Dataset Information

SP110 Polymorphisms Are Genetic Markers for Vulnerability to Latent and Active Tuberculosis Infection in Taiwan.

ABSTRACT: One-fourth of the human population is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) and carries the infection in its latent form. This latent infection presents a lifelong risk of developing active tuberculosis (TB) disease, and persons with latent TB infection (LTBI) are significant contributors to the pool of active TB cases. Genetic polymorphisms among hosts have been shown to contribute to the outcome of Mtb infection. The SP110 gene, which encodes an interferon-induced nuclear protein, has been shown to control host innate immunity to Mtb infection. In this study, we provide experimental data demonstrating the ability of the gene to control genetic susceptibility to latent and active TB infection. Genetic variants of the SP110 gene were investigated in the Taiwanese population (including 301 pulmonary TB patients, 68 LTBI individuals, and 278 healthy household contacts of the TB patients), and their association with susceptibility to latent and active TB infection was examined by performing an association analysis in a case-control study. We identified several SNPs (rs7580900, rs7580912, rs9061, rs11556887, and rs2241525) in the SP110 gene that are associated with susceptibility to LTBI and/or TB disease. Our studies further showed that the same SNPs may have opposite effects on the control of susceptibility to LTBI versus TB. In addition, our analyses demonstrated that the SP110 rs9061 SNP was associated with tumor necrosis factor-? (TNF?) levels in plasma in LTBI subjects. The results suggest that the polymorphisms within SP110 have a role in controlling genetic susceptibility to latent and active TB infection in humans. To the best of our knowledge, this is the first report showing that the SP110 variants are associated with susceptibility to LTBI. Our study also demonstrated that the identified SP110 SNPs displayed the potential to predict the risk of LTBI and subsequent TB progression in Taiwan.

SUBMITTER: Chang SY

PROVIDER: S-EPMC6304864 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SP110 Polymorphisms Are Genetic Markers for Vulnerability to Latent and Active Tuberculosis Infection in Taiwan.

Chang So-Yi SY Chen Mei-Ling ML Lee Meng-Rui MR Liang Yun-Chieh YC Lu Tzu-Pin TP Wang Jann-Yuan JY Yan Bo-Shiun BS

Disease markers 20181205

One-fourth of the human population is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) and carries the infection in its latent form. This latent infection presents a lifelong risk of developing active tuberculosis (TB) disease, and persons with latent TB infection (LTBI) are significant contributors to the pool of active TB cases. Genetic polymorphisms among hosts have been shown to contribute to the outcome of Mtb infection. The SP110 gene, which en ...[more]

PMID: 30627224

Similar Datasets

Project description:Despite a century of research into tuberculosis (TB), there is a dearth of reproducible, easily quantifiable, biomarkers that can predict disease onset and differentiate between host disease states. Due to the challenges associated with human sampling, nonhuman primates (NHPs) are utilized for recapitulating the closest possible modelling of human TB. To establish a predictive peripheral biomarker profile based on a larger cohort of rhesus macaques (RM), we analyzed results pertaining to peripheral blood serum chemistry and cell counts from RMs that were experimentally exposed to Mtb in our prior studies and characterized as having either developed active TB (ATB) disease or latent TB infection (LTBI). We compared lung CFU burdens and quantitative pathologies with a number of measurables in the peripheral blood. Based on our results, the investigations were then extended to the study of specific molecules and cells in the lung compartments of a subset of these animals and their immune responses. In addition to the elevated serum C-reactive protein (CRP) levels, frequently used to discern the level of Mtb infection in model systems, reduced serum albumin-to-globulin (A/G) ratios were also predictive of active TB disease. Furthermore, higher peripheral myeloid cell levels, particularly those of neutrophils, kynurenine-to-tryptophan ratio, an indicator of induced expression of the immunosuppressive molecule indoleamine dioxygenase, and an influx of myeloid cell populations could also efficiently discriminate between ATB and LTBI in experimentally infected macaques. These quantifiable correlates of disease were then used in conjunction with a regression-based analysis to predict bacterial load. Our results suggest a potential biomarker profile of TB disease in rhesus macaques, that could inform future NHP-TB research. Our results thus suggest that specific biomarkers may be developed from the myeloid subset of peripheral blood or plasma with the ability to discriminate between active and latent Mtb infection.

Project description:BackgroundGiven that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI.MethodsParticipants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded.ResultsA total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort.ConclusionsThe diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

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.

Dataset Information

SP110 Polymorphisms Are Genetic Markers for Vulnerability to Latent and Active Tuberculosis Infection in Taiwan.

Publications

<i>SP110</i> Polymorphisms Are Genetic Markers for Vulnerability to Latent and Active Tuberculosis Infection in Taiwan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets