Quantitative Proteomics Identifies Vasopressin- Responsive Nuclear Proteins in the Collecting Duct”
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
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. -
Arabidopsis Adaptor Protein 1G2 Is Required for Female and Male Gametogenesis
Arabidopsis adaptor protein 1G2 is required for female and male gametogenesis Yongmei Zhou Fujian Agriculture and Forestry University Wenqin Fang Fujian Agriculture and Forestry University Li-Yu Chen Fujian Agriculture and Forestry University Neha Pandey Fujian Agriculture and Forestry University Azam Syed Muhammad Fujian Agriculture and Forestry University Ray Ming ( [email protected] ) University of Illinois at Urbana-Champaign https://orcid.org/0000-0002-9417-5789 Research article Keywords: Arabidopsis, AP1G2, megagametogenesis, microgametogenesis, development. Posted Date: November 12th, 2019 DOI: https://doi.org/10.21203/rs.2.17134/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/22 Abstract Background: The gametophyte s are essential for the productive process in angiosperms. During sexual reproduction in owering plants, haploid spores are formed from meioses of spore mother cells. The spores then undergo mitosis and develop into female and male gametes and give rise to seeds after fertilization. Results: We identied a female sterile mutant from EMS mutagenesis, and a BC1F2 population was generated for map based cloning of the causal gene. Genome re-sequencing of mutant and non-mutant pools revealed a candidate gene, AP1G2 . Analyses of two insertions mutants, ap1g2-1 +/- in exon 7 and ap1g2-3 -/- in 3’ UTR, revealed partial female sterility. Complementation test using native promoter of AP1G2 restored the function in ap1g2-1 +/- and ap1g2-3 -/- . AP1G2 is a paralog of AP1G1 , encoding the large subunit (γ) of adaptor protein-1 (AP-1). ap1g2 mutation led to defective female and male gametophyte development was determined. -
Constitutive Activation of RAS/MAPK Pathway Cooperates with Trisomy 21 and Is Therapeutically Exploitable in Down Syndrome B-Cell Leukemia
Author Manuscript Published OnlineFirst on March 27, 2020; DOI: 10.1158/1078-0432.CCR-19-3519 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Constitutive activation of RAS/MAPK pathway cooperates with trisomy 21 and is therapeutically exploitable in Down syndrome B-cell Leukemia Anouchka P. Laurent1,2, Aurélie Siret1, Cathy Ignacimouttou1, Kunjal Panchal3, M’Boyba K. Diop4, Silvia Jenny5, Yi-Chien Tsai5, Damien Ross-Weil1, Zakia Aid1, Naïs Prade6, Stéphanie Lagarde6, Damien Plassard7, Gaelle Pierron8, Estelle Daudigeos-Dubus4, Yann Lecluse4, Nathalie Droin1, Beat Bornhauser5, Laurence C. Cheung3,9, John D. Crispino10, Muriel Gaudry1, Olivier A. Bernard1, Elizabeth Macintyre11, Carole Barin Bonnigal12, Rishi S. Kotecha3,9,13, Birgit Geoerger4, Paola Ballerini14, Jean-Pierre Bourquin5, Eric Delabesse6, Thomas Mercher1,15 and Sébastien Malinge1,3 1INSERM U1170, Gustave Roussy Institute, Université Paris Saclay, Villejuif, France 2Université Paris Diderot, Paris, France 3Telethon Kids Cancer Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia 4Gustave Roussy Institute Cancer Campus, Department of Pediatric and Adolescent Oncology, INSERM U1015, Equipe Labellisée Ligue Nationale contre le Cancer, Université Paris-Saclay, Villejuif, France 5Department of Pediatric Oncology, Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland 6Centre of Research on Cancer of Toulouse (CRCT), CHU Toulouse, Université Toulouse III, Toulouse, France 7IGBMC, Plateforme GenomEast, UMR7104 CNRS, Ilkirch, France 8Service de Génétique, Institut Curie, Paris, France 9School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Australia 10Division of Hematology/Oncology, Northwestern University, Chicago, USA 11Hematology, Université de Paris, Institut Necker-Enfants Malades and Assistance Publique – Hopitaux de Paris, Paris, France 12Centre Hospitalier Universitaire de Tours, Tours, France 1 Downloaded from clincancerres.aacrjournals.org on September 30, 2021. -
Transcriptomic Analysis of the Aquaporin (AQP) Gene Family
Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples. -
Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Supplementary Table S1. Table 1. List of Bacterial Strains Used in This Study Suppl
Supplementary Material Supplementary Tables: Supplementary Table S1. Table 1. List of bacterial strains used in this study Supplementary Table S2. List of plasmids used in this study Supplementary Table 3. List of primers used for mutagenesis of P. intermedia Supplementary Table 4. List of primers used for qRT-PCR analysis in P. intermedia Supplementary Table 5. List of the most highly upregulated genes in P. intermedia OxyR mutant Supplementary Table 6. List of the most highly downregulated genes in P. intermedia OxyR mutant Supplementary Table 7. List of the most highly upregulated genes in P. intermedia grown in iron-deplete conditions Supplementary Table 8. List of the most highly downregulated genes in P. intermedia grown in iron-deplete conditions Supplementary Figures: Supplementary Figure 1. Comparison of the genomic loci encoding OxyR in Prevotella species. Supplementary Figure 2. Distribution of SOD and glutathione peroxidase genes within the genus Prevotella. Supplementary Table S1. Bacterial strains Strain Description Source or reference P. intermedia V3147 Wild type OMA14 isolated from the (1) periodontal pocket of a Japanese patient with periodontitis V3203 OMA14 PIOMA14_I_0073(oxyR)::ermF This study E. coli XL-1 Blue Host strain for cloning Stratagene S17-1 RP-4-2-Tc::Mu aph::Tn7 recA, Smr (2) 1 Supplementary Table S2. Plasmids Plasmid Relevant property Source or reference pUC118 Takara pBSSK pNDR-Dual Clonetech pTCB Apr Tcr, E. coli-Bacteroides shuttle vector (3) plasmid pKD954 Contains the Porpyromonas gulae catalase (4) -
Genomic Aberrations Associated with Erlotinib Resistance in Non-Small Cell Lung Cancer Cells
ANTICANCER RESEARCH 33: 5223-5234 (2013) Genomic Aberrations Associated with Erlotinib Resistance in Non-small Cell Lung Cancer Cells MASAKUNI SERIZAWA1, TOSHIAKI TAKAHASHI2, NOBUYUKI YAMAMOTO2,3 and YASUHIRO KOH1 1Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute, Sunto-gun, Shizuoka, Japan; 2Division of Thoracic Oncology, Shizuoka Cancer Center Hospital, Sunto-gun, Shizuoka, Japan; 3Third Department of Internal Medicine, Wakayama Medical University, Kimiidera, Wakayama, Japan Abstract. Background/Aim: Mechanisms of resistance to mutations develop resistance, usually within one year of epidermal growth factor receptor (EGFR)-tyrosine kinase treatment. Therefore, there is an urgent need to elucidate the inhibitors (TKIs) in non-small cell lung cancer (NSCLC) underlying mechanisms of resistance in such tumors to are not fully-understood. In this study we aimed to overcome this obstacle (11-14, 17, 24). Recent studies elucidate remaining unknown mechanisms using erlotinib- suggest that mechanisms of acquired resistance to EGFR- resistant NSCLC cells. Materials and Methods: We TKIs can be categorized into three groups: occurrence of performed array comparative genomic hybridization genetic alterations, activation of downstream pathways via (aCGH) to identify genomic aberrations associated with bypass signaling, and phenotypic transformation (15, 16, 21); EGFR-TKI resistance in erlotinib-resistant PC-9ER cells. therapeutic strategies to overcome these resistance Real-time polymerase chain reaction (PCR) and mechanisms are under development. However, although the immunoblot analyses were performed to confirm the results causes of acquired resistance to EGFR-TKIs have been of aCGH. Results: Among the five regions with copy investigated, in more than 30% of patients with acquired number gain detected in PC-9ER cells, we focused on resistance to EGFR-TKI treatment, the mechanisms remain 22q11.2-q12.1 including v-crk avian sarcoma virus CT10 unknown (15). -
Formation of COPI-Coated Vesicles at a Glance Eric C
© 2018. Published by The Company of Biologists Ltd | Journal of Cell Science (2018) 131, jcs209890. doi:10.1242/jcs.209890 CELL SCIENCE AT A GLANCE Formation of COPI-coated vesicles at a glance Eric C. Arakel1 and Blanche Schwappach1,2,* ABSTRACT unresolved, this review attempts to refocus the perspectives of The coat protein complex I (COPI) allows the precise sorting of lipids the field. and proteins between Golgi cisternae and retrieval from the Golgi KEY WORDS: Arf1, ArfGAP, COPI, Coatomer, Golgi, Endoplasmic to the ER. This essential role maintains the identity of the early reticulum, Vesicle coat secretory pathway and impinges on key cellular processes, such as protein quality control. In this Cell Science at a Glance and accompanying poster, we illustrate the different stages of COPI- Introduction coated vesicle formation and revisit decades of research in the Vesicle coat proteins, such as the archetypal clathrin and the coat context of recent advances in the elucidation of COPI coat structure. protein complexes II and I (COPII and COPI, respectively) are By calling attention to an array of questions that have remained molecular machines with two central roles: enabling vesicle formation, and selecting protein and lipid cargo to be packaged within them. Thus, coat proteins fulfil a central role in the 1Department of Molecular Biology, Universitätsmedizin Göttingen, Humboldtallee homeostasis of the cell’s endomembrane system and are the basis 23, 37073 Göttingen, Germany. 2Max-Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. of functionally segregated compartments. COPI operates in retrieval from the Golgi to the endoplasmic reticulum (ER) and in intra-Golgi *Author for correspondence ([email protected]) transport (Beck et al., 2009; Duden, 2003; Lee et al., 2004a; Spang, E.C.A., 0000-0001-7716-7149; B.S., 0000-0003-0225-6432 2009), and maintains ER- and Golgi-resident chaperones and enzymes where they belong.