Project description:To further understand the host immune factors involved in the progression from latent tuberculosis infection (LTBI) to active tuberculosis (TB) and identify the potential signatures for discriminating TB from LTBI, the genome-wide transcriptional profile of the Mycobacterium.Tuberculosis (M.TB)–specific antigens stimulated peripheral blood mononuclear cells (PBMCs) from active TB, LTBI and health controls (HCs) were performed. A total of 209- and 234- differentially expressed genes (Fold change > 4, P < 0.05) were detected in comparisons of TB vs. LTBI and TB vs. HCs, respectively. Nineteen differentially expressed genes with top fold change between TB and the other two groups were validated in the same RNA samples by real-time PCR, and showed 94.7% consistent expression pattern with microarray test.
Project description:When exposed to M. tuberculosis, 25-50% become infected, of which 10-20% having several symptoms. Currently, there is no marker for judging the efficacy of M tuberculosis (Mtb) treatment, and it is judged based on the appearance of subjective symptoms and radiological findings. In this study, we attempted to establish a marker to distinguish patients who relapsed after TB treatment from patients who had recurrence after TB treatment using the information of circulating miRNA expression. Thirty two patients with tuberculosis infection aged 18 years or older were enrolled. 16 patients had onset or recurrence of TB and 16 patients with latent tuberculosis infection (LTBI) with positive test for Interferon gamma release assays (IGRA) and no chest X-ray examination. Total RNA from serum in exosome-rich fraction was extracted and then miRNA expression analysis was performed using a next-generation sequencer and NGS data analyzed using miRDeep2. Using expression information of nine miRNAs, TB and LTBI could be separated with 71.8% accuracy. The odds ratio also was 6.16, and the p-value was 3.20e-02, respectively. The expression of hsa-miR-122-5p was significantly different between TB and LTBI (p <0.05). Here we established a novel method to efficiently differentiate LTBI from patients with tuberculosis infection. Since this method can be performed in a low invasive manner such as blood sampling, it is promising tool for utilizing follow-up of TB treatment.
Project description:Treatment of latent tuberculosis infection (LTBI) in individuals at greatest risk for reactivation is an important component of TB control and elimination strategies. However, blood biomarkers for monitoring LTBI treatment have not been identified. We performed a microarray analysis to identify the gene expression profile in whole blood from healthy individuals reactive to the tuberculin skin test (TST) with/without a positive in-house interferon-γ release assay (IGRA) before and after isoniazid (INH) prophylactic therapy.
Project description:We applied a cell population transcriptomics strategy to sorted human memory CD8 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and understand the phenotype of tuberculosis (TB)-specific T cells. We found a 41-gene signature that could discriminate between memory CD8 T cells from healthy LTBI subjects and noninfected controls. The gene signature was dominated by genes known to be associated with mucosal associated invariant T cells (MAITs) and reflected the lower frequency of MAITs observed in individuals with LTBI. There was no evidence for a conventional CD8 T cell specific signature between the two cohorts. We therefore investigated the MAITs in more detail in these cohorts. Phenotyping based on Vα7.2 and CD161 expression and MR1 tetramers revealed 2 distinct populations of CD8+Vα7.2+CD161+ T cells: MR1 tetramer+ and MR1 tetramer−, both of which had a distinct gene expression profile compared to CD8 memory T cells. Transcriptomic analysis of LTBI vs. noninfected individuals did not reveal significant differences for MR1 tetramer+ cells. However, gene expression of MR1 tetramer− cells showed a very different profile with large inter-individual diversity and a TB-specific signature. This was further strengthened by a more diverse TCR-α and -β repertoire of MR1 tetramer− cells as compared to MR1 tetramer+. Thus, cell population transcriptomics revealed a dominant MAIT signature in CD8 memory T cells that upon detailed investigation provided novel insights into the phenotype of different MAIT populations implicated in tuberculosis.
Project description:We applied a cell population transcriptomics strategy to sorted human memory CD8 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and understand the phenotype of tuberculosis (TB)-specific T cells. We found a 41-gene signature that could discriminate between memory CD8 T cells from healthy LTBI subjects and noninfected controls. The gene signature was dominated by genes known to be associated with mucosal associated invariant T cells (MAITs) and reflected the lower frequency of MAITs observed in individuals with LTBI. There was no evidence for a conventional CD8 T cell specific signature between the two cohorts. We therefore investigated the MAITs in more detail in these cohorts. Phenotyping based on Vα7.2 and CD161 expression and MR1 tetramers revealed 2 distinct populations of CD8+Vα7.2+CD161+ T cells: MR1 tetramer+ and MR1 tetramer−, both of which had a distinct gene expression profile compared to CD8 memory T cells. Transcriptomic analysis of LTBI vs. noninfected individuals did not reveal significant differences for MR1 tetramer+ cells. However, gene expression of MR1 tetramer− cells showed a very different profile with large inter-individual diversity and a TB-specific signature. This was further strengthened by a more diverse TCR-α and -β repertoire of MR1 tetramer− cells as compared to MR1 tetramer+. Thus, cell population transcriptomics revealed a dominant MAIT signature in CD8 memory T cells that upon detailed investigation provided novel insights into the phenotype of different MAIT populations implicated in tuberculosis.
Project description:The study aimed to define transcriptional signatures for detection of active TB (TB) compared to latent TB infection (LTBI) as well as to other diseases (OD) with similar clinical phenotypes in patients with and without HIV in a paediatric cohort from Kenya Transcriptional signatures were identified that distinguished active TB from LTBI, active TB from other diseases, and active TB from both LTBI and other diseases in HIV+/- patients.
Project description:The study aimed to define transcriptional signatures for detection of active TB (TB) compared to latent TB infection (LTBI) as well as to other diseases (OD) with similar clinical phenotypes in patients with and without HIV in two African adult populations. Transcriptional signatures were identified that distinguished active TB from LTBI, active TB from other diseases, and active TB from both LTBI and other diseases in HIV+/- patients.
Project description:The study aimed to define transcriptional signatures for detection of active TB (TB) compared to latent TB infection (LTBI) as well as to other diseases (OD) with similar clinical phenotypes in patients with and without HIV in two African paediatric populations. Transcriptional signatures were identified that distinguished active TB from LTBI, and active TB from other diseases.
Project description:To study the role of miRNAs in the transition from latent to active TB and to discover candidate biomarkers that may help predict TB progression, we have employed miRNA microarray expression profiling as a discovery platform to probe the transcriptome of peripheral blood mononuclear cells (PBMCs) with active TB, latent TB infection (LTBI), and healthy donors.Patients were recruited at the Shanghai Public Health Clinical Centre (Shanghai, China) from December, 2008 to May, 2009. The diagnosis of active TB was based on clinical presentation, chest radiography, and acid-fast stain of sputum smear.All the patients were HIV negative, as diagnosed by the Livzon Anti-HIV1/2 EIA Kit (Livzon Pharmaceutical Group Inc., Guangdong, China). Additional tests were also performed to detect hepatitis B virus (HBV) and hepatitis C virus (HCV) by using the Abbott AxSYM anti-HBsAg and HCV 3.0 antibody assay kit (Abbott Laboratories, Illinois) to exclude HBV- and HCV-positive patients (these 2 diseases are highly prevalent in China). Patients with a diabetes history were also excluded because diabetes could increase the risk of TB. Peripheral venous blood was drawn before treatment. Subjects with LTBI and healthy donors both without a history of clinical TB or other infectious diseases were recruited from the staff at the Shanghai Public Health Clinical Centre. TST and IGRA (T-SPOTM-BM-..TB, Oxford Immunuotec, Oxfordshire, U.K) results were used to distinguish the two groups. The LTBI group was TST-positive (TST>10 mm) and IGRA-positive while the healthy donors were TST-negative (TST<5 mm) and IGRA-negative. RNA of PBMC from 6 patients with active TB, 6 donors with Latent TB, and 3 healthy controls (total of 15 biologically independent samples) were used to perform Agilent Human miRNA (version 3) microarray , No replicates were included
Project description:The study aimed to define transcriptional signatures for detection of active TB (TB) compared to latent TB infection (LTBI) as well as to other diseases (OD) with similar clinical phenotypes in patients with and without HIV in two African adult populations. Transcriptional signatures were identified that distinguished active TB from LTBI, active TB from other diseases, and active TB from both LTBI and other diseases in HIV+/- patients. Adults were recruited from Cape Town, South Africa (n=300) and Karonga, Malawi (n=237) who were either HIV+ or HIV - with either active TB, LTBI or OD. Blood was collected into PAX gene tubes (PreAnalytiX). Total RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). Labeled cRNA was hybridized to Illumina Human HT-12 Beadchips. Data were analysed in R.