Mitogen-Activated Protein Kinases and Phosphatases Synergistically Regulate 3-Hydroxy-3-Methylglutaryl-CoA Reductase To Enhance Squalene Biosynthesis in Camellia oleifera Fruits under Heat Stress Conditions

Mitogen-Activated Protein Kinases and Phosphatases Synergistically Regulate 3-Hydroxy-3-Methylglutaryl-CoA Reductase To Enhance Squalene Biosynthesis in Camellia oleifera Fruits under Heat Stress Conditions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mitogen-Activated Protein Kinases and Phosphatases Synergistically Regulate 3-Hydroxy-3-Methylglutaryl-CoA Reductase To Enhance Squalene Biosynthesis in Camellia oleifera Fruits under Heat Stress Conditions Jianwen Wu, Rong Qin, Yingying Chen, Jihua Guan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8118690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heat stress substantially influences lipid metabolism and triterpenoid biosynthesis in oil-tea ( Camellia oleifera ) seeds, yet the regulatory coordination between protein phosphorylation signaling and mevalonate (MVA) flux remains poorly understood. Here, we combined transcriptomic, metabolomic, and enzymatic analyses with RT-qPCR validation to elucidate how MAPK–phosphatase cascades modulate HMGR-dependent squalene biosynthesis during thermal exposure. Fully mature seed kernels harvested after Shuangjiang (late October to early November) were incubated at 35°C for 0, 12, and 24 h. Heat stress significantly enhanced HMGR activity and squalene accumulation, accompanied by transcriptional activation of HMGR-1 , which showed the most pronounced induction among the four HMGR isoforms. Multi-omics integration revealed that fatty-acid elongation and desaturation modules were positively correlated with oleic acid levels, while pyridoxine-associated genes in vitamin B₆ metabolism formed a strong co-expression subnetwork, reflecting enhanced membrane remodeling and coenzyme turnover under heat. RT-qPCR analyses further confirmed that MAPK (MPK3/MPK6-like) and phosphatase ( PP1c / PP2A ) genes exhibited synchronized transcriptional patterns with HMGR-1 , supporting a reversible phosphorylation mechanism that dynamically regulates carbon flux through the MVA pathway. Collectively, these findings establish a mechanistic framework in which MAPK–PP–HMGR signaling enhances triterpenoid synthesis and lipid homeostasis, thereby contributing to thermal resilience in C. oleifera seeds. This work provides mechanistic insights and candidate targets for metabolic engineering toward improved squalene productivity and heat tolerance in oil-tea germplasm. Camellia oleifera Heat stress Squalene biosynthesis Mitogen-activated protein kinase Phosphatase 3-hydroxy-3-methylglutaryl-CoA reductase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Plants have developed complex mechanisms to perceive and respond to external stresses under constantly changing environmental conditions. There are similarities among abiotic stresses, such as temperature extremes, high salinity, and drought, in terms of their effect on plants and how plants perceive them. For example, these abiotic stresses typically affect plant cell osmotic pressure, leading to a transient increase in the cytoplasmic Ca 2+ concentration. Therefore, Ca 2+ is considered to be a universal second messenger for major stress signals (Gong et al., 2020 ). However, different abiotic stresses have many distinct effects on plants. For example, cold and heat can decrease and increase cell membrane fluidity(Zhu, 2016 ), respectively, while salt stress, which is perceived via the feronia signaling pathway, can weaken the cell wall. The activation of the FER signaling pathway can prevent cell rupture (Feng et al., 2018 ). Under drought conditions, the abscisic acid (ABA) concentration can increase by 50-times, which is one of the most drastic changes in plant hormone concentrations observed to date in response to environmental stimuli (Zeevaart, 1980 ).Beyond thermal cues, other physical stimuli can also reprogram energy and lipid metabolism; for example, magneto-electric composite fields enhanced carbohydrate and fatty acid biosynthesis in filamentous algae by stimulating core metabolic routes(Kayani, 2023 ). Transcriptomics analyses are important for thoroughly investigating the mechanisms underlying plant responses and adaptations to environmental stress that continue to be identified. At low temperatures, CBF expression may be rapidly induced in plants (> 100-times), with calmodulin-binding transcriptional activator (CAMTA) functioning as a positive regulator of CBF expression (Doherty et al., 2009 ). Both CAMTA3 and CAMTA5 regulate the expression of CBF1 and CBF2 in response to a rapid decrease in temperature (Kidokoro et al., 2017 ). Heat stress transcription factor A1 (HsfA1) appears to be a key regulator of heat tolerance (Liu et al., 2011 ). Specifically, it activates the expression of heat stress response genes, including HsfA7 , HsfA2 , HsfB , MBF1c (multiprotein bridging factor 1c), and DREB2A , with the encoded proteins subsequently regulating the synthesis of molecular chaperones and enzymes involved in degrading unfolded proteins and scavenging reactive oxygen species (Ohama et al., 2017 ; Yoshida et al., 2011 ). HsfA1 also regulates the expression of the Copia -like retrotransposon ONSEN and contributes to heat stress memory (Sedaghatmehr et al., 2019 ). Camellia oleifera , which belongs to the genus Camellia in the family Theaceae, is one of the four major woody oil crop species worldwide. Its seeds contain 20%–30% oil and are rich in unsaturated fatty acids and various bioactive substances. Recent research on C. oleifera has clarified its adaptations to various environmental stresses, especially in terms of the mechanisms and regulatory pathways mediating the response to drought conditions. Drought stress can significantly alter C. oleifera growth, enzyme secretion, stomatal morphology, and leaf osmotic regulatory substances (He et al., 2020 ). Both miR398 and miR408-3p can enhance C. oleifera drought resistance by negatively regulating the expression of genes encoding downy mildew resistance 6 (DMR6) and enhanced disease resistance 2 (EDR2), respectively (He et al., 2022 ). Exogenous ABA effectively activates the antioxidant system by inhibiting stomatal conductance and moderately decreasing the photosynthetic rate, thereby alleviating oxidative damage caused by drought stress (Yang et al., 2024 ). The endophytic bacterium Streptomyces OSILF-2 reportedly protects C. oleifera from drought stress (He et al., 2023 ). Additionally, CoSWEET10, which is a sucrose and hexose transporter, plays a dual role in promoting seed development and enhancing plant drought resistance (Ye et al., 2023 ). Recent research has clarified that the C. oleifera HMGR gene family consists of four members ( CoHMGR1-4 ) exhibiting tissue- and stage-specific expression during seed development (Gu et al., 2025 ). Among them, CoHMGR2 acts as the dominant isoform regulating squalene and triterpenoid accumulation during the mid-maturation phase of the seed kernel. These findings indicate that the kernel is not merely a passive oil-storage organ but a metabolically dynamic tissue where HMGR-driven terpenoid biosynthesis is tightly coupled with oil-body formation and membrane biogenesis. Building on these insights, our study investigates how heat stress perturbs this kernel-specific regulatory network to modulate squalene accumulation. In this study, ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and second-generation sequencing technology (Illumina HiSeq ultra-high-throughput sequencing) were applied to explore changes in metabolic products and transcript levels in harvested C. oleifera fresh fruits during three post-ripening stages under high-temperature stress (35°C). Moreover, differences in gene expression among the harvested fresh fruits during different post-ripening stages were thoroughly investigated. Therefore, this study aimed to elucidate how heat stress reshapes squalene biosynthesis and membrane adaptation in C. oleifera seed-kernel tissues through integrated transcriptomic and metabolomic analyses. Consistently, hormone signaling can orchestrate lipid metabolic cascades; exogenous jasmonates markedly boosted ω-3 polyunsaturated fatty acid biosynthesis through transcriptional activation of key regulators in Tribonema minus (Kayani et al., 2025 ), supporting a signal-induced reprogramming paradigm that we examine under heat stress in C. oleifera . We propose that heat exposure triggers MAPK cascades ( MPK3/MPK6-like ) and PP1/PP2A -mediated dephosphorylation of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), enhancing its catalytic activity and flux through the mevalonate (MVA) pathway. The resulting elevation in squalene, sterols, and triterpenoids, together with increased unsaturated fatty acids, contributes to adaptive membrane remodeling and improved thermal tolerance. This working model provides a mechanistic framework linking signal transduction, enzyme regulation, and metabolic adaptation under heat stress. 2. Materials and methods 2.1 Metabolite extraction Fully mature fruits of C. oleifera were harvested from the same experimental plantation (Guangxi, China) after Shuangjiang (the first frost period, late October to early November), when the fruits had reached full maturity. After removing the pericarp and seed coat, the kernels were used for analyses. Samples were divided into three groups: untreated control (T-0) and heat-treated fruits incubated at 35°C for 12 h (T-12) and 24 h (T-24). Each group contained three biological replicates (n = 3). The kernels were immediately frozen in liquid nitrogen and stored at − 80°C for transcriptomic (RNA-seq) and metabolomic (LC-MS) analyses. For metabolite extraction, the frozen seeds were lyophilized using a vacuum freeze-dryer (Scientz-100F) and then crushed using a mixer mill (MM 400, Retsch) and a zirconia bead (1.5 min at 30 Hz). The powdered material (50 mg) was resuspended in 1.2 mL 70% methanol solution and vortexed six times (30 s every 30 min). The mixture was centrifuged (centrifuge at 80.5 ×g for 3 min) and then the supernatant (extract) was filtered (SCAA-104, 0.22 µm pores; ANPEL, Shanghai, China, http://www.anpel.com.cn/ ) for the subsequent UPLC-MS/MS analysis. 2.2 UPLC-MS/MS conditions Sample extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2, https://www.shimadzu.com.cn/ ; MS, Applied Biosystems 4500 QTRAP, https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html ). Analytical conditions were as follows: UPLC column: Agilent SB-C18 (1.8 µm, 2.1 mm × 100 mm); mobile phase: solvent A (pure water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid); gradient elution program: 95% A, 5% B; linear adjustment to 5% A, 95% B within 9 min and then hold for 1 min; adjustment to 95% A, 5.0% B within 1.1 min and then hold for 2.9 min; flow rate: 0.35 mL/min; column oven temperature: 40°C; injection volume: 4 µL. The eluate was analyzed using an ESI-triple quadrupole-linear ion trap (QTRAP)-MS system. 2.3 ESI-QTRAP-MS/MS The ESI source operation parameters were as follows: source temperature: 550°C; ion spray voltage: 5,500 V (positive ion mode)/−4,500 V (negative ion mode); ion source gas I, gas II, and curtain gas: 50, 60, and 25 psi, respectively; collision-activated dissociation: high. Instrument tuning and mass calibration were performed using 10 and 100 µmol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. QQQ scans were acquired in MRM experiments with the collision gas (nitrogen) set to medium. Declustering potential and collision energy were optimized for individual MRM transitions. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within that period. 2.4 Qualitative and quantitative metabolite analyses Metabolite data were log 2 -transformed for statistical analyses to improve normality and were normalized. An unsupervised principal component analysis (PCA) was performed using the prcomp function in R ( www.r-project.org ). Data were unit variance-scaled before completing the unsupervised PCA. A hierarchical cluster analysis (HCA) of samples and metabolites was performed and the results were visualized in heatmaps with dendrograms. Pearson correlation coefficients (PCC) were calculated using the cor function in R and presented in heatmaps. HCA was completed and PCC was calculated using the R package ComplexHeatmap. For HCA, normalized signal intensities of metabolites (after unit variance scaling) were visualized as a color spectrum. For a two-group analysis, differentially accumulated metabolites were determined using the following criteria: VIP ≥ 1 and |log 2 (fold-change)| ≥ 1.0. VIP values were extracted from OPLS-DA data, which were presented in score plots and permutation plots generated using the R package MetaboAnalystR. Data were log-transformed and mean-centered before performing OPLS-DA. To avoid overfitting, a permutation test (200 permutations) was completed. Identified metabolites were annotated using the KEGG Compound database ( http://www.kegg.jp/kegg/compound/ ), after which annotated metabolites were mapped using the KEGG Pathway database ( http://www.kegg.jp/kegg/pathway.html ). Pathways with significantly regulated metabolites were selected for a metabolite set enrichment analysis using the MSEA online server; their significance was determined on the basis of hypergeometric test p-values. To ensure annotation accuracy, all metabolite identifications were manually validated by matching retention times, precursor ions (m/z), and MS/MS fragmentation spectra with authentic standards in the MetWare MWDB database. KEGG assignments were curated to retain only plant-related metabolic pathways prior to visualization. 2.5 RNA extraction and RNA-seq analysis Total RNA was extracted using the Total RNA Extractor (Trizol) kit (Sangon, China) and then treated with RNase-free DNase I to remove any remaining genomic DNA. RNA integrity was evaluated by 1.0% agarose gel electrophoresis, whereas RNA quality and quantity were determined using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). High-quality RNA samples were used for library construction and sequencing by Sangon Biotech (Shanghai) Co., Ltd. For each sample, 2 µg RNA was used as the input material for constructing sequencing libraries using a VAHTSTM mRNA-seq v2 Library Prep Kit for Illumina®. Index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads and then fragmented using divalent cations at high temperatures in VAHTS™ First Strand Synthesis Reaction Buffer (5×). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). The second cDNA strand was synthesized using DNA polymerase I and RNase H. The remaining overhangs were converted to blunt ends via exonuclease/polymerase activities. After the 3′ ends of cDNA fragments were adenylated, an adapter was ligated to the fragments. An AMPure XP system (Beckman Coulter, Beverly, USA) was used to select cDNA fragments with the preferred length (150–200 bp), after which 3 µL USER Enzyme (NEB, USA) was added to the size-selected cDNA. The mixture was incubated at 37°C for 15 min and then at 95°C for 5 min. A PCR amplification was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers, and an Index (X) Primer. PCR products were purified (AMPure XP system) and library quality was assessed using the Agilent Bioanalyzer 2100 system. High-quality libraries were then quantified and pooled for the paired-end sequencing performed on a HiSeq XTen system (Illumina, San Diego, CA). 2.6 Data assessment and quality control FastQC (version 0.11.2) was used for evaluating the quality of sequenced data. Raw reads were filtered by Trimmomatic (version 0.36) according to several steps: 1) Removing adaptor sequence if reads contains; 2) Removing low quality bases from reads 3’to 5’(Q < 20); 3) Removing low quality bases from reads 5’to 3’(Q < 20); 4) Using a sliding window method to remove the base value less than 20 of reads tail (window size is 5 bp); 5) Removing reads with reads length less than 35nt and its pairing reads. And the remaining clean data was used for further analysis.. 2.7 Transcriptome assembly and gene annotation Clean reads were de novo assembled into transcripts using Trinity (version 2.0.6) (parameter: min_kmer_cov 2). Transcripts with a minimum length of 200 bp were clustered to minimize redundancy. For each cluster (representing the transcriptional complexity for the same gene), the longest sequence was preserved and designated as a unigene. Unigenes served as queries for a BLAST search of the following databases: NCBI Nr (non-redundant protein database), Swiss-Prot, TrEMBL, CDD (Conserved Domain Database), Pfam, and KOG (EuKaryotic Orthologous Groups) (E-value < 1e-5). The best alignments were used to determine unigene open reading frames and the encoded amino acid sequences. TransDecoder (version 3.0.1) was used to predict the coding sequences of the unaligned unigenes. Gene Ontology (GO) functional annotation information was obtained for the transcripts annotated by Swiss-Prot and TrEMBL. KAAS (KEGG Automatic Annotation Server version 2.1) was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. 2.8 RNA-seq and expression analysis Bowtie2 (version 2.3.2) was used for mapping the quality control sequences to the assembled transcripts, and RSeQC (version 2.6.1) was used for statistics the aligned result. Salmon (version 0.8.2) was used to calculate the reads count and expression value of unigenes. The TPM (Transcripts Per Million), eliminates the influence of gene lengths and sequencing discrepancies to enable direct comparison of gene expression between samples. Principal Component Analysis (PCA) and Principal co-ordinates analysis (PCoA) were performed to reflect the distance and difference between samples. DESeq2 (version 1.12.4) was used to determine differentially expressed genes (DEGs) between two samples. Genes were considered as significant differentially expressed if q-value 2. When the normalized expression of a gene was zero between two samples, its expression value was adjusted to 0.01 (as 0 cannot be plotted on a log plot). If the normalized expression of a certain gene in two libraries was all lower than 1, further differential expression analysis was conducted without this gene. 2.9 Functional characterization of DEGs GO and KEGG analyses were performed to functionally characterize DEGs. The GO database is part of an international standard classification system for gene functions. DEGs were annotated with GO terms (biological functions). The number of genes annotated with each term was recorded. A hypergeometric test was conducted to identify significantly enriched GO terms in the gene list. The KEGG database is a public database of pathways. A KEGG pathway analysis involving a hypergeometric test was completed to reveal significantly enriched metabolic pathways or signal transduction pathways among DEGs. A false discovery rate (q-value) < 0.05 was used as the threshold for determining the significance of GO terms and KEGG pathways. 2.10 Statistical analysis All experiments were performed with three biological replicates unless otherwise stated. Data are expressed as mean ± standard deviation (SD). Differences among treatments (T-0, T-12, and T-24) were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference (HSD) test for multiple comparisons. When data did not meet the assumptions of normality or homogeneity of variance, Kruskal-Wallis non-parametric testing was applied. Statistical significance was set at P < 0.05. All analyses were performed using SPSS Statistics 26.0 (IBM, Armonk, NY, USA). 3. Results and discussion 3.1 Phenotypic and physiological indices The effects of a high-temperature stress treatment on harvested C. oleifera fresh fruits were determined on the basis of phenotypic and physiological indices. In terms of phenotypic indices, the pericarp of fruits treated for 24 h exhibited obvious cracking (Fig. 1 ). In addition, a comparison with control fruits (0 h) indicated that the squalene content in seed kernels was essentially unaffected by an exposure to high-temperature stress for 12 h. At the 24 h time point, the squalene content was 1.77-times higher in the heat-stressed group than in the control group. According to physiological and phenotypic indices, transcriptome and metabolome analyses were conducted for samples at three time points (0, 12, and 24 h). All data were obtained from seed-kernel tissues (n = 3 biological replicates per group 3.2 Transcriptome analysis To investigate the molecular mechanism underlying squalene accumulation in harvested C. oleifera fresh fruits exposed to high-temperature stress, samples were incubated at 35°C for 0, 12, and 24 h, after which the seed coat was removed for a transcriptome analysis. After eliminating low-quality data, 62.38 Gb (62,387,688,331) clean data were obtained, with Q30 exceeding 92.85% and a GC content of 50.12%–54.48%, reflecting the relatively high quality of the transcriptome data. A total of 131,511 genes were annotated. To identify key factors in the transcriptome data, a PCA was performed to analyze the changes at different high-temperature stress treatment time points (Fig. 2A). The first two principal components (PC1 and PC2) could distinguish between different stress treatment time points. In addition, according to the correlation heatmap (Fig. 2B), biological replicates in the same sample group were highly correlated, reflecting the repeatability of the results for the selected sample groups. Comparisons of the three selected treatment time points revealed 3,917, 3,701, and 2,079 significant DEGs (q-value 2). A Venn diagram (Fig. 2C) was constructed to present the common and unique DEGs among the comparisons of stress treatment time points. A total of 171 DEGs were common to all three comparisons. The results showed that the exposure to high-temperature stress induced transcriptome-level changes in C. oleifera seeds. 3.3 Functional annotation of DEGs All DEGs were annotated according to the GO database (Fig. 3A) and classified into three categories (biological process, cellular component, and molecular function). The enriched biological process GO terms assigned to DEGs included response to stimulus, developmental process, biological regulation, regulation of biological process, metabolic process, cellular process, and cellular component organization or biogenesis. This suggests that high-temperature stress activates the rapid response of C. oleifera fruits to thermal stimuli, while also triggering complex biological processes, including metabolic regulation, cellular remodeling, and developmental regulation. Accordingly, the mechanism underlying the adaptation of C. oleifera fruits to heat stress may involve the production of heat-protective proteins expression, the activation of the antioxidant system, and the accumulation of secondary metabolites (e.g., squalene). The enriched cellular component GO terms among the DEGs included organelle, membrane, cell part, cell, and organelle part. Hence, under high-temperature stress conditions, the cell membrane, subcellular organelles (e.g., mitochondria, endoplasmic reticulum, and plastids), and their functional units may be restructured or regulated. This is consistent with the fact plants maintain membrane permeability and organelle homeostasis through membrane-stabilizing proteins, molecular chaperones, and other components under stress conditions. In terms of molecular functions, the enriched GO terms among the DEGs included catalytic activity and binding. This indicates that the high-temperature treatment triggered significant changes in key catalytic processes associated with metabolic pathways as well as in processes related to the recognition and transduction of signals involved in stress responses. Considered together, these findings indicate that heat stress significantly regulates changes to internal structures and functions of C. oleifera fruit cells, while also inducing the expression of various genes encoding stress response-related proteins with catalytic activities and molecular binding abilities, thereby promoting the adaptive response of C. oleifera fruits to stress. On the basis of a KEGG analysis (Fig. 3B), the DEGs in all samples were revealed to be associated with various pathways, including signal transduction, carbohydrate metabolism, translation, and amino acid metabolism. Therefore, under high-temperature stress, C. oleifera fruits may coordinate multiple physiological and metabolic processes, including signal perception and transduction, energy metabolism, protein synthesis, and cellular homeostasis, to optimize resource allocation and enhance stress adaptation. The enriched KEGG pathways among the DEGs in the T-12 vs T-0 included pyruvate metabolism, protein processing in endoplasmic reticulum, plant–pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, MAPK signaling pathway – plant, glycolysis/gluconeogenesis, cysteine and methionine metabolism, carbon metabolism, and carbon fixation in photosynthetic organisms. Therefore, in the early stage of the exposure to high-temperature stress, hormone signaling and MAPK phosphorylation signaling may synergistically regulate the early defense response and metabolic remodeling in C. oleifera fruits. The enriched KEGG pathways among the DEGs in the T-12 vs T-24 included alpha-linolenic acid metabolism, starch and sucrose metabolism, protein processing in endoplasmic reticulum, plant–pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, MAPK signaling pathway – plant, glutathione metabolism, estrogen signaling pathway, and antigen processing and presentation. This indicates that in the middle-to-late stages of the high-temperature stress treatment, the adaptive response of C. oleifera fruits may involve lipid signaling (e.g., jasmonic acid precursor metabolism) and reactive oxygen species scavenging mechanisms. A comprehensive analysis indicated that the following KEGG pathways were enriched among the DEGs in all three comparison groups: protein processing in endoplasmic reticulum, plant–pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, and MAPK signaling pathway – plant. This indicates that these biological processes have a central regulatory role throughout the heat stress response. Specifically, the enrichment of the MAPK signaling pathway and the plant hormone signal transduction pathway suggests that C. oleifera fruits may synergistically regulate the activities of key metabolic enzymes, such as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), through phosphorylation cascades and hormone signals, thereby affecting the synthesis of secondary metabolites (e.g., squalene). 3.4 Dynamic analysis of transcriptome data According to a K-means analysis (Fig. 4 ), the gene expression patterns in harvested C. oleifera fruits under high-temperature stress conditions were divided into subclasses 1–5. Genes in each subclass had different expression patterns at different treatment time points (T-0, T-12, and T-24) and were associated with different KEGG pathways. Analyses of gene expression patterns in different subclasses revealed significant spatiotemporal changes, reflecting the multiple biological responses of C. oleifera fruits to high-temperature stress. The expression level of subclass 1 genes stabilized after an initial rapid increase in the early stage of the high-temperature treatment (T-12); the enriched KEGG pathways among these genes were mainly carbon metabolism, TCA cycle, amino acid biosynthesis, phenolic compound synthesis, and plant hormone signaling. Considering their expression trends, the subclass 1 genes were rapidly activated during the early response to high-temperature stress, which helped maintain basic cellular metabolism and stress defense responses. These genes may be related to the mechanism mediating the rapid metabolic regulation of fruits exposed to heat stress. In addition, the enrichment of the Toll-like receptor and NF-kB signaling pathway suggests these genes are important for immune-like responses. Subclass 2 gene expression levels decreased rapidly after peaking at T-12; the enriched KEGG pathways were mainly related to stress responses, including MAPK signaling pathway, endoplasmic reticulum protein folding, protein degradation, and RNA splicing. These genes had typical stress-induced expression characteristics and may encode key regulators of heat stress memory, post-transcriptional modifications, and cellular homeostasis. The MAPK signaling pathway, which is critical for heat stress signal perception and transduction, was significantly enriched among subclass 2 genes, providing further evidence of its core regulatory role. Subclass 3 gene expression levels were lowest at T-12, but subsequently increased; the enriched KEGG pathways among these genes included glycolysis/gluconeogenesis, glutathione metabolism, ABC transporters, and PI3K Akt. This suggests that these genes may contribute to the temporary inhibition of energy metabolism during the middle stage of the response to high-temperature stress, which is followed by the resumption of energy metabolism and the activation of antioxidant and transporter functions. Hence, these genes may encode proteins with important roles for the high-temperature stress recovery stage. The expression of subclass 4 genes continued to decrease during the treatment period; the enriched KEGG pathways among these genes included lipid metabolism, amino acid metabolism, ubiquitin-mediated protein degradation, and Toll/NF-kB signaling. Their expression trends and enriched KEGG pathways indicated that subclass 4 genes may be related to non-critical growth and development, which are negatively regulated under high-temperature stress conditions to decrease energy consumption. Moreover, they may be associated with programmed cell death and aging processes. Subclass 5 gene expression levels were most significantly upregulated at T-24; the main enriched KEGG pathways among these genes included RNA splicing, carbon metabolism, TCA cycle, phenylpropanoid synthesis, and two-component systems. The expression levels of these genes were consistent with a typical “late repair” response trend. These genes may influence cell reconstruction, metabolic recovery, and physiological homeostasis-related regulation during the late stage of the high-temperature stress response. In summary, the K-means dynamic clustering analysis revealed the heterogeneity of the transcription-level responses of C. oleifera fruits under high-temperature stress conditions. Various genes were associated with key time-specific KEGG pathways, including energy metabolism, signal transduction, protein processing, and transport, reflecting the multi-stage dynamic regulatory mechanism in C. oleifera fruits (from “rapid response-regulation buffering-adaptation recovery”) after an exposure to high-temperature stress. Notably, the signaling pathway-related genes in subclasses 1 and 5 had highly consistent temporal expression trends that were similar to the transcriptional dynamics of genes encoding HMGR, a key enzyme upstream of squalene synthesis. This suggests that these genes may be closely related to the regulation of secondary metabolism in C. oleifera fruits exposed to stress. 3.5 Metabolome analysis To elucidate the effects of high-temperature stress on the metabolism of harvested C. oleifera fruits, metabolic changes in harvested fruits under high-temperature stress conditions were systematically analyzed using a UPLC-MS/MS platform, which identified 663 metabolites. PCA results (Fig. 5A) clearly distinguished the stress treatment group from the control group, with PC1 (33.92%) mainly explaining the differences between treatment and control groups, while PC2 (25.32%) reflected the gradient distribution of stress treatment time points. This result reflected the significant changes in C. oleifera fruit metabolic activities due to high-temperature stress. The heatmap clustering results (Fig. 5B) showed that the T-12 and T-24 stress treatment groups had highly similar metabolic profiles, indicating that the early response to high-temperature stress was established within 12 h and tended to stabilize after 24 h. Specific metabolites, such as tannins, quinones, and alkaloids, accumulated significantly under high-temperature stress conditions. These metabolites are commonly involved in plant abiotic stress responses and may enhance fruit stress tolerance by clearing reactive oxygen species and stabilizing cell structures. Further analyses of the T-0 vs T-12, T-0 vs T-24, and T-12 vs T-24 detected 126, 136, and 74 significant differentially accumulated metabolites (VIP ≥ 1, fold-change ≥ 2 or ≤ 0.5), respectively. Thus, the exposure to high-temperature stress appeared to induce extensive metabolic reprogramming, but there were differences in the response patterns at different time points. A Venn diagram analysis (Fig. 5C) showed that there were 12 stable core stress response-related metabolites among the selected time points, but there were also differentially accumulated metabolites that were unique to specific comparison groups (e.g., 24 each in the T-0 vs T-12 and T-0 vs T-24), further supporting the stage-specific characteristics of the heat stress response. These metabolites were mainly secondary metabolites (e.g., flavonoids and phenolic acids), energy metabolites (e.g., pyruvic acid and pentose phosphate), and amino acid metabolites, all of which are known to play critical roles in metabolic pathways under abiotic stress conditions. Interestingly, the enriched pathways among the differentially accumulated metabolites in the T-0 vs T-12 were mainly pyruvate metabolism and flavonoid biosynthesis, suggesting that energy metabolism and antioxidant defense are quickly activated during the early stage of the heat stress response. By contrast, the differentially accumulated metabolites in the T-0 vs T-24 and T-12 vs T-24 were associated with pathways related to long-term homeostasis, including nucleotide metabolism and phenylpropanoid biosynthesis, reflecting metabolic adjustments that mainly focused on structural maintenance and regulation during the stress adaptation period. Considered together, these findings suggest that high-temperature stress results in dynamic metabolic changes in C. oleifera fruits (i.e., “rapid response–adjustment transition–adaptive homeostasis”). These changes include enhanced carbon and nitrogen metabolism and the accumulation of antioxidant substances, but they also involve the selective allocation of core metabolites. These observations provided an important metabolic basis for the subsequent integrated analysis of transcriptome data and the mechanism regulating squalene production. 3.6 Analysis of differentially accumulated metabolites To further analyze the regulatory effects of high-temperature stress on the metabolic network of C. oleifera fruits, a KEGG pathway enrichment analysis of the differentially accumulated metabolites among three comparison groups (T-0 vs T-12, T-0 vs T-24, and T-12 vs T-24) was conducted (Fig. 6). The results showed that the significantly enriched KEGG pathways among the differentially accumulated metabolites in all comparison groups were typical secondary metabolic pathways, including biosynthesis of various plant secondary metabolites, and flavonoid biosynthesis, indicating that flavonoids and various secondary metabolites play key roles in the response to high-temperature stress. Specifically, in addition to the above-mentioned pathways, purine metabolism, pentose phosphate pathway, and pyruvate metabolism were identified as enriched KEGG pathways among the differentially accumulated metabolites in the T-0 vs T-12. Thus, the initial exposure to high-temperature stress may modulate the energy supply and stress resistance by affecting pathways related to nucleotide and carbon metabolism. Additional enriched KEGG pathways among the differentially accumulated metabolites in the T-0 vs T-24 were arginine and proline metabolism, vitamin B6 metabolism, starch and sucrose metabolism, and multiple sugar metabolism pathways. Accordingly, during a continuous exposure to heat stress, changes in carbon and nitrogen metabolism and increases in the antioxidant capacity are critical for the adaptive response. In the T-12 vs T-24, the differentially accumulated metabolites were significantly associated with amino acid and derivative metabolism; the enriched KEGG pathways included phenylalanine metabolism, beta-alanine metabolism, lysine biosynthesis, and phenylpropanoid biosynthesis, suggesting that in C. oleifera fruits, late-stage heat stress responses may be enhanced through amino acid metabolism and the accumulation of phenylpropanoids. To screen for core metabolites with potential regulatory significance, the top 20 metabolites in each comparison group were analyzed. These metabolites had the largest fold-changes in abundance, which were consistent with the changes in the expression levels of the corresponding key genes. In the T-0 vs T-12, the highly abundant differentially accumulated metabolites were mainly sugars (e.g., dulcitol and D-mannitol), organic acids (e.g., oxalic acid), lignans (e.g., syringaresinol), and phenolic acids and flavonoids (e.g., gallocatechin, aromadendrin, pinocembrin, and ferulic acid methyl ester). In the T-0 vs T-24, most of the screened metabolites were related to sugars, flavonoids, and lignans, such as phloretin, 3′-O-methyl-epicatechin, apigenin-6-C-(2″-glucosyl)arabinoside, and pinoresinol, reflecting the continuous accumulation of secondary metabolites. In the T-12 vs T-24, the main metabolites were nucleotides (e.g., 2′-deoxyadenosine and 2′-deoxyguanosine), flavonoids (e.g., apigenin-7-O-(2″-sinapoyl)glucuronide), and organic acids and amino acid derivatives (e.g., α-ketoglutaric acid, S-(methyl)glutathione, and 4-guanidinobutanal). These results provide further insights into the changes in metabolic homeostasis and the activation of the antioxidant system during the late stage of the C. oleifera fruit response to stress. 3.7 Validation of squalene accumulation and MVA-pathway enzyme activities under heat stress To experimentally validate the transcriptomic and metabolomic findings, we quantified the squalene content and determined the activities of two key enzymes-3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) and 2,3-oxidosqualene cyclase (OSCs)-in C. oleifera seed kernels exposed to heat stress (Table 1 ). The results showed that the squalene content increased markedly from 177.6 µg/g in the control (T-0) to 314.3 µg/g (T-12) and 309.1 µg/g (T-24), representing a 2.3-fold elevation (p < 0.05, n = 3). In parallel, HMGR activity rose from 10.70 IU/g to 13.74–14.03 IU/g, while OSCs activity increased from 18.14 U/g to 19.29–20.87 U/g. These concerted increases demonstrate that heat stress enhances both the upstream MVA pathway (via HMGR activation) and the downstream triterpenoid cyclization (via OSCs activation), thereby promoting squalene accumulation and subsequent triterpenoid biosynthesis. As sterols and triterpenes derived from squalene are essential constituents of cellular and organellar membranes, their coordinated induction likely contributes to membrane stabilization and thermal adaptation. Together with the enriched GO terms (“membrane”, “organelle”, “cell part”) and metabolomic enrichment of “biosynthesis of secondary metabolites”, these biochemical validations provide direct quantitative evidence that high-temperature exposure strengthens sterol/triterpenoid metabolism as an adaptive response in C. oleifera fruits. Table 1 Quantitative changes in squalene content and related enzyme activities in C. oleifera seed kernels under heat stress Treatment Squalene (µg /g ) HMGR activity (IU/g ) OSCs activity (U/g ) T-0 177.58 ± 8.2 10.70 ± 0.52 18.14 ± 0.46 T-12 314.30 ± 9.7* 13.74 ± 0.48* 19.29 ± 0.51 T-24 309.10 ± 7.5* 14.03 ± 0.55* 20.87 ± 0.47 Note: Values are mean ± SD (n = 3 biological replicates). Asterisks (*) indicate significant differences compared with the control (T-0) based on one-way ANOVA followed by Tukey’s HSD test (P < 0.05).. 3.8 Analysis of the correlation between the transcriptome and metabolome Table 2 Expression levels (TPM ± SD) of HMGR isoforms in C. oleifera fruits under heat stress Isoform ID Transcript ID T-0 (Mean ± SD) T-12 (Mean ± SD) T-24 (Mean ± SD) HMGR-1 TRINITY_DN57861_c3_g1 0.02 ± 0.03 0.15 ± 0.09 a 0.05 ± 0.04 HMGR-2 TRINITY_DN57861_c3_g3 2.92 ± 2.16 1.12 ± 0.37 0.34 ± 0.28 HMGR-3 TRINITY_DN67520_c1_g4 0.91 ± 0.63 0.67 ± 0.43 0.42 ± 0.24 HMGR-4 TRINITY_DN76432_c0_g1 0.14 ± 0.24 0.04 ± 0.07 0.18 ± 0.31 Note: TPM values are expressed as mean ± SD (n = 3 biological replicates). Superscript letters (ᵃ) indicate statistically significant induction compared with T-0 (P < 0.05, one-way ANOVA + Tukey’s HSD). To elucidate the isoform-level regulation of HMGR under heat stress, four HMGR transcripts were identified from the assembled transcriptome of C. oleifera , consistent with a recent report describing four CoHMGR genes in this species(Gu et al., 2025 ). Comparative expression profiling revealed distinct temporal patterns (Table 2 ). Among them, HMGR-1 ( TRINITY_DN57861_c3_g1 ) showed the most pronounced and transient induction (7-fold) at 12 h, whereas HMGR-2 (c3_g3), HMGR-3 (c1_g4), and HMGR-4 (c0_g1) maintained low or gradually decreasing expression levels. These findings indicate functional divergence within the HMGR family, in line with previous evidence that CoHMGR2 acts as the dominant regulator of squalene biosynthesis during seed development (Gu et al., 2025 ). Collectively, the data support a model in which HMGR-1 , a rate-limiting enzyme of the mevalonate pathway (L. Yang et al., 2022 ), serves as the principal inducible isoform driving enhanced enzymatic activity and squalene accumulation under heat stress. A nine-quadrant analysis revealed significant correlations between HMGR and multiple key metabolites. In quadrant 3 (both gene expression and metabolite accumulation were upregulated), HMGR was significantly positively correlated with 4-pyridine-O-glucoside, a vitamin B6-derived metabolite that functions as an antioxidant. This suggests that increasing HMGR production may be associated with the activation of the antioxidant system (Havaux et al., 2009 ). By contrast, in quadrant 9 (gene expression is upregulated, but metabolite accumulation is downregulated), HMGR was negatively correlated with various metabolites, such as D-fructose and vitexin glucoside, which may reflect the redistribution of carbon flow under high-temperature stress conditions, with metabolic pathways related to lipid and membrane stability prioritized (Obata & Fernie, 2012 ). A correlation heatmap validated these associations, showing a significant bidirectional regulatory relationship between HMGR and antioxidant and carbohydrate metabolites. This complex metabolic regulation suggests that HMGR may be critical for the heat stress response of C. oleifera fruits because it reconfigures the metabolic network to stabilize the cell membrane and increase the antioxidant capacity, with positive effects on heat tolerance (Falcone Ferreyra et al., 2012 ; Tholl, 2015 ). The specific function of HMGR in the high-temperature stress response network of C. oleifera fruits will need to be more thoroughly clarified by analyzing protein–protein interactions (e.g., in pull-down experiments) and the phosphorylation modification status (Shen et al., 2016 ). Under heat stress, C. oleifera fruits exhibited a comprehensive metabolic reprogramming that extended beyond the activation of the squalene biosynthetic route, encompassing a broader coordination between lipid remodeling and coenzyme metabolism. To delineate these cross-pathway linkages, pathway-level gene-metabolite correlation networks were established based on differential transcript-metabolite associations (Fig. 7G-H). In the fatty acid biosynthesis pathway (ko00061), TRINITY_DN64718_c2_g2 (Δ⁹-desaturase) and TRINITY_DN67038_c0_g1 (β-ketoacyl synthase) showed strong positive correlations with oleic acid (metaID pmf0395), all with |r| ≥ 0.70 and FDR < 0.05. The concerted upregulation of these nodes under heat stress suggests enhanced fatty-acid elongation and desaturation, leading to increased membrane unsaturation and fluidity-key adaptive mechanisms that stabilize cellular structures at elevated temperatures(M. He & Ding, 2020 ; Sharma et al., 2023 ). In the vitamin B₆ metabolism pathway (ko00750), pyridoxine (metaID pme1383) formed a significant positive subnetwork with TRINITY_DN60178_c0_g1 (pyridoxal phosphate-dependent oxidoreductase) and TRINITY_DN18884_c1_g2 (uncharacterized transcript co-expressed with pyridoxine), also meeting the thresholds of |r| ≥ 0.70 and FDR < 0.05. Given the central role of vitamin B₆ as a cofactor in transamination, decarboxylation, and redox-homeostasis reactions, this module implies an elevated B₆-dependent coenzyme flux that alleviates oxidative stress and sustains amino-acid metabolism under heat exposure. Collectively, these findings delineate a coordinated lipid-desaturation-vitamin B₆ metabolic axis that complements the MVA-HMGR pathway, jointly maintaining membrane homeostasis and reinforcing triterpenoid metabolic flux during thermal adaptation in C. oleifera fruits. 3.9 Phosphorylation site prediction In this study, we cloned the full-length cDNA sequence of a C. oleifera HMGR-encoding gene using rapid amplification of cDNA ends (RACE) technology (Fig. 8A) and obtained the full-length HMGR cDNA of C. oleifera by RACE (Fig. 8A) and analyzed the predicted phosphorylation sites using NetPhos 3.1 (Fig. 8B), multiple predicted phosphorylation sites were detected on HMGR in C. oleifera ; they were mainly concentrated on serine (Ser) and threonine (Thr) residues, including Ser47, Thr208, and Ser217. Among the predicted phosphorylation sites, Ser116, Thr246, and Thr238 had the three highest scores (predicted values of 0.960, 0.861, and 0.787, respectively). These sites were predicted as potential targets of related kinases, such as PKC, GSK3, p38 MAPK, and CDK5, suggesting that HMGR may contribute to the regulation of multiple kinases. HMGR activity is regulated by both phosphorylation and dephosphorylation. Previous studies showed that HMGR activity is usually inhibited via phosphorylation, while dephosphorylation can significantly activate this enzyme, thereby enhancing the synthesis of downstream terpenoid products, including squalene (Antolín-Llovera et al., 2011 ; Leivar et al., 2011 ). In Arabidopsis thaliana , protein phosphatase 2A (PP2A) directly interacts with the conserved N-terminal domain of HMGR through its B″ regulatory subunit, thereby regulating the HMGR phosphorylation status (Leivar et al., 2011 ). As environmental conditions change, the regulated phosphorylation state of HMGR may trigger different feedback regulatory mechanisms, indicating that its regulatory pathway may exhibit some environmental adaptability. Under various environmental conditions, such as high salinity, this interaction leads to PP2A-mediated dephosphorylation of HMGR and triggers HMGR degradation (Antolín-Llovera et al., 2011 ). Intriguingly, in A. thaliana , the dephosphorylation of AtHMGR1 by PP2A does not occur at the conserved Ser577 residue within the catalytic domain, which is phosphorylated by SnRK1 (Robertlee et al., 2017 ). This suggests that PP2A may target other phosphorylation sites on HMGR for a more precise regulation. Interestingly, when A. thaliana seedlings are treated with phosphatase inhibitors, the extent of the phosphorylation of HMGR at sites other than Ser577 increases significantly as does HMGR activity, further supporting its negative regulatory effect (Leivar et al., 2011 ). There are currently relatively few reports on how protein phosphatase 1 (PP1) regulates HMGR in plants. However, as another major Ser/Thr phosphatase, the potential role of PP1 cannot be ignored. There is evidence in other biological systems that PP1 is involved in the regulated dephosphorylation of HMGR. For example, in the mammalian liver, factors that inhibit PP1/PP2A activity significantly block the dephosphorylation of HMGR (Serra et al., 1990 ). In plants, PP1 facilitates Ser/Thr dephosphorylation in various biological processes, including ABA signaling, stomatal movement, and kinase regulation (Takemiya & Shimazaki, 2010 ). Therefore, we speculate that in plants, PP1 and PP2A cooperatively regulate the HMGR phosphorylation status, thereby maintaining HMGR activity at a suitable level. In this study, we identified multiple candidate genes encoding PP2A subunits on the basis of transcriptome data, including catalytic subunit PP2A-C (TRINITYDN60447_c1_g1) and structural subunit PP2A-A (TRINITYDN65830_c0_g1). Under high-temperature stress conditions, the expression levels of both genes tended to increase, especially after the T-24 treatment. A comparison with the control (T-0) revealed significant increases in gene expression levels. This differs from the findings of previous studies on the negative regulatory effects of PP2A on HMGR activity. Hence, under high-temperature stress conditions, PP2A may increase HMGR activity through dephosphorylation, thereby promoting squalene synthesis. Further supporting the above-mentioned hypothesis, the role of PP2A may vary under different physiological states. Under adverse conditions, PP2A may enhance the accumulation of metabolites by regulating the phosphorylation status of HMGR to help plants adapt to environmental stress. Increases in PP2A production and HMGR activity may optimize the metabolic balance of the isoprene pathway and enhance plant adaptations to stress. Additionally, in the present study, the expression of a PP1 catalytic subunit-encoding gene (TRINITYDN67318_c1_g3) had an upregulated trend similar to that of a gene encoding HMGR, with peak transcription at the T-12 time point (TPM value of 3.42), suggesting that the encoded enzyme may play an auxiliary dephosphorylation role during stress responses. In this study, we identified two key predicted phosphorylation sites on HMGR in C. oleifera (Ser47 and Thr208). On the basis of this finding combined with the results of earlier research on HMGR regulation, we propose a new hypothesis that high-temperature stress leads to the phosphorylation of HMGR at these two sites because it activates the MAPK cascade pathway, thereby regulating HMGR activity. In plant stress responses, the MAPK pathway is critical for the perception and amplification of environmental signals. A previous study showed that in plants, such as A. thaliana , heat stress can rapidly activate MAPKs (e.g., MPK3/MPK6) that recognize Ser/Thr-Pro sequences and mediate phosphorylation (Asai et al., 2002 ). The S47 and T208 loci identified in the current study exhibit typical MAPK recognition features and are located near the N-terminal regulatory region and catalytic domain of HMGR, respectively, implying that they may serve as functional regulatory sites for stress signal integration. We speculate that MAPK directly phosphorylates S47 and T208 after being activated by high-temperature stress, thereby decreasing HMGR catalytic activity or altering HMGR stability to rapidly regulate the isoprene metabolic flux, ultimately resulting in the temporary inhibition of squalene synthesis. Protein phosphatases PP2A and PP1, which catalyze Ser/Thr dephosphorylation, are key negative regulators of MAPK signaling. In A. thaliana , PP2A can directly dephosphorylate MPK6 and its downstream substrates and bind to HMGR through the B″ regulatory subunit to regulate HMGR activity and degradation (Leivar et al., 2011 ). Our transcriptome data indicated that the expression of genes encoding PP2A-C, PP2A-A, and PP1 subunits were significantly upregulated following the exposure to high-temperature stress, indicative of their possible involvement in HMGR dephosphorylation. We propose that in the early stages of an exposure to stress, MAPK-mediated phosphorylation inhibits HMGR, but PP2A/PP1 subsequently dephosphorylates S47 and T208 to partially restore HMGR activity and gradually increase the synthesis of defense-related metabolites, such as squalene, while also establishing metabolic homeostasis to maintain energy levels and satisfy stress response requirements. This hypothesis involves a complete multi-level signaling regulatory framework comprising kinase and phosphatase metabolic enzymes that can explain how C. oleifera fruits dynamically regulate squalene synthesis to adapt to high-temperature stress. Future studies should explore in vitro expression following the introduction of point mutations (e.g., S47A/T208A or S47E/T208E) and elucidate the molecular mechanism underlying this model by combining mass spectrometry-based detection of phosphorylation, Co-IP assay-based protein interactions, and other experimental approaches. 4.0 RT-qPCR validation of MAPK-PP-HMGR module expression under heat stress To confirm the transcriptomic reliability and clarify the molecular regulation underlying heat-induced squalene biosynthesis, RT-qPCR was conducted for eleven key genes, including HMGR-1, HMGR-2, HMGR-3, MPK3-like, MPK6-like, PP1c-1, PP1c-2, PP2A-A, PP2A-C, OSCs , and the internal control gene SAND (Table 3 ). The expression profiles were highly consistent with the RNA-seq results, confirming the robustness of transcriptomic data and the coordinated activation of the MAPK–PP–HMGR regulatory module. RT-qPCR results showed that HMGR-1 exhibited the strongest induction under heat stress, with a 2⁻ΔΔCt value of 7.7 at 12 h, followed by a moderate decrease to 3.4 at 24 h. In contrast, HMGR-2 and HMGR-3 displayed lower but significant up-regulation (1.6–3.2-fold). These results indicate that HMGR-1 serves as the major inducible isoform driving the mevalonate pathway flux enhancement during early stress response, while HMGR-2/3 play secondary or complementary roles. Upstream kinases MPK3 -like and MPK6 -like were also activated, consistent with their roles in heat-triggered phosphorylation cascades. MPK3 -like expression increased 7.1–10.1-fold, whereas MPK6 -like showed a 1.5–2.1-fold increase, supporting its function as a signaling intermediary that transmits stress cues to HMGR . Concurrently, phosphatase genes PP1c-1 and PP1c-2 were up-regulated by 1.7–2.8-fold, and PP2A-A and PP2A-C by 1.3–1.9-fold, suggesting the involvement of reversible dephosphorylation in sustaining HMGR enzymatic activation after MAPK signaling initiation. Interestingly, OSCs transcript levels decreased slightly after 12–24 h (2⁻ΔΔCt < 1), while OSCs enzymatic activity increased significantly (Section 3.7 ). This discrepancy implies that downstream regulation may occur at the post-transcriptional or post-translational level—through enzyme stabilization, substrate channeling, or activation state modification—rather than transcriptional control. Collectively, these data confirm a dynamic regulatory mechanism in which MAPKs mediate stress signal perception and transmission, PP1/PP2A phosphatases modulate dephosphorylation balance, and HMGR acts as the key enzymatic node enhancing squalene biosynthesis. The convergence of RT-qPCR, enzyme activity, and metabolite data strongly supports a heat-induced feedback system integrating phosphorylation signaling with metabolic adaptation C. oleifera fruits. Table 3 Relative expression levels (2⁻ΔΔCt) of selected genes involved in the MAPK–PP–HMGR regulatory module under heat stress. Gene Group Ct Mean ± SD ΔCt ± SD ΔΔCt (vs T-0) 2⁻ΔΔCt HMGR-1 T-0 26.12 ± 0.17 −0.78 ± 0.18 0.00 1.00 T-12 25.80 ± 0.06 −3.73 ± 0.06 −2.95 7.71 T-24 26.35 ± 0.10 −2.53 ± 0.10 −1.75 3.36 HMGR-2 T-0 26.94 ± 0.04 0.05 ± 0.04 0.00 1.00 T-12 28.69 ± 0.12 −0.83 ± 0.12 −0.88 1.84 T-24 27.28 ± 0.07 −1.61 ± 0.07 −1.66 3.16 HMGR-3 T-0 25.91 ± 0.04 −0.98 ± 0.04 0.00 1.00 T-12 27.85 ± 0.25 −1.68 ± 0.25 −0.70 1.62 T-24 27.09 ± 0.21 −1.79 ± 0.21 −0.81 1.75 HMGR-4 — — — — — MPK3-like T-0 27.19 ± 0.12 0.30 ± 0.12 0.00 1.00 T-12 27.00 ± 0.04 −2.53 ± 0.04 −2.83 7.12 T-24 25.84 ± 0.06 −3.04 ± 0.06 −3.34 10.06 MPK6-like T-0 24.87 ± 0.03 −2.02 ± 0.03 0.00 1.00 T-12 26.40 ± 0.03 −3.12 ± 0.03 −1.10 2.14 T-24 26.26 ± 0.17 −2.62 ± 0.17 −0.60 1.52 PP1c-1 T-0 25.56 ± 0.01 −1.33 ± 0.01 0.00 1.00 T-12 27.22 ± 0.12 −2.30 ± 0.12 −0.97 1.96 T-24 27.54 ± 0.06 −2.07 ± 0.06 −0.74 1.67 PP1c-2 T-0 24.90 ± 0.02 −1.99 ± 0.02 0.00 1.00 T-12 26.04 ± 0.06 −3.49 ± 0.06 −1.50 2.83 T-24 25.96 ± 0.01 −2.92 ± 0.01 −0.93 1.91 PP2A-A T-0 24.52 ± 0.02 −2.37 ± 0.02 0.00 1.00 T-12 26.72 ± 0.20 −2.80 ± 0.20 −0.43 1.35 T-24 26.03 ± 0.06 −2.85 ± 0.06 −0.48 1.39 PP2A-C T-0 27.24 ± 0.10 −0.59 ± 0.10 0.00 1.00 T-12 28.07 ± 0.06 −1.46 ± 0.06 −0.87 1.83 T-24 27.65 ± 0.14 −1.51 ± 0.14 −0.92 1.89 OSCs T-0 20.55 ± 0.13 −6.39 ± 0.13 0.00 1.00 T-12 23.48 ± 0.12 −6.05 ± 0.12 0.34 0.79 T-24 23.69 ± 0.18 −5.20 ± 0.18 1.19 0.44 Note: Relative expression was calculated using the 2⁻ΔΔCt method with SAND as the internal reference gene. Values represent the mean of three biological replicates for each treatment group (T-0, T-12, and T-24). Genes showing 2⁻ΔΔCt > 1 were considered upregulated, while values < 1 indicate downregulation relative to the control (T-0). 4. Conclusions This study integrates transcriptomic, metabolomic, and RT-qPCR validation analyses to elucidate the molecular basis of squalene accumulation in C. oleifera fruits under heat stress. The results reveal that heat exposure triggers a coordinated transcriptional reprogramming involving signal perception, MAPK cascade activation, phosphatase-mediated dephosphorylation, and downstream modulation of the mevalonate pathway. Notably, HMGR-1 was identified as the principal heat-inducible isoform, displaying a marked upregulation pattern verified by RT-qPCR, while its expression was concomitant with the activation of MPK6 -like and PP2A-C , suggesting a phosphorylation–dephosphorylation control loop within the MAPK-PP-HMGR axis. The integration of multi-omic evidence supports a regulatory model in which MAPK kinases and phosphatases synergistically fine-tune HMGR activity to optimize carbon flux toward squalene biosynthesis during thermal adaptation. Concurrent changes in OSCs and other terpenoid biosynthetic genes further reinforce the role of this axis in modulating triterpenoid metabolism. These findings not only provide mechanistic insight into lipid metabolic plasticity in C. oleifera fruits under abiotic stress but also highlight potential molecular targets ( HMGR-1 , MPK6 -like, PP2A-C ) for genetic improvement or biochemical modulation of oil quality and yield in woody oil crops Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The RNA-seq datasets generated and analyzed in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1355314 (Heat stress transcriptome of C. oleifera seed kernels, Oct 30, 2025). Additional data supporting the findings of this study are included in the article and its supplementary materials. Further information is available from the corresponding author upon reasonable request. Competing interests The authors declare no conflict of interest. During the preparation of this work, the authors did not use any generative AI or AI-assisted technologies. The entire manuscript was written and revised by the authors themselves, and the authors take full responsibility for the content of the published article. Funding This research was funded by the Central Guiding Local Science and Technology Development Fund (Grant no. Guike ZY23055025), the Guangxi Natural Science Foundation (Grant no. 2025GXNSFAA069040) and the Guangxi Forestry Science and Technology Promotion Demonstration Project (Grant no. 2023GXLK22). Authors’ contributions Jianwen Wu : conceptualization, funding acquisition, writing–original draft. Rong Qin : data curation, software, validation, writing–original draft. Yingying Chen : formal analysis, writing–original draft, project administration. Jihua Guan : investigation, methodology, writing–review and editing. All authors have read and approved the submitted version of the manuscript. Acknowledgements We thank Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript. References Antolín-Llovera M, Leivar P, Arró M, Ferrer A, Boronat A, Campos N. Modulation of plant HMG-CoA reductase by protein phosphatase 2A: Positive and negative control at a key node of metabolism. Plant Signal Behav. 2011;6(8):1127–31. https://doi.org/10.4161/psb.6.8.16363 . Asai T, Tena G, Plotnikova J, Willmann MR, Chiu W-L, Gomez-Gomez L, Boller T, Ausubel FM, Sheen J. MAP kinase signalling cascade in Arabidopsis innate immunity. Nature. 2002;415(6875):977–83. https://doi.org/10.1038/415977a . Doherty CJ, Van Buskirk HA, Myers SJ, Thomashow MF. Roles for Arabidopsis CAMTA Transcription Factors in Cold-Regulated Gene Expression and Freezing Tolerance. Plant Cell. 2009;21(3):972–84. https://doi.org/10.1105/tpc.108.063958 . Falcone Ferreyra ML, Rius SP, Casati P. Flavonoids: Biosynthesis, biological functions, and biotechnological applications. Front Plant Sci. 2012;3. https://doi.org/10.3389/fpls.2012.00222 . Feng W, Kita D, Peaucelle A, Cartwright HN, Doan V, Duan Q, Liu M-C, Maman J, Steinhorst L, Schmitz-Thom I, Yvon R, Kudla J, Wu H-M, Cheung AY, Dinneny JR. The FERONIA Receptor Kinase Maintains Cell-Wall Integrity during Salt Stress through Ca2 + Signaling. Curr Biol. 2018;28(5):666–e6755. https://doi.org/10.1016/j.cub.2018.01.023 . Gong Z, Xiong L, Shi H, Yang S, Herrera-Estrella LR, Xu G, Chao D-Y, Li J, Wang P-Y, Qin F, Li J, Ding Y, Shi Y, Wang Y, Yang Y, Guo Y, Zhu J-K. Plant abiotic stress response and nutrient use efficiency. Sci China Life Sci. 2020;63(5):635–74. https://doi.org/10.1007/s11427-020-1683-x . Gu Y, Zhu Z, Zeng Y, Pan X, Lu M, Huang X, Ge L, Li A, Bi W, Du Q, Cao H, Yang G, Tan X. CoHMGR2 as a Critical Regulator of Squalene Biosynthesis Key Period in Camellia oleifera Seed Kernels. Physiol Plant. 2025;177(3):e70352. https://doi.org/10.1111/ppl.70352 . Havaux M, Ksas B, Szewczyk A, Rumeau D, Franck F, Caffarri S, Triantaphylidès C. Vitamin B6 deficient plants display increased sensitivity to high light and photo-oxidative stress. BMC Plant Biol. 2009;9(1):130. https://doi.org/10.1186/1471-2229-9-130 . He M, Ding N-Z. Plant Unsaturated Fatty Acids: Multiple Roles in Stress Response. Front Plant Sci. 2020;11:562785. https://doi.org/10.3389/fpls.2020.562785 . He X, Xu L, Pan C, Gong C, Wang Y, Liu X, Yu Y. Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics. PLoS ONE. 2020;15(7):e0235795. https://doi.org/10.1371/journal.pone.0235795 . He Z, Cui K, Wang R, Xu T, Zhang Z, Wang X, Chen Y, Zhu Y. Multi-omics joint analysis reveals how Streptomyces albidoflavus OsiLf-2 assists Camellia oleifera to resist drought stress and improve fruit quality. Front Microbiol. 2023;14:1152632. https://doi.org/10.3389/fmicb.2023.1152632 . He Z, Liu C, Zhang Z, Wang R, Chen Y. Integration of mRNA and miRNA analysis reveals the differentially regulatory network in two different Camellia oleifera cultivars under drought stress. Front Plant Sci. 2022;13:1001357. https://doi.org/10.3389/fpls.2022.1001357 . Kayani S-I. Enhancing biomass productivity towards carbohydrates and fatty acids biosynthesis under the effects of magneto-electric composite fields on filamentous algae. Biomass and Bioenergy; 2023. Kayani S-I, Zhu F, Yu Z, Rehman OU, Zhang C, Rehman Shah MA, Li Y, Huo S. Jasmonates regulate metabolic cascade towards Eicosapentaenoic acid and Docosahexaenoic acid biosynthesis in filamentous microalga Tribonema minus. Bioresour Technol. 2025;435:132931. https://doi.org/10.1016/j.biortech.2025.132931 . Kidokoro S, Yoneda K, Takasaki H, Takahashi F, Shinozaki K, Yamaguchi-Shinozaki K. Different Cold-Signaling Pathways Function in the Responses to Rapid and Gradual Decreases in Temperature. Plant Cell. 2017;29(4):760–74. https://doi.org/10.1105/tpc.16.00669 . Leivar P, Antolín-Llovera M, Ferrero S, Closa M, Arró M, Ferrer A, Boronat A, Campos N. Multilevel Control of Arabidopsis 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase by Protein Phosphatase 2A. Plant Cell. 2011;23(4):1494–511. https://doi.org/10.1105/tpc.110.074278 . Liu H, Liao H, Charng Y. The role of class A1 heat shock factors (HSFA1s) in response to heat and other stresses in Arabidopsis . Plant Cell Environ. 2011;34(5):738–51. https://doi.org/10.1111/j.1365-3040.2011.02278.x . Obata T, Fernie AR. The use of metabolomics to dissect plant responses to abiotic stresses. Cell Mol Life Sci. 2012;69(19):3225–43. https://doi.org/10.1007/s00018-012-1091-5 . Ohama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional Regulatory Network of Plant Heat Stress Response. Trends Plant Sci. 2017;22(1):53–65. https://doi.org/10.1016/j.tplants.2016.08.015 . Robertlee J, Kobayashi K, Suzuki M, Muranaka T. AKIN 10, a representative Arabidopsis SNF 1-related protein kinase 1 (Sn RK 1), phosphorylates and downregulates plant HMG ‐CoA reductase. FEBS Lett. 2017;591(8):1159–66. https://doi.org/10.1002/1873-3468.12618 . Sedaghatmehr M, Thirumalaikumar VP, Kamranfar I, Marmagne A, Masclaux-Daubresse C, Balazadeh S. A regulatory role of autophagy for resetting the memory of heat stress in plants. Plant Cell Environ. 2019;42(3):1054–64. https://doi.org/10.1111/pce.13426 . Serra D, Asins G, Hegardt F. Purification and characterization of a protein inhibitor from rat liver that inhibits type 1 protein phosphatase when 3-hydroxy-3-methylglutaryl CoA reductase is the substrate. J Lipid Res. 1990;31(5):919–26. https://doi.org/10.1016/S0022-2275(20)42791-9 . Sharma P, Lakra N, Goyal A, Ahlawat YK, Zaid A, Siddique KHM. Drought and heat stress mediated activation of lipid signaling in plants: A critical review. Front Plant Sci. 2023;14:1216835. https://doi.org/10.3389/fpls.2023.1216835 . Shen Q, Lu X, Yan T, Fu X, Lv Z, Zhang F, Pan Q, Wang G, Sun X, Tang K. The jasmonate-responsive Aa MYC 2 transcription factor positively regulates artemisinin biosynthesis in Artemisia annua . New Phytol. 2016;210(4):1269–81. https://doi.org/10.1111/nph.13874 . Takemiya A, Shimazaki K. Phosphatidic Acid Inhibits Blue Light-Induced Stomatal Opening via Inhibition of Protein Phosphatase 1. Plant Physiol. 2010;153(4):1555–62. https://doi.org/10.1104/pp.110.155689 . Tholl D. (2015). Biosynthesis and Biological Functions of Terpenoids in Plants. In J. Schrader & J. Bohlmann, editors, Biotechnology of Isoprenoids (Vol. 148, pp. 63–106). Springer International Publishing. https://doi.org/10.1007/10_2014_295 Yang D, Chen Y, Wang R, He Y, Ma X, Shen J, He Z, Lai H. Effects of Exogenous Abscisic Acid on the Physiological and Biochemical Responses of Camellia oleifera Seedlings under Drought Stress. Plants. 2024;13(2):225. https://doi.org/10.3390/plants13020225 . Yang L, Gu Y, Zhou J, Yuan P, Jiang N, Wu Z, Tan X. Whole-Genome Identification and Analysis of Multiple Gene Families Reveal Candidate Genes for Theasaponin Biosynthesis in Camellia oleifera. Int J Mol Sci. 2022;23(12):6393. https://doi.org/10.3390/ijms23126393 . Ye Z, Du B, Zhou J, Cao Y, Zhang L. Camellia oleifera CoSWEET10 Is Crucial for Seed Development and Drought Resistance by Mediating Sugar Transport in Transgenic Arabidopsis. Plants. 2023;12(15):2818. https://doi.org/10.3390/plants12152818 . Yoshida T, Ohama N, Nakajima J, Kidokoro S, Mizoi J, Nakashima K, Maruyama K, Kim J-M, Seki M, Todaka D, Osakabe Y, Sakuma Y, Schöffl F, Shinozaki K, Yamaguchi-Shinozaki K. Arabidopsis HsfA1 transcription factors function as the main positive regulators in heat shock-responsive gene expression. Mol Genet Genomics. 2011;286(5–6):321–32. https://doi.org/10.1007/s00438-011-0647-7 . Zeevaart JAD. Changes in the Levels of Abscisic Acid and Its Metabolites in Excised Leaf Blades of Xanthium strumarium during and after Water Stress. Plant Physiol. 1980;66(4):672–8. https://doi.org/10.1104/pp.66.4.672 . Zhu J-K. Abiotic Stress Signaling and Responses in Plants. Cell. 2016;167(2):313–24. https://doi.org/10.1016/j.cell.2016.08.029 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8118690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554170759,"identity":"e5d41246-3bf4-400b-acc4-1a6e6f742363","order_by":0,"name":"Jianwen Wu","email":"","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jianwen","middleName":"","lastName":"Wu","suffix":""},{"id":554170760,"identity":"ea3c4b17-1c07-40e4-9f86-d5a90e56d042","order_by":1,"name":"Rong Qin","email":"","orcid":"","institution":"Guangxi Gaofeng State Owned Forest Farm","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Qin","suffix":""},{"id":554170761,"identity":"97796e83-8200-42b2-9aad-c7634a0cce75","order_by":2,"name":"Yingying Chen","email":"","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Chen","suffix":""},{"id":554170763,"identity":"f2faf039-47da-43bc-9aff-3b5c96c9bd4a","order_by":3,"name":"Jihua Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYLACCYMDcmzs7QdI02LMx3MmgSR7DiTOk3AwIE6tfETyswcWBXfS2yQYEhh+VGwjrMXwRpq5gYTBs9w26cYDjD1nbhOhZUaCmYSEweHcNpkDCcyMbURpSf8G0pLOJpFgQJwWeYkcsC0JxGsx4HlTBtJi2AYM5INE+UW+PX2btMSfw/Ly7e0HH/yoIMaWAwwMzBJQzgHC6kG2NDAwMH4gSukoGAWjYBSMWAAA2h86rTzVTAQAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Jihua","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2025-11-15 01:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8118690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8118690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97667968,"identity":"e36cccf6-d5ae-420d-a057-3e9469ad96a9","added_by":"auto","created_at":"2025-12-08 09:24:34","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9953403,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript2025.11.19.doc","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/b94fc822769f2d689e131e5d.doc"},{"id":97669354,"identity":"68e8abff-db2f-4b4f-9cf5-f9217424d474","added_by":"auto","created_at":"2025-12-08 09:27:50","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6146,"visible":true,"origin":"","legend":"","description":"","filename":"054fbd0df075441a98687ec85bbf5c74.json","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/88824d3fe353b1945e6f71e8.json"},{"id":97453716,"identity":"0a7a693a-6739-42d8-8e05-338a6fd832e3","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165936,"visible":true,"origin":"","legend":"","description":"","filename":"054fbd0df075441a98687ec85bbf5c741enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/3604c2814ff0bfce4cdd712d.xml"},{"id":97453722,"identity":"cde9fe83-ae0f-4127-b3b2-93ff83545b63","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":280115,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/95280533facc798ede402f4a.jpeg"},{"id":97668697,"identity":"20da5e14-e4d8-4c6e-b3a7-4156a4863ebc","added_by":"auto","created_at":"2025-12-08 09:26:04","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195844,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/173a08de53432b9c01d382ea.png"},{"id":97669506,"identity":"f0eab961-2c7d-41ec-a7a2-bf427ae2d174","added_by":"auto","created_at":"2025-12-08 09:28:06","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202231,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/84cad148b61686819efa3691.png"},{"id":97453723,"identity":"addc1461-a67a-496d-9548-ea56aa761404","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":531139,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/1f1b5da2b75d8d6dbce9f810.jpeg"},{"id":97453728,"identity":"61889f79-b78a-445b-8624-42299d1d1f94","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":524372,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/778585eaa08a7910bb47bf24.jpeg"},{"id":97669149,"identity":"abd69552-7d84-4ac6-8994-c6cac2ac51fa","added_by":"auto","created_at":"2025-12-08 09:27:26","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":485448,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/7ea32582eeb7f28336680cc0.jpeg"},{"id":97668152,"identity":"18b99ed9-a294-43d6-9b09-ebbb20922b4a","added_by":"auto","created_at":"2025-12-08 09:24:53","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78402,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/6cca03ad318daf05005e6b38.jpeg"},{"id":97453727,"identity":"347e9497-c3a1-4a37-8423-95c4d9d285d7","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2560,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/4961a0aa9df9de1c57171862.png"},{"id":97453725,"identity":"b89949ec-4953-41f4-ad17-5f6e6e34c1e4","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23540,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/82fb82ece9308a0b9bc2428b.png"},{"id":97667947,"identity":"8b645bf8-a188-46c3-bd7c-37537f2f9d58","added_by":"auto","created_at":"2025-12-08 09:24:30","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68238,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/d5821013fed79d803fe39cb8.png"},{"id":97668194,"identity":"969970ce-400a-4366-bb21-4870e09352c2","added_by":"auto","created_at":"2025-12-08 09:25:01","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46018,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/c644fe077ac6afc9e9c0baec.png"},{"id":97453734,"identity":"56bde202-b9ea-4558-9b86-728d47bd864b","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":691133,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/1efae32cc6ebfda64874bebd.jpeg"},{"id":97669680,"identity":"b9822a5f-8dab-41bc-ada7-e38522df93b5","added_by":"auto","created_at":"2025-12-08 09:28:41","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197819,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/bbd17e8430e242b597c87f3c.jpeg"},{"id":97669527,"identity":"94645891-1e0e-4872-9b70-a91240befaf6","added_by":"auto","created_at":"2025-12-08 09:28:08","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":287643,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/e07a58e56185c75396f75f27.jpeg"},{"id":97669331,"identity":"62478434-db5a-4e16-8dcc-11d16b22a53c","added_by":"auto","created_at":"2025-12-08 09:27:49","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53973,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/3efe3fe05f74a0a63bc7b477.png"},{"id":97453745,"identity":"f65218db-6d77-4144-9e6a-4025ed570c78","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196677,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/aa5087fd2956494962763e9e.png"},{"id":97453735,"identity":"6bfe10a3-0a0f-4b0e-9e59-f0d34bc1a64e","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40245,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/cac2dec5439552e0e63d0ed2.jpeg"},{"id":97668348,"identity":"9700527c-b073-437a-8a19-7be887cf6c5f","added_by":"auto","created_at":"2025-12-08 09:25:22","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50406,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/6a291209eb25fbabaff32670.png"},{"id":97669288,"identity":"b6405b58-6866-4519-8d7f-3aa5d6a0b4e9","added_by":"auto","created_at":"2025-12-08 09:27:45","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66509,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/72e1cb31e31f5da06fc604f2.png"},{"id":97453731,"identity":"1f84de45-1194-403e-88a4-91546f29e139","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68457,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/2310db9981d74a9f68b0db92.png"},{"id":97669111,"identity":"fb9a58a2-7829-4912-b694-1c116d6c455c","added_by":"auto","created_at":"2025-12-08 09:27:14","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126639,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/080d5c08fcb09062d81e3831.png"},{"id":97453741,"identity":"4d15af33-577e-48be-9730-8aed5eccd7a3","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130212,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/7dc68dcd7c4a0e32958ae9ca.png"},{"id":97667939,"identity":"56220875-fd1f-4fd7-b815-b15f3cbc1944","added_by":"auto","created_at":"2025-12-08 09:24:29","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109690,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/6fb13944a2d36cceaba45f0f.png"},{"id":97453738,"identity":"e41fbb6b-d697-4407-8648-72cea7caa5ea","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13705,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/4d9394ac0ee77eddb0a77e68.png"},{"id":97453750,"identity":"c133b946-c015-4e1a-a575-9a59fd20c89e","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3384,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/5d095fc2a89857cb3673942d.png"},{"id":97453755,"identity":"d907d74f-e4b2-4f04-a1b6-a7381cea8d69","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15644,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/7510d031c25a45e4740194de.png"},{"id":97667846,"identity":"c707b568-9fea-434b-982b-352075255553","added_by":"auto","created_at":"2025-12-08 09:24:22","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57046,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/80b98aa97c28e72e42987ee6.png"},{"id":97668556,"identity":"5a980e25-ce02-471c-aaab-a6ce7a1f617c","added_by":"auto","created_at":"2025-12-08 09:25:47","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36975,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/fe41852ff547b0997f68f280.png"},{"id":97453748,"identity":"48273d0b-9afa-452f-8b3f-0050db45d3f4","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132147,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/5eede9e8fbfa11f1c44712a7.png"},{"id":97453747,"identity":"1117de75-843c-4205-91fe-2b5c29c15747","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36771,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/67d4f5f736e93fcc35862731.png"},{"id":97668580,"identity":"15217c7d-3519-4e9b-9a17-ef2d480d5552","added_by":"auto","created_at":"2025-12-08 09:25:50","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45426,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/0846f36292e51002d8acf992.png"},{"id":97669511,"identity":"c1b59b30-4817-4dc9-92aa-a9ad17fda121","added_by":"auto","created_at":"2025-12-08 09:28:07","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16427,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/04ad8d0224c195b705511089.png"},{"id":97453753,"identity":"538140b7-bcc3-490b-acfa-daa989e394a0","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67191,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/41cd84c9f3bb0d90731978fb.png"},{"id":97453752,"identity":"ff342f7a-eea1-4ac6-b4fa-92e567a8ef8f","added_by":"auto","created_at":"2025-12-04 14:06:03","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32037,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/2a60dae5af06792b3244e6fd.png"},{"id":97453756,"identity":"12252eb4-ec51-423b-833d-7baa00cefee2","added_by":"auto","created_at":"2025-12-04 14:06:04","extension":"xml","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164096,"visible":true,"origin":"","legend":"","description":"","filename":"054fbd0df075441a98687ec85bbf5c741structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/d345f68edd5a156d93fea06c.xml"},{"id":97667795,"identity":"cb98affc-e70c-45db-9daf-fcc6b27ba636","added_by":"auto","created_at":"2025-12-08 09:24:16","extension":"html","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176022,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/97f6bfbe199284f89915fb16.html"},{"id":97453713,"identity":"54a33ae8-87cf-4e43-b243-3e8915703c2b","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":616451,"visible":true,"origin":"","legend":"\u003cp\u003eFresh Camellia oleifera fruits subjected to 35 ℃ stress treatment\u003c/p\u003e\n\u003cp\u003eAll data were obtained from seed-kernel tissues (n = 3 biological replicates per group\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/7019c3aa10e350d9f5949347.png"},{"id":97453710,"identity":"6c553b7a-a2c9-45a2-9e7a-e01947fac12d","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127889,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis, correlation heatmap analysis, and Venn diagram of differentially expressed genes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/0514d499f8ddd7bbc3f497c1.png"},{"id":97453711,"identity":"3ceb7974-9007-4371-9e26-ebc5d39b4f3d","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162394,"visible":true,"origin":"","legend":"\u003cp\u003eGO term distribution and KEGG enrichment scatter plot for DEGs in the T-12 vs T-0 comparison group..\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/8fb84918467dec24b3bb2c9b.png"},{"id":97453715,"identity":"d41ccb0c-1285-4839-943a-8fa138dc63b3","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":300337,"visible":true,"origin":"","legend":"\u003cp\u003eK-means transcriptomic analysis of the effects of high-temperature stress conditions. DEGs are divided into five subclasses. The top 10 KEGG pathways in each subclass are listed.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/7f4f4c5e049d99fa057796b3.png"},{"id":97668300,"identity":"d0e1ad2e-9b96-4954-b1ec-0e9559b0e1f2","added_by":"auto","created_at":"2025-12-08 09:25:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118731,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score plot, sample population clustering plot, and Venn diagram of comparison groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/fec061307251ba86f2e3d409.png"},{"id":97669302,"identity":"8cac42df-f717-45d4-8371-2f4f7e58e68e","added_by":"auto","created_at":"2025-12-08 09:27:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97788,"visible":true,"origin":"","legend":"\u003cp\u003eEnriched KEGG pathway bubble chart.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/df6a0727c83f0acc311b1680.png"},{"id":97453719,"identity":"34553544-87c6-487d-9d68-e8f5f6ab9cf3","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":561215,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between transcriptome and metabolome under heat stress.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/9248411f86e31c4d266dc8dc.png"},{"id":97453717,"identity":"5d827dc6-479b-4c77-8871-689d840ff570","added_by":"auto","created_at":"2025-12-04 14:06:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":80182,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted phosphorylation sites (NetPhos 3.1).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/583ae5537d6fa334aeac61c7.png"},{"id":100365998,"identity":"4f9f8d9d-e23e-4691-89f8-95fe83e54b02","added_by":"auto","created_at":"2026-01-16 07:55:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3395160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8118690/v1/8463a055-b27f-432d-8506-a2f1daebd9cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitogen-Activated Protein Kinases and Phosphatases Synergistically Regulate 3-Hydroxy-3-Methylglutaryl-CoA Reductase To Enhance Squalene Biosynthesis in Camellia oleifera Fruits under Heat Stress Conditions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlants have developed complex mechanisms to perceive and respond to external stresses under constantly changing environmental conditions. There are similarities among abiotic stresses, such as temperature extremes, high salinity, and drought, in terms of their effect on plants and how plants perceive them. For example, these abiotic stresses typically affect plant cell osmotic pressure, leading to a transient increase in the cytoplasmic Ca\u003csup\u003e2+\u003c/sup\u003e concentration. Therefore, Ca\u003csup\u003e2+\u003c/sup\u003e is considered to be a universal second messenger for major stress signals (Gong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, different abiotic stresses have many distinct effects on plants. For example, cold and heat can decrease and increase cell membrane fluidity(Zhu, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), respectively, while salt stress, which is perceived via the feronia signaling pathway, can weaken the cell wall. The activation of the FER signaling pathway can prevent cell rupture (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Under drought conditions, the abscisic acid (ABA) concentration can increase by 50-times, which is one of the most drastic changes in plant hormone concentrations observed to date in response to environmental stimuli (Zeevaart, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).Beyond thermal cues, other physical stimuli can also reprogram energy and lipid metabolism; for example, magneto-electric composite fields enhanced carbohydrate and fatty acid biosynthesis in filamentous algae by stimulating core metabolic routes(Kayani, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTranscriptomics analyses are important for thoroughly investigating the mechanisms underlying plant responses and adaptations to environmental stress that continue to be identified. At low temperatures, \u003cem\u003eCBF\u003c/em\u003e expression may be rapidly induced in plants (\u0026gt;\u0026thinsp;100-times), with calmodulin-binding transcriptional activator (CAMTA) functioning as a positive regulator of \u003cem\u003eCBF\u003c/em\u003e expression (Doherty et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Both CAMTA3 and CAMTA5 regulate the expression of \u003cem\u003eCBF1\u003c/em\u003e and \u003cem\u003eCBF2\u003c/em\u003e in response to a rapid decrease in temperature (Kidokoro et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Heat stress transcription factor A1 (HsfA1) appears to be a key regulator of heat tolerance (Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Specifically, it activates the expression of heat stress response genes, including \u003cem\u003eHsfA7\u003c/em\u003e, \u003cem\u003eHsfA2\u003c/em\u003e, \u003cem\u003eHsfB\u003c/em\u003e, \u003cem\u003eMBF1c\u003c/em\u003e (multiprotein bridging factor 1c), and \u003cem\u003eDREB2A\u003c/em\u003e, with the encoded proteins subsequently regulating the synthesis of molecular chaperones and enzymes involved in degrading unfolded proteins and scavenging reactive oxygen species (Ohama et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). HsfA1 also regulates the expression of the \u003cem\u003eCopia\u003c/em\u003e-like retrotransposon \u003cem\u003eONSEN\u003c/em\u003e and contributes to heat stress memory (Sedaghatmehr et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCamellia oleifera\u003c/em\u003e, which belongs to the genus \u003cem\u003eCamellia\u003c/em\u003e in the family Theaceae, is one of the four major woody oil crop species worldwide. Its seeds contain 20%\u0026ndash;30% oil and are rich in unsaturated fatty acids and various bioactive substances. Recent research on \u003cem\u003eC. oleifera\u003c/em\u003e has clarified its adaptations to various environmental stresses, especially in terms of the mechanisms and regulatory pathways mediating the response to drought conditions. Drought stress can significantly alter \u003cem\u003eC. oleifera\u003c/em\u003e growth, enzyme secretion, stomatal morphology, and leaf osmotic regulatory substances (He et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Both miR398 and miR408-3p can enhance \u003cem\u003eC. oleifera\u003c/em\u003e drought resistance by negatively regulating the expression of genes encoding downy mildew resistance 6 (DMR6) and enhanced disease resistance 2 (EDR2), respectively (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exogenous ABA effectively activates the antioxidant system by inhibiting stomatal conductance and moderately decreasing the photosynthetic rate, thereby alleviating oxidative damage caused by drought stress (Yang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The endophytic bacterium \u003cem\u003eStreptomyces\u003c/em\u003e OSILF-2 reportedly protects \u003cem\u003eC. oleifera\u003c/em\u003e from drought stress (He et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, CoSWEET10, which is a sucrose and hexose transporter, plays a dual role in promoting seed development and enhancing plant drought resistance (Ye et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent research has clarified that the \u003cem\u003eC. oleifera HMGR\u003c/em\u003e gene family consists of four members (\u003cem\u003eCoHMGR1-4\u003c/em\u003e) exhibiting tissue- and stage-specific expression during seed development (Gu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among them, \u003cem\u003eCoHMGR2\u003c/em\u003e acts as the dominant isoform regulating squalene and triterpenoid accumulation during the mid-maturation phase of the seed kernel. These findings indicate that the kernel is not merely a passive oil-storage organ but a metabolically dynamic tissue where HMGR-driven terpenoid biosynthesis is tightly coupled with oil-body formation and membrane biogenesis. Building on these insights, our study investigates how heat stress perturbs this kernel-specific regulatory network to modulate squalene accumulation.\u003c/p\u003e\u003cp\u003eIn this study, ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and second-generation sequencing technology (Illumina HiSeq ultra-high-throughput sequencing) were applied to explore changes in metabolic products and transcript levels in harvested \u003cem\u003eC. oleifera\u003c/em\u003e fresh fruits during three post-ripening stages under high-temperature stress (35\u0026deg;C). Moreover, differences in gene expression among the harvested fresh fruits during different post-ripening stages were thoroughly investigated. Therefore, this study aimed to elucidate how heat stress reshapes squalene biosynthesis and membrane adaptation in \u003cem\u003eC. oleifera\u003c/em\u003e seed-kernel tissues through integrated transcriptomic and metabolomic analyses. Consistently, hormone signaling can orchestrate lipid metabolic cascades; exogenous jasmonates markedly boosted ω-3 polyunsaturated fatty acid biosynthesis through transcriptional activation of key regulators in \u003cem\u003eTribonema minus\u003c/em\u003e (Kayani et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), supporting a signal-induced reprogramming paradigm that we examine under heat stress in \u003cem\u003eC. oleifera\u003c/em\u003e. We propose that heat exposure triggers \u003cem\u003eMAPK\u003c/em\u003e cascades (\u003cem\u003eMPK3/MPK6-like\u003c/em\u003e) and \u003cem\u003ePP1/PP2A\u003c/em\u003e-mediated dephosphorylation of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), enhancing its catalytic activity and flux through the mevalonate (MVA) pathway. The resulting elevation in squalene, sterols, and triterpenoids, together with increased unsaturated fatty acids, contributes to adaptive membrane remodeling and improved thermal tolerance. This working model provides a mechanistic framework linking signal transduction, enzyme regulation, and metabolic adaptation under heat stress.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Metabolite extraction\u003c/h2\u003e\u003cp\u003eFully mature fruits of \u003cem\u003eC. oleifera\u003c/em\u003e were harvested from the same experimental plantation (Guangxi, China) after Shuangjiang (the first frost period, late October to early November), when the fruits had reached full maturity. After removing the pericarp and seed coat, the kernels were used for analyses. Samples were divided into three groups: untreated control (T-0) and heat-treated fruits incubated at 35\u0026deg;C for 12 h (T-12) and 24 h (T-24). Each group contained three biological replicates (n\u0026thinsp;=\u0026thinsp;3). The kernels were immediately frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for transcriptomic (RNA-seq) and metabolomic (LC-MS) analyses. For metabolite extraction, the frozen seeds were lyophilized using a vacuum freeze-dryer (Scientz-100F) and then crushed using a mixer mill (MM 400, Retsch) and a zirconia bead (1.5 min at 30 Hz). The powdered material (50 mg) was resuspended in 1.2 mL 70% methanol solution and vortexed six times (30 s every 30 min). The mixture was centrifuged (centrifuge at 80.5 \u0026times;g for 3 min) and then the supernatant (extract) was filtered (SCAA-104, 0.22 \u0026micro;m pores; ANPEL, Shanghai, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.anpel.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.anpel.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the subsequent UPLC-MS/MS analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 UPLC-MS/MS conditions\u003c/h2\u003e\u003cp\u003eSample extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.shimadzu.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.shimadzu.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; MS, Applied Biosystems 4500 QTRAP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html\u003c/span\u003e\u003cspan address=\"https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Analytical conditions were as follows: UPLC column: Agilent SB-C18 (1.8 \u0026micro;m, 2.1 mm \u0026times; 100 mm); mobile phase: solvent A (pure water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid); gradient elution program: 95% A, 5% B; linear adjustment to 5% A, 95% B within 9 min and then hold for 1 min; adjustment to 95% A, 5.0% B within 1.1 min and then hold for 2.9 min; flow rate: 0.35 mL/min; column oven temperature: 40\u0026deg;C; injection volume: 4 \u0026micro;L. The eluate was analyzed using an ESI-triple quadrupole-linear ion trap (QTRAP)-MS system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 ESI-QTRAP-MS/MS\u003c/h2\u003e\u003cp\u003eThe ESI source operation parameters were as follows: source temperature: 550\u0026deg;C; ion spray voltage: 5,500 V (positive ion mode)/\u0026minus;4,500 V (negative ion mode); ion source gas I, gas II, and curtain gas: 50, 60, and 25 psi, respectively; collision-activated dissociation: high. Instrument tuning and mass calibration were performed using 10 and 100 \u0026micro;mol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. QQQ scans were acquired in MRM experiments with the collision gas (nitrogen) set to medium. Declustering potential and collision energy were optimized for individual MRM transitions. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within that period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Qualitative and quantitative metabolite analyses\u003c/h2\u003e\u003cp\u003eMetabolite data were log\u003csub\u003e2\u003c/sub\u003e-transformed for statistical analyses to improve normality and were normalized. An unsupervised principal component analysis (PCA) was performed using the prcomp function in R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data were unit variance-scaled before completing the unsupervised PCA. A hierarchical cluster analysis (HCA) of samples and metabolites was performed and the results were visualized in heatmaps with dendrograms. Pearson correlation coefficients (PCC) were calculated using the cor function in R and presented in heatmaps. HCA was completed and PCC was calculated using the R package ComplexHeatmap. For HCA, normalized signal intensities of metabolites (after unit variance scaling) were visualized as a color spectrum. For a two-group analysis, differentially accumulated metabolites were determined using the following criteria: VIP\u0026thinsp;\u0026ge;\u0026thinsp;1 and |log\u003csub\u003e2\u003c/sub\u003e(fold-change)| \u0026ge; 1.0. VIP values were extracted from OPLS-DA data, which were presented in score plots and permutation plots generated using the R package MetaboAnalystR. Data were log-transformed and mean-centered before performing OPLS-DA. To avoid overfitting, a permutation test (200 permutations) was completed. Identified metabolites were annotated using the KEGG Compound database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/compound/\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/compound/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), after which annotated metabolites were mapped using the KEGG Pathway database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pathways with significantly regulated metabolites were selected for a metabolite set enrichment analysis using the MSEA online server; their significance was determined on the basis of hypergeometric test p-values. To ensure annotation accuracy, all metabolite identifications were manually validated by matching retention times, precursor ions (m/z), and MS/MS fragmentation spectra with authentic standards in the MetWare MWDB database. KEGG assignments were curated to retain only plant-related metabolic pathways prior to visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 RNA extraction and RNA-seq analysis\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using the Total RNA Extractor (Trizol) kit (Sangon, China) and then treated with RNase-free DNase I to remove any remaining genomic DNA. RNA integrity was evaluated by 1.0% agarose gel electrophoresis, whereas RNA quality and quantity were determined using a NanoPhotometer\u0026reg; spectrophotometer (IMPLEN, CA, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). High-quality RNA samples were used for library construction and sequencing by Sangon Biotech (Shanghai) Co., Ltd. For each sample, 2 \u0026micro;g RNA was used as the input material for constructing sequencing libraries using a VAHTSTM mRNA-seq v2 Library Prep Kit for Illumina\u0026reg;. Index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads and then fragmented using divalent cations at high temperatures in VAHTS\u0026trade; First Strand Synthesis Reaction Buffer (5\u0026times;). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). The second cDNA strand was synthesized using DNA polymerase I and RNase H. The remaining overhangs were converted to blunt ends via exonuclease/polymerase activities. After the 3\u0026prime; ends of cDNA fragments were adenylated, an adapter was ligated to the fragments. An AMPure XP system (Beckman Coulter, Beverly, USA) was used to select cDNA fragments with the preferred length (150\u0026ndash;200 bp), after which 3 \u0026micro;L USER Enzyme (NEB, USA) was added to the size-selected cDNA. The mixture was incubated at 37\u0026deg;C for 15 min and then at 95\u0026deg;C for 5 min. A PCR amplification was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers, and an Index (X) Primer. PCR products were purified (AMPure XP system) and library quality was assessed using the Agilent Bioanalyzer 2100 system. High-quality libraries were then quantified and pooled for the paired-end sequencing performed on a HiSeq XTen system (Illumina, San Diego, CA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data assessment and quality control\u003c/h2\u003e\u003cp\u003eFastQC (version 0.11.2) was used for evaluating the quality of sequenced data. Raw reads were filtered by Trimmomatic (version 0.36) according to several steps: 1) Removing adaptor sequence if reads contains; 2) Removing low quality bases from reads 3\u0026rsquo;to 5\u0026rsquo;(Q\u0026thinsp;\u0026lt;\u0026thinsp;20); 3) Removing low quality bases from reads 5\u0026rsquo;to 3\u0026rsquo;(Q\u0026thinsp;\u0026lt;\u0026thinsp;20); 4) Using a sliding window method to remove the base value less than 20 of reads tail (window size is 5 bp); 5) Removing reads with reads length less than 35nt and its pairing reads. And the remaining clean data was used for further analysis..\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Transcriptome assembly and gene annotation\u003c/h2\u003e\u003cp\u003eClean reads were \u003cem\u003ede novo\u003c/em\u003e assembled into transcripts using Trinity (version 2.0.6) (parameter: min_kmer_cov 2). Transcripts with a minimum length of 200 bp were clustered to minimize redundancy. For each cluster (representing the transcriptional complexity for the same gene), the longest sequence was preserved and designated as a unigene. Unigenes served as queries for a BLAST search of the following databases: NCBI Nr (non-redundant protein database), Swiss-Prot, TrEMBL, CDD (Conserved Domain Database), Pfam, and KOG (EuKaryotic Orthologous Groups) (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5). The best alignments were used to determine unigene open reading frames and the encoded amino acid sequences. TransDecoder (version 3.0.1) was used to predict the coding sequences of the unaligned unigenes. Gene Ontology (GO) functional annotation information was obtained for the transcripts annotated by Swiss-Prot and TrEMBL. KAAS (KEGG Automatic Annotation Server version 2.1) was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 RNA-seq and expression analysis\u003c/h2\u003e\u003cp\u003eBowtie2 (version 2.3.2) was used for mapping the quality control sequences to the assembled transcripts, and RSeQC (version 2.6.1) was used for statistics the aligned result. Salmon (version 0.8.2) was used to calculate the reads count and expression value of unigenes. The TPM (Transcripts Per Million), eliminates the influence of gene lengths and sequencing discrepancies to enable direct comparison of gene expression between samples. Principal Component Analysis (PCA) and Principal co-ordinates analysis (PCoA) were performed to reflect the distance and difference between samples. DESeq2 (version 1.12.4) was used to determine differentially expressed genes (DEGs) between two samples. Genes were considered as significant differentially expressed if q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt;2. When the normalized expression of a gene was zero between two samples, its expression value was adjusted to 0.01 (as 0 cannot be plotted on a log plot). If the normalized expression of a certain gene in two libraries was all lower than 1, further differential expression analysis was conducted without this gene.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Functional characterization of DEGs\u003c/h2\u003e\u003cp\u003eGO and KEGG analyses were performed to functionally characterize DEGs. The GO database is part of an international standard classification system for gene functions. DEGs were annotated with GO terms (biological functions). The number of genes annotated with each term was recorded. A hypergeometric test was conducted to identify significantly enriched GO terms in the gene list. The KEGG database is a public database of pathways. A KEGG pathway analysis involving a hypergeometric test was completed to reveal significantly enriched metabolic pathways or signal transduction pathways among DEGs. A false discovery rate (q-value)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the threshold for determining the significance of GO terms and KEGG pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll experiments were performed with three biological replicates unless otherwise stated. Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Differences among treatments (T-0, T-12, and T-24) were analyzed using one-way analysis of variance (ANOVA) followed by Tukey\u0026rsquo;s honestly significant difference (HSD) test for multiple comparisons. When data did not meet the assumptions of normality or homogeneity of variance, Kruskal-Wallis non-parametric testing was applied. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using SPSS Statistics 26.0 (IBM, Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Phenotypic and physiological indices\u003c/h2\u003e\u003cp\u003eThe effects of a high-temperature stress treatment on harvested \u003cem\u003eC. oleifera\u003c/em\u003e fresh fruits were determined on the basis of phenotypic and physiological indices. In terms of phenotypic indices, the pericarp of fruits treated for 24 h exhibited obvious cracking (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, a comparison with control fruits (0 h) indicated that the squalene content in seed kernels was essentially unaffected by an exposure to high-temperature stress for 12 h. At the 24 h time point, the squalene content was 1.77-times higher in the heat-stressed group than in the control group. According to physiological and phenotypic indices, transcriptome and metabolome analyses were conducted for samples at three time points (0, 12, and 24 h).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll data were obtained from seed-kernel tissues (n\u0026thinsp;=\u0026thinsp;3 biological replicates per group\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Transcriptome analysis\u003c/h2\u003e\u003cp\u003eTo investigate the molecular mechanism underlying squalene accumulation in harvested \u003cem\u003eC. oleifera\u003c/em\u003e fresh fruits exposed to high-temperature stress, samples were incubated at 35\u0026deg;C for 0, 12, and 24 h, after which the seed coat was removed for a transcriptome analysis. After eliminating low-quality data, 62.38 Gb (62,387,688,331) clean data were obtained, with Q30 exceeding 92.85% and a GC content of 50.12%\u0026ndash;54.48%, reflecting the relatively high quality of the transcriptome data. A total of 131,511 genes were annotated. To identify key factors in the transcriptome data, a PCA was performed to analyze the changes at different high-temperature stress treatment time points (Fig.\u0026nbsp;2A). The first two principal components (PC1 and PC2) could distinguish between different stress treatment time points. In addition, according to the correlation heatmap (Fig.\u0026nbsp;2B), biological replicates in the same sample group were highly correlated, reflecting the repeatability of the results for the selected sample groups. Comparisons of the three selected treatment time points revealed 3,917, 3,701, and 2,079 significant DEGs (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |fold-change| \u0026gt;2). A Venn diagram (Fig.\u0026nbsp;2C) was constructed to present the common and unique DEGs among the comparisons of stress treatment time points. A total of 171 DEGs were common to all three comparisons. The results showed that the exposure to high-temperature stress induced transcriptome-level changes in \u003cem\u003eC. oleifera\u003c/em\u003e seeds.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Functional annotation of DEGs\u003c/h2\u003e\u003cp\u003eAll DEGs were annotated according to the GO database (Fig.\u0026nbsp;3A) and classified into three categories (biological process, cellular component, and molecular function). The enriched biological process GO terms assigned to DEGs included response to stimulus, developmental process, biological regulation, regulation of biological process, metabolic process, cellular process, and cellular component organization or biogenesis. This suggests that high-temperature stress activates the rapid response of \u003cem\u003eC. oleifera\u003c/em\u003e fruits to thermal stimuli, while also triggering complex biological processes, including metabolic regulation, cellular remodeling, and developmental regulation. Accordingly, the mechanism underlying the adaptation of \u003cem\u003eC. oleifera\u003c/em\u003e fruits to heat stress may involve the production of heat-protective proteins expression, the activation of the antioxidant system, and the accumulation of secondary metabolites (e.g., squalene). The enriched cellular component GO terms among the DEGs included organelle, membrane, cell part, cell, and organelle part. Hence, under high-temperature stress conditions, the cell membrane, subcellular organelles (e.g., mitochondria, endoplasmic reticulum, and plastids), and their functional units may be restructured or regulated. This is consistent with the fact plants maintain membrane permeability and organelle homeostasis through membrane-stabilizing proteins, molecular chaperones, and other components under stress conditions. In terms of molecular functions, the enriched GO terms among the DEGs included catalytic activity and binding. This indicates that the high-temperature treatment triggered significant changes in key catalytic processes associated with metabolic pathways as well as in processes related to the recognition and transduction of signals involved in stress responses. Considered together, these findings indicate that heat stress significantly regulates changes to internal structures and functions of \u003cem\u003eC. oleifera\u003c/em\u003e fruit cells, while also inducing the expression of various genes encoding stress response-related proteins with catalytic activities and molecular binding abilities, thereby promoting the adaptive response of \u003cem\u003eC. oleifera\u003c/em\u003e fruits to stress.\u003c/p\u003e\u003cp\u003eOn the basis of a KEGG analysis (Fig.\u0026nbsp;3B), the DEGs in all samples were revealed to be associated with various pathways, including signal transduction, carbohydrate metabolism, translation, and amino acid metabolism. Therefore, under high-temperature stress, \u003cem\u003eC. oleifera\u003c/em\u003e fruits may coordinate multiple physiological and metabolic processes, including signal perception and transduction, energy metabolism, protein synthesis, and cellular homeostasis, to optimize resource allocation and enhance stress adaptation. The enriched KEGG pathways among the DEGs in the T-12 \u003cem\u003evs\u003c/em\u003e T-0 included pyruvate metabolism, protein processing in endoplasmic reticulum, plant\u0026ndash;pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, MAPK signaling pathway \u0026ndash; plant, glycolysis/gluconeogenesis, cysteine and methionine metabolism, carbon metabolism, and carbon fixation in photosynthetic organisms. Therefore, in the early stage of the exposure to high-temperature stress, hormone signaling and MAPK phosphorylation signaling may synergistically regulate the early defense response and metabolic remodeling in \u003cem\u003eC. oleifera\u003c/em\u003e fruits. The enriched KEGG pathways among the DEGs in the T-12 \u003cem\u003evs\u003c/em\u003e T-24 included alpha-linolenic acid metabolism, starch and sucrose metabolism, protein processing in endoplasmic reticulum, plant\u0026ndash;pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, MAPK signaling pathway \u0026ndash; plant, glutathione metabolism, estrogen signaling pathway, and antigen processing and presentation. This indicates that in the middle-to-late stages of the high-temperature stress treatment, the adaptive response of \u003cem\u003eC. oleifera\u003c/em\u003e fruits may involve lipid signaling (e.g., jasmonic acid precursor metabolism) and reactive oxygen species scavenging mechanisms. A comprehensive analysis indicated that the following KEGG pathways were enriched among the DEGs in all three comparison groups: protein processing in endoplasmic reticulum, plant\u0026ndash;pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, and MAPK signaling pathway \u0026ndash; plant. This indicates that these biological processes have a central regulatory role throughout the heat stress response. Specifically, the enrichment of the MAPK signaling pathway and the plant hormone signal transduction pathway suggests that \u003cem\u003eC. oleifera\u003c/em\u003e fruits may synergistically regulate the activities of key metabolic enzymes, such as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), through phosphorylation cascades and hormone signals, thereby affecting the synthesis of secondary metabolites (e.g., squalene).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Dynamic analysis of transcriptome data\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to a K-means analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the gene expression patterns in harvested \u003cem\u003eC. oleifera\u003c/em\u003e fruits under high-temperature stress conditions were divided into subclasses 1\u0026ndash;5. Genes in each subclass had different expression patterns at different treatment time points (T-0, T-12, and T-24) and were associated with different KEGG pathways. Analyses of gene expression patterns in different subclasses revealed significant spatiotemporal changes, reflecting the multiple biological responses of \u003cem\u003eC. oleifera\u003c/em\u003e fruits to high-temperature stress.\u003c/p\u003e\u003cp\u003eThe expression level of subclass 1 genes stabilized after an initial rapid increase in the early stage of the high-temperature treatment (T-12); the enriched KEGG pathways among these genes were mainly carbon metabolism, TCA cycle, amino acid biosynthesis, phenolic compound synthesis, and plant hormone signaling. Considering their expression trends, the subclass 1 genes were rapidly activated during the early response to high-temperature stress, which helped maintain basic cellular metabolism and stress defense responses. These genes may be related to the mechanism mediating the rapid metabolic regulation of fruits exposed to heat stress. In addition, the enrichment of the Toll-like receptor and NF-kB signaling pathway suggests these genes are important for immune-like responses. Subclass 2 gene expression levels decreased rapidly after peaking at T-12; the enriched KEGG pathways were mainly related to stress responses, including MAPK signaling pathway, endoplasmic reticulum protein folding, protein degradation, and RNA splicing. These genes had typical stress-induced expression characteristics and may encode key regulators of heat stress memory, post-transcriptional modifications, and cellular homeostasis. The MAPK signaling pathway, which is critical for heat stress signal perception and transduction, was significantly enriched among subclass 2 genes, providing further evidence of its core regulatory role. Subclass 3 gene expression levels were lowest at T-12, but subsequently increased; the enriched KEGG pathways among these genes included glycolysis/gluconeogenesis, glutathione metabolism, ABC transporters, and PI3K Akt. This suggests that these genes may contribute to the temporary inhibition of energy metabolism during the middle stage of the response to high-temperature stress, which is followed by the resumption of energy metabolism and the activation of antioxidant and transporter functions. Hence, these genes may encode proteins with important roles for the high-temperature stress recovery stage. The expression of subclass 4 genes continued to decrease during the treatment period; the enriched KEGG pathways among these genes included lipid metabolism, amino acid metabolism, ubiquitin-mediated protein degradation, and Toll/NF-kB signaling. Their expression trends and enriched KEGG pathways indicated that subclass 4 genes may be related to non-critical growth and development, which are negatively regulated under high-temperature stress conditions to decrease energy consumption. Moreover, they may be associated with programmed cell death and aging processes. Subclass 5 gene expression levels were most significantly upregulated at T-24; the main enriched KEGG pathways among these genes included RNA splicing, carbon metabolism, TCA cycle, phenylpropanoid synthesis, and two-component systems. The expression levels of these genes were consistent with a typical \u0026ldquo;late repair\u0026rdquo; response trend. These genes may influence cell reconstruction, metabolic recovery, and physiological homeostasis-related regulation during the late stage of the high-temperature stress response.\u003c/p\u003e\u003cp\u003eIn summary, the K-means dynamic clustering analysis revealed the heterogeneity of the transcription-level responses of \u003cem\u003eC. oleifera\u003c/em\u003e fruits under high-temperature stress conditions. Various genes were associated with key time-specific KEGG pathways, including energy metabolism, signal transduction, protein processing, and transport, reflecting the multi-stage dynamic regulatory mechanism in \u003cem\u003eC. oleifera\u003c/em\u003e fruits (from \u0026ldquo;rapid response-regulation buffering-adaptation recovery\u0026rdquo;) after an exposure to high-temperature stress. Notably, the signaling pathway-related genes in subclasses 1 and 5 had highly consistent temporal expression trends that were similar to the transcriptional dynamics of genes encoding HMGR, a key enzyme upstream of squalene synthesis. This suggests that these genes may be closely related to the regulation of secondary metabolism in \u003cem\u003eC. oleifera\u003c/em\u003e fruits exposed to stress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Metabolome analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the effects of high-temperature stress on the metabolism of harvested \u003cem\u003eC. oleifera\u003c/em\u003e fruits, metabolic changes in harvested fruits under high-temperature stress conditions were systematically analyzed using a UPLC-MS/MS platform, which identified 663 metabolites. PCA results (Fig.\u0026nbsp;5A) clearly distinguished the stress treatment group from the control group, with PC1 (33.92%) mainly explaining the differences between treatment and control groups, while PC2 (25.32%) reflected the gradient distribution of stress treatment time points. This result reflected the significant changes in \u003cem\u003eC. oleifera\u003c/em\u003e fruit metabolic activities due to high-temperature stress. The heatmap clustering results (Fig.\u0026nbsp;5B) showed that the T-12 and T-24 stress treatment groups had highly similar metabolic profiles, indicating that the early response to high-temperature stress was established within 12 h and tended to stabilize after 24 h. Specific metabolites, such as tannins, quinones, and alkaloids, accumulated significantly under high-temperature stress conditions. These metabolites are commonly involved in plant abiotic stress responses and may enhance fruit stress tolerance by clearing reactive oxygen species and stabilizing cell structures.\u003c/p\u003e\u003cp\u003eFurther analyses of the T-0 \u003cem\u003evs\u003c/em\u003e T-12, T-0 \u003cem\u003evs\u003c/em\u003e T-24, and T-12 \u003cem\u003evs\u003c/em\u003e T-24 detected 126, 136, and 74 significant differentially accumulated metabolites (VIP\u0026thinsp;\u0026ge;\u0026thinsp;1, fold-change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5), respectively. Thus, the exposure to high-temperature stress appeared to induce extensive metabolic reprogramming, but there were differences in the response patterns at different time points. A Venn diagram analysis (Fig.\u0026nbsp;5C) showed that there were 12 stable core stress response-related metabolites among the selected time points, but there were also differentially accumulated metabolites that were unique to specific comparison groups (e.g., 24 each in the T-0 \u003cem\u003evs\u003c/em\u003e T-12 and T-0 \u003cem\u003evs\u003c/em\u003e T-24), further supporting the stage-specific characteristics of the heat stress response. These metabolites were mainly secondary metabolites (e.g., flavonoids and phenolic acids), energy metabolites (e.g., pyruvic acid and pentose phosphate), and amino acid metabolites, all of which are known to play critical roles in metabolic pathways under abiotic stress conditions. Interestingly, the enriched pathways among the differentially accumulated metabolites in the T-0 \u003cem\u003evs\u003c/em\u003e T-12 were mainly pyruvate metabolism and flavonoid biosynthesis, suggesting that energy metabolism and antioxidant defense are quickly activated during the early stage of the heat stress response. By contrast, the differentially accumulated metabolites in the T-0 \u003cem\u003evs\u003c/em\u003e T-24 and T-12 \u003cem\u003evs\u003c/em\u003e T-24 were associated with pathways related to long-term homeostasis, including nucleotide metabolism and phenylpropanoid biosynthesis, reflecting metabolic adjustments that mainly focused on structural maintenance and regulation during the stress adaptation period. Considered together, these findings suggest that high-temperature stress results in dynamic metabolic changes in \u003cem\u003eC. oleifera\u003c/em\u003e fruits (i.e., \u0026ldquo;rapid response\u0026ndash;adjustment transition\u0026ndash;adaptive homeostasis\u0026rdquo;). These changes include enhanced carbon and nitrogen metabolism and the accumulation of antioxidant substances, but they also involve the selective allocation of core metabolites. These observations provided an important metabolic basis for the subsequent integrated analysis of transcriptome data and the mechanism regulating squalene production.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Analysis of differentially accumulated metabolites\u003c/h2\u003e\u003cp\u003eTo further analyze the regulatory effects of high-temperature stress on the metabolic network of \u003cem\u003eC. oleifera\u003c/em\u003e fruits, a KEGG pathway enrichment analysis of the differentially accumulated metabolites among three comparison groups (T-0 \u003cem\u003evs\u003c/em\u003e T-12, T-0 \u003cem\u003evs\u003c/em\u003e T-24, and T-12 \u003cem\u003evs\u003c/em\u003e T-24) was conducted (Fig.\u0026nbsp;6). The results showed that the significantly enriched KEGG pathways among the differentially accumulated metabolites in all comparison groups were typical secondary metabolic pathways, including biosynthesis of various plant secondary metabolites, and flavonoid biosynthesis, indicating that flavonoids and various secondary metabolites play key roles in the response to high-temperature stress. Specifically, in addition to the above-mentioned pathways, purine metabolism, pentose phosphate pathway, and pyruvate metabolism were identified as enriched KEGG pathways among the differentially accumulated metabolites in the T-0 \u003cem\u003evs\u003c/em\u003e T-12. Thus, the initial exposure to high-temperature stress may modulate the energy supply and stress resistance by affecting pathways related to nucleotide and carbon metabolism. Additional enriched KEGG pathways among the differentially accumulated metabolites in the T-0 \u003cem\u003evs\u003c/em\u003e T-24 were arginine and proline metabolism, vitamin B6 metabolism, starch and sucrose metabolism, and multiple sugar metabolism pathways. Accordingly, during a continuous exposure to heat stress, changes in carbon and nitrogen metabolism and increases in the antioxidant capacity are critical for the adaptive response. In the T-12 \u003cem\u003evs\u003c/em\u003e T-24, the differentially accumulated metabolites were significantly associated with amino acid and derivative metabolism; the enriched KEGG pathways included phenylalanine metabolism, beta-alanine metabolism, lysine biosynthesis, and phenylpropanoid biosynthesis, suggesting that in \u003cem\u003eC. oleifera\u003c/em\u003e fruits, late-stage heat stress responses may be enhanced through amino acid metabolism and the accumulation of phenylpropanoids.\u003c/p\u003e\u003cp\u003eTo screen for core metabolites with potential regulatory significance, the top 20 metabolites in each comparison group were analyzed. These metabolites had the largest fold-changes in abundance, which were consistent with the changes in the expression levels of the corresponding key genes. In the T-0 \u003cem\u003evs\u003c/em\u003e T-12, the highly abundant differentially accumulated metabolites were mainly sugars (e.g., dulcitol and D-mannitol), organic acids (e.g., oxalic acid), lignans (e.g., syringaresinol), and phenolic acids and flavonoids (e.g., gallocatechin, aromadendrin, pinocembrin, and ferulic acid methyl ester). In the T-0 \u003cem\u003evs\u003c/em\u003e T-24, most of the screened metabolites were related to sugars, flavonoids, and lignans, such as phloretin, 3\u0026prime;-O-methyl-epicatechin, apigenin-6-C-(2\u0026Prime;-glucosyl)arabinoside, and pinoresinol, reflecting the continuous accumulation of secondary metabolites. In the T-12 \u003cem\u003evs\u003c/em\u003e T-24, the main metabolites were nucleotides (e.g., 2\u0026prime;-deoxyadenosine and 2\u0026prime;-deoxyguanosine), flavonoids (e.g., apigenin-7-O-(2\u0026Prime;-sinapoyl)glucuronide), and organic acids and amino acid derivatives (e.g., α-ketoglutaric acid, S-(methyl)glutathione, and 4-guanidinobutanal). These results provide further insights into the changes in metabolic homeostasis and the activation of the antioxidant system during the late stage of the \u003cem\u003eC. oleifera\u003c/em\u003e fruit response to stress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Validation of squalene accumulation and MVA-pathway enzyme activities under heat stress\u003c/h2\u003e\u003cp\u003eTo experimentally validate the transcriptomic and metabolomic findings, we quantified the squalene content and determined the activities of two key enzymes-3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) and 2,3-oxidosqualene cyclase (OSCs)-in \u003cem\u003eC. oleifera\u003c/em\u003e seed kernels exposed to heat stress (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results showed that the squalene content increased markedly from 177.6 \u0026micro;g/g in the control (T-0) to 314.3 \u0026micro;g/g (T-12) and 309.1 \u0026micro;g/g (T-24), representing a 2.3-fold elevation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;3). In parallel, HMGR activity rose from 10.70 IU/g to 13.74\u0026ndash;14.03 IU/g, while OSCs activity increased from 18.14 U/g to 19.29\u0026ndash;20.87 U/g. These concerted increases demonstrate that heat stress enhances both the upstream MVA pathway (via HMGR activation) and the downstream triterpenoid cyclization (via OSCs activation), thereby promoting squalene accumulation and subsequent triterpenoid biosynthesis. As sterols and triterpenes derived from squalene are essential constituents of cellular and organellar membranes, their coordinated induction likely contributes to membrane stabilization and thermal adaptation. Together with the enriched GO terms (\u0026ldquo;membrane\u0026rdquo;, \u0026ldquo;organelle\u0026rdquo;, \u0026ldquo;cell part\u0026rdquo;) and metabolomic enrichment of \u0026ldquo;biosynthesis of secondary metabolites\u0026rdquo;, these biochemical validations provide direct quantitative evidence that high-temperature exposure strengthens sterol/triterpenoid metabolism as an adaptive response in \u003cem\u003eC. oleifera\u003c/em\u003e fruits.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQuantitative changes in squalene content and related enzyme activities in \u003cem\u003eC. oleifera\u003c/em\u003e seed kernels under heat stress\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSqualene (\u0026micro;g /g )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHMGR activity (IU/g )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOSCs activity (U/g )\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e177.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e18.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e314.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e13.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e19.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e309.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e20.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (n\u0026thinsp;=\u0026thinsp;3 biological replicates). Asterisks (*) indicate significant differences compared with the control (T-0) based on one-way ANOVA followed by Tukey\u0026rsquo;s HSD test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)..\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Analysis of the correlation between the transcriptome and metabolome\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExpression levels (TPM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) of \u003cem\u003eHMGR\u003c/em\u003e isoforms in \u003cem\u003eC. oleifera\u003c/em\u003e fruits under heat stress\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsoform ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTranscript ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRINITY_DN57861_c3_g1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRINITY_DN57861_c3_g3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRINITY_DN67520_c1_g4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRINITY_DN76432_c0_g1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: TPM values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (n\u0026thinsp;=\u0026thinsp;3 biological replicates). Superscript letters (ᵃ) indicate statistically significant induction compared with T-0 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, one-way ANOVA\u0026thinsp;+\u0026thinsp;Tukey\u0026rsquo;s HSD).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the isoform-level regulation of \u003cem\u003eHMGR\u003c/em\u003e under heat stress, four \u003cem\u003eHMGR\u003c/em\u003e transcripts were identified from the assembled transcriptome of \u003cem\u003eC. oleifera\u003c/em\u003e, consistent with a recent report describing four \u003cem\u003eCoHMGR\u003c/em\u003e genes in this species(Gu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Comparative expression profiling revealed distinct temporal patterns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, \u003cem\u003eHMGR-1\u003c/em\u003e (\u003cem\u003eTRINITY_DN57861_c3_g1\u003c/em\u003e) showed the most pronounced and transient induction (7-fold) at 12 h, whereas \u003cem\u003eHMGR-2\u003c/em\u003e (c3_g3), \u003cem\u003eHMGR-3\u003c/em\u003e (c1_g4), and \u003cem\u003eHMGR-4\u003c/em\u003e (c0_g1) maintained low or gradually decreasing expression levels. These findings indicate functional divergence within the \u003cem\u003eHMGR\u003c/em\u003e family, in line with previous evidence that \u003cem\u003eCoHMGR2\u003c/em\u003e acts as the dominant regulator of squalene biosynthesis during seed development (Gu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Collectively, the data support a model in which \u003cem\u003eHMGR-1\u003c/em\u003e, a rate-limiting enzyme of the mevalonate pathway (L. Yang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), serves as the principal inducible isoform driving enhanced enzymatic activity and squalene accumulation under heat stress.\u003c/p\u003e\u003cp\u003eA nine-quadrant analysis revealed significant correlations between HMGR and multiple key metabolites. In quadrant 3 (both gene expression and metabolite accumulation were upregulated), HMGR was significantly positively correlated with 4-pyridine-O-glucoside, a vitamin B6-derived metabolite that functions as an antioxidant. This suggests that increasing HMGR production may be associated with the activation of the antioxidant system (Havaux et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). By contrast, in quadrant 9 (gene expression is upregulated, but metabolite accumulation is downregulated), HMGR was negatively correlated with various metabolites, such as D-fructose and vitexin glucoside, which may reflect the redistribution of carbon flow under high-temperature stress conditions, with metabolic pathways related to lipid and membrane stability prioritized (Obata \u0026amp; Fernie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A correlation heatmap validated these associations, showing a significant bidirectional regulatory relationship between HMGR and antioxidant and carbohydrate metabolites. This complex metabolic regulation suggests that HMGR may be critical for the heat stress response of \u003cem\u003eC. oleifera\u003c/em\u003e fruits because it reconfigures the metabolic network to stabilize the cell membrane and increase the antioxidant capacity, with positive effects on heat tolerance (Falcone Ferreyra et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tholl, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The specific function of HMGR in the high-temperature stress response network of \u003cem\u003eC. oleifera\u003c/em\u003e fruits will need to be more thoroughly clarified by analyzing protein\u0026ndash;protein interactions (e.g., in pull-down experiments) and the phosphorylation modification status (Shen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder heat stress, \u003cem\u003eC. oleifera\u003c/em\u003e fruits exhibited a comprehensive metabolic reprogramming that extended beyond the activation of the squalene biosynthetic route, encompassing a broader coordination between lipid remodeling and coenzyme metabolism. To delineate these cross-pathway linkages, pathway-level gene-metabolite correlation networks were established based on differential transcript-metabolite associations (Fig.\u0026nbsp;7G-H). In the fatty acid biosynthesis pathway (ko00061), TRINITY_DN64718_c2_g2 (Δ⁹-desaturase) and TRINITY_DN67038_c0_g1 (β-ketoacyl synthase) showed strong positive correlations with oleic acid (metaID pmf0395), all with |r| \u0026ge; 0.70 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The concerted upregulation of these nodes under heat stress suggests enhanced fatty-acid elongation and desaturation, leading to increased membrane unsaturation and fluidity-key adaptive mechanisms that stabilize cellular structures at elevated temperatures(M. He \u0026amp; Ding, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the vitamin B₆ metabolism pathway (ko00750), pyridoxine (metaID pme1383) formed a significant positive subnetwork with TRINITY_DN60178_c0_g1 (pyridoxal phosphate-dependent oxidoreductase) and TRINITY_DN18884_c1_g2 (uncharacterized transcript co-expressed with pyridoxine), also meeting the thresholds of |r| \u0026ge; 0.70 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Given the central role of vitamin B₆ as a cofactor in transamination, decarboxylation, and redox-homeostasis reactions, this module implies an elevated B₆-dependent coenzyme flux that alleviates oxidative stress and sustains amino-acid metabolism under heat exposure. Collectively, these findings delineate a coordinated lipid-desaturation-vitamin B₆ metabolic axis that complements the MVA-HMGR pathway, jointly maintaining membrane homeostasis and reinforcing triterpenoid metabolic flux during thermal adaptation in \u003cem\u003eC. oleifera\u003c/em\u003e fruits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Phosphorylation site prediction\u003c/h2\u003e\u003cp\u003eIn this study, we cloned the full-length cDNA sequence of a \u003cem\u003eC. oleifera\u003c/em\u003e HMGR-encoding gene using rapid amplification of cDNA ends (RACE) technology (Fig.\u0026nbsp;8A) and obtained the full-length HMGR cDNA of \u003cem\u003eC. oleifera\u003c/em\u003e by RACE (Fig.\u0026nbsp;8A) and analyzed the predicted phosphorylation sites using NetPhos 3.1 (Fig.\u0026nbsp;8B), multiple predicted phosphorylation sites were detected on HMGR in \u003cem\u003eC. oleifera\u003c/em\u003e; they were mainly concentrated on serine (Ser) and threonine (Thr) residues, including Ser47, Thr208, and Ser217. Among the predicted phosphorylation sites, Ser116, Thr246, and Thr238 had the three highest scores (predicted values of 0.960, 0.861, and 0.787, respectively). These sites were predicted as potential targets of related kinases, such as PKC, GSK3, p38 MAPK, and CDK5, suggesting that HMGR may contribute to the regulation of multiple kinases.\u003c/p\u003e\u003cp\u003eHMGR activity is regulated by both phosphorylation and dephosphorylation. Previous studies showed that HMGR activity is usually inhibited via phosphorylation, while dephosphorylation can significantly activate this enzyme, thereby enhancing the synthesis of downstream terpenoid products, including squalene (Antol\u0026iacute;n-Llovera et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Leivar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, protein phosphatase 2A (PP2A) directly interacts with the conserved N-terminal domain of HMGR through its B\u0026Prime; regulatory subunit, thereby regulating the HMGR phosphorylation status (Leivar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As environmental conditions change, the regulated phosphorylation state of HMGR may trigger different feedback regulatory mechanisms, indicating that its regulatory pathway may exhibit some environmental adaptability. Under various environmental conditions, such as high salinity, this interaction leads to PP2A-mediated dephosphorylation of HMGR and triggers HMGR degradation (Antol\u0026iacute;n-Llovera et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Intriguingly, in \u003cem\u003eA. thaliana\u003c/em\u003e, the dephosphorylation of AtHMGR1 by PP2A does not occur at the conserved Ser577 residue within the catalytic domain, which is phosphorylated by SnRK1 (Robertlee et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This suggests that PP2A may target other phosphorylation sites on HMGR for a more precise regulation. Interestingly, when \u003cem\u003eA. thaliana\u003c/em\u003e seedlings are treated with phosphatase inhibitors, the extent of the phosphorylation of HMGR at sites other than Ser577 increases significantly as does HMGR activity, further supporting its negative regulatory effect (Leivar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are currently relatively few reports on how protein phosphatase 1 (PP1) regulates HMGR in plants. However, as another major Ser/Thr phosphatase, the potential role of PP1 cannot be ignored. There is evidence in other biological systems that PP1 is involved in the regulated dephosphorylation of HMGR. For example, in the mammalian liver, factors that inhibit PP1/PP2A activity significantly block the dephosphorylation of HMGR (Serra et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). In plants, PP1 facilitates Ser/Thr dephosphorylation in various biological processes, including ABA signaling, stomatal movement, and kinase regulation (Takemiya \u0026amp; Shimazaki, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, we speculate that in plants, PP1 and PP2A cooperatively regulate the HMGR phosphorylation status, thereby maintaining HMGR activity at a suitable level.\u003c/p\u003e\u003cp\u003eIn this study, we identified multiple candidate genes encoding \u003cem\u003ePP2A\u003c/em\u003e subunits on the basis of transcriptome data, including catalytic subunit \u003cem\u003ePP2A-C\u003c/em\u003e (TRINITYDN60447_c1_g1) and structural subunit \u003cem\u003ePP2A-A\u003c/em\u003e (TRINITYDN65830_c0_g1). Under high-temperature stress conditions, the expression levels of both genes tended to increase, especially after the T-24 treatment. A comparison with the control (T-0) revealed significant increases in gene expression levels. This differs from the findings of previous studies on the negative regulatory effects of PP2A on HMGR activity. Hence, under high-temperature stress conditions, PP2A may increase HMGR activity through dephosphorylation, thereby promoting squalene synthesis. Further supporting the above-mentioned hypothesis, the role of PP2A may vary under different physiological states. Under adverse conditions, \u003cem\u003ePP2A\u003c/em\u003e may enhance the accumulation of metabolites by regulating the phosphorylation status of HMGR to help plants adapt to environmental stress. Increases in PP2A production and HMGR activity may optimize the metabolic balance of the isoprene pathway and enhance plant adaptations to stress. Additionally, in the present study, the expression of a PP1 catalytic subunit-encoding gene (TRINITYDN67318_c1_g3) had an upregulated trend similar to that of a gene encoding HMGR, with peak transcription at the T-12 time point (TPM value of 3.42), suggesting that the encoded enzyme may play an auxiliary dephosphorylation role during stress responses.\u003c/p\u003e\u003cp\u003eIn this study, we identified two key predicted phosphorylation sites on HMGR in \u003cem\u003eC. oleifera\u003c/em\u003e (Ser47 and Thr208). On the basis of this finding combined with the results of earlier research on HMGR regulation, we propose a new hypothesis that high-temperature stress leads to the phosphorylation of HMGR at these two sites because it activates the MAPK cascade pathway, thereby regulating HMGR activity. In plant stress responses, the MAPK pathway is critical for the perception and amplification of environmental signals. A previous study showed that in plants, such as \u003cem\u003eA. thaliana\u003c/em\u003e, heat stress can rapidly activate MAPKs (e.g., MPK3/MPK6) that recognize Ser/Thr-Pro sequences and mediate phosphorylation (Asai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The S47 and T208 loci identified in the current study exhibit typical MAPK recognition features and are located near the N-terminal regulatory region and catalytic domain of HMGR, respectively, implying that they may serve as functional regulatory sites for stress signal integration. We speculate that MAPK directly phosphorylates S47 and T208 after being activated by high-temperature stress, thereby decreasing HMGR catalytic activity or altering HMGR stability to rapidly regulate the isoprene metabolic flux, ultimately resulting in the temporary inhibition of squalene synthesis.\u003c/p\u003e\u003cp\u003eProtein phosphatases PP2A and PP1, which catalyze Ser/Thr dephosphorylation, are key negative regulators of MAPK signaling. In \u003cem\u003eA. thaliana\u003c/em\u003e, PP2A can directly dephosphorylate MPK6 and its downstream substrates and bind to HMGR through the B\u0026Prime; regulatory subunit to regulate HMGR activity and degradation (Leivar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our transcriptome data indicated that the expression of genes encoding PP2A-C, PP2A-A, and PP1 subunits were significantly upregulated following the exposure to high-temperature stress, indicative of their possible involvement in HMGR dephosphorylation. We propose that in the early stages of an exposure to stress, MAPK-mediated phosphorylation inhibits HMGR, but PP2A/PP1 subsequently dephosphorylates S47 and T208 to partially restore HMGR activity and gradually increase the synthesis of defense-related metabolites, such as squalene, while also establishing metabolic homeostasis to maintain energy levels and satisfy stress response requirements. This hypothesis involves a complete multi-level signaling regulatory framework comprising kinase and phosphatase metabolic enzymes that can explain how \u003cem\u003eC. oleifera\u003c/em\u003e fruits dynamically regulate squalene synthesis to adapt to high-temperature stress. Future studies should explore \u003cem\u003ein vitro\u003c/em\u003e expression following the introduction of point mutations (e.g., S47A/T208A or S47E/T208E) and elucidate the molecular mechanism underlying this model by combining mass spectrometry-based detection of phosphorylation, Co-IP assay-based protein interactions, and other experimental approaches.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e4.0 RT-qPCR validation of MAPK-PP-HMGR module expression under heat stress\u003c/h3\u003e\n\u003cp\u003eTo confirm the transcriptomic reliability and clarify the molecular regulation underlying heat-induced squalene biosynthesis, RT-qPCR was conducted for eleven key genes, including \u003cem\u003eHMGR-1, HMGR-2, HMGR-3, MPK3-like, MPK6-like, PP1c-1, PP1c-2, PP2A-A, PP2A-C, OSCs\u003c/em\u003e, and the internal control gene \u003cem\u003eSAND\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The expression profiles were highly consistent with the RNA-seq results, confirming the robustness of transcriptomic data and the coordinated activation of the \u003cem\u003eMAPK\u0026ndash;PP\u0026ndash;HMGR\u003c/em\u003e regulatory module.\u003c/p\u003e\u003cp\u003eRT-qPCR results showed that \u003cem\u003eHMGR-1\u003c/em\u003e exhibited the strongest induction under heat stress, with a 2⁻ΔΔCt value of 7.7 at 12 h, followed by a moderate decrease to 3.4 at 24 h. In contrast, \u003cem\u003eHMGR-2\u003c/em\u003e and \u003cem\u003eHMGR-3\u003c/em\u003e displayed lower but significant up-regulation (1.6\u0026ndash;3.2-fold). These results indicate that \u003cem\u003eHMGR-1\u003c/em\u003e serves as the major inducible isoform driving the mevalonate pathway flux enhancement during early stress response, while \u003cem\u003eHMGR-2/3\u003c/em\u003e play secondary or complementary roles.\u003c/p\u003e\u003cp\u003eUpstream kinases \u003cem\u003eMPK3\u003c/em\u003e-like and \u003cem\u003eMPK6\u003c/em\u003e-like were also activated, consistent with their roles in heat-triggered phosphorylation cascades. \u003cem\u003eMPK3\u003c/em\u003e-like expression increased 7.1\u0026ndash;10.1-fold, whereas \u003cem\u003eMPK6\u003c/em\u003e-like showed a 1.5\u0026ndash;2.1-fold increase, supporting its function as a signaling intermediary that transmits stress cues to \u003cem\u003eHMGR\u003c/em\u003e. Concurrently, phosphatase genes \u003cem\u003ePP1c-1\u003c/em\u003e and \u003cem\u003ePP1c-2\u003c/em\u003e were up-regulated by 1.7\u0026ndash;2.8-fold, and \u003cem\u003ePP2A-A\u003c/em\u003e and \u003cem\u003ePP2A-C\u003c/em\u003e by 1.3\u0026ndash;1.9-fold, suggesting the involvement of reversible dephosphorylation in sustaining \u003cem\u003eHMGR\u003c/em\u003e enzymatic activation after \u003cem\u003eMAPK\u003c/em\u003e signaling initiation.\u003c/p\u003e\u003cp\u003eInterestingly, \u003cem\u003eOSCs\u003c/em\u003e transcript levels decreased slightly after 12\u0026ndash;24 h (2⁻ΔΔCt\u0026thinsp;\u0026lt;\u0026thinsp;1), while \u003cem\u003eOSCs\u003c/em\u003e enzymatic activity increased significantly (Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e). This discrepancy implies that downstream regulation may occur at the post-transcriptional or post-translational level\u0026mdash;through enzyme stabilization, substrate channeling, or activation state modification\u0026mdash;rather than transcriptional control.\u003c/p\u003e\u003cp\u003eCollectively, these data confirm a dynamic regulatory mechanism in which \u003cem\u003eMAPKs\u003c/em\u003e mediate stress signal perception and transmission, \u003cem\u003ePP1/PP2A\u003c/em\u003e phosphatases modulate dephosphorylation balance, and \u003cem\u003eHMGR\u003c/em\u003e acts as the key enzymatic node enhancing squalene biosynthesis. The convergence of RT-qPCR, enzyme activity, and metabolite data strongly supports a heat-induced feedback system integrating phosphorylation signaling with metabolic adaptation \u003cem\u003eC. oleifera\u003c/em\u003e fruits.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative expression levels (2⁻ΔΔCt) of selected genes involved in the \u003cem\u003eMAPK\u0026ndash;PP\u0026ndash;HMGR\u003c/em\u003e regulatory module under heat stress.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCt Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔCt\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eΔΔCt (vs T-0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2⁻ΔΔCt\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMGR-4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMPK3-like\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMPK6-like\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePP1c-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePP1c-2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePP2A-A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePP2A-C\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOSCs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Relative expression was calculated using the 2⁻ΔΔCt method with \u003cem\u003eSAND\u003c/em\u003e as the internal reference gene. Values represent the mean of three biological replicates for each treatment group (T-0, T-12, and T-24). Genes showing 2⁻ΔΔCt\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered upregulated, while values\u0026thinsp;\u0026lt;\u0026thinsp;1 indicate downregulation relative to the control (T-0).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study integrates transcriptomic, metabolomic, and RT-qPCR validation analyses to elucidate the molecular basis of squalene accumulation in \u003cem\u003eC. oleifera\u003c/em\u003e fruits under heat stress. The results reveal that heat exposure triggers a coordinated transcriptional reprogramming involving signal perception, \u003cem\u003eMAPK\u003c/em\u003e cascade activation, phosphatase-mediated dephosphorylation, and downstream modulation of the mevalonate pathway. Notably, \u003cem\u003eHMGR-1\u003c/em\u003e was identified as the principal heat-inducible isoform, displaying a marked upregulation pattern verified by RT-qPCR, while its expression was concomitant with the activation of \u003cem\u003eMPK6\u003c/em\u003e-like and \u003cem\u003ePP2A-C\u003c/em\u003e, suggesting a phosphorylation\u0026ndash;dephosphorylation control loop within the \u003cem\u003eMAPK-PP-HMGR\u003c/em\u003e axis.\u003c/p\u003e\u003cp\u003eThe integration of multi-omic evidence supports a regulatory model in which MAPK kinases and phosphatases synergistically fine-tune HMGR activity to optimize carbon flux toward squalene biosynthesis during thermal adaptation. Concurrent changes in OSCs and other terpenoid biosynthetic genes further reinforce the role of this axis in modulating triterpenoid metabolism. These findings not only provide mechanistic insight into lipid metabolic plasticity in \u003cem\u003eC. oleifera\u003c/em\u003e fruits under abiotic stress but also highlight potential molecular targets (\u003cem\u003eHMGR-1\u003c/em\u003e, \u003cem\u003eMPK6\u003c/em\u003e-like, \u003cem\u003ePP2A-C\u003c/em\u003e) for genetic improvement or biochemical modulation of oil quality and yield in woody oil crops\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq datasets generated and analyzed in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1355314 (Heat stress transcriptome of C. oleifera seed kernels, Oct 30, 2025). Additional data supporting the findings of this study are included in the article and its supplementary materials. Further information is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. During the preparation of this work, the authors did not use any generative AI or AI-assisted technologies. The entire manuscript was written and revised by the authors themselves, and the authors take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Central Guiding Local Science and Technology Development Fund (Grant no. Guike ZY23055025), the Guangxi Natural Science Foundation (Grant no. 2025GXNSFAA069040) and the Guangxi Forestry Science and Technology Promotion Demonstration Project (Grant no. 2023GXLK22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJianwen Wu\u003c/strong\u003e: conceptualization, funding acquisition, writing\u0026ndash;original draft. \u003cstrong\u003eRong Qin\u003c/strong\u003e: data curation, software, validation, writing\u0026ndash;original draft. \u003cstrong\u003eYingying Chen\u003c/strong\u003e: formal analysis, writing\u0026ndash;original draft, project administration. \u003cstrong\u003eJihua Guan\u003c/strong\u003e: investigation, methodology, writing\u0026ndash;review and editing. All authors have read and approved the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntol\u0026iacute;n-Llovera M, Leivar P, Arr\u0026oacute; M, Ferrer A, Boronat A, Campos N. Modulation of plant HMG-CoA reductase by protein phosphatase 2A: Positive and negative control at a key node of metabolism. Plant Signal Behav. 2011;6(8):1127\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4161/psb.6.8.16363\u003c/span\u003e\u003cspan address=\"10.4161/psb.6.8.16363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsai T, Tena G, Plotnikova J, Willmann MR, Chiu W-L, Gomez-Gomez L, Boller T, Ausubel FM, Sheen J. MAP kinase signalling cascade in Arabidopsis innate immunity. Nature. 2002;415(6875):977\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/415977a\u003c/span\u003e\u003cspan address=\"10.1038/415977a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoherty CJ, Van Buskirk HA, Myers SJ, Thomashow MF. Roles for \u003cem\u003eArabidopsis\u003c/em\u003e CAMTA Transcription Factors in Cold-Regulated Gene Expression and Freezing Tolerance. Plant Cell. 2009;21(3):972\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.108.063958\u003c/span\u003e\u003cspan address=\"10.1105/tpc.108.063958\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalcone Ferreyra ML, Rius SP, Casati P. Flavonoids: Biosynthesis, biological functions, and biotechnological applications. Front Plant Sci. 2012;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2012.00222\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2012.00222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng W, Kita D, Peaucelle A, Cartwright HN, Doan V, Duan Q, Liu M-C, Maman J, Steinhorst L, Schmitz-Thom I, Yvon R, Kudla J, Wu H-M, Cheung AY, Dinneny JR. The FERONIA Receptor Kinase Maintains Cell-Wall Integrity during Salt Stress through Ca2\u0026thinsp;+\u0026thinsp;Signaling. Curr Biol. 2018;28(5):666\u0026ndash;e6755. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cub.2018.01.023\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2018.01.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong Z, Xiong L, Shi H, Yang S, Herrera-Estrella LR, Xu G, Chao D-Y, Li J, Wang P-Y, Qin F, Li J, Ding Y, Shi Y, Wang Y, Yang Y, Guo Y, Zhu J-K. Plant abiotic stress response and nutrient use efficiency. Sci China Life Sci. 2020;63(5):635\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11427-020-1683-x\u003c/span\u003e\u003cspan address=\"10.1007/s11427-020-1683-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu Y, Zhu Z, Zeng Y, Pan X, Lu M, Huang X, Ge L, Li A, Bi W, Du Q, Cao H, Yang G, Tan X. \u003cem\u003eCoHMGR2\u003c/em\u003e as a Critical Regulator of Squalene Biosynthesis Key Period in \u003cem\u003eCamellia oleifera\u003c/em\u003e Seed Kernels. Physiol Plant. 2025;177(3):e70352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ppl.70352\u003c/span\u003e\u003cspan address=\"10.1111/ppl.70352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHavaux M, Ksas B, Szewczyk A, Rumeau D, Franck F, Caffarri S, Triantaphylid\u0026egrave;s C. Vitamin B6 deficient plants display increased sensitivity to high light and photo-oxidative stress. BMC Plant Biol. 2009;9(1):130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2229-9-130\u003c/span\u003e\u003cspan address=\"10.1186/1471-2229-9-130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe M, Ding N-Z. Plant Unsaturated Fatty Acids: Multiple Roles in Stress Response. Front Plant Sci. 2020;11:562785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2020.562785\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2020.562785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe X, Xu L, Pan C, Gong C, Wang Y, Liu X, Yu Y. Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics. PLoS ONE. 2020;15(7):e0235795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0235795\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0235795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe Z, Cui K, Wang R, Xu T, Zhang Z, Wang X, Chen Y, Zhu Y. Multi-omics joint analysis reveals how Streptomyces albidoflavus OsiLf-2 assists Camellia oleifera to resist drought stress and improve fruit quality. Front Microbiol. 2023;14:1152632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2023.1152632\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2023.1152632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe Z, Liu C, Zhang Z, Wang R, Chen Y. Integration of mRNA and miRNA analysis reveals the differentially regulatory network in two different Camellia oleifera cultivars under drought stress. Front Plant Sci. 2022;13:1001357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.1001357\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.1001357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKayani S-I. Enhancing biomass productivity towards carbohydrates and fatty acids biosynthesis under the effects of magneto-electric composite fields on filamentous algae. Biomass and Bioenergy; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKayani S-I, Zhu F, Yu Z, Rehman OU, Zhang C, Rehman Shah MA, Li Y, Huo S. Jasmonates regulate metabolic cascade towards Eicosapentaenoic acid and Docosahexaenoic acid biosynthesis in filamentous microalga Tribonema minus. Bioresour Technol. 2025;435:132931. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biortech.2025.132931\u003c/span\u003e\u003cspan address=\"10.1016/j.biortech.2025.132931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKidokoro S, Yoneda K, Takasaki H, Takahashi F, Shinozaki K, Yamaguchi-Shinozaki K. Different Cold-Signaling Pathways Function in the Responses to Rapid and Gradual Decreases in Temperature. Plant Cell. 2017;29(4):760\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.16.00669\u003c/span\u003e\u003cspan address=\"10.1105/tpc.16.00669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeivar P, Antol\u0026iacute;n-Llovera M, Ferrero S, Closa M, Arr\u0026oacute; M, Ferrer A, Boronat A, Campos N. Multilevel Control of \u003cem\u003eArabidopsis\u003c/em\u003e 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase by Protein Phosphatase 2A. Plant Cell. 2011;23(4):1494\u0026ndash;511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.110.074278\u003c/span\u003e\u003cspan address=\"10.1105/tpc.110.074278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu H, Liao H, Charng Y. The role of class A1 heat shock factors (HSFA1s) in response to heat and other stresses in \u003cem\u003eArabidopsis\u003c/em\u003e. Plant Cell Environ. 2011;34(5):738\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3040.2011.02278.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3040.2011.02278.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObata T, Fernie AR. The use of metabolomics to dissect plant responses to abiotic stresses. Cell Mol Life Sci. 2012;69(19):3225\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00018-012-1091-5\u003c/span\u003e\u003cspan address=\"10.1007/s00018-012-1091-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional Regulatory Network of Plant Heat Stress Response. Trends Plant Sci. 2017;22(1):53\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tplants.2016.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.tplants.2016.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobertlee J, Kobayashi K, Suzuki M, Muranaka T. AKIN 10, a representative \u003cem\u003eArabidopsis\u003c/em\u003e SNF 1-related protein kinase 1 (Sn RK 1), phosphorylates and downregulates plant HMG ‐CoA reductase. FEBS Lett. 2017;591(8):1159\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/1873-3468.12618\u003c/span\u003e\u003cspan address=\"10.1002/1873-3468.12618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSedaghatmehr M, Thirumalaikumar VP, Kamranfar I, Marmagne A, Masclaux-Daubresse C, Balazadeh S. A regulatory role of autophagy for resetting the memory of heat stress in plants. Plant Cell Environ. 2019;42(3):1054\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pce.13426\u003c/span\u003e\u003cspan address=\"10.1111/pce.13426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSerra D, Asins G, Hegardt F. Purification and characterization of a protein inhibitor from rat liver that inhibits type 1 protein phosphatase when 3-hydroxy-3-methylglutaryl CoA reductase is the substrate. J Lipid Res. 1990;31(5):919\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0022-2275(20)42791-9\u003c/span\u003e\u003cspan address=\"10.1016/S0022-2275(20)42791-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma P, Lakra N, Goyal A, Ahlawat YK, Zaid A, Siddique KHM. Drought and heat stress mediated activation of lipid signaling in plants: A critical review. Front Plant Sci. 2023;14:1216835. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1216835\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1216835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen Q, Lu X, Yan T, Fu X, Lv Z, Zhang F, Pan Q, Wang G, Sun X, Tang K. The jasmonate-responsive Aa MYC 2 transcription factor positively regulates artemisinin biosynthesis in \u003cem\u003eArtemisia annua\u003c/em\u003e. New Phytol. 2016;210(4):1269\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.13874\u003c/span\u003e\u003cspan address=\"10.1111/nph.13874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakemiya A, Shimazaki K. Phosphatidic Acid Inhibits Blue Light-Induced Stomatal Opening via Inhibition of Protein Phosphatase 1. Plant Physiol. 2010;153(4):1555\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.110.155689\u003c/span\u003e\u003cspan address=\"10.1104/pp.110.155689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTholl D. (2015). Biosynthesis and Biological Functions of Terpenoids in Plants. In J. Schrader \u0026amp; J. Bohlmann, editors, \u003cem\u003eBiotechnology of Isoprenoids\u003c/em\u003e (Vol. 148, pp. 63\u0026ndash;106). Springer International Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/10_2014_295\u003c/span\u003e\u003cspan address=\"10.1007/10_2014_295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang D, Chen Y, Wang R, He Y, Ma X, Shen J, He Z, Lai H. Effects of Exogenous Abscisic Acid on the Physiological and Biochemical Responses of Camellia oleifera Seedlings under Drought Stress. Plants. 2024;13(2):225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants13020225\u003c/span\u003e\u003cspan address=\"10.3390/plants13020225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L, Gu Y, Zhou J, Yuan P, Jiang N, Wu Z, Tan X. Whole-Genome Identification and Analysis of Multiple Gene Families Reveal Candidate Genes for Theasaponin Biosynthesis in Camellia oleifera. Int J Mol Sci. 2022;23(12):6393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23126393\u003c/span\u003e\u003cspan address=\"10.3390/ijms23126393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe Z, Du B, Zhou J, Cao Y, Zhang L. Camellia oleifera CoSWEET10 Is Crucial for Seed Development and Drought Resistance by Mediating Sugar Transport in Transgenic Arabidopsis. Plants. 2023;12(15):2818. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants12152818\u003c/span\u003e\u003cspan address=\"10.3390/plants12152818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshida T, Ohama N, Nakajima J, Kidokoro S, Mizoi J, Nakashima K, Maruyama K, Kim J-M, Seki M, Todaka D, Osakabe Y, Sakuma Y, Sch\u0026ouml;ffl F, Shinozaki K, Yamaguchi-Shinozaki K. Arabidopsis HsfA1 transcription factors function as the main positive regulators in heat shock-responsive gene expression. Mol Genet Genomics. 2011;286(5\u0026ndash;6):321\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00438-011-0647-7\u003c/span\u003e\u003cspan address=\"10.1007/s00438-011-0647-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeevaart JAD. Changes in the Levels of Abscisic Acid and Its Metabolites in Excised Leaf Blades of \u003cem\u003eXanthium strumarium\u003c/em\u003e during and after Water Stress. Plant Physiol. 1980;66(4):672\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.66.4.672\u003c/span\u003e\u003cspan address=\"10.1104/pp.66.4.672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J-K. Abiotic Stress Signaling and Responses in Plants. Cell. 2016;167(2):313\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2016.08.029\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2016.08.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Camellia oleifera, Heat stress, Squalene biosynthesis, Mitogen-activated protein kinase, Phosphatase, 3-hydroxy-3-methylglutaryl-CoA reductase","lastPublishedDoi":"10.21203/rs.3.rs-8118690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8118690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeat stress substantially influences lipid metabolism and triterpenoid biosynthesis in oil-tea (\u003cem\u003eCamellia oleifera\u003c/em\u003e) seeds, yet the regulatory coordination between protein phosphorylation signaling and mevalonate (MVA) flux remains poorly understood. Here, we combined transcriptomic, metabolomic, and enzymatic analyses with RT-qPCR validation to elucidate how MAPK\u0026ndash;phosphatase cascades modulate HMGR-dependent squalene biosynthesis during thermal exposure. Fully mature seed kernels harvested after Shuangjiang (late October to early November) were incubated at 35\u0026deg;C for 0, 12, and 24 h. Heat stress significantly enhanced HMGR activity and squalene accumulation, accompanied by transcriptional activation of \u003cem\u003eHMGR-1\u003c/em\u003e, which showed the most pronounced induction among the four HMGR isoforms. Multi-omics integration revealed that fatty-acid elongation and desaturation modules were positively correlated with oleic acid levels, while pyridoxine-associated genes in vitamin B₆ metabolism formed a strong co-expression subnetwork, reflecting enhanced membrane remodeling and coenzyme turnover under heat. RT-qPCR analyses further confirmed that \u003cem\u003eMAPK\u003c/em\u003e (MPK3/MPK6-like) and phosphatase (\u003cem\u003ePP1c\u003c/em\u003e/\u003cem\u003ePP2A\u003c/em\u003e) genes exhibited synchronized transcriptional patterns with \u003cem\u003eHMGR-1\u003c/em\u003e, supporting a reversible phosphorylation mechanism that dynamically regulates carbon flux through the MVA pathway. Collectively, these findings establish a mechanistic framework in which \u003cem\u003eMAPK\u0026ndash;PP\u0026ndash;HMGR\u003c/em\u003e signaling enhances triterpenoid synthesis and lipid homeostasis, thereby contributing to thermal resilience in \u003cem\u003eC. oleifera\u003c/em\u003e seeds. This work provides mechanistic insights and candidate targets for metabolic engineering toward improved squalene productivity and heat tolerance in oil-tea germplasm.\u003c/p\u003e","manuscriptTitle":"Mitogen-Activated Protein Kinases and Phosphatases Synergistically Regulate 3-Hydroxy-3-Methylglutaryl-CoA Reductase To Enhance Squalene Biosynthesis in Camellia oleifera Fruits under Heat Stress Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 14:05:57","doi":"10.21203/rs.3.rs-8118690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6ef0e38-610a-4130-8c77-63567b1f8b4e","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T08:25:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 14:05:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8118690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8118690","identity":"rs-8118690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}