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Genetic variation among orthologous genes has been largely formed through neutral genetic drift while maintaining the functional role of these genes. However, because the evolution of gene occurs in the context of each host organism, their sequence changes are also associated with adaptation to a specific environment. Thus, genetic variation can create critical phenotypic variation, particularly when genes are transferred to a new host by horizontal gene transfer. Unveiling "hidden phenotypic variation" is particularly important for genes that confer resistance to antibiotics. However, our understanding of the molecular mechanisms that underlie phenotypic variation remains limited. Here we sought to determine the extent of phenotypic variation in the B1 metallo-β-lactamase (MBL) family and its molecular basis by systematically characterizing eight MBL orthologs, including NDM-1 and VIM-2 and IMP-1. We found that these MBLs confer diverse levels of resistance. The phenotypic variation cannot be explained by variation in catalytic efficiency alone; rather, it is the combination of the catalytic efficiency and abundance of functional periplasmic enzyme that best predicts the observed variation in resistance. The level of functional periplasmic expression varied dramatically between MBL orthologs. This was the result of changes at multiple levels of each ortholog's: (1) quantity of mRNA, (2) amount of MBL expressed, and (3) efficacy of functional enzyme translocation to the periplasm. Overall, it is the interaction between each gene and the host's underlying cellular processes (transcription, translation, and translocation) that determines MBL genetic incompatibility through horizontal gene transfer. These host-specific processes may constrain the effective spread and deployment of MBLs to certain host species and could explain the current observed distribution bias.
Pubmed ID: 30769117
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsData analysis, graphing, and management application that allows users to import, manipulate, analyze data, and create customized plots. Plots include x-y probability, histogram, box, percentile, horizontal bar, stack bar, column, stack column, polar, and pie. Binned data can be exported to a histogram, step plot, or spike plot. KaleidaGraph works with Windows and Macintosh systems.
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