COS 32-3 - A phylogenetic framework for trait innovation and selection in microbial communities

Tuesday, August 8, 2017: 8:40 AM
C125-126, Oregon Convention Center
Mario E. Muscarella and James P. O'Dwyer, Department of Plant Biology, University of Illinois, Urbana, IL

Patterns of diversity among microbial communities suggest that environmental conditions select for organisms with specific traits. However, the traits being selected are often unknown or unmeasured. A better understanding of traits and trait evolution, including quantitative signatures, would therefore increase our ability to understand the selective pressures within and between microbial communities. We hypothesized that microbial trait evolution will leave a specific pattern in microbial phylogenies, and that these patterns can be used as a proxy to understand selection and community assembly processes. To test our hypothesis, we combined simulation and exploratory approaches. First, we used Markov chain simulations to predict the phylogenetic patterns associated with different trait evolution models. Using the simulation results, we tested the ability to predict trait origins using ancestral state reconstruction and trait conservation approaches. Next, we used genomes to explore the phylogenetic patterns of traits across the microbial tree of life. Using a collection of microbial genomes, we generated a phylogenetic tree representing the shared evolutionary history and identified metabolic and physiological traits. Using these traits, we predicted trait origins using ancestral state reconstruction and trait conservation approaches. We then compared the phylogenetic patterns associated with the microbial traits and the simulations results.


First, while simulating trait evolutions, we found as expected that trait evolution parameters effected both trait origin and conservation in extant organisms: the rate of innovation best predicted the origin of the trait, while the rate of loss best predicted trait conservation. We found that we could accurately infer trait evolution parameters using ancestral state reconstructions, accounting for some systematic biases. Second, using microbial genomes, we predicted both trait origin and conservation in extant organisms. Based on ancestral state reconstructions we also quantified the rates of appearance and loss for a large suite of traits. For example, we found that innovation rates for motility traits were twice those for carbohydrate metabolism. In addition, we discovered distinct phylogenetic patterns associated with branch lengths adjacent to predicted nodes of trait origin. For example, we found that for some traits (e.g., sulfur metabolism) the branch lengths following trait origin were shorter than expected given the branches preceding. Using these phylogenetic patterns, we discuss the prospects for searching for similar patterns in microbial communities across environmental gradients and using the differences in the patterns to determine the types of traits which are being selected from in given habitats.