This study was approved by the Ethical Committee of “L. Spallanzani” National Institute of Infectious Diseases (INMI)-IRCCS, approval number 72/2015. Written informed consent was required to participate in the study conducted at INMI. We prospectively enrolled HIV-uninfected subjects with TBI with and without IMID and patients with pulmonary active-TB. Microbiologically diagnosed active-TB was defined based on the Mtb isolation from sputum culture. Active-TB patients were enrolled within 7 days of starting the specific TB treatment (T0) and at the end of therapy (T1).

In the absence of clinical, microbiological, and radiological signs of active-TB, TBI was defined based on a positive score to QFT-Plus (Qiagen, Hilden, Germany). The TBI cohort included subjects with a remote infection (contact with a smear-positive pulmonary TB patient at least 3 years before the enrolment) and subjects reporting a recent contact (within 3 months). TBI subjects reporting a time of exposure between 4 months and 3 years were not enrolled (38). TBI subjects and TBI-IMID patients were enrolled before starting the specific TB preventive therapy (T0) and at the end of treatment (T1). Demographic and epidemiological information were collected at enrolment and are reported in Table 1 . The information relative to the type of IMID and IMID-therapy are reported in Table 2 .

QFT-Plus assay was performed for each patient at T0 and T1. Two TBI-IMID and one active-TB patients did not perform the T1 evaluation. QFT-Plus kits were used according to manufacturer’s instructions (53). The QFT-Plus Analysis Software (available from www.quantiFERON.com) was used to analyze raw data and to calculate the IFN-γ results in international units per milliliter (IU/ml). The software performs a quality control assessment of the assay, generates a standard curve, and provides a test result for each subject. Test results were interpreted according to manufacturer’s criteria (53). TB1 tube contained peptides designed to induce mainly a CD4 T-cell response, whereas TB2 tube contained peptides to induce both a CD4 and CD8 T-cell response (48, 49).

Intracellular staining (ICS) was performed for each patient at T0 and T1. Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll density gradient centrifugation with the SepMate™ tubes (StemCell) and resuspended in complete RPMI-16-40 medium (Gibco, CA, USA) with 10% fetal bovine serum (PAA Laboratories GmbH, Pasching, Austria). To characterize by flow cytometry the Mtb-specific T-cell response, 1 × 10⁶ PBMC resuspended in 1 ml of medium were dispensed in TB1, TB2, Mitogen, and Nil tubes of the QFT-Plus kit. After a 1 h incubation, PBMCs were transferred in polystyrene round-bottom tubes, and 1 μl/ml of Golgi plug (BD Biosciences, San Josè, USA) was added to inhibit cytokine secretion. Anti-CD28 and anti-CD49d monoclonal antibodies (mAb) at 2 μg/ml each were added to co-stimulate cells.

Following an incubation of 16–24 h, the ICS was performed. As previously described (46), PBMCs were stained with anti-CD4 peridinin chlorophylprotein (PerCp)-Cy5.5 conjugate, anti-CD8 allophycocyanin (APC)-H7, anti-CD3 conjugate PE- cyanine 7 (Cy7), anti-IFN-γ Pacific Blue (PB) conjugate, anti-TNF-α fluorescein isothiocyanate (FITC), anti-IL-2 R-phycoerythrin (PE), anti-CD45RA APC, anti-CD27 Horizon V500 (all from BD Biosciences).

The Mtb-specific T-cell response was characterized evaluating the frequencies of CD4 and CD8 T cells producing IFN-γ, TNF-α, and IL-2 ( Figure 1 ). At least 100,000 lymphocytes were acquired with a FACS CANTO II (BD Biosciences). Cytometry data were analyzed using FlowJo software (Version 9.3). Background cytokine production in the Nil tube was subtracted from each stimulated condition. If the background was higher than half of the antigen-specific response, the results were scored as negative. A frequency of IFN-γ-producing T cells of at least 0.03% was considered as positive response. The cytokine profile has been evaluated only in patients with a positive total response to the Mtb stimulation using the Boolean gate function of FlowJo software. For responders, we also calculated the functional differentiation score (FDS) as previously described (29, 30) by applying the following equation:

The phenotype as well has been evaluated only in responders, assessing the proportion of CD45RA and CD27 on the gate of CD4 T cells able to produce IFN-γ, IL-2, or TNF-α (total CD4 T-cell response).

The number of CD4 and CD8 responders to TB1 and TB2 stimulation is reported in detail in Table 3 .

Data were analyzed using Graph Pad Prism (Version 8.2.1 for Windows) and Stata (Stata 15, StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). The median and interquartile ranges (IQRs) were calculated for continuous measures. Kruskal–Wallis test was used for comparison among several groups. The Chi-Square and Fisher tests were used for proportions. The Wilcoxon matched-pairs signed rank test was used to compare the different time points. Bonferroni correction was applied when appropriate.

Thirty-nine subjects at different TB stages were enrolled. The TB-IMID patients had the higher age compared to the other groups (p = 0.0002), they included a higher proportion of remote Mtb exposed subjects compared to TBI (p=0.006), and they have a similar number of lymphocytes before and after TB therapy completion (p = 0.75, Table 1 ). One TBI-IMID patient was taking biologic drugs at T0 and at T1, and four TBI-IMID patients were taking biologics at T1 ( Tables 2 , 4 ). Regarding the TB therapy, the majority of TBI-IMID patients received isionazid, whereas only three received isoniazid and rifampicin for 3 months; one subject received for 2 weeks isionazid and rifampicine then continued with isoniazid only. The duration of the preventive therapy regimen had a median of 6 months ( Table 1 ). The TBI subjects showed similar lymphocyte counts before and after therapy (p = 0.73, Table 1 ) and received mainly isoniazid. Finally, as expected, patients with active-TB showed a significant increase of the lymphocyte counts after TB therapy completion (p = 0.002, Table 1 ) and received the standard TB regimen.

In this study, the diagnosis of TBI is based on a positive IGRA and clinical and radiological parameters; therefore, the TBI-IMID and the TBI subjects have for definition a positive IGRA result. However, among the TBI-IMID patients, two patients had a negative QFT-Plus at T0 and a previously positive IGRA: one patient had a remote Mtb-infection, whereas the other one had a recent Mtb-infection and was taking an immunosuppressive drug. Among the subjects with TBI, one patient with a remote Mtb-infection had a negative QFT-Plus at T0 and a previously positive IGRA.

Differently, the assumption of a positive IGRA is not a criterion for the diagnosis of active-TB disease. Evaluating the QFT-Plus response at T0 and T1, we did not observe any significant differences in any groups ( Supplementary Figure 1 ), and remarkably, all patients responded to Mitogen stimulation (data not shown).

We evaluated the ability of CD4 and CD8 T cells to produce IFN-γ, IL-2, and TNF-α in response to TB1 or TB2 stimulation ( Figures 2 , 3 and Table 3 ). In Figure 1 , we showed a representative flow-cytometry analysis of Mtb-specific T cells for each studied group. Regarding the Mtb-specific CD4 T-cell response, we did not observe significant differences in terms of number of responders before and after TB treatment in any groups ( Table 3 ). The frequency of Mtb-specific CD4 T cells, producing IFN-γ, IL-2, and TNF-α, was similar among TBI-IMID ( Figures 2A, B ), TBI subjects ( Figures 2C, D ), and active-TB patients ( Figures 2E, F ) at T0 and T1 with the exception of a lower frequency of TB2-specific TNF-α⁺ CD4 T cells at T0 compared to T1 in TBI-IMID group ( Figure 2B ) and a higher frequency of TB1- and TB2-specific IL-2⁺ CD4 T cells at T0 compared to T1 in TBI group ( Figures 2C, D ). Moreover, all groups of patients produced IFN-γ, IL-2, and TNF-α in response to TB1 or TB2 stimulations ( Figure 2 and Table 3 ). Considering the different IMID therapy of TBI-IMID ( Table 2 ), we indicated in the graph the presence or not of an undergoing therapy with biologic drugs at each time point ( Figures 2A, B ). Stratifying for the presence of the undergoing biologic therapy ( Table 4 ), we found that four patients were taking biologics only at T1, one patient at both T0 and T1, and nine patients neither at T0 nor at T1. We did not find any significant differences among TBI-IMID comparing T0 and T1 in terms of total response (production of any cytokines by CD4 T cells) ( Table 4 ). Due to sample size, we could not stratify for type of biologic agents used and for the others IMID therapies.

Regarding the Mtb-specific CD8 T-cell response, we observed a not significant higher number of responders at T1 compared to T0 in response to TB2 stimulation ( Table 3 ). Moreover, the number of CD8 T-cell responders was lower than the number of CD4 T-cell responders ( Table 3 ). The frequency of Mtb-specific CD8 T cells was similar among TBI-IMID ( Figures 3A, B ), TBI subjects ( Figures 3C, D ), and active-TB ( Figures 3E, F ) patients at T0 and T1 and the responders produced mainly IFN-γ in response to TB1 or TB2 stimulations. Due to the low number of CD8 T-cell responders, we did not analyze the data according to the presence or not of biologic drugs.

Patients of all studied groups responded to the in vitro mitogen stimulation ( Supplementary Figure 2 ). Regarding the CD4 T-cell response, the TBI-IMID patients showed a higher frequency of T cells producing IFN-γ or TNF-α or IL-2 at T1 compared to T0, whereas no significant differences were observed in TBI subjects and active-TB patients ( Supplementary Figure 2A–E ). Regarding the CD8 T-cell response, the TBI subjects showed a higher frequency of T cells producing IL-2 at T0 compared to T1, whereas no significant differences were observed in TBI-IMID and active-TB patients ( Supplementary Figure 2B–F ).

We next compared the cytokine production of Mtb-specific CD4 T cells among the different groups at each time point ( Figure 4 ); we focused on CD4 T cells since the number of CD8 T cells responders was too low to allow a robust statistical analysis ( Table 3 ). The frequency of Mtb-specific CD4 T cells producing IFN-γ, IL-2, or TNF-α was similar in TB, TBI subjects, and TBI-IMID patients enrolled at T0 ( Figures 4A, C ) and at T1 ( Figures 4B, D ). Differently, in response to mitogen stimulation, we reported a higher level of TNF-α (p = 0.0013) and IL-2 in TBI-IMID compared to TBI ( Supplementary Figure 3 ).

We further investigated the functional cytokine profile of Mtb-specific T cells ( Figure 5 ). We focused on CD4 T cells since the number of CD8 T cells responders was too low to allow a robust statistical analysis ( Table 3 ).

In TBI-IMID group, we did not observe any significant changes comparing CD4 cytokine profile at T0 and T1 ( Figures 5A, B ) with the exception of a decrease of the IFN-γ⁺ IL-2⁺ CD4 T-cell subset at T1 compared to T0 in response to TB2 stimulation ( Figure 5B ). The CD4 T-cell response to TB1 and TB2 was mainly characterized by polyfunctional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4 T cells; among the monofunctional T cells, the IFN-γ⁺ IL-2^- TNF-α^- CD4 T cells were the most representative subset ( Figures 5A, B ).

In TBI group, we observed significant changes comparing CD4 T-cell cytokine profile at T0 and T1 ( Figures 5C, D ): the proportion of polyfunctional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4 T cells was significantly higher at T0 than at T1 in response to TB1 (p = 0.0039) and to TB2 (p = 0.0195). Differently, we reported a higher but not significant proportion of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4 T cells at T1 compared to T0 in response to both stimuli. The proportion of the monofunctional IFN-γ⁺ IL-2^- TNF-α^- T cells in TBI subjects after TB treatment was quite higher compared to TBI-IMID at the same time point (see below for the details).

In the active-TB group, we did not observe any significant change comparing CD4 cytokine profile at T0 and T1 ( Figures 5E, F ). In this case, the CD4 response to TB1 and TB2 was not similar. The TB1 response was mainly characterized by polyfunctional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4 T cells; among the monofunctional T cells, the IFN-γ^- IL-2^- TNF-α⁺ CD4 T cells and the IFN-γ⁺ IL-2^- TNF-α^- CD4 T cells were the most representative subsets ( Figure 5E ). In contrast, the TB2 response showed a low proportion of polyfunctional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4 T cells and an increased proportion of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4 T cells at both time points ( Figure 5F ).

It is known that Mtb-specific T cells change their functional capacity depending on antigen and bacterial load (25, 30, 31, 33). Based on these evidences, it has been proposed a single measurement of functional differentiation, FDS, of responders that describes the different type of Mtb-infection and Mtb burden (30). This analysis led us to synthetize the functional changes of T cells before and after therapy reported in Figure 5 . The FDS of Mtb-specific CD4 T cells before and after therapy was not significantly different between T0 and T1 in any groups of patients ( Supplementary Figure 4 ). The FDS comparison at the baseline did not show any significant differences among groups (data not shown). To note that, we could not include in the FDS calculation the patients without the IFN-γ^- IL-2^+/- TNF-α^+/- CD4⁺ T-cell subset (TBI-IMID T0 in response to TB1: two patients, TBI-IMID T1 in response to TB1: one patient; TBI-IMID T0 in response to TB2: one patient; TBI T1 in response to TB2: one patient; active-TB T0 in response to TB2: two patients).

Since the TBI-IMID and the TBI included both recent and remote Mtb-infection, we choose to analyze again the FDS including the most represented category: remote TBI-IMID (12 patients over 14 enrolled) and recent TBI (8 patients over 12 enrolled) ( Supplementary Figure 5 ). Even with this stratification, we did no observe significant differences before and after TB therapy in any studied groups.

Collectively, these data suggested that the TB treatment did not deeply affect the CD4 cytokine profile of the TBI-IMID patients. Differently, the TBI subjects showed a significant decrease of triple functional T cells after treatment and the active-TB a not significant but evident increase of the proportion of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells ( Figure 5 ).

Then, we compared the cytokine profile of Mtb-specific CD4 T cells among the different groups at each time point ( Figures 6 and 7 ). In response to TB1 stimulation at T0, we did not observe any significant differences ( Figure 6A ). However, the main involved subset was constituted by the triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells in all groups; in active-TB and TBI-IMID, the contribution of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells was also evident ( Figure 6A ). Similarly, in response to TB1 stimulation at T1, we did not observe any significant differences ( Figure 6B ). In particular, we observed: in active-TB a high proportion of monofunctional IFN-γ^+/- IL-2^- TNF-α^+/- CD4⁺ T cells and triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells; in TBI, a great contribution of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells; in TBI-IMID, a high proportion of monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells and triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells ( Figure 6B ).

In response to TB2 stimulation at T0, we did not observe any significant differences ( Figure 7A ). However, in active-TB, the monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells constituted the main involved subset, whereas in TBI and TBI-IMID the triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells was the major represented subset ( Figure 7A ). In response to TB2 stimulation at T1 ( Figure 7B ), we observed a high contribution of the monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells in active-TB and TBI. In TBI-IMID, we reported a high and significant percentage of double functional IFN-γ⁺ IL-2^- TNF-α⁺ CD4⁺ T cells compared to TBI (p = 0.0161) and a high contribution of triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells in TBI-IMID ( Figure 7B ).

Collectively, these data indicated at T0 a predominant TB1-induced polyfunctional profile and at T1 an increase of TB1-induced monofunctional IFN-γ⁺ IL-2^- TNF-α^- CD4⁺ T cells in all groups ( Figure 6 ). The TB2 cytokine profile was more heterogeneous, showing that triple functional IFN-γ⁺ IL-2⁺ TNF-α⁺ CD4⁺ T cells were mainly represented in TBI-IMID patients at both T0 and T1 ( Figure 7 ).

Next, we investigated if the TB treatment had or not an impact on the phenotype of Mtb-specific T cells, evaluating the proportion of CD45RA and CD27 ( Figure 8 ). Even in this case, we focused on CD4 T cells since the number of CD8 T cells responders was too low to perform a robust statistical analysis ( Table 3 ).

Since we did not observe differences in term of FDS and considering the low frequency of the Mtb-specific T cells, we decided to analyze the phenotype of Mtb-specific CD4 T cells able to produce any cytokines (total response based on IFN-γ, TNF-α, or IL-2 production). In this way, we had the maximum number of events available for the phenotype description, and we avoided as much as possible the interference of the background that could not be subtracted in the phenotype evaluation.

In TBI-IMID and TBI subjects, the Mtb-specific CD4 T-cell response was mainly represented by the CD45RA^-CD27⁺ subset both at T0 and T1 (with a frequency ranging from 55% to 79%) ( Figures 8A–D ). In TBI-IMID patients, we did not observe significant differences in response to TB1 and TB2 antigens and any evident trend ( Figures 8A, B ). In TBI subjects, we observed a lower proportion of Mtb-specific CD45RA^- CD27⁺ CD4 T cells at T1 compared to T0 (p = 0.0098) in response to TB2 and a similar trend in response to TB1 ( Figures 8C, D ).

In active-TB, the Mtb-specific CD4 T-cell response was mainly represented by the CD45RA^- CD27⁺ subset both a T0 and T1 (with a frequency ranging from 46% to 67%) ( Figures 8E, F ). We observed an increased Mtb-specific CD4⁺ CD45RA^- CD27⁺ subset and a contraction of the CD45RA^- CD27^- subset after therapy completion in response to TB1 and TB2 antigens ( Figures 8E, F ). The frequency of the CD45RA^- CD27⁺ subset was significantly increased in response to TB2 (p = 0.0195, Figure 8F ), whereas the frequency of the CD45RA^- CD27^- subset was significantly decreased in response to TB1 (p = 0.0195, Figure 8E ).

Then, we compared the phenotype profile of Mtb-specific CD4 T cells among the different groups at each time point ( Figure 9 ). In response to TB1 stimulation at T0, we found that the CD45RA^-CD27⁺ subset was significantly lower in active-TB compared to TBI and TBI-IMID (p = 0.0021; p = 0.0027) and the CD45RA^-CD27^- subset was significantly higher in active-TB compared to TBI and TBI-IMID (p = 0.0058; p = 0.0041) ( Figure 9A ). Differently, at T1, we did not find any significant differences ( Figure 9B ).

In response to TB2 stimulation at T0, we found a phenotype profile similar to TB1 stimulation for all groups; however, only the CD45RA^-CD27⁺ subset was significantly lower in active-TB compared to TBI-IMID (p = 0.001) ( Figure 9C ). At T1, we observed that the TBI subjects had lower proportion of the CD45RA^-CD27⁺ subset (p = 0.0002) and a higher proportion of CD45RA⁺CD27⁺ (p = 0.0005) compared to TBI-IMID ( Figure 9D ).

Collectively, these data highlighted a loss of CD27⁺ CD4 T cells in active-TB patients and this profile was more evident at T0.