Physiology mediates the response of microbial communities to temporally varying environments. Due to the inherent difficulties of investigating the physiology of natural communities, microbial ecologists have historically opted for sequence-based proxies, with ribosomal RNA transcripts being a prominent example still frequently used to make claims about the physiology and metabolic activity of microbiomes.

However, we currently lack an understanding of the quantitative benefits provided by sequencing ribosomal RNA relative to DNA, as well as their consistency with physiological predictions. In this talk, I will apply a macroecological approach to examine the extent that ribosomal RNA transcripts (i.e., rDNA) provide an advantage over ribosomal RNA genes (i.e., rDNA) when investigating the dynamics and composition of a freshwater microbial community.

Here, rRNA and rDNA follow highly concordant macroecological patterns across community members, a shared statistical structure that permits the development of a single minimal model of ecological dynamics capable of capturing both measureables. Using this model, I successfully predict macroecological properties of the commonly used ratio of rRNA relative to rDNA (rRNA:rDNA).

The capacity of rRNA:rDNA to capture physiological properties of natural communities was assessed by developing a mechanistic model of ribosomal activity consistent with our phenomenological model of ecology. Using this model, physiological interpretations were obtained for two community-wide signatures of physiology: a superlinear increase of rRNA:rDNA with 16S operon copy number across taxa and a systematic tendency for rRNA temporal fluctuations to precede those of rDNA fluctuations, a pattern consistent with ribosome buildup anticipating biomass accumulation.

Finally, I demonstrate that rRNA:rDNA is more sensitive than rDNA alone to shifts in environmental variables that index the oligotrophic-eutrophic axis of the community, linking physiological state directly to a well-established ecological gradient in aquatic systems.

Zoom meeting

Please use the following link for registration:

https://univienna.zoom.us/j/63980130049?pwd=NCe9u3TerlCISRVXdAOUgWJ585XatM.1

Meeting ID: 639 8013 0049

Passcode: 102856

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