Evolution of Gene Expression Between Closely Related Taxa of Mus

Total Page:16

File Type:pdf, Size:1020Kb

Evolution of Gene Expression Between Closely Related Taxa of Mus Evolution of gene expression between closely related taxa of Mus I n a u g u r a l – D i s s e r t a t i o n zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Universität zu Köln vorgelegt von Christian Voolstra aus Offenbach am Main Köln, 2006 Berichterstatter: Prof. Dr. Diethard Tautz Prof. Dr. Thomas Wiehe Tag der letzten mündlichen Prüfung: 29.05.2006 Table of Contents Table of Contents Table of Contents ........................................................................................................................i Danksagung................................................................................................................................ v Zusammenfassung.....................................................................................................................vi Abstract ...................................................................................................................................viii 1 Introduction ........................................................................................................................ 1 1.1 Microarrays as a tool to study evolution of gene expression ..................................... 1 1.2 Intra-specific transcriptome variation ........................................................................ 2 1.3 Inter-specific transcriptome variation - tempo and mode of transcriptome evolution3 1.4 Transcriptome divergence and speciation.................................................................. 4 1.5 The genetic basis of gene expression differences ...................................................... 5 1.6 Model/study system – the Mus species complex ....................................................... 6 1.7 Project description...................................................................................................... 8 2 Material & Methods........................................................................................................... 9 2.1 Animals ...................................................................................................................... 9 2.2 Microarrays .............................................................................................................. 11 2.3 Sample preparation................................................................................................... 11 2.4 Hybridization............................................................................................................ 11 2.5 Data Acquistion........................................................................................................ 12 2.6 Experimental design................................................................................................. 12 2.7 Genomic DNA hybridizations.................................................................................. 13 2.8 Data processing and statistical analyses................................................................... 13 2.9 Identification of differentially expressed genes with SAM (Significance Analysis of Microarrays) ......................................................................................................................... 13 i Table of Contents 2.10 Identification of lineage-specific genes of subspecies of Mus musculus................. 14 2.11 Mitochondrial D-loop sequencing............................................................................ 14 2.12 Confirmation of differentially expressed genes from the SAM analyses with quantitative real-time PCR (qRT-PCR) ............................................................................... 15 2.13 Identification of functional categories within lists of differentially expressed genes with DAVID (Database for Annotation, Visualization and Integrated Discovery).............. 16 2.14 Identification of biological processes within lists of differentially expressed genes with PANTHER (Protein ANalysis THrough Evolutionary Relationships)......................... 17 2.15 Scaled divergence analysis....................................................................................... 17 2.16 dN/dS analysis.......................................................................................................... 18 3 Results .............................................................................................................................. 19 3.1 Differentially expressed genes between species and subspecies of Mus ................. 19 3.1.1 Genomic DNA hybridizations (CGHs)............................................................ 19 3.1.2 SAM (Significance Analysis of Microarrays) analysis – number of differentially expressed genes between species/subspecies for the different tissues....... 21 3.1.2.1 Gene expression differences between Mus musculus and Mus spretus ....... 21 3.1.2.2 Gene expression differences between subspecies of Mus musculus............ 22 3.1.2.3 Comparison of gene expression differences between species and subspecies of Mus ...................................................................................................................... 22 3.1.2.4 Distribution of fold-changes (FC) among significantly differentially expressed genes............................................................................................................ 26 3.1.2.5 Lineage-specific genes in Mus musculus ..................................................... 29 3.2 Confirmation of SAM candidate genes with quantitative real-time PCR (qRT-PCR) .................................................................................................................................. 32 3.2.1 Genes differentially expressed between Mus spretus and Mus musculus........ 32 3.2.2 Genes differentially expressed between subspecies of Mus musculus............. 36 3.3 Identification of functional categories within lists of differentially expressed genes with DAVID (Database for Annotation, Visualization and Integrated Discovery).............. 37 3.3.1 Categories of functional classification within the list of differentially expressed genes identified in the brain ............................................................................................. 38 ii Table of Contents 3.3.2 Categories of functional classification within the list of differentially expressed genes identified in the liver/kidney .................................................................................. 40 3.3.3 Categories of functional classification within the list of differentially expressed genes identified in the testis ............................................................................................. 42 3.4 Identification of biological processes within lists of differentially expressed genes with PANTHER (Protein ANalysis THrough Evolutionary Relationships)......................... 44 3.5 Genome-wide patterns of expression divergence..................................................... 47 3.5.1 Scaled divergence analysis............................................................................... 47 3.5.2 dN/dS analysis.................................................................................................. 53 3.5.2.1 Distribution of dN/dS ratios ......................................................................... 55 3.5.2.2 Patterns of correlation of evolution of gene expression and sequence evolution ...................................................................................................................... 55 4 DISCUSSION .................................................................................................................. 61 4.1 Gene expression differences between species and subspecies of Mus..................... 61 4.2 Taxonomic status of Mus musculus ssp. .................................................................. 62 4.3 Confirmation of differentially expressed genes with qRT-PCR .............................. 63 4.4 Functional annotation of candidate genes ................................................................ 64 4.5 Correlations of expression divergence and nucleotide sequence divergence .......... 66 4.6 Towards evolutionary patterns of species divergence/speciation regarding gene expression divergence .......................................................................................................... 67 5 Literature .......................................................................................................................... 71 6 Appendix .......................................................................................................................... 77 6.1 Differentially expressed genes between Mus spretus and Mus musculus (SAM 2- class unpaired analysis, 200 permutations, FDR < 5%)....................................................... 77 6.1.1 brain – over-expressed genes in Mus spretus................................................... 77 6.1.2 brain – over-expressed genes in Mus musculus ............................................... 78 6.1.3 liver/kidney – over-expressed genes in Mus spretus........................................ 78 6.1.4 liver/kidney – over-expressed genes in Mus musculus .................................... 81 6.1.5 testis – over-expressed genes in Mus spretus................................................... 85 iii Table of Contents 6.1.6 testis – over-expressed genes in Mus musculus ............................................... 89 6.2 Differentially expressed genes across subspecies of Mus
Recommended publications
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • Molecular Basis for the Distinct Cellular Functions of the Lsm1-7 and Lsm2-8 Complexes
    bioRxiv preprint doi: https://doi.org/10.1101/2020.04.22.055376; this version posted April 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Molecular basis for the distinct cellular functions of the Lsm1-7 and Lsm2-8 complexes Eric J. Montemayor1,2, Johanna M. Virta1, Samuel M. Hayes1, Yuichiro Nomura1, David A. Brow2, Samuel E. Butcher1 1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. 2Department of Biomolecular Chemistry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. Correspondence should be addressed to E.J.M. ([email protected]) and S.E.B. ([email protected]). Abstract Eukaryotes possess eight highly conserved Lsm (like Sm) proteins that assemble into circular, heteroheptameric complexes, bind RNA, and direct a diverse range of biological processes. Among the many essential functions of Lsm proteins, the cytoplasmic Lsm1-7 complex initiates mRNA decay, while the nuclear Lsm2-8 complex acts as a chaperone for U6 spliceosomal RNA. It has been unclear how these complexes perform their distinct functions while differing by only one out of seven subunits. Here, we elucidate the molecular basis for Lsm-RNA recognition and present four high-resolution structures of Lsm complexes bound to RNAs. The structures of Lsm2-8 bound to RNA identify the unique 2′,3′ cyclic phosphate end of U6 as a prime determinant of specificity. In contrast, the Lsm1-7 complex strongly discriminates against cyclic phosphates and tightly binds to oligouridylate tracts with terminal purines.
    [Show full text]
  • Genome-Wide Analysis of Transcriptional Bursting-Induced Noise in Mammalian Cells
    bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. 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. Title: Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells Authors: Hiroshi Ochiai1*, Tetsutaro Hayashi2, Mana Umeda2, Mika Yoshimura2, Akihito Harada3, Yukiko Shimizu4, Kenta Nakano4, Noriko Saitoh5, Hiroshi Kimura6, Zhe Liu7, Takashi Yamamoto1, Tadashi Okamura4,8, Yasuyuki Ohkawa3, Itoshi Nikaido2,9* Affiliations: 1Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-0046, Japan 2Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama, 351-0198, Japan 3Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-0054, Japan 4Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 5Division of Cancer Biology, The Cancer Institute of JFCR, Tokyo, 135-8550, Japan 6Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8503, Japan 7Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA 8Section of Animal Models, Department of Infectious Diseases, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 9Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako, 351-0198, Japan *Corresponding authors Corresponding authors e-mail addresses Hiroshi Ochiai, [email protected] Itoshi Nikaido, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019.
    [Show full text]
  • Role of Glypican-6 and Ng2 As Metastasis Promoting Factors
    UNIVERSITA’ DEGLI STUDI DI PARMA Dottorato di ricerca in Fisiopatologia Sistemica Ciclo XX ROLE OF GLYPICAN-6 AND NG2 AS METASTASIS PROMOTING FACTORS Coordinatore: Chiar.mo Prof. Ezio Musso Tutor: Chiar.mo Prof.Roberto Perris Dottoranda: Katia Lacrima Anni Accademici 2005-2008 To Indy L'anima libera e' rara, ma quando la vedi la riconosci: soprattutto perché provi un senso di benessere, quando gli sei vicino. (Charles Bukowski ) Index Summary ……………………………………………………..................................................... 3 1. Introduction …………………………………………………………………………………… 5 1.1. Proteoglycans (PGs)………………………………………………………………......... 6 1.2. Membrane associated proteoglycans……………………………………………..…... 8 1.3. Syndecans……………………………………………………………………………..…. 9 1.4. Glypicans……………………………………………………………………………...….. 11 1.5. GPC6……………………………………………………………………………….…….. 13 1.6. NG2/CSPG4……………………………………………………………………….…….. 14 1.7. Metastasis………………………………………………………………………….….…. 16 1.8. Soft Tissue Sarcoma (STS)…………………………………………………………….. 17 1.9. Membrane PGs and tumour……………………………………………………………. 18 1.10. Membrane PGs in sarcoma…………………………………………………………….. 24 2. Material and Methods ……………………………………………………………………… 26 2.1. Cell Culture……………………………………………………………………….………. 27 2.2. RNA extraction……………………………………………………………………….…. 28 2.3. Real Time quantitative PCR……………………………………………….………….. 28 2.4. DNA extraction…………………………………………….……………………………. 30 2.5. Plasmids and Transfection………………………………………….………………… 30 2.6. Western Blotting………………………………………………………………………... 31 2.7. Preparation of ECM substrates………………………………………….……………
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]
  • UNIVERSITY of CALIFORNIA RIVERSIDE Investigations Into The
    UNIVERSITY OF CALIFORNIA RIVERSIDE Investigations into the Role of TAF1-mediated Phosphorylation in Gene Regulation A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Cell, Molecular and Developmental Biology by Brian James Gadd December 2012 Dissertation Committee: Dr. Xuan Liu, Chairperson Dr. Frank Sauer Dr. Frances M. Sladek Copyright by Brian James Gadd 2012 The Dissertation of Brian James Gadd is approved Committee Chairperson University of California, Riverside Acknowledgments I am thankful to Dr. Liu for her patience and support over the last eight years. I am deeply indebted to my committee members, Dr. Frank Sauer and Dr. Frances Sladek for the insightful comments on my research and this dissertation. Thanks goes out to CMDB, especially Dr. Bachant, Dr. Springer and Kathy Redd for their support. Thanks to all the members of the Liu lab both past and present. A very special thanks to the members of the Sauer lab, including Silvia, Stephane, David, Matt, Stephen, Ninuo, Toby, Josh, Alice, Alex and Flora. You have made all the years here fly by and made them so enjoyable. From the Sladek lab I want to thank Eugene, John, Linh and Karthi. Special thanks go out to all the friends I’ve made over the years here. Chris, Amber, Stephane and David, thank you so much for feeding me, encouraging me and keeping me sane. Thanks to the brothers for all your encouragement and prayers. To any I haven’t mentioned by name, I promise I haven’t forgotten all you’ve done for me during my graduate years.
    [Show full text]
  • 1 Mutational Heterogeneity in Cancer Akash Kumar a Dissertation
    Mutational Heterogeneity in Cancer Akash Kumar A dissertation Submitted in partial fulfillment of requirements for the degree of Doctor of Philosophy University of Washington 2014 June 5 Reading Committee: Jay Shendure Pete Nelson Mary Claire King Program Authorized to Offer Degree: Genome Sciences 1 University of Washington ABSTRACT Mutational Heterogeneity in Cancer Akash Kumar Chair of the Supervisory Committee: Associate Professor Jay Shendure Department of Genome Sciences Somatic mutation plays a key role in the formation and progression of cancer. Differences in mutation patterns likely explain much of the heterogeneity seen in prognosis and treatment response among patients. Recent advances in massively parallel sequencing have greatly expanded our capability to investigate somatic mutation. Genomic profiling of tumor biopsies could guide the administration of targeted therapeutics on the basis of the tumor’s collection of mutations. Central to the success of this approach is the general applicability of targeted therapies to a patient’s entire tumor burden. This requires a better understanding of the genomic heterogeneity present both within individual tumors (intratumoral) and amongst tumors from the same patient (intrapatient). My dissertation is broadly organized around investigating mutational heterogeneity in cancer. Three projects are discussed in detail: analysis of (1) interpatient and (2) intrapatient heterogeneity in men with disseminated prostate cancer, and (3) investigation of regional intratumoral heterogeneity in
    [Show full text]
  • Structure and Mechanism of the RNA Polymerase II Transcription Machinery
    Downloaded from genesdev.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Structure and mechanism of the RNA polymerase II transcription machinery Allison C. Schier and Dylan J. Taatjes Department of Biochemistry, University of Colorado, Boulder, Colorado 80303, USA RNA polymerase II (Pol II) transcribes all protein-coding ingly high resolution, which has rapidly advanced under- genes and many noncoding RNAs in eukaryotic genomes. standing of the molecular basis of Pol II transcription. Although Pol II is a complex, 12-subunit enzyme, it lacks Structural biology continues to transform our under- the ability to initiate transcription and cannot consistent- standing of complex biological processes because it allows ly transcribe through long DNA sequences. To execute visualization of proteins and protein complexes at or near these essential functions, an array of proteins and protein atomic-level resolution. Combined with mutagenesis and complexes interact with Pol II to regulate its activity. In functional assays, structural data can at once establish this review, we detail the structure and mechanism of how enzymes function, justify genetic links to human dis- over a dozen factors that govern Pol II initiation (e.g., ease, and drive drug discovery. In the past few decades, TFIID, TFIIH, and Mediator), pausing, and elongation workhorse techniques such as NMR and X-ray crystallog- (e.g., DSIF, NELF, PAF, and P-TEFb). The structural basis raphy have been complemented by cryoEM, cross-linking for Pol II transcription regulation has advanced rapidly mass spectrometry (CXMS), and other methods. Recent in the past decade, largely due to technological innova- improvements in data collection and imaging technolo- tions in cryoelectron microscopy.
    [Show full text]
  • Cpg Islands and the Regulation of Transcription
    Downloaded from genesdev.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW CpG islands and the regulation of transcription Aime´e M. Deaton and Adrian Bird1 The Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3JR, United Kingdom Vertebrate CpG islands (CGIs) are short interspersed DNA In spite of their link with transcription, the functional sequences that deviate significantly from the average significance of CGIs is only just beginning to emerge. CGI genomic pattern by being GC-rich, CpG-rich, and pre- promoters turn out to have distinctive patterns of tran- dominantly nonmethylated. Most, perhaps all, CGIs are scription initiation and chromatin configuration. Their sites of transcription initiation, including thousands that regulation involves proteins (some of which specifically are remote from currently annotated promoters. Shared bind nonmethylated CpG) that influence the modifica- DNA sequence features adapt CGIs for promoter function tion status of CGI chromatin. In addition, the CpG moieties by destabilizing nucleosomes and attracting proteins that themselves are sometimes subject to cytosine methylation, create a transcriptionally permissive chromatin state. which correlates with stable shutdown of the associated Silencing of CGI promoters is achieved through dense promoter. Here we examine the properties shared by ver- CpG methylation or polycomb recruitment, again using tebrate CGIs and how transcription is regulated at these their distinctive DNA sequence composition. CGIs are sites. Recent related reviews include Illingworth and Bird therefore generically equipped to influence local chroma- (2009), Mohn and Schubeler (2009), and Blackledge and tin structure and simplify regulation of gene activity. Klose (2011). Vertebrate genomes are methylated predominantly at the Evolutionary conservation of CGIs dinucleotide CpG, and consequently are CpG-deficient owing to the mutagenic properties of methylcytosine CGIs are distinct in vertebrates due to their lack of DNA (Coulondre et al.
    [Show full text]
  • Identification of Molecular Targets in Head and Neck Squamous Cell Carcinomas Based on Genome-Wide Gene Expression Profiling
    1489-1497 7/11/07 18:41 Page 1489 ONCOLOGY REPORTS 18: 1489-1497, 2007 Identification of molecular targets in head and neck squamous cell carcinomas based on genome-wide gene expression profiling SATOYA SHIMIZU1,2, NAOHIKO SEKI2, TAKASHI SUGIMOTO2, SHIGETOSHI HORIGUCHI1, HIDEKI TANZAWA3, TOYOYUKI HANAZAWA1 and YOSHITAKA OKAMOTO1 Departments of 1Otorhinolaryngology, 2Functional Genomics and 3Clinical Molecular Biology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Japan Received May 21, 2007; Accepted June 28, 2007 Abstract. DNA amplifications activate oncogenes and are patients and metastases develop in 15-25% of patients (1). hallmarks of nearly all advanced cancers including head and Many factors, such as TNM stage, pathological grade and neck squamous cell carcinoma (HNSCC). Some oncogenes tumor site, influence the prognosis of HNSCC but are not show both DNA copy number gain and mRNA overexpression. sufficient to predict outcome. In addition, treatment often Chromosomal comparative genomic hybridization and oligo- results in impairment of functions such as speech and nucleotide microarrays were used to examine 8 HNSCC cell swallowing, cosmetic disfiguration and mental pain. These lines and a plot of gene expression levels relative to their inflictions significantly erode quality of life. To overcome this position on the chromosome was produced. Three highly situation, there is a need to find novel biomarkers that classify up-regulated genes, NT5C3, ANLN and INHBA, were patients into prognostic groups, to aid identification of high- identified on chromosome 7p14. These genes were subjected risk patients who may benefit from different treatments. to quantitative real-time RT-PCR on cDNA and genomic Comparative genomic hybridization (CGH) has facilitated DNA derived from 8 HNSCC cell lines.
    [Show full text]
  • Histone H3Q5 Serotonylation Stabilizes H3K4 Methylation and Potentiates Its Readout
    Histone H3Q5 serotonylation stabilizes H3K4 methylation and potentiates its readout Shuai Zhaoa,1, Kelly N. Chuhb,1, Baichao Zhanga,1, Barbara E. Dulb, Robert E. Thompsonb, Lorna A. Farrellyc,d, Xiaohui Liue, Ning Xue, Yi Xuee, Robert G. Roederf, Ian Mazec,d,2, Tom W. Muirb,2, and Haitao Lia,g,2 aMinistry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China; bDepartment of Chemistry, Princeton University, Princeton, NJ 08540; cNash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029; dDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029; eNational Protein Science Technology Center, School of Life Sciences, Tsinghua University, Beijing 100084, China; fLaboratory of Biochemistry and Molecular Biology, The Rockefeller University, New York, NY 10065; and gTsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing 100084, China Edited by Peter Cheung, York University, Toronto, ON, Canada, and accepted by Editorial Board Member Karolin Luger November 12, 2020 (received for review August 7, 2020) Serotonylation of glutamine 5 on histone H3 (H3Q5ser) was recently established that H3K4me3 regulates gene expression through re- identified as a permissive posttranslational modification that coexists cruitment of nuclear factors that contain reader domains specific with adjacent lysine 4 trimethylation (H3K4me3). While the resulting for the mark. These include ATP-dependent chromatin remod- dual modification, H3K4me3Q5ser, is enriched at regions of active eling enzymes, as well as several transcription factors (13).
    [Show full text]
  • CR Cistrome: a Chip-Seq Database for Chromatin Regulators and Histone Modification Linkages in Human and Mouse
    CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Wang, Q., J. Huang, H. Sun, J. Liu, J. Wang, Q. Wang, Q. Qin, et al. 2013. “CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse.” Nucleic Acids Research 42 (D1): D450-D458. doi:10.1093/nar/gkt1151. http:// dx.doi.org/10.1093/nar/gkt1151. Published Version doi:10.1093/nar/gkt1151 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:12064382 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA D450–D458 Nucleic Acids Research, 2014, Vol. 42, Database issue Published online 18 November 2013 doi:10.1093/nar/gkt1151 CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse Qixuan Wang1, Jinyan Huang1, Hanfei Sun1, Jing Liu1, Juan Wang1, Qian Wang1, Qian Qin1, Shenglin Mei1, Chengchen Zhao1, Xiaoqin Yang1, X. Shirley Liu2,* and Yong Zhang1,* 1Shanghai Key Laboratory of Signaling and Disease Research, School of Life Science and Technology, Tongji University, Shanghai 200092, China and 2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard school of Public Health, 450 Brookline Avenue, Boston, MA 02215, USA Received July 24, 2013; Revised October 17, 2013; Accepted October 26, 2013 ABSTRACT composed of an octamer of core histones (two copies of H2A, H2B, H3 and H4) and 146 DNA base pairs of DNA Diversified histone modifications (HMs) are essential wrapped around the histone octamer (1).
    [Show full text]