番茄风味物质代谢途径解析与分子育种研究进展

doi:10.13560/j.cnki.biotech.bull.1985.2025-1358

摘要/Abstract

摘要：

番茄作为全球消费量最大的果蔬之一，其风味品质直接影响消费体验与市场价值。随着我国人均收入水平提升及消费结构升级，市场对高品质番茄的需求日益迫切。然而，受限于风味性状遗传调控的复杂性及检测技术的局限性，加之传统育种长期优先考虑产量、抗病性与耐贮运性等农艺性状，导致番茄果实风味品质普遍下降，难以满足消费需求。因此，深度解析风味代谢物的生物合成机制及其遗传调控网络，已成为精准改良番茄风味的关键突破口。本文系统综述了近年来国内外在番茄风味代谢物的合成代谢途径解析及其潜在遗传调控机制研究方面取得的前沿进展，重点探讨了基于多组学整合分析策略的番茄风味遗传改良技术体系：通过全面采集番茄种质资源的表型组、代谢组、转录组及基因组等多维度组学数据，结合机器学习与生物信息学分析方法，精准挖掘控制番茄风味形成的关键功能基因与调控位点，并利用现代分子育种技术手段，实现优质风味番茄新品种的高效定向选育。此外，本文还深入剖析了当前番茄风味育种研究在风味成分量化评价标准体系构建、风味性状遗传调控机制解析以及多性状（风味‒产量‒抗性等）协同改良等方面存在的关键技术瓶颈，并针对性地提出了未来研究的发展方向与策略建议。综上所述，本文旨在推动番茄风味育种理念从传统的“生产者导向”向“消费者导向”转型，为番茄及其他作物风味性状的精准遗传改良提供理论依据与技术路径，进而促进我国农业与种业的高质量发展。

关键词: 番茄, 风味, 多组学, 遗传机制, 代谢途径, 分子育种, 人工智能, 感官评价

Abstract:

As one of the most widely consumed fruits and vegetables worldwide, tomato flavor quality directly influences consumer experience and market value. With the rising of income per capita and the upgrading of consumption patterns in China, market demand for high-quality tomatoes has become increasingly urgent. However, the complexity of the genetic regulation of flavor traits, together with limitations in detection technologies and the long-standing emphasis of traditional breeding on agronomic traits such as yield, disease resistance, storage and transport tolerance, has led to a general decline in tomato fruit flavor quality, making it difficult to meet consumer expectations. Therefore, an in-depth understanding of the biosynthetic mechanisms of flavor metabolites and their genetic regulatory networks is critical for the precise improvement of tomato flavor. This review systematically summarizes recent cutting-edge advances, both domestically and internationally, in the biosynthetic and metabolic pathways of tomato flavor compounds and their underlying genetic regulatory mechanisms. It focuses on a multi-omics integration framework for the genetic improvement technology system of tomato flavor. By comprehensively collecting multidimensional omics data—including phenomics, metabolomics, transcriptomics, and genomics—from diverse tomato germplasm resources, and integrating machine learning and bioinformatics approaches, key functional genes and regulatory loci controlling flavor formation can be precisely identified. These discoveries, when combined with modern molecular breeding technologies, enable the efficient and targeted development of new tomato varieties with superior flavor. Furthermore, this review examines major technological bottlenecks in tomato flavor breeding, including the establishment of standardized quantitative evaluation systems for flavor components, the elucidation of genetic regulatory mechanisms underlying flavor traits, and the synergistic improvement of trade-off traits (e.g., flavor vs. yield and resistance). It also proposes targeted future research directions and strategies aimed at promoting a transition in tomato flavor breeding from a traditional producer-oriented paradigm to a consumer-oriented approach. Collectively, this framework provides theoretical foundations and technical pathways for the precise genetic improvement of flavor traits in tomatoes and other crops, thereby supporting the high-quality development of China's agricultural and seed industries.

Key words: tomato, flavor, multi-omics, genetic mechanisms, metabolic pathways, molecular breeding, artificial intelligence, sensory evaluation

姜喆卉, 王小龙, 王守创, 周科. 番茄风味物质代谢途径解析与分子育种研究进展[J]. 生物技术通报, 2026, 42(3): 60-78.

JIANG Zhe-hui, WANG Xiao-long, WANG Shou-chuang, ZHOU Ke. Advances in the Elucidation of Metabolic Pathways and Molecular Breeding for Tomato Flavor[J]. Biotechnology Bulletin, 2026, 42(3): 60-78.

图/表 5

图1 人类风味感知机制的多模态交互示意图人体形成风味感知的生物学机制共分为4个阶段：（1）食物摄取：食物提供挥发性香气化合物及非挥发性味觉与化学物理觉识别物质；（2）感官识别：通过鼻腔嗅觉、口腔味觉（酸甜苦咸鲜）以及三叉神经介导的化学物理觉（如辣椒素的痛热感、薄荷的凉感）进行多维度感知；（3）信号整合：外周神经将信号传入大脑，在味觉皮层形成味觉，随后大脑对多感官信号进行跨模态整合；（4）风味记忆：最终转化为客观的物体识别（如辨认出是番茄）与主观的享乐反应（即喜爱或厌恶的情绪），形成完整的风味体验。VOCs：挥发性化合物。该示意图由Germini3软件辅助修改

Fig. 1 Schematic diagram of multimodal interactions in human flavor perception mechanismsThe biological mechanism of flavor perception in humans comprises four stages: 1) Food intake: Food provides volatile aromatic compounds and non-volatile substances for taste and chemoreceptor identification. 2) Sensory recognition: Multidimensional perception occurs through nasal olfaction, oral taste (sweet, sour, bitter, salty, and umami), and chemoreceptors mediated by the trigeminal nerve (e.g., pain/heat sensation from capsaicin, and cooling sensation from menthol). 3) Signal integration: Signals from peripheral neural circuits enter the brain via the solitary nucleus in the brainstem, travel through the parabrachial nucleus and thalamus to the primary gustatory cortex to form taste perception, then project to regions like the amygdala and orbitofrontal cortex for cross-modal integration of multisensory signals. 4) Flavor memory: This ultimately translates into objective object recognition (e.g., identifying a tomato) and subjective hedonic responses (i.e., emotional preferences like liking or disliking), forming a complete flavor experience. VOCs: Volatile organic compounds. This schematic diagram was modified with the assistance of Germini3 software

图2 番茄糖类、有机酸类、苦味、挥发性代谢物相关的初生及次生代谢途径本图基于KEGG注释及文献参考构建，虚线箭头涉及多步反应。AADC：芳香族氨基酸脱羧酶；AAT：醇酰基转移酶；ADH：醇脱氢酶；BCAT：支链氨基转移酶；BCKDC：支链α-酮酸脱羧酶；CXE：羧甲基酯酶；DMAPP：二甲基烯丙基二磷酸；FRK：果糖激酶；GAME1/2/4/5/6/11/12/17/18/25/31/36/40：甾体糖苷生物碱代谢酶1/2/4/5/6/11/12/17/18/25/31/36/40；GGPP：香叶基香叶基二磷酸；GGPPS：香叶基香叶基二磷酸合成酶；GPP：香叶基二磷酸；GPPS：GPP合成酶；HDR：1-羟基-2-甲基-2-（E）-丁烯基-4-二磷酸还原酶；HMBPP：1-羟基-2-甲基-2-（E）-丁烯基-4-二磷酸；HPL：氢过氧化物裂解酶；Ile：异亮氨酸；INV：转化酶；Leu：亮氨酸；LOX：脂氧合酶；IPP：异戊烯基二磷酸；MEP：甲基-D-赤藓醇磷酸盐；MVA：甲羟戊酸；NAD-MDH：NAD-苹果酸脱氢酶；NADP-ME：NADP-苹果酸酶；OAA：草酰乙酸；PEP：磷酸烯醇式丙酮酸；PSY：八氢番茄红素合酶；SGA：甾体糖苷生物碱；SPP：蔗糖磷酸酯酶；SPS：蔗糖磷酸合酶；SUS：蔗糖合酶；TCA cycle：三羧酸循环；TNH1：四氢噻唑烷-N-羟化酶1；UDPG：二磷酸尿苷葡萄糖；VAL：缬氨酸

Fig. 2 Primary and secondary metabolic pathways related to sugars, organic acids, bitterness, and volatile metabolites in tomato (Solanum lycopersicum)This figure was created based on KEGG annotations and references, and the dotted line arrow is involved multi-step reactions. AADC: Aromatic amino acid decarboxylase. AAT: Alcohol acyltransferase. ADH: Alcohol dehydrogenase. BCAT: Branched-chain aminotransferase. BCKDC: Branched-chain α-ketoacid decarboxylase. CXE: Carboxymethylesterase. DMAPP: Dimethylallyl diphosphate. FRK: Fructokinase. GAME1/2/4/5/6/11/12/17/18/25/31/36/40: Glycoalkaloid metabolism enzyme 1/2/4/5/6/11/12/17/18/25/31/36/40. GGPP: Geranylgeranyl diphosphate. GGPPS: Geranylgeranyl diphosphate synthase. GPP: Geranyl diphosphate. GPPS: GPP synthase. HDR: 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase. HMBPP: 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate. HPL: Hydroperoxide lyase. Ile: Isoleucine. INV: Invertase. Leu: Leucine. LOX: Lipoxygenase. IPP: Isopentenyl diphosphate. MEP: 2-C-methyl-D-erythritol-4-phosphate. MVA: Mevalonate. NAD-MDH: NAD-malate dehydrogenase. NADP-ME: NADP-malic enzyme. OAA: Oxaloacetic acid. PEP: Phosphoenolpyruvate. PSY: Phytoene synthase. SGA: Steroidal glycoalkaloids. SPP: Sucrose phosphate phosphatase. SPS: Sucrose phosphate synthase. sus: sucrose synthase. TCA cycle: Tricarboxylic acid cycle. TNH1: Tetrahydrothiazolidine N-hydroxylase 1. UDPG: Uridine diphosphate glucose. Val: Valine

表1 番茄风味相关代谢物及基因列表

Table 1 List of tomato flavor-related metabolites and gene

物质类别 Primary pathway	代谢物 Metabolites	相关基因 Related genes	风味贡献Attributes	参考文献 Reference
糖类 Sugars	果糖、葡萄糖 Fructose， glucose	Lin5， STP11，SWEET， SlNAP2， SlFgr， SlGLK2， SlVPE5	Positive	［27， 80-83］
有机酸 Organic acids	苹果酸 Malic acid	SlME， SlTDT， SlALMT9， SlMIR164A	Positive	［39， 45， 84-85］
有机酸 Organic acids	柠檬酸 Citric acid	SlCS， SlAco， SlPEPCK	Positive	［39， 44， 86］
生物碱类 Alkaloids	甾体糖苷生物碱 Steroidal alkaloids	GAME1， GAME2， GAME5-9， GAME11， GAME12 GAME17， GAME18， GAME25， GAME31	Negative	［87］
氨基酸类 Amino Acids	脯氨酸 Proline	SlP5CS1， SlP5CR	Positive	［88］
氨基酸类 Amino Acids	天冬酰胺、γ-氨基丁酸 Asparagine， GABA	GDSL esterase/lipase	Unknown	［89］
	谷氨酰胺、苏氨酸 Glutamine， threonine		Unknown
醛类 Aldehydes	反式-2-己烯醛 Trans-2-Hexenal	LeHPL	Positive	［90］
	苯乙醛 Phenylacetaldehyde	LeAADCIA， LeAADCIB， LeAADC2	Positive	［91］
	苯甲醛 Benzaldehyde		Positive
	反式-2-庚烯醛 Trans-2-Heptenal		Unknown
	己醛 Hexanal	TomloxC	Unknown	［60］
	庚醛 Heptaldehyde		Unknown
	壬醛 Nonylaldehyde		Negative
	反式-2-戊烯醛 Trans-2-Pentenal	SlscADH1	Unknown	［92］
	顺式柠檬醛 Neral		Unknown
	香叶醛 Geranial	SlADH2	Unknown	［93］
	水杨醛 Salicylaldehyde		Negative
	β-环柠檬醛 β-Cyclocitral	LeCCD4	Unknown	［94］
含氮硫类挥发性代谢物 Nitrogen- and sulfur-containing volatile metabolites	2-异丁基噻唑 2-Isobutylthiazole	SlTNH1	Positive	［95-96］
	3-甲基丁醛肟 3-methylbutyraldehyde oxime		Negative	［95-96］
	苯乙腈 Benzylcyanide		Positive
	3-甲基丁腈 3-methylbutanenitrile		Negative	［95-96］
醇类 Alcohols	2-苯乙醇 2-Phenylethanol	LeAADCIA， LeAADC1B， LeAADC2， PPEAT， FLORAL4	Positive	［97］
	1-戊烯-3-醇 1-Penten-3-ol		Unknown
	3-甲基丁醇 3-Methylbutanol		Unknown
	1-辛烯-3-醇 1-Octen-3-ol	SlFAD7	Negative	［98］
	反式-3-己烯-1-醇 Trans-3-hexen-1-ol	SlADH1	Unknown	［99］
	1-戊醇 1-Pentanol	SlFAD7， Sl-LIP8	Unknown	［100］
	反式-2-己烯-1-醇 E-2-hexen-1-ol		Positive
	己醇 Hexylalcohol	Sl-LIP8	Unknown	［101］
	顺式-3-己烯-1-醇 Z-3-hexen-1-ol	ADH2	Unknown	［101］
	芳樟醇 Linalool	TPS3， TPS5， TPS20， TPS37， TPS39， SlMYB75	Unknown	［102-103］
	橙花叔醇 Nerolidol		Unknown	［102-103］
	2-甲基-1-丁醇 2-Methyl-1-butanol	BCKDC， SlBCAT1， SlBCAT2， AAT1	Unknown	［104］
	3-甲基-1-丁醇 3-Methyl-1-butanol		Positive
酚类 Phenols	丁香酚 Eugenol		Negative
酚类 Phenols	愈创木酚 Guaiacol	E8	Unknown	［105］
酯类 Esters	水杨酸甲酯 Methyl salicylate	SlSAMT	Negative	［106］
	顺式-3-己烯基乙酸酯 cis-3-hexenylacetate		Unknown
	乙酸己酯 Hexylacetate		Unknown
酮类 Ketones	1-戊烯-3-酮 1-Penten-3-one		Unknown
	香叶基丙酮、β-紫罗兰酮 Geranylacetone， β-Ionone	LeCCD1A， LeCCD1B	Positive	［107］
	6-甲基-5-庚烯-2-酮 6-Methyl-5-hepten-2-one		Positive
烷类Alkanes	1-硝基-2-苯乙烷 1-nitro-2-Phenylethane	SlTNH1	Positive	［95-96］
	1-硝基-3-甲基丁烷 1-nitro-3-methylbutane	SlTNH1	Negative	［95-96］

表1 番茄风味相关代谢物及基因列表

Table 1 List of tomato flavor-related metabolites and gene

物质类别 Primary pathway	代谢物 Metabolites	相关基因 Related genes	风味贡献Attributes	参考文献 Reference
糖类 Sugars	果糖、葡萄糖 Fructose， glucose	Lin5， STP11，SWEET， SlNAP2， SlFgr， SlGLK2， SlVPE5	Positive	［27， 80-83］
有机酸 Organic acids	苹果酸 Malic acid	SlME， SlTDT， SlALMT9， SlMIR164A	Positive	［39， 45， 84-85］
有机酸 Organic acids	柠檬酸 Citric acid	SlCS， SlAco， SlPEPCK	Positive	［39， 44， 86］
生物碱类 Alkaloids	甾体糖苷生物碱 Steroidal alkaloids	GAME1， GAME2， GAME5-9， GAME11， GAME12 GAME17， GAME18， GAME25， GAME31	Negative	［87］
氨基酸类 Amino Acids	脯氨酸 Proline	SlP5CS1， SlP5CR	Positive	［88］
氨基酸类 Amino Acids	天冬酰胺、γ-氨基丁酸 Asparagine， GABA	GDSL esterase/lipase	Unknown	［89］
	谷氨酰胺、苏氨酸 Glutamine， threonine		Unknown
醛类 Aldehydes	反式-2-己烯醛 Trans-2-Hexenal	LeHPL	Positive	［90］
	苯乙醛 Phenylacetaldehyde	LeAADCIA， LeAADCIB， LeAADC2	Positive	［91］
	苯甲醛 Benzaldehyde		Positive
	反式-2-庚烯醛 Trans-2-Heptenal		Unknown
	己醛 Hexanal	TomloxC	Unknown	［60］
	庚醛 Heptaldehyde		Unknown
	壬醛 Nonylaldehyde		Negative
	反式-2-戊烯醛 Trans-2-Pentenal	SlscADH1	Unknown	［92］
	顺式柠檬醛 Neral		Unknown
	香叶醛 Geranial	SlADH2	Unknown	［93］
	水杨醛 Salicylaldehyde		Negative
	β-环柠檬醛 β-Cyclocitral	LeCCD4	Unknown	［94］
含氮硫类挥发性代谢物 Nitrogen- and sulfur-containing volatile metabolites	2-异丁基噻唑 2-Isobutylthiazole	SlTNH1	Positive	［95-96］
	3-甲基丁醛肟 3-methylbutyraldehyde oxime		Negative	［95-96］
	苯乙腈 Benzylcyanide		Positive
	3-甲基丁腈 3-methylbutanenitrile		Negative	［95-96］
醇类 Alcohols	2-苯乙醇 2-Phenylethanol	LeAADCIA， LeAADC1B， LeAADC2， PPEAT， FLORAL4	Positive	［97］
	1-戊烯-3-醇 1-Penten-3-ol		Unknown
	3-甲基丁醇 3-Methylbutanol		Unknown
	1-辛烯-3-醇 1-Octen-3-ol	SlFAD7	Negative	［98］
	反式-3-己烯-1-醇 Trans-3-hexen-1-ol	SlADH1	Unknown	［99］
	1-戊醇 1-Pentanol	SlFAD7， Sl-LIP8	Unknown	［100］
	反式-2-己烯-1-醇 E-2-hexen-1-ol		Positive
	己醇 Hexylalcohol	Sl-LIP8	Unknown	［101］
	顺式-3-己烯-1-醇 Z-3-hexen-1-ol	ADH2	Unknown	［101］
	芳樟醇 Linalool	TPS3， TPS5， TPS20， TPS37， TPS39， SlMYB75	Unknown	［102-103］
	橙花叔醇 Nerolidol		Unknown	［102-103］
	2-甲基-1-丁醇 2-Methyl-1-butanol	BCKDC， SlBCAT1， SlBCAT2， AAT1	Unknown	［104］
	3-甲基-1-丁醇 3-Methyl-1-butanol		Positive
酚类 Phenols	丁香酚 Eugenol		Negative
酚类 Phenols	愈创木酚 Guaiacol	E8	Unknown	［105］
酯类 Esters	水杨酸甲酯 Methyl salicylate	SlSAMT	Negative	［106］
	顺式-3-己烯基乙酸酯 cis-3-hexenylacetate		Unknown
	乙酸己酯 Hexylacetate		Unknown
酮类 Ketones	1-戊烯-3-酮 1-Penten-3-one		Unknown
	香叶基丙酮、β-紫罗兰酮 Geranylacetone， β-Ionone	LeCCD1A， LeCCD1B	Positive	［107］
	6-甲基-5-庚烯-2-酮 6-Methyl-5-hepten-2-one		Positive
烷类Alkanes	1-硝基-2-苯乙烷 1-nitro-2-Phenylethane	SlTNH1	Positive	［95-96］
	1-硝基-3-甲基丁烷 1-nitro-3-methylbutane	SlTNH1	Negative	［95-96］

表2 正向与反向遗传学在番茄糖含量调控关键基因解析方面的比较分析

Table 2 Comparative analysis of forward genetics and reverse genetics in the identification of key genes regulating sugar content in tomatoes

研究策略

Research strategy

研究逻辑

Approach

技术手段

Technical means

研究场景

Research context

优势

Advantages

局限性

Limitations

代表性基因与功能

Representative genes and functions

参考文献Reference

正向遗传学

Forward genetics

从表型到基因

图位克隆/QTL定位

两个极端表型亲本杂交衍生的群体（如F2、RIL、NIL等）

1. 检测效能高；

2. 遗传背景简单；

3. 可直接用于育种转育

1. 构建群体耗时；

2. 多样性低；

3. 定位区间较大

Lin5，细胞壁转化酶，直接调控果实可溶性固形物（TSS）与甜度

［108］

全基因组关联分析

GWAS

自然种质群体

1. 分辨率高；

2. 遗传背景丰富；

3. 多性状同步分析；

4. 无需构建杂交群体

1. 易受群体结构干扰；

2. 假阳性率高；

3. 计算与检测成本高

STP1，糖转运蛋白，STP1^Insertion 中SSC水平约是STP1^Deletion 的1.2倍

［28］

反向遗传学

Reverse genetics

从基因到表型

RNA干扰

RNAi

基因敲降

避免基因敲除导致胚胎致死问题

1. 抑制不彻底；

2. 易脱靶；

3. 多代遗传稳定性问题；

4. 目前面临监管政策限制

HT1，己糖转运蛋白，沉默HT1基因的果实中己糖积累降低55%

［109］

基因编辑

CRISPR/Cas9

基因敲除

1. 定点精准编辑；

2. 实现单基因；

3. 多代稳定遗传

1. 基因敲除存在致死风险；

2. 功能冗余干扰；

3. 面临监管政策限制

SlSWEET12c，糖转运蛋白，其敲除突变体植株中果糖和葡萄糖含量降低约40%

［110］

过表达

Overexpression

获得功能

1. 直接验证基因的正向调控功能；

2. 克服功能冗余问题

1. 异位表达干扰植株正常生理；

2. 非自然遗传状态；

3. 共抑制现象；

4. 多代遗传稳定性问题；

5. 面临监管政策限制

SlTST3a，液泡糖转运蛋白，过表达植株中果糖和葡萄糖含量升高约1.3倍

［23］

表2 正向与反向遗传学在番茄糖含量调控关键基因解析方面的比较分析

Table 2 Comparative analysis of forward genetics and reverse genetics in the identification of key genes regulating sugar content in tomatoes

研究策略

Research strategy

研究逻辑

Approach

技术手段

Technical means

研究场景

Research context

优势

Advantages

局限性

Limitations

代表性基因与功能

Representative genes and functions

参考文献Reference

正向遗传学

Forward genetics

从表型到基因

图位克隆/QTL定位

两个极端表型亲本杂交衍生的群体（如F2、RIL、NIL等）

1. 检测效能高；

2. 遗传背景简单；

3. 可直接用于育种转育

1. 构建群体耗时；

2. 多样性低；

3. 定位区间较大

Lin5，细胞壁转化酶，直接调控果实可溶性固形物（TSS）与甜度

［108］

全基因组关联分析

GWAS

自然种质群体

1. 分辨率高；

2. 遗传背景丰富；

3. 多性状同步分析；

4. 无需构建杂交群体

1. 易受群体结构干扰；

2. 假阳性率高；

3. 计算与检测成本高

STP1，糖转运蛋白，STP1^Insertion 中SSC水平约是STP1^Deletion 的1.2倍

［28］

反向遗传学

Reverse genetics

从基因到表型

RNA干扰

RNAi

基因敲降

避免基因敲除导致胚胎致死问题

1. 抑制不彻底；

2. 易脱靶；

3. 多代遗传稳定性问题；

4. 目前面临监管政策限制

HT1，己糖转运蛋白，沉默HT1基因的果实中己糖积累降低55%

［109］

基因编辑

CRISPR/Cas9

基因敲除

1. 定点精准编辑；

2. 实现单基因；

3. 多代稳定遗传

1. 基因敲除存在致死风险；

2. 功能冗余干扰；

3. 面临监管政策限制

SlSWEET12c，糖转运蛋白，其敲除突变体植株中果糖和葡萄糖含量降低约40%

［110］

过表达

Overexpression

获得功能

1. 直接验证基因的正向调控功能；

2. 克服功能冗余问题

1. 异位表达干扰植株正常生理；

2. 非自然遗传状态；

3. 共抑制现象；

4. 多代遗传稳定性问题；

5. 面临监管政策限制

SlTST3a，液泡糖转运蛋白，过表达植株中果糖和葡萄糖含量升高约1.3倍

［23］

图3 多组学大数据驱动下的番茄风味性状精准改良技术路线人工智能驱动的番茄精准育种流程可分为4个阶段：（1）资源整合：从野生近缘种、地方品种到现代栽培种，建立丰富的番茄种质资源库；（2）多组学数据采集：利用电子舌、电子鼻（表型组）、质谱分析（代谢组）及高通量测序（基因组/转录组）获取海量生物信息；（3）AI集成分析：将多维度数据输入机器学习模型，进行QTL定位、mGWAS关联分析及差异表达基因（DEGs）鉴定，挖掘调控风味的关键位点；（4）育种应用：结合分子标记辅助选择（MAS）、双单倍体技术、基因编辑技术及全基因组选择（GS）技术，高效培育风味优异的新品种。LC-MS：液相色谱‒质谱联用技术；GC-MS：气相色谱‒质谱联用技术；NGS：高通量测序技术；QTL：数量性状位点；eQTL：表达量性状位点；mGWAS：代谢组全基因组关联研究；TWAS：转录组全基因组关联分析；DEGs：差异表达基因。该示意图由Germini3软件辅助修改

Fig. 3 Precision improvement strategy for tomato flavor traits driven by multi-omics big dataThe AI-driven precision breeding process for tomatoes comprises four stages: 1) Integration germplasm resources: Establish a comprehensive tomato germplasm repository spanning wild relatives, landraces, and modern cultivars. 2) Multi-omics data collection: Acquire vast biological information through electric tongue/nose (phenomics), mass spectrometry (metabolomics), and high-throughput sequencing (genomics/transcriptomics). 3) AI-integrated analysis: Input multidimensional data into machine learning models to perform QTL mapping, mGWAS association analysis, and differential gene expression (DEG) identification, uncovering key loci regulating flavor. 4) Breeding applications: Combine molecular marker-assisted selection (MAS), double haploid technology, gene editing techniques, and genome-wide selection (GS) to efficiently develop new varieties with superior flavor. LC-MS: Liquid chromatography-mass spectrometry. GC-MS: Gas chromatography-mass spectrometry. NGS: Next-generation sequencing. QTL: Quantitative trait locus. eQTL: Expression quantitative trait locus. mGWAS: Metabolome-wide association study. TWAS: Transcriptome-wide association study. DEGs: Differentially expressed genes. This schematic diagram was modified with the assistance of Germini3 software

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