Back

Promoting diversity in ecological systems through toxin production

Lui, G. C.; Goyal, S.

2023-03-06 biophysics
10.1101/2023.03.06.531399 bioRxiv
Show abstract

Toxins are used by microbes to kill and compete, and hence, unlike cross-feeding from secondary metabolites, they are less often considered as a candidate to promote diversity in a microbial community. In contrast, some natural communities hint at the possibility that the presence of toxin genes and externally supplied antibiotics may support higher diversity. We address this gap with a model that shows how toxins in small and medium-sized communities can indeed promote diversity. The central idea of the model emerges from the combination of bacterial growth laws of resource partitioning and recent experiments suggesting a trade-off between toxin sensitivity and nutrient efficiency. The second puzzle in natural data is the presence of both negative and positive regulation of toxin production under similar environmental stress. Our model shows how this diversity in regulation of toxin production can promote toxin-producer fitness. In general, we provide a framework to integrate the role of toxins in microbial communities. Significance statementStructured organization of microbial communities into functional groups (consumers and producers) has been a framework to understand diverse populations. This framework leaves out the impact of toxin producers, which kill rather than provide the others with benefits, on diversification. Surprisingly, natural microbial communities show a correlation between the number of toxin genes in a community and its diversity. Our work shows how physiological constraints and trade-offs for individual bacteria can lead to a diverse, structured population where toxin-producers serve as a keystone species. This provides a pathway towards toxin production being established as a viable strategy in a diverse population: releasing toxins allows the survival of the less competitive strains, which would otherwise be driven towards extinction by competitive exclusion.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.