DOCSLIB.ORG

Supplementary Tables

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Supplementary Tables Supplementary Table S1: Clinicopathologic characteristics of 64 patients with untreated primary myxofibrosarcoma Characteristic n (%) or median (range) Gender Male 23 (36%) Female 41 (64%) Age, years 63.5 (24-88) Location Extremity 55 (86%) Trunk/chest wall 8 (13%) Retroperitoneal 1 (1%) Size, cm ≤5 9 (14%) >5; ≤10 34 (53%) >10 21 (33%) Median 8.5 (3.1-25) Depth Superficial 12 (19%) Deep 52 (81%) Surgical margins R0 49 (77%) R1 15 (23%) Percent Myxoid 75% (10%-100%) Local Recurrence 14 (22%) Distant Recurrence 22 (34%) Died of Disease 17 (27%) Follow-up, years All patients 2.6 (0.1-9.2) Surviving patients 3.1 (0.4-9.2) Time to death, years 1.8 (0.7-3.3) 213338 at RIS1 3.00E-06 -2.45 219840 s at TCL6 1.30E-05 -0.25 201645 at TNC 2.00E-06 -2.15 208052 x at CEACAM3 1.40E-05 -0.25 205542 at STEAP1 1.70E-05 -1.74 210339 s at KLK2 1.40E-05 -0.25 212473 s at MICAL2 2.20E-05 -1.6 206612 at CACNG1 1.50E-05 -0.25 204337 at RGS4 <1.00E-06 -1.51 205531 s at GLS2 1.60E-05 -0.25 208025 s at HMGA2 4.50E-05 -1.51 215274 at SLC12A3 1.60E-05 -0.25 204464 s at EDNRA 6.00E-05 -1.48 216881 x at PRB4 1.60E-05 -0.25 208712 at CCND1 1.20E-05 -1.47 206869 at CHAD 1.80E-05 -0.25 201506 at TGFBI <1.00E-06 -1.32 207528 s at SLC7A11 1.80E-05 -0.25 203324 s at CAV2 <1.00E-06 -1.29 210577 at CASR 1.80E-05 -0.25 201617 x at CALD1 5.00E-06 -1.24 205815 at REG3A 1.90E-05 -0.25 206766 at ITGA10 2.60E-05 -1.23 208204 s at CAV3 1.90E-05 -0.25 209218 at SQLE <1.00E-06 -1.18 220581 at C6orf97 2.00E-05 -0.25 221773 at ELK3 <1.00E-06 -1.14 207007 at NR1I3 2.10E-05 -0.25 219926 at POPDC3 <1.00E-06 -1.14 208234 x at FGFR2 2.20E-05 -0.25 204017 at KDELR3 1.00E-05 -1.12 216600 x at ALDOB 2.50E-05 -0.25 209890 at TSPAN5 1.00E-05 -1.1 206812 at ADRB3 2.60E-05 -0.25 210845 s at PLAUR 9.00E-06 -1.07 207862 at UPK2 2.60E-05 -0.25 218424 s at STEAP3 9.00E-06 -1.07 204532 x at UGT1A10 2.70E-05 -0.25 212771 at C10orf38 6.00E-06 -1.05 211117 x at ESR2 2.70E-05 -0.25 221502 at KPNA3 <1.00E-06 -1.03 211689 s at TMPRSS2 2.70E-05 -0.25 207265 s at KDELR3 1.20E-05 -1.03 205840 x at GH1 2.90E-05 -0.25 212449 s at LYPLA1 <1.00E-06 -1 216128 at TBCD 2.90E-05 -0.25 218984 at FLJ20485 <1.00E-06 -0.99 220958 at ULK4 3.00E-05 -0.25 211924 s at PLAUR 2.30E-05 -0.99 221329 at OR52A1 3.00E-05 -0.25 202304 at FNDC3A <1.00E-06 -0.93 210641 at CAPN9 3.10E-05 -0.25 201300 s at PRNP 7.40E-05 -0.93 221456 at TAS2R3 3.20E-05 -0.25 221503 s at KPNA3 <1.00E-06 -0.9 31861 at IGHMBP2 3.20E-05 -0.25 208691 at TFRC 4.10E-05 -0.89 217017 at OSBPL10 3.50E-05 -0.25 201324 at EMP1 1.70E-05 -0.87 221184 at — 3.90E-05 -0.25 203323 at CAV2 9.70E-05 -0.87 204763 s at GNAO1 4.00E-05 -0.25 201063 at RCN1 4.00E-06 -0.86 216116 at — 4.30E-05 -0.25 220147 s at FAM60A 2.70E-05 -0.85 208139 s at — 4.90E-05 -0.25 214039 s at LAPTM4B 1.20E-05 -0.84 220902 at FLJ12616 4.90E-05 -0.25 204967 at APXL <1.00E-06 -0.83 215865 at SYT12 5.60E-05 -0.25 212887 at SEC23A 2.10E-05 -0.83 201974 s at C7orf28A 5.80E-05 -0.25 209286 at CDC42EP3 5.80E-05 -0.82 206630 at TYR 5.80E-05 -0.25 203999 at SYT1 3.90E-05 -0.81 218623 at HMP19 6.10E-05 -0.25 206805 at SEMA3A 8.00E-06 -0.8 220819 at FRMD1 6.10E-05 -0.25 204339 s at RGS4 <1.00E-06 -0.76 206679 at APBA1 6.20E-05 -0.25 202710 at BET1 1.10E-05 -0.76 207220 at DO 6.50E-05 -0.25 205453 at HOXB2 1.10E-05 -0.76 214088 s at FUT3 6.70E-05 -0.25 205199 at CA9 1.40E-05 -0.76 217134 at MTAP 7.30E-05 -0.25 212077 at CALD1 1.80E-05 -0.76 200724 at RPL10 7.50E-05 -0.25 204338 s at RGS4 8.30E-05 -0.76 207385 at TFDP3 7.60E-05 -0.25 204693 at CDC42EP1 <1.00E-06 -0.74 214956 at — 7.90E-05 -0.25 206713 at NTNG1 <1.00E-06 -0.73 216152 at PDZRN3 7.90E-05 -0.25 207431 s at DEGS1 <1.00E-06 -0.72 210744 s at IL5RA 8.70E-05 -0.25 219888 at SPAG4 2.00E-05 -0.72 217568 at FAM12A 8.70E-05 -0.25 205677 s at DLEU1 <1.00E-06 -0.71 206755 at CYP2B6 9.80E-05 -0.25 218796 at C20orf42 <1.00E-06 -0.7 217181 at — <1.00E-06 -0.24 216348 at RPS17* 2.00E-06 -0.7 220805 at HRH2 <1.00E-06 -0.24 212149 at KIAA0143 3.00E-06 -0.7 206930 at GLYAT <1.00E-06 -0.24 201921 at GNG10* 8.00E-06 -0.7 208311 at GPR50 <1.00E-06 -0.24 213102 at ACTR3 1.20E-05 -0.69 216085 at DKFZP434C153 <1.00E-06 -0.24 396 f at EPOR <1.00E-06 -0.68 201766 at ELAC2 2.00E-06 -0.24 200994 at IPO7 3.90E-05 -0.68 215829 at SHANK2 2.00E-06 -0.24 220603 s at MCTP2 3.00E-06 -0.67 217630 at ANGEL2 2.00E-06 -0.24 217975 at WBP5 1.40E-05 -0.67 220366 at ELSPBP1 2.00E-06 -0.24 221266 s at TM7SF4 <1.00E-06 -0.66 206290 s at RGS7 3.00E-06 -0.24 218516 s at IMPAD1 8.00E-06 -0.66 207368 at HTR1D 3.00E-06 -0.24 204575 s at MMP19 6.80E-05 -0.66 220732 at DEPDC2 3.00E-06 -0.24 218566 s at CHORDC1 <1.00E-06 -0.65 221459 at TAAR5 3.00E-06 -0.24 203438 at STC2 6.00E-06 -0.65 221644 s at SLC45A2 3.00E-06 -0.24 Myxofibrosarcoma 2 217266 at RPL15 1.80E-05 -0.65 208448 x at IFNA16 5.00E-06 -0.24 220167 s at TP53TG3 1.80E-05 -0.65 215003 at DGCR9 5.00E-06 -0.24 200639 s at YWHAZ <1.00E-06 -0.64 211786 at TNFRSF9 6.00E-06 -0.24 204729 s at STX1A <1.00E-06 -0.63 217619 x at — 7.00E-06 -0.24 214866 at PLAUR 3.00E-06 -0.63 220829 s at B3GALT1 9.00E-06 -0.24 205975 s at HOXD1 <1.00E-06 -0.62 206094 x at UGT1A6 1.10E-05 -0.24 216849 at FLJ16124 <1.00E-06 -0.62 91826 at EPS8L1 1.10E-05 -0.24 211936 at HSPA5 2.00E-06 -0.62 219943 s at FLJ11850 1.20E-05 -0.24 203119 at MGC2574 6.00E-06 -0.62 220930 s at MGC5590 1.20E-05 -0.24 218113 at TMEM2 1.20E-05 -0.62 216690 at OR7C1 1.30E-05 -0.24 221753 at SSH1 1.20E-05 -0.62 220886 at GABRQ 1.30E-05 -0.24 71933 at WNT6 <1.00E-06 -0.61 207214 at SPINK4 1.50E-05 -0.24 215479 at SEMA6A <1.00E-06 -0.61 208597 at CNTF 1.50E-05 -0.24 208161 s at ABCC3 6.00E-06 -0.61 215419 at KIAA1086 1.50E-05 -0.24 213100 at UNC5B 2.30E-05 -0.61 208507 at OR7C2 1.60E-05 -0.24 201358 s at COPB <1.00E-06 -0.6 216219 at AQP6 1.90E-05 -0.24 213577 at SQLE <1.00E-06 -0.6 219887 at FLJ10786 1.90E-05 -0.24 221347 at CHRM5 <1.00E-06 -0.6 220993 s at GPR63 1.90E-05 -0.24 206429 at F2RL1 3.90E-05 -0.6 220537 at MTMR8 2.10E-05 -0.24 219634 at CHST11 9.30E-05 -0.6 207951 at CSN2 2.30E-05 -0.24 209294 x at TNFRSF10B <1.00E-06 -0.59 216709 at — 2.30E-05 -0.24 220428 at CD207 <1.00E-06 -0.59 221429 x at TEX13A 2.30E-05 -0.24 209250 at DEGS1 2.00E-06 -0.59 215890 at GM2A 2.40E-05 -0.24 211452 x at LRRFIP1 1.40E-05 -0.59 216790 at — 2.70E-05 -0.24 217599 s at MDFIC 5.80E-05 -0.59 211384 s at CASR 2.80E-05 -0.24 221580 s at MGC5306 9.20E-05 -0.59 205845 at CACNA1H 3.10E-05 -0.24 200921 s at BTG1 9.90E-05 -0.59 216907 x at KIR3DL2 3.50E-05 -0.24 208203 x at KIR2DS5 <1.00E-06 -0.58 208062 s at NRG2 3.60E-05 -0.24 218644 at PLEK2 <1.00E-06 -0.58 216636 at — 3.80E-05 -0.24 219735 s at TFCP2L1 <1.00E-06 -0.58 205093 at PLEKHA6 4.00E-05 -0.24 211708 s at SCD 6.00E-06 -0.58 221285 at ST8SIA2 4.10E-05 -0.24 221330 at CHRM2 8.00E-06 -0.58 207421 at CA5A 4.20E-05 -0.24 214265 at ITGA8 1.60E-05 -0.58 211157 at — 4.30E-05 -0.24 201260 s at SYPL1 5.30E-05 -0.58 221035 s at TEX14 4.60E-05 -0.24 202488 s at FXYD3 <1.00E-06 -0.57 216986 s at IRF4 5.00E-05 -0.24 208520 at OR10H3 <1.00E-06 -0.57 208439 s at FCN2 5.10E-05 -0.24 210270 at RGS6 <1.00E-06 -0.57 214905 at LOC145899 5.10E-05 -0.24 219557 s at NRIP3 <1.00E-06 -0.57 208364 at INPP4A 5.20E-05 -0.24 205793 x at TNK1 <1.00E-06 -0.56 205772 s at AKAP7 5.50E-05 -0.24 216757 at GALNT7 <1.00E-06 -0.56 221422 s at C9orf45 5.70E-05 -0.24 217170 at — <1.00E-06 -0.56 206678 at GABRA1 6.10E-05 -0.24 206614 at GDF5 <1.00E-06 -0.56 91682 at EXOSC4 6.20E-05 -0.24 211945 s at ITGB1 5.00E-06 -0.56 211544 s at GHRHR 6.70E-05 -0.24 202211 at ARFGAP3 1.90E-05 -0.56 216079 at EPM2A 6.80E-05 -0.24 212245 at MCFD2 5.90E-05 -0.56 207874 s at CFHL4* 7.00E-05 -0.24 214805 at EIF4A1 6.00E-05 -0.56 216696 s at PRODH2 7.80E-05 -0.24 212364 at MYO1B 8.70E-05 -0.56 207698 at C6orf123 8.00E-05 -0.24 205832 at CPA4 <1.00E-06 -0.55 215824 at NUDT7 8.10E-05 -0.24 206186 at MPP3 <1.00E-06 -0.55 205064 at SPRR1B 8.30E-05 -0.24 211153 s at TNFSF11 <1.00E-06 -0.55 220282 at RIC3 8.40E-05 -0.24 215511 at TCF20 <1.00E-06 -0.55 220622 at LRRC31 8.80E-05 -0.24 211405 x at IFNA17 2.00E-06 -0.55 1494 f at CYP2A6 8.90E-05 -0.24 221112 at IL1RAPL2 2.00E-06 -0.55 207074 s at SLC18A1 8.90E-05 -0.24 219215 s at SLC39A4 1.90E-05 -0.55 204673 at MUC2 9.10E-05 -0.24 205031 at EFNB3 5.20E-05 -0.55 217008 s at GRM7 9.30E-05 -0.24 204411 at KIF21B <1.00E-06 -0.54 208593 x at CRHR1 9.50E-05 -0.24 217307 at — <1.00E-06 -0.54 220048 at EDAR 9.80E-05 -0.24 221114 at AMBN <1.00E-06 -0.54 216249 at PVT1 <1.00E-06 -0.23 202597 at IRF6 <1.00E-06 -0.54 207151 at ADCYAP1R1 <1.00E-06 -0.23 208378 x at FGF5 1.30E-05 -0.54 211214 s at DAPK1 <1.00E-06 -0.23 205137 x at USH1C <1.00E-06 -0.53 38707 r at E2F4 <1.00E-06 -0.23 211497 x at NKX3-1 <1.00E-06 -0.53 207770 x at CSH2 2.00E-06 -0.23 221313 at GPR52 <1.00E-06 -0.53 206199 at CEACAM7 4.00E-06 -0.23 205676 at CYP27B1 2.00E-06 -0.53 221395 at TAS2R13 6.00E-06 -0.23 214357 at C1orf105 2.00E-06 -0.53 211830 s at CACNA1I 7.00E-06 -0.23 211241 at ANXA2P3 1.60E-05 -0.53 211238 at ADAM7 9.00E-06 -0.23 200738 s at PGK1 1.70E-05 -0.53 216536 at OR7E19P 9.00E-06 -0.23 215823 x at PABPC3 2.20E-05 -0.53 206014 at ACTL6B 1.00E-05 -0.23 214053 at ERBB4 3.30E-05 -0.53 220192 x at SPDEF 1.00E-05 -0.23 209060 x at NCOA3 6.60E-05 -0.53 207751 at — 1.20E-05 -0.23 205402 x at PRSS2 <1.00E-06 -0.52 222373 at CIP29 1.30E-05 -0.23 207082 at CSF1 <1.00E-06 -0.52 216989 at SPAM1 1.50E-05 -0.23 208285 at OR7A5 <1.00E-06 -0.52 207227 x at RFPL2 1.60E-05 -0.23 210357 s at SMOX <1.00E-06 -0.52 220262 s at EGFL9 1.70E-05 -0.23 221090 s at FLJ10826 <1.00E-06 -0.52 220264
Recommended publications
  • Homozygous Deletion of Six Olfactory Receptor Genes in a Subset of Individuals with Beta-Thalassemia

    Homozygous Deletion of Six Olfactory Receptor Genes in a Subset of Individuals with Beta-Thalassemia

    Homozygous Deletion of Six Olfactory Receptor Genes in a Subset of Individuals with Beta-Thalassemia Jessica Van Ziffle, Wendy Yang, Farid F. Chehab* Department of Laboratory Medicine, University of California San Francisco, San Francisco, California, United States of America Abstract Progress in the functional studies of human olfactory receptors has been largely hampered by the lack of a reliable experimental model system. Although transgenic approaches in mice could characterize the function of individual olfactory receptors, the presence of over 300 functional genes in the human genome becomes a daunting task. Thus, the characterization of individuals with a genetic susceptibility to altered olfaction coupled with the absence of particular olfactory receptor genes will allow phenotype/genotype correlations and vindicate the function of specific olfactory receptors with their cognate ligands. We characterized a 118 kb b-globin deletion and found that its 39 end breakpoint extends to the neighboring olfactory receptor region downstream of the b-globin gene cluster. This deletion encompasses six contiguous olfactory receptor genes (OR51V1, OR52Z1, OR51A1P, OR52A1, OR52A5, and OR52A4) all of which are expressed in the brain. Topology analysis of the encoded proteins from these olfactory receptor genes revealed that OR52Z1, OR52A1, OR52A5, and OR52A4 are predicted to be functional receptors as they display integral characteristics of G- proteins coupled receptors. Individuals homozygous for the 118 kb b-globin deletion are afflicted with b-thalassemia due to a homozygous deletion of the b-globin gene and have no alleles for the above mentioned olfactory receptors genes. This is the first example of a homozygous deletion of olfactory receptor genes in human.
  • PARSANA-DISSERTATION-2020.Pdf

    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.
  • Arabidopsis Adaptor Protein 1G2 Is Required for Female and Male Gametogenesis

    Arabidopsis Adaptor Protein 1G2 Is Required for Female and Male Gametogenesis

    Arabidopsis adaptor protein 1G2 is required for female and male gametogenesis Yongmei Zhou Fujian Agriculture and Forestry University Wenqin Fang Fujian Agriculture and Forestry University Li-Yu Chen Fujian Agriculture and Forestry University Neha Pandey Fujian Agriculture and Forestry University Azam Syed Muhammad Fujian Agriculture and Forestry University Ray Ming ( [email protected] ) University of Illinois at Urbana-Champaign https://orcid.org/0000-0002-9417-5789 Research article Keywords: Arabidopsis, AP1G2, megagametogenesis, microgametogenesis, development. Posted Date: November 12th, 2019 DOI: https://doi.org/10.21203/rs.2.17134/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/22 Abstract Background: The gametophyte s are essential for the productive process in angiosperms. During sexual reproduction in owering plants, haploid spores are formed from meioses of spore mother cells. The spores then undergo mitosis and develop into female and male gametes and give rise to seeds after fertilization. Results: We identied a female sterile mutant from EMS mutagenesis, and a BC1F2 population was generated for map based cloning of the causal gene. Genome re-sequencing of mutant and non-mutant pools revealed a candidate gene, AP1G2 . Analyses of two insertions mutants, ap1g2-1 +/- in exon 7 and ap1g2-3 -/- in 3’ UTR, revealed partial female sterility. Complementation test using native promoter of AP1G2 restored the function in ap1g2-1 +/- and ap1g2-3 -/- . AP1G2 is a paralog of AP1G1 , encoding the large subunit (γ) of adaptor protein-1 (AP-1). ap1g2 mutation led to defective female and male gametophyte development was determined.
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception

    Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception

    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
  • Aquaporin Channels in the Heart—Physiology and Pathophysiology

    Aquaporin Channels in the Heart—Physiology and Pathophysiology

    International Journal of Molecular Sciences Review Aquaporin Channels in the Heart—Physiology and Pathophysiology Arie O. Verkerk 1,2,* , Elisabeth M. Lodder 2 and Ronald Wilders 1 1 Department of Medical Biology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] 2 Department of Experimental Cardiology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected]; Tel.: +31-20-5664670 Received: 29 March 2019; Accepted: 23 April 2019; Published: 25 April 2019 Abstract: Mammalian aquaporins (AQPs) are transmembrane channels expressed in a large variety of cells and tissues throughout the body. They are known as water channels, but they also facilitate the transport of small solutes, gasses, and monovalent cations. To date, 13 different AQPs, encoded by the genes AQP0–AQP12, have been identified in mammals, which regulate various important biological functions in kidney, brain, lung, digestive system, eye, and skin. Consequently, dysfunction of AQPs is involved in a wide variety of disorders. AQPs are also present in the heart, even with a specific distribution pattern in cardiomyocytes, but whether their presence is essential for proper (electro)physiological cardiac function has not intensively been studied. This review summarizes recent findings and highlights the involvement of AQPs in normal and pathological cardiac function. We conclude that AQPs are at least implicated in proper cardiac water homeostasis and energy balance as well as heart failure and arsenic cardiotoxicity. However, this review also demonstrates that many effects of cardiac AQPs, especially on excitation-contraction coupling processes, are virtually unexplored.
  • Table 2. Significant

    Table 2. Significant

    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
  • Seq2pathway Vignette

    Seq2pathway Vignette

    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
  • 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.
  • Transcriptome Analysis of Gravitational Effects on Mouse Skeletal Muscles Under Microgravity and Artificial 1 G Onboard Environm

    Transcriptome Analysis of Gravitational Effects on Mouse Skeletal Muscles Under Microgravity and Artificial 1 G Onboard Environm

    www.nature.com/scientificreports OPEN Transcriptome analysis of gravitational efects on mouse skeletal muscles under microgravity and artifcial 1 g onboard environment Risa Okada1,2, Shin‑ichiro Fujita3,4, Riku Suzuki5,6, Takuto Hayashi3,5, Hirona Tsubouchi5, Chihiro Kato5,7, Shunya Sadaki5, Maho Kanai5,6, Sayaka Fuseya3,5, Yuri Inoue3,5, Hyojung Jeon5, Michito Hamada5, Akihiro Kuno5,6, Akiko Ishii8, Akira Tamaoka8, Jun Tanihata9, Naoki Ito10, Dai Shiba1,2, Masaki Shirakawa1,2, Masafumi Muratani1,4, Takashi Kudo1,5* & Satoru Takahashi1,5* Spacefight causes a decrease in skeletal muscle mass and strength. We set two murine experimental groups in orbit for 35 days aboard the International Space Station, under artifcial earth‑gravity (artifcial 1 g; AG) and microgravity (μg; MG), to investigate whether artifcial 1 g exposure prevents muscle atrophy at the molecular level. Our main fndings indicated that AG onboard environment prevented changes under microgravity in soleus muscle not only in muscle mass and fber type composition but also in the alteration of gene expression profles. In particular, transcriptome analysis suggested that AG condition could prevent the alterations of some atrophy‑related genes. We further screened novel candidate genes to reveal the muscle atrophy mechanism from these gene expression profles. We suggest the potential role of Cacng1 in the atrophy of myotubes using in vitro and in vivo gene transductions. This critical project may accelerate the elucidation of muscle atrophy mechanisms. Gravity is the most constant factor afecting the entire process of evolution of organisms on Earth. As adapting to a changing environment is key for any organism’s survival, the constant mechanical stimulus of gravitational force has been shared by all organisms on Earth through evolution 1.
  • Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
  • Kras, Braf, Erbb2, Tp53

    Kras, Braf, Erbb2, Tp53

    Supplementary Table 1. SBT-EOC cases used for molecular analyses Fresh Frozen Tissue FFPE Tissue Tumour HRM Case ID PCR-Seq HRM component Oncomap (KRAS, BRAF, CNV GE CNV IHC (NRAS) (TP53 Only) ERBB2, TP53) Paired Cases 15043 SBT & INV √ √ √ √ √ √ 65662 SBT & INV √ √ √ √ √ 65661 SBT & INV √ √ √ √ √ √ 9128 SBT & INV √ √ √ √ √ 2044 SBT & INV √ √ √ √ √ 3960 SBT & INV √ √ √ √ √ 5899 SBT & INV √ √ √ √ √ 65663 SBT & INV √ √ √ Inv √ √ √ 65666 SBT √ Inv √ √ √ √ √ 65664 SBT √ √ √ Inv √ √ √ √ √ 15060 SBT √ √ √ Inv √ √ √ √ √ 65665 SBT √ √ √ Inv √ √ √ √ √ 65668 SBT √ √ √ Inv √ √ √ √ 65667 SBT √ √ √ Inv √ √ √ √ 15018 SBT √ Inv √ √ √ √ 15071 SBT √ Inv √ √ √ √ 65670 SBT √ Inv √ √ √ 8982 SBT √ Unpaired Cases 15046 Inv √ √ √ 15014 Inv √ √ √ 65671 Inv √ √ √ 65672 SBT √ √ √ 65673 Inv √ √ √ 65674 Inv √ √ √ 65675 Inv √ √ √ 65676 Inv √ √ √ 65677 Inv √ √ √ 65678 Inv √ √ √ 65679 Inv √ √ √ Fresh Frozen Tissue FFPE Tissue Tumour HRM Case ID PCR-Seq HRM component Oncomap (KRAS, BRAF, CNV GE CNV IHC (NRAS) (TP53 Only) ERBB2, TP53) 65680 Inv √ √ √ 5349 Inv √ √ √ 7957 Inv √ √ √ 8395 Inv √ √ √ 8390 Inv √ √ 2110 Inv √ √ 6328 Inv √ √ 9125 Inv √ √ 9221 Inv √ √ 10701 Inv √ √ 1072 Inv √ √ 11266 Inv √ √ 11368 Inv √ √ √ 12237 Inv √ √ 2064 Inv √ √ 3539 Inv √ √ 2189 Inv √ √ √ 5711 Inv √ √ 6251 Inv √ √ 6582 Inv √ √ 7200 SBT √ √ √ 8633 Inv √ √ √ 9579 Inv √ √ 10740 Inv √ √ 11766 Inv √ √ 3958 Inv √ √ 4723 Inv √ √ 6244 Inv √ √ √ 7716 Inv √ √ SBT-EOC, serous carcinoma with adjacent borderline regions; SBT, serous borderline component of SBT- EOC; Inv, invasive component of SBT-EOC;
  • LETTER Doi:10.1038/Nature09515

    LETTER Doi:10.1038/Nature09515

    LETTER doi:10.1038/nature09515 Distant metastasis occurs late during the genetic evolution of pancreatic cancer Shinichi Yachida1*, Siaˆn Jones2*, Ivana Bozic3, Tibor Antal3,4, Rebecca Leary2, Baojin Fu1, Mihoko Kamiyama1, Ralph H. Hruban1,5, James R. Eshleman1, Martin A. Nowak3, Victor E. Velculescu2, Kenneth W. Kinzler2, Bert Vogelstein2 & Christine A. Iacobuzio-Donahue1,5,6 Metastasis, the dissemination and growth of neoplastic cells in an were present in the primary pancreatic tumours from which the meta- organ distinct from that in which they originated1,2, is the most stases arose. A small number of these samples of interest were cell lines common cause of death in cancer patients. This is particularly true or xenografts, similar to the index lesions, whereas the majority were for pancreatic cancers, where most patients are diagnosed with fresh-frozen tissues that contained admixed neoplastic, stromal, metastatic disease and few show a sustained response to chemo- inflammatory, endothelial and normal epithelial cells (Fig. 1a). Each therapy or radiation therapy3. Whether the dismal prognosis of tissue sample was therefore microdissected to minimize contaminat- patients with pancreatic cancer compared to patients with other ing non-neoplastic elements before purifying DNA. types of cancer is a result of late diagnosis or early dissemination of Two categories of mutations were identified (Fig. 1b). The first and disease to distant organs is not known. Here we rely on data gen- largest category corresponded to those mutations present in all samples erated by sequencing the genomes of seven pancreatic cancer meta- from a given patient (‘founder’ mutations, mean of 64%, range 48–83% stases to evaluate the clonal relationships among primary and of all mutations per patient; Fig.