1
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie ME, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D’Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database (Oxford) 2024; 2024:baae031. [PMID: 38713862 PMCID: PMC11184451 DOI: 10.1093/database/baae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/23/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
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Affiliation(s)
- Marija Orlic-Milacic
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Adam Wright
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Veronica Shamovsky
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Quang Trinh
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marc E Gillespie
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ralf Stephan
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Bruce May
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Robin Haw
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Deidre Beavers
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Patrick Conley
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Lincoln D Stein
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Room 4386, Toronto, ON M5S 1A8, Canada
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
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2
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Dessimoz C, Thomas PD. AI and the democratization of knowledge. Sci Data 2024; 11:268. [PMID: 38443367 PMCID: PMC10915151 DOI: 10.1038/s41597-024-03099-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 02/28/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Christophe Dessimoz
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
| | - Paul D Thomas
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, USA.
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3
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562964. [PMID: 37904913 PMCID: PMC10614924 DOI: 10.1101/2023.10.18.562964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
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4
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Bulstrode H, Girdler GC, Gracia T, Aivazidis A, Moutsopoulos I, Young AMH, Hancock J, He X, Ridley K, Xu Z, Stockley JH, Finlay J, Hallou C, Fajardo T, Fountain DM, van Dongen S, Joannides A, Morris R, Mair R, Watts C, Santarius T, Price SJ, Hutchinson PJA, Hodson EJ, Pollard SM, Mohorianu I, Barker RA, Sweeney TR, Bayraktar O, Gergely F, Rowitch DH. Myeloid cell interferon secretion restricts Zika flavivirus infection of developing and malignant human neural progenitor cells. Neuron 2022; 110:3936-3951.e10. [PMID: 36174572 PMCID: PMC7615581 DOI: 10.1016/j.neuron.2022.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 08/10/2022] [Accepted: 09/01/2022] [Indexed: 02/02/2023]
Abstract
Zika virus (ZIKV) can infect human developing brain (HDB) progenitors resulting in epidemic microcephaly, whereas analogous cellular tropism offers treatment potential for the adult brain cancer, glioblastoma (GBM). We compared productive ZIKV infection in HDB and GBM primary tissue explants that both contain SOX2+ neural progenitors. Strikingly, although the HDB proved uniformly vulnerable to ZIKV infection, GBM was more refractory, and this correlated with an innate immune expression signature. Indeed, GBM-derived CD11b+ microglia/macrophages were necessary and sufficient to protect progenitors against ZIKV infection in a non-cell autonomous manner. Using SOX2+ GBM cell lines, we found that CD11b+-conditioned medium containing type 1 interferon beta (IFNβ) promoted progenitor resistance to ZIKV, whereas inhibition of JAK1/2 signaling restored productive infection. Additionally, CD11b+ conditioned medium, and IFNβ treatment rendered HDB progenitor lines and explants refractory to ZIKV. These findings provide insight into neuroprotection for HDB progenitors as well as enhanced GBM oncolytic therapies.
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Affiliation(s)
- Harry Bulstrode
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK.
| | - Gemma C Girdler
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Tannia Gracia
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | | | - Ilias Moutsopoulos
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Adam M H Young
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - John Hancock
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Xiaoling He
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Katherine Ridley
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Zhaoyang Xu
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - John H Stockley
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - John Finlay
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Clement Hallou
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Teodoro Fajardo
- Department of Virology, University of Cambridge, Cambridge CB2 0QQ, UK; Department of Virology, Royal London Hospital, Barts Health NHS Trust, London E1 2ES, UK
| | | | | | - Alexis Joannides
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Robert Morris
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Richard Mair
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Colin Watts
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, UK
| | - Thomas Santarius
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Stephen J Price
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Peter J A Hutchinson
- Division of Academic Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Emma J Hodson
- Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Steven M Pollard
- Centre for Regenerative Medicine and Cancer Research UK Edinburgh Centre, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK
| | - Irina Mohorianu
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Roger A Barker
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Trevor R Sweeney
- Department of Virology, University of Cambridge, Cambridge CB2 0QQ, UK; The Pirbright Institute, Guildford, Surrey GU24 0NF, UK
| | | | - Fanni Gergely
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK.
| | - David H Rowitch
- Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Wellcome Sanger Institute, Hinxton CB10 1SA, UK; Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK.
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5
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Wang Y, Wang Q, Huang H, Huang W, Chen Y, McGarvey PB, Wu CH, Arighi CN. A crowdsourcing open platform for literature curation in UniProt. PLoS Biol 2021; 19:e3001464. [PMID: 34871295 PMCID: PMC8675915 DOI: 10.1371/journal.pbio.3001464] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 12/16/2021] [Indexed: 11/29/2022] Open
Abstract
The UniProt Knowledgebase is a public database for protein sequence and function, covering the tree of life. This Community Page article present a community submission system to harness timely scientific knowledge via crowdsourcing of the literature, creating a research ecosystem where researchers play an active role in scaling up UniProt curation, while receiving proper attribution for their biocuration work.
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Affiliation(s)
- Yuqi Wang
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Qinghua Wang
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Hongzhan Huang
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Wei Huang
- College of Agriculture and Natural Resource, University of Delaware, Newark, Delaware, United States of America
| | - Yongxing Chen
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Peter B. McGarvey
- Protein Information Resource, Georgetown University Medical Center, District of Columbia, United States of America
| | - Cathy H. Wu
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
- Protein Information Resource, Georgetown University Medical Center, District of Columbia, United States of America
| | - Cecilia N. Arighi
- Protein Information Resource, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
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6
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Moutsopoulos I, Maischak L, Lauzikaite E, Vasquez Urbina S, Williams E, Drost HG, Mohorianu I. noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise. Nucleic Acids Res 2021; 49:e83. [PMID: 34076236 PMCID: PMC8373073 DOI: 10.1093/nar/gkab433] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/16/2021] [Accepted: 05/06/2021] [Indexed: 01/22/2023] Open
Abstract
High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.
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Affiliation(s)
- Ilias Moutsopoulos
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Lukas Maischak
- Computational Biology Group, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Max-Planck Ring 1, 72076 Tübingen, Germany
| | - Elze Lauzikaite
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Sergio A Vasquez Urbina
- Computational Biology Group, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Max-Planck Ring 1, 72076 Tübingen, Germany
| | - Eleanor C Williams
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Hajk-Georg Drost
- Computational Biology Group, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Max-Planck Ring 1, 72076 Tübingen, Germany
| | - Irina I Mohorianu
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
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7
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Varusai TM, Jupe S, Sevilla C, Matthews L, Gillespie M, Stein L, Wu G, D’Eustachio P, Metzakopian E, Hermjakob H. Using Reactome to build an autophagy mechanism knowledgebase. Autophagy 2021; 17:1543-1554. [PMID: 32486891 PMCID: PMC8204961 DOI: 10.1080/15548627.2020.1761659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 04/16/2020] [Accepted: 04/21/2020] [Indexed: 10/26/2022] Open
Abstract
The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process and as a response to stress and foreign material invasion. There is considerable knowledge of the molecular mechanisms of autophagy, and this is still growing as new modalities emerge. There is a need to investigate autophagy mechanisms reliably, comprehensively and conveniently. Reactome is a freely available knowledgebase that consists of manually curated molecular events (reactions) organized into cellular pathways (https://reactome.org/). Pathways/reactions in Reactome are hierarchically structured, graphically presented and extensively annotated. Data analysis tools, such as pathway enrichment, expression data overlay and species comparison, are also available. For customized analysis, information can also be programmatically queried. Here, we discuss the curation and annotation of the molecular mechanisms of autophagy in Reactome. We also demonstrate the value that Reactome adds to research by reanalyzing a previously published work on genome-wide CRISPR screening of autophagy components.Abbreviations: CMA: chaperone-mediated autophagy; GO: Gene Ontology; MA: macroautophagy; MI: microautophagy; MTOR: mechanistic target of rapamycin kinase; SQSTM1: sequestosome 1.
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Affiliation(s)
- Thawfeek Mohamed Varusai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridgeshire, UK
| | - Steven Jupe
- COSMIC, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY, USA
| | - Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Pharmaceutical Sciences, St.John’s University, Queens, NY, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health and Science University, Oregon, Portland, OR, USA
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY, USA
| | - Emmanouil Metzakopian
- UK Dementia Research Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences, Beijing, China
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8
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Tian L, Wang SL. Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200711171530] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Recently, ample researches show that microRNAs (miRNAs) not only
interact with coding genes but interact with a pool of different RNAs. Those RNAs are called
miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and
various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help
researchers to study the mechanisms of breast cancers.
Objective:
We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNAdisease-lncRNA and miRNA-target networks (MSNMDL).
Method:
Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-
lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA
to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA
pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of
breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly,
MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions
and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.
Results:
MSNMDL is superior to other methods in terms of biological significance and its identified modules might
act as module signatures for prognostication of breast cancer.
Conclusion:
MiRNA sponge networks identified by MSNMDL are biologically significant and are
closely associated with breast cancer, which makes MSNMDL a promising way for researchers to
study the pathogenesis of breast cancer.
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Affiliation(s)
- Lei Tian
- School of Information Science and Engineering, Hunan University, Changsha, China
| | - Shu-Lin Wang
- School of Information Science and Engineering, Hunan University, Changsha, China
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9
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Brulport A, Vaiman D, Bou-Maroun E, Chagnon MC, Corre LL. Hepatic transcriptome and DNA methylation patterns following perinatal and chronic BPS exposure in male mice. BMC Genomics 2020; 21:881. [PMID: 33297965 PMCID: PMC7727143 DOI: 10.1186/s12864-020-07294-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/28/2020] [Indexed: 11/21/2022] Open
Abstract
Background Bisphenol S (BPS) is a common bisphenol A (BPA) substitute, since BPA is virtually banned worldwide. However, BPS and BPA have both endocrine disrupting properties. Their effects appear mostly in adulthood following perinatal exposures. The objective of the present study was to investigate the impact of perinatal and chronic exposure to BPS at the low dose of 1.5 μg/kg body weight/day on the transcriptome and methylome of the liver in 23 weeks-old C57BL6/J male mice. Results This multi-omic study highlights a major impact of BPS on gene expression (374 significant deregulated genes) and Gene Set Enrichment Analysis show an enrichment focused on several biological pathways related to metabolic liver regulation. BPS exposure also induces a hypomethylation in 58.5% of the differentially methylated regions (DMR). Systematic connections were not found between gene expression and methylation profile excepted for 18 genes, including 4 genes involved in lipid metabolism pathways (Fasn, Hmgcr, Elovl6, Lpin1), which were downregulated and featured differentially methylated CpGs in their exons or introns. Conclusions This descriptive study shows an impact of BPS on biological pathways mainly related to an integrative disruption of metabolism (energy metabolism, detoxification, protein and steroid metabolism) and, like most high-throughput studies, contributes to the identification of potential exposure biomarkers. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07294-3.
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Affiliation(s)
- Axelle Brulport
- Université de Bourgogne Franche-Comté, LNC UMR1231, F-21000, Dijon, France.,AgroSup, LNC UMR1231, 1 Esplanade Erasme, 21000, Dijon, France.,Nutrition Physiology and Toxicology Team (NUTox), INSERM, LNC UMR1231, F-21000, Dijon, France
| | - Daniel Vaiman
- From Gametes to Birth Team (FGTB), INSERM, U1016, Institut Cochin, F-75014, Paris, France.,CNRS UMR8104, F-75014, Paris, France.,Université Sorbonne Paris Cité, F-75014, Paris, France
| | - Elias Bou-Maroun
- Université de Bourgogne Franche-Comté, AgroSup Dijon, PAM UMR A 02.102, Procédés Alimentaires et Microbiologiques, F-21000, Dijon, France
| | - Marie-Christine Chagnon
- Université de Bourgogne Franche-Comté, LNC UMR1231, F-21000, Dijon, France.,AgroSup, LNC UMR1231, 1 Esplanade Erasme, 21000, Dijon, France.,Nutrition Physiology and Toxicology Team (NUTox), INSERM, LNC UMR1231, F-21000, Dijon, France
| | - Ludovic Le Corre
- Université de Bourgogne Franche-Comté, LNC UMR1231, F-21000, Dijon, France. .,AgroSup, LNC UMR1231, 1 Esplanade Erasme, 21000, Dijon, France. .,Nutrition Physiology and Toxicology Team (NUTox), INSERM, LNC UMR1231, F-21000, Dijon, France.
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Lock A, Harris MA, Rutherford K, Hayles J, Wood V. Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5827230. [PMID: 32353878 PMCID: PMC7192550 DOI: 10.1093/database/baaa028] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/28/2020] [Accepted: 03/22/2020] [Indexed: 11/22/2022]
Abstract
Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases. To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments. Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase’s system. With 8 years’ experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project. We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.
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Affiliation(s)
- Antonia Lock
- Department of Genetics, Evolution and Environment, University College London, Gower street, London WC1E 6BT, UK
| | - Midori A Harris
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Kim Rutherford
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Jacqueline Hayles
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Valerie Wood
- Cell Cycle Laboratory, The Francis Crick Institute, Midland Rd, London NW1 1AT, UK
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