Reference genome sequence and population genomic analysis of peas provide insights into the genetic basis of Mendelian and other agronomic traits.

Peas are essential for human nutrition and played a crucial role in the discovery of Mendelian laws of inheritance. In this study, we assembled the genome of the elite vegetable pea cultivar 'Zhewan No. 1' at the chromosome level and analyzed resequencing data from 314 accessions, creating a comprehensive map of genetic variation in peas. We identified 235 candidate loci associated with 57 important agronomic traits through genome-wide association studies. Notably, we pinpointed the causal gene haplotypes responsible for four Mendelian traits: stem length (Le/le), flower color (A/a), cotyledon color (I/i) and seed shape (R/r). Additionally, we discovered the genes controlling pod form (Mendelian P/p) and hilum color. Our study also involved constructing a gene expression atlas across 22 tissues, highlighting key gene modules related to pod and seed development. These findings provide valuable pea genomic information and will facilitate the future genome-informed improvement of pea crops.

Comparative transcriptome analysis of vegetable soybean grain discloses genes essential for grain quality.

BACKGROUND: Vegetable soybean is an important vegetable crop in world. Seed size and soluble sugar content are considered crucial indicators of quality in vegetable soybean, and there is a lack of clarity on the molecular basis of grain quality in vegetable soybean. RESULTS: In this context, we performed a comprehensive comparative transcriptome analysis of seeds between a high-sucrose content and large-grain variety (Zhenong 6, ZN6) and a low-sucrose content and small-grain variety (Williams 82, W82) at three developmental stages, i.e. stage R5 (Beginning Seed), stage R6 (Full Seed), and stage R7 (Beginning Maturity). The transcriptome analysis showed that 17,107 and 13,571 differentially expressed genes (DEGs) were identified in ZN6 at R6 (vs. R5) and R7 (vs. R6), respectively, whereas 16,203 and 16,032 were detected in W82. Gene expression pattern and DEGs functional enrichment proposed genotype-specific biological processes during seed development. The genes participating in soluble sugar biosynthesis such as FKGP were overexpressed in ZN6, whereas those responsible for lipid and protein metabolism such as ALDH3 were more enhanced in W82, exhibiting different dry material accumulation between two genotypes. Furthermore, hormone-associated transcriptional factors involved in seed size regulation such as BEH4 were overrepresented in ZN6, exhibiting different seed size regulation processes between two genotypes. CONCLUSIONS: Herein, we not only discovered the differential expression of genes encoding metabolic enzymes involved in seed composition, but also identified a type of hormone-associated transcriptional factors overexpressed in ZN6, which may regulate seed size and soluble content. This study provides new insights into the underlying causes of differences in the soybean metabolites and appearance, and suggests that genetic data can be used to improve its appearance and textural quality.

Genome-wide transcriptome analysis reveals key regulatory networks and genes involved in the determination of seed hardness in vegetable soybean.

Seed hardness is an important quality trait of vegetable soybean. To determine the factors underlying seed hardness, two landraces with contrasting seed hardness, Niumaohuang (low seed hardness) and Pixiansilicao (high seed hardness), were selected from 216 soybean accessions originating from 26 provinces in China. The contents of the main components in vegetable soybean seeds such as water, soluble sugar, starch, protein and oil were measured, and transcriptome analyses performed during five stages of seed developmental. Transcriptome analysis indicates that during the middle and late stages of seed development, a large number of genes involved in the synthesis or degradation of starch, storage protein, and fatty acids were differentially expressed, leading to differences in the accumulation of stored substances during seed maturation among Niumaohuang and Pixiansilicao. The activity of cell proliferation and the formation of cell walls in the middle and late stages of seed development may also affect the hardness of seeds to a certain extent. In addition, weighted gene co-expression network analysis (WGCNA) was undertaken to identify co-expressed gene modules and hub genes that regulate seed hardness. Overexpression of a candidate seed hardness regulatory hub gene, GmSWEET2, resulted in increased seed hardness. In this study, the important role of GmSWEET2 in regulating the hardness of vegetable soybean seeds was verified and numerous potential key regulators controlling seed hardness and the proportion of seed components were identified, laying the groundwork for improving the texture of vegetable soybean.

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness.

The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.

Genome and Transcriptome Analysis of Ascochyta pisi Provides Insights into the Pathogenesis of Ascochyta Blight of Pea.

Ascochyta blight caused by Ascochyta pisi is a major constraint to pea (Pisum sativum L.) production worldwide. Deciphering the pathogenic mechanism of A. pisi on peas will help in breeding resistant pea varieties and developing effective approaches for disease management. However, little is known about the genomic features and pathogenic factors of A. pisi. In this study, we first report that A. pisi is one of the causal agents of ascochyta blight disease of pea in China. The genome of the representative isolate A. pisi HNA23 was sequenced using PacBio and Illumina sequencing technologies. The HNA23 genome assembly is almost 41.5 Mb in size and harbors 10,796 putative protein-encoding genes. We predicted 555 carbohydrate-active enzymes (CAZymes), 1,008 secreted proteins, 74 small secreted cysteine-rich proteins (SSCPs), and 26 secondary metabolite biosynthetic gene clusters (SMGCs). A comparison of A. pisi genome features with the features of 6 other available genomes of Ascochyta species showed that CAZymes, the secretome, and SMGCs of this genus are considerably conserved. Importantly, the transcriptomes of HNA23 during infection of peas at three stages were further analyzed. We found that 245 CAZymes and 29 SSCPs were upregulated at all three tested infection stages. SMGCs were also trigged, but most of them were induced at only one stage of infection. Together, our results provide important genomic information on Ascochyta spp. and offer insights into the pathogenesis of A. pisi. IMPORTANCE Ascochyta blight is a major disease of legumes worldwide. Ascochyta pisi and other Ascochyta species have been identified as pathogens of ascochyta blight. Here, we first report that A. pisi causes ascochyta blight of pea in China, and we report the high-quality, fully annotated genome of A. pisi. Comparative genome analysis was performed to elucidate the differences and similarities among 7 Ascochyta species. We predict abundant CAZymes (569 per species), secreted proteins (851 per species), and prolific secondary metabolite gene clusters (29 per species) in these species. We identified a set of genes that may be responsible for fungal virulence based on transcriptomes in planta, including CAZymes, SSCPs, and secondary metabolites. The findings from the comparative genome analysis highlight the genetic diversity and help in understanding the evolutionary relationship of Ascochyta species. In planta transcriptome analysis provides reliable information for further investigation of the mechanism of the interaction between Ascochyta spp. and legumes.

Promoter of Vegetable Soybean GmTIP1;6 Responds to Diverse Abiotic Stresses and Hormone Signals in Transgenic Arabidopsis.

Tonoplast intrinsic proteins (TIPs), a sub-family of aquaporins (AQPs), are known to play important roles in plant abiotic stress responses. However, evidence for the promoters of TIPs involvement in abiotic stress processes remains scarce. In this study, the promoter of the vegetable soybean GmTIP1;6 gene, which had the highest similarity to TIP1-type AQPs from other plants, was cloned. Expression pattern analyses indicated that the GmTIP1;6 gene was dramatically induced by drought, salt, abscisic acid (ABA), and methyl jasmonate (MeJA) stimuli. Promoter analyses revealed that the GmTIP1;6 promoter contained drought, ABA, and MeJA cis-acting elements. Histochemical staining of the GmTIP1;6 promoter in transgenic Arabidopsis corroborated that it was strongly expressed in the vascular bundles of leaves, stems, and roots. Beta-glucuronidase (GUS) activity assays showed that the activities of the GmTIP1;6 promoter were enhanced by different concentrations of polyethylene glycol 6000 (PEG 6000), NaCl, ABA, and MEJA treatments. Integrating these results revealed that the GmTIP1;6 promoter could be applied for improving the tolerance to abiotic stresses of the transgenic plants by promoting the expression of vegetable soybean AQPs.

Genome-wide association analysis for yield-related traits at the R6 stage in a Chinese soybean mini core collection.

BACKGROUND: Soybean (Glycine max (L.) Merr.) is an economically important crop for vegetable oil and protein production, and yield is a critical trait for grain/vegetable uses of soybean. However, our knowledge of the genes controlling the vegetable soybean yield remains limited. OBJECTIVE: To better understand the genetic basis of the vegetable soybean yield. METHODS: The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), kernel percent (KP) and moisture content of fresh seeds (MCFS) at the R6 stage are four yield-related traits for vegetable soybean. We investigated a soybean mini core collection composed of 224 germplasm accessions for four yield-related traits in two consecutive years. Based on 1514 single nucleotide polymorphisms (SNPs), genome-wide association studies (GWAS) were conducted using a mixed linear model (MLM). RESULTS: Extensive phenotypic variation existed in the soybean mini core collection and significant positive correlations were shown among most of traits. A total of 16 SNP markers for PFW, SFW, KP and MCFS were detected in all environments via GWAS. Nine SNP markers were repeatedly identified in two environments. Among these markers, eight were located in or near regions where yield-related QTLs have been reported in previous studies, and one was a novel genetic locus identified in this study. In addition, we conducted candidate gene analysis to the large-effect SNP markers, a total of twelve genes were proposed as potential candidate genes of soybean yield at the R6 stage. CONCLUSION: These results will be beneficial for understanding the genetic basis of soybean yield at the R6 stage and facilitating the pyramiding of favourable alleles for future high-yield breeding by marker-assisted selection in vegetable soybean.

Genome-Wide Analysis Reveals the Role of Mediator Complex in the Soybean-Phytophthora sojae Interaction.

The mediator complex is an essential link between transcription factors and RNA polymerase II, and mainly functions in the transduction of diverse signals to genes involved in different pathways. Limited information is available on the role of soybean mediator subunits in growth and development, and their participation in defense response regulation. Here, we performed genome-wide identification of the 95 soybean mediator subunits, which were unevenly localized on the 20 chromosomes and only segmental duplication events were detected. We focused on GmMED16-1, which is highly expressed in the roots, for further functional analysis. Transcription of GmMED16-1 was induced in response to Phytophthora sojae infection. Agrobacterium rhizogenes mediated soybean hairy root transformation was performed for the silencing of the GmMED16-1 gene. Silencing of GmMED16-1 led to an enhanced susceptibility phenotype and increased accumulation of P. sojae biomass in hairy roots of transformants. The transcript levels of NPR1, PR1a, and PR5 in the salicylic acid defense pathway in roots of GmMED16-1-silenced transformants were lower than those of empty-vector transformants. The results provide evidence that GmMED16-1 may participate in the soybean-P. sojae interaction via a salicylic acid-dependent process.

Genome-wide association study of four yield-related traits at the R6 stage in soybean.

BACKGROUND: The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), 100-seed dry weight (SDW) and moisture content of fresh seeds (MCFS) at the R6 stage are crucial factors for vegetable soybean yield. However, the genetic basis of yield at the R6 stage remains largely ambiguous in soybean. RESULTS: To better understand the molecular mechanism underlying yield, we investigated four yield-related traits of 133 soybean landraces in two consecutive years and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). The GWAS results revealed a total of 14, 15, 63 and 48 SNPs for PFW, SFW, SDW and MCFS, respectively. Among these markers, 35 SNPs were repeatedly identified in all evaluated environments (2015, 2016, and the average across the two years), and most co-localized with yield-related QTLs identified in previous studies. AX-90496773 and AX-90460290 were large-effect markers for PFW and MCFS, respectively. The two markers were stably identified in all environments and tagged to linkage disequilibrium (LD) blocks. Six potential candidate genes were predicted in LD blocks; five of them showed significantly different expression levels between the extreme materials with large PFW or MCFS variation at the seed development stage. Therefore, the five genes Glyma.16g018200, Glyma.16g018300, Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300 were regarded as candidate genes associated with PFW and MCFS. CONCLUSION: These results provide useful information for the development of functional markers and exploration of candidate genes in vegetable soybean high-yield breeding programs.

Combining QTL-seq and linkage mapping to fine map a wild soybean allele characteristic of greater plant height.

BACKGROUND: Plant height (PH) is an important agronomic trait and is closely related to yield in soybean [Glycine max (L.) Merr.]. Previous studies have identified many QTLs for PH. Due to the complex genetic background of PH in soybean, there are few reports on its fine mapping. RESULTS: In this study, we used a mapping population derived from a cross between a chromosome segment substitution line CSSL3228 (donor N24852 (G. Soja), a receptor NN1138-2 (G. max)) and NN1138-2 to fine map a wild soybean allele of greater PH by QTL-seq and linkage mapping. We identified a QTL for PH in a 1.73 Mb region on soybean chromosome 13 through QTL-seq, which was confirmed by SSR marker-based classical QTL mapping in the mapping population. The linkage analysis showed that the QTLs of PH were located between the SSR markers BARCSOYSSR_13_1417 and BARCSOYSSR_13_1421 on chromosome 13, and the physical distance was 69.3 kb. RT-PCR and sequence analysis of possible candidate genes showed that Glyma.13 g249400 revealed significantly higher expression in higher PH genotypes, and the gene existed 6 differences in the amino acids encoding between the two parents. CONCLUSIONS: Data presented here provide support for Glyma.13 g249400 as a possible candidate genes for higher PH in wild soybean line N24852.