Analysis of differential mechanisms in flavonoid synthesis in the exocarp of virescens and nigrescens fruits and development of KASP molecular markers

Abstract

Background

Oil palm is an important tropical woody oilseed crop. Its fruit is rich in flavonoids, which possess high nutritional value and exhibit antioxidant, anti-inflammatory, and antibacterial activities. The molecular mechanism underlying flavonoid biosynthesis in oil palm remains unclear. In this study, we performed LC-MS/MS and RNA-Seq analyses to investigate the molecular regulatory mechanism of flavonoid accumulation in oil palm exocarp. Exocarps from virescens (FS) and nigrescens (FT) at 95, 125, and 185 days after pollination were used as experimental materials to characterize the metabolome and transcriptome at different developmental stages. Our objectives were to identify flavonoid metabolite types, key biosynthetic genes, and related biological metabolic pathways in oil palm exocarp.

Results

A total of 274 flavonoids were identified in FS and 275 in FT. Across the three developmental stages, the flavonoid content in FT was higher than that in FS. The differentially accumulated metabolites were mainly enriched in three pathways: flavonoid biosynthesis, flavone and flavonol biosynthesis, and anthocyanin biosynthesis. Combined metabolomic and transcriptomic analyses allowed the reconstruction of the flavonoid biosynthetic pathway and the identification of enzyme genes and metabolites involved in flavonoid synthesis. For FS, six enzyme genes and seven differentially accumulated metabolites were involved. For FT, eight enzyme genes and nine metabolites were involved. This study preliminarily developed 9 pairs of KASP molecular markers (K2–K10). In field tests using 30 FS individuals and 30 FT individuals, these markers were used to evaluate the consistency between genotypes and corresponding phenotypes.The identification accuracy reached 100% within the tested population.These 9 marker pairs are closely linked to the EgDFR gene and can effectively distinguish FS from FT.

Conclusions

This study clarifies the flavonoid biosynthesis mechanism in the exocarp of oil palm fruits and develops markers associated with flavonoid biosynthesis. It helps improve the ornamental value of oil palm, and allows the identification of variety purity and type at the seedling stage. This shortens the breeding cycle of oil palm. It provides a resource basis for the breeding and utilization of oil palm varieties, the screening of high‑flavonoid germplasm, and the development of related products.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-08826-x.

Background

Overview of oil palm and biological value of flavonoids

Elaeis guineensis Jacq., commonly known as oil palm, is a perennial arborescent species belonging to the genus Elaeis in the family Arecaceae, and ranks as the oil crop with the highest oil production globally [1]. Flavonoids, as secondary metabolites widely present in plants, possess various biological activities and nutritional values such as stress resistance, antibacterial, antioxidant, and anti-inflammatory properties [2, 3]. For example, soybeans contain high levels of flavonoids, demonstrating tolerance [4]. Flavonoids in citrus not only regulate oxidative stress, but also inhibit the occurrence and development of tumors in humans, which is crucial for the development of the pharmaceutical industry [5].Some studies have shown that oil palm is rich in flavonoids [6], and increasing the flavonoid content in oil palm may enhance the antioxidant, antimicrobial, and anticancer effects of palm oil while improving its nutritional value. Understanding the synthesis of flavonoids in oil palm and exploring the key genes of flavonoid synthesis are of great significance to the expansion of palm oil consumption market.

Classification and biosynthetic pathway of flavonoids

Currently, analyzing the synthesis pattern of plant flavonoids based on combined transcriptome and metabolome analysis has become a hot direction in the field of plant secondary metabolism research. In view of the oxidation degree of the C - ring, the connection position of the B - ring, and the open or closed situation of the C - ring, flavonoids are mainly grouped as flavones, flavonols, flavanones, anthocyanins, isoflavones, chalcones, and so on [7–10]. Flavonoid synthesis begins within the phenylpropane metabolic pathway. PAL promotes the deamination of phenylalanine to generate trans-cinnamic acid. Then, C4H makes use of CYP450 enzymes to transform trans-cinnamic acid into p-coumaric acid through hydroxylation [11]. 4CL assists in the binding of p - coumaric acid to CoA to make p - coumaroyl - CoA [12]. In the central part of flavonoid synthesis, CHS acts on p - coumaroyl - CoA to produce chalcone. It is the core enzyme in flavonoid synthesis and decides the flow of flavonoid production. CHI takes part in the isomerization of chalcone to get naringenin. Flavone Synthase II (FNSII) makes possible the production of flavones from naringenin. Flavonol Synthase (FLS) creates flavonols by carrying out the oxidation of dihydroflavonols. F3H adds hydroxyl groups so as to produce dihydroflavonols. DFR and ANS help with the synthesis of anthocyanins, which give plants red, blue, or purple coloration [13]. FLS and LAR and so on. Play a role in the making process of catechins and proanthocyanidins [10].

Research progress of key enzyme genes in flavonoid biosynthesis

Numerous enzyme genes in the flavonoid biosynthetic pathway have been widely studied, including HCT, CHS, F3H, UFGT, DFR, FG2, FLS, and ANR [14–18].For example, UFGT is essential for anthocyanin biosynthesis, acylation, glycosylation, and flavonol synthesis in plants [19]. In tobacco flowers, RNA interference (RNAi)-mediated post-transcriptional gene silencing inhibits the expression of NtDFR-1 and NtDFR-2. Pink-flowered plants with silenced DFR show higher flavonol contents than white-flowered plants, suggesting that DFR suppression increases flavonol accumulation [20]. In poplar, genes including BZ1, FG2, ANS, and IF7MAT are positively or negatively correlated with 13 flavonoid differential metabolites during anthocyanin biosynthesis. Additionally, FLS and DFR compete for the common substrate dihydroflavonol to regulate flavonoid metabolism [21, 22]. Qian et al. reported that in Acer truncatum, downregulation of C4H, CHS, and other genes and upregulation of FLS reduce anthocyanin biosynthesis. Meanwhile, upregulation of the downstream gene ANR promotes the conversion of anthocyanins to proanthocyanidins and increases total flavonoid content, indicating that ANR is a key gene for breeding high-flavonoid varieties [23]. Studies on flavonoid-regulated red pulp formation in grapes have revealed that the ANS gene may be related to the red pulp phenotype [24]. At present, studies on flavonoid biosynthesis-related genes and metabolic pathways in the exocarp of oil palm fruits are limited. Only a few reports have focused on flavonoid content determination and exocarp anthocyanin biosynthesis in oil palm [25, 26].

Experimental materials, objectives and scientific hypothesis

In this study, integrated transcriptomic and metabolomic analyses of flavonoids were conducted in the exocarps of virescens (FS) and nigrescens (FT) at three developmental stages: 95 d (FS1/FT1), 125 d (FS2/FT2), and 185 d (FS3/FT3). The purposes were to find differential metabolites and genes that might play vital roles in flavonoid biosynthesis in the oil palm exocarp. At the same time, explore flavonoid - synthesis - associated genes in the exocarp of oil palm fruits, clarify the flavonoid synthesis signaling pathway, and identify and verify the regulatory mechanisms of key enzyme genes in the flavonoid synthesis pathway. Furthermore, during fruit development, the exocarp color changes from purplish black to purplish red and then to orange-red in virescens (FS), and from green to yellowish green and then to orange-yellow in nigrescens (FT).We hypothesized that the distinct fruit color differences between FS and FT oil palm are caused by the differential accumulation of flavonoids, which is attributed to the differential expression of key genes involved in flavonoid biosynthesis. Besides, single nucleotide polymorphism (SNP) loci were screened from transcriptomic data. Kompetitive Allele - Specific PCR (KASP) assays were used to see if the gene EgDFR could tell the difference between the oil palm varieties FS and FT.

Scientific significance and application value of this study

Virescens (FS) and nigrescens (FT) are two oil palm forms with obvious differences and dynamic changes in exocarp color. Their fruit color variations are closely related to the accumulation of flavonoids and anthocyanins. Elucidating the differential mechanisms of flavonoid biosynthesis between them can not only clarify the molecular basis of fruit color formation in oil palm, but also provide theoretical support for the breeding of high-flavonoid and high-quality oil palm varieties. This is of great agronomic and application value for improving the nutritional quality of palm oil and promoting the high-quality development of the oil palm industry.

Materials and methods

Plant materials

Collection LocationThe research base of oil palm at the Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, located in Wenchang City, Hainan Province.

Plant Materials Exocarps of two oil palm cultivars, Elaeis guineensis virescens (FS) and Elaeis guineensis nigrescens (FT).

Sampling Stages and Rationale Samples were collected at 95 (FS1/FT1), 125 (FS2/FT2), and 185 (FS3/FT3) days after female flower pollination, representing the early, middle, and late stages of fruit development and coloration, respectively.

Harvest Dates March 7, 2024; April 6, 2024; July 5, 2024.

Sampling: Each biological replicate comprised approximately 60 pooled fruits, with three independent biological replicates per developmental stage. The oil palm trees were randomly selected under natural field conditions.

Experimental Replicates Each sample was prepared with three biological replicates and three technical replicates.

Sample Processing and Storage Processing: The exocarp was carefully scraped off with a scalpel, divided into six 10‑mL centrifuge tubes, and filled completely.

Storage: Samples were immediately frozen in liquid nitrogen and stored at − 80 °C until further analysis (Table 1).

Table 1.

Experimental Instruments

Instrument	Model	Manufacturer
Electronic balance	G2204C	Shanghai Yoke Instrument Co., Ltd.
Autoclave	ICAVE VA-85	irayama
Benchtop high-speed refrigerated centrifuge	CT18RT	Shanghai Tianmei Scientific Instruments Co., Ltd.
Ultrapure water system	RODI-C-12A1	Xiamen Rescience Water Purification Technology Co., Ltd.
Constant temperature water bath	DF-101 S	Gongyi Yuhua Instrument Co., Ltd.
UV-Vis spectrophotometer	752 N	Shanghai INESA Analytical Instrument Co., Ltd.
-20℃ refrigerator	BCD-338WKRINPG	Haier Group
Ultra-low temperature freezer	DW-86,338 J	Haier Group
Ice maker	AF200-AS	Scotsman
Liquid chromatography-tandem mass spectrometer (LC-MS/MS)	QTRAP 6500+	SCIEX
Grinding machine	MM400	Retsch
Multi-tube vortex mixer	MIX-200	Shanghai Jingxin Industrial Development Co., Ltd.
Bioanalyzer	Agilent 2100	Agilent Technologies (China) Co., Ltd.
Electrophoresis apparatus	iuYi DYY-6 C	Beijing Liuyi Biotechnology Co., Ltd.
Nucleic acid gel imaging system	Syngene GBOX F3	Syngene
Real-time fluorescence quantitative PCR instrument	QuantStudio™7 Real-Time PCR Instrument	Thermo Fisher Scientific

Methods

The determination of flavonoid metabolome

Dry sample extraction: (1) Biological samples were vacuum freeze-dried using a Scientz-100 F freeze dryer. (2) The samples were ground into powder using an MM 400 mixer mill (Retsch) at 30 Hz for 1.5 min. (3) Exactly 50 mg of the sample powder was weighed on an MS105DΜ electronic balance, and 1200 µL of 70% methanol-water internal standard extract solution pre-cooled at − 20 °C was added. For samples less than 50 mg, the extract solution was added proportionally at 1200 µL per 50 mg of sample. (4) The mixture was vortexed 6 times, with each vortex lasting 30 s at 30‑min intervals. (5) After centrifugation at 12,000 rpm for 3 min, the supernatant was collected, filtered through a 0.22 μm microporous membrane, and transferred into a sample vial for UPLC-MS/MS analysis (Table 2).

Table 2.

Experimental Software

Analysis	Software	Data processing
Data quality control	fastp 0.19.3
Genome alignment	fastp 0.19.4
Gene quantification	fastp 0.19.5
Alternative splicing	fastp 0.19.6
Novel transcript prediction	fastp 0.19.7
Differential expression analysis	fastp 0.19.8
Venn diagram	fastp 0.19.9	With biological replicates, DESeq2
GO enrichment analysis	fastp 0.19.10
Transcription factor analysis (Plant)	fastp 0.19.11
Protein-protein interaction (PPI) analysis	fastp 0.19.12
SNP detection	fastp 0.19.13
Weighted gene co-expression network analysis (WGCNA)	fastp 0.19.14
Principal component analysis (PCA)	fastp 0.19.15
Heat map	R (base package) 3.5.1	UV(unit variance scaling)
Pearson correlation coefficient	R (base package) 3.5.2	UV(unit variance scaling)
Sample correlation plot	R (base package) 3.5.3
Orthogonal projections to latent structures discriminant analysis (OPLS-DA)	R (base package) 3.5.4	-
Radar chart	R (base package) 3.5.5	log2 transformation + centering
Chord diagram	R (base package) 3.5.6
Correlation network diagram	R (base package) 3.5.7
K-Means clustering plot	R (base package) 3.5.8
K-Means图	R (base package) 3.5.9	UV(unit variance scaling)

UPLC combined with MS/MS was employed for qualitative and quantitative analysis. Mass spectral data were processed and analyzed utilizing Analyst 1.6.3 software and Multi Quant 3.0.3 software. Differentially accumulated metabolites were screened using thresholds of |log₂FC| ≥ 1 (FC ≥ 2 or FC ≤ 0.5) and VIP ≥ 1. One-way analysis of variance (ANOVA) was performed using SPSS 26.0 software. The FDR were used for multiple testing correction.

RNA Seq Method

RNA quantification and qualification

RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer^® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit^® RNAAssay Kit in Qubit^®2.0 Flurometer (Life Technologies, CA, USA). RNAintegrity was assessed using the RNA Nano 6000Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

Library preparation for Transcriptome sequencing

Atotal amount of 1 µg RNAper sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext^®UltraTM RNA Library Prep Kit for Illumina^®(NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ends of DNA fragments, NEB Next Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 250 ~ 300 bp in length, the library ragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 µl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Atlast, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.

Clustering and sequencing

The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina platform and 150 bp paired-end reads were generated.

Data analysis

Data quality control

Use fastp v 0.19.3 to filter the original data, mainly to remove reads with adapters; when the N content in any sequencing reads exceeds 10% of the base number of the reads, remove the paired reads; when any sequencing reads When the number of low-quality (Q < = 20) bases contained in reads exceeds 50% of the bases of the reads, this paired reads will be removed. All subsequent analyses are based on clean reads.

Reads mapping to the reference genome

Download the reference genome and its annotation files from the designated website, use HISAT v2.1.0 to construct the index, and compare clean reads to the reference genome.

New transcript prediction

Use StringTie v1.3.4d for new gene prediction. StringTie applies network streaming algorithms and optional de novo to splice transcripts. Compared with Cufflinks and other software, StringTie can splice a more complete and accurate transcript, and the splicing speed is faster.

Quantification of gene expression levels

Use featureCounts v1.6.2 /StringTie v1.3.4d to calculate the gene alignment and FPKM. FPKM is currently the most commonly used method to estimate gene expression levels.

Difference analysis

Differential expression analysis between the two groups was performed using DESeq2 v1.22.1. The FDR was calculated by adjusting P-values using the Benjamini & Hochberg method. FDR and |log₂foldchange| were used as thresholds for identifying significantly differentially expressed genes.

Differential gene enrichment analysis

The enrichment analysis is performed based on the hypergeometric test. For KEGG, the hypergeometric distribution test is performed with the unit of pathway; for GO, it is performed based on the GO term.

Differential AS analysis

Use rMATS v3.1.0 to analyze variable splicing events, including five alternative splicing events: SE, RI, MXE, A5SS, andA3SS.

SNPanalysis

Use GATKv4.1.9.0 to analyze the variant sites, and use annovar to annotate the variant sites.

Differential gene protein interaction analysis

The protein interaction analysis of differentially expressed genes is based on the STRING database of known and predicted protein-protein interactions. For the species existing in the database, we construct the network by extracting the target gene list from the database; otherwise, use diamond v0.9.24.125 to compare the target gene sequence with the selected reference protein sequence, and then according to the selected reference species Know the interaction to build a network.

GSEA

Use gsea-3.0.jar for gene set enrichment analysis.

WGCNA

Use WGCNAv1.69 for weighted gene co-expression network analysis.

Integrated metabolomics and transcriptomics analysis

Pathways related to flavonoid genes and metabolites were screened from KEGG (https://www.genome.jp/kegg). The criteria were as follows: pathways with no less than 25 entries and ranking among the top 25 based on P-values, with FDR correction performed using the Benjamini & Hochberg method to avoid Type I errors.

We have clarified the sample size used for correlation analysis. The correlation analysis between metabolome and transcriptome was performed using all biological replicates (n = 9), with a threshold of R > 0.80 and FDR < 0.05.

Determination of total flavonoid content

Ultrasound-assisted extraction was used. Fresh samples (0.5 g) were ground into powder in liquid nitrogen, and extracted with 75% ethanol at a solid–liquid ratio of 30:1.Ultrasonic extraction was conducted for 20 min and repeated three times.The supernatant was collected after centrifugation at 1500 rpm for 5 min. A standard curve was prepared using rutin (0–1 mg/mL) as the standard. The standard curve equation was obtained with rutin concentration (mg/mL) as the abscissa and absorbance as the ordinate.

Total flavonoid content was determined by the NaNO₂‑AlCl₃ colorimetric method. A 0.5 mL sample was placed in a test tube. First, 50 µL of 5% NaNO₂ was added, and the mixture was reacted at room temperature for 6 min. Then, 100 µL of 10% AlCl₃ was added and reacted for 5 min. Finally, 0.5 mL of 1 mol/L NaOH was added. A 150 µL aliquot was loaded, and absorbance was measured at A_{500 nm}(Table 3).

Table 3.

Experimental procedures for flavonoid extraction

	Standard (Rutin)						sample extract (for testing)
Concentration(mg/mL)	0	0.2	0.4	0.6	0.8	1	/	/	/
Standard / Sample(mL)	0	0.1	0.2	0.3	0.4	0.5	0.5	0.5	0.5
ddH2O	0.5	0.4	0.3	0.2	0.1	0	/	/	/
Reagent 1		50µL 5% NaNO2, React at room temperature for 6 min
Reagent 2		100µL 10% AlCl3, React at room temperature for 5 min
Reagent 3		0.5mL1mol/L的NaOH
Mix well, load 150 µL, and measure the absorbance at A500 nm.

RNA extraction from oil palm exocarp

Grind the exocarp of oil palm fruits, add lysis buffer SL (Buffer SL), and centrifuge. Collect the supernatant, add ethanol, and centrifuge again to discard the waste liquid. Perform two washes each with RW1 (protein-removing solution) and RW (washing solution), followed by thorough centrifugation. Air-dry the adsorption membrane at room temperature, then add RNase-Free ddH₂O dropwise (suspended above the membrane to avoid direct contact), incubate at room temperature for 2 min, and centrifuge to obtain the RNA solution.

Reverse Transcription of RNA into cDNA

(1) RNA was thawed on ice. 5× FastKing-RT SuperMix and RNase-Free H₂O were thawed at room temperature and then placed on ice. All reagents were mixed thoroughly by vortexing and centrifuged briefly before use.

(2)Reverse transcription system (Table 4).

Table 4.

Reverse transcription reaction system

components	dosage
5×FastKing-RTSuperMix,	4µL
Total RNA	50ng-2 µg
RNase-FreeH2O	adjusted to 20 µL

(3)subjected to reverse transcription in the instrument (Table 5).

Table 5.

Reaction temperature and system

reaction temperature	reaction time	notes
42℃	15 min	genomic DNA removal, reverse transcription reaction
95℃	3 min	enzyme inactivation

qRT-PCR

Total RNA was reverse-transcribed into cDNA for subsequent experiments, and the following solutions were prepared: Solution 1: Each vial contains 190 µL of SYBR Green, 11.4 µL of forward primer, and 11.4 µL of reverse primer; 5 vials total were prepared. Solution 2: Each vial contains 32 µL of cDNA and 108.8 µL of ddH₂O; 6 vials total were prepared (Table 6).

Table 6.

Reaction program:

Stage	Cycle	Temperature	Time		Content	Fluorescence signal collection
Pre-denaturation	1×	95℃	2 min	Pre-denaturation		No
PCR Reaction	40×	95℃	5 s		Denaturation	No
		50–60℃	10 s		Annealing	No
		72℃	15 s		Extension	Yes

Melting/Dissociation Curve Stage

DNA Extraction from Oil Palm Leaves

(1) Weigh 50–100 mg of oil palm exocarp, grind into powder with liquid nitrogen, and transfer to a 1.5 mL centrifuge tube.

(2) Add 400 µL Buffer PCL and 8 µL β-mercaptoethanol, vortex to mix. Incubate in a 65 °C water bath for 45 min until the sample is completely lysed.

(3) Add 200 µL Buffer PP, mix thoroughly by inversion, and place at − 20 °C for 5 min.

(4) Centrifuge at 10,000 rpm for 5 min at room temperature, and transfer the supernatant to a new 1.5 mL centrifuge tube.

(5) Add 500 µL chloroform, mix by inversion, centrifuge at 12,000 rpm for 5 min at room temperature, and collect the supernatant.

(6) Add an equal volume of isopropanol, invert 5–8 times to mix thoroughly, leave at room temperature for 2–3 min, centrifuge at 10,000 rpm for 5 min at room temperature, and discard the supernatant.

(7) Add 1 mL 75% ethanol, invert to rinse for 1–3 min, centrifuge at 10,000 rpm for 2 min, and discard the supernatant. Repeat this step once.

(8) Open the lid and invert the tube at room temperature for 5–10 min until residual ethanol evaporates completely.

(9) Dissolve the extracted DNA in 50–100 µL TE Buffer. Store the DNA at − 20 °C.

Primer design and KASP assay protocol

Based on the SNP loci of the EgDFR gene identified from transcriptome sequencing data, sequences spanning 100 bp upstream and downstream of each SNP were extracted for KASP primer design. Primers were successfully designed for all 10 loci (Appendix Table 2).

DNA samples were quantified using agarose gel electrophoresis and a NanoDrop ND‑2000 spectrophotometer. DNA concentrations were adjusted according to the species tested.

A preliminary KASP assay was first performed on 24 DNA samples. If the preliminary genotyping results were successful, formal large‑scale KASP validation was then conducted on the remaining 36 DNA samples.

First, DNA concentrations were adjusted to 5–50 ng according to the sample type, diluted into 96‑well plates, and re‑quantified using a NanoDrop 2000.

Next, KASP validation was performed on the normalized DNA samples. Master mix was prepared according to the table provided. During preparation, reagent names and volumes were carefully verified following the manufacturer’s instructions. The mixture was gently mixed by pipetting 5 times and briefly centrifuged at 3000 rpm (Table X).

Then, 2.57 µL of the prepared master mix was dispensed into the corresponding wells of a 384‑well plate, followed by 2.5 µL of normalized DNA. NTC reactions were included at designated positions, containing 2.57 µL master mix and 2.5 µL RNase‑free water.

The plate was sealed with specialized sealing film and tightly pressed using a plate sealer. The 384‑well plate was briefly centrifuged at 1200 rpm at 4 °C to collect samples at the bottom of each well. Any bubbles were removed by gentle tapping, followed by another brief centrifugation at 1200 rpm.

Finally, the plate was removed and subjected to end‑point fluorescence scanning and genotyping (Tables 7 and 8).

Table 7.

Master Mix Preparation

KASP Genotyping Reaction System(DNA was diluted to a final concentration of 5–50 ng per reaction)
Components	Wet DNA method(µL)		DryDNAmethod(µL)
DNA*	2.5	5	N/A	N/A
2×Master Mix	2.5	5	2.5	5
Primer mix	0.07	0.14	0.07	0.14

Table 8.

PCR Amplification Program

Temperature(℃)	Time (second)	Cycle
94℃	15 min	Hot-start activation
94℃	20 s	10 cycles
61 − 55℃	60 s
94℃	20 s	26 cycles
55℃	60 s

Fluorescence detection and analysis

Following the completion of KASP amplification, fluorescence signal values were read, and sample genotypes were called and clustered. Samples with the allele linked to the FAM fluorescent tag were detected as one color, corresponding to homozygous wild-type plants without base mutations. Samples with the allele linked to the VIC fluorescent tag were detected as another color, corresponding to homozygous mutant plants with base mutations. Samples showing an intermediate color, with signals detected from both FAM and VIC probes, were identified as heterozygous mutant plants.

Method for validating the accuracy of KASP genotyping results

The accuracy of KASP genotyping was calculated by comparing the field phenotypic survey results of green-fruited and black-fruited oil palm samples with the corresponding KASP genotyping results.

Result

Changes in flavonoids content of oil palm exocarp

Flavonoids represent a class of crucial secondary metabolites in plants and serve as one of the primary pigments implicated in the formation of plant colors. During the three developmental stages of virescens (FS) and nigrescens (FT) fruits, the exocarp colors show significant differences. In FS, the exocarp is green at FS1, yellow-green at FS2, and orange-yellow at FS3. In FT, the exocarp is purple-black at FT1, purple-red at FT2, and orange-red at FT3(Fig. 1A). The total flavonoid contents (TFC) were: FS1 (1.368 mg/g), FS2 (3.133 mg/g), FS3 (1.923 mg/g) for the FS genotype; FT1 (4.322 mg/g), FT2 (3.701 mg/g), FT3 (2.683 mg/g) for the FT genotype. At the same developmental stage, FT1, FT2 and FT3 had 3.16-, 1.18- and 1.40-fold higher TFC than FS1, FS2 and FS3, respectively(Fig. 1B).

Fig. 1.

A Oil palm fruits at three periods (95d,125d,185d) in FS and FT. B Total exocarp flavonoid content of oil palm fruits at three periods (95d,125d,185d) in FS and FT

Notably, the total flavonoid content in FT1, FT2, and FT3 was 3.16, 1.18, and 1.4 times that in FS1, FS2, and FS3, respectively. Throughout the developmental stages, the total flavonoid content in FT was higher than that in FS. The content trend in FT showed a continuous decrease, while that in FS increased first and then decreased. The content change trends of FS and FT were consistent with the relative flavonoid content trends in the metabolome.

Comparative flavonoid metabolome analysis of two oil palm fruit types

Flavonoid classification in the exocarp of oil palm fruits at various developmental stages

Nine flavonoid subclasses were detected in the oil palm exocarp during flavonoid accumulation.These included chalcones, flavanones, dihydroflavonols, anthocyanins, flavones, flavonols, flavanols, isoflavones, and other flavonoids (Fig. 2). Comparison between FS and FT showed that FT had higher contents of 8 flavonoid subclasses, such as anthocyanins, flavonols and dihydroflavonols.Only the flavone content was higher in FS than in FT.

Fig. 2.

Dynamic changes in the relative contents of nine flavonoids in the exocarp of oil palm fruits at different developmental stages

Relative content analysis of flavonoids in the exocarp of oil palm fruits during various developmental periods

In this study, we analyzed the flavonoid metabolome in the exocarp of FS and FT oil palm.A total of 274 and 275 flavonoids were identified in FS and FT, respectively.

In FS, the relative content of flavonoids first increased and then decreased.Flavones and flavonols were the dominant components at all three developmental stages:44.74% and 38.46% at FS1,67.50% and 9.13% at FS2,and 71.30% and 21.62% at FS3. In FT, the flavonoid content decreased continuously.Anthocyanins and flavonols were dominant at FT1 (21.17% and 46.26%) and FT2 (19.45% and 47.96%).At FT3, flavones (23.76%) and flavonols (44.70%) were the main components (Fig. 3A).The flavonoid contents in the FT exocarp were significantly higher than those in FS at all three developmental stages (Fig. 3B).

Fig. 3.

A Percentage content of various flavonoids in the exocarp of fruits at three developmental periods in FS and FT. B Changes in exocarp flavonoid content of fruits during three developmental periods in FS and FT

In summary, flavones and flavonols were the most abundant flavonoids in FS during the three developmental stages. In FT, flavonols and anthocyanins were dominant at FT1 and FT2, whereas flavones and flavonols were the most abundant at FT3.

Statistical analysis of differential flavonoid metabolites in the exocarp of oil palm fruits at different developmental stages

Metabolomic data analysis was performed on two types of oil palm fruits. Based on the screening criteria (variable importance in projection, VIP ≥ 1; Fold Change ≥ 2 or ≤ 0.5), a total of 186 significantly differential metabolites were identified in FS, and 194 DEMs were identified in FT. In FS, 34 DEMs were shared between FS1 vs. FS2 and FS1 vs. FS3, 14 between FS1 vs. FS2 and FS2 vs. FS3, and 99 between FS2 vs. FS3 and FS1 vs. FS3. In FT, 30 DEMs were shared between FT1 vs. FT2 and FT1 vs. FT3, 30 between FT1 vs. FT2 and FT2 vs. FT3, and 116 between FT2 vs. FS3 and FT1 vs. FS3 (Fig. 4A-B). To identify key differential metabolites, the top 20 metabolites with the highest |log₂FC| values were selected by comparing FS and FT at the same developmental stage.Epicatechin and hesperetin-7-O-glucoside were common to all three comparison groups(Fig. 4C-E).

Fig. 4.

Statistics of different metabolites of flavonoids in the exocarp of FS and FT at 3 developmental stages. A-B Wayne’s plots of the differential metabolites in the exocarp of FS(a) and FT(b). C-E The top 20 metabolites of |log2FC| were screened by comparing the same periods in each of the three periods of FS and FT. Vertical coordinates indicate the names of different substances, and horizontal coordinates indicate the size of log2FC values. The length of the line:|log2FC|, the size of the circle: VIP value

Comparisons among different developmental stages in FS showed that: FS1 vs. FS2 had 25 up-regulated and 22 down-regulated differential metabolites; FS1 vs. FS3 had 28 up-regulated and 127 down-regulated differential metabolites; FS2 vs. FS3 had 12 up-regulated and 107 down-regulated differential metabolites. Comparisons among different developmental stages in FT showed that: FT1 vs. FT2 had 26 up-regulated and 32 down-regulated differential metabolites; FT1 vs. FT3 had 30 up-regulated and 117 down-regulated differential metabolites; FT2 vs. FT3 had 16 up-regulated and 131 down-regulated differential metabolites (Fig. 5).

Fig. 5.

Volcano plot of the number of differential metabolites in the exocarp of FS and FT fruiting types

These results indicate that more differential metabolites were identified among the three developmental stages in FT than in FS.FT1 vs. FT3 and FT2 vs. FT3 showed the largest number of differential metabolites, with 147 in each comparison.In FS1 vs. FS2, the numbers of up‑ and down‑regulated metabolites were equal.In all other comparisons, more metabolites were down‑regulated than up‑regulated.

Transcriptomic differential analysis of two fruit types in oil palm

Based on the screening criteria of |log₂Fold Change|≥1 and FDR < 0.05, transcriptome analysis of the two oil palm fruit types identified 8,669 differentially expressed genes (DEGs) in FS and 10,449 DEGs in FT.

The number of DEGs in each comparison was as follows: FS1 vs. FS2 (1,152 up/1,765 down), FS1 vs. FS3 (2,740 up/3,171 down), FS2 vs. FS3 (2,969 up/3,102 down); FT1 vs. FT2 (1,266 up/2,007 down), FT1 vs. FT3 (3,261 up/3,705 down), FT2 vs. FT3 (3,914 up/3,829 down)(Fig. 6A). In the comparisons of FS1 vs. FS2, FS1 vs. FS3, and FS2 vs. FS3, there were 676 identical significantly DEGs. Among them, the highest number of identical DEGs was 3,165 between FS1 vs. FS3 and FS2 vs. FS3. In the comparisons of FT1 vs. FT2, FT1 vs. FT3, and FT2 vs. FT3, there were 723 identical significantly DEGs, with the highest number of 4,047 identical differentially expressed genes observed between FT1 vs. FT3 and FT2 vs. FT3 (Fig. 6C). In addition, a heat map was drawn for the transcriptome genes, which directly reflected that each developmental stage had a distinct transcriptional profile. These genes may serve as potential regulatory factors for the differences in flavonoid content(Fig. 6B).

Fig. 6.

Genes that are differentially expressed in a developmental - stage - specific manner in the exocarp of FS and FT oil palm fruits. A The number of genes that have been up - regulated and down - regulated in each group comparison. B All - Gene Heatmap: Individual genes are shown by rows, and stage - specific samples by columns. Red shows high expression, and green represents relatively low expression. C Venn diagrams related to different stages

Obviously, the number of DEGs in the FT is larger than that of the FS. In particular, the greatest number of DEGs (6,071) in the FS is found in the comparison of FS2 vs. FS3 shows a higher proportion of down - regulated DEGs. While looking at the FT, the comparison of FT2 vs. FT3 shows the greatest quantity of DEGs (7,743), and a larger proportion of these are up - regulated.

Integrated metabolome and transcriptome analysis of FS and FT

KEGG enrichment analysis

KEGG enrichment analysis of DEGs and DEMs in the exocarp of FS and FT fruits were enriched in three pathways: Flavone and Flavonol, Flavonoid, and Anthocyanin Biosynthesis (Fig. 7).

Fig. 7.

Histogram of KEGG enrichment of differential metabolites and genes in FS and FT at different developmental periods. Note The asterisk (*) denotes isomers.

The flavonoid biosynthesis pathway (ko00941) was enriched with 15 DEMs and 40 DEGs in both FS and FT. Among these, in the comparison of FS1 vs. FT1, 12 flavonoids such as kaempferol, catechin, and afzelechin, along with 34 DEGs including CYP73A, DFR and LAR were enriched. In the comparison of FS2 vs. FT2, 10 flavonoids such as afzelechin, epicatechin, and catechin, along with 39 DEGs including HCT, ANS and C’3 H, were enriched. In the comparison of FS3 vs. FT3, 10 flavonoids such as epiafzelechin, epicatechin, and phloretin, along with 39 differentially expressed genes including F3H, CHS, and F3H were enriched (Fig. 7A).

In the Flavone and flavonol biosynthesis pathway, a total of 14 DEMs and 20 DEGs were enriched. In FS1 vs. FT1, 13 DEMs such as Nicotiflorin*, Isovitexin*, and Vitexin*, and 17 DEGs including CYP75A and FG2 were enriched. In FS2 vs. FT2, 12 DEMs such as Rutin and Trifolin, and 14 DEGs including CYP75A and FG2 were enriched. In FS3 vs. FT3, 12 DEMs such as Isoquercitrin and Baimaside, and 13 DEGs including CYP75B1 and FG2 were enriched (Fig. 7B).

In the anthocyanin biosynthesis pathway, a total of 6 DEMs and 2 DEGs were enriched. In FS1 vs. FT1, 6 DEMs including Keracyanin and Kuromanin, as well as 1 DEG BZI, were enriched. In FS2 vs. FT2, 6 DEMs such as Kuromanin and Peonidin-3-O-glucoside, together with 2 BZI, were enriched. In FS3 vs. FT3, 4 DEMs including Pelargonidin-3-O-rutinoside and Keracyanin, along with 1 BZI, were enriched (Fig. 7C).

Key flavonoid genes and metabolites: a correlation analysis

The analysis was done based on the FPKM values of differentially expressed genes of the transcriptome and the relative expression levels of differentially expressed metabolites of the metabolome. In FS, UFGT-2 was significantly negatively correlated with taxifolin, and HCT-3 was significantly negatively correlated with prunin (P < 0.05). F3H-1 showed significantly positive correlations with flavosativaside and trifolin (P < 0.05). In addition, significantly positive correlations were observed between C3’H and prunin, DFR-3 and quercetin-3-O-sambubioside, and HCT-2 and taxifolin (P < 0.05). In FT, two differentially accumulated metabolites, isovitexin and vitexin, were significantly negatively correlated with F3H-1, HCT-4, HCT-3, and HCT-2 (P < 0.05). Flavosativaside was significantly negatively correlated with ANR, while vitexin-2’’-rhamnoside was significantly positively correlated with F3H-2 (P < 0.05). Baimaside showed significantly positive correlations with F3H-1, HCT-4, HCT-3, and HCT-2, and phlorizin was significantly positively correlated with FG2-2 and HCT-1 (P < 0.05) (Fig. 8).

Fig. 8.

The graph is divided into two sides, displaying the correlation data between genes and metabolites of FT and FS. Left side (gene data): The outer heatmap shows each column representing a gene, each layer representing a stage, and the color of the grid indicating the relative abundance of the gene in the group. Right side (metabolite data): Displays metabolite information. Middle lines: Indicate positive and negative correlations between genes and metabolites

In summary, six enzyme genes including HCT-2, DFR-1, and C3’H may be involved in the synthesis of seven differential metabolites such as Prunin and Vitexin in FS. In FT, eight enzyme genes including HCT-1, F3H-1, and ANR may participate in the synthesis of nine metabolites such as Isovitexin and Baimaside.

Flavonoid biosynthetic pathway analysis in oil palm exocarp during various developmental stages

To comprehensively elucidate the molecular basis and metabolic pathway characteristics of the oil palm fruit exocarp, this study conducted KEGG enrichment analysis to investigate the pathways involved in flavonoid synthesis and accumulation, as well as their associated genes, and thereby identified a total of 24 flavonoid metabolites and 27 genes potentially related to flavonoid biosynthesis.

Among the 27 genes, CHS, E5.5.1.6, F3H, ANS, LAR, C3’H, CYP75B1, and BZI showed higher relative expression in FT oil palm, while genes in CYP73A, HCT, DFR, FG2, and CYP75A exhibited variable expression. Specifically: In CYP73A, CYP73A-1 (LOC105052309) had higher expression in FS, whereas CYP73A-2 (LOC105057212) was more highly expressed in FT. HCT-3 (LOC105037012) showed higher expression in FS, while HCT-1, HCT-2, and HCT-4 were highly expressed in FT. DFR-1 (LOC105040722) had higher expression in FS. CYP75A-1 (LOC105045491) was more highly expressed in FS, and FG2-3 (LOC105057418) showed higher expression in FT(Fig. 9). Among the 24 flavonoid metabolites, flavones had the highest relative content in FS, while anthocyanins were most abundant in FT. Except for flavosativaside, vitexin-2’’-rhamnoside, isovitexin, and vitexin, FT exhibited higher relative contents of differentially expressed metabolites than FS (Fig. 10, Table S4).

Fig. 9.

A The content of flavone of FS; B The content of anthocyanins of FT

Fig. 10.

The main pathways (Ko00941, Ko00942, Ko00944) and key enzyme genes involved in flavonoid biosynthesis in the exocarp of oil palm. (Note: CYP73A༈LOC105052309、LOC105057212)、HCT༈LOC105034344、LOC105036623、LOC105037012、LOC105039824༉. ANS(LOC105035842)、DFR(LOC105040722、LOC105040724、LOC105048473)、E.5.5.1.6༈LOC105045978༉、C3’H༈LOC105052441༉、F3H༈LOC105054663、LOC105059410༉、CHS༈LOC105060373)、CYP73B1(LOC105055971)、ANR(LOC105035639)、BZI༈LOC105039612、LOC105046326༉、CYP75A༈LOC105036168、LOC105037552、LOC105045491、LOC105045492、LOC105045493、LOC105053152、LOC105053153、LOC105053154༉、CYP75B1༈LOC105055971༉、FG2༈LOC105036426、LOC105054575、LOC105057418༉.

In summary, these 27 enzyme genes may be the key genes influencing the differential accumulation of flavonoids and playing important roles in the synthesis of flavonoids in the exocarps of FS and FT fruits.

Analysis of relative expression of flavonoid biosynthetic pathway genes in oil palm exocarp

In this piece of research, 9 genes associated with flavonoid biosynthesis in the exocarp of oil palm were randomly picked for qRT - PCR analysis (Fig. 11). With the growth and development of the fruit in the FS, the enzyme genes FG2, FG2–2, HCT − 2, and C3’H showed a pattern of rising first and then falling. In particular, the expression level of FG2 in the exocarp of FS was constantly higher than that in FT. As the fruit was in the process of growth and development, the enzyme genes HCT and LAR showed a downward movement. With the growth progress of the fruit, the enzyme genes DFR and ANR showed an upward - sloping expression pattern. Their levels in the exocarp of FT were always higher than those in FS. The expression of the enzyme gene HCT − 3 rose at first and then fell, which was the opposite of its expression in FT. In the FS, when the fruit was growing and developing, the enzyme genes FG2 and FG2–2 went down. In fact, the expression level of FG2–2 in the exocarp of FT was higher than that of green - fruited counterparts, the opposite of what was seen in FS. The enzyme genes HCT, DFR, and C3’H showed an upward shift.

Fig. 11.

The relative expression levels of genes FG2-1, FG2-2, ANR, DFR, C3’H, LAR, HCT-1, HCT-2, and HCT-3 in the flavonoid synthesis process of exocarps from FS and FT

A comparison to the transcriptome data showed that the relative expression levels of enzyme genes FG2, FG2–2, ANR, and C3’H were in harmony with the data, while DFR, LAR, and HCT were different.

Develop molecular markers for the EgDFR gene

Through the screening of transcriptome data and the flavonoid biosynthesis pathway, it was found that the gene EgDFR (LOC105048473) exhibited abnormal expression in virescens and 30 nigrescens individuals. NR annotation revealed that this gene encodes the DFR in oil palm. As a key component of the flavonoid biosynthesis pathway, it is linked with the biosynthesis of multiple anthocyanins. In consequence, the differences in its expression levels and site mutations may be closely correlated with the color change of the exocarp as oil - palm fruit goes through development and ripening. To investigate this issue, 10 SNP loci of the DFR gene were screened from the transcriptome data. Primers were designed for these 10 SNP loci, designated as K1-K10. Young leaves were randomly collected from 30 virescens and 30 nigrescens individuals in the field, which were used as experimental materials for KASP molecular marker analysis. The aim of this experiment was to verify whether K1-K10 could serve as specific primers for distinguishing FS and FT. The results showed that except for marker K1, the % accuracy of KASP genotyping for markers K2-K10 was 100%(Table 9; Fig. 12). This outcome confirms that these molecular markers are able to accurately tell apart virescens and nigrescens oil palm. This study demonstrates that EgDFR, a key gene involved in flavonoid synthesis in oil palm fruit, plays a critical role in exocarp pigment formation in the two fruit types.However, given the relatively small sample size in this study, further validation using an expanded and independent population is warranted.

Table 9.

The results of genotyping and field phenotypic surveys of k1–k10 markers in 60 oil palm individuals

ID	Entire population	KASP Genotyping Outcomes		KASP Genotyping and Field Phenotyping Plant Counts		KASP Geno. & Field Pheno. Difference Plants	% accuracy of KASP genotyping
		FS	FT	FS	FT
K1	60	CC(30)	CT(18)	30	18	2	80%
K2	60	AG(30)	AA(30)	30	30	0	100%
K3	60	CG(30)	CC(30)	30	30	0	100%
K4	60	GA(30)	GG(30)	30	30	0	100%
K5	60	TC(30)	TT(30)	30	30	0	100%
K6	60	GA(30)	GG(30)	30	30	0	100%
K7	60	GA(30)	GG(30)	30	30	0	100%
K8	60	CT(30)	CC(30)	30	30	0	100%
K9	60	GA(30)	GG(30)	30	30	0	100%
K10	60	GA(30)	GG(30)	30	30	0	100%

Fig. 12.

Genotyping results of markers K1-K10 in 30 virescens and 30 nigrescens individuals. (Blue and red represent homozygotes, green represents heterozygotes, and black represents negative controls)

Discussion

Secondary metabolites, flavonoids, that are of great importance, are widely found in vegetables, fruits, and medicinal plants in nature [27]. Flavonoids taken from plants show effects related to anti - hypertension, anti - oxidation, anti - inflammation, and anti - cancer [28, 29]. The high level of flavonoids in oil palm not only improves the nutritional value of palm oil but also has important physiological effects, like anti - oxidation and wound healing, in the development of the oil palm [30]. This study found that with the development and ripening of oil palm fruits, the flavonoid content in the exocarps of FT and FS fruits respectively showed a downward trend and a trend of increasing first and then going downwards, which was consistent with the change trend of the relative flavonoid content in the metabolomics data.

Currently, metabolomics has been widely employed in studies on plant flavonoids.Metabolomic analysis has also been extensively applied in various crops, including rice, purple rice and Torreya grandis [31–33]. Flavonoid metabolomics was conducted on the exocarps of FS and FT oil palm fruits. A total of 9 flavonoid subclasses were detected, including flavanones, flavanonols, anthocyanins, etc., which are similar to the main flavonoids identified in Ricinus communis [34]. In FS, flavonoids and flavonols are the main components, including 134 kinds of glycosides, which is similar to tartary buckwheat seeds and Chinese torreya fruits [35, 36]. The levels of anthocyanin in FT drop step - by - step, similar to what prior studies have shown about the decrease of anthocyanin accumulation in pineapple peel with fruit ripening [37]. From the above - shown results, it can be inferred that the difference in flavonoid content between the exocarps of FS and FT fruits may result in the different exocarp colors. In particular, the exocarp of FT contains abundant anthocyanins, which is similar to the finding that anthocyanin accumulation leads to purple pigmentation in Dendrobium orchids [38].

In recent years, transcriptomic technology has been widely used to screen key candidate genes involved in plant flavonoid biosynthesis.It has also been widely applied in various crops such as Coreopsis tinctoria and pumpkin [39–40]. In FT, eight enzyme genes including HCT-1, F3H-1, and ANR, and nine metabolites such as Isovitexin, Flavosativaside, and Baimaside were related to flavonoid biosynthesis. In FS, F3H-1 showed a significant positive correlation with Flavosativaside and Trifolin. In FT, the differential gene F3H-1was strongly negatively associated with Isovitexin and Vitexin, but strongly positively correlated with Baimaside. F3H-2 exhibited a significant positive association with Vitexin-2’’-rhamnoside. The F3H gene in melon can promote the accumulation of flavonoids, flavanones, flavonols and isoflavones, which is similar to this study [41]. A transcriptional and metabolic analysis of five sunflower varieties revealed that UFGT was highly expressed in red flowers, which was associated with anthocyanin synthesis [42]. The current study, UFGT affects the synthesis of Chrysanthemin and Taxifolin, and the enzyme gene UFGT-2 is significantly negatively correlated with the differential metabolite Taxifolin. Treating sorghum seeds with Citric acid can upregulate ANR and promote the accumulation of flavonoids [43]. When ANR expression is high in the exocarp, the accumulation of (-)-Epicatechin and (-)-Epiafzelechin goes up. A research on flavonoid synthesis in Ginkgo biloba showed that genes including DFR, HCT, and FG2 may take part in the acetylation of flavonoid biosynthesis, thereby impacting flavonoid synthesis [44]. In addition, the DFR gene is also associated with changes in anthocyanin accumulation and pigmentation in crabapple leaves [45]. Studies of Amaryllidaceae alkaloids have shown that CYP73A and C3’H are important components in phenylpropanoid biosynthesis [46]. If HCT or C3’H are deficient, it leads to problems in phenylpropanoid biosynthesis [47]. In the biosynthetic route of flavonoids in the exocarp of oil - palm fruits, CYP73A, C3’H, and HCT play critical roles in phenylpropanoid synthesis. DFR affects the making of Pelargonidin − 3 - O - rutinoside, Epiafzelechin, and Afzelechin. FG2 is engaged in the biosynthesis of flavones and flavonols, in line with earlier work. In addition, the ANR gene (LOC105035639) showed significant differential expression between FS and FT.The relative content of (-)-Epicatechin in FT was consistent with the FPKM expression pattern of ANR.(-)-Epicatechin is a precursor of anthocyanin biosynthesis.Anthocyanin is a key pigment that determines color variation in plants.

The KASP technology is a type of competitive allele-specific PCR technique. Specific primers bind to target SNP loci, and fluorescent signals are used to distinguish different genotypes. It features high flexibility, low cost, and strong accuracy, and has been applied in various crops such as wheat, rice, and radish [48–50]. In the study of the EgPSY2 gene in oil palm, our research group identified 10 SNP mutation loci and developed 3 pairs of KASP molecular markers. These markers achieved 100% accuracy and could effectively distinguish between the two oil palm varieties, virescens and nigrescens [51]. Although the K2-K10 markers achieved 100% genotyping accuracy in this study, the sample size was relatively small. Further validation using a larger and independent population is needed in future research. To date, no molecular markers associated with the DFR gene and fruit color have been reported. This study is the first to develop DFR-based KASP markers for these two oil palm varieties. This method can be applied to other oil palm varieties or related species in the future. In addition, exploring the linked SNPs between DFR and genes in other pathways will help establish a multi-gene KASP genotyping system. This will provide a more efficient basis for breeding oil palm varieties with high flavonoid content.

Conclusions

In summary, this study employed combined metabolomic and transcriptomic analysis.We investigated flavonoid accumulation patterns and the correlations between DEGs and DEMs in oil palm FS and FT across three developmental stages. The results revealed that flavonoid accumulation differed greatly between FS and FT. This accumulation difference may be related to the expression differences of genes involved in the flavonoid biosynthesis pathway.Among the 27 DEGs associated with flavonoid biosynthesis (such as HCT-3, CHS, and FG2), 20 DEGs (including HCT-1, ANS, and F3H) showed higher expression levels in FT than in FS. The high expression of these genes may be associated with the higher flavonoid content in FT. However, the causal relationship between them has not been verified by gene functional validation. Through correlation analysis of genes and metabolites, we identified six DEGs potentially related to flavonoid synthesis (HCT-2, HCT-3, DFR-1, DFR-3, CYP73A-1, C3’H) and seven DEMs (Trifolin, Prunin, Vitexin, etc.) in FS.In FT, eight enzyme genes potentially related to flavonoid synthesis (HCT-1, F3H-1, ANR, FG2-2, etc.) and nine metabolites (Isovitexin, Flavosativaside, Baimaside, etc.) were identified. The above correlations between genes and metabolites are only based on correlation analysis. Their specific regulatory mechanisms need further verification. In addition, this study preliminarily identified the EgDFR gene associated with flavonoid biosynthesis using KASP technology. The K2-K10 markers achieved 100% accuracy in distinguishing oil palm exocarp phenotypes.These results provide a reliable molecular marker basis for the rapid identification of oil palm exocarp phenotypes. Notably, only 30 FS and 30 FT individuals were used in this study. The sample size was relatively limited, no validation was conducted in an independent population, and field-grown samples may be potentially affected by environmental variations. In future work, we will expand the sample size and include oil palm materials with different genetic backgrounds.We will further verify the stability and universality of these markers.This will provide stronger support for their practical application in oil palm molecular breeding.

Author contributions

Zhang Ruimin: Conducted experiments and drafted the initial manuscript. Liu Xiaoyu: Assisted with experimental work and provided conceptual insights. Li Qihong & Cao Hongxing: Responsible for the research direction of the manuscript and supervised the project progress. Fu Dengqiang: Methodology. Jerome Jeyakumar John Martin: Reviewed and edited the manuscript. Li Xinyu, Zhou Lixia, and Li Rui gave guidance on the analysis of the experimental data. All the authors contributed to the article and approved the version finally submitted.

Funding

This research work was financially supported by the Open Funds of State Key Laboratory of Tropical Crop Breeding (No.SKLTCBKF202502), the National Key R&D Program of China (Grant No. 2023YFD2200700), National Project of State Key Laboratory for Tropical Breeding Crop (No. NKLTCB-ZCXTD3707), Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (No. 1630152023011).

Data availability

The datasets generated in this study are available in the NCBI database under BioProject accession number PRJNA1399727.

Declarations

Ethics approval and consent to participate

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Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.