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Novel biomarkers distinguishing active tuberculosis from latent infection identified by gene expression profile of peripheral blood mononuclear cells.

ABSTRACT: BACKGROUND: Humans infected with Mycobacterium tuberculosis (MTB) can delete the pathogen or otherwise become latent infection or active disease. However, the factors influencing the pathogen clearance and disease progression from latent infection are poorly understood. This study attempted to use a genome-wide transcriptome approach to identify immune factors associated with MTB infection and novel biomarkers that can distinguish active disease from latent infection. METHODOLOGY/PRINCIPAL FINDINGS: Using microarray analysis, we comprehensively determined the transcriptional difference in purified protein derivative (PPD) stimulated peripheral blood mononuclear cells (PBMCs) in 12 individuals divided into three groups: TB patients (TB), latent TB infection individuals (LTBI) and healthy controls (HC) (n?=?4 per group). A transcriptional profiling of 506 differentially expressed genes could correctly group study individuals into three clusters. Moreover, 55- and 229-transcript signatures for tuberculosis infection (TB<BI) and active disease (TB) were identified, respectively. The validation study by quantitative real-time PCR (qPCR) performed in 83 individuals confirmed the expression patterns of 81% of the microarray identified genes. Decision tree analysis indicated that three genes of CXCL10, ATP10A and TLR6 could differentiate TB from LTBI subjects. Additional validation was performed to assess the diagnostic ability of the three biomarkers within 36 subjects, which yielded a sensitivity of 71% and specificity of 89%. CONCLUSIONS/SIGNIFICANCE: The transcription profiles of PBMCs induced by PPD identified distinctive gene expression patterns associated with different infectious status and provided new insights into human immune responses to MTB. Furthermore, this study indicated that a combination of CXCL10, ATP10A and TLR6 could be used as novel biomarkers for the discrimination of TB from LTBI.

SUBMITTER: Lu C

PROVIDER: S-EPMC3164189 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Novel biomarkers distinguishing active tuberculosis from latent infection identified by gene expression profile of peripheral blood mononuclear cells.

Lu Chanyi C Wu Jing J Wang Honghai H Wang Sen S Diao Ni N Wang Feifei F Gao Yan Y Chen Jiazhen J Shao Lingyun L Weng Xinhua X Zhang Ying Y Zhang Wenhong W

PloS one 20110831 8

<h4>Background</h4>Humans infected with Mycobacterium tuberculosis (MTB) can delete the pathogen or otherwise become latent infection or active disease. However, the factors influencing the pathogen clearance and disease progression from latent infection are poorly understood. This study attempted to use a genome-wide transcriptome approach to identify immune factors associated with MTB infection and novel biomarkers that can distinguish active disease from latent infection.<h4>Methodology/princ ...[more]

PMID: 21904626

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Project description:AbstractTuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis.

Project description:In tuberculosis (TB), as in other infectious diseases, studies of small noncoding RNAs (sncRNA) in peripheral blood have focused on microRNAs (miRNAs) but have neglected the other major sncRNA classes in spite of their potential functions in host gene regulation. Using RNA sequencing of whole blood, we have therefore determined expression of miRNA, PIWI-interacting RNA (piRNA), small nucleolar RNA (snoRNA), and small nuclear RNA (snRNA) in patients with TB (n = 8), latent TB infection (LTBI; n = 21), and treated LTBI (LTBItt; n = 6) and in uninfected exposed controls (ExC; n = 14). As expected, sncRNA reprogramming was greater in TB than in LTBI, with the greatest changes seen in miRNA populations. However, substantial dynamics were also evident in piRNA and snoRNA populations. One miRNA and 2 piRNAs were identified as moderately accurate (area under the curve [AUC] = 0.70 to 0.74) biomarkers for LTBI, as were 1 miRNA, 1 piRNA, and 2 snoRNAs (AUC = 0.79 to 0.91) for accomplished LTBI treatment. Logistic regression identified the combination of 4 sncRNA (let-7a-5p, miR-589-5p, miR-196b-5p, and SNORD104) as a highly sensitive (100%) classifier to discriminate TB from all non-TB groups. Notably, it reclassified 8 presumed LTBI cases as TB cases, 5 of which turned out to have features of Mycobacterium tuberculosis infection on chest radiographs. SNORD104 expression decreased during M. tuberculosis infection of primary human peripheral blood mononuclear cells (PBMC) and M2-like (P = 0.03) but not M1-like (P = 0.31) macrophages, suggesting that its downregulation in peripheral blood in TB is biologically relevant. Taken together, the results demonstrate that snoRNA and piRNA should be considered in addition to miRNA as biomarkers and pathogenesis factors in the various stages of TB.IMPORTANCE Tuberculosis is the infectious disease with the worldwide largest disease burden and there remains a great need for better diagnostic biomarkers to detect latent and active M. tuberculosis infection. RNA molecules hold great promise in this regard, as their levels of expression may differ considerably between infected and uninfected subjects. We have measured expression changes in the four major classes of small noncoding RNAs in blood samples from patients with different stages of TB infection. We found that, in addition to miRNAs (which are known to be highly regulated in blood cells from TB patients), expression of piRNA and snoRNA is greatly altered in both latent and active TB, yielding promising biomarkers. Even though the functions of many sncRNA other than miRNA are still poorly understood, our results strongly suggest that at least piRNA and snoRNA populations may represent hitherto underappreciated players in the different stages of TB infection.

Project description:Background The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. Methods T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. Results A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. Conclusions Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07954-7. Highlights The first study to establish 28 models using machine learning for TB diagnosis. The first TB diagnostic model based on routine, TB-specific and non-specific tests. Cforest model presented excellent performance in discriminating ATB from LTBI. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07954-7.

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Novel biomarkers distinguishing active tuberculosis from latent infection identified by gene expression profile of peripheral blood mononuclear cells.

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Novel biomarkers distinguishing active tuberculosis from latent infection identified by gene expression profile of peripheral blood mononuclear cells.

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