Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

Abstract

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: A pipeline for biomarker verification and its application to a human model of myocardial injury.
Figure 2: Venn diagrams summarizing proteins identified in the coronary sinuses of PMI patients.
Figure 3: Kinetic analyses of known and putative biomarkers for acute myocardial infarction in PMI patients from discovery proteomics.
Figure 4: Verification of novel candidate biomarkers in peripheral blood of PMI patients by targeted, quantitative MS.
Figure 5: Verification of candidate biomarkers by western blot analysis and ELISA.

Similar content being viewed by others

References

  1. Edwards, A.V.G., White, M.Y. & Cordwell, S.J. The role of proteomics in clinical cardiovascular biomarker discovery. Mol. Cell. Proteomics 7, 1824–1837 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Jacquet, S. et al. Identification of cardiac myosin-binding protein C as a candidate biomarker of myocardial Infarction by proteomics analysis. Mol. Cell. Proteomics 8, 2687–2699 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fu, Q. & Van Eyk, J.E. Proteomics and heart disease: identifying biomarkers of clinical utility. Expert Rev. Proteomics 3, 237–249 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Anderson, N.L. The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin. Chem. 56, 177–185 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Kulasingam, V. & Diamandis, E.P. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat. Clin. Pract. Oncol. 5, 588–599 (2008).

    Article  CAS  PubMed  Google Scholar 

  6. Rifai, N., Gillette, M.A. & Carr, S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Paulovich, A.G., Whiteaker, J.R., Hoofnagle, A.N. & Wang, P. The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Proteomics Clin. Appl. 2, 1386–1402 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. & Carr, S.A. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 6, 2212–2229 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Keshishian, H. et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 8, 2339–2349 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jaffe, J.D. et al. Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification. Mol. Cell. Proteomics 7, 1952–1962 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sigwart, U. Non-surgical myocardial reduction for hypertrophic obstructive cardiomyopathy. Lancet 346, 211–214 (1995).

    Article  CAS  PubMed  Google Scholar 

  13. Knight, C. et al. Nonsurgical septal reduction for hypertrophic obstructive cardiomyopathy: outcome in the first series of patients. Circulation 95, 2075–2081 (1997).

    Article  CAS  PubMed  Google Scholar 

  14. de Lemos, J.A. et al. The prognostic value of serum myoglobin in patients with non–ST-segment elevation acute coronary syndromes: results from the TIMI 11B and TACTICS-TIMI 18 studies. J. Am. Coll. Cardiol. 40, 238–244 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. O'Donoghue, M. et al. Prognostic utility of heart-type fatty acid binding protein in patients with acute coronary syndromes. Circulation 114, 550–557 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Layne, M.D. et al. Impaired abdominal wall development and deficient wound healing in mice lacking aortic carboxypeptidase-like protein. Mol. Cell. Biol. 21, 5256–5261 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sheikh, F. et al. An FHL1-containing complex within the cardiomyocyte sarcomere mediates hypertrophic biomechanical stress responses in mice. J. Clin. Invest. 118, 3870–3880 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kishimoto, K., Liu, S., Tsuji, T., Olson, K.A. & Hu, G.F. Endogenous angiogenin in endothelial cells is a general requirement for cell proliferation and angiogenesis. Oncogene 24, 445–456 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Moretti, A. et al. Essential myosin light chain as a target for caspase-3 in failing myocardium. Proc. Natl. Acad. Sci. USA 99, 11860–11865 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fusaro, V.A., Mani, D.R., Mesirov, J.P. & Carr, S.A. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat. Biotechnol. 27, 190–198 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Richard, P. et al. Hypertrophic cardiomyopathy: distribution of disease genes, spectrum of mutations, and implications for a molecular diagnosis strategy. Circulation 107, 2227–2232 (2003).

    Article  PubMed  Google Scholar 

  22. Faca, V.M. et al. A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 5, e123 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. States, D.J. et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 24, 333–338 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Schenk, S., Schoenhals, G.J., de Souza, G. & Mann, M. A high confidence, manually validated human blood plasma protein reference set. BMC Med. Genomics 1, 41–68 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Abbatiello, S.E., Mani, D.R., Keshishian, H. & Carr, S.A. Automated detection of inaccurate and imprecise transitions in quantitative assays of peptides by multiple monitoring mass spectrometry. Clin. Chem. 56, 291–305 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Shaham, O. et al. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 4, 214 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lewis, G.D. et al. Metabolic signatures of exercise in human plasma. Sci. Transl. Med. 2, 33ra37 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Rhee, E.P. et al. Metabolite profiling identifies markers of uremia. J. Am. Soc. Nephrol. 21, 1041–1051 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. van Hall, G. et al. Leg and arm lactate and substrate kinetics during exercise. Am. J. Physiol. Endocrinol. Metab. 284, E193–E205 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Baggish, A.L. et al. Pathological effects of alcohol septal ablation for hypertrophic obstructive cardiomyopathy. Heart 92, 1773–1778 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Horiba, M. et al. Midkine plays a protective role against cardiac ischemia/reperfusion injury through a reduction of apoptotic reaction. Circulation 114, 1713–1720 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Feinstein, A.R. Principles of Medical Statistics (Chapman & Hall/CRC, 2002).

  33. Guilford, J.P. Fundamental Statistics in Psychology and Education (McGraw Hill, 1956).

  34. Futschik, M.E. & Carlisle, B. Noise-robust soft clustering of gene expression time-course data. J. Bioinform. Comput. Biol. 3, 965–988 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Kumar, L. & Futschik, M.E. Mfuzz: A software package for soft clustering of microarray data. Bioinformation 2, 5–7 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  36. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2009).

Download references

Acknowledgements

The authors gratefully acknowledge support from the US National Institutes of Health (NIH) National Heart, Lung, and Blood Institute U01HL083141 and R01HL096738-02 to R.E.G., S.A.C. and M.S.S., the Donald W. Reynolds Foundation (to R.E.G. and M.S.S.) and Foundation Leducq (to R.E.G.). S.A.C. also acknowledges support from the NIH 1U24 CA126476 as part of the National Cancer Institute (NCI)'s Clinical Proteomic Technologies Assessment in Cancer Program, from the Women's Cancer Research Fund of the Entertainment Industry Foundation and to D.R.M. from the NCI Clinical Proteomic Technologies Initiative (R01 CA126219). We also appreciate the excellent technical assistance of C. Bodycombe.

Author information

Authors and Affiliations

Authors

Contributions

T.A.A., S.A.C. and R.E.G. wrote the manuscript. S.A.C. and M.A.G. conceived of the biomarker pipeline used here. R.E.G. conceived of the PMI as a model for proteomic discovery, and along with M.A.F., M.S.S., G.D.L. and L.A.F. developed the human studies protocols included in the manuscript, and performed the phenotyping of the patient populations. H.K., X.S. and T.A.A. carried out all of the sample preparation, conducted the MS-based proteomics experiments for discovery and AIMS and interpreted the results. H.K. and M.B. conducted all of the MRM-MS experiments for assaying proteins by M.S.S. X.S. tested, developed and applied all antibody-based measurements, with contributions from D.S. M.A.G. was responsible for design of the AIMS experiments. K.R.C. designed and adapted the Spectrum Mill software for peptide and protein identification, label-free quantification and calculation of peptide-level FDR and participated in data analysis. D.R.M., M.S.S. and K.R.C. were responsible for statistical design and analysis.

Corresponding authors

Correspondence to Robert E Gerszten or Steven A Carr.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–9, Supplementary Figs. 1–6, Supplementary Results and Discussion, and Supplementary Methods (PDF 11166 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Addona, T., Shi, X., Keshishian, H. et al. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat Biotechnol 29, 635–643 (2011). https://doi.org/10.1038/nbt.1899

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1899

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research