Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
Cell Syst. 2019 Jan 23;8(1):3-14.e3. doi: 10.1016/j.cels.2018.12.002. Epub 2019 Jan 2.
Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.
细菌对抗生素耐药性发展所伴随的代谢适应仍知之甚少。为了研究这种关系，我们对实验室进化出的机会性病原体铜绿假单胞菌的抗生素耐药谱系在 190 种独特碳源上的生长进行了分析。我们的数据显示，抗生素耐药性的进化导致了生长动态和代谢表型的系统水平变化。对铜绿假单胞菌的基因组规模代谢网络重建与全基因组测序数据相结合，以预测导致代谢变化的基因。我们通过实验验证了计算预测，以确定耐药铜绿假单胞菌中影响分解代谢功能丧失的突变。最后，我们发现实验室进化的铜绿假单胞菌和具有相似突变景观的临床分离株之间存在共同的代谢表型。我们的研究结果建立在抗生素诱导的代谢适应的先前知识基础上，并为鉴定抗生素耐药病原体中的代谢限制提供了框架。
