Proteomic profiling of royal jelly produced by Apis mellifera L. exposed to food containing herbicide-based glyphosate

Royal jelly (RJ) is rich in protective elements associated with collective immune defenses in the hive of Apis mellifera. Exposure of bees to glyphosate-based herbicides causes ultrastructural changes in the hypopharyngeal glands and a reduction in the production of RJ. However, the effects of glyphosate-based herbicides on the protein composition of RJ and consequences for the hive are unknown. Thus, we performed proteomic profiling of royal jelly produced in hives of A. mellifera exposed to food containing 1,5 μL of Roundup® (2.16 mg. g-1 of glyphosate). The production of RJ was carried out in six hives, following the method of artificial production of queens. The combs containing 80 grafting cells were introduced into the hives, and the collection of royal jelly was performed after 72 h. Two treatments were determined based on hive feeding and the hive as the experimental unit: Control and "Roundup®". Royal jelly from the Roundup® treatment hives was compared to the Control hives. Proteins with differences in expression were identified by mass spectrometry. Only the proteins present in all three biological replicates were considered in the differential abundance analysis, using Student's t-test (p-value < 0.05, two-tailed). Hives that received food containing Roundup®, analysis showed alterations in protein profile in the RJ produced therein. In total, 24 proteins were identified, and the accumulation of Major royal jelly protein 3 (MRJP3) was downregulated, showing a significant reduction in hives exposed to food containing Roundup® in relation to control hives (t = 0.0017). MRJP3 acts analogously to polyclonal antigen-antibody reactions, performing functions related to immunity in bees of different ages and castes. To the best of our knowledge, this is the first study to demonstrate changes in the proteomic profile of RJ caused by glyphosate-based herbicides, indicating its negative effects on the nutrition and social immunity of bees.

Genetic divergence in the common bean (Phaseolus vulgaris L.) in the Cerrado-Pantanal ecotone

Evaluating genetic diversity among genotypes is important for providing parameters for the identification of superior genotypes, because the choice of parents that form segregating populations is crucial. Our objectives were to i) evaluate agronomic performance; ii) compare clustering methods; iii) ascertain the relative contributions of the variables evaluated; and iv) identify the most promising hybrids to produce superior segregating populations. The trial was conducted in 2015 at the State University of Mato Grosso do Sul, Brazil. We used a randomized block design with three replications, and recorded the days to emergence, days to flowering, days to maturity, plant height, number of branches, number of pods, number of seeds per pod, weight of 100 grains, and productivity. The genetic diversity of the genotypes was determined by cluster analysis using two dissimilarity measures: the Euclidean distance and the standardized mean Mahalanobis distance using the Ward hierarchical method. The genotypes 'CNFC 10762', 'IAC Dawn', and 'BRS Style' had the highest grain yields, and clusters that were based on the Euclidean distance differed from those based on the Mahalanobis distance, the second being more precise. The yield grain character has greater relevance to the dispute. Hybrids with a high heterotic effect can be obtained by crossing 'IAC Alvorada' with 'CNFC 10762', 'IAC Alvorada' with 'CNFC 10764', and 'BRS Style' with 'IAC Alvorada'.

Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stability

This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.