Application of GC–MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis ratBiochemical and Biophysical Research Communications

About

Authors
Shao Liu, Ji-Shi Liu, Ren-na Luo, Hui Xu, Wei-ru Zhang, Jie Meng, Yi-Zeng Liang, Li-Jian Tao
Year
2015
DOI
10.1016/j.bbrc.2015.04.031
Subject
Molecular Biology / Biochemistry / Biophysics / Cell Biology

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Keywords:

Biomarker

Chemometrics

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common analysis of the changes of metabolites in cells, tissues, or body rophy, tubular cavity expansion, and interstitial fibrosis, which eventually lead to the structural breakdown and dysfunction of the whole kidney. Screening metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis

Gas chromatographyemass spectrometry (GC/MS) is a robust es of metabolites. n power than the ty in the structure

S, GC/MS has been f the GC/MS are ed by comparing n samples at their , and made strucof electron impact (EI) and chemical ionization. Nevertherless, these methods show difficulty in accurately identifying and quantifying metabolites that are co-eluted with, or eluted close to high abundant overloaded metabolites due to overlapping chromatographic peaks [18,19]. In this case, chemometric resolutionmethodmay be useful and can be employed to improve the efficiency of metabolomics research.

In this study, the GC/MS coupled with two chemometric resolution methods, heuristic evolving latent projections (HELP) [20,21], and selective ion analysis (SIA) [22e24] was first used to * Corresponding author. Fax: þ86 731 8480 5215.

E-mail addresses: liushao999@hotmail.com (S. Liu), taolj@mail.csu.edu.cn

Contents lists availab

Biochemical and Biophysical .e l

Biochemical and Biophysical Research Communications xxx (2015) 1e7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116

YBBRC33777_proof ■ 15 April 2015 ■ 1/7(L.-J. Tao).fluids, has received increasing interests in recent years [1e5].

Chronic kidney disease (CKD) is a major public health problem worldwide [6e9]. Renal interstitial fibrosis (RIF) closely relates to

CKD and is regarded as the final common pathway in most cases of end-stage renal disease [10]. Early diagnosis of RIF contributes to timely treatment. Renal biopsy is, however, the golden standard for diagnosis of RIF. Therefore, to find non-invasive early biomarkers is becoming more and more urgent for risk assessment of RIF.

Unilateral ureteral obstruction (UUO) induces renal fibrosis in rats [11]. The UUO rat model is characterized by renal tubular atmethod for qualitative and quantative analys

Because of its greater sensitivity and separatio conventional NMR approach and better reliabili identification of candidate biomarkers than LC/M extensively used in the metabolomics [13e17].

Metabolites in the metabolomic profile o currently identified and structurally confirm samples with standards. Different laboratories ru own equipment, searched MS libraries available tural identifications based on mass spectral dataMetabolomics, which involves the nontargeted, whole-profiling with RIF induced by the UUO [12].Gas chromatographyemass spectrometry

Metabolites

Renal fibrosis 1. Introductionhttp://dx.doi.org/10.1016/j.bbrc.2015.04.031 0006-291X/© 2015 Published by Elsevier Inc.

Please cite this article in press as: S. Liu, et al. renal fibrosis rat, Biochemical and BiophysicHowever, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis © 2015 Published by Elsevier Inc. underlying renal fibrosis in UUO rats. Using 1H NMR-based metabonomics, Zhang et al. studied the metabolic changes of ratsAvailable online xxxemass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites.Received 28 March 2015 pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatographyApplication of GCeMS coupled with che serum metabolic biomarkers from renal

Shao Liu a, *, Ji-Shi Liu b, Ren-na Luo a, Hui Xu a, W

Yi-Zeng Liang c, Li-Jian Tao a a Xiangya Hospital, Central South University, Changsha, Hunan 410008, PR China b Xiangya's Third Affiliated Hospital, Central South University, Changsha, Hunan 41000 c College of Chemistry and Chemical Engineering, Central South University, Changsha, H a r t i c l e i n f o a b s t r a c t journal homepage: www, Application of GCeMS couple al Research Communicationsometrics for scanning brosis rat ru Zhang a, Jie Meng a,

China n 410008, PR China le at ScienceDirect

Research Communications sevier .com/locate/ybbrc117 118 119 dwith chemometrics for scanning serummetabolic biomarkers from (2015), http://dx.doi.org/10.1016/j.bbrc.2015.04.031 l Re 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129

YBBRC33777_proof ■ 15 April 2015 ■ 2/7investigate metabolites in sera from UUO rats. HELP resolved the partial overlapping chromatographic peaks, and SIA resolved some severely overlapping peaks and embedded peaks. Furthermore, the competitive adaptive reweighted sampling (CARS) method [25] coupled with partial least squares linear discriminant analysis (PLS-LDA) was employed to seek the most potential biomarkers to distinguish UUO rats from the controls. 2. Materials and methods 2.1. Chemicals and reagents