Personalized microbiotas (counter-)select for antibiotic resistant pathogens

Abstract

Antibiotic resistant pathogens are an increasing public health threat, as development of novel therapeutics is outpaced by resistance emergence and dissemination. Approaches to slow down or even revert antibiotic resistance are necessary to maintain efficacy of both existing and new antibiotics. Such approaches exploit the fitness cost of resistance elements, but have largely relied on assessing this cost in laboratory conditions that poorly reflect the native context in which pathogens reside. Here we present a method that allows to investigate the influence of personalized human gut microbiota compositions on the competitive fitness of antibiotic resistant pathogens. Using fecal matter-derived microbiomes we identified a specific community that selected for a carbapenem-resistant Klebsiella pneumoniae strain. This selective advantage was due to mutations arising in a LacI-type transcriptional regulator, GlyR, which upregulated expression of the downstream glycoporin GlyP, causing the effect. By deconvoluting the microbiome composition, we identified a focal E. coli strain as a central driver of the selection, which was further modulated by other microbiota members. We further demonstrate that the selective advantage was due to carbohydrate competition, and in particular for glycerol-containing compounds. Importantly, glyR mutations are under strong positive but conditional selection in clinical K. pneumoniae isolates. This implies a reduced competitiveness in other environments, which we experimentally validated in vitro . Overall, this study offers a path to identify microbiome-specific interactions that modulate the competitiveness of antibiotic resistant pathogens.

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19875736.

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.

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Personalized microbiotas (counter-)select for antibiotic resistant pathogens

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Competing interests

Use of Artificial Intelligence (AI)