{"title":"Quantifying phage-bacteria dynamics <i>in vitro:</i> rapid emergence of phage-resistant mutants for <i>Klebsiella pneumoniae</i>.","authors":"Marcos Rodríguez, Irene Cantallops, Pilar Domingo-Calap, Josep Sardanyés","doi":"10.17912/micropub.biology.001666","DOIUrl":null,"url":null,"abstract":"<p><p>In the quantitative description of evolving phage-bacterial systems, a central challenge lies in accurately identifying the key parameters governing the dynamics of both bacterial and phage populations. This is especially relevant in the case of multidrug-resistant pathogenic bacteria such as <i>Klebsiella sp</i> . This pathogen poses serious health problems due to antibiotic overuse, which causes the emergence of antibiotic-resistant strains and great difficulty in eradicating bacterial infections with antibiotics. Research on phage-bacteria thus becomes a very important topic to provide alternative strategies to eradicate multidrug-resistant bacteria, and thus quantitative descriptions of these processes are of paramount importance. Despite increasing research on this topic, key structural parameters of the populations, such as bacterial growth rates, the impact of phages on bacterial dynamics or the probability of emergence of phage-resistant strains, are often scarce. In this study, we investigated a battery of growth experiments for <i>Klebsiella pneumoniae</i> alone and with the presence of bacteriophage vB_Kpn_2-P4. Using mathematical models we estimate key parameters for these experiments, showing the rapid growth and emergence of phage-resistant mutants which outcompete the susceptible bacteria strains. Our results provide quantitative estimates of these processes and may be useful for understanding phage-bacterial dynamical systems and parameterizing future theoretical and computational models.</p>","PeriodicalId":74192,"journal":{"name":"microPublication biology","volume":"2025 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174997/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"microPublication biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17912/micropub.biology.001666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the quantitative description of evolving phage-bacterial systems, a central challenge lies in accurately identifying the key parameters governing the dynamics of both bacterial and phage populations. This is especially relevant in the case of multidrug-resistant pathogenic bacteria such as Klebsiella sp . This pathogen poses serious health problems due to antibiotic overuse, which causes the emergence of antibiotic-resistant strains and great difficulty in eradicating bacterial infections with antibiotics. Research on phage-bacteria thus becomes a very important topic to provide alternative strategies to eradicate multidrug-resistant bacteria, and thus quantitative descriptions of these processes are of paramount importance. Despite increasing research on this topic, key structural parameters of the populations, such as bacterial growth rates, the impact of phages on bacterial dynamics or the probability of emergence of phage-resistant strains, are often scarce. In this study, we investigated a battery of growth experiments for Klebsiella pneumoniae alone and with the presence of bacteriophage vB_Kpn_2-P4. Using mathematical models we estimate key parameters for these experiments, showing the rapid growth and emergence of phage-resistant mutants which outcompete the susceptible bacteria strains. Our results provide quantitative estimates of these processes and may be useful for understanding phage-bacterial dynamical systems and parameterizing future theoretical and computational models.