基于液质联用技术的代谢组学新方法的研究与应用
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摘要
代谢组学研究的是生物体系受到刺激产生的内源性代谢变化,可以对那些能描述代谢循环情况的关键化合物进行定性和定量分析。作为应用驱动的新兴科学,它已在医药相关领域得到了广泛的应用。但在其发展过程中,也出现了一系列的问题,面对这些问题,本研究从策略上提出了定量化和整合化的思想,并在数据处理方法上进行了新的尝试。将这些研究策略和方法用在重大疾病的具体研究中,取得了良好的效果。
针对目前代谢组学研究中存在的定量不准确的问题,将定量化策略用在代谢组学研究上,提出了自己的定量代谢组学的研究方法。针对糖尿病肾病,以嘌呤嘧啶类物质为主要目标,采用HPLC-UV-MS/MS分析技术,建立了同时对嘌呤嘧啶代谢循环中21种代谢物进行定量的定量代谢组学研究平台。将这一研究平台应用于糖尿病肾病的研究,筛查到七种潜在的代谢标志物,分别为尿酸、黄嘌呤、肌苷、腺苷、胞嘧啶、胞苷和胸腺嘧啶核苷,并推测了疾病可能的机理。针对神经管畸形,采用HPLC-MS/MS分析技术,建立了同时对叶酸、同型半胱氨酸和谷胱甘肽代谢循环中16种代谢物进行定量的定量代谢组学研究平台。将这一研究平台应用于营养素干预防止神经管畸形发生的研究,筛查到四种与干预相关的潜在生物标志物,分别为Hcy、5-MT、GSH和GluCys。结合神经管畸形病人的相关数据,说明干预是有效的,并对干预机理进行了推测。研究结果说明,定量化的研究策略解决了代谢组学研究定量不准确的缺点,使得研究结果更可靠,更容易被接受。
针对代谢组学研究内容较单一而对象较复杂的问题,将整合化策略用到代谢组学的研究中,提出了自己的整合化代谢组学的研究思想。首先进行了定量代谢组学与代谢指纹谱的整合,寻找更全面的代谢标志物,使得对疾病病理分期的判别分析的预测准确率达到了96%。然后进行了代谢组学研究与临床研究的整合,用代谢标志物与临床生化指标一起对疾病进行预测,推高了预测准确率,并将两者结合探讨糖尿病肾病的发病机理；最后将基因组学研究结果与代谢组学研究进行了整合,提出了两者结合寻找疾病发生相关的通路的研究模式,寻找到一个与糖尿病肾病相关的重要通路——亚油酸代谢通路。整合的策略加强了各研究之间的联系,使疾病的研究更深入,更透彻。
针对目前代谢组学数据处理方法对多类别样本分类和预测的能力较弱的问题,在数据处理上引入了人工智能技术,通过模糊逻辑和人工神经网络的结合,建立了一套人工智能技术寻找生物标志物的方法,并用在了糖尿病肾病的研究中。对于糖尿病肾病的定量代谢组学研究数据,最终寻找到四个标志物：尿酸、胞嘧啶、黄嘌呤和胸腺嘧啶核苷,与t检验找到的7个标志物相比,预测准确率相当；对于糖尿病肾病的代谢指纹谱研究数据,将4000个变量最终缩减到4个,且对疾病各分期样本的预测准确率超过0.92。此方法可以完成对多类样本的分类和预测,预测效果良好,而且技术的结合也实现了变量的缩减,寻找到了潜在的生物标志物。
Metabonomics, the scientific research of endogenous metabolic responses of living systems to stimuli, serves not only as a source of qualitative but also quantitative data of metabolites essential for the description of the metabolic cycle. As an application-driven science, metabonomics has been applied to the curatorial area broadly. For the questions of metabonomic research, the quantitative research strategy and integrated research strategy were proposed, and the data processing was carried out with a new try. These strategies and methods were used in the study of major diseases, which resulted in achieving good results.
For the inaccurate quantitation of nowaday metabonomics, quantitative research strategy was applied to the metabonomic research. And our own research methods were proposed. Firstly, aimed at purine and pyrimidine, an analytical platform for simultaneous quantification of 21 related metabolites was established using HPLC-UV-MS/MS. Then the platform was applied to the research on diabetic nephropathy. With the research, seven potential biomarkers were found out, which were uric acid, xanthine, inosine, adenosine, cytosine, cytidine and thymidine. And the possible mechanism of the disease was speculated. Afterwards, an analytical platform for simultaneous quantification of 16 metabolites involved in folic acid, homocysteine and glutathione metabolism was established using HPLC-MS/MS. Then the platform was applied to the research on the avoidance of neural tube defects (NTDs) with nutriment. With the research, four potential biomarkers were found out, which were Hcy, 5-MT, GSH and GluCys. The results illuminated that the nutriment was effective refered to the data of NTDs patients. And the possible mechanism of the nutriment was speculated. Through the complete research, the problem of poor quantitation was solved by using the quantitative research strategy. So the results of metabonomic research were more reliable and acceptable.
For the problem of the unilateral content and complex object, integrated research strategy was applied to the metabonomic research. And our own research methods were proposed. Firstly, we integrated quantitative metabonomics and metabolic fingerprinting to look for comprehensive biomarkers, which make the prediction accuracy rate of identification for disease stage reaching to 96%. Then the metabonomics was integrated with clinical research. With the combination of metabolic biomarkers and clinical biochemical parameters, the prediction accuracy for disease was increased. And the disease mechanism was speculated with the combination. Finally, a research mode was put forward for finding out metabolic pathway related to disease with the integration of genomics and metabonomics. With the research mode, a very important metabolic pathway related to diabetic nephropathy was found out, which is linoleic acid metabolism. Integrated strategy strengthen the links among the various studies, so that the research for the disease became deeper and more thorough.
For the poor capacity of solving multiclass and prediction, artificial intelligence technologies were applied to the data processing. With the combination of fuzzy logic and artificial neural network, a new method was established for finding out biomarkers, which was then applied to the research of diabetic nephropathy. For the data of quantitative metabonomics, we found out four potential biomarkers, which were uric acid, cytosine, xanthine and thymidine. Compared to seven biomarkers found out using student-t test, they had similar prediction accuracy. For the data of metabolic fingerprinting, the variables was reduced from 4000 to 4, while both of them had good prediction accuracy, which is higher than 0.92. From the research results, we can see that the established method can achieve multiclass classification with a good prediction accuracy. In addition, the variables can be reduced, therefore the potential biomarkers can be found out with the artificial intelligence technologies.
针对目前代谢组学研究中存在的定量不准确的问题,将定量化策略用在代谢组学研究上,提出了自己的定量代谢组学的研究方法。针对糖尿病肾病,以嘌呤嘧啶类物质为主要目标,采用HPLC-UV-MS/MS分析技术,建立了同时对嘌呤嘧啶代谢循环中21种代谢物进行定量的定量代谢组学研究平台。将这一研究平台应用于糖尿病肾病的研究,筛查到七种潜在的代谢标志物,分别为尿酸、黄嘌呤、肌苷、腺苷、胞嘧啶、胞苷和胸腺嘧啶核苷,并推测了疾病可能的机理。针对神经管畸形,采用HPLC-MS/MS分析技术,建立了同时对叶酸、同型半胱氨酸和谷胱甘肽代谢循环中16种代谢物进行定量的定量代谢组学研究平台。将这一研究平台应用于营养素干预防止神经管畸形发生的研究,筛查到四种与干预相关的潜在生物标志物,分别为Hcy、5-MT、GSH和GluCys。结合神经管畸形病人的相关数据,说明干预是有效的,并对干预机理进行了推测。研究结果说明,定量化的研究策略解决了代谢组学研究定量不准确的缺点,使得研究结果更可靠,更容易被接受。
针对代谢组学研究内容较单一而对象较复杂的问题,将整合化策略用到代谢组学的研究中,提出了自己的整合化代谢组学的研究思想。首先进行了定量代谢组学与代谢指纹谱的整合,寻找更全面的代谢标志物,使得对疾病病理分期的判别分析的预测准确率达到了96%。然后进行了代谢组学研究与临床研究的整合,用代谢标志物与临床生化指标一起对疾病进行预测,推高了预测准确率,并将两者结合探讨糖尿病肾病的发病机理；最后将基因组学研究结果与代谢组学研究进行了整合,提出了两者结合寻找疾病发生相关的通路的研究模式,寻找到一个与糖尿病肾病相关的重要通路——亚油酸代谢通路。整合的策略加强了各研究之间的联系,使疾病的研究更深入,更透彻。
针对目前代谢组学数据处理方法对多类别样本分类和预测的能力较弱的问题,在数据处理上引入了人工智能技术,通过模糊逻辑和人工神经网络的结合,建立了一套人工智能技术寻找生物标志物的方法,并用在了糖尿病肾病的研究中。对于糖尿病肾病的定量代谢组学研究数据,最终寻找到四个标志物：尿酸、胞嘧啶、黄嘌呤和胸腺嘧啶核苷,与t检验找到的7个标志物相比,预测准确率相当；对于糖尿病肾病的代谢指纹谱研究数据,将4000个变量最终缩减到4个,且对疾病各分期样本的预测准确率超过0.92。此方法可以完成对多类样本的分类和预测,预测效果良好,而且技术的结合也实现了变量的缩减,寻找到了潜在的生物标志物。
Metabonomics, the scientific research of endogenous metabolic responses of living systems to stimuli, serves not only as a source of qualitative but also quantitative data of metabolites essential for the description of the metabolic cycle. As an application-driven science, metabonomics has been applied to the curatorial area broadly. For the questions of metabonomic research, the quantitative research strategy and integrated research strategy were proposed, and the data processing was carried out with a new try. These strategies and methods were used in the study of major diseases, which resulted in achieving good results.
For the inaccurate quantitation of nowaday metabonomics, quantitative research strategy was applied to the metabonomic research. And our own research methods were proposed. Firstly, aimed at purine and pyrimidine, an analytical platform for simultaneous quantification of 21 related metabolites was established using HPLC-UV-MS/MS. Then the platform was applied to the research on diabetic nephropathy. With the research, seven potential biomarkers were found out, which were uric acid, xanthine, inosine, adenosine, cytosine, cytidine and thymidine. And the possible mechanism of the disease was speculated. Afterwards, an analytical platform for simultaneous quantification of 16 metabolites involved in folic acid, homocysteine and glutathione metabolism was established using HPLC-MS/MS. Then the platform was applied to the research on the avoidance of neural tube defects (NTDs) with nutriment. With the research, four potential biomarkers were found out, which were Hcy, 5-MT, GSH and GluCys. The results illuminated that the nutriment was effective refered to the data of NTDs patients. And the possible mechanism of the nutriment was speculated. Through the complete research, the problem of poor quantitation was solved by using the quantitative research strategy. So the results of metabonomic research were more reliable and acceptable.
For the problem of the unilateral content and complex object, integrated research strategy was applied to the metabonomic research. And our own research methods were proposed. Firstly, we integrated quantitative metabonomics and metabolic fingerprinting to look for comprehensive biomarkers, which make the prediction accuracy rate of identification for disease stage reaching to 96%. Then the metabonomics was integrated with clinical research. With the combination of metabolic biomarkers and clinical biochemical parameters, the prediction accuracy for disease was increased. And the disease mechanism was speculated with the combination. Finally, a research mode was put forward for finding out metabolic pathway related to disease with the integration of genomics and metabonomics. With the research mode, a very important metabolic pathway related to diabetic nephropathy was found out, which is linoleic acid metabolism. Integrated strategy strengthen the links among the various studies, so that the research for the disease became deeper and more thorough.
For the poor capacity of solving multiclass and prediction, artificial intelligence technologies were applied to the data processing. With the combination of fuzzy logic and artificial neural network, a new method was established for finding out biomarkers, which was then applied to the research of diabetic nephropathy. For the data of quantitative metabonomics, we found out four potential biomarkers, which were uric acid, cytosine, xanthine and thymidine. Compared to seven biomarkers found out using student-t test, they had similar prediction accuracy. For the data of metabolic fingerprinting, the variables was reduced from 4000 to 4, while both of them had good prediction accuracy, which is higher than 0.92. From the research results, we can see that the established method can achieve multiclass classification with a good prediction accuracy. In addition, the variables can be reduced, therefore the potential biomarkers can be found out with the artificial intelligence technologies.
引文
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