GC-MS代谢组学方法筛选造血干细胞移植后的aGVHD代谢标志物的研究

发布时间：2018-04-13 22:20

本文选题：造血干细胞移植(HSCT) + 急性移植物抗宿主病(aGVHD)　；参考：《苏州大学》2015年硕士论文

【摘要】：目的基于气象色谱质谱联用(GC-MS)的方法,探究造血干细胞移植后急性移植物抗宿主病(a GVHD)的血浆代谢标志物。方法本文采用GC-MS的研究方法分别对移植后7天内、移植后14天、28天及移植后29天至60天四个时间点的61位患者174例血浆样本进行处理,利用自动质谱退卷积定性系统(AMDIS)和美国国家标准与技术研究院(NIST)标准库并结合保留指数对血浆中的化合物进行鉴定,再应用正交偏最小二乘判别分析(OPLS-DA)、t检验、遗传算法(GA)等多种统计学方法选取特征代谢物,并应用Logistic回归合并受试者工作特征曲线(ROC曲线)对代谢标志物组合进行最终筛选与评价。结果(1)建立了稳定、可靠的适用于人血浆的GC-MS代谢组学分析方法。本研究对该分析方法的日内稳定性、日间稳定性和预处理的重现性进行考察。实验结果表明上述三项考察中共有峰面积相对标准偏差(RSD)15%的数目占到所有共有峰的95%以上,说明该方法能能够满足血浆中代谢物相对定量的需要。(2)GC-MS扫描获得的总离子图(TIC)凭借AMDIS的解析合并NIST 11库的检索并联合保留指数的比对共鉴定出54种化合物,包括氨基酸、脂肪酸和糖类等。(3)应用OPLS-DA方法分别对不同时间点的所有化合物信息进行分析,各个时间点阴性组阳性组分界清晰,联合OPLS-DA模型下的散点图(S-Plot)和变异权重参数值(VIP)值选取出不同时间点的a GVHD血浆特征代谢物,分别是:移植后7天内:尿素;移植后14天:尿素,棕榈酸;移植后28天:尿素;移植后29天至60天:乳酸,羟基乙酸,果糖,塔罗糖。(4)分别对4个时间点所有鉴定出的化合物行独立样本t检验,以p≤0.05为标准选取出相应时间点的数个a GVHD血浆特征代谢物,分别为:移植后7天内:丙氨酸;移植后14天:山梨醇,甘油酸;移植后28天:甘氨酸,山梨醇;移植后29天至60天:乳酸,丙酸,缬氨酸,尿素,甘氨酸,丝氨酸,山梨醇,葡萄糖,肌醇,丙二醇,α-生育酚,羟基乙酸,2,3-二羟基丁酸乙酯。(5)应用GA算法分别对不同时间点所有化合物信息进行计算,每个时间点计算5次,取位次靠前的十种化合物,其结果分别是:移植后7天内:丙氨酸,3-羟丁酸,甘油,苯丙氨酸,山梨醇,酪氨酸,油酸,鸟氨酸,磷酸,甘油酸;移植后14天:丙氨酸,尿素,山梨醇,尿酸,棕榈酸单甘油酯,门冬氨酸,甘油酸,甘露糖,十五酸,花生酸;移植后28天:甘油,脯氨酸,甘氨酸,丝氨酸,山梨醇,亚油酸,油酸,色氨酸,磷酸,甘油酸;移植后29天至60天:乳酸,草酸,缬氨酸,甘氨酸,苏氨酸,山梨醇,肌醇,丙二醇,α-生育酚,甘油酸。(6)将4个时间点所有化合物分别用Logistic回归方程进行计算,以α入=0.05,α出=0.10为标准选取各个时间点的特征代谢物,其结果为:移植后7天内:丙氨酸,尿素,酪氨酸;移植后14天:山梨醇,甘油酸,十五酸;移植后28天:甘氨酸,山梨醇,甘油酸;移植后29天至60天:乳酸,草酸,苏氨酸,丙二醇。(7)将上述每种方法选取的特征代谢物按照时间点进行合并,每个时间点化合物以不同的组合引入Logistic回归方程,计算相应的预测值,并用其绘制ROC曲线,直到某个组合满足:1)ROC曲线下组合的p值0.01;2)ROC曲线的AUC0.85;3)ROC曲线截点的灵敏度和特异性均70%为止,即将该组合作为这个时间点的代谢标志物组合。最终的组合是:移植后7天内:丙氨酸+尿素+酪氨酸;移植后14天:丙氨酸+尿素+山梨醇+尿酸+棕榈酸单甘油酯+门冬氨酸+甘油酸+正十五酸+花生酸;移植后28天:丙氨酸+丝氨酸+山梨醇+油酸+甘氨酸;移植后29天至60天:乳酸+草酸+苏氨酸+丙二醇。其中移植后7天内这个时间点筛选出的组合,其ROC曲线的AUC达到0.935,灵敏度和特异性分别达到90.5%和81.8%,是所有组合中对a GVHD的发生预测能力最强的。结论(1)基于GC-MS方法建立的代谢模型可以用于造血干细胞移植后a GVHD的预测。(2)造血干细胞移植后所选取的各个时间点均可筛选出预测和区分能力良好的a GVHD特异的代谢标志物组合。
[Abstract]:Based on Meteorological chromatography-mass spectrometry (GC-MS) method, inquiry after hematopoietic stem cell transplantation for acute graft-versus-host disease (a GVHD) of the plasma metabolic markers. Methods this paper adopts the research methods of GC-MS respectively for 7 days after transplantation, 14 days after transplantation, 28 days and 29 days to 60 days after transplantation four time points in 61 patients, 174 cases of plasma samples were processed by automated mass spectral deconvolution & identification system (AMDIS) and the National Institute of standards and Technology (NIST) standard library and identification of compounds in plasma combined with retention index, using orthogonal partial least squares discriminant analysis (OPLS-DA), t test the genetic algorithm (GA), and other statistical methods of characteristic metabolites, and the application of Logistic regression with receiver operating characteristic curve (ROC curve) of the final screening and evaluation of metabolic markers. Results (1) to establish a stable and reliable. For GC-MS metabolic plasma analysis method. The research on the analysis method of stability in a day, the day the stability and reproducibility of pretreatment were investigated. The experimental results show that the three peaks of the relative standard deviation (RSD) to the number 15% account for all of the common peaks above 95%, indicating this method can meet the needs of the relative quantification of metabolites in plasma. (2) the total ion map scanned by GC-MS (TIC) by comparison with NIST AMDIS 11 retrieval analysis library and combined retention index of 54 compounds were identified, including amino acids, fatty acids and carbohydrates (3) were. Analysis and application of OPLS-DA method at different time points of all the compounds, each time point negative group, positive group clear demarcation, scattered united under the OPLS-DA model diagram (S-Plot) and the variation of weight parameter value (VIP) values extracted at different time points A GVHD features of plasma metabolites, respectively: 7 days after transplantation: 14 days after transplantation: urea; urea, palmitic acid; urea; 28 days after transplantation: 29 to 60 days after transplantation: lactic acid, glycolic acid, fructose, talose. (4) of all 4 time points identified compounds for independent samples t test, respectively with P lower than 0.05 for the standard selected a GVHD plasma characteristics of metabolites, the corresponding time: 7 days after transplantation: alanine; 14 days after transplantation: sorbitol, glycerol acid; 28 days after transplantation: glycine, sorbitol; 29 to 60 days after transplantation lactic acid, propionic acid, urea, glycine, valine, serine, sorbitol, glucose, inositol, propylene glycol, tocopherol, glycolic acid, 2,3- two hydroxy ethyl butyrate. (5) the application of GA algorithm at different time points of all the compounds information is calculated, each time point is calculated from 5 times, ranking ten compound front, the results Don't is: 7 days after transplantation: alanine, 3- hydroxybutyric acid, glycerol, sorbitol, phenylalanine, tyrosine, oleic acid, ornithine, phosphoric acid, glycerol acid; 14 days after transplantation: alanine, urea, sorbitol, uric acid, palmitic acid monoglyceride, aspartic acid, glycerol acid, mannose, fifteen acid, arachidic acid 28 days after transplantation; glycerol, proline, glycine, serine, sorbitol, linoleic acid, oleic acid, tryptophan, phosphoric acid, glycerol acid; 29 to 60 days after transplantation: lactic acid, oxalic acid, valine, glycine, threonine, sorbitol, inositol, propylene glycol, alpha tocopherol, glyceric acid (6). The 4 time point of all the compounds were used Logistic regression equation to calculate, alpha =0.05, alpha =0.10 as the standard for selecting characteristic metabolites each time point, the results are as follows: 7 days after transplantation: urea, alanine, tyrosine; 14 days after transplantation: sorbitol, glycerol acid, fifteen acid; 28 days after transplantation: glycine, Yamanashi Alcohol, glycerol acid; 29 to 60 days after transplantation: lactic acid, oxalic acid, threonine, propylene glycol. (7) the characteristic metabolites of each method are combined according to the selected time points, each time point compounds in different combinations using the Logistic regression equation, calculate the predicted value, and draw the curve with ROC which, until a combination meet: 1) the combination of P ROC under the curve value of 0.01; 2) ROC curve AUC0.85; 3) the sensitivity and specificity of the ROC curve cut point are 70% so far, is the combination as the time point of metabolic markers. The final combination is: 7 days after transplantation: alanine + urea + tyrosine; 14 days after transplantation: alanine + urea + sorbitol + uric acid + palmitic acid monoglyceride + aspartic acid and glycerol is fifteen + acid + acid arachidonic acid; 28 days after transplantation: alanine serine + + sorbitol + oleic acid + glycine; 29 to 60 days after transplantation: lactic acid + oxalic acid + + threonine Propylene glycol. 7 days after transplantation the time point selected combination, the ROC curve of AUC reached 0.935, the sensitivity and specificity were respectively 90.5% and 81.8%, is the strongest predictive power of a GVHD in all combinations. Conclusion (1) metabolism model based on the GC-MS method can be used for blood a GVHD predicted stem cell transplantation. (2) hematopoietic stem cell transplantation selected after each time point can be selected to predict and distinguish the specific ability a GVHD metabolic markers.

【学位授予单位】：苏州大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R457.7

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