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Genetic and Genomics Laboratory Tools and Approaches

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Genetic and Genomics Laboratory Tools and Approaches Meredith Yeager, PhD Cancer Genomics Research Laboratory Division of Cancer Epidemiology and Genetics [email protected] DCEG Radiation Epidemiology and Dosimetry Course 2019 www.dceg.cancer.gov/RadEpiCourse (Recent) history of genetics 2 Sequencing of the Human Genome Science 291, 1304-1351 (2001) 3 The Human Genome – 2019 • ~3.3 billion bases (A, C, G, T) • ~20,000 protein-coding genes, many non-coding RNAs (~2% of the genome) • Annotation ongoing – the initial sequencing in 2001 is still being refined, assembled and annotated, even now – hg38 • Variation (polymorphism) present within humans – Population-specific – Cosmopolitan 4 Types of polymorphisms . Single nucleotide polymorphisms (SNPs) . Common SNPs are defined as > 5% in at least one population . Abundant in genome (~50 million and counting) ATGGAACGA(G/C)AGGATA(T/A)TACGCACTATGAAG(C/A)CGGTGAGAGG . Repeats of DNA (long, short, complex, simple), insertions/deletions . A small fraction of SNPs and other types of variation are very or slightly deleterious and may contribute by themselves or with other genetic or environmental factors to a phenotype or disease 5 Different mutation rates at the nucleotide level Mutation type Mutation rate (per generation) Transition on a CpG 1.6X10-7 Transversion on a CpG 4.4X10-8 Transition: purine to purine Transition out of CpG 1.2X10-8 Transversion: purine to pyrimidine Transversion out of CpG 5.5X10-9 Substitution (average) 2.3X10-8 A and G are purines Insertion/deletion (average) 2.3X10-9 C and T are pyrimidines Mutation rate (average) 2.4X10-8 . Size of haploid genome : 3.3X109 nucleotides . 80 new mutations per haploid genome per generation. Assume 2% of genome under natural selection . About 1.6 new deleterious mutations in each gamete . Most of these deleterious mutations are recessive Nachman & Crowell, Genetics 156:297-304 (2000) 6 Confusing terminology, SNPs versus mutations . Germline versus somatic . Inherited versus acquired . Common versus rare germline . Common variants are generally called ‘polymorphisms’ . However, all polymorphisms started as germline mutations (de novo mutations) . Singletons or exceedingly rare SNPs?? 7 Most SNPs are rare (n = 67,135,025 SNPs from 1000 Genomes) 8 7X106 Estimated number of common SNPs in the human genome as a function of their minor allele frequency 6X106 Estimated number of SNPs 5X106 4X106 3X106 2X106 106 0 >5% >10% >15% >20% >25% >30% >35% >40% >45% Minor Allele Frequency (MAF) Adapted from Reich et al. Nat Genet (2003) 9 SNPs & function . Vast majority are “silent” . No known functional change . Alter function of gene product . Change sequence of protein . Alter gene expression/regulation . Promoter/enhancer . mRNA stability . Small RNA binding sites . Disrupt CpG site 10 11 Which variants contribute to disease? . Loss of function – premature stops, frame-shifts . Splice variants . Amino acid changes . SNPs in regulatory regions . Structural variants . ?? . There are tools that help predict which variants might be important 12 Mapping Genetic Susceptibility 13 Trajectory of the Genetics of Cancer Susceptibility Pre 2001 ~2004 Very few candidate genes panned out • Underpowered studies? • Bad guesses? Candidate SNP Functional Data Candidate Genes Biological Plausibility Genetic Markers Candidate Pathway Biological Plausibility 14 Technological advances + community efforts 15 16 17 Trajectory of the Genetics of Cancer Susceptibility (2) Pre 2001 ~2004 2006 Candidate SNP Functional Data Genome Wide Association Studies (GWAS) Candidate Genes Biological Plausibility Genetic Markers Candidate Pathway Biological Plausibility 18 GWAS . Somewhat randomly-distributed SNPs are genotyped as “markers” . Capitalize on phenomenon known as linkage disequilibrium (non- random association of alleles at different loci) . “Agnostic” 19 Basic principle of genetic association studies in unrelated individuals 20 Published Cancer GWAS Etiology Hits: June 2019 TARDBP AJAP1 NOC2L ITPR3 NOTCH4 6p25.2 PEX14 RCC2 KIF1B EXOC2 UHRF1BP1HLA-DOA ATAT1 TRIM31 6p21.31HLA-G >1,100 Disease Loci marked by SNPs SERPINB6BTNL2 HCP5 6p21 6p21.1 KLHDC7A M DS2 WNT4 STG GPX6 DDX1 HLA- HLA- 2p25.1 HLA-DQFLJ22536 GRM 4 LACE1 1p34.3 DRB5 HLA DQB1 C1orf200 GNL2 2p25.2C2orf48 For > 30 “cancers” RSPO1 NOL10 TNF BAK1 C6orf106RREB1HLA-DRA HLA-A HLA-DRB6 PIK3R3 1p34.2 HIVEP3 M YCN GRHL1 GDF7 EGOT 3p26 M ICA 6p21.32 M ICB SOX4C6orf10DTNBP1CYP21A2 2 Loci marked by a CNV ADCY3 2p24.1 DAZL HLA- HLA- 1p32.3 FAF1 DTNB FOXQ1 HLA-F HIST1H1E DRB1 DPB2 GABBR1 GPN1 2p23.3 2p24 TGFBR2 SLC4A7 EOM ESITGA9 RAVER2 L1TD1 5p15.33 EXOC2 IRF4 RANBP9 C11orf21 TSPAN32 OR52N2 WDR43NCOA12p22.3 MAD1L1 CTNNB1ULK4 3p22.1 TERT/ CDKAL1PSORS1C1 CASC15 GATA4 AK5 QPCT EPAS1CYP1B1 CLPTM1L 5p15 AGR3 LSP1 PIDD1 LMO1 3p21.31 TACC3 HAUS6 CCND2 DENND5B UNC5CL AIF1 CCHCR1 ITGB8 DMRT1 IGF2 PPFIBP2 RNU6-491P NRXN1 EHBP1 THADA 5p15.1 AHRRTPPP SP8 AHR 8p23.3 NKX3-1 8p21 LARP4B MOB2 LM O4 1p22.3 CPZ 12p13.31 DOCK3 6p22.1 ATXN1 NEU1 DNAH11 DMRTA1 SPSB2 WNK1 REL 2p14 MEIS1-AS3 CREB5 SP4 EBF2 BNC2 9p21.3 LOC338591 5p15.33BASP1ELL2 HLA- NAT2 INTS10 GATA3 10p14 ETAA1 FHIT HLA-B 6p22.2 7p15 7p14.1 CDKN2A 12p13.1 ETV6 GSTM 1 ZNF638BCL11A 5p14.3 DAB2 5p13.1 DQA2 7p13 CHRNA2 CDKN2A AS1 WT1 CDKN1B BCL2L15 KCNIP4 HLA- 8p12 TNFRSF10 /B PIP4K2A DNAJC1 NEBL SLC25A26 LCORL 5p13.3 PRKAA1 TFEB CDKN1A IKZF1 TNS3 KIAA1551 ATF7IP deletion PPP1R21 FOXP1 SUGCT WAC SSPN GGCX 4p14 DQA1 8p11.23 NRG1 MTAP RN7SKP114 NAMPTL HSD17B12 11p11.2 FGF10 5p12 SLC45A2 6p12.1 6p21.32 EGFR 7p11.2 12p11.22 ITPR2 EM BP1 1p12 VGLL3 3p11 3p12 HPVC1 STK38L FADS1 EXOC1 COX18 KHDRBS2 OR8U8 11q12 10q11.23 PAX8 CM SS1 WDR52-AS1 CWC27 RAB3C PDE4D 10q11 2q13 2q14.1 CDKL2 SMARCAD1 11q13.1 MYRF INCENP KRT5 DIP2B ACVR1B DUBR 6q14.1 MYO6 ZNF365 BCL2L112q14.2 ACOXL ARID5B 10q21.2 HELQ HEL308 PDLIM5 5q11.2 5q11.1 DDX4 ABCB4ABCB1LMTK2 KRT8 PRPH 12q13 RNF115 OTUD7B NBPF23 ZBTB203q13.2 PRDM14 EGR2 11q13.3 POLD3 CCND1 TFCP2L1 ADH1B ZM Z1 AS1 ADAM15 GOLPH3LDENND4B 4q22.1 ADH6 5q12.3 12q1212q13.312q24.12 SLO2A1 3q22.1 HNF4G 8q21.13 NKX2-3 TYR 11q14.1 11q13 ATG10 ARRDC3 XRCC4 HACE1 LIN28B CHMP4C FOXE1 9q22.33 9q31.1 ATP1B3 4q24 CENPE 4q22.2 CUX1 7q21.3 ABCC2 FFAR4 RASSF3 RNU6-148P12q15 TRIM 46M UC1 ARNT NCK1 3q21 ATG5 TGFBR1 PLCE1 ATM CXCR5 KDM2A TET2 BANK1 5q14.3 ADGRV1 5q15 GPRC6A 8q22.3 KLF4 9q31.3 LEF1 OLIG3 RGS22 ODF1 ENTPD7 12q21.31 12q21.2 M TX1ASH1LDUSP12 ZEB2 ZBTB38 3q23 3q25.31 10q23.33 FAS POLD3 11q22.1 POU2AF1 NR1H4 CAMK2D ECHDC1PTPRKROS1 ZFHX4 ZBTB10 ZFPM3 9q31.2 TMEM38B SYNPO2 NREP POT1 ASZ1 9q34.2 10q25.1 10q24.33 RNLS KDELC2 TMPRSS5 C11orf93 ELK3 NTN4 POLR3B CLCN6SLC25A44 ITGA6 2q24.2 M BNL1 RSRC13q25.31 ALDH7A1 LINC00536EIF3H POU5FIP1 TCFL2 CYP17A1 4q28.1 L3MBTL3 AHI1 NEDD9 KLF14 FLJ43663LINC-PINT ASTN2LM X1B9q33 OBFC1 PHLDB1 SH2B3 ACAD10 BRAP UCK2 HOXD1HOXD32q31.1 M YNNLRRIQ4M DS1 11q23.3 DLG2 PITX1TMEM173 IL3 CCAT2 CCDC26 VTI1A TCF7L2 10q25.2 NR5A2 LRRN2 SLC41A1 LOC643623HEY2 HBS1L PVT1 LAMC3 11q24.1 GRAMD1BMPZL2 TBX3 CUX2 C12orf51 CDCA7 DLX2-AS1 TNFSF10 3q26.31 TERC 4q31.21 HSPA4 CSNK1A1 RNU6-456P CASC19POU5F1B SEC16A TOR1A 10q26.12 FGFR2 PRLHR NOS1 PHLDA3 ZC3H11A ESR1 6q25.1IPCEF1 NOBOX 8q24.21 ABO 11q24.3 SCN3BMMP20 OAS3 TBX5 MDM4 CLDN11 PPP2R2B HTR3CST6GAL1 CATSPER3SPRY4 CNNM2 TCF7L2 NABP1 STAT4NUP35 FSTL54q32.3 SLC22A3 SLC22A2 PSCA ANXA13 8q24.13 LHPP MMP8 12q24.31 VTI1A SCARB1 LGR6 ESRRG DUSP10 M ECOM TAB2 SMARCD3 NCAPG2 ETS1 C11orf30 EBF1 10q26 LHPP KITLG ALS2CR12 CASP8HIBCH TP63 LPP ADAM29 CCAT1 8q24 ATM PKNOX2 SH2B3 ALDH2 PARP1 TPRG1 MIR3939 RNASET2 RGS17 MYC HTR3B RHOU 2q33.1 C3orf21 12q24.31 TBX5 12q24.21 PCNXL2 ERBB4 PNKD 3q28 B3GAT1 GAB2 ZBTB16 GPR160 IRF2 ZFP42 5q35.1 BOD1 6q27 KATNA1 BARD1 LRRC34 DOCK2 FBRSL1 MSRB3 EXO1 2q35 DIRC3 COL23A1 SATB2 EFCAB2 2q36.3 SP140 ZNF257 ZNF728 FARP2 BOK-AS1 MLPH ZNF726 SHROOM2 GPR143 CLEC16A NFIX1 MYO9B TCF3 C20orf54 BM P2 ADCY9 LM F1 ZBTB7A MPG NXN SM G6 CHD3 PLIN3 TSPAN16 TGM 3 MCM8 HAO1 CIITA MPV17L BCAR4 MERIT40 19p13.12 19p13.11 20p12.2 20p12.3 TP53 17p13.1 DBIL5P CLUL1 JAG1 TNFRSF13B 19p12 20p11.22 MAPK3 PIRT PTPN2 FAM38B GATAD2A ELL KIZ NUDT10 XAGE3 GPATCH1 RHPN2 TNFRSF19 IKZF3 ATAD5HNF1B CHST918q11.2 GAREM CCNE1 RALY BP1FB1 CDC91L1 22q12.3 M IPEP PDX1 CEBPE MMP14NKX2-1 OCA2HERC2 GREM1 NRIP1 GRIK1 XBP1 MTMR3 AR 17q11.2 TCF2 17q12 KCCN4 GIPR 16q12.1 TOX3 FTO SLC14A1 PRKD2 20q13.12 PREX1 LAMA5 BRCA2 FRY PAX9 ATG14 BM F CRAC1 15q22.33 CDH2SM AD7 21q22.12 RUNX1 BACH1 CBX6 EMID1 CHEK2 NLGN3 SLC7A M BIP CNTNAP1 GSDMB HAP1 CYP2A6 IFNL319q13.2 13q13.3 SEMA6D CASC5 CHRNA5 16q12.2 AM FR HEATR3 GAREM1 MBD2 20q13.13 RPS21 20q13.33 TFF1 22q12.1 ZNRF3 TBX1 KANSL1 COX11 GRP THEG5 LINC00111 MX2 DDHD1 SIX1 RFX7 17q21.31 TGFB1SYM PK BMP4 RFX7 CYP19A1 CDH1 RFWD3 GPR56 SETBP1 RTEL1 CYP24A1 DNMT3B M KL1 PLA2G6 XRCC6 DCC M BD1 ATP5SL KLK3 TMPRSS2 MCM3APAS ESR2 TTC9 17q21.32 BRIP1 NGFR 19q13 14q23.1 CHRNA3 MAP2K1 PRTG PHLPP2 16q23.3 ZFPM1 ADNP 22q13.31 13q22 PMAIP1 18q21.1 LILRA3 POU2F2 19q11 GTPBP5 STMN3 SCO2 SLC16A8 RAD51B DPF3 BPTF ZNF652 RNF43 BCL2 CLK3 PCAT29 RPLP1 CDYL2 M C1R IRF8 BCAS1 22q13.1 CBX7 TTC28 13q22.1 KLF5 KLF12 TSHZ1 TCF4 OACYLP NLRP12 19q13.43 20q13.2 20q13.13 17q25.3 LINC00673 17q24 PRC1ANP32A 16q24.2 22q13 CCDC88C RIN3 ETFA FOXL1 16q24.1 SALL3 GNAS NCOA5 20q11.21 FAM19A5 AIFM3 SLC28A1SM AD3 DEF8 BCAR1 TEX14 SKAP1 EVI2A UBAC2 ADSSL1 16q23.2 AKT1 17q24.3 M CF2LUPF3A IRS2 13q34 TKTL1 36 Basal Cell 17 Bladder 184Breast 15 Cervical 81 CLL 122Colorectal 15Cutaneous Sq 22 Endometrial 29 Esophageal 6 Ewing Sarcoma 3 Gallbladder 14 Gastric 38 Glioma 29 Hodgkins 13 Kidney 7 Liver 48 Lung 22 Melanoma 103 Multiple 23Multiple Myeloma 14 Nasopharyngeal 18 Neuroblastoma 15Non-Hodgkin 10 Oral 2 Osteosarcoma 39 Ovary 28 Pancreas 16 Pediatric ALL 170Prostate 60 Testicular 17 Thyroid 3 Wilms 21 Trajectory of the Genetics of Cancer Susceptibility (3) Pre 2001 ~2004 2006 2010 Whole Genome Sequencing Candidate SNP Functional Data Genome Wide Association Exome Studies (GWAS) Sequencing Candidate Genes Biological Plausibility Genetic Markers Candidate Pathway Biological Plausibility Regional Sequencing GWAS & Linkage 22 23 24 25 https://gnomad.broadinstitute.org/ 125,748 exomes 15,708 whole genomes 26 Examples: Genetic studies .
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  • Constitutive Activation of RAS/MAPK Pathway Cooperates with Trisomy 21 and Is Therapeutically Exploitable in Down Syndrome B-Cell Leukemia

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    Author Manuscript Published OnlineFirst on March 27, 2020; DOI: 10.1158/1078-0432.CCR-19-3519 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Constitutive activation of RAS/MAPK pathway cooperates with trisomy 21 and is therapeutically exploitable in Down syndrome B-cell Leukemia Anouchka P. Laurent1,2, Aurélie Siret1, Cathy Ignacimouttou1, Kunjal Panchal3, M’Boyba K. Diop4, Silvia Jenny5, Yi-Chien Tsai5, Damien Ross-Weil1, Zakia Aid1, Naïs Prade6, Stéphanie Lagarde6, Damien Plassard7, Gaelle Pierron8, Estelle Daudigeos-Dubus4, Yann Lecluse4, Nathalie Droin1, Beat Bornhauser5, Laurence C. Cheung3,9, John D. Crispino10, Muriel Gaudry1, Olivier A. Bernard1, Elizabeth Macintyre11, Carole Barin Bonnigal12, Rishi S. Kotecha3,9,13, Birgit Geoerger4, Paola Ballerini14, Jean-Pierre Bourquin5, Eric Delabesse6, Thomas Mercher1,15 and Sébastien Malinge1,3 1INSERM U1170, Gustave Roussy Institute, Université Paris Saclay, Villejuif, France 2Université Paris Diderot, Paris, France 3Telethon Kids Cancer Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia 4Gustave Roussy Institute Cancer Campus, Department of Pediatric and Adolescent Oncology, INSERM U1015, Equipe Labellisée Ligue Nationale contre le Cancer, Université Paris-Saclay, Villejuif, France 5Department of Pediatric Oncology, Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland 6Centre of Research on Cancer of Toulouse (CRCT), CHU Toulouse, Université Toulouse III, Toulouse, France 7IGBMC, Plateforme GenomEast, UMR7104 CNRS, Ilkirch, France 8Service de Génétique, Institut Curie, Paris, France 9School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Australia 10Division of Hematology/Oncology, Northwestern University, Chicago, USA 11Hematology, Université de Paris, Institut Necker-Enfants Malades and Assistance Publique – Hopitaux de Paris, Paris, France 12Centre Hospitalier Universitaire de Tours, Tours, France 1 Downloaded from clincancerres.aacrjournals.org on September 30, 2021.
  • TBX15/Mir-152/KIF2C Pathway Regulates Breast Cancer Doxorubicin Resistance Via Preventing PKM2 Ubiquitination

    TBX15/Mir-152/KIF2C Pathway Regulates Breast Cancer Doxorubicin Resistance Via Preventing PKM2 Ubiquitination

    TBX15/miR-152/KIF2C Pathway Regulates Breast Cancer Doxorubicin Resistance via Preventing PKM2 Ubiquitination Cheng-Fei Jiang Nanjing Medical University Yun-Xia Xie Zhengzhou University Ying-Chen Qian Nanjing Medical University Min Wang Nanjing Medical University Ling-Zhi Liu Thomas Jefferson University - Center City Campus: Thomas Jefferson University Yong-Qian Shu Nanjing Medical University Xiao-Ming Bai ( [email protected] ) Nanjing Medical University Bing-Hua Jiang Thomas Jefferson University Research Keywords: TBX15, miR-152, KIF2C, PKM2, Doxorubicin resistance, breast cancer Posted Date: December 28th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-130874/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/29 Abstract Background Chemoresistance is a critical risk problem for breast cancer treatment. However, mechanisms by which chemoresistance arises remains to be elucidated. The expression of T-box transcription factor 15 (TBX- 15) was found downregulated in some cancer tissues. However, role and mechanism of TBX15 in breast cancer chemoresistance is unknown. Here we aimed to identify the effects and mechanisms of TBX15 in doxorubicin resistance in breast cancer. Methods As measures of Drug sensitivity analysis, MTT and IC50 assays were used in DOX-resistant breast cancer cells. ECAR and OCR assays were used to analyze the glycolysis level, while Immunoblotting and Immunouorescence assays were used to analyze the autophagy levels in vitro. By using online prediction software, luciferase reporter assays, co-Immunoprecipitation, Western blotting analysis and experimental animals models, we further elucidated the mechanisms. Results We found TBX15 expression levels were decreased in Doxorubicin (DOX)-resistant breast cancer cells.
  • Interplay Between Epigenetics and Metabolism in Oncogenesis: Mechanisms and Therapeutic Approaches

    Interplay Between Epigenetics and Metabolism in Oncogenesis: Mechanisms and Therapeutic Approaches

    OPEN Oncogene (2017) 36, 3359–3374 www.nature.com/onc REVIEW Interplay between epigenetics and metabolism in oncogenesis: mechanisms and therapeutic approaches CC Wong1, Y Qian2,3 and J Yu1 Epigenetic and metabolic alterations in cancer cells are highly intertwined. Oncogene-driven metabolic rewiring modifies the epigenetic landscape via modulating the activities of DNA and histone modification enzymes at the metabolite level. Conversely, epigenetic mechanisms regulate the expression of metabolic genes, thereby altering the metabolome. Epigenetic-metabolomic interplay has a critical role in tumourigenesis by coordinately sustaining cell proliferation, metastasis and pluripotency. Understanding the link between epigenetics and metabolism could unravel novel molecular targets, whose intervention may lead to improvements in cancer treatment. In this review, we summarized the recent discoveries linking epigenetics and metabolism and their underlying roles in tumorigenesis; and highlighted the promising molecular targets, with an update on the development of small molecule or biologic inhibitors against these abnormalities in cancer. Oncogene (2017) 36, 3359–3374; doi:10.1038/onc.2016.485; published online 16 January 2017 INTRODUCTION metabolic genes have also been identified as driver genes It has been appreciated since the early days of cancer research mutated in some cancers, such as isocitrate dehydrogenase 1 16 17 that the metabolic profiles of tumor cells differ significantly from and 2 (IDH1/2) in gliomas and acute myeloid leukemia (AML), 18 normal cells. Cancer cells have high metabolic demands and they succinate dehydrogenase (SDH) in paragangliomas and fuma- utilize nutrients with an altered metabolic program to support rate hydratase (FH) in hereditary leiomyomatosis and renal cell 19 their high proliferative rates and adapt to the hostile tumor cancer (HLRCC).
  • Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer

    Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer

    Downloaded from genesdev.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW What a difference a hydroxyl makes: mutant IDH, (R)-2-hydroxyglutarate, and cancer Julie-Aurore Losman1 and William G. Kaelin Jr.1,2,3 1Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 2Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA Mutations in metabolic enzymes, including isocitrate whether altered cellular metabolism is a cause of cancer dehydrogenase 1 (IDH1) and IDH2, in cancer strongly or merely an adaptive response of cancer cells in the face implicate altered metabolism in tumorigenesis. IDH1 of accelerated cell proliferation is still a topic of some and IDH2 catalyze the interconversion of isocitrate and debate. 2-oxoglutarate (2OG). 2OG is a TCA cycle intermediate The recent identification of cancer-associated muta- and an essential cofactor for many enzymes, including tions in three metabolic enzymes suggests that altered JmjC domain-containing histone demethylases, TET cellular metabolism can indeed be a cause of some 5-methylcytosine hydroxylases, and EglN prolyl-4-hydrox- cancers (Pollard et al. 2003; King et al. 2006; Raimundo ylases. Cancer-associated IDH mutations alter the enzymes et al. 2011). Two of these enzymes, fumarate hydratase such that they reduce 2OG to the structurally similar (FH) and succinate dehydrogenase (SDH), are bone fide metabolite (R)-2-hydroxyglutarate [(R)-2HG]. Here we tumor suppressors, and loss-of-function mutations in FH review what is known about the molecular mechanisms and SDH have been identified in various cancers, in- of transformation by mutant IDH and discuss their im- cluding renal cell carcinomas and paragangliomas.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.