Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion

Wang,Cheng; Peng,Jingjin; Kuang,Yanling; Zhang,Jiaqiang; Dai,Luming

doi:10.3892/mmr.2017.6758

Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion

Authors:
- Cheng Wang
- Jingjin Peng
- Yanling Kuang
- Jiaqiang Zhang
- Luming Dai
View Affiliations

Affiliations: The Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China
Published online on: June 12, 2017 https://doi.org/10.3892/mmr.2017.6758
Pages: 1147-1156
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting 1H nuclear magnetic resonance technology, and pattern‑recognition multi-variable statistical analysis was used to screen out different metabolites. One‑way analysis of variance, and Student‑Newman‑Keuls and the Kruskal‑Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure.

View Figures

View References

1	Maskell N: British Thoracic Society Pleural Disease Guideline Group: British thoracic society pleural disease guidelines-2010 update. Thorax. 65:667–669. 2010. View Article : Google Scholar : PubMed/NCBI
2	Light RW: Clinical practice: Pleural effusion. N Engl J Med. 346:1971–1977. 2002. View Article : Google Scholar : PubMed/NCBI
3	Daniil ZD, Zintzaras E, Kiropoulos T, Papaioannou AI, Koutsokera A, Kastanis A and Gourgoulianis KI: Discrimination of exudative pleural effusions based on multiple biological parameters. Eur Respir J. 30:957–964. 2007. View Article : Google Scholar : PubMed/NCBI
4	World Health Organization, . Global Tuberculosis report. Geneva, Switzerland: 2015
5	Light RW: Update on tuberculous pleural effusion. Respirology. 15:451–458. 2010. View Article : Google Scholar : PubMed/NCBI
6	Dodd PJ, Gardiner E, Coghlan R and Seddon JA: Burden of childhood tuberculosis in 22 high-burden countries: A mathematical modelling study. Lancet Glob Health. 2:e453–e459. 2014. View Article : Google Scholar : PubMed/NCBI
7	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
8	Schmidt CW: Metabolomics: What's happening downstream of DNA. Environ Health Perspect. 112:A410–A415. 2004. View Article : Google Scholar : PubMed/NCBI
9	Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R, Prehn C, Altmaier E, Kastenmüller G, Kato BS, Mewes HW, et al: A genome-wide perspective of genetic variation inhuman metabolism. Nat Genet. 42:137–141. 2010. View Article : Google Scholar : PubMed/NCBI
10	Yang J, Xu G, Zheng Y, Kong H, Pang T, Lv S and Yang Q: Diagnosis of liver cancer using HPLC-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases. J Chromatogr B Analyt Technol Biomed Life Sci. 813:59–65. 2004. View Article : Google Scholar : PubMed/NCBI
11	Chen J, Zhang X, Cao R, Lu X, Zhao S, Fekete A, Huang Q, Schmitt-Kopplin P, Wang Y, Xu Z, et al: Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J Proteome Res. 10:2625–2632. 2011. View Article : Google Scholar : PubMed/NCBI
12	Hodavance MS, Ralston SL and Pelczer I: Beyond blood sugar: The potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model. Anal Bioanal Chem. 387:533–537. 2007. View Article : Google Scholar : PubMed/NCBI
13	Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, Ingman P, Kaski K, Groop PH and Ala-Korpela M: FinnDiane Study Group: Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum. Magma. 19:281–296. 2006. View Article : Google Scholar : PubMed/NCBI
14	Messana I, Forni F, Ferrari F, Rossi C, Giardina B and Zuppi C: Proton nuclear magnetic resonance spectral profiles of urine in type II diabetic patients. Clin Chem. 44:1529–1534. 1998.PubMed/NCBI
15	Brindle JT, Nicholson JK, Schofield PM, Grainger DJ and Holmes E: Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst. 128:32–36. 2003. View Article : Google Scholar : PubMed/NCBI
16	Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE and Grainger DJ: 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
17	Sabatine MS, Liu E, Morrow DA, Heller E, Mc Carroll R, Wiegand R, Berriz GF, Roth FP and Gerszten RE: Metabolonmic identification of novel biomarkers of myocardial ischemia. Circulation. 112:3868–3875. 2005. View Article : Google Scholar : PubMed/NCBI
18	Barba I, de León G, Martín E, Cuevas A, Aguade S, Candell-Riera J, Barrabés JA and Garcia-Dorado D: Nuclear magnetic resonance-based metabolomics predicts exercise-induced ischemia in patients with suspected coronary artery disease. Magn Reson Med. 60:27–32. 2008. View Article : Google Scholar : PubMed/NCBI
19	Sun L, Hu W, Liu Q, Hao Q, Sun B, Zhang Q, Mao S, Qiao J and Yan X: Metabonomics reveals plasma metabolic changes and inflammatory marker in polycystic ovary syndrome patients. J Proteome Res. 11:2937–2946. 2012. View Article : Google Scholar : PubMed/NCBI
20	Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, Yang DY, Fan SH, Chen L, Fang L and Xie P: Plasma metabonomics as novel diagnostic approach for major depressive disorder. J Proteome Res. 11:1741–1748. 2012. View Article : Google Scholar : PubMed/NCBI
21	Xia J, Mandal R, Sinelnikov IV, Broadhurst D and Wishart DS: MetaboAnalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 40:W127–W133. 2012. View Article : Google Scholar : PubMed/NCBI
22	Xia J and Wishart DS: Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. Curr Protoc Bioinformatics Chapter. 14:Unit 14.102011.
23	Yang WB, Liang QL, Ye ZJ, Niu CM, Ma WL, Xiong XZ, Du RH, Zhou Q, Zhang JC and Shi HZ: Cell origins and diagnostic accuracy of interleukin 27 in pleural effusions. PLoS One. 7:e404502012. View Article : Google Scholar : PubMed/NCBI
24	Dong X and Yang J: High IL-35 pleural expression in patients with tuberculous pleural effusion. Med Sci Monit. 21:1261–1268. 2015. View Article : Google Scholar : PubMed/NCBI
25	Garrido V Villena, Viedma E Cases, Fernández Villar A, de Pablo Gafas A, Pérez Rodríguez E, Pérez JM Porcel, Rodríguez Panadero F, Martínez C Ruiz, Velázquez A Salvatierra and Valdés Cuadrado L: Recommendations of diagnosis and treatment of pleural effusion. Update. Arch Bronconeumol. 50:235–249. 2014.(In English, Spanish). View Article : Google Scholar
26	Frediani JK, Jones DP, Tukvadze N, Uppal K, Sanikidze E, Kipiani M, Tran VT, Hebbar G, Walker DI, Kempker RR, et al: Plasma metabolomics in human pulmonary tuberculosis disease: A pilot study. PLoS One. 9:e1088542014. View Article : Google Scholar : PubMed/NCBI
27	Deja S, Porebska I, Kowal A, Zabek A, Barg W, Pawelczyk K, Stanimirova I, Daszykowski M, Korzeniewska A, Jankowska R and Mlynarz P: Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. J Pharm Biomed Anal. 100:369–380. 2014. View Article : Google Scholar : PubMed/NCBI
28	Nandakumar M, Nathan C and Rhee KY: Isocitrate lyase mediates broad antibiotic tolerance in Mycobacterium tuberculosis. Nat Commun. 5:43062014. View Article : Google Scholar : PubMed/NCBI
29	Eoh H and Rhee KY: Multifunctional essentiality of succinate metabolism in adaptation to hypoxia in Mycobacterium tuberculosis. Proc Natl Acad Sci USA. 110:6554–6559. 2013. View Article : Google Scholar : PubMed/NCBI
30	Micklinghoff JC, Breitinger KJ, Schmidt M, Geffers R, Eikmanns BJ and Bange FC: Role of the transcriptional regulator RamB (Rv0465c) in the control of the glyoxylate cycle in Mycobacterium tuberculosis. J Bacteriol. 191:7260–7269. 2009. View Article : Google Scholar : PubMed/NCBI
31	Kusao I, Troelstrup D and Shiramizu B: Possible mitochondria-associated enzymatic role in Non-Hodgkin lymphoma residual disease. Cancer Growth Metastasis. 1:3–8. 2008.PubMed/NCBI
32	Schlichtholz B, Turyn J, Goyke E, Biernacki M, Jaskiewicz K, Sledzinski Z and Swierczynski J: Enhanced citrate synthase activity in human pancreatic cancer. Pancreas. 30:99–104. 2005. View Article : Google Scholar : PubMed/NCBI
33	Qiu Y, Cai G, Su M, Chen T, Liu Y, Xu Y, Ni Y, Zhao A, Cai S, Xu LX and Jia W: Urinary metabonomic study on colorectal cancer. J Proteome Res Mar. 9:1627–1634. 2010. View Article : Google Scholar
34	Chen L, Liu T, Zhou J, Wang Y, Wang X, Di W and Zhang S: Citrate synthase expression affects tumor phenotype and drug resistance in human ovarian carcinoma. PLoS One. 9:e1157082014. View Article : Google Scholar : PubMed/NCBI
35	Chajès V, Cambot M, Moreau K, Lenoir GM and Joulin V: Acetyl-CoA carboxylase alpha is essential to breast cancer cell survival. Cancer Res. 66:5287–5294. 2006. View Article : Google Scholar : PubMed/NCBI
36	Brusselmans K, de Schrijver E, Verhoeven G and Swinnen JV: RNA interference-mediated silencing of the acetyl-CoA-carboxylase-α gene induces growth inhibition and apoptosis of prostate cancer cells. Cancer Res. 65:6719–6725. 2005. View Article : Google Scholar : PubMed/NCBI
37	Warburg O: On the origin of cancer cells. Science. 123:309–314. 1956. View Article : Google Scholar : PubMed/NCBI
38	Kayser G, Kassem A, Sienel W, Schulte-Uentrop L, Mattern D, Aumann K, Stickeler E, Werner M, Passlick B and zur Hausen A: Lactate-dehydrogenase 5 is overexpressed in non-small cell lung cancer and correlates with the expression of the transketolase-like protein 1. Diagn Pathol. 5:222010. View Article : Google Scholar : PubMed/NCBI
39	Chen JQ and Russo J: Dysregulation of glucose transport, glycolysis, TCA cycle and glutaminolysis by oncogenes and tumor suppressors in cancer cells. Biochim Biophys Acta. 1826:370–384. 2012.PubMed/NCBI
40	Gilmore SA, Schelle MW, Holsclaw CM, Leigh CD, Jain M, Cox JS, Leary JA and Bertozzi CR: Sulfolipid-1 biosynthesis restricts Mycobacterium tuberculosis growth in human macrophages. ACS Chem Biol. 7:863–870. 2012. View Article : Google Scholar : PubMed/NCBI
41	Brown AK, Bhatt A, Singh A, Saparia E, Evans AF and Besra GS: Identification of the dehydratase component of the mycobacterial mycolic acid-synthesizing fatty acid synthase-II complex. Microbiology. 153:4166–4173. 2007. View Article : Google Scholar : PubMed/NCBI
42	Klink M, Brzezinska M, Szulc I, Brzostek A, Kielbik M, Sulowska Z and Dziadek J: Cholesterol oxidase is indispensable in the pathogenesis of Mycobacterium tuberculosis. PLoS One. 8:e733332013. View Article : Google Scholar : PubMed/NCBI
43	Ouellet H, Johnston JB and de Montellano PR: Cholesterol catabolism as a therapeutic target in Mycobacterium tuberculosis. Trends Microbiol. 19:530–539. 2011. View Article : Google Scholar : PubMed/NCBI
44	Szatmari I and Nagy L: Nuclear receptor signalling in dendritic cells connects lipids, the genome and immune function. EMBO J. 27:2353–2362. 2008. View Article : Google Scholar : PubMed/NCBI
45	Almeida PE, Silva AR, Maya-Monteiro CM, Töröcsik D, D'Avila H, Dezsö B, Magalhães KG, Castro-Faria-Neto HC, Nagy L and Bozza PT: Mycobacterium bovis bacillus Calmette-Guérin infection induces TLR2-dependent peroxisome proliferator-activated receptor gamma expression and activation: Functions in inflammation, lipid metabolism, and pathogenesis. J Immunol. 183:1337–1345. 2009. View Article : Google Scholar : PubMed/NCBI
46	Chen W, Zu Y, Huang Q, Chen F, Wang G, Lan W, Bai C, Lu S, Yue Y and Deng F: Study on metabonomic characteristics of human lung cancer using high resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis. Magn Reson Med. 66:1531–1540. 2011. View Article : Google Scholar : PubMed/NCBI
47	Collins MD, Goodfellow M and Minnikin DE: Fatty acid composition of some mycolic acid-containing coryneform bacteria. J Gen Microbiol. 128:2503–2509. 1982.PubMed/NCBI
48	Baracos VE and Mackenzie ML: Investigations of branched-chain amino acids and their metabolites in animal models of cancer. J Nutr. 136 Suppl 1:S237–S242. 2006.
49	Choudry HA, Pan M, Karinch AM and Souba WW: Branched-chain amino acid-enriched nutritional support in surgical and cancer patients. J Nutr. 136 Suppl 1:S314–S318. 2006.
50	Liu B, Cui C, Duan W, Zhao M, Peng S, Wang L, Liu H and Cui G: Synthesis and evaluation of anti-tumor activities of N4 fatty acyl amino acid derivatives of 1-beta-arabinofuranosylcytosine. Eur Med Chem. 44:3596–3600. 2009. View Article : Google Scholar