RESEARCH ARTICLE
Identification of plasma biomarkers for
discrimination between tuberculosis
infection/disease and pulmonary non
tuberculosis disease
Marco Pio La Manna1☯, Valentina Orlando1☯, Paolo Li Donni2, Guido Sireci1,3, Paola Di
Carlo4, Antonio Cascio4, Francesco Dieli1,3‡, Nadia Caccamo1,3‡*
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OPEN ACCESS
Citation: La Manna MP, Orlando V, Li Donni P,
Sireci G, Di Carlo P, Cascio A, et al. (2018)
Identification of plasma biomarkers for
discrimination between tuberculosis infection/
disease and pulmonary non tuberculosis disease.
PLoS ONE 13(3): e0192664. https://doi.org/
10.1371/journal.pone.0192664
Editor: Juraj Ivanyi, King’s College London,
UNITED KINGDOM
Received: November 3, 2017
Accepted: January 26, 2018
Published: March 15, 2018
Copyright: © 2018 La Manna et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper.
Funding: The study was supported by grants from
the European Union: HEALTH-F3-2009-241642
and EC FP7 NEWTBVAC (contract no. HEALTH.
F3.2009 241745), TBVAC2020 (contract number
H2020-PHC-643381) and EMITB (contract number
H2020-PHC-643558). The text represents the
authors’ views and does not necessarily represent
1 Central Laboratory of Advanced Diagnosis and Biomedical Research (CLADIBIOR), Azienda Universitaria
Ospedaliera Policlinico P. Giaccone, Palermo, Italy, 2 Dipartimento di Scienze Economiche, Aziendali e
Statistiche, Università di Palermo, Palermo, Italy, 3 Dipartimento di Biopatologia e Biotecnologie Mediche,
Università di Palermo, Palermo, Italy, 4 Department of Sciences for Health Promotion and Mother-Child Care
“G. D’Alessandro”, University of Palermo, Palermo, Italy
☯ These authors contributed equally to this work.
‡ FD and NC are joint senior authors on this work.
* nadia.caccamo@unipa.it
Abstract
We used the Luminex Bead Array Multiplex Immunoassay to measure cytokines, chemokines and growth factors responses to the same antigens used for RD1-based Interferon γ
Release Assay (IGRA) test. Seventy-nine individuals, 27 active TB, 32 latent infection subsets, 20 individuals derivative purified protein (PPD) negative (subjects that do not have any
indurative cutaneous reaction after 72 hrs of intradermal injection of PPD) and with other
pulmonary disease were retrospectively studied. Forty-eight analytes were evaluated by
Luminex Assay in plasma obtained from whole blood stimulated cells. The diagnostic accuracies of the markers detected were evaluated by ROC curve analysis and by the combination of multiple biomarkers to improve the potential to discriminate between infection/
disease and non infection. Among 48 cytokines, 13 analytes, namely IL-3, IL-12-p40, LIF,
IFNα2, IL-2ra, IL-13, b-NGF, SCF, TNF-β, TRAIL, IL-2, IFN-γ, IP-10, and MIG, were significantly higher in the active TB and LTBI groups, compared to NON-TB patients, while MIF
was significantly lower in active TB patients compared to NON-TB and LTBI groups. The
diagnostic accuracies of the markers detected in the culture supernatants evaluated by
ROC curve analysis revealed that 11 analytes (IL2, IP10, IFN-γ, IL13, MIG, SCF, b-NGF,
IL12-p40, TRAIL, IL2 Ra, LIF) discriminated between NON-TB and LTBI groups, with AUC
for all analytes 0.73, while 14 analytes (IL2, IP10, IFN-γ, MIG, SCF, b-NGF, IL12-p40,
TRAIL, IL2Ra, MIF, TNF-β, IL3, IFN-α2, LIF) discriminated between NON-TB and active TB
groups, with AUC 0.78, that is a moderate, value in terms of accuracy of a diagnostic test.
Finally, the combinations of seven biomarkers resulted in the accurate prediction of 88.89%
of active TB patients, 82.35% of subjects with latent infection and 90% of non-TB patients,
respectively. Taken together, our data suggest that combinations of whole blood
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
a position of the Commission who will not be liable
for the use made of such information.
Mycobacterium tuberculosis (Mtb) antigen dependent cytokines production could be useful
as biomarkers to determine tuberculosis disease states when compared to non TB cohort.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
Tuberculosis (TB) continues to be the cause of death of 4000 people per day [1], and the
World Health Organization (WHO) and all the people that are involved in the field are still
searching solutions able to control and prevent the socio-economic and medical problems
caused by this pathology. In 2014, the WHO approved a new strategy to reach the ambitious
target of ending the global TB epidemic by 2035 [2, 3]: the End TB Strategy. The goal will be
reached when TB related deaths and active TB disease incidence, compared to the data of
2015, will be reduced by 95% and 90%, respectively, with a decrease in incidence less than 10
per 100, 000 population [4, 5]. Therefore, the discovery, development, and rapid implementation of new diagnostic tools have been identified as important components of the End TB
strategy of WHO [6, 7].
One of the limits of the available diagnostic tests is that it is not possible to discriminate
infection from disease. Therefore, the search for simple and cheap diagnostic methods with
the aim to discriminate patients with active disease from subjects with latent infection, with
the objective to cure patients with active disease and to block the spread of the pathogen from
subjects with latent infection that are at high risk to develop the disease and that can cause the
transmission of infection to healthy subjects. The identification of biomarkers that are detectable using peripheral blood with simple technologies represents a crucial aspect in the field,
due to the limit of the IGRA tests commercially available as diagnostic tests, that are not able
to discriminate between infection and active disease. In order to bypass these limitations, new
techniques, including transcript microarrays, flow cytometry of intracellular cytokines, and
multiplex microbead-based immunoassay (Luminex assay) of cytokines, have recently been
introduced [8–12]. Moreover, these cytokines/chemokines were analyzed as a single biomarker or in combination, in order to find a tool to discriminate, in an unequivocal way, subjects with latent infection from that with active disease. In these studies, some biomarkers were
found at the high significant difference between patients with active disease and LTBI subjects;
while in other studies other cytokines have been indicated in order to discriminate the infection from disease, demonstrating that several factors, other than the pathogen, could influence
the cytokines/chemokines levels [13–15].
In the current study, on the basis of the WHO End TB strategy, we have evaluated the levels
of different cytokines and chemokines from plasma samples obtained from unstimulated and
or antigen specific stimulated cells with the objective to discriminate active TB cases from
latently infected contacts, or if the combination of different biomarkers could be useful as biosignature of disease/infection.
Materials and methods
Study participants
Seventy-nine individuals were retrospectively studied as here reported: (a) active TB group: 27
individuals diagnosed with TB (either with a positive culture for Mtb from sputum (48%) or
with positive genexpert MTB/RIF, Mtb DNA resistance to rifampicin (52%) amplification
results from biopsy specimens and/or biological fluids) 21 men, 6 women (age range 17–82
years) who started specific treatment within 8 days before enrolment; (b) latent infection
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
subsets (LTBI): 32 individuals (24 men, 8 women, age range 17–84 years) who reported household or equivalent close contact (work) with smear-positive pulmonary TB patients in the
previous 3 months, tested PPD positive (when the diameter was major of 10 mm) second ATS
guidelines and QuantiFERON Gold In-Tube (QFT IT), Qiagen, -positive(IFN-γ levels
0.35UI/ml), with negative chest radiographic results for pulmonary lesions and no prior preventive therapy; (c)other non-TB pulmonary infections (NON-TB): 20 individuals PPD negative (12 men, 8 women, age range 24–76 years); (d)healthy donors (HD): 20 individuals PPD
negative (14 men, 6 women, age range 21–68 years).
Patients were recruited from the Department of Infectious Disease, University Hospital of
Palermo. All patients were treated in accordance with Italian guidelines and received therapy
for 6 months. Treatment was successful in all participants, all of whom completed the full
course of anti-TB chemotherapy. None of the TB patients had evidence of HIV infection, or
was being treated with steroid or other immunosuppressive or anti-tubercular drugs at the
time of their first sampling. The study was approved by the Ethical Committee of the University Hospital in Palermo (approval number 13/2013), where the patients were recruited.
Informed consent was signed by all participants. QFT, IT (Qiagen, Carnegie, VA, Australia).
QFT-IT was performed as indicated by the manufacturer. Briefly, whole blood was collected
in the QFT-IT tubes (Nil Control, TB-Ag and Mitogen) and incubated at 37˚C for 16–24
hours. Following incubation, samples were centrifuged and the plasma was used to measure
the IFN-γ produced in response to Mtb antigens, phytohaemagglutinin (PHA) and the negative control. Data are presented as IU/ml of IFN-γ; the cut-off value for a positive test was 0.35
IU/ml, according to manufacturer’s instructions.
Luminex multiplex Immunoassay Bio-Rad
The concentrations of 48 host cytokines and chemokines, including biomarkers that have
been already validated, were investigated in samples from all the study participants. Experiments were performed in a blinded manner, according to the instructions of the kit
manufacturers.
The principle of the technique is based on developing color coded microspheres by combining different ratios of two dyes and this combination can give up to 100 different combinations,
which enables measurement of 100 different analytes. The technology combines ELISA and
flow cytometry together where the capture antibody is conjugated with beads or microspheres
whereas the secondary antibody is conjugated with fluorochrome which quantifies the antigenantibody reaction by measuring the relative fluorescence intensity. The assays were performed
according to the supplier’s instructions. Briefly, following pre wetting of plates, 50 μl precombined beads of all the 48 individual cytokines or chemokines were added and washed twice.
Plasma samples (25 μl) were diluted 1:2 with the kit serum matrix and added to the plate.
The plate was shaken for 30 s at 1000 RPM and then incubated for 1 h on plate shaken at 300
RPM at room temperature. The plates were washed twice and 25 μl of detection antibody was
added per well and incubated for one hour on a plate shaker. Then strepatavidin-PE conjugate
(50μl per well) was added and incubated for 30 min at room temperature. Finally, the plate
was washed three times and 150 μl of sheath fluid was added to each well and then the plate
was read on the Bio-Plex platform (Bio-Rad), with the Bio-Plex Software version 6.1 used for
bead acquisition and analysis.
QuantiFERON TB-gold in tube (QFT-IT)
QFT-IT (Cellestis Limited, Carnegie, Victoria, Australia) was performed using TB antigen-,
mitogen- and unstimulated tube (nil). The tube containing TB antigen uses overlapping
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
peptides from CFP-10 and ESAT-6 and TB7.7. QFT is a test for cell-mediated immune (CMI)
responses to peptide antigens that simulate mycobacterial proteins. These proteins, ESAT-6,
CFP-10, and TB7.7 (p4), are absent from all BCG strains and from most non tuberculous
mycobacteria with the exception of M. kansasii, M. szulgai, and M. marinum. The assay was
performed and the results were scored as indicated by the manufacturer’s instructions (the
cut-off value for a positive test was 0.35 IU/ml). An indeterminate score was assigned if the
IFN-γ response to the mitogen after subtracting the nil IFN-γ response was <0.5 IU/ml or if
the nil IFN-γ response was >8 IU/ml.
Statistical analysis
Differences in the concentrations of host biomarkers detected in plasma samples from TB
patients, LTBI subjects, NON-TB and HD were evaluated using the Kruskal- Wallis test distributed data. Biomarkers were compared between multiple groups using Kruskal Wallis tests.
A non-parametric test able to compare more than two experimental groups. Dunn’s multiple
comparison tests was used for post hoc correction to account for multiple comparisons.
Test performance in terms of sensitivity (ability of the test to identify the real positive subjects) and specificity (ability of the test to identify the real negative subjects) was evaluated for
each cytokine by a ROC (Receiving Operating Characteristic) curve, the selective validation
method of a quantitative diagnostic test in a population. The proportion of patients correctly
diagnosed, that is the test accuracy, is proportional to the area under the curve (AUC), which
can assume values between 0.5 (50% accuracy)and not accurate for AUC = 0.5, the test is
poorly accurate for 0.5 <AUC0.7, the test is moderately accurate for 0.7 <AUC0.9, the test
is highly accurate for 0.9 <AUC < 1 and the test is perfect for AUC = 1. The ROC curve also
allows to identify the best cut off value that maximizes the difference between true positive
subjects and false positives ones [16]. P-values <0.05were considered significant. The likelihood ratios was used for assessing the value of performing a diagnostic test.
General discriminant analysis (GDA) was used to evaluate the abilities of combinations of
biomarkers to discriminate between Mtb-infected and uninfected groups or between active TB
and LTBI. Optimal combinations of biomarkers were investigated by performing a best subsets
analysis. All statistical analyses were performed and graphics were prepared using Statistica
version 13 software (Statsoft, Ohio, USA), and GraphPad Prism software version 5.
Results
Measurement of host markers in LTBI subjects, TB patients and patients
with other pulmonary infections
The baseline concentrations of host markers in TB patients were compared to the levels
detected in subjects with LTBI and patients with pulmonary infections other than TB in order
to avoid any unspecific background levels independently on mycobacterial stimulation. Initially, we scored those cytokine levels which were over the normal range concentrations, as calculated by the manufacturer in n = 66 healthy individuals. For comparison, we measured the
48 analytes in the serum of an independent cohort of n = 20 age- and sex-matched healthy
individuals and found that reference values from the manufacturer were fully consistent with
those from our independent cohort of healthy subjects (Wilcoxon p value = 0.26 for comparison of the two series of data). (Table 1).
The concentrations of 30 out of the 48 tested analytes were over the normal range in LTBI
subjects and patients with active TB disease (Table 2). Baseline serum levels of 28 cytokines
were found elevated in both groups, with percentages ranging from 40 to 100% in LTBI
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 1. List of the cytokines, chemokines and growth factors evaluated by luminex assay Biorad (Germany) and their normal range assessed in healthy donors.
Analyte
pg/ml
(Range)
pg/ml
(Observed)
pg/ml
(Median)
pg/ml
(Mean)
IL-1α
0.50–1.40
0.40–1.40
0
0.12
IL-1β
<0.70
0.02–0.70
0
0.01
IL-1ra
6–665
0.20–665
23.94
42.01
IL-2
2–90
0.03–90
1.24
6.46
IL-2Ra
28–594
28–594
102.66
116.85
IL-3
13–170
13–170
41.06
44.54
IL-4
0.06–3
0.01–3
0
0.10
IL-5
1–7
0.01–7
0
0.15
IL-6
0.50–9
0.02–9
0
0.73
IL-7
0.60–13
0.01–14
0
0.27
IL-8
0.40–116
0.08–116
0
7.21
IL-9
2–500
0.38–500
19.40
37.50
IL-10
0.40–2
0.10–2
0
0.13
IL-12(p40)
36–646
36–646
0
60.19
IL-12(p70)
3–6
0.10–6
0
0.14
IL-13
0.80–9
0.01–9
0
0.33
IL-15
2–5
0.06–5
0
0.31
IL-16
10–1,270
10–1,270
77.50
94.79
IL-17
2–31
0.22–31
0
2.30
IL-18
9–812
9–812
68.05
75.71
IFN-α2
14–79
3.30–63
0
16.07
IFN-γ
7–124
0.60–124
8.68
13.43
TNF-α
6–98
0.10–98
0
5.92
TNF-β
1–13
0.71–13
0
0.31
TRAIL
8–272
8–272
66.61
65.91
CXCL1 (GRO-α)
9–365
9–365
22.35
36.33
CXCL9 (MIG)
86–7,911
86–7,911
289
617.05
CXCL10 (IP-10)
6–637
5.90–637
32.24
93.61
CXCL12 (SDF-1α)
8–92
8–92
0
13.79
CCL2 (MCP-1)
2–48
2–48
17.95
18.24
CCL3 (MIP-1α)
<2
0.01–2
0
0.15
CCL4 (MIP-1β)
5–47
1.70–47
11.24
14.75
CCL5 (RANTES)
100–2,282
100–2,282
0
203.64
CCL7 (MCP-3)
1–78
1–78
2.25
3.28
CCL11 (Eotaxin)
2–39
1.20–39
0
3.80
CCL27 (CTACK)
1–1,086
1–1,086
196
246.51
G-CSF
<1.50
<1.50
0
0.02
M-CSF
6–208
6–208
29.64
48.68
GM-CSF
3–122
0.80–122
6.78
12.47
SCF
16–837
16–837
167.57
172.88
SCGF-β
6,05–130,932
6,05–130,932
47,870
48,312
LIF
4–55
4–55
14.85
17.20
MIF
6–2,003
6–2,003
72.40
170.29
FGF-β
4–55
1.30–55
7.54
9
b-NGF
<1.10
<1.10
0
0.04
PDGF-BB
6–3,667
6–3,667
180.10
394.87
VEGF
0.50–9
0.01–9
0
0.43
HGF
63–1,868
63–1,868
195.20
255.24
https://doi.org/10.1371/journal.pone.0192664.t001
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
subjects and from 44 to 100% in TB patients. Baseline levels of no single analyte were differentially increased in sera from LTBI subjects. Conversely, baseline level of IFNα2 was slightly
increased over the maximum normal range value (1,14-fold) in patients with active TB, but
this was only detected in approximately one third of the patients, while the baseline of
GM-CSF was increased in LTBI compared to active TB (1,20-fold) in the 50% of LTBI
subjects.
To understand whether increased levels of several different analytes were a feature of TB
infection/disease or were found also in other pathologic conditions, we measured their baseline concentrations in sera from patients with other non-TB pulmonary infections (n.20). As
reported in Table 2, 29 out of 30 analytes were increased both in sera of TB patients and in
NON-TB patients over the normal range, suggesting that elevated baseline levels of these molecules reflect an active infection/inflammation state, irrespective of the etiology.
To check for potential biomarkers capable of differentiating between LTBI subjects and
patients with active TB, and between these latter and patients with other non-TB pulmonary
infections, we compared the median baseline analyte concentrations in the tested LTBI, TB
and NON-TB groups by Kruskal-Wallis tests. Results show (Table 2) that median baseline levels of 7 analytes, namely IL-1β, IL2, IL-8, IL-12p70, MCP-1, PDGF-BB, VEGF and LIF were
significantly different among LTBI subjects, TB and NON-TB patients; furthermore, after
post-test correction using Dunn’s multiple comparison tests, the median concentrations of IL1β, IL12p70 and VEGF were significantly higher in LTBI subjects compared to NON-TB
patients, and the median concentrations of PDGF-BB, IL-1β, IL-2, IL-8, IL12p70, MCP-1 and
LIF were significantly higher in TB patients compared to NON-TB patients (Fig 1).
Of note, however, median baseline concentrations of the 48 tested analytes did not significantly differ between LTBI subjects and patients with active TB, indicating that there was no
baseline biomarkers capable to discriminate between LTBI and active TB disease.
When the diagnostic accuracies of individual host markers were investigated by ROC curve
analysis, the area under the ROC curve (AUC) was 0.70 for the 8 markers found significantly
increased in LTBI subjects and active TB patients (Table 3). These markers have proven to be
very effective in distinguishing non TB from active TB. At their optimal unstimulated cut off
values, IL-8, IL-12p70 and MCP-1 had a sensitivity of 72%, 76% and 73% respectively, and IL1β, PDGF-BB and LIF had a specificity of 85%, 90% and 80% respectively. The best performance characteristic was with unstimulated IL12p70 with a sensitivity of 73% and specificity
of 78% (Table 3). Representative plots showing the 10 individual baseline host markers with
the best diagnostic accuracy are shown in Fig 2.
Measurement of mycobacterial antigen-specific host markers in LTBI
subjects, TB patients and patients with other pulmonary infections
We compared the median analyte concentrations in the supernatants of mycobacterial Ag
stimulated cells from LTBI, TB and NON-TB groups by Kruskal-Wallis tests. In order to
exclude the contribution of unstimulated marker level to the diagnosis of TB infection/disease,
for each study participant before the analysis of the data, the antigen specific responses of the
different cytokines and chemokines were evaluated by subtraction of the unstimulated levels
from the antigen-stimulated response.
Results show (Table 4) that in response to Mtb antigen specific stimulation by ESAT-6/
CFP-10, median concentrations of 14 analytes, namely IL-2, IP-10, IFN-γ, MIG, SCF, b-NGF,
IL-12-p40, MIF, TRAIL, IL-2ra, TNF-β, IL-3, IFNα2, LIF and IL-13 were significantly different
in the active TB and LTBI groups, compared to NON-TB patients. Furthermore, after post-test
correction using Dunn’s multiple comparison tests, the median concentrations of IL12-p40,
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 2. Medians and statistical analysis of the cytokines baseline levels.
Dunn’s multiple comparisons test
Non TB
Analyte
LTBI
Active TB
Normal range
% of patients upper % of patients upper % of patients upper
normal range
normal range
normal range
KruskalWallis test
p value
NIL NEG vs.
NIL LTBI
NIL NEG vs. NIL
ACTIVE TB
NIL LTBI vs. NIL
ACTIVE TB
p value
p value
p value
0,1197
Basic FGF
90%
78%
96%
1.30–55.00
0,0528 ns
>0,9999
0,1022
Eotaxin
85%
88%
93%
1.20–39.00
0,5465 ns
>0,9999
>0,9999
0,851
G-CSF
100%
97%
100%
<1.50
0,1157 ns
0,7201
0,1141
0,8366
GM-CSF
45%
50%
48%
0.80–122.00
0,8664 ns
>0,9999
>0,9999
>0,9999
IFN-γ
40%
75%
70%
0.60–124.00
0,0537 ns
0,0519
0,2127
>0,9999
IL-1β
100%
97%
100%
0.02–0.70
0,0179
0,0077
>0,9999
IL-1ra
20%
16%
15%
0.20–665.00
0,0562 ns
0,1565
0,0629
>0,9999
IL-2
10%
6%
4%
0.03–90.00
0,0341
0,1194
0,036
>0,9999
IL-4
85%
84%
93%
0.01–3.00
0,218
ns
0,253
0,6588
>0,9999
0,0051
IL-5
5%
6%
0%
0.01–7.00
0,7723 ns
>0,9999
>0,9999
>0,9999
IL-6
100%
97%
100%
0.02–9.00
0,0934 ns
0,0917
0,4415
>0,9999
>0,9999
IL-7
30%
38%
26%
0.01–14.00
0,4545 ns
IL-8
100%
97%
93%
0.08–116.00
0,0161
0,629
>0,9999
0,5306
0,0132
IL-9
20%
6%
22%
0.38–500.00
0,2461
0,1278 ns
0,6297
0,1279
>0,9999
>0,9999
IL-10
100%
97%
100%
0.10–2.00
0,7698 ns
>0,9999
>0,9999
IL-12(p70)
100%
91%
96%
0.10–6.00
0,0062
0,0167
0,01
>0,9999
IL-13
20%
28%
35%
0.01–9.00
0,0867 ns
0,2386
0,0994
>0,9999
IL-15
55%
69%
56%
0.06–5.00
0,2735 ns
0,3651
>0,9999
0,8823
IL-17
95%
100%
100%
0.22–31.00
0,0779 ns
>0,9999
0,0819
0,4117
IP-10
80%
81%
89%
5.90–637.00
0,2025 ns
0,6522
>0,9999
0,277
MCP-1
(MCAF)
95%
97%
100%
2.00–48.00
0,0102
0,1554
0,0075
0,5804
MIP-1α
95%
97%
96%
0.01–2.00
0,1592 ns
0,2462
0,2905
>0,9999
MIP-1β
85%
69%
85%
1.70–47.00
0,4969 ns
0,7958
>0,9999
>0,9999
PDGF–BB
0%
0%
7%
6.00–3,667.00
0,5805
0,0123
0,201
RANTES
85%
84%
81%
100,00–
2.282,00
0,3143 ns
0,7307
0,4309
>0,9999
0,0143
TNF-α
55%
81%
89%
0.10–98.00
0,051
ns
0,0652
0,1298
>0,9999
VEGF
100%
100%
100%
0.01–9.00
0,0298
0,039
0,0814
>0,9999
CTACK
60%
76%
100%
1.00–1,086.00
0,3238 ns
0,5438
0,5198
>0,9999
GRO-a
80%
94%
100%
9.00–365.00
0,6316 ns
>0,9999
>0,9999
> 0,9999
HGF
30%
24%
11%
63.00–
1,868.00
0,5387 ns
>0,9999
0,8739
>0,9999
>0,9999
IFN-α2
60%
59%
44%
3.30–63.00
0,6583 ns
>0,9999
>0,9999
IL-1a
59%
100%
100%
0.40–1.40
0,4344 ns
>0,9999
0,8889
0,7695
IL-2Ra
20%
12%
11%
28.00–594.00
0,7087 ns
>0,9999
>0,9999
>0,9999
IL-3
100%
94%
100%
13.00–170.00
0,8275 ns
>0,9999
>0,9999
>0,9999
IL-12(p40)
80%
71%
89%
36.00–646.00
0,5405 ns
>0,9999
>0,9999
0,8275
IL-16
0%
0%
0%
10.00–
1,270.00
0,1413
0,377
>0,9999
0,131
ns
IL-18
10%
6%
6%
9.00–812.00
0,2328 ns
LIF
80%
82%
94%
4.00–55.00
0,048
MCP-3
70%
82%
94%
1.00–78.00
M-CSF
40%
53%
28%
6.00–208.00
MIF
80%
65%
94%
6.00–2,003.00
0,2645
0,7535
>0,9999
>0,9999
0,0751
0,1916
0,2451 ns
>0,9999
0,3016
0,9322
0,6022 ns
>0,9999
>0,9999
0,9684
0,263
>0,9999
0,3635
0,7695
ns
(Continued)
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 2. (Continued)
Dunn’s multiple comparisons test
Non TB
Analyte
LTBI
Active TB
Normal range
% of patients upper % of patients upper % of patients upper
normal range
normal range
normal range
KruskalWallis test
p value
NIL NEG vs.
NIL LTBI
NIL NEG vs. NIL
ACTIVE TB
NIL LTBI vs. NIL
ACTIVE TB
p value
p value
p value
MIG
60%
18%
50%
86.00–
7,911.00
0,1655 ns
0,174
0,7619
>0,9999
b-NGF
100%
82%
94%
<1.10
0,4464 ns
0,7495
>0,9999
0,9538
SCF
0%
0%
0%
16.00–837.00
0,7475 ns
>0,9999
>0,9999
>0,9999
SCGF-b
0%
0%
0%
6,054.00–
130,932.00
0,1127 ns
0,2636
>0,9999
0,2024
SDF-1a
100%
94%
100%
8.00–92.00
0,9409 ns
>0,9999
>0,9999
>0,9999
TNF-β
30%
47%
33%
0.71–13.00
0,8992 ns
>0,9999
>0,9999
>0,9999
TRAIL
0%
0%
6%
8.00–272.00
0,6917 ns
>0,9999
>0,9999
>0,9999
Median baseline levels of cytokines analyzed in the three groups, Kruskal-Wallis test with Dunn’s correction.
p<0.05
p<0.01.
The bold cytokines show higher concentrations than the normal range.
Fr. is the frequency of subjects with a higher cytokine concentration than the normal range.
https://doi.org/10.1371/journal.pone.0192664.t002
IL-2ra, SCF, TRAIL, IL-2, IFN-γ, IP-10, b-NGF, LIF and MIG were significantly higher in the
active TB and LTBI subjects compared to NON-TB patients, and the median concentrations of
IFNα2, IL-3, and TNF-β were significantly higher in TB patients compared to NON-TB
patients (Fig 3). Finally, the median concentrations of IL-13 was significantly higher only in
LTBI compared to NON-TB patients. As with baseline levels of tested analytes, there were no
statistically significant differences in median concentrations of the tested analytes in Mtb antigen-stimulated samples from LTBI subjects and patients with active TB, except for MIF that
shows an appreciable and statistically significant decrease in the active TB group compared to
LTBI and non TB groups.
The biomarkers detected were analysed using receiver operating characteristic (ROC)
curves, the test accuracy that is proportional to the area under the curve (AUC), which can
assume values between 0.5 (50% accuracy) and 1 (100% accuracy). The ROC curve also allows
to identify the best cut off value that maximizes the difference between true positive subjects
and false positives ones. ESAT-6/CFP-10-specific levels of 11 analytes (IL2, MIG, SCF, TRAIL,
b-NGF, IL12-p40, IL2Ra, IL13, IFN-γ, IP10 and LIF) discriminated between NON-TB and
LTBI groups, with AUC 0.73. Out of these 8 markers, ESAT-6/CFP-10-specific level of MIG
and SCF had the best sensitivity and specificity of >80% (Table 5 and Fig 4).
Similarly, when the diagnostic accuracies of the markers detected in the culture supernatants were evaluated by ROC curve analysis, ESAT-6/CFP-10-specific levels of 14 analytes
(IL2, SCF, TRAIL, MIG, IL12-p40, b-NGF, MIF, TNF-β, IFN-α2, IFN-γ, IL3, IP10, LIF, IL2
Ra,) discriminated between NON-TB and active TB groups, with AUC 0.78. Two out of
these 14 markers, ESAT-6/CFP-10-specific level of MIG and SCF had again the best sensitivity
and specificity of >90% (Table 6 and Fig 5).
General discriminant analysis (GDA)
To determine whether combinations of multiple biomarkers could improve the ability to
detect Mtb-infected individuals, we examined the performance of all combinations of 23
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Fig 1. Scatter plots showing biomarker concentrations in unstimulated supernatants from participants with non TB disease
(triangles), LTBI (squares), active TB patients (circle). Biomarkers were measured by Luminex assay. Median concentrations are
indicated with horizontal bars. Statistical differences were analyzed using Kruskal-Wallis test with Dunn’s correction. p<0.05, p<0.01.
https://doi.org/10.1371/journal.pone.0192664.g001
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 3. ROC curves features of markers increased in LTBI and Active TB.
ROC curve of non TB vs LTBI
Area under the ROC
curve
Std.
Error
95% confidence
interval
Pvalue
cutoff
Sensitivity
%
95% CI
Specificity
%
95% CI
Likelihood
ratio
IL-1β
0.7283
0.0734
0.5844 to 0.8722
0.00669
> 373.1
63.33
43.86% to
80.07%
85
62.11% to
96.79%
4.222
IL12 p70
0.7257
0.07212
0.5843 to 0.8671
0.008629 > 72.69
53.13
34.74% to
70.91%
83.33
58.58% to
96.42%
3.188
IL-2
0.6713
0.08274
0.5091 to 0.8335
0.04891
> 17.20
63.33
43.86% to
80.07%
72.22
46.52% to
90.31%
2.28
IL-8
0.6161
0.08502
0.4495 to 0.7828
0.1649
> 10992
67.74
48.63% to
83.32%
60
36.05% to
80.88%
1.694
MCP-1
0.6613
0.07746
0.5094 to 0.8132
0.05765
> 1092
54.84
36.03% to
72.68%
73.68
48.80% to
90.85%
2.084
PDGF-BB
0.6094
0.08119
0.4502 to 0.7685
0.188
> 1230
53.13
34.74% to
70.91%
55
31.53% to
76.94%
1.181
VEGF
0.6938
0.07373
0.5492 to 0.8383
0.01972
> 260.9
50
31.89% to
68.11%
90
68.30% to
98.77%
5
LIF
0.5875
0.1156
0.3609 to 0.8141
0.4606
> 112.3
62.5
35.43% to
84.80%
70
34.75% to
93.33%
2.083
Area under the ROC
curve
Std.
Error
95% confidence
interval
Sensitivity
%
95% CI
Specificity
%
95% CI
Likelihood
ratio
IL-1β
0.7611
0.07001
0.6239 to 0.8984
0.002427 > 355.7
55.56
35.33% to
74.52%
85
62.11% to
96.79%
3.704
IL12 p70
0.7789
0.0725
0.6368 to 0.9210
0.002015 > 57.60
72
50.61% to
87.93%
77.78
52.36% to
93.59%
3.24
IL-2
0.7318
0.07671
0.5814 to 0.8822
0.009627 > 19.65
57.69
36.92% to
76.65%
77.78
52.36% to
93.59%
2.596
IL-8
0.744
0.08302
0.5812 to 0.9068
0.005345 > 14618
76
54.87% to
90.64%
70
45.72% to
88.11%
2.533
MCP-1
0.7692
0.07132
0.6294 to 0.9091
0.002252
> 1118
73.08
52.21% to
88.43%
73.68
48.80% to
90.85%
2.777
PDGF-BB
0.7462
0.07233
0.6044 to 0.8879
0.004583
> 1821
57.69
36.92% to
76.65%
90
68.30% to
98.77%
5.769
VEGF
0.7058
0.07795
0.5530 to 0.8586
0.01778
> 161.3
61.54
40.57% to
79.77%
70
45.72% to
88.11%
2.051
LIF
0.7444
0.1007
0.5469 to 0.9419
0.03494
> 155.7
61.11
35.75% to
82.70%
80
44.39% to
97.48%
3.056
ROC curve of non TB. vs Active TB
Pvalue
cutoff
https://doi.org/10.1371/journal.pone.0192664.t003
biomarker responses (PDGF NIL, IL1β NIL, IL2 NIL, IL8,NIL, IL12p70 NIL, MCP-1 NIL,
VEGF NIL, LIF NIL, IL2 Ag-NIL, IP10 Ag-NIL, IFN-γ Ag-NIL, MIG Ag-NIL, SCF Ag-NIL, bNGF Ag-NIL, IL12-p40, MIF Ag-NIL, TRAIL Ag-NIL, IL2RaAg-NIL, IL13 Ag-NIL, TNF-β
Ag-NIL, IL3 Ag–NIL, IFN-α2 Ag-NIL, LIF Ag-NIL) significantly differed between Mtbinfected cases (active TB n. 27, LTBI n. 32) and non TB group n. 20 (see also Figs 2 and 3).
Using a GDA, all of the top–six or seven biomarker combinations (Table 7) accurately predicted active TB patients, LTBI subjects and non TB patients. In particular, seven biomarker
combinations could predicted from 77.78 to 88.89% of active-TB patients, from 64.71 to
82.35% of LTBI subjects and from 90% to 100% of non TB patients. IL-2 was the biomarker
most frequently included in the top seven-biomarker models, followed, in order, by IFN-γ,
MIF, IL2Ra, LIF[Nil], followed by PDGF-BB [Nil], TRAIL and MCP-1[Nil] (Table 7).
The top six biomarker combinations could predicted from 77.78% to 88,89% of active TB
patients, from 64,71 to 70,59% of LTBI subjects and from 80% to 100% of non TB patients.
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Fig 2. Receiver operating characteristic (ROC) curves for the baseline cytokine values comparing LTBI subjects
and patients with active TB with non TB patients. The solid line shows the result of absolute values of each
biomarker. The area under the curve (AUC) is indicated.
https://doi.org/10.1371/journal.pone.0192664.g002
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 4. Medians and statistical analysis of cytokine levels after antigen specific stimulation with subtraction of baseline.
Dunn’s multiple comparisons test
Non TB
LTBI
Active TB
Normal range
Kruskal-Wallis
test
Analyte
% of patients upper
normal range
% of patients upper
normal range
% of patients upper
normal range
pg/ml
p value
Basic FGF
0%
0%
0%
1.30–55.00
0,3507
Eotaxin
0%
12%
4%
1.20–39.00
0,4187
G-CSF
50%
38%
36%
<1.50
0,2068
Ag-NIL NEG vs.
Ag—NIL LTBI
Ag-NIL NEG vs. Ag
—NIL Act. TB
Ag—NIL LTBI vs
Ag.-.NIL Act.TB
p value
p value
p value
ns
0,4541
0,9515
> 0,9999
ns
0,7966
0,6547
> 0,9999
ns
0,2764
0,4318
> 0,9999
GM-CSF
0%
4%
0%
0.80–122.00
0,8174
ns
> 0,9999
> 0,9999
> 0,9999
IFN-γ
10%
42%
48%
0.60–124.00
0,0003
0,0004
0,0027
> 0,9999
0,9416
IL-1β
45%
42%
64%
0.02–0.70
0,3585
ns
0,5116
> 0,9999
IL-1ra
0%
16%
4%
0.20–665.00
0,2291
ns
> 0,9999
0,2896
0,72
IL-2
0%
69%
68%
0.03–90.00
< 0,0001
< 0,0001
< 0,0001
0,5463
0,5554
IL-4
5%
15%
8%
0.01–3.00
0,311
ns
0,6375
> 0,9999
IL-5
0%
8%
4%
0.01–7.00
0,0339
0,8795
0,3191
0,028
IL-6
70%
46%
44%
0.02–9.00
0,6
ns
> 0,9999
> 0,9999
> 0,9999
IL-7
15%
15%
12%
0.01–14.00
0,6539
ns
> 0,9999
> 0,9999
> 0,9999
IL-8
45%
42%
36%
0.08–116.00
0,3979
ns
> 0,9999
0,5293
> 0,9999
IL-9
0%
0%
0%
0.38–500.00
0,6374
ns
> 0,9999
> 0,9999
> 0,9999
IL-10
30%
15%
20%
0.10–2.00
0,0397
0,035
0,2389
> 0,9999
IL-12(p70)
15%
15%
16%
0.10–6.00
0,0421
ns
0,0672
0,0792
> 0,9999
IL-13
0%
23%
28%
0.01–9.00
0,0033
0,0022
0,1727
0,3893
IL-15
25%
42%
36%
0.06–5.00
0,6884
ns
> 0,9999
> 0,9999
> 0,9999
IL-17
25%
42%
36%
0.22–31.00
0,7224
ns
> 0,9999
> 0,9999
> 0,9999
IP-10
45%
69%
84%
5.90–637.00
0,0026
0,0048
0,0088
> 0,9999
MCP-1
(MCAF)
45%
81%
68%
2.00–48.00
0,4415
ns
0,6271
> 0,9999
> 0,9999
> 0,9999
MIP-1a
40%
38%
32%
0.01–2.00
0,3835
ns
> 0,9999
0,5069
MIP-1b
50%
31%
60%
1.70–47.00
0,0835
ns
0,9293
0,8343
0,0778
PDGF–BB
0%
0%
0%
6.00–3,667.00
0,0423
0,1245
0,0486
> 0,9999
RANTES
20%
23%
24%
100.00–
2,282.00
0,7542
ns
> 0,9999
> 0,9999
> 0,9999
TNF-α
20%
35%
28%
0.10–98.00
0,79
ns
> 0,9999
> 0,9999
> 0,9999
VEGF
15%
8%
4%
0.01–9.00
0,0015
0,0034
0,0045
> 0,9999
CTACK
0%
0%
0%
1.00–1,086.00
0,5625
ns
0,9307
> 0,9999
> 0,9999
GRO-a
40%
59%
44%
9.00–365.00
0,7153
ns
> 0,9999
> 0,10000
> 0,10001
HGF
0%
0%
0%
63.00–1,868.00
0,1554
ns
0,1701
0,981
0,8104
IFN-α2
0%
12%
17%
3.30–63.00
0,0133
0,1087
0,0104
> 0,9999
ns
> 0,9999
0,9955
> 0,9999
0,0401
0,0275
> 0,9999
IL-1a
30%
47%
33%
0.40–1.40
0,6236
IL-2Ra
0%
0%
0%
28.00–594.00
0,0195
IL-3
0%
47%
67%
13.00–170.00
0,0419
0,1203
0,0451
> 0,9999
IL-12(p40)
0%
24%
22%
36.00–646.00
0,0037
0,0133
0,0046
> 0,9999
IL-16
0%
0%
0%
10.00–1,270.00
0,3716
ns
0,6493
> 0,9999
0,758
IL-18
0%
0%
0%
9.00–812.00
0,3452
ns
0,5321
0,6017
> 0,9999
LIF
0%
59%
50%
4.00–55.00
0,0346
0,0629
0,0519
> 0,9999
MCP-3
20%
53%
50%
1.00–78.00
0,1776
ns
0,1908
0,6126
> 0,9999
M-CSF
0%
6%
0%
6.00–208.00
0,3932
ns
0,8376
0,5487
> 0,9999
MIF
40%
47%
17%
6.00–2,003.00
0,0029
> 0,9999
0,007
0,0208
MIG
0%
53%
72%
86.00–7,911.00
0,0008
0,0029
0,0013
> 0,9999
b-NGF
20%
71%
89%
<1.10
0,0044
0,0102
0,0081
> 0,9999
0,0051
0,0004
> 0,9999
> 0,9999
0,4237
> 0,9999
SCF
0%
0%
0%
16.00–837.00
0,0004
SCGF-b
10%
6%
0%
6,054.00–
13,0932.00
0,3387
ns
SDF-1a
20%
41%
39%
8.00–92.00
0,2878
ns
0,68
0,36
> 0,9999
TNF-β
0%
41%
50%
0.71–13.00
0,0098
0,0832
0,0077
> 0,9999
(Continued)
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 4. (Continued)
Dunn’s multiple comparisons test
Non TB
LTBI
Active TB
Normal range
Kruskal-Wallis
test
Analyte
% of patients upper
normal range
% of patients upper
normal range
% of patients upper
normal range
pg/ml
p value
TRAIL
0%
6%
17%
8.00–272.00
0,0013
Ag-NIL NEG vs.
Ag—NIL LTBI
Ag-NIL NEG vs. Ag
—NIL Act. TB
Ag—NIL LTBI vs
Ag.-.NIL Act.TB
p value
p value
p value
0,0092
0,0013
> 0,9999
Bold cytokines show appreciable and statistically significant differences between non-TB and LTBI or active TB
p<0.05
p<0.01
p<0.001.
https://doi.org/10.1371/journal.pone.0192664.t004
IL2, IFN-γ, LIF, MIF were frequently occurring biomarkers, followed by MCP-1[Nil],
IL12-p40 and TRAIL. These results, clearly demonstrate that Mtb infection display a different
combination of cytokines/chemokines expression, highlighting that the immune response is
completely different between infection and non TB status.
Fig 3. Scatter plots showing biomarker concentrations in antigen stimulated supernatants with subtracted Nil value from participants with non TB disease
(triangles), LTBI (squares), active TB patients (circle). Biomarkers were measured by Luminex assay. Median concentrations are indicated with horizontal bars.
Statistical differences were analyzed using Kruskall Wallis tests. p<0.05, p<0.01.
https://doi.org/10.1371/journal.pone.0192664.g003
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 5. ROC curves features of the 11 markers increased in LTBI.
ROC curve of non TB vs LTBI (Ag-Nil)
Area under the
ROC curve
Std.
Error
95% confidence
interval
Pvalue
cutoff
Sensitivity
%
95% CI
Specificity
%
95% CI
Likelihood
ratio
IL-2
0.8968
0.04533
0.8079 to 0.9856
< 0.0001
> 42.13
74.19
55.39% to
88.14%
95
75.13% to
99.87%
14.84
IP10
0.7323
0.07094
0.5932 to 0.8713
0.005487
> 6226
67.74
48.63% to
83.32%
85
62.11% to
96.79%
4.516
IFN-γ
0.7323
0.07094
0.5932 to 0.8713
0.005487
> 64.24
64.52
45.37% to
80.77%
90
68.30% to
98.77%
6.452
IL-13
0.796
0.06165
0.6751 to 0.9168
0.000403
> 1.330
64.52
45.37% to
80.77%
90
68.30% to
98.77%
6.452
MIG
0.8941
0.06935
0.7582 to 1.030
0.000774
> 6.547
88.24
63.56% to
98.54%
90
55.50% to
99.75%
8.824
SCF
0.8765
0.06837
0.7424 to 1.011
0.00132
> 40.99
82.35
56.57% to
96.20%
90
55.50% to
99.75%
8.235
b-NGF
0.8156
0.08294
0.6530 to 0.9782
0.007802 > 0.5650
75
47.62% to
92.73%
80
44.39% to
97.48%
3.75
IL-12-p40
0.8
0.09117
0.6213 to 0.9787
0.01048
> 97.77
76.47
50.10% to
93.19%
90
55.50% to
99.75%
7.647
TRAIL
0.8471
0.07543
0.6992 to 0.9949
0.003067
> 37.66
70.59
44.04% to
89.69%
90
55.50% to
99.75%
7.059
IL-2 Ra
0.7778
0.09485
0.5918 to 0.9637
0.02539
> 22.65
60
32.29% to
83.66%
88.89
51.75% to
99.72%
5.4
LIF
0.7647
0.09289
0.5826 to 0.9468
0.0239
> 28.31
64.71
38.33% to
85.79%
90
55.50% to
99.75%
6.471
https://doi.org/10.1371/journal.pone.0192664.t005
Discussion
In the present study, we have investigated the potential role of 48 cytokines/chemokines in
plasma obtained from stimulated whole blood of adults, as biomarkers of infection/disease by
comparing patients with active TB disease with LTBI subjects and with non-TB patients. We
have shown that multiple biomarkers detected in the antigen stimulated supernatants can contribute to a diagnostic signature with the ability to discriminate between mycobacterial
infected patients and non TB patients.
Several groups of research have focused their studies on assessing the quality of several biomarkers, that could have poor specificity and sensitivity when used alone, but which show a
good performance when used in combination.
Preliminary data on the studies of biomarkers available in internet web such as PubMed,
Embase, and Web of Science have reported either diagnostic accuracy and statistical significance of TB biomarkers able to identify active TB even if lacking of validation; in particular,
for the majority of biomarkers (n = 399), diagnostic performance is not reported (161 biomarkers), or is based on testing of a non-blinded, usually retrospective set of conveniently
obtained samples (170 biomarkers), or on blinded testing in a single study (68 biomarkers) as
recently reported by Yerlikaya et al.[17].
From these studies, only 12 biomarkers have been confirmed in studies where these biomarkers have been evaluated prospectively, one of these biomarkers, has been evaluated as
host biomarker and urine test has been approved by the WHO [18], but they require validation
in a larger cohort of samples. Our data showed that the comparison of the median baseline of
7 analytes, namely IL-1β, IL2, IL-8, IL-12p70, MCP-1, PDGF-BB, VEGF responses significantly differed between Mtb-infected and non TB groups.
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Fig 4. Receiver operating characteristic (ROC) curves for the cytokine values with subtracted Nil value comparing LTBI subjects with non TB patients. The solid
line shows the result of the absolute values of each cytokine. The area under the curve (AUC) is indicated.
https://doi.org/10.1371/journal.pone.0192664.g004
Several recent studies that have highlighted that various biomarkers explored in whole
blood and peripheral blood mononuclear cells, investigated in adults or children, have been
detected as alternative biomarkers for IFN-γ to detect Mtb-infected individuals [19–29]. In
our study, cytokines, chemokines and growth factors showing a higher median concentration
in active TB and LTBI compared to non TB, were not able to detect any differences between
LTBI and active TB patients.
In contrast with our data, Anbarasu et al. have found an increase of PDGF in TB patients,
compared with HD in response to M. tuberculosis culture filtrate proteins (CFPs), with other
cytokines such as G-CSF, IL-1Ra, IL-6, IL-7, IL-8, IL-9 in endemic setting population, even if
the study was limited to the low number of subjects analysed [29]. In fact, the increase of this
cytokine probably is due to the different antigen used, very recently a distinct patterns of cytokines and chemokines has been demonstrated that are released in response to different innate
ligands that interact with specific pattern recognition receptors[30].
The diagnostic accuracies of the different cytokines was evaluated by ROC curve analysis in
order to discriminate the different study populations. We found that several cytokines were
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 6. ROC curves features of the 14 markers increased in active TB.
ROC curve of non TB vs Active TB (Ag-Nil)
Area under the ROC
curve
Std.
Error
95% confidence
interval
p value
cutoff
Sensitivity%
95% CI
Specificity%
95% CI
Likelihood
ratio
IL2
0.7778
0.09485
0.5918 to 0.9637
0.02539
> 42.13
81.48
61.92% to
93.70%
95
75.13% to
99.87%
16.3
IP10
0.7942
0.07397
0.6492 to 0.9393
0.000704 > 6557
80.77
60.65% to
93.45%
85
62.11% to
96.79%
5.385
IFN-γ
0.8269
0.06213
0.7051 to 0.9487
0.000148 > 57.38
70.37
49.82% to
86.25%
90
68.30% to
98.77%
7.037
MIG
0.8944
0.07156
0.7542 to 1.035
0.000669 > 6.456
94.44
72.71% to
99.86%
90
55.50% to
99.75%
9.444
SCF
0.9353
0.05365
0.8301 to 1.040
0.000205 > 39.37
94.12
71.31% to
99.85%
90
55.50% to
99.75%
9.412
b-NGF
0.8765
0.06869
0.7418 to 1.011
0.00132
> 1.965
70.59
44.04% to
89.69%
90
55.50% to
99.75%
7.059
IL12-p40
0.8944
0.06315
0.7706 to 1.018
0.000669 > 86.37
83.33
58.58% to
96.42%
90
55.50% to
99.75%
8.333
TRAIL
0.9028
0.06148
0.7823 to 1.023
0.000513 > 34.01
83.33
58.58% to
96.42%
90
55.50% to
99.75%
8.333
IL2 Ra
0.8333
0.08273
0.6711 to 0.9955
0.005499 > 21.69
55.56
30.76% to
78.47%
88.89
51.75% to
99.72%
5
MIF
0.8706
0.06827
0.7367 to 1.004
0.00157
< 110.1
76.47
50.10% to
93.19%
90
55.50% to
99.75%
7.647
TNF-β
0.8667
0.06776
0.7338 to 0.9995
0.001563 > 5.405
72.22
46.52% to
90.31%
90
55.50% to
99.75%
7.222
IL3
0.8
0.08311
0.6371 to 0.9629
0.009651 > 155.0
66.67
40.99% to
86.66%
90
55.50% to
99.75%
6.667
IFN-α
0.8667
0.0723
0.7249 to 1.008
0.001563 > 13.35
83.33
58.58% to
96.42%
90
55.50% to
99.75%
8.333
LIF
0.7778
0.08809
0.6051 to 0.9505
0.01655
66.67
40.99% to
86.66%
90
55.50% to
99.75%
6.667
> 25.70
https://doi.org/10.1371/journal.pone.0192664.t006
able to discriminate, with a good performance, LTBI from non TB. From this analysis, 11 cytokines showed a good performance with the area under the curve >0.8.
Similarly, from the analysis of the diagnostic accuracy of the markers evaluated between
active TB patients and non TB patients, 13 markers were able to discriminate with a AUC
0.78, with two markers MIG and SCF, able to reach the best sensitivity and specificity of 90%.
The concept of combining multiple biomarkers to improve the potential to discriminate
between LTBI and active TB has also been explored previously.
Our data revealed that the six or seven-marker combinations correctly classified from 80 to
100% of non TB subjects, while could correctly identified from 77.78% to 88,89% of active TB
patient and from 64–74% to 82,35% of LTBI subjects, highlighting that these combinations
considerably improved the ability to discriminate between active TB and LTBI from non TB
group, although the ability to predict non TB patients was found better reaching 90 of 100% of
subjects who not have Mtb infection/disease.
Won et al. have investigated on the ability of eight Mtb antigen-specific biomarkers in the
Mtb-infected group to be significantly different from those of the HCs, they have found that
the combination of five Mtb-specific biomarkers and two unstimulated biomarkers, and one
Mtb-specific biomarker ratio showed significant differences between active TB and LTBI.
Three unstimulated biomarkers and 5 Mtb-specific biomarkers were significantly different
between active TB and non TB groups [31]. Previous studies have reported that the
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Fig 5. Receiver operating characteristic (ROC) curves for the cytokines values with subtracted Nil value comparing active TB patients with non TB patients. The
solid line shows the result of the absolute values of each cytokine. The area under the curve (AUC) is indicated.
https://doi.org/10.1371/journal.pone.0192664.g005
combination of three cytokines, TNF-α, with IL-1Ra or IL-10 correctly classified 95.5% or
100% of cases, respectively [21]. Furthermore, Frahm et al. also reported that the combination
of MCP-1 and IL-15 correctly classified 86.4% of cases [28]. Collectively, these findings suggest
that combining cytokine biomarkers results in more accurate discrimination between Mtb
and non TB infection.
In a recent study, serum levels of eotaxin, MIP-1α, sIL-2Rα, and lipocalin 2 were found different expressed in pulmonary tuberculosis patients vs. non TB patients [32]. From these
markers, eotaxin was found to be the most accurate immunological biomarker, as evaluated by
ROC curve analysis, that differentiated pulmonary tuberculosis and non TB patients. The
power to differentiate between the different cohorts was increased by evaluating 4 different
biomarkers in combination (eotaxin, MIP-1α, sIL-2Rα, and lipocalin 2). This study did not
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Plasma biomarkers in Mycobacterium tuberculosis infection/disease
Table 7. General discriminant analysis of different biomarker combinations.
GDA
Wilks’ lambda
Active TB vs
LTBI
Active TB vs
Non TB
LTBI vs
Non TB
F
p
F
p
F
p
Seven biomarkers
Active TB
(%)
LTBI
(%)
Non TB
(%)
Total
(%)
MCP-1 [NIL], LIF [NIL],
IL2, IFN-γ, MIF, TRAIL, IL2Ra
88.89
82.35
90.00
86.67
0.242
5.12
0.0004
6.15
<0.0001
4.83
0.0007
PDGF [NIL], LIF [NIL],
IL2, IFN-γ, MIF, IL12-p40, IL2Ra
83.33
76.47
100.00
84.44
0.247
4.82
0.0007
6.62
<0.0001
4.81
0.0007
PDGF [NIL], LIF [NIL],
IL2, IFN-γ, MIF, TRAIL, IL2Ra
77.78
64.71
100.00
77.78
0.243
4.71
0.0008
6.43
<0.0001
4.78
0.0007
MCP-1 [NIL], LIF [NIL],
IL2, IFN-γ, MIF, TRAIL
88.89
70.59
90.00
82.22
0.280
6.06
0.0002
6.31
0.0001
4.20
0.0026
MCP-1 [NIL], LIF [NIL],
IL2, IFN-γ, MIF, L12-p40
83.33
70.59
80.00
77.78
0.279
5.71
0.0003
6.42
0.0001
4.52
0.0016
PDGF [NIL], LIF [NIL],
IL2, IFN-γ, MIF, IL12-p40
77.78
64.71
100.00
77.78
0.278
5.55
0.0004
6.91
<0.0001
4.43
0.0018
Sixbiomarkers
https://doi.org/10.1371/journal.pone.0192664.t007
analyze these biomarkers as a biosignature of infection in LTBI subjects, limiting their use as
diagnostic tools in discriminating between infection and disease.
In conclusion, our data add additional knowledge to the field of biomarkers that alone or in
combination might further understanding of pulmonary tuberculosis infection/disease. A paucity of knowledge regarding specific biomarkers for pulmonary tuberculosis represents a big
barrier to the development of diagnostic tests, specific treatment, and appropriate monitoring
of disease. An additional large-scale study which target various Mtb-infected populations
needs to be performed to validate the potential role of the different cytokines analyzed in detail
in this study in order to validate their potential use as diagnostic biomarkers of Mtb infection.
Author Contributions
Conceptualization: Marco Pio La Manna, Valentina Orlando, Nadia Caccamo.
Data curation: Marco Pio La Manna, Valentina Orlando.
Software: Paolo Li Donni.
Supervision: Guido Sireci, Paola Di Carlo, Francesco Dieli, Nadia Caccamo.
Validation: Marco Pio La Manna, Valentina Orlando, Paolo Li Donni, Francesco Dieli.
Visualization: Paola Di Carlo, Antonio Cascio.
Writing – original draft: Francesco Dieli, Nadia Caccamo.
Writing – review & editing: Nadia Caccamo.
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