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Funk et al. Journal of Biomedical Semantics (2015) 6:9 DOI 10.1186/s13326-015-0006-4 JOURNAL OF BIOMEDICAL SEMANTICS RESEARCH Open Access Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct Christopher S Funk1*, Indika Kahanda2, Asa Ben-Hur2 and Karin M Verspoor3,4 Abstract Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function = 0.408, Biological Process = 0.461, Cellular Component = 0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a “medium-throughput” pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated. Keywords: Text mining, Protein function prediction, Biomedical concept recognition Introduction Background Characterizing the functions of proteins is an important Literature mining has been shown to have substantial task in bioinformatics today. In recent years, many com- promise in the context of automated function prediction, putational methods to predict protein function have been although there has been limited exploration to date [2]. -
Analysis of the Indacaterol-Regulated Transcriptome in Human Airway
Supplemental material to this article can be found at: http://jpet.aspetjournals.org/content/suppl/2018/04/13/jpet.118.249292.DC1 1521-0103/366/1/220–236$35.00 https://doi.org/10.1124/jpet.118.249292 THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS J Pharmacol Exp Ther 366:220–236, July 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Analysis of the Indacaterol-Regulated Transcriptome in Human Airway Epithelial Cells Implicates Gene Expression Changes in the s Adverse and Therapeutic Effects of b2-Adrenoceptor Agonists Dong Yan, Omar Hamed, Taruna Joshi,1 Mahmoud M. Mostafa, Kyla C. Jamieson, Radhika Joshi, Robert Newton, and Mark A. Giembycz Departments of Physiology and Pharmacology (D.Y., O.H., T.J., K.C.J., R.J., M.A.G.) and Cell Biology and Anatomy (M.M.M., R.N.), Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada Received March 22, 2018; accepted April 11, 2018 Downloaded from ABSTRACT The contribution of gene expression changes to the adverse and activity, and positive regulation of neutrophil chemotaxis. The therapeutic effects of b2-adrenoceptor agonists in asthma was general enriched GO term extracellular space was also associ- investigated using human airway epithelial cells as a therapeu- ated with indacaterol-induced genes, and many of those, in- tically relevant target. Operational model-fitting established that cluding CRISPLD2, DMBT1, GAS1, and SOCS3, have putative jpet.aspetjournals.org the long-acting b2-adrenoceptor agonists (LABA) indacaterol, anti-inflammatory, antibacterial, and/or antiviral activity. Numer- salmeterol, formoterol, and picumeterol were full agonists on ous indacaterol-regulated genes were also induced or repressed BEAS-2B cells transfected with a cAMP-response element in BEAS-2B cells and human primary bronchial epithelial cells by reporter but differed in efficacy (indacaterol $ formoterol . -
Omics Knowledgebase for Mammalian Cellular Signaling Pathways
bioRxiv preprint doi: https://doi.org/10.1101/401729; this version posted August 27, 2018. 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. The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways Scott Ochsner, David Abraham*, Kirt Martin*, Wei Ding, Apollo McOwiti, Zichen Wang, Kaitlyn Andreano, Ross A. Hamilton, Yue Chen, Angelica Hamilton, Marin L. Gantner, Michael Dehart, Shijing Qu, Susan G. Hilsenbeck, Lauren B. Becnel, Dave Bridges, Avi Ma’ayan, Janice M. Huss, Fabio Stossi, Charles E. Foulds, Anastasia Kralli, Donald P. McDonnell and Neil J. McKenna Address Correspondence To: Neil J. McKenna Department of Molecular and Cellular Biology Baylor College of Medicine Houston, TX 77030 USA e: [email protected] t: 713-798-8568 *These authors contributed equally to this study 1 bioRxiv preprint doi: https://doi.org/10.1101/401729; this version posted August 27, 2018. 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. Summary Public transcriptomic and ChIP-Seq datasets have the potential to illuminate facets of transcriptional regulation by mammalian cellular signaling pathways not yet explored in the research literature. Unfortunately, a variety of obstacles prevent routine re-use of these datasets by bench biologists for hypothesis generation and data validation. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates stable community classifications of three major categories of cellular signaling pathway node (receptors, enzymes and transcription factors) and the bioactive small molecules (BSMs) known to modulate their functions. -
Omics Knowledgebase for Mammalian Cellular Signaling Pathways Scott A
www.nature.com/scientificdata OPEN The Signaling Pathways Project, an ARTICLE integrated ‘omics knowledgebase for mammalian cellular signaling pathways Scott A. Ochsner1, David Abraham1,8, Kirt Martin1,8, Wei Ding2, Apollo McOwiti2, Wasula Kankanamge2, Zichen Wang 3, Kaitlyn Andreano4, Ross A. Hamilton1, Yue Chen1, Angelica Hamilton5, Marin L. Gantner6, Michael Dehart2, Shijing Qu2, Susan G. Hilsenbeck2, Lauren B. Becnel2, Dave Bridges7, Avi Ma’ayan 3, Janice M. Huss5, Fabio Stossi1, Charles E. Foulds1, Anastasia Kralli6, Donald P. McDonnell4 & Neil J. McKenna 1* Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifcations of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus ‘omics signatures, or consensomes, which ranked genes based on measures of their signifcant diferential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specifc node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confrming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org. -
Identification of Common Gene Networks Responsive To
Cancer Gene Therapy (2014) 21, 542–548 © 2014 Nature America, Inc. All rights reserved 0929-1903/14 www.nature.com/cgt ORIGINAL ARTICLE Identification of common gene networks responsive to radiotherapy in human cancer cells D-L Hou1, L Chen2, B Liu1, L-N Song1 and T Fang1 Identification of the genes that are differentially expressed between radiosensitive and radioresistant cancers by global gene analysis may help to elucidate the mechanisms underlying tumor radioresistance and improve the efficacy of radiotherapy. An integrated analysis was conducted using publicly available GEO datasets to detect differentially expressed genes (DEGs) between cancer cells exhibiting radioresistance and cancer cells exhibiting radiosensitivity. Gene Ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein–protein interaction (PPI) networks analysis were also performed. Five GEO datasets including 16 samples of radiosensitive cancers and radioresistant cancers were obtained. A total of 688 DEGs across these studies were identified, of which 374 were upregulated and 314 were downregulated in radioresistant cancer cell. The most significantly enriched GO terms were regulation of transcription, DNA-dependent (GO: 0006355, P = 7.00E-09) for biological processes, while those for molecular functions was protein binding (GO: 0005515, P = 1.01E-28), and those for cellular component was cytoplasm (GO: 0005737, P = 2.81E-26). The most significantly enriched pathway in our KEGG analysis was Pathways in cancer (P = 4.20E-07). PPI network analysis showed that IFIH1 (Degree = 33) was selected as the most significant hub protein. This integrated analysis may help to predict responses to radiotherapy and may also provide insights into the development of individualized therapies and novel therapeutic targets. -
Genomic and Transcriptome Analysis Revealing an Oncogenic Functional Module in Meningiomas
Neurosurg Focus 35 (6):E3, 2013 ©AANS, 2013 Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas XIAO CHANG, PH.D.,1 LINGLING SHI, PH.D.,2 FAN GAO, PH.D.,1 JONATHAN RUssIN, M.D.,3 LIYUN ZENG, PH.D.,1 SHUHAN HE, B.S.,3 THOMAS C. CHEN, M.D.,3 STEVEN L. GIANNOTTA, M.D.,3 DANIEL J. WEISENBERGER, PH.D.,4 GAbrIEL ZADA, M.D.,3 KAI WANG, PH.D.,1,5,6 AND WIllIAM J. MAck, M.D.1,3 1Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California; 2GHM Institute of CNS Regeneration, Jinan University, Guangzhou, China; 3Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California; 4USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California; 5Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, California; and 6Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California Object. Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%–5% display malignant pathological features. The key molecular pathways involved in malignant trans- formation remain to be determined. Methods. Illumina expression microarrays were used to assess gene expression levels, and Illumina single- nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant me- ningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones). -
Skeletal Muscle in Aged Mice Reveals Extensive Transformation of Muscle
Lin et al. BMC Genetics (2018) 19:55 https://doi.org/10.1186/s12863-018-0660-5 RESEARCHARTICLE Open Access Skeletal muscle in aged mice reveals extensive transformation of muscle gene expression I-Hsuan Lin1†, Junn-Liang Chang3†, Kate Hua1, Wan-Chen Huang4, Ming-Ta Hsu2 and Yi-Fan Chen4* Abstract Background: Aging leads to decreased skeletal muscle function in mammals and is associated with a progressive loss of muscle mass, quality and strength. Age-related muscle loss (sarcopenia) is an important health problem associated with the aged population. Results: We investigated the alteration of genome-wide transcription in mouse skeletal muscle tissue (rectus femoris muscle) during aging using a high-throughput sequencing technique. Analysis revealed significant transcriptional changes between skeletal muscles of mice at 3 (young group) and 24 (old group) months of age. Specifically, genes associated with energy metabolism, cell proliferation, muscle myosin isoforms, as well as immune functions were found to be altered. We observed several interesting gene expression changes in the elderly, many of which have not been reported before. Conclusions: Those data expand our understanding of the various compensatory mechanisms that can occur with age, and further will assist in the development of methods to prevent and attenuate adverse outcomes of aging. Keywords: Aging, Skeletal muscle, Cardiac-related genes, RNA sequencing analysis, Muscle fibers, Defects on differentiation Background SIRT1 reduces the oxidative stress and inflammation Aging is a process whereby various changes were accu- associated with ameliorating diseases, such as vascular mulated over time, resulting in dysfunction in mole- endothelial disorders, neurodegenerative diseases, as cules, cells, tissues and organs. -
Live-Cell Imaging Rnai Screen Identifies PP2A–B55α and Importin-Β1 As Key Mitotic Exit Regulators in Human Cells
LETTERS Live-cell imaging RNAi screen identifies PP2A–B55α and importin-β1 as key mitotic exit regulators in human cells Michael H. A. Schmitz1,2,3, Michael Held1,2, Veerle Janssens4, James R. A. Hutchins5, Otto Hudecz6, Elitsa Ivanova4, Jozef Goris4, Laura Trinkle-Mulcahy7, Angus I. Lamond8, Ina Poser9, Anthony A. Hyman9, Karl Mechtler5,6, Jan-Michael Peters5 and Daniel W. Gerlich1,2,10 When vertebrate cells exit mitosis various cellular structures can contribute to Cdk1 substrate dephosphorylation during vertebrate are re-organized to build functional interphase cells1. This mitotic exit, whereas Ca2+-triggered mitotic exit in cytostatic-factor- depends on Cdk1 (cyclin dependent kinase 1) inactivation arrested egg extracts depends on calcineurin12,13. Early genetic studies in and subsequent dephosphorylation of its substrates2–4. Drosophila melanogaster 14,15 and Aspergillus nidulans16 reported defects Members of the protein phosphatase 1 and 2A (PP1 and in late mitosis of PP1 and PP2A mutants. However, the assays used in PP2A) families can dephosphorylate Cdk1 substrates in these studies were not specific for mitotic exit because they scored pro- biochemical extracts during mitotic exit5,6, but how this relates metaphase arrest or anaphase chromosome bridges, which can result to postmitotic reassembly of interphase structures in intact from defects in early mitosis. cells is not known. Here, we use a live-cell imaging assay and Intracellular targeting of Ser/Thr phosphatase complexes to specific RNAi knockdown to screen a genome-wide library of protein substrates is mediated by a diverse range of regulatory and targeting phosphatases for mitotic exit functions in human cells. We subunits that associate with a small group of catalytic subunits3,4,17. -
Robles JTO Supplemental Digital Content 1
Supplementary Materials An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma based on mRNA, microRNA and DNA Methylation Biomarkers Ana I. Robles1, Eri Arai2, Ewy A. Mathé1, Hirokazu Okayama1, Aaron Schetter1, Derek Brown1, David Petersen3, Elise D. Bowman1, Rintaro Noro1, Judith A. Welsh1, Daniel C. Edelman3, Holly S. Stevenson3, Yonghong Wang3, Naoto Tsuchiya4, Takashi Kohno4, Vidar Skaug5, Steen Mollerup5, Aage Haugen5, Paul S. Meltzer3, Jun Yokota6, Yae Kanai2 and Curtis C. Harris1 Affiliations: 1Laboratory of Human Carcinogenesis, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 2Division of Molecular Pathology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 3Genetics Branch, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 4Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 5Department of Chemical and Biological Working Environment, National Institute of Occupational Health, NO-0033 Oslo, Norway. 6Genomics and Epigenomics of Cancer Prediction Program, Institute of Predictive and Personalized Medicine of Cancer (IMPPC), 08916 Badalona (Barcelona), Spain. List of Supplementary Materials Supplementary Materials and Methods Fig. S1. Hierarchical clustering of based on CpG sites differentially-methylated in Stage I ADC compared to non-tumor adjacent tissues. Fig. S2. Confirmatory pyrosequencing analysis of DNA methylation at the HOXA9 locus in Stage I ADC from a subset of the NCI microarray cohort. 1 Fig. S3. Methylation Beta-values for HOXA9 probe cg26521404 in Stage I ADC samples from Japan. Fig. S4. Kaplan-Meier analysis of HOXA9 promoter methylation in a published cohort of Stage I lung ADC (J Clin Oncol 2013;31(32):4140-7). Fig. S5. Kaplan-Meier analysis of a combined prognostic biomarker in Stage I lung ADC. -
Cholesterol 25-Hydroxylase on Chromosome 10Q Is a Susceptibility Gene for Sporadic Alzheimer’S Disease
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2005 Cholesterol 25-hydroxylase on chromosome 10q is a susceptibility gene for sporadic Alzheimer’s disease Papassotiropoulos, A ; Lambert, J C ; Wavrant-De Vrièze, F ; Wollmer, M A ; von der Kammer, H ; Streffer, J R ; Maddalena, A ; Huynh, K D ; Wolleb, S ; Lütjohann, D ; Schneider, B;Thal,DR Grimaldi, L M E ; Tsolaki, M ; Kapaki, E ; Ravid, R ; Konietzko, U ; Hegi, T ; Pasch, T ; Jung, H ; Braak, H ; Amouyel, P ; Rogaev, E I ; Hardy, J ; Hock, C ; Nitsch, R M Abstract: Alzheimer’s disease (AD) is the most common cause of dementia. It is characterized by beta- amyloid (A beta) plaques, neurofibrillary tangles and the degeneration of specifically vulnerable brain neurons. We observed high expression of the cholesterol 25-hydroxylase (CH25H) gene in specifically vulnerable brain regions of AD patients. CH25H maps to a region within 10q23 that has been previ- ously linked to sporadic AD. Sequencing of the 5’ region of CH25H revealed three common haplotypes, CH25Hchi2, CH25Hchi3 and CH25Hchi4; CSF levels of the cholesterol precursor lathosterol were higher in carriers of the CH25Hchi4 haplotype. In 1,282 patients with AD and 1,312 healthy control subjects from five independent populations, a common variation in the vicinity of CH25H was significantly associated with the risk for sporadic AD (p = 0.006). Quantitative neuropathology of brains from elderly non- demented subjects showed brain A beta deposits in carriers of CH25Hchi4 and CH25Hchi3 haplotypes, whereas no A beta deposits were present in CH25Hchi2 carriers. -
Hypoxia-Inducible Factor 1-Mediated Regulation of PPP1R3C Promotes
FEBS Letters 584 (2010) 4366–4372 journal homepage: www.FEBSLetters.org Hypoxia-inducible factor 1-mediated regulation of PPP1R3C promotes glycogen accumulation in human MCF-7 cells under hypoxia ⇑ Guo-Min Shen, Feng-Lin Zhang, Xiao-Ling Liu, Jun-Wu Zhang National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China article info abstract Article history: Hundreds of genes can be regulated by hypoxia-inducible factor 1 (HIF1) under hypoxia. Here we Received 30 July 2010 demonstrated a HIF1-mediated induction of protein phosphatase 1, regulatory subunit 3C gene Revised 10 September 2010 (PPP1R3C) in human MCF7 cells under hypoxia. By mutation analysis we confirmed the presence Accepted 24 September 2010 of a functional hypoxia response element that is located 229 bp upstream from the PPP1R3C gene. Available online 1 October 2010 PPP1R3C induction correlates with a significant glycogen accumulation in MCF7 cells under hypoxia. Edited by Judit Ovádi Knockdown of either HIF1a or PPP1R3C attenuated hypoxia-induced glycogen accumulation signif- icantly. Knockdown of HIF2a reduced hypoxia-induced glycogen accumulation slightly (but not sig- nificantly). Our results demonstrated that HIF1 promotes glycogen accumulation through regulating Keywords: Hypoxia-inducible factor PPP1R3C expression under hypoxia, which revealed a novel metabolic adaptation of cells to hypoxia. Protein phosphatase 1, regulatory subunit Ó 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................