Introduction: In microbial metabolomics, the use of multivariate data analysis (MDVA) has not been comprehensively explored regarding the different techniques available and the information that each gives about the metabolome. To overcome these limitations, here we show the use of Fusarium oxysporum cultured in the presence of exogenous alkaloids as a model system to demonstrate a comprehensive strategy for metabolic profiling. Matherials and methods: F. oxysporum was harvested on different days of incubation after alkaloidal addition, and the chemical profiles were compared using LC–MS data and MDVA. We show significant innovation to evaluate the chemical production of microbes during their life cycle by utilizing the full capabilities of Partial Least Square (PLS) with microbial-specific modeling that considers incubation days, media culture availability, and growth rate in solid media. Results and Discusscion: Results showed that the treatment of the Y-data and the use of both PLS regression and discrimination (PLSr and PLS-DA) inferred complemental chemical information. PLSr revealed the metabolites that are produced/consumed during fungal growth, whereas PLS-DA focused on metabolites that are only consumed/produced at a specific period. Both regression and classificatory analysis were equally important to identify compounds that are regulated and/or selectively produced as a response to the presence of the alkaloids. Lastly, we report the annotation of analogs from the piperidine alkaloids biotransformed by F. oxysporum as a defense response to the toxic plant metabolites. These molecules do not show the antimicrobial potential of their precursors in the fungal extracts and were rapidly produced and consumed within 4 days of microbial growth.

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.