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Article

Molecular Mechanism of Oryza sativa L. Under Long Day Regime Based on Transcriptome Analysis

by
Wenju Luo
1,2,3,†,
Yufeng Li
1,2,†,
Xianbin Hou
1,2,
Chun Wei
1,2,
Zheng Teng
1,2,
Cuifeng Yang
1,2,
Hongzhu Su
1,2,
Xiaoli Wang
3,* and
Zhengjie Zhu
1,2,*
1
Guangxi Key Laboratory of Biology for Mongo, Baise University, Baise 533000, China
2
College of Agriculture and Food Engineering, Baise University, Baise 533000, China
3
Guizhou Institute of Prataculture, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diversity 2025, 17(9), 603; https://doi.org/10.3390/d17090603
Submission received: 24 July 2025 / Revised: 23 August 2025 / Accepted: 24 August 2025 / Published: 28 August 2025
(This article belongs to the Section Plant Diversity)
Browse Figures
Review Reports Versions Notes

Abstract

The growth of rice (Oryza sativa L.) is affected by long days, which occur throughout the year in southern China, leading to a sharp decline in rice varieties and yields. In order to conduct adaptive genetic breeding research on rice, it is urgent to understand the long days response mechanism of rice. This study used RNA-seq technology to analyze rice under long day conditions and found that RNA, Ehd1, chlorophyll a/b binding protein, were identified as key genes or proteins under long day conditions. MAPK is closely related to long day conditions. This study provides a unique theoretical basis for long-term research and genetic breeding of rice by screening key genes related to the photoperiod.

1. Introduction

Photoperiod is one of the environmental elements that significantly affects plant growth and development, fruit quality, and yield [1,2]. Changes in photoperiod are also significant environmental signals that control flowering in a variety of plants. Plants are categorized as long-day (LD), short-day (SD), or sun-neutral based on how flowering changes in response to variations in the length of sunshine. Many advances have been achieved in the study of photoperiod regulation using Arabidopsis thaliana L. as a typical LD model plant, including the understanding of the molecular regulatory mechanisms [3]. The core circadian oscillator of Arabidopsis thaliana L. was discovered to have a maximum expression of both LHY and CCA1. Response regulator (PSEUDORESPONSEREGULATOR, PRR) family genes peak between morning and evening [4], which is part of the negative feedback loop of LHY. GI, EARLY 3, and EARLY 4 show maxima at night, and they show repression of genes expressed earlier in the daytime [5]. Additionally, rice and Arabidopsis thaliana L. share the Os GI(GI)-Hd1(CO)-Hd3a/RFT1(FT) pathway; rice, a SD plant, promotes blooming under SD conditions, whereas LD conditions repress flowering. Under LD conditions, the Arabidopsis thaliana L. CO gene was driven to express and begin flowering, whereas under SD conditions, CO expression was suppressed but had no effect on flower development. The Ghd7-Ehd1-Hd3a/RFT1 pathway, which is unique to rice [6,7], controls the expression of the florigenin genes Hd3a and RFT1 to control rice flowering. Therefore, it is crucial to comprehend the blooming control mechanism in the model plant Arabidopsis thaliana L. in order to comprehend the mechanism of flowering regulation in rice.
With numerous planting adjustments, rice is one of the most significant cereal crops in the world [8,9,10]. In rice, flowering is a crucial component of seasonal adaptation and directly affects grain yield. The Hd1-dependent pathway and the Ehd1-dependent pathway are the two flowering pathways found in rice [11,12,13]. By controlling the expression of Hd3a and Hd1, a homologue of Arabidopsis thaliana L. CONSTANS (CO) enhances flowering under SD conditions but severely suppresses flowering under LD conditions [14]. On the other hand, Hd3a and the rice flowering locus RFT1 are two flower-forming hormones that function under SD and LD conditions, respectively [15], with Hd3a and its paralog RFT1 being more strongly flower-promoting under SD conditions [16]. In contrast, Ehd1 promotes flowering under both SD and LD conditions. For Hd3a to be transported from the leaf to the stem tip for blooming under SD circumstances, mobile flowering signaling proteins Hd3a and RFT1 are both required [17]. Ehd1 is controlled by several genes [18,19]. Under SD conditions, the expression of the Hd1 transcript peaks at night, and the Hd1 protein acts as an activator to induce the expression of the Hd3a transcript at night; under LD conditions, the expression of the Hd1 transcript peaks during the photosensitive period, Hd1 interacts with photosensitive pigments, and the Hd1 protein acts as a repressor of the Hd3a gene to prevent flowering. A brief exposure to light at night can change the activity of the Hd1 protein, promoting rather than suppressing the production of the Hd3a gene [20]. Light had no effect on Hd1 protein levels in Hd1 overexpressing plants, but photochromes’ alteration of Hd1 or Hd1 complex proteins caused Hd1 to convert from an enhancer to a repressor, which, in turn, controlled Hd3a transcriptional expression [21]. Rice photoperiodic blooming may be significantly influenced by the activity of the Hd1 protein complex, which is not essential for photoperiod-mediated control of Hd1 protein stability [22]. In both SD and LD circumstances, the rice Ostpr075 mutant showed late blooming, but the OsTPR075 overexpression line displayed early flowering [23]. These findings likely show that OsTPR075 improves the physical interaction between FTIP and florigenin and has a good impact on florigenin abundance in stem tips. Previous research has demonstrated that OsFTIP1 binds to and increases the export of RFT1, another florigenin, under LD [24]. Subudhi et al. (2018) [25] found that under LD conditions, weed rice exhibited late flowering of Hd1 in a cultivated rice background because early flowering and light insensitivity in weed rice are due to genetic interactions between Hd1 and a novel gene on chromosome 7 other than Ghd7. Therefore, analyzing the molecular response mechanism of rice under long-day conditions, cultivating long-day crops, and maintaining high quality and high yield under long-day conditions are important measures to ensure the sustainable growth of crop yield.
Although the regulatory mechanisms of rice under many stress conditions have been well described, we still lack a complete understanding of transgenic rice. In this study, the research group successfully introduced the FaGI gene of Festuca arundinacea into rice, we used RNA sequencing (Seq) technology to study the molecular differences of trans-FaGI rice varieties in different time periods under long day conditions. There were comparisons performed in nutrient growth period (four-leaf period, Y4 vs. F4) and flowering period (nine-leaf period, Y9 vs. F9), and we searched for photoperiod related genes through gene screening. Through this novel method, we are eager to reveal the potential mechanism of plant adaptability under long day conditions and provide a theoretical basis for the molecular mechanism and genetic breeding of transgenic rice.

2. Materials and Methods

2.1. Test Material

The material was Oryza sativa, L. transgenic Festuca arundinacea GIGANTEA (FaGI) and Oryza sativa, L., NIP, provided by the Grass Research Institute of Guizhou Provincial Academy of Agricultural Sciences. They were planted in an artificial climate chamber at the Grass Research Institute of Guizhou Province on 5 March 2023 under a temperature of 25 °C, humidity of 40%~50%, and light intensity of 86.4~108.0 μmoL/(m2·s). Leaves of two different varieties were collected separately at both the nutrient growth period (four-leaf period) (31 May 2023) and flowering period (nine-leaf period) (15 August 2023) and compared pairwise (Y4 vs. F4, Y9 vs. F9). The samples from the leaves of six plants at the time of sampling were mixed and three biological replicates were performed [26], respectively, and the sample numbers and information are shown in Table 1. The samples were quickly frozen in liquid nitrogen after sample collection and embedded in dry ice to be sent for transcriptome sequencing with Suzhou Panomics Biomedical Technology Co. (Panomic Biomedical Technology Co., Ltd., Suzhou, China).

2.2. Total RNA Extraction and Detection, cDNA Library Construction, and Transcriptome Sequencing

Total RNA was extracted from the leaves of Oryza sativa, L., FaGI and Oryza sativa, L., NIP at the nutrient growth period and the flowering period using the TIANGEN Magnetic Bead Method Plant Total RNA Extraction Kit (Tiangen Biochemical Technology Co., Ltd. in Peking, China, No. DP762-T1A). The directions were followed for each operation. Total RNA concentration and purity were evaluated using the Thermo Scientific Nano Drop 2000 (Thermofly Biotech, Shanghai, China), and sample integrity was assessed using the Agilent 2100 Bioanalyzer (Thermofly Biotech, Shanghai, China) and RNA 6000 Nano kit 5067-1511 (Thermofly Biotech, Shanghai, China). Quality inspection refers to the results of 2100 to judge the integrity of RNA. Eukaryotic mRNA is required to have a RIN value >6.5; in addition, if the general library detection concentration meets 20 μg/μL and the peak shape is normal, it will continue. The whole RNA, with a total amount of ≥1 µg, was chosen and examined using the NEBNext Ultra II RNA Library Prep Kit for Illumina (TIANGEN, Shanghai, China) (NEBNext Ultra Directional RNA Library Prep Kit for Illumina). To enrich for mRNAs with polyA tails, oligo(dT) magnetic beads were utilized. These mRNAs were then randomly interrupted by ion interruption using divalent cations. Fragmented mRNA was used as a template to produce cDNA, and randomly selected oligonucleotides were used as primers. The cDNA was sequenced using next-generation sequencing (NGS) technology built on Illumina HiSeq after passing quality control. This procedure was carried out by Suzhou Panomic Biopharmaceutical Technology Co., Ltd. in Suzhou, China.

2.3. Raw Data Processing and Comparison with Reference Genome Sequence

The downstream data contained some low-quality and spliced reads that were filtered using the cutadapt (v1.18) software. Using the fastp (0.22.0) software [27] (http://plants.ensembl.org/Oryza_sativa/Info/Index, accessed on 7 June 2023), the high-quality sequences (Clean Data) that were acquired after filtering were compared to the reference genome.

2.4. Analysis of Differential Gene Expressions (DEGs)

Expression was normalized using FPKM [28] to estimate the expression levels of genes in Oryza sativa L. at different developmental periods. Expression difference multiplicity |log2FoldChange| > 1 and significance p-value < 0.05 groups were used as screening criteria, and gene expression was differentially analyzed using DESeq (1.38.3) software.

2.5. Analysis of Gene Ontology (GO) Functional Enrichment

Cellular components, molecular functions, and biological processes involved were the three broad categories into which significantly enriched GO keywords [29] were divided. For GO keywords that were significantly enriched in differential genes, the p-value was determined using the hypergeometric distribution method, and the threshold for significant enrichment had a p-value of <0.05.

2.6. Analysis of KEGG Pathway Enrichment

The analysis of DEG enrichment was done using the database Kyoto Encyclopaedia of Genes and Genomes (KEGG) [30]. In order to identify the pathway with significant enrichment as determined by the rich factor, FDR value, and the number of enriched genes in this pathway, the extent of enrichment was analyzed.

2.7. Real-Time Fluorescent Quantitative PCR Analysis

Taking the rice gene UBC as the internal reference [31], we selected and screened the participating photoperiodic pathway according to the multiple change in DEG analysis. The nine differentially expressed genes OSHAP5C, OsLFL1, Lhca6, OsCRD1, OsBBX22, OsSIG6, OsIRL5, GH3-2, and GA20OX4 were identified by RT qPCR (Applied Biosystems, Forter City, CA, USA) using the design of the RT qPCR specific primers (Table 2). The relative expression of DEGs was calculated by the 2−ΔΔCT method. Three biological repetitions were set in the experiment, and Origin8.5 2022 was used for mapping.

3. Results

3.1. Analysis of RNA-Seq Sequence

At both the nutrient development (four-leaf period) and flowering stages (nine-leaf period) of the trans-FaGI rice, the WT-rice transcriptome sequencing of rice was performed. Each sample group had 601,913,038 reads in the raw data and 595,251,682 clean reads overall, with a Q30 > 94.46% for each sample. Because the sequencing data were of such high quality, it was clear that they could be trusted and applied to further analysis (Table 3).
The FPKM distribution was lower in the Y4 vs. F4 transgenic treatment, while the wild-type had the highest C value (Figure 1a), and the opposite was observed in Y9 vs. F9 (Figure 1e). The average correlation coefficients between variety and treatment were both greater than 0.8, indicating strong correlation and proving the reliability of replication (Figure 1b,e). Principal component analysis shows that processing tends to cluster together (Figure 1c,f), with PC1 and PC2 in Y4 vs. F4 and Y9 vs. F9 accounting for 63%, 13%, and 61%, and 20% of the variation, respectively.

3.2. Functional Annotation of Genes

In the total number of gene function annotations in rice leaves, the total number of Y4 vs. F4 was 44,879, and the total number of Y9 vs. F9 was 49,448 (Table 4).

3.3. Display of Differentially Expressed Genes in Rice at Different Periods of Time

The differently expressed genes of the two types at two distinct times are shown using a Venn diagram (Figure 2), in which, Y4 vs. F4 and Y9 vs. F9 shared 32 differentially expressed genes. Differentially expressed genes were unique to Y4 vs. F4 in 117 cases (21.47%) and to Y9 vs. F9 in 250 cases (28.55%). According to these findings, distinct rice varieties exhibited a greater number of genes exhibiting distinct expression patterns during one time period and a lower number of genes exhibiting distinct expression patterns during subsequent time periods within the same variety.

3.4. Comparison of Differentially Expressed Genes in Rice at Different Times

The analysis was done using Edge R 3.32.1 1 (empirical analysis of DGE in R) software to identify the differentially expressed genes among the two groups (Y4 vs. F4, Y9 vs. F9) of the two varieties. As seen in Figure 3, there were 545 differentially expressed genes in Y4 vs. F4, there were 885 genes that were differentially expressed in Y9 vs. F9, and there were 583 and 302 genes that were up- and down-regulated, respectively. This indicates that many genes are involved in the control of the rice flowering stage, which requires more genes than the nutrient growth period, and that the insertion of the tall fescue FaGI gene can cause transcriptional alterations in a significant number of genes in rice. Volcano plots were used to examine the importance of differences among the discovered genes (Figure 4).

3.5. GO Function Enrichment Analysis of Differential Genes

The primary biological roles of DEGs in Oryza sativa L. during nutrient growth period and flowering period may be reflected in gene ontology (GO) enrichment analysis. Data from the flowering and nutrient growth stages were compared and discovered in this study. In the cellular component (CC), the numbers of genes in Y4 vs. F4 and Y9 vs. F9 were 187 and 218 separately, which accounted for 10.9%, and 10.38% of their respective totals. In molecular function (MF), the numbers of genes were 435 and 554, accounting for 25.36% and 26.38% of their respective totals. In biological process (BP), the numbers of genes were 1093 and 1328, accounting for 63.73% and 63.24% of their respective total numbers (Figure 5). Overall, the trend of changes in the number and total number of CC, MF, and BP genes during the nutrient growth period and flowering periods is similar, with BP significantly higher during the flowering period than during the nutrient growth period.
In addition, Y4 vs. F4 and Y9 vs. F9 were enriched in 187 and 218 DEGs, respectively, in CC, with Y4 vs. F4 most enriched in thylakoid (15) and photosystem I (5); Y9 vs. F9 was mainly enriched in (thylakoid, 53) and (photosystem I, 20). In MF, Y4 vs. F4 and Y9 vs. F9 were enriched to 435 and 554 DEGs, with Y4 vs. F4 being most enriched in sigma factor activity (2), cyclohydrolase activity (3); Y9 vs. F9 was mainly enriched in chlorophyll binding (12) and polysaccharide binding (17). In BP, Y4 vs. F4 and Y9 vs. F9 were enriched to 1093 and 1328 DEGs, respectively, where Y4 vs. F4 was mainly enriched in photosynthesis (8), photosynthesis, light harvesting in photosynthesis, and light harvesting in photosystem I (1); Y9 vs. F9 was most enriched in photosynthesis (172) and photosynthesis (light harvesting in photosystem I, 12) (Figure 6).

3.6. KEGG Pathway Analysis of Differential Genes

Kyoto Encyclopaedia of Genes and Genomes (KEGG) can organically combine genomic information and its functional information, systematically analyze the metabolic pathways of each gene product in the cell, and then comprehensively analyze the functions of these gene products. Among them, Y4 vs. F4 had the highest number of DEGs enriched in glycine, serine, and threonine metabolism, with the amount of 5, followed by photosynthesis, glutathione metabolism, glycolysis/gluconeogenesis, protein processing in the endoplasmic reticulum, all with the amount of 4. Glycine, serine, and threonine metabolism; porphyrin metabolism; pentose phosphate pathway; inositol phosphate metabolism; glyoxylate and dicarboxylate metabolism; carbon fixation in photosynthetic organisms; and cysteine and methionine metabolism were enriched to the next highest number of DEGs, with the amount of 3 (Figure 7a). Y9 vs. F9 were enriched to the highest number of DEGs in photosynthesis, with the amount of 17, followed by photosynthesis—antenna proteins, with the amount of 12. Plant–pathogen interaction was enriched to the next highest number of DEGs, with the amount of 11 (Figure 7b).
The results of the KEGG enrichment analysis of this experiment are presented in the form of a scatter plot. As shown in Figure 7, the darker the color the more obvious the expression difference of the differentially expressed genes annotated to the biological pathway, and the larger the scatter plot indicates a higher number of differentially expressed genes annotated to the biological pathway. KEGG functional enrichment was performed on DEGs of Y4 vs. F4 and Y9 vs. F9, and 20 metabolic pathways were screened for significant enrichment, respectively (Figure 8). DEGs of Y4 vs. F4 and Y9 vs. F9 were most enriched in porphyrin metabolism and photosynthesis antenna proteins separately. In addition, Y4 vs. F4 had the most significant level and number of DEGs enriched in protein processing in the endoplasmic reticulum and photosynthesis (Figure 8a). Y9 vs. F9 had the most significant level of DEG enrichment in photosynthesis, antenna proteins, and the photosynthesis pathway, while having the highest number of enrichments in the photosynthesis pathway (Figure 8b).

3.7. Photoperiod-Related Differentially Expressed Genes

Combined with the annotation information of the NR database, from the 545 and 885 DEGs screened from the leaf transcriptome data of Y4 vs. F4 and Y9 vs. F9, respectively, we screened 10 photoperiod-associated DEGs in Y4 vs. F4 and 22 in Y9 vs. F9. They are involved in B3 DNA-binding domain-containing transcription factor, flowering time regulation, and repression of flowering activator Ehd1 and its downstream genes by binding to the promoter of the Ehd1 gene, chlorophyll a/b-binding protein type III (fragment), similar to the BZIP transcription factor (Fragment), chlorophyll a-b binding protein 2, chloroplast precursor (LHCII type I CAB-2) (LHCP), and other metabolic pathways. Among them, two photoperiod-related DEGs showed a trend of down-regulated expression and eight up-regulated expressions in Y4 vs. F4 and two down-regulated and 20 up-regulated DEGs in Y9 vs. F9 (Table 5).

3.8. Expression Patterns of Photoperiod-Related Differentially Expressed Genes in Rice

From the four-leaf period, according to the multiple changes in DEG analysis and the participation in the photoperiod pathway, nine related differential genes were screened, the differentially upregulated genes related to the photoperiod pathway are Os03g0251350 (OsHAP5C), Os01g0713600 (OsLFL1), Os09g0439500 (Lhca6), Os01g0279100 (OsCRD1), Os06g0713000 (OsBBX22), Os08g0242800 (OsSIG6), and Os10g0572300 (OsIRL5). Os01g0764800 (GH3-2) and os05g0421900 (GA20oX4) were differentially downregulated genes related to the photoperiod pathway (Figure 9a). To verify the reliability of RNA-seq results, RT qPCR analysis was performed on the selected photoperiod-related genes (Figure 9b). The transcriptome data are basically consistent with the results of quantitative validation, indicating that the data are reliable and can be used for subsequent data analysis.

3.9. Differential Gene Expression Specific to the Mid-Diameter in Y4 vs. F4 and Y9 vs. F9

The nutrient growth period stage is a crucial period in the growth process of rice, as the gene expression differences during this stage have biological significance.
a.
Differential genes in photoperiod pathway
Photoperiod profoundly affects plant growth and development by regulating physiological processes such as flowering time, dormancy cycle, and nutrient growth period. Therefore, we conducted further analysis on the expression changes of genes enriched in the photoperiod pathway during the nutrient growth period phase of leaves in two varieties. After removing unknown genes and duplicate genes, a total of 13 differentially expressed genes were identified (Figure 10a). Compared to wild-type rice, differentially expressed genes such as FNR (Os03g0784700) and OsLFNR2 (Os06g0107700) in transgenic rice showed upregulation during the nutrient growth period, but PsbO (Os01g0501800), OsLFNR1 (Os02g0103800), OsFdC1 (Os03g0659200), OsFdC2 (Os03g0685000), OsFd3 (Os03g0835900), Fd (Os05g0443500), OsPC (Os06g0101600), PsbP (Os07g0141400), petC (Os07g0556200), Fd1 (Os08g0104600), and PsbY (Os08g0119800) presented downregulated expression, during the flowering period, except for the downregulated expression of the OsFd3 (Os03g0835900) gene. All other genes showed an upregulated expression trend (Figure 10b). These genes may be the reason why rice leaves have improved photosynthesis.
b.
Differential expression of signaling-related genes
MAPK plays a crucial signaling role in plants, influencing growth, development, and adaptability to the environment by regulating gene expression and physiological metabolic processes. We analyzed differentially expressed genes associated with the MAPK signaling pathway and identified 17 and 16 genes related to the MAPK pathway during the nutrient growth period and flowering stages, respectively (Figure 10c,d). Compared to wild-type rice during its nutritional growth period, transgenic rice OsMAPK33 (Os02g0148100), OsHMA5 (Os02g0172600), HMA4 (Os02g0196600), YDA2 (Os02g0666300), OsXrn4 (Os03g0794800), SIK1 (Os06g0130100), and HMA9 (Os06g0665800) presented an upregulated expression pattern. OsHMA6 (Os03g0178100), OsOxi1 (Os04g0488700), HMA5 (Os04g0556000), YDA1 (Os04g0559800), OsMKK1 (Os06g0147800), OsMKK3 (Os06g0473200), OsMAPK4 (Os06g0699400), OsbZIP81 (Os11g0160500), OsbZIP84 (Os12g0162500), and OsNDPK2 (Os12g0548300) presented a downregulated expression pattern. During the flowering period, OsHMA5 (Os02g0172600), YDA2 (Os02g0666300), OsHMA6 (Os03g0178100), SIK1 (Os06g0130100), and OsMKK3 (Os06g0473200) manifested as upregulation of expression. OsMAPK33 (Os02g0148100), HMA4 (Os02g0196600), OsXrn4 (Os03g0794800), OsOxi1 (Os04g0488700), HMA5 (Os04g0556000), YDA1 (Os04g0559800), OsMKK1 (Os06g0147800), HMA9 (Os06g0665800), OsMAPK4 (Os06g0699400), OsbZIP81 (Os11g0160500), and OsbZIP84 (Os12g0162500) presented a downregulated expression pattern. These differentially expressed genes play crucial regulatory and metabolic roles during the nutritional growth period of rice.

4. Discussion

The results showed 595,251,682 clean reads in total. A Q30 > 94.46% was achieved for each sample in the transcriptome sequencing. There were 545 and 885 differentially expressed genes in Y4 vs. F4 and Y9 vs. F9, respectively. These genes were successfully annotated in the Gene Ontology (GO) as well as in four other databases, such as the Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis, Evolutionary Genealogy of Genes: Non-Supervised Orthologous Groups, and Swissprot. In the GO database, 187 and 218 were enriched to cellular components, 435 and 554 were enriched with molecular functions, and 1093 and 1328 were involved in biological processes, respectively. The KEGG pathway enrichment results revealed that in Y4 vs. F4 in glycine, Y4 vs. F4 was significantly enriched in glycine, serine, and threonine metabolism, while Y9 vs. F9 was significantly enriched in photosynthesis.
Comparative transcriptome analyses of FaGI-transformed rice and wild-type rice were carried out during the nutrient growth and flowering stages of this study, and differentially expressed genes related to photoperiod, such as the Ehd1 gene and the chlorophyll a/b-binding proteins (Table 5), were screened to examine the differences in photoperiod-related genes in rice leaves at the molecular level. Although the quality of different rice varieties is different, the Ehd1 gene and chlorophyll a/b binding protein (LHC) in rice leaves have a significant impact on rice growth [32,33]. End1, which is also a floral integrator that controls Hd3a and RFT1 [34], is a key flowering gene in tasseling that regulates the tasseling duration of japonica rice under SD conditions. Researchers have also altered the function of the Ehd1 gene in many japonica rice types, all of which, to varying degrees, delayed the tasseling stage under SD conditions and enhanced yield [35]. Therefore, the Ehd1 gene is a key target for genetically enhancing the tasseling duration in the “northern japonica moving southward” project in China. Molecular marker-assisted selection and other technical methods can be employed to genetically enhance the tasseling period of japonica rice. Nowadays, the majority of research on the Oryza sativa L. Ehd1 gene has concentrated on the functional validation of important genes of the photoperiodic pathway [36,37], with relatively few papers discussing photoperiodic DEGs throughout Oryza sativa L. types. This study found that most of the genes related to photoperiod were up-regulated. This may be the reason for the difference in the accumulation of genes related to Ehd1 in different rice varieties, but the specific regulatory mechanism is not yet known.
Chlorophyll a/b-binding proteins (Lhc) play an important role in regulating plant growth and development (Table 5). The Lhc family is divided into two main classes, Lhca and Lhcb, which are associated with photosystem I and photosystem II hapten proteins separately. They consist of three transmembrane helices and can combine harvester chlorophylls, carotenoids, and lipids [38]. In rice seedlings, iron deficiency significantly reduces Lhca 1-4 proteins, resulting in a decrease in chlorophyll and photosynthetic efficiency [39]. In addition, members of the Lhc family in kiwifruit and cotton have been shown to be involved in chlorophyll a synthesis. Transient overexpression of AcLhcb 3.1/3.2 in tobacco leaves significantly increased chlorophyll a content [40], whereas VIGS-mediated silencing of GhLhcb2.3 significantly reduced chlorophyll a content in cotton leaves [41]. In barley, some single nucleotide polymorphisms (snps) of the Lhcb1 gene have been found to be significantly correlated with a variety of agronomic traits, such as plant height and leaf color [42]. Therefore, the identification of gene families and expression analysis of Lhc in plants have become hot topics for many researchers. In this study, 1 and 7 DEGs related to Lhc were screened in Y4 vs. F4 and Y9 vs. F9, respectively, identifying a similar type to the type II chlorophyll a/b binding protein from the photosystem I precursor, a chlorophyll a/b-binding protein type III (fragment) similar to the photosystem II type II chlorophyll a/b binding protein (fragment), similar to the chlorophyll a/b-binding protein CP29 precursor, CCT(CONSTANS, CONSTANS-LIKE, and TIMING OF CHLOROPHYLL A/B BINDING1) domain protein, heading date, LD repression, regulator of growth, development, and stress-response, a/b-binding protein b (fragment), similar to the chlorophyll a/b-binding protein CP24 and photosystem II (fragment). A total of 16 components were significantly differentially expressed in trans-FaGI gene rice and wild-type rice. Although there were duplicated related genes in the intercomparison group, they were expressed differently and presented different trends. Under long day conditions, the LHC can promote the lutein cycle, dissipate excess energy, prevent the accumulation of harmful substances such as reactive oxygen species, and adapt to long day conditions to ensure normal plant growth.
MAPK plays a crucial signaling role in plants, influencing growth, development, and adaptability to the environment by regulating gene expression and physiological metabolic processes. Fujino et al. (2015) [43] compared and analyzed the genetic polymorphisms of ‘kitaake’ and ‘Nipponbare’ and found that there were abundant genetic variations in the MAPK pathway between them. It was speculated that genomic differences were the internal factors causing different growth cycles and plant phenotypes. Ding et al. (2023) [44] found that ROS, as a signal molecule, can activate the MAPK pathway, thereby causing the regulation of various downstream signaling pathways, indicating that the MAPK pathway plays an important role in plant antioxidant function. This study found that there were more differentially related genes of the MAPK pathway in the vegetative growth period than in the flowering period. It is speculated that under the condition of long sunshine, rice needs to accumulate more substances to promote growth in this period. At the flowering stage, most genes showed a downward trend, such as OsMAPK33 (Os02g0148100), HMA4 (Os02g0196600), OsXrn4 (Os03g0794800), and other genes (Figure 9). As a member of MAPK, these genes played an important role in adapting to long sunshine conditions by accumulating organic matter through downward regulation. However, the specific mechanism was unclear and needed further study.
In conclusion, Ehd1, and chlorophyll a/b-binding proteins were screened through a photoperiod-related pathway, which were three key genes in rice under long day conditions. MAPK, as an important signal transduction pathway, was closely related to photoperiod. Based on previous studies, a comprehensive understanding of the functions of these genes verified our findings and led to the development of strategies to use their potential to improve the adaptability of rice under long day conditions. In general, the complex interactions of related differential rice genes under long day conditions provide unique insights for the future study of plants under long day conditions at the molecular level.

5. Conclusions

Ehd1, chlorophyll a/b binding protein, play an important role in the photoperiod pathway and participate in the regulation of rice hormones. The MAPK signaling pathway is closely related to rice growth. The candidate genes related to photoperiod found in this study provide important value for studying the regulation mechanism of rice at the molecular level, in order to open up new possibilities for improving rice quality, early flowering, and new rice varieties.

Author Contributions

Conceptualization, X.W. and W.L.; investigation, Y.L., X.H., Z.T., C.Y., C.W., H.S. and Z.Z.; writing—original draft preparation, W.L. and Y.L.; writing—review and editing, W.L. and Y.L.; visualization, W.L.; funding acquisition, C.W., X.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Guangxi Youth Talent Support Program (Zhengjie Zhu); College of Agriculture and Food Engineering, Baise University. Guangxi First Class Discipline Construction Project Funding (Agricultural Resources and Environment, Guijiao Research [2022] No. 1); Screening and identification of typical cadmium tolerant microorganisms ([2024] KY0753) (Chun Wei).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. Transcriptome analysis of Y4 vs. F4 and Y9 vs. F9. (a,d) Density distribution plot of FPKM expression; (b,e) sample correlation plot; (c,f) principal component analysis of FPKM values of differential gene). Note: “Y” represents “Y4”, “F” represents “F4”, “KY” represents “Y9”, “KF” represents “F9”; the same applies below.
Figure 1. Transcriptome analysis of Y4 vs. F4 and Y9 vs. F9. (a,d) Density distribution plot of FPKM expression; (b,e) sample correlation plot; (c,f) principal component analysis of FPKM values of differential gene). Note: “Y” represents “Y4”, “F” represents “F4”, “KY” represents “Y9”, “KF” represents “F9”; the same applies below.
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Figure 2. Venn map of differentially expressed genes in rice at different periods. Note: The overlapping part is the DEGs jointly owned by two groups.
Figure 2. Venn map of differentially expressed genes in rice at different periods. Note: The overlapping part is the DEGs jointly owned by two groups.
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Figure 3. The number of different genes in rice at different periods.
Figure 3. The number of different genes in rice at different periods.
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Figure 4. Differential gene volcano map of rice in different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
Figure 4. Differential gene volcano map of rice in different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
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Figure 5. The number of differentially enriched GO genes in rice at different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
Figure 5. The number of differentially enriched GO genes in rice at different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
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Figure 6. GO functional classes of rice genes were differentially expressed at different periods. Note: (a) stands for Y4 vs. F4; (b) stands for Y9 vs. F9.
Figure 6. GO functional classes of rice genes were differentially expressed at different periods. Note: (a) stands for Y4 vs. F4; (b) stands for Y9 vs. F9.
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Figure 7. KEGG classification of rice DEGs at different periods ((a): Y4 vs. Y9 KEGG classification; (b): Y9 vs. F9 KEGG classification).
Figure 7. KEGG classification of rice DEGs at different periods ((a): Y4 vs. Y9 KEGG classification; (b): Y9 vs. F9 KEGG classification).
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Figure 8. KEGG enrichment of differentially expressed genes in rice at different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
Figure 8. KEGG enrichment of differentially expressed genes in rice at different periods ((a): Y4 vs. F4; (b): Y9 vs. F9).
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Figure 9. Expression patterns of photoperiod-related differentially expressed genes in rice ((a): Y4 vs. F4 Cluster diagram of photoperiod related differentially expressed genes; (b): RT qPCR validation analysis of photoperiod related genes).
Figure 9. Expression patterns of photoperiod-related differentially expressed genes in rice ((a): Y4 vs. F4 Cluster diagram of photoperiod related differentially expressed genes; (b): RT qPCR validation analysis of photoperiod related genes).
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Figure 10. Clustering diagram of differentially expressed genes in key pathways of rice ((a): Y4 vs. F4 differential genes in photoperiod pathway; (b): Y9 vs. F9 differential genes in photoperiod pathway; (c): Y4 vs. F4 differential expression of signaling-related genes; (d): Y9 vs. F9 differential expression of signaling-related genes).
Figure 10. Clustering diagram of differentially expressed genes in key pathways of rice ((a): Y4 vs. F4 differential genes in photoperiod pathway; (b): Y9 vs. F9 differential genes in photoperiod pathway; (c): Y4 vs. F4 differential expression of signaling-related genes; (d): Y9 vs. F9 differential expression of signaling-related genes).
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Table 1. Sample number.
Table 1. Sample number.
VarietyDevelopmental StagesSample NumberDuplicate Group Number
Oryza sativa, L., FaGINutrient growth periodF4F4-1, F4-2, F4-3
Flowering periodF9KF9-1, KF9-2, KF9-3
Oryza sativa, L., NIPNutrient growth periodY4Y4-1, Y4-2, Y4-3
Flowering periodY9Y9-1, Y9-2, Y9-3
Table 2. Primers of real-time fluorescent quantitative PCR.
Table 2. Primers of real-time fluorescent quantitative PCR.
GeneForward Primer Sequence (5′→3′)Reverse Primer Sequence (5′→3′)
OSHAP5CGCCATGGCGGTGCGGGCCATGGCGGTGCGG
OsLFL1ACGTAAGCCAGCTAGGGAGATGGGCCAATACCTGTACTTGAA
Lhca6 GGGCCCCATTGATAACCTCCGGACGTGAATGCCGAGAAGA
OsCRD1 CATTGCTGCGTACCTCATGCCAGTAGACAAGCTGGGGCTC
OsBBX22 ACGAGCAGTTCAACACCCCTAAGAACTCGTTGAGCGGCCA
OsSIG6 CCCCAGGGAGAAGGAGATCACCCAAACATGTCCCCGATGA
OsIRL5 CCAAGTGGCAAGCCTGATAGATCTCGGAAAGATCAAGCTCGG
GH3-2 CGGGGGAGAGGAAGCTAATGGCACGTACAAGTTCATGACGG
GA20OX4 CGATAATGGCGGTGCTAGGGCTGCTGTCCTCGAAGAACTCC
UBC CCGTTTGTAGAGCCATAATTGCAAGGTTGCCTGAGTCA-CAGTTAAGTG
Table 3. RNA-seq flux and quality detection of rice at different periods.
Table 3. RNA-seq flux and quality detection of rice at different periods.
SampleReads No.Clean
Reads No.		Q30 (%)Total MappedMultiple MappedUniquely Mapped
Y4-1561893445552806294.4697.53%4.02%95.98%
Y-4-2576066645692548894.5197.47%4.01%95.99%
Y-4-3551512005462340895.3497.62%3.78%96.22%
F-4-1575636025687347694.7096.89%4.18%95.82%
F-4-2509681585031349494.6497.61%6.35%93.65%
F-4-3539273145329499494.8097.46%4.04%95.96%
Y9-1440325124361448695.2098.91%4.35%95.65%
Y9-2398219143942578095.0098.51%8.64%91.36%
Y93465341564604432894.9198.53%4.99%95.01%
F9-1419747444150117694.7599.09%4.46%95.54%
F9-2518697525128916294.9098.82%4.56%95.44%
F9-3462736784581782894.9398.92%3.98%96.02%
Table 4. Functional annotation of rice leaf genes.
Table 4. Functional annotation of rice leaf genes.
Contrast
Numbering		DEGs Annotated in Each Database
NRGOKEGGEgg NOGSwissprotTotal
Y4 vs. F412,9176464485011,219942944,879
Y9 vs. F914,1157180538512,33710,43149,448
Table 5. Photoperiodic DEGs of rice leaves at different periods and their functional annotations.
Table 5. Photoperiodic DEGs of rice leaves at different periods and their functional annotations.
Development
Stage		Gene
ID		Database
Annotation		Gene
Function		Log2
(Fold Change)
Y4 vs. F4Os01g0713600response to light stimulusB3 DNA-binding domain-containing transcription factor1.64
Os03g0251350response to light stimulusHistone-fold domain containing protein1.88
Os03g0272400response to red or far red lightConserved hypothetical protein1.33
Os06g0713000response to light stimulusZinc finger, B-box domain containing protein1.07
Os08g0242800(OsSIG6)response to blue lightSimilar to Sigma factor SIG61.15
Os09g0439500(Lhca6)photosynthesis, light reactionSimilar to Type II chlorophyll a/b binding protein from photosystem I precursor1.48
Os10g0572300(OsIRL5)response to light stimulusLeucine-rich repeat, typical subtype containing protein1.16
Os01g0764800response to light stimulusIndole-3-acetic acid (IAA)-amido synthetase−2.40
Os05g0421900response to light stimulusGA 20-oxidase4−1.50
Os01g0279100(OsCRD1)light-independent chlorophyll biosynthetic processSubunit of magnesium-protoporphyrin IX monomethyl ester cyclase 1.23
Y9 vs. F9Os01g0600900(CAB2R)photosynthesis, light harvesting in photosystem IChlorophyll a-b binding protein 21.40
Os01g0720500(Lhcb1.1)photosynthesis, light harvesting in photosystem ISimilar to Type I chlorophyll a/b-binding protein b (Fragment)3.71
Os02g0197600photosynthesis, light harvesting in photosystem IChlorophyll a/b-binding protein type III (Fragment)1.03
Os02g0764500photosynthesis, light harvesting in photosystem ISimilar to Lhca5 protein1.07
Os03g0592500(LHCB)photosynthesis, light harvesting in photosystem ISimilar to Photosystem II type II chlorophyll a/b binding protein (Fragment)1.31
Os04g0457000(CP24)photosynthesis, light harvesting in photosystem ISimilar to Chlorophyll a/b-binding protein CP241.8
Os06g0320500photosynthesis, light harvesting in photosystem ISimilar to Light-harvesting complex I (Fragment)1.37
Os07g0558400(CP29)photosynthesis, light harvesting in photosystem ISimilar to Chlorophyll a/b-binding protein CP29 precursor1.54
Os07g0562700photosynthesis, light harvestingSimilar to Type III chlorophyll a/b-binding protein (Fragment)1.23
Os07g0577600photosynthesis, light reactionSimilar to Type II chlorophyll a/b binding protein from photosystem I precursor1.39
Os08g0435900(cab)photosynthesis, light reactionSimilar to LHC I type IV chlorophyll binding protein 1.38
Os11g0242800(ASCAB9-A)photosynthesis, light reactionSimilar to ASCAB9-A 1.50
Os06g0705100photosynthesis, light harvestingSimilar to Thylakoid lumenal 13.3 kDa protein 1.47
Os01g0501800(PsbO)regulation of photosynthesis, light reactionSimilar to Photosystem II oxygen-evolving complex protein 1 (Fragment)1.31
Os01g0773700(PSBW)regulation of photosynthesis, light reactionSimilar to Photosystem II reaction center W protein1.22
Os03g0333400photosynthesis, light reactionSimilar to photosystem II 11 kD protein1.62
Os07g0489800photosynthesis, light reactionBeta-grasp fold1.15
Os07g0544800photosynthesis, light reactionSimilar to oxygen-evolving enhancer protein 3-21.45
Os01g0869800(PSBS1)response to light intensity22-kDa Photosystem II protein, Photoprotection1.02
Os02g0729400(OsStr11)response to red or far red lightSimilar to extracellular calcium sensing receptor1.10
Os01g0764800(GH3-2)response to light stimulusIndole-3-acetic acid (IAA)-amido synthetase−1.33
Os11g0195500(PAD4)response to light stimulusHypothetical conserved gene−1.18
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Luo, W.; Li, Y.; Hou, X.; Wei, C.; Teng, Z.; Yang, C.; Su, H.; Wang, X.; Zhu, Z. Molecular Mechanism of Oryza sativa L. Under Long Day Regime Based on Transcriptome Analysis. Diversity 2025, 17, 603. https://doi.org/10.3390/d17090603

AMA Style

Luo W, Li Y, Hou X, Wei C, Teng Z, Yang C, Su H, Wang X, Zhu Z. Molecular Mechanism of Oryza sativa L. Under Long Day Regime Based on Transcriptome Analysis. Diversity. 2025; 17(9):603. https://doi.org/10.3390/d17090603

Chicago/Turabian Style

Luo, Wenju, Yufeng Li, Xianbin Hou, Chun Wei, Zheng Teng, Cuifeng Yang, Hongzhu Su, Xiaoli Wang, and Zhengjie Zhu. 2025. "Molecular Mechanism of Oryza sativa L. Under Long Day Regime Based on Transcriptome Analysis" Diversity 17, no. 9: 603. https://doi.org/10.3390/d17090603

APA Style

Luo, W., Li, Y., Hou, X., Wei, C., Teng, Z., Yang, C., Su, H., Wang, X., & Zhu, Z. (2025). Molecular Mechanism of Oryza sativa L. Under Long Day Regime Based on Transcriptome Analysis. Diversity, 17(9), 603. https://doi.org/10.3390/d17090603

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