{"paper_id":"05a64b4b-523b-42ed-95c3-b836d9f56ccb","body_text":"Temporal dynamics of the HSF-HSP regulatory network and flavonoid metabolism coordinate physiological adaptation to heat stress in Iris | 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 Temporal dynamics of the HSF-HSP regulatory network and flavonoid metabolism coordinate physiological adaptation to heat stress in Iris Jiayu Hu, Youli Li, Yang Lin, Nuoya Wu, Jiaxu Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9080835/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Heat stress threatens ornamental plant productivity. Iris tectorum ( Iris ), a prized ornamental species valued for its diverse flower colors and architectural forms, suffers from heat-induced physiological damage that diminishes its economic value. Here we conducted a time-series analysis (0, 1, 3, 6, 9, and 24 h at 40°C) integrating physiological assays, transcriptomics, and untargeted metabolomics, to elucidate the temporal dynamics of heat stress response in Iris tectorum 'Hot Spicy',. Physiologically, heat stress induced progressive membrane lipid peroxidation (MDA accumulation), transient activation of antioxidant enzymes (SOD and POD peaking at 6 h), and sustained proline accumulation, revealing a temporally phased stress response. Transcriptomic analysis identified a “Heat stress factors-Heat Shock Proteins” (HSF-HSP) network exhibited tightly coordinated temporal dynamics: small HSPs (such as HSP26.7 , HSP16.0 and HSP22.0 ) responded rapidly within 1–3 h (39.46-fold), broad HSP activation peaked at 6–9 h (56.50-fold), and sustained HSP70 -mediated protection persisted at 24 h (7.77-fold). HSFs showed hierarchical activation, with early-responsive HSFA5 (7.99-fold), mid-phase HSFA6b , and late-phase HSFA3/HSFA4a orchestrating the transcriptional cascade. The Abscisic acid signaling pathway was systematically activated, with progressive PYL receptor upregulation (5.87-fold) and late-stage ABF induction (4.30-fold), coinciding with activation of flavonoid biosynthesis genes. Metabolomic analysis confirmed coordinated accumulation of flavonoids (naringenin chalcone, (-)-epigallocatechin) from 6–24 h, mirroring transcriptional activation of PAL , CHS , and DFR . Weighted Gene Co-expression Network Analysis (WGCNA) revealed a core module (MEbrown4) enriched in phenylpropanoid biosynthesis and hormone signaling, with hub genes including KIN10 and HOP2 . This study provides the first time-resolved landscape of heat adaptation in Iris , revealing a phased cascade from early HSF-HSP activation to sustained flavonoid accumulation, and identifies key genetic targets for breeding thermotolerant ornamentals. Iris tectorum Heat stress Times-series analysis HSF-HSP regulatory axis flavonoid biosynthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Climate change-induced global warming, compounded by urban heat island effects, increasingly threatens the productivity and ornamental value of horticultural plants (Iba, 2002 ). Heat stress impairs plant growth, physiological functions, and metabolic homeostasis, triggering complex adaptive responses(Wu et al., 2023 ). In ornamental species, these responses directly impact aesthetic quality and economic viability (Mittler, 2002 ; Zinta et al., 2018 ). Iris tectorum Maxim.( Iris ) is a species of high ornamental value, prized for its diverse flower colors and architectural forms (Guo, 2016 ). The cultivar ‘ Hot Spicy ’ exhibits notable heat tolerance under field conditions, making it an ideal candidate for studying thermotolerance mechanisms in ornamental plants. Plants activate conserved defense mechanisms under heat stress, including antioxidant enzyme systems, osmolyte accumulation, and the heat shock response (Al-Whaibi, 2011 ; Suzuki et al., 2012 ; Niu and Xiang, 2018 ). The heat shock response is orchestrated by heat shock transcription factors (HSFs), which induce the expression of heat shock proteins (HSPs) and other protective genes, forming a critical signaling hub (Al-Whaibi, 2011 ; Jiang et al., 2020 ). However, how this HSF-HSP regulatory network coordinates downstream metabolic reprogramming, particularly the biosynthesis of stress-related secondary metabolites, which remains poorly characterized in non-model ornamental species. Multi-omics approaches have begun to decipher heat stress responses in ornamentals such as Rhododendron and Clematis , revealing both conserved and species-specific adaptive strategies (Wang et al., 2020 ). In Rhododendron , flavonoid biosynthesis (quercetin accumulation) and amino acid metabolism are markedly activated under high temperatures, assisting in mitigating oxidative damage (Zhao et al., 2018 ; Li et al., 2025 ). Similarly, In Clematis , the pronounced upregulation of small heat shock proteins (sHSPs) and enrichment of phenylpropanoid biosynthesis pathways highlight the importance of secondary metabolism in thermotolerance (Jiang et al., 2020 ). Despite these advances, the systemic response mechanisms in Iris species remain largely unexplored. Current research on Iris thermotolerance is confined to physiological evaluations, such as measuring malondialdehyde (MDA), antioxidant enzyme activities, and osmolytes, which is limited molecular insight (Mao et al., 2019 ). At the molecular level, a few studies have cloned HSF genes from Iris and suggested a role for calcium signaling (Zhang et al., 2009 ), but integrated multi-omics analyses bridging gene expression with physiological and metabolic phenotypes are lacking. To address this gap, we conducted an integrated time-series analysis (0, 1, 3, 6, 9, and 24 h at 40°C) on Iris ‘ Hot Spicy’ , combining physiological assays, transcriptome sequencing, and untargeted metabolomics. This study provides the first time-resolved multi-omics landscape of heat stress response in Iris, revealing a sequential cascade from early signal perception (1–3 h) to sustained metabolic defense (9–24 h). We demonstrate that the HSF-HSP network exhibits tightly coordinated temporal expression, with early HSF induction followed by sustained HSP70/90 expression and subsequent activation of flavonoid biosynthesis genes and metabolite accumulation, and suggesting a “HSF-HSP-flavonoid” regulatory axis as a key thermotolerance mechanism. Using WGCNA, we identify a core co-expression module (MEbrown4) strongly correlated with proline (Pro) and MDA, integrating hormone signaling, phenylpropanoid biosynthesis, and glutathione metabolism. Hub genes within this module, including KIN10 and HOP2, represent promising targets for functional validation and breeding. Collectively, this first comprehensive multi-omics study in Iris establishes a systems-level model of heat adaptation, providing both fundamental insights into stress resilience and practical genetic targets for developing thermotolerant ornamental cultivars. 2. Material and Methods 2.1. Plant Materials and Heat Stress Treatment The plant material used in this study was Iris tectorum Maxim. cv. ‘Hot Spicy’. Two-month-old seedlings of Iris ‘Hot Spicy’ were obtained from Xiaoshan Aquatic Plant Base, Hangzhou, China. The plants were clonally propagated from a single mother plant (Heat-resistant plants selected through preliminary heat tolerance tests. A voucher specimen (No. Iris-HS-2025-001) was desposited in the herbarium of Foshan key Laboratory of Agriculture and Biological Information), were grown in a growth chamber under a 16/8 h light/dark cycle (25/20°C, 70% relative humidity, 200 µmol m⁻² s⁻¹). After 30 days, uniform plants were subjected to 40 ± 0.5°C for 0 (control), 1, 3, 6, 9, and 24 h. Leaf samples from six biological replicates per time point were collected, immediately frozen in liquid nitrogen, and stored at − 80°C for subsequent analyses. 2.2. Physiological and Biochemical Assays Physiological assays were performed with three technical replicates per sample following established protocols. MDA content was determined by the thiobarbituric acid reaction (Zhou and Prognon, 2006 ). Superoxide dismutase (SOD) and peroxidase (POD) activities were assayed using the nitroblue tetrazolium and guaiacol methods, respectively (Krzysztof L and Anna, 2020 ; Brignot et al., 2025 ). Pro and soluble sugar (SS) contents were measured by the acid ninhydrin and anthrone-sulfuric acid methods, respectively (Ikegaya and Oishi, 2024 ; Aswani et al., 2025 ). All chemical reagents were of analytical grade. SOD, POD, MAD, Pro, and SS assay kits were purchased from Keming Biotechnology Co., Ltd (Suzhou, China). All exoeriments were performed with 15 biological replicates and 3 technical replicates. 2.3. Transcriptome Sequencing and Analysis Total RNA was extracted using TRIzol reagent. Library construction and sequencing (PE150) were performed on the Illumina NovaSeq 6000 platform (Illumiana, LingEn Bio, Shanghai, China). Raw reads were filtered using Fastp (NCBI accessiom number were SAMN56714318-SAMN56714353), and clean reads were aligned to the Iris reference transcriptome using HISAT2. Gene expression levels were quantified as FPKM using featureCounts. Differential expression analysis was performed with DESeq2 (|log₂FC| > 1, FDR < 0.05). Functional enrichment of Differential expressed genes (DEGs) via Gene Ontology (GO) and Further Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses (clusterProfiler, FDR < 0.05). For WGCNA, the top 75% most variable genes were used to construct a co-expression network (soft power β = 12, min module size = 30). Quantitative Real-Time PCR (qRT-PCR) Validation: To validate the RNA-seq expression profiles, 10 core DEGs were selected for qRT-PCR analysis. RNA extraction used the TB Green® Premix EX Taq™ (Takara, Shiga, Japan), Three independent biological replicates were analyzed for each sample, and each reaction was performed in triplicate technical replicates. The Iris actin gene was used as the internal reference for normalization (Zhang et al., 2009 ). Relative expression levels were calculated using the 2 −ΔΔCt method. The expression values were presented as mean ± standard deviation (SD). 2.4. Metabolomics Sequencing and Analysis Metabolites were extracted from 100 mg frozen powder with cold methanol/acetonitrile/water (2:2:1, v/v). Extracts were analyzed by LC/MS (Thermo, LingEn Bio, Shanghai, China). Raw data were processed using Progenesis QI for peak alignment and identification. Metabolites were annotated by matching m/z and MS/MS spectra against the HMDB, KEGG, and in-house databases. Differential accumulation of metabolites (DAMs) were selected based on |log₂FC| > 1 and p < 0.05 (t-test). Given our focus on flavonoid metabolism, pathway enrichment analysis was performed, with particular emphasis on the phenylpropanoid and flavonoid biosynthesis pathways (map00941, map00944). To elucidate the coordinated regulation of gene expression and metabolite accumulation, Pearson correlation coefficients were calculated between DEGs in the flavonoid pathway and their corresponding metabolites (|r| > 0.8, p < 0.05). Pathway-level integration was performed by mapping DEGs and differentially accumulated metabolites to KEGG pathways, with particular focus on the phenylpropanoid and flavonoid biosynthesis pathways. Co-enriched pathways were visualized using the clusterProfiler package. 3. Results 3.1 Physiological and Biochemical Responses of Iris to Heat Stress Heat stress induced progressive physiological changes in Iris leaves, with distinct temporal patterns across different stress indicators (Fig. 2). Membrane lipid peroxidation, measured as MDA content, increased continuously throughout the stress period, with a sharp rise observed at 9–24 h (p < 0.01), indicating cumulative oxidative damage to cellular membranes (Fig. 2A). The antioxidant enzyme system exhibited a transient activation followed by decline. Both SOD and POD activities increased significantly during early to mid-stress (1–6 h), peaking at 6 h, suggesting an initial proactive defense against reactive oxygen species (ROS) accumulation (Fig. 2B-C). However, enzyme activities declined markedly under prolonged stress (9–24 h), coinciding with the period of drastic MDA accumulation, indicating potential collapse of the enzymatic antioxidant system under severe stress. In contrast, osmoregulatory substances showed divergent accumulation patterns. Pro content accumulated steadily throughout the stress period, increasing progressively from 1–24 h (Fig. 2D), suggesting active osmotic adjustment as a sustained adaptive strategy. SS content exhibited a more transient response, peaking sharply at 3 h before declining to control levels by 6 h and remaining stable thereafter (Fig. 2E). This pattern implies rapid mobilization of soluble sugars as compatible solutes and energy sources during the initial shock phase, followed by metabolic shift as stress prolonged. Collectively, these physiological dynamics reveal a temporally phased stress response: early activation of enzymatic antioxidants and soluble sugar accumulation (1–6 h), followed by sustained Pro accumulation and progressive membrane damage (9–24 h). This time-resolved physiological framework provides the phenotypic basis for subsequent multi-omics analysis. 3.2 Global transcriptomic dynamics under heat stress Heat stress triggered a time-dependent increase in DEGs. Compared to control (0 h), 15,222 DEGs were identified at 1 h, rising to 33,668 at 24 h (Table 1 ). Notably, the transition from 9–24 h involved 20,967 DEGs, indicating a critical phase shift in the molecular response. GO enrichment of all DEGs revealed significant terms related to “response to heat,” “oxidative stress,” “protein folding,” and “antioxidant activity” (Fig. 3 A). KEGG enrichment highlighted pathways including “plant hormone signal transduction,” “MAPK signaling pathway,” “protein processing in endoplasmic reticulum,” and “phenylpropanoid biosynthesis” (Fig. 3 B). These results indicate coordinated activation of stress signal transduction, protein homeostasis, and secondary metabolism pathways. Temporal analysis (Appendix 1) revealed a phased response: early (1–3 h) enrichment of signal transduction and oxidative stress terms; mid-phase (6–9 h) shift toward protein folding and phenylpropanoid metabolism; late phase (24 h) involvement of programmed cell death and glutathione metabolism, reflecting progressive stress adaptation Table 1 Statistical Table of DEGs Number. Group All Up Down B vs A 15222 8569 6653 C vs A 22668 12344 10324 D vs A 21901 11326 10575 E vs A 23660 11776 11884 F vs A 33668 22473 11195 C vs B 1952 320 1632 D vs C 3732 2753 979 E vs D 7068 3890 3178 F vs E 20967 14507 6460 3.3 Heat Shock Proteins: a temporally coordinated chaperone network A total of 104 differentially expressed HSP genes were identified, including 34 small HSPs ( sHSPs ), 40 HSP70 , and 30 HSP90 members, whose expression exhibited a tightly regulated temporal cascade in response to heat stress (Fig. 4 A-C and detailed in Appendix 2). Within 1 h of stress onset, multiple sHSP genes were strongly upregulated, indicating an immediate protective response against protein misfolding. For example, HSP26.7 (TRINITY_DN62394_c2_g6) increased by 17.21-fold at 1 h and 39.46-fold at 3 h compared to 0 h, while HSP16.0 (TRINITY_DN68816_c1_g1), and HSP22.0 (TRINITY_DN68971_c0_g1) also showed marked upregulation exceeding 4.32-fold during this period. In contrast, most HSP70 and HSP90 family members exhibited only modest induction at this stage, with notable exceptions such as HSP90-1 (TRINITY_DN87102_c0_g1, 5.87-fold at 3 h compared to 0 h) and HSP70 (TRINITY_DN72233_c1_g2, 4.40-fold at 3 h compared to 0 h). As stress progressed into the mid-phase (6–9 h), most HSPs continued to accumulate, reaching their maximum expression levels. HSP26.7 peaked at 6 h with an extraordinary 56.50-fold increase, while HSP16.0 reached 11.86-fold at 9 h; HSP70 family members became more broadly activated, with HSP70 (TRINITY_DN38515_c0_g1) increasing by 5.98-fold at 9 h compared to 0 h. Notably, while most sHSPs began to decline after peaking at 6–9 h, HSP70 and HSP90 members generally maintained elevated expression, indicating a transition from rapid emergency response to sustained protein quality control. By 24 h of continuous stress, the expression profiles of HSP families diverged significantly: most sHSPs and HSP90 members declined substantially from their mid-phase peaks, whereas a subset of HSP70 genes continued to rise, reaching their highest expression at this late stage. Specifically, HSP70-16 (TRINITY_DN67914_c2_g2) and another HSP70 (TRINITY_DN72233_c0_g1) exhibited 7.29-fold and 7.77-fold increases, respectively, at 24 h. This sustained HSP70 activity suggests a shift toward long-term protein homeostasis maintenance, possibly involving refolding of damaged proteins and prevention of aggregation under prolonged stress. Collectively, these temporal dynamics reveal a phased chaperone strategy: rapid sHSP -dominated response during early stress, broad activation and peak accumulation across families during mid-phase, and sustained HSP70 -mediated protection at late stage, underscoring the central role of the HSP network in mitigating heat-induced proteotoxic stress throughout the entire stress continuum. 3.4 Heat Stress Factors: master regulators orchestrating the heat stress response HSFs , the master regulators of HSPs , showed early and sustained induction. A total of 29 differentially expressed HSFs were identified, encompassing members of the HSFA , HSFB , and HSFC families. Their expression dynamics revealed a temporally coordinated regulatory cascade. (Fig. 4 D, detailed in Appendix 3). Among HSFA family members, several exhibited pronounced upregulation with distinct temporal patterns. HSFA3 (TRINITY_DN66095_c1_g1) showed a progressive increase throughout stress, reaching 1.71-fold at 6 h, 2.35-fold at 9 h, and 2.54-fold at 24 h relative to control, indicating its role in sustained transcriptional activation. HSFA4a (TRINITY_DN57479_c0_g1) was markedly induced at late stage, with expression peaking at 24 h (2.95-fold), suggesting involvement in prolonged stress adaptation. HSFA6b (TRINITY_DN16518_c0_g1) displayed a mid-phase induction, with maximum levels at 6–9 h (2.15-fold and 2.20-fold, respectively), coinciding with the peak of antioxidant enzyme activities and HSP accumulation. Notably, HSFA5 (TRINITY_DN23603_c1_g2) exhibited a biphasic expression pattern: it was strongly upregulated as early as 1 h (4.46-fold) and 3 h (5.43-fold), declined moderately at 6–9 h, and then rose again at 24 h to 7.99-fold. This dual response suggests that HSFA5 may function both in rapid signal initiation and in late-stage regulatory reinforcement. Another HSFA5 (TRINITY_DN60959_c0_g2) also showed significant late induction (2.51-fold at 24 h). HSFA2b (TRINITY_DN64079_c1_g1), the most abundantly expressed HSF, maintained consistently high transcript levels throughout stress, with a moderate peak at 6 h (1.36-fold), implying a role as a constitutively active regulator that may integrate multiple stress signals. In contrast, HSFA1a (TRINITY_DN41618_c0_g2) exhibited relatively stable expression with only minor fluctuations, consistent with its proposed function as a master regulator whose activity is primarily controlled post-translationally. Among HSFB members, HSFB1 and HSFB2c showed varied responses. HSFB1 (TRINITY_DN16128_c0_g1) and HSFB2c (TRINITY_DN63448_c0_g1) displayed moderate changes without consistent upregulation, suggesting they may play auxiliary roles in fine-tuning the heat stress response. Collectively, the temporal expression patterns of HSFs reveal a hierarchical regulatory network: early-responsive HSFA5 initiate the transcriptional cascade, mid-phase HSFA6b coordinate with HSP accumulation and antioxidant defense, and late-phase HSFA3 and HSFA4a sustain long-term adaptation. This precisely timed HSF activation underpins the dynamic HSPs and biosynthesis genes, ultimately shaping the multifaceted heat stress response in Iris . 3.5 Systematic activation of ABA signaling components Phytohormones serve as central integrators of environmental stress signals, which playing a pivotal role in plant adaptation to diverse abiotic stresses including high temperature. KEGG enrichment analysis of DEGs revealed that “plant hormone signal transduction” was among the most significantly enriched pathways throughout the stress time course, with ABA signaling components exhibiting particularly pronounced and coordinated transcriptional reprogramming (Fig. 4 E, detailed in Appendix 4). ABA receptors (Pyrabactin Resistance 1, PYLs ) are progressively upregulated. Multiple genes encoding ABA receptors of the PYL family showed significant induction under heat stress, with expression levels increasing progressively over time. PYL3 (TRINITY_DN58402_c0_g1) exhibited a sustained upward trend, reaching 1.73-fold at 3 h, 3.39-fold at 9 h, and 5.87-fold at 24 h relative to control. Another PYL3 member (TRINITY_DN70111_c2_g6) displayed a similar pattern, peaking at 24 h with a 2.35-fold increase. Notably, PYL9 (TRINITY_DN30488_c0_g1) was specifically induced only at 6 h, suggesting a transient role during mid-stress phase. This progressive upregulation of ABA receptors indicates enhanced sensitivity to ABA signals under prolonged heat stress. PP2C negative regulators exhibit dynamic expression. As core components of the ABA signaling pathway, protein phosphatases type 2C ( PP2Cs ) act as negative regulators by inhibiting downstream Sucrose Non-Fermenting 1 (SNF1)-Related Protein Kinase 2 ( SnRK2 ) in the absence of ABA. Under heat stress, PP2C family genes displayed complex expression patterns. PP2C (TRINITY_DN44551_c0_g3) showed a biphasic response, with elevated expression at 6 h (1.12-fold) and a more pronounced peak at 24 h (1.27-fold). Similarly, PP2C (TRINITY_DN83622_c0_g1) was upregulated at 24 h (1.09-fold). The concurrent upregulation of both PYL receptors and certain PP2C members may reflect a finely tuned regulatory circuit that prevents overactivation of ABA signaling while maintaining responsiveness to persistent stress. Downstream transcription factors are activated at late stage. The ABA-responsive element binding factors ( ABFs ), which function as downstream transcription factors mediating ABA-dependent gene expression, showed marked induction primarily at late stress phase. ABF3 (TRINITY_DN33766_c0_g1) exhibited the most striking upregulation, increasing progressively from 1 h (2.69-fold) to 3 h (4.30-fold), and remained elevated through 24 h (3.47-fold). In contrast, ABF2 (TRINITY_DN56838_c1_g1) showed a transient peak specifically at 6 h (2.59-fold), suggesting a distinct temporal role. The late induction of ABF transcription factors coincides with the upregulation of flavonoid biosynthesis genes, such as Phenylalanine Ammonia-Lyase ( PAL ), Chalcone Synthase ( CHS ), and Dihydroflavonol 4-Reductase ( DFR ) and the subsequent accumulation of flavonoid metabolites (Fig. 6 ), suggesting a potential regulatory link between ABA signaling and the activation of secondary metabolic pathways under heat stress. Given that ABA has been implicated in the regulation of phenylpropanoid metabolism in other plant species, it is plausible that the systematic activation of the ABA pathway in Iris contributes to the enhanced biosynthesis of protective flavonoids, thereby strengthening cellular antioxidant capacity during prolonged heat exposure. 3.6 WGCNA identifies a core module linking gene expression to stress phenotypes To systematically elucidate the regulatory relationships between gene expression and physiological phenotypes, WGCNA was performed. WGCNA constructed a gene co-expression network and identified 10 modules (Fig. 5 A). Module-trait correlation analysis (Fig. 10C) revealed that the MEbrown4 module was positively correlated with SOD, POD, and MDA activities, and negatively correlated with Pro activity. GO enrichment of MEbrown4 genes highlighted “hormone-mediated signaling,” “regulation of RNA metabolism,” and “response to cadmium ion” (Fig. 5 B). KEGG enrichment revealed “phenylpropanoid biosynthesis,” “plant hormone signal transduction,” “ABC transporters,” and “glutathione metabolism” (Fig. 5 C). Hub genes within this module (top 20 by intramodular connectivity) included transcription factor s CH3, AP2/ERF, GARP , energy sensor KIN10 ( SnRK1 ,SNF1-related protein kinase catalytic subunit alpha), chaperone organizer HOP2 ( Hsp70-Hsp90 organizing protein 2), ascorbate-glutathione cycle gene MDAR2 (monodehydroascorbate reductase 2), and Proline catabolism gene POX1 . These hub genes represent potential integrators of stress signaling, energy status, and metabolic defense. 3.7 Transcriptome and metabolome association analysis of flavonoid synthesis in late stress phase Untargeted metabolomics identified 4,067 metabolites, with QC samples clustering tightly in PCA, confirming data reproducibility. Differential metabolite analysis revealed a progressive increase in DAMs. KEGG enrichment of DAMs showed that “flavone and flavonol biosynthesis” and “phenylpropanoid biosynthesis” were specifically enriched at late phase (9–24 h) (Appendix 5). Flavonoid biosynthesis pathway genes, including PAL , CHS , CHI , F3H , and DFR , showed coordinated upregulation starting from mid-phase (6 h) and peaking at 9–24 h (Fig. 6 , left panel). This temporal pattern closely followed the HSF-HSP activation (1–6 h), suggesting that HSF-HSP network may orchestrate flavonoid synthesis as part of the adaptive response. Focusing on the flavonoid pathway, key metabolites including naringenin chalcone, (-)-epigallocatechin, and quercetin derivatives accumulated significantly from 6 h onward, with peak levels at 24 h (Fig. 6 , right panel). The accumulation pattern mirrored the expression of flavonoid biosynthesis genes (Fig. 7 ), demonstrating coordinated transcriptional and metabolic activation of this pathway under prolonged heat stress. F3H, flavanone 3-hydroxylase; DFR, dihydroflavonol 4-reductase; COMT, Caffeic acid 3-O-methyltransferase; C4H, Cinnamate 4-hydroxylase; 4CL, 4-Methylcrotonyl-CoA Ligase; Phe, 3-phenyl-L-alanine; HCT, Hydroxycinnamoyl Transferase; CYP 84A, Cytochrome P450 84A; F3’5’H, lavanone 3’,5’-hydroxylase. 3.8 qRT-RCR To verify the reliability of the transcriptome sequencing results of Iris leaves under heat stress, 10 heat-responsive DEGs were selected for qRT-PCR analysis (Fig. 9). These genes including HSFB2C , HSP70 , HSP18.8 , HSP90 , PAL , CHS2 , PYL3 , PP2C , ABF2 , and MDAR2 (primer design details are provided in Appendix 7). The expression trends of all 10 DEGs detected by qRT-PCR were consistent with the RNA-Seq data, confirming the reliability of the transcriptome sequencing results. 4. Discussion 4.1 The HSF-HSP network exhibits a temporally phased chaperone strategy Iris employs a temporally reprogrammed HSF-HSP network that transitions from rapid emergency response to sustained protein protection. In the early phase (1–3 h), small HSPs (sHSPs) are rapidly induced, acting as \"first responders\" that bind to partially denatured proteins to prevent irreversible aggregation. During the mid-phase (6–9 h), the HSP family is broadly activated and reaches peak expression, with HSP70/90 beginning to dominate protein folding and repair. By the late phase (24 h), HSP70 continues to rise while sHSPs decline, indicating a shift toward HSP70 -mediated long-term protein homeostasis maintenance. This phased strategy aligns with findings in other ornamentals such as Clematis and lily ( Lilium longiflorum ) (Zhao et al., 2025 ; Ohama et al., 2017 ; Huang et al., 2023 ). The novel contribution of this study lies in revealing the sustained elevation of HSP70 at the late stage, suggesting its unique role in protein repair and aggregation prevention under prolonged stress (Zhou et al., 2022 ). The transcriptional cascade driving this chaperone network is orchestrated by functionally differentiated HSF family members. HSFA5 exhibits a biphasic expression pattern with peaks at both early and late phases, suggesting dual roles in signal initiation and late-stage reinforcement. HSFA6b peaks at mid-phase, coinciding with maximal HSP activation and antioxidant enzyme activities. HSFA3 and HSFA4a show sustained upregulation at the late phase, maintaining long-term adaptive responses. HSFA2b maintains constitutively high expression throughout stress, potentially serving as a signal integrator. This functional specialization is consistent with studies in Arabidopsis and rose ( Rosa rugosa Thunb.) (Wei et al., 2022 ), collectively establishing the HSF family as the core transcriptional hub governing heat stress responses. 4.2 ABA signaling cooperates with the HSF-HSP network to activate downstream metabolism The ABA signaling pathway, through late-stage induction of ABF transcription factors, cooperates with the HSF-HSP network to activate downstream metabolic responses. In this study, ABF transcription factors were significantly upregulated at the late stress phase, and their activation timing closely coincided with the upregulation of flavonoid biosynthesis genes ( PAL , CHS , DFR ). This temporal coupling suggests that ABFs may serve as a bridge between ABA signaling and the HSF-HSP network, mediating the regulation of secondary metabolism. Consistent with findings in rose where RcHsfA6 modulates ABA signaling to influence downstream gene expression (Gill and Tuteja, 2010 ; Wei et al., 2022 )), the synchronous activation of ABFs and flavonoid genes in Iris implies that ABA signaling may participate in HSF-HSP-mediated regulation of secondary metabolism. ABFs may directly bind to ABRE elements in the promoters of flavonoid biosynthesis genes, or cooperate with HSFs to regulate shared target genes, forming an \"HSF-HSP-ABA-flavonoid\" regulatory axis (Lee et al., 2019 ; Zhou et al., 2022 ). 4.3 Flavonoid biosynthesis is coordinately activated as a downstream metabolic output The flavonoid pathway, as a downstream target of HSF-HSP and ABA signaling, is coordinately activated during the late stress phase, establishing a sustained chemical defense. Flavonoid biosynthesis genes ( PAL , CHS , DFR ) were significantly upregulated from 6 to 24 h, following a clear temporal sequence after HSF-HSP network activation (1–6 h). At the metabolite level, key flavonoids including naringenin chalcone and (-)-epigallocatechin accumulated during the same period, showing high consistency between transcriptional and metabolic activation. These findings align with studies in Rhododendron , chrysanthemum , and rose (Szabados and Savoure, 2010 ; Guo et al., 2020 ). The novel contribution of this study lies in revealing the temporal coupling between flavonoid biosynthesis and HSF-HSP network activation (Wan et al., 2015 ). As antioxidants, flavonoids scavenge accumulated ROS and stabilize membrane structures during late stress, forming a second line of defense in Iris heat adaptation. 4.4 WGCNA identifies a core module integrating multiple adaptive pathways and provides targets for future studies WGCNA identified MEbrown4 as a core module significantly correlated with physiological indicators (SOD, POD, MDA), enriched in phenylpropanoid biosynthesis, hormone signaling, and glutathione metabolism pathways. This module provides systems-level validation of the integration among multiple adaptive pathways. Hub genes within this module include the energy sensor KIN10 ( SnRK1 ), the chaperone organizer HOP2 (Zhao et al., 2025 ), the antioxidant gene MDAR2 , and several transcription factors (Agati et al., 2012 ). KIN10 can activate secondary metabolism under energy stress (Wang et al., 2024 ); HOP2 coordinates protein homeostasis as a bridge between HSP70 and HSP90 (Zhao et al., 2025 ); MDAR2 directly scavenges ROS through the ascorbate-glutathione cycle (Nakabayashi and Saito, 2015 ). These hub genes represent priority targets for functional validation and molecular breeding. At all, this study reveals the dynamic network of heat stress response in Iris through multi-omics integration, but several limitations remain, including that direct regulation of flavonoid genes by HSFs (ie., HSFA2, HSFA5 , and HSFA3 ) has not been validated, the functions of HSP genes (HSP70/HSP90/sHSPs ) require validation through transgenic approaches or VIGS systems, investigating whether exogenous ABA application enhances flavonoid accumulation and thermotolerance to establish causality between ABA signaling and flavonoid biosynthesis, and the specific contributions of individual flavonoid compounds to thermotolerance need to be assessed through exogenous application or metabolic engineering. Future research should therefore focus on molecular dissection of the HSF-HSP-flavonoid regulatory axis, functional characterization of key hub genes ( KIN10 , HOP2 ), and application of core markers in screening and breeding heat-tolerant Iris germplasm. 5. Conclusion This study provides the first time-resolved multi-omics landscape of heat stress response in Iris tectorum ' Hot Spicy '. Heat stress triggers a temporally phased HSF-HSP chaperone network: small HSPs dominate the early emergency response, broad HSP family activation peaks at mid-phase, and sustained HSP70-mediated protection persists at late stage. HSF family members exhibit functional differentiation, with early-responsive HSFA5 , mid-phase HSFA6b , and late-phase HSFA3/HSFA4a orchestrating the transcriptional cascade. The ABA signaling pathway is activated, with late-stage induction of ABF transcription factors coinciding with the upregulation of flavonoid biosynthesis genes. Metabolomic analysis confirms coordinated accumulation of flavonoids from mid to late stress phase, mirroring the transcriptional activation of PAL , CHS , and DFR . WGCNA identifies MEbrown4 as a core module integrating hormone signaling, phenylpropanoid biosynthesis, and glutathione metabolism, with hub genes including energy sensor KIN10 , chaperone organizer HOP2 , and antioxidant MDAR2 representing key targets for functional validation. Collectively, these findings reveal a phased regulatory cascade from early HSF-HSP activation to sustained flavonoid accumulation, providing a systems-level framework for understanding heat adaptation in Iris and identifying genetic resources for breeding thermotolerant ornamental cultivars. Abbreviations Full name Abbreviation Full name Abbreviation soluble sugar SS Heat stress factors HSF 3-phenyl-L-alanine Phe Hydroxycinnamoyl Transferase HCT 4-Methylcrotonyl-CoA Ligase 4CL Iris tectorum Iris ABA-responsive element binding factors ABF Kyoto Encyclopedia of Genes and Genomes KEGG Abscisic acid ABA lavanone 3’,5’-hydroxylase F3’5’H Caffeic acid 3-O-methyltransferase COMT malondialdehyde MDA Chalcone Synthase CHS peroxidase POD Cinnamate 4-hydroxylase C4H Phenylalanine Ammonia-Lyase PAL Cytochrome P450 84A CYP84A proline Pro Differential accumulation of metabolites DAMs protein phosphatases type 2C PP2C differentially expressed genes DEGs Pyrabactin Resistance 1 PYL Dihydroflavonol 4-Reductase DFR reactive oxygen species ROS flavanone 3-hydroxylase F3H small heat shock proteins sHSPs Gene Ontology GO Sucrose Non-Fermenting 1 (SNF1)-Related Protein Kinase 2 SnRK2 Heat Shock Proteins HSP Superoxide dismutase SOD Weighted Gene Co-expression Network Analysis WGCNA Declarations Ethics approval and consent to participate: All authors ensured that every step of the research process complied with all ethical requirements mentioned by the BMC Plant Biology journal. All authors are aware of and agree to the submission, and guarantee that all research content is true and reliable. Consent for publication: All authors agree to submit the manuscript to BMC Plant Biology. By submitting our article we agree to pay this charge in full if ourarticle is accepted for publication. Competing Interests: We declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. We guarantee that there are no conflicts of interest related to the journal BMC Plant Biology in the article. Funding This research did receive funding. Jiayu Hu received funding from Basic and Applied Basic Research Foundation of Guangdong Province; Grant ID 2022A1515110779. Funding Information: Basic and Applied Basic Research Foundation of Guangdong Province (Grant ID: 2022A1515110779). Author Contribution Jiayu Hu reports financial support was provided by Guangdong Basic and Applied Basic Research Foundation. Jiayu Hu reports a relationship with Guangdong Basic and Applied Basic Research Foundation that includes: funding grants. Jiayu Hu has patent pending to Jiayu Hu. Each member is aware of and adheres to the submission guidelines. All members participated and contributed to the research process. Jiayu Hu was responsible for the overall planning and ideas of the research, as well as providing funding support; Youli Li was responsible for the progress of research experiments and data analysis; Yang Lin, Nuoya Wu, and Jiaxu Xie participated in experiments and data work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Supplementary Files Appendix1.GOEnrichment.docx AbstractGraphic.png Appendix1.KEGGEnrichment.docx Appendix6.docx Appendix3.xlsx Appendix4.xlsx Appendix2.xlsx Appendix5.KEGGEnrichmentDAMs.docx Appendix6.txt Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Editor invited by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 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. 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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-9080835\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":630749839,\"identity\":\"9b8f4258-f27f-4c73-b96d-7d459c673095\",\"order_by\":0,\"name\":\"Jiayu Hu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACxgYeBoYPNhCOBNFaGGekkaKFgYGHgZmHJC3MM3KPSdsk2NgbHGA+eJuHwS6PsMNm5KVJ5ySkJW44wJZszcOQXEyElhwz6dwfhxMMDvCYSfMwHEhsIEqLRcJhoMP4v5GghSHhMOOGAzxsRGrpeWNs2QP0y8zDbMaWcwySCWsxbM8xvPEDGGJ8x5sf3nhTYUeElgkJUBYziDAgpB4I5PkPEKFqFIyCUTAKRjYAAGmTNvxGHqHvAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Foshan University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jiayu\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":630749840,\"identity\":\"0efa032a-ba05-486b-a988-cbb425b356a6\",\"order_by\":1,\"name\":\"Youli Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Foshan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Youli\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":630749841,\"identity\":\"fc979274-1eb8-40e6-8055-dc1cf1bdb7a1\",\"order_by\":2,\"name\":\"Yang Lin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Foshan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yang\",\"middleName\":\"\",\"lastName\":\"Lin\",\"suffix\":\"\"},{\"id\":630749842,\"identity\":\"69647cc3-0720-4692-85ca-a3e667aec49a\",\"order_by\":3,\"name\":\"Nuoya Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Foshan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nuoya\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":630749843,\"identity\":\"3abd4044-42a1-4fb9-b655-121c8801e496\",\"order_by\":4,\"name\":\"Jiaxu Xie\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Foshan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiaxu\",\"middleName\":\"\",\"lastName\":\"Xie\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-10 07:54:55\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9080835/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9080835/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108490908,\"identity\":\"1dfaa7b3-d1e6-40da-a261-d5c4d71da4a7\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 09:49:48\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":974607,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePhenotypic response of Iris under heat stress. (A) 0 h, (B) 1 h, (C) 3 h, (D) 6 h, (E) 9 h, (F) 24 h.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/42e42a9f920693b47ba26389.png\"},{\"id\":108803748,\"identity\":\"deeeef72-34f0-41e3-8d6b-212ab8fe850e\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:05:34\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":127294,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDynamic changes in physiological and biochemical parameters in \\u003cem\\u003eIris \\u003c/em\\u003eplants under heat stress. (A) MDA content, (B) SOD activity, (C) POD activity, (D) Pro content, (E) SS content.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/b45efd53cf07d0537b456b36.png\"},{\"id\":108494353,\"identity\":\"cb457a4e-41b7-4de6-8e68-02fbddf97839\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 10:04:04\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":311417,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFunctional enrichment of DEGs. (A) GO enrichment, (B) KEGG enrichment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/3da6a11f4f8606c2cd80be13.png\"},{\"id\":108494356,\"identity\":\"65a59c8f-9847-49fc-bb4d-f55357d53009\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 10:04:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":709400,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeatmap of core genes under heat stress in \\u003cem\\u003eIris\\u003c/em\\u003e. (A) The expression of HSP70; (B) HSP90; and (C) sHSP, (D) HSFs, and (E) ABA signaling pathways. 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(A) Module-trait relationships form 10 modules labeled with different colors, (B) GO enrichment of MEbrown4 module, (C) KEGG enrichment of MEbrown4 module.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/632e1aa4638950a55ff82923.png\"},{\"id\":108494288,\"identity\":\"f82d5a9a-d392-4e4c-8993-1f90c878d129\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 10:03:34\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":930843,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlavonoid \\u0026nbsp;\\u0026nbsp;pathway metabolic expression profile of \\u003cem\\u003eIris \\u003c/em\\u003eunder heat stress. 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11:24:36\",\"extension\":\"xlsx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11975,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix4.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/b79e0e68c08fdeb5868ac007.xlsx\"},{\"id\":108494291,\"identity\":\"4ca87863-6309-4f6d-92c3-dd9a1e7c811c\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 10:03:37\",\"extension\":\"xlsx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":22923,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/0a5da9e8df83d3484a7e6fbb.xlsx\"},{\"id\":108803761,\"identity\":\"d28a1673-77ba-47c4-8448-018f27b13f29\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:05:59\",\"extension\":\"docx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":507441,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix5.KEGGEnrichmentDAMs.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/a5b06dc54948e2e6c5b69354.docx\"},{\"id\":108804274,\"identity\":\"4e58ceed-b852-40e7-a9f3-e99b6b1e3cd9\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:18:49\",\"extension\":\"txt\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1945,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix6.txt\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9080835/v1/ce551d0a7269abed2e79e1f2.txt\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Temporal dynamics of the HSF-HSP regulatory network and flavonoid metabolism coordinate physiological adaptation to heat stress in Iris\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eClimate change-induced global warming, compounded by urban heat island effects, increasingly threatens the productivity and ornamental value of horticultural plants (Iba, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). Heat stress impairs plant growth, physiological functions, and metabolic homeostasis, triggering complex adaptive responses(Wu et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In ornamental species, these responses directly impact aesthetic quality and economic viability (Mittler, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Zinta et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eIris tectorum\\u003c/em\\u003e Maxim.(\\u003cem\\u003eIris\\u003c/em\\u003e) is a species of high ornamental value, prized for its diverse flower colors and architectural forms (Guo, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The cultivar \\u0026lsquo;\\u003cem\\u003eHot Spicy\\u003c/em\\u003e\\u0026rsquo; exhibits notable heat tolerance under field conditions, making it an ideal candidate for studying thermotolerance mechanisms in ornamental plants.\\u003c/p\\u003e \\u003cp\\u003ePlants activate conserved defense mechanisms under heat stress, including antioxidant enzyme systems, osmolyte accumulation, and the heat shock response (Al-Whaibi, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Suzuki et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Niu and Xiang, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The heat shock response is orchestrated by heat shock transcription factors (HSFs), which induce the expression of heat shock proteins (HSPs) and other protective genes, forming a critical signaling hub (Al-Whaibi, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Jiang et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). However, how this HSF-HSP regulatory network coordinates downstream metabolic reprogramming, particularly the biosynthesis of stress-related secondary metabolites, which remains poorly characterized in non-model ornamental species.\\u003c/p\\u003e \\u003cp\\u003eMulti-omics approaches have begun to decipher heat stress responses in ornamentals such as \\u003cem\\u003eRhododendron\\u003c/em\\u003e and \\u003cem\\u003eClematis\\u003c/em\\u003e, revealing both conserved and species-specific adaptive strategies (Wang et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In \\u003cem\\u003eRhododendron\\u003c/em\\u003e, flavonoid biosynthesis (quercetin accumulation) and amino acid metabolism are markedly activated under high temperatures, assisting in mitigating oxidative damage (Zhao et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Li et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Similarly, In \\u003cem\\u003eClematis\\u003c/em\\u003e, the pronounced upregulation of small heat shock proteins (sHSPs) and enrichment of phenylpropanoid biosynthesis pathways highlight the importance of secondary metabolism in thermotolerance (Jiang et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Despite these advances, the systemic response mechanisms in \\u003cem\\u003eIris\\u003c/em\\u003e species remain largely unexplored. Current research on \\u003cem\\u003eIris\\u003c/em\\u003e thermotolerance is confined to physiological evaluations, such as measuring malondialdehyde (MDA), antioxidant enzyme activities, and osmolytes, which is limited molecular insight (Mao et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). At the molecular level, a few studies have cloned \\u003cem\\u003eHSF\\u003c/em\\u003e genes from \\u003cem\\u003eIris\\u003c/em\\u003e and suggested a role for calcium signaling (Zhang et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), but integrated multi-omics analyses bridging gene expression with physiological and metabolic phenotypes are lacking.\\u003c/p\\u003e \\u003cp\\u003eTo address this gap, we conducted an integrated time-series analysis (0, 1, 3, 6, 9, and 24 h at 40\\u0026deg;C) on \\u003cem\\u003eIris\\u003c/em\\u003e \\u0026lsquo;\\u003cem\\u003eHot Spicy\\u0026rsquo;\\u003c/em\\u003e, combining physiological assays, transcriptome sequencing, and untargeted metabolomics. This study provides the first time-resolved multi-omics landscape of heat stress response in Iris, revealing a sequential cascade from early signal perception (1\\u0026ndash;3 h) to sustained metabolic defense (9\\u0026ndash;24 h). We demonstrate that the HSF-HSP network exhibits tightly coordinated temporal expression, with early HSF induction followed by sustained \\u003cem\\u003eHSP70/90\\u003c/em\\u003e expression and subsequent activation of flavonoid biosynthesis genes and metabolite accumulation, and suggesting a \\u0026ldquo;HSF-HSP-flavonoid\\u0026rdquo; regulatory axis as a key thermotolerance mechanism. Using WGCNA, we identify a core co-expression module (MEbrown4) strongly correlated with proline (Pro) and MDA, integrating hormone signaling, phenylpropanoid biosynthesis, and glutathione metabolism. Hub genes within this module, including KIN10 and HOP2, represent promising targets for functional validation and breeding. Collectively, this first comprehensive multi-omics study in \\u003cem\\u003eIris\\u003c/em\\u003e establishes a systems-level model of heat adaptation, providing both fundamental insights into stress resilience and practical genetic targets for developing thermotolerant ornamental cultivars.\\u003c/p\\u003e\"},{\"header\":\"2. Material and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Plant Materials and Heat Stress Treatment\\u003c/h2\\u003e \\u003cp\\u003eThe plant material used in this study was \\u003cem\\u003eIris tectorum\\u003c/em\\u003e Maxim. cv. \\u0026lsquo;Hot Spicy\\u0026rsquo;. Two-month-old seedlings of \\u003cem\\u003eIris\\u003c/em\\u003e \\u0026lsquo;Hot Spicy\\u0026rsquo; were obtained from Xiaoshan Aquatic Plant Base, Hangzhou, China. The plants were clonally propagated from a single mother plant (Heat-resistant plants selected through preliminary heat tolerance tests. A voucher specimen (No. Iris-HS-2025-001) was desposited in the herbarium of Foshan key Laboratory of Agriculture and Biological Information), were grown in a growth chamber under a 16/8 h light/dark cycle (25/20\\u0026deg;C, 70% relative humidity, 200 \\u0026micro;mol m⁻\\u0026sup2; s⁻\\u0026sup1;). After 30 days, uniform plants were subjected to 40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u0026deg;C for 0 (control), 1, 3, 6, 9, and 24 h. Leaf samples from six biological replicates per time point were collected, immediately frozen in liquid nitrogen, and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C for subsequent analyses.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Physiological and Biochemical Assays\\u003c/h2\\u003e \\u003cp\\u003ePhysiological assays were performed with three technical replicates per sample following established protocols. MDA content was determined by the thiobarbituric acid reaction (Zhou and Prognon, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). Superoxide dismutase (SOD) and peroxidase (POD) activities were assayed using the nitroblue tetrazolium and guaiacol methods, respectively (Krzysztof L and Anna, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Brignot et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Pro and soluble sugar (SS) contents were measured by the acid ninhydrin and anthrone-sulfuric acid methods, respectively (Ikegaya and Oishi, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Aswani et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). All chemical reagents were of analytical grade. SOD, POD, MAD, Pro, and SS assay kits were purchased from Keming Biotechnology Co., Ltd (Suzhou, China). All exoeriments were performed with 15 biological replicates and 3 technical replicates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Transcriptome Sequencing and Analysis\\u003c/h2\\u003e \\u003cp\\u003eTotal RNA was extracted using TRIzol reagent. Library construction and sequencing (PE150) were performed on the Illumina NovaSeq 6000 platform (Illumiana, LingEn Bio, Shanghai, China). Raw reads were filtered using Fastp (NCBI accessiom number were SAMN56714318-SAMN56714353), and clean reads were aligned to the \\u003cem\\u003eIris\\u003c/em\\u003e reference transcriptome using HISAT2. Gene expression levels were quantified as FPKM using featureCounts. Differential expression analysis was performed with DESeq2 (|log₂FC| \\u0026gt; 1, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Functional enrichment of Differential expressed genes (DEGs) via Gene Ontology (GO) and Further Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses (clusterProfiler, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). For WGCNA, the top 75% most variable genes were used to construct a co-expression network (soft power β\\u0026thinsp;=\\u0026thinsp;12, min module size\\u0026thinsp;=\\u0026thinsp;30).\\u003c/p\\u003e \\u003cp\\u003eQuantitative Real-Time PCR (qRT-PCR) Validation: To validate the RNA-seq expression profiles, 10 core DEGs were selected for qRT-PCR analysis. RNA extraction used the TB Green\\u0026reg; Premix EX Taq\\u0026trade; (Takara, Shiga, Japan), Three independent biological replicates were analyzed for each sample, and each reaction was performed in triplicate technical replicates. The \\u003cem\\u003eIris\\u003c/em\\u003e actin gene was used as the internal reference for normalization (Zhang et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Relative expression levels were calculated using the 2\\u003csup\\u003e\\u0026minus;ΔΔCt\\u003c/sup\\u003e method. The expression values were presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Metabolomics Sequencing and Analysis\\u003c/h2\\u003e \\u003cp\\u003eMetabolites were extracted from 100 mg frozen powder with cold methanol/acetonitrile/water (2:2:1, v/v). Extracts were analyzed by LC/MS (Thermo, LingEn Bio, Shanghai, China). Raw data were processed using Progenesis QI for peak alignment and identification. Metabolites were annotated by matching m/z and MS/MS spectra against the HMDB, KEGG, and in-house databases. Differential accumulation of metabolites (DAMs) were selected based on |log₂FC| \\u0026gt; 1 and \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (t-test). Given our focus on flavonoid metabolism, pathway enrichment analysis was performed, with particular emphasis on the phenylpropanoid and flavonoid biosynthesis pathways (map00941, map00944).\\u003c/p\\u003e \\u003cp\\u003eTo elucidate the coordinated regulation of gene expression and metabolite accumulation, Pearson correlation coefficients were calculated between DEGs in the flavonoid pathway and their corresponding metabolites (|r| \\u0026gt; 0.8, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Pathway-level integration was performed by mapping DEGs and differentially accumulated metabolites to KEGG pathways, with particular focus on the phenylpropanoid and flavonoid biosynthesis pathways. Co-enriched pathways were visualized using the clusterProfiler package.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.1 Physiological and Biochemical Responses of \\u003cem\\u003eIris\\u003c/em\\u003e to Heat Stress\\u003c/h2\\u003e\\n\\u003cp\\u003eHeat stress induced progressive physiological changes in \\u003cem\\u003eIris\\u003c/em\\u003e leaves, with distinct temporal patterns across different stress indicators (Fig.\\u0026nbsp;2). Membrane lipid peroxidation, measured as MDA content, increased continuously throughout the stress period, with a sharp rise observed at 9\\u0026ndash;24 h (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), indicating cumulative oxidative damage to cellular membranes (Fig.\\u0026nbsp;2A).\\u003c/p\\u003e\\n\\u003cp\\u003eThe antioxidant enzyme system exhibited a transient activation followed by decline. Both SOD and POD activities increased significantly during early to mid-stress (1\\u0026ndash;6 h), peaking at 6 h, suggesting an initial proactive defense against reactive oxygen species (ROS) accumulation (Fig.\\u0026nbsp;2B-C). However, enzyme activities declined markedly under prolonged stress (9\\u0026ndash;24 h), coinciding with the period of drastic MDA accumulation, indicating potential collapse of the enzymatic antioxidant system under severe stress.\\u003c/p\\u003e\\n\\u003cp\\u003eIn contrast, osmoregulatory substances showed divergent accumulation patterns. Pro content accumulated steadily throughout the stress period, increasing progressively from 1\\u0026ndash;24 h (Fig.\\u0026nbsp;2D), suggesting active osmotic adjustment as a sustained adaptive strategy. SS content exhibited a more transient response, peaking sharply at 3 h before declining to control levels by 6 h and remaining stable thereafter (Fig.\\u0026nbsp;2E). This pattern implies rapid mobilization of soluble sugars as compatible solutes and energy sources during the initial shock phase, followed by metabolic shift as stress prolonged.\\u003c/p\\u003e\\n\\u003cp\\u003eCollectively, these physiological dynamics reveal a temporally phased stress response: early activation of enzymatic antioxidants and soluble sugar accumulation (1\\u0026ndash;6 h), followed by sustained Pro accumulation and progressive membrane damage (9\\u0026ndash;24 h). This time-resolved physiological framework provides the phenotypic basis for subsequent multi-omics analysis.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.2 Global transcriptomic dynamics under heat stress\\u003c/h2\\u003e\\n\\u003cp\\u003eHeat stress triggered a time-dependent increase in DEGs. Compared to control (0 h), 15,222 DEGs were identified at 1 h, rising to 33,668 at 24 h (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Notably, the transition from 9\\u0026ndash;24 h involved 20,967 DEGs, indicating a critical phase shift in the molecular response.\\u003c/p\\u003e\\n\\u003cp\\u003eGO enrichment of all DEGs revealed significant terms related to \\u0026ldquo;response to heat,\\u0026rdquo; \\u0026ldquo;oxidative stress,\\u0026rdquo; \\u0026ldquo;protein folding,\\u0026rdquo; and \\u0026ldquo;antioxidant activity\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). KEGG enrichment highlighted pathways including \\u0026ldquo;plant hormone signal transduction,\\u0026rdquo; \\u0026ldquo;MAPK signaling pathway,\\u0026rdquo; \\u0026ldquo;protein processing in endoplasmic reticulum,\\u0026rdquo; and \\u0026ldquo;phenylpropanoid biosynthesis\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). These results indicate coordinated activation of stress signal transduction, protein homeostasis, and secondary metabolism pathways.\\u003c/p\\u003e\\n\\u003cp\\u003eTemporal analysis (Appendix 1) revealed a phased response: early (1\\u0026ndash;3 h) enrichment of signal transduction and oxidative stress terms; mid-phase (6\\u0026ndash;9 h) shift toward protein folding and phenylpropanoid metabolism; late phase (24 h) involvement of programmed cell death and glutathione metabolism, reflecting progressive stress adaptation\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003cdiv class=\\\"colspec\\\" align=\\\"char\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eStatistical Table of DEGs Number.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGroup\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAll\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eUp\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDown\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eB vs A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e15222\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e8569\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e6653\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eC vs A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e22668\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e12344\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e10324\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eD vs A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e21901\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e11326\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e10575\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eE vs A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e23660\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e11776\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e11884\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF vs A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e33668\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e22473\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e11195\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eC vs B\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1952\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e320\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1632\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eD vs C\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e3732\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e2753\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e979\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eE vs D\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e7068\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e3890\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e3178\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF vs E\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e20967\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e14507\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e6460\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.3 Heat Shock Proteins: a temporally coordinated chaperone network\\u003c/h2\\u003e\\n\\u003cp\\u003eA total of 104 differentially expressed HSP genes were identified, including 34 small \\u003cem\\u003eHSPs\\u003c/em\\u003e (\\u003cem\\u003esHSPs\\u003c/em\\u003e), 40 \\u003cem\\u003eHSP70\\u003c/em\\u003e, and 30 \\u003cem\\u003eHSP90\\u003c/em\\u003e members, whose expression exhibited a tightly regulated temporal cascade in response to heat stress (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-C and detailed in Appendix 2). Within 1 h of stress onset, multiple \\u003cem\\u003esHSP\\u003c/em\\u003e genes were strongly upregulated, indicating an immediate protective response against protein misfolding. For example, \\u003cem\\u003eHSP26.7\\u003c/em\\u003e (TRINITY_DN62394_c2_g6) increased by 17.21-fold at 1 h and 39.46-fold at 3 h compared to 0 h, while \\u003cem\\u003eHSP16.0\\u003c/em\\u003e (TRINITY_DN68816_c1_g1), and \\u003cem\\u003eHSP22.0\\u003c/em\\u003e (TRINITY_DN68971_c0_g1) also showed marked upregulation exceeding 4.32-fold during this period. In contrast, most \\u003cem\\u003eHSP70\\u003c/em\\u003e and \\u003cem\\u003eHSP90\\u003c/em\\u003e family members exhibited only modest induction at this stage, with notable exceptions such as \\u003cem\\u003eHSP90-1\\u003c/em\\u003e (TRINITY_DN87102_c0_g1, 5.87-fold at 3 h compared to 0 h) and \\u003cem\\u003eHSP70\\u003c/em\\u003e (TRINITY_DN72233_c1_g2, 4.40-fold at 3 h compared to 0 h). As stress progressed into the mid-phase (6\\u0026ndash;9 h), most \\u003cem\\u003eHSPs\\u003c/em\\u003e continued to accumulate, reaching their maximum expression levels. \\u003cem\\u003eHSP26.7\\u003c/em\\u003e peaked at 6 h with an extraordinary 56.50-fold increase, while HSP16.0 reached 11.86-fold at 9 h; \\u003cem\\u003eHSP70\\u003c/em\\u003e family members became more broadly activated, with \\u003cem\\u003eHSP70\\u003c/em\\u003e (TRINITY_DN38515_c0_g1) increasing by 5.98-fold at 9 h compared to 0 h. Notably, while most \\u003cem\\u003esHSPs\\u003c/em\\u003e began to decline after peaking at 6\\u0026ndash;9 h, \\u003cem\\u003eHSP70\\u003c/em\\u003e and \\u003cem\\u003eHSP90\\u003c/em\\u003e members generally maintained elevated expression, indicating a transition from rapid emergency response to sustained protein quality control. By 24 h of continuous stress, the expression profiles of HSP families diverged significantly: most \\u003cem\\u003esHSPs\\u003c/em\\u003e and \\u003cem\\u003eHSP90\\u003c/em\\u003e members declined substantially from their mid-phase peaks, whereas a subset of HSP70 genes continued to rise, reaching their highest expression at this late stage. Specifically, \\u003cem\\u003eHSP70-16\\u003c/em\\u003e (TRINITY_DN67914_c2_g2) and another \\u003cem\\u003eHSP70\\u003c/em\\u003e (TRINITY_DN72233_c0_g1) exhibited 7.29-fold and 7.77-fold increases, respectively, at 24 h. This sustained \\u003cem\\u003eHSP70\\u003c/em\\u003e activity suggests a shift toward long-term protein homeostasis maintenance, possibly involving refolding of damaged proteins and prevention of aggregation under prolonged stress. Collectively, these temporal dynamics reveal a phased chaperone strategy: rapid \\u003cem\\u003esHSP\\u003c/em\\u003e-dominated response during early stress, broad activation and peak accumulation across families during mid-phase, and sustained \\u003cem\\u003eHSP70\\u003c/em\\u003e-mediated protection at late stage, underscoring the central role of the HSP network in mitigating heat-induced proteotoxic stress throughout the entire stress continuum.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.4 Heat Stress Factors: master regulators orchestrating the heat stress response\\u003c/h2\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eHSFs\\u003c/em\\u003e, the master regulators of \\u003cem\\u003eHSPs\\u003c/em\\u003e, showed early and sustained induction. A total of 29 differentially expressed \\u003cem\\u003eHSFs\\u003c/em\\u003e were identified, encompassing members of the \\u003cem\\u003eHSFA\\u003c/em\\u003e, \\u003cem\\u003eHSFB\\u003c/em\\u003e, and \\u003cem\\u003eHSFC\\u003c/em\\u003e families. Their expression dynamics revealed a temporally coordinated regulatory cascade. (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD, detailed in Appendix 3). Among HSFA family members, several exhibited pronounced upregulation with distinct temporal patterns. \\u003cem\\u003eHSFA3\\u003c/em\\u003e (TRINITY_DN66095_c1_g1) showed a progressive increase throughout stress, reaching 1.71-fold at 6 h, 2.35-fold at 9 h, and 2.54-fold at 24 h relative to control, indicating its role in sustained transcriptional activation. \\u003cem\\u003eHSFA4a\\u003c/em\\u003e (TRINITY_DN57479_c0_g1) was markedly induced at late stage, with expression peaking at 24 h (2.95-fold), suggesting involvement in prolonged stress adaptation. \\u003cem\\u003eHSFA6b\\u003c/em\\u003e (TRINITY_DN16518_c0_g1) displayed a mid-phase induction, with maximum levels at 6\\u0026ndash;9 h (2.15-fold and 2.20-fold, respectively), coinciding with the peak of antioxidant enzyme activities and \\u003cem\\u003eHSP\\u003c/em\\u003e accumulation. Notably, \\u003cem\\u003eHSFA5\\u003c/em\\u003e (TRINITY_DN23603_c1_g2) exhibited a biphasic expression pattern: it was strongly upregulated as early as 1 h (4.46-fold) and 3 h (5.43-fold), declined moderately at 6\\u0026ndash;9 h, and then rose again at 24 h to 7.99-fold. This dual response suggests that HSFA5 may function both in rapid signal initiation and in late-stage regulatory reinforcement. Another \\u003cem\\u003eHSFA5\\u003c/em\\u003e (TRINITY_DN60959_c0_g2) also showed significant late induction (2.51-fold at 24 h). \\u003cem\\u003eHSFA2b\\u003c/em\\u003e (TRINITY_DN64079_c1_g1), the most abundantly expressed HSF, maintained consistently high transcript levels throughout stress, with a moderate peak at 6 h (1.36-fold), implying a role as a constitutively active regulator that may integrate multiple stress signals. In contrast, \\u003cem\\u003eHSFA1a\\u003c/em\\u003e (TRINITY_DN41618_c0_g2) exhibited relatively stable expression with only minor fluctuations, consistent with its proposed function as a master regulator whose activity is primarily controlled post-translationally. Among \\u003cem\\u003eHSFB\\u003c/em\\u003e members, \\u003cem\\u003eHSFB1\\u003c/em\\u003e and \\u003cem\\u003eHSFB2c\\u003c/em\\u003e showed varied responses. \\u003cem\\u003eHSFB1\\u003c/em\\u003e (TRINITY_DN16128_c0_g1) and \\u003cem\\u003eHSFB2c\\u003c/em\\u003e (TRINITY_DN63448_c0_g1) displayed moderate changes without consistent upregulation, suggesting they may play auxiliary roles in fine-tuning the heat stress response.\\u003c/p\\u003e\\n\\u003cp\\u003eCollectively, the temporal expression patterns of \\u003cem\\u003eHSFs\\u003c/em\\u003e reveal a hierarchical regulatory network: early-responsive \\u003cem\\u003eHSFA5\\u003c/em\\u003e initiate the transcriptional cascade, mid-phase \\u003cem\\u003eHSFA6b\\u003c/em\\u003e coordinate with \\u003cem\\u003eHSP\\u003c/em\\u003e accumulation and antioxidant defense, and late-phase \\u003cem\\u003eHSFA3\\u003c/em\\u003e and \\u003cem\\u003eHSFA4a\\u003c/em\\u003e sustain long-term adaptation. This precisely timed HSF activation underpins the dynamic \\u003cem\\u003eHSPs\\u003c/em\\u003e and biosynthesis genes, ultimately shaping the multifaceted heat stress response in \\u003cem\\u003eIris\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.5 Systematic activation of ABA signaling components\\u003c/h2\\u003e\\n\\u003cp\\u003ePhytohormones serve as central integrators of environmental stress signals, which playing a pivotal role in plant adaptation to diverse abiotic stresses including high temperature. KEGG enrichment analysis of DEGs revealed that \\u0026ldquo;plant hormone signal transduction\\u0026rdquo; was among the most significantly enriched pathways throughout the stress time course, with ABA signaling components exhibiting particularly pronounced and coordinated transcriptional reprogramming (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE, detailed in Appendix 4).\\u003c/p\\u003e\\n\\u003cp\\u003eABA receptors (Pyrabactin Resistance 1, \\u003cem\\u003ePYLs\\u003c/em\\u003e) are progressively upregulated. Multiple genes encoding ABA receptors of the PYL family showed significant induction under heat stress, with expression levels increasing progressively over time. \\u003cem\\u003ePYL3\\u003c/em\\u003e (TRINITY_DN58402_c0_g1) exhibited a sustained upward trend, reaching 1.73-fold at 3 h, 3.39-fold at 9 h, and 5.87-fold at 24 h relative to control. Another \\u003cem\\u003ePYL3\\u003c/em\\u003e member (TRINITY_DN70111_c2_g6) displayed a similar pattern, peaking at 24 h with a 2.35-fold increase. Notably, \\u003cem\\u003ePYL9\\u003c/em\\u003e (TRINITY_DN30488_c0_g1) was specifically induced only at 6 h, suggesting a transient role during mid-stress phase. This progressive upregulation of ABA receptors indicates enhanced sensitivity to ABA signals under prolonged heat stress.\\u003c/p\\u003e\\n\\u003cp\\u003ePP2C negative regulators exhibit dynamic expression. As core components of the ABA signaling pathway, protein phosphatases type 2C (\\u003cem\\u003ePP2Cs\\u003c/em\\u003e) act as negative regulators by inhibiting downstream Sucrose Non-Fermenting 1 (SNF1)-Related Protein Kinase 2 (\\u003cem\\u003eSnRK2\\u003c/em\\u003e) in the absence of ABA. Under heat stress, \\u003cem\\u003ePP2C\\u003c/em\\u003e family genes displayed complex expression patterns. \\u003cem\\u003ePP2C\\u003c/em\\u003e (TRINITY_DN44551_c0_g3) showed a biphasic response, with elevated expression at 6 h (1.12-fold) and a more pronounced peak at 24 h (1.27-fold). Similarly, \\u003cem\\u003ePP2C\\u003c/em\\u003e (TRINITY_DN83622_c0_g1) was upregulated at 24 h (1.09-fold). The concurrent upregulation of both \\u003cem\\u003ePYL\\u003c/em\\u003e receptors and certain \\u003cem\\u003ePP2C\\u003c/em\\u003e members may reflect a finely tuned regulatory circuit that prevents overactivation of ABA signaling while maintaining responsiveness to persistent stress.\\u003c/p\\u003e\\n\\u003cp\\u003eDownstream transcription factors are activated at late stage. The ABA-responsive element binding factors (\\u003cem\\u003eABFs\\u003c/em\\u003e), which function as downstream transcription factors mediating ABA-dependent gene expression, showed marked induction primarily at late stress phase. \\u003cem\\u003eABF3\\u003c/em\\u003e (TRINITY_DN33766_c0_g1) exhibited the most striking upregulation, increasing progressively from 1 h (2.69-fold) to 3 h (4.30-fold), and remained elevated through 24 h (3.47-fold). In contrast, \\u003cem\\u003eABF2\\u003c/em\\u003e (TRINITY_DN56838_c1_g1) showed a transient peak specifically at 6 h (2.59-fold), suggesting a distinct temporal role.\\u003c/p\\u003e\\n\\u003cp\\u003eThe late induction of ABF transcription factors coincides with the upregulation of flavonoid biosynthesis genes, such as Phenylalanine Ammonia-Lyase (\\u003cem\\u003ePAL\\u003c/em\\u003e), Chalcone Synthase (\\u003cem\\u003eCHS\\u003c/em\\u003e), and Dihydroflavonol 4-Reductase (\\u003cem\\u003eDFR\\u003c/em\\u003e) and the subsequent accumulation of flavonoid metabolites (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), suggesting a potential regulatory link between ABA signaling and the activation of secondary metabolic pathways under heat stress. Given that ABA has been implicated in the regulation of phenylpropanoid metabolism in other plant species, it is plausible that the systematic activation of the ABA pathway in Iris contributes to the enhanced biosynthesis of protective flavonoids, thereby strengthening cellular antioxidant capacity during prolonged heat exposure.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.6 WGCNA identifies a core module linking gene expression to stress phenotypes\\u003c/h2\\u003e\\n\\u003cp\\u003eTo systematically elucidate the regulatory relationships between gene expression and physiological phenotypes, WGCNA was performed. WGCNA constructed a gene co-expression network and identified 10 modules (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). Module-trait correlation analysis (Fig.\\u0026nbsp;10C) revealed that the MEbrown4 module was positively correlated with SOD, POD, and MDA activities, and negatively correlated with Pro activity.\\u003c/p\\u003e\\n\\u003cp\\u003eGO enrichment of MEbrown4 genes highlighted \\u0026ldquo;hormone-mediated signaling,\\u0026rdquo; \\u0026ldquo;regulation of RNA metabolism,\\u0026rdquo; and \\u0026ldquo;response to cadmium ion\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). KEGG enrichment revealed \\u0026ldquo;phenylpropanoid biosynthesis,\\u0026rdquo; \\u0026ldquo;plant hormone signal transduction,\\u0026rdquo; \\u0026ldquo;ABC transporters,\\u0026rdquo; and \\u0026ldquo;glutathione metabolism\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). Hub genes within this module (top 20 by intramodular connectivity) included transcription factor\\u003cem\\u003es CH3, AP2/ERF, GARP\\u003c/em\\u003e, energy sensor \\u003cem\\u003eKIN10\\u003c/em\\u003e (\\u003cem\\u003eSnRK1\\u003c/em\\u003e,SNF1-related protein kinase catalytic subunit alpha), chaperone organizer \\u003cem\\u003eHOP2\\u003c/em\\u003e (\\u003cem\\u003eHsp70-Hsp90\\u003c/em\\u003e organizing protein 2), ascorbate-glutathione cycle gene \\u003cem\\u003eMDAR2\\u003c/em\\u003e (monodehydroascorbate reductase 2), and Proline catabolism gene \\u003cem\\u003ePOX1\\u003c/em\\u003e. These hub genes represent potential integrators of stress signaling, energy status, and metabolic defense.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.7 Transcriptome and metabolome association analysis of flavonoid synthesis in late stress phase\\u003c/h2\\u003e\\n\\u003cp\\u003eUntargeted metabolomics identified 4,067 metabolites, with QC samples clustering tightly in PCA, confirming data reproducibility. Differential metabolite analysis revealed a progressive increase in DAMs. KEGG enrichment of DAMs showed that \\u0026ldquo;flavone and flavonol biosynthesis\\u0026rdquo; and \\u0026ldquo;phenylpropanoid biosynthesis\\u0026rdquo; were specifically enriched at late phase (9\\u0026ndash;24 h) (Appendix 5).\\u003c/p\\u003e\\n\\u003cp\\u003eFlavonoid biosynthesis pathway genes, including \\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS\\u003c/em\\u003e, \\u003cem\\u003eCHI\\u003c/em\\u003e, \\u003cem\\u003eF3H\\u003c/em\\u003e, and \\u003cem\\u003eDFR\\u003c/em\\u003e, showed coordinated upregulation starting from mid-phase (6 h) and peaking at 9\\u0026ndash;24 h (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, left panel). This temporal pattern closely followed the HSF-HSP activation (1\\u0026ndash;6 h), suggesting that HSF-HSP network may orchestrate flavonoid synthesis as part of the adaptive response.\\u003c/p\\u003e\\n\\u003cp\\u003eFocusing on the flavonoid pathway, key metabolites including naringenin chalcone, (-)-epigallocatechin, and quercetin derivatives accumulated significantly from 6 h onward, with peak levels at 24 h (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, right panel). The accumulation pattern mirrored the expression of flavonoid biosynthesis genes (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e), demonstrating coordinated transcriptional and metabolic activation of this pathway under prolonged heat stress.\\u003c/p\\u003e\\n\\u003cp\\u003eF3H, flavanone 3-hydroxylase; DFR, dihydroflavonol 4-reductase; COMT, Caffeic acid 3-O-methyltransferase; C4H, Cinnamate 4-hydroxylase; 4CL, 4-Methylcrotonyl-CoA Ligase; Phe, 3-phenyl-L-alanine; HCT, Hydroxycinnamoyl Transferase; CYP 84A, Cytochrome P450 84A; F3\\u0026rsquo;5\\u0026rsquo;H, lavanone 3\\u0026rsquo;,5\\u0026rsquo;-hydroxylase.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003e3.8 qRT-RCR\\u003c/h2\\u003e\\n\\u003cp\\u003eTo verify the reliability of the transcriptome sequencing results of \\u003cem\\u003eIris\\u003c/em\\u003e leaves under heat stress, 10 heat-responsive DEGs were selected for qRT-PCR analysis (Fig.\\u0026nbsp;9). These genes including \\u003cem\\u003eHSFB2C\\u003c/em\\u003e, \\u003cem\\u003eHSP70\\u003c/em\\u003e, \\u003cem\\u003eHSP18.8\\u003c/em\\u003e, \\u003cem\\u003eHSP90\\u003c/em\\u003e, \\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS2\\u003c/em\\u003e, \\u003cem\\u003ePYL3\\u003c/em\\u003e, \\u003cem\\u003ePP2C\\u003c/em\\u003e, \\u003cem\\u003eABF2\\u003c/em\\u003e, and \\u003cem\\u003eMDAR2\\u003c/em\\u003e (primer design details are provided in Appendix 7). The expression trends of all 10 DEGs detected by qRT-PCR were consistent with the RNA-Seq data, confirming the reliability of the transcriptome sequencing results.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 The HSF-HSP network exhibits a temporally phased chaperone strategy\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003eIris\\u003c/em\\u003e employs a temporally reprogrammed HSF-HSP network that transitions from rapid emergency response to sustained protein protection. In the early phase (1\\u0026ndash;3 h), small HSPs (sHSPs) are rapidly induced, acting as \\\"first responders\\\" that bind to partially denatured proteins to prevent irreversible aggregation. During the mid-phase (6\\u0026ndash;9 h), the \\u003cem\\u003eHSP\\u003c/em\\u003e family is broadly activated and reaches peak expression, with \\u003cem\\u003eHSP70/90\\u003c/em\\u003e beginning to dominate protein folding and repair. By the late phase (24 h), \\u003cem\\u003eHSP70\\u003c/em\\u003e continues to rise while \\u003cem\\u003esHSPs\\u003c/em\\u003e decline, indicating a shift toward \\u003cem\\u003eHSP70\\u003c/em\\u003e-mediated long-term protein homeostasis maintenance. This phased strategy aligns with findings in other ornamentals such as \\u003cem\\u003eClematis\\u003c/em\\u003e and \\u003cem\\u003elily\\u003c/em\\u003e (\\u003cem\\u003eLilium longiflorum\\u003c/em\\u003e) (Zhao et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Ohama et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Huang et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The novel contribution of this study lies in revealing the sustained elevation of \\u003cem\\u003eHSP70\\u003c/em\\u003e at the late stage, suggesting its unique role in protein repair and aggregation prevention under prolonged stress (Zhou et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe transcriptional cascade driving this chaperone network is orchestrated by functionally differentiated \\u003cem\\u003eHSF\\u003c/em\\u003e family members. \\u003cem\\u003eHSFA5\\u003c/em\\u003e exhibits a biphasic expression pattern with peaks at both early and late phases, suggesting dual roles in signal initiation and late-stage reinforcement. \\u003cem\\u003eHSFA6b\\u003c/em\\u003e peaks at mid-phase, coinciding with maximal \\u003cem\\u003eHSP\\u003c/em\\u003e activation and antioxidant enzyme activities. \\u003cem\\u003eHSFA3\\u003c/em\\u003e and \\u003cem\\u003eHSFA4a\\u003c/em\\u003e show sustained upregulation at the late phase, maintaining long-term adaptive responses. \\u003cem\\u003eHSFA2b\\u003c/em\\u003e maintains constitutively high expression throughout stress, potentially serving as a signal integrator. This functional specialization is consistent with studies in \\u003cem\\u003eArabidopsis\\u003c/em\\u003e and \\u003cem\\u003erose\\u003c/em\\u003e (\\u003cem\\u003eRosa rugosa\\u003c/em\\u003e Thunb.) (Wei et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), collectively establishing the \\u003cem\\u003eHSF\\u003c/em\\u003e family as the core transcriptional hub governing heat stress responses.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 ABA signaling cooperates with the HSF-HSP network to activate downstream metabolism\\u003c/h2\\u003e \\u003cp\\u003eThe ABA signaling pathway, through late-stage induction of ABF transcription factors, cooperates with the HSF-HSP network to activate downstream metabolic responses. In this study, ABF transcription factors were significantly upregulated at the late stress phase, and their activation timing closely coincided with the upregulation of flavonoid biosynthesis genes (\\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS\\u003c/em\\u003e, \\u003cem\\u003eDFR\\u003c/em\\u003e). This temporal coupling suggests that \\u003cem\\u003eABFs\\u003c/em\\u003e may serve as a bridge between ABA signaling and the HSF-HSP network, mediating the regulation of secondary metabolism. Consistent with findings in \\u003cem\\u003erose\\u003c/em\\u003e where \\u003cem\\u003eRcHsfA6\\u003c/em\\u003e modulates ABA signaling to influence downstream gene expression (Gill and Tuteja, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Wei et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)), the synchronous activation of \\u003cem\\u003eABFs\\u003c/em\\u003e and flavonoid genes in Iris implies that ABA signaling may participate in HSF-HSP-mediated regulation of secondary metabolism. \\u003cem\\u003eABFs\\u003c/em\\u003e may directly bind to \\u003cem\\u003eABRE\\u003c/em\\u003e elements in the promoters of flavonoid biosynthesis genes, or cooperate with \\u003cem\\u003eHSFs\\u003c/em\\u003e to regulate shared target genes, forming an \\\"HSF-HSP-ABA-flavonoid\\\" regulatory axis (Lee et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Zhou et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Flavonoid biosynthesis is coordinately activated as a downstream metabolic output\\u003c/h2\\u003e \\u003cp\\u003eThe flavonoid pathway, as a downstream target of HSF-HSP and ABA signaling, is coordinately activated during the late stress phase, establishing a sustained chemical defense. Flavonoid biosynthesis genes (\\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS\\u003c/em\\u003e, \\u003cem\\u003eDFR\\u003c/em\\u003e) were significantly upregulated from 6 to 24 h, following a clear temporal sequence after HSF-HSP network activation (1\\u0026ndash;6 h). At the metabolite level, key flavonoids including naringenin chalcone and (-)-epigallocatechin accumulated during the same period, showing high consistency between transcriptional and metabolic activation. These findings align with studies in \\u003cem\\u003eRhododendron\\u003c/em\\u003e, \\u003cem\\u003echrysanthemum\\u003c/em\\u003e, and \\u003cem\\u003erose\\u003c/em\\u003e (Szabados and Savoure, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Guo et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The novel contribution of this study lies in revealing the temporal coupling between flavonoid biosynthesis and HSF-HSP network activation (Wan et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). As antioxidants, flavonoids scavenge accumulated ROS and stabilize membrane structures during late stress, forming a second line of defense in \\u003cem\\u003eIris\\u003c/em\\u003e heat adaptation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 WGCNA identifies a core module integrating multiple adaptive pathways and provides targets for future studies\\u003c/h2\\u003e \\u003cp\\u003eWGCNA identified MEbrown4 as a core module significantly correlated with physiological indicators (SOD, POD, MDA), enriched in phenylpropanoid biosynthesis, hormone signaling, and glutathione metabolism pathways. This module provides systems-level validation of the integration among multiple adaptive pathways. Hub genes within this module include the energy sensor \\u003cem\\u003eKIN10\\u003c/em\\u003e(\\u003cem\\u003eSnRK1\\u003c/em\\u003e), the chaperone organizer \\u003cem\\u003eHOP2\\u003c/em\\u003e (Zhao et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), the antioxidant gene \\u003cem\\u003eMDAR2\\u003c/em\\u003e, and several transcription factors (Agati et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). \\u003cem\\u003eKIN10\\u003c/em\\u003e can activate secondary metabolism under energy stress (Wang et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e); \\u003cem\\u003eHOP2\\u003c/em\\u003e coordinates protein homeostasis as a bridge between \\u003cem\\u003eHSP70\\u003c/em\\u003e and \\u003cem\\u003eHSP90\\u003c/em\\u003e (Zhao et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e); \\u003cem\\u003eMDAR2\\u003c/em\\u003e directly scavenges ROS through the ascorbate-glutathione cycle (Nakabayashi and Saito, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). These hub genes represent priority targets for functional validation and molecular breeding.\\u003c/p\\u003e \\u003cp\\u003eAt all, this study reveals the dynamic network of heat stress response in \\u003cem\\u003eIris\\u003c/em\\u003e through multi-omics integration, but several limitations remain, including that direct regulation of flavonoid genes by \\u003cem\\u003eHSFs\\u003c/em\\u003e (ie., \\u003cem\\u003eHSFA2, HSFA5\\u003c/em\\u003e, and \\u003cem\\u003eHSFA3\\u003c/em\\u003e) has not been validated, the functions of HSP genes \\u003cem\\u003e(HSP70/HSP90/sHSPs\\u003c/em\\u003e) require validation through transgenic approaches or VIGS systems, investigating whether exogenous ABA application enhances flavonoid accumulation and thermotolerance to establish causality between ABA signaling and flavonoid biosynthesis, and the specific contributions of individual flavonoid compounds to thermotolerance need to be assessed through exogenous application or metabolic engineering. Future research should therefore focus on molecular dissection of the HSF-HSP-flavonoid regulatory axis, functional characterization of key hub genes (\\u003cem\\u003eKIN10\\u003c/em\\u003e, \\u003cem\\u003eHOP2\\u003c/em\\u003e), and application of core markers in screening and breeding heat-tolerant Iris germplasm.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides the first time-resolved multi-omics landscape of heat stress response in \\u003cem\\u003eIris tectorum\\u003c/em\\u003e '\\u003cem\\u003eHot Spicy\\u003c/em\\u003e'. Heat stress triggers a temporally phased HSF-HSP chaperone network: small \\u003cem\\u003eHSPs\\u003c/em\\u003e dominate the early emergency response, broad HSP family activation peaks at mid-phase, and sustained HSP70-mediated protection persists at late stage. \\u003cem\\u003eHSF\\u003c/em\\u003e family members exhibit functional differentiation, with early-responsive \\u003cem\\u003eHSFA5\\u003c/em\\u003e, mid-phase \\u003cem\\u003eHSFA6b\\u003c/em\\u003e, and late-phase \\u003cem\\u003eHSFA3/HSFA4a\\u003c/em\\u003e orchestrating the transcriptional cascade. The ABA signaling pathway is activated, with late-stage induction of \\u003cem\\u003eABF\\u003c/em\\u003e transcription factors coinciding with the upregulation of flavonoid biosynthesis genes. Metabolomic analysis confirms coordinated accumulation of flavonoids from mid to late stress phase, mirroring the transcriptional activation of \\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS\\u003c/em\\u003e, and \\u003cem\\u003eDFR\\u003c/em\\u003e. WGCNA identifies MEbrown4 as a core module integrating hormone signaling, phenylpropanoid biosynthesis, and glutathione metabolism, with hub genes including energy sensor \\u003cem\\u003eKIN10\\u003c/em\\u003e, chaperone organizer \\u003cem\\u003eHOP2\\u003c/em\\u003e, and antioxidant \\u003cem\\u003eMDAR2\\u003c/em\\u003e representing key targets for functional validation. Collectively, these findings reveal a phased regulatory cascade from early HSF-HSP activation to sustained flavonoid accumulation, providing a systems-level framework for understanding heat adaptation in Iris and identifying genetic resources for breeding thermotolerant ornamental cultivars.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tabb\\\" border=\\\"1\\\"\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003cth style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFull name\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAbbreviation\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFull name\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAbbreviation\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003esoluble sugar\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSS\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHeat stress factors\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHSF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e3-phenyl-L-alanine\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePhe\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHydroxycinnamoyl Transferase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHCT\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e4-Methylcrotonyl-CoA Ligase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e4CL\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eIris tectorum\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eIris\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eABA-responsive element binding factors\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eABF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eKyoto Encyclopedia of Genes and Genomes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eKEGG\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAbscisic acid\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eABA\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003elavanone 3\\u0026rsquo;,5\\u0026rsquo;-hydroxylase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF3\\u0026rsquo;5\\u0026rsquo;H\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCaffeic acid 3-O-methyltransferase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOMT\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003emalondialdehyde\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMDA\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eChalcone Synthase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCHS\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eperoxidase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePOD\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCinnamate 4-hydroxylase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eC4H\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePhenylalanine Ammonia-Lyase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePAL\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCytochrome P450 84A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCYP84A\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eproline\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePro\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDifferential accumulation of metabolites\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDAMs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eprotein phosphatases type 2C\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePP2C\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003edifferentially expressed genes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDEGs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePyrabactin Resistance 1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePYL\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDihydroflavonol 4-Reductase\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDFR\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ereactive oxygen species\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eROS\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr style=\\\"height: 35px;\\\"\\u003e\\n\\u003ctd style=\\\"height: 35px;\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eflavanone 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align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eWGCNA\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35.2732px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd style=\\\"height: 35.2732px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eEthics approval and consent to participate:\\u003c/h2\\u003e \\u003cp\\u003e All authors ensured that every step of the research process complied with all ethical requirements mentioned by the BMC Plant Biology journal. All authors are aware of and agree to the submission, and guarantee that all research content is true and reliable.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e \\u003cp\\u003eAll authors agree to submit the manuscript to BMC Plant Biology. By submitting our article we agree to pay this charge in full if ourarticle is accepted for publication.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eCompeting Interests:\\u003c/h2\\u003e \\u003cp\\u003eWe declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. We guarantee that there are no conflicts of interest related to the journal BMC Plant Biology in the article.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eFunding\\u003c/strong\\u003e \\u003cp\\u003eThis research did receive funding. Jiayu Hu received funding from Basic and Applied Basic Research Foundation of Guangdong Province; Grant ID 2022A1515110779.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding Information:\\u003c/h2\\u003e \\u003cp\\u003eBasic and Applied Basic Research Foundation of Guangdong Province (Grant ID: 2022A1515110779).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eJiayu Hu reports financial support was provided by Guangdong Basic and Applied Basic Research Foundation. Jiayu Hu reports a relationship with Guangdong Basic and Applied Basic Research Foundation that includes: funding grants. Jiayu Hu has patent pending to Jiayu Hu. Each member is aware of and adheres to the submission guidelines. All members participated and contributed to the research process. Jiayu Hu was responsible for the overall planning and ideas of the research, as well as providing funding support; Youli Li was responsible for the progress of research experiments and data analysis; Yang Lin, Nuoya Wu, and Jiaxu Xie participated in experiments and data work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements:\\u003c/h2\\u003e \\u003cp\\u003eHeartfelt thanks are extended to every research participant, editor, and reviewer, for their invaluable contributions to the conduct of this study and the completion of this manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll RNA sequencing results have been uploaded to the NCBI database, and newly generated public data(Accession ID: SAMN57111650-SAMN57111685 corresponding URLs detailed in Appendix 6).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAgati G, Azzarello E, Pollastri S, Tattini M. Flavonoids as antioxidants in plants: location and functional significance. Plant Sci. 2012;196:67\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl-Whaibi MH. Plant heat-shock proteins: a mini review. J King Saud Univ - Sci. 2011;23:139\\u0026ndash;50.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArnao MB, Hern\\u0026aacute;ndez-Ruiz J. Melatonin: a new plant hormone and/or a plant master regulator? Trends Plant Sci. 2019;24:38\\u0026ndash;48.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAswani V, Rao DE, Bapatla RB, Sunil B, Saini D, Raghavendra AS. 2025. Deficiency of p5cs but not pdh decreases proline content and raises the ros levels during photo-oxidative stress in arabidopsis thaliana mutants. J Plant Growth Regul. 1\\u0026ndash;12.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBrignot H, Rayot C, Buiret G, Danguin TT, Feron G. Determination of the optimal method for measuring malondialdehyde in human saliva. 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New York, N.Y.: Rice; 2020. p. 61.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHuang Y, An J, Sircar S, Bergis C, Chlo\\u0026eacute; DL, He X, Costa D, Feng B, Bazin QT, Antunez Sanchez J, Maria J, Ravi FM, Chaouche SD, Bendahmane RB, Frugier A, Xia F, Rothan C, Aline C, Mohamed VP, Bergounioux Z, Delarue C, Zhang M, Zheng Y, Crespi S, Fragkostefanakis M, Magdy S, Ariel MM, Marcos FG, Raynaud J, Latrasse C, Benhamed D, M. Hsfa1a modulates plant heat stress responses and alters the 3d chromatin organization of enhancer-promoter interactions. Nat Commun. 2023;14:469.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIba K. Acclimative response to temperature stress in higher plants: approaches of gene engineering for temperature tolerance. Annu Rev Plant Biol. 2002;53:225\\u0026ndash;45.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIkegaya A, Oishi R. 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Front Plant Sci. 2018;9:915.\\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;22:53\\u0026ndash;65.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOzturk M, Turkyilmaz Unal B, Garcia-Caparros P, Khursheed A, Gul A, Hasanuzzaman M. Osmoregulation and its actions during the drought stress in plants. Physiol Plant. 2021;172:1321\\u0026ndash;35.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRiboni M, Galbiati M, Tonelli C, Conti L. Gigantea enables drought escape response via abscisic acid-dependent activation of the florigens and suppressor of overexpression of constans1. Plant Physiol. 2013;162:1706\\u0026ndash;19.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSami F, Yusuf M, Faizan M, Faraz A, Hayat S. Role of sugars under abiotic stress. 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Raw material enzymatic activity determination: a specific case for validation and comparison of analytical methods\\u0026ndash;the example of superoxide dismutase (sod). J Pharm Biomed Anal. 2006;40:1143\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou Y, Wang Y, Xu F, Song C, Yang X, Zhang Z, Yi M, Ma N, Zhou X, He J. 2022. Small hsps play an important role in crosstalk between hsf-hsp and ros pathways in heat stress response through transcriptomic analysis in lilies (lilium longiflorum). BMC Plant Biol. 22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZinta G, AbdElgawad H, Peshev D, Weedon JT, Wim VDE, Nijs I, Janssens IA, Beemste RGTS, Asard H. Dynamics of metabolic responses to periods of combined heat and drought in arabidopsis thaliana under ambient and elevated atmospheric co2. J Exp Bot. 2018;69:2159\\u0026ndash;70.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Iris tectorum, Heat stress, Times-series analysis, HSF-HSP regulatory axis, flavonoid biosynthesis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9080835/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9080835/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eHeat stress threatens ornamental plant productivity. \\u003cem\\u003eIris tectorum\\u003c/em\\u003e (\\u003cem\\u003eIris\\u003c/em\\u003e), a prized ornamental species valued for its diverse flower colors and architectural forms, suffers from heat-induced physiological damage that diminishes its economic value. Here we conducted a time-series analysis (0, 1, 3, 6, 9, and 24 h at 40\\u0026deg;C) integrating physiological assays, transcriptomics, and untargeted metabolomics, to elucidate the temporal dynamics of heat stress response in Iris tectorum 'Hot Spicy',. Physiologically, heat stress induced progressive membrane lipid peroxidation (MDA accumulation), transient activation of antioxidant enzymes (SOD and POD peaking at 6 h), and sustained proline accumulation, revealing a temporally phased stress response. Transcriptomic analysis identified a \\u0026ldquo;Heat stress factors-Heat Shock Proteins\\u0026rdquo; (HSF-HSP) network exhibited tightly coordinated temporal dynamics: small \\u003cem\\u003eHSPs\\u003c/em\\u003e (such as \\u003cem\\u003eHSP26.7\\u003c/em\\u003e, \\u003cem\\u003eHSP16.0\\u003c/em\\u003e and \\u003cem\\u003eHSP22.0\\u003c/em\\u003e) responded rapidly within 1\\u0026ndash;3 h (39.46-fold), broad \\u003cem\\u003eHSP\\u003c/em\\u003e activation peaked at 6\\u0026ndash;9 h (56.50-fold), and sustained \\u003cem\\u003eHSP70\\u003c/em\\u003e-mediated protection persisted at 24 h (7.77-fold). \\u003cem\\u003eHSFs\\u003c/em\\u003e showed hierarchical activation, with early-responsive \\u003cem\\u003eHSFA5\\u003c/em\\u003e (7.99-fold), mid-phase \\u003cem\\u003eHSFA6b\\u003c/em\\u003e, and late-phase \\u003cem\\u003eHSFA3/HSFA4a\\u003c/em\\u003e orchestrating the transcriptional cascade. The Abscisic acid signaling pathway was systematically activated, with progressive \\u003cem\\u003ePYL\\u003c/em\\u003e receptor upregulation (5.87-fold) and late-stage \\u003cem\\u003eABF\\u003c/em\\u003e induction (4.30-fold), coinciding with activation of flavonoid biosynthesis genes. Metabolomic analysis confirmed coordinated accumulation of flavonoids (naringenin chalcone, (-)-epigallocatechin) from 6\\u0026ndash;24 h, mirroring transcriptional activation of \\u003cem\\u003ePAL\\u003c/em\\u003e, \\u003cem\\u003eCHS\\u003c/em\\u003e, and \\u003cem\\u003eDFR\\u003c/em\\u003e. Weighted Gene Co-expression Network Analysis (WGCNA) revealed a core module (MEbrown4) enriched in phenylpropanoid biosynthesis and hormone signaling, with hub genes including \\u003cem\\u003eKIN10\\u003c/em\\u003e and \\u003cem\\u003eHOP2\\u003c/em\\u003e. This study provides the first time-resolved landscape of heat adaptation in \\u003cem\\u003eIris\\u003c/em\\u003e, revealing a phased cascade from early \\u003cem\\u003eHSF-HSP\\u003c/em\\u003e activation to sustained flavonoid accumulation, and identifies key genetic targets for breeding thermotolerant ornamentals.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Temporal dynamics of the HSF-HSP regulatory network and flavonoid metabolism coordinate physiological adaptation to heat stress in Iris\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-29 11:29:52\",\"doi\":\"10.21203/rs.3.rs-9080835/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-04-29T10:45:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-27T12:52:19+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"32099792354394948594933181037306017699\",\"date\":\"2026-04-22T11:31:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-22T01:23:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"310598805515790662252595524663130836761\",\"date\":\"2026-04-21T07:29:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"87064874605866363474251260778725763740\",\"date\":\"2026-04-21T03:13:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-21T03:03:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-16T01:03:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-04-07T15:23:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-07T12:05:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Plant Biology\",\"date\":\"2026-04-07T09:38:19+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b63f51df-ca07-482c-a9f0-9f2b4a098f51\",\"owner\":[],\"postedDate\":\"April 29th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-29T11:29:52+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-29 11:29:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9080835\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9080835\",\"identity\":\"rs-9080835\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}