Multi-trait association mapping for phosphorous efficiency reveals flexible root architectures in sorghum.

BACKGROUND: On tropical regions, phosphorus (P) fixation onto aluminum and iron oxides in soil clays restricts P diffusion from the soil to the root surface, limiting crop yields. While increased root surface area favors P uptake under low-P availability, the relationship between the three-dimensional arrangement of the root system and P efficiency remains elusive. Here, we simultaneously assessed allelic effects of loci associated with a variety of root and P efficiency traits, in addition to grain yield under low-P availability, using multi-trait genome-wide association. We also set out to establish the relationship between root architectural traits assessed in hydroponics and in a low-P soil. Our goal was to better understand the influence of root morphology and architecture in sorghum performance under low-P availability. RESULT: In general, the same alleles of associated SNPs increased root and P efficiency traits including grain yield in a low-P soil. We found that sorghum P efficiency relies on pleiotropic loci affecting root traits, which enhance grain yield under low-P availability. Root systems with enhanced surface area stemming from lateral root proliferation mostly up to 40 cm soil depth are important for sorghum adaptation to low-P soils, indicating that differences in root morphology leading to enhanced P uptake occur exactly in the soil layer where P is found at the highest concentration. CONCLUSION: Integrated QTLs detected in different mapping populations now provide a comprehensive molecular genetic framework for P efficiency studies in sorghum. This indicated extensive conservation of P efficiency QTL across populations and emphasized the terminal portion of chromosome 3 as an important region for P efficiency in sorghum. Increases in root surface area via enhancement of lateral root development is a relevant trait for sorghum low-P soil adaptation, impacting the overall architecture of the sorghum root system. In turn, particularly concerning the critical trait for water and nutrient uptake, root surface area, root system development in deeper soil layers does not occur at the expense of shallow rooting, which may be a key reason leading to the distinctive sorghum adaptation to tropical soils with multiple abiotic stresses including low P availability and drought.

Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.

In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.

Genome-Wide Association Study for Root Morphology and Phosphorus Acquisition Efficiency in Diverse Maize Panels.

Maximizing soil exploration through modifications of the root system is a strategy for plants to overcome phosphorus (P) deficiency. Genome-wide association with 561 tropical maize inbred lines from Embrapa and DTMA panels was undertaken for root morphology and P acquisition traits under low- and high-P concentrations, with 353,540 SNPs. P supply modified root morphology traits, biomass and P content in the global maize panel, but root length and root surface area changed differentially in Embrapa and DTMA panels. This suggests that different root plasticity mechanisms exist for maize adaptation to low-P conditions. A total of 87 SNPs were associated to phenotypic traits in both P conditions at -log10(p-value) ≥ 5, whereas only seven SNPs reached the Bonferroni significance. Among these SNPs, S9_137746077, which is located upstream of the gene GRMZM2G378852 that encodes a MAPKKK protein kinase, was significantly associated with total seedling dry weight, with the same allele increasing root length and root surface area under P deficiency. The C allele of S8_88600375, mapped within GRMZM2G044531 that encodes an AGC kinase, significantly enhanced root length under low P, positively affecting root surface area and seedling weight. The broad genetic diversity evaluated in this panel suggests that candidate genes and favorable alleles could be exploited to improve P efficiency in maize breeding programs of Africa and Latin America.

Genomic prediction applied to multiple traits and environments in second season maize hybrids.

Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.

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.

Back to Acid Soil Fields: The Citrate Transporter SbMATE Is a Major Asset for Sustainable Grain Yield for Sorghum Cultivated on Acid Soils.

Aluminum (Al) toxicity damages plant roots and limits crop production on acid soils, which comprise up to 50% of the world's arable lands. A major Al tolerance locus on chromosome 3, AltSB, controls aluminum tolerance in sorghum [Sorghum bicolor (L.) Moench] via SbMATE, an Al-activated plasma membrane transporter that mediates Al exclusion from sensitive regions in the root apex. As is the case with other known Al tolerance genes, SbMATE was cloned based on studies conducted under controlled environmental conditions, in nutrient solution. Therefore, its impact on grain yield on acid soils remains undetermined. To determine the real world impact of SbMATE, multi-trait quantitative trait loci (QTL) mapping in hydroponics, and, in the field, revealed a large-effect QTL colocalized with the Al tolerance locus AltSB, where SbMATE lies, conferring a 0.6 ton ha(-1) grain yield increase on acid soils. A second QTL for Al tolerance in hydroponics, where the positive allele was also donated by the Al tolerant parent, SC283, was found on chromosome 9, indicating the presence of distinct Al tolerance genes in the sorghum genome, or genes acting in the SbMATE pathway leading to Al-activated citrate release. There was no yield penalty for AltSB, consistent with the highly localized Al regulated SbMATE expression in the root tip, and Al-dependent transport activity. A female effect of 0.5 ton ha(-1) independently demonstrated the effectiveness of AltSB in hybrids. Al tolerance conferred by AltSB is thus an indispensable asset for sorghum production and food security on acid soils, many of which are located in developing countries.

Molecular analysis of endophytic bacteria from the genus Bacillus isolated from tropical maize (Zea mays L. ) / Análise molecular de bactérias endofíticas do gênero Bacillus isoladas de milho tropical (Zea mays L. )

Endophytic bacteria play an important role in agriculture by improving plant performance and adaptation against biotic and abiotic stresses. In the present study molecular methods were used for identifying Bacillus endophytic bacteria isolated from Brazilian sweet corn. SDS-PAGE of wholecell protein extract of fortytwo isolates revealed a high number of scrutinable bands. Twenty-four isolates were identified in nine different groups of duplicated bacteria and eighteen were identified as unique. Some high-accumulated polipeptides with variable length were observed in almost isolates. Partial sequencing of 16S ribosomal gene revealed that all isolates are Bacillus sp. and among thirteen isolates with similar protein profiles, two were different strains. Among the forty-two isolates identified by rDNA sequencing, Bacillus subitilis and B. pumilus were the most frequenty species (15 and 12 isolates, respectively) followed by B. licheniformes (7 isolates), B. cereus (5 isolates) and B. amiloliquefascens (3 isolates). According to present results, SDS-PAGE technique could be used as a fast and cheap first tool for identifying interspecific variation in maize endophytic bacterial collections while rDNA sequencing could be applied for analyzing intraspecific variation among isolates with similar protein profile as well as for taxonomic studies.

Bactérias endofíticas desempenham papel importante na agricultura, melhorando a performance e adaptação de plantas contra estresses bióticos e abióticos. No presente estudo, métodos moleculares foram empregados para identificar bactérias endofíticas do gênero Bacillus isoladas de cultivares de milho doce brasileiro. SDS-PAGE de extratos protéicos totais de quarenta e dois isolados revelaram elevado número de bandas escrutináveis. Vinte e quatro isolados formaram nove grupos diferentes de réplicas bactérianas e dezoito foram considerados como únicos. Entre os isolados, alguns polipeptídios, de tamanhos variados, foram altamente acumulados. Seqüenciamento parcial do gene ribosomal 16S revelou que todos os isolados pertencem ao gênero Bacillus e que, entre treze isolados com padrão protéico similar, dois eram linhagens diferentes. Entre os quarenta e dois isolados identificados por seqüenciamento de rDNA, Bacillus subtilis e B. pumilus foram mais frequentes (15 e 12 isolados, respectivamente), seguido por, B. licheniformes (7 isolados), B. cereus (5 isolados) e B. amiloliquefascens (3 isolados). Baseado nos resultados, conclui-se que a técnica de SDS-PAGE poderá ser usada como primeiro procedimento, rápido e barato, para identificar variação inter-específica em coleções de bactérias endofíticas isoladas do milho, enquanto o método de seqüenciamento de rDNA poderá ser aplicado para analisar variações intra-específica entre isolados com padões similares de proteínas e estudos de taxonomia.

Molecular analysis of endophytic bacteria from the genus Bacillus isolated from tropical maize (Zea mays L.).

Endophytic bacteria play an important role in agriculture by improving plant performance and adaptation against biotic and abiotic stresses. In the present study molecular methods were used for identifying Bacillus endophytic bacteria isolated from Brazilian sweet corn. SDS-PAGE of whole-cell protein extract of forty-two isolates revealed a high number of scrutinable bands. Twenty-four isolates were identified in nine different groups of duplicated bacteria and eighteen were identified as unique. Some high-accumulated polipeptides with variable length were observed in almost isolates. Partial sequencing of 16S ribosomal gene revealed that all isolates are Bacillus sp. and among thirteen isolates with similar protein profiles, two were different strains. Among the forty-two isolates identified by rDNA sequencing, Bacillus subitilis and B. pumilus were the most frequenty species (15 and 12 isolates, respectively) followed by B. licheniformes (7 isolates), B. cereus (5 isolates) and B. amiloliquefascens (3 isolates). According to present results, SDS-PAGE technique could be used as a fast and cheap first tool for identifying inter-specific variation in maize endophytic bacterial collections while rDNA sequencing could be applied for analyzing intra-specific variation among isolates with similar protein profile as well as for taxonomic studies.

Tolerance to stress and environmental adaptability of Chromobacterium violaceum.

Chromobacterium violaceum is a Gram-negative bacterium, abundant in a variety of ecosystems in tropical and subtropical regions, including the water and borders of the Negro River, a major component of the Amazon Basin. As a free-living microorganism, C. violaceum is exposed to a series of variable conditions, such as different sources and abundance of nutrients, changes in temperature and pH, toxic compounds and UV rays. These variations, and the wide range of environments, require great adaptability and strong protective systems. The complete genome sequencing of this bacterium has revealed an enormous number and variety of ORFs associated with alternative pathways for energy generation, transport-related proteins, signal transduction, cell motility, secretion, and secondary metabolism. Additionally, the limited availability of iron in most environments can be overcome by iron-chelating compounds, iron-storage proteins, and by several proteins related to iron metabolism in the C. violaceum genome. Osmotically inducible proteins, transmembrane water-channel, and other membrane porins may be regulating the movement of water and maintaining the cell turgor, activities which play an important role in the adaptation to variations in osmotic pressure. Several proteins related to tolerance against antimicrobial compounds, heavy metals, temperature, acid and UV light stresses, others that promote survival under starvation conditions, and enzymes capable of detoxifying reactive oxygen species were also detected in C. violaceum. All these features together help explain its remarkable competitiveness and ability to survive under different types of environmental stress.