Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota

Abstract

The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

“…Interestingly, the three highest growth rates belonged to the rare pathogens Escherichia , Burkholderia and Achromobacter , while the next three highest growth rates belonged to the common pathogens Pseudomonas , Streptococcus and Staphylococcus (Figure 3A; species numbered as in Table 1). These predictions were consistent with our modeling results for the gut microbiome (39) where opportunistic pathogens consistently had higher growth rates than commensal species. The other two species Prevotella and Haemophilus commonly observed in the 75 patient samples were predicted to have much lower in silico growth rates.…”

Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism

“…For example, by simulating western and high‐fiber diets to yield the maximal growth of 28 representative species from the gut microbiota, FBA suggested low species diversity and metabolic imbalances in SCFA production. On the other hand, FVA on the 28‐species and 20‐species community under suboptimal growth suggested higher species diversity and more balanced SCFA production, as well as a lower rate of metabolic exchanges between species …”

“…On the other hand, FVA on the 28-species and 20-species community under suboptimal growth suggested higher species diversity and more balanced SCFA production, as well as a lower rate of metabolic exchanges between species. [77,82]…”

Systematic interrogation of the distinct metabolic potential in gut microbiomes of inflammatory bowel disease patients with dysbiosis

“…Ribose has been found to be increased in serum of dogs with IBD (Minamoto et al, 2015). Hydrogen sulfide has been proposed to both worsen (Ijssennagger et al, 2016) and protect against (Wallace et al, 2018) gastrointestinal inflammation. On the other hand, a reduced secretion potential was predicted for 58 metabolites, among them being butyrate, nicotinamide, nicotinic acid, reduced riboflavin, and degradation products of mucins and other glycans (Figure 2a-f, Table S4b).…”

“…AGORA enables the creation of personalized microbiome models from metagenomics data (Baldini et al, 2018) and has valuable applications in studying microbe-microbe and host-microbe interactions . AGORA has been successfully applied to investigating host-microbe interactions in colorectal cancer (Hale et al, 2018), predicting microbial cross-feeding at suboptimal growth (Henson and Phalak, 2018), modelling the lung microbiome in Cystic Fibrosis patients (Henson et al, 2019), predicting dietary supplements that promote short-chain fatty acid production in Crohn's Disease patients (Bauer and Thiele, 2018), and analyzing microbial network patterns in relapsing Crohn's Disease (Yilmaz et al, 2019).…”