In this study we have shown 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 LTBI. This both reinforces and substantially extends our previous findings where we first showed that proteomic patterns could be used as a diagnostic approach for active TB [12] . 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 LTBI. (a,b) active TB vs. all symptomatic controls using crude or pre-fractionated plasma respectively; (c,d) active TB vs. symptomatic controls with latent TB using crude or pre-fractionated plasma respectively; (e,f) active TB vs. symptomatic controls without latent TB using crude or pre-fractionated plasma respectively. The ROCs are derived from 1000 random train/test resamplings of the data. Error bars show standard deviations. The Area Under the Curve (AUC) is shown in the centre of each plot. doi:10.1371/journal.pone.0038080.g005 Table 2 . Discrimination of active from latent tuberculosis in symptomatic patients. The ability to discriminate rapidly in a symptomatic patient between active TB and non-tuberculous disease has profound implications for both individual clinical management and TB control programs [21] . For example, current diagnostic limitations frequently result in many patients in resource-poor settings being treated empirically for community acquired pneumonia before eventual diagnosis of active TB. This may lead to on-going transmission during the interval preceding diagnosis as well as greater individual morbidity. The alternative strategy of empirical anti-TB chemotherapy is sometimes employed, but cost, toxicity and logistics often preclude this. Adjuncts to conventional microbiology for diagnosis of active TB in widespread use include the TST and IGRAs. The use of TSTs in the diagnosis of active TB in high prevalence settings is greatly limited by its poor specificity for active TB as reactivity is also seen in LTBI, previous BCG vaccination and exposure to environmental mycobacteria. Nor has the recent introduction of IGRAs into clinical practice resolved this key diagnostic issue. This is because of their inability to distinguish active TB from LTBI [6] and frequent false negative results in acute active TB [22] , limitations which are especially problematic in high prevalence settings [23] . Thus a diagnostic that overcomes these limitations is urgently required and would be a major advance in the management of the global TB pandemic. Recently it has been reported that a TNF-alpha + TB-specific CD4+response can be used to differentiate latent infection from active TB but the sensitivity was just 67% [24] . Moreover, that study relied on polychromatic flow cytometry limiting the feasibility of being translated in high prevalence settings. In contrast, our approach provides improved accuracy, 87%, by detecting relevant protein biomarkers in plasma. Despite the discovery-phase of our approach using sophisticated proteomic methodologies, the identification of relevant plasma proteins leads to a clear translational path for antibody-based point-of-care devices that can be used to measure these plasma proteins in the future.