The development of expressed sequence tags (ESTs) from pea has provided a useful source for mining novel simple sequence repeat (SSR) markers. In the present research, in order to find EST-derived SSR markers, 18 552 pea ESTs from the National Center for Biotechnology Information (NCBI) database were downloaded and assembled into 10 086 unigenes. A total of 586 microsatellites in 530 unigenes were identified, indicating that merely 5.25% of sequences contained SSRs. The most abundant SSRs within pea were tri-nucleotide repeat motifs, and among all the tri-nucleotide repeats, the motif GAA was the most abundant type. In total, 49 SSRs were used for primer design. EST-SSR loci were subsequently screened on 10 widely adapted varieties in China. Of these, nine loci showed polymorphic profiles that revealed two to three alleles per locus. The polymorphism information content value ranged from 0.18 to 0.58 with an average of 0.41. Furthermore, transferable analysis revealed that some of these loci showed transferability to faba bean. Because of their polymorphism and transferability, these nine novel EST-SSRs will be valuable tools for marker-assisted breeding and comparative mapping of pea in the future.

Aquaporins (AQPs) are one diverse family of membrane channel proteins that play crucial regulatory roles in plant stress physiology. However, the heat stress responsiveness of AQP genes in soybean remains poorly understood. In this study, 75 non-redundant AQP encoding genes were identified in soybean. Multiple sequence alignments showed that all GmAQP proteins possessed the conserved regions, which contained 6 trans-membrane domains (TM1 to TM6). Different GmAQP members consisted of distinct Asn-Pro-Ala (NPA) motifs, aromatic/arginine (ar/R) selectivity filters and Froger’s positions (FPs). Phylogenetic analyses distinguished five sub-families within these GmAQPs: 24 GmPIPs, 24 GmTIPs, 17 GmNIPs, 8 GmSIPs, and 2 GmXIPs. Promoter cis-acting elements analyses revealed that distinct number and composition of heat stress and hormone responsive elements existed in different promoter regions of GmAQPs. QRT-PCR assays demonstrated that 12 candidate GmAQPs with relatively extensive expression in various tissues or high expression levels in root or leaf exhibited different expression changes under heat stress and hormone cues (abscisic acid (ABA), l-aminocyclopropane-l-carboxylic acid (ACC), salicylic acid (SA) and methyl jasmonate (MeJA)). Furthermore, the promoter activity of one previously functionally unknown AQP gene-GmTIP2;6 was investigated in transgenic Arabidopsis plants. The beta-glucuronidase (GUS) activity driven by the promoter of GmTIP2;6 was strongly induced in the heat- and ACC-treated transgenic plants and tended to be accumulated in the hypocotyls, vascular bundles, and leaf trichomes. These results will contribute to uncovering the potential functions and molecular mechanisms of soybean GmAQPs in mediating heat stress and hormone signal responses.

Chilling stress is a major factor limiting the yield and quality of vegetable soybean (Glycine max L.) on a global scale. In the present study, systematic identification and functional analysis of miRNAs under chilling stress were carried out to clarify the molecular mechanism of chilling resistance. Two independent small RNA libraries from leaves of soybean were constructed and sequenced with the high-throughput Illumina Solexa system. A total of 434 known miRNAs and 3 novel miRNAs were identified. Thirty-five miRNAs were verified by qRT-PCR analysis. Furthermore, their gene targets were identified via high-throughput degradome sequencing. A total of 898 transcripts were targeted by 54 miRNA families attributed to five categories. More importantly, we identified 51 miRNAs differentially expressed between chilling stress and control conditions. The targets of these miRNAs were enriched in oxidation-reduction, signal transduction and metabolic process functional categories. Our qRT-PCR analysis confirmed a negative relationship among the miRNAs and their targets under chilling stress. Our work thus provides comprehensive molecular evidence supporting the involvement of miRNAs in chilling-stress responses in vegetable soybean.

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.