Surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry to identify patients with chronic obstructive pulmonary disease

Proteomic profiling of peripheral blood neutrophils identifies two inflammatory phenotypes in stable COPD patients

BACKGROUND

COPD is a heterogeneous chronic inflammatory disease of the airways and it is well accepted that the GOLD classification does not fully represent the complex clinical manifestations of COPD and this classification therefore is not well suited for phenotyping of individual patients with COPD. Besides the chronic inflammation in the lung compartment, there is also a systemic inflammation present in COPD patients. This systemic inflammation is associated with elevated levels of cytokines in the peripheral blood, but the precise composition is unknown. Therefore, differences in phenotype of peripheral blood neutrophils in vivo could be used as a read out for the overall systemic inflammation in COPD.

METHOD

Our aim was to utilize an unsupervised method to assess the proteomic profile of peripheral neutrophils of stable COPD patients and healthy age matched controls to find potential differences in these profiles as read-out of inflammatory phenotypes. We performed fluorescence two-dimensional difference gel electrophoresis with the lysates of peripheral neutrophils of controls and stable COPD patients.

RESULTS

We identified two groups of COPD patients based on the differentially regulated proteins and hierarchical clustering whereas there was no difference in lung function between these two COPD groups. The neutrophils from one of the COPD groups were less responsive to bacterial peptide N-formyl-methionyl-leucyl-phenylalanine (fMLF).

CONCLUSION

This illustrates that systemic inflammatory signals do not necessarily correlate with the GOLD classification and that inflammatory phenotyping can significantly add in an improved diagnosis of single COPD patients.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT00807469 registered December 11th 2008.

Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry

Background

Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction.

Methodology/Principal Findings

A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS).

Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity.

Conclusions/Significance

MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

Identification of lipocalin and apolipoprotein A1 as biomarkers of chronic obstructive pulmonary disease

RATIONALE

Much effort is being made to discover noninvasive biomarkers of chronic airway disease that might enable better management, predict prognosis, and provide new therapeutic targets.

OBJECTIVES

To undertake a comprehensive, unbiased proteomic analysis of induced sputum and identify novel noninvasive biomarkers for chronic obstructive pulmonary disease (COPD).

METHODS

Induced sputum was obtained from patients with COPD with a spectrum of disease severity and from control subjects. Two-dimensional gel electrophoresis and mass spectrometric identification of differentially expressed proteins were first applied to induced sputum from patients with GOLD stage 2 COPD and healthy smoker control subjects. Initial results thus obtained were validated by a combination of immunoassays (Western blotting and ELISA) applied to a large subject cohort. The biomarkers were localized to bronchial mucosa by immunohistochemistry.

MEASUREMENTS AND MAIN RESULTS

Of 1,325 individual protein spots identified, 37 were quantitatively and 3 qualitatively different between the two groups (P < 0.05%). Forty protein spots were subjected to tandem mass spectrometry, which identified 15 separate protein species. Seven of these were further quantified in induced sputum from 97 individuals. Using this sequential approach, two of these potential biomarkers (apolipoprotein A1 and lipocalin-1) were found to be significantly reduced in patients with COPD when compared with healthy smokers. Their levels correlated with FEV(1)/FVC, indicating their relationship to disease severity.

CONCLUSIONS

A potential role for apolipoprotein A1 and lipocalin-1 in innate defense has been postulated previously; our discovery of their reduction in COPD indicates a deficient innate defense system in airway disease that could explain increased susceptibility to infectious exacerbations.

SELDI-TOF biomarker signatures for cystic fibrosis, asthma and chronic obstructive pulmonary disease

OBJECTIVES

The aim of this work was to establish protein profiles in serum and nasal epithelial cells of cystic fibrosis individuals in comparison with controls, asthma and chronic obstructive pulmonary disease patients for specific biomarker signatures identification.

DESIGN AND METHODS

Protein extracts were analyzed by Surface Enhanced Laser Desorption/Ionization Time-Of-Flight Mass-Spectrometry (SELDI-TOF-MS).

RESULTS

The mass spectra revealed a set of peaks with differential expression in serum and nasal cells among the different groups studied, resulting into peak signatures representative/specific of each pathology. Logistic regressions were applied to those peaks; sensitivity, specificity, Youden's indexes and area under the curve (AUC) of the respective receiver operating characteristic (ROC) curves were compared.

DISCUSSION

Multivariate analysis demonstrated that combination of peaks has a better predictive value than the individual ones. These protein signatures may serve as diagnostic/prognostic markers for the studied diseases with common clinical features, or as follow-up assessment markers of therapeutic interventions.

Proteomics methods and applications for the practicing clinician

OBJECTIVE

To describe clinical proteomics from discovery techniques and their limitations, to applications in allergy, asthma, and immunology, and finally to how proteomics can be integrated into clinical practice.

DATA SOURCES

Despite many inherent challenges, proteomics-based methods have become a powerful and popular means of profiling clinical samples for the purpose of biomarker discovery. Although several strategies exist, clinical proteomics for the purpose of biomarker discovery generally focuses on 1 of 3 basic workflows: (1) 2-dimensional gel electrophoresis to quantitate relative protein levels followed by mass spectrometry (MS) to identify proteins of interest, (2) non-gel-based methods that rely on liquid chromatography MS (LCMS) for both quantitation and identification of proteins, and (3) protein profiling methods that do not directly result in the identification of proteins but rather generate "fingerprints" that are compared among individuals or samples.

STUDY SELECTION

Regardless of the strategy being pursued, a few general experimental steps are followed that will be expounded on in the text. These proteomics techniques have been applied to discover new biomarkers in biofluids and tissues from individuals with a variety of conditions, including allergy, asthma, atopic dermatitis, inflammatory diseases, chronic obstructive pulmonary disease, and other lung diseases.

RESULTS

After biomarker discovery, LCMS-based proteomics offers several advantages over traditional antibody-based clinical assays, including greater specificity, cost- and time-effectiveness, and the potential to multiplex up to hundreds of peptides in a single assay.

CONCLUSION

With many guidelines now in place and model studies on which to design future experiments, there is reason to be optimistic that candidate protein biomarkers will be discovered using proteomics and translated into clinical assays.

Proteomics in pulmonary medicine

Proteomics is the study of the entire protein complement of the genome (the proteome) in a biological system. Proteomic studies require a multidisciplinary approach and have only been practical with the convergence of technical and methodologic improvements including the following: advances in mass spectrometry and genomic sequencing that now permit the identification and relative quantization of small amounts (femtomole) of nearly any single protein; new methods in gel electrophoresis that allow the detection of subtle changes in protein expression, including posttranslational modifications; automation and miniaturization that permit high-throughput analysis of clinical samples; and new bioinformatics and computational methods that facilitate analysis and interpretation of the abundant data that are generated by proteomics experiments. This convergence makes proteomics studies practical for pulmonary researchers using BAL fluid, lung tissue, blood, and exhaled breath condensates, and will facilitate the research of complex, multifactorial lung diseases such as acute lung injury and COPD. This review describes how proteomics experiments are conducted and interpreted, their limitations, and how proteomics has been used in clinical pulmonary medicine.