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Aluminum (Al) toxicity on acid soils adversely affects maize yields, which can be overcome by combining soil amendments with genetic tolerance. In maize, ZmMATE1 confers Al tolerance via Al-activated citrate release, whereby citrate forms non-toxic complexes with Al3+ in the rhizosphere. Here, we investigated Al tolerance mechanisms in maize germplasm originated from Kenya based on quantitative trait loci (QTL) mapping. Five QTLs and four epistatic interactions explained ~51% of the phenotypic variation for Al tolerance. The lack of Al tolerance QTL on chromosome 6 and the much lower expression of ZmMATE1 in both Kenyan lines than in Cateto Al237, which donates the superior allele of ZmMATE1, strongly indicate that this gene does not play a significant role in Al tolerance in neither parent. In turn, maize homologs to genes previously implicated in Al tolerance in other species, ZmNrat1, ZmMATE3, ZmWRKY and ZmART1, co-localized with Al tolerance QTL and were more highly expressed in the parent that donate favorable QTL alleles. However, these candidate genes will require further studies for functional validation on maize Al tolerance. The existence of Al tolerance mechanisms independent from ZmMATE1 suggests it is possible to develop highly Al tolerant cultivars by pyramiding complementary Al tolerance genes in maize.

During the past decade, sweet sorghum (Sorghum bicolor Moench L.) has shown great potential for bioenergy production, especially biofuels. In this study, 223 recombinant inbred lines (RILs) derived from a cross between two sweet sorghum lines (Brandes Wray) were evaluated in three trials. Single-nucleotide polymorphisms (SNPs) derived from genotyping by sequencing of 272 RILs were used to build a high-density genetic map comprising 3,767 SNPs spanning 1,368.83 cM. Multitrait multiple interval mapping (MT-MIM) was carried out to map quantitative trait loci (QTL) for eight bioenergy traits. A total of 33 QTLs were identified for flowering time, plant height, total soluble solids and sucrose (five QTLs each), fibers (four QTLs), and fresh biomass yield, juice extraction yield, and reducing sugars (three QTLs each). QTL hotspots were found on chromosomes 1, 3, 6, 9, and 10, in addition to other QTLs detected on chromosomes 4 and 8. We observed that 14 out of the 33 mapped QTLs were found in all three trials. Upon further development and validation in other crosses, the results provided by the present study have a great potential to be used in marker-assisted selection in sorghum breeding programs for biofuel production. Made available in DSpace on 2021-10-22T12:00:59Z (GMT). No. of bitstreams: 1 QTL-mapping.pdf: 757265 bytes, checksum: 1af729a3c697df5e85fedb3abadb4a74 (MD5) Previous issue date: 2021

O desenvolvimento de culturas geneticamente modificadas (GM) inclui a descoberta de genes candidatos através de análises de bioinformática, usando dados genômicos, expressão gênica, entre outros. As proteínas de função desconhecida (PFD) são alvos interessantes para pipelines de culturas GM devido a novidade associada a esses alvos e também para evitar proteções de direitos autorais. Um método para inferir a possível função das PFD é relacioná-las a fatores de interesse, como estresses abióticos, usando redes de ortologia e coexpressão, aplicando a abordagem de culpa por associação. O objetivo desse trabalho é desenvolver e disponibilizar a versão 2 da ferramenta PlantAnnot (PlantAnnot2) e o objetivo do PlantAnnot2 é a descoberta de PFD envolvidas em respostas a estresses abióticos em plantas. Para isso, foram processados dados genômicos de 67 plantas, com algumas importantes espécies tolerantes a stresses abióticos. Os softwares Diamond e InterproScan foram usados para descobrir PFDs em todas as plantas. Dados de expressão gênica (RNA-seq) relacionados a estresses abióticos foram baixados do NCBI / GEO e usados no software LSTrAP para construir clusters de coexpressão cujos membros estão mais provavelmente relacionados aos mecanismos moleculares associados ao estresse abiótico em plantas. Grupos de ortólogos foram criados com OrthoFinder, depois foram buscadas PFDs associadas a esses grupos de ortólogos e também a clusters de coexpressão simultâneamente. Como resultado, foram armazenados no Machado 2.136.336 genes e 2.714.161 mRNA, juntamente com suas proteínas traduzidas. Recuperados 78.416 PFDs com as análises de Diamond e Interproscan, criados 91.172 grupos de ortólogos e 1.975 clusters de coexpressão. Foi desenvolvido um protocolo para busca de anotações de PFDs, que recupera PFDs que pertençam a algum grupo de ortólogos que contenha também proteínas cujo mRNA faz parte de clusteres de coexpressão relacionados à estresses abióticos. Dessa forma, foram recuperadas s 4.673 PFDs, na versão 1 do sistema tinha sido 1.364 PFDs. Foi realizado uma pesquisa bibliográfica sobre as proteínas que pertencem aos grupos ortólogos, para todos os PFDs pertencentes às espécies Pearl millet(Cenchrus americanus), Populus simonii, Oropethium thomaeum e Boea hygrometrica, todos conhecidos por serem tolerantes a estresses abiótico (517 PFDs). Encontrados estudos relacionados a estresses abióticos, em média, para 67,5% das PFDs, na versão 1 do sistema o percentual era de 35%. Um servidor web https://www.machado.cnptia.embrapa.br/plantannot2 está disponível gratuitamente e fornece consultas indexadas de PFDs possivelmente associadas a estresses abióticos. Espera-se que o PlantAnnot2 seja útil para pesquisadores que buscam obter genes relacionados a respostas a estresses abióticos para a produção de novos cultivos GM tolerantes aos riscos das mudanças climáticas. The development of genetically modified (GM) crops includes the discovery of candidate genes through bioinformatics analysis, using genomic data, gene expression, among others. Proteins of unknown function (PUF) are interesting targets for GM crop pipelines due to the novelty associated with these targets and also to avoid copyright protections. One method to infer the possible function of PUF is to relate them to factors of interest, such as abiotic stresses, using orthology and coexpression networks, applying the guilt by association approach. The objective of this work is to develop and make available version 2 of the PlantAnnot tool (PlantAnnot2) and the objective of PlantAnnot2 is the discovery of PFD involved in responses to abiotic stresses in plants. For that, genomic data from 67 plants were processed, with some important species tolerant to abiotic stresses. Diamond and InterproScan software were used to discover PUF in all plants. Gene expression data (RNA-seq) related to abiotic stresses were downloaded from NCBI / GEO and used in the LSTrAP software to build coexpression clusters whose members are most likely related to the molecular mechanisms associated with abiotic stress in plants. Groups of orthologous were created with OrthoFinder, then PUF associated with these groups of orthologous and also with coexpression clusters were searched simultaneously. As a result, 2,136,336 genes and 2,714,161 mRNA were stored in Machado, along with their translated proteins. 78,416 PUF were recovered with Diamond and Interproscan analyzes, 91,172 groups of orthologous and 1,975 coexpression clusters were created. A protocol for searching PUF annotations was developed, which retrieves PUF that belong to a group of orthologous that also contain proteins whose mRNA is part of coexpression clusters related to abiotic stresses. Thus, 4,673 PUF were recovered, in version 1 of the system it had been 1,364 PUF. A bibliographic search was carried out on proteins belonging to orthologous groups, for all PUF belonging to the species Pearl millet (Cenchrus americanus), Populus simonii, Oropethium thomaeum and Boea hygrometrica, all known to be tolerant to abiotic stresses (517 PUF). Studies related to abiotic stresses were found, on average, for 67.5% of PUF, in version 1 of the system the percentage was 35%. A web server https://www.machado.cnptia.embrapa.br/plantannot2 is available for free and provides indexed queries for PUF possibly associated with abiotic stresses. PlantAnnot2 is expected to be useful for researchers looking to obtain genes related to responses to abiotic stresses for the production of new GM crops tolerant to the risks of climate change.

Weighted outperformed unweighted genomic prediction using an unbalanced dataset representative of a commercial breeding program. Moreover, the use of the two cycles preceding predictions as training set achieved optimal prediction ability. Predicting the performance of untested single-cross hybrids through genomic prediction (GP) is highly desirable to increase genetic gain. Here, we evaluate the predictive ability (PA) of novel genomic strategies to predict single-cross maize hybrids using an unbalanced historical dataset of a tropical breeding program. Field data comprised 949 single-cross hybrids evaluated from 2006 to 2013, representing eight breeding cycles. Hybrid genotypes were inferred based on their parents’ genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GP analyses were fitted using genomic best linear unbiased prediction via a stage-wise approach, considering two distinct cross-validation schemes. Results highlight the importance of taking into account the uncertainty regarding the adjusted means at each step of a stage-wise analysis, due to the highly unbalanced data structure and the expected heterogeneity of variances across years and locations of a commercial breeding program. Further, an increase in the size of the training set was not always advantageous even in the same breeding program. The use of the two cycles preceding predictions achieved optimal PA of untested single-cross hybrids in a forward prediction scenario, which could be used to replace the first step of field screening. Finally, in addition to the practical and theoretical results applied to maize hybrid breeding programs, the stage-wise analysis performed in this study may be applied to any crop historical unbalanced data.

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, BayesC?, 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. Made available in DSpace on 2018-09-25T00:42:59Z (GMT). No. of bitstreams: 1 Genomicprediction.pdf: 1227569 bytes, checksum: 4d1c64b7f50c1e67001257591223fc3b (MD5) Previous issue date: 2018-09-24

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. Made available in DSpace on 2017-08-25T19:13:00Z (GMT). No. of bitstreams: 1 Phenotypicmolecular.pdf: 4534558 bytes, checksum: 7e98a0d3b0adcbdf79eb8bd2f106509c (MD5) Previous issue date: 2017-08-24

Sorghum bicolor (L.) Moench is cultivated in several tropical and subtropical regions in the world. Among the diseases, the mosaic caused by potyvirus is an important constraint for the agricultural production causing reduction in grain and forage sorghum production. In Brazil, only Sugarcane mosaic virus (SCMV) had previously been reported as the potyvirus species causing mosaic in sorghum and maize. A survey was carried out in sorghum plantations of the State of Minas Gerais, Brazil, during the 2014/2015 crop season for monitoring mosaic disease. Samples of sorghum plants expressing virus disease symptoms were collected for molecular analyzes. Molecular characterization of coat protein (CP) of the potyviruses naturally infecting sorghum, allowed us to identify the Johnsongrass mosaic virus (JGMV) as a new causal agent of mosaic disease in sorghum in Brazil. The sequences of the Brazilian JGMV sorghum-infecting (JGMV-Sr) isolates were deposited in the GenBank under the accession numbers KY952241, KY952242, and KY952243. Comparisons of the CP gene sequences of these Brazilian JGMV-Sr isolates revealed high nucleotide (nt) and amino acid (aa) sequence identities, ranging from 97.93 to 98.23%, and 99.12 to 99.20%, respectively, with the U07218.1 (JGMV-MDKS1) isolate. The Brazilian JGMV-Sr isolates were distinct from the Brazilian forage grasses-infecting (JGMV-Fg) isolates (KT833782 and KT289893). Transmission evaluations showed susceptibility of the teosinte, Sorghum verticilliflorum and Sorghum bicolor (L.) Moench, except line QL3.Maize and sugarcane genotypes were not infected by the Brazilian JGMV-Sr isolate. However, it is important to test more genotypes. This is the first report showing the identification and molecular characterization of the JGMV species naturally infecting sorghum at field conditions, expanding the knowledge about the dynamic and range of the mosaic causal agent for this crop in Brazil. Made available in DSpace on 2018-01-23T16:04:50Z (GMT). No. of bitstreams: 1 Johnsongrassmosaic.pdf: 771661 bytes, checksum: 54e279ad7c4671a4dd3af758a11bfafb (MD5) Previous issue date: 2018-01-16

The 'maize common mosaic', caused by potyvirus, is among the major virus diseases of this crop in Brazil. Although there were evidences indicating Sugarcane mosaic virus (SCMV) as the most common potyvirus species in maize (Zea mays L.) in Brazil, information about those species that infect sorghum plants [Sorghum bicolor(L.) Moench] are few. Leaves showing characteristic mosaic symptoms were collected from maize and sorghum and used in serological and sequencing analysis of the coat protein (CP) gene for potyvirus species identification. Amino acid (aa) analysis of the CP N-terminal sequence of our samples showed a different repeated sequence, a higher content of the dipeptide GT, and a 15 aa longer than the majority of the SCMV sequences used for comparisons. The Brazilian maize and sorghum potyviruses formed a monophyletic group, suggesting that they can be classified within a new SCMV strain. Studies using potyvirus CP gene sequencing from Brazilian sorghum potyvirus have been reported for the first time.

O Al tóxico é um dos principais fatores limitantes ao crescimento radicular em solos ácidos, acarretando sérias limitações á produção agrícola. Estes solos estão amplamente distribuídos nas regiões tropicais e subtropicais, englobando cerca de 50% das terras agricultáveis no planeta. O desenvolvimento de genótipos tolerantes ao Al é uma alternativa sustentável para superar as limitações causadas pelos solos ácidos. A tolerância ao Al em milho é uma característica complexa, envolvendo possivelmente múltiplos genes e mecanismos, que não estão bem compreendidos até o momento. No presente trabalho, foram avaliados 36.147 marcadores SNPs gerados pela técnica de genotipagem por sequenciamento (GBS), 39 SSRs e três genes candidatos em uma população de RILs derivada do cruzamento entre duas linhagens altamente contrastantes quanto à tolerância ao Al. A associação entre marcadores e fenótipo foi realizada por meio de modelos lineares generalizados (GLM) e do mapeamento de QTLs por intervalos múltiplos (MIM), sendo identificados oito QTLs nos cromossomos 2, 3, 4, 5, 6 e 8. Um QTL de efeito maior, explicando 22% da variância genotípica da tolerância ao Al, foi identificado no cromossomo 6 (bin 6.00), corroborando com resultados previamente publicados. Nessa região, além do gene candidato ZmMATE1, dois QTLs de expressão (eQTL) foram mapeados flanqueando o gene alvo, sendo considerados como cis eQTLs. Essa região genômica foi transferida para linhagens semi-isogênicas de milho, resultando em um aumento de duas vezes na tolerância ao Al, associados com um aumento na expressão do ZmMATE1. Tais resultados validam o QTL6 como uma região genômica capaz de aumentar a tolerância ao Al em milho. O gene candidato ZmMATE2 foi co-localizado com o segundo QTL de maior efeito para a tolerância ao Al, mas não foi validado nas linhagens semi-isogências de milho. No entanto, um trans eQTL explicando 24% da variação genotípica da expressão do ZmMATE2 foi mapeado no cromossomo 3. O uso de uma elevada densidade de marcadores permitiu um aumento considerável na precisão dos QTLs identificados, cujos intervalos de confiança foram restringidos entre 1,7 e 31,7 Mb. A integração entre informações do mapa genético com distância física foi possível devido ao alinhamento das sequências dos SNP no genoma de referência do milho, outra grande vantagem dos marcadores baseados na técnica de GBS. Assim, os resultados gerados contribuem com informações relevantes que podem ser aplicadas diretamente no melhoramento assistido visando o desenvolvimento de genótipos de milho mais tolerantes ao Al. Adicionalmente, as demais regiões de QTLs associadas com buscas in silico possibilitaram elencar novos genes candidatos, que poderão ser alvos para estudos avançados contribuindo para uma melhor compreensão dos mecanismos e genesenvolvidos na tolerância ao Al em milho. The Al toxicity is a major constraint for root growth on acid soils, leading to serious limitations in crop production. These soils are widely distributed in tropical and subtropical regions, comprising about 50% of arable land on the planet. The development of Al tolerant genotypes is a sustainable alternative to overcome the limitations caused by acid soils.Aluminum tolerance is a complex trait in maize, possibly involving multiple genes and mechanisms that are still not well understood. In our current work, 36,147 SNPs based on genotyping-by-sequencing technology (GBS), 39 SSRs and three candidate genes wereassessed in a population of RILs derived from a cross between two maize lines highly contrasting for Al tolerance. Marker-trait associations were performed using generalized linear models (GLM) and multiple interval mapping (MIM), been identified eight QTL on chromosomes 2, 3, 4, 5, 6 and 8. A major QTL, explaining 22% of the genotypic variance ofAl tolerance, was mapped on chromosome 6 (bin 6.00), confirming previously published results. The candidate gene ZmMATE1 was mapped in this region, as well as two expression QTL (eQTL) flanking the target gene, which were considered as cis eQTL. This genomic region was transferred to maize near-isogenic line, resulting in a two-fold increase of Al tolerance associated with an enhanced ZmMATE1 expression. These results validated the QTL6 as capable to improve Al tolerance in maize. The candidate gene ZmMATE2 was colocated with the Al tolerance QTL5.1, but it was not validated in the lines the maize NILs. However, a trans eQTL explaining 24% of genotypic variation of ZmMATE2 expression was mapped to chromosome 3. A high density of GBS-based markers allowed a considerable precision improvement of QTL identified, whose confidence intervals were restricted from 1.7 to 31.7 Mb. Integration of genetic map information with physical genomic position was possible due to the alignment of SNP sequences in the reference maize genome, which is another great advantage of these GBS-based markers. Thus, the results generated here can be directly applied on marker-assisted breeding to develop maize genotypes with improved Al tolerance. Moreover, the other QTL regions combined with in silico search allowed to select new candidate genes to be target for advanced studies, which can contribute to a better understanding of the mechanisms and genes involved in maize Al tolerance.

Background: This 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. Methods: Male 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. Results: A 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. Conclusion: Most 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.