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Differential Digestive Stability of Food-Derived microRNAs: The Case of miR-30c-5p and miR-92a-3p in Polyfloral Honey.

Diana Marisol Abrego-GuandiqueOlubukunmi Amos IloriMaria Cristina CaroleoRoberto CannataroErika CionePaola Tucci
Published in: Current issues in molecular biology (2024)
Dietary microRNAs (miRs) represent a new area in food science. Although they have been found in many foods, including honey, more research is needed about their stability and fate during digestion. Hence, this study aimed to analyze the digestive stability of two selected miRs in honey. We extracted miR-92a-3p and miR-30c-5p from pasteurized and unpasteurized forms of polyfloral honey using two different methods and kits: a column-based manual method and a phenol-free semi-automated magnetic-bead-based method. The latter option was used for the subsequent analysis of samples according to the INFOGEST static in vitro digestion protocol. Also, the honey samples were examined for exosome-like particles using dynamic light scattering. Although the expression levels of both miRs were significantly lower following intestinal digestion, we found a difference in the resilience of the miRs to gastrointestinal conditions, with miR-30c-5p being relatively stable compared to miR-92a-3p following digestion, regardless of the honey's pasteurization treatment. Moreover, there was marked heterogeneity in the extracellular vesicle profile of the pasteurized sample. We identified the presence of two broadly conserved miRs in honey: miR-92a-3p and miR-30c-5p. Despite honey exhibiting high digestibility, miR-92a-3p was less resilient than miR-30c-5p, demonstrating considerable resistance under gastrointestinal conditions. Although further research is needed, the results obtained from this study may represent a starting point for utilizing honey as a source of exogenous miRNAs for preventive strategies and more "natural" treatments.
Keyphrases
  • cell proliferation
  • long non coding rna
  • poor prognosis
  • randomized controlled trial
  • public health
  • anaerobic digestion
  • machine learning
  • deep learning
  • human milk
  • mass spectrometry
  • risk assessment