DOCSLIB.ORG

University of Groningen GSTCD and INTS12 Regulation And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
University of Groningen GSTCD and INTS12 Regulation And University of Groningen GSTCD and INTS12 Regulation and Expression in the Human Lung Obeidat, Ma'en; Miller, Suzanne; Probert, Kelly; Billington, Charlotte K.; Henry, Amanda P.; Hodge, Emily; Nelson, Carl P.; Stewart, Ceri E.; Swan, Caroline; Wain, Louise V. Published in: PLoS ONE DOI: 10.1371/journal.pone.0074630 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2013 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Obeidat, M., Miller, S., Probert, K., Billington, C. K., Henry, A. P., Hodge, E., Nelson, C. P., Stewart, C. E., Swan, C., Wain, L. V., Artigas, M. S., Melen, E., Ushey, K., Hao, K., Lamontagne, M., Bosse, Y., Postma, D. S., Tobin, M. D., Sayers, I., & Hall, I. P. (2013). GSTCD and INTS12 Regulation and Expression in the Human Lung. PLoS ONE, 8(9), [e74630]. https://doi.org/10.1371/journal.pone.0074630 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 06-10-2021 GSTCD and INTS12 Regulation and Expression in the Human Lung Ma’en Obeidat1,4☯, Suzanne Miller1☯, Kelly Probert1, Charlotte K. Billington1, Amanda P. Henry1, Emily Hodge1, Carl P. Nelson1, Ceri E. Stewart1, Caroline Swan1, Louise V. Wain2, María Soler Artigas2, Erik Melén3, Kevin Ushey4, Ke Hao5, Maxime Lamontagne7, Yohan Bossé6,7, Dirkje S. Postma8, Martin D. Tobin2,9, Ian Sayers1, Ian P. Hall1* 1 Division of Respiratory Medicine, University of Nottingham, Queen’s Medical Center, Nottingham, United Kingdom, 2 Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, United Kingdom, 3 Institute of Environmental Medicine, Karolinska Institutet and Sachs’ Children’s Hospital, Stockholm, Sweden, 4 James Hogg Research Centre, Institute for Heart and Lung Health, University of British Columbia, Vancouver, British Columbia, Canada, 5 Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, United States of America, 6 Department of Molecular Medicine, Laval University, Québec City, Canada, 7 Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Québec City, Canada, 8 Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 9 National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom Abstract Genome-Wide Association Study (GWAS) meta-analyses have identified a strong association signal for lung function, which maps to a region on 4q24 containing two oppositely transcribed genes: glutathione S-transferase, C- terminal domain containing (GSTCD) and integrator complex subunit 12 (INTS12). Both genes were found to be expressed in a range of human airway cell types. The promoter regions and transcription start sites were determined in mRNA from human lung and a novel splice variant was identified for each gene. We obtained the following evidence for GSTCD and INTS12 co-regulation and expression: (i) correlated mRNA expression was observed both via Q-PCR and in a lung expression quantitative trait loci (eQTL) study, (ii) induction of both GSTCD and INTS12 mRNA expression in human airway smooth muscle cells was seen in response to TGFβ1, (iii) a lung eQTL study revealed that both GSTCD and INTS12 mRNA levels positively correlate with percent predicted FEV1, and (iv) FEV1 GWAS associated SNPs in 4q24 were found to act as an eQTL for INTS12 in a number of tissues. In fixed sections of human lung tissue, GSTCD protein expression was ubiquitous, whereas INTS12 expression was predominantly in epithelial cells and pneumocytes. During human fetal lung development, GSTCD protein expression was observed to be highest at the earlier pseudoglandular stage (10-12 weeks) compared with the later canalicular stage (17-19 weeks), whereas INTS12 expression levels did not alter throughout these stages. Knowledge of the transcriptional and translational regulation and expression of GSTCD and INTS12 provides important insights into the potential role of these genes in determining lung function. Future work is warranted to fully define the functions of INTS12 and GSTCD. Citation: Obeidat M, Miller S, Probert K, Billington CK, Henry AP, et al. (2013) GSTCD and INTS12 Regulation and Expression in the Human Lung. PLoS ONE 8(9): e74630. doi:10.1371/journal.pone.0074630 Editor: Rory Edward Morty, University of Giessen Lung Center, Germany Received May 2, 2013; Accepted August 5, 2013; Published September 18, 2013 Copyright: © 2013 Obeidat et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The work performed at the University of Nottingham and included in this manuscript was funded by the Medical Research Council (G1000861). Martin D. Tobin holds a Medical Research Council Senior Clinical Fellowship (G0902313) (www.MRC.ac.uk). The resequencing study performed at the Univserity of Leicester was funded by Pfizer. Yohan Bossé is a research scholar from the Heart and Stroke Foundation of Canada. Erik Melen has received funding from The Swedish Research Council, The Swedish Heart-Lung Foundation and Stockholm County Council (ALF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Competing interests: The resequencing study performed at the University of Leicester was funded by Pfizer. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. * E-mail: [email protected] ☯ These authors contributed equally to this work. PLOS ONE | www.plosone.org 1 September 2013 | Volume 8 | Issue 9 | e74630 GSTCD and INTS12 Regulation in the Human Lung Introduction correlation=0.48, P=0.0002) [15]. This, and the fact that the 4q24 locus has the strongest novel association with lung Forced expiratory volume in one second (FEV1), forced vital function in the multiple GWAS meta-analyses conducted so far, capacity (FVC), and the ratio of FEV1 to FVC (FEV 1/FVC) are have provided the rationale to further investigate this locus to commonly used to assess pulmonary function and these unravel the mechanisms underlying this association. measurements are integral to the diagnosis of chronic Here we report the genetic architecture of GSTCD and obstructive pulmonary disease (COPD). Reduced FEV 1/FVC INTS12, and show that both genes are expressed in a range of defines airway obstruction, whereas reduced FEV1 grades the cell types present in the lung. We report one novel splice severity of airway obstruction [1]. These measures also predict variant for each gene. Furthermore we show that the genome- population morbidity and mortality [2]. wide significant SNPs at 4q24 act as expression quantitative Pulmonary function is determined both by environmental and trait loci (eQTL) for INTS12, and provide evidence that the genetic factors. Tobacco smoking is the major environmental peak association signal maps to a region rich in elements likely risk factor for the development of COPD in the developed to be important for transcriptional regulation of these genes. world. A genetic contribution to pulmonary function is well Interestingly, these appear to be coordinately regulated in the established with heritability estimates reaching as high as 77% lung. Expression levels were also shown to correlate with lung for FEV1 [3]. Three large scale meta-analyses of genome-wide function measures. Finally, we describe the expression of association studies (GWAS) of lung function measures have GSTCD and INTS12 proteins in human lung tissue and assess recently been published [4-6]: these studies identified a total of how expression is altered during human fetal development. 26 novel loci associated with either FEV1 or FEV 1/FVC. Genome-wide interaction analyses with smoking later identified Results three additional regions of potential importance for lung function [7]. One of the strongest association signals identified Gene arrangement via PCR and RACE was with intronic Single Nucleotide Polymorphisms (SNPs) in a Using cDNA synthesised from RNA extracted from total lung,
Load more

Recommended publications
  • Protein Biomarkers Analysis Within Muscular Dystrophies
    EXAMENSARBETE INOM BIOTEKNIK, AVANCERAD NIVÅ, 30 HP STOCKHOLM, SVERIGE 2017 Protein biomarkers analysis within muscular dystrophies SANDRA MENA KTH SKOLAN FÖR BIOTEKNOLOGI PROTEIN BIOMARKERS ANALYSIS WITHIN MUSCULAR DYSTROPHIES Master thesis Author: Sandra Carolina Mena Pérez Supervisor: Cristina Al-Khalili Szigyarto Stockholm 2017 Master’s program: Medical Biotechnology Kungliga Tekniska Högskolan Content Abstract .........................................................................................................................................................1 Abstrakt .........................................................................................................................................................1 Introduction .................................................................................................................................................1 Materials and Methods ............................................................................................................................ 3 Results and Discussion ............................................................................................................................ 4 Conclusion .................................................................................................................................................. 11 Future Perspectives ................................................................................................................................. 11 Aknowledgments ....................................................................................................................................
    [Show full text]
  • Variation in Protein Coding Genes Identifies Information
    bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number.
    [Show full text]
  • Integration of the Drug-Gene Interaction Database (Dgidb) with Open Crowdsource Efforts
    bioRxiv preprint doi: https://doi.org/10.1101/2020.09.18.301721; this version posted September 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Integration of the Drug-Gene Interaction Database (DGIdb) with open crowdsource efforts Sharon Freshour1,2,†, Susanna Kiwala2,†, Kelsy C. Cotto1,2,†, Adam C. Coffman2, Joshua F. McMichael2, Jonathan Song1,2, Malachi Griffith1,2,3,4,*, Obi L. Griffith1,2,3,4,*, Alex H. Wagner1,2,5,6,* 1 Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO, 63110, USA 2 McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA 3 Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA 4 Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, 63110, USA 5 The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, 43215, USA 6 Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA * To whom correspondence should be addressed. Tel: 1-614-355-1645; Fax: 1-614-355-6833; Email: [email protected] Correspondence may also be addressed to Obi L. Griffith. Tel: 1-314-747-9248; Fax: 1-314-286-1810; Email: [email protected] Correspondence may also be addressed to Malachi Griffith. Tel: 1-314-286-1274; Fax: 1-314-286-1810; Email: [email protected] † The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors ABSTRACT The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from various sources including publications, databases, and other web-based sources in one resource.
    [Show full text]
  • Anti-CKB Monoclonal Antibody, Clone 904DU30.2.2 (DCABH-6668) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
    Anti-CKB monoclonal antibody, clone 904DU30.2.2 (DCABH-6668) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Product Overview Mouse monoclonal to Creatine kinase B type Antigen Description Reversibly catalyzes the transfer of phosphate between ATP and various phosphogens (e.g. creatine phosphate). Creatine kinase isoenzymes play a central role in energy transduction in tissues with large, fluctuating energy demands, such as skeletal muscle, heart, brain and spermatozoa. Immunogen Recombinant full length protein (His-tag) corresponding to Creatine kinase B type aa 1- 381.Database link: P12277 Isotype IgG Source/Host Mouse Species Reactivity Mouse, Human Clone 904DU30.2.2 Purity Protein G purified Purification This antibody is purified through a protein G column, eluted with high and low pH buffers and neutralized immediately, followed by dialysis against PBS. Conjugate Unconjugated Applications WB Positive Control MDA-MB453; 293 and Y-79 cell lysates; Mouse stomach and Mouse brain tissue lysates Format Liquid Size 400 μl Buffer Preservative: 0.09% Sodium azide; Constituent: 99% PBS Storage Store at +4°C short term (1-2 weeks). Upon delivery aliquot. Store at -20°C long term. Avoid freeze / thaw cycle. Ship Shipped at 4°C. 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved GENE INFORMATION Gene Name CKB creatine kinase, brain [ Homo sapiens ] Official Symbol CKB Synonyms CKB; creatine
    [Show full text]
  • JAX-CKB), Which Can Be Queried Readily to Access Comprehensive Data for Clinical Reporting Via Customized Reporting Queries
    Patterson et al. Human Genomics (2016) 10:4 DOI 10.1186/s40246-016-0061-7 PRIMARY RESEARCH Open Access The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies Sara E. Patterson, Rangjiao Liu, Cara M. Statz, Daniel Durkin, Anuradha Lakshminarayana and Susan M. Mockus* Abstract Background: Precision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer- associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult. Results: To support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants.
    [Show full text]
  • PDF Output of CLIC (Clustering by Inferred Co-Expression)
    PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 14 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 14. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Cpsf3l Polr2b Ints3 Ints7 Ints1 Ints4 Ints9 Ints2
    [Show full text]
  • PHF22 (INTS12) (NM 020395) Human Over-Expression Lysate Product Data
    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 LC412498 PHF22 (INTS12) (NM_020395) Human Over-expression Lysate Product data: Product Type: Over-expression Lysates Description: INTS12 HEK293T cell transient overexpression lysate (as WB positive control) Species: Human Expression Host: HEK293T Expression cDNA Clone TrueORF Clone RC204838 or AA Sequence: Tag: C-Myc/DDK Detection Antibodies: Clone OTI4C5, Anti-DDK (FLAG) monoclonal antibody (TA50011-100) Accession Number: NM_020395, NP_065128 Synonyms: INT12; PHF22; SBBI22 Predicted MW: 48.8 kDa Components: 1 vial of 20 ug lyophilized gene specific transient over-expression cell lysate Storage: The lysate can be shipped at ambient temperature. Upon receiving, store the sample at - 20°C. Lysate samples can be reconstituted with SDS Sample Buffer. Avoid repeated freeze- thaw cycles after reconstitution. Lysate samples are stable for 12 months from date of receipt when stored at -20°C. Preparation: HEK293T cells in 10-cm dishes were transiently transfected withM egaTran Transfection Reagent (TT200002) and 5ug TrueORF cDNA plasmid. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix (Sigma), 1mM PMSF and 1mM Na3VO4), and then centrifuged to clarify the lysate. Protein concentration was measured by BCA kit (Thermo Scientific Inc.). To facilitate transportation and protein, the products are supplied as lyophilized proteins. RefSeq: NP_065128 Locus ID: 57117 Cytogenetics: 4q24 Protein Families: Druggable Genome, Transcription Factors This product is to be used for laboratory only.
    [Show full text]
  • (Q36.1;Q24) with a Concurrent Submicroscopic Del(4)(Q23q24) in an Adult with Polycythemia Vera
    cancers Case Report A Novel Acquired t(2;4)(q36.1;q24) with a Concurrent Submicroscopic del(4)(q23q24) in An Adult with Polycythemia Vera Eigil Kjeldsen Cancer Cytogenetic Section, HemoDiagnostic Laboratory, Department of Hematology, Aarhus University Hospital, Tage-Hansens Gade 2, DK-8000 Aarhus C, Denmark; [email protected]; Tel.: +45-7846-7398; Fax: +45-7846-7399 Received: 6 June 2018; Accepted: 21 June 2018; Published: 25 June 2018 Abstract: Background: Polycythemia vera (PV) is a clonal myeloid stem cell disease characterized by a growth-factor independent erythroid proliferation with an inherent tendency to transform into overt acute myeloid malignancy. Approximately 95% of the PV patients harbor the JAK2V617F mutation while less than 35% of the patients harbor cytogenetic abnormalities at the time of diagnosis. Methods and Results: Here we present a JAK2V617F positive PV patient where G-banding revealed an apparently balanced t(2;4)(q35;q21), which was confirmed by 24-color karyotyping. Oligonucleotide array-based Comparative Genomic Hybridization (aCGH) analysis revealed an interstitial 5.4 Mb large deletion at 4q23q24. Locus-specific fluorescent in situ hybridization (FISH) analyses confirmed the mono-allelic 4q deletion and that it was located on der(4)t(2;4). Additional locus-specific bacterial artificial chromosome (BAC) probes and mBanding refined the breakpoint on chromosome 2. With these methods the karyotype was revised to 46,XX,t(2;4)(q36.1;q24)[18]/46,XX[7]. Conclusions: This is the first report on a PV patient associated with an acquired novel t(2;4)(q36.1;q24) and a concurrent submicroscopic deletion del(4)(q23q24).
    [Show full text]
  • Profiling Haplotype Specific Cpg and Cph Methylation Within A
    www.nature.com/scientificreports OPEN Profling haplotype specifc CpG and CpH methylation within a schizophrenia GWAS locus on chromosome 14 in schizophrenia and healthy subjects Margarita Alfmova*, Nikolay Kondratyev, Arkadiy Golov & Vera Golimbet Interrogating DNA methylation within schizophrenia risk loci holds promise to identify mechanisms by which genes infuence the disease. Based on the hypothesis that allele specifc methylation (ASM) of a single CpG, or perhaps CpH, might mediate or mark the efects of genetic variants on disease risk and phenotypes, we explored haplotype specifc methylation levels of individual cytosines within a genomic region harbouring the BAG5, APOPT1 and KLC1 genes in peripheral blood of schizophrenia patients and healthy controls. Three DNA fragments located in promoter, intronic and intergenic areas were studied by single-molecule real-time bisulfte sequencing enabling the analysis of long reads of DNA with base-pair resolution and the determination of haplotypes directly from sequencing data. Among 1,012 cytosines studied, we did not fnd any site where methylation correlated with the disease or cognitive defcits after correction for multiple testing. At the same time, we determined the methylation profle associated with the schizophrenia risk haplotype within the KLC1 fourth intron and confrmed ASM for cytosines located in the vicinity of rs67899457. These genetically associated DNA methylation variations may be related to the pathophysiological mechanism diferentiating the risk and non-risk haplotypes and merit further investigation. Schizophrenia is a common, highly heritable disorder characterized by positive, negative, and cognitive symp- toms. Large genome-wide association studies (GWAS) of the Psychiatric Genomics Consortium (PGC) have identifed more than 100 genomic regions that are signifcantly associated with schizophrenia1,2.
    [Show full text]
  • Gene Network Analysis in a Pediatric Cohort Identifies Novel Lung Function Genes
    Gene Network Analysis in a Pediatric Cohort Identifies Novel Lung Function Genes Bruce A. Ong1.¤,JinLi2., Joseph M. McDonough1, Zhi Wei3, Cecilia Kim2, Rosetta Chiavacci2, Frank Mentch2, Jason B. Caboot4, Jonathan Spergel5,7,8, Julian L. Allen1,7, Patrick M. A. Sleiman2,6,7*, Hakon Hakonarson1,2,6,7* 1 Division of Pulmonary Medicine and Cystic Fibrosis Center, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 2 Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 3 Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America, 4 Division of Pediatric Pulmonology, Madigan Army Medical Center, Tacoma, Washington, United States of America, 5 Center for Pediatric Eosinophilic Disorders, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 6 Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 7 Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 8 Division of Allergy and Immunology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America Abstract Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS) of four pulmonary function traits, including FVC, FEV1, FEV1/FVC and FEF25–75% in 1556 children.
    [Show full text]
  • Identification of Expression Qtls Targeting Candidate Genes For
    ISSN: 2378-3648 Salleh et al. J Genet Genome Res 2018, 5:035 DOI: 10.23937/2378-3648/1410035 Volume 5 | Issue 1 Journal of Open Access Genetics and Genome Research RESEARCH ARTICLE Identification of Expression QTLs Targeting Candidate Genes for Residual Feed Intake in Dairy Cattle Using Systems Genomics Salleh MS1,2, Mazzoni G2, Nielsen MO1, Løvendahl P3 and Kadarmideen HN2,4* 1Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark Check for 2Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark updates 3Department of Molecular Biology and Genetics-Center for Quantitative Genetics and Genomics, Aarhus University, AU Foulum, Tjele, Denmark 4Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark *Corresponding author: Kadarmideen HN, Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark, E-mail: [email protected] Abstract body weight gain and net merit). The eQTLs and biological pathways identified in this study improve our understanding Background: Residual feed intake (RFI) is the difference of the complex biological and genetic mechanisms that de- between actual and predicted feed intake and an important termine FE traits in dairy cattle. The identified eQTLs/genet- factor determining feed efficiency (FE). Recently, 170 can- ic variants can potentially be used in new genomic selection didate genes were associated with RFI, but no expression methods that include biological/functional information on quantitative trait loci (eQTL) mapping has hitherto been per- SNPs. formed on FE related genes in dairy cows. In this study, an integrative systems genetics approach was applied to map Keywords eQTLs in Holstein and Jersey cows fed two different diets to eQTL, RNA-seq, Genotype, Data integration, Systems improve identification of candidate genes for FE.
    [Show full text]
  • Screening for the Proteins That Can Interact with Grouper Nervous Necrosis Virus Capsid Protein
    viruses Article Screening for the Proteins That Can Interact with Grouper Nervous Necrosis Virus Capsid Protein 1, 2, 2 2 3 Po-Yu Huang y, Han-Chia Hsiao y, Szu-Wen Wang , Shao-Fu Lo , Ming-Wei Lu and Li-Li Chen 1,2,* 1 Center of Excellence for the Oceans, National Taiwan Ocean University, No. 2, Pei-Ning Road, Keelung 20224, Taiwan; [email protected] 2 Institute of Marine Biology, National Taiwan Ocean University, No. 2, Pei-Ning Road, Keelung 20224, Taiwan; [email protected] (H.-C.H.); [email protected] (S.-W.W.); [email protected] (S.-F.L.) 3 Department of Aquaculture, National Taiwan Ocean University, No. 2, Pei-Ning Road, Keelung 20224, Taiwan; [email protected] * Correspondence: [email protected] These authors contributed equally to this work. y Received: 17 August 2020; Accepted: 1 September 2020; Published: 4 September 2020 Abstract: Nervous necrosis virus (NNV) can infect many species of fish and has an 80–100% mortality rate. NNV capsid protein (NNVCP) is the only structural protein of NNV, but there are few studies on the protein–protein interaction between NNVCP and the host cell. To investigate NNV morphogenesis, native NNV capsid protein (NNVCP) was used to screen for protein–protein interactions in this study. The results identified that 49 grouper optic nerve proteins can interact with NNVCP and may function as putative receptor or co-receptor, cytoskeleton, glucose metabolism and ATP generation, immunity, mitochondrial ion regulation, and ribosomal proteins. Creatine kinase B-type (CKB) is one of those 49 optic nerve proteins.
    [Show full text]