Three-species gut community
The human gut microbiome is composed of complex communities of microorganisms which are influenced by several factors, including age, genetics, environment, lifestyle, and diet. While diet represents an important ecological driver of gut microbiota composition, the mechanisms through which dietary perturbations influence community dynamics remain incompletely understood. Here, we take an ecological approach to gut microbial community analysis and investigate how diet-dependent changes affect microbial dynamics through growth and interactions. Using data from a defined artificial consortium subjected to controlled dietary perturbations, we were able to predict and recover the temporal dynamics of the microbiota under a regime of diet switching. In the treatment starting with a low-fat, high–plant polysaccharide diet (LF0), bacterial populations reach stabilization during the abiotic growth phase, with dynamics initially governed by intrinsic growth rates and subsequently by competitive interactions and self-regulation. Under this regime, community dynamics are primarily structured by density-dependent regulation and predominantly negative interactions among taxa. In contrast, in the treatment starting with a high-fat, high-sugar diet (HF0), populations do not reach stability during the same phase, reflecting comparatively lower growth rates likely associated with the dietary context and host-mediated effects. Under HF0, the interaction network is sparser, and species dynamics depend more strongly on intrinsic growth properties and metabolic flexibility than on strong interspecies coupling. Keystoneness analyses further reveal that under LF0, species abundances depend strongly on their interaction partners, whereas under HF0 they are largely determined by intrinsic growth. Finally, parameters inferred from a generalized Lotka–Volterra model fitted to a defined 12-species gut consortium retained predictive power when applied to a reduced three-species consortium, capturing interaction directions but not their magnitudes. Altogether, this study highlights the role of ecological modeling in resolving the relative contributions of microbial interactions and intrinsic growth to diet-induced community dynamics.
The human gut microbiome is composed of complex communities of microorganisms which are influenced by several factors, including age, genetics, environment, lifestyle, and diet. While diet represents an important ecological driver of gut microbiota composition, the mechanisms through which dietary perturbations influence community dynamics remain incompletely understood. Here, we take an ecological approach to gut microbial community analysis and investigate how diet-dependent changes affect microbial dynamics through growth and interactions. Using data from a defined artificial consortium subjected to controlled dietary perturbations, we were able to predict and recover the temporal dynamics of the microbiota under a regime of diet switching. In the treatment starting with a low-fat, high–plant polysaccharide diet (LF0), bacterial populations reach stabilization during the abiotic growth phase, with dynamics initially governed by intrinsic growth rates and subsequently by competitive interactions and self-regulation. Under this regime, community dynamics are primarily structured by density-dependent regulation and predominantly negative interactions among taxa. In contrast, in the treatment starting with a high-fat, high-sugar diet (HF0), populations do not reach stability during the same phase, reflecting comparatively lower growth rates likely associated with the dietary context and host-mediated effects. Under HF0, the interaction network is sparser, and species dynamics depend more strongly on intrinsic growth properties and metabolic flexibility than on strong interspecies coupling. Keystoneness analyses further reveal that under LF0, species abundances depend strongly on their interaction partners, whereas under HF0 they are largely determined by intrinsic growth. Finally, parameters inferred from a generalized Lotka–Volterra model fitted to a defined 12-species gut consortium retained predictive power when applied to a reduced three-species consortium, capturing interaction directions but not their magnitudes. Altogether, this study highlights the role of ecological modeling in resolving the relative contributions of microbial interactions and intrinsic growth to diet-induced community dynamics.
biologiamolecularteoricaevolutiva