Supplemental Table 2: UC Vs. NL Gene List Gene Name Symbol

Total Page:16

File Type:pdf, Size:1020Kb

Supplemental Table 2: UC Vs. NL Gene List Gene Name Symbol Supplemental Table 2: UC vs. NL gene list Gene Name Symbol Entrez UC vs. Gene NL fold ID change solute carrier family 6 (amino acid transporter), member 14 SLC6A14 11254 79.97 dual oxidase 2 DUOX2 50506 73.48 matrix metallopeptidase 1 (interstitial collagenase) MMP1 4312 65.16 matrix metallopeptidase 3 (stromelysin 1, progelatinase) MMP3 4314 60.20 matrix metallopeptidase 10 (stromelysin 2) MMP10 4319 40.71 chemokine (C-X-C motif) ligand 6 (granulocyte chemotactic protein CXCL6 6372 36.11 2) matrix metallopeptidase 12 (macrophage elastase) MMP12 4321 35.94 carboxypeptidase A3 (mast cell) CPA3 1359 33.94 chemokine (C-X-C motif) ligand 5 CXCL5 6374 30.84 cholesterol 25-hydroxylase CH25H 9023 28.08 myeloid cell nuclear differentiation antigen MNDA 4332 24.66 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating CXCL1 2919 22.55 activity, alpha) tryptophan 2,3-dioxygenase TDO2 6999 21.66 chitinase 3-like 1 (cartilage glycoprotein-39) CHI3L1 1116 20.78 S100 calcium binding protein A8 S100A8 6279 19.81 cysteine-rich, angiogenic inducer, 61 CYR61 3491 19.71 carboxypeptidase, vitellogenic-like CPVL 54504 18.77 matrix metallopeptidase 7 (matrilysin, uterine) MMP7 4316 16.92 collagen triple helix repeat containing 1 CTHRC1 115908 15.47 proprotein convertase subtilisin/kexin type 1 PCSK1 5122 15.26 hemoglobin, beta HBB 3043 13.55 chemokine (C-C motif) ligand 2 CCL2 6347 13.44 lumican LUM 4060 13.43 tenascin C (hexabrachion) TNC 3371 12.19 dual oxidase maturation factor 2 DUOXA2 405753 12.07 TNFAIP3 interacting protein 3 TNIP3 79931 11.96 angiotensinogen (serpin peptidase inhibitor, clade A, member 8) AGT 183 11.94 phosphodiesterase 4B, cAMP-specific (phosphodiesterase E4 dunce PDE4B 5142 11.64 homolog, Drosophila) chemokine (C-X-C motif) ligand 9 CXCL9 4283 11.25 chemokine (C-X-C motif) ligand 11 CXCL11 6373 11.12 toll-like receptor 1 TLR1 7096 11.05 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase PTGS2 5743 10.93 and cyclooxygenase) annexin A1 ANXA1 301 10.78 wingless-type MMTV integration site family, member 5A WNT5A 7474 10.69 TIMP metallopeptidase inhibitor 1 TIMP1 7076 10.65 transmembrane protein 158 TMEM15 25907 10.55 8 cadherin 11, type 2, OB-cadherin (osteoblast) CDH11 1009 10.53 serine peptidase inhibitor, Kazal type 4 SPINK4 27290 10.32 interleukin 8 IL8 3576 10.30 tissue factor pathway inhibitor 2 TFPI2 7980 10.24 Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide FCER1G 2207 10.14 complement component 4 binding protein, alpha C4BPA 722 10.12 purinergic receptor P2Y, G-protein coupled, 13 P2RY13 53829 10.01 hypothetical protein LOC441168 - 441168 9.99 complement component 4 binding protein, beta C4BPB 725 9.79 similar to omega protein - 91353 9.78 prokineticin 2 PROK2 60675 9.72 carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 CHST2 9435 9.65 chemokine (C-C motif) ligand 11 CCL11 6356 9.25 transmembrane protein 45A TMEM45 55076 9.05 A similar to RIKEN cDNA 2310016C16 - 493869 8.84 tumor necrosis factor receptor superfamily, member 17 TNFRSF1 608 8.75 7 ectonucleoside triphosphate diphosphohydrolase 1 ENTPD1 953 8.56 nitric oxide synthase 2A (inducible, hepatocytes) NOS2A 4843 8.55 Fc fragment of IgG, low affinity IIIb, receptor (CD16b) FCGR3B 2215 8.46 KIAA0125 KIAA012 9834 8.34 5 C-type lectin domain family 4, member A CLEC4A 50856 7.97 coagulation factor II (thrombin) receptor F2R 2149 7.89 sulfatase 1 SULF1 23213 7.84 SLAM family member 7 SLAMF7 57823 7.79 likely ortholog of mouse neighbor of Punc E11 - 57722 7.78 chemokine (C-X-C motif) ligand 3 CXCL3 2921 7.67 collagen, type VI, alpha 3 COL6A3 1293 7.63 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog KIT 3815 7.61 guanylate binding protein 5 GBP5 115362 7.59 vanin 1 VNN1 8876 7.58 chondroitin sulfate proteoglycan 2 (versican) CSPG2 1462 7.42 pim-2 oncogene PIM2 11040 7.31 neutrophil cytosolic factor 2 (65kDa, chronic granulomatous disease, NCF2 4688 7.27 autosomal 2) plasminogen activator, urokinase PLAU 5328 7.25 complement component 1, s subcomponent C1S 716 7.25 indoleamine-pyrrole 2,3 dioxygenase INDO 3620 7.16 chemokine (C-X3-C motif) receptor 1 CX3CR1 1524 7.12 complement component 4A (Rodgers blood group) C4A 720 7.06 complement component 4B (Childo blood group) C4B 721 7.06 complement component 1, q subcomponent, B chain C1QB 713 7.03 phosphoserine aminotransferase 1 PSAT1 29968 6.97 FK506 binding protein 11, 19 kDa FKBP11 51303 6.97 lymphocyte cytosolic protein 2 (SH2 domain containing leukocyte LCP2 3937 6.84 protein of 76kDa) chemokine (C-X-C motif) ligand 2 CXCL2 2920 6.84 protein C receptor, endothelial (EPCR) PROCR 10544 6.83 stress 70 protein chaperone, microsome-associated, 60kDa STCH 6782 6.83 regulator of G-protein signalling 18 RGS18 64407 6.82 IKK interacting protein - 121457 6.80 tumor suppressor candidate 3 TUSC3 7991 6.77 retinoic acid induced 2 RAI2 10742 6.77 major histocompatibility complex, class II, DP alpha 1 HLA- 3113 6.65 DPA1 chromosome 20 open reading frame 100 C20orf10 84969 6.63 0 interleukin 10 receptor, alpha IL10RA 3587 6.56 transforming growth factor, beta-induced, 68kDa TGFBI 7045 6.53 chemokine (C-X-C motif) ligand 10 CXCL10 3627 6.43 lysyl oxidase-like 1 LOXL1 4016 6.31 insulin receptor substrate 1 IRS1 3667 6.31 lymphocyte transmembrane adaptor 1 LAX1 54900 6.24 PDZK1 interacting protein 1 PDZK1IP 10158 6.21 1 hyaluronan synthase 2 HAS2 3037 6.13 immunoglobulin superfamily, member 6 IGSF6 10261 6.05 cat eye syndrome chromosome region, candidate 1 CECR1 51816 6.02 zinc finger protein, multitype 2 ZFPM2 23414 6.01 RAB31, member RAS oncogene family RAB31 11031 5.93 acyl-CoA synthetase long-chain family member 4 ACSL4 2182 5.91 B cell RAG associated protein - 51363 5.91 hemoglobin, alpha 1 HBA1 3039 5.88 adenomatosis polyposis coli down-regulated 1 APCDD1 147495 5.87 proapoptotic caspase adaptor protein - 51237 5.86 cathepsin K (pycnodysostosis) CTSK 1513 5.79 CD38 molecule CD38 952 5.76 latrophilin 2 LPHN2 23266 5.76 major histocompatibility complex, class II, DR alpha HLA- 3122 5.71 DRA apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like APOBEC 60489 5.69 3G 3G complement component 1, r subcomponent C1R 715 5.68 SEC24 related gene family, member D (S. cerevisiae) SEC24D 9871 5.63 ADP-ribosylation factor GTPase activating protein 3 ARFGAP 26286 5.60 3 interleukin 1, beta IL1B 3553 5.55 guanine nucleotide binding protein (G protein), alpha 15 (Gq class) GNA15 2769 5.52 dual specificity phosphatase 4 DUSP4 1846 5.50 mesenchyme homeobox 1 MEOX1 4222 5.50 integrin, beta 2 (complement component 3 receptor 3 and 4 subunit) ITGB2 3689 5.50 phospholipase A1 member A PLA1A 51365 5.45 fibrillin 1 FBN1 2200 5.44 lipoma HMGIC fusion partner LHFP 10186 5.40 collagen, type V, alpha 2 COL5A2 1290 5.38 chemokine orphan receptor 1 CMKOR1 57007 5.35 vascular endothelial growth factor C VEGFC 7424 5.35 cathepsin C CTSC 1075 5.34 protein kinase, cAMP-dependent, regulatory, type II, beta PRKAR2 5577 5.34 B calcitonin receptor-like CALCRL 10203 5.32 hemoglobin, alpha 2 HBA2 3040 5.25 G protein-coupled receptor 109B GPR109B 8843 5.24 complement factor I CFI 3426 5.24 elongation factor, RNA polymerase II, 2 ELL2 22936 5.21 epithelial membrane protein 3 EMP3 2014 5.17 matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa MMP2 4313 5.13 type IV collagenase) tumor necrosis factor (ligand) superfamily, member 13b TNFSF13 10673 5.09 B guanine nucleotide binding protein (G protein), alpha 14 GNA14 9630 5.02 interleukin 8 receptor, beta IL8RB 3579 5.01 pleckstrin homology-like domain, family A, member 1 PHLDA1 22822 4.95 collagen, type I, alpha 2 COL1A2 1278 4.91 procollagen C-endopeptidase enhancer PCOLCE 5118 4.89 matrix-remodelling associated 5 MXRA5 25878 4.86 RAB38, member RAS oncogene family RAB38 23682 4.86 G protein-coupled receptor 137B GPR137B 7107 4.84 S100 calcium binding protein P S100P 6286 4.82 annexin A3 ANXA3 306 4.79 serpin peptidase inhibitor, clade G (C1 inhibitor), member 1, SERPING 710 4.76 (angioedema, hereditary) 1 CD74 molecule, major histocompatibility complex, class II invariant CD74 972 4.76 chain family with sequence similarity 46, member C FAM46C 54855 4.74 damage-regulated autophagy modulator - 55332 4.74 tyrosinase-related protein 1 TYRP1 7306 4.72 CD86 molecule CD86 942 4.71 BCL2-associated athanogene 2 BAG2 9532 4.71 PALM2-AKAP2 protein - 445815 4.70 A kinase (PRKA) anchor protein 2 AKAP2 11217 4.70 transmembrane protein 23 TMEM23 259230 4.68 Rho guanine nucleotide exchange factor (GEF) 3 ARHGEF 50650 4.68 3 caspase recruitment domain family, member 6 CARD6 84674 4.66 bone morphogenetic protein 6 BMP6 654 4.64 interferon, gamma-inducible protein 30 IFI30 10437 4.60 oncostatin M receptor OSMR 9180 4.56 cadherin 13, H-cadherin (heart) CDH13 1012 4.55 toll-like receptor 8 TLR8 51311 4.55 colony stimulating factor 1 receptor, formerly McDonough feline CSF1R 1436 4.51 sarcoma viral (v-fms) oncogene homolog Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homolog FGR 2268 4.45 interleukin 2 receptor, beta IL2RB 3560 4.44 adrenomedullin ADM 133 4.43 caldesmon 1 CALD1 800 4.37 dedicator of cytokinesis 4 DOCK4 9732 4.32 lipocalin 2 (oncogene 24p3) LCN2 3934 4.31 BH-protocadherin (brain-heart) PCDH7 5099 4.31 NAD(P)H dehydrogenase, quinone 2 NQO2 4835 4.30 degenerative spermatocyte homolog 1, lipid desaturase (Drosophila) DEGS1 8560 4.30 SLAM family member 8 SLAMF8 56833 4.27 ADAM metallopeptidase with thrombospondin type 1 motif, 1 ADAMTS 9510 4.27 1 collagen, type III, alpha 1 (Ehlers-Danlos syndrome type IV, COL3A1 1281 4.24 autosomal dominant) major histocompatibility complex, class II, DQ beta 1 HLA- 3119 4.20 DQB1 SEC14 and spectrin domains 1 SESTD1 91404 4.20 caspase recruitment domain family, member 15 CARD15 64127 4.19 thioredoxin domain containing 5 TXNDC5 81567 4.16 ERO1-like beta (S.
Recommended publications
  • Acetyl Group Coordinated Progression Through the Catalytic Cycle of an Arylalkylamine N-Acetyltransferase
    RESEARCH ARTICLE Acetyl group coordinated progression through the catalytic cycle of an arylalkylamine N-acetyltransferase Adam A. Aboalroub, Ashleigh B. Bachman, Ziming Zhang, Dimitra Keramisanou, David J. Merkler, Ioannis Gelis* Department of Chemistry, University of South Florida, Tampa, Florida, United States of America * [email protected] a1111111111 a1111111111 a1111111111 Abstract a1111111111 a1111111111 The transfer of an acetyl group from acetyl-CoA to an acceptor amine is a ubiquitous bio- chemical transformation catalyzed by Gcn5-related N-acetyltransferases (GNATs). Although it is established that the reaction proceeds through a sequential ordered mecha- nism, the role of the acetyl group in driving the ordered formation of binary and ternary com- OPEN ACCESS plexes remains elusive. Herein, we show that CoA and acetyl-CoA alter the conformation of the substrate binding site of an arylalkylamine N-acetyltransferase (AANAT) to facilitate Citation: Aboalroub AA, Bachman AB, Zhang Z, Keramisanou D, Merkler DJ, Gelis I (2017) Acetyl interaction with acceptor substrates. However, it is the presence of the acetyl group within group coordinated progression through the the catalytic funnel that triggers high affinity binding. Acetyl group occupancy is relayed catalytic cycle of an arylalkylamine N- through a conserved salt bridge between the P-loop and the acceptor binding site, and is acetyltransferase. PLoS ONE 12(5): e0177270. manifested as differential dynamics in the CoA and acetyl-CoA-bound states. The capacity https://doi.org/10.1371/journal.pone.0177270 of the acetyl group carried by an acceptor to promote its tight binding even in the absence of Editor: Viswanathan V. Krishnan, California State CoA, but also its mutually exclusive position to the acetyl group of acetyl-CoA underscore its University Fresno, UNITED STATES importance in coordinating the progression of the catalytic cycle.
    [Show full text]
  • ALS2CR2 (STRADB) 406-418) Goat Polyclonal Antibody – AP08962PU-N
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for AP08962PU-N ALS2CR2 (STRADB) 406-418) Goat Polyclonal Antibody Product data: Product Type: Primary Antibodies Applications: ELISA, IHC, WB Recommended Dilution: ELISA: 1/32000. Immunohistochemistry on Paraffin Sections: 3.75 µg/ml. Western Blot: 1 - 3 µg/ml. Reactivity: Canine, Human Host: Goat Clonality: Polyclonal Immunogen: Synthetic peptide from C-terminus of human ALS2CR2 Specificity: This antibody reacts to STE20-Related Kinase Adaptor Beta (STRADB/ALS2CR2) at aa 406-418. It is expected to recognise both human isoforms: ILPIP-alpha (NP_061041.2) and ILPIP-beta (AAF71042.1). Formulation: Tris saline buffer, pH 7.3, 0.5% BSA, 0.02% sodium azide State: Aff - Purified State: Liquid purified Ig Concentration: lot specific Purification: Immunoaffinity Chromatography Conjugation: Unconjugated Storage: Store the antibody undiluted at 2-8°C for one month or (in aliquots) at -20°C for longer. Avoid repeated freezing and thawing. Stability: Shelf life: one year from despatch. Database Link: Entrez Gene 55437 Human Q9C0K7 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 3 ALS2CR2 (STRADB) 406-418) Goat Polyclonal Antibody – AP08962PU-N Background: Amyotrophic lateral sclerosis 2 (juvenile) chromosome region, candidate 2, is connected to transferase/kinase activity and ATP binding, it has recently been shown to interact with XIAP, a member of the IAP (Inhibitor of Apoptosis) protein family.
    [Show full text]
  • Dysregulated Hepatic Methionine
    Virginia Commonwealth University VCU Scholars Compass Internal Medicine Publications Dept. of Internal Medicine 2015 Dysregulated Hepatic Methionine Metabolism Drives Homocysteine Elevation in Diet-Induced Nonalcoholic Fatty Liver Disease Tommy Pacana Virginia Commonwealth University, [email protected] Sophie Cazanave Virginia Commonwealth University Aurora Verdianelli Virginia Commonwealth University See next page for additional authors Follow this and additional works at: http://scholarscompass.vcu.edu/intmed_pubs Part of the Medicine and Health Sciences Commons Copyright: © 2015 Pacana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Downloaded from http://scholarscompass.vcu.edu/intmed_pubs/98 This Article is brought to you for free and open access by the Dept. of Internal Medicine at VCU Scholars Compass. It has been accepted for inclusion in Internal Medicine Publications by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. Authors Tommy Pacana, Sophie Cazanave, Aurora Verdianelli, Viashali Patel, Hae-Ki Min, Faridoddin Mirshahi, Eoin Quinlavin, and Arun J. Sanyal This article is available at VCU Scholars Compass: http://scholarscompass.vcu.edu/intmed_pubs/98 RESEARCH ARTICLE Dysregulated Hepatic Methionine Metabolism Drives Homocysteine Elevation in Diet-Induced Nonalcoholic Fatty
    [Show full text]
  • Systems and Chemical Biology Approaches to Study Cell Function and Response to Toxins
    Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences Presented by MSc. Yingying Jiang born in Shandong, China Oral-examination: Systems and chemical biology approaches to study cell function and response to toxins Referees: Prof. Dr. Rob Russell Prof. Dr. Stefan Wölfl CONTRIBUTIONS The chapter III of this thesis was submitted for publishing under the title “Drug mechanism predominates over toxicity mechanisms in drug induced gene expression” by Yingying Jiang, Tobias C. Fuchs, Kristina Erdeljan, Bojana Lazerevic, Philip Hewitt, Gordana Apic & Robert B. Russell. For chapter III, text phrases, selected tables, figures are based on this submitted manuscript that has been originally written by myself. i ABSTRACT Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragment- chemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database.
    [Show full text]
  • ASPH-Notch Axis Guided Exosomal Delivery of Prometastatic Secretome
    Lin et al. Molecular Cancer (2019) 18:156 https://doi.org/10.1186/s12943-019-1077-0 RESEARCH Open Access ASPH-notch Axis guided Exosomal delivery of Prometastatic Secretome renders breast Cancer multi-organ metastasis Qiushi Lin1†, Xuesong Chen2†, Fanzheng Meng3†, Kosuke Ogawa4†, Min Li5, Ruipeng Song3, Shugeng Zhang3, Ziran Zhang3, Xianglu Kong3, Qinggang Xu1,6, Fuliang He1,7, Xuewei Bai8, Bei Sun8, Mien-Chie Hung9,10, Lianxin Liu3,11*, Jack Wands4* and Xiaoqun Dong12,1* Abstract Background: Aspartate β-hydroxylase (ASPH) is silent in normal adult tissues only to re-emerge during oncogenesis where its function is required for generation and maintenance of malignant phenotypes. Exosomes enable prooncogenic secretome delivering and trafficking for long-distance cell-to-cell communication. This study aims to explore molecular mechanisms underlying how ASPH network regulates designated exosomes to program development and progression of breast cancer. Methods: Stable cell lines overexpressing or knocking-out of ASPH were established using lentivirus transfection or CRISPR-CAS9 systems. Western blot, MTT, immunofluorescence, luciferase reporter, co-immunoprecipitation, 2D/3-D invasion, tube formation, mammosphere formation, immunohistochemistry and newly developed in vitro metastasis were applied. Results: Through physical interactions with Notch receptors, ligands (JAGs) and regulators (ADAM10/17), ASPH activates Notch cascade to provide raw materials (especially MMPs/ADAMs) for synthesis/release of pro-metastatic exosomes. Exosomes orchestrate EMT, 2-D/3-D invasion, stemness, angiogenesis, and premetastatic niche formation. Small molecule inhibitors (SMIs) of ASPH’s β-hydroxylase specifically/efficiently abrogated in vitro metastasis, which mimics basement membrane invasion at primary site, intravasation/extravasation (transendothelial migration), and colonization/outgrowth at distant sites.
    [Show full text]
  • Supplemental Table S1
    Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1
    [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]
  • Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [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]
  • Bioinformatics Analyses of Genomic Imprinting
    Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________
    [Show full text]
  • Origins and Functional Impact of Copy Number Variation in the Human Genome
    doi:10.1038/nature08516 ARTICLES Origins and functional impact of copy number variation in the human genome Donald F. Conrad1*, Dalila Pinto2*, Richard Redon1,3, Lars Feuk2,4, Omer Gokcumen5, Yujun Zhang1, Jan Aerts1, T. Daniel Andrews1, Chris Barnes1, Peter Campbell1, Tomas Fitzgerald1, Min Hu1, Chun Hwa Ihm5, Kati Kristiansson1, Daniel G. MacArthur1, Jeffrey R. MacDonald2, Ifejinelo Onyiah1, Andy Wing Chun Pang2, Sam Robson1, Kathy Stirrups1, Armand Valsesia1, Klaudia Walter1, John Wei2, Wellcome Trust Case Control Consortium{, Chris Tyler-Smith1, Nigel P. Carter1, Charles Lee5, Stephen W. Scherer2,6 & Matthew E. Hurles1 Structural variations of DNA greater than 1 kilobase in size account for most bases that vary among human genomes, but are still relatively under-ascertained. Here we use tiling oligonucleotide microarrays, comprising 42 million probes, to generate a comprehensive map of 11,700 copy number variations (CNVs) greater than 443 base pairs, of which most (8,599) have been validated independently. For 4,978 of these CNVs, we generated reference genotypes from 450 individuals of European, African or East Asian ancestry. The predominant mutational mechanisms differ among CNV size classes. Retrotransposition has duplicated and inserted some coding and non-coding DNA segments randomly around the genome. Furthermore, by correlation with known trait-associated single nucleotide polymorphisms (SNPs), we identified 30 loci with CNVs that are candidates for influencing disease susceptibility. Despite this, having assessed the completeness of our map and the patterns of linkage disequilibrium between CNVs and SNPs, we conclude that, for complex traits, the heritability void left by genome-wide association studies will not be accounted for by common CNVs.
    [Show full text]
  • Review Article Cystathionine -Synthase in Physiology and Cancer
    Hindawi BioMed Research International Volume 2018, Article ID 3205125, 11 pages https://doi.org/10.1155/2018/3205125 Review Article Cystathionine �-Synthase in Physiology and Cancer Haoran Zhu,1,2 Shaun Blake,1,2 Keefe T. Chan,1 Richard B. Pearson ,1,2,3,4 and Jian Kang 1 1 Division of Research, Peter MacCallum Cancer Centre, 305 Grattan Street, Melbourne, Victoria 3000, Australia 2Sir Peter MacCallum Department of Oncology, Australia 3Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, Victoria 3052, Australia 4Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3168, Australia Correspondence should be addressed to Richard B. Pearson; [email protected] Received 23 March 2018; Accepted 29 May 2018; Published 28 June 2018 Academic Editor: Maria L. Tornesello Copyright © 2018 Haoran Zhu et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cystathionine �-synthase (CBS) regulates homocysteine metabolism and contributes to hydrogen sulfde (H2S) biosynthesis through which it plays multifunctional roles in the regulation of cellular energetics, redox status, DNA methylation, and protein modifcation. Inactivating mutations in CBS contribute to the pathogenesis of the autosomal recessive disease CBS-defcient homocystinuria. Recent studies demonstrating that CBS promotes colon and ovarian cancer growth in preclinical models highlight a newly identifed oncogenic role for CBS. On the contrary, tumor-suppressive efects of CBS have been reported in other cancer types, suggesting context-dependent roles of CBS in tumor growth and progression. Here, we review the physiological functions of CBS, summarize the complexities regarding CBS research in oncology, and discuss the potential of CBS and its key metabolites, including homocysteine and H2S, as potential biomarkers for cancer diagnosis or therapeutic targets for cancer treatment.
    [Show full text]