Biology subjects

Boddu, S. S.

Publications and source records attributed to Boddu, S. S..

3 recordsLinked to original sources

bioRxiv2024

Variance in C. elegans gut bacterial load suggests complex host-microbe dynamics

Variation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.

biophysics↗

bioRxiv2024

Correlations in microbial abundance data reveal host-bacteria and bacteria-bacteria interactions jointly shaping the C. elegans microbiome

Compositional structure of host-associated microbiomes is potentially affected by interactions among the microbes and between the microbes and the host. To quantify the relative importance of these contributions to the microbiome composition and variation, here we analyze absolute abundance (count) data for a minimal eight-species native microbiome in the Caenorhabditis elegans intestine. We find that a simple neutral model only considering migration, birth, death, and competition for space among the bacteria can capture the means and variances of bacterial abundance, but not the experimental bacteria-bacteria covariances. We find that either bacteria-bacteria interactions or correlations among bacterial population dynamics parameters induced by the host can qualitatively recapitulate the observed correlations among bacterial taxa. However, neither model is uniquely or completely sufficient to explain the data. Further, we observe that different interactions are required to explain (co)variance data in microbiota associated with different host genotypes, suggesting different community dynamics associated with these host types. Finally, we find that many of these signals are obscured when data are converted to proportions from counts, consistent with a growing literature on the limitations of compositional data for inference of population dynamics. We end with discussing the limitations of Lotka-Volterra type assumptions for microbial community data analysis revealed by our results.

biophysics↗

bioRxiv2023

Maximum Likelihood Estimators For Colony Forming Units

There is a recognized need to measure the abundance of microbes in hospital environments, in the sanitation industry, and in food preparation. Doctors, microbiologists, and food safety experts have been addressing this need by using serial dilution methods to grow bacterial colonies in small enough numbers to count and, from these counts, to infer bacterial concentrations measured in Colony Forming Units (CFUs). There are two primary types of such methods: plating bacteria on a growth medium and counting their resulting colonies or counting the number of tubes at a given dilution that have growth. Traditionally, these types of data have been analyzed separately using different analytic methods. Here we build a direct correspondence between these approaches, which allows one to extend the use of the Most Probable Number (MPN) method from the liquid tubes experiments, for which it was developed, to the growth plates. We also discuss how to combine measurements taken at different dilutions, and we review several ways of analyzing colony counts, including the Poison and truncated Poison methods. For all methods, we discuss their relevant error bounds, assumptions, strengths, and weaknesses. We provide an online calculator for these estimators.

biophysics↗