Abstract
RATIONALE: Sarcoidosis is a granulomatous disease of unknown etiology, although M. tuberculosis may play a role in the pathogenesis. The traditional view holds that inflammation in sarcoidosis is compartmentalized to involved organs. OBJECTIVES: To determine whether whole blood gene expression signatures reflect inflammatory pathways in the lung in sarcoidosis and whether these signatures overlap with tuberculosis. METHODS: We analyzed transcriptomic data from blood and lung biopsies in sarcoidosis and compared these profiles with blood transcriptomic data from tuberculosis and other diseases. MEASUREMENTS AND MAIN RESULTS: Applying machine learning algorithms to blood gene expression data, we built a classifier that distinguished sarcoidosis from health in derivation and validation cohorts (92% sensitivity, 92% specificity). The most discriminative genes were confirmed by quantitative PCR and correlated with disease severity. Transcript profiles significantly induced in blood overlapped with those in lung biopsies and identified shared dominant inflammatory pathways (e.g., Type-I/II interferons). Sarcoidosis and tuberculosis shared more overlap in blood gene expression compared with other diseases using the 86-gene signature reported to be specific for tuberculosis and the sarcoidosis signature presented herein, although reapplication of machine learning algorithms could identify genes specific for sarcoidosis. CONCLUSIONS: These data indicate that blood transcriptome analysis provides a noninvasive method for identifying inflammatory pathways in sarcoidosis, that these pathways may be leveraged to complement more invasive procedures for diagnosis or assessment of disease severity, and that sarcoidosis and tuberculosis share overlap in gene regulation of specific inflammatory pathways.