Prediction of antibiotic resistance by gene expression profiles

• Published in: Nature communications Vol. 5; no. 1; p. 5792
• Main Authors: Suzuki, Shingo, Horinouchi, Takaaki, Furusawa, Chikara
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.12.2014; Nature Publishing Group; Nature Pub. Group
• Subjects: 631/326/22/1434; 631/326/41/2529; Anti-Bacterial Agents - classification; Anti-Bacterial Agents - pharmacology; Drug Resistance, Multiple, Bacterial - genetics; Escherichia coli - drug effects; Escherichia coli - genetics; Escherichia coli - growth & development; Escherichia coli - metabolism; Gene Expression Profiling; Gene Expression Regulation, Bacterial; Genes, Bacterial; Genotype; Humanities and Social Sciences; Microbial Sensitivity Tests; Models, Statistical; multidisciplinary; Mutation; Phenotype; Science; Science (multidisciplinary); Transcriptome
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms6792

Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances. The relationship between mutations and phenotypic changes associated with drug resistance in bacteria remains unclear. Here, the authors use antibiotic-resistant E. coli strains, obtained by laboratory evolution, to show that resistance profiles can be predicted by changes in expression of a few genes.