Unmasking “Hidden” Adhesives in Pseudomonas: Real-time c-di-GMP sensing during wrinkly-spreader evolution

Pentz et al. (2024) uncovered deviations from expected evolutionary outcomes in Pseudomonas, including absence of wspF mutations and no fitness drop after deleting known exopolysaccharide loci, suggesting unrecognized adhesive components under c-di-GMP control. This project evolves WS phenotypes in P. syringae, P. savastanoi, and P. protegens while simultaneously measuring intracellular c-di-GMP dynamics and matrix composition. It uses near-infrared aptamer nanosensors adapted from SELEC/HT-SELEX platforms to create sensors for c-di-GMP and candidate secreted matrix metabolites, paired with barcoded CRISPRi/CRISPRa libraries targeting diguanylate cyclases and putative polysaccharide/adhesin operons. Evolve-and-resequence (E&R), RNA-seq, and glyco/proteomics will map compensation routes in real time. This approach directly monitors intracellular signaling and matrix flux during evolution rather than inferring pathways post hoc, revealing how c-di-GMP signaling rewires to find hidden adhesins. The research extends prior forecasting studies by instrumenting the genotype–phenotype–fitness map and tests hypothesized additional adhesive components. It clarifies why molecular-level forecasts fail, identifies new biofilm targets, and yields principles for compensatory adaptation in regulatory networks, relevant for biofilm control and evolutionary steering.

References:

1. Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas syringae and Pseudomonas savastanoi. Jennifer T. Pentz, Aparna Biswas, Bassel Alsaed, Peter A. Lind (2024). bioRxiv.
2. Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas. Peter A. Lind (2018). bioRxiv.
3. Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas protegens. Jennifer T. Pentz, Peter A. Lind (2021). PLoS Genetics.
4. High-Throughput Evolution of Near-Infrared Oxytocin Nanosensors. Jaquesta Adams, N. Navarro, Natsumi Komatsu, Esther Leem, Xiaoqi Sun, M. Landry (2024). ECS Meeting Abstracts.