Complete genome sequence and genome-scale metabolic modelling of Acinetobacter baumannii type strain ATCC 19606

Zhu, Yan; Lü, Jing; Zhao, Jinxin; Xinru, Zhang; Yu, Heidi H.; Velkov, Tony; Li, Jian

doi:10.1016/j.ijmm.2020.151412

Cited by 19 publications

(31 citation statements)

References 70 publications

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“…To the best of our knowledge, the only GSMMs previously developed for A. baumannii are for strains ATCC 19606 and AYE [ 14 , 16 , 39 , 40 ]. However, ATCC 19606 and AYE were isolated in the 1950s and 2003, respectively, and have significant differences in their genomic content and virulence phenotypes compared to more recent clinical isolates [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…In silico single-gene deletion was conducted using both FBA and minimization of metabolic adjustment (MOMA) algorithms [ 14 ]. FBA predicts growth and metabolic fluxes based on the assumption that growth efficiency has evolved to an optimal point using linear programming.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast to FBA, MOMA does not assume optimality of growth. Instead, MOMA relaxes the assumption of optimal growth flux for gene deletions by performing distance minimization in flux space using quadratic programming [ 14 ]. Nutrient uptake constraints were set according to ingredients of chemically defined M9, MH, and Luria-Bertani (LB) media.…”

Section: Methodsmentioning

confidence: 99%

“…The total abundance of metabolic enzyme ( P ) was set to 0.25 g⋅gDW −1 according to previous Escherichia coli data due to the limited quantitative proteomics data in A. baumannii [ 14 ]. The molecular weight ( MW k ) of k th protein was calculated based on its amino acid sequence:

where reaction flux

is limited by the enzyme turnover rate k cat, k and enzyme molar abundance

as previously described [ 14 , 29 ]. Due to the lack of enzyme kinetic data in A. baumannii , an averaged k cat (65 s −1 ) in E. coli was used unless specific k cat values were available in BRENDA database (e.g., 2740 s −1 for xanthine dehydrogenase catalyzing reaction R_HXAND [ 30 ], 1 s −1 for methionyl aminopeptidase catalyzing reaction R_AMPTALAGLN [ 31 ], 11.14 s −1 for D-Alanine- d -alanine ligase catalyzing reaction R_ALAALAr [ 32 ], 34 s −1 for NAD + synthase catalyzing reaction R_NADS1 [ 33 ]).…”

Section: Methodsmentioning

confidence: 99%

“…Genome-scale metabolic modeling (GSMM) is increasingly used to decipher the metabolic changes in pathogens under infection or antibiotic treatment conditions [ 14 , 15 , 16 ]. Integration with multi-omics data enabled GSMMs to accurately describe cellular metabolism [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

et al. 2020

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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.

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