De Nicola, Donato (2020) NEW FRONTIERS OF THE RESEARCH IN BUFFALO BREEDING. [Tesi di dottorato]

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The interest around buffalo breeding in Italy has noticeably augmented during the last years, as demonstrated from the increase of buffalo population that passed from about 200,000 heads in 2000 to more than 400,000 in 2020. This is due to the interest around the main product derived from buffalo milk: the mozzarella cheese, also known as Mozzarella di Bufala Campana, that received in 1996 the Protected Designation of Origin (PDO). In Italy buffalo breeding is carried out in intensive and semi-intensive systems, similar to those utilized in dairy cattle: thus, similar issues in terms of productive and reproductive efficiency have been risen in this species. Furthermore, in the Mediterranean region, buffaloes show annual fluctuations in reproduction with distinct breeding and non-breeding seasons. As buffaloes are short-day breeders, the annual peak in fertility coincides with decreasing day length from autumn to winter. Therefore, in order to guarantee milk production throughout the year for mozzarella cheese production, some strategies are applied to increase the reproductive efficiency out of the breeding season, when a greater incidence of anestrus, a decline in the function of the corpus luteum (CL), and an increase in embryonic mortality are usually recorded. Furthermore, natural mating is often preferred to assisted reproduction (estrus synchronization and artificial insemination), because of some peculiarities of buffalo species, such as low estrus behavior and large variability in estrus duration, which makes difficult the individuation of the optimal moment for artificial insemination (AI). In this scenario, the purpose of this project was to improve reproductive efficiency and management in buffaloes through the application of new methodological approaches, such as synchronization of estrus, eco-color Doppler and metabolomics. In particular, a great attention has been paid to the improvement of AI in buffalo, in both heifers and pluriparous buffaloes. In the first experiment (Chapter 5), the efficiency of two synchronization protocols for oestrus synchronization and the influence of live body weight (LBW) and age on reproductive performance was evaluated in buffalo heifers. The animals were synchronized by Ovsynch-TAI Program (OVS; n=72) or double prostaglandin administered 12 days apart (PGF; n=74) and all the buffaloes were inseminated twice (24 days apart). 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. In the second experiment (Chapter 6), a deep study of CL development was carried out in buffaloes out of breeding season through the application of eco-color Doppler technique. Adult Mediterranean buffaloes (n=29) were synchronized by Ovsynch-TAI Program 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 non-pregnant and retrospectively analyzed. In the third experiment (Chapter 7) a metabolomic approach on milk was used on 10 pregnant and 10 non-pregnant buffaloes in order to identify potential biomarkers of early pregnancy. The study was carried out on 10 pregnant and 10 non-pregnant buffaloes that were synchronized by Ovsynch-TAI Program and have undergone the first AI. 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. Data were analyzed retrospectively by dividing pregnant and non-pregnant subjects. Statistical analysis was carried out by using Mass Profile Professional. In the fourth experiment (Chapter 8) an advanced GC-MS and metabolite identification approach was also utilized to characterize the metabolome of buffalo milk and mozzarella cheese in a robust and repeatable technology platform. 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 and maintained at -80°C until analysis. Metabolomic assay was carried out through gas-chromatography and mass spectroscopy and differently expressed metabolites were identified. Statistical analysis of the results was carried out by ANOVA. The results of the first experiment showed that 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. In the second experiment, the total pregnancy rate was 50.0% (13/26) on Day 45. A significant difference between CL average area in pregnant and non-pregnant 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 non-pregnant 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 and pregnancy maintenance during the transitional period in buffalo. During the third experiment, 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 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. Finally, during the fourth experiment 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" PDO.

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