Identification of a UDP-glucuronosyl/UDP-glucosyltransferase involved in rice seedling height regulation

Abstract

Seedling height is an important agronomic trait for early vigor and crop establishment in rice. It is controlled by multiple genes, as shown by the continuous variation and normal distribution observed in the Cheongcheong × Nagdong double haploid population measured from 6 to 24 days after sowing. This study identified 32 quantitative trait loci QTLs associated with seedling height across multiple chromosomes. Subsequent candidate gene analysis within overlapping QTL regions identified 44 genes associated with growth, metabolism, and stress responses. A key finding was the identification of Os06g0282000 on chromosome 6 between RM540 and RM586, which encodes a putative UDP-glucuronosyl/UDP-glucosyltransferase (UGT). This gene showed consistently higher expression in tall CNDH lines and increased expression during the 12–24 DAS period, indicating a positive correlation with seedling growth. Gene Ontology enrichment showed the role of QTL-associated genes in hormone signaling and carbohydrate metabolism, emphasizing their roles in early seedling development. Comparative genomic analysis showed that Os06g0282000 is part of a conserved gene family that includes 51 homologous genes in rice. Phylogenetic analysis supported its close relationship with UGTs involved in regulating plant growth. These findings position Os06g0282000 as a strong candidate gene that may play a key role in seedling height QTLs and emphasize the role of UGT-mediated regulation of phytohormone homeostasis in controlling rice seedling growth and development.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-08925-9.

Introduction

Rice (Oryza sativa L.) is one of the world’s most important staple crops, feeding over half the world’s population and providing an important source of calories and nutrition [1]. Improving rice productivity and stability remains a continuous challenge due to increasing population demands, limited arable land, and frequent environmental fluctuations [2]. Early seedling establishment is a crucial stage in rice growth that determines plant population density, uniformity, and yield potential. In particular, the ability of seedlings to grow rapidly and vigorously during the early stages referred to as seedling vigor is essential for ensuring successful establishment under both nursery and direct-seeded conditions [3].

Seedling vigor encompasses several morphological and physiological characteristics, including seed germination rate, seedling height, root length, and biomass accumulation [4]. Among these, seedling height is one of the most direct and easily measurable parameters reflecting early growth potential. Taller seedlings typically possess greater photosynthetic capacity and light-capturing ability, which improves competitiveness against weeds and enhances nutrient uptake efficiency [5, 6]. Furthermore, vigorous seedlings often exhibit improved survival after transplanting and increased tolerance to early-season abiotic stresses such as submergence, drought, or cold [7]. Therefore, understanding the genetic and physiological mechanisms regulating seedling height is critical for breeding rice cultivars with improved establishment and stress resilience.

The genetic control of seedling height is quantitative, governed by multiple loci and influenced by environmental factors. Numerous studies have identified quantitative trait loci (QTLs) for seedling vigor traits in rice using various mapping populations, such as doubled haploid (DH) lines, backcross inbred lines (BILs), and recombinant inbred lines (RILs) [8, 9]. QTLs associated with seedling height, shoot length, and root traits have been reported across almost all rice chromosomes [10–12]. Some QTLs have been linked to genes regulating plant hormones such as gibberellins (GA), auxins, and brassinosteroids, which are key mediators of shoot elongation and cell expansion [13]. Despite these advances, most studies have relied on single time-point measurements, providing a static view of growth regulation.

Seedling development, however, is a dynamic process, and the genetic determinants of height may vary across different growth stages [14]. Some genes are active during early elongation, while others influence later developmental transitions or internode expansion. Therefore, stage-specific QTL analysis offers a more comprehensive understanding of how genetic control of seedling height changes over time. Identifying stage-dependent and stable QTLs can help breeders select lines that maintain consistent growth performance throughout early development, a key trait under field variability [15].

The use of double haploid (DH) populations provides a significant advantage for QTL mapping of complex traits. DH lines are completely homozygous, which ensures genetic uniformity and enhances mapping precision [16]. Because of their genetic stability, DH populations can be repeatedly evaluated across environments and developmental stages, facilitating high-confidence detection of QTLs with consistent effects. This makes them ideal for dissecting quantitative traits such as seedling height, which are often influenced by subtle and interacting genetic effects [17].

While QTL mapping identifies genomic regions associated with phenotypic variation, it typically covers large chromosomal intervals containing many possible genes. Hence, functional validation is essential to pinpoint the causal genes underlying these QTLs [18]. Integrating QTL analysis with gene expression studies helps bridge the gap between genomic loci and biological function. With advances in genome annotation and transcriptomic resources, candidate genes within QTL regions can be predicted based on functional annotation, gene ontology (GO) categories, and known involvement in growth-related pathways [19, 20]. Among various validation techniques, quantitative real-time PCR (qRT-PCR) remains the most reliable method for confirming expression differences of candidate genes between contrasting genotypes [21].

Plant hormones play a central role in regulating seedling height, particularly gibberellins, auxins, and brassinosteroids, which control cell elongation and division [22]. For example, the gibberellin biosynthesis gene OsGA20ox2, the brassinosteroid receptor gene OsBRI1, and the auxin-responsive gene OsIAA3 are known to regulate elongation and early seedling growth [23, 24]. The plant hormone activity is highly regulated by conjugation and deconjugation processes in addition to biosynthesis and signaling [25]. UDP-glucuronosyl/UDP-glucosyltransferase (UGTs) play a key role in this regulation by attaching sugars moieties to hormones such as gibberellins, auxin, abscisic acid, and cytokinins thereby modulating their bioactivity [26]. Several studies have reported that UGT-mediated hormone conjugation effects seedling growth and developmental plasticity in rice and other plant species [27, 28]. However, little research has been done on UGT encoding genes located within seedling height QTLs, and their contribution to seedling growth remains poorly understood.

Despite progress in identifying seedling vigor QTLs, few studies have integrated multi-stage QTL mapping with expression profiling in rice DH populations. Most previous efforts emphasized either static phenotypic analysis or genome-wide association studies without temporal validation. As a result, our understanding of how genetic regulation of seedling height shifts during development remains incomplete [8]. A comprehensive approach that combines phenotypic measurement at multiple time points, QTL mapping, and gene expression validation is needed to unravel both stable and dynamic genetic components controlling early growth.

In this study, we used Cheongcheong/Nagdong double haploid (CNDH) population derived from a cross between Cheongcheong (Indica) and Nagdong (Japonica) lines. DH population were used to investigate the genetic architecture of seedling height at five days intervals after transplanting to pots. The population exhibited wide phenotypic variation, enabling the identification of QTLs associated with seedling height across stages. Major QTLs were further examined to predict candidate genes based on rice genome annotation, and expression validation was performed using qRT-PCR in parental lines and six contrasting DH lines (three tall and three short). Seedling height was selected as a key trait in this study because it is major determinant of early vigor, light capture influence, competitiveness, and overall crop establishment. Compared to other traits such as root length, seedling height provides a direct and integrative measure of shoot growth and is closely linked to hormone-mediated elongation process.

The main objectives of this study were to evaluate phenotypic variation for seedling height at multiple growth stages, identify stage-specific and stable QTLs, predict and annotate candidate genes within these loci, and validate their expression pattern across contrasting lines. By integrating QTL mapping and gene expression analysis, this study provides novel insights into the genetic and molecular regulation of rice seedling height. The findings contribute to a better understanding of early-stage growth dynamics and may offer useful molecular markers and candidate genes for marker-assisted breeding aimed at enhancing seedling vigor and establishment in rice.

Materials and methods

Plant materials

In this study 120 Cheongcheong/Nagdong double haploid (CNDH) population developed by another culture from the F1 cross between ‘Cheongcheong’ (Oryza sativa L. ssp. Indica) and ‘Nagdong’ (Oryza sativa L. ssp. Japonica) were used as plant materials. The CNDH population was used in many studies since 2010 at Kyungpook National University fields located experimental field in Gunwi [10].

Linkage map

A linkage map comprising 12 chromosomes of rice was developed using 778 SSR markers, of which 423 were identified as polymorphic, along with genotypic data from 120 double haploid lines. The linkage map covered a cumulative genetic distance of 2121.7 cM across the rice genome, with an average interval of 10.6 cM between each marker. The QTL analysis for seedling height was performed with Win QTL Cartographer 2.5 software (North Carolina State University, Raleigh, NC, USA). Markers names, chromosome number, distance between markers, PCR data, and target trait values were used as input data to run this software. A fixed logarithm of the odds (LOD) 3.0 was used for Composite Interval Mapping (CIM) throughout the genome in order to ensure statistical significance. Although permutation testing was not performed to determine an empirical LOD threshold, the chosen threshold was based on commonly used standards in similar QTL studies. However, permutation-based thresholds could provide more robust significance estimates and should be considered in future analyses. Names were given to the identified QTLs using McCough method [29].

Growth and phenotypic evaluation of seedlings

The seeds of CNDH population and their parental cultivars were surface sterilized with 500 μL/L Spotak (Hankooksamgong, Seoul, South Korea) pesticide and soaked in water for three days under dark conditions at 34 ◦C in an incubator. Successful germinated seeds were planted in a tray with 50 holes in a soil mixture (Doobaena plus) (composition and percentage: Cocopeat 27, peatmoss 10, vermiculite 34, Masato 10, diatomite 13, bara mesh 5.5, fertilizer 0.48, and humectant 0.2) provided by Nongkyung Co. Ltd, Korea and transferred to controlled growth conditions under a photoperiod of 16 h light/8 h dark, with temperature of 25–30 ℃, and relative humidity of 50–70%. The height of the seedlings was measured from the surface of the soil in the seedling tray to the tip of the tallest plant in each sample. Measurements were taken with eight replications. The height was recorded at four different time points: 6, 12, 18, and 24 days after sowing (DAS). Growth rates were determined for three intervals 6–12 DAS, 6–18 DAS, and 6–24 DAS using the following formula:

where x represents 12, 18, or 24 DAS.

All statistical analyses were conducted with GraphPad Prism. The mean, standard deviation (SD), and coefficient of variation (CV) were calculated for seedling height and growth rate at various developmental stages. Skewness and kurtosis were also calculated in order to evaluate the distribution patterns of these traits within the CNDH population. Differences between parental cultivars were evaluated using an independent t-test, where statistical significance was defined at p < 0.05.

Identification of candidate genes through QTL mapping

Candidate genes were identified using RiceXpro (https://ricexpro.dna.affrc.go.jp) and RAP-DB (https://rapdb.dna.affrc.go.jp). All annotated genes located within the QTL intervals defined by markers were initially extracted based on their physical positions. Functional annotation of these genes was obtained from the Rice Genome Annotation Project (http://rice.uga.edu/index.shtml), and genes associated with biological processes related to plant growth, hormone signaling, and stress response were selected.

Expression level of candidate genes

To confirm the role of candidate genes associated with seedling height, the expression of screened genes were assessed. For relative gene expression, two parental cultivars Cheongcheong and Nagdong as well as three tall CNDH lines (CNDH-30, CNDH-31 and CNDH-95) and three short CNDH lines (CNDH-48–1, CNDH-59–1 and CNDH-59–2) were selected. At 12, 18, and 24 days after sowing the selected lines leaves were collected for RNA extraction. RNA was extracted from the leaves, according to the manufacturer’s protocol using RNeasy plant mini kit (QIAGEN, Hilden, Germany), and using the UltraScript 2.0 cDNA Synthesis Kit, cDNA was synthesized. For gene expression, qRT-PCR was performed using the StepOnePlusTM RT-PCR System (Thermo Fisher Scientific, Seoul, Korea). The reaction include, 10 μL 2X real-time PCR Mix (including SYBR Green 1) (BIOFACT, Daejon, Korea), 1 μL forward and reverse primer (20 pmol/μL), 100 ng of cDNA, and 7 μL nuclease-free water to make 20 μL of final volume. The PCR condition was: Incubation at 95℃ for 2 min, followed by 35 cycles at 94℃ for 10 s and at 60℃ and 72℃ for 10 and 40 s, respectively. The OsActin was used as a housekeeping gene and each PCR was run with three replicates. The accession numbers and primer sequences of selected genes are present in (Table S1).

Functional annotation, gene family and evolutionary analysis

Genes located within the detected QTLs intervals were first identified from rice reference genome database. To categorize candidate genes, Gene Ontology (GO) enrichment and expression based filtering were performed using ShinyGO 0.77 (https://bioinformatics.sdstate.edu/go77/), allowing selection of genes with functional relevance to seedling height. Based on GO terms and gene expression analysis, potential candidate gene was selected. To further investigate the sequence similarity and gene family context of the selected candidate gene, PLAZA platform (https://www.vandepoelelab.be/plaza/) was used. Based on PLAZA homology group assignment, homologous genes were found, and sequence conservation between homologs in the same gene family was visualized using similarity heatmap. To find orthologs of the selected candidate gene across plant species, the protein sequence was blast in the FunPlantGenes database (https://funplantgenes.henau.edu.cn/). The FunPlantGenes identified homologous were shown as a gene similarity network, and the corresponding protein sequences were used to perform multiple sequence alignment, and a phylogenetic tree was constructed using MEGA 11 (https://www.megasoftware.net). Using common phylogenetic construction technique, the evolutionary relationship between the selected candidate gene and its homologs from other plant species were determined, and bootstrap analysis was used to assess branch support.

Results

To provide an overview of the experimental design and the major findings of this study, a schematic representation is shown in (Fig. 1).

Fig. 1.

Schematic overview of the experimental design and major findings of the study

Phenotypic variation

In this study, the seedling height of CNDH population and their parental cultivars were assessed. Seedling height was measured at four different intervals of time: 6, 12, 18, and 24 DAS. Similarly, growth rates were assessed for three intervals 6–12, 6–18, and 6–24 DAS. The frequency distribution of all time points and growth rates were assessed using a histogram, which showed a normal distribution, indicating that seedling height measured at each time point and their growth rates are quantitative and regulated by polygenes (Fig. 2). The mean value and standard deviation (SD) of each time point in the parental cultivars and CNDH population, along with the skewness, and kurtosis test for measuring the significant differences between the parents, are shown in (Table S2). Differences in seedling height between Cheongcheong and Nagdong were time dependent, with statistically significant difference observed at specific time points (p < 0.05), while no significant differences were detected at others. The CNDH population showed significant phenotypic variation with coefficients of variations (CV) ranging from 14.30% to 40.95% at different time points, indicating a high level of variability that is suitable for QTL analysis. In the first two time point the Cheongcheong showed higher seedling height, however in the last 2 time points and in growth rates the Nagdong showed higher seedling height, suggesting that Nagdong have more seedling height then Cheongcheong. Based on seedling height, among the CNDH population, three tall CNDH lines (CNDH-30, CNDH-31 and CNDH-95) and three short CNDH lines (CNDH-48–1, CNDH-59–1 and CNDH-59–2) along with two parental cultivars Cheongcheong and Nagdong were selected for further study.

Fig. 2.

The frequency distribution of seedling height in the CNDH population. Seedling height at different days show a normal distribution and are controlled by polygenes. (C, Cheongcheong; N, Nagdong)

QTL mapping related to seedling height

Total 32 QTLs were detected related to seedling height on different chromosomes (Table S3). QTLs associated with plant height at 6 DAS were mapped on chromosome 1 between RM1297 and RM14323, on chromosome 6 between RM588 and RM527, and on chromosome 9 between RM3769 and RM219. QTLs associated with plant height at 12 DAS were mapped on chromosome 6 between RM586 and RM527, on chromosome 7 between RM21438 and RM21105, and on chromosome 8 between RM22861 and RM6999. QTLs associated with plant height at 18 DAS were mapped on chromosome 6 between RM20017 and RM540 and between RM586 and RM50, on chromosome 7 between RM21438 and RM21105, and on chromosome 8 between RM3689 and RM22499 (Fig. 3).

Fig. 3.

QTL mapping associated with seedling height in the CNDH population. 32 QTLs were detected on different chromosomes and eight QTLs were selected for candidate genes screening

QTLs associated with plant height at 24 DAS were mapped on chromosome 1 between RM3482 and RM11849, on chromosome 2 between RM555 and RM6639, on chromosome 3 between RM1221 and RM16146, on chromosome 7 between RM1364 and RM21105, on chromosome 8 between RM3689 and RM6999, on chromosome 9 between RM3808 and RM24288 and between RM1896 and RM3769, and on chromosome 12 between RM28816 and RM1221. QTLs associated with the average plant height across all sampling dates were mapped on chromosome 6 between RM1163 and RM50, on chromosome 7 between RM21438 and RM21105, on chromosome 8 between RM3689 and RM404, on chromosome 9 between RM24288 and RM444, and on chromosome 12 between RM28816 and RM1221 (Fig. 3).

QTLs associated with growth rates at 6–12 DAS were mapped on chromosome 2 between RM13713 and RM6, on chromosome 3 between RM218 and RM6931, on chromosome 6 between RM540 and RM586, on chromosome 9 between RM1896 and RM444, and on chromosome 11 between RM206 and RM26002. Similarly, QTLs associated with growth rate at 6–18 DAS were mapped on chromosome 1 between RM11966 and RM212, on chromosome 3 between RM14330 and RM218, on chromosome 6 between RM588 and RM1163 and between RM1163 and RM19621, and on chromosome 9 between RM3769 and RM23914 (Fig. 3). The detected QTLs exhibited LOD scores ranging from 3.1 to 7.38 and a phenotypic variance of 26% to 50%. The detailed information, including additive effects, is provided in (Table S3).

Candidate genes screening related to seedling height

A total of 32 QTLs were detected on different chromosomes, however some of the QTLs overlapped on same chromosome. On chromosome 1, between RM3482 and RM11849 and between RM1297 and RM14323, on chromosome 2 between RM555 and RM12662, on chromosome 3 between RM14330 and RM218, on chromosome 6 between RM20017 and RM540 and between RM540 and RM527 were screened for candidate genes. Similarly, on chromosome 7, between RM21438 and RM21105, on chromosome 8 between RM44 and RM404, on chromosome 9 between RM3808 and RM24288 and between RM24288 and RM444, and on chromosome 11 between RM3668 and RM26002 were screened for candidate genes (Fig. 4). The upper panels in Fig. 4 represents the LOD score indicating significant QTL regions, while the lower panels display the physical distribution of candidate genes within these intervals. Based on gene ontology and predicted gene function, 44 candidate genes with molecular function, response to stress and biological process functions were screened on all different selected QTLs (Table S4).

Fig. 4.

QTL analysis and physical mapping of candidate genes across rice chromosomes. Upper panels on each chromosome show QTL mapping for seedling height across different chromosomes. The horizontal red dashed line indicates the LOD threshold (LOD = 3.0). Peaks above this threshold represent significant QTL regions. The lower panels on each chromosome display the physical positions of candidate genes identified within QTL intervals. Genes are categorized based on Gene Ontology annotations into molecular function, response to stress, and biological process, indicated by different colors

Relative gene expression of the candidate genes related to seedling height

The relative gene expression of selected candidate genes were assessed at 12, 18, and 24 DAS (Fig. 5). For gene expression two parental cultivars (Cheongcheong and Nagdong), three tall CNDH lines (CNDH-30, CNDH-31 and CNDH-95), and three short CNDH lines (CNDH-48–1, CNDH-59–1 and CNDH-59–2) were selected among all CNDH population. A total of 44 genes have been chosen based on gene ontology and their sequence annotations, with the functions of molecular function, response to stress, and biological process (Fig. 5). On chromosome 1, twelve gene were screened, and the gene expression of all twelve genes showed upregulation at 12 DAS, while at 18 and 24 DAS the gene expression became reduced, except one gene (Os01g0823100) which showed higher expression at 24 DAS compared to 12 and 18 DAS. On chromosome 2, two genes were screened and gene expression was higher at 12 DAS. The gene (Os02g0266800) showed higher expression in tall CNDH lines while low expression was seen in short CNDH lines. On chromosome 3, total six genes were screened, and four genes (Os03g0118700, Os03g0132200, Os03g0289100, and Os03g0337900) showed higher expression at 12 DAS, while the two genes (Os03g0180800, and Os03g0284100) showed higher expression at 24 DAS. The two genes have comparatively less expressed than other four genes on chromosome 3. Similarly, on chromosome 6, six genes were screened and three genes (Os06g0154200, Os06g0256500, and Os06g0281800) showed higher expression at 12 DAS, while the other three genes (Os06g0157000, Os06g0199100, and Os06g0282000) showed higher expression at 24 DAS. The gene Os06g0282000 exhibited higher expression levels in tall CNDH lines, whereas lower expression was observed in short CNDH lines. Furthermore, this gene showed elevated expression in the parental cultivars and displayed a progressive increase in expression from 12 to 24 DAS. The remaining genes located on chromosome 7 (two genes), chromosome 8 (one gene), chromosome 9 (11 genes), and chromosome 11 (4 genes) exhibited higher expression levels at 12 days after sowing (DAS) compared with those at 18 and 24 DAS. Finally, UDP-glucuronosyl/UDP-glucosyltransferase family protein (Os06g0282000) was selected as final candidate gene for further analysis based on its significantly higher expression in tall lines compared to short lines (p < 0.05) and a consistent increase in expression from 12 to 24 DAS, indicating a strong association with seedling elongation (Fig. 5).

Fig. 5.

qRT–PCR results of genes related to seedling height in Cheongcheong and Nagdong parental cultivars as well as the three tall height and three short height CNDH lines. The relative expression levels of the selected genes were analyzed after 12, 18, and 24 days. The error bars represent the standard error of each mean (n = 3). The error bars with “ns” indicate nonsignificant differences, and those with asterisks indicate a significant difference (* p < 0.05, ** p < 0.01, and *** p < 0.001), according to the two-way ANOVA analysis and the Bonferroni post hoc test

Functional enrichment and candidate gene identification

GO enrichment analysis of 44 genes within the detected QTL regions shown a significant enrichment of biological processes related to hormone signaling, stress response, and carbohydrate metabolism (Fig. 6A). The significantly enriched categories included response to chemical, response to hormone, response to endogenous stimulus, and hormone mediated signaling pathways, indicating that genes detected on QTLs are highly involved in hormonal and environmental signal perception. Additionally, metabolic processes such as gluconeogenesis, glycolysis, hexose biosynthetic process, and glucose metabolic process were enriched. Network analysis revealed that metabolic and hormone related GO terms were highly interconnected through common genes (Fig. 6B), emphasizing the coordinated regulation between energy metabolism and signaling pathways. Hierarchical clustering further grouped metabolic pathways, stress response, and hormone response into closely related functional modules ((Fig. 6C). This suggests that genes involved in integrated control of metabolic pathways, stress adaption, and hormonal regulation are present in the QTL regions.

Fig. 6.

Gene Ontology (GO) enrichment analysis of genes located within seedling height QTL regions. A Bar plot of significantly enriched GO biological process terms, where dot size indicates gene number and color shows − log10 (FDR). B Network of enriched GO terms based on shared genes. C Hierarchical clustering of GO biological processes associated with QTL localized genes

Gene family analysis of Os06g0282000

Among 44 genes located within the QTL regions, Os06g0282000 (UDP-glucuronosyl/UDP-glucosyltransferase family protein) was selected as a strong candidate gene based on its gene expression and functional annotation. This gene encodes a putative UDP-glucuronosyl/UDP-glucosyltransferase (UGT), a class of enzymes that work through glycosylation to regulate plant hormones, secondary metabolites, and stress related compounds. The PLAZA analysis showed that Os06g0282000 is part of the conserved homology group HOM05M000060, which has 51 homologous genes in Oryza sativa ssp. Japonica. The similarity heatmap showed significant sequence conservation between Os06g0282000 and various members of this group, indicating that it is part of the multigene UGT family with shared structural and functional attributes (Fig. 7). As this Os06g0282000 is located within the QTL region, these results suggest that this conserved UGT family may contribute to the genetic regulation of the seedling height trait.

Fig. 7.

Similarity heatmap of the Os06g0282000 (UDP-glucuronosyl/UDP-glucosyltransferase family protein) and its homologs in Oryza sativa. The heatmap represents pairwise sequence similarity among 51 japonica rice genes belonging to the homology group HOM05M000060. Green colors indicate higher sequence similarity while pink color indicate less sequence similarity, reflecting conserved protein domains and evolutionary relatedness among family members. The position of Os06g0282000 is highlighted with a red box

Evolutionary conservation of Os06g0282000

Blast analysis using FunPlantGenes platform identified multiple homologous proteins of Os06g0282000 from different plant species, including Oryza sativa, Arabidopsis thaliana, Aethionema arabicum, Prunus dulcis, and Phyllostachys heterocylla (Fig. 8A). Strong interaction between Os06g0282000 and a number of monocot and dicot homologs was found by the gene similarity network, suggesting that this gene is evolutionarily conserved among angiosperms. This relationship was further supported by phylogenetic analysis based on protein sequences from (Fig. 8A). Os06g0282000 formed a well-supported clade by closely clustering with homologous Arabidopsis thaliana and Oryza sativa genes (Fig. 8B). These results suggests that Os06g0282000 shares a common evolutionary origin with functionally related genes in other species, supporting its importance and candidacy as a QTL associated gene.

Fig. 8.

Evolutionary analysis of Os06g0282000 (UDP-glucuronosyl/UDP-glucosyltransferase family protein). A Gene similarity network of Os06g0282000 and its homologs generated based on protein sequence similarity. Each node represents a gene, and connecting ribbons indicate sequence similarity among homologs. B Phylogenetic tree constructed from the protein sequences of Os06g0282000 and its homologs using MEGA. Os06g0282000 is highlighted, and bootstrap values are shown at branch nodes

Discussion

UDP glycosyltransferases (UGTs) are a broad group of vital enzymes that are involved in many aspects of life. They are particularly important in plant development because they transfer glycosyl residues from activated nucleotide-sugars to a variety of acceptor substances [30, 31]. Glycosyl transfer plays an essential function in the stability, bioactivity, solubility, and transportation of some chemicals. Because of their role in plant development and stress responses, UDP glycosyltransferases are considered important. UDP glycosyltransferases are essential to plant function. It helps attach glycosyl residues from specific molecules, usually uridine diphosphate (UDP) molecules that take up different compounds, like UDP-glucose, UDP-galactose, UDP-xylose, and UDP-rhamnose [32, 33]. UGTs also play an important role in regulating phytohormone, such as gibberellins, cytokinin, auxins, and salicylic acid. Glycosylation can help regulate hormonal homeostasis and plant growth and development by inactivating hormones, storing them in less active forms, or facilitating their movement throughout the plant [32]. Homologous UGT genes in Arabidopsis thaliana have been reported to regulate hormone homeostasis and influence plant growth and development [34]. For example, UGT84B1 and UGT74D1 are involved in the glycosylation of indole-3-acetic acid (IAA) and its derivatives, thereby controlling auxin levels during plant development. Since auxin plays a key role in cell elongation, such regulation is directly linked to growth–related traits, including seedling height [35]. This regulatory mechanism is essential for processes like seed development, shoot development, and root growth.

In rice, UDP- glycosyltransferases (UGTs) play a crucial role in maintaining hormone homeostasis and regulating growth through the glucose conjugation of auxin. A rice gene, OsIAGT1, also known as OsIAAGLU, has been reported for its catalytic activity in conjugating indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA) with glucose [25, 36]. This mechanism results change auxin content, and when OsIAGT is overexpressed, shoot and root growth reduced, linking UGT activity directly to plant development in rice [28, 36]. In rice, several UGTs have been identified with different functions, such as OsUGT90A1 in chilling stress tolerance [37], OsUGT75A in submergence tolerance during seed germination and seedling emergence [27, 38], and overexpression of OsIAAGLU in reduced tiller formation [28]. These finding reveal the critical importance of UGTs in rice plant adaptation and development.

In the present study, Os06g0282000, encoding a putative UDP-glucuronosyl/UDP-glucosyltransferase, was identified as a candidate gene within a major QTL on chromosome 6 between RM540 and RM586 for seedling height. The gene expression analysis shows a higher expression of this gene in tall CNDH lines compared to short CNDH lines, as well as increase expression is seen over developmental time (12–24 DAS), which shows a positive correlation between UGT expression and seedling height (Fig. 5). The expression of Os06g0282000 suggests a complex regulatory role for this specific UGT gene in rice seedling height, distinct from the previous study of OsIAGT1. Previously, OsIAGT1 (OsIAAGLU) was found to negatively regulate shoot and root development by conjugating auxins, leading to their inactivation and less active auxin level [25, 26, 36]. This variation highlights the diverse role of UGTs, which are determined by their substrate specificity and patterns of spatial–temporal expression.

The identification of Os06g0282000 as a candidate gene is supported by the combination of various independent lines of evidence. First, the gene is in a stable QTL region that has constantly been linked to seedling height. Second, out of the 44 candidate genes found in QTL intervals, Os06g0282000 had a clear and consistent expression pattern. It was expressed significantly higher in tall lines and was progressively upregulated during important stages of seedling elongation (12–24 DAS). Third, its identification as an UDP-glucosyltransferase places it within a well-defined enzyme family known for regulating plant growth via hormone alteration. The combination of positional, gene expression, and functional annotation based evidence enhances the eligibility of Os06g0282000 relative to other genes within the same QTL regions.

Given that Os06g0282000 expression positively corelates with increased seedling height, it is possible that unlike OsIAGT1, its activity requires the glycosylation of different hormonal or metabolic substrates. Rather than deactivating auxins that promote growth, Os06g0282000 might be involved in stabilizing or activating other growth promoting compounds. For example, some UGTs have been shown to maintain optimal level of growth promoting secondary metabolites or to conjugate precursors to active form of hormones [35, 39]. Alternatively, this gene may involve in the inactivation of growth inhibiting compounds, such as phytohormones or phytotoxins, as a result indirectly promoting elongation [40]. For instance, the previously mentioned OsUGT75A was discovered to affects ABA and jasmonate (JA) levels, and that its activity was connected to seedling emergence and submergence tolerance [27, 38]. For example a UGT that inactivates ABA, may results in increased growth, as ABA is typically considered a growth inhibiting hormone under stress conditions [41].

From a mechanistic view, UGT-mediated glycosylation is seen as a reversible regulatory system that adjusts the activity of hormones rather than just turning on or off signaling pathways. In seedling elongation, the accurate management of hormone gradients, especially auxin and gibberellins, is crucial for both cell elongation and division [42]. Hence, it can be considered possible that Os06g0282000 has some role in either spatial or temporal regulation concerning hormone availability during early developmental stages. The expression increase during (12–24 DAS) gives additional evidence for this gene’s possible participation in active growth phases where balance among hormones is necessary for processes related to elongation.

There are some limitations that should be considered even though these findings have been presented. The functional role of Os06g0282000 has not yet been confirmed through transgenic or gene-editing approaches, and its enzymatic activity as well as substrate specificity are still uncharacterized. Moreover, gene expression analysis was restricted to leaf tissues; therefore, it may not completely reflect the role of this gene in elongating regions such as shoot apices or internodes. In addition, the resolution of QTL mapping is limited by marker density and may contain other linked genes within mapped intervals. Thus, although strong evidence is provided by this study for Os06g0282000 being a candidate gene, further functional and biochemical studies will be required for conclusive proof regarding its involvement in the control of seedling height.

In summary, the findings here identify Os06g0282000 as an UDP-glucuronosyl/UDP-glucosyltransferase candidate gene underlying a seedling height QTL in rice. Therefore, further studies is needed to identify its specific substrates. Molecular profiling of overexpression and knockout lines of Os06g0282000 could be useful to detect changes in various phytohormones conjugates and other primary or secondary metabolites during early seedling growth. The positive correlations with seedling height would be particularly useful to analyze its interaction with gibberellins, cytokinins or other hormones related to plant growth. To fully elucidate its role in rice plant development and its potential for agricultural applications it will be crucial to understand the precise biochemical reactions catalyzed by Os06g0282000.

Conclusion

In this study, we identified several QTLs related to rice seedling height and combined them with gene expression analysis, functional annotation, and comparative genomics to identify Os06g0282000 as a candidate gene on chromosome 6 between RM540 and RM586, may contribute to seedling height. This gene encodes UDP-glucuronosyl/UDP-glucosyltransferase and showed higher gene expression over time during active seedling growth in tall CNDH lines compared to the short CNDH lines, suggesting a key role in the regulation of seedling height. Our findings suggest that UGT-mediated regulation of hormone homeostasis is an important mechanism underlying rice seedling height. In addition, SSR markers flanking the major QTL region on chromosome 6 (RM540–RM586) may serve as potential candidates for marker-assisted selection to improve early seedling vigor in rice breeding programs. These results provide a key molecular target for improving plant early vigor using breeding and functional studies.

Authors’ contributions

S.A. (Saleem Asif), J-R.P., and K.M.K. designed the study; S.A. (Saleem Asif), and N.K. performed the experiments. S.A. (Saleem Asif), J-R.P., N.K., and R.J. performed the analyses; S.A. (Saleem Asif) and S.A. (Sajjad Asaf) wrote the manuscript; S.A. (Saleem Asif) and S.A. (Sajjad Asaf) performed the statistical analyses. K.M.K. supervised the study and provided resources. All authors have read and agreed to the published version of the manuscript.

Funding

Not applicable.

Data availability

The data presented in this study are available upon request from the corresponding author.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.