The immune response to Mycobacterium tuberculosis is complex and incompletely characterized, hindering the development of new diagnostics, treatments and vaccines. A systems-biology approach has now been used to compare the transcriptional profiles of expressed genes in patients with active and latent tuberculosis and in healthy controls. Active pulmonary disease is shown to correlate with a neutrophil-driven interferon-inducible gene profile. About 10% of people with latent tuberculosis go on to develop the active disease. In this study, 10% of latent patients displayed a transcriptional signature similar to that in the active disease, suggesting that the biomarker may be useful in both prognosis and diagnosis. Here, the human immune response to infection with Mycobacterium tuberculosis has been characterized by transcriptional profiling. The results show that active tuberculosis correlates with a particular transcriptional signature that is dominated by a neutrophil-driven interferon-inducible gene profile. The study provides a broad range of transcriptional biomarkers with potential as diagnostic and prognostic tools to combat the tuberculosis epidemic. Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis, is a major cause of morbidity and mortality worldwide. Efforts to control it are hampered by difficulties with diagnosis, prevention and treatment1,2. Most people infected with M. tuberculosis remain asymptomatic, termed latent TB, with a 10% lifetime risk of developing active TB disease. Current tests, however, cannot identify which individuals will develop disease3. The immune response to M. tuberculosis is complex and incompletely characterized, hindering development of new diagnostics, therapies and vaccines4,5. Here we identify 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 after treatment. A subset of patients with latent TB had signatures similar to those in patients with active TB. We also identify a specific 86-transcript signature that discriminates 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 IFN-αβ signalling. Comparison with transcriptional signatures in purified cells and flow cytometric analysis suggest that this TB signature reflects changes in cellular composition and altered gene expression. Although an IFN-inducible 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 underappreciated role of type I IFN-αβ signalling in the pathogenesis of TB, 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.

The informal settlements of the Global South are the least prepared for the pandemic of COVID-19 since basic needs such as water, toilets, sewers, drainage, waste collection, and secure and adequate housing are already in short supply or non-existent. Further, space constraints, violence, and overcrowding in slums make physical distancing and self-quarantine impractical, and the rapid spread of an infection highly likely. Residents of informal settlements are also economically vulnerable during any COVID-19 responses. Any responses to COVID-19 that do not recognize these realities will further jeopardize the survival of large segments of the urban population globally. Most top-down strategies to arrest an infectious disease will likely ignore the often-robust social groups and knowledge that already exist in many slums. Here, we offer a set of practice and policy suggestions that aim to (1) dampen the spread of COVID-19 based on the latest available science, (2) improve the likelihood of medical care for the urban poor whether or not they get infected, and (3) provide economic, social, and physical improvements and protections to the urban poor, including migrants, slum communities, and their residents, that can improve their long-term well-being. Immediate measures to protect residents of urban informal settlements, the homeless, those living in precarious settlements, and the entire population from COVID-19 include the following: (1) institute informal settlements/slum emergency planning committees in every urban informal settlement; (2) apply an immediate moratorium on evictions; (3) provide an immediate guarantee of payments to the poor; (4) immediately train and deploy community health workers; (5) immediately meet Sphere Humanitarian standards for water, sanitation, and hygiene; (6) provide immediate food assistance; (7) develop and implement a solid waste collection strategy; and (8) implement immediately a plan for mobility and health care. Lessons have been learned from earlier pandemics such as HIV and epidemics such as Ebola. They can be applied here. At the same time, the opportunity exists for public health, public administration, international aid, NGOs, and community groups to innovate beyond disaster response and move toward long-term plans.

A major impediment to tuberculosis control in Africa is the difficulty in diagnosing active tuberculosis (TB), particularly in the context of HIV infection. We hypothesized that a unique host blood RNA transcriptional signature would distinguish TB from other diseases (OD) in HIV-infected and -uninfected patients, and that this could be the basis of a simple diagnostic test. Adult case-control cohorts were established in South Africa and Malawi of HIV-infected or -uninfected individuals consisting of 584 patients with either TB (confirmed by culture of Mycobacterium tuberculosis [M.TB] from sputum or tissue sample in a patient under investigation for TB), OD (i.e., TB was considered in the differential diagnosis but then excluded), or healthy individuals with latent TB infection (LTBI). Individuals were randomized into training (80%) and test (20%) cohorts. Blood transcriptional profiles were assessed and minimal sets of significantly differentially expressed transcripts distinguishing TB from LTBI and OD were identified in the training cohort. A 27 transcript signature distinguished TB from LTBI and a 44 transcript signature distinguished TB from OD. To evaluate our signatures, we used a novel computational method to calculate a disease risk score (DRS) for each patient. The classification based on this score was first evaluated in the test cohort, and then validated in an independent publically available dataset (GSE19491). In our test cohort, the DRS classified TB from LTBI (sensitivity 95%, 95% CI [87–100]; specificity 90%, 95% CI [80–97]) and TB from OD (sensitivity 93%, 95% CI [83–100]; specificity 88%, 95% CI [74–97]). In the independent validation cohort, TB patients were distinguished both from LTBI individuals (sensitivity 95%, 95% CI [85–100]; specificity 94%, 95% CI [84–100]) and OD patients (sensitivity 100%, 95% CI [100–100]; specificity 96%, 95% CI [93–100]). Limitations of our study include the use of only culture confirmed TB patients, and the potential that TB may have been misdiagnosed in a small proportion of OD patients despite the extensive clinical investigation used to assign each patient to their diagnostic group. In our study, blood transcriptional signatures distinguished TB from other conditions prevalent in HIV-infected and -uninfected African adults. Our DRS, based on these signatures, could be developed as a test for TB suitable for use in HIV endemic countries. Further evaluation of the performance of the signatures and DRS in prospective populations of patients with symptoms consistent with TB will be needed to define their clinical value under operational conditions. Please see later in the article for the Editors' Summary Tuberculosis (TB), caused by Mycobacterium tuberculosis, is curable and preventable, but according to the World Health Organization (WHO), in 2011, 8.7 million people had symptoms of TB (usually a productive cough and fever) and 1.4 million people—95% from low- and middle-income countries—died from this infection. Worldwide, TB is also the leading cause of death in people with HIV. For over a century, diagnosis of TB has relied on clinical and radiological features, sputum microscopy, and tuberculin skin testing but all of these tests have major disadvantages, especially in people who are also infected with HIV (HIV/TB co-infection) in whom results are often atypical or falsely negative. Furthermore, current tests cannot distinguish between inactive (latent) and active TB infection. Therefore, there is a need to identify biomarkers that can differentiate TB from other diseases common to African populations, where the burden of the HIV/TB pandemic is greatest. Previous studies have suggested that TB may be associated with specific transcriptional profiles (identified by microarray analysis) in the blood of the infected patient (host), which might make it possible to differentiate TB from other conditions. However, these studies have not included people co-infected with HIV and have included in the differential diagnosis diseases that are unrepresentative of the range of conditions common to African patients. In this study of patients from Malawi and South Africa, the researchers investigated whether blood RNA expression could distinguish TB from other conditions prevalent in African populations and form the basis of a diagnostic test for TB (through a process using transcription signatures). The researchers recruited patients with suspected TB attending one clinic in Cape Town, South Africa between 2007 and 2010 and in one hospital in Karonga district, Malawi between 2007 and 2009 (the training and test cohorts). Each patient underwent a series of tests for TB (and had a blood test for HIV) and was diagnosed as having TB if there was microbiological evidence confirming the presence of Mycobacterium tuberculosis. At recruitment, each patient also had blood taken for microarray analysis and following this assessment, the researchers selected minimal transcript sets that distinguished TB from latent TB infection and TB from other diseases, even in HIV-infected individuals. In order to help form the basis of a simple, low cost, diagnostic test, the researchers then developed a statistical method for the translation of multiple transcript RNA signatures into a disease risk score, which the researchers then checked using a separate cohort of South African patients (the independent validation cohort). Using these methods, after screening 437 patients in Malawi and 314 in South Africa, the researchers recruited 273 patients to the Malawi cohort and 311 adults to the South African cohort (the training and test cohorts). Following technical failures, 536 microarray samples were available for analysis. The researchers identified a set of 27 transcripts that could distinguish between TB and latent TB and a set of 44 transcripts that could distinguish TB from other diseases. These multi-transcript signatures were then used to calculate a single value disease risk score for every patient. In the test cohorts, the disease risk score had a high sensitivity (95%) and specificity (90%) for distinguishing TB from latent TB infection (sensitivity is a measure of true positives, correctly identified as such and specificity is a measure of true negatives, correctly identified as such) and for distinguishing TB from other diseases (sensitivity 93% and specificity 88%). In the independent validation cohort, the researchers found that patients with TB could be distinguished from patients with latent TB infection (sensitivity 95% and specificity 94%) and also from patients with other diseases (sensitivity 100% and specificity 96%). These findings suggest that a distinctive set of RNA transcriptional signatures forming a disease risk score might provide the basis of a diagnostic test that can distinguish active TB from latent TB infection (27 signatures) and also from other diseases (44 signatures), such as pneumonia, that are prevalent in African populations. There is a concern that using transcriptional signatures as a clinical diagnostic tool in resource poor settings might not be feasible because they are complex and costly. The relatively small number of transcripts in the signatures described here may increase the potential for using this approach (transcriptional profiling) as a clinical diagnostic tool using a single blood test. In order to make most use of these findings, there is an urgent need for the academic research community and for industry to develop innovative methods to translate multi-transcript signatures into simple, cheap tests for TB suitable for use in African health facilities. Please access these websites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001538.

Paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) is a frequent complication of antiretroviral therapy in resource-limited countries. We aimed to assess whether a 4-week course of prednisone would reduce morbidity in patients with paradoxical TB-IRIS without excess adverse events. A randomized, double-blind, placebo-controlled trial of prednisone (1.5 mg/kg per day for 2 weeks then 0.75 mg/kg per day for 2 weeks). Patients with immediately life-threatening TB-IRIS manifestations were excluded. The primary combined endpoint was days of hospitalization and outpatient therapeutic procedures, which were counted as one hospital day. One hundred and ten participants were enrolled (55 to each arm). The primary combined endpoint was more frequent in the placebo than the prednisone arm {median hospital days 3 [interquartile range (IQR) 0-9] and 0 (IQR 0-3), respectively; P = 0.04}. There were significantly greater improvements in symptoms, Karnofsky score, and quality of life (MOS-HIV) in the prednisone vs. the placebo arm at 2 and 4 weeks, but not at later time points. Chest radiographs improved significantly more in the prednisone arm at weeks 2 (P = 0.002) and 4 (P = 0.02). Infections on study medication occurred in more participants in prednisone than in placebo arm (27 vs. 17, respectively; P = 0.05), but there was no difference in severe infections (2 vs. 4, respectively; P = 0.40). Isolates from 10 participants were found to be resistant to rifampicin after enrolment. Prednisone reduced the need for hospitalization and therapeutic procedures and hastened improvements in symptoms, performance, and quality of life. It is important to investigate for drug-resistant tuberculosis and other causes for deterioration before administering glucocorticoids.

Globally 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. To determine if blood transcriptional signatures change in response to antituberculosis treatment and could act as early biomarkers of a successful response. Blood 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. An 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. Significant 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.

Many low and middle-income countries are experiencing colliding epidemics of chronic infectious (ID) and non-communicable diseases (NCD). As a result, the prevalence of multiple morbidities (MM) is rising. We conducted a study to describe the epidemiology of MM in a primary care clinic in Khayelitsha. Adults with at least one of HIV, tuberculosis (TB), diabetes (DM), and hypertension (HPT) were identified between Sept 2012-May 2013 on electronic databases. Using unique patient identifiers, drugs prescribed across all facilities in the province were linked to each patient and each drug class assigned a condition. These 4 diseases accounted for 45% of all prescription visits. Among 14364 chronic disease patients, HPT was the most common morbidity (65%). 22.6% of patients had MM, with an increasing prevalence with age; and a high prevalence among younger antiretroviral therapy (ART) patients (26% and 30% in 18-35 yr and 36–45 year age groups respectively). Among these younger ART patients with MM, HPT and DM prevalence was higher than in those not on ART. We highlight the co-existence of multiple ID and NCD. This presents both challenges (increasing complexity and the impact on health services, providers and patients), and opportunities for chronic diseases screening in a population linked to care. It also necessitates re-thinking of models of health care delivery and requires policy interventions to integrate and coordinate management of co-morbid chronic diseases.