抗白叶枯病转基因粳稻C418-Xα21代谢组学研究
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摘要
白叶枯病是世界上影响水稻产量最严重的病害之一,控制这种病害最经济、最有效的方法是将抗性基因渗入水稻栽培品种。Xa21是最早克隆的水稻白叶枯病抗性基因,来源于非洲马里的长药野生稻,在分蘖后期具有广谱抗性,已广泛用于水稻基因工程育种；Xa23是从我国普通野生稻中鉴定发掘,目前已知的抗谱最广和抗性最强的显性基因,全生育期抗性,具有广阔的应用前景,虽尚未被克隆,已通过传统杂交结合分子标记辅助选择育种技术用于抗性水稻育种。
本文首次利用基于气相色谱质谱联用(GC-MS)的代谢组学分析方法,结合主成分分析(PCA)、层次聚类分析(HCA)和偏最小二乘法-判别分析(PLS-DA)研究了海南陵水南繁育种基地和北京昌平环境释放基地不同播种期和环境对农杆菌介导的转化系统获得的转基因白叶枯病抗性粳稻恢复系C418-Xa21与非转基因易感亲本C418及分子标记辅助选择育种获得的抗性新品系C418/Xa23糙米代谢指纹的影响。研究发现无论是海南陵水南繁育种基地,或是北京昌平环境释放基地,C418-Xa21和C418糙米的代谢指纹图谱均表现为相似度更高,而C418/Xa23糙米具有与它们明显不同的、独特的代谢指纹图谱。两种抗性水稻品系分别与C418糙米代谢指纹图谱相比较,C418/Xa23糙米的代谢表型差异明显大于转基因水稻C418-Xa21。此外,海南陵水南繁育种基地独特的地理位置和气候条件对转基因水稻C418-Xa21和非转基因亲本C418糙米之间代谢水平上的差异程度具有显著影响,该产区的这两个不同基因型水稻糙米样本组间在第一个主成分(PCI)上不能完全分型,呈交叉分布,仅在第二个主成分(PC2)上能基本实现分型,而北京昌平环境释放基地产的C418-Xa21和C418糙米样本组间在PCI上能很好的获得完全区分。
此外,两个产地不同播种期和环境对这三种水稻品系糙米代谢影响显著,与北京昌平基地相比较,发现在海南陵水南繁育种基地生产的这三种水稻品系糙米中一致下调的已鉴定差异代谢物分别为棕榈酸、硬脂酸、油酸、亚油酸、葡糖酸、丙氨酸和甘油,其中两个白叶枯病抗性水稻品系糙米中脂肪酸类成分下调趋势明显低于亲本C418。结合三种基因型水稻糙米GC-MS代谢指纹分析和GC-FID脂肪酸定量分析结果,我们证实了转基因水稻品系C418-Xa21糙米中易受播种期和环境影响而发生显著变化的代谢物主要为脂肪酸类成分,棕榈酸、硬脂酸、油酸和亚油酸等只在海南陵水南繁育种基地产的白叶枯病抗性水稻糙米中明显上调(P<0.05),而在北京昌平环境释放基地产的白叶枯病抗性水稻糙米中没有发现脂肪酸含量上的显著变化。另一个重要发现是,果糖和塔格糖在两种白叶枯病抗性水稻糙米中稳定下调(P<0.05),不受播种期和环境的影响,这类差异代谢物有可能作为白叶枯病抗性的潜在标志物。
利用电感耦合等离子体原子发射光谱法(ICP-AES)技术对这三种基因型水稻糙米中的5种重要元素(钾、磷、钠、锌和铁)进行定量分析,结果表明,相对于C418水稻糙米钾元素含量在两种抗性品系水稻糙米中均显著降低10%；钠元素含量只在C418/Xa23糙米中显著减少17%锌元素含量分别在C418-Xa21和C418/Xa23糙米中显著增加24%和12%；磷元素含量只在C418/Xa23糙米中显著降低11%；铁元素含量没有发生明显变化。
为了系统研究农杆菌介导转Xa21基因对粳稻C418不同组织(糙米、根和叶)代谢的影响,本文采用水稻温室水培技术,排除播种期、环境和土壤成分等因素对水稻代谢的干扰,以非转基因易感水稻品系C418和抗性水稻品系C418/Xa23为对照,实现了不同组织GC-MS代谢指纹图谱的快速判别分类和差异可视化。我们发现农杆菌介导Xa21基因转移对亲本C418水稻不同组织代谢上的干扰程度不同。Xa21转基因水稻糙米和根组织代谢受影响均较小,糙米中检测到9个差异代谢物,包括1个特有差异代谢物和2个与C418/Xa23糙米中相同下调趋势的果糖和塔格糖,验证了果糖和塔格糖可作为白叶枯病抗性潜在标志物的研究结果；根组织中检测到3个差异代谢物,含有2个特有差异代谢物和1个与C418/Xa23根中共有的差异代谢物海藻糖；叶组织受干扰程度最大,检测到18个差异代谢物,含有7个特有差异代谢物和11个C418/Xa23根中共有的差异代谢物。与非转基因易感水稻品系C418相比较,非转基因抗病水稻品系C418/Xa23在水稻的糙米、根和叶组织代谢水平上均具有独特的代谢表型,未预料显著差异代谢物均明显多于转基因水稻C418-Xa21,而转基因水稻C418-Xa21和亲本C418糙米和根的代谢指纹图谱非常相似,只在叶组织代谢水平上具有明显差异。
此外,我们还采用基于液相色谱飞行时间质谱联用(LC-TOF/MS)的代谢组学分析方法,检测到转基因水稻C418-Xa21和亲本C418叶组织对菲律宾白叶枯病菌PX099具有不同代谢反应。感染白叶枯病菌PX099导致转基因水稻C418-Xa21叶组织代谢发生了本质变化,转基因水稻对白叶枯细菌的感染应答反应导致了抗性发生,生物碱类次生代谢产物的生物合成受到明显影响,大多数含氮显著差异代谢物在Xa21介导的对PX099的反应过程中增加,这有助于更好的理解宿主对病原菌感染的复杂应答,为抗性检测提供一种新的依据。
总之,本研究揭示了分子标记辅助选择技术获得白叶枯病抗性新品系C418/Xa23代谢水平上的变化与农杆菌介导的转化系统产生的白叶枯病抗性品系C418-Xa21代谢上的未预料效应至少在一个可比较的幅度上。建立了以遗传背景一致的非转基因易感和抗性水稻品系为对照来评价转基因作物的未预料代谢产物变化的研究模式,广泛适用于更复杂的包含多个外源基因的转基因杂交稻代谢指纹图谱的层次聚类分析和未预料效应分析,只有超出自然变异范围的差异代谢物需要予以着重考虑,这对以后的转基因作物安全评价研究将具有重要参考价值。
Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The C418cultivar is susceptible to infection by Xoo strain PXO99. The transgenic variety, C418-Xa21, and introgression line C418/Xa23through marker assisted selection (MAS) of Xa23gene are resistant.
The polar compounds of brown rice were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS) from Lingshui, Hainan island and Changping, Beijing. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to differentiate transgenic and non-transgenci variants. As part of our exploratory phase we performed Hierachical Cluster Analysis (HCA) by grouping the samples into clusters based on the similarity of their metabolite abundance profiles. It was found that both sowing and environment had remarkable impacts on the compositions of transgenic rice and its counterpart.
Whether growing in Lingshui, Hainan Island or Changping, Beijing, the metabolic profiles of transgenic and non-transgenic rice were higher similarity compared with C418/Xa23. It possessed distinctive metabolite profile and were more significant differences in metabolic phenotype than transgenic rice compared with the negative control. Furthermore, unique geographical location and climatic conditions had remarkable inpacts on the brown rice metabolism of transgenic and wild rice in Lingshui, Hainan Isaland. It was found that the samples of two different genotypes growing in Hainan Island could not be discriminated on the first principle component and showd cross distribution. Meanwhile, their browen rice samples could be fully distinguished growing in Beijing.
Another major finding from the present study was that sowing and environment had no impacts on fructose and tagatose (P<0.05) as the potential biomarkers in brown rice from the two bacterial blight resistance rice varieties. Therefore, when the risk of transgenic rice is assessed in subsequent studies, these steady changed compositions must be taken into account. The unintended compositional changes detected in our study laid a good foundation for further safety assessment of transgenic rice. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation and not limited to recongnized and known metabolites that can be applicable to a wild range of transgenic hybrid rice with more than one foreign gene with no previous optimization.
According to metabolic profiles analysis based on GC-MS and quantitative analysis of the resultsof GC-FID, we confirmed that the fatty acid in transgenic rice were vulnerable to the effects of the sowing and environment. It was found that palmitic acid, stearic acid, oleic acid and linoleic acid (P<0.05) only remarkably increased in the Hainan Island, but no significant differences in transgenic rice growing in Beijing. Therefore, these potentional biomarkers may not to be taken into account in futher studies. In addition, the elements (K, P, Na, Zn, and Fe) that varied between transgenic rice and non-transgenic rice growing in Hainnan Island were also analyzed by ANOVA by use of ICP-AES. Except element Fe, other elements were proved to be significantly different. This showed that in the two resistant rice variants C418-Xa21and C418/Xa23, the concentration of element K both decreased at10%, whereas the concentration of elements Na merely decreased17%. The difference of C418-Xa2Iand C418/Xa23was that the concentration of Zn increased24%and12%, respectively, whereas P content decreased11%, although still in the reference range value reported for rice.
To rule out the effect of sowing, environment and soil compositon on the rice metabolism, Non-transgenic japonica restorer line C418and C418/Xa23were firstly as the controls under hydroponics in the green house, metabolic profiles of transgenic rice variant C418-Xa21and non-transgenic varieties were compared to assess the unintended effects related to gene modification. The polar compounds of brown rice, root and leaf were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS). It was found that the degree of interference was significant differences on different tissues, least affected on brown rice and root tissues, followed by leaves in the largest. Insterestingly, fructose and tagatose (P<0.05) still decreased in brown rice from the two bacterial blight resistance rice varieties and validated them as biomarkers. In addition, C418/Xa23on the metabolism with a distinctive metabolite profile in the brown rice, root and leaf tissues, the transgenic rice C418-Xa21and its counterpart C418only in the rice leaf tissue metabolism have significantly different levels of metabolic phenotypes. However, the leaf tissue metabolic profiles of the transgenic rice and its counterpart showed stronger similarity compared with C418/Xa23. As for brown rice and root tissue, HCA results showed that the samples of transgenic rice and its counterpart were shown to cross distribution and could not be discriminated.
In addition, unsupervised and supervised classification tools clearly discriminate C418-Xa21challenged PXO99based LC-TOF/MS analysis. Unbiased, discovery-based metabolomics analyses yielded novel insights into the rice response to Xoo. Our results reveal global metabolic changes in leaf tissues of the XA21transgenic variant challenged with PXO99. While central carboncatabolism is reduced in correlationmetabolite expression in C418-Xa21rice genotype, some alkaloids were increased specifically in the XA21-mediated response to PXO99. The outcome of metabolomics studies such as these will aid in a better understanding of complex response to pathogen infection.
In conclusion, our results suggested metabolic profiles analyses can be used to the identification and classification in the metabolic level from innovative rice varieties, and this study provides experimental evidence for application of metabolomics in genetic modified varieties satefy evaluation in the future. Furthermore, the results in the present study confirmed that a single gene change has little effect on biochemical pathway, this was interpreted as a pleiotropic effect of the primary gentic alteration but not found in pathway that were later determined to be involved in resistance to bacterial infection. According to the GC-MS data, supervised multivariate statistics demonstrated the metabolic changes caused throught marker assisted selection techniques were, in these cases, at least of a comparable magnitude to those resulting as an unintended effect of genetic engineering techniques. For the first time using non-transgenic susceptible and resistant controls with the same genetic background to evaluate the unintended effects and metabolic profiles of GM crops will be provided an important reference value in further safety assessment of GM crops.
本文首次利用基于气相色谱质谱联用(GC-MS)的代谢组学分析方法,结合主成分分析(PCA)、层次聚类分析(HCA)和偏最小二乘法-判别分析(PLS-DA)研究了海南陵水南繁育种基地和北京昌平环境释放基地不同播种期和环境对农杆菌介导的转化系统获得的转基因白叶枯病抗性粳稻恢复系C418-Xa21与非转基因易感亲本C418及分子标记辅助选择育种获得的抗性新品系C418/Xa23糙米代谢指纹的影响。研究发现无论是海南陵水南繁育种基地,或是北京昌平环境释放基地,C418-Xa21和C418糙米的代谢指纹图谱均表现为相似度更高,而C418/Xa23糙米具有与它们明显不同的、独特的代谢指纹图谱。两种抗性水稻品系分别与C418糙米代谢指纹图谱相比较,C418/Xa23糙米的代谢表型差异明显大于转基因水稻C418-Xa21。此外,海南陵水南繁育种基地独特的地理位置和气候条件对转基因水稻C418-Xa21和非转基因亲本C418糙米之间代谢水平上的差异程度具有显著影响,该产区的这两个不同基因型水稻糙米样本组间在第一个主成分(PCI)上不能完全分型,呈交叉分布,仅在第二个主成分(PC2)上能基本实现分型,而北京昌平环境释放基地产的C418-Xa21和C418糙米样本组间在PCI上能很好的获得完全区分。
此外,两个产地不同播种期和环境对这三种水稻品系糙米代谢影响显著,与北京昌平基地相比较,发现在海南陵水南繁育种基地生产的这三种水稻品系糙米中一致下调的已鉴定差异代谢物分别为棕榈酸、硬脂酸、油酸、亚油酸、葡糖酸、丙氨酸和甘油,其中两个白叶枯病抗性水稻品系糙米中脂肪酸类成分下调趋势明显低于亲本C418。结合三种基因型水稻糙米GC-MS代谢指纹分析和GC-FID脂肪酸定量分析结果,我们证实了转基因水稻品系C418-Xa21糙米中易受播种期和环境影响而发生显著变化的代谢物主要为脂肪酸类成分,棕榈酸、硬脂酸、油酸和亚油酸等只在海南陵水南繁育种基地产的白叶枯病抗性水稻糙米中明显上调(P<0.05),而在北京昌平环境释放基地产的白叶枯病抗性水稻糙米中没有发现脂肪酸含量上的显著变化。另一个重要发现是,果糖和塔格糖在两种白叶枯病抗性水稻糙米中稳定下调(P<0.05),不受播种期和环境的影响,这类差异代谢物有可能作为白叶枯病抗性的潜在标志物。
利用电感耦合等离子体原子发射光谱法(ICP-AES)技术对这三种基因型水稻糙米中的5种重要元素(钾、磷、钠、锌和铁)进行定量分析,结果表明,相对于C418水稻糙米钾元素含量在两种抗性品系水稻糙米中均显著降低10%；钠元素含量只在C418/Xa23糙米中显著减少17%锌元素含量分别在C418-Xa21和C418/Xa23糙米中显著增加24%和12%；磷元素含量只在C418/Xa23糙米中显著降低11%；铁元素含量没有发生明显变化。
为了系统研究农杆菌介导转Xa21基因对粳稻C418不同组织(糙米、根和叶)代谢的影响,本文采用水稻温室水培技术,排除播种期、环境和土壤成分等因素对水稻代谢的干扰,以非转基因易感水稻品系C418和抗性水稻品系C418/Xa23为对照,实现了不同组织GC-MS代谢指纹图谱的快速判别分类和差异可视化。我们发现农杆菌介导Xa21基因转移对亲本C418水稻不同组织代谢上的干扰程度不同。Xa21转基因水稻糙米和根组织代谢受影响均较小,糙米中检测到9个差异代谢物,包括1个特有差异代谢物和2个与C418/Xa23糙米中相同下调趋势的果糖和塔格糖,验证了果糖和塔格糖可作为白叶枯病抗性潜在标志物的研究结果；根组织中检测到3个差异代谢物,含有2个特有差异代谢物和1个与C418/Xa23根中共有的差异代谢物海藻糖；叶组织受干扰程度最大,检测到18个差异代谢物,含有7个特有差异代谢物和11个C418/Xa23根中共有的差异代谢物。与非转基因易感水稻品系C418相比较,非转基因抗病水稻品系C418/Xa23在水稻的糙米、根和叶组织代谢水平上均具有独特的代谢表型,未预料显著差异代谢物均明显多于转基因水稻C418-Xa21,而转基因水稻C418-Xa21和亲本C418糙米和根的代谢指纹图谱非常相似,只在叶组织代谢水平上具有明显差异。
此外,我们还采用基于液相色谱飞行时间质谱联用(LC-TOF/MS)的代谢组学分析方法,检测到转基因水稻C418-Xa21和亲本C418叶组织对菲律宾白叶枯病菌PX099具有不同代谢反应。感染白叶枯病菌PX099导致转基因水稻C418-Xa21叶组织代谢发生了本质变化,转基因水稻对白叶枯细菌的感染应答反应导致了抗性发生,生物碱类次生代谢产物的生物合成受到明显影响,大多数含氮显著差异代谢物在Xa21介导的对PX099的反应过程中增加,这有助于更好的理解宿主对病原菌感染的复杂应答,为抗性检测提供一种新的依据。
总之,本研究揭示了分子标记辅助选择技术获得白叶枯病抗性新品系C418/Xa23代谢水平上的变化与农杆菌介导的转化系统产生的白叶枯病抗性品系C418-Xa21代谢上的未预料效应至少在一个可比较的幅度上。建立了以遗传背景一致的非转基因易感和抗性水稻品系为对照来评价转基因作物的未预料代谢产物变化的研究模式,广泛适用于更复杂的包含多个外源基因的转基因杂交稻代谢指纹图谱的层次聚类分析和未预料效应分析,只有超出自然变异范围的差异代谢物需要予以着重考虑,这对以后的转基因作物安全评价研究将具有重要参考价值。
Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The C418cultivar is susceptible to infection by Xoo strain PXO99. The transgenic variety, C418-Xa21, and introgression line C418/Xa23through marker assisted selection (MAS) of Xa23gene are resistant.
The polar compounds of brown rice were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS) from Lingshui, Hainan island and Changping, Beijing. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to differentiate transgenic and non-transgenci variants. As part of our exploratory phase we performed Hierachical Cluster Analysis (HCA) by grouping the samples into clusters based on the similarity of their metabolite abundance profiles. It was found that both sowing and environment had remarkable impacts on the compositions of transgenic rice and its counterpart.
Whether growing in Lingshui, Hainan Island or Changping, Beijing, the metabolic profiles of transgenic and non-transgenic rice were higher similarity compared with C418/Xa23. It possessed distinctive metabolite profile and were more significant differences in metabolic phenotype than transgenic rice compared with the negative control. Furthermore, unique geographical location and climatic conditions had remarkable inpacts on the brown rice metabolism of transgenic and wild rice in Lingshui, Hainan Isaland. It was found that the samples of two different genotypes growing in Hainan Island could not be discriminated on the first principle component and showd cross distribution. Meanwhile, their browen rice samples could be fully distinguished growing in Beijing.
Another major finding from the present study was that sowing and environment had no impacts on fructose and tagatose (P<0.05) as the potential biomarkers in brown rice from the two bacterial blight resistance rice varieties. Therefore, when the risk of transgenic rice is assessed in subsequent studies, these steady changed compositions must be taken into account. The unintended compositional changes detected in our study laid a good foundation for further safety assessment of transgenic rice. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation and not limited to recongnized and known metabolites that can be applicable to a wild range of transgenic hybrid rice with more than one foreign gene with no previous optimization.
According to metabolic profiles analysis based on GC-MS and quantitative analysis of the resultsof GC-FID, we confirmed that the fatty acid in transgenic rice were vulnerable to the effects of the sowing and environment. It was found that palmitic acid, stearic acid, oleic acid and linoleic acid (P<0.05) only remarkably increased in the Hainan Island, but no significant differences in transgenic rice growing in Beijing. Therefore, these potentional biomarkers may not to be taken into account in futher studies. In addition, the elements (K, P, Na, Zn, and Fe) that varied between transgenic rice and non-transgenic rice growing in Hainnan Island were also analyzed by ANOVA by use of ICP-AES. Except element Fe, other elements were proved to be significantly different. This showed that in the two resistant rice variants C418-Xa21and C418/Xa23, the concentration of element K both decreased at10%, whereas the concentration of elements Na merely decreased17%. The difference of C418-Xa2Iand C418/Xa23was that the concentration of Zn increased24%and12%, respectively, whereas P content decreased11%, although still in the reference range value reported for rice.
To rule out the effect of sowing, environment and soil compositon on the rice metabolism, Non-transgenic japonica restorer line C418and C418/Xa23were firstly as the controls under hydroponics in the green house, metabolic profiles of transgenic rice variant C418-Xa21and non-transgenic varieties were compared to assess the unintended effects related to gene modification. The polar compounds of brown rice, root and leaf were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS). It was found that the degree of interference was significant differences on different tissues, least affected on brown rice and root tissues, followed by leaves in the largest. Insterestingly, fructose and tagatose (P<0.05) still decreased in brown rice from the two bacterial blight resistance rice varieties and validated them as biomarkers. In addition, C418/Xa23on the metabolism with a distinctive metabolite profile in the brown rice, root and leaf tissues, the transgenic rice C418-Xa21and its counterpart C418only in the rice leaf tissue metabolism have significantly different levels of metabolic phenotypes. However, the leaf tissue metabolic profiles of the transgenic rice and its counterpart showed stronger similarity compared with C418/Xa23. As for brown rice and root tissue, HCA results showed that the samples of transgenic rice and its counterpart were shown to cross distribution and could not be discriminated.
In addition, unsupervised and supervised classification tools clearly discriminate C418-Xa21challenged PXO99based LC-TOF/MS analysis. Unbiased, discovery-based metabolomics analyses yielded novel insights into the rice response to Xoo. Our results reveal global metabolic changes in leaf tissues of the XA21transgenic variant challenged with PXO99. While central carboncatabolism is reduced in correlationmetabolite expression in C418-Xa21rice genotype, some alkaloids were increased specifically in the XA21-mediated response to PXO99. The outcome of metabolomics studies such as these will aid in a better understanding of complex response to pathogen infection.
In conclusion, our results suggested metabolic profiles analyses can be used to the identification and classification in the metabolic level from innovative rice varieties, and this study provides experimental evidence for application of metabolomics in genetic modified varieties satefy evaluation in the future. Furthermore, the results in the present study confirmed that a single gene change has little effect on biochemical pathway, this was interpreted as a pleiotropic effect of the primary gentic alteration but not found in pathway that were later determined to be involved in resistance to bacterial infection. According to the GC-MS data, supervised multivariate statistics demonstrated the metabolic changes caused throught marker assisted selection techniques were, in these cases, at least of a comparable magnitude to those resulting as an unintended effect of genetic engineering techniques. For the first time using non-transgenic susceptible and resistant controls with the same genetic background to evaluate the unintended effects and metabolic profiles of GM crops will be provided an important reference value in further safety assessment of GM crops.
引文
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