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Conwill, A.

Publications and source records attributed to Conwill, A..

2 recordsLinked to original sources

Rapid, inexpensive measurement of synthetic bacterial community composition by Sanger sequencing

Simple synthetic bacterial communities are powerful tools for studying microbial ecology and evolution, as they enable rapid iteration between controlled laboratory experiments and theoretical modeling. However, their utility is hampered by the lack of fast, inexpensive, and accurate methods for quantifying bacterial community composition. For instance, while next-generation amplicon sequencing can be very accurate, high costs (>$30 per sample) and turnaround times (>1 month) limit the nature and pace of experiments. Here, we introduce a new approach for quantifying composition in synthetic bacterial communities based on Sanger sequencing. First, for a given community, we PCR-amplify a universal marker gene (here, the 16S rRNA gene), which yields a mixture of amplicons. Second, we sequence this amplicon mixture in a single Sanger sequencing reaction, which produces a \"mixed\" electropherogram with contributions from each community member. We also sequence each community members marker gene individually to generate \"individual\" electropherograms. Third, we fit the mixed electropherogram as a linear combination of time-warped individual electropherograms, thereby allowing us to estimate the fractional amplicon abundance of each strain within the community. Importantly, our approach accounts for retention-time variability in electrophoretic signals, which is crucial for accurate compositional estimates. Using synthetic communities of marine bacterial isolates, we show that this approach yields accurate and reproducible abundance estimates for two-, four-, and seven-strain bacterial communities. Furthermore, this approach can provide results within one day and costs ~$5 USD per sample. We envision this approach will enable new insights in microbial ecology by increasing the number of samples that can be analyzed and enabling faster iteration between experiments and theory. We have implemented our method in a free and open-source R package called CASEU (\"Compositional Analysis by Sanger Electropherogram Unmixing\"), available at https://bitbucket.org/DattaManoshi/caseu.

bioinformatics

Migration alters oscillatory dynamics and promotes survival in connected bacterial populations

Migration influences population dynamics on networks, thereby playing a vital role in scenarios ranging from species extinction to epidemic propagation. While low migration rates prevent local populations from becoming extinct, high migration rates enhance the risk of global extinction by synchronizing the dynamics of connected populations. Here, we investigate this trade-off using two mutualistic strains of E. coli that exhibit population oscillations when co-cultured. In experiments, as well as in simulations using a mechanistic model, we observe that high migration rates lead to in-phase synchronization whereas intermediate migration rates perturb the oscillations and change their period. Further, our simulations predict, and experiments show, that connected populations subjected to more challenging antibiotic concentrations have the highest probability of survival at intermediate migration rates. Finally, we identify altered population dynamics, rather than recolonization, as the primary cause of extended survival.

ecology