1 Introduction

Sweet corn (Zea mays L.) is a highly valued vegetable crop, particularly in the United States, where it ranks second in farm value for processing vegetables, just behind tomatoes. It is also gaining popularity in Asia and Europe, with significant production in countries like Japan, Canada, France, and Taiwan. Sweet corn is distinct from other types of corn due to specific genes that affect starch synthesis in the endosperm, making it suitable for use as a vegetable. The crop is not only consumed fresh but also processed, with approximately 40% of the processed corn being frozen and the remainder canned. The economic significance of sweet corn is underscored by its high market value and its role in the food industry, both for fresh consumption and as a processed product (Dey et al., 2018).

The sugar content in sweet corn is a critical quality trait that significantly influences consumer preference and market value. High sugar levels contribute to the sweetness and overall taste, which are primary factors for consumer satisfaction (Becerra-Sanchez and Taylor, 2021). The presence of specific genes that alter carbohydrate composition in the endosperm is what differentiates sweet corn from other corn varieties, making it sweeter and more desirable. Additionally, the high sugar content is essential for maintaining the quality of sweet corn during post-harvest handling and processing, as it affects taste, aroma, and color, which are key attributes appreciated by consumers (Becerra-Sanchez and Taylor, 2021).

This study provides a comprehensive review of the key genes associated with high sugar content in sweet corn and their applications in breeding programs. It explores the genetic basis of sugar accumulation in sweet corn, the methodologies employed for gene mapping, and the broader implications of these findings for the development of high-sugar-content varieties. The scope of this study includes an analysis of recent advancements in the field of sweet corn genetics, the challenges encountered in breeding for enhanced sugar content, and future strategies for improving sweet corn quality through genetic innovation. The aim is to provide breeders with a foundation to develop superior sweet corn varieties that meet consumer demands and enhance market competitiveness.

2 High Sugar Content in Sweet Corn: A Trait of Interest

2.1 Definition and measurement

The sugar content in sweet corn is primarily determined by the types and amounts of sugars present in the kernels, including sucrose, glucose, fructose, and maltose. The sugary enhancer (se) gene, for instance, has been shown to increase sucrose levels significantly while also contributing to the accumulation of maltose during kernel development (Ferguson et al., 1979; Carey et al., 1984). Additionally, genes involved in glycolysis, starch, and sucrose metabolism, such as hexokinases and beta-glucosidase, play crucial roles in the biochemical pathways that lead to sugar accumulation in sweet corn kernels (Figure 1) (Chen et al., 2022b).

Several methods are employed to measure sugar levels in sweet corn. Traditional biochemical assays involve gas-liquid chromatography to quantify individual sugars like sucrose, glucose, and maltose (Ferguson et al., 1979). More advanced techniques include genome-wide transcriptome analysis to identify differentially expressed genes related to sugar metabolism (Chen et al., 2022b). Near-infrared spectroscopy (NIR) combined with chemometrics has also been developed as a rapid, simple, and environmentally friendly method for quantifying soluble sugar content in super sweet corn (Yang et al., 2020).

2.2 Genetic basis of sugar content

The genetic basis of sugar content in sweet corn is complex and involves multiple genes and genetic interactions. Key genes such as shrunken2 (sh2), which encodes ADP-glucose pyrophosphorylase, play a significant role in starch metabolism and consequently affect sugar levels in the endosperm (Ruanjaichon et al., 2021). The sugary enhancer (se) gene is another critical genetic factor that modifies the sugary-1 (su-1) endosperm mutation, leading to higher sugar levels without reducing phytoglycogen content (Bonte and Juvik, 1990). Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with sugar content, providing valuable markers for breeding programs (Chen et al., 2022a; Ruanjaichon et al., 2021).

Breeding for high sugar content in sweet corn has a long history, with early efforts focusing on the selection of naturally occurring mutations such as Su-1 and Sh2. These mutations were found to significantly increase sugar levels in the kernels, making the corn sweeter and more desirable for consumption (Bonte and Juvik, 1990). Marker-assisted breeding has further advanced the development of sweet corn varieties by enabling the precise introgression of favorable alleles, such as those responsible for high β-carotene content, into existing sweet corn lines (Saha et al., 2022).

2.3 Importance in consumer preferences and market trends

Consumer preferences have increasingly shifted towards sweeter varieties of sweet corn, driven by the desire for better taste and improved postharvest storage properties. Super sweet cultivars, which retain higher levels of sugar and moisture for longer periods after harvest, have become particularly popular in urban markets (Bonte and Juvik, 1990). The demand for these varieties has led to significant increases in the import and export values of sweet corn as a fresh, processed, and preserved commodity (Saha et al., 2022).

The market for sweet corn has seen substantial growth over the past decade, with a notable increase in the cultivation and commercialization of high-sugar varieties. This trend is supported by the development of new breeding techniques and genetic markers that facilitate the production of sweet corn with enhanced sugar content and other desirable traits (Chen et al., 2022a; Saha et al., 2022). The introduction of biofortified inbreds with high β-carotene content, for example, has expanded the market potential of sweet corn by combining nutritional benefits with high sugar levels (Saha et al., 2022).

3 Key Genes Associated with Sugar Content

3.1 Identification of key genes

The primary genes associated with high sugar content in sweet corn include Su (sugary), Sh2 (shrunken-2), and Bt (brittle). The Sh2 gene, which encodes ADP-glucose pyrophosphorylase (AGPase), plays a crucial role in starch metabolism in the maize endosperm. Variants of the Sh2 gene have been identified through genome-wide association studies (GWAS), highlighting its significant impact on sugar content (Ruanjaichon et al., 2021; Chhabra et al., 2022). The Su1 gene, another key player, is involved in the biosynthesis of starch and has been shown to influence sugar levels in sweet corn (Bonte and Juvik, 1990; Finegan et al., 2022). Additionally, the Bt gene, particularly the bt-A allele, has been found to condition higher sucrose levels in kernels compared to the Su gene (Hannah and Bassett, 1977).

Advances in molecular studies have identified other candidate genes that contribute to sugar content. For instance, genome-wide transcriptome analyses have revealed differentially expressed genes (DEGs) related to sugar metabolism, such as hexokinases and beta-glucosidase, which are involved in glycolysis and sucrose metabolism (Chen et al., 2022b). These findings provide a broader understanding of the genetic basis of sugar accumulation in sweet corn.

3.2 Functional analysis of key genes

The identified genes play significant roles in sugar biosynthesis and metabolism. The Sh2 gene, encoding AGPase, is essential for starch biosynthesis, and its mutation leads to increased sugar levels in the endosperm (Figure 2) (Ruanjaichon et al., 2021; Chhabra et al., 2022) . The Su1 gene, which encodes isoamylase, is involved in the breakdown of starch into simpler sugars, thereby enhancing sweetness (Bonte and Juvik, 1990; Finegan et al., 2022). The bt-A allele of the Bt gene has been shown to double the sucrose content in kernels compared to the Su gene (Hannah and Bassett, 1977).

Expression patterns of these genes vary under different conditions. For example, the mutation of the Su1 gene results in minimal changes in the endosperm transcriptome, while the loss of Sh2 function leads to increased expression of sugar transporters and genes involved in ABA signaling (Finegan et al., 2022). Additionally, the expression levels of candidate genes identified through GWAS, such as those involved in stalk sugar content, differ significantly between genotypes with varying sugar levels (Chen et al., 2022a).

3.3 Mutations and variants

Allelic diversity in the key genes associated with sweetness traits is crucial for breeding programs. Variants of the Sh2 gene, identified through SNP and InDel markers, have been shown to differentiate between sweet corn and other types of corn, providing valuable tools for marker-assisted breeding (Ruanjaichon et al., 2021; Chhabra et al., 2022). The Su1 gene also exhibits significant allelic variation, with different alleles contributing to varying levels of sweetness and other desirable traits (Bonte and Juvik, 1990; Mehta et al., 2017).

The importance of allelic diversity is further highlighted by the identification of unique and rare alleles in sweet corn inbreds, which can be utilized to develop novel hybrids with enhanced sweetness (Mehta et al., 2017). Understanding the genetic diversity and functional implications of these mutations and variants is essential for improving sweet corn breeding strategies.

4 Techniques for Mapping Key Genes

4.1 Quantitative trait loci (QTL) mapping

Quantitative trait loci (QTL) mapping has been extensively used to identify regions of the genome associated with sugar content in sweet corn. This technique involves evaluating the genetic variance and heritability of traits across different genotypes. For instance, a study on sugar cane identified 36 QTLs associated with brix, a measure of sugar content, across 32 chromosomes, highlighting the genetic complexity of sugar traits (Zhang et al., 2023). Similarly, in maize, QTL mapping has been used to identify loci influencing various yield-related traits, which can be extrapolated to sugar content traits in sweet corn (Zhang et al., 2020). The integration of QTL findings into breeding programs involves using these identified markers for marker-assisted selection (MAS), which accelerates the breeding of high-sugar content varieties by selecting individuals with desirable QTLs (Zhang et al., 2020; Zhang et al., 2023).

4.2 Genome-wide association studies (GWAS)

Genome-Wide Association Studies (GWAS) have been pivotal in identifying loci associated with sugar traits in sweet corn. GWAS involves scanning the entire genome for SNPs that correlate with the trait of interest. For example, a GWAS study on sweet corn identified 12 significant SNPs associated with sweetness, with the most significant SNP located on chromosome 3 linked to the shrunken2 (sh2) gene, which plays a crucial role in starch metabolism (Figure 3) (Ruanjaichon et al., 2021). This approach allows for the identification of candidate genes and the development of functional markers for MAS. When comparing QTL mapping and GWAS outcomes, GWAS provides higher resolution and can identify specific genes within QTL regions, offering more precise targets for breeding programs (Ruanjaichon et al., 2021; Hu et al., 2023).

4.3 CRISPR and functional genomics approaches

CRISPR and other gene-editing techniques have revolutionized the validation of gene functions related to sugar content in sweet corn. These techniques allow for precise modifications of target genes to observe phenotypic changes. For instance, CRISPR has been used to edit genes involved in carbohydrate metabolism, validating their roles in sugar accumulation (Kaur et al., 2021). Additionally, functional genomics approaches, such as transcriptome analysis, have provided insights into regulatory elements influencing sugar content. By identifying and manipulating these regulatory elements, researchers can enhance sugar content in sweet corn through targeted breeding strategies (Li et al., 2020; Kaur et al., 2021).

5 Case Studies

5.1 Successful mapping studies in sweet corn

Recent studies have made significant strides in mapping genes associated with high sugar content in sweet corn. One notable study utilized genome-wide association studies (GWAS) to identify genetic variations and candidate genes responsible for stalk sugar content and other agronomic traits in fresh corn. This study identified 92 significant single nucleotide polymorphisms (SNPs), with seven SNPs consistently associated with stalk sugar content across multiple environments, explaining 13.68~17.82% of the phenotypic variation (Chen et al., 2022a). Another GWAS focused on the sweetness trait in sweet corn identified 12 significant SNPs on chromosomes 3, 4, 5, and 7, with the most associated SNP located on chromosome 3. This SNP was linked to the Shrunken2 (Sh2) gene, which encodes ADP-glucose pyrophosphorylase, a key enzyme in starch metabolism (Ruanjaichon et al., 2021).

Further, a genome-wide transcriptome analysis of two supersweet corn cultivars revealed 45 748 differentially expressed genes (DEGs) in kernels and 596 DEGs in leaves. This study highlighted the role of genes involved in glycolysis, starch, and sucrose metabolism in contributing to high sugar content in sweet corn kernels (Chen et al., 2022b). Additionally, a comprehensive characterization of the sh2 gene in various maize inbreds identified 686 SNPs and 372 InDels, providing valuable markers for assessing genetic diversity and aiding in the selection of sweet corn varieties with high sugar content (Chhabra et al., 2022).

5.2 Breeding programs utilizing key gene information

Several breeding programs have successfully incorporated mapped genes to develop sweet corn varieties with enhanced sugar content and other desirable traits. For instance, the introgression of the crtRB1 allele, responsible for high β-carotene content, into the sweet corn inbred SC11-2 resulted in biofortified lines with significantly higher β-carotene levels while maintaining high sugar content (Saha et al., 2022) (Figure 4; Table 1). Another breeding effort utilized marker-assisted backcross breeding (MABB) to stack multiple genes, including Sh2, Opaque2 (O2), Lycopene epsilon cyclase (lcyE), and CrtRB1, in the parents of four sweet corn hybrids. These hybrids exhibited high levels of essential nutrients such as lysine, tryptophan, and provitamin-A, along with high kernel sweetness (Baveja et al., 2021).

Moreover, the development of functional SNP markers for the sh2 gene has facilitated the classification and selection of sh2-based sweet corn varieties, streamlining the breeding process for improved sweetness (Ruanjaichon et al., 2021). Similarly, the development and validation of functional markers for the Sugary1 (Su1) gene, which encodes a starch de-branching enzyme, have provided breeders with tools to enhance kernel sweetness in maize (Chhabra et al., 2019).

6 Evolutionary Insights into Sweet Corn Sugar Content

6.1 Origins of sweet corn variants

The evolutionary background of sweetness traits in maize is deeply rooted in the domestication events that have shaped modern sweet corn. The primary mutation responsible for the high sugar content in sweet corn is the Shrunken2 (Sh2) gene, which encodes ADP-glucose pyrophosphorylase, a key enzyme in starch metabolism. This mutation leads to the accumulation of sugars instead of starch in the kernels, a trait that has been selected for its desirable sweet taste (Ruanjaichon et al., 2021; Chhabra et al., 2022). The domestication of maize, including sweet corn, likely began in northern Mexico, where early farmers selected for traits such as increased sugar content (Hu et al., 2021).

Domestication events have significantly influenced sugar-related genes in maize. For instance, the Sugary1 (Su1) locus, another critical gene for sweetness, has undergone multiple independent mutations, suggesting a recurrent selection for sweet phenotypes across different maize populations (Tracy et al., 2006). These mutations have been pivotal in the development of sweet corn varieties that are now widely cultivated and consumed.

6.2 Comparative genomics with other crops

Comparative genomics provides valuable insights into the evolution of sugar content in sweet corn by comparing it with related cereal crops. For example, the genetic mechanisms underlying sugar accumulation in sweet sorghum (Sorghum bicolor) show high levels of sequence similarity with sweet corn, despite their phenotypic differences. This similarity highlights the shared evolutionary pressures and genetic pathways that have been selected for high sugar content in both crops (Cooper et al., 2019).

Lessons from parallel evolution in other sweet crops, such as sugarcane and sorghum, reveal that changes in gene expression and the activity of sugar transporters play crucial roles in sugar metabolism. In sweet sorghum, for instance, the timing and localization of sugar metabolism genes are critical for the high sugar phenotype, a pattern that is also observed in sweet corn (Cooper et al., 2019). These comparative studies underscore the importance of specific genetic changes and regulatory mechanisms in the evolution of sweetness traits across different plant species.

7 Breeding Applications for High Sugar Content

7.1 Marker-assisted selection (MAS)

Marker-assisted selection (MAS) leverages mapped genes to enhance breeding programs by enabling the precise selection of desirable traits. In sweet corn, MAS has been instrumental in identifying and utilizing specific single nucleotide polymorphisms (SNPs) associated with high sugar content. For instance, the Shrunken2 (Sh2) gene, which encodes ADP-glucose pyrophosphorylase, has been identified as a key determinant of sweetness in corn. SNP markers developed for this gene have proven highly effective in distinguishing sweet corn varieties from other types, thereby facilitating targeted breeding efforts (Ruanjaichon et al., 2021). The benefits of MAS include increased breeding efficiency, reduced time to develop new varieties, and improved accuracy in selecting for high sugar content traits (Kumawat et al., 2020; Hasan et al., 2021).

7.2 Genomic selection (GS)

Genomic selection (GS) integrates high-throughput genotyping and phenotyping to predict the performance of breeding lines, thereby accelerating the development of high-sugar sweet corn varieties. GS has been successfully applied in various crops, including sugarcane, where it has demonstrated the potential to enhance breeding cycles and improve prediction accuracy for traits like sucrose content (Sandhu et al., 2022; Xiong et al., 2023). In sweet corn, GS can be used to predict the sweetness of new varieties by incorporating data from genome-wide association studies (GWAS) and other genetic analyses. This approach allows breeders to select superior genotypes without extensive field testing, thus speeding up the breeding process and increasing the likelihood of achieving desired traits (Liu et al., 2019).

7.3 Development of hybrid varieties

The development of hybrid sweet corn varieties involves combining mapped genes to achieve hybrid vigor, particularly in traits related to sweetness. By utilizing genes identified through MAS and GS, breeders can create hybrids that exhibit enhanced sugar content and other desirable agronomic traits. For example, the integration of SNP markers associated with high sugar content can be used to select parent lines that, when crossed, produce offspring with superior sweetness traits (Chen et al., 2021; Ruanjaichon et al., 2021). This approach not only improves the quality of sweet corn but also ensures consistency and reliability in the performance of hybrid varieties.

8 Challenges and Opportunities

8.1 Challenges in mapping key genes

Current gene mapping techniques, such as genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping, face several limitations. One significant challenge is the complexity of the genetic architecture of traits like sugar content in sweet corn, which often involves multiple genes with small effects. For instance, a study identified 92 significant SNPs associated with stalk sugar content, but these only explained a portion of the phenotypic variation, indicating the presence of many minor-effect genes that are difficult to detect (Chen et al., 2022a). Additionally, the resolution of QTL mapping can be limited by the density of markers and the size of the mapping population, which can result in broad confidence intervals that complicate the identification of specific candidate genes (Qi et al., 2009).

Translating genetic findings into practical breeding applications is another major hurdle. Even when key genes or markers are identified, their integration into breeding programs can be slow and resource-intensive. For example, while functional SNP markers for the Shrunken2 (Sh2) gene have been developed and validated for predicting sweetness in corn, the process of marker-assisted selection (MAS) and backcrossing to incorporate these markers into elite lines can be time-consuming and requires extensive field testing to ensure the desired traits are expressed consistently (Ruanjaichon et al., 2021). Moreover, environmental interactions can affect the expression of these traits, making it challenging to achieve stable performance across different growing conditions (Chen et al., 2022a).

8.2 Opportunities in genomic technologies

Next-generation sequencing (NGS) and omics technologies offer promising opportunities to overcome some of the limitations of traditional mapping techniques. NGS allows for high-resolution mapping and the identification of a large number of genetic variants across the genome. This can enhance the power of GWAS and QTL studies by providing a more comprehensive understanding of the genetic basis of complex traits like sugar content in sweet corn. Additionally, transcriptome analysis can reveal differentially expressed genes involved in sugar metabolism, providing insights into the regulatory mechanisms underlying these traits (Chen et al., 2022b).

Integrated approaches, such as systems biology, can further accelerate the mapping and utilization of key genes in breeding programs. By combining data from genomics, transcriptomics, proteomics, and metabolomics, researchers can construct comprehensive models of the biological pathways involved in sugar accumulation and metabolism. This holistic view can identify key regulatory nodes and interactions that might be missed by single-omics approaches. For instance, integrating transcriptome data with QTL mapping has identified genes involved in both photosynthesis and sugar metabolism, offering new targets for improving sugar content in sweet corn (Chen et al., 2022b). Such integrated approaches can also facilitate the development of multi-trait breeding strategies, enabling the simultaneous improvement of multiple desirable traits, such as sweetness, nutritional content, and yield (Baveja et al., 2021).

9 Environmental and Epigenetic Influences

9.1 Impact of environmental factors on sugar accumulation

Environmental factors such as temperature, light, and soil nutrients play a crucial role in the sugar traits of sweet corn. Temperature influences the rate of photosynthesis and respiration, which are critical for sugar metabolism. For instance, higher temperatures can enhance the activity of enzymes involved in sugar synthesis, thereby increasing sugar content in the kernels. Light intensity and duration also significantly affect sugar accumulation by impacting photosynthetic efficiency. The presence of adequate light can boost the expression of genes related to photosynthesis, such as PsbS, which in turn influences sugar accumulation in the kernels (Chen et al., 2022b). Soil nutrients, particularly nitrogen and potassium, are essential for the optimal growth and metabolic activities of sweet corn. These nutrients support the synthesis of sugars and other carbohydrates, contributing to higher sugar content in the stalks and kernels (Chen et al., 2022a).

Interactions between these environmental factors and genetic pathways are complex and multifaceted. For example, the expression of genes involved in sugar metabolism, such as hexokinases and beta-glucosidase, can be modulated by environmental conditions, leading to variations in sugar content (Chen et al., 2022b). Additionally, genetic variations identified through genome-wide association studies (GWAS) have shown that certain single nucleotide polymorphisms (SNPs) are associated with sugar content across different environmental conditions, indicating a strong interaction between genetic makeup and environmental factors (Chen et al., 2022a).

9.2 Epigenetic regulation of sugar traits

Epigenetic mechanisms, including DNA methylation and histone modifications, play a significant role in regulating gene expression related to sugar traits in sweet corn. DNA methylation can suppress or enhance the expression of genes involved in sugar metabolism, thereby affecting the overall sugar content in the kernels and stalks. Histone modifications, such as acetylation and methylation, can alter chromatin structure and accessibility, influencing the transcriptional activity of sugar-related genes (Chen et al., 2023).

The potential for exploiting epigenetics in breeding programs is immense. By understanding the epigenetic regulation of key genes involved in sugar metabolism, breeders can develop strategies to modify these epigenetic marks to enhance sugar content. For instance, the use of plant water-content-enabled micro-Ribonucleic acids (PWC-miRNAs) has been shown to regulate gene expression during the sucrose accumulation process, providing a new avenue for increasing sugar content in corn stalks (Chen et al., 2023). This approach can be integrated into molecular marker-assisted breeding programs to select for desirable epigenetic traits, thereby improving the efficiency and effectiveness of breeding high-sugar-content sweet corn varieties.

10 Future Directions

10.1 Emerging research areas

Recent advancements in epigenetics have opened new avenues for understanding the regulation of sugar content traits in sweet corn. Epigenetic modifications, such as DNA methylation and histone modifications, can influence gene expression without altering the DNA sequence itself. These modifications can play a crucial role in the regulation of genes associated with sugar metabolism and accumulation. For instance, the identification of differentially expressed genes (DEGs) in sweet corn kernels has highlighted the importance of genes involved in glycolysis, starch, and sucrose metabolism, which are regulated at multiple levels, including epigenetic mechanisms (Chen et al., 2022b). Future research should focus on mapping the epigenetic landscape of sweet corn to identify key regulatory elements that control sugar content traits.

The complexity of sugar content traits in sweet corn is often governed by the interaction of multiple genes. Studies have shown that quantitative trait loci (QTL) analysis can identify multiple QTLs associated with sugar-related traits, which often exhibit additive, dominant, and epistatic effects (Ritter et al., 2008; Shiringani et al., 2010). For example, the identification of QTLs for kernel soluble sugar content in super-sweet corn has revealed the presence of both additive and non-additive genetic effects, indicating the potential for synergistic interactions among different genes (Qi et al., 2009). Future research should aim to explore these synergistic effects through advanced genetic mapping and functional genomics approaches to develop sweet corn varieties with enhanced sugar content.

10.2 Bridging research and practical breeding

The translation of genetic research into practical breeding applications is essential for the development of high-sugar sweet corn varieties that can benefit farmers. Marker-assisted selection (MAS) has proven to be an effective tool in this regard. For instance, the development of functional SNP markers for genes like shrunken2 (sh2) has facilitated the breeding of sweet corn with improved sweetness (Ruanjaichon et al., 2021). Additionally, the introgression of favorable alleles for traits such as β-carotene content using MAS has demonstrated the potential for combining multiple desirable traits in sweet corn (Saha et al., 2022). Future efforts should focus on integrating these genetic tools into breeding programs to create scalable solutions that can be readily adopted by farmers.

Collaboration among researchers, breeders, and industry stakeholders is crucial for the successful implementation of genetic advancements in sweet corn breeding. The identification of candidate genes and QTLs for sugar-related traits provides a valuable resource for breeding programs (Khanbo et al., 2020; Chen et al., 2022a). By fostering partnerships between academic institutions, breeding companies, and farmers, it is possible to accelerate the development and dissemination of high-sugar sweet corn varieties. Collaborative efforts can also facilitate the sharing of genetic resources, data, and expertise, thereby enhancing the overall efficiency and impact of breeding programs.

Acknowledgments

Thank you to the anonymous peer review for providing targeted revision suggestions for the manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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