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Variation analysis and quantitative trait loci mapping of 16 free amino acid traits in the tea plant (Camellia sinensis)

BMC Plant Biology volume 25, Article number: 194 (2025) Cite this article

Abstract

Background

The levels of free amino acids (FAAs) and the timing of bud flush (TBF) are among the the most economic traits of tea plants (Camellia sinensis). Investigating the genetic variation characteristics of FAAs and their potential associations with TBF is critical for the breeding of new tea cultivars with high economic value.

Methods

In this study, we utilized the ‘Emei Wenchun’ (♀) × ‘Chuanmu 217’ (♂) filial 1 (F1) genetic population (n = 208) and measured their FAA contents in the “one bud and two leaves” samples across two spring seasons and one summer season using high-performance liquid chromatography combined with the Waters AccQ-Tag method. The sprouting index (SPI) was observed over two springs to quantify the TBF trait. A genetic map previously constructed based on the same population was employed for quantitative trait loci (QTL) mapping.

Results

A total of 16 FAAs were measured, and the average total FAA contents were 28.1 and 25.4 mg/g (dry weight) in the two spring seasons and 14.29 mg/g in the summer season. Within the population, the coefficients of variation (CV) for the FAAs ranged from 23 to 41% within each season, and the correlation coefficients (r) varied from 0.15 to 0.35 across seasons. ANOVA analyses revealed that 13 out of the 16 FAAs exhibited significant genetic variation, with the estimated broad-sense heritability (h2) ranging between 10.33% and 57.10%. Interestingly, three FAAs and the total FAA contents showed significant positive correlations (r = 0.21–0.34, P < 0.01) with the SPI trait in both spring seasons. QTL mapping identified 13 FAA-associated QTLs distributed across eight linkage groups.

Conclusion

Within the F1 population, the FAAs exhibited considerable variation across seasons, their heritabilities were generally low (most ≤ 50%). There was a weak but significant positive correlation between FAAs and TBF. Additionally, 13 FAA-associated QTLs were identified. The results of this study enhance our understanding of the genetic variation characteristics of FAAs and provide insights for breeding tea cultivars with both higher FAAs and earlier TBF.

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Introduction

Tea (Camellia sinensis) is considered one of the most prominent non-alcoholic beverages globally due to its distinctive flavor profile and associated health advantages [1]. A key focus of research among tea breeders has centered on enhancing the flavor profile of tea. Tea plants possess young shoots that are notably abundant in secondary metabolites, such as catechins, caffeine, as well as primary metabolites, such as free amino acids (FAAs), which influence the flavor and aroma of the tea infusion and are pivotal in determining the overall tea quality [2].

During the growth and development of tea plants, FAAs function as precursors for the synthesis of protein and various secondary metabolites, thereby exerting a substantial influence on tea quality [3]. The predominant umami flavor in green tea is attributed to FAAs, with an increase in the amino acid levels corresponding to heightened umami flavor. The umami flavor primarily relies on the presence of theanine (Thea) and glutamic acid (Glu). Although certain FAAs, such as arginine (Arg) and histidine (His), contribute to the bitterness of green tea, the overall FAA content is positively correlated with the umami flavor and sweetness of the tea [4,5,6]. A total of 26 FAAs has been identified in tea plants, comprising 20 protein amino acids and 6 non-protein amino acids, with a notable abundance of Thea, Glu, aspartic acid (Asp), and Arg [7, 8]. Thea is the most prevalent amino acid in tea plants, constituting approximately 60–70% of the total FAA (TFAA) content in the young shoots [3]. Thea not only enhances the flavor of the tea but also exhibits antioxidant properties, reduces blood pressure, alleviates anxiety, and acts as an antidepressant [9, 10]. Therefore, a primary goal among tea breeders has been to develop tea cultivars with elevated levels of Thea and TFAA contents.

As a quantitative trait, the levels of FAAs in tea plants are influenced by numerous micro-effect genes and environmental factors, such as temperature, light intensity, fertilization, etc [3, 11]. Therefore, deciphering the genetic structure of FAAs in tea plants is essential for tea breeding and genetic improvement. Quantitative trait loci (QTL) mapping is an effective analytical approach for studying quantitative traits and identifying target genes. Additionally, the results of QTL mapping can serve as valuable guides for cultivar selection and breeding, offering insights into the inheritance patterns of quantitative traits [12]. A large number of QTLs associated with agronomic traits have been detected in tea plants using this method [8, 13, 14]. Previously, Li [15] employed the ‘Yingshuang’ (♀) × ‘Beiyue Danzhu’ (♂) F1 population to map 19 FAA-associated QTLs in tea plants, while Wang [16] and Huang et al. [8] mapped 25 FAA-associated QTLs in tea plants using ‘Longjing 43’ (♀) × ‘Baijiguan’ (♂) F1 population.

The FAA content in tea is significantly influenced by the harvest season. It is widely recognized that spring-harvested tea exhibits both a higher FAA content and superior quality compared to the tea harvested in other seasons [17, 18]. One explanation for this seasonal variation is the accumulation of FAAs, especially Thea, in the roots and stems of the tea plant during the winter dormancy (WD) period [3].

The timing of bud flush (TBF) in spring is another important agronomic trait of tea plants. The cultivar ‘Fuding Dabaicha’ (FD) often serves as a standard control in tea breeding for its early bud flush (EBF) trait. Cultivars that exhibit bud flush ≥ 10 days ahead of FD are classified as extra-early bud flush (EEBF) cultivars [19]. EEBF tea plants offer an extended growth and harvesting period, resulting in increased economic advantages as they can be marketed earlier in the spring [14, 20]. However, EEBF tea plants experience a considerably shorter WD compared to other tea plants, owing to their earlier spring bud flush and later fall bud set. However, the relationship between the FAA content and spring TBF in tea remains unclear.

‘Emei Wenchun’ (EW), an EEBF tea cultivar, has a nearly non-existent endo-dormancy stage and a short WD period (~ 45 d shorter compared to other tea cultivars) [21]. Furthermore, the FAA content in the spring tea of EW was notably lower in comparison to other tea cultivars [22]. Consequently, we postulated that the WD period in EW may lead to a reduction in its FAA content, thus raising concerns about the compatibility between the breeding objectives of achieving EEBF and a high FAA content. To validate this hypothesis, in the study, we measured the FAA content in the ‘Emei Wenchun’ (♀) × ‘Chuanmu 217’ (♂) (EW × CM) F1 population (n = 208) in three seasons, and analyzed their extent of variation and heritability. Additionally, the relationships between FAA contents and the sprouting index (SPI, a quantitative indicator of TBF) were also examined. Subsequently, we conducted correlation and QTL analyses of the collected data. The results of this study improve our understanding of the genetic variations associated with the FAA traits in tea plants and serve as a valuable reference for enhancing the quality of EEBF tea cultivars.

Results

Phenotypic data and variation analyses

A total of 16 FAAs were detected (Fig. 1). The average TFAA contents of the F1 population in the spring of 2021 and 2022 (28.1 and 25.4 mg/g, respectively) were significantly higher than that in the summer of 2022 (14.29 mg/g) (Table 1). Thea had the highest content in the F1 population, followed by Glu, Asp, Arg, and His, which together accounted for ≥ 80% of the TFAA content in the F1 population. Compared to the SPI, the TFAA content was more variable with CV ranging between 23 and 41%. The CV of each FAA in the F1 population ranged between 21 and 201% in the same season. These results indicate that FAAs have a high level of variation in the F1 population. The frequency distribution of the predominant FAAs and TFAA in the three seasons of the F1 population are shown in Fig. 2. Detailed information about FAA contents for each offspring can be found in Table S2. The correlation coefficient of each FAA across the three seasons ranged between − 0.09 and 0.47 (Figure S1), indicating seasonal variations.

Fig. 1
figure 1

High-performance liquid chromatography analysis of the predominant free amino acids in a standard sample (top) and the tea sample (bottom)

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Table 1 Phenotypic variation of free amino acids (mg/g) in the EW × CM F1 population
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Fig. 2
figure 2

Distribution patterns of the contents of theanine (Thea), glutamate (Glu), aspartic acid (Asp), arginine (Arg), Histidine (His), and the total amino acid content (TFAA) in the EW × CM F1 population. Parental values are indicated with green (EW) and yellow (CM) arrows

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The SPI distribution of the EW × CM F1 population is shown in Fig. 3. The average SPI values of the F1 population in 2021 and 2022 were 4.66 and 4.99, respectively, with a CV of 17.09% and 19.42%, indicating a moderate degree of variation. The mean SPI of the F1 population was lower than that of EW but higher than that of CM and FD. Furthermore, > 90% of the F1 progeny exhibited higher SPI than that of the FD, indicating that the F1 population has an EBF characteristic.

Fig. 3
figure 3

Distribution of the EW × CM F1 population based on the sprouting index values in 2021 and 2022

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Correlation analyses of the phenotypic data

Pearson’s correlation between each FAA in the F1 population across the three seasons is shown in Fig. 4. The five major FAAs (Thea, Glu, Asp, Arg, and His) were significantly positively correlated with each other (r = 0.30–0.75, p < 0.01). In addition, lysine (Lys), tyrosine (Tyr), leucine (Leu), isoleucine (Ile), and phenylalanine (Phe) were highly positively correlated with each other in all three seasons (r > 0.6, p < 0.01). The TFAA content showed a significant positive correlation with the SPI in both the spring seasons (0.27 and 0.31 in 2021 and 2022, respectively, p < 0.01, Table 2). Additionally, Thea, Ala, and Arg also showed significant positive correlations with the SPI in both the spring seasons (0.21–0.36, p < 0.01).

Fig. 4
figure 4

Heatmap of the Pearson’s correlation between the free amino acids in the EW × CM F1 population in spring 2021 and 2022 and summer 2022

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Table 2 Pearson’s correlation between the free amino acids and sprouting index of the EW × CM F1 population
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Estimation of heritability

In this study, the genetic variance (\(\:{\sigma\:}_{V}^{2}\)) and environmental variance (\(\:{\sigma\:}_{E}^{2}\)) were estimated using ANOVA analyses. The broad-sense heritability (h2) was estimated, with results presented in Table 3. Among the 16 amino acid components, genetic variances of 13 were found to be significant (P < 0.05), with their h2 values ranging from 10.33 to 57.10%. The h2 of Thea was 30.59%, whereas that of TFAA was significantly lower at 17.67%.

Table 3 Variance components and broad-sense heritability estimated from amino acid content data across three seasons in the EW × CM F1 population
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QTL mapping for FAA traits

A total of 13 QTLs were identified using the FAAs, TFAA, and Thea/Glu data. These QTLs were distributed on chr01, chr02, chr04, chr06, chr12, chr13, chr14, and chr15 (Table 4; Fig. 5). For Thea, a QTL (qThea12) with a LOD and PVE of 5.83 and 13.9, respectively, was identified on chr12 using the spring 2021 data. Moreover, three other QTLs were also mapped (qTFAA12, qGlu12, and qHis12) adjacent to the qThea12. Furthermore, four QTLs were mapped for Thr (qThr2, qThr6-1, qThr6-2, and qThr13) with a PVE ranging between 10.1 and 11.8%, among which two were on the same linkage group. In addition, one QTL each was mapped for Ser, Ile, Leu, and Val, while one QTL associated with the Thea/Glu ratio trait was mapped on chr04. However, all the detected QTLs were season-specific.

Table 4 The 13 quantitative trait loci detected in the EW × CM F1 population by interval mapping
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Fig. 5
figure 5

Distribution of the free amino acid-associated quantitative trait loci identified in the EW × CM F1 population

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Discussion

The FAA content in tender leaves of tea plants is subject to various influences, including genotype, tea-picking season, plant age, light intensity and temperature [23, 24]. Our results revealed that the TFAA content during summer, from the perspective of the entire population, was approximately half of that in spring. Similar results were reported by Zaman et al. [25] and Gong et al. [26]. The seasonal variations can be ascribed to multiple factors, including stronger solar radiation, enhanced photosynthetic efficiency, and accelerated growth rates during the summer. Additionally, we found that the average TFAA content in the F1 population in 2022 was significantly lower (-9.6%) than that in 2021, corroborating the established hypothesis that FAA levels decline with tea plant age increasing [27]. Liu and his colleagues have demonstrated, based on multiple years’ data of another genetic population, that the level of TFAA declines with increasing tree age over a period of 3 to 6 years [27].

Our results further demonstrate that genetic factors still play a significant role in the variation of FAA in tea plants, as the most of measured FAAs exhibited significant genetic variation in ANOVA analyses. Their estimated h2 range from 10.33 to 57.10%. The h2 for theanine is 30.59%. Previous studies have reported h2 for secondary metabolites in young tea leaves (such as catechins, caffeine and FAA) ranging from 35 to 92%, with theanine h2 at 56% [28] or 60% [29]. In comparison, the lower h2 of FAAs in this study may be attributed to different types of genetic populations used across studies. The population in this study was derived from a full-sib family with a relatively narrow genetic background, and we considered both spring and summer seasons, which increased environmental variation. However, these findings collectively indicate that FAA traits are co-regulated by both genetic and environmental factors, with the latter accounting for half or even more of the phenotypic variation.

The accumulation of nitrogen reserves in tea plants may persist throughout the WD and may facilitate the increase in FAA content [7, 30], thereby enhancing the quality of spring tea. Contrary to our hypothesis, the contents of TFAA and three major FAAs in the F1 population exhibited a significant positive correlation with the SPI in both observed years (Table 2). We hypothesize that the positive correlation between FAAs and SPI may be an indirect reflection of the influence of temperature on the FAA content in tea plants. The buds flushing earlier in spring experience lower environmental temperatures, which may facilitate the accumulation of amino acids. Since a higher SPI corresponds to a shorter WD period, the result implies that the shortened WD is unlikely to be the limiting factor for FAA accumulation in the EEBF tea plants. Recently, Li et al. [31] reported that the theanine content in tea plant roots exhibits a declining trend from late winter to early spring, contrary to the expected increase. The theanine content in roots significantly increased only before the buds on the branches began to flush. Together, these results suggest that the tea plants may not necessarily require a long WD to accumulate FAAs for producing high-FAA tea.

The preceding rationale indicates the feasibility of concurrently attaining the objectives of EEBF and high FAA content in tea breeding. To validate this hypothesis, we examined the EW × CM F1 population and identified 20 individuals with comparatively high FAA content across all observed seasons. In both 2021 and 2022, these individuals exhibited SPI values surpassing those of the male parent CM and the FD control, with over half of these individuals classified as EEBF (Table S3). Consequently, they can serve as promising candidates for the development of tea cultivars characterized by both high FAA contents and EEBF.

Numerous QTLs associated with quality-related traits, like catechins, caffeine, and FAAs have been successfully identified in tea plants [13, 32]. In this study, we used the EW × CM F1 population for QTL analysis of FAA content and identified 13 QTLs, distributed across eight linkage groups; however, none of these QTLs were detected in all three seasons. It is speculated that climatic variations during different seasons decrease the stability of QTL for traits with a relatively low heritability. This seasonal fluctuation likely makes it more challenging to detect stable and consistent QTLs across multiple seasons. These results are consistent with a previous study by Li [15] in which the FAA-associated QTLs were not detected in the ‘Yingshuang’ × ‘Beiyue Danzhu’ F1 population in two consecutive years. Huang et al. [8] identified 4 FAA-associated QTLs in the ‘Longjing 43’ × ‘Baijiguan’ F1 population, and although some QTL were identified in both years, they reported significant discrepancies in the QTL count, QTL interval positions, and FAA contents between two consecutive years. These findings indicate that the FAA content in tea plants is an unstable trait, prone to environmental influences. Among the FAA-associated QTLs, we observed a proximity between one QTL associated with Thea/Glu (qThea/Glu4) and a previously identified QTL associated with Thea (qTHN-4.1-2015) [15], implying that this locus potentially houses a pivotal gene governing Thea content.

Despite the availability of chromosome-level reference genomes of tea plants [33, 34], the identification of functional genes associated with the FAA trait is still challenging due to the scarcity of stable QTLs with a high PVE. In the future, employing more superior and accurate techniques, such as bulked segregant RNA sequencing or genome-wide association studies, is recommended for mapping FAA-controlling genes in tea plants. The FAA-associated QTLs identified in this study, along with those reported in other studies, could serve as valuable genetic evidence for identifying and characterizing these functional genes.

Materials and methods

Plant materials

The F1 population selecting for the TBF trait was established using a female cultivar ‘Emei Wenchun’ (EW) and a male EBF tea cultivar ‘Chuanmu 217’ (CM) [14]. This F1 population contains 208 seedlings, the corresponding ID for each offspring can be found in Table S2. The F1 seedlings were planted in 2018 at the tea breeding base in Mingshan County, Sichuan Province, China (N28°59’, E103°53’). Fertilization and pruning during the trial period were conducted according to the conventional cultivation and management practices of adult tea plants, including one basal fertilization (November), two follow-up fertilization (February and July), two light prunings (June and November).

SPI analysis

The SPI was recorded as described previously [14]. Briefly, a scale of 0–8 was used to record the state of the bud flush, where 0 represents dormant and a higher number represents the bud flush with more leaves. A total of ten buds were observed for each individual and the average SPI was calculated. The SPI analyses were conducted on Feb 20th, 2021 (as reported in Tan et al. [14]) and Mar 10th, 2022. As it takes approximately 6 d to flush one leaf from the vegetative bud of tea plants in early spring [35], a difference of 1 in the SPI represents a six-day difference in TBF.

Sampling, FAA extraction, and measurement

The spring tea (“one bud and two leaves”) samples were collected from each F1 individual and the two parents from late February to mid-March in 2021 and 2022, while the summer tea samples were collected in July 2022. Fresh samples were heated in a microwave for approximately 2 min for passivating enzyme activity and dried at 80 ℃ for approximately 4 h until constant weight. Due to the variations in TBF, samples were collected every 10 days during the sampling period. The samples collected in batches were then pooled for subsequent analysis.

FAA extraction was conducted according to the Chinese national standard GB/T 30,987 − 2020. First, 400 mg of powdered sample was extracted using 40 mL boiling water at 95 ℃ for 10 min. Thereafter, the samples were centrifuged (5000 r/min, 15 min) and the extracts were made up to 50 mL and filtered through a 0.45 μm filter. The FAAs in the samples were detected using high-performance liquid chromatography (HPLC) carried out using an Agilent system (Agilent, 1260 infinity II), with an AccQ-Tag reversed-phase HPLC column (particle size 4 μm; column size 3.9 × 150 mm; Waters). The column temperature was maintained at 37 °C. The flow rate was 1.1 mL min− 1 and detection were conducted at 395 nm. The mobile phase was composed of AccQ-Tag Eluent A (A) and 60% acetonitrile (B). The elution procedure was conducted as described previously [36], with small modifications, as shown in Table S1. A total of 16 FAAs were detected and their total content was reported as the TFAA. Considering that both Thea and Glu are major amino acids in tea plants, and Glu has been confirmed as a precursor in the synthesis of Thea [37], we regarded the ratio of these two amino acids as a trait for QTL mapping.

QTL mapping

The FAA data and the genetic map of the F1 population [14] were used for phenotypic QTL mapping using MapQTL 6.0 [38]. The interval mapping method was used for QTL mapping, and the locus with the logarithm of the odds (LOD) score ≥ 4 was considered as a QTL. The phenotypic variance explained (PVE, %) of each QTL was calculated to estimate its effect. The distribution of QTLs was plotted using the R package LinkageMapView [39].

Data analysis

All statistical analyses were conducted using the R software (v4.3.0). The means, standard deviations, coefficients of variation (CV), kurtosis, and skewness were calculated separately for each FAA in the F1 population. Additionally, the Pearson correlations between the FAAs in the three seasons and between the FAAs and TBF in the spring of 2021 and 2022 were calculated for the F1 population. The graphs were plotted using ggplot2 [40].

ANOVA were performed using software R on the collected amino acid contents data from three sampled seasons to obtain the mean square among offspring (MSV), season (MSE ) and the error (MSe). The broad-sense heritability (h2) was calculated using the formulas:

h² = \(\:{\sigma\:}_{V}^{2}\)/ (\(\:{\sigma\:}_{V}^{2}\)+\(\:{\sigma\:}_{E}^{2}\) ) × 100%,

$$\:{\sigma\:}_{V}^{2}\:=\:\frac{{MS}_{V}\:-\:{MS}_{e}}{b}\:,$$
$$\:{\sigma\:}_{E}^{2}\:=\:\frac{{MS}_{E}\:-\:{MS}_{e}}{a},$$

where \(\:{\sigma\:}_{V}^{2}\) represents genetic variance, and \(\:{\sigma\:}_{E}^{2}\) represents environmental variance, a represents the number of offspring and b represents the number of the seasons.

Conclusion

In this study, we conducted a comprehensive analysis of the variation patterns, correlations, and heritability of FAA traits in a full-sib population of tea plants. Our findings indicate that both genetic factors and seasonal influences play important roles in regulating FAAs. We also observed a significant positive correlation between TFAA content and the SPI, highlighting the potential for breeding tea cultivars with elevated FAA levels and EEBF traits. Furthermore, we identified 13 FAA-associated quantitative trait loci (QTLs), which exhibited distinct seasonal specificity. Overall, this study enhances our understanding of the genetic variability underlying FAA traits and offers valuable insights for the development of tea cultivars characterized by higher FAA levels and earlier bud flush.

Data availability

Data is provided within the manuscript or supplementary information files and also can be obtained by contacting the corresponding author (Email: tanliqiang@sicau.edu.cn).

Abbreviations

FAA:

Free amino acid

TFAA:

Total free amino acid

TBF:

Timing of bud flush

SPI:

Sprouting index

QTL:

Quantitative trait loci

EBF:

Early bud flush

EEBF:

Extra-early bud flush

WD:

Winter dormancy

EW:

Emei Wenchun

CM:

Chuanmu 217

Asp:

Aspartic acid

Ser:

Serine

Glu:

Glutamic acid

Gly:

Glycine

His:

Histidine

Arg:

Arginine

Thr:

Threonine

Ala:

Alanine

Pro:

Proline

Thea:

Theanine

Tyr:

Tyrosine

Val:

Valine

Lys:

Lysine

Ile:

Isoleucine

Leu:

Leucine

Phe:

Phenylalanine

HPLC:

High-performance liquid chromatography

LOD:

Logarithm of the odds

PVE:

Phenotypic variance explained

CV:

Coefficients of variation

References

  1. Pan SY, Nie Q, Tai HC, Song XL, Tong YF, Zhang LJF, Wu XW, Lin ZH, Zhang YY, Ye DY, Zhang Y, Wang XY, Zhu PL, Chu ZS, Yu ZL, Liang C. Tea and tea drinking: China’s outstanding contributions to the mankind. Chin Med. 2022;17(1):27.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Zhang L, Cao QQ, Granato D, Xu YQ, Ho CT. Association between chemistry and taste of tea: a review. Trends Food Sci Technol. 2020;101:139–49.

    Article  CAS  Google Scholar 

  3. Lin SJ, Chen ZP, Chen TT, Deng WW, Wan XC, Zhang ZL. Theanine metabolism and transport in tea plants (Camellia sinensis L.): advances and perspectives. Crit Rev Biotechnol. 2023;43(3):327–41.

    Article  PubMed  CAS  Google Scholar 

  4. Kaneko S, Kumazawa K, Masuda H, Henze A, Hofmann T. Molecular and sensory studies on the Umami taste of Japanese Green Tea. J Agric Food Chem. 2006;54(7):2688–94.

    Article  PubMed  CAS  Google Scholar 

  5. Namal Senanayake SPJ. Green tea extract: Chemistry, antioxidant properties and food applications – A review. J Funct Foods. 2013;5(4):1529–41.

    Article  CAS  Google Scholar 

  6. Yu ZM, Yang ZY. Understanding different regulatory mechanisms of proteinaceous and non-proteinaceous amino acid formation in tea (Camellia sinensis) provides new insights into the safe and effective alteration of tea flavor and function. Crit Rev Food Sci Nutr. 2020;60(5):844–58.

    Article  PubMed  CAS  Google Scholar 

  7. Wan XC. Tea biochemistry (in Chinese). 3rd ed. Beijing, China: China Agriculture; 2003.

    Google Scholar 

  8. Huang R, Wang JY, Yao MZ, Ma CL, Chen L. Quantitative trait loci mapping for free amino acid content using an albino population and SNP markers provides insight into the genetic improvement of tea plants. Hortic Res. 2022, 9.

  9. Unno K, Noda S, Kawasaki Y, Yamada H, Morita A, Iguchi K, Nakamura Y. Reduced stress and Improved Sleep Quality caused by Green Tea Are Associated with a reduced caffeine content. Nutrients. 2017;9(7):777.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Li MY, Liu HY, Wu DT, Kenaan A, Geng F, Li H-B, Gunaratne A, Li H, Gan R-Y. L-Theanine: a unique functional amino acid in tea (Camellia sinensis L.) with multiple health benefits and food applications. Front Nutr. 2022, 9.

  11. Huang R, Wang JY, Yao MZ, Ma CL, Chen L. Quantitative trait loci mapping for free amino acid content using an albino population and SNP markers provides insight into the genetic improvement of tea plants. Hortic Res. 2022;9:uhab029.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Raj SRG, Nadarajah K. QTL and candidate genes: techniques and Advancement in Abiotic stress resistance breeding of major cereals. Int J Mol Sci. 2023;24(1):6.

    Article  CAS  Google Scholar 

  13. An YL, Chen LB, Tao LL, Liu SG, Wei CL. QTL mapping for Leaf Area of Tea plants (Camellia sinensis) based on a high-quality genetic map constructed by whole genome resequencing. Front Plant Sci. 2021, 12.

  14. Tan LQ, Cui D, Wang LB, Liu QL, Zhang DY, Hu XL, Fu Y, Chen SX, Zou Y, Chen W, Wen WQ, Yang XM, Yang Y, Li PW, Tang Q. Genetic analysis of the early bud flush trait of tea plants (Camellia sinensis) in the cultivar ‘Emei Wenchun’ and its open-pollinated offspring. Hortic Res. 2022, 9.

  15. Li XJ. QTL mapping and genetic analysis for free amino acid in tea plant. Master, Chinese Academy of Agricultural Sciences Dissertation, 2018.

  16. Wang SL. QTL Mapping of Specific Traits of Pigment and Free Amino Acid in Chlorotic Tea Cultivar ‘Baijiguan’. Master, Chinese Academy of Agricultural Sciences Dissertation, 2018.

  17. Ma BS, Wang JC, Zhou BX, Wang ZH, Huang YY, Ma CQ, Li XH. Impact of harvest season on bioactive compounds, amino acids and in vitro antioxidant capacity of white tea through multivariate statistical analysis. LWT. 2022;164:113655.

    Article  CAS  Google Scholar 

  18. Ye F, Guo XB, Li B, Chen HQ, Qiao XY. Characterization of effects of different tea Harvesting Seasons on Quality Components, Color and sensory quality of Yinghong 9 and Huangyu Large-Leaf-Variety Black Tea. In Molecules, 2022; 27.

  19. Jiang CJ. Tea plant breeding science. 3rd ed. Beijing, China: China Agriculture; 2021.

    Google Scholar 

  20. Wang RJ, Gao XF, Yang J, Kong XR. Genome-wide Association study to identify favorable SNP allelic variations and candidate genes that control the timing of spring bud flush of tea (Camellia sinensis) using SLAF-seq. J Agric Food Chem. 2019;67(37):10380–91.

    Article  PubMed  CAS  Google Scholar 

  21. Tan LQ, Wang LB, Zhou B, Liu QL, Chen SX, Sun DL, Zou Y, Chen W, Li PW, Tang Q. Comparative transcriptional analysis reveled genes related to short winter-dormancy regulation in Camellia sinensis. Plant Growth Regul. 2020;92(2):401–15.

    Article  CAS  Google Scholar 

  22. Xie WG, Chen W, Tan LQ, Yang Y, Qian T. Analysis of main biochemical components in new shoots of tea cultivars Emeiwenchun and Chuancha 2. Acta Agriculturae Zhejiangensis. 2021;33(09):1592–601.

    Google Scholar 

  23. Jia XL, Ye JH, Wang HB, Li L, Wang FQ, Zhang Q, Chen JB, Zheng XY, He HB. Characteristic amino acids in tea leaves as quality indicator for the evaluation of Wuyi Rock Tea in different culturing regions. J Appl Bot Food Qual. 2018;91:187–93.

    CAS  Google Scholar 

  24. Sun LT, Fan K, Wang LL, Ma DX, Wang Y, Kong XJ, Li HY, Ren YL, Ding ZT. Correlation among metabolic changes in Tea Plant Camellia sinensis (L.) shoots, Green Tea Quality and the application of cow manure to Tea Plantation soils. Molecules. 2021;26(20):6180.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Zaman F, Zhang E, Xia L, Deng XL, Ilyas M, Ali A, Guo F, Wang P, Wang M, Wang Y, Ni DJ, Zhao H. Natural variation of main biochemical components, morphological and yield traits among a panel of 87 tea [Camellia sinensis (L.) O. Kuntze] cultivars. Hortic Plant J. 2023;9(3):563–76.

    Article  CAS  Google Scholar 

  26. Gong AD, Lian SB, Wu NN, Zhou YJ, Zhao SQ, Zhang LM, Cheng L, Yuan HY. Integrated transcriptomics and metabolomics analysis of catechins, caffeine and theanine biosynthesis in tea plant (Camellia sinensis) over the course of seasons. BMC Plant Biol. 2020;20(1):294.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Liu DD, Wei K, Zhang CY, Liu HR, Gong Y, Ye YY, Chen JD, Yao MZ, Chen L, Ma CL. The potential effects of chlorophyll-deficient mutation and tree_age on the accumulation of amino acid components in tea plants. Food Chem. 2023;411:135527.

    Article  PubMed  CAS  Google Scholar 

  28. Lubanga N, Massawe F, Mayes S. Genomic and pedigree-based predictive ability for quality traits in tea (Camellia sinensis (L.) O. Kuntze). Euphytica. 2021;217(3):32.

    Article  CAS  Google Scholar 

  29. Fang K, Xia Z, Li H, Jiang X, Qin D, Wang Q, Wang Q, Pan C, Li B, Wu H. Genome-wide association analysis identified molecular markers associated with important tea flavor-related metabolites. Hortic Res. 2021, 8.

  30. Ma LF, Shi YZ, Ruan JY. Nitrogen absorption by field-grown tea plants (Camellia sinensis) in winter dormancy and utilization in spring shoots. Plant Soil. 2019;442(1):127–40.

    Article  CAS  Google Scholar 

  31. Li F, Dong CX, Yang TY, Ma JZ, Zhang SP, Wei CL, Wan XC, Zhang ZL. Seasonal Theanine Accumulation and related gene expression in the roots and Leaf buds of Tea plants (Camellia Sinensis L). Front Plant Sci. 2019;10:1397.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wei K, Wang XC, Hao XY, Qian YH, Li X, Xu LY, Ruan L, Wang YX, Zhang YZ, Bai PX, Li Q, Aktar S, Hu XL, Zheng GY, Wang LB, Liu BY, He WZ, Cheng H, Wang LY. Development of a genome-wide 200K SNP array and its application for high-density genetic mapping and origin analysis of Camellia sinensis. Plant Biotechnol J. 2022;20(3):414–6.

    Article  PubMed  CAS  Google Scholar 

  33. Xia EH, Tong W, Hou Y, An Y, Chen L, Wu Q, Liu Y, Yu J, Li F, Li R, Li P, Zhao H, Ge R, Huang J, Mallano AI, Zhang Y, Liu S, Deng W, Song C, Zhang Z, Zhao J, Wei S, Zhang Z, Xia T, Wei C, Wan X. The reference genome of tea plant and resequencing of 81 diverse accessions provide insights into its genome evolution and adaptation. Mol Plant. 2020;13(7):1013–26.

    Article  PubMed  CAS  Google Scholar 

  34. Zhang XT, Chen S, Shi LQ, Gong D, Zhang S, Zhao Q, Zhan D, Vasseur L, Wang Y, Yu J, Liao Z, Xu X, Qi R, Wang W, Ma Y, Wang P, Ye N, Ma D, Shi Y, Wang H, Ma X, Kong X, Lin J, Wei L, Ma Y, Li R, Hu G, He H, Zhang L, Ming R, Wang G, Tang H, You M. Haplotype-resolved genome assembly provides insights into evolutionary history of the tea plant Camellia sinensis. Nat Genet. 2021;53(8):1250–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Luo YP, Tang M, Cai WZ, Wen DH, Wen ZJ. Study on the optimum machine plucking period for high quality tea. Tea Sci. 2008;01:9–13.

    Google Scholar 

  36. Ning JM, Ding D, Song YS, Zhang ZZ, Luo XJL, Wan XC. Chemical constituents analysis of white tea of different qualities and different storage times. Eur Food Res Technol. 2016;242(12):2093–104.

    Article  CAS  Google Scholar 

  37. Cheng S, Fu X, Wang X, Liao Y, Zeng L, Dong F, Yang Z. Studies on the biochemical formation pathway of the amino acid l-Theanine in tea (Camellia sinensis) and other plants. J Agric Food Chem. 2017;65(33):7210–6.

    Article  PubMed  CAS  Google Scholar 

  38. Van Ooijen JW. MapQTL6, Software for the mapping of quantitative trait loci in experimental populations of diploid species. Kyazma BV. 2009.

  39. Ouellette LA, Reid RW, Blanchard SG, Brouwer CR. LinkageMapView—rendering high-resolution linkage and QTL maps. Bioinformatics. 2017;34(2):306–7.

    Article  PubMed Central  Google Scholar 

  40. Wickham H. ggplot2: elegant graphics for data analysis. New York: New York,: Springer-; 2016.

    Book  Google Scholar 

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Funding

This work was supported by National Natural Science Foundation of China (32372767, 31800589), the Department of Science and Technology of Sichuan Province (2022JDRC0035, 2021YFYZ0025), and the Sichuan Innovation Team of National Modern Agricultural Industry System (sccxtd-2024-10).

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Authors and Affiliations

  1. College of Horticulture, Sichuan Agricultural University, Chengdu, Sichuan, 611130, China

    Dongyang Zhang, Xujiao Wei, Jing Zhang, Dong Cui, Peng Zhang, Shengxiang Chen, Yao Zou, Wei Chen, Dandan Tang, Chen Liu, Jinlin Bian, Qian Tang & Liqiang Tan

  2. Tea Refining and Innovation Key Laboratory of Sichuan Province, Chengdu, Sichuan, 611130, China

    Dongyang Zhang, Xujiao Wei, Jing Zhang, Dong Cui, Peng Zhang, Shengxiang Chen, Yao Zou, Wei Chen, Dandan Tang, Chen Liu, Jinlin Bian, Qian Tang & Liqiang Tan

  3. No.211, Huimin Road, Wenjiang District, Chengdu City, 611130, China

    Qian Tang

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  1. Dongyang Zhang
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  2. Xujiao Wei
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  3. Jing Zhang
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  11. Jinlin Bian
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  12. Qian Tang
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  13. Liqiang Tan
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Contributions

L.T., Q.T. and D.Z. conceived and designed the experiment. D.Z., X. W., P.Z., J.Z. and D.C performed the experiments and data collection. D.Z. conducted data analysis. D.Z. wrote the manuscript. J.Z., S.C., Y.Z., W.C., D.T., C.L. and J.B. revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Qian Tang or Liqiang Tan.

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Zhang, D., Wei, X., Zhang, J. et al. Variation analysis and quantitative trait loci mapping of 16 free amino acid traits in the tea plant (Camellia sinensis). BMC Plant Biol 25, 194 (2025). https://doi.org/10.1186/s12870-024-06038-9

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Keywords

BMC Plant Biology

ISSN: 1471-2229

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