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

doi:10.3724/SP.J.1123.2019.07030

Abstract

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

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.

Figures/Tables 7

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.

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

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

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

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

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

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

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