Methods and kits for determining tuberculosis infection status

Abstract

There is provided methods of determining tuberculosis (TB) infection status in an individual comprising: (i) providing a sample comprising T-cells; (ii) exposing the sample of (i) to one or more TB antigens; (iii) identifying T-cells in the sample that are CD4 positive and (a) secrete TNF-α without secreting IFN-γ; or (b) secrete IFN-γ without secreting TNF-α; (iv) identifying those cells of (iii) which are also CCR7 and, CD127 negative; and optionally (v) calculating the cells identified in (iv) as a percentage of those identified in (iii); wherein the identification of cells in (iv) and/or the percentage of T-cells calculated in (v) correlates to TB infection status of the individual, and wherein steps (iii) and (iv) can be carried out either sequentially or simultaneously. There are also provided compositions and kits for use in such methods.

Claims

1. A method of determining the risk of a latent tuberculosis (TB) infection progressing to an active TB infection in an individual comprising: (i) providing a sample comprising T-cells from said individual; (ii) exposing the sample of (i) to one or more TB antigens; (iii) identifying T-cells in the sample that are CD4 positive and (a) secrete TNF-α without secreting IFN-γ; or (b) secrete IFN-γ without secreting TNF-α; (iv) assessing the number of CD4+ T cells of (iii) (a) or (b) for CCR7 and CD127 expression; and (v) calculating the number of CD4+, CCR7−, CD127− cells identified in (iv) as a percentage of the CD4+ T cells identified in (iii) (a) or (b); wherein the higher the percentage of CD4+ cells that are CCR7 and CD127 negative calculated in (v), the greater the risk of a latent (TB) infection progressing to an active TB infection in the individual, and wherein steps (iii) and (iv) can be carried out either sequentially or simultaneously.

2. The method of claim 1, wherein the CD4+ T cells of (iii) (a) or (b) are additionally assessed for CD45RA expression in (iv), and wherein in (v) the CD4+, CCR7−, CD127− cells are CD4+, CCR7−, CD127−, CD45RA− cells.

3. The method of claim 1, wherein the cells identified in step (iii) (a) or (b) additionally do not secrete IL-2.

4. The method of claim 1, wherein the cells identified in step (iii) (a) or (b) are additionally CD3 positive.

5. The method of claim 1, wherein the T-cells identified in step (iii) (a) or (b) are identified as live T-cells.

6. The method of claim 1, wherein the risk of a latent TB infection progressing to an active TB infection is proportional to the percentage value calculated in step (v) of the method.

7. The method of claim 1, wherein the individual is infected with HIV.

8. The method of claim 1, wherein the individual has been infected with TB at any site.

9. The method of claim 1, wherein the sample is a blood sample, PBMC sample, a bronochoalveolar lavage (BAL) sample or a cerebral spinal fluid (CSF) sample.

10. The method of claim 1, wherein steps (iii) and/or (iv) are performed by multi-parameter flow cytometry.

11. The method of claim 1, further comprising first performing an assay to determine whether the sample of cells is infected with TB or the individual is infected with TB.

12. The method of claim 11, wherein said assay is an ELISpot platform, an interferon gamma release assay (IGRA) and/or a tuberculin skin test.

13. A method of treating a subject infected with TB comprising: (a) conducting the method of claim 1; and (b) administrating treatment to the subject with an increased risk of a latent TB infection progressing to an active TB infection.

14. The method claim 5, wherein the T-cells identified in step (iii) (a) or (b) are identified as live T-cells by use of a dead cell marker.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIGS. 1A-1C show the frequency of IFN-γ and TNF-α secreting CD4+ and CD8+ cell subsets are increased in ATB

(2) (FIG. 1A) Example gating strategy for the CD4+ TNF-α-only-secreting subset using representative plots from an individual with ATB whose cells were stimulated overnight with PPD is shown. Cells were gated on live singlets (not shown) and CD3+CD4+ cells (top row), then according to IFN-γ, IL-2 and TNF-α expression using FMOs (middle row). Boolean gating was used to define individual non-overlapping functional subsets, for example the TNF-α-only subset which did not express IFN-γ or IL-2 (bottom row).

(3) Graphs show frequency and median of CD4+ (FIG. 1B) and CD8+ (FIG. 1C) cells secreting IFN-γ and TNF-α in response to overnight stimulation with PPD or RD1-peptides in participants with ATB versus LTBI. Those with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(4) FIGS. 2A-2B show the cell surface phenotype of CD4+ cell functional subsets is influenced by TB disease stage

(5) CD4+ cell functional subsets were examined for CD45RA and CCR7 expression in active versus LTBI in those with a positive response.

(6) (FIG. 2A) An example gating strategy for a PPD-specific CD4+ IFN-γ-only secreting subset is demonstrated using representative plots from an individual with ATB. Each CD3+CD4+ (top row) functional subset e.g. IFN-γ-only-secreting cells (middle row) was analysed for expression of CD45RA and CCR7 (bottom row).

(7) (FIG. 2B) Graphs show the percentage (and median percentage) of PPD-stimulated CD4+ IFN-γ-only (page 7/22), TNF-α-only (page 8/22) and IL-2-only (page 9/22) cells that were CD45RA−CCR7+ (TCM) (top chart) and CD45RA−CCR7−(TEM) (bottom chart) in patients with ATB and LTBI. The first two pages are representative of changes observed in active versus LTBI in all MTB-specific (responding to PPD and RD-1-peptides) CD4+ functional subsets except IL-2-only-secreting cells. Those with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(8) FIGS. 3A-3C show the percentage of MTB-specific CD4+ functional T-cell subsets expressing CD127 is influenced by stage of TB infection and CD4 count

(9) CD3+CD4+ functional cell subsets were examined for CD127 expression.

(10) (FIG. 3A) A representative gating strategy is shown. PBMCs from an individual with LTBI infection were stimulated overnight with PPD, and CD3+CD4+ cells (top row) were gated for cytokine secretion e.g. IFN-γ-only-secreting subset (middle row) and analysed for expression of CD127 (bottom row).

(11) (FIG. 3B) Graphs show percentage (and median percentage) of cells expressing CD127 from positive responders in CD4+ IFN-γ-only—(top row), TNF-α-only—(middle row) and IFN-γ- and TNF-α-dual-secreting (bottom row) cell subsets following stimulation with PPD (page 11/22) and RD1-peptides (page 12/22) in ATB and LTBI. Patients with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(12) (FIG. 3C) Graphs show correlations of CD4 cells/μl with the percentage of CD4+ PPD-specific cells secreting all three cytokines (top left), IFN-γ-only (top right), TNF-α and IL-2 (bottom left) and TNF-α-only (bottom right) that expressed CD127 in patients with HIV co-infection. Patients with TB (filled circles) and LTBI (open circles) are indicated. Spearman's rank correlation coefficient, corresponding p values and lines of best fit are shown.

(13) FIGS. 4A-4D show combining functional subset analysis with memory phenotype reveals a potentially powerful biomarker to distinguish active and LTBI

(14) Boolean gating was used to analyse the percentage of PPD-specific CD4+ TNF-α-only-secreting cells that had the phenotype TEFF (CD45RA−CCR7−CD127−) in active and LTBI.

(15) A representative gating strategy is shown for individuals with ATB (FIG. 4A) and LTBI (FIG. 4B).

(16) (FIG. 4C) Graphs at the top of each page show the percentage (and median percentage) of PPD or RD-1 peptide-specific CD4+ TNFα-only and IFNγ-only cells from responders that were TEFF. Cut off values for distinguishing ATB from LTBI are shown as dotted lines, and corresponding ROC curves are shown in bottom chart on each page. A cut off of 17.3% of TNF-α-only cells of TEFF phenotype distinguished ATB from LTBI with 100% sensitivity (95% CI 73.5-100.0) and 92.9% specificity (95% CI 66.1-99.8) (table 2). In ROC analysis, area under the curve was 0.99 (95% CI 0.97-1.01; p<0.0001) (FIG. 4C). A cut off of 34.5% for PPD-specific IFN-γ-only cells gave a sensitivity of 100% (95% CI 73.5 to 100.0) and specificity of 84.2% (95% CI 60.42-96.6); a cut off of 33.8% for RD-1-specific TNF-α-only cells gave a sensitivity of 100% (95% CI 54.07 to 100.0) and specificity of 75.0% (95% CI 34.91 to 96.8); and a cut off of 49.3% of RD-1-specific IFN-γ-only cells gave a sensitivity of 100% (95% CI 66.4-100.0) and specificity of 75.0% (95% CI 42.8-94.5).

(17) (FIG. 4D) Graphs show the percentage (and median percentage) of PPD-specific CD4+ TNFα-only and IFNγ-only cells that were TEFF in patients with extrapulmonary TB (EPTB) and pulmonary TB (PTB). Patients with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(18) FIG. 5 shows there was no difference in the frequency of tri-functional cells between those with ATB or LTBI or HIV-infected versus uninfected responding to PPD (top row) or RD-1 peptides (bottom row)

(19) Patients with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(20) FIG. 6 shows there was no difference in the proportion of any CD4+ functional subsets that were TCM except for RD-1-peptides-specific cells secreting IFN-γ only (top row) and TNF-α and IL-2 (bottom row)

(21) Patients with HIV co-infection (filled circles) and without HIV co-infection (open circles) are indicated. Results were analysed by Mann Whitney U test; and p values of <0.05 were considered significant.

(22) FIGS. 7A-7B shows the inverse correlation of the proportion of PPD-specific or ECR-specific TNF-α-only T cells that are CD45RA−CCR7−CD127− with time since exposure to TB

(23) Proportion of (FIG. 7A) PPD-specific and (FIG. 7B) ECR-specific TNF-α-only T cells that are CD45RA−CCR7−CD127− against estimated time since TB exposure. Results were analysed by Spearman's Rank Correlation Coefficient.

EXAMPLES

(24) Methods Used in the Examples

(25) Participants were prospectively enrolled from three clinical centres in London, UK during the period January 2008-February 2011 under National Research Ethics Service approval (07/H0712/85). Participants were ≥18 years, provided written, informed consent and were eligible if under clinical investigation for ATB (active TB), undergoing LTBI (latent tuberculosis infection) screening or had recognised TB risk factors e.g. known TB contact.

(26) Suspected ATB was confirmed microbiologically by the clinical diagnostic laboratory. LTBI was defined as a positive response to RD-1 antigens in either T-SPOT.TB (carried out in routine clinical work up) or MTB IFN-γ ELISpot (carried out for the current study) in the absence of symptomatic, microbiological or radiological evidence of ATB.

(27) Presence of HIV infection was confirmed by 3rd or 4th generation sero-assay performed by the clinical diagnostic laboratory and using HIV-1 type specific EIA, according to national standards. HIV viral load (VL) and CD4 T-lymphocyte counts were assayed in the local Clinical Pathology Association-accredited diagnostic laboratories at the time of study recruitment. HIV diagnostics were available for all patients with ATB (in line with the national screening policy) and the majority of those with LTBI; the remainder had no risk factors for HIV and normal CD4:CD8 lymphocyte ratios, and were classified as HIV-uninfected.

(28) The demographic characteristics of the study population are shown in Table 1.

(29) TABLE-US-00002 TABLE 1 Demographics and clinical test results of participants HIV & TB TB HIV & LTBI LTBI Total 7 (%) 6 (%) 10 (%) 11 (%) 34 (%) Median 43  (40.5-52.4)  34.5 (28.0-56.0) 36  (24.0-39.0) 33  (31.0-35.5)   35.5 (31.3-40.8) (IQR) age Sex Male 4 (57.1) 3 (50.0) 6 (60.0) 4 (36.4) 17 (50.0) Female 3 (42.9) 3 (50.0) 4 (40.0) 7 (63.6) 17 (50.0) Ethnicity Black African 5 (71.4) 1 (16.7) 8 (80.0) 6 (54.5) 20 (58.8) Asian 1 (14.3) 3 (50.0) 0  (0.0) 3 (27.3)  7 (20.6) Caucasian 1 (14.3) 2 (33.3) 2 (20.0) 2 (18.2)  7 (20.6) BCG vaccination Yes 5 (71.4) 3 (50.0) 9 (90.0) 8 (72.7) 25 (73.5) No 1 (14.3) 2 (33.3) 0  (0.0) 2 (18.2)  5 (14.7) unknown 1 (14.3) 1 (16.7) 1 (10.0) 1  (9.1)  4 (11.8) Microbiological (smear/culture) confirmation Positive  7.sup.a (100.0)   6.sup.b (100.0)  NA NA NA NA 13 (100.0)  negative 0  (0.0) 0  (0.0) NA NA NA NA  0  (0.0) HIV test Positive 7 (100.0)  0  (0.0) 10  (100.0)  0  (0.0) 17 (50.0) negative 0  (0.0) 6 (100.0)  0  (0.0) 6 (54.5) 12 (35.3) not done 0  (0.0) 0  (0.0) 0  (0.0)  5.sup.b (45.5)  5 (14.7) All subjects tested positive in one or more of the tuberculin skin test, TSPOT.TB, QuantiFERON ®-TB Gold In-Tube or MTB IFN-γ ELISpot, performed clinically or for the current study. IQR = interquartile range .sup.aTen patients with TB had not started treatment at the point of recruitment, two had received <14 days treatment, and one had received ≥14 days treatment .sup.bAll participants without clinical need for HIV testing had normal CD4:CD8 ratios

(30) IFN-γ MTB ELISpot

(31) Fresh or frozen peripheral blood mononuclear cells (PBMCs), 2.5×10.sup.5 per well, were stimulated overnight (37° C., 5% CO.sub.2, 16-20 h) in an IFN-γ ELISpot plate (Mabtech) with phytohemagglutinin (PHA; positive control; Sigma-Aldrich), Tuberculin Purified Protein Derivative (PPD; Statens Serum Institute) or pools of MTB-specific 15-mer overlapping peptides covering each of ESAT-6, CFP-10, EspC, TB7.7, Rv3879c, Rv3873 and Rv3878. Unstimulated cells were used as a negative control. The IFN-γ ELISpot assay was performed as previously described (Dosanjh D P et al. Ann Intern Med 2008; 148:325-36).

(32) Intracellular cytokine staining and polychromatic flow cytometry: Thawed PBMCs (3-5×10.sup.6 per well) were cultured for 16 h (37° C., 5% CO.sub.2) in 10% human serum (Sigma-Aldrich) in RPMI-1640 (Sigma-Aldrich) at a concentration of 1×10.sup.7 cells/ml. Cells were stimulated with PMA-lonomycin (positive control); (Sigma-Aldrich; 500 ng/ml final concentration), PPD (16.7 μg/ml final concentration) or a cocktail of peptides spanning the length of three highly immunodominant MTB-specific RD1-associated antigens, ESAT-6, CFP-10 and EspC (10 pg/ml final concentration per peptide) (Millington K A et al. Proc Natl Acad Sci USA 2011; 108:5730-5). Unstimulated cells were used as a negative control.

(33) After two hours, monensin (2 μM final concentration) was added. Following stimulation, cells were washed and stained with a dead cell marker (LIVE/DEAD® Fixable Dead Cell Stain Kits, aqua, Invitrogen) for 30 minutes at 4° C. in phosphate buffered saline (PBS). Cells were then washed in PBS and placed in FC block buffer (10% human serum in filtered FACS solution (0.5% bovine serum albumin and 2 mM EDTA in PBS)) for 20 minutes at 4° C. before staining with a pre-titrated and optimised antibody cocktail with fluorochrome-conjugated antibodies against CD3−APC-Alexa Fluor®750, CD4−Qdot®605, CD45RA−Qdot® 655 (Invitrogen), CD8−APC, CCR7−PE-CyTM7, CD127−FITC (BD Biosciences) and PD-1-PerCP/Cy5.5 (Biolegend). After washing, the cells were fixed and permeabilised using BD Cytofix/Cytoperm™ Fixation/Permeabilization kit (BD Biosciences) for 20 minutes at 4° C. The cells were washed twice with Perm/Wash solution (BD Biosciences) then stained with pre-titrated fluorochrome-conjugated antibodies in Perm/Wash solution with IFN-γ-V450, IL-2-PE and TNF-α-AlexaFluor 700 (BD Biosciences) for 30 minutes at 4° C. 1×10.sup.6 events (where possible) were acquired straightaway using a BD LSR-II flow cytometer. Anti-Rat and Anti-Mouse Ig compensation beads (BD Biosciences) were used to set compensation parameters. Fluorescence minus one (FMO) controls were used in each experiment to set gates.

(34) Data Analysis and Thresholds

(35) The data was analysed on FlowJo version 9.4.4 ©TreeStar, Inc. Events were gated on live cells, singlets and lymphocytes using forward and side scatter properties. CD3+CD4+ and CD3+CD8+ subsets were defined. Gating controls were used to define IFN-γ, IL-2 and TNF-α responses and surface marker expression.

(36) For phenotypic analysis of MTB-specific cells, only participants with a positive response were included. Positive responders were defined as those with a response that was ≥2 times the background (in unstimulated but fully stained samples) and >0.001% of CD3+CD4+ or CD3+CD8+ cells. This cut-off was used because we did not use co-stimulation to enhance responses, and we normalised to background (unstimulated) data before applying the cut-off (rather than classifying background as negligible). A strict cut-off meant only antigen-specific cells were included in the phenotypic analysis.

(37) Statistical Analysis

(38) Statistical analysis was conducted using IBM SPSS Statistics version 20 and GraphPad Prism version 5.00 for Mac OS X, GraphPad Software, California USA. TB disease stage compared all TB (n=13) vs. all LTBI (n=21) regardless of HIV status, the impact of HIV compared all HIV-infected (n=17) vs. uninfected (n=17) regardless of TB disease stage and across all four subgroups. The 2-tailed Mann-Whitney U test was used for non-parametric two-sample comparisons. Spearman's rank correlation coefficients were used to test correlations. Receiver operator characteristic (ROC) curve defined the sensitivity and specificity of the diagnostic approach.

Example 1—The Frequency of CD4+ and CD8+ Cells with an IFN-γ+ and TNF-α+ Response was Increased in ATB

(39) We first examined the frequency of CD4+ and CD8+ functional effector cell subsets.

(40) Boolean gating was used to create individual non-overlapping subsets by combining data in 3 dimensions (FIG. 1A). The frequency of PPD-specific CD4+ IFN-γ-only, TNF-α-only and IFN-γ/TNF-α-dual-secreting cells was higher in ATB compared with LTBI (p=0.003, 0.002 and 0.002, respectively) (FIG. 1B). A similar relationship was seen for RD1-peptide-specific CD4+ IFN-γ-only-secreting cells (not significant; data not shown), and IFN-γ/TNF-α-dual-secreting cells (p=0.017) (FIG. 1B).

(41) The presence of HIV-infection was not associated with an altered frequency of these cell subsets. We observed no difference in the frequency of tri-functional cells in patients with ATB compared with those with LTBI (FIG. 5).

(42) The majority of participants with ATB, but not LTBI, had a CD8+ IFN-γ response (PPD: 12/13 for ATB vs 6/21 for LTBI, RD1-peptides: 10/13 for ATB vs 11/21 for LTBI). The frequencies of PPD- and RD1-peptide-specific CD8+ IFN-γ-only-producing cells were significantly higher in ATB than in LTBI (p=0.017 and 0.016, respectively), as was the frequency of CD8+ PPD-specific cells secreting both IFN-γ and TNF-α (p=0.013) (FIG. 10).

(43) HIV co-infection was (non-significantly) associated with a reduced frequency of PPD-specific IFN-γ/TNF-α-dual and TNF-α-only responses in ATB compared with HIV-negativity (p=0.051 for both). In the HIV-uninfected TB group the percentages of these PPD-specific CD8+ cells were significantly higher than in LTBI (p=0.008 and 0.022 respectively).

(44) Similarly the frequency of cells secreting IFN-γ-only were significantly higher in ATB compared with LTBI in HIV-uninfected (p=0.023). These CD8+ effector functional subsets were therefore related to mycobacterial load analogously to equivalent CD4+ subsets but the impact of HIV co-infection was more profound.

Example 2—PPD-Specific and RD1-Peptide-Specific CD4+ Cellular Differentiation was Increased in ATB Versus LTBI

(45) We analysed the memory phenotype of the CD4+ functional subsets as putative correlates of mycobacterial pathogen load. Non-responders were excluded. Each T-cell functional subset was gated for expression of CD45RA and CCR7 (FIG. 2A). Memory phenotypes of functional subsets were defined as naive CD45RA+CCR7+, central memory (TCM) CD45RA−CCR7+, effector memory (TEM) CD45RA−CCR7− and CD45RA+ effectors (TEMRA) CD45RA+CCR7−. This last subset was mainly evident in CD8+ cells.

(46) PPD- and RD1-peptide-specific CD4+ cell effector functional subsets were principally TCM in LTBI compared to TEM in ATB, for example fewer PPD-specific CD4+ IFN-γ-only secreting cells were TCM in ATB compared with LTBI (p=0.005) (FIG. 2B). Comparisons of HIV-infected with uninfected patients were non-significant except for the CD4+ RD1-peptide-specific IFN-γ-only- and TNF-α/IL-2-dual-secreting subsets, fewer of which were TCM in HIV-infected than uninfected subjects (p=0.030 and 0.006) (FIG. 6).

(47) In contrast to the CD4+ effector functional subsets, CD4+ IL-2-only PPD- (FIG. 2B) and RD1-peptide-specific cells (data not shown) were principally TCM in both ATB and LTBI and were therefore unaffected by TB disease stage. CD8+ IFN-γ-only cellular responses to PPD and RD1-peptides were mostly TEM and TEMRA (data not shown).

(48) CD127 (IL7Rα) expression is reduced following antigen stimulation in effector T-cells (Schluns et al. Nat Rev Immunol 2003; 3:269-79.) and defines CD4+ and CD8+ subsets of differentiated murine T effector cells distinct from effector memory cells (Kaech et al. Nat Immunol 2003; 4:1191-8; Harrington et al. Nature 2008; 452:356-60). We therefore measured CD127 expression on antigen-specific CD4+ functional T-cell subsets (FIG. 3A).

(49) A smaller percentage of PPD- and RD1-peptide-specific CD4+ cells expressed CD127 in ATB compared with LTBI, for example a lower percentage of PPD-specific CD4+ IFN-γ-only- and TNF-α-only-secreting cells expressed CD127 in ATB compared with LTBI (p<0.001 and p=0.003 respectively) (FIG. 3B). Expression of CD127 on antigen-specific cells was unaffected by HIV status (FIG. 3B).

(50) However, in patients with HIV co-infection, frequencies of several subsets of PPD-specific T-cells expressing CD127 correlated with CD4 count (FIG. 3C). Similarly, for RD-1-peptide-specific cells, IFN-γ/IL-2-dual-producing cells expressing CD127 correlated with CD4 count (Rho=0.647; p=0.047) and HIV VL correlated inversely with IFN-γ-only (Rho=−0.780; p=0.013) and IFN-γ/TNF-α-dual-secreting CD127-expressing cells (Rho=−0.727; p=0.005) (data not shown).

Example 3—Expansion of Differentiated CD4+ Functional Effector T-Cells in ATB Versus LTBI

(51) We next investigated the potential of combined phenotypic and functional measurement as a clinical biomarker. We determined the percentage of PPD- and RD1-peptide-specific CD4+ cells secreting IFN-γ-only or TNF-α-only that were differentiated effector cells (TEFF; CD45RA−CCR7−CD127−) (FIG. 4A (ATB) and B (LTBI)).

(52) In ATB, compared with LTBI, a higher percentage of CD4+ cells secreting IFN-γ-only or TNF-α-only in response to PPD and RD1-peptides were TEFF. This was most significant for CD4+ PPD-specific CD4+ cells secreting TNF-α-only (p<0.0001) and IFN-γ-only (p<0.0001). A cut off of >17.3% of TNF-α-only cells of TEFF phenotype distinguished ATB from LTBI with 100% sensitivity (95% CI 73.5-100.0) and 92.9% specificity (95% CI 66.1-99.8) (Table 2).

(53) In ROC analysis, area under the curve was 0.99 (95% CI 0.97-1.01; p<0.0001) (FIG. 4C). Similar although slightly less discriminatory ROC curves were generated for PPD-specific IFN-γ-only cells and for RD-1-specific cells (FIG. 4C).

(54) To test whether this approach was robust to differences in disease site we compared individuals with active pulmonary (PTB) and extra-pulmonary disease (EPTB). We found no significant difference in the proportion of PPD-specific cells secreting IFN-γ-only, or TNF-α-only that were TEFF (FIG. 4D) when stratified by site of disease or HIV co-infection. There was also no difference in the proportion of RD-1 peptide-specific cells that were TEFF when stratified by disease site (data not shown).

(55) The operator conducting analyses (KMP) was also involved in participant recruitment and therefore not blinded to patient categorization. To demonstrate integrity and reproducibility of the results, data for all study participants was re-gated and re-analysed by a second independent operator (HSW) who was blinded to patient diagnoses.

(56) Correlations between results obtained by operators 1 and 2 for the percentage of CD3+CD4+ cells secreting TNF-α-only (Rho=0.97, p<0.0001), the percentage of CD3+CD4+ TNF-α-only-secreting cells that are CD127+ (Rho=0.96, p<0.0001) and the percentage of CD3+CD4+ TNF-α-only-secreting cells that are CD45RA−CCR7− (Rho=0.88, p<0.0001) were very strong. Using the 17.3% cut-off for TNF-α-only-secreting cells of TEFF phenotype to distinguish TB from LTBI, operator 2 misclassified 1 case of TB as LTBI, and 1 further case of LTBI as TB (data not shown).

(57) TABLE-US-00003 TABLE 2 Clinical and radiological characteristics of cases sorted by percentage of TNF-α-only-secreting cells that were T.sub.EFF (CD45RA-CCR7-CD127-) % T.sub.EFF/ TNF-α Sputum MTB Culture Radiology TB final No. only HIV CD4 VL smear culture site (CXR or CT) diagnosis S135 78.9 + 190 281671 − + BAL Bilateral pleural pulmonary and effusions, lung pleural and splenic fluid nodules, peritoneal thickening S126 75.0 + 69 601000 + + Sputum Azygos lobe focal pulmonary consolidation in cavity, pleural effusions, no lymphadenopathy S221 72.4 − na na nt + Lymph Enlarged low extra node density lymph pulmonary nodes in mediastinum and left axilla S059 46.9 + 140 <100 + + Sputum Effusion, pulmonary and thickened pleura, BAL loss of volume left lung, ground glass change S184 40.5 − na na nt + Lymph Multiple extra node mediatstianal, pulmonary coeliac axis lymph nodes with nodules in spleen and breast S193 37.6 + 136 28958 nt + Lymph Axillary, para- extra node aortic and pulmonary abdominal lymphadenopathy, subpleural nodules, liver lesions S083 30.0 − na na − + Lymph Right pleural pulmonary node, collection and BAL, right paratracheal peritoneal lymphadenopathy S076 29.2 + 200 <50 + + Left Consolidation pulmonary upper and cavitation lobe, upper lobe, BAL, interstitial sputum opacities, linear atelectasis S115 28.6 − na na nt + Lymph Mediastianal extra node lymphadenopathy pulmonary S146 20.1 + 250 52205 nt + Lymph Supraclavicular, extra node mediastinal and pulmonary abdominal lymphadenopathy, nodular infiltrates S195 18.0 − na na + + BAL Mediastinal, hilar pulmonary and and lymph supraclavicular node lymph nodes, patchy consolidation S153 17.8 − na na nt nt nt nil of note LTBI S082 17.5 − na na − + Lymph nil of note extra node pulmonary S074 17.1 − na na − M. fortuitum BAL Opacification LTBI right upper lobe, small volume axillary and mediastinal lymph nodes S050 16.7 + 480 12479 nt nt nt nil of note LTBI S052 13.3 + 520 45719 − − Sputum nil of note LTBI S177 12.8 − na na nt nt nt nil of note LTBI S094 10.9 + 660 <50 nt nt nt Heavily calcified LTBI nodule and small lymph nodes right upper lobe S092 10.3 nt na na nt nt nt nil of note LTBI S145 9.4 − na na nt nt nt nt LTBI S098 8.6 − na na nt nt nt (Fractured ribs LTBI T4-9 posteriorly) S047 8.5 + 360 <50 nt nt nt nil of note LTBI S079 6.2 nt na na nt nt nt nil of note LTBI S001 5.9 + 430 775 nt nt nt nil of note LTBI S099 5.8 nt na na nt nt nt nil of note LTBI S120 4.7 nt na na nt nt nt Fibrosis both LTBI apices, hilar lymphadenopathy, pleural thickening S097 na + 177 19906 + − Sputum Subcarinal and pulmonary axillary lymph nodes S029 na + 530 <50 nt nt nt nil of note LTBI S025 na + 330 <50 nt nt nt nil of note LTBI S197 na + 210 <50 nt nt nt nil of note LTBI S201 na − na na nt nt nt nil of note LTBI S171 na + 365 <50 nt nt nt nil of note LTBI S191 na + 530 13328 nt nt nt nt LTBI S121 na − na na nt nt nt nil of note LTBI TB = tuberculosis, LTBI = Latent TB infection, Pos = positive, Neg = negative, nt = not tested, na = not applicable, BAL = bronchoalveolar lavage, CXR = chest x-ray, CT = computerised tomography. Those above the top bold dividing line would be predicted to have ATB and those below it LTBI. Those below the second bold dividing line did not have a positive TNF-α-only response to PPD.

Example 4—Diagnosis of Infection with MTB

(58) ELISpot platforms such that use MTB-specific antigens such as the T-SPOT®. TB are a highly sensitive and highly specific method to demonstrate the presence of infection with MTB (Dinnes, Deeks et al. 2007). The antigens utilised are either encoded by or associated with the region of difference 1 (RD1) including ESAT-6, CFP-10, Rv3879c, Rv3873 and Rv3615c which are both specific and immunodominant for MTB infection (Dosanjh, Hinks et al. 2008) (Dosanjh, Bakir et al. 2011). This approach is now recommended by health protection organisations worldwide for the diagnosis of LTBI. The ELISpot platform has the following advantages a) Standardisation of cell number in each well, this is a theoretical advantage in immunosuppression and data suggests that this test retains sensitivity when used in HIV co-infected individuals, unlike the TST (Chapman, Munkanta et al. 2002). b) Highly sensitive method of detection. This allows the use of several MTB-specific antigens, which can be used to distinguish MTB infection without confounding by prior BCG vaccination. Use of these antigens in the flow cytometry method of Example 5 is also possible, but responses can be too small to fully characterise. The most sensitive platform as the initial screening assay is therefore preferable to attain the highest diagnostic rate.

(59) Method: 1. Take 5-10 mls peripheral blood from the patient into a TSpot. TB assay tube. 2. Separate out the peripheral blood mononuclear cells. 3. Place the counted cells into a 96-well TSpot. TB plate with negative control, positive control and test wells in duplicate. 4. Stimulate overnight with MTB-specific antigens. 5. Read on an AIDS ELISpot reader. 6. Stratify into positive and negative according to manufacturer's predefined threshold.

(60) This method of diagnosing MTB infection may preferably then be followed by the method described in Example 5.

Example 5: Distinction of Active and Latent TB Infection and Indication of Mycobacterial Disease Activity

(61) Multi-parameter flow cytometry is a powerful tool for the interrogation of cellular immunity for measurement of responses to PPD, RD1, live or dead MTB and BCG. Simple flow cytometry procedures are used for monitoring in HIV infection (CD4 counts). Complex staining procedures are used for diagnostics of immune cancers such as leukaemia. There are currently no multi-colour flow cytometry assays for diagnostic use in the field of TB.

(62) The test (which may preferably be performed as a second step after the test described in Example 4) measures a novel combination of cellular targets, not previously investigated simultaneously:

(63) 1. Dead cell marker (allows removal of dead cells from analysis)

(64) 2. Cluster of differentiation (CD) 3 fluorochrome-conjugated antibody (identifies T-cells)

(65) 3. CD4 fluorochrome-conjugated antibody (identifies major T helper cell subset)

(66) 4. C—C chemokine receptor 7 (CCR7) fluorochrome-conjugated antibody

(67) 5. CD127 also known as interleukin-7 receptor alpha fluorochrome-conjugated antibody (this is particularly under-investigated in TB)

(68) 6. Interferon-gamma (IFN-γ) fluorochrome-conjugated antibody

(69) 7. Interleukin-2 (IL-2) fluorochrome-conjugated antibody

(70) 8. Tumour necrosis factor-alpha (TNF-α) fluorochrome-conjugated antibody

(71) 9. CD45RA fluorochrome-conjugated antibody (loss of this marker identifies experienced T-cell memory subset, this may be dispensable in the final assay)

(72) 10. CD8 fluorochrome-conjugated antibody (identifies major T-cytotoxic cell subset, this marker may be dispensable in the final assay)

(73) This method quantifies the proportion of live CD3+ CD4+ cells secreting TNF-α or IFN-γ that do not express CD45RA, CCR7 or CD127 (effector phenotype). This is to distinguish active from latent infection and stratify those at risk of progression to active TB. The higher the percentage of TNF-α-only secreting cells that have this effector phenotype, the higher the likelihood that a patient has active TB (higher threshold) or risk of progression to active TB (lower threshold). The combined measurement of these MTB-specific phenotype and function markers and test-specific approach to flow cytometry data analysis has not previously been described.

(74) Method:

(75) 1. Take 30 mls whole blood from the patient with a positive response from the test described in Example 1. (This could be done simultaneously with the step 1 phlebotomy).

(76) 2. Separate PBMCs using Ficoll-Paque solution.

(77) 3. Take 3-5 million cells per well and leave unstimulated (negative control) or stimulate for 16 hours with mitogen (positive control) and purified protein derivative (PPD) in duplicate. After 2 hours add a golgi apparatus blocking compound e.g. Monensin.

(78) 4. Stain cells with a live cell marker then with a cocktail of cell surface marker fluorochrome-conjugated antibodies.

(79) 5. Fix and permeabilise cells and stain with a cocktail of intracellular cytokine fluorochrome-conjugated antibodies.

(80) 6. Acquire at least 1 million events per well on a flow cytometer

(81) 7. Analyse data using a gating strategy specific for this test with flow cytometry software package e.g FlowJo, Treestarinc.

(82) Discussion of Results of Examples 1-3

(83) Our detailed interrogation of antigen-specific T-cell phenotype and function has delineated the association of TB disease stage with MTB-specific cellular immunity. ATB was associated with an increased frequency of mono- or dual-functional CD4+ and CD8+ MTB-specific T-cells that secrete IFN-γ and/or TNF-α making these subsets potential biomarkers of disease activity.

(84) Simultaneous evaluation of memory phenotype of responding cells provided a more sensitive and specific surrogate than CD4+ functional profile alone. Expression of CD45RA, CCR7 and CD127 on MTB-specific T-cells secreting only IFN-γ or TNF-α was lowest in those with ATB. Memory phenotype was not exclusively linked to the functional profile (except for IL-2-only cells which were mainly T.sub.CM) but was closely related to underlying TB stage. These markers might therefore serve as indicators of TB activation.

(85) Combined measurement of both functional profile and differentiation phenotype provided a highly discriminatory immunological read-out for ATB and LTBI. In those with ATB, >17.3% of PPD-specific CD4+ TNF-α-only-secreting cells were CD45RA−CCR7−CD127− and this phenotype was therefore strongly associated with activated infection. Given that responses to PPD are less specific for MTB infection than responses to RD-1 antigens, this PPD-specific T cell signature may be used to distinguish TB from LTBI in the second step of a two-step diagnostic testing strategy, where MTB infection has been ruled-in at step one by a positive result in an RD-1-based immunodiagnostic test (e.g. IGRA).

(86) Our data shows that the frequency of CD8+ MTB-specific cells, and therefore proportion of responders, was increased in ATB and this approach holds promise for the discrimination of TB disease stage especially in HIV co-infection where all participants had a positive response to MTB peptides. This association precludes the comparison of combined function and phenotype of MTB-specific CD8+ cells, however, because non-responders were by default mainly in the LTBI group. Measurement of CD8+ functional subsets in ATB and LTBI was therefore not sufficiently discriminatory for active and LTBI in our cohort.

(87) In this study we included individuals with HIV co-infection to compare and distinguish the impact of ATB on MTB-specific cellular immunity with the impact of HIV co-infection. Where CD4+ MTB-specific effector-like cells were influenced by TB disease stage, the impact of HIV co-infection per se was rarely significantly associated with these changes. This may have been partially due to the inclusion of patients who were treated for HIV infection.

(88) However, in the case of CD127, stratification by CD4 count showed that the stage of HIV disease influenced expression of this marker on MTB-specific T-cells. Reduced CD127 expression on HIV-specific CD8+ T-cells (reviewed in Crawley et al. Immunol Cell Biol 2011 Aug. 23. doi: 10.1038/icb.2011.66) and CD4+ T-cells (Dunham R M, et al. J Immunol 2008; 180:5582-92) is observed with HIV disease progression, but a relationship between HIV disease progression and CD127 expression on MTB-specific T-cells has not previously been noted. Our finding indicates that in HIV co-infection, MTB-antigen-specific CD4+ cells lose CD127 expression with advancing HIV disease and are therefore potentially more differentiated. This effect could be directly virus-induced or secondary to increasing subclinical mycobacterial burden with advancing HIV infection.

(89) Our cohort included individuals with both pulmonary and extra-pulmonary infection. HIV infection is more commonly associated with TB dissemination as evidenced by the widespread involvement in some of these individuals. Despite some variation in clinical phenotype of those with ATB, our biomarker reliably distinguished TB stage, regardless of site of disease, suggesting that it may remain robust across the clinical TB disease spectrum and therefore have wide applicability. No individuals with LTBI developed ATB during 12 months of follow-up suggesting that, in our cohort (recruited in a TB non-endemic area), subclinical TB was not present in those classified as LTBI. The lack of continuous exogenous priming or re-stimulation due to TB exposure distinguishes our cohort from others recruited from TB endemic areas and removes this is as a possible confounding effect on the immunological changes we observed.

(90) Through dissection of the impact of varying TB stage as a surrogate for mycobacterial pathogen burden, with and without HIV co-infection, we have identified cellular changes that are highly sensitive to TB activity.

Example 6: Inverse Correlation with Time Since Estimated Exposure to TB

(91) In further work carried out amongst an entirely distinct cohort of HIV-uninfected individuals with latent TB infection (LTBI) (Table 3), the inventors have demonstrated that the immunological signature of the proportion of PPD-specific or ECR-specific TNF-α-only T cells that are CD45RA−CCR7−CD127−) inversely correlates with time since estimated exposure to TB (FIGS. 7(a) and (b)).

(92) “ECR” refers to the combination of ESAT-6 and CFP-10 and Rv3615c (otherwise known as EspC). Each of these are antigens that are strongly and widely recognised by T cells in MTB-infected people but not in BCG-vaccinated, MTB-uninfected people. Hence, unlike PPD, ECR is a MTB-specific cocktail of T cell antigens.

(93) This second cohort includes recent (<6 months) contacts of known pulmonary TB cases and individuals from high TB incidence countries who have been in the UK for at least 2 years with no known TB exposure within that time (‘remote’ contacts). For remote contacts, time since entry to the UK was used as a proxy measure for time since TB exposure. Given the far higher risk of progression to TB disease within the first few years following MTB infection, and the decline in risk over time thereafter, the inventor's novel T cell signature can stratify individuals with LTBI by their likelihood of developing disease.

(94) TABLE-US-00004 TABLE 3 Demographics of HIV-uninfected LTBI cohort. Time Patient TST IGRA since TB ID Age Sex (+/−) (+/−) Ethnicity exposure* T1002 57 M + + Black Caribbean 5 months T1070 50 M + + White(British) 2 months T1204 21 F + + Bangladeshi 1 month T1208 24 M + + White (British) 4 months T1215 22 M + + White (British) 4 months T1263 41 M + + White(British) 3 months T496 33 M NT + White (Eastern 6 months European) T508 31 F + + White (Western 2 months European) T566 30 M + + Hispanic (South 4 months American) T846 43 F + NT Black Caribbean Ongoing T854 44 M + + White (British) 3 months T475 29 M NT + Indian 3 months T505 40 F + + Black African 2 months T752 41 F + + Asian (Other) Ongoing T845 33 M − − Black Africa 3 months T1044 35 F − + Black African 2 years T1169 53 M + + Middle Eastern 28 years (Other) T1224 25 M NT + Indian 3 years T1225 21 F NT + Chinese 7 years T1234 24 F NT + Indian 3 years T1235 24 M NT + Indian 2 years T1250 26 F NT + Indian 3 years T1265 25 M + + Indian 3 years T489 36 F + + Black Africa 14 years T491 34 F NT + Black African 13 years T515 37 M − + Asian (Other) 2 years T957 29 F + + Pakistani 2 years T1206 26 M − + Asian (other) 2 years M: male; F: female; NT: not tested *Estimated based on time since exposure to a known TB index case (for recent contacts) or time since emigration from a high TB incidence country (for remote contacts).

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