Molecular Determinants of the Cytotoxicity of Platinum Compounds: the Contribution of in Silico Research
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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. -
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Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Structure-Based Screen Identification of a Mammalian Ste20-Like Kinase 4 (MST4) Inhibitor with Therapeutic Potential for Pituitary Tumors
Published OnlineFirst December 31, 2015; DOI: 10.1158/1535-7163.MCT-15-0703 Small Molecule Therapeutics Molecular Cancer Therapeutics Structure-Based Screen Identification of a Mammalian Ste20-like Kinase 4 (MST4) Inhibitor with Therapeutic Potential for Pituitary Tumors Weipeng Xiong1,3, Christopher J. Matheson2, Mei Xu1,3, Donald S. Backos2, Taylor S. Mills1, Smita Salian-Mehta1, Katja Kiseljak-Vassiliades1,3, Philip Reigan2, and Margaret E. Wierman1,3 Abstract Pituitary tumors of the gonadotrope lineage are often large described as an Aurora kinase inhibitor, exhibited potent inhi- and invasive, resulting in hypopituitarism. No medical treat- bition of the MST4 kinase at nanomolar concentrations. The ments are currently available. Using a combined genetic and LbT2 gonadotrope pituitary cell hypoxic model was used to test genomic screen of individual human gonadotrope pituitary the ability of this inhibitor to antagonize MST4 actions. Under tumor samples, we recently identified the mammalian ster- short-term severe hypoxia (1% O2), MST4 protection from ile-20 like kinase 4 (MST4) as a protumorigenic effector, driving hypoxia-induced apoptosis was abrogated in the presence of increased pituitary cell proliferation and survival in response to hesperadin. Similarly, under chronic hypoxia (5%), hesperadin a hypoxic microenvironment. To identify novel inhibitors of blocked the proliferative and colony-forming actions of MST4 the MST4 kinase for potential future clinical use, computation- as well as the ability to activate specific downstream signaling al-based virtual library screening was used to dock the Sell- and hypoxia-inducible factor-1 effectors. Together, these data eckChem kinase inhibitor library into the ATP-binding site of identify hesperadin as the first potent, selective inhibitor of the the MST4 crystal structure. -
Bioinformatic Analysis of Next‑Generation Sequencing Data to Identify Dysregulated Genes in Fibroblasts of Idiopathic Pulmonary Fibrosis
INTERNATIONAL JOURNAL OF MOleCular meDICine 43: 1643-1656, 2019 Bioinformatic analysis of next‑generation sequencing data to identify dysregulated genes in fibroblasts of idiopathic pulmonary fibrosis CHAU‑CHYUN SHEU1-3, WEI‑AN CHANG1,2, MING‑JU TSAI1-3, SSU‑HUI LIAO1, INN‑WEN CHONG2,3 and PO-LIN KUO1 1Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University; 2Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital; 3Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan, R.O.C. Received October 7, 2018; Accepted January 29, 2019 DOI: 10.3892/ijmm.2019.4086 Abstract. Idiopathic pulmonary fibrosis (IPF) is a lethal and miRNA expression data, combined with GEO verifica- fibrotic lung disease with an increasing global burden. It is tion, finally identified Homo sapiens (hsa)-miR-1254-INKA2 hypothesized that fibroblasts have a number of functions that and hsa-miR-766-3p-INKA2 as the potential miRNA-mRNA may affect the development and progression of IPF. However, interactions in IPF fibroblasts. In summary, the results the present understanding of cellular and molecular mecha- of the present study suggest that dysregulation of PAX8, nisms associated with fibroblasts in IPF remains limited. hsa-miR-1254-INKA2 and hsa-miR-766-3p-INKA2 may The present study aimed to identify the dysregulated genes promote the proliferation and survival of IPF fibroblasts. In in IPF fibroblasts, elucidate their functions and explore the functional analysis of the dysregulated genes, a marked potential microRNA (miRNA)‑mRNA interactions. mRNA association between fibroblasts and the ECM was identified. -
Identification of Tgfβ-Related Genes Regulated in Murine Osteoarthritis and Chondrocyte Hypertrophy by Comparison of Multiple Microarray Datasets
http://hdl.handle.net/1765/109466 Identification of TGFβ-related genes regulated in murine osteoarthritis and chondrocyte hypertrophy by comparison of multiple microarray datasets Laurie M.G. de Kroon1,2,&, Guus G.H. van den Akker1,&, Bent Brachvogel3,4, Roberto Narcisi2, Daniele Belluoccio5, Florien Jenner6, John F. Bateman5, Christopher B. Little7, Pieter Brama8, Esmeralda N. Blaney Davidson1, Peter M. van der Kraan1, Gerjo J.V.M. van Osch2,9* 1Department of Rheumatology, Experimental Rheumatology, Radboud University Medical Center, Nijmegen, the Netherlands 2Department of Orthopedics, Erasmus MC University Medical Center, Rotterdam, the Netherlands 3Center for Biochemistry, Medical Faculty, University of Cologne, Cologne, Germany 4Department of Pediatrics and Adolescent Medicine, Experimental Neonatology, Medical Faculty, University of Cologne, Cologne, Germany 5Murdoch Childrens Research Institute, Royal Children’s Hospital, Parkville, Victoria, Australia 6Equine University Hospital, University of Veterinary Medicine, Vienna, Austria 7Raymond Purves Bone and Joint Research Laboratories, Kolling Institute of Medical Research, University of Sydney, St Leonards, New South Wales, Australia 8Veterinary Clinical Sciences, School of Veterinary Medicine, University College Dublin, Dublin, Ireland 9Department of Otorhinolaryngology, Erasmus MC University Medical Center, Rotterdam, the Netherlands &Both authors contributed equally *Corresponding author: Gerjo van Osch ([email protected]) Address for correspondence: Erasmus MC, Departments -
Breakpoint Mapping and Haplotype Analysis of Translocation T(1;12)(Q43;Q21.1) in Two Apparently Independent Families with Vascular Phenotypes
Received: 7 August 2017 | Revised: 9 October 2017 | Accepted: 11 October 2017 DOI: 10.1002/mgg3.346 ORIGINAL ARTICLE Breakpoint mapping and haplotype analysis of translocation t(1;12)(q43;q21.1) in two apparently independent families with vascular phenotypes Tiia Maria Luukkonen1,2 | Mana M. Mehrjouy3 | Minna Poyh€ onen€ 4,5 | Anna-Kaisa Anttonen6 | Paivi€ Lahermo1 | Pekka Ellonen1 | Lars Paulin7 | Niels Tommerup3 | Aarno Palotie1,8 | Teppo Varilo2,5 1Institute for molecular medicine Finland FIMM, University of Helsinki, Helsinki, Abstract Finland Background: The risk of serious congenital anomaly for de novo balanced 2Department of Health, National Institute translocations is estimated to be at least 6%. We identified two apparently inde- for Health and Welfare, Helsinki, Finland pendent families with a balanced t(1;12)(q43;q21.1) as an outcome of a “System- 3Wilhelm Johannsen Centre for atic Survey of Balanced Chromosomal Rearrangements in Finns.” In the first Functional Genome Research, Department of Cellular and Molecular family, carriers (n = 6) manifest with learning problems in childhood, and later Medicine, University of Copenhagen, with unexplained neurological symptoms (chronic headache, balance problems, Copenhagen, Denmark tremor, fatigue) and cerebral infarctions in their 50s. In the second family, two 4Clinical Genetics, Helsinki University Hospital, University of Helsinki, carriers suffer from tetralogy of Fallot, one from transient ischemic attack and one Helsinki, Finland from migraine. The translocation cosegregates with these vascular phenotypes and 5Department of Medical Genetics, neurological symptoms. University of Helsinki, Helsinki, Finland Methods and Results: We narrowed down the breakpoint regions using mate 6Laboratory of Genetics, HUSLAB, Helsinki, Finland pair sequencing. We observed conserved haplotypes around the breakpoints, 7Institute of Biotechnology, University of pointing out that this translocation has arisen only once. -
A Semantic Approach for Knowledge Capture of Microrna-Target Gene Interactions
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) A semantic approach for knowledge capture of microRNA-target gene interactions Jingshan Huang* Fernando Gutierrez Dejing Dou School of Computing, University of South Alabama CIS Dept., University of Oregon CIS Dept., University of Oregon Mobile, Alabama 36688, U.S.A. Eugene, Oregon 97403, U.S.A. Eugene, Oregon 97403, U.S.A. Judith A. Blake Karen Eilbeck Darren A. Natale The Jackson Laboratory School of Medicine, University of Utah Georgetown University Medical Center Bar Harbor, Maine 04609, U.S.A. Salt Lake City, Utah 84112, U.S.A. Washington D.C. 20007, U.S.A. Barry Smith Yu Lin Xiaowei Wang Dept. Philosophy, University at Buffalo University of Miami Washington University School of Medicine Buffalo, New York 14260, U.S.A. Miami, Florida 33146, U.S.A. St. Louis, Missouri 63110, U.S.A. Zixing Liu Ming Tan Alan Ruttenberg MCI, University of South Alabama MCI, University of South Alabama Dental Medicine, University at Buffalo Mobile, Alabama 36604, U.S.A. Mobile, Alabama 36604, U.S.A. Buffalo, New York 14214, U.S.A. Abstract—Research has indicated that microRNAs (miRNAs), Conventionally, data end users (that is, biologists, bioin- a special class of non-coding RNAs (ncRNAs), can perform formaticians, and clinical investigators) need to search for important roles in different biological and pathological processes. (1) biologically validated miRNA targets (for example, by miRNAs’ functions are realized by regulating their respective querying the PubMed database [3]) and (2) computationally target genes (targets). It is thus critical to identify and analyze putative miRNA targets (for example, by initiating inquiries miRNA-target interactions for a better understanding and de- on various prediction databases or websites such as miRDB lineation of miRNAs’ functions. -
STRIPAK Complexes in Cell Signaling and Cancer
Oncogene (2016), 1–9 © 2016 Macmillan Publishers Limited All rights reserved 0950-9232/16 www.nature.com/onc REVIEW STRIPAK complexes in cell signaling and cancer Z Shi1,2, S Jiao1 and Z Zhou1,3 Striatin-interacting phosphatase and kinase (STRIPAK) complexes are striatin-centered multicomponent supramolecular structures containing both kinases and phosphatases. STRIPAK complexes are evolutionarily conserved and have critical roles in protein (de) phosphorylation. Recent studies indicate that STRIPAK complexes are emerging mediators and regulators of multiple vital signaling pathways including Hippo, MAPK (mitogen-activated protein kinase), nuclear receptor and cytoskeleton remodeling. Different types of STRIPAK complexes are extensively involved in a variety of fundamental biological processes ranging from cell growth, differentiation, proliferation and apoptosis to metabolism, immune regulation and tumorigenesis. Growing evidence correlates dysregulation of STRIPAK complexes with human diseases including cancer. In this review, we summarize the current understanding of the assembly and functions of STRIPAK complexes, with a special focus on cell signaling and cancer. Oncogene advance online publication, 15 February 2016; doi:10.1038/onc.2016.9 INTRODUCTION in the central nervous system and STRN4 is mostly abundant in Recent proteomic studies identified a group of novel multi- the brain and lung, whereas STRN3 is ubiquitously expressed in 5–9 component complexes named striatin (STRN)-interacting phos- almost all tissues. STRNs share a -
Identifying Genetic Interactions Associated with Late-Onset
1 Identifying Genetic Interactions Associated with Late-Onset 2 Alzheimer’s Disease 3 Charalampos S. Floudas, M.D, Ph.D. 1§ , Nara Um, M.D, M.S. 1, M. Ilyas Kamboh, Ph.D. 2, 4 Michael M. Barmada, Ph.D. 2, Shyam Visweswaran, M.D, Ph.D. 1,3 5 6 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA s t 7 2Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA n i r 8 3The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA P e r 9 P 10 §Corresponding author 11 12 13 14 15 16 17 Email addresses : 18 CSF: [email protected] 19 SV: [email protected] 20 1 PeerJ PrePrints | https://peerj.com/preprints/123v2/ | v2 received: 11 Dec 2013, published: 11 Dec 2013, doi: 10.7287/peerj.preprints.123v2 21 Abstract 22 Background 23 Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) 24 can help in understanding the genetic basis of complex diseases. The large number of single 25 nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic 26 interactions computationally challenging. We developed the Bayesian Combinatorial Method s t n 27 (BCM) that can identify pairs of SNPs that in combination have high statistical association with i r P 28 disease. e r 29 Results P 30 We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify 31 SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For 32 evaluation we compared our results with those from logistic regression, as implemented in 33 PLINK. -
RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA- 3 Seq Data
bioRxiv preprint doi: https://doi.org/10.1101/2020.11.06.371724; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 1 Title 2 NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA- 3 Seq Data 4 Authors/Affiliations 5 Eliah G. Overbey⇞,1, Amanda M. Saravia-Butler⇞,2,3, Zhe Zhang4, Komal S. Rathi4, Homer 6 Fogle5,3, Willian A. da Silveira6, Richard J. Barker7, Joseph J. Bass8, Afshin Beheshti37,38, Daniel 7 C. Berrios39, Elizabeth A. Blaber9, Egle Cekanaviciute3, Helio A. Costa10, Laurence B. Davin11, 8 Kathleen M. Fisch12, SamraWit G. Gebre3,37, MattheW Geniza13, Rachel Gilbert14, Simon Gilroy7, 9 Gary Hardiman6,15, Raúl Herranz16, Yared H. Kidane17, Colin P.S. Kruse18, Michael D. Lee19,20, 10 Ted Liefeld21, Norman G. LeWis11, J. Tyson McDonald22, Robert Meller23, TejasWini Mishra24, 11 Imara Y. Perera25, Shayoni Ray26, Sigrid S. Reinsch3, Sara Brin Rosenthal12, Michael Strong27, 12 Nathaniel J SzeWczyk28, Candice G.T. Tahimic29, Deanne M. Taylor30, Joshua P. Vandenbrink31, 13 Alicia Villacampa16, Silvio Weging32, Chris Wolverton33, Sarah E. Wyatt34,35, Luis Zea36, 14 Sylvain V. Costes*,3, Jonathan M. Galazka*,3 15 1: Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA 16 2: Logyx, LLC, Mountain VieW, CA 94043, USA 17 3: Space Biosciences Division, NASA