Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Profiling antibody responses to M. tuberculosis proteins in TB suspects’ sera using full-proteome microarrays.

ABSTRACT: Understanding the immune response to tuberculosis requires greater knowledge of humoral responses. To characterize antibody targets and the effect of disease parameters on target recognition, we developed a systems immunology approach that integrated detection of antibodies against the entire Mycobacterium tuberculosis proteome, bacterial metabolic and regulatory pathway information, and patient data. Probing ~4,000 M. tuberculosis proteins with sera from >500 suspected tuberculosis patients worldwide revealed that antibody responses recognized ~10% of the bacterial proteome. This result defines the immunoproteome of M. tuberculosis, which is rich in membrane-associated and extracellular proteins. Most serum reactivity during active tuberculosis focused onto ~0.5% of the proteome. Within this pool, which is selectively enriched for extracellular proteins (but not for membrane-associated proteins), relative target preference varied among patients. The shift in relative M. tuberculosis protein reactivity observed with active tuberculosis defines the evolution of the humoral immune response during M. tuberculosis infection and disease. Peripheral blood was collected from prospectively enrolled TB suspects among subjects seeking care for pulmonary symptoms at clinics associated with national TB control programs in 11 countries. M. tuberculosis proteome microarrays representing 4099 bacterial protein spots were probed with sera from 561 TB suspects. Based on the final diagnosis, they belonged to two classes: TB (n=254) and Non-TB Disease (n=307). In addition, healthy individuals negative to Latent TB Infection (LTBI neg, n=64) were also tested (negative control sera). Each serum was tested with a single array and no replicate experiments were performed. The reactivity of a serum to an M. tuberculosis protein (reactivity call) was defined based on the distribution of negative control sera intensity for that protein using Z-statistics. Based on the distribution of reactivity calls, 27 outlier samples reacting with more than 20 proteins were excluded from further analysis. The association of reactivity calls of each protein with TB/NTBD status of TB suspects was determined by estimating odds ratio and 95% confidence interval.

ORGANISM(S): Homo sapiens

SUBMITTER: Shajo Kunnath-Velayudhan

PROVIDER: E-GEOD-19433 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Dynamic antibody responses to the Mycobacterium tuberculosis proteome.

Kunnath-Velayudhan Shajo S Salamon Hugh H Wang Hui-Yun HY Davidow Amy L AL Molina Douglas M DM Huynh Vu T VT Cirillo Daniela M DM Michel Gerd G Talbot Elizabeth A EA Perkins Mark D MD Felgner Philip L PL Liang Xiaowu X Gennaro Maria L ML

Proceedings of the National Academy of Sciences of the United States of America 20100728 33

Considerable effort has been directed toward controlling tuberculosis, which kills almost two million people yearly. High on the research agenda is the discovery of biomarkers of active tuberculosis (TB) for diagnosis and for monitoring treatment outcome. Rational biomarker discovery requires understanding host-pathogen interactions leading to biomarker expression. Here we report a systems immunology approach integrating clinical data and bacterial metabolic and regulatory information with high- ...[more]

PMID: 20668240

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Project description:Human infection with Mycobacterium tuberculosis results in a continuum of ill-defined, clinical manifestations with stable latent M. tuberculosis infection (LTBI) and severe active disease at the ends. Identifying different states of infection is of importance to tuberculosis (TB) control since risk of developing active disease varies among different asymptomatic states while infectiousness varies among patients with different bacterial burden. We investigated changes in proteome-scale antibody responses during disease progression in a non-human primate model of tuberculosis. We probed M. tuberculosis proteome microarrays with serial sera collected from three infection-outcome groups (active, reactivation, and latent). We found that each infection outcome is associated with characteristic changes in the antibody levels and number of antigenic targets, which suggested an association between antibody responses and bacillary burden. Additional proteome-scale serological profiling of > 400 human TB suspects established that antibody responses are positively associated with bacterial load. Thus tuberculosis-specific antibody levels and number of antigenic targets increases with disease progression. Serum samples collected from adult patients with suspected tuberculosis during a multi-site study was used to probe whole proteome microarrays. Subject recruitment was conducted under uniform protocols approved by the institutional ethics committee at each site. Final diagnosis of active TB was based on positive M. tuberculosis culture results. The active TB patients were further subdivided into smear-positive and negative disease based on results of Ziehl-Neelsen staining of sputum smears for acid fast bacilli. Active TB was excluded as a diagnosis (Non-TB Disease [NTBD] patients) based on having negative M. tuberculosis culture and smear results and on having an alternate diagnosis. All subjects were presumably negative for HIV infection given the very low incidence of HIV infection in the study sites. Sera from 169 TB and 242 NTBD patients were selected for microarray probing. The control sera (n = 14) which was used to generate negative control distribution for each protein were negative to latent M. tuberculosis infection, as indicated by negative results to tuberculin skin test.

Project description:More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, protein biomarkers to help diagnosis TB, which is critical in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One biomarker, serum CXCL1, met the World Health Organization’s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune mediators and four proteins corresponding to a subset of highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB.

Dataset Information

Profiling antibody responses to M. tuberculosis proteins in TB suspects’ sera using full-proteome microarrays.

Publications

Dynamic antibody responses to the Mycobacterium tuberculosis proteome.

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