Mechanism Underlying Flavor Quality Formation during Withering Process of Niangniang Tea, a Compressed Large-Leaf Yellow Tea

Mechanism Underlying Flavor Quality Formation during Withering Process of Niangniang Tea, a Compressed Large-Leaf Yellow Tea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mechanism Underlying Flavor Quality Formation during Withering Process of Niangniang Tea, a Compressed Large-Leaf Yellow Tea Yanxia Wang, Hao Guan, Li Lu, Yunan Zhao, Jinjie Shi, Xiaosong Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5265030/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Niangniang tea (NNT) is a traditional compressed large-leaf yellow tea shaped as Chinese writing brush. Withering time affects the flavor characteristics. The sensory evaluation revealed the highest score was achieved at 8 hours of withering (Yd). Metabolomics analysis revealed a decrease in 18 bitter metabolites, accompanied by an increase in sweet metabolites and amino acids during the withering process. Transcriptome analysis showed, the relative expression of CsSCPL (Serine carboxypeptidase-like) decreased rapidly, while the relative expression of CsTA (Tannase) showed an increasing trend, which inhibited the acylation of non-ester type catechins to ester type, and promoted the conversion of bitter and heavy ester type catechins to non-ester type catechins with lighter bitterness. The withering process of NNT reduced the bitter taste but enhanced sweetness, and the tea tastes more sweet and mellow. Metabolomics and transcriptomics result conducive to a more comprehensive and systematic understanding of the formation mechanism of flavor quality in the withering process. Biological sciences/Chemical biology/Metabolomics Physical sciences/Chemistry/Biochemistry/Dna Yellow tea Flavor Withering time Metabolites Transcriptome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Tea is renowned for its health benefits, primarily attributed to its abundant and diverse secondary metabolites (Liao et al., 2022). Fermentation processing is an important factor in the change of secondary metabolism content, which affects the taste and aroma of tea (Sun et al., 2024). Based on the level of fermentation, tea is categorized into various types, including green tea, yellow tea, white tea, oolong tea, black tea, and dark tea. The biotransformation of chemical components in tea leaves after various processing makes tea have unique flavor and biological activity (Leung et al., 2016; Liu et al., 2013). Yellow tea is a unique historical tea in China and has gradually become popular in the world due to its unique flavor and health function (Kujawska et al., 2016). Niangniang tea (NNT) is a kind of compressed large-leaf yellow tea origin in Guizhou province from Ming Dynasty. Its shape is similar to that of Chinese writing brush, and it is also called ‘champion pen’ (Fig. 1 a). Traditional NNT is made from one bud with three to four leaves of the local ancient tea plant ( Camellia sinensis (L.) O.Kuntze), generally about 3–4 inches long. After fixed, rolled, and molded into the shape of Chinese writing brush, and then stringed, dried with charcoal fire or directly in the sun. The dried NNT was with rich aroma and bright orange liquor. Withering is the first step in the manufacture of NNT. The withering time is usually 8–12 h. Withering processing can increase the concentration of tea cell fluid, enhance the activity of hydrolase and oxidase, promote the oxidative degradation of compounds, and increase the role of water-soluble products. It has an important effect on the bitter, astringent and sweet taste of tea(Xu et al., 2018). Metabolomics and transcriptomics are powerful tools for simultaneous measurement of hundreds of differential metabolites and related gene expression, and have been widely used in food and beverage analysis and quality assessment (Zhang et al., 2022; Zhou et al., 2022). The withering process of NNT was studied by metabolomics and transcriptomics, aiming to elucidate the changes of metabolites in the withering processing from the perspective of omics, and to explore the effect of withering on the flavor of NNT, so as to provide a basis for further optimization of NNT processing. Results and Discussion Effects of withering processing on the sensory characteristics of NNT Withering is an essential process affecting the flavor of NNT. Different withering time NNT were sensory evaluated. A s showed in Fig. 1 , there was no significant difference in the appearance of the dried tea, the liquor color, the aroma and the taste although are significantly different. Especially the aroma and the taste, with the increasing of withering time, the NNT showed a sweet flavor, both sweet aroma and sweet taste. Of which, Yd got a higher score than the other treatments. These results indicated that the withering time of fresh leaf could affect the flavor quality of NNT, and the best withering time may be 8 hours. However, the sweet metabolite compositions and the mechanisms that regulate the sweet flavor formation during withering of NNT remain unclear. For this reason, we performed metabolomic and transcriptomic analysis to elucidate these mechanisms. Metabolomic analysis of Niangniang tea Non-targeted liquid chromatography-mass spectrometry (UPLC-MS/MS) was used to analyze CK and 15 NNT samples at five different withering stages in positive and negative ion modes. During the withering processing, the tea leaves gradually lost water, and the morphology changed significantly (Fig. 2 a). The generated ion chromatograms show that the data have high overlap and good sample preparation process (Fig. 2 b). The high Pearson correlation R 2 > 0.95 indicated that the whole process was stable and reliable (Fig. 2 c). QC samples were added to the test to evaluate the metabolomics performance. Principal component analysis was performed on the samples to determine the overall metabolite differences between the groups and the changes in metabolites within the group. The score plot showed that the QC samples were clustered in the center, and the three repeated distances of the samples in each time period were close to each other, indicating that the metabolomics analysis was repeatable and reliable (Fig. 2 d). The samples in different withering periods in the PCA diagram were completely separated, indicating that the withering caused a significant change in the metabolite profile. Identification of differentially accumulated metabolites (DAMs) After peak extraction and comparison, 622 cationic differential metabolites and 442 anionic differential metabolites were obtained in the positive and negative ion modes, respectively. The cationic mode could detect more compounds than the anionic mode. Therefore, the cationic differential metabolites were selected for analysis in subsequent experiments. The 622 DAMs were divided into 10 different categories (117 Lipids and lipid-like molecules, 87 Phenylpropanoids and polyketides, 66 Organic acids and derivatives, 63 Organ heterocyclic compounds, 41 Benzenoids, 31 Organic oxygen compounds, 21 Nucleosides, nucleotides, and analogues, 10 Alkaloids and derivatives, 9 Organic nitrogen compounds, 7 Lignans, neolignans and related compounds. In addition, 170 substances have not been classified). The quantitative results and annotations of all DAMs are shown in Supplementary table S1 . Figure 2 a, b shows an overview of the tea metabolite profiles at different withering stages (Fig. 3 a, b). It is worth noting that Phenylpropanoids and polyketides are the second most abundant DAMs in different withering periods. Polyphenols are one of the most concerned secondary metabolites in tea, which have important medicinal and edible values. Organic acids were the third most abundant DAMs found during the entire withering period, which may play an important role in the formation of ester compounds. Through KEGG enrichment analysis, DAMs were clustered into different pathways to explore the physiological changes during the withering process. 622 differential metabolites were enriched in 13 KEGG pathways. Among them, 81 DAMs were enriched in the Global and overview maps pathway; forty DAMs were enriched in the Biosynthesis of other secondary metabolites pathway. A total of 26 DAMs were enriched in the Amino acid metabolism pathway (Fig. 3 c). The DAMs of tea leaves with different withering time (Ya-e) were compared with those of control check (CK). Venn diagram showed that there were 27 common DAMs in all groups. Venn diagram showed that there were 27 DAMs in all groups (Fig. 3 d). Comparing the overlapping metabolites of each treatment, many DAMs showed time-dependent regulation during the whole withering process of NNT. Influence of withering on the quality development of NNT Differential analysis showed that 87 differential metabolites were up-regulated and 54 differential metabolites were down-regulated in Ya/CK, which were enriched in KEGG pathways such as ABC transporters and stilbenoid, diarylheptanoid and gingerol biosynthesis (Fig. 4 a); There were 92 differential metabolites up-regulated and 53 differential metabolites down-regulated in Yb/CK, which were enriched in KEGG pathways such as Aminoacyl-tRNA biosynthesis and ABC transporters (Fig. 4 b); There were 87 differential metabolites up-regulated and 69 differential metabolites down-regulated in Yc/CK, which were enriched in KEGG pathways such as ABC transporters and Aminoacyl-tRNA biosynthesis (Fig. 4 c); There were 99 differential metabolites up-regulated and 78 differential metabolites down-regulated in Yd/CK, which were enriched in KEGG pathways such as Biosynthesis of secondary metabolites and Phenylpropanoid biosynthesis (Fig. 4 d); There were 139 differential metabolites up-regulated and 76 differential metabolites down-regulated in Ye/CK, which were enriched in KEGG pathways such as Metabolic pathways and ABC transporters (Fig. 4 e). These results indicate that the longer the withering time, the more differential metabolites. Biosynthesis of secondary metabolites metabolic pathway regulates secondary metabolites and affects flavanols and flavone glycosides related to bitterness and astringency. The regulation of ABC transporters can change some sugars, sugar alcohols and amino acids related to sweetness, thus affecting the sweet flavor quality of NNT. A large number of differential metabolites with flavor characteristics were identified by analyzing the metabolites with flavor characteristics during the withering process. The down-regulated bitter metabolites were valine, 4-hydroxyisoleucine, L-phenylalanine, L-lysine, adenosine, kaempferol, quercetin, myricetin, isorhamnetin, rhamnetin, laricitrin, nicotinamide, riboflavin, adenosine, guanine, 2-(dimethylamino) guanosine, catechins and so on (Du et al., 2023; Sun et al., 2024). The astringent metabolites with down-regulated content are protocatechuic acid and 4-hydroxybenzoic acid. At the same time, flavonols and flavonol glycosides such as bitter quercetin and catechins also have important effects on astringency (Qin et al., 2020). Theaflavin, theaflavine-3,3-digallate, theophylline, catechins and other substances have a sense of folding convergence, and the taste threshold is low. Identification analysis found that their content has also been reduced to varying degrees. The content of sweet metabolite L-alanine and 3'-adenosine monophosphate (3'-AMP) was up-regulated. The results showed that, the bitter taste reduced and the sweetness enhanced in NNT during the withering process, making the tea taste sweeter (Supplementary table S1 ). Changes of tea polyphenols, catechins and caffeine contents at different withering time L-theanine, catechin and caffeine are the most concerned secondary metabolites in tea. They jointly regulate the taste of tea. Studies showed that theanine has a significant improvement effect on the umami taste of tea. The content of theanine showed a downward trend during the withering process, and the decrease became larger at 8h (Fig. 5 a). It was speculated that the theanine was hydrolyzed. Caffeine is one of the main components of the bitter and astringent taste of tea. The content of caffeine reached the highest at 4h, but then decreased, and showed a no significant change during the whole withering process (Fig. 5 b). Tea polyphenols were the main active compounds in tea, composed of catechins, flavonols, anthocyanins, deep phenols and polymeric phenols. Among them, catechins account for 65–80% of total tea polyphenols, which is the mainly causing tea bitterness. Catechins were the general term for soluble flavan-3-ols in tea, including (+)-catechin (C), (−)-gallocatechin (GC), (−)-catechin gallate (CG), (−)-gallocatechin gallate (GCG), (−)-epicatechin (EC), (−)-epigallocatechin (EGC), (−)-epicatechin-3-gallate (ECG) and (−)-epigallocatechin-3-gallate (EGCG) (Sajilata et al., 2008)。According to the complexity of the molecular structure, catechins are divided into ester type catechins and non-ester type catechins. Ester type catechins include CG, GCG, ECG and EGCG, which have strong bitterness and astringency. They are the main flavor components of tea and easily oxidized. In tea plants, the content of gallic acid catechins is much higher than that of non-gallic acid catechins. Ester type catechins are much more abundant than non-ester type catechins in tea plants. With the progress of withering, the content of ester type catechins showed a downward trend. Non-ester type catechins including C, GC, EC and EGC with simple molecular structure, its bitter taste is lower. With the process of withering, the content of non-ester type catechins increased first and then decreased. There is a high point at 4h, in which the content of EGC accounts for the largest proportion. The change trend of content is consistent with that of non-ester type catechins, and the content also has a high point at 4h (Fig. 5 c). The content of C reached the highest at 8h, while EC was different from the first two substances, and its content showed a downward trend with withering (Fig. 5 d). It showed that the content of ester type catechins decreased gradually, the content of non-ester type catechins increased first and then decreased, and the content of gallic acid also showed an increasing trend. According to this, it is speculated that during the withering process, the enzymatic reaction is active, and ester type catechins may be catalyzed by polyphenol oxidase to generate non-ester type catechins or other substances. The bitter and astringent taste of tea is weakened, and the mellow taste is promoted. Transcriptomic analysis of Niangniang tea In order to further understand the physiological process of metabolite transformation during the withering process, we performed transcriptome sequencing and comparison. In the comparison of Ya/CK, Yb/Ya, Yc/Yb, Yd/Yc and Ye/Yd, differentially expressed genes (DEGs) were identified. As shown in Table 1 , the number of up-regulated and down-regulated DEGs at 2h was much higher than that at 10h. It can be inferred that a large number of transcription and translation may be activated or inhibited during the withering process. The down-regulation of DEGs at the beginning of withering (Ya/CK) and the late stage of withering (Ye/Yd) was greater than the up-regulation, while the DEGs at the middle stage of withering (Yb/Ya, Yc/Yb, Yd/Yc) were opposite, and the up-regulation was greater than the down-regulation, indicating that many physiological processes were activated during the withering process. Through the overlapping analysis of up-regulated and down-regulated DEGs in the withering process, it was found that many DEGs were always up-regulated or down-regulated. These genes may play a vital role in the transformation of metabolites of tea during the withering process, so as to affect the taste and quality of tea. The results showed that the differential expression of genes during the withering changed greatly in a time-dependent manner, which was consistent with the results of metabolomics. Table 1 The differentially expressed genes of Ya/CK, Yb/Ya, Yc/Yb, Yd/Yc and Ye/Yd were analyzed. Ya/CK Yb/Ya Yc/Yb Yd/Yc Ye/Yd All-differential gene 6677 4666 3638 2539 1532 Up- differential gene 3198 2406 1483 1287 745 Down-differential gene 3479 2260 2155 1252 787 DEGs were enriched into different pathways by KEGG enrichment analysis. A total of 2265, 1495, 1348, 858, and 881 DEGs were annotated on the 120, 115, 113, 108, and 104 pathways of Ya / CK, Yb / Ya, Yc / Yb, Yd / Yc, and Ye / Yd, respectively. These pathways were divided into 5 categories, and the number of DEGs included in their secondary classification was analyzed. The most DEGs were enriched in 88 pathways of Metabolism classification (Fig. 6 ). Among them, DEGs in Glycolysis /Gluconeogenesis, Starch and sucrose metabolism, Amino sugar and nucleotide sugar metabolism, Cysteine and methionine metabolism and Oxidative phosphorylation pathways were down-regulated more. The DEGs of Phenylpropanoid biosynthesis, Cysteine and methionine metabolism, Phenylpropanoid biosynthesis, Starch and sucrose metabolism and Galactose metabolism pathways were up-regulated more, indicating that a large number of metabolic activities were more active during the withering process, and a large number of metabolites were transformed. A large number of DEGs genes were up-regulated or down-regulated in the Ribosome, RNA degradation, Spliceosome and Nucleocytoplasmic transport pathways, indicating that there was a large amount of protein synthesis and decomposition during the withering process. The reason is that a large number of messenger RNAs are synthesized at specific genomic sites in the nucleoplasm during the synthesis and decomposition of substances, and are transported to specific cytoplasmic locations at the appropriate time, and proteins are locally produced in the cytoplasm (Basyuk et al., 2021). In Cellular Processes, the Protein processing in endoplasmic reticulum pathway enriched the most DEGs during the withering process, followed by the Endocytosis pathway, which further verified that there was a large amount of protein synthesis, decomposition and transportation during the withering process. Effect of withering processing on L-theanine biosynthesis The integrated analysis of transcriptomics and metabolomics is expected to elucidate the regulatory mechanism of NNT in the process of withering, further clarify the mechanism of DEGs regulating important secondary metabolites, and construct the pathway diagram of DEGs in theanine (Fig. 7 ) and catechin pathway (Fig. 8 ). The coding genes of metabolic enzymes involved in theanine synthesis and hydrolysis were identified and analyzed. The expression of genes involved in theanine synthesis was positively correlated with theanine content to a large extent (Fig. 7 ). CsALT , CsAIDA , CsGDH , CsGOGAT , CsTS and CsGS were structural genes encoding enzymes related to theanine synthesis (no specific enzyme encoded by AIDA was found in tea plant. CsSAMDC and CsADC shared similar domains with CsAIDA were found). Most of their gene expression decreased with the increasing of withering time, that is, the withering process inhibited the accumulation of theanine in tea (Z.-W. Liu et al., 2017; Zhang et al., 2015). Under the action of theanine hydrolase, theanine decomposed into L-glutamic acid and ethylamine (Chang et al., 2023; Yu et al., 2020). CsThYD and CsAO encode enzymes that hydrolyze theanine. We found that most of the transcripts of CsAO had a high expression level after a period of withering, and the expression of CsAO promoted the hydrolysis of theanine. Previous studies have shown that CsPDX2.1 (L-theanine hydrolysis gene, CSS003722 ) was the key gene for L-theanine metabolism during the withering process (Cheng et al., 2022). With the withering progress of NNT, its relative expression increased first and then decreased, reaching a peak at 8h. The theanine content decreasing data in the previous metabolomics analysis was consistent with the change of CsPDX2.1 expression. Effect of withering processing on catechin biosynthesis Genes encoding metabolic enzymes involved in catechin synthesis and hydrolysis in NNT withering were identified and analyzed. These genes include CsPAL , CsC4H , Cs4CH , CsCHS , CsCHI , CsF3H , CsF3'5'H , CsDFR , CsLAR , CsANS , CsANR (Fig. 8 ). The relative expression levels are shown in the figure: the relative expression levels of CsPAL , CsC4H , CsCHS , CsCHI , and CsF3H showed a downward trend. The relative expression levels of Cs4CH and CsF3H increased first and then decreased. These genes are also located in the flavonoid synthesis pathway and are widely present in other species. The complexity of flavonoid biosynthesis and the presence of gene isomers, so the expression levels of these genes are not necessarily related to the content of catechins (Wu et al., 2014). CsLAR , CsANS and CsANR are unique in the synthesis pathway of catechins. They were involved in the synthesis of non-ester type catechins in tea plants, and their relative expression levels show a decreasing trend during the withering, and jointly regulate and control the accumulation of non-ester type catechins in NNT. Non-ester type catechins are widely distributed, while ester type catechins (ECG and EGCG) are only abundant in tea plants. In addition, galloylated catechins are much more abundant than nongalloylated catechins in tea plants (Kim et al., 2004; Liao et al., 2022). Therefore, non-ester type catechins and ester type catechins have mutual transformation in tea plants. CsSCPL has high acyltransferase activity and can catalyze the work of gallic acylation of EC and EGC (Yao et al., 2022). The results showed that the relative expression of CsSCPL was rapidly reduced during the withering, which reduced the conversion of non-ester type catechins to Ester type catechins. CsTA ( C. sinensis tannase) is a type of tannin acyl-hydrolase hydrolyzing HTs, CT monomer gallates and depsides, which is involved in the hydrolysis of EGCG and ECG to EGC and EC. Through literature review, we obtained the CDS sequence of CsTA in tea plant (NCBI database accession number: MK381269) (Dai et al., 2020). It was used to align with the tea plant genome and obtained 6 copies. The metabolome data showed that the relative expression of four copies of CsTA increased with the increase of withering time, while the relative expression of two copies of CsTA increased first and then decreased. The process of withering promoted the hydrolysis of EGCG and ECG into EGC and EC in tea. Effect of withering processing on the flavor formation of NNT Suitable withering processing can reduce the grass aroma and lead to the formation of sweet aroma and taste. UPLC-MS/MS combined with sensory review was used to analyze the changes of composition and sweet flavor of ‘Niangniang tea’ during the withering process. The expression of genes related to the synthesis of L-theanine and catechins, which are important secondary metabolites was further studied by transcriptome. The analysis of differential metabolites showed that the content of 18 bitter metabolites decreased during the withering process, mainly nicotinamide, flavonols and flavonol glycosides, and the contents of protocatechuic acid and 4-hydroxybenzoic acid with obvious astringency also showed a downward trend. On the contrary, the sweet metabolite L-ascorbic acid, 3 ' -adenosine monophosphate (3'-AMP) content increased. All in all, the withering process of NNT reduced the bitter taste, and its sweetness was enhanced, making the NNT more sweet and mellow. Sensory evaluation proved it. The integrated analysis of transcriptomics and metabolomics provides an idea for elucidating the regulatory mechanism of withering reaction. The identified metabolites were mainly Fatty Acyls, Prenol lipids, Flavonoids, Isoflavonoids, Amino acids, peptides, and analogues, which are the most concerned secondary metabolites in tea, and also the signal of metabolism and development process during tea withering, producing various molecules and affecting the flavor quality of tea (Chen, 2021; Zuo et al., 2023). Flavonoids and isoflavonoids have a significant impact on the flavor of tea, and are major contributors to astringency and bitterness (Ahmad et al., 2020; Wilson et al., 2016). Amino acids, peptides, and analogues are important components of the umami taste in tea (Shao et al., 2022). Accordingly, the withering process increased the concentration of tea cell sap, enhanced the activity of hydrolase and oxidase, promoted the oxidative degradation of compounds, and increased the effect of water-soluble products. At the same time, starch, protein, protopectin and other substances undergo decomposition and transformation reactions to generate glucose, amino acids, soluble pectin, etc., and polyphenols also undergo oxidation reactions. Metabolomics analysis showed that many DEGs were found enriched in Ribosome, RNA degradation, Spliceosome, Protein processing in endoplasmic reticulum pathway. Ribosome and RNA degradation are the beginning of protein synthesis. Endoplasmic reticulum is an important place for the synthesis, processing and sorting of various enzymes in eukaryotic cells (Qu et al., 2021). Therefore, these pathways have an important relationship with protein synthesis, decomposition and transport. It is speculated that a large amount of protein synthesis and decomposition are accompanied during the withering process. A large number of DEGs expressing in these pathways during the withering process activates various enzymes, participates in the transformation process of various substances, and improves the flavor of tea. For example, protease is involved in the degradation process of protein, which converts the protein into amino acids and increases the sweet taste. Amylase is involved in the degradation process of starch, which converts starch into soluble sugar and also improves the sweetness of tea. L-theanine and catechins are the core metabolites that determine the flavor of tea. During the withering process, the content of L-theanine and catechin showed a downward trend. The results showed that the expression levels of L-theanine synthesis genes such as CsALT , CsAIDA , CsGDH , CsGOGAT , CsTS , CsGS , CsSAMDC and CsADC decreased during the processing of withering. It is speculated that withering may reduce the accumulation of L-theanine by reducing the expression of theanine synthesis genes. Cheng proved that water loss in tea is a key factor leading to the hydrolysis of L-theanine, and the key gene for hydrolysis is CsPDX2.1 (Cheng et al., 2022). The results showed that the relative expression of CsPDX2.1 increased first and then decreased, that is, the hydrolysis rate of L-theanine showed an accelerated trend during the withering, which was consistent with the decrease of L-theanine content. Previous studies have showed that L-theanine is mainly synthesized by glutamic acid and ethylamine in the roots of tea plants, and then transported to the buds through amino acid permease family members. The hydrolysis of L-theanine occurs in the leaves (Ashihara, 2015; Fu et al., 2020). In summary, the main reason for the decrease of L-theanine accumulation during the withering process is the hydrolysis of L-theanine. Amino acids (including L-theanine) are the main source of sweet taste of tea, and the decrease of L-theanine content will weaken the umami taste of tea infusion. However, the hydrolysis product of L-theanine exists glutamic acid, and the degradation of protein during the withering process increases the content of other amino acids, so the withering makes the NNT taste sweeter. Catechins play an important role in bitter taste of tea. It showed that the relative expression level of CsLAR , CsANS and CsANR decreased during the withering process, while the content of non-ester type catechins increased first and then decreased, which was inconsistent with the expression levels of CsLAR , CsANS and CsANR . Yao showed that ester type catechins were synthesized by galloylation of nonester type catechins with CsSCPL (Yao et al., 2022). Dai et al. demonstrated that CsTA can catalyze the hydrolysis of ester type catechins to non-ester type catechins (Dai et al., 2020). The results showed that the relative expression of CsSCPL decreased rapidly and the relative expression of CsTA showed an increasing trend during the withering process. The withering inhibited the acylation of non-ester type catechins to ester type catechins and promoted the hydrolysis of ester type catechins to non-ester type catechins. As an important enzyme in tea, polyphenol oxidase participates in the oxidation reaction of polyphenols in tea and promotes the oxidative degradation of catechins. In summary, we speculated that the content of ester type catechins decreased under the catalysis of polyphenol oxidase and SCPL acyltransferase, while the content of non-ester type catechins increased first and then decreased under the action of polyphenol oxidase and CsTA tannase. Bitterness is highly correlated with the concentrations of EGCG and ECG, and astringency is highly correlated with the concentrations of ECG and flavonol glycosides (Qin et al., 2020; Zhao et al., 2020). Therefore, the withering process reduces the bitterness and promotes the sweet taste of NNT. Conclusions Withering is the first step in the processing of NNT. Understanding the metabolomic transformation in the withering process leads to different flavor characteristics, is helpful to optimize the processing technology. Metabolomics and transcriptomics, combined with sensory evaluation, were used to analyze the changes of metabolomic composition and flavor of NNT during withering. The changes of important quality substances at different withering time were further studied by transcriptome. During the withering process, the content of 18 bitter metabolites decreased, and the total content of sweet metabolites and amino acids increased. Transcriptome analysis showed that the relative expression of L-theanine synthesis-related genes decreased during the withering process, while the relative expression of hydrolysis-related genes increased, resulting in a decrease in L-theanine accumulation. At the same time, the bitter and heavy taste ester type catechins were transformed into non-ester type catechins with lighter bitterness. Especially at the 8h withering, the bitterness and astringency reduced and the sweetness was enhanced the most. Metabolomics and transcriptomics were used to monitor the dynamic changes of compounds during the withering process of NNT, which was conducive to a more comprehensive and systematic understanding of the formation mechanism of flavor quality in the withering process, and provided a theoretical basis for the future processing and quality control of NNT. Materials and methods Preparation of tea samples In this research, one bud and three leave of fresh tea leaves were used as experimental materials, which were obtained from Poliu Village, Longchang Town, Zhenfeng City, Guizhou Province, China, on July 4, 2023. The freshly picked tea leaves were immediately transported to the tea processing room, where they were evenly spread on a sieve with a thickness of 2–3 cm, under ambient temperatures ranging from 23–26℃ and a relative humidity of 55%-65%. The samples were obtained by five-point sampling method at 2, 4, 6, 8, and 10 h after withering, and fresh leaves (0 h) as control. The remaining samples were processed into Niangniang tea (labelled as ck, Ya, Yb, Yc, Yd and Ye). The NNT tea was processed according to the traditional technology as recorded. Three parallel replicates were set for each treatment. Sensory evaluation Sensory of the tea samples were valued in accordance by the Chinese national standard GB/T23776-2018. Three grams of tea were weighed and brewed for 5 min with 100℃ boiled water and a tea-water ratio of 1:50. Subsequently, the tea liquor was filtered to a bowl. Sensory evaluations were executed based on soup color, aroma, taste, and leaf appearance. Corresponding review terminology and scoring criteria were provided, and weighted scoring was used to determine the total score. Metabolomic analysis Tea metabolite extracts were prepared according to the method of Shen et al. (Shen et al., 2022). Specifically, 2.0 g of freeze-dried tea powder was added to 40 mL of 70% methanol solution (methanol / water, 7/3, v/v), ultrasonically mixed for 30 min, and allowed to stand for 4 h. After repeated ultrasonic standing once, the supernatant was transferred to a clean container and shaken. 1 ml of the extract was centrifuged in a 2 ml centrifuge tube at 10000 g, centrifuged for 10 min, diluted 40 times with 70% methanol, and filtered with a 0.22 µm organic filter membrane for analysis. Quality control (QC) samples were mixed with all tea samples in equal quantities, and each sample was repeated three batches. Untargeted metabolomics profiling was conducted by a business services company (Novogene, Beijing, China). The metabolites were analyzed using a liquid chromatograph mass spectrometer (LC-MS) system triple quadrupole (Thermo Fisher Scientific, TSQ Fortis TM Plus, Massachusetts, USA). Equipped with ESI Turbo IonSpray interface, it can be used in positive ion and negative ion mode, and adjusted by Analyst 1.6.3 software (AB Sciex). The operating parameters of the ESI source were referred to the published literature (Sun et al., 2023). The data were analyzed by Analyst 1.6.3 software (AB Sciex). The detailed parameters of chromatographic conditions and spray ionization source were set according to the study of Zheng et al. (Zheng et al., 2021). All raw MS data were analyzed using Analyst (v1.6.3) and Multiquanta (Tsugawa et al., 2015). Transcriptome sequencing Total RNA content was extracted using the TaKaRa MiniBEST Plant RNA Extraction Kit (TaKaRa, Japan), according to the manufacturer’s instructions. The high-quality total RNAs, validated by gel electrophoresis and an ND-1000 spectrophotometer (Thermo Fisher, USA), were sent to Biomarker Technology Co. Ltd. (Beijing, China) for cDNA library construction and transcriptome sequencing. Eighteen libraries (0, 2, 4, 6, 8, and 10 h × three biological replicates) were constructed and sequenced using the Illumina NovaSeq 6000 platform based on the Paired-End 150 (PE150) strategy. The RNA-seq data analysis, identification of differentially expressed genes (DEGs) and quantitative realtime PCR (qRT-PCR) analysis were based on the methods described in our previous study (Qiao et al., 2021). 10 DEGs were selected to validate the RNA-Seq results. The cDNAs were synthesized from the same RNA samples used for transcriptome sequencing. The clean reads data of this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA869358. KEGG analysis The relative abundance of FPKM as a metric of normalised gene expression. Differentially expressed genes (DEGs) were identified using DEGseq (Love et al., 2014) and screened using specific parameters (fold change ≥ 2.00 and adjusted p ≤ 0.05) as thresholds. Enrichment analysis was performed in accordance with the Kyoto Encyclopedia of Genes and Genomes pathway annotation classification to further study the potential biological pathways of these dysregulated metabolites ( http://www.kegg.jp/kegg/compound/ ) enrichment analysis, and used Python-3.5.0 and R-4.0.3R software packages for data processing, P values indicate significance. Declarations Data analysis The data were processed by one-way analysis of ANOVA using SPSS version 25.0 software (SPSS Inc., Chicago, IL, USA) at test significant difference (p < 0.05) to evaluate significant difference between groups. Origin 2023 (OriginLab Co., Northampton, MA, USA) was used to create figures. Using Design-expert (version 13.0; stat-ease, Minneapolis, MN, USA) performed least squares regression analysis of the data. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the data of 6 samples using SIMCA-P version 14.0 to determine the significantly different accumulated metabolites of HS and LS. TB tools (v1.082, Guangzhou, China) was applied to visualize the KEGG and GO enrichment results and heatmap analysis(Chen et al., 2023). Competing interests The authors declare no competing interests. Author Contribution W.Y.X and L.L.T. prepared and designed the study. G.H, L.L., Z.Y.N, S.J.J, L.X.S, and H.H. designed the detailed experiments, and evaluated and analyzed the performance data. W.Y.X. drafted the original manuscript which was subsequently re-examined following various discussions regarding data interpretation by W.Y.X.and L.L.T. Acknowledgement We wish to express our gratitude for the financial support provided by the Guizhou Province Science and Technology Planning Project (Qiankehe Support [2023] 087，[2023] 012); Southwest Guizhou Science and Technology Planning Project . Innovative Capacity Construction Project for Protection and Utilization of Crop Germplasm Resources in Tongren City (Qiankehefuqi[2022]012). Data Availability The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. Source data are provided with this paper and its Supplementary Information files. . References Ahmad, M.Z., Li, P., She, G., Xia, E., Benedito, V.A., Wan, X.C., Zhao, J., 2020. Genome-Wide Analysis of Serine Carboxypeptidase-Like Acyltransferase Gene Family for Evolution and Characterization of Enzymes Involved in the Biosynthesis of Galloylated Catechins in the Tea Plant (Camellia sinensis). Front. Plant Sci. 11. https://doi.org/10.3389/fpls.2020.00848 Ashihara, H., 2015. Occurrence, Biosynthesis and Metabolism of Theanine (γ-Glutamyl-L-ethylamide) in Plants: A Comprehensive Review. Natural Product Communications10,1934578X1501000.https://doi.org/10.1177/1934578X1501000525 Basyuk, E., Rage, F., Bertrand, E., 2021. RNA transport from transcription to localized translation: a single molecule perspective. RNA Biology 18, 1221–1237. https://doi.org/10.1080/15476286.2020.1842631 Chang, M., Ma, J., Sun, Y., Tian, L., Liu, L., Chen, Q., Zhang, Z., Wan, X., Sun, J., 2023. γ-Glutamyl-transpeptidase CsGGT2 functions as light-activated theanine hydrolase in tea plant (Camellia sinensis L.). Plant, Cell & Environment 46, 1596–1609. https://doi.org/10.1111/pce.14561 Chen, C., Wu, Y., Li, J., Wang, X., Zeng, Z., Xu, J., Liu, Y., Feng, J., Chen, H., He, Y., Xia, R., 2023. TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining. Molecular Plant 16, 1733–1742. https://doi.org/10.1016/j.molp.2023.09.010 Chen, M., 2021. The Tea Plant Leaf Cuticle: From Plant Protection to Tea Quality. Front. Plant Sci. 12. https://doi.org/10.3389/fpls.2021.751547 Cheng, H., Wu, W., Liu, X., Wang, Y., Xu, P., 2022. Transcription factor CsWRKY40 regulates L-theanine hydrolysis by activating the CsPDX2.1 promoter in tea leaves during withering. Horticulture Research 9, uhac025. https://doi.org/10.1093/hr/uhac025 Dai, X., Liu, Y., Zhuang, J., Yao, S., Liu, L., Jiang, X., Zhou, K., Wang, Y., Xie, D., Bennetzen, J.L., Gao, L., Xia, T., 2020. Discovery and characterization of tannase genes in plants: roles in hydrolysis of tannins. New Phytologist 226, 1104–1116. https://doi.org/10.1111/nph.16425 Du, J., Wu, X., Sun, S., Qin, Y., Liao, K., Liu, X., Qiu, R., Long, Z., Zhang, L., 2023. Study on inoculation fermentation by fungi to improve the taste quality of summer green tea. Food Bioscience 51, 102321. https://doi.org/10.1016/j.fbio.2022.102321 Fu, X., Cheng, S., Liao, Y., Xu, X., Wang, X., Hao, X., Xu, P., Dong, F., Yang, Z., 2020. Characterization of l-Theanine Hydrolase in Vitro and Subcellular Distribution of Its Specific Product Ethylamine in Tea (Camellia sinensis). J. Agric. Food Chem. 68, 10842–10851. https://doi.org/10.1021/acs.jafc.0c01796 Kujawska, M., Ewertowska, M., Ignatowicz, E., Adamska, T., Szaefer, H., Gramza-Michałowska, A., Korczak, J., Jodynis-Liebert, J., 2016. Evaluation of Safety and Antioxidant Activity of Yellow Tea ( Camellia sinensis ) Extract for Application in Food. Journal of Medicinal Food 19, 330–336. https://doi.org/10.1089/jmf.2015.0114 Leung, F.**, Yung, L.M., Ngai, C.Y., Cheang, W.S., Tian, **ao Yu, Lau, C.W., Zhang, Y., Liu, J., Chen, Z.Y., Bian, Z.-**ang, Yao, **aoqiang, Huang, Y., 2016. Chronic black tea extract consumption improves endothelial function in ovariectomized rats. Eur J Nutr 55, 1963–1972. https://doi.org/10.1007/s00394-015-1012-0 Liu, K., Zhou, R., Wang, B., Chen, K., Shi, L.-Y., Zhu, J.-D., Mi, M.-T., 2013. Effect of green tea on glucose control and insulin sensitivity: a meta-analysis of 17 randomized controlled trials. The American journal of clinical nutrition 98, 340–348. Liu, M.-Y., Burgos, A., Ma, L., Zhang, Q., Tang, D., Ruan, J., 2017. Lipidomics analysis unravels the effect of nitrogen fertilization on lipid metabolism in tea plant (Camellia sinensis L.). BMC Plant Biology 17, 165. https://doi.org/10.1186/s12870-017-1111-6 Liu, Z.-W., Wu, Z.-J., Li, H., Wang, Y.-X., Zhuang, J., 2017. L-Theanine Content and Related Gene Expression: Novel Insights into Theanine Biosynthesis and Hydrolysis among Different Tea Plant (Camellia sinensis L.) Tissues and Cultivars. Front. Plant Sci. 8. https://doi.org/10.3389/fpls.2017.00498 Qin, D., Wang, Qiushuang, Li, H., Jiang, X., Fang, K., Wang, Qing, Li, B., Pan, C., Wu, H., 2020. Identification of key metabolites based on non-targeted metabolomics and chemometrics analyses provides insights into bitterness in Kucha [ Camellia kucha (Chang et Wang) Chang]. Food Research International 138, 109789. https://doi.org/10.1016/j.foodres.2020.109789 Qu, J., Zou, T., Lin, Z., 2021. The Roles of the Ubiquitin–Proteasome System in the Endoplasmic Reticulum Stress Pathway. International Journal of Molecular Sciences 22, 1526. https://doi.org/10.3390/ijms22041526 Sajilata, M.G., Bajaj, P.R., Singhal, R.S., 2008. Tea Polyphenols as Nutraceuticals. Comp Rev Food Sci Food Safe 7, 229–254. https://doi.org/10.1111/j.1541-4337.2008.00043.x Shao, C., Jiao, H., Chen, Jiahao, Zhang, C., Liu, J., Chen, Jianjiao, Li, Y., Huang, J., Yang, B., Liu, Z., Shen, C., 2022. Carbon and Nitrogen Metabolism Are Jointly Regulated During Shading in Roots and Leaves of Camellia Sinensis. Front. Plant Sci. 13. https://doi.org/10.3389/fpls.2022.894840 Shen, S., Huang, J., Li, T., Wei, Y., Xu, S., Wang, Y., Ning, J., 2022. Untargeted and targeted metabolomics reveals potential marker compounds of an tea during storage. LWT 154, 112791. https://doi.org/10.1016/j.lwt.2021.112791. Sun, L., Wen, S., Zhang, S., Li, Qiuhua, Cao, J., Chen, R., Chen, Z., Zhang, Z., Li, Z., Li, Qian, Lai, Z., Sun, S., 2024. Study on flavor quality formation in green and yellow tea processing by means of UPLC-MS approach. Food Chemistry: X 22, 101342. https://doi.org/10.1016/j.fochx.2024.101342 Sun, L., Zhang, S., Li, Qiuhua, Yuan, E., Chen, R., Yan, F., Lai, X., Zhang, Z., Chen, Z., Li, Qian, Sun, S., 2023. Metabolomics and electronic tongue reveal the effects of different storage years on metabolites and taste quality of Oolong Tea. Food Control 152, 109847. https://doi.org/10.1016/j.foodcont.2023.109847 Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., Arita, M., 2015. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12, 523–526. https://doi.org/10.1038/nmeth.3393 Wilson, A.E., Matel, H.D., Tian, L., 2016. Glucose ester enabled acylation in plant specialized metabolism. Phytochem Rev 15, 1057–1074. https://doi.org/10.1007/s11101-016-9467-z Yao, S., Liu, Y., Zhuang, J., Zhao, Y., Dai, X., Jiang, C., Wang, Z., Jiang, X., Zhang, S., Qian, Y., Tai, Y., Wang, Y., Wang, H., Xie, D.-Y., Gao, L., Xia, T., 2022. Insights into acylation mechanisms: co-expression of serine carboxypeptidase-like acyltransferases and their non-catalytic companion paralogs. The Plant Journal 111, 117–133. https://doi.org/10.1111/tpj.15782 Zhang, H.-B., Xia, E.-H., Huang, H., Jiang, J.-J., Liu, B.-Y., Gao, L.-Z., 2015. De novo transcriptome assembly of the wild relative of tea tree (Camellia taliensis) and comparative analysis with tea transcriptome identified putative genes associated with tea quality and stress response. BMC Genomics 16, 298. https://doi.org/10.1186/s12864-015-1494-4 Zhang, X., Wen, B., Zhang, Y., Li, Y., Yu, C., Peng, Z., Wang, K., Liu, Z., Huang, J., Xiong, L., Li, J., 2022. Transcriptomic and biochemical analysis reveal differential regulatory mechanisms of photosynthetic pigment and characteristic secondary metabolites between high amino acids green-leaf and albino tea cultivars. Scientia Horticulturae 295, 110823. https://doi.org/10.1016/j.scienta.2021.110823 Zhao, J., Li, P., Xia, T., Wan, X., 2020. Exploring plant metabolic genomics: chemical diversity, metabolic complexity in the biosynthesis and transport of specialized metabolites with the tea plant as a model. Critical Reviews in Biotechnology 40, 667–688. https://doi.org/10.1080/07388551.2020.1752617 Zheng, Y., Wang, P., Chen, X., Yue, C., Guo, Y., Yang, J., Sun, Y., Ye, N., 2021. Integrated transcriptomics and metabolomics provide novel insight into changes in specialized metabolites in an albino tea cultivar (Camellia sinensis (L.) O. Kuntz). Plant Physiology and Biochemistry 160, 27–36. https://doi.org/10.1016/j.plaphy.2020.12.029 Additional Declarations No competing interests reported. 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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-5265030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375810146,"identity":"27314513-8428-4345-a16e-1f0b0ccce718","order_by":0,"name":"Yanxia Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPACGzkIzUa8ljRjkrUcTmwgWovBjRzDzwW/mNPn+58xYPhQdpiBf3YDAS1nzhhLz+xjy9144IwB44xzhxkk7hwgoOV47wZp3h6e3I2NPQbMvG2HGQwkEghoOcy7+Tdvj0S6YTOPAfNforQc790mzfPDIEGeDaiFkRgtkmfOf7PmbUgw3MDDVnCw51w6j8QNAlr4bqQl3+b5819evv/wxgc/yqzl+GcQ0KJwAEgwtgFdCGSA2Dz41QOBfAOI/ANjjIJRMApGwSjAAgCehER07NcLxAAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Yanxia","middleName":"","lastName":"Wang","suffix":""},{"id":375810147,"identity":"dfdeb013-74d7-48bb-8d2e-32b81fda12a3","order_by":1,"name":"Hao Guan","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Guan","suffix":""},{"id":375810148,"identity":"b999f42f-8eb3-48e6-8457-2c1693af033a","order_by":2,"name":"Li Lu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Lu","suffix":""},{"id":375810149,"identity":"9714fd68-6770-478f-8ad3-3a4adef8cf30","order_by":3,"name":"Yunan Zhao","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yunan","middleName":"","lastName":"Zhao","suffix":""},{"id":375810150,"identity":"9e1267eb-f8df-42e3-bb52-222502157e24","order_by":4,"name":"Jinjie Shi","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Jinjie","middleName":"","lastName":"Shi","suffix":""},{"id":375810151,"identity":"fcd8c403-2f75-431c-9807-af5201176f5a","order_by":5,"name":"Xiaosong Li","email":"","orcid":"","institution":"Tongren Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaosong","middleName":"","lastName":"Li","suffix":""},{"id":375810152,"identity":"072ea435-9c6c-4979-bd7e-5c22f8712298","order_by":6,"name":"Hao huang","email":"","orcid":"","institution":"Guizhou Poliu Tribute Tea Agricultural Development Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"huang","suffix":""},{"id":375810153,"identity":"c62dfa6d-92ef-4b6a-9c04-a5a78c04a1b3","order_by":7,"name":"Litang Lu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Litang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-10-15 04:53:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5265030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5265030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68732554,"identity":"dfc87f0b-b3fe-4281-a274-87ccf67935c6","added_by":"auto","created_at":"2024-11-11 12:48:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21410734,"visible":true,"origin":"","legend":"\u003cp\u003eNiangniang tea sensory evaluation results\u003c/p\u003e","description":"","filename":"Figure1.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/a7da8818d0f4ee5128ff5741.png"},{"id":68733843,"identity":"ffccba3f-28e0-42c8-b8b0-86afc3f51961","added_by":"auto","created_at":"2024-11-11 13:04:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6755694,"visible":true,"origin":"","legend":"\u003cp\u003ereveals the metabolites during the withering process of niangniang tea through a widely targeted metabolite profile. (a) The morphological changes of leaves gradually dehydrated and wilted during the withering process of tea leaves. (b) Multi-peak diagram of metabolite detection. (c) Metabolite detection Pearson analysis. (d) Principal component analysis of the identified differential metabolites.\u003c/p\u003e","description":"","filename":"Figure2.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/982d2a322d09073ad7608c4d.png"},{"id":68733629,"identity":"c472d21f-be0b-4446-8ac2-da1c08319a9a","added_by":"auto","created_at":"2024-11-11 12:56:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3487455,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomics analysis of NNT during withering. (a) Pie chart of the categories of differential metabolite. (b) The cluster heat map of differential metabolites during the withering process, blue and red indicate the low and high levels of metabolite accumulation. (c) KEGG pathway related to the enrichment of differential metabolites during the withering process. (d) The Venn diagram shows the number of differential metabolites in the comparison.\u003c/p\u003e","description":"","filename":"Figure3.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/27b350b9ce5b1ea52df79a43.png"},{"id":68732556,"identity":"0ec3f399-4821-46c0-8412-4e2ff585452a","added_by":"auto","created_at":"2024-11-11 12:48:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3402916,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolites and related KEGG pathways during the withering process. (a) KEGG enrichment of differential metabolites between Ya and CK. (b) KEGG enrichment of differential metabolites between Yb and Ya. (c) KEGG enrichment of differential metabolites between Yc and Yd. (d) KEGG enrichment of differential metabolites between Yd and Yc. (e) KEGG enrichment of differential metabolites between Ye and Yd.\u003c/p\u003e","description":"","filename":"Figure4.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/780001bfee6e9f8f3e8fb067.png"},{"id":68732552,"identity":"4dab4b8e-0554-40e3-bf8e-1609a395c0ae","added_by":"auto","created_at":"2024-11-11 12:48:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1629229,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the content of theanine, caffeine and catechins in NNT during the withering process. (a) Changes in theanine content during the withering process. (b) Changes in caffeine content during the withering process. (c) Changes in the content of Ester type catechins during the withering process. (d) Changes in the content of nonester type catechins during the withering process. Ya, Yb, Yc, Yd, Ye and CK represent 2h, 4h, 6h, 8h, 10h and control check (fresh leaves).\u003c/p\u003e","description":"","filename":"Figure5.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/40308c6c21d47529db9341e4.png"},{"id":68732555,"identity":"3dc2d346-8ad1-4124-8c17-fb7a39ed19ce","added_by":"auto","created_at":"2024-11-11 12:48:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4006883,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathways enriched in all comparisons of Ya/CK, Yb/Ya, Yc/Yb, Yd/Yc, and Ye/Yd and classification of DEGs.\u003c/p\u003e","description":"","filename":"Figure6.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/c2ceefb7d77cb5503d419792.png"},{"id":68734898,"identity":"3da649fb-c800-4fe5-a1a5-6a48affae873","added_by":"auto","created_at":"2024-11-11 13:12:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2520589,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs are related to the L-theanine metabolic pathway enriched by the KEGG pathway. Color represents the FPKM value of the gene, and red and blue represent relatively high and low expression levels, respectively. Ya, Yb, Yc, Yd, Ye and CK represent 2h, 4h, 6h, 8h, 10h and control check (fresh leaves).\u003c/p\u003e","description":"","filename":"Figure7.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/d87988cb5e9404363bfbc15f.png"},{"id":68732549,"identity":"1e711e06-3d5b-4219-b463-5562f63bd11f","added_by":"auto","created_at":"2024-11-11 12:48:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3265746,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs are related to the catechin metabolic pathway enriched by the KEGG pathway. Color represents the FPKM value of the gene, and red and blue represent relatively high and low expression levels, respectively. Ya, Yb, Yc, Yd, Ye and CK represent 2h, 4h, 6h, 8h, 10h and control check (fresh leaves).\u003c/p\u003e","description":"","filename":"Figure8.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/a572a19c78517f33167da281.png"},{"id":78640235,"identity":"78b865ef-b8e0-4e08-87e6-c49e45460af6","added_by":"auto","created_at":"2025-03-17 06:17:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40886719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/be7f5cdd-0cb7-47b8-aa7b-54dd34780adb.pdf"},{"id":68733632,"identity":"0e7a58d2-e2e6-4199-91ba-9377971c8b1f","added_by":"auto","created_at":"2024-11-11 12:56:19","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20844431,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5265030/v1/1227c45d96c54dd861ea7d1c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanism Underlying Flavor Quality Formation during Withering Process of Niangniang Tea, a Compressed Large-Leaf Yellow Tea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTea is renowned for its health benefits, primarily attributed to its abundant and diverse secondary metabolites (Liao et al., 2022). Fermentation processing is an important factor in the change of secondary metabolism content, which affects the taste and aroma of tea (Sun et al., 2024). Based on the level of fermentation, tea is categorized into various types, including green tea, yellow tea, white tea, oolong tea, black tea, and dark tea. The biotransformation of chemical components in tea leaves after various processing makes tea have unique flavor and biological activity (Leung et al., 2016; Liu et al., 2013).\u003c/p\u003e\u003cp\u003eYellow tea is a unique historical tea in China and has gradually become popular in the world due to its unique flavor and health function (Kujawska et al., 2016). Niangniang tea (NNT) is a kind of compressed large-leaf yellow tea origin in Guizhou province from Ming Dynasty. Its shape is similar to that of Chinese writing brush, and it is also called \u0026lsquo;champion pen\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Traditional NNT is made from one bud with three to four leaves of the local ancient tea plant (\u003cem\u003eCamellia sinensis\u003c/em\u003e (L.) O.Kuntze), generally about 3\u0026ndash;4 inches long. After fixed, rolled, and molded into the shape of Chinese writing brush, and then stringed, dried with charcoal fire or directly in the sun. The dried NNT was with rich aroma and bright orange liquor. Withering is the first step in the manufacture of NNT. The withering time is usually 8\u0026ndash;12 h. Withering processing can increase the concentration of tea cell fluid, enhance the activity of hydrolase and oxidase, promote the oxidative degradation of compounds, and increase the role of water-soluble products. It has an important effect on the bitter, astringent and sweet taste of tea(Xu et al., 2018).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMetabolomics and transcriptomics are powerful tools for simultaneous measurement of hundreds of differential metabolites and related gene expression, and have been widely used in food and beverage analysis and quality assessment (Zhang et al., 2022; Zhou et al., 2022). The withering process of NNT was studied by metabolomics and transcriptomics, aiming to elucidate the changes of metabolites in the withering processing from the perspective of omics, and to explore the effect of withering on the flavor of NNT, so as to provide a basis for further optimization of NNT processing.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eEffects of withering processing on the sensory characteristics of NNT\u003c/p\u003e \u003cp\u003eWithering is an essential process affecting the flavor of NNT. Different withering time NNT were sensory evaluated. A s showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was no significant difference in the appearance of the dried tea, the liquor color, the aroma and the taste although are significantly different. Especially the aroma and the taste, with the increasing of withering time, the NNT showed a sweet flavor, both sweet aroma and sweet taste. Of which, Yd got a higher score than the other treatments. These results indicated that the withering time of fresh leaf could affect the flavor quality of NNT, and the best withering time may be 8 hours. However, the sweet metabolite compositions and the mechanisms that regulate the sweet flavor formation during withering of NNT remain unclear. For this reason, we performed metabolomic and transcriptomic analysis to elucidate these mechanisms.\u003c/p\u003e \u003cp\u003eMetabolomic analysis of Niangniang tea\u003c/p\u003e \u003cp\u003eNon-targeted liquid chromatography-mass spectrometry (UPLC-MS/MS) was used to analyze CK and 15 NNT samples at five different withering stages in positive and negative ion modes. During the withering processing, the tea leaves gradually lost water, and the morphology changed significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The generated ion chromatograms show that the data have high overlap and good sample preparation process (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The high Pearson correlation R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.95 indicated that the whole process was stable and reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). QC samples were added to the test to evaluate the metabolomics performance. Principal component analysis was performed on the samples to determine the overall metabolite differences between the groups and the changes in metabolites within the group. The score plot showed that the QC samples were clustered in the center, and the three repeated distances of the samples in each time period were close to each other, indicating that the metabolomics analysis was repeatable and reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The samples in different withering periods in the PCA diagram were completely separated, indicating that the withering caused a significant change in the metabolite profile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIdentification of differentially accumulated metabolites (DAMs)\u003c/p\u003e \u003cp\u003eAfter peak extraction and comparison, 622 cationic differential metabolites and 442 anionic differential metabolites were obtained in the positive and negative ion modes, respectively. The cationic mode could detect more compounds than the anionic mode. Therefore, the cationic differential metabolites were selected for analysis in subsequent experiments. The 622 DAMs were divided into 10 different categories (117 Lipids and lipid-like molecules, 87 Phenylpropanoids and polyketides, 66 Organic acids and derivatives, 63 Organ heterocyclic compounds, 41 Benzenoids, 31 Organic oxygen compounds, 21 Nucleosides, nucleotides, and analogues, 10 Alkaloids and derivatives, 9 Organic nitrogen compounds, 7 Lignans, neolignans and related compounds. In addition, 170 substances have not been classified). The quantitative results and annotations of all DAMs are shown in Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b shows an overview of the tea metabolite profiles at different withering stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). It is worth noting that Phenylpropanoids and polyketides are the second most abundant DAMs in different withering periods. Polyphenols are one of the most concerned secondary metabolites in tea, which have important medicinal and edible values. Organic acids were the third most abundant DAMs found during the entire withering period, which may play an important role in the formation of ester compounds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough KEGG enrichment analysis, DAMs were clustered into different pathways to explore the physiological changes during the withering process. 622 differential metabolites were enriched in 13 KEGG pathways. Among them, 81 DAMs were enriched in the Global and overview maps pathway; forty DAMs were enriched in the Biosynthesis of other secondary metabolites pathway. A total of 26 DAMs were enriched in the Amino acid metabolism pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The DAMs of tea leaves with different withering time (Ya-e) were compared with those of control check (CK). Venn diagram showed that there were 27 common DAMs in all groups. Venn diagram showed that there were 27 DAMs in all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Comparing the overlapping metabolites of each treatment, many DAMs showed time-dependent regulation during the whole withering process of NNT.\u003c/p\u003e \u003cp\u003eInfluence of withering on the quality development of NNT\u003c/p\u003e \u003cp\u003eDifferential analysis showed that 87 differential metabolites were up-regulated and 54 differential metabolites were down-regulated in Ya/CK, which were enriched in KEGG pathways such as ABC transporters and stilbenoid, diarylheptanoid and gingerol biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea); There were 92 differential metabolites up-regulated and 53 differential metabolites down-regulated in Yb/CK, which were enriched in KEGG pathways such as Aminoacyl-tRNA biosynthesis and ABC transporters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb); There were 87 differential metabolites up-regulated and 69 differential metabolites down-regulated in Yc/CK, which were enriched in KEGG pathways such as ABC transporters and Aminoacyl-tRNA biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec); There were 99 differential metabolites up-regulated and 78 differential metabolites down-regulated in Yd/CK, which were enriched in KEGG pathways such as Biosynthesis of secondary metabolites and Phenylpropanoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed); There were 139 differential metabolites up-regulated and 76 differential metabolites down-regulated in Ye/CK, which were enriched in KEGG pathways such as Metabolic pathways and ABC transporters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). These results indicate that the longer the withering time, the more differential metabolites. Biosynthesis of secondary metabolites metabolic pathway regulates secondary metabolites and affects flavanols and flavone glycosides related to bitterness and astringency. The regulation of ABC transporters can change some sugars, sugar alcohols and amino acids related to sweetness, thus affecting the sweet flavor quality of NNT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA large number of differential metabolites with flavor characteristics were identified by analyzing the metabolites with flavor characteristics during the withering process. The down-regulated bitter metabolites were valine, 4-hydroxyisoleucine, L-phenylalanine, L-lysine, adenosine, kaempferol, quercetin, myricetin, isorhamnetin, rhamnetin, laricitrin, nicotinamide, riboflavin, adenosine, guanine, 2-(dimethylamino) guanosine, catechins and so on (Du et al., 2023; Sun et al., 2024). The astringent metabolites with down-regulated content are protocatechuic acid and 4-hydroxybenzoic acid. At the same time, flavonols and flavonol glycosides such as bitter quercetin and catechins also have important effects on astringency (Qin et al., 2020). Theaflavin, theaflavine-3,3-digallate, theophylline, catechins and other substances have a sense of folding convergence, and the taste threshold is low. Identification analysis found that their content has also been reduced to varying degrees. The content of sweet metabolite L-alanine and 3'-adenosine monophosphate (3'-AMP) was up-regulated. The results showed that, the bitter taste reduced and the sweetness enhanced in NNT during the withering process, making the tea taste sweeter (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChanges of tea polyphenols, catechins and caffeine contents at different withering time\u003c/p\u003e \u003cp\u003eL-theanine, catechin and caffeine are the most concerned secondary metabolites in tea. They jointly regulate the taste of tea. Studies showed that theanine has a significant improvement effect on the umami taste of tea. The content of theanine showed a downward trend during the withering process, and the decrease became larger at 8h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). It was speculated that the theanine was hydrolyzed. Caffeine is one of the main components of the bitter and astringent taste of tea. The content of caffeine reached the highest at 4h, but then decreased, and showed a no significant change during the whole withering process (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTea polyphenols were the main active compounds in tea, composed of catechins, flavonols, anthocyanins, deep phenols and polymeric phenols. Among them, catechins account for 65\u0026ndash;80% of total tea polyphenols, which is the mainly causing tea bitterness. Catechins were the general term for soluble flavan-3-ols in tea, including (+)-catechin (C), (\u0026minus;)-gallocatechin (GC), (\u0026minus;)-catechin gallate (CG), (\u0026minus;)-gallocatechin gallate (GCG), (\u0026minus;)-epicatechin (EC), (\u0026minus;)-epigallocatechin (EGC), (\u0026minus;)-epicatechin-3-gallate (ECG) and (\u0026minus;)-epigallocatechin-3-gallate (EGCG) (Sajilata et al., 2008)。According to the complexity of the molecular structure, catechins are divided into ester type catechins and non-ester type catechins. Ester type catechins include CG, GCG, ECG and EGCG, which have strong bitterness and astringency. They are the main flavor components of tea and easily oxidized. In tea plants, the content of gallic acid catechins is much higher than that of non-gallic acid catechins. Ester type catechins are much more abundant than non-ester type catechins in tea plants. With the progress of withering, the content of ester type catechins showed a downward trend. Non-ester type catechins including C, GC, EC and EGC with simple molecular structure, its bitter taste is lower. With the process of withering, the content of non-ester type catechins increased first and then decreased. There is a high point at 4h, in which the content of EGC accounts for the largest proportion. The change trend of content is consistent with that of non-ester type catechins, and the content also has a high point at 4h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The content of C reached the highest at 8h, while EC was different from the first two substances, and its content showed a downward trend with withering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). It showed that the content of ester type catechins decreased gradually, the content of non-ester type catechins increased first and then decreased, and the content of gallic acid also showed an increasing trend. According to this, it is speculated that during the withering process, the enzymatic reaction is active, and ester type catechins may be catalyzed by polyphenol oxidase to generate non-ester type catechins or other substances. The bitter and astringent taste of tea is weakened, and the mellow taste is promoted.\u003c/p\u003e \u003cp\u003eTranscriptomic analysis of Niangniang tea\u003c/p\u003e \u003cp\u003eIn order to further understand the physiological process of metabolite transformation during the withering process, we performed transcriptome sequencing and comparison. In the comparison of Ya/CK, Yb/Ya, Yc/Yb, Yd/Yc and Ye/Yd, differentially expressed genes (DEGs) were identified. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the number of up-regulated and down-regulated DEGs at 2h was much higher than that at 10h. It can be inferred that a large number of transcription and translation may be activated or inhibited during the withering process. The down-regulation of DEGs at the beginning of withering (Ya/CK) and the late stage of withering (Ye/Yd) was greater than the up-regulation, while the DEGs at the middle stage of withering (Yb/Ya, Yc/Yb, Yd/Yc) were opposite, and the up-regulation was greater than the down-regulation, indicating that many physiological processes were activated during the withering process. Through the overlapping analysis of up-regulated and down-regulated DEGs in the withering process, it was found that many DEGs were always up-regulated or down-regulated. These genes may play a vital role in the transformation of metabolites of tea during the withering process, so as to affect the taste and quality of tea. The results showed that the differential expression of genes during the withering changed greatly in a time-dependent manner, which was consistent with the results of metabolomics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe differentially expressed genes of Ya/CK, Yb/Ya, Yc/Yb, Yd/Yc and Ye/Yd were analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYa/CK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYb/Ya\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYc/Yb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYd/Yc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYe/Yd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-differential gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp- differential gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown-differential gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDEGs were enriched into different pathways by KEGG enrichment analysis. A total of 2265, 1495, 1348, 858, and 881 DEGs were annotated on the 120, 115, 113, 108, and 104 pathways of Ya / CK, Yb / Ya, Yc / Yb, Yd / Yc, and Ye / Yd, respectively. These pathways were divided into 5 categories, and the number of DEGs included in their secondary classification was analyzed. The most DEGs were enriched in 88 pathways of Metabolism classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among them, DEGs in Glycolysis /Gluconeogenesis, Starch and sucrose metabolism, Amino sugar and nucleotide sugar metabolism, Cysteine and methionine metabolism and Oxidative phosphorylation pathways were down-regulated more. The DEGs of Phenylpropanoid biosynthesis, Cysteine and methionine metabolism, Phenylpropanoid biosynthesis, Starch and sucrose metabolism and Galactose metabolism pathways were up-regulated more, indicating that a large number of metabolic activities were more active during the withering process, and a large number of metabolites were transformed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA large number of DEGs genes were up-regulated or down-regulated in the Ribosome, RNA degradation, Spliceosome and Nucleocytoplasmic transport pathways, indicating that there was a large amount of protein synthesis and decomposition during the withering process. The reason is that a large number of messenger RNAs are synthesized at specific genomic sites in the nucleoplasm during the synthesis and decomposition of substances, and are transported to specific cytoplasmic locations at the appropriate time, and proteins are locally produced in the cytoplasm (Basyuk et al., 2021). In Cellular Processes, the Protein processing in endoplasmic reticulum pathway enriched the most DEGs during the withering process, followed by the Endocytosis pathway, which further verified that there was a large amount of protein synthesis, decomposition and transportation during the withering process.\u003c/p\u003e \u003cp\u003eEffect of withering processing on L-theanine biosynthesis\u003c/p\u003e \u003cp\u003eThe integrated analysis of transcriptomics and metabolomics is expected to elucidate the regulatory mechanism of NNT in the process of withering, further clarify the mechanism of DEGs regulating important secondary metabolites, and construct the pathway diagram of DEGs in theanine (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and catechin pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The coding genes of metabolic enzymes involved in theanine synthesis and hydrolysis were identified and analyzed. The expression of genes involved in theanine synthesis was positively correlated with theanine content to a large extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eCsALT\u003c/em\u003e, \u003cem\u003eCsAIDA\u003c/em\u003e, \u003cem\u003eCsGDH\u003c/em\u003e, \u003cem\u003eCsGOGAT\u003c/em\u003e, \u003cem\u003eCsTS\u003c/em\u003e and \u003cem\u003eCsGS\u003c/em\u003e were structural genes encoding enzymes related to theanine synthesis (no specific enzyme encoded by \u003cem\u003eAIDA\u003c/em\u003e was found in tea plant. \u003cem\u003eCsSAMDC\u003c/em\u003e and \u003cem\u003eCsADC\u003c/em\u003e shared similar domains with \u003cem\u003eCsAIDA\u003c/em\u003e were found). Most of their gene expression decreased with the increasing of withering time, that is, the withering process inhibited the accumulation of theanine in tea (Z.-W. Liu et al., 2017; Zhang et al., 2015).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder the action of theanine hydrolase, theanine decomposed into L-glutamic acid and ethylamine (Chang et al., 2023; Yu et al., 2020). \u003cem\u003eCsThYD\u003c/em\u003e and \u003cem\u003eCsAO\u003c/em\u003e encode enzymes that hydrolyze theanine. We found that most of the transcripts of \u003cem\u003eCsAO\u003c/em\u003e had a high expression level after a period of withering, and the expression of \u003cem\u003eCsAO\u003c/em\u003e promoted the hydrolysis of theanine. Previous studies have shown that \u003cem\u003eCsPDX2.1\u003c/em\u003e (L-theanine hydrolysis gene, CSS003722 ) was the key gene for L-theanine metabolism during the withering process (Cheng et al., 2022). With the withering progress of NNT, its relative expression increased first and then decreased, reaching a peak at 8h. The theanine content decreasing data in the previous metabolomics analysis was consistent with the change of \u003cem\u003eCsPDX2.1\u003c/em\u003e expression.\u003c/p\u003e \u003cp\u003eEffect of withering processing on catechin biosynthesis\u003c/p\u003e \u003cp\u003eGenes encoding metabolic enzymes involved in catechin synthesis and hydrolysis in NNT withering were identified and analyzed. These genes include \u003cem\u003eCsPAL\u003c/em\u003e, \u003cem\u003eCsC4H\u003c/em\u003e, \u003cem\u003eCs4CH\u003c/em\u003e, \u003cem\u003eCsCHS\u003c/em\u003e, \u003cem\u003eCsCHI\u003c/em\u003e, \u003cem\u003eCsF3H\u003c/em\u003e, \u003cem\u003eCsF3'5'H\u003c/em\u003e, \u003cem\u003eCsDFR\u003c/em\u003e, \u003cem\u003eCsLAR\u003c/em\u003e, \u003cem\u003eCsANS\u003c/em\u003e, \u003cem\u003eCsANR\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The relative expression levels are shown in the figure: the relative expression levels of \u003cem\u003eCsPAL\u003c/em\u003e, \u003cem\u003eCsC4H\u003c/em\u003e, \u003cem\u003eCsCHS\u003c/em\u003e, \u003cem\u003eCsCHI\u003c/em\u003e, and \u003cem\u003eCsF3H\u003c/em\u003e showed a downward trend. The relative expression levels of \u003cem\u003eCs4CH\u003c/em\u003e and \u003cem\u003eCsF3H\u003c/em\u003e increased first and then decreased. These genes are also located in the flavonoid synthesis pathway and are widely present in other species. The complexity of flavonoid biosynthesis and the presence of gene isomers, so the expression levels of these genes are not necessarily related to the content of catechins (Wu et al., 2014). \u003cem\u003eCsLAR\u003c/em\u003e, \u003cem\u003eCsANS\u003c/em\u003e and \u003cem\u003eCsANR\u003c/em\u003e are unique in the synthesis pathway of catechins. They were involved in the synthesis of non-ester type catechins in tea plants, and their relative expression levels show a decreasing trend during the withering, and jointly regulate and control the accumulation of non-ester type catechins in NNT.\u003c/p\u003e \u003cp\u003eNon-ester type catechins are widely distributed, while ester type catechins (ECG and EGCG) are only abundant in tea plants. In addition, galloylated catechins are much more abundant than nongalloylated catechins in tea plants (Kim et al., 2004; Liao et al., 2022). Therefore, non-ester type catechins and ester type catechins have mutual transformation in tea plants. \u003cem\u003eCsSCPL\u003c/em\u003e has high acyltransferase activity and can catalyze the work of gallic acylation of EC and EGC (Yao et al., 2022). The results showed that the relative expression of \u003cem\u003eCsSCPL\u003c/em\u003e was rapidly reduced during the withering, which reduced the conversion of non-ester type catechins to Ester type catechins. \u003cem\u003eCsTA\u003c/em\u003e (\u003cem\u003eC. sinensis\u003c/em\u003e tannase) is a type of tannin acyl-hydrolase hydrolyzing HTs, CT monomer gallates and depsides, which is involved in the hydrolysis of EGCG and ECG to EGC and EC. Through literature review, we obtained the CDS sequence of \u003cem\u003eCsTA\u003c/em\u003e in tea plant (NCBI database accession number: MK381269) (Dai et al., 2020). It was used to align with the tea plant genome and obtained 6 copies. The metabolome data showed that the relative expression of four copies of \u003cem\u003eCsTA\u003c/em\u003e increased with the increase of withering time, while the relative expression of two copies of \u003cem\u003eCsTA\u003c/em\u003e increased first and then decreased. The process of withering promoted the hydrolysis of EGCG and ECG into EGC and EC in tea.\u003c/p\u003e \u003cp\u003eEffect of withering processing on the flavor formation of NNT\u003c/p\u003e \u003cp\u003eSuitable withering processing can reduce the grass aroma and lead to the formation of sweet aroma and taste. UPLC-MS/MS combined with sensory review was used to analyze the changes of composition and sweet flavor of \u0026lsquo;Niangniang tea\u0026rsquo; during the withering process. The expression of genes related to the synthesis of L-theanine and catechins, which are important secondary metabolites was further studied by transcriptome. The analysis of differential metabolites showed that the content of 18 bitter metabolites decreased during the withering process, mainly nicotinamide, flavonols and flavonol glycosides, and the contents of protocatechuic acid and 4-hydroxybenzoic acid with obvious astringency also showed a downward trend. On the contrary, the sweet metabolite L-ascorbic acid, 3 ' -adenosine monophosphate (3'-AMP) content increased. All in all, the withering process of NNT reduced the bitter taste, and its sweetness was enhanced, making the NNT more sweet and mellow. Sensory evaluation proved it.\u003c/p\u003e \u003cp\u003eThe integrated analysis of transcriptomics and metabolomics provides an idea for elucidating the regulatory mechanism of withering reaction. The identified metabolites were mainly Fatty Acyls, Prenol lipids, Flavonoids, Isoflavonoids, Amino acids, peptides, and analogues, which are the most concerned secondary metabolites in tea, and also the signal of metabolism and development process during tea withering, producing various molecules and affecting the flavor quality of tea (Chen, 2021; Zuo et al., 2023). Flavonoids and isoflavonoids have a significant impact on the flavor of tea, and are major contributors to astringency and bitterness (Ahmad et al., 2020; Wilson et al., 2016). Amino acids, peptides, and analogues are important components of the umami taste in tea (Shao et al., 2022). Accordingly, the withering process increased the concentration of tea cell sap, enhanced the activity of hydrolase and oxidase, promoted the oxidative degradation of compounds, and increased the effect of water-soluble products. At the same time, starch, protein, protopectin and other substances undergo decomposition and transformation reactions to generate glucose, amino acids, soluble pectin, etc., and polyphenols also undergo oxidation reactions.\u003c/p\u003e \u003cp\u003eMetabolomics analysis showed that many DEGs were found enriched in Ribosome, RNA degradation, Spliceosome, Protein processing in endoplasmic reticulum pathway. Ribosome and RNA degradation are the beginning of protein synthesis. Endoplasmic reticulum is an important place for the synthesis, processing and sorting of various enzymes in eukaryotic cells (Qu et al., 2021). Therefore, these pathways have an important relationship with protein synthesis, decomposition and transport. It is speculated that a large amount of protein synthesis and decomposition are accompanied during the withering process. A large number of DEGs expressing in these pathways during the withering process activates various enzymes, participates in the transformation process of various substances, and improves the flavor of tea. For example, protease is involved in the degradation process of protein, which converts the protein into amino acids and increases the sweet taste. Amylase is involved in the degradation process of starch, which converts starch into soluble sugar and also improves the sweetness of tea.\u003c/p\u003e \u003cp\u003eL-theanine and catechins are the core metabolites that determine the flavor of tea. During the withering process, the content of L-theanine and catechin showed a downward trend. The results showed that the expression levels of L-theanine synthesis genes such as \u003cem\u003eCsALT\u003c/em\u003e, \u003cem\u003eCsAIDA\u003c/em\u003e, \u003cem\u003eCsGDH\u003c/em\u003e, \u003cem\u003eCsGOGAT\u003c/em\u003e, \u003cem\u003eCsTS\u003c/em\u003e, \u003cem\u003eCsGS\u003c/em\u003e, \u003cem\u003eCsSAMDC\u003c/em\u003e and \u003cem\u003eCsADC\u003c/em\u003e decreased during the processing of withering. It is speculated that withering may reduce the accumulation of L-theanine by reducing the expression of theanine synthesis genes. Cheng proved that water loss in tea is a key factor leading to the hydrolysis of L-theanine, and the key gene for hydrolysis is \u003cem\u003eCsPDX2.1\u003c/em\u003e (Cheng et al., 2022). The results showed that the relative expression of \u003cem\u003eCsPDX2.1\u003c/em\u003e increased first and then decreased, that is, the hydrolysis rate of L-theanine showed an accelerated trend during the withering, which was consistent with the decrease of L-theanine content. Previous studies have showed that L-theanine is mainly synthesized by glutamic acid and ethylamine in the roots of tea plants, and then transported to the buds through amino acid permease family members. The hydrolysis of L-theanine occurs in the leaves (Ashihara, 2015; Fu et al., 2020). In summary, the main reason for the decrease of L-theanine accumulation during the withering process is the hydrolysis of L-theanine. Amino acids (including L-theanine) are the main source of sweet taste of tea, and the decrease of L-theanine content will weaken the umami taste of tea infusion. However, the hydrolysis product of L-theanine exists glutamic acid, and the degradation of protein during the withering process increases the content of other amino acids, so the withering makes the NNT taste sweeter.\u003c/p\u003e \u003cp\u003eCatechins play an important role in bitter taste of tea. It showed that the relative expression level of \u003cem\u003eCsLAR\u003c/em\u003e, \u003cem\u003eCsANS\u003c/em\u003e and \u003cem\u003eCsANR\u003c/em\u003e decreased during the withering process, while the content of non-ester type catechins increased first and then decreased, which was inconsistent with the expression levels of \u003cem\u003eCsLAR\u003c/em\u003e, \u003cem\u003eCsANS\u003c/em\u003e and \u003cem\u003eCsANR\u003c/em\u003e. Yao showed that ester type catechins were synthesized by galloylation of nonester type catechins with \u003cem\u003eCsSCPL\u003c/em\u003e (Yao et al., 2022). Dai et al. demonstrated that \u003cem\u003eCsTA\u003c/em\u003e can catalyze the hydrolysis of ester type catechins to non-ester type catechins (Dai et al., 2020). The results showed that the relative expression of \u003cem\u003eCsSCPL\u003c/em\u003e decreased rapidly and the relative expression of \u003cem\u003eCsTA\u003c/em\u003e showed an increasing trend during the withering process. The withering inhibited the acylation of non-ester type catechins to ester type catechins and promoted the hydrolysis of ester type catechins to non-ester type catechins. As an important enzyme in tea, polyphenol oxidase participates in the oxidation reaction of polyphenols in tea and promotes the oxidative degradation of catechins. In summary, we speculated that the content of ester type catechins decreased under the catalysis of polyphenol oxidase and SCPL acyltransferase, while the content of non-ester type catechins increased first and then decreased under the action of polyphenol oxidase and CsTA tannase. Bitterness is highly correlated with the concentrations of EGCG and ECG, and astringency is highly correlated with the concentrations of ECG and flavonol glycosides (Qin et al., 2020; Zhao et al., 2020). Therefore, the withering process reduces the bitterness and promotes the sweet taste of NNT.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWithering is the first step in the processing of NNT. Understanding the metabolomic transformation in the withering process leads to different flavor characteristics, is helpful to optimize the processing technology. Metabolomics and transcriptomics, combined with sensory evaluation, were used to analyze the changes of metabolomic composition and flavor of NNT during withering. The changes of important quality substances at different withering time were further studied by transcriptome. During the withering process, the content of 18 bitter metabolites decreased, and the total content of sweet metabolites and amino acids increased. Transcriptome analysis showed that the relative expression of L-theanine synthesis-related genes decreased during the withering process, while the relative expression of hydrolysis-related genes increased, resulting in a decrease in L-theanine accumulation. At the same time, the bitter and heavy taste ester type catechins were transformed into non-ester type catechins with lighter bitterness. Especially at the 8h withering, the bitterness and astringency reduced and the sweetness was enhanced the most. Metabolomics and transcriptomics were used to monitor the dynamic changes of compounds during the withering process of NNT, which was conducive to a more comprehensive and systematic understanding of the formation mechanism of flavor quality in the withering process, and provided a theoretical basis for the future processing and quality control of NNT.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePreparation of tea samples\u003c/p\u003e \u003cp\u003eIn this research, one bud and three leave of fresh tea leaves were used as experimental materials, which were obtained from Poliu Village, Longchang Town, Zhenfeng City, Guizhou Province, China, on July 4, 2023. The freshly picked tea leaves were immediately transported to the tea processing room, where they were evenly spread on a sieve with a thickness of 2\u0026ndash;3 cm, under ambient temperatures ranging from 23\u0026ndash;26℃ and a relative humidity of 55%-65%. The samples were obtained by five-point sampling method at 2, 4, 6, 8, and 10 h after withering, and fresh leaves (0 h) as control. The remaining samples were processed into Niangniang tea (labelled as ck, Ya, Yb, Yc, Yd and Ye). The NNT tea was processed according to the traditional technology as recorded. Three parallel replicates were set for each treatment.\u003c/p\u003e \u003cp\u003eSensory evaluation\u003c/p\u003e \u003cp\u003eSensory of the tea samples were valued in accordance by the Chinese national standard GB/T23776-2018. Three grams of tea were weighed and brewed for 5 min with 100℃ boiled water and a tea-water ratio of 1:50. Subsequently, the tea liquor was filtered to a bowl. Sensory evaluations were executed based on soup color, aroma, taste, and leaf appearance. Corresponding review terminology and scoring criteria were provided, and weighted scoring was used to determine the total score.\u003c/p\u003e \u003cp\u003eMetabolomic analysis\u003c/p\u003e \u003cp\u003eTea metabolite extracts were prepared according to the method of Shen et al. (Shen et al., 2022). Specifically, 2.0 g of freeze-dried tea powder was added to 40 mL of 70% methanol solution (methanol / water, 7/3, v/v), ultrasonically mixed for 30 min, and allowed to stand for 4 h. After repeated ultrasonic standing once, the supernatant was transferred to a clean container and shaken. 1 ml of the extract was centrifuged in a 2 ml centrifuge tube at 10000 g, centrifuged for 10 min, diluted 40 times with 70% methanol, and filtered with a 0.22 \u0026micro;m organic filter membrane for analysis. Quality control (QC) samples were mixed with all tea samples in equal quantities, and each sample was repeated three batches. Untargeted metabolomics profiling was conducted by a business services company (Novogene, Beijing, China). The metabolites were analyzed using a liquid chromatograph mass spectrometer (LC-MS) system triple quadrupole (Thermo Fisher Scientific, TSQ Fortis TM Plus, Massachusetts, USA). Equipped with ESI Turbo IonSpray interface, it can be used in positive ion and negative ion mode, and adjusted by Analyst 1.6.3 software (AB Sciex). The operating parameters of the ESI source were referred to the published literature (Sun et al., 2023). The data were analyzed by Analyst 1.6.3 software (AB Sciex). The detailed parameters of chromatographic conditions and spray ionization source were set according to the study of Zheng et al. (Zheng et al., 2021). All raw MS data were analyzed using Analyst (v1.6.3) and Multiquanta (Tsugawa et al., 2015).\u003c/p\u003e \u003cp\u003eTranscriptome sequencing\u003c/p\u003e \u003cp\u003e Total RNA content was extracted using the TaKaRa MiniBEST Plant RNA Extraction Kit (TaKaRa, Japan), according to the manufacturer\u0026rsquo;s instructions. The high-quality total RNAs, validated by gel electrophoresis and an ND-1000 spectrophotometer (Thermo Fisher, USA), were sent to Biomarker Technology Co. Ltd. (Beijing, China) for cDNA library construction and transcriptome sequencing. Eighteen libraries (0, 2, 4, 6, 8, and 10 h \u0026times; three biological replicates) were constructed and sequenced using the Illumina NovaSeq 6000 platform based on the Paired-End 150 (PE150) strategy. The RNA-seq data analysis, identification of differentially expressed genes (DEGs) and quantitative realtime PCR (qRT-PCR) analysis were based on the methods described in our previous study (Qiao et al., 2021). 10 DEGs were selected to validate the RNA-Seq results. The cDNAs were synthesized from the same RNA samples used for transcriptome sequencing. The clean reads data of this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA869358.\u003c/p\u003e \u003cp\u003eKEGG analysis\u003c/p\u003e \u003cp\u003eThe relative abundance of FPKM as a metric of normalised gene expression. Differentially expressed genes (DEGs) were identified using DEGseq (Love et al., 2014) and screened using specific parameters (fold change\u0026thinsp;\u0026ge;\u0026thinsp;2.00 and adjusted p\u0026thinsp;\u0026le;\u0026thinsp;0.05) as thresholds. Enrichment analysis was performed in accordance with the Kyoto Encyclopedia of Genes and Genomes pathway annotation classification to further study the potential biological pathways of these dysregulated metabolites (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/compound/\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/compound/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) enrichment analysis, and used Python-3.5.0 and R-4.0.3R software packages for data processing, P values indicate significance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe data were processed by one-way analysis of ANOVA using SPSS version 25.0 software (SPSS Inc., Chicago, IL, USA) at test significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to evaluate significant difference between groups. Origin 2023 (OriginLab Co., Northampton, MA, USA) was used to create figures. Using Design-expert (version 13.0; stat-ease, Minneapolis, MN, USA) performed least squares regression analysis of the data. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the data of 6 samples using SIMCA-P version 14.0 to determine the significantly different accumulated metabolites of HS and LS. TB tools (v1.082, Guangzhou, China) was applied to visualize the KEGG and GO enrichment results and heatmap analysis(Chen et al., 2023).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.Y.X and L.L.T. prepared and designed the study. G.H, L.L., Z.Y.N, S.J.J, L.X.S, and H.H. designed the detailed experiments, and evaluated and analyzed the performance data. W.Y.X. drafted the original manuscript which was subsequently re-examined following various discussions regarding data interpretation by W.Y.X.and L.L.T.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe wish to express our gratitude for the financial support provided by the Guizhou Province Science and Technology Planning Project (Qiankehe Support [2023] 087，[2023] 012); Southwest Guizhou Science and Technology Planning Project . Innovative Capacity Construction Project for Protection and Utilization of Crop Germplasm Resources in Tongren City (Qiankehefuqi[2022]012).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. Source data are provided with this paper and its Supplementary Information files. .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad, M.Z., Li, P., She, G., Xia, E., Benedito, V.A., Wan, X.C., Zhao, J., 2020. Genome-Wide Analysis of Serine Carboxypeptidase-Like Acyltransferase Gene Family for Evolution and Characterization of Enzymes Involved in the Biosynthesis of Galloylated Catechins in the Tea Plant (Camellia sinensis). Front. Plant Sci. 11. https://doi.org/10.3389/fpls.2020.00848\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshihara, H., 2015. Occurrence, Biosynthesis and Metabolism of Theanine (γ-Glutamyl-L-ethylamide) in Plants: A Comprehensive Review. Natural Product Communications10,1934578X1501000.https://doi.org/10.1177/1934578X1501000525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasyuk, E., Rage, F., Bertrand, E., 2021. RNA transport from transcription to localized translation: a single molecule perspective. RNA Biology 18, 1221\u0026ndash;1237. https://doi.org/10.1080/15476286.2020.1842631\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, M., Ma, J., Sun, Y., Tian, L., Liu, L., Chen, Q., Zhang, Z., Wan, X., Sun, J., 2023. γ-Glutamyl-transpeptidase CsGGT2 functions as light-activated theanine hydrolase in tea plant (Camellia sinensis L.). Plant, Cell \u0026amp; Environment 46, 1596\u0026ndash;1609. https://doi.org/10.1111/pce.14561\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C., Wu, Y., Li, J., Wang, X., Zeng, Z., Xu, J., Liu, Y., Feng, J., Chen, H., He, Y., Xia, R., 2023. TBtools-II: A \u0026ldquo;one for all, all for one\u0026rdquo; bioinformatics platform for biological big-data mining. Molecular Plant 16, 1733\u0026ndash;1742. https://doi.org/10.1016/j.molp.2023.09.010\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, M., 2021. The Tea Plant Leaf Cuticle: From Plant Protection to Tea Quality. Front. Plant Sci. 12. https://doi.org/10.3389/fpls.2021.751547\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, H., Wu, W., Liu, X., Wang, Y., Xu, P., 2022. Transcription factor CsWRKY40 regulates L-theanine hydrolysis by activating the CsPDX2.1 promoter in tea leaves during withering. Horticulture Research 9, uhac025. https://doi.org/10.1093/hr/uhac025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai, X., Liu, Y., Zhuang, J., Yao, S., Liu, L., Jiang, X., Zhou, K., Wang, Y., Xie, D., Bennetzen, J.L., Gao, L., Xia, T., 2020. Discovery and characterization of tannase genes in plants: roles in hydrolysis of tannins. New Phytologist 226, 1104\u0026ndash;1116. https://doi.org/10.1111/nph.16425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu, J., Wu, X., Sun, S., Qin, Y., Liao, K., Liu, X., Qiu, R., Long, Z., Zhang, L., 2023. Study on inoculation fermentation by fungi to improve the taste quality of summer green tea. Food Bioscience 51, 102321. https://doi.org/10.1016/j.fbio.2022.102321\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, X., Cheng, S., Liao, Y., Xu, X., Wang, X., Hao, X., Xu, P., Dong, F., Yang, Z., 2020. Characterization of l-Theanine Hydrolase in Vitro and Subcellular Distribution of Its Specific Product Ethylamine in Tea (Camellia sinensis). J. Agric. Food Chem. 68, 10842\u0026ndash;10851. https://doi.org/10.1021/acs.jafc.0c01796\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKujawska, M., Ewertowska, M., Ignatowicz, E., Adamska, T., Szaefer, H., Gramza-Michałowska, A., Korczak, J., Jodynis-Liebert, J., 2016. Evaluation of Safety and Antioxidant Activity of Yellow Tea ( \u003cem\u003eCamellia sinensis\u003c/em\u003e ) Extract for Application in Food. Journal of Medicinal Food 19, 330\u0026ndash;336. https://doi.org/10.1089/jmf.2015.0114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung, F.**, Yung, L.M., Ngai, C.Y., Cheang, W.S., Tian, **ao Yu, Lau, C.W., Zhang, Y., Liu, J., Chen, Z.Y., Bian, Z.-**ang, Yao, **aoqiang, Huang, Y., 2016. Chronic black tea extract consumption improves endothelial function in ovariectomized rats. Eur J Nutr 55, 1963\u0026ndash;1972. https://doi.org/10.1007/s00394-015-1012-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, K., Zhou, R., Wang, B., Chen, K., Shi, L.-Y., Zhu, J.-D., Mi, M.-T., 2013. Effect of green tea on glucose control and insulin sensitivity: a meta-analysis of 17 randomized controlled trials. The American journal of clinical nutrition 98, 340\u0026ndash;348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, M.-Y., Burgos, A., Ma, L., Zhang, Q., Tang, D., Ruan, J., 2017. Lipidomics analysis unravels the effect of nitrogen fertilization on lipid metabolism in tea plant (Camellia sinensis L.). BMC Plant Biology 17, 165. https://doi.org/10.1186/s12870-017-1111-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Z.-W., Wu, Z.-J., Li, H., Wang, Y.-X., Zhuang, J., 2017. L-Theanine Content and Related Gene Expression: Novel Insights into Theanine Biosynthesis and Hydrolysis among Different Tea Plant (Camellia sinensis L.) Tissues and Cultivars. Front. Plant Sci. 8. https://doi.org/10.3389/fpls.2017.00498\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin, D., Wang, Qiushuang, Li, H., Jiang, X., Fang, K., Wang, Qing, Li, B., Pan, C., Wu, H., 2020. Identification of key metabolites based on non-targeted metabolomics and chemometrics analyses provides insights into bitterness in Kucha [\u003cem\u003eCamellia kucha\u003c/em\u003e (Chang et Wang) Chang]. Food Research International 138, 109789. https://doi.org/10.1016/j.foodres.2020.109789\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu, J., Zou, T., Lin, Z., 2021. The Roles of the Ubiquitin\u0026ndash;Proteasome System in the Endoplasmic Reticulum Stress Pathway. International Journal of Molecular Sciences 22, 1526. https://doi.org/10.3390/ijms22041526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSajilata, M.G., Bajaj, P.R., Singhal, R.S., 2008. Tea Polyphenols as Nutraceuticals. Comp Rev Food Sci Food Safe 7, 229\u0026ndash;254. https://doi.org/10.1111/j.1541-4337.2008.00043.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, C., Jiao, H., Chen, Jiahao, Zhang, C., Liu, J., Chen, Jianjiao, Li, Y., Huang, J., Yang, B., Liu, Z., Shen, C., 2022. Carbon and Nitrogen Metabolism Are Jointly Regulated During Shading in Roots and Leaves of Camellia Sinensis. Front. Plant Sci. 13. https://doi.org/10.3389/fpls.2022.894840\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, S., Huang, J., Li, T., Wei, Y., Xu, S., Wang, Y., Ning, J., 2022. Untargeted and targeted metabolomics reveals potential marker compounds of an tea during storage. LWT 154, 112791. https://doi.org/10.1016/j.lwt.2021.112791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, L., Wen, S., Zhang, S., Li, Qiuhua, Cao, J., Chen, R., Chen, Z., Zhang, Z., Li, Z., Li, Qian, Lai, Z., Sun, S., 2024. Study on flavor quality formation in green and yellow tea processing by means of UPLC-MS approach. Food Chemistry: X 22, 101342. https://doi.org/10.1016/j.fochx.2024.101342\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, L., Zhang, S., Li, Qiuhua, Yuan, E., Chen, R., Yan, F., Lai, X., Zhang, Z., Chen, Z., Li, Qian, Sun, S., 2023. Metabolomics and electronic tongue reveal the effects of different storage years on metabolites and taste quality of Oolong Tea. Food Control 152, 109847. https://doi.org/10.1016/j.foodcont.2023.109847\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., Arita, M., 2015. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12, 523\u0026ndash;526. https://doi.org/10.1038/nmeth.3393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson, A.E., Matel, H.D., Tian, L., 2016. Glucose ester enabled acylation in plant specialized metabolism. Phytochem Rev 15, 1057\u0026ndash;1074. https://doi.org/10.1007/s11101-016-9467-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao, S., Liu, Y., Zhuang, J., Zhao, Y., Dai, X., Jiang, C., Wang, Z., Jiang, X., Zhang, S., Qian, Y., Tai, Y., Wang, Y., Wang, H., Xie, D.-Y., Gao, L., Xia, T., 2022. Insights into acylation mechanisms: co-expression of serine carboxypeptidase-like acyltransferases and their non-catalytic companion paralogs. The Plant Journal 111, 117\u0026ndash;133. https://doi.org/10.1111/tpj.15782\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, H.-B., Xia, E.-H., Huang, H., Jiang, J.-J., Liu, B.-Y., Gao, L.-Z., 2015. De novo transcriptome assembly of the wild relative of tea tree (Camellia taliensis) and comparative analysis with tea transcriptome identified putative genes associated with tea quality and stress response. BMC Genomics 16, 298. https://doi.org/10.1186/s12864-015-1494-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Wen, B., Zhang, Y., Li, Y., Yu, C., Peng, Z., Wang, K., Liu, Z., Huang, J., Xiong, L., Li, J., 2022. Transcriptomic and biochemical analysis reveal differential regulatory mechanisms of photosynthetic pigment and characteristic secondary metabolites between high amino acids green-leaf and albino tea cultivars. Scientia Horticulturae 295, 110823. https://doi.org/10.1016/j.scienta.2021.110823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, J., Li, P., Xia, T., Wan, X., 2020. Exploring plant metabolic genomics: chemical diversity, metabolic complexity in the biosynthesis and transport of specialized metabolites with the tea plant as a model. Critical Reviews in Biotechnology 40, 667\u0026ndash;688. https://doi.org/10.1080/07388551.2020.1752617\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y., Wang, P., Chen, X., Yue, C., Guo, Y., Yang, J., Sun, Y., Ye, N., 2021. Integrated transcriptomics and metabolomics provide novel insight into changes in specialized metabolites in an albino tea cultivar (Camellia sinensis (L.) O. Kuntz). Plant Physiology and Biochemistry 160, 27\u0026ndash;36. https://doi.org/10.1016/j.plaphy.2020.12.029\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Yellow tea, Flavor, Withering time, Metabolites, Transcriptome","lastPublishedDoi":"10.21203/rs.3.rs-5265030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5265030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNiangniang tea (NNT) is a traditional compressed large-leaf yellow tea shaped as Chinese writing brush. Withering time affects the flavor characteristics. The sensory evaluation revealed the highest score was achieved at 8 hours of withering (Yd). Metabolomics analysis revealed a decrease in 18 bitter metabolites, accompanied by an increase in sweet metabolites and amino acids during the withering process. Transcriptome analysis showed, the relative expression of \u003cem\u003eCsSCPL\u003c/em\u003e (Serine carboxypeptidase-like) decreased rapidly, while the relative expression of \u003cem\u003eCsTA\u003c/em\u003e (Tannase) showed an increasing trend, which inhibited the acylation of non-ester type catechins to ester type, and promoted the conversion of bitter and heavy ester type catechins to non-ester type catechins with lighter bitterness. The withering process of NNT reduced the bitter taste but enhanced sweetness, and the tea tastes more sweet and mellow. Metabolomics and transcriptomics result conducive to a more comprehensive and systematic understanding of the formation mechanism of flavor quality in the withering process.\u003c/p\u003e","manuscriptTitle":"Mechanism Underlying Flavor Quality Formation during Withering Process of Niangniang Tea, a Compressed Large-Leaf Yellow Tea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 12:48:13","doi":"10.21203/rs.3.rs-5265030/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a0eeeb9-a0e2-4bc5-aacd-4ad03f5f1f97","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40012102,"name":"Biological sciences/Chemical biology/Metabolomics"},{"id":40012103,"name":"Physical sciences/Chemistry/Biochemistry/Dna"}],"tags":[],"updatedAt":"2025-03-17T06:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 12:48:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5265030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5265030","identity":"rs-5265030","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}