Diagnostic biomarkers for active tuberculosis: progress and challenges

Nogueira, Betânia M. F.; Krishnan, Sonya; Barreto-Duarte, Beatriz; Araújo-Pereira, Mariana; Queiroz, Artur T. L.; Ellner, Jerrold J.; Salgame, Padmini; Scriba, Thomas J.; Gupta, Amita; Andrade, Bruno B.

doi:10.15252/emmm.202114088

Cited by 47 publications

(28 citation statements)

References 106 publications

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“…This lowers the power to determine differences and utility of the same biomarker(s) in this sub-group that is prominent in many low-resource, TB-endemic nations. The extent of immunodeficiency from HIV infection also varies widely, hence a large and diverse population of co-infected patients need to be recruited to study the relationship between the accuracy of biomarkers and the extent of immunodeficiency 199 . Therefore, multinational centre-based collaborations are essential to increase patient groups, introduce geographic diversity and introduce an international consortium for standardized and feasible “gold standard” diagnostic comparison.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Ludi¹,

Sule²,

Samy³

et al. 2023

Theranostics

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Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis (Mtb) and can manifest both pulmonary and extrapulmonary disease, including ocular tuberculosis (OTB). Accurate diagnosis and swift optimal treatment initiation for OTB is faced by many challenges combined with the lack of standardized treatment regimens this results in uncertain OTB outcomes. The purpose of this study is to summarize existing diagnostic approaches and recently discovered biomarkers that may contribute to establishing OTB diagnosis, choice of anti-tubercular therapy (ATT) regimen, and treatment monitoring. The keywords ocular tuberculosis, tuberculosis, Mycobacterium, biomarkers, molecular diagnosis, multi-omics, proteomics, genomics, transcriptomics, metabolomics, T-lymphocytes profiling were searched on PubMed and MEDLINE databases. Articles and books published with at least one of the keywords were included and screened for relevance. There was no time limit for study inclusion. More emphasis was placed on recent publications that contributed new information about the pathogenesis, diagnosis, or treatment of OTB. We excluded abstracts and articles that were not written in the English language. References cited within the identified articles were used to further supplement the search. We found 10 studies evaluating the sensitivity and specificity of interferon-gamma release assay (IGRA), and 6 studies evaluating that of tuberculin skin test (TST) in OTB patients. IGRA (Sp = 71-100%, Se = 36-100%) achieves overall better sensitivity and specificity than TST (Sp = 51.1-85.7%; Se = 70.9-98.5%). For nuclear acid amplification tests (NAAT), we found 7 studies on uniplex polymerase chain reaction (PCR) with different Mtb targets, 7 studies on DNA-based multiplex PCR, 1 study on mRNA-based multiplex PCR, 4 studies on loop-mediated isothermal amplification (LAMP) assay with different Mtb targets, 3 studies on GeneXpert assay, 1 study on GeneXpert Ultra assay and 1 study for MTBDRplus assay for OTB. Specificity is overall improved but sensitivity is highly variable for NAATs (excluding Ivyspring

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Section: Discussionmentioning

confidence: 99%

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Ludi¹,

Sule²,

Samy³

et al. 2023

Theranostics

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“…An increasing number of laboratories are turning their focus towards selecting and applying host diagnostic markers. However, the current emphasis is predominantly on the development of RNA and protein biomarkers (McNerney et al, 2012;Guo et al, 2022;Nogueira et al, 2022). Yet, the instability of RNA and the requirement for antigen stimulation in protein diagnosis often lead to diagnoses at later stages, thus limiting diagnostic effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis

Zhang,

Chen,

Zhang

et al. 2024

Front. Genet.

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Background:Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a persistent infectious disease threatening human health. The existing diagnostic methods still have significant shortcomings, including a low positivity rate in pathogen-based diagnoses and the inability of immunological diagnostics to detect active TB. Hence, it is urgent to develop new techniques to detect TB more accurate and earlier. This research aims to scrutinize and authenticate DNA methylation markers suitable for tuberculosis diagnosis. Concurrently, Providing a new approach for tuberculosis diagnosis.Methods:Blood samples from patients with newly diagnosed tuberculosis and healthy controls (HC) were utilized in this study. Examining methylation microarray data from 40 whole blood samples (22TB + 18HC), we employed two procedures: signature gene methylated position analysis and signature region methylated position analysis to pinpoint distinctive methylated positions. Based on the screening results, diagnostic classifiers are constructed through machine learning, and validation was conducted through pyrosequencing in a separate queue (22TB + 18HC). Culminating in the development of a new tuberculosis diagnostic method via quantitative real-time methylation specific PCR (qMSP).Results:The combination of the two procedures revealed a total of 10 methylated positions, all of which were located in the promoter region. These 10 signature methylated positions facilitated the construction of a diagnostic classifier, exhibiting robust diagnostic accuracy in both cross-validation and external test sets. The LDA model demonstrated the best classification performance, achieving an AUC of 0.83, specificity of 0.8, and sensitivity of 0.86 on the external test set. Furthermore, the validation of signature methylated positions through pyrosequencing demonstrated high agreement with screening outcomes. Additionally, qMSP detection of 2 potential hypomethylated positions (cg04552852 and cg12464638) exhibited promising results, yielding an AUC of 0.794, specificity of 0.720, and sensitivity of 0.816.Conclusion:Our study demonstrates that the validated signature methylated positions through pyrosequencing emerge as plausible biomarkers for tuberculosis diagnosis. The specific methylation markers in the TSPAN4 gene, identified in whole blood samples, hold promise for improving tuberculosis diagnosis. This approach could significantly enhance diagnostic accuracy and speed, offering a new avenue for early detection and treatment.

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“…These studies have paved the way for the diagnosis and identification of novel biomarkers of PTB. While there has been success in clinical use of pathogen-based biomarkers in the form of Cepheid GeneXpert and Urine Lipoarabinomannan (LAM), host-based biomarkers are in less advanced stages of development ( Nogueira et al, 2022 ). Based on previous literature, the present study aimed to identify more specific biomarkers of PTB using blood samples.…”

Section: Introductionmentioning

confidence: 99%

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Zhu

Liu

2023

Front. Genet.

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Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

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Diagnostic biomarkers for active tuberculosis: progress and challenges

Cited by 47 publications

References 106 publications

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

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