SoyBase, the USDA-ARS soybean genetics and genomics database

Contextualized Metabolic Modelling Revealed Factors Affecting Isoflavone Accumulation in Soybean Seeds

Isoflavones, secondary metabolites with numerous health benefits, are predominantly found in legume seeds, especially soybean; however, their contents in domesticated soybean seeds are highly variable. Wild soybeans are known for higher seed isoflavone contents than cultivars. Here we used experimental and modelling approaches on wild soybean (W05) and cultivated soybean (C08) to delineate factors influencing isoflavone accumulation. We found imported nutrients were converted into storage compounds, with isoflavone accumulation in W05 seeds being faster than in C08 ones. The isoflavone accumulation during seed development was simulated using context-specific cotyledon metabolic models of four developmental stages on cultivar C08, and the metabolic burden imposed by increasing biomass was evaluated. Trade-off analyses between biomass and isoflavone suggest that high biomass requirement in cultivars could limit the reallocation of resources for secondary metabolite production. Isoflavone production in mature seeds was also influenced by biomass compositions. Seeds with higher carbohydrate contents favour isoflavone production, while those with highest protein and oil contents had lowest isoflavone contents. Although seeds could synthesize isoflavones on their own, the predicted fluxes from biosynthesis alone were lower than the empirical levels. Shadow price analyses indicated that isoflavone accumulation depended on both intrinsic biosynthesis and direct contribution from the plant.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations' elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01489-2.

afila, the origin and nature of a major innovation in the history of pea breeding

The afila (af) mutation causes the replacement of leaflets by a branched mass of tendrils in the compound leaves of pea - Pisum sativum L. This mutation was first described in 1953, and several reports of spontaneous af mutations and induced mutants with a similar phenotype exist. Despite widespread introgression into breeding material, the nature of af and the origin of the alleles used remain unknown. Here, we combine comparative genomics with reverse genetic approaches to elucidate the genetic determinants of af. We also investigate haplotype diversity using a set of AfAf and afaf cultivars and breeding lines and molecular markers linked to seven consecutive genes. Our results show that deletion of two tandemly arranged genes encoding Q-type Cys(2)His(2) zinc finger transcription factors, PsPALM1a and PsPALM1b, is responsible for the af phenotype in pea. Eight haplotypes were identified in the af-harbouring genomic region on chromosome 2. These haplotypes differ in the size of the deletion, covering more or less genes. Diversity at the af locus is valuable for crop improvement and sheds light on the history of pea breeding for improved standing ability. The results will be used to understand the function of PsPALM1a/b and to transfer the knowledge for innovation in related crops.

Combining GWAS and comparative genomics to fine map candidate genes for days to flowering in mung bean

BACKGROUND

Mung bean (Vigna radiata (L.) Wilczek), is an important pulse crop in the global south. Early flowering and maturation are advantageous traits for adaptation to northern and southern latitudes. This study investigates the genetic basis of the Days-to-Flowering trait (DTF) in mung bean, combining genome-wide association studies (GWAS) in mung bean and comparisons with orthologous genes involved with control of DTF responses in soybean (Glycine max (L) Merr) and Arabidopsis (Arabidopsis thaliana).

RESULTS

The most significant associations for DTF were on mung bean chromosomes 1, 2, and 4. Only the SNPs on chromosomes 1 and 4 were heavily investigated using downstream analysis. The chromosome 1 DTF association is tightly linked with a cluster of locally duplicated FERONIA (FER) receptor-like protein kinase genes, and the SNP occurs within one of the FERONIA genes. In Arabidopsis, an orthologous FERONIA gene (AT3G51550), has been reported to regulate the expression of the FLOWERING LOCUS C (FLC). For the chromosome 4 DTF locus, the strongest candidates are Vradi04g00002773 and Vradi04g00002778, orthologous to the Arabidopsis PhyA and PIF3 genes, encoding phytochrome A (a photoreceptor protein sensitive to red to far-red light) and phytochrome-interacting factor 3, respectively. The soybean PhyA orthologs include the classical loci E3 and E4 (genes GmPhyA3, Glyma.19G224200, and GmPhyA2, Glyma.20G090000). The mung bean PhyA ortholog has been previously reported as a candidate for DTF in studies conducted in South Korea.

CONCLUSION

The top two identified SNPs accounted for a significant proportion (~ 65%) of the phenotypic variability in mung bean DTF by the six significant SNPs (39.61%), with a broad-sense heritability of 0.93. The strong associations of DTF with genes that have orthologs with analogous functions in soybean and Arabidopsis provide strong circumstantial evidence that these genes are causal for this trait. The three reported loci and candidate genes provide useful targets for marker-assisted breeding in mung beans.

Common signatures of selection reveal target loci for breeding across soybean populations

Understanding the underlying genetic bases of yield-related selection and distinguishing these changes from genetic drift are critical for both improved understanding and future success of plant breeding. Soybean [Glycine max (L.) Merr.] is a key species for world food security, yet knowledge of the mechanism of selective breeding in soybean, such as the century-long program of artificial selection in U.S. soybean germplasm, is currently limited to certain genes and loci. Here, we identify genome-wide signatures of selection in separate populations of soybean subjected to artificial selection for increased yield by multiple breeding programs in the United States. We compared the alternative soybean breeding population (AGP) created by USDA-ARS to the conventional public soybean lines (CGP) developed at three different stages of breeding (ancestral, intermediate, and elite) to identify shared signatures of selection and differentiate these from drift. The results showed a strong selection for specific haplotypes identified by single site frequency and haplotype homozygosity methods. A set of common selection signatures was identified in both AGP and CGP that supports the hypothesis that separate breeding programs within similar environments coalesce on the fixation of the same key haplotypes. Signatures unique to each breeding program were observed. These results raise the possibility that selection analysis can allow the identification of favorable alleles to enhance directed breeding approaches.

In Silico Comparison of WRKY Transcription Factors in Wild and Cultivated Soybean and Their Co-expression Network Arbitrating Disease Resistance

WRKY Transcription factors (TFs) play critical roles in plant defence mechanisms that are activated in response to biotic and abiotic stresses. However, information on the Glycine soja WRKYs (GsoWRKYs) is scarce. Owing to its importance in soybean breeding, here we identified putative WRKY TFs in wild soybean, and compared the results with Glycine max WRKYs (GmaWRKYs) by phylogenetic, conserved motif, and duplication analyses. Moreover, we explored the expression trends of WRKYs in G. max (oomycete, fungi, virus, bacteria, and soybean cyst nematode) and G. soja (soybean cyst nematode), and identified commonly expressed WRKYs and their co-expressed genes. We identified, 181 and 180 putative WRKYs in G. max and G. soja, respectively. Though the number of WRKYs in both studied species is almost the same, they differ in many ways, i.e., the number of WRKYs on corresponding chromosomes, conserved domain structures, WRKYGQK motif variants, and zinc-finger motifs. WRKYs in both species grouped in three major clads, i.e., I-III, where group-II had sub-clads IIa-IIe. We found that GsoWRKYs expanded mostly through segmental duplication. A large number of WRKYs were expressed in response to biotic stresses, i.e., Phakospora pachyrhizi, Phytoplasma, Heterodera glycines, Macrophomina phaseolina, and Soybean mosaic virus; 56 GmaWRKYs were commonly expressed in soybean plants infected with these diseases. Finally, 30 and 63 GmaWRKYs and GsoWRKYs co-expressed with 205 and 123 non-WRKY genes, respectively, indicating that WRKYs play essential roles in biotic stress tolerance in Glycine species.

GmGLU1 and GmRR4 contribute to iron deficiency tolerance in soybean

Iron deficiency chlorosis (IDC) is a form of abiotic stress that negatively impacts soybean yield. In a previous study, we demonstrated that the historical IDC quantitative trait locus (QTL) on soybean chromosome Gm03 was composed of four distinct linkage blocks, each containing candidate genes for IDC tolerance. Here, we take advantage of virus-induced gene silencing (VIGS) to validate the function of three high-priority candidate genes, each corresponding to a different linkage block in the Gm03 IDC QTL. We built three single-gene constructs to target GmGLU1 (GLUTAMATE SYNTHASE 1, Glyma.03G128300), GmRR4 (RESPONSE REGULATOR 4, Glyma.03G130000), and GmbHLH38 (beta Helix Loop Helix 38, Glyma.03G130400 and Glyma.03G130600). Given the polygenic nature of the iron stress tolerance trait, we also silenced the genes in combination. We built two constructs targeting GmRR4+GmGLU1 and GmbHLH38+GmGLU1. All constructs were tested on the iron-efficient soybean genotype Clark grown in iron-sufficient conditions. We observed significant decreases in soil plant analysis development (SPAD) measurements using the GmGLU1 construct and both double constructs, with potential additive effects in the GmRR4+GmGLU1 construct. Whole genome expression analyses (RNA-seq) revealed a wide range of affected processes including known iron stress responses, defense and hormone signaling, photosynthesis, and cell wall structure. These findings highlight the importance of GmGLU1 in soybean iron stress responses and provide evidence that IDC is truly a polygenic trait, with multiple genes within the QTL contributing to IDC tolerance. Finally, we conducted BLAST analyses to demonstrate that the Gm03 IDC QTL is syntenic across a broad range of plant species.

Isoflavonoid metabolism in leguminous plants: an update and perspectives

Isoflavonoids constitute a well-investigated category of phenylpropanoid-derived specialized metabolites primarily found in leguminous plants. They play a crucial role in legume development and interactions with the environment. Isoflavonoids usually function as phytoalexins, acting against pathogenic microbes in nature. Additionally, they serve as signaling molecules in rhizobial symbiosis. Notably, owing to their molecular structure resembling human estrogen, they are recognized as phytoestrogens, imparting positive effects on human health. This review comprehensively outlines recent advancements in research pertaining to isoflavonoid biosynthesis, transcriptional regulation, transport, and physiological functions, with a particular emphasis on soybean plants. Additionally, we pose several questions to encourage exploration into novel contributors to isoflavonoid metabolism and their potential roles in plant-microbe interactions.

Dissection of the E8 locus in two early maturing Canadian soybean populations

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibility and chromosomal interaction, as well as genetic variation data from 24 501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Frequency of cytosine methylation in the adjacent regions of soybean retrotransposon SORE-1 depends on chromosomal location

Mobilization of transposable elements (TEs) is suppressed by epigenetic mechanisms involving cytosine methylation. However, few studies have focused on clarifying relationships between epigenetic influences of TEs on the adjacent DNA regions and time after insertion of TEs into the genome and/or their chromosomal location. Here we addressed these issues using soybean retrotransposon SORE-1. We analyzed SORE-1, inserted in exon 1 of the GmphyA2 gene, one of the newest insertions in this family so far identified. Cytosine methylation was detected in this element but was barely present in the adjacent regions. These results were correlated, respectively, with the presence and absence of the production of short interfering RNAs. Cytosine methylation profiles of 74 SORE-1 elements in the Williams 82 reference genome indicated that methylation frequency in the adjacent regions of SORE-1 was profoundly higher in pericentromeric regions than in euchromatic chromosome arms and was only weakly correlated with the length of time after insertion into the genome. Notably, the higher level of methylation in the 5' adjacent regions of SORE-1 coincided with the presence of repetitive elements in pericentromeric regions. Together, these results suggest that epigenetic influence of SORE-1 on the adjacent regions is influenced by its location on the chromosome.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed

Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.

Genomic analysis and characterization of new loci associated with seed protein and oil content in soybeans

Breeding for increased protein without a reduction in oil content in soybeans [Glycine max (L.) Merr.] is a challenge for soybean breeders but an expected goal. Many efforts have been made to develop new soybean varieties with high yield in combination with desirable protein and/or oil traits. An elite line, R05-1415, was reported to be high yielding, high protein, and low oil. Several significant quantitative trait loci (QTL) for protein and oil were reported in this line, but many of them were unstable across environments or genetic backgrounds. Thus, a new study under multiple field environments using the Infinium BARCSoySNP6K BeadChips was conducted to detect and confirm stable genomic loci for these traits. Genetic analyses consistently detected a single major genomic locus conveying these two traits with remarkably high phenotypic variation explained (R2 ), varying between 24.2% and 43.5%. This new genomic locus is located between 25.0 and 26.7 Mb, distant from the previously reported QTL and did not overlap with other commonly reported QTL and the recently cloned gene Glyma.20G085100. Homolog analysis indicated that this QTL did not result from the paracentric chromosome inversion with an adjacent genomic fragment that harbors the reported QTL. The pleiotropic effect of this QTL could be a challenge for improving protein and oil simultaneously; however, a further study of four candidate genes with significant expressions in the seed developmental stages coupled with haplotype analysis may be able to pinpoint causative genes. The functionality and roles of these genes can be determined and characterized, which lay a solid foundation for the improvement of protein and oil content in soybeans.

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

Mechanisms of Primed Defense: Plant Immunity Induced by Endophytic Colonization of a Mycovirus-Induced Hypovirulent Fungal Pathogen

How mycovirus-induced hypovirulence in fungi activates plant defense is still poorly understood. The changes in plant fitness and gene expression caused by the inoculation of the fungus Sclerotinia sclerotiorum harboring and made hypovirulent by the mycovirus soybean leaf-associated gemygorvirus-1 (SlaGemV-1) of the species Gemycircularvirus soybe1 were examined in this study. As the hypovirulent fungus (DK3V) colonized soybean Glycine max, plant transcriptomic analysis indicated changes in defense responses and photosynthetic activity, supported by an upregulation of individual genes and overrepresentation of photosystem gene ontology groups. The upregulated genes include genes relating to both pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity as well as various genes relating to the induction of systemic acquired resistance and the biosynthesis of jasmonic acid. Plants colonized with DK3V showed a resistant phenotype to virulent S. sclerotiorum infection. Plant height and leaf area were also determined to be larger in plants grown with the virus-infected fungus. Here, we hypothesize that inoculation of soybean with DK3V can result in the triggering of a wide range of defense mechanisms to prime against later infection. The knowledge gained from this study about plant transcriptomics and phenotype will help prime plant immunity with mycovirus-infected hypovirulent fungal strains more effectively. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Genetic Mapping for QTL Associated with Seed Nickel and Molybdenum Accumulation in the Soybean 'Forrest' by 'Williams 82' RIL Population

Understanding the genetic basis of seed Ni and Mo is essential. Since soybean is a major crop in the world and a major source for nutrients, including Ni and Mo, the objective of the current research was to map genetic regions (quantitative trait loci, QTL) linked to Ni and Mo concentrations in soybean seed. A recombinant inbred line (RIL) population was derived from a cross between 'Forrest' and 'Williams 82' (F × W82). A total of 306 lines was used for genotyping using 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted and included the parents and the RIL population. One experiment was conducted in 2018 in North Carolina (NC), and the second experiment was conducted in Illinois in 2020 (IL). Logarithm of the odds (LOD) of ≥2.5 was set as a threshold to report identified QTL using the composite interval mapping (CIM) method. A wide range of Ni and Mo concentrations among RILs was observed. A total of four QTL (qNi-01, qNi-02, and qNi-03 on Chr 2, 8, and 9, respectively, in 2018, and qNi-01 on Chr 20 in 2020) was identified for seed Ni. All these QTL were significantly (LOD threshold > 2.5) associated with seed Ni, with LOD scores ranging between 2.71-3.44, and with phenotypic variance ranging from 4.48-6.97%. A total of three QTL for Mo (qMo-01, qMo-02, and qMo-03 on Chr 1, 3, 17, respectively) was identified in 2018, and four QTL (qMo-01, qMo-02, qMo-03, and qMo-04, on Chr 5, 11, 14, and 16, respectively) were identified in 2020. Some of the current QTL had high LOD and significantly contributed to the phenotypic variance for the trait. For example, in 2018, Mo QTL qMo-01 on Chr 1 had LOD of 7.8, explaining a phenotypic variance of 41.17%, and qMo-03 on Chr 17 had LOD of 5.33, with phenotypic variance explained of 41.49%. In addition, one Mo QTL (qMo-03 on Chr 14) had LOD of 9.77, explaining 51.57% of phenotypic variance related to the trait, and another Mo QTL (qMo-04 on Chr 16) had LOD of 7.62 and explained 49.95% of phenotypic variance. None of the QTL identified here were identified twice across locations/years. Based on a search of the available literature and of SoyBase, the four QTL for Ni, identified on Chr 2, 8, 9, and 20, and the five QTL associated with Mo, identified on Chr 1, 17, 11, 14, and 16, are novel and not previously reported. This research contributes new insights into the genetic mapping of Ni and Mo, and provides valuable QTL and molecular markers that can potentially assist in selecting Ni and Mo levels in soybean seeds.

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Deciphering the genetic architecture of resistance to Corynespora cassiicola in soybean (Glycine max L.) by integrating genome-wide association mapping and RNA-Seq analysis

Target spot caused by Corynespora cassiicola is a problematic disease in tropical and subtropical soybean (Glycine max) growing regions. Although resistant soybean genotypes have been identified, the genetic mechanisms underlying target spot resistance has not yet been studied. To address this knowledge gap, this is the first genome-wide association study (GWAS) conducted using the SoySNP50K array on a panel of 246 soybean accessions, aiming to unravel the genetic architecture of resistance. The results revealed significant associations of 14 and 33 loci with resistance to LIM01 and SSTA C. cassiicola isolates, respectively, with six loci demonstrating consistent associations across both isolates. To identify potential candidate genes within GWAS-identified loci, dynamic transcriptome profiling was conducted through RNA-Seq analysis. The analysis involved comparing gene expression patterns between resistant and susceptible genotypes, utilizing leaf tissue collected at different time points after inoculation. Integrating results of GWAS and RNA-Seq analyses identified 238 differentially expressed genes within a 200 kb region encompassing significant quantitative trait loci (QTLs) for disease severity ratings. These genes were involved in defense response to pathogen, innate immune response, chitinase activity, histone H3-K9 methylation, salicylic acid mediated signaling pathway, kinase activity, and biosynthesis of flavonoid, jasmonic acid, phenylpropanoid, and wax. In addition, when combining results from this study with previous GWAS research, 11 colocalized regions associated with disease resistance were identified for biotic and abiotic stress. This finding provides valuable insight into the genetic resources that can be harnessed for future breeding programs aiming to enhance soybean resistance against target spot and other diseases simultaneously.

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

SoyDBean: a database for SNPs reconciliation by multiple versions of soybean reference genomes

Due to the development of sequence technology and decreased cost, many whole genome sequences have been obtained. As a result, extensive genetic variations have been discovered from many populations and germplasms to understand the genetic diversity of soybean (Glycine max [L.] Merr.). However, assessing the quality of variation is essential because the published variants were collected using different bioinformatic methods and parameters. Furthermore, despite the enhanced genome contiguity and more efficient filling of "N" stretches in the new reference genome, there remains a dearth of endeavors to verify the caliber of variations present in it. The primary goal of this research was to discern a dependable set of SNPs that can withstand reconciliation across multiple reference genomes. Additionally, the investigation aimed to reconfirm the variations through the utilization of numerous whole genome sequencing data obtained from publicly available databases. Based on the result, we created datasets that comprised the thoroughly verified SNP coordinates between the reference assemblies. The resulting "SoyDBean" database is now publicly accessible through the following URL: http://soydbean.plantprofile.net/ .

JustRNA: a database of plant long noncoding RNA expression profiles and functional network

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Genome-wide analysis of cold imbibition stress in soybean, Glycine max

In Canada, the length of the frost-free season necessitates planting crops as early as possible to ensure that the plants have enough time to reach full maturity before they are harvested. Early planting carries inherent risks of cold water imbibition (specifically less than 4°C) affecting seed germination. A marker dataset developed for a previously identified Canadian soybean GWAS panel was leveraged to investigate the effect of cold water imbibition on germination. Seed from a panel of 137 soybean elite cultivars, grown in the field at Ottawa, ON, over three years, were placed on filter paper in petri dishes and allowed to imbibe water for 16 hours at either 4°C or 20°C prior to being transferred to a constant 20°C. Observations on seed germination, defined as the presence of a 1 cm radicle, were done from day two to seven. A three-parameter exponential rise to a maximum equation (3PERM) was fitted to estimate germination, time to the one-half maximum germination, and germination uniformity for each cultivar. Genotype-by-sequencing was used to identify SNPs in 137 soybean lines, and using genome-wide association studies (GWAS - rMVP R package, with GLM, MLM, and FarmCPU as methods), haplotype block analysis, and assumed linkage blocks of ±100 kbp, a threshold for significance was established using the qvalue package in R, and five significant SNPs were identified on chromosomes 1, 3, 4, 6, and 13 for maximum germination after cold water imbibition. Percent of phenotypic variance explained (PVE) and allele substitution effect (ASE) eliminated two of the five candidate SNPs, leaving three QTL regions on chromosomes 3, 6, and 13 (Chr3-3419152, Chr6-5098454, and Chr13-29649544). Based on the gene ontology (GO) enrichment analysis, 14 candidate genes whose function is predicted to include germination and cold tolerance related pathways were identified as candidate genes. The identified QTLs can be used to select future soybean cultivars tolerant to cold water imbibition and mitigate risks associated with early soybean planting.

Determining Genetic Markers and Seed Compositions Related to High Test Weight in Glycine max

Test weight, one of the primary indicators of soybean seed quality, is measured as the amount of soybean seeds in kilograms that can fit into one hectoliter. The price that growers receive for their soybean is dependent on test weight. Over the past 50 years, growers have observed a decreasing trend in test weight. Therefore, it is imperative to understand better the relationship between soybean test weight and other traits to enable breeders to select parental lines with high test weights in breeding programs to ensure the grower's profitability. The objectives of the study were to identify genetic markers associated with high test weight in soybean and to determine the correlation between high test weight and five important seed composition traits (protein, oil, sucrose, raffinose, and stachyose content). Maturity group IV and V germplasms from the USDA soybean germplasm collection were grown in Blacksburg and Warsaw in Virginia from 2019 to 2021 and were measured for all of the above traits. Results show that test weight values ranged from 62-77 kg/hL over the three years. Multiple single-nucleotide polymorphisms (SNPs) significantly associated with high test weight were found on chromosome (Chr.) 15 along with a couple on chromosome 14, and 11 candidate genes were found near these SNPs. Test weight was found to be significantly negatively correlated with oil content, inconsistently correlated with protein content in all environments, and negatively correlated but not significantly with all three sugars except for raffinose in Blacksburg 2019. We concluded that the genes that underlie test weight might be on chromosome 15, and the validated associated SNPs might be used to assist breeding selection of test weight. Breeders should pay special attention to test weight while selecting for high oil content in soybean due to their negative correlation.

Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

Functional annotation of proteins for signaling network inference in non-model species

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Circular RNAs modulate the floral fate acquisition in soybean shoot apical meristem

BACKGROUND

Soybean (Glycine max), a major oilseed and protein source, requires a short-day photoperiod for floral induction. Though key transcription factors controlling flowering have been identified, the role of the non-coding genome is limited. Circular RNAs (circRNAs) recently emerged as a novel class of RNAs with critical regulatory functions. However, a study on circRNAs during the floral transition of a crop plant is lacking. We investigated the expression and potential function of circRNAs in floral fate acquisition by soybean shoot apical meristem in response to short-day treatment.

RESULTS

Using deep sequencing and in-silico analysis, we denoted 384 circRNAs, with 129 exhibiting short-day treatment-specific expression patterns. We also identified 38 circRNAs with predicted binding sites for miRNAs that could affect the expression of diverse downstream genes through the circRNA-miRNA-mRNA network. Notably, four different circRNAs with potential binding sites for an important microRNA module regulating developmental phase transition in plants, miR156 and miR172, were identified. We also identified circRNAs arising from hormonal signaling pathway genes, especially abscisic acid, and auxin, suggesting an intricate network leading to floral transition.

CONCLUSIONS

This study highlights the gene regulatory complexity during the vegetative to reproductive transition and paves the way to unlock floral transition in a crop plant.

SoybeanGDB: A comprehensive genomic and bioinformatic platform for soybean genetics and genomics

Soybean (Glycine max (L.) Merr.) is a globally significant crop, widely cultivated for oilseed production and animal feeds. In recent years, the rapid growth of multi-omics data from thousands of soybean accessions has provided unprecedented opportunities for researchers to explore genomes, genetic variations, and gene functions. To facilitate the utilization of these abundant data for soybean breeding and genetic improvement, the SoybeanGDB database (https://venyao.xyz/SoybeanGDB/) was developed as a comprehensive platform. SoybeanGDB integrates high-quality de novo assemblies of 39 soybean genomes and genomic variations among thousands of soybean accessions. Genomic information and variations in user-specified genomic regions can be searched and downloaded from SoybeanGDB, in a user-friendly manner. To facilitate research on genetic resources and elucidate the biological significance of genes, SoybeanGDB also incorporates a variety of bioinformatics analysis modules with graphical interfaces, such as linkage disequilibrium analysis, nucleotide diversity analysis, allele frequency analysis, gene expression analysis, primer design, gene set enrichment analysis, etc. In summary, SoybeanGDB is an essential and valuable resource that provides an open and free platform to accelerate global soybean research.

Pinpointing Rcs3 for frogeye leaf spot resistance and tracing its origin in soybean breeding

Frogeye leaf spot is a yield-reducing disease of soybean caused by the pathogen Cercospora sojina. Rcs3 has provided durable resistance to all known races of C. sojina since its discovery in the cultivar Davis during the 1980s. Using a recombinant inbred line population derived from a cross between Davis and the susceptible cultivar Forrest, Rcs3 was fine-mapped to a 1.15 Mb interval on chromosome 16. This single locus was confirmed by tracing Rcs3 in resistant and susceptible progeny derived from Davis, as well as three near-isogenic lines. Haplotype analysis in the ancestors of Davis indicated that Davis has the same haplotype at the Rcs3 locus as susceptible cultivars in its paternal lineage. On the basis of these results, it is hypothesized that the resistance allele in Davis resulted from a mutation of a susceptibility allele. Tightly linked SNP markers at the Rcs3 locus identified in this research can be used for effective marker-assisted selection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01397-x.

Soybean Root Transcriptomics: Insights into Sucrose Signaling at the Crossroads of Nutrient Deficiency and Biotic Stress Responses

Soybean (Glycine max) is an important agricultural crop, but nutrient deficiencies frequently limit soybean production. While research has advanced our understanding of plant responses to long-term nutrient deficiencies, less is known about the signaling pathways and immediate responses to certain nutrient deficiencies, such as P_i and Fe deficiencies. Recent studies have shown that sucrose acts as a long-distance signal that is sent in increased concentrations from the shoot to the root in response to various nutrient deficiencies. Here, we mimicked nutrient deficiency-induced sucrose signaling by adding sucrose directly to the roots. To unravel transcriptomic responses to sucrose acting as a signal, we performed Illumina RNA-sequencing of soybean roots treated with sucrose for 20 min and 40 min, compared to non-sucrose-treated controls. We obtained a total of 260 million paired-end reads, mapping to 61,675 soybean genes, some of which are novel (not yet annotated) transcripts. Of these, 358 genes were upregulated after 20 min, and 2416 were upregulated after 40 min of sucrose exposure. GO (gene ontology) analysis revealed a high proportion of sucrose-induced genes involved in signal transduction, particularly hormone, ROS (reactive oxygen species), and calcium signaling, in addition to regulation of transcription. In addition, GO enrichment analysis indicates that sucrose triggers crosstalk between biotic and abiotic stress responses.

The genetic architecture of soybean photothermal adaptation to high latitudes

Soybean is a major plant protein source for both human food and animal feed, but to meet global demands as well as a trend towards regional production, soybean cultivation needs to be expanded to higher latitudes. In this study, we developed a large diversity panel consisting of 1503 early-maturing soybean lines and used genome-wide association mapping to dissect the genetic architecture underlying two crucial adaptation traits, flowering time and maturity. This revealed several known maturity loci, E1, E2, E3, and E4, and the growth habit locus Dt2 as causal candidate loci, and also a novel putative causal locus, GmFRL1, encoding a homolog of the vernalization pathway gene FRIGIDA-like 1. In addition, the scan for quantitative trait locus (QTL)-by-environment interactions identified GmAPETALA1d as a candidate gene for a QTL with environment-dependent reversed allelic effects. The polymorphisms of these candidate genes were identified using whole-genome resequencing data of 338 soybeans, which also revealed a novel E4 variant, e4-par, carried by 11 lines, with nine of them originating from Central Europe. Collectively, our results illustrate how combinations of QTL and their interactions with the environment facilitate the photothermal adaptation of soybean to regions far beyond its center of origin.

Genome-Wide Identification and Comparative Analysis of RALF Gene Family in Legume and Non-Legume Species

Rapid alkalinization factor (RALF) are small secreted peptide hormones that can induce rapid alkalinization in a medium. They act as signaling molecules in plants, playing a critical role in plant development and growth, especially in plant immunity. Although the function of RALF peptides has been comprehensively analyzed, the evolutionary mechanism of RALFs in symbiosis has not been studied. In this study, 41, 24, 17 and 12 RALFs were identified in Arabidopsis, soybean, Lotus and Medicago, respectively. A comparative analysis including the molecular characteristics and conserved motifs suggested that the RALF pre-peptides in soybean represented a higher value of isoelectric point and more conservative motifs/residues composition than other species. All 94 RALFs were divided into two clades according to the phylogenetic analysis. Chromosome distribution and synteny analysis suggested that the expansion of the RALF gene family in Arabidopsis mainly depended on tandem duplication, while segment duplication played a dominant role in legume species. The expression levels of most RALFs in soybean were significantly affected by the treatment of rhizobia. Seven GmRALFs are potentially involved in the release of rhizobia in the cortex cells. Overall, our research provides novel insights into the understanding of the role of the RALF gene family in nodule symbiosis.

Loss of function of SSIIIa and SSIIIb coordinately confers high RS content in cooked rice

The sedentary lifestyle and refined food consumption significantly lead to obesity, type 2 diabetes, and related complications, which have become one of the major threats to global health. This incidence could be potentially reduced by daily foods rich in resistant starch (RS). However, it remains a challenge to breed high-RS rice varieties. Here, we reported a high-RS mutant rs4 with an RS content of ~10.8% in cooked rice. The genetic study revealed that the loss-of-function SSIIIb and SSIIIa together with a strong Wx allele in the background collaboratively contributed to the high-RS phenotype of the rs4 mutant. The increased RS contents in ssIIIa and ssIIIa ssIIIb mutants were associated with the increased amylose and lipid contents. SSIIIb and SSIIIa proteins were functionally redundant, whereas SSIIIb mainly functioned in leaves and SSIIIa largely in endosperm owing to their divergent tissue-specific expression patterns. Furthermore, we found that SSIII experienced duplication in different cereals, of which one SSIII paralog was mainly expressed in leaves and another in the endosperm. SSII but not SSIV showed a similar evolutionary pattern to SSIII. The copies of endosperm-expressed SSIII and SSII were associated with high total starch contents and low RS levels in the seeds of tested cereals, compared with low starch contents and high RS levels in tested dicots. These results provided critical genetic resources for breeding high-RS rice cultivars, and the evolutionary features of these genes may facilitate to generate high-RS varieties in different cereals.

The elite variations in germplasms for soybean breeding

The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.

MDSi: Multi-omics Database for Setaria italica

BACKGROUND

Foxtail millet (Setaria italica) harbors the small diploid genome (~ 450 Mb) and shows the high inbreeding rate and close relationship to several major foods, feed, fuel and bioenergy grasses. Previously, we created a mini foxtail millet, xiaomi, with an Arabidopsis-like life cycle. The de novo assembled genome data with high-quality and an efficient Agrobacterium-mediated genetic transformation system made xiaomi an ideal C₄ model system. The mini foxtail millet has been widely shared in the research community and as a result there is a growing need for a user-friendly portal and intuitive interface to perform exploratory analysis of the data.

RESULTS

Here, we built a Multi-omics Database for Setaria italica (MDSi, http://sky.sxau.edu.cn/MDSi.htm ), that contains xiaomi genome of 161,844 annotations, 34,436 protein-coding genes and their expression information in 29 different tissues of xiaomi (6) and JG21 (23) samples that can be showed as an Electronic Fluorescent Pictograph (xEFP) in-situ. Moreover, the whole-genome resequencing (WGS) data of 398 germplasms, including 360 foxtail millets and 38 green foxtails and the corresponding metabolic data were available in MDSi. The SNPs and Indels of these germplasms were called in advance and can be searched and compared in an interactive manner. Common tools including BLAST, GBrowse, JBrowse, map viewer, and data downloads were implemented in MDSi.

CONCLUSION

The MDSi constructed in this study integrated and visualized data from three levels of genomics, transcriptomics and metabolomics, and also provides information on the variation of hundreds of germplasm resources that can satisfies the mainstream requirements and supports the corresponding research community.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

Genome-wide association study reveals novel loci and a candidate gene for resistance to frogeye leaf spot (Cercospora sojina) in soybean

Frogeye leaf spot, caused by the fungus Cercospora sojina, is a threat to soybeans in the southeastern and midwestern United States that can be controlled by crop genetic resistance. Limited genetic resistance to the disease has been reported, and only three sources of resistance have been used in modern soybean breeding. To discover novel sources and identify the genomic locations of resistance that could be used in soybean breeding, a GWAS was conducted using a panel of 329 soybean accessions selected to maximize genetic diversity. Accessions were phenotyped using a 1-5 visual rating and by using image analysis to count lesion number and measure the percent of leaf area diseased. Eight novel loci on eight chromosomes were identified for three traits utilizing the FarmCPU or BLINK models, of which a locus on chromosome 11 was highly significant across all model-trait combinations. KASP markers were designed using the SoySNP50K Beadchip and variant information from 65 of the accessions that have been sequenced to target SNPs in the gene model Glyma.11g230400, a LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE. The association of a KASP marker, GSM990, designed to detect a missense mutation in the gene was the most significant with all three traits in a genome-wide association, and the marker may be useful to select for resistance to frogeye leaf spot in soybean breeding.

Genetic architecture of protein and oil content in soybean seed and meal

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress

Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.

Genome-Wide Detection of Quantitative Trait Loci and Prediction of Candidate Genes for Seed Sugar Composition in Early Mature Soybean

Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.

MOTHER-OF-FT-AND-TFL1 regulates the seed oil and protein content in soybean

Soybean is a major crop that produces valuable seed oil and protein for global consumption. Seed oil and protein are regulated by complex quantitative trait loci (QTLs) and have undergone intensive selections during the domestication of soybean. It is essential to identify the major genetic components and understand their mechanism behind seed oil and protein in soybean. We report that MOTHER-OF-FT-AND-TFL1 (GmMFT) is the gene of a classical QTL that has been reported to regulate seed oil and protein content in many studies. Mutation of MFT decreased seeds oil content and weight in both Arabidopsis and soybean, whereas increased expression of GmMFT enhanced seeds oil content and weight. Haplotype analysis showed that GmMFT has undergone selection, which resulted in the extended haplotype homozygosity in the cultivated soybean and the enriching of the oil-favorable allele in modern soybean cultivars. This work unraveled the GmMFT-mediated mechanism regulating seed oil and protein content and seed weight, and revealed a previously unknown function of MFT that provides new insights into targeted soybean improvement and breeding.

Soybean (Glycine max) isoflavone conjugate hydrolysing β-glucosidase (GmICHG): a promising candidate for soy isoflavone bioavailability enhancement

Isoflavones are a sub-class of phenylpropanoids having health benefits and a role in plant defence and plant-rhizobium interaction. Isoflavone conjugate hydrolysis is crucial in determining the bioactivity and bioavailability of these isoflavones inside the human body. This study examined the different characteristics of soy isoflavone conjugate hydrolysing β-glucosidase (GmICHG) to explore its potential for isoflavone bioavailability enhancement. We cloned the full-length GmICHG cDNA from the soybean seedling roots from the DS2706 variety of 1545 bp. The bioinformatics analysis revealed secretion and glycosylation of this protein. The evolutionary relatedness of this gene to the other glucosidases interestingly had related sequences outside the Papilionaceae family. The protein had a pI above neutral of 7.62 and optimum pH of 6.0, indicating its activity in the extracellular acidic environment. The GmICHG gene expression at three stages of seedling roots gradually rose to 1.84 ± 0.54 fold and a concomitant increase in the β-glucosidase activity. The enzyme kinetics of GmICHG showed a K m of 6.38 mM and V max of 2.82 U/ml and an optimum temperature of 40 °C. These hint that soy ICHG can be a potent candidate for the isoflavone bioavailability enhancement by hydrolysing their β-glycosidic bonds.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03427-5.

Identification and Mapping of QTLs for Sulfur-Containing Amino Acids in Soybean (Glycine max L.)

Soybean is a major source of high-quality protein for humans and animals. The content of sulfur-containing amino acids (SAA) in soybean is insufficient, which has become the main factor limiting soybean nutrition. In this study, we used the high-density genetic maps derived from Guizao 1 and Brazil 13 to evaluate the quantitative trait loci of cysteine (Cys), methionine (Met), SAA, glycinin (7S), β-conglycinin (11S), ratio of glycinin to β-conglycinin (RGC), and protein content (PC). In genetic map linkage analysis, the major and stable 44 QTLs were detected, which shared nine bin intervals. Among them, the bin interval (bin157-bin160) on chromosome 5 was detected in multiple environments as a stable QTL, which was linked to 11S, 7S, RGC, and SSA. Based on the analysis of bioinformatics and RNA-sequencing data, 16 differentially expressed genes (DEGs) within these QTLs were selected as candidate genes. These results will help to elucidate the genetic mechanism of soybean SAA-related traits and provide the basis for the gene mining of sulfur-containing amino acids.

P-TarPmiR accurately predicts plant-specific miRNA targets

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

Quantitative proteomics reveals key pathways in the symbiotic interface and the likely extracellular property of soybean symbiosome

An effective symbiosis between legumes and rhizobia relies largely on diverse proteins at the plant-rhizobium interface for material transportation and signal transduction during symbiotic nitrogen fixation. Here, we report a comprehensive proteome atlas of the soybean symbiosome membrane (SM), peribacteroid space (PBS), and root microsomal fraction (RMF) using state-of-the-art label-free quantitative proteomic technology. In total, 1759 soybean proteins with diverse functions are detected in the SM, and 1476 soybean proteins and 369 rhizobial proteins are detected in the PBS. The diversity of SM proteins detected suggests multiple origins of the SM. Quantitative comparative analysis highlights amino acid metabolism and nutrient uptake in the SM, indicative of the key pathways in nitrogen assimilation. The detection of soybean secretory proteins in the PBS and receptor-like kinases in the SM provides evidence for the likely extracellular property of the symbiosome and the potential signaling communication between both symbionts at the symbiotic interface. Our proteomic data provide clues for how some of the sophisticated regulation between soybean and rhizobium at the symbiotic interface is achieved, and suggest approaches for symbiosis engineering.

Amino acids induce high seed-specific expression driven by a soybean (Glycine max) glycinin seed storage protein promoter

We characterize GFP expression driven by a soybean glycinin promoter in transgenic soybean. We demonstrate specific amino acid-mediated induction of this promoter in developing soybean seeds in vitro. In plants, gene expression is primarily regulated by promoter regions which are located upstream of gene coding sequences. Promoters allow transcription in certain tissues and respond to environmental stimuli as well as other inductive phenomena. In soybean, seed storage proteins (SSPs) accumulate during seed development and account for most of the monetary and nutritional value of this crop. To better study the regulatory functions of a SSP promoter, we developed a cotyledon culture system where media and media addenda were evaluated for their effects on cotyledon development and promoter activity. Stably transformed soybean events containing a glycinin SSP promoter regulating the green fluorescent protein (GFP) were generated. Promoter activity, as visualized by GFP expression, was only observed in developing in planta seeds and in vitro-cultured isolated embryos and cotyledons from developing seeds when specific media addenda were included. Asparagine, proline, and especially glutamine induced glycinin promoter activity in cultured cotyledons from developing seeds. Other amino acids did not induce the glycinin promoter. Here, we report, for the first time, induction of a reintroduced glycinin SSP promoter by specific amino acids in cotyledon tissues during seed development.

LDRGDb - Legumes disease resistance genes database

Legumes comprise one of the world's largest, most diverse, and economically important plant families, known for their nutritional and medicinal benefits. Legumes are susceptible to a wide range of diseases, similar to other agricultural crops. Diseases have a considerable impact on the production of legume crop species, resulting in large yield losses worldwide. Due to continuous interactions between plants and their pathogens in the environment and the evolution of new pathogens under high selection pressure; disease resistant genes emerge in plant cultivars in the field against those pathogens or disease. Thus, disease resistant genes play critical roles in plant resistance responses, and their discovery and subsequent use in breeding programmes aid in reducing yield loss. The genomic era, with its high-throughput and low-cost genomic tools, has revolutionised our understanding of the complex interactions between legumes and pathogens, resulting in the identification of several critical participants in both the resistant and susceptible relationships. However, a substantial amount of existing information about numerous legume species has been disseminated as text or is preserved across fractions in different databases, posing a challenge for researchers. As a result, the range, scope, and complexity of these resources pose challenges to those who manage and use them. Therefore, there is an urgent need to develop tools and a single conjugate database to manage genetic information for the world's plant genetic resources, allowing for the rapid incorporation of essential resistance genes into breeding strategies. Here, developed the first comprehensive database of disease resistance genes named as LDRGDb - LEGUMES DISEASE RESISTANCE GENES DATABASE comprises 10 legumes [Pigeon pea (Cajanus cajan), Chickpea (Cicer arietinum), Soybean (Glycine max), Lentil (Lens culinaris), Alfalfa (Medicago sativa), Barrelclover (Medicago truncatula), Common bean (Phaseolus vulgaris), Pea (Pisum sativum),Faba bean (Vicia faba), and Cowpea (Vigna unguiculata)]. The LDRGDb is a user-friendly database developed by integrating a variety of tools and software that combine knowledge about resistant genes, QTLs, and their loci, with proteomics, pathway interactions, and genomics (https://ldrgdb.in/).