Transcriptome and metabolome analyses of Rosa chinensis identify heat stress response genes and metabolite pathways

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In the rose flower industry, high-temperature stress leads to bud dormancy or even death, reducing ornamental value and incurring in economic loss. Understanding the molecular mechanisms underlying the response and resistance of roses to high-temperature stress can serve as an important reference for the cultivation of high-temperature-stress-resistant roses. Results To evaluate the impact of high temperature on rose plants, we initially measured physiological indices in rose leaves after heat stress. We observed a significant decrease in protein and chlorophyll content, while proline and malondialdehyde (MDA) levels, as well as peroxidase (POD) activity, increased. Subsequently, transcriptomics and metabolomics analyses were conducted to detect changes in gene expression and metabolite content after high-temperature stress. Compared to the untreated control (T0), the number of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) in rose plants subjected to heat peaked at time points T6-T9. This trend closely aligned with the observed physiological changes. Enrichment analysis showed that most DEGs and DAMs primarily involved in the mitogen-activated protein kinases (MAPK) signaling pathway, plant hormone signal transduction, alpha-linolenic acid metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis, etc. After heat stress, the DEGs and DAMs combined analysis revealed a predominant downregulation of genes and metabolites related to the flavonoid biosynthesis pathway. Similarly, genes involved in the jasmonic acid pathway within the MAPK signaling pathway exhibited decreased expression, but genes associated with the ethylene pathway were mostly upregulated, suggesting a role in roses’ heat stress responses. Furthermore, heterologous overexpression of the heat stress-responsive gene RcHP70 in Arabidopsis thaliana increased resistance against heat stress. Conclusion The present study provides new insights on the genes and metabolites induced in roses in response to high temperature; the present results provide a reference for analyzing the molecular mechanism underlying resistance to heat stress in roses. The obtained candidate genes and metabolites could be valuable resources for breeding of heat stress resistant roses. Rosa chinensis heat stress Transcriptome Metabolome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Heat stress due to global warming poses a significant threat to food security and agricultural sustainability [ 1 ]. The elevated temperatures associated with heat stress can decrease water content, disrupting cellular homeostasis and compromising vital biological processes [ 2 ]. These physiological disturbances can have devastating effects, leading to reduced crop yields and, in extreme cases, plant death [ 1 ]. Rosa chinensis is an important ornamental plant, with an optimum temperature to grow and bloom of 22–26°C. Roses are sensitive to high temperatures, which often decreases the number or size of flowers, makes them enter dormancy, and not blossom [ 3 , 4 ]. Plants have evolved complex and diverse systems to cope with environmental heat stress [ 1 , 2 , 5 ]. At the molecular level, plants rely on transcriptional regulatory networks to orchestrate their stress response. Heat shock proteins (HSPs) and reactive oxygen species (ROS) are major makers used for detecting plant responses to heat stress [ 6 – 9 ]. The transcriptional regulatory network activated by heat stress is a highly dynamic and coordinated system. For instance, tomato and Arabidopsis heat shock transcription factor A1s (HfA1s) play critical roles in the heat shock response by decreasing induction of heat stress-responsive genes and heat stress sensitive phenotypes [ 10 , 11 ]. Among them, some genes encode proteins involved in protecting cellular components from damage caused by heat stress, while others encode proteins involved in repairing damage or restoring cellular homeostasis [ 12 – 15 ]. In addition to transcriptional regulation, post-translational modifications play a pivotal role in the plant’s heat stress response. These modifications, which include phosphorylation [ 16 ], ubiquitination [ 17 ], and SUMOylation [ 18 – 20 ], can alter the activity, localization, or stability of proteins involved in stress signaling and responses. For instance, Ca 2+ and ROS may be involved in heat stress sensing through MAPK signaling [ 21 – 23 ]. Moreover, plant hormones such as jasmonic acid play a key role in abiotic stress responses [ 24 ] and early signaling enhances heat tolerance in Arabidopsis [ 25 ]. In roses, Li et al. found Ca 2+ signaling pathways and transcription factors associated with rapid sensing and signal transduction in heat stress responses [ 4 ]. Despite these advances in understanding the molecular mechanisms underlying plant heat stress responses, the molecular mechanism underlying the response to heat stress in roses remains unknown. In this study, the “red apple” rose variety was used and subjected to RNA-seq and metabolomic analysis to identify candidate genes and metabolites. The results indicated that the genes and metabolites related to flavonoid biosynthesis and MAPK signaling pathway may involve in heat stress response. The results provide valuable information for molecular breeding of resistance rose variety. Materials and Methods Plant materials and heat stress treatment Two-year-old “red apple” rose cuttings (rootstock, white-flowered roses) were used as experimental materials. After being purchased from a nursery, they were pre-cultured in an artificial climatic chamber for 10 days under the following conditions: temperature of 24°C, humidity of 55%, light duration of 12 h (day) / 12 h (night), and a light intensity of 100%. Subsequently, high-temperature treatments at 42°C were conducted for 0 h (Treatment 0 h, T0), 3 h (T3), 6 h (T6), 9 h (T9), and 12 h (T12), respectively. After each treatment, functional leaves were collected, immediately frozen in liquid nitrogen, and stored in a ˗80°C freezer for subsequent transcriptome and metabolome sequencing. The transcriptome analysis included three biological replicates and the metabolome analysis included six biological replicates. Photosynthetic and antioxidant enzyme analysis Plant leaves were collected to determine the concentration of photosynthetic pigments and proline. Samples (0.5 g fresh weight per treatment) were extracted in 80% (v/v) methanol and thoroughly ground. The concentration in the supernatant of this mixture was determined spectrophotometrically as described by Wang and Huang [ 26 ]. Malondialdehyde (MDA), peroxidase (POD), and protein levels were analyzed using specific assay kits obtained from Nanjing Mo Fan Biotechnology Co., Ltd (Nanjing, China). RNA extraction and sequencing The total RNA of leaves was extracted according to the protocol for extraction of plant RNA ( https://www.yuque.com/yangyulan-ayaeq/oupzan ). After measuring the RNA level and quality, RNAs were used to construct libraries according to the protocol for mRNA library preparation (BGI, China). Then, the libraries were sequenced by DNBSEQ (BGI, China). The SOAPnuke (v1.5.6) software was used for quality control of the raw data [ 27 , 28 ]. The reference R. chinensis genome and annotation files were downloaded from the NCBI database (accession no. GCF_002994745.2) [ 29 ]. The clean data was mapped to the reference genome by HISAT (v2.1.0) [ 30 ]. Bowtie2 was applied to align the clean reads to the gene set, where known and novel as well as coding and noncoding transcripts were included [ 31 ]. Gene expression levels were calculated by RSEM (v1.3.1) [ 32 ]. Differentially expressed gene (DEG) analysis was performed using DESeq2 (v1.4.5) with a q -value ≤ 0.05 [ 33 ]. The time series analysis was performed using Mfuzz (v2.34.0) [ 34 ], and gene co-expression network analysis was performed with WGCNA (v1.48). Metabolites extraction and analysis A total of 50 mg tissues were extracted by directly adding 800 µL of precooled extraction reagent (MeOH: H 2 O) (70:30, v/v, precooled at -20°C); then, 20 µL of internal standards mix was added for quality control of the sample preparation. Two small steel balls were added to the Eppendorf tube. After homogenizing at 50 Hz for 5 min by using TissueLyser (JXFSTPRP, China), samples were sonicated for 30 min at 4°C and incubated at -20°C for 1 h. Then, samples were further centrifuged for 15 min at 14,000 rpm and 4°C. Next, 600 µL of the supernatants were filtered through 0.22 µm microfilters and transferred to autosampler vials for liquid chromatography mass spectrometry (LC-MS) analysis. To evaluate the reproducibility and stability of the whole LC-MS analysis, a quality control (QC) sample was prepared by pooling 20 µL of the supernatant from each sample. Sample analysis was performed on a Waters ACQUITY UPLC 2D (Waters, USA), coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific, USA) with a heated electrospray ionization source. Chromatographic separation was performed on a Hypersil GOLD aQ column (2.1 × 100 mm, 1.9 µm, Thermo Fisher Scientific, USA), with mobile phase A consisting 0.1% formic acid in water and mobile phase B consisting 0.1 formic acid in acetonitrile. The column temperature was maintained at 40°C. The gradient conditions were as follows: 5% B over 0.0–2.0 min, 5–95% B over 2.0–22.0 min, held constant at 95% B over 22.0–27.0 min and washed with 95% B over 27.1–30 min. The flow rate was 0.3 mL/min and the injection volume was 5 µL. The mass spectrometric settings for positive/negative ionization modes were as follows: spray voltage, 3.8/–3.2 kV; sheath gas flow rate, 40 arbitrary units (arb); aux gas flow rate, 10 arb; aux gas heater temperature, 350°C; and capillary temperature, 320°C. The full scan range was 100–1,500 m/z with a resolution of 70,000, and the automatic gain control (AGC) target for MS acquisitions was set to 1e6 with a maximum ion injection time of 100 ms. Top three precursors were selected for subsequent mass spectrometry fragmentation with a maximum ion injection time of 50 ms and a resolution of 30,000, the AGC was 2e5. The stepped normalized collision energy was set to 20, 40, and 60 eV. Metabolome data preprocessing The raw data collected by LC-MS/MS was imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) for data processing. The molecular weight, retention time, peak area and identification were derived from this analysis. Metabolites were identified using the BGI self-built standard library and mzCloud database. Data preprocessing was performed using metaX [ 35 ]. The DAMs between groups were screened by multivariate statistical analysis using principal component analysis (PCA) and discriminant analysis, partial least squares method-discriminant analysis (PLS-DA) [ 36 , 37 ], and univariate analysis using fold-change analysis and T test (Student’s T test). DAM screening thresholds were as follows: the variable importance in projection (VIP) values of the first two principal components of the PLS-DA model ≥ 1, Fold-Change ≥ 1.2 or ≤ 0.83, and q -value < 0.05. Function enrichment analysis Gene ontology (GO) ( http://www.geneontology.org/ ) and Kyoto Encyclopedia of Genes and Genomes (KEGG) ( https://www.kegg.jp/ ) enrichment analysis were performed by Phyper based on a hypergeometric test. The significant levels of terms and pathways were corrected by a q -value with a rigorous threshold ( q -value ≤ 0.05). Correlation analysis of RNA‑seq and metabolomic data The correlation between DEGs and DAMs was analyzed based on regularized canonical correlation analysis (rCCA). Sparse partial least squares discriminant analysis (SPLSDA) was performed using mixOmics package in R [ 38 ]. Quantitative real-time PCR analysis Total RNA was extracted and reverse transcribed into cDNA using the abovementioned method. Real-time qRT-PCR was performed using 20 µL of cDNA using the TB GreenTM Premix Ex TaqTM II reagent (Takara; Tli RNaseH Plus); 16S RNA was used as internal reference gene. The primers of all detected genes are listed in Supplementary Table 1. Three biological replicates (each with three technical replicates) were subjected to the QuantStudioTM 6 Flex System (Applied Biosystems, USA) with the following amplification parameters: activation at 50°C for 2 min, predenaturation at 95°C for 2 min, denaturation at 95°C for 15 s, and annealing at 60°C for 1 min for 40 cycles. The relative gene expression level was calculated using the 2 −ΔΔCt method. RcHP70 overexpression in Arabidopsis and heat stress treatment The vector 35S::RcHP70 was constructed and transformed into A. tumefaciens GV3101. Transformation of Arabidopsis plants was performed using the floral dip method. For selection, seeds were planted in aseptic conditions on MS agar containing 25 mg L − 1 hygromycin. T3 lines displaying 100% hygromycin resistance were considered homozygous and used for further experiments. Young seeding under high-temperature treatment at 42°C was conducted for 0 h, 0.5 h, 1 h, 2 h, and 3 h, respectively. Results Heat stress affects photosynthetic and antioxidant enzyme activities in Rosa chinensis The physiological function of R. chinensis was affected by heat stress. Analysis of physiological and biochemical indices at various periods after heat stress indicated that the content of chlorophyll and total protein decreased significantly over time, reaching a minimum at 9 h before increasing/recovering (Fig. 1AB). Similarly, the proline content increased significantly after heat stress, reaching a maximum at 9 h, before slightly decreasing (Fig. 1 C). Moreover, the POD activity level increased significantly and reached a maximum at 6 h, followed by a rapid decrease (Fig. 1 D). MDA content showed a similar trend to that of POD, but the increase was relatively weak (Fig. 1 E). RNA-sequencing and screening for DEGs After the five groups of samples were sequenced and processed, clean reads were mapped to the rose genome and the fragment per kilobase of transcript per million mapped value of each gene was calculated. PCA showed that the control group (T0) was significantly distinct from the treatment groups (T3–T12), with some treatment groups being relatively close to each other, such as T3 and T9. The samples within each group could also be effectively clustered into multiple replicates, indicating high reproducibility (Fig. 2 A). Since multiple treatment groups were not clustered with the control group, indicating that significant changes occurred in multiple treatment groups, comparing multiple treatment group time points with the control group showed 11,233 DEGs (5,855 upregulated and 5,378 downregulated genes) at T3-vs-T0, and 13,403 DEGs at T6-vs-T0 (6,738 upregulated and 6,665down-regulated genes), T9-vs-T0 had 13,050 DEGs (6,448 upregulated and 6,602 downregulated genes), and T12-vs-T0 had 11,676 DEGs (5,693 upregulated and 5,983 downregulated genes; Fig. 2 B). Functional enrichment analysis of DEGs in the four compared groups showed that the common enriched pathways including: alpha-linolenic acid metabolism, sulfur metabolism, pentose phosphate pathway, glycerophospholipid metabolism, porphyrin and chlorophyll metabolism, etc. (Fig. 2 C–F). In addition, some pathways were enriched only in the T3-vs-T0 comparison group, such as homologous recombination, nucleotide excision repair, terpenoid backbone biosynthesis, RNA degradation, spliceosome, plant hormone signal transduction, ether lipid metabolism (Fig. 2 C); and the glyoxylate and dicarboxylate metabolism, glycosphingolipid biosynthesis, propanoate metabolism, nicotinate and nicotinamide metabolism, citrate cycle, and SNARE interactions in vesicular transport pathways were enriched only in the T6-vs-T0 comparison group (Fig. 2 D). Further, limonene and pinene degradation was only enriched in the T9-vs-T0 comparison group (Fig. 2 E) and the fructose and mannose metabolism, phosphonate and phosphinate metabolism, beta-amylase metabolism, alanine metabolism, beta-amylase metabolism, and pyrimidine metabolism were only enriched in the T12-vs-T0 group (Fig. 2 F). Function enrichment of DEGs in response to heat stress Further analysis of the four comparison groups showed 4,652 common DEGs (Fig. 3 A); the corresponding enriched metabolic pathways were plant-pathogen interaction, MAPK signaling pathway, spliceosome, mismatch repair, pentose phosphate pathway, homologous recombination, DNA replication, etc. (Fig. 3 B). According to the pattern of gene expression changes during heat stress, the expression of genes in cluster 6 gradually increased after heat stress treatment, reaching the highest point at 6–9 h. The expression of genes in cluster 8 gradually decreased after heat stress treatment reaching the lowest point at 6–12 h (Fig. 3 C). The genes in clusters 6 and 8 were basically consistent with the trends of changes detected in some physiological indices and antioxidant activities. Functional analysis of the genes in cluster 6 showed that they were mainly related to spliceosome, mismatch repair, DNA replication, sulfur metabolism, etc (Fig. 3 D). The pathways significantly enriched of cluster 8 genes included plant-pathogen interaction, phenylpropanoid biosynthesis, flavonoid biosynthesis, MAPK signaling pathway, etc. (Fig. 3 E). Metabolomic changes in R. chinensis in response to heat stress Further metabolomic examination of the heat treated samples showed high reproducibility of both positive ion mode (pos) metabolites and negative ion mode (neg) metabolites in samples from multiple time points (Fig. 4AB, Supplementary Table 2). A total of 723 pos metabolites and 432 neg metabolites were identified (Fig. 4 C), mainly containing flavonoids (60 positive metabolites and 52 negative metabolites), terpenoids (39 positive metabolites and 33 negative metabolites), phenylpropanols (33 positive metabolites and 23 negative metabolites), phenols (15 positive metabolites and 15 negative metabolites), phenolic acids (16 positive metabolites and 11 negative metabolites), and others (Fig. 4 D). Comparison of DAMs between the four treatment groups and the control group showed 243 DAMs (including 144 positive and 89 negative metabolites) in the T3-vs-T0 comparison group, 254 DAMs (including 157 positive and 97 negative) in the T6-vs-T0 comparison group, 246 DAMs in the T9-vs-T0 comparison group (including 165 positives and 81 negatives), and 265 DAMs in the T12-vs-T0 comparison group (including 174 positives and 91 negatives; Fig. 4 E). Enrichment analysis of the DAMs in four comparison groups showed that the enriched pos DAMs in KEGG metabolic pathways include alpha-linolenic acid metabolism and arginine biosynthesis, while the neg DAMs were enriched in pathways such as plant hormone signal transduction, cyanoamino acid metabolism, tropane, piperidine and pyridine alkaloid biosynthesis, aminoacyl-tRNA biosynthesis, phenylpropanoid biosynthesis, and glucosinolate biosynthesis (Fig. 4 F–I). In addition, pos DAMs were significantly enriched in ABC transporters in the T6/9/12-vs-T0 comparison group except at the early stage of heat stress treatment (T3-vs-T0), related to the transmembrane transport of metabolites at the later stage of the treatment (Fig. 4 F–I). DAMs in response to heat stress Further analysis showed that 57 common DAMs in the four comparison groups (Fig. 5 A). These 57 DAMs were significantly enriched in the pathway of aminoacyl-tRNA biosynthesis, glucosinolate biosynthesis, and cyanoamino acid metabolism, and associated with two major metabolites, L-Isoleucine and L-Phenylalanine (Fig. 5 B). Analysis of the changes in the content of these DAMs showed three clusters, cluster Ⅰ showed the lowest content at T0, increasing significantly from T3 to T12; the changes in these metabolites were consistent with the trend in the changes in POD activities, proline, and MDA content. In cluster III, the trend was almost the opposite, with the highest content at T0, decreasing significantly thereafter; the trend was also consistent with the trend of changes to chlorophyll and total protein contents. In addition, the contents of cluster Ⅱ metabolites decreased at T3 followed by a rapid increase at T6 and a subsequent rapid decrease (Fig. 5 C). Combined RNA‑seq and metabolomic analysis The Spearman correlation coefficient was calculated for DEGs and DAMs. Network diagrams were plotted for DEGs and DAMs with absolute correlation coefficient values > 0.9 and p-value < 0.05 (Fig. 6 A). The significant nodes in the four comparison groups were L-Phenylalanine, jasmonic acid, 5-Fluoro, biocytin may play key roles in the plant’s response to heat stress (Figs. 6 B–E). The patterns in content changes divided the metabolites into three subgroups: cluster 1 upregulated after heat shock (T3–T12); cluster 2 downregulated after heat shock (T3–T12); cluster 3 increased at T6 and later decreased (Fig. 6 F). This trend was consistent with the results shown in Fig. 5 C. In addition, DAMs and DEGs in the four comparison groups were mainly enriched in alpha-linolenic acid metabolism, plant hormone signal transduction, phenylpropanoid biosynthesis, etc. (Fig. 6 G). Heat stress response pathways After heat stress, phenylpropanoid biosynthesis and downstream flavonoid biosynthesis were enriched in roses. Multiple hormones are involved in the regulation of the heat stress process. In the flavonoid biosynthesis pathway, the expression level of CHI , HCT (excluding 112197593 and 112165685), FLS , and DFR genes was downregulated after heat stress. Four metabolites in this pathway showed significant changes, pinocembrin and dihydroquercetin decreased after heat treatment, and eriodictyol and dihydromyricetin showed an increasing trend after heat stress (Fig. 7 A). The significant enrichment of plant hormone signal transduction and MAPK signaling indicates a close relationship between these two pathways. We analyzed jasmonic acid and ethylene related pathway in the MAPK signaling pathway. The expression levels of MKK3 and MPK6 were significantly downregulated after heat stress, which play an inhibitor of MYC2 and caused upregulated of MYC2 homologies (except 112200522). In addition, ERF1 repression (whose expression levels increased after heat stress) also play a role in regulated VSP2 expression (Fig. 7 B). In the ethylene signaling pathway, RAN1 , ETR , and CTR1 (excluding 112178901) significant increased their expression levels while MPK3/6 expression was inhibited. In addition, MYC2 , involved in the ethylene pathway, and which coordinates the regulation of ChiB expression levels, upregulated the expression of a ChiB homologous after heat stress (Fig. 7 B). RNA‑seq expression validation by qRT‑PCR qRT-PCR was used to confirm the reliability of our RNA-seq data. The results of this analysis revealed that the expression patterns of 16 selected DEGs were consistent with the RNA-seq dataset (Fig. 8 ). Among these genes, HSP70 , a typical maker gene in response to heat stress, showed significant up-regulation after heat stress. WRKY transcript factors (four homologies), RBOP, and PR1, crucial genes in the MAPK signaling pathway, were downregulated after heat stress, which suggested that heat stress may regulate the resistant of rose to high temperatures (Fig. 8 ). RcHSP70 overexpression decreases rose’s timely responses to high temperature To determine whether RcHSP70 has a function in resistance to heat stress, RcHP7 was introduced into Arabidopsis wild-type (WT). RcHSP70 overexpression (OE) plants showed obviously stronger than WT (Fig. 9 A, 0h). After high-temperature treatment, RcHSP7 OE plants showed higher resistance to high temperature, as well as slowed water loss and wilting rate than WT plants (Fig. 9 A). High RcHSP70 expression was also detected in Arabidopsis OE plants by qRT-PCR (Fig. 9 B, 0h). After high-temperature treatment, RcHSP70 expression significantly increased in both OE and WT plants, indicating that heat stress induces RcHSP70 expression. In OE plants, RcHSP70 expression was significantly lower than that in WT. RcHSP70 expression was approximately ≥ 20-fold than that of untreated plants, but OE2 and OE3 plants increased approximately 4- to 14-fold (OE5 increasing similarly to WT; Fig. 9 B). Discussion As an important ornamental plant, rose holds a high market share in flower industry and has important economic value. Breeding varieties resistant to high temperatures is crucial for increasing the production of rose flowers. Although key genes involved in heat stress responses have been reported in other species, whether the regulatory and responsive mechanisms in roses are consistent with those species remains unclear. Li et al. conducted a preliminary exploration of heat stress-responsive genes in roses through transcriptome analysis, but there is still a lack of systematic research on the relationship between gene and metabolite changes after heat stress [ 4 ]. This study further explored the characteristics of gene and metabolite changes in roses induced by high temperature using transcriptomic and metabolomic methods. In this study, we found a higher number of DEGs at T6 and T9 time points than at T3 and T12, indicating that the impact of heat stress on roses peaks at T6 and T9. This finding is generally consistent with multiple physiological indicators, such as the lowest chlorophyll and protein content at T9 and the highest proline content at T9. The POD and MAD activities reached their peaks at T6. Typically, the activity of plant is often inhibited under high-temperature stress, leading to suppressed protein and chlorophyll synthesis. Both substances reached their lowest levels around 9 h after heat stress, suggesting that the impact of heat stress on roses may be less severe during early stages or within 9 h of continuous heat stress. The accumulation of proline helps plants tolerating high-temperature stress [ 39 – 41 ]. In roses, the proline content peaked at 9 h of high-temperature stress. POD and MDA activity are usually associated with a plant’s ability to cope with stress, indicating that roses gradually accumulate substances to resist high temperatures during stress responses. This study employed transcriptomic and metabolomic approaches. Despite the vast amount of gene data changed in roses after high-temperature treatment, the enrichment of DEGs and DAMs exhibited high similarity. Significant enrichments were observed in pathways such as alpha-linolenic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, plant hormone signal transduction, and MAPK signaling pathway, suggesting a strong correlation between DEGs and DAMs. The accumulation of flavonoids plays a crucial role in increasing a plant’s tolerance to heat stress. For instance, flavonoids have a short-term heat stress effect in Anoectochilus roxburghii [ 42 ], and flavonoid accumulation regulation through hormones reduced heat stress in rice [ 43 ]. In this study, flavonoid biosynthesis, a downstream pathway of phenylpropanoid biosynthesis, exhibited a decreasing trend in the expression levels of multiple genes. Similarly, the contents of several metabolites (eriodictyol, dihydroquercetin, and pinocembrin) in this pathway decreased during the early stages of heat stress, consistent with gene expression trends. However, the metabolite levels increased at T12, possibly to facilitate adaptation and recovery after heat stress. Plant hormones and MAPK signaling are involved in various stress responses and crosstalk closely [ 44 – 48 ]. The MAPK pathway not only responds to pathogen infection but also plays a crucial role in responses to plant hormone regulation, cold, salt, drought, and wounding [ 47 , 48 ]. In this study, plant hormones and MAPK signaling were significantly enriched after high-temperature stress, indicating that this type of stress may share response pathways with the aforementioned stresses. In particular, regulatory pathways involving jasmonic acid and ethylene showed different expression patterns. Multiple genes in the jasmonic acid pathway tended to decrease their expression levels after high-temperature stress, while genes in the ethylene pathway did the opposite. Although both pathways are involved in defense responses, further research is needed to determine whether the gene expression changes are due to heat stress induction or resistance responses. HSPs are crucial response proteins and markers for detecting heat stress in plants [ 6 , 49 , 50 ]. HSPs are diverse and possess the important function of restoring proteins denatured due to heat stress to their undenatured state [ 7 ]. Therefore, the high expression and constitutive presence of HSPs in plants provide the potential for timely restoration of denatured proteins. In this study, we heterologously overexpressed a selected HSP70 protein in Arabidopsis thaliana , and the results confirmed that overexpressing RcHSP70 in Arabidopsis plants confers a significant advantage in resisting high-temperature stress. Conclusions This study examined genetic and biochemical changes in rose plants at five time points after heat stress. DEGs and DAMs were enriched in pathways including phenylpropanoid biosynthesis, MAPK signaling, alpha-linolenic acid metabolism, etc. The present results suggest that flavonoids and plant hormones play crucial roles in rose plant’s resistance to heat stress. Our findings provide insights into rose response to high temperature and can serve as foundation to improve rose plant resistance against heat stress. Abbreviations MDA Malondialdehyde POD Peroxidase DEG Differentially expressed gene DAMs Differentially abundant metabolites MAPK Mitogen-activated protein kinases HSP Heat shock proteins ROS Reactive oxygen species HfA1 Heat shock transcription factor A1 LC-MS Liquid chromatography mass spectrometry QC Quality control AGC Automatic gain control PCA Principal component analysis PLS-DA Partial least squares method-discriminant analysis VIP Variable importance in projection GO Gene ontology KEGG Kyoto Encyclopedia of Genes and Genomes rCCA regularized Canonical correlation analysis SPLSDA Sparse partial least squares discriminant analysis pos positive ion mode neg negative ion mode Declarations Acknowledgements Not applicable. Authors' contributions H.W. conceived the study; W.X., X.Z.and S.Z. analyzed the data; S.Z. was involved in data interpretation; R.L. and A.R. prepared figures and tables; S. J.and W.L.collected the samples and performed the experiments; H.W. and L.W.wrote the article. All authors read and approved the final version of the manuscript. Funding This work was supported by the key research and development project in Anhui Province (202104a06020017). Data availability All the raw data used in this study have been deposited at NCBI BioProject ID: PRJNA1090540 (http://www.ncbi.nlm.nih.gov/bioproject/1090540). Ethics approval and consent to participate The use of plant parts in the present study complies with international, national and/or institutional guidelines. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Lesk C, Rowhani P, Ramankutty N. Influence of extreme weather disasters on global crop production. Nature. 2016;529(7584):84–7. Hasanuzzaman M, Nahar K, Alam MM, Roychowdhury R, Fujita M. Physiological, biochemical, and molecular mechanisms of heat stress tolerance in plants. 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Proline accumulation is inhibitory to Arabidopsis seedlings during heat stress. Plant Physiol. 2011;156(4):1921–33. Rajametov SN, Yang EY, Cho MC, Chae SY, Jeong HB, Chae WB. Heat-tolerant hot pepper exhibits constant photosynthesis via increased transpiration rate, high proline content and fast recovery in heat stress condition. Sci Rep. 2021;11(1):14328. Kavi Kishor PB, Suravajhala P, Rathnagiri P, Sreenivasulu N. Intriguing Role of Proline in Redox Potential Conferring High Temperature Stress Tolerance. Front Plant Sci. 2022;13:867531. Cui M, Liang Z, Liu Y, Sun Q, Wu D, Luo L, Hao Y. Flavonoid profile of Anoectochilus roxburghii (Wall.) Lindl. Under short-term heat stress revealed by integrated metabolome, transcriptome, and biochemical analyses. Plant Physiol biochemistry: PPB. 2023;201:107896. Jan R, Kim N, Lee SH, Khan MA, Asaf S, Lubna, Park JR, et al. Enhanced flavonoid accumulation reduces combined salt and heat stress through regulation of transcriptional and hormonal mechanisms. Front Plant Sci. 2021;12:796956. Danquah A, de Zelicourt A, Colcombet J, Hirt H. The role of ABA and MAPK signaling pathways in plant abiotic stress responses. Biotechnol Adv. 2014;32(1):40–52. Hettenhausen C, Schuman MC, Wu J. MAPK signaling: a key element in plant defense response to insects. Insect Sci. 2015;22(2):157–64. Meng X, Zhang S. MAPK cascades in plant disease resistance signaling. Annu Rev Phytopathol. 2013;51:245–66. Smékalová V, Doskočilová A, Komis G, Samaj J. Crosstalk between secondary messengers, hormones and MAPK modules during abiotic stress signalling in plants. Biotechnol Adv. 2014;32(1):2–11. Zhang M, Su J, Zhang Y, Xu J, Zhang S. Conveying endogenous and exogenous signals: MAPK cascades in plant growth and defense. Curr Opin Plant Biol. 2018;45(Pt A):1–10. Takayama S, Xie Z, Reed JC. An Evolutionarily Conserved Family of Hsp70/Hsc70 Molecular Chaperone Regulators. J Biol Chem. 1999;274(2):781–6. Zhang LS, Wu SD, Chang XJ, Wang XY, Zhao YP, Xia YP, Trigiano RN, et al. The ancient wave of polyploidization events in flowering plants and their facilitated adaptation to environmental stress. Plant Cell Environ. 2020;43(12):2847–56. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 08 May, 2024 Reviews received at journal 08 May, 2024 Reviews received at journal 06 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers invited by journal 29 Apr, 2024 Editor assigned by journal 29 Apr, 2024 Submission checks completed at journal 29 Apr, 2024 First submitted to journal 19 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4292491","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298770092,"identity":"83c6fc55-37d5-43ef-882c-8a5df3291102","order_by":0,"name":"Hua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACfmbGxgcfDCR47NsbiNQi2c582HBGhYWMAc8BIrUYnGdLE+Y5U2FjIJFArC3NPGbMvG0SPOaSjzfeYKixiSaohZ+Zx+zhXKAWy9lpxRYMx9JyG4iwxdzgLVALw+0cMwnGhsOEtRgc5jGTADmM4eYZorWwpUnynJHgMbjBQ6QWyWZwIEvwSPYA/ZJAjF/4+Q+CorLOnp/98MYbH2psCGtBcSTRUYOkhVQdo2AUjIJRMDIAACvuOx4bC5V4AAAAAElFTkSuQmCC","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Wang","suffix":""},{"id":298770094,"identity":"f97a058f-90f2-48b8-9748-5e4816604297","order_by":1,"name":"Wanting Xu","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wanting","middleName":"","lastName":"Xu","suffix":""},{"id":298770096,"identity":"4e36635a-0f1c-4872-b096-02638a4208fc","order_by":2,"name":"Xiaojuan Zhang","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Zhang","suffix":""},{"id":298770098,"identity":"2aebefbd-a9c6-4549-ad90-d1b94badc943","order_by":3,"name":"Lian Wang","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Wang","suffix":""},{"id":298770100,"identity":"b5542e8d-c387-423f-b799-af91773a5601","order_by":4,"name":"Suqi Jia","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Suqi","middleName":"","lastName":"Jia","suffix":""},{"id":298770102,"identity":"2a682128-7e67-4ed0-9409-072c791847de","order_by":5,"name":"Shuwei Zhao","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shuwei","middleName":"","lastName":"Zhao","suffix":""},{"id":298770104,"identity":"cff3e66d-e43f-4d80-bedb-7fe644316db5","order_by":6,"name":"Wan Li","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"","lastName":"Li","suffix":""},{"id":298770106,"identity":"c9938242-4795-489a-8504-052e84746acd","order_by":7,"name":"Rongqianyi Lu","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rongqianyi","middleName":"","lastName":"Lu","suffix":""},{"id":298770109,"identity":"65d10207-d3a3-43e3-9c4f-f77c5d1350ce","order_by":8,"name":"Aihua Ren","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Ren","suffix":""},{"id":298770111,"identity":"d3fc677d-22a4-4c17-8a43-33c30758c7f6","order_by":9,"name":"Shuiming Zhang","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shuiming","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-19 10:10:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4292491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4292491/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05543-1","type":"published","date":"2024-09-20T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55893306,"identity":"5f4519f1-482a-4131-a40b-13f7d679341b","added_by":"auto","created_at":"2024-05-06 03:06:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77544,"visible":true,"origin":"","legend":"\u003cp\u003ePhotosynthetic and antioxidant enzyme activities of \u003cem\u003eRosa chinensis \u003c/em\u003eafter heat stress. (A) Total chlorophyll content. (B) Total protein content. (C) Proline content. (D)POD activities. (E) MDA content. Values are means ± SD (\u003cem\u003en\u003c/em\u003e = 3 biological replicates); Asterisks indicate statistically significant differences as determined by Student’s t-test: ns, no significant; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/c698c4a7e5ff4813b0168ff9.png"},{"id":55893870,"identity":"e7a9dc89-46db-4290-9315-9ae5551b0ddc","added_by":"auto","created_at":"2024-05-06 03:14:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":469382,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DEGs and the main KEGG pathways enriched by DEGs in \u003cem\u003eRosa chinensis\u003c/em\u003e response to heatstress. (A) PCA analysis of gene expression datasets. (\u003cstrong\u003eB\u003c/strong\u003e) Number of DEGs among compared groups. (C) KEGG analysis of DEGs between T0 and R3h. (D) KEGG analysis of DEGs between T0 and T6. (E) KEGG analysis of DEGs between T0 and R9h. (F) KEGG analysis of DEGs between T0 and R12h. The color and size of the bubbles indicate significant enrichment and gene number, respectively.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/d18760974fed167e5d2dc90b.png"},{"id":55893304,"identity":"ec7ea658-5347-45b0-80b4-940e9a195c53","added_by":"auto","created_at":"2024-05-06 03:06:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":679939,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of the comparison groups and function enrichment analysis. (A) Venn diagram of the four comparison groups. (B) KEGG analysis of common DEGs in four comparison groups. (C) The trend pattern analysis of all genes expression level. (D) KEGG analysis of cluster 6 genes in Figure 3C. (E) KEGG analysis of cluster 8 genes in Figure 3C. The color and size of the bubbles indicate significant enrichment and gene number, respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/63660653f2bf76ca801f75d4.png"},{"id":55893307,"identity":"3e24c40d-8bbe-4dd6-9497-b4126eccd87f","added_by":"auto","created_at":"2024-05-06 03:06:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":468876,"visible":true,"origin":"","legend":"\u003cp\u003eDAMs of \u003cem\u003eRosa chinensis\u003c/em\u003e in response to heat stress. (A) Principal component analysis of pos metabolites. \u0026nbsp;(B) Principal component analysis of neg metabolites. \u0026nbsp;(C) Number of identify metabolites. (D) Classified of metabolites. (E) Number of DAMs among compared groups. (F) KEGG analysis of DAMs between T3-vs-T0. (G) KEGG analysis of DAMs between T6-vs-T0. (H)KEGG analysis of DAMs between T9-vs-T0. (I) KEGG analysis of DAMs between T12-vs-T0.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/c9fa665f2b5d01c3100703a7.png"},{"id":55893309,"identity":"df26836d-1d49-4849-b953-43ae50c522e2","added_by":"auto","created_at":"2024-05-06 03:06:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":289470,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of DAMs in the comparison groups and function enrichment analysis. (A) Venn diagram of DAMs in the four comparison groups. (B)KEGG analysis of common DAMs in four comparison groups. (C) The heatmap of common DAMs in the four comparison groups. Three subgroups marked with cluster I, II, III.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/bd784988c93b247c0df2beb0.png"},{"id":55893311,"identity":"760b4dff-d8b2-4cba-a2da-e21e778c607b","added_by":"auto","created_at":"2024-05-06 03:06:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2639646,"visible":true,"origin":"","legend":"\u003cp\u003eThe connection network between DEGs and DAMs. (A)The schematic of network between DEGs and DAMs. The rhomboid and dots represent genes and metabolites, respectively. (B) The top10 core nodes DAMs in compared group T3-vs-T0. (C) The top10 core nodes DAMs in compared group T6-vs-T0. (D) The top10 core nodes DAMs in compared group T9-vs-T0. (E) The top10 core nodes DAMs in compared group T12-vs-T0. (F) The heatmap of core DAMs in four compared groups. Log2-scaled metabolites content are shown, ranging from low (blue) to high (red). Three subgroups marked with cluster I, II, III. (G) Sankey diagram showing the relationship between pathways and DAMs/DEGs, the DEGs number were more than 4000, the gene name were not showed.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/eb6c413aa82c7aabeda0ae77.png"},{"id":55893313,"identity":"6a57132b-0523-4412-b67d-a45ec9cf2d10","added_by":"auto","created_at":"2024-05-06 03:06:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":318186,"visible":true,"origin":"","legend":"\u003cp\u003eThe pathways of flavonoid biosynthesis and MAPK signaling pathway response to heat stress. (A) The flavonoid biosynthesis pathways. HCT: shikimate O-hydroxycinnamoyltransferase; FLS: flavonol synthase; DFR: bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase; ANS: anthocyanidin synthase; CHI: chalcone isomerase. (B) The MAPK signaling pathway. MKK3: mitogen-activated protein kinase kinase 3; MPK6: mitogen-activated protein kinase 6; MYC2: transcription factor MYC2; VSP2: vegetative storage protein 2; RAN1/copA, P-type Cu+ transporter; ETR/ERS: ethylene receptor; CTR1: serine/threonine-protein kinase; MPK3: mitogen-activated protein kinase 3; MPK6: mitogen-activated protein kinase 6; EIN2/3: ethylene-insensitive protein 2/3; ERF1: ethylene-responsive transcription factor 1; ChiB: basic endochitinase B. Log2-scaled FPKM or metabolites content are shown in different time points of leaf (here T0-T12, from left to right in each heatmap panel) are presented in the heatmap alongside the gene id. Low to high expression is indicated by a change in color from blue to red.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/3477e8cc194b145f473a3a96.png"},{"id":55893310,"identity":"ea516f37-b011-4abe-967b-90d9c9b8579b","added_by":"auto","created_at":"2024-05-06 03:06:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131444,"visible":true,"origin":"","legend":"\u003cp\u003eExpression pattern of the selected 16 genes. The first and third lines figures showed the expression level detect by qRT-PCR, and the corresponding second and fourth lines figures showed the gene expression data of RNA-seq.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/a4b302d641ceaeca122ca8a9.png"},{"id":55893871,"identity":"8652c221-8dae-44ba-8b1d-dacf9ee7afa2","added_by":"auto","created_at":"2024-05-06 03:14:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":361127,"visible":true,"origin":"","legend":"\u003cp\u003eThe \u003cem\u003eRcHSP70\u003c/em\u003e gene enhance the resistance of Arabidopsis to heat stress. (A) Representative images showed the resistance of Arabidopsis to heat stress. (B) qRT-PCR analysis of \u003cem\u003eRcHSP70\u003c/em\u003e expression in WT, OE2, OE3, and OE5 lines. 18S was used as the reference gene. Values are means ± SD of three technical replicates.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/106d06ab6af6bc6f463b73f8.png"},{"id":65103943,"identity":"16dae0e4-c045-4744-ae3d-6d1ece5ca340","added_by":"auto","created_at":"2024-09-23 16:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5007070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4292491/v1/7c4ade7e-8a41-4441-9154-834434252359.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome and metabolome analyses of Rosa chinensis identify heat stress response genes and metabolite pathways","fulltext":[{"header":"Background","content":"\u003cp\u003eHeat stress due to global warming poses a significant threat to food security and agricultural sustainability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The elevated temperatures associated with heat stress can decrease water content, disrupting cellular homeostasis and compromising vital biological processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These physiological disturbances can have devastating effects, leading to reduced crop yields and, in extreme cases, plant death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. \u003cem\u003eRosa chinensis\u003c/em\u003e is an important ornamental plant, with an optimum temperature to grow and bloom of 22\u0026ndash;26\u0026deg;C. Roses are sensitive to high temperatures, which often decreases the number or size of flowers, makes them enter dormancy, and not blossom [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlants have evolved complex and diverse systems to cope with environmental heat stress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. At the molecular level, plants rely on transcriptional regulatory networks to orchestrate their stress response. Heat shock proteins (HSPs) and reactive oxygen species (ROS) are major makers used for detecting plant responses to heat stress [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The transcriptional regulatory network activated by heat stress is a highly dynamic and coordinated system. For instance, tomato and \u003cem\u003eArabidopsis\u003c/em\u003e heat shock transcription factor A1s (HfA1s) play critical roles in the heat shock response by decreasing induction of heat stress-responsive genes and heat stress sensitive phenotypes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among them, some genes encode proteins involved in protecting cellular components from damage caused by heat stress, while others encode proteins involved in repairing damage or restoring cellular homeostasis [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to transcriptional regulation, post-translational modifications play a pivotal role in the plant\u0026rsquo;s heat stress response. These modifications, which include phosphorylation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], ubiquitination [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and SUMOylation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], can alter the activity, localization, or stability of proteins involved in stress signaling and responses. For instance, Ca\u003csup\u003e2+\u003c/sup\u003e and ROS may be involved in heat stress sensing through MAPK signaling [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, plant hormones such as jasmonic acid play a key role in abiotic stress responses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and early signaling enhances heat tolerance in \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In roses, Li et al. found Ca\u003csup\u003e2+\u003c/sup\u003e signaling pathways and transcription factors associated with rapid sensing and signal transduction in heat stress responses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advances in understanding the molecular mechanisms underlying plant heat stress responses, the molecular mechanism underlying the response to heat stress in roses remains unknown. In this study, the \u0026ldquo;red apple\u0026rdquo; rose variety was used and subjected to RNA-seq and metabolomic analysis to identify candidate genes and metabolites. The results indicated that the genes and metabolites related to flavonoid biosynthesis and MAPK signaling pathway may involve in heat stress response. The results provide valuable information for molecular breeding of resistance rose variety.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and heat stress treatment\u003c/h2\u003e \u003cp\u003eTwo-year-old \u0026ldquo;red apple\u0026rdquo; rose cuttings (rootstock, white-flowered roses) were used as experimental materials. After being purchased from a nursery, they were pre-cultured in an artificial climatic chamber for 10 days under the following conditions: temperature of 24\u0026deg;C, humidity of 55%, light duration of 12 h (day) / 12 h (night), and a light intensity of 100%. Subsequently, high-temperature treatments at 42\u0026deg;C were conducted for 0 h (Treatment 0 h, T0), 3 h (T3), 6 h (T6), 9 h (T9), and 12 h (T12), respectively. After each treatment, functional leaves were collected, immediately frozen in liquid nitrogen, and stored in a ˗80\u0026deg;C freezer for subsequent transcriptome and metabolome sequencing. The transcriptome analysis included three biological replicates and the metabolome analysis included six biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhotosynthetic and antioxidant enzyme analysis\u003c/h2\u003e \u003cp\u003ePlant leaves were collected to determine the concentration of photosynthetic pigments and proline. Samples (0.5 g fresh weight per treatment) were extracted in 80% (v/v) methanol and thoroughly ground. The concentration in the supernatant of this mixture was determined spectrophotometrically as described by Wang and Huang [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Malondialdehyde (MDA), peroxidase (POD), and protein levels were analyzed using specific assay kits obtained from Nanjing Mo Fan Biotechnology Co., Ltd (Nanjing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and sequencing\u003c/h2\u003e \u003cp\u003eThe total RNA of leaves was extracted according to the protocol for extraction of plant RNA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.yuque.com/yangyulan-ayaeq/oupzan\u003c/span\u003e\u003cspan address=\"https://www.yuque.com/yangyulan-ayaeq/oupzan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After measuring the RNA level and quality, RNAs were used to construct libraries according to the protocol for mRNA library preparation (BGI, China). Then, the libraries were sequenced by DNBSEQ (BGI, China). The SOAPnuke (v1.5.6) software was used for quality control of the raw data [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The reference \u003cem\u003eR. chinensis\u003c/em\u003e genome and annotation files were downloaded from the NCBI database (accession no. GCF_002994745.2) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The clean data was mapped to the reference genome by HISAT (v2.1.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Bowtie2 was applied to align the clean reads to the gene set, where known and novel as well as coding and noncoding transcripts were included [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Gene expression levels were calculated by RSEM (v1.3.1) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Differentially expressed gene (DEG) analysis was performed using DESeq2 (v1.4.5) with a \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The time series analysis was performed using Mfuzz (v2.34.0) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and gene co-expression network analysis was performed with WGCNA (v1.48).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetabolites extraction and analysis\u003c/h2\u003e \u003cp\u003eA total of 50 mg tissues were extracted by directly adding 800 \u0026micro;L of precooled extraction reagent (MeOH: H\u003csub\u003e2\u003c/sub\u003eO) (70:30, v/v, precooled at -20\u0026deg;C); then, 20 \u0026micro;L of internal standards mix was added for quality control of the sample preparation. Two small steel balls were added to the Eppendorf tube. After homogenizing at 50 Hz for 5 min by using TissueLyser (JXFSTPRP, China), samples were sonicated for 30 min at 4\u0026deg;C and incubated at -20\u0026deg;C for 1 h. Then, samples were further centrifuged for 15 min at 14,000 rpm and 4\u0026deg;C. Next, 600 \u0026micro;L of the supernatants were filtered through 0.22 \u0026micro;m microfilters and transferred to autosampler vials for liquid chromatography mass spectrometry (LC-MS) analysis. To evaluate the reproducibility and stability of the whole LC-MS analysis, a quality control (QC) sample was prepared by pooling 20 \u0026micro;L of the supernatant from each sample.\u003c/p\u003e \u003cp\u003eSample analysis was performed on a Waters ACQUITY UPLC 2D (Waters, USA), coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific, USA) with a heated electrospray ionization source. Chromatographic separation was performed on a Hypersil GOLD aQ column (2.1 \u0026times; 100 mm, 1.9 \u0026micro;m, Thermo Fisher Scientific, USA), with mobile phase A consisting 0.1% formic acid in water and mobile phase B consisting 0.1 formic acid in acetonitrile. The column temperature was maintained at 40\u0026deg;C. The gradient conditions were as follows: 5% B over 0.0\u0026ndash;2.0 min, 5\u0026ndash;95% B over 2.0\u0026ndash;22.0 min, held constant at 95% B over 22.0\u0026ndash;27.0 min and washed with 95% B over 27.1\u0026ndash;30 min. The flow rate was 0.3 mL/min and the injection volume was 5 \u0026micro;L.\u003c/p\u003e \u003cp\u003eThe mass spectrometric settings for positive/negative ionization modes were as follows: spray voltage, 3.8/\u0026ndash;3.2 kV; sheath gas flow rate, 40 arbitrary units (arb); aux gas flow rate, 10 arb; aux gas heater temperature, 350\u0026deg;C; and capillary temperature, 320\u0026deg;C. The full scan range was 100\u0026ndash;1,500 m/z with a resolution of 70,000, and the automatic gain control (AGC) target for MS acquisitions was set to 1e6 with a maximum ion injection time of 100 ms. Top three precursors were selected for subsequent mass spectrometry fragmentation with a maximum ion injection time of 50 ms and a resolution of 30,000, the AGC was 2e5. The stepped normalized collision energy was set to 20, 40, and 60 eV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome data preprocessing\u003c/h2\u003e \u003cp\u003eThe raw data collected by LC-MS/MS was imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) for data processing. The molecular weight, retention time, peak area and identification were derived from this analysis. Metabolites were identified using the BGI self-built standard library and mzCloud database. Data preprocessing was performed using metaX [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The DAMs between groups were screened by multivariate statistical analysis using principal component analysis (PCA) and discriminant analysis, partial least squares method-discriminant analysis (PLS-DA) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and univariate analysis using fold-change analysis and T test (Student\u0026rsquo;s T test). DAM screening thresholds were as follows: the variable importance in projection (VIP) values of the first two principal components of the PLS-DA model\u0026thinsp;\u0026ge;\u0026thinsp;1, Fold-Change\u0026thinsp;\u0026ge;\u0026thinsp;1.2 or \u0026le;\u0026thinsp;0.83, and \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunction enrichment analysis\u003c/h2\u003e \u003cp\u003eGene ontology (GO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) enrichment analysis were performed by Phyper based on a hypergeometric test. The significant levels of terms and pathways were corrected by a \u003cem\u003eq\u003c/em\u003e-value with a rigorous threshold (\u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of RNA‑seq and metabolomic data\u003c/h2\u003e \u003cp\u003eThe correlation between DEGs and DAMs was analyzed based on regularized canonical correlation analysis (rCCA). Sparse partial least squares discriminant analysis (SPLSDA) was performed using mixOmics package in R [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted and reverse transcribed into cDNA using the abovementioned method. Real-time qRT-PCR was performed using 20 \u0026micro;L of cDNA using the TB GreenTM Premix Ex TaqTM II reagent (Takara; Tli RNaseH Plus); 16S RNA was used as internal reference gene. The primers of all detected genes are listed in Supplementary Table\u0026nbsp;1. Three biological replicates (each with three technical replicates) were subjected to the QuantStudioTM 6 Flex System (Applied Biosystems, USA) with the following amplification parameters: activation at 50\u0026deg;C for 2 min, predenaturation at 95\u0026deg;C for 2 min, denaturation at 95\u0026deg;C for 15 s, and annealing at 60\u0026deg;C for 1 min for 40 cycles. The relative gene expression level was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRcHP70 overexpression in Arabidopsis and heat stress treatment\u003c/h2\u003e \u003cp\u003eThe vector \u003cem\u003e35S::RcHP70\u003c/em\u003e was constructed and transformed into \u003cem\u003eA. tumefaciens\u003c/em\u003e GV3101. Transformation of \u003cem\u003eArabidopsis\u003c/em\u003e plants was performed using the floral dip method. For selection, seeds were planted in aseptic conditions on MS agar containing 25 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e hygromycin. T3 lines displaying 100% hygromycin resistance were considered homozygous and used for further experiments. Young seeding under high-temperature treatment at 42\u0026deg;C was conducted for 0 h, 0.5 h, 1 h, 2 h, and 3 h, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHeat stress affects photosynthetic and antioxidant enzyme activities in Rosa chinensis\u003c/h2\u003e \u003cp\u003eThe physiological function of \u003cem\u003eR. chinensis\u003c/em\u003e was affected by heat stress. Analysis of physiological and biochemical indices at various periods after heat stress indicated that the content of chlorophyll and total protein decreased significantly over time, reaching a minimum at 9 h before increasing/recovering (Fig.\u0026nbsp;1AB). Similarly, the proline content increased significantly after heat stress, reaching a maximum at 9 h, before slightly decreasing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Moreover, the POD activity level increased significantly and reached a maximum at 6 h, followed by a rapid decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). MDA content showed a similar trend to that of POD, but the increase was relatively weak (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRNA-sequencing and screening for DEGs\u003c/h2\u003e \u003cp\u003eAfter the five groups of samples were sequenced and processed, clean reads were mapped to the rose genome and the fragment per kilobase of transcript per million mapped value of each gene was calculated. PCA showed that the control group (T0) was significantly distinct from the treatment groups (T3\u0026ndash;T12), with some treatment groups being relatively close to each other, such as T3 and T9. The samples within each group could also be effectively clustered into multiple replicates, indicating high reproducibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Since multiple treatment groups were not clustered with the control group, indicating that significant changes occurred in multiple treatment groups, comparing multiple treatment group time points with the control group showed 11,233 DEGs (5,855 upregulated and 5,378 downregulated genes) at T3-vs-T0, and 13,403 DEGs at T6-vs-T0 (6,738 upregulated and 6,665down-regulated genes), T9-vs-T0 had 13,050 DEGs (6,448 upregulated and 6,602 downregulated genes), and T12-vs-T0 had 11,676 DEGs (5,693 upregulated and 5,983 downregulated genes; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis of DEGs in the four compared groups showed that the common enriched pathways including: alpha-linolenic acid metabolism, sulfur metabolism, pentose phosphate pathway, glycerophospholipid metabolism, porphyrin and chlorophyll metabolism, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;F). In addition, some pathways were enriched only in the T3-vs-T0 comparison group, such as homologous recombination, nucleotide excision repair, terpenoid backbone biosynthesis, RNA degradation, spliceosome, plant hormone signal transduction, ether lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC); and the glyoxylate and dicarboxylate metabolism, glycosphingolipid biosynthesis, propanoate metabolism, nicotinate and nicotinamide metabolism, citrate cycle, and SNARE interactions in vesicular transport pathways were enriched only in the T6-vs-T0 comparison group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Further, limonene and pinene degradation was only enriched in the T9-vs-T0 comparison group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and the fructose and mannose metabolism, phosphonate and phosphinate metabolism, beta-amylase metabolism, alanine metabolism, beta-amylase metabolism, and pyrimidine metabolism were only enriched in the T12-vs-T0 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunction enrichment of DEGs in response to heat stress\u003c/h2\u003e \u003cp\u003eFurther analysis of the four comparison groups showed 4,652 common DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA); the corresponding enriched metabolic pathways were plant-pathogen interaction, MAPK signaling pathway, spliceosome, mismatch repair, pentose phosphate pathway, homologous recombination, DNA replication, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). According to the pattern of gene expression changes during heat stress, the expression of genes in cluster 6 gradually increased after heat stress treatment, reaching the highest point at 6\u0026ndash;9 h. The expression of genes in cluster 8 gradually decreased after heat stress treatment reaching the lowest point at 6\u0026ndash;12 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The genes in clusters 6 and 8 were basically consistent with the trends of changes detected in some physiological indices and antioxidant activities. Functional analysis of the genes in cluster 6 showed that they were mainly related to spliceosome, mismatch repair, DNA replication, sulfur metabolism, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The pathways significantly enriched of cluster 8 genes included plant-pathogen interaction, phenylpropanoid biosynthesis, flavonoid biosynthesis, MAPK signaling pathway, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic changes in R. chinensis in response to heat stress\u003c/h2\u003e \u003cp\u003eFurther metabolomic examination of the heat treated samples showed high reproducibility of both positive ion mode (pos) metabolites and negative ion mode (neg) metabolites in samples from multiple time points (Fig.\u0026nbsp;4AB, Supplementary Table\u0026nbsp;2). A total of 723 pos metabolites and 432 neg metabolites were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), mainly containing flavonoids (60 positive metabolites and 52 negative metabolites), terpenoids (39 positive metabolites and 33 negative metabolites), phenylpropanols (33 positive metabolites and 23 negative metabolites), phenols (15 positive metabolites and 15 negative metabolites), phenolic acids (16 positive metabolites and 11 negative metabolites), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Comparison of DAMs between the four treatment groups and the control group showed 243 DAMs (including 144 positive and 89 negative metabolites) in the T3-vs-T0 comparison group, 254 DAMs (including 157 positive and 97 negative) in the T6-vs-T0 comparison group, 246 DAMs in the T9-vs-T0 comparison group (including 165 positives and 81 negatives), and 265 DAMs in the T12-vs-T0 comparison group (including 174 positives and 91 negatives; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eEnrichment analysis of the DAMs in four comparison groups showed that the enriched pos DAMs in KEGG metabolic pathways include alpha-linolenic acid metabolism and arginine biosynthesis, while the neg DAMs were enriched in pathways such as plant hormone signal transduction, cyanoamino acid metabolism, tropane, piperidine and pyridine alkaloid biosynthesis, aminoacyl-tRNA biosynthesis, phenylpropanoid biosynthesis, and glucosinolate biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u0026ndash;I). In addition, pos DAMs were significantly enriched in ABC transporters in the T6/9/12-vs-T0 comparison group except at the early stage of heat stress treatment (T3-vs-T0), related to the transmembrane transport of metabolites at the later stage of the treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u0026ndash;I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDAMs in response to heat stress\u003c/h2\u003e \u003cp\u003eFurther analysis showed that 57 common DAMs in the four comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These 57 DAMs were significantly enriched in the pathway of aminoacyl-tRNA biosynthesis, glucosinolate biosynthesis, and cyanoamino acid metabolism, and associated with two major metabolites, L-Isoleucine and L-Phenylalanine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Analysis of the changes in the content of these DAMs showed three clusters, cluster Ⅰ showed the lowest content at T0, increasing significantly from T3 to T12; the changes in these metabolites were consistent with the trend in the changes in POD activities, proline, and MDA content. In cluster III, the trend was almost the opposite, with the highest content at T0, decreasing significantly thereafter; the trend was also consistent with the trend of changes to chlorophyll and total protein contents. In addition, the contents of cluster Ⅱ metabolites decreased at T3 followed by a rapid increase at T6 and a subsequent rapid decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCombined RNA‑seq and metabolomic analysis\u003c/h2\u003e \u003cp\u003eThe Spearman correlation coefficient was calculated for DEGs and DAMs. Network diagrams were plotted for DEGs and DAMs with absolute correlation coefficient values\u0026thinsp;\u0026gt;\u0026thinsp;0.9 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The significant nodes in the four comparison groups were L-Phenylalanine, jasmonic acid, 5-Fluoro, biocytin may play key roles in the plant\u0026rsquo;s response to heat stress (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026ndash;E). The patterns in content changes divided the metabolites into three subgroups: cluster 1 upregulated after heat shock (T3\u0026ndash;T12); cluster 2 downregulated after heat shock (T3\u0026ndash;T12); cluster 3 increased at T6 and later decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). This trend was consistent with the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. In addition, DAMs and DEGs in the four comparison groups were mainly enriched in alpha-linolenic acid metabolism, plant hormone signal transduction, phenylpropanoid biosynthesis, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHeat stress response pathways\u003c/h2\u003e \u003cp\u003eAfter heat stress, phenylpropanoid biosynthesis and downstream flavonoid biosynthesis were enriched in roses. Multiple hormones are involved in the regulation of the heat stress process. In the flavonoid biosynthesis pathway, the expression level of \u003cem\u003eCHI\u003c/em\u003e, \u003cem\u003eHCT\u003c/em\u003e (excluding 112197593 and 112165685), \u003cem\u003eFLS\u003c/em\u003e, and \u003cem\u003eDFR\u003c/em\u003e genes was downregulated after heat stress. Four metabolites in this pathway showed significant changes, pinocembrin and dihydroquercetin decreased after heat treatment, and eriodictyol and dihydromyricetin showed an increasing trend after heat stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe significant enrichment of plant hormone signal transduction and MAPK signaling indicates a close relationship between these two pathways. We analyzed jasmonic acid and ethylene related pathway in the MAPK signaling pathway. The expression levels of \u003cem\u003eMKK3\u003c/em\u003e and \u003cem\u003eMPK6\u003c/em\u003e were significantly downregulated after heat stress, which play an inhibitor of \u003cem\u003eMYC2\u003c/em\u003e and caused upregulated of \u003cem\u003eMYC2\u003c/em\u003e homologies (except 112200522). In addition, \u003cem\u003eERF1\u003c/em\u003e repression (whose expression levels increased after heat stress) also play a role in regulated \u003cem\u003eVSP2\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In the ethylene signaling pathway, \u003cem\u003eRAN1\u003c/em\u003e, \u003cem\u003eETR\u003c/em\u003e, and \u003cem\u003eCTR1\u003c/em\u003e (excluding 112178901) significant increased their expression levels while \u003cem\u003eMPK3/6\u003c/em\u003e expression was inhibited. In addition, \u003cem\u003eMYC2\u003c/em\u003e, involved in the ethylene pathway, and which coordinates the regulation of \u003cem\u003eChiB\u003c/em\u003e expression levels, upregulated the expression of a ChiB homologous after heat stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRNA‑seq expression validation by qRT‑PCR\u003c/h2\u003e \u003cp\u003eqRT-PCR was used to confirm the reliability of our RNA-seq data. The results of this analysis revealed that the expression patterns of 16 selected DEGs were consistent with the RNA-seq dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among these genes, \u003cem\u003eHSP70\u003c/em\u003e, a typical maker gene in response to heat stress, showed significant up-regulation after heat stress. WRKY transcript factors (four homologies), RBOP, and PR1, crucial genes in the MAPK signaling pathway, were downregulated after heat stress, which suggested that heat stress may regulate the resistant of rose to high temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eRcHSP70 overexpression decreases rose\u0026rsquo;s timely responses to high temperature\u003c/h2\u003e \u003cp\u003eTo determine whether \u003cem\u003eRcHSP70\u003c/em\u003e has a function in resistance to heat stress, \u003cem\u003eRcHP7\u003c/em\u003e was introduced into \u003cem\u003eArabidopsis\u003c/em\u003e wild-type (WT). \u003cem\u003eRcHSP70\u003c/em\u003e overexpression (OE) plants showed obviously stronger than WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, 0h). After high-temperature treatment, \u003cem\u003eRcHSP7\u003c/em\u003e OE plants showed higher resistance to high temperature, as well as slowed water loss and wilting rate than WT plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). High \u003cem\u003eRcHSP70\u003c/em\u003e expression was also detected in \u003cem\u003eArabidopsis\u003c/em\u003e OE plants by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, 0h). After high-temperature treatment, \u003cem\u003eRcHSP70\u003c/em\u003e expression significantly increased in both OE and WT plants, indicating that heat stress induces \u003cem\u003eRcHSP70\u003c/em\u003e expression. In OE plants, \u003cem\u003eRcHSP70\u003c/em\u003e expression was significantly lower than that in WT. \u003cem\u003eRcHSP70\u003c/em\u003e expression was approximately\u0026thinsp;\u0026ge;\u0026thinsp;20-fold than that of untreated plants, but OE2 and OE3 plants increased approximately 4- to 14-fold (OE5 increasing similarly to WT; Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs an important ornamental plant, rose holds a high market share in flower industry and has important economic value. Breeding varieties resistant to high temperatures is crucial for increasing the production of rose flowers. Although key genes involved in heat stress responses have been reported in other species, whether the regulatory and responsive mechanisms in roses are consistent with those species remains unclear. Li et al. conducted a preliminary exploration of heat stress-responsive genes in roses through transcriptome analysis, but there is still a lack of systematic research on the relationship between gene and metabolite changes after heat stress [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This study further explored the characteristics of gene and metabolite changes in roses induced by high temperature using transcriptomic and metabolomic methods.\u003c/p\u003e \u003cp\u003eIn this study, we found a higher number of DEGs at T6 and T9 time points than at T3 and T12, indicating that the impact of heat stress on roses peaks at T6 and T9. This finding is generally consistent with multiple physiological indicators, such as the lowest chlorophyll and protein content at T9 and the highest proline content at T9. The POD and MAD activities reached their peaks at T6. Typically, the activity of plant is often inhibited under high-temperature stress, leading to suppressed protein and chlorophyll synthesis. Both substances reached their lowest levels around 9 h after heat stress, suggesting that the impact of heat stress on roses may be less severe during early stages or within 9 h of continuous heat stress. The accumulation of proline helps plants tolerating high-temperature stress [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In roses, the proline content peaked at 9 h of high-temperature stress. POD and MDA activity are usually associated with a plant\u0026rsquo;s ability to cope with stress, indicating that roses gradually accumulate substances to resist high temperatures during stress responses.\u003c/p\u003e \u003cp\u003eThis study employed transcriptomic and metabolomic approaches. Despite the vast amount of gene data changed in roses after high-temperature treatment, the enrichment of DEGs and DAMs exhibited high similarity. Significant enrichments were observed in pathways such as alpha-linolenic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, plant hormone signal transduction, and MAPK signaling pathway, suggesting a strong correlation between DEGs and DAMs.\u003c/p\u003e \u003cp\u003eThe accumulation of flavonoids plays a crucial role in increasing a plant\u0026rsquo;s tolerance to heat stress. For instance, flavonoids have a short-term heat stress effect in \u003cem\u003eAnoectochilus roxburghii\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and flavonoid accumulation regulation through hormones reduced heat stress in rice [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this study, flavonoid biosynthesis, a downstream pathway of phenylpropanoid biosynthesis, exhibited a decreasing trend in the expression levels of multiple genes. Similarly, the contents of several metabolites (eriodictyol, dihydroquercetin, and pinocembrin) in this pathway decreased during the early stages of heat stress, consistent with gene expression trends. However, the metabolite levels increased at T12, possibly to facilitate adaptation and recovery after heat stress.\u003c/p\u003e \u003cp\u003ePlant hormones and MAPK signaling are involved in various stress responses and crosstalk closely [\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The MAPK pathway not only responds to pathogen infection but also plays a crucial role in responses to plant hormone regulation, cold, salt, drought, and wounding [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, plant hormones and MAPK signaling were significantly enriched after high-temperature stress, indicating that this type of stress may share response pathways with the aforementioned stresses. In particular, regulatory pathways involving jasmonic acid and ethylene showed different expression patterns. Multiple genes in the jasmonic acid pathway tended to decrease their expression levels after high-temperature stress, while genes in the ethylene pathway did the opposite. Although both pathways are involved in defense responses, further research is needed to determine whether the gene expression changes are due to heat stress induction or resistance responses.\u003c/p\u003e \u003cp\u003eHSPs are crucial response proteins and markers for detecting heat stress in plants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. HSPs are diverse and possess the important function of restoring proteins denatured due to heat stress to their undenatured state [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the high expression and constitutive presence of HSPs in plants provide the potential for timely restoration of denatured proteins. In this study, we heterologously overexpressed a selected HSP70 protein in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, and the results confirmed that overexpressing RcHSP70 in \u003cem\u003eArabidopsis\u003c/em\u003e plants confers a significant advantage in resisting high-temperature stress.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined genetic and biochemical changes in rose plants at five time points after heat stress. DEGs and DAMs were enriched in pathways including phenylpropanoid biosynthesis, MAPK signaling, alpha-linolenic acid metabolism, etc. The present results suggest that flavonoids and plant hormones play crucial roles in rose plant\u0026rsquo;s resistance to heat stress. Our findings provide insights into rose response to high temperature and can serve as foundation to improve rose plant resistance against heat stress.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMDA\u0026nbsp;\u0026nbsp; Malondialdehyde\u003c/p\u003e\n\u003cp\u003ePOD\u0026nbsp;\u0026nbsp;\u0026nbsp; Peroxidase\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEG\u0026nbsp;\u0026nbsp; Differentially expressed gene\u003c/p\u003e\n\u003cp\u003eDAMs\u0026nbsp; Differentially abundant metabolites\u003c/p\u003e\n\u003cp\u003eMAPK\u0026nbsp; Mitogen-activated protein kinases\u003c/p\u003e\n\u003cp\u003eHSP\u0026nbsp;\u0026nbsp; Heat shock proteins\u003c/p\u003e\n\u003cp\u003eROS\u0026nbsp;\u0026nbsp; Reactive oxygen species\u003c/p\u003e\n\u003cp\u003eHfA1\u0026nbsp; Heat shock transcription factor A1\u003c/p\u003e\n\u003cp\u003eLC-MS\u0026nbsp; Liquid chromatography mass spectrometry\u003c/p\u003e\n\u003cp\u003eQC\u0026nbsp;\u0026nbsp;\u0026nbsp; Quality control\u003c/p\u003e\n\u003cp\u003eAGC\u0026nbsp; \u0026nbsp;Automatic gain control\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA\u0026nbsp;\u0026nbsp;\u0026nbsp; Principal component analysis\u003c/p\u003e\n\u003cp\u003ePLS-DA\u0026nbsp; Partial least squares method-discriminant analysis\u003c/p\u003e\n\u003cp\u003eVIP\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Variable importance in projection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Gene ontology\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp;\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003erCCA\u0026nbsp;\u0026nbsp; regularized Canonical correlation analysis\u003c/p\u003e\n\u003cp\u003eSPLSDA\u0026nbsp; Sparse partial least squares discriminant analysis\u003c/p\u003e\n\u003cp\u003epos\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; positive ion mode\u003c/p\u003e\n\u003cp\u003eneg\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; negative ion mode\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.W. \u0026nbsp; conceived the study; W.X., X.Z.and S.Z. \u0026nbsp;analyzed the data; S.Z. was involved in data interpretation; R.L. and A.R. prepared figures and tables; \u0026nbsp; S. J.and W.L.collected the samples and performed the experiments; H.W. and L.W.wrote the article. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the key research and development project in Anhui Province (202104a06020017).\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eAll the raw data used in this study have been deposited at NCBI BioProject ID: PRJNA1090540 (http://www.ncbi.nlm.nih.gov/bioproject/1090540).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of plant parts in the present study complies with international, national and/or institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLesk C, Rowhani P, Ramankutty N. 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Plant Cell Environ. 2020;43(12):2847\u0026ndash;56.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"Rosa chinensis, heat stress, Transcriptome, Metabolome","lastPublishedDoi":"10.21203/rs.3.rs-4292491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4292491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGlobal warming has made high–temperature stress one of the most important factors causing crop yield reduction and death. In the rose flower industry, high-temperature stress leads to bud dormancy or even death, reducing ornamental value and incurring in economic loss. Understanding the molecular mechanisms underlying the response and resistance of roses to high-temperature stress can serve as an important reference for the cultivation of high-temperature-stress-resistant roses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the impact of high temperature on rose plants, we initially measured physiological indices in rose leaves after heat stress. We observed a significant decrease in protein and chlorophyll content, while proline and malondialdehyde (MDA) levels, as well as peroxidase (POD) activity, increased. Subsequently, transcriptomics and metabolomics analyses were conducted to detect changes in gene expression and metabolite content after high-temperature stress. Compared to the untreated control (T0), the number of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) in rose plants subjected to heat peaked at time points T6-T9. This trend closely aligned with the observed physiological changes. Enrichment analysis showed that most DEGs and DAMs primarily involved in the mitogen-activated protein kinases (MAPK) signaling pathway, plant hormone signal transduction, alpha-linolenic acid metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis, etc.\u003c/p\u003e\n\u003cp\u003eAfter heat stress, the DEGs and DAMs combined analysis revealed a predominant downregulation of genes and metabolites related to the flavonoid biosynthesis pathway. Similarly, genes involved in the jasmonic acid pathway within the MAPK signaling pathway exhibited decreased expression, but genes associated with the ethylene pathway were mostly upregulated, suggesting a role in roses’ heat stress responses. Furthermore, heterologous overexpression of the heat stress-responsive gene \u003cem\u003eRcHP70\u003c/em\u003e in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e increased resistance against heat stress.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe present study provides new insights on the genes and metabolites induced in roses in response to high temperature; the present results provide a reference for analyzing the molecular mechanism underlying resistance to heat stress in roses. The obtained candidate genes and metabolites could be valuable resources for breeding of heat stress resistant roses.\u003c/p\u003e","manuscriptTitle":"Transcriptome and metabolome analyses of Rosa chinensis identify heat stress response genes and metabolite pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-06 03:06:50","doi":"10.21203/rs.3.rs-4292491/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-08T11:25:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T08:15:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-06T05:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264815848800135290013024040787461203600","date":"2024-04-29T23:17:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36685937947814095675892121400495395835","date":"2024-04-29T19:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-29T15:57:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-29T14:34:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-29T14:34:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-04-19T10:09:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"7efd60b7-c45c-4ffd-bf9f-6cbbc04b925b","owner":[],"postedDate":"May 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-4292491","link":"https://doi.org/10.1186/s12870-024-05543-1","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-09-20 15:57:18","publishedOnDateReadable":"September 20th, 2024"},"versionCreatedAt":"2024-05-06 03:06:50","video":"","vorDoi":"10.1186/s12870-024-05543-1","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05543-1","workflowStages":[]},"version":"v1","identity":"rs-4292491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4292491","identity":"rs-4292491","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}