Mycobacterium tuberculosis
            Secreted Proteins As Potential Biomarkers for the Diagnosis of Active Tuberculosis and Latent Tuberculosis Infection

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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Diagnostic Potential of IgG and IgA Responses to Mycobacterium tuberculosis Antigens for Discrimination among Active Tuberculosis, Latent Tuberculosis Infection, and Non-Infected Individuals

Microorganisms ◽

10.3390/microorganisms8070979 ◽

2020 ◽

Vol 8 (7) ◽

pp. 979

Author(s):

Ji Yeon Lee ◽

Byoung-Jun Kim ◽

Hyeon-Kyoung Koo ◽

Junghyun Kim ◽

Jee-min Kim ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Latent Tuberculosis Infection ◽

Latent Tuberculosis ◽

Public Health Problem ◽

Active Tuberculosis ◽

Antibody Responses ◽

Chorismate Mutase ◽

Tuberculosis Infection ◽

Major Public Health Problem ◽

Diagnostic Potential

Tuberculosis remains a major public health problem. Conventional tests are inadequate to distinguish between active tuberculosis (ATB) and latent tuberculosis infection (LTBI). We measured antibody responses to Mycobacterium tuberculosis antigens (Mycobacterium tuberculosis chorismate mutase (TBCM), antigen 85B (Ag85B), early secreted antigen-6 (ESAT-6), and culture filtrate protein-10 (CFP-10) in ATB, LTBI, and non-infected (NI) individuals. Serum immunoglobulin G (IgG) and immunoglobulin A (IgA) levels were measured and the QuantiFERON-TB Gold In-Tube assay was used to diagnose LTBI. IgG levels against TBCM were significantly higher in LTBI than NI subjects. IgG and IgA levels against Ag85B and IgG levels against CFP-10 were significantly higher in ATB, followed by LTBI, and then NI. When the ATB group was subdivided, IgG levels against Ag85B and CFP-10 were significantly higher in each subgroup compared with those in LTBI and NI groups. Positive correlation trends between interferon-gamma and IgG levels against Ag85B, TBCM, and CFP-10 and IgA levels against Ag85B in LTBI and NI subjects were observed. Age- and sex-adjusted models showed that IgG against TBCM and CFP-10 was independently related to LTBI diagnosis, and IgG against Ag85B was independently related to the diagnosis of ATB and could distinguish between LTBI and ATB. Overall, IgG antibody responses to TBCM, Ag85B, and CFP-10 can discriminate among ATB, LTBI, and NI groups.

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Perbedaan Antara Jumlah Sel T Subset Gamma-Delta di Darah Tepi pada Penderita Tuberkulosis dan Orang dengan Latent Tuberculosis Infection

Jurnal Biosains Pascasarjana ◽

10.20473/jbp.v18i2.2016.162-171 ◽

2016 ◽

Vol 18 (2) ◽

pp. 162

Author(s):

Ryzky Widi Atmaja ◽

Jusak Nugraha

Keyword(s):

Mycobacterium Tuberculosis ◽

Heat Shock ◽

Heat Shock Protein ◽

Latent Tuberculosis Infection ◽

Latent Tuberculosis ◽

Tuberculosis Infection ◽

Gamma Delta

Abstrak Latar Belakang. Imunitas memiliki peranan penting untuk melindungi host dari bacilli Mycobacterium tuberculosis (M.tb), bakteri Obligat intraseluler yang menyebabkan Tuberkulosis (TB) dan latent tuberculosis infection (LTBI). Sel T subset gamma-delta (T-γδ) adalah sel-sel potensial tersembunyi yang bermain peran di imunitas innate dan adaptive pada TB. Tetapi, hingga kini perananya di LTBI masih menjadi misteri. Bahan dan Metode. Penelitian dilakukan dengan melibatkan 10 penderita TB serta 10 orang dengan LTBI. Mereka didapatkan dari Rumah Sakit Paru Surabaya melalui suatu persetujuan kelaikan etik dari Universitas Airlangga. Sampel-sampel tersebut akan dihitung jumlah sel T-γδ menggunakan F A C S C a l i b u r. Hasil. Jumlah sel T-γδ meningkat pada TB (10,7%) dan LTBI (15, 4%). Jumlah dari kedua kelompok tersebut melebihi rerata normal di darah tepi (1% - 5%). Kesimpulan. Penigkatan jumlah sel T-γδ pada TB disebabkan melimpahnya kadar IL-12 yang dilepas oleh makrofag selama infeksi. Sementara, peningkatan jumlah sel T-γδ pada LTBI diasumsikan karena banyaknya heat shock protein (HSPs) yang dilepas oleh M.tb di bawah kondisi stres. ...Kata kunci: tuberkulosis, latent tuberculosis infection, Mycobacterium tuberclosis, sel T subset gamma-d e l t a.

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