Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE

Abstract

Acinetobacter baumannii has emerged as a new clinical threat to human health, particularly to ill patients in the hospital environment. Current lack of effective clinical solutions to treat this pathogen urges us to carry out systems-level studies that could contribute to the development of an effective therapy. Here we report the development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses. First, a genome-scale metabolic network of A. baumannii AYE, a drug-resistant strain, was reconstructed based on its genome annotation data, and biochemical knowledge from literatures and databases. In order to evaluate the performance of the in silico model, constraints-based flux analysis was carried out with appropriate constraints. Simulations were performed from both reaction (gene)- and metabolite-centric perspectives, each of which identifies essential genes/reactions and metabolites critical to the cell growth. The gene/reaction essentiality enables validation of the model and its comparative study with other known organisms' models. The metabolite essentiality approach was undertaken to predict essential metabolites that are critical to the cell growth. The EMFilter, a framework that filters initially predicted essential metabolites to find the most effective ones as drug targets, was also developed. EMFilter considers metabolite types, number of total and consuming reaction linkage with essential metabolites, and presence of essential metabolites and their relevant enzymes in human metabolism. Final drug target candidates obtained by this system framework are presented along with implications of this approach.

Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes

“…The solute pool or cofactor pool is often added as substrate in one of those reactions (Kim et al, 2010). The macromolecular composition and detailed content of building blocks, together with energetic costs of growth and maintenance, can be sufficient to simulate growth of wild-type organisms (Liao et al, 2011).…”

Genome‐scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production

“…Use of the genome-scale metabolic models has enabled designing strategies for increasing the production of target compounds [1], such as nutritional supplements [2] and biofuels [3]. Metabolic models have also been utilized for identifying targets for new drugs against pathogenic microorganisms [4], and have been useful in dis-covering new information on the biological systems that they represent. Currently, most of the genomescale metabolic models describe bacterial systems, due to their widespread use in the field of biotechnology and their lower degree of cellular complexity compared to eukaryotic systems.…”

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

“…FBA investigations of pathogenic organisms have been used to search for novel drug targets and have pointed to potential metabolic targets not affected by current therapeutics, such as amino acid production or fatty acid metabolism [53][54][55][56][57][58][59][60]. Oberhardt and colleagues have constructed a genome-based model of metabolism and transport in P. aeruginosa [61], and used flux balance analysis (FBA) with transcript data from two CF clinical strains to investigate P. aeruginosa's metabolic capabilities and potential metabolic changes during prolonged infection [62].…”