R. W. Noda

Soybean glycinin improves HDL-C and suppresses the effects of rosuvastatin on hypercholesterolemic rats

BackgroundThis study was an investigation of the effects of ingesting a daily dose of isolated glycinin soy protein (11S globulin), in association with rosuvastatin, on the control of hypercholesterolemia in experimental animals.MethodsMale Wistar rats were kept in individual cages under appropriate controlled conditions of temperature, light and humidity. The animals were divided into five groups (n = 9): 1) standard (STD): fed on casein as protein source; 2) hypercholesterolemic (HC): STD plus 1% cholesterol and 0.5% cholic acid; 3) HC+11S: hypercholesterolemic + glycinin (300 mg/kg/day); 4) HC+ROS: hypercholesterolemic + rosuvastatin (10 mg/kg/day); 5) HC+11S+ROS: HC diet, the 11S protein and the drug in the doses given in (3) and (4). The protein and the drug were administered by gavage for 28 days. The results indicated that the addition of 1% cholesterol and 0.5% cholic acid induced hypercholesterolemia in the animals without interfering with their weight gain.ResultsA single daily dose of glycinin contributed an additional 2.8% of dietary protein intake and demonstrated its functional role, particularly in raising HDL-C, decreasing triglycerides in the liver and improving the atherogenic index in animals exposed to a hypercholesterolemic diet.ConclusionMost of the beneficial effects of the isolated treatments disappeared when the drug (rosuvastatin) and the protein (glycinin) were taken simultaneously. The association was shown not to interact additively, as noted in the plasma levels of total cholesterol and non-HDL cholesterol, and in the significant increase of cholesterol in the liver. Studies are in progress to identify the effects of peptides derived from the 11S globulin and their role in cholesterol metabolism.

Genetic dissection of Al tolerance QTLs in the maize genome by high density SNP scan

BackgroundAluminum (Al) toxicity is an important limitation to food security in tropical and subtropical regions. High Al saturation on acid soils limits root development, reducing water and nutrient uptake. In addition to naturally occurring acid soils, agricultural practices may decrease soil pH, leading to yield losses due to Al toxicity. Elucidating the genetic and molecular mechanisms underlying maize Al tolerance is expected to accelerate the development of Al-tolerant cultivars.ResultsFive genomic regions were significantly associated with Al tolerance, using 54,455 SNP markers in a recombinant inbred line population derived from Cateto Al237. Candidate genes co-localized with Al tolerance QTLs were further investigated. Near-isogenic lines (NILs) developed for ZmMATE2 were as Al-sensitive as the recurrent line, indicating that this candidate gene was not responsible for the Al tolerance QTL on chromosome 5, qALT5. However, ZmNrat1, a maize homolog to OsNrat1, which encodes an Al3+ specific transporter previously implicated in rice Al tolerance, was mapped at ~40 Mbp from qALT5. We demonstrate for the first time that ZmNrat1 is preferentially expressed in maize root tips and is up-regulated by Al, similarly to OsNrat1 in rice, suggesting a role of this gene in maize Al tolerance. The strongest-effect QTL was mapped on chromosome 6 (qALT6), within a 0.5 Mbp region where three copies of the Al tolerance gene, ZmMATE1, were found in tandem configuration. qALT6 was shown to increase Al tolerance in maize; the qALT6-NILs carrying three copies of ZmMATE1 exhibited a two-fold increase in Al tolerance, and higher expression of ZmMATE1 compared to the Al sensitive recurrent parent. Interestingly, a new source of Al tolerance via ZmMATE1 was identified in a Brazilian elite line that showed high expression of ZmMATE1 but carries a single copy of ZmMATE1.ConclusionsHigh ZmMATE1 expression, controlled either by three copies of the target gene or by an unknown molecular mechanism, is responsible for Al tolerance mediated by qALT6. As Al tolerant alleles at qALT6 are rare in maize, marker-assisted introgression of this QTL is an important strategy to improve maize adaptation to acid soils worldwide.

FluxTransgenics: a flexible LIMS-based tool for management of plant transformation experimental data

BackgroundThe production and commercial release of Genetically Modified Organisms (GMOs) are currently the focus of important discussions. In order to guarantee the quality and reliability of their trials, companies and institutions working on this subject must adopt new approaches on management, organization and recording of laboratory conditions where field studies are performed. Computational systems for management and storage of laboratory data known as Laboratory Information Management Systems (LIMS) are essential tools to achieve this.ResultsIn this work, we have used the SIGLa system – a workflow based LIMS as a framework to develop the FluxTransgenics system for a GMOs laboratory of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Maize and Sorghum (Sete Lagoas, MG - Brazil). A workflow representing all stages of the transgenic maize plants generation has been developed and uploaded in FluxTransgenics. This workflow models the activities involved in maize and sorghum transformation using the Agrobacterium tumefaciens method. By uploading this workflow in the SIGLa system we have created Fluxtransgenics, a complete LIMS for managing plant transformation data.ConclusionsFluxTransgenics presents a solution for the management of the data produced by a laboratory of genetically modified plants that is efficient and supports different kinds of information. Its adoption will contribute to guarantee the quality of activities and products in the process of transgenic production and enforce the use of Good Laboratory Practices (GLP).The adoption of the transformation protocol associated to the use of FluxTransgenics has made it possible to increase productivity by at least 300%, increasing the efficiency of the experiments from between 0.5 and 1 percent to about 3%. This has been achieved by an increase in the number of experiments performed and a more accurate choice of parameters, all of which have been made possible because it became easier to identify which were the most promising next steps of the experiments. The FluxTransgenics system is available for use by other laboratories, and the workflows that have been developed can be adapted to other contexts.

Genomic prediction applied to high-biomass sorghum for bioenergy production

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids’ genotypes were inferred based on their parents’ genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.

Quantitative Trait Loci and Resistance Gene Analogs Associated with Maize White Spot Resistance

Maize white spot (MWS), caused by the bacterium Pantoea ananatis, is one of the most important maize foliar diseases in tropical and subtropical regions, causing significant yield losses. Despite its economic importance, genetic studies of MWS are scarce. The aim of this study was to map quantitative trait loci (QTL) associated with MWS resistance and to identify resistance gene analogs (RGA) underlying these QTL. QTL mapping was performed in a tropical maize F2:3 population, which was genotyped with simple-sequence repeat and RGA-tagged markers and phenotyped for the response to MWS in two Brazilian southeastern locations. Nine QTL explained approximately 70% of the phenotypic variance for MWS resistance at each location, with two of them consistently detected in both environments. Data mining using 112 resistance genes cloned from different plant species revealed 1,697 RGA distributed in clusters within the maize genome. The RGA Pto19, Pto20, Pto99, and Xa26.151.4 were genetically mapped within MWS resistance QTL on chromosomes 4 and 8 and were preferentially expressed in the resistant parental line at locations where their respective QTL occurred. The consistency of QTL mapping, in silico prediction, and gene expression analyses revealed RGA and genomic regions suitable for marker-assisted selection to improve MWS resistance.

Phenotypic and molecular characterization of sweet sorghum accessions for bioenergy production.

Sweet sorghum [Sorghum bicolor (L.) Moench] is a type of cultivated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass accumulation, making this crop an important feedstock for bioenergy production. Sweet sorghum breeding programs that focus on bioenergy have two main goals: to improve quantity and quality of sugars in the juicy stem and to increase fresh biomass productivity. Genetic diversity studies are very important for the success of a breeding program, especially in the early stages, where understanding the genetic relationship between accessions is essential to identify superior parents for the development of improved breeding lines. The objectives of this study were: to perform phenotypic and molecular characterization of 100 sweet sorghum accessions from the germplasm bank of the Embrapa Maize and Sorghum breeding program; to examine the relationship between the phenotypic and the molecular diversity matrices; and to infer about the population structure in the sweet sorghum accessions. Morphological and agro-industrial traits related to sugar and biomass production were used for phenotypic characterization, and single nucleotide polymorphisms (SNPs) were used for molecular diversity analysis. Both phenotypic and molecular characterizations revealed the existence of considerable genetic diversity among the 100 sweet sorghum accessions. The correlation between the phenotypic and the molecular diversity matrices was low (0.35), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. Furthermore, the clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis. The low correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program.