A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children

Togun, Toyin; Hoggart, Clive; Agbla, Schadrac C.; Gomez, Marie P; Egere, Uzochukwu; Sillah, Abdou K; Saidy, Binta; Mendy, Francis; Pai, Madhukar; Kampmann, Beate

doi:10.1016/j.ebiom.2020.102909

Cited by 21 publications

(16 citation statements)

References 56 publications

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“…Multiple limitations registered by conventional tests of etiology hurdles to the timely diagnosis of disease and contribute to promote clinical progression as well as continued transmission. Recent advances in genomics (16,17), transcriptomics (18)(19)(20), proteomics (21)(22)(23), and metabolomics (24)(25)(26) have effectively facilitated the diagnosis of TB. But these emerging methods often require prohibitively complex equipment and operations, which hinder their promotion of clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

et al. 2021

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BackgroundEasily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC).MethodsA total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously.ResultsCompared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4+ T cells, CD8+ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR+CD3+ T cells, Treg, CD4+ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%).ConclusionsOur study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.

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Section: Introductionmentioning

confidence: 99%

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

et al. 2021

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“…Nevertheless, these two tests were not useful in distinguishing ATB from LTBI (29)(30)(31). Moreover, a variety of host biomarkers identified by omics including transcriptome (32)(33)(34), proteome (35,36), and metabolome (37)(38)(39) have been proposed as potential biomarkers for diagnosing MTB infection. Howbeit, few studies validated their actual benefit in various areas with different demographic, ethnic settings, as well as TB prevalence.…”

Section: Introductionmentioning

confidence: 99%

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Luo

Xue

et al. 2021

Front. Immunol.

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BackgroundRapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue.MethodsParticipants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4+ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation.ResultsA total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α+ or IL-2+ cells had superiority over that on IFN-γ+ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α+IL-2+ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α+IL-2+ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity.ConclusionsWe demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.

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“…In recent years, there have been more studies attempted to identify protein signature for TB disease in adults ( 9 , 18 , 41 , 42 ) and children ( 43 ). A recent study hypothesized that a blood protein-based host response signature for active PTB could discriminate it from other TB-like disease (OTD) in adult patients with persistent cough and provide the foundation for a community-based triage test for PTB.…”

Section: Discussionmentioning

confidence: 99%

Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults

et al. 2021

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To achieve the ambitious targets for tuberculosis (TB) prevention, care, and control stated by the End TB Strategy, new health care strategies, diagnostic tools are warranted. Host-derived biosignatures are explored for their TB diagnostic potential in accordance with the WHO target product profiles (TPPs) for point-of-care (POC) testing. We aimed to identify sputum-independent TB diagnostic signatures in newly diagnosed adult pulmonary-TB (PTB) patients recruited in the context of a prospective household contact cohort study conducted in Andhra Pradesh, India. Whole-blood mRNA samples from 158 subjects (PTB, n = 109; age-matched household controls, n = 49) were examined by dual-color Reverse-Transcriptase Multiplex Ligation-dependent Probe-Amplification (dcRT-MLPA) for the expression of 198 pre-defined genes and a Mesoscale discovery assay for the concentration of 18 cytokines/chemokines in TB-antigen stimulated QuantiFERON supernatants. To identify signatures, we applied a two-step approach; in the first step, univariate filtering was used to identify and shortlist potentially predictive biomarkers; this step may be seen as removing redundant biomarkers. In the second step, a logistic regression approach was used such that group membership (PTB vs. household controls) became the binary response in a Lasso regression model. We identified an 11-gene signature that distinguished PTB from household controls with AUCs of ≥0.98 (95% CIs: 0.94–1.00), and a 4-protein signature (IFNγ, GMCSF, IL7 and IL15) that differentiated PTB from household controls with AUCs of ≥0.87 (95% CIs: 0.75–1.00), in our discovery cohort. Subsequently, we evaluated the performance of the 11-gene signature in two external validation data sets viz, an independent cohort at the Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, UK (GSE107994 data set), and the Catalysis treatment response cohort (GSE89403 data set) from South Africa. The 11-gene signature validated and distinguished PTB from healthy and asymptomatic M. tuberculosis infected household controls in the GSE107994 data set, with an AUC of 0.95 (95% CI: 0.91–0.98) and 0.94 (95% CI: 0.89–0.98). More interestingly in the GSE89403 data set, the 11-gene signature distinguished PTB from household controls and patients with other lung diseases with an AUC of 0.93 (95% CI: 0.87–0.99) and 0.73 (95% CI: 0.56–0.89). These criteria meet the WHO TTP benchmarks for a non–sputum-based triage test for TB diagnosis. We suggest that further validation is required before clinical implementation of the 11-gene signature we have identified markers will be possible.

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A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children

Cited by 21 publications

References 56 publications

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults

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