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.

Keywords: cystic fibrosis; drug discovery; gut microbiome; metabolic modeling; metabolite cross-feeding.

FIGURE 1

Metabolic modeling to understand health and disease. Microbial interactions observed in the human gut and in the context of chronic lung disease such as in pwCF can be predicted through metabolic modeling to pinpoint metabolic cross-feeding interactions (denoted by letters) driving community structure and associated with healthy or diseased states. These predictions are facilitated by the capacity of this in silico approach to integrate in vivo-like nutritional and physico-chemical parameters, and ultimately help guide experimentation. Figure designed using BioRender

FIGURE 2

Iterative workflow between clinical observations, metabolic modeling and experimental work. (i) Several recalcitrant biofilm-based chronic infectious diseases are polymicrobial in nature and next-generation tools have allowed us to (ii) identify “who is there”. (iii) Metabolic modeling can leverage sequencing information by reconstructing metabolic networks and predicting key metabolites responsible of driving community structure and function. (iv) Metabolic predictions can then be experimentally tested and the model fine-tuned with the integration of this new experimental data. (v) Through modeling predictions, novel drugs can then be designed or repurposed to negatively impact polymicrobial communities. Figure designed using BioRender.