DENR Promotes Translation Reinitiation Via Ribosome Recycling to Drive Expression of Oncogenes Including ATF4
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Mena regulates the LINC complex to control actin–nuclear lamina associations, trans-nuclear membrane signalling and cancer gene expression Frederic Li Mow Chee!, Bruno Beernaert!, Alexander Loftus!, Yatendra Kumar", Billie G. C. Griffith!, Jimi C. Wills!, Ann P. Wheeler#, J. Douglas Armstrong$, Maddy Parsons%, Irene M. Leigh,(, Charlotte M. Proby&, Alex von Kriegsheim!, Wendy A. Bickmore", Margaret C. Frame,* & Adam Byron,* Supplementary Information Supplementary Figure 1 Supplementary Figure 2 Supplementary Figure 3 Supplementary Table 1 Supplementary Table 2 Supplementary Table 3 Supplementary Table 4 !Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH< =XR, UK. "MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH< =XU, UK. #Advanced Imaging Resource, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH< =XU, UK. $Simons Initiative for the Developing Brain, School of Informatics, University of Edinburgh, Edinburgh EHH IYL, UK. %Randall Centre for Cell and Molecular Biophysics, King’s College London, London SEM MUL, UK. &Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee DD <HN, UK. 'Institute of Dentistry, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EM =AT, UK. *email: [email protected] or [email protected] 1 a cSCC IAC correlation b cSCC IAC pathways c Core adhesome network ENAH −log10(q) MACF1 CSRP1 Met1 Met4 0 5 10 + + CORO2A Integrin signalling + CFL1 pathway PRNP ILK + HSPB1 PALLD PPFIA1 TES RDX Cytoskeletal regulation + VASP + + ARPC2 by Rho GTPase PPP2CA + Met1 + LASP1 MYH9 + VIM TUBA4A Huntington ITGA3 + disease ITGB4 VCL CAV1 ACTB ROCK1 KTN1 FLNA+ CALR DNA FBLIM1 CORO1B RAC1 + replication +ACTN1 ITGA6 + Met4 ITGAV Parkinson ITGB1 disease Actin cytoskel. -
Activities of Ligatin and MCT-1/DENR in Eukaryotic Translation Initiation and Ribosomal Recycling
Downloaded from genesdev.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Activities of Ligatin and MCT-1/DENR in eukaryotic translation initiation and ribosomal recycling Maxim A. Skabkin,1,3 Olga V. Skabkina,1,3 Vidya Dhote,1 Anton A. Komar,2 Christopher U.T. Hellen,1,4 and Tatyana V. Pestova1,5 1Department of Cell Biology, State University of New York Downstate Medical Center, Brooklyn, New York 11203, USA; 2Center for Gene Regulation in Health and Disease, Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, Ohio 44115, USA Eukaryotic translation initiation begins with ribosomal recruitment of aminoacylated initiator tRNA (Met- Met Met tRNA i) by eukaryotic initiation factor eIF2. In cooperation with eIF3, eIF1, and eIF1A, Met-tRNA i/eIF2/ GTP binds to 40S subunits yielding 43S preinitiation complexes that attach to the 59-terminal region of mRNAs and then scan to the initiation codon to form 48S initiation complexes with established codon–anticodon base- pairing. Stress-activated phosphorylation of eIF2a reduces the level of active eIF2, globally inhibiting translation. However, translation of several viral mRNAs, including Sindbis virus (SV) 26S mRNA and mRNAs containing hepatitis C virus (HCV)-like IRESs, is wholly or partially resistant to inhibition by eIF2 phosphorylation, despite Met requiring Met-tRNA i. Here we report the identification of related proteins that individually (Ligatin) or together (the oncogene MCT-1 and DENR, which are homologous to N-terminal and C-terminal regions of Ligatin, Met respectively) promote efficient eIF2-independent recruitment of Met-tRNA i to 40S/mRNA complexes, if attachment of 40S subunits to the mRNA places the initiation codon directly in the P site, as on HCV-like IRESs and, as we show here, SV 26S mRNA. -
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. -
Crystal Structure of the DENR-MCT-1 Complex Revealed Zinc-Binding Site Essential for Heterodimer Formation
Crystal structure of the DENR-MCT-1 complex revealed zinc-binding site essential for heterodimer formation Ivan B. Lomakina,1, Sergey E. Dmitrievb, and Thomas A. Steitza,c,2 aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8114; bBelozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119234 Moscow, Russia; and cHoward Hughes Medical Institute, Yale University, New Haven, CT 06520-8114 Edited by Jennifer A. Doudna, University of California, Berkeley, CA, and approved November 29, 2018 (received for review June 5, 2018) The density-regulated protein (DENR) and the malignant T cell- of the ribosomal complex with DENR-MCT-1 at 6-Åresolution amplified sequence 1 (MCT-1/MCTS1) oncoprotein support nonca- (13). It showed that the structure of the C-terminal domain of nonical translation initiation, promote translation reinitiation on a DENR (C-DENR) is similar to that of the canonical translation specific set of mRNAs with short upstream reading frames, and initiation factor 1 (eIF1), which controls the fidelity of trans- regulate ribosome recycling. DENR and MCT-1 form a heterodimer, lation initiation and scanning. Moreover, C-DENR binds the 40S which binds to the ribosome. We determined the crystal structure of ribosomal subunit at the same site as eIF1. These data suggest a the heterodimer formed by human MCT-1 and the N-terminal do- similar mechanism for DENR-MCT-1 and eIF1 in discriminating main of DENR at 2.0-Å resolution. The structure of the heterodimer the initiator tRNA in the P site of the 40S subunit, which is reveals atomic details of the mechanism of DENR and MCT-1 inter- crucial for translation initiation, reinitiation, and ribosomal action. -
Phenomic Profiling Through Live-Cell Imaging in a Panel of Reporter Cell
www.nature.com/scientificreports OPEN Tales of 1,008 small molecules: phenomic profling through live‑cell imaging in a panel of reporter cell lines Michael J. Cox1,5, Stefen Jaensch1,5*, Jelle Van de Waeter1, Laure Cougnaud2, Daan Seynaeve2, Soulaiman Benalla1, Seong Joo Koo1, Ilse Van Den Wyngaert1, Jean‑Marc Neefs1, Dmitry Malkov3, Mart Bittremieux1, Margino Steemans1, Pieter J. Peeters1, Jörg Kurt Wegner1, Hugo Ceulemans1, Emmanuel Gustin1, Yolanda T. Chong1,4 & Hinrich W. H. Göhlmann1 Phenomic profles are high‑dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profling of compound libraries can be used for compound target identifcation or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profling assays, we assembled a library of 1,008 approved drugs and well‑characterized tool compounds manually annotated to 218 unique MoAs, and we profled each compound at four concentrations in live‑cell, high‑content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fuorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profles accurately ranked the reference compounds (AUC‑ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fuorescent markers increased the number of distinguishable MoAs but this efect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study’s conditions. -
Deconvolution of Expression for Nascent RNA Sequencing Data 3 1 2 (≤ 25 Bp) (Supplementary Fig
Page 1 of 10 Bioinformatics 1 2 3 Downloaded from https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab582/6348164 by Cold Spring Harbor Laboratory user on 18 August 2021 4 5 6 7 Subject Section XXXXX 8 9 10 Deconvolution of Expression for Nascent RNA 11 12 sequencing data (DENR) highlights pre-RNA 13 14 isoform diversity in human cells 15 Yixin Zhao 1,3, Noah Dukler 1,3, Gilad Barshad 2, Shushan Toneyan 1, Charles 16 2 1,∗ 17 G. Danko and Adam Siepel 18 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA 19 2Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA 20 3These authors contributed equally to this work. 21 22 ∗To whom correspondence should be addressed. 23 Associate Editor: XXXXXXX 24 25 Received on XXXXX; revised on XXXXX; accepted on XXXXX 26 27 Abstract 28 Motivation: Quantification of isoform abundance has been extensively studied at the mature-RNA level 29 using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing. 30 Results: We address this problem with a new computational method called Deconvolution of Expression 31 for Nascent RNA sequencing data (DENR), which models nascent RNA sequencing read counts as a 32 mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions 33 of active transcription start sites and an adjustment for the typical “shape profile” of read counts along a 34 transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene 35 and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with 36 frequent differences between cell types. -
2020 Program Book
PROGRAM BOOK Note that TAGC was cancelled and held online with a different schedule and program. This document serves as a record of the original program designed for the in-person meeting. April 22–26, 2020 Gaylord National Resort & Convention Center Metro Washington, DC TABLE OF CONTENTS About the GSA ........................................................................................................................................................ 3 Conference Organizers ...........................................................................................................................................4 General Information ...............................................................................................................................................7 Mobile App ....................................................................................................................................................7 Registration, Badges, and Pre-ordered T-shirts .............................................................................................7 Oral Presenters: Speaker Ready Room - Camellia 4.......................................................................................7 Poster Sessions and Exhibits - Prince George’s Exhibition Hall ......................................................................7 GSA Central - Booth 520 ................................................................................................................................8 Internet Access ..............................................................................................................................................8 -
Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2. -
Intrinsic Indicators for Specimen Degradation
Laboratory Investigation (2013) 93, 242–253 & 2013 USCAP, Inc All rights reserved 0023-6837/13 $32.00 Intrinsic indicators for specimen degradation Jie Li1, Catherine Kil1, Kelly Considine1, Bartosz Smarkucki1, Michael C Stankewich1, Brian Balgley2 and Alexander O Vortmeyer1 Variable degrees of molecular degradation occur in human surgical specimens before clinical examination and severely affect analytical results. We therefore initiated an investigation to identify protein markers for tissue degradation assessment. We exposed 4 cell lines and 64 surgical/autopsy specimens to defined periods of time at room temperature before procurement (experimental cold ischemic time (CIT)-dependent tissue degradation model). Using two-dimen- sional fluorescence difference gel electrophoresis in conjunction with mass spectrometry, we performed comparative proteomic analyses on cells at different CIT exposures and identified proteins with CIT-dependent changes. The results were validated by testing clinical specimens with western blot analysis. We identified 26 proteins that underwent dynamic changes (characterized by continuous quantitative changes, isoelectric changes, and/or proteolytic cleavages) in our degradation model. These changes are strongly associated with the length of CIT. We demonstrate these proteins to represent universal tissue degradation indicators (TDIs) in clinical specimens. We also devised and implemented a unique degradation measure by calculating the quantitative ratio between TDIs’ intact forms and their respective degradation- -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses 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 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. -
Signature Redacted Thesis Supervisor Certified By
Single-Cell Transcriptomics of the Mouse Thalamic Reticular Nucleus by Taibo Li S.B., Massachusetts Institute of Technology (2015) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2017 @ Massachusetts Institute of Technology 2017. All rights reserved. A uthor ... ..................... Department of Electrical Engineering and Computer Science May 25, 2017 Certified by. 3ignature redacted Guoping Feng Poitras Professor of Neuroscience, MIT Signature redacted Thesis Supervisor Certified by... Kasper Lage Assistant Professor, Harvard Medical School Thesis Supervisor Accepted by . Signature redacted Christopher Terman Chairman, Masters of Engineering Thesis Committee MASSACHUSETTS INSTITUTE 0) OF TECHNOLOGY w AUG 14 2017 LIBRARIES 2 Single-Cell Transcriptomics of the Mouse Thalamic Reticular Nucleus by Taibo Li Submitted to the Department of Electrical Engineering and Computer Science on May 25, 2017, in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science Abstract The thalamic reticular nucleus (TRN) is strategically located at the interface between the cortex and the thalamus, and plays a key role in regulating thalamo-cortical in- teractions. Current understanding of TRN neurobiology has been limited due to the lack of a comprehensive survey of TRN heterogeneity. In this thesis, I developed an integrative computational framework to analyze the single-nucleus RNA sequencing data of mouse TRN in a data-driven manner. By combining transcriptomic, genetic, and functional proteomic data, I discovered novel insights into the molecular mecha- nisms through which TRN regulates sensory gating, and suggested targeted follow-up experiments to validate these findings.