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Table S1. List of Proteins in the BAHD1 Interactome
Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Single-Trait and Multi-Trait Genome-Wide Association Analyses Identify Novel Loci for Blood Pressure in African-Ancestry Populations
RESEARCH ARTICLE Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations Jingjing Liang1, Thu H. Le2, Digna R. Velez Edwards3, Bamidele O. Tayo4, Kyle J. Gaulton5, Jennifer A. Smith6, Yingchang Lu7,8,9, Richard A. Jensen10, Guanjie Chen11, Lisa R. Yanek12, Karen Schwander13, Salman M. Tajuddin14, Tamar Sofer15, Wonji Kim16, James Kayima17,18, Colin A. McKenzie19, Ervin Fox20, Michael A. Nalls21, J. Hunter Young12, Yan V. Sun22, a1111111111 Jacqueline M. Lane23,24,25, Sylvia Cechova2, Jie Zhou11, Hua Tang26, Myriam Fornage27, a1111111111 Solomon K. Musani20, Heming Wang1, Juyoung Lee28, Adebowale Adeyemo11, Albert a1111111111 W. Dreisbach20, Terrence Forrester19, Pei-Lun Chu29, Anne Cappola30, Michele K. Evans14, a1111111111 Alanna C. Morrison31, Lisa W. Martin32, Kerri L. Wiggins10, Qin Hui22, Wei Zhao6, Rebecca a1111111111 D. Jackson33, Erin B. Ware6,34, Jessica D. Faul34, Alex P. Reiner35, Michael Bray3, Joshua C. Denny36, Thomas H. Mosley20, Walter Palmas37, Xiuqing Guo38, George J. Papanicolaou39, Alan D. Penman20, Joseph F. Polak40, Kenneth Rice15, Ken D. Taylor41, Eric Boerwinkle31, Erwin P. Bottinger7, Kiang Liu42, Neil Risch43, Steven C. Hunt44, Charles Kooperberg35, Alan B. Zonderman14, Cathy C. Laurie15, Diane M. Becker12, OPEN ACCESS Jianwen Cai45, Ruth J. F. Loos7,8,46, Bruce M. Psaty10,47, David R. Weir34, Sharon L. R. Kardia6, Donna K. Arnett48, Sungho Won16,49, Todd L. Edwards50, Susan Redline51, Citation: Liang J, Le TH, Edwards DRV, Tayo BO, Richard S. Cooper4, D. C. Rao13, Jerome I. Rotter41, Charles Rotimi11, Daniel Levy52, Gaulton KJ, Smith JA, et al. (2017) Single-trait and Aravinda Chakravarti53, Xiaofeng Zhu1☯*, Nora Franceschini54☯* multi-trait genome-wide association analyses identify novel loci for blood pressure in African- 1 Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, ancestry populations. -
Subcellular Localization and Mitotic Interactome Analyses Identify SIRT4 As a Centrosomally Localized and Microtubule Associated Protein
cells Article Subcellular Localization and Mitotic Interactome Analyses Identify SIRT4 as a Centrosomally Localized and Microtubule Associated Protein 1 1, 1 1 Laura Bergmann , Alexander Lang y , Christoph Bross , Simone Altinoluk-Hambüchen , Iris Fey 1, Nina Overbeck 2, Anja Stefanski 2, Constanze Wiek 3, Andreas Kefalas 1, Patrick Verhülsdonk 1, Christian Mielke 4, Dennis Sohn 5, Kai Stühler 2,6, Helmut Hanenberg 3,7, Reiner U. Jänicke 5, Jürgen Scheller 1, Andreas S. Reichert 8 , Mohammad Reza Ahmadian 1 and Roland P. Piekorz 1,* 1 Institute of Biochemistry and Molecular Biology II, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; [email protected] (L.B.); [email protected] (A.L.); [email protected] (C.B.); [email protected] (S.A.-H.); [email protected] (I.F.); [email protected] (A.K.); [email protected] (P.V.); [email protected] (J.S.); [email protected] (M.R.A.) 2 Molecular Proteomics Laboratory, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; [email protected] (N.O.); [email protected] (A.S.); [email protected] (K.S.) 3 Department of Otolaryngology and Head/Neck Surgery, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; [email protected] (C.W.); [email protected] (H.H.) 4 Institute of Clinical Chemistry and Laboratory Diagnostics, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; [email protected] -
Iii COMPUTATIONAL APPROACHES for ASSESSING KINOME
COMPUTATIONAL APPROACHES FOR ASSESSING KINOME FUNCTION AND DEREGULATION A Dissertation Presented to the Faculty of the Weill Cornell Graduate School of Medical Sciences in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Charles Joseph Murphy August 2018 iii © 2018 Charles Joseph Murphy ALL RIGHTS RESERVED COMPUTATIONAL APPROACHES FOR ASSESSING KINOME FUNCTION AND DEREGULATION Charles Joseph Murphy, PhD Cornell University 2018 Protein kinases are a diverse family of about 500 proteins that all share the common ability to catalyze phosphorylation of the side chains of amino acids in proteins. Kinases play a vital role across diverse biological functions including proliferation, differentiation, cell migration, and cell-cycle control. Moreover, they are frequently altered across most cancers types and have been a focus for development of anti- cancer drugs, which has led to the development of 38 approved kinase inhibitors as of 2018. In this thesis, I developed two orthogonal computational approaches for investigating kinase function and deregulation. Starting with data from a large cohort of mouse triple negative breast cancer (TNBC) tumors, I use a combination of whole exome sequencing (WES) and RNA-seq to identify somatic alterations that drive individual tumors. I discovered that a large number of these alterations involve protein kinases and subsequent therapeutic targeting led to tumor regression. For my second approach, I utilized a large peptide library dataset from about 300 kinases. Which kinase phosphorylate which phosphorylation site is determined by both kinase- intrinsic and contextual factors. Peptide library approaches provide kinase-intrinsic amino acid specificity, which I used to predict novel kinase substrates and map out kinase phosphorylation networks. -
List of Genes Associated with Sudden Cardiac Death (Scdgseta) Gene
List of genes associated with sudden cardiac death (SCDgseta) mRNA expression in normal human heart Entrez_I Gene symbol Gene name Uniprot ID Uniprot name fromb D GTEx BioGPS SAGE c d e ATP-binding cassette subfamily B ABCB1 P08183 MDR1_HUMAN 5243 √ √ member 1 ATP-binding cassette subfamily C ABCC9 O60706 ABCC9_HUMAN 10060 √ √ member 9 ACE Angiotensin I–converting enzyme P12821 ACE_HUMAN 1636 √ √ ACE2 Angiotensin I–converting enzyme 2 Q9BYF1 ACE2_HUMAN 59272 √ √ Acetylcholinesterase (Cartwright ACHE P22303 ACES_HUMAN 43 √ √ blood group) ACTC1 Actin, alpha, cardiac muscle 1 P68032 ACTC_HUMAN 70 √ √ ACTN2 Actinin alpha 2 P35609 ACTN2_HUMAN 88 √ √ √ ACTN4 Actinin alpha 4 O43707 ACTN4_HUMAN 81 √ √ √ ADRA2B Adrenoceptor alpha 2B P18089 ADA2B_HUMAN 151 √ √ AGT Angiotensinogen P01019 ANGT_HUMAN 183 √ √ √ AGTR1 Angiotensin II receptor type 1 P30556 AGTR1_HUMAN 185 √ √ AGTR2 Angiotensin II receptor type 2 P50052 AGTR2_HUMAN 186 √ √ AKAP9 A-kinase anchoring protein 9 Q99996 AKAP9_HUMAN 10142 √ √ √ ANK2/ANKB/ANKYRI Ankyrin 2 Q01484 ANK2_HUMAN 287 √ √ √ N B ANKRD1 Ankyrin repeat domain 1 Q15327 ANKR1_HUMAN 27063 √ √ √ ANKRD9 Ankyrin repeat domain 9 Q96BM1 ANKR9_HUMAN 122416 √ √ ARHGAP24 Rho GTPase–activating protein 24 Q8N264 RHG24_HUMAN 83478 √ √ ATPase Na+/K+–transporting ATP1B1 P05026 AT1B1_HUMAN 481 √ √ √ subunit beta 1 ATPase sarcoplasmic/endoplasmic ATP2A2 P16615 AT2A2_HUMAN 488 √ √ √ reticulum Ca2+ transporting 2 AZIN1 Antizyme inhibitor 1 O14977 AZIN1_HUMAN 51582 √ √ √ UDP-GlcNAc: betaGal B3GNT7 beta-1,3-N-acetylglucosaminyltransfe Q8NFL0 -
SOX4-Mediated Repression of Specific Trnas Inhibits Proliferation of Human Glioblastoma Cells
SOX4-mediated repression of specific tRNAs inhibits proliferation of human glioblastoma cells Jianjing Yanga,b,c, Derek K. Smithc,d, Haoqi Nia,b,c,KeWua,b, Dongdong Huanga,b, Sishi Pana,b,c, Adwait A. Sathee, Yu Tangc,d, Meng-Lu Liuc,d, Chao Xinge,f,g, Chun-Li Zhangc,d,1, and Qichuan Zhugea,b,1 aDepartment of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; bZhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; cDepartment of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390; dHamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390; eMcDermott Center of Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390; fDepartment of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390; and gDepartment of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390 Edited by S. Altman, Yale University, New Haven, CT, and approved February 5, 2020 (received for review November 15, 2019) Transfer RNAs (tRNAs) are products of RNA polymerase III (Pol III) indicates that tRNA expression may also be under cell state- and essential for mRNA translation and ultimately cell growth and dependent regulations (12–16). proliferation. Whether and how individual tRNA genes are specif- In this study, we performed a systematic analysis on how ically regulated is not clear. Here, we report that SOX4, a well- NGN2/SOX4-mediated cell-fate reprogramming leads to cell known Pol II-dependent transcription factor that is critical for neuro- cycle exit of human glioblastoma cells. -
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. -
Evidence for Differential Alternative Splicing in Blood of Young Boys With
Stamova et al. Molecular Autism 2013, 4:30 http://www.molecularautism.com/content/4/1/30 RESEARCH Open Access Evidence for differential alternative splicing in blood of young boys with autism spectrum disorders Boryana S Stamova1,2,5*, Yingfang Tian1,2,4, Christine W Nordahl1,3, Mark D Shen1,3, Sally Rogers1,3, David G Amaral1,3 and Frank R Sharp1,2 Abstract Background: Since RNA expression differences have been reported in autism spectrum disorder (ASD) for blood and brain, and differential alternative splicing (DAS) has been reported in ASD brains, we determined if there was DAS in blood mRNA of ASD subjects compared to typically developing (TD) controls, as well as in ASD subgroups related to cerebral volume. Methods: RNA from blood was processed on whole genome exon arrays for 2-4–year-old ASD and TD boys. An ANCOVA with age and batch as covariates was used to predict DAS for ALL ASD (n=30), ASD with normal total cerebral volumes (NTCV), and ASD with large total cerebral volumes (LTCV) compared to TD controls (n=20). Results: A total of 53 genes were predicted to have DAS for ALL ASD versus TD, 169 genes for ASD_NTCV versus TD, 1 gene for ASD_LTCV versus TD, and 27 genes for ASD_LTCV versus ASD_NTCV. These differences were significant at P <0.05 after false discovery rate corrections for multiple comparisons (FDR <5% false positives). A number of the genes predicted to have DAS in ASD are known to regulate DAS (SFPQ, SRPK1, SRSF11, SRSF2IP, FUS, LSM14A). In addition, a number of genes with predicted DAS are involved in pathways implicated in previous ASD studies, such as ROS monocyte/macrophage, Natural Killer Cell, mTOR, and NGF signaling. -
Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79
www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g. -
SETD1B-Associated Neurodevelopmental Disorder
Genotype- phenotype correlations J Med Genet: first published as 10.1136/jmedgenet-2019-106756 on 16 June 2020. Downloaded from Original research SETD1B- associated neurodevelopmental disorder Alexandra Roston ,1 Dan Evans,2 Harinder Gill,1,3 Margaret McKinnon,1 Bertrand Isidor,4 Benjamin Cogné ,4,5 Jill Mwenifumbo,1,6 Clara van Karnebeek,7,8 Jianghong An,9 Steven J M Jones,9 Matthew Farrer,2 Michelle Demos,10 Mary Connolly,10 William T Gibson ,1 CAUSES Study, EPGEN Study ► Additional material is ABSTRACT same gene allows causal inference for those geno- published online only. To view Background Dysfunction of histone methyltransferases type–phenotype correlations. This is especially true please visit the journal online (http:// dx. doi. org/ 10. 1136/ and chromatin modifiers has been implicated in complex when perturbation of the general biological process jmedgenet- 2019- 106756). neurodevelopmental syndromes and cancers. SETD1B has, in similar contexts, been shown reproducibly encodes a lysine- specific methyltransferase that assists to result in similar phenotypes. For numbered affiliations see in transcriptional activation of genes by depositing Individual case reports have appeared in the end of article. H3K4 methyl marks. Previous reports of patients with literature describing neurodevelopmental delays rare variants in SETD1B describe a distinctive phenotype associated with rare coding variants in SETD1B, Correspondence to Dr Alexandra Roston, that includes seizures, global developmental delay and and with hemizygosity for SETD1B due to CNVs. Department of Medical intellectual disability. Here we show that pathogenic variants within Genetics, The University of Methods Two of the patients described herein were SETD1B contribute to a conserved phenotype that British Columbia, Vancouver, BC identified via genome-wide and exome-wide testing, includes intellectual disability and childhood- onset, V6T 1Z4, Canada; with microarray and research- based exome, through treatment- refractory seizures. -
Development of a Human Mitochondrial Oligonucleotide
BMC Genomics BioMed Central Research article Open Access Development of a human mitochondrial oligonucleotide microarray (h-MitoArray) and gene expression analysis of fibroblast cell lines from 13 patients with isolated F1Fo ATP synthase deficiency Alena Жížková1,2,5, Viktor Stránecký1,2, Robert Ivánek1,2,4, Hana Hartmannová1,2, Lenka Nosková2, Lenka Piherová1,2, Markéta Tesařová1,3, Hana Hansíková1,3, Tomáš Honzík3, JiříZeman1,3, Petr Divina4, Andrea Potocká1,5, Jan Paul1,5, Wolfgang Sperl6, Johannes A Mayr6, Sara Seneca7, Josef Houštĕk1,5 and Stanislav Kmoch*1,2 Address: 1Center for Applied Genomics, 1st Faculty of Medicine, Charles University, Prague, Czech Republic, 2Institute of Inherited Metabolic Disorders, 1st Faculty of Medicine, Charles University, Prague, Czech Republic, 3Department of Pediatrics, 1st Faculty of Medicine, Charles University, Prague, Czech Republic, 4Institute of Molecular Genetics, Academy of Science of the Czech Republic, Prague, Czech Republic, 5Department of Bioenergetics, Institute of Physiology, Academy of Science of the Czech Republic, Prague, Czech Republic, 6Department of Pediatrics, Paracelsus Medical University, Salzburg, Austria and 7Center of Medical Genetics, Free University Brussels, Brussels, Belgium Email: Alena Жížková - [email protected]; Viktor Stránecký - [email protected]; Robert Ivánek - [email protected]; Hana Hartmannová - [email protected]; Lenka Nosková - [email protected]; Lenka Piherová - [email protected]; Markéta Tesařová - [email protected]; Hana Hansíková -