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作物学报 ›› 2022, Vol. 48 ›› Issue (5): 1103-1118.doi: 10.3724/SP.J.1006.2022.14055

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

大豆叶片响应CO2浓度升高、干旱及其交互作用的转录组分析

李阿立(), 冯雅楠, 李萍, 张东升, 宗毓铮, 林文, 郝兴宇*()   

  1. 山西农业大学农学院, 山西太谷 030800
  • 收稿日期:2021-04-06 接受日期:2021-09-09 出版日期:2022-05-12 网络出版日期:2021-10-09
  • 通讯作者: 郝兴宇
  • 作者简介:E-mail: lal19950713@126.com
  • 基金资助:
    山西农业大学省部共建有机旱作农业国家重点实验室自主研发项目(202105D121008-3-7);国家自然科学基金项目资助(31871517);国家自然科学基金项目资助(31971773);国家自然科学基金项目资助(31601212)

Transcriptome analysis of leaves responses to elevated CO2 concentration, drought and interaction conditions in soybean [Glycine max (Linn.) Merr.]

LI A-Li(), FENG Ya-Nan, LI Ping, ZHANG Dong-Sheng, ZONG Yu-Zheng, LIN Wen, HAO Xing-Yu*()   

  1. College of Agriculture, Shanxi Agricultural University, Taigu 030800, Shanxi, China
  • Received:2021-04-06 Accepted:2021-09-09 Published:2022-05-12 Published online:2021-10-09
  • Contact: HAO Xing-Yu
  • Supported by:
    State Key Laboratory of Integrative Sustainable Dryland Agriculture, the Shanxi Agricultural University(202105D121008-3-7);National Natural Science Foundation of China(31871517);National Natural Science Foundation of China(31971773);National Natural Science Foundation of China(31601212)

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摘要：

气候变暖及大气CO2浓度升高成为全球共识, 由此增加极端天气气候事件(干旱)发生的频率和强度并对大豆生产带来不确定性。本研究通过大豆表型和叶片转录组测序(RNA-seq)分析, 阐释CO2浓度升高、干旱及其交互条件对大豆基因表达影响, 明确CO2浓度升高影响大豆耐旱性的调控途径, 并在两个不同遗传背景品种中验证, 从分子水平为未来气候变化背景下大豆抗旱育种提供理论参考。表型结果表明, CO2浓度升高促进了大豆的生长并缓解干旱胁迫的负面效应。叶片转录组测序分析共筛选到89个CO2响应基因, KEGG分类显示这些基因主要参与抗氧化物质(萜类、黄酮类等)代谢, 同时特异性差异表达基因功能主要集中在细胞组分和生长发育方面。干旱条件下筛选的1006个差异表达(16倍)基因主要参与各类氨基酸(脯氨酸、色氨酸等)代谢途径, 绝大多数蛋白质合成与转运相关基因上调, 表明干旱胁迫下大豆叶片内物质合成交换过程加强。交互条件下筛选出的8566个差异表达基因主要参与碳水化合物代谢, 光合作用-天线蛋白途径的相关基因几乎全部下调表达, 表明交互条件下大豆光合能力下降。34个基因在3种条件下均差异表达, 这些基因主要集中在抗氧化物质(黄酮类物质、谷胱甘肽、苯丙素等)代谢方面, 且多数参与各类植物激素代谢和刺激响应。6个具有抗旱性功能的差异表达基因在两个不同遗传背景品种中的qRT-PCR结果说明RNA-seq数据准确。总之, CO2浓度升高提高了大豆叶片抗氧化物质代谢和生长发育相关基因的表达; 干旱胁迫诱导各类氨基酸代谢和蛋白质合成途径相关基因表达; 交互条件下大豆光合能力受限, CO2浓度升高主要通过调控激素代谢、抗氧化物质(抗氧化酶类、黄酮类、苯丙素等)代谢、碳水化合物代谢等途径提高大豆对干旱胁迫的耐受性。

关键词: 大豆, CO2浓度升高, 干旱, RNA-seq, 差异表达基因

Abstract:

Global consensus on climate warming and elevated atmospheric CO2 concentrations has increased the frequency and intensity of extreme weather events (droughts) and brought uncertainty about soybean production. In this study, the effects of elevated CO2 concentration, drought and their interaction on gene expression in soybean were elucidated by phenotypic and leaf transcriptome sequencing (RNA-seq) analysis. To provide theoretical reference for soybean breeding under the background of future climate change, we identified the regulatory pathway of CO2 affecting soybean drought tolerance. The phenotypic results showed that elevated CO2 concentration promoted the growth and alleviated the negative effects of drought stress on soybean. The results revealed that a total of 89 CO2-responsive genes were identified by transcriptome sequencing analysis. KEGG classification demonstrated that these genes were mainly involved in antioxidant metabolism (terpenoid, flavonoid, etc.), meanwhile, Functional of the specific differentially expressed gene mainly focused on cell components, growth, and development. Under drought condition, 1006 highly differentially expressed (16-fold) genes were screened out. These genes were mainly involved in various amino acid (proline, tryptophan, etc.) metabolic pathways, and almost all genes involved in protein synthesis and transport were up-regulated, indicating that there were a lot of material exchange processes in soybean leaves under drought stress. A total of 8566 differentially expressed genes, mainly involved in carbohydrate metabolism pathway, were detected under the interaction, and almost all genes related to the photosynthesis-antenna protein pathway were down-regulated, suggesting that the photosynthetic capacity of soybean was decreased under the interaction. 34 genes were found to be differentially expressed under all three conditions. These genes were mainly concentrated in antioxidant metabolism (flavonoids, glutathione, phenylpropanoids, etc.), and most of these genes were involved in the metabolism of various plant hormones and stimulus responses. The qRT-PCR results of six differentially expressed genes related to drought resistance in two soybean varieties with different genetic background showed that the RNA-seq data were accurate. In conclusion, elevated CO2 concentration could increase the relative expression levels of genes related to antioxidant metabolism, growth and development in soybean leaves. Drought stress induced the relative expression levels of genes related to amino acid metabolism and protein synthesis pathway. The photosynthetic capacity of soybean was inhibited under the interactive condition. Elevated CO2 concentration enhanced the tolerance of soybean to drought stress by regulating hormone metabolism, antioxidant (antioxidant enzyme, flavonoid, phenylpropanoid) metabolism and carbohydrate metabolism.

Key words: soybean, elevated CO2 concentration, drought, RNA-seq, differentially expressed genes

表1

qRT-PCR引物信息"

基因名称
Gene ID		 GO注释
GO annotation		 引物序列
Primer sequence (5′-3′)
Actin — F: GGTGGTTCTATCTTGGCATC
R: CTTTCGCTTCAATAACCCTA
Glyma.11G155000.Wm82.a2.v1 侧根发育 Lateral root development (GO:0048527)
根毛伸长 Root hair elongation (GO:0048767)		 F: CTACTTATCCTTGCTTGTCT
R: GTTGTCTTGAACTGGTGG
Glyma.19G247500.Wm82.a2.v1 细胞水分缺失响应 Response to water deprivation (GO:0009414)
响应脱落酸 Response to abscisic acid (GO:0009737)		 F: ATTCAGCATCCACCACTT
R: GTCCTCAGGGACCTTTCT
Glyma.05G149900.Wm82.a2.v1 响应渗透胁迫 Response to osmotic stress (GO:0006970) F: TCTATCGGAGGGCACAGG
R: TGCGTCCTTCTTGTTGTG
Glyma.U012100.Wm82.a2.v1 响应脱落酸 Response to abscisic acid (GO:0009737) F: GGGTTCTAAGTCTGGTGCT
R: GTAATCTTCTGCCCGTTC
Glyma.02G125100.Wm82.a2.v1 类黄酮生物合成 Flavonoid biosynthetic process (GO:0009813)
氧化还原过程 Oxidation-reduction process (GO:0055114)		 F: CTTGTTTGGTGCCTTCTG
R: GATGGGAACTTGTGGTAAAG
Glyma.01G010200.Wm82.a2.v1 调节过氧化氢代谢
Regulation of hydrogen peroxide metabolic process (GO:0010310)		 F: CCAGATGACAACGAGGGT
R: TTGATGCCAGGGTAGGAG

图1

CO2浓度升高、干旱和交互作用对大豆表型的影响 图A为Williams 82, 图B为中黄35。CK: 正常CO2浓度+正常水分处理; AC-D: 正常CO2浓度+PEG处理; EC-C: 高CO2浓度+正常水分处理; EC-D: 高CO2浓度+PEG处理。"

表2

不同处理对大豆品种Williams 82形态指标的影响"

处理
Treatment		 株高
Height (cm)		 茎粗
Stem diameter (mm)		 叶干重
Dry weight of leaves (g)		 茎干重
Dry weight of stem (g)		 根干重
Dry weight of root (g)
CK 16.40±0.92 ab 2.97±0.07 ab 0.45±0.06 c 0.15±0.01 b 0.29±0.05 b
AC-D 16.03±0.23 b 2.44±0.04 bc 0.25±0.02 d 0.16±0.01 b 0.24±0.03 b
EC-C 22.40±0.61 a 3.08±0.06 a 0.98±0.03 a 0.38±0.02 a 0.54±0.02 a
EC-D 18.13±0.32 b 2.78±0.06 b 0.66±0.07 b 0.35±0.03 a 0.65±0.03 a

表3

不同处理对大豆品种中黄35形态指标的影响"

处理
Treatment		 株高
Height (cm)		 茎粗
Stem diameter (mm)		 叶干重
Dry weight of leave (g)		 茎干重
Dry weight of stem (g)		 根干重
Dry weight of root (g)
CK 13.32±0.54 a 3.63±0.02 b 1.27±0.02 ab 0.44±0.02 a 0.72±0.03 a
AC-D 12.87±0.20 a 3.01±0.18 c 0.74±0.17 c 0.31±0.02 b 0.53±0.02 b
EC-C 11.33±0.72 ab 4.02±0.15 a 1.45±0.04 a 0.44±0.01 a 0.75±0.05 a
EC-D 10.67±0.61 b 3.75±0.06 ab 1.05±0.05 b 0.38±0.03 a 0.71±0.06 a

表4

转录组测序数据质量评估"

样本
Sample		 总数据
Total reads		 比对数据
Mapped reads		 比对率
Mapped ratio (%)		 GC含量
GC content (%)		 Q30
(%)
CK-1 50,556,622 47,044,145 93.05 45.23 94.45
CK-2 51,658,426 49,028,122 94.91 45.72 94.30
CK-3 43,820,024 41,614,743 94.97 45.33 94.34
AC-D-1 43,120,330 41,119,245 95.36 44.83 94.01
AC-D-2 44,531,268 42,172,536 94.70 44.91 93.91
AC-D-3 45,104,672 40,243,601 89.22 45.90 94.10
EC-C-1 45,547,956 43,138,374 94.71 45.84 94.47
EC-C-2 41,472,902 35,771,431 86.25 45.22 93.95
EC-C-3 50,234,668 47,881,951 95.32 45.45 94.20
EC-D-1 50,456,872 48,117,672 95.36 45.42 93.99
EC-D-2 44,848,264 41,773,279 93.14 45.06 93.97
EC-D-3 46,186,506 44,120,098 95.53 45.07 93.76

图2

CO2浓度升高、干旱和交互条件下大豆叶片DEG的韦恩图 处理同图1。"

表5

差异基因数目总览"

分组
Grouping		 总基因数目
Total number of genes		 上调基因数目
No. of up-regulated genes		 下调基因数目
No. of down-regulated genes
CK vs AC-D 10,081 3932 6149
CK vs EC-C 89 75 14
CK vs EC-D 8566 3599 4967

表6

CO2浓度升高条件下上/下调差异倍数最大的5个基因信息"

基因名称
Gene ID		 平均FPKM值
Average FPKM value		 log2 FC 相关性
Correlation		 基因注释
Gene annotation
CK EC-C
Glyma.02G058400.Wm82.a2.v1 0.016 3.199 3.842 上调 Up 预测: 蛋白质未定义结构域-2类。
PREDICTED: protein indeterminate-domain 2-like.
Glyma.15G199700.Wm82.a2.v1 5.073 65.190 2.649 上调 Up 假定蛋白GLYMA09G093000。
Hypothetical protein GLYMA09G093000.
Glyma.11G155000.Wm82.a2.v1 24.801 157.097 2.283 上调 Up 早期结瘤素-12A。
Early nodulin-12A.
Glyma.13G336600.Wm82.a2.v1 1.459 8.3240 1.806 上调 Up 预测: 膨胀素-A4。
PREDICTED: expansin-A4.
Glyma.15G054600.Wm82.a2.v1 2.069 18.329 1.607 上调 Up 预测: 蛋白质EXORDIUM类。
PREDICTED: protein EXORDIUM-like 2.
Glyma.01G003000.Wm82.a2.v1 42.373 16.676 -1.114 下调 Down MYB转录因子部分。
Transcription factor MYB129, partial.
Glyma.17G090500.Wm82.a2.v1 2.964 0.891 -1.202 下调 Down 未定义蛋白质LOC100787505。
Uncharacterized protein LOC100787505.
Glyma.17G242600.Wm82.a2.v1 11.048 3.420 -1.358 下调 Down 假定的钙离子结合蛋白CML15。
Putative calcium-binding protein CML15.
Glyma.09G149000.Wm82.a2.v1 14.753 3.794 -1.600 下调 Down 预测: 短截转录因子花椰菜D-类异构X1。
PREDICTED: truncated transcription factor
CAULIFLOWER D-like isoform X1.
Glyma.10G124300.Wm82.a2.v1 6.547 1.832 -1.677 下调 Down 预测: 未定义蛋白LOC100780762。
PREDICTED: uncharacterized protein
LOC100780762.

表7

干旱条件下上/下调差异倍数最大的5个基因信息"

基因名称
Gene ID		 平均FPKM值
Average FPKM value		 log2 FC 相关性
Correlation		 基因注释
Gene annotation
CK AC-D
Glyma.06G157000.Wm82.a2.v1 0 53.699 11.538 上调 Up 预测: 未定义蛋白质LOC100778708。
PREDICTED: uncharacterized protein LOC100778708.
Glyma.17G040800.Wm82.a2.v1 0.031 96.173 11.027 上调 Up Lea蛋白前体。
Lea protein precursor.
Glycine_max_newGene_3981 0 19.590 10.767 上调 Up 假定蛋白GLYMA14G121700。
Hypothetical protein GLYMA14G121700.
Glyma.05G065800.Wm82.a2.v1 0.027 86.385 10.701 上调 Up 预测: 膨胀素-类B1。
PREDICTED: expansin-like B1.
Glyma.09G185500.Wm82.a2.v1 3.423 5737.775 10.468 上调 Up 假定蛋白GLYMA09G185500。
Hypothetical protein GLYMA09G185500.
Glyma.10G200800.Wm82.a2.v1 12.755 0 -9.720 下调 Down 假定蛋白GLYMA10G200800。
Hypothetical protein GLYMA10G200800.
Glyma.04G169600.Wm82.a2.v1 92.106 0 -10.029 下调 Down 预测: 赤霉素调节蛋白4。
PREDICTED: gibberellin-regulated protein 4.
Glyma.16G007700.Wm82.a2.v1 357.220 0.059 -10.503 下调 Down 预测: 生长素结合蛋白ABP19a-类。
PREDICTED: auxin-binding protein ABP19a-like.
Glyma.07G038500.Wm82.a2.v1 454.705 0.186 -10.984 下调 Down 立方形超级蛋白家族前体。
Cupin-like superfamily protein precursor.
Glyma.17G212200.Wm82.a2.v1 288.954 0.059 -11.088 下调 Down AAI-LTSS超级蛋白家族前体。
AAI-LTSS superfamily protein precursor.

图3

CO2浓度升高和干旱条件下大豆叶片DEG分析 A: CO2浓度升高条件下大豆叶片DEG的KEGG分类图; B: 干旱条件下大豆叶片DEG的KEGG富集图。"

图4

交互条件下大豆叶片DEG分析 A: 交互条件下大豆叶片DEG的火山图; B: 交互条件下大豆叶片DEG的COG蛋白分析图; C: 交互条件下大豆叶片DEG的KEGG富集图。"

图5

交互条件下氧化应激DEG表达谱热图分析 A: 细胞水分缺失响应基因热图; B: 谷胱甘肽转移酶基因热图。"

图6

CO2浓度升高、干旱和交互条件下重叠DEG分析 A: CO2浓度升高、干旱和交互条件下重叠DEG的GO分类图; B: CO2浓度升高、干旱和交互条件下重叠DEG的KEGG富集图。"

图7

转录组测序结果的qRT-PCR验证 A~F为6个基因的表达趋势图; G为相关性分析图。"

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