(Adcy4) in Y1 ADRENOCORTICAL TUMOR CELLS
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Supplemental Table S1
Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 -
Gene and Pathway-Based Second-Wave Analysis of Genome-Wide Association Studies
European Journal of Human Genetics (2010) 18, 111–117 & 2010 Macmillan Publishers Limited All rights reserved 1018-4813/10 $32.00 www.nature.com/ejhg ARTICLE Gene and pathway-based second-wave analysis of genome-wide association studies Gang Peng1, Li Luo2, Hoicheong Siu1, Yun Zhu1, Pengfei Hu1, Shengjun Hong1, Jinying Zhao3, Xiaodong Zhou4, John D Reveille4, Li Jin1, Christopher I Amos5 and Momiao Xiong*,2 Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression -
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 -
HHS Public Access Author Manuscript
HHS Public Access Author manuscript Author Manuscript Author ManuscriptBreast Cancer Author Manuscript Res Treat Author Manuscript . Author manuscript; available in PMC 2016 June 01. Published in final edited form as: Breast Cancer Res Treat. 2015 June ; 151(2): 453–463. doi:10.1007/s10549-015-3401-8. Body mass index associated with genome-wide methylation in breast tissue Brionna Y. Hair1, Zongli Xu2, Erin L. Kirk1, Sophia Harlid2, Rupninder Sandhu3, Whitney R. Robinson1,3, Michael C. Wu4, Andrew F. Olshan1, Kathleen Conway1,3, Jack A. Taylor2, and Melissa A. Troester1 1 Department of Epidemiology, University of North Carolina at Chapel Hill, CB #7435, 2101 McGavran-Greenberg Hall, Chapel Hill, NC 27599-7435, USA 2 Epidemiology Branch, and Epigenomics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences (NIH), Research Triangle Park, NC, USA 3 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 4 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Abstract Gene expression studies indicate that body mass index (BMI) is associated with molecular pathways involved in inflammation, insulin-like growth factor activation, and other carcinogenic processes in breast tissue. The goal of this study was to determine whether BMI is associated with gene methylation in breast tissue and to identify pathways that are commonly methylated in association with high BMI. Epigenome-wide methylation profiles were determined using the Illumina HumanMethylation450 BeadChip array in the non-diseased breast tissue of 81 women undergoing breast surgery between 2009 and 2013 at the University of North Carolina Hospitals. Multivariable, robust linear regression was performed to identify methylation sites associated with BMI at a false discovery rate q value <0.05. -
Transcriptome Atlas of Eight Liver Cell Types Uncovers Effects of Histidine
c Indian Academy of Sciences RESEARCH ARTICLE Transcriptome atlas of eight liver cell types uncovers effects of histidine catabolites on rat liver regeneration C. F. CHANG1,2,J.Y.FAN2, F. C. ZHANG1,J.MA1 and C. S. XU2,3∗ 1College of Life Science and Technology, Xinjiang University, 14# Shengli Road, Urumqi 830046, Xinjiang, People’s Republic of China 2Key Laboratory for Cell Differentiation Regulation, 3College of Life Science, Henan Normal University, 46# East of Construction Road, Xinxiang 453007, People’s Republic of China Abstract Eight liver cell types were isolated using the methods of Percoll density gradient centrifugation and immunomagnetic beads to explore effects of histidine catabolites on rat liver regeneration. Rat Genome 230 2.0 Array was used to detect the expression profiles of genes associated with metabolism of histidine and its catabolites for the above-mentioned eight liver cell types, and bioinformatic and systems biology approaches were employed to analyse the relationship between above genes and rat liver regeneration. The results showed that the urocanic acid (UA) was degraded from histidine in Kupffer cells, acts on Kupffer cells itself and dendritic cells to generate immune suppression by autocrine and paracrine modes. Hepatocytes, biliary epithelia cells, oval cells and dendritic cells can convert histidine to histamine, which can promote sinusoidal endothelial cells proliferation by GsM pathway, and promote the proliferation of hepatocytes and biliary epithelia cells by GqM pathway. [Chang C. F., Fan J. Y., Zhang F. C., Ma J. and Xu C. S. 2010 Transcriptome atlas of eight liver cell types uncovers effects of histidine catabolites on rat liver regeneration. -
Protein Family Members. the GENE.FAMILY
Table 3: Protein family members. The GENE.FAMILY col- umn shows the gene family name defined either by HGNC (superscript `H', http://www.genenames.org/cgi-bin/family_ search) or curated manually by us from Entrez IDs in the NCBI database (superscript `C' for `Custom') that we have identified as corresonding for each ENTITY.ID. The members of each gene fam- ily that are in at least one of our synaptic proteome datasets are shown in IN.SYNAPSE, whereas those not found in any datasets are in the column OUT.SYNAPSE. In some cases the intersection of two HGNC gene families are needed to specify the membership of our protein family; this is indicated by concatenation of the names with an ampersand. ENTITY.ID GENE.FAMILY IN.SYNAPSE OUT.SYNAPSE AC Adenylate cyclasesH ADCY1, ADCY2, ADCY10, ADCY4, ADCY3, ADCY5, ADCY7 ADCY6, ADCY8, ADCY9 actin ActinsH ACTA1, ACTA2, ACTB, ACTC1, ACTG1, ACTG2 ACTN ActininsH ACTN1, ACTN2, ACTN3, ACTN4 AKAP A-kinase anchoring ACBD3, AKAP1, AKAP11, AKAP14, proteinsH AKAP10, AKAP12, AKAP17A, AKAP17BP, AKAP13, AKAP2, AKAP3, AKAP4, AKAP5, AKAP6, AKAP8, CBFA2T3, AKAP7, AKAP9, RAB32 ARFGEF2, CMYA5, EZR, MAP2, MYO7A, MYRIP, NBEA, NF2, SPHKAP, SYNM, WASF1 CaM Endogenous ligands & CALM1, CALM2, EF-hand domain CALM3 containingH CaMKK calcium/calmodulin- CAMKK1, CAMKK2 dependent protein kinase kinaseC CB CalbindinC CALB1, CALB2 CK1 Casein kinase 1C CSNK1A1, CSNK1D, CSNK1E, CSNK1G1, CSNK1G2, CSNK1G3 CRHR Corticotropin releasing CRHR1, CRHR2 hormone receptorsH DAGL Diacylglycerol lipaseC DAGLA, DAGLB DGK Diacylglycerol kinasesH DGKB, -
Autocrine IFN Signaling Inducing Profibrotic Fibroblast Responses By
Downloaded from http://www.jimmunol.org/ by guest on September 23, 2021 Inducing is online at: average * The Journal of Immunology , 11 of which you can access for free at: 2013; 191:2956-2966; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication August 2013; doi: 10.4049/jimmunol.1300376 http://www.jimmunol.org/content/191/6/2956 A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Autocrine IFN Signaling Feng Fang, Kohtaro Ooka, Xiaoyong Sun, Ruchi Shah, Swati Bhattacharyya, Jun Wei and John Varga J Immunol cites 49 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130037 6.DC1 This article http://www.jimmunol.org/content/191/6/2956.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 23, 2021. The Journal of Immunology A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Inducing Autocrine IFN Signaling Feng Fang,* Kohtaro Ooka,* Xiaoyong Sun,† Ruchi Shah,* Swati Bhattacharyya,* Jun Wei,* and John Varga* Activation of TLR3 by exogenous microbial ligands or endogenous injury-associated ligands leads to production of type I IFN. -
Mrna Expression in Human Leiomyoma and Eker Rats As Measured by Microarray Analysis
Table 3S: mRNA Expression in Human Leiomyoma and Eker Rats as Measured by Microarray Analysis Human_avg Rat_avg_ PENG_ Entrez. Human_ log2_ log2_ RAPAMYCIN Gene.Symbol Gene.ID Gene Description avg_tstat Human_FDR foldChange Rat_avg_tstat Rat_FDR foldChange _DN A1BG 1 alpha-1-B glycoprotein 4.982 9.52E-05 0.68 -0.8346 0.4639 -0.38 A1CF 29974 APOBEC1 complementation factor -0.08024 0.9541 -0.02 0.9141 0.421 0.10 A2BP1 54715 ataxin 2-binding protein 1 2.811 0.01093 0.65 0.07114 0.954 -0.01 A2LD1 87769 AIG2-like domain 1 -0.3033 0.8056 -0.09 -3.365 0.005704 -0.42 A2M 2 alpha-2-macroglobulin -0.8113 0.4691 -0.03 6.02 0 1.75 A4GALT 53947 alpha 1,4-galactosyltransferase 0.4383 0.7128 0.11 6.304 0 2.30 AACS 65985 acetoacetyl-CoA synthetase 0.3595 0.7664 0.03 3.534 0.00388 0.38 AADAC 13 arylacetamide deacetylase (esterase) 0.569 0.6216 0.16 0.005588 0.9968 0.00 AADAT 51166 aminoadipate aminotransferase -0.9577 0.3876 -0.11 0.8123 0.4752 0.24 AAK1 22848 AP2 associated kinase 1 -1.261 0.2505 -0.25 0.8232 0.4689 0.12 AAMP 14 angio-associated, migratory cell protein 0.873 0.4351 0.07 1.656 0.1476 0.06 AANAT 15 arylalkylamine N-acetyltransferase -0.3998 0.7394 -0.08 0.8486 0.456 0.18 AARS 16 alanyl-tRNA synthetase 5.517 0 0.34 8.616 0 0.69 AARS2 57505 alanyl-tRNA synthetase 2, mitochondrial (putative) 1.701 0.1158 0.35 0.5011 0.6622 0.07 AARSD1 80755 alanyl-tRNA synthetase domain containing 1 4.403 9.52E-05 0.52 1.279 0.2609 0.13 AASDH 132949 aminoadipate-semialdehyde dehydrogenase -0.8921 0.4247 -0.12 -2.564 0.02993 -0.32 AASDHPPT 60496 aminoadipate-semialdehyde -
(QUEENIE) CHEONG, BS Submitted in Partial
MOLECULAR AND PHYSIOLOGICAL RESPONSES TO HYPOXIA by HOI I (QUEENIE) CHEONG, B.S. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Molecular Medicine CASE WESTERN RESERVE UNIVERSITY May, 2017 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/ dissertation of Hoi I (Queenie) Cheong Candidate for the degree of Ph.D. Committee Chair Kingman P. Strohl, M.D. Committee Members Mitchell Drumm, Ph.D. George R. Stark, Ph.D. Sathyamangla V. Naga Prasad, Ph.D. Serpil C. Erzurum, M.D. (Thesis Advisor) Date of Defense February 27th, 2017 * We also certify that written approval has been obtained for any proprietary material contained therein. DEDICATION To my parents, Lai Wa Lam and Kuok Kin Cheong. For they are the reason for whom I have become today. For their strength and love to support me to leave home since 2003 to broaden my vision and knowledge. To my mentor, Serpil C. Erzurum. For providing such wonderful learning opportunities and experience for science, medicine, collaborative spirits and leadership. To my husband, Emir Charles Roach. For his continuous love and kind support. For his intellectual curiosity to stir daily scientific discussions. For his excitements and encouragements. Table of Contents List of Tables iii List of Figures iv Acknowledgements vi List of Abbreviations viii Abstract ix Chapters 1. Introduction I. Hypoxia 1 II. HIF-1 in Hypoxia Sensing Ø History of discovery 1 Ø Function 2 Ø Regulation 3 III. Beta-Adrenergic Receptors and Hypoxia Responses Ø βAR subtypes, expression, structure and history 4 Ø Ligand binding 5 Ø β2AR signaling 5 Ø βAR function 6 Ø βAR under hypoxia 8 IV. -
Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein -
Maternal Folic Acid Impacts DNA Methylation Profile in Male Rat Offspring Implicated in Neurodevelopment and Learning/Memory
Wang et al. Genes & Nutrition (2021) 16:1 https://doi.org/10.1186/s12263-020-00681-1 RESEARCH Open Access Maternal folic acid impacts DNA methylation profile in male rat offspring implicated in neurodevelopment and learning/memory abilities Xinyan Wang1, Zhenshu Li1, Yun Zhu2,3, Jing Yan3,4, Huan Liu1,3, Guowei Huang1,3 and Wen Li1,3* Abstract Background: Periconceptional folic acid (FA) supplementation not only reduces the incidence of neural tube defects, but also improves cognitive performances in offspring. However, the genes or pathways that are epigenetically regulated by FA in neurodevelopment were rarely reported. Methods: To elucidate the underlying mechanism, the effect of FA on the methylation profiles in brain tissue of male rat offspring was assessed by methylated DNA immunoprecipitation chip. Differentially methylated genes (DMGs) and gene network analysis were identified using DAVID and KEGG pathway analysis. Results: Compared with the folate-normal diet group, 1939 DMGs were identified in the folate-deficient diet group, and 1498 DMGs were identified in the folate-supplemented diet group, among which 298 DMGs were overlapped. The pathways associated with neurodevelopment and learning/memory abilities were differentially methylated in response to maternal FA intake during pregnancy, and there were some identical and distinctive potential mechanisms under FA deficiency or FA-supplemented conditions. Conclusions: In conclusion, genes and pathways associated with neurodevelopment and learning/memory abilities were differentially -
Discovery of Pharmaceutically-Targetable Pathways and Prediction of Survivorship for Pneumonia and Sepsis Patients from the View Point of Ensemble Gene Noise
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.10.035717; this version posted April 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise Tristan de Jong1,4, Victor Guryev1,4, Yury M. Moshkin2,3,4 1 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands, 2 Federal Research Centre, Institute of Cytology and Genetics, SB RAS, Novosibirsk, Russia, 3 Institute of Molecular and Cellular Biology, SB RAS, Novosibirsk, Russia, 4 Gene Learning Association, Geneva, Switzerland Correspondence: [email protected]; [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.04.10.035717; this version posted April 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Abstract Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is rather challenging. This stems from the heterogenous response of individuals to disease which is also reflected in the inter-individual variability of gene expression responses. This in turn obscures differential gene expression analysis (DGE). In the midst of the COVID-19 pandemic, we wondered whether an alternative to DGE approaches could be applied to dissect the molecular nature of a host-response to infection exemplified here by an analysis of H1N1 influenza, community/hospital acquired pneumonia (CAP) and sepsis.