DOCSLIB.ORG

S1- Alphabetical List of Aging-Related Genes

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
S1- Alphabetical List of Aging-Related Genes Young vs Aged MSI Correlations Symbol Description Young CA Aged CA Young DG Aged DG CA v DG in CA in DG in CA in DG AARSL alanyl-tRNA synthetase like 672 +/- 50 780 +/- 24 666 +/- 26 777 +/- 38 -0.959643 0.0904189 0.04684 0.0097575 0.0922137 AASDH 2-aminoadipic 6-semialdehyde dehydrogenase 587 +/- 19 771 +/- 21 501 +/- 29 664 +/- 26 -6.83E-05 8.644E-05 0.002318 0.0046175 0.0347906 ABCG2 ATP-binding cassette, sub-family G (WHITE), member 2 437 +/- 47 523 +/- 31 335 +/- 10 398 +/- 11 -0.001844 0.1582204 0.002045 0.1826101 0.0544598 ABHD14A abhydrolase domain containing 14A 1760 +/- 56 1515 +/- 91 1973 +/- 103 1818 +/- 29 0.010547 -0.048824 -0.200935 0.0371071 0.3262792 ABP1 Amiloride binding protein 1 (amine oxidase (copper-containing)) 105 +/- 10 165 +/- 23 154 +/- 17 181 +/- 18 0.021482 0.0527017 0.302632 5.18E-05 0.0770133 ABTB2 Ankyrin repeat and BTB (POZ) domain containing 2 654 +/- 122 485 +/- 39 505 +/- 32 342 +/- 18 -0.030184 -0.236531 -0.002265 0.5021444 0.0832426 ACAA2 acetyl-Coenzyme A acyltransferase 2 (mitochondrial 3-oxoacyl-Coenzyme A thiolase) 589 +/- 56 854 +/- 62 529 +/- 31 645 +/- 33 -0.03897 0.0097889 0.030153 0.000594 0.1078687 ACACB acetyl-Coenzyme A carboxylase beta 124 +/- 17 175 +/- 13 138 +/- 12 164 +/- 12 0.943167 0.0376355 0.152219 0.0293754 0.3358733 ACADL acyl-Coenzyme A dehydrogenase, long chain 234 +/- 12 184 +/- 12 226 +/- 9 179 +/- 19 -0.359297 -0.013599 -0.065477 0.0959391 0.0845069 ACAT1 acetyl-Coenzyme A acetyltransferase 1 (acetoacetyl Coenzyme A thiolase) 4099 +/- 188 3515 +/- 106 3250 +/- 137 3238 +/- 137 -0.007467 -0.027046 -0.954273 0.1128106 0.9705821 ACCN1 amiloride-sensitive cation channel 1, neuronal (degenerin) 1925 +/- 136 1421 +/- 164 1179 +/- 59 900 +/- 42 -0.000254 -0.040656 -0.004392 0.0273253 0.0121481 ACIN1 apoptotic chromatin condensation inducer 1 1859 +/- 171 1790 +/- 102 2092 +/- 94 1822 +/- 45 0.172929 -0.735092 -0.03529 0.8562892 0.0993413 ACO1 aconitase 1, soluble 964 +/- 82 811 +/- 43 950 +/- 45 803 +/- 46 -0.754764 -0.139533 -0.048938 0.1183283 0.0289747 ACOT9 acyl-CoA thioesterase 9 370 +/- 33 344 +/- 40 284 +/- 9 218 +/- 20 -0.001741 -0.63831 -0.024981 0.6715461 0.2839666 ACP1 acid phosphatase 1, soluble 2644 +/- 165 2074 +/- 157 2357 +/- 91 2031 +/- 147 -0.085298 -0.031306 -0.10182 0.009087 0.0106821 ACP6 acid phosphatase 6, lysophosphatidic 881 +/- 49 984 +/- 51 742 +/- 28 837 +/- 8 -0.002935 0.171551 0.018151 0.0355159 0.0710112 ACSS1 acyl-CoA synthetase short-chain family member 1 229 +/- 21 311 +/- 28 196 +/- 16 228 +/- 23 -0.013777 0.0442198 0.295382 0.092173 0.4151929 ACTC actin, alpha, cardiac muscle 84 +/- 15 206 +/- 35 98 +/- 11 212 +/- 35 0.021907 0.0155645 0.029736 0.0980108 0.1657953 ACTR10 actin-related protein 10 homolog (S. cerevisiae) 6172 +/- 108 6822 +/- 248 6301 +/- 210 7510 +/- 229 0.046595 0.0483003 0.003988 0.5901922 0.0845271 ACTR1A ARP1 actin-related protein 1 homolog A, centractin alpha (yeast) 3191 +/- 134 2571 +/- 136 2763 +/- 211 2529 +/- 81 -0.241727 -0.008683 -0.339256 0.0015617 0.5937013 ACVR1B activin A receptor, type IB 1767 +/- 140 1532 +/- 52 1675 +/- 66 1338 +/- 98 -0.117997 -0.163381 -0.023761 0.2017275 0.249096 ACY1 aminoacylase 1 493 +/- 28 523 +/- 20 435 +/- 30 520 +/- 22 -0.072713 0.3972113 0.048724 0.6064414 0.155183 ACY1L2 aminoacylase 1-like 2 224 +/- 39 230 +/- 24 450 +/- 41 321 +/- 40 0.001236 0.8951819 -0.050237 0.5937618 0.3209364 ADA adenosine deaminase 265 +/- 19 352 +/- 29 341 +/- 51 373 +/- 25 0.130605 0.0331452 0.593305 0.0330362 0.5864965 ADAM10 ADAM metallopeptidase domain 10 1557 +/- 99 1808 +/- 84 1420 +/- 70 1612 +/- 46 -0.053577 0.0837127 0.049117 0.0627916 0.3649355 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1022 +/- 24 1299 +/- 94 1046 +/- 106 1001 +/- 50 -0.089311 0.0308546 -0.711056 0.1417549 0.7624836 ADCK4 aarF domain containing kinase 4 430 +/- 24 546 +/- 40 383 +/- 31 503 +/- 38 -0.139029 0.0374119 0.039519 0.2730918 0.306371 ADD1 adducin 1 (alpha) 4262 +/- 135 3716 +/- 140 3921 +/- 116 3527 +/- 163 -0.000231 -0.018613 -0.086088 0.0313122 0.2337141 ADD3 adducin 3 (gamma) 2311 +/- 330 3058 +/- 364 3069 +/- 265 3733 +/- 91 0.020001 0.1598374 0.054267 0.1569602 0.2721863 ADI1 acireductone dioxygenase 1 841 +/- 61 1306 +/- 144 924 +/- 99 1052 +/- 80 -0.633769 0.0213659 0.342331 0.0411467 0.8502112 ADPRH ADP-ribosylarginine hydrolase 189 +/- 23 309 +/- 20 168 +/- 16 281 +/- 20 -0.015382 0.0030101 0.00234 0.0028139 0.0090525 AFF3 AF4/FMR2 family, member 3 128 +/- 17 237 +/- 14 224 +/- 20 358 +/- 44 0.000454 0.0007359 0.034613 0.0013776 0.0003071 AFTIPHILINaftiphilin protein 689 +/- 9 552 +/- 54 739 +/- 50 699 +/- 28 0.034714 -0.050685 -0.502967 0.1353892 0.6185892 AGPAT3 1-acylglycerol-3-phosphate O-acyltransferase 3 124 +/- 10 226 +/- 20 169 +/- 8 232 +/- 26 0.32873 0.0024393 0.066576 0.0077687 0.0848176 AGXT2L1 alanine-glyoxylate aminotransferase 2-like 1 3918 +/- 414 5586 +/- 247 3574 +/- 289 4607 +/- 398 -0.017396 0.0082839 0.070131 0.0534143 0.1478785 AHCYL1 S-adenosylhomocysteine hydrolase-like 1 5935 +/- 600 7815 +/- 704 6059 +/- 511 8209 +/- 330 0.89046 0.0702645 0.007299 0.178801 0.1949046 AHSA1 AHA1, activator of heat shock 90kDa protein ATPase homolog 1 (yeast) 3672 +/- 96 3351 +/- 98 3165 +/- 122 3121 +/- 103 -0.010338 -0.04181 -0.788981 0.0745863 0.7081707 AIM1 absent in melanoma 1 368 +/- 97 238 +/- 44 114 +/- 15 175 +/- 22 -0.04556 -0.265509 0.050898 0.4169037 0.3555645 AKAP2 A kinase (PRKA) anchor protein 2 119 +/- 16 187 +/- 25 136 +/- 11 180 +/- 30 0.449089 0.048877 0.230583 0.1043416 0.7033942 AKAP8L A kinase (PRKA) anchor protein 8-like 115 +/- 15 71 +/- 9 120 +/- 16 99 +/- 3 0.145062 -0.038473 -0.237324 0.0940107 0.464845 AKR7A2 aldo-keto reductase family 7, member A2 (aflatoxin aldehyde reductase) 1175 +/- 77 1379 +/- 69 853 +/- 46 1153 +/- 50 -0.000463 0.0781494 0.001841 0.3070654 0.0892846 AKR7A3 aldo-keto reductase family 7, member A3 (aflatoxin aldehyde reductase) 722 +/- 43 917 +/- 15 718 +/- 9 959 +/- 30 0.634132 0.0049548 0.000763 0.0088467 0.0075985 AKT3 V-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma) 4120 +/- 100 3342 +/- 250 3311 +/- 184 3045 +/- 90 -0.019216 -0.025143 -0.234272 0.0121962 0.4146659 ALDH3A1 aldehyde dehydrogenase 3 family, memberA1 99 +/- 11 267 +/- 57 115 +/- 22 357 +/- 54 0.192629 0.0317146 0.008136 0.1405832 0.1298736 S1- alphabetical list of aging-related genes.xls Page 1 Young vs Aged MSI Correlations Symbol Description Young CA Aged CA Young DG Aged DG CA v DG in CA in DG in CA in DG ALDH3B1 aldehyde dehydrogenase 3 family, member B1 147 +/- 9 263 +/- 37 91 +/- 15 124 +/- 5 -0.002691 0.024391 0.084644 0.0284083 0.1508073 ALG5 asparagine-linked glycosylation 5 homolog (S. cerevisiae, dolichyl-phosphate beta-glucosylt 601 +/- 24 662 +/- 22 529 +/- 14 623 +/- 25 -0.004397 0.089144 0.015616 0.0442575 0.0523303 ALKBH8 alkB, alkylation repair homolog 8 (E. coli) 473 +/- 41 658 +/- 40 571 +/- 39 657 +/- 38 0.100357 0.009148 0.144295 0.026047 0.1802512 ALS2CR7 amyotrophic lateral sclerosis 2 (juvenile) chromosome region, candidate 7 167 +/- 14 134 +/- 8 171 +/- 14 126 +/- 13 -0.89375 -0.070817 -0.048406 0.0693331 0.0090109 AMY1A amylase, alpha 1A; salivary 217 +/- 46 706 +/- 123 210 +/- 34 692 +/- 108 -0.311506 0.0086487 0.008796 0.20853 0.1553613 ANAPC1 anaphase promoting complex subunit 1 942 +/- 48 810 +/- 32 776 +/- 30 736 +/- 25 -0.002459 -0.048252 -0.328608 0.0962622 0.410849 ANAPC10 anaphase promoting complex subunit 10 140 +/- 9 189 +/- 9 212 +/- 8 217 +/- 21 0.001658 0.003833 0.804792 0.0042811 0.1671211 ANAPC11 APC11 anaphase promoting complex subunit 11 homolog (yeast) 1418 +/- 65 1478 +/- 46 1036 +/- 22 1185 +/- 40 -2.42E-05 0.4651226 0.015793 0.9601436 0.2593954 ANAPC13 anaphase promoting complex subunit 13 1133 +/- 61 1351 +/- 62 1288 +/- 122 1286 +/- 100 0.659716 0.0312862 -0.989156 0.0454783 0.4677743 ANGPT1 angiopoietin 1 1046 +/- 93 1388 +/- 95 1366 +/- 231 1407 +/- 71 0.128906 0.0281217 0.870727 0.0085604 0.9933055 ANKH ankylosis, progressive homolog (mouse) 2365 +/- 119 2794 +/- 121 2527 +/- 92 2834 +/- 84 0.309715 0.0303359 0.035778 0.1664892 0.3065043 ANKHD1 ankyrin repeat and KH domain containing 1 870 +/- 60 1083 +/- 30 945 +/- 57 1001 +/- 44 -0.975161 0.0145608 0.45834 0.0060792 0.436999 ANKIB1 ankyrin repeat and IBR domain containing 1 1928 +/- 106 2973 +/- 269 1694 +/- 82 1917 +/- 65 -0.007949 0.009743 0.063334 9E-05 0.0839082 ANKMY1 ankyrin repeat and MYND domain containing 1 80 +/- 6 123 +/- 12 89 +/- 10 110 +/- 19 -0.871513 0.0124915 0.377353 0.0331304 0.1804862 ANKRD10 ankyrin repeat domain 10 912 +/- 51 1028 +/- 46 829 +/- 38 1027 +/- 65 -0.235634 0.116825 0.035519 0.1665156 0.2166634 ANKRD11 ankyrin repeat domain 11 147 +/- 24 240 +/- 28 262 +/- 22 250 +/- 72 0.044845 0.0307121 -0.879094 0.0139103 0.3395989 ANKRD16 ankyrin repeat domain 16 117 +/- 11 155 +/- 15 124 +/- 12 175 +/- 8 0.207757 0.078603 0.005667 0.4099566 0.0824376 ANKRD20Aankyrin repeat domain 20 family, member A1 143 +/- 19 247 +/- 40 57 +/- 20 106 +/- 21 -0.004106 0.0508737 0.130608 0.0214372 0.1929429 ANKRD34 ankyrin repeat domain 34 1681 +/- 72 1443 +/- 63 1781 +/- 88 1714 +/- 34 0.039068 -0.031915 -0.501221 0.0025436 0.8755633 ANKRD37 ankyrin repeat domain 37 571 +/- 66 630 +/- 24 455 +/- 38 593 +/- 37 -0.099949 0.4327419 0.027995 0.4128942 0.0187851 ANKRD43 ankyrin repeat domain 43 1626 +/- 115 1329 +/- 135 1930 +/- 112 1590 +/- 92 0.041881 -0.125069 -0.044003 0.1207345 0.1464073 ANKRD52 ankyrin repeat domain 52 1020 +/- 66 863 +/- 45 1073 +/- 53 917 +/- 18 0.324193 -0.080713 -0.030652 0.1436219 0.1729886 ANKS1B ankyrin
Load more

Recommended publications
  • 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]
  • 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.
    [Show full text]
  • Sphingolipid Metabolism Diseases ⁎ Thomas Kolter, Konrad Sandhoff
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Biochimica et Biophysica Acta 1758 (2006) 2057–2079 www.elsevier.com/locate/bbamem Review Sphingolipid metabolism diseases ⁎ Thomas Kolter, Konrad Sandhoff Kekulé-Institut für Organische Chemie und Biochemie der Universität, Gerhard-Domagk-Str. 1, D-53121 Bonn, Germany Received 23 December 2005; received in revised form 26 April 2006; accepted 23 May 2006 Available online 14 June 2006 Abstract Human diseases caused by alterations in the metabolism of sphingolipids or glycosphingolipids are mainly disorders of the degradation of these compounds. The sphingolipidoses are a group of monogenic inherited diseases caused by defects in the system of lysosomal sphingolipid degradation, with subsequent accumulation of non-degradable storage material in one or more organs. Most sphingolipidoses are associated with high mortality. Both, the ratio of substrate influx into the lysosomes and the reduced degradative capacity can be addressed by therapeutic approaches. In addition to symptomatic treatments, the current strategies for restoration of the reduced substrate degradation within the lysosome are enzyme replacement therapy (ERT), cell-mediated therapy (CMT) including bone marrow transplantation (BMT) and cell-mediated “cross correction”, gene therapy, and enzyme-enhancement therapy with chemical chaperones. The reduction of substrate influx into the lysosomes can be achieved by substrate reduction therapy. Patients suffering from the attenuated form (type 1) of Gaucher disease and from Fabry disease have been successfully treated with ERT. © 2006 Elsevier B.V. All rights reserved. Keywords: Ceramide; Lysosomal storage disease; Saposin; Sphingolipidose Contents 1. Sphingolipid structure, function and biosynthesis ..........................................2058 1.1.
    [Show full text]
  • Structural Characterization of Polysaccharides from Cordyceps Militaris and Their Hypolipidemic Effects Cite This: RSC Adv.,2018,8,41012 in High Fat Diet Fed Mice†
    RSC Advances View Article Online PAPER View Journal | View Issue Structural characterization of polysaccharides from Cordyceps militaris and their hypolipidemic effects Cite this: RSC Adv.,2018,8,41012 in high fat diet fed mice† Zhen-feng Huang, ‡ Ming-long Zhang,‡ Song Zhang,* Ya-hui Wang and Xue-wen Jiang Cordyceps militaris is a crude dietary therapeutic mushroom with high nutritional and medicinal values. Mushroom-derived polysaccharides have been found to possess antihyperglycemic and antihyperlipidemic activities. This study aimed to partially clarify the structural characterization and comparatively evaluate hypolipidemic potentials of intracellular- (IPCM) and extracellular polysaccharides of C. militaris (EPCM) in high fat diet fed mice. Results indicated that IPCM-2 is a-pyran polysaccharide with an average molecular weight of 32.5 kDa, was mainly composed of mannose, glucose and galactose with mass percentages of 51.94%, 10.54%, and 37.25%, respectively. EPCM-2 is an a-pyran Creative Commons Attribution 3.0 Unported Licence. polysaccharide with an average molecular weight of 20 kDa that is mainly composed of mannose, glucose and galactose with mass percentages of 44.51%, 18.33%, and 35.38%, respectively. In in vivo study, EPCM-1 treatment (100 mg kgÀ1 dÀ1) showed potential effects on improving serum lipid profiles of hyperlipidemic mice, reflected by decreasing serum total cholesterol (TC), triglyceride (TG) and low density lipoprotein-cholesterol (LDL-C) levels by 20.05%, 45.45% and 52.63%, respectively, while IPCM-1 treatment
    [Show full text]
  • Beta-Arrestin-Mediated Signaling in the Heart
    SPECIAL ARTICLE Circ J 2008; 72: 1725–1729 Beta-Arrestin-Mediated Signaling in the Heart Priyesh A. Patel, BS; Douglas G. Tilley, PhD*; Howard A. Rockman, MD*,** Beta-arrestin is a multifunctional adapter protein well known for its role in G-protein-coupled receptor (GPCR) desensitization. Exciting new evidence indicates thatβ-arrestin is also a signaling molecule capable of initiating its own G-protein-independent signaling at GPCRs. One of the best-studiedβ-arrestin signaling pathways is the one involvingβ-arrestin-dependent activation of a mitogen-activated protein kinase cascade, the extracellular regulated kinase (ERK). ERK signaling, which is classically activated by agonist stimulation of the epidermal growth factor receptor (EGFR), can be activated by a number of GPCRs in aβ-arrestin-dependent manner. Recent work in animal models of heart failure suggests thatβ-arrestin-dependent activation of EGFR/ERK signaling by theβ-1-adrenergic receptor, and possibly the angiotensin II Type 1A receptor, are cardioprotective. Hence, a new model of signaling at cardiac GPCRs has emerged and implicates classical G-protein-mediated signaling with promoting harmful remodeling in heart failure, while concurrently linkingβ-arrestin-dependent, G-protein-inde- pendent signaling with cardioprotective effects. Based on this paradigm, a new class of drugs could be identified, termed “biased ligands”, which simultaneously block harmful G-protein signaling, while also promoting cardio- protectiveβ-arrestin-dependent signaling, leading to a potential breakthrough
    [Show full text]
  • BMC Evolutionary Biology Biomed Central
    BMC Evolutionary Biology BioMed Central Research article Open Access On the origins of arrestin and rhodopsin Carlos E Alvarez1,2,3 Address: 1Center for Molecular and Human Genetics, The Research Institute at Nationwide Children's Hospital, Columbus, OH, 43205, USA, 2Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA and 3Novartis Institutes of BioMedical Research, CH-4002 Basel, Switzerland Email: Carlos E Alvarez - [email protected] Published: 29 July 2008 Received: 11 January 2008 Accepted: 29 July 2008 BMC Evolutionary Biology 2008, 8:222 doi:10.1186/1471-2148-8-222 This article is available from: http://www.biomedcentral.com/1471-2148/8/222 © 2008 Alvarez; 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: G protein coupled receptors (GPCRs) are the most numerous proteins in mammalian genomes, and the most common targets of clinical drugs. However, their evolution remains enigmatic. GPCRs are intimately associated with trimeric G proteins, G protein receptor kinases, and arrestins. We conducted phylogenetic studies to reconstruct the history of arrestins. Those findings, in turn, led us to investigate the origin of the photosensory GPCR rhodopsin. Results: We found that the arrestin clan is comprised of the Spo0M protein family in archaea and bacteria, and the arrestin and Vps26 families in eukaryotes. The previously known animal arrestins are members of the visual/beta subfamily, which branched from the founding "alpha" arrestins relatively recently.
    [Show full text]
  • Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
    Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897
    [Show full text]
  • Transcriptomic Analysis of the Aquaporin (AQP) Gene Family
    Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples.
    [Show full text]
  • Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
    Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together.
    [Show full text]
  • The Endocytic Membrane Trafficking Pathway Plays a Major Role
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository RESEARCH ARTICLE The Endocytic Membrane Trafficking Pathway Plays a Major Role in the Risk of Parkinson’s Disease Sara Bandres-Ciga, PhD,1,2 Sara Saez-Atienzar, PhD,3 Luis Bonet-Ponce, PhD,4 Kimberley Billingsley, MSc,1,5,6 Dan Vitale, MSc,7 Cornelis Blauwendraat, PhD,1 Jesse Raphael Gibbs, PhD,7 Lasse Pihlstrøm, MD, PhD,8 Ziv Gan-Or, MD, PhD,9,10 The International Parkinson’s Disease Genomics Consortium (IPDGC), Mark R. Cookson, PhD,4 Mike A. Nalls, PhD,1,11 and Andrew B. Singleton, PhD1* 1Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 2Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain 3Transgenics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 4Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 5Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom 6Department of Pathophysiology, University of Tartu, Tartu, Estonia 7Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 8Department of Neurology, Oslo University Hospital, Oslo, Norway 9Department of Neurology and Neurosurgery, Department of Human Genetics, McGill University, Montréal, Quebec, Canada 10Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada 11Data Tecnica International, Glen Echo, Maryland, USA ABSTRACT studies, summary-data based Mendelian randomization Background: PD is a complex polygenic disorder.
    [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]
  • Characterization of a Mutation in a Family with Saposin B Deficiency
    Proc. Nadl. Acad. Sci. USA Vol. 87, pp. 2541-2544, April 1990 Genetics Characterization of a mutation in a family with saposin B deficiency: A glycosylation site defect (sphingolipid activator protein/SAP-1/metachromatic leukodystrophy/arylsulfatase A) KEITH A. KRETZ*, GEOFFREY S. CARSON*, SATOSHI MORIMOTO*t, YASUO KISHIMOTO*, ARVAN L. FLUHARTYt, AND JOHN S. O'BPUEN*§ *Department of Neurosciences and Center for Molecular Genetics, University of California, San Diego, School of Medicine, M-034J, La Jolla, CA 92093; and tUniversity of California, Los Angeles, Mental Retardation Research Center Group at Lanterman Developmental Center, Pomona, CA 91766 Communicated by Dan L. Lindsley, January 19, 1990 ABSTRACT Saposins are small, heat-stable glycoproteins these four saposin proteins has now been isolated and their required for the hydrolysis of sphingolipids by specific lyso- activating properties have been determined (3-14). somal hydrolases. Saposins A, B, C, and D are derived by Saposins A and C specifically activate hydrolysis of glu- proteolytic processing from a single precursor protein named cocerebroside byB-glucosylceramidase (D-glucosyl-N-acyl- prosaposin. Saposin B, previously known as SAP-1 and sul- sphingosine glucohydrolase; EC 3.2.1.45) and ofgalactocere- fatide activator, stimulates the hydrolysis of a wide variety of broside by galactosylceramidase (D-galactosyl-N-acyl- substrates including cerebroside sulfate, GM1 ganglioside, and sphingosine galactohydrolase; EC 3.2.1.46) (3, 4). Saposin D globotriaosylceramide by arylsulfatase A, acid 8-galacto- specifically activates the hydrolysis of sphingomyelin by sidase, and a-galactosidase, respectively. Human saposin B sphingomyelin phosphodiesterase (sphingomyelin choline- deficiency, transmitted as an autosomal recessive trait, results phosphohydrolase; EC 3.1.4.12) (5).
    [Show full text]