Figure S1-S6, Table S1

Total Page:16

File Type:pdf, Size:1020Kb

Figure S1-S6, Table S1 Supplementary Figure Legends Supplementary Figure S1. CETSA assay showing on target binding of JQ1: CHLA-10 and CADO-ES1 cells were treated with DMSO or 500nM JQ1 for 3h and incubated at indicated temperatures for CETSA (cellular thermal shift assay). Cells w ere lysed and soluble proteins detected by immunoblotting with the indicated antibody. Shown are representative blots from two independent experiments. b) EWS/ERG and BRD4 interaction is acetylation independent. Top-panel, CADO-ES1 cells were treated with DM SO or HDAC inhibitors (TSA and SAHA) for 48h followed by nuclear extractions. Immunoprecipitation followed by immunoblotting for the indicated target was performed. IgG was used as negative control. Lower panel, HDAC treatment control. Increased pan-acetylated histone levels in TSA and SAHA treated nuclear lysates compared to DMSO control. c) Endogenous association of EWS/FLI1 and BRD4. CHLA-10 nuclear extracts were subjected to immunoprecipitation using anti-FLI1 antibody. Immunoprecipitates were analyzed for the presence of BRD4 by immunoblotting. Supplementary Figure S2. a) Table showing high-throughput sequencing read information for RNA-seq libraries of indicated samples performed for this study. b) Lack of MYC downregulation upon knockdown of BRD2, BRD3 or BRD4. qRT-PCR showing the GAPDH normalized expression of MYC in the indicated cells. c) RT-qPCR validation of PHF19 downregulation upon JQ1 treatment in ES cells. d) RNAseq FPKM values for FLI1 and ERG in the indicated cell lines treated with DMSO or 500nM JQ1 for 48hrs. Supplementary Figure S3. PHF19 is the only gene affected transcriptionally by EWS/FLI1 or BRD4 knockdown or JQ1 treatment in the chromosome 9 loci that include genes PSMD5, FBXW2, MEGF9 and TRAF1 flanking PHF19. a) Genome browser view of H3K27ac and EWS/FLI1 at PHF19 loci in SK-NM-C and A675 cells. The ChIP-seq data were downloaded from NCBI GEO with accession number GSE619534. b) Normalized FPKM values for PHF19, PSMD5, FBXW2, MEGF9 and TRAF1 transcripts in the SK-N-MC and A673 cells expressing control shRNA or shFLI1 (GSE61953). For CHLA-10, data for shBRD4 is also included. c) As in b, for CHLA10, RD-ES, SK-N-MC and CADO-ES1 control versus JQ1 treatment. Supplementary Figure S4. ChIP with the indicated antibodies using chromatin extracted from DMSO or 0.5uM JQ1 for 24h. qPCR was performed using a primer set amplifying a region flanking the EWS/FLI1 binding site located approximately 20kb from the PHF19 gene (hg19 - chr9:123,636,835-123,636,855). Fold enrichment relative to the IgG control from DMSO is plotted with standard deviation. b) qRT-PCR and immunoblot validation of PHF19 knockdown in SK-N-MC cells transfected with 50nM si-nontarget or siPHF19 for 48hrs. c) cells as in b were treated with or without 500nM JQ1. Cells were counted every alternate day for 6 days. Inset shows zoomed growth curves for JQ1 and siPHF19 + JQ1 treated cells. d) Increased sensitivity to JQ1 in PHF19 knockout cells. Cell viability curves for control or PHF19 knockout SK- N-MC cells treated with JQ1 for 4-days followed by cell-titer glow assay for viability. Mean ± s.e.m from n = 4 is plotted. Supplementary Figure S5. PHF19 overexpression correlates with overall survival in Ewing sarcoma patients. Kaplan-Meier analysis for PHF19 expression under the condition of overall survival probability in Ewing sarcoma patients using SurvExpress database. HR denotes hazard ratio. Insert, boxplot for the mRNA levels of PHF19 in high (red) and low (green)-risk cohorts. Supplementary Figure S6. a) Individual tumor volume for all three Ewing sarcoma xenograft models treated with vehicle or 50mg/kg JQ1 for 5days/week. b) Animal weights for tumor growth studies displayed as mean ±SEM. c) qRT-PCR for PHF19 and MYC in the CHLA-10 tumors from control and JQ1 treated mice. N =5 tumors from each group. d) Immunoblot analysis of PHF19 and MYC in CHLA-10 tumors as in c. GAPDH was used as loading control. e) Normalized quantification of PHF19 and MYC from d. P – values determined by Student’s t-test using GraphPad Prism. Supplementary Figure S1 a CADO-ES-1 cells DMSO JQ1 (500nM) 39 42 45 48 51 54 57 60 39 42 45 48 51 54 57 60 Temp (° C) BRD4 GAPDH CHLA-10 cells DMSO JQ1 (500nM) 39 42 45 48 51 54 57 60 39 42 45 48 51 54 57 60 Temp (° C) BRD4 GAPDH b IP-BRD4 c Input IgG IP-FLI1 Input IgG DMSO TSA SAHA BRD4 EWS/ERG EWS/FLI1 * IgG Heavy * IgG Heavy BRD4 DMSO TSA SAHA pan-acetylated histone Total H3 Supplementary Figure 2 a # Sample ID Reads Number Of % Total Reads Aligned 1 CHLA10-DMSO-1 151/151 13,213,552 94.45% 2 CHLA10-DMSO-2 151/151 10,756,525 93.40% 3 CHLA10-shFL1-1 151/151 35,988,604 93.36% 4 CHLA10-shFLI1-2 151/151 38,358,307 95.10% 5 CHLA10-shBRD4-1 151/151 39,119,332 94.33% 6 CHLA10-shBRD4-2 151/151 72,529,289 94.37% 7 CHLA10-JQ1-1 151/151 25,263,070 94.72% 8 CHLA10-JQ1-2 151/151 17,252,188 93.42% 9 RD-ES-DMSO-1 151/151 34,920,613 91.87% 10 RD-ES-DMSO-2 151/151 27,102,776 93.09% 11 RD-ES-JQ1-1 151/151 34,041,127 91.04% 12 RD-ES-JQ1-2 151/151 49,410,157 90.94% 13 Cado-ES-DMSO-1 151/151 11,777,772 91.50% 14 Cado-ES-DMSO-2 151/151 37,251,074 91.36% 15 Cado-ES-JQ1-1 151/151 15,570,290 91.23% 16 Cado-ES-JQ1-2 151/151 34,188,934 91.08% 17 SK-N-MC -DMSO-1 75/75 18,053,003 94.28% 18 SK-N-MC -DMSO-2 75/75 18,720,436 93.81% 19 SK-N-MC -JQ1-1 75/75 19,668,526 93.88% 20 SK-N-MC -JQ1-2 75/75 14,486,565 93.62% 21 SK-N-MC -PHF19_KO-1 75/75 84,317,814 92.83% 22 SK-N-MC -PHF19_KO-2 75/75 23,938,928 92.65% b CHLA-10 RD-ES CADO-ES1 c CHLA-10 RD-ES SK-N-MC CADO-ES1 2 1.2 1.2 1.2 1.2 1.4 1.2 1.0 1.0 1 1 1.2 1.5 1 0.8 0.8 0.8 1 0.6 0.6 0.6 0.4 0.4 0.4 0.5 0.2 0.2 0.2 Relative MYC mRNA 0 0 0 0 0 0 0 Relative PHF19 mRNA JQ1 JQ1 JQ1 JQ1 DMSO DMSO sh NT sh NT sh NT DMSO DMSO sh BRD2sh BRD3sh BRD4 sh BRD2sh BRD3sh BRD4 sh BRD2sh BRD3sh BRD4 d CHLA-10 RD-ES SK-N-MC CADO-ES1 Normalized FPKM (EWS-FLI1) (EWS-FLI1) (EWS-FLI1) (EWS-ERG) Supplementary Figure 3 a Scale 100kb hg19 Chr9: 123,500,000 123,550,000 123,600,000 123,650,000 123,700,000 _ A673 FLI1 _ SK-N-MC FLI1 _ A673 H3K27ac _ SK-N-MC H3K27ac PSMD5 MEGF9 FBXW2 PHF19 TRAF1 b SK-N-MC A673 CHLA-10 dmso sh Ct. 50 35 30 sh Ct. sh FLI1 sh FLI1 45 sh FLI1 30 sh BRD4 25 40 35 25 20 30 20 15 25 20 15 10 15 10 10 Normalized FPKM 5 5 5 0 0 0 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 c CHLA-10 RD-ES SK-N-MC CADO-ES1 dmso dmso dmso dmso JQ1 JQ1 JQ1 JQ1 Normalized FPKM PHF19 PSMD5 FBXW2 MEGF9 TRAF1 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 PHF19 PSMD5 FBXW2 MEGF9 TRAF1 Supplementary Figure 4 a ChIP qPCR * P < 0.05 24 FLI1 ab BRD4 ab 20 IgG 16 12 n.s. Fold enrichment 8 4 n.s. 0 JQ1 JQ1 JQ1 DMSO DMSO DMSO b c si-NT si-PHF19 600 JQ1 PHF19 10000 siNT siPHF19 + JQ1 500 siPHF19 GAPDH JQ1 400 1.2 siPHF19 + JQ1 300 * P < 0.05 1 1000 * P < 0.0001 200 * P < 0.05 0.8 100 Percentage Cell Number * P < 0.05 * P < 0.05 0 0.6 * P < 0.05 0 2 4 6 100 * P < 0.05 0.4 Days 0.2 Percentage Cell Number Fold phf19 mRNA expression Fold phf19 mRNA 0 10 si-NT si-PHF19 0 2 4 6 Days d Control 1 00 PHF19 KO y t i l i * b a i v * l 5 0 l e C * * p < 0.001 * 0 * 0 .001 0 .01 0 .1 1 1 0 J Q1 (µM ) Supplementary Figure S5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 PHF19 mRNA expression PHF19 mRNA 4.5 Low risk High risk Time (months) Supplementary Figure S6 a SK-N-MC b SK-N-MC 30 2500 Vehicle treatment 2500 JQ1 treatment ) ) 3 3 2000 2000 (grams) 20 t 1500 1500 vehicle (n = 8) weigh 10 1000 1000 al JQ1 (n = 12) m ni 500 500 A Tumor Volume (mm Volume Tumor 0 Tumor Volume (mm Volume Tumor 0 0 1 4 7 9 11 14 16 18 21 22 1 4 7 9 11 14 16 18 21 22 1 4 7 9 11 14 16 18 21 22 Days of drug treatment Days Days CHLA-10 CHLA-10 Vehicle treatment JQ1 treatment 3000 3000 30 ) 3 ) 3 2500 2500 2000 2000 (grams) 20 t 1500 1500 vehicle (n = 8) weigh 1000 1000 10 al m JQ1 (n = 8) 500 500 ni Tumor Volume (mm Volume Tumor A Tumor Volume (mm Volume Tumor 0 0 0 1 4 7 9 11 14 16 18 22 1 4 7 9 11 14 16 18 22 1 4 7 9 11 14 16 18 22 Days Days Days of drug treatment CADO-ES1 CADO-ES1 2000 Vehicle treatment 2000 JQ1 treatment 30 ) 3 ) 3 1500 1500 20 (grams) t 1000 1000 vehicle (n = 10) weigh 10 al 500 m JQ1 (n = 10) 500 ni Tumor Volume (mm Volume Tumor A Tumor Volume (mm Volume Tumor 0 0 0 1 4 6 8 11 14 18 1 4 8 11 14 16 18 1 4 8 11 14 16 18 Days of drug treatment Days Days Vehicle tumors (n = 5) Vehicle tumors (n = 5) c JQ1 tumors (n = 5) d e JQ1 tumors (n = 5) 1.2 * p <0.0001 n.s.
Recommended publications
  • WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).
    [Show full text]
  • Mapping of Craniofacial Traits in Outbred Mice Identifies Major Developmental Genes Involved in Shape Determination
    Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination Luisa F Pallares1, Peter Carbonetto2,3, Shyam Gopalakrishnan2,4, Clarissa C Parker2,5, Cheryl L Ackert-Bicknell6, Abraham A Palmer2,7, Diethard Tautz1 # 1Max Planck Institute for Evolutionary Biology, Plön, Germany 2University of Chicago, Chicago, Illinois, USA 3AncestryDNA, San Francisco, California, USA 4Museum of Natural History, Copenhagen University, Copenhagen, Denmark 5Middlebury College, Department of Psychology and Program in Neuroscience, Middlebury VT, USA 6Center for Musculoskeletal Research, University of Rochester, Rochester, NY USA 7University of California San Diego, La Jolla, CA, USA # corresponding author: [email protected] short title: craniofacial shape mapping Abstract The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination 1 is accumulating, we are still far from understanding how such variation at the genetic level is translated into craniofacial shape variation. Here we used 3D geometric morphometrics to map genes involved in shape determination in a population of outbred mice (Carworth Farms White, or CFW). We defined shape traits via principal component analysis of 3D skull and mandible measurements. We mapped genetic loci associated with shape traits at ~80,000 candidate single nucleotide polymorphisms in ~700 male mice. We found that craniofacial shape and size are highly heritable, polygenic traits. Despite the polygenic nature of the traits, we identified 17 loci that explain variation in skull shape, and 8 loci associated with variation in mandible shape. Together, the associated variants account for 11.4% of skull and 4.4% of mandible shape variation, however, the total additive genetic variance associated with phenotypic variation was estimated in ~45%.
    [Show full text]
  • 1471-2105-8-217.Pdf
    BMC Bioinformatics BioMed Central Software Open Access GenMAPP 2: new features and resources for pathway analysis Nathan Salomonis1,2, Kristina Hanspers1, Alexander C Zambon1, Karen Vranizan1,3, Steven C Lawlor1, Kam D Dahlquist4, Scott W Doniger5, Josh Stuart6, Bruce R Conklin1,2,7,8 and Alexander R Pico*1 Address: 1Gladstone Institute of Cardiovascular Disease, 1650 Owens Street, San Francisco, CA 94158 USA, 2Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, 513 Parnassus Avenue, San Francisco, CA 94143, USA, 3Functional Genomics Laboratory, University of California, Berkeley, CA 94720 USA, 4Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8220, Los Angeles, CA 90045 USA, 5Computational Biology Graduate Program, Washington University School of Medicine, St. Louis, MO 63108 USA, 6Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064 USA, 7Department of Medicine, University of California, San Francisco, CA 94143 USA and 8Department of Molecular and Cellular Pharmacology, University of California, San Francisco, CA 94143 USA Email: Nathan Salomonis - [email protected]; Kristina Hanspers - [email protected]; Alexander C Zambon - [email protected]; Karen Vranizan - [email protected]; Steven C Lawlor - [email protected]; Kam D Dahlquist - [email protected]; Scott W Doniger - [email protected]; Josh Stuart - [email protected]; Bruce R Conklin - [email protected]; Alexander R Pico* - [email protected] * Corresponding author Published: 24 June 2007 Received: 16 November 2006 Accepted: 24 June 2007 BMC Bioinformatics 2007, 8:217 doi:10.1186/1471-2105-8-217 This article is available from: http://www.biomedcentral.com/1471-2105/8/217 © 2007 Salomonis et al; licensee BioMed Central Ltd.
    [Show full text]
  • Comprehensive Biological Information Analysis of PTEN Gene in Pan-Cancer
    Comprehensive biological information analysis of PTEN gene in pan-cancer Hang Zhang Shanghai Medical University: Fudan University https://orcid.org/0000-0002-5853-7754 Wenhan Zhou Shanghai Medical University: Fudan University Xiaoyi Yang Shanghai Jiao Tong University School of Medicine Shuzhan Wen Shanghai Medical University: Fudan University Baicheng Zhao Shanghai Medical University: Fudan University Jiale Feng Shanghai Medical University: Fudan University Shuying Chen ( [email protected] ) https://orcid.org/0000-0002-9215-9777 Primary research Keywords: PTEN, correlated genes, TCGA, GEPIA, UALCAN, GTEx, expression, cancer Posted Date: April 12th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-388887/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract Background PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISEq.
    [Show full text]
  • Endocrine System Local Gene Expression
    Copyright 2008 By Nathan G. Salomonis ii Acknowledgments Publication Reprints The text in chapter 2 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC, Dahlquist KD, Doniger SW, Stuart J, Conklin BR, Pico AR. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics. 2007 Jun 24;8:218. The co-authors listed in this publication co-wrote the manuscript (AP and KH) and provided critical feedback (see detailed contributions at the end of chapter 2). The text in chapter 3 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Cotte N, Zambon AC, Pollard KS, Vranizan K, Doniger SW, Dolganov G, Conklin BR. Identifying genetic networks underlying myometrial transition to labor. Genome Biol. 2005;6(2):R12. Epub 2005 Jan 28. The co-authors listed in this publication developed the hierarchical clustering method (KP), co-designed the study (NC, AZ, BC), provided statistical guidance (KV), co- contributed to GenMAPP 2.0 (SD) and performed quantitative mRNA analyses (GD). The text of this dissertation contains a reproduction of a figure from: Yeo G, Holste D, Kreiman G, Burge CB. Variation in alternative splicing across human tissues. Genome Biol. 2004;5(10):R74. Epub 2004 Sep 13. The reproduction was taken without permission (chapter 1), figure 1.3. iii Personal Acknowledgments The achievements of this doctoral degree are to a large degree possible due to the contribution, feedback and support of many individuals. To all of you that helped, I am extremely grateful for your support.
    [Show full text]
  • Genomic Approach in Idiopathic Intellectual Disability Maria De Fátima E Costa Torres
    ESTUDOS DE 8 01 PDPGM 2 CICLO Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres D Autor. Maria de Fátima e Costa Torres D.ICBAS 2018 Genomic approach in idiopathic intellectual disability Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres SEDE ADMINISTRATIVA INSTITUTO DE CIÊNCIAS BIOMÉDICAS ABEL SALAZAR FACULDADE DE MEDICINA MARIA DE FÁTIMA E COSTA TORRES GENOMIC APPROACH IN IDIOPATHIC INTELLECTUAL DISABILITY Tese de Candidatura ao grau de Doutor em Patologia e Genética Molecular, submetida ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto Orientadora – Doutora Patrícia Espinheira de Sá Maciel Categoria – Professora Associada Afiliação – Escola de Medicina e Ciências da Saúde da Universidade do Minho Coorientadora – Doutora Maria da Purificação Valenzuela Sampaio Tavares Categoria – Professora Catedrática Afiliação – Faculdade de Medicina Dentária da Universidade do Porto Coorientadora – Doutora Filipa Abreu Gomes de Carvalho Categoria – Professora Auxiliar com Agregação Afiliação – Faculdade de Medicina da Universidade do Porto DECLARAÇÃO Dissertação/Tese Identificação do autor Nome completo _Maria de Fátima e Costa Torres_ N.º de identificação civil _07718822 N.º de estudante __ 198600524___ Email institucional [email protected] OU: [email protected] _ Email alternativo [email protected] _ Tlf/Tlm _918197020_ Ciclo de estudos (Mestrado/Doutoramento) _Patologia e Genética Molecular__ Faculdade/Instituto _Instituto de Ciências
    [Show full text]
  • Cell Culture-Based Profiling Across Mammals Reveals DNA Repair And
    1 Cell culture-based profiling across mammals reveals 2 DNA repair and metabolism as determinants of 3 species longevity 4 5 Siming Ma1, Akhil Upneja1, Andrzej Galecki2,3, Yi-Miau Tsai2, Charles F. Burant4, Sasha 6 Raskind4, Quanwei Zhang5, Zhengdong D. Zhang5, Andrei Seluanov6, Vera Gorbunova6, 7 Clary B. Clish7, Richard A. Miller2, Vadim N. Gladyshev1* 8 9 1 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard 10 Medical School, Boston, MA, 02115, USA 11 2 Department of Pathology and Geriatrics Center, University of Michigan Medical School, 12 Ann Arbor, MI 48109, USA 13 3 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, 14 MI 48109, USA 15 4 Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 16 48109, USA 17 5 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10128, USA 18 6 Department of Biology, University of Rochester, Rochester, NY 14627, USA 19 7 Broad Institute, Cambridge, MA 02142, US 20 21 * corresponding author: Vadim N. Gladyshev ([email protected]) 22 ABSTRACT 23 Mammalian lifespan differs by >100-fold, but the mechanisms associated with such 24 longevity differences are not understood. Here, we conducted a study on primary skin 25 fibroblasts isolated from 16 species of mammals and maintained under identical cell culture 26 conditions. We developed a pipeline for obtaining species-specific ortholog sequences, 27 profiled gene expression by RNA-seq and small molecules by metabolite profiling, and 28 identified genes and metabolites correlating with species longevity. Cells from longer-lived 29 species up-regulated genes involved in DNA repair and glucose metabolism, down-regulated 30 proteolysis and protein transport, and showed high levels of amino acids but low levels of 31 lysophosphatidylcholine and lysophosphatidylethanolamine.
    [Show full text]
  • Anti-UBE2D2 (Aa 99-147) Polyclonal Antibody (DPABH-17109) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
    Anti-UBE2D2 (aa 99-147) polyclonal antibody (DPABH-17109) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Antigen Description Accepts ubiquitin from the E1 complex and catalyzes its covalent attachment to other proteins. In vitro catalyzes Lys-48-linked polyubiquitination. Mediates the selective degradation of short-lived and abnormal proteins. Functions in the E6/E6-AP-induced ubiquitination of p53/TP53. Mediates ubiquitination of PEX5 and autoubiquitination of STUB1 and TRAF6. Involved in the signal- induced conjugation and subsequent degradation of NFKBIA, FBXW2-mediated GCM1 ubiquitination and degradation, MDM2-dependent degradation of p53/TP53 and the activation of MAVS in the mitochondria by DDX58/RIG-I in response to viral infection. Essential for viral activation of IRF3. Immunogen A region within synthetic peptide: ISKVLLSICS LLCDPNPDDP LVPEIARIYK TDREKYNRIA REWTQKYAM, corresponding to C terminal amino acids 99-147 of Human UBE2D2 Isotype IgG Source/Host Rabbit Species Reactivity Mouse, Rat, Dog, Human Purification Protein A purified Conjugate Unconjugated Applications WB, ELISA, IHC-P Format Liquid Size 100 μl Buffer Constituents: 2% Sucrose, PBS Preservative None Storage Shipped at 4°C. Upon delivery aliquot and store at -20°C or -80°C. Avoid repeated freeze / thaw cycles. GENE INFORMATION 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved Gene Name UBE2D2 ubiquitin-conjugating
    [Show full text]
  • Whole-Exome Sequencing in Familial Parkinson Disease
    Supplementary Online Content Farlow JL, Robak LA, Hetrick K, et al. Whole-exome sequencing in familial Parkinson disease. JAMA Neurol. Published online November 23, 2015. doi:10.1001/jamaneurol.2015.3266. eMethods. Supplemental Methods eFigure 1. Pedigrees of Discovery Cohort Families A-I eFigure 2. Pedigrees of Discovery Cohort Families J-Q eFigure 3. Pedigrees of Discovery Cohort Families R-Z eFigure 4. Pedigrees of Discovery Cohort Families AA-AF eTable 1. Single-Nucleotide Variants Identified in the Filtered Genes With GO Annotation eTable 2. Primer Sequences for Sanger Sequencing Verification of Identified Variants of Interest This supplementary material has been provided by the authors to give readers additional information about their work. © 2015 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/27/2021 eMethods. Supplemental Methods Whole exome sequencing Discovery cohort. All members of each family were sequenced at the same center (44 samples/15 families at the Center for Inherited Disease Research [CIDR], and 53 samples/18 families at the HudsonAlpha Institute for Biotechnology). One family with 4 members was sequenced at both centers (eMethods). The Agilent SureSelect 50Mb Human All Exon Kit (CIDR) and Nimblegen 44.1Mb SeqCap EZ Exome Capture version 2.0 (HudsonAlpha) were used for capture, and the Illumina HiSeq 2000 system was used for 100bp paired-end sequencing. Samples were aligned to the human genome (build hg19) using Burrows Wheeler Aligner1. The Genome Analysis Toolkit (GATK)2 was used for local realignment, base quality score recalibration, and multi-sample variant calling (Unified Genotyper) for the samples sequenced at CIDR and HudsonAlpha separately.
    [Show full text]
  • Supplemental Table 3 Two-Class Paired Significance Analysis of Microarrays Comparing Gene Expression Between Paired
    Supplemental Table 3 Two‐class paired Significance Analysis of Microarrays comparing gene expression between paired pre‐ and post‐transplant kidneys biopsies (N=8). Entrez Fold q‐value Probe Set ID Gene Symbol Unigene Name Score Gene ID Difference (%) Probe sets higher expressed in post‐transplant biopsies in paired analysis (N=1871) 218870_at 55843 ARHGAP15 Rho GTPase activating protein 15 7,01 3,99 0,00 205304_s_at 3764 KCNJ8 potassium inwardly‐rectifying channel, subfamily J, member 8 6,30 4,50 0,00 1563649_at ‐‐ ‐‐ ‐‐ 6,24 3,51 0,00 1567913_at 541466 CT45‐1 cancer/testis antigen CT45‐1 5,90 4,21 0,00 203932_at 3109 HLA‐DMB major histocompatibility complex, class II, DM beta 5,83 3,20 0,00 204606_at 6366 CCL21 chemokine (C‐C motif) ligand 21 5,82 10,42 0,00 205898_at 1524 CX3CR1 chemokine (C‐X3‐C motif) receptor 1 5,74 8,50 0,00 205303_at 3764 KCNJ8 potassium inwardly‐rectifying channel, subfamily J, member 8 5,68 6,87 0,00 226841_at 219972 MPEG1 macrophage expressed gene 1 5,59 3,76 0,00 203923_s_at 1536 CYBB cytochrome b‐245, beta polypeptide (chronic granulomatous disease) 5,58 4,70 0,00 210135_s_at 6474 SHOX2 short stature homeobox 2 5,53 5,58 0,00 1562642_at ‐‐ ‐‐ ‐‐ 5,42 5,03 0,00 242605_at 1634 DCN decorin 5,23 3,92 0,00 228750_at ‐‐ ‐‐ ‐‐ 5,21 7,22 0,00 collagen, type III, alpha 1 (Ehlers‐Danlos syndrome type IV, autosomal 201852_x_at 1281 COL3A1 dominant) 5,10 8,46 0,00 3493///3 IGHA1///IGHA immunoglobulin heavy constant alpha 1///immunoglobulin heavy 217022_s_at 494 2 constant alpha 2 (A2m marker) 5,07 9,53 0,00 1 202311_s_at
    [Show full text]
  • Analysis of the Expression and Single-Nucleotide Variant Frequencies of the Buyrophilin-Like 2 Gene in Patients with Uveal Melanoma
    Supplementary Online Content Amaro A, Parodi F, Diedrich K, et al. Analysis of the expression and single-nucleotide variant frequencies of the buyrophilin-like 2 gene in patients with uveal melanoma. JAMA Ophthalmol. Published online August 11, 2016. doi: 10.1001/jamaophthalmol.2016.2691. eTable 1. M1- and M2-poloarization genes eTable 2. M1- and M2-polarization genes that are associated with disease-free survival of patients with uveal melanoma This supplementary material has been provided by the authors to give readers additional information about their work. © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 eTable 1. M1- and M2-poloarization genes 203308_x NM_000195 | HPS1 3257 Hs.40456 203308_x 10 GO:00070 - AI185798 Hermansky-Pudlak syndrome 1 chr10:100 1E+08 1E+08 -1.13131 6.095084 0.859709 0.14105 1.819564 205022_s NM_001085471 FOXN3 1112 Hs.43428 205022_s 14 GO:00000 - NM_0051 forkhead box N3 chr14:896 89622516 90085494 -0.83339 6.106332 1.983495 0.324826 3.850649 204226_a NM_001164380 STAU2 27067 Hs.56181 204226_a 8 GO:00068 - NM_0143 staufen, RNA binding protein, chr8:7446 74461843 74659943 -0.95841 6.106973 0.850648 0.139291 1.804766 208639_x NM_005742 | PDIA6 10130 Hs.21210 208639_x 2 GO:00064 - BC001312 protein disulfide isomerase chr2:1092 10923518 10952960 -0.37258 6.110976 1.043652 0.170783 2.061971 204295_a NA NA NA NA 204295_a NA NA NA NM_0031 NA chrNA:-1-- -1 -1 -0.94773 6.113623 0.686679 0.112319 1.613194 202609_a NM_004447 | EPS8 2059 Hs.59116 202609_a
    [Show full text]
  • Single-Gene Deletions Contributing to Loss of Heterozygosity in Saccharomyces Cerevisiae: Genome-Wide Screens and Reproducibilit
    G3: Genes|Genomes|Genetics Early Online, published on July 3, 2019 as doi:10.1534/g3.119.400429 1 1 2 Single-gene deletions contributing to loss of heterozygosity in Saccharomyces cerevisiae: 3 genome-wide screens and reproducibility 4 5 Kellyn M. Hoffert* and Erin D. Strome* 6 *Department of Biological Sciences, Northern Kentucky University, Highland Heights, Kentucky, 7 41099 8 9 10 11 12 13 14 15 16 17 18 19 © The Author(s) 2013. Published by the Genetics Society of America. 2 20 21 S. cerevisiae heterozygous LOH screen 22 Keywords: S. cerevisiae, loss of heterozygosity, MAT, MET15, screen comparison 23 Erin D. Strome, FH359G Nunn Drive, Highland Heights, KY 41099, 859-572-6635, 24 [email protected] 25 26 27 28 29 30 31 32 33 34 35 36 37 3 38 ABSTRACT 39 Loss of heterozygosity (LOH) is a phenomenon commonly observed in cancers; the loss of 40 chromosomal regions can be both causal and indicative of underlying genome instability. Yeast 41 has long been used as a model organism to study genetic mechanisms difficult to study in 42 mammalian cells. Studying gene deletions leading to increased LOH in yeast aids our 43 understanding of the processes involved, and guides exploration into the etiology of LOH in 44 cancers. Yet, before in-depth mechanistic studies can occur, candidate genes of interest must 45 be identified. Utilizing the heterozygous Saccharomyces cerevisiae deletion collection (≈ 6500 46 strains), 217 genes whose disruption leads to increased LOH events at the endogenously 47 heterozygous mating type locus were identified. Our investigation to refine this list of genes to 48 candidates with the most definite impact on LOH includes: secondary testing for LOH impact at 49 an additional locus, gene ontology analysis to determine common gene characteristics, and 50 positional gene enrichment studies to identify chromosomal regions important in LOH events.
    [Show full text]