Proteomic Analysis of Liver from Finishing Beef Cattle Supplemented with a Rumen-Protected B-Vitamin Blend and Hydroxy Trace Minerals.

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.

Intramuscular collagen characteristics and expression of related genes in skeletal muscle of cull cows receiving a high-energy diet.

We aimed to investigate differences in the synthesis and metabolism of intramuscular collagen in the Longissimus thoracis (LT) muscle between heifers and cull-cows fed high-energy diet. Ten cull-cows, (74.9 ± 3.2 months age, weighing 536 ± 14.55 kg) and ten heifers (18.4 ± 3.2 months age, weighting 310.5 ± 14.5 kg) were fed with high-energy diets for 150 days. The total collagen content did not differ between treatments. Greater collagen solubility was observed in heifers than cull-cows, although no differences in lysyl oxidase activity were observed between treatments. No differences were observed for mRNA expression of CO1A1, MMP2, MMP9 and TIMP2 between treatments. However, cull-cows presented greater mRNA expression of COL3A1, TIMP1 and TIMP3 than heifers. Our data give no indication that feeding a high-energy diet to cull-cows decreases the concentration of intramuscular collagen in the LT muscle or increases its solubility in respect to the collagen solubility in LT muscles from heifers on the same diet.

Quantile regression for genome-wide association study of flowering time-related traits in common bean.

Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits (Days to first flower-DFF; Days for flowering-DTF; Days to end of flowering-DEF) in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.

Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data.

Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together.