Science for Health
07 June 2012
Tuberculosis is the leading bacterial cause of death worldwide, particularly in the developing world. Rapid diagnosis and treatment are critical for the prevention of transmission. The global burden of active TB occurs on a background of quiescent or latent infection affecting one third of the world’s population. A key challenge is distinguishing patients with active TB from patients with overlapping clinical symptoms who have latent TB infection. However, current methods are insufficiently accurate.
Traditional serological analysis of single circulating proteins is notoriously unreliable for TB diagnosis but patterns of circulating proteins could provide an accessible readout of pathophysiological status. Proteomic analysis using Surface Enhanced Laser Desorption Ionisation Time of Flight (SELDI-ToF) mass spectrometry is a high throughput profiling methodology, which enables rapid comparison of protein patterns from large numbers of patients. It is based on the principle that distinctive combinations of circulating proteins characterize different disease states. It can provide a molecular snapshot defining disease state that can be used to develop point-of-care diagnostics.
Delmiro Fernandez-Reyes (pictured), and collaborators at Imperial College, USA and Peru, hypothesized that plasma proteomic differences would also distinguish patients with active TB from those without active TB. Plasma and clinical data were obtained prospectively from patients attending community TB clinics in Peru and from household contacts. Plasma was subjected to high-throughput proteomic profiling by mass spectrometry. Statistical pattern recognition methods were used to define mass spectral patterns that distinguished patients with active TB from symptomatic controls with or without latent infection.
The researchers found that a distinctive pattern of plasma proteins distinguishes patients with active TB from non-TB patients with overlapping clinical features, even in the presence of latent infection. This both reinforces and substantially extends their previous findings which first showed that proteomic patterns could be used as a diagnostic approach for active TB.
We have now shown that the proteomic pattern does not merely reflect the presence of TB infection per se. Rather, it can be used to identify active TB even in a highly TB-endemic setting with high prevalence of both respiratory symptoms and background latent TB infection. Translation of biomarkers derived from this study into a robust and affordable point-of-care format will have significant implications for recognition and control of active TB in high prevalence settings.
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Clustering of patients with active TB and symptomatic controls with or without latent TB using principal component analysis. a. Crude plasma spectra; b. Fractionated plasma spectra.
Gurjinder Sandhu, Francesca Battaglia, Barry K. Ely, Dimitrios Athanasakis, Rosario Montoya, Teresa Valencia, Robert H. Gilman, Carlton A. Evans, Jon S. Friedland, Delmiro Fernandez-Reyes, Daniel D. Agranoff. (2012)
Discriminating active from latent tuberculosis in patients presenting to community clinics.
PLoS ONE 7(5): e38080. Full-text.
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