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.

Many vaccines induce protective immunity via antibodies. Recent studies have used systems biological approaches to determine signatures that predict vaccine immunity in humans, but whether there is a ‘universal signature’ that can predict antibody responses to any vaccine, is unknown. Here we performed systems analyses of immune responses to the meningococcal polysaccharide and conjugate vaccines in healthy adults, in the broader context of our previous studies with the yellow fever and two influenza vaccines. To achieve this, we performed a large-scale network integration of public human blood transcriptomes, and systems-scale databases in specific biological contexts, and deduced a set of blood transcription modules. These modules revealed distinct transcriptional signatures of antibody responses to different classes of vaccines providing key insights into primary viral, protein recall and anti-polysaccharide responses. These results illuminate the early transcriptional programs orchestrating vaccine immunity in humans, and demonstrate the power of integrative network modeling.

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.

Summary Little is known on the functional differences of the human skin myeloid DC subsets, epidermal CD207+ Langerhans cells (LCs) and dermal CD14+ DCs. We show that CD14+ DCs prime CD4+ T cells into cells that induce naïve B cells to switch isotype and become plasma cells. LCs preferentially induce the differentiation of CD4+ T cells secreting Th2 cytokines and are remarkably efficient at priming and crosspriming naïve CD8+ T cells. A third DC population, CD14-CD207-CD1a+ DC population, which resides in the dermis can activate CD8+ T cells better than CD14+ DCs but less efficiently than LCs. Thus, human skin display three DC subsets, two of them i.e. CD14+ DCs and LCs, display functional specializations; the preferential activation of humoral or cellular immunity respectively.