Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease

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However, the roles of defense genes and metabolites in different SM-infected canopy layers of tea plants remain largely unclear. To investigate the immune mechanisms of tea plants, we utilized hyperspectral, transcriptomic, and metabolomic data from leaves of three canopy layers infected by SM (A1, A2, and A3). Results The hyperspectral analysis indicated that the spectral characteristics of the three canopy layers significantly differed, and three inflection spots were located close to the 552, 673, and 800 nm wavelengths. Transcriptome analysis showed that the differentially expressed genes were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction. The cluster analysis revealed an elevated immune response in the A2 and A3 samples. A total of 733 metabolites were co-changed in leaves infected by SM, with alcohol, lipids (free fatty acids), hydrocarbons, and amino acids significantly accumulating in A1, while flavonoids were predominantly upregulated in A2 and A3. WGCNA analysis revealed that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) have significant relationships with the DEGs and metabolites. Further co-expression showed that tea plants mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to defend against SM. Conclusion This study established a foundation for elucidating the immune mechanisms of different canopy layers of tea plants infected by SM, and provided candidate genes and metabolites for improving tea plant breeding. tea plants sooty mold disease resistance hyperspectral transcriptomic metabolomic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Tea plants ( Camellia sinensis (L.) O. Kuntze) typically grow in subtropical areas, where warm, humid weather is conducive to disease growth [ 1 , 2 ]. Tea cultivation has been the subject of more research in recent times because of its high nutritional content and financial significance. However, biotic and abiotic pressures pose several obstacles to the tea industry, making it difficult to produce enough tea to fulfill the rising demand. However, tea production faces several challenges caused by biotic and abiotic stresses, resulting in insufficient tea supply to meet the high demand [ 3 , 4 ]. Pathogens can affect plant growth and fitness by modifying their host plant's resource availability, metabolism, and physiology [ 5 ]. Infected plants are expected to have reduced resources for growth and reproduction as defense is costly, and pathogens withdraw nutrients from the host [ 6 ]. More than 500 diseases have been found in 37 countries worldwide, of which 138 have been reported in China [ 7 ]. Among these, sooty mold disease (SM) caused by Cladosporium species was the most widespread and devastating in Shandong Province. The disease mainly affects tea leaves, covering diseased branches and leaves with a layer of black mold, affecting tea photosynthesis. In addition, sooty mold disease fungi colonize the leaves of plants and use honeydew secreted by Aleurocanthus spiniferus , scale insects, Aphidoidea , or the secretions of plants as a source of nutrition [ 8 , 9 ]. Many secondary metabolites produced by tea plants are considered to represent the quality of tea and have been found to reduce disease infection [ 4 , 10 ]. The primary metabolic pathways for phenolic compounds involve phenylpropane and flavonoid metabolic pathways, which entail several enzymes' cooperative actions [ 11 ]. Many genes related to the phenylpropanoid catabolic process are involved in plant resistance to disease. For example, phenylalanine ammonia-lyase, a critical enzyme, regulates the flow of primary metabolism into secondary metabolism, producing anti-microbial phytoalexins as a response to pathogen attacks [ 12 , 13 ]. Plants use the metabolites produced by the phenylpropane metabolism pathway, such as lignin, pollen, anthocyanins, and organic acids, to regulate plant immunity [ 14 ]. Lignin accumulation reinforces defensive structures, like the cell wall, by thickening lignin layers [ 15 ]. The sugar alcohol mannitol is an essential carbohydrate with documented roles in metabolism and osmoprotection in many plants and fungi [ 16 ]. In mannitol-producing plants, the accumulation of mannitol is largely influenced by mannitol dehydrogenase (MTD). While a pathogen-induced MTD in the apparent absence of mannitol was initially unexpected, several lines of evidence suggest a unique role for MTD and mannitol in plant-pathogen interactions [ 16 ]. In the present study, we employed hyperspectral to identify the phenotypes of tea leaves from different canopies infected with Cladosporium species. Further transcriptomic and metabolomic analyses were conducted to view the metabolites and gene profiles of the tea plant defense against SM disease. Hence, the overarching aim of this study was to examine the variations in infection by Cladosporium species on the leaves of different canopy layers of tea plants and to explore the immune molecular mechanisms of tea plants against sooty mold disease. Materials and methods Plant materials and sampling The tea leaf samples were collected from a tea plantation in October 2023 in Linyi (N 36°65′, E 117°26′), Shandong Province, China. Three groups of samples at different canopies of tea plants were taken: up (A1), middle (A2), and bottom (A3), which were infected by Cladosporium species. This experiment was a randomized block design of three replicates, and all samples were immediately placed in liquid nitrogen and stored at -80°C for further processing. Hyperspectral image acquisition Hyperspectral image data was obtained and calibrated using the procedure outlined by [ 17 ]. The hyperspectral image data was collected using a hyperspectral camera (GaiaField-Pro-V10, Jiangsu Dualix Spectral Image Technology Co. Ltd, China) housed in a specially designed dark box cube. SpecVIEW software was used to apply lens and reflectance corrections to the data. The entire leaf sample was selected as the region of interest (ROI) after opening the preprocessed RAW format hyperspectral images with ENVI 5.3 (Research System Inc., Boulder, CO, USA). The ROI tool was then utilized to extract the samples' mean reflectance spectral data. RNA extraction, library construction, and transcriptome sequencing The transcriptome library was constructed using the RNAprep Pure Plant Kit (DP441, Tiangen, China), which is also used to extract total RNA. The transcriptome sequencing was carried out on the Illumina Novaseq6000 platform, with the GO enrichment and KEGG pathway analysis performed using the Metware Cloud ( https://cloud.metware.cn ), with the GO terms with corrected P-values (FDR < 0.05) retained. The fragments per kilobase of transcript per million mapped reads (FPKM) method was used to estimate the gene expression levels. Log2 (Fold change) ≥ |2| and FDR < 0.05 was used as a cut-off. Extraction and analysis of non-volatile metabolites by UPLC-MS Using vacuum freeze-drying technology, place the biological samples in a lyophilizer (Scientz-100F), then grind (30 Hz, 1.5 min) the samples to powder form by using a grinder (MM 400, Retsch). Next, weigh 50 mg of sample powder using an electronic balance (MS105DΜ) and add 1200 µL of -20°C pre-cooled 70% methanolic aqueous internal standard extract (less than 50 mg added at the rate of 1200 µL extractant per 50 mg sample). Vortex once every 30 min for 30 sec, for a total of six times. After centrifugation (rotation speed 12000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 µm pore size) and stored in the injection vial for UPLC-MS/MS analysis. The sample extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, ExionLC™ AD; MS, Applied Biosystems 6500 Q TRAP). The analytical conditions were as follows: UPLC: column, AgilentSB-C18 (1.8 µm, 2.1 mm * 100 mm); The mobile phase consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1 min. Subsequently, a composition of 95% A and 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was 0.35 mL per minute; the column oven was set to 40°C, and the injection volume was 2 µL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS. The ESI source operation parameters were as follows: source temperature 500°C; ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode); ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation (CAD) was high. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions were done with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period. Extraction and analysis of volatile metabolites by GC-MS Samples were ground to a powder in liquid nitrogen. 500 mg (1 mL) of the powder was transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA) containing a NaCl-saturated solution to inhibit any enzyme reaction. The vials were sealed using crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was placed at 60°C for 5 min, and then a 120 µm DVB/CWR/PDMS fiber (Agilent) was exposed to the sample's headspace for 15 min at 60°C. After sampling, desorption of the VOCs from the fiber coating was carried out in the injection port of the GC apparatus (Model 8890; Agilent) at 250°C for 5 min in the splitless mode. The identification and quantification of VOCs were carried out using an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent) equipped with a 30 m × 0.25 mm × 0.25 µm DB-5MS (5% phenyl-polydimethylsiloxane) capillary column. Helium was used as the carrier gas at a 1.2 mL/min linear velocity. The injector temperature was kept at 250°C and the detector at 280°C. The oven temperature was programmed from 40°C (3.5 min), increasing at 10°C/min to 100°C, at 7°C/min to 180°C, at 25°C/min to 280°C, and held for 5 min. Mass spectra were recorded in electron impact (EI) ionization mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set at 150, 230, and 280°C. The MS was selected in ion monitoring (SIM) mode to identify and quantify analytes. Co-expression analysis The R package WGCNA (Langfelder and Horvath, 2008) was used for co-expression network analysis. A gene expression adjacency matrix was developed to analyze the network topology with the following settings: soft threshold power, 18; TOMType, unsigned; and minModuleSize and mergeCutHeight, 50 and 0.25, respectively. The blockwise module function was used to obtain the modules using default settings. Cytoscape v.3.9.1 was used to visualize the gene co-expression networks. Quantitative real-time PCR (qRT-PCR) Total RNA was sampled for deep sequencing to validate the gene expression analysis results. qRT-PCR was performed as described previously. Genes were assayed using TB Green® Premix Ex TaqTM II (TaKaRa) on a CFX96 real-time PCR system (Bio-Rad). Three separate biological repeats were used for the analysis, and each biological replication was subjected to three sets of measurements to obtain technical replicates. The relative expression of genes was calculated using the 2 −ΔΔCt method, and the relative expression levels were presented as log2 fold changes. CsGAPDH was used as the reference. The primers are listed in Table S6. Results Changes in hyperspectral characteristics during sooty mold disease infection To illustrate the differences between different canopy layers, an average of the original spectrum was created (Fig. 1). The reflectance trends of the three canopy layers increased in the 397–552 nm wavelength range and declined in the 552–673 nm wavelength range. There was a noticeable variation in the reflectance of reaching the inflection point with canopy layers. The reflectance of all three canopy layers increased exponentially in the wavelength range of 673–800 nm. Based on the spectral characteristics, the average spectral reflectance of the three canopy layers in the designated wavelength range displayed a similar overall pattern. In the spectral characteristics, three inflection spots were located close to the 552, 673, and 800 nm wavelengths. Transcriptomic analysis and screening of DEGs Nine cDNA libraries were generated from the leaf samples at three canopy layers: A1, A2, and A3. Transcriptomic sequences were obtained after the removal of adapters and low-quality reads. An average of 44.8 million clean reads per sample were received, with a Q20 base composition percentage exceeding 97.56% and a Q30 base composition percentage exceeding 93.02%. The GC content measured greater than 43.56% (Table S1 ). A total of 2404 genes showed significantly differential expression under the log 2 (fold change) ≥ |2| and p-value < 0.5 (Table S2 ). The number of differentially expressed genes (DEGs) ranged from 322 (231 upregulated, 91 downregulated) between A3 and A2 to 1994 (1265 upregulated, 729 downregulated) between A3 and Al (Fig. 2 A). The number of upregulated DEGs was higher than the downregulated DEGs in A2 and A3 samples, showing that many DEGs were highly expressed in the leaves in down canopy layers. The overlap in gene expression revealed that numerous DEGs were found to be unique to each of the combinations. The most unique DEGs were identified at A3 vs A1 (1254), and the fewest were found at A3 vs A2 (78) (Fig. 2 B). To further explore the functions of DEGs in response to sooty mold disease, Gene Ontology (GO) annotation was conducted. The most significantly enriched terms were oxidoreductase activity, secondary metabolite process, phenylpropanoid catabolic process, lipid biosynthetic process, and cell wall organization or biogenesis (Fig. 2 C). An analysis of the enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the most enriched pathways were metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction (Fig. 2 D). Different expression patterns of DEGs Under pathogen infection, DEGs exhibit diverse expression patterns in tea plants. A heatmap with GO enrichment was employed to visualize the expression patterns of DEGs in the nine samples. As shown in Fig. 3 , many genes are differentially expressed in A1 samples. These DEGs were mainly enriched in carbohydrate binding, oxidoreductase activity, isoprenoid metabolic and biosynthetic processes, and terpenoid metabolic processes. In Cluster II, the DEGs were upregulated in A2 and A3 samples. The genes involved in the homeostatic process, secondary active transmembrane transporter activity, UDP-glycosyltransferase activity, and response to oxidative activity. In A3 samples, numerous genes associated with the secondary metabolic process, phenylpropanoid catabolic process, cell wall biogenesis, and flavonoid metabolic process exhibited induced expression in Cluster III. These findings show an elevated immune response in the A2 and A3 samples. In addition, UDP-glucosyltransferase and oxidoreductase activities are observed in all samples, suggesting their essential roles in the tea plant response to sooty mold disease. Metabolome profiling of tea leaves infected by sooty mold disease A multivariate analysis method, PLS-DA, was performed to investigate metabolite changes under sooty mold disease infection. Score plots showed that the differentially expressed metabolites in A1, A2, and A3 were statistically separated (Fig. 4A). In total, 733 metabolites were detected (Table S3). The identified compounds include flavonoids (18.76%), phenolic acids (12.48%), amino acids and derivatives (6.34%), organic acids (5.01%), alkaloids ( 5.01% ), and others (Fig. 4B). A heatmap was used to visualize the differentially expressed metabolites between different canopy layers infected by SM (Fig. 4C). A1 mainly drives the accumulation of alcohol, lipids (free fatty acids), hydrocarbons, and amino acids and derivatives. Much differently, flavonoids were predominantly upregulated in A2 and A3, particularly in A3. To elucidate the critical metabolites in tea plants that resist SM, scatter plots were used to display the differential metabolites between A2 and A3 as compared to A1 (Fig. 4D). The alkaloids, amino acids and derivatives, and flavonoids were significantly expressed in A2 samples compared with A1. Much differently, alcohol, aldehyde, ester, flavonoids, heterocyclic compounds, hydrocarbons, ketone, lignans, and coumarins were significantly enriched in A3 samples. The results suggest that A3 employs more metabolites to protect against SM than A2. Correlations between the DEGs and metabolites The differentially expressed genes and metabolites were used to construct a network of six and three distinct modules with similar expression trends among different samples, respectively (Figs. S1 and S2). The differentially expressed metabolites and genes upregulated in A3 samples, which showed more defense against SM, were selected to construct the network. WGCNA analysis demonstrates that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, Wound-induced protein, and uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) have significant relationships with the DEGs and metabolites (Table S4). The hub genes and metabolites were developed into a subnetwork comprising three groups linked to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction (Fig. 5 , Table S5). Out of the 98 genes, 40 genes in the biosynthesis of secondary metabolites category included cytochrome P450, gibberellin 20-oxidase, alcohol dehydrogenase, and mannitol dehydrogenase. The high expression of mannitol dehydrogenase was consistent with the enrichment of D-mannitol. The plant-pathogen interaction category includes numerous WRKY, LRR, and PR members. In addition, the genes related to auxin and ethylene show high enrichment in the plant hormone signal introduction category. These results suggested that the tea plant mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to defend against SM. qRT-PCR verification of DEGs To assess the authenticity and reliability of the transcriptome data and the extent of DEGs. Nine DEGs across the network were selected for validation via qRT-PCR. Of these, Dormancy-associated protein 1 (CSS0035295), uncharacterized protein LOC114300808 (CSS0008624), wound-induced protein (CSS0009105), Serine/threonine-protein phosphatase (CSS0030286), and ABC transporter (CSS0044987) were hub genes. The mannitol dehydrogenase (CSS0031337) and cytochrome P450 (CSS0017190) were involved in the biosynthesis of secondary metabolites. The LRR receptor (CSS0020320) and pathogenesis-related protein (CSS0037023) were related to plant-pathogen interaction. As shown in Fig. 6, qRT–PCR analyses showed the same expression trend for each of the analyzed candidates. These results suggest that the transcriptomic data were accurate and could be used for further functional analysis. Discussion Sooty mold is an important plant disease in tropical and subtropical regions. It can affect plants' growth, development, and metabolism and reduce their commodity and economic value [ 18 , 19 ]. Recently, SM disease has been detected in the tea-producing area of Shandong Province, China, and has severely affected the development of the local tea industry. Hence, exploring the effect and molecular mechanism of SM disease is useful for producing and breeding tea plants. In this study, we employed hyperspectral, transcriptome, and metabolome analysis to investigate the immune mechanisms of tea plants infected by SM. Hyperspectral imaging is a promising non-invasive technique for identifying plant diseases in agriculture, which has several benefits over conventional techniques [ 17 , 20 ]. Hyperspectral imaging has demonstrated major benefits regarding objective, precise, non-destructive, and easily understandable plant disease diagnosis outcomes. With the help of hyperspectral imaging, disease images, and spectral data can be obtained without endangering crops [ 21 ]. Stresses on tea plants break down the chloroplast structure of the leaf, which increases reflectance and decreases matching band absorption [ 22 ]. Meanwhile, the red edge shifts in the direction of the shortwave due to a drop in chlorophyll concentration [ 23 ]. These damages can reduce the spectral reflectance in the near-infrared region. In this study, the average spectral reflectance in the designated wavelength range displayed a similar overall pattern while showing a noticeable variation in the reflectance of reaching the inflection point with the canopy layers. The results were consistent with the previous studies that hyperspectral imaging can non-destructively distinguish the different degrees of symptoms infected by SM [ 17 ]. A total of 2404 genes demonstrated differential expression patterns, and most DEGs were exhibited in A3 samples compared with A1 and A2 samples. The significantly enriched GO terms were oxidoreductase activity, secondary metabolite process, phenylpropanoid catabolic process, lipid biosynthetic process, and cell wall organization or biogenesis. Phenolic compounds are essential for chemical resistance, as numerous studies have shown [ 24 , 25 ]. Once an infection has taken hold, these chemicals can eradicate dangerous microbes and the materials they create [ 26 , 27 ]. Under SM infection, numerous genes associated with the secondary metabolic process, phenylpropanoid catabolic process, cell wall biogenesis, and flavonoid metabolic process were upregulated in the A3 sample (Fig. 3 ). The results suggest that tea plants resisted the invasion of pathogens by regulating the secondary metabolite content and cell wall thickness in severely SM-infected leaves, consistent with previous studies' findings [ 27 – 29 ]. The sugar alcohol mannitol is an antioxidant, and current research suggests that pathogenic fungi can secrete mannitol into the plant’s extracellular spaces during infection to suppress reactive oxygen-mediated host defenses [ 16 ]. In plants, the accumulation of mannitol is largely influenced by the activity of mannitol dehydrogenase [ 30 ]. Down-regulation of Mtd expression by salt, osmotic stress, and ABA results in decreased mannitol catabolism, with a concomitant accumulation of mannitol for osmoprotectant [ 31 ]. Previous studies also suggest that regulation of mannitol accumulation by MTD could influence initial events in plant-pathogen interaction [ 32 ]. In the co-expression network, the mannitol dehydrogenase (from the transcriptome) and D-mannitol (from the metabolism) were both enriched in the A3 sample (Fig. 5 ). The qRT-PCR results improved the mannitol dehydrogenase enriched in the A3 samples (Fig. 6). In this study, the D-mannitol (mws1155) is a hub gene linked to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to regulate tea plants' response to SM. Conclusions In summary, fundamental insights into the defense genes and metabolites in the different SM-infected canopy layers of tea plants were acquired through integrated hyperspectral, transcriptomics, and metabolomics analyses. The primary findings of this study are outlined below: (1) The hyperspectral analysis indicated that the spectral characteristics of the three canopy layers significantly differed, and three inflection spots were located close to the 552, 673, and 800 nm wavelengths. (2) Transcriptome analysis showed that the DEGs were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction. (3) Integrated transcriptome and metabolomic analysis indicated that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) shown important roles in tea plants response to SM. Abbreviations SM sooty mold DEGs differentially expressed genes KEGG Kyoto encyclopedia of genes and genomes GO Gene ontology FPKM Fragments Per Kilobase per Million FC Fold change PCR Polymerase chain reaction qRT-PCR Real-time quantitative PCR FDR False discovery rate MTD mannitol dehydrogenase ROI region of interest DP declustering potential CE collision energy CAD collision-activated dissociation Declarations Author contributions WSS and DZT conceived and designed the research. XY and SJZ do the hyperspectral data collection and analysis. WSS, XY, SJZ, and CH collected the plant materials. All authors contributed to the article and approved the submitted version. Funding This study was supported by the Natural Science Foundation of Shandong Province (ZR2021QC092). Ethics approval and consent to participate Not applicable. All methods were carried out according to relevant guidelines and regulations. Consent to publish Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials Raw sequence data for small RNAs, degradome, and transcriptome in this study are available at the NCBI Sequence Read Archive (http://www.ncbi.nlm. nih.gov/sra) under accession number PRJNA1161365. Author details 1 Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China. 2 Tea Research Institute, Qingdao Agricultural University, Qingdao, China. References Lu R, Su J, Li T, Sun J, Wu G. Molecular regulation of immunity in tea plants. Mol Biol Rep. 2023;50(3):2883–92. Li XB, Niu GQ, Fan YH, Liu WY, Wu Q, Yu C, Wang J, Xiao YH, Hou L, Jin D et al. Synthetic dual hormone-responsive promoters enable engineering of plants with broad-spectrum resistance. Plant Commun 2023, 4(4). Zuo H, Chen J, Lv Z, Shao C, Chen Z, Zhou Y, Shen C. Tea-Derived Polyphenols Enhance Drought Resistance of Tea Plants (Camellia sinensis) by Alleviating Jasmonate–Isoleucine Pathway and Flavonoid Metabolism Flow. Int J Mol Sci. 2024;25(7):3817. Zhang J, Yu Y, Qian X, Zhang X, Li X, Sun X. Recent Advances in the Specialized Metabolites Mediating Resistance to Insect Pests and Pathogens in Tea Plants (Camellia sinensis). Plants. 2024;13(2):323. Dijk LJAv, Ehrlén J, Tack AJM. Direct and insect-mediated effects of pathogens on plant growth and fitness. J Ecol. 2021;109(7):2769–79. Denancé N, Sánchez-Vallet A, Goffner D, Molina A. Disease resistance or growth: the role of plant hormones in balancing immune responses and fitness costs. Front Plant Sci. 2013;4:155. Pandey AK, Sinniah GD, Babu A, Tanti A. How the Global Tea Industry Copes With Fungal Diseases - Challenges and Opportunities. Plant Dis. 2021;105(7):1868–79. Chen Z. Chemical ecology of tea pests. Shanghai Science and Technology; 2013. Thangaraj K, Liu S, Li J, Zhao Z, Han R, Mei H, Jeyaraj A, Chen X, Li X. Exogenous melatonin alleviates sooty mould on tea plants (Camellia sinensis L). Sci Hort. 2022;299:111056. Zheng S, Du Z, Wang X, Zheng C, Wang Z, Yu X. Metabolic Rewiring in Tea Plants in Response to Gray Blight Disease Unveiled by Multi-Omics Analysis. Metabolites. 2023;13(11):1122. Han S, Cai H, Yu H, Yu Z, Wu Y. Targeted metabolomic and transcriptomic reveal the regulation of UV–C on phenolics biosynthesis of peach fruit during storage. LWT. 2023;190:115573. Wu Y, Wang W, Li Y, Dai X, Ma G, Xing D, Zhu M, Gao L, Xia T. Six phenylalanine ammonia-lyases from Camellia sinensis: Evolution, expression, and kinetics. Plant Physiol Biochem. 2017;118:413–21. Das S, Chakraborty P, Mandal P, Saha D, Saha A. Phenylalanine ammonia-lyase gene induction with benzothiadiazole elevates defence against Lasiodiplodia theobromae in tea in India. J Phytopathol. 2017;165(11–12):755–61. Pieterse CMJ, Leon-Reyes A, Van der Ent S, Van Wees SCM. Networking by small-molecule hormones in plant immunity. Nat Chem Biol. 2009;5(5):308–16. Sels J, Mathys J, De Coninck BMA, Cammue BPA, De Bolle MFC. Plant pathogenesis-related (PR) proteins: A focus on PR peptides. Plant Physiol Biochem. 2008;46(11):941–50. Williamson JD, Desai A, Krasnyanski SF, Ding F, Guo W-w, Nguyen T-T, Olson HA, Dole JM, Allen GC. Overexpression of mannitol dehydrogenase in zonal geranium confers increased resistance to the mannitol secreting fungal pathogen Botrytis cinerea. Plant Cell Tissue Organ Cult (PCTOC). 2013;115(3):367–75. Xu Y, Mao Y, Li H, Sun L, Wang S, Li X, Shen J, Yin X, Fan K, Ding Z, et al. A deep learning model for rapid classification of tea coal disease. Plant Methods. 2023;19(1):98. Ponmurugan P, Manjukarunambika K, Gnanamangai BM. Impact of various foliar diseases on the biochemical, volatile and quality constituents of green and black teas. Australas Plant Pathol. 2016;45(2):175–85. Ponmurugan P, Baby UI, Rajkumar R. Growth, photosynthetic and biochemical responses of tea cultivars infected with various diseases. Photosynthetica. 2007;45(1):143–6. Ali MM, Bachik NA, Muhadi NA, Tuan Yusof TN, Gomes C. Non-destructive techniques of detecting plant diseases: A review. Physiol Mol Plant Pathol. 2019;108:101426. Pu Y-Y, Feng Y-Z, Sun D-W. Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Compr Rev Food Sci Food Saf. 2015;14(2):176–88. Wang X, Shen S. Effects of temperature and strain on thermal properties of Ni/Al laminated structure. Comput Mater Sci. 2014;84:13–7. Zhao X, Zhang J, Huang Y, Tian Y, Yuan L. Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis. Comput Electron Agric. 2022;193:106717. Shalaby S, Horwitz BA. Plant phenolic compounds and oxidative stress: integrated signals in fungal–plant interactions. Curr Genet. 2015;61(3):347–57. Nicholson RL, Hammerschmidt R. Phenolic compounds and their role in disease resistance. Annu Rev Phytopathol. 1992;30(1):369–89. Zhang L, Zhang Z, Ahammed GJ, Wang X, Fang H, Yan P, Zhang L-P, Ge S, Fu J, Han W-Y, et al. 24-Epibrassinolide Enhances Resistance Against Colletotrichum fructicola by Promoting Lignin Biosynthesis in Camellia sinensis L. J Plant Growth Regul. 2023;42(3):1558–66. Sanaeifar A, Ye D, Li X, Luo L, Tang Y, He Y. A Spatial-Temporal Analysis of Cellular Biopolymers on Leaf Blight-Infected Tea Plants Using Confocal Raman Microspectroscopy. Front Plant Sci. 2022;13:846484. Wang S, Liu L, Mi X, Zhao S, An Y, Xia X, Guo R, Wei C. Multi-omics analysis to visualize the dynamic roles of defense genes in the response of tea plants to gray blight. Plant J. 2021;106(3):862–75. Liu R, Liu H, Wang Y, Chen J, Qiu Z, Zheng Y, Sun B, Tan X, Shu C, Liu S, et al. Study on the Effect of Sooty Mould Disease in Tea Plants. Plants. 2024;13(16):2321. Stoop JMH, Williamson JD, Pharr DM. Mannitol metabolism in plants: A method for coping with stress. Trends Plant Sci. 1996;1(5):139–44. Zamski E, Guo WW, Yamamoto YT, Pharr DM, Williamson JD. Analysis of celery ( Apium graveolens) mannitol dehydrogenase ( Mtd) promoter regulation in Arabidopsis suggests roles for MTD in key environmental and metabolic responses. Plant Mol Biol. 2001;47(5):621–31. Grant M, Lamb C. Systemic immunity. Curr Opin Plant Biol. 2006;9(4):414–20. Additional Declarations No competing interests reported. Supplementary Files SupplementTables.xlsx SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Editor assigned by journal 20 Sep, 2024 Submission checks completed at journal 19 Sep, 2024 First submitted to journal 12 Sep, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5075569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356822094,"identity":"f5a7f512-a4b5-49db-b965-69479396ed0e","order_by":0,"name":"Shuangshuang Wang","email":"","orcid":"","institution":"Tea Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Wang","suffix":""},{"id":356822096,"identity":"48da2389-df53-455f-8d3e-17a451067be2","order_by":1,"name":"Yang Xu","email":"","orcid":"","institution":"Tea Research Institute, Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Xu","suffix":""},{"id":356822098,"identity":"2f52da73-302f-4adf-b40c-687efdf832c7","order_by":2,"name":"Jiazhi Shen","email":"","orcid":"","institution":"Tea Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiazhi","middleName":"","lastName":"Shen","suffix":""},{"id":356822099,"identity":"7f994b50-c971-45c8-bd11-d72488f3dc24","order_by":3,"name":"Hao Chen","email":"","orcid":"","institution":"Tea Research Institute, Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""},{"id":356822100,"identity":"77a0a4a8-617c-4703-a9eb-fcbfba3bb815","order_by":4,"name":"Yu Wang","email":"","orcid":"","institution":"Tea Research Institute, Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":356822101,"identity":"a7c8a658-3754-43bd-af30-1bfa6908bf54","order_by":5,"name":"Zhaotang Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDCCA2DShoefv4FoLcyMQLVpMpIzDpCm5bCNQUMCkTr4zp8//uBDzXkeA4YDjB8+5hChRfLAYcbGGcdu85gzNzBLztxGhBaDg82MzTxst3ksGw6wMfMSpeUwM2Pzn3/neAwOJBCr5RhQC2PbARK0SJ5hNpzZ25fMIznjYDNxfuE7f/DBhx/f7Oz5+ZsPfvhIjBYkAIqfUTAKRsEoGAXUAQAhOTkJTrncNwAAAABJRU5ErkJggg==","orcid":"","institution":"Tea Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zhaotang","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-09-12 07:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5075569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5075569/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05806-x","type":"published","date":"2024-11-15T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67914758,"identity":"d3d0695b-56c1-4fda-9e56-7d55c24d7d46","added_by":"auto","created_at":"2024-10-31 06:03:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264256,"visible":true,"origin":"","legend":"\u003cp\u003eThe original hyperspectral spectrum of the tea plant canopy infected by sooty mold diseases.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/ebf370b45b62e8787492c16a.jpg"},{"id":67915356,"identity":"5375bb62-80bb-4e26-83df-20057ea0ebd6","added_by":"auto","created_at":"2024-10-31 06:11:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343065,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of differentially expressed genes responding to sooty mold disease. A: the DEG number in different treatments; B: Venn diagram of three treatments; Scatter plot of the most enriched GO terms (C) and KEGG pathways (D) of all DEGs.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/74ead2395cb26eef527cae80.jpg"},{"id":67914763,"identity":"9ae3d10b-ccc0-4162-a523-710fffc7cfcc","added_by":"auto","created_at":"2024-10-31 06:03:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":570144,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of differentially expressed genes (DEGs). The clustering was performed based on the FPKM for each gene. The genes showing similar expression patterns were grouped into three clusters (Clusters I–III). The original expression values of DEGs were normalized by Z-score normalization.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/4cd3f795e78572c83e48415d.jpg"},{"id":67915357,"identity":"349f0af9-8764-4400-99da-f3103a670527","added_by":"auto","created_at":"2024-10-31 06:11:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":517381,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of differentially expressed metabolites responding to sooty mold disease. A: PLS-DA plot of all samples; B: circular diagram of metabolite category composition; C: cluster analysis of differentially expressed metabolites; D: scatter plots of the differential metabolites.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/00ac77bd4a6eca55b5771d92.jpg"},{"id":67914765,"identity":"f62ba5b6-c540-4666-84ee-ce8ad3872d83","added_by":"auto","created_at":"2024-10-31 06:03:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":584880,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression subnetwork of DEGs and metabolites. Circles in the pink ring represent DEGs, and circles in the blue ring represent metabolites. Circles in the center represent the different functional categories.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/cf2cf7718a4177e4647913cf.jpg"},{"id":67914760,"identity":"0042650c-66b9-4f67-9d44-7d959d19cae4","added_by":"auto","created_at":"2024-10-31 06:03:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":304162,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR validation of 9 key genes. The left Y-axis represents the relative expression of samples in qRT-PCR, and the right Y-axis represents the FPKM value in transcriptomics. The column represents the relative qRT-PCR expression of each component in each comparison group, corresponding to the left Y-axis. The green circle represents the FPKM value of each component in each comparison group, corresponding to the right Y-axis.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/5bfdda4a6619d339a5300a49.jpg"},{"id":69274535,"identity":"7384dad4-340f-4342-b0b0-b9dc8883c605","added_by":"auto","created_at":"2024-11-18 16:05:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3115075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/2f1f612d-8123-482b-b0d9-193108f33c45.pdf"},{"id":67915358,"identity":"d781fb98-490a-495b-83c1-71febabc74c8","added_by":"auto","created_at":"2024-10-31 06:11:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1931718,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/297b649318a3cf7369707219.xlsx"},{"id":67914762,"identity":"3caba4bb-f59c-43e4-a562-9a37069496c6","added_by":"auto","created_at":"2024-10-31 06:03:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5786984,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5075569/v1/d829552263754b7711a89c4f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease","fulltext":[{"header":"Background","content":"\u003cp\u003eTea plants (\u003cem\u003eCamellia sinensis\u003c/em\u003e (L.) O. Kuntze) typically grow in subtropical areas, where warm, humid weather is conducive to disease growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Tea cultivation has been the subject of more research in recent times because of its high nutritional content and financial significance. However, biotic and abiotic pressures pose several obstacles to the tea industry, making it difficult to produce enough tea to fulfill the rising demand. However, tea production faces several challenges caused by biotic and abiotic stresses, resulting in insufficient tea supply to meet the high demand [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pathogens can affect plant growth and fitness by modifying their host plant's resource availability, metabolism, and physiology [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Infected plants are expected to have reduced resources for growth and reproduction as defense is costly, and pathogens withdraw nutrients from the host [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. More than 500 diseases have been found in 37 countries worldwide, of which 138 have been reported in China [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among these, sooty mold disease (SM) caused by \u003cem\u003eCladosporium\u003c/em\u003e species was the most widespread and devastating in Shandong Province. The disease mainly affects tea leaves, covering diseased branches and leaves with a layer of black mold, affecting tea photosynthesis. In addition, sooty mold disease fungi colonize the leaves of plants and use honeydew secreted by \u003cem\u003eAleurocanthus spiniferus\u003c/em\u003e, scale insects, \u003cem\u003eAphidoidea\u003c/em\u003e, or the secretions of plants as a source of nutrition [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany secondary metabolites produced by tea plants are considered to represent the quality of tea and have been found to reduce disease infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The primary metabolic pathways for phenolic compounds involve phenylpropane and flavonoid metabolic pathways, which entail several enzymes' cooperative actions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Many genes related to the phenylpropanoid catabolic process are involved in plant resistance to disease. For example, phenylalanine ammonia-lyase, a critical enzyme, regulates the flow of primary metabolism into secondary metabolism, producing anti-microbial phytoalexins as a response to pathogen attacks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Plants use the metabolites produced by the phenylpropane metabolism pathway, such as lignin, pollen, anthocyanins, and organic acids, to regulate plant immunity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Lignin accumulation reinforces defensive structures, like the cell wall, by thickening lignin layers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The sugar alcohol mannitol is an essential carbohydrate with documented roles in metabolism and osmoprotection in many plants and fungi [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In mannitol-producing plants, the accumulation of mannitol is largely influenced by mannitol dehydrogenase (MTD). While a pathogen-induced MTD in the apparent absence of mannitol was initially unexpected, several lines of evidence suggest a unique role for MTD and mannitol in plant-pathogen interactions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, we employed hyperspectral to identify the phenotypes of tea leaves from different canopies infected with \u003cem\u003eCladosporium\u003c/em\u003e species. Further transcriptomic and metabolomic analyses were conducted to view the metabolites and gene profiles of the tea plant defense against SM disease. Hence, the overarching aim of this study was to examine the variations in infection by \u003cem\u003eCladosporium\u003c/em\u003e species on the leaves of different canopy layers of tea plants and to explore the immune molecular mechanisms of tea plants against sooty mold disease.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and sampling\u003c/h2\u003e \u003cp\u003eThe tea leaf samples were collected from a tea plantation in October 2023 in Linyi (N 36\u0026deg;65\u0026prime;, E 117\u0026deg;26\u0026prime;), Shandong Province, China. Three groups of samples at different canopies of tea plants were taken: up (A1), middle (A2), and bottom (A3), which were infected by \u003cem\u003eCladosporium\u003c/em\u003e species. This experiment was a randomized block design of three replicates, and all samples were immediately placed in liquid nitrogen and stored at -80\u0026deg;C for further processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHyperspectral image acquisition\u003c/h2\u003e \u003cp\u003eHyperspectral image data was obtained and calibrated using the procedure outlined by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The hyperspectral image data was collected using a hyperspectral camera (GaiaField-Pro-V10, Jiangsu Dualix Spectral Image Technology Co. Ltd, China) housed in a specially designed dark box cube. SpecVIEW software was used to apply lens and reflectance corrections to the data. The entire leaf sample was selected as the region of interest (ROI) after opening the preprocessed RAW format hyperspectral images with ENVI 5.3 (Research System Inc., Boulder, CO, USA). The ROI tool was then utilized to extract the samples' mean reflectance spectral data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction, library construction, and transcriptome sequencing\u003c/h2\u003e \u003cp\u003eThe transcriptome library was constructed using the RNAprep Pure Plant Kit (DP441, Tiangen, China), which is also used to extract total RNA. The transcriptome sequencing was carried out on the Illumina Novaseq6000 platform, with the GO enrichment and KEGG pathway analysis performed using the Metware Cloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.metware.cn\u003c/span\u003e\u003cspan address=\"https://cloud.metware.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the GO terms with corrected P-values (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) retained. The fragments per kilobase of transcript per million mapped reads (FPKM) method was used to estimate the gene expression levels. Log2 (Fold change) \u0026ge; |2| and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as a cut-off.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExtraction and analysis of non-volatile metabolites by UPLC-MS\u003c/h2\u003e \u003cp\u003eUsing vacuum freeze-drying technology, place the biological samples in a lyophilizer (Scientz-100F), then grind (30 Hz, 1.5 min) the samples to powder form by using a grinder (MM 400, Retsch). Next, weigh 50 mg of sample powder using an electronic balance (MS105DΜ) and add 1200 \u0026micro;L of -20\u0026deg;C pre-cooled 70% methanolic aqueous internal standard extract (less than 50 mg added at the rate of 1200 \u0026micro;L extractant per 50 mg sample). Vortex once every 30 min for 30 sec, for a total of six times. After centrifugation (rotation speed 12000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 \u0026micro;m pore size) and stored in the injection vial for UPLC-MS/MS analysis.\u003c/p\u003e \u003cp\u003eThe sample extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, ExionLC\u0026trade; AD; MS, Applied Biosystems 6500 Q TRAP). The analytical conditions were as follows: UPLC: column, AgilentSB-C18 (1.8 \u0026micro;m, 2.1 mm * 100 mm); The mobile phase consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1 min. Subsequently, a composition of 95% A and 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was 0.35 mL per minute; the column oven was set to 40\u0026deg;C, and the injection volume was 2 \u0026micro;L. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS.\u003c/p\u003e \u003cp\u003eThe ESI source operation parameters were as follows: source temperature 500\u0026deg;C; ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode); ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation (CAD) was high. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions were done with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExtraction and analysis of volatile metabolites by GC-MS\u003c/h2\u003e \u003cp\u003eSamples were ground to a powder in liquid nitrogen. 500 mg (1 mL) of the powder was transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA) containing a NaCl-saturated solution to inhibit any enzyme reaction. The vials were sealed using crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was placed at 60\u0026deg;C for 5 min, and then a 120 \u0026micro;m DVB/CWR/PDMS fiber (Agilent) was exposed to the sample's headspace for 15 min at 60\u0026deg;C. After sampling, desorption of the VOCs from the fiber coating was carried out in the injection port of the GC apparatus (Model 8890; Agilent) at 250\u0026deg;C for 5 min in the splitless mode.\u003c/p\u003e \u003cp\u003eThe identification and quantification of VOCs were carried out using an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent) equipped with a 30 m \u0026times; 0.25 mm \u0026times; 0.25 \u0026micro;m DB-5MS (5% phenyl-polydimethylsiloxane) capillary column. Helium was used as the carrier gas at a 1.2 mL/min linear velocity. The injector temperature was kept at 250\u0026deg;C and the detector at 280\u0026deg;C. The oven temperature was programmed from 40\u0026deg;C (3.5 min), increasing at 10\u0026deg;C/min to 100\u0026deg;C, at 7\u0026deg;C/min to 180\u0026deg;C, at 25\u0026deg;C/min to 280\u0026deg;C, and held for 5 min. Mass spectra were recorded in electron impact (EI) ionization mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set at 150, 230, and 280\u0026deg;C. The MS was selected in ion monitoring (SIM) mode to identify and quantify analytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCo-expression analysis\u003c/h2\u003e \u003cp\u003eThe R package WGCNA (Langfelder and Horvath, 2008) was used for co-expression network analysis. A gene expression adjacency matrix was developed to analyze the network topology with the following settings: soft threshold power, 18; TOMType, unsigned; and minModuleSize and mergeCutHeight, 50 and 0.25, respectively. The blockwise module function was used to obtain the modules using default settings. Cytoscape v.3.9.1 was used to visualize the gene co-expression networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was sampled for deep sequencing to validate the gene expression analysis results. qRT-PCR was performed as described previously. Genes were assayed using TB Green\u0026reg; Premix Ex TaqTM II (TaKaRa) on a CFX96 real-time PCR system (Bio-Rad). Three separate biological repeats were used for the analysis, and each biological replication was subjected to three sets of measurements to obtain technical replicates. The relative expression of genes was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, and the relative expression levels were presented as log2 fold changes. \u003cem\u003eCsGAPDH\u003c/em\u003e was used as the reference. The primers are listed in Table S6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChanges in hyperspectral characteristics during sooty mold disease infection\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the differences between different canopy layers, an average of the original spectrum was created (Fig.\u0026nbsp;1). The reflectance trends of the three canopy layers increased in the 397\u0026ndash;552 nm wavelength range and declined in the 552\u0026ndash;673 nm wavelength range. There was a noticeable variation in the reflectance of reaching the inflection point with canopy layers. The reflectance of all three canopy layers increased exponentially in the wavelength range of 673\u0026ndash;800 nm. Based on the spectral characteristics, the average spectral reflectance of the three canopy layers in the designated wavelength range displayed a similar overall pattern. In the spectral characteristics, three inflection spots were located close to the 552, 673, and 800 nm wavelengths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic analysis and screening of DEGs\u003c/h2\u003e \u003cp\u003eNine cDNA libraries were generated from the leaf samples at three canopy layers: A1, A2, and A3. Transcriptomic sequences were obtained after the removal of adapters and low-quality reads. An average of 44.8\u0026nbsp;million clean reads per sample were received, with a Q20 base composition percentage exceeding 97.56% and a Q30 base composition percentage exceeding 93.02%. The GC content measured greater than 43.56% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 2404 genes showed significantly differential expression under the log\u003csub\u003e2\u003c/sub\u003e (fold change) \u0026ge; |2| and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.5 (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The number of differentially expressed genes (DEGs) ranged from 322 (231 upregulated, 91 downregulated) between A3 and A2 to 1994 (1265 upregulated, 729 downregulated) between A3 and Al (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The number of upregulated DEGs was higher than the downregulated DEGs in A2 and A3 samples, showing that many DEGs were highly expressed in the leaves in down canopy layers. The overlap in gene expression revealed that numerous DEGs were found to be unique to each of the combinations. The most unique DEGs were identified at A3 vs A1 (1254), and the fewest were found at A3 vs A2 (78) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo further explore the functions of DEGs in response to sooty mold disease, Gene Ontology (GO) annotation was conducted. The most significantly enriched terms were oxidoreductase activity, secondary metabolite process, phenylpropanoid catabolic process, lipid biosynthetic process, and cell wall organization or biogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). An analysis of the enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the most enriched pathways were metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferent expression patterns of DEGs\u003c/h2\u003e \u003cp\u003eUnder pathogen infection, DEGs exhibit diverse expression patterns in tea plants. A heatmap with GO enrichment was employed to visualize the expression patterns of DEGs in the nine samples. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, many genes are differentially expressed in A1 samples. These DEGs were mainly enriched in carbohydrate binding, oxidoreductase activity, isoprenoid metabolic and biosynthetic processes, and terpenoid metabolic processes. In Cluster II, the DEGs were upregulated in A2 and A3 samples. The genes involved in the homeostatic process, secondary active transmembrane transporter activity, UDP-glycosyltransferase activity, and response to oxidative activity. In A3 samples, numerous genes associated with the secondary metabolic process, phenylpropanoid catabolic process, cell wall biogenesis, and flavonoid metabolic process exhibited induced expression in Cluster III. These findings show an elevated immune response in the A2 and A3 samples. In addition, UDP-glucosyltransferase and oxidoreductase activities are observed in all samples, suggesting their essential roles in the tea plant response to sooty mold disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome profiling of tea leaves infected by sooty mold disease\u003c/h2\u003e \u003cp\u003eA multivariate analysis method, PLS-DA, was performed to investigate metabolite changes under sooty mold disease infection. Score plots showed that the differentially expressed metabolites in A1, A2, and A3 were statistically separated (Fig.\u0026nbsp;4A). In total, 733 metabolites were detected (Table S3). The identified compounds include flavonoids (18.76%), phenolic acids (12.48%), amino acids and derivatives (6.34%), organic acids (5.01%), alkaloids ( 5.01% ), and others (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003eA heatmap was used to visualize the differentially expressed metabolites between different canopy layers infected by SM (Fig.\u0026nbsp;4C). A1 mainly drives the accumulation of alcohol, lipids (free fatty acids), hydrocarbons, and amino acids and derivatives. Much differently, flavonoids were predominantly upregulated in A2 and A3, particularly in A3. To elucidate the critical metabolites in tea plants that resist SM, scatter plots were used to display the differential metabolites between A2 and A3 as compared to A1 (Fig.\u0026nbsp;4D). The alkaloids, amino acids and derivatives, and flavonoids were significantly expressed in A2 samples compared with A1. Much differently, alcohol, aldehyde, ester, flavonoids, heterocyclic compounds, hydrocarbons, ketone, lignans, and coumarins were significantly enriched in A3 samples. The results suggest that A3 employs more metabolites to protect against SM than A2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between the DEGs and metabolites\u003c/h2\u003e \u003cp\u003eThe differentially expressed genes and metabolites were used to construct a network of six and three distinct modules with similar expression trends among different samples, respectively (Figs. S1 and S2). The differentially expressed metabolites and genes upregulated in A3 samples, which showed more defense against SM, were selected to construct the network.\u003c/p\u003e \u003cp\u003eWGCNA analysis demonstrates that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, Wound-induced protein, and uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) have significant relationships with the DEGs and metabolites (Table S4). The hub genes and metabolites were developed into a subnetwork comprising three groups linked to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S5). Out of the 98 genes, 40 genes in the biosynthesis of secondary metabolites category included cytochrome P450, gibberellin 20-oxidase, alcohol dehydrogenase, and mannitol dehydrogenase. The high expression of mannitol dehydrogenase was consistent with the enrichment of D-mannitol. The plant-pathogen interaction category includes numerous WRKY, LRR, and PR members. In addition, the genes related to auxin and ethylene show high enrichment in the plant hormone signal introduction category. These results suggested that the tea plant mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to defend against SM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR verification of DEGs\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the authenticity and reliability of the transcriptome data and the extent of DEGs. Nine DEGs across the network were selected for validation via qRT-PCR. Of these, Dormancy-associated protein 1 (CSS0035295), uncharacterized protein LOC114300808 (CSS0008624), wound-induced protein (CSS0009105), Serine/threonine-protein phosphatase (CSS0030286), and ABC transporter (CSS0044987) were hub genes. The mannitol dehydrogenase (CSS0031337) and cytochrome P450 (CSS0017190) were involved in the biosynthesis of secondary metabolites. The LRR receptor (CSS0020320) and pathogenesis-related protein (CSS0037023) were related to plant-pathogen interaction. As shown in Fig.\u0026nbsp;6, qRT\u0026ndash;PCR analyses showed the same expression trend for each of the analyzed candidates. These results suggest that the transcriptomic data were accurate and could be used for further functional analysis.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eSooty mold is an important plant disease in tropical and subtropical regions. It can affect plants' growth, development, and metabolism and reduce their commodity and economic value [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recently, SM disease has been detected in the tea-producing area of Shandong Province, China, and has severely affected the development of the local tea industry. Hence, exploring the effect and molecular mechanism of SM disease is useful for producing and breeding tea plants. In this study, we employed hyperspectral, transcriptome, and metabolome analysis to investigate the immune mechanisms of tea plants infected by SM.\u003c/p\u003e \u003cp\u003eHyperspectral imaging is a promising non-invasive technique for identifying plant diseases in agriculture, which has several benefits over conventional techniques [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hyperspectral imaging has demonstrated major benefits regarding objective, precise, non-destructive, and easily understandable plant disease diagnosis outcomes. With the help of hyperspectral imaging, disease images, and spectral data can be obtained without endangering crops [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Stresses on tea plants break down the chloroplast structure of the leaf, which increases reflectance and decreases matching band absorption [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, the red edge shifts in the direction of the shortwave due to a drop in chlorophyll concentration [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These damages can reduce the spectral reflectance in the near-infrared region. In this study, the average spectral reflectance in the designated wavelength range displayed a similar overall pattern while showing a noticeable variation in the reflectance of reaching the inflection point with the canopy layers. The results were consistent with the previous studies that hyperspectral imaging can non-destructively distinguish the different degrees of symptoms infected by SM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA total of 2404 genes demonstrated differential expression patterns, and most DEGs were exhibited in A3 samples compared with A1 and A2 samples. The significantly enriched GO terms were oxidoreductase activity, secondary metabolite process, phenylpropanoid catabolic process, lipid biosynthetic process, and cell wall organization or biogenesis. Phenolic compounds are essential for chemical resistance, as numerous studies have shown [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Once an infection has taken hold, these chemicals can eradicate dangerous microbes and the materials they create [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Under SM infection, numerous genes associated with the secondary metabolic process, phenylpropanoid catabolic process, cell wall biogenesis, and flavonoid metabolic process were upregulated in the A3 sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results suggest that tea plants resisted the invasion of pathogens by regulating the secondary metabolite content and cell wall thickness in severely SM-infected leaves, consistent with previous studies' findings [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sugar alcohol mannitol is an antioxidant, and current research suggests that pathogenic fungi can secrete mannitol into the plant\u0026rsquo;s extracellular spaces during infection to suppress reactive oxygen-mediated host defenses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In plants, the accumulation of mannitol is largely influenced by the activity of mannitol dehydrogenase [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Down-regulation of \u003cem\u003eMtd\u003c/em\u003e expression by salt, osmotic stress, and ABA results in decreased mannitol catabolism, with a concomitant accumulation of mannitol for osmoprotectant [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies also suggest that regulation of mannitol accumulation by MTD could influence initial events in plant-pathogen interaction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the co-expression network, the mannitol dehydrogenase (from the transcriptome) and D-mannitol (from the metabolism) were both enriched in the A3 sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The qRT-PCR results improved the mannitol dehydrogenase enriched in the A3 samples (Fig.\u0026nbsp;6). In this study, the D-mannitol (mws1155) is a hub gene linked to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to regulate tea plants' response to SM.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, fundamental insights into the defense genes and metabolites in the different SM-infected canopy layers of tea plants were acquired through integrated hyperspectral, transcriptomics, and metabolomics analyses. The primary findings of this study are outlined below: (1) The hyperspectral analysis indicated that the spectral characteristics of the three canopy layers significantly differed, and three inflection spots were located close to the 552, 673, and 800 nm wavelengths. (2) Transcriptome analysis showed that the DEGs were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction. (3) Integrated transcriptome and metabolomic analysis indicated that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) shown important roles in tea plants response to SM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSM\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;sooty mold\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp;\u0026nbsp;differentially expressed genes\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp;Kyoto encyclopedia of genes and genomes\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene ontology\u003c/p\u003e\n\u003cp\u003eFPKM\u0026nbsp;Fragments Per Kilobase per Million\u003c/p\u003e\n\u003cp\u003eFC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fold change\u003c/p\u003e\n\u003cp\u003ePCR\u0026nbsp; \u0026nbsp;\u0026nbsp;Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eqRT-PCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Real-time quantitative PCR\u003c/p\u003e\n\u003cp\u003eFDR\u0026nbsp; \u0026nbsp;\u0026nbsp;False discovery rate\u003c/p\u003e\n\u003cp\u003eMTD\u0026nbsp; \u0026nbsp;mannitol dehydrogenase\u003c/p\u003e\n\u003cp\u003eROI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;region of interest\u003c/p\u003e\n\u003cp\u003eDP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;declustering potential\u003c/p\u003e\n\u003cp\u003eCE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;collision energy\u003c/p\u003e\n\u003cp\u003eCAD \u0026nbsp; collision-activated dissociation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWSS\u0026nbsp;and DZT conceived and designed the research. XY and SJZ do the hyperspectral data collection and analysis. WSS, XY, SJZ, and CH collected the plant materials. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Shandong Province (ZR2021QC092).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. All methods were carried out according to relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequence data for small RNAs, degradome, and transcriptome in this study are available at the NCBI Sequence Read Archive (http://www.ncbi.nlm. nih.gov/sra) under accession number PRJNA1161365.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China. \u003csup\u003e2\u0026nbsp;\u003c/sup\u003eTea Research Institute, Qingdao Agricultural University, Qingdao, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLu R, Su J, Li T, Sun J, Wu G. Molecular regulation of immunity in tea plants. Mol Biol Rep. 2023;50(3):2883\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi XB, Niu GQ, Fan YH, Liu WY, Wu Q, Yu C, Wang J, Xiao YH, Hou L, Jin D et al. Synthetic dual hormone-responsive promoters enable engineering of plants with broad-spectrum resistance. Plant Commun 2023, 4(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo H, Chen J, Lv Z, Shao C, Chen Z, Zhou Y, Shen C. Tea-Derived Polyphenols Enhance Drought Resistance of Tea Plants (Camellia sinensis) by Alleviating Jasmonate\u0026ndash;Isoleucine Pathway and Flavonoid Metabolism Flow. Int J Mol Sci. 2024;25(7):3817.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Yu Y, Qian X, Zhang X, Li X, Sun X. Recent Advances in the Specialized Metabolites Mediating Resistance to Insect Pests and Pathogens in Tea Plants (Camellia sinensis). Plants. 2024;13(2):323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDijk LJAv, Ehrl\u0026eacute;n J, Tack AJM. Direct and insect-mediated effects of pathogens on plant growth and fitness. J Ecol. 2021;109(7):2769\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenanc\u0026eacute; N, S\u0026aacute;nchez-Vallet A, Goffner D, Molina A. Disease resistance or growth: the role of plant hormones in balancing immune responses and fitness costs. Front Plant Sci. 2013;4:155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey AK, Sinniah GD, Babu A, Tanti A. How the Global Tea Industry Copes With Fungal Diseases - Challenges and Opportunities. Plant Dis. 2021;105(7):1868\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z. Chemical ecology of tea pests. Shanghai Science and Technology; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThangaraj K, Liu S, Li J, Zhao Z, Han R, Mei H, Jeyaraj A, Chen X, Li X. Exogenous melatonin alleviates sooty mould on tea plants (Camellia sinensis L). Sci Hort. 2022;299:111056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng S, Du Z, Wang X, Zheng C, Wang Z, Yu X. Metabolic Rewiring in Tea Plants in Response to Gray Blight Disease Unveiled by Multi-Omics Analysis. Metabolites. 2023;13(11):1122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan S, Cai H, Yu H, Yu Z, Wu Y. Targeted metabolomic and transcriptomic reveal the regulation of UV\u0026ndash;C on phenolics biosynthesis of peach fruit during storage. LWT. 2023;190:115573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Wang W, Li Y, Dai X, Ma G, Xing D, Zhu M, Gao L, Xia T. Six phenylalanine ammonia-lyases from Camellia sinensis: Evolution, expression, and kinetics. Plant Physiol Biochem. 2017;118:413\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Chakraborty P, Mandal P, Saha D, Saha A. Phenylalanine ammonia-lyase gene induction with benzothiadiazole elevates defence against Lasiodiplodia theobromae in tea in India. J Phytopathol. 2017;165(11\u0026ndash;12):755\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePieterse CMJ, Leon-Reyes A, Van der Ent S, Van Wees SCM. Networking by small-molecule hormones in plant immunity. Nat Chem Biol. 2009;5(5):308\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSels J, Mathys J, De Coninck BMA, Cammue BPA, De Bolle MFC. Plant pathogenesis-related (PR) proteins: A focus on PR peptides. Plant Physiol Biochem. 2008;46(11):941\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson JD, Desai A, Krasnyanski SF, Ding F, Guo W-w, Nguyen T-T, Olson HA, Dole JM, Allen GC. Overexpression of mannitol dehydrogenase in zonal geranium confers increased resistance to the mannitol secreting fungal pathogen Botrytis cinerea. Plant Cell Tissue Organ Cult (PCTOC). 2013;115(3):367\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Mao Y, Li H, Sun L, Wang S, Li X, Shen J, Yin X, Fan K, Ding Z, et al. A deep learning model for rapid classification of tea coal disease. Plant Methods. 2023;19(1):98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonmurugan P, Manjukarunambika K, Gnanamangai BM. Impact of various foliar diseases on the biochemical, volatile and quality constituents of green and black teas. Australas Plant Pathol. 2016;45(2):175\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonmurugan P, Baby UI, Rajkumar R. Growth, photosynthetic and biochemical responses of tea cultivars infected with various diseases. Photosynthetica. 2007;45(1):143\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli MM, Bachik NA, Muhadi NA, Tuan Yusof TN, Gomes C. Non-destructive techniques of detecting plant diseases: A review. Physiol Mol Plant Pathol. 2019;108:101426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu Y-Y, Feng Y-Z, Sun D-W. Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Compr Rev Food Sci Food Saf. 2015;14(2):176\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Shen S. Effects of temperature and strain on thermal properties of Ni/Al laminated structure. Comput Mater Sci. 2014;84:13\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Zhang J, Huang Y, Tian Y, Yuan L. Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis. Comput Electron Agric. 2022;193:106717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShalaby S, Horwitz BA. Plant phenolic compounds and oxidative stress: integrated signals in fungal\u0026ndash;plant interactions. Curr Genet. 2015;61(3):347\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholson RL, Hammerschmidt R. Phenolic compounds and their role in disease resistance. Annu Rev Phytopathol. 1992;30(1):369\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zhang Z, Ahammed GJ, Wang X, Fang H, Yan P, Zhang L-P, Ge S, Fu J, Han W-Y, et al. 24-Epibrassinolide Enhances Resistance Against Colletotrichum fructicola by Promoting Lignin Biosynthesis in Camellia sinensis L. J Plant Growth Regul. 2023;42(3):1558\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanaeifar A, Ye D, Li X, Luo L, Tang Y, He Y. A Spatial-Temporal Analysis of Cellular Biopolymers on Leaf Blight-Infected Tea Plants Using Confocal Raman Microspectroscopy. Front Plant Sci. 2022;13:846484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Liu L, Mi X, Zhao S, An Y, Xia X, Guo R, Wei C. Multi-omics analysis to visualize the dynamic roles of defense genes in the response of tea plants to gray blight. Plant J. 2021;106(3):862\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Liu H, Wang Y, Chen J, Qiu Z, Zheng Y, Sun B, Tan X, Shu C, Liu S, et al. Study on the Effect of Sooty Mould Disease in Tea Plants. Plants. 2024;13(16):2321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoop JMH, Williamson JD, Pharr DM. Mannitol metabolism in plants: A method for coping with stress. Trends Plant Sci. 1996;1(5):139\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamski E, Guo WW, Yamamoto YT, Pharr DM, Williamson JD. Analysis of celery (\u0026lt;\u0026thinsp;i\u0026thinsp;\u0026gt;\u0026thinsp;Apium graveolens) mannitol dehydrogenase (\u0026lt;\u0026thinsp;i\u0026thinsp;\u0026gt;\u0026thinsp;Mtd) promoter regulation in \u0026lt;\u0026thinsp;i\u0026thinsp;\u0026gt;\u0026thinsp;Arabidopsis\u0026thinsp;suggests roles for MTD in key environmental and metabolic responses. Plant Mol Biol. 2001;47(5):621\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrant M, Lamb C. Systemic immunity. Curr Opin Plant Biol. 2006;9(4):414\u0026ndash;20.\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":"tea plants, sooty mold disease, resistance, hyperspectral, transcriptomic, metabolomic","lastPublishedDoi":"10.21203/rs.3.rs-5075569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5075569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSooty mold (SM) is one of the most destructive diseases of tea plants, causing considerable damage and productivity losses. However, the roles of defense genes and metabolites in different SM-infected canopy layers of tea plants remain largely unclear. To investigate the immune mechanisms of tea plants, we utilized hyperspectral, transcriptomic, and metabolomic data from leaves of three canopy layers infected by SM (A1, A2, and A3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe hyperspectral analysis indicated that the spectral characteristics of the three canopy layers significantly differed, and three inflection spots were located close to the 552, 673, and 800 nm wavelengths. Transcriptome analysis showed that the differentially expressed genes were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, and plant-pathogen interaction. The cluster analysis revealed an elevated immune response in the A2 and A3 samples. A total of 733 metabolites were co-changed in leaves infected by SM, with alcohol, lipids (free fatty acids), hydrocarbons, and amino acids significantly accumulating in A1, while flavonoids were predominantly upregulated in A2 and A3. WGCNA analysis revealed that five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) have significant relationships with the DEGs and metabolites. Further co-expression showed that tea plants mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to defend against SM.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study established a foundation for elucidating the immune mechanisms of different canopy layers of tea plants infected by SM, and provided candidate genes and metabolites for improving tea plant breeding.\u003c/p\u003e","manuscriptTitle":"Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-31 06:03:01","doi":"10.21203/rs.3.rs-5075569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-20T11:56:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-20T11:52:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-19T04:31:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-09-12T07:18:57+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":"4006eceb-c5d6-41fc-a307-6e4ca0ce0e7d","owner":[],"postedDate":"October 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T15:58:43+00:00","versionOfRecord":{"articleIdentity":"rs-5075569","link":"https://doi.org/10.1186/s12870-024-05806-x","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-11-15 15:56:51","publishedOnDateReadable":"November 15th, 2024"},"versionCreatedAt":"2024-10-31 06:03:01","video":"","vorDoi":"10.1186/s12870-024-05806-x","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05806-x","workflowStages":[]},"version":"v1","identity":"rs-5075569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5075569","identity":"rs-5075569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}