Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases
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ABSTRACT:
SUBMITTER: Petrilli J
PROVIDER: S-EPMC7745039 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We report the accuracy of various existing blood transcriptional signatures in distinguishing TB and LTBI individuals in a South Indian cohort
2018-01-24 | GSE101705 | GEO
Project description:Background:Although the incidence of tuberculosis (TB) has dropped substantially, it still is a serious threat to human health. And in recent years, the emergence of resistant bacilli and inadequate disease control and prevention has led to a significant rise in the global TB epidemic. It is known that the cause of TB is Mycobacterium tuberculosis infection. But it is not clear why some infected patients are active while others are latent. Methods:We analyzed the blood gene expression profiles of 69 latent TB patients and 54 active pulmonary TB patients from GEO (Transcript Expression Omnibus) database. Results:By applying minimal redundancy maximal relevance and incremental feature selection, we identified 24 signature genes which can predict the TB activation. The support vector machine predictor based on these 24 genes had a sensitivity of 0.907, specificity of 0.913, and accuracy of 0.911, respectively. Although they need to be validated in a large independent dataset, the biological analysis of these 24 genes showed great promise. Conclusion:We found that cytokine production was a key process during TB activation and genes like CYBB, TSPO, CD36, and STAT1 worth further investigation.
| S-EPMC6363485 | biostudies-literature
Project description:This series regroups different datasets (training set, test set, validation set, longitudinal set, separated cell set) to identify and characterise a specific transcriptional signature for patients with active TB, distinct from patients with latent TB and healthy controls. The training set dataset was used to identify a whole blood transcriptional signature for active TB patients in London, across a range of ethnicity. This signature was then validated in an independent cohort of patients, also recruited in London (the test set), and then further confirmed in an additional independent cohort recruited in Cape Town, South Africa (validation set), in order to confirm that the defined signature was present in both high (Cape Town, South Africa) and medium incidence regions (London, UK). The longitudinal dataset was then used to explore how successful TB treatment modifies this transcriptional signature. The separated cell set compares the transcriptional profiles in purified cell subsets (neutrophils, monocytes and T cells) to assess which cell types are contributing to the whole blood signature, and in what way. These studies may ultimately help to improve the diagnosis of active tuberculosis which normally relies on culture of the bacilli, which can take up to 6 weeks, and sometimes the bacilli cannot be obtained from sputum thus requiring invasive techniques such as bronchoalveolar lavage (BAL). In some cases (30%) the bacill cannot be grown from sputum or BAL. Any diagnostic tool would need to be valid across a range of ethnicities, and be valid in both high and low incidence countries. A further aim was to determine whether latent TB patients have a distinct homogeneous or heterogeneous signature, since it is not currently possible to determine using present tests (Tuberculin skin test - TST - or MTb antigen responsiveness of blood cells to produce IFN-gamma - IGRA assay) whether the mycobacteria have been cleared, are still present but are controlled by an active immune response, or to predict which patients will develop active TB. Defining heterogeneity in the latent TB patients would be an important step in developing diagnostics which could detect those most at risk of developing active TB, and thus enable targeted preventive therapy. The latter situation may be determined if Latent patients have a blood transcriptional signature similar to that in Active patients. The transcriptional signature in whole blood and cell subsets from Active TB patients may also provide information as to the factors leading to immunopathogenesis, thus possibly identifying therapeutic targets. The transcriptional profile in latent TB may give information regarding protective factors controlling the infection, important for vaccine development. Finally, definition of a transcriptional signature which responds to therapy could facilitate the development of surrogate biomarkers for drug or vaccine studies. Since any active TB signature may reflect common inflammatory responses evoked during many diseases, we also performed analysis of significance, comparing transcriptional profiles from patients with TB to those from patients with other bacterial and inflammatory diseases to identify a TB specific signature. The resulting signature was then tested against patients normalized to their own controls from 7 independent datasets: TB (Training and Validation Sets), Staphylococcus infection, Group A Streptococcus infection, Still's disease, and adult and pediatric SLE. This SuperSeries is composed of the SubSeries listed below.
2010-08-11 | GSE19491 | GEO
Project description:This series regroups different datasets (training set, test set, validation set, longitudinal set, separated cell set) to identify and characterise a specific transcriptional signature for patients with active TB, distinct from patients with latent TB and healthy controls. The training set dataset was used to identify a whole blood transcriptional signature for active TB patients in London, across a range of ethnicity. This signature was then validated in an independent cohort of patients, also recruited in London (the test set), and then further confirmed in an additional independent cohort recruited in Cape Town, South Africa (validation set), in order to confirm that the defined signature was present in both high (Cape Town, South Africa) and medium incidence regions (London, UK). The longitudinal dataset was then used to explore how successful TB treatment modifies this transcriptional signature. The separated cell set compares the transcriptional profiles in purified cell subsets (neutrophils, monocytes and T cells) to assess which cell types are contributing to the whole blood signature, and in what way. These studies may ultimately help to improve the diagnosis of active tuberculosis which normally relies on culture of the bacilli, which can take up to 6 weeks, and sometimes the bacilli cannot be obtained from sputum thus requiring invasive techniques such as bronchoalveolar lavage (BAL). In some cases (30%) the bacill cannot be grown from sputum or BAL. Any diagnostic tool would need to be valid across a range of ethnicities, and be valid in both high and low incidence countries. A further aim was to determine whether latent TB patients have a distinct homogeneous or heterogeneous signature, since it is not currently possible to determine using present tests (Tuberculin skin test - TST - or MTb antigen responsiveness of blood cells to produce IFN-gamma - IGRA assay) whether the mycobacteria have been cleared, are still present but are controlled by an active immune response, or to predict which patients will develop active TB. Defining heterogeneity in the latent TB patients would be an important step in developing diagnostics which could detect those most at risk of developing active TB, and thus enable targeted preventive therapy. The latter situation may be determined if Latent patients have a blood transcriptional signature similar to that in Active patients. The transcriptional signature in whole blood and cell subsets from Active TB patients may also provide information as to the factors leading to immunopathogenesis, thus possibly identifying therapeutic targets. The transcriptional profile in latent TB may give information regarding protective factors controlling the infection, important for vaccine development. Finally, definition of a transcriptional signature which responds to therapy could facilitate the development of surrogate biomarkers for drug or vaccine studies. Since any active TB signature may reflect common inflammatory responses evoked during many diseases, we also performed analysis of significance, comparing transcriptional profiles from patients with TB to those from patients with other bacterial and inflammatory diseases to identify a TB specific signature. The resulting signature was then tested against patients normalized to their own controls from 7 independent datasets: TB (Training and Validation Sets), Staphylococcus infection, Group A Streptococcus infection, Still's disease, and adult and pediatric SLE. This SuperSeries is composed of the following subset Series: GSE19435: Transcriptional profiles in Blood of patients with Tuberculosis - Longitudinal Study GSE19439: Blood Transcriptional Profiles in Active and Latent Tuberculosis UK (Training Set) GSE19442: Blood Transcriptional Profiles of TB in South Africa GSE19443: Blood Transcriptional Profiles of Active TB (UK Test Set Separated) GSE19444: Blood Transcriptional Profiles of Active and Latent TB (UK Test Set) GSE22098: Whole blood transcriptional profiles of patients with active tuberculosis (TB) and other inflammatory and infectious diseases Active Pulmonary TB: PTB - All patients were confirmed by isolation of Mycobacterium Tuberculosis on culture of sputum or bronchoalvelolar lavage fluid. Latent TB: LTB - All patients were screened at a tuberculosis clinic, being either new entrants to the UK from endemic countries or being household contacts of infectious cases, or in the case of the validation set recruited in South Africa, were residents of a high incidence country. All UK patients were positive by tuberculin skin test (>14mm if BCG vaccinated, >5mm if not vaccinated) and were also positive by Interferon-Gamma Release assay(IGRA); specifically Quantiferon Gold In-Tube Assay (Cellestis, Australia). The South African latent TB patients were all positive by Interferon-Gamma Release assay (IGRA); specifically Quantiferon Gold In-Tube Assay. Latent patients had no clinical, radiological or microbiological evidence of active infection and were asymptomatic. Healthy controls - these were volunteers without exposure to TB who were negative by both tuberculin skin test (<15mm if BCG vaccinated, <6mm if unvaccinated); and IGRA (as described above). Experimental variables : Patient group: Active PTB; Latent TB, Healthy controls (BCG vaccinated and unvaccinated). Ethnicity - a wide range of ethnic groups is represented. The active PTB group incorporates a range of smear positive and smear negative disease and a spectrum of disease extent/severity. Experimental methods: Whole blood was collected into Tempus tubes (Applied Biosystems, Foster City, CA, USA) and stored between -20degrees Celsius and -80 degrees Celsius before RNA extraction. For the training set cohort, and the active TB patients baseline samples in the longitudinal cohort, total RNA was isolated from whole blood using the PerfectPure RNA Blood kit (5 PRIME Inc, Gaithersburg, MD, USA). For the separated cell samples, total RNA was isolated using the Qiagen RNeasy Mini Kit. For all other cohorts Total RNA was isolated from whole blood using the MagMAX 96 well RNA isolation kit (Applied Biosystems, Foster City, CA, USA). Isolated total RNA was then globin reduced using the GLOBINclear 96-well format kit (Ambion, Austin, TX, USA) according to the manufacturer's instructions. Total and globin-reduced RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). Biotinylated, amplified RNA targets (cRNA) were then prepared from the globin-reduced RNA using the Illumina CustomPrep RNA amplification kit (Ambion, Austin, TX, USA). Labeled cRNA was hybridized overnight to Sentrix HT12 V3 BeadChip arrays (>48,000 probes, Illumina Inc, San Diego, CA, USA), washed, blocked, stained and scanned on an Illumina BeadStation 500 following the manufacturer's protocols. Illumina's BeadStudio version 2 software was used to generate signal intensity values from the scans, substract background, and scale each microarray to the median average intensity for all samples (per-chip normalisation). This normalised data was used for all subsequent data analysis.
2010-08-11 | E-GEOD-19491 | biostudies-arrayexpress
Project description:Rapid diagnosis of active Mycobacterium tuberculosis (Mtb) infection remains a clinical and laboratory challenge. We have analyzed the cytokine profile (interferon-? (IFN-?), tumor necrosis factor-? (TNF-?) and interleukin-2 (IL-2)) of Mtb-specific T cells by polychromatic flow cytometry. We studied Mtb-specific CD4+ T cell responses in subjects with latent Mtb infection and active tuberculosis disease. The results showed substantial increase in the proportion of single-positive TNF-? Mtb-specific CD4+ T cells in subjects with active disease, and this parameter was the strongest predictor of diagnosis of active disease versus latent infection. We validated the use of this parameter in a cohort of 101 subjects with tuberculosis diagnosis unknown to the investigator. The sensitivity and specificity of the flow cytometry-based assay were 67% and 92%, respectively, the positive predictive value was 80% and the negative predictive value was 92.4%. Therefore, the proportion of single-positive TNF-? Mtb-specific CD4+ T cells is a new tool for the rapid diagnosis of active tuberculosis disease.
| S-EPMC6570988 | biostudies-literature
Project description:Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional immune system is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray gene expression analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma receptor 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and natural killer cell activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB. Three infection stages. Two-color arrays with independent swap design
2010-11-23 | E-GEOD-25534 | biostudies-arrayexpress
Project description:Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional immune system is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray gene expression analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma receptor 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and natural killer cell activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB.
2010-11-23 | GSE25534 | GEO
Project description:Bacterial culture of M. tuberculosis (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change > 1.2; p < 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.
| S-EPMC8282789 | biostudies-literature
Project description: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. 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. The immune response to M. tuberculosis is complex and incompletely characterized, hindering development of new diagnostics, therapies and vaccines. The goals of this study include: 1. Identify a 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; 2. Identify a specific transcript signature that discriminated active TB from other inflammatory and infectious diseases; 3. Classify TB signature using modular and pathway analysis tools. Three milliliters of whole blood was collected in Tempus tubes from 12 pediatric streptococcus, 40 pediatric staphylococcus, 31 still’s disease, 82 pediatric systemic lupus erythematosus (SLE) and 28 adult SLE patients. RNA was extracted and globin reduced. Labeled cRNA was hybridized to Illumina Human HT-12 Beadchips. Healthy controls were included to match patients’ demographic data. Genespring software was used to analyze active TB transcript signatures, comparing with healthy controls and other inflammatory and infectious diseases.
2010-08-11 | E-GEOD-22098 | biostudies-arrayexpress
Project description:Changes in expression of membrane antigens may accompany the transition of Mycobacterium tuberculosis (Mtb) from 'dormant' to 'active' states. We have determined whether antibody and T cell responses to Mtb membrane (MtM)-associated antigens, especially the latency-induced protein alpha crystallin (Acr), can discriminate between latent tuberculosis infection (LTBI) and active TB (ATB) disease. Study subjects comprised a previously described cohort of healthcare workers (HCWs, n = 43) and smear-positive ATB patients (n = 10). HCWs were further categorized as occupational contacts (OC, n = 30), household contacts of TB (HC, n = 8) and cured TB (CTB, n = 5). Levels (?OD) of serum antibody isotypes (IgG, IgA and IgM) were determined by ELISA and blood T cell proliferative responses were determined by flow cytometry using Ki67 protein as marker for DNA synthesis. Antibodies to MtM and Acr were predominantly IgG and their levels in HCWs and ATB did not differ significantly. However, HCWs showed a significantly higher level of anti-MtM IgM and a significantly lower level of anti-Acr IgA antibodies than the ATB patients. Also, a larger proportion of HCWs showed a high (>1) ?ODAcr/?ODMtM ratio for IgG. HCWs also showed a higher, though not significantly different from ATB, avidity of anti-MtM (IgG) antibodies. A higher proportion of HCWs (35% of OC, 62.5% of HC and 20% of CTB), compared with ATB (10%) showed a positive T cell response to Acr along with significant difference (P <0.05) between HC and ATB. A significant correlation (r = 0.60, P <0.0001) was noted between T cell responses of HCWs towards Acr and MtM (reported earlier by us) and both responses tended to decline with rising exposure to the infection. Even so, positive responses to Acr (38.5%) were significantly lower than to MtM (92%). Neither antibody nor T cell responses to either antigen appeared affected by BCG vaccination or reactivity to tuberculin. Results of the study suggest that the levels of IgM antibodies to MtM, IgA antibodies to Acr and proliferative T cell responses to both the antigens can potentially discriminate between LTBI and active TB disease. They also underscore the necessity of SOPs for antibody assays.
| S-EPMC6994005 | biostudies-literature