Milk Metabolomics Reveals Potential Biomarkers for Early Prediction of Pregnancy in Buffaloes Having Undergone Artificial Insemination

Simple Summary Today, the ability to determine the pregnancy status of cows as soon as possible after artificial insemination (AI) has become the most important thing to obtain in an ideal farm. Several efforts have been made to discover biomarkers of early pregnancy but, as of today, without any particular result. Most of the studies were carried out on non-invasive and cheap biological fluids, such as milk. Therefore, in order to identify potential biomarkers of early pregnancy a metabolomic approach on milk of 10 pregnant and 10 non-pregnant buffaloes was used. Milk was recovered in different days before and after the AI, and the data were analyzed retrospectively. The results revealed significant differences between pregnant and non-pregnant buffaloes, as well as in the expression of five metabolites. These data suggest the effectiveness of the metabolomic analysis for the identification of novel potential biomarkers in early prediction of pregnancy in buffaloes after AI, and these findings would give breeders the opportunity to rebreed animals at the next estrus event, saving most of the days as open. Abstract This study aimed to identify potential biomarkers for early pregnancy diagnosis in buffaloes subjected to artificial insemination (AI). The study was carried out on 10 pregnant and 10 non-pregnant buffaloes that were synchronized by Ovsynch-Timed Artificial Insemination Program and have undergone the first AI. Furthermore, milk samples were individually collected ten days before AI (the start of the synchronization treatment), on the day of AI, day 7 and 18 after AI, and were analyzed by LC-MS. Statistical analysis was carried out by using Mass Profile Professional (Agilent Technologies, Santa Clara, CA, USA). Metabolomic analysis revealed the presence of several metabolites differentially expressed between pregnant and non-pregnant buffaloes. Among these, a total of five metabolites were identified by comparison with an online database and a standard compound as acetylcarnitine (3-Acetoxy-4-(trimethylammonio)butanoate), arginine-succinic acid hydrate, 5 '-O-{[3-({4-[(3aminopropyl)amino]butyl}amino)propyl]carbamoyl}-2 '-deoxyadenosine, N-(1-Hydroxy-2-hexadecanyl)pentadecanamide, and N-[2,3-Bis(dodecyloxy)propyl]-L-lysinamide). Interestingly, acetylcarnitine was dominant in milk samples collected from non-pregnant buffaloes. The results obtained from milk metabolic profile and hierarchical clustering analysis revealed significant differences between pregnant and non-pregnant buffaloes, as well as in the metabolite expression. Overall, the findings indicate the potential of milk metabolomics as a powerful tool to identify biomarkers of early pregnancy in buffalo undergoing AI.