基于气相色谱-质谱联用技术的血清氨基酸代谢谱在尿石症中的应用

doi:10.3724/SP.J.1123.2019.07030

色谱 ›› 2020, Vol. 38 ›› Issue (5): 587-594.DOI: 10.3724/SP.J.1123.2019.07030

基于气相色谱-质谱联用技术的血清氨基酸代谢谱在尿石症中的应用

高瑶¹^,^*(), 龚思思¹, 张天闻², 唐敏洁³, 张蓓英¹, 陈敏¹^,^*(), 欧启水³, 毛厚平³

¹ 福建医科大学医学技术与工程学院, 福建福州 350004
² 福建省渔业资源监测中心, 福建福州 350003
³ 福建医科大学附属第一医院, 福建福州 350005

收稿日期:2019-07-27 出版日期:2020-05-08 发布日期:2020-12-10
通讯作者: 高瑶,陈敏
作者简介:陈敏.Tel:059183569236, E-mail:cmjy503@163.com
高瑶.Tel:059183569236, E-mail:yaogao@fjmu.edu.cn;
基金资助:
国家自然科学基金(21405017);福建省自然科学基金(2018J01676);福建省大学生创新创业训练计划(201610392079);福建医科大学医学技术与工程学院青-科研基金计划项目(2018xy001)

Application of serum amino acid profile in urolithiasis based on gas chromatography-mass spectrometry

GAO Yao¹^,^*(), GONG Sisi¹, ZHANG Tianwen², TANG Minjie³, ZHANG Beiying¹, CHEN Min¹^,^*(), OU Qishui³, MAO Houping³

¹ School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350004, China
² Fujian Fishery Resources Monitoring Center, Fuzhou 350003, China
³ the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China

Received:2019-07-27 Online:2020-05-08 Published:2020-12-10
Contact: GAO Yao,CHEN Min
Supported by:
National Natural Science Foundation of China(21405017);Fujian Natural Science Foundation(2018J01676);College Students’ Innovation and Entrepreneurship Training Program of Fujian Province(201610392079);Youth Scientific Research Fund Project of School of Medical Technology and Engi- neering of Fujian Medical University(2018xy001)

摘要/Abstract

摘要：

建立了基于气相色谱-质谱联用技术（GC-MS）的血清氨基酸检测平台，研究病例组（n=80，年龄（46.82±13.39）岁）和对照组（n=37，年龄（43.46±12.79）岁）的血清氨基酸代谢谱差异，运用多元统计分析模型，结合两样本t检验、逻辑回归分析、受试者工作特征（ROC）曲线筛选与尿石症相关差异氨基酸。该方法中目标氨基酸的线性相关系数（R²）均大于0.9985，检出限（LOD）在0.1~4.0 μmol/L之间。研究结果显示，通过血清氨基酸代谢谱能够很好地区分病例组和对照组，根据变量重要性投影（VIP）>1和p < 0.05筛选出的5种差异氨基酸可作为尿石症的潜在生物标志物，其联合指标的诊断灵敏度达97.3%；其中，丝氨酸ROC曲线下面积（AUC）达0.819，具有较好的临床价值，有望辅助临床进行尿石症的早期筛查诊断。

关键词: 气相色谱-质谱, 氨基酸代谢谱, 潜在生物标志物, 尿石症

Abstract:

An overall workflow based on gas chromatography-mass spectrometry (GC-MS) was established for the analysis of the serum amino acid profile between urolithiasis patients (n=80, age (46.82±13.39) years) and healthy controls (n=37, age (43.46±12.79) years). The raw data from GC-MS analysis were processed by multivariate statistical methods to build the model. Following this, student's t-test and logistic regression were performed and receiver operator characteristic (ROC) curve was plotted to identify the potential biomarkers. Good linearities were observed for the target amino acids, with correlation coefficients (R²) greater than 0.9985. The limits of detection (LODs) were 0.1-4.0 μmol/L. The results indicated a significant discrimination between the urolithiasis and control groups. Five significantly differentially expressed amino acids (variable importance in projection (VIP)>1 and p < 0.05) were found to provide the scientific evidence for the early diagnosis of urolithiasis, while the sensitivity of the integrated five differential amino acids was up to 97.3%. In particular, the area under the curve (AUC) of serine reached 0.819, which suggested a great clinical screening value.

Key words: gas chromatography-mass spectrometry (GC-MS), amino acid profile, potential biomarkers, urolithiasis

高瑶, 龚思思, 张天闻, 唐敏洁, 张蓓英, 陈敏, 欧启水, 毛厚平. 基于气相色谱-质谱联用技术的血清氨基酸代谢谱在尿石症中的应用[J]. 色谱, 2020, 38(5): 587-594.

GAO Yao, GONG Sisi, ZHANG Tianwen, TANG Minjie, ZHANG Beiying, CHEN Min, OU Qishui, MAO Houping. Application of serum amino acid profile in urolithiasis based on gas chromatography-mass spectrometry[J]. Chinese Journal of Chromatography, 2020, 38(5): 587-594.

图/表 7

图1 16种目标氨基酸和内标CPA的总离子流(TIC)色谱图

Fig. 1 Total ion current (TIC) chromatogram of the 16 target amino acids and CPA (IS) CPA: chlorphenylalanine; Ala: alanine; Gly: glycine; CySS: cystine; Val: valine; Leu: leucine; Ile: isoleucine; Pro: proline; Ser: serine; Asp: aspartic acid; Met: methionine; Cys: cysteine; Glu: glutamate; Phe: phenylalanine; Lys: lysine; Try: tryptophan; Tyr: tyrosine.

图2 16种目标氨基酸的特征质谱图

Fig. 2 Typical mass spectra of the 16 target amino acids * Quantitative ion.

表1 16种目标氨基酸的线性范围、回归方程、相关系数、检出限和定量限

Table 1 Linear ranges, regression equations, correlation coefficients (R2), limits of detection (LODs) and limits of quantification (LOQs) of the 16 target amino acids

Amino acid	Linear range/(μmol/L)	Regression equation	R²	LOD/(μmol/L)	LOQ/(μmol/L)
Y: peak area ratio of amino acid to internal standard; X: concentration, μ mol/L.
Ala	1.0-250	Y=6.649×10^-5X+9.315×10^-4	0.9998	0.1	0.3
Gly	1.0-250	Y=2.222×10^-5X-1.010×10^-4	0.9991	0.1	0.3
CySS	0.5-125	Y=2.392×10^-5X+9.737×10^-4	0.9993	2.0	6.6
Val	1.0-250	Y=1.454×10^-4X+7.568×10^-3	0.9990	0.1	0.3
Leu	1.0-250	Y=2.011×10^-4X+7.682×10^-3	0.9996	0.1	0.3
Ile	1.0-250	Y=1.558×10^-4X+7.080×10^-3	0.9993	0.1	0.3
Pro	1.0-250	Y=8.862×10^-5X+5.382×10^-3	0.9991	0.1	0.3
Ser	1.0-250	Y=6.299×10^-6X-1.221×10^-4	0.9993	2.0	6.6
Asp	1.0-250	Y=4.101×10^-5X-1.242×10^-3	0.9998	0.2	0.7
Met	1.0-250	Y=4.128×10^-5X-1.470×10^-3	0.9992	0.5	1.7
Cys	1.0-250	Y=2.560×10^-5X-6.778×10^-3	0.9995	4.0	13.2
Glu	1.0-250	Y=1.081×10^-6X-6.531×10^-5	0.9985	2.0	6.6
Phe	1.0-250	Y=2.001×10^-4X+5.518×10^-3	0.9991	0.1	0.3
Lys	1.0-250	Y=1.958×10^-5X-1.254×10^-3	0.9993	0.2	0.7
Try	1.0-250	Y=1.665×10^-4X-6.690×10^-2	0.9992	0.1	0.3
Tyr	1.0-250	Y=2.574×10^-4X-2.068×10^-2	0.9986	0.1	0.3

图3 血清样本的(a)PCA、(b)OPLS-DA得分图和(c)T预测散点图

Fig. 3 (a) Principal component analysis (PCA), (b) orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots, and (c)T-predicted scatter plot of the serum samples

图4 差异氨基酸在病例组(红色)和对照组(蓝色)中含量的箱形图

Fig. 4 Box charts of differential amino acids between disease group (red) and control group (blue)

图5 5种差异氨基酸联合的ROC曲线

Fig. 5 Receiver operator characteristic (ROC) curve of the five differential amino acids by integrating AUC: area under the curve.

图6 潜在生物标志物的代谢通路图

Fig. 6 Metabolic pathways of potential biomarkers TCA: tricarboxylic acid; acetyl-CoA: acetyl coenzyme A.

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基于气相色谱-质谱联用技术的血清氨基酸代谢谱在尿石症中的应用

Application of serum amino acid profile in urolithiasis based on gas chromatography-mass spectrometry

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