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Walvoort, M. T. C.

Publications and source records attributed to Walvoort, M. T. C..

2 recordsLinked to original sources

Digestion, fermentation, and pathogen anti-adhesive properties of the hMO-mimic di-fucosyl-β-cyclodextrin

ScopeHuman milk is widely acknowledged as the best food for infants, and that is not just because of nutritional features. Human milk also contains a plethora of bioactive molecules, including a large set of human milk oligosaccharides (hMOs). Especially fucosylated hMOs have received attention for their anti-adhesive effects on pathogens by preventing attachment to the intestinal wall. Because hMOs are generally challenging to produce in sufficient quantities to study and ultimately apply in (medical) infant formula, hMO mimics are interesting compounds to produce and evaluate for their biological effects. Methods and resultsWe investigated the digestion, fermentation, and pathogen anti-adhesive capacity of the novel hMO mimic di-fucosyl-{beta}-cyclodextrin (DF{beta}CD). We establish that DF{beta}CD is not digested by -amylase and also resists fermentation by the microbiota from a 9 month-old infant. In addition, we reveal that DF{beta}CD blocks adhesion of enterotoxigenic E. coli (ETEC) to Caco-2 cells, especially when DF{beta}CD is pre-incubated with ETEC prior to addition to the Caco-2 cells. ConclusionOur results suggests that DF{beta}CD functions through a decoy effect. We expect that our results inspire the generation and biological evaluation of other fucosylated hMOs and mimics, to obtain a comprehensive overview of the anti-adhesive power of fucosylated glycans.

microbiology

Robust and independent metabolic transcriptional components in 34,494 patient-derived samples and cell lines: a resource for translational cancer research

Patient-derived expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We, therefore, performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components and their activation score in individual samples were defined. Here we demonstrate how this landscape can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.

bioinformatics