ABSTRACT
Background and Aim: Intestinal infections are associated with Gram-negative bacteria like Escherichia coli. When eliminated by treatments during replication, E. coli release lipopolysaccharides (LPS) that can activate the intestinal immune system and increase the expression of cytokines, such as interleukin (IL)-8, IL-18, tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ), by the intestinal epithelium under pathological conditions. This study aimed to evaluate the addition of Bacillus subtilis to the duodenal gene expression of pro-inflammatory and anti-inflammatory cytokines in broilers exposed to LPS from E. coli. Materials and Methods: RNA was extracted using the Zymo Research total RNA commercial kit, according to the manufacturer's specifications, from the intestinal tissue of the duodenum previously resuspended in the lysis buffer of the kit. The expression of the cytokines of interest was measured using the QuantiNova SYBR green real-time polymerase chain reaction kit (Qiagen). Transcript quantification was performed by the ΔΔC(t) method using glyceraldehyde 3-phosphate dehydrogenase as a normalizing constitutive gene. Results: For the measurement of pro-inflammatory (IL-8, IL-18, TNF-α, and IFN-γ) and anti-inflammatory (IL-10) cytokines, there was no statistically significant difference (p > 0.05) between the basal diet and the diet with antibiotic (avilamycin). There was a statistical difference (p < 0.05) between diets with LPS. The diet with B. subtilis presented the lowest expression; the results differed on each sampling day (days 14, 28, and 42). Conclusion: A decrease in the expression of pro-inflammatory cytokines (IL-8, IL-18, TNF-α, and IFN-γ) and an increase in IL-10 (anti-inflammatory) was observed; in this way, a balance of the inflammatory response to bacterial infection is achieved, suggesting that the use of B. subtilis as an additive in a broiler diet has a similar effect to that produced with antibiotic growth promoter.
ABSTRACT
RESUMEN Objetivo. El objetivo de este estudio fue determinar la precisión y el sesgo de predicción de valores genómicos directos (VGD) usando genotipos imputados a densidad media, en características productivas y reproductivas en ganado Holstein de Antioquia, Colombia. Materiales y métodos. Fueron genotipificados 31 animales con el chip Illumina BovineLD, 64 con el chip Illumina BovineSNP50v2 y 48 con el chip Illumina BovineHD. La imputación se realizó usando dos paneles de SNPs (6K y 40K) a una densidad 44K, usando el programa FINDHAP.f90 v4. Los efectos de los SNPs fueron estimados mediante el método bayes C, usando genotipos de baja densidad (6K) y genotipos imputados a una densidad media (44_imputado). La precisión y el sesgo de los VGDs fueron determinados mediante validación cruzada. Las características evaluadas fueron: producción de leche (PL), porcentaje de proteína (PRO), porcentaje de grasa (GRA), puntaje de células somáticas (SCS), intervalo entre partos (IEP) y días abiertos (DA). Resultados. Las precisiones de VGD (rpVGD.EBV) en todas las características evaluadas oscilaron entre 0.19 y 0.24 y el sesgo (bVGD.EBV) entre 0.03 y 0.16 cuando se usó el panel 6K y usando el panel 44K_imputado las precisiones fueron mayores, oscilado entre 0.24 y 0.33 y sesgo entre 0.03 y 0.26. Conclusiones. La precisión de predicción de los VGDs fue mayor cuando se usaron genotipos imputados a densidad media, en comparación con la precisión de predicción obtenida empleando genotipos de baja densidad. Por lo cual, en este estudio se concluye que la imputación de genotipos es muy útil dado que aumenta la confiabilidad de la evaluación genómica.
ABSTRACT Objective. The goal of this study was to determine the accuracy and bias of direct genomic values (DGV) using imputed genotypes at medium density in yield- and reproduction-related traits for Holstein cattle from Antioquia, Colombia. Materials and Methods. A total of 31 animals were genotyped with the Illumina BovineLD chip, 64 with Illumina BovineSNP50v2 and 48 with Illumina BovineHD. Two SNP panels (6K and 40K) were imputed to a density of 44K using the FINDHAP.f90 v4 program. The effects of the SNPs were estimated using the Bayes C method, using low-density (6K) genotypes as well as medium-density imputed genotypes (44_imputed). The accuracy and bias of the DGVs were determined by cross-validation. The evaluated traits were: milk yield (MY), percentage of protein (PP), percentage of fat (PF), somatic cell score (SCS), calving interval (CI) and open days (OD). Results. When using the 6K panel, the accuracy values for DGV (rpDGV.EBV) in all the studied traits ranged from 0.19 to 0.24, and the bias (bDGV.EBV) from 0.03 to 0.16. In contrast, using the 44K_imputed panel generated higher accuracy values ranging from 0.24 to 0.33 and a bias ranging from 0.03 to 0.26. Conclusions. The accuracy of prediction the DGV was higher with genotypes imputed to medium densities when compared to the accuracy of prediction obtained using low-density genotypes. Therefore, in this study it is concluded that the imputation of genotypes is very useful, because it improves the reliability of the genomic evaluation.
