Project description:Purpose: Accurate diagnosis of complete inactivation of tuberculosis lesions is still a challenge with respect to sputum-negative tuberculosis. RNA-sequencing was conducted to uncover potential lncRNA indicators of metabolic activity in tuberculosis lesions. Methods: Lung tissues with high metabolic activity (PET-high) and low metabolic activity (PET-low) demonstrated by fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) were collected from five sputum-negative tuberculosis patients for RNA-sequencing. Differentially expressed mRNAs and lncRNAs were identified, and their correlations were evaluated to constructed lncRNA-mRNA co-expression network. Afterwards, cis- or trans-targets of differentially expressed lncRNAs were predicted, followed by establishing lncRNA-target-pathway network, in which lncRNAs and mRNAs with high degrees were measured by quantitative real-time PCR using samples collected from 11 patients. Prediction efficiencies of lncRNA indicators were assessed by receiver operating characteristic curve analysis, Bioinformatics analysis was performed for potential lncRNAs. Results: In total, 386 mRNAs and 44 lncRNAs were identified to be differentially expressed. Differentially expressed mRNAs in lncRNA-mRNA co-expression network were significantly associated with the fibrillar collagen, platelet-derived growth factor binding, and leukocyte migration involved in inflammatory response. One cis-target gene and 440 trans-target genes were predicted for differentially expressed lncRNAs. Seven genes (C1QB, CD68, CCL5, CCL19, MMP7, HLA-DMB, and CYBB) and two lncRNAs (ENST00000429730.1 and MSTRG.93125.4) were validated to be significantly up-regulated. The area under the curve of ENST00000429730.1 and MSTRG.93125.4 was 0.750 and 0.813, respectively. Conclusion: Two lncRNAs ENST00000429730.1 and MSTRG.93125.4 might be considered as potential indicators of metabolic activity in tuberculosis lesions for sputum-negative tuberculosis.

Project description:Human infection with Mycobacterium tuberculosis results in a continuum of ill-defined, clinical manifestations with stable latent M. tuberculosis infection (LTBI) and severe active disease at the ends. Identifying different states of infection is of importance to tuberculosis (TB) control since risk of developing active disease varies among different asymptomatic states while infectiousness varies among patients with different bacterial burden. We investigated changes in proteome-scale antibody responses during disease progression in a non-human primate model of tuberculosis. We probed M. tuberculosis proteome microarrays with serial sera collected from three infection-outcome groups (active, reactivation, and latent). We found that each infection outcome is associated with characteristic changes in the antibody levels and number of antigenic targets, which suggested an association between antibody responses and bacillary burden. Additional proteome-scale serological profiling of > 400 human TB suspects established that antibody responses are positively associated with bacterial load. Thus tuberculosis-specific antibody levels and number of antigenic targets increases with disease progression. Serum samples collected from adult patients with suspected tuberculosis during a multi-site study was used to probe whole proteome microarrays. Subject recruitment was conducted under uniform protocols approved by the institutional ethics committee at each site. Final diagnosis of active TB was based on positive M. tuberculosis culture results. The active TB patients were further subdivided into smear-positive and negative disease based on results of Ziehl-Neelsen staining of sputum smears for acid fast bacilli. Active TB was excluded as a diagnosis (Non-TB Disease [NTBD] patients) based on having negative M. tuberculosis culture and smear results and on having an alternate diagnosis. All subjects were presumably negative for HIV infection given the very low incidence of HIV infection in the study sites. Sera from 169 TB and 242 NTBD patients were selected for microarray probing. The control sera (n = 14) which was used to generate negative control distribution for each protein were negative to latent M. tuberculosis infection, as indicated by negative results to tuberculin skin test.

Project description:Despite more than a century fighting against tuberculosis, the World Health Organisation has estimated that around 1.7 million people died of tuberculosis in 2016 and over a quarter of the world’s population is infected. One of the critical hurdles for stopping tuberculosis transmission is early and effective diagnosis of patients with the active pulmonary disease. Although important innovations in molecular diagnosis have been recently developed (e.g. Xpert MTB/RIF, Cepheid Inc., USA), there are no suitable tests for population screening at point-of-care. The current tuberculosis diagnosis pipeline presents a highly variable performance and requires access to reference laboratory facilities. A non-sputum based rapid test with high specificity and sensitivity could save ~400,000 lives per year. Therefore, new biomarkers for diagnosis are urgently required for identifying patients with early symptoms and to expedite treatment. Variable sensitivity and specificity can be overcome using a combination of multiple biomarkers (5). Proteins, as ultimate biological effectors, are ideal candidates for diagnostic biomarkers; consequently, proteomic studies are a crucial platform for biomarker discovery in tuberculosis. This work aims to develop a multi-marker panel for tuberculosis diagnosis with high performance capable of differentiating tuberculosis patients from relevant controls. Quantitative Multidimensional Protein Identification Technology (qMudPIT) is applied for biomarker discovery identifying candidates for early diagnosis of tuberculosis. The multidimensional method optimised in this work led to the identification of 5022 plasma proteins and 3577 quantified proteins using iTRAQ labelling. Known and completely novel markers for active tuberculosis in plasma were identified including a peptide derived from Mycobacterium tuberculosis. Complementary statistical and bioinformatic analysis were applied to prioritise candidates for validation in one or two independent cohorts. The plasma proteomic profile here described represents a power strategy for biomarker discovery and the panel proposed has the potential to be translated to a rapid test and which might contribute to tuberculosis control.

Project description:The aim of the project was to define transcriptional signatures in whole blood of TB patients (before drug treatment) and healthy controls to distinguish the signature of Latent and Active TB patients from each other and from healthy controls. This will help in diagnosis of active tuberculosis which normally relies on culture of the bacilli, which can take up to 6 wks, and sometimes the bacilli cannot be obtained from sputum thus requiring invasive techniques obtaining bronchoalveolar lavage (BAL). In some cases the bacill cannot be grown from sputum or BAL. Secondly the aim was to determine whether Latent patients have a homogeneous or heterogeneous signature, one may expect the latter since it is not possible to determine by the present tests (Tuberculin skin test - TST - or MTb antigen responsiveness of blood cells to produce IFN-gamma - IGRA assay) whether the mycobacteria has been cleared, is still present but is controlled, or if patients are recently infected or reactivated and will develop active TB. The latter situation may be determined if Latent patients have a blood transcriptional signature similar to that in Active patients. The transcriptional signature in 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 as to the protective factors controlling the infection, thus important for monitoring vaccine development. A further aim was to examine the transcriptional blood profile of active TB patients at the time of recruitment (before drug treatment) and then subsequently at specific time points after drug treatment to determine whether the signature is extingsuihed with treatment and when. London is a site of intermediate burden of TB - the study was initiated in London, across a broad range of ethnicities to obtain a robust signature that could be used in different developing countries where there is a high burden TB disease. Experimental design : Whole blood collected in tempus tubes from patients with different spectra of TB disease and healthy controls. All patients were sampled prior to the initiation of any antimycobacterial therapy. Active Pulmonary TB: PTB - All patients 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. All 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). 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); who were also negative by IGRA (as described above). In this dataset: PTB n= 13; LTB n = 17; BCG+ n = 6, BCG-, n =6. Experimental variables : Patient group: Active PTB; Latent TB, Healthy controls (BCG vaccinated and unvaccinated). ethnicity - a wide range of ethnic groups is represented. However all controls in this dataset are broadly Caucasian/White. The active PTB group incorporates a range of smear positive and smear negative disease and a spectrum of disease extent/severity.

			Action	DRS
	ERR10225400.fastq.gz	Fastqsanger.gz
	ERR10225500_1.fastq.gz	Fastqsanger.gz
	ERR10225500_2.fastq.gz	Fastqsanger.gz
	ERR10225401.fastq.gz	Fastqsanger.gz
	ERR10225501_1.fastq.gz	Fastqsanger.gz

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