Population and community genomics-based insights into eco-evolutionary microbial dynamics

Background/Question/Methods

Microbial populations can respond to change in three ways: by abundance shifts (becoming more or less competitive), by gene expression adjustments, and by selection of genetic variants. Few studies have addressed such responses in situ, particularly how evolutionary responses may contribute. We have developed methods to distinguish, directly in the environment, small difference in the genetic makeup and protein expression of microbial populations. This so-called microdiversity is commonly observed and most microbiologists tend to group these populations together in ecological analyses based on poorly founded assumptions that taxonomic relatedness serves as a proxy for functional relatedness. We rely on high-throughput sequencing to generate sufficient data to reconstruct the genomes of these populations from highly complex microbial community DNA extracted from environmental samples. We also use these highly genetically resolved genomic data to identify, using mass spectrometry, proteins extracted from the same environmental samples that differ by as a little as a single amino acid. When making spatiotemporal comparisons between microbial communities in the context of varying environmental constraints, our approach allows us to track not only major shifts in species composition, but also shifts in abundance of and protein expression by genetic variants of a particular species. This approach allows us to go beyond current a priori determinations of what constitutes a functionally relevant level of genetic variation.

Results/Conclusions

We demonstrated how this approach, in a natural system, shows that evolutionary differences, previously considered small enough to allow functional grouping of organisms, translate in changes in environmental distribution and ecological role. Our data also indicate the in situ rates at which microbial populations evolve are high enough to be relevant at ecological timescales. The main challenge remaining is to identify how these eco-evolutionary dynamics affect overall ecosystem functioning.