Despite the limited adoption of DDGS in South America, its potential benefits in pig nutrition have not been thoroughly evaluated, we assessed the effects of increasing levels of HP-DDGS in corn/soybean meal-based diets fed to pigs from the initial I nursery phase to the finishing phase on performance, health parameters, and quality characteristics of carcass, pork, and belly. Five hundred piglets were distributed in randomized blocks to five HPDDGS inclusion levels. HP-DDGS was introduced 14 days into the experiment and maintained until the end of the 147-day experimental period. It replaced corn and soybean meal in isoenergetic diets at increasing levels of 0 %, 10 %, 20 %, 30 %, and 40 %. Increasing levels of HP-DDGS positively affected average daily feed intake (P = 0.001), average daily gain (P = 0.0002), and body weight (P = 0.0003) in the nursery phase, while in the growing and finishing phase negatively impacted feed conversion ratio (P = 0.002 and P < 0.0001, respectively) and body weight (P = 0.018 and P = 0.0003, respectively). HP-DDGS inclusion also positively influenced fecal score; animals fed 40 % HP-DDGS in the nursery phase and 30 % and 40 % HP-DDGS in the growing and finishing phase had highest proportion of well-formed feces (fecal score 1), while those fed 10 % or no HP-DDGS showed more mild diarrhea (fecal score 2) (P < 0.0001 to P = 0.0024). Increased HP-DDGS inclusion levels had a negative impact on body weight at slaughterhouse (P < 0.0001), hot carcass weight (P < 0.0001), chilled carcass weight (P < 0.0001), backfat thickness (P = 0.042), belly weight (P = 0.0002), belly thickness (P < 0.0001), belly external flexibility (P < 0.0001), belly internal flexibility (P < 0.0001), and a* color component (P = 0.037), but had a positive effect on L* color component (P = 0.044). Overall, increased HP-DDGS levels in pig diets modified the fatty acid profile of belly fat. Although this belly fat shows increased oxidation rates and flexibility, it is rendered more suitable for human consumption in terms of health benefits. In this sense, including HP-DDGS in pig diets benefits early growth by improving gut health and reducing diarrhea, enhancing weight gain and fecal consistency. However, high HP-DDGS inclusions levels negatively affected carcass traits. While HP-DDGS improved the polyunsaturated lipid profile of pork, its overall impact on meat quality varied. Therefore, HP-DDGS should be used early in growth, with a transition to a low-fiber diet during finishing to mitigate these effects.

Conducting an economic analysis is essential to evaluate the feasibility of including HP-DDGS in feed, aiming to optimize costs and improve economic returns for producers. In this sense, we conducted an economic analysis of high-protein Distiller's Dried Grains with Solubles (HP-DDGS) inclusion in pig diets, aiming to guide pig producers in decision-making for efficient use of production factors and favorable economic outcomes. Performance data were obtained from a nursery-to-finishing experiment with 500 piglets, assigned to one of five HP-DDGS inclusion levels: 0%, 10%, 20%, 30%, and 40%. The economic analysis, based on Neoclassical Economic Theory, considered implicit and explicit costs across production settings in two economic distinct scenarios. Additionally, two tools were developed to optimize HP-DDGS inclusion for the best cost-benefit ratio and economic return, focusing on the nutrition cost per kilogram of pig produced. Results indicated that HP-DDGS inclusion in the nursery phase could reduce feed costs and enhance economic returns. Despite being costlier than corn, HP-DDGS offered a better cost-benefit than diets without HP-DDGS. Even when feed costs rose in the scenario where HP-DDGS was pricier than corn and soybean meal, improved feed conversion and higher weights partially offset increased costs, maintaining economic viability. However, in nursery-to-finishing phases, higher HP-DDGS levels led to reduce performance. Under intermediate HP-DDGS pricing, lower production costs compensated for decrease in revenue, resulting in higher profitable. In contrast, high HP-DDGS pricing, coupled with reduced performance, led to economic inefficiency, failing to cover production costs. The challenge of HP-DDGS inclusion is balancing price and levels to minimize costs without affecting performance and revenue. Tools developed showed that when HP-DDGS costs up to 97.02% of soybean meal and 66.75% above corn, its inclusion at 40% allows optimal cost-benefit and maximized returns.

Chromium is an essential trace mineral in insulin-mediated glucose metabolism, potentially affecting protein synthesis and improving performance and meat quality. This study aimed to assess the impact of chromium on performance, carcass characteristics, meat quality, and the gene expression in the skeletal muscle of lambs fed a high-concentrate diet. Sixteen just just-weaned Dorper × Santa Inês crossbred lambs, weighing 24.95 ± 1.97 kg, were allocated into a control group, a basal diet without chromium in the diet supplementation (CTL) or chromium supplementation (Cr3.0), with the addition of 3.0 mg of chromium per kg of dry matter (Cr3.0DM). The animals were housed in individual pens and fed a diet comprising 6

Since 2021, the emergence of variants of concern (VOC) has led Brazil to experience record numbers of in COVID-19 cases and deaths. The expanded spread of the SARS-CoV-2 combined with a low vaccination rate has contributed to the emergence of new mutations that may enhance viral fitness, leading to the persistence of the disease. Due to limitations in the real-time genomic monitoring of new variants in some Brazilian states, we aimed to investigate whether genomic surveillance, coupled with epidemiological data and SARS-CoV-2 variants spatiotemporal spread in a smaller region, can reflect the pandemic progression at a national level. Our findings revealed three SARS-CoV-2 variant replacements from 2021 to early 2022, corresponding to the introduction and increase in the frequency of Gamma, Delta, and Omicron variants, as indicated by peaks of the Effective Reproductive Number (Reff). These distinct clade replacements triggered two waves of COVID-19 cases, influenced by the increasing vaccine uptake over time. Our results indicated that the effectiveness of vaccination in preventing new cases during the Delta and Omicron circulations was six and eleven times higher, respectively, than during the period when Gamma was predominant, and it was highly efficient in reducing the number of deaths. Furthermore, we demonstrated that genomic monitoring at a local level can reflect the national trends in the spread and evolution of SARS-CoV-2.

The meat market has enormous importance for the world economy, and the quality of the product offered to the consumer is fundamental for the success of the sector. In this study, we analyzed a database which contained information on 2470 animals from a commercial farm in the state of São Paulo, Brazil. Of this total, 2181 animals were genotyped, using 777,962 single-nucleotide polymorphisms (SNPs). After quality control analysis, 468,321 SNPs provided information on the number of genotyped animals. Genome-wide association analyses (GWAS) were performed for the characteristics of the rib eye area (REA), subcutaneous fat thickness (SFT), shear force at 7 days’ ageing (SF7), and intramuscular fat (IMF), with the aid of the single-step genomic best linear unbiased prediction (ssGBLUP) method, with the purpose of identifying possible genomic windows (~1 Mb) responsible for explaining at least 0.5% of the genetic variance of the traits under analysis (≥0.5%). These genomic regions were used in a gene search and enrichment analyses using MeSH terms. The distributed heritability coefficients were 0.14, 0.20, 0.18, and 0.21 for REA, SFT, SF7, and IMF, respectively. The GWAS results indicated significant genomic windows for the traits of interest in a total of 17 chromosomes. Enrichment analyses showed the following significant terms (FDR ≤ 0.05) associated with the characteristics under study: for the REA, heat stress disorders and life cycle stages; for SFT, insulin and nonesterified fatty acids; for SF7, apoptosis and heat shock proteins (HSP27); and for IMF, metalloproteinase 2. In addition, KEGG (Kyoto encyclopedia of genes and genomes) enrichment analysis allowed us to highlight important metabolic pathways related to the studied phenotypes, such as the growth hormone synthesis, insulin-signaling, fatty acid metabolism, and ABC transporter pathways. The results obtained provide a better understanding of the molecular processes involved in the expression of the studied characteristics and may contribute to the design of selection strategies and future studies aimed at improving the productivity of Nellore cattle.

Since 2021, the emergence of variants of concern (VOC) has led Brazil to experience record numbers of in COVID-19 cases and deaths. The expanded spread of the SARS-CoV-2 combined with a low vaccination rate has contributed to the emergence of new mutations that may enhance viral fitness, leading to the persistence of the disease. Due to limitations in the real-time genomic monitoring of new variants in some Brazilian states, we aimed to investigate whether genomic surveillance, coupled with epidemiological data and SARS-CoV-2 variants spatiotemporal spread in a smaller region, can reflect the pandemic progression at a national level. Our findings revealed three SARS-CoV-2 variant replacements from 2021 to early 2022, corresponding to the introduction and increase in the frequency of Gamma, Delta, and Omicron variants, as indicated by peaks of the Effective Reproductive Number (Reff). These distinct clade replacements triggered two waves of COVID-19 cases, influenced by the increasing vaccine uptake over time. Our results indicated that the effectiveness of vaccination in preventing new cases during the Delta and Omicron circulations was six and eleven times higher, respectively, than during the period when Gamma was predominant, and it was highly efficient in reducing the number of deaths. Furthermore, we demonstrated that genomic monitoring at a local level can reflect the national trends in the spread and evolution of SARS-CoV-2.

Abstract Feed efficiency (FE) selection is crucial for improving the economic and environmental sustainability of beef cattle production. Residual feed intake (RFI) is the most commonly used parameter for FE because it is independent of metabolic body weight (MBW) and average daily gain (ADG). Low-RFI (LRFI) animals are more efficient, as they present less feed intake than expected, decreasing dry matter intake (DMI) and methane production per unit of body weight gain (BW). However, considering the time and resources required for RFI measurement, omics methods have been applied to distinguish between LRFI and high-RFI (HRFI) animals. This study performed a stepwise approach as an alternative for selecting important plasma metabolites related to RFI in beef cattle. Black Angus bulls (n = 64) with an initial BW of 321.8 ± 59.98 kg and 12.9 ± 1.08 mo from rearing under continuous grazing were allocated to feedlot pens and fed a diet containing 80:20 roughage:concentrate. The animals were evaluated for DMI and BW using an integrated real-time data collection system (Intergado, Brazil) for 56 d, and ADG was estimated as the slope of the linear regression between BW and days on feed. Blood samples were collected at the end of the finishing phase by puncturing the coccygeal vessels. Through targeted metabolomics, concentrations of plasma amino acids, biogenic amines, and hexoses were determined using an AB Sciex 5500 QTRAP mass spectrometer in liquid chromatography-mass-spectrometry and flow-injection analysis mode. The fixed effect of MBW, ADG, age and batch, and the random effect of sire were considered in the mixed model to determine RFI and to categorize the 20 greatest and 20 least values as HRFI and LRFI, respectively, using the lmer4 R package. A stepwise approach in both directions was used in the MASS R package. The model with the lowest AIC (31.15) presented 23 of the 31 metabolites, followed by ANOVA (Table 1). Serotonin, glutamate, phenylalanine, and tyrosine concentrations were greater in HRFI, whereas aspartate, citrulline, tryptophan, ornithine, taurine, creatinine, and trans-4-hydroxyproline concentrations were greater in LRFI (P < 0.05). The gluconeogenic potential of glutamate, phenylalanine, and tyrosine suggests that an impact on energy availability may affect DMI. Conversely, differences in creatinine and trans-4-hydroxyproline abundance can reflect changes in muscular body mass, while the greater concentrations of aspartate, citrulline, and ornithine in LRFI animals suggest that a lower DMI can be associated with lower urea cycle activity. The stepwise approach appears to be a feasible alternative as a pre-selection method to identify plasma metabolites that could be used to categorize beef cattle for RFI. Further validation is required to confirm the potential of these metabolites as biomarkers for FE selection.

The risk of chronic diseases such as cardiovascular disease, cancer, inflammation, obesity, and autoimmune disease is linked to the quality of dietary fats, with lower intake of saturated and higher intake of n-6 and n-3 polyunsaturated fatty acids (PUFA) considered beneficial to health. This study investigated the effect of supplementing sows’ diets with cow’s milk biofortified with n-6 or n-3 PUFA, at varying n-6/n-3 ratios (8.26, 7.92, and 2.72) during their growing phase and throughout gestation and lactation on their reproductive performance and immune-inflammatory status. Specifically, we analyzed circulating cholesterol and fatty acid profiles of serum, colostrum and milk, sow body weight, and neonate colostrum intake, Apgar scores, muscle composition, and embryo viability. Analysis of circulating immunoglobulins (Ig), interleukins, and eicosanoids and complement system hemolytic activity were used to evaluate inflammatory and immune responses of sows and piglets. Expression of lipolysis and lipogenic genes in the liver were investigated in sows and piglets, with additional investigation of hypothalamus genes regulating appetite in sows. Feeding sows milk biofortified with n-6 and n-3 PUFA altered serum fatty acid profiles, reduced triglycerides (TG), increased embryo total number, increased early gestation backfat, and reduced colostrum IgG. Piglets of biofortified sow had higher circulating IgA, IgM and TNF-α, and lower IL-10. Sows fed n-3 biofortified milk had higher very low-density lipoproteins (VLDL) and TNF-α in circulation. Offspring from sows fed n-6 versus n-3 biofortified milk had lower IL-10 and expression levels of SREBP-1. N-3 versus n-6 also lowered arachidonic acid (ARA) levels in sow’s milk and piglet viability 1. Findings offer insights into the potential health benefits of dietary supplementation with biofortified milk in swine, which serve as good model of diet-nutrition studies of humans, and therefore can potentially be considered in dietary recommendations both human and animal populations.

Thermotolerance has become an essential factor in the prevention of the adverse effects of heat stress, but it varies among animals. Identifying genes related to heat adaptability traits is important for improving thermotolerance and for selecting more productive animals in hot environments. The primary objective of this research was to find candidate genes in the liver that play a crucial role in the heat stress response of Santa Ines sheep, which exhibit varying levels of heat tolerance. To achieve this goal, 80 sheep were selected based on their thermotolerance and placed in a climate chamber for 10 days, during which the average temperature was maintained at 36 °C from 10 a.m. to 4 p.m. and 28 °C from 4 p.m. to 10 a.m. A subset of 14 extreme animals, with seven thermotolerant and seven non-thermotolerant animals based on heat loss (rectal temperature), were selected for liver sampling. RNA sequencing and differential gene expression analysis were performed. Thermotolerant sheep showed higher expression of genes GPx3, RGS6, GPAT3, VLDLR, LOC101108817, and EVC. These genes were mainly related to the Hedgehog signaling pathway, glutathione metabolism, glycerolipid metabolism, and thyroid hormone synthesis. These enhanced pathways in thermotolerant animals could potentially mitigate the negative effects of heat stress, conferring greater heat resistance.

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.

The skin plays an important role in thermoregulation. Identification of genes on the skin that contribute to increased heat tolerance can be used to select animals with the best performance in warm environments. Our objective was to identify candidate genes associated with the heat stress response in the skin of Santa Ines sheep. A group of 80 sheep assessed for thermotolerance was kept in a climatic chamber for 8 days at a stress level temperature of 36 °C (10 am to 04 pm) and a maintenance temperature of 28 °C (04 pm to 10 am). Two divergent groups, with seven animals each, were formed after ranking them by thermotolerance using rectal temperature. From skin biopsy samples, total RNA was extracted, quantified, and used for RNA-seq analysis. 15,989 genes were expressed in sheep skin samples, of which 4 genes were differentially expressed (DE; FDR < 0.05) and 11 DE ( FDR 0.05–0.177) between the two divergent groups. These genes are involved in cellular protection against stress ( HSPA1A and HSPA6 ), ribosome assembly (28S, 18S, and 5S ribosomal RNA), and immune response ( IGHG4 , GNLY , CXCL1 , CAPN14 , and SAA-4). The candidate genes and main pathways related to heat tolerance in Santa Ines sheep require further investigation to understand their response to heat stress in different climatic conditions and under solar radiation. It is essential to verify whether these genes and pathways are present in different breeds and to understand the relationship between heat stress and other genes identified in this study.

The objective of this study was to investigate the potential of creating a pipeline to classify the marbling score obtained from ribeye area (REA) images using computer vision and machine learning methods. Our database consisted of images and measurements (N = 2,446) from the transversal cut between the 12th and 13th ribs of the Longissimus dorsi muscle from carcasses of a beef cattle population (Bos taurus). Each sample was previously labeled by the industry using a low, medium or high marbling score. The prediction accuracies of two tree-based Machine Learning (ML) algorithms (Decision Tree - DT and Random Forest - RF) were compared, as well as different measures for extracting features from the REA images, which were used as input for the ML algorithms. In order to extract features based on detectable color patterns and textures contained in smaller parts of the grayscale image, we proposed the application of the local binary pattern (LBP) method prior to the adoption of ML methods. Mean classification accuracies for the test set ranged from 45.78% to 91.25% for different test scenarios. The results were mostly impacted by the feature extraction metrics, ML methods, potential subjectivity during the classification process by the industry, and the number of classes evaluated together. The best prediction accuracy results were achieved after performing the cross-validation (20% in each balanced group, 5 folds, and 10 repetitions), considering solely the extreme groups (low and high marbling scores) and pre-selecting from each group the 400 most visually representative samples. The RF algorithm outperformed the DT for most scenarios. After increasing the number of images to 580 samples for the same two groups, the highest testing accuracies were reduced to 83.05% for RF and 75.58% for DT. Such a decrease in the classification accuracies may be associated with the addition of erroneously classified images, due to the subjective nature of the industry evaluation. In conclusion, our preliminary studies showed the LBP method as a powerful feature extraction strategy considering a scenario where the labels were well defined. Our results revealed high accuracies for the classification of marbling extremes, but there is an evident need to improve the understanding of the biological and visual aspects that led to a sharp drop in classification accuracy after the insertion of the intermediate groups of marbling. In addition, the authors highlight the importance of an accurate labeling process for achieving better classification accuracy when applying supervised classification methods.

Pork is of great importance in world trade and represents the largest source of fatty acids in the human diet. Lipid sources such as soybean oil (SOY), canola (CO), and fish oil (FO) are used in pig diets and influence blood parameters and the ratio of deposited fatty acids. In this study, the main objective was to evaluate changes in gene expression in porcine skeletal muscle tissue resulting from the dietary oil sources and to identify metabolic pathways and biological process networks through RNA-Seq. The addition of FO in the diet of pigs led to intramuscular lipid with a higher FA profile composition of C20:5 n-3, C22:6 n-3, and SFA (C16:0 and C18:0). Blood parameters for the FO group showed lower cholesterol and HDL content compared with CO and SOY groups. Skeletal muscle transcriptome analyses revealed 65 differentially expressed genes (DEG, FDR 10%) between CO vs SOY, and 32 DEG for CO vs FO, and 531 DEG for SOY vs FO comparison. Several genes, including AZGP1, PDE3B, APOE, PLIN1, and LIPS, were found to be down-regulated in the diet of the SOY group compared to the FO group. The enrichment analysis revealed DEG involved in lipid metabolism, metabolic diseases, and inflammation between the oil groups, with specific gene functions in each group and altered blood parameters. The results provide mechanisms to help us understand the behavior of genes according to fatty acids.

São Paulo is the financial center of Brazil, with a population of over 12 million, that receives travelers from all over the world for business and tourism. It was the first city in Brazil to report a case of COVID-19 that rapidly spread across the city despite the implementation of the restriction measures. Despite many reports, much is still unknown regarding the genomic diversity and transmission dynamics of this virus in the city of São Paulo. Thus, in this study, we provide a retrospective overview of the COVID-19 epidemic in São Paulo City, Southeastern, Brazil, by generating a total of 9995 near-complete genome sequences from all the city’s different macro-regions (North, West, Central, East, South, and Southeast). Our analysis revealed that multiple independent introduction events of different variants (mainly Gamma, Delta, and Omicron) occurred throughout time. Additionally, our estimates of viral movement within the different macro-regions further suggested that the East and the Southeast regions were the largest contributors to the Gamma and Delta viral exchanges to other regions. Meanwhile, the North region had a higher contribution to the dispersion of the Omicron variant. Together, our results reinforce the importance of increasing SARS-CoV-2 genomic monitoring within the city and the country to track the real-time evolution of the virus and to detect earlier any eventual emergency of new variants of concern that could undermine the fight against COVID-19 in Brazil and worldwide.

Delta VOC is highly diverse with more than 120 sublineages already described as of November 30, 2021. In this study, through active monitoring of circulating severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) variants in the state of São Paulo, southeast Brazil, we identified two emerging sublineages from the ancestral AY.43 strain which were classified as AY.43.1 and AY.43.2. These sublineages were defined by the following characteristic nonsynonymous mutations ORF1ab:A4133V and ORF3a:T14I for the AY.43.1 and ORF1ab:G1155C for the AY.43.2 and our analysis reveals that they might have a likely‐Brazilian origin. Much is still unknown regarding their dissemination in the state of São Paulo and Brazil as well as their potential impact on the ongoing vaccination process. However, the results obtained in this study reinforce the importance of genomic surveillance activity for timely identification of emerging SARS‐CoV‐2 variants which can impact the ongoing SARS‐CoV‐2 vaccination and public health policies.

São Paulo state has been the epicenter of the Coronavirus Disease 2019 (COVID-19) in Brazil, ranking first by state with over six million reported cases. In February 2021, the P.4 lineage was reported in 21 cities across the state by public health authorities due to the L452R mutation. Here, by analyzing 17,304 genome sequences of SARS-CoV-2 sampled between February and August of 2021 in 476 distinct cities in São Paulo, we assess the transmission dynamics of the P.4 lineage and other SARS-CoV-2 variants that were, at the time of the study, co-circulating in the state. Additionally, clinical parameters from the city of Araras, São Paulo (N = 251) were considered to estimate the potential risk and mortality rate associated with the P.4 lineage since its higher prevalence was observed in that city. Our data suggest a low frequency (0.55%) of the P.4 lineage across the state, with the gamma variant being the dominant form in all regions (90%) at that time. Furthermore, no evidence of increased transmissibility and disease severity related to the P.4 lineage was observed. The displacement through the time of different lineages in São Paulo highlights how challenging genomic surveillance appears to track the emergence of new SARS-CoV-2 lineages, which could better guide the implementation of control measures.

Data integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (-0.47), ribeye area (REA) and protein 4 (prot4) (-0.33), REA and protein 2 (prot2) (-0.3), carcass and prot4 (-0.31), carcass and prot2 (-0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (-0.25). Positive correlations were observed among the four protein factors (0.45-0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships.

Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR < 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle.

This study investigated the effect of prenatal nutrition on liver metabolome and on body (BW) and liver weight (LW) of Nellore bulls at slaughter. Three treatments were applied in 126 cows during pregnancy: NP—control (mineral supplementation); PP—protein-energy supplementation in the third trimester; and FP—protein-energy supplementation during the entire pregnancy. Offspring BW and LW were evaluated, and a targeted metabolomics analysis was performed on their livers (n = 18, 22.5 ± 1 months of age). Data were submitted to principal component analysis (PCA), analysis of variance (ANOVA), enrichment analysis, and Pearson’s correlation analysis. The phenotypes did not show differences between treatments (p > 0.05). Metabolites PCA showed an overlap of treatment clusters in the analysis. We found significant metabolites in ANOVA (p ≤ 0.05; Glycine, Hydroxytetradecadienylcarnitine, Aminoadipic acid and Carnosine). Enrichment analysis revealed some biological processes (Histidine metabolism, beta-Alanine metabolism, and Lysine degradation). Pearson’s correlation analysis showed 29 significant correlated metabolites with BW and 1 metabolite correlated with LW. In summary, prenatal nutrition did not show effects on the phenotypes evaluated, but affected some metabolites and biological pathways, mainly related to oxidative metabolism. In addition, BW seems to influence the hepatic metabolome more than LW, due to the amount and magnitude of correlations found.

•The addition of 4% yerba mate to diet of lambs modified branched-chain fatty acids’ concentration in the meat fatty acid profile.•The diet influenced the color of the meat without affecting quality.•The inclusion of YME does not affect the overall sensory parameters of acceptance in lamb meat.