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Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.

ABSTRACT: Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

SUBMITTER: Grana-Miraglia L

PROVIDER: S-EPMC10506723 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.

Graña-Miraglia Lucía L Morales-Lizcano Nadia N Wang Pauline W PW Hwang David M DM Yau Yvonne C W YCW Waters Valerie J VJ Guttman David S DS

PLoS computational biology 20230906 9

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we gener ...[more]

PMID: 37672526

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Project description:Survival of Pseudomonas aeruginosa in cystic fibrosis (CF) chronic infections is based on a genetic adaptation process consisting of mutations in specific genes, which can produce advantageous phenotypic switches and ensure its persistence in the lung. Among these, mutations inactivating the regulators MucA (alginate biosynthesis), LasR (quorum sensing) and MexZ (multidrug-efflux pump MexXY) are the most frequently observed, with those inactivating the DNA mismatch repair system (MRS) being also highly prevalent in P. aeruginosa CF isolates, leading to hypermutator phenotypes that could contribute to this adaptive mutagenesis by virtue of an increased mutation rate. Here, we characterized the mutations found in the mucA, lasR, mexZ and MRS genes in P. aeruginosa isolates obtained from Argentinean CF patients, and analyzed the potential association of mucA, lasR and mexZ mutagenesis with MRS-deficiency and antibiotic resistance. Thus, 38 isolates from 26 chronically infected CF patients were characterized for their phenotypic traits, PFGE genotypic patterns, mutations in the mucA, lasR, mexZ, mutS and mutL gene coding sequences and antibiotic resistance profiles. The most frequently mutated gene was mexZ (79%), followed by mucA (63%) and lasR (39%) as well as a high prevalence (42%) of hypermutators being observed due to loss-of-function mutations in mutL (60%) followed by mutS (40%). Interestingly, mutational spectra were particular to each gene, suggesting that several mechanisms are responsible for mutations during chronic infection. However, no link could be established between hypermutability and mutagenesis in mucA, lasR and mexZ, indicating that MRS-deficiency was not involved in the acquisition of these mutations. Finally, although inactivation of mucA, lasR and mexZ has been previously shown to confer resistance/tolerance to antibiotics, only mutations in MRS genes could be related to an antibiotic resistance increase. These results help to unravel the mutational dynamics that lead to the adaptation of P. aeruginosa to the CF lung.

Project description:RationalePseudomonas aeruginosa, the predominant cause of chronic airway infections of patients with cystic fibrosis, exhibits extensive phenotypic diversity among isolates within and between sputum samples, but little is known about the underlying genetic diversity.ObjectivesTo characterize the population genetic structure of transmissible P. aeruginosa Liverpool Epidemic Strain in chronic infections of nine patients with cystic fibrosis, and infer evolutionary processes associated with adaptation to the cystic fibrosis lung.MethodsWe performed whole-genome sequencing of P. aeruginosa isolates and pooled populations and used comparative analyses of genome sequences including phylogenetic reconstructions and resolution of population structure from genome-wide allele frequencies.Measurements and main resultsGenome sequences were obtained for 360 isolates from nine patients. Phylogenetic reconstruction of the ancestry of 40 individually sequenced isolates from one patient sputum sample revealed the coexistence of two genetically diverged, recombining lineages exchanging potentially adaptive mutations. Analysis of population samples for eight additional patients indicated coexisting lineages in six cases. Reconstruction of the ancestry of individually sequenced isolates from all patients indicated smaller genetic distances between than within patients in most cases.ConclusionsOur population-level analysis demonstrates that coexistence of distinct lineages of P. aeruginosa Liverpool Epidemic Strain within individuals is common. In several cases, coexisting lineages may have been present in the infecting inoculum or assembled through multiple transmissions. Divergent lineages can share mutations via homologous recombination, potentially aiding adaptation to the airway during chronic infection. The genetic diversity of this transmissible strain within infections, revealed by high-resolution genomics, has implications for patient segregation and therapeutic strategies.

Dataset Information

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.

Publications

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.

Similar Datasets

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