Gprofiler2 -- an R Package for Gene List Functional Enrichment Analysis and Namespace Conversion Toolset G:Profiler [Version 2; Peer Review: 2 Approved]
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Cooperative Gene Regulation by Microrna Pairs and Their
Published online 29 May 2014 Nucleic Acids Research, 2014, Vol. 42, No. 12 7539–7552 doi: 10.1093/nar/gku465 Cooperative gene regulation by microRNA pairs and their identification using a computational workflow Ulf Schmitz1,*, Xin Lai2, Felix Winter1, Olaf Wolkenhauer1,3, Julio Vera2 and Shailendra K. Gupta1,4 Downloaded from 1Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany, 2Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Germany, 3Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa and 4Department of Bioinformatics, CSIR-Indian Institute of Toxicology Research, 226001 Lucknow, Uttar Pradesh, India http://nar.oxfordjournals.org/ Received December 22, 2013; Revised April 18, 2014; Accepted May 10, 2014 ABSTRACT fine-tuned through a cellular context-dependent regulation by multiple miRNAs, where miRNAs can either induce MicroRNAs (miRNAs) are an integral part of gene reg- translational repression or target mRNA degradation (4). ulation at the post-transcriptional level. Recently, it Thereby, the miRNA-target regulation machinery can real- at Universitaet Erlangen-Nuernberg, Wirtschafts- und Sozialwissenschaftliche Z on August 3, 2016 has been shown that pairs of miRNAs can repress the ize elaborate gene control functions, including noise buffer- translation of a target mRNA in a cooperative man- ing or homeostasis, and can ultimately mediate distinct tar- ner, which leads to an enhanced effectiveness and get expression patterns appropriate to the demand of differ- specificity in target repression. However, it remains ent biological processes (3,5,6). However, deregulated miR- unclear which miRNA pairs can synergize and which NAs have also been associated with the pathogenesis and genes are target of cooperative miRNA regulation. -
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. -
INPP5A Monoclonal Antibody (M05), Clone 3D8
INPP5A monoclonal antibody (M05), clone 3D8 Catalog # : H00003632-M05 規格 : [ 100 ug ] List All Specification Application Image Product Mouse monoclonal antibody raised against a partial recombinant Western Blot (Recombinant protein) Description: INPP5A. Immunoprecipitation Immunogen: INPP5A (NP_005530, 288 a.a. ~ 387 a.a) partial recombinant protein with GST tag. MW of the GST tag alone is 26 KDa. Sequence: YFNQEVFRDNNGTALLEFDKELSVFKDRLYELDISFPPSYPYSEDARQG EQYMNTRCPAWCDRILMSPSAKELVLRVSVCCPSPGHRGMWSAGSGL AQPW enlarge Host: Mouse Sandwich ELISA (Recombinant Reactivity: Human protein) Isotype: IgG2a Kappa Quality Control Antibody Reactive Against Recombinant Protein. Testing: enlarge ELISA Western Blot detection against Immunogen (36.74 KDa) . Storage Buffer: In 1x PBS, pH 7.4 Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Instruction: MSDS: Download Datasheet: Download Applications Western Blot (Recombinant protein) Protocol Download Immunoprecipitation Page 1 of 3 2016/5/21 Immunoprecipitation of INPP5A transfected lysate using anti-INPP5A monoclonal antibody and Protein A Magnetic Bead (U0007), and immunoblotted with INPP5A MaxPab rabbit polyclonal antibody. Protocol Download Sandwich ELISA (Recombinant protein) Detection limit for recombinant GST tagged INPP5A is 0.1 ng/ml as a capture antibody. Protocol Download ELISA Gene Information Entrez GeneID: 3632 GeneBank NM_005539 Accession#: Protein NP_005530 Accession#: Gene Name: INPP5A Gene Alias: 5PTASE,DKFZp434A1721,MGC116947,MGC116949 Gene inositol polyphosphate-5-phosphatase, 40kDa Description: Omim ID: 600106 Gene Ontology: Hyperlink Gene Summary: The protein encoded by this gene is a membrane-associated type I inositol 1,4,5-trisphosphate (InsP3) 5-phosphatase. InsP3 5- phosphatases hydrolyze Ins(1,4,5)P3, which mobilizes intracellular calcium and acts as a second messenger mediating cell responses to various stimulation. -
Identification of Holocarboxylase Synthetase Chromatin Binding Sites in Human Mammary Cell Lines Using the Damid Technology
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Biological Systems Engineering--Dissertations, Theses, and Student Research Biological Systems Engineering 12-2010 IDENTIFICATION OF HOLOCARBOXYLASE SYNTHETASE CHROMATIN BINDING SITES IN HUMAN MAMMARY CELL LINES USING THE DAMID TECHNOLOGY Dipika Singh University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/biosysengdiss Part of the Biological Engineering Commons Singh, Dipika, "IDENTIFICATION OF HOLOCARBOXYLASE SYNTHETASE CHROMATIN BINDING SITES IN HUMAN MAMMARY CELL LINES USING THE DAMID TECHNOLOGY" (2010). Biological Systems Engineering--Dissertations, Theses, and Student Research. 11. https://digitalcommons.unl.edu/biosysengdiss/11 This Article is brought to you for free and open access by the Biological Systems Engineering at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Biological Systems Engineering--Dissertations, Theses, and Student Research by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. IDENTIFICATION OF HOLOCARBOXYLASE SYNTHETASE CHROMATIN BINDING SITES IN HUMAN MAMMARY CELL LINES USING THE DAMID TECHNOLOGY by Dipika Singh A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science Major: Agricultural and Biological Systems Engineering Under the Supervision of Professors Angela K. Pannier and Janos Zempleni Lincoln, Nebraska December, 2010 Identification of holocarboxylase synthetase chromatin binding sites in human mammary cell lines using the DamID technology Dipika Singh, M.S. University of Nebraska, 2010 Advisers: Angela Pannier and Janos Zempleni Holocarboxylase synthetase (HCS) is a chromatin protein that is essential for mediating the covalent binding of biotin to histones. -
In Human Metabolism
Supporting Information (SI Appendix) Framework and resource for more than 11,000 gene-transcript- protein-reaction associations (GeTPRA) in human metabolism SI Appendix Materials and Methods Standardization of Metabolite IDs with MNXM IDs Defined in the MNXref Namespace. Information on metabolic contents of the Recon 2Q was standardized using MNXM IDs defined in the MNXref namespace available at MetaNetX (1-3). This standardization was to facilitate the model refinement process described below. Each metabolite ID in the Recon 2Q was converted to MNXM ID accordingly. For metabolite IDs that were not converted to MNXM IDs, they were manually converted to MNXM IDs by comparing their compound structures and synonyms. In the final resulting SBML files, 97 metabolites were assigned with arbitrary IDs (i.e., “MNXMK_” followed by four digits) because they were not covered by the MNXref namespace (i.e., metabolite IDs not converted to MNXM IDs). Refinement or Removal of Biochemically Inconsistent Reactions. Recon 2 was built upon metabolic genes and reactions collected from EHMN (4, 5), the first genome-scale human liver metabolic model HepatoNet1 (6), an acylcarnitine and fatty-acid oxidation model Ac-FAO (7), and a small intestinal enterocyte model hs_eIEC611 (8). Flux variability analysis (9) of the Recon 2Q identified blocked reactions coming from these four sources of metabolic reaction data. The EHMN caused the greatest number of blocked reactions in the Recon 2Q (1,070 reactions corresponding to 69.3% of all the identified blocked reactions). To refine the EHMN reactions, following reactions were initially disregarded: 1) reactions having metabolite IDs not convertible to MNXM IDs; and 2) reactions without genes. -
Inhibiting TRK Proteins in Clinical Cancer Therapy
cancers Review Inhibiting TRK Proteins in Clinical Cancer Therapy Allison M. Lange 1 and Hui-Wen Lo 1,2,* 1 Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; [email protected] 2 Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA * Correspondence: [email protected] Received: 25 February 2018; Accepted: 29 March 2018; Published: 4 April 2018 Abstract: Gene rearrangements resulting in the aberrant activity of tyrosine kinases have been identified as drivers of oncogenesis in a variety of cancers. The tropomyosin receptor kinase (TRK) family of tyrosine receptor kinases is emerging as an important target for cancer therapeutics. The TRK family contains three members, TRKA, TRKB, and TRKC, and these proteins are encoded by the genes NTRK1, NTRK2, and NTRK3, respectively. To activate TRK receptors, neurotrophins bind to the extracellular region stimulating dimerization, phosphorylation, and activation of downstream signaling pathways. Major known downstream pathways include RAS/MAPK/ERK, PLCγ, and PI3K/Akt. While being rare in most cancers, TRK fusions with other proteins have been well-established as oncogenic events in specific malignancies, including glioblastoma, papillary thyroid carcinoma, and secretory breast carcinomas. TRK protein amplification as well as alternative splicing events have also been described as contributors to cancer pathogenesis. For patients harboring alterations in TRK expression or activity, TRK inhibition emerges as an important therapeutic target. To date, multiple trials testing TRK-inhibiting compounds in various cancers are underway. In this review, we will summarize the current therapeutic trials for neoplasms involving NTKR gene alterations, as well as the promises and setbacks that are associated with targeting gene fusions. -
Overlapping Role of SCYL1 and SCYL3 in Maintaining Motor Neuron Viability
The Journal of Neuroscience, March 7, 2018 • 38(10):2615–2630 • 2615 Neurobiology of Disease Overlapping Role of SCYL1 and SCYL3 in Maintaining Motor Neuron Viability Emin Kuliyev,1 Sebastien Gingras,4 XClifford S. Guy,1 Sherie Howell,2 Peter Vogel,3 and XStephane Pelletier1 1Departments of Immunology, 2Pathology, 3Veterinary Pathology Core, Advanced Histology Core, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, and 4Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213 Members of the SCY1-like (SCYL) family of protein kinases are evolutionarily conserved and ubiquitously expressed proteins character- ized by an N-terminal pseudokinase domain, centrally located Huntingtin, elongation factor 3, protein phosphatase 2A, yeast kinase TOR1 repeats, and an overall disorganized C-terminal segment. In mammals, three family members encoded by genes Scyl1, Scyl2, and Scyl3 have been described. Studies have pointed to a role for SCYL1 and SCYL2 in regulating neuronal function and viability in mice and humans, but little is known about the biological function of SCYL3. Here, we show that the biochemical and cell biological properties of SCYL3aresimilartothoseofSCYL1andbothproteinsworkinconjunctiontomaintainmotorneuronviability.Specifically,althoughlack of Scyl3 in mice has no apparent effect on embryogenesis and postnatal life, it accelerates the onset of the motor neuron disorder caused by Scyl1 deficiency. Growth abnormalities, motor dysfunction, hindlimb paralysis, muscle wasting, neurogenic atrophy, motor neuron degeneration, and loss of large-caliber axons in peripheral nerves occurred at an earlier age in Scyl1/Scyl3 double-deficient mice than in Scyl1-deficientmice.DiseaseonsetalsocorrelatedwiththemislocalizationofTDP-43inspinalmotorneurons,suggestingthatSCYL1and SCYL3 regulate TDP-43 proteostasis. Together, our results demonstrate an overlapping role for SCYL1 and SCYL3 in vivo and highlight the importance the SCYL family of proteins in regulating neuronal function and survival. -
Loss of Inositol Polyphosphate 5-Phosphatase Is an Early Event in Development of Cutaneous Squamous Cell Carcinoma
Published OnlineFirst September 28, 2010; DOI: 10.1158/1940-6207.CAPR-10-0058 Published OnlineFirst on September 28, 2010 as 10.1158/1940-6207.CAPR-10-0058 Research Article Cancer Prevention Research Loss of Inositol Polyphosphate 5-Phosphatase Is an Early Event in Development of Cutaneous Squamous Cell Carcinoma Aleksandar Sekulic1, Su Y. Kim2, Galen Hostetter2, Stephanie Savage2, Janine G. Einspahr3, Anil Prasad4, Paul Sagerman4, Clara Curiel-Lewandrowski4, Robert Krouse4, G. Timothy Bowden3, James Warneke3, David S. Alberts3, Mark R. Pittelkow5, David DiCaudo1, Brian J. Nickoloff6, Jeffrey M. Trent2, and Michael Bittner2 Abstract Cutaneous squamous cell carcinoma (SCC) occurs commonly and can metastasize. Identification of specific molecular aberrations and mechanisms underlying the development and progression of cutane- ous SCC may lead to better prognostic and therapeutic approaches and more effective chemoprevention strategies. To identify genetic changes associated with early stages of cutaneous SCC development, we analyzed a series of 40 archived skin tissues ranging from normal skin to invasive SCC. Using high- resolution array-based comparative genomic hybridization, we identified deletions of a region on chro- mosome 10q harboring the INPP5A gene in 24% of examined SCC tumors. Subsequent validation by immunohistochemistry on an independent sample set of 71 SCC tissues showed reduced INPP5A protein levels in 72% of primary SCC tumors. Decrease in INPP5A protein levels seems to be an early event in SCC development, as it also is observed in 9 of 26 (35%) examined actinic keratoses, the earliest stage in SCC development. Importantly, further reduction of INPP5A levels is seen in a subset of SCC patients as the tumor progresses from primary to metastatic stage. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Recount Brain Example with Data from SRP027383
recount_brain example with data from SRP027383 true Abstract This is an example on how to use recount_brain applied to the SRP027383 study. We show how to download data from recount2, add the sample metadata from recount_brain, explore the sample metadata and the gene expression data, and perform a gene expression analysis. Introduction This document is an example of how you can use recount_brain. We will use the data from the SRA study SRP027383 which is described in “RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas” (Bao, Chen, Yang, Zhang, et al., 2014). As you can see in Figure @ref(fig:runselector) a lot of the metadata for these samples is missing from the SRA Run Selector which makes it a great case for using recount_brain. We will show how to add the recount_brain metadata and perform a gene differential expression analysis using this information. Sample metadata Just like any study in recount2 (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017), we first need to download the gene count data using recount::download_study(). Since we will be using many functions from the recount package, lets load it first1. ## Load the package library('recount') Download gene data Having loaded the package, we next download the gene-level data. if(!file.exists(file.path('SRP027383', 'rse_gene.Rdata'))) { download_study('SRP027383') } load(file.path('SRP027383', 'rse_gene.Rdata'), verbose = TRUE) ## Loading objects: ## rse_gene 1If you are a first time recount user, we recommend first reading the package vignette at bioconductor.org/packages/recount. 1 Figure 1: SRA Run Selector information for study SRP027383. -
Gene Ontology Functional Annotations and Pleiotropy
Network based analysis of genetic disease associations Sarah Gilman Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2013 Sarah Gilman All Rights Reserved ABSTRACT Network based analysis of genetic disease associations Sarah Gilman Despite extensive efforts and many promising early findings, genome-wide association studies have explained only a small fraction of the genetic factors contributing to common human diseases. There are many theories about where this “missing heritability” might lie, but increasingly the prevailing view is that common variants, the target of GWAS, are not solely responsible for susceptibility to common diseases and a substantial portion of human disease risk will be found among rare variants. Relatively new, such variants have not been subject to purifying selection, and therefore may be particularly pertinent for neuropsychiatric disorders and other diseases with greatly reduced fecundity. Recently, several researchers have made great progress towards uncovering the genetics behind autism and schizophrenia. By sequencing families, they have found hundreds of de novo variants occurring only in affected individuals, both large structural copy number variants and single nucleotide variants. Despite studying large cohorts there has been little recurrence among the genes implicated suggesting that many hundreds of genes may underlie these complex phenotypes. The question -
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.