PREreview del Personalized microbiotas (counter-)select for antibiotic resistant pathogens

This study evaluates integrating personalized gut microbiota screening with genetic and proteomic analysis to look at carbapenem resistant Klebsiella pneumoniae. The main research question is, can the integration of personalized microbiome screening with genomic analysis provide a higher resolution for tracking competitive fitness and evolution of resistant pathogens compared to other laboratory methods? The study tracks the competition and evolution of the pathogen over several months using a longitudinal vivo design.

Main findings are that personalized microbiota integrated screening expands coverage and resolution of resistance monitoring to advance pathogen control. By utilizing a proteome profiling, the researchers found competitive links with the E. coli strain that's not seen with traditional laboratory monitoring. This shows how personalized microbiome data is useful in surveillance, for how gut environments drive pathogen evolution to monitor resistance. These support correlation between specific metabolic niches like glycerol compounds, and the selection of resistant mutants.

For strengths, the personalized fecal microbiomes let authors find competitive interactions in the glyR-glyP axis that other surveillance misses. A limitation of the study is in low-density settings for the sensitivity; it's effective when specific competitors are abundant, but the selective advantage of the K. pneumoniae mutants may decrease as the concentration of competing E. coli decreases. In microbiomes with low competitors, surveillance might detect the pathogen's presence but may not provide enough genomic data.

A major concern is that the study was conducted using optimized ex vivo conditions, making it easier to capture metabolic interactions. Could still be evaluated if this tool is sensitive enough to work when the pathogen or its competitors are at lower frequencies in gut.

To address nutrient bias, the authors should discuss on whether the glyR mutation remains advantageous under different carbon sources beyond glycerol containing compounds. This could help generalize findings to people on different dietary profiles, and to also evaluate the concentrations of glycerol.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.