Rat serum and heart tissue metabolomics in myocardial ischemia based on liquid chromatography-mass spectrometry

doi:10.3724/SP.J.1123.2019.07019

Abstract

Abstract:

A rat model with isoproterenol (ISO)-induced myocardial ischemia was obtained. Liquid chromatography-mass spectrometry (LC-MS)was conducted on the serum and heart tissue metabolites of normal and model rats. Principal component analysis (PCA) and differentiation analysis of supervised partial least-squares method (PLS-DA) were applied to the metabolomics data for multidimensional statistical analysis, and the resulting biomarkers were screened. Compared with the normal group, 18 different endogenous metabolites in plasma and tissues were detected, including the products of arginine and proline metabolism; glycine, serine, and threonine metabolism; glutamine and glutamate metabolism; and taurine and hypotaurine metabolism. These metabolites can be used as important biomarkers for myocardial ischemia. The results of this study can help reveal the pathogenesis of myocardial ischemia and aid clinical diagnosis.

Key words: liquid chromatography-mass spectrometry (LC-MS), biomarkers, metabolomics, myocardial ischemia

YIN Chunyuan, SUN Mingqian, JIN Long, LIN Li, MIAO Lan, LIU Jianxun. Rat serum and heart tissue metabolomics in myocardial ischemia based on liquid chromatography-mass spectrometry[J]. Chinese Journal of Chromatography, 2020, 38(2): 206-211.

Figures/Tables 5

References

1	Zhao Q B . Guide of China Medicine, 2015, 13 (31): 28
1	赵曲波. 中国医药指南, 2015, 13 (31): 28
2	Hu S S , Gao R L , Liu L S , et al. Chinese Circulation Journal, 2019, 34 (3): 209
2	胡盛寿, 高润霖, 刘力生, et al. 中国循环杂志, 2019, 34 (3): 209
3	Gowda G N , Zhang S , Gu H , et al. Expert Rev Mol Diagn, 2008, 8 (5): 617
4	Yang X J , Yang Z J , Li S , et al. Chinese Journal of Chromatography, 2019, 37 (1): 71
4	杨秀娟, 杨志军, 李硕, et al. 色谱, 2019, 37 (1): 71
5	Wu Y , Zhang X , Zhang J , et al. Chinese Journal of Chromatography, 2019, 37 (8): 897
5	吴衍, 张西, 张洁, et al. 色谱, 2019, 37 (8): 897
6	Cao B , Wang D , Brietzke E , et al. Amino Acids, 2018, 50 (8): 1013
7	Ganzinelli S , Joensen L , Borda E , et al. Br J Pharmacol, 2007, 151 (2): 175
8	Dong W Q , Zhang M X . Progress in Physiological Sciences, 2012, 43 (5): 334
8	董文倩, 张铭湘. 生理科学进展, 2012, 43 (5): 334
9	Rajapakse N W , Head G A , Kaye D M . Hypertension, 2016, 67 (5): 813
10	Zhang X , Lin Q , Chen J , et al. Molecules, 2018, 23 (4): 807
11	Wang H , Hu Y H , Meng H , et al. Beijing Journal of Traditional Chinese Medicine, 2013, 32 (8): 613
11	王欢, 胡元会, 孟浩, et al. 北京中医药, 2013, 32 (8): 613
12	Ruiz M , Labarthe F , Fortier A , et al. Am J Physiol Heart Circ Physiol, 2017, 313 (4): H768
13	Zhang J Z . Herald of Medicine, 2001 3): 160
13	张金枝. 医药导报, 2001 3): 160
14	Whaley-Connell A , Habibi J , Cooper S A , et al. Am J Physiol Endocrinol Metab, 2008, 295 (1): E103
15	Liao J D , Wang P C , Zhang Y , et al. Chinese Archives of Traditional Chinese Medicine, 2016, 34 (8): 1968
15	廖佳丹, 王鹏程, 张艳, et al. 中华中医药学刊, 2016, 34 (8): 1968
16	Ashrafian H , Frenneaux M P . Circulation, 2007, 116 (4): 434
17	Liu Y T , Zhou C , Jia H M , et al. Sci Rep, 2016, 26 (6): 30208
18	Liu X , Zhang C , Qian L , et al. Pathol Res Pract, 2015, 211 (12): 955
19	Li D , Li J , An Y , et al. Eur Rev Med Pharmacol Sci, 2013, 17 (17): 2323
20	Li D Q , Wu M J , Hu X H , et al. Progress in Modern Biomedicine, 2011, 11 (2): 390
20	李大庆, 吴明均, 胡晓华, et al. 现代生物医学进展, 2011, 11 (2): 390
21	Liu X , Zhang C , Qian L , et al. Pathol Res Pract, 2015, 211 (12): 955
22	Singleton K D , Beckey V E , Wischmeyer P E . Shock, 2005, 24 (6): 583
23	Ma X , Li Y C . Chinese Journal of Arteriosclerosis, 2007, 15 (11): 873
23	马欣, 李玉成. 中国动脉硬化杂志, 2007, 15 (11): 873
24	Ferdinandy P , Hausenloy D J , Heusch G , et al. Pharmacol Rev, 2014, 66 (4): 1142

Group	CK/(U/L)	LDH/(U/L)	CK-MB/(U/L)
CK: creatine kinase; LDH: lactate dehydrogenase; CK-MB: creatine kinase isoenzyme MB. ** Compared with control group, p＜0.01.
Control group	463±130	548±210	603±157
Model group	743±139^**	680±267	1167±243^**

Group	CK/(U/L)	LDH/(U/L)	CK-MB/(U/L)
CK: creatine kinase; LDH: lactate dehydrogenase; CK-MB: creatine kinase isoenzyme MB. ** Compared with control group, p＜0.01.
Control group	463±130	548±210	603±157
Model group	743±139^**	680±267	1167±243^**

No.	m/z	Biomarker	Pathways	Trend^*
No.	m/z	Biomarker	Pathways	Serum	Heart tissue
* Change trend compared with control group.
1	89.09	alanine	alanine, aspartate and glutamate metabolism	↑
2	175.19	citrulline	arginine and proline metabolism	↑
3	113.12	creatinine	arginine and proline metabolism	↓
4	131.13	hydroxyproline	arginine and proline metabolism	↓	↑
5	132.16	ornithine	arginine and proline metabolism	↑	↑
6	161.20	L-carnitine	carnitine synthesis	↑
7	117.15	betaine	glycine, serine and threonine metabolism	↓	↑
8	75.07	glycine	glycine, serine and threonine metabolism		↑
9	146.14	glutamine	glutamine and glutamate metabolism	↓	↓
10	147.13	glutamate	glutamine and glutamate metabolism	↓
11	105.09	serine	glutamine and glutamate metabolism		↑
12	188.22	n6-acetyl-L-lysine	lysine degradation		↓
13	297.33	5′-methylthioadenosine	methionine metabolism		↑
14	111.10	cytosine	pyrimidine metabolism		↑
15	376.36	riboflavin	riboflavin metabolism	↓	↑
16	125.15	taurine	taurine and hypotaurine metabolism	↓	↓
17	117.15	indole	tryptophan metabolism	↓
18	117.15	valine	valine, leucine and isoleucine degradation		↓

No.	m/z	Biomarker	Pathways	Trend^*
No.	m/z	Biomarker	Pathways	Serum	Heart tissue
* Change trend compared with control group.
1	89.09	alanine	alanine, aspartate and glutamate metabolism	↑
2	175.19	citrulline	arginine and proline metabolism	↑
3	113.12	creatinine	arginine and proline metabolism	↓
4	131.13	hydroxyproline	arginine and proline metabolism	↓	↑
5	132.16	ornithine	arginine and proline metabolism	↑	↑
6	161.20	L-carnitine	carnitine synthesis	↑
7	117.15	betaine	glycine, serine and threonine metabolism	↓	↑
8	75.07	glycine	glycine, serine and threonine metabolism		↑
9	146.14	glutamine	glutamine and glutamate metabolism	↓	↓
10	147.13	glutamate	glutamine and glutamate metabolism	↓
11	105.09	serine	glutamine and glutamate metabolism		↑
12	188.22	n6-acetyl-L-lysine	lysine degradation		↓
13	297.33	5′-methylthioadenosine	methionine metabolism		↑
14	111.10	cytosine	pyrimidine metabolism		↑
15	376.36	riboflavin	riboflavin metabolism	↓	↑
16	125.15	taurine	taurine and hypotaurine metabolism	↓	↓
17	117.15	indole	tryptophan metabolism	↓
18	117.15	valine	valine, leucine and isoleucine degradation		↓

[1]	WANG Yating, YANG Yang, SUN Xiulan, JI Jian. Development of a widely-targeted metabolomics method based on gas chromatography-mass spectrometry [J]. Chinese Journal of Chromatography, 2023, 41(6): 520-526.
[2]	ZHAO Yuan, LIU Xin, ZHANG Yidan, ZHANG Jian, LIU Xiang, YANG Guofeng. Tandem mass tag-based quantitative proteomics analysis of plasma and plasma exosomes in Parkinson’s disease [J]. Chinese Journal of Chromatography, 2023, 41(12): 1073-1083.
[3]	QU Tao, CHEN Yang, YANG Changjun, LIU Qisong, CHEN Hui, HE Zhiyong, WANG Zhaojun, CHEN Jie, ZENG Maomao. Pseudotargeted metabolomics analysis of pine pollen intervention in the liver of premature ovarian failure rats [J]. Chinese Journal of Chromatography, 2023, 41(1): 47-57.
[4]	GU Yan, ZANG Peng, LI Jinxia, YAN Yanyan, WANG Jia. Plasma metabolomics in a deep vein thrombosis rat model based on ultra-high performance liquid chromatography-electrostatic field orbitrap high resolution mass spectrometry [J]. Chinese Journal of Chromatography, 2022, 40(8): 736-745.
[5]	MA Zhanjun, LI Zhenguo, WANG Huan, WANG Renjun, HAN Xiaofei. Screening of serum oxysterol biomarkers for colon cancer by liquid chromatography-tandem mass spectrometry [J]. Chinese Journal of Chromatography, 2022, 40(6): 541-546.
[6]	GUO Minkang, ZHANG Jian. Lipid metabolomic analysis in exosomes of osteonecrosis of the femoral head based on ultra performance liquid chromatography-tandem mass spectrometry [J]. Chinese Journal of Chromatography, 2022, 40(2): 123-129.
[7]	ZHU Shuyun, ZHAO Xian-En, LIU Huwei. Recent advances in chemical derivatization-based chromatography-mass spectrometry methods for analysis of aldehyde biomarkers [J]. Chinese Journal of Chromatography, 2021, 39(8): 845-854.
[8]	HE Zhian, LIN Houwei, GUI Juan, ZHU Weichao, HE Jianhua, WANG Hang, FENG Lei. Urine metabolomics analysis based on ultra performance liquid chromatography-high resolution mass spectrometry combined with osmolality calibration sample concentration variability [J]. Chinese Journal of Chromatography, 2021, 39(4): 391-398.
[9]	OUYANG Yang, CHI Lei, XU Chao, ZHAO Xinjie, CUI Zhenze. Liquid chromatography-mass spectrometry-based metabolomics study of the efficacy of Chinese medicine asthma-relieving decoction on respiratory syncytial virus infection [J]. Chinese Journal of Chromatography, 2021, 39(3): 281-290.
[10]	SHEN Shi, YANG Yi, WANG Jingbo, CHEN Xi, LIU Tingting, ZHUO Qin. Analysis of differences between unifloral honeys from different botanical origins based on non-targeted metabolomics by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry [J]. Chinese Journal of Chromatography, 2021, 39(3): 291-300.
[11]	YANG Kaige, WANG Weiwei, WANG Yan, YAN Chao. Proteomic analysis of serum and serum exosomes, and their application in intrahepatic cholangiocarcinoma [J]. Chinese Journal of Chromatography, 2021, 39(11): 1191-1202.
[12]	MU Hongmei, CI Zhijuan, Aisajan MAMAT, LIANG Yanping, LIU Xiaohong, DU Xiaoyun, YU Qiang, LI Qingyu, LI Yuanjun. Analysis of metabolite differences in skin between Clapp’s Favorite and its mutant Red Clapp’s Favorite through non-targeted metabolomics [J]. Chinese Journal of Chromatography, 2021, 39(11): 1203-1212.
[13]	WANG Wen, CHEN Di, PIAO Hailong. Identification of LAMTOR1-regulated metabolites using ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry in malignant transformation of liver inflammation [J]. Chinese Journal of Chromatography, 2021, 39(10): 1118-1127.
[14]	WANG Fang, WANG Song, CONG Hailin, YU Bing. Analysis of metabolomics and proteomics based on capillary electrophoresis-mass spectrometry [J]. Chinese Journal of Chromatography, 2020, 38(9): 1013-1021.
[15]	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.