Impact of transient acquired hypermutability on the inter- and intra-species competitiveness of Pseudomonas aeruginosa.

Once acquired, hypermutation is unrelenting, and in the long-term, leads to impaired fitness due to its cumulative impact on the genome. This raises the question of why hypermutators arise so frequently in microbial ecosystems. In this work, we explore this problem by examining how the transient acquisition of hypermutability affects inter- and intra-species competitiveness, and the response to environmental insults such as antibiotic challenge. We do this by engineering Pseudomonas aeruginosa to allow the expression of an important mismatch repair gene, mutS, to be experimentally controlled over a wide dynamic range. We show that high levels of mutS expression induce genomic stasis (hypomutation), whereas lower levels of induction lead to progressively higher rates of mutation. Whole-genome sequence analyses confirmed that the mutational spectrum of the inducible hypermutator is similar to the distinctive profile associated with mutS mutants obtained from the airways of people with cystic fibrosis (CF). The acquisition of hypermutability conferred a distinct temporal fitness advantage over the wild-type P. aeruginosa progenitor strain, in both the presence and the absence of an antibiotic selection pressure. However, over a similar time-scale, acquisition of hypermutability had little impact on the population dynamics of P. aeruginosa when grown in the presence of a competing species (Staphylococcus aureus). These data indicate that in the short term, acquired hypermutability primarily confers a competitive intra-species fitness advantage.

Therapeutic interventions alter ecological interactions among cystic fibrosis airway microbiota.

The airways of people with cystic fibrosis (CF) often harbor a diverse microbiota and in recent years, much effort has been invested in cataloguing these. In spite of providing a wealth of insight, this cataloguing tells us little about how the organisms interact with one another in the CF airways. However, such relationships can be inferred using the theoretical framework of the Lotka-Volterra (LV) model. In the current work, we use a generalized Lotka-Volterra model to interrogate the nationwide data collected and curated by the UK CF Registry. This longitudinal dataset (covering the period 2008-2020) contains annual depositions that record the presence/absence of microbial taxa in each patient, their medication, and their CF genotype. Specifically, we wanted to identify trends in ecological relationships between the CF microbiota at a nationwide level, and whether these are potentially affected by medication. Our results show that some medications have a distinct influence on the microbial interactome, especially those that potentially influence the "gut-lung axis" or mucus viscosity. In particular, we found that patients treated with a combination of antimicrobial agents (targeting the airway microbiota), digestive enzymes (assisting in the assimilation of dietary fats and carbohydrates), and DNase (to reduce mucus viscosity) displayed a distinctly different airway interactome compared with patients treated separately with these medications.