Background: N6-methyladenosine (m6A) is an important epigenetic modification involved in RNA stability and translation regulation. Manipulating the expression of RNA m6A methyltransferases or demethylases makes it difficult to study the effect of specific RNA methylation. Results: In this study, we report the development of Plant m6A Editors (PMEs) using dLwaCas13a (from L. wadei) and human m6A demethylase ALKBH5 catalytic domain. PMEs specifically demethylates m6A of targeted mRNAs (WUS, STM, FT, SPL3 and SPL9) to increase mRNAs stability. In addition, we discovered that a double ribozyme system can significantly improve the efficiency of RNA editing. Conclusion: PMEs specifically demethylates m6A of targeted mRNAs to increase mRNAs stability, suggesting that this engineered tool is instrumental for biotechnological applications.

Harvest duration and postharvest shelf life are two of the most important characters for sweet corn. However, the regulatory mechanism remains unclear. We performed a comparative transcriptome analysis of long harvest -duration and shelf-life sweet corn (LHS) and short harvest-duration and shelf-life field corn (SHS) at three stages, i.e. 10 days after pollination (10DAP), 22 days after pollination (22DAP), and 7 days after harvest (7DAH). We have observed the major transcriptome changes accompanying the harvest process in LHS corn. Gene expression pattern analysis and differentially expressed genes (DEGs) functional enrichments suggested an association between ROS metabolism in kernels with harvest duration and postharvest shelf life. The genes encoding cytochrome P450, peroxidase, peroxiredoxin, glutathione peroxidase, and glutathione S-transferase were upregulated specifically in LHS kernels during and after harvest compared to SHS kernels. These novel findings reveal a new regulatory mechanism of corn post-harvest shelf life and should be useful for extending harvest duration and shelf life for sweet corn.

• EBR delayed the decline in quality of sweet corn during and after harvest. • ROS levels in sweet corn kernels were reduced by treatment with EBR. • EBR promoted the activity and transcriptions of antioxidant enzymes in kernels. • EBR retarded the soluble sugar content drops during and after harvest. • The expression of sucrose transporter genes in kernels were markedly induced by EBR.

Micronutrients, including vitamins, minerals, and other bioactive compounds, have tremendous impacts on human health. Much progress has been made in improving the micronutrient content of inbred lines in various crops through biofortified breeding. However, biofortified breeding still falls short for the rapid generation of high-yielding hybrids rich in multiple micronutrients. Here, we bred multi-biofortified sweet corn hybrids efficiently through marker-assisted selection. Screening by molecular markers for vitamin E and folic acid, we obtained 15 inbred lines carrying favorable alleles (six for vitamin E, nine for folic acid, and three for both). Multiple biofortified corn hybrids were developed through crossing and genetic diversity analysis.

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.

Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation.