Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Saa, Pedro A.; Urrutia, Arles; Silva-Andrade, Claudia; Martin, Alberto J. M.; Garrido, Daniel

doi:10.1016/j.csbj.2021.12.006

Cited by 24 publications

(11 citation statements)

References 157 publications

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“…The relation between metabolites and microbiome is often described through a consumption/production network [66,67,47,48,49]. Such networks are based on three assumptions.…”

Section: Resultsmentioning

confidence: 99%

Microbiome-metabolome interactions predict host phenotype

Shtossel

Koren

Shai

et al. 2022

Preprint

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The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolitc profiles from microbial taxa frequencies, assuming a direct relation between the gut microbiome composition and blood metabolite concentrations. However, the microbiome-metabolite relation may depend on host demographics or condition. We show that the relation between microbiome and metabolites is best predicted at the log concentration level. We further develop LOCATE (Latent Of miCrobiome And meTabolites rElations), a machine learning (ML) tool based on latent representation which predicts the log normalized metabolites composition based on the log normalized microbiome composition. LOCATE has a higher overall accuracy than all current state-of-the-art predictors in both 16S rRNA gene and shotgun gene sequencing. The accuracy of LOCATE and all other predictors significantly decreases when predicting on one dataset and testing on a different dataset, or on a different condition in the same dataset, especially in 16S rRNA gene sequence based data. We propose an intermediate representation between the microbiome and the metabolite concentrations and show that this representation can be used to predict the host phenotype better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics, including age, gender and diet and can be used to improve ML predictions of host phenotypes in comparison with either microbiome or metabolome using a large microbiome sample combined with a small number of metabolome samples (~ 50)

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“…The relation between metabolites and microbiome is often described through a consumption/production network [66,67,47,48,49]. Such networks are based on three assumptions.…”

Section: Resultsmentioning

confidence: 99%

Microbiome-metabolome interactions predict host phenotype

Shtossel

Koren

Shai

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Dynamic interactions between gut microbes are constantly taking place and involve the production of and the sensing of secondary metabolites. 91 Although the mechanisms by which some of these metabolites protect against CDI are still unclear, 81 , 87 , 88 , 90 their identification provides important data that can be directly tested as potential treatment options for CDI. Moreover, a microbiome-centric approach for CDI treatment will most likely find other metabolites that show protection against CDI and could be explored for therapeutic approaches.…”

Section: Non Bile Acid Metabolites That Correlate or Show Protection ...mentioning

confidence: 99%

Gut associated metabolites and their roles in Clostridioides difficile pathogenesis

Aguirre

Sorg

2022

Gut Microbes

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The nosocomial pathogen Clostridioides difficile is a burden to the healthcare system. Gut microbiome disruption, most commonly by broad-spectrum antibiotic treatment, is well established to generate a state that is susceptible to CDI. A variety of metabolites produced by the host and/or gut microbiota have been shown to interact with C. difficile . Certain bile acids promote/inhibit germination while other cholesterol-derived compounds and amino acids used in the Stickland metabolic pathway affect growth and CDI colonization. Short chain fatty acids maintain intestinal barrier integrity and a myriad of other metabolic compounds are used as nutritional sources or used by C. difficile to inhibit or outcompete other bacteria in the gut. As the move toward non-antibiotic CDI treatment takes place, a deeper understanding of interactions between C. difficile and the host’s gut microbiome and metabolites becomes more relevant.

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“…uptake or secretion of nutrients). A wide range of mathematical modelling approaches have been used to study metabolic interactions in complex microbial communities, for instance by postulating mechanistic population dynamic models that can be used to evaluate the eco-evolutionary dynamics, although they are often limited by modelling and parametric uncertainties [51]. Other studies have posed coarse-grained models that associate growth rate with how bacterial cells allocate resources towards protein synthesis and metabolic functions [41,[52][53][54][55].…”

Section: Modelling Bacterial Growth Under Resource Limitationmentioning

confidence: 99%

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

et al. 2022

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Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

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Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Cited by 24 publications

References 157 publications

Microbiome-metabolome interactions predict host phenotype

Microbiome-metabolome interactions predict host phenotype

Gut associated metabolites and their roles in Clostridioides difficile pathogenesis

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

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