Blood transcriptomics for diagnosis, risk, and treatment monitoring in tuberculosis reveal the evolution and resolution of TB disease: Does one signature capture all?
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE157657 | GEO | 2021/09/04
REPOSITORIES: GEO
Similar Datasets
Project description:Changes in the blood transcriptome upon treatment were studied in a cohort of 42 latent tuberculosis (TB) subjects and 8 active TB subiects. Samples were collected at diagnosis (prior the start of treatment) and post treatment and gene expression studied with Illumina microarrays. We hypothesize that individuals with latent TB at risk of developing active disease are immunologically closer to those with active TB and will thus display a blood transcriptomic signature similar to active TB subjects upon treatment. This signature should significantly differ from the one mounted by latent TB individuals at low risk of progression. Thus, monitoring blood transcriptomic changes following anti-TB therapy might inform on which latent TB subjects should be prioritized for receiving therapeutic intervention in order to prevent further transmission.
2021-12-08 | GSE152532 | GEO
Project description:Blood transcriptomics for diagnosis, risk, and treatment monitoring in tuberculosis reveal the evolution and resolution of TB disease: Does one signature capture all?
| PRJNA662344 | ENA
Project description:Mycobacterium tuberculosis (Mtb) antigen-specific cellular response is promising for detectionof Mtb infection, but not efficient for diagnosis of TB. We firstly identified 16 TB disease-specific protein markers measured in the culture supernatant of Mtb-stimulated whole blood using a 640 human proteins array, the highest throughput antibody-based protein array available at the time when we did this study. Potential TB-related proteins were then analyzed across three different patient cohorts comprised of healthy controls, LTBI, non-TB pneumonia, and TB patients to evaluate how the biomarkers performed in diagnosing TB in the real clinical setting. The data finally reveal an eight-protein biosignature of TB.
2019-06-25 | GSE133249 | GEO
Project description:Mycobacterium tuberculosis (Mtb) antigen-specific cellular response is promising for detectionof Mtb infection, but not efficient for diagnosis of TB. We firstly identified 16 TB disease-specific protein markers measured in the culture supernatant of Mtb-stimulated whole blood using a 640 human proteins array, the highest throughput antibody-based protein array available at the time when we did this study. Potential TB-related proteins were then analyzed across three different patient cohorts comprised of healthy controls, LTBI, non-TB pneumonia, and TB patients to evaluate how the biomarkers performed in diagnosing TB in the real clinical setting. The data finally reveal an eight-protein biosignature of TB.
2019-12-12 | GSE141848 | GEO
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.
2010-08-11 | GSE22098 | GEO
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.
2012-07-05 | E-GEOD-30722 | biostudies-arrayexpress
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:Understanding the immune response to tuberculosis requires greater knowledge of humoral responses. To characterize antibody targets and the effect of disease parameters on target recognition, we developed a systems immunology approach that integrated detection of antibodies against the entire Mycobacterium tuberculosis proteome, bacterial metabolic and regulatory pathway information, and patient data. Probing ~4,000 M. tuberculosis proteins with sera from >500 suspected tuberculosis patients worldwide revealed that antibody responses recognized ~10% of the bacterial proteome. This result defines the immunoproteome of M. tuberculosis, which is rich in membrane-associated and extracellular proteins. Most serum reactivity during active tuberculosis focused onto ~0.5% of the proteome. Within this pool, which is selectively enriched for extracellular proteins (but not for membrane-associated proteins), relative target preference varied among patients. The shift in relative M. tuberculosis protein reactivity observed with active tuberculosis defines the evolution of the humoral immune response during M. tuberculosis infection and disease. Peripheral blood was collected from prospectively enrolled TB suspects among subjects seeking care for pulmonary symptoms at clinics associated with national TB control programs in 11 countries. M. tuberculosis proteome microarrays representing 4099 bacterial protein spots were probed with sera from 561 TB suspects. Based on the final diagnosis, they belonged to two classes: TB (n=254) and Non-TB Disease (n=307). In addition, healthy individuals negative to Latent TB Infection (LTBI neg, n=64) were also tested (negative control sera). Each serum was tested with a single array and no replicate experiments were performed. The reactivity of a serum to an M. tuberculosis protein (reactivity call) was defined based on the distribution of negative control sera intensity for that protein using Z-statistics. Based on the distribution of reactivity calls, 27 outlier samples reacting with more than 20 proteins were excluded from further analysis. The association of reactivity calls of each protein with TB/NTBD status of TB suspects was determined by estimating odds ratio and 95% confidence interval.
2010-07-13 | E-GEOD-19433 | biostudies-arrayexpress
Project description:Globally, the anti-tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success rates are lower among people with diabetes mellitus (DM). Predicting treatment failure early after diagnosis would allow early treatment adaptation and may improve global TB control. Methods Samples were collected in a longitudinal cohort study of adult TB patients with or without concomitant DM from South Africa and Indonesia to characterize whole blood transcriptional profiles before and during anti-TB treatment, using unbiased RNA-Seq and targeted gene dcRT-MLPA. Findings We report differences in whole blood transcriptome profiles between patients with a good versus poor anti-TB treatment outcome, which were observed before initiation of treatment and throughout treatment. An eight-gene and 22-gene blood transcriptional signatures distinguished patients with a good treatment outcome from patients with a poor treatment outcome at diagnosis (AUC=0·815) or two weeks (AUC=0·834) after initiation of anti-TB treatment, respectively. Importantly, high accuracy was obtained by cross-validating this signature in an external cohort (AUC=0·749). Interpretation These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM.
2022-08-02 | GSE193979 | GEO
Project description:Whole blood transcriptional profiles of patients with (1) active pulmonary ['AdjuVIT active TB' and 'New active pulmonary'] and (2) extrapulmonary TB ['New active-extrapulmonary'] at time of diagnosis, (3) long-term recovery after treatment for active pulmonary TB ['AdjuVIT active TB'], (4) febrile illnesses presenting to hospital ['Fever mixed infection'] and (5) febrile pneumonia ['Fever pneumonia'] before antibiotic treatment, and (6) healthy vounteers. Each array sample represents a separate individual in each group. This submission includes two human whole genome Agilent Array Designs: A-MEXP-2104 and A-AGIL-28. Each of the individual raw array files are included as well as a single processed file representing the data matrix of all the merged and normalised data for the probes that are shared by the two array designs.
2016-10-28 | E-MTAB-4257 | biostudies-arrayexpress