The Emerging Roles of Mtorc1 in Macromanaging Autophagy
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Sensing and Relayaing of the Autophagic Signal by the ULK1
SENSING AND RELAYING THE AUTOPHAGIC SIGNAL BY THE ULK1 KINASE COMPLEX by Cindy Puente A Dissertation Presented to the Faculty of the Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan-Kettering Cancer Center in Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy New York, NY May, 2016 __________________________ __________________________ Xuejun Jiang, PhD Date Dissertation Mentor Copyright © 2016 by Cindy Puente To my husband, son, family and friends for their indefinite love and support iii ABSTRACT Autophagy is a conserved catabolic process that utilizes a defined series of membrane trafficking events to generate a double-membrane vesicle termed the autophagosome, which matures by fusing to the lysosome. Subsequently, the lysosome facilitates the degradation and recycling of the cytoplasmic cargo. Autophagy plays a vital role in maintaining cellular homeostasis, especially under stressful conditions, such as nutrient starvation. As such, it is implicated in a plethora of human diseases, particularly age-related conditions such as neurodegenerative disorders. The long-term goals of this proposal were to understand the upstream molecular mechanisms that regulate the induction of mammalian autophagy and understand how this signal is transduced to the downstream, core machinery of the pathway. In yeast, the upstream signals that modulate the induction of starvation- induced autophagy are clearly defined. The nutrient-sensing kinase Tor inhibits the activation of autophagy by regulating the formation of the Atg1-Atg13-Atg17 complex, through hyper-phosphorylation of Atg13. However, in mammals, the homologous complex ULK1-ATG13-FIP200 is constitutively formed. As such, the molecular mechanism by which mTOR regulates mammalian autophagy is unknown. -
A Minimum-Labeling Approach for Reconstructing Protein Networks Across Multiple Conditions
A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions Arnon Mazza1, Irit Gat-Viks2, Hesso Farhan3, and Roded Sharan1 1 Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. Email: [email protected]. 2 Dept. of Cell Research and Immunology, Tel Aviv University, Tel Aviv 69978, Israel. 3 Biotechnology Institute Thurgau, University of Konstanz, Unterseestrasse 47, CH-8280 Kreuzlingen, Switzerland. Abstract. The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to fa- cilitate the interpretation and representation of these data. A fundamen- tal challenge in this domain is the reconstruction of a protein-protein sub- network that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorith- mic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza in- fection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes. 1 Introduction With the increasing availability of high-throughput data, network biol- arXiv:1307.7803v1 [q-bio.QM] 30 Jul 2013 ogy has become the method of choice for filtering, interpreting and rep- resenting these data. -
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. -
Coxsackievirus Infection Induces a Non-Canonical Autophagy Independent of the ULK and PI3K Complexes
www.nature.com/scientificreports OPEN Coxsackievirus infection induces a non‑canonical autophagy independent of the ULK and PI3K complexes Yasir Mohamud1,2, Junyan Shi1,2, Hui Tang1,3, Pinhao Xiang1,2, Yuan Chao Xue1,2, Huitao Liu1,2, Chen Seng Ng1,2 & Honglin Luo1,2* Coxsackievirus B3 (CVB3) is a single‑stranded positive RNA virus that usurps cellular machinery, including the evolutionarily anti‑viral autophagy pathway, for productive infections. Despite the emergence of double‑membraned autophagosome‑like vesicles during CVB3 infection, very little is known about the mechanism of autophagy initiation. In this study, we investigated the role of established autophagy factors in the initiation of CVB3‑induced autophagy. Using siRNA‑mediated gene‑silencing and CRISPR‑Cas9‑based gene‑editing in culture cells, we discovered that CVB3 bypasses the ULK1/2 and PI3K complexes to trigger autophagy. Moreover, we found that CVB3‑ induced LC3 lipidation occurred independent of WIPI2 and the transmembrane protein ATG9 but required components of the late‑stage ubiquitin‑like ATG conjugation system including ATG5 and ATG16L1. Remarkably, we showed the canonical autophagy factor ULK1 was cleaved through the catalytic activity of the viral proteinase 3C. Mutagenesis experiments identifed the cleavage site of ULK1 after Q524, which separates its N‑terminal kinase domain from C‑terminal substrate binding domain. Finally, we uncovered PI4KIIIβ (a PI4P kinase), but not PI3P or PI5P kinases as requisites for CVB3‑induced LC3 lipidation. Taken together, our studies reveal that CVB3 initiates a non‑canonical form of autophagy that bypasses ULK1/2 and PI3K signaling pathways to ultimately converge on PI4KIIIβ‑ and ATG5–ATG12–ATG16L1 machinery. -
The Schizophrenia Risk Gene Product Mir-137 Alters Presynaptic Plasticity
The schizophrenia risk gene product miR-137 alters presynaptic plasticity The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Siegert, S., J. Seo, E. J. Kwon, A. Rudenko, S. Cho, W. Wang, Z. Flood, et al. 2015. “The schizophrenia risk gene product miR-137 alters presynaptic plasticity.” Nature neuroscience 18 (7): 1008-1016. doi:10.1038/nn.4023. http://dx.doi.org/10.1038/nn.4023. Published Version doi:10.1038/nn.4023 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:24983896 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author Manuscript Author ManuscriptNat Neurosci Author Manuscript. Author manuscript; Author Manuscript available in PMC 2016 January 01. Published in final edited form as: Nat Neurosci. 2015 July ; 18(7): 1008–1016. doi:10.1038/nn.4023. The schizophrenia risk gene product miR-137 alters presynaptic plasticity Sandra Siegert1,2, Jinsoo Seo1,2,*, Ester J. Kwon1,2,*, Andrii Rudenko1,2,*, Sukhee Cho1,2, Wenyuan Wang1,2, Zachary Flood1,2, Anthony J. Martorell1,2, Maria Ericsson4, Alison E. Mungenast1,2, and Li-Huei Tsai1,2,3,5 1Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA 2Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA 3Broad Institute of MIT and Harvard, Cambridge, MA, USA 4Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA Abstract Non-coding variants in the human MIR137 gene locus increase schizophrenia risk at a genome- wide significance level. -
Supplementary Tables
Supplementary Tables Supplementary Table S1: Preselected miRNAs used in feature selection Univariate Cox proportional hazards regression analysis of the endpoint freedom from recurrence in the training set (DKTK-ROG sample) allowed the pre-selection of 524 miRNAs (P< 0.5), which were used in the feature selection. P-value was derived from log-rank test. miRNA p-value miRNA p-value miRNA p-value miRNA p-value hsa-let-7g-3p 0.0001520 hsa-miR-1304-3p 0.0490161 hsa-miR-7108-5p 0.1263245 hsa-miR-6865-5p 0.2073121 hsa-miR-6825-3p 0.0004257 hsa-miR-4298 0.0506194 hsa-miR-4453 0.1270967 hsa-miR-6893-5p 0.2120664 hsa-miR-668-3p 0.0005188 hsa-miR-484 0.0518625 hsa-miR-200a-5p 0.1276345 hsa-miR-25-3p 0.2123829 hsa-miR-3622b-3p 0.0005885 hsa-miR-6851-3p 0.0531446 hsa-miR-6090 0.1278692 hsa-miR-3189-5p 0.2136060 hsa-miR-6885-3p 0.0006452 hsa-miR-1276 0.0557418 hsa-miR-148b-3p 0.1279811 hsa-miR-6073 0.2139702 hsa-miR-6875-3p 0.0008188 hsa-miR-3173-3p 0.0559962 hsa-miR-4425 0.1288330 hsa-miR-765 0.2141536 hsa-miR-487b-5p 0.0011381 hsa-miR-650 0.0564616 hsa-miR-6798-3p 0.1293342 hsa-miR-338-5p 0.2153079 hsa-miR-210-5p 0.0012316 hsa-miR-6133 0.0571407 hsa-miR-4472 0.1300006 hsa-miR-6806-5p 0.2173515 hsa-miR-1470 0.0012822 hsa-miR-4701-5p 0.0571720 hsa-miR-4465 0.1304841 hsa-miR-98-5p 0.2184947 hsa-miR-6890-3p 0.0016539 hsa-miR-202-3p 0.0575741 hsa-miR-514b-5p 0.1308790 hsa-miR-500a-3p 0.2185577 hsa-miR-6511b-3p 0.0017165 hsa-miR-4733-5p 0.0616138 hsa-miR-378c 0.1317442 hsa-miR-4515 0.2187539 hsa-miR-7109-3p 0.0021381 hsa-miR-595 0.0629350 hsa-miR-3121-3p -
Syntaxin 16'S Newly Deciphered Roles in Autophagy
cells Perspective Syntaxin 16’s Newly Deciphered Roles in Autophagy Bor Luen Tang 1,2 1 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore; [email protected]; Tel.: +65-6516-1040 2 NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119077, Singapore Received: 19 November 2019; Accepted: 6 December 2019; Published: 17 December 2019 Abstract: Syntaxin 16, a Qa-SNARE (soluble N-ethylmaleimide-sensitive factor activating protein receptor), is involved in a number of membrane-trafficking activities, particularly transport processes at the trans-Golgi network (TGN). Recent works have now implicated syntaxin 16 in the autophagy process. In fact, syntaxin 16 appears to have dual roles, firstly in facilitating the transport of ATG9a-containing vesicles to growing autophagosomes, and secondly in autolysosome formation. The former involves a putative SNARE complex between syntaxin 16, VAMP7 and SNAP-47. The latter occurs via syntaxin 16’s recruitment by Atg8/LC3/GABARAP family proteins to autophagosomes and endo-lysosomes, where syntaxin 16 may act in a manner that bears functional redundancy with the canonical autophagosome Qa-SNARE syntaxin 17. Here, I discuss these recent findings and speculate on the mechanistic aspects of syntaxin 16’s newly found role in autophagy. Keywords: ATG9; autophagy; autophagosome; SNARE; syntaxin 16; syntaxin 17; VAMP7 1. Introduction Vesicular membrane trafficking [1] and macroautophagy [2] are two highly conserved cellular membrane remodeling processes in eukaryotes. The former mediates the transfer of materials between membrane compartments, while the latter serves to degrade and recycle cytosolic as well as membranous cellular materials. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
12Q Deletions FTNW
12q deletions rarechromo.org What is a 12q deletion? A deletion from chromosome 12q is a rare genetic condition in which a part of one of the body’s 46 chromosomes is missing. When material is missing from a chromosome, it is called a deletion. What are chromosomes? Chromosomes are the structures in each of the body’s cells that carry genetic information telling the body how to develop and function. They come in pairs, one from each parent, and are numbered 1 to 22 approximately from largest to smallest. Additionally there is a pair of sex chromosomes, two named X in females, and one X and another named Y in males. Each chromosome has a short (p) arm and a long (q) arm. Looking at chromosome 12 Chromosome analysis You can’t see chromosomes with the naked eye, but if you stain and magnify them many hundreds of times under a microscope, you can see that each one has a distinctive pattern of light and dark bands. In the diagram of the long arm of chromosome 12 on page 3 you can see the bands are numbered outwards starting from the point at the top of the diagram where the short and long arms meet (the centromere). Molecular techniques If you magnify chromosome 12 about 850 times, a small piece may be visibly missing. But sometimes the missing piece is so tiny that the chromosome looks normal through a microscope. The missing section can then only be found using more sensitive molecular techniques such as FISH (fluorescence in situ hybridisation, a technique that reveals the chromosomes in fluorescent colour), MLPA (multiplex ligation-dependent probe amplification) and/or array-CGH (microarrays), a technique that shows gains and losses of tiny amounts of DNA throughout all the chromosomes. -
Table S2.Up Or Down Regulated Genes in Tcof1 Knockdown Neuroblastoma N1E-115 Cells Involved in Differentbiological Process Anal
Table S2.Up or down regulated genes in Tcof1 knockdown neuroblastoma N1E-115 cells involved in differentbiological process analysed by DAVID database Pop Pop Fold Term PValue Genes Bonferroni Benjamini FDR Hits Total Enrichment GO:0044257~cellular protein catabolic 2.77E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 537 13588 1.944851 8.64E-07 8.64E-07 5.02E-07 process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0051603~proteolysis involved in 4.52E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 534 13588 1.93519 1.41E-06 7.04E-07 8.18E-07 cellular protein catabolic process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0044265~cellular macromolecule 6.09E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 609 13588 1.859332 1.90E-06 6.32E-07 1.10E-06 catabolic process ISG15, RBM8A, ATG7, LOC100046898, PSENEN, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, XRN2, USP17L5, FBXO11, RAD23B, UBE2V2, NED GO:0030163~protein catabolic process 1.81E-09 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 556 13588 1.87839 5.64E-06 1.41E-06 3.27E-06 ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, -
Exploring Autophagy with Gene Ontology
Autophagy ISSN: 1554-8627 (Print) 1554-8635 (Online) Journal homepage: https://www.tandfonline.com/loi/kaup20 Exploring autophagy with Gene Ontology Paul Denny, Marc Feuermann, David P. Hill, Ruth C. Lovering, Helene Plun- Favreau & Paola Roncaglia To cite this article: Paul Denny, Marc Feuermann, David P. Hill, Ruth C. Lovering, Helene Plun- Favreau & Paola Roncaglia (2018) Exploring autophagy with Gene Ontology, Autophagy, 14:3, 419-436, DOI: 10.1080/15548627.2017.1415189 To link to this article: https://doi.org/10.1080/15548627.2017.1415189 © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 17 Feb 2018. Submit your article to this journal Article views: 1097 View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=kaup20 AUTOPHAGY, 2018 VOL. 14, NO. 3, 419–436 https://doi.org/10.1080/15548627.2017.1415189 RESEARCH PAPER - BASIC SCIENCE Exploring autophagy with Gene Ontology Paul Denny a,†,§, Marc Feuermann b,§, David P. Hill c,f,§, Ruth C. Lovering a,§, Helene Plun-Favreau d and Paola Roncaglia e,f,§ aFunctional Gene Annotation, Institute of Cardiovascular Science, University College London, London, UK; bSIB Swiss Institute of Bioinformatics, Geneva, Switzerland; cThe Jackson Laboratory, Bar Harbor, ME, USA; dDepartment of Molecular Neuroscience, UCL Institute of Neurology, London, UK; eEuropean Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, UK; fThe Gene Ontology Consortium ABSTRACT ARTICLE HISTORY Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the Received 18 May 2017 strategies that cells use to catabolize substances in a controlled way.