Effect of potassium on anthocyanin metabolism in blueberry fruit
The results of PCA and OPLS-DA demonstrated significant differences in metabolites between the sample groups, confirming the reliability and accuracy of subsequent metabolomic analyses (Supplementary Fig. 1). Metabolites exhibiting differences between the three treatments were analyzed by mapping the identified differential metabolites to the KEGG database, which provided comprehensive pathway information (Fig. 2A, B, and C). Notably, the metabolites that differed between KH-10 and CK were primarily associated with the biosynthetic processes of plant secondary metabolites, such as flavonoids, flavonols, and isoflavonoids. Additionally, the metabolites that varied among KH-10, YG-10, and CK showed significant enrichment in the biosynthesis pathways of anthocyanins.
KEGG enrichment maps of different metabolites in different groups. (A) (B) (C) represents the comparison of CK_vs_KH-10, CK_vs_YG-10, and KH-10_vs_YG-10 corresponding to different treatments, respectively. The dots in the KEGG enrichment maps indicate the number of different metabolites, with the color scale from red to purple indicating P-value levels.
In comparison to CK, a total of 163 distinct metabolites were identified in the anthocyanin synthesis pathway of blueberries under potassium regulation across various fertilization treatments (Fig. 3A). For subsequent cluster analysis among the three treatments, 25 anthocyanins were identified (Fig. 3B). Notably, the control group exhibited the fewest types of anthocyanins, with only four identified. These are pelargonidin-3-O-rutinoside, delphinidin-3-O-(6”-O-caffeoyl) glucoside, luteolinidin, cyanidin-3-O-sambubioside-[Cyanidin − 3-O-(2”-O-xylosyl) glucoside], and their expression level was low in fertilization treatments. The content of five anthocyanins, malvidin-3,5-di-O-glucoside (Malvin), malvidin-3-O-glucoside, delphinidin-3-0-(6’’-O-feruloyl) glucoside, peonidin-3-O-glucoside, and peonidin-3-O-arabinoside, was higher in KH-10, being 13.48-23253.67 times higher than in CK and 1.23–2.26 times higher than in YG-10. There were nine types of anthocyanins in YG-10, which were 5.25-214.54 times higher than in CK and 1.47–2.91 times higher than in KH-10. Delphinidin-3-O-(6”-O-p-coumaroyl)glucoside, peonidin-3-O-(6”-O-p-coumaroyl)glucoside, petunidin-3-O-(6”-O-p-coumaroyl)glucoside, petunidin-3-O-(6”-O-acetyl)glucoside, malvidin-3-O-(6”-O-p-coumaroyl)glucoside, malvidin-3-O-(6”-O-malonyl)glucoside, cyanidin-3,5-O-diglucoside (Cyanin), cyanidin-3-O-(6”-O-p-coumaroyl)glucoside, and cyanidin-3-O-(6”-O-acetyl)glucoside.
The application of potassium fertilizer during the flowering and young fruit stages significantly enhanced the levels of peonidin-3-O-rutinoside, peonidin-3-O-(6’’-O-acetyl) glucose, pelargonidin-3-O-glucose, cyanidin-3-O-glucose, malvidin-3-O-(6’’-O-acetyl) glucose, and petunidin-3-O-glucose. These findings suggest that these six anthocyanins may be involved in the coloration process of blueberry fruits under potassium regulation, with their increased accumulation potentially contributing to the observed color development.
Venn diagram of metabolite differences between samples (A) and a cluster heat map of relative contents of anthocyanin metabolites (B) and statistical histogram of differentially expressed genes in fruits under different potassium levels (C). In the cluster heat map (B), red indicates high relative metabolite content, while blue indicates low relative metabolite content.
Sequencing and gene functional annotation
To ensure data quality, the raw data were filtered before conducting bioinformatics analysis. Fastp was used for quality control of raw reads, filtering low-quality data, resulting in a total of 397,664,760 clean reads (Supplementary Table 2). Following filtration, the guanine-cytosine (GC) content in every sample exceeded 46%, with average Q20 and Q30 values of 97.79% and 93.67%, respectively. The results showed that 79.51-81.03% of clean reads were successfully mapped to the reference genome. The unique match rate was above 77.18%, indicating that the sequencing results were of high quality and suitable for further analysis. A new gene’s sequence was obtained from the genome, and diamond software was employed to compare it with sequences from the KEGG, GO, NR, Swiss Prot, TrEMBL, KOG, and Pfam databases to achieve annotated results, utilizing alignment criteria with an E-value of 1e-5. (Supplementary Table 3). The findings indicated that the majority of novel genes were recorded in the TrEMBL database, totaling 4,219 sequences. In comparison, the NR and GO databases had 3,855 and 3,004 unique genes annotated, respectively.
GO enrichment and KEGG pathway analysis of DEGs
A differential expression analysis was performed between the sample groups utilizing DESeq2, whereby differentially expressed genes were identified based on the criteria of |log2Fold Change| >= 1 and FDR < 0.05 (Supplementary Fig. 2). The findings revealed that there were 3010 DEGs common to the CK_vs_KH-10 and CK_vs_YG-10 subgroups, with a cumulative total of 4093 genes identified in the CK_vs_KH-10 comparison, showcasing significant expression variations between these two sample sets. Among these, 2702 genes exhibited higher expression levels in CK compared to lower levels in KH-10. In the CK_vs_YG-10 analysis, the total count of differential genes reached 4518, which was the highest among all comparative analyses, including 1681 genes that were up-regulated and 2837 genes that were down-regulated. The KH-10_vs_YG-10 comparison revealed the fewest DEGs, totaling only 946, indicating a difference in potassium application between the flowering stage and the young fruit stage (Fig. 3C).
A total of 8,813 differentially expressed genes were annotated with GO terms across various sample combinations. Among these, 5,638 genes showed enrichment in the biological process category, making it the most represented category. Furthermore, 175 GO terms that were significantly enriched (p < 0.05) were linked to the DEGs, which included 115 secondary terms in the biological process category, 38 in the molecular function category, and 22 in the cellular component category (Supplementary Table 4). The enriched GO terms for DEGs within the biological process, cellular component, and molecular function categories cover essential genes associated with anthocyanoside and sugar synthesis. These include terms like “flavonoid biosynthetic process, GO:0046148,” “carbohydrate catabolic process, GO:0016052,” “pigment metabolic process, GO:0042440,” “cellular glucan metabolic process, GO:0006073,” “glucan metabolic process, GO:0044042,” and “glucosyltransferase activity, GO:0046527” (Fig. 4A, B and C). The differential genes displayed significant enrichment in the flavonoid anabolic pathway and glucan metabolic process, indicating a possible connection between glucan metabolism and flavonoid synthesis.
KEGG enrichment (Fig. 4D, E, and F) revealed 51 DEGs linked to the phenylpropane biosynthesis pathway, 26 with the flavonoid biosynthesis pathway, and 72 with the sugar metabolism pathway in CK_vs_KH-10. In CK_vs_YG-10, 47 DEGs were linked to the phenylpropane biosynthesis pathway, 27 to the flavonoid biosynthesis pathway, and 83 to the sugar metabolism pathway. Comparison of fertilization periods (KH-10_vs_YG-10) showed 15 DEGs in the phenylpropane biosynthesis pathway, 4 in the flavonoid biosynthesis pathway, and 6 in the sugar metabolism pathway. Anthocyanidins, being a type of flavonoid, are closely related to the flavonoid synthesis pathway. Supplementary Table 5 shows that 34 DEGs are associated with the flavonoid synthesis pathway. Notably, 14 of these genes were up-regulated in fruits treated with potassium during the flowering stage, while another set of 14 genes exhibited up-regulation during the young fruiting stage in comparison to CK. Ten genes were found to be involved with enzymes in the anthocyanin synthesis pathway, including F3H, CHS, CHI, ANR (anthocyanidin reductase), DFR, F3’5’H, and LAR (leucoanthocyanidin reductase). This suggests that these enzyme activities are closely related to anthocyanin synthesis under potassium influence.
In the phenylpropane synthesis pathway (Supplementary Table 6), 67 genes were associated with this pathway, with 16 genes up-regulated in fruits across all fertilization periods under potassium regulation. These genes were linked to Prx (peroxidase), CCoAOMT (caffeoyl-CoA O-methyltransferase), PAL, REF1 (coniferyl-aldehyde dehydrogenase), HCT (shikimate O-hydroxycinnamoyltransferase), COMT (caffeic acid 3-O-methyltransferase/acetylserotonin O-methyltransferase), 4CL, and UGT (coniferyl-alcohol glucosyltransferase). Four genes were up-regulated only at the flowering stage when potassium was applied, and two genes were significantly up-regulated at the YG stage under potassium application compared to CK and the flowering stage. PAL and 4CL are initiating enzymes of flavonoid biosynthesis, catalyzing the synthesis of phenylalanine and providing precursors for the flavonoid biosynthesis pathway. CoAOMT, HCT, and CHS generate dihydroflavonoids in different ways, which are precursors for the production of anthocyanins and other related compounds.
In the comprehensive analysis, 102 genes in the carbohydrate metabolism pathway (Supplementary Table 7) were found to be related to sugar metabolism. Among these, 24 genes were up-regulated in fruit across all fertilization periods under potassium regulation. These genes were associated with SUS (sucrose synthase), malQ (4-alpha-glucanotransferase), HK (hexokinase), UGP2 (UTP-glucose-1-phosphate uridylyltransferase), glgA (starch synthase), glgC (glucose-1-phosphate adenylyltransferase), otsB (trehalose 6-phosphate phosphatase), AMY (alpha-amylase), EGL (endoglucanase), BG (beta-glucosidase), BFR (beta-fructofuranosidase), PGM (phosphoglucomutase), TPS (trehalose 6-phosphate synthase/phosphatase), and GN (glucan endo-1,3-beta-glucosidase) synthesis. Therefore, changes in enzyme expression in these glycolytic pathways are likely related to the potassium-influenced synthesis and accumulation of anthocyanins.
As shown in Supplementary Table 8, the KEGG pathway enrichment identified genes related to potassium transport. These genes were mainly divided into two categories: Potassium channel proteins (AKT, KAT, GORK, SKOR, KHA) and potassium transporter proteins (KUP). A total of 12 DEGs were detected, with only 2 up-regulated and 10 down-regulated in the potassium application treatment. This indicates that the expression of AKT and KUP genes in fruit is down-regulated by potassium.
GO and KEGG enrichment classification histograms of DEGs in fruits treated with potassium at different stages. The ordinate is the GO term category and KEGG pathway category of differential genes and is represented by different colors, and the abscissa is the number and percentage of enriched genes.
Analysis of related genes in the biosynthesis pathway of blueberry anthocyanin
Further analysis of the DEGs identified through GO and KEGG functional classification and enrichment analysis revealed key enzyme genes encoding anthocyanin synthesis. The findings indicated that under potassium regulation, a total of 13 gene families were detected, all exhibiting differential expression (Supplementary Table 9). PAL, CHS, CHI, F3’5’H, F3H, DFR, GST, and HCT were primarily upregulated under potassium treatment, with relatively low expression levels in CK treatment. These genes are essential for the potassium-induced synthesis and accumulation of blueberry anthocyanins. Under KH-10 treatment alone, the highly expressed genes include PAL, F3’5’H, DFR, and GST, whereas CHS, CHI, LAR, ANR, F3H, and HCT are only highly expressed in YG-10.
The expression heat map was created to analyse the key metabolite concentrations of structural genes in the anthocyanin biosynthesis pathway of blueberry fruit, based on the reported route for anthocyanin biosynthesis (Fig. 5). P-Coumaroyl-CoA produces Caffeoyl-CoA and Naringenin chalcone, respectively, through the actions of HCT and CHS, with CHS being the first rate-limiting enzyme in the pathway. Metabolomic and transcriptomic correlation analyses revealed that CHS regulation significantly increased the content of the downstream product Naringenin chalcone. This increase, in turn, promoted the synthesis of Naringenin via the action of CHI. Additionally, the up-regulation of DFR expression favored the synthesis of Pelargonidin. Eriodictyol generated various types of anthocyanins through the actions of F3H, DFR, and F3’5’H. Increased F3H activity promoted the accumulation of downstream Cyanidin. F3’5’H increases the content of dihydromyricetin, the latter under the action of DRF, and then produce Delphinidin, Peonidin, Cyanidin and Malvidin. By combining the metabolites of these pathways with gene expression, we can infer that potassium regulation promotes the synthesis of blueberry Cyanidin, Delphinidin, Malvidin, and Peonidin. The F3’5’H, F3H, and DFR genes catalyze the synthesis of flavonoids and anthocyanidins in the phenylpropanoid synthesis pathway. The differential expression of these genes is the primary reason for the differences in flavonoid content between fruits grown under different conditions: Potassium application and CK.
Changes in the content of major metabolites and gene expression during anthocyanin synthesis in blueberry. (The bar chart represents the change in metabolite abundance, and the heat map represents the change in gene expression)
Integrated analysis of the transcriptome and metabolome
The substances enriched in the flavonoid and anthocyanin pathways, as identified by transcriptome and metabolome analyses, were examined. Analysis of the common differentially expressed genes and metabolites between fertilization treatment and CK revealed that UFGT, F3H, CHI, HCT, C12RT1, DFR, and F3’5’H were associated with metabolite production and catalyzed the synthesis of flavonoids and anthocyanins. Among the KH-10 and YG-10 treatments, BZ1, ANR, and shikimate O-hydroxycinnamoyltransferase were linked to the formation of metabolites and facilitated the synthesis of flavonoids and anthocyanins (Supplementary Table 10).
Analysis of gene expression through qRT-PCR
Based on the transcriptome and metabolome results, six structural genes involved in anthocyanin synthesis (F3H, DFR, F3’5’H, C12RT1, HCT, UFGT) were selected for qRT-PCR analysis, with blueberry GAPDH serving as the internal reference (Fig. 6A). The qRT-PCR results indicated that the expressions of the F3H and F3’5’H genes were significantly higher under potassium treatment compared to CK treatment, whereas the expressions of UFGT and C12RT1 were inhibited. Specifically, the expressions of F3’5’H and HCT genes increased significantly at the flowering stage by 266.97% and 106.64%, and by 77.41% and 39.44% compared to CK and YG-10, respectively. Additionally, the expression of the F3H gene in young fruit increased significantly by 192.28% and 59.03% compared to CK and KH-10, respectively. The expression patterns of genes differentially related to flavonoid biosynthesis in the transcriptome were in agreement with the qRT-PCR findings.
The activity of F3H, F3’5’H, and UFGT enzymes in the anthocyanin biosynthesis pathway
Anthocyanin biosynthesis depends on a series of enzymes. Based on the qRT-PCR results, the activities of F3H, F3’5’H, and UFGT enzymes were determined at different periods under potassium fertilizer treatment (Fig. 6B). All potassium-treated enzymes had significantly greater activity than CK. Specifically, F3H, F3’5’H, and UFGT in KH-10 were 63.65%, 121.99%, and 107.13% higher than in CK, respectively, while in YG-10, they were 59.14%, 134.49%, and 90.36% higher than in CK. Except for F3H, there were significant differences in F3’5’H and UFGT enzyme activities between KH-10 and YG-10. The results indicated that potassium significantly promotes the activity of key enzymes in the anthocyanin synthesis pathway, and its effect varies across different growth stages of blueberry.
Expression levels (A) of genes related to the flavonoid metabolism pathway and histograms (B) of related enzymes during anthocyanin synthesis in blueberry. Different lowercase letters indicate significant differences between treatments (p < 0.05).