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. 2026 Aug 7:10.1111/pbi.70731. Online ahead of print. doi: 10.1111/pbi.70731

Natural and CRISPR/Cas9 Editing Variations of Dual‐uORFs Synergistically Increase the Nutritional Value of Silage Maize Feed

Jing Zhou 1, Xi Wang 1, Yajing Zhang 1, Xinhao Luo 1, Shuyao Quan 1, Jie Ji 1, Lixue Sun 1, Xinyang Chen 1, Jin Chen 1, Jianbo Cheng 2, Jun Fan 1, Changsheng Li 1,✉, Beijiu Cheng 1,✉, Xiaoyu Li 1,✉
PMCID: PMC13448561  PMID: 42563555

ABSTRACT

The precise enhancement of nutritional quality in silage maize is a core strategy for increasing livestock production efficiency. Through evolutionary analysis of multiple plant species, we identified two functionally synergistic upstream open reading frames (uORF1 and uORF2) within the 5′ untranslated region of the gene encoding GDP‐L‐galactose phosphorylase (GGP). By leveraging a natural translation‐enhancing haplotype of uORF1 (Hap2) and performing CRISPR/Cas9‐mediated targeted mutagenesis of the highly conserved uORF2, we successfully engineered an elite dual‐uORFs variant. This variant significantly increased vitamin C (Vc) content and concurrently improved key silage quality traits, including crude protein and phosphorus levels, without inducing growth penalties. Transcriptomic profiling further elucidated the molecular mechanisms by which the dual‐uORFs variation coordinately regulates Vc biosynthesis and the improvement of silage quality. Our findings deepen the understanding of the conventional paradigm of single‐uORF regulation and provide a novel strategy and superior germplasm resources for the precision breeding of high‐Vc, high‐quality silage maize.

Keywords: dual‐uORFs, GGP, haplotype, nutritional quality, silage maize, Vc

1. Introduction

Maize ( Zea mays L.) stands as the preeminent global forage crop, with more than half of its total grain and biomass production dedicated to ruminant nutrition (Erenstein et al. 2022; Shiferaw et al. 2011; Zhou et al. 2026). The biomass yield and nutritional profile of silage maize are pivotal determinants of livestock productivity and farm profitability. High‐quality silage provides a stable nutrient supply for enhancing animal growth and reproductive performance (Kamiya et al. 2023; Khan et al. 2015). However, traditional breeding paradigms often encounter a trade‐off between quality improvement and biomass or yield (Zhao et al. 2022). Therefore, circumventing this inverse correlation to simultaneously enhance nutritional traits and maintain high yields remains a central challenge in contemporary maize breeding.

Vitamin C (Vc), also known as L‐ascorbic acid, is a quintessential antioxidant in plants for maintaining cellular redox homeostasis, stress resistance and metabolic equilibrium (Fotopoulos and Kanellis 2013; Gallie 2013b; Gest et al. 2013). During the ensiling process, the potent reducing capacity of Vc facilitates the maintenance of anaerobic conditions and promotes the activity of lactic acid bacteria, which enhances feed stability and palatability (Li et al. 2022). Dietary Vc fortification bolsters livestock immunity and mitigates oxidative stress (Akinmoladun 2021; Carr and Maggini 2017; Pogge et al. 2015). Despite its profound breeding value, endogenous Vc levels in maize vegetative and reproductive tissues are typically suboptimal (Gallie 2013a). Although CRISPR/Cas9‐mediated editing of Vc biosynthetic genes has shown promise, strong constitutive alleles or loss‐of‐function mutations frequently incur growth penalties or metabolic pleiotropy (Bulley et al. 2012; Chen and Gallie 2004; Fitzgerald et al. 2004; Khan et al. 2025; Schoof et al. 2000; Xu et al. 2015). Thus, developing precise regulatory strategies to increase Vc accumulation without compromising agronomic fitness is imperative.

Plant phenotypic diversity and adaptive evolution are driven by extensive genetic variation. Such variation modulates agronomic traits through diverse molecular mechanisms, ranging from structural changes in protein‐coding sequences to the fine‐tuned regulation of gene expression mediated by cis‐regulatory elements, including promoters, enhancers, 5′ untranslated regions (5′UTRs) and upstream open reading frames (uORFs) (von Arnim et al. 2014; Wang and Zhang 2017). While conventional studies have focused predominantly on transcriptional control, genome‐wide association studies have revealed a significant enrichment of trait‐associated variants within UTRs (Gaut et al. 2018; Yano et al. 2016). Given the frequent discordance between mRNA abundance and protein levels in plants, translational regulation emerges as a primary driver of phenotypic variation (Wu et al. 2022; Xiong et al. 2020). Therefore, dissecting genetic variation within UTRs, particularly uORFs, provides a critical foundation for elucidating trait evolution and advancing precision molecular breeding.

uORFs are key cis‐regulatory elements located within the 5′UTRs of eukaryotic mRNAs and modulate gene expression by controlling the translation efficiency of the primary ORF (pORF) (Luo and Sachs 1996; Mize and Morris 2001; Morris and Geballe 2000; Ruan et al. 1996; Zhong et al. 2023). In recent years, targeted engineering of uORFs has emerged as an effective strategy for improving crop yield, stress resistance, and quality traits (Xing et al. 2020; Xu et al. 2017; Yang et al. 2024; Zhang et al. 2018; Zhang et al. 2025). Although uORFs are known to play important roles in plant development and stress responses, the extent of their natural variation and their contribution to phenotypic diversity remain poorly understood. GDP‐L‐galactose phosphorylase (GGP) acts as the primary rate‐limiting enzyme in the plant Vc biosynthetic pathway, and its translational regulation serves as a core mechanism for governing Vc accumulation (Dowdle et al. 2007; Fenech et al. 2021). The highly conserved ACG‐uORF has been established as a pivotal element in the modulation of GGP translation, enabling the successful biofortification of Vc across diverse plant species through genome editing (Li et al. 2018; Sang et al. 2025; Zhang et al. 2018). Nevertheless, whether a more intricate uORF system exists within the 5′UTR of the maize GGP, and how the synergistic regulatory mechanisms among multiple uORFs dictate translational output, remains fundamental yet unresolved scientific questions. Furthermore, the existence of elite natural uORF variants capable of bypassing translational repression to drive downstream translation, as well as the molecular mechanisms by which Vc enrichment reconfigures silage quality through metabolic remodelling, have not been systematically investigated.

In this study, we identified two uORFs in the ZmGGP 5′UTR, including a polymorphic uORF1 with substantial natural variation and a highly conserved uORF2, by integrated evolutionary analysis of the 5′UTRs of GGP homologues across diverse plant species and haplotype analysis of 479 maize accessions. Using CRISPR/Cas9, we generated multiple edited variants of uORF2 and systematically evaluated the regulatory effects of both natural uORF1 variation and engineered uORF2 mutation on downstream pORF translation efficiency, Vc accumulation in stems, leaves, and kernels, and overall silage quality. In addition, molecular markers were developed for the elite haplotype Hap2. Integrated transcriptomic analyses revealed that the metabolic networks were reprogrammed by these regulatory changes. Overall, this study not only provides outstanding germplasm resources for precision breeding in maize but also establishes a generalizable theoretical framework and strategic paradigm for balancing high yield and superior quality in crops through the regulation of translational elements.

2. Results

2.1. Two Functional uORFs Are Present in the 5′UTR of ZmGGP

Prior investigations have established that the 5′UTRs of GGP genes typically contain a highly conserved uORF initiated by a noncanonical ACG start codon. By analysing 224 GGP homologues identified across 117 different species, this study demonstrated that although monocotyledonous and dicotyledonous plants form two distinct evolutionary clades, their 5′UTR sequence alignments consistently show that this ACG‐initiated uORF structure is highly retained. In addition, a second uORF initiated by a canonical ATG start codon was characterized in the GGP 5′UTRs of maize and sorghum. In maize, these two uORFs were designated uORF1 and uORF2, respectively (Figure 1a). uORF1 initiates at an ATG codon and spans 399 bp, encoding a peptide of 133 amino acids, whereas uORF2 initiates at the noncanonical ACG codon and spans 177 bp, encoding a peptide of 59 amino acids. The two uORFs terminate at a shared stop codon TGA.

FIGURE 1.

FIGURE 1

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Identification and functional characterization of two uORFs in the 5′UTR of ZmGGP. (a) Phylogenetic analysis and sequence alignment of GGP 5′UTRs across representative plant species. The conserved noncanonical start codon (ACG) and the monocot‐specific canonical start codon (ATG) are highlighted. (b) Schematic representation of the dual‐luciferase reporter constructs (not to scale). The WT ZmGGP 5′UTR and its mutated versions (uATA‐uACG and uATG‐uACA) were fused upstream of the FLUC reporter gene. (c, d, e) Effects of uATA–uACG and uATG–uACA mutations in the ZmGGP uORFs on relative FLUC/RLUC activity (c, d) and relative mRNA levels (e). Luciferase activity and mRNA abundance were determined by DLRA and RT–qPCR, respectively. The data are presented as the means ± SDs (n = 4). p‐values in this figure and others were calculated by a two‐tailed Student's t test. ‘ns’ indicates no significant difference.

To elucidate the functional roles of the two uORFs in the 5′UTR of ZmGGP, we individually disrupted their initiation codons via site‐directed mutagenesis. The ATG in uORF1 was mutated to ATA, and the ACG in uORF2 was changed to ACA. These two mutated versions of the uORFs were individually cloned upstream of the firefly luciferase (FLUC) gene, with the Renilla luciferase (RLUC) gene serving as an internal control. We transfected each reporter construct into Nicotiana benthamiana leaves and determined the relative mRNA abundance of FLUC/RLUC, as well as the relative FLUC/RLUC activity (Figure 1b). We found that mutation of the start codons ATG in uORF1 and ACG in uORF2 increased the relative FLUC/RLUC activity but did not significantly affect gene transcription (Figure 1c–e). These results indicated that two uORFs in the ZmGGP 5′UTR act as negative regulators of pORF translation.

2.2. Natural Variation in ZmGGP uORF1 Activates Downstream pORF Translation and Increases the Vc Content

To explore the biological impact of uORF variation in ZmGGP during maize domestication, a haplotype analysis was conducted using the genome sequences from a maize association panel comprising 479 accessions. uORF1 displayed three single‐nucleotide polymorphisms (SNPs) and one insertion (indel), generating six major haplotypes. In contrast, the uORF2 sequence was highly conserved. Among the changes in uORF1 tested, Hap1 and Hap2 predominated. These two haplotypes differ at position −351 bp; Hap1 carries an ACC sequence, whereas Hap2 has a 1‐bp insertion (ACCC) at this site (Figure 2a,b).

FIGURE 2.

FIGURE 2

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Natural variations in ZmGGP uORF1 are associated with Vc content. (a) Haplotype analysis of ZmGGP uORF1. The polymorphic nucleotides, sequence variations, and positions are shown (not to scale). Poi: Position; Hap: Haplotype. (b) Sequencing chromatograms of the Indel site in Hap1 and Hap2. The red box or text highlights the single‐base insertion variation (from ACC to ACCC) in Hap2 relative to that in Hap1. (c) Sequence alignment of ZmGGP uORF1 from the maize inbred lines B73 and KN5585. (d) Schematic representation of the constructs used for the DLRA (not to scale). The 5′UTR of ZmGGP from B73 or KN5585 was cloned upstream of FLUC. (e–f) Effects of ZmGGP uORF1 from B73 (Hap1B73) and KN5585 (Hap2KN5585) on relative FLUC/RLUC activity (e) and relative mRNA levels (f), as quantified by DLRA and RT–qPCR, respectively. The data are presented as the means ± SDs (n = 4). (g) Vc contents in maize leaves from the Hap1 and Hap2 varieties at the V6 stage. The data are the means ± SDs (n = 30).

To assess the functional relevance of this natural variation, representative inbred lines B73 (Hap1) and KN5585 (Hap2) were selected for further analysis. Sequence alignment revealed that uORF1 in B73 is 399 bp in length, whereas the 1‐bp insertion in KN5585 introduces a premature termination codon, resulting in a much shorter uORF1 of 231 bp (Figure 2c). We tested the effects of uORF1B73 and uORF1KN5585 on transcript levels and translation by cloning them individually upstream of the FLUC reporter gene for a dual‐luciferase reporter assay (DLRA) in N. benthamiana leaves (Figure 2d). Compared with uORF1B73, uORF1KN5585 manifested markedly higher FLUC activity, but no significant differences in transcript abundance were detected between the two uORF variants (Figure 2e,f). These results implied that truncation of uORF1 alleviated translational repression and increased downstream translation of the ZmGGP pORF.

In alignment with these findings, population‐level analyses revealed a significant association between the uORF1 haplotype and Vc accumulation. Compared with those with Hap1 (ACC‐type), maize varieties with Hap2 (ACCC‐type) presented markedly higher leaf Vc contents (Figure 2g). The results revealed that the natural variation in ZmGGP uORF1 modulated translational efficiency and contributed to the phenotypic diversity in the Vc content during maize evolution.

2.3. Synergistic Effects of uORF1 Natural Variation and uORF2 Edited Mutation Significantly Increase Downstream pORF Translation and Vc Content

As pivotal cis‐regulatory elements, uORFs repress gene translation by competing for ribosome binding or inhibiting ribosome reinitiation at the downstream pORF. Accordingly, targeted disruption of uORFs using CRISPR/Cas9 has emerged as an effective strategy to relieve translational repression and increase protein expression (Figure 3a). Given that ZmGGP uORF2 is highly conserved in natural maize populations, we selected KN5585 (Hap2 haplotype) as the genetic background for targeted editing of uORF2 to generate high‐Vc maize lines. Using CRISPR/Cas9, we obtained six representative Hap2 allelic variants. Specifically, Hap2 #1 to #4 contained deletions of varying lengths (46–76 bp), Hap2 #5 harboured a 46‐bp deletion accompanied by a point mutation, and Hap2 #6 harboured a 60‐bp deletion (Figure 3b). Subsequent off‐target analysis via Sanger sequencing confirmed that Hap2 #1 to #6 represent six ZmGGP uORF2‐knockout mutants (Figures S1 and S2).

FIGURE 3.

FIGURE 3

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Targeted editing of ZmGGP uORF2 by CRISPR/Cas9 enhanced downstream pORF translation and Vc content. (a) Schematic diagram (not to scale) showing the mechanism by which the CRISPR/Cas9‐mediated disruption of uORFs enhances the translation efficiency of the downstream pORF. (b) CRISPR/Cas9‐induced mutations in the ZmGGP uORF2 gene in the KN5585 line. The sgRNA was designed to target an approximately 80‐bp region within the ZmGGP uORF2 gene. Orange arrows indicate the positions of the sgRNAs, and blue boxes represent the exon regions. (c) Schematic representation of the dual‐luciferase reporter constructs (not to scale). The 5′ UTR sequences from the KN5585 line and Hap2‐uorf2 mutants (Hap2 #1–6) were subsequently cloned upstream of FLUC. (d) Representative fluorescence images of Nicotiana benthamiana leaves and the relative FLUC/RLUC activities of Hap2 #1–6 mutants. (e) Schematic diagram of Hap1 #1–3 constructs and Hap2 #1–3, with the KN5585 line as the control. (f) Representative fluorescence images of N. benthamiana leaves and the relative FLUC/RLUC activities of Hap2 #1–3 and Hap1 #1–3. (g) Relative ZmGGP expression levels detected by RT–qPCR in the KN5585 line and Hap2 #1–4 mutants. The data are the means ± SDs (n = 4). Statistical significance was determined using a two‐tailed Student's t test: **p < 0.01, ***p < 0.001. (h) Immunoblot analysis of ZmGGP. ZmGAPDH was used as the internal control. (i) Vc contents in the leaves of the KN5585 line and its Hap2‐uorf2 mutants at the V6 stage. The data are the means ± SDs (n = 3). In all figures, different letters above the bars indicate significant differences as determined by one‐way ANOVA with Tukey's HSD test.

First, we tested whether these variants increased the translation of the downstream pORF mRNA. The 5′UTRs from KN5585 and each of the six edited lines were fused upstream of the FLUC reporter gene. Compared with KN5585, Hap2 #1–#4 substantially increased the relative FLUC/RLUC activity, whereas the effects of Hap2 #5 and Hap2 #6 did not significantly differ (Figure 3c,d). To assess whether the natural variation in uORF1 (Hap2) and targeted editing of uORF2 have additive effects on translation, we performed a comparative analysis between the combined genotypes generated in the Hap2 background and their counterparts in the Hap1 background. The results revealed that compared with the natural Hap2 variant (KN5585), targeted editing (Hap1 #1–#3) resulted in stronger translational activation. Furthermore, the overall translational enhancement in the Hap2 background (Hap2 #1–#3) was significantly greater than that observed in the corresponding Hap1 edited lines (Hap1 #1–#3). (Figure 3e,f).

Four edited lines (Hap2 #1–#4) displaying pronounced translational activation were further characterized. RT–qPCR and immunoblot analyses confirmed that these lines presented higher ZmGGP transcript levels and protein abundance than did the KN5585 line (Figure 3g,h). Consistent with these molecular changes, compared with KN5585, all four edited lines presented markedly elevated leaf Vc contents (Figure 3i). These results indicated that editing of the conserved uORF2 facilitated precise modulation of downstream pORF translation. Most importantly, the combination of uORF editing with natural variation can have additive positive effects in specific allelic contexts.

2.4. Natural Variation in ZmGGP uORF1 Improves the Vc Content and Nutritional Quality of Silage Maize

The Vc levels across multiple tissues in the two haplotype populations at the R2 stage were quantified. Compared with the Hap1 haplotype, the Hap2 haplotype had a distinct advantage in terms of Vc accumulation in leaves, stems, and kernels (Figure 4a–c). We subsequently assessed the impact of this natural variation on the comprehensive nutritional quality of the silage. Physicochemical analysis revealed that compared with Hap1, the Hap2 haplotype synergistically improved multiple quality traits; in addition to the elevated Vc levels, Hap2 resulted in substantially higher crude protein amounts in kernels and higher stover phosphorus content. Furthermore, the crude fibre content of Hap2 stover was considerably decreased (Figure 4d–f). No apparent alterations were detected between the edited lines and KN5585 in terms of dry matter, crude fat, crude ash, acid detergent fibre (ADF), neutral detergent fibre (NDF) or calcium content (Figure S3). The results suggested that Hap2 increased the nutritional density of the feed and improved the palatability and digestibility of the selected silage.

FIGURE 4.

FIGURE 4

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The Hap2 haplotype of ZmGGP uORF1 significantly increased the Vc content and improved the nutritional quality of the maize silage. (a–c) Comparison of the Vc contents between the Hap1 and Hap2 haplotypes in different tissues and stages, including leaves (a), stems (b), and kernels (c), at the R2 stage. (d–f) Analysis of nutritional indicators in silage maize stover and fresh maize kernels from the Hap1 and Hap2 haplotypes, including the contents of phosphorus (d), crude protein (e), and crude fibre (f). The data in these figures are the means ± SDs (n = 30). (g) Genotyping analysis was conducted on commercially available mainstream silage maize. (h) Development and validation of a diagnostic molecular marker for the ZmGGP Hap2 haplotype. PCR‐based genotyping was performed based on the Indel polymorphism in the uORF1 region. The 353‐bp target fragment was specifically amplified in Hap2 accessions, whereas no amplification product was detected in Hap1 varieties. M, DNA molecular weight marker; lanes 2–7, Hap1 cultivars; lanes 8–13, Hap2 cultivars.

To investigate the breeding potential of the Hap2 haplotype in contemporary maize production, we genotyped a panel of 40 leading commercial silage cultivars. Among these, only 13 varieties carried the Hap2 haplotype, whereas the remaining 27 were non‐Hap2 types, representing a frequency of only 32% (Figure 4g). These findings indicate that nearly 70% of current cultivars lack this favourable allele, suggesting a substantial opportunity for further genetic improvement through molecular breeding. To facilitate rapid and efficient selection, we developed a molecular marker targeting the Indel polymorphism within the uORF1 region. PCR validation confirmed that this marker specifically amplified a 353‐bp target fragment in the Hap2 accessions, whereas no amplification occurred in the Hap1 varieties (Figure 4h). The successful development of this marker is effective for the precise discrimination of elite ZmGGP alleles.

2.5. Combined Natural Variation and Gene Editing Improved the Nutritional Value of Maize Silage

To systematically evaluate the practical application potential of ZmGGP uORF2‐targeted editing in silage maize production, we quantified Vc accumulation in Hap2‐uorf2 mutants across multiple tissues, including leaves and stover at the blister stage (R2 stage), as well as the aboveground biomass after silage processing (Figure 5a). In leaves at the R2 stage, the Vc contents in Hap2 #1–#4 increased by 1.02‐, 0.46‐, 0.56‐ and 0.31‐fold, respectively, compared with that in KN5585 (Figure 5b). In mature stems, the Vc contents were increased by 0.8‐, 1.43‐ and 0.66‐fold in Hap2 #2, #3 and #4, respectively. The Vc content in KN5585 was comparable to that in Hap2 #1 (Figure 5c). Notably, the increase in the Vc content was particularly pronounced in the kernels of Hap2 #1, #2 and #3, whose contents increased by 1.35‐, 0.73‐, and 0.65‐fold, respectively, with concentrations reaching 15.9–22.7 mg/100 g FW. Similarly, the Vc content in Hap2 #4 was increased 0.5‐fold (Figure 5d). Furthermore, following 30 days of silage fermentation under simulated agricultural practices, the Vc contents in Hap2 #1, #3 and #4 remained 1.0–1.2 times higher than those in KN5585, reaching 32–35 mg/100 g FW (Figure S4). These results suggested that the Hap2‐uorf2 mutation not only increased Vc accumulation in the plants but also improved their nutritional retention during silage processing.

FIGURE 5.

FIGURE 5

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ZmGGP uORF2 mutations increased the Vc content and improved the nutritional quality of the silage. (a) Schematic illustration of the silage preparation process. Whole maize plants (including ears) harvested at 30 days after pollination were chopped and fermented to produce silage. (b–d) Vc contents in leaves (b), stems (c) and kernels (d) of the KN5585 and Hap2‐uorf2 mutants at the R2 stage. (e) Analysis of nutritional indicators in silage maize stover and fresh maize kernels from Hap2 #1, Hap2 #3 and Hap2 #4 compared with KN5585, including the contents of phosphorus, crude protein, and crude fibre. The data in the figures are the means ± SDs (n = 4).

To further assess the impact of uORF2 editing on the overall nutritional value of silage maize, we analysed multiple nutritional parameters in stover and kernels. Hap2 #1, while maintaining high Vc levels, resulted in appreciable increases in crude protein in the kernels and phosphorus contents in the stover, which were accompanied by a decrease in crude fibre content in the stover. In addition, compared with KN5585, Hap2 #3 resulted in a significantly greater phosphorus content in stover (Figure 5e). No discernible changes in other conventional parameters, including dry matter, crude fat, crude ash, ADF, NDF and calcium content, were observed between the edited lines and KN5585 (Figure S5).

These results demonstrate that Hap2 #1, in particular, clearly enhances maize Vc biosynthesis while concurrently improving key nutritional traits, highlighting the substantial potential of uORF2‐targeted editing for the development of high‐nutritional‐value silage maize cultivars.

2.6. Hap2 #1 Promoted Vegetative Growth While Maintaining Yield Stability

To evaluate whether uORF2 editing for quality improvement has detrimental effects on plant development and grain yield, we performed a systematic evaluation of various agronomic traits in KN5585 and its mutants at the R2 stage (Figure 6a). The measured parameters included aboveground biomass (fresh and dry weight), plant height, and stem diameter. Among all the engineered lines, Hap2 #1 exhibited the most favourable phenotype. The aboveground fresh weight, dry weight, plant height, and stem diameter of Hap2 #1 were greater than those of KN5585 (Figure 6b–e). These results indicated that the variations in Hap2 #1 do not negatively affect plant growth and development; instead, they confer a discernible growth‐promoting effect. Crucially, the individual plant yield and hundred‐kernel weight of Hap2 #1 did not significantly decrease, indicating that this specific variation successfully maintained yield stability while synergistically increasing biomass accumulation. Conversely, compared with KN5585, both Hap2 #2 and Hap2 #3 displayed severe growth retardation and biomass loss, with substantially lower yields and kernel weights. In comparison, the growth indicators of Hap2 #4 were comparable to those of the WT, and the yield‐related traits still tended to decrease (Figure 6f–h).

FIGURE 6.

FIGURE 6

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Yield and agronomic trait analysis of Hap2‐uorf2 mutants. (a) Plant morphologies of the KN5585 and Hap2‐uorf2 mutants. Scale bar = 2 cm. (b–e) Analysis of biomass‐related traits, including aboveground fresh weight (b), aboveground dry weight (c), stem diameter (d) and plant height (e). (f) Comparison of grain phenotypes between the KN5585 line and the mutants. (g–h) S tatistical analysis of 100‐kernel weight (g) and grain yield per plant (h) in the plants. The data presented in the figures are the means ± SDs (n ≥ 10).

Furthermore, kernel phenotypic analysis revealed no observable differences in kernel length or width between any of the edited mutants and the KN5585 (Figure S6). In summary, these findings demonstrated that the Hap2 #1 allele achieved an optimal balance between high‐Vc biofortification and agronomic fitness.

2.7. Transcriptome Analysis Revealed That Hap2 #1 Triggers Broad Transcriptional Reprogramming

To elucidate the molecular mechanisms underlying the diverse phenotypic changes observed in Hap2 #1, we performed RNA‐seq analysis on mature leaf tissues. Differential expression analysis revealed 3694 differentially expressed genes (DEGs), of which 2085 were upregulated and 1609 were downregulated. Principal component analysis and hierarchical clustering both showed a clear separation between Hap2 #1 and the control KN5585, with high consistency among biological replicates (Figure 7a; Figure S7).

FIGURE 7.

FIGURE 7

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Transcriptomic analysis of maize leaves in the Hap2 #1 mutant. (a) Hierarchical clustering heatmap of DEGs between mature leaves of the KN5585 line and Hap2 #1, illustrating distinct transcriptional profiles between the two genotypes. The colour scale represents Z score‐normalized expression values. (b–c) GO enrichment analysis of upregulated (b) and downregulated (c) DEGs. The bar height represents the gene count, and the colour gradient indicates the significance of enrichment. (d) KEGG pathway enrichment analysis of upregulated and downregulated DEGs. Bubble size represents the gene count, and colour depth indicates enrichment significance (p value). (e) Expression profiles of genes involved in the L‐galactose pathway of Vc biosynthesis. The heatmap displays the differential expression (Z score) of key genes, including ZmGMP2, ZmGGP (vtc2) and ZmGPP (vtc4), between the KN5585 line and Hap2 #1. All genes are annotated according to the MaizeGDB database.

To investigate whether editing ZmGGP uORF2 triggered transcriptional responses with association of changes in Vc levels, we first examined genes in the L‐galactose pathway of Vc biosynthesis. Consistent with the increased ZmGGP expression and Vc accumulation observed in Hap2 #1, several key genes in the Vc biosynthetic pathway, including ZmGMP2, ZmGGP and ZmGPP, were significantly upregulated (Figure 7e; Table S2), indicating that disruption of the uORF2 led to the overall activation of the Vc biosynthesis.

Beyond the Vc biosynthetic pathway, transcriptomic changes were mainly enriched in functional modules related to plant growth and nutrient metabolism. GO enrichment analysis of upregulated DEGs revealed significant enrichment in cellulose synthase activity and cell wall organization (Figure 7b; Table S2). Consistently, representative genes involved in cell wall loosening and cellulose biosynthesis, such as expa1 and cesa1, were significantly upregulated, consistent with the enhanced vegetative growth and biomass accumulation observed in Hap2 #1 (Figure S8c; Table S2). In addition, genes involved in nitrogen metabolism (nnr2 and nii2) and amino acid transport (aaap18 and aaap57) also showed significant upregulation (Figure S8a; Table S2), suggesting a coordinated adjustment of nitrogen assimilation processes. Genes associated with the SPX–PHR regulatory network controlling phosphate homeostasis were also differentially expressed (Figure S8b; Table S2), indicating coordinated regulation of phosphate balance following uORF perturbation.

In contrast, GO enrichment analysis of downregulated DEGs showed significant enrichment in ribosome‐related functions and protein folding processes (Figure 7c; Table S2), suggesting an overall suppression of translation and protein synthesis. KEGG pathway analysis further supported this result (Figure 7d; Table S2). Upregulated genes were mainly enriched in ‘biosynthesis of various plant secondary metabolites’ and ‘starch and sucrose metabolism’, whereas ‘ribosome’ and ‘protein processing in endoplasmic reticulum’ pathways were significantly downregulated, indicating a systemic remodelling of cellular translation and protein processing machinery. This may contribute to the reduced endogenous resource consumption and increased allocation of amino acids to develop kernels.

Overall, these results demonstrated that disruption of the ZmGGP uORFs enhanced the transcriptional activity of the Vc biosynthetic pathway and was accompanied by the coordinated transcriptional reprogramming of multiple growth and nutrient metabolism related processes. These findings suggested that Vc probably acted as a key metabolic node linking uORFs regulation to broad transcriptional and phenotypic changes.

3. Discussion

The trade‐off between biomass and nutritional quality is a central challenge in maize breeding, governed by complex translational regulation. Here, we revealed a dynamic regulatory architecture within the ZmGGP 5′UTR, where dual uORFs (ATG‐uORF1 and ACG‐uORF2) acted synergistically to fine‐tune translational output and metabolic flux, ultimately improving silage quality.

Distinct from the single, highly conserved ACG‐uORF mode previously reported in dicots such as Arabidopsis, lettuce and tomato (Li et al. 2018; Zhang et al. 2018), our study breaked this conventional consensus by demonstrating that the maize ZmGGP gene has evolved a unique dual‐uORFs architecture initiated by an upstream canonical ATG (uORF1) and a downstream non‐canonical ACG (uORF2) (Figure 1a). Genetic evidence robustly substantiated that uORF2 repressed downstream translation primarily through a cis‐translational ribosome trapping mechanism. When its non‐canonical initiation codon was precisely mutated from ACG to ACA, translation initiation was completely abolished, leading to a marked increase in ZmGGP expression (Figure 1c–e). Subsequent intensive evaluations of six independent CRISPR/Cas9‐induced mutant lines, all harbouring structural truncations within uORF2, further validated this model: lines Hap2 #1–4 successfully de‐repressed ZmGGP expression, whereas lines Hap2 #5–6 exhibited expression levels that remained virtually indistinguishable from the wild type (Figure 3d). These highly convergent results consistently support the conclusion that the regulatory function of uORF2 depends strictly on its role as a cis‐acting ribosomal ‘roadblock,’ rather than on its translated peptide product. Notably, along with the relief of translational repression, all uORF2‐edited lines also manifested a significant increase in ZmGGP mRNA levels (Figure 3g). This observation aligns with previous reports indicating that large structural deletions can alter mRNA abundance; such an unexpected accumulation implies the involvement of more complex, multi‐layered indirect regulatory systems. This notable phenomenon may be explained by multiple mechanisms, including increased mRNA accumulation, enhanced translational efficiency of the ZmGGP gene or improved mRNA stability in the absence of uof‐mediated regulation (Sang et al. 2025; Zhang et al. 2018). Intriguingly, our deeper analysis revealed that across the four constructed dual‐uORFs mutants, the mRNA levels of ZmGGP, its corresponding protein contents, and the final biosynthesized Vc levels did not exhibit a strictly synchronized, linear correlation (Figure 3g–i). This decoupling highlights the intricate nature of post‐transcriptional and translational regulation governing the ZmGGP protein.

This dynamic, evolutionary control architecture implies that natural variation within these regulatory modules provides an invaluable genetic reservoir for dismantling complex trait trade‐offs. Beyond classic variations in promoter transcription or protein function, recent studies have highlighted the profound impact of natural variations within untranslated regions on crop phenotypic diversity (Li et al. 2019; Wallace et al. 2014). Natural uORF polymorphisms influence nutrient uptake, yield and developmental trade‐offs, including PHO1‐mediated phosphate acquisition, GmPHF1‐dependent phosphorus efficiency, and grain‐size regulation via HvDEP1 and ZmSWEET4c (Chen et al. 2023; Guo et al. 2022; Reis et al. 2020; Watt et al. 2020). In this study, we identified a natural indel variation within ZmGGP uORF1 that truncates the uORF1 and relieves translational repression, thereby increasing ZmGGP expression, Vc accumulation, and silage quality (Figures 2 and 4). Importantly, combining this natural allele with CRISPR/Cas9‐mediated editing of uORF2 produced a strong additive activation effect (Figure 3f), demonstrating that layered uORFs perturbations can be rationally integrated to engineer translational output. This establishes a general framework that merges superior natural haplotypes with precise genome editing for quantitative control of protein synthesis.

Using the elite natural haplotype Hap2 as the genetic baseline, our targeted editing of uORF2 successfully yielded the optimized line Hap2 #1. Notably, this line not only further augmented Vc accumulation and silage quality (Figure 5) but also exhibited an unexpected promotion of vegetative growth while maintaining a stable grain yield. Conversely, a striking ‘phenotypic divergence’ emerged across our broader edited population, where several lines with sequence disruptions manifested severe dwarfism and yield penalties (Figure 6). This divergence strongly underscores that maintaining homeostasis in Vc levels is essential for normal plant vegetative growth and general metabolic stability. A highly analogous phenomenon was recently documented in rice for OsTBF1, where the complete deletion of its uORF or severe truncation of upstream sequences induced catastrophic growth defects and compromised disease resistance; in contrast, a precise, moderate truncation of the uORF successfully elevated protein abundance without incurring a growth penalty (Tian et al. 2025). Therefore, when utilizing uORFs as vital regulatory modules in molecular design breeding, a comprehensive evaluation encompassing vegetative development, yield performance and quality traits is strictly required. Such holistic assessment is indispensable to avoid negative trade‐offs triggered by metabolic disequilibrium, ensuring that nutritional fortification does not come at the expense of agricultural productivity.

Concomitant with this upstream translational de‐repression, targeted editing of ZmGGP uORF2 precipitated extensive transcriptomic reprogramming (Figure 7), an observation highly congruent with the genome‐wide cascades previously documented for Vc‐driven networks (Deslous et al. 2021). As a central redox metabolite linking metabolic status and developmental signalling, Vc can broadly reshape gene expression networks in response to changes in its cellular abundance (Foyer and Noctor 2011; Pastori et al. 2003), suggesting that it serves as the proximal effector connecting uORF perturbation to downstream transcriptional changes. Because GGP catalyses the rate‐limiting step of Vc biosynthesis, enhanced translation of GGP is expected to increase cellular Vc levels and alter the intracellular redox state (Fenech et al. 2021), thereby affecting nitrogen and phosphorus metabolism as well as pathways related to cell wall biosynthesis and cell expansion, ultimately triggering a series of downstream phenotypic responses.

At the nexus of these redox‐driven responses, this metabolic shift directly rewires the plant's macronutrient utilization networks, characteristically manifesting as a profound optimisation of nitrogen metabolism. Specifically, genes involved in nitrate reduction and ammonium assimilation, including nnr2/nnr4, nii1/nii2, gln1/gln3 and gdh2, were consistently upregulated, whereas the vegetative storage protein genes vsp1 and vsp2 were induced and asn3/asn4 were downregulated (Figure S8a; Table S2). These expression changes indicate a shift from nitrogen storage in the form of asparagine toward more active nitrogen assimilation and transport (Hirel et al. 2001; Martin et al. 2006). Given that nitrate reductase activity is regulated by cellular redox status (Chamizo‐Ampudia et al. 2017; Trimboli et al. 1996), these transcriptional changes are likely attributable to Vc‐mediated redox remodelling that enhances nitrogen assimilation.

In parallel, perturbations in Vc homeostasis also impinge upon phosphate metabolism, reflecting the well‐established crosstalk between Vc biosynthesis and inositol phosphate signalling pathways (Dong et al. 2019; Ried et al. 2021; Zhou et al. 2021). VTC4 (ZmGPP), a key enzyme in Vc biosynthesis with additional inositol monophosphatase activity, is also a direct transcriptional target of PHR1 (Mora‐Macías et al. 2017). Its upregulation may promote reprogramming of the InsP8–SPX–PHR signalling pathway, thereby inducing phosphate starvation‐responsive genes such as pap13 and pap15 (Figure S8b; Table S2) and ultimately enhancing phosphate acquisition and utilization.

Ultimately, the integrated reprogramming of nitrogen and phosphorus metabolism converges with redox‐dependent regulation of cell wall dynamics, providing a mechanistic basis for the observed increases in fresh biomass and reductions in crude fibre content (Figure S8c; Table S2). Apoplastic ascorbate promotes cell‐wall loosening and cell expansion (Fry 1998; Pignocchi and Foyer 2003), and Vc is the activating/reactivating cofactor of prolyl‐4‐hydroxylases and of 2‐oxoglutarate‐dependent dioxygenases that act in cell‐wall protein maturation and in lignin/flavonoid and hormone metabolism (Smirnoff 2018); in maize, ascorbate redox state further governs quiescent‐centre cell‐cycle progression (Kerk and Feldman 1995). Consistent with this, multiple expansin genes are induced in Hap2 #1 (expb4/expa5/expa1/expb6/expb17), with strong enrichment of the GO term ‘cell wall organization’. The phenylpropanoid pathway was also enriched (18 genes), with divergent expression among laccase family members. Given the functional diversification of LACs in lignin polymerization and related wall processes (Khandal et al. 2020), altered laccase expression could reshape lignin crosslinking patterns (Berthet et al. 2011), further contributing to reduced fibre content while enhanced cell expansion increases biomass.

In summary, this study identified, for the first time, two synergistically regulatory uORFs in the 5′ UTR of ZmGGP. We discovered a naturally occurring indel variation in uORF1 that significantly promotes Vc biosynthesis. Based on this superior haplotype (Hap2), we further applied CRISPR/Cas9‐mediated precise knockout of uORF2, successfully generating and screening a dual‐uORFs synergistically improved line, Hap2 #1. The experimental results demonstrated that this strategy effectively disrupted the antagonistic relationship between nutritional enhancement and plant growth and development, resulting in coordinated improvements in the Vc content and silage quality (Figure 8). These findings highlight the great potential of precise translational regulation of multiple uORFs in crop breeding and provide important theoretical support and valuable germplasm resources for the molecular design and breeding of high‐nutrition, high‐efficiency silage maize.

FIGURE 8.

FIGURE 8

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Proposed model for the role of ZmGGP uORFs in regulating Vc biosynthesis and silage maize quality. The natural variation in uORF1 located in the 5′UTR of ZmGGP, together with uORF2‐edited variants, synergistically increased the Vc contents and silage maize quality.

4. Methods

4.1. Plant Materials

The N. benthamiana and maize inbred lines used in this study were provided by the National Engineering Laboratory of Crop Stress Resistance Breeding, Anhui Agricultural University. To investigate the biological function of uORF2 under the ZmGGP Hap2 genetic background, various plasmid vectors were constructed. For the CRISPR/Cas9 constructs, two specific target sites, 5′‐CCCCGCAGCTGCGCAGGTGGAGC‐3′ and 5′‐GCGTCCAGCCCCGCTCCGCACGG‐3′, were designed for the ZmGGP uORF2 region using the online tool CRISPR‐P 2.0 (http://cbi.hzau.edu.cn/crispr/). After synthesis, the sgRNA sequences were cloned into the CRISPR/Cas9 expression vector. Following a previously published protocol (Xing et al. 2014), the CRISPR/Cas9 constructs were introduced into the elite inbred line KN5585 (Hap2 background) as the receptor via maize genetic transformation to generate transgenic plants. Transgenic positive plants were back‐crossed with KN5585, and six knockout lines were generated in the self‐pollinated offspring. Seeds from the transgene‐free homozygous T3 generation of transgenic plants were used in experiments.

4.2. Dual‐Luciferase Reporter Assay

Each of the ZmGGP 5′UTR sequences derived from the selected maize lines was inserted into the pGreenII 0800‐LUC vector, and the resulting vectors were independently transformed into Agrobacterium tumefaciens strain GV3101 (pSoup‐p19). The transformed A. tumefaciens suspensions were then infiltrated into the leaves of 4‐week‐old N. benthamiana plants. After 48 h of incubation, the activities of FLUC (at 560 nm) and RLUC (at 480 nm) in the leaf extracts were measured using a DLRA system (Uelandy, F6075M) on a microplate reader (PerkinElmer, USA). The pGreenII 0800‐LUC empty vector served as the negative control. The relative ratio of FLUC/RLUC was used as an indicator of translation efficiency.

4.3. RNA Isolation and qRT–PCR

Total RNA was isolated using the phenol–chloroform extraction method. First‐strand cDNA was synthesized from the total RNA using a HiScript II First‐Strand cDNA Synthesis Kit (Vazyme, Nanjing, China). Quantitative real‐time PCR (qRT–PCR) was performed on a PikoReal Real‐Time PCR System (Thermo Scientific, Waltham, MA, USA) using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). For each sample, three independent biological replicates were conducted. The relative expression levels of target genes were calculated using the 10−(∆Ct/3) method, with ZmGAPDH serving as the internal control. The sequences of the primers used in this work are listed in Table S1.

4.4. Protein Extraction and Immunoblotting

Approximately 0.1 g of plant tissue was ground into a fine powder in liquid nitrogen. Total protein was extracted using protein extraction buffer (50 mM Tris–HCl, 2.5 mM EDTA, 0.1% IGEPAL, 150 mM NaCl, 10% glycerol, 1 mM phenylmethylsulfonyl fluoride, 1× Roche protease inhibitor cocktail, 10 mM b‐mercaptoethanol and 10 μM leupeptin). After centrifugation at 4°C, the supernatant was collected, and total proteins were separated by SDS–PAGE and subsequently transferred onto a polyvinylidene fluoride membrane. For immunoblotting, anti‐GGP (Agrisera, AS10723) and anti‐GAPDH (EnoGene, E12‐042) antibodies were used for immunodetection.

4.5. Measurement of Vc

The Vc content in the maize tissues was determined using a Vc Assay Kit (Solarbio, BC1235). Based on the biochemical oxidation of Vc to dehydroascorbic acid, the reaction is catalysed by ascorbate oxidase. The Vc content was monitored by measuring the rate of decrease in absorbance at 265 nm (Garchery et al. 2013; Yamaguchi et al. 2003).

4.6. Silage Quality Analysis

Silage maize was harvested at the R2 (mid‐milk) stage. Three uniform plants from each inbred line and edited line were randomly selected; the stover and kernel from each plant were collected separately, and the stover was processed to small segments less than 2 cm in length. Stovers or kernels were dried in an oven at 65°C for 48 h. The dried samples were ground and passed through a 1‐mm sieve for analysis of their nutritional characteristics. The contents of dry matter, crude ash, crude protein, crude fat, crude fibre, ADF, NDF, calcium and phosphorus were measured using standard methods (Horwitz 1925).

4.7. RNA‐Seq Data Analysis

Transcriptomic sequencing and analysis were conducted by Wuhan Metware Biotechnology Company (Wuhan, China). Total RNA was extracted and purified from each biological replicate using an RNAprep Pure Plant Kit (Tiangen, DP441). The concentration and structural integrity of the harvested RNA were assessed using a NanoPhotometer (IMPLEN, CA, USA), a Qubit 2.0 fluorometer (Life Technologies, CA, USA), and an Agilent 2100 bioanalyzer (Agilent Technologies, CA, USA).

The RNA was randomly fragmented using specialized fragmentation buffer, followed by the synthesis of first‐strand cDNA guided by random hexamer primers. Subsequently, second‐strand cDNA was synthesized through catalysis by DNA polymerase I. The resulting cDNA fragments (~200 bp) were ligated to sequencing adapters, followed by size selection using AMPure XP beads. After PCR amplification and purification, the finalized cDNA libraries were subjected to high‐throughput sequencing on an Illumina NovaSeq 6000 system (Cheng et al. 2011). Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM) (Liao et al. 2014). Differentially expressed genes (DEGs) between groups were identified using the DESeq2 R package, with significance thresholds set at |log2Fold‐Change| ≥ 1 and a false discovery rate (FDR) < 0.05, adjusted via the Benjamini–Hochberg method (Love et al. 2014).

4.8. Statistical Analysis and Reproducibility

All the statistical analyses were performed using GraphPad Prism 8.0. To determine statistical significance, a two‐tailed unpaired Student's t‐test was employed for pairwise comparisons, while one‐way analysis of variance (ANOVA) followed by Tukey's HSD test for multiple comparisons was used for comparisons involving three or more groups. No outliers or datasets were excluded from the final analysis. Details regarding the number of independent biological replicates are indicated in the respective figure legends.

Author Contributions

Beijiu Cheng and Xiaoyu Li conceived the project and designed the experiments. Jing Zhou and Yajing Zhang performed most of the experiments. Xinhao Luo, Shuyao Quan, Jie Ji, Lixue Sun and Xinyang Chen conducted part of the experiments. Jing Zhou and Changsheng Li wrote the manuscript. Xi Wang, Jianbo Cheng and Jun Fan oversaw the entire study and revised the manuscript. Xi Wang, Changsheng Li and Jianbo Cheng provided critical guidance for the revision of the manuscript. All authors discussed the data and contributed to the manuscript.

Funding

This work was supported by Anhui Provincial Key Research and Development Program (2023n06020023). Bio‐breeding Laboratory of Anhui Province (2025SWYZ0200).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Off‐target effect analysis of sgRNA1 in the ZmGGP uORF2‐knockout mutants. (a) Alignment of the sgRNA1 target sequence with the potential off‐target sites predicted by Crispr‐P 2.0. (b) Representative Sanger sequencing chromatograms of the predicted off‐target sites amplified from the KN5585 and the six ZmGGP uORF2‐knockout lines. No off‐target phenomenon was detected in these potential off‐target sites tested.

Figure S2: Off‐target effect analysis of sgRNA2 in the ZmGGP uORF2‐knockout mutants. (a) Alignment of the sgRNA2 target sequence with the potential off‐target sites predicted by Crispr‐P 2.0. (b) Representative Sanger sequencing chromatograms of the predicted off‐target sites amplified from the KN5585 and the six ZmGGP uORF2‐knockout lines. No off‐target phenomenon was detected in these potential off‐target sites tested.

Figure S3: Comparison of silage nutritional quality between stover and kernels in two haplotypes. (a) Dry matter; (b) crude fat; (c) calcium content; (d) crude ash; (e) neutral detergent fibre (NDF); (f) acid detergent fibre (ADF). The data are presented as the means ± SDs (n = 30). Statistical significance was determined using two‐tailed Student's t tests (ns, no significant difference).

Figure S4: Vc contents in Hap2‐uorf2 mutants after silage processing. The data are the means ± SDs (n = 3). Different letters above the bars in this and other figures in the supplemental materials, indicate significant differences as determined by one‐way ANOVA with Tukey's HSD test.

Figure S5: Nutritional quality of silage derived from stover and kernels of Hap2‐uorf2 mutants. (a) Dry matter; (b) crude fat; (c) calcium content; (d) crude ash; (e) neutral detergent fibre (NDF); (f) acid detergent fibre (ADF). The data are presented as the means ± SDs (n = 4).

Figure S6: Seed trait analysis of the Hap2‐uorf2 mutants. (a–b) Grain morphology of Hap2‐uorf2 mutants and the KN5585 line. Scale bars represent 2 cm. (c–d) Comparison of grain length (c) and grain width (d) between Hap2‐uorf2 mutants and the KN5585 line. The data are presented as the means ± SDs (n ≥ 10).

Figure S7: Principal component analysis (PCA) of the RNA‐seq data for all the samples. Biological replicates of the same material are indicated by the same colour.

Figure S8: Proposed molecular mechanisms underlying the four phenotypic shifts in Hap2 #1. (a) Mechanism of increased crude protein content in kernels. (b) Mechanism of increased P content in stover. (c) Mechanism of increased fresh weight and reduced crude fibre content. (a–c) Red arrows indicate upregulation, while green arrows indicate downregulation. Gene names are shown in italics, followed by their corresponding z score‐normalized expression values. All genes are annotated according to the MaizeGDB database.

PBI-9999-0-s002.docx (38MB, docx)

Table S1: List of the primers used in this study.

Table S2: Transcriptome analysis.

PBI-9999-0-s001.xlsx (7.7MB, xlsx)

Contributor Information

Changsheng Li, Email: csli@ahau.edu.cn.

Beijiu Cheng, Email: cbj@ahau.edu.cn.

Xiaoyu Li, Email: lixiaoyu@ahau.edu.cn.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Off‐target effect analysis of sgRNA1 in the ZmGGP uORF2‐knockout mutants. (a) Alignment of the sgRNA1 target sequence with the potential off‐target sites predicted by Crispr‐P 2.0. (b) Representative Sanger sequencing chromatograms of the predicted off‐target sites amplified from the KN5585 and the six ZmGGP uORF2‐knockout lines. No off‐target phenomenon was detected in these potential off‐target sites tested.

Figure S2: Off‐target effect analysis of sgRNA2 in the ZmGGP uORF2‐knockout mutants. (a) Alignment of the sgRNA2 target sequence with the potential off‐target sites predicted by Crispr‐P 2.0. (b) Representative Sanger sequencing chromatograms of the predicted off‐target sites amplified from the KN5585 and the six ZmGGP uORF2‐knockout lines. No off‐target phenomenon was detected in these potential off‐target sites tested.

Figure S3: Comparison of silage nutritional quality between stover and kernels in two haplotypes. (a) Dry matter; (b) crude fat; (c) calcium content; (d) crude ash; (e) neutral detergent fibre (NDF); (f) acid detergent fibre (ADF). The data are presented as the means ± SDs (n = 30). Statistical significance was determined using two‐tailed Student's t tests (ns, no significant difference).

Figure S4: Vc contents in Hap2‐uorf2 mutants after silage processing. The data are the means ± SDs (n = 3). Different letters above the bars in this and other figures in the supplemental materials, indicate significant differences as determined by one‐way ANOVA with Tukey's HSD test.

Figure S5: Nutritional quality of silage derived from stover and kernels of Hap2‐uorf2 mutants. (a) Dry matter; (b) crude fat; (c) calcium content; (d) crude ash; (e) neutral detergent fibre (NDF); (f) acid detergent fibre (ADF). The data are presented as the means ± SDs (n = 4).

Figure S6: Seed trait analysis of the Hap2‐uorf2 mutants. (a–b) Grain morphology of Hap2‐uorf2 mutants and the KN5585 line. Scale bars represent 2 cm. (c–d) Comparison of grain length (c) and grain width (d) between Hap2‐uorf2 mutants and the KN5585 line. The data are presented as the means ± SDs (n ≥ 10).

Figure S7: Principal component analysis (PCA) of the RNA‐seq data for all the samples. Biological replicates of the same material are indicated by the same colour.

Figure S8: Proposed molecular mechanisms underlying the four phenotypic shifts in Hap2 #1. (a) Mechanism of increased crude protein content in kernels. (b) Mechanism of increased P content in stover. (c) Mechanism of increased fresh weight and reduced crude fibre content. (a–c) Red arrows indicate upregulation, while green arrows indicate downregulation. Gene names are shown in italics, followed by their corresponding z score‐normalized expression values. All genes are annotated according to the MaizeGDB database.

PBI-9999-0-s002.docx (38MB, docx)

Table S1: List of the primers used in this study.

Table S2: Transcriptome analysis.

PBI-9999-0-s001.xlsx (7.7MB, xlsx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Plant Biotechnology Journal are provided here courtesy of Society for Experimental Biology (SEB) and the Association of Applied Biologists (AAB) and John Wiley and Sons, Ltd

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