1 Phosphorylation of Syntaxin 17 by TBK1 Controls Autophagy Initiation
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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. -
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). -
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, -
(12) United States Patent (10) Patent No.: US 7.873,482 B2 Stefanon Et Al
US007873482B2 (12) United States Patent (10) Patent No.: US 7.873,482 B2 Stefanon et al. (45) Date of Patent: Jan. 18, 2011 (54) DIAGNOSTIC SYSTEM FOR SELECTING 6,358,546 B1 3/2002 Bebiak et al. NUTRITION AND PHARMACOLOGICAL 6,493,641 B1 12/2002 Singh et al. PRODUCTS FOR ANIMALS 6,537,213 B2 3/2003 Dodds (76) Inventors: Bruno Stefanon, via Zilli, 51/A/3, Martignacco (IT) 33035: W. Jean Dodds, 938 Stanford St., Santa Monica, (Continued) CA (US) 90403 FOREIGN PATENT DOCUMENTS (*) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 WO WO99-67642 A2 12/1999 U.S.C. 154(b) by 158 days. (21)21) Appl. NoNo.: 12/316,8249 (Continued) (65) Prior Publication Data Swanson, et al., “Nutritional Genomics: Implication for Companion Animals'. The American Society for Nutritional Sciences, (2003).J. US 2010/O15301.6 A1 Jun. 17, 2010 Nutr. 133:3033-3040 (18 pages). (51) Int. Cl. (Continued) G06F 9/00 (2006.01) (52) U.S. Cl. ........................................................ 702/19 Primary Examiner—Edward Raymond (58) Field of Classification Search ................... 702/19 (74) Attorney, Agent, or Firm Greenberg Traurig, LLP 702/23, 182–185 See application file for complete search history. (57) ABSTRACT (56) References Cited An analysis of the profile of a non-human animal comprises: U.S. PATENT DOCUMENTS a) providing a genotypic database to the species of the non 3,995,019 A 1 1/1976 Jerome human animal Subject or a selected group of the species; b) 5,691,157 A 1 1/1997 Gong et al. -
CORVET, CHEVI and HOPS – Multisubunit Tethers of the Endo
© 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs189134. doi:10.1242/jcs.189134 REVIEW SUBJECT COLLECTION: CELL BIOLOGY AND DISEASE CORVET, CHEVI and HOPS – multisubunit tethers of the endo-lysosomal system in health and disease Jan van der Beek‡, Caspar Jonker*,‡, Reini van der Welle, Nalan Liv and Judith Klumperman§ ABSTRACT contact between two opposing membranes and pull them close Multisubunit tethering complexes (MTCs) are multitasking hubs that together to allow interactions between SNARE proteins (Murray form a link between membrane fusion, organelle motility and signaling. et al., 2016). SNARE assembly then provides additional fusion CORVET, CHEVI and HOPS are MTCs of the endo-lysosomal system. specificity and drives the actual fusion process (Ohya et al., 2009; They regulate the major membrane flows required for endocytosis, Stroupe et al., 2009). In this Review, we focus on the role of tethering lysosome biogenesis, autophagy and phagocytosis. In addition, proteins in endo-lysosomal fusion events. individual subunits control complex-independent transport of specific Tethering proteins can be divided into two main groups: long cargoes and exert functions beyond tethering, such as attachment to coiled-coil proteins (Gillingham and Munro, 2003) and microtubules and SNARE activation. Mutations in CHEVI subunits multisubunit tethering complexes (MTCs). MTCs form a lead to arthrogryposis, renal dysfunction and cholestasis (ARC) heterogenic group of protein complexes that consist of up to ten ∼ syndrome, while defects in CORVET and, particularly, HOPS are subunits resulting in a general length of 50 nm (Brocker et al., associated with neurodegeneration, pigmentation disorders, liver 2012; Chou et al., 2016; Hsu et al., 1998; Lürick et al., 2018; Ren malfunction and various forms of cancer. -
Genes and Symptoms of Schizophrenia: Modifiers, Networks, and Interactions in Complex Disease
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2009 Genes and Symptoms of Schizophrenia: Modifiers, Networks, and Interactions in Complex Disease Sarah Bergen Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Medical Genetics Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/1940 This Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. Genes and Symptoms of Schizophrenia: Modifiers, Networks, and Interactions in Complex Disease A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University by: Sarah E. Bergen B.A., Macalester College, 2000 M.S., University of Pittsburgh, 2004 Director: Kenneth S. Kendler, M.D. Distinguished Professor, Departments of Psychiatry and Human and Molecular Genetics Virginia Commonwealth University Richmond, VA September, 2009 Acknowledgements First, I want to thank my parents whose unconditional love and only slightly conditional support has carried me through every bump and rough patch on my journey through grad school (and life). None of my work would have been accomplished without all you have done for me, and I am profoundly grateful. I have been extremely fortunate in my experiences here to not only have been quite productive scientifically, but also to have enjoyed the graduate school experience. This is almost entirely due to the brilliant and kind people I have had the opportunity to work with. -
Lysosomal Biology and Function: Modern View of Cellular Debris Bin
cells Review Lysosomal Biology and Function: Modern View of Cellular Debris Bin Purvi C. Trivedi 1,2, Jordan J. Bartlett 1,2 and Thomas Pulinilkunnil 1,2,* 1 Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS B3H 4H7, Canada; [email protected] (P.C.T.); jjeff[email protected] (J.J.B.) 2 Dalhousie Medicine New Brunswick, Saint John, NB E2L 4L5, Canada * Correspondence: [email protected]; Tel.: +1-(506)-636-6973 Received: 21 January 2020; Accepted: 29 April 2020; Published: 4 May 2020 Abstract: Lysosomes are the main proteolytic compartments of mammalian cells comprising of a battery of hydrolases. Lysosomes dispose and recycle extracellular or intracellular macromolecules by fusing with endosomes or autophagosomes through specific waste clearance processes such as chaperone-mediated autophagy or microautophagy. The proteolytic end product is transported out of lysosomes via transporters or vesicular membrane trafficking. Recent studies have demonstrated lysosomes as a signaling node which sense, adapt and respond to changes in substrate metabolism to maintain cellular function. Lysosomal dysfunction not only influence pathways mediating membrane trafficking that culminate in the lysosome but also govern metabolic and signaling processes regulating protein sorting and targeting. In this review, we describe the current knowledge of lysosome in influencing sorting and nutrient signaling. We further present a mechanistic overview of intra-lysosomal processes, along with extra-lysosomal processes, governing lysosomal fusion and fission, exocytosis, positioning and membrane contact site formation. This review compiles existing knowledge in the field of lysosomal biology by describing various lysosomal events necessary to maintain cellular homeostasis facilitating development of therapies maintaining lysosomal function. -
The Emerging Roles of Mtorc1 in Macromanaging Autophagy
cancers Review The Emerging Roles of mTORC1 in Macromanaging Autophagy Akpedje S. Dossou and Alakananda Basu * Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-817-735-2487 Received: 25 August 2019; Accepted: 23 September 2019; Published: 24 September 2019 Abstract: Autophagy is a process of self-degradation that enables the cell to survive when faced with starvation or stressful conditions. The mechanistic target of rapamycin (mTOR), also known as the mammalian target of rapamycin, plays a critical role in maintaining a balance between cellular anabolism and catabolism. mTOR complex 1 (mTORC1) was unveiled as a master regulator of autophagy since inhibition of mTORC1 was required to initiate the autophagy process. Evidence has emerged in recent years to indicate that mTORC1 also directly regulates the subsequent steps of the autophagy process, including the nucleation, autophagosome elongation, autophagosome maturation and termination. By phosphorylating select protein targets of the autophagy core machinery and/or their regulators, mTORC1 can alter their functions, increase their proteasomal degradation or modulate their acetylation status, which is a key switch of the autophagy process. Moreover, it phosphorylates and alters the subcellular localization of transcription factors to suppress the expression of genes needed for autophagosome formation and lysosome biogenesis. The purpose of this review article is to critically analyze current literatures to provide an integrated view of how mTORC1 regulates various steps of the autophagy process. Keywords: macroautophagy; autophagy regulation; mTORC1 substrates; AMPK; ULK1; autophagy initiation; nucleation; elongation; autophagosome maturation; transcriptional regulation 1.