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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. -
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. -
A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults
A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies. -
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. -
Effects of Neonatal Stress and Morphine on Murine Hippocampal Gene Expression
0031-3998/11/6904-0285 Vol. 69, No. 4, 2011 PEDIATRIC RESEARCH Printed in U.S.A. Copyright © 2011 International Pediatric Research Foundation, Inc. Effects of Neonatal Stress and Morphine on Murine Hippocampal Gene Expression SANDRA E. JUUL, RICHARD P. BEYER, THEO K. BAMMLER, FEDERICO M. FARIN, AND CHRISTINE A. GLEASON Department of Pediatrics [S.E.J., C.A.G.], Department of Environmental and Occupational Health Sciences [R.P.B., T.K.B., F.M.F.], University of Washington, Seattle, Washington 98195 ABSTRACT: Critically ill preterm infants experience multiple to severe impairment occurring in close to 50% of ex- stressors while hospitalized. Morphine is commonly prescribed to tremely LBW infants (4). Autism, attention deficit disorder, ameliorate their pain and stress. We hypothesized that neonatal and school failure also occur more frequently in NICU stress will have a dose-dependent effect on hippocampal gene survivors (5). Although some degree of impairment might expression, and these effects will be altered by morphine treat- ment. Male C57BL/6 mice were exposed to five treatment condi- be inevitable, it is likely that the stress and treatments these tions between postnatal d 5 and 9: 1) control, 2) mild stress ϩ infants undergo impact neurologic outcome. Improved un- saline, 3) mild stress ϩ morphine, 4) severe stress ϩ saline, and derstanding of these factors will provide the basis of better 5) severe stress ϩ morphine. Hippocampal RNA was extracted treatments and subsequent improvement in outcomes. and analyzed using Affymetrix Mouse Gene 1.0 ST Arrays. Single Many preterm infants receive opiates for sedation or gene analysis and gene set analysis were used to compare groups analgesia during their NICU stay. -
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. -
Lipid Droplets Protect Human Β Cells from Lipotoxic-Induced Stress and Cell
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.19.449124; this version posted June 20, 2021. 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. Lipid droplets protect human β cells from lipotoxic-induced stress and cell identity changes Xin Tong1 and Roland Stein1,2 1Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 2Corresponding author: [email protected]; Tel: 615-322-7026 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.19.449124; this version posted June 20, 2021. 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. Abstract (200 words) Free fatty acids (FFAs) are often stored in lipid droplet (LD) depots for eventual metabolic and/or synthetic use in many cell types, such a muscle, liver, and fat. In pancreatic islets, overt LD accumulation was detected in humans but not mice. LD buildup in islets was principally observed after roughly 11 years of age, increasing throughout adulthood under physiologic conditions, and also enriched in type 2 diabetes. To obtain insight into the role of LDs in human islet β cell function, the levels of a key LD structural protein, perilipin2 (PLIN2), were manipulated by lentiviral-mediated knock-down (KD) or over-expression (OE) in EndoCβH2-Cre cells, a human cell line with adult islet β-like properties. -
Figure S1. SYT1 and SYT3 Are Induced Under Abiotic Stress (Related to Figure 1)
Figure S1. SYT1 and SYT3 are induced under abiotic stress (related to Figure 1). (A) Phylogenetic clustering of the Arabidopsis protein coding sequence of SYT1, SYT2, SYT3, SYT4, SYT5 and the Homo sapiens E-Syt1. Phylogenetic tree was inferred using the Neighbor-Joining method. The optimal tree with the sum of branch length = 3.26 is shown. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. This analysis involved 11 amino acid sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There were a total of 1752 positions in the final dataset. Evolutionary analyses were conducted using MEGA X (Kumar et al., 2018). (B) Gene expression profiles based on RNA-seq analysis of SYT1, SYT2, SYT3, SYT4, and SYT5 in vegetative tissues at different developmental stages from TRAVA database http://travadb.org/). Each dot represent a RNAseq value and the bar represent the median. (C) Gene expression profiles based on RNA-seq analysis of SYT1, SYT3, and SYT5 in eFP-seq Browser after various abiotic stresses (https://bar.utoronto.ca/eFP-Seq_Browser/).https://bar.utoronto.ca/eFP- Seq_Browser/). (D) Heatmap representing the expression responses to abiotic stress of SYT1, SYT3 and SYT5. Expression levels genes are represented as the fold-change relative to the control. Red represents a gene that is induced, and blue represents a gene that is repressed in response to the indicated abiotic stress. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Figure S1. HAEC ROS Production and ML090 NOX5-Inhibition
Figure S1. HAEC ROS production and ML090 NOX5-inhibition. (a) Extracellular H2O2 production in HAEC treated with ML090 at different concentrations and 24 h after being infected with GFP and NOX5-β adenoviruses (MOI 100). **p< 0.01, and ****p< 0.0001 vs control NOX5-β-infected cells (ML090, 0 nM). Results expressed as mean ± SEM. Fold increase vs GFP-infected cells with 0 nM of ML090. n= 6. (b) NOX5-β overexpression and DHE oxidation in HAEC. Representative images from three experiments are shown. Intracellular superoxide anion production of HAEC 24 h after infection with GFP and NOX5-β adenoviruses at different MOIs treated or not with ML090 (10 nM). MOI: Multiplicity of infection. Figure S2. Ontology analysis of HAEC infected with NOX5-β. Ontology analysis shows that the response to unfolded protein is the most relevant. Figure S3. UPR mRNA expression in heart of infarcted transgenic mice. n= 12-13. Results expressed as mean ± SEM. Table S1: Altered gene expression due to NOX5-β expression at 12 h (bold, highlighted in yellow). N12hvsG12h N18hvsG18h N24hvsG24h GeneName GeneDescription TranscriptID logFC p-value logFC p-value logFC p-value family with sequence similarity NM_052966 1.45 1.20E-17 2.44 3.27E-19 2.96 6.24E-21 FAM129A 129. member A DnaJ (Hsp40) homolog. NM_001130182 2.19 9.83E-20 2.94 2.90E-19 3.01 1.68E-19 DNAJA4 subfamily A. member 4 phorbol-12-myristate-13-acetate- NM_021127 0.93 1.84E-12 2.41 1.32E-17 2.69 1.43E-18 PMAIP1 induced protein 1 E2F7 E2F transcription factor 7 NM_203394 0.71 8.35E-11 2.20 2.21E-17 2.48 1.84E-18 DnaJ (Hsp40) homolog. -
TAF10 Complex Provides Evidence for Nuclear Holo&Ndash;TFIID Assembly from Preform
ARTICLE Received 13 Aug 2014 | Accepted 2 Dec 2014 | Published 14 Jan 2015 DOI: 10.1038/ncomms7011 OPEN Cytoplasmic TAF2–TAF8–TAF10 complex provides evidence for nuclear holo–TFIID assembly from preformed submodules Simon Trowitzsch1,2, Cristina Viola1,2, Elisabeth Scheer3, Sascha Conic3, Virginie Chavant4, Marjorie Fournier3, Gabor Papai5, Ima-Obong Ebong6, Christiane Schaffitzel1,2, Juan Zou7, Matthias Haffke1,2, Juri Rappsilber7,8, Carol V. Robinson6, Patrick Schultz5, Laszlo Tora3 & Imre Berger1,2,9 General transcription factor TFIID is a cornerstone of RNA polymerase II transcription initiation in eukaryotic cells. How human TFIID—a megadalton-sized multiprotein complex composed of the TATA-binding protein (TBP) and 13 TBP-associated factors (TAFs)— assembles into a functional transcription factor is poorly understood. Here we describe a heterotrimeric TFIID subcomplex consisting of the TAF2, TAF8 and TAF10 proteins, which assembles in the cytoplasm. Using native mass spectrometry, we define the interactions between the TAFs and uncover a central role for TAF8 in nucleating the complex. X-ray crystallography reveals a non-canonical arrangement of the TAF8–TAF10 histone fold domains. TAF2 binds to multiple motifs within the TAF8 C-terminal region, and these interactions dictate TAF2 incorporation into a core–TFIID complex that exists in the nucleus. Our results provide evidence for a stepwise assembly pathway of nuclear holo–TFIID, regulated by nuclear import of preformed cytoplasmic submodules. 1 European Molecular Biology Laboratory, Grenoble Outstation, 6 rue Jules Horowitz, 38042 Grenoble, France. 2 Unit for Virus Host-Cell Interactions, University Grenoble Alpes-EMBL-CNRS, 6 rue Jules Horowitz, 38042 Grenoble, France. 3 Cellular Signaling and Nuclear Dynamics Program, Institut de Ge´ne´tique et de Biologie Mole´culaire et Cellulaire, UMR 7104, INSERM U964, 1 rue Laurent Fries, 67404 Illkirch, France. -
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.