Bacterial Involvements in Ulcerative Colitis: Molecular and Microbiological Studies

BACTERIAL INVOLVEMENTS IN ULCERATIVE COLITIS: MOLECULAR AND MICROBIOLOGICAL STUDIES

Samia Alkhalil

A thesis submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of the University of Portsmouth

Institute of Biomedical and biomolecular Sciences School of Pharmacy and Biomedical Sciences

October 2017

AUTHORS’ DECLARATION

I declare that whilst registered as a candidate for the degree of Doctor of Philosophy at University of Portsmouth, I have not been registered as a candidate for any other research award. The results and conclusions embodied in this thesis are the work of the named candidate and have not been submitted for any other academic award.

Samia Alkhalil

ABSTRACT

Inflammatory bowel disease (IBD) is a series of disorders characterised by chronic intestinal inflammation, with the principal examples being Crohn’s Disease (CD) and ulcerative colitis (UC). A paradigm of these disorders is that the composition of the colon microbiota changes, with increases in bacterial numbers and a reduction in diversity, particularly within the Firmicutes. Sulfate reducing bacteria (SRB) are believed to be involved in the etiology of these disorders, because they produce hydrogen sulfide which may be a causative agent of epithelial inflammation, although little supportive evidence exists for this possibility. The purpose of this study was (1) to detect and compare the relative levels of gut bacterial populations among patients suffering from ulcerative colitis and healthy individuals using PCR-DGGE, sequence analysis and biochip technology; (2) develop a rapid detection method for SRBs and (3) determine the susceptibility of Desulfovibrio indonesiensis in biofilms to Manuka honey with and without antibiotic treatment. Mucosal biopsy DNA from 4 colitis patients and one healthy individual was used to amplify 16S rRNA fragments, which were separated either by DGGE or by molecular cloning prior to sequencing, and dissimilatory sulphite reductase (dsr) gene fragments, which were cloned and sequenced. The number of bands separated by DGGE varied from 3 to 12 for an individual. The profiles from the UC patients had a greater similarity than the one from the healthy individual. In total, 25 bands were excised for sequence analysis but only 4 produced usable sequences. The 16S rRNA gene library comprised 250 clones, the sequences of which showed great changes in diversity from the healthy individual to the UC patients. The sequences from the healthy individual represented members of the Bacteroidetes, Clostridiaceae, Ruminococcaceae, Enterobacteriaceae, Coriobacteriaceae and Lactobacillaceae, whereas those form the UC patients comprise only members of the Enterobacteriaceae. The library for the dsr gene comprised 30 clones, with sequences from the healthy individual representing members of the Desulfobacteraceae (45%), and the Desulfovibrionaceae (33%), with minor components from the Desulfohalobiaceae, Desulfomicrobiaceae and Desulfonatronaceae. Sequences from the UC patients were less diverse, being composed primarily of Desulfovibrionaceae sequences.

Oligonucleotide probes targeting SRB and other bacterial species found to be involved in UC were developed or selected from published sources. Cassettes with multiple probes were used to evaluate the hybridisation probes for inclusion in a biochip. Validated probes (23), with similar binding profiles, were then used to assess the levels of SRB and other bacteria associated with UC in healthy control and UC patient’s samples. The DNA from the healthy individual gave a strong hybridisation signal for Desulfovibrio piger, and weaker signals for Escherichia coli, Bacteroides fragilis, and a Clostridium species, whereas the signals Desulfobacter and Desulfovibrio gigas were present in all of the samples from the UC patients. Signals were also recorded for species of Fuscobacterium, which have been implicated in colitis. In this investigation, procedure for rapid detection and quantification of SRB was developed. The SRB growth was monitored in the presence and absence of Escherichia coli BP, the copy number of the dsrA and adenosine-5′-phosphosulfate reductase (aps) genes was detected in DNA and RNA purified from different ages of SRB culture using TaqMan qPCR. The result showed that in the presence of E.coli BP enhanced the growth of SRB and allowed the detection of SRB in low dilutions comparing with the growth of SRB only. E. coli and SRB are both implicated in colonic infections. They pose several mechanisms for immune evasion and antibiotic resistance, one of these being their ability to grow in a biofilm. The present investigation has highlighted that Manuka honey and antibiotic treatment might have a protective role against SRBs and enteric bacteria. However, mixed bacterial population in biofilms exhibit greater resistance to both treatments, and this needs to be taken into account when devising a treatment based on ingestion of honey. The present investigation were conducted as a baseline study for determination the role of SRB in the pathogenesis of colitis through the use of novel methodology allowing for rapid detection and screening investigations into species and genera of interest present in colonic mucosa; that may allowed in further investigation to evaluate the bacterial role in UC through extensive studies in broad multinational cohort of patients suffering from this UC.

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ACKNOWLEDGEMENTS

First of all, all thanks and gratitude go to Almighty ALLAH for guiding and inspiring me during this research process. Secondly, I offer my sincerest gratitude to my parents who supported me during my study, without them and their help this mission would have been impossible. My deepest gratitude to my husband Tariq for his patience and support while I’m doing this research and to my sons, Mohammed, Abdulaziz and my daughter Monera with love to all of you.

I would like to express my thanks to my first supervisor Dr. Vitaly Zinkevich for giving me the opportunity to give a great hand in this project, this thesis would not be achieved without his presence and support.

I am sincerely grateful to my supervisor Dr. Julian Mitchell for his supervision, motivation for friendly guidance and enormous support and help during the stages of this thesis. Without his constructive feedback and tremendous support this thesis would not have seen the light of day. Thank you, Dr. Julian for all positive energy that you gave me.

I would like to tank Dr. Irina Bogdarina for her continues help and also Dr. Nelly Sapojnikova who was always available for help and Dr. Tamar Kartvelishvili for the warm welcome and for their assistance during the stay in the University of Tbilisi. Thanks to my sponsor Ministry for Higher Education in Saudi Arabia for their financial and academic support represented by Saudi Arabian Cultural Bureau in London.

Finally, I offer my regards and blessings to all of those who have supported me and given assistance to me in any respect during the completion of this thesis.

LIST OF ABBREVIATIONS

APS Ammonium persulphate

APS Adenosine phosphosulfate

ATCC American type culture collection

ATP Adenosine triphosphate

BLAST Basic Local Alignment Search Tool bp Base pairs cDNA Complementary DNA

CD Crohn’s Disease

Ct Cycle threshold

Cy3 Cyanine 3

DGGE Denaturing gradient gel electrophoresis

DIEA Diisopropylethyl amine

DMF Anhydrous amine free N,N-dimethylformamide

DNA Deoxyribonucleic acid dsDNA double-stranded DNA

Dsr Dissimilatory sulphite reductase

EDTA Ethyleno-diamino tetra-acetic acid

FAM 6-carboxy-fluorescein, a blue fluorescent dye

FISH Fluorescence in situ hybridization

GIT Gastrointestinal Tract

H2S Hydrogen sulphide

IDT Integrated DNA Techenlogy

IPTG Isopropyl-β-D-thiogalactopyranoside kb Kilobase pairs

LB Lysogeny or Luria Broth MBC Minimum Bactericidal Concentration

MGO Methylglyoxal

MIC Minimum Inhibitory Concentration mRNA Messenger RNA

MW Molecular weight

NA Avogadro’s number

PCR Polymerase chain reaction

PFGE Pulsed Filed gel Electrophoresis

QPCR Quantitative PCR

QS Quorum sensing

RNA Ribonucleic acid rpm Revolutions per minute rRNA Ribosomal RNA RT Room Temperature sp. Species

SRB Sulfate-reducing bacteria ssDNA single stranded DNA

TAMRA Tetramethylrhodamine

TEMED N,N,N’,N’Tetramethylethylenediamine

Tm Melting temperature, the temperature at which a DNA double helix dissociates into single strands Tris-HCl Tris (hydroxylmethyl) aminomethane UC Ulcerative Colitis UV Ultra Violet light

VM Vitamin Medium

VMI Vitamin Medium with Iron

VMR Vitamin Medium with Reduced iron

X-Gal 5-bromo-4-chloro-3-indolyl-β-D- galactopyranoside %(v/v) Percentage volume/volume

%(w/v) Percentage mass/volume

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TABLE OF CONTENTS

AUTHORS’ DECLARATION…………………………………………….……………………………………………..I ABSTRACT ...... II ACKNOWLEDGEMENTS ...... IV LIST OF ABBREVIATIONS ...... V TABLE OF CONTENTS ...... VIII LIST OF FIGURES ...... XII LIST OF TABLES ...... XVIII DISSEMINATION ...... XXI CHAPTER 1: INTRODUCTION ...... 1 Section 1 ...... 1 The Microbiota of the Human Large Intestine ...... 2 1.1 .1 General Overview ...... 2 1.1.2 Development of the intestinal Microbiota ...... 3 1.1.3 Normal intestinal microbiota a substantial factor in health ...... 5 1.1.4 Intestinal Microbiota in Disease ...... 5 1.1.4.1 Ulcerative colitis (UC) ...... 5 1.1.4.2 Role of bacteria in UC ...... 8 1.1.4.3 Bacterial composition and UC ...... 9 1.1.5 Dysbiosis of the intestinal microbiota in UC ...... 10 1.1.6 Miscellaneous Bacterial/Host Product in Colitis ...... 12 Section 2 ...... 14 The contribution of sulphate reducing bacteria in UC ...... 14 1.2.1 Sulphate Reducing Bacteria in the colon ...... 14 1.2.2 Interaction of SRB with other organisms ...... 16 1.2.3 Role of SRB in the aetiology of UC ...... 17

1.2.4 Sulphate Reducing Bacteria, H2S, and Colitis – A potential Nexus ...... 18 1.2.4 Genetic Diversity of SRB – 5’-phosphosulphate reductase (apsr1) and dissimilatory sulphite reductase (dsrAB) ...... 20 Section 3 ...... 22 Molecular techniques for the detection and identification bacteria in human colon ...... 22 1.3.1 General Overview ...... 22 1.3.2 Polymerase chain reaction Amplification (PCR) ...... 26 1.3.3 16S rRNA Gene Sequencing and Phylogenetic analysis ...... 27 1.3.4 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 28

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1.3.5 Pulsed field gel electrophoresis (PFGE)...... 30 1.3.6 Quantitative PCR (qPCR) ...... 31 1.3.7 Microarray ...... 32 Section 4 ...... 35 The effect of antibiotics and honey on the growth of bacterial biofilms ...... 35 1.4.1 Bacterial biofilms ...... 35 1.4.2 The aspects of bacterial Quorum Sensing in the colon ...... 36 1.4.3 Existing UC therapies ...... 37 1.4.4 Traditional medicine as a potential treatment for UC ...... 39 AIMS OF THE RESEARCH ...... 41 CHAPTER 2: MATERIALS AND METHODS ...... 42 Molecular characterisation of mucosa-associated bacterial communities from human colon ...... 43 2.1.1 Bacterial growth conditions ...... 43 2.1.1.1 Purification of genomic bacterial DNA ...... 44 2.1.2 Polymerase chain reaction (PCR) ...... 45 2.1.2.1 PCR for bacterial 16S rRNA genes ...... 45 2.1.2.2 PCR detection of SRB (for dsr, Desulfovibrio 16S rRNA and aps genes) ..... 45 2.1.2.3 Positive controls for PCR reactions ...... 47 2.1.3 Analysis of PCR products using agarose gel electrophoresis ...... 48 2.1.4 Cloning: construction of genomic libraries and sequencing ...... 48 2.1.4.1 Ligation of plasmid DNA and competent cells transformation ...... 48 2.1.4.2 Purification of plasmid DNA ...... 49 2.1.4.3 Purification of PCR product and Sequencing ...... 50 2.1.5 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 50 2.1.6 DNA extraction from DGGE gels and Sequencing ...... 51 2.1.7 Phylogenetic analysis ...... 52 Section 2 ...... 53 Probe designing and testing ...... 53 2.2.1. DNA probes design ...... 53 2.2.2 Cassette construction for fine-tuning of biochips ...... 54 2.2.3 Development of a biochip using dendrimeric matrix ...... 54 2.2.4 Hybridization ...... 55 2.2.5 Genomic DNA preparation for hybridization on a biochip ...... 57

2.2.5.1 DNA amplification ...... 57 2.2.5.2 Purification of amplified products ...... 57 2.2.5.3 DNA Fragmentation ...... 58 2.2.5.4 DNA labelling and hybridization ...... 58 Section 3 ...... 60 Detection and quantification of sulphate reducing bacteria using microbiological and quantitative PCR techniques ...... 60 2.3.1 Bacterial culture preparation ...... 60 2.3.2 Purification of total bacterial RNA ...... 61 2.3.2 .1 cDNA Synthesis ...... 62 2.3.3 Preparation of PCR fragment of the apsA and the dsrA genes ...... 62 2.3.4 Purification of DNA fragments from the agarose gel ...... 63 2.3.5 Quantitative real-time PCR (Q PCR) ...... 63

2.3.6 Measurements of hydrogen sulfide (H2S) ...... 64 2.3.7 Pulsed field gel electrophoresis (PFGE)...... 64 2.3.7.1 Preparation of Agarose Embedded Bacterial DNA ...... 64 2.3.7.2 Restriction Enzyme Digestion of Plugs ...... 66 2.3.7.3 Casting the gel ...... 66 2.3.7.4 Removing and staining the gel ...... 66 Section 4 ...... 68 The effect of Manuka honey and antibiotics on the growth of pure and mixed sulphate reducing bacterial biofilms ...... 68 2.4.1 Honey samples ...... 68 2.4.2 Antibiotics...... 68 2.4.3 Bacterial growth conditions ...... 68 2.4.4 Preparation of honey samples ...... 69 2.4.5 Determination of the Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of honey and antibiotics ...... 69 2.4.6 Preparation of Methylglyoxal (MGO) ...... 71 CHAPTER 3: RESULTS AND DISCUSSION ...... 72 Section 1 ...... 73 Analysis of Biopsy DNA samples using 16S rRNA and DSR genes sequences ...... 73 3.1.1 PCR for bacterial 16S rRNA genes ...... 73 3.1.2 DGGE analysis of microbial communities in the biopsy samples...... 78

3.1.3 Diversity analysis of PCR products of bacterial 16S rRNA and dsr genes by sequencing ...... 83 3.1.4 Phylogenetic analysis ...... 107 Section 2 ...... 120 Design of a Biochip for screening of mucosa associated bacteria in healthy and colitis patients ...... 120 3.2.1 Oligonucleotide probe design ...... 120 3.2.2 Fine-tuning of biochips using cassette approach ...... 128 3.2.3 Analysis of DNA samples of healthy control and patients suffering from UC using biochip ...... 134 Section 3 ...... 144 Detection and quantification of SRB using microbiological and quantitative PCR techniques ...... 144 3.3.1 The growth of different age of Desulfovibrio species culture in VMR and VMI media ...... 144 3.3.2 Quantification of D. indonesiensis culture using q PCR ...... 154 3.3.3 Quantification of Hydrogen sulfide produced by D. indonesiensis grown with and without E. coli BP ...... 159 3.4 Genome Sizes of D. indonesiensis estimated by Pulsed-Field Gel Electrophoresis ..161 Section 4 ...... 165 The effect of antibiotics and Manuka honey on the growth of sulfate reducing bacterial biofilm ...... 165 3.4.1 The MIC and MBC values of different Manuka honey on D. indonesiensis in pure culture and mixed culture with E. coli BP ...... 166 3.4.2 The susceptibility of D. indonesiensis in pure culture and mixed culture with E. coli BP when treated with Manuka honey and antibiotics ...... 177 CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION ...... 188 FUTURE WORK ...... 196 REFERENCES ...... 199 References ...... 200 APPENDICES ...... 240 Appendix 1: Growth media composition ...... 241 Appendix 2: Phylogenetic analysis ...... 244 Appendix 3: Taxonomic report ...... 275

LIST OF FIGURES Figure 1.1.1 The link between global diet, bacterial composition and the risk of colonic disease development (Simpson & Campbell, 2015; Vipperla K & O’Keefe, 2016)………………………………………………………………………………………………………………………...7 Figure 1.2.1. Metabolic pathway used by SRBs to reduce sulphate (adapted Carbonero et al., 2012); APS: adenosine 5’- phosphosulfate…………………………………………………………………………………………………...……19 Figure1.2.2. Sulphide as a Mucus Barrier-Breaker in IBD (Adapted from Ijssennagger et al., 2016)…………..…………………………………… …………………………………………………………….…20 Figure 1.3.1. Schematic diagram of molecular techniques used for colonic bacterial analysis (adapted from Muyzer & Smalla, 1998; Vaughan et al., 2000)………………………………………………………………………………………………………………………26 Figure 1.3.2. The principles of DGGE………………………………………………………………………..30 Figure 1.3.3. Comparison of and A) SYBR®-Green, B) TaqMan®- Based Detection Workflows Adapted from (Cao & Shockey, 2012)………………………………………………………32 Figure 1.3.4. Principle of the microarray technique (adapted from Namsolleck et al, 2004)………………………………………………………………………………………………………………………34 Figure 1.4.1 biofilm formation – 1. Cell adhesion 2. Formation of micro colonies, 3. Accompanied by EPS production. 4. The formation and maturation5. The dispersion cells from biofilm (Kjelleberg et al., 2007)…………………………………………………………………35 Figure 2.2.1. Photograph for the hybridization chamber………………………………………...... 56 Figure 3.1.1. A photograph of agarose gel showing the PCR amplified products of 16SrRNA gene (550 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of Patient 1 suffering from UC; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5: PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7: Negative control; lane 8: DNA D. alaskensis……………………………………………………………….74 Figure 3.1.2. A photograph of agarose gel showing the PCR amplified products of 16SrRNA gene (1500 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5:PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7: Negative control; lane 8: DNA D. alaskensis……………………………………………………………….75 Figure 3.1.3. A photograph of agarose gel showing the PCR amplified products of desulfovibrios 16S rRNA gene (135 bp) with primers: DSV691F and DSV826R. Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of

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Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer…………..76 Figure 3.1.4. A photograph of agarose gel showing the PCR amplified products of dsrA gene (221 bp) with primers: DSR1F+and DSR-R Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer…………………………………………………..77 Figure 3.1.5. DGGE profile demonstrating diversity of bacterial populations in biopsy samples obtained from healthy volunteer and patients suffering from UC based on PCR products of 550 bp of bacterial 16S rRNA. Lane 1: UC Patient 1, Lane 2: UC Patient 2, Lane 3: UC Patient 3, Lane 4: UC Patient 4, Lane 5: Negative control, Lane 6: healthy volunteer…………………………………………………………………………………………………………………80 Figure 3.1.6. UPGMA dendogram showing similarity of DGGE profiles of microbial communities in all colonic biopsy samples of UC patients and healthy control used in this study. Bands from each sample were coded and a UMPGA tree was produced using the distance algorithm described by Nei and Li (1979) and executed in PAUP* 4.0. P1: UC patient number 1, P2: UC patient number 2 , P3: UC patient number 3 , P4: UC patient number 4 , H: healthy individual……………………………………………………………………………….81 Figure 3.1.7. A histogram demonstrating the Family level of bacterial composition present in healthy patient and patients suffering from UC (16S rRNA gene)………………84 Figure 3.1.8. Proportion of bacterial genera present in the colon of UC patients and healthy control (16S rRNA)………………………………………………………………………………………93 Figure 3.1.9. A histogram demonstrating the Family level of SRB composition present in healthy patient and patients suffering from UC (dsrA gene)……………………………………….96 Figure 3.1.10. Distribution of genera from heathy and UC patients. The numbers above each histogram represents the number of species identified as Desulfovibrio from each patient…………………………………………………………………………………………………………………….98 Figure 3.1.11. Species of Desulfovibrio present in a healthy individual and four UC suffers……………………………………………………………………………………………………………………105 Figure 3.1.12. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Firmicutes and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown…………………………………………………………………………………………………………….109 Figure 3.1.13. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Bacteroidetes and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown…………………………………………………………………………………………………………….112

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Figure 3.1.14. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Proteobacteria and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown…………………………………………………………………………………………………………….113 Figure 3.1.15. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Actinobacteria and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown……………………………………………………………………………………………………………114 Figure 3.1.16. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Proteobacteria and those of random clones from patients suffering from UC the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown……………………………………………………………………………………………………………………116 Figure 3.1.17. Deltaproteobacteria maximum-likelihood (model TN93) dsr gene based phylogenetic tree. The tree reflected the relative placement of the dsr gene clones from healthy control and patients suffering from UC among sulfate-reducing Deltaproteobacteria. Numbers placed above the branches were the support values estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown…………………………………………………………………………………………………………….118 Figure 3.2.1. The composition of the ss cassettes………………………………………………………………………………………………………..………127 Figure 3.2.2. Schematic diagram and the results of the designed cassette N1 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4,B1,C1,D1, E1 – Probes position markers; B2,C2 – Dslpig for Desulfovibrio piger ;D2,E2 – Dslgig for Desulfovibrio gigas ;B3,C3 – Dslsp for Desulfovibrio sp; D3,E3 – S1 for SRB; B4,C4 -16S Negative control; D4,E4 – were left empty to give background signal.…………………………………………………………………………………………………………………….129 Figure 3.2.3. Schematic diagram and the results of the designed cassette N2 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Dsbub for Desulfobulbus; D2, E2 – Dsbac for Desulfobacter; B3, C3 – Dsldes for Desulfovibrio desulfuricans; D3, E3 – Desfot for Desulfotomaculum; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.…………………………………………………....130 Figure 3.2.4. Schematic diagram and the results of the designed cassette N3 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Ent1 for

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Enterobacteriaceae; D2, E2 –EC for E.coli; B3, C3 – Bacfr for Bacteriodes fragilis; D3, E3 –Bacsp for Bacteriodes sp; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.…………………………………………………………………………………………130 Figure 3.2.5. Schematic diagram and the results of the designed cassette N4 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes.The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Cldif for Clostridium difficile; D2, E2 –Clsp1 for Clostridium sp; B3,C3 –Fusv for Fusobacterium varium; D3, E3 –Bif for Bifidobacterium; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.…………………………………………………………………………………………131 Figure 3.2.6. Schematic diagram and the results of the designed cassette N5 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Fusnu for Fusobacterium nucleatum; D2, E2 –Rum for Ruminococcus; B3, C3 –Fusn for Fusobacterium necrophorum; D3, E3 –Lac for Lactobacillus; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.…………………………………..131 Figure 3.2.7. Schematic diagram and the analysis of the designed cassettes hybridization with biochip. The results of the hybridization as signal to noise (S/N) ration. The data presented are mean values±SD.……………………………………………………………………………………………………………..133 Figure 3.2.8. Schematic diagram and the hybridization analysis of the DNA obtained from healthy individual. The hybridization results represented as signal to noise (S/N) ratio.……………………………………………………………………………………………………………………...135 Figure 3.2.9. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 1. The hybridization results represented as signal to noise (S/N) ratio…...…………………………………………………………………………………………………………………136 Figure 3.2.10. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 2. The hybridization results represented as signal to noise (S/N) ratio…...……………………………………………………………………………………………………...…………..137 Figure 3.2.11. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 3. The hybridization results represented as signal to noise (S/N) ratio. …...…………………………………………………………………………………………………….………………….138 Figure 3.2.12. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 4. The hybridization results represented as signal to noise (S/N) ratio. …...…………………………………………………………………………………………………….…………………...139 Figure 3.2.13. Heat map of bacteria detected in the samples of healthy individual and UC patients. Cells are coloured according to the S/N ratio calculated for each sample and probe as per the key provided. ………………………………………………………………………………………………….…………………………..141

Figure 3.3.1. Growth of 7days old D. indonesiensis culture in VMR medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.………………………………………………………………………….……………………………….146 Figure 3.3.2. Growth of 3 months old D. indonesiensis culture in VMR medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.……………………………………………………………………….………………………………….147 Figure 3.3.3. Growth of 1 year old D. indonesiensis culture in VMR medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.……………………………………………………………………….…………………………………..149 Figure 3.3.4. Growth of 7 days old D. indonesiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.…………………………………………………………………….……………………………………150 Figure 3.3.5. Growth of 3 months old D. indonensiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonensiensis, (B) Growth of D. indonensiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.…………………………………………………………………….……………………...152 Figure 3.3.6. Growth of 1 year old D. indonesiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.…………………………………………………………………….……………………...... 153 Figure 3.3.7. Standard curve for apsA gene……………………………………………………………………………………………………..……….……….155 Figure 3.3.8. Standard curve for dsrA gene…………………………………………………………………………………………………………….………...156

Figure 3.3.9. The concentration of H2S of 7 days D. indonesiensis culture with and without E.coli BP. three independent experiments were done in triplicate. The data presented are mean values±SD.…………………………………………………….………...... 159 Figure 3.3.10. Pulsed-field gel sizing of SRB genomes from (a) D. indonensiensis and (b) E.coli: Lanes (1) Schizosaccharomyces pombe size range 3.5-5.7 Mb; (2) lambda ladder size range 0.05–1 Mb ;(3) D. indonensiensis digested – PmeI; (4) D. indonensiensis; (5) D. indonensiensis digested-NotI; (6) Saccharomyces cerevisiae ladder size range 240- 2200 Kb; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI;(10) D. indonensiensis. Sizes of selected DNA standards (BIO-RAD DNA Size Markers–Yeast Chromosomal; BIO-RAD DNA Size Markers lambda and BIO-RAD DNA Size Markers–S. pombe Chromosomal DNA) are indicated…………………………………………………….………….163

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Figure 3.4.1. The MIC values for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added simultaneously with culture and then incubated for 7 days) when treated with Manuka honey UMF® 5+, UMF® 15+, UMF® 25+ and commercial honey. The MIC values are the average from 5 independent experiments and are displayed with standard deviation.………………………………………………….………………...167 Figure 3.4.2. The MBC values for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added simultaneously with culture and then incubated for 7 days) when treated with Manuka honey UMF®5+, UMF®15+, UMF®25+ and commercial honey. The mean MBC values were calculated from 5 independent repeat experiments and shown with standard deviation……………………………………………….……170 Figure 3.4.3: The MIC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ and commercial honey for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added after incubating the culture for 6 days). The mean values were calculated form 5 independent experiments and shown with standard deviation……..173 Figure 3.4.4. The MBC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ Manuka honey and commercial honey for E. coli BP and D. indonesiensis grown as pure and mixed population (the honey was added after incubated the culture for 6 days). The mean values were calculated from 5 independent experiments and shown with standard deviation.…………………………………………….………………………………………………….……………175 Figure 3.4.5. The MIC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics and honey were added simultaneously to the cultures), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics were added after 6 days incubation with Manuka UMF® 15+). Mean values were estimated from 5 independent experiments and are shown with standard deviation.………………………………………….……………………………..179 Figure 3.4.6. The MBC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding the antibiotics and honey simultaneously), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding antibiotics after 6 days of incubation with Manuka UMF® 15+). The mean values were estimated from 5 independent experiments and are shown with standard deviation………………………………………….…………………………………………………………………….182 Figure 3.4.7: The MIC value of Methylglyoxal (MGO) of D. indonensiensis as pure and mixed culture with E.coli BP. The mean values were estimated from 5 independent experiments and are shown with standard deviation.….…………………………………….…….185 Figure 3.4.8. The MBC value of Methylglyoxal (MGO) of D. indonensiensis as pure and mixed culture with E.coli BP. Mean MBC values were estimated from 5 independent experiments and are shown with standard deviation.….…………………………………..………186

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LIST OF TABLES

Table 1.2.1 The Characteristics and metabolic activities of some key genera of SRB in human colon………………………………………………………………………………………….…………………15

Table 1.3.1. The role of intestinal bacteria in human health and disease………………...... 23

Table 2.1.1. Sequence of primers used in PCR……………………………………………….………….47

Table 3.1.1. Sequencing results of bacterial 16S rRNA from the bands cut from DGGE gel……………………………………………………………………………………………………………………………83

Table 3.1.2. The species level of bacterial composition present in the colon of UC patients and healthy control (16S rRNA)…………………………………………………………………...88 Table 3.1.2 contd.……………………………………………………………………………………………………89 Table 3.1.2 contd.……………………………………………………………………………………………………90 Table 3.1.2 contd..………………………………………………………………………………………………..….91

Table 3.1.3 The level of SRB species present in the colon of UC patients and healthy individual (dsr gene) ……………………………………………………………………………………………..100 Table 3.1.3 contd.………………………………………………………………………………………………….101 Table 3.1.3 contd..…………………………………………………………………………………………………102 Table 3.1.3 contd..…………………………………………………………………………………………………103

Table 3.2.1. List of design probes……………………………………………………………………..…….122 Table 3.2.1. Cont.…………………………………………………………………………………….……………123 Table 3.2.2. List of published DNA probes.……………………………………………………………...124

Table 3.3.1. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.………………………………………145

Table 3.3.3. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.……………………………………….148

Table 3.3.4. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.………………………………………150

XVIII

Table 3.3.5. Comparison of 3 months old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth…..151

Table 3.3.6. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate……………………………….……….153

Table 3.3.7. Quantification of the dsrA and ApsA genes in genomic DNA purified from 7 days old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical analysis was based on several independent experiments.…………………...…………………...157

Table 3.3.8. A comparison of the copy number of dsrA and apsA genes detected in genomic DNA isolated from 7 days, 3months and 1 year old D. indonesiensis cultures using Kruskal-Wallis statistic test based on ten replicates……………….……………………….157

Table 3.3.9. Quantification of the dsrA and ApsA genes in RNA purified from 7 days, 3 months old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical analysis was based on several independent experiments.………………………………………...158

Table 3.3.10. A comparison of the copy number of dsrA and apsA genes detected in transcripts in total RNA isolated from 7 days and 3 months old D. indonesiensis cultures using Mann-Whitney test statistic based on ten replicates of dsr and aps genes………..158

Table 3.3.11. Estimated gel size Measurement of the genomes from (a) D. indonensiensis and (b) E.coli: (3) D. indonensiensis digested – PmeI; (5) D. indonensiensis digested-NotI; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI.……………………………………………………………………………………………………………..………..163

Table 3.4.1: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Only comparisons which reached significance are shown………………………………………………..169

Table 3.4.2: A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics…….171

Table 3.4.3: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics……..174

Table 3.4.4. A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics……..176

Table 3.4.5. A comparison of the MIC values obtained when different concentrations antibiotics and UMF®15+ were applied Kruskal-Wallis test statistics………………………180

Table 3.4.6. A comparison of the MBC values obtained when different concentrations antibiotics and UMF®15+ were applied using Kruskal-Wallis test statistics……………..183

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Table 3.4.7. A comparison of the MIC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics.……………………186

Table 3.4.8. A comparison of the MBC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics……………………187

DISSEMINATION

1. Faculty of Science Research Degree Students, Poster, June 2012, University of Portsmouth. 2. Faculty of Science Research Degree Students Poster, Jun 2013, University of Portsmouth. 3. Nutrition Society conference, Diet, gut microbiology and human health, Oral Presentation, December 2013, Royal College of Surgeons, London, UK. 4. Seventh Saudi Students Conference (SSC2014), Edinburgh international Conference Centre (EICC), Poster, Feb 2014, Edinburgh, UK. 5. Alkhalil S, Chopra M and Zinkevich V. Antibacterial activity of Manuka honey on the growth of pure and mixed bacterial biofilms formed by sulphate reducing bacteria and Escherichia coli. Proceedings of the Nutrition Society. (2014), 73 (OCE1), E36, Abstract. 6. Zinkevich, V., Sapojnikova, N., Mitchell, J., Kartvelishvili, T., Asatiani, N., Alkhalil, S., Al- Humam, A. A. (2014). A novel cassette method for probe evaluation in the designed biochips. PLoS One, 9(6), e98596. doi:10.1371/journal.pone.0098596 7. Training on biochip technology, Tbilisi University, Georgia. 8. The 8th Saudi Students Conference (SSC2015), Imperial College London, Poster, Feb 2015, London, UK. 9. The 9th Saudi Students Conference (SSC2016). The International Convention Centre, University of Birmingham, Poster, Feb 2016, Birmingham, UK.

XXI

CHAPTER 1: INTRODUCTION

Section 1 The Microbiota of the Human Large Intestine

1.1 .1 General Overview

The human body plays host to a complex community consisting of taxa from across the tree of life. Bacteria are the major microbial organism present in the gut, however, many other organisms can be found such as protozoa, fungi and viruses. The interactions of these microbes with each other and the host have a profound influence on physiology in both health and disease (Macpherson & Harris, 2004; O’Hara & Shanahan, 2006; Clemente et al., 2012). The microorganisms that coexist peacefully with their hosts are referred to as normal microflora. It is estimated that in humans this microflora community contains between 1013 to 1014 bacteria, 10 times greater than the number of human cells present in the body (Hooper & Gordon, 2001). Almost the entire human body, including the respiratory and urogenital tracts and the skin, are colonized by microbiota, however, it is the gastrointestinal tract (GIT) which is most heavily colonized due to the abundance of nutrients as a food source. Within the GIT the colon has been found to contain more than 70% of the total number of microbes in human body (Whitman et al., 1998; Ley et al., 2006). This complex microbial community within the GIT plays a crucial role in human health with an influence on host metabolism, physiology, protection, nutrition and immune function (Ley et al., 2008). Therefore any alteration, imbalance (dysbiosis) or change in bacterial function within the GIT ecosystem, or changes in bacterial interactions with each other can impact on human health and is thought to contribute to the development of key intestinal diseases such as inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (Underwood, 2014; Carding et al., 2015).

Bacteria are usually found in a dynamic environment containing numerous different microbial species populations in which frequent interactions occur (Shoaie et al., 2013). Therefore, a better understanding of the interactions between bacteria will help to improve our understanding of how bacterial genetic information can impact on human health and disease. Several studies have highlighted a link between intestinal bacteria

CHAPTER 1: INTRODUCTION and human disease (Frank et al., 2007; Guinane & Cotter, 2013; Carding et al., 2015). Additionally, determining the growth conditions required for culturing single isolates remains challenging. Although much progress has been made using culture-based methods to characterize bacterial diversity within the GIT, with over 400-500 different microbial species fully identified and assessed (Hiergeist et al., 2015), it is thought that this method has only identified a small fraction of the full diversity existing within the GIT. Molecular ecological studies allow for more accurate analysis of the microbiota within the GIT, revealing that the intestinal bacterial ecosystem is more complex and diverse than previously expected with the vast majority of microbial species remaining uncultivated (Zoetendal et al., 2004). Most of these studies rely on analysis of the highly conserved ribosomal RNA (rRNA) sequences of bacteria. Molecular approaches used to study the microbial population colonizing the human colon can facilitate both high- detail and high-throughput analysis of phylogenetic diversity (Rajendhran & Gunasekaran, 2011). Molecular techniques previously used include temperature or denaturing gradient gel electrophoresis (TGGE or DGGE), quantitative polymerase chain reaction (qPCR), and Microarray technology (Houpikian & Raoult, 2002). The estimation of the structure of the bacterial population in the large intestine is moving closer to absolute quantification (Swidsinski et al., 2005; Casen et al., 2015). However, whilst absolute quantification of bacterial populations may soon be possible, the major future goal of colonic bacterial studies will be to investigate the interactions of these bacteria with each other and the host, and how these interactions can impact on human health and disease. To this end recent major advances have been made in colonic microbiota studies aiming to determine the identity of specific bacterial groups that may contribute to host colonic diseases (Guinane & Cotter, 2013; Ohland & Jobin, 2015).

1.1.2 Development of the intestinal Microbiota

As well as bacteria the gut microflora also contains archaea, fungi and viruses, advancement in sequencing technologies and the use of metagenomics approaches are helping to characterise these total populations; including identifying various factors involved in their sustenance (Hoffmann et al., 2013). All humans are born with an almost sterile colon, however both bottle and breastfed infants rapidly start

CHAPTER 1: INTRODUCTION accumulating Gram-positive and Gram-negative intestinal bacteria (Koenig et al., 2011). The early colonizers are typically Enterococci and Enterobacteria, which are mainly facultative anaerobes (Zetterstrom et al., 1994; Dogra et al., 2015; Avershina et al.,

2016). Both these bacterial species significantly reduce oxygen pressure (pO2) within the GIT creating an environment suitable for the growth of other anaerobes mainly Bacteroides and Bifidobacteria (Roberts et al., 1992; Knol et al., 2005). It has been observed that bacterial populations in the colon varies among infants depending on region of birth and type of feeding, i.e. breastfeeding or formula milk (Gomez-Llorente et al., 2013; Patel et al., 2015). In adults, colonic microorganisms are mainly anaerobic in nature and the population is typically stable (Hoffmann et al., 2013). Differences do exist in the diversity of bacterial population among individuals from different geographical regions of the world. An evaluation of 13,355 prokaryotic ribosomal RNA genes from multiple colonic mucosal sites and faeces from healthy subjects demonstrated significant intersubjective variability as well as the differences between the stool and mucosal bacterial population (Eckburg et al., 2005).

The composition and functionality of the human intestinal microbiota has long been of great interest. More than 1000 different species have been reported to colonize the human intestine, with the majority being of unknown species belonging to anaerobic strains (Bäckhed et al., 2005; Zoetendal et al., 2006; Rajilić-Stojanović et al., 2007; Jandhyala et al., 2015). Microbial colonization occurs throughout the length of the large intestinal gut with marked differences in the density and composition of colonization. The colonization of bacteria in the large intestine is influenced by many factors such as host secretions, environmental conditions, substrate availability, transit rates, and the organization of the gut wall. Thus, the large intestine mostly supports the establishment of a dense population dominated by anaerobic bacteria (Graf et al., 2015). Information on the composition of colonic microbiota is derived mainly from molecular characterisation of faecal samples, which do not usually reflect mucosa-associated bacteria. Consequently, relatively little is known about the composition of the microflora of the large intestine.

CHAPTER 1: INTRODUCTION

1.1.3 Normal intestinal microbiota a substantial factor in health

Bacterial colonization of the human colon plays a crucial role in modulating the immunological, physiological and metabolic activities in the human body which can have a large impact on both health and disease. Bacteria can play a beneficial role by contributing to the host gut defence system and by supporting and maintaining normal gut function (Jandhyala et al., 2015). The metabolic processes of primary fermenters lead to the production of key nutrients that the human body is unable to manufacture or derive itself such as vitamins (vitamin K, vitamin B12 and folic acid), amino acids, short- chain fatty acids (SCFA), and yielding some gases such as hydrogen (H2), Methane (CH4) and carbon dioxide (CO2) and hydrogen sulphide (H2S) (Wong et al., 2006; Fischbach & Sonnenburg, 2011). These products are important to the host but are also used as a carbon and energy source for other bacteria within in the human colon. Therefore, these fermenting bacteria must maintain the balance between oxidation and reduction process whilst at the same time producing the required energy (Macfarlane & Macfarlane, 2011). In addition to this, bacteria can breakdown non-digestible foods, such as resistant starches and dietary fibres generating energy (Parker, 1976; Bergman, 1990; Maier et al., 2015).

The disposal of the hydrogen produced by bacteria in the human colon during anaerobic fermentation along with the final products produced from their metabolism plays a key role in optimal large bowel function and has an important impact on host health (Conlon & Bird, 2015). The major protective role of resident bacteria is through barrier effect, colonizing the intestinal mucosa thus leaving no space for the pathogenic bacteria attachment and subsequent proliferation (Kamada et al., 2013).

1.1.4 Intestinal Microbiota in Disease 1.1.4.1 Ulcerative colitis (UC)

UC is both an acute and chronic disorder of the large intestine, and one of the main forms of IBD. UC is marked by an inflammation affecting the mucosal layer of the colon and rectum (Di Sabatino et al., 2012). Patients with UC present symptoms such as idiopathic diarrhoea, abdominal pain, fever, rectal bleeding and weight loss; these

CHAPTER 1: INTRODUCTION usually range from mild to severe depending on the extent and severity of the inflammation (Zhang et al., 2015). Epidemiological studies have found that the occurrence of UC is between 1.5 to 24.5 per 100,000 cases, with wide variation depending upon geographical location. A significant increase in the occurrence of UC during the last 20 years has also been described (Cosnes et al., 2011; Burisch & Munkholm, 2013; da Silva et al., 2014), with a higher prevalence amongst people from the West, potentially indicating a strong influence of the western lifestyle on UC prevalence (Pajares & Gisbert, 2001; Suk-Kyun et al., 2001) (Figure 1.1.1).

CHAPTER 1: INTRODUCTION

Figure1.1. 1 The link between global diet, bacterial composition and the risk of colonic disease development (Simpson & Campbell, 2015; Vipperla K & O’Keefe, 2016)

CHAPTER 1: INTRODUCTION

Although the cause of UC remains uncertain, several factors are recognized to have a role in the development of the disease. Among these factors, the involvement of bacteria in the pathogenesis of UC has been strongly implicated. However, because there appears to be no single established cause of UC, this condition seems to have multi-factorial disease susceptibility (Fries & Comunale, 2011). The pathogenesis of UC and underlying mechanisms of the disease are an active area of investigation, with several longitudinal studies describing the mechanisms underlying the pathogenesis of this disease. However, the exact aetiological factors associated with UC are still unknown.

1.1.4.2 Role of bacteria in UC

The involvement of intestinal bacteria in the aetiology of UC is supported by studies that have shown that the inflammation occurs in regions of the intestine with the greatest concentration of bacteria, and the observation that germ-free animals do not develop the disease (Thompson-Chagoyan et al., 2005; Nell et al., 2010; Nagalingam et al., 2011; Jiminez et al., 2015). In humans, altered bacterial combinations combined with high numbers of bacteria in UC patients compared with healthy subjects have been reported by several studies (Cummings et al., 2003; Round & Mazmanian, 2009; Matsuoka & Kanai, 2015). Further evidence supporting the role of bacteria in UC comes from studies where the efficacy of using antibacterial agents as a therapy was assessed (Azad Khan et al., 1977; Sutherland et al., 1993; Moshkovska & Mayberry, 2007; Freeman, 2012). A meta- analysis evaluating 40 years of available scientific literature (1966 -2006) relevant to the efficacy of antibacterial therapies in UC suggested significant clinical remission among UC patients taking antibacterial therapy (Rahimi et al., 2007). Another later meta-analysis described a similar benefit for antibacterial treatment in the clinical trials for UC (Wang et al., 2012).

The key role of bacteria in the aetiology of UC was first proposed in the 1950’s (Marshall et al., 1950, Seneca & Henderson, 1950). In the following decades, some studies demonstrated that it was not just overall bacterial load, but also the types of bacteria associated with UC. Changes in the anaerobic environment of the colon, associated bacterial population and the generation of short chain fatty acids as fermentation

CHAPTER 1: INTRODUCTION products which used as energy source for colonic epithelial cells brought the concept of colitis as an energy deficiency diseases to the fore (Roediger, 1980). This was confirmed by several studies suggesting that bacteria disturb the synthesis of short chain fatty acids generation from carbohydrates during the pathogenesis of UC (Kim, 1998). The question of whether a specific pathogen could cause UC has been increasingly investigated. Although several bacteria have been reported to contribute to UC susceptibility, to date no specific bacteria have been confirmed to be solely responsible for the disease.

1.1.4.3 Bacterial composition and UC

The human colon contains a rich microbial population, predominated by 4 divisions including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Most bacteria within the colon have been identified as belonging to the Firmicutes and Bacteroides. Firmicutes Represent ~64% of the microbiota community, whereas Bacteroidetes ~23% of the normal microbiota (Eckburg et al., 2005; Mao et al., 2015).

There have been numerous attempts to discern the bacteria involved in UC. However, due to the invasive nature of the procedures, initial efforts were limited to evaluating the microbial content of faecal material. With the availability of colonoscopy, it has been easier to collect samples from the colon for further evaluation. A study focusing on the colon in UC reported that the bacterial population were mainly Bacteroides (Poxton et al., 1997). In this study the bacterial population present in biopsy samples of colon mucosa, collected during colonoscopy from the proximal colon and rectum of 12 patients, (six with UC and six controls without inflammation) was characterized. The study revealed no difference in the bacterial counts from proximal colonic and rectal biopsy samples. Overall, the study reported 235 isolates representing 11 species of Bacteroides. These species were uniformly distributed along the colon and differed from the communities recovered from faeces (Zoetendal et al., 2002).

Technological limitations also hampered the identification of bacterial species. Whilst initially cell culture techniques were used; the emergence of genomic techniques broadened the evaluation process. Subsequent to the completion of human genome

CHAPTER 1: INTRODUCTION project several techniques like fluorescent in-situ hybridization (FISH), restriction fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE), quantitative dot blot hybridization, and large-scale 16S rDNA sequencing emerged as powerful techniques able to overcome issues with conventional microbiological plating methods (Mai & Morris, 2004).

A GI Health Foundation sponsored supplement published in the American Journal of Gastroenterology in 2012 stated that there is a need for “additional therapeutic strategies as the overall potential of emerging techniques have not yet been validated by controlled clinical trials. These include blocking attachment of adherent bacteria, such as adherent/invasive E. coli; enhancing defective bacterial killing in genetically susceptible hosts; and faecal transplant to correct dysbiosis” further highlighting the role of bacteria in the pathogenesis of UC (Sartor & Mazmanian, 2012).

1.1.5 Dysbiosis of the intestinal microbiota in UC

Microbial diversity within the bacterial communities residing in the colon plays a significant role in modulating human health. A recent review concludes that “A healthy gut environment is regulated by the balance of its intestinal microbiota, metabolites, and the host's immune system. Imbalance of these factors in genetically susceptible persons may promote a disease state. Manipulation of the intestinal microbiota with probiotics, which can selectively stimulate the growth of beneficial bacteria, might help to maintain a healthy intestinal environment or improve diseased one” (Kataoka, 2016).

It is important to note that culture-independent techniques, such as the molecular methodologies previously described have significantly aided our understanding of the composition and diversity of colonic microbial populations. The compositional diversity of gut can be evaluated at two levels. Firstly, diversity amongst resident bacteria of the gut; and secondly, the transient bacteria introduced from external environments through ingestion or other sources such as the regulary influx of bacteria by intake of food. Therefore, understanding the factors influencing the diversity of the mucosa associated bacteria is essiential in order to underlying the pathology and a critical next

CHAPTER 1: INTRODUCTION step in further evaluating the contribution of bacteria to establishing and promoting the chronicity of colonic disease.

In 2007 the number of bacterial species described to be present in the large intestine was approximately 400, although it was reconginsed that this did not describe the full extent of bacteiral species present in the gut (Rajilić-Stojanović et al., 2007). Three years later, a catalogue of microbial genes present in the human gut, established by metagenomics sequencing, suggested that more 1000 species-level phylotypes were present in the GI tract of humans (Qin et al., 2010). It is important to mention here that the metagenomics analysis used for predicting phylotypes is based on non-redundant genes contained by an average-sized genome, so it is possible that this number will be revised as more information becomes available.

The linkage between bacteria and UC is well established, however, the exact species or strains involved in the pathogenesis of UC remain unknown limiting the potential for targeted therapies. An increase in overall bacterial population and reduction in protective bacteria like lactobacilli and bifidobacteria has been reported (Cummings et al., 2003). Additionally, probiotic modulation of bacterial populations identified key changes in the microbial ecosystem and certain bacterial species that may be involved in UC (Gerritsen et al., 2011).

Interestingly Sasaki and Klapproth (2012) performed an elaborate study to evaluate imbalances in bacterial population during the pathogenesis of colitis. Based on their evaluation of existing literature they conclude that “quantitative and qualitative microbial imbalance in UC, defined as dysbiosis, has been characterized by an increase in Rhodococcus spp., Shigella spp., and Escherichia spp., but a decrease in certain Bacteroides spp. More specifically, Campylobacter spp., Enterobacteriae, and enterohepatic Helicobacter were more prevalent in a tissue sample from UC patients subjected to molecular detection methods, but not controls. Also, serologic testing identified Fusobacterium varium as a potential contributor to the intestinal inflammation in UC. Interestingly, in-situ hybridization studies have shown anti- inflammatory Lactobacillus spp. and Pediococcus spp. were absent in the patients’ samples suffreing from UC. Therefore, dysbiosis is a factor in the pathogenesis of UC”.

CHAPTER 1: INTRODUCTION

1.1.6 Miscellaneous Bacterial/Host Product in Colitis

The human colon harbours both pathogenic and non-pathogenic bacteria. In deciphering the etiologic elements involved in colitis, several bacteria and their products have been thought to be involved in either the initiation or overall pathogenesis of the disease. Nitric oxide (NO) one of the bacterial metabolic products was considered to participate in human colitis related inflammation. Neut et al (1997) compared the quantity and quality of nitrate reducing bacteria in diversion colitis (colon inflammation occurring among patients undergoing ileostomy or colostomy) with healthy controls. The relative percentage of nitrate reducing bacteria showed statistically significant elevation among individuals suffering from diversion colitis.

Indirect observation has also provided support for the role of bacterial populations in the induction of colitis. The therapeutic use of bacterial populations as probiotics, mainly Escherichia coli strain Nissle 1917, is acknowledged as one of the most important uses of bacteria (Konturek et al., 2009, Sha et al., 2014, Souza et al., 2016). It is believed that in healthy individuals their intestinal microflora occupies all the available space inhibiting colonization by pathogenic bacteria. Under certain conditions, when the normal intestinal microflora is disturbed it provides an opportunity for either the opportunistic or disease-causing bacteria to find an attachment place followed by colonization leading to disease like colitis and others.

Johansson et al., (2014) have provided substantial evidence regarding the involvement of bacteria in UC. They proposed that the whole human sigmoid colon inner mucous layer, which is typically recalcitrant to microorganisms, becomes permeable providing access to the bacteria causing colitis and associated dysfunctions. These findings are mainly based on observations from a mouse model which revealed the presence of bacteria in epithelial areas supporting the idea of bacterial penetration. The breakage of colon mucus barrier and colitis has been supported by several studies (Chen et al., 2014). The existing animal colitis models including the dextran sulphate colitis model (Johansson et al., 2010) and Muc2 deficient mice model closely match human colitis (Wenzel et al., 2014). Using these models no direct evidence exists to support barrier

CHAPTER 1: INTRODUCTION crossing by the sulphate-reducing bacteria. However, this might be a very plausible mechanism to be explored in future studies for providing a proof of concept.

CHAPTER 1: INTRODUCTION

Section 2 The contribution of sulphate reducing bacteria in UC

1.2.1 Sulphate Reducing Bacteria in the colon

The human gut microbiota helps in extracting nutrients and energy from our daily diet. A variety of dietary components not metabolized in the small intestine are metabolized by fermentation when they reach the colon, and this is driven by bacteria. Maintaining the redox balance is a major problem faced during fermentation. The H2 produced during the fermentation must be consumed, otherwise its high concentrations leads to several adverse effects such as the accumulation of toxic compounds like butyrate.

Overcoming such toxicity by H2 consuming bacteria is likely to be a significant role in the gut. (Gibson et al. 1990; Macfarlane & Macfarlane, 2012). Approximately 30-50% of individuals in the West harbour bacteria capable of producing methane from H2 in their gut. These individuals have methanogenic bacteria in their large intestine converting hydrogen into methane and are called methanogenic individuals (Triantafyllou et al., 2014). However, these are not the only bacteria capable of consuming hydrogen.

In the human colon, the H2 consuming microbial population (hydrogenotrophic) can be split into three classes; methanogens, acetogens and sulphate reducing bacteria (SRB). This classification is based on the products they release upon utilizing hydrogen. The methanogens produce methane, acetogens acetate and SRBs H2S. and of the three classes the SRB are the most efficient in utilizing sulphate (Gibson et al., 1988a). It has also been reported that the human colon mainly harbours Desulfovibrio of the class δ- Proteobacteria, the main genus of SRB (Scanlan et al., 2009). Sulphate reducing bacteria (SRB) are a group of unrelated microorganisms that can utilize oxidized sulphur compounds, as final electron acceptors; during growth under anaerobic conditions.

Through utilizing H2 SRB produce methane, acetate and H2S according to the following equation: 4 H2 + SO42- + H+ → HS- + 4 H2O (Plugge et al. 2011 ; Carbonero & Gaskins, 2014).

CHAPTER 1: INTRODUCTION

In general, the SRB are a highly diverse group of microorganisms including Desulfovibrio, Desulfomicrobium, Desulfobulbus, Desulfobacter, Desulfococcus, Desulfosarcina, Desulfonema, Desulfotomaculum, and Thermodesulfobacterium. The metabolic activities and characteristics of main key genera of SRB that present in the human colon are sumrise in Table 1.2.1. Table 1.2.1 The Characteristics and metabolic activities of some key genera of SRB in human colon Genus Characteristics and metabolic activities Desulfovibrio The most predominant genus in the large intestine (64–81%), commonly utilize lactate, ethanol, and hydrogen as electron donors for sulfate reduction. The SRB use sulfate as a terminal electron acceptor for respiration with typical electron donors consisting of H2, ethanol, lactate, succinate and other organic acids, with the concomitant production of H2S which is toxic to colonic epithelial cells leading to several colonic disease including UC (Wagner et al., 1998, Carbonero et al., 2012) Desulfobacter Desulfobacter utilizes only acetate as electron donor and oxidizes it to CO2 via the citric acid cycle only 9–16% of this group are present in human colon mucosa (Carbonero et al., 2012). Desulfobulbus Desulfobulbus present in the human intestinal mucosa in 5–8%. Desulfobulbus utilize propionate and hydrogen and can ferment organic materials under certain conditions (Kuever et al., 2005) Desulfomicrobium Desulfomicrobium reduce sulfate to hydrogen sulfide. They oxidize simple organic compounds in the human intestine (lactate, pyruvate, ethanol, formate and hydrogen) incompletely, with the formation of acetate as an end product. They can grow using sulfate as an electron acceptor during sulfate anaerobic respiration. Desulfomicrobium are often found in the intestines of both humans and animals (Kushkevych, 2014). Desulfococcus Desulfococcus are found as one of SRBs members that associated with mucosa in human colon. This group can oxidized organic compounds such as carbohydrates, fatty acids, alkanes, aromatic hydrocarbons completely to CO2 (Kushkevych, 2016). Desulfosarcina One of the sulfate reducer that plays a crucial role in sulfur cycle.They can grow autotrophically with hydrogen gas as electron donor and CO2 sole carbon source. Desulfosarcina has been implicated to have role in colon cancer (Huycke & Gaskins, 2004).

Bilophila Bilophila are common inhabitant of the human colon and has been reported as infective agent. Bilophila are differs from the other colonic associated members of Desulfovibrionaceae by not being able to reduce sulfate and the absences of desulfoviridin. In addition, this group are non-saccharolytic bacteria, which produce acetic acid as major and succinic and lactic acids as minor fermentation products from peptone-yeast broth. (Baron et al., 1989; Marchesi et al., 2016).

CHAPTER 1: INTRODUCTION

Desulfuromonas Desulfuromonas are asporogenous bacteria that oxidize organic substrates completely to CO2. This group of bacteria use sulfur as an electron acceptor. It has been found that the growth of Desulfuromonas in human colon is associated with excessive protein ingestion (protein diet) as these metabolites products will promote the overgrowth of this group in human colon which may lead to the inflammation (Muyzer & Stams, 2008 ;Kushkevych,2013)

SRB groups found in the gut are mainly of the genera Desulfobacter, Desulfovibrio, Desuflbulbus, Desulfomonas, Desulfomicrobium (Gibson, 1990). Reports have also shown that that among these five genera the Desulfovibrio are predominant in the human colon (Gibson et al., 1988c ; Willis et al., 1997). Desulfovibrio are characterized by the presence of a pigment, desulfoviridin which displays differential fluorescence; red in alkaline and blue-green in acidic under long wavelength UV 365 millimicron (Warren et al., 2005). Additionally, these bacteria are all anaerobic, Gram-negative bacilli. Two desulfovibrio species the D. piger and D. fairfieldensis were first identified in 1976 from human faeces. These species are unique to the human intestinal tract and have never been isolated from outside the human body (Gibson et al., 1988b). Desulfomonas pigra was the first SRB identified that was later renamed as Desulfovibrio piger (Loubinoux et al., 2002). Following this identification several other species belonging to the genera Desulfobacter and Desulfobulbus were also isolated and characterized (Gibson et al., 1993b). The presence of SRB in human faeces varies geographically, and may be associated with dietary habits or, perhaps, certain genetic factors. This presumption is based on a study reporting the differential carriage rates of SRB between the UK and South Africa faecal donors. With over 70% of UK stool samples containing SRB compared to only 15% of South African samples ( Section 1, Figure 1.1.1) (Gibson et al., 1988c) .

1.2.2 Interaction of SRB with other organisms

Co-existence of SRB with other organisms has been observed in natural and artificial environments. SRB can coexist and interact with several anaerobic bacteria through using an alternative electron donor or can compete by consuming the same substrate (Dar et al., 2008). A number of processes, such as conjugation, multicellular symbiosis, quorum sensing and niche adaption, combating host defence mechanisms, production of

CHAPTER 1: INTRODUCTION secondary metabolites, benefit from inter- and intra-species co-operation (Williams et al., 2007). It is feasible that the interactions between different metabolic groups of bacteria in the colon may impact on human health, however before such a hypothesis can be formed it is necessary to have a greater knowledge about the exact nature of the bacterial populations and their possible interactions.

1.2.3 Role of SRB in the aetiology of UC

SRB are capable of metabolizing hydrogen as a source of energy and fall into the category of hydrogenotrophic bacteria. The H2S produced by SRB is considered a primary pathogenic mechanism in UC. The high production of acetate and H2S, both of which have a deleterious effect on the intestinal epithelial cells if they are not removed quickly (Kushkevych, 2014). The cells lining the colon can detoxify this gaseous material, however in UC patients this detoxification system is deteriorated so that H2S accumulates. This accumulation can lead to several other pathologies including an increase in the epithelial permeability and barrier function of the colon thus impacting its defensive and protective role, as well as inhibiting butyrate oxidation (Pitcher & Cummings, 1996). There are several strains of SRB present in the colon, however, some of these have demonstrated a relatively higher production of H2S identifying them as a potential nexus within the aetiology of UC. Kushkevych (2013) compared the production of H2S amongst SRB strains and revealed that Desulfovibrio sp. strain Vib-7 produced the highest concentration (up to 3.23 mM) of H2S, consuming 99% of sulphate presented in the medium. In vitro characterization of SRB isolates demonstrated that rates of H2S production were higher in SRBs isolated from UC patients compared to those obtained from healthy ones (0.55 versus 0.25 mM). Furthermore, faecal samples of UC patients mainly contain SRB of the genus Desulfovibrio (Gibson et al., 1991).

Although the pathogenesis of UC remains poorly understood several studies have linked SRB with the development of UC. Loubinoux et al (2002) compared the presence of SRB in the faeces of patients with IBD to healthy volunteers and demonstrated that the amount of Desulfovibrio piger (formerly Desulfomonas pigra) was significantly increased in IBD patients (55%) when compared with healthy individuals (12%) or patients with other symptoms (25%) (p<0.05). A study comparing the levels of these groups of

CHAPTER 1: INTRODUCTION bacteria in various parts of the colon of 25 healthy subjects and using sequence analyses of genus specific genes revealed that mucosa-associated SRBs were related to Desulfovibrio piger, Desulfovibrio desulphuricans and Bilophila wadsworthia (Nava et al., 2012).

It has been proposed that a higher dietary sulphate intake promotes SRB associated with UC, however, a study using Genome-wide transposon mutagenesis and insertion- site sequencing, RNA-Seq, with mass spectrometry in a murine model revealed that “genes involved in hydrogen consumption and sulphate reduction are necessary for its colonization, varying dietary-free sulphate levels did not significantly alter levels of D. piger, which can obtain sulphate from the host in part via cross-feeding mediated by Bacteroides-encoded sulphatases” (Rey et al., 2013).

As SRB play important roles associated with hydrogen removal in the intestines, it is unsurprising that they have been linked to UC syndrome (Levine et al., 1998; Jørgensen & Mortensen, 2001; Nyangale et al., 2012). The use of molecular techniques has aided the identification of pathogens, and techniques such as PCR have identified the relative richness of SRB in individuals with colitis issues (Zinkevich & Beech, 2000).

1.2.4 Sulphate Reducing Bacteria, H2S, and Colitis – A potential Nexus

SRB use sulphate as an electron acceptor in their energy supply pathway producing H2S as a metabolite (Attene-Ramos et al., 2007) (Figure 1.2.1).

CHAPTER 1: INTRODUCTION

Figure 1.2.1.Metabolic pathway used by SRBs to reduce sulphate (adapted Carbonero et al., 2012); APS: adenosine 5’-phosphosulfate

H2S produced from SRB is hypothesized to disrupt mucin polymers of the gut epithelium. These polymers contain disulphide bonds and sulphide produced by SRB is thought to reduce these bonds, leading to breakage of mucus barrier thus allowing pathogenic bacteria access to the gut epithelium leading to the development of inflammation (Ijssennagger et al., 2016) (Figure 1.2.2). The penetration of hydrogen sulphide through the cell membrane is due to its high solubility in lipophilic solvents and it exerts an inhibitory effect on butyrate production (Rowan et al., 2009; Cuevasanta et al., 2012). Butyrate oxidation is the main source of energy the epithelial cells of the gut, when inhibited the resulting energy deficient environment leads to apoptosis of the cells and induction of inflammation ( Moore et al., 1997; Babidge et al., 1998).

H2S has further deleterious effects on host cellular metabolism including activation of adenosine 5’-triphosphate (ATP) dependent potassium channels, inhibition of mitochondrial cytochrome c oxidase and induction of DNA damage (Szabó, 2007; Popov,

2013). Higher concentrations of H2S in the millimolar range lead to oxidative stress through the formation of a complex with the ferric ion of cytochrome c oxidase, which ultimately inhibits cellular respiration (Caro et al., 2011). In support of this, a recent study by Beaumont et al (2016) reported that colonocyte exposure to low concentrations of the sulphide donor NaHS reversibly inhibited colonocyte mitochondrial oxygen consumption, concomitantly enhancing the expression of hypoxia inducible factor 1alpha (Hif-1alpha) gene along with inflammation-related genes such as those for inducible nitric oxide synthase (iNos) and interleukin-6 (Il-6).

CHAPTER 1: INTRODUCTION

It is important to mention here that the effect of H2S on bacterial cells are contrasting compared to host epithelial cells and is time and environment dependent (Wang , 2012).

Figure 1.2.2. Sulphide as a Mucus Barrier-Breaker in IBD (Adapted from Ijssennagger et al., 2016)

1.2.4 Genetic Diversity of SRB – 5’-phosphosulphate reductase (apsr1) and dissimilatory sulphite reductase (dsrAB)

The SRB have two essential genes which allow them to utilise sulphate as a metabolite, adenosine 5’-phosphosulphate reductase (apsr1) and dissimilatory sulphite reductase (dsrAB). Both are highly conserved among SRB and have potential as biomarker. There are limited studies describing the role of both apsr1 and dsrAB in the overall pathogenic mechanisms of these bacteria. However, dsrAB was used to confirm SRB in colonic biopsy samples (Nava et al., 2012). Through utilizing dsrAB 1.9 kb amplicon a unique gel retardation-based technique, involving fingerprinting methodology, efficiently increases the chance of detecting rare and novel SRB (Wagner et al., 2005). Additionally, there are several interesting studies describing the role of the dsrAB gene in characterizing changes in the environment and various ecosystems (Perez-Jimenez et al., 2001; Perez- Jimenez & Kerkhof, 2005). There are several ongoing metagenomics and molecular analysis to evaluate the role of gut-residing SRB in the pathogenesis of IBDs underway.

CHAPTER 1: INTRODUCTION

However, there is limited literature relevant to both these genes as far as their involvement in colitis disorders is concerned.

CHAPTER 1: INTRODUCTION

Section 3 Molecular techniques for the detection and identification bacteria in human colon 1.3.1 General Overview

The human colon harbours hundreds of different species of bacteria (Poxton et al., 1997). Earlier research on the detection of colonic bacteria mainly involved traditional culture techniques for the isolation of bacteria followed by their characterization. Analysis of the microbial community in the human gut was largely dependent on the use of enrichment procedures and the ability to grow strict anaerobes. The isolates were identified and characterized by various phenotypic methods. In a pioneering study, Moore & Holdeman (1974) analysed the faecal flora of 20 male Japanese-Hawaiians. The total number of different bacterial species was estimated to exceed 400, although the actual number of identified species was only 113. Subsequent studies confirmed the great diversity of intestinal bacteria. The Bacteroides, Bifidobacterium, Clostridium, Eubacterium, Fusobacterium, Lactobacillus, Peptostreptococcus, Ruminococcus and Streptococcus were the first dominant taxa identified (Eckburg et al., 2005; Martínez et al., 2013; Donaldson et al., 2016). Altough the exact diversity and function of the bacteria in human colon continue to be revealed; the role of these microbial communities in colonic health and disease has received considerable attention. It thought that certain bacteria inhabit the colon play a role in the onset of some colonic disease, whereas other bacteria are considered protective. The role of some key bacteria in the colon is shown in Table 1.3.1.

CHAPTER 1: INTRODUCTION Table 1.3.1. The role of intestinal bacteria in human health and disease Bacterial genera Role of bacteria in the human colon A dominant genus present in the human colon as a normal microflora, however, causes disease if Bacteroides get into the bloodstream either through surgery or invades various tissues. Suggesting the role of these bacteria as an aetiological agent in infection diseases such as UC (Waidmann et al.,2003; Singh et al., 2013) Bifidobacterium The first bacteria to colonize the human colon and are believed to have positive benefits on their host health. They have important probiotic and protection role in the colon from pathogens invasion by competitive exclusion. They involved in the promoting of the immune system and in the digestion of energy substrates. (Toumi et al., 2013; Satish et al., 2017).

Campylobacter Involved in IBD through producing toxins in the oral cavity. Enteric Campylobacter infections produce an inflammatory, bloody diarrhea (Man et al.,2010 ; Zhang et al.,2014) Clostridium Play a crucial role in colonic homeostasis through the interaction with the other bacterial populations. They provide specific and fundamental maintenance functions in the colon. However, some Clostridium species such as C. difficile is considered as the most important cause of infectious of the human colon; they linked to large bowel malignancies. Additionally has been associated with immunosuppression. These bacteria increased sharply among UC patients (Zhang et al.,2007 ; Marteau, 2009; Kolho et al., 2015; Autenrieth & Baumgart, 2017; Sokol et al., 2017) Escherichia Commensal strains of Escherichia are involved in the homeostasis of human colon. However, in case of dysbiosis most beneficial bacteria are damaged, favouring the overgrowth of pathogenic strains that exert toxic effects on the colonic epithelium and causes chronic diarrhea. Some of Escherichia strains have been associated with IBD by promoting the inflammation leading to the development of colon cancer (Waidmann et al., 2003; Joossens et al., 2011) Faecalibacterium Faecalibacterium are one of the most abundant bacterial members in the human colon found in ileal, colonic, and rectal biopsies from healthy individuals. These organisms produce butyrate and have anti-inflammatory properties. Lower levels of these bacteria are associated with Crohn’s disease and ulcerative colitis (Martinez-Medina et al., 2006; Hansen et al., 2012; Machiels et al.,2014; Vrakas et al.,2017)

CHAPTER 1: INTRODUCTION Lactobacillus Lactobacillus in the colon has a very important role in the host health promoting properties. They are commonly used as probiotics, which are known as live microorganisms that give a health benefit on the host by creating balanced microbial populations in human colon. In addition, protect the host against colonic infections and tumor by their antibacterial properties and immune system activation. (Ott et al., 2008)

Peptostreptococcus Induces intracellular cholesterol biosynthesis and degrade complex indigestible compounds in the colon. However, they can become pathogenic to their host. Numbers of these organisms were found in IBD and Colorectal cancer patients (Verma et al., 2010; Zhang et al.,2013) Ruminococcus Dominant members of the human intestinal bacteria that play a key role in the digestion of the complex carbohydrate present in the high fibre dietary to provide the energy that is necessary for their host. (Joossens et al., 2011) Eubacterium Eubacterium is considered as a key orgnasim within the human colon that has very important role in the intestinal metabolic balance with final impact on host health. They utilize glucose and ferment acetate and lactate, to form butyrate and hydrogen supporting the trophic interactions of microbes in the human colon (Engels et al, 2016). Fusobacterium Part of the normal flora of the human colon. However; Fusobacterium recently found to be in a high abundance in colon cancer patients suggesting that they may provide an important contribution in disease stages (Kostic et al., 2012). Streptococcus Part of the commensal human intestinal microbiota which involved in the physiological health by promoting the immunomodulatory functions (Jandhyala et al., 2015).

CHAPTER 1: INTRODUCTION

Characterization of SRB was initially dependent on 1) phenotypic characteristics of bacteria involving nutritional aspects and morphology and 2) biochemical characterization, mainly the presence of desulphoviridin or lipid fatty acids. The advent of molecular techniques for evaluating bio-ecological microbial communities of the gut has provided a wealth of information beneficial for overall understanding the role of resident gut bacteria in human health and disease (Furrie, 2006). There are advantage and disadvantages to both the classical bacterial culturing techniques and more modern molecular techniques. The choice of techniques is dependent the question being addressed. For example, techniques involving fingerprinting are good for evaluating bacterial community structure; dot blots and fluorescent in situ hybridization are helpful to measure the density of a bacterial species. Whereas, techniques like microarrays can be used to identify pathogenesis mechanisms as well as for bacterial identification purposes. Molecular techniques based microbial diagnostic have emerged as an active discipline, and several commercial companies have developed tools for the identification and characterization of bacterial species (Namsolleck et al., 2004) (Figure 1.3.1)

CHAPTER 1: INTRODUCTION

Figure 1.3.1. Schematic diagram of molecular techniques used for colonic bacterial analysis (adapted from Muyzer & Smalla, 1998; Vaughan et al., 2000)

1.3.2 Polymerase chain reaction Amplification (PCR)

PCR was developed by Kary B. Mullis in 1983, and rapidly became a fundamental tool in molecular biology. Without specifically isolating the target bacteria; PCR can detect specific nucleic acid of individual bacterial species from a mixture of nucleic acids of different sources (Mullis, 1990). In theory, the products generated by PCR reflect the mix of microbial gene signatures present within a sample. Amplifying conserved genes with PCR like 16S rRNA obtained from a sample, is a widespread practice for studying colonic microbial ecology mainly because conserved genes are 1) universal in prokaryotes, 2) are functionally and structurally conserved, 3) are composed of both highly conserved and variable regions (Hugenholtz, 2002). An alternative is to use specially designed primers that exclusively target regions of DNA that encode functional genes, verifying the presence of specific metabolic pathways. Several primers are available that specifically target key species of colonic mucosa bacteria. PCR products

CHAPTER 1: INTRODUCTION that result from amplifying DNA obtained from colonic samples are generally analysed by using 1) the clone library method, 2) DNA micro-arrays, 3) genetic fingerprinting or through a combination of any of these (Coenye & Vandamme, 2003; Frank et al., 2007).

There are several types of PCR available to investigate populations of microbes both qualitatively and quantitatively. In addition to being able to detect the genetic signature of bacteria that cannot be cultivated by conventional means, the advantage of PCR techniques which includes real-time PCR, quantitative PCR is that the technology is very sensitive and demonstrates selectivity (Ott et al., 2004).

1.3.3 16S rRNA Gene Sequencing and Phylogenetic analysis

During the last decade, it has become apparent that there has been a significant miscalculation of the taxonomic and phylogenetic diversity of GIT microbiota. This situation arose from the combination of being unable to isolate and cultivate microbes together with the inability to identify new species. The technological development of using 16S rRNA gene sequences to identify and classify bacteria has transformed taxonomy. By comparing the rRNA sequence, taxonomists can determine the evolutionary relationships between specimens. The extent of sequence differences in different samples provides a form of measurement of evolutionary distance. Some regions in the rRNA molecules are highly conserved, whereas others display high degrees of variability. Evolutionarily closer samples demonstrate greater homology (Ritari et al., 2015). This method allows organisms to be differentiated at phylogenetic levels ranging from species to domain, with relationships visualised by creating a phylogenetic tree (Janda & Abbott, 2007).

The 16S rRNA genes display a relatively slower rate of evolution making them ideal for use in phylogenies (Woese & Fox, 1977; Janda & Abbott, 2007). The usefulness of the 16S rRNA in detecting clinically relevant bacterial species can be ascertained from the fact that there are more than three 16S database used for taxonomic classification of microbial species. Among these the ribosome database project (RDP) (Bacci et al., 2015), SILVA (Quast et al., 2013), and Greengenes (DeSantis et al., 2006) have received relatively more attention.

CHAPTER 1: INTRODUCTION

Today, a library of tens of thousands of 16S rRNA gene sequences is available; novel sequences that have been isolated can be compared to these known existing sequences making this technique an effective tool in the identification of novel human GIT bacteria (Lahti et al., 2014).

The phylogenetic identification of cultures of hundreds of obligate anaerobes has been achieved by sequencing short fragments of amplified rDNA (approximately 500 bases); these sequences includes variable regions V1, V2 and V3 and V6 that are used for diagnosis. More than 90% of the organisms have easily been identified as belonging to recognised species, but many novel isolates remain unmatched (Schloss & Westcott, 2011). Detailed phylogenetic analysis has demonstrated that these isolates belong to previously unknown species or genera with many new GIT species being officially recorded; meanwhile the research into the phenotypes of the unknown isolates continues (Li et al., 2014). It has been reported that 16S rRNA sequencing technology has been helpful mainly in the identification of bacteria with an unusual phenotypic profile, rare and hard to culture bacterial species. According to very conservative estimates; 29 bacterial species, out of 215 belonged to new genus, which were achived between 2001 and 2007 (Woo et al., 2008). Additionally, the 16S rRNA technique has been helpful in defining the changes in bacterial characteristics over time. Therefore, it is important to mention here that one should be cautious in describing the data generated from the 16S rRNA sequencing strategy particularly in clinical settings (Christophersen et al., 2011; O'Sullivan et al., 2014).

1.3.4 Denaturing Gradient Gel Electrophoresis (DGGE)

Molecular methods that have been developed to study the microbial composition are referred to fingerprinting. A common feature of all molecular fingerprinting methods is that they depend upon multi-template PCR reactions to assess microbial community structures or ‘fingerprints’. Fingerprinting techniques have been widely applied to the ecology of colonic microbes; the methods are also proving invaluable to understanding the composition and dynamics of GIT microbes (Suchodolski et al., 2004).

CHAPTER 1: INTRODUCTION

Denaturing gradient gel electrophoresis (DGGE), introduced in 1993 by Muyzer et al. can be used to analyse the diversity of complex microbial ecosystems. This method, demonstrated in Figure 1.3.2, separates 16S rRNA fragments that have been amplified by PCR on polyacrylamide gels that contain a gradient of denaturing agents. Hetero- duplexes of different amplicons (with different G/C content) are migrated to different positions; thereby fragments of the same length but different sequences can be differentiated. The result is a pattern of bands in the gel with each band representing hypothetically, a single species. The bands separated through this methodology can then be identified through sequence analysis by extracting DNA fragments from the gel (Muyzer et al. 1993). A benefit associated with DGGE is several samples can be analyzed on a single gel run. In addition to the biases presented by the PCR technique and DNA extraction inadequacies, DGGE is also vulnerable to general biases related to sample handling, storage and type (Green et al. 2006). Furthermore, there are some DGGE specific limitations, including the co-migration of DNA fragments with different sequences and limited detection sensitivity for under-represented species where only the dominant species of the community are displayed. In addition, relatively small fragments of DNA can be difficult to separate, as are the molecules generated by the different rRNA operons of a single organism.

Irrespective of its shortcomings and despite the advancement of high-throughput sequence analysis techniques, DGGE is the current best method available to obtain an accurate outline of the composition of a microbial communities; from this, sequence analyses can be explored in greater detail (Possemiers et al., . 2004; Vanhoutte et al., 2004).

CHAPTER 1: INTRODUCTION

Figure 1.3.2. The principles of DGGE

1.3.5 Pulsed field gel electrophoresis (PFGE)

Pulsed field gel electrophoresis (PFGE) is a reliable and fast method to resolve DNA molecules larger than 30 kb in an electric field that periodically changes direction. This technique can be used to estimate genome size of a microorganism. (Alduina & Pisciotta., 2015). The PFGE fingerprinting method is commonly used by the epidemiologists for differentiating pathogenic bacterial strains from the non-pathogenic counterparts (Falsafi et al., 2014). In addition, it is also possible to use PFGE to compare the genome of bacterial species. The PGFE of linearized full-length chromosomal DNA of Desulfovibrio desulphuricans, Desulfovibrio vulgaris, and Desulfobulbus propionicus (Devereux et al., 1997). There are several studies describing the utility of PFGE for comparative studies of bacterial diversity (Said et al., 2010; Fendri et al., 2013; Castiaux et al., 2014).

CHAPTER 1: INTRODUCTION

1.3.6 Quantitative PCR (qPCR)

Quantitative PCR (qPCR) technique is used for direct quantification of the number of bacteria present in a sample. qPCR uses fluorescent reporter molecules that can be 1) fluorescent dye-tagged probes made of sequence-specific oligonucleotides, together with a quencher (e.g. Molecular Beacons, TaqMan probes (hydrolysis probes) or (Scorpions). 2) Nonspecific DNA binding dyes, for example SYBR Green, which when bound to double-stranded DNA (dsDNA), emit fluorescence (Brukner et al., 2015).

DNA binding dyes, such as SYBR Green present an easy to use, cost-effective visualisation tool and are a popular choice for optimising qPCR reactions. Fluorescence increases in proportion with the concentration of dsDNA, which presents the dye with more binding sites. This feature provides information about the accumulation of PCR products as the fluorescence signal increases in line with the augmented levels of dsDNA and this can be directly measured (Figure 1.3.3). SYBR Green is a reasonably straightforward method; however, non-specificity is a limitation that is inherent to assays that rely upon dyes binding to DNA. The binding of dye molecules that increases in parallel with the increase in dsDNA means that reaction specificity can only be governed by the primers (Giglio et al., 2003; Agrimonti et al., 2013).

The other most commonly employed detection chemistry for qPCR is linear probes, such as TaqMan. As well as PCR primers, an oligonucleotide probe is incorporated into the reaction in this detection chemistry. Attached to the 5’ end of the probe is fluorescent reporter dye, such as FAM, and at the 3’ end is a quencher, for example TAMRA. TaqMan system developed by relies on the release and detection of a fluorescent signal following the cleavage of a fluorescent labelled probe by the 5-exonuclease activity of Taq polymerase. In the intact state, the fluorescent signal on the probe, such as 6- carboxyfluorescein (6- FAM), is quenched by the proximity on the probe of a second dye, 6-carboxy-tetramethylrhodamine (TAMRA). The rapid detection of fluorescence is achieved after each round of amplification as fluorescent dye is released(Figure 1.3.3) (Heid et al., 1996; Kang et al., 2007; Kubota et al., 2014; Wang et al., 2014).

CHAPTER 1: INTRODUCTION qPCR allows for high throughput analysis of numerous samples. Provisional upon the creation of a universal probe for use with real-time PCR, conserved regions of 16S rRNA theoretically enable the detection and enumeration of all complex populations of bacteria (Farrelly et al., 1995). A functional gene specific for a given group or species to increase specificity was also applied in this technique (Nadkarni et al., 2002). qPCR has been used to identify and evaluate the distribution of faecal desulfovirbios in healthy samples also determining Desulfovibrio numbers in crypt-associated mucous (Fite et al., 2004; Rowan et al., 2010).

Figure 1.3.3. Comparison of and A) SYBR®-Green, B) TaqMan®- Based Detection Workflows Adapted from (Cao & Shockey, 2012)

1.3.7 Microarray

A biochip is a collection of miniaturized test sites (microarrays) arranged on a solid surface allowing in a single experiment to analyse the expression of thousands of genes, or the same gene from thousands of organisms in parallel.

CHAPTER 1: INTRODUCTION

Total DNA or RNA is isolated from clinical samples and then fragmented. The fragments are amplified by PCR and simultaneously labelled using labelled nucleotides (referred to as DNA arrays) or the fragments are directly labelled chemically (referred to as RNA arrays) and hybridized to oligonucleotide probes which immobilized on support matrix, termed probes. After that the immobilized probes are washed to remove excess product before the probe/target complex can be visualized with a fluorescent scanner (Namsolleck et al., 2004) (Figure 1.3.4).

In standard oligonucleotide or cDNA microarray probes are organised directly on a solid surface. Either a collection of organised spotted probes immobilized on a solid surface like glass microscope slide, nylon membrane or silicon or alternatively a bead array which involves a collection of microscopic polystyrene beads, each with a specific probe. (Lyons, 2003; Mah et al., 2004; Mocellin et al., 2005).

2D solid substrate arrays rely on probes bound in a two-dimensional fashion directly to the surface. However, in 3D solid substrate arrays the probes are bound in multiple layers within a three-dimensional microdroplet. A 3D format increases the surface area and increase probe quantities, therefore increasing the sensitivity of the microarray (Tang et al., 2009; Bumgarner, 2013).

A review of the literature indicates that micro-array systems have been used to analyse the diversity of bacterial populations based on their functional genes or the 16S rRNA gene (Cho & Tiedje 2001; Paliy et al., 2009). Microarrays have also previously been used in the study of the composition of bacteria present in the human colon (Wang et al., 2004; Kim et al., 2005; Palmer et al., 2006).

For characterization of bacterial composition several types of microarrays have been used. Genome arrays which are established using pure bacterial culture genomic DNA (Wu et al., 2004). Functional gene arrays which contain genes encoding key enzymes that are involved in the metabolic pathway of the bacteria which allow controlling any physiological changes within bacterial population (Wu et al., 2001). Phylogenetic oligonucleotide arrays are used for analysis of bacteria structure and variance and their probes are designed from rRNA sequence database (Loy et al., 2002).

CHAPTER 1: INTRODUCTION

Figure 1.3.4. Principle of the microarray technique (adapted from Namsolleck et al, 2004).

CHAPTER 1: INTRODUCTION

Section 4 The effect of antibiotics and honey on the growth of bacterial biofilms 1.4.1 Bacterial biofilms

Bacterial populations utilize several strategies to preserve their existence in adverse environments. One such strategy is the formation of bacterial biofilms in which a group of bacteria form a multicellular structure, protecting individuals through the production of extracellular polymeric substances (EPS) (Figure 1.4.1) (Kjelleberg et al., 2007). Whilst biofilms have been investigated those present on living tissues, and in particular those of the gut, have not been explored in detail (Dongari-Bagtzoglou, 2008). The human colon harbours a huge number of bacterial including both beneficial bacteria, and under certain circumstances pathological or opportunistic bacteria which can give rise to conditions such as UC (Sasaki and Klapproth, 2012). As far as the formation of biofilms in the colonic bacterial population is concerned, there have been relatively few studies ( Probert & Gibson, 2002; Macfarlane & Dillon, 2007; von Rosenvinge et al., 2013). Whilst the usage of antibiotics for controlling a particular bacterial species within colon can be effective; they demonstrate reduced effect against bacterial biofilms and can impact on key metabolic activities, such as fermentation and enzyme synthesis (Newton et al., 2013).

Figure 1.4.1 biofilm formation – 1. Cell adhesion 2. Formation of micro colonies, 3. Accompanied by EPS production. 4. The formation and maturation5. The dispersion cells from biofilm (Kjelleberg et al., 2007).

CHAPTER 1: INTRODUCTION

1.4.2 The aspects of bacterial Quorum Sensing in the colon

Quorum sensing (QS) is a mechanism that can regulate the bacterial colonization and the dissemination in the gut by coordinating gene expression and cell behaviour according to the population density of the gut bacteria (Yang and Jobin, 2014). This phenomenon involves bacterial release of various classes of signalling molecules, “autoinducers”, into their environment such as acyl-homoserine lactones (AHLs) (Fuqua et al., 1994). The accumulation of these autoinducer molecules then enable a single cell to sense the number of bacteria (cell density) and also measure the number (concentration) of the molecules within a population (Miller and Bassler, 2001; Thompson et al., 2015). In this way, the bacteria share information about cell density and regulate individual gene expression by cell-to-cell communication (Rutherford and Bassler, 2012). When the signalling molecules attain a critical threshold concentration, the targeted QS genes are activated or repressed (Jacobi et al., 2012). The role of QS in the growth and survival of colonic bacteria is likely to be important. It is believed that upon colonization of the gut bacteria undergo metabolic changes where genes involved in transportation and the metabolism of carbohydrate and amino acid become highly induced (Yuan et al., 2008; Alpert et al., 2009). The colon microbiota is known to consist of a wide variety of bacteria with a variety of roles in the gut ranging from modulation of the immune system to protection against pathogens, and nutrient absorption (Poxton et al., 1997). The diversity of the colonic bacteria determine the state of the human gut, with a high diversity being associated with a healthy human gut. On the other hand, a reduction in diversity is associated with dysbiosis where an abnormal ratio of commensal and pathogenic bacterial species exists in the colon, favouring the emergence and augmentation of certain bacterial species in the gut which is considered to be the hallmark of the colonic disease, such as UC (Manichanh et al., 2006; Bäckhed et al., 2012; Belizário and Napolitano, 2015). Because of the diversity and the increase in number of the bacteria inhabiting the colon there is a need for a survival mechanism that would enhance the growth in the presence of bacterial competition. QS could be such a mechanism. Bacteria motility, surface proteins biofilm formation, and virulence are also controlled by the secretion of QS signalling molecules which are released into the colon environment (Di Cagno et al 2011; Piras et al., 2012; Shao et al., 2012; Thompson et al., 2015). It has been reported that chemicals mediating QS are also

CHAPTER 1: INTRODUCTION pathogenic factors for certain bacterial species and that their levels define the relative bacterial number particularly in diseases like UC (Raut et al., 2013). Several studies have described the role of QS the in their pathogenesis. It is worth mentioning here that one of the emerging bacterial species appearing during the pathogenesis of UC was Enterococcus faecalis. According to these studies, microbes utilize several QS regulatory proteins including gelatinase, serine protease, enterocin O16 and cytolysin as the primary pathogenic attributes ( Zhou et al., 2016; Ali et al., 2017). Among these various molecular moieties, gelatinase has been associated with the disruption of intestinal epithelial barriers thus providing pathogenic bacteria to invade intestinal barrier (Maharshak et al., 2015). Furthermore, data from animal model studies showed that bacterial invasion of intestinal barrier is dependent on the quorum-sensing transcriptional regulator SdiA and bacterial species lacking this regulatory protein have relatively lesser capability to penetrate into the deep layers of the gut (Sharma and Bearson, 2013; Sharma and Casey, 2014). The bacteria involved in UC are no exception to this control. These bacteria, amongst others, synchronize their social behavior, to communicate among themselves, and to regulate gene expression in response to their population density (Rutherford and Bassler 2012). In additiona, antibiotic treatment eliminates vulnerable bacteria from the bacterial population in the gut, leaving resistant bacteria to grow, multiply and perform their physiological functions. It is possible, therefore, that the acquired and intrinsic resistance to a variety of antibiotics in UC could be associated with QS mechanisms of the bacteria involved (Miller et al., 2014; Ali et al., 2017).

1.4.3 Existing UC therapies

The UC symptoms are mainly inflammation of the colon associated with diarrhoea with blood. Symptoms are mainly a refractory and hard to treat even with conventional medications. Mostly anti-inflammatory drugs prescribed like aminosalicylates and corticosteroids followed by a variety of immune system suppressors that reduce inflammation. The most prominent immunosuppressants in practice are azathioprine, mercaptopurine, cyclosporine, infliximab, adalimumab and golimumab and vedolizumab (Monterubbianesi et al., 2014; Rietdijk & D'Haens, 2014; Kim et al., 2015; Shahidi et al., 2016). With our increasing understanding of the gut microbiome and the role of

CHAPTER 1: INTRODUCTION bacterial biofilms colonic disease; it is timely to evaluate usage of antibiotics amongst UC patients. In treating UC clinicians must account for numerous factors when designing a therapeutic regime. It has been observed that alongside bacterial associated colitis, the Clostridium difficile secreted toxins (CD toxins) can also induce antibiotic-associated colitis, or pseudomembranous colitis (PMC) (Kawaratani et al., 2010). Two prominent antibiotics rifaximin and metronidazole are commonly prescribed to treat UC. However, the usage of metronidazole is controversial, whilst considered as a cost-effective drug which inhibits the nucleic acid synthesis by disrupting the DNA of microbial cells resulting in bacterial cell death with good activity against pathogenic anaerobic bacteria (Lofmark et al., 2010). Issues remain surrounding the generation of resistant bacteria involved in UC with a capability to produce nitroreductase (Rafii et al., 2003). Because of this and due to other factors, including genetic susceptibility, and abnormal immune response potentially due to Clostridium difficile colonisation, metronidazole is often administered in conjuction with other agents. A case report suggested that vancomycin and metronidazole when administered in conjunction were helpful in overcoming refractory UC (Miner et al., 2005). A more in depth study of metronidazole-ciprofloxacin administration demonstrated that the antibiotic pairing transiently disrupted the colon bacterial population with selective disruption of bacterial biofilms associated with UC. The beneficial bacterial population return to normal within a week upon the cessation of antibiotic administration (Swidsinski et al., 2008). Rifaximin is another antibiotic that selectively targets the microbial population involved in IBD acts by inhibiting RNA synthesis in susceptible bacteria by binding to the β- subunit of bacterial RNA polymerase (Guslandi, 2011). This antibiotic has a very promising in vitro antimicrobial profile when compared with other antibiotics like ampicillin-sulbactam, neomycin, nitazoxanide, teicoplanin, and vancomycin. A study comparing rifaximin with all these antimicrobial pharmacological agents revealed its inhibitory effect on 90% of the anaerobic bacterial strains evaluated in this study. Furthermore, the minimal inhibitory concentration was 0.25 microgram/ml comparable with teicoplanin and vancomycin, however, less than those of nitazoxanide and ampicillin-sulbactam (Finegold et al., 2009). Rifaximin is approved in the United States for the treatment of travellers’ diarrhoea and colonic infections. Due to its safety profile and beneficial impact on gastroenteritis and associated disorder, this antibiotic is

CHAPTER 1: INTRODUCTION approved in more than 30 other countries to treat a variety of gastrointestinal disorders (Koo & DuPont, 2010). Rifaximin has shown very promising data, with remission in 59% of individuals with Crohn’s disease and 76% with UC (Guslandi, 2011). Promising results with rifaximin have also led to the development of rifaximin-extended intestinal release variants (Scribano, 2015) as well as a nano-suspension formulation instead of orally administered tablets (Newton & Kumar, 2016). IBD are associated with gut microbiota dysbiosis, and a review of existing studies suggested that, besides direct bactericidal effect, the rifaximin can also modulate the gut environment maintaining normal microflora (Newton & Kumar, 2016). However, these findings and the proposed mechanism of action of rifaximin are yet to be validated.

1.4.4 Traditional medicine as a potential treatment for UC

A wide variety of traditional medicines are also used to treat UC. The most popular include aloe Vera gel, wheat grass juice, Boswellia serrata, and bovine colostrum enemas (Ke et al., 2012). A proposed advantage of using herbal remedies is reduced side effects when compared with synthetic medicine. However, there is limited evidence about the efficacy of herbal medicine, although its use is widely accepted for herbal remedies due to relative safety and cost benefits. A series of clinical trials using herbal medicine in UC have also been reported (Salaga et al., 2014, Teschke et al., 2015, Triantafyllidi et al., 2015). The use of honey as a natural remedy for UC is increasing. Natural honey showed highly promising results in protecting rodents from acetic induced colitis. Honey is composed of a mixture of sugars and as such the control in this study involved glucose, fructose, sucrose and maltose mixture showing no significant protective effect confirming that natural honey has certain essential elements inhibiting the induction of colitis (Mahgoub et al., 2002). Intriguingly a similar antibacterial effect was observed with a natural product, honey, at a low concentration of 0.5 % (v/v). At this dose biofilm formation in enterohemorrhagic Escherichia coli O157: H7 was significantly reduced, without inhibiting the growth commensal E. coli K-12 biofilm formation (Lee et al., 2011). Follow-up studies compared Manuka honey (MH) alone or in combination with sulfasalazine a medicine used for colitis treatment. MH showed an additive effect when used as combination therapeutics

CHAPTER 1: INTRODUCTION thus confirming its efficacy and safety profile without any interactions with a conventional drug (Medhi et al., 2008).

Besides benefitting morphological and histological scores, biochemical parameters like lipid peroxidation were also improved with combination therapeutic utilizing MH (Medhi et al., 2008). The use of honey in the treatment of colitis has been reported both with the oral administration or rectal administration. A study compared the benefits of honey, prednisolone and disulfiram in the trinitrobenzene sulfonic acid induced colitis rat model. Intriguingly honey showed a higher potential to treat chronic colitis when compared with prednisolone (Bilsel et al., 2002).

The emerging therapeutic potentials of honey in UC are due to its potential in overcoming inflammation and oxidative stress. A recent study has evaluated the benefits of using honey in dextran sodium sulfate (DSS) induced colitis model. The study revealed that honey-treated UC animals showed improvements in both microscopic and macroscopic scores. A significant down-regulation of oxidative, inflammatory and apoptotic markers manifest the beneficial impact of honey in UC (Nooh & Nour-Eldien, 2016). This study supports further investigations into the effect of honey alone, or in combination with other conventional medications, to treat UC.

AIMS OF THE RESEARCH

The exact causes of ulcerative colitis (UC) are unknown, with possible candidates being an autoimmune response, genetics and diet/environment. It is the contention of this thesis that the microflora of the colon plays an important role in the development of UC, in particular that the Sulfate Reducing Bacteria (SRB) are the primary etiological agent involved in UC. A sub-hypothesis of the current study is that the consumption of Mauka honey can be an effective approach in the control of possible UC-causing bacteria. The major hypothesis and the associated sub-hypothesis are linked with the following research questions:  Does bacterial diversity change in patients suffering from UC, with particular reference to SRBs?  Is there any link between SRB and the presence of specific bacterial population that can help in early detection of SRB?  Can the use of Manuka honey improve the efficiency of antibiotics in controlling the bacteria involve in UC? The overall aim of the study is to estimate the relative levels of SRB, compared to predominant species colonizing the mucosal surface of the human colon, in healthy and patients suffering from UC, and to design procedures for the detection and quantification of SRBs. Specific aims of the investigation were as follows:  Test bacterial DNA samples obtained from patients suffering from UC and healthy controls.  Identify existing oligonucleotide (DNA) probes for selected bacteria known to be implicated in the aetiology of UC; and designing de-novo probes where required.  Evaluate DNA probes using a cassette based approach for fine-tuning of biochips.  Test DNA probes for hybridization with bacterial DNA samples obtained from patients suffering from UC and healthy controls.  Evaluate a sensitive method for rapid enumeration of SRB  Investigate the influence of honey on the growth of SRB biofilms in the presence of antibiotics to determine whether this agent influences bacterial growth and activity.

CHAPTER 2: MATERIALS AND METHODS

Section 1

Molecular characterisation of mucosa-associated bacterial communities from human colon

2.1.1 Bacterial growth conditions

The bacterial strains used in this study were Desulfovibrio alaskensis NCIMB 13491, Desulfovibrio indonensiensis NCIMB 13468, Desulfovibrio vulgaris (strain Hildenborough, NCIB 8303), Desulfovibrio vietnamensis DSMZ 10520, and Escherichia coli (BP) obtained from the University of Portsmouth, School of Pharmacy and Biomedical Sciences bacterial culture collection. The Desulfovibrio bacteria were used because they represent the main genus of SRB that found in the human colon and known to be implicated in UC. Moreover, unpublished data reported a level of serum antibody against D. indonensiensis in UC patients (Zinckivch, Unpublished). E. coli (BP) was used because previous experiments have shown that the presence of E. coli in the human colon correlates significantly with SRB presence (Zinckivch, Unpublished). SRB strains (Desulfovibrio sp.) were cultivated anaerobically in Vitamin Medium with iron (VMI medium) or Vitamin medium with reduced iron (VMR) at 37°C for 7 days. It has been found that these media supplemented with lactate and sulfate which can ofered a higher sensitivity of SRB detection (one to two orders of magnitude) (Zinkevich et al., 1996; Zinkevich & Beech, 2000). E. coli BP were cultivated aerobically at 37°C for 18 h in either Lysogeny Broth (LB) medium which is the most commonly used a nutrient-rich medium for fast growth of E.coli (adapted from Bertani, 1951) (Fisher, UK); or in MacConkey Agar which is usually used for the differentiation and identification of enteric bacteria based on their lactose fermentation (MacConkey, 1905). The bacterial inoculum was adjusted for all experiments of approximately 2X108 cells/ml for E. coli BP and 7X106 cells/ml for SRB. The composition of VMI, VMR, LB medium nutrient broth and MacConkey Agar is shown in Appendix 1.

CHAPTER 2: MATERIALS AND METHODS

2.1.1.1 Purification of genomic bacterial DNA

Chromosomal DNA was extracted from bacterial cultured cells using QIAGEN DNeasy Blood and Tissue Kit (QIAGEN, UK) following the manufacturer’s instructions (DNeasy Blood and Tissue Handbook 07/2006 p.29-44). The bacterial cells harvested in eppendorf tubes (1.5 ml) by centrifugation at 13 000 x g for 10 min at room tempreture (Heraeus Fresco 21, Thermo Scientific, UK) and were resuspended in 180 μl of Tissue Lysis buffer (ATL buffer). 20 µl of Proteinase K was added to the samples and mixed thoroughly by vortexing and incubated at 56°C from 1-3 h until the samples completely lysed. The samples were vortexed occasionally (every hour). After incubation the samples were vortexed for 15 seconds before adding 200µl of Lysis buffer (AL buffer) and mixed thoroughly by vortexing. Then 200µl of 100% ethanol was added to the samples and mixed again thoroughly by vortexing. The mixture was placed into the DNeasy Mini Spin column (placed in a 2 ml collection tubes) and centrifuged at 6 000 x g for 1 min at room tempreture, the flow through was discarded and the DNeasy Mini Spin column was placed in a new collection tube. 500 µl of Wash buffer 1 (buffer AW1) was added to the column, which was then centrifuge at 6 000 x g for 1 min at at room tempreture, the flow through was discarded and the DNeasy Mini Spin column placed in a new collection tube. 500 µl of Wash buffer 2 (buffer AW2) was added to the column, which was then centrifuged at 13 000 x g for 3 min at room tempreture and the flow through was discarded. Finally, the DNA was eluted by placing DNeasy Mini Spin column in a clean 1.5 ml Eppendorf tubes after adding the 50 µl of Elution buffer (buffer AE) directly onto the centre of DNeasy membrane, incubated at room temperature for 1 min and then centrifuged at 6 000 x g for 1 min at room temperature.

Bacterial DNA isolated following the sampling of specimens of colonic mucosa which were taken during colonoscopy from the proximal colon of one healthy individual and four patients suffering from UC. The samples were kept at -20°C until used (Zinkevich & Beech, 2000). These samples were provided by Dr. Zinkevich.

CHAPTER 2: MATERIALS AND METHODS

2.1.2 Polymerase chain reaction (PCR)

Aliquots of 2-10 ng of DNA were added to a PCR master mixture and the volume adjusted to 25 µl or 50 µl with nuclease-free water (SIGMA). The PCR products were analysed by 0.9 or 1.2 % agarose gel electrophoresis. Standard PCR reactions were carried out in the RoboCycler® Gradient 96 (Stratagene, UK) or T100™ Thermal Cycler (Bio-Rad, UK). Touch down PCR reactions were carried out in the T100™ Thermal Cycler (Bio-Rad, UK). All primers used in this study were purchased from Life Technologies, UK. GoTaq® Green Master Mix was purchased from Promega, UK.

2.1.2.1 PCR for bacterial 16S rRNA genes

The V3, V4 and V5 region of the bacterial 16S rRNA gene was amplified using the primers pair 341F+GC and 907R (Table 2.1.1). Final reactions contained: 2x GoTaq® Green Master Mix and 10 µM of each oligonucleotide primers. Reactions for the V3, V4 and V5 region were initially denatured at 94°C for 4 min; followed by a touch down PCR: 20 cycles of 94°C for 1 min, 63-54 °C for 1 min and 72°C for 1 min followed by 15 cycles of 94°C for 1 min, 53°C for 1 min and 72°C for 1 min plus an additional 10-min cycle at 72°C. Approximately 1.5kb of 16S rRNA gene was amplified using primers 8F and 1492R (Table 2.1.1). Final reactions contained: 2x GoTaq® Green Master Mix and 10 µM of each oligonucleotide primers. Reactions were initially denatured at 94°C for 2 min; followed by 30 cycles of 94°C for 30s, 55°C for 1 min and 72°C for 45s followed by a final extension step at 72°C for 15 min.

2.1.2.2 PCR detection of SRB (for dsr, Desulfovibrio 16S rRNA and aps genes)

The dsr, Desulfovibrio 16S rRNA and aps genes were amplified using the primers set DSR1F+,DSR-R, DSV691F, DSV826R and Aps F, Aps R respectively (Table 2.1.1). Reactions contained: 2x GoTaq® Green Master Mix and 10μM of each oligonucleotide primers. Reactions for the dsr were initially denatured at 95°C for 3 min; followed by 35 cycles of 95°C for 15s, 67°C for 30s and 72°C for 30s followed by a final extension step at

CHAPTER 2: MATERIALS AND METHODS

72°C for 10 min. The reactions for the Desulfovibrio 16S rRNA gene were initially denatured at 95°C for 3 min; followed by 35 cycles of 95°C for 30s, 62°C for 1min and 72°C for 45s followed by a final extension step at 72°C for 10 min. The reactions for the Aps gene were initially denatured at 95°C for 3 min; followed by 34 cycles of 95°C for 15s, 59°C for 1min and 72°C for 1 min followed by a final extension step at 72°C for 10 min.

CHAPTER 2: MATERIALS AND METHODS

Table 2.1.1. Sequence of primers used in PCR Primer Sequences of + and – Primers Gene Target Reference (nucleotide) 8F 5'-AGAGTTTGATCCTGCCTCAG-3' Turner et al., 1999 1492R 5'-GGTTACCTTGTTACGACTT-3'

341F+GC 5’- Bacterial Muyzer et CGCCCGCCGCGCGCGGGCGGGGCGGGGGCA 16S rRNA al., 1998 CGGGGGGCCTACGGGAGGCAGCAG-3’ 907R 5’-CCGTCAATTCMTTTGAGTTT-3’

DSR1F+ 5’-ACSCACTGGAAGCACGGCGG-3’ dsrA Kondo et al., 2006 DSR-R 5’ -GTGGMRCCGTGCAKRTTGG-3’

DSV691F 5′-CCGTAGATATCTGGAGGAACATCAG-3′ Desulfovibrio Fite et al., 16S rRNA 2004 DSV826R 5′-ACATCTAGCATCCATCGTTTACAGC-3′

APS F 5’-CGCTGGCAGATGATGATCAA-3’ apsA This study

APS R 5’-ATGCGGTTGGGCTCGTT-3’

Legend: R=G/A, Y=T/C, W=A/T, I = Inosine, S=G /C , M= A/C , K= G/T.

2.1.2.3 Positive controls for PCR reactions

Genomic DNA, extracted from laboratory type strains obtained from University of Portsmouth strains collections were used as positive controls and Nuclease free Water as negative controls for all PCR reactions. Details of laboratory type strains and microbial cultivation are given in subsection (2.1.1) and media composition in Appendix 1.

CHAPTER 2: MATERIALS AND METHODS

2.1.3 Analysis of PCR products using agarose gel electrophoresis

All the amplified PCR products (10 µl) were analysed on either 0.9 % (w/v) or 1.2% (w/v) agarose gel made by dissolving the appropriate amount of agarose (Sigma Chemical Ltd, UK) in 1xTAE (20 mM Tris acetate, 10 mM sodium acetate, 0.5 mM Na2- EDTA) buffer. DNA molecular weight markers (1 kb DNA ladder and 100 bp, Promega, UK) were used for size determination of the PCR products. The gels were run in a horizontal electrophoresis chamber (Horizontal gel electrophoresis unit, Scie-Plas, UK). A voltage of 50 V was applied for the first 10 min and 140 V for the remaining 40 min. The gels were stained with the SYBR® Safe DNA gel stain (Life Technologies), (6 µl of stain added into 100 ml of agarose gel) and was photographed under UV light using molecular imager® Gel Doc™ XR+ Imaging system (BIORAD).

2.1.4 Cloning: construction of genomic libraries and sequencing

The total bacterial DNA extracted from biopsy sample was used as the template for PCR amplification of 16S rRNA and dsrA genes, which were subsequently cloned using pGEM®-T Easy Vector cloning system (Promega, UK) and TOPO® TA Cloning® Kit for Sequencing (life technologies, UK).

2.1.4.1 Ligation of plasmid DNA and competent cells transformation

The ligation reaction (final volume 10 µl) was prepared using 50 ng (1 µl) of pGEM®-T Easy vector, 5 µl of 2X rapid ligation buffer, 3 µl of insert (PCR product) and 1 µl of T4 DNA ligase. The molar ratio of vector to the insert of 1:3 was used as recommended by the manufacturer (technical manual no. 042). The ligation reactions were incubated at RT for 1 h then centrifuged and 2 µl of the ligation reaction was added to sterile 1.5 ml tube placed on ice. 50 µl of JM109 high efficiency competent cells (Messing et al., 1981) were carefully transferred to ligated mixture and incubated on ice for 20 min.

The cells were heat-shocked for 50 seconds at 42°C in a water bath and were immediately returned to the ice for 2 min. Room temperature SOC medium (950 µl)

CHAPTER 2: MATERIALS AND METHODS

(Sigma, UK) was and cells were incubated at 37°C for 1.5 hours with aeration (~150 rpm). 100 µl of each transformation culture were plated onto LB/Ampicillin/IPTG/X-Gal plates in duplicate. The solid LB medium was supplemented with 100 µg ampicillin ml- 1, 100 µg X-Gal ml-1 and 0.5 mM IPTG. After incubation overnight at 37°C the plates were placed at 4°C overnight for better blue/white screening. TOPO cloning reaction (final volume 6 µl) was prepared according to manufacture instruction. 4 µl of PCR product, 1 µl of salt solution and 1 µl TOPO®TA vector, was mixed gently and incubated for 5 minutes at RT. 2 µl of the TOPO® cloning reaction was added to One Shot® TOP10 chemically competent cells (Life Technologies, UK), mixed gently and incubated on ice for 30 minutes. The cells were heat-shocked for 30 seconds at 42°C without shaking. The tube was immediately transferred to the ice for few minutes. 250 µl of room temperature S.O.C medium was added to the cells and shaked horizontally (200rpm) at 37°C. 100 µl of each transformation culture were plated onto LB/Ampicillin plates in duplicate and incubated overnight at 37°C .

2.1.4.2 Purification of plasmid DNA

The white bacterial colonies were picked from the plates and inoculated into sterile universal tubes containing 5 ml of LB supplemented with 100 µg/ml ampicillin (Fisher, UK) and incubated overnight at 37°C with aeration (~200 rpm). Recombinant plasmid DNA was purified using Zyppy™ Plasmid Miniprep Kit (Zymo Research, Irvine, CA) following the manufacturer’s instructions. 3 ml of E.coli LB culture harvested in Eppendorf tubes (1.5 ml) by centrifugation at 11 000 x g for 1 min (Heraeus Fresco 21, Thermo Scientific, UK). Bacterial cells were re-suspended in 600 µl water. 100 µl of 7X Lysis Buffer was added and mixed by inverting the tube 4-6 times until complete lysis was obtained. 350 µl of Neutralization Buffer was added and mixed by inverting the tube several times until the neutralization was completed. The samples were span at 11,000 x g for 4 minutes. The supernatant (~900 µl) was transferred into the Zymo-Spin™ IIN column without disturbing the cell debris pellet. The column centrifuged 11,000 x g for 15 seconds and flow through was discarded (this step was repeated until all supernatant has been loaded). 200 µl of Endo-Wash Buffer was added to the column and centrifuged for 30

CHAPTER 2: MATERIALS AND METHODS seconds then 400 µl of Zyppy™ Wash Buffer and centrifuged for 1 minute. Finally the plasmid DNA was eluted by placing Zymo-Spin™ IIN column into a clean 1.5 ml

Eppendorf tube and then 30 µl of PCR grade H2O was added directly onto the membrane, incubated at RT for 1 min and then centrifuged at 11 000 x g for 1 min. Insertion of the PCR product of interest into the plasmid was verified by PCR using primers 8F and 1492R and DSR1F+ and DSR-R (Table 2.1.1) and by agarose gel electrophoresis analysis (subsection 2.1.3).

2.1.4.3 Purification of PCR product and Sequencing

Amplified PCR products of plasmid DNA (bacterial 16S rRNA and dsr genes) were purified using QIAquick® PCR purification kit (Qiagen, UK) following the manufacturer’s instructions (quick-start protocol, 09/2011 p.1-2) before to be sent to the GATC Biotech UK (Cambridge, UK) DNA sequencing service. 5 volumes of buffer PB was mixed with 1 volume of the PCR reaction applied onto the QIAquick column and centrifuged for 1 min. Flow through was discarded. The column was washed using 750 µl buffer PE and centrifuged for 1 min. After flow through was discarded the QIAquick column was centrifuged once more for 1 min to remove residual wash buffer. Finally, the PCR product was eluted with 50 µl of ultra-pure water. Purified PCR products were sent for DNA sequencing (GATC Biotech, Cambridge, UK). The DNA fragments were identified by sequencing using PCR primers (subsections 2.1.2.1 and 2.1.2.2) and homology searches in BLAST of the GenBank.

2.1.5 Denaturing Gradient Gel Electrophoresis (DGGE)

PCR products (bacterial 16S rRNA gene) were separated on denaturing gradient gel (INGENYphorU, Ingeny International BV, The Netherlands). 30 µl of PCR products were loaded on 6% (w/v) polyacrylamide gel in 0.5x TAE buffer. The electrophoresis was run at 60°C using the gel containing 30% to 80% urea-formamide denaturing gradient (100% corresponded to 7M urea and 40% (v/v) formamide) increasing in the direction of electrophoresis. The gel was run for 15 min at 200 V and for 20 hours at 90 V.

CHAPTER 2: MATERIALS AND METHODS

After electrophoresis the gel was removed gently from the tank, placed in appropriate container and stained using 12 µl of SYBR Safe DNA gel stain (Life technologies, UK) in 750 ml of double distilled water for 20 minutes in darkness, then rinsed thoroughly with distilled water. The gel was photographed under UV light using Alpha Innotech Gel Documentation System (Alpha Innotech Corporation, USA).

2.1.6 DNA extraction from DGGE gels and Sequencing

All bands were cut out from the DGGE gels using a sterile scalpel, transferred into a sterile 1.5 ml Eppendorf tubes containing 40 µl of ultra-pure water (Machado et al., 2007). The samples were incubated for 24 h at 4°C, followed by 1 hour incubation at 37°C. Samples were then centrifuged at 13 000 x g for 1 min (Heraeus Fresco 21, Thermo Scientific, UK). The supernatants (5 µl) were used for PCR re-amplification as described above (subsection 2.1.3.1). Re-amplified PCR products were purified using QIAquick® PCR purification kit (Qiagen, UK) (as described in subsection 2.1.4.3) and sent for DNA sequencing (GATC Biotech, Cambridge, UK). The DNA fragments were identified by sequencing using PCR primers (subsections 2.1.2.1) and homology searches in BLAST of the GenBank.

CHAPTER 2: MATERIALS AND METHODS

2.1.7 Phylogenetic analysis

Sequences were aligned using Muscle as implemented in SeaView ver4.0 (Galtier et al., 1996; Edgar, 2004). Phylogenetic trees were constructed using the maximum likelihood optimal criteria which are part of SeaView ver4.0. The Tamura and Nei 1993 (TN93) model was the substitution model employed using a rate category of 4. Trees were constructed using BioNJ method (Gascuel, 1997) with NNI tree searching operations. Support for nodes in the produced trees was estimated with a bootstrap analysis, implemented in SeaView ver4.0. A bootstrap search of 100 repetitions was used in this analysis. The DGGE gel was scored for the presence and absence of bands for each sample tested. A comparative table was constructed with band presence score as 1, and absence scored as 0. The comparative table was used to construct a matrix from which genetic distance was calculated according to the algoritm of Nei and Li (1979) as applied in PAUP* 4.0 (Swofford, 2003). The genetic distance was used to construct a UPGMA tree.

CHAPTER 2: MATERIALS AND METHODS

Section 2 Probe designing and testing 2.2.1. DNA probes design

The groups of colonic bacteria that can be involved in the etiology and pathogenesis of UC were used as targets for Probes design. The NCBI database (http://www.ncbi.nlm.nih.gov) was used to derive sequences for probe design (Appendix 3). The complete nucleotide sequences for each functional and structural gene were downloaded and saved in FASTA format. The sequences were loaded in SeaView software (Galtier et al., 1996). Region variations in the sequences were identified from multiple alignments generated by Clustalo (Sievers et al., 2011) or Muscle (Edgar, 2004). Based on final alignments, conserved parts of the sequences of each gene were chosen and used for probe designing using the Oligo 7 program (Rychlik, 2007). Published probes and an established rRNA-targeted oligonucleotide probe database named 'probeBase' (Loy et al., 2007) were also used for probe selection.

The probe design meets the following well-established requirements:  Unique to the target agent.  The homogeneous predicted melting temperatures (Tm) of the probes.  The homogeneous length of oligonucleotides and for the developing array the probe size is preferentially limited to between 18 and 25 bases.

Hybridization kinetics of nucleic acids is temperature dependent, and the specificity and efficiency depend on the hybridization temperature. One of the main problems with designing oligonucleotide microarrays is to achieve nearly identical melting temperatures for all probes on the array. There are several approaches for the equalizing of the probes hybridization ability. In the first stage, the oligonucleotide probes should be design with the same predicted melting temperature (Tm±2⁰C) by using one of the algorithms based on thermodynamic properties of nucleic acid duplex formation and dissociation in solution. Mainly all the algorithms are based on the nearest neighbour model (Breslauer et al., 1986).

CHAPTER 2: MATERIALS AND METHODS

The predicted Tm is calculated using Integrated DNA Techenlogy (IDT) OligoAnalyzer 3.1program (http://eu.idtdna.com/analyzer/Applications/OligoAnalyzer/Default.aspx). For the Tm calculation, the program demands not only the oligonucleotide sequence, but the buffer conditions as well. Tm and consequently the hybridization temperature depend on the buffer composition. The length of each probe was established ranging from 18 to 25 bases. The GC content was calculated and only probes with GC content between 40 and 60% were selected for further analysis. The designed probes were synthesized and C6-Amine-group was inserted at 5´-end during the synthesis (Sigma- Aldrich, UK). All designed probes were verified against the GeneBank nucleic acid database for specificity using the BLAST program (Altschul et al., 1990).

2.2.2 Cassette construction for fine-tuning of biochips

Cassette approach is a novel method used for evaluation designed probes in single reaction. The cassette comprising all of the probes in equimolar proportions for a quick reveal the probes unsuitable for inclusion in the biochip (Zinkevich et al., 2014). Single stranded (ss) DNA cassette was used as the targets for hybridization with the probes that were coupled to the matrix so that the hybridization capacity of each probes to be evaluated. The ss DNA cassette is a lineal array of sequences complimentary to the studied set of probes. Cy3 fluorescent dye was inserted at the 5´-end of the ss cassette during the synthesis. Each DNA cassettes was constructed to contain the four chosen oligonucleotides and were synthesized by Bioneer Corporation (Daejeon, Republic of South Korea). The size of the cassette was varied from 93 to 100 bp.

2.2.3 Development of a biochip using dendrimeric matrix

Ready to use dendrimeric matrix activated for the probes immobilization was provided by Dr Nelly Sapojnikova (I. Javakhishvili Tbilisi State University, Andronikashvili Institute of Physics). Dendrimeric matrix is formed by the microscopic glass slide treatment in a special manner (Anne-Marie et al., 2006). The oligonucleotide probes were immobilized onto the activated slides using spotting pins (V&P Scientific, Inc, San Diego, USA). 200 nl/spot of oligonucleotide probes with two replicate spots of each probe being applied to activated slides. Concentrations of oligonucleotide probes were 250 pmol/0.1µl in 1% Diisopropylethyl amine (DIEA)

CHAPTER 2: MATERIALS AND METHODS reagentPlus®, 99% (Sigma-Aldrich, UK), in water. The slides were then incubated overnight in a humid chamber at 37⁰C and subsequently washed with nuclease-free sterile water then with 100 % ethanol. The surface of the glass slides with the immobilized probes was deactivated by treatment with a solution made of 6-amino-1- hexanol 97% (50mM), (Sigma Adrich, UK) and DIEA (150 mM) in 20ml of anhydrous amine free N,N-dimethylformamide (DMF) molecular biology grade (Applichem, Germany) for 2 hours, in order to prevent the binding of the fluorescently labelled DNA with the matrix surface. Finally, the deactivated glass slides with the immobilized probes (biochips) were washed with DMF, acetone, water and dried. The biochips were then ready to be used in hybridization experiments and can be stored at 4ᵒC until use at least for 6 months.

2.2.4 Hybridization

The hybridization buffer for the ss-DNA cassette was SSARC (4xSSC [600 mM NaCl, 60 mM Na-citrate], 7.2% (v/v) Na-sarcosyl). The solution of Cy3-labelled ss cassette (100 pmol/µl) was prepared by dissolving the dry pellet in nuclease free water. The cassette quantity in the hybridization buffer was 100 pmol. The sample and the slide with immobilized probes were preheated to the hybridization temperature (45⁰C) using chilling/heating block (Cole-Parmer®,USA) for 5 minutes. The fluorescent samples solution was loaded onto the biochip and covered immediately with the cover slip. The final volume of the hybridization solution was 10 µl. The volume of the hybridization solution (10 µl) is from the calculation 2 µl per 1 cm2 of the covered square. The area under a cover slip was 2.2cm x 2.2cm=4.84 cm2. Before placing the biochip in the hybridization chamber (Arrayit, USA) 10 µl of nuclease- free water was added to one of the grooves to keep 100 % humidity in the chamber during the hybridization reaction. The slide was immediately placed in the hybridization chamber covered by the chamber lid and sealed by screws (Figure 2.2.1). The hybridization proceeds at 45°C for 4 hours. Slide was removed from the hybridization chamber and immersed in the washing buffer 1 (2XSSC/0.2% SDS) in a Petri dish for removing the cover slip. Then the slide was placed in a Falcon tube for washing in buffer 1 by shaking for 2 minutes on a shaker multi Bio 3D (BIOSAN, Latvia). Then the buffer 1 was discarded and next wash was performed in the washing buffer 2 (0.2XSSC/0.2%

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SDS) and the washing buffer 3 (0.2XSSC) subsequently on a shaker multi Bio 3D (BIOSAN, Latvia) for 2 minutes each at 30 rpm at room temperature. The slide was dried by centrifugation in a Microarray High Speed Centrifuge (ArrayIt, USA), and visualized using a Portable Imager 5000 (Aurora Photonic, USA) with 532 nm green Laser and 580 nm filter. The signal intensity of each point on the biochip was calculated using MicroChip Imager software (Aurora Photonics, USA).

Figure 2.2.1. Photograph for the hybridization chamber

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2.2.5 Genomic DNA preparation for hybridization on a biochip

DNA purified from the bacterial strains described in subsection 2.1.1 and the genomic DNA from biopsy samples subsection 2.1.1.1 was used in the hybridization procedure.

2.2.5.1 DNA amplification

Genomic DNA from bacteria and from biopsy samples were amplified using illustra™ GenomiPhi HY DNA Amplification Kit (GE Healthcare, Life Science, USA). 22.5 µl of sample buffer was added to 2.5 µl of 10 ng DNA and mixed thoroughly. The samples were heated at 95 ᵒC for 3 min and immediately chilled on ice. The master mix was prepared by combine 22.5 µl of reaction buffer and 2.5 µl of enzyme mix on ice .25 µl of prepared master mix was transferred to each cooled samples on ice. The samples were incubated at 30ᵒC for 4 hours. The reaction was stopped by incubation at 65°C for 10 min and followed by immediate chilling on ice for 10 min. The extent of amplification was verified using 1.5% agarose gel in 1X TAE buffer. The amplified reactions were store at -20 °C until used.

2.2.5.2 Purification of amplified products

The amplified products were purified using PureLink Quick PCR Purification Kit (Invitrogen, USA). Four volumes of PureLink® Binding Buffer (B2) with isopropanol was added to one volume of the amplified product and mixed well. The samples were loaded into the PureLink® Spin Column placed in collection tube. The column was centrifuged at 10,000 × g for 1 min at room temperature and the flow through was discarded. 650 µl of washing buffer with ethanol was added to the column and centrifuged at 10,000 × g for 1 min at room temperature and the flow through was discarded. The column was centrifuged again at maximum speed (13,000× g) at room temperature for 3 min. Finally, the DNA was eluted by placing the PureLink® Spin Column in a clean 1.5 ml Eppendorf tubes after that 50 µl elution Buffer (10 mM Tris- HCl, pH 8.5) was added directly onto the centre of column , incubated at room temperature for 1 min and then centrifuged at 13, 000 x g for 2 min.

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2.2.5.3 DNA Fragmentation

The amplified purified products were fragmented using NEBNext® dsDNA Fragmentase® (New England biolabs, UK). The fragmentation reaction was prepared by mixing 1-3 µg of amplified purified products, 2µl 10X reaction buffer v2, 2µl of

NEBNext® dsDNA Fragmentase and 4 µl of 200 Mm MgCl2. The volume was adjusted to 20 µl by nuclease-free water. The reaction was incubated at 37°C for 25 min and the enzyme was inactivated by heating the reaction for 5 min at 65°C. The restirictase FastDigest SaqAI (Thermo Scientific, USA) was used for the fragmentation of the amplified purified products as well. . The fragmentation reaction was prepared by mixing 3 µg of amplified purified products, 3 µl 10xSaqAI Buffer, 3 µl of SaqAI enzyme. The volume was adjusted to 30 µl by nuclease-free water. The reaction was incubated at 37°C 30 min, and the enzyme was inactivated by heating the reaction for 5 min at 65°C. The digestion conditions were selected to form a range of fragment sizes from 50 to 200 bp. The fragments sizes were checked in 1.5 % agarose gel in 1XTAE buffer. The fragmented DNA was purified using PureLink Quick PCR Purification Kit (Invitrogen, USA) as described in subsection 2.2.5.2

2.2.5.4 DNA labelling and hybridization

Fragmented DNA was labelling using BioPrime® Plus Array CGH Genomic Labelling System (Invitrogen, USA) Alexa Fluor® 555-labeled primers and nucleotides. For labelling reaction approximately 1 µg of fragmented DNA and 20 µl of Alexa Fluor® 555 Panomer™ 9(31 nmole was re-suspended in 110 µl of 2.5X Reaction Buffer) were mixed and nuclease free water was added up to 44 µl, . The reaction was incubated at 95°C for 10 minutes then chilled immediately on an ice and was protected from light, for 5 minutes. One ice, 5 µl of 10X Nucleotide Mix with Alexa Fluor® 555-aha-dCTP and 1 µl Exo-Klenow Fragment were added to the mixture with DNA to reach the final volume 50 µl, The isothermal amplification of DNA and simultaneous labelling in the samples proceeds at 37ᵒC for 8 hours. The amplified and labelled products were purified using BioPrime® purification Module (Invitrogen, USA). 200 µl of Binding Buffer B2 was added to to each tube contained the labelled samples. The samples were loaded onto the PureLink™ Spin Column and centrifuged at 10,000×g for 1 minute, then flow-through

CHAPTER 2: MATERIALS AND METHODS was discarded and the column was placed back in the tube. 650 µl of Wash Buffer W1 was added to the column and centrifuged at 10,000×g for 1 minute, then flow-through was discarded and the column was placed back in the tube. The samples were spin at maximum speed (14,000 xg) for an additional 3 minutes to remove any residual wash buffer. Finally, the Spin Column was placed in a new, sterile amber collection tube and 55 µl of elution buffer E1 was added to the centre of column, incubated at room temperature for 1 minute and centrifuged at maximum speed (14,000 xg) for 2 minutes. The yield of amplified labelled purified DNA was estimated by spectrophotometer (NanoDrop, Technologies Inc.). The DNA was precipitated by adding 10 volumes of 2%

LiClO4 in acetone for overnight at - 20°C. Centrifugation was performed to pellet DNA at 14,000 x g for 30 min, the supernatant was discarded, and the pellet was washed with 1 ml acetone, and then centrifuged at 14,000xg for 20 min. The pellet was dried on air or at 37°C 10-20 min until the red/pink colour was clearly visible. The pellet was dissolved in the hybridization buffer (1 M GuSCN (guanidine thyocianate), 5 mM EDTA, 50 mM HEPES (pH 7.5), 0.2 mg/ml BSA (bovine serum albumin) (Sigma, UK), then was heated at 95°C for 5 min followed by immediate chilling on ice for 2 min. The fluorescent samples solution was loaded onto the slide of immobilized probes and covered immediately with the cover slip. The cover slip was prepared by soaking in washing detergent for 20 min, and then extensively rinsed in distillate water; after that, the cover slip was soaked in the concentrated sulfuric acid for 20 min, and rinsed extensively in distillate water. Finally, the cover slip was rinsed in 95% ethanol and dry at 80°C. The area under a cover slip was 2.2cm x6.4cm=14.08 cm2; the volume of the hybridization buffer (30 µl) is calculated from 2 µl per 1 cm2. Before placing the biochip in the hybridization chamber 10 µl of nuclease-free water was added to one of the grooves to keep 100 % humidity in the chamber during the hybridization reaction. The samples were hybridized at 25°C for 4 hours followed by washing in 4xSSC; 7.2 % Sarcosyl for 30 sec. The slide was dried by centrifugation in a Microarray High Speed Centrifuge (ArrayIt, USA), and visualized using a Portable Imager 5000 (Aurora Photonic, USA) with 532 nm green Laser and 580 nm filter. The signal intensity of each point on the biochip was calculated using MicroChip Imager software (Aurora Photonics, USA).

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Section 3 Detection and quantification of sulphate reducing bacteria using microbiological and quantitative PCR techniques

2.3.1 Bacterial culture preparation

Desulfovibrio indonesiensis NCIMB 13468 were cultivated anaerobically in Vitamin Medium Reducing iron (VMR medium) at 37°C for 7 days. E. coli BP was cultivated anaerobically in VMR medium at 37°C overnight (~17-18 h). The bacterial inoculum was adjusted for all experiments of approximately 2X108 cells/ml for E. coli BP and 7X106 cells/ml for D. indonesiensis. Seven days old culture of D. indonesiensis was used for subculturing D. indonesiensis in different time of growth for 7 days, 3 months and 1 year in VMR and VMI media at 37 °C. The SRB growth stages divided into exponential phase, stable state and decline period. It has been found that the number of SRB increased rapidly (from day ~3-30) achieving the maximum in day seven. After that the growing process of SRB reached the stable state (~2-6 months) before it started the last stage which is the decline phase (Butlin et al., 1949; Zhang et al., 2011; Mahat et al., 2015). In these experiments three growth stages (7days, 3 months and 1 year old culture) were used. This is meant to observe the stage by stage progress of SRB gene expression over time as well as to evaluate the ability of E.coli BP to induce the growth of SRB in low cell density. Each age of D. indonesiensis cultures (7 days, 3months, and 1 year) were serially diluted in VMR and VMI media. 0.5ml of E. coli BP was inoculated to each dilution of different age D. indonesiensis cultures and incubated anaerobically at 37°C for 28 days and the growth of D. indonesiensis , was monitored every 24 hours visually for the production of black iron sulfide. The blackening of the medium is evidence of SRB growth in the medium (Vester & Ingvorsen, et al., 1998). Viable count of E. coli BP was determined before co-culture with D. indonesiensis by ten-fold serially diluting 0.1 ml of E.coli BP culture in 0.9 % NaCl; and plated on a freshly prepared MacConkey agar and incubation at 37°C. The colonies were counted and expressed as colony forming unit per ml of culture (cfu/ml). The composition of VMR, VMI and MacConkey agar is shown in Appendix 1.

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Genomic DNA was extracted from 5ml of D. indonesiensis original culture cells which was used for serial dilution (of all ages) QIAGEN DNeasy Blood and Tissue Kit (QIAGEN, Germany) following the manufacturer’s instructions (as described in subsection 2.1.1.1).

2.3.2 Purification of total bacterial RNA

RNA was purified from D. indonesiensis original culture which were used for serial dilution (of all ages) using the QIAGEN RNeasy Mini kit according to the manufacturer’s instructions (RNAprotect bacteria reagent Handbook, 07/2001 p.13-35). Before proceeding to RNA purification 4 ml of D. indonesiensis culture were stabilized by adding 1 volume of the culture to 2 volumes of RNAprotect bacteria solution placed in a reaction tube. The samples were mixed by vortexing for 5 seconds, incubated for 5 min at room temperature and centrifuged for 10 min at 5000x g (Heraeus Fresco 21, Thermo Scientific, UK). The supernatant was discarded. 200µl of TE buffer containing lysozyme (1mg/ml) was added to the samples and mixed by vortexing for 10 seconds then incubated for 5 min at room temperature. 700 µl of buffer RLT containing β- Mercaptoethanol (10µl β-ME per 1ml buffer RLT), (sigma Aldrich, UK) was added to the samples and votexed vigorously then centrifuged at the maximum speed (13000xg) for 2 min. The supernatant was collected in a new eppendorf tube and 500 µl of 100% ethanol was added to the lysate, mixed by votexing. The lysate containing ethanol was applied to RNeasy mini column placed in a 2ml collection tube and centrifuged for 15 seconds at 8000x g. DNase digestion was performed during RNA purification using RNase-free DNase set (QIAGEN, UK). 350 µl of RW1 buffer was added into the RNeasy mini column and centrifuged for 15 seconds at 8000 x g and the flow through was discarded. 80 µl of DNase I incubation mixture was added into each RNeasy silica-gel membrane, and incubated in the room temperature for 15 min. 350 µl of buffer RW1 was added into the RNeasy column , incubated at the room temperature for 5 min and centrifuged for 15 seconds at 8000 x g. The RNeasy column was transferred into new collection tube, washed by 500 µl RPE buffer and centrifuged for 15 seconds at 8000 x g and the flow through was discarded. RNeasy column was washed again with 500 µl RPE buffer and centrifuged for 2 min at 8000 x g. The RNeasy column was placed in new 2 ml collection tube and centrifuged for 1min at the maximum speed (14000 x g). Finally, RNA was eluted with 40 µl RNase free water onto the centre of RNeasy silica-gel

CHAPTER 2: MATERIALS AND METHODS membrane was placed in a new 1.5 ml Eppendorf tube and centrifuged for 1 min at 8 000 x g.

2.3.2 .1 cDNA Synthesis

Random primers (2.5 µl, 60 µM, Promega, UK) were annealed to RNA (10 µl, approx. 1-2 µg) in a total volume of 15 µl at 70°C for 10 minutes and immediately placed on ice for 5 minutes. 2.5 µl of 5x buffer for MMLV, 1µl of 10mM dNTP mix, 1µl RNase inhibitor, 4.5 µl of water and 1µl of MMLV reverse transcriptase (Promega, UK) were mixed and added to each RNA/primer mixture; the total volume of each reaction was 25 μl. Reverse transcription was carried out at 37°C for 60 minutes. The reaction was stopped by heating for 15 minutes at 70°C. An equal volume of H2O was added to each cDNA. A negative control was included by omitting MMLV-RT from the reaction mixture. cDNA samples were stored at -20°C before real-time PCR analysis.

2.3.3 Preparation of PCR fragment of the apsA and the dsrA genes

The apsA and dsrA genes fragments were used for standard curve generation for qPCR. PCR fragments were amplified (five tubes reactions; final volume of 50 µl) using genomic DNA of D. indonesiensis as a template as describes in subsection 2.1.2.2. The PCR products were verified in 1.2% agarose gel. The rest of PCR reactions of were combined and DNA was precipitated. One tenth of the PCR products volume of 3M Sodium Acetate pH 5.2 was added (Sigma adrich, UK). 2.5 volumes of 100 % ethanol was added to the samples and placed in a -20°C overnight. DNA was precipitated by centrifugation at 14 000 x g for 10 min, washed twice with 400µl of 70% ethanol and centrifuged at 14 000 x g for 10 min each. After removal of ethanol the pelleted DNA was air dried and resuspended in 25 µl of nuclease free water. The DNA samples were mixed with 6X loading dye (Promega, UK) and analysed on 1.2 % agrose gel in 1 X TAE buffer (90 min, 100 V). The gel was photographed after staining as described in section 2.1.3.

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2.3.4 Purification of DNA fragments from the agarose gel

PCR fragments of dsrA gene (221 bp) and apsA gene (135 bp) were cut off from agarose gel and purified using QIAquick ® Gel Extraction kit (Qiagen, UK) according to the manufacturer's instructions (QIAquick Spin Handbook 04/2014 p24-25). 3 volumes of Buffer QG were added to 1 volume of gel slice (100 mg ~ 100 μl). The gel slice was incubated at 50°C for 10 min (until agarose has completely dissolved). The tube was vortexed occasionally. 1 gel volume of isopropanol was added to the sample and mixed thoroughly. The samples were applied to the QIAquick column, centrifuged for 1 min, washed with 500 µl of buffer QG, and then washed with 750 µl of buffer PE. After centrifugation the flow-through was discarded. The column was centrifuged for an additional 1min at 14 000 x g to dry the column. DNA was eluted with 50 µl of Buffer EB (10mMTris·Cl, pH 8.5).

2.3.5 Quantitative real-time PCR (Q PCR)

All real-time experiments were performed on Stratagene Mx3005P (Agilent Technologies, UK). A quantitative real-time PCR assay targeting two functional genes of SRB dsrA and apsA was used to determine the copy numbers of these genes and finally enumerate the amount of SRB. Specific primers DSR-F1, DSR-R (Table 2.1.1) and TaqMan probe (6FAM) - 5’-CCGATAACRCYGCCGCCGTAACCGA - 3’ - (TAMRA) (Bourne et al., 2011) were used for dsrA gene. For the apsA gene; TaqMan assay using the primers APS-F, APS-R (Table 2.1.1) and the probe – (6FAM-5’ CGCATCATGGAGCGCATGTTCATC- 3’ - TAMRA); (This study). Standard curves for quantification of the dsrA and the apsA genes were constructed with 10-fold dilutions of gel purified PCR fragments from 10-1 – 10-10. PCR reaction contained 5 µl of 2 X Brilliant Q PCR master mix (Agilent, UK), 0.5 µl of 10 µM of the DSR or APS forward and reverse primers mix, 0.25 µl of 5 uM specific dsr or aps TaqMan probes, 2 µl of template, plus nuclease-free water to bring the total volume to 10 µl. The reaction condition for amplification was 95°C for 3 min followed by 40 cycles of 95°C for 15 sec, 67°C for 30 sec and 72°C for 30 sec. All runs included a no template control (NTC). Standard curves were obtained by plotting the Ct (threshold cycle) values of 10-fold dilutions of the dsrA and the apsA fragments. Ct values for each well were calculated

CHAPTER 2: MATERIALS AND METHODS using the manufacturer’s software. Three experiments in triplicate were performed for each gene. For data analysis, Ct and DNA quantity in ng were exported into a Microsoft Excel Word spread sheets. The copy number was calculated on the basis of the measured concentration of standard (ng/ µl), an average molecular mass of 660 for a base pair in a double-stranded DNA and the size of PCR fragment (221 bp for dsrA gene and 135 bp for apsA) and Avogadro’s number (6,02 x 1023 mol-1). Normality of data was assessed using the Shapiro-Wilk normality test. The data was found to be not normally distributed; therefore Kruskal-Wallis with Dunn’s multiple comparisons or Mann- Whitney statistical tests were performed to compare the SRB g DNA copy numbers and the gene expression in different time of growth.

2.3.6 Measurements of hydrogen sulfide (H2S)

The serial dilution of a 7 days old D. indonesiensis culture was grown in either VMR or VMI medium at 37°C for 7 days. The last two vials of the serial dilution which gave positive indication of D. indonesiensis growth as pure and mixed culture with E. coli BP were used for measuring H2S concentration using H2S meter (Sensorcon, Inc. ,USA), with a lower detection limit of 1 ppm. Three independent experiments were done in triplicate. The averages of the 3 readings of each repeats were taken and the mean values were determined. Error bars indicate standard deviation. * indicate significance (p ≤ 0.05).

2.3.7 Pulsed field gel electrophoresis (PFGE)

The Pulsed Field Gel Electrophoresis (PFGE) was performed according to manufacture instruction (CHEF-DR®II pulsed field electrophoresis systems, instruction manual and application guide p12-23) with some minor modification adapted from (Devereux et al., 1997; Ribeiro et al., 2009) See below.

2.3.7.1 Preparation of Agarose Embedded Bacterial DNA

The bacterial culture used in this study was D. indonesiensis and E.coli BP. D. indonesiensis was grown anaerobically in VMR medium at 37°C for 24h, 48h and 6 days and E. coli BP aerobically in LB Broth for 4h at 37°C. 180 g/ml chloramphenicol (sigma

CHAPTER 2: MATERIALS AND METHODS

Aldrich, UK) was added to the bacterial cultures and continued to grow for 1 hour. A twenty-fold dilution of the above bacterial suspension was made and small amount of this suspension was placed on the hemocytometer for counting bacterial cells under light microscope with a 40 X objective. Approximately 5X10⁸ bacterial cells were taken for each ml of agarose plugs to be made. The cells were harvested from culture by centrifugation at 10,000 x g for 5 minutes at 4 °C. The pellet was washed twice with TE buffer(10 mM Tris-HCl and 1 mM EDTA, pH 8.0) (sigma, UK) then the cells were resuspend in one-half the final volume of plugs to be made using Cell Suspension Buffer (10 mM Tris, pH 7.2, 20 mM NaCl, 50 mM EDTA) and the cell suspension were equilibrated to 50 °C. 2% of low melt agarose (CleanCut agarose, Biorad, US) was prepared and equilibrated to 50 °C in a water bath. The cell suspension combined with an equal volume of 2% CleanCut agarose and mixed gently but thoroughly. The cell/agarose mixtures were kept at 50 °C, and the mixture was transferred to plug molds which were placed initially on ice using sterile transfer pipettes. The agarose was allowed to solidify by placing the molds at 4 °C for 10–15 min. Using 50 ml of falcon tube, 5ml of lysozyme buffer was transferred (10 mM Tris, pH 7.2, 50 mM NaCl, 0.5% Brij 58, 0.2% sodium deoxycholate, 0.5% sodium lauryl sarcosine, 1 mg/ml lysozyme), (sigma, UK) for each ml of agarose plugs. The solidified agarose plugs were pushed using the snap off tool with plug molds into the 50 ml centrifuge tube containing lysozyme buffer then the plugs Incubated for 1 hour at 37 °C without agitation. The lysozyme buffer was removed and the plugs was rinsed with 25 ml of 1x wash buffer (20 mM Tris, pH 8.0, 50 mM EDTA) (sigma, UK). 5 ml of Proteinase K Reaction Buffer (100 mM EDTA, pH 8.0, 0.2% sodium deoxycholate, 1% sodium lauryl sarcosine, 1 mg/ml Proteinase K) (sigma, UK) was used for each ml of agarose plugs. The plugs were transferred to the tubes contain the Proteinase K Reaction Buffer then were Incubated at 50°C until the plug goes clear after 2 hours-overnight without agitation. The plugs were washed with sterile water twice, followed by 6 washes with 50 ml of 1X wash buffer (20 mM Tris, pH 8.0, 50 mM EDTA)(sigma, UK) 1 hour each at room temperature with gentle agitation. The plugs were kept in TE buffer and store at 4°C until used.

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2.3.7.2 Restriction Enzyme Digestion of Plugs

Tow restriction enzymes were used for plugs digestion PmeI which recognizes 5’GTTT↓AAAC 3’ sites and NotI recognizes 5’GC↓GGCCGC 3’ sites (Thermo scientific, US). In a sterile 1.5 ml microcentrifuge tube one plug was placed with 1 ml of 1X Buffer B for PmeI or 1X Buffer H for Not I then were incubated at room temperature for 1 hour with gentle agitation. The buffer was aspirated off then 0.3 ml of fresh 1X Buffer was added and 50U of the restriction enzyme Pme I or Not I per 100 µl plug and was incubated at 37°C for 2 hours. After the incubation time, the buffer was removed and 1 ml of UltraPure™ 0.5 X TBE Buffer (made from 10X stock solution:1.0M Tris, 0.9M boric Acid, 0.01M EDTA pH 8.0)(Invitrogen by Life Technologies, UK) was added and was incubated for 30 minutes at 37°C with gentle agitation.

2.3.7.3 Casting the gel

The gel was run using CHEF-DR® II Pulsed Field Electrophoresis Systems (Biorad, US). 3 litters of 0.5X TBE was poured into in the electrophoresis chamber and the buffer was allowed to circulate at ~ 0.75 L/min to equilibrate to the desire temperature (14°C). The plugs were placed on each tooth of the comb then 1% Pulsed Field Certified™agrose gel (Biorad, USA) in 0.5X TBE was poured gently and allowed to solidify for 15 minutes. The comb was removed gently and the gel wells was filled with 1% agrose gel in 0.5X TBE allowed to solidified for 10 minutes and placed in the electrophoresis chamber containing 0.5X TBE. The run time was 22 h at 6 V/cm with a 60 to 120 second switch time ramp at an included angle of 120°. Three markers were used as as size standards in the gel; Lambda ladder 1000 kb, Saccharomyces cerevisiae 2200 kb and Schizosaccharomyces pombe 5.7 mb (Biorad, USA).

2.3.7.4 Removing and staining the gel

After the run was completed the gel was removed gently and slided off into 0.5µg/ml of SYBR® Safe DNA gel stain (Life Technologies) in 0.5 X TBE buffer and the gel was left in dark for 20 min then destained in 0.5 X TBE for 5min. Finally, the gel was photographed under UV light using molecular imager® Gel Doc™ XR+ Imaging system (BIORAD). The

CHAPTER 2: MATERIALS AND METHODS genome size was estimated based on the observation of linearized chromosomal DNA which migrated into the agarose gel during PFGE.

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Section 4

The effect of Manuka honey and antibiotics on the growth of pure and mixed sulphate reducing bacterial biofilms

2.4.1 Honey samples

Manuka honey of different strength: UMF® 5+, UMF® 15+, UMF®25+ and (Comvita), as commercial honey (Rowse) were compared for their effects on biofilm formation. The methylglyoxal (MGO) content of Manuka honey is used to define the antimicrobial activity of this honey and is referred to as the unique Manuka Honey Factor (UMF®) (Mavric et al., 2008). All samples were diluted in VMR medium for use in all assays and stored in the dark at 4°C. All honey concentrations are expressed as % w/v. To distinguish the antimicrobial effect of honey from any osmotic effects of sugars, an artificial honey, simulating the sugar composition of honey, 30.5% glucose, 37.5% fructose and 1.5% sucrose dissolved in sterile water (Shannon et al., 1979) were also tested.

2.4.2 Antibiotics

The antibiotics used in this study included rifaximin and metronidazole (sigma aldrich, UK). The two antibiotics most commonly used as antimicrobial drugs in the treatment of patients suffering from UC (Wang and Yang , 2012).

2.4.3 Bacterial growth conditions

The bacterial strains which were used in this study were D. indonesiensis and E. coli BP. Approximately 2X108 cells/ml for E. coli BP and 7X106 cells/ml for D. indonesiensis had been used as adjusted inoculum for all the further studies. Both strains were subcultured anaerobically in VMR medium at 37oC. E.coli BP culture was ten-fold serially diluted in 0.9 ml of 0.9 % NaCl; then plated on MacConkey agar and incubated at 37°C for 24 h for counting of viable bacterial cells before inoculation. The initial D.

CHAPTER 2: MATERIALS AND METHODS indonesiensis inoculum size was determined using a haemocytometer. The result of counting was expressed as cells per millilitre (cells/ml).

2.4.4 Preparation of honey samples

Stock solution of the honey were prepared in VMR medium (w/v %). Honey solution was kept in closed jars in dark by covering the jars with aluminium foil. Prepared honey solution was placed in shaker incubator innova 4000 (New Brunswick Scientific, USA) at 220 rpm for approximately 1 h at 37oC to obtain a homogenous solution. Aseptically, the prepared honey solution was then pre-filtered with 0.45μm Nalgene syringe pre-filters (Themo Scientific, USA) into new autoclaved-containers in order to purify the highly viscous solutions and prevent clogging of final filtration. Pre-filtrated honey solution was then subsequently filtered with sterile 0.22μm pore sized "Millex Syringe Filters"(Merck Chemicals Ltd.) into autoclaved glass vials and degassed under a stream of nitrogen (N2) for 30 mins, sealed and kept in the 4⁰C until further use.

2.4.5 Determination of the Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of honey and antibiotics

The Minimum Inhibitory Concentration (MIC) which is the lowest concentration of an antimicrobial agent required to inhibit the visible growth of bacteria; and the Minimum Bactericidal Concentration (MBC) which is the lowest concentration of antimicrobial treatment that required to kill the bacteria was determined. The effect of honey, rifaximin and metronidazole alone and in combination against D. indonesiensis as pure and mixed culture with E. coli BP in biofilms were examined. These experiments were performed in different aspects. First, for the evaluation of the ability of the honey to prevent the biofilms formation; the honey concentrations were prepared in increments of 5 in the range of 0- 60 % (w/v) in VMR medium. D. indonesiensis was inoculated as pure and mixed culture with E.coli BP (approximately 106 cells/ml for D. indonesiensis and 108 cells/ml for E. coli BP) simultaneously with the honey and incubated anaerobically at 37⁰C for 7days. After 7 days incubation the vials were treated carefully in order to not disrupted the biofilms. The supernatant solution was gently removed by syringe and the uniform biofilms that been adhere, colonize in the vials were rinsed by

CHAPTER 2: MATERIALS AND METHODS

0.5 ml of fresh VMI medium. 1 ml of the solution was transferred into fresh VMI medium and ten fold serial dilutions were performed. The media with cultures were incubated at 37⁰C for 7days. The second experiment was performed to assess wither the honey was able to eradicate the already established biofilms. The culture of pure and mixed D. indonesiensis with E.coli BP was allowed to grow at 37⁰C for 6 days. Then different concentrations of honey (0- 60 % (w/v) were added to the bacterial cultures and incubated at 37⁰C overnight. The bacterial biofilms assessments were repeated as described above for all experiments. Third, the effect of antibiotics on the growth of pure and mixed D. indonesiensis biofilms was evaluated. Antibiotics solutions were prepared from stock solutions of 25mg/ml and diluted to 256- 1 µg/ml (v/v) through doubling dilution in VMR medium. D. indonesiensis was inoculated as pure and mixed culture with E. coli BP in different concentrations of antibiotics alone and in combination then incubated anaerobically at 37⁰C for 7days before the biofilms were assed as described above. Fourth, the synergistic effect of honey and antibiotics was investigated. The concentration of Manuka UMF®15+ that inhibited the growth of pure and mixed D. indonesiensis biofilms was used to be tested with different concentrations of antibiotics to assess whether manuka honey will be able to improve the efficiency and reduce the MIC value of the antibiotics. Manuka UMF®15+ was used to be tested synergistically with antibiotics because it is commercially available. The volume of antibiotics necessary to achieve the required concentrations (256- 1 µg/ml (v/v) was aseptically added simultaneously into sterile vials containing the bacterial culture and the required Manuka UMF®15+ concentration in VMR medium then incubated anaerobically at 37⁰C for 7days. In the fifth experiment, to evaluate whether manuka honey can inhibit the growth of biofilms therefore the antibiotics will have better effect on the weak biofilms. As described above the required Manuka UMF®15+ concentration in VMR medium were added simultaneously to the pure and mixed D. indonesiensis. The cultures were incubated with Manuka UMF®15+ at 37⁰C for for 6 days; then to achieve the required concentrations of the antibiotics combination; appropriate volume was added. The cultures were incubated overnight before the biofilms were assesd.

CHAPTER 2: MATERIALS AND METHODS

2.4.6 Preparation of Methylglyoxal (MGO)

Methylglyoxal (MGO) is the major antibacterial component of Manuka honey. MGO is found in high concentration in the nectar of manuka flowers and belived to give manuka honey its antibacterial properties (Mavric et al., 2008). In order to assess the contribution role of MGO to the activity of Manuka honey this experiment was performed. MGO levels can be used to estimate the UMF based on the relationship between MGO and UMF identified previously (Adams et al., 2008; Cokcetin et al., 2016). MGO levels in Manuka honey found to be ranging from 38 to 1123 mg/kg. Manuka UMF®15+ was predicted to has the mnimum MGO content of approximately 514 mg/kg (Adams et al., 2008; Atrott and Henle, 2009; Roberts et al., 2015). MGO (40 % solution in water (w/w) (Sigma-Aldrich, UK) was diluted in VMR medium to obtain the equivalent concentrations of MGO content in Manuka UMF®15+ (ranging from 0-0.25% v/v). D. indonesiensis was inoculated as pure and mixed culture with E.coli BP in the different concentrantions of MGO and incubated for 7 days at 37°C before the biofilms were assed as described in section 2.4.5. In all experiments the mean values were estimated from five independent experiements. All values are presented as mean standard deviation. Normality of data was assessed using the Shapiro-Wilk normality test. In all instances the data was found to be not normally distributed. As such Kruskal-Wallis with Dunn’s multiple comparisons or Mann-Whitney statistical tests were performed to compare the differences between treatments and among honey samples on the pure and mixed D. indonesiensis biofilms as required. .

CHAPTER 3: RESULTS AND DISCUSSION

Section 1 Analysis of Biopsy DNA samples using 16S rRNA and DSR genes sequences

It has been widely excepted that the bacteria that colonize the human colon play a fundamental role in the human physiology however, little is known about the bacterial composition that colonize mucosal surfaces in the human colon. In this investigation the bacterial composition of the colonic mucosa associated bacteria were characterised for microbial identification and phylogeny construction. Rapid molecular fingerprinting analysis based on 16S rRNA and DSR genes including DGGE and sequencing were used to detect and identified bacteria in samples obtained from patients suffering from UC and healthy individual.

3.1.1 PCR for bacterial 16S rRNA genes

The 16S rRNA gene is used frequently as a target for many assays, and universal PCR primers are routinely used for species identification (Weisburg et al., 1991). To confirm that bacterial DNA has been successfully extracted and to exclude the presence of inhibitors of the PCR reaction, the universal bacterial 16S rRNA primers were used as a positive PCR amplification control (subsection 2.1.2.1). PCR gels demonstrating the presence of bacterial DNA are depicted in Figures 3.1.1 and 3.1.2. The 16S rRNA PCR products of the correct size (550 bp and 1500 bp) for DNA extracted from the colonic mucosa of healthy volunteer and patients suffering from UC showed that all the extracted DNA products were efficiently PCR amplifiable in all samples, which indicated that bacteria were always present and that the PCR were conducted without significant amounts of inhibitors in biopsy samples. Negative controls yielded no bands, which demonstrated that contaminants were not present. In addition, the fact that no band occurred in all samples examined confirmed that PCR reagents were free of contaminants.

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.1. A photograph of agarose gel showing the PCR amplified products of 16SrRNA gene (550 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of Patient 1 suffering from UC; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5: PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7: Negative control; lane 8: DNA D. alaskensis

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.2. A photograph of agarose gel showing the PCR amplified products of 16SrRNA gene (1500 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5:PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7: Negative control; lane 8: DNA D. alaskensis

Highly specific primers for Desulfovibrios 16S rRNA gene were used to confirm the presence of the genus of Desulfovibrio in the samples. The result showed strong PCR positivity of the expected size and mucosa associated Desulfovibrio were present in all of UC tissues and in healthy volunteer (Figure 3.1.3). The microbiota present in the colon has been mostly described from faeces samples, which do not accurately represent the mucosa-associated microbiota (Zoetendal et al., 2002; Eckburg et al., 2005). Therefore, little is known about the diversity and ecology of mucosa-associated SRB in the human colon. This study provides some information on the molecular characterization of abundant of microbes that are associated with the colonic mucosa of healthy human and UC suffers. These observations confirmed that Desulfovibrio were the predominant SRB populations and that they were found associated with the mucosa throughout the large intestine in both UC sufferers and healthy individual, supporting previous evidence that indicated Desulfovibrio was the dominant genus of SRB found in human colon (Gibson et al., 1988a; Loubinoux et al., 2002; Stewart et al., 2006; Kushkevych, 2014).

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.3. A photograph of agarose gel showing the PCR amplified products of desulfovibrios 16S rRNA gene (135 bp) with primers: DSV691F and DSV826R. Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer

To further analyze the SRB population in biopsy samples of UC and healthy volunteer, genomic DNAs from these samples were amplified using the dsr primers set DSR1F+and DSR-R. All samples tested yielded unambiguously a positive result and distinct amplicons of the right size were obtained and that indicated the presence of SRB in all samples (Figures 3.1.4). DSR gene involved in dissimilatory sulphate reduction pathway in SRB is evolutionarily conserved among SRB, including delta proteobacteria and was selected as the preferred marker for SRB. (Wagner,1998; Ben-Dov et al.,, 2007).

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.4. A photograph of agarose gel showing the PCR amplified products of dsrA gene (221 bp) with primers: DSR1F+and DSR-R Lane 1: PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer

Functional marker genes encoding key enzymes of characteristic of SRB metabolic energy pathways, such as sulfate reduction, have been found to be useful in the detection of microorganisms belonging to different ecophysiological classes in complex microbial assemblages from diverse habitats in faeces samples. However, no information exists about the diversity of functional genes of SRB in colonic mucosa associated bacteria (Gibson et al., 1991; Fite et al., 2004; Wagner et al., 2005; Frank et al., 2007; Christophersen et al., 2011; Rey et al., 2013).

Perhaps, in order to generate information on microflora of the colon, a molecular approach was taken, involving PCR, Cloning, Sequencing and DGGE-PCR analysis, to provide a structural and functional analysis of the colon from healthy and UC patients.

CHAPTER 3: RESULTS AND DISCUSSION

3.1.2 DGGE analysis of microbial communities in the biopsy samples

It is evident that there is a strong association between UC and colonic mucosa microflora. However, the entire picture remains unclear. Using molecular techniques, to analyze the bacteria existing in colonic mucus that were considered to be influencing the inflammation of UC helped to clarify the relationship between colonic inflammation and microflora diversity in UC. PCR-DGGE approach has been used extensively to analysis human microbiota and was found that this approach is a useful tool for comparing complex microbial community profiles present in human colonic samples (Scanlan et al., 2006; Kinross et al., 2008). The 16S rRNA gene profiles of the bacterial collections associated with biopsies were generated by PCR coupled with DGGE. The PCR amplified products of 16S rRNA gene (550 bp) were analysed with DGGE for the visualisation of DNA bands representing dominant bacterial species in the samples. DGGE allows separation of DNA fragment mixtures of equal length depending on their sequence. To some extent the community profiles originating in the patients suffering from UC resembled each other as far as the 16S rRNA band numbers and migration patterns are concerned. However, significant differences in the healthy volunteer sample profile were observed. Examples of profiles are shown in Figure 3.1.5.

The high-density bands which represent the dominant microbial populations showed the differences in microbial structure of the various biopsy samples. The differences in localization of dominant bands in the adjacent lanes suggested that there were various degrees of changes and differences in the microbial community structure between UC patients and healthy control and, even within UC patients.

The DGGE patterns obtained from each sample contained a number of bands at relatively different positions and with differing intensities. The community profiles of samples obtained from UC patients number one and two (Fig. 3.1.5, lanes 1 and 2) showed relatively few strong bands (1-3), while the profile of the samples from UC patient number 3 and 4 (Fig 3.1.5, lanes 3 and 4) had between 5-7 dominant bands with a greater number of weaker bands. In contrast, the lane (Fig. 3.1.5., lane 6) containing amplicons from the healthy patient displayed up to 9 bands, representing the presence

CHAPTER 3: RESULTS AND DISCUSSION of at least 9 different bacterial phylotypes. This suggests that there has been a reduction in diversity for UC patients compared with healthy one. Several bands were shared between all samples, and these included bands 2, 4, 10, 15 and 24. The bands 1,7,13 and 19 were present in UC patient 1, 3, 4 and healthy control. However, these bands were absence in UC patient 2. Band 5 and 12 were present only in UC patients 3 and 4, while band 11 was unique to UC patient 3 (Figure 3.1.5). The lower band counts found in the UC patients indicate the possible absence or reduction in the abundance of specific bacterial classes amongst their predominant bacteria compared with healthy control (Figure 3.1.5). DGGE analyses in this investigation showed that the richness of UC patient’s profiles was lower compared to healthy individual. These results collectively are consistent with many comparative studies of colonic microbiota of patients with UC and non-UC controls, confirming that changes occur in the microbial communities in UC patients colon when compared to healthy individuals (Frank et al., 2007; Arumugam et al., 2011; Rigottier-Gois, 2013). This was subsequently confirmed by sequencing and biochip analysis (Section 2).

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.5. DGGE profile demonstrating diversity of bacterial populations in biopsy samples obtained from healthy volunteer and patients suffering from UC based on PCR products of 550 bp of bacterial 16S rRNA. Lane 1: UC Patient 1, Lane 2: UC Patient 2, Lane 3: UC Patient 3, Lane 4: UC Patient 4, Lane 5: Negative control, Lane 6: healthy volunteer

CHAPTER 3: RESULTS AND DISCUSSION

The dendrogram in Figure 3.1.6 was constructed based on DGGE banding similarity patterns, and clearly shows that the banding profiles from all of the patients biopsy samples formed a single clade, supported by a bootstrap value of 75%. Within this patient cluster two other clusters formed, one composed of patient 1 with 2 (cluster I) and the other containing patients 3 with 4 (cluster II), supported by bootstrap values of 80% and 81% respectively. The banding profile from the healthy individual grouped outside from the main cluster. This result showed the distinct separation of patients suffering from UC and healthy individual thus indicating a difference in the composition of the intestinal microbiota present in both group.

Figure 3.1.6. UPGMA dendogram showing similarity of DGGE profiles of microbial communities in all colonic biopsy samples of UC patients and healthy control used in this study. Bands from each sample were coded and a UMPGA tree was produced using the distance algorithm described by Nei and Li (1979) and executed in PAUP* 4.0. P1: UC patient number 1, P2: UC patient number 2 , P3: UC patient number 3 , P4: UC patient number 4 , H: healthy individual.

CHAPTER 3: RESULTS AND DISCUSSION

The strength of a PCR-DGGE analysis is that it is able to show the changes in structure of dominant bacterial communities between different environments by simple changes in the banding profiles. The bands can be identified after they have been re-amplified and sequenced by performing a BLAST analysis using the target sequence against a database, such as GenBank. 25 bands, identified in Fig. 3.1.6, were excised, re-amplified and sequenced. Although 25 bands were excised, not all were successfully sequenced. The results of 16S rRNA gene sequencing of DGGE-bands is shown in (Table 3.1.1). Only 4 bands were successfully sequenced. There are a number of reasons why the majority of bands failed to generate a sequence: (1) insufficient DNA for sequencing, (2) contamination of the extracted DNA with inhibitors, (3) degradation of the extracted DNA so that the primers fail to anneal, and (4) the band may contain heterogeneous DNA fragments that give indistinct sequences (Sekiguchi et al., 2001; Sun et al., 2013). In addition to this, some PCR fragments may not separate during DGGE so that they form indistinct smears of other separated PCR fragment (Kisand & Wikner, 2003). The sequences from the 4 bands were matched with those from Escherichia coli (98% match) for band 5, and Enterobacter aerogenes (99% match) for band 13, both of which were present in the UC patients. The other sequences from bands 23 and 25, derived from a healthy individual, matched those from an uncultured Ruminococcus species (98% match) and Eggerthella lenta (99% match) resepectively. Both of these species are Gram-positive gut inhabiting, anaerobic bacteria. Eggerthella species have been implicated in systematic bacteremia (Lau et al., 2004), and are candidates for involvement in UC, and the cause of liver and anal abscesses. Although the sample size here is small, the results suggest that the microflora of UC patients is different to that of a healthy person, with it having a lower diversity and, potentially, species that differ considerably.

CHAPTER 3: RESULTS AND DISCUSSION

Table 3.1.1. Sequencing results of bacterial 16S rRNA from the bands cut from DGGE gel Band(s)* Closest relative % Identity Accession no 5 Escherichia coli 1303 98% |HE616528.1| 13 Enterobacter aerogenes EA1509E 99% |FO203355.1| 23 Uncultured Ruminococcus sp. M6-41 98% gb|EU530431.1| 25 Eggerthella lenta strain W02018C 99% KP944193.1 *Bands are numbered as indicated on the DGGE gels shown in Figure 3.1.5

3.1.3 Diversity analysis of PCR products of bacterial 16S rRNA and dsr genes by sequencing

Analysis of amplified bacterial 16S rRNA gene provides in depth exploring of bacterial diversity from complex bacterial communities. In this investigation, bacterial diversity of the mucosal biopsies from 4 UC patients and one healthy control were compared by analysis of PCR-amplified 16S rRNA clone libraries. A total of 250 clones from the mucosal biopsies were sequenced (control 100 clones; UC patient1 25 clones; UC patient2 25 clones; UC patient3 50 clones; UC patient4 50 clones) to obtain an overall estimation of bacterial diversity present in both groups.

The 16S rRNA sequence data from the healthy individual identified bacteria pre- dominantly from families including Enterobacteriaceae, Bacterioidaceae, Clostridiacease, Coriobacteriaceae, Ruminococcaceae, Eggerthellaceae, Lactobacilaceae and Porphyromonoadaceae. All the four colitis patients had sequences from the Enterobacteriaceae (Figure 3.1.7) and no other. The 16S rRNA have been used in several studies to evaluate the bacterial diversity of the mucosal biopsies from different parts of the gastrointestinal tract (GIT) like human jejunum, ileum, colon as well as the rectum. A study focusing on evaluating the diversity of human GIT revealed that upon sequencing 347 clones bacteria from various phyla including Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Verrucomicrobia, and Actinobacteria were observed within one healthy individual (Wang et al., 2005). It was intriguing to note in this study that microbial population varies from different parts of the colonic mucosa. For example jejunum, ileum, colon, and rectum have highly diverse bacterial population. This suggests that the differences of microbial population can occur within the patient.

CHAPTER 3: RESULTS AND DISCUSSION

However, other studies showed that the major group of bacteria were similar throughout the different parts of the colon between the individuals (Eckburg et al., 2005; Costello et al., 2009 ; Momozawa et al., 2011)

Figure 3.1.7. A histogram demonstrating the Family level of bacterial composition present in healthy patient and patients suffering from UC (16S rRNA gene)

This data shown (Figure 3.1.7) which obtained from this study clearly demonstrate that colitis leads to an imbalance in colon microbial population strengthening the population of Enterobacteriaceae, whereas, bacteria from all other families had vanish among colitis individuals. Since imbalance of normal colonic bacterial population is precondition for colon inflammation (Gophna et al., 2006). Higher abundance of Enterobacteriaceae, in UC patients has also been reported previously (Kotlowski et al., 2007). Enterobacteriaceae have been observed to be active in inflammatory environments; the pathogenic bacteria can start the inflammation process in genetic susceptibility people, weakening the mucosal lining by the production of high concentration of butyric acid. This leads to activation of the immune system provoking an attack on the mucosa cells, causing a shift in the colonic commensal microbiota that favours members of the Enterobacteriaceae (Ohkusa et al., 2003; Nedialkova et al., 2014). The imbalance of

CHAPTER 3: RESULTS AND DISCUSSION microbial population in the colon leads to a metabolic imbalance which inflames the colonic mucosa which may result in changing of bacterial composition (Ott et al., 2004). A reduction of bacteroidetes was also observed in UC microbiota which comes in line with our findings. However, this study found also that there was an increase in clostridia in UC patients ( Hansen et al., 2011) Regarding the relative abundance of various bacterial families present in the healthy colon, it was observed that Bacteroidaceae was the most abundant bacterial family in the colon of the healthy patient, having a 48% share of the total, followed by members from the Clostridiaceae (16%), the Ruminococcaceae (13%), the Enterobacteriaceae (12%), and the Coriobacteriaceae (8%). Three other families, Eggerthellaceae, Porphyromonadaceae and Lactobacilaceae had sequences representing 1% of the total. It is interesting to note that colitis patients having disrupted this balance of various bacterial families and have 100% predominance of bacteria from Enterobacteriaceae family. Several studies have compared the bacteria present in patients with active UC and non UC patients. Nishikawa et al (2009) shown that there was a loss of bacterial belonging to Clostridia associated with active colitis. Ríos-Covián et al (2016) indicated that species from Bacteriodes and Firmcute promote good health in the colon by producing long chain fatty acid, which are an essential source of energy for epithelial cells lining the colon. It is possible that after the inflammation and damaging the mucosa layer of the colon, these bacteria may be depleted which result of the reduction of this bacterial group during active UC disease.

A reduction of bacterial diversity has been observed in other studies. Walujkar et al (2014) using 16S rRNA gene sequences reported dysbiosis and dysanaerobiosis in microbial populations of patients suffering mild to server UC, with an overall decrease in the proportion of obligate anaerobes and an increase in facultative anaerobes and some aerobes. This study also aligns well with data generated from other studies that suggest a reduction in the diversity of the colonic mucosa associated bacterial microflora (Ott et al., 2004). The mechanisms causing this imbalance remain unknown.

The features of a healthy colon include low oxygen concentrations and the presence of anaerobic bacteria in great numbers. The decrease of obligate anaerobes observed in

CHAPTER 3: RESULTS AND DISCUSSION this study, including Bacteroidetes, Clostridium, Ruminococcus present in the colon of a healthy individual, and the shift to facultative anaerobes, members of the Enterobacteriaceae in the colons of patients suffreing from UC, can be explained by increases in the levels of oxygen, which will kill the obligate anaerobes allowing facultative anaerobes to take over (Lind Due et al., 2003; Lupp et al., 2007, Rigottier- Gois, 2013)

Based on the information generated from sequences of 16S rRNA the next step was to identify relative key species of each bacterium among UC patients and healthy control. The healthy individual gut microbiome is highly complex. It has several species of bacteria. However, the ulcerative colitis patients mainly harbor bacteria from Enterobacteriaceae family. The major species of Enterobacteriaceae in the colon of colitis patients varied amongst the patients. A breakdown for relative levels of each bacterial species in healthy is shown in Table 3.1.2. In the healthy individual 16S RNA gene sequences from Bacteriodes frgilis were recovered at a frequency of 14% followed by 10% for Bacteroides dorei, Bacteroides vulgatus (8%), Ruminococcus torques (6%), Clostridum ramosum (4%) and Clostridum saccharogumia , Clostridum cocleatum , Clostridum spiroforme and Barnesiella intestinihominis each with (3%) while the other sequences represented the species Enterobacter aerogenes, Lactobacillus aogosae, Faecalibacterium prausnitzii, Eggerthella lenta, klebsiella pneumoniae at a frequency of 1%. Although all of the bacterial sequences from the colons of the UC patients represented species of the Enterbacteriaceae, there were not a single common bacterial species found among all the four patients. The majority of 16S rRNA gene sequences recovered from UC patient 1 belonged to Citrobacter species, with 29% of the sequences belonging to Citrobacter freundii, 10% to Citrobacter murliniae, and 8% each to Citrobacter braakii, Citrobacter werkmanii and Enterobacter aerogenes in addition to other species displayed in Table 3.1.2.

16S rRNA sequences from UC patient 2 were found to have identities similar to some of the common species found in UC patient 1, although the relative levels varied. These included matches for Citrobacter freundii and Enterobacter aerogenes, each representing 22% of the sequences recovered, Citrobacter braakii, representing 19%, Raoultella

CHAPTER 3: RESULTS AND DISCUSSION ornithinolytica (13%), Kluyvera cryocrescens (11%) in addition to other species including Klebsiella pneumoniae, Lelliottia amnigena, Acinetobacter calcoaceticus, Citrobacter murliniae and Klebsiella oxytoca all showed in Table 3.1.2.

UC patient 3 showed a different profile to all of the other UC patients. UC patient 3 had sequence matches for five species, 48% of which were for Escherichia coli followed by 28% for Shigella sonnei and 10% for Serratia marcescens, and 7% each for Shigella flexneri, Shigella boydii (Table 3.1.2). Sequences from UC patient 4 matched those from Enterobacter aerogenes (24% of the total), Raoultella ornithinolytica (22%), Kluyvera cryocrescens (19%), Citrobacter freundii (12%) followed by the presence of Citrobacter braakii, Klebsiella oxytoca and Yokenella regensburgei at 4% each with other more species all showed in Table 3.1.2.

CHAPTER 3: RESULTS AND DISCUSSION Table 3.1.2. The species level of bacterial composition present in the colon of UC patients and healthy control (16S rRNA)

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Bacteroides fragilis 14% - - - - Bacteroides dorei 10% - - - - Bacteroides vulgatus 8% - - - - Ruminococcus torques 6% - - - - Clostridium ramosum 4% - - - - Clostridium spiroforme, C.cocleatum , C.saccharogumia, C.hathewayi 3% - - - - Clostridium leptum, C. lavalense, C.asparagiforme, C. bolteae, C. 1% - - - - clostridioforme, C.citroniae, C.coccoides, C. aldenense Ruminococcus productus, R. gnavus 1% - - - - Bacteroides merdae, B. eggerthii, B. gallinarum, B. clarus, B. uniformis 1% - - - - Odoribacter splanchnicus 1% - - - - Butyricimonas paravirosa, Butyricimonas faecihominis, Butyricimonas virosa 1% - - - - Barnesiella intestinihominis 3% - - - - Blautia hansenii, Blautia stercoris 1% - - - - Lactobacillus rogosae 1% - - - - Adlercreutzia equolifaciens 1% - - - - Asaccharobacter celatus 1% - - - - Gordonibacter faecihominis,G. pamelaeae 1% - - - - Enterorhabdus mucosicola, E. caecimuris 1% - - - -

CHAPTER 3: RESULTS AND DISCUSSION

Table 3.1.2 cont.

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Parvibacter caecicola 1% - - - - Denitrobacterium detoxificans 1% - - - - Slackia faecicanis 1% - - - - Faecalibacterium prausnitzii 1% - - - - Averyella dalhousiensis 1% - - - - Kluyvera ascorbate 1% - - - - Hungatella effluvii 1% - - - - Erwinia billingiae 1% - - - - Raoultella ornithinolytica 1% 4% 13% - 22% Pantoea agglomerans 1% - - - 3% Lelliottia amnigena 1% - 1% - - Eggerthella lenta,Eggerthella sinensis,Eggerthella hongkongensis 1% - - - - Enterobacter soli 1% - - - - Klebsiella oxytoca - - 2% - 4% Citrobacter youngae - 3% - - -

CHAPTER 3: RESULTS AND DISCUSSION

Table 3.1.2 cont.

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Escherichia coli - - - 48%- - Shigella sonnei - - - 28% - Serratia marcescens - - - 10% - Citrobacter freundii 1% 29% 22% - 12% Citrobacter murliniae - 10% 5% - - Citrobacter braakii 1% 8% 19% - 4% Citrobacter werkmanii - 8% - - 3% Enterobacter aerogenes 1% 8% 22% - 24% Kluyvera cryocrescens 1% 6% 11% - 19% Kluyvera intermedia - 2% - - - klebsiella pneumoniae 1% - 1% - - Shigella flexneri - - - 7% - Shigella boydii - - - 7% - Raoultella terrigena - 2% - - 3% Raoultella planticola - - - - 1%

CHAPTER 3: RESULTS AND DISCUSSION

Table 3.1.2 cont.

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Yokenella regensburgei - 1% - - 4% Enterobacter asburiae - 2% - - 1% Acinetobacter calcoaceticus - 2% 4% - - Enterobacter cloacae - 4% - - - Enterobacter ludwigii - 3% - - - Cedecea neteri, Cedecea lapagei - 1% - - - Leclercia adecarboxylata - 1% - - - Escherichia vulneris - 1% - - - Pantoea eucalypti - 1% - - - Proteus mirabilis - 1% - - - Enterobacter hormaechei - 2% - - -

CHAPTER 3: RESULTS AND DISCUSSION

Among the four UC patients, each has certain unique bacterial species suggesting pathogenesis of diseases varies from individual to individual. Although the species differ between patients, similar genera are present in 3 of the patients (Figure 3.1.8). Genera of Citrobacter, Enterobacter, Kluyvera and Roultella are present in patients 1, 2 and 4. In contrast, patient 3 has less diversity, dominated by genera of Escherichia and Shigella. Healthy control was dominated by high proportion of Bactriodes , Ruminococcus and clostridium and low level of Kluyvera, Roultella and Citrobacter when compared with UC patients.

Becker et al (2015) highlighted the unmet need to identify bacterial species involved in UC. The authors stated “It is currently unclear whether the composition of the microbial flora or individual bacterial strains or pathogens induces or supports the pathogenesis of UC. Further research will be necessary to carefully dissect the contribution of individual bacterial species to this disease and to ascertain whether specific modulation of the intestinal microbiome may represent a valuable further option for future therapeutic strategies.” Although this study was a pilot in nature, it does identify bacterial populations involved in colitis disorders at the genus and family level and highlights the need for future genomic studies to understand better diseases associated with human colonic mucosa.

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.8. Proportion of bacterial genera present in the colon of UC patients and healthy control (16S rRNA).

CHAPTER 3: RESULTS AND DISCUSSION

In the literature various species have been sporadically identified as being involved in colitis. According to Kaakoush et al (2014) bacteria from Campylobacter species are involved in colitis disorders. Among the various bacteria adherent and invasive Escherichia coli, Helicobacter, Fusobacteria, and Campylobacter species have also been identified (Bohr et al., 2004; Conte et al., 2006; Minami et al., 2009; Mukhopadhya et al., 2011; Thomson et al., 2011). Data have started to emerge to indicate that the bacterial genotype has a significant role in its invasiveness to gut (Kaakoush et al., 2014). Murine model data further revealed that it is not only the presence of a particular bacterial species that is important but it is the interaction of microbes with other species present in the gut that determine the pathogenic aspects (Liang et al., 2014). A study by Winter and Baumler (2014) agreed with our findings at the molecular level. According to this study, high levels of facultative anaerobic bacteria mainly from the Enterobacteriaceae family were involved in inflamed colon. In addition, Ahmed et al (2007) concluded that pro-inflammatory bacterial species such as Shigella, Citrobacter were associated with the early stages of inflammation, as possible bacterial operators that can drive colitis to colon cancer in patients suffering from UC. It may be important to recognise that several types of inflammatory pathways that could manifest, which could be influenced by different bacteria. Lapthorne et al (2015), in an animal study, noticed a reduction in the Enterobacteriaceae family bacteria associated with colonic disorders. This study confirms these observations associating members of the Enterobacteriaceae with colitis, particularly, Citrobacter and Shigella species, as well as recording that dysbiosis in colonic mucosa is generally characterized by bacterial reduction in diversity and decrease of obligate anaerobes in parallel with increase facultative anaerobes or the presences of unusual aerobes bacteria.

CHAPTER 3: RESULTS AND DISCUSSION

In the current study, primary interest was to detect SRB in samples of healthy control and patients suffering from UC and to estimate if there is any difrences or the precense of specific SRB species in both groups. SRB 16S rRNA gene sequences were not detected in this study. This might be because these bacteria were not present in high enough quantities as aresult of their slow growth. In order to detect SRBs, a different target gene was used which is specific to SRBs. In next set of experiments primer specific for dissimilatory sulfite reductase gene was used to amplify dsr gene of healthy and UC patients DNA then PCR products cloned. A total of 30 clones from the mucosal biopsies were sequenced (control 7 clones; UC patient1 6 clones; UC patient2 7 clones; UC patient3 5 clones; UC patient4 5 clones). The sequences were compared with database sequences. As shown in Figure 3.1.9, SRB of five different families were identified. These include Desulfovibrionaceae, Desulfohalobiaceae, Desulfomicrobiacease, Desulfobacteraceae, Desulfonatronaceae, and Syntrophobacteraceae. Importantly, all four colitis patients revealed a loss of the Desulfonatronaceae bacterial family. Furthermore, bacteria from Syntrophobacteraceae family were only present in patient 2 and 4. Based on the data from Figure 3.1.9, it is not possible to make any conclusive statement, except that members of the Desulfovibrionaceae and Desulfomicrobiaceae are present in healthy and all UC patients and Desulfontronaceae was only present in healthy control and was absence in all UC patients. The microbiome diversity appears to be the major issue encountered by patients with colitis disorders.

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.9. A histogram demonstrating the Family level of SRB composition present in healthy patient and patients suffering from UC (dsrA gene)

CHAPTER 3: RESULTS AND DISCUSSION

Sequences from Desulfovibrionaceae, Desulfobacteraceae and Desulfomicrobiaceae were the most abundant of SRB groups present in the samples, including samples from patients with UC and the healthy individual and which were recovered with similar frequencies in general. The exception being sequences from patient 1 which showed the absence of Desulfobacteraceae .

The gut microbiome is highly diverse in nature, and overall bacterial population varies between individuals. A dietary habit of the people and environmental factors affects the composition of bacterial populations. The diversity of SRBs, however, is limited to two families and 2 main genera. In total, sequences from 14 genera were detected from the patients and the healthy individual. The presence of sequences from two genera, Desulfovibrio and Desulfomicobium, were recovered from all patients and the healthy individual. All of the other genera were present in one, two, three or four of the individuals sampled. The greatest number of species recovered, according to the dsr sequences, was found within the Desulfovibrio. Sequences representing 18 Desulfovibrio species were recovered from the healthy individual. In three of the four patients, this number increased to 20, 27 and 28 (Figure 3.1.10). Only in patient 1 did the number of Desulfovibrio species sequences drop, to 5. The diversity of SRBs in this individual was greatly reduced compared to the other patients, reflecting the dominance of 7 species in total. Sequences from the other genus present in all of the individuals matched Desulfomicrobium species. The healthy individual had sequences representing 3 species of Desulfomicrobium. This number increased in two UC patients, stayed the same in one and decreased in the other (Figure 3.1.10).

CHAPTER 3: RESULTS AND DISCUSSION

Figure 3.1.10. Distribution of genera from heathy and UC patients. The numbers above each histogram represents the number of species identified as Desulfovibrio from each patient.

CHAPTER 3: RESULTS AND DISCUSSION

The levels and diversity of SRB in UC patient 1 are of interest. The clinical data relevant to disease severity and how long the patient has been suffering from the disease was not described for the patients. In future, it will be quite intriguing to link bacterial population particularly SRB with the UC pathogenic aspect and disease stage. It is a quite possible that the levels and type of bacteria species might be defining factors in the disease outcomes. In comparing with health control, it is quite evident that existing population of SRB was significantly elevated in this patient, although the diversity was low. However, the other three individuals also manifested emergence of new bacterial species.

The healthy individual had dsr sequences representing a complex and diverse SRB population with no significant increase for any specific species. The sequence identities recovered from UC patient 1 are shown in Table 3.1.3, along with those from the other patients. Seven species of bacteria were identified including Desulfohalobium retbaense (15%), Desulfomicrobium salsuginis (15%), Desulfovibrio burkinensis (14%), Desulfovibrio intestinalis (14%), Desulfovibrio carbinolicus (14%), Desulfovibrio magneticus (14%), and Desulfovibrio desulfuricans (14%). Twenty-one new sequences were recovered from the rest of the patients (2 to 4), representing 21 new species. The diversity from patients 2, 3 and 4 resembled that of the healthy individual. The largest group of sequences representing single species were recovered from UC patient 2, and these were for Desulfovibrio gigas and Desulfovibrio hydrothermalis, both representing 7% of the total sequences recovered.

CHAPTER 3: RESULTS AND DISCUSSION Table 3.1.3 The level of SRB species present in the colon of UC patients and healthy individual (dsr gene)

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Desulfovibrio africanus 7% - 5% 4% 4% Desulfovibrio intestinalis 6% 14% 5% 3% 4% Desulfovibrio gabonensis 6% - 2% 1% 3% Bilophila wadsworthia 6% - 5% 1% 4% Desulfovibrio vulgaris 4% - 2% 1% 3% Desulfovibrio piger 4% - 5% 1% 3% Desulfovibrio desulfuricans 4% 14% 5% 3% 3% Desulfovibrio fructosivorans 3% - - 1% 1% Desulfovibrio fairfieldensis 3% - 2% 1% 3% Desulfovibrio simplex 3% - - 1% 3% Desulfovibrio burkinensis 3% 14% 2% 4% 3% Desulfovibrio oxyclinae 3% - 2% 3% 3% Desulfovibrio oceani 3% - - 1% 1% Desulfovibrio magneticus 3% 14% 2% 4% 3% Desulfovibrio halophilus 2% - 2% 3% 3% Desulfovibrio longus 2% - - 1% 1% Desulfovibrio aminophilus 2% - - 3% 1% Desulfovibrio termitidis 1% - 2% 3% 3%

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Table 3.1.3 cont

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Desulfovibrio carbinolicus 2% 14% 3% 4% 3% Desulfovibrio alkalitolerans 1% - - 3% 1% Desulfovibrio alaskensis 2% - 3% 3% 1% Desulfovibrio piezophilus - - 5% 3% 4% Desulfovibrio portus - - 2% 1% 3% Desulfovibrio hydrothermalis - - 7% 1% - Desulfovibrio gigas - - 7% 4% 4% Desulfovibrio zosterae - - 3% 3% 1% Desulfovibrio salexigens - - 5% 1% 3% Desulfovibrio aespoeensis - - 3% - 1% Desulfovibrio acrylicus - - - 3% 1% Desulfovibrio aerotolerans - - - 3% 1% Desulfovibrio carbinoliphilus - - - 3% 1% Desulfovibrio cuneatus - - - 3% 1% Desulfohalobium retbaense 1% 15% - 3% 1% Desulfomicrobium salsuginis 1% 15% 3% 3% 3% Desulfosalsimonas propionicica 1% - - 3% 1%

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Table 3.1.3 cont

Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Desulfonatronospira thiodismutans 2% - - - - Desulfatibacillum alkenivorans 3% - - 1% 3% Desulfococcus oleovorans 4% - 2% - 1% Desulfomicrobium apsheronum 1% - - 2% - Desulfatitalea tepidiphila 2% - - - - Desulfonatronovibrio halophilus 2% - - - - Desulfomicrobium aestuarii 1% - - 3% 1% Desulfonatronum thioautotrophicum 1% - - - - Desulfonatronum lacustre 1% - - - - Desulfatibacillum aliphaticivorans 3% - - 1% 1% Desulfobacter vibrioformis 3% 2% - - Desulfobacter postgatei 2% - 2% - 1% Desulfobacter latus 2% - 2% - 1% Thermodesulforhabdus norvegicus - - 3% - 1% Desulfosarcina variabilis - - 3% - 2% Desulfomicrobium escambiense - - 2% 2% 3% Desulfomicrobium baculatum - - 2% 2% 1%

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Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4 Desulfomicrobium thermophilum - - - 3% 2% Desulfomicrobium orale - - - 3% 2% Desulfomicrobium salsuginisstrain - - - - 2%

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The number of dsr sequences that represented a single species totaled 36 for the healthy individual, 7 for patient 1, 31 for patient 2, 42 for pateint 3 and 47 for pateint 4. This confusing data is simplified if the number of genera for each individual is considered. The healthy individual had sequences representing 11 genera, whereas the UC patients had 4, 7, 6 and 9 for patients 1, 2, 3 and 4 respectively. A reduction in diversity for each individual patient. This reduction coincides with an increase in the propotion of sequences representing the genus Desulfovibrio. In the healthy individual, 53% of sequences recovered belonged to Desulfovibrio, this proportion increased to 71% and 70% in patients 1 and 3, and to 66% and 67% in patients 4 and 2.

Sequences representing 32 species of Desulfovibrio were recovered from all individuals. Of these, only 5 sequences were present in the healthy individual and UC patients, and these represented the species Desulfovibrio intestinalis, Desulfovibrio desulfuricans, Desulfovibrio burkinensis, Desulfovibrio carbinolicus and Desulfovibrio magnetus (Figure 3.1.11). Sequences present in the healthy individual and three of the patients included those presenting the species Desulfovibrio fairfieldensis, Desulfovibrio termitidis, Desulfovibrio alaskensis and Desulfovibrio halophilus. Sequences representing species only found in three of the UC suffers included: Desulfovibrio piger, D. piezophilus, D. gigas, D. zosterae, and D. salexigens. The only other species present as a dsr sequence in the healthy individual and three of the UC patients was Bilophila wadsworthia.

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Figure 3.1.11. Species of Desulfovibrio present in a healthy individual and four UC suffers

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Jia et al (2012) reported that distribution of SRB was reduced among patients suffering from UC. This study concluded that for a healthy functioning colon two bacterial species were needed, Desulfovibrio piger and B. wadsworthia, where their presence was noted at levels of 93.5% in the healthy control group. In the present study, a slightly different response was observed. There was a reduction in diversity of SRBs at the genera level associated with UC, possibly at the expense of an increase in the diversity of one genus, Desulfovibrio. Desulfovibrio piger sequences were not present in the healthy individual but those for Bilophila wadsworthia were. In UC patient 1, which exhibited the greatest diversity loss, B. wadsworthia was absent, but it was present in the other three UC patients. This study suggests that it is, perhaps, the loss of diversity at the genera level that is important in the development of UC, allowing one group of SRBs to become dominant.

There are several studies describing dysbiosis, but these have produced different sets of information about the bacterial populations. Ott et al (2004) studied the dynamics of the mucosa-associated flora in the UC patients using 16S rRNA sequences, reporting the loss of bacterial diversity including Prevotella sp, Porphyromonas sp, however, none of the bacteria described in this study were reported in the UC patients. It is quite possible, that the amplification strategies as well as the quality of DNA also impact the overall recovery of bacterial species for 16S rRNA and these findings still awaits confirmation with future studies. No SRB sequences were detected in the current study when the 16S rRNA gene was targeted.

Although 16S rRNA is widely used for bacterial species characterization from environments, it is possible that some species sequences are missed in such studies, particularly those from rare or low abundant species. Targeting functional genes in PCR amplifications may be the only option to get conclusive information about rare bacterial species. There are several reports highlighting this issue. Salzman et al (2002) studied the characterization and presence of Lactobacillus acidophilus from mouse small intestine, large intestine and faeces. 16S rRNA gene sequences were helpful in the identification of this particular bacterium, but also picked out certain other bacterial species. Similarly, there are several other reports in the literature in which gene-specific

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PCR has been the final resort in the characterization of bacterial species (Parra et al., 1991; Spierings et al., 1992; Gloux et al., 2011). The major focus of interst in this study was on sulfate reducing bacteria, and to our surprise, it was not possible to get a reliable picture of sulfate reducing bacteria in the UC and healthy individual using 16S rRNA gene sequences. However, a gene specific PCR involving dsr gene was selected to find out their potential contributions in UC.

This study based on combining 16S rRNA bacterial identification strategy combined with specific gene for SRB (dsr gene) utilization in evaluating the relative levels of colon microbiota about UC has opened up a new avenue of research. It has identified that bacterial diversity changes dramatically in UC sufferers at several levels; there is an overwhelming increase in the number of species from the Enterobacteriaceae, and an increase of species from Desulfovibrio.

3.1.4 Phylogenetic analysis

The sequences of 16S rRNA and dsrA genes obtained by cloning and sequencing were used for Phylogenetic analysis construction. All sequences and their closest relatives were aligned using Muscle implemented in Seaview (Galtier et al., 1996; Edgar, 2004) (Appendices 2). Based on the sequence result from 16S rRNA; the majority of the inhabitants of the colon of UC patient and healthy control belonged to Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. These bacteria have been recorded as present in healthy colon by other studies, supporting our findings (Eckburg et al., 2005; Huse et al., 2012). Phylogenetic analyses were performed for each of these bacterial groups, with the significance of each clade estimated by bootstrapping. The taxa in the Firmicutes 16S rRNA Phylogenetic tree (Figure 3.1.12) divide into three deeply branched clades, named Group-A, Group-B, and Group-C. Group-C contained members of the class Negativicutes and sequences from clones were absent from this group. Within Group-B, representing class Bacilli and Clostridia, 5 out of 27 clones from the healthy colon sample (K53, K55, K23, P81, and P68) were grouped with sequences from Clostridium ramosum. Most of the clones, 22 out of 27 clones from the healthy control sample, belonged to the larger group, Group-A. Within the Group-A, 2 clones (K24 and K31) were grouped with Clostridium lavalense and 2 clones (C_P2100 and K8)

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CHAPTER 3: RESULTS AND DISCUSSION with sequences from Hungatella hathewayi. 7 clone sequences (p87, p28, p69, p21, p67, k10, and k5) formed a distinct clade that was closely related to Ruminococcus torques sequences. 2 clones (K29 and P24) had sequences that grouped with sequences from Clostridium nexile. 2 clone sequences (K30 and K50) formed a clade with those from Ruminococcus gnavus. 2 clones (P41 and P47) formed their own clade, which was closely related to Subdoligranulum variabile sequences. 5 clones (K53, k55, k23, p81, and p68) were clustered with Clostridium raosum. Clostridia species participate in immune homeostasis, inhibition of autoimmunity and harmful inflammation to colonic mucosa, and they also contribute to the overall maintenance of colon function, however, it still unknown if the reduction of Clostridia species in the colon can lead to chronic inflation in UC patients (Frank et al., 2007; Rajilic et al., 2007). Clones K25, P76, K13, K52, and K32 were classified as Blautia coccoides, Eubacterium eligens, Clostridium leptum, Faecalibacterium prausnitzii, Flavonifractor plautii respectively; which may indicate that these clones are belonging to these bacteria.

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Figure 3.1.12. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Firmicutes and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown

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Both S. variabile and Faecalibacterium prausnitzii are abundant in the healthy human colon and they have an important function in human health and protection against pathogenic bacteria (Nava et al., 2005).

The taxa representing the order Bacteroidales formed a maximum likelihood tree (Figure 3.1.13) that divided into four deeply branched clades named group-A to group- D. Most of the clones, 37 out of 43 Bacteroidales clones from healthy control sample, belonged to the larger group, group-A. The Bacteroidales, an order within the phylum Bacteroidetes, contains four families including the Bacteroidaceae, the Porphyromonaceae, the Rikenellaceae, and the Prevotellaceae. Members of the Bacteroidales have been shown to predominate and occupy a vital niche at the colonic mucosal surface, which gives these bacteria the ability to interact with the host and therefore they have role(s) in the human health either beneficial or detrimental (Hooper et al., 2003; Mazmanian et al., 2005). Within the group-A, containing general Bacteroides taxa, clones and reference taxa formed seven independent sub-clades termed BF1, BF2, BA1, BV, BD, BU, and BA2. Sequences from 14 out of 43 clones (p45, p46, k26, p44, p82, p25, p64, k4, p89, k16, k51, k6, p49, and p26) formed clades with BF1 and BF2 subgroups with various B. fragilis strains. Sub-clade BA1 comprised 6 sequences out of 43 clones (k2, k3, k1, p78, p212, k28), which was basal to clades BF1 and BF2. Blast searching results showed k2 and k3 clones had 99% sequence homologies with B. fragilis, as did sequences from clones p78, p212 and k28 at 97%, 96% and 98% homology matches respectively. It would appear that clades BF1, BF2 and BA1 contain strains of B. fragilis, with the exception k1 clone, which had 99% sequence homology with that from Bacteroides clarus. Poxton et al (1997) found mucosa associated B. fragilis in the colon representing 42% of total bacteroides isolated, which is in agreement with the high frequency of this species in healthy human colon of this study.

Clade BV contained 5 clone sequences (k54, p85, p48, p83, p86) with representatives of B. vulgatus sequences at a basal location, while clade BD containing 7 clone sequences (k22, p29, p66, p62, p88, p65, p710) with a basal B. dorei sequence. The taxonomic affiliation of the BA2 clade, which contains 4 clone sequences (p77, k27, p61, and p410) only, was hard to infer from the tree. The blast searching results showed that the

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CHAPTER 3: RESULTS AND DISCUSSION sequence from clone P410 had 96% sequence identity with that from B. uniformis. Whereas, the sequence from clone P61 had 99% sequence homology with that from B. fragilis. Clones k27 and p77 were closely related to B. vulgatus. Clade BA2 is sister to clade BU, which contained 1 clone (p412) and many B. uniformis strains. In this study the dominating species including B. thetaiotaomicron, B. vulgatus, B. uniformis and B. fragilis which were observed to be commonly present in the healthy control sample came in line with other studies (Frank et al., 2007;Noor et al., 2010; Sekirov et al., 2010). The protection role of B. vulgatus and B. ovatus against the development of colitis and intestinal inflammation has previously been demonstrated (Waidmann et al., 2003; Conte et al., 2006; Hudcovic et al., 2009).

The remaining 3 clades (group B, group C and group D) all contained clone sequences. In general, group-B contained members of the Parabacterioides and clone k15, which grouped with the 16S rRNA sequence from Parabacteroides merdea. Group-C contained member of the Barnesiella as a sister group to three clones (P211, P611, p210) where sequences from Barnesiella viscericola represented to nearest taxonomic relation. Basal to this main clade was the sequence from clone L31 and Barnesiella intestinihominis. Group-D contained members of the Butyricimonas and clone p27, which formed a clade with the sequence from Butyricimonas virosa. This species has been recorded before as being a member of the human colonic mucosa, and reinforces the usefulness of 16S rRNA sequences to provide a rapid identification of environmental bacteria, providing a broader understanding of unknown bacteria present in mucosa of human colon (Toprak et al., 2015; Mehta et al., 2015; Enemchukwu et al., 2016).

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Figure 3.1.13. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Bacteroidetes and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown

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The 16S rRNA phylogenetic tree of Proteobacteria in healthy control sample (Figure 3.1.14) appeared divided into two major branches named Group-A, Group-B, containing the Gamma Proteobacteria and the Delta Proteobacteria, as an outgroup. Group A comprised members of the orders Pseudomonadales and Enterobacterales, and two clones from the healthy control sample (P79 and P610) which grouped inside of the Enterobacterales subgroup, closely related to the sequences of Enterobacter aerogenes. Enterobacter aerogenes is recognised as part of the normal intestinal flora, however under certain conditions these bacteria have been found to infect the human colon and lead to several symptoms that can cause debilitation of the host colon that leads to inflammation (Grimont & Grimont, 2006).

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The last bacterial group from the healthy individual was the Actinobacteria and the phylogenetic tree for this is shown in Figure 3.1.15. It contained three major branches Group-A, Group-B, and Group-C corresponding to the Coriobacteriia, Bifidobacteriales, and Actinobacteri. Within the Group-A, one clone (K14) from healthy control sample formed a clade with Eggerthella lenta. E. lenta found as member of normal bowel mucosa their presence contributes to the host defense through the anti-inflammation activity that certain E. lenta strains have however their pathogenicity is not as low as previously suggested (Kageyama et al., 1999; Saunders et al., 2009; Longman & Littman, 2015).

Figure 3.1.15. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Actinobacteria and those of random clones from healthy control; the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown

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Sequences recovered from UC suffers gave homology matches to members of the Enterobacteriaceae, and a phylogenetic tree (Figure 3.1.16) based on maximum likelihood analysis was constructed using clone rRNA sequences and public available reference sequences for this group. The clone sequences from all patients suffering from UC grouped into three distinct groups termed Group-A, Group-B, and Group-C. Group-A contained 11 clone sequences (C_23/24, C_23/32, C_A1A6, C23/22, C23/33, C_A1A17, C_A1A20, C_A1A15, C_A1A11, C_A1A18, and C_A1A14), which formed a clade with sequences from Escherichia coli. The clones belonged to Group-A , were all from patient 3. Group-B contained 10 clone sequences formed a clade with those from Citrobacter freundii and these sequences were from patients 2 and 4. Group-C contained 7 clones from patient 1 and patient 2 and these sequences also formed a clade with sequences from Citrobacter freundii. Several studies have observed imbalances in the bacteria present in the colon of patients suffering from UC such as the increase of certain group of bacteria (Proteobacteria) which was observed this study (Ott et al., 2004; Frank et al., 2007; Packey& Sartor., 2009). The taxonomic differences between control and UC patients were distinguished in this study and our results are similar in terms of the reduction the reduction of biodiversity of Firmicutes, Bacteroidetes and Actinobacteria in patients of UC and an increase in members of Gamma Proteobacteria was also observed (Sokol & Seksik, 2010).

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Figure 3.1.16. A maximum likelihood phylogenetic tree (model TN93) showing the relationships between derived 16S rRNA gene sequences of known taxa belonging to Proteobacteria and those of random clones from patients suffering from UC the support value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown

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SRB belong to the mucosa associated bacteria of both humans and animals (Cummings et al., 2003), and 67- 91% of all dominant SRB species are belonging to Desulfovibrio genus (Kushkevych, 2013). A pathogenic role in UC disease may came from this genus of SRB, however not one 16S rRNA sequence for these species was detected from healthy or UC patients. One reason that could explain this absence at the 16S rRNA level is the rarity of SRB cells, sequences from which might be out-completed in PCR amplifications by those from commoner species. In order to characterise the SRBs present in the human colon, a different gene was targeted, one that would be present pre-dominantly in SRB cells – the dissimilatory sulfite reducatase (dsr) gene. Dissimilatory sulfate reduction and the production of hydrogen sulphide by SRB in the human colon have been proposed as one of the main causes of colonic mucosa inflammation (Rowan et al., 2009). Previous studies on the diversity of SRB in complex microbial communities such as human colon have been based on 16SrRNA gene analysis (Deplancke et al., 2000; Nava et al., 2012; Kushkevych, 2014). The dsr gene has been widely used as ideal phylogenetic marker gene for assessing the diversity of SRB in complex environments, other than the human colon (Wagner et al., 1998; Loy et al., 2004; Nakagawa et al., 2004). The dsr gene phylogenetic tree (Figure 3.1.17) was constructed from an alignment of taxa including dsr sequences from cloned PCR products. A maximum likelihood tree divided the taxa into five deeply branched clades named group-A to group-E. The Group-A, Group-B, Group-C, Group-D, and Group-E clades corresponding to the Desulfovibrionales, Desulfobacterales, Desulfovibrionales, Desulfarculales, and Syntrophobacterales taxonomic lineage respectively. The dsr gene clone sequences were from both healthy (clones F1, F4, F6, and E5) and UC patients (clones A7, B8, B6, C4, C11, C1, D4, D12, and D5) and all were placed inside the larger clade, the Group-A for the Delsulfovibrionales, but not at the same location. All the clone sequences from healthy control samples were clustered together, and were closely related to Desulfovibrio aminophilus and Desulfovibrio longus. All the clone sequences from three patient samples were clustered together with Desulfovibrio alaskensis G20, a gram- negative mesophilic sulfate-reducing bacterium. The presence and increased members of the genus of Desulfovibrio subspecies (Specifically, strains of D. piger and D. vulgaris) were reported as human opportunistic pathogen in UC patients (Gibson et al., 1990; Zinkevich & Beech, 2000; Rowan et al., 2009; Rowan et al., 2010).

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Figure 3.1.17. Deltaproteobacteria maximum-likelihood (model TN93) dsr gene based phylogenetic tree. The tree reflected the relative placement of the dsr gene clones from healthy control and patients suffering from UC among sulfate-reducing Deltaproteobacteria. Numbers placed above the branches were the support values estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50 only shown

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Based on the phylogenetic analysis obtained from this study distinct bacterial taxa accounted the differences between healthy control and UC patients the overall bacterial profile showed a major shifts in the bacterial taxa that discriminated mucosal communities in healthy control and UC patients samples. The ability of 16S rRNA gene to distinguish SRB species was limited however the functional gene, dsr, was able to distinguish ecologically distinct bacterial populations (Kondo et al., 2006).

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Section 2 Design of a Biochip for screening of mucosa associated bacteria in healthy and colitis patients

The low-density biochips have been developed to analyse a relatively small number of genes. A notable example being the usage of a biochip to monitor the common intestinal pathogenic bacteria (Jin et al., 2005). These multi target biochips are possibly ideal for detecting pathogens that interact to produce symptoms and as such would be of great use for rapidly screening the bacterial populations of the colonic mucosa in UC. In this chapter a set of oligonucleotide probes targeting some of bacteria known to be involved on UC was designed. The hybridization potential of each probe was evaluated using cassette method (Zinkevich et al., 2014); and subsequently the probes were used to design biochips for bacterial detection and identification on healthy and UC patients samples.

3.2.1 Oligonucleotide probe design

The colonic bacteria, which are likely to be implicated in the etiology and pathogenesis of UC, were selected and the DNA probes for targeting these bacterial genes were designed. The selection of these bacteria was based on previous studies as well as on the analysis of biopsy samples of healthy individual and UC patients from this thesis (Cummings et al., 2003; Sasaki & Klapproth, 2012; Kushkevych, 2013; Matsuoka & Kanai, 2015). Additional probes obtained from ProbeBase an rRNA-targeted oligonucleotide probe database were also included in the analysis (Loy et al., 2007). Designed and published probes shown in Table 3.2.1 and Table 3.2.2. The probes were designed from the alignments of species- or genus-specific variable regions. The sequences of selected marker genes for a range of taxa were obtained from NCBI. Alignment maps were generated to allow for the detection of conserved sequences which would allow for the generation of taxa probes. All probes were checked for their specificity and taxon coverage by BLAST searches against the GenBank database. Probe length is important to modulate specificity and sensitivity, with longer

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CHAPTER 3: RESULTS AND DISCUSSION Table 3.2.1. List of design probes

Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C Dslpig Desulfovibrio piger dissimilatory sulfite reductase beta-subunit-dsrB TCAAAAAGAATTTCGGCAAGTGGCT 56.1 Dslgig Desulfovibrio gigas Sulfate adenylyltransferase-sat CCTGAAGATCGAAGAAAAGTACG 55.7 Dsbub Desulfobulbus Sulfate adenylyltransferase-sat GATCATTATTTCGTCAAAGAGAACG 54.4 Dsbac Desulfobacter Sulfate adenylyltransferase-sat AAGATCGCTATTGAAGTATGTGACG 56.1 Dslsp Desulfovibrio sp adenosine-5'-phosphosulfate reductase alpha- TCATGATCAACGGTGAATCCTACAA 56.1 subunit-aprA Dsldes Desulfovibrio dissimilatory sulfite reductase alpha –subunit- CAAGTACTATCACAGCAAATTCCTG 56.1 desulfuricans dsrA Desfot Desulfotomaculum Sulfate adenylyltransferase-sat CTGAAGAATACAAAGGTGTTTACCT 54.4 S1 SRB dissimilatory sulfite reductase alpha –subunit-dsrA TAAGTTCTACACCTCCGAATACCT 55.9 Ent1 Enterobacteriaceae β-galactosidaes-β-gal TATTCGATTTCTGATAAGAGCGTGG 56.1 EC E. coli β-galactosidaes-β-gal GGAAAACACCTAACGCATATTAACG 56.1 Bacfr Bacteriodes fragilis Neuraminidase gene-nanH GACAAGGATTCTACCAGCTTTATAC 56.1

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CHAPTER 3: RESULTS AND DISCUSSION Table 3.2.1. Cont. Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C Bacsp Bacteriodes sp beta-isopropylmalate TGTATTTCGGTGAGAAGTATCAGGA 56.1 dehydrogenase -leuB Cldif Clostridium difficile Toxin Clostridium difficile gene- GTGAAGCGTTCTTAGTCAATACACT 56.1 TcdC Clsp1 Clostridium sp [Fe-Fe]-hydrogenases- hydA GGGGATAGCTAGTGGATTTAGTATA 56.1 Fusv Fusobacterium varium Fusobacterium adhesin A-fadA TAACTATAAAGTATCAGGAGGGCTT 54.4 Fusnu Fusobacterium nucleatum Fusobacterium adhesin A-fadA GATTTAATGAAGAAAGAGCACAAGC 54.4 Fusne Fusobacterium necrophorum Fusobacterium adhesin A-fadA CTATTATTTCGGTCAAAGTTCCACA 54.4 Ent2 Enterobacteriaceae β-galactosidaes-β-gal GGCTGAACAACAAGAAGATTTACAC 56.1 Clsp2 Clostridium sp [Fe-Fe]-hydrogenases- hydA GACCGTATAAGAGATAACTATGAAG 54.4

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CHAPTER 3: RESULTS AND DISCUSSION Table 3.2.2. List of published DNA probes

Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C Reference Rum Ruminococcus 16SrRNA CCA ATT GGA AAC GAT TGT TAA TAC C 54.4 Harmsen et al., 2002 Bif Bifidobacterium CATCCGGCATTACCACCC 56.9 Reichardt et al., 2011 Lac Lactobacillus CCG TCA ACC CTT GAA CAG TT 55.2 Demaneche et al., 2008 SR2 SRB aprA GGCCTGTCCGCCATCAATA 57.1 Zinkevich & Beech, 2000

16S Bacteria 16S rRNA CCTACGGGAGGC AGCAG 55 Muyzer et al.,1993

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Probes used in this study vary between 18 and 25 oligigonucleotides in length. The probes with similar Tm values (Tm±2°C) were also considered. The identical hybridization behaviour of the oligonucleotide probes immobilized onto a solid surface have to be achieved and controlled. Five cassettes were constructed. Each cassette contains four of the chosen oligonucleotide probes and functions as the target DNA in hybridisation reactions with the biochip were constructed. Therefore, appropriate and poorly performing probes can be rapidly identified in the hybridization reactions. The composition of the ss cassettes is shown in Figure 3.2.1.

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Cassette 1- 5’- piger (25/56.1)* gigas (23/55.7) * Desulfovibrio sp (25/56.1)* SRB1 (24/55.9) *- 3’ Cassette 1 - SEQUENCE 5’TCAAAAAGAATTTCGGCAAGTGGCTCCTGAAGATCGAAGAAAAGTACGTCATGATCAACG GTGAATCCTACAATAAGTTCTACACCTCCGAATACCT3’ Cassette 1_COMPL 5’ CY3- AGGTATTCGGAGGTGTAGAACTTATTGTAGGATTCACCGTTGATCATGACGTACTTTTCTTC GATCTTCAGGAGCCACTTGCCGAAATTCTTTTTGA 3’ (97 bp) ______Cassette 2 -5’- Desulfobulbus (25/54.4)* Desulfobacter (25/56.1)* Desulfovibrio desulfuricans (25/56.1)* Desulfotomaculum (25/54.4)*- 3’ Cassette 2 – SEQUENCE 5’ GATCATTATTTCGTCAAAGAGAACGAAGATCGCTATTGAAGTATGTGACG CAAGTACTATCACAGCAAATTCCTGCTGAAGAATACAAAGGTGTTTACCT 3’ Cassette 2_COMPL 5’ CY3- AGGTAAACACCTTTGTATTCTTCAGCAGGAATTTGCTGTGATAGTACTTGCGTCACATACTT CAATAGCGATCTTCGTTCTCTTTGACGAAATAATGATC’3 (100 bp) ______Cassette 3 -5’ Enterobacteriaceae (25/56.1)* E.coli (25/56.1)* Bacteriodes fragilis (25/56.1)* Bacteriodes sp (25/56.1)*-3’ Cassette 3 – SEQUENCE 5’ TATTCGATTTCTGATAAGAGCGTGG GGAAAACACCTAACGCATATTAACG GACAAGGATTCTACCAGCTTTATAC TGTATTTCGGTGAGAAGTATCAGGA’3 Cassette 3_COMPL 5’ CY3- TCCTGATACTTCTCACCGAAATACAGTATAAAGCTGGTAGAATCCTTGTCCGTTAATATGCG TTAGGTGTTTTCCCCACGCTCTTATCAGAAATCGAATA’3 (100 bp) ______

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Cassette 4 -5’ Clostridium difficile (25/56.1)* Clostridium sp (25/56.1)* Fusobacterium varium (25/54.4)* Bifidobacterium (18/56.9)*’3 Cassette 4 – SEQUENCE 5’ GTGAAGCGTTCTTAGTCAATACACTGGGGATAGCTAGTGGATTTAGTATA TAACTATAAAGTATCAGGAGGGCTTCATCCGGCATTACCACCC ’3 Cassette 4_COMPL 5’ CY3- GGGTGGTAATGCCGGATGAAGCCCTCCTGATACTTTATAGTTATATACTAAATCCACTAGCT ATCCCCAGTGTATTGACTAAGAACGCTTCAC’3 (93 bp) ______Cassette 5 -5’ Fusobacterium nucleatum (25/54.4)* Ruminococcus (25/54.4) *Fusobacterium necrophorum (25/54.4)* Lactobacillus (20/55.2)* ’3 Cassette 5 – SEQUENCE 5’ GATTTAATGAAGAAAGAGCACAAGCCCAATTGGA AACGATTGTTAATACC CTATTATTTCGGTCAAAGTTCCACACCGTCAACCCTTGAACAGTT’3 Cassette 5_COMPL 5’ CY3- AACTGTTCAAGGGTTGACGGTGTGGAACTTTGACCGAAATAATAGGGTATTAACAATCGTTT CCAATTGGGCTTGTGCTCTTTCTTCATTAAATC’3 (95 bp) ______Figure 3.2.1. The composition of the ss cassettes

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3.2.2 Fine-tuning of biochips using cassette approach

A critical step in biochips design is the evaluation of probes, which are going to be used on a biochip. These probes have to satisfy several requirements such as a uniform melting temperature (Tm) and being a similar size so that their signal intensities are comparable under the same hybridization conditions. Whilst in silico modelling of biochip probes is developing rapidly there is often a large difference between the theoretical and real world activity of probes. Approximately a quarter of probes are non-specific when in situ (Hager, 2006). Determination of individual probe efficacy can be time consuming and a novel cassette method for the rapid evaluation of probe efficacy has been developed (Zinkevich et al., 2014).

This methodology was adapted to investigate the role of SRB in the development of UC. Glass or other silica derivatives are typically used as the base material for microarrays as they are chemically resistant and display good optical character. However, they feature a limited DNA binding capacity resulting in a low-density surface coverage. Various techniques have been used to improve binding efficacy, for example the polyacrylamide gel pads coupled with 3-methyluridine probes (Zinkevich et al., 2014). For this study a dendrimeric matrix was used which allows for a higher loading of probes, promotes improved hybridization and are more robust allowing for several hybridization, de-hybridization cycles (Caminade et al., 2006). Probes to detect bacterial species or genera identified from the published literature as potentially playing a role in UC were designed.

Single stranded (ss) cassettes were used as the targets for the hybridization with the probes coupled into the dendrimeric matrix. The cassettes present the lineal set of sequences complimentary to the probes. The Cy3 fluorescent dye was inserted at 5´-end of each cassette during the synthesis.

Five cassettes were used to evaluate the hybridization potential of each probe. The cassettes were composed of four probes; and the size of cassettes varied from 93 to 100 bp. The hybridization behaviours of the probes targeting Desulfovibrio piger, Desulfovibrio gigas, Desulfovibrio sp and SRB were assessed using cassette N1. All of the

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CHAPTER 3: RESULTS AND DISCUSSION probes showed comparable hybridization capacities (Figure 3. 2.2), indicating that each of these probes were suitable for inclusion in the biochip.

Figure 3.2.2. Schematic diagram and the results of the designed cassette N1 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4,B1,C1,D1, E1 – Probes position markers; B2,C2 – Dslpig for Desulfovibrio piger ;D2,E2 – Dslgig for Desulfovibrio gigas ;B3,C3 – Dslsp for Desulfovibrio sp; D3,E3 – S1 for SRB; B4,C4 -16S Negative control; D4,E4 – were left empty to give background signal.

Four probes for the identification of Desulfobulbus, Desulfobacter, Desulfovibrio desulfuricans and Desulfotomaculum included in cassette N2. Only two of these probes for Desulfobulbus and Desulfotomaculum exhibited similar hybridization profiles however the probes for Desulfobacter and Desulfovibrio desulfuricans failed to detect their antisense (Figure 3. 2.3)

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Figure 3.2.3. Schematic diagram and the results of the designed cassette N2 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Dsbub for Desulfobulbus; D2, E2 – Dsbac for Desulfobacter; B3, C3 – Dsldes for Desulfovibrio desulfuricans; D3, E3 – Desfot for Desulfotomaculum; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.

The probes for Enterobacteriaceae, Escherichia coli, Bacteriodes fragilis and Bacteriodes sp. in cassette N3 reveal the same binding capacity while the probe for Bacteriodes sp. failed to give any signal (Figure 3. 2.4).

Figure 3.2.4. Schematic diagram and the results of the designed cassette N3 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Ent1 for Enterobacteriaceae; D2, E2 –EC for E.coli; B3, C3 – Bacfr for Bacteriodes fragilis; D3, E3 – Bacsp for Bacteriodes sp; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.

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The hybridization efficiency of the probes for Clostridium sp, and Bifidobacterium in cassette N4 was the same. The probes for Clostridium difficile and Fusobacterium varium failed to hybridize with its single-stranded compliment (Figure 3. 2.5).

Figure 3.2.5. Schematic diagram and the results of the designed cassette N4 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes.The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Cldif for Clostridium difficile; D2, E2 –Clsp1 for Clostridium sp; B3,C3 –Fusv for Fusobacterium varium; D3, E3 –Bif for Bifidobacterium; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.

The probes in cassette N5 showed comparable hybridization capacities except for probe Lactobacillus which gave negative result (Figure 3.2.6).

Figure 3.2.6. Schematic diagram and the results of the designed cassette N5 hybridization as signal to noise (S/N) ration. The purple circles are the probes position markers the gray circles are the probes. The arrangement of the probes was as follows: A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Fusnu for Fusobacterium nucleatum; D2, E2 –Rum for Ruminococcus; B3, C3 –Fusn for Fusobacterium necrophorum; D3, E3 –Lac for Lactobacillus; B4, C4 -16S Negative control; D4, E4 – were left empty to give background signals.

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The 6 probes (for Desulfobacter; Desulfovibrio desulfuricans; Bacteriodes sp; Clostridium difficile; Fusobacterium varium and Lactobacillus) which failed to produce any hybridization signal were reordered and were tested again. The biochip experiment is a multi-stage process in which the accuracy of each individual step may influence the target detection. Therefore, to exclude the possibility of the influence of other factors caused the detection failure, such as the probe design or the human factor; the probes were reorder as separate sets. The newly synthesized probes showed comparable hybridization signal with other probes (Figure 3.2.7). This indicated that the negative results obtained with the first set of the probes occurred due to the error during the synthesis. The probe manufacturing errors were reported previously that it is very common (Jonathan et al., 1997; McGall et al., 1997; Jakubek & Cutler, 2012). Another possibility is that the chips often may have different binding properties if these matrixes manufactured on different days (batch effect). However, several fundamental observations of microarray behaviour remain poorly understood (Hong et al., 2008).

As a result most of the probes exhibited comparable hybridization signals and were characterized by the mean S/N ratio=7 (dashed line) (Figure 3.2.7). Only probes for Desulfobacter, Desulfotomaculum, Enterobacteriaceae and Clostridium sp were slightly out of the mean range ±SD. The probes, S/N ratio which were higher than the mean S/N of the majority of the probes; were re-design and tested against DNA obtained from patients suffering from UC and healthy individual.

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Figure 3.2.7. Schematic diagram and the analysis of the designed cassettes hybridization with biochip. The results of the hybridization as signal to noise (S/N) ration. The data presented are mean values±SD.

Panel A. Schematic diagram represents the arrangement of the probes on the biochip. The probes were spotted in duplicate. The red circles are the probes position markers. B2, C2- Dslpig for D. piger; B3, C3- Dsbub for Desulfobulbus; B4, C4- Ent1 for Enterobacteriaceae; B5, C5- Cldif for C. Difficile, B6, C6- Fusnu for F. nucleatum; D2, E2- Dslgig for D. gigas ; D3, E3- Dsbac for Desulfobacter; D4, E4- EC for E.coli; D5, E5- Clsp1 for Clostridium sp.; D6, E6- Rum for Ruminococcus; F2, G2- Dslsp for Desulfovibrio sp; F3, G3- Dsldes for D. desulfuricans; F4, G4- Bacfr for B. fragilis; F5, G5- Fusv for F. varium; F6, G6- Fusn for F. necrophorum; H2, I2- S1 for SRB; H3, I3- Desfot for Desulfotomaculum; H4, I4- Bacsp for Bacteriodes sp.; H5, I5- Bif for Bifidobacterium; H6, I6-Lac for Lactobacillus.

Panel B. The analysis of the designed cassettes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- Dslsp for Desulfovibrio sp; 3- Dslpig for D. piger; 4- Dslgig for D. gigas; 5- Dsldes for D. desulfuricans; 6- Dsbub for Desulfobulbus; 7- Dsbac for Desulfobacter; 8- Desfot for Desulfotomaculum; 9- Ent1 for Enterobacteriaceae; 10- EC for E.coli; 11- Bacsp for Bacteriodes sp.; 12- Bacfr for B. fragilis; 13- Clsp1 for Clostridium sp; 14- Cldif for C. difficile; 15- Rum for Ruminococcus; 16- Fusv for F. varium; 17- Fusnu for F. nucleatum; 18- Fusn for F. necrophorum;19- Bif for Bifidobacterium; 20- Lac for Lactobacillus

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3.2.3 Analysis of DNA samples of healthy control and patients suffering from UC using biochip

The designed biochip was used to study the bacterial profiling using DNA purified from biopsy samples of healthy individual and patients suffering from UC. DNA of healthy individual and patients suffering from UC were prepared for hybridization with the biochip by amplification, fragmentation and labelling. Peaks equal and above a S/N of 2 were counted as positive samples. The biochip for healthy individual sample analysis was constructed using 23 probes in duplicate. Probes 1 and 2 targeted SRB while probes 3-9 targeting specific SRB species. Probes 10 and above target other bacteria which may have a role in the development of UC. Probes 10 and 11 targeted Enterobacteriaceae species generically and probes 15 and 16 targeted Clostridium species. In the healthy control, DNA produced high levels of the fluorescent signal intensity for the probes targeting D. piger and Enterobacteriaceaee family members (Probe 2 which was re-designed but not Probe 1) were detected. Low levels of SRB (Probe 1 and 2), E. coli, Bacteriodes sp., Bacteriodes fragilis , Clostridium sp1 (Probe 1; but not probe 2 which was re-designed) and Fusobacterium nucleatum were detected (Figure 3.2.8). The obtained results showed the presence of these bacterial species in healthy individual sample. The weak or absence of hybridization signal (probes 3, 5-10, 17-19, 23) could mean either the absence of the appropriate group of bacteria in the studied sample or very low presentation of these bacteria.

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Figure 3.2.8. Schematic diagram and the hybridization analysis of the DNA obtained from healthy individual. The hybridization results represented as signal to noise (S/N) ratio. Panels A. Schematic diagram represents the arrangement of the probes on the biochips of healthy individual sample. The probes were spotted in duplicate. The red circles are the probes position markers. B2, C2- S1 for SRB1; B3, C3- SR2 for SRB; B4, C4- Dslsp for Desulfovibrio sp; B5, C5- Dslpig for D. piger; B6, C6- Dslgig for D. gigas; D2, E2- Dsldes for D. desulfuricans; D3, E3- Dsbub for Desulfobulbus; D4, E4- Dsbac for Desulfobacter; D5, E5- Desfot for Desulfotomaculum; D6, E6- Ent1 for Enterobacteriaceae; F2, G2- Ent2 for Enterobacteriaceae; F3, G3- EC for E.coli; F4, G4- Bacsp for Bacteriodes sp.; F5, G5- Bacfr for B. fragilis; F6, G6- Empty; H2, I2- Clsp1 for Clostridium sp.; H3, I3- Clsp2 for Clostridium sp.; H4, I4- Cldif for C. Difficile; H5, I5- Rum for Ruminococcus; H6, I6- Fusv for F. varium; J2, K2- Fusnu for F. nucleatum; J3, K3- Fusn for F. necrophorum; J4, K4- Bif for Bifidobacterium; J5, K5- Lac for Lactobacillus.

Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus; 8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15 - Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum; 21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus.

The biochip for UC patients’ samples analysis was constructed using 23 probes in duplicate. UC patient 1 sample all bacteria were detected in this sample with the exception of Desulfobulbus and Bacteriodes sp, Clostridium sp (Probe 1 but not Probe 2) and Clostridium difficile. SRB 1, SRB 2, D. piger, Desulfobacter, Enterobacteriaceaee

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(Probe 1 but not Probe 2), and Fuscobacterium varium were detected at high levels comparing with other probes (Figure 3.2.9).

Figure 3.2.9. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 1. The hybridization results represented as signal to noise (S/N) ratio.

Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 1 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5, C5- Cldif for C. Difficile; B6, C6- Fusn for F. necrophorum; B7, C7- SR2 for SRB2; D2, E2- Dslgig for D. gigas; D3, E3- Dsldes for D. desulfuricans; D4, E4- EC for E.coli; D5, E5- Clsp1 for Clostridium sp.; D6, E6- Rum for Ruminococcus; D7, E7- Ent1 for Enterobacteriaceae; F2, G2- Dsbub for Desulfobulbus; F3, G3- Desfot for Desulfotomaculum; F4, G4- Bacfr for B. fragilis; F5, G5- Fusv for F. varium; F6, G6- Bif for Bifidobacterium; F7, G7- Clsp2 for Clostridium sp.; H2, I2- Dsbac for Desulfobacter; H3, I3- S1 for SRB1; H4, I4- Bacsp for Bacteriodes sp.; H5, I5- Fusnu for F. nucleatum; H6, I6- Lac for Lactobacillus; H7, I7- S1 for SRB

Panel B. The analysis of the designed probes. The hybridization results represented as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.

In UC patient 2 sample; SRB (Probe 1 but not 2), Desulfovibrio sp, D. piger, D. gigas, Desulfobacter, Bacteriodes sp., Bacteriodes fragilis, Desulfotomaculum,

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Enterobacteriaceae (Probe 1 not 2) and Lactobacillus were detected at high levels of hybridization signals. However, low levels of Clostridium sp. were detected (Figure 3.2.10).

Figure 3.2.10. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 2. The hybridization results represented as signal to noise (S/N) ratio.

Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 2 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus; H7,I7- S1 for SRB

Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.

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In UC patient 3 high levels of fluorescent signal of the probes detecting SRB, D. piger and D. gigas were observed. However, low levels of Desulfobulbus, Enterobacteriaceae (Probe 1), Fuscobacterium nucleatum and Bifidobacerium were detected (Figure 3.2.11).

Figure 3.2.11. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 3. The hybridization results represented as signal to noise (S/N) ratio.

Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 3 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus;H7,I7-S1 for SRB

Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.

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In UC patient 4 sample SRB (with both probes), D. piger, D. gigas and Desulfobacter were detected at high levels comparing with low levels of hybridization signals of Enterobacteriaceae, Fusobacterium nucleatum and Fuscobacterium necrophorum (Figure 3.2.12).

Figure 3.2.12. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 4. The hybridization results represented as signal to noise (S/N) ratio.

Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 4 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus; H7,I7-S1 for SRB

Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus; 8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.

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When comparing the samples, several key trends are visible (Figure 3.2.13). Pan-SRB probes 1 and 2 detected SRB in all samples, however in UC patients 1 and 4 this was highly elevated compared to healthy individual sample. The probe for D. piger was detected in all samples and at elevated levels in the healthy control individual. Conversely, the probes for D. gigas and Desulfobacter sp were detected in all UC patients samples, albeit at low levels in some UC patients, but they were not detected in the control sample. Whilst no other specific SRB were detected in the control sample it was not possible to discern a trend due to a high degree of variation in UC patient samples. The presence of D. piger was unsurprising as it has previously been described as the most common SRB present in the human colon (Scanlan et al., 2009, Rey et al., 2013). The converse trend was observed with D. gigas and Desulfobacter sp in this instance no hybridization signal was detected in the control sample whereas a consistent level signal was detected in all UC patients samples. There are no reports in the literature of D. gigas or Desulfobacter sp specifically being associated with UC so this is a potential novel finding. An interesting possibility is that the reduction in D. piger and increase in D. gigas and Desulfobacter sp may be linked through mechanisms unknown, contribution to the dysbiosis associated with UC. In addition, other explanations of the association of D. gigas with UC patients samples that D. gigas may has physiological features that cause inflammation or participate in the prolongation of chronic inflammatory or may these species are preferred by inflammation condition in UC patients (Rowan et al., 2010). However, investigation of sufficient number of samples would be required to make a proper conclusion.

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Probe Target Healthy UC1 UC2 UC3 UC4 S1 SRB SR2 SRB Dslsp Desulfovibrio sp. Dslpig D. piger Dslgig D. gigas Dsldes Desulfovibrio desulfuricans Dsbub Desulfobulbus Dsbac Desulfobacter Desfot Desulfotomaculum Ent1 Enterobacteriaceaee Ent2 Enterobacteriaceaee EC E. coli Bacsp Bacteriodes sp. Bacfr Bacteriodes fragillis Clsp1 Clostridium sp Clsp2 Clostridium sp Cldif Clostridium difficile Rum Ruminococcus Fusv Fuscobacterium varium Fusnu Fuscobacterium nucleatum Fusne Fuscobacterium Bif Bifidobaceriumnecrophorum Lac Lactobacillus Key >2 2-4 4-6 6-8 8+

Figure 3.2.13. Heat map of bacteria detected in the samples of healthy individual and UC patients. Cells are coloured according to the S/N ratio calculated for each sample and probe as per the key provided.

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The probes assessing Enterobacteriaceaee displayed a marked difference, with a strong detection with Enterobacteriaceaee probe 1 in UC patients 1, 2 and 3 samples but not UC patient 4 and control, whereas Enterobacteriaceaee probe 2 detected in the healthy control sample at a high level, and only in UC patient 1 , 2 and 4 at a low level. These data suggest that the pan-Enterobacteriaceae probes were detecting different, non- overlapping populations. Enterobacteriaceae have been associated with UC previously, although specific strains have not (Lavelle et al., 2015). These findings suggest that investigation into specific species of Enterobacteriaceae may prove useful in determining if certain species display a protective or deleterious effect in UC. Clostridium difficile colonization can induce antibiotic-associated colitis (Kawaratani et al., 2010). However, there was no noticeable trend in the detection of C. difficile with specific probes in all samples analysed. Interestingly similar amount of Clostridium genera with non-specific probes was detected in UC patients 1 and 2 as well as healthy control sample in the case of the latter suggesting that the generic probes did not detect C. difficile. Whilst C. difficile is undoubtedly pathogenic and able to induce colitis however these species was not detected in all samples tested in this study. Therefore, it is not possible to draw strong conclusions from the data obtained. As Clostridium genera were detected in the healthy control it is possible that non-pathogenic genera displaces the C. difficile in control sample. However, investigation of sufficient number of samples would be required to make a proper conclusion. One other key difference between the control and UC patient samples was the detection of Fuscobacterium nucleatum probe in all UC patients except UC patient 2. The probe for Fuscobacterium necrophorum was detected in UC patients samples together with presence of the Fuscobacterium varium probe in UC patient 1 and UC patient 2 with notable increase on UC patient 1. Members of the Fusobacterium genus, including Fuscobacterium nucleatum, F. necrophorum, and Fuscobacterium varium which have been detected in the studied samples, have been reported by previous studies as gastrointestinal bacteria that have a significant role on UC (Ohkusa et al. 2003; Legaria et al., 2005; Afra et al., 2013; Tahara et al., 2015). Although Fuscobacterium varium is conserved generally to be a rare bacterium and found to be correlated with UC in a minority cases around the world, the present study findings was in conjunction with previous studies which demonstrated the F. varium have long been associated with the development of UC.

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All samples tested in this study displayed differences in the heterogeneity of resident bacteria, which may conform to the hypothesis that UC arises from dysbiosis (Cummings et al., 2003). A loss of protective bacteria such as Lactobacilli and Bifidobacteria was also proposed as a mechanism in the pathogenesis of UC, however this was not shown in the studied samples. Indeed, all UC patients displayed a marked increase in the detection of Bifidobacteria and Lactobacillus was detected only in UC patient 1 and 2 in high level. The increasing level of Lactobacillus in UC patients was previously reported (Wang et al., 2014). The presence of Bifidobacteria and the high level of Lactobacillus in UC patients samples could be explained that these bacteria have a role in defence through the promotion of a mucous secretion which will help to protect colonic epithelial cells from pathogenic bacteria during inflammation (Mack et al., 2003). Together this suggests that whilst UC may be linked to bacteria, it is not currently possible to identify a single bacterial species or change that is associated with UC. Elevated levels of SRB bacteria detected in UC patients 1 , 3 and 4 suggests that SRB may play a role in the development of UC, but low level results of SRB in patient 2 suggests that this may be limited to a subset of cases. The analysis of the patients samples for the presence of main bacterial species or groups that may have role on UC pathogenesis by biochip showed that hybridization signals are absent for some probes. This may indicate that either the absence of the appropriate target group of bacteria in the studied samples or their low abundance. The biochip/cassette methodology used in this study would provide an ideal framework to rapidly and accurately assess bacteria associated with UC in patient samples. In future studies it would be worthwhile incorporating of multiple probes specific to species of Enterobacteriaceae as the changes in the level of hybridization signals of these bacteria were potentially the most novel finding of these experiments.

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Section 3 Detection and quantification of SRB using microbiological and quantitative PCR techniques

The role of SRBs, particularly Desulfovibrio species, in etiology of UC has been widely suggested. SRBs are toxic to human epithelial intestinal cells chiefly because they produce H2S, which damage the colonic mucosa layer leading to inflammation (Medani et al., 2011; Chan & Wallace, 2013; Kushkevych, 2014). In order to extend understanding further from these reports and to control SRB effects, a rapid method to quantification and detect SRB colonisation is needed. Unfortunately, this has posed a huge challenge. This is largely because of low recovery rate of SRB making it difficult to recognize the early stages of infection. In this study rapid method for detection and quantification of Desulfovibrio species was developed.

3.3.1 The growth of different age of Desulfovibrio species culture in VMR and VMI media

Strains of Desulfovibrio species are frequently recovered at a greater frequency (when Escherichia coli is present, even when the SRB is present at low cell densities (102- 103cells/ml) (Zinkevich, unpublished data). This suggests that a relationship might exist between the two organisms. When both species are co-cultured together their growth behaviour changes (Bharati et al., 1980), the growth yield constant increases and sulfate is reduced accompanied by the total consumption of formate, lactate and alcohol. Co- culture of SRBs with methanogens and dehalorespiring bacteria are well-known examples of syntrophism (Drzyzga et al, 2001). The relationship with Escherichia coli appears to lead to enhanced growth of the SRB (generation time for this organisms is usually greater than 3-8 days) (Rabus et al., 1993; Galushko et al., 1999; Gerardi, 2003; Ingvorsen et al., 2003). This can, potentially, provide a rapid detection of the sulphate reducer by stimulating its growth. SRBs often operate at low cell densities and have a slow growth (- > 28 days) which could be changed by co-culturing with E. coli so that detection can be recorded earlier.

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For these experiments, the effect of E.coli BP on the growth of different age cultures (7 days, 3 months and 1-year old) of Desulfovibrio indonesiensis was assessed. 10-fold serial dilutions of 7-day old D. indonesiensis cultures were incubated in VMR media. Two groups of experiments were designed: (1) where E. coli BP was added into each dilution of D. indonesiensis, and (2) where no E.coli BP was added to the dilution. The growth of D. indonesiensis was monitored daily for 28 days and the SRB was detected by the production of the metal sulphide in culture. The blackening of the medium was indication of SRB growth in the medium (Andrade, et al., 2000).

The results showed that the addition of E.coli BP to the culture induced the growth of D. indonesiensis. The growth of pure D. indonesiensis was observed in the first 5 dilutions only, whereas in the presence of E. coli BP growth of D. indonesiensis was observed up to 10-10 (Table 3.3.1, Figure 3.3.1). These results indicate, that E. coli BP induces D. indonesiensis growth or acts as an enhancer that activates dormant SRB. This result suggests that inclusion of E. coli increase the detection of SRB which otherwise would have stayed undetected due to their extremely low density.

Dilution in VMR medium 7 days old -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 culture used for inoculation D. indonesiensis + + + + + ------D. indonesiensis + + + + + + + + + + - - + E.coli BP

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To test whether E.coli BP can activate dormant SRB cells older cultures of D. indonesiensis were used. When a 3 months old D. indonesiensis culture was used the growth induction was observed as well. However, the difference between pure SRB growth (10-1) and growth of SRB in the presence of E. coli BP (10-5) was 4 orders of magnitude (Table 3.3.2, Figure 3.3.2). Although the detection level had dropped; it is worth noting that there is a difference in detection level between D. indonesiensis with or without the addition of E. coli BP.

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Table 3.3.2. Comparison of 3 months old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate. Dilution in VMR medium 3 months old -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 culture used for inoculation D. indonesiensis + ------D. indonesiensis + + + + + ------+E.coli BP

Figure 3.3.2. Growth of 3 months old D. indonesiensis culture in VMR medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.

A similar result was obtained when a 1-year old pre-culture of D. indonesiensis was used as the inoculum for the serial dilution in the presence or absence of E. coli BP (Table 3.3.3, Figure 3.3.3). Interestingly, this result showed that D. indonesiensis could still be detected even though the culture was 1 year old. Although both groups, had growth it

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CHAPTER 3: RESULTS AND DISCUSSION was evident that the addition of E. coli enhanced the level of detection of D. indonesiensis significantly when compared to the group without. This result further suggests that E. coli plays a very important role in re-activating SRBs. This result is consistent with the previous results obtained when the 7-day and the 3-month old inoculates were used, implying that E. coli BP may be acting as a growth inducer or activator, thereby making the SRB cells detectable. All three experiments showed that although D. indonesiensis was detectable without the inclusion of E. coli, its addition greatly increased the lower level of detection.

Dilution in VMR medium 1 year old culture -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 used for inoculation

D. indonesiensis + ------D. indonesiensis + + + + ------+E.coli BP

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Figure 3.3.3. Growth of 1 year old D. indonesiensis culture in VMR medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.

Species of Desulfovibrio have a requirement for inorganic iron to stimulate growth (Postgate, 1965; Gibson, et al., 1999). The amount of inorganic iron present in the medium used, VMR, to culture D. indonesiensis previously was 0.05 g of iron (II) sulphate heptahydrate (FeSO4.7H2O). It is feasible that increasing this quantity of iron in the media will further promote growth. If this can be achieved, then the level of detection might be increased. For this reason, the inorganic iron in the medium was increased to 0.5 g (medium VMI). The experiments to characterise the relationship between E. coli BP and D. indonesiensis were repeated using VMI medium.

The cultures of D. indonesiensis used aged for 7 days, 3 months and 1 year, as before, and growth assessed with and without E.coli BP. A difference in the growth of the 7-day old culture of D. indonesiensis in VMI medium was observed when E.coli BP was included in the dilution series (Table 3.3.4, Figure 3.3.4). The addition of E. coli BP greatly enhanced the ability to detect D. indonesiensis at greater dilutions. A result that was similar to the one obtained when VMR medium was used except that detection was made at a greater dilution. However, the difference between the levels of detection for the co-culture and monoculture was the same in both sets of experiments. It can be

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concluded that the increase of inorganic iron to the medium does promote growth of the SRB but does not influence the effect that E.coli has on growth.

Dilution in VMI medium 7 days old culture -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 used for inoculation D. indonesiensis + + + + + + ------D. indonesiensis +E. + + + + + + + + + + - - coli BP

Figure 3.3.4. Growth of 7 days old D. indonesiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.

When the age of the D. indonesiensis culture was increased to 3 months a similar result was obtained. The level of detection of the monoculture and co-culture improved, but the difference between the treatments did not, which remained at the 104 level (Table 3.3.5, Figure 3.3.5).

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Dilution in VMI medium 3 months old culture -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 used for inoculation D. indonesiensis + + ------D. indonesiensis + + + + + + + ------E.coli BP

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Figure 3.3.5. Growth of 3 months old D. indonensiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonensiensis, (B) Growth of D. indonensiensis with E.coli BP. The black precipitate at the bottom of the vials indicates SRB growth.

The difference in detection levels between the two media was not observed when a 1- year old culture of D. indonesiensis was used as the inoculum. (Table 3.3.6 and Figure 3.3.6). There was no increase in the level of detection of D. indonesiensis with or without the inclusion of E. coli when either medium was used. This presumably reflects the physiological state of the older culture, where fewer cells capable of responding to the increased iron levels exist. Nevertheless, the co-cultures with E. coli BP retained their enhanced level of detection ability; this result strongly suggests a role for E.coli in increasing the detection of SRB in that E.coli has a commensal effect which improves the efficiency of growth of SRB at a low density of cells.

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Dilution in VMI medium 1 year old culture -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 used for inoculation D. indonesiensis + ------D. indonesiensis + + + + ------+E.coli BP

Figure 3.3.6. Growth of 1 year old D. indonesiensis culture in VMI medium in the presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D. indonesiensis with E. coli BP. The black precipitate at the bottom of the vials indicates SRB growth.

The growth experiments described above indicate that there is a significant relationship between E. coli BP and SRBs. The addition of E. coli greatly enhanced the lower level of detection of D. indonesiensis when compared to growth without E. coli BP in all the ages of D. indonesiensis pre-cultures tested (7 days old, 3 month’s old and 1 year old pre- cultures). This suggests that co-culturing SRB with E. coli provides a method to recover aged SRB cultures and sustain their growth. Both SRB and E. coli have been reported to

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CHAPTER 3: RESULTS AND DISCUSSION be involved in UC etiology, and the relationship exhibited between the two may be a survival mechanism. The experiments described in this section indicate the influence of E.coli on SRB growth and the role for E. coli as an inducer of SRB growth. This is probably because E. coli may be producing a substance(s) capable of either enriching the medium or supporting the growth of SRB by an unknown mechanism. The inclusion of E.coli in the culture medium for SRB increases detection level, thereby improving identification and quantification of SRB, and enhancing the chance of having cells physiologically capable of growth for further investigation (Gibson et al., 1991; Kushkevyeh & Moroz, 2012). Additionally, the inclusion of E.coli may improve the growth of SRB because it is a facultative anaerobe, and as such it is possible that it makes use of the traces of oxygen available in the environment, which will aid the growth of SRB, which are obligate anaerobes. E.coli has an iron (II) transport system and its ability to obtain this crucial element for fundamental living processes (Kammler et al., 1993). This may be the critical factor that allowed the survival and growth of E.coli to continue and therefore improve the efficiency of SRB growth.

3.3.2 Quantification of D. indonesiensis culture using q PCR

Viable SRB cell counts are believed to be underestimated when standard synthetic enumeration media is used. Improved count estimates are produced when natural media are used (Vester & Ingvorsen, 1998), presumably because of the presence of additional nutrients not found in synthetic media, however it is not always convenient to use natural media. Most enumeration methods using culture are evaluated by the production of a black ferrous sulfide precipitate. This provides a useful method for detection but an inaccurate one for quantification and it is time consuming. The slow growth rates of SRBs and their low cell densities add to the problems associated with their enumeration. Therefore, there is a need to identify the best method to quantify SRB for experimental purposes. The accurate quantification of genes and their transcripts allow an exact measurement of microbial activity and, possibly, phylogenetic identity (Neretin et al 2003). Real time or qPCR is a specific quantification method that accurately evaluates the number of bacteria in a culture by estimating the number of transcripts or DNA molecules directly in an environment that can be amplified by PCR (Wagner et al., 2005; Barton & Fauque, 2009; Christophersen et al., 2011).

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Dissimilatory sulfite reductase (DSR) and adenosine phosphosulfate reductase (APS) are key enzymes in sulfate reduction pathways present in SRBs. Estimation of the copy number for the genes and transcripts for these enzymes can be related to the number of cells in culture. Such an approach can be used to confirm the results obtained in the previous section regarding the age of the culture and its activity. The genomic DNA and total RNA were extracted from the samples taken from the original culture of different ages used for serial dilution (7 days, 3 months and 1year) and analyzed by qPCR. A series of tenfold dilutions of the target amplicons were analyzed in parallel with DNA and RNA samples for estimations of absolute abundance of dsrA and apsA. Ct values in each dilution were measured in triplicate using qPCR with the apsA primers-probe set to generate the standard curve. Ct values were plotted against the logarithm of their initial template (in ng). Efficiency of PCR for apsA was 99.3% with R2 = 0.995 and for dsrA was 100.3% with R2 = 0.992; both the reaction efficiencies and the R values indicated the validity of the standard curves. (Figure 3.3.7; Figure 3.3.8).

Figure 3.3.7. Standard curve for apsA gene

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Figure 3.3.8. Standard curve for dsrA gene

The dsrA and the apsA copy numbers were expressed as number of SRB cells per ml of D. indonesiensis culture, taking into account dilution factors and the volume of nucleic acid extract. All calculations were based on the assumption that an average of one dsrA gene copy (Klein et al., 2001; Kondo et al., 2008) and only one apsA (Friedrich, 2002) gene copy per SRB cell.

For genomic DNA samples (Table 3.3.7), 2.13 x1011 cells/ml were revealed in a 7-day old culture, based on the detection of dsrA copies in genomic DNA isolated from these cells. SRB cultures that were 3 months old contained 6.9 x1010 cells/ml, while the 1-year old culture gave 2.85 x1010 cells/ml. Quantification of the cells in different age SRB cultures based on the detection of the apsA copy numbers in genomic DNA showed that the greatest cell count was found in the 7-day old culture at 4.40 x 1011 cells/ml, followed by the 3-month old culture at 1.68 x1011 cells/ml and the least was for the 1- year oldlture at 7.68 x1010 cells/ml (Table 3.3.7). The same trend was shown for both genes - the older SRB culture contained the fewest number of cells.

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The age of The copy number of dsrA and apsA genes D. indonesiensis per ml of D. indonesiensis culture culture dsr A Aps A

7 days 2.13 x1011 4.40 x 1011 3 months 6.9 x1010 1.68 x1011 1 year 2.85 x1010 7.68x1010

Furthermore, according to Kruskal-Wallis analysis (Table 3.3.8) there was a significant difference in the copy number for the dsrA gene detected in genomic DNA between the 7-day and 1 year old cultures (P = 0.0005). On the other hand (Table 3.3.8) a comparison of the of the copy number of aps gene detected in genomic DNA showed significant difference between 7days and 3 month old cultures (P = 0.0373) and 7 days and 1 year cultures (P = 0.0023). However, when comparing 3 months and 1 year old cultures there was no significant was observed.

Comparison Significant P Value dsrA 7 Days vs. 3 Months No 0.1348 7 Days vs. 1 Year Yes 0.0005 (***) 3 Months vs. 1 Year No 0.2813 ApsA 7 Days vs. 3 Months Yes 0.0373 (*) 7 Days vs. 1 Year Yes 0.0023 (**) 3 Months vs. 1 Year No 0.9418

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Detection of dsrA and apsA transcripts in total RNA isolated from the different age of SRB cultures showed the same trend - a higher cell numbers in the 7-day old culture with 1.43 x 1010 (dsr A) and 6.37 x 109 (aps A) cells/ ml of D. indonesiensis culture when compared to the 2.85 x 109 (dsr A) and 6.98 x 108 (aps A) present in the 3-month old culture (Table 3.3.9). The Mann-Whitney test statistic suggested that there was a significant different between the 7-day old and 3-month old cultures of D. indonesiensis culture for both genes (dsr A, P=0.0018 and Aps A, P=0.0079) (Table 3.3.10). This may be due to the age of the inoculum of the cultures and the number of viable cells present. Older cultures would be expected to have fewer viable cells.

The age of The copy number of dsrA and apsA genes D. indonesiensis per ml of D. indonesiensis culture culture dsr A aps A

7 days 1.43 x 1010 6.37 x 109 3 months 2.85 x 109 6.98 x 108

Comparison Significant P Value dsrA 7 Days vs. 3 Months YES 0.0018 (**) ApsA 7 Days vs. 3 Months YES 0.0079 (**)

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3.3.3 Quantification of Hydrogen sulfide produced by D. indonesiensis grown with and without E. coli BP

SRB are known to produce toxic hydrogen sulfide (H2S) in the gastrointestinal tract of humans and animals (Reis et al., 1992). It is also believed that only very little of the H2S is produced by dissimilatory sulfate reduction, which is integrated in to the cell for cell synthesis, while the rest is expelled into the environment (Singh & Lin, 2015). The production of hydrogen sulfide by D. indonesiensis was estimated in order to correlate it to the production of ferrous sulfide. In the experiment a 7-day old culture of D. indonesiensis was diluted serially in VMR medium and cultured with and without E. coli

BP, and the H2S concentration was measured in the vial with the lowest level of detection (positive for culture of D. indonesiensis). This was the 10-5 dilution without the addition of E.coli BP and the 10-10 dilution when D. indonesiensis was cultured with E.coli

BP. The average concentration of H2S present in the D. indonesiensis and E. coli culture was 131 ppm, which was 3 times greater than the concentration (51 ppm) measured in the D. indonesiensis culture, despite the difference in dilution (Figure 3.3.9). The presence of E.coli BP with D. indonesiensis enhanced the H2S production when compared to D. indonesiensis alone culture. Both data sets resulted in the same gap difference in the detection of growth of the original experiment, a magnitude of 5 (-5 to -10). The difference made by the addition of E. coli; this result indicates that E.coli is capable of enhancing growth in SRB.

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Previous studies have shown that in order to determine the etiology of SRB in gastrointestinal diseases, it is important to accurately estimate its cell growth (Christophersen et al., 2011). Therefore in the process of determining the best method of measuring and quantifying the number of cell growth of Desulfovibrio in a culture, the use of qPCR was examined in this study.

The qPCR data obtained from a 7-day old pre-culture and its serially diluted post- cultures in the vials labeled 10-1 to 10-12 resulted in a similar data to those obtained when levels of detection in growth medium was examined. The qPCR data in this chapter correlated with the growth data in corresponding vial. This result thus showed that when the dsrA and aps -TaqMan qPCR were used, the number of SRB cells estimated per ml of cultures had an impressive correlation with the data of culture obtained from 7 days old D. indonesiensis. This result therefore supports previous data that showed that qPCR is an accurate and time saving method of measuring and quantifying Desulfovibrio (Christophersen et al., 2011; Neretin et al., 2003).

The H2S level in vials result in serially diluting a 7 day old pre-culture D. indonesiensis with and without E.coli supports previous reports that H2S is a key product resulting from the metabolic processes of SBR (Christophersen et al., 2011). Studies have also shown that H2S is toxic to human colon; extreme levels of H2S coupled with profuse levels of SRB have also been implicated in patients having UC (Gibson et al., 1991; Roediger et al., 1993; Gardiner et al., 1995; Christl et al., 1996; Pitcher et al., 2000). The findings obtained from this study showed the differences of concentration average level of H2S in D. indonesiensis co-cultured with E. coli BP. The difference was greatly increased ~3-folds in concentration when compared to D. indonesiensis alone which suggest that E. coli BP supporting the growth of D. indonesiensis and therefore increase the H2S production. During the metabolic activity, E. coli produces Lactate which is one of the substrates used by SRB Desulfovibrio in the gut for metabolism (Barbetti et al., 1988; Vernia et al., 1988; Bourriaud et al., 2005; Marquet et al., 2009; Kushkevych and Moroz, 2012). Although, the presence of sulfate and lactate in the gut increases cell growth, it also results in enhanced production of acetate and hydrogen sulfide which are toxic to humans (Gibson et al., 1993b; Marquet et al., 2009; Rey et al., 2013).

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Culture technique allows detecting the SRB growth in the presence of E. coli BP in low dilutions comparing with the growth of SRB only. This suggests an underestimation of SRB growth in agreement with other reports (Gibson et al., 1987; Levine et al., 1998; Jørgensen, 2009). Using the dsrA-apsA TaqMan qPCR the number of SRB cells was estimated and that was in a good correlation with the data of culture techniques. The inclusion of E. coli BP in the culture medium for these SRB allows a faster identification of their presence.

3.4 Genome Sizes of D. indonesiensis estimated by Pulsed-Field Gel Electrophoresis

The genome size of D. indonesiensis was estimated by Pulsed-Field Gel Electrophoresis (PGFE). The size was determined from the estimation of the migration of the full-length bands relative to the sizes of DNA standard fragments. PFGE is an analytical tool employed in estimating the actual genome size of bacteria; this method separates the bacteria DNA into several chromosomes of various sizes that can be measured (Römling et al., 1992; Boyle et al 1993; Devereux et al., 1997; Klaassen et al., 2002; Fernández- Cuenca, 2004). Interestingly, PFGE resolves DNA fragments into several megabases between 0.6 and 10 Mb thus making it easier to evaluate and also map out the bacterial genomes physically (Römling et al., 1992).

In the past researchers have been able to use PFGE to identify other family members of a bacterium genus. When accurately interpreted these data and structure could be used in genetic engineering, drug and vaccine discovery and other areas of Molecular Biology and Biotechnology (Kullen & Klaenhammer 2000; Klaassen et al., 2002; Fernández- Cuenca, 2004). It is however worth noting that this method may not be effective in resolving some bacterial chromosomes and in some instances the DNA degrades (Kato et al 1996; Izumiya et al 1997; Liesegang & Tschäpe, 2002). To solve this problem some workers recommended the use of thiourea which has the ability to neutralize the nucleolytic peracid derivative of Tris buffer produced at the anode during electrophoresis, thereby preventing DNA degradation but this method was not effective for all bacterial species (Ray et al., 1995; Silbert et al., 2003). However, over the years the use of Restriction endonuclease was discovered and tested. This involves cleaving

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CHAPTER 3: RESULTS AND DISCUSSION the bacterial chromosome with a restriction endonuclease that has a 6 bp or 8 bp recognition site (genome finger printing), resulting in about three to one hundred DNA fragments that are resolvable on the PFGE gel (Pfaller et al., 1992; Römling et al., 1992). Summation of these fragments allows an estimation of the genome size. For this section, the bacterial cultures used were D. indonesiensis and E. coli BP, as these were used in the detection studies.

Figure 3.3.10 shows the PGFE gel with the following samples loaded in corresponding wells/lane as labelled below: Lanes (1) Schizosaccharomyces pombe ladder; (2) lambda ladder (3) D. indonesiensis DNA digested with PmeI; (4) D. indonesiensis DNA; (5) D. indonesiensis DNA digested with NotI; (6) Saccharomyces cerevisiae ladder size range; (7) E.coli DNA digested with PmeI; (8) E.coli DNA; (9) E.coli DNA digested with NotI.

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Figure 3.3.10. Pulsed-field gel sizing of SRB genomes from (a) D. indonesiensis and (b) E.coli: Lanes (1) Schizosaccharomyces pombe size range 3.5-5.7 Mb; (2) lambda ladder size range 0.05–1 Mb ;(3) D. indonesiensis digested – PmeI; (4) D. indonesiensis ; (5) D. indonesiensis digested-NotI; (6) Saccharomyces cerevisiae ladder size range 240-2200 Kb; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI;(10) D. indonesiensis. Sizes of selected DNA standards (BIO-RAD DNA Size Markers–Yeast Chromosomal; BIO- RAD DNA Size Markers lambda and BIO-RAD DNA Size Markers–S. pombe Chromosomal DNA) are indicated.

Table 3.3.11 below showed the estimation of the total genome observed. E.coli was used as control in this experiment largely because it has a known genomic size (Lukjancenko et al., 2010).

Table 3.3.11. Estimated gel size Measurement of the genomes from (a) D. indonesiensis and (b) E.coli: (3) D. indonesiensis digested – PmeI; (5) D. indonesiensis digested-NotI; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI.

Lane 3 5 8 9

Fragments 392 181 2100 2150

512 1673 1600 1600

1965 1695 405 378

Total (Mb) 2869 3549 4105 4128

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DNA degradation was observed when samples were analyzed by electrophoresis without restriction digestion, as seen in the D. indonesiensis genomic DNA (lane 4 above). The reason for the degradation was unclear but this may probably be due the large size of the genome or to the nucleolytic peracid derivatives formed by Tris buffer at the anode during electrophoresis thus, supporting observations from other research workers (Ray et al., 1995). The restriction enzymes PmeI and NotI were selected and used individually to digest genomic DNA from SRB and E.coli and the resultant fragments were separated by PFGE (Figure 3.3.10). The electrophoretic profiles and genome fingerprinting results above showed three DNA fragments on Lane 3- SRB digested – PmeI of a total genome size ~2.5 Mb (392 kb, 512 kb and 1965 kb respectively). Lane 5- SRB digested-NotI resulted in a total genome size of ~3.5 Mb (181 kb, 1673 kb and 1695 kb respectively), Lane 8- E. coli resulted in a total genome size of ~4.1 Mb (2100 kb, 1600 kb and 405 kb respectively) while Lane 9- E.coli digested – NotI resulted in a total genome size of ~4.1 Mb (2150 kb, 1600 kb and 378 kb respectively). From the results of this study, the D. indonesiensis estimated genome size obtained above showed a very close similarity to those obtained in previous research work where the genome sizes of Desulfovibrio desulfuricans, Desulfovibrio vulgaris, and Desulfobulbus propionicus were estimated to be 3.1, 3.6, and 3.7 Mb, respectively (Devereux et al., 1997).

The results obtained in this section thus indicates that Pulse Field Gel Electrophoresis accurately estimates the genome size of bacteria after the DNA has first been digested with restriction enzymes; with the restriction enzymes preventing DNA degradation during electropghoresis (Pfaller et al., 1992; Römling et al., 1992; Boyle et al 1993; Devereux et al., 1997; Klaassen et al., 2002; Fernández-Cuenca, 2004).

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Section 4 The effect of antibiotics and Manuka honey on the growth of sulfate reducing bacterial biofilm

A biofilm is a microbial community formed within a complex matrix where bacteria and other microbes co-exist and support the growth of each other (Costerton et al., 1995; Jackson et al., 2002a). In adapting to their way of life, these bacteria have developed intricate methods of growing and surviving by attaching themselves to and inhabiting solid surfaces (Costerton et al., 1995, 1999; Jackson et al., 2002 a,b). Biofilms protects bacteria from damaging circumstances, the effect of which is widespread universally through all environments allowing biofilms to escalate bacterial infections making them difficult to treat (Römling & Balsalobre, 2012). Previous reports have highlighted the difficulty in treating chronic and difficult-to-treat infections caused by the bacterial inhabitants of the colon; a source of concern here is related to one of the advantages of that biofilms provide their ability to evade attacks from the human immune system (Dongari-Bagtzoglou, 2008; Macfarlane et al., 2011; Willem, 2015). Antimicrobial efficiency of Manuka honey which is produced from manuka tree (Leptospermum scoparium) in New Zealand is known to supersede other honey and its effectiveness is attributed to a joint action of methylglyoxal (MGO) and other compounds which give it an extra antiseptic and therapeutic property against several antibiotic resistance microorganisms. The MGO content of Manuka honey is used to define the antimicrobial activity of this honey and is referred to as the unique Manuka Honey Factor (UMF®) (Molan & Russell, 1988; Mavric et al., 2008; Maddocks et al., 2012). In the previous sections, it was observed that UC was associated with a change in bacterial diversity favoring members of the Enterbacteriaceae and the Desulfovibrio. Furthermore, it was demonstrated that a relationship between E. coli and Desulfovibrio indonesiensis existed, where co-culturing the SRB with E. coli enhanced its growth. Therefore, does the use of manuka honey in combination with antibiotics can help in controlling the bacteria involved in UC ? In this section Manuka honey with different UMF number was compared with the commercial honey for their efficacy against E. coli BP and D. indonesiensis microbes both of which are associated with UC. Effect of honey against pure and mixed of these

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CHAPTER 3: RESULTS AND DISCUSSION bacteria was examined on the biofilm formation as well as disruption of the existing biofilms. The effect of two commonly used antibiotics for UC rifaximin and metronidazole was also examined in the presence and absence of honey.

3.4.1 The MIC and MBC values of different Manuka honey on D. indonesiensis in pure culture and mixed culture with E. coli BP

The growth characteristics of D. indonesiensis and E. coli BP before treatment with honey and antibiotics were determined. The growth of E. coli for all experiments ranged between 1.95X107 to 2X108 cells/ml after 18 hours incubation at 37C; while the growth for D. indonesiensis was 3.5X105 to 7X106 cells/ml after 6 days incubation at 37 C. Minimum inhibitory concentrations (MIC values) and minimum bactericidal concentration (MBC values) were determined for D. indonesiensis biofilms cultured with and without E.coli BP when treated with different concentrations of Manuka honey (UMF® 5+, UMF® 15+ and UMF® 25+) , artificial honey and commercial honey. Here, the honey was added simultaneously with the D. indonesiensis biofilm with and without E. coli; and incubated for 7 days after which the inhibitory effect of honey on biofilm formation was examined. Although artificial honey has the same amount of sugars presented in the honey; it exhibited no effect on the growth of pure and mixed D. indonesiensis biofilms with E. coli BP. This result clearly suggested that sugar content is not the only antibacterial factor however there might be other mechanisms than the high osmotic pressure are involved in the inhibition of bacterial growth (Nassar et al., 2012). The data shown in Figure 3.4.1 gives the mean MIC values for D. indonesiensis, in single and mixed culture, when incubated simulataneously with 4 different honeys, Manuka UMF®5+, Manuka UMF®15+, Manuka UMF®25+ and commercial honey. Differences in MIC values exist between treatments and between single culture or mixed biofilms.

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Significant differences between treatments were observed when assessed via Kruskal- Wallis test with Dunn’s multiple comparisons (Table 3.4.1) for MIC values for the SRB when in single or mixed culture. The differences between the mean MIC values when the SRB in single or mixed culture was treated with different grades of Manuka honey showed some inconsistencies. The mean MIC value for all of the treatments increased when D. indonesiensis was cultured with E. coli, however none of these differences were significant according (Table 3.4.1). Successive treatments with higher grades of Manuka honey, whether the SRB was in single or mixed culture, resulted in lower mean MIC values although significance not detected indicating some differences in susceptibility to Manuka honey (Table 3.4.1). However, a significant effect was observed when comparing the UMF®15+ and UMF®25+ single biofilms to the commercial honey single and mixed biofilms (UMF®15 vs CH P value of 0.009, UMF®15 vs CH Mix value of 0.003 & UMF®25+ vs CH value of 0.005, UMF® 25+ vs CH Mix value of 0.002).

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Similarly, no significant difference between the means was found for the SRB in single or mixed culture when the treatments were for Manuka UMF®15+ and UMF®25+ (MIC values from 14% to 12% respectively). However, when the mean MIC values for UMF® 5+ and UMF® 25+ treatments were compared there was a significant difference between these two concentrations (UMF®25+ had a lower MIC value) indicating that both cultures had a greater susceptible to higher grades of Manuka honey. The mean MIC value for all of the treatments increased when D. indonesiensis was cultured with E. coli, however not all of these differences were significant according to Kruskal-Wallis test statistics (Table 3.4.1). Comparison of the means for the mixed and single culture biofilms treated with commercial honey (42% and 46% respectively) were not significantly different at P = > 0.9999. Similarly, the difference between the mean MIC values was not significant for D. indonesiensis in either biofilm when treated with Manuka UMF®5, UMF®15+ and UMF®25 (Table 3.4.1).

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Table 3.4.1: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Only comparisons which reached significance are shown. Asterisks indicate significant differences (** P ≤ 0.01).

Comparison Significant p-value UMF®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999 UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No > 0.9999 UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No 0.6890 UMF®5+ vs. CH mix No 0.3041 UMF®5+ mix vs. UMF®15+ No 0.3980 UMF®5+ mix vs. UMF®15+ mix No > 0.9999 UMF®5+ mix vs. UMF®25+ No 0.2703 UMF®5+ mix vs. UMF®25+ mix No > 0.9999 UMF®5+ mix vs. CH No > 0.9999 UMF®5 mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999 UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999 UMF®15+ vs. CH Yes 0.0090 (**) UMF®15+ vs. CH mix Yes 0.0027 (**) UMF®15+ mix vs. UMF®25+ No > 0.9999 UMF®15+ mix vs. UMF®25+ mix No > 0.9999 UMF®15+ mix vs. CH No 0.4291 UMF®15+ mix vs. CH mix No 0.1805 UMF®25+ vs. UMF®25+ mix No > 0.9999 UMF®25+ vs. CH Yes 0.0053 (**) UMF®25+ vs. CH mix Yes 0.0015 (**) UMF®25+ mix vs. CH No 0.5974 UMF®25+ mix vs. CH mix No 0.2598 CH vs. CH mix No > 0.9999

In these situations, the addition of E. coli to the biofilm appears to have increased the resistance of D. indonesiensis to the inhibitory effects of the Manuka honey. A number of possibilities could explain these results. The apparent reduction in susceptibility of the SRB cells to the antimicrobial component of honey could have happened because their contact with it had been reduced. In the mixed culture, a greater number of E. coli cells would have been present to interact with the antimicrobial agent, preventing interaction with the SRB cells. E. coli cells in culture are sensitive to the active ingredient in Manuka honey, perhaps more so than other bacteria, and they are particularly sensitive to the higher grades of honey (Badawy et al., 2004; Adebolu, 2005; Wilkinson & Cavanagh, 2005; Mandal & Mandal, 2011). Another possibility is that the

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E.coli biofilm provides protection against the uptake of the antimicrobial agent. It has been reported that these biofilms exhibit a greater resistance to antibiotics because E.coli changes its expression of the rpoS gene, which codes for one of the sigma factors (sigma 38) of RNA polymerase. This controls stationary phase genes and protects the cell against stress. Genes activated at this time include sodC – the gene that codes for superoxide dismutase, and gor – glutathione reductase (Corona-Izquierdo & Membrillo- Herna’ndez 2002; Schembri et al., 2003; Ito et al 2009). Presumably the reduction in activity against D. indonesiensis results from some degree of protection afforded by the presence of another bacterium. In this respect it can be considered that the difference in the inhibitory effect is due to complementary activity of the two species taken together (Cooper et al., 1999). The mean MBC values for D. indonesiensis biofilms cultured with and without E.coli when treated with Manuka honey of different grades and commercial honey, which was added simultaneously with the inoculum and cultured for 7 days, are shown in the histogram in Figure 3.4.2.

Figure 3.4.2. The MBC values for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added simultaneously with culture and then incubated for 7 days) when treated with Manuka honey UMF®5+, UMF®15+, UMF®25+ and commercial honey. The mean MBC values were calculated from 5 independent repeat experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).

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The pattern for the MBC values was similar to the one observed for MIC values, although the values were marginally greater, which was expected. No significant difference was observed within or between Manuka honey treatments with the exception of UMF®5 mixed SRB and E. coli culture compared to UMF® 25+ pure SRB (P = 0.021) ;UMF® 15+ compared to commercial honey on pure SRB culture (P =0.0211); UMF® 15+ compared with commercial honey on mixed culture (P =0.0017); UMF® 25+ with compared with commercial pure SRB culture (P=0.0035) and UMF® 25+ pure SRB culture with compared with commercial honey (P=0.0002) (Table 3.4.2).

Table 3.4.2: A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001). Comparison Significant p-value UMF®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999 UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No 0.8417 UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999 UMF®5+ vs. CH mix No 0.5915 UMF®5+ mix vs. UMF®15+ No 0.1025 UMF®5+ mix vs. UMF®15+ mix No > 0.9999 UMF®5+ mix vs. UMF®25+ Yes 0.0211 (*) UMF®5+ mix vs. UMF®25+ mix No > 0.9999 UMF®5+ mix vs. CH No > 0.9999 UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999 UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999 UMF®15+ vs. CH Yes 0.0211 (*) UMF®15+ vs. CH mix Yes 0.0017 (**) UMF®15+ mix vs. UMF®25+ No > 0.9999 UMF®15+ mix vs. UMF®25+ mix No > 0.9999 UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No 0.2369 UMF®25+ vs. UMF 25 mix No > 0.9999 UMF®25+ vs. CH Yes 0.0035 (**) UMF®25+ vs. CH mix Yes 0.0002 (***) UMF®25+ mix vs. CH No 0.7854 UMF®25+ mix vs. CH mix No 0.1272 CH vs. CH mix No > 0.9999

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The mean MBC values for D. indonesiensis present in biofilms varied when it was present alone or with E.coli. In all cases the MBC values were greater when the SRB was in a mixed biofilm although this effect never reached significance (Table 3.4.2). When the grade of Manuka honey was increased to UMF® 15+, the mean MBC value was reduced compared to UMF®5+ for both cultures (Figure 3.4.2); with the pure D. indonesiensis biofilm having 16 % MBC mean value and the D. indonesiensis with E. coli BP mixed having 23 % MBC mean value although the difference was not significant according to Kruskal-Wallis test statistics (Table 3.4.2), this suggests that increase in concentration of honey increases the susceptibility of both cultures but the inclusion of E. coli further emphasizes the resistance of D. indonesiensis to the treatments. Treatment with a higher UMF® 25+ Manuka honey resulted in a much lower MBC mean value when compared to UMF® 5+ and UMF® 15+ (Figure 3.4.2); which was statistically significant between the single UMF®25+ and the mixed UMF®5+ biofilm cultures (Table 3.4.2) according to Kruskal-Wallis test statistics. Treatment of the D. indonesiensis biofilm resulted in a 13.8 % mean value and the mixed culture showed a 21.4 % mean value, suggesting that the MBC value is dependent on the concentration of the Manuka honey where the higher the grades of the honey results in a lower MBC value. The inclusion of E. coli BP enhanced the resistance of D. indonesiensis to these treatments. On the other hand, treatment with the commercial honey resulted in an increased MBC mean value for both cultures when compared to their mean values obtained when Manuka honey were used. The mean MBC values for D. indonesiensis alone or with E. coli BP, were 45% and 53% respectively, with the difference between these not being statistically significant (Table 3.4.2); this data also showed that the addition of E. coli BP decreased the sensitivity of the mixed biofilm to honey thus, the slightly higher MBC values of D. indonesiensis with E. coli BP when compared to the pure D. indonesiensis. In order to determine whether Manuka honey could disrupt established biofilms, the honey was added to preformed biofilms before MIC and MBC values were estimated. D. indonesiensis biofilms and D. indonesiensis with E. coli biofilms were formed after 6 days growth, when various honey treatments (Manuka UMF ®5+, Manuka UMF®15+, Manuka UMF®25+ and commercial honey) were applied to the biofilms followed by overnight incubation. The mean MIC values for D. indonesiensis were estimated for each treatment (Figure 3.4.3). The pattern observed was similar to the one obtained when honey was,

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CHAPTER 3: RESULTS AND DISCUSSION added to the growing cultures, except that the mean MIC values were greater. The mean MIC value decreases with the different Manuka honey treatments, with the lowest values being observed for the highest-grade honey applied to the SRB biofilm.

Figure 3.4.3. The MIC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ and commercial honey for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added after incubating the culture for 6 days). The mean values were calculated form 5 independent experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).

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Table 3.4.3: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (** P ≤ 0.01). Comparison Significant p-value UMF®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999 UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No 0.8016 UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999 UMF®5+ vs. CH mix No > 0.9999 UMF®5+ mix vs. UMF®15+ No 0.0767 UMF®5+ mix vs. UMF®15+ mix No > 0.9999 UMF®5+ mix vs. UMF®25+ Yes 0.0019 (**) UMF®5+ mix vs. UMF®25+ mix No 0.1743 UMF®5+ mix vs. CH No > 0.9999 UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999 UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999 UMF®15+ vs. CH No > 0.9999 UMF®15+ vs. CH mix No 0.0767 UMF®15+ mix vs. UMF®25+ No 0.4172 UMF®15+ mix vs. UMF®25+ mix No > 0.9999 UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No > 0.9999 UMF®25+ vs. UMF®25+ mix No > 0.9999 UMF®25+ vs. CH No 0.0701 UMF®25+ vs. CH mix Yes 0.0019 (**) UMF®25+ mix vs. CH No > 0.9999 UMF®25+ mix vs. CH mix No 0.1743 CH vs. CH mix No > 0.9999

Table 3.4.3 showed a comparison of the different treatments used in this experiment for both biofilms according to Kruskal-Wallis test statistics. The MIC mean values of D. indonesiensis alone were only significant for UMF®5+ mixed compared to UMF®25+ single (P= 0.002) although a trend of decreasing MIC with increasing Manuka concentration as observed. The mean MIC values for the SRB alone at UMF®25+ are significantly different compared to the values obtained for commercial honey with a mixed biofilm, with the Manuka honey producing the lower values (P = 0.002). In other words, the only successful treatment of the SRB in mixed culture was with the highest grade of Manuka honey, where the mean MIC was 37%.

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This contrast with a previous study where the reported percentage of honey needed to completely prevent the growth of Proteus mirabilis and E. coli in biofilms was between 6-6.5 % v/v (Willix et al. 1992; Ahmed et al. 2014). This concentration was lower than that obtained in this study for the tested honeys. These differences probably due to the different of antibacterial activity of honey of different batch or the differences in the tested bacteria and tested methods.

Figure 3.4.4. The MBC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ Manuka honey and commercial honey for E. coli BP and D. indonesiensis grown as pure and mixed population (the honey was added after incubated the culture for 6 days). The mean values were calculated from 5 independent experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05)

The mean MBC values for D. indonesiensis in pure and mixed pre-formed biofilms when treated with different grades of honey are shown in Figure 3.4.4. In general, the mean values have increased from those observed when the honey was included with the growing culture (Figure 3.4.2), and they were greater than the mean MIC values (Figure 3.4.3). The pattern observed was similar to the previous susceptibility results, where greater mean MBC values were observed for the SRB in mixed culture and decreasing mean MBC values recorded for the higher grades of Manuka honey (values of 47%, 37%

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CHAPTER 3: RESULTS AND DISCUSSION and 37% for pure culture biofilms treated with Manuka UMF®5+, UMF®15+ and UMF®25+ respectively, and 57%, 45% and 41% for mixed cultures treated with Manuka UMF®5+, UMF®15+ and UMF®25+ respectively). The greatest mean MBC values (52% and 57% for the SRB in pure and mixed culture respectively) were for the cells in biofilms treated with commercial honey.

Table 3.4.4. A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05). Comparison Significant p-value UMF ®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999 UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No > 0.9999 UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999 UMF®5+ vs. CH mix No > 0.9999 UMF®5+ mix vs. UMF®15+ Yes 0.0156 (*) UMF®5+ mix vs. UMF®15+ mix No 0.9377 UMF®5+ mix vs. UMF®25+ Yes 0.0103 (*) UMF®5+ mix vs. UMF®25+ mix No 0.1047 UMF®5+ mix vs. CH No > 0.9999 UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999 UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999 UMF®15+ vs. CH No 0.3626 UMF®15+ vs. CH mix Yes 0.0156 (*) UMF®15+ mix vs. UMF®25+ No > 0.9999 UMF®15+ mix vs. UMF®25+ mix No > 0.9999 UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No 0.9377 UMF®25+ vs. UMF®25+ mix No > 0.9999 UMF®25+ vs. CH No 0.2644 UMF®25+ vs. CH mix Yes 0.0103 (*) UMF®25+ mix vs. CH No > 0.9999 UMF®25+ mix vs. CH mix No 0.1047 CH vs. CH mix No > 0.9999

Comparison of the mean MBC values for D. indonesiensis treated with the different honeys using the Kruskal-Wallis test statistic (Table 3.4.4) revealed differences with the various treatments. The trends were the same for the single culture and mixed culture biofilms. Significant differences between the means were observed for the Manuka

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UMF®5+ on mix SRB culture and UMF®15+ on pure SRB (P = 0.016) and UMF®25+ (P = 0.010), and the UMF®15+ and UMF®25+ on pure SRB culture compared with the commercial honey on mixed SRB biofilms (P = 0.0156 and 0.010 respectively) (Table 3.4.4) The susceptibility of D. indoneseisis to Manuka honey when present in an existing biofilm, pure or mixed, was less than when the honey was added to the cells prior to the establishment of the biofilm. The mean MIC and MBC values were greater for every treatment, although there was still greater susceptibility when the higher grades of honey were used. Presumably the increase in MIC and MBC mean values reflects that greater permeability barriers exist when the biofilm is established, and that higher concentrations of honey are needed to disrupt the biofilm. Previous studies have shown honey’s ability to prevent the formation as well as elimination of established biofilms (Cooper et al., 2010; Lu et al., 2014). In agreement with previous studies the results obtained from this investigation showed that the effective concentration of honey needed to prevent biofilm formation is lower than the concentration required to disrupt an established biofilm (Allen et al., 1991; Probert & Gibson, 2002; Lin et al., 2011). Manuka UMF®25+ was more effective at interrupting the growth and survival of matured biofilm when compared to other concentrations of honey tested. This is result agrees with previous studies by Sherlock et al (2010), who showed in their study that the biofilm formation of E.coli is inhibited by Manuka honey UMF®25+ at concentrations ≥ 12.5%.

3.4.2 The susceptibility of D. indonesiensis in pure culture and mixed culture with E. coli BP when treated with Manuka honey and antibiotics

The susceptibility of D. indonesiensis to Manuka honey supplemented with antibiotics was assessed to explore whether application of the honey improves the prevention of SRB biofilm establishment or disruption. The antibiotics chosen for study were rifaxmin and metronidazole, both of which are frequently used in the treatment of UC (Guslandi, 2011; Miner et al., 2005). Rifaxmin is a semi-synthetic antibiotic that binds to the Beta sub-unit of RNA polymerase, preventing transcription. It is used as a treatment against traveller’s diarrhoea, caused by E. coli, irritable bowel syndrome and hepatic encephalopathy. Metronidazole is a nitroimidazole that inhibits DNA synthesis by

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CHAPTER 3: RESULTS AND DISCUSSION disrupting DNA. This only happens when the antibiotic is reduced, which takes place in anaerobic cells. It, therefore, has little effect against human cells or aerobic bacteria. The susceptibility of the SRB was tested against these antibiotics given singularly and in mixtures with and without Manuka UMF®15+ honey. The MIC values for D. indonesiensis in pure and mixed cultures after they had been treated with the antibiotics rifaximin, metronidazole, and combinations of these with and without honey (Figure 3.4.5). The greatest effect of these antibiotics was on the pure cultures of D. indonesiensis when they were given individually or in combination, and when the honey was added, either at the beginning of growth or after 6 days. The greatest mean MIC value was 230.4 µg/ml, which was obtained when the SRB was in mixed culture treated with either rifaxmin, metronidole or a combination of both. The mean MIC values dropped considerably to 57.6, 22.4 and 22.4 µg/ml respectively when treated with rifaxmin, metronidole and a combination of both. In this study these antibiotics were combined in order to reduce the occurrence of antibiotic resistance to rifaximin and metronidazole. Combination of antibiotics agents used in this way form a synergy that increases the antimicrobial properties. The increased sensitivities to the antibiotics provide the best way to eradicate both test bacteria, which are involved in the etiology and pathogenesis of UC (Burke, 1997; Ohge et al., 2003; Rowan et al., 2009; Leibovici et al., 2010; Mirsepasi-Lauridsen et al., 2016). However, what was observed is that these two bacteria were able to increases their chance of survival at high concentrations of both antibiotics even in combination when were grown together.

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Figure 3.4.5. The MIC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics and honey were added simultaneously to the cultures), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics were added after 6 days incubation with Manuka UMF® 15+). Mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).

According to Kruskal-Wallis test statistics (Table 3.4.5), although a large difference existed between the mean MIC values for D. indonesiensis in pure culture than mixed with E. coli BP when they were grown simultaneously with rifaximin or metronidazole alone and in combination, these values were not significant. The pure D. indonesiensis culture was sensitive to these antibiotics while the mixed culture showed high resistance probably because the co-culturing both bacteria enhances resistance via a number of mechanisms (Cooper et al., 1999; Wilkinson & Cavanagh, 2005; Mittal et al., 2015). These include speed with which they develop extracellular polymeric matrix as well as altered activity of metabolic enzymes and the growth rate leading to a shift from the TCA cycle to fermentation-dervied energy acquisition, endogenous oxidative stress and their ability to transfer genes which enables them to develop resistance to the therapeutic agents (Bradshaw et al., 1996; Shimizu, 2014). When D. indonesiensis and cultures of D. indonesiensis and E. coli were treated with rifaximin, metronidazole and Manuka honey 15+, added simultaneously to the culture, the difference between the mean MIC value for the SRB in the two cultures were again different but not significantly so according to the Kruskal-Wallis test statistics (Table

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3.4.5). The mean MIC value was lowest when D. indonesiensis was in pure culture, with the smallest value being 14.4µg/ml. Table 3.4.5. A comparison of the MIC values obtained when different concentrations antibiotics and UMF®15+ were applied Kruskal-Wallis test statistics. Asterisks indicate significant differences (** P ≤ 0.01; *** P ≤ 0.001).

Treatment Significant p-value Rif. vs. Rif. mix No > 0.9999 Rif. vs. Met. No > 0.9999 Rif. vs. Met. mix No > 0.9999 Rif. vs. Rif. + Met. No > 0.9999 Rif. vs. Rif. + Met. mix No > 0.9999 Rif. vs. Rif. + Met. + UMF®15+ No > 0.9999 Rif. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.5913 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Met. No 0.1223 Rif. mix vs. Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. No 0.1223 Rif. mix vs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**) Rif. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Met. vs. Met. mix No 0.1223 Met. vs. Rif. + Met. No > 0.9999 Met. vs. Rif. + Met. mix No 0.1223 Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Met. mix vs. Rif. + Met. No 0.1223 Met. mix vs. Rif. + Met. mix No > 0.9999 Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**) Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. vs. Rif. + Met. mix No 0.1223 Rif. + Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Rif. + Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**) Rif. + Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. + UMF®15 vs. Rif. + Met. + UMF®15 mix No > 0.9999

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Treatment Significant p-value Rif. + Met. + UMF®15 vs. UMF®15 treated cells with Subs. Rif. + No > 0.9999 Met. Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. + No > 0.9999 Met. mix Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. No 0.0947 Rif. + Met. Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. No > 0.9999 Rif. + Met. mix UMF®15+ treated cells with Subs. Rif. + Met. vs. UMF®15+ treated No 0.8500 cells with Subs. Rif. + Met. mix

Differences in mean MIC values between the treatments were observed that were statistically significant according to Kruskal-Wallis tests (Table 3.4.5). Considering the susceptibility of the SRB to the antibiotics alone, the mean MIC value recorded was 22.4 µg/ml for metronidazole, which was not significantly different to the mean MIC value of 57.6 µg/ml for rifaximin. Similarly, when the two antibiotics were combined in a treatment, the mean MIC value (22.4 µg/ml) was not significantly different to that for rifaximin alone or metronidazole alone. The mean MIC value for D. indonesiensis fell to 14.4 µg/ml when Manuka UMF®15 honey was included with the dual antibiotic treatment of the pure biofilm, however this was not significantly different to the metronidazole or rifaximin and metronidazole treatments. The mean MIC values for D. indonesiensis changed considerably when the biofilm contained E. coli as well as the SRB. The value, 230.4 µg/ml, did not change for treatments with rifaximin, metronidazole or the mixture, but dropped significantly to 76.8 µg/ml for the treatment with the honey (Table 3.4.5). Interestingly, the addition of honey influenced the MIC value for the SRB in mixed culture only. Increased sensitivity of D. indonesiensis to the honey and antibiotic treatment was achieved by prolonged incubation of the SRB, grown with E.coli, with Manuka UMF®15 honey for 6 days before the addition of rifaximin and metronidazole. For the pure culture biofilm, the mean MIC value for the SRB decreased to 4.8 µg/ml, whereas the mean value dropped to 44.8 µg/ml for the mixed biofilm. This reduction for single cultures was significant compared to previous treatments with rifaximin and metronidazole in mixed cultures (Table 3.4.5). It is feasible, under these circumstances, that the honey is preventing the formation and establishment of the biofilm which, in

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CHAPTER 3: RESULTS AND DISCUSSION turn, reduces the protection it provides, and increases the sensitivity of the cells to the antibiotics, when they are applied.

Figure 3.4.6. The MBC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding the antibiotics and honey simultaneously), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding antibiotics after 6 days of incubation with Manuka UMF® 15+). The mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (** P ≤ 0.01).

The mean MBC values for D. indonesiensis in pure or mixed cultures with E. coli treated with the antibiotics, singularly and in combination, with and without Manuka UMF®15+ honey are shown in Figure 3.4.6. The overall pattern observed was similar to the one described for mean MIC values. The mean values when the SRB biofilms were treated with rifaximin and metronidazole were 51.2 and 70.4 µg/ml, which were not significantly different according to Kruskal-Wallis test statistics (Table 3.4.6), while the value when the antibiotics were used in combination was 57.6 µg/ml, which was also not significantly different to the ones obtained for the rifaximin or metronidazole treatments. The MBC values decreased to 14.4 and 7.2 µg/ml when the combined antibiotic treatment included Manuka honey UMF®15+ and when the cells were treated with Manuka UMF®15+ honey prior to combined antibiotic treatment. Both of these treatments produced mean MBC values that were significantly different to the values obtained with the other treatments (Table 3.4.6).

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Table 3.4.6. A comparison of the MBC values obtained when different concentrations antibiotics and UMF®15+ were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001).

Treatment Significant p-value Rif. vs. Rif. mix No > 0.9999 Rif. vs. Met. No > 0.9999 Rif. vs. Met. mix No 0.6438 Rif. vs. Rif. + Met. No > 0.9999 Rif. vs. Rif. + Met. mix No 0.6438 Rif. vs. Rif. + Met. + UMF®15+ No > 0.9999 Rif. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Met. No > 0.9999 Rif. mix vs. Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. No > 0.9999 Rif. mix vs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. + UMF®15+ Yes 0.0112 (*) Rif. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0017 (**) Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.6844 Met. vs. Met. mix No > 0.9999 Met. vs. Rif. + Met. No > 0.9999 Met. vs. Rif. + Met. mix No > 0.9999 Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.8698 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Met. mix vs. Rif. + Met. No 0.6638 Met. mix vs. Rif. + Met. mix No > 0.9999 Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0044 (**) Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 ® Met. mix vs. UMF 15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.3526 Rif. + Met. vs. Rif. + Met. mix No 0.6638 Rif. + Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Rif. + Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0044 (**) Rif. + Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.3526 Rif. + Met. + UMF®15+ vs. Rif. + Met. + UMF®15+ mix No 0.2704 Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999

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Treatment Significant p-value Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.0596 Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 UMF®15+ treated cells with Subs. Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999

The mean MBC values increased when the treatments were against mixed culture biofilms of D. indonesiensis and E. coli. When rifaximin was used the mean value was 204.8 µg/ml, which increased to 230.4 µg/ml when metronidazole was used. When the antibiotics were used in combination the mean MBC value was 230.4 µg/ml. All of these values were not significantly different according to Kruskal-Wallis test statistics (Table 3.4.6). The mean values dropped to 115.2 µg/ml when Manuka honey UMF®15+ was included in the treatment, and to 44.8 µg/ml when the cells were treated with Manuka honey UMF®15+ before antibiotic treatment. The differences between the mean MBC values for the SRB in pure or mixed culture biofilms were not significantly different when each treatment was compared (Table 3.4.6). However, there was a trend of the mixed biofilms having greater MIC and MBC mean values than those for D. indonesiensis alone, indicating that the SRB strain was less susceptible to the antibiotics and antibiotics/Manuka honey combinations when in mixed culture.

A similar pattern of significance, as described for figure 3.4.5, when comparing mixed cultures with rifaximin and metronidazole to single cultures with honey was observed. With a significant reduction for all values observed following the addition of Manuka honey. The lowest MBC values were obtained when Manuka honey UMF®15+ was used to treat the cells in either the pure or mixed biofilm before antibiotic treatment. Desulfovibrio species and E. coli have been reported to be sensitive to rifaximin and metronidazole (Huang & DuPont, 2005; Finegold et al., 2009; Löfmark et al., 2010), and this appears to be true for D. indonesiensis, however it also appears that the SRB becomes less susceptible to both antibiotics when it is co-cultured with E. coli. How this sensitivity is altered is unknown, but there does appear to be a relationship between

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CHAPTER 3: RESULTS AND DISCUSSION these organisms as both grow together in the human colon and they have both been implicated in the pathogenesis of UC (Burke & Axon, 1987; Pitcher et al., 2000). E. coli strains have a known ability to increase the cell density of other organisms by producing a signalling factor (Scott, 2002). This signalling factor may be part of a quorum sensing system or the production of an auto-inducer for growth. If the growth of the SRB is stimulated by the presence of E. coli, it is possible that this could lead to the greater production of hydrogen sulfide, which might act against the antibiotics.

In order to determine if the inhibitory effect of Manuka honey was due to its antimicrobial agent, Methylglyoxal (MGO) (Adams et al., 2008; Mavric et al., 2008; Müller et al., 2013), rather than some other compound found in the honey, the effect of MGO on the pure and mixed cultures was examined.

Figure 3.4.7. The MIC value of Methylglyoxal (MGO) of D. indonesiensis as pure and mixed culture with E.coli BP. The mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (** P ≤ 0.01).

The mean MIC value for D. indonesiensis in pure culture was 0.08% v/v which increased to 0.19% v/v when the SRB was co-cultured with E. coli (Figure 3.4.7). This indicated that MGO had an antimicrobial effect on the pure and the mixed culture of D. indonesiensis. The difference in MIC level between the pure D. indonesiensis and D. indonesiensis co- cultured with E. coli BP was statistically significant according to Mann- Whitney test statistics (P = 0.008) (Table 3.4.7).

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Table 3.4.7. A comparison of the MIC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics. Asterisks indicate significant differences (** P ≤ 0.01).

Comparison Significant p-value MGO: SRB vs SRB + EC Yes 0.008 (**)

The pattern recorded here was similar to the one observed with Manuka honey, where a decrease in susceptibility occurred when the SRB was co-cultured with E. coli. This result thus agreed with previous research work showed that the antimicrobial activity of Manuka honey against bacterial cultures is due to the presence of the unique MGO as the active antimicrobial component in Manuka honey (Adams et al., 2008; Müller et al., 2013)

Figure 3.4.8. The MBC value of Methylglyoxal (MGO) of D. indonesiensis as pure and mixed culture with E.coli BP. Mean MBC values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05).

The mean MBC values of MGO for D. indonesiensis in pure and mixed culture were 0.14% v/v and 0.23% v/v respectively (Figure 3.4.8), mirroring the pattern observed for the mean MIC values of MGO and Manuka honey. The difference between the two values was significant according Mann-Whitney analysis (P = 0.047) (Table 3.4.8). The magnitude of change, however, was not as great as observed for the Manuka honey.

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Table 3.4.8. A comparison of the MBC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics. Asterisks indicate significant differences (* P ≤ 0.05).

Comparison Significant p-value MGO: SRB vs SRB + EC Yes 0.047 (*)

The present study showed that Manuka honey both inhibits as well as disrupts existing biofilms both in pure and mixed culture of Desulfovibrio with and without E. coli. The effectiveness of this increased with the higher UMF grade of Manuka honey. The protection of the SRB by E. coli was observed by a reduction in its susceptibility, recorded as increases in mean MIC and MBC values. The effectiveness of any treatment with the antibiotics rifaximin and metronidazole is enhanced if Manuka honey UMF®15+ is applied prior to their delivery. It was also shown that the believed active ingredient in Manuka honey, methylglyoxal, inhibited D. indonesiensis in a similar way observed by treatment with Manuka honey, suggesting that this is indeed the active component.

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The primary aims of the work described in this thesis were to compare and contrast the diversity of bacteria present in the colon of healthy people with that from patients suffering from ulcerative colitis with specific reference to SRBs, develop a system to enhance the detection of SRBs in natural systems, and to assess the susceptibility of Desulfovibrio indonesiensis, cultured alone and co-cultured with Escherichia coli, to Manuka honey.

The colon harbours a complex microbial ecosystem, reaching densities of 109- 1012 cfu/ml (Blaut & Clavel, 2007) that provides anaerobic fermentative of resistant starches, dietary fibres and unabsorbed sugars and alcohols, as well as proteins to generate short chain fatty acids, acetate, propionate, and butyrate as well as peptides and amino acids. Lactic acid, ethanol, succinic acid and formate are important intermediates in this breakdown. In addition to these compounds, branch chain fatty acids, ammonia, hydrogen sulfide, amines, phenols, indoles and mercaptanes are also formed. This requires the cooperative action of different microbial groups. It has been estimated that there are approximately 500 different species present in the colon, of which 75% are believed to be uncultrable (Duncan et al., 2007). Although great diversity of bacterial species and groups exists, dependent upon diet and health, approximately 60-80% of colonic or faecal bacteria belong to either the Cytophaga- Flavobacterium-Bacteroides (CFB) group or to the low GC Firmicutes. The remaining 20-25% is often represented by members of the Bacteriodetes and 3-5% by high GC content Actinomycetes. Proteobacteria usually form a minor component of the colon microflora.

In this study a number of molecular techniques were employed to assess the molecular diversity of bacteria from the colon of healthy people and UC suffers. These included PCR-DGGE, and the cloning and sequencing of 16S rRNA gene fragments, the dsr gene fragments from colon derived DNA. PCR/DGGE profiles were used initially as a rapid method to compare the bacterial diversity associated with UC patients and healthy individual samples and the result showed that there was a change in microbiota composition associated with the disease. The 16S rRNA sequences from the healthy person provided a broad diversity that included members from the Bacteriodaceae, Clostridiaceae, Coriobacteriaceae,

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Ruminococcaceae, Eggerthellaceae, Lactobacilaceae, Porphyromonadaceae, and the Enterobacteriaceae, with the greatest proportion being members of the Bacteriodaceae followed by the Clostridiaceae and Ruminococcaceae. The frequency of Enterobacteriaceae was approximately 12 %, which is greater than previously reported (Duncan, et al., 2007) but it is clear that this group is not one of the pre-dominant ones. This level of diversity is consistent with previous reports (Eckburg et al., 2005; Rajilić- Stojanović, 2007; Bik et al., 2010) and reflects the metabolic complexity expected of bacteria present in the colon. In contrast, the mucosal bacterial community of the colon from UC patients was not so diverse, with a total reduction in bacteria belonging to the Firmicutes and Bacteriodetes and an increase in the number belonging to the Enterobacteriaceae, in particular species of Citrobacter and Enterobacter.

It is tempting to speculate that the observed dysbiosis in the microflora from UC patients maybe characteristic, and could be used as an initial marker for diagnosing the UC disease, however not all studies report such a reduction. Hansen et al (2011) reported a reduction in alpha diversity of colonic bacteria for suffers of Crohn’s Disease (CD) but not for those with ulcerative colitis. In this study, 37 patients (13 with CD, 12 with UC and 12 healthy) had mucosal biopsies, from which DNA was extracted and sequenced for 16S rRNA gene spanning V3 to V6 regions. Sequences representing members of the Proteobacteria represented 10 % of the total for UC patients, which was higher than that for CD patients and healthy people but the proportion for Firmicutes and Bacterioidetes remained the same. In the present study biopsies from 4 patients were analysed, which is less than the number examined in Hansen et al (2011). It is possible that if a greater number of samples were analysed the recorded change in diversity might have been less. Lennon et al (2014) reported that the mucus gel layer in UC suffers is thinner than that of healthy people. This reduction of the layer is accompanied by an increase of bacteria that can degrade the gel, such as species of Rumminococcus, and the reduction in butyrate producing bacteria. The degradation of the mucus layer releases other compounds that support the growth of other bacteria, such as SRBs through the release of oxidised sulphur compounds.

The reduction in 16S rRNA molecular diversity in UC patients reported in the current study clearly showed an increase in the proportion of Enterobacteriaceae at the expense

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CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION of other bacteria commonly associated with a healthy functioning colon; however it did not show the presence of any SRB sequences. Sulfate reducing bacteria have been implicated in UC condition through the production of hydrogen sulphide (Pitcher & Cummings, 1996; Rowan et al., 2009). The presence of hydrogen sulfide in the colon (Jørgensen & Mortensen, 2001; Linden et al., 2010); Bacteria degradation of cysteine and methionine results in the formation of hydrogen sulfide, presence as HS- at neutral pH, and the build up of this in the colon is toxic, inhibiting butyrate oxidation. Hydrogen sulfide can also be generated by the oxidation of lactate, ethanol, succinate and H2 when sulfate is reduced by SRBs. Oxidised forms of sulfur are present in foods, and these can act as the substrate for sulfide production. The absence of 16S rRNA gene sequences for any SRB does not mean that they were not present in the biopsy samples, it is possible that they were present but at low densities. Under these conditions, bacteria present at greater cell densities would be always preferentially detected. In order to test whether SRB were present in the mucosa biopsies different target genes were chosen.

Dissimilatory sulphite reductase (DSR) is a key enzyme produced by SRBs, and the dsr AB genes make suitable targets for detection of these bacteria ( Dar et al., 2007). Few studies have been conducted to detect mucosa associated SRBs in the colon of UC patients (Zinkevich & Beech, 2000; Fite et al., 2004), most studies have limited the detection of SRBs to faeces. Although the targeting of dsr A gene, including real time PCR, to detection SRBs has been used in healthy human fecal (Christophersen et al., 2011), it has not been used to detect them in colonic mucosa of UC patients. A variety of morphologically and nutritionally different SRBs have been isolated from human faeces, which belong to 5 genera with Desulfovibrio being the pre-dominant genus (Gibson et al., 1988c). The recovery of dsr A gene sequences from the 4 biopsies of UC patients revealed a diverse range of Desulfovibrio species, including Desulfovibrio intestinalis, Desulfovibrio desulfuricans, Desulfovibrio burkinensis, Desulfovibrio carbinolicus, Desulfovibrio magneticus, but with limitations for other SRB genera.

The detection of Desulfovibrio species directly with mucosal biopsy samples agrees with the study of Loubinoux et al. (2002) where it was observed that the incident of SRBs (specifically Desulfovibrios) was higher in the faeces of IBD (CD and UC) patients than healthy indivuals. It is also in agreement with (Rowan et al., 2010) who used laser

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CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION capture microdisection and PCR to show that the Desulfovibrio increased in colonic mucosal biopsies from acute and chronic UC patients, It would appear that the mucosal layer undergoes a dysbiosis in UC patients where the proportion of Enterobacteriaceae and the number of Desulfovibrio species increases.

A possible link between SRB and colonic epithelial inflammation has been suggested, as a result of hydrogen sulphide production but with little supporting evidence (Rowan et al., 2009). Desulfovibrio sp are well documented as possible causative agent of UC and in some extreme cases they may cause clone cancer in the individual. This makes investigate the role of SRB important bacteria of interest. However, the growth of SRB is slow and making the early detection these bacteria at low cell density very difficult after their recovery from natural habitats (human colon) and therefore controlling these bacteria is important.

The detection of SRBs and their control was investigated by the development of a biochip to allow rapid screening and by identifying dietary additions for which SRBs have susceptibility. The challenging step in biochip design is the selection of probes with identical hybridisation characteristics. A novel cassette based methodology, which allowed for a rapid validate the de-novo designed probes and those which identified from the literature for fine-tuning of biochips, was used (Zinkevich et al., 2014) to assess the biochip. After probe evaluation, DNA from four UC biopsies and one healthy control were hybridised against the biochip to assess changes in bacterial population number and heterogeneity. It was not possible to identify a particular species or genera of bacteria that had a significant association with UC or confirm the role for SRB, although this may be due to the relatively small samples number of the study. However, several key trends were identified which may prove useful for further research. Possible variations in levels of Enterobacteriaceae were detected between UC patients and the control. Additionally, a potentially novel change in the relative amounts of D. piger and D. gigas were detected between UC patients and the control. Whilst, unable to identify a pathological mechanism in the literature this novel description requires further investigation. To summarize; whilst unable to identify a single species or genera of bacteria which underlies the eaitology of UC, this project has advanced the field and identified several possible strands of future development. Namely, more detailed

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CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION investigation into identifying particular Enterobacteriaceae species along with further investigation into the interaction between D. piger and D. gigas may prove informative. However, the precise role of SRB in UC remains unclear. Additionally, the novel, rapid biochip/cassette based screening method represents proof of concept study and should prove useful to expand the sample size of this research, for further validation to confirm the clinical usefulness of this test and also for other researchers for UC diagnosis before any test could be rolled out. As our understanding of the dysbioisis associated with UC expands, a methodology to rapidly optimize probes for simultaneous detection bacterial species and genera from UC patient samples will prove invaluable.

Previous observation have shown that there is substantial association between SRBs and E. coli in the gut (Unpublished), where SRB always seem to be associated with the presence of enteric bacteria, such as E. coli, in colonic mucosa tissue. In addition to this it has been observed that the colonic bacteria E.coli can induce the recovery and growth of SRBs from the colon at low cell density. Co-culturing of SRBs with E. coli might provide a method to enhance its growth so that early detection of these bacteria can be made. SRB may play an important role in the eitology of UC and the key to understanding the role of SRB in UC could be in determining the real quantity of SRB between UC and non UC patients. The results obtained in this study take a closer look at their relationship and further emphasizes the effect of E. coli on the aiding the growth and survival of SRBs and how they can exist together in the colon as model in vitro.

SRBs are well known to act co-operatively with other organisms as a result of their need to act synergistically when searching for exogenous electron acceptors (Walker et al., 2009). The nature of the relationship between E. coli and SRBs is believed to one where the enteric stimulates the growth of the SRB. This is not unusual, E.coli has been described to enhance growth of other organisms, such as Chorella (Higgins et al., 2015). Co-culturing of Desulfovibrio indonesiensis with Escherichia coli BP resulted in increased levels of detection of the SRB. The strain of E.coli, recovered from an UC biopsy sample, was able to promote the growth of the SRB from aged pre-cultures, even when the SRB was present in low densities. This growth study was supported by changes in the levels of dsrA and apsA transcripts in the cultures detected by qPCR.

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This observation is novel and has not been previously reported, and the co-culturing appears to give a novel way to detect SRBs.

The control of SRBs can be achieved by treatment with antibiotics. However the mucosa associated bacteria are able to form biofilms which increases their chance of survival at high concentrations of antibiotics. This may be a contributing factor to the observation that UC patients receiving antibiotic treatment do not necessarily show any improvements in their condition. The low level of success in using antibiotics to treat UC suffers may be improved if the biofilm associated with the mucosal layer could be disrupted prior to treatment. The use of honey to do this was an option here, because honey has been widely used in treating bacterial infection (Badawy et al., 2004; Mandal & Mandal, 2011; Maddocks et al., 2013). No such investigation examining the effect of honey on the bacteria involved in UC and mixed biofilms in combinations with antibiotics has previously been reported.

The effect of Manuka honey of various strengths with and without rifaximin and Metronidazole antibiotics on the growth of D. indonesiensis alone and with E. coli was examined. It was observed that the addition of honey to pure biofilm D. indonesiensis reduced its MIC and MBC values in comparison to D. indonesiensis with E. coli, and that the strength of this reduction was associated with the higher grades of Manuka honey, in particular UMF®25+. It was shown also that the addition of the antibiotics to the bacterial culture after incubation with Manuka honey improved the antibiotics effect on the growth of pure D. indonesiensis biofilms and slightly for mixed D. indonesiensis with E.coli biofilms. The honey was able to inhibit the development of biofilms rather than inhibit the growth of establish biofilms this finding suggest that after early detection of bacteria causing the disease the honey before starting the antibiotics course will improve the efficiency of antibiotics used for treating the condition. This study thus showed that although co-culturing of SRB with E. coli increases the detection level of SRB in culture making isolation, identification quantification effective, it also increases its resistance to antimicrobials thus, increasing their survival

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In conclusion, SRB has been linked to UC as possible causes by numbers of investigations, but whether these bacteria are causative agents, or other bacteria such as E. coli taking an advantage of changes in the gut intestinal bacteria and promoting the SRB growth; will be the main challenge for the future research.

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Ulcerative colitis is a well characterized medical condition associated with bacterial dysbiosis, where changes in the intestinal bacterial population influence an individual’s health (Chen et al., 2017). It is possible that the establishment of a particular bacterial community is a predisposing factor for this disorder. Previous work has focussed on evaluating dynamic changes in the microbiome of the colon or identifying the predominant species present. Individual dietary habits can influence the microflora of the colon, and may have implications for the overall pathogenicity of the disease. The present study consisted of two separate, yet contextually highly relevant, studies investigating the role of sulfate reducing bacteria in the overall pathogenesis of the UC and the role that Manuka honey might have on the colon resident bacterial populations when used to treat SRBs. Kumamoto (2016) has previously reported that rare gut microbes might have a significant role in the inflammatory bowel disease. This study mainly considered the fungal population, but data presented in this thesis suggests that it may be equally true for the bacterial population. Here, the crucial role of SRB as producers of hydrogen sulfide fits well with the notion that microbes with a low population density might have a significant impact on the pathogenesis of UC disease. The microbial population changes observed in this study revealed that members of the Enterobacteriaceae predominated the colon in UC suffers, reflecting an overall reduction in bacterial diversity, while there was also an increase in species of Desulfovibrio. Although a reduction in bacterial diversity has been previously observed in UC suffers, the link between the enterics and SRBs has not been reported, particularly the induction of Desulfovibrio species growth in the presence of Escherichia coli. Nevertheless, to make these findings more meaningful, there is a need for further study relevant to this aspect.  The samples obtained for this study were colonic biopsies from a healthy individual or UC suffers. Furture work should focus on samples from inflamed and non-inflamed regions of biopsies from the same patient, and from patients who are in the same stage of the disease. This would allow the identification of specific strains that might be associated with the development of the disease.  Biopsy samples from different colonic regions of the same patient and taken at different stages of the disease would also allow an assessment how SRB profiles can change, and if there is link between particular SRB strains and bacterial population with UC pathogenesis.

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 The potential relationship, commensal or symbiotic, between SRBs and E.coli needs to be investigated further. This is probably best approached by studying the association of bacterial strains, isolated from UC suffers, in animal model systems. By growing the strains together in vitro or in germ free animal models it would be possible to check whether they could induce inflammation to the model tissue, allowing this information to be translated to a clinical settings.  The co-culture of SRB strains with different organisms isolated from UC patient at different disease stages might allow the identification of their interactions leading to colonisation, and the reduction in bacterial diversity. This approach might answer the questions whether different strains of SRB can transmit the disease phenotype and if some bacteria can improve or prevent the disease.

 H2S is the major byproduct of SRB growth that affects the mucosa layer of human colon leading to the inflammation. It is suspected that anything that promotes SRB growth will lead to an increase in the concentration of hydrogen sulphide production. This needs to be tested in situ.  The reduction in bacterial diversity associated with UC has been reported on a number of occasions (Zoetendal et al., 2002; Ott et al., 2004 ;Rigottier-Gois, 2013; Wang et al., 2014; Carding et al., 2015), however the reduction observed in this study was extreme, resulting in only members of the Enterobactericeae and Desulfovibrio being present from UC biopsies. This may reflect a low sequence sampling effort or that the change in diversity is progressive. In addition to this, the relationship between E. coli and Desulfovibrio species, described in this thesis, suggests a mechanism for the diversity reduction observed in the SRB group. The possible progressive nature in the reduction of diversity at the general and specific levels can be investigated further by evaluating the relationship between diet and the microbial diversity of UC sufferers at different levels of the disease.  The use of Manuka honey and its impact on controlling bacterial population existing in the form of biofilms is intriguing and is of future research interest. For the future direction clinical trials worth to conducted to evaluate the beneficial impact of Manuka honey with and without antibiotics among patients suffering from UC.

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Appendix 1: Growth media composition

Growth media composition used in this study is described below.  Vitamin Medium, Vitamin Medium with iron and Vitamin medium with reduced iron (VM, VMI, VMR medium) Medium was prepared as follows (composition per litre) (Table A.1.1). Table A.1.1: VM, VMI and VMR medium composition.

Compound g/liter Potassium dihydrogen phosphate 0.5 g (KH2PO4)

Ammonium chloride (NH4Cl) 1 g

Sodium sulphate (Na2SO4) 4.5 g Calcium chloride (CaCl2∙2 H2O) 0.04 g MgSO4∙7 H2O 0.06 g FeSO4∙7 H2O 0.004 g* Sodium citrate (Na3C6H5O7∙2 H2O) 0.3 g NaCl 25.0 g Casamino acids 2.0 g

Tryptone 2.0 g

Sodium lactate (NaC3H5O3) 6.0 g

* FeSO4∙7 H2O for VMI medium 0.5 g and for VMR 0.05 g Medium pH was adjusted to 7.5 by adding NaOH. Sterile trace elements (1 ml stock solution) (Table A.1.2) and 1 ml sterile vitamin stock solution (Table A.1.3) were added to the medium. 10 ml vials were filled with a medium prepared. The medium was subsequently degassed with an O2 free-N2 flux to achieve anaerobic condition, the vial was covered with a stopper and crimp with aluminum and autoclaved for 30 min at 121°C.

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Table A.1.2: Modified Wolfe’s mineral elixir (per liter). Compound g/liter C6H9NO6 1.5 g

MgSO4∙7 H2O 3.0 g

MnSO4∙ H2O 0.5 g NaCl 1.0 g

FeSO4∙7 H2O 0.1 g

CoSO4∙7 H2O 0.1 g

NiCl2∙6 H2O 0.1 g

CuCl2∙2 H2O 0.1 g

ZnSO4∙7 H2O 0.1 g

CuSO4∙5 H2O 0.01 g

AlK(SO4) ∙12 H2O 0.01 g

H3BO3 0.01 g

NaMoO4∙2 H2O 0.01 g

Na2SeO4 0.001 g

The pH was adjusted to 6,5 to 7 then Distilled water was added up to 1L.

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Table A.1.3: Vitamin stock solution. Vitamins Weight Vitamin C, ascorbic acid 10 g Vitamin B1, thiamine 60 mg Vitamin B2, riboflavin 20 mg Vitamin B12 5 mg Vitamin B3, nicotinic acid 50 mg Vitamin B5, D-pentanthonic acid 60 mg Vitamin B6, pyridoxime 61 mg Vitamin H , d-biotin 1 mg Distilled water 100 ml  LB medium

LB Medium was prepared as follows (composition per litre) (Table A.1.4).

Table A.1.4: Composition of LB medium. Compound Weight Sodium Chloride 10 Yeast Extract 5 Tryptone 10

The pH for medium was adjusted to 7.2 using 5M NaOH; then autoclaved at 121°C for 30 minutes.  MacConkey agar Table A.1.5: Composition of MacConkey agar. Compound Weight

MacConkey agar powder 52

Distilled H2O Up to 1L

The MacConkey agar was dissolved; then autoclaving at 121⁰C for 30 minutes. Approximately 20 ml of MacConkey agar was poured into each Petri dish.

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APPENDICES

Appendix 2: Phylogenetic analysis

Table A.2.1. Accession numbers of 16SrRNA sequences used for Bacteriodes phylogenic tree construction Bacteria Accession number Bacteroides thetaiotaomicron (ATCC 29148) 16S ribosomal RNA L16489.1 Bacteroides vulgatus 16S ribosomal RNA gene, partial sequence M58762.2 Bacteroides vulgatus strain mpk genome CP013020.1 Bacteroides fragilis gene for 16S ribosomal RNA, partial cds, AB618792.1 strain: JCM 17586 Bacteroides dorei strain Z5 16S ribosomal RNA gene, partial KJ145327.1 sequence Bacteroides uniformis gene for 16S ribosomal RNA, partial AB908393.1 sequence, strain: EFEL002 Parabacteroides distasonis gene for 16S ribosomal RNA strain: AB640686.1 JCM 1294 Parabacteroides goldsteinii strain JCM13446 16S ribosomal RNA EU136697.1 gene Bacteroidetes bacterium Smarlab 3301186 16S ribosomal RNA AY538684.1 gene B.ovatus 16S rRNA (ATCC 8483T) X83952.1 Barnesiella viscericola strain C46 16S ribosomal RNA gene NR_041508.1 Uncultured Bacteroidetes bacterium clone M0035_096 16S EF071264.1 ribosomal RNA gene Butyricimonas virosa strain MT12 16S ribosomal RNA gene NR_041691.1 Parabacteroides merdae 16S ribosomal RNA gene EU722738.1

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Table A.2.1.Cont

Bacteria Accession number

Bacteroides sp. WAL 10018 16S ribosomal RNA gene, partial sequence AY608696.1 Bacteroides salyersiae strain JCM 12988 16S ribosomal RNA gene, NR_112942.1 partial sequence Uncultured Bacteroidetes bacterium clone M0011_019 16S ribosomal EF071259.1 RNA gene, partial sequence Bacteroides thetaiotaomicron strain VPI-5482 16S ribosomal RNA gene NR_074277.1

Bacteroides nordii strain JCM 12987 16S ribosomal RNA gene, partial NR_112939.1 sequence Bacteroides sp. Marseille-P2653 partial 16S rRNA gene, strain Marseille- LT558803.1 P2653 Bacteroides distasonis 16S ribosomal RNA M86695.1 Bacteroides sp. WH2 16S ribosomal RNA gene, partial sequence AY895180.1 Bacteroides uniformis ATCC 8492 16S ribosomal RNA gene, complete L16486.1 sequence Uncultured Bacteroides sp. clone RML172 16S ribosomal RNA gene, KU161522.1 partial sequence Bacteroides uniformis strain ZL1 16S ribosomal RNA gene, partial gb|JN873344.1 sequence Bacteroides sp. WH302 16S ribosomal RNA gene, partial sequence AY895184.1 B.fragilis 16S rRNA (ATCC 25285T) X83935.1 B.caccae 16S rRNA (ATCC 43185T) X83951.1

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APPENDICES

Table A.2.1.Cont Bacteria Accession number Uncultured Bacteroidetes bacterium clone LI3-81 16S GU956524.1 ribosomal RNA gene Barnesiella intestinihominis strain JCM 15079 16S NR_113073.1 ribosomal RNA gene Bacteroides fragilis gene for 16S ribosomal RNA, strain: AB618793.1 JCM 17587 Barnesiella intestinihominis strain JCM 15079 16S NR_113073.1 ribosomal RNA gene Bacteroides fragilis gene for 16S ribosomal RNA, strain: AB618793.1 JCM 17587 Uncultured Bacteroides sp. clone RML151 16S ribosomal KU161501.1 RNA gene Bacteroides vulgatus strain W05002A 16S ribosomal RNA KP944149.1 gene Bacteroides vulgatus strain 39a-cc-B-5824-ARE 16S KR364743.1 ribosomal RNA gene, partial sequence Bacteroides fragilis strain DNF00264 16S ribosomal RNA KU726649.1 gene, partial sequence Bacteroides uniformis strain 19006C 16S ribosomal RNA KP944146.1 gene, partial sequence Uncultured Bacteroides sp. clone RML172 16S ribosomal KU161522.1 RNA gene, partial sequence

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APPENDICES

* 700 * 720 * 740 * 760 * 780 * 8 AY608696.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC NR_112942. : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC EF071259.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC NR074277.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC NR112939.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGTTACTGACACTGAGGCTCGAAAGTGTGGGTATCAAAC LT558803.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC M86695.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCCAAGCCATTACTGACGCTGATGCACGAAAGCGTGGGGATCAAAC AY895180.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC L16486.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATC-AAC KU161522.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC JN873344.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC AY895184.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC X83935.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC X83951.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC L16489.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC M58762.2 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAG-CTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC CP013020.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC AB618792.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC KJ145327.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC AB908393.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC NR126194.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCTTAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC AB640686.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCTTAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCCAAGCCATGACTGACGCTGATGCACGAAAGCGTGGGGATCAAAC EU136697.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACTATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC AY538684.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCTTAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC X83952.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTG--ACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC NR041508.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCAACGGACGCTGAGGCACGAAAGCGTGGGGATCGAAC EF071264.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC NR041691.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCATAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGTTCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC EU722738.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC GU956524.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTGTCGAAC NR_113073. : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC AB618793.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC KU161501.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC KP944149.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC KR364743.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC KU726649.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC KP944146.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC KU161522.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC K1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC K4 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC k16 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC k22 : GGCAGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC k28 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC k51 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC k54 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC p25 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC p26 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC

247

APPENDICES p64 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC p65 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC p66 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC p78 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC p710 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_k2 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_k3 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_k6 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_k15 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC C_k26 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACCGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_k27 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_L31 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC C_P29 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P44 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P45 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P46 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P48 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P49 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P61 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P62 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P77 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P82 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P83 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P85 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P86 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P88 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC C_P89 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_PP210 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC C_P211 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC C_P212 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC C_P410 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC C_P412 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC C_P611 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGGCAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC C_P27 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCATAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC GGCgGAAttcGTGgtGTAGCGGTGAAATGCtTAGATATCACgaaGAACtCCGATtGCGAAGGCAGC Ct ctg aaCtGAC TGA GCtCGAaAGtGTGGGtatCaAAC

Figure A.2.1. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Bacteriodes and those of random clones from healthy control.

248

APPENDICES

Table A.2.2. Accession numbers of 16SrRNA sequences used for Firmicutes phylogenic tree construction

Bacteria Accession number Clostridium leptum 16S ribosomal RNA M59095.1 Clostridium sp. MB2-A37 16S ribosomal RNA gene KT935669.1 Uncultured Clostridium sp. b2-173 16S ribosomal RNA gene gb|JX576090.1 Ruminococcus bromii strain L2-63 16S ribosomal RNA gene gb|EU266549.1

Clostridium sp. JCD partial 16S rRNA gene, isolate JCD HG726040.1

Clostridium boltei partial 16S rRNA gene, strain 16351 AJ508452.1

Clostridium lavalense strain CCRI-9929 16S ribosomal RNA gene EF564278.1 Clostridium asparagiforme strain N6 16S ribosomal RNA gene NR_042200.1 Ruminococcus sp. 14565 partial 16S rRNA gene, strain 14565 AJ315980.1 Ruminococcus albus 16S ribosomal RNA gene AF079847.1

Clostridium sp. YIT 12069 gene for 16S rRNA AB491207.1 Ruminococcus flavefaciens strain JM1 16S ribosomal RNA gene AY445595.1 Ruminococcus sp. NML 00-0124 16S ribosomal RNA gene EU815223.1 Clostridium orbiscindens strain AIP162.06 16S ribosomal RNA gene EU541435.1 Clostridium sp. gene for 16S ribosomal RNA AB622833.1 Clostridium bartlettii strain WAL 16138 16S ribosomal RNA gene AY438672.1 Uncultured Clostridium sp. clone 5.17h43 16S ribosomal RNA gene JN679181.1

249

APPENDICES

Table A.2.2. Cont

Bacteria Accession number Clostridium sp. TM-40 gene for 16S rRNA AB249652.1 Faecalibacterium prausnitzii A2165 a 16S rRNA gene AJ270469.2 Uncultured Faecalibacterium sp. M4-59 16S ribosomal RNA gene EU530347.1 Uncultured Faecalibacterium sp. M4-28 16S ribosomal RNA gene EU530316.1 Clostridium celerecrescens strain AIP 148.09 16S ribosomal RNA gene JQ246092.1 Clostridium sp. K10 gene for 16S rRNA AB610561.1 Clostridium lavalense 16S rRNA gene, strain Marseille-P2117 LT223652.1 Clostridium hathewayi strain 1313 16S ribosomal RNA gene NR_036928.1| Clostridium citroniae strain RMA 16102 16S ribosomal RNA gene NR_043681.1 Blautia coccoides strain DSM 29138 16S ribosomal RNA gene KU196081.1 Blautia producta strain JCM 1471 16S ribosomal RNA gene NR_113270.1 Clostridium nexile 16S rRNA gene, strain DSM 1787 X73443.1 Flavonifractor plautii gene for 16S rRNA sequence, strain: MT42 AB693937.1 Hungatella hathewayi strain R4 16S ribosomal RNA gene KU234408.1 Ruminococcus gnavus gene for 16S ribosomal RNA AB910745.1 Coprococcus eutactus gene for 16S ribosomal RNA D14148.1 Gemella haemolysans ATCC 10379 16S ribosomal RNA gene L14326.1 Gemella morbillorum strain 2917B 16S ribosomal RNA gene NR_025904.1 Gemella sp. oral clone ASCF12 16S ribosomal RNA gene AY923143.1 Uncultured Gemella sp. DSD10 16S ribosomal RNA gene AY672072.1

250

APPENDICES

Table A.2.2. Cont Bacteria Accession number Roseburia hominis A2-183 16S rRNA gene, type strain A2-183T AJ270482.2 Parvimonas micra ATCC 33270 16S ribosomal RNA gene AF542231.1 Peptostreptococcus sp. oral clone FJ023 16S ribosomal RNA gene AY349390.1 Eubacterium ventriosum strain ATCC 27560 16S ribosomal RNA gene L34421.2 Eubacterium hallii strain ATCC 27751 16S ribosomal RNA gene L34621.2 Coprococcus catus gene for 16S rRNA AB038359.1 Eubacterium siraeum strain ATCC 29066 16S ribosomal RNA gene L34625.2 Roseburia intestinalis 16S rRNA gene, strain L1-82 AJ312385.1 Roseburia inulinivorans 16S rRNA gene, type strain A2-194T AJ270473.3 Eubacterium eligens 16S ribosomal RNA L34420.1 Clostridium sp. 14774 16S rRNA gene, strain 14774 AJ315981.1 Butyrate-producing bacterium A2-166 16S rRNA gene AJ270489.1 Butyrate-producing bacterium A2-175 16S rRNA gene AJ270485.2 Roseburia faecis strain M6/1 16S ribosomal RNA gene AY804149.1 Ruminococcus obeum ribosomal RNA (16S rRNA) gene L76601.1 Ruminococcus torques gene for 16S ribosomal RNA isolate: GIFU 12126 D14137.1 Subdoligranulum variabile 16S rRNA gene, type strain BI 114T AJ518869.1 Butyrate-producing bacterium L2-6 16S rRNA gene AJ270470.2 Dorea formicigenerans strain ATCC 27755 16S ribosomal RNA gene L34619.2

Table A.2.2. Cont Bacteria Accession number Dialister sp. E2_20 16S ribosomal RNA gene AF481209.1 Uncultured Dialister sp. clone oral taxon JKAS-116 16S ribosomal RNA JX548458.1 gene Anaerostipes hadrus 16S ribosomal RNA gene AY305319.1 Eubacterium rectale 16S ribosomal RNA L34627.1 Faecalibacterium prausnitzii strain ATCC 27768 AJ413954.1 L.lactis ribosomal RNA encoding 16S ribosomal RNA, transfer RNA X64887.1 Lactococcus lactis subsp. lactis strain NM152-3 16S ribosomal RNA HM218600.1 gene Streptococcus mitis 16S ribosomal RNA gene AF003929.1 Streptococcus sp. 2418 16S ribosomal RNA gene JN590018.1 Streptococcus oralis Uo5 strain Uo5 16S ribosomal RNA NR_102809.1

251

APPENDICES * 820 * 840 * 860 * 880 * 900 * M59095.1 : GAAGTAGAGG-TAGGCGGAATT-CCCNGTGTAGCGGT-AAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAAGCA KT935669.1 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGGCTTT-AACTGACGCTGAGGCA JX576090.1 : GAAGTAGAGG-CAGGTGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA EU266549.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA HG726040.1 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAGGCA AJ508452.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT EF564278.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT NR_042200. : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT AJ315980.1 : GAAGTAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCT AF079847.1 : GAAGTAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGACGAA-GGC-GGCTTACT-GGGCTTT-AACTGACGCTGAGGCT AB491207.1 : GAAGTAGAGG-CAGGCGGAATT-CCGAGTGTAGCGGTGAAATGCGTAGATATTC-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-TACTGACGCTGAGGCT AY445595.1 : GAAGTAGAGG-TAAGCGGAATT-CCTGGTGTAGCGGTGAAATGCGTAGATATCA-GGAGGAACA-CCGGTGGCGAA-GGC-GGCTTACT-GGGCTTT-TACTGACGCTGAGGCT EU815223.1 : GAAGTAGAGG-TTGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGGCGAA-GGC-GGCCAACT-GGGCTTT-TACTGACGCTGAGGCT EU541435.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATAC-GGAGGAACA-CCAGTGGCGAA-GGC-GGATTGCT-GGACAGT-AACTGACGCTGAGGCG AB622833.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATACGGAGGGAACA-CCAGTGGCGAA-GGCGGGATTGCTGGGACAGT-AACTGACGCTGAGGCG AY438672.1 : GCAGGAGAGG-AGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTAGCGAA-GGC-GGCTCTCT-GGACTGT-AACTGACGCTGAGGCA JN679181.1 : GCAGGAGAGG-AGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTAGCGAAGGGC-GGCTCTCT-GGACTGT-AACTGACGCTGAGGCA L76600.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA EU530308.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA EU728796.1 : A-AGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA M59112.2 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CNAGTGGCGAA-GGC-GACTTACT-GGACGAT-AACTGACGTTGAGGCT X76747.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT HM008264.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT AF262238.1 : GTCGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACGAT-GACTGACGTTGAGGCT AF028349.1 : GTCGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACGAT-GACTGACGTTGAGGCT AB117566.1 : GTCGGAGAGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGACGAT-GACTGACGTTGAGGCT JN713312.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT NR036800.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT HM235650.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT X73441.1 : GCAGGAGAGG-ATCGTGGAATT--CCTGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGGTCT-GGCCTGT-AACTGACGCTCATTCC AY699288.1 : GCAGGAGAGGAATCGTGGAATTCCCATGTGTAGCGGTGAAATGCGTAGATATATGGGAGGAACA-CCAGTGGCGAAGGGC-GACGATCT-GGCCTGCAAACTGACGCTCAGTCC NR119286.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGT-AACTGACGCTCAGTCC FJ538172.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC AB249652.1 : GCATCAGAGG-ATCGCGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GGCGGTCT-GGGGTGC-AGCTGACGCTCAGTCC AJ270469.2 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT EU530347.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT EU530316.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT JQ246092.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT AB610561.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT LT223652.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT NR036928.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT NR043681.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT

252

APPENDICES KU196081.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT NR113270.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT X73443.1 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT AB693937.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATAC-GGAGGAACA-CCAGTGGCGAA-GGC-GGATTGCT-GGACAGT-AACTGACGCTGAGGCG KU234408.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT JQ271545.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT AB910745.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT D14148.1 : ACAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GACTTACT-GGACTGC-TACTGACACTGAGGCA L14326.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG NR025904.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG AY923143.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG AY672072.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACACCCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG AJ270482.2 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT AF542231.1 : GAAGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAATA-CCGGTGGCGAA-GGC-GACTTTCT-GGACTTT-TACTGACGCTCAGGTA AY349390.1 : GAAGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAATA-CCGGTGGCGAA-GGC-GACTTTCT-GGACTTT-TACTGACGCTCAGGTA L34421.2 : GTCGGAGGGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCGGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT _L34621.2 : ACAGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACTGT-TACTGACACTGAGGCA AB038359.1 : TCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGA-CAATGACGCTGAGGCT L34625.2 : GAAGTAGAGG-CAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGCTTTT--ACTGACGCTGAGGCT AJ312385.1 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT AJ270473.3 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT L34420.1 : GCAGGAGGGG-TGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTCACT-GGACTGT-AACTGACACTGAGGCT AJ315981.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGT-AACTGACGCTCAGTCC AJ270489.1 : ACAGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACTGA-AACTGACACTGAGGCA AJ270485.2 : TCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAG-GGCGAA-GGC-GGCTTACT-GGACTGA-CAATGACGCTGAGGCT AY804149.1 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT L76601.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT D14137.1 : GT-GGAGAGG--TAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT AJ518869.1 : AGTGCAGAGG-TAGGTGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GACCTACT-GGGCACC-AACTGACGCTGAGGCT _AJ270470. : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCA-TGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT L34619.2 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGC-TACT-GGACGAT-GACTGACGTTGAGGCT AF481209.1 : ATCGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAAGAACA-CCGGTGGCGAA-GGC-GACTTTCT-GGACGAA-AACTGACGCTGAGGCG JX548458.1 : ATCGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAAGAACA-CCGGTGGCGAA-GGC-GACTTTCT-GGACGAA-AACTGACGCTGAGGCG AY305319.1 : GCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGGCGAA-GGC-GGCTTACT-GGACTGA-AACTGACACTGAGGCA L34627.1 : GTCGGAGGGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT AJ413954.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT X64887.1 : GCAGGAGAGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCCTGT-AACTGACACTGAGGCT HM218600.1 : GCAGGAGAGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCCTGT-AACTGACACTGAGGCT AF003929.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT JN590018.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT NR102809.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT p76 : GCAGGAGGGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGACTGT-AACTGACACTGAGGCT K8 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT

253

APPENDICES k10 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT k13 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAGGCA k50 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAACGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT k52 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT k53 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC k55 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC p21 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT p67 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT C_k5 : GCTGGAGAGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT C_k23 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC C_k24 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT C_k29 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT C_k30 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT C_k31 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT C_k32 : ACTGGAGAGG-CAGACGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGTCTGCT-GGACAGC-AACTGACGCTGAGGCG C_P24 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT C_P41 : GGTGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACT-AACTGACGCTGAGGCT C_P47 : GGTGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACT-AACTGACGCTGAGGCT C_P68 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC C_P69 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT C_P81 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC C_P87 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT C_P2100 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT C_P28 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT C_k25 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT g g AGaGg a g GGAATT cC GTGTAGCGGTgaAAtGCGTAGAtAT GgagGAAcA ccagtggCGAA gGC Ggc t cT GG c acTGACg TgAggc

Figure A.2.2. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Firmicutes and those of random clones from healthy control.

254

APPENDICES

Table A.2.3. Accession numbers of 16SrRNA sequences used for Proteobacteria phylogenic tree construction for healthy control

Bactria Accession number Pseudomonas aeruginosa PAO1 chromosome NC_002516.2

Citrobacter freundii 16S rRNA gene, isolate MBRG 7.3 AJ514240.1 Escherichia coli CFT073 NC_004431.1 Escherichia coli O157:H7 EDL933 NC_002655.2 K.pneumoniae 16S rRNA gene X87276.1 Salmonella bongori strain BR 1859 16S ribosomal RNA AF029227.1 gene Y.enterocolitica (serotype 0:3) for 16S rRNA X68674.1 Enterobacter aerogenes strain BAC006 16S ribosomal KU161309.1 RNA gene Enterobacter sp. ChroAq 66 16S ribosomal RNA gene KU951452.1 Enterobacter xiangfangensis strain SDI-37 16S KT021517.1 ribosomal RNA gene Enterobacter cloacae strain KLHD_03 16S ribosomal KU158291.1 RNA gene Citrobacter sp. UIWRF1269 16S ribosomal RNA gene KR189389.1 Citrobacter braakii strain 519C1 16S ribosomal RNA KT764983.1 gene Enterobacter sp. AR15 16S ribosomal RNA gene HM027902.1 Escherichia coli strain B10 16S ribosomal RNA gene KU870318.1| Escherichia coli str. K-12 substr. MG1655 strain K-12 NR_102804.1 16S ribosomal RNA Escherichia coli strain ATCC 43893 16S ribosomal HM194886.1 RNA

255

APPENDICES

Table A.2.3. Cont

Bacteria Accession number Citrobacter freundii gene for 16S ribosomal AB548831.1 RNA strain: JCM 24066 Klebsiella pneumoniae strain QLR-2 16S KM096434.1 ribosomal RNA gene Campylobacter coli 16S ribosomal RNA M59073.1 E. coli genomic sequence M87049.1 Bilophila wadsworthia ribosomal RNA L35148.1 (rRNA) gene Bilophila wadsworthia strain CCUG 32349 KU749296.1 16S ribosomal RNA gene Shigella sonnei strain AK102 insertion KU255074.1 sequence IS1012 Escherichia coli strain SYW001 16S EF620921.1 ribosomal RNA gene, partial sequence

256

APPENDICES * 820 * 840 * 860 * 880 * 900 * NC002516.2 : GAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCG-AAAGCGTGGGGAGCAAACAGG AJ514240.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCA-GTGCG-AAAGCGTGGGGAGCAAACAGG NC004431.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG NC002655.2 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG _X87276.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG AF029227.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG X68674.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KU234408.1 : GAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAACTGACGTTGAGGCTCG-AAAGCGTGGGGAGCAAACAGG KU161309.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KU951452.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KT021517.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KU158291.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KR189389.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KT764983.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG HM027902.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KU870318.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KU255074.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG EF620921.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG NR_102804. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG HM194886.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG AB548831.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG KM096434.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG M59073.1 : GAATTGGTGGTGTAGGGGTAAAATCCGTAGAGATCACCAAGAATACCCATTGCGAAGGCGATCTGCTAGAACTCAACTGACGCTAA--TGCGTAAAGCGTGGGGAGCAAACAGG M87049.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG L35148.1 : GAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCG-AAAGCGTGGGTAGCAAACAGG KU749296.1 : GAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCG-AAAGCGTGGGTAGCAAACAGG p79 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG C_P610 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG GAATTccagGTGTAGcgGTgAAATgCGTAGAgATctggAgGAAtACCggTgGCGAAGGCGgccccCTgGAc aagACTGACgcTcAggtgCG AAAGCGTGGGgAGCAAACAGG

Figure A.2.3. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Proteobacteria and those of random clones from healthy control.

257

APPENDICES

Table A.2.4. Accession numbers of 16SrRNA sequences used for Actinobacteria phylogenic tree construction

Bacteria Accession number Corynebacterium durum 16S ribosomal RNA Z97069.1 Uncultured Corynebacterium sp. 24BA82 16S FJ976416.1 ribosomal RNA gene Bifidobacterium adolescentis 16S ribosomal AY305304.1 RNA gene Bifidobacterium sp. TM-7 gene for 16S rRNA AB218972.1 Bifidobacterium coryneforme strain ATCC NR044690.2 25911 16S ribosomal RNA gene Bifidobacterium sp. MRM 5.9 16S ribosomal KP718941.1 RNA gene Bifidobacterium breve strain KB 90 16S AY513711.1 ribosomal RNA gene Uncultured Bifidobacterium sp. clone HM046575.1 ACW_P1 16S ribosomal RNA gene Bifidobacterium minimum strain 20E 16S KC787351.1 ribosomal RNA gene Actinomyces graevenitzii 16S rRNA gene, AJ540309.1 strain CCUG 27294T Corynebacterium sundsvallense 16S rRNA Y09655.1 gene, strain CCUG 36622 Actinomyces odontolyticus 16S rRNA X53227.1 Eubacterium aerofaciens gene for 16S rRNA AB011816.1 Eggerthella lenta gene for 16S ribosomal RNA LC145578.1 strain: JCM 10766 Eggerthella sp. SDG-2 16S ribosomal RNA EF413638.1 gene

Table A.2.4.Cont Bacteria Accession number Slackia heliotrinireducens strain JCM 14554 16S NR_113176.1 ribosomal RNA gene Gordonibacter pamelaeae strain 7-10-1-b 16S NR_102934.1 ribosomal RNA gene Adlercreutzia equolifaciens gene for 16S AB306660.1 ribosomal RNA strain: FJC-A10 Eggerthella lenta strain W02018C 16S KP944193.1 ribosomal RNA gene Adlercreutzia equolifaciens gene for 16S rRNA AB693938.1 strain: MT4s-5 Gordonibacter faecihominis strain CAT-2 16S KF785806.1 ribosomal RNA gene

258

APPENDICES

460 * 480 * 500 * 520 * 540 * 560 * Z97069.1 : GGTGACGGTACCGTGGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGAGCTCGTAGGTGGTG FJ976416.1 : GGTGACGGTACCGTGGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGAGCTCGTAGGTGGTG AY305304.1 : GGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT AB218972.1 : GGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT NR044690.2 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGC-AGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT KP718941.1 : AGTGAGTGTACC-CGTTGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT AY513711.1 : GTTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT HM046575.1 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGATTTATTGGGCGTAAAGAGCTCGTAGGCGGTT KC787351.1 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT AJ540309.1 : GTTGAGGGTACC-TGGATAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGCT _Y09655.1 : TGTGACGGTAGG-TTGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTGTCCGGAATTACTGGGCGTAAAGAGCTCGTAGGTGGTT X53227.1 : GGTGACGGTAG--TGGGTAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGC-AGCGTTGTCCGGAATTATTGGGCGTAAAGGGCT--TAGGCGGT- AB011816.1 : CAAGACTGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC LC145578.1 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC EF413638.1 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC NR113176.1 : CATGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGCAGGCGGCC |NR102934. : -GTGACGGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC AB306660.1 : ---GACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC _KP944193. : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC AB693938.1 : -TAGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC KF785806.1 : -TTGACGGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC k14 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC GA GTAcc t gAA AAGC CCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGG GC AGCGTT TCCGGA T AtTGGGCGTAAAG GC cgtAGGcGG

Figure A.2.4. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Actinobacteria and those of random clones from healthy control.

259

APPENDICES

Table A.2.5. Accession numbers of 16SrRNA sequences used for Proteobacteria phylogenic tree construction for UC patients

Bacteria Accession number Pseudomonas aeruginosa PAO1 chromosome NC_002516.2 Citrobacter freundii 16S rRNA gene, isolate MBRG 7.3 AJ514240.1 Escherichia coli CFT073 NC_004431.1 Escherichia coli O157:H7 EDL933 NC_002655.2 K.pneumoniae 16S rRNA gene X87276.1 Salmonella bongori strain BR 1859 16S ribosomal RNA AF029227.1 gene Y.enterocolitica (serotype 0:3) 16SS rRNA X68674.1 Enterobacter aerogenes strain BAC006 16S ribosomal KU234408.1 RNA gene Enterobacter sp. ChroAq 66 16S ribosomal RNA gene KU951452.1 Enterobacter xiangfangensis strain SDI-37 16S ribosomal KT021517.1 RNA gene Enterobacter cloacae strain KLHD_03 16S ribosomal |KU158291.1 RNA gene Citrobacter sp. UIWRF1269 16S ribosomal RNA gene KR189389.1 Citrobacter braakii strain 519C1 16S ribosomal RNA KT764983.1| gene Enterobacter sp. AR15 16S ribosomal RNA gene HM027902.1 Escherichia coli strain B10 16S ribosomal RNA gene KU870318.1| Shigella sonnei strain AK102 insertion sequence IS1012 KU255074.1 Escherichia coli strain SYW001 16S ribosomal RNA EF620921.1 gene

260

APPENDICES

Table A.2.5. Contd.

Bacteria Accession number Escherichia coli str. K-12 substr. MG1655 strain NR_102804.1 K-12 16S ribosomal RNA Escherichia coli strain ATCC 43893 16S HM194886.1 ribosomal RNA gene Citrobacter freundii gene for 16S ribosomal RNA AB548831.1 strain: JCM 24066 Klebsiella pneumoniae strain QLR-2 16S KM096434.1 ribosomal RNA gene Campylobacter coli 16S ribosomal RNA M59073.1 E. coli genomic sequence M87049.1

261

APPENDICES

* 700 * 720 * 740 * 760 * 780 * 8 C_23/22_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_23/24_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_23/32_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_23/33_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_26/36_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_26/41_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_B13U_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A6_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A11_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A14_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A15_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A17_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A18_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_A1A20_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_B1B3_UC_ : GAATTNCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_B1B16_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_6_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_7_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_19/14_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_19/20_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_1A_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA1_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA4_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA5_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA7_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA10_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_AA20_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA C_BB5_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA NC002516.2 : GAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGA _AJ514240. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCA-GTGCGAAAGCGTGGGGAGCAAACAGGA NC004431.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA NC002655.2 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA X87276.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA AF029227.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA X68674.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KU161309.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KU951452.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KT021517.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA |KU158291. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KR189389.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KT764983.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA

262

APPENDICES HM027902.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KU870318.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KU255074.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA EF620921.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA NR_102804. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA HM194886.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA AB548831.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA KM096434.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA _M59073.1 : GAATTGGTGGTGTAGGGGTAAAATCCGTAGAGATCACCAAGAATACCCATTGCGAAGGCGATCTGCTAGAACTCAACTGACGCTAATGCGT-AAAGCGTGGGGAGCAAACAGGA M87049.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA GAATTccagGTGTAGcGGTgAAATgCGTAGAgATctggAgGAAtACCggTgGCGAAGGCGgcCccCTgGAc aagACTGACgCTcAgGtGcgAAAGCGTGGGGAGCAAACAGGA

Figure A.2.5. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Proteobacteria and those of random clones from UC Patients.

263

APPENDICES

Table A.2.6. Accession numbers of dsr sequences used for Deltaproteobacteria phylogenic tree construction for UC patients and healthy individua

Bacteria Accession number Desulfomicrobium sp. MSL97 dsrA gene for AB373130 dissimilatory sulfite reductase alpha subunit Desulfomicrobium sp. MSL95 dsrA gene for AB373129 dissimilatory sulfite reductase alpha subunit Desulfomicrobium sp. MSL94 dsrA gene for AB373128 dissimilatory sulfite reductase alpha subunit Desulfomicrobium sp. MSL93 dsrA gene for AB373127 dissimilatory sulfite reductase alpha subunit Desulfomicrobium sp. MSL65 dsrA gene for AB373124| dissimilatory sulfite reductase alpha subunit Desulfomicrobium escambiense dsrA, dsrB AB061531 genes for sulfite reductase alpha and beta subunits Desulfomicrobium salsuginis dsrA gene AM493693.1 ADR28 Desulfomicrobium baculatum dsrA, dsrB AB061530 genes for sulfite reductase alpha and beta subunits Desulfomicrobium baculatum DSM 4028 CP001629 Desulfococcus oleovorans Hxd3 strain DSM AF418201.1 6200 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes

264

APPENDICES

Table A.2.6.Cont Bacteria Accession number Desulfococcus oleovorans Hxd3 strain DSM 6200 AF482464 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfococcus oleovorans Hxd3 CP000859.1 Desulfatibacillum alkenivorans dissimilatory sulfite AY504426 reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfatibacillum aliphaticivorans strain CV2803 AY215506.1 dissimilatory sulfite reductase Desulfatibacillum aliphaticivorans dissimilatory sulfite AY215505.1 reductase alpha subunit Desulfosarcina sp. SD1 dissimilatory sulfite reductase FJ416306.1 alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes, partial cds Desulfosarcina variabilis dissimilatory sulfite reductase AF191907 alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfosarcina variabilis dissimilatory sulfite reductase AF360643.1 alpha subunit (dsrA) gene Desulfobacter latus dissimilatory sulfite reductase alpha U58124.2 subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfobacter vibrioformis dissimilatory sulfite redutase AJ250472 Desulfobacter postgatei 2ac9 strain DSM 2034 AF418198.1 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes

Table. A.2.6.Cont Bacteria Accession number Desulfobacterium cetonicum dissimilatory AF420282 sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfohalobium retbaense DSM 5692 CP001735.1 Thermodesulforhabdus norvegicus AJ277293.1 dissimilatory sulphite reductase Desulfovibrio africanus dissimilatory sulfite AF271772.1 reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio africanus dsrA, dsrB genes for AB061535.1 sulfite reductase alpha and beta subunits Desulfovibrio africanus subsp. uniflagellum EU716165 strain dissimilatory sulfite reductase alpha subunit and dissimilatory sulfite reductase beta subunit genes

265

APPENDICES

Table. A.2.6.Cont Bacteria Accession number Desulfovibrio africanus str. Walvis Bay NC_016629.1

Desulfovibrio alaskensis G20, complete CP000112.1 genome Desulfovibrio alkalitolerans DSM 16529 ctg3 NZ_ATHI01000026.1 Desulfovibrio carbinolicus strain DSM 3852 AY626026 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes

Desulfovibrio desulfuricans subsp. AY820828 desulfuricans dissimilatory sulfite reductase A subunit gene, partial cds Desulfovibrio desulfuricans strain F28-1 DQ092635.1 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio desulfuricans dissimilatory AF273034 sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio desulfuricans subsp. AY015495 desulfuricans dissimilatory sulfite reductase subunit A (dsrA) gene, partial cds

Table. A.2.6.Cont Bacteria Accession number Desulfovibrio fairfieldensis strain CCUG 45958, CP014229 complete genome Desulfovibrio gabonensis strain DSM 10636 AY626027 2 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585 Desulfovibrio halophilus strain DSM 5663 AF482461.1 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio intestinalis dsrA, dsrB genes for AB061539 sulfite reductase alpha and beta subunits Desulfovibrio intestinalis strain DSM 11275 AF418183 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio magneticus RS-1 DNA AP010904 Desulfovibrio oxyclinae strain DSM 11498 AY626033.1

266

APPENDICES dissimilatory bisulfite reductase alpha subunit (dsrA) gene Desulfovibrio piezophilus C1TLV30 FO203427.1 Desulfovibrio piger ATCC 29098 strain DSM 749 AF482462 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio portus dsrA gene for dissimilatory AB373125 sulfite reductase alpha subunit Desulfovibrio salexigens DSM 2638 CP001649 Desulfomonile tiedjei DSM 6799 CP003360

Table. A.2.6.Cont Bacteria Accession number Desulfovibrio sp. CME3 dissimilatory sulfite AF360649 reductase alpha subunit (dsrA) gene Desulfovibrio sp. dissimilatory sulfite reductase DSU58116 alpha subunit gene Desulfovibrio sp. enrichment culture clone 83 HM800739 1 dissimilatory sulfite reductase (dsrAB) gene Desulfovibrio sp. J2, complete genome CP014206 Desulfovibrio sp. LVS-10 partial rA gene for AM494494 dissimilatory sulfite reductase alpha subunit, strain LVS-10 Desulfovibrio sp. LVS-13 gene for dissimilatory AM494495 sulfite reductase alpha subunit, strain LVS-13 Desulfovibrio sp. LVS-21 partial rA gene for AM494497 dissimilatory sulfite reductase alpha subunit Desulfovibrio sp. sul5 dissimilatory sulfite KJ801800.1 reductase subunit A (dsrA) and dissimilatory sulfite reductase subunit B (dsrB) genes Desulfovibrio sp. wp6 dissimilatory sulfite KJ801801 reductase subunit A (dsrA) and dissimilatory sulfite reductase subunit B (dsrB) genes Desulfovibrio termitidis dsrA, dsrB genes for AB061542.1 sulfite reductase alpha and beta subunits Desulfovibrio termitidis HI1 strain DSM 5308 AF418185 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio vulgaris subsp. vulgaris DQ826729 dissimilatory sulfite reductase subunit A Desulfovibrio vulgaris RCH1 CP002297.1

267

APPENDICES

Table. A.2.6.Cont Bacteria Accession number Desulfovibrio vulgaris DP4 CP000527 Desulfovibrio vulgaris subsp. vulgaris str. AE017285 Hildenborough Desulfovibrio vulgaris dissimilatory sulfite reductase U16723.1 alpha Desulfovibrio zosterae strain DSM 11974 AY626028.2 dissimilatory sulfite reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio simplex dsrA, dsrB genes for sulfite AB061541 reductase alpha and beta subunits Desulfovibrio fructosivorans dsrA, dsrB genes for AB061538 sulfite reductase alpha and beta subunits Desulfovibrio aerotolerans dissimilatory sulfite AY749039.1 reductase alpha subunit (dsrA) and dissimilatory sulfite reductase beta subunit (dsrB) genes Desulfovibrio aminophilus strain DSM 12254 AY626029 dissimilatory sulfite reductase alpha subunit (dsrA) Desulfovibrio simplex dissimilatory sulfite reductase U78738 alpha Desulfovibrio aespoeensis Aspo-2 CP002431 Desulfovibrio longus dsrA, dsrB genes for sulfite AB061540 reductase alpha and beta subunits Desulfarculus baarsii DSM 2075 CP002085 Desulfovibrio fructosivorans JJ strain DSM 3604 AF418187 dissimilatory sulfite reductase alpha subunit (dsrA) Desulfobacula toluolica Tol2 FO203503

268

APPENDICES

* 240 * 260 * 280 * 300 * 320 * 340 AB373130 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA AB373129 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA AB373128 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA AB373127 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA _AB373124| : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA AB061531 : ------ATGTTCCCCGGCGTTGCTCATTTTCATACCATCCGTGTCGCTCAGCCCGCGGGCATGTACTACACCACGGAG AM493693.1 : ------ATGTTCCCCGGCGTTGCTCATTTTCATACCATCCGTGTCGCTCAGCCCGCGGGCATGTACTACACCACGGAG _AB061530 : -ATGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCCAGATGTTCCCCGGTGTTGCTCATTTCCACACCGTTCGTGTCGCTCAGCCTGCGGGCATGTACTACACGACTGAT CP001629 : TATGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCCAGATGTTCCCCGGTGTTGCTCATTTCCACACCGTTCGTGTCGCTCAGCCTGCGGGCATGTACTACACGACTGAT AF418201 : ------ATGCGCGTGGCCCAGCCCGCGGGCAAGTTCTACACCACCAAG _AF482464 : ------ATGCGCGTGGCCCAGCCGC---GCAAGTTCTACACCACCAAG CP000859 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTTCCCGGCGTGGCTCACTTTCACACCATGCGCGTGGCCCAGCCCGCGGGCAAGTTCTACACCACCAAG _AY504426 : ------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA AY215506.1 : ------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA AY215505 : ------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA FJ416306 : ------ATGCGTATCAACCAGCCCGGCGGAAAATACTACACCACTGAT AF191907 : ------ATGCGTATCAACCAGCCTGGCGGGAAATACTATACCTCCGAT AF360643 : ------ATGCGTATCAACCAGCCTGGCGGGAAATACTACACCACCGAT DLU58124 : ------ATGCGTGTAAACCAGCCCTGCGGTAAATATTACAGCACTGAA _AJ250472 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCAAACTGTTCCCCGGTGTTGAACATTTTCACACCATGCGCGTAAATCAGCCCTGCGGTAAGTACTACAGCACAGAA _AF418198 : ------ATGCGTGTAAACCAGCCTTGCGGTAAATACTATAGCACCGAG _AF420282 : -ATGGCGGTGGTGTTATCGGTCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCCCACTTTCATACCATGCGCATCAACCAGCCCGGCGGCAAATATTACACCACCGAG _CP001735. : TATGGTGGCGGCGTTATCGGCCGGTACTGTGATCAGCCGGAAATGTTCCCCGGCGTGGCCCACTTCCACACCATGCGTGTCAACCAGCCTGCCGGCAAGTACTACACCAGTGAG AJ277293.1 : TACGGCGGCGGCGTTATCGGACGATACTGCGACAGACCCGACCTGTTTCCCAATGTTGCCCATTTCCACACGATGCGTGTTAATCAGCCGGCAAGCAAGTTTTACACTACCGAC AF269147 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCTCACTTCCACACCGTCCGCGTGGCTCAGCCCTCCGGTAAGTACTACTCCACCGAA _CP003221 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG AF327308 : ------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG AB061535 : ------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTACTACAACAGCAAG AF271772 : ------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGCGAAGTACTACACGACCAAG EU716165 : ------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG ____NC_016 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG CP000112.1 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA NZ_ATHI010 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACGTTCCCAAGCAGTTCCCGGGCGTGGCCCACTTCCACACCGTCCGCGTCAACCAGCCCGGCGGCAAGTACTACACCACCGAG _AB061536 : ------ATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAA _AY626026 : ------ATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT ____DQ0926 : ------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG AF273034 : ------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG AY015495 : ------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG _DQ450464 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG CP014229 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAACAGTTCCCCGGCGTGGCCCACTTCCACACCATGCGTGTGGCCCAGCCCGCCGGCAAATACTATGACACCAAG

269

APPENDICES _CP006585 : TACGGTGGCGGCGTCATTGGTCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCGCACTTCCACACTGTCCGTCTGGCCCAGCCTGCAGCCAAGTATTACACGGCTGAG AF482461.1 : ------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCGTGGCTCAGCCNGCCGGTAAGTTCTACACCACCAAG AB061539 : ------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTACTACAACAGCAAG AF418183 : ------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTTCTACAACAGCAAG AP010904 : TACGGCGGCGGCGTCATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT AY626033.1 : ------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCGTGGCTCAGCCCGCCGGTAAGTTCTACACCACCAAG FO203427 : TACGGCGGCGGCGTTATTGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCTCACTTCCACACTGTTCGCGTGGCTCAGCCCATGGGCATGTGGTACAAGACCGAC AF482462 : ------ATGCGTGTGGCCCAGCCCGCTGCCAAATACTACCACACCAAA _CP001649 : TACGGCGGCGGCGTTATCGGTCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTTGCACACTTTCACACCGTTCGTGTTGCACAGCCCACCGCTAAGTACTACACCACCGAG DSU58116 : ------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCATGGCTCAGCCCGCCGGTAAGTTCTACACCACCAAG HM800739_1 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAA CP014206 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCGCAGATGTTCCCCGGCGTTGCTCACTTCCACACCGTCCGCGTGGCTCAGCCCAACGGCAAGTGGTACAACACCAAG AM494494 : ------ATGTTCCCCGGTGTGGCCCACTTCCACACCATGCGTGTGGCTCAGCCTTCGGGCAAGTACTACCACAGCAAG AM494495 : -ATGGCGGCGGCGTTATCGGCCGCTACTGTGACCAGCCCGAAATGTTCCCCGGCGTCGCCCACTTCCATACGGTTCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCACCGAG AM494497 : ------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG _KJ801800 : ------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTCCGCCTGGACCAGCCCTCGGGCAAGTACTACACTTCCGAG KJ801801 : ------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGTAAGTACTACCACAGCAAG CP002297 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC CP000527 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC AE017285 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC DVU16723 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC AB061541 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTTGCTCACTTCCACACCATGCGTGTGGCCCAGCCCGCGGGCAAGTTCTACCACACCAAG AB061538 : TATGGCGGCGGCGTTATCGGCCGCTACTGTGACCAGCCCGAGATGTTCCCCGGCGTGGCCCATTTTCATACGGTTCGTGTGGCCCAGCCCTCCGGCAAGTACTACACCACCGAG AY749039 : TACGGCGGCGGCGTCATCGGACGTTACTGCGACCAGCCCGAAATGTTCCCGGGTGTGGCCCACTTCCACACCGTTCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT _AY626029 : TACGGCGGCAGCGTCATCGGCCGTTACTGCGACCAGCCCCAGATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCCTCCGGCCTGTACTACAAGACCGAC _U78738 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTTGCTCACTTCCACACCATGCGTGTGGCCCAGCCCGCGGGCAAGTTCTACCACACCAAG CP002431 : TACGGCGGCGGCGTTATCGGTCGTTACTGTGACCAGCCCCAGATGTTCCCCGGCGTGGCTCACTTCCACACCGTGCGTGTTGCCCAGCCCATGGGCATGTGGTACAACACCGAG AB061540 : TACGGCGGCGGCGTCATCGGTCGTTACTGCGACCAGCCTGAGATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCCTCCGGCAAATTCTACACCACCGAA CP002085 : TATGGCGGAGGCGTCATCGGCCGTTATTCCGACGTCCCCAACCTTTTCCCTGGCGTCGAACACTTCCACACCGTGCGCGTCAACCAGCCCGCGTCCAAGTTCTACAAGACCGAG FO203503 : TATGGCGGTGGCGTTATCGGTAGATATTGTGATCAGCCTCAGGCATTTCCAGGTGTTGAGCATTTTCATACAATGCGTGTAAACCAGCCTGCAGGTAAGTATTATACAACTGAT CP003360 : TACGGCGGAGGGATCATCGGAAGATACTGCGATCAGCCGCAGCAGTTCCCTGGTGTTGCTCATTTCCATACTGTTCGTGTGAACCAGCCCTCAAGCAAGTATTACACCACCAAG F4 : ----GCNGCGGCGTTNTCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA F6 : ----GCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA E5 : ------GCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGAGCAAGTACTACACCAGCGAA C4 : ----GNGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA C11 : ----GNGGCGGCGTTA-CGGCCGTTACTGTGACCAGCCCGGAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAGCCAGCCTGCAGGTAAGTTCTACACCTCCGAA D4 : ----GCGGCNGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA D12 : ----GCGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA A7 : ------GGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA B8 : ----GCGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA C1 : ----GNGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA C_B6 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA C_D5 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA

270

APPENDICES F1 : ------GGCNGCGTTNTCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA gttccccggcgt gc ca tt ca acc T CG gT cCAGCC c gg aagT cTAca cc A

Figure A.2.6. Alignment showing the dsr sequences of known taxa from database belonging to Deltaproteobacteria and those of random clones from healthy control and UC patients.

271

APPENDICES

* 360 * 380 * 400 * 420 * 440 * AB373130 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA AB373129 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA AB373128 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA AB373127 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA _AB373124| : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA AB061531 : TTCCTGAAGCAGCTCTGCGATCTTTGGGAAATGCGCGGTTCCGGCCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTGTTGGGCACCACTACTCCCCAGCTGGAAGAG AM493693.1 : TTCCTGAAGCAGCTCTGCGATCTGTGGGAAATGCGCGGTTCCGGCCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTGTTGGGCACCACTACTCCCCAGCTGGAAGAG _AB061530 : TTCCTGAAGCAGCTCTGCGACCTGTGGGATATGCGCGGCTCCGGTTTGACCAACATGCATGGCGCCACCGGCGACATCGTGCTTCTGGGAACAACCACTCCTCAGCTGGAAGAA CP001629 : TTTCTGAAGCAGCTCTGCGACCTGTGGGATATGCGCGGCTCCGGTTTGACCAACATGCATGGCGCCACCGGCGACATCGTGCTTCTGGGAACAACCACTCCTCAGCTGGAAGAA AF418201 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCACCACGCCCCAGCTGGAAGAG _AF482464 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCTGCACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCTCCACGCCCCAGCTGGAAGAG CP000859 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCACCACGCCCCAGCTGGAAGAG _AY504426 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA AY215506.1 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA AY215505 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA FJ416306 : TTTCTGAAAAAACTCTGCGATCTGTGGGAGTTCCGCGGTTCCGGCATCACCAACATGCACGGTTCTACCGGTGACATCATCTTTATCGGCACCTCCACTCCGCAGTTGGAAGAA AF191907 : TTTCTGAAAAAACTGTGTGACCTGTGGGAGTTCCGCGGCTCCGGCATCACCAATATGCACGGCTCCACCGGTGACATCATTTTTATCGGTACCTCCACCCCGCAGTTGGAAGAA AF360643 : TTTCTGAAAAAACTGTGTGACCTGTGGGAGTTCCGCGGCTCCGGCATCACCAATATGCACGGCTCCACCGGTGACATCATTTTTATCGGTACCTCCACCCCGCAGTTGGAAGAA DLU58124 : TATCTCAGACAACTGACTGACCTGTGGGATTTCAGGGGTTCCGGCGTAACCAATATGCATGGCGCCACCGGGAATATCATCCTGCTGGGTACCACCACTCCCCAGTTGGAAGAA _AJ250472 : TACTTGAGAAAACTGACGGATCTGTGGGACTTCAGAGGTTCCGGAGTTACCAATATGCATGGCGCCACCGGCGATATCATTCTTCTGGGTACCACCACTCCCCAGCTTGAAGAA _AF418198 : TATCTTAGACAATTGACTGAGCTGTGGGACTTCAGGGGCTCCGGAATTACCAATATGCACGGCGCCACCGGCGACATCATTCTGCTGGGTACCACTACGCCCCAGCTGGAAGAG _AF420282 : TTTCTGAAAAAGCTGTGCGACCTGTGGGAATTCCGCGGTTCCGGCGTCACCAACATGCACGGCTCCACCGGTGACATCATCTTCATCGGTACCTCCACGCCTCAGCTGGAAGAG _CP001735. : TACCTCCGTCAGCTGTGCGACCTTTGGGATTTCCGCGGCAGCGGTCTGACCAACATGCATGGGTCCACCGGTGATATCGTTTTCCTCGGTACCCGGACCGAACAGTTGGAAGAA AJ277293.1 : GTCCTCAGAAAGCTTTGCGACATCTGGGAAGAAAAGGGAAGCGGGCTTTTCAATTTCCATGGTTCCACCGGAGACATCATTCTTCTGGGAACCACGACGGATCAGCTGGAGCCG AF269147 : TTCCTGCGCGGCCTGTGCGACATTTGGGAACTCCGTGGCTCCGGTCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTCATCGGCACCCAGACCCCGCAGCTTGAAGAG _CP003221 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG AF327308 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG AB061535 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA AF271772 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGGA EU716165 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG ____NC_016 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG CP000112.1 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTTCTGCTGGGTACCACCACTCCCCAGCTGGAAGAG NZ_ATHI010 : TACCTGCGCGGCCTGATCGACATCTGGGACATGCGCGGCTCCGGCCTGACCAACATGCACGGTTCCACCGGCGACATGGTGTGGCTGGGCACCTTCACCGAGCAGCTCGAAGAG _AB061536 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTGGAAGAA _AY626026 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTGGGCACCACCACCCCGCAGCTGGAAGAA ____DQ0926 : TTCCTGCGCGACCTGTGCGATATCTGGGACATGCGCGGCTCTGGCCTGACTAACATGCACGGTTCCACCGGCGATATCGTGCTGCTCGGTACCCAGACCCCCCAGCTGGAAGAA AF273034 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA AY015495 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA _DQ450464 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA CP014229 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGTTCCACGGGCGACATCGTGCTGCTGGGCACCCAGACCGTCCAGCTGGAAGAA

272

APPENDICES _CP006585 : TACCTGGAAGCCATCTGCGACGTCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGGTCCACCGGCGACATCGTGCTTCTGGGCACCCAGACCCCGCAGCTCGAAGAA AF482461.1 : TTCCTGCGTGATCTCTGCGACCTGTGGGACCTCCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACCCAGACCCCGCAGCTCGAAGAA AB061539 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA AF418183 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA AP010904 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTGGAAGAA AY626033.1 : TTCCTGCGTGATCTCTGCGACCTGTGGGACCTCCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACTCAGACCCCGCAGCTCGAAGAA FO203427 : TTCCTGCGCTCCTTGATGGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGTGACGTTGTCCTGCTTGGTACATCCACCCCGCAGCTCGAAGAA AF482462 : TTCCTGCGTGACCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTATGACCAACATGCACGGTTCCACCGGTGACATCGTGCTCCTGGGCACCCAGACCCCGCAGCTGGAAGAA _CP001649 : TTCCTGAACAAGATCATCGACATTTGGGATATGCGCGGTTCCGGTCTGACCAACATGCACGGTTCTACCGGTGACATCGTCTTCCTCGGTACCACCACTCCTCAGCTCGAAGAA DSU58116 : TTCCTGCGTGATCTCTGCNACCTGTGGGACCTGCGCNGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACTCAGACCCCGCAGCTCGAAGAA HM800739_1 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTTCTGCTGGGCACCACCACCCCGCAGCTCGAAGAA CP014206 : CTGCTCAACGGCCTGATGGACATCTGGGAACTCCGTGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTCCTGGGCACCACCACTCCCCAGCTCGAAGAG AM494494 : TTCCTGCGCGACCTGTGCGACATTTGGGACCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTTGGCACCCAGACCGCCCAGCTGGAAGAA AM494495 : TATCTGCGCCAGATCATGGATATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTCGAGGAA AM494497 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGTGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA _KJ801800 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGCTCGGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTCCTGCTCGGCACCACCACGGCCCAATTGGAAGAG KJ801801 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGTGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA CP002297 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA CP000527 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA AE017285 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA DVU16723 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA AB061541 : TTCCTGCGCGACCTTTGCGACATTTGGGACATGCGTGGCTCCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTTCTTGGCACCCAGACCGCTCAGCTCGAAGAA AB061538 : TATCTCCGCCAGATCATGGACATCTGGGATCTGCGCGGCTCGGGCCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTCGAGGAA AY749039 : TACCTGCGCGGCATCATGGACATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACGGGCGACATCGTGCTGCTCGGCACCACCACGCCGCAGCTGGAAGAA _AY626029 : TTCCTCCGTCAGCTCTGCGACCTGTGGGAACTCCGCGGTTCGGGCCTGACCAACATGCACGGCTCCACGGGCGACATCGTGCTCCTGGGCACGACCACCCCGCAGCTGGAAGAG _U78738 : TTCCTGCGCGACCTTTGCGACATTTGGGACATGCGTGGCTCCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTTCTTGGCACCCAGACCGCTCAGCTCGAAGAA CP002431 : TTCCTGAACAGCCTCATGGACATCTGGGAACTGCGCGGCTCCGGCCTTACCAACATGCACGGCGCCACCGGCGACATCGTGCTGCTTGGCACGTCCACCCCGCAGCTTGAGGAG AB061540 : TTCCTCCGTCAGCTCTGCGACCTCTGGGAGTTCCGCGGCTCCGGTCTGACCAACATGCATGGCGCCACCGGCGACATCGTGTTCATCGGCACCACCACCCCGCAGCTCGAAGAG CP002085 : CGTCTGCGCGAGATCATGGACATCTGGAACCGCCGTGGCTCCAGCATGCTCAACATGCACGGCTCCACCGGCGACATCATTCTGCTGGGCGCCTTCACCGAGCAGCTCGAGCCC FO203503 : TATCTTAGAAAATTGACCGACCTTTGGGACTTCAGGGGTTCCGGTGTTACCAATATGCATGGCGCAACCGGTGATATCATTCTTCTGGGAACAACTACTCCTCAGTTAGAAGAA CP003360 : TTCCTGCGCGAACTCTGTGATCTGTGGGACAGACACGGAAGCGGTTTGACGAACATGCACGGATCGACCGGTGATATCGTCTTCTTGGGAACTACCACCGATCATCTCGAGCCC F4 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTG------F6 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACC------E5 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTGCACGGCNCCNCA------C4 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC------C11 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGCNNGG------D4 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC------D12 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC------A7 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC------B8 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC------C1 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGCNC------C_B6 : TACCTGCGCNAGNTCTGCGA------C_D5 : TACCTGCGCAAGCTNTGCGA------

273

APPENDICES F1 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTGCNCGG------t ccT a T gA t tggga t cg gg tccgg tgaccaacatgca gg ccac gg ga atc t t t gg ac ac cag t ga ga

Figure A.2.6. Cont Alignment showing the dsr sequences of known taxa from database belonging to Deltaproteobacteria and those of random clones from healthy control and UC patients.

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APPENDICES

Appendix 3: Taxonomic report

Table A.3.1 Accesion number of dsrB gene sequences used in this study Bacteria Accession number

Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585.1 Desulfatibacillum alkenivorans AK-01 CP001322.1 Desulfovibrio desulfuricans isolate SRDQC DQ450464.1 Desulfovibrio desulfuricans dsrA gene for sulfite AJ249777.1 reductase and dsrB gene for sulfite reductase Desulfovibrio vulgaris DP4 CP000527.1 Desulfovibrio piger ATCC 29098 Scfld439 NZ_DS996396.1 Desulfocapsa sulfexigens DSM 10523 CP003985.1 Desulfovibrio piezophilus C1TLV30 FO203427.1 Desulfovibrio vulgaris str. 'Miyazaki F' CP001197.1 Desulfovibrio salexigens DSM 2638 CP001649.1 Desulfovibrio vulgaris RCH1 CP002297.1 Desulfovibrio alaskensis G20 CP000112.1 Desulfobacter vibrioformis dissimilatory sulfite redutase AJ250472.1 Desulfovibrio aespoeensis Aspo-2 CP002431.1

Table A.3.2 Accesion number of sat gene sequences used in this study

Bacteria Accession number Desulfobulbus japonicus DSM 18378 KE386997.1 Desulfobulbus propionicus DSM 2032 CP002364.1 Desulfobacter curvatus DSM 3379 KB893012.1 Desulfobacula sp. TS genomic scaffold TS-13370 KL662031.1 Desulfobulbus elongatus DSM 2908 JHZB01000003.1 Desulfobulbus mediterraneus DSM 13871 AUCW01000004.1 Desulfobulbus sp. Tol-SR contig_611 JROS01000122.1 Desulfocurvus vexinensis DSM 17965 JAEX01000001.1 Desulfonauticus sp. A7A AVAG01000038.1 Desulforegula conservatrix Mb1Pa AUEY01000017.1 I997DRAFT_scaffold00017.17_C Desulfosarcina sp. BuS5 AXAM01000009.1 Desulfovibrio alaskensis DSM 16109 AXWQ01000004.1 Desulfovibrio aminophilus DSM 12254 AUMA01000020.1 Desulfovibrio magneticus str. ALAO01000129.1 Desulfovibrio gigas strain ATCC 19364 KF113861.1 Desulfovibrio longus DSM 6739 ATVA01000004.1 Desulfatibacillum aliphaticivorans DSM 15576 AUCT01000002.1

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APPENDICES

Table A.3.3 Accesion number of aprA gene sequences used in this study Bacteria Accession number Thiobacillus denitrificans strain DSM 12475 NZ_AQWL00000000.1 Thiohalocapsa halophila strain DSM 6210 EF618603.1

Thiorhodovibrio winogradskyi strain DSM 6702 AprB EF641946.1 (aprB) and AprA (aprA) genes Desulfococcus multivorans strain DSM 2059 NZ_CP015381.1 Desulfobacter sp. DSM 2057 AprB (aprB) and AprA EF442909.1 (aprA) genes Desulfobacterium autotrophicum strain DSM 3382 AF418108.1 Desulfovibrio sulfodismutans strain DSM 3969 AprB EF442897.1 (aprB) and AprA (aprA) genes, Desulfomicrobium baculatum DSM 4028 NC_013173.1 Desulfovibrio piger ATCC 29098 Scfld442 genomic NZ_DS996397.1 scaffold Desulfovibrio ferrophilus strain DSM 15579 AprB EF442884.1 (aprB) and AprA (aprA) genes Desulfocaldus sp. Hobo AprB (aprB) and AprA (aprA) EF442898.1 genes Desulfohalobium retbaense DSM 5692 adenosine-5'- AF418125.1 phosphosulfate reductase alpha subunit (apsA) gene Desulfovibrio alaskansis G20 CP000112.1

Desulfovibrio desulfuricans subsp. desulfuricans AF226708.1

Table A.3.4 Accesion number of dsrA gene sequences used in this study Bacteria Accession number Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585.1 Desulfomonile tiedjei DSM 6799 CP003360.1 Desulfovibrio desulfuricans dsrA gene for sulfite AJ249777.1 reductase and dsrB gene for sulfite reductase Desulfovibrio vulgaris DP4 CP000527.1 Desulfocapsa sulfexigens DSM 10523 CP003985.1 Desulfovibrio piezophilus C1TLV30 FO203427.1 Desulfovibrio vulgaris str. 'Miyazaki F' CP001197.1 Desulfurivibrio alkaliphilus AHT2 CP001940.1 Desulfovibrio salexigens DSM 2638 CP001649.1 Desulfobacter vibrioformis dissimilatory sulfite AJ250472.1 redutase Desulfovibrio hydrothermalis str. FO203519.1 Desulfohalobium retbaense DSM 5692 CP001734.1 Desulfococcus oleovorans Hxd3 CP000859.1 Desulfobacula toluolica Tol2 NC_018645.1 Bilophila wadsworthia dissimilatory sulfite reductase AF269147.2 A and dissimilatory sulfite reductase B genes Desulfobacter postgatei 2ac9 CM001488.1 Desulfohalobium retbaense DSM 5692 NC_013223. Desulfobulbus propionicus DSM 2032 CP002364.1

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APPENDICES

Desulfarculus baarsii DSM 2075 CP002085.1 Desulfotalea psychrophila LSv54 CR522870.1

Table A.3.5 Accesion number of beta-gal gene sequences used in this study Bacteria Accession number Escherichia coli EBG repressor (ebgR), EBG M64441.1 enzyme alpha subunit E.coli beta-gal gene M38327.1 Escherichia coli strain 86-24 EU889485.1 Enterobacter cloacae gene for beta-gal D42077.1 Escherichia coli strain TW14359 EU889487.1 Escherichia coli strain ST540 CP007265.1 Enterobacter cloacae CHS 79 aeeaF- JMUS01000016.1 supercont1.1.C16 Klebsiella pneumoniae beta-gal HQ324708.1 Klebsiella pneumoniae UCI 68 aeeav- JMZU01000007.1 supercont1.3.C7 Citrobacter freundii MGH 56 aedZq- JMUJ01000034.1 supercont1.2.C34 Lelliottia amnigena CHS 78 aedZL- JMZV01000011.1 supercont1.2.C11 Salmonella enterica subsp. enterica serovar CP002099.1 Typhi str Shigella sonnei Ss046 CP000038.1 Enterobacter aerogenes MGH 61 aeeaS- JMUL01000016.1 supercont1.1.C16 Enterobacter cloacae JMUU01000020.1 Raoultella sp. UIWRF1100 beta-gal KR424228.1

Table A.3.6 Accesion number of nan-H gene sequences used in this study Bacteria Accession number Bacteroides fragilis neuraminidase (nanH) M31663.1 gene Bacteroides fragilis neuraminidase (nanH) AF031639.1 gene Bacteroides fragilis strain KLE1758 LTZK01000072.1 Bacteroides fragilis strain IQS-1 KU378646.1 neuraminidase (nanH) gene Bacteroides fragilis NCTC 9343 CR626927.1 Bacteroides intestinalis strain KLE1704 LTDF01000084.1

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APPENDICES

Table A.3.7 Accesion number of leuB gene sequences used in this study Bacteria Accession number Bacteroides caecimuris strain I48 CP015401.1 Bacteroides cellulosilyticus strain WH2 NZ_CP012801.1 Bacteroides dorei CP007619.1 Bacteroides fragilis strain BOB25 CP011073.1 Bacteroides helcogenes P 36-108 NC_014933.1 Bacteroides intestinalis DSM 17393 NZ_ABJL02000008.1 Bacteroides mediterraneensis NZ_LT635783.1 Bacteroides nordii CL02T12C05 NZ_JH724315.1 Bacteroides ovatus strain ATCC 8483 NZ_CP012938.1 Bacteroides salyersiae strain NZ_CYXS01000012.1 2789STDY5608871 Bacteroides sp. NZ_FNVX01000005.1 Bacteroides thetaiotaomicron strain 7330 NZ_CP012937.1

Table A.3.7.Cont

Bacteria Accession number Bacteroides uniformis strain NZ_CZAO01000004.1 2789STDY5834898 Bacteroides vulgatus ATCC 8482 CP000139.1 Bacteroides xylanisolvens XB1A FP929033.1

Table A.3.8 Accesion number of TcdC gene sequences used in this study Bacteria Accession number C.difficile tcdA, tdcC genes and cdd1 gene Y10689.1 C.difficile tcdC gene X92982.1 Clostridium difficile tcdC gene for TcdC AJ428943.1 strain M7 Clostridium difficile strain ATCC 43594 DQ870674.1 Cdd1 (cdd1) gene, partial cds; and TcdC (tcdC) gene Clostridium difficile strain 79A292 Cdd1 DQ870676.1 (cdd1) gene, partial cds; and TcdC (tcdC) gene Clostridium difficile strain CH6186 tcdC FJ409548. (tcdC) gene, tcdC-mb1

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Table A.3.9 Accesion number of hydA gene sequences used in this study

Bcteria Accession number Clostridium stercorarium subsp. leptospartum DSM NZ_CP014673.1 9219 Clostridium autoethanogenum DSM 10061, CP006763.1 Clostridium perfringens hydA gene for hydrogenase AB016820.1

Clostridium felsineum strain DSM794 FeFe- JF720844.1 hydrogenase gene

Clostridium kluyveri DSM 555 CP000673.1 [Clostridium] celerecrescens strain 152B JPME01000007.1 Clostridium sp. hydA gene GQ180214.1 Table A.3.10 Accesion number of fadA gene sequences used in this study

Bcteria Accession number Fusobacterium equinum strain CMW8396 LRPX01000104.1 Fusobacterium sp. GG657973.1 Fusobacterium nucleatum strain PK1594 DQ012979.1 adhesin A (fadA) gene Fusobacterium gonidiaformans ATCC 25563 ACET02000016.1 Fusobacterium hwasookii ChDC F128 ALVD01000022.1 Fusobacterium massiliense strain Marseille- NZ_LT608327.1 P2749 Fusobacterium necrophorum strain ATCC FMXX01000074.1 25286 Fusobacterium sp. GL988028.1 Fusobacterium nucleatum strain 3349 adhesin DQ012978.1 A (fadA) gene Fusobacterium periodonticum strain 33693 DQ012980.1 adhesin A (fadA) gene Fusobacterium ulcerans ATCC 49185 ACDH02000047.1 Fusobacterium varium ATCC 27725 ACIE02000038.1

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280

APPENDICES

281