Table 1 the Statistical Metrics for Key Differentially Expressed Genes (Degs)
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Anti-ARL4A Antibody (ARG41291)
Product datasheet [email protected] ARG41291 Package: 100 μl anti-ARL4A antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes ARL4A Tested Reactivity Hu, Ms, Rat Tested Application ICC/IF, IHC-P Host Rabbit Clonality Polyclonal Isotype IgG Target Name ARL4A Antigen Species Human Immunogen Recombinant fusion protein corresponding to aa. 121-200 of Human ARL4A (NP_001032241.1). Conjugation Un-conjugated Alternate Names ARL4; ADP-ribosylation factor-like protein 4A Application Instructions Application table Application Dilution ICC/IF 1:50 - 1:200 IHC-P 1:50 - 1:200 Application Note * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 23 kDa Properties Form Liquid Purification Affinity purified. Buffer PBS (pH 7.3), 0.02% Sodium azide and 50% Glycerol. Preservative 0.02% Sodium azide Stabilizer 50% Glycerol Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. Note For laboratory research only, not for drug, diagnostic or other use. www.arigobio.com 1/2 Bioinformation Gene Symbol ARL4A Gene Full Name ADP-ribosylation factor-like 4A Background ADP-ribosylation factor-like 4A is a member of the ADP-ribosylation factor family of GTP-binding proteins. ARL4A is similar to ARL4C and ARL4D and each has a nuclear localization signal and an unusually high guaninine nucleotide exchange rate. -
HHS Public Access Author Manuscript
HHS Public Access Author manuscript Author Manuscript Author ManuscriptAm J Med Author Manuscript Genet B Neuropsychiatr Author Manuscript Genet. Author manuscript; available in PMC 2015 December 01. Published in final edited form as: Am J Med Genet B Neuropsychiatr Genet. 2014 December ; 0(8): 673–683. doi:10.1002/ajmg.b.32272. Association and ancestry analysis of sequence variants in ADH and ALDH using alcohol-related phenotypes in a Native American community sample Qian Peng1,2,*, Ian R. Gizer3, Ondrej Libiger2, Chris Bizon4, Kirk C. Wilhelmsen4,5, Nicholas J. Schork1, and Cindy L. Ehlers6,* 1 Department of Human Biology, J. Craig Venter Institute, La Jolla, CA 92037 2 Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037 3 Department of Psychological Sciences, University of Missouri-Columbia, Columbia, MO 65211 4 Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517 5 Department of Genetics and Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 6 Department of Molecular and Cellular Neuroscience, The Scripps Research Institute, La Jolla, CA 92037 Abstract Higher rates of alcohol use and other drug-dependence have been observed in some Native American populations relative to other ethnic groups in the U.S. Previous studies have shown that alcohol dehydrogenase (ADH) genes and aldehyde dehydrogenase (ALDH) genes may affect the risk of development of alcohol dependence, and that polymorphisms within these genes may differentially affect risk for the disorder depending on the ethnic group evaluated. We evaluated variations in the ADH and ALDH genes in a large study investigating risk factors for substance use in a Native American population. -
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. -
Download on 20
bioRxiv preprint doi: https://doi.org/10.1101/850776; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Intramembrane protease RHBDL4 cleaves oligosaccharyltransferase subunits to target them for ER-associated degradation Julia D. Knopf1, Nina Landscheidt1, Cassandra L. Pegg2, Benjamin L. Schulz2, Nathalie Kühnle1, Chao-Wei Chao1, Simon Huck1 and Marius K. Lemberg1, # 1Centre for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany. 2School of Chemistry and Molecular Biosciences, ARC Training Centre for Biopharmaceutical Innovation, The University of Queensland, St Lucia QLD 4072, Australia. #Corresponding author: [email protected] Running title: RHBDL4 triggers ERAD of OST subunits Key words: Rhomboid serine protease, Rhbdd1, ubiquitin-dependent proteolysis, post- translational protein abundance control, N-linked glycosylation. Abbreviations ERAD, ER-associated degradation; OST, oligosacharyltransferase; TM, transmembrane; UIM, ubiquitin-interacting motif. Abstract The Endoplasmic Reticulum (ER)-resident intramembrane rhomboid protease RHBDL4 generates metastable protein fragments and together with the ER-associated degradation (ERAD) machinery provides a clearance mechanism for aberrant and surplus proteins. However, the endogenous substrate spectrum and with that the role of RHBDL4 in physiological ERAD is mainly unknown. Here, we use a substrate trapping approach in combination with quantitative proteomics to identify physiological RHBDL4 substrates. This revealed oligosacharyltransferase (OST) complex subunits such as the catalytic active subunit STT3A as substrates for the RHBDL4-dependent ERAD pathway. RHBDL4-catalyzed cleavage inactivates OST subunits by triggering dislocation into the cytoplasm and subsequent proteasomal degradation. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
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. -
MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. -
Genome-Wide Association Study for Circulating Fibroblast Growth Factor
www.nature.com/scientificreports OPEN Genome‑wide association study for circulating fbroblast growth factor 21 and 23 Gwo‑Tsann Chuang1,2,14, Pi‑Hua Liu3,4,14, Tsui‑Wei Chyan5, Chen‑Hao Huang5, Yu‑Yao Huang4,6, Chia‑Hung Lin4,7, Jou‑Wei Lin8, Chih‑Neng Hsu8, Ru‑Yi Tsai8, Meng‑Lun Hsieh9, Hsiao‑Lin Lee9, Wei‑shun Yang2,9, Cassianne Robinson‑Cohen10, Chia‑Ni Hsiung11, Chen‑Yang Shen12,13 & Yi‑Cheng Chang2,9,12* Fibroblast growth factors (FGFs) 21 and 23 are recently identifed hormones regulating metabolism of glucose, lipid, phosphate and vitamin D. Here we conducted a genome‑wide association study (GWAS) for circulating FGF21 and FGF23 concentrations to identify their genetic determinants. We enrolled 5,000 participants from Taiwan Biobank for this GWAS. After excluding participants with diabetes mellitus and quality control, association of single nucleotide polymorphisms (SNPs) with log‑transformed FGF21 and FGF23 serum concentrations adjusted for age, sex and principal components of ancestry were analyzed. A second model additionally adjusted for body mass index (BMI) and a third model additionally adjusted for BMI and estimated glomerular fltration rate (eGFR) were used. A total of 4,201 participants underwent GWAS analysis. rs67327215, located within RGS6 (a gene involved in fatty acid synthesis), and two other SNPs (rs12565114 and rs9520257, located between PHC2-ZSCAN20 and ARGLU1-FAM155A respectively) showed suggestive associations with serum FGF21 level (P = 6.66 × 10–7, 6.00 × 10–7 and 6.11 × 10–7 respectively). The SNPs rs17111495 and rs17843626 were signifcantly associated with FGF23 level, with the former near PCSK9 gene and the latter near HLA-DQA1 gene (P = 1.04 × 10–10 and 1.80 × 10–8 respectively). -
Plenary and Platform Abstracts
American Society of Human Genetics 68th Annual Meeting PLENARY AND PLATFORM ABSTRACTS Abstract #'s Tuesday, October 16, 5:30-6:50 pm: 4. Featured Plenary Abstract Session I Hall C #1-#4 Wednesday, October 17, 9:00-10:00 am, Concurrent Platform Session A: 6. Variant Insights from Large Population Datasets Ballroom 20A #5-#8 7. GWAS in Combined Cancer Phenotypes Ballroom 20BC #9-#12 8. Genome-wide Epigenomics and Non-coding Variants Ballroom 20D #13-#16 9. Clonal Mosaicism in Cancer, Alzheimer's Disease, and Healthy Room 6A #17-#20 Tissue 10. Genetics of Behavioral Traits and Diseases Room 6B #21-#24 11. New Frontiers in Computational Genomics Room 6C #25-#28 12. Bone and Muscle: Identifying Causal Genes Room 6D #29-#32 13. Precision Medicine Initiatives: Outcomes and Lessons Learned Room 6E #33-#36 14. Environmental Exposures in Human Traits Room 6F #37-#40 Wednesday, October 17, 4:15-5:45 pm, Concurrent Platform Session B: 24. Variant Interpretation Practices and Resources Ballroom 20A #41-#46 25. Integrated Variant Analysis in Cancer Genomics Ballroom 20BC #47-#52 26. Gene Discovery and Functional Models of Neurological Disorders Ballroom 20D #53-#58 27. Whole Exome and Whole Genome Associations Room 6A #59-#64 28. Sequencing-based Diagnostics for Newborns and Infants Room 6B #65-#70 29. Omics Studies in Alzheimer's Disease Room 6C #71-#76 30. Cardiac, Valvular, and Vascular Disorders Room 6D #77-#82 31. Natural Selection and Human Phenotypes Room 6E #83-#88 32. Genetics of Cardiometabolic Traits Room 6F #89-#94 Wednesday, October 17, 6:00-7:00 pm, Concurrent Platform Session C: 33. -
Controls Homeostatic Splicing of ARGLU1 Mrna Stephan P
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, No. 6 3473–3486 doi: 10.1093/nar/gkw1140 An Ultraconserved Element (UCE) controls homeostatic splicing of ARGLU1 mRNA Stephan P. Pirnie, Ahmad Osman, Yinzhou Zhu and Gordon G. Carmichael* Department of Genetics and Genome Sciences, UCONN Health Center, 400 Farmington Avenue, Farmington, CT 06030, USA Received January 13, 2016; Revised October 25, 2016; Editorial Decision October 28, 2016; Accepted October 31, 2016 ABSTRACT or inhibit the usage of a particular splice site (1,2). The ex- pression of trans-acting factors in a developmental and tis- Arginine and Glutamate-Rich protein 1 (ARGLU1) is sue specific manner results in regulated splicing that isof- a protein whose function is poorly understood, but ten cell specific. Most notably, trans-acting proteins such as may act in both transcription and pre-mRNA splicing. the NOVA (3–6), the RBFOX (7) and SR protein (8–11) We demonstrate that the ARGLU1 gene expresses families, and a number of hnRNP (12,13) proteins compete at least three distinct RNA splice isoforms – a fully to bind nascent RNAs at specific motifs and drive regula- spliced isoform coding for the protein, an isoform tion of alternative splicing in a tissue and developmentally containing a retained intron that is detained in the specific manner. Alternative splicing is an important pro- nucleus, and an isoform containing an alternative cess that is seen in at least 95% of multi-exon genes in the exon that targets the transcript for nonsense medi- human transcriptome (14). Furthermore, alternative splic- ated decay. -
Macropinocytosis Requires Gal-3 in a Subset of Patient-Derived Glioblastoma Stem Cells
ARTICLE https://doi.org/10.1038/s42003-021-02258-z OPEN Macropinocytosis requires Gal-3 in a subset of patient-derived glioblastoma stem cells Laetitia Seguin1,8, Soline Odouard2,8, Francesca Corlazzoli 2,8, Sarah Al Haddad2, Laurine Moindrot2, Marta Calvo Tardón3, Mayra Yebra4, Alexey Koval5, Eliana Marinari2, Viviane Bes3, Alexandre Guérin 6, Mathilde Allard2, Sten Ilmjärv6, Vladimir L. Katanaev 5, Paul R. Walker3, Karl-Heinz Krause6, Valérie Dutoit2, ✉ Jann N. Sarkaria 7, Pierre-Yves Dietrich2 & Érika Cosset 2 Recently, we involved the carbohydrate-binding protein Galectin-3 (Gal-3) as a druggable target for KRAS-mutant-addicted lung and pancreatic cancers. Here, using glioblastoma patient-derived stem cells (GSCs), we identify and characterize a subset of Gal-3high glio- 1234567890():,; blastoma (GBM) tumors mainly within the mesenchymal subtype that are addicted to Gal-3- mediated macropinocytosis. Using both genetic and pharmacologic inhibition of Gal-3, we showed a significant decrease of GSC macropinocytosis activity, cell survival and invasion, in vitro and in vivo. Mechanistically, we demonstrate that Gal-3 binds to RAB10, a member of the RAS superfamily of small GTPases, and β1 integrin, which are both required for macro- pinocytosis activity and cell survival. Finally, by defining a Gal-3/macropinocytosis molecular signature, we could predict sensitivity to this dependency pathway and provide proof-of- principle for innovative therapeutic strategies to exploit this Achilles’ heel for a significant and unique subset of GBM patients. 1 University Côte d’Azur, CNRS UMR7284, INSERM U1081, Institute for Research on Cancer and Aging (IRCAN), Nice, France. 2 Laboratory of Tumor Immunology, Department of Oncology, Center for Translational Research in Onco-Hematology, Swiss Cancer Center Léman (SCCL), Geneva University Hospitals, University of Geneva, Geneva, Switzerland. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.