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.

Plant architecture traits are closely related to plant biomass, lodging, and photosynthetic efficiency, which in turn affect soybean yield. In this study, we investigated a Chinese soybean mini core collection consisting of 224 germplasm accessions for four plant architecture-related traits (plant height (PH), number of nodes on main stem (NN), branch number (BN), and stem diameter (DI)) under three environments and conducted a genome-wide association study (GWAS) based on 1514 single nucleotide polymorphisms (SNPs). A total of 41 SNPs were found to be significantly associated with PH, NN, BN, and DI in two or more environments. Among these SNPs, 15 were located in regions in which plant architecture-related QTLs had been reported in previous studies, and 26 were new genetic loci. In addition, 18 potential candidate genes for plant architecture-related traits were obtained by predicting the genes in the interval of four large-effect markers (BARC-017097-02199, Map-2213, BARC-014639-01604, and Map-2223). This research will help to illuminate the genetic basis of soybean plant architecture-related traits and accelerate the process of plant architecture breeding by molecular marker-assisted selection in soybean.

Stay-green syndrome (SGS) is an important factor that causes soybean (Glycine max) yield reduction. Despite progress being made, the regulatory mechanism remains largely unclear. Therefore, in this study, an SGS-sensitive soybean variety, “HD0702”, was employed to investigate the underlying mechanism. Transcriptomic analyses were performed in a tissue-specific manner to investigate differentially expressed genes (DEGs) in soybeans impacted by SGS and in those without SGS. A total of 1858 DEGs were identified in the pods, and 2814 DEGs were identified in the leaves. Further investigation revealed that SGS mainly affected the expression levels of key genes involved in the regulation of photosynthesis, starch and sucrose metabolism, and plant hormone signal transduction. To support this finding, the chlorophyll content of the pods was to be found increased by 320% for chlorophyll a and 260% for chlorophyll b. In leaves, soluble sugar levels significantly increased, whereas phytohormones IAA and ABA decreased in SGS pods. DEGs were classified using gene ontology (GO) terms, and photosynthesis-related genes α-glucosidase, β-mannosidase, β-amylase 5 (GmBAM5), and starch synthase 2 (GmSS2) were up-regulated. This study demonstrates a molecular and physiological basis for SGS that merits further investigation to allow for SGS management.

Soybean production is significantly impacted by Phytophthora root rot (PRR), which is caused by Phytophthora sojae. The nucleotide-binding leucine-rich repeat (NLR) gene family plays a crucial role in plant disease resistance. However, current understanding of the function of soybean NLR genes in resistance to PRR is limited. To address this knowledge gap, transgenic soybean plants overexpressing the NLR gene (Glyma.18g283200) were generated to elucidate the molecular mechanism of resistance. Here, transcript changes and metabolic differences were investigated at three time points (12, 24, and 36 h) after P. sojae infection in hypocotyls of two soybean lines, Dongnong 50 (susceptible line, WT) and Glyma.18g283200 overexpression line (resistant line, OE). Based on the changes in differentially expressed genes (DEGs) in response to P. sojae infection in different lines and at different time points, it was speculated that HOPZ-ACTIVATED RESISTANCE 1 (ZAR1), valine, leucine, and isoleucine degradation, and phytohormone signaling may be involved in the defense response of soybean to P. sojae at the transcriptome level by GO term and KEGG pathway enrichment analysis. Differentially accumulated metabolites (DAMs) analysis revealed that a total of 223 and 210 differential metabolites were identified in the positive ion (POS) and negative ion (NEG) modes, respectively. An integrated pathway-level analysis of transcriptomics (obtained by RNA-seq) and metabolomics data revealed that isoflavone biosynthesis was associated with disease resistance. This work provides valuable insights that can be used in breeding programs aiming to enhance soybean resistance against PRR.

Phytophthora root rot, caused by Phytophthora sojae (P. sojae), is one of the most devastating diseases limiting soybean production worldwide. microRNAs (miRNAs) play major roles in regulating plant defense against pathogens. To understand the roles of soybean miRNAs during P. sojae infection, we analyzed four small RNA libraries from two soybean germplasms before and after P. sojae isolate JS08-12 infection. The cultivar Nannong 10-1 was resistant to JS08-12, whereas the 06-070583 line was susceptible to JS08-12. In total, 528 known and 555 putative novel miRNAs in soybean were identified from 97 million reads; 74 known miRNAs and 75 novel miRNAs that might be specifically related to Nannong10-1 responses to P. sojae; and 55 known and 43 novel miRNAs expressed before and after infection in the susceptible line 06-070583. qRT-PCR provided similar miRNA expression patterns to those obtained by the small-RNA sequencing of the four libraries. Then, the potential target genes of these differentially expressed miRNA were predicted, which encoded transcriptional factors, resistance proteins and transporters. Finally, we focused on the targets of the three legume-specific miRNAs (gma-miR1508, gma-miR1509, and gma-miR1510) and charted the miRNA–target interactions and networks based on the published degradome data.