Soybean (Glycine max) seed is primarily composed of a mature embryo that provides a major source of protein and oil for humans and other animals. Early in development, the tiny embryos grow rapidly and acquire large quantities of sugars from the liquid endosperm of developing seeds. An insufficient supply of nutrients from the endosperm to the embryo results in severe seed abortion and yield reduction. Hence, an understanding of the molecular basis and regulation of assimilate partitioning involved in early embryo development is important for improving soybean seed yield and quality. Here, we used expression profiling analysis to show that two paralogous sugar transporter genes from the SWEET (Sugars Will Eventually be Exported Transporter) family, GmSWEET15a and GmSWEET15b, were highly expressed in developing soybean seeds. In situ hybridization and quantitative real-time PCR showed that both genes were mainly expressed in the endosperm at the cotyledon stage. GmSWEET15b showed both efflux and influx activities for sucrose in Xenopus oocytes. In Arabidopsis (Arabidopsis thaliana), knockout of three AtSWEET alleles is required to see a defective, but not lethal, embryo phenotype, whereas knockout of both GmSWEET15 genes in soybean caused retarded embryo development and endosperm persistence, resulting in severe seed abortion. In addition, the embryo sugar content of the soybean knockout mutants was greatly reduced. These results demonstrate that the plasma membrane sugar transporter, GmSWEET15, is essential for embryo development in soybean by mediating Suc export from the endosperm to the embryo early in seed development.

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Although CRISPR-Cas9 technology has been rapidly applied in soybean genetic improvement, it is difficult to achieve the targeted editing of the specific loci in the soybean complex genome due to the limitations of the classical protospacer adjacent motif (PAM). Here, we developed a PAM-less genome editing system mediated by SpRY in soybean. By performing targeted editing of representative agronomic trait targets in soybean and evaluating the results, we demonstrate that the SpRY protein can achieve efficient targeted mutagenesis at relaxed PAM sites in soybean. Furthermore, the SpRY-based cytosine base editor SpRY-hA3A and the adenine base editor SpRY-ABE8e both can accurately induce C-to-T and A-to-G conversion in soybean, respectively. Thus, our data illustrate that the SpRY toolbox can edit the soybean genomic sequence in a PAM-free manner, breaking restrictive PAM barriers in the soybean genome editing technology system. More importantly, our research enriches soybean genome editing tools, which has important practical application value for precise editing and molecular design in soybean breeding.

The new gene-editing technology CRISPR/Cas system has been widely used for genome engineering in various organisms. Since the CRISPR/Cas gene-editing system has a certain possibility of low efficiency and the whole plant transformation of soybean is time-consuming and laborious, it is important to evaluate the editing efficiency of designed CRISPR constructs before the stable whole plant transformation process starts. Here, we provide a modified protocol for generating transgenic hairy soybean roots to assess the efficiency of guide RNA (gRNA) sequences of the CRISPR/Cas constructs within 14 days. The cost- and space-effective protocol was first tested in transgenic soybean harboring the GUS reporter gene for the efficiency of different gRNA sequences. Targeted DNA mutations were detected in 71.43–97.62% of the transgenic hairy roots analyzed as evident by GUS staining and DNA sequencing of the target region. Among the four designed gene-editing sites, the highest editing efficiency occurred at the 3′ terminal of the GUS gene. In addition to the reporter gene, the protocol was tested for the gene-editing of 26 soybean genes. Among the gRNAs selected for stable transformation, the editing efficiency of hairy root transformation and stable transformation ranged from 5% to 88.8% and 2.7% to 80%, respectively. The editing efficiencies of stable transformation were positively correlated with those of hairy root transformation with a Pearson correlation coefficient (r) of 0.83. Our results demonstrated that soybean hairy root transformation could rapidly assess the efficiency of designed gRNA sequences on genome editing. This method can not only be directly applied to the functional study of root-specific genes, but more importantly, it can be applied to the pre-screening of gRNA in CRISPR/Cas gene editing.