Genome Sequence of the Necrotrophic Fungus Penicillium Digitatum, the Main Postharvest Pathogen of Citrus
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Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics
Gamwell et al. Orphanet Journal of Rare Diseases 2013, 8:33 http://www.ojrd.com/content/8/1/33 RESEARCH Open Access Small cell ovarian carcinoma: genomic stability and responsiveness to therapeutics Lisa F Gamwell1,2, Karen Gambaro3, Maria Merziotis2, Colleen Crane2, Suzanna L Arcand4, Valerie Bourada1,2, Christopher Davis2, Jeremy A Squire6, David G Huntsman7,8, Patricia N Tonin3,4,5 and Barbara C Vanderhyden1,2* Abstract Background: The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Method: The tumourigenic potential of BIN-67 cells was determined and the tumours formed in a xenograft model was compared to human SCCOHT. DNA sequencing, spectral karyotyping and high density SNP array analysis was performed. The sensitivity of the BIN-67 cells to standard chemotherapeutic agents and to vesicular stomatitis virus (VSV) and the JX-594 vaccinia virus was tested. Results: BIN-67 cells were capable of forming spheroids in hanging drop cultures. When xenografted into immunodeficient mice, BIN-67 cells developed into tumours that reflected the hypercalcemia and histology of human SCCOHT, notably intense expression of WT-1 and vimentin, and lack of expression of inhibin. Somatic mutations in TP53 and the most common activating mutations in KRAS and BRAF were not found in BIN-67 cells by DNA sequencing. -
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. -
Sla2 (NM 029983) Mouse Untagged Clone – MC201875 | Origene
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for MC201875 Sla2 (NM_029983) Mouse Untagged Clone Product data: Product Type: Expression Plasmids Product Name: Sla2 (NM_029983) Mouse Untagged Clone Tag: Tag Free Symbol: Sla2 Synonyms: A930009E21Rik; AI430952; SLAP-2; SLAP2 Vector: PCMV6-Kan/Neo E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin Fully Sequenced ORF: >BC052655 sequence for NM_029983 AAGAGGTAATCCGGCCACGCTCTTTGTCCCTGCTGTGCTGGGTGGGAGGGCCCAACCTGGTTTCTCTGAG AAGCAAAGGACTGCTGTACTAGTTTCGTGGAGATTGTCTGCTGACAAAGAAGCTTGATCACAGTACCTCA GCCTACTCTGACTCCTTTCTGGTGACCGATCCTCCAGGCTGCTGGGGCCTGAGATGCCGACTACCTTAGG ACCTGCAAAGGCCTGACCTGTCGGGTCAGTGTGCACATTGGCTGACTACCCTCATCAAACGTCTGATGGC AAACCTTTCCCTTTCCAGGTTCAGTGTGCTTGTGAGCGTCTGCTGAGTGATGGGAAGTTTGTCCAGCAGA GGGAAAACCTCCAGCCCCAGCCCCAGCTCCTCTGGTCCAGACCAGGAACCCGTGTCCATGCAACCAGAAA GACGCAAGGTCACAGCTGTGGCCCTGGGCAGTTTCCCAGCAGGTGAACAGGCCAGACTATCTCTGAGACT CGGGGAGCCGCTGACCATCATCTCTGAGGATGGAGATTGGTGGACAGTCCAGTCGGAAGTCTCAGGCAGA GAGTACCACATGCCCAGTGTGTATGTGGCTAAAGTCGCCCACGGGTGGCTGTACGAGGGCCTGAGCCGGG AGAAAGCCGAGGAACTACTCCTGTTACCTGGGAACCCCGGAGGGGCCTTCCTCATCCGGGAGAGCCAGAC CAGGAGAGGCTGCTATTCCCTGTCCGTCCGACTCAGCCGCCCTGCATCTTGGGACCGGATCAGACACTAC AGGATACAGCGTCTTGACAATGGCTGGCTGTACATCTCACCTCGCCTCACCTTCCCCTCACTCCACGCCT TGGTGGAGCATTACTCTGAGCTAGCAGATGGCATCTGCTGTCCCCTCAGGGAGCCGTGTGTCCTGCAGAA GCTTGGGCCACTACCTGGCAAAGATACACCTCCACCTGTGACTGTGCCAACATCATCACTAAATTGGAAA -
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. -
Analysis of Viral RNA-Host Protein Interactomes Enables Rapid Antiviral Drug Discovery
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.25.441316; this version posted April 26, 2021. 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. 1 Analysis of viral RNA-host protein interactomes 2 enables rapid antiviral drug discovery 3 Shaojun Zhang1,5, Wenze Huang1,5, Lili Ren2,3,5, Xiaohui Ju4,5, Mingli Gong4,5, 4 Jian Rao2,3, 5, Lei Sun1, Pan Li1, Qiang Ding4,*, Jianwei Wang2,3,*, Qiangfeng Cliff 5 Zhang1,* 6 7 1 MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for 8 Structural Biology & Frontier Research Center for Biological Structure, Center for 9 Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, 10 School of Life Sciences, Tsinghua University, Beijing, 100084, China 11 2 NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux 12 Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & 13 Peking Union Medical College, Beijing, 100730, China 14 3 Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical 15 Sciences and Peking Union Medical College, Beijing, 100730, China 16 4 Center for Infectious Disease Research, School of Medicine, Tsinghua University, 17 Beijing, 100084, China 18 5 Co-first author 19 * Correspondence: [email protected] (Q.D.), [email protected] (J.W.), 20 [email protected] (Q.C.Z.) 21 Short title: Viral RNA and host protein interactome 22 Keywords: RNA-protein interaction, SARS-CoV-2, EBOV, ZIKV, drug discovery 23 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.25.441316; this version posted April 26, 2021. -
Efficient Prediction of Human Protein-Protein Interactions at a Global Scale
Efficient prediction of human protein-protein interactions at a global scale. Andrew Schoenrock, Bahram Samanfar, Sylvain Pitre, Mohsen Hooshyar, Ke Jin, Charles A Phillips, Hui Wang, Sadhna Phanse, Katayoun Omidi, Yuan Gui, Md Alamgir, Alex Wong, Fredrik Barrenäs, Mohan Babu, Mikael Benson, Michael A Langston, James R Green, Frank Dehne and Ashkan Golshani Linköping University Post Print N.B.: When citing this work, cite the original article. Original Publication: Andrew Schoenrock, Bahram Samanfar, Sylvain Pitre, Mohsen Hooshyar, Ke Jin, Charles A Phillips, Hui Wang, Sadhna Phanse, Katayoun Omidi, Yuan Gui, Md Alamgir, Alex Wong, Fredrik Barrenäs, Mohan Babu, Mikael Benson, Michael A Langston, James R Green, Frank Dehne and Ashkan Golshani, Efficient prediction of human protein-protein interactions at a global scale., 2014, BMC bioinformatics, (15), 1, 383. http://dx.doi.org/10.1186/s12859-014-0383-1 Copyright: BioMed Central http://www.biomedcentral.com/ Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-114135 Schoenrock et al. BMC Bioinformatics 2014, 15:383 http://www.biomedcentral.com/1471-2105/15/383 RESEARCH ARTICLE Open Access Efficient prediction of human protein-protein interactions at a global scale Andrew Schoenrock1, Bahram Samanfar2, Sylvain Pitre1†, Mohsen Hooshyar2†, Ke Jin3, Charles A Phillips4, Hui Wang5,6, Sadhna Phanse3, Katayoun Omidi2, Yuan Gui2, Md Alamgir2, Alex Wong2, Fredrik Barrenäs5,6, Mohan Babu7, Mikael Benson5,6, Michael A Langston4, James R Green8, Frank Dehne1 and Ashkan Golshani2* Abstract Background: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. -
A Graph-Theoretic Approach to Model Genomic Data and Identify Biological Modules Asscociated with Cancer Outcomes
A Graph-Theoretic Approach to Model Genomic Data and Identify Biological Modules Asscociated with Cancer Outcomes Deanna Petrochilos A dissertation presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: Neil Abernethy, Chair John Gennari, Ali Shojaie Program Authorized to Offer Degree: Biomedical Informatics and Health Education UMI Number: 3588836 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 3588836 Published by ProQuest LLC (2013). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 ©Copyright 2013 Deanna Petrochilos University of Washington Abstract Using Graph-Based Methods to Integrate and Analyze Cancer Genomic Data Deanna Petrochilos Chair of the Supervisory Committee: Assistant Professor Neil Abernethy Biomedical Informatics and Health Education Studies of the genetic basis of complex disease present statistical and methodological challenges in the discovery of reliable and high-confidence genes that reveal biological phenomena underlying the etiology of disease or gene signatures prognostic of disease outcomes. This dissertation examines the capacity of graph-theoretical methods to model and analyze genomic information and thus facilitate using prior knowledge to create a more discrete and functionally relevant feature space. -
Systematic Elucidation of Neuron-Astrocyte Interaction in Models of Amyotrophic Lateral Sclerosis Using Multi-Modal Integrated Bioinformatics Workflow
ARTICLE https://doi.org/10.1038/s41467-020-19177-y OPEN Systematic elucidation of neuron-astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow Vartika Mishra et al.# 1234567890():,; Cell-to-cell communications are critical determinants of pathophysiological phenotypes, but methodologies for their systematic elucidation are lacking. Herein, we propose an approach for the Systematic Elucidation and Assessment of Regulatory Cell-to-cell Interaction Net- works (SEARCHIN) to identify ligand-mediated interactions between distinct cellular com- partments. To test this approach, we selected a model of amyotrophic lateral sclerosis (ALS), in which astrocytes expressing mutant superoxide dismutase-1 (mutSOD1) kill wild-type motor neurons (MNs) by an unknown mechanism. Our integrative analysis that combines proteomics and regulatory network analysis infers the interaction between astrocyte-released amyloid precursor protein (APP) and death receptor-6 (DR6) on MNs as the top predicted ligand-receptor pair. The inferred deleterious role of APP and DR6 is confirmed in vitro in models of ALS. Moreover, the DR6 knockdown in MNs of transgenic mutSOD1 mice attenuates the ALS-like phenotype. Our results support the usefulness of integrative, systems biology approach to gain insights into complex neurobiological disease processes as in ALS and posit that the proposed methodology is not restricted to this biological context and could be used in a variety of other non-cell-autonomous communication -
A Genome-Scale Yeast Library with Inducible Expression of Individual Genes
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.30.424776; this version posted January 1, 2021. 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. A genome-scale yeast library with inducible expression of individual genes 1,2,3,* 4,* 1,2,* 1,2 4 4 Yuko Arita , Griffin Kim , Zhijian Li , Helena Friesen , Gina Turco , Rebecca Y. Wang , 1,2 1,2 4 4 4 Dale Climie , Matej Usaj , Manuel Hotz , Emily Stoops , Anastasia Baryshnikova , Charles 1,2,3,^ 4,^ 1,2,^ 4,^ Boone , David Botstein , Brenda J. Andrews , R. Scott McIsaac 1. Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. 2. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada. 3. RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan. 4. Calico Life Sciences LLC, South San Francisco, CA 94080, USA. *Authors contributed equally to this study. ^Corresponding authors: R. Scott McIsaac ([email protected]); David Botstein ([email protected]); Charles Boone ([email protected]); Brenda J. Andrews ([email protected]) For requesting the YETI library in pooled format, contact R. Scott McIsaac. Running title: The YETI strain library Keywords: BAR-seq / gene overexpression / yeast genomics / yeast mutant array / CRISPRi ORCID ID for R. Scott McIsaac: https://orcid.org/0000-0002-5339-6032 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.30.424776; this version posted January 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Chromatin Accessibility Dynamics of Chlamydia-Infected Epithelial Cells
Hayward et al. Epigenetics & Chromatin (2020) 13:45 https://doi.org/10.1186/s13072-020-00368-2 Epigenetics & Chromatin RESEARCH Open Access Chromatin accessibility dynamics of Chlamydia-infected epithelial cells Regan J. Hayward1, James W. Marsh2, Michael S. Humphrys3, Wilhelmina M. Huston4 and Garry S. A. Myers1,4* Abstract Chlamydia are Gram-negative, obligate intracellular bacterial pathogens responsible for a broad spectrum of human and animal diseases. In humans, Chlamydia trachomatis is the most prevalent bacterial sexually transmitted infec- tion worldwide and is the causative agent of trachoma (infectious blindness) in disadvantaged populations. Over the course of its developmental cycle, Chlamydia extensively remodels its intracellular niche and parasitises the host cell for nutrients, with substantial resulting changes to the host cell transcriptome and proteome. However, little infor- mation is available on the impact of chlamydial infection on the host cell epigenome and global gene regulation. Regions of open eukaryotic chromatin correspond to nucleosome-depleted regions, which in turn are associated with regulatory functions and transcription factor binding. We applied formaldehyde-assisted isolation of regulatory elements enrichment followed by sequencing (FAIRE-Seq) to generate temporal chromatin maps of C. trachomatis- infected human epithelial cells in vitro over the chlamydial developmental cycle. We detected both conserved and distinct temporal changes to genome-wide chromatin accessibility associated with C. trachomatis infection. The observed diferentially accessible chromatin regions include temporally-enriched sets of transcription factors, which may help shape the host cell response to infection. These regions and motifs were linked to genomic features and genes associated with immune responses, re-direction of host cell nutrients, intracellular signalling, cell–cell adhesion, extracellular matrix, metabolism and apoptosis. -
Entrez ID Gene Name Fold Change Q-Value Description
Entrez ID gene name fold change q-value description 4283 CXCL9 -7.25 5.28E-05 chemokine (C-X-C motif) ligand 9 3627 CXCL10 -6.88 6.58E-05 chemokine (C-X-C motif) ligand 10 6373 CXCL11 -5.65 3.69E-04 chemokine (C-X-C motif) ligand 11 405753 DUOXA2 -3.97 3.05E-06 dual oxidase maturation factor 2 4843 NOS2 -3.62 5.43E-03 nitric oxide synthase 2, inducible 50506 DUOX2 -3.24 5.01E-06 dual oxidase 2 6355 CCL8 -3.07 3.67E-03 chemokine (C-C motif) ligand 8 10964 IFI44L -3.06 4.43E-04 interferon-induced protein 44-like 115362 GBP5 -2.94 6.83E-04 guanylate binding protein 5 3620 IDO1 -2.91 5.65E-06 indoleamine 2,3-dioxygenase 1 8519 IFITM1 -2.67 5.65E-06 interferon induced transmembrane protein 1 3433 IFIT2 -2.61 2.28E-03 interferon-induced protein with tetratricopeptide repeats 2 54898 ELOVL2 -2.61 4.38E-07 ELOVL fatty acid elongase 2 2892 GRIA3 -2.60 3.06E-05 glutamate receptor, ionotropic, AMPA 3 6376 CX3CL1 -2.57 4.43E-04 chemokine (C-X3-C motif) ligand 1 7098 TLR3 -2.55 5.76E-06 toll-like receptor 3 79689 STEAP4 -2.50 8.35E-05 STEAP family member 4 3434 IFIT1 -2.48 2.64E-03 interferon-induced protein with tetratricopeptide repeats 1 4321 MMP12 -2.45 2.30E-04 matrix metallopeptidase 12 (macrophage elastase) 10826 FAXDC2 -2.42 5.01E-06 fatty acid hydroxylase domain containing 2 8626 TP63 -2.41 2.02E-05 tumor protein p63 64577 ALDH8A1 -2.41 6.05E-06 aldehyde dehydrogenase 8 family, member A1 8740 TNFSF14 -2.40 6.35E-05 tumor necrosis factor (ligand) superfamily, member 14 10417 SPON2 -2.39 2.46E-06 spondin 2, extracellular matrix protein 3437 -
Integration of Protein Binding Interfaces and Abundance
INTEGRATION OF PROTEIN BINDING INTERFACES AND ABUNDANCE DATA REVEALS EVOLUTIONARY PRESSURES IN PROTEIN NETWORKS by David Orestis Holland A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland January, 2018 © David O. Holland 2018 All rights reserved Abstract Networks of protein-protein interactions have received considerable interest in the past two decades for their insights about protein function and evolution. Traditionally, these networks only map the functional partners of proteins; they lack further levels of data such as binding affinity, allosteric regulation, competitive vs noncompetitive binding, and protein abundance. Recent experiments have made such data on a network-wide scale available, and in this thesis I integrate two extra layers of data in particular: the binding sites that proteins use to interact with their partners, and the abundance or “copy numbers” of the proteins. By analyzing the networks for the clathrin-mediated endocytosis (CME) system in yeast and the ErbB signaling pathway in humans, I find that this extra data reveals new insights about the evolution of protein networks. The structure of the binding site or interface interaction network (IIN) is optimized to allow higher binding specificity; that is, a high gap in strength between functional binding and nonfunctional mis-binding. This strongly implies that mis-binding is an evolutionary error-load constraint shaping protein network structure. Another method to limit mis-binding is to balance protein copy numbers so that there are no “leftover” proteins available for mis-binding. By developing a new method to quantify balance in IINs, I show that ii the CME network is significantly balanced when compared to randomly sampled sets of copy numbers.