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Background: Identifying molecular mechanisms responsible for the response to heat stress is essential to increase production, reproduction, health, and welfare. This study aimed to identify early biological responses and potential biomarkers involved in the response to heat stress and animal's recovery in tropically adapted beef cattle through proteomic analysis of blood plasma. Methods: Blood samples were collected from 14 Caracu males during the heat stress peak (HSP) and 16 h after it (heat stress recovery-HSR) assessed based on wet bulb globe temperature index and rectal temperature. Proteome was investigated by liquid chromatography-tandem mass spectrometry from plasma samples, and the differentially regulated proteins were evaluated by functional enrichment analysis using DAVID tool. The protein-protein interaction network was evaluated by STRING tool. Results: A total of 1,550 proteins were detected in both time points, of which 84 and 65 were downregulated and upregulated during HSR, respectively. Among the differentially regulated proteins with the highest absolute log-fold change values, those encoded by the GABBR1, EPHA2, DUSP5, MUC2, DGCR8, MAP2K7, ADRA1A, CXADR, TOPBP1, and NEB genes were highlighted as potential biomarkers because of their roles in response to heat stress. The functional enrichment analysis revealed that 65 Gene Ontology terms and 34 pathways were significant (P < 0.05). We highlighted those that could be associated with the response to heat stress, such as those related to the immune system, complement system, hemostasis, calcium, ECM-receptor interaction, and PI3K-Akt and MAPK signaling pathways. In addition, the protein-protein interaction network analysis revealed several complement and coagulation proteins and acute-phase proteins as important nodes based on their centrality and edges. Conclusion: Identifying differentially regulated proteins and their relationship, as well as their roles in key pathways contribute to improve the knowledge of the mechanisms behind the response to heat stress in naturally adapted cattle breeds. In addition, proteins highlighted herein are potential biomarkers involved in the early response and recovery from heat stress in tropically adapted beef cattle.
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BACKGROUND: In this study we integrated the CNV (copy number variation) and WssGWAS (weighted single-step approach for genome-wide association) analyses to increase the knowledge about number of piglets born alive, an economically important reproductive trait with significant impact on production efficiency of pigs. RESULTS: A total of 3892 samples were genotyped with the Porcine SNP80 BeadChip. After quality control, a total of 57,962 high-quality SNPs from 3520 Duroc pigs were retained. The PennCNV algorithm identified 46,118 CNVs, which were aggregated by overlapping in 425 CNV regions (CNVRs) ranging from 2.5 Kb to 9718.4 Kb and covering 197 Mb (~ 7.01%) of the pig autosomal genome. The WssGWAS identified 16 genomic regions explaining more than 1% of the additive genetic variance for number of piglets born alive. The overlap between CNVR and WssGWAS analyses identified common regions on SSC2 (4.2-5.2 Mb), SSC3 (3.9-4.9 Mb), SSC12 (56.6-57.6 Mb), and SSC17 (17.3-18.3 Mb). Those regions are known for harboring important causative variants for pig reproductive traits based on their crucial functions in fertilization, development of gametes and embryos. Functional analysis by the Panther software identified 13 gene ontology biological processes significantly represented in this study such as reproduction, developmental process, cellular component organization or biogenesis, and immune system process, which plays relevant roles in swine reproductive traits. CONCLUSION: Our research helps to improve the understanding of the genetic architecture of number of piglets born alive, given that the combination of GWAS and CNV analyses allows for a more efficient identification of the genomic regions and biological processes associated with this trait in Duroc pigs. Pig breeding programs could potentially benefit from a more accurate discovery of important genomic regions.
Assuntos
Estudo de Associação Genômica Ampla , Animais , Animais Recém-Nascidos , Mapeamento Cromossômico , Variações do Número de Cópias de DNA , Genótipo , Fenótipo , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , SuínosRESUMO
The objective of this study was to estimate genetic parameters for body weight of beef cattle in performance tests. Different random regression models with quadratic B-splines and heterogeneous residual variance were fitted to estimate covariance functions for body weights of Nellore and crossbred Charolais × Nellore bulls. The criteria −2 residual log-likelihood (−2RLL), Akaike Information Criterion (AIC), and consistent AIC (CAIC) were used to choose the most appropriate model. For Nellore bulls, residual variance was modeled with six classes of age, and direct additive genetic and permanent environment effects were modeled with quadratic B-splines with two and one intervals, respectively. For crossbred bulls, quadratic B-splines with one interval fitted direct additive genetic and permanent environment effects and nine classes of age were needed to fit residual variance. Pooling classes of age with up to 40% in difference of residual variances does not compromise the fit of the model. Heritability for body weight in performance tests are moderate (>0.25, for crossbred bulls) to high (>0.5, for Nellore bulls) and genetic correlation between weights over the test are also high (>0.65). Then, selection of young bulls in performance test is an efficient tool to increase body weight in beef cattle.(AU)