Hogardt, M. | bioRxiv Science

bioRxiv2025

Metabolites produced by gut bacteria under anoxic conditions drive the suppression of Acinetobacter baumannii

Multidrug-resistant (MDR) bacteria pose a significant global health threat. Among these, Acinetobacter baumannii, particularly carbapenem-resistant A. baumannii, is a leading cause of healthcare-associated infections. Emerging evidence links gut colonization to systemic infections, highlighting opportunities for new control strategies. We demonstrate that A. baumannii can survive under anaerobic conditions ([≤]1%) and shows limited growth under low oxygen conditions ([≥]1%), underscoring its adaptation to niches in the intestine. However, specific carbohydrates, including maltose, provide the gut bacteria Klebsiella oxytoca with a competitive advantage under anoxic conditions, enabling it to actively suppress A. baumannii through its metabolism. Notably, maltose induces this suppressive capacity in other commensals and complex gut microbiomes as well. Force-feeding experiments in Galleria mellonella larvae corroborate that K. oxytoca in combination with maltose significantly reduces A. baumannii recovery in gut environments. These findings suggest that targeted carbohydrate supplementation could enhance probiotic strategies, creating an environment unfavorable to A. baumannii.

microbiology↗

bioRxiv2019

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

microbiology↗