Artificial selection for microbial collective composition can succeed or fail depending on the initial and target values
Year: 2024
Type: preprint
Abstract: Microbial collectives, capable of functions beyond the reach of individual populations, can be enhanced through artificial selection. However, this process presents unique challenges. Here, we explore the ‘waterfall’ phenomenon, a metaphor describing how the success in achieving a desired genotype or species composition in microbial collectives can depend on both the target characteristics and initial conditions. We focus on collectives comprising fast-growing (F) and slow-growing (S) types, aiming to achieve specific S frequencies. Through simulations and analytical calculations, we show that intermediate target S frequencies might be elusive, akin to maintaining a raft’s position within a waterfall, rather than above or below it. This challenge arises because intra-collective selection, favoring F during growth, is the strongest at intermediate S frequencies, which can overpower counteracting inter-collective selection effects. Achieving low target S frequencies is consistently possible as expected, but high target S frequencies require an initially high S frequency — similar to a raft that can descend but not ascend a waterfall. The range of attainable target frequencies is significantly influenced by the initial population size of the collectives, while the number of collectives under selection plays a less critical role. In scenarios involving more than two types, the evolutionary trajectory must navigate entirely away from the metaphorical ‘waterfall drop.’ Our findings illustrate that the strength of intra-collective evolution is frequency-dependent, with implications in experimental planning.
Source: eLife
Authors: Juhee Lee, Wenying Shou, Hye Jin Park
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,500
Awards: G6064810067, G633575054
OpenAlex ID: W4399140785