The aim of this study is to combine advanced GC-MS and metabolite identification in a robust and repeatable technology platform to characterize the metabolome of buffalo milk and mozzarella cheese. The study utilized eleven dairies located in a protected designation of origin (PDO) region and nine dairies located in non-PDO region in Italy. Samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy. A total of 185 metabolites were consistently detected in both milk and mozzarella cheese. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples. For milk samples, it was possible to divide metabolites into two classes according to region: those with lower concentrations in PDO samples (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with lower concentrations in non-PDO samples (talopyranose, pantothenic acid, mannobiose, etc.,). The same was observed for mozzarella samples with the proportion of some metabolites (talopyranose, 2, 3-dihydroxypropyl icosanoate, etc.,) higher in PDO samples while others (tagatose, lactic acid dimer, ribitol, etc.,) higher in non-PDO samples. The findings establish the utility of GC-MS together with mass spectral libraries as a powerful technology platform to determine the authenticity, and create market protection, for “Mozzarella di Bufala Campana.”

No abstract

This study evaluated corpus luteum (CL) development in buffaloes out of breeding season and assessed an early pregnancy diagnosis. Mediterranean buffaloes (n = 29) were synchronized and artificially inseminated. CL B-mode/color Doppler ultrasonography examinations were performed daily from Days 5 to 10 post-synchronization, recording CL dimensions and blood flow parameters. Blood samples were collected on the same days for the progesterone (P4) assay. Data were grouped into pregnant or nonpregnant and retrospectively analyzed. The total pregnancy rate was 50.0% (13/26) on Day 45. A significant difference between CL average area in pregnant and nonpregnant buffaloes was recorded only on Day 10. Pregnant buffaloes showed a significantly higher mean P4 concentration and higher mean time average medium velocity (TAMV) values from Day 5 to Day 10 compared to nonpregnant buffaloes. Linear regression analysis showed a significant relationship between P4 levels and TAMV. Multiple logistic regression highlighted a significant influence of TAMV on pregnancy outcome, particularly on Day 8. This is probably due to the strong relationship between TAMV and P4 production. Both TAMV and P4 could be used to predict pregnancy starting on Day 6, although a more reliable result was obtained at Day 10. Thus, the period between Days 5 and 10 is critical for CL development during the transitional period in buffalo.

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.

This study aimed to assess the influence of live body weight (LBW) and age on reproductive performance in buffalo heifers synchronized by different treatments.The study was carried out on 146 Mediterranean buffalo heifers (mean age 25.3±13.4 months, LBW 424±47 kg), divided into 2 homogeneous groups and synchronized by Ovsynch-TAI Program (OVS; n = 72) or double prostaglandin administered 12 days apart (PGF; n = 74). All the buffaloes were inseminated twice and follicle dimensions and ovulation rate (OR) were assessed by ultrasound 24 and 48 h post-insemination. Pregnancy was assessed on day 25, 45 and 90 post-insemination and the incidence of late embryonic (LEM) and fetal (FM) mortality were respectively recorded. Data were analyzed by ANOVA, Chi-square test and multiple logistic regression. The LBW was significantly (P<0.05) higher in inseminated animals, compared to those that did not respond to the treatments (450.0±3.2 vs. 423.2±9.6 kg in inseminated and not inseminated heifers, respectively). Total OR was similar between groups, although OR at 24 h tended to be higher (P = 0.06) in OVS (86.7 vs. 72.9% in OVS and PGF, respectively). A (P<0.01) higher LBW was observed in ovulated heifers of PGF, while no differences were recorded in OVS. LBW affected OR (odds ratio = 1,032; P<0.05) only in PGF, while no effects were recorded in OVS. Total pregnancy rate, LEM and FM were similar between groups. In conclusion, the LBW would be considered before including buffalo heifers in a synchronization program and both synchronization treatments can be useful.