Identifying ecological differentiation in microbial communities across taxonomic scales

Microbial diversity, both genetic and phenotypic, is a product of evolutionary and ecological processes interacting at multiple levels of biological organization. High-throughput sequencing is providing a means to resolve the vast amount of genetic diversity in microbial assemblages. However, a current fundamental challenge is associating genetic diversity, whether at the community or population scale, to phenotypic variation that defines ecological roles and dictates how microbial community diversity and ecosystem functioning respond to environmental change. In this body of work, I have used sequencing-based techniques to identify ecological differentiation at taxonomic scales ranging from a whole bacterial community to a single population of methanogenic archaea. At the broadest taxonomic scale, I used a whole-ecosystem disturbance along with 16S rRNA 454-pyrosequencing to identify ecologically relevant distinctions among taxonomic groups defined at various taxonomic scales. The findings showed that bacterial lineages require different taxonomic definitions to capture ecological patterns. At a more refined taxonomic breadth, I used a culture-independent approach to elucidate how methanogen community diversity was distributed within and between a set of freshwater lakes and also identified the ecological processes dictating this spatial distribution of diversity. Finally, at the highest resolution, I employed a comparative genomics approach to identify genomic signals of adaptive evolution in a Methanosarcina mazei population in order to link genetic variation to the ecological processes that define spatial and temporal distributions of methanogen populations. Together, this work has helped to resolve the interdependencies between genetic diversity and ecological processes, which concomitantly act to create and maintain microbial diversity across time and space

SIPSim: A Modeling Toolkit to Predict Accuracy and Aid Design of DNA-SIP Experiments

DNA Stable isotope probing (DNA-SIP) is a powerful method that links identity to function within microbial communities. The combination of DNA-SIP with multiplexed high throughput DNA sequencing enables simultaneous mapping of in situ assimilation dynamics for thousands of microbial taxonomic units. Hence, high throughput sequencing enabled SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. There are several different methods for analyzing DNA-SIP data and despite the power of SIP experiments, it remains difficult to comprehensively evaluate method accuracy across a wide range of experimental parameters. We have developed a toolset (SIPSim) that simulates DNA-SIP data, and we use this toolset to systematically evaluate different methods for analyzing DNA-SIP data. Specifically, we employ SIPSim to evaluate the effects that key experimental parameters (e.g., level of isotopic enrichment, number of labeled taxa, relative abundance of labeled taxa, community richness, community evenness, and beta-diversity) have on the specificity, sensitivity, and balanced accuracy (defined as the product of specificity and sensitivity) of DNA-SIP analyses. Furthermore, SIPSim can predict analytical accuracy and power as a function of experimental design and community characteristics, and thus should be of great use in the design and interpretation of DNA-SIP experiments