Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (M. tuberculosis), is a major cause of morbidity and mortality worldwide and efforts to control TB are hampered by difficulties with diagnosis, prevention and treatment 1,2. Most people infected with M. tuberculosis remain asymptomatic, termed latent TB, with a 10% lifetime risk of developing active TB disease, but current tests cannot identify which individuals will develop disease 3. The immune response to M. tuberculosis is complex and incompletely characterized, hindering development of new diagnostics, therapies and vaccines 4,5. We identified a whole blood 393 transcript signature for active TB in intermediate and high burden settings, correlating with radiological extent of disease and reverting to that of healthy controls following treatment. A subset of latent TB patients had signatures similar to those in active TB patients. We also identified a specific 86-transcript signature that discriminated active TB from other inflammatory and infectious diseases. Modular and pathway analysis revealed that the TB signature was dominated by a neutrophil-driven interferon (IFN)-inducible gene profile, consisting of both IFN-γ and Type I IFNαβ signalling. Comparison with transcriptional signatures in purified cells and flow cytometric analysis, suggest that this TB signature reflects both changes in cellular composition and altered gene expression. Although an IFN signature was also observed in whole blood of patients with Systemic Lupus Erythematosus (SLE), their complete modular signature differed from TB with increased abundance of plasma cell transcripts. Our studies demonstrate a hitherto under-appreciated role of Type I IFNαβ signalling in TB pathogenesis, which has implications for vaccine and therapeutic development. Our study also provides a broad range of transcriptional biomarkers with potential as diagnostic and prognostic tools to combat the TB epidemic.

Summary The analysis of patient blood transcriptional profiles offers a means to investigate immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically-relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease datasets. Mapping changes in gene expression at the module-level generated disease-specific transcriptional fingerprints which provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

The survival of young children with sickle cell disease (SCD) has improved, but less is known about older children and adolescents. We studied the Dallas Newborn Cohort (DNC) to estimate contemporary 18-year survival for newborns with SCD and document changes in the causes and ages of death over time. We also explored whether improvements in the quality of medical care were temporally associated