Integrated Metabolic Modeling, Culturing, and Transcriptomics Explain Enhanced Virulence of Vibrio cholerae during Coinfection with Enterotoxigenic Escherichia coli.
AuthorsAbdel-Haleem, Alyaa M.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Online Publication Date2020-09-08
Print Publication Date2020-10-27
Permanent link to this recordhttp://hdl.handle.net/10754/665078
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AbstractGene essentiality is altered during polymicrobial infections. Nevertheless, most studies rely on single-species infections to assess pathogen gene essentiality. Here, we use genome-scale metabolic models (GEMs) to explore the effect of coinfection of the diarrheagenic pathogen Vibrio cholerae with another enteric pathogen, enterotoxigenic Escherichia coli (ETEC). Model predictions showed that V. cholerae metabolic capabilities were increased due to ample cross-feeding opportunities enabled by ETEC. This is in line with increased severity of cholera symptoms known to occur in patients with dual infections by the two pathogens. In vitro coculture systems confirmed that V. cholerae growth is enhanced in cocultures relative to single cultures. Further, expression levels of several V. cholerae metabolic genes were significantly perturbed as shown by dual RNA sequencing (RNAseq) analysis of its cocultures with different ETEC strains. A decrease in ETEC growth was also observed, probably mediated by nonmetabolic factors. Single gene essentiality analysis predicted conditionally independent genes that are essential for the pathogen's growth in both single-infection and coinfection scenarios. Our results reveal growth differences that are of relevance to drug targeting and efficiency in polymicrobial infections.IMPORTANCE Most studies proposing new strategies to manage and treat infections have been largely focused on identifying druggable targets that can inhibit a pathogen's growth when it is the single cause of infection. In vivo, however, infections can be caused by multiple species. This is important to take into account when attempting to develop or use current antibacterials since their efficacy can change significantly between single infections and coinfections. In this study, we used genome-scale metabolic models (GEMs) to interrogate the growth capabilities of Vibrio cholerae in single infections and coinfections with enterotoxigenic E. coli (ETEC), which cooccur in a large fraction of diarrheagenic patients. Coinfection model predictions showed that V. cholerae growth capabilities are enhanced in the presence of ETEC relative to V. cholerae single infection, through cross-fed metabolites made available to V. cholerae by ETEC. In vitro, cocultures of the two enteric pathogens further confirmed model predictions showing an increased growth of V. cholerae in coculture relative to V. cholerae single cultures while ETEC growth was suppressed. Dual RNAseq analysis of the cocultures also confirmed that the transcriptome of V. cholerae was distinct during coinfection compared to single-infection scenarios where processes related to metabolism were significantly perturbed. Further, in silico gene-knockout simulations uncovered discrepancies in gene essentiality for V. cholerae growth between single infections and coinfections. Integrative model-guided analysis thus identified druggable targets that would be critical for V. cholerae growth in both single infections and coinfections; thus, designing inhibitors against those targets would provide a broader spectrum of coverage against cholera infections.
CitationAbdel-Haleem, A. M., Ravikumar, V., Ji, B., Mineta, K., Gao, X., Nielsen, J., … Mijakovic, I. (2020). Integrated Metabolic Modeling, Culturing, and Transcriptomics Explain Enhanced Virulence of Vibrio cholerae during Coinfection with Enterotoxigenic Escherichia coli. mSystems, 5(5). doi:10.1128/msystems.00491-20
SponsorsWe are very grateful to insightful comments from Nathan Lewis and Neema Jamshidi. We thank Abdallah Abdallah and Mohammed Alarawi from the bioscience core lab at KAUST, Hajime Ohyanagi, and Yoshi Saito for helpful discussions. This research was supported by funding from KAUST, BAS/1/1624-01-01 and BAS/ 1/1059-01-01 and SEED funding FCS/1/2448-01-01 (A.M.A.-H., X.G., T.G., and K.M.) as well as by grants from the Novo Nordisk Foundation (I.M. and V.R.), the Swedish National Research Council (V.R.), and the Danish National Research Council (DFF) (to I.M.).A.M.A.-H. performed the modeling, simulations, and data analysis and wrote the paper. V.R. performed the experiments. B.J. provided support for the modeling and data analysis. J.N. provided support for the modeling. K.M. provided support for the DNA and RNA sequencing. X.G. contributed to data analysis. I.M. and T.G. conceived the project, oversaw the project, and wrote the paper. All authors read and approved the final manuscript. We declare no competing interests.
PublisherAmerican Society for Microbiology
CollectionsArticles; Biological and Environmental Science and Engineering (BESE) Division; Bioscience Program; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
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