Antibiotic use is widespread and a key weapon against bacterial infections. However, their behavior against complex microbial communities is still unknown, as they are typically developed against distinct, individual pathogens. Understanding the effect of specific antibiotics on microbial communities is important for the effective treatment of polymicrobial infections, such as those found in cystic fibrosis (CF) airways. To investigate this, Ghuneim et. al. (2022) applied a mathematical model to 24 CF microbial communities treated with 11 different antibiotics. The model predicted the following outcomes: 1) community death, 2) community resistance, 3) pathogen killing, and 4) fermenter killing. However, in vivo validation of the models showed two additional unexpected outcomes which were 5) community profile shifts where the total bacterial load (TBL) was slightly changed, and 6) TBL increase. The TBL increase was observed in 17.8% of the samples. The authors suggested that the TBL increase was due to antibiotic effects mediating pH-dependent inhibition of pathogens by anaerobe fermentation. Metagenomic sequencing of sputum samples from CF patients during antibiotic therapy done by CosmosID validated the predicted in silico outcomes in a clinical setting. Altogether, the findings of the study suggest that the distinct polymicrobial ecology of specific infections impacts antibiotic mode of action and treatment outcome. Click here for the paper.
Modeling and Microbiome Analyses Reveal Unexpected Outcomes of Antimicrobial Therapy in Polymicrobial Cystic Fibrosis
5 October 2022by Barış Özdinç0