Leia o texto publicado no Guardian e ao final o texto da Nature
TB genetic signature may help predict active cases
Study finds molecular fingerprint that could help doctors target patients with active form of tuberculosis
Scientists have uncovered a molecular fingerprint in the blood of patients with the active form tuberculosis. The finding could help doctors predict which infected patients will become ill, and which will carry the infection without effects.
TB, a disease primarily of the lungs, kills up to 1.7 million people a year. Approximately one-third of the world's population has been exposed or infected with Mycobacterium tuberculosis, but only 10% of these people become ill. "The question is why and what is it that determines which people actually get active TB," said Anne O'Garra an immunologist at the MRC National Institute for Medical Research in London, who led the latest study.
The latent form of TB can be diagnosed by either a skin or blood test that looks for a reaction to the bacterium that causes the disease, but are unable to predict which infected people will develop the active disease.
O'Garra's team analysed blood samples from more than 400 people and identified hundreds of molecules that were present in a specific pattern only in the blood of those with the active form of TB. These molecules are the products of certain genes, acting in the presence of the TB bacterium and, as such, provide a genetic signature in the blood for the active form of the disease.
"This signature reflected the severity and extent of the lung disease," said Matthew Berry of Imperial College, co-author of the study published today in Nature. "When we treated the patients successfully, this signature disappears. This really gives the potential that these approaches could be developed and used to diagnose people with TB and also to monitor them during their treatment and highlight people earlier who are not responding."
The study, which analysed people in London and Cape Town, found that 10% of the people with latent TB infections had the genetic fingerprint for the active disease. If further research confirms the results, the findings could be used to predict which people with latent TB are at greatest risk of developing the active version, and therefore receive earlier treatment.
O'Garra's research also provides valuable insights into how TB might cause disease in the body. "The signature that is in active TB and in 10% of latent is dominated by genes turned on by a molecule called type-1 interferon," she said. "This is well-known to have antiviral activity, however it can really aggravate bacterial infections such as TB. Finding these molecules expressed in TB suggest that they may be contributing to make the disease work."
The molecules induced by the type-1 interferon were present in cells called neutrophils, which are abundant in the blood but have received little attention from TB researchers until now. "Our cells are suggesting that type-1 interferon and neutrophils maybe contributing to the extent of damage in TB disease," O'Garra said.
Artigo da Nature
An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis
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 treatment 1 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 disease 3. The immune response to M. tuberculosis is complex and incompletely characterized, hindering development of new diagnostics, therapies and vaccines 4 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.
Blood transcriptional profiling has improved diagnosis and understanding of disease pathogenesis 6 7 8 9. Such a comprehensive unbiased survey will provide insights into the immunopathogenesis of TB, leading to advances in control of this complex disease. Genome-wide transcriptional profiles were generated from blood from patients with active TB (before treatment), patients with latent TB and healthy controls ( Supplementary Fig. 1, and Supplementary Tables 1 and 2). A distinct 393-transcript signature was defined in patients with active TB (training set, London), using a combination of expression-level and statistical filters and hierarchical clustering ( Supplementary Fig. 2a, b(i), Supplementary Table 3 and Methods). We then applied the 393-transcript list to two independent cohorts (UK test set; South African validation set). Hierarchical clustering of transcriptional profiles showed patients with active TB cluster independently of latent TB and healthy controls, in both intermediate (London) and high-burden (South Africa) regions, with a significant association between cluster and study group (Fisher’s exact test: P = 0.00001365, UK (Fig. 1a); P = 5.79 × 10−10, South Africa (Fig. 1b)). This was independent of ethnicity, age or gender ( Supplementary Fig. 2b(ii), c, d). The transcriptional profiles of 10–25% of patients with latent TB (5/21 test set, 3/31 validation set) clustered with patients with active TB (Fig. 1a, b). The k-nearest neighbour class prediction, using the 393-transcript list, gave a sensitivity of 61.67%, specificity of 93.75% and an indeterminate rate of 1.9% for the test set ( Supplementary Table 4), with five patients with latent TB classified as active TB and four patients with active TB misclassified. In the validation set the sensitivity was 94.12%, specificity 96.67% and indeterminate rate 7.8%. The UK patients were of diverse ethnicity, potentially infected with different M. tuberculosis lineages, suggesting the signature may be independent of bacterial clade, although molecular typing was not available. The proportion of latent patients having a transcriptional signature similar to that of active TB was equal to the expected frequency of patients at risk of progression to active disease 3, potentially identifying patients with latent TB with sub-clinical active disease or higher burden latent infection.
Four out of 21 patients with active TB in the test set, also misclassified by class prediction, clustered with healthy controls and patients with latent TB (filled circle, hash symbol, and filled square and diamond in Fig. 1a), demonstrating molecular heterogeneity that could reflect clinical variance. To address this, radiographic extent of disease was assessed by three physicians, blinded to clinical diagnosis and transcriptional profile ( Supplementary Fig. 3) 10. The median ‘molecular distance to health’ 11, a composite of the number of transcripts in a profile that significantly differ from the healthy control baseline, and the degree of that difference, was significantly higher for those with advanced disease than for those with minimal or no disease (Fig. 1c). We show for the first time that the transcriptional signature in blood correlates with extent of disease in patients with active TB, and reflects changes at the site of disease. The transcriptional signature was diminished in patients with active TB after 2 months, and completely extinguished by 12 months after treatment, with ‘molecular distance to health’ at 12 months significantly lower than at baseline pretreatment (Fig. 1d and Supplementary Fig. 4), reflecting radiographic improvement. Thus the blood transcriptional signature of patients with active TB could be used to monitor efficacy of treatment, and is reflective of the host response to infection with M. tuberculosis.
The 393-gene active TB signature may reflect common inflammatory responses evoked during many diseases. We therefore identified a TB-specific 86-gene whole-blood signature through analysis of significance 12, compared with patients with other bacterial and inflammatory diseases ( Supplementary Fig. 5, and Supplementary Tables 5 and 6). This 86-gene signature was then tested against patients normalized to their own controls from seven independent data sets by class prediction (k-nearest neighbours) (Fig. 2a). Sensitivities in the TB training and validation sets were 92% and 90% respectively, distinguishing active TB from other diseases with a pooled specificity of 83% ( Supplementary Table 7). As with the 393-gene signature, this 86-gene signature was diminished in response to treatment (Fig. 2b) and reflected the same heterogeneity in identical samples from patients ( Supplementary Fig. 6).
To identify functional components of the transcriptional host response during active TB, we used a modular data-mining strategy, using sets of genes that are coordinately expressed in different diseases and defined as specific modules, often demonstrating coherent functional relationships through unbiased literature profiling 7. The blood modular signature of patients with active TB compared with healthy controls (filtering out only undetected transcripts, α = 0.01, in at least two individuals) was similar in all three TB data sets (Fig. 3a and Supplementary Fig. 7), confirming the reproducibility of the transcriptional signature. The modular TB signature revealed decreased abundance of B-cell (Module, M1.3) and T-cell (M2.8) transcripts and increased abundance of myeloid-related transcripts (M1.5 and M2.6). The largest proportion of transcripts changing in a given module in TB was within the IFN-inducible module (M3.1; 75–82% of IFN-module transcripts (Fig. 3a and Supplementary Fig. 7)). Because a type I IFN-inducible signature, linked with disease pathogenesis, has been demonstrated in peripheral blood mononuclear cells from patients with SLE 13 14, we compared whole-blood modular signatures from patients with other diseases. Patients with SLE demonstrated over-representation of the IFN-inducible module (M3.1 (Fig. 3a) quantified in Supplementary Fig. 8), but displayed a plasma-cell-related module absent in TB (M1.1 (Fig. 3a and Supplementary Fig. 8)). The blood modular signature from patients with group A Streptococcus or Staphylococcus infection, or Still’s disease, showed minimal to no change in the IFN-inducible module (M3.1) but marked over-representation of the neutrophil-related module (M2.2), distinguishing these diseases from TB (Fig. 3a and Supplementary Fig. 8). Thus the IFN-inducible signature is not common to all inflammatory responses, but is preferentially induced during some diseases, potentially reflecting protection or pathogenesis. Although SLE and TB share common inflammatory components such as an IFN-inducible response, the overall pattern of transcriptional changes (Fig. 3a) and their amplitude ( Supplementary Fig. 8) distinguishes one disease from another.
The TB blood-transcriptional signature could represent altered cell composition or changes in gene expression in discrete cellular populations. Percentages of B cells, and of T cells carrying the CD4 and CD8 antigens, assessed by flow cytometry, were significantly diminished in patients with active TB, with reduced numbers of total and central memory T cells carrying the CD4 antigen (Fig. 3b and Supplementary Fig. 9a, b), in keeping with previous studies 15. That the reduction in T-cell transcripts revealed by the modular analysis (Fig. 3a) resulted from changes in cell numbers in the blood, was further confirmed because expression of these transcripts in purified T cells from the same individuals did not differ between patients with TB and healthy controls ( Supplementary Fig. 9c). In contrast, the increase in myeloid transcripts (M1.5, M2.6 (Fig. 3a and Supplementary Fig. 7)) in the blood of patients with active TB was not accounted for by changes in monocytes (CD14+, CD16−) or neutrophils (CD16+, CD14−) although inflammatory monocytes (CD14+, CD16+) were increased (Fig. 3c and Supplementary Fig. 10a), as in other diseases 16. Increased abundance of myeloid transcripts was less pronounced in purified monocytes (CD14+) ( Supplementary Fig. 10b), which suggests involvement of other cells.
Pathway analysis confirmed IFN signalling as the most significantly over-represented pathway in the 393-gene signature (Fisher’s exact test, Benjamini–Hochberg correction for multiple testing, P < 0.0000001 ( Supplementary Fig. 11)). Genes downstream of both IFN-γ and type I IFN-αβ receptor signalling were significantly over-represented in blood from patients with active TB (Fig. 4a–c). IFN-α2 and IFN-γ proteins were not elevated in serum from patients with active TB, although the IFN-inducible chemokine CXCL10 (IP10) was significantly increased ( Supplementary Fig. 11c–e).
Although IFN-γ is protective during immune responses to intracellular pathogens, including mycobacteria 4 17 18, the role of type I IFN-αβ is less clear. Type I IFN signalling is crucial for defence against viral infections but may be detrimental during bacterial 19, including mycobacterial, infections 20 21. Absence of IFN-αβ signalling in mice improved outcome after infection with highly virulent 20 21 22, but not less virulent, strains of M. tuberculosis 23. Highly virulent strains of M. tuberculosis induce higher levels of type I IFNs 20. There are reports of TB reactivation during IFN-α treatment for hepatitis D viral infection 24. The increase in type I IFN-αβ-inducible transcripts in the blood of patients with active TB (Fig. 4c), correlating with disease severity, provides the first data in human disease to support a role for type I IFNs in the pathogenesis of TB. These IFN-inducible transcripts were overexpressed in purified blood neutrophils and to a lesser extent monocytes, but not T cells carrying the CD4 and CD8 antigens, from patients with active TB, compared with healthy controls (Fig. 4d; top to bottom: OAS1, IFI6, IFI44, IFI44L, OAS3, IRF7, IFIH1, IFI16, IFIT3, IFIT2, OAS2, IFITM3, IFITM1, GBP1, GBP5, STAT1, GBP2, TAP1, STAT1, STAT2, IFI35, TAP2, CD274, SOCS1, CXCL10, IFIT5). Neutrophils are the predominant cell type infected with rapidly replicating M. tuberculosis in patients with TB 25. Evidence from genetically susceptible mice suggests that neutrophils contribute to pathology during infection with M. tuberculosis 26. Our studies support a role for neutrophils in the pathogenesis of TB, which may result from over-activation by IFN-γ and type I IFNs.
Earlier microarray studies, limited by small numbers of patients and custom microarrays, reported a small number of genes in blood associated with TB 27 28. Here we provide the first complete description of the human blood transcriptional signature of TB. The signature of active TB, observed in 10–20% of patients with latent TB, may identify those individuals who will develop active disease, facilitating targeted preventative therapy, but longitudinal studies are needed to assess this. That the TB signature is dominated by type I IFN-signalling and reflects extent of lung disease, may indicate the process leading to disease susceptibility. These data improve our understanding of the fundamental biology of TB and may offer future leads for diagnosis and treatment.
Whole blood was collected into Tempus tubes (Applied Biosystems) from patients as follows: those with active TB (confirmed by culture for M. tuberculosis); those with latent TB (defined by a positive tuberculin-skin test (TST) (London)) and/or a positive M. tuberculosis antigen-specific IFN-γ release assay (IGRA); healthy controls (recruited in London; TST/IGRA negative). RNA was extracted from whole blood and purified (by Dynabeads, Invitrogen) neutrophils, monocytes and T cells carrying the CD4 and CD8 antigens, and genome-wide transcriptional profiles were generated using Illumina HT12 V3 Beadarrays, and analysed using Genespring GX (see Methods). Calculations of ‘molecular distance to health’ 11, transcriptional modular analysis 7 and analysis of significance 12 were performed as previously described. Pathway analysis was performed using Ingenuity (Ingenuity Systems). Multiplex serum protein measurement was performed using the Milliplex Multi-Analyte Profiling System by Millipore UK. Flow cytometry was performed on a Beckman Coulter Cyan using Summit software version 3.02, followed by FlowJo analysis.
Full methods accompany this paper on the web.
M.P.R.B., D.C., O.M.K and A.O’G. designed the study on TB with input from J.B. and R.J.W. and for other diseases with input from V.P. and O.R.; M.P.R.B., S.A.A.B., T.O., K.A.W., J.J.C., A.M., R.B. and O.M.K. recruited, sampled and collected data about patients; M.P.R.B., R.B., A.M. and C.M.G. processed whole blood for microarray experiments with help from J.S.; C.G. performed blood-cell subset separations and processing for microarray experiments with help from J.S.; M.P.R.B., C.M.G. and Z.X. performed microarray data analysis, with advice and input from J.S., D.C. and V.P.; M.P.R.B. and Z.X. performed Ingenuity, modular and ‘molecular distance to health’ analyses; M.P.R.B. performed multiplex serum analyses; F.W.McN. performed flow cytometry analysis; D.C., V.P. and A.O’G. supervised data analysis; M.P.R.B. and D.B. performed statistical analysis; M.P.R.B., S.A.A.B., R.D. and O.M.K performed analyses of radiology; A.O’G. and M.P.R.B. wrote the manuscript, with early input from C.M.G., F.W.McN., J.B., D.C. and J.S., and subsequently all authors provided advice and approved the final manuscript.
Downloaded by firstname.lastname@example.org on 18th Aug 2010 from 126.96.36.199