Back

Mutations in filamentous bacteriophages spark eco-evolutionary feedbacks in Pseudomonas aeruginosa

Houpt, N. S.; Hernandez, C. A.; Turner, P. E.

2026-01-23 evolutionary biology
10.64898/2026.01.21.699487 bioRxiv
Show abstract

Microbial populations strongly shape their environment, which can re-route adaptation toward organism-generated fitness optima. However, the conditions that promote these eco-evolutionary feedbacks are unclear. Here, we used experimental evolution to test whether high population density, by strengthening environmental construction, drives eco-evolutionary feedbacks in the bacterial pathogen Pseudomonas aeruginosa MPAO1. Unexpectedly, we found that endpoint populations had higher performance than the ancestral strain in organism-modified media across nearly all evolutionary lines regardless of population density. This was caused by the emergence of hyperactive filamentous bacterio(phage) mutants during experimental passaging, which inhibited the ancestral strain but not endpoint populations in modified media. Hyperactive phages emerged from one of two avirulent prophages in MPAO1s genome during experimental passaging. Hyperactive phages drove the evolution of phage resistance in bacterial populations via mutations in the type IV pilus (TIVP), the phages surface receptor. TIVP mutations pleiotropically reduced motility and decreased susceptibility to a TIVP-targeting virulent phage, both of which are important traits for P. aeruginosa infection and treatment. Overall, this work suggests that filamentous phage evolution can act as a driver of eco-evolutionary feedbacks in bacterial populations, causing phenotypic and genetic changes that would not be anticipated from adaptation to the extrinsic environment alone.

Published in The ISME Journal (predicted rank #10) · training set

Matching journals

The top 6 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.