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Kiciatovas, D.

Publications and source records attributed to Kiciatovas, D..

2 recordsLinked to original sources

Universal bacterial clade dynamics dominate under predation despite altered phenotypes and mutation targets

Recent studies have revealed bacterial genome-wide evolution to be complex and dynamic even in a constant environment, characterized by the emergence of new clades competing or temporarily coexisting as each clade undergoes evolutionary change. Previous studies on predator-prey dynamics tracking simple ecological and phenotypic metrics have shown predation to fundamentally alter prey evolution, facilitating defense evolution followed by coevolution and frequency dependent selection between defended and undefended prey genotypes. Here we sought to consolidate these fields by examining genome-wide evolution in five bacterial prey species separately subjected to long-term evolution under ciliate predation. We hypothesized that the presence of predation could change the pattern of clonal dynamics, for example, by more frequently producing selective sweeps if predation-defense-related mutations are under strong selection. For all species, we found mutational signals of prey adaptation, with phenotypic data and genomic mutation targets demonstrating changes in composition between the experimental treatments. Intriguingly, despite higher variant counts, overall temporal clade dynamics across the coevolved prey species were strikingly similar to those of bacteria evolving alone, with constant emergence, competition and quasi-stable coexistence of clades. This study shows that long-term molecular evolution in bacterial prey under predation is more interesting and less predictable than we might expect based on existing coevolutionary theories.

evolutionary biology↗

Quantifying massively parallel microbial growth with spatially mediated interactions

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterized at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to an interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments, i.e. interpopulation interactions are mediated via the shared environment. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses. Author summaryImage-based platforms allow obtaining population size estimates for massively parallel growth experiments on substrate plates at relatively low cost. However, such population size data has been shown to display a high degree of spatial variability, which occurs even with isogenic populations. Here we first quantified the importance of spatial location on growth variation using a machine learning approach, and then developed spatially aware population growth models to explain the spatial structure of the growth data. Ultimately, we showed that a spatial consumer-resource model with local microhabitats connected via diffusion can fully explain the observed spatial variation in growth while allowing the inference of intrinsic growth parameters of specific populations. This result provides a method for systematic extraction of spatial growth models and paves the way for massively parallel eco-evolutionary experimentation.

ecology↗