Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections

Elemental sulfur concentration can be used as a rapid, reliable, and cost-effective predictor of sulfur amino acid content of soybean seeds

In this study, we have examined the feasibility of using elemental sulfur content of soybean seeds as a proxy for the overall sulfur amino acid content of soybean seeds. Earlier, we have identified by high throughput ionomic phenotyping several high and low sulfur containing soybean lines from the USDA Soybean Germplasm Collection. Here, we measured the cysteine and methionine content of select soybean lines by high-performance liquid chromatography. Our results demonstrate that those soybean lines which had high elemental sulfur content also had a higher cysteine and methionine content when compared to soybean lines with low elemental sulfur. SDS-PAGE and immunoblot analysis revealed that the accumulation of Bowman Birk protease inhibitor and lunasin in soybean seeds may only be marginally correlated with the elemental sulfur levels. However, we found a positive correlation between the levels of trypsin and chymotrypsin inhibitor activities and elemental sulfur and sulfur amino acid content of the seeds. Thus, elemental sulfur content and/or protease inhibitor activity measurement can be utilized as a rapid and cost-effective method to predict the overall sulfur amino acid content of soybean seeds. Our findings will benefit breeders in their endeavors to develop soybean cultivars with enhanced sulfur amino acid content.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Climate change challenges, plant science solutions

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.

Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Temporal and environmental factors interact with rootstock genotype to shape leaf elemental composition in grafted grapevines

Plants take up elements through their roots and transport them to their shoot systems for use in numerous biochemical, physiological, and structural functions. Elemental composition of above-ground plant tissues, such as leaves, reflects both above- and below-ground activities of the plant, as well the local environment. Perennial, grafted plants, where the root system of one individual is fused to the shoot system of a genetically distinct individual, offer a powerful experimental system in which to study how genetically distinct root systems influence the elemental composition of a common shoot system. We measured elemental composition of over 7,000 leaves in the grapevine cultivar "Chambourcin" growing ungrafted and grafted to three rootstock genotypes. Leaves were collected over multiple years and phenological stages (across the season) and along a developmental time series. Temporal components of this study had the largest effect on leaf elemental composition, and rootstock genotype interacted with year, phenological stage, and leaf age to differentially modulate leaf elemental composition. Further, the local, above-ground environment affected leaf elemental composition, an effect influenced by rootstock genotype. This work highlights the dynamic nature by which root systems interact with shoot systems to respond to temporal and environmental variation.

Traditional soybean (Glycine max) breeding increases seed yield but reduces yield stability under non-phosphorus supply

Traditional soybean (Glycine max L.) breeding has improved seed yield in high-input agricultural systems, under high nitrogen (N), phosphorus (P) and potassium (K) supply. The seed yield improvements under non-P supply and the seed protein and mineral content dilution by yield improvement were evaluated in 18 soybean cultivars released from 1995 to 2016 in south-east China. Soybean varieties were grown under rainfed conditions in the field under 0 and 35kgPha-1 in four sites: Dafang and Shiqian in the growing season of 2017 and Dafang and Puding in the 2018 season. The seed yield, seed protein content and nine seed nutrition concentration were examined. Soybean seed yield increased with the year of release at rates of 5.5-6.7gm-2 year-1 under 35kgPha-1 and 3.9-4.8gm-2 year-1 under non-P supply in the four experiments. The increase resulted from increases in the number of filled-pods and total seed number rather than from single seed weight and number of seeds per pod. Seed protein content and seed nutrition concentration has not changed with the year of release under 0 and 35kgPha-1 . Grain yield was positively correlated with the seed Fe concentration. The cultivar superiority of seed yield, seed P, Zn and Ca concentration was negatively correlated with their static stability coefficient. Traditional soybean breeding increased yield under both P and non-P supply, without affecting seed protein content and mineral concentrations. A trade-off between high seed yield and seed P, Zn and Ca concentration and their stability under different environments was shown.

Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Genetic architecture of root and shoot ionomes in rice (Oryza sativa L.)

KEY MESSAGE

Association analysis for ionomic concentrations of 20 elements identified independent genetic factors underlying the root and shoot ionomes of rice, providing a platform for selecting and dissecting causal genetic variants. Understanding the genetic basis of mineral nutrient acquisition is key to fully describing how terrestrial organisms interact with the non-living environment. Rice (Oryza sativa L.) serves both as a model organism for genetic studies and as an important component of the global food system. Studies in rice ionomics have primarily focused on above ground tissues evaluated from field-grown plants. Here, we describe a comprehensive study of the genetic basis of the rice ionome in both roots and shoots of 6-week-old rice plants for 20 elements using a controlled hydroponics growth system. Building on the wealth of publicly available rice genomic resources, including a panel of 373 diverse rice lines, 4.8 M genome-wide single-nucleotide polymorphisms, single- and multi-marker analysis pipelines, an extensive tome of 321 candidate genes and legacy QTLs from across 15 years of rice genetics literature, we used genome-wide association analysis and biparental QTL analysis to identify 114 genomic regions associated with ionomic variation. The genetic basis for root and shoot ionomes was highly distinct; 78 loci were associated with roots and 36 loci with shoots, with no overlapping genomic regions for the same element across tissues. We further describe the distribution of phenotypic variation across haplotypes and identify candidate genes within highly significant regions associated with sulfur, manganese, cadmium, and molybdenum. Our analysis provides critical insight into the genetic basis of natural phenotypic variation for both root and shoot ionomes in rice and provides a comprehensive resource for dissecting and testing causal genetic variants.

Ionomic Approaches for Discovery of Novel Stress-Resilient Genes in Plants

Plants, being sessile, face an array of biotic and abiotic stresses in their lifespan that endanger their survival. Hence, optimized uptake of mineral nutrients creates potential new routes for enhancing plant health and stress resilience. Recently, minerals (both essential and non-essential) have been identified as key players in plant stress biology, owing to their multifaceted functions. However, a realistic understanding of the relationship between different ions and stresses is lacking. In this context, ionomics will provide new platforms for not only understanding the function of the plant ionome during stresses but also identifying the genes and regulatory pathways related to mineral accumulation, transportation, and involvement in different molecular mechanisms under normal or stress conditions. This article provides a general overview of ionomics and the integration of high-throughput ionomic approaches with other "omics" tools. Integrated omics analysis is highly suitable for identification of the genes for various traits that confer biotic and abiotic stress tolerance. Moreover, ionomics advances being used to identify loci using qualitative trait loci and genome-wide association analysis of element uptake and transport within plant tissues, as well as genetic variation within species, are discussed. Furthermore, recent developments in ionomics for the discovery of stress-tolerant genes in plants have also been addressed; these can be used to produce more robust crops with a high nutritional value for sustainable agriculture.

Development of soybean experimental lines with enhanced protein and sulfur amino acid content

Soybean is the preferred protein source for both poultry and swine feed. However, this preferred status is being challenged due to competition from alternative feed ingredients. To overcome this, it becomes necessary for breeders to develop soybean cultivars that contain higher protein and better nutritional composition. In this study, we have developed experimental soybean lines that not only contain significantly higher amounts of protein but also improved sulfur amino acid content. This objective was achieved by crossing a O-acetylserine sulfhydrylase (OASS) overexpressing transgenic soybean line with elevated levels of sulfur amino acid content (CS) with a high protein Korean soybean cultivar (Lee 5). Introgression of high protein and overexpression of OASS was monitored in the experimental lines at each successive generation (F2-F6) by measuring protein content and OASS activity. The average protein content of transgenic CS and Lee 5 seeds were 34.8 % and 44.7 %, while in the experimental soybean lines the protein content ranged from 41.3 %-47.7 %, respectively. HPLC and inductively coupled plasma-mass spectrometry analyses revealed that all the experimental lines developed in this study contained significantly higher amounts of sulfur containing amino acids and elemental sulfur in the seeds. The sulfur amino acid (cysteine + methionine) content of the experimental lines ranged from 1.1 % to 1.26 % while the parents Lee 5 and CS had 0.79 % and 1.1 %, respectively. SDS-PAGE and western blot analysis demonstrated that the accumulation of Bowman-Birk protease inhibitor and lunasin, two sulfur amino acid rich peptides, were elevated in experimental soybean lines. High-resolution 2D-gel electrophoresis and Delta2D gel analysis validated that an overall increase in the different subunits of 7S β-conglycinin and 11S glycinin were mainly responsible for the observed increase in the total amount of protein in experimental lines.

1,135 ionomes reveal the global pattern of leaf and seed mineral nutrient and trace element diversity in Arabidopsis thaliana

Soil is a heterogeneous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically based variation in the elemental composition, the 'ionome', of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool, Ion Explorer, to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory genetic mapping studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.

Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods

BACKGROUND

Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers.

RESULTS

To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10- 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods.

CONCLUSIONS

While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.

Identification of loci controlling mineral element concentration in soybean seeds

BACKGROUND

Mineral nutrients play a crucial role in the biochemical and physiological functions of biological systems. The enhancement of seed mineral content via genetic improvement is considered as the most promising and cost-effective approach compared alternative means for meeting the dietary needs. The overall objective of this study was to perform a GWAS of mineral content (Ca, K, P and S) in seeds of a core set of 137 soybean lines that are representative of the diversity of early maturing soybeans cultivated in Canada (maturity groups 000-II).

RESULTS

This panel of 137 soybean lines was grown in five environments (in total) and the seed mineral content was measured using a portable x-ray fluorescence (XRF) spectrometer. The association analyses were carried out using three statistical models and a set of 2.2 million SNPs obtained from a combined dataset of genotyping-by-sequencing and whole-genome sequencing. Eight QTLs significantly associated with the Ca, K, P and S content were identified by at least two of the three statistical models used (in two environments) contributing each from 17 to 31% of the phenotypic variation. A strong reproducibility of the effect of seven out these eight QTLs was observed in three other environments. In total, three candidate genes were identified involved in transport and assimilation of these mineral elements.

CONCLUSIONS

There have been very few GWAS studies to identify QTLs associated with the mineral element content of soybean seeds. In addition to being new, the QTLs identified in this study and candidate genes will be useful for the genetic improvement of soybean nutritional quality through marker-assisted selection. Moreover, this study also provides details on the range of phenotypic variation encountered within the Canadian soybean germplasm.

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur-containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two-trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

The guard cell ionome: Understanding the role of ions in guard cell functions

The ionome is critical for plant growth, productivity, defense, and it eventually affects human food quantity and quality. Located on the leaf surface, stomatal guard cells are critical gatekeepers for water, gas, and pathogens. Insights form ionomics (metallomics) is imperative as we enter an omics-driven systems biology era where an understanding of guard cell function and physiology is advanced through efforts in genomics, transcriptomics, proteomics, and metabolomics. While the roles of major cations (K, Ca) and anions (Cl) are well known in guard cell function, the related physiology, movement and regulation of trace elements, metal ions, and heavy metals are poorly understood. The majority of the information on the role of trace elements in guard cells emanates from classical feeding experiments, field or in vitro fortification, micropropagation, and microscopy studies, while novel insights are available from limited metal ion transporter and ion channel studies. Given the rejuvenated and recent interest in the constantly changing ionome in plant mineral balance and eventually in human nutrition and health, we looked into the far from established guard cell ionome in lieu of the modern omics era of high throughput research endeavors. Newer technologies and tools i.e., high resolution mass spectrometry, advanced imaging, and phenomics are now available to delve into the guard cell ionomes. In this review, research efforts on guard cell ionomes were collated and categorized, and we highlight the underlying role of the largely unknown ionome in guard cell function towards a systems physiology understanding of plant health and productivity.