key: cord-0848428-wbmdubbj
authors: Kam, Yiu‐Wing; Ahmed, Mohamed Yousif; Amrun, Siti Naqiah; Lee, Bernett; Refaie, Tarik; Elgizouli, Kamla; Fong, Siew‐Wai; Renia, Laurent; Ng, Lisa FP
title: Systematic analysis of disease‐specific immunological signatures in patients with febrile illness from Saudi Arabia
date: 2020-08-22
journal: Clin Transl Immunology
DOI: 10.1002/cti2.1163
sha: c6d0ec4da238afd5bd981aa5c089622b464437a7
doc_id: 848428
cord_uid: wbmdubbj

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‐1α, MCP‐1), malaria (TNF‐α), streptococcal and meningococcal (eotaxin, GRO‐α, RANTES, SDF‐1α and PIGF‐1) infections, with high specificity and sensitivity profiles. Notably, based on the conditional inference model, six of these mediators (MIP‐1α, TNF‐α, GRO‐α, RANTES, SDF‐1α and 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.

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] [4] [5] [6] [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] [10] [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 indepth profiling of disease prevalence in Saudi Arabia, revealing key predictors of pathogenspecific 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.

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 coinfection, 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.

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 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) .

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 neutrophils showed opposing trend between viral and bacterial infections ( Figure 2a ). Compared to the healthy controls, DENV and DENV-CHIKV coinfections 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 ).

In order to identify and define disease-specific immune mediators, serum samples of healthy controls and DENV-, Plasmodium-, N. meningitidisand 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. Twentyfour out of 45 immune mediators did not show any significant differences between the disease groups and healthy controls (Figure 3a 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 diseasespecific 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-1a and MCP-1, and TNF-a, respectively (Figure 3b ). In contrast, GRO-a, RANTES, eotaxin, SDF-1a and PIGF-1 were significantly decreased in the sera of N. meningitidis-and GAS-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 ).

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 crossvalidation yielded an optimal signature composing of six cytokines, MIP-1a, TNF-a, GRO-a, RANTES, SDF-1a and PIGF-1, with a high accuracy of 68.4% (Figure 4a ). Patients showing serum concentrations (Log 10 scale, pg mL À1 ) between À0.255 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-a were most likely suffering from DENV infections (88.9% probability; Figure 4a ). In contrast, those with > 0.825 for TNF-a and MIP-1a concentrations between 0.648 and 1.733 could be Plasmodium-infected (71.4% probability), and patients with GRO-a concentration of ≤ 1.029 and 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 ).

Infectious diseases represent one of the global causes of morbidity and mortality, 1 with vectorborne 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 coinfections 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- 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 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 warranting an in-depth biomarker identification in 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, diseasespecific biomarkers for dengue, malaria and bacterial infections were identified. MIP-1a and MCP-1 were found to be dengue-specific, as reported in previous studies. [40] [41] [42] TNF-a, however, is a unique identifier of malaria in this cohort, correlating well with previous studies reporting increased TNF-a levels 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-1a and PIGF-1 were observed, and these mediators play integral roles in host immune response to bacterial infection. [45] [46] [47] [48] [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 diseasespecific 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. 

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.

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 [granulocytemacrophage 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 xPONENTâ software (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.

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.

Writing-original draft; Writing-review & editing. Laurent Renia: Conceptualization; Funding acquisition; Writingreview & editing

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Clin Infect Dis 2020: ciaa248. e-pub ahead of print 12

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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. 

The authors declare no conflict of interests.

Additional supporting information may be found online in the Supporting Information section at the end of the article.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.