scholarly journals Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Guoxing Tang ◽  
Qun Lin ◽  
Huijuan Song ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Roc Curve ◽  
Latent Tuberculosis ◽  
Diagnostic Model ◽  
City Hospital ◽  
Roc Curve Analysis ◽  
Hla Dr ◽  
Tnf Α ◽  
Ifn Γ ◽  
Latent Tb Infection

BackgroundNovel approaches for tuberculosis (TB) diagnosis, especially for distinguishing active TB (ATB) from latent TB infection (LTBI), are urgently warranted. The present study aims to determine whether the combination of HLA-DR on Mycobacterium tuberculosis (MTB)-specific cells and TB antigen/phytohemagglutinin (TBAg/PHA) ratio could facilitate MTB infection status discrimination.MethodsBetween June 2020 and June 2021, participants with ATB and LTBI were recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort), respectively. The detection of HLA-DR on MTB-specific cells upon TB antigen stimulation and T-SPOT assay were simultaneously performed on all subjects.ResultsA total of 116 (54 ATB and 62 LTBI) and another 84 (43 ATB and 41 LTBI) cases were respectively enrolled from Qiaokou cohort and Caidian cohort. Both HLA-DR on IFN-γ+TNF-α+ cells and TBAg/PHA ratio showed discriminatory value in distinguishing between ATB and LTBI. Receiver operator characteristic (ROC) curve analysis showed that HLA-DR on IFN-γ+TNF-α+ cells produced an area under the ROC curve (AUC) of 0.886. Besides, TBAg/PHA ratio yield an AUC of 0.736. Furthermore, the combination of these two indicators resulted in the accurate discrimination with an AUC of 0.937. When the threshold was set as 0.36, the diagnostic model could differentiate ATB from LTBI with a sensitivity of 92.00% and a specificity of 81.82%. The performance obtained in Qiaokou cohort was further validated in Caidian cohort.ConclusionsThe combination of HLA-DR on MTB-specific cells and TBAg/PHA ratio could serve as a robust tool to determine TB disease states.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Liyan Mao ◽  
Qun Lin ◽  
Guoxing Tang ◽  
...  
Keyword(s):  
T Cells ◽  
Mycobacterium Tuberculosis ◽  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Antigen Stimulation ◽  
Hla Dr ◽  
Tnf Α ◽  
Ifn Γ

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.

scholarly journals The effect of BKV reactivation on cytokines behavior in kidney transplanted patients

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Zahra Rahimi ◽  
Ramin Yaghobi ◽  
Afsoon Afshari ◽  
Jamshid Roozbeh ◽  
Mohammad Javad Mokhtari ◽  
...  
Keyword(s):  
Real Time ◽  
Roc Curve ◽  
Real Time Pcr ◽  
Curve Analysis ◽  
Expression Level ◽  
Control Group ◽  
Roc Curve Analysis ◽  
Tnf Α ◽  
Ifn Γ ◽  
Inactive Group

Abstract Background BK virus associated nephropathy (BKVAN) is one of the common causes of graft loss among kidney transplanted recipients (KTRs). The current treatment for BKV nephropathy is decreasing the immunosuppressive regimen in KTRs. Interleukin-27 (IL-27) is a multifunctional cytokine that might be the front-runner of an important pathway in this regard. Therefore, in current study it is tried to evaluate the changes in the expression level of IL-27 and some related molecules, resulting from BKV reactivation in KTR patients. Methods EDTA-treated blood samples were collected from all participants. Patients were divided into two groups, 31 kidney transplant recipients with active and 32 inactive BKV infection, after being monitored by Real time PCR (Taq-Man) in plasma. Total of 30 normal individuals were considered as healthy control group. Real time PCR (SYBR Green) technique is used to determine the expression level of studied genes. Results The results of gene expression comparisons showed that the expression level of IL-27, IFN-γ, TNF-α, TNFR2 and IRF7 genes was significantly higher in inactive group in comparison to active group. The expression level of TLR4 was lower in both active and inactive groups in comparison to control group. ROC curve analysis showed that IL-27 and IRF7 are significantly different amongst other studied genes. Finally, the analyses revealed that the expression level of most of the studied genes (except for TNF-α and TLR4) have significant correlation with viral load. Conclusions Our findings revealed that IL-27, IFN-γ, TNF-α, TNFR2 and IRF7 expression level is higher in inactive group and TLR4 expression level is lower in patients’ groups in comparison to control group. Also, ROC curve analysis showed IL-27 and IRF7 can significantly differentiate studied groups (BKV active vs. inactive). Therefore, these results might help elucidating the pattern in charge of BKV reactivation in kidney transplanted patients.

scholarly journals Screening for Mycobacterium tuberculosis Infection Using Beijing/K Strain-Specific Peptides in a School Outbreak Cohort

2021 ◽  
Vol 11 ◽  
Author(s):  
Ji Young Hong ◽  
Ahreum Kim ◽  
So Yeong Park ◽  
Sang-Nae Cho ◽  
Hazel M. Dockrell ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Mycobacterium Tuberculosis Infection ◽  
Tb Treatment ◽  
Diagnostic Potential ◽  
Tnf Α ◽  
Latent Tb ◽  
Ifn Γ ◽  
Latent Tb Infection ◽  
Tb Diagnostics ◽  
Active Disease

BackgroundThe Beijing strain of Mycobacterium tuberculosis (M. tb) has been most frequently isolated from TB patients in South Korea, and the hyper-virulent Beijing/K genotype is associated with TB outbreaks. To examine the diagnostic potential of Beijing/K-specific peptides, we performed IFN-γ release assays (IGRA) using a MTBK antigen tube containing Beijing/K MTBK_24800, ESAT-6, and CFP-10 peptides in a cohort studied during a school TB outbreak.MethodsA total of 758 contacts were investigated for M. tb infection, and 43 contacts with latent TB infection (LTBI) and 25 active TB patients were enrolled based on serial screening with QuantiFERON-TB Gold In-Tube tests followed by clinical examinations. Blood collected in MTBK antigen tubes was utilized for IGRA and multiplex cytokine bead arrays. Immune responses were retested in 24 patients after TB treatment, and disease progression was investigated in subjects with LTBI.ResultsTotal proportions of active disease and LTBI during the outbreak were 3.7% (28/758) and 9.2% (70/758), respectively. All clinical isolates had a Beijing/K M. tb genotype. IFN-γ responses to the MTBK antigen identified M. tb infection and distinguished between active disease and LTBI. After anti-TB treatment, IFN-γ responses to the MTBK antigen were significantly reduced, and strong TNF-α responses at diagnosis were dramatically decreased.ConclusionsMTBK antigen-specific IFN-γ has diagnostic potential for differentiating M. tb infection from healthy controls, and between active TB and LTBI as well. In addition, TNF-α is a promising marker for monitoring therapeutic responses. These data provide informative readouts for TB diagnostics and vaccine studies in regions where the Beijing/K strain is endemic.

scholarly journals Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Qun Lin ◽  
Liyan Mao ◽  
Guoxing Tang ◽  
...  
Keyword(s):  
Bacterial Meningitis ◽  
Cell Count ◽  
Tuberculous Meningitis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Specific Antigen ◽  
Diagnostic Model ◽  
City Hospital ◽  
Roc Curve Analysis ◽  
New City

BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

scholarly journals TNF-α Carried by Plasma Extracellular Vesicles Predicts Knee Osteoarthritis Progression

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Zhang ◽  
Ming-Feng Hsueh ◽  
Janet L. Huebner ◽  
Virginia B. Kraus
Keyword(s):  
Knee Osteoarthritis ◽  
Extracellular Vesicles ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Surface Markers ◽  
Roc Curve Analysis ◽  
Tnf Α ◽  
Osteoarthritis Progression ◽  
Ifn Γ

ObjectivesTo identify plasma extracellular vesicles (EVs) associated with radiographic knee osteoarthritis (OA) progression.MethodsEVs of small (SEV), medium (MEV) and large (LEV) sizes from plasma of OA participants (n=30) and healthy controls (HCs, n=22) were profiled for surface markers and cytokine cargo by high-resolution flow cytometry. The concentrations of cytokines within (endo-) and outside (exo-) EVs were quantified by multiplex ELISA. EV associations with knee radiographic OA (rOA) progression were assessed by multivariable linear regression (adjusted for baseline clinical variables of age, gender, BMI and OA severity) and receiver operating characteristic (ROC) curve analysis.ResultsBased on integrated mean fluorescence intensity (iMFI), baseline plasma MEVs carrying CD56 (corresponding to natural killer cells) predicted rOA progression with highest area under the ROC curve (AUC) 0.714 among surface markers. Baseline iMFI of TNF-α in LEVs, MEVs and SEVs, and the total endo-EV TNF-α concentration, predicted rOA progression with AUCs 0.688, 0.821, 0.821, 0.665, respectively. In contrast, baseline plasma exo-EV TNF-α (the concentration in the same unit of plasma after EV depletion) did not predict rOA progression (AUC 0.478). Baseline endo-EV IFN-γ and exo-EV IL-6 concentrations were also associated with rOA progression, but had low discriminant capacity (AUCs 0.558 and 0.518, respectively).ConclusionsPlasma EVs carry pro-inflammatory cargo that predict risk of knee rOA progression. These findings suggest that EV-associated TNF-α may be pathogenic in OA. The sequestration of pathogenic TNF-α within EVs may provide an explanation for the lack of success of systemic TNF-α inhibitors in OA trials to date.

scholarly journals 1405. The Accuracy of Mycobacterium tuberculosis Specific IFN-γ/IL-2/TNF-α- FluoroSpot in Differential Diagnosis of Active Tuberculosis and Latent Tuberculosis Infection: A Case-Control Study

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S786-S787
Author(s):  
Lifan Zhang ◽  
Huimin Ma ◽  
Qiping Ge ◽  
Yueqiu Zhang ◽  
Xiaochun Shi ◽  
...  
Keyword(s):  
T Cells ◽  
Differential Diagnosis ◽  
Mycobacterium Tuberculosis ◽  
Latent Tuberculosis Infection ◽  
Specific Antigen ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tnf Α ◽  
Ifn Γ ◽  
Stimulation Of

Abstract Background To establish the Mycobacterium tuberculosis (MTB) specific IFN-γ/IL-2/TNF-α-FluoroSpot assay, and preliminarily evaluate its accuracy of differential diagnosis of active tuberculosis (ATB) and latent tuberculosis infection (LTBI). Methods Patients with pathologically confirmed and clinically diagnosed ATB in Peking Union Medical College Hospital and Beijing Chest Hospital from April 2020 to May 2021 were enrolled as case group, while patients with LTBI in the same period were enrolled as control group. The FluoroSpot assay was used to simultaneously detect the secretion of IFN-γ, IL-2 and TNF-α in T cells stimulated by the MTB specific antigens ESAT-6 and CFP-10 at the single-cell level. A binary logistic regression model was used to fit the combined diagnostic parameters, and the sensitivity, specificity, predictive value and likelihood ratio of the differential diagnosis of ATB and LTBI were calculated. Figure 1. Schematic diagram of FluoroSpot (IFN-γ/IL-2/TNF-α) detecting cytokine-secreting specific T cells after stimulation with MTB specific antigen. A. The green spots are the total IFN-γ-secreting T cells; B. The red spots are the total IL-2-secreting T cells; C. The blue spots are the total TNF-α-secreting T cells; D. The green spots are the single IFN-γ-secreting T cells; the red spots are the single IL-2-secreting T cells; the blue spots are the single TNF-α-secreting T cells; the yellow spots are the dual IFN-γ/IL-2-secreting T cells; the cyan spots are the dual IFN-γ/TNF-α-secreting T cells; the purple spots are the dual IL-2/TNF-α-secreting T cells; the white spots are the triple IFN-γ/IL-2/TNF-α-secreting T cells. Results 62 patients with ATB (37 pathogen-confirmed ATB, 25 clinical diagnosed ATB), 87 patients with LTBI were included. There was significant correlation of the frequencies of total IFN-γ-secreting T cells detected by IFN-γ/IL-2/TNF-α-FluoroSpot assay compared with T-SPOT.TB after stimulation of MTB-specific antigen (r=0.829 for ESAT-6, P< 0.001, r=0.804 for CFP-10, P< 0.001). ROC curve was drawn for both T-SPOT.TB and Fluorospot. For T-SPOT.TB, the AUROC was 0.669 (95%CI 0.574-0.765), the sensitivity and specificity of differentiating ATB from LTBI were 70.97% (95%CI 58.05%-81.80%) and 56.32% (95CI 45.26%-66.94%) respectively. While for Fluorospot, the AUROC was 0.906 (95 CI 0.856-0.957), the sensitivity and specificity of differentiating ATB from LTBI were 80.65% (95%CI 68.63% - 89.58%) and 88.51% (95%CI 79.88% - 94.35%) respectively. Figure 2. Correlation between the frequencies of total IFN-γ-secreting T cells detected by FluoroSpot assay and those of T-SPOT.TB. (A) Stimulated by EAST-6. (B) Stimulated by CFP-10. Figure 3. ROC curves and the corresponding AUROC for measurement of frequencies of specific T cells in differentiating ATB and LTBI under stimulation of ESAT-6 or CFP-10. The blue line is drawn with the frequency of IFN-γ-secreting T cells detected by T-SPOT.TB, and the AUC is 0.669 (95%CI, 0.574-0.765). The red line is drawn with combination of the frequencies and proportion of single IFN-γ-'single IL-2-'single TNF-α-'dual IFN-γ/IL-2-'dual IFN-γ/ /TNF-α-'dual IL-2/TNF-α-secreting T cells detected by FluoroSpot, and the AUC is 0.906 (95% CI, 0.856-0.957). Table 1. Diagnostic value of T-SPOT.TB and FluoroSpot for differentiating ATB from LTBI Conclusion Compared with T-SPOT.TB, the IFN-γ/IL-2/TNF-α-Fluorospot assay may be helpful to distinguish ATB from LTBI, and the results need to be verified by large sample prospective cohort study. Disclosures All Authors: No reported disclosures

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