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Reconsidering Tree Fruit as Candidate Crops Through the Use of Rapid Cycle Crop Breeding Technologies
Tree fruit, although desirable from a crew nutrition and menu diversity perspective, have long been dismissed as candidate crops based on their long juvenile phase, large architecture, low short-term harvest index, and dormancy requirements. Recent developments in Rapid Cycle Crop Breeding (RCCB) have overcome these historical limitations, opening the door to a new era in candidate crop research. Researchers at the United States Department of Agriculture (USDA) have developed FT-construct (Flowering Locus T) dwarf plum lines that have a very short juvenile phase, vine-like architecture, and no obligate dormancy period. In a collaborative research effort, NASA and the USDA are evaluating the performance of these FT-lines under controlled environment conditions relevant to spaceflight.
High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field
Effective implementation of technology that facilitates accurate and high-throughput screening of thousands of field-grown lines is critical for accelerating crop improvement and breeding strategies for higher yield and disease tolerance. Progress in the development of field-based high throughput phenotyping methods has advanced considerably in the last 10 years through technological progress in sensor development and high-performance computing. In this paper, we review recent advances in high throughput field phenotyping technologies designed to inform the genetics of quantitative traits, including crop yield and disease tolerance. Successful application of phenotyping platforms to advance crop breeding and identify and monitor disease requires: (1) high resolution of imaging and environmental sensors; (2) quality data products that facilitate computer vision, machine learning and GIS; (3) capacity infrastructure for data management and analysis; and (4) automated environmental data collection. Finally, accelerated breeding for agriculturally relevant crop traits is key to the development of improved varieties and is critically dependent on high-resolution, high-throughput field-scale phenotyping technologies that can efficiently discriminate better performing lines within a larger population and across multiple environments.
Is genetic engineering ever going to take off in forage, turf and bioenergy crop breeding?
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Is more better? Polyploidy in crops with diverse end uses and the potential for future applications
Increasing the number of chromosome sets can increase cell size and improve yields in some crops. Breeding polyploid crops introduces unique challenges compared to diploid species, which has deterred many from exploring the potential benefits. Despite this, recent technological advancements have alleviated some of the challenges related to complex genomes and enabled the improvement of many polyploid crops. Given these advancements, there is a need to review the use of higher ploidy crops and explore potential opportunities for increased chromosome number. Many of the leading bioenergy crops are polyploids and there may be additional opportunities to further diversify feedstocks for emerging bioenergy markets. Such diversification would help to meet the anticipated increase in renewable and sustainable energy demands. Here, in this perspective review, we review polyploid crops and the extent to which ploidy level impacts improvement and production. The advantages and disadvantages of each crop are discussed in the context of their ploidy level and end-use. Particular emphasis is given to the current role and potential of polyploidy in creating the next generation of bioenergy feedstocks. Polyploids present challenges to crop improvement due to their complex genomes, but many of these difficulties can and have been overcome with technological advancements. Approaches that facilitate the use of higher ploidy crops open a path to capturing the many benefits of polyploidy, such as increased fruit and seed size, vigour, diversity, biomass and yield quality. However, these benefits are not observed across all species. This further emphasizes the need to study higher ploidy in traditionally diploid crops.
Data‐driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates
Summary Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well‐studied major crops. Using 727 maize ( Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre‐ and postanthesis maximum temperature, mid‐season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well‐studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.
Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.
A large sequenced mutant library – valuable reverse genetic resource that covers 98% of sorghum genes
SUMMARY Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum , a drought and heat‐tolerant C4 species, requires a vast, large‐scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large‐scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)‐induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis ‐regulatory elements. A comparison of the distribution of mutations in the large‐scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS‐induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5′ UTR, gene body, and 3′‐UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the K a / K s value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB ( https://www.depts.ttu.edu/igcast/sorbmutdb.php ) and SorghumBase ( https://sorghumbase.org/ ). This current large‐scale sequence‐indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.
Alternative splicing of OsGS1;1 affects nitrogen‐use efficiency, grain development, and amylose content in rice
SUMMARY Excessive nitrogen fertilizer application is harmful to the environment and reduces the quality of cereal crops. Maintaining crop yields under low nitrogen (LN) conditions and improving quality are important goals for cereal crop breeding. Although the effects of nitrogen assimilation on crop nitrogen‐use efficiency (NUE) have been intensively studied, natural variations of the key assimilation genes underlying grain development and quality are largely unclear. Here, we identified an NUE‐associated gene, OsGS1;1 , encoding glutamine synthase, through genome‐wide association analysis, followed by validation experiments and functional analysis. Fifteen single‐nucleotide polymorphisms in the OsGS1;1 region led to alternative splicing that generated two functional transcripts: OsGS1;1a and OsGS1;1b . The elite haplotype of OsGS1;1 showed high OsGS1;1b activity, which improved NUE, affected grain development, and reduced amylose content. The results show that OsGS1;1 , which is induced under LN conditions, affects grain formation by regulating sugar metabolism and may provide a new avenue for the breeding of high‐yield and high‐quality rice ( Oryza sativa ).
Selection Factors for Space Crops
NASA is actively researching space crop production to determine its potential to contribute to food system security on long duration missions beyond Low Earth Orbit. Our near-term focus is on nutrient and variety supplementation of prepackaged food with fresh produce that requires little or no processing. The longer-term goal is caloric replacement to become less dependent on Earth, and this will require cultivation of staple crops, processing and cooking equipment, integration with spacecraft air, water, and power systems, and automation. There are numerous technology and knowledge gaps remaining for sustainable space crop production systems, but one high-impact area is in the development of crops specifically customized to meet the needs of controlled environment crop production, astronaut health and well-being, and space-unique environments. Modern crop breeding and genome engineering tools are allowing for rapid development of new genotypes with incredible specificity. Targeted aspects to optimize crops for space have been identified and characterized into five categories: plant growth and development, plant physiology, produce nutrition, produce organoleptic acceptability, and postharvest characteristics. Within each category there are several targets that further the development of crop production systems for spaceflight, such as crop size and harvest index, tolerance to specific environmental stresses, optimizing target nutrients that are low or degrade in the packaged diet, maintenance time requirements, and less indigestible structural material. NASA-funded PIs are already beginning to develop candidate crops, and spaceflight testing and validation of novel space crops is on the horizon. Crops developed for space also have the potential to benefit terrestrial controlled environment agriculture crop production systems. This research was supported by NASA’s Space Biology and Human Research Programs.
Selection Factors for Space Crops
NASA is actively researching space crop production to determine its potential to contribute to food system security on long duration missions beyond Low Earth Orbit. Our near-term focus is on nutrient and variety supplementation of prepackaged food with fresh produce that requires little or no processing. The longer-term goal is caloric replacement to become less dependent on Earth, and this will require cultivation of staple crops, processing and cooking equipment, integration with spacecraft air, water, and power systems, and automation. There are numerous technology and knowledge gaps remaining for sustainable space crop production systems, but one high-impact area is in the development of crops specifically customized to meet the needs of controlled environment crop production, astronaut health and well-being, and space-unique environments. Modern crop breeding and genome engineering tools are allowing for rapid development of new genotypes with incredible specificity. Targeted aspects to optimize crops for space have been identified and characterized into five categories: plant growth and development, plant physiology, produce nutrition, produce organoleptic acceptability, and postharvest characteristics. Within each category there are several targets that further the development of crop production systems for spaceflight, such as crop size and harvest index, tolerance to specific environmental stresses, optimizing target nutrients that are low or degrade in the packaged diet, maintenance time requirements, and less indigestible structural material. NASA-funded PIs are already beginning to develop candidate crops, and spaceflight testing and validation of novel space crops is on the horizon. Crops developed for space also have the potential to benefit terrestrial controlled environment agriculture crop production systems. This research was supported by NASA’s Space Biology and Human Research Programs.
Major impacts of widespread structural variation on sorghum
Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype–phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.
Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence
Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.
Precision genome editing in plants using gene targeting and prime editing: existing and emerging strategies
Precise modification of plant genomes, such as seamless insertion, deletion, or replacement of DNA sequences at a predefined site, is a challenging task. Gene targeting (GT) and prime editing are currently the best approaches for this purpose. However, these techniques are inefficient in plants, which limits their applications for crop breeding programs. Recently, substantial developments have been made to improve the efficiency of these techniques in plants. Several strategies, such as RNA donor templating, chemically modified donor DNA template, and tandem-repeat homology-directed repair, are aimed at improving GT. Additionally, improved prime editing gRNA design, use of engineered reverse transcriptase enzymes, and splitting prime editing components have improved the efficacy of prime editing in plants. These emerging strategies and existing technologies are reviewed along with various perspectives on their future improvement and the development of robust precision genome editing technologies for plants.
Crystal structure of WA352 provides insight into cytoplasmic male sterility in rice
Highlights: • Identification of a C-terminal structural and functional core domain of WA352. • The crystal structure of C-terminus of WA352 was determined at 1.3 Å resolution. • Molecular mechanism whereby WA352 perturbs COX11 function to cause CMS is proposed. Plant cytoplasmic male sterility (CMS) is an important phenomenon and is widely utilized in hybrid crop breeding. The Wild Abortive CMS (CMS-WA), a well-known CMS type, has been successfully applied in the commercial production of hybrid rice seeds for more than 40 years. The CMS-WA causal gene WA352 encodes a novel transmenbrane protein and the interacts with the mitochondrial copper chaperone COX11, triggering reactive oxygen species production and resulting in male sterility in CMS-WA lines. However, the structure of WA352 is currently unknown, and the structural mechanism whereby WA352 perturbs COX11 function to cause CMS remains largely unknown. Here, we report the crystal structure of the C-terminal functional domain of WA352 at 1.3 Å resolution. This functional domain, consisting of five α helices, is spindle-shaped with a length of 42 Å, and a diameter of 28 Å. Notably, the absence of any structural similarity to a known protein structure suggests that the WA352 functional domain is a novel fold. In addition, surface conservation analysis and structural modeling of the WA352-COX11 complex revealed details about the WA352-COX11 interaction. Further structural analysis suggested that the WA352-COX11 interaction blocks the copper ion transportation activity of COX11, which is essential for the assembly of cytochrome c oxidase, resulting in male sterility in CMS-WA lines. Our study paves the way toward structural determination of the WA352-COX11 complex and provides new insight into the mechanism of plant CMS.
Genetic loci simultaneously controlling lignin monomers and biomass digestibility of rice straw
Lignin content and composition are crucial factors affecting biomass digestibility. Exploring the genetic loci simultaneously affecting lignin-relevant traits and biomass digestibility is a precondition for lignin genetic manipulation towards energy crop breeding. In this study, a high-throughput platform was employed to assay the lignin content, lignin composition and biomass enzymatic digestibility of a rice recombinant inbred line population. Correlation analysis indicated that the absolute content of lignin monomers rather than lignin content had negative effects on biomass saccharification, whereas the relative content of p-hydroxyphenyl unit and the molar ratio of p-hydroxyphenyl unit to guaiacyl unit exhibited positive roles. Eight QTL clusters were identified and four of them affecting both lignin composition and biomass digestibility. Here, the additive effects of clustered QTL revealed consistent relationships between lignin-relevant traits and biomass digestibility. Pyramiding rice lines containing the above four positive alleles for increasing biomass digestibility were selected and showed comparable lignin content, decreased syringyl or guaiacyl unit and increased molar percentage of p-hydroxyphenyl unit, the molar ratio of p-hydroxyphenyl unit to guaiacyl unit and sugar releases. More importantly, the lodging resistance and eating/cooking quality of pyramiding lines were not sacrificed, indicating the QTL information could be applied to select desirable energy rice lines.
A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression
Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. Further, we provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.