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ENSG Gene Encodes Effector TCR Pathway Costimulation Inhibitory/Exhaustion Synapse/Adhesion Chemokines/Receptors
ENSG Gene Encodes Effector TCR pathway Costimulation Inhibitory/exhaustion Synapse/adhesion Chemokines/receptors ENSG00000111537 IFNG IFNg x ENSG00000109471 IL2 IL-2 x ENSG00000232810 TNF TNFa x ENSG00000271503 CCL5 CCL5 x x ENSG00000139187 KLRG1 Klrg1 x ENSG00000117560 FASLG Fas ligand x ENSG00000121858 TNFSF10 TRAIL x ENSG00000134545 KLRC1 Klrc1 / NKG2A x ENSG00000213809 KLRK1 Klrk1 / NKG2D x ENSG00000188389 PDCD1 PD-1 x x ENSG00000117281 CD160 CD160 x x ENSG00000134460 IL2RA IL-2 receptor x subunit alpha ENSG00000110324 IL10RA IL-10 receptor x subunit alpha ENSG00000115604 IL18R1 IL-18 receptor 1 x ENSG00000115607 IL18RAP IL-18 receptor x accessory protein ENSG00000081985 IL12RB2 IL-12 receptor x beta 2 ENSG00000186810 CXCR3 CXCR3 x x ENSG00000005844 ITGAL CD11a x ENSG00000160255 ITGB2 CD18; Integrin x x beta-2 ENSG00000156886 ITGAD CD11d x ENSG00000140678 ITGAX; CD11c x x Integrin alpha-X ENSG00000115232 ITGA4 CD49d; Integrin x x alpha-4 ENSG00000169896 ITGAM CD11b; Integrin x x alpha-M ENSG00000138378 STAT4 Stat4 x ENSG00000115415 STAT1 Stat1 x ENSG00000170581 STAT2 Stat2 x ENSG00000126561 STAT5a Stat5a x ENSG00000162434 JAK1 Jak1 x ENSG00000100453 GZMB Granzyme B x ENSG00000145649 GZMA Granzyme A x ENSG00000180644 PRF1 Perforin 1 x ENSG00000115523 GNLY Granulysin x ENSG00000100450 GZMH Granzyme H x ENSG00000113088 GZMK Granzyme K x ENSG00000057657 PRDM1 Blimp-1 x ENSG00000073861 TBX21 T-bet x ENSG00000115738 ID2 ID2 x ENSG00000176083 ZNF683 Hobit x ENSG00000137265 IRF4 Interferon x regulatory factor 4 ENSG00000140968 IRF8 Interferon -
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. -
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d. -
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 -
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 -
An Uncommon Clinical Presentation of Relapsing Dilated Cardiomyopathy with Identification of Sequence Variations in MYNPC3, KCNH2 and Mitochondrial Trna Cysteine M
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by George Washington University: Health Sciences Research Commons (HSRC) Himmelfarb Health Sciences Library, The George Washington University Health Sciences Research Commons Pediatrics Faculty Publications Pediatrics 6-2015 An uncommon clinical presentation of relapsing dilated cardiomyopathy with identification of sequence variations in MYNPC3, KCNH2 and mitochondrial tRNA cysteine M. J. Guillen Sacoto Kimberly A. Chapman George Washington University D. Heath M. B. Seprish Dina Zand George Washington University Follow this and additional works at: http://hsrc.himmelfarb.gwu.edu/smhs_peds_facpubs Part of the Pediatrics Commons Recommended Citation Guillen Sacoto, M.J., Chapman, K.A., Heath, D., Seprish, M.B., Zand, D.J. (2015). An uncommon clinical presentation of relapsing dilated cardiomyopathy with identification of sequence variations in MYNPC3, KCNH2 and mitochondrial tRNA cysteine. Molecular Genetics and Metabolism Reports, 3, 47-54. doi:10.1016/j.ymgmr.2015.03.007 This Journal Article is brought to you for free and open access by the Pediatrics at Health Sciences Research Commons. It has been accepted for inclusion in Pediatrics Faculty Publications by an authorized administrator of Health Sciences Research Commons. For more information, please contact [email protected]. Molecular Genetics and Metabolism Reports 3 (2015) 47–54 Contents lists available at ScienceDirect Molecular Genetics and Metabolism Reports journal homepage: http://www.journals.elsevier.com/molecular-genetics-and- metabolism-reports/ Case Report An uncommon clinical presentation of relapsing dilated cardiomyopathy with identification of sequence variations in MYNPC3, KCNH2 and mitochondrial tRNA cysteine Maria J. Guillen Sacoto a,1, Kimberly A. -
Novel Myosin Mutations for Hereditary Hearing Loss Revealed by Targeted Genomic Capture and Massively Parallel Sequencing
European Journal of Human Genetics (2014) 22, 768–775 & 2014 Macmillan Publishers Limited All rights reserved 1018-4813/14 www.nature.com/ejhg ARTICLE Novel myosin mutations for hereditary hearing loss revealed by targeted genomic capture and massively parallel sequencing Zippora Brownstein1,6, Amal Abu-Rayyan2,6, Daphne Karfunkel-Doron1, Serena Sirigu3, Bella Davidov4, Mordechai Shohat1,4, Moshe Frydman1,5, Anne Houdusse3, Moien Kanaan2 and Karen B Avraham*,1 Hereditary hearing loss is genetically heterogeneous, with a large number of genes and mutations contributing to this sensory, often monogenic, disease. This number, as well as large size, precludes comprehensive genetic diagnosis of all known deafness genes. A combination of targeted genomic capture and massively parallel sequencing (MPS), also referred to as next-generation sequencing, was applied to determine the deafness-causing genes in hearing-impaired individuals from Israeli Jewish and Palestinian Arab families. Among the mutations detected, we identified nine novel mutations in the genes encoding myosin VI, myosin VIIA and myosin XVA, doubling the number of myosin mutations in the Middle East. Myosin VI mutations were identified in this population for the first time. Modeling of the mutations provided predicted mechanisms for the damage they inflict in the molecular motors, leading to impaired function and thus deafness. The myosin mutations span all regions of these molecular motors, leading to a wide range of hearing phenotypes, reinforcing the key role of this family of proteins in auditory function. This study demonstrates that multiple mutations responsible for hearing loss can be identified in a relatively straightforward manner by targeted-gene MPS technology and concludes that this is the optimal genetic diagnostic approach for identification of mutations responsible for hearing loss. -
Using Large Sequencing Data Sets to Refine Intragenic Disease Regions and Prioritize Clinical Variant Interpretation
ORIGINAL RESEARCH ARTICLE © American College of Medical Genetics and Genomics Using large sequencing data sets to refine intragenic disease regions and prioritize clinical variant interpretation Sami S. Amr, PhD1,2, Saeed H. Al Turki, PhD1, Matthew Lebo, PhD1,2, Mahdi Sarmady, PhD3, Heidi L. Rehm, PhD1,2 and Ahmad N. Abou Tayoun, PhD3 Purpose: Classification of novel variants is a major challenge fac- commonly impacted in symptomatic individuals. Our data show ing the widespread adoption of comprehensive clinical genomic that a significant number of exons and domains in genes strongly sequencing and the field of personalized medicine in general. This is associated with disease can be defined as disease-sensitive or disease- largely because most novel variants do not have functional, genetic, tolerant, leading to potential reclassification of at least 26% (450 out or population data to support their clinical classification. of 1,742) of variants of uncertain clinical significance in the 132 genes. Methods: To improve variant interpretation, we leveraged the Exome Conclusion: This approach leverages domain functional annotation Aggregation Consortium (ExAC) data set (N = ~60,000) as well as and associated disease in each gene to prioritize candidate disease 7,000 clinically curated variants in 132 genes identified in more than variants, increasing the sensitivity and specificity of novel variant 11,000 probands clinically tested for cardiomyopathies, rasopathies, assessment within these genes. hearing loss, or connective tissue disorders -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
Genome-Wide Identification, Characterization and Expression
G C A T T A C G G C A T genes Article Genome-Wide Identification, Characterization and Expression Profiling of myosin Family Genes in Sebastes schlegelii Chaofan Jin 1, Mengya Wang 1,2, Weihao Song 1 , Xiangfu Kong 1, Fengyan Zhang 1, Quanqi Zhang 1,2,3 and Yan He 1,2,* 1 MOE Key Laboratory of Molecular Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; [email protected] (C.J.); [email protected] (M.W.); [email protected] (W.S.); [email protected] (X.K.); [email protected] (F.Z.); [email protected] (Q.Z.) 2 Laboratory of Tropical Marine Germplasm Resources and Breeding Engineering, Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China 3 Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China * Correspondence: [email protected]; Tel.: +86-0532-82031986 Abstract: Myosins are important eukaryotic motor proteins that bind actin and utilize the energy of ATP hydrolysis to perform a broad range of functions such as muscle contraction, cell migration, cytokinesis, and intracellular trafficking. However, the characterization and function of myosin is poorly studied in teleost fish. In this study, we identified 60 myosin family genes in a marine teleost, black rockfish (Sebastes schlegelii), and further characterized their expression patterns. myosin showed divergent expression patterns in adult tissues, indicating they are involved in different types and Citation: Jin, C.; Wang, M.; Song, W.; compositions of muscle fibers. Among 12 subfamilies, S. schlegelii myo2 subfamily was significantly Kong, X.; Zhang, F.; Zhang, Q.; He, Y. -
Epithelial-To-Mesenchymal Transition Enhances Cancer Cell Sensitivity to Cytotoxic Effects of Cold Atmospheric Plasmas in Breast and Bladder Cancer Systems
cancers Article Epithelial-to-Mesenchymal Transition Enhances Cancer Cell Sensitivity to Cytotoxic Effects of Cold Atmospheric Plasmas in Breast and Bladder Cancer Systems Peiyu Wang 2,3 , Renwu Zhou 4 , Patrick Thomas 2,3,5 , Liqian Zhao 6, Rusen Zhou 4, Susmita Mandal 7, Mohit Kumar Jolly 7, Derek J. Richard 2,3, Bernd H. A. Rehm 8, Kostya (Ken) Ostrikov 9, Xiaofeng Dai 1,*, Elizabeth D. Williams 2,3,4 and Erik W. Thompson 2,3 1 Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China 2 Queensland University of Technology (QUT), School of Biomedical Sciences, Brisbane 4059, Australia 3 Translational Research Institute, Woolloongabba 4102, Australia 4 School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney 2006, Australia 5 Queensland Bladder Cancer Initiative (QBCI), Woolloongabba 4102, Australia 6 The First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China 7 Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India 8 Centre for Cell Factories and Biopolymers, Griffith Institute for Drug Discovery, Griffith University, Nathan 4111, Australia 9 School of Chemistry and Physics, Queensland University of Technology, Brisbane 4000, Australia; Citation: Wang, P.; Zhou, R.; * Correspondence: [email protected] Thomas, P.; Zhao, L.; Zhou, R.; Mandal, S.; Jolly, M.K.; Richard, D.J.; Simple Summary: Cold atmospheric plasma (CAP) and plasma-activated medium (PAM) are known Rehm, B.H.A.; Ostrikov, K.; et al. to selectively kill cancer cells, however the efficacy of CAP in cancer cells following epithelial- Epithelial-to-Mesenchymal Transition mesenchymal transition (EMT), a process which endows cancer cells with increased stemness, Enhances Cancer Cell Sensitivity to metastatic potential, and resistance to conventional therapies, has not been previously examined. -
Invasive Bladder Cancer: Genomic Insights and Therapeutic Promise Jaegil Kim1, Rehan Akbani2, Chad J
Review Clinical Cancer Research Invasive Bladder Cancer: Genomic Insights and Therapeutic Promise Jaegil Kim1, Rehan Akbani2, Chad J. Creighton3, Seth P. Lerner3, John N. Weinstein2, Gad Getz1,4, and David J. Kwiatkowski1,5 Abstract Invasive bladder cancer, for which there have been few thera- unusually frequent in comparison with other cancers, and muta- peutic advances in the past 20 years, is a significant medical tion or amplification of transcription factors is also common. problem associated with metastatic disease and frequent mortal- Expression clustering analyses organize bladder cancers into four ity. Although previous studies had identified many genetic altera- principal groups, which can be characterized as luminal, immune tions in invasive bladder cancer, recent genome-wide studies have undifferentiated, luminal immune, and basal. The four groups provided a more comprehensive view. Here, we review those show markedly different expression patterns for urothelial differ- recent findings and suggest therapeutic strategies. Bladder cancer entiation (keratins and uroplakins) and immunity genes (CD274 has a high mutation rate, exceeded only by lung cancer and and CTLA4), among others. These observations suggest numerous melanoma. About 65% of all mutations are due to APOBEC- therapeutic opportunities, including kinase inhibitors and anti- mediated mutagenesis. There is a high frequency of mutations body therapies for genes in the canonical signaling pathways, and/or genomic amplification or deletion events that affect many histone deacetylase inhibitors and novel molecules for chromatin of the canonical signaling pathways involved in cancer develop- gene mutations, and immune therapies, which should be targeted ment: cell cycle, receptor tyrosine kinase, RAS, and PI-3-kinase/ to specific patients based on genomic profiling of their cancers.