Multi-Omics Data Integration and Data Model Optimization in Proteomicsdb
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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. -
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. -
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. -
1 Title 1 Loss of PABPC1 Is Compensated by Elevated PABPC4
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.07.430165; this version posted February 15, 2021. 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. 1 1 Title 2 Loss of PABPC1 is compensated by elevated PABPC4 and correlates with transcriptome 3 changes 4 5 Jingwei Xie1, 2, Xiaoyu Wei1, Yu Chen1 6 7 1 Department of Biochemistry and Groupe de recherche axé sur la structure des 8 protéines, McGill University, Montreal, Quebec H3G 0B1, Canada 9 10 2 To whom correspondence should be addressed: Dept. of Biochemistry, McGill 11 University, Montreal, QC H3G 0B1, Canada. E-mail: [email protected]. 12 13 14 15 Abstract 16 Cytoplasmic poly(A) binding protein (PABP) is an essential translation factor that binds to 17 the 3' tail of mRNAs to promote translation and regulate mRNA stability. PABPC1 is the 18 most abundant of several PABP isoforms that exist in mammals. Here, we used the 19 CRISPR/Cas genome editing system to shift the isoform composition in HEK293 cells. 20 Disruption of PABPC1 elevated PABPC4 levels. Transcriptome analysis revealed that the 21 shift in the dominant PABP isoform was correlated with changes in key transcriptional 22 regulators. This study provides insight into understanding the role of PABP isoforms in 23 development and differentiation. 24 Keywords 25 PABPC1, PABPC4, c-Myc 26 bioRxiv preprint doi: https://doi.org/10.1101/2021.02.07.430165; this version posted February 15, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
RAB9 Antibody (R32125)
RAB9 Antibody (R32125) Catalog No. Formulation Size R32125 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity Buffer Lyophilized from 1X PBS with 2.5% BSA and 0.025% sodium azide UniProt P51151 Applications Western blot : 0.1-0.5ug/ml Limitations This RAB9 antibody is available for research use only. Western blot testing of 1) rat brain, 2) mouse brain, 3) human HeLa and 4) human MCF7 lysate with RAB9 antibody. Expected/observed molecular weight: ~23 kDa. Description Ras-related protein Rab-9A, also called RAB9, is a protein that in humans is encoded by the RAB9A gene. This gene is mapped to Xp22.2. RAB9 has been localized to components of the endocytic/exocytic pathway and has been implicated in recycling of membrane receptors. It has been found that downregulation of RAB9A gene expression in HeLa cells induced severe cell vacuolation. RAB9A GTPase is directly bound by TIP47 in its active, GTP-bound conformation. Moreover, RAB9A increases the affinity of TIP47 for its cargo. What’s more, this gene may involved in the transport of proteins between the endosomes and the trans Golgi network. RAB9A has been shown to interact with RABEPK and TIP47. Application Notes Optimal dilution of the RAB9 antibody should be determined by the researcher. Immunogen Amino acids KDATNVAAAFEEAVRRVLATEDRSDHLIQTDTVNLHRK of human RAB9A were used as the immunogen for the RAB9 antibody. Storage After reconstitution, the RAB9 antibody can be stored for up to one month at 4oC. -
Bioinformatics Analysis for the Identification of Differentially Expressed Genes and Related Signaling Pathways in H
Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells Dingyu Chen*, Chao Li, Yan Zhao, Jianjiang Zhou, Qinrong Wang and Yuan Xie* Key Laboratory of Endemic and Ethnic Diseases , Ministry of Education, Guizhou Medical University, Guiyang, China * These authors contributed equally to this work. ABSTRACT Aim. Helicobacter pylori cytotoxin-associated protein A (CagA) is an important vir- ulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA- induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods. AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, | logFC |> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Submitted 20 August 2020 Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the Accepted 11 March 2021 key genes derived from the PPI network. Further analysis of the key gene expressions Published 15 April 2021 in gastric cancer and normal tissues were performed based on The Cancer Genome Corresponding author Atlas (TCGA) database and RT-qPCR verification. -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in -
Mapping of Craniofacial Traits in Outbred Mice Identifies Major Developmental Genes Involved in Shape Determination
Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination Luisa F Pallares1, Peter Carbonetto2,3, Shyam Gopalakrishnan2,4, Clarissa C Parker2,5, Cheryl L Ackert-Bicknell6, Abraham A Palmer2,7, Diethard Tautz1 # 1Max Planck Institute for Evolutionary Biology, Plön, Germany 2University of Chicago, Chicago, Illinois, USA 3AncestryDNA, San Francisco, California, USA 4Museum of Natural History, Copenhagen University, Copenhagen, Denmark 5Middlebury College, Department of Psychology and Program in Neuroscience, Middlebury VT, USA 6Center for Musculoskeletal Research, University of Rochester, Rochester, NY USA 7University of California San Diego, La Jolla, CA, USA # corresponding author: [email protected] short title: craniofacial shape mapping Abstract The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination 1 is accumulating, we are still far from understanding how such variation at the genetic level is translated into craniofacial shape variation. Here we used 3D geometric morphometrics to map genes involved in shape determination in a population of outbred mice (Carworth Farms White, or CFW). We defined shape traits via principal component analysis of 3D skull and mandible measurements. We mapped genetic loci associated with shape traits at ~80,000 candidate single nucleotide polymorphisms in ~700 male mice. We found that craniofacial shape and size are highly heritable, polygenic traits. Despite the polygenic nature of the traits, we identified 17 loci that explain variation in skull shape, and 8 loci associated with variation in mandible shape. Together, the associated variants account for 11.4% of skull and 4.4% of mandible shape variation, however, the total additive genetic variance associated with phenotypic variation was estimated in ~45%. -
INTS14 Form a Functional Module of Integrator That Binds Nucleic Acids and the Cleavage Module
ARTICLE https://doi.org/10.1038/s41467-020-17232-2 OPEN INTS10–INTS13–INTS14 form a functional module of Integrator that binds nucleic acids and the cleavage module Kevin Sabath1, Melanie L. Stäubli1, Sabrina Marti1, Alexander Leitner 2, Murielle Moes 1 & ✉ Stefanie Jonas 1 ′ 1234567890():,; The Integrator complex processes 3 -ends of spliceosomal small nuclear RNAs (snRNAs). Furthermore, it regulates transcription of protein coding genes by terminating transcription after unstable pausing. The molecular basis for Integrator’s functions remains obscure. Here, we show that INTS10, Asunder/INTS13 and INTS14 form a separable, functional Integrator module. The structure of INTS13-INTS14 reveals a strongly entwined complex with a unique chain interlink. Unexpected structural homology to the Ku70-Ku80 DNA repair complex suggests nucleic acid affinity. Indeed, the module displays affinity for DNA and RNA but prefers RNA hairpins. While the module plays an accessory role in snRNA maturation, it has a stronger influence on transcription termination after pausing. Asunder/INTS13 directly binds Integrator’s cleavage module via a conserved C-terminal motif that is involved in snRNA processing and required for spermatogenesis. Collectively, our data establish INTS10-INTS13- INTS14 as a nucleic acid-binding module and suggest that it brings cleavage module and target transcripts into proximity. 1 Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, CH-8093 Zurich, Switzerland. 2 Institute of Molecular Systems Biology, ETH ✉ Zurich, Zurich, Switzerland. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:3422 | https://doi.org/10.1038/s41467-020-17232-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-17232-2 NA polymerase II (RNAPII) requires essential processing development in Drosophila and Xenopus and shown to be factors to terminate transcription and to release its primary required for correct mitosis in human cells36,38,39. -
Understanding and Improving the Identification of Somatic Variants
Understanding and Improving the Identification of Somatic Variants Vinaya Vijayan Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Genetics, Bioinformatics and Computational Biology Chair: Liqing Zhang Christopher Franck Lenwood S. Heath Xiaowei Wu August 12th, 2016 Blacksburg, VA, USA Keywords: Somatic variants, Somatic variant callers, Somatic point mutations, Short tandem repeat variation, Lung squamous cell carcinoma Understanding and Improving the Identification of Somatic Variants Vinaya Vijayan ABSTRACT It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of better diagnostic, prognostic and therapeutic tools. This thesis outlines our work in understanding somatic variant calling, improving the identification of somatic variants from whole genome and whole exome platforms and identification of biomarkers for lung cancer. Integrating somatic variants from whole genome and whole exome platforms poses a challenge as variants identified in the exonic regions of the whole genome platform may not be identified on the whole exome platform and vice-versa. Taking a simple union or intersection of the somatic variants from both platforms would lead to inclusion of many false positives (through union) and exclusion of many true variants (through intersection). We develop the first framework to improve the identification of somatic variants on whole genome and exome platforms using a machine learning approach by combining the results from two popular somatic variant callers. Testing on simulated and real data sets shows that our framework identifies variants more accurately than using only one somatic variant caller or using variants from only one platform. -
Mouse Rabepk Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Rabepk Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Rabepk conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Rabepk gene (NCBI Reference Sequence: NM_145522 ; Ensembl: ENSMUSG00000070953 ) is located on Mouse chromosome 2. 8 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 8 (Transcript: ENSMUST00000145903). Exon 3 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Rabepk gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-347M2 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 6.84% of the coding region. The knockout of Exon 3 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 2045 bp, and the size of intron 3 for 3'-loxP site insertion: 4516 bp. The size of effective cKO region: ~658 bp. The cKO 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 2 3 8 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Rabepk Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Proteomicsdb: a Multi-Omics and Multi-Organism Resource for Life Science
Published online 30 October 2019 Nucleic Acids Research, 2020, Vol. 48, Database issue D1153–D1163 doi: 10.1093/nar/gkz974 ProteomicsDB: a multi-omics and multi-organism resource for life science research Patroklos Samaras 1, Tobias Schmidt 1,MartinFrejno1, Siegfried Gessulat1,2, Maria Reinecke1,3,4, Anna Jarzab1, Jana Zecha1, Julia Mergner1, Piero Giansanti1, Hans-Christian Ehrlich2, Stephan Aiche2, Johannes Rank5,6, Harald Kienegger5,6, Helmut Krcmar5,6, Bernhard Kuster1,7,* and Mathias Wilhelm1,* 1Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany, 2Innovation Center Network, SAP SE, Potsdam, Germany, 3German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, 4German Cancer Research Center (DKFZ), Heidelberg, Germany, 5Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany, 6SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany and 7Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising, Bavaria, Germany Received September 14, 2019; Revised October 11, 2019; Editorial Decision October 11, 2019; Accepted October 15, 2019 ABSTRACT INTRODUCTION ProteomicsDB (https://www.ProteomicsDB.org) ProteomicsDB (https://www.ProteomicsDB.org) is an in- started as a protein-centric in-memory database for memory database initially developed for the exploration of the exploration of large collections of quantitative large quantities of quantitative human mass spectrometry- mass spectrometry-based proteomics data. The based proteomics data including the first draft of the hu- data types and contents grew over time to include man proteome (1). Among many features, it allows the real- time exploration and retrieval of protein abundance values RNA-Seq expression data, drug-target interactions across different tissues, cell lines, and body fluids via inter- and cell line viability data.