Supplimentry Figure 1- GSEA Plot of All the Significantly Enriched Data Sets
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Translational Resistance of Late Alphavirus Mrna to Eif2␣ Phosphorylation: a Strategy to Overcome the Antiviral Effect of Protein Kinase PKR
Downloaded from genesdev.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Translational resistance of late alphavirus mRNA to eIF2␣ phosphorylation: a strategy to overcome the antiviral effect of protein kinase PKR Iván Ventoso,1,3 Miguel Angel Sanz,2 Susana Molina,2 Juan José Berlanga,2 Luis Carrasco,2 and Mariano Esteban1 1Departamento de Biología Molecular y Celular, Centro Nacional de Biotecnología/CSIC, Cantoblanco, E-28049 Madrid, Spain; 2Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Facultad de Ciencias, Cantoblanco, E-28049 Madrid, Spain The double-stranded RNA-dependent protein kinase (PKR) is one of the four mammalian kinases that phosphorylates the translation initiation factor 2␣ in response to virus infection. This kinase is induced by interferon and activated by double-stranded RNA (dsRNA). Phosphorylation of eukaryotic initiation factor 2␣ (eIF2␣) blocks translation initiation of both cellular and viral mRNA, inhibiting virus replication. To counteract this effect, most viruses express inhibitors that prevent PKR activation in infected cells. Here we report that PKR is highly activated following infection with alphaviruses Sindbis (SV) and Semliki Forest virus (SFV), leading to the almost complete phosphorylation of eIF2␣. Notably, subgenomic SV 26S mRNA is translated efficiently in the presence of phosphorylated eIF2␣. This modification of eIF2 does not restrict viral replication; SV 26S mRNA initiates translation with canonical methionine in the presence of high levels of phosphorylated eIF2␣. Genetic and biochemical data showed a highly stable RNA hairpin loop located downstream of the AUG initiator codon that is necessary to provide translational resistance to eIF2␣ phosphorylation. This structure can stall the ribosomes on the correct site to initiate translation of SV 26S mRNA, thus bypassing the requirement for a functional eIF2. -
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. -
Structure of the Mammalian 80S Initiation Complex with Initiation Factor 5B on HCV-IRES RNA
ARTICLES Structure of the mammalian 80S initiation complex with initiation factor 5B on HCV-IRES RNA Hiroshi Yamamoto1,3, Anett Unbehaun1,3, Justus Loerke1, Elmar Behrmann1, Marianne Collier1, Jörg Bürger1,2, Thorsten Mielke1,2 & Christian M T Spahn1 The universally conserved eukaryotic initiation factor (eIF) 5B, a translational GTPase, is essential for canonical translation initiation. It is also required for initiation facilitated by the internal ribosomal entry site (IRES) of hepatitis C virus (HCV) RNA. Met eIF5B promotes joining of 60S ribosomal subunits to 40S ribosomal subunits bound by initiator tRNA (Met-tRNAi ). However, the exact molecular mechanism by which eIF5B acts has not been established. Here we present cryo-EM reconstructions of the Met mammalian 80S–HCV-IRES–Met-tRNAi –eIF5B–GMPPNP complex. We obtained two substates distinguished by the rotational state of the ribosomal subunits and the configuration of initiator tRNA in the peptidyl (P) site. Accordingly, a combination of Met conformational changes in the 80S ribosome and in initiator tRNA facilitates binding of the Met-tRNAi to the 60S P site and redefines the role of eIF5B as a tRNA-reorientation factor. Met Eukaryotic translation initiation is a highly regulated process within stable ribosomal binding of Met-tRNAi and elongation competence the translation cycle that proceeds via 48S and 80S initiation-complex in vivo11–13. However, the molecular mechanism linking eIF5B–GTP Met Met intermediates. At least 12 eIFs facilitate recruitment of Met-tRNAi hydrolysis and proper placement of Met-tRNAi in the ribosomal and mRNA to the ribosomal 40S subunit and regulate the interaction of P site is not known. -
Membrane Tension Buffering by Caveolae: a Role in Cancer?
Cancer and Metastasis Reviews (2020) 39:505–517 https://doi.org/10.1007/s10555-020-09899-2 Membrane tension buffering by caveolae: a role in cancer? Vibha Singh1 & Christophe Lamaze1 Published online: 30 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Caveolae are bulb-like invaginations made up of two essential structural proteins, caveolin-1 and cavins, which are abundantly present at the plasma membrane of vertebrate cells. Since their discovery more than 60 years ago, the function of caveolae has been mired in controversy. The last decade has seen the characterization of new caveolae components and regulators together with the discovery of additional cellular functions that have shed new light on these enigmatic structures. Early on, caveolae and/ or caveolin-1 have been involved in the regulation of several parameters associated with cancer progression such as cell migration, metastasis, angiogenesis, or cell growth. These studies have revealed that caveolin-1 and more recently cavin-1 have a dual role with either a negative or a positive effect on most of these parameters. The recent discovery that caveolae can act as mechanosensors has sparked an array of new studies that have addressed the mechanobiology of caveolae in various cellular functions. This review summarizes the current knowledge on caveolae and their role in cancer development through their activity in membrane tension buffering. We propose that the role of caveolae in cancer has to be revisited through their response to the mechanical forces encountered by cancer cells during tumor mass development. Keywords Caveolae . Cancer . Mechanosensing . Mechanotransdcution . Membrane tension . -
Chr21 Protein-Protein Interactions: Enrichment in Products Involved in Intellectual Disabilities, Autism and Late Onset Alzheimer Disease
bioRxiv preprint doi: https://doi.org/10.1101/2019.12.11.872606; this version posted December 12, 2019. 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. Chr21 protein-protein interactions: enrichment in products involved in intellectual disabilities, autism and Late Onset Alzheimer Disease Julia Viard1,2*, Yann Loe-Mie1*, Rachel Daudin1, Malik Khelfaoui1, Christine Plancon2, Anne Boland2, Francisco Tejedor3, Richard L. Huganir4, Eunjoon Kim5, Makoto Kinoshita6, Guofa Liu7, Volker Haucke8, Thomas Moncion9, Eugene Yu10, Valérie Hindie9, Henri Bléhaut11, Clotilde Mircher12, Yann Herault13,14,15,16,17, Jean-François Deleuze2, Jean- Christophe Rain9, Michel Simonneau1, 18, 19, 20** and Aude-Marie Lepagnol- Bestel1** 1 Centre Psychiatrie & Neurosciences, INSERM U894, 75014 Paris, France 2 Laboratoire de génomique fonctionnelle, CNG, CEA, Evry 3 Instituto de Neurociencias CSIC-UMH, Universidad Miguel Hernandez-Campus de San Juan 03550 San Juan (Alicante), Spain 4 Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA 5 Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon 34141, Republic of Korea 6 Department of Molecular Biology, Division of Biological Science, Nagoya University Graduate School of Science, Furo, Chikusa, Nagoya, Japan 7 Department of Biological Sciences, University of Toledo, Toledo, OH, 43606, USA 8 Leibniz Forschungsinstitut für Molekulare Pharmakologie -
Neural Stem Cell-Derived Exosomes Revert HFD-Dependent Memory Impairment Via CREB-BDNF Signalling
International Journal of Molecular Sciences Article Neural Stem Cell-Derived Exosomes Revert HFD-Dependent Memory Impairment via CREB-BDNF Signalling Matteo Spinelli 1, Francesca Natale 1,2, Marco Rinaudo 1, Lucia Leone 1,2, Daniele Mezzogori 1, Salvatore Fusco 1,2,* and Claudio Grassi 1,2 1 Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; [email protected] (M.S.); [email protected] (F.N.); [email protected] (M.R.); [email protected] (L.L.); [email protected] (D.M.); [email protected] (C.G.) 2 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy * Correspondence: [email protected] Received: 6 October 2020; Accepted: 25 November 2020; Published: 26 November 2020 Abstract: Overnutrition and metabolic disorders impair cognitive functions through molecular mechanisms still poorly understood. In mice fed with a high fat diet (HFD) we analysed the expression of synaptic plasticity-related genes and the activation of cAMP response element-binding protein (CREB)-brain-derived neurotrophic factor (BDNF)-tropomyosin receptor kinase B (TrkB) signalling. We found that a HFD inhibited both CREB phosphorylation and the expression of a set of CREB target genes in the hippocampus. The intranasal administration of neural stem cell (NSC)-derived exosomes (exo-NSC) epigenetically restored the transcription of Bdnf, nNOS, Sirt1, Egr3, and RelA genes by inducing the recruitment of CREB on their regulatory sequences. Finally, exo-NSC administration rescued both BDNF signalling and memory in HFD mice. Collectively, our findings highlight novel mechanisms underlying HFD-related memory impairment and provide evidence of the potential therapeutic effect of exo-NSC against metabolic disease-related cognitive decline. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
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. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
EHD2 Is a Mechanotransducer Connecting Caveolae Dynamics with Gene Transcription
EHD2 is a mechanotransducer connecting caveolae dynamics with gene transcription Satish Kailasam Mani, Cesar Valades-Cruz, Stéphanie Torrino, Wei-Wei Shen, Cedric Blouin, Satish Kailasam Mani, Christine Viaris de Lesegno, Pierre Bost, Alexandre Grassart, Darius Köster, et al. To cite this version: Satish Kailasam Mani, Cesar Valades-Cruz, Stéphanie Torrino, Wei-Wei Shen, Cedric Blouin, et al.. EHD2 is a mechanotransducer connecting caveolae dynamics with gene transcription. Journal of Cell Biology, Rockefeller University Press, 2018, 217 (12), pp.4092-4105. 10.1083/jcb.201801122. inserm- 02426440 HAL Id: inserm-02426440 https://www.hal.inserm.fr/inserm-02426440 Submitted on 2 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - ShareAlike| 4.0 International License REPORT EHD2 is a mechanotransducer connecting caveolae dynamics with gene transcription Stéphanie Torrino1,2,3*, Wei‑Wei Shen1,2,3*, Cédric M. Blouin1,2,3, Satish Kailasam Mani1,2,3, Christine Viaris de Lesegno1,2,3, Pierre Bost4,5, Alexandre Grassart6, Darius Köster7, Cesar Augusto Valades‑Cruz2,3,8, Valérie Chambon2,3,8, Ludger Johannes2,3,8, Paolo Pierobon9, Vassili Soumelis4, Catherine Coirault10, Stéphane Vassilopoulos10, and Christophe Lamaze1,2,3 Caveolae are small invaginated pits that function as dynamic mechanosensors to buffer tension variations at the plasma membrane. -
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.