DOCSLIB.ORG

Supplementary Table 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Table 1 Supplementary Table 1: Clinical and Pathological characteristics of tumor samples Serum Sample Tumor histology Age Gender DM HTN ESRD Creatinine Metastasis ID (gm/dl) Extends beyond Renal cell renal capsule, Tumor 1 carcinoma, clear cell 84 F N Y N 0.9 thrombus in renal type vein, Adenopathy Renal cell 3 carcinoma, clear cell 62 F N Y N 1.2 N type Renal cell 4 carcinoma, clear cell 57 F Y Y N 1.3 N type Renal cell 6 carcinoma, clear cell 63 F N N N 1.3 N type Renal cell 8 carcinoma, clear cell 58 M Y Y Y 1 N type Renal cell carcinoma, clear cell 10 69 M Y Y N 1 N type with focal papillary architecture Extension into Renal cell perinephric tissue, 11 carcinoma, clear cell 48 M Y Y N 1.3 renal sinus, renal type vein and lung Renal cell 12 carcinoma, clear cell 66 M N Y CKD 2.4 N type Renal cell 13 carcinoma, clear cell 54 F Y N N 0.9 Adenopathy+ type Renal cell carcinoma, clear cell Bilateral kidney 14 47 M Y Y Y 12.6 type multilocular lesions cystic variant Renal cell Extends into renal 19 carcinoma, clear cell 69 F N Y N 1 vein type Renal cell 21 carcinoma, clear cell 18 F N N N 1 N type Renal cell 22 carcinoma, clear cell 65 F N N Y 5.8 N type HTN: Hypertension, ESRD: End stage renal disease, DM: Diabetes Mellitus, Supplementary Table 2: Aberrantly methylated and underexpressed loci with overlapping enhancer (H3K4me1) marks Gene Name Refseq ID Chromosomal Location Promoter Intragenic Gene AGPAT3 NM_020132 chr21:44173228-44173889 0 1 1-acylglycerol-3-phosphate O-acyltransferase 3 ALDH4A1 NM_003748 chr1:19087954-19088127 0 1 aldehyde dehydrogenase 4 family, member A1 ARHGEF3 NM_019555 chr3:56756502-56756626 0 1 Rho guanine nucleotide exchange factor (GEF) 3 ARNT2 NM_014862 chr15:78494479-78494996 0 1 aryl-hydrocarbon receptor nuclear translocator 2 ARNT2 NM_014862 chr15:78573982-78574898 0 1 aryl-hydrocarbon receptor nuclear translocator 2 ATP6V0A4 NM_020632 chr7:138113827-138114236 0 1 ATPase, H+ transporting, lysosomal V0 subunit a4 BANP NM_017869 chr16:86595763-86596862 0 1 BTG3 associated nuclear protein C12orf49 NM_024738 chr12:115646042-115646624 0 1 chromosome 12 open reading frame 49 C12orf49 NM_024738 chr12:115642036-115642761 0 1 chromosome 12 open reading frame 49 CACNB4 NM_000726 chr2:152614969-152615444 0 1 calcium channel, voltage-dependent, beta 4 subunit CNNM2 NM_017649 chr10:104819168-104819290 0 1 cyclin M2 CNNM2 NM_017649 chr10:104790991-104792018 0 1 cyclin M2 CNNM2 NM_017649 chr10:104828109-104828622 0 1 cyclin M2 CTNNBIP1 NM_020248 chr1:9883307-9884482 0 1 catenin, beta interacting protein 1 CYP24A1 NM_000782 chr20:52217893-52218110 0 1 cytochrome P450, family 24, subfamily A, polypeptide 1 DDC NM_000790 chr7:50566647-50567423 0 1 dopa decarboxylase (aromatic L-amino acid decarboxylase) EPN1 NM_013333 chr19:60888891-60889508 0 1 epsin 1 EPN1 NM_013333 chr19:60889903-60890539 0 1 epsin 1 EPN1 NM_013333 chr19:60890581-60891050 0 1 epsin 1 FHOD3 NM_025135 chr18:32215178-32215984 0 1 formin homology 2 domain containing 3 FHOD3 NM_025135 chr18:32538791-32539245 0 1 formin homology 2 domain containing 3 FHOD3 NM_025135 chr18:32134301-32134536 0 1 formin homology 2 domain containing 3 GMDS NM_001500 chr6:1568864-1569225 0 1 GDP-mannose 4,6-dehydratase GMDS NM_001500 chr6:1746328-1746930 0 1 GDP-mannose 4,6-dehydratase GMDS NM_001500 chr6:1663538-1663811 0 1 GDP-mannose 4,6-dehydratase GMDS NM_001500 chr6:1653712-1653952 0 1 GDP-mannose 4,6-dehydratase HSD11B2 NM_000196 chr16:66024649-66025060 0 1 hydroxysteroid (11-beta) dehydrogenase 2 HSD11B2 NM_000196 chr16:66028570-66029814 0 1 hydroxysteroid (11-beta) dehydrogenase 2 HSD11B2 NM_000196 chr16:66027123-66027242 0 1 hydroxysteroid (11-beta) dehydrogenase 2 HSD11B2 NM_000196 chr16:66028209-66028379 0 1 hydroxysteroid (11-beta) dehydrogenase 2 IQGAP2 NM_006633 chr5:75828704-75829645 0 1 IQ motif containing GTPase activating protein 2 ITGB5 NM_002213 chr3:126044913-126045276 0 1 integrin, beta 5 ITGB5 NM_002213 chr3:125981166-125982064 0 1 integrin, beta 5 KIF13B NM_015254 chr8:29148303-29149459 0 1 kinesin family member 13B LETM1 NM_012318 chr4:1813924-1814512 0 1 leucine zipper-EF-hand containing transmembrane protein 1 LETM1 NM_012318 chr4:1793513-1793723 0 1 leucine zipper-EF-hand containing transmembrane protein 1 MAL NM_002371 chr2:95069524-95070748 0 1 mal, T-cell differentiation protein MAL NM_002371 chr2:95070749-95071008 0 1 mal, T-cell differentiation protein MAL NM_002371 chr2:95079186-95079377 0 1 mal, T-cell differentiation protein MAN1C1 NM_020379 chr1:25887729-25888154 0 1 mannosidase, alpha, class 1C, member 1 MAN1C1 NM_020379 chr1:25866389-25867076 0 1 mannosidase, alpha, class 1C, member 1 MPPED2 NM_001584 chr11:30505570-30506470 0 1 metallophosphoesterase domain containing 2 NISCH NM_007184 chr3:52462145-52462859 1 0 nischarin PCGF2 NM_007144 chr17:34144023-34144172 0 1 polycomb group ring finger 2 PLCG2 NM_002661 chr16:80442915-80444292 0 1 phospholipase C, gamma 2 (phosphatidylinositol-specific) PLEKHA6 NM_014935 chr1:202490710-202491435 0 1 pleckstrin homology domain containing, family A member 6 PLEKHA6 NM_014935 chr1:202557246-202558125 0 1 pleckstrin homology domain containing, family A member 6 PLEKHA6 NM_014935 chr1:202535289-202535653 0 1 pleckstrin homology domain containing, family A member 6 PLEKHA6 NM_014935 chr1:202499059-202499640 0 1 pleckstrin homology domain containing, family A member 6 PLEKHA6 NM_014935 chr1:202554265-202554691 0 1 pleckstrin homology domain containing, family A member 6 RALGPS1 NM_014636 chr9:128765901-128766442 0 1 Ral GEF with PH domain and SH3 binding motif 1 RALGPS1 NM_014636 chr9:128981567-128981870 0 1 Ral GEF with PH domain and SH3 binding motif 1 RALGPS1 NM_014636 chr9:128764778-128765900 0 1 Ral GEF with PH domain and SH3 binding motif 1 RXRA NM_002957 chr9:136468548-136469076 0 1 retinoid X receptor, alpha S100A2 NM_005978 chr1:151802759-151803151 0 1 S100 calcium binding protein A2 S100A2 NM_005978 chr1:151802759-151803151 1 0 S100 calcium binding protein A2 SELENBP1 NM_003944 chr1:149607800-149608122 0 1 selenium binding protein 1 SEMA3B NM_004636 chr3:50283452-50283534 0 1 semaphorin 3B SLC25A38 NM_017875 chr3:39401457-39402313 0 1 solute carrier family 25, member 38 SLC25A38 NM_017875 chr3:39401457-39402313 1 0 solute carrier family 25, member 38 SLC2A9 NM_020041 chr4:9540027-9540442 0 1 solute carrier family 2 (facilitated glucose transporter), member9 SMAD6 NM_005585 chr15:64827758-64828360 0 1 SMAD family member 6 SMAD6 NM_005585 chr15:64820486-64821212 0 1 SMAD family member 6 SMAD6 NM_005585 chr15:64804315-64804393 0 1 SMAD family member 6 SMAD6 NM_005585 chr15:64826825-64827332 0 1 SMAD family member 6 TACC2 NM_006997 chr10:123964741-123964996 0 1 transforming, acidic coiled-coil containing protein 2 TFCP2L1 NM_014553 chr2:121728700-121729880 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121744467-121744956 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121752377-121753466 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121717020-121717483 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121753467-121754391 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121716227-121716396 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121754663-121755146 0 1 transcription factor CP2-like 1 TFCP2L1 NM_014553 chr2:121746786-121747338 0 1 transcription factor CP2-like 1 TGFBR3 NM_003243 chr1:92027599-92027753 0 1 transforming growth factor, beta receptor III TMCO3 NM_017905 chr13:113246036-113246401 0 1 transmembrane and coiled-coil domains 3 TMCO3 NM_017905 chr13:113246726-113246914 0 1 transmembrane and coiled-coil domains 3 TMCO3 NM_017905 chr13:113246915-113247655 0 1 transmembrane and coiled-coil domains 3 TMCO3 NM_017905 chr13:113250736-113251115 0 1 transmembrane and coiled-coil domains 3 VAV2 NM_003371 chr9:135804920-135805577 0 1 vav 2 guanine nucleotide exchange factor VAV2 NM_003371 chr9:135728436-135729001 0 1 vav 2 guanine nucleotide exchange factor VAV2 NM_003371 chr9:135638784-135639447 0 1 vav 2 guanine nucleotide exchange factor VAV2 NM_003371 chr9:135804813-135804919 0 1 vav 2 guanine nucleotide exchange factor VAV2 NM_003371 chr9:135718971-135719481 0 1 vav 2 guanine nucleotide exchange factor VAV2 NM_003371 chr9:135798780-135798946 0 1 vav 2 guanine nucleotide exchange factor Supplementary Table 3: Transcription factor binding sites enriched in differentially methylated regions in RCC. Transcription factor Hits P Value AP2alpha 26903 0 AhR 3690 1.352787E-316 Hairy 3208 2.67E-306 Sp-1 3302 2.68E-305 Pax-4 3042 3.22E-246 MAZ 2615 1.47E-194 Max 2777 3.65E-192 E2F 5394 3.43E-189 Pax-3 4054 3.69E-161 CREB 4405 2.00E-154 MyoD 1904 3.54E-149 E2A 3080 1.52E-144 VDR 3130 3.70E-138 E12 2218 4.49E-137 Arnt 1965 2.05E-131 USF 1876 3.28E-127 c-MycMax 1765 4.66E-110 E2F-1 2449 7.52E-109 Bsap 1536 1.31E-102 HIF-1 1226 1.53E-99 Supplementary Table 4: List of Gene and Pathways hypermethylated and underexpressed in RCC Category P- Molecules Value Cellular 3.86E- AGTR1,ALDOA,ANGPT2,BID,CD9,CD97,CD99,CDH13,CDH4,CSF1R,CTBP1,DDR1,DIAPH2,DISC1,DKK3,DOCK Movement 16 4,DPYSL2,EGFR,ELMO1,ENG,ERBB3,FHL1,FLT1,FOXO3,FSCN1,GNA11,HOXA7,ICAM1,ICAM2,IGF2R,IL32,IQ GAP1,ITGAM,ITGB2,JAK2,KAL1,KDR,LAMB1,LAMC1,LIMS1,MAP3K14,MCM3,MET,MMP2,NUAK1,PALLD,PCSK 6,PDGFRB,RPS19,SCARB1,SDCBP,SLC2A1,SMAD7,SPARC,STAT1,TAGLN,TCIRG1,TGFA,THBD,THBS1,THBS 2,TIMP3,TLR2,TNFAIP8,TNFRSF1B,TP53,TPM1 (includes EG:22003),VEGFB,ZEB2 Cancer 1.90E- ABCG1,AGTR1,AKAP13,ALDOA,ALOX5,AMFR,ANGPT2,ANP32A,BCL6,C18orf1,CAPG,CBFB,CD9,CD97,CD99, 14 CDH13,CDH4,CLDN7,COL15A1,COL1A2,COL6A3,CP,CSF1R,CTBP1,DDB2,DDR1,DIAPH2,DMD,DPYD,DPYSL2
Load more

Recommended publications
  • Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
    Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent
    [Show full text]
  • PARSANA-DISSERTATION-2020.Pdf
    DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
    [Show full text]
  • Analysis of Gene–Environment Interactions in Postnatal
    – Analysis of gene environment interactions in postnatal INAUGURAL ARTICLE development of the mammalian intestine Seth Rakoff-Nahouma,b,c,1, Yong Kongd, Steven H. Kleinsteine, Sathish Subramanianf, Philip P. Ahernf, Jeffrey I. Gordonf, and Ruslan Medzhitova,b,1 aHoward Hughes Medical Institute, bDepartment of Immunobiology, dDepartment of Molecular Biophysics and Biochemistry, W. M. Keck Foundation Biotechnology Resource Laboratory, and eInterdepartmental Program in Computational Biology and Bioinformatics and Department of Pathology, Yale University School of Medicine, New Haven, CT 06510; fCenter for Genome Sciences and Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108; and cDivision of Infectious Diseases, Department of Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115 This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2010. Contributed by Ruslan Medzhitov, December 31, 2014 (sent for review December 25, 2014; reviewed by Alexander V. Chervonsky and Alexander Y. Rudensky) Unlike mammalian embryogenesis, which takes place in the relatively immediately after birth, the intestine is exposed to mother’s milk predictable and stable environment of the uterus, postnatal develop- and undergoes initial colonization with microorganisms. Second, ment can be affected by a multitude of highly variable environmental after weaning, the intestinal tract becomes exposed to solid foods factors, including diet, exposure to noxious substances, and micro- and is no longer exposed to mother’s milk components, the host organisms. Microbial colonization of the intestine is thought to play a immune system matures, and the microbiota shifts. particularly important role in postnatal development of the gastroin- Although it is widely recognized that these transitions have testinal, metabolic, and immune systems.
    [Show full text]
  • Epha4/Tie2 Crosstalk Regulates Leptomeningeal Collateral Remodeling Following Ischemic Stroke
    EphA4/Tie2 crosstalk regulates leptomeningeal collateral remodeling following ischemic stroke Benjamin Okyere, … , John B. Matson, Michelle H. Theus J Clin Invest. 2019. https://doi.org/10.1172/JCI131493. Research In-Press Preview Neuroscience Vascular biology Leptomeningeal anastomoses or pial collateral vessels play a critical role in cerebral blood flow (CBF) restoration following ischemic stroke. The magnitude of this adaptive response is postulated to be controlled by the endothelium, although the underlying molecular mechanisms remain under investigation. Here we demonstrated that endothelial genetic deletion, using EphA4f/f/Tie2-Cre and EphA4f/f/VeCahderin-CreERT2 mice and vessel painting strategies, implicated EphA4 receptor tyrosine kinase as a major suppressor of pial collateral remodeling, CBF and functional recovery following permanent middle cerebral artery occlusion. Pial collateral remodeling is limited by the cross talk between EphA4-Tie2 signaling in vascular endothelial cells, which is mediated through p-Akt regulation. Furthermore, peptide inhibition of EphA4 resulted in acceleration of the pial arteriogenic response. Our findings demonstrate EphA4 is a negative regulator of Tie2 receptor signaling which limits pial collateral arteriogenesis following cerebrovascular occlusion. Therapeutic targeting of EphA4 and/or Tie2 represents an attractive new strategy for improving collateral function, neural tissue health and functional recovery following ischemic stroke. Find the latest version: https://jci.me/131493/pdf 1 EphA4/Tie2
    [Show full text]
  • Supplemental Material Table of Contents
    Supplemental material Table of Contents Detailed Materials and Methods ......................................................................................................... 2 Perioperative period ........................................................................................................................... 2 Ethical aspects ................................................................................................................................... 4 Evaluation of heart failure ................................................................................................................. 4 Sample preparation for ANP mRNA expression .................................................................................. 5 Sample preparation for validative qRT-PCR (Postn, Myh7, Gpx3, Tgm2) ............................................ 6 Tissue fibrosis .................................................................................................................................... 7 Ventricular remodeling and histological tissue preservation ................................................................ 8 Evaluation of the histological preservation of cardiac tissue ................................................................ 9 Sample preparation and quantitative label-free proteomics analyses .................................................. 10 Statistical methods ........................................................................................................................... 12 References ........................................................................................................................................
    [Show full text]
  • Merlin Is a Potent Inhibitor of Glioma Growth
    Research Article Merlin Is a Potent Inhibitor of Glioma Growth Ying-Ka Ingar Lau,1 Lucas B. Murray,1 Sean S. Houshmandi,2 Yin Xu,1 David H. Gutmann,2 and Qin Yu1 1Department of Oncological Sciences, Mount Sinai School of Medicine, New York, New York and 2Department of Neurology, Washington University School of Medicine, St. Louis, Missouri Abstract The NF2 gene shares sequence similarity with the members of Neurofibromatosis 2 (NF2) is an inherited cancer syndrome in Band 4.1 superfamily (4, 5). In particular, the NF2 protein, merlin (or schwannomin), most closely resembles proteins of the ezrin- which affected individuals develop nervous system tumors, including schwannomas, meningiomas, and ependymomas. radixin-moesin (ERM) subfamily. Similar to the ERM proteins, The NF2 protein merlin (or schwannomin) is a member of the merlin serves as a linker between transmembrane proteins and the Band 4.1superfamily of proteins, which serve as linkers actin cytoskeleton and regulates cytoskeleton remodeling and cell between transmembrane proteins and the actin cytoskeleton. motility (6–8). Unlike ERM proteins, merlin functions as a negative In addition to mutational inactivation of the NF2 gene in NF2- growth regulator or tumor suppressor. In this regard, mutational associated tumors, mutations and loss of merlin expression inactivation of the NF2 gene is sufficient to result in the have also been reported in other types of cancers. In the development of NF2-associated nervous system tumors. Moreover, present study, we show that merlin expression is dramatically NF2 mutations have also been reported in other tumor types, reduced in human malignant gliomas and that reexpression of including melanoma and mesothelioma (9), suggesting that merlin plays an important role, not only in NF2-associated tumors but also functional merlin dramatically inhibits both subcutaneous and intracranial growth of human glioma cells in mice.
    [Show full text]
  • 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.
    [Show full text]
  • Gene PMID WBS Locus ABR 26603386 AASDH 26603386
    Supplementary material J Med Genet Gene PMID WBS Locus ABR 26603386 AASDH 26603386 ABCA1 21304579 ABCA13 26603386 ABCA3 25501393 ABCA7 25501393 ABCC1 25501393 ABCC3 25501393 ABCG1 25501393 ABHD10 21304579 ABHD11 25501393 yes ABHD2 25501393 ABHD5 21304579 ABLIM1 21304579;26603386 ACOT12 25501393 ACSF2,CHAD 26603386 ACSL4 21304579 ACSM3 26603386 ACTA2 25501393 ACTN1 26603386 ACTN3 26603386;25501393;25501393 ACTN4 21304579 ACTR1B 21304579 ACVR2A 21304579 ACY3 19897463 ACYP1 21304579 ADA 25501393 ADAM12 21304579 ADAM19 25501393 ADAM32 26603386 ADAMTS1 25501393 ADAMTS10 25501393 ADAMTS12 26603386 ADAMTS17 26603386 ADAMTS6 21304579 ADAMTS7 25501393 ADAMTSL1 21304579 ADAMTSL4 25501393 ADAMTSL5 25501393 ADCY3 25501393 ADK 21304579 ADRBK2 25501393 AEBP1 25501393 AES 25501393 AFAP1,LOC84740 26603386 AFAP1L2 26603386 AFG3L1 21304579 AGAP1 26603386 AGAP9 21304579 Codina-Sola M, et al. J Med Genet 2019; 56:801–808. doi: 10.1136/jmedgenet-2019-106080 Supplementary material J Med Genet AGBL5 21304579 AGPAT3 19897463;25501393 AGRN 25501393 AGXT2L2 25501393 AHCY 25501393 AHDC1 26603386 AHNAK 26603386 AHRR 26603386 AIDA 25501393 AIFM2 21304579 AIG1 21304579 AIP 21304579 AK5 21304579 AKAP1 25501393 AKAP6 21304579 AKNA 21304579 AKR1E2 26603386 AKR7A2 21304579 AKR7A3 26603386 AKR7L 26603386 AKT3 21304579 ALDH18A1 25501393;25501393 ALDH1A3 21304579 ALDH1B1 21304579 ALDH6A1 21304579 ALDOC 21304579 ALG10B 26603386 ALG13 21304579 ALKBH7 25501393 ALPK2 21304579 AMPH 21304579 ANG 21304579 ANGPTL2,RALGPS1 26603386 ANGPTL6 26603386 ANK2 21304579 ANKMY1 26603386 ANKMY2
    [Show full text]
  • Clinical and Prognostic Significance of MYH11 in Lung Cancer
    ONCOLOGY LETTERS 19: 3899-3906, 2020 Clinical and prognostic significance ofMYH11 in lung cancer MENG-JUN NIE1,2, XIAO-TING PAN1,2, HE-YUN TAO1,2, MENG-JUN XU1,2, SHEN-LIN LIU1, WEI SUN1, JIAN WU1 and XI ZOU1 1Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029; 2No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China Received May 14, 2019; Accepted February 21, 2020 DOI: 10.3892/ol.2020.11478 Abstract. Myosin heavy chain 11 (MYH11), encoded by 5-year survival rate is estimated to approximately 18% (2). the MYH11 gene, is a protein that participates in muscle Therefore, novel targets for drug treatment and prognosis of contraction through the hydrolysis of adenosine triphosphate. lung cancer are needed. Although previous studies have demonstrated that MYH11 Myosin heavy chain 11 (MYH11), which is encoded by gene expression levels are downregulated in several types of the MYH11 gene, is a smooth muscle myosin belonging to the cancer, its expression levels have rarely been investigated in myosin heavy chain family (3). MYH11 is a contractile protein lung cancer. The present study aimed to explore the clinical that slides past actin filaments to induce muscle contraction significance and prognostic value of MYH11 expression levels via adenosine triphosphate hydrolysis (4,5). Previous findings in lung cancer and to further study the underlying molecular have shown that in aortic tissue, destruction of MYH11 can mechanisms of the function of this gene. The Oncomine lead to vascular smooth muscle cell loss, disorganization database showed that the MYH11 expression levels were and hyperplasia, which is one of the mechanisms leading to decreased in lung cancer compared with those noted in the thoracic aortic aneurysms and dissections (6,7).
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • A Multistep Bioinformatic Approach Detects Putative Regulatory
    BMC Bioinformatics BioMed Central Research article Open Access A multistep bioinformatic approach detects putative regulatory elements in gene promoters Stefania Bortoluzzi1, Alessandro Coppe1, Andrea Bisognin1, Cinzia Pizzi2 and Gian Antonio Danieli*1 Address: 1Department of Biology, University of Padova – Via Bassi 58/B, 35131, Padova, Italy and 2Department of Information Engineering, University of Padova – Via Gradenigo 6/B, 35131, Padova, Italy Email: Stefania Bortoluzzi - [email protected]; Alessandro Coppe - [email protected]; Andrea Bisognin - [email protected]; Cinzia Pizzi - [email protected]; Gian Antonio Danieli* - [email protected] * Corresponding author Published: 18 May 2005 Received: 12 November 2004 Accepted: 18 May 2005 BMC Bioinformatics 2005, 6:121 doi:10.1186/1471-2105-6-121 This article is available from: http://www.biomedcentral.com/1471-2105/6/121 © 2005 Bortoluzzi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs. Results: Novel software (COOP) was developed for extraction of sequence motifs, based on clustering of exact or approximate patterns according to the frequency of their overlapping occurrences.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]