Proteomics: beginning to realize its promise?

Proteomics in the Assessment of the Therapeutic Response of Antineoplastic Drugs: Strategies and Practical Applications

Uncovering unknown pathological mechanisms and body response to applied medication are the driving forces toward personalized medicine. In this post-genomic era, all eyes are turned to the proteomics field, searching for answers and explanations by investigating the gene end point functional units-proteins and their proteoforms. The development of cutting-edge mass spectrometric technologies and bioinformatics tools have allowed the life-science community to discover disease-specific proteins as biomarkers, which are often concealed by high sample complexity and dynamic range of abundance. Currently, there are several proteomics-based approaches to investigate the proteome. This chapter focuses on gold standard proteomics strategies and related issues toward candidate biomarker discovery, which may have diagnostic/prognostic as well as mechanistic utility in cancer drug resistance.

Maximizing the sensitivity and reliability of peptide identification in large-scale proteomic experiments by harnessing multiple search engines

Despite recent advances in qualitative proteomics, the automatic identification of peptides with optimal sensitivity and accuracy remains a difficult goal. To address this deficiency, a novel algorithm, Multiple Search Engines, Normalization and Consensus is described. The method employs six search engines and a re-scoring engine to search MS/MS spectra against protein and decoy sequences. After the peptide hits from each engine are normalized to error rates estimated from the decoy hits, peptide assignments are then deduced using a minimum consensus model. These assignments are produced in a series of progressively relaxed false-discovery rates, thus enabling a comprehensive interpretation of the data set. Additionally, the estimated false-discovery rate was found to have good concordance with the observed false-positive rate calculated from known identities. Benchmarking against standard proteins data sets (ISBv1, sPRG2006) and their published analysis, demonstrated that the Multiple Search Engines, Normalization and Consensus algorithm consistently achieved significantly higher sensitivity in peptide identifications, which led to increased or more robust protein identifications in all data sets compared with prior methods. The sensitivity and the false-positive rate of peptide identification exhibit an inverse-proportional and linear relationship with the number of participating search engines.

Comparison of multiplex immunoassay platforms

BACKGROUND

Candidate biomarkers discovered with high-throughput proteomic techniques (along with many biomarkers reported in the literature) must be rigorously validated. The simultaneous quantitative assessment of multiple potential biomarkers across large cohorts presents a major challenge to the field. Multiplex immunoassays represent a promising solution, with the potential to provide quantitative data via parallel analyses. These assays also require substantially less sample and reagents than the traditional ELISA (which is further limited by its ability to measure only a single antigen). We have measured the reproducibility, reliability, robustness, accuracy, and throughput of commercially available multiplex immunoassays to ascertain their suitability for serum biomarker analysis and validation.

METHODS

Assay platforms MULTI-ARRAY (Meso Scale Discovery), Bio-Plex (Bio-Rad Laboratories), A(2) (Beckman Coulter), FAST Quant (Whatman Schleicher & Schuell BioScience), and FlowCytomix (Bender MedSystems) were selected as representative examples of technologies currently used for high-throughput immunoanalysis. All assays were performed according to protocols specified by the manufacturers and with the reagents (diluents, calibrators, blocking reagents, and detecting-antibody mixtures) included with their kits.

RESULTS

The quantifiable interval determined for each assay and antigen was based on precision (CV < 25%) and percentage recovery (measured concentration within 20% of the actual concentration). The MULTI-ARRAY and Bio-Plex assays had the best performance with the lowest limits of detection, and the MULTI-ARRAY system had the most linear signal output over the widest concentration range (10(5) to 10(6)). Cytokine concentrations in unspiked and cytokine-spiked serum samples from healthy individuals were further investigated with the MULTI-ARRAY and Bio-Plex assays.

CONCLUSIONS

The MULTI-ARRAY and Bio-Plex multiplex immunoassay systems are the most suitable for biomarker analysis or quantification.

Stratification and monitoring of juvenile idiopathic arthritis patients by synovial proteome analysis

Juvenile idiopathic arthritis (JIA) comprises a poorly understood group of chronic, childhood onset, autoimmune diseases with variable clinical outcomes. We investigated whether profiling of the synovial fluid (SF) proteome by a fluorescent dye based, two-dimensional gel (DIGE) approach could distinguish patients in whom inflammation extends to affect a large number of joints, early in the disease process. SF samples from 22 JIA patients were analyzed: 10 with oligoarticular arthritis, 5 extended oligoarticular and 7 polyarticular disease. SF samples were labeled with Cy dyes and separated by two-dimensional electrophoresis. Multivariate analyses were used to isolate a panel of proteins which distinguish patient subgroups. Proteins were identified using MALDI-TOF mass spectrometry with expression further verified by Western immunoblotting and immunohistochemistry. Hierarchical clustering based on the expression levels of a set of 40 proteins segregated the extended oligoarticular from the oligoarticular patients (p < 0.05). Expression patterns of the isolated protein panel have also been observed over time, as disease spreads to multiple joints. The data indicates that synovial fluid proteome profiles could be used to stratify patients based on risk of disease extension. These protein profiles may also assist in monitoring therapeutic responses over time and help predict joint damage.

Proteomic diagnosis of Sjögren's syndrome

In the last few years, a growing interest has arisen in the application of proteomic analysis to rheumatic disease. Sjögren's syndrome is a systemic disease that affects exocrine glands directly, and is therefore expected to influence the composition of the whole human saliva and lachrymal fluid. Therefore, a rising number of studies have been performed in an attempt to characterize the salivary and lachrymal protein profiles of patients with Sjögren's syndrome by using a proteomic approach. This review summarizes the state of the art and the potential application of proteomics in the systematic search for diagnostic biomarkers in Sjögren's syndrome.

Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues

Successful treatment of multiple cancer types requires early detection and identification of reliable biomarkers present in specific cancer tissues. To test the feasibility of identifying proteins from archival cancer tissues, we have developed a methodology, termed direct tissue proteomics (DTP), which can be used to identify proteins directly from formalin-fixed paraffin-embedded prostate cancer tissue samples. Using minute prostate biopsy sections, we demonstrate the identification of 428 prostate-expressed proteins using the shotgun method. Because the DTP method is not quantitative, we employed the absolute quantification method and demonstrate picogram level quantification of prostate-specific antigen. In depth bioinformatics analysis of these expressed proteins affords the categorization of metabolic pathways that may be important for distinct stages of prostate carcinogenesis. Furthermore, we validate Wnt-3 as an upregulated protein in cancerous prostate cells by immunohistochemistry. We propose that this general strategy provides a roadmap for successful identification of critical molecular targets of multiple cancer types.

Development of a novel 2D proteomics approach for the identification of proteins secreted by primary chondrocytes after stimulation by IL-1 and oncostatin M

OBJECTIVES

To develop a proteomics approach to study changes in the secreted protein levels of primary human chondrocytes after stimulation by the pro-inflammatory cytokines interleukin-1 and oncostatin M.

METHODS

Using both the primary human articular and bovine nasal chondrocyte-conditioned mediums, methods were investigated to enable the separation of proteins by two-dimensional (2D) gel electrophoresis. Differentially regulated proteins were identified using tandem electrospray mass spectrometery.

RESULTS

We discovered that proteoglycans and glycosylaminoglycans (GAGs) secreted by chondrocytes significantly interfered with 2D gel focusing. Several different methods for GAG removal were attempted including enzymic digestion, cetyl pyridinium chloride precipitation and anion exchange in high salt. The anion exchange proved to be the most effective. Even from these initial gels, we were able to identify eight proteins produced by human chondrocytes: matrix metalloproteinase (MMP)-1, MMP-3, YKL40, cyclophilin A, beta2-microglobulin, transthyretin, S100A11, peroxidine 1 and cofilin. MMP-1, MMP-3, YKL40 and cyclophilin A were all identified as processed, smaller peptide fragments.

CONCLUSIONS

We were able to develop a novel sample preparation protocol to allow the reproducible sample preparation of secreted proteins from human chondrocytes. From the initial data, we were able to show that at least some of the proteins produced were cleaved to smaller fragments as a result of proteolysis. Therefore, this technique provides valuable information about protein processing which gene-based arrays do not.