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      Identification of tuberculosis-associated proteins in whole blood supernatant

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          Abstract

          Background

          Biological parameters are useful tools for understanding and monitoring complicated disease processes. In this study, we attempted to identify proteins associated with active pulmonary tuberculosis (TB) using a proteomic approach.

          Methods

          To assess TB-associated changes in the composition of human proteins, whole blood supernatants were collected from patients with active TB and healthy control subjects. Two-dimensional difference gel electrophoresis (2D-DIGE) was performed to analyze proteins with high molecular weights (approximately >20 kDa). Baseline protein levels were initially compared between patients with active TB and control subjects. Possible changes of protein patterns in active TB were also compared ex vivo between whole blood samples incubated with Mycobacterium tuberculosis ( Mtb)-specific antigens (stimulated condition) and under unstimulated conditions. Immunoblot and enzyme-linked immunosorbent assays (ELISA) were performed to confirm differences in identified proteins.

          Results

          Under the baseline condition, we found that the levels of retinol-binding protein 4 (RBP4), fetuin-A (also called α-HS-glycoprotein), and vitamin D-binding protein differed between patients with active TB and control subjects on 2D gels. Immunoblotting results confirmed differential expression of RBP4 and fetuin-A. ELISA results further confirmed significantly lower levels of these two proteins in samples from patients with active TB than in control subjects ( P < 0.0001). Mtb-specific antigen stimulation ex vivo altered clusterin expression in whole blood samples collected from patients with active TB.

          Conclusions

          We identified TB-associated proteins in whole blood supernatants. The dynamics of protein expression during disease progression may improve our understanding of the pathogenesis of TB.

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          Most cited references14

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          Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis.

          Infection with Mycobacterium tuberculosis is controlled by an efficacious immune response in about 90% of infected individuals who do not develop disease. Although essential mediators of protection, e.g., interferon-gamma, have been identified, these factors are insufficient to predict the outcome of M. tuberculosis infection. As a first step to determine additional biomarkers, we compared gene expression profiles of peripheral blood mononuclear cells from tuberculosis patients and M. tuberculosis-infected healthy donors by microarray analysis. Differentially expressed candidate genes were predominantly derived from monocytes and comprised molecules involved in the antimicrobial defense, inflammation, chemotaxis, and intracellular trafficking. We verified differential expression for alpha-defensin 1, alpha-defensin 4, lactoferrin, Fcgamma receptor 1A (cluster of differentiation 64 [CD64]), bactericidal permeability-increasing protein, and formyl peptide receptor 1 by quantitative polymerase chain reaction analysis. Moreover, we identified increased protein expression of CD64 on monocytes from tuberculosis patients. Candidate biomarkers were then assessed for optimal study group discrimination. Using a linear discriminant analysis, a minimal group of genes comprising lactoferrin, CD64, and the Ras-associated GTPase 33A was sufficient for classification of (1) tuberculosis patients, (2) M. tuberculosis-infected healthy donors, and (3) noninfected healthy donors.
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            Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum

            Summary Background We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. Methods We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. Findings Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93·5%, specificity 94·9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. Interpretation The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.
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              Fetuin-A and incident diabetes mellitus in older persons.

              Fetuin-A is a hepatic secretory protein that binds the insulin receptor and inhibits insulin action in vitro. In prior cross-sectional studies in humans, higher fetuin-A levels were associated with insulin resistance. However, the longitudinal association of fetuin-A with incident type 2 diabetes mellitus is unknown. To determine whether fetuin-A levels are associated with incident diabetes in older persons. Observational study among 3075 well-functioning persons aged 70 to 79 years. In this case-cohort study, we retrospectively measured fetuin-A levels in baseline serum among 406 randomly selected participants without prevalent diabetes, and all participants who developed incident diabetes mellitus during a 6-year follow-up (to August 31, 2005). Incident diabetes mellitus. Incident diabetes developed in 135 participants (10.1 cases/1000 person-years). Participants with fetuin-A levels within the highest tertile (> 0.97 g/L) had an increased risk of incident diabetes (13.3 cases/1000 person-years) compared with participants in the lowest tertile (< or = 0.76 g/L) (6.5 cases/1000 person-years) in models adjusted for age, sex, race, waist circumference, body weight, physical activity, blood pressure level, fasting glucose level, high-density lipoprotein cholesterol concentration, triglyceride concentration, and C-reactive protein level (adjusted hazard ratio, 2.41; 95% confidence interval, 1.28-4.53; P = .007). The association was not affected by adipocytokine levels but was moderately attenuated by adjustment for visceral adiposity (adjusted hazard ratio of highest vs lowest tertile 1.72; 95% confidence interval, 0.98-3.05; P = .06). Among well-functioning older persons, serum fetuin-A is associated with incident diabetes, independent of other markers of insulin resistance.
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                Author and article information

                Journal
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central
                1471-2334
                2011
                22 March 2011
                : 11
                : 71
                Affiliations
                [1 ]Molecular Enzymology, Graduate School of Agricultural Science, Tohoku University, 1-1 Amamiya-machi, Tsutsumidori, Aoba-ku, Sendai, Miyagi 981-8555, Japan
                [2 ]Department of Respiratory Diseases, Research Institute, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
                [3 ]Department of Diabetic Complications, Research Institute, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
                [4 ]Department of Metabolic Disorder, Research Institute, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
                [5 ]Supramolecular Biology, International Graduate School of Art and Sciences, Yokohama City University, 1-7-29 Suehirocho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
                [6 ]Department of Respiratory Medicine, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
                [7 ]National Center for Global Health and Medicine - Bach Mai Hospital (NCGM-BMH) Medical Collaboration Center, 78 Giai Phong St., Hanoi, Vietnam
                [8 ]Hanoi Tuberculosis and Lung Disease Hospital, 44 Thanh Nhan Road, Hanoi, Vietnam
                Article
                1471-2334-11-71
                10.1186/1471-2334-11-71
                3072329
                21418657
                0ab3caa0-5ac2-44be-ad63-6cb9b32d1d6c
                Copyright ©2011 Tanaka et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 October 2010
                : 22 March 2011
                Categories
                Research Article

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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