Ontology highlight
ABSTRACT:
PROVIDER: PRJNA906938 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22461600_1.fastq.gz | Fastqsanger.gz | |||
| SRR22461600_2.fastq.gz | Fastqsanger.gz | |||
| SRR22461700_1.fastq.gz | Fastqsanger.gz | |||
| SRR22461700_2.fastq.gz | Fastqsanger.gz | |||
| SRR22461601_1.fastq.gz | Fastqsanger.gz |
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Project description:People with diabetes are more likely to develop tuberculosis (TB) and to have poor TB treatment outcomes than those without. We previously showed that blood transcriptomes in people with TB-diabetes (TB-DM) co-morbidity have excessive inflammatory and reduced interferon responses at TB diagnosis. It is unknown whether this persists through treatment, potentially underlying adverse outcomes. Methods Pulmonary TB patients were recruited in South Africa, Indonesia and Romania, and classified as having TB-DM, TB with prediabetes, TB-related hyperglycaemia or uncomplicated TB, based on glycated haemoglobin (HbA1c) concentration at TB diagnosis and after 6 months of TB treatment. Gene expression in blood samples collected at diagnosis and at regular intervals throughout treatment was measured by unbiased RNA-Seq and targeted Multiplex Ligation-dependent Probe Amplification. Results Gene expression was modulated by TB treatment in all groups but to different extents, such that differences remained in people with TB-DM relative to TB-only throughout, including genes involved in innate responses, anti-microbial immunity and the inflammasome. People with prediabetes or with TB-related hyperglycaemia had gene expression more similar to people with TB-DM than TB-only throughout treatment. The overall pattern of change was similar across clinical groups irrespective of glycaemic index, permitting models predictive of TB treatment to be developed. Conclusions The exacerbated transcriptome changes seen in TB-DM take longer to resolve during TB treatment, indicating that prolonged treatment or host-directed therapy may be needed to improve TB treatment outcomes. Development of transcriptome-based biomarker signatures of TB-treatment response should include people with diabetes to be useful across populations.
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Project description:72 candidate biomarkers evaluated for TB diagnosis using mRNA purified from whole blood of Active TB patients recruited in the UK and India, LTB and two groups of controls from the UK (i) from a low incidence region and (ii) individuals variably domiciled in the UK and India. Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB) and pulmonary (PTB)) and latent TB (LTBI) remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. PBL mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new Tuberculosis diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs)
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