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.

RationaleNew approaches to define factors underlying the immunopathogenesis of pulmonary diseases including sarcoidosis and tuberculosis are needed to develop new treatments and biomarkers. Comparing the blood transcriptional response of tuberculosis to other similar pulmonary diseases will advance knowledge of disease pathways and help distinguish diseases with similar clinical presentations.ObjectivesTo determine the factors underlying the immunopathogenesis of the granulomatous diseases, sarcoidosis and tuberculosis, by comparing the blood transcriptional responses in these and other pulmonary diseases.MethodsWe compared whole blood genome-wide transcriptional profiles in pulmonary sarcoidosis, pulmonary tuberculosis, to community acquired pneumonia and primary lung cancer and healthy controls, before and after treatment, and in purified leucocyte populations.Measurements and Main ResultsAn Interferon-inducible neutrophil-driven blood transcriptional signature was present in both sarcoidosis and tuberculosis, with a higher abundance and expression in tuberculosis. Heterogeneity of the sarcoidosis signature correlated significantly with disease activity. Transcriptional profiles in pneumonia and lung cancer revealed an over-abundance of inflammatory transcripts. After successful treatment the transcriptional activity in tuberculosis and pneumonia patients was significantly reduced. However the glucocorticoid-responsive sarcoidosis patients showed a significant increase in transcriptional activity. 144-blood transcripts were able to distinguish tuberculosis from other lung diseases and controls.ConclusionsTuberculosis and sarcoidosis revealed similar blood transcriptional profiles, dominated by interferon-inducible transcripts, while pneumonia and lung cancer showed distinct signatures, dominated by inflammatory genes. There were also significant differences between tuberculosis and sarcoidosis in the degree of their transcriptional activity, the heterogeneity of their profiles and their transcriptional response to treatment.

RationaleGlobally there are approximately 9 million new active tuberculosis cases and 1.4 million deaths annually. Effective antituberculosis treatment monitoring is difficult as there are no existing biomarkers of poor adherence or inadequate treatment earlier than 2 months after treatment initiation. Inadequate treatment leads to worsening disease, disease transmission and drug resistance.ObjectivesTo determine if blood transcriptional signatures change in response to antituberculosis treatment and could act as early biomarkers of a successful response.MethodsBlood transcriptional profiles of untreated active tuberculosis patients in South Africa were analysed before, during (2 weeks and 2 months), at the end of (6 months) and after (12 months) antituberculosis treatment, and compared to individuals with latent tuberculosis. An active-tuberculosis transcriptional signature and a specific treatment-response transcriptional signature were derived. The specific treatment response transcriptional signature was tested in two independent cohorts. Two quantitative scoring algorithms were applied to measure the changes in the transcriptional response. The most significantly represented pathways were determined using Ingenuity Pathway Analysis.ResultsAn active tuberculosis 664-transcript signature and a treatment specific 320-transcript signature significantly diminished after 2 weeks of treatment in all cohorts, and continued to diminish until 6 months. The transcriptional response to treatment could be individually measured in each patient.ConclusionsSignificant changes in the transcriptional signatures measured by blood tests were readily detectable just 2 weeks after treatment initiation. These findings suggest that blood transcriptional signatures could be used as early surrogate biomarkers of successful treatment response.

IL-10 regulates the balance of an immune response between pathogen clearance and immunopathology. We show here that Mycobacterium tuberculosis (Mtb) infection in the absence of IL-10 (IL-10−/− mice) results in reduced bacterial loads in the lung. This reduction was preceded by an accelerated and enhanced IFN-γ response in the lung, an increased influx of CD4+ T cells into the lung, and enhanced production of chemokines and cytokines, including CXCL10 and IL-17, in both the lung and the serum. Neutralization of IL-17 affected neither the enhanced production of CXCL10 nor the accumulation of IFN-γ-producing T cells in the lungs, but led to reduced numbers of granulocytes in the lung and reduced bacterial loads in the spleens of Mtb-infected mice. This suggests that IL-17 may contribute to dissemination of Mtb.

Transcriptional profiles and host response biomarkers are used increasingly to investigate the severity, subtype and pathogenesis of disease. We now describe whole blood mRNA signatures, local and systemic immune mediator concentrations in 131 adults hospitalised with influenza from which extensive clinical and investigational data were obtained by the MOSAIC consortium. Signatures reflecting interferon-related antiviral pathways were common up to day 4 of symptoms in cases not requiring mechanical ventilatory support; in those needing mechanical ventilation an inflammatory, activated neutrophil and cell stress/death (‘bacterial’) pattern was seen, even early in disease. Identifiable bacterial co-infection was not necessary for this ‘bacterial’ signature but could enhance its development, while attenuating the early ‘viral’ signature. Our findings emphasise the importance of timing and severity in the interpretation of host responses to acute viral infection, and identify specific patterns of immune activation that may enable the development of novel diagnostic and therapeutic tools for severe influenza.

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise the information from the entire transcriptome, which includes overabundance of type I interferon-inducible genes and underabundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals or those with acute viral and bacterial infections. We suggest that methods targeting gene selection across multiple discriminant modules can improve the development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach, we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

Tuberculosis is classically divided into states of latent infection and active disease. Using combined positron emission and computed tomography in 35 asymptomatic, antiretroviral therapy naïve, HIV-1 infected adults with latent tuberculosis, we identified ten individuals with pulmonary abnormalities suggestive of subclinical, active disease who were significantly more likely to progress to clinical disease. Our findings challenge the conventional two-state paradigm and may aid future identification of biomarkers predictive of progression.

Whole blood transcriptional signatures distinguishing patients with active tuberculosis from asymptomatic latently infected individuals have been described but, no consensus exists for the composition of optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. We have recapitulated a blood transcriptional signature of active tuberculosis using RNA-Seq, previously reported by microarray that discriminates active tuberculosis from latently infected and healthy individuals, also validated in an independent cohort. We show that an advanced modular approach, which preserves and presents a signature of the entire transcriptome, can better discriminate patients with active tuberculosis from both latently infected and acute viral and bacterial infections. We suggest a method of targeted gene selection across modules for constructing diagnostic biomarkers, more representative of the transcriptome that overcomes some limitations of existing techniques. Finally, we utilise the modular approach to demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.Tuberculosis (TB) is the leading cause of global mortality from an infectious disease. In 2016, there were 6.3 million new cases of TB disease and 1.67 million deaths and its diagnosis is problematic 1 . However, clinical disease represents one end of a spectrum of infection states. It is estimated that up to one third of all individuals worldwide have been infected with the causative pathogen, Mycobacterium tuberculosis, but the vast majority remain clinically asymptomatic with no radiological or microbiological evidence for active infection. This is termed latent TB infection (LTBI) and conceptually denotes a state in which M. tuberculosis persists within its host, while maintaining viability with the potential to replicate and cause symptomatic disease. Indeed, LTBI represents the primary reservoir for future incident TB, with 90% of all TB cases estimated to arise from reactivation of existing infection 1,2 . The risk of incident TB arising from existing LTBI is heterogeneous, poorly characterised and modifiable with anti-tuberculous treatment. Modelling studies indicate effective TB prevention to significantly reduce future TB incidence requires policies directed at the identification and treatment of LTBI 3 . However, implementation of mass screening programmes for this purpose are severely constrained by the size of the target population. Transformative advances in diagnostic tools that can effectively stratify TB risk in the LTBI population are therefore implicit to the realisation of systematic screening.The basis for LTBI heterogeneity rests with the limited scope of the tools we have available to identify the state. LTBI is inferred solely through evidence that immune sensitization has occurred, by the tuberculin skin test (TST) or the M. tuberculosis antigen-specific interferon-gamma (IFN-g) release assay (IGRA). Although these tests are both sensitive and specific for identi...