Reverse-engineering human gut microbiome synthetic ecologies

The human gut microbiome is a diverse and interconnected network that has a significant impact on health, nutrition and development. Developing the capability to design functional, stable, energy efficient and resilient multi-species communities to mediate human health is an obtainable grand challenge. Central to this problem is systematically mapping inter-species interactions that generate emergent community-level properties and the development of predictive computational models of ecosystem function and dynamics. We integrated experiment and computational modeling to decipher inter-species interactions among members of a human gut microbiome synthetic ecology by leveraging a bottom-up and top-down combinatorial community assembly approach. These communities demonstrated a myriad of population dynamic responses and patterns of co-existence. Three hub organisms were identified in the network that we hypothesize play a central role in shaping community function and dynamics. Metabolic profiling of single species as a function of time provided key insights into the mechanisms driving inter-species interactions. 50% of interactions could be explained by resource competition or metabolite cross-feeding among species based on known metabolites. We systematically characterized the response of a multi-species community to temporal perturbations based on the hypothesis that diverse species growth parameters and inter-species interactions could generate differential sensitivity to periodic environmental shifts. The abundance of specific species varied significantly as a function of transfer period, demonstrating that time-dependent inputs can be used to modulate community diversity and stability. In sum, these methods will be used to extract generalizable design principles for ecosystem engineering including stability, resilience to perturbations and resistance to invasion.