This study investigated changes in meat and subcutaneous fat metabolomes and possible metabolic pathways related to prenatal nutrition in beef cattle. For this purpose, 18 Nellore bulls were used for meat sampling and 15 for fat sampling. The nutritional treatments during the gestation were: NP—not programmed or control, without protein-energy supplementation; PP—partially programmed, with protein-energy supplementation (0.3% of body weight (BW)) only in the final third of pregnancy; and FP—full programming, with protein-energy supplementation (0.3% of BW) during the entire pregnancy. The meat and fat samples were collected individually 24 h after slaughter, and the metabolites were extracted using a combination of chemical reagents and mechanical processes and subsequently quantified using liquid chromatography or flow injection coupled to mass spectrometry. The data obtained were submitted to principal component analysis (PCA), analysis of variance (ANOVA), and functional enrichment analysis, with a significance level of 5%. The PCA showed an overlap between the treatments for both meat and fat. In meat, 25 metabolites were statistically different between treatments (p ≤ 0.05), belonging to four classes (glycerophospholipids, amino acids, sphingolipids, and biogenic amine). In fat, 10 significant metabolites (p ≤ 0.05) were obtained in two classes (phosphatidylcholine and lysophosphatidylcholine). The functional enrichment analysis showed alterations in the aminoacyl-tRNA pathway in meat (p = 0.030); however, there was no pathway enriched for fat. Fetal programming influenced the meat and fat metabolomes and the aminoacyl-tRNA metabolic pathway, which is an important candidate for the biological process linked to meat quality and related to fetal programming in beef cattle.

This study aimed to evaluate the effects of different prenatal nutrition treatments on pregnant cows and their progeny. One hundred and twenty-six pregnant Nellore cows (455.3 ± 8.1 kg) were allocated in three different nutritional treatments during pregnancy: NP—control, PP—protein-energy supplementation in the last 3 months of pregnancy, and FP—the same supplementation throughout pregnancy. After parturition, all cows and calves received the same environmental and nutrition condition. The body condition score (BCS), body weight (BW), ribeye area (REA), backfat thickness (BFT), and rumpfat thickness (RFT) were collected on four occasions during pregnancy in the cows and from birth to finishing in calves. All data (cows and calves) were submitted to an analysis of variance (p < 0.05) using a linear model (MIXED procedure; SAS software). The BW, RFT, and BCS from the cows showed significant differences in the middle third of pregnancy and pre-delivery and RFT postpartum (p < 0.05). For the offspring, the weaning weight showed a tendency (NP lighter than others). In terms of gain, the PP group tended to be higher in RFT at calving (p = 0.06), in REA at finishing (p = 0.09), and in ADG in the same period (p = 0.09). The prenatal nutrition strategies had little or no effect on the beef cattle postnatal performance.

The objective of this study was to evaluate and develop equations to predict forage intake and growth of calves throughout the suckling period of beef calves grazing on forage or dairy calves fed harvested forage. Milk and forage intake and body weight data for individual animals were collected from published theses (one using bottle-fed dairy calves and one using suckling beef calves). A nutrition model was constructed using milk and forage intake equations and growth equations. Additional datasets were compiled from the literature to develop equations to adjust the original nutrition model for forage digestibility, milk composition, and growth. In general, the original nutrition model predicted the forage intake and body weight of dairy calves with moderate-to-high precision (CCC = 0.234 to 0.929) and poor accuracy (MB = −341.16 to −1.58%). Additionally, the original nutrition model predicted forage intake and body weight in beef calves with poor-to-moderate precision (CCC = 0.348 to 0.766) and accuracy (MB = 6.39 to 57.67%). Adjusted nutrition models performed better with the best model precisely (CCC = 0.914) predicting forage intake and precisely (CCC = 0.978) and accurately (MB = 2.83%) predicting body weight in dairy calves. The best adjusted nutrition model predicted forage intake and body weight with high precision (CCC = 0.882 and 0.935) and moderate accuracy (MB = −7.01 and −7.34) in beef calves. Nutrition models were able to adequately predict the forage intake and growth of calves with adjustments made to standard milk energy concentrations and growth equations.

Anais do XXI Encontro dos Grupos do Programa de Educação Tutorial da Região Sudeste (Sudeste PET) (978-65-5941-298-3) - O Grupo Pet Zootecnia E A Criação De Soluções Para O Enfrentamento Da Pandemia