bioRxivMicrobiologyPreprint
Signatures of Microbial Diversity at Multiple Scales of Resolution within Engineered Enrichment Communities
E. A. McDaniel, F. Moya-Flores, D. Mendez, C. Wethersby, B. O. Oyserman, J. Flowers, S. He, F. Petriglieri, C. Singleton, P. H. Nielsen, K. McMahon
Abstract
Microbial community dynamics are dictated by both abiotic environmental conditions and biotic interactions. These communities consist of individual microorganisms across the continuum of phylogenetic diversity, ranging from coexisting members of different domains of life and phyla to multiple strains with only a handful of single nucleotide variants. Ecological forces act on a shifting template of population-level diversity that is shaped by evolutionary processes. However, understanding the ecological and evolutionary forces contributing to microbial community interactions and overall ecosystem function is difficult to interrogate for complex, naturally occurring microbial communities. Here, we use two time series of lab-scale engineered enrichment microbial communities simulating phosphorus removal to explore signatures of microbial diversity at multiple phylogenetic scales. We characterized microbial community dynamics and diversity over the course of reactor start-up and long-term dynamics including periods of eubiosis and dysbiosis as informed by the intended ecosystem function of phosphorus removal. We then compared these signatures to lineages from full-scale WWTPs performing phosphorus removal. We found that enriched lineages in lab-scale bioreactors harbor less intra-population diversity than lineages from the full-scale WWTP overall. Our work establishes a foundation for using engineered enrichment microbial communities as a semi-complex model system for addressing the fundamental ecological and evolutionary processes necessary for developing stable microbiome based biotechnologies.
- organism: bacteria (other)
- assay: metagenomics
- research focus: ecology & biodiversity
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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