Steady-state reduction of generalized Lotka-Volterra systems in the microbiome

Interactions-based classification of a single microbial sample

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

Fast growth can counteract antibiotic susceptibility in shaping microbial community resilience to antibiotics

SignificanceAntibiotic exposure stands among the most used interventions to drive microbial communities away from undesired states. How the ecology of microbial communities shapes their recovery-e.g., posttreatment shifts toward Clostridioides difficile infections in the gut-after antibiotic exposure is poorly understood. We study community response to antibiotics using a model community that can reach two alternative states. Guided by theory, our experiments show that microbial growth following antibiotic exposure can counteract antibiotic susceptibility in driving transitions between alternative community states. This makes it possible to reverse the outcome of antibiotic exposure through modifying growth dynamics, including cooperative growth, of community members. Our research highlights the relevance of simple ecological models to better understand the long-term effects of antibiotic treatment.

Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiome

The generalized Lotka-Volterra (gLV) equations are a mathematical proxy for ecological dynamics. We focus on a gLV model of the gut microbiome, in which the evolution of the gut microbial state is determined in part by pairwise interspecies interaction parameters that encode environmentally mediated resource competition between microbes. We develop an in silico method that controls the steady-state outcome of the system by adjusting these interaction parameters. This approach is confined to a bistable region of the gLV model. In this method, a dimensionality reduction technique called steady-state reduction (SSR) is first used to generate a two-dimensional (2D) gLV model that approximates the high-dimensional dynamics on the 2D subspace spanned by the two steady states. Then a bifurcation analysis of the 2D model analytically determines parameter modifications that drive an initial condition to a target steady state. This parameter modification of the reduced 2D model guides parameter modifications of the original high-dimensional model, resulting in a change of steady-state outcome in the high-dimensional model. This control method, called SSR-guided parameter change (SPARC), bypasses the computational challenge of directly determining parameter modifications in the original high-dimensional system. SPARC could guide the development of indirect bacteriotherapies, which seek to change microbial compositions by deliberately modifying gut environmental variables such as gut acidity or macronutrient availability.