DOCSLIB.ORG

Protein Structure-Guided Approaches to Identify Functional Mutations in Cancer Sohini Sengupta Washington University in St

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Protein Structure-Guided Approaches to Identify Functional Mutations in Cancer Sohini Sengupta Washington University in St Washington University in St. Louis Washington University Open Scholarship Arts & Sciences Electronic Theses and Dissertations Arts & Sciences Winter 12-15-2018 Protein Structure-Guided Approaches to Identify Functional Mutations in Cancer Sohini Sengupta Washington University in St. Louis Follow this and additional works at: https://openscholarship.wustl.edu/art_sci_etds Part of the Bioinformatics Commons Recommended Citation Sengupta, Sohini, "Protein Structure-Guided Approaches to Identify Functional Mutations in Cancer" (2018). Arts & Sciences Electronic Theses and Dissertations. 1688. https://openscholarship.wustl.edu/art_sci_etds/1688 This Dissertation is brought to you for free and open access by the Arts & Sciences at Washington University Open Scholarship. It has been accepted for inclusion in Arts & Sciences Electronic Theses and Dissertations by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS Division of Biology and Biomedical Sciences Computational and Systems Biology Dissertation Examination Committee: Li Ding, Chair Greg Bowman Barak Cohen Cynthia Ma Chris Maher Protein Structure-Guided Approaches to Identify Functional Mutations in Cancer by Sohini Sengupta A dissertation presented to the Graduate School of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy December 2018 St. Louis, Missouri © 2018, Sohini Sengupta Table of Contents Acknowledgments ................................................................................................................... v Abstract of the Dissertation .................................................................................................... ix Chapter 1: Introduction .......................................................................................................... 1 1.1 Existing computational methods to identify driver mutations ..................................................... 3 1.1.1 Shortcomings of Current Computational Methods .................................................................... 7 Chapter 2: HotSpot3D: A computational algorithm to identify intra- and inter-molecular mutation clusters in protein structure ................................................................................... 11 Preface ........................................................................................................................................... 11 2.1 Abstract .................................................................................................................................... 13 2.2 Introduction ............................................................................................................................. 14 2.3 Results ...................................................................................................................................... 15 2.3.1 Intra- and inter-mutation clusters across 19 cancer types ....................................................... 15 2.3.2 Significant mutation clusters with cancer type specificity ........................................................ 17 2.3.3 Rare and medium recurrence functional mutation discovery .................................................. 18 2.3.4 Validation by protein array and functional experiment ........................................................... 20 2.3.5 Mutation-drug networks and clinical implications ................................................................... 23 2.4 Discussion ................................................................................................................................ 26 2.5 Methods ................................................................................................................................... 27 2.5.1 HotSpot3D and code comparison ............................................................................................. 28 2.5.2 Data preprocessing ................................................................................................................... 28 2.5.3 3D proximal pairs analysis ........................................................................................................ 29 2.5.4 Drug interaction module .......................................................................................................... 31 2.5.5 Cancer mutation data set and cancer types ............................................................................. 31 2.5.6 Identifying mutation and drug-mutation clusters .................................................................... 32 2.5.7 Prioritizing clusters with high cluster closeness ....................................................................... 33 2.5.8 Cluster conservation score ....................................................................................................... 34 2.5.9 Cluster validation ...................................................................................................................... 35 2.5.10 Mutation and drug annotations ............................................................................................. 36 2.5.11 Prioritized variant list for functional validation ...................................................................... 37 2.5.12 Software engineering aspects ................................................................................................ 37 2.6 Supplementary Note ................................................................................................................ 39 2.6.1 Performance assessment and comparison to existing tools .................................................... 39 2.6.2 Intra- and inter-mutation clusters across 19 cancer types ....................................................... 40 2.6.3 Significant mutation clusters with cancer type specificity ........................................................ 40 ii 2.6.4 Mutation-drug networks and clinical implications ................................................................... 41 2.6.5 SUPPLEMENTARY REFERENCES ................................................................................................ 42 2.7 Figures ...................................................................................................................................... 43 Table 1. Top (cluster closeness > 2.5) drug-mutation clusters with HGNC gene families and drug classifications from NIH and DrugBank. .......................................................................................... 56 References ..................................................................................................................................... 58 Chapter 3: Integrative Omics Analyses Broadens Treatment Targets in Human Cancer .......... 61 Preface ........................................................................................................................................... 61 3.2 Abstract .................................................................................................................................... 63 3.3 Background .............................................................................................................................. 64 3.4 Methods ................................................................................................................................... 66 3.4.1 Construction of Database of Evidence for Precision Oncology (DEPO) .................................... 66 3.4.2 Pan-Cancer Cohort and Cancer Types ...................................................................................... 67 3.4.3 Collection of Mutations in Pan-Cancer Cohort ......................................................................... 68 3.4.4 Drug-associated Mutations in Pan-Cancer Cohort ................................................................... 68 3.4.5 Proximity-Based Clustering of Drug-associated Mutations with Pan-Cancer Cohort ............... 70 3.4.6 Druggable Expression Outliers in Pan-Cancer Cohort ............................................................... 71 3.4.7 Fusion Analysis .......................................................................................................................... 72 3.4.8 Proteomic Analysis with CPTAC Mass-Spectrometry Data ....................................................... 72 3.4.9 Cell Line Based Validation ......................................................................................................... 73 3.4.10 Experimental Validation ......................................................................................................... 75 3.4.11 Integrative Omics Analysis of Druggability ............................................................................. 76 3.4.12 Druggability and Demographics .............................................................................................. 76 3.5 Results ...................................................................................................................................... 77 3.5.1 Database of Evidence for Precision Oncology .......................................................................... 77 3.5.2 Drug-associated Mutations in Pan-Cancer Cohort ................................................................... 78 3.5.3 Protein Structure-Based Clustering of Drug-Associated Mutations ......................................... 81 3.5.4 Druggable Gene and Protein
Load more

Recommended publications
  • Histone Isoform H2A1H Promotes Attainment of Distinct Physiological
    Bhattacharya et al. Epigenetics & Chromatin (2017) 10:48 DOI 10.1186/s13072-017-0155-z Epigenetics & Chromatin RESEARCH Open Access Histone isoform H2A1H promotes attainment of distinct physiological states by altering chromatin dynamics Saikat Bhattacharya1,4,6, Divya Reddy1,4, Vinod Jani5†, Nikhil Gadewal3†, Sanket Shah1,4, Raja Reddy2,4, Kakoli Bose2,4, Uddhavesh Sonavane5, Rajendra Joshi5 and Sanjay Gupta1,4* Abstract Background: The distinct functional efects of the replication-dependent histone H2A isoforms have been dem- onstrated; however, the mechanistic basis of the non-redundancy remains unclear. Here, we have investigated the specifc functional contribution of the histone H2A isoform H2A1H, which difers from another isoform H2A2A3 in the identity of only three amino acids. Results: H2A1H exhibits varied expression levels in diferent normal tissues and human cancer cell lines (H2A1C in humans). It also promotes cell proliferation in a context-dependent manner when exogenously overexpressed. To uncover the molecular basis of the non-redundancy, equilibrium unfolding of recombinant H2A1H-H2B dimer was performed. We found that the M51L alteration at the H2A–H2B dimer interface decreases the temperature of melting of H2A1H-H2B by ~ 3 °C as compared to the H2A2A3-H2B dimer. This diference in the dimer stability is also refected in the chromatin dynamics as H2A1H-containing nucleosomes are more stable owing to M51L and K99R substitu- tions. Molecular dynamic simulations suggest that these substitutions increase the number of hydrogen bonds and hydrophobic interactions of H2A1H, enabling it to form more stable nucleosomes. Conclusion: We show that the M51L and K99R substitutions, besides altering the stability of histone–histone and histone–DNA complexes, have the most prominent efect on cell proliferation, suggesting that the nucleosome sta- bility is intimately linked with the physiological efects observed.
    [Show full text]
  • G Protein Alpha 13 (GNA13) (NM 006572) Human Tagged ORF Clone Lentiviral Particle Product Data
    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 RC207762L3V G protein alpha 13 (GNA13) (NM_006572) Human Tagged ORF Clone Lentiviral Particle Product data: Product Type: Lentiviral Particles Product Name: G protein alpha 13 (GNA13) (NM_006572) Human Tagged ORF Clone Lentiviral Particle Symbol: GNA13 Synonyms: G13 Vector: pLenti-C-Myc-DDK-P2A-Puro (PS100092) ACCN: NM_006572 ORF Size: 1131 bp ORF Nucleotide The ORF insert of this clone is exactly the same as(RC207762). Sequence: OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_006572.3 RefSeq Size: 4744 bp RefSeq ORF: 1134 bp Locus ID: 10672 UniProt ID: Q14344, A0A024R8M0 Domains: G-alpha Protein Families: Druggable Genome Protein Pathways: Long-term depression, Regulation of actin cytoskeleton, Vascular smooth muscle contraction MW: 44 kDa 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 / 2 G protein alpha 13 (GNA13) (NM_006572) Human Tagged ORF Clone Lentiviral Particle – RC207762L3V Gene Summary: Guanine nucleotide-binding proteins (G proteins) are involved as modulators or transducers in various transmembrane signaling systems (PubMed:15240885, PubMed:16787920, PubMed:16705036, PubMed:27084452).
    [Show full text]
  • Histone Variants: Deviants?
    Downloaded from genesdev.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Histone variants: deviants? Rohinton T. Kamakaka2,3 and Sue Biggins1 1Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA; 2UCT/National Institutes of Health, Bethesda, Maryland 20892, USA Histones are a major component of chromatin, the pro- sealing the two turns of DNA. The nucleosome filament tein–DNA complex fundamental to genome packaging, is then folded into a 30-nm fiber mediated in part by function, and regulation. A fraction of histones are non- nucleosome–nucleosome interactions, and this fiber is allelic variants that have specific expression, localiza- probably the template for most nuclear processes. Addi- tion, and species-distribution patterns. Here we discuss tional levels of compaction enable these fibers to be recent progress in understanding how histone variants packaged into the small volume of the nucleus. lead to changes in chromatin structure and dynamics to The packaging of DNA into nucleosomes and chroma- carry out specific functions. In addition, we review his- tin positively or negatively affects all nuclear processes tone variant assembly into chromatin, the structure of in the cell. While nucleosomes have long been viewed as the variant chromatin, and post-translational modifica- stable entities, there is a large body of evidence indicat- tions that occur on the variants. ing that they are highly dynamic (for review, see Ka- makaka 2003), capable of being altered in their compo- Supplemental material is available at http://www.genesdev.org. sition, structure, and location along the DNA. Enzyme Approximately two meters of human diploid DNA are complexes that either post-translationally modify the packaged into the cell’s nucleus with a volume of ∼1000 histones or alter the position and structure of the nucleo- µm3.
    [Show full text]
  • PLXNB1 (Plexin
    Atlas of Genetics and Cytogenetics in Oncology and Haematology OPEN ACCESS JOURNAL AT INIST-CNRS Gene Section Mini Review PLXNB1 (plexin B1) José Javier Gómez-Román, Montserrat Nicolas Martínez, Servando Lazuén Fernández, José Fernando Val-Bernal Department of Anatomical Pathology, Marques de Valdecilla University Hospital, Medical Faculty, University of Cantabria, Santander, Spain (JJGR, MN, SL, JFVB) Published in Atlas Database: March 2009 Online updated version: http://AtlasGeneticsOncology.org/Genes/PLXNB1ID43413ch3p21.html DOI: 10.4267/2042/44702 This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 France Licence. © 2010 Atlas of Genetics and Cytogenetics in Oncology and Haematology Identity Pseudogene No. Other names: KIAA0407; MGC149167; OTTHUMP00000164806; PLEXIN-B1; PLXN5; SEP Protein HGNC (Hugo): PLXNB1 Location: 3p21.31 Description Local order: The Plexin B1 gene is located between 2135 Amino acids (AA). Plexins are receptors for axon FBXW12 and CCDC51 genes. molecular guidance molecules semaphorins. Plexin signalling is important in pathfinding and patterning of both neurons and developing blood vessels. Plexin-B1 is a surface cell receptor. When it binds to its ligand SEMA4D it activates several pathways by binding of cytoplasmic ligands, like RHOA activation and subsequent changes of the actin cytoskeleton, axon guidance, invasive growth and cell migration. It monomers and heterodimers with PLXNB2 after proteolytic processing. Binds RAC1 that has been activated by GTP binding. It binds PLXNA1 and by similarity ARHGEF11, Note ARHGEF12, ERBB2, MET, MST1R, RND1, NRP1 Size: 26,200 bases. and NRP2. Orientation: minus strand. This family features the C-terminal regions of various plexins. The cytoplasmic region, which has been called DNA/RNA a SEX domain in some members of this family is involved in downstream signalling pathways, by Description interaction with proteins such as Rac1, RhoD, Rnd1 and other plexins.
    [Show full text]
  • Pathway-Based Genome-Wide Association Analysis of Coronary Heart Disease Identifies Biologically Important Gene Sets
    European Journal of Human Genetics (2012) 20, 1168–1173 & 2012 Macmillan Publishers Limited All rights reserved 1018-4813/12 www.nature.com/ejhg ARTICLE Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets Lisa de las Fuentes1,4, Wei Yang2,4, Victor G Da´vila-Roma´n1 and C Charles Gu*,2,3 Genome-wide association (GWA) studies of complex diseases including coronary heart disease (CHD) challenge investigators attempting to identify relevant genetic variants among hundreds of thousands of markers being tested. A selection strategy based purely on statistical significance will result in many false negative findings after adjustment for multiple testing. Thus, an integrated analysis using information from the learned genetic pathways, molecular functions, and biological processes is desirable. In this study, we applied a customized method, variable set enrichment analysis (VSEA), to the Framingham Heart Study data (404 467 variants, n ¼ 6421) to evaluate enrichment of genetic association in 1395 gene sets for their contribution to CHD. We identified 25 gene sets with nominal Po0.01; at least four sets are previously known for their roles in CHD: vascular genesis (GO:0001570), fatty-acid biosynthetic process (GO:0006633), fatty-acid metabolic process (GO:0006631), and glycerolipid metabolic process (GO:0046486). Although the four gene sets include 170 genes, only three of the genes contain a variant ranked among the top 100 in single-variant association tests of the 404 467 variants tested. Significant enrichment for novel gene sets less known for their importance to CHD were also identified: Rac 1 cell-motility signaling pathway (h_rac1 Pathway, Po0.001) and sulfur amino-acid metabolic process (GO:0000096, Po0.001).
    [Show full text]
  • The U2AF1S34F Mutation Induces Lineage-Specific Splicing Alterations in Myelodysplastic Syndromes
    RESEARCH ARTICLE The Journal of Clinical Investigation The U2AF1S34F mutation induces lineage-specific splicing alterations in myelodysplastic syndromes Bon Ham Yip,1 Violetta Steeples,1 Emmanouela Repapi,2 Richard N. Armstrong,1 Miriam Llorian,3 Swagata Roy,1 Jacqueline Shaw,1 Hamid Dolatshad,1 Stephen Taylor,2 Amit Verma,4 Matthias Bartenstein,4 Paresh Vyas,5 Nicholas C.P. Cross,6 Luca Malcovati,7 Mario Cazzola,7 Eva Hellström-Lindberg,8 Seishi Ogawa,9 Christopher W.J. Smith,3 Andrea Pellagatti,1 and Jacqueline Boultwood1 1Bloodwise Molecular Haematology Unit, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, and BRC Blood Theme, National Institute for Health Research (NIHR) Oxford Biomedical Centre, Oxford University Hospital, Oxford, United Kingdom. 2The Computational Biology Research Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom. 3Department of Biochemistry, Downing Site, University of Cambridge, Cambridge, United Kingdom. 4Albert Einstein College of Medicine, Bronx, New York, USA. 5Medical Research Council, Molecular Hematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, and Department of Hematology, Oxford University Hospital National Health Service Trust, Oxford, United Kingdom. 6Faculty of Medicine, University of Southampton, Southampton, and National Genetics Reference Laboratory (Wessex), Salisbury, United Kingdom. 7Fondazione IRCCS Policlinico San Matteo and University of Pavia, Pavia, Italy. 8Center for Hematology and Regenerative Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden. 9Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan. Mutations of the splicing factor–encoding gene U2AF1 are frequent in the myelodysplastic syndromes (MDS), a myeloid malignancy, and other cancers. Patients with MDS suffer from peripheral blood cytopenias, including anemia, and an increasing percentage of bone marrow myeloblasts.
    [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]
  • Differences for Ectopic Versus Eutopic Cells
    556 RBMO VOLUME 39 ISSUE 4 2019 ARTICLE Chemosensitivity and chemoresistance in endometriosis – differences for ectopic versus eutopic cells BIOGRAPHY Andres Salumets is Professor of Reproductive Medicine at the University of Tartu, and Scientific Head at the Competence Centre on Health Technologies, Tartu, Estonia. He has been involved in assisted reproduction for 20 years, first as an embryologist and later as a researcher. His major interests are endometriosis, endometrial biology and implantation. Darja Lavogina1,2,*, Külli Samuel1, Arina Lavrits1,3, Alvin Meltsov1, Deniss Sõritsa1,4,5, Ülle Kadastik6, Maire Peters1,4, Ago Rinken2, Andres Salumets1,4,7, 8 KEY MESSAGE Akt/PKB inhibitor GSK690693, CK2 inhibitor ARC-775, MAPK pathway inhibitor sorafenib, proteasome inhibitor bortezomib, and microtubule-depolymerizing toxin MMAE showed higher cytotoxicity in eutopic cells. In contrast, 10 µmol/l of the anthracycline toxin doxorubicin caused cellular death in ectopic cells more effectively than in eutopic cells, underlining the potential of doxorubicin in endometriosis research. ABSTRACT Research question: Endometriosis is a common gynaecological disease defined by the presence of endometrium-like tissue outside the uterus. This complex disease, often accompanied by severe pain and infertility, causes a significant medical and socioeconomic burden; hence, novel strategies are being sought for the treatment of endometriosis. Here, we set out to explore the cytotoxic effects of a panel of compounds to find toxins with different efficiency in eutopic versus ectopic cells, thus highlighting alterations in the corresponding molecular pathways. Design: The effect on cellular viability of 14 compounds was established in a cohort of paired eutopic and ectopic endometrial stromal cell samples from 11 patients.
    [Show full text]
  • Supplementary Table 2 Supplementary Table 1
    Supplementary table 1 Rai/ Binet IGHV Cytogenetic Relative viability Fludarabine- Sex Outcome CD38 (%) IGHV gene ZAP70 (%) Treatment (s) Stage identity (%) abnormalities* increase refractory 1 M 0/A Progressive 14,90 IGHV3-64*05 99,65 28,20 Del17p 18.0% 62,58322819 FCR n.a. 2 F 0/A Progressive 78,77 IGHV3-48*03 100,00 51,90 Del17p 24.8% 77,88052021 FCR n.a. 3 M 0/A Progressive 29,81 IGHV4-b*01 100,00 9,10 Del17p 12.0% 36,48 Len, Chl n.a. 4 M 1/A Stable 97,04 IGHV3-21*01 97,22 18,11 Normal 85,4191657 n.a. n.a. Chl+O, PCR, 5 F 0/A Progressive 87,00 IGHV4-39*07 100,00 43,20 Del13q 68.3% 35,23314039 n.a. HDMP+R 6 M 0/A Progressive 1,81 IGHV3-43*01 100,00 20,90 Del13q 77.7% 57,52490626 Chl n.a. Chl, FR, R-CHOP, 7 M 0/A Progressive 97,80 IGHV1-3*01 100,00 9,80 Del17p 88.5% 48,57389901 n.a. HDMP+R 8 F 2/B Progressive 69,07 IGHV5-a*03 100,00 16,50 Del17p 77.2% 107,9656878 FCR, BA No R-CHOP, FCR, 9 M 1/A Progressive 2,13 IGHV3-23*01 97,22 29,80 Del11q 16.3% 134,5866919 Yes Flavopiridol, BA 10 M 2/A Progressive 0,36 IGHV3-30*02 92,01 0,38 Del13q 81.9% 78,91844953 Unknown n.a. 11 M 2/B Progressive 15,17 IGHV3-20*01 100,00 13,20 Del11q 95.3% 75,52880995 FCR, R-CHOP, BR No 12 M 0/A Stable 0,14 IGHV3-30*02 90,62 7,40 Del13q 13.0% 13,0939004 n.a.
    [Show full text]
  • ARHGEF12 Regulates Erythropoiesis and Is Involved in Erythroid Regeneration After Ferrata Storti Foundation Chemotherapy in Acute Lymphoblastic Leukemia Patients
    Red Cell Biology & its Disorders ARTICLE ARHGEF12 regulates erythropoiesis and is involved in erythroid regeneration after Ferrata Storti Foundation chemotherapy in acute lymphoblastic leukemia patients Yangyang Xie,1* Li Gao,2* Chunhui Xu,3 Liming Chu,3,7 Lei Gao,4 Ruichi Wu,1 Yu Liu,1 Ting Liu,1 Xiao-jian Sun,5 Ruibao Ren,5 Jingyan Tang,1 Yi Zheng,6 Yong Zhou7 and Shuhong Shen1 1Key Lab of Pediatrics Hematology/Oncology, Ministry of Health, Department of Haematologica 2020 Hematology/Oncology, Shanghai Children's Medical Center, Shanghai Jiao Tong University, Volume 105(4):925-936 Shanghai, China; 2Department of Hematology and Oncology, Children's Hospital of Soochow University, Suzhou, China; 3Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; 4CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Beijing, China; 5State Key Laboratory for Medical Genomics, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai, China; 6Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Research Foundation, Cincinnati, OH, USA and 7CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China *YX and LG contributed equally to this work. ABSTRACT ematopoiesis is a finely regulated process in vertebrates under both homeostatic and stress conditions. By whole exome sequencing, we Correspondence: Hstudied the genomics of acute lymphoblastic leukemia (ALL) YI ZHENG patients who needed multiple red blood cell (RBC) transfusions after inten- [email protected] sive chemotherapy treatment. ARHGEF12, encoding a RhoA guanine YONG ZHOU nucleotide exchange factor, was found to be associated with chemothera- [email protected] py-induced anemia by genome-wide association study analyses.
    [Show full text]
  • Identification of Reproduction-Related Gene Polymorphisms Using Whole
    INVESTIGATION Identification of Reproduction-Related Gene Polymorphisms Using Whole Transcriptome Sequencing in the Large White Pig Population Daniel Fischer,* Asta Laiho,† Attila Gyenesei,‡ and Anu Sironen*,1 *Natural Resources Institute Finland (Luke), Green Technology, Animal and Plant Genomics and Breeding, FI-31600 Jokioinen, Finland, †The Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland, and ‡Campus Science Support Facilities, Vienna Biocenter, A-1030 Vienna, Austria ORCID ID: 0000-0003-2064-6960 (D.F.) ABSTRACT Recent developments in high-throughput sequencing techniques have enabled large-scale KEYWORDS analysis of genetic variations and gene expression in different tissues and species, but gene expression oviduct patterns and genetic variations in livestock are not well-characterized. In this study, we have used high- testis throughput transcriptomic sequencing of the Finnish Large White to identify gene expression patterns and gene expression coding polymorphisms within the breed in the testis and oviduct. The main objective of this study was to polymorphism identify polymorphisms within genes that are highly and specifically expressed in male and/or female SNP reproductive organs. The differential expression (DE) analysis underlined 1234 genes highly expressed in pig the testis and 1501 in the oviduct. Furthermore, we used a novel in-house R-package hoardeR for the transcriptome identification of novel genes and their orthologs, which underlined 55 additional DE genes based on RNAseq orthologs in the human, cow, and sheep. Identification of polymorphisms in the dataset resulted in a total of reproduction 29,973 variants, of which 10,704 were known coding variants.
    [Show full text]
  • The UVB-Induced Gene Expression Profile of Human Epidermis in Vivo Is Different from That of Cultured Keratinocytes
    Oncogene (2006) 25, 2601–2614 & 2006 Nature Publishing Group All rights reserved 0950-9232/06 $30.00 www.nature.com/onc ORIGINAL ARTICLE The UVB-induced gene expression profile of human epidermis in vivo is different from that of cultured keratinocytes CD Enk1, J Jacob-Hirsch2, H Gal3, I Verbovetski4, N Amariglio2, D Mevorach4, A Ingber1, D Givol3, G Rechavi2 and M Hochberg1 1Department of Dermatology, The Hadassah-Hebrew University Medical Center, Jerusalem, Israel; 2Department of Pediatric Hemato-Oncology and Functional Genomics, Safra Children’s Hospital, Sheba Medical Center and Sackler School of Medicine, Tel-Aviv University,Tel Aviv, Israel; 3Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel and 4The Laboratory for Cellular and Molecular Immunology, Department of Medicine, The Hadassah-Hebrew University Medical Center, Jerusalem, Israel In order to obtain a comprehensive picture of the radiation. UVB, with a wavelength range between 290 molecular events regulating cutaneous photodamage of and 320 nm, represents one of the most important intact human epidermis, suction blister roofs obtained environmental hazards affectinghuman skin (Hahn after a single dose of in vivo ultraviolet (UV)B exposure and Weinberg, 2002). To protect itself against the were used for microarray profiling. We found a changed DNA-damaging effects of sunlight, the skin disposes expression of 619 genes. Half of the UVB-regulated genes over highly complicated cellular programs, including had returned to pre-exposure baseline levels at 72 h, cell-cycle arrest, DNA repair and apoptosis (Brash et al., underscoring the transient character of the molecular 1996). Failure in selected elements of these defensive cutaneous UVB response.
    [Show full text]