Metabolomics study on model rats of chronic obstructive pulmonary disease treated with Bu‑Fei Jian‑Pi

Li,Jiansheng; Yang,Liping; Li,Ya; Tian,Yange; Li,Suyun; Jiang,Suli; Wang,Ying; Li,Xinmin

doi:10.3892/mmr.2014.2843

Metabolomics study on model rats of chronic obstructive pulmonary disease treated with Bu‑Fei Jian‑Pi

Authors:
- Jiansheng Li
- Liping Yang
- Ya Li
- Yange Tian
- Suyun Li
- Suli Jiang
- Ying Wang
- Xinmin Li
View Affiliations

Affiliations: Basic Discipline of Integrated Chinese and Western Medicine, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450008, P.R. China, Respiratory Disease Institute, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450000, P.R. China
Published online on: November 3, 2014 https://doi.org/10.3892/mmr.2014.2843
Pages: 1324-1333

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

The therapeutic effect of Traditional Chinese Medicine (TCM) on chronic obstructive pulmonary disease (COPD) has been know for numerous years; however, the mechanism of action of the beneficial effects of TCM remains to be elucidated. The present study aimed to investigate the molecular mechanisms of COPD through metabolomic analysis as well as explore the targets and intervention mechanisms of TCM therapy using the common TCM granules Bu‑Fei Jian‑Pi. COPD rat models were established using smoke inhalations and recurrent bacterial infections. Rats were then divided into three groups as follows: A1, control healthy rats; B1, COPD model; and D1, Bu‑Fei Jian‑Pi‑treated COPD rats. Following administration of the medicine, the metabolomic proﬁle of the lung tissue of rats in each group was assessed using high‑performance liquid chromatography/quadrupole‑time‑of‑flight mass spectrometry. The results demonstrated that there was a significanlty different spectrum of metabolites in the lung tissue of the model group compared to that of the control group as well as the Bu‑Fei Jian‑Pi‑treated COPD group; in addition, following treatment with Bu‑Fei Jian‑Pi, the metabolites of COPD rats were comparable with those of the control. Notable changes were observed in 31 metabolites between the Bu‑Fei Jian‑Pi‑treated group and the model group; however, there were 13 comparable metabolites between the Bu‑Fei Jian‑Pi and control groups as well as the model and control groups. Eleven metabolites showed a negative fold change in the Bu‑Fei Jian‑Pi‑treated groups compared to concentrations in the model group; however, minimal changes were observed in phenylpyruvic acid and α‑D‑fucose expression. In conclusion, the results of the present study demonstrated that Bu‑Fei Jian‑Pi granules had beneficial effects on measured outcomes in a rat model of stable COPD, indicated by a significantly different spectrum of metabolites. This therefore indicated that the metabolites which had significantly altered expression in the model group compared with that of the control and Bu‑Fei Jian‑Pi‑treated groups may be potential biomarkers of COPD.

View Figures

View References

1	Raherison C and Girodet PO: Epidemiology of COPD. Eur Respir Rev. 18:213–221. 2009. View Article : Google Scholar
2	Lin HH, et al: Effects of smoking and solid-fuel use on COPD, lung cancer, and tuberculosis in China: a time-based, multiple risk factor, modelling study. Lancet. 372:1473–1483. 2008. View Article : Google Scholar : PubMed/NCBI
3	Qaseem A, Wilt TJ, Weinberger SE, et al: American college of Physicians; American Collage of Chest Physicians; American Thoracic Society and European Respiratory Society: Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med. 155:179–191. 2011. View Article : Google Scholar : PubMed/NCBI
4	Calverley PM, Anderson JA, Celli B, et al: Satmeterol and flutieasene propionate and survival in chronic obstructive pulmonary disease. N Engl J Med. 356:775–789. 2007. View Article : Google Scholar : PubMed/NCBI
5	Li JS, Wang ZW, Yu XQ, et al: Clinical efficacy and safety of TCM for COPD at stable phase: A Systematic review. Traditional Chinese Medicine of Liaoning. 37:229–232. 2010.
6	Li JS, Wang ZW, Yu XQ, et al: Systemic evaluation of TCM for COPD in the acute stage of exacerbation. Traditional Chinese Medicine of Tianjin. 25:428–431. 2008.
7	Zhang WJ and Zhang YP: Recent research in chronic obstructive pulmonary disease treated with TCM. Clinical Traditional Chinese Medicine of Beijing. 14:39–41. 2007.
8	Lu AP and Chen KJ: Chinese medicine pattern diagnosis could lead to innovation in medical sciences. Chin J Integr Med. 17:811–817. 2011. View Article : Google Scholar : PubMed/NCBI
9	Wang ZW, Li JS, et al: Symptom diagnosis criteria for chronic obstructive pulmonary diseases at stationary phase from literature. The Science of Traditional Chinese Medicine. 218:55–58. 2008.
10	Nicholls AW, Nicholson JK, Haselden JN and Waterfield CJ: A metabonomic approach to the investigation of drug-induced phospholipidosis: an NMR spectroscopy and pattern recognition study. Biomarkers. 5:410–423. 2000. View Article : Google Scholar
11	Nicholson JK and Wilson ID: Understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov. 2:668–676. 2003. View Article : Google Scholar : PubMed/NCBI
12	Nicholson JK, Lindon JC and Holmes E: ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 29:1181–1189. 1999. View Article : Google Scholar : PubMed/NCBI
13	Holmes E, Nicholls AW, Lindon JC, et al: Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem Res Toxicol. 13:471–478. 2000. View Article : Google Scholar : PubMed/NCBI
14	Lindon JC, Nicholson JK, Holmes E, et al: Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson. 12:289–320. 2000. View Article : Google Scholar
15	Brindle JT, Antti H, Holmes E, et al: Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med. 8:1439–1444. 2002. View Article : Google Scholar : PubMed/NCBI
16	Keun HC, Ebbels TM, Bollard ME, et al: Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles. Chem Res Toxicol. 17:579–587. 2004. View Article : Google Scholar : PubMed/NCBI
17	Lindon JC, Nicholson JK, Holmes E, et al: Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol Appl Pharmacol. 187:137–146. 2003. View Article : Google Scholar : PubMed/NCBI
18	Holmes E, Nicholson JK and Tranter G: Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chem Res Toxicol. 14:182–191. 2001. View Article : Google Scholar : PubMed/NCBI
19	Solanky KS, Bailey NJ, et al: Application of biofluid 1H nuclear magnetic resonance-based metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal Biochem. 323:197–204. 2003. View Article : Google Scholar : PubMed/NCBI
20	Holmes E and Antti H: Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst. 127:1549–1557. 2002. View Article : Google Scholar
21	Gavaghan CL, Wilson ID and Nicholson JK: Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and PLS-DA. FEBS Lett. 530:191–196. 2002. View Article : Google Scholar : PubMed/NCBI
22	Ebbels T, Keun H, Beckonert O, et al: Toxicity classification from metabonomic data using a density superposition approach: ‘CLOUDS’. Anal Chim Acta. 490:109–122. 2003. View Article : Google Scholar
23	Młynarz P, Barg W, Deja S and Jankowska R: Application of metabolomic in COPD diagnosing. Pol Merkur Lekarski. 33:207–212. 2012.(In Polish).
24	Li Y, Li SY, Li JS, et al: A rat model for stable chronic obstructive pulmonary disease induced by cigarette smoke inhalation and repetitive bacterial infection. Biol Pharm Bull. 35:1752–1760. 2012. View Article : Google Scholar : PubMed/NCBI