Genome-scale metabolic modeling reveals metabolic alterations of multidrug-resistant acinetobacter Baumannii in a murine bloodstream infection model

Jinxin Zhao, Yan Zhu, Jiru Han, Yu Wei Lin, Michael Aichem, Jiping Wang, Ke Chen, Tony Velkov, Falk Schreiber, Jian Li

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

Original languageEnglish
Article number1793
Number of pages18
JournalMicroorganisms
Volume8
Issue number11
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Acinetobacter baumannii
  • Bacteremia
  • Genome-scale metabolic modeling
  • RNA-seq
  • Transcriptomics

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