Artificial selection for microbial collective composition can succeed or fail depending on the initial and target values

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..

Medienart:

Preprint

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

bioRxiv.org - (2024) vom: 10. Jan. Zur Gesamtaufnahme - year:2024

Sprache:

Englisch

Beteiligte Personen:

Lee, Juhee [VerfasserIn]
Shou, Wenying [VerfasserIn]
Park, Hye Jin [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

570
Biology

doi:

10.1101/2023.03.07.531234

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XBI03889730X