DOCSLIB.ORG

The Genetic Landscape of Renal Complications in Type 1 Diabetes

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Genetic Landscape of Renal Complications in Type 1 Diabetes Supplementary information: Genome-wide dissection of diabetic kidney disease The Genetic Landscape of Renal Complications in Type 1 Diabetes SUPPLEMENTAL INFORMATION Niina Sandholm, Natalie Van Zuydam, Emma Ahlqvist, Thorhildur Juliusdottir, Harshal A. Deshmukh, N. William Rayner, Barbara Di Camillo, Carol Forsblom, Joao Fadista, Daniel Ziemek, Rany M. Salem, Linda T. Hiraki, Marcus Pezzolesi, David Trégouët, Emma Dahlström, Erkka Valo, Nikolay Oskolkov, Claes Ladenvall, M. Loredana Marcovecchio, Jason Cooper, Francesco Sambo, Alberto Malovini, Marco Manfrini, Amy Jayne McKnight, Maria Lajer, Valma Harjutsalo, Daniel Gordin, Maija Parkkonen, Jaakko Tuomilehto, Valeriya Lyssenko, Paul M. McKeigue, Stephen S. Rich, Mary Julia Brosnan, Eric Fauman, Riccardo Bellazzi, Peter Rossing, Samy Hadjadj, Andrzej Krolewski, Andrew D. Paterson, Jose C. Florez, Joel N. Hirschhorn, Alexander P. Maxwell, David Dunger, The DCCT/EDIC Study Group, GENIE Consortium, The FinnDiane Study Group, Claudio Cobelli, Helen M. Colhoun, Leif Groop, Mark I. McCarthy, Per- Henrik Groop, on behalf of The SUMMIT Consortium 1 Supplementary information: Genome-wide dissection of diabetic kidney disease COMPLETE METHODS ........................................................................................................................................... 4 Subjects with Type 1 diabetes (T1D) .............................................................................................................................. 4 Genotypes ....................................................................................................................................................................... 7 Statistical methods ......................................................................................................................................................... 9 SUPPLEMENTAL TABLES ...................................................................................................................................... 14 Supplemental Table 1: Proportion of phenotypic variance explained by the GWAS genotypes in FinnDiane, estimated with GCTA .................................................................................................................................................... 14 Supplemental Table 2: Information on genotyping, and clinical characteristics of the discovery and replication patients divided based on the seven case – control definitions .................................................................................. 15 Supplemental Table 3: Number of subjects included in the analysis in the discovery and in silico replication cohorts ...................................................................................................................................................................................... 16 Supplemental Table 4: Statistical power to detect association with ‘combined DKD’ with genome-wide significance (p<5×10-8) at the discovery stage. ............................................................................................................................... 17 Supplemental Table 5: Association analysis results for the 101 GWAS SNPs selected for in silico replication. .......... 18 Supplemental Table 6: Statistical power to detect association with ‘Combined DKD’ with genome-wide significance (p<5x10-8) with the two-stage study design. ................................................................................................................ 19 Supplemental Table 7: Association at the AFF3 locus with ‘ESRD vs. non-ESRD’ phenotypic comparison, conditional on the previously reported lead SNP rs7583877 .......................................................................................................... 20 Supplemental Table 8: Statistical power to detect association with the ‘Late DKD’ phenotype for varying odds ratio and risk allele frequency. .............................................................................................................................................. 21 Supplemental Table 9: Evaluation of previously reported candidate genes or GWAS loci on kidney complications in type 1 and type 2 diabetes, or GWAS on CKD in the general population. ................................................................... 22 Supplemental Table 10: Association between diabetic kidney complications and genetic risk scores of related phenotypes ................................................................................................................................................................... 24 Supplemental Table 11: MAGENTA Gene set enrichment results with FDR<0.05 ....................................................... 28 Supplemental Table 12: Characteristics of the patients selected for the whole exome sequencing .......................... 30 Supplemental Table 13. Top 20 results of single variant analysis for WES ‘ESRD vs. no DKD’ using the score test .... 31 Supplemental Table 14. Top 20 associations for WES ‘Late DKD’ using the single variant score test ......................... 32 Supplemental Table 15: Top 10 associations to WES ‘Late DKD’ with VT with 4 masks .............................................. 33 Supplemental Table 16: Top 10 associations to WES ‘Late DKD’ using SKAT-O with 4 different masks. ..................... 34 Supplemental Table 17: Top 10 associations to WES ‘Late DKD’ using SKAT with 4 masks ......................................... 35 Supplemental table 18: Top 10 associations to WES ‘ESRD vs. no DKD’ with VT and 4 masks .................................... 36 Supplemental Table 19: Top 10 associations to WES ‘ESRD vs. no DKD’ with SKAT-O with 4 masks .......................... 37 Supplemental Table 20: Top 10 associations to WES ‘ESRD vs. no DKD’ with SKAT with 4 masks .............................. 38 Supplemental Table 21: Gene-sets which showed enrichment in WES association data on the ‘Late DKD’ phenotype, with permutation (N=100) results ................................................................................................................................ 39 Supplemental Table 22: ABACUS association analysis results for the top SNPs from the GWAS discovery ................ 41 2 Supplementary information: Genome-wide dissection of diabetic kidney disease Supplemental Table 23: ABACUS association analysis results for the top SNPs from the WES discovery ................... 43 Supplemental Table 24: DAVID functional clustering of the genes mapped by the SNPs selected by the ABACUS software on GWAS data ............................................................................................................................................... 44 Supplemental Table 25: DAVID functional clustering of the genes mapped by the SNPs selected by the ABACUS software on WES data .................................................................................................................................................. 46 Supplemental Table 26: Phenotype definitions. Table A: albuminuria- and eGFR based definitions. Table B: Case – control phenotypes. ..................................................................................................................................................... 48 Supplemental Table 27: Membership of the GENIE Consortium ................................................................................. 49 Supplemental Table 28: List of the FinnDiane centers and participating physicians and nurses. ............................... 50 Supplemental Table 29: Membership of the SUMMIT Consortium ............................................................................. 53 SUPPLEMENTAL FIGURES .................................................................................................................................... 61 Supplemental Figure 1: Schematic picture of the study plan. In the GWAS setting, the stage 1 included T1D patients from the FinnDiane, EURODIAB, SDR, and Cambridge studies. Stage 1 GWAS meta-analysis results were used for evaluation of the previously reported loci, analysis of genetic risk scores, LD score regression, and for the pathway analyses. Stage 2 included patients from the UK-ROI, GoKinD US, French-Danish effort, DCCT/EDIC, and Joslin studies. Stage 3 replication consisted of additional T1D FinnDiane patients not part of the FinnDiane GWAS. Whole exome sequencing (WES) included patients from the FinnDiane, SDR, and Steno studies. Finally, the bivariate association analyses were performed in all GWAS stage 1 studies and in WES studies. ............................................. 61 Supplemental Figure 2: Manhattan and QQ-plots for the seven studied phenotype definitions. .............................. 62 Supplemental Figure 3: LocusZoom and Forest plots of the top loci. .......................................................................... 65 Supplemental Figure 4: P-value distribution of association at the previously reported loci for DKD or CKD in the general population. ...................................................................................................................................................... 66 Supplemental Figure 5: Association at previously reported loci plotted by the previously reported A) p-values and by B) Z-scores. ............................................................................................................................................................... 67 Supplemental Figure 6: Genome-wide comparison of the
Load more

Recommended publications
  • Familial Nephropathy and Multiple Exostoses with Exostosin-1 (EXT1) Gene Mutation
    PATHOPHYSIOLOGY of the RENAL BIOPSY www.jasn.org Familial Nephropathy and Multiple Exostoses With Exostosin-1 (EXT1) Gene Mutation Ian S. D. Roberts* and Jonathan M. Gleadle† *Department of Cellular Pathology, John Radcliffe Hospital, Headley Way, Headington, Oxford, United Kingdom; and †Renal Unit, Level 6, Flinders Medical Centre, Bedford Park, South Australia, Australia ABSTRACT Glomerular deposition of fibrillar collagen is a characteristic finding of genetically mained in remission with trace protein- distinct conditions, including nail-patella syndrome and collagen type III glomeru- uria until cyclosporine was stopped 3.5 lopathy. A case of familial nephropathy in which steroid-sensitive nephrotic syn- yr later. Six months after this, she suf- drome and glomerular deposits of fibrillar collagen are associated with multiple fered another relapse of nephrotic exostoses due to mutation of the EXT1 gene is described. This gene encodes a syndrome that responded to 60 mg pred- glycosyltransferase required for synthesis of heparan sulfate glycosaminoglycans. nisolone and reintroduction of cyclo- There is deficiency of heparan sulfate and perlecan, together with accumulation of sporine. After a further relapse 18 mo collagens, in the matrix of EXT1-associated osteochondromas. Similar glomerular later and because of the development of basement membrane abnormalities could offer an explanation for both the renal adverse corticosteroid effects, she was ultrastructural changes and steroid-sensitive nephrotic syndrome. treated with a 2-mo course of cyclophos- phamide (2.5 mg/kg, orally). Ten years J Am Soc Nephrol 19: 450–453, 2008. doi: 10.1681/ASN.2007080842 after her initial presentation, she remains in full remission and off steroids. Renal function has remained normal through- A 37-yr-old woman presented with the history of renal disease and hearing im- out with a current serum creatinine of nephrotic syndrome.
    [Show full text]
  • CRISPR Screening of Porcine Sgrna Library Identifies Host Factors
    ARTICLE https://doi.org/10.1038/s41467-020-18936-1 OPEN CRISPR screening of porcine sgRNA library identifies host factors associated with Japanese encephalitis virus replication Changzhi Zhao1,5, Hailong Liu1,5, Tianhe Xiao1,5, Zichang Wang1, Xiongwei Nie1, Xinyun Li1,2, Ping Qian2,3, Liuxing Qin3, Xiaosong Han1, Jinfu Zhang1, Jinxue Ruan1, Mengjin Zhu1,2, Yi-Liang Miao 1,2, Bo Zuo1,2, ✉ ✉ Kui Yang4, Shengsong Xie 1,2 & Shuhong Zhao 1,2 1234567890():,; Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that causes ence- phalitis and reproductive disorders in mammalian species. However, the host factors critical for its entry, replication, and assembly are poorly understood. Here, we design a porcine genome-scale CRISPR/Cas9 knockout (PigGeCKO) library containing 85,674 single guide RNAs targeting 17,743 protein-coding genes, 11,053 long ncRNAs, and 551 microRNAs. Subsequently, we use the PigGeCKO library to identify key host factors facilitating JEV infection in porcine cells. Several previously unreported genes required for JEV infection are highly enriched post-JEV selection. We conduct follow-up studies to verify the dependency of JEV on these genes, and identify functional contributions for six of the many candidate JEV- related host genes, including EMC3 and CALR. Additionally, we identify that four genes associated with heparan sulfate proteoglycans (HSPGs) metabolism, specifically those responsible for HSPGs sulfurylation, facilitate JEV entry into porcine cells. Thus, beyond our development of the largest CRISPR-based functional genomic screening platform for pig research to date, this study identifies multiple potentially vulnerable targets for the devel- opment of medical and breeding technologies to treat and prevent diseases caused by JEV.
    [Show full text]
  • Environmental Influences on Endothelial Gene Expression
    ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community.
    [Show full text]
  • Selectin Ligand Sialyl-Lewis X Antigen Drives Metastasis of Hormone-Dependent Breast Cancers
    Published OnlineFirst October 24, 2011; DOI: 10.1158/0008-5472.CAN-11-1139 Cancer Tumor and Stem Cell Biology Research Selectin Ligand Sialyl-Lewis x Antigen Drives Metastasis of Hormone-Dependent Breast Cancers Sylvain Julien1, Aleksandar Ivetic2, Anita Grigoriadis3, Ding QiZe1, Brian Burford1, Daisy Sproviero1, Gianfranco Picco1, Cheryl Gillett4, Suzanne L. Papp5, Lana Schaffer5, Andrew Tutt3, Joyce Taylor-Papadimitriou1, Sarah E. Pinder4, and Joy M. Burchell1 Abstract The glycome acts as an essential interface between cells and the surrounding microenvironment. However, changes in glycosylation occur in nearly all breast cancers, which can alter this interaction. Here, we report that profiles of glycosylation vary between ER-positive and ER-negative breast cancers. We found that genes involved in the synthesis of sialyl-Lewis x (sLex; FUT3, FUT4, and ST3GAL6) are significantly increased in estrogen receptor alpha-negative (ER-negative) tumors compared with ER-positive ones. SLex expression had no influence on the survival of patients whether they had ER-negative or ER-positive tumors. However, high expression of sLex in ER- positive tumors was correlated with metastasis to the bone where sLex receptor E-selectin is constitutively expressed. The ER-positive ZR-75-1 and the ER-negative BT20 cell lines both express sLex but only ZR-75-1 cells could adhere to activated endothelial cells under dynamic flow conditions in a sLex and E-selectin–dependent manner. Moreover, L/P-selectins bound strongly to ER-negative MDA-MB-231 and BT-20 cell lines in a heparan sulfate (HS)–dependent manner that was independent of sLex expression. Expression of glycosylation genes involved in heparan biosynthesis (EXT1 and HS3ST1) was increased in ER-negative tumors.
    [Show full text]
  • 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.
    [Show full text]
  • Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
    Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen,
    [Show full text]
  • In Vivo Dual RNA-Seq Analysis Reveals the Basis for Differential Tissue Tropism of Clinical Isolates of Streptococcus Pneumoniae
    In Vivo Dual RNA-Seq Analysis Reveals the Basis for Differential Tissue Tropism of Clinical Isolates of Streptococcus pneumoniae Vikrant Minhas,1,4 Rieza Aprianto,2,4 Lauren J. McAllister,1 Hui Wang,1 Shannon C. David,1 Kimberley T. McLean,1 Iain Comerford,3 Shaun R. McColl,3 James C. Paton,1,5,6,* Jan-Willem Veening,2,5 and Claudia Trappetti,1,5 Supplementary Information Supplementary Table 1. Pneumococcal differential gene expression in the lungs 6 h post-infection, 9-47-Ear vs 9-47M. Genes with fold change (FC) greater than 2 and p < 0.05 are shown. FC values highlighted in blue = upregulated in 9-47-Ear, while values highlighted in red = upregulated in 9- 47M. Locus tag in 9-47- Product padj FC Ear Sp947_chr_00844 Sialidase B 3.08E-10 313.9807 Sp947_chr_02077 hypothetical protein 4.46E-10 306.9412 Sp947_chr_00842 Sodium/glucose cotransporter 2.22E-09 243.4822 Sp947_chr_00841 N-acetylneuraminate lyase 4.53E-09 227.7963 scyllo-inositol 2-dehydrogenase Sp947_chr_00845 (NAD(+)) 4.36E-09 221.051 Sp947_chr_00848 hypothetical protein 1.19E-08 202.7867 V-type sodium ATPase catalytic subunit Sp947_chr_00853 A 1.29E-06 100.5411 Sp947_chr_00846 Beta-glucoside kinase 3.42E-06 98.18951 Sp947_chr_00855 V-type sodium ATPase subunit D 8.34E-06 85.94879 Sp947_chr_00851 V-type sodium ATPase subunit C 2.50E-05 72.46612 Sp947_chr_00843 hypothetical protein 2.17E-05 65.97758 Sp947_chr_00839 HTH-type transcriptional regulator RpiR 3.09E-05 61.28171 Sp947_chr_00854 V-type sodium ATPase subunit B 1.32E-06 50.86992 Sp947_chr_00120 hypothetical protein 3.00E-04
    [Show full text]
  • Promiscuity and Specificity of Eukaryotic Glycosyltransferases
    Biochemical Society Transactions (2020) 48 891–900 https://doi.org/10.1042/BST20190651 Review Article Promiscuity and specificity of eukaryotic glycosyltransferases Ansuman Biswas and Mukund Thattai Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bangalore, India Correspondence: Mukund Thattai ([email protected]) Glycosyltransferases are a large family of enzymes responsible for covalently linking sugar monosaccharides to a variety of organic substrates. These enzymes drive the synthesis of complex oligosaccharides known as glycans, which play key roles in inter-cellular interac- tions across all the kingdoms of life; they also catalyze sugar attachment during the syn- thesis of small-molecule metabolites such as plant flavonoids. A given glycosyltransferase enzyme is typically responsible for attaching a specific donor monosaccharide, via a spe- cific glycosidic linkage, to a specific moiety on the acceptor substrate. However these enzymes are often promiscuous, able catalyze linkages between a variety of donors and acceptors. In this review we discuss distinct classes of glycosyltransferase promiscuity, each illustrated by enzymatic examples from small-molecule or glycan synthesis. We high- light the physical causes of promiscuity, and its biochemical consequences. Structural studies of glycosyltransferases involved in glycan synthesis show that they make specific contacts with ‘recognition motifs’ that are much smaller than the full oligosaccharide sub- strate. There is a wide range in the sizes of glycosyltransferase recognition motifs: highly promiscuous enzymes recognize monosaccharide or disaccharide motifs across multiple oligosaccharides, while highly specific enzymes recognize large, complex motifs found on few oligosaccharides. In eukaryotes, the localization of glycosyltransferases within compartments of the Golgi apparatus may play a role in mitigating the glycan variability caused by enzyme promiscuity.
    [Show full text]
  • Sex Differences in Glutamate Receptor Gene Expression in Major Depression and Suicide
    Molecular Psychiatry (2015) 20, 1057–1068 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp IMMEDIATE COMMUNICATION Sex differences in glutamate receptor gene expression in major depression and suicide AL Gray1, TM Hyde2,3, A Deep-Soboslay2, JE Kleinman2 and MS Sodhi1,4 Accumulating data indicate that the glutamate system is disrupted in major depressive disorder (MDD), and recent clinical research suggests that ketamine, an antagonist of the N-methyl-D-aspartate (NMDA) glutamate receptor (GluR), has rapid antidepressant efficacy. Here we report findings from gene expression studies of a large cohort of postmortem subjects, including subjects with MDD and controls. Our data reveal higher expression levels of the majority of glutamatergic genes tested in the dorsolateral prefrontal cortex (DLPFC) in MDD (F21,59 = 2.32, P = 0.006). Posthoc data indicate that these gene expression differences occurred mostly in the female subjects. Higher expression levels of GRIN1, GRIN2A-D, GRIA2-4, GRIK1-2, GRM1, GRM4, GRM5 and GRM7 were detected in the female patients with MDD. In contrast, GRM5 expression was lower in male MDD patients relative to male controls. When MDD suicides were compared with MDD non-suicides, GRIN2B, GRIK3 and GRM2 were expressed at higher levels in the suicides. Higher expression levels were detected for several additional genes, but these were not statistically significant after correction for multiple comparisons. In summary, our analyses indicate a generalized disruption of the regulation of the GluRs in the DLPFC of females with MDD, with more specific GluR alterations in the suicides and in the male groups.
    [Show full text]
  • 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
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • NICU Gene List Generator.Xlsx
    Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2
    [Show full text]