Metabonomic profiling of diet-induced hyperlipidaemia in a rat model

被引:40
作者
Zhang, Qi [1 ,2 ]
Wang, Guangji [1 ]
Jiye A [1 ]
Ma, Bo [1 ,2 ]
Dua, Yu
Zhu, Lingling [2 ]
Wu, Di [3 ]
机构
[1] China Pharmaceut Univ, Key Lab Drug Metab & Pharmacokinet, Nanjing 210009, Peoples R China
[2] Nanjing Univ Technol, Sch Pharmaceut Sci, Nanjing 210009, Peoples R China
[3] Childrens Hosp Philadelphia, Div Clin Pharmacol & Therapeut, Lab Appl Pharmacokinet Pharmacodynam, Philadelphia, PA 19104 USA
关键词
Metabonomics; hyperlipidaemia; GC-MS; principal component analysis; partial least squares discriminant analysis; early diagnosis; DISEASE DIAGNOSIS; SYSTEMS BIOLOGY; DATA SETS; METABOLOMICS; ATHEROSCLEROSIS; DYSLIPIDEMIA; CHEMOMETRICS; EXTRACTION; SAMPLES;
D O I
10.3109/13547500903419049
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 [微生物学]; 090105 [作物生产系统与生态工程];
摘要
This study describes the metabolic profiles of the development of hyperlipidaemia in a rat model, utilizing metabonomics by gas chromatography-mass spectrometry (GC-MS) determination coupled with multivariate statistical analysis. Rat plasma samples were collected before and during a high-lipid diet at days 0, 7, 14, 21 and 28, and were analysed for lipid levels using kit assays or metabonomics using GC-MS. Forty-one endogenous metabolites were separated, identified and quantified using GC-MS. The data matrix was processed by principal component analysis or partial least squares discriminant analysis. Dynamic modification of the rat metabonome can be clearly identified and tracked at different stages of hyperlipidaemia in the rat model. Potential biomarkers, including beta-hydroxybutyrate, tyrosine and creatinine, were identified. The current work suggests that metabonomics is able to provide an overview of biochemical profiles of disease progress in animal models. Using a metabonomic approach to identify physiopathological states promises to establish a new methodology for the early diagnosis of human diseases.</.
引用
收藏
页码:205 / 216
页数:12
相关论文
共 31 条
[1]
Approach to the diagnosis and management of lipoprotein disorders [J].
Alwaili, Khalid ;
Alrasadi, Khalid ;
Awan, Zuhier ;
Genest, Jacques .
CURRENT OPINION IN ENDOCRINOLOGY DIABETES AND OBESITY, 2009, 16 (02) :132-140
[2]
Molekulares Profiling und prädiktive SignaturenBiomarkeranalysen beim OvarialkarzinomMolecular profiling and predictive signaturesBiomarker analysis in ovarian cancer [J].
C. Denkert .
Der Pathologe, 2008, 29 (Suppl 2) :168-171
[3]
Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) [J].
Eriksson, L ;
Antti, H ;
Gottfries, J ;
Holmes, E ;
Johansson, E ;
Lindgren, F ;
Long, I ;
Lundstedt, T ;
Trygg, J ;
Wold, S .
ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2004, 380 (03) :419-429
[4]
Eriksson L., 2013, Multi-and megavariate data analysis basic principles and applications, V3rd ed.
[5]
New Insights Into Cardiovascular and Lipid Metabolomics [J].
Giovane, Alfonso ;
Balestrieri, Antonio ;
Napoli, Claudio .
JOURNAL OF CELLULAR BIOCHEMISTRY, 2008, 105 (03) :648-654
[6]
Dyslipidemia prevalence, treatment, and control in the Multi-Ethnic Study of Atherosclerosis (MESA) - Gender, ethnicity, and coronary artery calcium [J].
Goff, DC ;
Bertoni, AG ;
Kramer, H ;
Bonds, D ;
Blumenthal, RS ;
Tsai, MY ;
Psaty, BM .
CIRCULATION, 2006, 113 (05) :647-656
[7]
Metabolomics-based methods for early disease diagnostics [J].
Gowda, G. A. Nagana ;
Zhang, Shucha ;
Gu, Haiwei ;
Asiago, Vincent ;
Shanaiah, Narasimhamurthy ;
Raftery, Daniel .
EXPERT REVIEW OF MOLECULAR DIAGNOSTICS, 2008, 8 (05) :617-633
[9]
Metabolic phenotyping in health and disease [J].
Holmes, Elaine ;
Wilson, Ian D. ;
Nicholson, Jeremy K. .
CELL, 2008, 134 (05) :714-717
[10]
Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets [J].
Jonsson, P ;
Bruce, SJ ;
Moritz, T ;
Trygg, J ;
Sjöström, M ;
Plumb, R ;
Granger, J ;
Maibaum, E ;
Nicholson, JK ;
Holmes, E ;
Antti, H .
ANALYST, 2005, 130 (05) :701-707