The Interferon-Inducible P47 (IRG) Gtpases In
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Database of Cattle Candidate Genes and Genetic Markers for Milk Production and Mastitis
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PubMed Central doi:10.1111/j.1365-2052.2009.01921.x Database of cattle candidate genes and genetic markers for milk production and mastitis J. Ogorevc*, T. Kunej*, A. Razpet and P. Dovc Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia Summary A cattle database of candidate genes and genetic markers for milk production and mastitis has been developed to provide an integrated research tool incorporating different types of information supporting a genomic approach to study lactation, udder development and health. The database contains 943 genes and genetic markers involved in mammary gland development and function, representing candidates for further functional studies. The candidate loci were drawn on a genetic map to reveal positional overlaps. For identification of candidate loci, data from seven different research approaches were exploited: (i) gene knockouts or transgenes in mice that result in specific phenotypes associated with mam- mary gland (143 loci); (ii) cattle QTL for milk production (344) and mastitis related traits (71); (iii) loci with sequence variations that show specific allele-phenotype interactions associated with milk production (24) or mastitis (10) in cattle; (iv) genes with expression profiles associated with milk production (207) or mastitis (107) in cattle or mouse; (v) cattle milk protein genes that exist in different genetic variants (9); (vi) miRNAs expressed in bovine mammary gland (32) and (vii) epigenetically regulated cattle genes associated with mammary gland function (1). Fourty-four genes found by multiple independent analyses were suggested as the most promising candidates and were further in silico analysed for expression levels in lactating mammary gland, genetic variability and top biological func- tions in functional networks. -
Identifying Novel Disease-Associated Variants and Understanding The
Identifying Novel Disease-variants and Understanding the Role of the STAT1-STAT4 Locus in SLE A dissertation submitted to the Graduate School of University of Cincinnati In partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Immunology Graduate Program of the College of Medicine by Zubin H. Patel B.S., Worcester Polytechnic Institute, 2009 John B. Harley, M.D., Ph.D. Committee Chair Gurjit Khurana Hershey, M.D., Ph.D Leah C. Kottyan, Ph.D. Harinder Singh, Ph.D. Matthew T. Weirauch, Ph.D. Abstract Systemic Lupus Erythematosus (SLE) or lupus is an autoimmune disorder caused by an overactive immune system with dysregulation of both innate and adaptive immune pathways. It can affect all major organ systems and may lead to inflammation of the serosal and mucosal surfaces. The pathogenesis of lupus is driven by genetic factors, environmental factors, and gene-environment interactions. Heredity accounts for a substantial proportion of SLE risk, and the role of specific genetic risk loci has been well established. Identifying the specific causal genetic variants and the underlying molecular mechanisms has been a major area of investigation. This thesis describes efforts to develop an analytical approach to identify candidate rare variants from trio analyses and a fine-mapping analysis at the STAT1-STAT4 locus, a well-replicated SLE-risk locus. For the STAT1-STAT4 locus, subsequent functional biological studies demonstrated genotype dependent gene expression, transcription factor binding, and DNA regulatory activity. Rare variants are classified as variants across the genome with an allele-frequency less than 1% in ancestral populations. -
DEDD (NM 001039712) Human Untagged Clone 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 SC310813 DEDD (NM_001039712) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: DEDD (NM_001039712) Human Untagged Clone Tag: Tag Free Symbol: DEDD Synonyms: CASP8IP1; DEDD1; DEFT; FLDED1; KE05 Vector: pCMV6-Entry (PS100001) E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin Fully Sequenced ORF: >NCBI ORF sequence for NM_001039712, the custom clone sequence may differ by one or more nucleotides ATGGCGGGCCTAAAGCGGCGGGCAAGCCAGGTGTGGCCAGAAGAGCATGGTGAGCAGGAACATGGGCTGT ACAGCCTGCACCGCATGTTTGACATCGTGGGCACTCATCTGACACACAGAGATGTGCGCGTGCTTTCTTT CCTCTTTGTTGATGTCATTGATGACCACGAGCGTGGACTCATCCGAAATGGACGTGACTTCTTATTGGCA CTGGAGCGCCAGGGCCGCTGTGATGAAAGTAACTTTCGCCAGGTGCTGCAGCTGCTGCGCATCATCACTC GCCACGACCTGCTGCCCTACGTCACCCTCAAGAGGAGACGGGCTGTGTGCCCTGATCTTGTAGACAAGTA TCTGGAGGAGACATCAATTCGCTATGTGACCCCCAGAGCCCTCAGTGATCCAGAACCAAGGCCTCCCCAG CCCTCTAAAACAGTGCCTCCCCACTATCCTGTGGTGTGTTGCCCCACTTCGGGTCCTCAGATGTGTAGCA AGCGGCCAGCCCGAGGGAGAGCCACACTTGGGAGCCAGCGAAAACGCCGGAAGTCAGTGACACCAGATCC CAAGGAGAAGCAGACATGTGACATCAGACTGCGGGTTCGGGCTGAATACTGCCAGCATGAGACTGCTCTG CAGGGCAATGTCTTCTCTAACAAGCAGGACCCACTTGAGCGCCAGTTTGAGCGCTTTAACCAGGCCAACA CCATCCTCAAGTCCCGGGACCTGGGCTCCATCATCTGTGACATCAAGTTCTCTGAGCTCACCTACCTCGA TGCATTCTGGCGTGACTACATCAATGGCTCTTTATTAGAGGCACTTAAAGGTGTCTTCATCACAGACTCC CTCAAGCAAGCTGTGGGCCATGAAGCCATCAAGCTGCTGGTAAATGTAGACGAGGAGGACTATGAGCTGG -
Supplementary Information
Supplementary Information PathwayMatcher: multi-omics pathway mapping and proteoform network generation Luis Francisco Hernández Sánchez1,2,3, Bram Burger4,5, Carlos Horro4,5, Antonio Fabregat3, Stefan Johansson1,2, Pål Rasmus Njølstad1,6, Harald Barsnes4,5, Henning Hermjakob3,7, and Marc Vaudel1,2,* 1 K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway 2 Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway 3 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom 4 Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway 5 Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway 6 Department of Pediatrics, Haukeland University Hospital, Bergen, Norway 7 Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing, China * To whom correspondence should be addressed Abstract Mapping biomedical data to functional knowledge is an essential task in biomedicine and can be achieved by querying gene or protein identifiers in pathway knowledgebases. Here, we demonstrate that including fine-granularity information such as post-translational modifications greatly increases the specificity of the analysis. We present PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher), a bioinformatic application for mapping multi-omics data to pathways and show how this enables the -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Kids First Pediatric Research Program (Kids First) Poster Session at ASHG Accelerating Pediatric Genomics Research Through Collaboration October 15Th, 2019
The Gabriella Miller Kids First Pediatric Research Program (Kids First) Poster Session at ASHG Accelerating Pediatric Genomics Research through Collaboration October 15th, 2019 Background The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans- NIH Common Fund program initiated in response to the 2014 Gabriella Miller Kids First Research Act. The program’s vision is to alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and support data sharing within the pediatric research community. This is implemented through developing the Gabriella Miller Kids First Data Resource (Kids First Data Resource) and populating this resource with whole genome sequence datasets and associated clinical and phenotypic information. Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. Purpose During this evening poster session, attendees will gain a broad understanding of the utility of the genomic data generated by Kids First, learn about the progress of Kids First X01 cohort projects, and observe demonstrations of the tools and functionalities of the recently launched Kids First Data Resource Portal. The session is an opportunity for the scientific community and public to engage with Kids First investigators, collaborators, and a growing community of researchers, patient foundations, and families. Several other NIH and external data efforts will present posters and be available to discuss collaboration opportunities as we work together to accelerate pediatric research. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79
www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
C-Terminal Region (ARP58987 P050) Data Sheet
CASP3 antibody - C-terminal region (ARP58987_P050) Data Sheet Product Number ARP58987_P050 Product Name CASP3 antibody - C-terminal region (ARP58987_P050) Size 50ug Gene Symbol CASP3 Alias Symbols CPP32; CPP32B; SCA-1 Nucleotide Accession# NM_032991 Protein Size (# AA) 277 amino acids Molecular Weight 12kDa Product Format Lyophilized powder NCBI Gene Id 836 Host Rabbit Clonality Polyclonal Official Gene Full Name Caspase 3, apoptosis-related cysteine peptidase Gene Family CASP This is a rabbit polyclonal antibody against CASP3. It was validated on Western Blot by Aviva Systems Biology. At Aviva Systems Biology we manufacture rabbit polyclonal antibodies on a large scale (200-1000 Description products/month) of high throughput manner. Our antibodies are peptide based and protein family oriented. We usually provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: NLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFG CASP3 is a protein which is a member of the cysteine-aspartic acid protease (caspase) family. Sequential activation of caspases plays a central role in the execution-phase of cell apoptosis. Caspases exist as inactive proenzymes which undergo proteolytic processing at conserved aspartic residues to produce two subunits, Description of Target large and small, that dimerize to form the active enzyme. This protein cleaves and activates caspases 6, 7 and 9, and the protein itself is processed by caspases 8, 9 and 10. It is the predominant caspase involved in the cleavage of amyloid-beta 4A precursor protein, which is associated with neuronal death in Alzheimer's disease. -
Analysis of the Indacaterol-Regulated Transcriptome in Human Airway
Supplemental material to this article can be found at: http://jpet.aspetjournals.org/content/suppl/2018/04/13/jpet.118.249292.DC1 1521-0103/366/1/220–236$35.00 https://doi.org/10.1124/jpet.118.249292 THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS J Pharmacol Exp Ther 366:220–236, July 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Analysis of the Indacaterol-Regulated Transcriptome in Human Airway Epithelial Cells Implicates Gene Expression Changes in the s Adverse and Therapeutic Effects of b2-Adrenoceptor Agonists Dong Yan, Omar Hamed, Taruna Joshi,1 Mahmoud M. Mostafa, Kyla C. Jamieson, Radhika Joshi, Robert Newton, and Mark A. Giembycz Departments of Physiology and Pharmacology (D.Y., O.H., T.J., K.C.J., R.J., M.A.G.) and Cell Biology and Anatomy (M.M.M., R.N.), Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada Received March 22, 2018; accepted April 11, 2018 Downloaded from ABSTRACT The contribution of gene expression changes to the adverse and activity, and positive regulation of neutrophil chemotaxis. The therapeutic effects of b2-adrenoceptor agonists in asthma was general enriched GO term extracellular space was also associ- investigated using human airway epithelial cells as a therapeu- ated with indacaterol-induced genes, and many of those, in- tically relevant target. Operational model-fitting established that cluding CRISPLD2, DMBT1, GAS1, and SOCS3, have putative jpet.aspetjournals.org the long-acting b2-adrenoceptor agonists (LABA) indacaterol, anti-inflammatory, antibacterial, and/or antiviral activity. Numer- salmeterol, formoterol, and picumeterol were full agonists on ous indacaterol-regulated genes were also induced or repressed BEAS-2B cells transfected with a cAMP-response element in BEAS-2B cells and human primary bronchial epithelial cells by reporter but differed in efficacy (indacaterol $ formoterol . -
University of California, Merced
UNIVERSITY OF CALIFORNIA, MERCED SUMOylation is a regulator of regional cell fate and genomic integrity in planarians by Manish Thiruvalluvan A dissertation submitted in satisfaction of the requirements for the degree Doctor of Philosophy in Quantitative and Systems Biology Committee in Charge: Professor Kirk Jensen, Chair Professor Jennifer Manilay, Member Professor Anna Beaudin, Member Professor Néstor J. Oviedo, Advisor 2018 i Copyright Manish Thiruvalluvan, 2018 All rights reserved ii The dissertation of Manish Thiruvalluvan is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Jennifer Manilay Anna Beaudin Kirk Jensen, Chair University of California, Merced 2018 iii TABLE OF CONTENTS vi. List of figures vii. List of abbreviations viii. Acknowledgements ix. Curriculum vitae xii. Abstract 1. Introduction 1.1. Regional signals influence cell fate decisions in health and disease 1.2. SUMOylation – a type of posttranslational modification 1.3. The planarian model Schmidtea mediterranea 1.4. Research summary 2. Materials and Methods 2.1. Materials 2.1.1. Organisms 2.1.2. Selection of primers and cloning 2.1.3. Antibodies, enzymes and other reagents 2.1.4. Solutions and buffers 2.2. Methods 2.2.1. Planarian husbandry 2.2.2. Identification of orthologs and phylogenetic analysis 2.2.3. PCR amplification and gel electrophoresis 2.2.4. Planarian Amputation 2.2.5. Irradiation 2.2.6. RNAi by bacteria feeding 2.2.7. Fixation protocols 2.2.8. RNA extraction 2.2.9. Planarian cell dissociation 2.2.10. Immunocytochemistry 2.2.11. RNA probe synthesis 2.2.12. In situ hybridization 2.2.13.