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Chemical and Biological Technologies in Agriculture
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Multi-omics reveals the metabolic changes and genetic basis of post-flowering rice caryopsis under blue light

Chemical and Biological Technologies in Agriculture volume 11, Article number: 128 (2024) Cite this article

Abstract

Background

The effects of blue light on photosynthetic organs have been studied. However, its effects on non-photosynthetic organs, in particular, on the early stages of rice caryopsis development, are unclear. Thus, we aimed to determine the metabolic characteristics of caryopsis development under blue light to improve the metabolic quality of crop kernels.

Results

We conducted a multi-omics analysis of each of the three periods from the beginning of cellular differentiation to the end of morphogenesis in post-pollination seeds of a japonica rice variety to explore the effect of blue light on metabolic levels during these metabolic changes and its genetic basis. It was found that blue light caused a gradual decrease in auxin content, a significant increase in the accumulation of JA and flavonoids, and a downregulation of the expression of many starch-related genes and proteins, leads to reduced starch synthesis and smaller starch granules. In addition, the gene co-expression network identified three transcription factors that may regulate starch and two that may regulate flavonoids.

Conclusions

It was found through multi-omics testing that hormones such as jasmonic acid and auxins, and metabolites including alkaloids, flavonoids, lipids, organic acids, phenolic acids, and terpenoids altered significantly. Transcriptome and proteome analyses showed that blue light affected the seed nutrient repository activity. Specifically, starch- and gluten-related genes and proteins were significantly downregulated. Co- and WGCNA analyses identified several transcription factors that were regulated under blue light and identified key regulators of starch. Our study provides an understanding of the effects of blue light on post-flowering development in Gramineae and provides a framework for blue light-induced synthesis of secondary metabolites.

Graphical Abstract

Highlights

  • Evaluate the impact of blue light on the secondary metabolite accumulation.

  • Confirm the accumulation of hormones and flavonoids in different blue light.

  • Identify key candidate genes related to starch and flavonoid.

  • Blue light leads to reduced starch synthesis and smaller starch granules.

Background

Light is essential for plant growth and development, and it is a source of energy for photosynthesis. Therefore, light intensity, quality, and photoperiod are important environmental factors that affect many plant physiological processes [1]. These processes are primarily initiated through the activation of different photoreceptor genes and the production of photosynthetic pigments, signaling them to regulate further physiological functions [2, 3]. These processes encompass photosynthesis regulation, primary and secondary metabolism, morphogenesis, and molecular physiological responses that affect many aspects of plant growth and development [4]. The absorption spectra of plants primarily correspond to blue (400–500 nm) and red (600–700 nm) lights because the wavelengths of 400–700 nm are the most photosynthetically effective [5], with important effect on morphogenesis and metabolism. Light quality has been shown to induce secondary metabolite biosynthesis via photoreceptor-driven photoreceptor networks, and is involved in photomorphogenesis, mineral uptake [6, 7], and nitrate metabolism in plants [8, 9].

Monochromatic blue light resulted in lower root weights in tomato seedlings than a mixture of red and blue light [10, 11]. Blue light also affects leaf area and thus photosynthesis and biomass accumulation [12]. Fruit coloration was darker and faster under blue light, which was associated with the upregulation of some structural genes in pigment metabolism [13, 14]. Blue light is involved in the regulation of cellular secondary metabolites and the biosynthesis of functional metabolites [15, 16]. In strawberries, blue and red light treatments resulted in significantly higher levels of total anthocyanins; however, the highest and fastest rate of accumulation was observed under blue light alone [17]. Moreover, fruit anthocyanin synthesis increased in peppers under higher-intensity blue light, while genes associated with senescence were downregulated [18]. Similarly, the use of a single spectrum of blue color in lettuce has been shown to increase biomass and anthocyanin concentrations [19, 20]. It has been revealed that the auxin polar transport protein PIN2 is regulated by the blue photoreceptor PHOT1 and the signaling factor NPH3 in the root tip [21]. Similarly, the polar localization of PIN3 is regulated by blue light [22]. Blue light also activates the expression of PIF4/5, which can negatively regulate auxin-mediated phototropic growth through transcriptional upregulation of signaling pathways [23]. Moreover, blue light triggers the interaction between CRY1 and GID1 to control photomorphogenesis by inhibiting GA-induced degradation of DELLA protein and GA signaling [24]. These suggest that blue light has a significant species-specific effect on plants both intrinsically and extrinsically.

Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks [25]. Study shows that both the changes in physiological characteristics, including leaf photosynthesis, and the changes in morphological characteristics, including leaf development, contributed to the enhancement of biomass production under blue light conditions [26]. After 10 days of exposure to various wavelengths of LEDs, leaf area and shoot biomass were greater in seedlings grown under white and blue LEDs than those of green and red LEDs [27]. In addition, the combination of red and blue lights could regulate the expression of genes related to photosynthesis in rice leaves, affect the activity of the Rubisco enzyme, and then affect the photosynthesis of rice seedlings. These results indicate that red and blue lights have direct synergistic effects, which can regulate the growth of rice seedlings and promote the morphogenesis of rice seedlings [28].

The current research on blue light has focused on the effects on photosynthetic organs, i.e., on leaf source material. However, little research has been reported on non-photosynthetic organs. Different genotypic materials used in different studies and under different environmental conditions make general conclusions about the function of blue light difficult to ascertain. The effects of red and blue light on the metabolism of wheat kernels at 14 days after pollination have been reported in our previous studies. To further understand and unravel the metabolic characteristics of caryopsis development under blue light, the present study focuses on the period of morphogenesis of kernel development after pollination, aiming to provide a practical basis for the improvement of the metabolic quality of crop kernels in a controlled environment.

Methods

Plant materials and growth conditions

The widespread local rice (Oryza sativa L. spp. Japonica) cv. Chugeng 27, grown at the modern education base of Yunnan Agricultural University (102° 41ʹ E, 25° 20ʹ N; Kunming, China), was utilized as the material in this study. The rice was grown following standard cultivation practices (mid-may 2022), other management practices adhering to conventional cultivation methods. The experiment was conducted using three treatment methods: (1) normal plants without any treatment as the control (CK); (2) blue hybrid bag treatment after pollination (BU); and (3) dark treatment after pollination (BA). Each treatment collected samples at three critical time points in development, with three repetitions for each time point, resulting in a total of 27 samples. Detailed experimental design information is provided in the Supplementary Materials (Experimental design).

Scanning electron microscope and (SEM) transmission electron microscopy (TEM)

For the observation of starch granules, intact seeds were taken on days 7 and 14 after anthesis and immediately fixed with electron microscope fixative, followed by 1% (Ted Pella Inc) post-fixation. Dehydrated, embedded, polymerized, sectioned, and stained for analysis. The analysis is described in the Supplementary Materials (SEM and TEM analyses).

Hormone detection

Fresh plant samples were harvested, immediately frozen in liquid nitrogen, ground into powder, and stored at − 80 °C until needed. 50 mg of plant sample was weighed into a 2 mL plastic microtube, frozen in liquid nitrogen, dissolved in 1 mL methanol/water/formic acid (15:4:1, V/V/V). 10 μL internal standard mixed solution (100 ng/mL) were added to the extract as internal standards (IS) for quantification. The mixture was vortexed for 10 min, then centrifuged for 5 min (12,000 r/min, and 4 °C), the supernatant was transferred to clean plastic microtubes, followed by evaporation to dryness and dissolved in 100 μL 80% methanol (V/V), and filtered through a 0.22-μm membrane filter for further LC–MS/MS analysis [29].

Metabolomics analysis

Sample preparation and extraction followed the method described by Chen et al. [30]. The biological samples were freeze-dried and ground (30 Hz, 1.5 min) into a powder. Next, 50 mg of sample powder was weighed and 1200 μL of − 20 °C pre-cooled 70% methanolic aqueous internal standard extract was added. After vortexing six times and centrifugation at 13,400g for 10 min, the samples were filtered through a microporous filter membrane (0.22 μm pore size) and preserved in an injection vial for UPLC–MS/MS analysis. The analysis is described in the Supplementary Materials (Metabolomics analysis).

Proteomics analysis

After removing the sample from the − 80 °C freezer, it is ground into a powder using liquid nitrogen, and transferred to a centrifuge tube. Following procedures such as lysis, incubation, standing, and air drying, the sample is prepared for LC–MS/MS detection. The details are described in the Supplementary Materials (Proteomics analysis). To thoroughly understand the functional properties of different proteins, a comprehensive functional annotation of the identified proteins and the differentially expressed proteins in each comparative group was conducted. This included Gene Ontology (GO), KOG functional classification, KEGG pathway, protein domain, subcellular localization, and signal peptide (SignalP). In the proteomics analysis, 3 biological replicates were performed for each group. The false discovery rate (FDR) was set to a value of ≤ 0.01. A fold change (FC) > 1.5 or FC < 0.6667 with a P-value < 0.05 was defined as a significantly differentially expressed protein (DEP). The classification and function of differentially enriched proteins were analyzed using the Gene Ontology (GO) database, the Clustering of Homologous Groups (COG) database, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The StringDB (http://string-db.org/) protein interaction database was utilized for protein–protein interaction (PPI) analysis. Protein sequences of the corresponding species were directly extracted and interactions were determined based on a confidence score > 400 (medium). The network relationships were then imported into Cytoscape software [31] for visualization and editing using R (graph) network graphs simultaneously.

Transcriptome sequencing and data analysis

The detailed description of RNA extraction and sequencing is in the Supplementary Materials (Transcriptome sequencing and data analysis). For data analysis, paired reads were mapped to the Oryza sativa L. (IRGSP-1.0_genome.fasta.gz, available at https://rapdb.dna.affrc.go.jp/download/irgsp1.html) genome assembly using HISAT2 with default parameters [32]. Quantification of gene expression levels uses FeatureCounts v1.6.2 to calculate gene alignment and FPKM [33]. Difference analysis was conducted using DESeq2 v1.22.1 to analyze the differential expression between the two groups, and the p-value was corrected using the Benjamini and Hochberg method [34]. The corrected p-value and |log2FC| are used as the threshold for significant differential expression. Differentially expressed genes (DEGs) were identified using a false discovery rate threshold of < 0.05. GO enrichment analysis of DEGs was conducted using the GOseq R package [35], and enriched KEGG pathways among the differentially expressed genes (DEGs) were identified using the KOBAS program [36].

Transcription factor identification analysis, weighted correlation network analysis (WCGNA) and gene network visualization

ITAK software (IAITAM, Canton, OH, USA) [37, 38] was used to identify and annotate transcription factor families. After discarding undetectable genes or genes with relatively low expression, DEGs were used to create co-expression network modules through the WGCNA package in R. Modules were generated using automatic network constraints (block-wise Modules) with default parameters. Genes were classified into several gene modules using an appropriate soft threshold power, calculated using the Soft Threshold function. The topological overlap matrix, which can reflect the similarity of the co-expression relationship between two genes, was further constructed by calculating the soft threshold-based adjacency matrix. The gene-clustering tree obtained after hierarchical clustering was then cut using the dynamic shear tree algorithm, and colors were randomly assigned to each module for division. Eigenvectors for each module were calculated using PCA and used to calculate the correlations between modules and traits. A network map was created using Cytoscape software v.3.9.1 [31], and default parameters were utilized for all analyses. The structural gene promoter region (1000 bp upstream and 200 bp downstream of the transcription start site) and cis-acting element information were determined with reference to Wang et al. [39] and combined with the Pearson correlation coefficient (PCC > 0.8) between structural genes and transcription factors to generate the transcriptional regulatory network.

Validation of RT-qPCR analysis for expression of key genes in seeds

RNA was extracted from seeds of the nine treatment groups and used for qPCR analysis. The OsTubulin (LOC_Os12g39650, Chr12, CDS: 24478869 ~ 24482531)gene served as an internal control [40]. The relative transcript levels were determined using the 2-ΔΔCt method. Specific primer pairs for the selected genes were designed using Beacon Design 7.9 software (Table S1). QPCR was then performed in triplicate in 96-well plates with a StepOnePlus instrument (Applied Biosystems, Foster City, CA, USA) using PerfectStartTM SYBR qPCR Supermix (TransGen Biotech, Beijing, China) according to the manufacturer’s instructions. The 20-µL reaction mixtures contained 10 µL of 2 × PerfectStartTM SYBR qPCR Supermix, 0.4 µL of passive reference dye, 3.8 µL of nuclease-free water, 0.4 µL of each primer (10 mM), and 5 μL (200 μg/μL) of cDNA. The thermal cycles were as follows: 94 °C for 30 s, 40 cycles of 94 °C for 5 s, and 60 °C for 30 s.

Results

Histological analysis of seeds under blue light

Seed slices were observed for starch morphology and accumulation as shown by SEM in Fig. 1A (a, b, c). On day 7, BU (Fig. 1A-b) had smaller starch granules than BA (Fig. 1A-a) and CK (Fig. 1A-c), although no significant difference in starch quantity could be seen. BA and BU had large, loose fragmented starch granules. CK had larger unseparated complex starch granules. TEM also showed (Fig. 1A-d, e, f) that BA (Fig. 1A-d) and CK (Fig. 1A-f) accumulated more starch than BU (Fig. 1A-e). On the 14th day, SEM (Fig. 1B-g, h, i) clearly showed that BA (Fig. 1B-g) and CK (Fig. 1B-i) had larger starch granules than BU (Fig. 1B-h), although unlike on day 7, the starch granules of BA were larger and more intact than those of BU and CK on day 14. This is even more evident under TEM (Fig. 1B-j, k, l), where quantitatively, although there is no significant difference, the starch granules are larger, rounder, and more intact in BA (Fig. 1B-j) and CK (Fig. 1B-l) than in BU (Fig. 1B-k).

Fig. 1
figure 1

Representative endosperm starch morphology in seeds under blue light. AM amyloplast, BSG broken compound granule, CMSG compressed starch granules, CSG compound starch granule, CW cell wall, ISG irregular starch granule, LB lipid body, PB protein body, PSV protein storage vacuole, SG starch granule, SSG spherical starch granule, V vacuoles. Bars = 5 mm

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Similarly, we observed the accumulation of starch in the aleurone layer and the morphology among the groups, as shown in SEM Fig. 2A (a, b, c). Significantly fewer starch granules were accumulated in the cell chambers of BU (Fig. 2A-b) and BA (Fig. 2A-a) at day 7 than in those of CK. No significant difference was found among the three groups as observed by TEM (Fig. 2A-d, e, f). On day 14, BA (Fig. 2B-g) and CK (Fig. 2B-i) had more complete starch granules than BU (Fig. 2B-h) as observed by SEM (Fig. 2B-g, h, i). Specifically, CK and BA were not only larger but also fuller, while BU had a very obvious folding phenomenon. Larger and more abundant starch granules were also found using TEM (Fig. 2B- j, k, l).

Fig. 2
figure 2

Representative map of starch morphology of seed aleurone layer under blue light. AM amyloplast, BSG broken compound granule, CSG compound starch granule, CW cell wall, ISG irregular storage vacuole, LB lipid body, MSG mildly angular starch granules, PB protein body, PSV protein storage vacuole, SG starch granule, SSG spherical starch granule, V vacuoles, WSG wrinkled starch grains. Bars = 5 mm

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Hormone content analysis of seeds under blue light

Qualitative and quantitative analyses of hormones in 27 samples were performed using LC–MS/MS. A total of 88 hormones were detected in 8 classes, including 2 abscisic acids (ABA), 26 auxins, 36 cytokinins (CK), 1 ethylene (ETH), 10 gibberellins (GA), 9 jasmonic acids (JA), 2 salicylic acids (SA), and 2 solanocarpine lactones (SL) (Table S2). Under BU and BA, 13 (mainly auxin, CK, GA, and JA) and 10 (mainly auxin, CK, and JA) hormones, respectively, differentially accumulated at all three stages of seed development (Table S3 and Fig. 3A). Table S4 and Fig. 3B show the types of hormones that varied significantly across groups, with upregulated TRA (auxin) and JA classes under blue light in BU at stage 1; upregulation of JA classes, and downregulation of auxin at stage 2; upregulation of JA classes at stage 3, and downregulation of SA classes at stages 2 and 3. In BA, the JA class is upregulated at all three stages, and the SA class is downregulated at stages 2 and 3. In BA, TRA was only upregulated at stage 3. At stage 2, BU upregulated more JA-like substances and downregulated more auxin-like substances. Furthermore, more JA- and ABA-like substances and less auxin-like substances were accumulated in BU than in BA. Notably, active GA1, GA3, GA4, and GA7 were weakly detected in BU.

Fig. 3
figure 3

Wayne plots (A) and differential accumulation of hormones (B) under blue light. Error bars represent means ± SE (n = 3), Different letters (a–c) show statistically significant differences at P < 0.05

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Metabolite analysis of seeds under blue light

Metabolome analysis was conducted to characterize DAM in seeds under blue light, and 1,677 metabolites were detected. Based on the clustered heat map, a large number of metabolites were highly accumulated at stage 1, and the accumulation was significantly upregulated in the early stage under blue light. In the PCA score plot, the nine sample groups were distributed across distinct regions, facilitating clear differentiation between groups, treatments, and sampling periods characterized by significant variations in metabolite profiles (Fig. S1). Metabolites defining VIP > 1 were significantly different, with 1,190 DAMs identified (Table S5). The main categories were alkaloids, amino acids and their derivatives, flavonoids, lipids, organic acids, phenolic acids, and terpenoids. Fig. S2 shows a K-means plot with all DAMs categorized into eight clusters. Among them, the ones that were highly accumulated in all three periods under BU were clusters 4 and 5, with 104 (mainly lipids [n = 11], terpenoids [n = 10], alkaloids [n = 13], flavonoids [n = 24], and phenolic acids [20]) and 361 (including lipids [n = 26], organic acids [n = 24], terpenoids [n = 25], alkaloids [n = 21], flavonoids [n = 124], phenolic acids [n = 55], and amino acid and its derivatives [n = 42]) metabolites, respectively. Under BA, there were 6 clusters with 5 lipids, 7 organic acids, 7 terpenoids, 5 alkaloids, 11 flavonoids, and 17 amino acids and their derivatives.

Between groups, a difference of > 2 or < 0.5 was defined as DAM. Table S6 shows the statistics of DAM between groups, with an upregulated accumulation of DAM in all three periods for BU and different for BA. DAM was highly accumulated under BU compared with that under BA. Notably, all three groups had upregulated accumulation of DAM at stage 1 as the seeds continued to develop.

Table S7 shows that the DAM between groups, stages 1, 2, and 3 under BU were mostly flavonoids and alkaloids. Under BA, stage 1 was mainly lipids and flavonoids while stages 2 and 3 were terpenoids and flavonoids. Compared to BA, stage 1 under blue light is primarily lipids and terpenes, while stages 2 and 3 are predominantly flavonoids and terpenes. Figure 4A depicts Wayne plots of DAM for the three comparison groups with significant accumulation across all three periods. Specifically, there were 56 significant differential metabolites accumulating under BU, 48 under BA, and 62 under BU compared with those under BA. These majorly constituted flavonoids and alkaloids in both BU and BA; however, there were more lipids, terpenoids, and flavonoids under BU than under BA. These DAMs under BU were predominantly upregulated; whereas under BA they were mostly downregulated; compared with BA, BU had upregulation predominantly in stages 2 and 3 (Fig. 4B). Plant hormone signal transduction and flavonoid-related pathways were significantly altered in all three periods under BU, whereas diterpenoid biosynthesis, amino acid metabolism, and the flavonoid-related pathway were significantly changed under BA. Similarly, diterpenoid biosynthesis and the flavonoid-related pathway were significantly altered under BU compared with those under BA (Table S8).

Fig. 4
figure 4

Wayne plots (A) and significantly changed DAM thermograms (B) for all three periods for each treatment

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Proteomic analysis of seeds under blue light

A 4D-DIA proteomics assay was performed with the samples, as demonstrated in Fig. S3A. The number of database protein sequences selected was 48,899, of which 12,481 were identified as known sequences and were all quantified. A total of 118,681 peptides were identified, with most of the peptides distributed within amino acids 7–20. As demonstrated in Fig. S3B, the treatments and periods were well distinguished from each other, indicating that the sampling and experimental procedures were stable and reliable. Moreover, protein subcellular localization predictions showed that these proteins were predominantly localized to chloroplast (36.1%), nucleus (22.9%), and cytoplasm (20.2%). Proteins were considered significantly differentiated at FC > 1.5 or FC < 0.6667 with P-value < 0.05. The differential proteins at stages 1, 2, and 3 were 205 (97 were upregulated and 108 were downregulated), 616 (248 upregulated and 368 downregulated), and 339 (219 upregulated and 120 downregulated), respectively, under BU; 283 (129 upregulated and 154 downregulated), 449 (153 upregulated and 296 downregulated) and 605 (284 upregulated and 321 downregulated) under BA, respectively. A total of 40 DEPs were expressed in all three periods under BU (Table S9 and Fig. 5). These proteins were predominantly enriched in pathways related to protein processing in the endoplasmic reticulum, metabolic pathways, biosynthesis of secondary metabolites, and glycerophospholipid metabolism. Notably, most of these proteins exhibited upregulation under blue light conditions. Ninety-nine DEPs under BA were expressed in all three periods, majorly related to the pathways of protein processing in endoplasmic reticulum, diterpenoid biosynthesis, carbon fixation in photosynthetic organisms, glyoxylate and dicarboxylate metabolism, and photosynthesis. Organisms, glyoxylate and dicarboxylate metabolism, and photosynthesis, and most of the proteins were downregulated under blue light. Compared with BA, BU had 34 DEPs, which were related to photosynthesis, carbon fixation in photosynthetic organisms, diterpenoid biosynthesis, and protein processing in endoplasmic reticulum. Notably, nine of the DEPs in all three periods under blue light belonged to heat stress proteins (HSP). Two Bowman–Birk type proteinase inhibitor proteins were also highly expressed under blue light. The jasmonate-related lipoxygenase (LOX) was also highly expressed under blue light.

Fig. 5
figure 5

Wayne plots under different treatments

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The highly expressed proteins under BU were primarily involved in starch and sucrose metabolism, whereas those with the largest fold difference were involved in protein processing in the endoplasmic reticulum, and most of the highly expressed proteins were downregulated relative to the control (Table S10). The proteins with high expression and large fold difference under BA were mainly involved in protein processing in endoplasmic reticulum, and these proteins were all upregulated compared with those under CK. For example, two pyruvate, phosphate dikinase 1 (PPDK1), and two glucose-1-phosphate adenylyltransferase large subunit 2 (AGPL2) proteins were downregulated. However, non-specific lips-transfer protein and Bowman–Birk type proteinase inhibitor were upregulated. Moreover, among the proteins with the largest fold difference, heat stress protein, linoleate 9S-lipoxygenase, and Bowman–Birk type bran trypsin inhibitor were primarily upregulated.

Table S11 shows the top 20 terms of GO enrichment among the comparison groups. Under BU, response to abiotic stimulus, lipid metabolic process, and hydrolase activity were involved. Under BA, the enriched GO terms were thylakoid part, thylakoid membrane, thylakoid, response to abiotic stimulus, plastid thylakoid membrane, photosystem, photosynthesis, generation of precursor metabolites and energy, and membrane protein complex. Among them, nutrient reservoir activity (GO:0045735) under BU was downregulated in 22 of the 23 proteins involved at stage 2. The lipid metabolic process (GO:0006629) was upregulated in the expression of most proteins involved in all the three periods. Table S12 shows the pathways that were significantly different in each group. Under BU, biosynthesis of secondary metabolites, linoleic acid metabolism, phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and protein processing in endoplasmic reticulum changed significantly at all three stages. Similarly, under BA, metabolic pathways, diterpenoid biosynthesis, carbon fixation in photosynthetic organisms, glyoxylate and dicarboxylate metabolism, alanine, aspartate, and glutamate metabolism, photosynthesis-antenna proteins and photosynthesis, were altered.

Transcription profiling of seeds under blue light

To understand the differences in transcript levels in rice seeds under BU and BA, we subjected 27 samples to transcriptome analysis. A total of 182.1 Gb of clean data was obtained, with each sample yielding 6 Gb of clean data. The percentage of Q30 bases across all samples was ≥ 94%. Subsequently, 9510 differentially expressed genes were identified from the 441,203 expressed genes (Table S13). The differential gene heat maps showed that BU and BA differed significantly in gene expression levels (Fig. S4). We defined differential genes at |log2FC|≥ 1 and FDR < 0.05, which were 456,201, and 741 for stages 1, 2, and 3 of seed development, respectively, under BU; and 631, 107, and 2441 under BA, respectively. The differential genes of BU and BA are concentrated in the early and late stages. Table S14 demonstrates the top 20 GO terms that were significantly enriched in each group, and the genes under BU were mainly involved in endopeptidase inhibitor activity, peptidase inhibitor activity, endopeptidase regulator activity, peptidase regulator activity, serine-type endopeptidase inhibitor activity, alpha-amino acid metabolic process, enzyme inhibitor activity, and nutrient reservoir activity. GO terms under BA were enriched in nutrient reservoir activity, enzyme inhibitor activity, response to heat, response to ethylene, phosphorelay signal transduction system, and protein folding. Compared with those under BA, the genes under BU were involved in response to heat, protein folding, unfolded protein binding, aleurone grain, and nutrient reservoir activity. Table S15 shows the pathways that significantly changed in each subgroup. Under BU, plant hormone signal transduction, fatty acid metabolism, and biosynthesis of unsaturated fatty acids significantly changed; whereas, under BA, the main altered pathways were protein processing in the endoplasmic reticulum, fatty acid metabolism, and diterpenoid biosynthesis. Fatty acid biosynthesis and diterpenoid biosynthesis pathways significantly changed in BU compared with that in BA.

As shown in Table S16, among the differentially expressed genes at all three stages under BU, the highest expressed were Bowman–Birk type trypsin inhibitor (Os01t0124200-02) and jasmonate zimdomain (JAZ) protein and regulation of coleoptile length under submergence (Os07t0615200-01). Among the differentially expressed genes at all three stages under BA, mannose-binding lectin domain-containing protein\jacalin-like lectin domain-containing protein (Os03t0399800-01), 16.9-kDa class I small heat shock protein (Os01t0136100-01), chloroplast-localized small heat shock protein (Os03t0245800-02) and fructose-bisphosphate aldolase, and chloroplast precursor (EC 4.1.2.13) (ALDP) (Os11t0171300-01) had the highest expression.

Glutenins, lipid transfer proteins, and protease inhibitors were primarily involved under blue light (Table S17). Alcohol-soluble proteins and lipid transfer proteins were predominantly involved under BA. In the three stages under BU, and BU compared to BA, lipid transfer protein class II (Os12t0115100-00), glutenin A type III precursor (Os03t0427300-01), glutenin-type A2 precursor (Os10t0400200-01), glutenin-like (Os02t0249600-01), seed endosperm developing (Os02t0453600-01), a gluten-like protein (Os02t0453600-01), overexpression of α-amylase/trypsin inhibitor (Os07t0214300-01), which increases seed size, and lipid transfer protein (Os11t0115400-01), which are gene duplicates of these genes, were highly differentially expressed. Moreover, two of them were involved in lipid transport, with the five being related to the library capacity size. All of these genes had significantly decreased expression under blue light. Trend analysis of DEGs compared the trend of these expression characteristics under BU and BA (Fig. S5). Both BU and BA had the highest number of genes from profile 0 and were in a decreasing trend in three stages, while profiles 1 and 6 had a steady trend after decline and rise, respectively. The genes with changing patterns of expression concentrated in profiles 0, 1, and 6, and the expression trends of the three were the same under BU and BA. However, profile 7 changed significantly under BU but not under BA (Fig. S5A, B). We performed a KEGG enrichment analysis of profile 7 on 628 genes and the result showed that most of the genes were significantly enriched in metabolic pathways and biosynthesis of secondary metabolites (Fig. S5C). Notably, starch and sucrose metabolism and plant hormone signal transduction were significantly enriched. Additionally, noteworthy entries for GO enrichment included nutrient reservoir activity, aleurone grain, and lipids.

Correlation analysis of significantly changed hormones and metabolites with transcripts and proteins under blue light

The transcriptome and proteome were correlated using hormones that changed significantly under blue light, and it was found that 5DS, ABA-GE, ACC, TRP, and JA-ile did not associate with genes, whereas 5DS, ABA-GE, and TRP did not associate with proteins. A network diagram was constructed by selecting hormones that were associated with both genes and proteins (P < 0.05, | Cor|> 0.9; Fig. S6A). The hormones found to be significantly associated with transcription and protein were tZR, IAA, cZROG, OPDA, and DH7ZG. Similarly, the highly differential accumulation of DAMs under blue light was analyzed in association with the transcriptome and proteome (Fig. S6B). Among the 54 most significantly associated metabolites were 24 flavonoids, 9 alkaloids, 7 lipids and terpenes, 5 phenolic acids, and 2 organic acids. The 79 significantly associated proteins were related to ko01100 (metabolic pathways), ko00500 (starch and sucrose metabolism), ko04141 (protein processing in endoplasmic reticulum), ko04075 (plant hormone signal transduction), ko00940 (phenylpropanoid biosynthesis), ko00904 (diterpenoid biosynthesis), ko00592 (alpha-linolenic acid metabolism), and ko00591 (linoleic acid metabolism). Similarly, most of the significantly associated genes were related to ko01100 (metabolic pathways), ko04141 (protein processing in endoplasmic reticulum), ko04075 (plant hormone signal transduction), ko00940 (phenylpropanoid biosynthesis), ko00941 (flavonoid biosynthesis), ko00196 (photosynthesis-antenna proteins), and ko00195 (photosynthesis). O2PLS analysis also showed that the 10 hormones with the greatest impact on the transcriptome were IAA-Trp, SAG, JA, MEJA, IAA-Phe, GA24, MEIAA, JA-ILE, IAA, and OxIAA. Similarly, the 10 hormones with the greatest impact on the proteome were JA, MEJA, JA-ILE, JA-Val, IAA-Val, JA-Phe, IAA-Trp, SAG, OPC-4, and TRA. Among them, IAA-Trp, SAG, JA, MEJA, and JA-ILE had large effects on both transcription and protein. Metabolites with effects on the transcriptome and proteome were lssp210344 (craiobioside B), ma10107783 (3-[(1-carboxyvinyl) oxy]benzoic acid), lmgn000160 (3-ureidopropionic acid), mws0001 (l-asparagine), and lskp211268 (3-ethyl-7-hydroxyphthslide) (Fig. S6C).

Combined metabolome, proteome, and transcriptome analysis under blue light

As demonstrated in Fig. S7, metabolic pathways in the auxin precursor TRA (mws0005) associated with five genes and three proteins; two proteins and one gene related to JA (pme1654); and the most abundant was n-acetyl-l-phenylalanine (zmgn002106) with four genes and 13 proteins. The secondary metabolite biosynthesis pathway is essentially the same as the metabolic pathways; however, flavonoids (pma6389) associated with six proteins. In the plant hormone signal transduction pathway, only jasmonic acid (pme1654) was associated with the gene Os07t0615200-01. The gamma-aminobutyric (pme3011) component of the alanine, aspartate, and glutamate metabolism pathway associated with a gene and protein during stage 2. Similar findings were obtained for the biosynthetic pathway of the amino acid homocitrate (wayn001528). Plant hormone signal transduction was the same as in stage 1. In metabolic pathways, indole-3-acetic acid (IAA) was significantly positively correlated with three proteins, with correlations above 0.95 for all. It was also negatively correlated with one protein (correlation of − 0.96). In the secondary metabolite biosynthetic pathway, flavonoids (mws0064) were positively correlated with Q53KM3, and l-ornithine (pme2527) was negatively correlated with Q7XMP7. At stage 3 of development, biosynthesis of amino acids was significantly associated with n-acetyl-l-glutamic acid (pme0075) with three genes and two proteins. The metabolic pathways featured TRA (pme2024) with the highest number of genes and proteins associated with n-acetyl-l-glutamic acid (pme2024).

The correlation nine quadrant analysis is shown in Fig. S8, where we focused on quadrants 3 and 7 because changes in metabolite expression may be positively regulated by genes. Stage 1 (A) consisted of lathyrol (terpenoids), feruloylmalic acid (phenolic acids), jasmonic acid (organic acids), 9 s, 13r-12-oxophytodienoic acid (lipids), apigenin-6,8-di-c-arabinoside (flavonoids), quercetin-3-o-(2ʺ-o-acetyl) glucuronide (flavonoids), homoproline (amino acids and derivatives), tryptamine (alkaloid), glycine (alkaloid), glycine (alkaloid), glycine (alkaloid), tryptamine (alkaloids), n-feruloyltryptamine (alkaloids), n-feruloylserotonin (alkaloids), and cantleyine (alkaloids). Similarly, at stage 2 (B), the alkaloids distinguished from stage 1 were indole-3-acetic acid (IAA), 2-oxo-3,4-dihydro-1h-quinoline-3-carboxylic acid, methyl indole-3-acetate, and methyl indole-3-acetate. The organic acids were mainly 2-picolinic acid* and 2-aminoethanesulfinic acid. By stage 3, the predominant metabolites were alkaloids and flavonoids. In an analysis of expression trends with the proteome (Fig. S8B), we found that the predominant metabolites in the three stages were alkaloids, flavonoids, terpenoids, organic acids, and lipids (Fig. S8C).

Joint metabolomic, proteomic, and transcriptomic resolution of flavonoid changes under blue light

Flavonoids, which are produced during secondary plant metabolism, influence multiple plant developmental processes. Moreover, flavonoid-related transcription factors may play a role in directing metabolic resources from central metabolism to flavonoid biosynthesis. The transcriptome, proteome, and metabolome were united to resolve the mechanism of flavonoid accumulation under blue light. The up- and down-regulation of genes, proteins, and metabolism in the phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways among the groups are shown in Fig. S9A. Most of the genes (Fig. S9B, left) and proteins (Fig. S9B, center) were upregulated under BU; similarly, the metabolites that were altered under BU were significantly accumulated (Fig. S9B, right). Specifically, the genes and proteins of PAL in the phenylpropanoid biosynthesis pathway were upregulated and expressed in both stages 1 and 2, PTAL-related proteins were upregulated and expressed in stage 3, 4CL genes were upregulated and expressed in both stages 1 and 3, and the genes and proteins of CCR were upregulated and expressed in both stages 1 and 3, and POD genes and proteins were upregulated in all three periods. Genes and proteins related to CHS, CHI, FLS, and ANR in the flavone and flavonol biosynthesis pathway were upregulated during all three stages. In contrast, these genes were downregulated under BA. Moreover, the genes and proteins related to CAD and F3H were not significantly changed under BU but were downregulated under BA. Only the FG2 gene was upregulated and expressed at stages 1 and 3 in the flavonoid biosynthesis pathway, while the expression of the UGT73C6 gene was downregulated under BA. For metabolite accumulation, p-coumaroyl quinic acid, hesperetin 7-o-glucoside, hesperetin 7-o-neohesperidoside, sakuranetin, dihydrokaempferol, butin luteolin, eriodictyol, quercetin, 5,7-dihydroxy-4ʹ-methoxyflavone and naringenin were predominant under BU and their expression was upregulated during each of the three stages. Under BA, the significantly altered substances were caffeoyl quinic acid, p-coumaryl alcohol, coniferyl alcohol, coniferyl alcohol, prunin, and dihydrokaempferol, and were downregulated. In summary, the accumulation of flavonoids was predominantly upregulated under BU, and downregulated under BA. Similarly, related genes and proteins were predominantly upregulated under BU and downregulated under BA. The genes, proteins, and metabolites were predominant in the stages 1, 2, and 3, respectively.

WGCNA analysis of candidate regulators of significantly changed metabolites under blue light

WGCNA analysis showed a highly significant positive correlation of the turquoise module with IAA (0.86), OxIAA (0.85), and IAA.Asp (0.85), and IAA.Glu (0.87); and highly significant negative correlation with ICAld (− 0.84), TZ (− 0.92), OPDA (− 0.9), DZ (− 0.91), DHZ7G (− 0.87), cZROG (− 0.85), OPC.4 (− 0.7), GA9 (− 0.71), and GA4 (− 0.73). The green module was positively correlated with TZ (0.81), ICAld (0.72), OPDA (0.74), GA9 (0.8), GA24 (0.78), DZ (0.71), DHZROG (0.72), DHZ7G (0.78), cZROG (0.83), and SAG (0.7); and negatively correlated with IAA.Asp (− 0.72) and IAA.Glu (− 0.77) (Fig. S10A). Similarly, multiple compounds in the turquoise and green modules were found to have high correlations with amino acids, phenolic acids, flavonoids, alkaloids, terpenoids, and lipids (Fig. S10B). Thus, focusing on these two modules, hub screening was conducted based on |KME|> 0.8 (eigengene connectivity) (Table S18). The turquoise and green modules yielded 1031 and 683 hub genes, respectively. The genes from both modules were analyzed by GO and KEGG, respectively (Fig. S11). It was found that the biological process of the turquoise module involves starch metabolism, post-embryonic plant morphogenesis, alpha-amino acid biosynthesis, cellular amino acid biosynthesis, specification of floral organ identity, and starch biosynthesis. The cellular component was primarily involved in the monolayer-surrounded lipid storage body, aleurone grain and lipid droplet, and molecular function involved immunoglobulin E binding and nutrient reservoir activity. The cellular components of the green module were concentrated in the inner mitochondrial membrane protein and aleurone grain, and molecular functions were in nutrient reservoir activity, transcriptional activator activity, and immunoglobulin binding. KEGG enrichment indicated that the turquoise module was mainly involved in valine, leucine, isoleucine, phenylalanine, tyrosine, and tryptophan biosynthesis, metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of amino acids and 2-oxocarboxylic acid metabolism. The green module focused on the ribosomal pathway, protein export, metabolic pathways, fatty acid elongation, cutin, suberin, and wax biosynthesis, and alanine, aspartate, and glutamate metabolism.

Considering that the turquoise and green modules have a high correlation with most hormones and differential generation metabolism, they may have potential genes that influence changes in the accumulation of these substances under blue light. Therefore, these two modules were used for further analysis. To identify candidate regulatory genes involved in these differential metabolites, we focused on DEGs in both modules. Setting |GS| (Gene Significance) > 0.8 and |MM| (Module Membership) > 0.8, the genes from the module associated with high levels of each significantly differentiated metabolite were screened for key genes again. The turquoise and green modules screened 341 and 237 hub genes, respectively (Table S19). The turquoise module identified 46 TF and 33 structural genes. Subsequently, the betweenness (BC) values of the candidate genes were calculated using the cytoNCA plug-in in Cytoscape 3.9.1 software to screen the core genes. Combined with the weight values from the WGCNA analysis, five core genes were identified namely, Os01t0104500, Os05t0415400, Os08t0565200, Os03t0182800, and Os10t0191900 (Fig. 6A). Examination of the transcript profiles of these genes revealed that the expression of Os01t0104500, Os05t0415400, Os08t0565200, and Os03t0182800 was all downregulated under BU, which was further verified by RT-qPCR of 27 samples (Fig. S12). Some focused structural genes (outer circle) regulated by TF (inner circle), such as the hydroporin OsNIP1 (Os02t0232900-01) and the adenosine diphosphate glucose pyrophosphorylase OsAGPL1 (Os05t0580000-01) were upregulated, with OsAGPL1 being associated with starch synthesis and accumulation during early endosperm development. However, most of the structural genes, such as starch debranching enzyme PUL (Os04t0164900-01), branched-chain starch biosynthesis enzyme PHS8/ISA1 (Os08t0520900-01), subgroup glutenin GluB7 (Os02t0249600-01), glutamine synthetase OsGS1 (Os03t0712800-02), sucrose synthase kinase OsCDPK23 (Os10t0539600-01), sucrose transporter protein Os-11N3/OsSWEET14 (Os11t0508600-01), α-amylase/trypsin inhibitor RAG2 (Os07t0214300-01), glutenin OsGluA2 (Os10t0400200-01), glutenin GluC-1 (Os02t0453600-01), powdered endosperm FLO12 (Os10t0390500-01), and waxy synthesis OsBDG (Os06t0132500-01) were downregulated. Moreover, these structural genes have high connectivity with core TF (Fig. 6A). Similarly, the green module identified 19 TF and 18 structural genes. The core transcription factors were Os12t0572000-01, Os02t0104500, Os10t0419200, Os11t0512200, Os02t0132500, Os03t0707600, and Os12t0122000 (Fig. 6B). Under BU, Os12t0572000, Os02t0104500, Os11t0512200, and Os03t0707600 were at stage 1 and their expression was upregulated (Fig. S12 and Table 1). The expression of structural genes such as the RING Finger E3 ubiquitin ligase OsMHZ2 (Os04t0101800-01) and the gibberellin-regulated genes OsGAE1 (Os01t0201600-01), the alginate-6-phosphate synthase OsTPS4 (Os03t0224300-01), and OsABCB14 (Os04t0459000-01) was upregulated and cytoplasmic phosphorylase Pho2 (Os01t0851700-01) and OsABCC1 (Os04t0620000-01) were not significantly altered. Notably, the expression of NAC (Os05t0415400-01, A0A0P0WML5), PUL (Os04t0164900-01, A0A0P0W7K6), GluC-1 (Os02t0453600-01, A1YQH2), and FLO12 (Os10t0390500-01, A0A0P0XU67) proteins was downregulated under BU. Similarly, we performed a WGCNA analysis of the proteome with the significantly changed hormones and metabolites described above, and the 67 most important proteins in this module were identified from the highly correlated module according to weight. Ppdk2, ppdk1, agpl2, ssii-3, isa1, ssiiia, and 19 kDa globulin were significantly downregulated under BU, whereas the 70 kDa heat shock protein was significantly upregulated (Table S20). These findings imply a potential regulatory function of the TFs induced by blue light on the structural genes, leading to a decrease in pool capacity because of reduced accumulation of seed starch under blue light conditions. However, further investigation is required to elucidate the specific molecular mechanisms involved.

Fig. 6
figure 6

Turquoise A and green module B transcription factor and structural gene network interaction plots. Red lines indicate a positive correlation, and blue lines indicate a negative correlation. Each node in the network represents a gene. A Outer circles represent structural genes, while inner circles represent transcription factors. Red font is used to indicate identified significant transcription factors. B Diamond is transcription factor and ellipse is structural gene

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Table 1 Core transcription factors identified by the Turquoise and Green modules
Full size table

Discussion

Blue light is as effective as green or red light in driving photosynthesis. Therefore, while blue light is somewhat dim to us, it is highly energizing and useful for plant growth. In a study on wheat breeding, significant morphological and physiological differences were found between spicules on day 14 after pollination under dark, red, and blue lights [41]. To further understand the genetic basis of the pre-morphological building stage, this study chose rice and focused on the metabolite levels during grain morphogenesis. With a particular focus on the influence of blue light, this study unraveled the genetic basis of pre-morphological variations leading to final morphological differences. This was accomplished through multi-omics analysis, laying the groundwork for future in-depth research in this area. The four categories that changed significantly under blue light were IAA, CK, GA, and JA. It was anticipated that light signaling and hormonal pathways would interact and mutually influence each other [42]. JA was found to be significantly upregulated as a stressor in blue light, and elevated blue light intensity led to its increased accumulation [41]. In the present experiment, JA accumulated significantly under blue light and throughout the morphogenetic period. Multiple JAZ family proteins were upregulated and highly expressed under blue light. These proteins act as a repressor in the JA signaling pathway to inhibit the activity of related transcription factors at the core of the JA signaling pathway. Studies have demonstrated that this pathway has an important role in the partitioning of labor and mechanisms in ketone and phenolic acid biosynthesis [43]. In the hormonal assay, found that four substances with strong activity, GA1, GA3, GA4, and GA7, were not strongly detectable under blue light. We suspect this to be related to the high expression of JA under blue light [44]. Metabolome analysis revealed that lipids and alkaloids were also significantly accumulated under blue light. This could be related to the significant accumulation of JA, as observed in previous studies [45,46,47]. Alternatively, this may be attributed to the fact that blue light affects the degradation of fatty acids, changing the fatty acid composition of membrane lipids and increasing the lipid content of plants [48, 49]. Although growth hormone expression was downregulated under blue light, the growth hormone synthesis precursor substance TRA (auxin) was markedly upregulated. Since morphogenesis regulated by the light environment involves alterations in auxin homeostasis [50], changes in upward auxin have a large impact on morphology and intrinsic physiology during the period of seed morphogenesis, and this impact could be directly caused by blue light. Given that there is evidence indicating that both the blue light receptor, CRY1, and the auxin receptor share common downstream receptors, and that they both play a role in regulating the elongation of hypocotyl cells, it implies that signaling pathways for light and hormones can jointly influence growth and developmental processes [51, 52]. The decline in auxin may also be related to the accumulation of flavonoids. This is because flavonoids can act as regulators of endogenous auxin transport [53], and flavonoids affect auxin levels, PIN1 (protein that transports auxin) protein levels, and the expression of auxin-related genes [54]. O2PLS analysis also revealed that the hormones with the greatest impact on the transcriptome and proteome were primarily growth hormone analogs and JAs. Similarly, correlation analyses showed that cytokinins and jasmonates were strongly correlated with transcription and protein. These suggest that light signaling continuously promotes plant growth and developmental remodeling, and that these dynamic regulations are influenced by plant hormones such as auxin, ethylene, cytokinin, abscisic acid, and gibberellin.

Metabolome analysis showed that significant changes under blue light were in lipids, terpenoids, alkaloids, flavonoids, and phenolic acids, with significant accumulation of flavonoids and alkaloids. Expression trend analysis also revealed that flavonoids were positively regulated by genes and proteins. In the joint analysis, PAL, PTAL, 4CL, CCR, and POD-related genes and proteins were found to be upregulated and expressed in each of the three periods under blue light. CHS, CHI, FLS, and ANR genes and proteins were upregulated and expressed during one of three periods. Previous studies have also found that blue light stimulates the transcription of key flavonoid biosynthesis genes in plants that interact to regulate flavonoid content [55, 56]. WGCNA analysis identified two MYB family genes that were upregulated for expression at stage 1, which is consistent with previous findings [57]. Studies have shown that blue light regulates lipid and flavonoid metabolism and that MYB plays an important role in this process [58]. NAC transcription factor family genes were identified in two important modules of interest to WGCNA. Based on the important role of the NAC family of transcription factors in starch synthesis, we examined the expression of these genes under blue light and found that the expression of genes related to starch accumulation, such as starch debranching enzyme, branched-chain starch biosynthesis enzyme, sucrose synthase kinase, sucrose transport protein, and α-amylase/trypsin repressor, were markedly downregulated under blue light. Even genes and proteins involved in library capacity size, such as glutenin GluB7, glutamine synthetase OsGS1, glutenin OsGluA2, glutenin GluC-1, and powdered endosperm FLO12 were significantly downregulated. Histology also demonstrated that blue light accumulated less starch and had smaller starch granules. Therefore, we speculate that blue light indirectly affects starch accumulation by influencing the expression of NAC gene family to modify the activity of the seed bank. Soluble starch synthase, isoamylase, glucose-1-phosphate adenylyltransferase large subunit, and pyruvate, phosphate dikinase were significantly downregulated in the WGCNA analysis of the proteome. Similarly, cryptochrome 1a (CRY1a)-mediated blue light signaling is essential for regulating starch accumulation through HY5-induced starch degradation [59]. Additionally, in guard cells, starch is degraded in the light [60]. Among the core genes we identified, Os05t0415400 (NAC) and Os12t0572000 (MYB) have been confirmed [61, 62].

Light is the most influential environmental factor in starch metabolism, and daily changes in starch accumulation can often be explained by the dynamics of photosynthesis since light provides plants with the energy for this biological process [63]. It seems that blue light is not conducive to starch accumulation. In plants, starch metabolism is regulated by several factors such as intrinsic carbon status, circadian rhythms, redox homeostasis, hormones, and environmental factors such as light, temperature, water, and nutrient availability [64,65,66,67,68]. Whether all this is caused by blue light may need to be studied in depth. What is certain, however, is that phytohormones such as ABA have an important role to play in stimulating starch breakdown under stress [69]. Further exploration is warranted to investigate whether blue light and the metabolites induced by blue light act as photomodulators. The endoplasmic reticulum plays a critical role in the biosynthesis, folding, assembly, and transportation of secreted and transmembrane proteins. In our investigation, observed significant alterations in protein processing within the endoplasmic reticulum pathway, evident in both transcriptome and proteome analyses. Additionally, through metabolite-associated transcriptome and proteome analyses, found that genes and proteins associated with metabolic substance accumulation were enriched in the protein processing within the endoplasmic reticulum pathway. This prompts speculation about whether blue light influences cellular protein processing to modulate metabolic levels, suggesting a potential avenue for future in-depth research. Moreover, in the proteomic analysis, JA-related LOX was highly expressed under blue light. Similarly, most of the proteins involved in the lipid metabolic process (GO:0006629) were upregulated in the three periods of time, and PPDK1 and AGPL2 proteins, which are involved in starch accumulation, were significantly downregulated. Several soluble starch synthase enzymes were also downregulated. Furthermore, nutrient reservoir activity (GO:0045735) was downregulated in almost all the proteins involved in grain development, which verified our speculation.

In this study, we focused on secondary metabolism because of the significant differences in the physiological phenotypes of plants grown under red and blue light, which are manifested in morphological, photosynthetic, and secondary metabolites [70]. We believe that morphological differences begin with metabolism; hence, this study focuses on genetic mechanisms at the metabolic level in hopes of gaining a deeper understanding of the more comprehensive effects of blue light on plants. Note that although measures were taken to minimize the effect, the different bag colors could alter the internal temperature and humidity, impacting growth. Fortunately, our bagging results for unpollinated spikes clearly demonstrate that temperature and humidity had negligible effects, as the grain size only decreased when exposed to sunlight. In particular, BL exposure had the greatest effect. This agrees with the results of Millet et al., who found that the temperature and humidity did not exert significant effects grain on size [41, 71]. Based on the observed effects of blue light and the contrasting seed morphology responses to red and blue light in previous studies on wheat, we believe that blue light could serve as a viable alternative to chemical growth inhibitors [12]. This prompts us to consider whether light manipulation technology could be used to alter the intrinsic physiology and extrinsic morphology of Gramineae produced in controlled environments [72, 73]. Furthermore, we believe that this study will help to deepen understanding of rice genetics. Since the effects of blue light are so profound, the effects of UV light should not be ignored, and this is one of the key points that deserve continued in-depth research in the future.

Conclusion

Through multi-omics testing, it was found that hormones such as jasmonic acid and auxins, and metabolites including alkaloids, flavonoids, lipids, organic acids, phenolic acids, and terpenoids altered significantly. Transcriptome and proteome analyses showed that blue light affected the seed nutrient repository activity. Specifically, starch- and gluten-related genes and proteins were significantly downregulated. Co- and WGCNA analyses identified several transcription factors that were regulated under blue light and identified key regulators of starch. Our study provides an understanding of the effects of blue light on post-flowering development in Gramineae and provides a framework for blue light-induced synthesis of secondary metabolites.

Availability of data and materials

The original contributions presented in the study are publicly available. This data can be found here National Center for Biotechnology Information (NCBI) SRA database under accession number PRJNA1091402.

Abbreviations

CK:

Control

BA:

Black

BU:

Blue

14DAH:

The 14th day after hybridization

FPKM:

Fragments per kilo base per million mapped reads

DEGs:

Differentially expressed genes

DAMs:

Differentially accumulated metabolites

DEPs:

Differentially expressed proteins

PCA:

Principal component analysis

TFs:

Transcription factors

BPs:

Biological processes

MFs:

Molecular functions

CCs:

Cellular components

WGCNA:

Weighted gene co-expression network analysis

UPLC–MS/MS:

Ultra-high liquid chromatography and tandem mass spectrometry

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GO:

Gene Ontology

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Acknowledgements

We thank the staff of Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China), for their support during the metabolite data analysis. We would like to thank Editage (www.editage.cn) for English language editing. We would like to thank the Rice Research Institute of Yunnan Agricultural University for providing the research material.

Funding

We gratefully acknowledge the financial support of the Yunnan Expert Workstation (202205AF150001).

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  1. Ping Zhang and Yongsheng Tang are co first authors and have contributed equally to this work

Authors and Affiliations

  1. College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, People’s Republic of China

    Ping Zhang, Junna Liu, Qianchao Wang, Li Li, Hanxue Li, Xuqin Wang, Lingyuan Zhang, Yutao Bai, Guofei Jiang, Liubin Huang & Peng Qin

  2. Qujing Academy of Agricultural Science, Qujing, 655000, People’s Republic of China

    Yongsheng Tang

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Contributions

PZ, YST, contributed equally to this article. PZ: Writing-Original Draft, Methodology. YST: Conceptualization, Writing- Review & Editing. XQW and YTB.: Formal analysis, Methodology. JNL. and QCW.: Data Curation, Visualization. LL and GFJ: Data Curation, Investigation. HXL. and LBH: Methodology, Visualization. LYZ: Formal analysis, Investigation. PQ: Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript.

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Correspondence to Peng Qin.

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Zhang, P., Tang, Y., Liu, J. et al. Multi-omics reveals the metabolic changes and genetic basis of post-flowering rice caryopsis under blue light. Chem. Biol. Technol. Agric. 11, 128 (2024). https://doi.org/10.1186/s40538-024-00654-1

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