clearly observed that the ceruloplasmin level was much ranged between 12 to 146 mm/hr with a mean value of 62.5 mm/hr more elevated in the arthritic group in comparison to the positive [20]. In another study conducted in Chandigarh, the mean CRP and control group. However the administrations of Aloe vera plant ESR values were found to be 22.8 mg/dl and 51.3 mm/hr respectively extract in different doses have shown considerable reduction in the [21]. Thus these already published results are very much comparable ceruloplasmin levels in varying degrees. In the EG125 group, it was to our reports. CRP is produced in the liver induced by monocytes found that the ceruloplasmin level was almost reduced to normal level and macrophages derived pro-inflammatory responses. These when measured on the 21st day whereas the ceruloplasmin level went proinflammatory responses triggers increased secretion of interleukin high on the 28th day. In case of EG250 and EG500 groups, we found –1β (IL-1β) and tumor necrosis factor – α(TNF-α), which via the release that the ceruloplasmin level was not only reduced from that of the AG of interleukin 6 (IL-6) stimulate the liver to secrete CRP. Recent studies but was also maintained at a steady state both on the 21st and 28th day. have suggested the direct contribution of the CRP in the inflammatory Interestingly, it was found that the Protection Group (PG) did not activities, where it stimulates secretion of inflammatory cytokines show any elevations in their plasma ceruloplasmin during both the 21st such as IL-1β, IL-6 and TNF-α from the monocytes and also directly and 28th day of the experiments. In serum creatinine assay, it was found provides pro-inflammatory stimulus to phagocytic cells [22,23]. CRP that all the experimental groups showed lowered levels compared to is also the only independent determinant of microvascular endothelial the Arthritic Group (AG). However, unlike the ceruloplasmin results, dysfunction in patients with RA [24]. On the other hand, the basic we found no significant reduction in creatinine level in the PG group principle underlying the estimation of ESR is that the erythrocytes compared to the negative control group. normally repel each other due to the net negative charges. However, at the time of acute phase reactions, positively charged high molecular Discussion weight proteins such as fibrinogens, present in the blood, increase in amount and promote rouleaux formation which further increases the A good number of observations were derived from the results ESR. Inflammatory processes play a pivotal role in the pathogenesis of of the estimation of the different clinical parameters that we have RA and therefore these two parameters are also considered important conducted on the blood samples of 50 RA patients and 50 control measures of RA prognosis. However, sometimes severe inflammation subjects. Interestingly it was observed that none of the four clinical does not corroborate the prognosis of RA and clinicians have to depend parameters were 100% positive (above the threshold value) in all the on other diagnostic features for determining RA. As evident from our affected individuals as were also evident from their sensitivity and results, both CRP and ESR are not always present above the threshold specificity measures. These discrepancies have been explained below. value in case of the RA patients though they are positive for 74% and In case of RA factor, it was found that 92% of the patients had values 78% respectively. This may happen due to the lack of prominent acute above the threshold level while 8% had lower values. When compared phase reactions in some of the patients. This may also occur because of to data of other populations, it was found that in case of the Korean the tendency of these parameters to return to lower values if measured population, 80.56% of the RA patients were found to be RF positive during early phases of the disease. However the sensitivities and while 19.44 were RF negative [17]. In a similar study conducted on the specificities of both these tests were quite high thereby assuring us to Iranian population, it was found that 66.5% of the RA patients were keep our faith on these tests in RA diagnostic purposes. The ASO titre RF positive while 33.5 were negative [18]. In another study based on is another essential parameter for the diagnosis of RA. This test was Turkish patients, it was found that 53.335 were RF positive while 46.67 employed in our experiments to detect the rheumatic fever caused were RF negative [19]. These variations may be the outcome of some by environmental triggers like streptococci infection. It measures peculiar fundamental facts. RA factor is the antibody against the FC the plasma levels of anti-streptolysin O antibodies produced against
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
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Parameters Normal Range RA patients (n=50) Control Subjects (n=50) T-value Odds Ratio Relative Risk Sensitivity (%) Specificity (%) Age 47.2 ± 14.0 35.1 ± 11.4 RA factor (IU/ml) 0-20.0 IU/ml 135.9 ± 91.4 14.0 ± 5.5 9.45*** 84.33 7.67 92 88 CRP (mg/dl) 0-6 mg/dl 46.4 ± 42.9 2.2 ± 1.6 7.32*** 44.59 12.33 74 94 ASO (IU/ml) 0-200 IU/ml 147.5 ± 107.8 83.1 ± 49.7 3.66*** 29.41 23.00 22 100 ESR (mm/hour) 0-20 mm/hour 37.6 ± 23.0 12.7 ± 4.5 8.01*** 31.91 7.80 78 90 Ceruloplasmin (mg/dl) 20-35 mg/dl 42.1 ± 11 23.8 ± 6.1 8.96*** 34.62 12.50 65.79 94.74 Creatinine (mg/dl) 0.6-1.2 mg/dl 1.5 ± 0.6 0.9 ± 0.2 5.79*** 11.69 5.50 57.89 89.47
*p<0.05, **p<0.01, ***p<0.001. Table 1: Statistical analyses of the different clinical parameters of the Rheumatoid Arthritic patients and control subjects.
RA CRP ASO ESR Cerulo plasmin Creatinine RA 1.00 CRP 0.73 1.00 ASO 0.65 0.48 1.00 ESR 0.71 0.72 0.63 1.00 Ceruloplasmin 0.57 0.52 0.38 0.40 1.00 Creatinine 0.60 0.65 0.22 0.48 0.62 1.00 Table 2: Correlation coefficient of the different clinical parameters in the RheumatoidArthritis patients.
Days Groups 1 4 7 10 13 16 19 23 27 + Control(NT) 22.57 23.76 22.46 23.24 22.84 22.56 22.29 22.14 21.99 - Control(AG) 22.38 32.00 28.28 26.84 26.02 25.14 32.85 30.80 26.98 EG-125 21.63 27.17 25.54 23.63 23.21 23.27 29.21 27.55 25.04 EG-250 22.75 27.67 25.84 24.75 24.00 23.44 26.53 26.42 24.36 EG-500 22.86 26.00 25.47 25.17 23.94 22.92 26.43 26.25 24.69 Protective (PG) 22.21 22.39 22.14 22.24 21.98 21.78 24.18 22.92 22.95 Table 3: Measurement of paw circumference (mm) of different rat groups in different days. streptolysin of different streptococcal strains. It has been observed that [26] and may have considerable protective role [27,28] especially in out of 50 samples only 11 samples had higher than 200 IU/ml ASO presence of tissue damage or destruction. In a study conducted in titre values. However it is clearly evident that the mean ASO titre value Poland, mean ceruloplasmin level was found to be 0.3 g/L which is very was higher in case of the RA patients compared to the control samples much comparable to our data [29]. Serum creatinine estimation has (Table 1) which may have resulted due to the presence of ASO positive been carried out in this study as it is an important metabolic by-product patients having ASO titre above threshold value (200 IU/ml). Francois of creatine which in turn is a very crucial amino acid for building and (1965) [25] has shown significantly higher prevalence of 3-haemolytic repairing of muscular tissues. streptococci of Groups A, C, and G at any point of the study duration in Increased level of plasma ceruloplasmin level during inflammation the RA patients compared to control samples which he assumed to be may occur due to an increased production of interleukins such as due in part to a slightly higher acquisition rate, but mainly to a reduced IL1 and IL6 in the RA patients which stimulate the hepatocytes to elimination rate. Thus due to lower elimination rate, prolonged release and increase the amount of ceruloplasmin into the blood. As infection may lead to higher antibody response and this statement may evident from our study, such rise of ceruloplasmin level in case of justify the facts that although raised anti-streptolysin-O titres (>150 RA patients was also evident from other previously published reports IU/ml) were found in both the groups, their numbers were higher in [26]. Previous reports have also shown highly significant correlation RA patients compared to the controls and also more higher titres (>300 between the serum ceruloplasmin and serum antioxidant activity U) were significantly found in the RA patients but such high values [26] and thereby supporting the concept that ceruloplasmin may be of ASO titre were not observed in the control samples (Figure 1b). responsible for actively governing the serum antioxidant activity [30] One interesting observation was that the sensitivity of the test was as which when increased may be an important component of the systemic low as 22% which may result due to the presence of only 11 patients inflammatory response. On the other hand the creatinine level showed having positive titre result i.e. ASO>200 IU/ml, but on the contrary, moderate differences in between the RA and the control blood samples. the specificity was 100% indicating the negative outcome of the test Blood creatinine generally shows a rise only after marked damage of result in absence of acute phase reactions which is an well-established nephrons which is observed in patients having long term RA and pathophysiological condition in case of rheumatoid arthritis. Thus inflammatory responses. Thus it may not serve as a very potent tool ASO titre may also prove useful in the diagnosis of RA. for early diagnosis of RA but can be useful for detection and treatment It is interesting to note that the plasma ceruloplasmin and creatinine of nephropathy during late Rheumatoid Arthritis. Moreover these two showed significant differences in their levels in the RA patient and tests have considerable sensitivity and specificity indicating that their the control blood samples though they are not generally considered results may prove affirmative with that of the other above mentioned as essential diagnostic tool for RA diagnosis. Serum ceruloplasmin tests for RA diagnosis. was selected for the study because of its high correlation with serum In case of the rat model it was found that considerable foot swelling antioxidant property which have already been reported elsewhere
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
Page 6 of 7 and increased paw circumference induced by FCA injection in the References Arthritic rat group were also accompanied by significant increase in 1. Firestein GS (2003) Evolving concepts of rheumatoid arthritis. Nature 423: 356-361. serum ceruloplasmin and creatinine levels in this group compared to 2. Newman SP, Fitzpatrick R (1995) Understanding rheumatoid arthritis, Burns st that of the non-treated or the positive control groups. In both the 21 & Oates. and 28th days, there was significant increase of ceruloplasmin levels in 3. Nejatzadeh-Barandozi F (2013) Antibacterial activities and antioxidant capacity arthritic group. Thus the above mentioned similarity between human of Aloe vera. Org Med Chem Lett 3: 5. and the rat models in the distribution of ceruloplasmin and creatinine 4. Surjushe A, Vasani R, Saple DG (2008) Aloe vera: a short review. Indian J in the arthritic and the normal population strongly suggest the efficacy Dermatol 53: 163-166. of the rat model for various pharmaceutical and clinical experiments 5. Davis RH, Leitner MG, Russo JM, Byrne ME (1989) Wound healing. Oral and targeted towards the Rheumatoid Arthritis therapy in the humans. topical activity of Aloe vera. J Am Podiatr Med Assoc 79: 559-562. It was found that the paw circumference was decreased towards 6. Davis RH, Donato JJ, Hartman GM, Haas RC (1994) Anti-inflammatory and wound healing activity of a growth substance in Aloe vera. J Am Podiatr Med the normal state on applying Aloe vera gel in the experimental rat Assoc 84: 77-81. groups. It clearly indicated the rapid regression of the foot swelling 7. Domkat LC, Habibu T (2013) Comparative studies of the aqueous extracts of in the experimental groups compared to the negative control or the Ocimun gratissmum, Aloe vera, Brassica oleracea and Ipopoea batatas on arthritic groups (Figure 3 and Table 3). The differences in the mean some biochemical parameters in diabetic rats. J Pharm Biol Sci 6: 23-29. paw circumference between the members of the experimental and the 8. Paul S, Dutta S, Chaudhuri TK, Bhattacharjee S (2014) Anti-Inflammatory and negative control (AG) groups started to increase from the 3rd day. Paw Protective Properties of Aloe vera Leaf Crude Gel in Carrageenan Induced swelling reverted back almost to the normal condition within 10th day Acute Inflammatory Rat Models. Int J Ph Pharma Biol 6: 368-371. in all the experimental groups while the swelling was retained in the 9. Tan ZJ, Li FF, Xu XL (2013) Extraction and purification of anthraquinones negative control (Arthritic group) rats throughout the experiment. derivatives from Aloe vera L. using alcohol/salt aqueous two-phase system. Bioprocess Biosyst Eng 36: 1105-1113. The same feature was also observed after the booster injection of FCA. Moreover, X-ray photographs (Figure 3) have clearly indicated 10. Ng T (1997) Erythrocyte sedimentation rate, plasma viscosity and C-reactive protein in clinical practice. Br J Hosp Med 58: 521-523. that in case of the arthritis induced rats, the hind paw joints appeared loosened and the spaces increased in between the joints, confirming 11. Caswell M, Pike LA, Bull BS, Stuart J (1993) Effect of patient age on tests of the acute-phase response. Arch Pathol Lab Med 117: 906-910. the diseased state in the arthritic rats [31]. Thus, it can be clearly said that these two parameters can be used as effective visual tool for 12. Sunderman FW Jr, Nomoto S (1970) Measurement of human serum ceruloplasmin by its p-phenylenediamine oxidase activity. Clin Chem 16: 903-910. understanding the disease severity. The treatment groups showed a tendency to restore the joint structure towards the normal state (Figure 13. Lustgarten JA, Wenk RE (1972) Simple, rapid, kinetic method for serum creatinine measurement. Clin Chem 18: 1419-1422. 3). Moreover, the decreased levels of ceruloplasmin in the experimental groups were evident compared to the negative controls which confirm 14. Bendele A, McComb J, Gould T, McAbee T, Sennello G, et al. (1999) Animal models of arthritis: relevance to human disease. Toxicol Pathol 27: 134-142. the efficacious role ofAloe vera gel in lowering the ceruloplasmin level. Interestingly, the protective group showed no significant difference in 15. Paquet J, Goebel JC, Delaunay C, Pinzano A, Grossin L, et al. (2012) Cytokines profiling by multiplex analysis in experimental arthritis: which pathophysiological the ceruloplasmin levels when compared with the Non-Treated (NT) relevance for articular versus systemic mediators? Arthritis Res Ther 14: R60. group rats, both on 21st and 28th day, indicating the strong protective 16. Rathore B, Paul B, Chaudhury BP, Saxena AK, Sahu AP, et al. (2007) role of Aloe vera in the amelioration of rheumatoid arthritis. Comparative studies of different organs of Nyctanthes arbortristis in modulation of cytokines in murine model of arthritis. Biomed Environ Sci 20: 154-159. Serum creatinine concentration increased in the negative control arthritic group rats when compared to the normal rats (p>0.05) but 17. Choi SW, Lim MK, Shin DH, Park JJ, Shim SC (2005) Diagnostic performances of anti-cyclic citrullinated peptides antibody and antifilaggrin antibody in Korean rats of all the other experimental groups showed no significant change patients with rheumatoid arthritis. J Korean Med Sci 20: 473-478. when compared to the positive control rats. This observation indicated 18. Binesh F, Salehabadi HS, Behniafard N, Ranginkaman K, Behniafard N (2014) that the level of creatinine is normal (p>0.05). However the protective A Comparative Assessment of the Diagnostic Value of Anti-cyclic Citrullinated group did not show any effect on the creatinine level probably because Peptide Antibodies and Rheumatoid Factor in Rheumatoid Arthritis. J Clin Exp this factor increases due to increasing nephropathy in case of long term Pathol 4: 158. RA. In correlation with other biochemical parameters examined in this 19. Kuru O, Bilgici A, Birinci A, Ulusoy H, Durupinar B (2009) Prognostic value of part of the study, it can be said that Aloe vera plays a significant role in anti-cyclic citrullinated peptide antibodies and rheumatoid factor in patients with rheumatoid arthritis. Bratisl Lek Listy 110: 650-654. amelioration of rheumatoid arthritis induced paw edema. 20. Naqi N, Ahmed TA, Malik JM, Ahmed M, Bashir MM (2012) Identification Of In conclusion, the work can be summarized with three principal Laboratory Markers Of Disease Activity In Rheumatoid Arthritis. Pak Arm F sentences. Firstly, six clinical parameters that we have selected for Med J 1: 1-6. the study like, RA factor, CRP, ASO, ESR, ceruloplasmin and serum 21. Taneja SK, Mandal R (2009) Assessment of mineral status (Zn, Cu, Mg and creatinine, are all essential for the differential diagnosis of RA during its Mn) in rheumatoid arthritis patients in Chandigarh, India. Rheum Rep 1: e5. early and later stages. Secondly, this study has supported the justification 22. Bharadwaj D, Stein MP, Volzer M, Mold C, Du Clos TW (1999) The major receptor for C-reactive protein on leukocytes is fcgamma receptor II. J Exp of the experimentation on rat model for designing the therapeutic Med 190: 585-590. strategies against Rheumatoid Arthritis in humans. Lastly, as evident 23. Stancíková M, Rovenský J (1993) Effect of cyclosporin on the activity of cytidine from our study, Aloe vera extracts can be beneficial for the reduction of deaminase and adenosine deaminase in the serum and polymorphonuclear inflammatory edema and also for the reduction of ceruloplasmin in RA leukocytes of patients with rheumatoid arthritis. Int J Tissue React 15: 169-174. condition in rat model. However, further investigations are necessary 24. Galarraga B, Khan F, Kumar P, Pullar T, Belch JJ (2008) C-reactive protein: for more refined therapeutic usage ofAloe vera for the treatment of RA the underlying cause of microvascular dysfunction in rheumatoid arthritis. in humans. Rheumatology (Oxford) 47: 1780-1784.
Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Citation: Guha P, Subhashis P, Das A, Halder B, Bhattacharjee S, et al. (2014) Analyses of Human and Rat Clinical Parameters in Rheumatoid Arthritis Raise the Possibility of Use of Crude Aloe vera Gel in Disease Amelioration. Immunome Res 10: 081. doi: 10.4172/17457580.1000081
Page 7 of 7
25. François RJ (1965) Beta-haemolytic streptococci and antistreptolysin-O titres 29. Strecker D, Mierzecki A, Radomska K (2013) Copper levels in patients with in patients with rheumatoid arthritis and a matched control group. Ann Rheum rheumatoid arthritis. Ann Agric Environ Med 20: 312-316. Dis 24: 369-377. 30. Stocks J, Gutteridge JM, Sharp RJ, Dormandy TL (1974) The inhibition of lipid 26. Scudder PR, Al-Timimi D, McMurray W, White AG, Zoob BC, et al. (1978) autoxidation by human serum and its relation to serum proteins and alpha- Serum copper and related variables in rheumatoid arthritis. Ann Rheum Dis tocopherol. Clin Sci Mol Med 47: 223-233. 37: 67-70. 31. Ekambaram S, Perumal SS, Subramanian V (2010) Evaluation of antiarthritic 27. Al-Timimi DJ (1977) Serum antioxidant activity. MPhill thesis, University of activity of Strychnos potatorum Linn seeds in Freund’s adjuvant induced London. arthritic rat model. BMC Complement Altern Med 10: 56.
28. Al-Timimi DJ, Dormandy TL (1977) The inhibition of lipid autoxidation by human caeruloplasmin. Biochem J 168: 283-288.
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Immunome Res ISSN: 1745-7580 IMR, an open access journal Volume 10 • Issue 2 • 1000081 Human Immunology 76 (2015) 789–794
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Rapid Communication Study of genetic diversity of KIR and TLR in the Rabhas, an endogamous primitive tribe of India ⇑ Pokhraj Guha a, Avishek Das a, Somit Dutta a, Soumen Bhattacharjee b, Tapas Kumar Chaudhuri a, a Cellular Immunology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal 734013, India b Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal 734013, India article info abstract
Article history: The Rabha tribe is a little known small endogamous population belonging to Indo-mongoloid group of Received 30 December 2014 north-eastern India. We have analyzed 16 KIR and 5 TLR gene polymorphisms in the Rabha population Revised 13 July 2015 of northern West Bengal, India for the first time. The observed frequencies of the KIR genes (except frame- Accepted 27 September 2015 work and pseudogene loci) ranged between 0.26 (KIR2DS3) and 0.96 (KIR2DL1). Comparisons based on Available online 30 September 2015 KIR polymorphism have revealed that although the Rabhas are of Indian origin the presence of mongoloid component in their gene pool cannot be denied. The frequencies of the 5 TLR genes ranged between 0.90 Keywords: (TLR4) and 0.46 (TLR5). TLR variations found in the Rabhas may play a synergistic role in fighting against KIR the bacterial invasions. Our results may contribute to the understanding of (1) genetic background and TLR Rabha extent of genetic admixture in the Rabhas, (2) population migration events and (3) KIR-disease-TLR PCR-SSP interactions. Genetic polymorphism Ó 2015 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.
1. Introduction The TLR genes, located in different chromosomes, act in syner- gism to combat pathogens that infect the body. Recently there is a Rabha is a very little known small endogamous scheduled tribe trend to establish the immunological connection between KIR and community of India [1–3] with a relatively conserved gene pool of TLRs [7], following which we have also genotyped 5 TLR genes their own. In West Bengal, they are mainly distributed in the forest (TLR1–TLR5) in the Rabha population. Sivori et al. [8] mentioned in villages of the Dooars region of Jalpaiguri and Coochbehar districts. one of their publications that KIR3DL2 directly bind unmethylated Historically they are considered to be the primitive inhabitants of microbial cysteine guanine dinucleotide (CpG) DNA and deliver it the region who remained isolated from other neighboring popula- to endosome-resident TLR9 resulting in its activation and signaling tions due to strict endogamy. According to H.H. Risley, the Rabhas [8]. Following this, Held [7] commented that immune cells such as belong to the Indo-mongoloid stock [4], having a unique genetic NK and T-cell subsets co-expressing specific KIR and TLR9 may play constitution. Thus, there is an urgency to undertake genetic a prominent role in directly recognizing and responding to patho- diversity studies in this group. gens or to host-cell death [7]. Another study by Artavanis- Herein, we have used two very recent genetic marker groups Tsakonas et al. [9] has shown a significant correlation between the namely Killer Cell Ig-like Receptor (KIR) and Toll-like Receptor carriers of a particular KIR3DL2 allele and the induction of an (TLR) genes to unravel the genetic profile of the Rabhas. The KIR efficient NK-cell response to Plasmodium falciparum infected genes cluster together on chromosome 19q13.4. [5,6]. They erythrocytes which lack HLA molecules [9]. These evidences may directly interact with HLA Class I molecules and play significant signify the importance of the KIR–TLR connection(s) [7]. Therefore role in modulation of immune responses. We have genotyped 16 in this study, we have analyzed TLR genes along with KIR genes in KIR genes in the Rabha population and compared our data with the Rabha population. that of other previously published reports from different parts of the world to trace the phylogenetic relationship of the Rabha 2. Materials and methods population with that of other populations around the world. 2.1. Sample collection, DNA extraction and KIR specific PCR-SSP typing
⇑ Corresponding author. E-mail addresses: [email protected], [email protected] Blood samples were collected from 50 medically examined (T.K. Chaudhuri). healthy Rabha individuals from the Tufanganj Block (26°190N/89°40 http://dx.doi.org/10.1016/j.humimm.2015.09.024 0198-8859/Ó 2015 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved. 790 P. Guha et al. / Human Immunology 76 (2015) 789–794
0E) of Coochbehar District of West Bengal who provided their con- Pakistani [34], Chinese, Malay and Indian in Singapore [35], Indian sent in written form after knowing the purpose of the study. Individ- Parsis and Maharashtrian [36], Tibetans [37] and Iranian Arabs and uals with common ancestry for earlier 3 generations were excluded Persians [38]. from the analyses. The investigation was approved by the Human Based on gene content, two distinct KIR haplotype groups can Ethics Committee of the Department of Zoology, University of North be designated, namely A and B. Certain assumptions were made Bengal, India. while assigning genes to a specific haplotype which are as follows: Genomic DNAs were extracted by phenol–chloroform method (i) the framework genes (KIR3DL3, 2DL4, 3DL2 and 3DP1) are [10]. The purity of the DNA samples was checked by UV present in all the haplotypes; (ii) KIR3DL1 and 3DS1 are likely Spectrophotometry. The extracted DNAs were then amplified using equivalent to alleles and (iii) either KIR2DL2 or KIR2DL3 are SSP-PCR reactions for detecting 16 KIR genes (KIR2DL1-5, present in a haplotype but not both. Assignment of haplotypes KIR2DS1-5, 3DL1-3, 3DS1, 2DP1 and 3DP1). The KIR primer was performed as described by Marsh et al. [39] with group B hap- sequences were similar to that of Guha et al. [11] which were gen- lotypes defined by the presence of one or more of the following erously provided by R. Rajalingam on enquiry. The PCR reactions genes: KIR2DL5, KIR2DL2, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS5, were performed according to Guha et al. [11]. We have also and KIR3DS1. Conversely, the absence of all these genes define genotyped 5 TLR genes (TLR1–TLR5) in the Rabha population. The group A haplotypes [39]. Therefore, if any of the B haplotype genes primer sequences generated by Primer-Blast [12] are shown in are present, the genotype is considered as Bx. If none of these is Table 2. The PCR reactions and amplification cycles were modified present then the genotype is considered as AA. as per requirement. The numbers were assigned to the KIR genotypes as provided in the Allele Frequencies website (http://www.allelefrequencies.net) [13]. The KIR genes and genotypic frequencies of the reference 2.2. KIR genotyping and statistical analyses populations were also extracted from the ‘http://www.allelefre- quencies.net’ database [13] and Guha et al. [11]. The frequency data of KIR genes and genotypes in populations to be compared to the Bengalis were extracted from the following publications and from the ‘http://www.allelefrequencies.net’ data- 2.3. Statistical analyses base [13] as follows: Indian Rajbanshis [11], South Indian Paravar, Kanikar and Mollukurumba [14], Finnish, French Caucasian, Sene- The observed frequency (OF) for each KIR gene was estimated as gal African, Guadeloupe Caribbean, and Reunion, a population from the ratio of the number of individuals carrying the gene to the total Indian Ocean origin [15], North Indian [16], Cook Island, Samoan, number of individuals in the sample population. KIR Locus Fre- Tokelau, Tongan [17], Mestizo, Huichol, Purepecha, Tarahumara quencies (KLFs) were calculated by using the formula: p [18], Amazonian Amerindian [19], Wichis and Chiriguanos [20], KLF = 1 (1 f), where f is the OF of a particular KIR gene in a Northern Irish [21], Basque population [22], Eastern Mainland Chi- population. The standard v2 test was executed using Kyplot 2.0 nese [23], Chinese Han [24], Korean [25], Japanese [26], Warao, beta 15 to compare the differences of the Rabha population KIR Bari, Yucpa [27], Vietnamese and Australian Aborigine [28], gene content with that of some other neighboring populations. American Caucasian, Hispanic, African American [29], Thai, British Nei genetic distances were calculated from 1000 bootstrap repli- Caucasian, Palestinian [30], Australian Caucasian [31], New York cates of the KIR frequency data from the Rabhas and few other Caucasian [32], Greeks [33], Afro-Caribbean, Trinidad Asian, Asian populations and consensus NJ tree was constructed using
Table 1 KIR gene and genotypic frequencies in the Rabha population. Black boxes (j) indicate presence and white boxes (h) absence of KIR genes. Genotype IDs are considered according to www.allelefrequencies.net. The Observed Phenotypic Frequency (OF) for each KIR gene was calculated as the ratio of the number of individuals in the population carrying the gene to the total number of individuals in the sample population. KIR Locus Frequencies (KLF) were estimated from the OFs of the KIR genes. Geno. ID 3DL1 2DL1 2DL3 2DS4 2DL2 2DL5 3DS1 2DS1 2DS2 2DS3 2DS5 2DL4 3DL2 3DL3 2DP1 3DP1 No. Freq. 1 17 34.00 2 4 8.00 3 2 4.00 4 4 8.00 6 3 6.00 8 2 4.00 15 2 4.00 22 1 2.00 68 2 4.00 70 4 8.00 101 1 2.00 161 1 2.00 192 2 4.00 277 1 2.00 300 1 2.00 486 1 2.00 530 1 2.00 N 1 2.00 Number 42 48 46 38 23 22 20 23 22 13 19 50 50 50 49 50 50 100.00 OF 0.84 0.96 0.92 0.76 0.46 0.44 0.40 0.46 0.44 0.26 0.38 1.00 1.00 1.00 0.98 1.00 KLF 0.60 0.80 0.72 0.51 0.27 0.25 0.23 0.27 0.25 0.14 0.21 1.00 1.00 1.00 0.86 1.00 P. Guha et al. / Human Immunology 76 (2015) 789–794 791
Fig. 1. Principle Component Analysis (PCA) of carrier frequencies of nine variable KIR genes (2DL1-3, 2DS1-4, 3DL1, and 3DS1) in the Rabhas and other world populations developed by MINITAB v16. The PCA graph shows a global relationship between Rabhas and other previously reported world populations. (Jap, Japanese; Chi, Eastern Mainland Chinese; Zhe, Zhejiang Chinese; Kor, Korean; SpC, Singapore Chinese; Jia, Jiansu Chinese; Hkc; Hongkong Chinese; Hui, Huichol; Pur, Purepecha; Tar, Tarahumara; Tib, Tibetans; Vie, Vietnamese; Tha, Thailand; Fin, Finnish; Chg, Chiriguanos; War, Warao; Bar, Bari; AmA, Amazonian Amerindian; Yuc, Yucpa; ReU, Reunion; Gre, Greek; CkI, Cook Island; Sam, Samoan; Tok, Tokelau; Ton, Tongan; Mez, Mestizo; Bri, British Caucasian; NIr, Northern Irish; Fre, French Caucasian; AmC, American Caucasian; SpM, Singapore Malay; His, Hispanic; MlK, Mollukurumba; Sen, Senegal African; GuC, Guadeloupe Caribbean; AfA, African American; AfC, Afro-Caribbean; Arb, Arabian; Pal, Palestinian; Raj, Rajbanshis; Rab, Rabhas; Mah, Maharashtrian; Tri, Trinidad Asian; Pak, Pakistani; NIn, North Indian; Par, Paravar; Kan, Kanikar; MuP, Indian Mumbai Parsi; Bas, Basque population; SpI, Singapore Indians and AuA, Australian Aborigine). The Rabha population has been marked in bold (j).
Fig. 2. Phylogenetic dendrogram based on REML analysis of the KIR genotypic profiles in the Rabhas and other previously studied World populations using PHYLIP package. Clusters relating to the Asian Indians, North East Asians, Africans, Europeans, Mexicans and American Natives can be distinguished separately. Populations having incomplete genotypes were excluded from the analyses. Bootstrap values were calculated for 100 replicates and the values are provided only to the branches supported by P75%. 792 P. Guha et al. / Human Immunology 76 (2015) 789–794
Table 2 Frequencies of the TLR genes and the genotypes in the Rabha population. Black boxes (j) indicate presence and white boxes (h) absence of TLR genes. The Observed Phenotypic Frequency (OF) for each TLR gene was calculated as the ratio of the number of individuals in the population carrying the gene to the total number of individuals in the sample population. TLR Locus Frequencies (TLRF) were estimated from the OFs of the TLR genes. Gene Primers OF TLR Genotypes Forward (5’-3’) Reverse (5’-3’) F 1 2345678910 11 12 13 14 15 16 17 18 TLR1 TCAACCAGGAATTGGAAT AGTTCCAGATTTGCTACA AC GT 0.76 0.51 TLR2 GGATGGTTGTGCTTTTAAG AAGATCCCAACTAGACAA TACTG AGACTG 0.6 0.37 TLR3 ATTGGGTCTGGGAACATTT GTGAGATTTAAACATTCCT CTCTTC CTTCGC 0.74 0.49 TLR4 TTCTTCTAACTTCCTCTCCT TTAGCTGTTCGGCTCTACT GTG ATGG 0.9 0.68 TLR5 CATTGTATGCACTGTCACT CCACCACCATGATGAGAG C CA 0.46 0.27
Number 1186333222211111111
Frequency 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 6 2 6 6 6 4 4 4 4 2 2 2 2 2 2 2 2
PHYLIP software version 3.69 [40]. The Principal Components Anal- phylogenetic tree, computed from the genotypic frequencies yses (PCA) was computed using MINITAB software version 16.0 (Fig. 2), that the Rabhas have branched out from the Tibetan–Japa based on KIR gene frequencies of the Rabha population and other nese–Korean–Chinese clade and have occupied a position in reference populations. Restricted Maximum Likelihood (REML) between the Indian and the North East Asian Cluster. analysis was computed using PHYLIP version 16.0 based on the The TLR frequencies were shown in Table 2. Interestingly, it was KIR genotypic frequencies and the phylogenetic tree was devel- found that TLR4 has the highest frequency (0.90) among the stud- oped using FigTree version 1.3.1 (http://tree.bio.ed.ac.uk). ied TLR genes while on the other hand TLR5 has a considerably low frequency (0.46). Genotype ID 1 consisting of TLR1-5 was the most frequent among the genotypes. 3. Results
The OFs of KIR genes in the Rabhas (except the framework and 4. Discussion pseudogene loci) ranged between 0.26 (KIR2DS3) and 0.96 (KIR2DL1) (Table 1). The A haplotype associated KIR genes were Historically, the Koch Rabhas and the Rajbanshis are considered much frequent compared to the B haplotype associated KIR genes to be very close relatives and they were thought to share the same in the studied population. Certain significant observations ancestry [42]. This fact is evident from the v2 analyses whereby no can be revealed from the above findings. Based on v2 analyses significant differences were observed for any of the KIR genes (Supplementary Table 1), it was found that the frequency of between the Rabhas and the Rajbanshis. Moreover, genetic dis- KIR2DL5 in the Rabhas differed significantly from all the tance measures also showed the least distance of the Rabhas from reference populations of the Indian subcontinent, except from the Rajbanshis in comparison to other Indian populations. An ear- the Mollukurumbas and the Rajbanshis [11]. Another striking lier study by Guha et al. [11] suggested that the Indo-European observation from the v2 analyses was that the Rabha population speaking Rajbanshis from the northern part of Bengal has Tibeto- showed no significant difference for any of the KIR loci with that Burman influence in spite of having an Indian origin. Thus, the of the Rajbanshi population. Moreover, it was also observed that genetic proximity of the Rabhas with the Rajbanshis clearly indi- except for KIR2DS3 locus, the Rabhas did not differ significantly cates the inclination of the Rabhas toward mongoloid ethnicity. from the Tibetan population. Furthermore, both PCA score plot and NJ tree, based on KIR gene Principal Component analyses (PCA), based on OFs of frequencies, showed the deviation of the Rabha population from the KIR genes, revealed that the Rabhas have occupied a slightly the Indian cluster and also suggested their proximity to the mon- distant position from the Indian cluster in the score plot and have goloid ethnicity. This view was further strengthened by the REML moved towards the lower left half of the score plot which have analysis where the Rabhas shared the same clade with the Tibetan been mostly occupied by the North East and South East Asians population. Thus the results of the above analyses helped us to (Fig. 1). Furthermore, the Neighbor Joining (NJ) dendrogram ascertain the fact that the mongoloid lineage has strongly influ- (Supplementary Fig. 1) based on Nei genetic distances enced the gene pool of the Rabha population. The findings of our (Supplementary Table 2) (computed using PHYLIP statistical pack- study were well supported by Y-chromosome haplogroup diversity age) also supported the result of the PCA score plot. The NJ tree study which have also extended similar views, where the Rabhas showed that the Rabha population has branched away from the have clustered with the North East Asians [43]. Another study on Indian cluster as an outlier group occupying a position somewhat Y-chromosome haplogroup diversity by Su et al. [44] also intermediate between the Indian cluster and that of the other suggested the prehistoric migration of people belonging to Asian populations i.e. the North East and South East Asian clusters. Tibeto-Burman lineage to the Himalayas. Another earlier study Altogether 18 KIR genotypes were reported in this study by Chakraborty et al. [45] have also shown the presence of (Table 1). One significant observation was that 17 out of 50 Rabha mongoloid element in the Rabha gene pool. individuals have genotype ID 1 (34%) which has been found to The TLR profile of a population in conjunction with the sur- occur predominantly among the North East Asian populations. rounding environment plays a complex role in disease pathogene- Interestingly, it was also found that genotype IDs 161 and 300 that sis [46]. Numerous published reports are available regarding the were found in the Rabhas were only reported in the North Indians role of TLR in disease association studies [47–49]. Moreover, the till date. Genotype ID 486, found in the Rabha population was presence of TLR genes can be documented from genetic pools of reported earlier only in the Chinese Shaanxi Han population [41]. each and every population of the world. Thus, this marker may It was evident from Restricted Maximum Likelihood (REML) based be considered to clarify genetic diversity and relatedness among P. 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Editorial Open Access HLA: Facts and Findings Tapas Kumar Chaudhuri* and Pokhraj Guha Department of Zoology, Cellular Immunology Laboratory, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India
Human Major Histocompatibility Complex also known as Human like TNF-α and different immune cells like NK cells. This regulation Leukocyte Antigen (HLA) system is an extended portion of human may be disrupted due to lowered HLA-G expression following maternal genome spanning a region of about 4000 Kb on the short arm of infection resulting in upregulation of detrimental inflammatory chromosome 6 between 6p21.31 and 6p21.32.1 [1]. This region contains cytokines which in turn may result in neuro-developmental disorder a large number of polymorphic genes with variable expression and leading to schizophrenia [11]. Lama et al. also documented significant function which are arranged close together and are generally inherited higher frequency of HLA-DRB1*03 in asthmatic children compared to as a haplotype [2]. These genes have been grouped into different clusters controls [12]. based on their structural and functional characteristics namely: Class I, Future researches on HLA shall focus on exploring the intricate II and III. Due to their diploid nature, an individual inherits two alleles connection between genetic diversities and molecular attributes. of each HLA loci, one each from either parent. Furthermore, due to very low recombination frequency (less than 1%), a complete set of References alleles on the same chromosome is usually inherited as a haplotype. 1. Naik S (2003) The Human HLA system. J Indian Rheumatol Assoc 11: 79-83. Approximately 150 billion or even more genotypic combinations are 2. Mehra NK, Rajalingam R, Giphart MJ (1996) Generation of DR51-associated possible in the HLA system. According to Klein, such a polymorphism DQA1, DQB1 haplotypes in Asian Indians. Tissue Antigens 47: 85-89. is not only advantageous for an individual, but also enhances the 3. Klein J (1987) Origin of major histocompatibility complex polymorphism: the survivality of the species surrounded by plethora of pathogens [3]. trans-species hypothesis. Hum Immunol 19: 155-162. Additionally, racial admixture can lead to the formation of new recombinants as a result of exchange of genetic material between alleles 4. Debnath M, Chaudhuri TK (2006) HLA-A and HLA-B distribution in Toto - a vanishing sub-Himalayan tribe of India. Tissue Antigens 67: 64-65. of the same locus or due to point mutation and other genetic events [2]. All these features made the human HLA system a lucrative subject for 5. Debnath M, Chaudhuri TK (2006) Study of Genetic Relationships of Indian Gurkha Population on the Basis of HLA - A and B Loci Antigens. Int J Hum genome based researches worldwide. Genet 6: 159-162.
Analyses of inter-population HLA polymorphism has also gained 6. Agrawal S, Srivastava SK, Borkar M, Chaudhuri TK (2008) Genetic affinities of importance in the recent past. Debnath and Chaudhuri documented north and northeastern populations of India: inference from HLA-based study. highest known frequency of HLA-B14 in the Toto population of India Tissue Antigens 72: 120-130. (32.5%) when compared to that of the other world populations [4]. 7. Singh B, Mallick GC, Bandopadhyay S, Nayak CR, Chaudhuri TK (2009) Study Another study by Debnath et al. suggested the origin of the Gurkhas of Selected HLA-A and -B Antigens by PCR-SSP Method in Bengali Population of Siliguri and Adjoining Areas of West Bengal. Int J Hum Genet 9: 245. from mongoloid/Tibetan stock [5]. Studies on HLA polymorphism in north and north-eastern populations of India have also been carried 8. Debnath M, Das SK, Bera NK, Nayak CR, Chaudhuri TK (2006) Genetic associations between delusional disorder and paranoid schizophrenia: A novel out by Agrawal et al., where it was shown that based on HLA alleles etiologic approach. Can J Psychiatry 51: 342-349. and haplotype similarity, the Rajbanshis have close proximity with the mongoloids [6]. Singh et al. reported highest frequency of HLA-B*37 9. Singh B, Bera NK, De S, Nayak C, Chaudhuri TK (2011) Study of HLA Class I gene in Indian schizophrenic patients of Siliguri, West Bengal. Psychiatry Res and B*08 in the Bengalis compared to other populations of India [7]. 189: 215-219. The application of HLA system in disease association studies also 10. Singh B, Banerjee S, Bera NK, Nayak CR, Chaudhuri TK (2008) Analysis of the revealed certain important facts. Debnath et al. reported significant role of human leukocyte antigen class-I genes to understand the etiopathology positive association of HLA-A*03 gene with delusional disorder as of schizophrenia. Indian J Psychiatry 50: 166-170. well as paranoid schizophrenia [8], while Singh et al., further showed 11. Debnath M, Chaudhuri TK (2006) The role of HLA-G in cytokine homeostasis considerable decrease in the frequency of HLA-A*25, A*31 and HLA during early pregnancy complicated with maternal infections: a novel etiopathological approach to the neurodevelopmental understanding of B*51 in schizophrenia [9,10]. Furthermore, Debnath and Chaudhuri, schizophrenia. Med Hypotheses 66: 286-293. hypothesized HLA-G molecules as the novel regulator of immune 12. Lama M, Chatterjee M, Chaudhuri TK (2014) A study of the association of homeostasis during early pregnancy to protect developing fetus from childhood asthma with HLA alleles in the population of Siliguri, West Bengal, maternal immune attack by interacting with proinflammatory cytokines India. Tissue Antigens 84: 316-320.
*Corresponding author: Tapas Kumar Chaudhuri, Department of Zoology, Cellular Immunology Laboratory, University of North Bengal, Raja Rammohunpur, Siliguri, West Bengal, India, 734013, Tel: 9434377127; E-mail: [email protected]
Received: August 28, 2015; Accepted: August 31, 2015; Published: September 03, 2015
Citation: Chaudhuri TK, Guha P (2015) HLA: Facts and Findings. Transcriptomics 3: e112. doi:10.4172/2329-8936.1000e112
Copyright: © 2015 Chaudhuri TK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Transcriptomics Volume 3 • Issue 2 • 1000e113 ISSN: 2329-8936 TOA, an open access journal