Genome-Wide Meta-Analysis Associates HLA-DQA1/DRB1 And
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Supplementary Table 1. Pain and PTSS Associated Genes (N = 604
Supplementary Table 1. Pain and PTSS associated genes (n = 604) compiled from three established pain gene databases (PainNetworks,[61] Algynomics,[52] and PainGenes[42]) and one PTSS gene database (PTSDgene[88]). These genes were used in in silico analyses aimed at identifying miRNA that are predicted to preferentially target this list genes vs. a random set of genes (of the same length). ABCC4 ACE2 ACHE ACPP ACSL1 ADAM11 ADAMTS5 ADCY5 ADCYAP1 ADCYAP1R1 ADM ADORA2A ADORA2B ADRA1A ADRA1B ADRA1D ADRA2A ADRA2C ADRB1 ADRB2 ADRB3 ADRBK1 ADRBK2 AGTR2 ALOX12 ANO1 ANO3 APOE APP AQP1 AQP4 ARL5B ARRB1 ARRB2 ASIC1 ASIC2 ATF1 ATF3 ATF6B ATP1A1 ATP1B3 ATP2B1 ATP6V1A ATP6V1B2 ATP6V1G2 AVPR1A AVPR2 BACE1 BAMBI BDKRB2 BDNF BHLHE22 BTG2 CA8 CACNA1A CACNA1B CACNA1C CACNA1E CACNA1G CACNA1H CACNA2D1 CACNA2D2 CACNA2D3 CACNB3 CACNG2 CALB1 CALCRL CALM2 CAMK2A CAMK2B CAMK4 CAT CCK CCKAR CCKBR CCL2 CCL3 CCL4 CCR1 CCR7 CD274 CD38 CD4 CD40 CDH11 CDK5 CDK5R1 CDKN1A CHRM1 CHRM2 CHRM3 CHRM5 CHRNA5 CHRNA7 CHRNB2 CHRNB4 CHUK CLCN6 CLOCK CNGA3 CNR1 COL11A2 COL9A1 COMT COQ10A CPN1 CPS1 CREB1 CRH CRHBP CRHR1 CRHR2 CRIP2 CRYAA CSF2 CSF2RB CSK CSMD1 CSNK1A1 CSNK1E CTSB CTSS CX3CL1 CXCL5 CXCR3 CXCR4 CYBB CYP19A1 CYP2D6 CYP3A4 DAB1 DAO DBH DBI DICER1 DISC1 DLG2 DLG4 DPCR1 DPP4 DRD1 DRD2 DRD3 DRD4 DRGX DTNBP1 DUSP6 ECE2 EDN1 EDNRA EDNRB EFNB1 EFNB2 EGF EGFR EGR1 EGR3 ENPP2 EPB41L2 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 EPHX2 ERBB2 ERBB4 EREG ESR1 ESR2 ETV1 EZR F2R F2RL1 F2RL2 FAAH FAM19A4 FGF2 FKBP5 FLOT1 FMR1 FOS FOSB FOSL2 FOXN1 FRMPD4 FSTL1 FYN GABARAPL1 GABBR1 GABBR2 GABRA2 GABRA4 -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Downregulation of Glial Genes Involved in Synaptic Function
RESEARCH ARTICLE Downregulation of glial genes involved in synaptic function mitigates Huntington’s disease pathogenesis Tarik Seref Onur1,2,3†, Andrew Laitman2,4,5†, He Zhao2, Ryan Keyho2, Hyemin Kim2, Jennifer Wang2, Megan Mair1,2,3, Huilan Wang6, Lifang Li1,2, Alma Perez2, Maria de Haro1,2, Ying-Wooi Wan2, Genevera Allen2,7, Boxun Lu6, Ismael Al-Ramahi1,2, Zhandong Liu2,4,5, Juan Botas1,2,3,4* 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States; 2Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, United States; 3Genetics & Genomics Graduate Program, Baylor College of Medicine, Houston, United States; 4Quantitative & Computational Biosciences, Baylor College of Medicine, Houston, United States; 5Department of Pediatrics, Baylor College of Medicine, Houston, United States; 6State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; 7Departments of Electrical & Computer Engineering, Statistics and Computer Science, Rice University, Houston, United States Abstract Most research on neurodegenerative diseases has focused on neurons, yet glia help form and maintain the synapses whose loss is so prominent in these conditions. To investigate the contributions of glia to Huntington’s disease (HD), we profiled the gene expression alterations of *For correspondence: Drosophila expressing human mutant Huntingtin (mHTT) in either glia or neurons and compared [email protected] these changes to what is observed in HD human and HD mice striata. A large portion of conserved genes are concordantly dysregulated across the three species; we tested these genes in a high- †These authors contributed throughput behavioral assay and found that downregulation of genes involved in synapse assembly equally to this work mitigated pathogenesis and behavioral deficits. -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
Strand Breaks for P53 Exon 6 and 8 Among Different Time Course of Folate Depletion Or Repletion in the Rectosigmoid Mucosa
SUPPLEMENTAL FIGURE COLON p53 EXONIC STRAND BREAKS DURING FOLATE DEPLETION-REPLETION INTERVENTION Supplemental Figure Legend Strand breaks for p53 exon 6 and 8 among different time course of folate depletion or repletion in the rectosigmoid mucosa. The input of DNA was controlled by GAPDH. The data is shown as ΔCt after normalized to GAPDH. The higher ΔCt the more strand breaks. The P value is shown in the figure. SUPPLEMENT S1 Genes that were significantly UPREGULATED after folate intervention (by unadjusted paired t-test), list is sorted by P value Gene Symbol Nucleotide P VALUE Description OLFM4 NM_006418 0.0000 Homo sapiens differentially expressed in hematopoietic lineages (GW112) mRNA. FMR1NB NM_152578 0.0000 Homo sapiens hypothetical protein FLJ25736 (FLJ25736) mRNA. IFI6 NM_002038 0.0001 Homo sapiens interferon alpha-inducible protein (clone IFI-6-16) (G1P3) transcript variant 1 mRNA. Homo sapiens UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 15 GALNTL5 NM_145292 0.0001 (GALNT15) mRNA. STIM2 NM_020860 0.0001 Homo sapiens stromal interaction molecule 2 (STIM2) mRNA. ZNF645 NM_152577 0.0002 Homo sapiens hypothetical protein FLJ25735 (FLJ25735) mRNA. ATP12A NM_001676 0.0002 Homo sapiens ATPase H+/K+ transporting nongastric alpha polypeptide (ATP12A) mRNA. U1SNRNPBP NM_007020 0.0003 Homo sapiens U1-snRNP binding protein homolog (U1SNRNPBP) transcript variant 1 mRNA. RNF125 NM_017831 0.0004 Homo sapiens ring finger protein 125 (RNF125) mRNA. FMNL1 NM_005892 0.0004 Homo sapiens formin-like (FMNL) mRNA. ISG15 NM_005101 0.0005 Homo sapiens interferon alpha-inducible protein (clone IFI-15K) (G1P2) mRNA. SLC6A14 NM_007231 0.0005 Homo sapiens solute carrier family 6 (neurotransmitter transporter) member 14 (SLC6A14) mRNA. -
Evidence for a Gene Influencing Haematocrit on Chromosome 6Q23–24: Genomewide Scan in the Framingham Heart Study J-P Lin, C J O’Donnell, D Levy, L a Cupples
75 LETTER TO JMG J Med Genet: first published as 10.1136/jmg.2004.021097 on 5 January 2005. Downloaded from Evidence for a gene influencing haematocrit on chromosome 6q23–24: genomewide scan in the Framingham Heart Study J-P Lin, C J O’Donnell, D Levy, L A Cupples ............................................................................................................................... J Med Genet 2005;42:75–79. doi: 10.1136/jmg.2004.021097 or more than 40 years, a number of studies have revealed that high haematocrit (HCT) levels are associated with Key points increased risk for cerebrovascular disease,1–3 cardiovas- F 4–6 7–9 cular disease (CVD), peripheral vascular disease, and all N Elevated haematocrit (HCT) levels are associated with cause mortality.5 10–11 During 34 years of follow up in more increased risk for vascular diseases. We carried out than 5200 individuals, a Framingham investigation demon- genome scans for quantitative trait loci (QTL) on HCT strated that increased HCT was significantly associated with and on haemoglobin (HGB), a correlated trait. increased risks for CVD, coronary heart disease, and N The heritabilities were estimated as 41% for HCT and 5 myocardial infarction in both men and women. A significant 45% for HGB. increase in all cause mortality for individuals with very low or N The genomewide linkage analysis revealed evidence of high HCT was also observed.5 Although HCT levels were related to other vascular risk factors, the risk associated with significant linkage for HCT to chromosome 6q23-24, an elevated HCT persisted after accounting for other risk with a lod score of 3.4 at location 136 cM. -
Genome Wide Study of Tardive Dyskinesia in Schizophrenia
bioRxiv preprint doi: https://doi.org/10.1101/386227; this version posted August 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Title: Genome Wide Study of Tardive Dyskinesia in Schizophrenia Authors: Max Lam1,9, Keane Lim1,9, Jenny Tay1, Nina Karlsson4, Smita N Deshpande5, BK Thelma6, Norio Ozaki7, Toshiya Inada8, Kang Sim1, Siow-Ann Chong1,2, Jianjun Liu4,10, Jimmy Lee1,2,3,10 Affiliations: 1Research Division, Institute of Mental Health, Singapore 2Department of Psychosis, Institute of Mental Health, Singapore 3Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 4Genome Institute of Singapore, Singapore 5Department of Psychiatry & De-addiction Services, Post Graduate Institute of Medical Education & Research-Dr. Ram Manohar Lohia Hospital, New Delhi, India 6Department of Genetics, University of Delhi south campus, New Delhi, India 7Department of Psychiatry, Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan 8Department of Psychiatry and Psychobiology, Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan 9Authors contributed equally to this work 10Authors jointly supervised this work Corresponding Authors: Jianjun Liu, PhD Deputy Executive Director Senior Group Leader, Human Genetics Genome Institute of Singapore Email: [email protected] Jimmy Lee, MBBS Psychiatrist & Senior Consultant Department of Psychosis Institute of Mental Health, Singapore Assistant Professor, Neuroscience & Mental Health Lee Kong Chian School of Medicine Nanyang Technological University, Singapore Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/386227; this version posted August 7, 2018. -
Entrez ID 1 Symbol 1 Entrez ID 2 Symbol 2 Data Source (R
Supporting Information Table 4. List of human protein-protein interactons. Entrez ID 1 Symbol 1 Entrez ID 2 Symbol 2 Data Source (R: Rual et al; S: Stelzl et al; L: Literature curation) 1 A1BG 10321 CRISP3 L 2 A2M 259 AMBP L 2 A2M 348 APOE L 2 A2M 351 APP L 2 A2M 354 KLK3 L 2 A2M 567 B2M L 2 A2M 1508 CTSB L 2 A2M 1990 ELA1 L 2 A2M 3309 HSPA5 L 2 A2M 3553 IL1B L 2 A2M 3586 IL10 L 2 A2M 3931 LCAT L 2 A2M 3952 LEP L 2 A2M 4035 LRP1 L 2 A2M 4803 NGFB L 2 A2M 5047 PAEP L 2 A2M 7045 TGFBI L 2 A2M 8728 ADAM19 L 2 A2M 9510 ADAMTS1 L 2 A2M 10944 SMAP S 2 A2M 55729 ATF7IP L 9 NAT1 8260 ARD1A L 12 SERPINA3 351 APP L 12 SERPINA3 354 KLK3 L 12 SERPINA3 1215 CMA1 L 12 SERPINA3 1504 CTRB1 L 12 SERPINA3 1506 CTRL L 12 SERPINA3 1511 CTSG L 12 SERPINA3 1990 ELA1 L 12 SERPINA3 1991 ELA2 L 12 SERPINA3 2064 ERBB2 L 12 SERPINA3 2153 F5 L 12 SERPINA3 3817 KLK2 L 12 SERPINA3 4035 LRP1 L 12 SERPINA3 4485 MST1 L 12 SERPINA3 5422 POLA L 12 SERPINA3 64215 DNAJC1 L 14 AAMP 51497 TH1L S 15 AANAT 7534 YWHAZ L 18 ABAT 7915 ALDH5A1 L 19 ABCA1 335 APOA1 L 19 ABCA1 6645 SNTB2 L 19 ABCA1 8772 FADD L 20 ABCA2 55755 CDK5RAP2 L 22 ABCB7 2235 FECH L 23 ABCF1 3692 ITGB4BP S 24 ABCA4 1258 CNGB1 L 25 ABL1 27 ABL2 L 25 ABL1 472 ATM L 25 ABL1 613 BCR L 25 ABL1 718 C3 L 25 ABL1 867 CBL L 25 ABL1 1501 CTNND2 L 25 ABL1 2048 EPHB2 L 25 ABL1 2547 XRCC6 L 25 ABL1 2876 GPX1 L 25 ABL1 2885 GRB2 L 25 ABL1 3055 HCK L 25 ABL1 3636 INPPL1 L 25 ABL1 3716 JAK1 L 25 ABL1 4193 MDM2 L 25 ABL1 4690 NCK1 L 25 ABL1 4914 NTRK1 L 25 ABL1 5062 PAK2 L 25 ABL1 5295 PIK3R1 L 25 ABL1 5335 PLCG1 L 25 ABL1 5591 -
Transdifferentiation of Human Mesenchymal Stem Cells
Transdifferentiation of Human Mesenchymal Stem Cells Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Tatjana Schilling aus San Miguel de Tucuman, Argentinien Würzburg, 2007 Eingereicht am: Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Martin J. Müller Gutachter: PD Dr. Norbert Schütze Gutachter: Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am: Hiermit erkläre ich ehrenwörtlich, dass ich die vorliegende Dissertation selbstständig angefertigt und keine anderen als die von mir angegebenen Hilfsmittel und Quellen verwendet habe. Des Weiteren erkläre ich, dass diese Arbeit weder in gleicher noch in ähnlicher Form in einem Prüfungsverfahren vorgelegen hat und ich noch keinen Promotionsversuch unternommen habe. Gerbrunn, 4. Mai 2007 Tatjana Schilling Table of contents i Table of contents 1 Summary ........................................................................................................................ 1 1.1 Summary.................................................................................................................... 1 1.2 Zusammenfassung..................................................................................................... 2 2 Introduction.................................................................................................................... 4 2.1 Osteoporosis and the fatty degeneration of the bone marrow..................................... 4 2.2 Adipose and bone -
Modulating Glial Genes Involved in Synaptic Function Mitigates Pathogenesis and Behavioral Deficits in a Drosophila Model Of
bioRxiv preprint doi: https://doi.org/10.1101/2020.11.03.367326; this version posted November 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Modulating glial genes involved in synaptic function mitigates pathogenesis and behavioral deficits in a 2 Drosophila model of Huntington’s Disease 3 4 Tarik S. Onur1,2,3,+, Andrew Laitman2,4,5,+, He Zhao2, Ryan Keyho2, Hyemin Kim2, Jennifer Wang2, 5 Megan Mair1,2,3, Alma Perez2, Maria de Haro1,2, Huilan Wang6, Ying-Wooi Wan2, Genevera Allen2,7, 6 Boxun Lu6, Ismael Al-Ramahi1,2, Zhandong Liu2,4,5, Juan Botas1,2,3,4,8* 7 8 1 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA 9 2 Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, Texas, 10 USA 11 3 Genetics & Genomics Graduate Program, Baylor College of Medicine, Houston, Texas, USA 12 4 Quantitative & Computational Biosciences, Baylor College of Medicine, Houston, Texas, USA 13 5 Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA 14 6 State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of 15 Life Sciences, Fudan University, Shanghai, China 16 7 Departments of Electrical and Computer Engineering, Statistics and Computer Science, Rice University, 17 Houston, Texas, USA 18 8 Lead Contact 19 * Correspondence: [email protected] 20 + Co-first authors 21 22 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.03.367326; this version posted November 4, 2020. -
An Expanded Proteome of Cardiac T-Tubules☆
Cardiovascular Pathology 42 (2019) 15–20 Contents lists available at ScienceDirect Cardiovascular Pathology Original Article An expanded proteome of cardiac t-tubules☆ Jenice X. Cheah, Tim O. Nieuwenhuis, Marc K. Halushka ⁎ Department of Pathology, Division of Cardiovascular Pathology, Johns Hopkins University SOM, Baltimore, MD, USA article info abstract Article history: Background: Transverse tubules (t-tubules) are important structural elements, derived from sarcolemma, found Received 27 February 2019 on all striated myocytes. These specialized organelles create a scaffold for many proteins crucial to the effective Received in revised form 29 April 2019 propagation of signal in cardiac excitation–contraction coupling. The full protein composition of this region is un- Accepted 17 May 2019 known. Methods: We characterized the t-tubule subproteome using 52,033 immunohistochemical images covering Keywords: 13,203 proteins from the Human Protein Atlas (HPA) cardiac tissue microarrays. We used HPASubC, a suite of Py- T-tubule fi Proteomics thon tools, to rapidly review and classify each image for a speci c t-tubule staining pattern. The tools Gene Cards, Caveolin String 11, and Gene Ontology Consortium as well as literature searches were used to understand pathways and relationships between the proteins. Results: There were 96 likely t-tubule proteins identified by HPASubC. Of these, 12 were matrisome proteins and 3 were mitochondrial proteins. A separate literature search identified 50 known t-tubule proteins. A comparison of the 2 lists revealed only 17 proteins in common, including 8 of the matrisome proteins. String11 revealed that 94 of 127 combined t-tubule proteins generated a single interconnected network. Conclusion: Using HPASubC and the HPA, we identified 78 novel, putative t-tubule proteins and validated 17 within the literature.