Fine Mapping of a Linkage Peak with Integration of Lipid Traits Identifies
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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Mouse Spock1 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Spock1 Knockout Project (CRISPR/Cas9) Objective: To create a Spock1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Spock1 gene (NCBI Reference Sequence: NM_009262 ; Ensembl: ENSMUSG00000056222 ) is located on Mouse chromosome 13. 12 exons are identified, with the ATG start codon in exon 2 and the TAG stop codon in exon 12 (Transcript: ENSMUST00000185502). Exon 5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a targeted null mutation display no obvious morphological or behavioral abnormalities, are fertile, and have normal life spans. Adult homozygotes exhibit normal brain morphology and EEG recordings. Exon 5 starts from about 18.25% of the coding region. Exon 5 covers 8.67% of the coding region. The size of effective KO region: ~115 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 5 12 Legends Exon of mouse Spock1 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 5 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. -
Investigation of Functional Genes at Homologous Loci Identified Based
Journal of Atherosclerosis and Thrombosis Vol.22, No.5 455 Original Article Investigation of Functional Genes at Homologous Loci Identified Based on Genome-wide Association Studies of Blood Lipids via High-fat Diet Intervention in Rats using an in vivo Approach Koichi Akiyama, Yi-Qiang Liang, Masato Isono and Norihiro Kato Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan Aim: It is challenging to identify causal (or target) genes at individual loci detected using genome- wide association studies (GWAS). In order to follow up GWAS loci, we investigated functional genes at homologous loci identified using human lipid GWAS that responded to a high-fat, high-choles- terol diet (HFD) intervention in an animal model. Methods: The HFD intervention was carried out for four weeks in male rats of the spontaneously hypertensive rat strain. The liver and adipose tissues were subsequently excised for analyses of changes in the gene expression as compared to that observed in rats fed normal rat chow (n=8 per group). From 98 lipid-associated loci reported in previous GWAS, 280 genes with rat orthologs were initially selected as targets for the two-staged analysis involving screening with DNA microarray and validation with quantitative PCR (qPCR). Consequently, genes showing a differential expression due to HFD were examined for changes in the expression induced by atorvastatin, which was indepen- dently administered to the rats. Results: Using the HFD intervention in the rats, seven known (Abca1, Abcg5, Abcg8, Lpl, Nr1h3, Pcsk9 and Pltp) and three novel (Madd, Stac3 and Timd4) genes were identified as potential signifi- cant targets, with an additional list of 23 suggestive genes. -
Recombinant T-Cell Immunoglobulin and Mucin Domain Containing Protein 4 (TIMD4)
RPH854Hu01 100µg Recombinant T-Cell Immunoglobulin And Mucin Domain Containing Protein 4 (TIMD4) Organism Species: Homo sapiens (Human) Instruction manual FOR RESEARCH USE ONLY NOT FOR USE IN CLINICAL DIAGNOSTIC PROCEDURES 12th Edition (Revised in Aug, 2016) 1 / 4 [ PROPERTIES ] Source: Prokaryotic expression Host: E.coli Residues: Glu25~Gln314 Tags: N-terminal His Tag Subcellular Location: Secreted Purity: > 95% Traits: Freeze-dried powder Buffer formulation: 100mMNaHCO3, 500mMNaCl, pH8.3, containing 0.01% SKL, 5% Trehalose. Original Concentration: 100µg/mL Applications: Positive Control; Immunogen; SDS-PAGE; WB. (May be suitable for use in other assays to be determined by the end user.) Predicted isoelectric point: Predicted Molecular Mass: 35.0kDa Accurate Molecular Mass: 40kDa as determined by SDS-PAGE reducing conditions. Phenomenon explanation: The possible reasons that the actual band size differs from the predicted are as follows: 1.Splice variants: Alternative splicing may create different sized proteins from the same gene. 2. Relative charge: The composition of amino acids may affects the charge of the protein. 3. Post-translational modification: Phosphorylation, glycosylation, methylation etc. 4. Post-translation cleavage: Many proteins are synthesized as pro-proteins, and then cleaved to give the active form. 5. Polymerization of the target protein: Dimerization, multimerization etc. [ USAGE ] Reconstitute in 100mM NaHCO3, 500mM NaCl (pH8.3) to a concentration of 0.1-1.0 mg/mL. Do not vortex. 2 / 4 [ STORAGE AND STABILITY ] Storage: Avoid repeated freeze/thaw cycles. Store at 2-8ºC for one month. Aliquot and store at -80ºC for 12 months. Stability Test: The thermal stability is described by the loss rate. -
Neurodegenerative Triplet Repeat Expansion Disorders
The Pharma Innovation Journal 2018; 7(11): 34-40 ISSN (E): 2277- 7695 ISSN (P): 2349-8242 NAAS Rating: 5.03 Neurodegenerative triplet repeat expansion disorders: TPI 2018; 7(11): 34-40 © 2018 TPI A review www.thepharmajournal.com Received: 15-09-2018 Accepted: 20-10-2018 Mitesh Patel, RK Patel, Tushar Chauhan, Jigar Suthar and Sanjay Dave Mitesh Patel Genetics Group of Gujarat Abstract Diagnostic Centre, Mehsana, Epigenetic alterations are the major causes of triplet repeat expansion. The repetitive DNA expands of its Gujarat, India normal length results in sever neurodegenerative conditions. The common types of triplet repeat expansion (TNE) disorders are: Huntington disease, Friedreich ataxia, myotonic dystrophy, SBMA and RK Patel SCA1 out of which Huntington disease, SBMA and SCA1 are categorized as a poly glutamine disorder Sandor Animal Biogenics Pvt. due to the repeat of CAG. In contrast, the friedreich ataxia is occurred due to expansion of the GAA Ltd., Hyderabad, Telangana, whereas myotonic dystrophy is due to the expansion of CTG. The triplet disease follows the mechanism India of anticipation in which the onset of the disease increases with age. Conclusively, no clear mechanism can explain the origin of the disease. The pre mutation can be expanded in full mutation in successive Tushar Chauhan Genetics Group of Gujarat generations and the number of repeats increased with each generation. TNE can observe in both somatic Diagnostic Centre, Mehsana, as well as germ line tissues. Gujarat, India Keywords: triplet repeat expansion disorder, trinucleotide repeats, Huntington disease, SBMA, SCA1, Jigar Suthar friedreich ataxia Genetics Group of Gujarat Diagnostic Centre, Mehsana, Introduction Gujarat, India Since the early 1990s, a new class of molecular disease has been characterized based upon the Sanjay Dave presence of unstable and abnormal expansions of DNA-triplets (Trinucleotides). -
Anti- DDX17 Antibody
anti- DDX17 antibody Product Information Catalog No.: FNab02296 Size: 100μg Form: liquid Purification: Immunogen affinity purified Purity: ≥95% as determined by SDS-PAGE Host: Rabbit Clonality: polyclonal Clone ID: None IsoType: IgG Storage: PBS with 0.02% sodium azide and 50% glycerol pH 7.3, -20℃ for 12 months (Avoid repeated freeze / thaw cycles.) Background RNA-dependent ATPase activity. Involved in transcriptional regulation. Transcriptional coactivator for estrogen receptor ESR1. Increases ESR1 AF-1 domain-mediated transactivation. Synergizes with DDX5 and SRA1 RNA to activate MYOD1 transcriptional activity and probably involved in skeletal muscle differentiation. Required for zinc-finger antiviral protein ZC3HAV1- mediated mRNA degradation. Immunogen information Immunogen: DEAD(Asp-Glu-Ala-Asp) box polypeptide 17 Synonyms: DDX17, DEAD box protein 17, DEAD box protein p72, P72, RH70, RNA dependent helicase p72 Observed MW: 72 kDa, 80 kDa Uniprot ID : Q92841 Application Reactivity: Human, Mouse, Rat Tested Application: ELISA, IHC, IF, WB, IP 1 Wuhan Fine Biotech Co., Ltd. B9 Bld, High-Tech Medical Devices Park, No. 818 Gaoxin Ave.East Lake High-Tech Development Zone.Wuhan, Hubei, China(430206) Tel :( 0086)027-87384275 Fax: (0086)027-87800889 www.fn-test.com Recommended dilution: WB: 1:500-1:2000; IP: 1:500-1:2000; IHC: 1:500-1:2000; IF: 1:10-1:100 Image: Immunohistochemistry of paraffin-embedded human kidney using FNab02296(DDX17 antibody) at dilution of 1:100 Immunofluorescent analysis of Hela cells, using DDX17 antibody FNab02296 at 1:25 dilution and Rhodamine-labeled goat anti-rabbit IgG (red). IP Result of anti-DDX17,P72 (IP: FNab02296, 4ug; Detection: FNab02296 1:1000) with mouse brain tissue lysate 3000ug. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
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, -
Association Weight Matrix for the Genetic Dissection of Puberty in Beef Cattle
Association weight matrix for the genetic dissection of puberty in beef cattle Marina R. S. Fortesa,b,c, Antonio Revertera,b, Yuandan Zhanga,d, Eliza Collisa,b, Shivashankar H. Nagarajb,NickN.Jonssona,c,e, Kishore C. Prayagaa,b,1, Wes Barrisa,b, and Rachel J. Hawkena,b,2 aCooperative Research Centre for Beef Genetic Technologies; bCommonwealth Scientific and Industrial Research Organization, division of Livestock Industries, Queensland Bioscience Precinct, Brisbane QLD 4067, Australia; cThe University of Queensland, School of Veterinary Science, Gatton QLD 4343, Australia; dAnimal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia; and eFaculty of Veterinary Medicine, University of Glasgow, Glasgow G61 1QH, United Kingdom Edited by George Seidel, Colorado State University, Fort Collins, CO, and approved June 21, 2010 (received for review February 23, 2010) We describe a systems biology approach for the genetic dissection tional data on traits related to puberty are available. For example, of complex traits based on applying gene network theory to the re- weight and condition score are often measured on occasions sults from genome-wide associations. The associations of single- throughout an animal’s development. Hence, understanding ge- nucleotide polymorphisms (SNP) that were individually associated netics of cattle puberty and its biology serves two purposes: as with a primary phenotype of interest, age at puberty in our study, a strategy to develop efficient livestock resources and as a model were explored across 22 related traits. Genomic regions were sur- for human biology. veyed for genes harboring the selected SNP. As a result, an asso- The focus of this work is to demonstrate a unique systems ap- ciation weight matrix (AWM) was constructed with as many rows proach, which we call an association weight matrix (AWM), ap- as genes and as many columns as traits. -
Mice Fed Rapamycin Have an Increase in Lifespan Associated with Major Changes in the Liver Transcriptome
Mice Fed Rapamycin Have an Increase in Lifespan Associated with Major Changes in the Liver Transcriptome Fok WC, Chen Y, Bokov A, Zhang Y, Salmon AB, et al. (2014) Mice Fed Rapamycin Have an Increase in Lifespan Associated with Major Changes in the Liver Transcriptome. PLoS ONE 9(1): e83988. doi:10.1371/journal.pone.0083988 10.1371/journal.pone.0083988 Public Library of Science Version of Record http://hdl.handle.net/1957/47220 http://cdss.library.oregonstate.edu/sa-termsofuse Mice Fed Rapamycin Have an Increase in Lifespan Associated with Major Changes in the Liver Transcriptome Wilson C. Fok1, Yidong Chen3,4,5, Alex Bokov2,3, Yiqiang Zhang2,6, Adam B. Salmon2,7,11, Vivian Diaz2, Martin Javors8, William H. Wood, 3rd9, Yongqing Zhang9, Kevin G. Becker9, Viviana I. Pe´rez10, Arlan Richardson1,2,11* 1 Department of Cellular and Structural Biology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America, 2 Barshop Institute for Longevity and Aging Studies, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America, 3 Department of Epidemiology & Biostatistics, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America, 4 Greehey Children’s Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America, 5 Cancer Therapy and Research Center, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
Rats and Axolotls Share a Common Molecular Signature After Spinal Cord Injury Enriched in Collagen-1
Rats and axolotls share a common molecular signature after spinal cord injury enriched in collagen-1 Athanasios Didangelos1, Katalin Bartus1, Jure Tica1, Bernd Roschitzki2, Elizabeth J. Bradbury1 1Wolfson CARD King’s College London, United Kingdom. 2Centre for functional Genomics, ETH Zurich, Switzerland. Running title: spinal cord injury in rats and axolotls Correspondence: A Didangelos: [email protected] SUPPLEMENTAL FIGURES AND LEGENDS Supplemental Fig. 1: Rat 7 days microarray differentially regulated transcripts. A-B: Protein-protein interaction networks of upregulated (A) and downregulated (B) transcripts identified by microarray gene expression profiling of rat SCI (4 sham versus 4 injured spinal cord samples) 7 days post-injury. Microarray expression data and experimental information is publicly available online (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45006) and is also summarised in Supplemental Table 1. Protein-protein interaction networks were performed in StringDB using the full range of protein interaction scores (0.15 – 0.99) to capture maximum evidence of proteins’ interactions. Networks were then further analysed for betweeness centrality and gene ontology (GO) annotations (BinGO) in Cytoscape. Node colour indicates betweeness centrality while edge colour and thickness indicate interaction score based on predicted functional links between nodes (green: low values; red: high values). C-D: The top 10 upregulated (C) or downregulated (D) transcripts sorted by betweeness centrality score in protein-protein interaction networks shown in A & B. E-F: Biological process GO analysis was performed on networks of upregulated and downregulated genes using BinGO in Cytoscape. Graphs indicate the 20 most significant GO categories and the number of genes in each category.