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Rondeau, D.

Publications and source records attributed to Rondeau, D..

2 recordsLinked to original sources

Varied mutual growth inhibition between commensal yeasts and strains

Interkingdom interactions between bacteria and fungi are an emerging research field that provides insights into pathological, environmental, and microbiota-related relationships. However, the mechanisms governing these interactions, particularly in the context of microbial resistance, remain largely unknown. This study aims to enhance our understanding of the complex interactions between different Candida, Nakaseomyces and Sacharomyces species from the human microbiota and two not isogenic strains of Escherichia coli (antibiotic-susceptible E. coli-ATCC and multidrug-resistant E. coli-OXA48). Forty-nine Candida strains were co-cultured with the two E. coli strains. Both bacterial and yeast growth was monitored using flow cytometry and compared to monocultures. The effect of yeast culture supernatants on E. coli proliferation was also investigated. Metabolomic fingerprints and metabolite identification were performed using mass spectrometry-based approaches followed by multiblock statistical analyses. The inhibitory powers (IP) of yeasts against E. coli and vice versa varied significantly among fungal species. N. glabrata exhibited the strongest inhibition against E. coli-ATCC, while Candida lusitaniae, C. kefyr, C. krusei, C. tropicalis, and C. dubliniensis showed lower IPs. C. parapsilosis and Saccharomyces cerevisiae had no inhibitory effects. Against E. coli-OXA48, most yeasts displayed no inhibition, except for N. glabrata. Conversely, E. coli inhibited yeast growth more effectively, particularly Candida albicans. Fungal supernatants from S. cerevisiae, C. lusitaniae, and N. glabrata showed the highest inhibitory effects on E. coli-ATCC, while S. cerevisiae, C. krusei, and C. lusitaniae were most effective against E. coli-OXA48. Unsupervised metabolite profiling data analysis with multiblock approach highlighted a clustering of samples according to yeast species. Regarding inhibitory power on E. coli (ATCC or OXA48), active supernatants tend to cluster together suggesting the presence of similar metabolites; some were further characterized. This study highlights the diverse interactions between E. coli and commensal yeasts. From an applied perspective, these findings pave the way for identifying probiotics or postbiotics with potential applications in combating multidrug-resistant bacteria through novel antimicrobial compounds.

microbiology↗

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecules calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/473622v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@730ecaorg.highwire.dtl.DTLVardef@1d012e1org.highwire.dtl.DTLVardef@187a1e5org.highwire.dtl.DTLVardef@195c53f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗