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The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.

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Due to the co-evolved intricate relationships and mutual influence between changes in the microbiome and silage fermentation quality, we explored the effects of Lactobacillus plantarum and Propionibacterium acidipropionici (Inoc1) or Lactobacillus buchneri (Inoc2) inoculants on the diversity and bacterial and fungal community succession of rehydrated corn (CG) and sorghum (SG) grains and their silages using Illumina Miseq sequencing after 0, 3, 7, 21, 90, and 360 days of fermentation. The effects of inoculants on bacterial and fungal succession differed among the grains. Lactobacillus and Weissella species were the main bacteria involved in the fermentation of rehydrated corn and sorghum grain silage. Aspergillus spp. mold was predominant in rehydrated CG fermentation, while the yeast Wickerhamomyces anomalus was the major fungus in rehydrated SG silages. The Inoc1 was more efficient than CTRL and Inoc2 in promoting the sharp growth of Lactobacillus spp. and maintaining the stability of the bacterial community during long periods of storage in both grain silages. However, the bacterial and fungal communities of rehydrated corn and sorghum grain silages did not remain stable after 360 days of storage.

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Copy number variations (CNV) are an important source of genetic variation. CNV has been increasingly studied and frequently associated with diseases and productive traits in livestock animals. However, CNV-based genome-wide association studies (GWAS) in Santa Inês sheep, one of the principal sheep breeds in Brazil, have not yet been reported. Thus, the aim of this study was to investigate the association between CNV and growth, efficiency and carcass traits in sheep. The Illumina OvineSNP50 BeadChip array was used to detect CNV in 491 Santa Inês individuals. Then, CNV-based GWAS was performed with a linear mixed model approach considering a genomic relationship matrix, for ten traits: (1) growth: body weight at three (W3) and six (W6) months of age; (2) efficiency: residual feed intake (RFI) and feed efficiency (FE) and (3) carcass: external carcass length (ECL), leg length (LL), carcass yield (CY), commercial cuts weight (CCW), loin eye area (LEA) and subcutaneous fat thickness (SFT). We identified 1,167 autosomal CNV in 438 sheep, with 294 non-redundant CNV, ranging from 21.8 to 861.9 kb, merged into 216 distinct copy number variation regions (CNVRs). One significant CNV segment (pFDR -value<0.05) in OAR3 was associated with CY, while another significant CNV in OAR6 was associated with RFI. Additionally, another 5 CNV segments were considered relevant for investigation in the future studies. The significant segments overlapped 4 QTLs and spanned 8 genes, including the SPAST,TGFA and ADGRL3 genes, involved in cell differentiation and energy metabolism. Therefore, the results of the present study increase knowledge about CNV in sheep, their possible impacts on productive traits, and provide information for future investigations, being especially useful for those interested in structural variations in the sheep genome.

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Multitrait models can increase the accuracy of breeding value prediction and reduce bias due to selection by using traits measured before and after it has occurred. However, as the number of traits grows, a similar trend is expected for the number of parameters to be estimated, which directly affects the computing power and the amount of data required. The aim of the present study was to apply reduced rank (principal components model-PCM) and factor analytical models (FAM), to estimate (co)variance components for nineteen traits, jointly evaluated in a single analysis in Campolina horses. A total of 18 morphometric traits (MT) and one gait visual score (GtS), along with genealogical records of 48,806 horses, were analysed under a restricted maximum likelihood framework. Nine PCM, nine FAM and one standard multitrait model (MTM) were fitted to the data and compared to find the best suitable model. Based on Bayesian information criterion, the best model was the FAM option, considering five common factors (FAM5). After performing an intraclass analysis, none of MT were genetically negatively correlated, whereas GtS was negatively related to all MT, except for the genetic correlations among GtS and BLL, and between GtS and BLLBL (0.01 and 0.10 respectively). From all MT, two traits were derived computing ratios involving other traits, those had negative correlations with others MT, but all favourable for selection. Similar patterns were observed between the genetic parameters obtained from MTM and FAM5 respectively. The heritability estimates ranged from 0.09 (head width) to 0.47 (height at withers). Our results indicated that FAM was efficient to reduce the multitrait analysis dimensionality, and therefore, traits can be combined based on the first three eigenvectors from the additive genetic (co)variance matrix. In addition, there was sufficient genetic variation for selection, benefiting its potential implementation in a breeding program.

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Cattle population history, breeding systems, and geographic subdivision may be reflected in runs of homozygosity (ROH), effective population size (N e), and linkage disequilibrium (LD) patterns. Thus, the assessment of this information has become essential to the implementation of genomic selection on purebred and crossbred cattle breeding programs. In this way, we assessed the genotype of 19 cattle breeds raised in Brazil belonging to taurine, indicine, synthetic crossbreds, and Iberian-derived locally adapted ancestries to evaluate the overall LD decay patterns, N e, ROH, and breed composition. We were able to obtain a general overview of the genomic architecture of cattle breeds currently raised in Brazil and other tropical countries. We found that, among the evaluated breeds, different marker densities should be used to improve the genomic prediction accuracy and power of genome-wide association studies. Breeds showing low N e values indicate a recent inbreeding, also reflected by the occurrence of longer ROH, which demand special attention in the matting schemes to avoid extensive inbreeding. Candidate genes (e.g., ABCA7, PENK, SPP1, IFNAR1, IFNAR2, SPEF2, PRLR, LRRTM1, and LRRTM4) located in the identified ROH islands were evaluated, highlighting biological processes involved with milk production, behavior, rusticity, and fertility. Furthermore, we were successful in obtaining the breed composition regarding the taurine and indicine composition using single-nucleotide polymorphism (SNP) data. Our results were able to observe in detail the genomic backgrounds that are present in each breed and allowed to better understand the various contributions of ancestor breeds to the modern breed composition to the Brazilian cattle.

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Vitamin B and trace minerals are crucial molecular signals involved in many biological pathways; however, their bioavailability is compromised in high-producing ruminant animals. So far, studies have mainly focused on the effects of these micronutrients on animal performance, but their use in a rumen-protected form and their impact on liver metabolism in finishing beef cattle is poorly known. We used a shotgun proteomic approach combined with biological network analyses to assess the effects of a rumen-protected B-vitamin blend, as well as those of hydroxy trace minerals, on the hepatic proteome. A total of 20 non-castrated Nellore males with 353 ± 43 kg of initial body weight were randomly assigned to one of the following treatments: CTRL-inorganic trace minerals without supplementation of a protected vitamin B blend, or SUP-supplementation of hydroxy trace minerals and a protected vitamin B blend. All animals were fed the same amount of the experimental diet for 106 days, and liver biopsies were performed at the end of the experimental period. Supplemented animals showed 37 up-regulated proteins (p < 0.10), and the enrichment analysis revealed that these proteins were involved in protein folding (p = 0.04), mitochondrial respiratory chain complex I (p = 0.01) and IV (p = 0.01), chaperonin-containing T-complex 2 (p = 0.01), glutathione metabolism (p < 0.01), and other aspects linked to oxidative-stress responses. These results indicate that rumen-protected vitamin B and hydroxy trace mineral supplementation during the finishing phase alters the abundance of proteins associated with the electron transport chain and other oxidation-reduction pathways, boosting the production of reactive oxygen species, which appear to modulate proteins linked to oxidative-damage responses to maintain cellular homeostasis.

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BACKGROUND: Selenium (Se) has been recognized as an essential micronutrient for nearly all forms of life. In recent decades, broiler responses to dietary Se supplemental levels and sources have received considerable attention. On environmental grounds, organic trace mineral utilization in practical broiler feeds has been defended due to its higher bioavailability. In such feeds, trace minerals are provided simultaneously in the same supplement as inorganic salts or organic chelates, a fact commonly ignored in assays conducted to validate organic trace mineral sources. The current assay aimed to investigate growth and biochemical responses, as well as Se retention of growing chicks fed diets supplemented with organic and inorganic Se levels and where the trace minerals (zinc, copper, manganese, and iron) were provided as organic chelates or inorganic salts according to Se source assessed. In so doing, a 2 × 5 factorial arrangement was used to investigate the effects of sodium selenite (SS) and selenium-yeast (SY) supplemented in feeds to provide the levels of 0, 0.08, 0.16, 0.24, and 0.32 mg Se/kg. RESULTS: Chicks fed selenium-yeast diets had body weight (BW), and average daily gain (ADG) maximized at 0.133 and 0.130 mg Se/kg, respectively. Both Se sources linearly increased (P < 0.05) the glutathione peroxidase (GSH-Px) activity in chick blood but higher values were observed in sodium selenite fed chicks (P < 0.05). Both Se sources influenced thyroid hormone serum concentrations (P < 0.05). Chicks fed SY exhibited greater retention of Se in the feathers (P < 0.05). Relative bioavailability of selenium yeast compared with SS for the Se content in carcass, feathers, total and Se retention were, 126, 116, 125 and 125%, respectively. SY supplementation resulted in lower liver Se concentration as Se supplementation increased (P < 0.05). CONCLUSIONS: Based on performance traits, the supplemental level of organic Se as SY in organic trace minerals supplement to support the maximal growth of broiler chicks is 0.133 mg Se/kg.

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Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

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Cow stayability plays a major role on the overall profitability of the beef cattle industry, as it is directly related to reproductive efficiency and cow's longevity. Stayability (STAY63) is usually defined as the ability of the cow to calve at least three times until 76 months of age. This is a late-measured and lowly heritable trait, which consequently constrains genetic progress per time unit. Thus, the use of genomic information associated with novel stayability traits measured earlier in life will likely result in higher prediction accuracy and faster genetic progress for cow longevity. In this study, we aimed to compare pedigree-based and single-step GBLUP (ssGBLUP) methods as well as to estimate genetic correlations between the proposed stayability traits: STAY42, STAY53 and STAY64, which are measured at 52, 64 and 76 months of cow's age, considering at least 2, 3 and 4 calving, respectively. ssGBLUP yielded the highest prediction accuracy for all traits. The heritability estimates for STAY42, STAY53, STAY63 and STAY64 were 0.090, 0.151, 0.152 and 0.143, respectively. The genetic correlations between traits ranged from 0.899 (STAY42 and STAY53) to 0.985 (STAY53 and STAY63). The high genetic correlation between STAY42 and STAY53 suggests that besides being related to cow longevity, STAY53 is also associated with the early-stage reproductive efficiency. Thus, STAY53 is recommended as a suitable selection criterion for reproductive efficiency due to its higher heritability, favourable genetic correlation with other traits, and measured earlier in life, compared with the conventional stayability trait, that is STAY63.

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The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters' average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo-phenotypes in multiple-step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple-step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.

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