INTRODUCTION
Tuberculosis (TB) is one of the top 10 causes of death worldwide1. One third of the world’s population is latently infected withMycobacterium tuberculosis(Mtb), and up to 10% of infected individuals develop active TB in their lifetime2–3. Biomarkers are urgently needed to indicate progression from latent infection to clinical disease, to predict risk of reactivation after curing, and to provide accurate end points for drug and vaccine trials4–6. A recent study showed that 12 biomarkers were associated with the following clinical groups: “upstream” towards culture-positive TB on the TB disease spectrum (CD14, FCGR1A, FPR1, MMP9, RAB24, SEC14L1, and TIMP2), or “downstream” towards a decreased likelihood of TB disease (BLR1, CD3E, CD8A, IL7R, and TGFBR2). This suggested a correlation with M. tuberculosis-related pathology7–8. Matrix metalloproteinase or MMP activity is an essential component of resistance to pulmonary mycobacterial infection, and MMP9 specifically is required for macrophage recruitment and tissue remodeling to allow for the formation of tight, well-organized granulomas8. Other authors have identified the tissue inhibitor of metalloproteinases 1 (TIMP-1) as a diagnostic biomarker for TB diagnosis, with findings showing that plasma TIMP-1 and the relative mRNA expression levels may be a potential biomarker for TB diagnosis9. Recently, combinations of immune biomarkers such as CXCL9, sCD14, MMP9, and uPAR proteins were evaluated, in addition to anti-synthetic peptides ESAT-6 and Ag85A, covering certain sequences of M. tuberculosis. The findings showed that the anti-P-12034/uPAR combination could be a potential biomarker for identifying clinical TB patients with 96.7% sensitivity10.
Tuberculosis is a public health problem among Warao communities of the Venezuelan delta11–12. We studied transcriptional immune biomarkers in Warao indigenous individuals by applying a real-time reverse transcription polymerase chain reaction (RT-qPCR) assay. We also studied the relative gene expression levels of: interferon-gamma (IFN-γ; the soluble phosphatidylinositol-linked membrane glycoprotein (sCD14); matrix metalloproteinases (MMP-9); CC receptor 5 (CCR5), which recognizes CXC chemokines such as MIP-1a, RANTES, and MIP-1b; CC chemokine eotaxin (CCL11), which signals through the CCR3 receptor, the monokine induced by IFN-γ (MIG/CXCL9); and the urokinase-type plasminogen activator receptor (uPAR/PLAUR). These were examined as tools for the diagnosis of active TB and for identifying the risk of developing active TB from latent infection.
METHODS
Subject recruitment and stratification
A transversal study was carried out among individuals of both sexes from the Warao indigenous population living in the Orinoco delta area. Indigenous groups were enrolled under strict clinical and laboratory criteria as described previously13. Subjects were stratified as follows: 1) Indigenous with active pulmonary tuberculosis (ATB, n=4), the latter according to clinical evaluation of smear and sputum culture-positive tuberculosis14; and 2) Healthy volunteer controls (n=46). Peripheral blood samples from all healthy indigenous controls without TB symptoms or disease activity were drawn for QuantiFERON-TB Gold In-Tube (QFT-IT) testing (Commercial test QuantiFERON-TB Gold In-Tube, Cellestis, Victoria, Australia). These indigenous individuals were additionally subjected to the tuberculin skin test (TST). The results were used as the basis for classification as the latent TB infection group (LTBI, n=18), which consisted of Warao indigenous individuals classified as QFT-IT positive (QFT-IT+, n=4) and TST positive (TST+, n=14). The healthy non-infected controls or Warao indigenous individuals were TST-negative (TST-) and QFT-IT negative (QFT-IT-), (NIC, n=28).
Ethical considerations
This study was approved by the Ethical Committee of the Biomedicine Institute. (Protocol number FONACIT-2013002319/2013-UNU-BIOLAC/2016). Voluntary informed consent forms were signed by all individuals.
Quantification of IFN-γ and tuberculin skin test
Measurement of IFN-γ levels in plasma was performed by an enzyme-linked immunosorbent assay (ELISA) using the QuantiFERON-TB Gold in tube or QFT-IT test kit, as previously reported 15.
A tuberculin skin test (TST) was carried out in Warao indigenous individuals without TB symptoms or disease activity. The TST was administered according to the Mantoux method, as previously reported16.
Isolated Blood Cells and gene biomarker amplifications
Blood samples (n=100) under non-stimulation conditions (NS=50) and stimulation conditions (S=50) with M. tuberculosis antigens, Early Secretory Antigenic Target-6 (ESAT-6), Culture Filtrate Protein-10 (CFP10), and TB antigen 7.7 (TB7.7) were studied. Samples of 3-5 ml of peripheral blood were collected in a vacutainer tube with EDTA as an anticoagulant, and stimulated with antigens for 24 h at 37 °C. After incubation, both non-stimulated and stimulated blood cells were obtained by centrifugation. Total RNA was extracted from blood cells using the Total RNA Isolation System kit (Promega Corporation, WI, US) following the instructions of the supplier, and the RNA content was measured using a spectrophotometer at 260 nm. cDNA was generated from 5 μg of RNA using the Reverse Transcription System kit (Promega Corporation, WI US), following the manufacturer’s protocol. The qPCR assay was performed as follows: 2 µl of cDNA (50 ng) was mixed with 8 µl of reaction cocktail mix, which was composed of 5 µl of SsoFast™ EvaGreen® 2x (BioRad, USA), 2.5 µl of sterile and nuclease-free water, and 0.25 µl of both forward and reverse primers of host immune biomarkers (20 μM each). Primers for selected genes including GAPDH were designed using the GenBank database and commercially synthesized by the Genomics Institute (IDT®, USA) for qPCR (Table 1). All primers were designed to anneal at 60 °C, and primer specificities and assay efficiencies were tested on control cDNA. The reaction mix was placed in a Light Cycler 480® (Roche Life Sciences, USA). Cycling parameters were 1 cycle for enzyme activation at 95 °C for 30 s, 45 cycles of amplification at 95 °C for 5 s and 60 °C for 10 s, 1 cycle for melting curve analysis at 60 °C and 90 °C for 15 s, and 1 cycle for cooling at 40 °C for 30 s.
TABLE 1: Primers for reverse transcription-quantitative polymerase chain reaction.
| Gene | Direction | Sequence (5’-3’) | Product | GeneBank |
|---|---|---|---|---|
| Size (bp) | accession no. | |||
| GAPDH | F | AGCCACATCGCTCAGACAC | 66 | NM-02046.3 |
| R | GCCCAATACGACCAAATCC | |||
| IFN-γ | F | GTTTTGGGTTCTCTTGGCTGTTA | 112 | NM-00619.2 |
| R | AAAAGAGTTCCATTATCCGCTACATC | |||
| CD14 | F | GTTCGGAAGACTTATCGACCAT | 95 | NM-000591 |
| R | ACAAGGTTCTGGCGTGGT | |||
| MMP9 | F | GAACCAATCTCACCGACAGG | 120 | NM-004994 |
| R | GCCACCCGAGTGTAACCATA | |||
| CCR5 | F | GCCTCTGAATATGAACGGTGA | 60 | NM-00579.3 |
| R | ACATTTCCCTTCGTTGCTTC | |||
| uPAR/PLAUR | F | CTGCAAGGGGAACAGCAC | 118 | NM-002659.3 |
| R | GCTTTGGTTTTTCGGTTCG | |||
| MIG/CXCL9 | F | GGAGTGCAAGGAACCCCAGTA | 234 | NM-002416 |
| R | CTT TTG GCT GAC CTG TTT CTC | |||
| CCL11 | F | CCCTTCAGCGACTAGAGAGC | 90 | NM-02986.2 |
| R | TATCCTTGGCCAGTTTGGTC |
Gene: IFN-γ: Interferon gamma cytokine; sCD14: the soluble phosphatidylinositol-linked membrane glycoprotein (sCD14); MMP9: Matrix metalloproteinases; CCR5: chemokine CC receptor 5; CCL11: CC chemokine Eotaxin; F: forward; R: reverse. Product size (bp) and Gene bank accession no.
Quantification of gene expression
The mRNA relative expression of each gene was calculated based on the values of Cp. Normalization of Cp was performed against corresponding values for the constitutive gene GAPDH, using the following equation: 2-ΔΔct17.
Data analysis
Kruskal-Wallis and Dunn’s multiple comparison tests were performed to determine differences among the ATB, LTBI, and NIC groups. Receiver operating characteristic (ROC) curves and areas under the curves (AUC) were obtained to identify biomarkers with the best discriminatory accuracy. The significance level was set at 0.05. Statistical analyses were performed using GraphPad Prism, 6.0-Windows (San Diego, Ca. USA), SPSS (PASW Statistics ver. 18; SPSS, Chicago, IL, USA), and Epidat (Epidat ver. 3.1; Galicia, Spain) software.
RESULTS
Characteristics of study participants
Table 2 shows the demographic and clinical data for the participants. A total of 50 participants were enrolled into this study, 4 of whom were found to have alcohol/acid-fast bacilli and/or culture-positive pulmonary TB or active TB (ATB). Of 46 participants, 18 were classified as having latent TB infection (LTBI (QFT-IT+ or/and TST+), and 28 were healthy non-infected controls (NIC) with no reactivity to QFT-IT (QFT-IT-) or TST (TST-). The age results are shown as mean ± standard deviation (X – SD). The average age was 32.0-17.5 years for the ATB group, 34.0-15.6 years for the LTBI group, and 40.3-7.6 years for the NIC group (Table 2). There were no statistically significant differences between females and males, with differences of 1/3, 11/7, and 20/8 in the ATB, LTBI, and NIC groups, respectively (Table 2).
TABLE 2: Characteristics of age, gender, bacteriological and immunological markers.
| Markers | Patients | Contacts | Controls | p-value |
|---|---|---|---|---|
| (ATB) | (LTBI) | (NIC) | ||
| n=4 | n=18 | n=28 | ||
| Mean ages in years | 32.0±17.5 | 34.3±15.6 | 40.3±7.6 | ns |
| Number of Female/Male | 1/3 | 11/7 | 20/8 | ns |
| Smear+ (%) | 63.0a | 0b | ND | 0.0001 |
| Culture+ (%) | 100.0c | 0d | ND | 0.0001 |
| QFT-IT + (%) | 25.0 | 22.2e | 0.0f | 0.01 |
| TST+ (%) | ND | 88.8g | 0.0h | 0.0001 |
Indigenous individuals with active pulmonary TB (ATB). Indigenous individuals positive for QFT-IT/TST positive or with the latent TB infection (LTBI). Healthy non-infected controls (NIC). There were statistically significant differences between the percentage of smear (a &b), culture (c &d), the QFT-IT test (e &f), and the TST test (g &h). Not done (ND).
Bacteriological, QFT-IT, and TST
Results for the bacteriological, QFT-IT, and TST tests are shown in Table 2. Smears or smear plus culture were performed for all indigenous individuals with TB symptoms or disease activity (Table 2). Bacteriological assays revealed a statistically significant difference (P< 0.01) between the ATB and LTBI groups, both for smears as well as cultures (Table 2). QFT-IT results showed a statistically significant difference of P= 0.01 between indigenous QFT-IT+ and QFT-IT- in the LTBI group (4/18) and the NIC group (0/28) (Table 2). The skin reactivity test was carried out in all indigenous individuals without TB symptoms to study delayed-type hypersensitivity (DTH), for which a transversal diameter of indurations ³10mm were considered positive. There was a statistically significant difference of P< 0.01 between indigenous TST+ and TST- in the LTBI group (14/18) and the NIC group (0/28) (Table 2).
The mRNA expression levels of immune biomarkers by quantitative assay
The relative gene expression levels of immune biomarkers were detected by qPCR and normalized to GAPDH gene expression levels. Expression levels from the crossing point (Cp) of each gene were classified as very high (Cp < 25), high (Cp = 26-30), moderate (Cp = 31-35), low (Cp = 36-39), or not detected (Cp = 40). Figure 1 shows comparisons of the medians and interquartile ranges (IQRs) of transcriptional immune biomarkers or the relative gene expression levels of MMP9, CCR5, CCL11, IFN-γ, and CD14 among the ATB, LTBI, and NIC groups. This was done for direct ex vivo or non-stimulated conditions (ATB-NS, LTBI-NS, and NIC-NS) and in Mtb-antigen stimulated conditions (ATB-S, LTBI-S, and NIC-S). With the use of Kruskal-Wallis and Dunn’s multiple comparison tests for the medians of the transcriptional immune biomarker of MMP9, the results (mean – standard error (X – SE)) of the relative gene expression levels of MMP9 between the LTBI and NIC groups showed significantly higher MMP9 levels in the LTBI-NS group (150.7-74.4) than in the NIC-NS (21.74-15.20) and NIC-S (3.75-1.84) groups, with a significance of P< 0.01 and P< 0.01, respectively. There was also a significant difference in expression between the LTBI-S group (29.44-10.75) and the NIC groups, NIC-NS (21.74-15.20 (P= 0.02)) and NIC-S (3.75-1.84 (P< 0.01)) (Figure 1A).
The transcriptional immune biomarker in direct ex vivo and Mtb-antigen stimulated conditions of CCR5 levels are shown in Figure 1B. The Kruskal-Wallis and Dunn’s multiple comparison tests for the medians of the transcriptional immune biomarker of the CCR5 relative gene expression levels showed significantly lower levels in the LTBI-S group (0.899-0.547) than in the NIC groups, NIC-NS (9.14-3.30, P< 0.01)) and NIC-S (7.20-2.04, P< 0.01)) (Figure 1B). A Spearman’s rank correlation analysis determined a negative association between CCR5 and MMP9 when LTBI-NS and LTBI-S groups were compared (rho = -0.5743, P= 0.03 and rho = -0.5756, P= 0.03, respectively).
The relative gene expression levels of CCL11 and the transcriptional immune biomarker in direct ex vivoand Mtb-antigen stimulated conditions are shown in Figure 1C. The Kruskal-Wallis and Dunn’s multiple comparison tests for the medians of the transcriptional immune biomarker of the relative CCL11 gene expression levels showed significantly lower CCL11 levels in the LTBI-S group (1.68 – 1.26) than in the NIC-NS group (7.33 – 2.39 (P< 0.01)) (Figure 1C).
The Kruskal-Wallis and Dunn’s multiple comparison tests for the medians of the transcriptional immune biomarkers of IFN-γ and sCD14 did not show statistically significant differences among the groups (Figure 1D and 1E). In addition, there was complete absence of expression for the selected gene for qPCR to determine the gene expression levels of MIG/CXCL9 and uPAR/PLAUR immune biomarkers. This is probably due to the primer sequences used, and therefore further studies must be performed to establish whether different primers determine the mRNA expression levels of these immune biomarkers.
FIGURE 1: Relative expression of immune biomarkers between groups studied. Horizontal bars are medians and interquartile range (IQR) of IFN-γ for non stimulated (NS) conditions; NIC-NS (●), LTBI-NS (■) and ATB-NS (▲) and for stimulated conditions (S); NIC-S (▼), LTBI-S (◆) and ATB-S (○). Kruskal-Wallis and Dunn’s multiple comparison tests were used to compare the groups. (2A)*** Represents significance of differences for the MMP9 levels in the LTBI-NS and LTBI-S groups compared with the NIC-NS groups, P= 0.0006 and P= 0.0009, respectively, and NIC-S groups, P= 0.0002. (2B)*** and ** Represent significance of differences for the CCR5 levels in the LTBI-S group compared with the NIC-NS group, P= 0.0006, and in the LTBI-S group compared with the NIC-S group, P= 0.002. (2C)** Represents significance differences for the CCL11 levels in the LTBI-S group compared with the NIC-NS group, P= 0.001.
Receiver operating characteristic analysis of selected genes
Receiver operating characteristic (ROC) curves were constructed for evaluating immune biomarker diagnostic powers to distinguish LTBI indigenous individuals from NIC indigenous individuals. Figure 2shows ROC curve comparison analyses. The area under curve – standard error (AUC – SE) for each immune biomarker was: MMP9 (AUC= 0.771-0.080: 0.613-0.928 95% CI, P< 0.01), CCR5 (AUC= 0.317-0.090: 0.139-0.494 95% CI, P= 0.06), CCL11 (AUC= 0.352-0.101: 0.154-0.550 95% CI, P= 0.13), IFN-γ (AUC= 0.429-0.100: 0.233-0.625 95% CI, P= 0.47) and sCD14 (AUC= 0.609-0.106: 0.402-0.817 95% CI, P= 0.27) (Figure 2).
FIGURE 2: Receiver operating characteristic curve (ROC) analyses of immune biomarker tests. ROC analyses were constructed for evaluating immune biomarker diagnostic powers to distinguish between LTBI and NIC indigenous individuals. The area under the curves (AUC), standard error (SE), p value and 95% confidence interval (95% CI) of immune biomarkers; IFN-γ, sCD14, MMP9, CCR5 and CCL11.
Sensitivity and specificity tests
Table 3 illustrates the sensitivity and specificity of the immune biomarkers for diagnosis of Mtb infection. Statistical analyses demonstrated that sensitivities ranged between 94.12% and 21.43%, while specificities ranged between 88.46% and 23.08%. The MMP9 immune biomarker provided the best sensitivity (94.12%) for diagnosing LTBI but had 37.04% specificity for detecting subjects without Mtbinfection. The positive predictive value was 48.48 (29.92/67.05), and the negative predictive value was 90.91 (69.37/100.0), (Table 3). The CCL11 immune biomarker provided the sensitivity of 46.15% and specificity of 23.08%. The positive predictive value was 23.08 (4.96/41.19) and the negative predictive value was 46.15 (15.21/77.1), (Table 3). The sCD14 immune biomarker provided the sensitivity of 43.75% and specificity of 88.46%. The positive predictive value was 70.0 (36.6/100.0) and the negative predictive value was 71.88 (54.73/89.02), (Table 3). The IFN-γ immune biomarker provided the sensitivity of 35.29% and specificity of 29.63%. The positive predictive value was 24.0 (5.26/42.74) and the negative predictive value was 42.11 (17.27/66.94), (Table 3). The CCR5 immune biomarker provided the sensitivity of 21.43% and specificity of 28.57%. The positive predictive value was 13.04 (0.0/28.98 95% CI) and the negative predictive value was 42.11 (17.27/66.94 95% CI), (Table 3).
TABLE 3: Diagnostic accuracy of the immune biomarker tests.
| MMP9 | CCL11 | sCD14 | IFN-γ | CCR5 | |
|---|---|---|---|---|---|
| Sensitivity (%) | 94.12 | 46.15 | 43.75 | 35.29 | 21.43 |
| (79.99/100.0) | (15.21/77.1) | (16.32/71.18) | (9.64/60.95) | (0.0/46.49) | |
| Specificity (%) | 37.04 | 23.08 | 88.46 | 29.63 | 28.57 |
| (16.97/57.1) | (4.96/41.19) | (74.26/100) | (10.55/49.71) | (10.05/47.09) | |
| PPV (%) | 48.48 | 23.08 | 70.0 | 24.0 | 13.04 |
| (29.92/67.05 | (4-96/41.19) | (36.6/100) | (5.26/42.74) | (0.0/28.98) | |
| NPV (%) | 90.91 | 46.15 | 71.88 | 42.11 | 42.11 |
| (69.37/100.0) | (15.21/77.1) | (54.73/89.02) | (17.27/66.94) | (17.27/66.94) | |
| LR + | 1.49 | 0.6 | 3.79 | 0.5 | 0.3 |
| (1.09/2.04) | (0.32/1.12) | (1.14/12.6) | (0.25/1.0) | (0.11/0.84) | |
| LR – | 0.16 | 2.33 | 0.64 | 2.18 | 2.75 |
| (0.02/1.13) | (0.98/5.53) | (0.4/1.0) | (1.11/4.31) | (1.44/5.25) | |
| YI | 0.31 | -0.312 | 0.32 | -0.35 | -0.5 |
| (0.1/0.53) | (-0.62/-0.01) | (0.05/59) | (-0.64/-0.07) | (-0.77/-0.23) | |
| COV | 0.315 | 5.355 | 4.165 | 0.310 | 0.393 |
Sensitivity, Specificity, PPV: Positive predictive value; NPV: Negative predictive value; LR+: Likelihood ratio (positive); LR-: Likelihood ratio (negative); YI: Youden index; COV: Cut off value.
DISCUSSION
Several transcriptional immune biomarkers detected in direct ex vivo conditions have been evaluated in adult individuals with a diagnosis of TB. In addition, LTBI or PPD+ and non-infected individuals or PPD- have been detected using a Custom Array 4X2K “UIMZ-IMSS-MX microarray18,19. Authors identified 108 genes in Mexican ATB subjects. Serum proteins produced by these genes were over-expressed in whole blood from ATB patients. This made them excellent candidates for biomarkers, as the gene elevations could differentiate ATB patients from LTBI or NIC individuals without infection19.
Immunogenicity factors that contribute to susceptibility and progression of pulmonary TB among Warao indigenous individuals provided an opportunity to study these individuals, as different genetic backgrounds exist between Warao indigenous individuals and white Americans20,21. In the present study, differential gene expression in indigenous individuals with Mtb infection versus healthy indigenous individuals was performed. The results showed that for the medians of the relative MMP9 gene expression levels, there was no statistically significant difference between the ATB and NIC groups in direct ex vivo or non-stimulated and stimulated conditions. However, between the LTBI and NIC groups, there were statistically significant differences between the high MMP9 levels for the LTBI-NS and LTBI-S groups compared with the low MMP9 levels for the NIC-NS and NIC-S groups. The high MMP9 levels associated with TST+ in LTBI-NS and LTBI-S groups, or indigenous individuals, compared with the low MMP9 levels for the NIC-NS and NIC-S groups, or healthy indigenous individuals, was notable. This could correlate with findings that support the idea that early MMP activity is an essential component of resistance to pulmonary mycobacterial infection, and that MMP9 specifically is required for macrophage recruitment and tissue remodeling for the formation of tight, well-organized granulomas22. Authors reported that following aerosol infection withMtb, dissemination of bacilli occurred earlier in the C57BL/6 resistant mouse strain than in the susceptible CBA/J strain, as was evident from an increased number of bacteria in the blood, spleen, and liver at day 14 after infection. Non-specific blocking of MMPs in C57BL/6 mice early during infection reduced hematogenous spread of the bacilli, suggesting that MMPs indeed play a role in facilitating dissemination23.
A recent ex vivo transcriptional immune biomarker study was performed in a cohort of 99 Indian children with intrathoracic tuberculosis and during anti-TB treatment. Expression of three genes down-regulated during anti-TB treatment (FCGR1A, FPR1, and MMP9) exhibited a positive correlation with the extent of TB disease, whereas expression of eight up-regulated genes (BCL, BLR1, CASP8, CD3E, CD4, CD19, IL-7R, and TGFBR2) exhibited a negative correlation with the extent of disease23. A study restricted to Ethiopian adult TB patients and household contacts also identified MMP9 in addition to BLR1, FCGR1A, and IL7R as differentiating markers24. This agreement across different populations strengthens the likelihood of the MMP9 immune biomarker being relevant to a future diagnostic test for TST+ in Warao indigenous individuals. Taken together; the transcripts of MMP9 provided the highest sensitivity of 94.12% for diagnosing LTBI.
In regards to the CCR5 immune biomarker, comparison tests for the medians of CCR5 showed that between the LTBI and NIC groups, there were statistically significant differences between the low CCR5 levels in the LTBI-S group compared with the high CCR5 levels in the NIC-NS and NIC-S groups. In addition, there were no statistically significant differences between the NIC and ATB groups for comparison tests for the medians of the CCR5 transcriptional immune biomarker. This was probably due to the low number of ATB indigenous individuals included in the study. During Mtb infection, the pathogen modulates C-C Chemokine Receptor 5 (CCR5) to enhance IL-10 production, indicating the possible involvement of CCR5 in regulation of the host immune response. These events culminate in up-regulation of the production of immunosuppressive cytokine IL-10, which is associated with the down-regulation of macrophage MHC-II expression and up-regulation of CCR5 expression via engagement of STAT-3 in a positive feedback loop25. The CCR5 immune biomarker has been reported as an immunological marker that differentiates between different outcomes of Mtb infection. CCR5, as well as CXCR4, has been connected with the diagnosis of the extent of TB disease25–27. When we used Spearman’s rank correlation analysis, a significant negative association between MMP9 and CCR5 in the LTBI-NS and LTBI-S groups was determined (rho = -0.5743 and rho = -0.5756, respectively). This indicates that when MMP9 increases, CCR5 decreases, suggesting that CCR5 is not useful for detecting infected cases among adult Warao indigenous individuals at high risk of developing the disease. Considering that low CCR5 levels were found in the LTBI-S group compared with the high CCR5 levels in the NIC-NS and NIC-S groups, further studies must be performed to rule out the possibility that polymorphisms in this gene may influence the low levels of CCR5 found among the LTBI groups. Further studies are also necessary to determine whether expression of this gene could be diminished by the influence of Mtb infection.
In regards to the results for the gene expression levels of CCL11, these showed a statistically significant difference between the low CCL11 levels in the LTBI-S group compared with the high CCL11 levels in the NIC-NS group. In addition, there was no statistically significant difference between the ATB and NIC groups. Eotaxin or CCL11 is a CC-chemokine that signals through the CCR3 receptor. It is produced by IFN-γ -stimulated endothelial cells and TNF-activated monocytes28. It has been reported that CCL11, CCL24, and CCL26, which are produced by Th2 cells and other cells that induce Th2 development, are increased in TB patients compared to the controls. It appears that TB suppresses Th1 and subsequently, the classic function of macrophages, by inducing chemokine expression28. Studies must be conducted to understand the statistically significant differences found for CCL11 levels among the indigenous individuals that comprised the LTBI group.
The IFN-γ immune biomarker was expressed at low levels in all groups. Thus, comparisons of the medians and the relative gene expression levels of IFN-γ did not show statistically significant differences among the groups, neither in direct ex vivo or non-stimulated and stimulated conditions. The present IFN-γ findings correlate with those of our previous study, which examined the capacity of antigen-induced proliferation by PBMCs and IFN-γ production in Warao indigenous individuals with pulmonary TB and in healthy controls. We previously reported on the Creole population in another larger cohort of Venezuelan adults. In the previous study, IFN-γ production in Warao patients and controls was significantly lower after stimulation for 24 h and 48 h compared with that in the Creole group11. Studies in mice and humans showed that IFN-γ, Il-12, and TNF-α are cytokines involved in the control ofMtbinfections29,30. Several studies have evaluated the diagnostic potential of cytokine biomarkers for discrimination between TB infection states. Authors have identified cytokines as host biosignatures (IL-17F, MIP-3a, IL-13, IL-17A, IL-5, IL-9, IL-1, IL-2, and IFN-γ) that could identify and uniquely discriminate between TB infection states31,32.
For the median results of the relative sCD14 gene expression levels for discrimination between TB infection states and controls, there were no statistically significant differences among the studied groups. Though the comparison tests for the medians of the transcriptional immune biomarkers of sCD14 showed high levels of this immune biomarker in the ATB-S group compared with the ATB-NS and the LTBI-S and NIC-S groups, this was probably due to the low number of ATB indigenous individuals included in the study.
A recent study focused on delineating the immune factors associated with the asymptomatic states comprising latent tuberculosis infection (LTBI)33. To broadly characterize the immune state, latently infected and uninfected control individuals from South Africa were analyzed by CyTOF and flow cytometry. The results showed no significant changes in peripheral monocyte or lymphocyte counts compared to uninfected controls. Significant differences were identified in the percentage of immune effector cell subsets between samples from uninfected controls and individuals with LTBI. Granzyme B (GZMB) and perforin (PRF)-expressing cells were significantly higher in individuals with LTBI. These mostly consisted of NK cells and GZMB+PRF+IFNγ+TNF+ polyfunctional cells, which largely comprised CD27−CD8+ αβ T cells, but also included NK cells and γδ T cells. Authors therefore showed that latent tuberculosis is associated with enhanced cytotoxic responses, which are mostly mediated by CD16 (also known as FcγRIIIa) and natural killer cells, and continuous inflammation coupled with immune deviations in both T and B cell compartments33. Furthermore, authors using cell-type deconvolution of transcriptomic data from several cohorts of different ages, genetic backgrounds, geographical locations, and infection stages showed that although deviations in peripheral B and T cell compartments generally start at latency, they are heterogeneous across cohorts. However, an increase in the abundance of circulating natural killer cells in tuberculosis latency, with a corresponding decrease during active disease and a return to baseline levels upon clinical cure, are features that are common to all cohorts. Furthermore, by analysing three longitudinal cohorts, it was found that changes in peripheral levels of natural killer cells can inform disease progression and treatment responses, and inversely correlate with the inflammatory state of the lungs of patients with active tuberculosis. Together, these findings offer crucial insights into the underlying pathophysiology of tuberculosis latency, and identify factors that may influence infection outcomes33.
No studies applying gene expression profiling of Warao indigenous adults with TB infection states have been published so far. Authors have reported a study which identified a 116-gene signature set for discriminating TB and LTBI among Warao indigenous children34. Ten genes selected that are involved in calcium signaling and calcium metabolism were highly discriminative between TB, LTBI, and healthy controls. After validation by qPCR, 5 of the 10 genes (S100P, HBD, PIGC, CHRM2, and ACOT7) were sufficient to classify with 78% sensitivity and 96% specificity of TB cases correctly with no LTBI indigenous children wrongly classified as having TB34.
Regarding TB diagnostics, progress towards elimination of tuberculosis has remained elusive despite intensified standard measures of control. After a period of global acceleration in 2001-05, the case detection rate worldwide decelerated in 2006 and 2007, reaching 63% in 2007. Thus, the target of a case detection rate of at least 70% by 2005 has not yet been achieved, and is unlikely to be met until 2014. Insufficient access to advanced diagnostic tests (real-time reverse transcription polymerase chain reaction (RT-qPCR), flow cytometry, lateral-flow assay, CyTOF, etc.) has contributed to this suboptimal performance. Furthermore, national tuberculosis programs in disease endemic countries continue to rely largely on antiquated and inaccurate methods, such as direct smear microscopy, solid culture, chest radiography, and tuberculin skin testing. There is no rapid, point-of-care test that allows early detection of active tuberculosis at health clinics. Diagnostic delays, misdiagnosis, and inadequate implementation of existing tests result in increased morbidity and mortality in patients, and allow continued transmission of TB.
Insufficient access to advanced diagnostic tests in countries with limited resources such as Venezuela is an ongoing issue. However, we consider the next approach is to provide access to advanced diagnostic tests such as CyTOF. This is a proteomics technology that accesses the abundance of cell subsets, protein expression, and activation of signaling pathways at the single-cell level, as reported by Roy Chowdhury R et al.,33. Application of qRT-PCR detection of immune biomarkers at the mRNA level allowed us to establish the diagnostic potential of 7 immune biomarkers studied. Of these, MMP9 could identify LTBI indigenous from healthy indigenous with 94.12% sensitivity. Therefore, MMP9 has potential for further development and studies for confirming the diagnostic accuracy of the proposed biomarker. We consider that the analysis of MIG/CXCL9, uPAR/PLAUR, and alternative biomarkers consistently associated with the different stages of M. tuberculosis infection could be done using CyTOF technology. This could offer a better understanding of the immune state of latent M. tuberculosis infection to active disease, in addition to understanding the relevance of these biomarkers in a future point-of-care (POC) or a diagnostic POC test. If validated, this would contribute significantly to indicating progression from latent infection to clinical disease, in addition to predicting risk of reactivation after cure, especially in Warao indigenous TB-endemic communities.
