Integrated metabolomics and transcriptomics reveal the role of calcium sugar alcohol in the regulation of phenolic acid biosynthesis in 
Plant growth and development are influenced by various genetic and environmental factors, including transcription factors and nutrients . Although the extensive application of chemical fertilizers can increase crop yield, it poses a considerable threat to the environment . Biofortification is a process of adding essential nutrients and growth-promoting chemicals to improve the nutritional value of plants . With an improvement in the living standards of people, organic agriculture has recieved more attention recently Foliar fertilizers sprayed on the leaves of plants can provide essential nutrients and mitigate the accumulation of heavy metals in the aboveground parts . As an environmentally friendly fertilizer, foliar fertilizer mainly consists of growth regulators, organic acids, and various mineral elements . For example, the foliar application of Zn increased the production and quality of fruit crops . The foliar application of silicon enhanced the growth of crops and improved the efficiency of carbon and phosphorus (P) utilization . Foliar-applied iron oxide nanoparticles increased the plant biomass, chlorophyll contents, and other growth parameters of maize seedlings . More experiments confirmed that the contents of nitrogen, P, and magnesium can be increased by applying corresponding foliar fertilizers . Calcium (Ca) is an essential component of the cell structure and also serves as a crucial secondary messenger in various signal transduction pathways . The application of Ca improved the tolerance of plants to stresses by activating the antioxidant system . CaCl 2 can alleviate the negative effects of drought-triggered and-related stresses . Considering the impeded translocation of Ca fertilizers from the underground parts to leaves, the spraying of Ca 2+ on developing leaves is reported . The primary Ca-based fertilizers, including calcium chloride, calcium acetate, and calcium nitrate, are limited by their solubility and relatively low utilization rates . Recently, calcium sugar alcohol (CSA), a newly developed chelated Ca fertilizer with good water solubility, has been widely applied in agriculture . Torreya grandis is a notable tree species indigenous to the subtropical region of China . An evergreen coniferous tree, T. grandis possesses a drupe-like fruit containing a nut that is a rich source of nutrients and bioactive ingredients, such as alkaloids, flavonoids, and tannins . T. grandis nuts are often used as food supplements to promote cardiovascular health. Due to increasing demand, the production of nuts has expanded significantly . Since T. grandis trees mainly grow in subtropical mountainous regions, they face severe nutrient deficiencies . Chemical fertilizers at high doses have severe adverse impacts, such as water pollution and soil acidification . Although foliar fertilizers have been widely applied, their roles in the secondary metabolism of T. grandis are mainly unknown. Utilizing large-scale identification technology has enabled investigations of the metabolic pathways responsive to foliar fertilizers . In this study, nuts from the control and CSA-treated T. grandis trees were harvested for an integrated metabolomic and transcriptomic analysis. The results can provide a scientific basis for cultivating and managing T. grandis nuts by investigating the effects of CSA on the metabolism of phenolics in them. 

Plant material and growth conditions Thirty 15-year old Torreya grandis 'Merrilli' trees with the same site condition, growth and fruit quantity were selected as the research objects in Panmugang Chinese Torreya base (119°57′ E,30°30′ N), and their plant height and fruit quantity were recorded. The voucher specimens were identified by Prof. Wensheng Dai and deposited in the Zhejiang A&F University Intelligent Experimental Building Plant Specimen Room N205.Foliar fertilizer was applied from March to April 2022. The different leaf surface fertilizer treatments were CK (control group; pure water) and CSA (calcium sugar alcohol; high strength, water-soluble calcium fertilizer). Leaf fertilizer was purchased from Shenzhen Dugao Biological New Technology Co., Ltd. The fertilizer was diluted 1000-fold, once every 20 days, a total of three times. The CK and CSA treatment groups included five trees each. And there are three biological replicates each. A 10 L electric spray bottle was employed to spray 1 L of fertilizer per tree at a time. The samples were taken once during May, June, July, August, and September 2022. They were named 5 M, 6 M, 7 M, 8 M, and 9 M, respectively. All materials were stored in the refrigerator of −80 ℃ in the State Key Laboratory of Subtropical Forest Cultivation of Zhejiang Agriculture and Forestry University. Determination of the physiological and nutritional parameters Mature fresh T. grandis nuts were randomly collected from independent trees with different treatments. Then, the seed traits were determined based on our previous work. Soluble sugar and starch contents in seeds were determined using commercial kits from Solarbio Life Science Co, Ltd (codes: BC0030 and BC0700). The kernel oil content was ascertained by the national standard GB 5009.6–2016 Soxhlet extraction method. The extracted oil was methylated, and the fatty acid components were estimated by the peak area normalization method. Metabolite extraction The seed kernel samples (80 mg each) were flash-frozen in liquid N 2 and powdered. For metabolite extraction, 1,000 μL of methanol–acetonitrile–H 2 O solution (2:2:1, v/v/v) was added to the sample. The extract was centrifuged at 12,000 g for 20 min at 4 °C. The supernatant was vacuum-dried and redissolved in 100 μL of acetonitrile–H 2 O (1:1, v/v) solution. The resulting solution was used for LC–MS/MS analysis. For each time point and each treatment, three biological replicates were collected. LC–MS/MS analysis An LC–MS/MS analysis was carried out using a TripleTOF 6600 quadrupole time-of-flight (AB Sciex) coupled to a 290 Infinity LC ultrahigh performance liquid chromatography (Agilent Technologies). The column temperature was 40 °C, the flow rate was 0.4 mL/min, and the injection volume was 2 μL. Following were the ESI source conditions: the ion source gas1 (Gas1) was set to 60, the ion source gas2 (Gas2) to 60, and the curtain gas (CUR) to 30. The source temperature was 600 °C, and IonSpray Voltage Floating (ISVF) was 5,500 V. During auto MS/MS acquisition, the instrument was set to collect within the m/z range of 25–1,000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. Metabolite ion scanning was performed in an Information Based Acquisition (IDA) mode with high sensitivity. The parameters settings were: the collision energy (CE) at 35 V ± 15 eV; declustering potential (DP) at ± 60 V; excluded isotopes were < 4 Da; and candidate ions to be monitored per cycle were set at 10. Untargeted metabolomics The original MS scanning data was converted into MzXML files by employing the ProteoWizard MSConvert software. The following parameters were used when selecting peaks: center wave m/z set to 10 ppm, peak width to c (10, 60), and prefilter to c (10, 100). When grouping peaks, bw was set to 5, mz wid to 0.025, and min frac to 0.5. Isotopes and adducts were annotated utilizing the Metabolite Profile Annotation software algorithm. Metabolites were identified by comparing the accurate m/z values (< 10 ppm) with the MS/MS spectra established from an internal database of available standards. Metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database by matching the precise molecular weight data (m/z). An internal metabolite fragment spectral library was applied to validate metabolite identification. The Wilcoxon test was employed to detect the variations in metabolite concentrations between two groups of samples. The P -values of multiple tests were adjusted using the FDR (Benjamin Hochberg) method. RNA isolation and sequencing RNA was isolated from the T. grandis nuts using TRIzol reagent (Invitrogen, Shanghai, China) by following the instructions. The high-quality RNAs (integrity number > 7.0) were selected to construct the cDNA library. They were fragmented and reverse-transcribed to cDNA. RNA was sequenced on a DNBSEQ-T7 platform at LC-Bio, Hangzhou, China, to 150 bp paired-end reads. Transcriptomics Reads containing unknown nucleotides ( N < 5%) and low-quality bases (Q value < 19) were removed. The HISAT2 (ver.2.0.5) program with default parameters aligned all clean reads to the T. grandis genome . By calculating TPM (Transcripts Per Million), the FeatureCounts (ver.1.5.0) software tallied the location reads of each gene. The DESeq R (ver.4.0.4) software distinguished the differentially expressed genes (DEGs) in each compared group with a P value < 0.05 and a multiple change ≥ twofold. Gene cloning, promoter analysis and qRT-PCR validation The complete coding sequences (CDSs) of TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 were isolated from the T. grandis genome and PCR-amplified. The sequences of the primers used are presented in Table . The genomic DNAs from the T. grandis tissues were isolated using a kit. The 2,000-bp sequence of the TgHCT promoter was extracted from the T. grandis genome. For gene expression analysis, qRT-PCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) with a reaction volume of 20 μL on a real-time quantitative PCR instrument (BIO-RAD CFX96). This was done for three independent biological replicates.Calculate the relative expression using the 2 −ΔΔCT method . Subcellular localization The CDS was cloned into the pCAMBIA1300-GFP vector and fused with the c-terminus of the artificial green fluorescent protein (GFP). All gene sequences obtained in this study are listed in (Table ). The empty GFP vector was used as a control. All vectors were transiently expressed in tobacco epidermal cells through Agrobacterium tumefaciens (GV3101)-mediated transformation. The fluorescence of the GFP fusion protein was detected employing an LSM710 confocal microscope (Carl Zeiss AG, Oberkochen, Germany). Dual-luciferase assay The TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 cDNAs were inserted into pGreenII62-SK vectors as the effectors, and the TgHCT promoter was inserted into the pGreenII0800-LUC vector as the reporter. The constructed vectors were co-expressed in tobacco leaves by utilizing a transient transformation system mediated by Mycobacterium-induced root cancer (GV3101). The binding activities of the TFs with their specific promoters were calculated based on the LUC–REN ratio using a dual-LUC assay kit (Promega, Beijing, China). Yeast one-hybrid (Y1-H) assay In this study, Y1H was conducted per the Yeast Protocols Handbook (Clontech) . The promoter was subjected to cis acting element analysis via website - http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ , truncated and cloned into pLacZi vector with EcoRI and XhoI sites. The transcription factors cloned into PB42AD vector with EcoRI and XhoI sites. Yeast cells were then co-transformed using pLacZi + TgHCT with pB42AD + TgAP2-1 or pB42AD + TgAP2-3 vectors. Next, six yeast cells were randomly selected for the detection of positive clones after coating the transformation products with SD/-Trp-Ura medium at 30 °C for three days. For observing the color reaction, the PCR-confirmed bacteria were identified by detecting the appropriate yeast cells by spreading them on SD/-Trp-Ura/Gal/Raf/X-Gal agar color plates. Transient expression of TgHCT in tobacco leaves Transient expression of TgHCT in tobacco leaves The full length CDS of TgHCT with no stop codon was cloned into 1300-GFP vector with BamHI and SalI sites (Table ). The Agrobacterium tumefaciens strain GV3101 carrying TgHCT -GFP or GFP was transformed into the four-week-old Nicotiana benthamiana leaves as described by Lou et al. . After culturing for 3 days, the leaves expressing TgHCT were collected and used to measure the flavonoid content. 

Thirty 15-year old Torreya grandis 'Merrilli' trees with the same site condition, growth and fruit quantity were selected as the research objects in Panmugang Chinese Torreya base (119°57′ E,30°30′ N), and their plant height and fruit quantity were recorded. The voucher specimens were identified by Prof. Wensheng Dai and deposited in the Zhejiang A&F University Intelligent Experimental Building Plant Specimen Room N205.Foliar fertilizer was applied from March to April 2022. The different leaf surface fertilizer treatments were CK (control group; pure water) and CSA (calcium sugar alcohol; high strength, water-soluble calcium fertilizer). Leaf fertilizer was purchased from Shenzhen Dugao Biological New Technology Co., Ltd. The fertilizer was diluted 1000-fold, once every 20 days, a total of three times. The CK and CSA treatment groups included five trees each. And there are three biological replicates each. A 10 L electric spray bottle was employed to spray 1 L of fertilizer per tree at a time. The samples were taken once during May, June, July, August, and September 2022. They were named 5 M, 6 M, 7 M, 8 M, and 9 M, respectively. All materials were stored in the refrigerator of −80 ℃ in the State Key Laboratory of Subtropical Forest Cultivation of Zhejiang Agriculture and Forestry University. 

Mature fresh T. grandis nuts were randomly collected from independent trees with different treatments. Then, the seed traits were determined based on our previous work. Soluble sugar and starch contents in seeds were determined using commercial kits from Solarbio Life Science Co, Ltd (codes: BC0030 and BC0700). The kernel oil content was ascertained by the national standard GB 5009.6–2016 Soxhlet extraction method. The extracted oil was methylated, and the fatty acid components were estimated by the peak area normalization method. 

The seed kernel samples (80 mg each) were flash-frozen in liquid N 2 and powdered. For metabolite extraction, 1,000 μL of methanol–acetonitrile–H 2 O solution (2:2:1, v/v/v) was added to the sample. The extract was centrifuged at 12,000 g for 20 min at 4 °C. The supernatant was vacuum-dried and redissolved in 100 μL of acetonitrile–H 2 O (1:1, v/v) solution. The resulting solution was used for LC–MS/MS analysis. For each time point and each treatment, three biological replicates were collected. 

An LC–MS/MS analysis was carried out using a TripleTOF 6600 quadrupole time-of-flight (AB Sciex) coupled to a 290 Infinity LC ultrahigh performance liquid chromatography (Agilent Technologies). The column temperature was 40 °C, the flow rate was 0.4 mL/min, and the injection volume was 2 μL. Following were the ESI source conditions: the ion source gas1 (Gas1) was set to 60, the ion source gas2 (Gas2) to 60, and the curtain gas (CUR) to 30. The source temperature was 600 °C, and IonSpray Voltage Floating (ISVF) was 5,500 V. During auto MS/MS acquisition, the instrument was set to collect within the m/z range of 25–1,000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. Metabolite ion scanning was performed in an Information Based Acquisition (IDA) mode with high sensitivity. The parameters settings were: the collision energy (CE) at 35 V ± 15 eV; declustering potential (DP) at ± 60 V; excluded isotopes were < 4 Da; and candidate ions to be monitored per cycle were set at 10. 

The original MS scanning data was converted into MzXML files by employing the ProteoWizard MSConvert software. The following parameters were used when selecting peaks: center wave m/z set to 10 ppm, peak width to c (10, 60), and prefilter to c (10, 100). When grouping peaks, bw was set to 5, mz wid to 0.025, and min frac to 0.5. Isotopes and adducts were annotated utilizing the Metabolite Profile Annotation software algorithm. Metabolites were identified by comparing the accurate m/z values (< 10 ppm) with the MS/MS spectra established from an internal database of available standards. Metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database by matching the precise molecular weight data (m/z). An internal metabolite fragment spectral library was applied to validate metabolite identification. The Wilcoxon test was employed to detect the variations in metabolite concentrations between two groups of samples. The P -values of multiple tests were adjusted using the FDR (Benjamin Hochberg) method. 

RNA was isolated from the T. grandis nuts using TRIzol reagent (Invitrogen, Shanghai, China) by following the instructions. The high-quality RNAs (integrity number > 7.0) were selected to construct the cDNA library. They were fragmented and reverse-transcribed to cDNA. RNA was sequenced on a DNBSEQ-T7 platform at LC-Bio, Hangzhou, China, to 150 bp paired-end reads. 

Reads containing unknown nucleotides ( N < 5%) and low-quality bases (Q value < 19) were removed. The HISAT2 (ver.2.0.5) program with default parameters aligned all clean reads to the T. grandis genome . By calculating TPM (Transcripts Per Million), the FeatureCounts (ver.1.5.0) software tallied the location reads of each gene. The DESeq R (ver.4.0.4) software distinguished the differentially expressed genes (DEGs) in each compared group with a P value < 0.05 and a multiple change ≥ twofold. 

The complete coding sequences (CDSs) of TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 were isolated from the T. grandis genome and PCR-amplified. The sequences of the primers used are presented in Table . The genomic DNAs from the T. grandis tissues were isolated using a kit. The 2,000-bp sequence of the TgHCT promoter was extracted from the T. grandis genome. For gene expression analysis, qRT-PCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) with a reaction volume of 20 μL on a real-time quantitative PCR instrument (BIO-RAD CFX96). This was done for three independent biological replicates.Calculate the relative expression using the 2 −ΔΔCT method . 

The CDS was cloned into the pCAMBIA1300-GFP vector and fused with the c-terminus of the artificial green fluorescent protein (GFP). All gene sequences obtained in this study are listed in (Table ). The empty GFP vector was used as a control. All vectors were transiently expressed in tobacco epidermal cells through Agrobacterium tumefaciens (GV3101)-mediated transformation. The fluorescence of the GFP fusion protein was detected employing an LSM710 confocal microscope (Carl Zeiss AG, Oberkochen, Germany). 

The TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 cDNAs were inserted into pGreenII62-SK vectors as the effectors, and the TgHCT promoter was inserted into the pGreenII0800-LUC vector as the reporter. The constructed vectors were co-expressed in tobacco leaves by utilizing a transient transformation system mediated by Mycobacterium-induced root cancer (GV3101). The binding activities of the TFs with their specific promoters were calculated based on the LUC–REN ratio using a dual-LUC assay kit (Promega, Beijing, China). 

In this study, Y1H was conducted per the Yeast Protocols Handbook (Clontech) . The promoter was subjected to cis acting element analysis via website - http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ , truncated and cloned into pLacZi vector with EcoRI and XhoI sites. The transcription factors cloned into PB42AD vector with EcoRI and XhoI sites. Yeast cells were then co-transformed using pLacZi + TgHCT with pB42AD + TgAP2-1 or pB42AD + TgAP2-3 vectors. Next, six yeast cells were randomly selected for the detection of positive clones after coating the transformation products with SD/-Trp-Ura medium at 30 °C for three days. For observing the color reaction, the PCR-confirmed bacteria were identified by detecting the appropriate yeast cells by spreading them on SD/-Trp-Ura/Gal/Raf/X-Gal agar color plates. 

Transient expression of TgHCT in tobacco leaves The full length CDS of TgHCT with no stop codon was cloned into 1300-GFP vector with BamHI and SalI sites (Table ). The Agrobacterium tumefaciens strain GV3101 carrying TgHCT -GFP or GFP was transformed into the four-week-old Nicotiana benthamiana leaves as described by Lou et al. . After culturing for 3 days, the leaves expressing TgHCT were collected and used to measure the flavonoid content. 

Effect of CSA treatment on the quality parameters of T. grandis nuts To analyze the effects of CSA treatment on the quality of T. grandis nuts, a series of quality parameters, including protein, starch, soluble sugar, and oil contents, were determined. These contents were significantly increased by CSA (Fig. a-d). Considering the complexity of oil composition, the levels of ten individual oil components were determined. CSA slightly down-regulated the levels of linoleic acid and unsaturated fatty acids (Fig. e). These results indicated a marked impact of CSA treatment on the contents of total protein, starch, and soluble sugars in T. grandis nuts. Overview of the untargeted metabolomes Nuts harvested at different time points were subjected to LC–MS/MS analysis to reveal the differences in metabolites between the control and CSA-treated groups. A total of 20,006 ion features were identified, which were grouped into 1,394 annotated metabolites (Table ). All these metabolites were categorized into 326 lipids and lipid-like molecules, 272 phenylpropanoids and polyketides, 153 benzenoids, 131 organoheterocyclic compounds, and 122 organic oxygen compounds (Fig. a). Clustering of metabolites under CSA treatment at different time points The metabolomic profiling revealed remarkable variations in the metabolomes of the CK and CSA treatment groups (Fig. b). All metabolites were grouped into eight different clusters to identify the accumulation of metabolites in a time-dependent manner. In detail, Cluster I contained 2,915 5 M-, Cluster II contained 2,569 8 M-, and Cluster III contained 4,480 9 M-specifically accumulated metabolites. Clusters VII and VIII contained 1,583 and 1,535 metabolites that CSA significantly induced at time points 7 M and 5 M, respectively (Fig. c). Interestingly, the impact of harvest time on metabolite accumulation levels was more remarkable than that of CSA treatment. Variations in the contents of phenolic acids under CSA treatment A variety of phenolic acids, including 94 flavonoids, 24 iso-flavonoids, and 25 coumarins, were identified by untargeted metabolomics. A heatmap showed the accumulation pattern of all identified phenolic acids under CSA treatment at various time points (Fig. a). Quercetin and eriocitrin, as well as naringenin, were mainly accumulated at 9 M and 5 M, respectively, in both the CK(control kind) and CSA groups. Moreover, compared to the control, CSA treatment significantly increased the content of 5 M Ginkgetin, 9 M Glabrone, and both 7 M and 8 M Glycitin, respectively (Fig. b). Analysis of the differentially expressed genes (DEGs) under CSA treatment Transcriptome sequencing revealed the differences in gene expression between CK and CSA-treated groups. In the T. grandis genome, 47,064 transcripts were detected. In PCA, PC1 and PC2 explained 31.8% and 21.6% of the explained values, respectively (Fig. ). PCA clearly separated the samples into various groups, suggesting major differences between the two treatments. CSA groups at the transcriptional level. A number of DEGs were identified at multiple time points, including 1,028 at 5 M; 731 at 6 M; 1,307 at 7 M; 1,542 at 8 M; and 1,274 at 9 M (Fig. a). At 7 M–9 M, most of the DEGs were up-regulated by CSA. The numbers of overlapping DEGs are shown as a Venn diagram (Fig. b). Enrichment analysis of the DEGs responsive to CSA treatment KEGG enrichment analysis classified all DEGs into various metabolism-related categories, including ‘amino acid ‘, ‘secondary metabolites’, ‘carbohydrate metabolism’, ‘energy’, ‘lipid’, ‘cofactors and vitamins’, and ‘terpenoids’. For the ‘secondary metabolites’ category, the DEGs were enriched in three KEGG pathways, including flavonoid; tropane, piperidine, and pyridine; alkaloid; and isoquinoline alkaloid biosynthesis. For the ‘carbohydrate metabolism’ category, the DEGs exhibited a high level of enrichment in the KEGG pathways related to pyruvate metabolism; starch and sucrose metabolism; and galactose metabolism. For the terpenoids category, the DEGs demonstrated a high level of enrichment in the KEGG terms brassinosteroid biosynthesis; zeatin biosynthesis; and ‘carotenoid biosynthesis’ (Fig. c). The number of DEGs in each enriched KEGG pathway was counted. Our data showed that most of the secondary metabolites-related DEGs were elevated by CSA. Interestingly, most of the starch and sucrose metabolism- and zeatin biosynthesis-related genes were suppressed by CSA (Fig. d). Analysis of the phenolic acid biosynthesis pathway-related genes A schematic diagram of the phenolic acid biosynthesis pathway is shown in Fig. a. In the present study, five critical intermediates involved in phenolic acid biosynthesis, including cinnamic acid, p-coumaroyl shikimic acid, naringenin chalcone, naringenin, and caffeoyl-CoA, were detected by untargeted metabolomics. A number of crucial enzyme-encoding genes, such as 4CL (two transcripts), C4H (one transcript), COMT (12 transcripts), HCT (four transcripts), CHS (six transcripts), CHI (five transcripts), F3H (two transcripts), and FLS (one transcript), were detected by transcriptomics. The caffeoyl-CoA contents were significantly increased at 9 M. The p-Coumaroyl shikimic acid contents were reduced at 6 M and 7 M,and significantly increased at 8 M.The naringenin and naringenin chalcone levels were markedly reduced at 5 M (Fig. b). Most of the 4CL-, C4H-, and F5H-encoding genes were remarkably elevated at 8 M. Several HCT- and CHS-encoding genes were conspicuously enhanced at 8 M and 9 M. The expression of CHI- and FLS-encoding genes was significantly suppressed by CSA (Fig. c). Analysis of the transcription factors (TFs) responsive to CSA treatment Promoter analysis indicated several binding sites for WRKY and AP2 TFs in the promoters of phenolic acid biosynthesis pathway-related genes. Thus, the expression patterns of the WRKY and AP2 families are shown in Fig. a. The correlation analysis revealed a close positive association between the differentially expressed TFs and DAMs. The correlation analysis was also performed between TFs and phenolic acid biosynthesis pathway-related genes (Fig. ). Our data identified four key TFs, including TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4, positively correlated with the accumulation of phenolic acids (Fig. b). In subsequent experiments, we focused on TgHCT to investigate the regulation of phenolic acid biosynthesis in T. grandis . The expression of TgHCT gene was validated using fluorescence quantitative PCR method (Fig. ). Verification of the potential binding of TFs to their targets The promoters of several phenolic acid biosynthesis-related genes were extracted from the T. grandis genome and screened. Interestingly, the TgHCT promoter contains a number of TgWKRY and TgAP2 binding sites, suggesting that TgHCT is a potential target of CSA-responsive TFs. The in vivo transcription activation abilities of TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 were determined by a dual-LUC experiment carried out in Nicotiana benthamiana leaves (Fig. a). Dual-LUC image analysis showed that TgAP2-1 and TgAP2-3 significantly activated TgHCT expression (Fig. b). The full-length CDS of TgHCT was cloned. Enhanced GFP-tagged TgHCT was transiently expressed in tobacco epidermal cells to identify the subcellular localization. Our data showed that TgHCT was localized to the cell membrane (Fig. c). Furthermore, the binding activities of TgAP2-1 to the TgHCT promoter were confirmed using a yeast one-hybrid assay (Fig. d). In addition, the heterologous overexpression of TgHCT enhanced the flavonoid content of tobacco (Fig. ). 

T. grandis nuts To analyze the effects of CSA treatment on the quality of T. grandis nuts, a series of quality parameters, including protein, starch, soluble sugar, and oil contents, were determined. These contents were significantly increased by CSA (Fig. a-d). Considering the complexity of oil composition, the levels of ten individual oil components were determined. CSA slightly down-regulated the levels of linoleic acid and unsaturated fatty acids (Fig. e). These results indicated a marked impact of CSA treatment on the contents of total protein, starch, and soluble sugars in T. grandis nuts. 

Nuts harvested at different time points were subjected to LC–MS/MS analysis to reveal the differences in metabolites between the control and CSA-treated groups. A total of 20,006 ion features were identified, which were grouped into 1,394 annotated metabolites (Table ). All these metabolites were categorized into 326 lipids and lipid-like molecules, 272 phenylpropanoids and polyketides, 153 benzenoids, 131 organoheterocyclic compounds, and 122 organic oxygen compounds (Fig. a). 

The metabolomic profiling revealed remarkable variations in the metabolomes of the CK and CSA treatment groups (Fig. b). All metabolites were grouped into eight different clusters to identify the accumulation of metabolites in a time-dependent manner. In detail, Cluster I contained 2,915 5 M-, Cluster II contained 2,569 8 M-, and Cluster III contained 4,480 9 M-specifically accumulated metabolites. Clusters VII and VIII contained 1,583 and 1,535 metabolites that CSA significantly induced at time points 7 M and 5 M, respectively (Fig. c). Interestingly, the impact of harvest time on metabolite accumulation levels was more remarkable than that of CSA treatment. 

A variety of phenolic acids, including 94 flavonoids, 24 iso-flavonoids, and 25 coumarins, were identified by untargeted metabolomics. A heatmap showed the accumulation pattern of all identified phenolic acids under CSA treatment at various time points (Fig. a). Quercetin and eriocitrin, as well as naringenin, were mainly accumulated at 9 M and 5 M, respectively, in both the CK(control kind) and CSA groups. Moreover, compared to the control, CSA treatment significantly increased the content of 5 M Ginkgetin, 9 M Glabrone, and both 7 M and 8 M Glycitin, respectively (Fig. b). 

Transcriptome sequencing revealed the differences in gene expression between CK and CSA-treated groups. In the T. grandis genome, 47,064 transcripts were detected. In PCA, PC1 and PC2 explained 31.8% and 21.6% of the explained values, respectively (Fig. ). PCA clearly separated the samples into various groups, suggesting major differences between the two treatments. CSA groups at the transcriptional level. A number of DEGs were identified at multiple time points, including 1,028 at 5 M; 731 at 6 M; 1,307 at 7 M; 1,542 at 8 M; and 1,274 at 9 M (Fig. a). At 7 M–9 M, most of the DEGs were up-regulated by CSA. The numbers of overlapping DEGs are shown as a Venn diagram (Fig. b). 

KEGG enrichment analysis classified all DEGs into various metabolism-related categories, including ‘amino acid ‘, ‘secondary metabolites’, ‘carbohydrate metabolism’, ‘energy’, ‘lipid’, ‘cofactors and vitamins’, and ‘terpenoids’. For the ‘secondary metabolites’ category, the DEGs were enriched in three KEGG pathways, including flavonoid; tropane, piperidine, and pyridine; alkaloid; and isoquinoline alkaloid biosynthesis. For the ‘carbohydrate metabolism’ category, the DEGs exhibited a high level of enrichment in the KEGG pathways related to pyruvate metabolism; starch and sucrose metabolism; and galactose metabolism. For the terpenoids category, the DEGs demonstrated a high level of enrichment in the KEGG terms brassinosteroid biosynthesis; zeatin biosynthesis; and ‘carotenoid biosynthesis’ (Fig. c). The number of DEGs in each enriched KEGG pathway was counted. Our data showed that most of the secondary metabolites-related DEGs were elevated by CSA. Interestingly, most of the starch and sucrose metabolism- and zeatin biosynthesis-related genes were suppressed by CSA (Fig. d). 

A schematic diagram of the phenolic acid biosynthesis pathway is shown in Fig. a. In the present study, five critical intermediates involved in phenolic acid biosynthesis, including cinnamic acid, p-coumaroyl shikimic acid, naringenin chalcone, naringenin, and caffeoyl-CoA, were detected by untargeted metabolomics. A number of crucial enzyme-encoding genes, such as 4CL (two transcripts), C4H (one transcript), COMT (12 transcripts), HCT (four transcripts), CHS (six transcripts), CHI (five transcripts), F3H (two transcripts), and FLS (one transcript), were detected by transcriptomics. The caffeoyl-CoA contents were significantly increased at 9 M. The p-Coumaroyl shikimic acid contents were reduced at 6 M and 7 M,and significantly increased at 8 M.The naringenin and naringenin chalcone levels were markedly reduced at 5 M (Fig. b). Most of the 4CL-, C4H-, and F5H-encoding genes were remarkably elevated at 8 M. Several HCT- and CHS-encoding genes were conspicuously enhanced at 8 M and 9 M. The expression of CHI- and FLS-encoding genes was significantly suppressed by CSA (Fig. c). 

Promoter analysis indicated several binding sites for WRKY and AP2 TFs in the promoters of phenolic acid biosynthesis pathway-related genes. Thus, the expression patterns of the WRKY and AP2 families are shown in Fig. a. The correlation analysis revealed a close positive association between the differentially expressed TFs and DAMs. The correlation analysis was also performed between TFs and phenolic acid biosynthesis pathway-related genes (Fig. ). Our data identified four key TFs, including TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4, positively correlated with the accumulation of phenolic acids (Fig. b). In subsequent experiments, we focused on TgHCT to investigate the regulation of phenolic acid biosynthesis in T. grandis . The expression of TgHCT gene was validated using fluorescence quantitative PCR method (Fig. ). 

The promoters of several phenolic acid biosynthesis-related genes were extracted from the T. grandis genome and screened. Interestingly, the TgHCT promoter contains a number of TgWKRY and TgAP2 binding sites, suggesting that TgHCT is a potential target of CSA-responsive TFs. The in vivo transcription activation abilities of TgWRKY1, TgAP2-1, TgAP2-3, and TgAP2-4 were determined by a dual-LUC experiment carried out in Nicotiana benthamiana leaves (Fig. a). Dual-LUC image analysis showed that TgAP2-1 and TgAP2-3 significantly activated TgHCT expression (Fig. b). The full-length CDS of TgHCT was cloned. Enhanced GFP-tagged TgHCT was transiently expressed in tobacco epidermal cells to identify the subcellular localization. Our data showed that TgHCT was localized to the cell membrane (Fig. c). Furthermore, the binding activities of TgAP2-1 to the TgHCT promoter were confirmed using a yeast one-hybrid assay (Fig. d). In addition, the heterologous overexpression of TgHCT enhanced the flavonoid content of tobacco (Fig. ). 

With the development of the economy, the demand for high-nutrient nut-based complementary foods is increasing daily. Nuts of T. grandis are considered an important source of nutrients and bioactive ingredients and are frequently employed as food supplements . T. grandis trees are widely cultivated in China’s subtropical mountainous areas, where the soil is very barren . Although a large amount of chemical fertilizers has been applied, the effective fertilizer utilization rate is meager . The excessive use of chemical fertilizers causes massive damage to the environment . More importantly, chemical fertilizers are quickly fixed in the soil and converted into an insoluble form . Previous studies have reported the remarkable effects of foliar spray application on the quality of crops. However, the understanding of the impact of foliar fertilizers on the cultivation of T. grandis trees is still limited . As an essential element, Ca 2+ is the third most abundant in plants . Clear evidence confirmed the significance of Ca 2+ in various biological processes, such as signal transduction, secondary metabolism, growth, and development . In our study, CSA, a newly developed chelated calcium fertilizer, was applied to provide soluble Ca 2+ to T. grandis trees. Proteins from T. grandis nuts are valuable nutritional sources in the food industry . T. grandis kernels are enriched in unsaturated fatty acids, which are essential bioactive components for human health . Analysis of quality parameters showed that CSA significantly up-regulated the contents of total protein and unsaturated fatty acids, suggesting the positive effects of CSA on the quality of T. grandis nuts. Recently, integrated transcriptomic and metabolomic analyses revealed the regulatory mechanism underlying the secondary metabolism of T. grandis . A multi-omic analysis revealed the responses of terpenoid- and flavonoid-biosynthesis pathways to nanoplastic pollutants . An integrated omic analysis investigated the involvement of WRKY1 in the regulation of age-induced amino acid biosynthesis in T. grandis nuts . Another study used metabolome analysis and transcriptome analysis to study flavonoid biosynthesis in T. grandis seeds, and a total of 124 flavonoids were identified, 9 of which were highly correlated with antioxidant activity . Through metabolome integrated transcriptome data analysis, genes and metabolic pathways related to flavonoid biosynthesis were identified, and T. Grandis “shishengfei” has the highest flavonoid diversity and content, while other varieties are relatively low . By referring to the newly published genome of T. grandis , our multi-omic analysis detected 47,064 transcripts, providing sufficient genetic information for uncovering the role of CSA in the activation of the phenolic acid biosynthesis pathway of T. grandis nuts. KEGG enrichment analysis revealed that most of the CSA-responsive genes were enriched in ‘secondary metabolism’, ‘energy metabolism’, and ‘hormone signaling’, indicating that CSA has a comprehensive impact on T. grandis nuts. In plants, calcium sensors might participate in the regulation of secondary metabolite biosynthesis, such as stilbenes, phenolic precursors, and hormones . Our data showed that most of the secondary metabolism-related DEGs were up-regulated by CSA, suggesting an active role of CSA in the secondary metabolism of T. grandis nuts. A previous study reported that starch and sucrose metabolism participated in the modulation of seed development in T. grandis . Interestingly, most starch and sucrose metabolism-related genes were down-regulated by CSA. Thus, CSA treatment may modulate the processes involved in seed development in T. grandis by inhibiting energy metabolism. In T. grandis , phytohormones play an essential role in regulating the biosynthesis of squalene and β-sitosterol during the post-ripening process . In our study, the zeatin biosynthesis pathway was inhibited by CSA, indicating an influence of CSA on the hormone signaling pathway. In plants, flavonoids are classical phenolics containing a 15-C skeleton . Although most of the phenolic acid biosynthesis-related genes have been identified in T. grandis , the impacts of CSA on the regulation of phenolic acid biosynthesis are largely unknown . Recently, a number of TFs involved in the regulation of phenolic acid biosynthesis have been recognized in various plants. For example, several Salvia miltiorrhiza TFs, such as SPL7, MYB1, and NPR1-TGA2/NPR4 modules, are involved in phenolic acid biosynthesis . In our study, two members of the AP2/ERF family, AP2-1 and AP2-3, were identified as regulators of phenolic acid biosynthesis in T. grandis. In S . miltiorrhiza , overexpression of the two AP2/ERF family TFs, ERF1L1, and ERF115, markedly elevated tanshinone production by regulating the expression of DXR , PAL3, 4CL5, TAT3, and RAS4 , respectively . We identified HCT as a new downstream target of the AP2 subfamily members in T. grandis . The HCT enzyme, a significant regulator of phenolic acid biosynthesis, has been extensively investigated as a prominent member of the BAHD acyltransferase family . In T. grandis , AP2-1/AP2-3 participated in phenolic acid biosynthesis by inducing HCT expression. In Arabidopsis , HCT can inhibit the synthesis of lignin, which redirects metabolic flux to flavonoids via chalcone synthase activity. HCT might be a positive regulator of flavonoid biosynthesis and thereby affect the color of organs and tissues in Salvia miltiorrhiza . Although HCT theoretically competes with the flavonoid pathway for substrates, overexpression of HCT did not result in a depletion of flavonoid content, but rather increased the total flavonoid content. This indicates that HCT may play a positive role in the flavonoid biosynthesis pathway, suggesting that overexpression of HCT may through some mechanism, promote flavonoid synthesis or affect other metabolic pathways, indirectly enhancing flavonoid accumulation . The roles of exogenous Ca 2+ in the regulation of phenolic accumulation have been well-investigated in various plants. For example, exogenous Ca 2+ regulates phenolic metabolism and physiological responses of wheat seedlings to UV-B radiation . CaCl 2 remarkably elevated phenolic acid contents by up-regulating the activities of C4H, PAL, and F5H . The application of CSA could induce a higher direct entry of Ca 2+ through the leaf vascular system . We revealed a new regulatory module, Ca 2+ -AP2-HCT, that participated in the regulation of phenolic acid biosynthesis in T. grandis . Our data provides a theoretical basis for the application of Ca 2+ fertilizer for the management of daily fertilizer application in T. grandis trees. 

We employed LC–MS/MS-based metabolomic approaches to investigate the accumulation of metabolites in T. grandis seeds subjected to CK and CSA treatments during maturation from May through September. The accumulate content of phenolic acid compounds, including Quercetin, eriocitrin, Ginkgetin, Glabrone, and Glycitin, varied across different treatment conditions and harvesting months. A transcriptomic and metabolomic analysis was performed to reveal the effects of CSA application on the secondary metabolism in T. grandis nuts. CSA significantly altered the levels of various phenolic acids. We elucidated the role of AP2-HCT in the transcriptional regulation of phenolic acid biosynthesis-related genes and expanded the understanding of the role of calcium fertilizers on plant secondary metabolism. 

Supplementary Material 1. Table S1. Primers sequence used in this study. Supplementary Material 2. Table S2. The gene sequences used in this study. Supplementary Material 3. Table S3. All identified metabolites in study. Supplementary Material 4. Figure S1. The PCA data of the samples from ten different groups. Supplementary Material 5. Figure S2. Correlation analysis was conducted on transcription factors and flavonoid pathway genes among all differentially expressed genes. Supplementary Material 6. Figure S3. qRT-PCR analysis was performed on the expression of TgHCT and related TFs. 

