Untargeted metabolomic profiling of accessory sex gland fluid from Morada Nova rams.
Solange D SousaChristophe El-NakhelPaolo Ajmone MarsanMauricio Fraga van TilburgArlindo de Alencar Araripe Noronha MouraPublished in: Molecular reproduction and development (2020)
The present study was conducted to characterize the metabolome of accessory gland fluid (AGF) of locally adapted Morada Nova rams, raised in the Brazilian Northeast. AGF was collected by an artificial vagina from five vasectomized rams. Metabolites were identified by gas chromatography-mass spectrometry (GC/MS) and high-performance liquid chromatography-mass spectrometry (LC/MS), with the support of Human Metabolome Database, PubChem, LIPID Metabolites, Pathways Strategy databases, and MetaboAnalyst platforms. There were 182 and 190 metabolites detected by GC/MS and LC/MS, respectively, with an overlap of one molecule. Lipids and lipid-like molecules were the most abundant class of metabolites in the ram AGF (127 compounds), followed by amino acids, peptides, and analogs(103 metabolites). Considering all GC/MS and LC/MS, fructose, glycerol, citric acid, d-mannitol, d-glucose, and l-(+)-lactic acid were the most abundant single metabolites present in the ram AGF. Meaningful pathways associated with AGF metabolites included glycine, serine and threonine metabolism; pantothenate and CoA biosynthesis; galactose metabolism; glutamate metabolism and phenylalanine metabolism, and so forth. In conclusion, the combined use of LC/MS and GC/MS was essential for getting a holistic view of the compounds embedded in the ram AGF. Chemical analysis of the accessory sex gland secretion is relevant for understanding sperm function and fertilization.
Keyphrases
- ms ms
- mass spectrometry
- high performance liquid chromatography
- gas chromatography mass spectrometry
- solid phase extraction
- lactic acid
- liquid chromatography
- tandem mass spectrometry
- emergency department
- amino acid
- blood pressure
- adipose tissue
- big data
- gas chromatography
- metabolic syndrome
- blood glucose
- deep learning
- weight loss
- capillary electrophoresis
- machine learning
- electronic health record
- adverse drug
- high resolution mass spectrometry