Author:
Malekian Negin,Agrawal Amay A.,Berendonk Thomas U.,Al-Fatlawi Ali,Schroeder Michael
Abstract
AbstractAntibiotic resistance is a global health threat and consequently, there is a need to understand the mechanisms driving its emergence. Here, we hypothesize that genes and mutations under positive selection may contribute to antibiotic resistance. We explored wastewater E. coli, whose genomes are highly diverse. We subjected 92 genomes to a statistical analysis for positively selected genes. We obtained 75 genes under positive selection and explored their potential for antibiotic resistance. We found that eight genes have functions relating to antibiotic resistance, such as biofilm formation, membrane permeability, and bacterial persistence. Finally, we correlated the presence/absence of non-synonymous mutations in positively selected sites of the genes with a function in resistance against 20 most prescribed antibiotics. We identified mutations associated with antibiotic resistance in two genes: the porin ompC and the bacterial persistence gene hipA. These mutations are located at the surface of the proteins and may hence have a direct effect on structure and function. For hipA, we hypothesize that the mutations influence its interaction with hipB and that they enhance the capacity for dormancy as a strategy to evade antibiotics. Overall, genomic data and positive selection analyses uncover novel insights into mechanisms driving antibiotic resistance.
Funder
Bundesministerium für Bildung und Forschung
Technische Universität Dresden
Publisher
Springer Science and Business Media LLC
Reference43 articles.
1. Fleming, A. Penicillin. Nobel Lecture, December 11, 1945. Nobel Prize.org [online]. https://www.nobelprize.org/uploads/2018/06/fleming-lecture.pdf.
2. Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14(4), 243–248 (2013).
3. Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24(8), 1586–1591 (2007).
4. Nielsen, R. & Yang, Z. Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148(3), 929–936 (1998).
5. Kim, K. H. et al. Genome-wide association and epistatic interactions of flowering time in soybean cultivar. PloS One 15(1), e0228114 (2020).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献