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Liu W, Cen H, Wu Z, Zhou H, Chen S, Yang X, Zhao G, Zhang G. Mycobacteriaceae Phenome Atlas (MPA): A Standardized Atlas for the Mycobacteriaceae Phenome Based on Heterogeneous Sources. Phenomics 2023; 3:439-456. [PMID: 37881319 PMCID: PMC10593683 DOI: 10.1007/s43657-023-00101-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/23/2023] [Accepted: 03/03/2023] [Indexed: 10/27/2023]
Abstract
The bacterial family Mycobacteriaceae includes pathogenic and nonpathogenic bacteria, and systematic research on their genome and phenome can give comprehensive perspectives for exploring their disease mechanism. In this study, the phenotypes of Mycobacteriaceae were inferred from available phenomic data, and 82 microbial phenotypic traits were recruited as data elements of the microbial phenome. This Mycobacteriaceae phenome contains five categories and 20 subcategories of polyphasic phenotypes, and three categories and eight subcategories of functional phenotypes, all of which are complementary to the existing data standards of microbial phenotypes. The phenomic data of Mycobacteriaceae strains were compiled by literature mining, third-party database integration, and bioinformatics annotation. The phenotypes were searchable and comparable from the website of the Mycobacteriaceae Phenome Atlas (MPA, https://www.biosino.org/mpa/). A topological data analysis of MPA revealed the co-evolution between Mycobacterium tuberculosis and virulence factors, and uncovered potential pathogenicity-associated phenotypes. Two hundred and sixty potential pathogen-enriched pathways were found by Fisher's exact test. The application of MPA may provide novel insights into the pathogenicity mechanism and antimicrobial targets of Mycobacteriaceae. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00101-5.
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Affiliation(s)
- Wan Liu
- National Genomics Data Center & Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Hui Cen
- National Genomics Data Center & Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Zhile Wu
- National Genomics Data Center & Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
- Shanghai Southgene Technology Co., Ltd., Shanghai, 201210 China
| | - Haokui Zhou
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Shuo Chen
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Xilan Yang
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Guoping Zhao
- National Genomics Data Center & Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024 China
| | - Guoqing Zhang
- National Genomics Data Center & Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
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