Vornhagen et al. introduced a model combining gut microbiota structure and Klebsiella pneumoniae genotype to assess infection risk in K. pneumoniae-colonized patients. Building on their findings, we investigated the gut microbiota composition and K. pneumoniae genotype in 16 colonized patients, five of whom had bloodstream infections at the time of fecal sampling. Importantly, we did not apply the original machine learning model due to the small sample size of our cohort. Instead, we explored the distribution of key antimicrobial resistance and stress resistance genes and analyzed gut community structure based on amplicon sequence variants (ASVs) of the V3–V4 16S rRNA region. Notably, distinct gene profiles were observed in both infected and non-infected patients, and three patients without bloodstream infections showed no detectable Klebsiella ASVs despite microbiological confirmation of colonization. These findings highlight the need to integrate gut microbiota composition data into infection risk assessment and address limitations in taxonomic resolution and sample size. Future studies should aim to develop streamlined tools for clinical application in K. pneumoniae-colonized patients.
De Maio, F., Bianco, D. M., Santarelli, G., Rosato, R., Monzo, F. R., Fiori, B., Sanguinetti, M., Posteraro, B., Profiling the gut microbiota to assess infection risk in Klebsiella pneumoniae-colonized patients, <<GUT MICROBES>>, 2025; 17 (1): N/A-N/A. [doi:10.1080/19490976.2025.2468358] [https://hdl.handle.net/10807/316456]
Profiling the gut microbiota to assess infection risk in Klebsiella pneumoniae-colonized patients
De Maio, Flavio;Santarelli, Giulia;Rosato, Roberto;Fiori, Barbara;Sanguinetti, Maurizio
;Posteraro, Brunella
2025
Abstract
Vornhagen et al. introduced a model combining gut microbiota structure and Klebsiella pneumoniae genotype to assess infection risk in K. pneumoniae-colonized patients. Building on their findings, we investigated the gut microbiota composition and K. pneumoniae genotype in 16 colonized patients, five of whom had bloodstream infections at the time of fecal sampling. Importantly, we did not apply the original machine learning model due to the small sample size of our cohort. Instead, we explored the distribution of key antimicrobial resistance and stress resistance genes and analyzed gut community structure based on amplicon sequence variants (ASVs) of the V3–V4 16S rRNA region. Notably, distinct gene profiles were observed in both infected and non-infected patients, and three patients without bloodstream infections showed no detectable Klebsiella ASVs despite microbiological confirmation of colonization. These findings highlight the need to integrate gut microbiota composition data into infection risk assessment and address limitations in taxonomic resolution and sample size. Future studies should aim to develop streamlined tools for clinical application in K. pneumoniae-colonized patients.| File | Dimensione | Formato | |
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