Persistent infections require bacteria to evolve from their naïve colonization state by optimizing fitness in the host via simultaneous adaptation of multiple traits, which can obscure evolutionary trends and complicate infection management. Accordingly, here we screen 8 infection-relevant phenotypes of 443 longitudinalPseudomonas aeruginosaisolates from 39 young cystic fibrosis patients over 10 years. Using statistical modeling, we map evolutionary trajectories and identify trait correlations accounting for patient-specific influences. By integrating previous genetic analyses of 474 isolates, we provide a window into early adaptation to the host, finding: (1) a 2–3 year timeline of rapid adaptation after colonization, (2) variant “naïve” and “adapted” states reflecting discordance between phenotypic and genetic adaptation, (3) adaptive trajectories leading to persistent infection via three distinct evolutionary modes, and (4) new associations between phenotypes and pathoadaptive mutations. Ultimately, we effectively deconvolute complex trait adaptation, offering a framework for evolutionary studies and precision medicine in clinical microbiology.

Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

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The histidine autokinase CheA functions as the central processing unit in the Escherichia coli chemotaxis signaling machinery. CheA receives autophosphorylation control inputs from chemoreceptors and in turn regulates the flux of signaling phosphates to the CheY and CheB response regulator proteins. Phospho-CheY changes the direction of flagellar rotation; phospho-CheB covalently modifies receptor molecules during sensory adaptation. The CheA phosphorylation site, His-48, lies in the N-terminal P1 domain, which must engage the CheA ATP-binding domain, P4, to initiate an autophosphorylation reaction cycle. The docking determinants for the P1-P4 interaction have not been experimentally identified. We devised mutant screens to isolate P1 domains with impaired autophosphorylation or phosphotransfer activities. One set of P1 mutants identified amino acid replacements at surface-exposed residues distal to His-48. These lesions reduced the rate of P1 transphosphorylation by P4. However, once phosphorylated, the mutant P1 domains transferred phosphate to CheY at the wild-type rate. Thus, these P1 mutants appear to define interaction determinants for P1-P4 docking during the CheA autophosphorylation reaction.