Microbes frequently evolve in reproducible ways. Here, we show that differences in specific metabolic regulation rather than inter-strain interactions explain the frequent presence of lasR loss-of-function mutations in the bacterial pathogen Pseudomonas aeruginosa. While LasR contributes to virulence through its role in quorum sensing, lasR mutants have been associated with more severe disease. A model based on the intrinsic growth kinetics for a wild type strain and its LasR- derivative, in combination with an experimental evolution based genetic screen and further genetics analyses, indicated that differences in metabolism were sufficient to explain the rise of these common mutant types. The evolution of LasR- lineages in laboratory and clinical isolates depended on activity of the two-component system CbrAB, which modulates substrate prioritization through the catabolite repression control pathway. LasR- lineages frequently arise in cystic fibrosis lung infections and their detection correlates with disease severity. Our analysis of bronchoalveolar lavage fluid metabolomes identified compounds that negatively correlate with lung function, and we show that these compounds support enhanced growth of LasR- cells in a CbrB-controlled manner. We propose that in vivo metabolomes contribute to pathogen evolution, which may influence the progression of disease and its treatment.

No abstract

No abstract

1The opportunistic pathogen Pseudomonas aeruginosa damages hosts through the 2 production of diverse secreted products, many of which are regulated by quorum 3 sensing. The lasR gene, which encodes a central quorum-sensing regulator, is 4 frequently mutated, and loss of LasR function impairs the activity of downstream 5 regulators RhlR and PqsR. We found that in diverse models, the presence of P. 6 aeruginosa wild type causes LasR loss-of-function strains to hyperproduce RhlR/I-7 regulated antagonistic factors, and autoinducer production by the wild type is not 8 required for this effect. We uncovered a reciprocal interaction between isogenic wild 9 type and lasR mutant pairs wherein the iron-scavenging siderophore pyochelin, 10 specifically produced by the lasR mutant, induces citrate release and cross-feeding from 11 wild type. Citrate stimulates RhlR signaling and RhlI levels in LasR-but not in LasR+ 12 strains, and the interactions occur in diverse media. Co-culture interactions between 13 strains that differ by the function of a single transcription factor may explain worse 14 outcomes associated with mixtures of LasR+ and LasR loss-of-function strains. More 15 broadly, this report illustrates how interactions within a genotypically diverse population, 16 similar to those that frequently develop in natural settings, can promote net virulence 17 factor production. (19, 20). Further, some LasR-strains exhibit rewired regulation of quorum sensing 35 (QS)-controlled exoproducts (21) and LasR-strains can activate QS signaling in 36 response to products from other species (22) or in specific culture conditions (23, 24). 37LasR-strains are also frequently found among LasR+ P. aeruginosa strains where 38 exoproducts can be shared or signal cross-feeding can occur (13). 39 P. aeruginosa LasR participates in the regulation of QS in conjunction with two 40 transcription factors: RhlR and PqsR (MvfR). Each of these three regulators has one or 41 more autoinducer ligands: 3-oxo-C12-homoserine lactone (3OC12HSL) for LasR, C4-42 homoserine lactone (C4HSL) for RhlR, and hydroxyalkylquinolones (Pseudomonas 43Quinolone Signal (PQS) and hydroxy-heptyl quinolone (HHQ)) for PqsR (25). In the 44 regulatory networks described in the widely-used P. aeruginosa model strains, LasR is 45 an upstream regulator of RhlR and PqsR signaling, and together these regulators 46 control the expression of a suite of genes associated with virulence including redox-47 active small molecule phenazines (26-28), cyanide (29), proteases (30-33), and 48 rhamnolipid surfactants important for surface motility, biofilm dispersal, and host cell 49 damage (34)(35)(36). 50Other traits that are often heterogeneous across P. aeruginosa isolates relate to 51 strategies for iron acquisition. P. aeruginosa procures iron through the use of 52 siderophores, including pyochelin (37-39) and pyoverdine (40), from heme or through a 53 direct iron uptake system (41-43). It is common to encounter P. aeruginosa strains with 54 loss of function mutations in genes encoding ...

Genome-wide transcriptome profiling identifies genes that are prone to differential expression across contexts ("common DEGs"), as well as specific changes relevant to the experimental manipulation. Distinguishing common DEGs from those that are specifically changed in a context of interest will allow more efficient inference of relevant mechanisms and a more systematic understanding of the biological process under scrutiny. Currently, common changes can only be identified through the laborious manual curation of highly controlled experiments, an inordinately time-consuming and impractical endeavor. Here we pioneer a method for identifying common patterns using generative neural networks. This method produces a background set of transcriptomic experiments from which a gene and pathway-specific null distribution can be generated. By comparing the set of differentially expressed genes found in a target experiment against the background set, common results can easily be separated from specific ones. This "Specific cOntext Pattern Highlighting In Expression data" (SOPHIE) method is broadly applicable to new platforms or any species with a large collection of unannotated gene expression data. We apply SOPHIE to diverse datasets including human, including human cancer, and bacterial datasets. SOPHIE recapitulates previously described common DEGs, and our molecular validation indicates it detects highly specific, but low magnitude, biologically relevant, transcriptional changes. SOPHIE's measure of specificity can complement log fold change activity generated from traditional differential expression analyses by, for example, filtering the set of changed genes to identify those that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions.