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ORIGINAL ARTICLE
Systematic analysis of disease-specific immunological
signatures in patients with febrile illness from Saudi Arabia
Yiu-Wing Kam
1†
, Mohamed Yousif Ahmed
1,2†
, Siti Naqiah Amrun
1,3†
, Bernett Lee
1
, Tarik Refaie
2
,
Kamla Elgizouli
2
, Siew-Wai Fong
1,3,4
, Laurent Renia
1,3
& Lisa FP Ng
1,3,5,6,7
1
Singapore Immunology Network, Agency for Science, Technology and Research (A*STAR), Singapore
2
Department of Infectious Diseases Clinic and Medical Microbiology, King Fahad Central Hospital, Jazan, Saudi Arabia
3
Infectious Diseases Horizontal Technology Centre (ID HTC), Agency for Science, Technology and Research (A*STAR), Singapore
4
Department of Biological Sciences, National University of Singapore, Singapore
5
National Institute of Health Research, Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool,
Liverpool, UK
6
Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
7
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
Correspondence
Lisa FP Ng, Laboratory of Microbial Immunity,
Infectious Diseases Horizontal Technology
Centre (ID HTC), and Singapore Immunology
Network (SIgN), A*STAR, 8A Biomedical
Grove, Immunos #04-06, Singapore 138648,
Singapore.
E-mail: lisa_ng@immunol.a-star.edu.sg
†
Equal contributors.
Received 9 March 2020;
Revised 9 June 2020;
Accepted 6 July 2020
doi: 10.1002/cti2.1163
Clinical & Translational Immunology
2020; 0: e1163
Abstract
Objectives. Little is known about the prevalence of febrile illness
in the Arabian region as clinical, laboratory and immunological
profiling remains largely uncharacterised. Methods. A total of
2018 febrile patients from Jazan, Saudi Arabia, were recruited
between 2014 and 2017. Patients were screened for dengue and
chikungunya virus, Plasmodium,Brucella,Neisseria meningitidis,
group A streptococcus and Leptospira. Clinical history and
biochemical parameters from blood tests were collected. Patient
sera of selected disease-confirmed infections were quantified for
immune mediators by multiplex microbead-based immunoassays.
Results. Approximately 20% of febrile patients were tested
positive for one of the pathogens, and they presented overlapping
clinical and laboratory parameters. Nonetheless, eight disease-
specific immune mediators were identified as potential biomarkers
for dengue (MIP-1a, MCP-1), malaria (TNF-a), streptococcal and
meningococcal (eotaxin, GRO-a, RANTES, SDF-1aand PIGF-1)
infections, with high specificity and sensitivity profiles. Notably,
based on the conditional inference model, six of these mediators
(MIP-1a, TNF-a, GRO-a, RANTES, SDF-1aand PIGF-1) were revealed
to be 68.4% accurate in diagnosing different febrile infections,
including those of unknown diseases. Conclusions. This study is
the first extensive characterisation of the clinical analysis and
immune biomarkers of several clinically important febrile
infections in Saudi Arabia. Importantly, an immune signature with
robust accuracy, specificity and sensitivity in differentiating several
febrile infections was identified, providing useful insights into
patient disease management in the Arabian Peninsula.
Keywords: biomarkers, cytokines, infectious diseases, patients,
Saudi Arabia
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
2020 | Vol. 0 | e1163
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Clinical & Translational Immunology 2020; e1163. doi: 10.1002/cti2.1163
www.wileyonlinelibrary.com/journal/cti
INTRODUCTION
The Kingdom of Saudi Arabia hosts annual Islamic
religious events that attract millions of pilgrims
worldwide.
1,2
The constant human movement
puts the region in a unique and vulnerable
position, where the risk of import and export of
communicable diseases can lead to the spread of
global outbreaks.
1,2
Several bacterial infections are endemic in Saudi
Arabia, including brucellosis and meningococcal
diseases caused by the Gram-negative Brucella
and Neisseria meningitidis, respectively.
3–7
Human
movement and the importation of significant
numbers of livestock during the Hajj season are
reasons for endemicity,
3,6–8
high morbidity and
mortality rates.
4,7
Similarly, group A streptococcus
(GAS) or dominantly Streptococcus pyogenes
infections have high mortality rates of 20%.
9–11
Over the past decades, invasive and severe
manifestations of GAS infections have occurred
more frequently, likely because of the emergence
of new virulent strains.
9,12
Leptospirosis, caused by
spirochaete Leptospira species, can also present a
diverse range of symptoms in humans, including
meningitis, pulmonary haemorrhage and
death.
13,14
Given the wide spectrum of clinical
presentations, the incidence of leptospirosis in
Saudi Arabia is likely to be underestimated.
15
Vector-borne infectious diseases have gained
prominence in recent years as a result of recurring
outbreaks, especially in the tropics and
subtropics.
16,17
The climate of Saudi Arabia favors
breeding of Anopheles and Aedes mosquitoes, the
arthropod vectors responsible for the transmission
of protozoan parasite Plasmodium species, and
dengue (DENV) and chikungunya (CHIKV)
viruses.
2,16
Malaria, caused by the protozoan
parasite Plasmodium, is mostly concentrated in the
south-western parts of Saudi Arabia, Aseer and
Jazan.
18,19
While malaria has been reported in
Saudi Arabia since the 1940s,
20
DENV and CHIKV
emerged only recently. The first DENV outbreak in
Saudi Arabia was in 1994 with 289 confirmed
cases,
21,22
while the first autochthonous case of
CHIKV infection was in 2013.
23
The clinical symptoms and manifestations
presented by these tropical diseases are common
and indistinguishable, thus making differential
diagnosis difficult.
24,25
Laboratory tests are
therefore required to make a confirmatory
diagnosis, but often, these are unavailable or too
expensive in developing countries.
25,26
In this
study, we first characterised the clinical and
laboratory parameters of febrile diagnosed
patients from Jazan, Saudi Arabia. Comprehensive
multiplex microbead-based assays were performed
to identify specific immune mediators associated
with the disease. This study thus provides an in-
depth profiling of disease prevalence in Saudi
Arabia, revealing key predictors of pathogen-
specific infections that can aid in improving the
current practices of clinical management in the
region and elsewhere. This is especially so in the
current ongoing coronavirus disease 2019 (COVID-
19) outbreak
27
in which febrile patients present
overlapping symptoms.
RESULTS
One-fifth of febrile illnesses are known
pathogen infections
Between January 2014 and December 2017, 2018
symptomatic patients visited the clinic and were
recruited into the study upon hospital admission.
All febrile patients’ sera (median sampling day of
3 days post-illness onset) were screened by qRT-
PCR, serology ELISAs or bacterial culturing
methods for pathogen identification. Only 401
patients, or 19.9% of the cohort, were diagnosed
for at least one of the seven pathogens tested
(Table 1). A majority of the diagnosed cases was
because of DENV (42.4%), followed by GAS
(16.7%), Plasmodium (13.2%) and N. meningitidis
(13.0%) (Table 1). CHIKV, DENV-CHIKV co-
infection, Brucella and Leptospira infections
accounted for <10% of the total diagnosed cases
(Table 1). Given that there were only two cases of
leptospirosis throughout the 4 years of
recruitment, this group was excluded from further
analysis in subsequent sections.
Plasmodium infections display seasonal
variation
From 2014 to 2017, cases of DENV, Plasmodium,
N. meningitidis and GAS infections were recorded
throughout the years with a stable frequency
(Figure 1a). However, a surge of CHIKV cases was
observed in mid-2014 and declined in 2015, with no
reported cases until the end of the study (Figure 1a).
Naturally, a similar trend was observed for DENV-
CHIKV co-infections (Figure 1a). Conversely, Brucella
infections occurred sporadically over the years
with no clear trend (Figure 1a). Notably, incidences
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ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
Disease-specific predictive biomarkers Y-W Kam et al.
Table 1. Demographics and clinical characteristics of patients from Saudi Arabia with febrile known infections
Parameters
Febrile infections
Healthy controlsDENV CHIKV DENV-CHIKV co-infection Plasmodium Brucella
Neisseria
meningitidis GAS Leptospira
Number of patients, N170 34 15 53 8 52 67 2 31
Percentage of cases (%) 42.4 8.5 3.7 13.2 2.0 13.0 16.7 0.5 N.A.
Age, median (range),
years
26 (14–70) 47 (31–69) 56 (29–65) 31 (15–63) 46 (33–50) 45 (18–75) 59 (20–75) 52 (43–60) 41 (21–63)
Gender ratio (male/
female)
1.2 (93M/77F) 1.1 (18M/16F) 2 (10M/5F) 0.9 (25M/28F) 7 (7M/1F) 1.6 (32M/20F) 1.5 (40M/27F) (2M/0F) 0.8 (14M/17F)
Fever (%) 100 100 100 100 100 100 100 100 0
Rash (%) 99.4 100 93.3 1.9 0 73.1 11.9 100 0
Back pain (%) 82.4 64.7 100 83 100 69.2 83.6 0 0
Headache (%) 62.9 100 86.7 100 100 100 89.6 100 0
Joint pain (%) 97.1 100 100 54.7 100 53.9 64.2 0 0
Vomiting (%) 71.2 35.3 100 71.7 25 92.3 80.6 100 0
Nausea (%) 90.6 61.8 100 98.1 37.5 100 95.5 100 0
Chills (%) 98.2 100 100 98.1 12.5 63.5 73.1 100 0
Neutrophils, mean (%) 36.1 5.4 45.2 6.6 31.5 4.5 58.1 11.9 42.1 6.9 90.1 4.9 87.8 8748.5 58.6 5.3
Lymphocytes, mean (%) 15.8 3.0 15.2 2.5 13 1.3 31.1 4.4 21.9 6.3 48.2 8.7 46.4 9.4 47.5 3.5 33.5 3.8
Erythrocyte sedimentation
rate (ESR), mean
(mm h
-1
)
11.2 4.9 11.8 4.5 17.7 7.8 11.2 5.8 31.9 18.2 20.2 8.5 23.5 10 24.5 9.2 12.5 3.3
Haemoglobin, mean
(g dL
-1
)
11.9 1.0 12 0.9 10.5 0.8 12.5 1.1 11.3 1121.0 12.1 1.1 11.5 0.7 13.5 0.9
Platelet *1000 95.9 14.7 274.7 67.2 78.2 10.8 195 76.6 211.4 113.3 422.3 134 439.8 121.8 268.5 78.5 342.9 48.5
Alanine aminotransferase
(ALT), mean
75.2 21.2 42.9 2.6 102.9 7.2 43.4 5.6 85.8 16.5 42.9 5.4 40.5 6.9 103.5 13.4 31.3 7.2
Aspartate
aminotransferase (AST),
mean
64 20.1 31.2 4.2 91.3 7.1 33 6.1 73.8 16.2 32.2 3.7 30.6 5 90.5 6.4 27.3 5.7
C-reactive protein (CRP),
mean (mg dL
-1
)
28.9 8.9 20.2 7.4 34.9 8.1 23.4 13 46.8 17.6 74.7 36.4 83.4 40.5 36 29.7 4.8 0.9
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
2020 | Vol. 0 | e1163
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Y-W Kam et al.Disease-specific predictive biomarkers
of malaria demonstrated a seasonal pattern.
Plasmodium infections were higher in the months
ranging from May to October each year (Figure 1a),
coinciding with higher temperatures in Jazan
(Supplementary figure 1a).
Acute febrile infections present overlapping
clinical and laboratory parameters
Reported clinical symptoms were next analysed
and compared (Figure 1b and Table 1). With the
exception of brucellosis, all other febrile infections
largely showed overlapping clinical manifestations,
such as headache, chills and back and joint pains
(Figure 1b and Table 1). However, rashes were only
observed in few Plasmodium- and GAS-infected
patients (1.9% and 11.9% cases, respectively;
Figure 1b and Table 1). Furthermore, patients with
DENV-CHIKV co-infections reported more
symptoms such as vomiting, nausea and back pain,
than the single virus infections during the hospital
admission period (Figure 1b and Table 1).
Majority of the medical laboratory parameters
recorded from febrile patients in this cohort did
not show disease-specific patterns (Figure 2). In
terms of immune cell profiling, the percentage of
Figure 1. Longitudinal pattern of disease cases and clinical parameters of febrile diagnosed patients during the acute phase of disease. (a)
Percentage of positively diagnosed cases between 2014 and 2017 (n=401) collected in Jazan, Saudi Arabia. Data were expressed as percentage
of disease-specific [(number of disease-specific cases per month/total number of disease-specific cases between 2014 and 2017) 9100%] and
presented in a heatmap format, with white and red colours representing low and high percentage of cases, respectively. Grey colour indicates no
disease cases. (b) Clinical parameters reported during the acute phase of disease (within 7 days post-illness onset) were analysed and presented
in a heatmap of normalised scores. In the heatmap, patients were grouped according to the collection time (year) and the clinical parameters
were scaled between 0 in green (minimum) and 100 in red (maximum) for each recorded clinical parameter of the respective disease group. Grey
colour indicates no reports of clinical symptoms.
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ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
Disease-specific predictive biomarkers Y-W Kam et al.
neutrophils showed opposing trend between viral
and bacterial infections (Figure 2a). Compared to
the healthy controls, DENV and DENV-CHIKV co-
infections showed a significant reduction in
neutrophil population, while infections with
N. meningitidis and GAS showed a significant
increase (Figure 2a). Interestingly, brucellosis cases
presented a similar neutrophil profile as the viral
infections, and no differences were reported
between malaria patients and the healthy controls
(Figure 2a). A similar trend was also observed for
the percentage of lymphocytes in this cohort
(Figure 2b), while platelet counts decreased
significantly only for DENV, DENV-CHIKV and
malaria cases (Figure 2c). In terms of haematological
parameters, levels of nonspecific inflammation
parameter erythrocyte sedimentation rate (ESR)
were significantly high for all bacterial infection
cases (Figure 2d) whereas infections with any of the
seven pathogens would cause a significant decrease
in haemoglobin content (Figure 2e). In terms of
liver inflammation markers, the levels of C-reactive
protein (CRP) and alanine aminotransferase (ALT)
significantly increased in all of the studied diseases
(Figure 2f and g). However, levels of aspartate
aminotransferase (AST) increased significantly only
for DENV, DENV-CHIKV and brucellosis cases
(Figure 2h).
Cytokine responses vary for different
febrile infections
In order to identify and define disease-specific
immune mediators, serum samples of healthy
controls and DENV-, Plasmodium-, N. meningitidis-
and GAS-infected febrile patients were subjected to
a multiplex microbead-based immunoassay. The
remaining diseases were excluded because of
insufficient sample size and sera availability. Twenty-
four out of 45 immune mediators did not show any
significant differences between the disease groups
and healthy controls (Figure 3a and Supplementary
table 1). Levels of 10 other factors [interferon
IFN-c, interleukins IL-6, IL-7, IL-18, IL-10, IL-1RA,
interferon-c-induced protein 10 kDa (IP-10), platelet-
derived growth factor-BB (PDGF-BB), brain-derived
neurotrophic factor (BDNF) and epidermal growth
factor (EGF)] demonstrated significant differences
in more than one group of patients when compared
to healthy controls (Figure 3a and Supplementary
figure 2). Only eight immune mediators
[macrophage inflammatory protein-1a(MIP-1a),
monocyte chemoattractant protein-1 (MCP-1), tumor
necrosis factor-a(TNF-a), growth-regulated
oncogene-a(GRO-a), regulated on activation,
normal T cell expressed and secreted (RANTES),
eotaxin, stromal cell-derived factor-1a(SDF-1a)and
placental growth factor-1 (PIGF-1)] showed disease-
specific profiles that were significantly different
from the healthy controls and also distinct from
other infection groups (Figure 3b). DENV- and
Plasmodium-infected patients showed elevated
levels of MIP-1aand MCP-1, and TNF-a, respectively
(Figure 3b). In contrast, GRO-a, RANTES, eotaxin,
SDF-1aand PIGF-1 were significantly decreased in
theseraofN. meningitidis-andGAS-infected
patients (Figure 3b).
To further evaluate the robustness of these
selected immune mediators as disease-specific
biomarkers, receiver operating characteristic (ROC)
analysis was performed. The eight immune
mediators showed high area under curve (AUC),
and specificity and sensitivity values in disease
prediction (Table 2). Remarkably, RANTES for
N. meningitidis and GAS infections had the best
specificity (0.968) and sensitivity (0.923 and 1.000)
profile (Table 2). Altogether, a combination of
cytokines and chemokines can be used in tandem
to act as robust biomarkers to positively predict
diseases with indistinguishable clinical
presentations (Figure 3c).
Immune signature for differential diagnosis
of febrile infections
In order to further evaluate the performance of the
potential biomarkers identified, a multivariate
analysis was performed. To ensure a more robust
prediction, the levels of eight immune mediators
from 30 patients with febrile unknown infections
were also interrogated (Supplementary table 2).
Conditional inference analysis with 10-fold cross-
validation yielded an optimal signature composing
of six cytokines, MIP-1a, TNF-a,GRO-a, RANTES, SDF-
1aand PIGF-1, with a high accuracy of 68.4%
(Figure 4a). Patients showing serum concentrations
(Log
10
scale, pg mL
1
)between0.255 and 1.701 for
RANTES were likely to be infected with
N. meningitidis or GAS (80.4% probability), whereas
patients with >1.701 for RANTES, >1.029 for GRO-a
and ≤0.825 for TNF-awere most likely suffering
from DENV infections (88.9% probability; Figure 4a).
In contrast, those with >0.825 for TNF-aand MIP-1a
concentrations between 0.648 and 1.733 could be
Plasmodium-infected (71.4% probability), and
patients with GRO-aconcentration of ≤1.029 and
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
2020 | Vol. 0 | e1163
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Y-W Kam et al.Disease-specific predictive biomarkers
RANTES concentration of >2.153 were likely to be
infected with febrile diseases distinct from these
four pathogens (68.4% probability; Figure 4a).
The serum levels of the six cytokines from
conditional inference tree were then used to build
an immune signature, and the radar plot revealed
clear differential profiles among the four groups of
febrile diseases (Figure 4b). In this cohort, DENV
infections induced high levels of GRO-a, MIP-1a,
PIGF-1, RANTES and SDF-1a, but low levels of TNF-a
(Figure 4b). However, Plasmodium infections
showed the opposite phenomenon, while bacterial
infections presented even lower levels of these
cytokines (Figure 4b). Interestingly, febrile unknown
infections showed an intermediate response of the
six cytokines (Figure 4b).
DISCUSSION
Infectious diseases represent one of the global
causes of morbidity and mortality,
1
with vector-
borne diseases garnering increased attention
because of the emergence of pathogens.
1,17
Indeed,
arthropod-borne infections accounted for 67.8% of
the febrile cases in this cohort, with majority being
DENV. Although DENV transmission is generally
reported to be sensitive to environmental factors,
28
the number of DENV cases was constant throughout
the study period despite changes in temperature,
humidity and rainfall (Supplementary figure 1).
Instead, seasonality was observed for malaria, and it
has been shown that changes in temperature
control the Anopheles mosquito life cycle and
parasite development, thus regulating disease
transmission.
29
This suggests that the activity of
different vectors and pathogens could be regulated
by environmental factors at different levels, and
understanding the dynamics of climate and vector is
important in the effective planning and
implementation of routine control measures.
28
Because of the similar clinical presentations and
overlapping endemicity of malaria, DENV and CHIKV
infections in tropical regions, simultaneous
infections with more than one of the pathogen are
very likely to be underreported and misdiagnosed as
a mono-infection.
16
DENV-CHIKV co-infections have
been described in 26 different countries, including
Yemen.
16,30,31
To our knowledge, this is the first
report showing the incidence of DENV-CHIKV co-
infections in Saudi Arabia. Interaction of multiple
pathogens can potentially lead to different
outcomes
16
and this was observed clinically in our
study, as well as in a cohort in Bengal, India, in
which the co-infected patients experienced more
symptoms than those with single infections.
32
Furthermore, the laboratory parameters of DENV-
Figure 2. Laboratory parameter profiles of febrile diagnosed patients during the acute phase of the disease. Blood and serum samples of healthy
controls (n=31) and patients infected with DENV (n=170), CHIKV (n=34), DENV-CHIKV co-infection (n=15), Plasmodium (n=53), Brucella
(n=8), Neisseria meningitidis (n=52) or GAS (n=67) at acute phase (within 7 days post-illness onset) were tested for (a–c) levels of blood cells
and platelets, (d, e) haematological parameters and (f–h) liver inflammation markers. Results are depicted as dot plots with mean SD, and
statistical analysis between disease groups and healthy controls was conducted with Kruskal–Wallis test with Dunn’s post hoc tests (*P<0.05,
**P<0.01, ***P<0.001).
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Disease-specific predictive biomarkers Y-W Kam et al.
CHIKV co-infected patients, such as neutrophil
percentage, platelet count and levels of serum AST
and ALT, resembled closely to those of DENV
infections rather than CHIKV (Figure 2) and were
similarly observed in co-infected patients in
India.
33
Although DENV–malaria, CHIKV–malaria
and DENV–CHIKV–malaria co-infections have been
demonstrated elsewhere,
16
none of the different
Figure 3. Immune signature for differential diagnosis of febrile infections during the acute phase of disease. Serum samples of healthy controls
(n=31) and patients infected with DENV (n=100), Plasmodium (PLAS; n=30), Neisseria meningitidis (N.MEN; n=21), group A streptococcus
(GAS; n=26) or febrile unknown (n=30) at acute phase (within 7 days post-illness onset) were subjected to multiplex microbead-based
immunoassay. (a) Levels of immune mediators were analysed and presented in a heatmap of normalised scores. The concentrations of each
immune mediator were scaled between 0 and 1, and the average scaled value was computed for each group. (b) Selected immune mediators
with disease-specific profiles are depicted as Tukey box plots with the results of post hoc t-tests shown as asterisks. One-way ANOVAs were
conducted on the logarithmically transformed concentration with post hoc t-tests corrected using the method of Bonferroni. ANOVA results were
corrected for multiple testing using the method of Benjamini and Hochberg (*P<0.05, **P<0.01, ***P<0.001). Data in band care of one
independent experiment. (c) Immune mediator signature profiles of febrile patients in the acute phase of disease. Venn diagram shows the
generic febrile and virus-specific immune mediators for each infection compared to healthy controls.
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
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Y-W Kam et al.Disease-specific predictive biomarkers
combinations was detected in this study. Given the
limited knowledge on concurrent infections, more
investigations are required to understand the
interplay of different pathogens on the host
immune system and pathogenesis.
Consistent with previous reports, low platelet
levels were found in DENV patients,
34,35
while
patients suffering from bacterial infections had
significantly high level of circulating neutrophils.
36,37
However, other laboratory markers did not show
any disease specificity. Infections with any of the
pathogens induced a systematic inflammation in
patients, as indicated by elevated serum levels of CRP
and ALT (Figure 2f and g). Overall, laboratory
parameters are not distinctive in predicting
pathogen-specific infections in febrile patients, thus
warrantinganin-depthbiomarkeridentificationin
this study.
In this cohort, infections with any pathogen
triggered a common response of inflammatory
cytokines. Elevated levels of anti-inflammatory IL-
1RA, pro-inflammatory IL-18 and IL-6, as well as
decreased BDNF level, were observed, and data
were mostly congruent with previous reports for
DENV and malaria.
38,39
Nonetheless, disease-
specific biomarkers for dengue, malaria and
bacterial infections were identified. MIP-1aand
MCP-1 were found to be dengue-specific, as
reported in previous studies.
40–42
TNF-a, however,
is a unique identifier of malaria in this cohort,
correlating well with previous studies reporting
increased TNF-alevels in Plasmodium vivax and
Plasmodium falciparum infections.
43,44
For
patients with N. meningitidis and GAS infections,
decreased concentrations of eotaxin, GRO-a,
RANTES, SDF-1aand PIGF-1 were observed, and
these mediators play integral roles in host
immune response to bacterial infection.
45–49
This study has identified a signature of six
immune mediators that could accurately
distinguish DENV, Plasmodium, and N. meningitidis
or GAS infections. Specifically, with the ongoing
COVID-19 outbreaks,
27
the symptom overlap in
febrile illness underscores the necessity of
completing a differential diagnosis for all patients
globally. With the current knowledge that severe
acute respiratory syndrome coronavirus 2 (SARS-
CoV-2) infection triggers a cytokine storm with
markedly increased levels of generic febrile
immune mediators IL-6, IL-1RA and IL-18 in COVID-
19 patients,
50–53
the pathogen-specific immune
mediators identified in this study will be useful
in effective patient stratification during this
uncontrolled COVID-19 outbreak. Nonetheless,
more investigations are required to determine
the classification power of the immune signature
and how they can be implemented in the
diagnosis practice in hospitals in the Arabian
Peninsula. Taken together, the combination of
clinical observations and profiles of laboratory
parameters as well as the detection of disease-
specific immune mediators can aid in the
differential diagnosis of febrile patients. This will
thus greatly improve the accuracy of disease
prediction, alleviating the burden placed on the
public health system. Moreover, to prepare for
future outbreaks, the health authorities in
Saudi Arabia could implement new strategies to
better forecast the evolving trends of infectious
diseases.
Table 2. Receiver operating characteristic (ROC) analysis of biomarkers
Immune mediator Infection Area under curve Specificity Sensitivity
Virus MIP-1aDENV 0.814 0.774 0.730
MCP-1 DENV 0.769 0.903 0.580
Parasite TNF-aPlasmodium 0.811 0.935 0.633
Bacteria Eotaxin Neisseria meningitidis 0.740 0.935 0.619
Eotaxin GAS 0.779 1.000 0.692
GRO-aNeisseria meningitidis 0.866 0.806 0.762
GRO-aGAS 0.804 0.774 0.731
RANTES Neisseria meningitidis 0.998 0.968 1.000
RANTES GAS 0.970 0.968 0.923
SDF-1aNeisseria meningitidis 0.860 0.677 0.905
SDF-1aGAS 0.793 0.903 0.731
PIGF-1 Neisseria meningitidis 0.729 0.871 0.714
PIGF-1 GAS 0.847 0.935 0.731
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Disease-specific predictive biomarkers Y-W Kam et al.
METHODS
Ethical approval
Written informed consent was obtained from all
participants, participants’ parents or legal guardians, and
the study was conducted according to Declaration of
Helsinki principles. The collection of samples was
approved by the Ethical Committee Panel with IRB number
17/EA/002/1436H in King Fahad Central Hospital, Jazan,
Saudi Arabia.
Study participants
A total of 2018 febrile patients admitted to the infectious
diseases clinic (IDC) at King Fahad Central Hospital between
January 2014 and December 2017 enrolled in the study. Full
personal details of patients including recent travel history
and exposure to mosquitoes, and detailed clinical histories
and clinical examinations were recorded. The patients in
this study did not have severe disease outcomes. Thirty-one
healthy donors, who were staff at King Fahad Central
Hospital, were also recruited as control participants.
Figure 4. Multivariate analysis of immune mediators of acute febrile patients with DENV, Plasmodium, bacteria (Neisseria meningitidis and GAS)
and unknown infections. (a) Conditional inference tree analysis on serum concentrations of eight disease-specific cytokines from infected patients
of DENV (n=100), Plasmodium (PLAS; n=30), N. meningitidis (N.MEN; n=21), group A streptococcus (GAS; n=26) and patients with febrile
unknown diseases (FUD; n=30) resulted in an optimum immune signature comprising six cytokines (MIP-1a, TNF-a, GRO-a, RANTES, SDF-1aand
PIGF-1) with overall classification accuracy of 68.6%. The cytokines are displayed in square nodes. Numbers between nodes indicate Log
10
concentration of cytokine in pg mL
1
for each split. Each node in conditional inference tree represents a decision to go down one branch or the
other depending upon the cut-off values depicted along the line connecting the successive nodes. Finally, each sample ends up in one of the
terminal nodes. Terminal nodes display the relative proportion of samples from DENV (light green), Plasmodium (teal), bacterial (red) and febrile
unknown (orange) infections. (b) Radar chart summarising the signature of six immune mediators from conditional inference tree. Serum
concentrations for each immune mediator were graphed on separate axes, with all axes being scaled equally from 0% to 100%.
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
2020 | Vol. 0 | e1163
Page 9
Y-W Kam et al.Disease-specific predictive biomarkers
Sample collection and patient diagnosis
Acute-phase (within 7 days post-illness onset) blood and
serum samples were collected from patients upon hospital
admission. With the diagnostic tests available in the hospital,
samples from both patients and healthy donors were
processed and tested for seven febrile-related pathogens:
DENV, CHIKV, Plasmodium,Brucella,N. meningitidis, GAS
and Leptospira. Confirmatory investigations for acute
DENV infection include PCR and ELISAs for NS1 antigen
detection (Bio-Rad Laboratories, Inc., Hercules, CA, USA)
and IgM and IgG quantifications (Bioactiva Diagnostica
GmbH, Bad Homburg, Germany). For CHIKV diagnosis,
PCR and ELISAs for IgM and IgG quantifications
(EUROIMMUN AG, L€
ubec, Germany) were performed.
Bacterial infections were identified by traditional culture
methods. Immunochromatographic test (ICT malaria p.f/p.v)
and thick and thin blood films were used for confirmation of
acute malaria infection.
Laboratory analysis
Routine laboratory tests were carried out for all participants.
Complete blood count, haemoglobin level, lymphocyte and
neutrophil percentages, platelet (PLT) count, CRP level, ESR
and liver enzymes AST and ALT were quantified with
automated haematology and blood chemistry analysers.
Multiplex microbead-based immunoassay
Levels of immune mediators from serum samples of DENV-,
Plasmodium-, N. meningitidis- and GAS-infected patients, as
well as febrile unknown patients, were measured using the
ProcartaPlex Human Cytokine 45-Plex [granulocyte–
macrophage colony-stimulating factor (GM-CSF), EGF, BDNF,
beta-nerve growth factor (bNGF), basic fibroblast growth
factor 2 (FGF-2), hepatocyte growth factor (HGF), monocyte
chemoattractant protein-1 (MCP-1/CCL2), macrophage
inflammatory protein-1a(MIP-1a/CCL3), MIP-1b, regulated
on activation, normal T cell expressed and secreted
(RANTES/CCL5), growth-regulated oncogene-a(GRO-a/
CXCL1), stromal cell-derived factor-1a(SDF-1a/CXCL12a),
interferon-c-induced protein 10 kDa (IP-10/CXCL10),
eotaxin, interferon-a(IFN-a), IFN-c, interleukin-1a(IL-1a), IL-
1b, IL-1RA, IL-10, IL-13, IL-15, IL-17A, IL-18, IL-2, IL-21, IL-22,
IL-23, IL-27, IL-31, IL-4, IL-5, IL-6, IL-7, IL-8 (CXCL8), IL-9, IL-12
p70, leukaemia inhibitory factor (LIF), stem cell factor (SCF),
TNF-a, TNF-b, vascular endothelial growth factor A (VEGF-
A), VEGF-D, PDGF-BB and placental growth factor-1 (PIGF-
1)] immunoassay kits (Thermo Fisher Scientific, Waltham,
MA, USA) according to manufacturer’s instructions as
previously described.
54
Briefly, magnetic beads were
aliquoted in 96-well plates followed by addition of
standards and sera from patients and control subjects. After
an incubation period, plates were washed using a magnetic
wash station according to manufacturer’s instructions,
followed with addition of a detection antibody. Plates were
incubated for 30 min and washed, followed by an
incubation of 10 min in the presence of streptavidin-PE.
Results were acquired using the Bio-Plex 200 (Bio-Rad
Laboratories, Inc.) with xPONENTsoftware (Luminex
Corporation, Austin, TX, USA) based on standard curves
plotted through a 5-parameter logistic curve setting. IFN-a,
IL-5 and IL-9 were found to be below detection limit and
hence excluded from subsequent analysis.
Data analysis
Sample randomisation for the Luminex assays could not be
performed for the various sample groups as the processing of
the samples was dependent on the collection at the hospital.
Once sufficient samples were collected, the Luminex assays
were performed. To remove any potential plate effects, an
additional plate was assayed which contained a selected
number of samples from all assayed plates. These samples
were then used to normalise the assayed plates. A correction
factor was obtained from the difference observed between
the original assayed plate data and the replicates on the
additional plate. This correction factor was then applied to
the rest of the samples of the original assayed plate. The
concentrations were logarithmically transformed to ensure
normality. One-way ANOVA with the post hoc test corrected
using the method of Bonferroni was used to detect for
differences between the various sample groups. One-way
ANOVA results were corrected for multiple testing using the
method of Benjamini and Hochberg. P-values <0.05 were
considered to be statistically significant. Plots were
generated using GraphPad Prism version 7 (GraphPad
Software, San Diego, CA, USA).
Receiver operating characteristic curve analysis for
analytes that were differentially expressed between
pathogen-infected patients and healthy controls was
performed, and the AUCs were calculated. Analytes with
AUCs of >0.65, and specificity and sensitivity values of >0.70
are considered to be potential markers of disease-specific
infections. Conditional inference tree analysis and radar
plots of immune mediators of interest were analysed using
the R party package (R Foundation for Statistical Computing,
Vienna, Austria). The conditional tree was generated with a
10-fold cross-validation to avoid overfitting of data.
ACKNOWLEDGMENTS
We thank the study participants and healthy volunteers for
their participation, and clinical staffs from King Fahad
Central Hospital for assistance in patient enrolment and care,
blood sample preparation, study coordination and data
entry. This work is supported by core research grants
provided to the Singapore Immunology Network by the
Biomedical Research Council (BMRC) and Immunomonitoring
Service Platform (grant #NRF2017_SISFP09). The funders had
no role in study design, data collection and analysis, decision
to publish or preparation of the manuscript.
AUTHOR CONTRIBUTIONS
Yiu-Wing Kam: Data curation; Investigation; Visualization;
Writing-original draft. Mohamed Yousif Ahmed: Data
curation; Investigation; Resources; Writing-original draft. Siti
Naqiah Amrun: Visualization; Writing-original draft; Writing-
review & editing. Bernett Lee: Formal analysis; Visualization;
Writing-review & editing. Tarik Refaie: Data curation;
Investigation; Resources. Kamla Elgizouli: Data curation;
2020 | Vol. 0 | e1163
Page 10
ª2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of
Australian New Zealand Society for Immunology, Inc.
Disease-specific predictive biomarkers Y-W Kam et al.
Investigation; Resources. Siew-Wai Fong: Visualization;
Writing-original draft; Writing-review & editing. Laurent
Renia: Conceptualization; Funding acquisition; Writing-
review & editing. Lisa FP Ng: Conceptualization; Funding
acquisition; Project administration; Resources; Writing-
original draft; Writing-review & editing.
CONFLICT OF INTEREST
The authors declare no conflict of interests.
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Supporting Information
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the end of the article.
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