Prioritizing Candidate Genes Post-GWAS Using Multiple Sources
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Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice
Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications 2013 Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice Mihaela Palicev Gunter P. Wagner James P. Noonan Benedikt Hallgrimsson James M. Cheverud Loyola University Chicago, [email protected] Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs Part of the Biology Commons Recommended Citation Palicev, M, GP Wagner, JP Noonan, B Hallgrimsson, and JM Cheverud. "Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice." Genome Biology and Evolution 5(10), 2013. This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. © Palicev et al., 2013. GBE Genomic Correlates of Relationship QTL Involved in Fore- versus Hind Limb Divergence in Mice Mihaela Pavlicev1,2,*, Gu¨ nter P. Wagner3, James P. Noonan4, Benedikt Hallgrı´msson5,and James M. Cheverud6 1Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria 2Department of Pediatrics, Cincinnati Children‘s Hospital Medical Center, Cincinnati, Ohio 3Yale Systems Biology Institute and Department of Ecology and Evolutionary Biology, Yale University 4Department of Genetics, Yale University School of Medicine 5Department of Cell Biology and Anatomy, The McCaig Institute for Bone and Joint Health and the Alberta Children’s Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Canada 6Department of Anatomy and Neurobiology, Washington University *Corresponding author: E-mail: [email protected]. -
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. -
A Yeast Phenomic Model for the Gene Interaction Network Modulating
Louie et al. Genome Medicine 2012, 4:103 http://genomemedicine.com/content/4/12/103 RESEARCH Open Access A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis Raymond J Louie3†, Jingyu Guo1,2†, John W Rodgers1, Rick White4, Najaf A Shah1, Silvere Pagant3, Peter Kim3, Michael Livstone5, Kara Dolinski5, Brett A McKinney6, Jeong Hong2, Eric J Sorscher2, Jennifer Bryan4, Elizabeth A Miller3* and John L Hartman IV1,2* Abstract Background: The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically. Methods: To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a ‘phenomic’ model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T (51) International Patent Classification: David [US/US]; 13539 N . 95th Way, Scottsdale, AZ C12Q 1/68 (2006.01) 85260 (US). (21) International Application Number: (74) Agent: AKHAVAN, Ramin; Caris Science, Inc., 6655 N . PCT/US20 12/0425 19 Macarthur Blvd., Irving, TX 75039 (US). (22) International Filing Date: (81) Designated States (unless otherwise indicated, for every 14 June 2012 (14.06.2012) kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, English (25) Filing Language: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, Publication Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, KR, (30) Priority Data: KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, ME, 61/497,895 16 June 201 1 (16.06.201 1) US MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 61/499,138 20 June 201 1 (20.06.201 1) US OM, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SC, SD, 61/501,680 27 June 201 1 (27.06.201 1) u s SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, 61/506,019 8 July 201 1(08.07.201 1) u s TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. -
Supplementary Material Contents
Supplementary Material Contents Immune modulating proteins identified from exosomal samples.....................................................................2 Figure S1: Overlap between exosomal and soluble proteomes.................................................................................... 4 Bacterial strains:..............................................................................................................................................4 Figure S2: Variability between subjects of effects of exosomes on BL21-lux growth.................................................... 5 Figure S3: Early effects of exosomes on growth of BL21 E. coli .................................................................................... 5 Figure S4: Exosomal Lysis............................................................................................................................................ 6 Figure S5: Effect of pH on exosomal action.................................................................................................................. 7 Figure S6: Effect of exosomes on growth of UPEC (pH = 6.5) suspended in exosome-depleted urine supernatant ....... 8 Effective exosomal concentration....................................................................................................................8 Figure S7: Sample constitution for luminometry experiments..................................................................................... 8 Figure S8: Determining effective concentration ......................................................................................................... -
Novel Transcriptional Targets of the SRY-HMG Box Transcription Factor SOX4 Link Its Expression to the Development of Small Cell Lung Cancer
Published OnlineFirst November 14, 2011; DOI: 10.1158/0008-5472.CAN-11-3506 Cancer Molecular and Cellular Pathobiology Research Novel Transcriptional Targets of the SRY-HMG Box Transcription Factor SOX4 Link Its Expression to the Development of Small Cell Lung Cancer Sandra D. Castillo1, Ander Matheu2, Niccolo Mariani1, Julian Carretero3, Fernando Lopez-Rios4, Robin Lovell-Badge2, and Montse Sanchez-Cespedes1 Abstract The HMG box transcription factor SOX4 involved in neuronal development is amplified and overexpressed in a subset of lung cancers, suggesting that it may be a driver oncogene. In this study, we sought to develop this hypothesis including by defining targets of SOX4 that may mediate its involvement in lung cancer. Ablating SOX4 expression in SOX4-amplified lung cancer cells revealed a gene expression signature that included genes involved in neuronal development such as PCDHB, MYB, RBP1, and TEAD2. Direct recruitment of SOX4 to gene promoters was associated with their upregulation upon ectopic overexpression of SOX4. We confirmed upregulation of the SOX4 expression signature in a panel of primary lung tumors, validating their specific response by a comparison using embryonic fibroblasts from Sox4-deficient mice. Interestingly, we found that small cell lung cancer (SCLC), a subtype of lung cancer with neuroendocrine characteristics, was generally characterized by high levels of SOX2, SOX4, and SOX11 along with the SOX4-specific gene expression signature identified. Taken together, our findings identify a functional role for SOX genes in SCLC, particularly for SOX4 and several novel targets defined in this study. Cancer Res; 72(1); 1–11. Ó2011 AACR. Introduction on chromosome 6p22, containing the SOX4 gene, which was expressed at very high levels and was the best candidate for an As with other types of cancer, lung cancer is undergoing a oncogene in the amplicon (2). -
Genetic Identification of Brain Cell Types Underlying Schizophrenia
bioRxiv preprint doi: https://doi.org/10.1101/145466; this version posted June 2, 2017. 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. Genetic identification of brain cell types underlying schizophrenia Nathan G. Skene 1 †, Julien Bryois 2 †, Trygve E. Bakken3, Gerome Breen 4,5, James J Crowley 6, Héléna A Gaspar 4,5, Paola Giusti-Rodriguez 6, Rebecca D Hodge3, Jeremy A. Miller 3, Ana Muñoz-Manchado 1, Michael C O’Donovan 7, Michael J Owen 7, Antonio F Pardiñas 7, Jesper Ryge 8, James T R Walters 8, Sten Linnarsson 1, Ed S. Lein 3, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Patrick F Sullivan 2,6 *, Jens Hjerling- Leffler 1 * Affiliations: 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 3 Allen Institute for Brain Science, Seattle, Washington 98109, USA. 4 King’s College London, Institute of Psychiatry, Psychology and Neuroscience, MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK. 5 National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, UK. 6 Departments of Genetics, University of North Carolina, Chapel Hill, NC, 27599-7264, USA. 7 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK. -
Supplementary Table 1. List of Genes Up-Regulated in Abiraterone-Resistant Vcap Xenograft Samples PIK3IP1 Phosphoinositide-3-Kin
Supplementary Table 1. List of genes up-regulated in abiraterone-resistant VCaP xenograft samples PIK3IP1 phosphoinositide-3-kinase interacting protein 1 TMEM45A transmembrane protein 45A THBS1 thrombospondin 1 C7orf63 chromosome 7 open reading frame 63 OPTN optineurin FAM49A family with sequence similarity 49, member A APOL4 apolipoprotein L, 4 C17orf108|LOC201229 chromosome 17 open reading frame 108 | hypothetical protein LOC201229 SNORD94 small nucleolar RNA, C/D box 94 PCDHB11 protocadherin beta 11 RBM11 RNA binding motif protein 11 C6orf225 chromosome 6 open reading frame 225 KIAA1984|C9orf86|TMEM14 1 KIAA1984 | chromosome 9 open reading frame 86 | transmembrane protein 141 KIAA1107 TLR3 toll-like receptor 3 LPAR6 lysophosphatidic acid receptor 6 KIAA1683 GRB10 growth factor receptor-bound protein 10 TIMP2 TIMP metallopeptidase inhibitor 2 CCDC28A coiled-coil domain containing 28A FBXL2 F-box and leucine-rich repeat protein 2 NOV nephroblastoma overexpressed gene TSPAN31 tetraspanin 31 NR3C2 nuclear receptor subfamily 3, group C, member 2 DYNC2LI1 dynein, cytoplasmic 2, light intermediate chain 1 C15orf51 dynamin 1 pseudogene SAMD13 sterile alpha motif domain containing 13 RASSF6 Ras association (RalGDS/AF-6) domain family member 6 ZNF167 zinc finger protein 167 GATA2 GATA binding protein 2 NUDT7 nudix (nucleoside diphosphate linked moiety X)-type motif 7 DNAJC18 DnaJ (Hsp40) homolog, subfamily C, member 18 SNORA57 small nucleolar RNA, H/ACA box 57 CALCOCO1 calcium binding and coiled-coil domain 1 RLN2 relaxin 2 ING4 inhibitor of -
Genome-Wide Association Study Identifies Genetic Susceptibility Loci
Yang et al. J Transl Med (2020) 18:224 https://doi.org/10.1186/s12967-020-02390-0 Journal of Translational Medicine RESEARCH Open Access Genome-wide association study identifes genetic susceptibility loci and pathways of radiation-induced acute oral mucositis Da‑Wei Yang1,2†, Tong‑Min Wang1†, Jiang‑Bo Zhang1, Xi‑Zhao Li1, Yong‑Qiao He1, Ruowen Xiao1, Wen‑Qiong Xue1, Xiao‑Hui Zheng1, Pei‑Fen Zhang1, Shao‑Dan Zhang1, Ye‑Zhu Hu1, Guo‑Ping Shen3, Mingyuan Chen1,4, Ying Sun1,5 and Wei‑Hua Jia1,2,6* Abstract Background: Radiation‑induced oral mucositis (OM) is one of the most common acute complications for head and neck cancer. Severe OM is associated with radiation treatment breaks, which harms successful tumor management. Radiogenomics studies have indicated that genetic variants are associated with adverse efects of radiotherapy. Methods: A large‑scale genome‑wide scan was performed in 1467 nasopharyngeal carcinoma patients, including 753 treated with 2D‑CRT from Genetic Architecture of the Radiotherapy Toxicity and Prognosis (GARTP) cohort and 714 treated with IMRT (192 from the GARTP and 522 newly recruited). Subgroup analysis by radiotherapy technique was further performed in the top associations. We also performed physical and regulatory mapping of the risk loci and gene set enrichment analysis of the candidate target genes. Results: We identifed 50 associated genomic loci and 64 genes via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping and gene‑based analysis, and 36 of these loci were replicated in subgroup analysis. Interestingly, one of the top loci located in TNKS, a gene relevant to radiation toxicity, was associ‑ 6 ated with increased OM risk with OR 3.72 of the lead SNP rs117157809 (95% CI 2.10–6.57; P 6.33 10− ). -
The Pdx1 Bound Swi/Snf Chromatin Remodeling Complex Regulates Pancreatic Progenitor Cell Proliferation and Mature Islet Β Cell
Page 1 of 125 Diabetes The Pdx1 bound Swi/Snf chromatin remodeling complex regulates pancreatic progenitor cell proliferation and mature islet β cell function Jason M. Spaeth1,2, Jin-Hua Liu1, Daniel Peters3, Min Guo1, Anna B. Osipovich1, Fardin Mohammadi3, Nilotpal Roy4, Anil Bhushan4, Mark A. Magnuson1, Matthias Hebrok4, Christopher V. E. Wright3, Roland Stein1,5 1 Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 2 Present address: Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 3 Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 4 Diabetes Center, Department of Medicine, UCSF, San Francisco, California 5 Corresponding author: [email protected]; (615)322-7026 1 Diabetes Publish Ahead of Print, published online June 14, 2019 Diabetes Page 2 of 125 Abstract Transcription factors positively and/or negatively impact gene expression by recruiting coregulatory factors, which interact through protein-protein binding. Here we demonstrate that mouse pancreas size and islet β cell function are controlled by the ATP-dependent Swi/Snf chromatin remodeling coregulatory complex that physically associates with Pdx1, a diabetes- linked transcription factor essential to pancreatic morphogenesis and adult islet-cell function and maintenance. Early embryonic deletion of just the Swi/Snf Brg1 ATPase subunit reduced multipotent pancreatic progenitor cell proliferation and resulted in pancreas hypoplasia. In contrast, removal of both Swi/Snf ATPase subunits, Brg1 and Brm, was necessary to compromise adult islet β cell activity, which included whole animal glucose intolerance, hyperglycemia and impaired insulin secretion. Notably, lineage-tracing analysis revealed Swi/Snf-deficient β cells lost the ability to produce the mRNAs for insulin and other key metabolic genes without effecting the expression of many essential islet-enriched transcription factors. -
Functional Test of PCDHB11, the Most Human-Specific Neuronal Surface Protein Guilherme Braga De Freitas, Rafaella Araújo Gonçalves and Matthias Gralle*
de Freitas et al. BMC Evolutionary Biology (2016) 16:75 DOI 10.1186/s12862-016-0652-x RESEARCHARTICLE Open Access Functional test of PCDHB11, the most human-specific neuronal surface protein Guilherme Braga de Freitas, Rafaella Araújo Gonçalves and Matthias Gralle* Abstract Background: Brain-expressed proteins that have undergone functional change during human evolution may contribute to human cognitive capacities, and may also leave us vulnerable to specifically human diseases, such as schizophrenia, autism or Alzheimer’s disease. In order to search systematically for those proteins that have changed the most during human evolution and that might contribute to brain function and pathology, all proteins with orthologs in chimpanzee, orangutan and rhesus macaque and annotated as being expressed on the surface of cells in the human central nervous system were ordered by the number of human-specific amino acid differences that are fixed in modern populations. Results: PCDHB11, a beta-protocadherin homologous to murine cell adhesion proteins, stood out with 12 substitutions and maintained its lead after normalizing for protein size and applying weights for amino acid exchange probabilities. Human PCDHB11 was found to cause homophilic cell adhesion, but at lower levels than shown for other clustered protocadherins. Homophilic adhesion caused by a PCDHB11 with reversion of human- specific changes was as low as for modern human PCDHB11; while neither human nor reverted PCDHB11 adhered to controls, they did adhere to each other. A loss of function in PCDHB11 is unlikely because intra-human variability did not increase relative to the other human beta-protocadherins. Conclusions: The brain-expressed protein with the highest number of human-specific substitutions is PCDHB11.