Analytical and Bioanalytical Chemistry

, Volume 397, Issue 5, pp 1809–1819 | Cite as

Proteomic biomarkers in plasma that differentiate rapid and slow decline in lung function in adult cigarette smokers with chronic obstructive pulmonary disease (COPD)

  • Gaurav S. J. B. Rana
  • Timothy P. York
  • Jeffery S. Edmiston
  • Barbara K. Zedler
  • Joel G. Pounds
  • Joshua N. Adkins
  • Richard D. Smith
  • Zaigang Liu
  • Guoya Li
  • Bradley T. Webb
  • Edward L. Murrelle
  • Jason W. Flora
Original Paper


Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of morbidity and mortality in the United States and cigarette smoking is a primary determinant of the disease. COPD is characterized by chronic airflow limitation as measured by the forced expiratory volume in one second (FEV1). In this study, the plasma proteomes of 38 middle-aged or older adult smokers with mild to moderate COPD, with FEV1 decline characterized as either rapid (RPD, n = 20) or slow or absent (SLW, n = 18), were interrogated using a comprehensive high-throughput proteomic approach, the accurate mass and time (AMT) tag technology. This technology is based upon a putative mass and time tag database (PMT), high-resolution LC separations and high mass accuracy measurements using FT-ICR MS with a 9.4-T magnetic field. The peptide and protein data were analyzed using three statistical approaches to address ambiguities related to the high proportion of missing data inherent to proteomic analysis. The RPD and SLW groups were differentiated by 55 peptides which mapped to 33 unique proteins. Twelve of the proteins have known roles in the complement or coagulation cascade and, despite an inability to adjust for some factors known to affect lung function decline, suggest potential mechanistic biomarkers associated with the rate of lung function decline in COPD. Whether these proteins are the cause or result of accelerated decline will require further research.


Amino acids/peptides Bioanalytical methods Biological samples Clinical/biomedical analysis Genomics/proteomics 



accurate mass and time


chronic obstructive pulmonary disease


forced expiratory volume in one second


forced vital capacity


Genetics of Addiction Project


Lung Health Study


National Heart, Lung, and Blood Institute


putative mass and time


FEV1 rapid decliner group


FEV1 slow decliner group


cross-correlation score



The authors gratefully acknowledge the contributions to this study and manuscript by Michael S. Paul, Ph.D. and Alex Lindell from LineaGen, Inc., Salt Lake City, Utah and George J. Patskan, Ph.D. from Altria Client Services. The authors also acknowledge the comments of reviewers Rutger Van der Hoeven, Ph.D. and Marc R. Kraus, Ph.D. and the editorial assistance of Eileen Ivasauskas of Accuwrit Inc. Financial support was provided by Philip Morris USA Inc. and LineaGen, Inc.

Conflicts of interest

The authors of this manuscript have no financial/commercial conflicts of interest.

Supplementary material

216_2010_3742_MOESM1_ESM.xls (4 mb)
ESM 1 A total of 3,549 non-redundant peptides were identified from 80 independent MS runs (two technical replicates per sample), representing 533 proteins. A collection of all peptides, abundances (log-transformed), and related proteins can be found in the following Excel Table (XLS 4039 kb)


  1. 1.
    Rabe KF, Hurd S, Anzueto A, Barnes PJ et al (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176:532–555CrossRefGoogle Scholar
  2. 2.
    Lokke A, Lange P, Scharling H, Fabricius P et al (2006) Developing COPD: a 25 year follow up study of the general population. Thorax 61:935–939CrossRefGoogle Scholar
  3. 3.
    Lundbäck B, Lindberg A, Lindström M, Rönmark E et al (2003) Not 15 but 50% of smokers develop COPD? Report from the Obstructive Lung Disease in Northern Sweden Studies. Respir Med 97:115–122CrossRefGoogle Scholar
  4. 4.
    Mannino D, Braman S (2007) The epidemiology and economics of chronic obstructive pulmonary disease. Proc Am Thorac Soc 4:502–506CrossRefGoogle Scholar
  5. 5.
    Meng Z, Veenstra TD (2007) Proteomic analysis of serum, plasma, and lymph for the identification of biomarkers. Proteomics Clin Appl 1:747–757CrossRefGoogle Scholar
  6. 6.
    Lee HJ, Lee EY, Kwon MS, Paik YK (2006) Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics. Curr Opin Chem Biol 10:42–49CrossRefGoogle Scholar
  7. 7.
    Pasa-Tolic L, Masselon C, Barry RC, Shen Y et al (2004) Proteomic analyses using an accurate mass and time tag strategy. Biotechniques 37:626–633Google Scholar
  8. 8.
    Anderson KK, Monroe ME, Daly DS (2004) Estimating probabilities of peptide assignments to LC-FTICR-MS observations. In: Valafar F, Valafar H (eds) Proceedings of the international conference on mathematics and engineering techniques in medicine and biological sciences (METMBS '04). CSREA, Las Vegas, pp 151–156Google Scholar
  9. 9.
    Smith RD, Anderson GA, Lipton MS, Pasa-Tolic L et al (2002) An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics 2:513–523CrossRefGoogle Scholar
  10. 10.
    Adkins JN, Monroe ME, Auberry KJ, Shen Y et al (2005) A proteomic study of HUPO's plasma proteome project pilot samples using an accurate mass and time tag strategy. Proteomics 5:3454–3466CrossRefGoogle Scholar
  11. 11.
    Anthonisen NR, Connett JE, Kiley JP, Altose MD et al (1994) Effects of smoking intervention and the use of an inhaled anticholinergic bronchodilator on the rate of decline of FEV1: the Lung Health Study. JAMA 272:1497–1505CrossRefGoogle Scholar
  12. 12.
    Anthonisen NR, Connett JE, Murray RP (2002) Smoking and lung function of Lung Health Study participants after 11 years. Am J Respir Crit Care Med 166:675–679CrossRefGoogle Scholar
  13. 13.
    Qian WJ, Jacobs JM, Camp DG II, Monroe ME et al (2005) Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry. Proteomics 5:572–584CrossRefGoogle Scholar
  14. 14.
    Belov ME, Anderson GA, Wingerd MA, Udseth HR et al (2004) An automated high performance capillary liquid chromatography–Fourier transform ion cyclotron resonance mass spectrometer for high-throughput proteomics. J Am Soc Mass Spectrom 15:212–232CrossRefGoogle Scholar
  15. 15.
    Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989CrossRefGoogle Scholar
  16. 16.
    Link AJ, Eng J, Schieltz DM, Carmack E et al (1999) Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 17:676–682CrossRefGoogle Scholar
  17. 17.
    Washburn MP, Wolters D, Yates JR III (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19:242–247CrossRefGoogle Scholar
  18. 18.
    Rocke DM, Durbin B (2001) A model for measurement error for gene expression arrays. J Comput Biol 8:557–569CrossRefGoogle Scholar
  19. 19.
    van den Oord EJ (2005) Controlling false discoveries in candidate gene studies. Mol Psychiatry 10:230–231CrossRefGoogle Scholar
  20. 20.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
  21. 21.
    Storey JD, Tibshirani R (2003) Statistical methods for identifying differentially expressed genes in DNA microarrays. Meth Mol Biol 224:149–157Google Scholar
  22. 22.
    Storey JD, Tibshirani R (2003) Statistical significance for genome-wide studies. Proc Natl Acad Sci USA 100:9440–9445CrossRefGoogle Scholar
  23. 23.
    Kanehisa M, Araki M, Goto S, Hattori M et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484CrossRefGoogle Scholar
  24. 24.
    Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF et al (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354–D357CrossRefGoogle Scholar
  25. 25.
    Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30CrossRefGoogle Scholar
  26. 26.
    Donaldson GC, Seemungal TA, Patel IS, Bhowmik A et al (2005) Airway and systemic inflammation and decline in lung function in patients with COPD. Chest 128:1995–2004CrossRefGoogle Scholar
  27. 27.
    Gan WQ, Man SF, Senthilselvan A, Sin DD (2004) Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis. Thorax 59:574–580CrossRefGoogle Scholar
  28. 28.
    Wedzicha JA, Seemungal TA, Maccallum PK, Paul EA et al (2000) Acute exacerbations of chronic obstructive pulmonary disease are accompanied by elevations of plasma fibrinogen and serum IL-6 levels. Thromb Haemost 84:210–215Google Scholar
  29. 29.
    Bolger MS, Ross DS, Jiang H, Frank MM et al (2007) Complement levels and activity in the normal and LPS-injured lung. Am J Physiol Lung Cell Mol Physiol 292:L748–L759CrossRefGoogle Scholar
  30. 30.
    Markiewski MM, Lambris JD (2007) The role of complement in inflammatory diseases from behind the scenes into the spotlight. Am J Pathol 171:715–727CrossRefGoogle Scholar
  31. 31.
    Rennard SI (1999) Inflammation and repair processes in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 160:S12–S16Google Scholar
  32. 32.
    Celli BR, Roger S (2006) Mitchell lecture. Chronic obstructive pulmonary disease phenotypes and their clinical relevance. Proc Am Thorac Soc 3:461–465CrossRefGoogle Scholar
  33. 33.
    Wedzicha JA, Seemungal TA (2007) COPD exacerbations: defining their cause and prevention. Lancet 370:786–796CrossRefGoogle Scholar
  34. 34.
    Hurst JR, Perera WR, Wilkinson TM, Donaldson GC et al (2006) Systemic and upper and lower airway inflammation at exacerbation of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 173:71–78CrossRefGoogle Scholar
  35. 35.
    Sethi S, Maloney J, Grove L, Wrona C et al (2006) Airway inflammation and bronchial bacterial colonization in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 173:991–998CrossRefGoogle Scholar
  36. 36.
    Agusti A, MacNee W, Donaldson K, Cosio M (2003) Hypothesis: does COPD have an autoimmune component? Thorax 58:832–834CrossRefGoogle Scholar
  37. 37.
    Feghali-Bostwick CA, Gadgil AS, Otterbein LE, Pilewski JM et al (2007) Autoantibodies in patients with COPD. Am J Respir Crit Care Med 177:156–163CrossRefGoogle Scholar
  38. 38.
    Thyagarajan B, Jacobs DR, Apostol GG, Smith LJ et al (2006) Plasma fibrinogen and lung function: the CARDIA Study. Int J Epidemiol 35:1001–1008CrossRefGoogle Scholar
  39. 39.
    Cupples LA, Arruda HT, Benjamin EJ, D'Agostino RB Sr et al (2007) The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports. BMC Med Genet 8:S1CrossRefGoogle Scholar
  40. 40.
    Yanbaeva DG, Dentener MA, Creutzberg EC, Wesseling G et al (2007) Systemic effects of smoking. Chest 131:1557–1566CrossRefGoogle Scholar
  41. 41.
    Walter RE, Wilk JB, Larson MG, Vasan RS et al (2008) Systemic inflammation and COPD: the Framingham Heart Study. Chest 133:19–25CrossRefGoogle Scholar
  42. 42.
    Volanakis JE (1995) Transcriptional regulation of complement genes. Annu Rev Immunol 13(277–305):277–305CrossRefGoogle Scholar
  43. 43.
    Liu C, Xu Z, Gupta D, Dziarski R (2001) Peptidoglycan recognition proteins: a novel family of four human innate immunity pattern recognition molecules. J Biol Chem 276:34686–34694CrossRefGoogle Scholar
  44. 44.
    Lee PS, Waxman AB, Cotich KL, Chung SW et al (2007) Plasma gelsolin is a marker and therapeutic agent in animal sepsis. Crit Care Med 35:849–855CrossRefGoogle Scholar
  45. 45.
    Jourdain M, Carrette O, Tournoys A, Fourrier F et al (1997) Effects of inter-alpha-inhibitor in experimental endotoxic shock and disseminated intravascular coagulation. Am J Respir Crit Care Med 156:1825–1833Google Scholar
  46. 46.
    Furie B, Furie BC (2005) Thrombus formation in vivo. J Clin Invest 115:3355–3362CrossRefGoogle Scholar
  47. 47.
    Fay WP, Garg N, Sunkar M (2007) Vascular functions of the plasminogen activation system. Arterioscler Thromb Vasc Biol 27:1231–1237CrossRefGoogle Scholar
  48. 48.
    Ashitani J, Mukae H, Arimura Y, Matsukura S (2002) Elevated plasma procoagulant and fibrinolytic markers in patients with chronic obstructive pulmonary disease. Intern Med 41:181–185CrossRefGoogle Scholar
  49. 49.
    Alessandri C, Basili S, Violi F, Ferroni P et al (1994) Hypercoagulability state in patients with chronic obstructive pulmonary disease. Chronic Obstructive Bronchitis and Haemostasis Group. Thromb Haemost 72:343–346Google Scholar
  50. 50.
    Tapson VF (2005) The role of smoking in coagulation and thromboembolism in chronic obstructive pulmonary disease. Proc Am Thorac Soc 2:71–77CrossRefGoogle Scholar
  51. 51.
    Voelkel NF, Cool CD (2003) Pulmonary vascular involvement in chronic obstructive pulmonary disease. Eur Respir J Suppl 46:28s–32sCrossRefGoogle Scholar
  52. 52.
    Watson L, Vonk JM, Lofdahl CG, Pride NB et al (2006) Predictors of lung function and its decline in mild to moderate COPD in association with gender: results from the Euroscop study. Respir Med 100:746–753CrossRefGoogle Scholar
  53. 53.
    Soriano JB, Sin DD, Zhang X, Camp PG et al (2007) A pooled analysis of FEV1 decline in COPD patients randomized to inhaled corticosteroids or placebo. Chest 131:682–689CrossRefGoogle Scholar
  54. 54.
    Hogg JC (2006) Why does airway inflammation persist after the smoking stops? Thorax 61:96–97CrossRefGoogle Scholar
  55. 55.
    Agusti A, Soriano JB (2008) COPD as a systemic disease. COPD 5:138Google Scholar
  56. 56.
    Fabbri LM, Luppi F, Beghe B, Rabe KF (2008) Complex chronic comorbidities of COPD. Eur Respir J 31:204–212CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Gaurav S. J. B. Rana
    • 1
  • Timothy P. York
    • 2
  • Jeffery S. Edmiston
    • 1
  • Barbara K. Zedler
    • 1
  • Joel G. Pounds
    • 3
  • Joshua N. Adkins
    • 3
  • Richard D. Smith
    • 3
  • Zaigang Liu
    • 1
  • Guoya Li
    • 1
  • Bradley T. Webb
    • 2
  • Edward L. Murrelle
    • 1
  • Jason W. Flora
    • 1
  1. 1.Health Sciences, Altria Client Services, Research Development & EngineeringRichmondUSA
  2. 2.Departments of Human and Molecular Genetics and Pharmacy, Institute for Biomarker Discovery and Personalized MedicineVirginia Commonwealth University School of MedicineRichmondUSA
  3. 3.Biological Sciences DivisionPacific Northwest National LaboratoryRichlandUSA

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