The SSU Processome Interactome in Saccharomyces Cerevisiae Reveals Novel Protein Subcomplexes
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Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
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. -
(TEX) Genes: a Review Focused on Spermatogenesis and Male Fertility
Bellil et al. Basic and Clinical Andrology (2021) 31:9 https://doi.org/10.1186/s12610-021-00127-7 REVIEW ARTICLE Open Access Human testis-expressed (TEX) genes: a review focused on spermatogenesis and male fertility Hela Bellil1, Farah Ghieh2,3, Emeline Hermel2,3, Béatrice Mandon-Pepin2,3 and François Vialard1,2,3* Abstract Spermatogenesis is a complex process regulated by a multitude of genes. The identification and characterization of male-germ-cell-specific genes is crucial to understanding the mechanisms through which the cells develop. The term “TEX gene” was coined by Wang et al. (Nat Genet. 2001; 27: 422–6) after they used cDNA suppression subtractive hybridization (SSH) to identify new transcripts that were present only in purified mouse spermatogonia. TEX (Testis expressed) orthologues have been found in other vertebrates (mammals, birds, and reptiles), invertebrates, and yeasts. To date, 69 TEX genes have been described in different species and different tissues. To evaluate the expression of each TEX/tex gene, we compiled data from 7 different RNA-Seq mRNA databases in humans, and 4 in the mouse according to the expression atlas database. Various studies have highlighted a role for many of these genes in spermatogenesis. Here, we review current knowledge on the TEX genes and their roles in spermatogenesis and fertilization in humans and, comparatively, in other species (notably the mouse). As expected, TEX genes appear to have a major role in reproduction in general and in spermatogenesis in humans but also in all mammals such as the mouse. Most of them are expressed specifically or predominantly in the testis. -
Essential Genes and Their Role in Autism Spectrum Disorder
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Essential Genes And Their Role In Autism Spectrum Disorder Xiao Ji University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, and the Genetics Commons Recommended Citation Ji, Xiao, "Essential Genes And Their Role In Autism Spectrum Disorder" (2017). Publicly Accessible Penn Dissertations. 2369. https://repository.upenn.edu/edissertations/2369 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2369 For more information, please contact [email protected]. Essential Genes And Their Role In Autism Spectrum Disorder Abstract Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific oler in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality. -
Constructing and Analyzing Biological Interaction Networks for Knowledge Discovery
Constructing and Analyzing Biological Interaction Networks for Knowledge Discovery Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Duygu Ucar Graduate Program in Computer Science and Engineering The Ohio State University 2009 Dissertation Committee: Srinivasan Parthasarathy, Advisor Yusu Wang Umit Catalyurek c Copyright by Duygu Ucar 2009 ABSTRACT Many biological datasets can be effectively modeled as interaction networks where nodes represent biological entities of interest such as proteins, genes, or complexes and edges mimic associations among them. The study of these biological network structures can provide insight into many biological questions including the functional characterization of genes and gene products, the characterization of DNA-protein bindings, and the under- standing of regulatory mechanisms. Therefore, the task of constructing biological interac- tion networks from raw data sets and exploiting information from these networks is critical, but is also fraught with challenges. First, the network structure is not always known in a priori; the structure should be inferred from raw and heterogeneous biological data sources. Second, biological networks are noisy (containing unreliable interactions) and incomplete (missing real interactions) which makes the task of extracting useful information difficult. Third, typically these networks have non-trivial topological properties (e.g., uneven degree distribution, small world) that limit the effectiveness of traditional knowledge discovery al- gorithms. Fourth, these networks are usually dynamic and investigation of their dynamics is essential to understand the underlying biological system. In this thesis, we address these issues by presenting a set of computational techniques that we developed to construct and analyze three specific types of biological interaction networks: protein-protein interaction networks, gene co-expression networks, and regulatory networks. -
Making Ribosomes: Biochemical and Structural Studies of Early Ribosome Biogenesis in Yeast Malik Chaker-Margot
Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2018 Making Ribosomes: Biochemical and Structural Studies of Early Ribosome Biogenesis in Yeast Malik Chaker-Margot Follow this and additional works at: https://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons Recommended Citation Chaker-Margot, Malik, "Making Ribosomes: Biochemical and Structural Studies of Early Ribosome Biogenesis in Yeast" (2018). Student Theses and Dissertations. 472. https://digitalcommons.rockefeller.edu/student_theses_and_dissertations/472 This Thesis is brought to you for free and open access by Digital Commons @ RU. It has been accepted for inclusion in Student Theses and Dissertations by an authorized administrator of Digital Commons @ RU. For more information, please contact [email protected]. MAKING RIBOSOMES: BIOCHEMICAL AND STRUCTURAL STUDIES OF EARLY RIBOSOME BIOGENESIS IN YEAST A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Malik Chaker-Margot June 2018 © Copyright by Malik Chaker-Margot 2018 MAKING RIBOSOMES: BIOCHEMICAL AND STRUCTURAL STUDIES OF EARLY RIBOSOME BIOGENESIS IN YEAST Malik Chaker-Margot, Ph.D. The Rockefeller University 2018 The ribosome is a complex macromolecule responsible for the synthesis of all proteins in the cell. In yeast, it is made of four ribosomal RNAs and 79 proteins, asymmetrically divided in a small and large subunit. In a growing yeast cell, more than 2000 ribosomes are assembled every minute. The ribosome is assembled through a highly complex process involving more than 200 trans-acting factors. Ribosome assembly begins in the nucleolus where RNA polymerase I transcribes a long polycistronic RNA, the 35S pre- ribosomal RNA which contains the sequences for three of the four ribosomal RNAs, as well as spacer sequences which are transcribed and removed during assembly. -
Pathogenic Variants in the DEAH-Box RNA Helicase DHX37 Are a Frequent Cause of 46,XY Gonadal Dysgenesis and 46,XY Testicular Regression Syndrome
ARTICLE © American College of Medical Genetics and Genomics Pathogenic variants in the DEAH-box RNA helicase DHX37 are a frequent cause of 46,XY gonadal dysgenesis and 46,XY testicular regression syndrome Ken McElreavey, PhD 1, Anne Jorgensen, PhD 2, Caroline Eozenou, PhD1, Tiphanie Merel, MSc1, Joelle Bignon-Topalovic, BSc1, Daisylyn Senna Tan, BSc3, Denis Houzelstein, PhD 1, Federica Buonocore, PhD 4, Nick Warr, PhD 5, Raissa G. G. Kay, PhD5, Matthieu Peycelon, MD, PhD 6,7,8, Jean-Pierre Siffroi, MD, PhD6, Inas Mazen, MD9, John C. Achermann, MD, PhD 4, Yuliya Shcherbak, MD10, Juliane Leger, MD, PhD11, Agnes Sallai, MD 12, Jean-Claude Carel, MD, PhD 11, Laetitia Martinerie, MD, PhD11, Romain Le Ru, MD13, Gerard S. Conway, MD, PhD14, Brigitte Mignot, MD15, Lionel Van Maldergem, MD, PhD 16, Rita Bertalan, MD, PhD17, Evgenia Globa, MD, PhD 18, Raja Brauner, MD, PhD19, Ralf Jauch, PhD 3, Serge Nef, PhD 20, Andy Greenfield, PhD5 and Anu Bashamboo, PhD1 Purpose: XY individuals with disorders/differences of sex devel- specifically associated with gonadal dysgenesis and TRS. opment (DSD) are characterized by reduced androgenization Consistent with a role in early testis development, DHX37 is caused, in some children, by gonadal dysgenesis or testis regression expressed specifically in somatic cells of the developing human during fetal development. The genetic etiology for most patients and mouse testis. with 46,XY gonadal dysgenesis and for all patients with testicular Conclusion: DHX37 pathogenic variants are a new cause of an regression syndrome (TRS) is unknown. autosomal dominant form of 46,XY DSD, including gonadal Methods: We performed exome and/or Sanger sequencing in 145 dysgenesis and TRS, showing that these conditions are part of a individuals with 46,XY DSD of unknown etiology including clinical spectrum. -
Mouse Utp15 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Utp15 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Utp15 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Utp15 gene (NCBI Reference Sequence: NM_178918 ; Ensembl: ENSMUSG00000041747 ) is located on Mouse chromosome 13. 13 exons are identified, with the ATG start codon in exon 2 and the TAA stop codon in exon 13 (Transcript: ENSMUST00000040972). Exon 3~4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Utp15 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-267C22 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 5.74% of the coding region. The knockout of Exon 3~4 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 1332 bp, and the size of intron 4 for 3'-loxP site insertion: 1090 bp. The size of effective cKO region: ~862 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 5 6 13 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Utp15 Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
The Genetic Basis for Individual Differences in Mrna Splicing and APOBEC1 Editing Activity in Murine Macrophages
The genetic basis for individual differences in mRNA splicing and APOBEC1 editing activity in murine macrophages The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hassan, M. A., V. Butty, K. D. C. Jensen, and J. P. J. Saeij. “The Genetic Basis for Individual Differences in mRNA Splicing and APOBEC1 Editing Activity in Murine Macrophages.” Genome Research 24, no. 3 (March 1, 2014): 377–389. As Published http://dx.doi.org/10.1101/gr.166033.113 Publisher Cold Spring Harbor Laboratory Press Version Final published version Citable link http://hdl.handle.net/1721.1/89091 Terms of Use Creative Commons Attribution-Noncommerical Detailed Terms http://creativecommons.org/licenses/by-nc/3.0/ Downloaded from genome.cshlp.org on August 27, 2014 - Published by Cold Spring Harbor Laboratory Press The genetic basis for individual differences in mRNA splicing and APOBEC1 editing activity in murine macrophages Musa A. Hassan, Vincent Butty, Kirk D.C. Jensen, et al. Genome Res. 2014 24: 377-389 originally published online November 18, 2013 Access the most recent version at doi:10.1101/gr.166033.113 Supplemental http://genome.cshlp.org/content/suppl/2014/01/07/gr.166033.113.DC1.html Material References This article cites 95 articles, 32 of which can be accessed free at: http://genome.cshlp.org/content/24/3/377.full.html#ref-list-1 Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the Commons first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). -
Utpa and Utpb Chaperone Nascent Pre-Ribosomal RNA and U3 Snorna to Initiate Eukaryotic Ribosome Assembly
ARTICLE Received 6 Apr 2016 | Accepted 27 May 2016 | Published 29 Jun 2016 DOI: 10.1038/ncomms12090 OPEN UtpA and UtpB chaperone nascent pre-ribosomal RNA and U3 snoRNA to initiate eukaryotic ribosome assembly Mirjam Hunziker1,*, Jonas Barandun1,*, Elisabeth Petfalski2, Dongyan Tan3, Cle´mentine Delan-Forino2, Kelly R. Molloy4, Kelly H. Kim5, Hywel Dunn-Davies2, Yi Shi4, Malik Chaker-Margot1,6, Brian T. Chait4, Thomas Walz5, David Tollervey2 & Sebastian Klinge1 Early eukaryotic ribosome biogenesis involves large multi-protein complexes, which co-transcriptionally associate with pre-ribosomal RNA to form the small subunit processome. The precise mechanisms by which two of the largest multi-protein complexes—UtpA and UtpB—interact with nascent pre-ribosomal RNA are poorly understood. Here, we combined biochemical and structural biology approaches with ensembles of RNA–protein cross-linking data to elucidate the essential functions of both complexes. We show that UtpA contains a large composite RNA-binding site and captures the 50 end of pre-ribosomal RNA. UtpB forms an extended structure that binds early pre-ribosomal intermediates in close proximity to architectural sites such as an RNA duplex formed by the 50 ETS and U3 snoRNA as well as the 30 boundary of the 18S rRNA. Both complexes therefore act as vital RNA chaperones to initiate eukaryotic ribosome assembly. 1 Laboratory of Protein and Nucleic Acid Chemistry, The Rockefeller University, New York, New York 10065, USA. 2 Wellcome Trust Centre for Cell Biology, University of Edinburgh, Michael Swann Building, Max Born Crescent, Edinburgh EH9 3BF, UK. 3 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA. -
The Complete Structure of the Small-Subunit Processome
ARTICLES The complete structure of the small-subunit processome Jonas Barandun1,4 , Malik Chaker-Margot1,2,4 , Mirjam Hunziker1,4 , Kelly R Molloy3, Brian T Chait3 & Sebastian Klinge1 The small-subunit processome represents the earliest stable precursor of the eukaryotic small ribosomal subunit. Here we present the cryo-EM structure of the Saccharomyces cerevisiae small-subunit processome at an overall resolution of 3.8 Å, which provides an essentially complete near-atomic model of this assembly. In this nucleolar superstructure, 51 ribosome-assembly factors and two RNAs encapsulate the 18S rRNA precursor and 15 ribosomal proteins in a state that precedes pre-rRNA cleavage at site A1. Extended flexible proteins are employed to connect distant sites in this particle. Molecular mimicry and steric hindrance, as well as protein- and RNA-mediated RNA remodeling, are used in a concerted fashion to prevent the premature formation of the central pseudoknot and its surrounding elements within the small ribosomal subunit. Eukaryotic ribosome assembly is a highly dynamic process involving during which the four rRNA domains of the 18S rRNA (5′, central, in excess of 200 non-ribosomal proteins and RNAs. This process 3′ major and 3′ minor) are bound by a set of specific ribosome-assem- starts in the nucleolus where rRNAs for the small ribosomal subunit bly factors7,8. Biochemical studies indicated that these factors and the (18S rRNA) and the large ribosomal subunit (25S and 5.8S rRNA) 5′-ETS particle probably contribute to the independent maturation are initially transcribed as part of a large 35S pre-rRNA precursor of the domains of the SSU7,8. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.