(I)We use thousands of genome-scale bacterial metabolic reconstructions to investigate the interaction between metabolic network structure, function, and bacterial lifestyles. We demonstrate that bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We also find that bacterial metabolic networks with a certain number of reactions display a sharp percolation transition that coincides with marked differences in the fraction of obligate bacterial symbionts and the probability of metabolic cross-feeding among pairs of bacteria. (II) Bacterial species usually function in complex and dynamic communities. Based on multiple high-resolution time series data obtained from humans and mice we demonstrate that despite their inherent complexity, microbiota dynamics can be characterized by several robust scaling relationships. These patterns are highly similar to those previously observed across diverse ecological communities and economic systems. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. Our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamical processes across microbiomes.
Faculty Host: Robin Lee (University of Pittsburgh)