Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

Abstract

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

Growing a cystic fibrosis-relevant polymicrobial biofilm to probe community phenotypes

“…that are known to be important in the context of CF lung disease. 20 However, we argue that this polymicrobial model can be used as a starting point to identify mechanisms that can be further vetted in more in vivo -like systems.…”

Development of a four species biofilm to mimic the upper gut microbial community

“…Other applications could be the con rmation of results obtained when using metabolic, in spatial distribution, and inter-intra-species competition modeling. Metabolic modeling has recently been described to be used to probe interactions hypothesized to drive gut health and microbial community structure to better understand microbial disease and microbial imbalance 37 . Spatial distribution, interspecies and intraspecies competition, resistance to host defense and antimicrobial agents, has been widely modeled before, for various communities 24,[38][39][40] .…”

Modeling Cystic Fibrosis Chronic Infection Using Engineered Mucus-like Hydrogels

“…Other airway model studies have also reported changes in P. aeruginosa behavior when grown on lung epithelial cells, such as altered gene expression or an increase in antibiotic tolerance 35,51 . A potential cause of increased P. aeruginosa growth in the healthy model could be due to the influence of epithelial cell metabolites 52,53 . While host-microbe metabolic interactions in CF airway infections have yet to be fully understood due to the complex and polymicrobial nature of these infections, there is evidence that the effectiveness of antibiotics can be modulated by host metabolites 54 such as lactate 55 .…”