Detection of Selection Signatures Among Brazilian, Sri Lankan, And

Total Page:16

File Type:pdf, Size:1020Kb

Detection of Selection Signatures Among Brazilian, Sri Lankan, And Animal Science Publications Animal Science 1-2019 Detection of Selection Signatures Among Brazilian, Sri Lankan, and Egyptian Chicken Populations Under Different Environmental Conditions Muhammed Walugembe Iowa State University, [email protected] Francesca Bertolini Iowa State University Chandraratne Mahinda B. Dematawewa University of Peradeniya Matheus P. Reis São Paulo State University Ahmed R. Elbeltagy AFonilmloawl P thirodsu cationdn Raeddseaitrcionh Inalstit wutorke, Gsiza, at :Eghyttpps://lt ib.dr.iastate.edu/ans_pubs Part of the Agriculture Commons, Climate Commons, Genetics and Genomics Commons, and See next page for additional authors the Poultry or Avian Science Commons The ompc lete bibliographic information for this item can be found at https://lib.dr.iastate.edu/ ans_pubs/457. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Animal Science at Iowa State University Digital Repository. It has been accepted for inclusion in Animal Science Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Detection of Selection Signatures Among Brazilian, Sri Lankan, and Egyptian Chicken Populations Under Different Environmental Conditions Abstract Extreme environmental conditions are a major challenge in livestock production. Changes in climate, particularly those that contribute to weather extremes like drought or excessive humidity, may result in reduced performance and reproduction and could compromise the animal’s immune function. Animal survival within extreme environmental conditions could be in response to natural selection and to artificial selection for production traits that over time together may leave selection signatures in the genome. The aim of this study was to identify selection signatures that may be involved in the adaptation of indigenous chickens from two different climatic regions (Sri Lanka = Tropical; Egypt = Arid) and in non-indigenous chickens that derived from human migration events to the generally tropical State of São Paulo, Brazil. To do so, analyses were conducted using fixation index (Fst) and hapFLK analyses. Chickens from Brazil (n = 156), Sri Lanka (n = 92), and Egypt (n = 96) were genotyped using the Affymetrix Axiom®600k Chicken Genotyping Array. Pairwise Fst analyses among countries did not detect major regions of divergence between chickens from Sri Lanka and Brazil, with ecotypes/breeds from Brazil appearing to be genetically related to Asian-Indian (Sri Lanka) ecotypes. However, several differences were detected in comparisons of Egyptian with either Sri Lankan or Brazilian populations, and common regions of difference on chromosomes 2, 3 and 8 were detected. The ah pFLK analyses for the three separate countries suggested unique regions that are potentially under selection on chromosome 1 for all three countries, on chromosome 4 for Sri Lankan, and on chromosomes 3, 5, and 11 for the Egyptian populations. Some of identified regions under selection with hapFLK analyses contained genes such as TLR3, SOCS2, EOMES, and NFAT5 whose biological functions could provide insights in understanding adaptation mechanisms in response to arid and tropical environments. Keywords chickens, environment, selection signatures, adaptation, immune system Disciplines Agriculture | Animal Sciences | Climate | Genetics and Genomics | Poultry or Avian Science Comments This article is published as Walugembe, Muhammed, Francesca Bertolini, Chandraratne Mahinda Dematawewa, Matheus P. Reis, Ahmed R. Elbeltagy, Carl Joseph Schmidt, Susan J. Lamont, and Max F. Rothschild. "Detection of selection signatures among Brazilian, Sri Lankan, and Egyptian chicken populations under different environmental conditions." Frontiers in Genetics 9 (2018): 737. doi: 10.3389/ fgene.2018.00737. Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License. This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ans_pubs/457 Authors Muhammed Walugembe, Francesca Bertolini, Chandraratne Mahinda B. Dematawewa, Matheus P. Reis, Ahmed R. Elbeltagy, Carl J. Schmidt, Susan J. Lamont, and Max F. Rothschild This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ans_pubs/457 fgene-09-00737 January 9, 2019 Time: 19:8 # 1 ORIGINAL RESEARCH published: 14 January 2019 doi: 10.3389/fgene.2018.00737 Detection of Selection Signatures Among Brazilian, Sri Lankan, and Egyptian Chicken Populations Under Different Environmental Conditions Muhammed Walugembe1*, Francesca Bertolini1, Chandraratne Mahinda B. Dematawewa2, Matheus P. Reis3, Ahmed R. Elbeltagy4, Carl J. Schmidt5, Susan J. Lamont1 and Max F. Rothschild1 1 Department of Animal Science, Iowa State University, Ames, IA, United States, 2 Department of Animal Science, Faculty of Agriculture, University of Peradeniya, Kandy, Sri Lanka, 3 Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University, Jaboticabal, Brazil, 4 Department of Animal Biotechnology, Animal Production Research Institute, Giza, Egypt, 5 Animal and Food Sciences, University of Delaware, Newark, DE, United States Extreme environmental conditions are a major challenge in livestock production. Changes in climate, particularly those that contribute to weather extremes like drought Edited by: or excessive humidity, may result in reduced performance and reproduction and Peng Xu, Xiamen University, China could compromise the animal’s immune function. Animal survival within extreme Reviewed by: environmental conditions could be in response to natural selection and to artificial Yu Jiang, selection for production traits that over time together may leave selection signatures Northwest A&F University, China Keliang Wu, in the genome. The aim of this study was to identify selection signatures that may be China Agricultural University, China involved in the adaptation of indigenous chickens from two different climatic regions *Correspondence: (Sri Lanka = Tropical; Egypt = Arid) and in non-indigenous chickens that derived Muhammed Walugembe from human migration events to the generally tropical State of São Paulo, Brazil. [email protected] To do so, analyses were conducted using fixation index (Fst) and hapFLK analyses. Specialty section: Chickens from Brazil (n = 156), Sri Lanka (n = 92), and Egypt (n = 96) were genotyped This article was submitted to using the Affymetrix Axiom R 600k Chicken Genotyping Array. Pairwise Fst analyses Livestock Genomics, a section of the journal among countries did not detect major regions of divergence between chickens from Frontiers in Genetics Sri Lanka and Brazil, with ecotypes/breeds from Brazil appearing to be genetically Received: 13 April 2018 related to Asian-Indian (Sri Lanka) ecotypes. However, several differences were detected Accepted: 22 December 2018 Published: 14 January 2019 in comparisons of Egyptian with either Sri Lankan or Brazilian populations, and common Citation: regions of difference on chromosomes 2, 3 and 8 were detected. The hapFLK analyses Walugembe M, Bertolini F, for the three separate countries suggested unique regions that are potentially under Dematawewa CMB, Reis MP, selection on chromosome 1 for all three countries, on chromosome 4 for Sri Lankan, Elbeltagy AR, Schmidt CJ, Lamont SJ and Rothschild MF (2019) Detection and on chromosomes 3, 5, and 11 for the Egyptian populations. Some of identified of Selection Signatures Among regions under selection with hapFLK analyses contained genes such as TLR3, SOCS2, Brazilian, Sri Lankan, and Egyptian Chicken Populations Under Different EOMES, and NFAT5 whose biological functions could provide insights in understanding Environmental Conditions. adaptation mechanisms in response to arid and tropical environments. Front. Genet. 9:737. doi: 10.3389/fgene.2018.00737 Keywords: chickens, environment, selection signatures, adaptation, immune system Frontiers in Genetics| www.frontiersin.org 1 January 2019| Volume 9| Article 737 fgene-09-00737 January 9, 2019 Time: 19:8 # 2 Walugembe et al. Environmental Selection Signatures in Chickens INTRODUCTION Fixation Index, Fst (Elferink et al., 2012; Gholami et al., 2015; Fleming et al., 2017) and hapFLK (Gholami et al., 2015) analyses Extreme environmental conditions are a major challenge to detect selection signatures in both commercial and non- in livestock production. Changes in climate, particularly commercial breeds. Inter-population statistics are reported to those that contribute to weather extremes like drought or have more statistical power to detect selection signatures in extreme temperatures or humidity may result in reduced recently diverged populations (Yi et al., 2010). The major concern performance, reproduction and could compromise the animal’s with Fst is that it assumes the populations have same effective immune function (St-Pierre et al., 2003). In chickens, extreme population size and are derived independently from one ancestral environmental temperatures lead to generation of reactive population (Price et al., 2010). HapFLK is a method that is oxygen species (ROS), causing oxidative stress and lipid based on extension of the FLK statistic and accounts for both peroxidation (Altan et al., 2003). However, chickens particularly the hierarchical structure and haplotype information, and its use the local (indigenous)
Recommended publications
  • Hypertrophic Cardiomyopathy- Associated Mutations in Genes That Encode Calcium-Handling Proteins
    Current Molecular Medicine 2012, 12, 507-518 507 Beyond the Cardiac Myofilament: Hypertrophic Cardiomyopathy- Associated Mutations in Genes that Encode Calcium-Handling Proteins A.P. Landstrom and M.J. Ackerman* Departments of Medicine, Pediatrics, and Molecular Pharmacology & Experimental Therapeutics, Divisions of Cardiovascular Diseases and Pediatric Cardiology, and the Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, Minnesota, USA Abstract: Traditionally regarded as a genetic disease of the cardiac sarcomere, hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease and a significant cause of sudden cardiac death. While the most common etiologies of this phenotypically diverse disease lie in a handful of genes encoding critical contractile myofilament proteins, approximately 50% of patients diagnosed with HCM worldwide do not host sarcomeric gene mutations. Recently, mutations in genes encoding calcium-sensitive and calcium- handling proteins have been implicated in the pathogenesis of HCM. Among these are mutations in TNNC1- encoded cardiac troponin C, PLN-encoded phospholamban, and JPH2-encoded junctophilin 2 which have each been associated with HCM in multiple studies. In addition, mutations in RYR2-encoded ryanodine receptor 2, CASQ2-encoded calsequestrin 2, CALR3-encoded calreticulin 3, and SRI-encoded sorcin have been associated with HCM, although more studies are required to validate initial findings. While a relatively uncommon cause of HCM, mutations in genes that encode calcium-handling proteins represent an emerging genetic subset of HCM. Furthermore, these naturally occurring disease-associated mutations have provided useful molecular tools for uncovering novel mechanisms of disease pathogenesis, increasing our understanding of basic cardiac physiology, and dissecting important structure-function relationships within these proteins.
    [Show full text]
  • In This Table Protein Name, Uniprot Code, Gene Name P-Value
    Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2-
    [Show full text]
  • DOE Systems Biology Knowledgebase Implementation Plan
    DOE Systems Biology Knowledgebase Implementation Plan As part of the U.S. Department of Energy’s (DOE) Office of Science, the Office of Biological and Environmental Research (BER) supports fundamental research and technology development aimed at achieving predictive, systems-level understand- ing of complex biological and environmental systems to advance DOE missions in energy, climate, and environment. DOE Contact Susan Gregurick 301.903.7672, [email protected] Office of Biological and Environmental Research U.S. Department of Energy Office of Science www.science.doe.gov/Program_Offices/BER.htm Acknowledgements The DOE Office of Biological and Environmental Research appreciates the vision and leadership exhibited by Bob Cottingham and Brian Davison (both from Oak Ridge National Laboratory) over the past year to conceptualize and guide the effort to create the DOE Systems Biology Knowledgebase Implementation Plan. Furthermore, we are grateful for the valuable contributions from about 300 members of the scientific community to organize, participate in, and provide the intellectual output of 5 work- shops, which culminated with the implementation plan. The plan was rendered into its current form by the efforts of the Biological and Environmental Research Information System (Oak Ridge National Laboratory). The report is available via • www.genomicscience.energy.gov/compbio/ • www.science.doe.gov/ober/BER_workshops.html • www.systemsbiologyknowledgebase.org Suggested citation for entire report: U.S. DOE. 2010. DOE Systems Biology Knowledgebase Implementation Plan. U.S. Department of Energy Office of Science (www.genomicscience.energy.gov/compbio/). DOE Systems Biology Knowledgebase Implementation Plan September 30, 2010 Office of Biological and Environmental Research The document is available via genomicscience.energy.gov/compbio/.
    [Show full text]
  • Anti-SRI / Sorcin Antibody (ARG42959)
    Product datasheet [email protected] ARG42959 Package: 50 μg anti-SRI / Sorcin antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes SRI / Sorcin Tested Reactivity Hu, Ms, Rat Tested Application FACS, IHC-P, WB Host Rabbit Clonality Polyclonal Isotype IgG Target Name SRI / Sorcin Antigen Species Human Immunogen Synthetic peptide corresponding to a sequence of Human SRI / Sorcin. (TVDPQELQKALTTMGFRLSPQAVNSIAKRY) Conjugation Un-conjugated Alternate Names SCN; 22 kDa protein; V19; CP-22; Sorcin; CP22 Application Instructions Application table Application Dilution FACS 1:150 - 1:500 IHC-P 1:200 - 1:1000 WB 1:500 - 1:2000 Application Note IHC-P: Antigen Retrieval: Heat mediation was performed in Citrate buffer (pH 6.0) for 20 min. * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 22 kDa Observed Size ~ 22 kDa Properties Form Liquid Purification Affinity purification with immunogen. Buffer 0.2% Na2HPO4, 0.9% NaCl, 0.05% Sodium azide and 4% Trehalose. Preservative 0.05% Sodium azide Stabilizer 4% Trehalose Concentration 0.5 - 1 mg/ml www.arigobio.com 1/3 Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C or below. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. Note For laboratory research only, not for drug, diagnostic or other use. Bioinformation Gene Symbol SRI Gene Full Name sorcin Background This gene encodes a calcium-binding protein with multiple E-F hand domains that relocates from the cytoplasm to the sarcoplasmic reticulum in response to elevated calcium levels.
    [Show full text]
  • Genetic Determinants of Sporadic Breast Cancer in Sri Lankan Women Nirmala Dushyanthi Sirisena1*, Adebowale Adeyemo2, Anchala I
    Sirisena et al. BMC Cancer (2018) 18:180 https://doi.org/10.1186/s12885-018-4112-4 RESEARCH ARTICLE Open Access Genetic determinants of sporadic breast cancer in Sri Lankan women Nirmala Dushyanthi Sirisena1*, Adebowale Adeyemo2, Anchala I. Kuruppu1, Nilaksha Neththikumara1, Nilakshi Samaranayake3 and Vajira H. W. Dissanayake1 Abstract Background: While a range of common genetic variants have been identified to be associated with risk of sporadic breast cancer in several Western studies, little is known about their role in South Asian populations. Our objective was to examine the association between common genetic variants in breast cancer related genes and risk of breast cancer in a cohort of Sri Lankan women. Methods: A case-control study of 350 postmenopausal women with breast cancer and 350 healthy postmenopausal women was conducted. Genotyping using the iPLEX GOLD assay was done for 56 haplotype-tagging single nucleotide polymorphisms (SNPs) in 36 breast cancer related genes. Testing for association was done using an additive genetic model. Odds ratios and 95% confidence intervals were calculated using adjusted logistic regression models. Results: Four SNPs [rs3218550 (XRCC2), rs6917 (PHB), rs1801516 (ATM),andrs13689(CDH1)] were significantly associated with risk of breast cancer. The rs3218550 T allele and rs6917 A allele increased breast cancer risk by 1.5-fold and 1.4-fold, respectively. The CTC haplotype defined by the SNPs rs3218552|rs3218550|rs3218536 on chromosome 7 (P = 0.0088) and the CA haplotype defined by the SNPs rs1049620|rs6917 on chromosome 17 (P = 0.0067) were significantly associated with increased risk of breast cancer. The rs1801516 A allele and the rs13689 C allele decreased breast cancer risk by 0.6-fold and 0.7-fold, respectively.
    [Show full text]
  • Annexins: Putative Linkers in Dynamic Membrane–Cytoskeleton
    Protoplasma (2007) 230: 203–215 DOI 10.1007/s00709-006-0234-7 PROTOPLASMA Printed in Austria Annexins: putative linkers in dynamic membrane–cytoskeleton interactions in plant cells D. Konopka-Postupolska Laboratory of Plant Pathogenesis, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw Received January 10, 2006; accepted March 14, 2006; published online April 24, 2007 © Springer-Verlag 2007 Summary. The plasma membrane, the most external cellular structure, proteins with similar characteristics in plant cells (Boustead is at the forefront between the plant cell and its environment. Hence, it is et al. 1989, Blackbourn et al. 1991). In vertebrates the annex- naturally adapted to function in detection of external signals, their trans- duction throughout the cell, and finally, in cell reactions. Membrane ins were grouped into 13 families. In contrast, plant annexins lipids and the cytoskeleton, once regarded as simple and static structures, seem to represent a relatively simpler, smaller, and less have recently been recognized as significant players in signal transduc- diverse family of proteins. Nevertheless, in all analysed tion. Proteins involved in signal detection and transduction are organised in specific domains at the plasma membrane. Their aggregation allows to plant species, at least two distinct proteins with molecular bring together and orient the downstream and upstream members of sig- masses between 33 and 36 kDa have been discovered nalling pathways. The cortical cytoskeleton provides a structural frame- (Smallwood et al. 1990, Shin et al. 1995, Proust et al. 1996, work for rapid signal transduction from the cell periphery into the Thonat et al. 1997). However, a search through the nucleus.
    [Show full text]
  • The Biocyc Collection of Microbial Genomes and Metabolic Pathways Peter D
    Briefings in Bioinformatics, 2017, 1–9 doi: 10.1093/bib/bbx085 Paper Downloaded from https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbx085/4084231 by guest on 02 May 2019 The BioCyc collection of microbial genomes and metabolic pathways Peter D. Karp, Richard Billington, Ron Caspi, Carol A. Fulcher, Mario Latendresse, Anamika Kothari, Ingrid M. Keseler, Markus Krummenacker, Peter E. Midford, Quang Ong, Wai Kit Ong, Suzanne M. Paley and Pallavi Subhraveti Corresponding author: Peter D. Karp, Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. E-mail: [email protected] Abstract BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc in- clude an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome; an expansion in the amount of curated content within BioCyc; and new developments in the BioCyc soft- ware tools including redesigned gene/protein pages and metabolite pages; new search tools; a new sequence-alignment tool; a new tool for visualizing groups of related metabolic pathways; and a facility called SmartTables, which enables biolo- gists to perform analyses that previously would have required a programmer’s assistance. Key words: genome databases; microbial genome databases; metabolic pathway databases Peter Karp is the Director of the SRI Bioinformatics Research Group (BRG).
    [Show full text]
  • The Sri Lankan Personal Genome Project: an Overview
    Leading Article The Sri Lankan Personal Genome Project: an overview Prof. Vajira H. W. Dissanayake MBBS, PhD Professor, Department of Anatomy; Medical Geneticist, Human Genetics Unit, Faculty of Medicine, University of Colombo, Sri Lanka E-Mail address: [email protected] Pubudu S. Samarakoon BSc, MSc Tutor, Biomedical Informatics Course, Postgraduate Institute of Medicine, University of Colombo, Sri Lanka. Current address: Research Fellow, Department of Medical Genetics, University of Oslo, Norway. E-Mail address: [email protected] Dr. Vinod Scaria MBBS Scientist, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India E-Mail address: [email protected] Ashok Patowary BSc, MSc Scientist, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India E-Mail address: [email protected] Dr. Sridhar Sivasubbu PhD Scientist, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India E-Mail address: [email protected] Dr. Rajesh S. Gokhale PhD Director, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India E-mail address: [email protected] Sri Lanka Journal of Bio-Medical Informatics 2011;2(1):4-8 DOI: http://dx.doi.org/10.4038/sljbmi.v2i1.3711 Abstract The first Sri Lankan Personal Genome was sequenced heralding the entry of Sri Lanka into the new era of whole genome sequencing. This paper explains the background and the rationale for the project, gives a brief overview of what was found in the Sri Lankan Personal Genome, and discusses the future directions of the project. Keywords - Sri Lankan Personal Genome Project; Sri Lanka; Genome; Sinhalese; Whole Genome Sequencing Background With the completion of the Human Genome Project (HGP)(1,2) and a decade of human genomics, we are at an interesting juncture in the history of mankind.
    [Show full text]
  • An Expanded Proteome of Cardiac T-Tubules☆
    Cardiovascular Pathology 42 (2019) 15–20 Contents lists available at ScienceDirect Cardiovascular Pathology Original Article An expanded proteome of cardiac t-tubules☆ Jenice X. Cheah, Tim O. Nieuwenhuis, Marc K. Halushka ⁎ Department of Pathology, Division of Cardiovascular Pathology, Johns Hopkins University SOM, Baltimore, MD, USA article info abstract Article history: Background: Transverse tubules (t-tubules) are important structural elements, derived from sarcolemma, found Received 27 February 2019 on all striated myocytes. These specialized organelles create a scaffold for many proteins crucial to the effective Received in revised form 29 April 2019 propagation of signal in cardiac excitation–contraction coupling. The full protein composition of this region is un- Accepted 17 May 2019 known. Methods: We characterized the t-tubule subproteome using 52,033 immunohistochemical images covering Keywords: 13,203 proteins from the Human Protein Atlas (HPA) cardiac tissue microarrays. We used HPASubC, a suite of Py- T-tubule fi Proteomics thon tools, to rapidly review and classify each image for a speci c t-tubule staining pattern. The tools Gene Cards, Caveolin String 11, and Gene Ontology Consortium as well as literature searches were used to understand pathways and relationships between the proteins. Results: There were 96 likely t-tubule proteins identified by HPASubC. Of these, 12 were matrisome proteins and 3 were mitochondrial proteins. A separate literature search identified 50 known t-tubule proteins. A comparison of the 2 lists revealed only 17 proteins in common, including 8 of the matrisome proteins. String11 revealed that 94 of 127 combined t-tubule proteins generated a single interconnected network. Conclusion: Using HPASubC and the HPA, we identified 78 novel, putative t-tubule proteins and validated 17 within the literature.
    [Show full text]
  • PATHWAY TOOLS Ii Pathway Tools User’S Guide, Version 13.0
    VERSION 13.0 User’s Guide PATHWAY TOOLS ii Pathway Tools User’s Guide, Version 13.0 Copyright c 1996, 1999-2009 SRI International, 1997-1999 DoubleTwist, Inc. All rights reserved. Printed in the U.S.A. We gratefully acknowledge contributions to Pathway Tools, used by permission, from: Jeremy Zucker, Harvard Medical School The Laboratory of Christos Ouzounis, European Bioinformatics Institute DoubleTwist is a registered trademark of DoubleTwist, Inc. Medline is a registered trademark of the National Library of Medicine. Oracle is a registered trademark of Oracle Corporation. MySQL R is a registered trademark of MySQL AB in the United States, the European Union and other countries. The Generic Frame Protocol is Copyright c 1996, The Board of Trustees of the Leland Stanford Junior University and SRI International. All Rights Reserved. The Pathway Tools Software is Copyright c 1997-1999 DoubleTwist, Inc., SRI Interna- tional 1996, 1999-2007. All Rights Reserved. The EcoCyc Database is Copyright c SRI International 1996, 1999-2009, Marine Biologi- cal Laboratory 1996-2001, DoubleTwist Inc. 1997-1999. All Rights Reserved. The MetaCyc Database is Copyright c SRI International 1999-2009, Marine Biological Laboratory 1998-2001, DoubleTwist Inc. 1998, 1999. All Rights Reserved. Allegro Common Lisp is Copyright c 1985-2009, Franz Inc. All Rights Reserved. All other trademarks are property of their respective owners. Any rights not expressly granted herein are reserved. This product may include data from BIND (http://blueprint.org/bind/bind. php) to which the following two notices apply: (1) Bader GD, Betel D, Hogue CW. (2003) BIND: the Biomolecular Interaction Network Database.
    [Show full text]
  • The Human GCOM1 Complex Gene Interacts with the NMDA Receptor and Internexin-Alpha☆
    HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Gene. Author Manuscript Author manuscript; Manuscript Author available in PMC 2018 June 20. Published in final edited form as: Gene. 2018 March 30; 648: 42–53. doi:10.1016/j.gene.2018.01.029. The human GCOM1 complex gene interacts with the NMDA receptor and internexin-alpha☆ Raymond S. Roginskia,b,*, Chi W. Laua,1, Phillip P. Santoiemmab,2, Sara J. Weaverc,3, Peicheng Dud, Patricia Soteropoulose, and Jay Yangc aDepartment of Anesthesiology, CMC VA Medical Center, Philadelphia, PA 19104, United States bDepartment of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States cDepartment of Anesthesia, University of Wisconsin at Madison, Madison, WI 53706, United States dBioinformatics, Rutgers-New Jersey Medical School, Newark, NJ 07103, United States eDepartment of Genetics, Rutgers-New Jersey Medical School, Newark, NJ 07103, United States Abstract The known functions of the human GCOM1 complex hub gene include transcription elongation and the intercalated disk of cardiac myocytes. However, in all likelihood, the gene's most interesting, and thus far least understood, roles will be found in the central nervous system. To investigate the functions of the GCOM1 gene in the CNS, we have cloned human and rat brain cDNAs encoding novel, 105 kDa GCOM1 combined (Gcom) proteins, designated Gcom15, and identified a new group of GCOM1 interacting genes, termed Gints, from yeast two-hybrid (Y2H) screens. We showed that Gcom15 interacts with the NR1 subunit of the NMDA receptor by co- expression in heterologous cells, in which we observed bi-directional co-immunoprecipitation of human Gcom15 and murine NR1.
    [Show full text]
  • TOPSAN: a Dynamic Web Database for Structural Genomics Kyle Ellrott1,2, Christian M
    D494–D496 Nucleic Acids Research, 2011, Vol. 39, Database issue Published online 20 October 2010 doi:10.1093/nar/gkq902 TOPSAN: a dynamic web database for structural genomics Kyle Ellrott1,2, Christian M. Zmasek3,4, Dana Weekes1,4, S. Sri Krishna2,4, Constantina Bakolitsa1,4, Adam Godzik1,2,3,4,* and John Wooley1,2,4,* 1Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, 2Center for Research in Biological Systems, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, 3Joint Center for Molecular Modeling and 4Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA Received August 16, 2010; Revised September 21, 2010; Accepted September 22, 2010 ABSTRACT structure determination. Thus, currently over 90% of SG deposited structures are not yet described in literature. The Open Protein Structure Annotation Network The rate and volume of protein structures being produced (TOPSAN) is a web-based collaboration platform requires novel mechanisms to ensure that the knowledge for exploring and annotating structures determined gained by these structures is disseminated in a timely by structural genomics efforts. Characterization of manner. those structures presents a challenge since the Several new protein structure annotation platforms, majority of the proteins themselves have not yet using wiki-based methods, have been described (3–5). been characterized. Responding to this challenge, However, their content is largely static and derived from the TOPSAN platform facilitates collaborative anno- peer-reviewed publications, aspects that do not easily lend tation and investigation via a user-friendly web- themselves to exploring new knowledge about structures.
    [Show full text]