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.

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...

Blood transcriptomics in tuberculosis have revealed an IFN-inducible signature that diminished upon successful treatment, promising improved diagnostics and treatment monitoring, essential to eradicate tuberculosis. Sensitive radiography revealing lung abnormalities and blood transcriptomics have demonstrated heterogeneity in active tuberculosis patients and exposed asymptomatic latent individuals, suggesting a continuum of infection and immune states. Here, we describe the immune response to M. tuberculosis infection revealed using transcriptomics, and differences between clinical phenotypes of infection that may inform temporal changes in host immunity associated with evolving infection. We also review the diverse reduced blood transcriptional gene signatures that have been proposed for tuberculosis diagnosis and identification of at-risk asymptomatic individuals, and suggest novel approaches for developing such biomarkers for clinical use.

Blood transcriptomics have revealed major characteristics of the immune response in active TB, but the signature early after infection is unknown. In a unique clinically and temporally well-defined cohort of household contacts of active TB patients that progressed to TB, we define minimal changes in gene expression in incipient TB increasing in subclinical and clinical TB. While increasing with time, changes in gene expression were highest at 30 d before diagnosis, with heterogeneity in the response in household TB contacts and in a published cohort of TB progressors as they progressed to TB, at a bulk cohort level and in individual progressors. Blood signatures from patients before and during anti-TB treatment robustly monitored the treatment response distinguishing early and late responders. Blood transcriptomics thus reveal the evolution and resolution of the immune response in TB, which may help in clinical management of the disease.

Ventilator-associated pneumonia (VAP) is the most common infection in critically ill patients. Initial antibiotic therapy is often broad spectrum, which promotes antibiotic resistance so new techniques are under investigation to obtain early microbiological identification and quantification. This trial compares the performance of a new real-time quantitative molecular-based method with conventional culture in patients with suspected VAP. Patients with suspected VAP who were ventilated for at least 48 h were eligible. An endotracheal aspirate (ETA) and a bronchoalveolar lavage (BAL) were performed at each suspected VAP episode. Both samples were analysed by conventional culture and molecular analysis. For the latter, bacterial DNA was extracted from each sample and real-time PCR were run. In all, 120 patients were finally included; 76% (91) were men; median age was 65 years, and clinical pulmonary infection score was ≥6 for 73.5% (86) of patients. A total of 120 BAL and 103 ETA could be processed and culture results above the agreed threshold were obtained for 75.0% (90/120) of BAL and 60.2% (62/103) of ETA. The main isolated bacteria were Staphylococcus aureus, Pseudomonas aeruginosa and Haemophilus influenzae. Performance was 89.2% (83.2%-93.6%) sensitivity and 97.1% (96.1%-97.9%) specificity for BAL samples and 71.8% (61.0%-81.0%) sensitivity and 96.6% (95.4%-97.5%) specificity for ETA samples when the molecular biology method was compared with conventional culture method (chosen as reference standard). This new molecular method can provide reliable quantitative microbiological data and is highly specific with good sensitivity for common pathogens involved in VAP.