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Yang D, Zheng X, Jiang L, Ye M, He X, Jin Y, Wu R. Functional Mapping of Phenotypic Plasticity of Staphylococcus aureus Under Vancomycin Pressure. Front Microbiol 2021; 12:696730. [PMID: 34566908 PMCID: PMC8458881 DOI: 10.3389/fmicb.2021.696730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
Phenotypic plasticity is the exhibition of various phenotypic traits produced by a single genotype in response to environmental changes, enabling organisms to adapt to environmental changes by maintaining growth and reproduction. Despite its significance in evolutionary studies, we still know little about the genetic control of phenotypic plasticity. In this study, we designed and conducted a genome-wide association study (GWAS) to reveal genetic architecture of how Staphylococcus aureus strains respond to increasing concentrations of vancomycin (0, 2, 4, and 6 μg/mL) in a time course. We implemented functional mapping, a dynamic model for genetic mapping using longitudinal data, to map specific loci that mediate the growth trajectories of abundance of vancomycin-exposed S. aureus strains. 78 significant single nucleotide polymorphisms were identified following analysis of the whole growth and development process, and seven genes might play a pivotal role in governing phenotypic plasticity to the pressure of vancomycin. These seven genes, SAOUHSC_00020 (walR), SAOUHSC_00176, SAOUHSC_00544 (sdrC), SAOUHSC_02998, SAOUHSC_00025, SAOUHSC_00169, and SAOUHSC_02023, were found to help S. aureus regulate antibiotic pressure. Our dynamic gene mapping technique provides a tool for dissecting the phenotypic plasticity mechanisms of S. aureus under vancomycin pressure, emphasizing the feasibility and potential of functional mapping in the study of bacterial phenotypic plasticity.
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Affiliation(s)
- Dengcheng Yang
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Xuyang Zheng
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Libo Jiang
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Meixia Ye
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Xiaoqing He
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Yi Jin
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,Department of Public Health Sciences and Statistics, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA, United States
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