Browsing by Author "Garrido, Daniel"

Now showing 1 - 4 of 4
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    Anti-inflammatory effect of microbial consortia during the utilization of dietary polysaccharides
    (2018) Thomson, Pamela; Medina, Daniel A.; Ortuzar, Veronica; Gotteland, Martin; Garrido, Daniel
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    Cross-feeding interactions of gut microbes mediated by O-linked glycans from casein glycomacropeptide
    (2024) González-Morelo, Kevin J.; Garrido, Daniel
    The human gut microbiota plays an essential role in metabolizing complex compounds that host enzymes cannot degrade in the diet. In turn, the gut microbiota has the ability to interact with the host through mucus that protects epithelium cells. O-glycans have been proposed as emerging prebiotics due to their similarity to host-associated glycans instead of plant-derived prebiotics. Some gut microbes have been described to utilize the O-glycans as carbon sources for colonization. Changes in the protective barrier of host cells are linked to intestinal diseases. For this reason, trophic networks of microbial interactions play an important role in establishing a microbiome beneficial to the host’s health. Glycromacropeptide (GMP) is an O-glycopeptide obtained from whey during cheese manufacture. GMP could be considered as a simple model of O-glycans to analyze the molecular mechanisms involved in metabolic interactions between gut microbes. This work aimed to determine the molecular strategies and microbial interactions while utilizing glycomacropeptide as a carbon source. Individual cultures of representative bacteria allowed the identification of the major GMP-degraders. Unidirectional assays identified galacto-N-biose, galactose, N-Acetylgalactosamine, and sialic acid as by-products, providing a perspective on microbial interactions during GMP fermentation. Bidirectional assays demonstrated cross-feeding activity and competition between gut microbes, in addition to the promotion of butyrate from the fatty acids derived from the use of GMP. O-glycan-specific enzyme expression was identified for B. infantis ATCC 15697 and B. bifidum JCM 1254 during GMP cross-feeding consumption. This study highlights strategies for utilizing O-glycans in GMP consumption among gut microbes.
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    Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis
    (2024) Román Lagos, Loreto Andrea; Melis-Arcos, Felipe; Pröschle, Tomás; Saa, Pedro A.; Garrido, Daniel; Gilbert, Jack A.
    Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.
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    Using metabolic networks to predict cross-feeding and competition interactions between microorganisms
    (2024) Silva-Andrade, Claudia; Rodriguez-Fernández, María; Garrido, Daniel; Martin, Alberto J. M.; Jensen, Paul A.
    Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.