Back

Modification of social cheating facilitates the stabilization and chronic infection of polymorphic Pseudomonas aeruginosa population

Zhao, K.; Yang, X.; Zeng, Q.; Zhang, Y.; Li, H.; Li, J. S.; Liu, H.; Du, L.; Wu, Y.; Huang, G.; Huang, T.; Zhang, Y.; Zhou, H.; Wang, X.; Chu, Y.; Zhou, X.

2023-03-09 microbiology
10.1101/2023.03.08.531804 bioRxiv
Show abstract

Chronic infection of the common bacterial pathogen Pseudomonas aeruginosa frequently leads to the coexistence of heterogeneous individuals to engage in several group behaviors. However, further evolution of the polymorphic P. aeruginosa population, including the dynamic change of social cooperation and its impact on host immune system, still remain elusive. We show that the evolution of P. aeruginosa in the patients with chronic obstructive pulmonary disease frequently selects the isolates deficient in producing the costly and sharable extracellular products for nutrient acquisition. The evolution of polymorphic P. aeruginosa population is mainly concentrated on modifying the adaptability of lasR mutants, which are typical cheaters in the competition of quorum-sensing-controlled extracellular proteases. Importantly, lasR mutants with varying degrees of evolution interact with wild-type P. aeruginosa in a framework termed cascaded public goods game to compete for extracellular proteases and siderophores, and thus perpetuate social cooperation under different conditions. Finally, we find that a polymorphic population comprised of lasR-intact P. aeruginosa and evolved lasR-mutant can minimize the host immune fluctuation for persistent colonization. This study demonstrates the multistage evolution and complex interaction of P. aeruginosa in adaptation to host lungs, and provides an explanation for the success of cooperation in public goods game.

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.