DOCSLIB.ORG

Gene-Based and Pathway-Based Genome-Wide Association Study of Alcohol Dependence

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

• 111 • Shanghai Archives of Psychiatry, 2015, Vol. 27, No. 2 •Original research article• Gene-based and pathway-based genome-wide association study of alcohol dependence Lingjun ZUO1, Clarence K. ZHANG2,3*, Frederick G. SAYWARD4,5, Kei-Hoi CHEUNG4, Kesheng WANG6, John H. KRYSTAL1, Hongyu ZHAO2, Xingguang LUO1* Background: The organization of risk genes within signaling pathways may provide clues about the converging neurobiological effects of risk genes for alcohol dependence. Aims: Identify risk genes and risk gene pathways for alcohol dependence. Methods: We conducted a pathway-based genome-wide association study (GWAS) of alcohol dependence using a gene-set-rich analytic approach. Approximately one million genetic markers were tested in the discovery sample which included 1409 European-American (EA) alcohol dependent individuals and 1518 EA healthy comparison subjects. An additional 681 African-American (AA) cases and 508 AA healthy subjects served as the replication sample. Results: We identified several genome-wide replicable risk genes and risk pathways that were significantly associated with alcohol dependence. After applying the Bonferroni correction for multiple testing, the ‘cell- extracellular matrix interactions’ pathway (p<2.0E-4 in EAs) and the PXN gene (which encodes paxillin) (p=3.9E-7 in EAs) within this pathway were the most promising risk factors for alcohol dependence. There were also two nominally replicable pathways enriched in alcohol dependence-related genes in both EAs (0.015≤p≤0.035) and AAs (0.025≤p≤0.050): the ‘Na+/Cl- dependent neurotransmitter transporters’ pathway and the ‘other glycan degradation’ pathway. Conclusions: These findings provide new evidence highlighting several genes and biological signaling processes that may be related to the risk for alcohol dependence. Key words: gene-based GWAS; pathway-based GWAS; cell-extracellular matrix interaction pathway; PXN; paxillin; alcohol dependence [Shanghai Arch Psychiatry. 2015; 27(2): 111-118. doi: http://dx.doi.org/10.11919/j.issn.1002-0829.215031] 1. Introduction complex interplay of variants within genes, genomic [8] Conventional genome-wide association studies regions, or gene pathways. Gene variants that (GWASs) focused on the impact of single nucleotide individually contribute slightly to alcoholism risk but that polymorphisms (SNPs) have identified a large number may have a more important effect in moderating the impact of other risk genes may be missed by the single- of significant or suggestive risk genes for alcohol [9] dependence and alcohol consumption.[1-7] However, SNP analytic strategy. This problem may be reduced single-SNP analysis often identifies only a few of the by employing gene-based and pathway-based analytic most significant SNPs of the genome and they can approaches. only explain a small proportion of the genetic risk Gene- and pathway-based methods have many for diseases. Accumulating evidence suggests that advantages over the single-SNP approach.[9,10] First, susceptibility to alcohol dependence emerges from a the functions of many individual SNPs are not well 1 Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States 2 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, United States 3 Biostatistics Resource, Keck Laboratory, Department of Genetics, Yale University School of Medicine, New Haven, CT, United States 4 Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, United States 5 Cooperative Studies Program Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, United States. 6 Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN, United States *correspondence: XG Luo, [email protected]; CK Zhang, [email protected] A full-text Chinese translation of this article will be available at http://dx.doi.org/10.11919/j.issn.1002-0829.215031 on June 6, 2015. Shanghai Archives of Psychiatry, 2015, Vol. 27, No. 2 • 112 • characterized but the functions of whole genes and data were all approved by the respective institutional particular gene pathways are more clearly characterized; review boards, all subjects participating in the projects many functional studies (e.g., gene expression studies) provided written informed consent, and the current have been conducted at the gene or pathway level analysis was approved by the institutional review board making it possible to assess the association of biological of Yale University. functions with specific genes and pathways. Second, locus heterogeneity (i.e., alleles at different loci cause Figure 1. Enrollment of subjects in the study diseases in different populations) make it difficult to replicate association findings for a single marker, but replication at the gene or pathway level might still be SAGE dataset COGA dataset possible when locus heterogeneity exists because a (Study of Addiction: (Collaborative Study On The Genetics and Environment) Genetics of Alcoholism) gene or pathway can harbor multiple alleles of the 4316 subjects 1957 subjects heterogeneous risk markers. Finally, because the numbers of genes and pathways across the genome are 1477 overlapping subjects much less than the number of single markers, gene- excluded based and pathway-based analyses can substantially reduce the number of comparisons considered and, 4796 subjects thus, lead to better statistical power. In the present study, we aimed to identify the risk 680 subjects do not meet inclusion criteria genes for alcohol dependence and the pathways that are enriched in alcohol dependence-related genes. In view of the fact that the effects of an entire gene that 4116 subjects integrates many SNPs would be different from those of a single SNP, and the effects of an entire pathway that integrates many genes would be different from those 2927 European Americans 1189 African-Americans of a single gene, it is anticipated that the results from gene-based analyses might not be completely consistent 1518 controls 681 cases 508 controls with those from pathway-based analyses that use the 1409 cases same dataset, and, similarly, the results from gene- and pathway-based analyses might not be completely consistent with those from SNP-based analyses in 2.2 Genotyping previous GWASs on the same datasets. In other words, All subjects were genotyped on the Illumina Human 1M gene- and pathway-based analysis may lead to novel beadchip. Phenotype and genotype data were rigorously findings. cleaned before association analysis. Subjects with poor genotypic data, allele discordance, problematic sample identification (relatedness, misidentification, 2. Materials and Methods misspecification), duplicated identifiers, gender or 2.1 Subjects chromosomal anomalies, ethnicity issues (including missing information, non-EA or AA, mismatch between The identification of the GWAS data used in this analysis self- and genetically-inferred ethnicity), or with a is shown in Figure 1. Data from 1409 European-American missing genotype call rate ≥2% across all SNPs were (EA) cases with alcohol dependence (based on DSM-IV excluded. Furthermore, SNPs with allele discordance, criteria),1518 EA healthy controls,681 African-American chromosomal anomalies or batch effect, SNPs with an (AA) cases, and 508 AA healthy controls were included overall missing genotype call rate ≥2%, monomorphic in this analysis. Detailed demographic data on these [11,12] SNPs, SNPs with minor allele frequencies <0.01 in subjects were presented in previous GWASs. These either EAs or AAs, and SNPs that deviated from Hardy- data came from the merged SAGE (Study of Addiction: Weinberg equilibrium (p<10−4) within EA or AA controls Genetics and Environment) and COGA (Collaborative were also excluded. This selection process yielded Study On The Genetics of Alcoholism) datasets, which are 805,814 SNPs in EAs and 895,714 SNPs in AAs.[11,12] available on the database of Genotypes and Phenotypes (dbGaP)( https://www.ncbi.nlm.nih.gov/gap). The SAGE dataset (dbGaP access number: phs000092. 2.3 Statistical methods v1.p1) included COGA, COGEND (Collaborative Genetic The genotyping data on autosomes were extracted from Study of Nicotine Dependence), and FSCD (Family an Oracle database and stored efficiently in flat files for Study of Cocaine Dependence) subsets. This COGA gene- and pathway-based analysis. subset included in the SAGE dataset was a subset of the main dbGaP COGA dataset (access number: phs000125.v1.p1), so when we merged the SAGE and 2.3.1 Gene-based GWAS analysis COGA datasets, one copy of 1477 overlapping subjects The genotype was configured into a genotype score of were excluded.[11] The projects that collected these 1, 2, or 3: 1 represented a minor allele homozygote, 2 • 113 • Shanghai Archives of Psychiatry, 2015, Vol. 27, No. 2 represented a heterozygote, and 3 represented a major was significantly associated with alcohol dependence allele homozygote. SNPs were mapped to known genes/ (p=3.9E-7). If flanking regions were reduced to ±10kb, exons/introns boundaries obtained from the National PXN (±10kb) remained significantly associated with Center for Biotechnology Information (NCBI). Principal alcohol dependence in EAs (p<E-8), and the other 19 component analysis was applied to the SNPs within top-ranked risk genes remained nominally significant the defined gene boundary and then the components (p<0.05). which explained at least 85% of the variation were used
Recommended publications
  • Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

    Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

    Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources Chung-Feng Kao1, Yu-Sheng Fang2, Zhongming Zhao3, Po-Hsiu Kuo1,2,4* 1 Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan, 2 Institute of Clinical Medicine, School of Medicine, National Cheng-Kung University, Tainan, Taiwan, 3 Departments of Biomedical Informatics and Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 4 Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan Abstract Background: Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression. Methods: Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues. Results: A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources.
  • Transcriptional Regulation of RKIP in Prostate Cancer Progression

    Transcriptional Regulation of RKIP in Prostate Cancer Progression

    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Sciences Transcriptional Regulation of RKIP in Prostate Cancer Progression Submitted by: Sandra Marie Beach In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Sciences Examination Committee Major Advisor: Kam Yeung, Ph.D. Academic William Maltese, Ph.D. Advisory Committee: Sonia Najjar, Ph.D. Han-Fei Ding, M.D., Ph.D. Manohar Ratnam, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: May 16, 2007 Transcriptional Regulation of RKIP in Prostate Cancer Progression Sandra Beach University of Toledo ACKNOWLDEGMENTS I thank my major advisor, Dr. Kam Yeung, for the opportunity to pursue my degree in his laboratory. I am also indebted to my advisory committee members past and present, Drs. Sonia Najjar, Han-Fei Ding, Manohar Ratnam, James Trempe, and Douglas Pittman for generously and judiciously guiding my studies and sharing reagents and equipment. I owe extended thanks to Dr. William Maltese as a committee member and chairman of my department for supporting my degree progress. The entire Department of Biochemistry and Cancer Biology has been most kind and helpful to me. Drs. Roy Collaco and Hong-Juan Cui have shared their excellent technical and practical advice with me throughout my studies. I thank members of the Yeung laboratory, Dr. Sungdae Park, Hui Hui Tang, Miranda Yeung for their support and collegiality. The data mining studies herein would not have been possible without the helpful advice of Dr. Robert Trumbly. I am also grateful for the exceptional assistance and shared microarray data of Dr.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Modulating Hallmarks of Cholangiocarcinoma

    Modulating Hallmarks of Cholangiocarcinoma

    University of Nebraska Medical Center DigitalCommons@UNMC Theses & Dissertations Graduate Studies Fall 12-14-2018 Modulating Hallmarks of Cholangiocarcinoma Cody Wehrkamp University of Nebraska Medical Center Follow this and additional works at: https://digitalcommons.unmc.edu/etd Part of the Molecular Biology Commons Recommended Citation Wehrkamp, Cody, "Modulating Hallmarks of Cholangiocarcinoma" (2018). Theses & Dissertations. 337. https://digitalcommons.unmc.edu/etd/337 This Dissertation is brought to you for free and open access by the Graduate Studies at DigitalCommons@UNMC. It has been accepted for inclusion in Theses & Dissertations by an authorized administrator of DigitalCommons@UNMC. For more information, please contact [email protected]. MODULATING HALLMARKS OF CHOLANGIOCARCINOMA by Cody J. Wehrkamp A DISSERTATION Presented to the Faculty of the University of Nebraska Graduate College in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Biochemistry and Molecular Biology Graduate Program Under the Supervision of Professor Justin L. Mott University of Nebraska Medical Center Omaha, Nebraska November 2018 Supervisory Committee: Kaustubh Datta, Ph.D. Melissa Teoh‐Fitzgerald, Ph.D. Richard G. MacDonald, Ph.D. Acknowledgements This endeavor has led to scientific as well as personal growth for me. I am indebted to many for their knowledge, influence, and support along the way. To my mentor, Dr. Justin L. Mott, you have been an incomparable teacher and invaluable guide. You upheld for me the concept that science is intrepid, even when the experience is trying. Through my training, and now here at the end, I can say that it has been an honor to be your protégé. When you have shaped your future graduates to be and do great, I will be privileged to say that I was your first one.
  • Serum Albumin OS=Homo Sapiens

    Serum Albumin OS=Homo Sapiens

    Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo
  • S41467-020-18249-3.Pdf

    S41467-020-18249-3.Pdf

    ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible.
  • WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).
  • Quantitative Live Cell Imaging Reveals Influenza Virus Manipulation Of

    Quantitative Live Cell Imaging Reveals Influenza Virus Manipulation Of

    ARTICLE https://doi.org/10.1038/s41467-019-13838-3 OPEN Quantitative live cell imaging reveals influenza virus manipulation of Rab11A transport through reduced dynein association Amar R. Bhagwat 1, Valerie Le Sage1, Eric Nturibi1, Katarzyna Kulej2, Jennifer Jones 1, Min Guo3, Eui Tae Kim 2, Benjamin A. Garcia4,5, Matthew D. Weitzman2,5,6, Hari Shroff3 & Seema S. Lakdawala 1,7* fl 1234567890():,; Assembly of infectious in uenza A viruses (IAV) is a complex process involving transport from the nucleus to the plasma membrane. Rab11A-containing recycling endosomes have been identified as a platform for intracellular transport of viral RNA (vRNA). Here, using high spatiotemporal resolution light-sheet microscopy (~1.4 volumes/second, 330 nm isotropic resolution), we quantify Rab11A and vRNA movement in live cells during IAV infection and report that IAV infection decreases speed and increases arrest of Rab11A. Unexpectedly, infection with respiratory syncytial virus alters Rab11A motion in a manner opposite to IAV, suggesting that Rab11A is a common host component that is differentially manipulated by respiratory RNA viruses. Using two-color imaging we demonstrate co-transport of Rab11A and IAV vRNA in infected cells and provide direct evidence that vRNA-associated Rab11A have altered transport. The mechanism of altered Rab11A movement is likely related to a decrease in dynein motors bound to Rab11A vesicles during IAV infection. 1 Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, 450 Technology Drive, Pittsburgh, PA 15219, USA. 2 The Children’s Hospital of Philadelphia Research Institute, 3501 Civic Center Dr., Philadelphia, PA 19104, USA. 3 Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 13 South Drive, Building 13, Bethesda, MD 20892, USA.
  • NATURAL KILLER CELLS, HYPOXIA, and EPIGENETIC REGULATION of HEMOCHORIAL PLACENTATION by Damayanti Chakraborty Submitted to the G

    NATURAL KILLER CELLS, HYPOXIA, and EPIGENETIC REGULATION of HEMOCHORIAL PLACENTATION by Damayanti Chakraborty Submitted to the G

    NATURAL KILLER CELLS, HYPOXIA, AND EPIGENETIC REGULATION OF HEMOCHORIAL PLACENTATION BY Damayanti Chakraborty Submitted to the graduate degree program in Pathology and Laboratory Medicine and the Graduate Faculty of the University of Kansas in partial fulfillment ofthe requirements for the degree of Doctor of Philosophy. ________________________________ Chair: Michael J. Soares, Ph.D. ________________________________ Jay Vivian, Ph.D. ________________________________ Patrick Fields, Ph.D. ________________________________ Soumen Paul, Ph.D. ________________________________ Michael Wolfe, Ph.D. ________________________________ Adam J. Krieg, Ph.D. Date Defended: 04/01/2013 The Dissertation Committee for Damayanti Chakraborty certifies that this is the approved version of the following dissertation: NATURAL KILLER CELLS, HYPOXIA, AND EPIGENETIC REGULATION OF HEMOCHORIAL PLACENTATION ________________________________ Chair: Michael J. Soares, Ph.D. Date approved: 04/01/2013 ii ABSTRACT During the establishment of pregnancy, uterine stromal cells differentiate into decidual cells and recruit natural killer (NK) cells. These NK cells are characterized by low cytotoxicity and distinct cytokine production. In rodent as well as in human pregnancy, the uterine NK cells peak in number around mid-gestation after which they decline. NK cells associate with uterine spiral arteries and are implicated in pregnancy associated vascular remodeling processes and potentially in modulating trophoblast invasion. Failure of trophoblast invasion and vascular remodeling has been shown to be associated with pathological conditions like preeclampsia syndrome, hypertension in mother and/or fetal growth restriction. We hypothesize that NK cells fundamentally contribute to the organization of the placentation site. In order to study the in vivo role of NK cells during pregnancy, gestation stage- specific NK cell depletion was performed in rats using anti asialo GM1 antibodies.
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
  • Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses

    Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses

    medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened.
  • Convergent Functional Genomics of Schizophrenia: from Comprehensive Understanding to Genetic Risk Prediction

    Convergent Functional Genomics of Schizophrenia: from Comprehensive Understanding to Genetic Risk Prediction

    Molecular Psychiatry (2012) 17, 887 -- 905 & 2012 Macmillan Publishers Limited All rights reserved 1359-4184/12 www.nature.com/mp IMMEDIATE COMMUNICATION Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction M Ayalew1,2,9, H Le-Niculescu1,9, DF Levey1, N Jain1, B Changala1, SD Patel1, E Winiger1, A Breier1, A Shekhar1, R Amdur3, D Koller4, JI Nurnberger1, A Corvin5, M Geyer6, MT Tsuang6, D Salomon7, NJ Schork7, AH Fanous3, MC O’Donovan8 and AB Niculescu1,2 We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-- coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease.