A genome scale model of Pseudomonas aeruginosa published recently in GigaScience will help scientists to fight multi-drug-resistant superbugs.
Pseudomonas aeruginosa is one of the world’s most dangerous pathogens, causing life-threatening infections. It is increasingly resistant to all antibiotics. The antibiotic polymyxin is a weapon of last resort against the superbug, but P. aeruginosa is increasingly gaining resistance against polymyxin as well.
In GigaScience, researchers from Monash University’s Biomedicine Discovery Institute (BDI) present a new computational tool to better understand how P. aeruginosa responds to antibiotic treatments (article here, supporting data in our repository GigaDB here and in Metabolights at MTBLS630). An interdisciplinary team, led by antibiotics expert Jian Li, developed a “genome-scale metabolic model”(GSMM) of P. aeruginosa. With the help of the model, scientists can now explore bacterial responses to antibiotics more easily and it provides a short-cut to time-consuming and costly experimental work.
This is not the first genome scale model of P. aeruginosa, but it is the most complete metabolic reconstruction for this nasty bacterium to date. “The model accounts for the largest number of reactions and metabolites in this ‘superbug’ so far and enables accurate predictions of bacterial metabolism”, first author Dr Zhu explains.
The GigaScience authors included 3022 metabolites and 4265 reactions in the model, 1458 genes are represented. One of the strengths of the new genome scale model, also mentioned during the review process, is that it includes the space between the bacterial inner and the outer membranes, the “periplasmic space” (as GigaScience are advocates of open peer review you can read the reviews yourself via Publons.)
The inclusion of the periplasmic space is important to understand how the antibiotic polymixin attacs the bacterium. Specifically, polymixin binds to lipopolysaccharids (LPS) in the outer membrane and modifies a component called “Lipid A” . The genome scale model includes a detailed representation of LPS synthesis.
However, the details of the mode of polymyxin action are still unclear. The new model will help to close these gaps in our knowledge, which is urgently needed to combat multi-drug-resistant infections.
For more work integrating metabolomics data see our “Metabolomics: approaches, applications, and integration” series:
Zhu, Y; Czauderna, T; Zhao, J; Klapperstueck, M; Maifiah, M, H; Han, M, L; Lu, J; Sommer, B; Velkov, T; Lithgow, T; Song, J; Schreiber, F; Li, J Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa. GigaScience 2018, doi:10.1093/gigascience/giy021; https://doi.org/10.1093/gigascience/giy021
Supporting data in our GigaDB repository:
Zhu, Y; Czauderna, T; Zhao, J; Klapperstueck, M; Maifiah, M, H; Han, M, L; Lu, J; Sommer, B; Velkov, T; Lithgow, T; Song, J; Schreiber, F; Li, J Supporting data for “Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa.” GigaScience Database 2018. http://dx.doi.org/10.5524/100414
See also the authors’ press release.