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Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent -
Nuclear and Mitochondrial Genome Defects in Autisms
UC Irvine UC Irvine Previously Published Works Title Nuclear and mitochondrial genome defects in autisms. Permalink https://escholarship.org/uc/item/8vq3278q Journal Annals of the New York Academy of Sciences, 1151(1) ISSN 0077-8923 Authors Smith, Moyra Spence, M Anne Flodman, Pamela Publication Date 2009 DOI 10.1111/j.1749-6632.2008.03571.x License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California THE YEAR IN HUMAN AND MEDICAL GENETICS 2009 Nuclear and Mitochondrial Genome Defects in Autisms Moyra Smith, M. Anne Spence, and Pamela Flodman Department of Pediatrics, University of California, Irvine, California In this review we will evaluate evidence that altered gene dosage and structure im- pacts neurodevelopment and neural connectivity through deleterious effects on synap- tic structure and function, and evidence that the latter are key contributors to the risk for autism. We will review information on alterations of structure of mitochondrial DNA and abnormal mitochondrial function in autism and indications that interactions of the nuclear and mitochondrial genomes may play a role in autism pathogenesis. In a final section we will present data derived using Affymetrixtm SNP 6.0 microar- ray analysis of DNA of a number of subjects and parents recruited to our autism spectrum disorders project. We include data on two sets of monozygotic twins. Col- lectively these data provide additional evidence of nuclear and mitochondrial genome imbalance in autism and evidence of specific candidate genes in autism. We present data on dosage changes in genes that map on the X chromosomes and the Y chro- mosome. -
The Role of the S6K2 Splice Isoform in Mtor/S6K Signalling and Cellular Functions
The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions Olena Myronova A thesis submitted to the University College London in fulfilment with the requirements for the degree of Doctor of Philosophy London, November 2015 Research Department of Structural and Molecular Biology Division of Biosciences University College London Gower Street London, WC1E 6BT United Kingdom Ludwig Institute for Cancer Research 666 Third Avenue, 28th floor New York, N.Y. 10017 USA The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions 1 Declaration I, Olena Myronova, declare that all the work presented in this thesis is the result of my own work. The work presented here does not constitute part of any other thesis. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. The work here in was carried out while I was a graduate research student at University College London, Research Department of Structural and Molecular Biology under the supervision of Professor Ivan Gout. Olena Myronova The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions 2 Abstract Ribosomal S6 kinase (S6K) is a member of the AGC family of serine/threonine protein kinases and plays a key role in diverse cellular processes, including cell growth, survival and metabolism. Activation of S6K by growth factors, amino acids, energy levels and hypoxia is mediated by the mTOR and PI3K signalling pathways. Dysregulation of S6K activity has been implicated in a number of human pathologies, including cancer, diabetes, obesity and ageing. -
309 in PTPRCAP Is Associated with Susceptibility to Diffuse-Type Gastric
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Volume 11 Number 12 December 2009 pp. 1340–1347 1340 www.neoplasia.com Hyoungseok Ju*, Byungho Lim*, Minjin Kim*, A Regulatory Polymorphism at † ‡ § Yong Sung Kim , Woo Ho Kim , Chunhwa Ihm , − PTPRCAP Seung-Moo Noh¶,DongSooHan#, Hang-Jong Yu**, Position 309 in Is †† Associated with Susceptibility Bo Youl Choi and Changwon Kang* to Diffuse-type Gastric Cancer *Department of Biological Sciences, Korea Advanced 1 Institute of Science and Technology, Daejeon, South Korea; and Gene Expression †Medical Genomics Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea; ‡Department of Pathology, Seoul National University College of Medicine, Seoul, South Korea; §Department of Laboratory Medicine, Eulji University College of Medicine, Daejeon, South Korea; ¶Department of General Surgery, Chungnam National University College of Medicine, Daejeon, South Korea; #Department of Medicine, Hanyang University Guri Hospital, Guri, South Korea; **Korea Gastric Cancer Center, Seoul Paik Hospital, Seoul, South Korea; ††Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, South Korea Abstract PTPRCAP (CD45-AP) is a positive regulator of protein tyrosine phosphatase PTPRC (CD45), which activates Src family kinases implicated in tumorigenesis. Single-nucleotide polymorphism (SNP) rs869736 located at position −309 of the PTPRCAP promoter was associated with susceptibility to diffuse-type gastric cancer in the current case-control study. The minor-allele homozygote was significantly associated with a 2.5-fold increased susceptibility to diffuse- type gastric cancer (P = .0021, n = 252), but not to intestinal-type (P =.30,n =178),versus the major-allele homo- zygote, when comparing unrelated Korean patients with healthy controls (n = 406). -
Xo GENE PANEL
xO GENE PANEL Targeted panel of 1714 genes | Tumor DNA Coverage: 500x | RNA reads: 50 million Onco-seq panel includes clinically relevant genes and a wide array of biologically relevant genes Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 -
Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum
SUPPLEMENTARY MATERIAL Comprehensive assessment of Indian variations in the druggable kinome landscape highlights distinct insights at the sequence, structure and pharmacogenomic stratum Gayatri Panda1‡, Neha Mishra1‡, Disha Sharma2,3, Rahul C. Bhoyar3, Abhinav Jain2,3, Mohamed Imran2,3, Vigneshwar Senthilvel2,3, Mohit Kumar Divakar2,3, Anushree Mishra3, Priyanka Banerjee4, Sridhar Sivasubbu2,3, Vinod Scaria2,3, Arjun Ray1* 1 Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India. 2 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India. 3 CSIR-Institute of Genomics and Integrative Biology, Mathura Road, Delhi-110020, India. 4 Institute for Physiology, Charite-University of Medicine, Berlin, 10115 Berlin, Germany. ‡These authors contributed equally to this work. * [email protected] TABLE OF CONTENTS Name Title Supplemental_Figure_S1 Fauchere and Pliska hydrophobicity scale for variations in structure data Supplemental_Figure_S2 Phenotypic drug-drug correlogram Supplemental_Table_S1 545 kinase coding genes used in the study Supplemental_Table_S2 Classes and count of kinase coding genes Supplemental_Table_S3 Allele frequency Indian v/s other populations from 1000 genome data(1000g2015). Supplemental_Table_S4 IndiGen Structure Data- consisting of 12 genes and their 22 variants Supplemental_Table_S5 Genes, PDB ids, mutations in IndiGen data and associated drugs (FDA approved) Supplemental_Table_S6 Data used for docking and binding pocket similarity analysis Supplemental_Table_S7 -
Research Article Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis
Hindawi BioMed Research International Volume 2020, Article ID 3650935, 15 pages https://doi.org/10.1155/2020/3650935 Research Article Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis Jian-Qin Xie ,1,2,3 Ya-Peng Lu ,1,3 Hong-Li Sun ,1,3 Li-Na Gao ,2,3 Pei-Pei Song ,2,3 Zhi-Jun Feng ,3 and Chong-Ge You 2,3 1Department of Anesthesiology, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China 2Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China 3The Second Clinical Medical College of Lanzhou University, Lanzhou, Gansu 730030, China Correspondence should be addressed to Chong-Ge You; [email protected] Received 13 April 2020; Revised 8 October 2020; Accepted 18 November 2020; Published 3 December 2020 Academic Editor: Rudolf K. Braun Copyright © 2020 Jian-Qin Xie et al. This 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. Ischemic stroke (IS) greatly threatens human health resulting in high mortality and substantial loss of function. Recent studies have shown that the outcome of IS has sex specific, but its mechanism is still unclear. This study is aimed at identifying the sexually dimorphic to peripheral immune response in IS progression, predicting potential prognostic biomarkers that can lead to sex- specific outcome, and revealing potential treatment targets. Gene expression dataset GSE37587, including 68 peripheral whole blood samples which were collected within 24 hours from known onset of symptom and again at 24-48 hours after onset (20 women and 14 men), was downloaded from the Gene Expression Omnibus (GEO) datasets. -
MIKULASOVA Et Al. MMEJ DRIVES 8Q24 REARRANGEMENTS in MYELOMA
MIKULASOVA et al. MMEJ DRIVES 8q24 REARRANGEMENTS IN MYELOMA Microhomology-mediated end joining drives complex rearrangements and over-expression of MYC and PVT1 in multiple myeloma Authors Aneta Mikulasova1,2, Cody Ashby1, Ruslana G. Tytarenko1, Pingping Qu3, Adam Rosenthal3, Judith A. Dent1, Katie R. Ryan1, Michael A. Bauer1, Christopher P. Wardell1, Antje Hoering3, Konstantinos Mavrommatis4, Matthew Trotter5, Shayu Deshpande1, Erming Tian1, Jonathan Keats6, Daniel Auclair7, Graham H. Jackson8, Faith E. Davies1, Anjan Thakurta4, Gareth J. Morgan1, and Brian A. Walker1 Affiliations 1Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA 2Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom 3Cancer Research and Biostatistics, Seattle, WA, USA 4Celgene Corporation, Summit, NJ, USA 5Celgene Institute for Translational Research Europe, Seville, Spain 6Translational Genomics Research Institute, Phoenix, AZ, USA. 7Multiple Myeloma Research Foundation, Norwalk, CT, USA. 8Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, United Kingdom MIKULASOVA et al. MMEJ DRIVES 8q24 REARRANGEMENTS IN MYELOMA Contents Supplementary Methods ............................................................................................................... 2 Data Analysis .............................................................................................................................. 2 External Datasets ...................................................................................................................... -
Single-Cell Transcriptomes Reveal a Complex Cellular Landscape in the Middle Ear and Differential Capacities for Acute Response to Infection
fgene-11-00358 April 9, 2020 Time: 15:55 # 1 ORIGINAL RESEARCH published: 15 April 2020 doi: 10.3389/fgene.2020.00358 Single-Cell Transcriptomes Reveal a Complex Cellular Landscape in the Middle Ear and Differential Capacities for Acute Response to Infection Allen F. Ryan1*, Chanond A. Nasamran2, Kwang Pak1, Clara Draf1, Kathleen M. Fisch2, Nicholas Webster3 and Arwa Kurabi1 1 Departments of Surgery/Otolaryngology, UC San Diego School of Medicine, VA Medical Center, La Jolla, CA, United States, 2 Medicine/Center for Computational Biology & Bioinformatics, UC San Diego School of Medicine, VA Medical Center, La Jolla, CA, United States, 3 Medicine/Endocrinology, UC San Diego School of Medicine, VA Medical Center, La Jolla, CA, United States Single-cell transcriptomics was used to profile cells of the normal murine middle ear. Clustering analysis of 6770 transcriptomes identified 17 cell clusters corresponding to distinct cell types: five epithelial, three stromal, three lymphocyte, two monocyte, Edited by: two endothelial, one pericyte and one melanocyte cluster. Within some clusters, Amélie Bonnefond, Institut National de la Santé et de la cell subtypes were identified. While many corresponded to those cell types known Recherche Médicale (INSERM), from prior studies, several novel types or subtypes were noted. The results indicate France unexpected cellular diversity within the resting middle ear mucosa. The resolution of Reviewed by: Fabien Delahaye, uncomplicated, acute, otitis media is too rapid for cognate immunity to play a major Institut Pasteur de Lille, France role. Thus innate immunity is likely responsible for normal recovery from middle ear Nelson L. S. Tang, infection. The need for rapid response to pathogens suggests that innate immune The Chinese University of Hong Kong, China genes may be constitutively expressed by middle ear cells. -
NRF1) Coordinates Changes in the Transcriptional and Chromatin Landscape Affecting Development and Progression of Invasive Breast Cancer
Florida International University FIU Digital Commons FIU Electronic Theses and Dissertations University Graduate School 11-7-2018 Decipher Mechanisms by which Nuclear Respiratory Factor One (NRF1) Coordinates Changes in the Transcriptional and Chromatin Landscape Affecting Development and Progression of Invasive Breast Cancer Jairo Ramos [email protected] Follow this and additional works at: https://digitalcommons.fiu.edu/etd Part of the Clinical Epidemiology Commons Recommended Citation Ramos, Jairo, "Decipher Mechanisms by which Nuclear Respiratory Factor One (NRF1) Coordinates Changes in the Transcriptional and Chromatin Landscape Affecting Development and Progression of Invasive Breast Cancer" (2018). FIU Electronic Theses and Dissertations. 3872. https://digitalcommons.fiu.edu/etd/3872 This work is brought to you for free and open access by the University Graduate School at FIU Digital Commons. It has been accepted for inclusion in FIU Electronic Theses and Dissertations by an authorized administrator of FIU Digital Commons. For more information, please contact [email protected]. FLORIDA INTERNATIONAL UNIVERSITY Miami, Florida DECIPHER MECHANISMS BY WHICH NUCLEAR RESPIRATORY FACTOR ONE (NRF1) COORDINATES CHANGES IN THE TRANSCRIPTIONAL AND CHROMATIN LANDSCAPE AFFECTING DEVELOPMENT AND PROGRESSION OF INVASIVE BREAST CANCER A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in PUBLIC HEALTH by Jairo Ramos 2018 To: Dean Tomás R. Guilarte Robert Stempel College of Public Health and Social Work This dissertation, Written by Jairo Ramos, and entitled Decipher Mechanisms by Which Nuclear Respiratory Factor One (NRF1) Coordinates Changes in the Transcriptional and Chromatin Landscape Affecting Development and Progression of Invasive Breast Cancer, having been approved in respect to style and intellectual content, is referred to you for judgment. -
Quantitative Interactomics in Primary T Cells Unveils TCR Signal
Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics Guillaume Voisinne, Kristof Kersse, Karima Chaoui, Liaoxun Lu, Julie Chaix, Lichen Zhang, Marisa Goncalves Menoita, Laura Girard, Youcef Ounoughene, Hui Wang, et al. To cite this version: Guillaume Voisinne, Kristof Kersse, Karima Chaoui, Liaoxun Lu, Julie Chaix, et al.. Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics. Nature Immunology, Nature Publishing Group, 2019, 20 (11), pp.1530 - 1541. 10.1038/s41590-019-0489-8. hal-03013469 HAL Id: hal-03013469 https://hal.archives-ouvertes.fr/hal-03013469 Submitted on 23 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. RESOURCE https://doi.org/10.1038/s41590-019-0489-8 Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics Guillaume Voisinne 1, Kristof Kersse1, Karima Chaoui2, Liaoxun Lu3,4, Julie Chaix1, Lichen Zhang3, Marisa Goncalves Menoita1, Laura Girard1,5, Youcef Ounoughene1, Hui Wang3, Odile Burlet-Schiltz2, Hervé Luche5,6, Frédéric Fiore5, Marie Malissen1,5,6, Anne Gonzalez de Peredo 2, Yinming Liang 3,6*, Romain Roncagalli 1* and Bernard Malissen 1,5,6* The activation of T cells by the T cell antigen receptor (TCR) results in the formation of signaling protein complexes (signalo somes), the composition of which has not been analyzed at a systems level. -
Supplemental File
SUPPLEMENTARY MATERIALS AND METHODS ### reorder as minimun to maximum relative induction. allks2=cbind(allks,apply(allks,1,sum)) For the different analyses, the macros are as following: allks3=allks2[order(allks2[,length(colnames(allks2))]),] allks5=allks3[,-length(colnames(allks2))] -Heatmap and correlation analyses # check allks5 source("http://bioconductor.org/biocLite.R") biocLite() -Heatmap install.packages("gplots") library("gplots") #Set a color scale "zero centered" #Set Graph title (do one by one) breaks=c(seq(-(max(allks5)), max(allks5) , by = 0.05)) titre = "allkinases SASP " mycol <- colorpanel(n=length(breaks)- titre = "allkinases SASP + p16" 1,low="green",mid="black",high="red") titre = "allkinases SASP + p16 + NFkB" #Load Normalized RT-QPCR table (do one by one) # Heatmap, "symkey = T" allks<-read.table("allkS.txt", header = TRUE, sep = "\t", dec = ",", fill = TRUE, , row.names=1 ) dev.off() allks<-read.table("allkSp16.txt", header = TRUE, sep = heatmap.2(allks5, scale = "none", col = mycol, "\t", dec = ",", fill = TRUE, , row.names=1 ) breaks=breaks, symkey = F, allks<-read.table("allkSp16NFkB.txt", header = TRUE, trace = 'none', cexRow=0.8, cexCol = 1.6, srtCol sep = "\t", dec = ",", fill = TRUE, , row.names=1 ) = 0, keysize = 1.3, -Log2 relative fold changes induction calculation key.title = "", margins = c(8, 10), # matrix of minimun values main = paste("relative FC"," ", titre), min=apply(allks, 2, min ) Rowv=F mat<-matrix(min, length(row.names(allks)),length(colnames(allks)), -corrgram byrow=TRUE) # check mat install.packages("corrgram")