1
|
Hagenauer MH, Sannah Y, Hebda-Bauer EK, Rhoads C, O'Connor AM, Flandreau E, Watson SJ, Akil H. Resource: A curated database of brain-related functional gene sets (Brain.GMT). MethodsX 2024; 13:102788. [PMID: 39049932 PMCID: PMC11267058 DOI: 10.1016/j.mex.2024.102788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024] Open
Abstract
Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.
Collapse
Affiliation(s)
- Megan H. Hagenauer
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yusra Sannah
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Cosette Rhoads
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
- National Institutes of Health, Bethesda, MD 20892, USA
| | - Angela M. O'Connor
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Stanley J. Watson
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huda Akil
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
2
|
Grady SK, Peterson KA, Murray SA, Baker EJ, Langston MA, Chesler EJ. A graph theoretical approach to experimental prioritization in genome-scale investigations. Mamm Genome 2024:10.1007/s00335-024-10066-z. [PMID: 39191873 DOI: 10.1007/s00335-024-10066-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/14/2024] [Indexed: 08/29/2024]
Abstract
The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.
Collapse
Affiliation(s)
- Stephen K Grady
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA.
| | | | | | - Erich J Baker
- Department of Computer Science, Baylor University, Waco, TX, USA
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | | |
Collapse
|
3
|
Oba GM, Nakato R. Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach. Genes Cells 2024; 29:456-470. [PMID: 38602264 PMCID: PMC11163938 DOI: 10.1111/gtc.13119] [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: 01/05/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024]
Abstract
Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.
Collapse
Affiliation(s)
- Gina Miku Oba
- Laboratory of Computational Genomics, Institute for Quantitative BiosciencesUniversity of TokyoTokyoJapan
- Department of Computational Biology and Medical Science, Graduate School of Frontier ScienceUniversity of TokyoTokyoJapan
| | - Ryuichiro Nakato
- Laboratory of Computational Genomics, Institute for Quantitative BiosciencesUniversity of TokyoTokyoJapan
- Department of Computational Biology and Medical Science, Graduate School of Frontier ScienceUniversity of TokyoTokyoJapan
| |
Collapse
|
4
|
Salgado JCS, Alnoch RC, Polizeli MDLTDM, Ward RJ. Microenzymes: Is There Anybody Out There? Protein J 2024; 43:393-404. [PMID: 38507106 DOI: 10.1007/s10930-024-10193-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20-60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.
Collapse
Affiliation(s)
- Jose Carlos Santos Salgado
- Department of Chemistry, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-900, São Paulo, Brazil.
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil.
| | - Robson Carlos Alnoch
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| | - Maria de Lourdes Teixeira de Moraes Polizeli
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| | - Richard John Ward
- Department of Chemistry, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-900, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| |
Collapse
|
5
|
Hagenauer MH, Sannah Y, Hebda-Bauer EK, Rhoads C, O'Connor AM, Watson SJ, Akil H. Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.05.588301. [PMID: 38645214 PMCID: PMC11030436 DOI: 10.1101/2024.04.05.588301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation.
Collapse
Affiliation(s)
- Megan H Hagenauer
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| | - Yusra Sannah
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| | - Elaine K Hebda-Bauer
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| | - Cosette Rhoads
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
- National Institutes of Health, Bethesda, MD 20892, USA
| | - Angela M O'Connor
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| | - Stanley J Watson
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| | - Huda Akil
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor; MI 48109, USA
| |
Collapse
|
6
|
Wang L, Yilmaz O, Veeneman BA, Zhang Y, Dhanasekaran SM, Mehra R. Gene of the month: VSTM2A. J Clin Pathol 2024; 77:73-76. [PMID: 38124011 DOI: 10.1136/jcp-2023-208839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2023] [Indexed: 12/23/2023]
Abstract
The V-set and transmembrane domain containing 2A (VSTM2A) gene is located on chromosome 7. In the physiological state, VSTM2A regulates preadipocyte cell differentiation. VSTM2A is highly expressed in normal human brain tissue and minimally expressed in other normal tissues. Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a distinct renal tumour subtype with signature chromosomal copy number alterations and an indolent outcome in the majority of cases. VSTM2A overexpression is highly enriched in this renal cancer subtype and has been shown to have potential diagnostic value in distinguishing MTSCC from renal tumours with overlapping histological appearances.
Collapse
Affiliation(s)
- Lisha Wang
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Osman Yilmaz
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Brendan A Veeneman
- Synthetic Lethality Research Unit, Oncology, GlaxoSmithKline, Cambridge, Massachusetts, USA
| | - Yuping Zhang
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Rohit Mehra
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan, USA
| |
Collapse
|
7
|
Bárez-López S, Gadd GJ, Pauža AG, Murphy D, Greenwood MP. Isoflurane Rapidly Modifies Synaptic and Cytoskeletal Phosphoproteomes of the Supraoptic Nucleus of the Hypothalamus and the Cortex. Neuroendocrinology 2023; 113:1008-1023. [PMID: 37271138 DOI: 10.1159/000531352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/24/2023] [Indexed: 06/06/2023]
Abstract
INTRODUCTION Despite the widespread use of general anaesthetics, the mechanisms mediating their effects are still not understood. Although suppressed in most parts of the brain, neuronal activity, as measured by FOS activation, is increased in the hypothalamic supraoptic nucleus (SON) by numerous general anaesthetics, and evidence points to this brain region being involved in the induction of general anaesthesia (GA) and natural sleep. Posttranslational modifications of proteins, including changes in phosphorylation, enable fast modulation of protein function which could be underlying the rapid effects of GA. In order to identify potential phosphorylation events in the brain-mediating GA effects, we have explored the phosphoproteome responses in the rat SON and compared these to cingulate cortex (CC) which displays no FOS activation in response to general anaesthetics. METHODS Adult Sprague-Dawley rats were treated with isoflurane for 15 min. Proteins from the CC and SON were extracted and processed for nano-LC mass spectrometry (LC-MS/MS). Phosphoproteomic determinations were performed by LC-MS/MS. RESULTS We found many changes in the phosphoproteomes of both the CC and SON in response to 15 min of isoflurane exposure. Pathway analysis indicated that proteins undergoing phosphorylation adaptations are involved in cytoskeleton remodelling and synaptic signalling events. Importantly, changes in protein phosphorylation appeared to be brain region specific suggesting that differential phosphorylation adaptations might underlie the different neuronal activity responses to GA between the CC and SON. CONCLUSION In summary, these data suggest that rapid posttranslational modifications in proteins involved in cytoskeleton remodelling and synaptic signalling events might mediate the central mechanisms mediating GA.
Collapse
Affiliation(s)
- Soledad Bárez-López
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
- Instituto de Investigaciones Biomédicas Alberto Sols, Consejo Superior de Investigaciones Científicas (CSIC)-Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - George J Gadd
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - Audrys G Pauža
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
- Translational Cardio-Respiratory Research Group, Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - Michael P Greenwood
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| |
Collapse
|
8
|
Shao Y, Zhou L, Li F, Zhao L, Zhang BL, Shao F, Chen JW, Chen CY, Bi X, Zhuang XL, Zhu HL, Hu J, Sun Z, Li X, Wang D, Rivas-González I, Wang S, Wang YM, Chen W, Li G, Lu HM, Liu Y, Kuderna LFK, Farh KKH, Fan PF, Yu L, Li M, Liu ZJ, Tiley GP, Yoder AD, Roos C, Hayakawa T, Marques-Bonet T, Rogers J, Stenson PD, Cooper DN, Schierup MH, Yao YG, Zhang YP, Wang W, Qi XG, Zhang G, Wu DD. Phylogenomic analyses provide insights into primate evolution. Science 2023; 380:913-924. [PMID: 37262173 DOI: 10.1126/science.abn6919] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/26/2023] [Indexed: 06/03/2023]
Abstract
Comparative analysis of primate genomes within a phylogenetic context is essential for understanding the evolution of human genetic architecture and primate diversity. We present such a study of 50 primate species spanning 38 genera and 14 families, including 27 genomes first reported here, with many from previously less well represented groups, the New World monkeys and the Strepsirrhini. Our analyses reveal heterogeneous rates of genomic rearrangement and gene evolution across primate lineages. Thousands of genes under positive selection in different lineages play roles in the nervous, skeletal, and digestive systems and may have contributed to primate innovations and adaptations. Our study reveals that many key genomic innovations occurred in the Simiiformes ancestral node and may have had an impact on the adaptive radiation of the Simiiformes and human evolution.
Collapse
Affiliation(s)
- Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
| | - Long Zhou
- Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Fang Li
- Section for Ecology and Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark
- Institute of Animal Sex and Development, ZhejiangWanli University, Ningbo 315100, China
| | - Lan Zhao
- Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Bao-Lin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
| | - Feng Shao
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University School of Life Sciences, Chongqing 400715, China
| | | | - Chun-Yan Chen
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xupeng Bi
- Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xiao-Lin Zhuang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650204, China
| | | | - Jiang Hu
- Grandomics Biosciences, Beijing 102206, China
| | - Zongyi Sun
- Grandomics Biosciences, Beijing 102206, China
| | - Xin Li
- Grandomics Biosciences, Beijing 102206, China
| | - Depeng Wang
- Grandomics Biosciences, Beijing 102206, China
| | | | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
| | - Yun-Mei Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
| | - Wu Chen
- Guangzhou Zoo & Guangzhou Wildlife Research Center, Guangzhou 510070, China
| | - Gang Li
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Hui-Meng Lu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yang Liu
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Lukas F K Kuderna
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
- Illumina Artificial Intelligence Laboratory, Illumina Inc, San Diego, CA 92122, USA
| | - Kyle Kai-How Farh
- Illumina Artificial Intelligence Laboratory, Illumina Inc, San Diego, CA 92122, USA
| | - Peng-Fei Fan
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Li Yu
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, School of Life Sciences, Yunnan University, Kunming 650091, China
| | - Ming Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhi-Jin Liu
- College of Life Sciences, Capital Normal University, Beijing 100048, China
| | - George P Tiley
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Anne D Yoder
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Christian Roos
- Gene Bank of Primates and Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, 37077 Göttingen, Germany
| | - Takashi Hayakawa
- Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Japan Monkey Centre, Inuyama, Aichi 484-0081, Japan
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010 Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Jeffrey Rogers
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peter D Stenson
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
| | | | - Yong-Gang Yao
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650204, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650107, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650107, China
| | - Wen Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
| | - Xiao-Guang Qi
- Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Hangzhou 310058, China
- Section for Ecology and Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 311121, China
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650107, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650204, China
| |
Collapse
|
9
|
Elsamad G, Mecawi AS, Pauža AG, Gillard B, Paterson A, Duque VJ, Šarenac O, Žigon NJ, Greenwood M, Greenwood MP, Murphy D. Ageing restructures the transcriptome of the hypothalamic supraoptic nucleus and alters the response to dehydration. NPJ AGING 2023; 9:12. [PMID: 37264028 PMCID: PMC10234251 DOI: 10.1038/s41514-023-00108-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/04/2023] [Indexed: 06/03/2023]
Abstract
Ageing is associated with altered neuroendocrine function. In the context of the hypothalamic supraoptic nucleus, which makes the antidiuretic hormone vasopressin, ageing alters acute responses to hyperosmotic cues, rendering the elderly more susceptible to dehydration. Chronically, vasopressin has been associated with numerous diseases of old age, including type 2 diabetes and metabolic syndrome. Bulk RNAseq transcriptome analysis has been used to catalogue the polyadenylated supraoptic nucleus transcriptomes of adult (3 months) and aged (18 months) rats in basal euhydrated and stimulated dehydrated conditions. Gene ontology and Weighted Correlation Network Analysis revealed that ageing is associated with alterations in the expression of extracellular matrix genes. Interestingly, whilst the transcriptomic response to dehydration is overall blunted in aged animals compared to adults, there is a specific enrichment of differentially expressed genes related to neurodegenerative processes in the aged cohort, suggesting that dehydration itself may provoke degenerative consequences in aged rats.
Collapse
Affiliation(s)
- Ghadir Elsamad
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
| | - André Souza Mecawi
- Laboratory of Molecular Neuroendocrinology, Department of Biophysics, Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | - Audrys G Pauža
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
- Translational Cardio-Respiratory Research Group, Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Benjamin Gillard
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
| | - Alex Paterson
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
- Insilico Consulting Ltd., Wapping Wharf, Bristol, England
| | - Victor J Duque
- Laboratory of Molecular Neuroendocrinology, Department of Biophysics, Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | - Olivera Šarenac
- Institute of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
- Department of Safety Pharmacology, Abbvie, North Chicago, Illinois, USA
| | - Nina Japundžić Žigon
- Institute of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Mingkwan Greenwood
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
| | - Michael P Greenwood
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England.
| |
Collapse
|
10
|
Callahan TJ, Stefanski AL, Kim JD, Baumgartner WA, Wyrwa JM, Hunter LE. Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:371-382. [PMID: 36540992 PMCID: PMC9782728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.
Collapse
Affiliation(s)
- Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA,
| | | | | | | | | | | |
Collapse
|
11
|
Pauza AG, Murphy D, Paton JFR. Transcriptomics of the Carotid Body. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1427:1-11. [PMID: 37322330 DOI: 10.1007/978-3-031-32371-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The carotid body (CB) has emerged as a potential therapeutic target for treating sympathetically mediated cardiovascular, respiratory, and metabolic diseases. In adjunct to its classical role as an arterial O2 sensor, the CB is a multimodal sensor activated by a range of stimuli in the circulation. However, consensus on how CB multimodality is achieved is lacking; even the best studied O2-sensing appears to involve multiple convergent mechanisms. A strategy to understand multimodal sensing is to adopt a hypothesis-free, high-throughput transcriptomic approach. This has proven instrumental for understanding fundamental mechanisms of CB response to hypoxia and other stimulants, its developmental niche, cellular heterogeneity, laterality, and pathophysiological remodeling in disease states. Herein, we review this published work that reveals novel molecular mechanisms underpinning multimodal sensing and reveals numerous gaps in knowledge that require experimental testing.
Collapse
Affiliation(s)
- Audrys G Pauza
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand.
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Julian F R Paton
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand
| |
Collapse
|
12
|
Delmas M, Filangi O, Duperier C, Paulhe N, Vinson F, Rodriguez-Mier P, Giacomoni F, Jourdan F, Frainay C. Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors. Gigascience 2022; 12:giad065. [PMID: 37712592 PMCID: PMC10502579 DOI: 10.1093/gigascience/giad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 06/13/2023] [Accepted: 07/28/2023] [Indexed: 09/16/2023] Open
Abstract
In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).
Collapse
Affiliation(s)
- Maxime Delmas
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France
| | - Olivier Filangi
- IGEPP, INRAE, Institut Agro, Université de Rennes, Domaine de la Motte, 35653 Le Rheu, France
| | - Christophe Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
| | - Nils Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
| | - Florence Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31300, France
| | - Pablo Rodriguez-Mier
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France
| | - Franck Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
| | - Fabien Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31300, France
| | - Clément Frainay
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France
| |
Collapse
|
13
|
Plasil SL, Collins VJ, Baratta AM, Farris SP, Homanics GE. Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking phenotypes. ADVANCES IN DRUG AND ALCOHOL RESEARCH 2022; 2:10831. [PMID: 36908580 PMCID: PMC10004261 DOI: 10.3389/adar.2022.10831] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The molecular mechanisms regulating the development and progression of alcohol use disorder (AUD) are largely unknown. While noncoding RNAs have previously been implicated as playing key roles in AUD, long-noncoding RNA (lncRNA) remains understudied in relation to AUD. In this study, we first identified ethanol-responsive lncRNAs in the mouse hippocampus that are transcriptional network hub genes. Microarray analysis of lncRNA, miRNA, circular RNA, and protein coding gene expression in the hippocampus from chronic intermittent ethanol vapor- or air- (control) exposed mice was used to identify ethanol-responsive competing endogenous RNA (ceRNA) networks. Highly interconnected lncRNAs (genes that had the strongest overall correlation to all other dysregulated genes identified) were ranked. The top four lncRNAs were novel, previously uncharacterized genes named Gm42575, 4930413E15Rik, Gm15767, and Gm33447, hereafter referred to as Pitt1, Pitt2, Pitt3, and Pitt4, respectively. We subsequently tested the hypothesis that CRISPR/Cas9 mutagenesis of the putative promoter and first exon of these lncRNAs in C57BL/6J mice would alter ethanol drinking behavior. The Drinking in the Dark (DID) assay was used to examine binge-like drinking behavior, and the Every-Other-Day Two-Bottle Choice (EOD-2BC) assay was used to examine intermittent ethanol consumption and preference. No significant differences between control and mutant mice were observed in the DID assay. Female-specific reductions in ethanol consumption were observed in the EOD-2BC assay for Pitt1, Pitt3, and Pitt4 mutant mice compared to controls. Male-specific alterations in ethanol preference were observed for Pitt1 and Pitt2. Female-specific increases in ethanol preference were observed for Pitt3 and Pitt4. Total fluid consumption was reduced in Pitt1 and Pitt2 mutants at 15% v/v ethanol and in Pitt3 and Pitt4 at 20% v/v ethanol in females only. We conclude that all lncRNAs targeted altered ethanol drinking behavior, and that lncRNAs Pitt1, Pitt3, and Pitt4 influenced ethanol consumption in a sex-specific manner. Further research is necessary to elucidate the biological mechanisms for these effects. These findings add to the literature implicating noncoding RNAs in AUD and suggest lncRNAs also play an important regulatory role in the disease.
Collapse
Affiliation(s)
- SL Plasil
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - VJ Collins
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - AM Baratta
- Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - SP Farris
- Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - GE Homanics
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| |
Collapse
|
14
|
Huang X, Li L, Zhou S, Kong D, Luo J, Lu L, Xu HM, Wang X. Multi-omics analysis reveals expression complexity and functional diversity of mouse kinome. Proteomics 2022; 22:e2200120. [PMID: 35856475 DOI: 10.1002/pmic.202200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/01/2022] [Accepted: 07/15/2022] [Indexed: 12/29/2022]
Abstract
Protein kinases are a crucial component of signaling pathways involved in a wide range of cellular responses, including growth, proliferation, differentiation, and migration. Systematic investigation of protein kinases is critical to better understand phosphorylation-mediated signaling pathways and may provide insights into the development of potential therapeutic drug targets. Here we perform a systems-level analysis of the mouse kinome by analyzing multi-omics data. We used bulk and single-cell transcriptomic data from the C57BL/6J mouse strain to define tissue- and cell-type-specific expression of protein kinases, followed by investigating variations in sequence and expression between C57BL/6J and DBA/2J strains. We then profiled a deep brain phosphoproteome from C57BL/6J and DBA/2J strains as well as their reciprocal hybrids to infer the activity of the mouse kinome. Finally, we performed phenome-wide association analysis using the BXD recombinant inbred (RI) mice (a cross between C57BL/6J and DBA/2J strains) to identify any associations between variants in protein kinases and phenotypes. Collectively, our study provides a comprehensive analysis of the mouse kinome by investigating genetic sequence variation, tissue-specific expression patterns, and associations with downstream phenotypes.
Collapse
Affiliation(s)
- Xin Huang
- Institute of Crop Science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Ling Li
- Department of Biology, University of North Dakota, Grand Forks, North Dakota, USA
| | - Suiping Zhou
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Dehui Kong
- Department of Biology, University of North Dakota, Grand Forks, North Dakota, USA
| | - Jie Luo
- Central Laboratory of Zhejiang Academy of Agricultural Sciences, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Lu Lu
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Hai-Ming Xu
- Institute of Crop Science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, North Dakota, USA
| |
Collapse
|
15
|
de Crécy-lagard V, Amorin de Hegedus R, Arighi C, Babor J, Bateman A, Blaby I, Blaby-Haas C, Bridge AJ, Burley SK, Cleveland S, Colwell LJ, Conesa A, Dallago C, Danchin A, de Waard A, Deutschbauer A, Dias R, Ding Y, Fang G, Friedberg I, Gerlt J, Goldford J, Gorelik M, Gyori BM, Henry C, Hutinet G, Jaroch M, Karp PD, Kondratova L, Lu Z, Marchler-Bauer A, Martin MJ, McWhite C, Moghe GD, Monaghan P, Morgat A, Mungall CJ, Natale DA, Nelson WC, O’Donoghue S, Orengo C, O’Toole KH, Radivojac P, Reed C, Roberts RJ, Rodionov D, Rodionova IA, Rudolf JD, Saleh L, Sheynkman G, Thibaud-Nissen F, Thomas PD, Uetz P, Vallenet D, Carter EW, Weigele PR, Wood V, Wood-Charlson EM, Xu J. A roadmap for the functional annotation of protein families: a community perspective. Database (Oxford) 2022; 2022:baac062. [PMID: 35961013 PMCID: PMC9374478 DOI: 10.1093/database/baac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/28/2022] [Accepted: 08/03/2022] [Indexed: 12/23/2022]
Abstract
Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.
Collapse
Affiliation(s)
- Valérie de Crécy-lagard
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | | | - Cecilia Arighi
- Department of Computer and Information Sciences, University of Delaware, Newark, DE 19713, USA
| | - Jill Babor
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Ian Blaby
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Crysten Blaby-Haas
- Biology Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Alan J Bridge
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva 4 CH-1211, Switzerland
| | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stacey Cleveland
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Lucy J Colwell
- Departmenf of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ana Conesa
- Spanish National Research Council, Institute for Integrative Systems Biology, Paterna, Valencia 46980, Spain
| | - Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, i12, Boltzmannstr. 3, Garching/Munich 85748, Germany
| | - Antoine Danchin
- School of Biomedical Sciences, Li KaShing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Road, Pokfulam, SAR Hong Kong 999077, China
| | - Anita de Waard
- Research Collaboration Unit, Elsevier, Jericho, VT 05465, USA
| | - Adam Deutschbauer
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Raquel Dias
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Yousong Ding
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, USA
| | - Gang Fang
- NYU-Shanghai, Shanghai 200120, China
| | - Iddo Friedberg
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA
| | - John Gerlt
- Institute for Genomic Biology and Departments of Biochemistry and Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Joshua Goldford
- Physics of Living Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Mark Gorelik
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Geoffrey Hutinet
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Marshall Jaroch
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | | | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20817, USA
| | - Aron Marchler-Bauer
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20817, USA
| | - Maria-Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Claire McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Gaurav D Moghe
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Paul Monaghan
- Department of Agricultural Education and Communication, University of Florida, Gainesville, FL 32611, USA
| | - Anne Morgat
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva 4 CH-1211, Switzerland
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Darren A Natale
- Georgetown University Medical Center, Washington, DC 20007, USA
| | - William C Nelson
- Biological Sciences Division, Pacific Northwest National Laboratories, Richland, WA 99354, USA
| | - Seán O’Donoghue
- School of Biotechnology and Biomolecular Sciences, University of NSW, Sydney, NSW 2052, Australia
| | - Christine Orengo
- Department of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | | | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Colbie Reed
- Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL 32611, USA
| | | | - Dmitri Rodionov
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Irina A Rodionova
- Department of Bioengineering, Division of Engineering, University of California at San Diego, La Jolla, CA 92093-0412, USA
| | - Jeffrey D Rudolf
- Department of Chemistry, University of Florida, Gainesville, FL 32611, USA
| | - Lana Saleh
- New England Biolabs, Ipswich, MA 01938, USA
| | - Gloria Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - Francoise Thibaud-Nissen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20817, USA
| | - Paul D Thomas
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, USA
| | - Peter Uetz
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - David Vallenet
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS, Evry 91057, France
| | - Erica Watson Carter
- Department of Plant Pathology, University of Florida Citrus Research and Education Center, 700 Experiment Station Rd., Lake Alfred, FL 33850, USA
| | | | - Valerie Wood
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jin Xu
- Department of Plant Pathology, University of Florida Citrus Research and Education Center, 700 Experiment Station Rd., Lake Alfred, FL 33850, USA
| |
Collapse
|
16
|
Wang Y, Herzig G, Molano C, Liu A. Differential expression of the Tmem132 family genes in the developing mouse nervous system. Gene Expr Patterns 2022; 45:119257. [PMID: 35690356 DOI: 10.1016/j.gep.2022.119257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/01/2022] [Accepted: 06/03/2022] [Indexed: 11/15/2022]
Abstract
The family of novel transmembrane proteins (TMEM) 132 have been associated with multiple neurological disorders and cancers in humans, but have hardly been studied in vivo. Here we report the expression patterns of the five Tmem132 genes (a, b, c, d and e) in developing mouse nervous system with RNA in situ hybridization in wholemount embryos and tissue sections. Our results reveal differential and partially overlapping expression of multiple Tmem132 family members in both the central and peripheral nervous system, suggesting potential partial redundancy among them.
Collapse
Affiliation(s)
- Yuan Wang
- Department of Occupational and Environmental Health, School of Public Health, China Medical University, Shenyang, PR China; Department of Biology, Eberly College of Science and Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Graham Herzig
- Department of Biology, Eberly College of Science and Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Cassandra Molano
- Department of Biology, Eberly College of Science and Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Aimin Liu
- Department of Biology, Eberly College of Science and Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA, USA.
| |
Collapse
|
17
|
Higgins DP, Weisman CM, Lui DS, D'Agostino FA, Walker AK. Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0. Genetics 2022; 221:6588682. [PMID: 35587742 PMCID: PMC9339291 DOI: 10.1093/genetics/iyac085] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/04/2022] [Indexed: 12/03/2022] Open
Abstract
Omics tools provide broad datasets for biological discovery. However, the computational tools for identifying important genes or pathways in RNA-seq, proteomics, or GWAS (Genome-Wide Association Study) data depend on Gene Ontogeny annotations and are biased toward well-described pathways. This limits their utility as poorly annotated genes, which could have novel functions, are often passed over. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, which provides an intuitive visualization output. Unlike Gene Ontogeny-based enrichment tools, which exclude genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and can include genes with biological functions identified in published datasets. Poorly annotated genes are often considered to be potentially species-specific and thus, of reduced interest to the biomedical community. Instead, we find that around 3% of the UNASSIGNED genes have human orthologs, including some linked to human diseases. These human orthologs themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that the failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes. This suggests that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows the association of UNASSIGNED genes with specific phenotypes and known pathways. Building these associations in C. elegans, with its robust genetic tools, provides a path to further functional study and insight into these understudied genes.
Collapse
Affiliation(s)
- Daniel P Higgins
- Program in Molecular Medicine, UMASS Chan Medical School, Worcester MA 01605, USA
| | - Caroline M Weisman
- Lewis-Sigler Institute for Quantitative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Dominique S Lui
- Program in Molecular Medicine, UMASS Chan Medical School, Worcester MA 01605, USA
| | - Frank A D'Agostino
- Department of Applied Mathematics, Harvard University, Cambridge MA 02138, USA
| | - Amy K Walker
- Program in Molecular Medicine, UMASS Chan Medical School, Worcester MA 01605, USA
| |
Collapse
|
18
|
Pinson MR, Chung DD, Mahnke AH, Salem NA, Osorio D, Nair V, Payne EA, Del Real JJ, Cai JJ, Miranda RC. Gag-like proteins: Novel mediators of prenatal alcohol exposure in neural development. Alcohol Clin Exp Res 2022; 46:556-569. [PMID: 35187673 PMCID: PMC9018584 DOI: 10.1111/acer.14796] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/24/2022] [Accepted: 02/15/2022] [Indexed: 01/08/2023]
Abstract
Background We previously showed that ethanol did not kill fetal neural stem cells (NSCs), but that their numbers nevertheless are decreased due to aberrant maturation and loss of self‐renewal. To identify mechanisms that mediate this loss of NSCs, we focused on a family of Gag‐like proteins (GLPs), derived from retroviral gene remnants within mammalian genomes. GLPs are important for fetal development, though their role in brain development is virtually unexplored. Moreover, GLPs may be transferred between cells in extracellular vesicles (EVs) and thereby transfer environmental adaptations between cells. We hypothesized that GLPs may mediate some effects of ethanol in NSCs. Methods Sex‐segregated male and female fetal murine cortical NSCs, cultured ex vivo as nonadherent neurospheres, were exposed to a dose range of ethanol and to mitogen‐withdrawal‐induced differentiation. We used siRNAs to assess the effects of NSC‐expressed GLP knockdown on growth, survival, and maturation and in silico GLP knockout, in an in vivo single‐cell RNA‐sequencing dataset, to identify GLP‐mediated developmental pathways that were also ethanol‐sensitive. Results PEG10 isoform‐1, isoform‐2, and PNMA2 were identified as dominant GLP species in both NSCs and their EVs. Ethanol‐exposed NSCs exhibited significantly elevated PEG10 isoform‐2 and PNMA2 protein during differentiation. Both PEG10 and PNMA2 were mediated apoptosis resistance and additionally, PEG10 promoted neuronal and astrocyte lineage maturation. Neither GLP influenced metabolism nor cell cycle in NSCs. Virtual PEG10 and PNMA2 knockout identified gene transcription regulation and ubiquitin‐ligation processes as candidate mediators of GLP‐linked prenatal alcohol effects. Conclusions Collectively, GLPs present in NSCs and their EVs may confer apoptosis resistance within the NSC niche and contribute to the abnormal maturation induced by ethanol.
Collapse
Affiliation(s)
- Marisa R Pinson
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Dae D Chung
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Amanda H Mahnke
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA.,Women's Health in Neuroscience Program, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Nihal A Salem
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Daniel Osorio
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA
| | - Vijay Nair
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Elizabeth A Payne
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - Jonathan J Del Real
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA
| | - James J Cai
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA.,Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.,Interdisciplinary Program of Genetics, Texas A&M University, College Station, Texas, USA.,Center for Statistical Bioinformatics, Texas A&M University, College Station, Texas, USA
| | - Rajesh C Miranda
- Department of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, Bryan, Texas, USA.,Women's Health in Neuroscience Program, Texas A&M University Health Science Center, Bryan, Texas, USA.,Interdisciplinary Program of Genetics, Texas A&M University, College Station, Texas, USA
| |
Collapse
|
19
|
Davis KW, Bilancia CG, Martin M, Vanzo R, Rimmasch M, Hom Y, Uddin M, Serrano MA. NeuroSCORE is a genome-wide omics-based model that identifies candidate disease genes of the central nervous system. Sci Rep 2022; 12:5427. [PMID: 35361823 PMCID: PMC8971396 DOI: 10.1038/s41598-022-08938-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 03/08/2022] [Indexed: 02/06/2023] Open
Abstract
To identify candidate disease genes of central nervous system (CNS) phenotypes, we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We identified five genome-wide metrics highly associated with CNS phenotypes to score 19,601 protein-coding genes. Genes scored one point per metric (range: 0-5), identifying 8298 scored genes (scores ≥ 1) and 1601 "high scoring" genes (scores ≥ 3). Using logistic regression, we determined the odds ratio that genes with a NeuroSCORE from 1 to 5 would be associated with known CNS-related phenotypes compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts and aggregate mouse model data. High scoring genes are associated with CNS phenotypes (OR = 5.5, p < 2E-16), enriched in case CNVs, and mouse ortholog genes that cause behavioral and nervous system abnormalities. We identified 1058 high scoring genes with no disease association in OMIM. Transforming the logistic regression results indicates high scoring genes have an 84-92% chance of being associated with a CNS phenotype. Top scoring genes include GRIA1, MAP4K4, SF1, TNPO2, and ZSWIM8. Finally, we interrogated CNVs in the Clinical Genome Resource, finding the majority of clinically significant CNVs contain high scoring genes. These findings can direct future research and improve molecular diagnostics.
Collapse
Affiliation(s)
- Kyle W Davis
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Colleen G Bilancia
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Megan Martin
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Rena Vanzo
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Megan Rimmasch
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Yolanda Hom
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA
| | - Mohammed Uddin
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
- Cellular Intelligence (Ci) Lab, GenomeArc Inc., Toronto, ON, Canada
| | - Moises A Serrano
- Bionano Genomics, Lineagen Division, Inc., 9540 Towne Center, Dr. #100, San Diego, CA, 92121, USA.
| |
Collapse
|
20
|
Stoeger T, Nunes Amaral LA. The characteristics of early-stage research into human genes are substantially different from subsequent research. PLoS Biol 2022; 20:e3001520. [PMID: 34990452 PMCID: PMC8769369 DOI: 10.1371/journal.pbio.3001520] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/19/2022] [Accepted: 12/21/2021] [Indexed: 11/19/2022] Open
Abstract
Throughout the last 2 decades, several scholars observed that present day research into human genes rarely turns toward genes that had not already been extensively investigated in the past. Guided by hypotheses derived from studies of science and innovation, we present here a literature-wide data-driven meta-analysis to identify the specific scientific and organizational contexts that coincided with early-stage research into human genes throughout the past half century. We demonstrate that early-stage research into human genes differs in team size, citation impact, funding mechanisms, and publication outlet, but that generalized insights derived from studies of science and innovation only partially apply to early-stage research into human genes. Further, we demonstrate that, presently, genome biology accounts for most of the initial early-stage research, while subsequent early-stage research can engage other life sciences fields. We therefore anticipate that the specificity of our findings will enable scientists and policymakers to better promote early-stage research into human genes and increase overall innovation within the life sciences.
Collapse
Affiliation(s)
- Thomas Stoeger
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, Illinois, United States of America
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Luís A. Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, Illinois, United States of America
- Department of Molecular Bioscience, Northwestern University, Evanston, Illinois, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois, United States of America
- Department of Medicine, Northwestern University School of Medicine, Chicago, Illinois, United States of America
| |
Collapse
|
21
|
Stupp D, Sharon E, Bloch I, Zitnik M, Zuk O, Tabach Y. Co-evolution based machine-learning for predicting functional interactions between human genes. Nat Commun 2021; 12:6454. [PMID: 34753957 PMCID: PMC8578642 DOI: 10.1038/s41467-021-26792-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 10/09/2021] [Indexed: 12/20/2022] Open
Abstract
Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il. With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.
Collapse
Affiliation(s)
- Doron Stupp
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, 9112001, Jerusalem, Israel
| | - Elad Sharon
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, 9112001, Jerusalem, Israel
| | - Idit Bloch
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, 9112001, Jerusalem, Israel
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, MA, 02115, USA
| | - Or Zuk
- Department of Statistics and Data Science, The Hebrew University of Jerusalem, Jerusalem, 9190501, Israel.
| | - Yuval Tabach
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, 9112001, Jerusalem, Israel.
| |
Collapse
|
22
|
Hu Y, Tan H, Li C, Zhang H. Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method. Genet Epidemiol 2021; 45:710-720. [PMID: 34184773 PMCID: PMC8434958 DOI: 10.1002/gepi.22423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 06/07/2021] [Accepted: 06/17/2021] [Indexed: 02/05/2023]
Abstract
Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.
Collapse
Affiliation(s)
- Yue Hu
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
| | - Haizhu Tan
- Department of Preventive Medicine, Shantou University Medical College, Xinling Road 22, Shantou, Guangdong, P. R. China
| | - Cai Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
| | - Heping Zhang
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
- Correspondence Author: Heping Zhang, 300 George Street, Ste 523, New Haven, CT, 06511. . Phone: 203-785-5185
| |
Collapse
|
23
|
Silva-Gomes R, Mapelli SN, Boutet MA, Mattiola I, Sironi M, Grizzi F, Colombo F, Supino D, Carnevale S, Pasqualini F, Stravalaci M, Porte R, Gianatti A, Pitzalis C, Locati M, Oliveira MJ, Bottazzi B, Mantovani A. Differential expression and regulation of MS4A family members in myeloid cells in physiological and pathological conditions. J Leukoc Biol 2021; 111:817-836. [PMID: 34346525 PMCID: PMC9290968 DOI: 10.1002/jlb.2a0421-200r] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The MS4A gene family encodes 18 tetraspanin-like proteins, most of which with unknown function. MS4A1 (CD20), MS4A2 (FcεRIβ), MS4A3 (HTm4), and MS4A4A play important roles in immunity, whereas expression and function of other members of the family are unknown. The present investigation was designed to obtain an expression fingerprint of MS4A family members, using bioinformatics analysis of public databases, RT-PCR, and protein analysis when possible. MS4A3, MS4A4A, MS4A4E, MS4A6A, MS4A7, and MS4A14 were expressed by myeloid cells. MS4A6A and MS4A14 were expressed in circulating monocytes and decreased during monocyte-to-Mϕ differentiation in parallel with an increase in MS4A4A expression. Analysis of gene expression regulation revealed a strong induction of MS4A4A, MS4A6A, MS4A7, and MS4A4E by glucocorticoid hormones. Consistently with in vitro findings, MS4A4A and MS4A7 were expressed in tissue Mϕs from COVID-19 and rheumatoid arthritis patients. Interestingly, MS4A3, selectively expressed in myeloid precursors, was found to be a marker of immature circulating neutrophils, a cellular population associated to COVID-19 severe disease. The results reported here show that members of the MS4A family are differentially expressed and regulated during myelomonocytic differentiation, and call for assessment of their functional role and value as therapeutic targets.
Collapse
Affiliation(s)
- Rita Silva-Gomes
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,ICBAS-Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde and Instituto Nacional de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
| | | | - Marie-Astrid Boutet
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Regenerative Medicine and Skeleton, RMeS, Inserm UMR 1229, Oniris, CHU Nantes, Université de Nantes, Nantes, France
| | - Irene Mattiola
- Laboratory of Innate Immunity, Department of Microbiology, Infectious Diseases and Immunology, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Mucosal and Developmental Immunology, Berlin, Germany
| | - Marina Sironi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Fabio Grizzi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Domenico Supino
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Silvia Carnevale
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Fabio Pasqualini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | - Rémi Porte
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,Infinity, Université Toulouse, CNRS, Inserm, UPS, Toulouse, France
| | - Andrea Gianatti
- Unit of Pathology, Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, Bergamo, Italy
| | - Constantino Pitzalis
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Massimo Locati
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,Department of Medical Biotechnologies and Translational Medicine, University of Milan, Milan, Italy
| | - Maria José Oliveira
- ICBAS-Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde and Instituto Nacional de Engenharia Biomédica, Universidade do Porto, Porto, Portugal.,Department of Pathology and Oncology, Faculty of Medicine, University of Porto, Porto, Portugal
| | | | - Alberto Mantovani
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| |
Collapse
|
24
|
Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia. Sci Rep 2021; 11:15139. [PMID: 34301992 PMCID: PMC8302622 DOI: 10.1038/s41598-021-94442-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/08/2021] [Indexed: 11/09/2022] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.
Collapse
|
25
|
Pauža AG, Mecawi AS, Paterson A, Hindmarch CCT, Greenwood M, Murphy D, Greenwood MP. Osmoregulation of the transcriptome of the hypothalamic supraoptic nucleus: A resource for the community. J Neuroendocrinol 2021; 33:e13007. [PMID: 34297454 DOI: 10.1111/jne.13007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/25/2021] [Accepted: 06/20/2021] [Indexed: 01/13/2023]
Abstract
The hypothalamic supraoptic nucleus (SON) is a core osmoregulatory control centre that deciphers information about the metabolic state of the organism and orchestrates appropriate homeostatic (endocrine) and allostatic (behavioural) responses. We have used RNA sequencing to describe the polyadenylated transcriptome of the SON of the male Wistar Han rat. These data have been mined to generate comprehensive catalogues of functional classes of genes (enzymes, transcription factors, endogenous peptides, G protein coupled receptors, transporters, catalytic receptors, channels and other pharmacological targets) expressed in this nucleus in the euhydrated state, and that together form the basal substrate for its physiological interactions. We have gone on to show that fluid deprivation for 3 days (dehydration) results in changes in the expression levels of 2247 RNA transcripts, which have similarly been functionally catalogued, and further mined to describe enriched gene categories and putative regulatory networks (Regulons) that may have physiological importance in SON function related plasticity. We hope that the revelation of these genes, pathways and networks, most of which have no characterised roles in the SON, will encourage the neuroendocrine community to pursue new investigations into the new 'known-unknowns' reported in the present study.
Collapse
Affiliation(s)
- Audrys G Pauža
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - André Souza Mecawi
- Laboratory of Neuroendocrinology, Department of Biophysics, Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | - Alex Paterson
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
- Bristol Genomics Facility, University of Bristol, Bristol, UK
| | - Charles C T Hindmarch
- Queen's Cardiopulmonary Unit (QCPU), Department of Medicine, Translational Institute of Medicine (TIME), Queen's University, Kingston, ON, Canada
| | - Mingkwan Greenwood
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - Michael P Greenwood
- Molecular Neuroendocrinology Research Group, Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| |
Collapse
|
26
|
Wang X, Jiang W, Luo S, Yang X, Wang C, Wang B, Dang Y, Shen Y, Ma DK. The C. elegans homolog of human panic-disorder risk gene TMEM132D orchestrates neuronal morphogenesis through the WAVE-regulatory complex. Mol Brain 2021; 14:54. [PMID: 33726789 PMCID: PMC7962252 DOI: 10.1186/s13041-021-00767-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/03/2021] [Indexed: 01/11/2023] Open
Abstract
TMEM132D is a human gene identified with multiple risk alleles for panic disorders, anxiety and major depressive disorders. Defining a conserved family of transmembrane proteins, TMEM132D and its homologs are still of unknown molecular functions. By generating loss-of-function mutants of the sole TMEM132 ortholog in C. elegans, we identify abnormal morphologic phenotypes in the dopaminergic PDE neurons. Using a yeast two-hybrid screen, we find that NAP1 directly interacts with the cytoplasmic domain of human TMEM132D, and mutations in C. elegans tmem-132 that disrupt interaction with NAP1 cause similar morphologic defects in the PDE neurons. NAP1 is a component of the WAVE regulatory complex (WRC) that controls F-actin cytoskeletal dynamics. Decreasing activity of WRC rescues the PDE defects in tmem-132 mutants, whereas gain-of-function of TMEM132D in mammalian cells inhibits WRC, leading to decreased abundance of select WRC components, impaired actin nucleation and cell motility. We propose that metazoan TMEM132 family proteins play evolutionarily conserved roles in regulating NAP1 protein homologs to restrict inappropriate WRC activity, cytoskeletal and morphologic changes in the cell.
Collapse
Affiliation(s)
- Xin Wang
- Cardiovascular Research Institute and Department of Physiology, University of California San Francisco, San Francisco, CA, 94158, USA.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, 650091, Yunnan, China
| | - Wei Jiang
- Key Laboratory of Metabolism and Molecular Medicine, the Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Fudan University, Shanghai, 200032, China
| | - Shuo Luo
- Cardiovascular Research Institute and Department of Physiology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Changnan Wang
- Cardiovascular Research Institute and Department of Physiology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Bingying Wang
- Cardiovascular Research Institute and Department of Physiology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Yongjun Dang
- Key Laboratory of Metabolism and Molecular Medicine, the Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Fudan University, Shanghai, 200032, China
| | - Yin Shen
- Institute for Human Genetics, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Dengke K Ma
- Cardiovascular Research Institute and Department of Physiology, University of California San Francisco, San Francisco, CA, 94158, USA.
| |
Collapse
|
27
|
Kim JD, Wang Y, Fujiwara T, Okuda S, Callahan TJ, Cohen KB. Open Agile text mining for bioinformatics: the PubAnnotation ecosystem. Bioinformatics 2020; 35:4372-4380. [PMID: 30937439 PMCID: PMC6821251 DOI: 10.1093/bioinformatics/btz227] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 03/16/2019] [Accepted: 03/29/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. RESULTS This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. AVAILABILITY AND IMPLEMENTATION The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.
Collapse
Affiliation(s)
- Jin-Dong Kim
- Database Center for Life Science, Research Organization of Information and Systems, Kashiwa, Chiba, Japan
| | - Yue Wang
- Database Center for Life Science, Research Organization of Information and Systems, Kashiwa, Chiba, Japan
| | - Toyofumi Fujiwara
- Database Center for Life Science, Research Organization of Information and Systems, Kashiwa, Chiba, Japan
| | - Shujiro Okuda
- Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - K Bretonnel Cohen
- Computational Bioscience Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.,Université Paris-Saclay, LIMSI-ILES, France
| |
Collapse
|
28
|
Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63:177-189. [PMID: 32199228 PMCID: PMC7308189 DOI: 10.1016/j.copbio.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
Collapse
Affiliation(s)
- Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Current address: Institute of Molecular Biology, Mainz, Germany.
| |
Collapse
|
29
|
Bayraktar EC, La K, Karpman K, Unlu G, Ozerdem C, Ritter DJ, Alwaseem H, Molina H, Hoffmann HH, Millner A, Atilla-Gokcumen GE, Gamazon ER, Rushing AR, Knapik EW, Basu S, Birsoy K. Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism. Nat Metab 2020; 2:487-498. [PMID: 32694732 PMCID: PMC7384252 DOI: 10.1038/s42255-020-0206-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 04/15/2020] [Indexed: 12/14/2022]
Abstract
Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.
Collapse
Affiliation(s)
- Erol C Bayraktar
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Konnor La
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Kara Karpman
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
| | - Gokhan Unlu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Ceren Ozerdem
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Dylan J Ritter
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Hanan Alwaseem
- Proteomics Resource Center, The Rockefeller University, New York, NY, USA
| | - Henrik Molina
- Proteomics Resource Center, The Rockefeller University, New York, NY, USA
| | - Hans-Heinrich Hoffmann
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA
| | - Alec Millner
- Department of Chemistry, University at Buffalo, The State University of New York (SUNY), Buffalo, NY, USA
| | - G Ekin Atilla-Gokcumen
- Department of Chemistry, University at Buffalo, The State University of New York (SUNY), Buffalo, NY, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy R Rushing
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ela W Knapik
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Sumanta Basu
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Kıvanç Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.
| |
Collapse
|
30
|
Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations. Sci Rep 2020; 10:4106. [PMID: 32139709 PMCID: PMC7057977 DOI: 10.1038/s41598-020-60456-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 02/10/2020] [Indexed: 12/14/2022] Open
Abstract
Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.
Collapse
|
31
|
Abstract
The 11 existing FDA-approved osteoporosis drug treatments include hormone replacement therapy, 2 SERMs (raloxifene and bazedoxifene), 5 inhibitors of bone-resorbing osteoclasts (4 bisphosphonates and anti-RANKL denosumab), 2 parathyroid hormone analogues (teriparatide and abaloparatide), and 1 WNT signaling enhancer (romosozumab). These therapies are effective and provide multiple options for patients and physicians. As the genomic revolution continues, potential novel targets for future drug development are identified. This review takes a wide perspective to describe potentially rewarding topics to explore, including knowledge of genes and pathways involved in bone cell metabolism, the utility of animal models, targeting drugs to bone, and ongoing advances in drug design and delivery.
Collapse
|
32
|
Lu L, Huang J, Xu F, Xiao Z, Wang J, Zhang B, David NV, Arends D, Gu W, Ackert-Bicknell C, Sabik OL, Farber CR, Quarles LD, Williams RW. Genetic Dissection of Femoral and Tibial Microarchitecture. JBMR Plus 2019; 3:e10241. [PMID: 31844829 PMCID: PMC6894729 DOI: 10.1002/jbm4.10241] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 09/09/2019] [Accepted: 09/16/2019] [Indexed: 12/29/2022] Open
Abstract
Our understanding of the genetic control of bone strength has relied mainly on estimates of bone mineral density. Here we have mapped genetic factors that influence femoral and tibial microarchitecture using high‐resolution x‐ray computed tomography (8‐μm isotropic voxels) across a family of 61 BXD strains of mice, roughly 10 isogenic cases per strain and balanced by sex. We computed heritabilities for 25 cortical and trabecular traits. Males and females have well‐matched heritabilities, ranging from 0.25 to 0.75. We mapped 16 genetic loci most of which were detected only in females. There is also a bias in favor of loci that control cortical rather than trabecular bone. To evaluate candidate genes, we combined well‐established gene ontologies with bone transcriptome data to compute bone‐enrichment scores for all protein‐coding genes. We aligned candidates with those of human genome‐wide association studies. A subset of 50 strong candidates fell into three categories: (1) experimentally validated genes already known to modulate bone function (Adamts4, Ddr2, Darc, Adam12, Fkbp10, E2f6, Adam17, Grem2, Ifi204); (2) candidates without any experimentally validated function in bone (eg, Greb1, Ifi202b), but linked to skeletal phenotypes in human cohorts; and (3) candidates that have high bone‐enrichment scores, but for which there is not yet any functional link to bone biology or skeletal system disease (including Ifi202b, Ly9, Ifi205, Mgmt, F2rl1, Iqgap2). Our results highlight contrasting genetic architecture between sexes and among major bone compartments. The alignment of murine and human data facilitates function analysis and should prove of value for preclinical testing of molecular control of bone structure. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
Collapse
Affiliation(s)
- Lu Lu
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Center Memphis TN USA
| | - Jinsong Huang
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Center Memphis TN USA
| | - Fuyi Xu
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Center Memphis TN USA
| | - Zhousheng Xiao
- Department of Medicine University of Tennessee Health Science Center Memphis TN USA
| | - Jing Wang
- Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA
| | - Bing Zhang
- Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA
| | - Nicolae Valentin David
- Department of Medicine Northwestern University Feinberg School of Medicine Chicago IL USA
| | - Danny Arends
- Breeding Biology and Molecular Animal Breeding Humboldt University Berlin Germany
| | - Weikuan Gu
- Department of Orthopaedic Surgery and Biomedical Engineering University of Tennessee Health Science Center Memphis TN USA
| | | | - Olivia L Sabik
- Center for Public Health Genomics University of Virginia Charlottesville VA USA
| | - Charles R Farber
- Center for Public Health Genomics University of Virginia Charlottesville VA USA
| | - Leigh Darryl Quarles
- Department of Medicine University of Tennessee Health Science Center Memphis TN USA
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Center Memphis TN USA
| |
Collapse
|
33
|
Li H, Rukina D, David FPA, Li TY, Oh CM, Gao AW, Katsyuba E, Bou Sleiman M, Komljenovic A, Huang Q, Williams RW, Robinson-Rechavi M, Schoonjans K, Morgenthaler S, Auwerx J. Identifying gene function and module connections by the integration of multispecies expression compendia. Genome Res 2019; 29:2034-2045. [PMID: 31754022 PMCID: PMC6886503 DOI: 10.1101/gr.251983.119] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/31/2019] [Indexed: 12/13/2022]
Abstract
The functions of many eukaryotic genes are still poorly understood. Here, we developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multispecies expression compendia—1700 data sets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast—collected in this study. G-MAD identifies novel functions of genes—for example, DDT in mitochondrial respiration and WDFY4 in T cell activation—and also suggests novel components for modules, such as for cholesterol biosynthesis. By applying G-MAD on data sets from respective tissues, tissue-specific functions of genes were identified—for instance, the roles of EHHADH in liver and kidney, as well as SLC6A1 in brain and liver. Using M-MAD, we identified a list of module-module associations, such as those between mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available as an open resource, which will facilitate the identification of connections linking genes, modules, phenotypes, and diseases.
Collapse
Affiliation(s)
- Hao Li
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Daria Rukina
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Fabrice P A David
- Gene Expression Core Facility, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.,SV-IT, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Terytty Yang Li
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Chang-Myung Oh
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Arwen W Gao
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Elena Katsyuba
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Andrea Komljenovic
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne 1015, Switzerland
| | - Qingyao Huang
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, Tennessee 38163, USA
| | - Marc Robinson-Rechavi
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne 1015, Switzerland
| | - Kristina Schoonjans
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Stephan Morgenthaler
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| |
Collapse
|
34
|
Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
Collapse
MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
Collapse
Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| |
Collapse
|
35
|
Oprea TI. Exploring the dark genome: implications for precision medicine. Mamm Genome 2019; 30:192-200. [PMID: 31270560 DOI: 10.1007/s00335-019-09809-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 06/15/2019] [Indexed: 01/08/2023]
Abstract
The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.
Collapse
Affiliation(s)
- Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA. .,UNM Comprehensive Cancer Center, Albuquerque, NM, USA. .,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden. .,Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
36
|
Brommage R, Powell DR, Vogel P. Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns. Dis Model Mech 2019; 12:dmm038224. [PMID: 31064765 PMCID: PMC6550044 DOI: 10.1242/dmm.038224] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Two large-scale mouse gene knockout phenotyping campaigns have provided extensive data on the functions of thousands of mammalian genes. The ongoing International Mouse Phenotyping Consortium (IMPC), with the goal of examining all ∼20,000 mouse genes, has examined 5115 genes since 2011, and phenotypic data from several analyses are available on the IMPC website (www.mousephenotype.org). Mutant mice having at least one human genetic disease-associated phenotype are available for 185 IMPC genes. Lexicon Pharmaceuticals' Genome5000™ campaign performed similar analyses between 2000 and the end of 2008 focusing on the druggable genome, including enzymes, receptors, transporters, channels and secreted proteins. Mutants (4654 genes, with 3762 viable adult homozygous lines) with therapeutically interesting phenotypes were studied extensively. Importantly, phenotypes for 29 Lexicon mouse gene knockouts were published prior to observations of similar phenotypes resulting from homologous mutations in human genetic disorders. Knockout mouse phenotypes for an additional 30 genes mimicked previously published human genetic disorders. Several of these models have helped develop effective treatments for human diseases. For example, studying Tph1 knockout mice (lacking peripheral serotonin) aided the development of telotristat ethyl, an approved treatment for carcinoid syndrome. Sglt1 (also known as Slc5a1) and Sglt2 (also known as Slc5a2) knockout mice were employed to develop sotagliflozin, a dual SGLT1/SGLT2 inhibitor having success in clinical trials for diabetes. Clinical trials evaluating inhibitors of AAK1 (neuropathic pain) and SGLT1 (diabetes) are underway. The research community can take advantage of these unbiased analyses of gene function in mice, including the minimally studied 'ignorome' genes.
Collapse
Affiliation(s)
- Robert Brommage
- Department of Metabolism Research, Lexicon Pharmaceuticals, 8800 Technology Forest Place, The Woodlands, TX 77381, USA
| | - David R Powell
- Department of Metabolism Research, Lexicon Pharmaceuticals, 8800 Technology Forest Place, The Woodlands, TX 77381, USA
| | - Peter Vogel
- St. Jude Children's Research Hospital, Pathology, MS 250, Room C5036A, 262 Danny Thomas Place, Memphis, TN 38105, USA
| |
Collapse
|
37
|
Boone DR, Weisz HA, Willey HE, Torres KEO, Falduto MT, Sinha M, Spratt H, Bolding IJ, Johnson KM, Parsley MA, DeWitt DS, Prough DS, Hellmich HL. Traumatic brain injury induces long-lasting changes in immune and regenerative signaling. PLoS One 2019; 14:e0214741. [PMID: 30943276 PMCID: PMC6447179 DOI: 10.1371/journal.pone.0214741] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 03/19/2019] [Indexed: 12/19/2022] Open
Abstract
There are no existing treatments for the long-term degenerative effects of traumatic brain injury (TBI). This is due, in part, to our limited understanding of chronic TBI and uncertainty about which proposed mechanisms for long-term neurodegeneration are amenable to treatment with existing or novel drugs. Here, we used microarray and pathway analyses to interrogate TBI-induced gene expression in the rat hippocampus and cortex at several acute, subchronic and chronic intervals (24 hours, 2 weeks, 1, 2, 3, 6 and 12 months) after parasagittal fluid percussion injury. We used Ingenuity pathway analysis (IPA) and Gene Ontology enrichment analysis to identify significantly expressed genes and prominent cell signaling pathways that are dysregulated weeks to months after TBI and potentially amenable to therapeutic modulation. We noted long-term, coordinated changes in expression of genes belonging to canonical pathways associated with the innate immune response (i.e., NF-κB signaling, NFAT signaling, Complement System, Acute Phase Response, Toll-like receptor signaling, and Neuroinflammatory signaling). Bioinformatic analysis suggested that dysregulation of these immune mediators—many are key hub genes—would compromise multiple cell signaling pathways essential for homeostatic brain function, particularly those involved in cell survival and neuroplasticity. Importantly, the temporal profile of beneficial and maladaptive immunoregulatory genes in the weeks to months after the initial TBI suggests wider therapeutic windows than previously indicated.
Collapse
Affiliation(s)
- Deborah R. Boone
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Harris A. Weisz
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Hannah E. Willey
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | | | - Michael T. Falduto
- GenUs Biosystems, Northbrook, Illinois, United States of America
- Paradise Genomics, Inc., Northbrook, Illinois, United States of America
| | - Mala Sinha
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Heidi Spratt
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Ian J. Bolding
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Kathea M. Johnson
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Margaret A. Parsley
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Douglas S. DeWitt
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Donald S. Prough
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Helen L. Hellmich
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, United States of America
- * E-mail:
| |
Collapse
|
38
|
Abstract
The identification of genes that are differentially expressed provides a molecular foothold onto biological questions of interest. Whether some genes are more likely to be differentially expressed than others, and to what degree, has never been assessed on a global scale. Here, we reanalyze more than 600 studies and find that knowledge of a gene’s prior probability of differential expression (DE) allows for accurate prediction of DE hit lists, regardless of the biological question. This result suggests redundancy in transcriptomics experiments that both informs gene set interpretation and highlights room for growth within the field. Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.
Collapse
|
39
|
Rinschen MM, Limbutara K, Knepper MA, Payne DM, Pisitkun T. From Molecules to Mechanisms: Functional Proteomics and Its Application to Renal Tubule Physiology. Physiol Rev 2019; 98:2571-2606. [PMID: 30182799 DOI: 10.1152/physrev.00057.2017] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Classical physiological studies using electrophysiological, biophysical, biochemical, and molecular techniques have created a detailed picture of molecular transport, bioenergetics, contractility and movement, and growth, as well as the regulation of these processes by external stimuli in cells and organisms. Newer systems biology approaches are beginning to provide deeper and broader understanding of these complex biological processes and their dynamic responses to a variety of environmental cues. In the past decade, advances in mass spectrometry-based proteomic technologies have provided invaluable tools to further elucidate these complex cellular processes, thereby confirming, complementing, and advancing common views of physiology. As one notable example, the application of proteomics to study the regulation of kidney function has yielded novel insights into the chemical and physical processes that tightly control body fluids, electrolytes, and metabolites to provide optimal microenvironments for various cellular and organ functions. Here, we systematically review, summarize, and discuss the most significant key findings from functional proteomic studies in renal epithelial physiology. We also identify further improvements in technological and bioinformatics methods that will be essential to advance precision medicine in nephrology.
Collapse
Affiliation(s)
- Markus M Rinschen
- Department II of Internal Medicine, University Hospital Cologne , Cologne , Germany ; Center for Molecular Medicine Cologne, University of Cologne , Cologne , Germany ; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne , Cologne , Germany ; Division of Nephrology, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand ; Epithelial Systems Biology Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland ; and Center of Excellence in Systems Biology, Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand
| | - Kavee Limbutara
- Department II of Internal Medicine, University Hospital Cologne , Cologne , Germany ; Center for Molecular Medicine Cologne, University of Cologne , Cologne , Germany ; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne , Cologne , Germany ; Division of Nephrology, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand ; Epithelial Systems Biology Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland ; and Center of Excellence in Systems Biology, Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand
| | - Mark A Knepper
- Department II of Internal Medicine, University Hospital Cologne , Cologne , Germany ; Center for Molecular Medicine Cologne, University of Cologne , Cologne , Germany ; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne , Cologne , Germany ; Division of Nephrology, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand ; Epithelial Systems Biology Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland ; and Center of Excellence in Systems Biology, Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand
| | - D Michael Payne
- Department II of Internal Medicine, University Hospital Cologne , Cologne , Germany ; Center for Molecular Medicine Cologne, University of Cologne , Cologne , Germany ; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne , Cologne , Germany ; Division of Nephrology, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand ; Epithelial Systems Biology Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland ; and Center of Excellence in Systems Biology, Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand
| | - Trairak Pisitkun
- Department II of Internal Medicine, University Hospital Cologne , Cologne , Germany ; Center for Molecular Medicine Cologne, University of Cologne , Cologne , Germany ; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne , Cologne , Germany ; Division of Nephrology, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand ; Epithelial Systems Biology Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland ; and Center of Excellence in Systems Biology, Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok , Thailand
| |
Collapse
|
40
|
Brommage R, Ohlsson C. High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders. Front Endocrinol (Lausanne) 2019; 10:934. [PMID: 32117046 PMCID: PMC7010808 DOI: 10.3389/fendo.2019.00934] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
UNLABELLED The 2019 International Skeletal Dysplasia Society nosology update lists 441 genes for which mutations result in rare human skeletal disorders. These genes code for enzymes (33%), scaffolding proteins (18%), signal transduction proteins (16%), transcription factors (14%), cilia proteins (8%), extracellular matrix proteins (5%), and membrane transporters (4%). Skeletal disorders include aggrecanopathies, channelopathies, ciliopathies, cohesinopathies, laminopathies, linkeropathies, lysosomal storage diseases, protein-folding and RNA splicing defects, and ribosomopathies. With the goal of evaluating the ability of mouse models to mimic these human genetic skeletal disorders, a PubMed literature search identified 260 genes for which mutant mice were examined for skeletal phenotypes. These mouse models included spontaneous and ENU-induced mutants, global and conditional gene knockouts, and transgenic mice with gene over-expression or specific base-pair substitutions. The human X-linked gene ARSE and small nuclear RNA U4ATAC, a component of the minor spliceosome, do not have mouse homologs. Mouse skeletal phenotypes mimicking human skeletal disorders were observed in 249 of the 260 genes (96%) for which comparisons are possible. A supplemental table in spreadsheet format provides PubMed weblinks to representative publications of mutant mouse skeletal phenotypes. Mutations in 11 mouse genes (Ccn6, Cyp2r1, Flna, Galns, Gna13, Lemd3, Manba, Mnx1, Nsd1, Plod1, Smarcal1) do not result in similar skeletal phenotypes observed with mutations of the homologous human genes. These discrepancies can result from failure of mouse models to mimic the exact human gene mutations. There are no obvious commonalities among these 11 genes. Body BMD and/or radiologic dysmorphology phenotypes were successfully identified for 28 genes by the International Mouse Phenotyping Consortium (IMPC). Forward genetics using ENU mouse mutagenesis successfully identified 37 nosology gene phenotypes. Since many human genetic disorders involve hypomorphic, gain-of-function, dominant-negative and intronic mutations, future studies will undoubtedly utilize CRISPR/Cas9 technology to examine transgenic mice having genes modified to exactly mimic variant human sequences. Mutant mice will increasingly be employed for drug development studies designed to treat human genetic skeletal disorders. SIGNIFICANCE Great progress is being made identifying mutant genes responsible for human rare genetic skeletal disorders and mouse models for genes affecting bone mass, architecture, mineralization and strength. This review organizes data for 441 human genetic bone disorders with regard to heredity, gene function, molecular pathways, and fidelity of relevant mouse models to mimic the human skeletal disorders. PubMed weblinks to citations of 249 successful mouse models are provided.
Collapse
Affiliation(s)
- Robert Brommage
- Department of Internal Medicine and Clinical Nutrition, Centre for Bone and Arthritis Research, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- *Correspondence: Robert Brommage
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Centre for Bone and Arthritis Research, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| |
Collapse
|
41
|
Stoeger T, Gerlach M, Morimoto RI, Nunes Amaral LA. Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol 2018; 16:e2006643. [PMID: 30226837 PMCID: PMC6143198 DOI: 10.1371/journal.pbio.2006643] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 08/10/2018] [Indexed: 01/04/2023] Open
Abstract
Biomedical research has been previously reported to primarily focus on a minority of all known genes. Here, we demonstrate that these differences in attention can be explained, to a large extent, exclusively from a small set of identifiable chemical, physical, and biological properties of genes. Together with knowledge about homologous genes from model organisms, these features allow us to accurately predict the number of publications on individual human genes, the year of their first report, the levels of funding awarded by the National Institutes of Health (NIH), and the development of drugs against disease-associated genes. By explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, we describe gene-specific strategies for the identification of important but hitherto ignored genes that can open novel directions for future investigation.
Collapse
Affiliation(s)
- Thomas Stoeger
- Center for Genetic Medicine, Northwestern University, Chicago, United States of America
- Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, United States of America
| | - Martin Gerlach
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, United States of America
| | - Richard I. Morimoto
- Department of Molecular Bioscience, Northwestern University, Evanston, United States of America
| | - Luís A. Nunes Amaral
- Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, United States of America
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, United States of America
- Department of Molecular Bioscience, Northwestern University, Evanston, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, United States of America
| |
Collapse
|
42
|
Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM, Guha R, Hersey A, Holmes J, Jadhav A, Jensen LJ, Johnson GL, Karlson A, Leach AR, Ma’ayan A, Malovannaya A, Mani S, Mathias SL, McManus MT, Meehan TF, von Mering C, Muthas D, Nguyen DT, Overington JP, Papadatos G, Qin J, Reich C, Roth BL, Schürer SC, Simeonov A, Sklar LA, Southall N, Tomita S, Tudose I, Ursu O, Vidovic D, Waller A, Westergaard D, Yang JJ, Zahoránszky-Köhalmi G. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 2018; 17:317-332. [PMID: 29472638 PMCID: PMC6339563 DOI: 10.1038/nrd.2018.14] [Citation(s) in RCA: 209] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.
Collapse
Affiliation(s)
- Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cristian G. Bologa
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Shawn M. Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Anne Hersey
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Jayme Holmes
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Ajit Jadhav
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gary L. Johnson
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Anneli Karlson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Present addresses: SciBite Limited, BioData Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Andrew R. Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Avi Ma’ayan
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Subramani Mani
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Stephen L. Mathias
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | | | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Daniel Muthas
- Respiratory, Inflammation and Autoimmunity Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, Mölndal, Sweden
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - John P. Overington
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Medicines Discovery Catapult, Alderley Edge, UK
| | - George Papadatos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- GlaxoSmithKline, Stevenage, UK
| | - Jun Qin
- Baylor College of Medicine, Houston, TX, USA
| | | | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Larry A. Sklar
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Noel Southall
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Susumu Tomita
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Ilinca Tudose
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Google Germany GmbH, München, Germany
| | - Oleg Ursu
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dušica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anna Waller
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeremy J. Yang
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Gergely Zahoránszky-Köhalmi
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- NIH-NCATS, Rockville, MD, USA
| |
Collapse
|
43
|
Abstract
Neural basic helix-loop helix (bHLH) transcription factors promote progenitor cell differentiation by activation of downstream target genes that coordinate neuronal differentiation. Here we characterize a neural bHLH target gene in Xenopus laevis, vexin (vxn; previously sbt1), that is homologous to human c8orf46 and is conserved across vertebrate species. C8orf46 has been implicated in cancer progression, but its function is unknown. Vxn is transiently expressed in differentiating progenitors in the developing central nervous system (CNS), and is required for neurogenesis in the neural plate and retina. Its function is conserved, since overexpression of either Xenopus or mouse vxn expands primary neurogenesis and promotes early retinal cell differentiation in cooperation with neural bHLH factors. Vxn protein is localized to the cell membrane and the nucleus, but functions in the nucleus to promote neural differentiation. Vxn inhibits cell proliferation, and works with the cyclin-dependent kinase inhibitor p27Xic1 (cdkn1b) to enhance neurogenesis and increase levels of the proneural protein Neurog2. We propose that vxn provides a key link between neural bHLH activity and execution of the neurogenic program.
Collapse
|
44
|
Sanchez-Pulido L, Ponting CP. TMEM132: an ancient architecture of cohesin and immunoglobulin domains define a new family of neural adhesion molecules. Bioinformatics 2018; 34:721-724. [PMID: 29088312 PMCID: PMC6030884 DOI: 10.1093/bioinformatics/btx689] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 09/29/2017] [Accepted: 10/26/2017] [Indexed: 11/26/2022] Open
Abstract
Summary The molecular functions of TMEM132 genes remain poorly understood and under-investigated despite their mutations associated with non-syndromic hearing loss, panic disorder and cancer. Here we show the full domain architecture of human TMEM132 family proteins solved using in-depth sequence and structural analysis. We reveal them to be five previously unappreciated cell adhesion molecules whose domain architecture has an early holozoan origin prior to the emergence of choanoflagellates and metazoa. The extra-cellular portions of TMEM132 proteins contain five conserved domains including three tandem immunoglobulin domains, and a cohesin domain homologue, the first such domain found in animals. These findings strongly predict a cellular adhesion function for TMEM132 family, connecting the extracellular medium with the intracellular actin cytoskeleton. Contact luis.sanchez-pulido@igmm.ed.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Luis Sanchez-Pulido
- Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, UK
| | - Chris P Ponting
- Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
45
|
Brown LA, Peirson SN. Improving Reproducibility and Candidate Selection in Transcriptomics Using Meta-analysis. J Exp Neurosci 2018; 12:1179069518756296. [PMID: 29511359 PMCID: PMC5833209 DOI: 10.1177/1179069518756296] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 01/08/2018] [Indexed: 11/15/2022] Open
Abstract
Transcriptomic experiments are often used in neuroscience to identify candidate genes of interest for further study. However, the lists of genes identified from comparable transcriptomic studies often show limited overlap. One approach to addressing this issue of reproducibility is to combine data from multiple studies in the form of a meta-analysis. Here, we discuss recent work in the field of circadian biology, where transcriptomic meta-analyses have been used to improve candidate gene selection. With the increasing availability of microarray and RNA-Seq data due to deposition in public databases, combined with freely available tools and code, transcriptomic meta-analysis provides an ideal example of how open data can benefit neuroscience research.
Collapse
Affiliation(s)
- Laurence A Brown
- Sleep and Circadian Neuroscience Institute (SCNi),
Nuffield Department of Clinical Neurosciences, Sir William Dunn School of
Pathology, University of Oxford, Oxford, UK
| | - Stuart N Peirson
- Sleep and Circadian Neuroscience Institute (SCNi),
Nuffield Department of Clinical Neurosciences, Sir William Dunn School of
Pathology, University of Oxford, Oxford, UK
| |
Collapse
|
46
|
Haynes WA, Tomczak A, Khatri P. Gene annotation bias impedes biomedical research. Sci Rep 2018; 8:1362. [PMID: 29358745 PMCID: PMC5778030 DOI: 10.1038/s41598-018-19333-x] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 12/28/2017] [Indexed: 12/21/2022] Open
Abstract
We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.
Collapse
Affiliation(s)
- Winston A Haynes
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Aurelie Tomczak
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA
| | - Purvesh Khatri
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA.
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA.
| |
Collapse
|
47
|
Identification of 22q13 genes most likely to contribute to Phelan McDermid syndrome. Eur J Hum Genet 2018; 26:293-302. [PMID: 29358616 PMCID: PMC5838980 DOI: 10.1038/s41431-017-0042-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 09/04/2017] [Accepted: 10/31/2017] [Indexed: 01/02/2023] Open
Abstract
Chromosome 22q13.3 deletion (Phelan McDermid) syndrome (PMS) is a rare genetic neurodevelopmental disorder resulting from deletions or other genetic variants on distal 22q. Pathological variants of the SHANK3 gene have been identified, but terminal chromosomal deletions including SHANK3 are most common. Terminal deletions disrupt up to 108 protein-coding genes. The impact of these losses is highly variable and includes both significantly impairing neurodevelopmental and somatic manifestations. The current review combines two metrics, prevalence of gene loss and predicted loss pathogenicity, to identify likely contributors to phenotypic expression. These genes are grouped according to function as follows: molecular signaling at glutamate synapses, phenotypes involving neuropsychiatric disorders, involvement in multicellular organization, cerebellar development and functioning, and mitochondrial. The likely most impactful genes are reviewed to provide information for future clinical and translational investigations.
Collapse
|
48
|
Rozman J, Rathkolb B, Oestereicher MA, Schütt C, Ravindranath AC, Leuchtenberger S, Sharma S, Kistler M, Willershäuser M, Brommage R, Meehan TF, Mason J, Haselimashhadi H, Hough T, Mallon AM, Wells S, Santos L, Lelliott CJ, White JK, Sorg T, Champy MF, Bower LR, Reynolds CL, Flenniken AM, Murray SA, Nutter LMJ, Svenson KL, West D, Tocchini-Valentini GP, Beaudet AL, Bosch F, Braun RB, Dobbie MS, Gao X, Herault Y, Moshiri A, Moore BA, Kent Lloyd KC, McKerlie C, Masuya H, Tanaka N, Flicek P, Parkinson HE, Sedlacek R, Seong JK, Wang CKL, Moore M, Brown SD, Tschöp MH, Wurst W, Klingenspor M, Wolf E, Beckers J, Machicao F, Peter A, Staiger H, Häring HU, Grallert H, Campillos M, Maier H, Fuchs H, Gailus-Durner V, Werner T, Hrabe de Angelis M. Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Nat Commun 2018; 9:288. [PMID: 29348434 PMCID: PMC5773596 DOI: 10.1038/s41467-017-01995-2] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 10/30/2017] [Indexed: 12/20/2022] Open
Abstract
Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.
Collapse
Affiliation(s)
- Jan Rozman
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Birgit Rathkolb
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Ludwig-Maximilians-Universität München, Gene Center, Institute of Molecular Animal Breeding and Biotechnology, Feodor-Lynen Strasse 25, 81377, Munich, Germany
| | - Manuela A Oestereicher
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Christine Schütt
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Aakash Chavan Ravindranath
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Stefanie Leuchtenberger
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Martin Kistler
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Monja Willershäuser
- Chair of Molecular Nutritional Medicine, Technical University of Munich, TUM School of Life Sciences Weihenstephan, 85354, Freising, Germany
- EKFZ - Else Kröner-Fresenius Center for Nutritional Medicine, Technical University of Munich, 85354, Freising, Germany
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354, Freising, Germany
| | - Robert Brommage
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Terrence F Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Jeremy Mason
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Hamed Haselimashhadi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Tertius Hough
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Oxfordshire, OX11 0RD, UK
| | - Ann-Marie Mallon
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Oxfordshire, OX11 0RD, UK
| | - Sara Wells
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Oxfordshire, OX11 0RD, UK
| | - Luis Santos
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Oxfordshire, OX11 0RD, UK
| | - Christopher J Lelliott
- The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Jacqueline K White
- The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | - Tania Sorg
- CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), 1 Rue Laurent Fries, 67404, Illkirch-Graffenstaden, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Parc d'innovation, 1 Rue Laurent Fries - BP 10142, 67404, Illkirch, France
- Centre National de la Recherche Scientifique, UMR7104, 67404, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U964, 67404, Illkirch, France
- Université de Strasbourg, 67404, Illkirch, France
| | - Marie-France Champy
- CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), 1 Rue Laurent Fries, 67404, Illkirch-Graffenstaden, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Parc d'innovation, 1 Rue Laurent Fries - BP 10142, 67404, Illkirch, France
- Centre National de la Recherche Scientifique, UMR7104, 67404, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U964, 67404, Illkirch, France
- Université de Strasbourg, 67404, Illkirch, France
| | - Lynette R Bower
- Mouse Biology Program, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Corey L Reynolds
- Department of Molecular and Human Genetics, Baylor College of Medicine, 7702 Main St, Houston Medical Center, Houston, TX, 77030-4406, USA
| | - Ann M Flenniken
- The Centre for Phenogenomics, 25 Orde St, Toronto, M5T 3H7, ON, Canada
- The Hospital for Sick Children, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
| | - Stephen A Murray
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | - Lauryl M J Nutter
- The Centre for Phenogenomics, 25 Orde St, Toronto, M5T 3H7, ON, Canada
- The Hospital for Sick Children, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
| | - Karen L Svenson
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | - David West
- Children's Hospital Oakland Research Institute, 5700 Martin Luther King Jr. Way, Oakland, CA, 94609, USA
| | - Glauco P Tocchini-Valentini
- Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, Adriano Buzzati-Traverso Campus, Via E. Ramarini 32, Monterotondo Scalo, RM, 00015, Italy
| | - Arthur L Beaudet
- The Centre for Phenogenomics, 25 Orde St, Toronto, M5T 3H7, ON, Canada
- The Hospital for Sick Children, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
| | - Fatima Bosch
- Center of Animal Biotechnology and Gene Therapy and Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Robert B Braun
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | - Michael S Dobbie
- Australian Phenomics Network, John Curtin School of Medical Research, Australian National University, 131 Garran Road, Canberra, ACT, 2601, Australia
| | - Xiang Gao
- SKL of Pharmaceutical Biotechnology and Model Animal Research Center, Collaborative Innovation Center for Genetics and Development, Nanjing Biomedical Research Institute, Nanjing University, Nanjing, 210061, China
| | - Yann Herault
- CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), 1 Rue Laurent Fries, 67404, Illkirch-Graffenstaden, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Parc d'innovation, 1 Rue Laurent Fries - BP 10142, 67404, Illkirch, France
- Centre National de la Recherche Scientifique, UMR7104, 67404, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U964, 67404, Illkirch, France
- Université de Strasbourg, 67404, Illkirch, France
| | - Ala Moshiri
- Department of Ophthalmology & Vision Science, School of Medicine, U.C. Davis, 77 Cadillac Drive, Sacramento, 95825, CA, USA
| | - Bret A Moore
- William R. Pritchard Veterinary Medical Teaching Hospital, School of Veterinary Medicine, U.C. Davis, One Shields Avenue, Davis, 95616, CA, USA
| | - K C Kent Lloyd
- Mouse Biology Program, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Colin McKerlie
- The Centre for Phenogenomics, 25 Orde St, Toronto, M5T 3H7, ON, Canada
- The Hospital for Sick Children, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
| | - Hiroshi Masuya
- RIKEN BioResource Center, 3-1-1 Koyadai, Tsukuba, Ibaraki, 305-0074, Japan
| | - Nobuhiko Tanaka
- RIKEN BioResource Center, 3-1-1 Koyadai, Tsukuba, Ibaraki, 305-0074, Japan
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Helen E Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Radislav Sedlacek
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Prumyslova 595, 252 50, Vestec, Czech Republic
| | - Je Kyung Seong
- Korea Mouse Phenotyping Consortium (KMPC) and BK21 Program for Veterinary Science, Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul, 151-742, South Korea
| | - Chi-Kuang Leo Wang
- National Laboratory Animal Center, National Applied Research Laboratories (NARLabs), 128 Yen-Chiou-Yuan Rd., Sec. 2, Nankang, Taipei, 11529, Taiwan
| | | | - Steve D Brown
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Oxfordshire, OX11 0RD, UK
| | - Matthias H Tschöp
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764, Neuherberg, Germany
- Division of Metabolic Diseases, Department of Medicine, Technische Universität München, 80333, Munich, Germany
| | - Wolfgang Wurst
- Institute of Developmental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
- Chair of Developmental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
- Deutsches Institut für Neurodegenerative Erkrankungen (DZNE) Site Munich, Feodor-Lynen-Str. 17, 81377, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Adolf-Butenandt-Institut, Ludwig-Maximilians-Universität München, Feodor-Lynen-Str. 17, 81377, Munich, Germany
| | - Martin Klingenspor
- Chair of Molecular Nutritional Medicine, Technical University of Munich, TUM School of Life Sciences Weihenstephan, 85354, Freising, Germany
- EKFZ - Else Kröner-Fresenius Center for Nutritional Medicine, Technical University of Munich, 85354, Freising, Germany
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354, Freising, Germany
| | - Eckhard Wolf
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Ludwig-Maximilians-Universität München, Gene Center, Institute of Molecular Animal Breeding and Biotechnology, Feodor-Lynen Strasse 25, 81377, Munich, Germany
| | - Johannes Beckers
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Chair of Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Alte Akademie 8, 85354, Freising, Germany
| | - Fausto Machicao
- Department of Internal Medicine, Division of Endocrinology, Diabetology, Vascular Medicine, Nephrology and Clinical Chemistry, University of Tübingen, 72076, Tübingen, Germany
| | - Andreas Peter
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology, Vascular Medicine, Nephrology and Clinical Chemistry, University of Tübingen, 72076, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the Eberhard-Karls-University of Tuebingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany
| | - Harald Staiger
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the Eberhard-Karls-University of Tuebingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany
- Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Eberhard Karls University Tübingen, 72076, Tübingen, Germany
| | - Hans-Ulrich Häring
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology, Vascular Medicine, Nephrology and Clinical Chemistry, University of Tübingen, 72076, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the Eberhard-Karls-University of Tuebingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Clinical Cooperation Group Type 2 Diabetes, Helmholtz Zentrum München and Ludwig-Maximilians Universität München, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Monica Campillos
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Holger Maier
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Helmut Fuchs
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Valerie Gailus-Durner
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Thomas Werner
- Internal Medicine Nephrology and Center for Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Martin Hrabe de Angelis
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
- Chair of Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Alte Akademie 8, 85354, Freising, Germany.
| |
Collapse
|
49
|
Secco B, Camiré É, Brière MA, Caron A, Billong A, Gélinas Y, Lemay AM, Tharp KM, Lee PL, Gobeil S, Guimond JV, Patey N, Guertin DA, Stahl A, Haddad É, Marsolais D, Bossé Y, Birsoy K, Laplante M. Amplification of Adipogenic Commitment by VSTM2A. Cell Rep 2017; 18:93-106. [PMID: 28052263 PMCID: PMC5551894 DOI: 10.1016/j.celrep.2016.12.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 10/31/2016] [Accepted: 12/06/2016] [Indexed: 11/05/2022] Open
Abstract
Despite progress in our comprehension of the mechanisms regulating adipose tissue development, the nature of the factors that functionally characterize adipose precursors is still elusive. Defining the early steps regulating adipocyte development is needed for the generation of tools to control adipose tissue size and function. Here, we report the discovery of V-set and transmembrane domain containing 2A (VSTM2A) as a protein expressed and secreted by committed preadipocytes. VSTM2A expression is elevated in the early phases of adipogenesis in vitro and adipose tissue development in vivo. We show that VSTM2A-producing cells associate with the vasculature and express the common surface markers of adipocyte progenitors. Overexpression of VSTM2A induces adipogenesis, whereas its depletion impairs this process. VSTM2A controls preadipocyte determination at least in part by modulating BMP signaling and PPARγ2 activation. We propose a model in which VSTM2A is produced to preserve and amplify the adipogenic capability of adipose precursors.
Collapse
Affiliation(s)
- Blandine Secco
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Étienne Camiré
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Marc-Antoine Brière
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Alexandre Caron
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Armande Billong
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Yves Gélinas
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Anne-Marie Lemay
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Kevin M Tharp
- Program for Metabolic Biology, Department of Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Peter L Lee
- University of Massachusetts Medical School, Program in Molecular Medicine, Worcester, MA 01605, USA
| | - Stéphane Gobeil
- Centre hospitalier universitaire de Québec (CHU de Québec), Université Laval, Faculté de médecine, 2705 Boulevard Laurier, QC G1V 4G2, Canada
| | - Jean V Guimond
- CIUSSS du Centre-Sud-de-l'ile-de-Montréal, CLSC des Faubourgs, 66 rue Sainte-Catherine Est, Montréal, QC H2X 1K6, Canada
| | - Natacha Patey
- Centre Hospitalier Universitaire de Sainte-Justine (CHU de Sainte-Justine), Faculté de Médecine, Département de pathologie et biologie cellulaire, Université de Montréal, 3175 Chemin Côte Ste-Catherine, Montréal, QC H3T 1C5, Canada
| | - David A Guertin
- University of Massachusetts Medical School, Program in Molecular Medicine, Worcester, MA 01605, USA
| | - Andreas Stahl
- Program for Metabolic Biology, Department of Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Élie Haddad
- Centre Hospitalier Universitaire de Sainte-Justine (CHU de Sainte-Justine), Faculté de Médecine, Département de pédiatrie et Département de microbiologie, infectiologie et immunologie, Université de Montréal, 3175 Chemin Côte Ste-Catherine, Montréal, QC H3T 1C5, Canada
| | - David Marsolais
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Yohan Bossé
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada
| | - Kivanc Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
| | - Mathieu Laplante
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec (CRIUCPQ), Université Laval, Faculté de médecine, 2725 Chemin Ste-Foy, QC G1V 4G5, Canada.
| |
Collapse
|
50
|
Ponting CP. Big knowledge from big data in functional genomics. Emerg Top Life Sci 2017; 1:245-248. [PMID: 33525805 PMCID: PMC7288990 DOI: 10.1042/etls20170129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 09/12/2017] [Accepted: 09/12/2017] [Indexed: 02/07/2023]
Abstract
With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.
Collapse
Affiliation(s)
- Chris P Ponting
- MRC Human Genetics Unit, The Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, U.K
| |
Collapse
|