IL-18/IL-37/IP-10 signalling complex as a potential biomarker for discriminating active and latent TB

Sebastian Wawrocki; Michal Seweryn; Grzegorz Kielnierowski; Wieslawa Rudnicka; Marcin Wlodarczyk; Magdalena Druszczynska

doi:10.1371/journal.pone.0225556

. 2019 Dec 10;14(12):e0225556. doi: 10.1371/journal.pone.0225556

IL-18/IL-37/IP-10 signalling complex as a potential biomarker for discriminating active and latent TB

Sebastian Wawrocki ¹, Michal Seweryn ², Grzegorz Kielnierowski ³, Wieslawa Rudnicka ¹, Marcin Wlodarczyk ¹, Magdalena Druszczynska ^1,^*

Editor: Selvakumar Subbian⁴

PMCID: PMC6903724 PMID: 31821340

Abstract

Background

Currently, there are serious limitations in the direct diagnosis of active tuberculosis (ATB). We evaluated the levels of the IL-18/IL-37/IP-10 signalling complex proteins in Mycobacterium tuberculosis (M.tb)-specific antigen-stimulated QuantiFERON® Gold In-Tube (QFT) cultures and in serum samples from ATB patients, healthy individuals with latent M.tb infection (LTBI) and healthy controls (HC) to examine whether combined analyses of these proteins were useful in the differentiation of M.tb states.

Methods

The concentrations of IL-18, IL-18BP, IFN-γ, IL-37 and IP-10 in the serum and QFT supernatants were measured using specific enzyme-linked immunosorbent assay (ELISA) kits. Free IL-18 levels were calculated using the law of mass action.

Results

Increased concentrations of total and free IL-18, IL-18BP, IFN-γ and IP-10 in the sera of ATB patients were detected. These increases were not counterbalanced by the overproduction of IL-37. Complex co-expression of serum IL-18BP and IL-37, IP-10 and IFN-γ was identified as the highest discriminative biomarker set for the diagnosis of ATB.

Conclusions

Our results suggest that the IL-18 signalling complex might be exploited by M. tuberculosis to expand the clinical manifestations of pulmonary TB. Therefore, direct analysis of the serum components of the IL-18/IL-37 signalling complex and IP-10 may be applicable in designing novel diagnostic tests for ATB.

Introduction

Tuberculosis (TB) affects approximately 10 million people causing 2 million deaths annually [1]. Approximately 1/3 of the human population is infected with Mycobacterium tuberculosis (M.tb), the causative agent of TB, and 5–10% of this population develop active tuberculosis (ATB) disease during their lifetime. The remaining 90–95% of individuals mount an immune response and develop latent tuberculosis infection (LTBI) [2]. TB is predominantly a disease of the lungs and the transmission of M.tb bacilli occurs through airborne granulomatous particles released into the air by individuals suffering from pulmonary ATB. Tuberculous granulomas are formed in infected lungs and are aggregates of macrophages surrounded by a lymphocyte marginal zone that encloses the infecting mycobacteria [3]. M.tb persists in a dormant state inside macrophages for long periods of time. The immune status of the host’s macrophages and T cells and the activation of cytokines, primarily IFN-γ, control the granuloma structure and M.tb replication in asymptomatic LTBI. Due to an unpredictable reason, the bacilli reactivate in 5–10% of LTBI subjects, and caseous granulomas develop and initiate a clinical disease and the spread of virulent bacilli in the environment. The risk of progression from LTBI to active TB may be increased by some factors such as HIV infection, chronic renal failure, diabetes, organ transplantation or therapy using tumor necrosis factor-alpha blockers. The identification of M.tb-infected individuals and the appropriate treating those who develop ATB and preventing those with an increased risk of TB progression are undoubtedly crucial for effective TB control. Currently, there are limitations in the direct ATB detection via the microscopic visualization of acid-fast bacteria in the sputum and observation of M.tb growth in long-term cultures. Interferon-gamma release assays (IGRAs) are used to diagnose LTBI. These tests measure the release of IFN-γ in response to M.tb-specific antigens in whole blood cultures. However, IGRAs in combination with tuberculin skin tests are not sufficiently accurate to diagnose ATB [1,4]. To satisfy the demand for rapid and accurate TB diagnostic tests, we performed analyses of several classification algorithms to rank proteins of the IL-18/IL-37/IP-10 signalling complex according to their usefulness in the differentiation of M.tb infection states. Progression towards tuberculosis disease correlates with the loss of organization in the granulomas [5]. The immunological and inflammatory environments of granulomas change due to the recirculation of immune cells and the release of cytokines that reach the periphery. Therefore, evaluations of cytokine/chemokine profiles in the blood are promising for the differentiation of infectious states in TB [6–8].

The cytokine IL-18 is implicated in the protective and pathological processes of M.tb infection [9–11]. The activity of IL-18 occurs via an IL-18 binding receptor (IL-18R) complex formed by two chains: a ligand-binding alpha chain (IL-18Rα), and a beta chain (IL-18Rβ), which is responsible for the induction of a proinflammatory signal [12]. The formation of an IL-18Rα/IL-18Rβ heterodimer triggers the signalling cascade that leads to activation of the transcription factor NF-κβ [13]. The excessive inflammatory signalling of IL-18 is reduced by a constitutively secreted IL-18 binding protein (IL-18BP), which neutralizes circulating IL-18 to lower free IL-18 compared to total IL-18 levels. Therefore, the production of IFN-γ and other proinflammatory cytokines is reduced [14]. IL-18BP also shows a high affinity for IL-37, which is an inhibitor of the innate inflammatory responses involved in curbing excessive inflammation [15,16]. After binding to IL-18BP, IL-37 subsequently binds IL-18Rβ, which inhibits the proinflammatory activity of IL-18 [17,18]. We performed a combined analysis of the proteins, free and total IL-18, IL-18BP, IL-37, IFN-γ in QFT supernatants and directly in serum samples from pulmonary TB patients and healthy individuals with or without latent M.tb infection to identify new markers for the diagnosis of ATB. In parallel with the proteins’ analysis, we measured serum IFN-γ-inducible protein 10 (IP-10/CXCL10) levels, a chemokine mediating leukocyte recruitment and activation.

Materials and methods

Study population

A total population of 238 M. bovis Bacillus Calmette-Guerin (BCG)-vaccinated adults of both genders, 18–81 years of age, was enrolled in the present study. All participants were unrelated Poles who signed a formal written consent for the use of their blood for research purposes. The study protocol complied with the most recent Declaration of Helsinki, and the Ethics Committee of the University in Lodz, Poland approved the protocol. The study cohort included 95 patients with active pulmonary TB (ATB), which was microbiologically confirmed (51) or not confirmed (44) in a triple sputum culture. They were recruited from the Regional Specialised Hospital of Tuberculosis, Lung Diseases and Rehabilitation in Tuszyn, Poland. A full history was taken from all patients, and experienced physicians completed general and clinical examinations. The final diagnosis was based on the clinical symptoms, a chest X-ray image, microscopic and microbiological evaluations of sputum samples and a proper response to anti-tuberculous treatment. Blood samples were taken prior to the start of therapy. Healthy individuals (143) entering the study were classified as M.tb-infected (LTBI) (52) or M.tb-uninfected (91) based on the interferon-gamma release assay results (QuantiFERON-TB Gold Plus; QFT). None of the healthy volunteers had a history of TB.

The age, sex, BCG vaccination status as well as tuberculin skin test (TST) and QFT results of the study participants are summarized in Table 1. The group of the study comprised 95 patients diagnosed with active pulmonary TB (ATB), microbiologically confirmed (51) or not confirmed (44) by a triple sputum culture, 52 individuals without TB history, who were latently infected with M.tb (LTBI), and 91 healthy controls without M.tb infection (HC). There were no significant differences between studied groups regarding age or BCG vaccination rate. The proportion of men in the ATB group (56%) was significantly higher than in LTBI (31%) and HC (31%) groups (p<0.05). Fifty one (54%) ATB patients had a positive sputum culture for M.tb. Forty four (46%) M.tb culture negative patients were diagnosed on the basis of clinical manifestations, typical features’ on chest radiographs and proper response to anti-tuberculous treatment. Fifty-five % of ATB patients exhibited a positive TST result with an induration diameter of more than 10 mm, whereas a positive QFT result was found in 61% of individuals from this group. Both subgroups of ATB patients were characterized by a similar proportion of TST-positive and QFT-positive results. Nine out of 95 (9%) ATB patients had a history of healed pulmonary TB. Six percent of ATB patients suffered from diabetes or chronic renal failure, whereas cardiovascular or neurological diseases were diagnosed in 15% and 3% patients, respectively.

Table 1. Characteristics of the study participants.

	ATB	LTBI	HC
N	95	52	91
Ethnicity	Caucasians	Caucasians	Caucasians
Country of origin	Poland	Poland	Poland
Sex, N (%)
Men	53 (56%)	16 (31%)^a	28 (31%)^b
Women	42 (44%)	36 (69%)	63 (69%)
Age
Median	51	52	41
range	19–81	20–81	18–75
years (IQR)	33–63	41–56	33–52
BCG vaccination, N (%)	95 (100%)	52 (100%)	91 (100%)
TST induration diameter (mm)
median	12
range	0–35
TST result, N (%)		n.d	n.d
positive	52 (55%)
negative	43 (45%)
QFT result, N (%)
positive	58 (61%)	52 (100%)	0 (0%)
negative	37 (39%)	0 (0%)	91 (100%)
M.tb culture
positive	51 (54%)	n.d	n.d
negative	44 (46%)
History of healed ATB	9 (9%)	0 (100%)	0 (100%)

Open in a new tab

Abbreviations: ATB–active tuberculosis, HC- healthy controls, LTBI–latent M.tb infection, BCG–Bacillus Calmette-Guerin, QFT–QuantiFERON TB Gold Plus, TST- tuberculin skin test, IQR—interquartile range, n.d–not determined.

There was a significant difference among studied groups

^amale LTBI vs ATB (p<0.05) and

^bmale HC vs ATB (p<0.05) (χ² test).

Blood samples

A 5-ml volume of venous blood samples was used to prepare the sera and perform a QuantiFERON-TB® Gold Plus test (QFT, Qiagen, Hilden, Germany). Blood was collected in four 1-ml tubes (Nil control, TB antigen-specific 1 (TB1), TB antigen-specific 2 (TB2), Mitogen control), and IFN-γ levels in the supernatants were measured immunoenzymatically after a 24-hour incubation.

IL-18, IL-18BP, IL-37, IFN-γ, IP-10 estimation and calculation of free IL-18

The concentrations of IL-18, IL-18BP and IL-37 in the sera and QFT cell-free culture supernatants were determined using commercially available specific ELISA kits: Human Total IL-18 DuoSet ELISA (R&D, Minneapolis, USA), Human IL-18BPa DuoSet ELISA (R&D) and Human IL-37/IL-1F7 Duoset ELISA (R&D). IFN-γ and IP-10 concentrations in sera were assessed using Human IFN-γ Duoset ELISA (R&D) and Human IP-10 Duoset ELISA (R&D). The law of mass action was used to calculate the level of free IL-18 [19]. It is known that one IL-18BP molecule binds a single molecule of IL-18, and this interaction has a dissociation constant (Kd) of 0.4 nM. Therefore, the level of free IL-18 was calculated from the equation x = (-b±(b²-4ac)^1/2)/2a, where x is [IL-18free], b is = [IL-18BP]–[IL-18] + Kd, and c is -Kd·IL-18][13,19,20]. The levels of the studied proteins in the QFT supernatants were calculated after subtraction of baseline levels obtained from NIL tube.

Statistical analyses

Statistical analyses were performed using Statistica 12 PL (Statsoft, Poland). Comparisons of the frequencies were tested using the χ² or Fisher’s exact test. Differences in the levels of the studied proteins were analysed using the non-parametric Kruskal-Wallis test. Differences with p < 0.05 were considered statistically significant.

Target statistical and machine-learning methods were used as implemented in R. ‘pROC’, ‘ROCR’ packages as well as custom codes (available upon request) were used in the ROC analysis. For the classical analysis of association between levels of expression of single proteins and protein ratios logistic regression was used in order to make the comparison of performance of single markers to all available markers (full logistic model) 'fair'. The AUC values were estimated via 5-fold cross-validation and based on at least 500 bootstrap replicates. The Random Forest algorithm was used as implemented in the ‘randomForest’ package. Pearson’s correlation coefficient was used for co-expression analyses. For the network analysis, the custom R code was used (available upon request), and data discretization for the estimation of Renyi divergence was performed using the ‘infotheo’ and ‘equalfreq’ options. The t-SNE algorithm was used as implemented in ‘Rtsne’. The ordinal elastic-net algorithm was applied as implemented in ‘ordinalNet’.

Results

Serum IL-18, IL-18BP, IL-37, IFN-γ and IP-10 levels in the studied groups

The concentration of total IL-18 in the sera from the ATB patients (median (Me) 568.1 (IQR 371.9, 980.3) pg/ml) was significantly higher than those found in the LTBI (Me 261.6 (IQR 109.3, 492.3) pg/ml) and HC (Me 283.1 (IQR 177.5, 441.4) pg/ml) groups (p<0.001) (Fig 1A).

Fig 1 — Boxplots with median (horizontal line within the box), interquartile range (box limits), and extremes (whiskers) of serum levels of total IL-18 (A), IL-18BP (B), free IL-18 (C), IL-37 (D), IFN-γ (E) and IP-10 (F) in the groups of patients with active tuberculosis (ATB) and latent M.tb infection (LTBI) and healthy controls (HC).

Total IL-18 levels were increased in both culture-positive (Me 719.5 (IQR 416.3, 1146.0) pg/ml) and culture-negative (Me 444.2 (IQR 305.8, 828.1) pg/ml) ATB patients (data not shown). Similarly to the total IL-18 level, the serum concentration of IL-18BP was significantly higher in ATB patients (Me 43.5 (IQR 31.7, 60.4) ng/ml, culture-positive (Me 44.9 (IQR 32.5, 59.3) ng/ml, culture-negative (Me 43.4 (IQR 27.8, 77.2) ng/ml) than in LTBI (Me 28.8 (IQR 21.1, 42.8) ng/ml; p<0.001) or HC (Me 32.7 (IQR 22.7, 43.7) ng/ml; p<0.001) groups (Fig 1B).

The serum concentration of free IL-18 calculated from IL-18 and IL-18BP levels was significantly higher in ATB patients (Me 5.5 (IQR 3.0, 10.5) pg/ml; culture-positive (Me 5.5 (IQR 3.7, 10.9) pg/ml, culture-negative (Me 3.6 (IQR 2.1, 8.3) pg/ml) than LTBI (Me 3.6 (IQR 1.7, 5.5) pg/ml; p = 0.012) or HC (Me 4.0 (IQR 2.1, 6.5) pg/ml; p = 0.036) groups (Fig 1C). In contrast, the serum concentrations of IL-37 were similar among studied groups: ATB (Me 102.6 (IQR 12.6, 236.6) pg/ml, LTBI (Me 78.4 (IQR 23.3, 323.3) pg/ml, HC (Me 103.5 (IQR 9.8, 357.3) pg/ml (Fig 1D). On the other hand, the level of IFN-γ, in the ATB patients’ sera was significantly higher (Me 6.7 (IQR 3.5, 15.0) pg/ml; culture-positive (Me 8.8 (IQR 4.8, 20.4) pg/ml, culture-negative (Me 5.7 (IQR 3.2, 12.2) pg/ml) than that observed in the LTBI (Me 5.0 (IQR 3.3, 7.5) pg/ml; p = 0.049) or HC (Me 5.7 (IQR 3.0, 8.4) pg/ml; p = 0.002) individuals (Fig 1E). As shown in Fig 1F, the serum level of IP-10 was significantly higher in the ATB group (Me 43.5 (IQR 19.9, 75.7) pg/ml; culture-positive (Me 49.3 (IQR 27.4, 83.9) pg/ml, culture-negative (Me 35.8 (IQR 13.6, 57.2) pg/ml) than in the groups of LTBI (Me 13.7 (IQR 8.2, 28.0) pg/ml) and HC (Me 14.0 (IQR 7.8, 25.3) pg/ml) (p<0.001). There were no significant differences in the serum levels of the total IL-18, IL-18BP, free IL-18, IL-37, IFN-γ and IP-10 between ATB patients with positive or negative QFT and TST results (Fig 2A–2F).

Fig 2 — Boxplots with median (horizontal line within the box), interquartile range (box limits), and extremes (whiskers) of serum levels of total IL-18 (A), IL-18BP (B), free IL-18 (C), IL-37 (D), IFN-γ (E) and IP-10 (F) in the group of active tuberculosis patients with positive or negative QuantiFERON (QFT) and tuberculin skin test (TST) results.

Analysis of the area under the ROC revealed that the full logistic model resulted in an AUC = 0.82 (CI = (0.82; 0.83)) for the HC vs. ATB comparison, an AUC = 0.65 (CI = (0.61; 0.70)) for HC vs. LTBI, and an AUC = 0.79 (CI = (0.73; 0.80)) for LTBI vs. ATB. Table 2 shows the AUCs for individual proteins.

Table 2. Predictive values (median AUC and 95% CI) of individual protein levels measured in serum.

Parameter	HC vs. ATB	HC vs. LTBI	LTBI vs. ATB
Parameter	median AUC (95% CI)
IL-18	0.76 (0.76;0.77)	0.71 (0.65;0.77)	0.76 (0.75;0.77)
IL-18BP	0.67 (0.66;0.67)	0.62 (0.56;0.69)	0.69 (0.68;0.70)
IL-37	0.59 (0.52;0.59)	0.76 (0.69;0.82)	0.64 (0.59;0.69)
IFN-γ	0.57 (0.56;0.58)	0.61 (0.58;0.67)	0.52 (0.50;0.56)
IP-10	0.78 (0.77;0.78)	0.81 (0.75;0.86)	0.75 (0.75;0.76)

Open in a new tab

ATB–active tuberculosis (n = 95), HC- healthy controls (n = 91), LTBI–latent M.tb infection (n = 52)

The analysis of dependence between the levels of all proteins revealed certain correlations, that were specific for two groups of the study (IL-18BP and IP-10 as well as IL-18 and IL-18BP for ATB and LTBI) and some relationships specific only for certain study groups (e.g. IL-18 and IL-37 or il-18 and IP-10 for HC, IL-18 and IFN-γ or IFN-γ and IP-10 for ATB, and no correlation specific for LTBI) (Table 3).

Table 3. Correlation (r) and p values between protein levels in serum.

Parameters	HC		LTBI		ATB
	n = 91		n = 52		n = 95
	r	p	r	p	r	p
IL-18 ~ IL-18BP	0.021	0.765	0.258	0.006	0.180	0.009
IL-18 ~ IL-37	-0.130	0.070	-0.012	0.899	-0.005	0.940
IL-18 ~ IFN-γ	0.025	0.718	-0.100	0.293	0.210	0.002
IL-18 ~ IP-10	0.152	0.033	0.154	0.107	0.112	0.107
IL-18BP ~ IL-37	0.093	0.195	-0.122	0.203	-0.106	0.128
IL-18BP ~ IFN-γ	0.048	0.495	0.043	0.647	0.186	0.007
IL-18BP ~ IP-10	0.100	0.163	0.188	0.049	0.239	<0.001
IL-37 ~ IFN-γ	0.081	0.255	0.046	0.629	0.003	0.956
IL-37 ~ IP-10	0.018	0.796	-0.089	0.350	0.042	0.546
IFN-γ ~ IP-10	0.066	0.355	0.086	0.364	0.263	<0.001

Open in a new tab

ATB–active tuberculosis, HC- healthy controls, LTBI–latent M.tb infection

M.tb antigens–stimulated IL-18, IL-18BP, IL-37 and IFN-γ levels in QFT supernatants

As shown in Fig 3A, the M.tb antigens-stimulated levels of total IL-18 were significantly increased in the patients with ATB (Me 765.5 (IQR 431.6, 1122.0) pg/ml, culture-positive (Me 810.0 (IQR 440.4, 1142.0) pg/ml, culture-negative (Me 734.0 (IQR 403.7, 1131.0) pg/ml) compared to those of the LTBI (Me 445.7 (IQR 29.6, 656.1) pg/ml; p<0.001) or the HC group (Me 397.5 (IQR 222.8, 511.6) pg/ml; p<0.001). The level of IL-18BP was significantly higher (p<0.001) in ATB patients (Me 43.1 (IQR 32.5, 63.8) ng/ml) than HC (Me 32.2 (IQR 18.5, 50.9) ng/ml) (Fig 3B). There were no significant differences in the levels of free IL-18 or IL-37 in the QFT supernatants among studied groups (Fig 3C and 3D). The IFN-γ concentration in QFT supernatants was significantly higher in LTBI (Me 91.8 (IQR 37.5, 262.0) pg/ml) than ATB (Me 39.3 (IQR 13.0, 144.2) pg/ml, culture-positive (Me 49.6 (IQR 18.6, 144.2) pg/ml, culture negative (Me 25.3 (IQR 10.8, 159.0) pg/ml; p = 0.003) or HC groups (Me 7.9 (IQR 6.3, 10.6) pg/ml; p<0.001) (Fig 3E). However, there was no association between the results of QFT or TST and the levels of IL-18, IL-18BP or IL-37 measured in the QFT supernatants (Fig 4A–4D).

Fig 4 — Boxplots with median (horizontal line within the box), interquartile range (box limits), and extremes (whiskers) of levels of total IL-18 (A), IL-18BP (B), free IL-18 (C), IL-37 (D) in M.tb antigens-stimulated QFT supernatants from active tuberculosis patients with positive or negative QuantiFERON (QFT) and tuberculin skin test (TST) results.

Analysis based on the area under the ROC showed full logistic model of an AUC = 0.91 (CI = (0.90; 0.91)) for the HC vs. ATB comparison, an AUC = 0.98 (CI = (0.95; 0.99)) for HC vs. LTBI, and an AUC = 0.70 (CI = (0.69; 0.71)) for LTBI vs. ATB. Table 4 shows the discriminative power for the levels of individual proteins.

Table 4. Predictive values (median AUC and 95% CI) of individual protein levels in QFT supernatants.

Parameter	HC vs. ATB	HC vs. LTBI	LTBI vs. ATB
Parameter	median AUC (95% CI)
IL-18	0.76 (0.76;0.77)	0.51 (0.50;0.54)	0.71 (0.69;0.72)
IL-18BP	0.64 (0.64;0.65)	0.48 (0.45;0.55)	0.58 (0.56;0.59)
IL-37	0.54 (0.50;0.58)	0.75 (0.69;0.83)	0.59 (0.55;0.63)
IFN-γ	0.83 (0.83;0.84)	0.99 (0.99;0.99)	0.58 (0.55;0.62)

Open in a new tab

ATB–active tuberculosis (n = 95), HC- healthy controls (n = 91), LTBI–latent M.tb infection (n = 52)

Correlation analyses revealed pairs of proteins that were specific for certain study groups (IL-18 and IFN-γ and IL-18 and IL-37 for HC; IL-18BP and IL-37 for LTBI; and IL-18 and IL-18BP for ATB). A summary of these results is provided in Table 5.

Table 5. Correlation (r) and p-values between protein levels in QFT supernatants.

Parameters	HC		LTBI		ATB
	n = 91		n = 52		n = 95
	r	p	r	p	r	p
IL-18 ~ IL-18BP	-0.164	0.021	-0.006	0.943	0.148	0.033
IL-18 ~ IL-37	-0.136	0.056	-0.006	0.949	-0.055	0.427
IL-18 ~ IFN-γ	-0.139	0.051	-0.075	0.430	0.011	0.867
IL-18BP ~ IL-37	0.042	0.550	-0.252	0.008	-0.053	0.446
IL-18BP ~ IFN-γ	0.023	0.742	-0.020	0.831	0.016	0.816
IL-37 ~ IFN-γ	0.257	<0.001	-0.092	0.335	0.023	0.733

Open in a new tab

ATB–active tuberculosis, HC- healthy controls, LTBI–latent M.tb infection

Protein ratios in the sera and QFT supernatants

The discriminative powers of the ratios of serum and QFT supernatants between any two proteins of the IL-18 signalling complex were analysed. The highest discriminative powers in the sera (1) were IL-18/IL-37 for HC and ATB (AUC = 0.69, CI = (0.68; 0.69)), (2) IL-18/IL-18BP for HC and LTBI (AUC = 0.82, CI = (0.76; 0.88)) and (3) IL-18/IL-18BP for LTBI and ATB (AUC = 0.66, CI = (0.64; 0.67)). On the contrary, the highest discriminative powers in the QFT supernatants were (1) IL-37/IFN-γ for HC and ATB (AUC = 0.75, CI = (0.74; 0.76)), (2) IL-18BP/IFN-γ for HC and LTBI (AUC = 0.91, CI = (0.90; 0.91)), and (3) IL-18/IFN-γ for LTBI and ATB (AUC = 0.72, CI = (0.71; 0.74)).

Selection of the most informative protein ratios using random forest

An approach based on the random forest, which is a sample classification and feature selection algorithm, returned an importance score for each ratio of protein levels and an optimal number of features to be used in classifications estimated using cross-validation. Even with no statistically significant differences between studied groups, the IL-18, IL-18BP and IFN-γ from QFT supernatants were most informative (in terms of AUC) in the LTBI vs. HC comparison. The IL-18/IL-37, IL-18/IFN-γ, IL-18BP/IFN-γ and IL-37/IFN-γ protein ratios had the highest importance scores, and the levels of IL-18, IL-18BP and IFN-γ and the ratios between IL-18BP/IFN-γ and IL-37/IFN-γ remained informative in joint analyses. IL-18, IL-18BP and IFN-γ were most informative for the ATB vs. HC comparison, and the IL-18/IL-37, IL-18/IFN-γ, IL-18BP/IFN-γ and IL-37/IFN-γ protein ratios had the highest importance scores. The levels of IL-18, IL-18BP and IFN-γ and the ratios between IL-18/IL-37 and IL-37/IFN-γ remained informative in the joint analyses. IL-18 and IFN-γ were most informative for the LTBI vs. ATB comparison, and the IL-18/IL-18BP, IL-18/IL-37, IL-18/IFN-γ, IL-18BP/IL-37 and IL-18BP/IFN-γ protein ratios had the highest importance scores. The levels of IL-18, IL-18BP and IFN-γ and the IL-18/IL-18BP, IL-18/IL-37, IL-18/IFN-γ and IL-18BP/IFN-γ protein ratios remained informative in the joint analyses.

IL-18 and IL-37 were the most informative serum markers for the LTBI vs. HC comparison. The protein ratios IL-18/IFN-γ, IL-18BP/IFN-γ, and IL-18BP/IP-10 had the highest importance scores, and the levels of IL-18, IL-18BP and IFN-γ and the IL-18/IFN-γ, IL-18BP/IL-37, IL-18BP/IFN-γ and IL-18BP/IP-10 protein ratios remained informative in the joint analyses. IL-18 and IP-10 were most informative for ATB vs. HC comparison, and all of the ratios, except IL-18/IP-10 and IL-37/IFN-γ, were informative. The levels of IL-18 and IP-10 had the highest discriminative power in the joint analysis. IL-18, IL-18BP and IP-10 were the most informative for the LTBI vs. ATB comparison, and the IL-18/IL-18BP, IL-18/IL-37, IL-18/IFN-γ and IL-18BP/IP-10 ratios had highest importance scores. The levels of IL-18, IL-18BP and IP-10 and the IL-18/IL-18BP, IL-18/IL-37, IL-18/IFN-γ and IL-18BP/IP-10 ratios remained informative in the joint analysis. The AUC values are summarized in Table 6.

Table 6. AUC values for selected decision trees.

Comparison	QFT supernatant			Serum
	expression	ratio	both	expression	ratio	both
HC vs. LTBI	0.997	0.940	0.976	0.561	0.501	0.502
HC vs. ATB	0.883	0.722	0.906	0.796	0.733	0.798
LTBI vs. ATB	0.751	0.722	0.773	0.758	0.661	0.739

Open in a new tab

ATB–active tuberculosis (n = 95), HC- healthy controls (n = 91), LTBI–latent M.tb infection (n = 52)

For each case, the first value (expression) refers to the model with levels of single proteins only, the second value (ratio) to the ratios between protein levels and the third value (both) to the joint model.

The three-class comparison of the markers quantified in QFT supernatants revealed that all single protein levels were informative with a multi-class AUC = 0.7728. The IL-18/IFN-γ, IL-18BP/IFN-γ and IL-37/IFN-γ protein ratios had the highest importance scores with an AUC = 0.6026. IL-18 and IFN-γ and the IL-18/IFN-γ and IL-18BP/IFN-γ ratios were the most informative in the joint model with an AUC = 0.772.

Only IL-18 and IP-10 in the serum were informative with a multi-class AUC = 0.6507. All of the ratios, except IL-37/IFN-γ, were informative with an AUC = 0.6192. IL-18, IL-18BP and IP-10 as wells as the IL-18/IL-37, IL-18/IFN-γ and IL-18BP/IP-10 protein ratios were informative in the joint model with an AUC = 0.657.

Variability of expression and co-expression levels

To shed some further light on the issue of insufficient discriminative power of single protein levels and protein ratios, we aim to perform a deeper study of the variability and co-expression of the selected markers. In addition to the differential expression between the study groups, IFN-γ levels had significantly different variance between HC and ATB and HC and LTBI in QFT supernatants (p<10⁻¹⁰ in both cases). IL-18 was differentially variable between HC and LTBI in QFT supernatants (p~10⁻⁴). IL-18, IL-18BP, IFN-γ and IP-10 in the serum samples were differentially variable between HC and LTBI, and IL-18 and IP-10 were differentially variable between LTBI and ATB.

We used the co-expression network-building method introduced by Hartmann et al. [21] and found that pairs of proteins were stably co-expressed in one study group and co-expressed in a varying fashion in another study group, which is a phenomenon that we call differential co-expression. The co-expression networks generated using this approach are presented in Fig 5. The relationships between (a) IP-10 and IFN-γ as well as between IL-18BP and IL-37 in the serum samples were most robust in the ATB group, (b) IL-18BP and IP-10 as well as IL-18 and IFN-γ were more robust in the ATB or LTBI groups than the HC group, and (c) IL-18 and IP-10, IL-18BP and IFN-γ as well as between IL-37 and IFN-γ were most robust in the HC group (Fig 5A). The relationship between (a) IL-18 and IL-37 in QFT supernatants was most robust in the ATB groups, (b) IL-18BP and IL-37 were more robust in the ATB or LTBI group than in the HC group, and (c) IL-37 and IFN-γ were most robust in the HC group (Fig 5B).

Fig 5 — Network patterns among studied proteins measured in serum (A) and QFT supernatants (B) from pulmonary TB patients (ATB), latently infected subjects (LTBI) and non-infected healthy controls (HC) The numbers, all between 0 and 1, correspond to the 'likelihood' of observing a given co-expression in a network generated from a random subset of (at least 80% of) individuals in the group of interest.

Ordinal Elastic Net for the detection of protein levels and ratios predictive of the transition from HC through LTBI to ATB

The feature selection method based on ordinal regression with elastic net penalty revealed several important predictors in the levels and ratios of the sera and QFT supernatant proteins. All available predictors (5 levels of proteins and 10 ratios between these proteins in serum together with 4 protein levels and 6 ratios between proteins in QFT supernatants) were used to model the distribution of a random variable, Y, which equalled 1 for HC, 2 for LTBI and 3 for ATB. The performance of the model is presented in Table 7. Notably, the expression levels of all proteins in the serum and QFT supernatants were informative predictors of the infection status. However, a number of non-informative expression ratios were found: IL-18/IP-10, IL-18BP/IFN-γ, and IL-37/IFN-γ in the serum and IL-18/IL-37 in QFT supernatants.

Table 7. Results for ordinal regression (elastic net)–coefficients.

All available predictors (5 levels of proteins and 10 ratios between these proteins in serum together with 4 protein levels and 6 ratios between proteins in QFT supernatants) were used to model the distribution of an ordinal random variable, Y, which equalled 1 for HC, 2 for LTBI and 3 for ATB. The non-zero coefficients are regarded as being informative of the (conditional) distribution of Y.

Parameters	Coefficients (95% CI)
Parameters	logit(P[Y = 1\|Y> = 1])	logit(P[Y = 2\|Y> = 2])
Serum
(Intercept)	5.007 (2.320; 5.241)	3.745 (2.144; 3.779)
IL-18	-0.115 (-0.232; 0)	-0.073 (-0.232; 0)
IL-18BP	-0.237 (-0.311; -0.082)	-0.237 (-0.317; -0.093)
IL-37	0.129 (0; 0.047)	0.046 (0; 0.002)
IFN-γ	-0.848 (-0.633; -0,020)	-0.848 (-0.633; -0.020)
IP-10	-0.113 (-0.149; -0.062)	-0.157 (-0.175; -0.079)
IL-18/IL-18BP	-0.545 (-0.811; 0)	-1.025 (-0.858; 0)
IL-18/IL-37	0.129 (0;0.015)	0.129 (0; 0.015)
IL-18/IFN-γ	-0.051 (-0.12; 0)	0.079 (-0.007; 0.056)
IL-18/IP-10	0 (0; 0.154)	0 (0; 0.088)
IL-18BP/IL-37	0 (-0.019; 0)	-0.153 (-0.081; 0)
IL-18BP/IFN-γ	0 (-0.155; 0)	0 (-0.209; 0)
IL-18BP/IP-10	0.222 (0.150; 0.431)	-0.005 (0; 0.430)
IL-37/IFN-γ	0 (0; 0.023)	0 (-0.007; 0.021)
IL-37/IP-10	0 (-0.034; 0)	-0.200 (-0.063; 0)
IFN-γ/IP-10	1.300 (0; 0.851)	2.427 (0; 1.589)
QFT supernatant
IL-18	-0.371 (-0.419; -0.063)	0 (-0.123; 0)
IL-18BP	-0.097 (-0.540; -0.217)	-0.028 (-0.125; 0)
IL-37	-0.086 (-0.067; 0)	0.011 (-0.032; 0.004)
IFN-γ	-0.981 (-0.478; -0.153)	-0.027 (-0.027; 0)
IL-18/IL-18BP	0.433 (0; 0)	-0.106 (-0.252; 0)
IL-18/IL-37	0 (-0.028; 0)	0 (-0.035; 0)
IL-18/IFN-γ	0.301 (0; 0.602)	-0.363 (-0.418; -0.021)
IL-18BP /IL-37	-0.543 (-0.514; -0.074)	0.089 (-0.022; 0.232)
IL-18BP/IFN-γ	0.266 (0.411; 1. 116)	-0.659 (-0.947; -0.247)
IL-37/IFN-γ	0.047 (0.031; 0.211)	-0.009 (0; 0.049)

Open in a new tab

The CI's were estimated via boostrap procedure based on 300 repetitions with the size of the subsample equal to 80% of the original sample. This approach is proposed due to lack of closed form formulas for the distributions of the coefficients. Due to differences in sample size, there are instances where the coefficient lies outside of the CI indicating lack of robustness.

Unsupervised dimension reduction

We also asked whether the molecular signature based on the expression of the studied proteins and ratios may be used to define a meaningful partition of our cohort. We used an unsupervised dimension reduction technique, t-SNE, which is a non-linear alternative to standard Principal Component Analysis.

First, we performed t-SNE on serum and QFT supernatants separately, with no prior information on the studied individuals (Fig 6). For the serum samples, there was no clear clustering or separation between the studied groups. At the same time, there was a separation between the IGRA-negative and IGRA-positive samples for the QFT supernatants as confirmed in the analysis of the similarity of distributions of the three components (derived from t-SNE) between the IGRA-positive and -negative groups (see qq-plots in Fig 7). Then, we used the t-SNE conditionally on the IGRA result. In other words, we performed the dimension reduction separately for the IGRA-positive and -negative individuals. This prior information allowed us to identify two clusters for the IGRA-positive and IGRA-negative samples in the serum with a clearer separation between the ATB and HC groups than between the ATB and LTBI groups (only the active TB and HC could have a negative IGRA in our study) (Fig 6). For QFT supernatant samples and IGRA-negative samples, we separated a group of healthy controls from the two clusters of the mixed samples (ATB and HC), and we noticed that the likelihood of the sample being classified as LTBI rather than ATB increased for IGRA-positive individuals with the increase in the value of the third projection of the embedding (Fig 6, Fig 7).

Fig 7 — The top panel corresponds to the serum and the bottom to the QFT supernatant samples. The leftmost plot in each panel corresponds to the first coordinate of the embedding, the central plot to the second and the rightmost to the third coordinate of the embedding. The x-axis corresponds to IGRA negative and the y-axis to the IGRA positive samples. The red line is the diagonal. In the top panel (serum) the distribution of the consecutive components of the embedding is much more similar between IGRA positive and negative individuals, then in the bottom panel (QFT supernatants).

Discussion

We estimated whether the levels of individual proteins of the IL-18 signalling complex i.e. total and free IL-18, IL-18BP, IL-37, IFN-γ and the IP-10 chemokine, and their mutual relationships and ratios, were useful as auxiliary biomarkers of ATB. The analyses showed a significant increase in the serum levels of total and free IL-18, IL-18BP, IFN-γ and IP-10 in ATB patients compared to LTBI or HC individuals. In contrast, a slightly lower serum concentration of the anti-inflammatory IL-37 was measured in ATB than in LTBI or HC groups [16]. This observation indicates a significant loss of balance in the range of the IL-18 signaling complex in ATB. An elevated serum IL-18 concentration in ATB had been previously demonstrated [22–25]. However, our study analysed the IL-18 signalling complex in a wider extent, by evaluating the total and free IL-18, IL-18BP, IL-37, IFN-γ and IP-10 chemokine, in two M.tb infection states, which to the best of our knowledge, had not been done previously. We performed statistical analyses of several classification algorithms to rank the measured proteins for their usefulness in the differentiation of M.tb infection states using unstimulated serum samples and M.tb antigen-stimulated QuantiFERON culture supernatants. Our data showed that individual serum proteins, except IL-37, were able to discriminate between ATB and LTBI in both groups of positive and negative QFT tests. However, the highest discriminative biomarker set was a complex co-expression of serum IL-18BP and IL-37 and IP-10 and IFN-γ, which may be useful in the rapid differentiation between ATB patients and LTBI individuals. Our data are consistent with the opinion that a complex biomarker panel is more robust than single markers for TB screening [5–7,26]. The set of seven serum biosignatures, comprised of apolipoprotein-A1, CRP, complement factor H, IFN-γ, IP-10, serum amyloid A and transthyretin, and a panel of five other serum biomarkers, including IFN-γ, IL-6, IL-18, CRP and MIG, showed potential in screening for TB in African countries endemic for HIV infection [8,27].

Our data suggest that the unstimulated biomarker performance is a better approach to evaluate the systemic manifestation of active TB compared to M.tb-stimulated marker expression. In contrast to the five individual serum proteins of the IL-18 signalling complex that were able to discriminate between the ATB and LTBI groups, only total IL-18 and IFN-γ showed significantly different values in QFT supernatants from ATB patients and LTBI individuals. Other results demonstrated that the cytokine ratios might provide specific and sensitive TB indicators [28–30]. A potential role of the IFN-γ/IL-2 ratio in the diagnosis of extrapulmonary TB was reported [31]. The IFN-γ/IL-4 and IL-4δ2/IL-4 mRNA ratios may serve as valuable markers for TB susceptibility or resistance [31,32]. La Manna et al. indicated that 14 analytes (IL-2, IP-10, IFN-γ, MIG, SCF, b-NGF, IL-12-p40, TRAIL, IL2Ra, MIF, TNF-β, IL-3, IFN-α 2, and LIF) allowed discriminating between ATB and non-TB groups [33].

The present study revealed significantly higher levels of circulating IFN-γ in ATB patients than in the LTBI or HC groups. In contrast, the IFN-γ concentration in M.tb-stimulated QFT cultures was lower in ATB compared to LTBI subjects. This difference suggests that the activation of an antimycobacterial immune response during ATB occurs concomitantly with the signs of immune depression [34]. However, it cannot be excluded that the most M.tb-reactive T cells are redistributed from the periphery to the site of infection, and consequently, only less responsive M.tb-specific T cells remain in the circulation. The elevated levels of serum IP-10 in ATB patients support this hypothesis because this chemokine recruits Th1 lymphocytes and NK cells toward infected areas [35]. We previously reported that circulating leukocytes from healthy BCG-vaccinated individuals become effector cells producing IFN-γ upon stimulation with mycobacterial antigens [36]. The proportion of T CD4⁺ Th1 cells synthesizing IFN-γ in these cultures significantly exceeded the proportion of T CD8⁺ cells and NK cells. The IL-18 enhanced IFN-γ production by naïve rather than memory CD4⁺ Th1 cells [37]. To perform this function, CD4⁺ Th1 cells recognized M.tb antigens that were presented via major histocompatibility complex (MHC) class II molecules at the surface of dendritic cells. It is possible that antigen presentation to CD4⁺ Th1 cells in active TB might not be optimal for the IFN-γ response in QFT cultures. With this in mind, the search for novel antigens, other than those used in IFN-γ-release assays (ESAT-6/CFP-10/TB7.7), was undertaken by Chegou et al. [38]. Alternatively, we also speculate that new cell culture models that mimic the microenvironment of human lung tissue [39] may be used for the development of a robust immunodiagnosis of TB. In regards to such a supposition, the co-expression of the proteins of the IL-18/IL37 signalling complex and IP-10 should be further analysed in patients with pulmonary disease including those with pulmonary disease other than TB.

Conclusion

Our results show that the IL-18 signalling complex may be exploited by M. tuberculosis to expand the clinical manifestation of pulmonary TB. Therefore, direct analysis of serum components of the IL-18/IL-37 signalling complex and IP-10 may be applicable in designing novel rapid screening tests for pulmonary TB.

Supporting information

S1 File. Raw data serum.

(XLSX)

Click here for additional data file.^{(25.1KB, xlsx)}

S2 File. Raw data QFT.

(XLSX)

Click here for additional data file.^{(22.6KB, xlsx)}

Acknowledgments

This work was supported by the National Science Centre grants no 2015/19/N/NZ6/01385 and 2016/21/B/NZ7/01771.

Data Availability

All relevant data are within the article and its Supporting Information files.

Funding Statement

This work was supported by the National Science Centre grants no 2015/19/N/NZ6/01385 and 2016/21/B/NZ7/01771.

References

1.Organization WHO. Global Tuberculosis Report 2018. 2018; Available: https://apps.who.int/iris/bitstream/handle/10665/274453/9789241565646-eng.pdf [Google Scholar]
2.Takenami I, Loureiro C, Machado A, Emodi K, Riley LW, Arruda S. Blood cells and interferon-gamma levels correlation in latent tuberculosis infection. ISRN Pulmonol. 2013;2013: 1–8. 10.1155/2013/256148 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Russell DG, Cardona P-J, Kim M-J, Allain S, Altare F. Foamy macrophages and the progression of the human tuberculosis granuloma. Nat Immunol. 2009;10: 943–948. 10.1038/ni.1781 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Wlodarczyk M, Rudnicka W, Janiszewska-Drobinska B, Kielnierowski G, Kowalewicz-Kulbat M, Fol M, et al. Interferon-gamma assay in combination with tuberculin skin test are insufficient for the diagnosis of culture-negative pulmonary tuberculosis. PLoS One. Public Library of Science; 2014;9: e107208 10.1371/journal.pone.0107208 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Kunnath-Velayudhan S, Gennaro ML. Immunodiagnosis of tuberculosis: a dynamic view of biomarker discovery. Clin Microbiol Rev. American Society for Microbiology (ASM); 2011;24: 792–805. 10.1128/CMR.00014-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Weiner J, Kaufmann SHE. High-throughput and computational approaches for diagnostic and prognostic host tuberculosis biomarkers. Int J Infect Dis. 2017;56: 258–262. 10.1016/j.ijid.2016.10.017 [DOI] [PubMed] [Google Scholar]
7.Awoniyi DO, Teuchert A, Sutherland JS, Mayanja-Kizza H, Howe R, Mihret A, et al. Evaluation of cytokine responses against novel Mtb antigens as diagnostic markers for TB disease. J Infect. 2016;73: 219–230. 10.1016/j.jinf.2016.04.036 [DOI] [PubMed] [Google Scholar]
8.Farr K, Ravindran R, Strnad L, Chang E, Chaisson LH, Yoon C, et al. Diagnostic performance of blood inflammatory markers for tuberculosis screening in people living with HIV. Ivanyi J, editor. PLoS One. 2018;13: e0206119 10.1371/journal.pone.0206119 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Barksby HE, Lea SR, Preshaw PM, Taylor JJ. The expanding family of interleukin-1 cytokines and their role in destructive inflammatory disorders. Clin Exp Immunol. 2007;149: 217–225. 10.1111/j.1365-2249.2007.03441.x [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Dima E, Koltsida O, Katsaounou P, Vakali S, Koutsoukou A, Koulouris NG, et al. Implication of Interleukin (IL)-18 in the pathogenesis of chronic obstructive pulmonary disease (COPD). Cytokine. 2015;74: 313–317. 10.1016/j.cyto.2015.04.008 [DOI] [PubMed] [Google Scholar]
11.Schneider BE, Korbel D, Hagens K, Koch M, Raupach B, Enders J, et al. A role for IL-18 in protective immunity against Mycobacterium tuberculosis. Eur J Immunol. 2010;40: 396–405. 10.1002/eji.200939583 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Dinarello CA. IL-18: A TH1-inducing, proinflammatory cytokine and new member of the IL-1 family. J Allergy Clin Immunol. 1999;103: 11–24. Available: http://www.ncbi.nlm.nih.gov/pubmed/9893178 10.1016/s0091-6749(99)70518-x [DOI] [PubMed] [Google Scholar]
13.Dinarello CA, Novick D, Kim S, Kaplanski G. Interleukin-18 and IL-18 Binding Protein. Front Immunol. 2013;4: 289 10.3389/fimmu.2013.00289 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Nakanishi K, Yoshimoto T, Tsutsui H, Okamura H. Interleukin -18 regulates bothTH1 and TH2 responses. Annu Rev Immunol. 2001;19: 423–474. 10.1146/annurev.immunol.19.1.423 [DOI] [PubMed] [Google Scholar]
15.Nold MF, Nold-Petry CA, Zepp JA, Palmer BE, Bufler P, Dinarello CA. IL-37 is a fundamental inhibitor of innate immunity. Nat Immunol. NIH Public Access; 2010;11: 1014–22. 10.1038/ni.1944 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Dinarello CA, Nold-Petry C, Nold M, Fujita M, Li S, Kim S, et al. Suppression of innate inflammation and immunity by interleukin-37. Eur J Immunol. 2016;46: 1067–1081. 10.1002/eji.201545828 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Banda NK, Vondracek A, Kraus D, Dinarello CA, Kim S-H, Bendele A, et al. Mechanisms of inhibition of collagen-induced arthritis by murine IL-18 binding protein. J Immunol. 2003;170: 2100–5. Available: http://www.ncbi.nlm.nih.gov/pubmed/12574381 10.4049/jimmunol.170.4.2100 [DOI] [PubMed] [Google Scholar]
18.Bufler P, Azam T, Gamboni-Robertson F, Reznikov LL, Kumar S, Dinarello CA, et al. A complex of the IL-1 homologue IL-1F7b and IL-18-binding protein reduces IL-18 activity. Proc Natl Acad Sci U S A. 2002;99: 13723–8. 10.1073/pnas.212519099 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Migliorini P, Anzilotti C, Pratesi F, Quattroni P, Bargagna M, Dinarello CA, et al. Serum and urinary levels of IL-18 and its inhibitor IL-18BP in systemic lupus erythematosus. Eur Cytokine Netw. 2010;21: 264–71. 10.1684/ecn.2010.0210 [DOI] [PubMed] [Google Scholar]
20.Kim SH, Eisenstein M, Reznikov L, Fantuzzi G, Novick D, Rubinstein M, et al. Structural requirements of six naturally occurring isoforms of the IL-18 binding protein to inhibit IL-18. Proc Natl Acad Sci U S A. National Academy of Sciences; 2000;97: 1190–5. Available: http://www.ncbi.nlm.nih.gov/pubmed/10655506 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Hartmann K, Seweryn M, Handleman SK, Rempała GA, Sadee W. Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles. BMC Genomics. 2016;17: 738 10.1186/s12864-016-3075-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Morosini M, Meloni F, Marone Bianco A, Paschetto E, Uccelli M, Pozzi E, et al. The assessment of IFN-gamma and its regulatory cytokines in the plasma and bronchoalveolar lavage fluid of patients with active pulmonary tuberculosis. Int J Tuberc Lung Dis. 2003;7: 994–1000. http://www.ncbi.nlm.nih.gov/pubmed/14552571 [PubMed] [Google Scholar]
23.Yamada G, Shijubo N, Shigehara K, Okamura H, Kurimoto M, Abe S. Increased levels of circulating interleukin-18 in patients with advanced tuberculosis. Am J Respir Crit Care Med. 2000;161: 1786–9. 10.1164/ajrccm.161.6.9911054 [DOI] [PubMed] [Google Scholar]
24.Vankayalapati R, Wizel B, Weis SE, Samten B, Girard WM, Barnes PF. Production of Interleukin‐18 in human tuberculosis. J Infect Dis. 2000;182: 234–239. 10.1086/315656 [DOI] [PubMed] [Google Scholar]
25.El-Masry S, Lotfy M, Nasif W, El-Kady I, Al-Badrawy M. Elevated serum level of interleukin (IL)-18, interferon (IFN)-γ and soluble fas in patients with pulmonary complications in Tuberculosis. Acta Microbiol Immunol Hung. 2007;54: 65–77. 10.1556/AMicr.54.2007.1.7 [DOI] [PubMed] [Google Scholar]
26.Won E-J, Choi J-H, Cho Y-N, Jin H-M, Kee HJ, Park Y-W, et al. Biomarkers for discrimination between latent tuberculosis infection and active tuberculosis disease. J Infect. 2017;74: 281–293. 10.1016/j.jinf.2016.11.010 [DOI] [PubMed] [Google Scholar]
27.Chegou NN, Sutherland JS, Malherbe S, Crampin AC, Corstjens PLAM, Geluk A, et al. Diagnostic performance of a seven-marker serum protein biosignature for the diagnosis of active TB disease in African primary healthcare clinic attendees with signs and symptoms suggestive of TB. Thorax. 2016;71: 785–794. 10.1136/thoraxjnl-2015-207999 [DOI] [PubMed] [Google Scholar]
28.Joshi L, Ponnana M, Sivangala R, Chelluri LK, Nallari P, Penmetsa S, et al. Evaluation of TNF-α, IL-10 and IL-6 Cytokine Production and their correlation with genotype variants amongst tuberculosis patients and their household contacts. Subbian S, editor. PLoS One. 2015;10: e0137727 10.1371/journal.pone.0137727 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Sun Q, Wei W, Sha W. Potential role for Mycobacterium tuberculosis specific IL-2 and IFN-γ responses in discriminating between latent infection and active disease after long-term stimulation. Cao C, editor. PLoS One. 2016;11: e0166501 10.1371/journal.pone.0166501 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Suter-Riniker F, Berger A, Mayor D, Bittel P, Iseli P, Bodmer T. Clinical significance of interleukin-2/gamma interferon ratios in Mycobacterium tuberculosis-specific T-cell signatures. Clin Vaccine Immunol. 2011;18: 1395–6. 10.1128/CVI.05013-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Goyal N, Kashyap B, Kaur IR. Significance of IFN-ɤ/IL-2 ratio as a circulating diagnostic biomarker in extrapulmonary tuberculosis. Scand J Immunol. 2016;83: 338–344. 10.1111/sji.12424 [DOI] [PubMed] [Google Scholar]
32.Wassie L, Demissie A, Aseffa A, Abebe M, Yamuah L, Tilahun H, et al. Ex vivo cytokine mRNA levels correlate with changing clinical status of ethiopian TB patients and their contacts over time. Ratner A, editor. PLoS One. 2008;3: e1522 10.1371/journal.pone.0001522 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.La Manna MP, Orlando V, Li Donni P, Sireci G, Di Carlo P, Cascio A, et al. Identification of plasma biomarkers for discrimination between tuberculosis infection/disease and pulmonary non tuberculosis disease. Ivanyi J, editor. PLoS One. 2018;13: e0192664 10.1371/journal.pone.0192664 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Vanham G, Toossi Z, Hirsch CS, Wallis RS, Schwander SK, Rich EA, et al. Examining a paradox in the pathogenesis of human pulmonary tuberculosis: immune activation and suppression/anergy. Tuber Lung Dis. 1997;78: 145–58. Available: http://www.ncbi.nlm.nih.gov/pubmed/9713647 10.1016/s0962-8479(97)90021-6 [DOI] [PubMed] [Google Scholar]
35.Nie CQ, Bernard NJ, Norman MU, Amante FH, Lundie RJ, Crabb BS, et al. IP-10-mediated T cell homing promotes cerebral inflammation over splenic immunity to malaria infection. Riley EM, editor. PLoS Pathog. 2009;5: e1000369 10.1371/journal.ppat.1000369 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Druszczynska M, Kowalewicz-Kulbat M, Maszewska A, Rudnicka K, Szpakowski P, Wawrocki S, et al. The balance between pro- and anti-inflammatory cytokines in the immune responses to BCG and DTwP vaccines. Acta Biochim Pol. 2015;62 10.18388/abp.2015_1160 [DOI] [PubMed] [Google Scholar]
37.Szpakowski P, Biet F, Locht C, Paszkiewicz M, Rudnicka W, Druszczyńska M, et al. Dendritic cell activity driven by recombinant Mycobacterium bovis BCG producing human il-18, in healthy BCG vaccinated adults. J Immunol Res. Hindawi Limited; 2015;2015: 359153 10.1155/2015/359153 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Chegou NN, Black GF, Loxton AG, Stanley K, Essone PN, Klein MR, et al. Potential of novel Mycobacterium tuberculosis infection phase-dependent antigens in the diagnosis of TB disease in a high burden setting. BMC Infect Dis. BioMed Central; 2012;12: 10 10.1186/1471-2334-12-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Braian C, Svensson M, Brighenti S, Lerm M, Parasa VR. A 3D human lung tissue model for functional studies on Mycobacterium tuberculosis infection; Infection. J Vis Exp. 2015; 10.3791/53084 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0225556.r001

Decision Letter 0

Francesco Dieli

16 Jul 2019

PONE-D-19-15207

IL-18/IL-37/IP-10 signaling complex as a potential biomarker for discriminating active and latent TB

PLOS ONE

Dear Dr Druszczynska,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by 60 days. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Francesco Dieli, M.D., Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include in your Methods section the date ranges over which you recruited participants to this study.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this paper the authors analyse the levels of IL18 signalling complex (IL18, IL18 BP, IL37 and free IL18) and the levels of IFNγ and IP10 in sera and in QFT plasma of Active TB patients (ATB), latent TB infected subjects (LTBI) and healthy controls (HC). They study, using several statistical tools, the better combination of Cytokines concentrations or ratio able to distinguish ATB from LTBI.

Even if the rationale of this study is interesting, I observed a certain number of critical points.

The authors do not specify where the original data are available

Introduction

On the contrary of what written at line 95, the authors measured the levels of IP10 only in serum and not in QFT plasma.

Methods

It is not clear if the authors in the analysis of QFT supernatants calculated the levels of the studied cytokines after subtraction of baseline levels obtained from NIL tube.

Results

Paragraph 2.2 and 2.3.

Line 203. About the ROC analysis of cytokines levels paragraph 2.2 the authors do not explain how is performed the full logistic model analysis. For none of the studied parameters they provide any threshold level with values of specificity and sensibility, the results are expressed in only AUC.

Line 225 the authors write that no significant differences were observed for the levels of IL18, IL18 BP, IL37 and free IL18 among the groups in QFT supernatants but in figure 3B is highlited a significant difference of the IL18BP levels between ATB and HC.

Line 226 the analysis of IFN-γ among IGRA+ and IGRA- and TST+ and TST-, in my opinion it should be deeply revised or eliminated because it is quite obvious that a comparison of IFN-γ levels between QFT+ and QFT- give a significant difference, these data are consistent with the algorithm used in QFT TB GOLD in tube test, so they do not add any original information (fig 4E).

Line 228 the values of IFN-γ reported in this line do not fit with those showed in the Figure 3E for the group of HC which in the line is reported as “culture negative” I suppose.

Paragraph 2.4

Line 265 the authors write that IL18, IL18BP and IFN-g from QFT supernatants are the most informative but at line 225 they write that there was no significant difference among the groups about the levels of IL18 and IL18BP.

Paragraph 2.5

The results of this work are based on the analysis of the levels of cytokines studied using different statistical tests. In this way the authors, carrying out a meticulous statistical analysis, generate a considerable quantity of results. the exposure of the results, in some cases, seems to me rather confusing. It would be useful especially for the paragraph 2.5, a table that summarizes the values of the different ratios studied and not only the table with the AUC obtained from the combined analyses of the levels of cytokines and ratio.

About the tables in which the authors show the AUC resulted from the analysis of the levels of single cytokine in serum or in QFT supernatants, in my opinion they should also show the confidence interval for each AUC and for the parameters that show highest discrimination power among the groups they should propose a threshold value with relative sensibility and specificity.

Conclusion

The author suggests that the analysis of IL18 Signalling complex in serum may be applicable for a rapid screening test for pulmonary TB. In my opinion it would be necessary to add another group of patients, those with pulmonary disease other than TB. The levels of Cytokine observed in serum are baseline levels and not obtained with specific antigen stimulation so they reflect an inflammatory status rather than a cytokine pattern caused by a specific antigenic response, as that observed in QFT supernatants. This inflammatory status could be observed also in other pulmonary diseases, for this reason the results obtained from serum analysis in any case should be coupled with an IGRA test to be sure that that the subject is infected with Mtb.

Reviewer #2: The study suggest the evaluation of IL8 signalling to design a novel screening test for pulmonary TB. In the discussion the authors should argument also the application of immunological biomarkers as correlate of protection or disease: it could be interesting to study the modulation of IL8 signalling in LTBI and active TB population over time-therapy, in order to find an eventually find a correlation between the resolution of the disease and the cytokine level. Moreover it could be interesting to monitor the IL8 signaling in a latent population not taking preventive therapy, with the aim to find biomarkers of incipient active TB disease. Identify the LTBI subjects with the higher risk to develop active TB is currently one of the most important research topic in the TB field. In high TB endemic country it is not possible to offer TB preventive therapy to all LTBI subjects. Therefore, to find correlates of disease or protection is a way to offer TB preventive therapy to a selected population. Considering the impossibility to isolate Mtb in LTBI population, the use of immunological biomarkers is an alternative and promising strategy.

The study is well augmented but need some minor revision. In some part of the text, the description of figures and table is to poor.

• In the table 1 should be added:

o the origin of the enrolled patients

o appropriate statistical analysis among groups

o results about microbiological and clinical diagnosis of TB patients reported in the text

• line 132 correct QFT

• line 184, specify that these data are not reported in the figure

• line 200 the labels of the figure in not corrected, it should be 2 A,D,F

• figure 2B, 2C,2E and 2D are not discussed in the text

• line 203, in order to better understand the results, the data related to the full logistic model should be included in the table 2

• line 224-226 : in the text it is reported “There were no significant differences in the levels of free IL-18, IL-18BP or IL-37 in the QFT 226 supernatants among studied groups (Fig. 3B-D)”. In the figure 3B it is reported a significant differences comparing active TB with HC. Please clarify

• 229-230 “ IFN-g level was significantly higher in than in QFT-/TST+/TST- ATB patients (Fig. 4E).” “QFT-/TST+/TST-“ is not the correct definition of the group reported in the figure 4. Please clarify.

• In the figure 4 is not reported the legend, it is not clear what circle and triangle represents.

• The figure legend of all manuscript should be improved with a brief description of the data, for instance: serum, QFT plasma, analyzed groups, statistic used, graph legend.

• line 238, in order to better understand the results, the data related to the full logistic model should be included in the table 4

• line 353 : please specify that only the active TB and HC could have a negative IGRA.

• Regarding the table 3 I did not understand the correspondent comment reported in the text: Line 221 you wrote that the pair wise relationships is independent of the Mtb infection for IL18BP and IP 10 but in the table it is reported a significant relationship in LTBI and active TB. Please clarify what you mean. I found similar discrepancy also in the other description, please describe better this part.

• Regarding the table 7, the author should describe better and clearer what it reported in the table.

Reviewer #3: From my view, the first issue is only notational, but it must be fixed. Methodologically, my only concern is the skewness of the distributions. According to this, the authors apply non-parametric methods but they also use some indexes for symmetrical distributions as I will comment below.

Regarding notation:

I guess that BCG stands for Bacillus Calmette-Guerin and TST stands for Tuberculin Skin Test, but these acronyms are not properly introduced in the manuscript

According to usual statistical guidelines (e.g. APA), p values should be reported with 3 figures, unless they are lower than .001, in that case, it is enough reporting p<.001. It is senseless a p-value with 7 figures (line 179 and Fig. 1A). If the authors consider relevant to report p-values with higher accuracy (my view is that this is not the case), they must consider exponential notation. There are more p-values reported with an excess of decimal figures (lines 187, 224, tables 3, 5, figure 3, …) and also with few decimals (lines 190, 191, 194, …). Please, fix this issue.

This is just a comment, not an issue: I wander what is the interest of reporting AUC with four decimals.

Standard deviation must have the same accuracy or one decimal figure more than the mean value. Please, fix the expression in line 194.

In tables 2 and 4, change the decimal point to a point.

This is just another comment, using “±” to indicate mean and the standard deviation is increasingly discouraged in most of the guidelines for statistical reporting.

I miss the SE of the AUC estimates

Regarding methodology:

Figures 1-4 show that the IL distributions seems to be quite skewed. In fact, the authors have considered (properly) a non-parametric K-S test in order to check the homogeneity between groups. But they report the descriptive measures as mean and standard deviations. This can be misleading. For example, IL-8 in figure A has a variation coefficient greater than 100% in two groups. My view is that the descriptive statistics should be given in the same vein (eg median and IQR). An alternative choice is to transform data in order to induce symmetry.

This issue also affects the choice of the Pearson correlation coefficient in order to assess correlations. Pearson coefficient can be very sensitive to the presence of outliers. I wonder why the authors have not considered a non-parametric correlation coefficient, as Spearman's rho.

My view is that coefficients in fig 5, and the quartile plots in fig 7, should have some diagnostic words to help the reader to interpret them.

Line 430, please, change “demonstrate” by show, illustrate, evidence ...

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Pedro Femia

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2019 Dec 10;14(12):e0225556. doi: 10.1371/journal.pone.0225556.r002

Author response to Decision Letter 0

20 Sep 2019

To Reviewer #1:

We would like to thank you kindly for reading our manuscript “IL-18/IL-37/IP-10 signaling complex as a potential biomarker for discriminating active and latent TB” and for all the questions and suggestions.

Our answers to the questions are as follows:

1. „The authors do not specify where the original data are available.”

All the data are available in the Supporting information file.

2. „Introduction. On the contrary of what written at line 95, the authors measured the levels of IP10 only in serum and not in QFT plasma.”

The sentence was corrected (p.4-5, lines 96-101).

3. „Methods. It is not clear if the authors in the analysis of QFT supernatants calculated the levels of the studied cytokines after subtraction of baseline levels obtained from NIL tube.”

The levels of the studied cytokines were calculated after subtraction of baseline levels obtained from NIL tube. The appropriate sentence was included in the text (p. 7, lines 161-162).

4. „Results. Paragraph 2.2 and 2.3. Line 203. About the ROC analysis of cytokines levels paragraph 2.2 the authors do not explain how is performed the full logistic model analysis. For none of the studied parameters they provide any threshold level with values of specificity and sensibility, the results are expressed in only AUC.”

The appropriate text was added in the Methods section: “For the classical analysis of association between levels of expression of single proteins and protein ratios logistic regression was used in order to make the comparison of performance of single markers to all available markers (full logistic model) 'fair'. The AUC values were estimated via 5-fold cross-validation and based on at least 500 bootstrap replicates” (p.8 , lines 171-175). The 95% CI for the AUC was added to the appropriate tables (Table 2 and 4) and to the text (p. 10, lines 232-235, p. 12, lines 285-287, p.13, lines 307-312)

5. „Line 225 the authors write that no significant differences were observed for the levels of IL18, IL18 BP, IL37 and free IL18 among the groups in QFT supernatants but in figure 3B is highlited a significant difference of the IL18BP levels between ATB and HC.”

The appropriate sentences were corrected (p. 11, lines 256-259).

6. „Line 226 the analysis of IFN-γ among IGRA+ and IGRA- and TST+ and TST-, in my opinion it should be deeply revised or eliminated because it is quite obvious that a comparison of IFN-γ levels between QFT+ and QFT- give a significant difference, these data are consistent with the algorithm used in QFT TB GOLD in tube test, so they do not add any original information (fig 4E).”

The figure 4E and its description in the text were removed.

7. „Line 228 the values of IFN-γ reported in this line do not fit with those showed in the Figure 3E for the group of HC which in the line is reported as “culture negative” I suppose.”

The sentence was modified (p. 11-12, lines 260-263).

8. „Paragraph 2.4. Line 265 the authors write that IL18, IL18BP and IFN-g from QFT supernatants are the most informative but at line 225 they write that there was no significant difference among the groups about the levels of IL18 and IL18BP.”

The sentence was adjusted: “Even with no statistically significant differences between studied groups, the IL-18, IL-18BP and IFN-γ from QFT supernatants were most informative (in terms of AUC) in the LTBI vs. HC comparison” (p. 14, lines 318-320)

9. „Paragraph 2.5. The results of this work are based on the analysis of the levels of cytokines studied using different statistical tests. In this way the authors, carrying out a meticulous statistical analysis, generate a considerable quantity of results. the exposure of the results, in some cases, seems to me rather confusing. It would be useful especially for the paragraph 2.5, a table that summarizes the values of the different ratios studied and not only the table with the AUC obtained from the combined analyses of the levels of cytokines and ratio.”

The aim of paragraph 2.5 was not to evaluate the performance (in terms of AUC) of the selected proteins and protein ratios, but to shed some light on the poor performance of some of these features in terms of discriminative power. One of the reasons we highlight is differential variability and differential co-expression, which affects both the results of significance tests as well as predictive power. A sentence at the beginning of 2.5 was added: “To shed some further light on the issue of insufficient discriminative power of single protein levels and protein ratios, we aim to perform a deeper study of the variability and co-expression of the selected markers.” (p. 16, lines 356-358).

10. „About the tables in which the authors show the AUC resulted from the analysis of the levels of single cytokine in serum or in QFT supernatants, in my opinion they should also show the confidence interval for each AUC and for the parameters that show highest discrimination power among the groups they should propose a threshold value with relative sensibility and specificity.”

The CI for the logistic models was added. The cutoff value we choose is based on bootstrap replicates, thus we cannot guarantee (due to the lack of appropriate model/distribution for the cutoff value) that the median (or mean) cutoff will be in any sense optimal. We feel that the analysis of sensitivity and specificity of the proposed tests (and the relative trade-off between these two) goes beyond the scope of this short article. We enclose tables of both sensitivity and specificity for the Reviewer convenience.

Serum

QFT supernatant

11. „Conclusion. The author suggests that the analysis of IL18 Signalling complex in serum may be applicable for a rapid screening test for pulmonary TB. In my opinion it would be necessary to add another group of patients, those with pulmonary disease other than TB. The levels of Cytokine observed in serum are baseline levels and not obtained with specific antigen stimulation so they reflect an inflammatory rather than a cytokine pattern caused by a specific antigenic response, as that observed in QFT supernatants. This inflammatory status could be observed also in other pulmonary diseases, for this reason the results obtained from serum analysis in any case should be coupled with an IGRA test to be sure that that the subject is infected with Mtb.”

We agree that the concentrations of the proteins of IL-18 signalling complex should be investigated in patients with nonmycobacterial pulmonary diseases and in the future studies, we are planning to analyze the levels of studied immunomarkers in patients with pulmonary disease including those with pulmonary disease other than TB. We introduced such sentence in the Discussion section of the manuscript (p.21, lines 497-500).

In our study, 37% of patients with active TB showed negative IGRA test, Table 1.

To Reviewer #2

Our answers to the questions are as follows:

1.„The study suggest the evaluation of IL8 signalling to design a novel screening test for pulmonary TB. In the discussion the authors should argument also the application of immunological biomarkers as correlate of protection or disease: it could be interesting to study the modulation of IL8 signalling in LTBI and active TB population over time-therapy, in order to find an eventually find a correlation between the resolution of the disease and the cytokine level. Moreover it could be interesting to monitor the IL8 signaling in a latent population not taking preventive therapy, with the aim to find biomarkers of incipient active TB disease. Identify the LTBI subjects with the higher risk to develop active TB is currently one of the most important research topic in the TB field. In high TB endemic country it is not possible to offer TB preventive therapy to all LTBI subjects. Therefore, to find correlates of disease or protection is a way to offer TB preventive therapy to a selected population. Considering the impossibility to isolate Mtb in LTBI population, the use of immunological biomarkers is an alternative and promising strategy.”

We agree that the identification of M.tb-infected individuals and the appropriate treatment of people with the higher TB development risk are undoubtedly crucial for effective TB control (Introduction section, p. 3, lines 65-72).In our many years of research, we found that about 13% of adult Poles are latently infected with M.tb [Druszczynska et al, Clin Dev Immunol 2013, doi: 10.1155/2013/851452]. It is impossible to embrace such a large group of people with preventive therapy.

2. „In some part of the text, the description of figures and table is to poor.”

The description of figures and tables was revised.

3. „ In the table 1 should be added:

o the origin of the enrolled patients

o appropriate statistical analysis among groups

o results about microbiological and clinical diagnosis of TB patients reported in the text”

Table 1 was modified and all suggested information was included.

4.”• line 132 correct QFT”

The abbreviation was corrected (p. 7, line 152).

5”• line 184, specify that these data are not reported in the figure”

The information is provided in brackets (p. 9, lines 200-201).

6”• line 200 the labels of the figure in not corrected, it should be 2 A,D,F

• figure 2B, 2C,2E and 2D are not discussed in the text”

The sentence was modified and all the figures were discussed in the text (p. 10, lines 220-222)

7”• line 203, in order to better understand the results, the data related to the full logistic model should be included in the table 2”

The performance of the full logistic model is included in the main text. The CIs for the AUC were added.

8”• line 224-226 : in the text it is reported “There were no significant differences in the levels of free IL-18, IL-18BP or IL-37 in the QFT 226 supernatants among studied groups (Fig. 3B-D)”. In the figure 3B it is reported a significant differences comparing active TB with HC. Please clarify”

The appropriate sentences were corrected (p. 11, lines 256-259).

9”• 229-230 “ IFN-g level was significantly higher in than in QFT-/TST+/TST- ATB patients (Fig. 4E).” “QFT-/TST+/TST-“ is not the correct definition of the group reported in the figure 4. Please clarify.”

We agree that it was not the correct definition of the group reported in the Fig. 4.The sentence was removed from the text as suggested by the Reviewer 1.

10”• In the figure 4 is not reported the legend, it is not clear what circle and triangle represents.”

The figure was changed.

11”• The figure legend of all manuscript should be improved with a brief description of the data, for instance: serum, QFT plasma, analyzed groups, statistic used, graph legend.”

The description of figures was revised.

12”• line 238, in order to better understand the results, the data related to the full logistic model should be included in the table 4”

The performance of the full logistic model is included in the main text. The CIs for the AUC were added.

13”• line 353 : please specify that only the active TB and HC could have a negative IGRA”.

The information was added (p. 18, lines 421-422).

14”• Regarding the table 3 I did not understand the correspondent comment reported in the text: Line 221 you wrote that the pair wise relationships is independent of the Mtb infection for IL18BP and IP 10 but in the table it is reported a significant relationship in LTBI and active TB. Please clarify what you mean. I found similar discrepancy also in the other description, please describe better this part.”

The corresponding text was modified (p.10-11, lines 241-245).

15”• Regarding the table 7, the author should describe better and clearer what it reported in the table”.

The following text was added: “All available predictors (5 levels of proteins and 10 ratios between these proteins in serum together with 4 protein levels and 6 ratios between proteins in QFT supernatants) were used to model the distribution of an ordinal random variable, Y, which equalled 1 for HC, 2 for LTBI and 3 for ATB. The non-zero coefficients are regarded as being informative of the (conditional) distribution of Y.”

To Reviewer #3:

Our answers to the questions are as follows:

1. „Regarding notation: I guess that BCG stands for Bacillus Calmette-Guerin and TST stands for Tuberculin Skin Test, but these acronyms are not properly introduced in the manuscript.”

The abbreviations were explained in the sentences where they first appeared (p. 5, lines 105 and 120).

2. „According to usual statistical guidelines (e.g. APA), p values should be reported with 3 figures, unless they are lower than .001, in that case, it is enough reporting p<.001. It is senseless a p-value with 7 figures (line 179 and Fig. 1A). If the authors consider relevant to report p-values with higher accuracy (my view is that this is not the case), they must consider exponential notation. There are more p-values reported with an excess of decimal figures (lines 187, 224, tables 3, 5, figure 3, …) and also with few decimals (lines 190, 191, 194, …). Please, fix this issue.”

We reported the p values as p<.001 when they were lower than 0.001. We fixed also all the values in lines, tables and figures with an excess or shortage of the decimal places as suggested.

3. „This is just a comment, not an issue: I wander what is the interest of reporting AUC with four decimals.”

We apologize for the inconvenience, the numbers in tables are now corrected. We reported AUC up to four decimals as the CIs for the AUC were rather narrow in some of the studied cases. Additionally, as the estimates were based on the bootstrap, the four decimals (with narrow CIs) allowed to detect cases in which the detailed investigation of whether the bootstrap does not produce heavily biased estimates was needed.

4. „Standard deviation must have the same accuracy or one decimal figure more than the mean value. Please, fix the expression in line 194.”

We fixed all the values in lines, tables and figures with an excess or shortage of the decimal places as suggested.

5. „In tables 2 and 4, change the decimal point to a point.”

It was corrected.

6. „This is just another comment, using “±” to indicate mean and the standard deviation is increasingly discouraged in most of the guidelines for statistical reporting.”

We agree. We modified Fig 1-4 as the Reviewer suggested showing median and IQR instead of mean and standard deviation. We modified also the corresponding text in the Results section (p. 9-10, lines 199-220, p. 11-12, lines 252-263).

7. „I miss the SE of the AUC estimates”

95% CIs were added.

8. „Regarding methodology: Figures 1-4 show that the IL distributions seems to be quite skewed. In fact, the authors have considered (properly) a non-parametric K-S test in order to check the homogeneity between groups. But they report the descriptive measures as mean and standard deviations. This can be misleading. For example, IL-8 in figure A has a variation coefficient greater than 100% in two groups. My view is that the descriptive statistics should be given in the same vein (eg median and IQR). An alternative choice is to transform data in order to induce symmetry.”

We modified Fig 1-4 as the Reviewer suggested showing median and IQR instead of mean and standard deviation. We modified also the corresponding text in the Results section (p. 9 lines 197-218, p. 11 lines 246-257).

9. „This issue also affects the choice of the Pearson correlation coefficient in order to assess correlations. Pearson coefficient can be very sensitive to the presence of outliers. I wonder why the authors have not considered a non-parametric correlation coefficient, as Spearman's rho”.

We investigated the relation by two other commonly used rank-based association measures (Spearman's rho and Kendall's tau) – both of these gave consistent results, therefore, we used the simplest estimate.

10. „My view is that coefficients in fig 5, and the quartile plots in fig 7, should have some diagnostic words to help the reader to interpret them.”

For Fig 5. the following text was added: “The numbers, all between 0 and 1, correspond to the 'likelihood' of observing a given co-expression in a network generated from a random subset of (at least 80% of) individuals in the group of interest.”

Also, for Fig 7. the following text was added: “In the top panel (serum) the distribution of the consecutive components of the embedding is much more similar between IGRA positive and negative individuals, then in the bottom panel (QFT supernatants).”

11. „Line 430, please, change “demonstrate” by show, illustrate, evidence ...”

We changed the verb „demonstrate” to „show” (p. 22, line 503).

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(32.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0225556.r003

Decision Letter 1

Selvakumar Subbian

11 Oct 2019

PONE-D-19-15207R1

IL-18/IL-37/IP-10 signaling complex as a potential biomarker for discriminating active and latent TB

PLOS ONE

Dear Dr Druszczynska,

==============================

ACADEMIC EDITOR: Please address all the issues raised by the reviewers on the manuscript, not just on the rebuttal letter.

==============================

We would appreciate receiving your revised manuscript by Nov 25 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Selvakumar Subbian, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: The authors have improved the paper according to almost all the suggestions of reviewers. The paper is suitable for publication after some minor revision.

Line 130 Fifty-two % should be replaced by fifty-five% according to the number in brackets.

Line 187 I assume that "Me" is the abbreviation for median but it was not introduced previously in the manuscript.

Line 210 I suppose that average should be replaced by median.

Line 285 please write also the CI of AUC for HC vs ATB.

Line 365 and fig 5 the group ATB in the manuscript is written TB in the figure 5, could you please replace TB with ATB in the figure.

Line 414: in the manuscript the authors mentioned “squares” as symbol for IGRA negative patients in figure 6, but I don’t see squares in figure 6, only circles of different colours. The authors should fix this point

Lines 488, 489, 490 and 493 please write CD4+ with the symbol + superscript.

Reviewer #3: My view is that this manuscript still has several minor issues and it needs some review from the authors.

Regarding decimals homogeneity

- P-values in the text (lines 209 to 215, p-values are still shown with two -rather than three- decimal places)

- CI values in table 4 (IFN-gamma/HC vs LTBI CI)

- Table 6 (decimals and n)

Tables (e.g. tables 3 and 5) should show always the sample sizes involved in comparisons and correlations, regardless this information has already given in the text

Table 7: please show the SE of the coefficients or their CI

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

PLoS One. 2019 Dec 10;14(12):e0225556. doi: 10.1371/journal.pone.0225556.r004

Author response to Decision Letter 1

5 Nov 2019

To Reviewer #1:

Our answers to the questions are as follows:

1. Line 130 Fifty-two % should be replaced by fifty-five% according to the number in brackets.

We modified the sentence as suggested (p.6, line 130).

2. Line 187 I assume that "Me" is the abbreviation for median but it was not introduced previously in the manuscript.

The abbreviation was introduced in the sentence where it first appeared (p.8, line 187)

3. Line 210 I suppose that average should be replaced by median.

The word “average” was removed (p. 9, line 210)

4. Line 285 please write also the CI of AUC for HC vs ATB.

It was added (p. 12, line 285)

5. Line 365 and fig 5 the group ATB in the manuscript is written TB in the figure 5, could you please replace TB with ATB in the figure.

It was corrected.

6. Line 414: in the manuscript the authors mentioned “squares” as symbol for IGRA negative patients in figure 6, but I don’t see squares in figure 6, only circles of different colours. The authors should fix this point.

It was fixed (p. 18, lines 415-418)

7. Lines 488, 489, 490 and 493 please write CD4+ with the symbol + superscript.

The “+” symbol was written with the superscript (p.21, lines 490, 491, 492, 495).

To Reviewer #3:

Our answers to the questions are as follows:

1. - P-values in the text (lines 209 to 215, p-values are still shown with two -rather than three- decimal places)

We shown p-values in the text with three decimal places (p.9, lines 209-215).

2. - CI values in table 4 (IFN-gamma/HC vs LTBI CI)

It was added.

3. - Table 6 (decimals and n)

It was corrected.

4. Tables (e.g. tables 3 and 5) should show always the sample sizes involved in comparisons and correlations, regardless this information has already given in the text

The information on the sample sizes was added.

5. Table 7: please show the SE of the coefficients or their CI

The CI’s of the coefficients were added.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(16.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0225556.r005

Decision Letter 2

Selvakumar Subbian

7 Nov 2019

IL-18/IL-37/IP-10 signaling complex as a potential biomarker for discriminating active and latent TB

PONE-D-19-15207R2

Dear Dr. Druszczynska,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Selvakumar Subbian, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0225556.r006

Acceptance letter

Selvakumar Subbian

21 Nov 2019

PONE-D-19-15207R2

IL-18/IL-37/IP-10 signaling complex as a potential biomarker for discriminating active and latent TB

Dear Dr. Druszczynska:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Selvakumar Subbian

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Raw data serum.

(XLSX)

Click here for additional data file.^{(25.1KB, xlsx)}

S2 File. Raw data QFT.

(XLSX)

Click here for additional data file.^{(22.6KB, xlsx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(32.8KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(16.6KB, docx)}

Data Availability Statement

All relevant data are within the article and its Supporting Information files.

[pone.0225556.ref001] 1.Organization WHO. Global Tuberculosis Report 2018. 2018; Available: https://apps.who.int/iris/bitstream/handle/10665/274453/9789241565646-eng.pdf [Google Scholar]

[pone.0225556.ref002] 2.Takenami I, Loureiro C, Machado A, Emodi K, Riley LW, Arruda S. Blood cells and interferon-gamma levels correlation in latent tuberculosis infection. ISRN Pulmonol. 2013;2013: 1–8. 10.1155/2013/256148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref003] 3.Russell DG, Cardona P-J, Kim M-J, Allain S, Altare F. Foamy macrophages and the progression of the human tuberculosis granuloma. Nat Immunol. 2009;10: 943–948. 10.1038/ni.1781 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref004] 4.Wlodarczyk M, Rudnicka W, Janiszewska-Drobinska B, Kielnierowski G, Kowalewicz-Kulbat M, Fol M, et al. Interferon-gamma assay in combination with tuberculin skin test are insufficient for the diagnosis of culture-negative pulmonary tuberculosis. PLoS One. Public Library of Science; 2014;9: e107208 10.1371/journal.pone.0107208 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref005] 5.Kunnath-Velayudhan S, Gennaro ML. Immunodiagnosis of tuberculosis: a dynamic view of biomarker discovery. Clin Microbiol Rev. American Society for Microbiology (ASM); 2011;24: 792–805. 10.1128/CMR.00014-11 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref006] 6.Weiner J, Kaufmann SHE. High-throughput and computational approaches for diagnostic and prognostic host tuberculosis biomarkers. Int J Infect Dis. 2017;56: 258–262. 10.1016/j.ijid.2016.10.017 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref007] 7.Awoniyi DO, Teuchert A, Sutherland JS, Mayanja-Kizza H, Howe R, Mihret A, et al. Evaluation of cytokine responses against novel Mtb antigens as diagnostic markers for TB disease. J Infect. 2016;73: 219–230. 10.1016/j.jinf.2016.04.036 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref008] 8.Farr K, Ravindran R, Strnad L, Chang E, Chaisson LH, Yoon C, et al. Diagnostic performance of blood inflammatory markers for tuberculosis screening in people living with HIV. Ivanyi J, editor. PLoS One. 2018;13: e0206119 10.1371/journal.pone.0206119 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref009] 9.Barksby HE, Lea SR, Preshaw PM, Taylor JJ. The expanding family of interleukin-1 cytokines and their role in destructive inflammatory disorders. Clin Exp Immunol. 2007;149: 217–225. 10.1111/j.1365-2249.2007.03441.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref010] 10.Dima E, Koltsida O, Katsaounou P, Vakali S, Koutsoukou A, Koulouris NG, et al. Implication of Interleukin (IL)-18 in the pathogenesis of chronic obstructive pulmonary disease (COPD). Cytokine. 2015;74: 313–317. 10.1016/j.cyto.2015.04.008 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref011] 11.Schneider BE, Korbel D, Hagens K, Koch M, Raupach B, Enders J, et al. A role for IL-18 in protective immunity against Mycobacterium tuberculosis. Eur J Immunol. 2010;40: 396–405. 10.1002/eji.200939583 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref012] 12.Dinarello CA. IL-18: A TH1-inducing, proinflammatory cytokine and new member of the IL-1 family. J Allergy Clin Immunol. 1999;103: 11–24. Available: http://www.ncbi.nlm.nih.gov/pubmed/9893178 10.1016/s0091-6749(99)70518-x [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref013] 13.Dinarello CA, Novick D, Kim S, Kaplanski G. Interleukin-18 and IL-18 Binding Protein. Front Immunol. 2013;4: 289 10.3389/fimmu.2013.00289 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref014] 14.Nakanishi K, Yoshimoto T, Tsutsui H, Okamura H. Interleukin -18 regulates bothTH1 and TH2 responses. Annu Rev Immunol. 2001;19: 423–474. 10.1146/annurev.immunol.19.1.423 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref015] 15.Nold MF, Nold-Petry CA, Zepp JA, Palmer BE, Bufler P, Dinarello CA. IL-37 is a fundamental inhibitor of innate immunity. Nat Immunol. NIH Public Access; 2010;11: 1014–22. 10.1038/ni.1944 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref016] 16.Dinarello CA, Nold-Petry C, Nold M, Fujita M, Li S, Kim S, et al. Suppression of innate inflammation and immunity by interleukin-37. Eur J Immunol. 2016;46: 1067–1081. 10.1002/eji.201545828 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref017] 17.Banda NK, Vondracek A, Kraus D, Dinarello CA, Kim S-H, Bendele A, et al. Mechanisms of inhibition of collagen-induced arthritis by murine IL-18 binding protein. J Immunol. 2003;170: 2100–5. Available: http://www.ncbi.nlm.nih.gov/pubmed/12574381 10.4049/jimmunol.170.4.2100 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref018] 18.Bufler P, Azam T, Gamboni-Robertson F, Reznikov LL, Kumar S, Dinarello CA, et al. A complex of the IL-1 homologue IL-1F7b and IL-18-binding protein reduces IL-18 activity. Proc Natl Acad Sci U S A. 2002;99: 13723–8. 10.1073/pnas.212519099 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref019] 19.Migliorini P, Anzilotti C, Pratesi F, Quattroni P, Bargagna M, Dinarello CA, et al. Serum and urinary levels of IL-18 and its inhibitor IL-18BP in systemic lupus erythematosus. Eur Cytokine Netw. 2010;21: 264–71. 10.1684/ecn.2010.0210 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref020] 20.Kim SH, Eisenstein M, Reznikov L, Fantuzzi G, Novick D, Rubinstein M, et al. Structural requirements of six naturally occurring isoforms of the IL-18 binding protein to inhibit IL-18. Proc Natl Acad Sci U S A. National Academy of Sciences; 2000;97: 1190–5. Available: http://www.ncbi.nlm.nih.gov/pubmed/10655506 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref021] 21.Hartmann K, Seweryn M, Handleman SK, Rempała GA, Sadee W. Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles. BMC Genomics. 2016;17: 738 10.1186/s12864-016-3075-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref022] 22.Morosini M, Meloni F, Marone Bianco A, Paschetto E, Uccelli M, Pozzi E, et al. The assessment of IFN-gamma and its regulatory cytokines in the plasma and bronchoalveolar lavage fluid of patients with active pulmonary tuberculosis. Int J Tuberc Lung Dis. 2003;7: 994–1000. http://www.ncbi.nlm.nih.gov/pubmed/14552571 [PubMed] [Google Scholar]

[pone.0225556.ref023] 23.Yamada G, Shijubo N, Shigehara K, Okamura H, Kurimoto M, Abe S. Increased levels of circulating interleukin-18 in patients with advanced tuberculosis. Am J Respir Crit Care Med. 2000;161: 1786–9. 10.1164/ajrccm.161.6.9911054 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref024] 24.Vankayalapati R, Wizel B, Weis SE, Samten B, Girard WM, Barnes PF. Production of Interleukin‐18 in human tuberculosis. J Infect Dis. 2000;182: 234–239. 10.1086/315656 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref025] 25.El-Masry S, Lotfy M, Nasif W, El-Kady I, Al-Badrawy M. Elevated serum level of interleukin (IL)-18, interferon (IFN)-γ and soluble fas in patients with pulmonary complications in Tuberculosis. Acta Microbiol Immunol Hung. 2007;54: 65–77. 10.1556/AMicr.54.2007.1.7 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref026] 26.Won E-J, Choi J-H, Cho Y-N, Jin H-M, Kee HJ, Park Y-W, et al. Biomarkers for discrimination between latent tuberculosis infection and active tuberculosis disease. J Infect. 2017;74: 281–293. 10.1016/j.jinf.2016.11.010 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref027] 27.Chegou NN, Sutherland JS, Malherbe S, Crampin AC, Corstjens PLAM, Geluk A, et al. Diagnostic performance of a seven-marker serum protein biosignature for the diagnosis of active TB disease in African primary healthcare clinic attendees with signs and symptoms suggestive of TB. Thorax. 2016;71: 785–794. 10.1136/thoraxjnl-2015-207999 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref028] 28.Joshi L, Ponnana M, Sivangala R, Chelluri LK, Nallari P, Penmetsa S, et al. Evaluation of TNF-α, IL-10 and IL-6 Cytokine Production and their correlation with genotype variants amongst tuberculosis patients and their household contacts. Subbian S, editor. PLoS One. 2015;10: e0137727 10.1371/journal.pone.0137727 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref029] 29.Sun Q, Wei W, Sha W. Potential role for Mycobacterium tuberculosis specific IL-2 and IFN-γ responses in discriminating between latent infection and active disease after long-term stimulation. Cao C, editor. PLoS One. 2016;11: e0166501 10.1371/journal.pone.0166501 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref030] 30.Suter-Riniker F, Berger A, Mayor D, Bittel P, Iseli P, Bodmer T. Clinical significance of interleukin-2/gamma interferon ratios in Mycobacterium tuberculosis-specific T-cell signatures. Clin Vaccine Immunol. 2011;18: 1395–6. 10.1128/CVI.05013-11 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref031] 31.Goyal N, Kashyap B, Kaur IR. Significance of IFN-ɤ/IL-2 ratio as a circulating diagnostic biomarker in extrapulmonary tuberculosis. Scand J Immunol. 2016;83: 338–344. 10.1111/sji.12424 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref032] 32.Wassie L, Demissie A, Aseffa A, Abebe M, Yamuah L, Tilahun H, et al. Ex vivo cytokine mRNA levels correlate with changing clinical status of ethiopian TB patients and their contacts over time. Ratner A, editor. PLoS One. 2008;3: e1522 10.1371/journal.pone.0001522 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref033] 33.La Manna MP, Orlando V, Li Donni P, Sireci G, Di Carlo P, Cascio A, et al. Identification of plasma biomarkers for discrimination between tuberculosis infection/disease and pulmonary non tuberculosis disease. Ivanyi J, editor. PLoS One. 2018;13: e0192664 10.1371/journal.pone.0192664 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref034] 34.Vanham G, Toossi Z, Hirsch CS, Wallis RS, Schwander SK, Rich EA, et al. Examining a paradox in the pathogenesis of human pulmonary tuberculosis: immune activation and suppression/anergy. Tuber Lung Dis. 1997;78: 145–58. Available: http://www.ncbi.nlm.nih.gov/pubmed/9713647 10.1016/s0962-8479(97)90021-6 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref035] 35.Nie CQ, Bernard NJ, Norman MU, Amante FH, Lundie RJ, Crabb BS, et al. IP-10-mediated T cell homing promotes cerebral inflammation over splenic immunity to malaria infection. Riley EM, editor. PLoS Pathog. 2009;5: e1000369 10.1371/journal.ppat.1000369 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref036] 36.Druszczynska M, Kowalewicz-Kulbat M, Maszewska A, Rudnicka K, Szpakowski P, Wawrocki S, et al. The balance between pro- and anti-inflammatory cytokines in the immune responses to BCG and DTwP vaccines. Acta Biochim Pol. 2015;62 10.18388/abp.2015_1160 [DOI] [PubMed] [Google Scholar]

[pone.0225556.ref037] 37.Szpakowski P, Biet F, Locht C, Paszkiewicz M, Rudnicka W, Druszczyńska M, et al. Dendritic cell activity driven by recombinant Mycobacterium bovis BCG producing human il-18, in healthy BCG vaccinated adults. J Immunol Res. Hindawi Limited; 2015;2015: 359153 10.1155/2015/359153 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref038] 38.Chegou NN, Black GF, Loxton AG, Stanley K, Essone PN, Klein MR, et al. Potential of novel Mycobacterium tuberculosis infection phase-dependent antigens in the diagnosis of TB disease in a high burden setting. BMC Infect Dis. BioMed Central; 2012;12: 10 10.1186/1471-2334-12-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0225556.ref039] 39.Braian C, Svensson M, Brighenti S, Lerm M, Parasa VR. A 3D human lung tissue model for functional studies on Mycobacterium tuberculosis infection; Infection. J Vis Exp. 2015; 10.3791/53084 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

IL-18/IL-37/IP-10 signalling complex as a potential biomarker for discriminating active and latent TB

Sebastian Wawrocki

Michal Seweryn

Grzegorz Kielnierowski

Wieslawa Rudnicka

Marcin Wlodarczyk

Magdalena Druszczynska

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study population

Table 1. Characteristics of the study participants.

Blood samples

IL-18, IL-18BP, IL-37, IFN-γ, IP-10 estimation and calculation of free IL-18

Statistical analyses

Results

Serum IL-18, IL-18BP, IL-37, IFN-γ and IP-10 levels in the studied groups

Fig 1. Serum levels of total and free IL-18, IL-18BP, IL-37, IFN-γ and IP-10 in the groups of the study.

Fig 2. Serum levels of total IL-18, IL-18BP, free IL-18, IL-37, IP-10 and IFN-γ versus QFT/TST results.

Table 2. Predictive values (median AUC and 95% CI) of individual protein levels measured in serum.

Table 3. Correlation (r) and p values between protein levels in serum.

M.tb antigens–stimulated IL-18, IL-18BP, IL-37 and IFN-γ levels in QFT supernatants

Fig 3. Total IL-18, IL-18BP, free IL-18, IL-37 and IFN-γ levels in M.tb antigens-stimulated QuantiFERON (QFT) supernatants.

Fig 4. Levels of total IL-18, IL-18BP, free IL-18 and IL-37 in M.tb antigens-stimulated QuantiFERON (QFT) supernatants versus QFT/TST result.

Table 4. Predictive values (median AUC and 95% CI) of individual protein levels in QFT supernatants.

Table 5. Correlation (r) and p-values between protein levels in QFT supernatants.

Protein ratios in the sera and QFT supernatants

Selection of the most informative protein ratios using random forest

Table 6. AUC values for selected decision trees.

Variability of expression and co-expression levels

Fig 5.

Ordinal Elastic Net for the detection of protein levels and ratios predictive of the transition from HC through LTBI to ATB

Table 7. Results for ordinal regression (elastic net)–coefficients.

Unsupervised dimension reduction

Fig 6.

Fig 7. qq-plots for the components of the t-SNE projection.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Francesco Dieli

Roles

Author response to Decision Letter 0

Decision Letter 1

Selvakumar Subbian

Roles

Author response to Decision Letter 1

Decision Letter 2

Selvakumar Subbian

Roles

Acceptance letter

Selvakumar Subbian

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases