Back

A targeted metabolomic method to detect epigenetically relevant metabolites

Miro-Blanch, J.; Junza, A.; Capellades, J.; Balvay, A.; Moudet, C.; Kovatcheva, M.; Raineri, S.; Rabot, S.; Mellor, J.; Serrano, M.; Yanes, O.

2023-11-30 biochemistry
10.1101/2023.11.30.569455 bioRxiv
Show abstract

Metabolites play a central role in the chemical crosstalk between metabolism and epigenetic marks. Epigenetically relevant metabolites are substrates, products and cofactors that can act as activators or inhibitors of epigenetic enzymes, which control gene expression by adding or removing chemical marks in the DNA, RNA and histones. Diet composition, and biosynthetic pathways encoded in the gut microbiome and the host genome are the main sources of these metabolites for mammals. Despite the increasing interest in the study of the microbiota-nutrient metabolism-host epigenetic axis to understand health and disease, there is a lack of a sensitive and easy analytical method to detect epigenetically relevant metabolites simultaneously. Here, we show an straightforward biphasic extraction where the organic phase is directly analyzed by GC-EI MS to detect short-chain fatty acids and formate without chemical derivatization, and the aqueous phase is analyzed by HILIC coupled to ESI-MS/MS, which together can cover >30 epigenetically relevant metabolites in biological samples such as liver, plasma or feces. In addition, we propose a stable isotope tracing method based on multiple-reaction monitoring (MRM) transitions by LC-QqQ MS to understand how 13C-labeled glucose or glutamine are used to build SAM and acetyl-CoA, the main methyl and acetyl group donors in epigenetic modifications, respectively. We anticipate that our methods will complement epigenomic and proteomic analyses adding another layer of molecular information towards mechanistic insights. HighlightsO_LIHost and microbiota metabolites link metabolism with epigenetic regulation. C_LIO_LIChemical structure diversity in epigenetically relevant metabolites challenges its analysis with a single method. C_LIO_LIA biphasic extraction with no chemical derivatization is able to recover SCFAs and other epigenetically relevant metabolites. C_LIO_LIA novel isotope trace experiment approach allows isotopomer resolution using MS2 data. C_LI

Published in Molecular Metabolism · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.