RESUMO

Sex segregation increases the cost of Carica papaya production through seed-based propagation. Therefore, in vitro techniques are an attractive option for clonal propagation, especially of hermaphroditic plants. Here, we performed a temporal analysis of the proteome of C. papaya calli aiming to identify the key players involved in embryogenic callus formation. Mature zygotic embryos used as explants were treated with 20 µM 2,4-dichlorophenoxyacetic acid to induce embryogenic callus. Total proteins were extracted from explants at 0 (zygotic embryo) and after 7, 14, and 21 days of induction. A total of 1407 proteins were identified using a bottom-up proteomic approach. The clustering analysis revealed four distinct patterns of protein accumulation throughout callus induction. Proteins related to seed maturation and storage are abundant in the explant before induction, decreasing as callus formation progresses. Carbohydrate and amino acid metabolisms, aerobic respiration, and protein catabolic processes were enriched throughout days of callus induction. Protein kinases associated with auxin responses, such as SKP1-like proteins 1B, accumulated in response to callus induction. Additionally, regulatory proteins, including histone deacetylase (HD2C) and argonaute 1 (AGO1), were more abundant at 7 days, suggesting their role in the acquisition of embryogenic competence. Predicted protein-protein networks revealed the regulatory role of proteins 14-3-3 accumulated during callus induction and the association of proteins involved in oxidative phosphorylation and hormone response. Our findings emphasize the modulation of the proteome during embryogenic callus initiation and identify regulatory proteins that might be involved in the activation of this process.

RESUMO

The use of longitudinal measurements is an essential practice both in Psidium guajava L. breeding and in other perennial crops in which covariance structures can be introduced to explain the form of dependence between measurements. Hence, this study aimed to analyze six covariance structures to identify one that best described the correlation between the repeated measurements in time in traits of guava full-sib families. The repeatability coefficient for each trait was estimated and the minimum number of evaluations required for estimates representing the population was determined. The work was performed based on average data of three yield-related variables from nine harvests of a guava tree population evaluated from 2011 to 2018. The best model was chosen based on the Akaike and Schwarz Bayesian information criterion. The autoregressive covariance structure best represented the dependencies among families between crops for all traits. The number of variables of fruits and total yield per plant presented repeatability estimates higher than 0.5 and may be essential traits for indirect selection of others, such as fruit mass, which had an estimated repeatability of 0.24, proving low regularity in the repetition of the character from one cycle to another. It was also possible to define four harvests as the minimum acceptable number of observations necessary on the same individual for these traits; therefore, the repetitions represented the individuals.(AU)

Assuntos

RESUMO

This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.

RESUMO

The objective of this work was to use the Bayesian approach, modeling the interaction of coffee genotypes with the environment, using a bisegmented regression to identify stable and adapted genotypes. A group of 43 promising genotypes of Coffea canephora was chosen. The genotypes were arranged in a randomized block design with three replications of seven plants each. The experimental plot was harvested four years in the study period, according to the maturation cycle of each genotype. The proposed Bayesian methodology was implemented in the free program R using rstanarm and coda packages. It was possible to use previous information on coffee genotypes as prior information on parameter distributions of an Adaptability and Stability model, which allowed obtaining shorter credibility intervals and good evidence of low bias in the model by the determination coefficient. After fine adjustments in the approach, it was possible to make inferences about the significant GxE interaction and to discriminate the coffee genotypes regarding production, adaptability, and stability. This is still a new approach for perennials, and since it allows more accurate estimates it can be advantageous when planning breeding programs. The Z21 genotype is recommended to compose part of selected genetic material for highly technical farmers, as it responds very well to the favorable environment, being one of the most productive and with excellent stability.

Assuntos

RESUMO

We investigated the proteomic profiles of two popcorn inbred lines, P2 (N-efficient and N-responsive) and L80 (N-inefficient and nonresponsive to N), under low (10% of N supply) and high (100% of N supply) nitrogen environments, associated with agronomic- and physiological-related traits to NUE. The comparative proteomic analysis allowed the identification of 79 differentially accumulated proteins (DAPs) in the comparison of high/low N for P2 and 96 DAPs in the comparison of high/low N for L80. The NUE and N uptake efficiency (NUpE) presented high means in P2 in comparison to L80 at both N levels, but the NUE, NUpE, and N utilization efficiency (NUtE) rates decreased in P2 under a high N supply. DAPs involved in energy and carbohydrate metabolism suggested that N regulates enzymes of alternative pathways to adapt to energy shortages and that fructose-bisphosphate aldolase may act as one of the key primary nitrate responsive proteins in P2. Proteins related to ascorbate biosynthesis and nitrogen metabolism increased their regulation in P2, and the interaction of L-ascorbate peroxidase and Fd-NiR may play an important role in the NUE trait. Taken together, our results provide new insights into the proteomic changes taking place in contrasting inbred lines, providing useful information on the genetic improvement of NUE in popcorn.

Assuntos

RESUMO

The method of regional heritability mapping (RHM) has become an important tool in the identification of quantitative trait loci (QTLs) controlling traits of interest in plants. Here, RHM was first applied in a breeding population of popcorn, to identify the QTLs and candidate genes involved in grain yield, plant height, kernel popping expansion, and first ear height, as well as determining the heritability of each significant genomic region. The study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14, which were genetically evaluated in two environments (ENV1 and ENV2). Seventeen and five genomic regions were mapped by the RHM method in ENV1 and ENV2, respectively. Subsequent genome-wide analysis based on the reference genome B73 revealed associations with forty-six candidate genes within these genomic regions, some of them are considered to be biologically important due to the proteins that they encode. The results obtained by the RHM method have the potential to contribute to knowledge on the genetic architecture of the growth and yield traits of popcorn, which might be used for marker-assisted selection in breeding programs.

RESUMO

Drought is a common abiotic stress in tropical and subtropical regions that limits the growth and development of agricultural crops, mainly impacting grain yield. Acting through plant breeding is the most viable alternative for obtaining genotypes more tolerant of environments with stress. This work aims to select popcorn genotypes for environments with drought and to identify discriminating traits for the evaluation of drought tolerance in popcorn germplasm. Fifteen Latin American populations of popcorn were evaluated in water stress (WS) and well-watered (WW) conditions. The evaluated traits were based in morpho-agronomic, physiological and radicular descriptors. Data were submitted to individual and joint ANOVA and GT Biplot analysis. Variability was detected between populations for all traits in both conditions. The drought caused a reduction of 30.61% and 3.5% in grain yield and popping expansion, respectively. Based in GT biplot analysis, 880POP was the most stable in WS and WW, being indicated as a promising population for cultivation in environments with water limitation. This study is going to allow the establishment of a collection of great importance to maize germplasm and to provide information to facilitate the process of selection in breeding programs focused on drought tolerance.

RESUMO

Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to [Formula: see text] = [Formula: see text]), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual's genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.

RESUMO

Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.

Assuntos

RESUMO

Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix XX. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.(AU)

Assuntos