Análise da expressão dos genes da subfamília DREB em resposta à seca em plantas de Coffea canephora Conilon

Coffea canephora is one of the most economically important coffee species and in Brazil, Conilon is the most widely cultivated plant of this species. Abiotic stresses such as temperature variations and drought periods are factors that significantly affect their production and tend to worsen with globally recognized climate changes. In an attempt to understand the molecular responses of coffee plants in water deficit conditions, recent studies have identified candidate genes (CGs) as CcDREB1D. This gene showed increased expression in response to drought in the leaves of clone 14 (drought tolerant) in relation to the clone 22 (sensitive to drought) of C. canephora Conilon. Based on these results, the identification of DREB genes and their subgroups (SGs) of C. canephora, the objective is to analyze in silico and also in vivo these genes expression in leaf and root of tolerant (14, 73 and 120) and sensitive clones (22) of C. canephora Conilon submitted or not to a water deficit. In silico expressions of all DREB genes were analyzed from the Coffee Genome Hub Database and in vivo expression was performed by the technique "reverse transcription-quantitative PCR" (RT-qPCR). In silico gene expression analysis was possible to identify DREB genes with potential responses to abiotic stresses, corroborating some validated in vivo results. In this analysis, several genes showed differential expression in response to drought among the SGs (IIV), the tolerant and sensitive clones and the leaf and root. These differentially expressed genes were identified as potential CGs and among them, it was found that most tolerant clones showed increased expression in relation to sensitive in response to drought, with higher expression levels for clones 14 and 73. These highest levels were observed in leaves compared to the roots and SG-I stood at greater number of genes expressed in response to drought. These results suggest that DREB CGs, as Cc05_g06840, Cc02_g03420 e Cc08_g13960, play an important role in the regulatory mechanisms of response to drought in C. canephora Conilon.

Capacidade combinatória de linhagens e seleção de híbridos de sorgo sacarino

Sweet sorghum figure as an alternative feedstock for ethanol production. The establishment of this culture in Brazilian production chain depends on the development of more productive and adapted cultivars. The aim of this study was to evaluate the general combining ability (GCA) of sweet sorghum lines and specific combining ability (SCA) of hybrid combinations as the agronomic and technological traits, and additionally to identify promising hybrid combinations for evaluation in advanced trials. Five restorer lines (R) and four male-sterile lines (A) were used in a partial cross diallel yielding 20 hybrids. The parental lines, hybrids and one check were evaluated in experiments carried out in a rectangular lattice design 5x6 with three replicates in two locations. The following traits were measured: flowering time, plant height, green mass yield, dry matter percentage, dry matter yield, juice extraction, total soluble solids content, sucrose content, purity, reducing sugars content, fiber content, sugars reducing total content, total recoverable sugars, hydrous ethanol, tons of per hectare, and ethanol production. There were differences between locations and genotypes for the traits. There was a significant effect of the genotype by environment interaction for most characters, except juice extraction, purity and reducing sugars content. There were a significant effect of GCA and SCA for most traits, indicating that additive and non-additive effects affect the phenotypic expression. Considering the effects of the GCA, the A line 201402B022-A, and R lines BRS 511, CMSXS643, and CMSXS646 were considered promising for exploration as parents in breeding programs of sweet sorghum in order to increase the ethanol production and the quality of the feedstock.The hybrids 201402B010-A x BRS 511, 201402B010-A x BRS 508, 201402B010-A x CMSXS646, 201402B022-A x BRS 511, 201402B022-A x CMSXS643, 201402B022-A x CMSXS646, 201402B022-A x CMSXS647 were the most promising for ethanol yield.

Arginina para matrizes suínas primíparas em lactação

With the emergence of new genetic lines due to intense breeding improvement on swine production in recent years, there is the need to adapt more accurately diets for the current sows, which have higher nutritional demands. The use of functional amino acids aimsto optimize the sows production and among these amino acids arginine has excelled. Arginine is involved in several important metabolic pathways, for example, it serves as a substrate forsynthesis of protein, creatine, nitric oxide, polyamines, citrulline, agmatine, ornithine, proline, and glutamate. It also helps to stimulate the secretion of some hormones such as insulin, prolactin, and growth hormone.As arginine plays such important roles, its supplementation has been suggested in lactation feed once it may enhance the development of the mammary gland and milk nutritional profile, thus, providing a better piglet development.Thus, the objective was to evaluate the effect of lactation feed supplementation with L-Arginine on the productive performance of primiparoussows and their respective litter.One hundred forty sows from the same genetic lineage on a commercial farm, located in the city of Oliveira, MG were used in this study, in a completely randomized design with five treatments: control diet without amino acid supplementation and four diets with increasing levels of L-Arginine supplementation (containing 98.5% purity) - 0.5, 1.0, 1.5, and 2.0%. Each treatment hadtwenty-eight swine sows, and the experimental unit was the sowand its litter.It was used ‘on top’ amino acid supplementation.All data was submitted to variance analysis using the SAEG Software: version 9.1 (SAEG, 2005).The data relating to days of lactation were compared by Tukey test (5%). L-Arginine supplementation levels in lactation feed did not influence (P>0.05) average daily feed intake, body condition variables, and blood parameters of the sows (urea, creatinine, and non-esterified fatty acids) as well as it did not affect the dry matter, crude protein, and amino acid profile of milk and the litter performance. There was effect (P<0.05) of days of lactation on the percentage of crude protein and amino acids in milk, which reduced througout the days of lactation. The L-Arginine supplementation on the lactation diet at levels of 0.5, 1.0, 1.5, and 2.0% did not influence the sow and its respective litter performance.

Estabilidade e adaptabilidade de híbridos de milho utilizando a abordagem AMMI-bayesiano

Biplot graphics are widely employed in the study of the genotypeenvironment interactions, but they are only a graphical tool without a statistical hypothesis test. The singular values and scores (singular vectors) used in biplots correspond to specific estimates of its parameters, and the use of uncertainty measures may lead to different conclusions from those provided by a simple visual evaluation. The aim of this work is to estimate the genotype-environment interactions, using AMMI analysis, through Bayesian approach. Therefore the credibility intervals can be used for decision-making in different situations of analyses. It allows to verify the consistency of the selection and recommendation of cultivars. Two analyses were performed. The first analysis looked into 10 regular commercial hybrids and all possible 45 hybrids obtained from them. They were assessed in 15 locations. The second analysis evaluated 28 hybrids in 35 different environments, with imbalance data. The ellipses were grouped according to the standard of interaction in the biplot. The AMMI analysis with a Bayesian approach proved to be a complete analysis of stability and adaptability, which provides important information that may help the breeder in their decisions. The regions of credibility, built in the biplots, allow to perform an accurate selection and a precise genotype recommendation, with a level of credibility. Genotypes and environments can be grouped according to the existing interaction pattern, which makes possible to formulate specific recommendations. Moreover the environments can be evaluated, in order to find out which ones contribute similarly to the interaction and those to be discarted. The method makes possible to deal with imbalanced data in a natural way, showing efficiency for multienvironment trials. The prediction takes into account instability and the interaction standard of the observed data, in order to establish a direct comparison between genotypes of both 1st and 2nd seasons.