Abstract
The dynamic changes of metabolites and their regulatory mechanisms
during black tea processing are not yet fully clear. In this study,
flavonoid glycosides, tea pigments, VTs, and FADVs were primarily
influenced. The content of these components continuously increased
during processing, reaching their maximum after fermentation, and then
decreased after drying. Withering upregulated AM and GT genes,
promoting the glycosylation of flavonoids; the upregulation of ANR and
PPO genes facilitated the oxidative polymerization of catechins; and
the upregulation of TPS, LOX, and HPL genes promoted terpenoid
synthesis and fatty acid degradation. This led to an increase in the
content of these components in withered leaves. The accumulation of
these components during fermentation was mainly due to the disruption
of cells during rolling, allowing enzymes and substrates to fully
integrate and react during the prolonged fermentation process. The
decline in compound during the drying was primarily attributed to
thermal degradation.
Subject terms: Biochemistry, Plant sciences, Agriculture
Introduction
Black tea is one of the most consumed tea beverages globally,
accounting for approximately 75% of worldwide tea consumption^[44]1. It
not only possesses various health benefits such as anti-inflammatory,
antibacterial, and antioxidant properties^[45]2, but also attracts
consumers around the world due to its excellent and rich quality and
flavor^[46]3. The processing of traditional black tea involves several
steps: withering, rolling, fermentation, and drying, each playing a
crucial role in the formation of its unique flavor profile. During the
withering, the water loss increases the concentration of cell sap in
tea leaves, enhancing the activity of various endogenous enzymes
involved in the transformation of metabolites^[47]4. The rolling
disrupts the tissue cells of the tea leaves, releasing a large number
of enzymes and substrates. In the fermentation stage, these enzymes and
substrates come into full contact and react, accelerating the
conversion of flavor compounds^[48]5. These quality components undergo
further transformation and recombination during the drying process,
contributing to the development of the color, aroma, and taste
characteristics of black tea. The intrinsic quality of tea encompasses
taste, aroma, infusion color, and infused leaves. Among them, taste and
aroma are the core attributes of tea quality^[49]6,[50]7, which largely
influence the intuitive evaluation of tea drinkers. The taste compounds
in tea are primarily composed of flavonoids, organic acids, phenolic
acids, amino acids, tea pigments, and carbohydrates. Meanwhile, the
aroma of black tea is mainly derived from alcohols, aldehydes, esters,
and ketones, with over 600 distinct aroma components having been
identified^[51]3.
Currently, extensive research has been conducted on the dynamic changes
of non-volatile^[52]2,[53]8,[54]9, volatile metabolites^[55]3,[56]10
and gene expression^[57]11,[58]12 during the processing of black tea.
However, the majority of research has relied solely on headspace
solid-phase microextraction (HS-SPME) to investigate the variation
patterns of volatiles, which may not comprehensively capture all aroma
compounds and could overlook some important volatiles. Additionally,
the regulatory mechanisms underlying the evolution of key taste and
aroma compounds during the entire processing of black tea are not fully
understood. Therefore, ultra-performance liquid
chromatography-quadrupole time-of-flight mass spectrometry
(UPLC-QTOF-MS), push-pull dynamic headspace collection (PPDH) combined
with gas chromatography-mass spectrometry (GC-MS), HS-SPME-GC-MS, and
transcriptomics were employed to systematically analyze the formation
and regulatory mechanisms of key taste and aroma compounds during the
processing of black tea. This study lays a scientific foundation for
improving the quality of black tea products in the future.
Results and discussion
Changes of non-volatile metabolites during processing
To elucidate the dynamic changes of non-volatile compounds during black
tea processing, non-volatile metabolites in tea leaves at various
processing stages were measured using UPLC-QTOF-MS, followed by
multivariate statistical analysis (Fig. [59]1). Correlation analysis
revealed strong intra-replicate correlations (Supplementary Fig.
[60]1), indicating high reliability and accuracy of the metabolite
measurements. The principal component analysis (PCA) (Fig. [61]1a)
clearly distinguished samples from different processing stages,
suggesting differences in non-volatile metabolites among the tea
samples. Additionally, the QC samples clustered near the origin,
demonstrating the stability and precision of the measurement system and
the reliability of the results. The partial least squares discriminant
analysis (PLS-DA) results (Fig. [62]1b) also indicated differences in
metabolites among the samples, consistent with the PCA results. The
permutation test of the PLS-DA model confirmed its validity and absence
of overfitting (Fig. [63]1c).
Fig. 1. Multivariate statistical analysis.
[64]Fig. 1
[65]Open in a new tab
a Principal component analysis (PCA). b Partial least squares
discriminant analysis (PLS-DA). c Permutation test. d Heatmap of
differential metabolites. e Differential metabolite composition in
dried finished tea, bubble size represents metabolite abundace. FT
represents the sample of fresh tea leaves, W represents the sample of
withered leaves, F represents the sample of fermented leaves, D
represents the sample of dried finished tea, QC represents the sample
of quality control. Similarly hereinafter.
Based on P < 0.05, VIP > 1, and fold change > 2, a total of 44
differential metabolites were identified, including flavonoid
glycosides, amino acids, phenolic acids, theaflavins, and
proanthocyanidins (Supplementary Data [66]1). The clustering heatmap
revealed significant changes in the levels of these differential
metabolites during processing (Fig. [67]1d). As tea processing
progressed, these differential metabolites generally showed an initial
increase followed by a decrease, with fermentation being the critical
stage for the synthesis of metabolites, consistent with previous
research^[68]12. Flavonoid glycosides were the primary differential
metabolites identified in this study, primarily consisting of
kaempferol glycosides, which significantly contribute to the color and
taste of tea infusion^[69]6,[70]7,[71]13. In this study, these
flavonoid glycosides continued to increase until drying, after which
they declined (Fig. [72]1d). This trend is largely consistent with
previous research^[73]14. Theaflavins, crucial contributors to the
infusion color of black tea, also significantly impact its
flavor^[74]8,[75]13. In this study, theaflavins accumulated to their
maximum levels during the fermentation stage (Fig. [76]1d), aligning
with earlier finding^[77]8. Besides these metabolites, others such as
amino acids, proanthocyanidins, and linalool glycosides consistently
decreased throughout the processing (Fig. [78]1d). Theanine, the most
abundant amino acid in tea, has been reported to continuously decrease
during black tea processing^[79]8,[80]10, consistent with the results
of this study (Fig. [81]1d). The reduction in theanine is primarily due
to the interruption of its transport to new shoots after the tea leaves
are harvested^[82]8. Additionally, the decrease in theanine may be
related to the increased expression levels of CSWRKY40 and CSPDX2.1.
The reduction in amino acids may contribute to the formation of tea
color and aroma through the Maillard reaction^[83]15.
Proanthocyanidins, typical tea pigments, are considered bitter
compounds^[84]16. Studies showed a declining trend of proanthocyanidins
during processing^[85]15,[86]17, consistent with the findings of this
study (Fig. [87]1d).
The composition of non-volatile compounds in finished tea determines
the taste quality. Thus, the differential metabolites in sample D were
visualized using a network diagram (Fig. [88]1e; compound codes
correspond to those listed in Supplementary Table [89]1). The diagram
clearly showed that flavonoid glycosides and theaflavins had higher
total abundances, indicating that high levels of these compounds were
key to forming the color characteristics of black tea infusion. For
instance, studies showed^[90]6,[91]13,[92]18 that higher theaflavin
content resulted in a darker tea infusion color and some flavonoid
glycosides were found to be significantly positively correlated with
infusion color. In this study, M4 (kaempferol 3-O-glucosyl rutinoside)
had the highest abundance, followed by M5 (quercetin 3-O-glucosyl
rutinoside), M13 (apigenin 7-arabinoside), and M14 (capilliposide I
isomer 1). One study showed^[93]13 that the abundance of kaempferol
3-O-glucosyl rutinoside was significantly negatively correlated with
the color of beauty tea infusion and positively contributes to the
sweetness and mellowness, while capilliposide I isomer 1 shows the
opposite effect. Quercetin 3-O-glucosyl rutinoside was also considered
one of important contributors to the infusion color and taste of
Qingxiang Tieguanyin tea^[94]7.
The accumulation of flavonoid glycosides enhances both the flavor and health
benefits of tea
Flavonoids predominantly exist in plants in the form of glycosides,
primarily formed through oxidation (including hydroxylation and
methoxylation) at key positions on the flavonoid backbone, exhibiting
significant biological potential for antioxidant, hepatoprotective,
anti-inflammatory, anticancer, and antiviral activities^[95]19. Sugar
substituents are typically linked to the flavonoid carbon skeleton in
hydroxylated forms, forming O-glycosides, or directly connected to the
C atoms on the flavonoid A-ring to form C-glycosides. Flavonols such as
myricetin, kaempferol, and quercetin are the most widely distributed
flavonoids, and their glycosides generally exist as O-glycosides, with
the glycosyl group primarily attached at the C-3 position, a conclusion
supported by our research findings (Supplementary Data [96]1).
From a flavor perspective, flavonoid glycosides have a velvety,
silky-astringent, and mouth-coating sensation^[97]20. Flavonoid
glycosides have very low taste thresholds, making them essential
contributors to the taste quality of tea. For instance, the threshold
of quercetin-3-O-[α-l-rhamnopyranosyl-(1 → 6)-O-β-D-glucopyranoside],
quercetin 3-O-rutinoside, and kaempferol 3-O-rutinoside are as low as
0.001, 0.00115, and 0.25 μmol/L, respectively, which is far lower than
for EGCG (190 μmol/L) and ECG (260 μmol/L)^[98]19. Flavonoid glycosides
are hydrolytically stable, enhancing their in vitro absorption^[99]21.
From a nutritional and dietary perspective, the substantial
accumulation of flavonoid glycosides results in higher concentrations
of bioactive compounds in brewed tea, enhancing its health benefits and
contributing to a mellower, thicker, and more harmonious taste^[100]19.
Previously, it was believed that the enhancement of tea flavor was
closely related to the increase in catechins^[101]22,[102]23, and that
a reduction in catechins could decrease the bitterness and astringency
of tea infusion^[103]24, significantly improving tea flavor. Based on
metabolomic analysis and the role of these compounds in food flavor and
health, we propose that the accumulation of flavonoid glycosides,
rather than changes in catechin content, can enhance tea flavor. Given
the importance of O-glycosylflavonoids in stress resistance and human
health, further in-depth research is needed.
Changes in volatiles monitored in real-time during processing
To monitor the dynamic changes of volatiles in tea leaves during
processing in real-time, the PPDH method combined with GC-MS was used
to measure the volatiles. A total of 73 volatiles were identified
(Supplementary Data [104]2; no volatiles were detected in sample D). As
processing progressed, the number of volatiles increased (27 volatiles
in FT, 56 in W, and 59 in F). These volatiles primarily included
alcohols, esters, alkenes, and aldehydes (Fig. [105]2a), consistent
with previous research^[106]10. Additionally, the total content of
volatiles also continuously increased. Among them, alcohols and ketones
increased throughout the processing, while alkenes showed the opposite
trend. Aldehydes and esters initially increased and then slightly
decreased, whereas other compounds such as alkanes and aromatic
compounds showed little change during processing (Fig. [107]2b). These
results indicate that aldehydes are mainly formed during the withering
stage, and fermentation is the critical step for the accumulation of
alcohols. It has been reported that the primary aroma compounds of
black tea are alcohols and aldehydes^[108]25, which aligns with the
findings of this study.
Fig. 2. Multivariate statistical analysis.
[109]Fig. 2
[110]Open in a new tab
a The proportion of volatiles. b Stacked bar chart of volatiles. c PCA.
d PLS-DA. e Permutation test. f Variable importance in projection (VIP)
analysis. g Heatmap of key volatiles. h Heatmap of correlation between
non-volatile metabolites and key volatiles.
The PCA (Fig. [111]2c) and PLS-DA (Fig. [112]2d) showed clear
separation of tea samples from different processing stages, indicating
differences in volatiles among the samples. The permutation test of the
PLS-DA confirmed its validity and absence of overfitting (Fig.
[113]2e). Based on the variable importance in projection (VIP) analysis
from PLS-DA, a total of 15 volatiles with VIP > 1 exhibited significant
changes during processing (Fig. [114]2f), primarily consisting of fatty
acid derivatives (FADVs) and volatile terpenes (VTs). As shown in Fig.
[115]2g, these key volatiles showed an increasing trend as processing
progressed, with most of them sharply increasing during the
fermentation stage, suggesting that fermentation significantly promotes
fatty acid degradation and terpenoid synthesis. In summary, the PPDH
analysis results indicate that processing primarily promotes fatty acid
degradation and terpenoid synthesis, leading to the substantial
generation of aldehydes and alcohols during processing, which become
the main aroma components. This also drives the continuous increase in
the number and total content of volatiles up to the drying stage.
Additionally, a correlation analysis was conducted between the
representative non-volatile compounds and these 15 key volatiles. As
shown in Fig. [116]2h, linalool oxide primeveroside (C1), theanine
(C2), and glutathione (C3) exhibited highly significant negative
correlations (P < 0.01) with 10 key volatiles and a highly significant
positive correlation (P < 0.01) with β-myrcene. In contrast, kaempferol
3-O-glucosyl rutinoside (C5) showed the opposite trend. Sucrose (C4)
showed a highly significant positive correlation (P < 0.01) with
cis-β-ocimene but no significant correlations with other compounds.
During tea processing, particularly under the action of rolling, the
damaged tea tissues release enzymes that hydrolyze glycosidic bonds.
This releases monoterpenols such as linalool and its oxides, and
geraniol^[117]26. Furthermore, the aglycones produced during hydrolysis
form O-flavonoid glycosides with flavonols through hydroxylation. This
may explain the highly significant positive correlation between
flavonoid glycosides and linalool and its oxides, as well as the highly
significant negative correlation between linalool oxide primeveroside
and linalool oxide (Fig. [118]2h).
Changes in volatiles in freeze-dried samples during processing
To elucidate the dynamic changes of volatiles in freeze-dried samples
during processing, HS-SPME coupled with GC-MS was used to measure the
volatiles. A total of 64 volatiles were identified (Supplementary Data
[119]3), primarily including alcohols, alkenes, esters, and aldehydes
(Fig. [120]3a). As processing progressed, the total content of
volatiles in the tea leaves showed an initial increase followed by a
decrease (Fig. [121]3b). These results were consistent with the aroma
analysis obtained using the PPDH method. Alcohols, aldehydes, esters,
and alkenes all increased during processing and peaked after
fermentation (Fig. [122]3b). The aroma characteristics of the finished
tea are determined by the composition of its volatiles. To provide a
more intuitive understanding of the volatile composition in the
finished tea, the volatiles in sample D were visualized using a network
diagram (Fig. [123]3c; with volatile codes corresponding to those
listed in Supplementary Table [124]2). Among them, alcohols were the
main aroma components, followed by aldehydes and esters. The content of
A8 (nerol) was higher than that of other volatiles, while the contents
of the remaining volatiles were relatively similar (Fig. [125]3c). Venn
diagram results showed that there were 25 common volatiles among these
tea samples, and the number of unique volatiles formed at each stage
increased as processing progressed (Fig. [126]3d). PCA and PLS-DA
results (Fig. [127]3e, f) revealed clear separation of tea samples from
different processing stages, indicating differences in volatiles among
the freeze-dried samples. The permutation test of the PLS-DA confirmed
its validity and absence of overfitting (Fig. [128]3g). Additionally,
based on VIP > 1, a total of 14 volatiles exhibiting significant
changes during processing were identified (Fig. [129]3h). Unlike the
results from the PPDH method, these key volatiles were primarily VTs
and amino acid derivatives (AADVs), with fewer FADVs.
Fig. 3. Multivariate statistical analysis.
[130]Fig. 3
[131]Open in a new tab
a The proportion of volatiles. b Bar chart of various volatiles and
stacked bar chart of total volatiles. c Volatile metabolite compostion
in dried finished tea, bubble size represents the content of volatile
metabolite. d Venn diagram. e PCA. f PLS-DA. g Permutation test. h VIP
analysis, i Heatmap of characteristic volatiles.
The final contribution of volatiles to the aroma quality of tea depends
on their odor activity value (OAV)^[132]16. To further understand the
characteristic volatiles that contribute to the formation of aroma
quality during black tea processing, the OAVs of the volatiles were
calculated and 8 characteristic volatiles were identified (VIP > 1 &
OAVs ≥ 1). Linalool, nerol, and benzeneacetaldehyde were identified as
the primary aroma contributors. The remaining volatiles, such as
β-myrcene, methyl salicylate, indole, (E)-2-hexenal, and phenylethyl
alcohol, also significantly contributed to the aroma characteristics of
the tea during processing (Supplementary Table [133]3). Compared to FT,
the contents of the 7 characteristic volatiles, except for C5 (indole),
increased during processing. The levels of C6 (benzeneacetaldehyde) and
C7 (phenylethyl alcohol) peaked after drying. The contents of C1
(nerol), C3 (β-myrcene), and C4 (methyl salicylate) peaked after
fermentation, while the contents of C2 (linalool) and C8
((E)-2-hexenal) were highest after withering and fermentation (Fig.
[134]3i). Among the numerous volatiles, phenylethyl alcohol,
benzeneacetaldehyde, (E)-2-hexenal, linalool, and methyl salicylate
have been identified as key aroma-active compounds in black tea^[135]5,
consistent with the findings of this study. Additionally, β-myrcene,
linalool, and methyl salicylate were also considered key aroma-active
substances in beauty tea made from different fresh leaf
materials^[136]27. Although these characteristic volatiles are decisive
for the aroma quality of black tea, the potential additive effects of
other volatiles (OAVs < 1), such as synergistic or antagonistic
effects, should not be overlooked. In summary, the HS-SPME analysis
results indicate that processing primarily promotes terpenoid synthesis
and amino acid degradation, leading to the continuous increase of
alcohols, aldehydes, and esters during processing, peaking after
fermentation, thereby driving the increase in the total content of
volatiles. Furthermore, 8 characteristic volatiles, including linalool,
nerol, and β-myrcene, were identified, with phenylalanine-derived
products being largely produced during the drying stage, while
glycoside-derived products were mainly formed during the fermentation
stage.
Transcriptomic analysis
After removing low-quality reads, ambiguous reads, and adapter
sequences, each library contained 22.21-25.00 million clean reads
(Supplementary Table [137]4). These clean reads were mapped to the tea
reference genome, with the alignment efficiency of reads to the
reference genome ranging from 87.61% to 95.06% across samples
(Supplementary Table [138]5). Additionally, 24,425 genes predicted from
the genome were expressed in at least one sample (Supplementary Data
[139]4). Furthermore, 4405 new genes, not included in the reference
genome, were identified. Ten genes were selected for quantitative
real-time polymerase chain reaction (qRT-PCR) validation. The results
showed that the relative expression levels were consistent with the
RNA-Seq data (Supplementary Fig. [140]2), supporting the accuracy and
reliability of the transcriptomic analysis results.
Multivariate statistical analysis showed differences in gene expression
among tea samples at different processing stages, with the total
explained variance exceeding 91%. Additionally, PCA (Fig. [141]4a) and
PLS-DA (Fig. [142]4b) showed low variability between biological
replicates, and the correlation heatmap also indicated strong
correlations among replicates (Supplementary Fig. [143]3),
demonstrating high reproducibility in gene expression patterns. The
permutation test of the PLS-DA model confirmed its validity (Fig.
[144]4c). Through differential expression analysis across the three
processing stages, 21,057 differentially expressed genes (DEGs) were
identified. Among them, 16,132 DEGs were identified in the FT vs W
comparison, including 7761 upregulated and 8371 downregulated genes
(Fig. [145]4d, f), suggesting that downregulated genes may play a more
significant role during the withering stage of black tea processing. In
the W vs F comparison, 11,067 DEGs were identified, including 5598
upregulated and 5469 downregulated genes (Fig. [146]4d, g), indicating
that upregulated genes may play a more important role during the
fermentation stage of black tea processing. In the FT vs F comparison,
12,159 DEGs were identified, including 5791 upregulated and 6368
downregulated genes (Fig. [147]4d, h). Among the three comparison
groups, 2704 common DEGs and 5460 unique DEGs were identified (Fig.
[148]4e). Among the 2704 common DEGs, genes involved in metabolic
processes within biological processes played a dominant role
(Supplementary Data [149]5), indicating significant changes in
metabolites in tea leaves in response to abiotic stress during
processing. The remaining 18,353 DEGs accounted for 87.16% of the total
DEGs, suggesting a broad range of genetic variation among tea samples
at different processing stages.
Fig. 4. Multivariate statistical analysis and enrichment analysis of
differential expressed genes (DEGs).
[150]Fig. 4
[151]Open in a new tab
a PCA. b PLS-DA. c Permutation test. d Bar chart of DEGs. e Venn plot.
f–h Volcano plot of the FT vs W group, W vs F group, and FT vs F group,
respectively. i–k Genetic ontological (GO) enricment analysis of the FT
vs W group, W vs F group, and FT vs F group, respectively. l–n Kyoto
encyclopedia of genes and genomes (KEGG) pathway enrichment analysis,
green dots represent upregulated genes, while purple dots represent
downregulated genes.
As shown in Fig. [152]4i–k, Genetic ontological (GO) enrichment
analysis revealed that glutathione metabolic process (GO:0006749),
galactose metabolic process (GO:0006012), and sucrose metabolic process
(GO:0005985) were the primary metabolic processes. Kyoto encyclopedia
of genes and genomes (KEGG) pathway enrichment analysis was also
conducted. As shown in Fig. [153]4l–n, the DEGs in all three groups
were significantly enriched in amino acid metabolism pathways
(P < 0.05), followed by fatty acid and carbohydrate metabolism
pathways. In the FT vs W group, ko00280 (Valine, leucine, and
isoleucine degradation), ko00410 (beta-Alanine metabolism), and ko00480
(Glutathione metabolism) were the most significant (P < 0.001), with
most DEGs in these pathways being upregulated. In the W vs F group,
ko00280 (Valine, leucine, and isoleucine degradation), ko00592
(α-Linolenic acid metabolism), and ko00480 (Glutathione metabolism)
were the most significant (P < 0.001), with most DEGs in these pathways
being downregulated. However, in the FT vs F group, carbohydrate
metabolism pathways were the most significant, and most DEGs in these
pathways were also downregulated. In summary, amino acid, fatty acid,
and carbohydrate metabolism were the most prominent during tea
processing, and the expression levels of most genes involved in these
metabolic pathways decreased during processing.
Processing enhances the content of flavonoid glycosides by promoting the
glycosylation of flavonoids
In this study, flavonoid glycosides were the main differential
metabolites. Given the notable changes in flavonoid glycosides and
their importance to flavor and infusion color quality, the regulatory
mechanisms of flavonoid glycosides during processing were emphasized. A
total of 60 genes related to the differential accumulation of
flavonoids during black tea processing were identified (Fig. [154]5a,
Supplementary Data [155]6) and a flavonoid metabolic pathway was
further constructed. As shown in Fig. [156]5b, most enzyme genes
involved in flavonoid biosynthesis were downregulated during
processing. This is because gene regulation primarily occurs before
harvesting, and the expression of most genes rapidly decreases after
fresh leaves are plucked^[157]2. It has been reported that the
expression of chalcone synthase (CHS), chalcone isomerase (CHI),
flavanone 3-hydroxylase (F3H), dihydroflavonol 4-reductase (DFR),
leucoanthocyanidin reductase (LAR), anthocyanidin reductase (ANR), and
flavonol synthase (FLS) in Wuyi black tea significantly decreased
during processing^[158]2, further supporting our results.
Interestingly, the total abundance of flavonoid glycosides in this
study continuously increased as processing progressed (Fig. [159]5c),
indicating that the accumulation of flavonoid glycosides is mainly
regulated by other key enzyme genes. It has been reported that the
accumulation of flavonoid glycosides is associated with the
glycosylation of flavonoids^[160]14. Therefore, several amylase (AM)
and glycosyltransferase (GT) genes were identified (Supplementary Data
[161]7 and [162]8). As shown in Fig. [163]5d, among these highly
expressed AM genes, their expression levels in FT were significantly
lower than in W and F (P < 0.05). This leads to the production of more
monosaccharides during the withering and fermentation. These
monosaccharides can easily form flavonoid glycosides with flavonoids
(such as kaempferol and quercetin) under the action of GTs^[164]14. The
expression levels of most GT genes were higher in W and lower in F
(Fig. [165]5e). During withering, prolonged water loss stress activates
AMs and GTs. Simultaneously, cell membrane permeability increases, and
the concentrations of enzymes and substrates rise^[166]8. This results
in a highly significant increase in the content of flavonoid glycosides
in W (P < 0.01, Fig. [167]5c). Rolling disrupts the tissue cells of tea
leaves, further enhancing the contact between enzymes and substrates
during fermentation and making the related enzymatic reactions more
thorough. However, since the expression levels of GT genes decreased
during the fermentation (Fig. [168]5e), this may explain why the rate
of flavonoid glycoside generation was lower during fermentation than
during withering, although the overall content of flavonoid glycosides
still increased (Fig. [169]5c), consistent with previous
research^[170]2,[171]10. In summary, withering enhances the expression
of AM and GT genes, while fermentation increases the expression of AM
genes and, through the action of rolling, significantly enhances the
contact between relevant enzymes and substrates. This provides ample
substrates for the glycosylation of flavonoids, catalyzed by GTs,
leading to the continuous accumulation of flavonoid glycosides during
processing.
Fig. 5. The regulatory mechanism of the flavonoid biosynthesis pathway.
[172]Fig. 5
[173]Open in a new tab
a Venn plot. b Flavonoid metabolic pathway. c Flavonoid glycosides
abundance in tea at each processing stage. “*” indicates significant
difference (P < 0.05), “**” indicates highly significant difference
(P < 0.01), “***” indicates extremely significant difference
(P < 0.001). d Heatmap of amylase (AM) gene expression. e Heatmap of
glycosyltransferase (GT) gene expression. f Correlation between genes
of related enzymes in flavonoid metabolism pathway. CHS Chalcone
synthase, CHI Chalcone isomerase, F3H Flavanone 3-hydroxylase, FLS
Flavonol synthase, DFR Dihydroflavonol 4-reductase, LAR
Leucoanthocyanidin reductase, ANR Anthocyanidin reductase, PKSB Type
III polyketide synthase B, CYP75B2 Flavonoid 3’-monooxygenase.
Processing enhances the content of tea pigments by increasing the expression
of ANR and PPO genes
Theaflavins are typical tea pigments in black tea. In this study, the
abundance of theaflavins increased during processing and peaked during
the fermentation stage (Fig. [174]1d), consistent with previous
research^[175]2. ANR is an enzyme at the end of the flavonoid metabolic
pathway, involved in the formation of epicatechin in tea plants, which
is a precursor for the synthesis of theaflavins. In this study, the
expression levels of some ANR genes showed an increasing trend during
processing (Fig. [176]5b), aligning with the trend of theaflavin
changes. The upregulation of ANR promoted the accumulation of
epicatechin in tea leaves, providing more substrates for theaflavin
formation. Catechins can be oxidatively polymerized to form theaflavins
under the action of polyphenol oxidase (PPO) and peroxidase
(POD)^[177]14. Therefore, two PPO genes were identified and found that
their expression levels initially increased and then decreased during
processing, with the lowest expression in FT (Supplementary Fig.
[178]4), consistent with previous research^[179]2. Dehydration during
withering not only enhances the concentration of enzymes and substrates
but also increases the expression of ANR and PPO genes. This
accelerates the oxidative polymerization of catechins and promotes the
accumulation of theaflavins. During fermentation, the expression of ANR
genes further increased, strengthening the generation of epicatechin.
Although the expression of PPO genes decreased, the disruption of tea
leaf cells caused by rolling enhanced the interaction between enzymes
and substrates^[180]28, leading to more thorough enzymatic oxidation of
catechins during the prolonged fermentation process, thereby increasing
the content of theaflavins. In summary, the high expression of ANR and
PPO during processing, as well as the sufficient integration and
reaction between the enzymes and substrates, promote the generation and
accumulation of theaflavins.
Proanthocyanidins are also typical tea pigments. In this study, some
proanthocyanidins, such as procyanidin isomer and ECG-EGCG dimer
isomer, showed trends similar to those of theaflavins (Fig. [181]1d).
Given that proanthocyanidins are also formed through the oxidative
polymerization of catechins, we believe that the accumulation of these
two proanthocyanidins is regulated by the expression of ANR and PPO
genes. Previous studies found^[182]9,[183]10,[184]17 that the content
of proanthocyanidins continuously decreased during processing. In this
study, the abundance of some proanthocyanidins, such as procyanidin B
isomer, prodelphinidin B isomer, and procyanidin trimer isomer 1, also
decreased during processing (Fig. [185]1d), indicating that the changes
in these proanthocyanidins are primarily influenced by other key enzyme
genes. One study showed that the ANS gene played an important role in
the formation of ECG and EGCG, and the downregulation of this gene led
to a reduction in catechins and proanthocyanidins^[186]19. In this
study, the DFR gene upstream of cyanidin and delphinidin was
significantly downregulated during the withering and fermentation
stages (P < 0.05), and it was speculated that the downregulation of the
DFR gene contributed to the reduction of some proanthocyanidins.
Additionally, enzymatic and spontaneous oxidation of proanthocyanidins
may be potential factors in their decreased content^[187]14. In
summary, the activation of ANR and PPO during processing promotes the
generation of some proanthocyanidins, while the reduction of others is
related to the inhibition of key enzymes such as DFR.
Additionally, the R package was employed to perform correlation
analysis (P < 0.05 & |r | > 0.7) on the enzyme genes in the flavonoid
biosynthesis pathway (Fig. [188]5f). The results showed that these
genes were significantly correlated (P < 0.05) and primarily exhibited
positive correlations, indicating that the expression of gene groups
within the same metabolic pathway tends to be similar.
Regulation mechanism of key volatiles during processing
Based on their precursors, the key volatiles identified by the two
extraction methods are primarily classified into VTs, FADVs, and AADVs.
VTs: The analysis results of both extraction methods indicated that the
content of VTs increased during processing and peaked during the
fermentation stage. VTs originate from the monoterpene biosynthesis
pathway and are catalyzed by terpene synthases (TPSs) (Fig. [189]6).
The results of this study showed that among the highly expressed TPS
genes (Supplementary Data [190]9), their expression was upregulated
during the withering stage (Fig. [191]6). This suggests that
dehydration stress readily activates TPS, promoting the synthesis of
VTs. However, the expression of TPS genes decreased during the
fermentation stage (Fig. [192]6), which did not reduce the generation
and release of VTs. This is because, during the rolling process, tissue
cells are disrupted, releasing a large number of enzymes and
substrates, which further fully integrate and react in the prolonged
warm and humid environment, thus further promoting the generation and
accumulation of VTs during fermentation. Notably, the level of VTs
decreased during the drying process, which may be attributed to the
recombination of VTs with glycosides to form non-volatile compounds in
the early stages of drying. Subsequently, prolonged high temperatures
deactivated the enzymes, causing extensive decomposition due to their
high thermal sensitivity^[193]29. In summary, withering activates TPS,
while rolling and fermentation enhance the integration and reaction of
enzymes and substrates, thereby continuously generating and
accumulating VTs before drying.
Fig. 6. Metabolic pathways of key volatiles.
[194]Fig. 6
[195]Open in a new tab
HPL Hydroperoxide lyase, LOX Lipoxygenases, ADH Alcohol dehydrogenase,
PAAS Phenylacetaldehyde synthase, PAR Phenylacetaldehyde reductase, TPS
Terpene synthase.
FADVs: Similar to VTs, the levels of FADVs increased during processing
and peaked during the fermentation stage. These FADVs are derived from
the oxidative degradation of unsaturated fatty acids, a pathway that is
a major route for the formation of tea flavor^[196]26. Fatty acids are
first oxidized by lipoxygenases (LOXs) into lipid hydroperoxides, which
are then cleaved by hydroperoxide lyases (HPLs) into six-carbon
aliphatic aroma compounds such as hexanal. Subsequently, these
aldehydes can be further reduced to their corresponding alcohols or
isomerized into trans-isomers and then reduced to alcohols by alcohol
dehydrogenases (ADHs)^[197]26. In this study, several LOX, HPL, and ADH
genes were identified (Supplementary Data [198]10-[199]12). Among the
highly expressed genes, the expression of LOX and HPL genes was
upregulated during the withering stage (Fig. [200]6). This suggests
that dehydration stress readily activates LOX and HPL, thereby
promoting the synthesis of aldehyde fatty acid derivatives. The
expression of LOX and HPL genes decreased during the fermentation stage
(Fig. [201]6), which may explain the decline in the content of
2-hexanal, 3-hexanal, and (E)-2-hexanal. Compared to FT, the expression
of ADH genes was downregulated during both the withering and
fermentation stages (Fig. [202]6), which may explain the decrease in
2-ethyl-1-hexanol levels. However, the content of other alcohol fatty
acid derivatives was highest during the fermentation stage. This
indicates that alcohol fatty acid derivatives are primarily influenced
by other factors. Continuous dehydration during withering increases the
concentration of enzymes and substrates, promoting related enzymatic
reactions^[203]8; rolling and fermentation further enhance the
integration of relevant enzymes and substrates, thereby facilitating
the reduction reactions catalyzed by ADH. This may be the main reason
for the increase in the content of alcohol fatty acid derivatives and
also explains why the downregulation of LOX and HPL genes during the
fermentation stage did not lead to a decrease in the content of hexanal
and (E)-2-hexanal. Similarly, the reduction in FADVs content during the
drying process may be attributed to the thermal degradation of
volatiles. In summary, withering activates LOX and HPL and increases
the concentration of related enzymes and substrates, thereby promoting
the synthesis of FADVs, while the further accumulation of FADVs during
the fermentation stage is mainly due to the thorough reaction of
enzymes and substrates.
AADVs: The contents of benzeneacetaldehyde and phenylethyl alcohol
increased during processing and peaked during the drying stage (Fig.
[204]3i). Benzeneacetaldehyde can be directly derived from
phenylalanine^[205]3. During tea processing, phenylalanine can be
converted into benzeneacetaldehyde under the catalysis of
phenylacetaldehyde synthase (PAAS). Benzeneacetaldehyde can be further
transformed into phenylethyl alcohol by phenylacetaldehyde reductase
(PAR), while phenylethyl alcohol can be oxidized back to
benzeneacetaldehyde^[206]3. This explains why the trends of
benzeneacetaldehyde and phenylethyl alcohol are consistent (Fig.
[207]3i). Interestingly, in this study, the expression of PAAS
(Supplementary Data [208]13) and PAR genes (Supplementary Data [209]14)
decreased during processing (Fig. [210]6), indicating that
benzeneacetaldehyde and phenylethyl alcohol primarily originate from
other pathways. In addition to enzymatic synthesis, the Strecker
degradation of amino acids is considered the main reason for the
increase in benzeneacetaldehyde content. High temperatures increase the
content of phenylethyl alcohol by promoting the Strecker reaction of
amino acids and the Maillard reaction between amino acids and sugars.
In summary, the increase in benzeneacetaldehyde and phenylethyl alcohol
content during processing is mainly attributed to high temperatures
promoting the Strecker reaction of amino acids and the Maillard
reaction between amino acids and sugars.
In summary, the processing of black tea mainly affected flavonoid
glycosides, tea pigments, VTs, and FADVs. The content of these
components continuously increased during processing, reaching their
maximum during the fermentation stage, and then rapidly decreased
during the drying stage. Withering activated AM and GT, promoting the
glycosylation of flavonoids, thereby leading to the accumulation of
flavonoid glycosides. The activation of ANR and PPO facilitated the
oxidative polymerization of catechins, resulting in the accumulation of
tea pigments. The activation of TPS promoted the generation of VTs,
while the activation of LOX and HPL enhanced the synthesis of FADVs.
The accumulation of these components during fermentation was mainly due
to the disruption of cells during rolling, allowing enzymes and
substrates to fully integrate and react during the prolonged
fermentation process. During the drying stage, prolonged high
temperatures deactivated the enzymes, causing thermal degradation of
these quality components and leading to a rapid decline in their
content. Furthermore, eight characteristic volatiles, including
linalool, nerol, and benzeneacetaldehyde, are the main contributors to
the aroma during black tea processing. However, the key enzymes and
regulatory networks involved in the changes of these critical quality
components during black tea processing have not yet been thoroughly
investigated. In the future, integrating multi-omics approaches such as
proteomics with functional validation could provide deeper insights
into the molecular mechanisms underlying the processing stages. This
knowledge would enable the optimization of processing techniques by
modulating the expression of key genes and enzymes, ultimately
enhancing black tea quality and boosting the development and economic
benefits of the black tea industry.
Materials and methods
Tea preparation
One bud and two to three leaves from Camellia sinensis cv. Jinmudan
were picked in the tea garden of Zhangdun Town, Jianyang District,
Fujian Province (elevation 252.1 m, 27°25'33'' N, 118°28'41'' E) and
processed using the traditional black tea manufacturing techniques
(Fig. [211]7a). The process is as follows: The fresh tea leaves (FT)
were naturally withered indoors until the leaf color turned dark green
and the grassy aroma faded. They were then transferred to a specialized
withering room (22 ± 1 °C, 45 ± 5% RH) to continue withering until the
leaves became soft, the stems could be bent without breaking, and a
fresh fragrance emerged, resulting in withered leaves (W). Next, the
withered leaves were rolled. The rolling was considered adequate when
the tea juice sufficiently exuded and adhered to the surface of the
leaves without dripping. Immediately after rolling, the leaves were
placed in a fermentation room at 27 ± 2 °C and 95% RH to ferment until
70% of the leaves turned red, indicating proper fermentation, thus
obtaining the fermented leaves (F). The fermented leaves were then
baked in a tea drying machine at 105 °C for 15 min, followed by further
baking at 80 °C for 30 min. The tea was considered properly dried when
it turned reddish-brown and could be easily crumbled by hand. This
completed the preparation of the dried finished tea (D). The moisture
content of tea samples at each stage is shown in Supplementary Table
[212]6. A portion of the samples was immediately flash-frozen in liquid
nitrogen and stored at −80 °C. Another portion was promptly placed into
the apparatus shown in Fig. [213]7b for the collection of volatiles.
Fig. 7. The traditional processing techniques of black tea and the working
principle of the PPDH.
[214]Fig. 7
[215]Open in a new tab
a Black tea processing technology. b Collection device of volatiles.
Determination of non-volatile metabolites in tea
The freeze-dried tea samples were ground into powder, and 1 mL of 70%
methanol (containing 3 ng/μL of 2′,7′-dichlorofluorescein as an
internal standard) was added to 30 mg (±0.5 mg) of the tea powder. A
10 µL aliquot of the sample was further diluted 100-fold with 70%
methanol and filtered through a 0.22 µm polyvinylidene fluoride (PVDF)
filter (Millipore, Billerica, MA, USA) before analysis.
The analysis was performed using a Waters Acquity UPLC system coupled
with a Waters photodiode array (PDA) detector and a SYNAPT G2-Si HDMS
QTOF mass spectrometer (Waters, Manchester, UK), equipped with a Waters
Acquity UPLC HSS T3 column (100 × 2.1 mm, 1.8 µm) for gradient elution.
The mobile phase consisted of 0.1% formic acid in water (A) and 0.1%
formic acid in acetonitrile (B), with an injection volume of 1 μL. The
elution gradient was as follows: 0 min: 1% B; 2 min: 7% B; 13 min: 40%
B; 17 min: 60% B, immediately increased to 99% B at 17 min and held for
5 min. The MS parameters were as follows: source temperature at 120 °C,
desolvation temperature at 450 °C, collision energy at 4 eV, cone
voltage at 40 eV, m/z range of 50–1200 Da, desolvation gas flow at
800 L/h, and cone gas flow at 50 L/h. Quality control (QC) samples were
prepared by pooling equal amounts of all samples and were injected
after every ten samples throughout the analytical run to monitor
instrument performance.
The raw data were imported into Progenesis QI (Nonlinear Dynamics,
Newcastle upon Tyne, UK), and data preprocessing was performed using
default settings, with each sample normalized to the internal standard.
The identification of metabolites was initially conducted using
reliable standards, verifying them through the comparison of their
retention times (RT) and MS/MS fragments. If there was no standard, the
mass spectra information of metabolites was compared with HMDB,
MassBank, ReSpect, Metlin, and KNApSAcK databases for preliminary
identification, followed by verification through relevant literature.
If necessary, collision-induced dissociation (CID) fragmentation of
selected ions was performed to confirm structural assignments, and
ultraviolet spectroscopy was used for identification as much as
possible^[216]30.
Real-time determination of tea volatiles
Volatiles were collected using the PPDH method^[217]31. Briefly, 15 g
of tea samples from each processing stage were accurately weighed and
placed into an assembled glass tube (Fig. [218]7b). The air purified
through activated carbon was continuously introduced into the glass
tube using a gas sampler (QC-1B), thereby drawing the volatiles
released from the tea samples into a glass adsorption column containing
35 mg of Super-Q adsorbent (80–100 mesh) at a gas flow rate of
800 mL·min^-1. The collection of volatiles lasted for 30 min, with each
sample being replicated three times. After the collection, the
volatiles were immediately eluted with 500 μL of chromatographic grade
CH[2]Cl[2] in three aliquots (200 μL, 200 μL, and 100 μL) into a 1.5 mL
sample vial (Agilent) equipped with an insert. Then, 10 µL of 50 ppm
ethyl decanoate was added as an internal standard.
The volatile components were analyzed using a SHIMADZU Nexis GC-2030
equipped with an autosampler (AOC-20i Plus) and a DB-5MS capillary
column (30 m × 0.25 mm, 0.25 μm). High-purity helium (99.999%) was used
as the carrier gas at a flow rate of 1.8 mL/min. The initial column
temperature was maintained at 40 °C for 10 min, then increased at a
rate of 3.5 °C/min to 210 °C without holding. Finally, the temperature
was raised to 240 °C at a rate of 30 °C/min and held for 10 min. The
ion source temperature was set at 230 °C, the interface temperature at
250 °C, with an electron energy of 70 eV, and a mass scan range of
40–400 m/z. Volatile peaks were identified by matching mass spectra
with the National Institute of Standards and Technology (NIST 11)
database (match factor ≥80). The chemical structures and names of the
volatiles were determined using PubChem
([219]https://pubchem.ncbi.nlm.nih.gov) and NIST
([220]https://webbook.nist.gov/chemistry/cas-ser/), and odor
descriptions were confirmed by consulting relevant literature. The
relative content of volatiles was calculated using the internal
standard method (Eq. ([221]1)).
[MATH:
Ci=<
/mo>A/B×Di :MATH]
1
Note: C[i] (ng) represents the relative content of volatile compounds;
A represents content of internal standard; B represents peak area of
internal standard; D[i] represents the peak area of volatile compounds.
Determination of volatile metabolites in tea
The freeze-dried tea samples were ground into powder, and 0.5 g of the
tea powder was accurately weighed and mixed with 10 mL of boiling
water, followed by the addition of 10 µL of 50 ppm ethyl decanoate. The
headspace vial, equipped with an SPME injection handle, was placed in a
65 °C water bath for equilibration for 5 min. During this time, the
fiber in the injection handle was not exposed. After equilibration, the
fiber was exposed for 50 min to adsorb volatiles, and then the
injection handle was immediately inserted into the GC-MS injection port
for a 3-min desorption while the instrument was running. The GC-MS
instrument and column specifications were as described in section 2.3,
and manual injection was used. High-purity helium (99.999%) was used as
the carrier gas at a flow rate of 1.8 mL/min. The initial column
temperature was maintained at 40 °C for 3.5 min, then increased at a
rate of 2 °C/min to 120 °C and held for 2 min. Finally, the temperature
was raised to 230 °C at a rate of 10 °C/min and held for 2 min. The ion
source temperature was set at 230 °C, the interface temperature at
250 °C, with an electron energy of 70 eV, and a mass scan range of
40-400 m/z.
[MATH:
y=13742175.38x−2
4960691.31 :MATH]
2
Note: x represents the ethyl decanoate volume; y represents the ethyl
decanoate peak area. R^2 = 0.99.
Volatile peaks were identified by matching mass spectra with the NIST11
database (match factor ≥80) and retention indices (RI, using n-alkanes
C[7]-C[40], with an error <20). Three samples of ethyl decanoate with
different volumes were prepared and analyzed alongside the tea samples
to establish a standard curve for ethyl decanoate volume and peak area
(Eq. ([222]2)). The content of volatiles was quantified using the
internal standard method (Eq. ([223]3)). The OAV of the volatile
compounds is calculated by Eq. ([224]4).
[MATH:
Ei=<
/mo>(Fi×G×H)/(I
×M) :MATH]
3
Note: E[i] (μg/kg) represents the content of volatile compounds; F[i]
represents the peak area of volatile compounds; G represents the volume
of internal standard, which is calculated by Eq. ([225]2); H represents
the concentration of internal standard; I represents the peak area of
internal standard; M represents tea sample weight.
[MATH: OAVi=<
/mo>Ei/OTi
:MATH]
4
Note: E[i] represents the content of volatile compounds, which is
calculated by Eq. ([226]3); OT[i] represents the odor threshold of
volatile compounds.
RNA sequencing and analysis
The total RNA was extracted using the RNAprep Pure Plant Kit (Tiangen,
Beijing, China) according to the instructions provided by the
manufacturer. RNA concentration and purity were measured using NanoDrop
2000 (Thermo Fisher Scientific, Wilmington, DE). RNA integrity was
assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer
2100 system (Agilent Technologies, CA, USA). Sequencing libraries were
generated using the Hieff NGS Ultima Dual-mode mRNA Library Prep Kit
for Illumina (Yeasen Biotechnology (Shanghai) Co., Ltd.) following the
manufacturer’s recommendations and index codes were added to attribute
sequences to each sample, and 150 bp paired-end reads were generated on
an Illumina NovaSeq platform. Clean reads were generated and aligned
with the Huangdan genome
([227]https://ngdc.cncb.ac.cn/gwh/Assembly/17995/show), and gene
expression levels were estimated by fragments per kilobase of
transcript per million fragments mapped. DESeq2 was used to perform
differential expression analysis between the following groups: FT vs W,
W vs F, and FT vs F. The resulting P-values were adjusted using the
Benjamini and Hochberg’s approach for controlling the false discovery
rate. Genes with an adjusted P-value < 0.01 & Fold Change≥2 found by
DESeq2 were assigned as differentially expressed genes (DEGs). The
sequencing data generated in this study are available in the NCBI SRA
repository (BioProject ID: PRJNA1258312).
qRT-PCR verification of RNA sequencing results
The cDNA templates for each sample were obtained using NovoScript® Plus
All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) (Nvoprotein,
Shanghai, China), and qRT-PCR were performed using NovoStart® SYBR qPCR
SuperMix Plus (E096; Nvoprotein, Shanghai, China). Primers were
designed using Primer 5.0 and are listed in Supplementary Table [228]7.
Each 20 μL reaction mixture was prepared independently as follows:
10 μL of fluorescent dye, 8 μL of RNase-Free Water, 0.5 μL each of
forward and reverse primers, and 1 μL of cDNA template. The PCR
reactions were carried out using the Quant Gene 9600 (TC-96/G/H(b)B;
Bioer Technology, Hangzhou, China) real-time fluorescence quantitative
PCR system with the following program: pre-denaturation at 95 °C for
1 min, followed by 40 cycles of denaturation at 95 °C for 20 s,
annealing at 57 °C for 20 s, and extension at 72 °C for 30 s. Each
sample was evaluated with three biological replicates and three
technical replicates. The relative gene expression levels were
calculated using the 2^−ΔΔCT method, with GAPDH as the reference gene.
Statistics analysis
Quantitative data are presented as mean ± standard deviation
(mean ± SD). Each sample was analyzed in triplicate. A one-way ANOVA
and Tukey post hoc test were performed using SPSS 20.0, with a P
value < 0.05 considered statistically significant. Multivariate
statistical analysis was conducted using SIMCA 13.0, and correlation
analysis, as well as the generation of heatmaps, network diagrams,
volcano plots, rose diagrams, and bar charts, were performed using the
R package.
Supplementary information
[229]Supplementary Data 1^ (18KB, xlsx)
[230]Supplementary Data 2^ (23.4KB, xlsx)
[231]Supplementary Data 3^ (24.7KB, xlsx)
[232]Supplementary Data 4^ (2.4MB, xlsx)
[233]Supplementary Data 5^ (1.1MB, xlsx)
[234]Supplementary Data 6^ (23.9KB, xlsx)
[235]Supplementary Data 7^ (14.2KB, xlsx)
[236]Supplementary Data 8^ (18KB, xlsx)
[237]Supplementary Data 9^ (14.4KB, xlsx)
[238]Supplementary Data 10^ (12.8KB, xlsx)
[239]Supplementary Data 11^ (10KB, xlsx)
[240]Supplementary Data 12^ (11.9KB, xlsx)
[241]Supplementary Data 13^ (10.2KB, xlsx)
[242]Supplementary Data 14^ (13.3KB, xlsx)
[243]Supplementary materials^ (884.1KB, pdf)
Acknowledgements