- Email: [email protected]
Towards Predicting Progression to Severe Dengue
TIMI 1767 No. of Pages 9
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Review
Towards Predicting Progression to Severe Dengue Makeda Robinson1,2 and Shirit Einav1,2,* There is an urgent need for prognostic assays to predict progression to severe dengue infection, which is a major global threat. While the majority of symptomatic dengue patients experience an acute febrile illness, 5–20% progress to severe infection associated with significant morbidity and mortality. Early monitoring and administration of supportive care reduce mortality and clinically usable biomarkers to predict severe dengue are needed. Here, we review recent discoveries of gene sets, anti-dengue antibody properties, and inflammatory markers with potential utility as predictors of disease progression. Upon larger scale validation and development of affordable sample-to-answer technologies, some of these biomarkers may be utilized to develop the first prognostic assay for improving patient care and allocating healthcare resources more effectively in dengue endemic countries.
Highlights
The Urgent Need for Predictive Biomarkers
Recently discovered anti-dengue antibody properties, inflammatory, apoptotic and metabolic markers, some of which are generalizable across cohorts, also have potential utility as predictors of dengue disease progression.
Until recently there have been no clinically usable biomarkers to accurately predict which patients will progress to severe dengue, a major global threat. A 20-gene set that is strongly associated with the progression to severe dengue and represents a predictive signature that is generalizable across ages, host genetic factors, virus strains, and sample types was recently discovered using a multicohort analysis framework that integrates biologically heterogeneous data sets.
Dengue is the most common human arboviral disease worldwide, with an estimated 400 million infections occurring annually in over 100 countries and a growing distribution in the developed world due to climate changes and urbanization [1–3]. The four dengue virus serotypes (DENV 1–4) are transmitted via a mosquito vector. The majority of symptomatic individuals present with acute dengue fever, yet a fraction (5–20%) of these patients progresses to severe dengue (SD), which can manifest by bleeding, plasma leakage, shock, organ failure, and sometimes death [4,5].
Some of these biomarkers may be utilized to develop the first prognostic assay for improving patient care and allocating healthcare resources more effectively in dengue endemic countries.
Dengue severity has been shown to be determined by host genetic factors, the specific viral serotype, and the level of viremia [6,7]. The greatest risk factor for SD, however, is the immunological status of the patient. Specifically, the presence of preexisting, nonneutralizing anti-DENV antibodies at a specific titer range can predispose individuals to antibody-dependent enhancement (ADE) upon secondary infection with a heterologous DENV serotype [8–11]. Aberrant activation of crossreactive T cells may also play a role [10]. The complex interplay of protective and enhancing components of the human immune response to DENV infection has challenged the vaccine development effort [12] and has hampered the understanding of mechanisms that underlie progression to severe disease and hence the discovery of predictive biomarkers. Early admission to an inpatient facility and timely administration of supportive care have been shown to improve clinical outcomes and reduce mortality in patients with SD [13]. The World Health Organization (WHO) has therefore defined a set of criteria to classify dengue infection based on its severity. The currently used (2009) criteria classify patients into uncomplicated dengue, dengue with warning signs and SD, whereas the former (1997) criteria define dengue fever (DF) and two forms of severe dengue: dengue hemorrhagic fever (DHF) and/or dengue shock syndrome (DSS) [4,14,15]. The implementation of the currently utilized warning signs to identify patients at risk of progressing to SD has improved the sensitivity in capturing patients at risk, but has led to a substantial increase in the number of patients admitted to hospitals, resulting Trends in Microbiology, Month 2019, Vol. xx, No. xx
1
Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA 2 Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
*Correspondence: [email protected] (S. Einav).
https://doi.org/10.1016/j.tim.2019.12.003 © 2019 Elsevier Ltd. All rights reserved.
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in ineffective resource allocation [16]. Moreover, although improved, the sensitivity of the utilized warning signs, which are based on clinical parameters that often develop late during the course of disease, is still limited leading to continued morbidity and mortality [17,18]. There is thus an urgent need for novel biomarkers to predict progression to SD. Here, we review prior efforts to identify such predictive biomarkers and highlight several recent discoveries of candidate clinically usable biomarkers to predict the development of severe complications associated with dengue infection. The potential translational impact of these biomarkers and current challenges in the development of sample-to-answer assays for use in resource-limited settings are also discussed (Figure 1 and Table 1, Key Table).
Transcriptomic Signatures for Predicting Progression to SD Prior efforts to identify molecular biomarkers for dengue severity have focused primarily on bulk transcriptomics; that is, measuring gene expression in bulk RNA samples extracted from human blood and peripheral blood mononuclear cells (PBMCs) [19–29] or from primary cells infected with DENV [30]. These studies identified alterations in either the timing and/or magnitude of gene transcript abundance that were associated with dengue severity, yet had several limitations. First, the majority of these studies identified genes whose altered expression is associated with, but does not precede, the onset of SD and therefore cannot be used as prognostic biomarkers. Second, these studies did not yield parsimonious gene sets to enable their translation into a prognostic assay. Third, being single-cohort transcriptomics studies, none of the generated gene sets has yet been shown to be generalizable across independent cohorts.
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Figure 1. Schematic of the Various Categories of Candidate Biomarkers Showing Potential Promise for Prediction of Progression to Severe Dengue (SD). 2
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Key Table
Table 1. Classes and Examples of Candidate Predictive Biomarkers of SD and Their Translational Potential Category
Transcriptomic
Anti-DENV antibody properties
Other inflammatory
Endothelial activation
Candidate biomarker
Generalizability across cohorts
Timing of testing
Sample
Discovery platform
Potential sample-toanswer platform
Refs
20-gene set
Yes
Upon infection
Whole blood
Multiplexed qRT-PCR
Multiplexed qRT-PCR
[37]
MX2
NDa
Upon infection
B cells
viscRNA-Seq
qRT-PCR
[41]
IFIT1
ND
Upon infection
Monocytes
viscRNA-Seq
qRT-PCR
[41]
CD163
ND
Upon infection
Monocytes
viscRNA-Seq
qRT-PCR
[41]
Serum
Indirect (i) ELISA
Multiplexed ELISA
[9,11]
Anti-DENV antibody titer
Yes
Preinfection
Afucosylated IgG1
ND
Before or upon infection
Serum
LC-MS
?
[8]
IgG1/IgG2 ratio
ND
Before or upon infection
Serum
LC-MS
ELISA
[8]
Chymase
Yes
Upon infection
Serum
ELISA
ELISA
[46,48–50]
IL-10
Yes
Upon infection
Serum
Immunoassay
ELISA
[51]
IFN-γ
Yes
Upon infection
Serum
Immunoassay
ELISA
[51]
Endoglin
ND
Upon infection
Serum
ELISA
ELISA
[54]
CXCL10
ND
Upon infection
Serum
ELISA
ELISA
[54]
ICAM-1
ND
Upon infection
Serum
ELISA
ELISA
[54]
VCAM-1
ND
Upon infection
Serum
ELISA
ELISA
[55]
Angiopoetin-2
ND
Upon infection
Serum
ELISA
ELISA
[55]
Serotonin
ND
Upon infection
Serum
LC-MS
LC-MS
[56]
Kynurenine
ND
Upon infection
Serum
LC-MS
LC-MS
[56]
Plasma
Fluorescent assay
Fluorescent assay
[59]
b
Metabolic
Apoptotic a b
cfDNA
ND
Upon infection
Key: ND, not determined; NA, not available; ?, no known available sample-to-answer platform. Predictive utility of pre-existing antibodies measured upon infection remains to be determined.
To overcome these limitations, a multicohort analysis framework was recently used to integrate biologically heterogeneous data sets to identify robust host gene signatures that are generalizable and prospectively validated. This framework was previously used to identify discrete diagnostic or prognostic gene sets in sepsis, viral infections, active tuberculosis, organ transplant, vaccination, and systemic sclerosis [31–36]. To identify a set of conserved genes that is predictive of SD and generalizable across cohorts, this integrated multicohort analysis framework was used to analyze blood samples of dengue patients from seven publicly accessible gene expression datasets (446 samples, five countries) [37]. A 20-gene set was identified to predict progression to SD [37]. The predictive power of this 20-gene set was then validated in three retrospective independent publicly available dengue datasets (84 samples, three countries) and achieved an area under the receiver operating characteristic curve (AUCROC) of 0.80 [95% confidence interval (CI) 0.68–0.88]. This 20-gene set was also validated in a prospective Colombia cohort (34 patients), with an AUCROC of 0.89 (95% CI 0.81–0.97). To avoid misclassification of SD patients, the test sensitivity was set to 100% and calculated a specificity of 76% for the gene set to separate uncomplicated from SD [37]. This 20-gene set is strongly associated with the progression to SD and represents a predictive signature that is generalizable across ages, host genetic factors, virus
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strains, and sample types. While further prospective validation is ongoing, this 20-gene set has potential implications for the development of a host response-based dengue prognostic assay. Another novel transcriptomics approach that has been applied for the discovery of candidate predictive biomarkers of SD is coupling single-cell transcriptomics with fluorescence activated cell sorting (FACS). This approach was designed to overcome a different limitation of standard bulk transcriptomics: its limited ability to capture tissue heterogeneity. Specifically, averaging the signal over various cell populations is confounded by changes both in the abundance of cell types (e.g., B and T lymphocytes) and cell states (activation of SD specific genes) [38,39]. A novel virus-inclusive single-cell RNA sequencing (viscRNA-Seq) approach was developed to probe the host transcriptome together with intracellular viral RNA (vRNA) at the single-cell level [40]. Applying viscRNA-Seq to monitor DENV and Zika virus infections in cultured human hepatoma (Huh7) cells revealed extreme heterogeneity in the level of virus abundance, and enabled identification of host factors required for infection with one or both viruses that correlated with intracellular viral abundance [40]. More recently, FACS technology was combined with viscRNA-Seq to study the molecular signatures preceding the development of SD infection and identify cells with vRNA in PBMC samples derived from human dengue patients [41]. The use of antibodies against surface proteins via FACS enabled enrichment for specific cell populations and high-resolution screening of the entire human transcriptome for changes in gene expression at a single-cell level. Analyzing blood samples obtained prior to the progression to SD combined with sampling a wide range of cell types and activation states via the single-cell resolution of viscRNA-Seq provided a unique opportunity to discover candidate biomarkers of disease progression. For example, the expression of MX2, one of only four interferon (IFN)-induced genes previously shown to be induced in an IFN-regulatory factor (IRF)3 and IRF7 independent manner in DENV-infected mice [42], is greatly upregulated in naïve B cells before the development of SD [41]. Similarly, the expression of IFN-induced protein with tetratricopeptide repeats (IFIT)1 and CD163, previously shown to contribute to the pathogenesis of SD [43,44], within CD14+ CD16+ monocytes is greatly induced [41]. The predictive power of these cell-type-specific markers to identify patients at risk for SD is high (AUCROC ≥0.95), yet given the small number of subjects analyzed and the female predominance in this study, it warrants further validation in larger, gender-balanced cohorts. Nevertheless, these findings underscore the utility of the FACS-assisted viscRNA-Seq approach to identify candidate prognostic biomarkers for dengue.
Anti-DENV Antibodies as Candidate Predictive Biomarkers Two independent cohort studies in dengue endemic regions have recently identified preinfection levels of anti-DENV antibodies as an important risk factor for disease severity [9,11]. In a Nicaraguan cohort, patients with intermediate titers of preexisting anti-DENV antibodies (1:21–1:80) were found to have the greatest risk for SD, whereas patients with low or high preexisting antibody titers had a low risk for severe infection [9]. Preexisting anti-DENV antibody levels were also found to correlate with disease severity in a cohort of dengue patients in Thailand, albeit low titers (≤1:40) were associated with severe disease and higher titers (N1:40) were protective in this cohort [11]. These findings suggest that the preexisting DENV antibody level may be used as a candidate biomarker for disease severity. Yet, the clinical utility of this approach may be challenged by the need for preinfection samples, unless comparable findings are observed in samples obtained upon infection. Moreover, since the precise titer cutoffs appear to vary with the genetic background and the specific assay utilized, these will have to be determined in distinct populations and laboratories. Another recent finding that further addresses the question why only a subset of patients previously exposed to dengue develop severe disease is the discovery that some individuals respond 4
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to DENV infection by producing IgGs with higher affinity for FcγRIIIA [45]. More specifically, it was shown that patients who develop DHF/DSS have anti-DENV IgG antibodies with an elevated level of afucosylated Fc and increased IgG1/IgG2 ratio relative to patients with DF [45]. These findings correlated with the presence of thrombocytopenia, a criterion of DHF diagnosis, and the lowest platelet count recorded for each patient during hospitalization, attesting for the role of these enhanced affinity anti-DENV IgG antibodies in mediating disease enhancement [45]. While the predictive power of these determinants and their generalizability across cohorts remain to be studied, measurement of the fraction of afucosylated Fc anti-DENV IgG antibodies and/or the IgG1/IgG2 ratio may provide additional means to identify DENV exposed patients at risk for SD prior to or during a subsequent infection.
Other Inflammatory and Endothelial Activation Factors as Candidate Biomarkers Chymase, a serine protease and angiotensin-converting enzyme released from mast cells upon their activation by anti-DENV antibodies, was also proposed as a candidate biomarker for dengue severity [46,47]. In a Sri Lankan cohort, the serum chymase level at presentation demonstrated a prognostic potential with 1.32 (95% CI 1.21–1.44) fold higher odds for developing DHF than DF per unit increase in serum chymase level [48]. Similarly, in Vietnamese and Singaporean cohorts, chymase serum levels were higher at the time of presentation in patients who progressed to DHF/DSS than those with uncomplicated dengue [46,49]. Notably, even though the chymase level during acute dengue is altered by certain comorbidities, such as diabetes and respiratory and cardiovascular diseases, its predictive power is maintained with an AUCROC ranging from 0.6–0.77 in adults to 0.84–0.93 in children [48]. In contrast, in another independent study in Vietnam, no differences in chymase level were measured between various dengue categories during the early stage of illness, but rather only in later stages [50]. Accordingly, the utility of chymase as a predictive biomarker and its generalizability to other cohorts remain to be determined. Since dengue pathogenesis is largely linked to aberrant immune responses to the virus, other circulating immunomodulating proteins and cell surface markers have been widely studied as candidate predictive biomarkers. A comprehensive review that evaluated the relevant collective data generated in numerous (N700) papers revealed elevations of interleukin (IL)-10 and IFN-γ early in the course of disease as potential predictive biomarkers [51]. However, significant heterogeneity in methodologies, patient cohorts, and protein or cell levels raise questions about the utility of these factors as prognostic markers [52]. Similarly, large heterogeneity within cohorts presents challenges in the interpretation of data concerning alterations in cellular abundance such as in NK and T cell populations and expression level of cell surface markers in patients who progress to SD [52,53]. Since an important clinical feature of SD is an increase in vascular permeability, identification of biomarkers indicative of endothelial activation may provide another potential avenue to identify patients at risk to progress to SD. Indeed, a prospective cohort study of dengue in Colombia that measured the level of 19 serum biomarkers by ELISA revealed elevated levels of soluble intercellular adhesion molecule (ICAM)-1, endoglin and chemokine CXC ligand (CXCL)10/IFN-γ-induced protein 10 in subjects who subsequently developed DHF/DSS [54]. Elevated soluble ICAM-1 at presentation was independently associated with progression to DHF/DSS [54]. However, while the levels of the other known endothelial activation markers vascular cell adhesion molecule (VCAM)-1 and angiopoetin-2 were elevated during acute dengue, they did not significantly correlate with the degree of plasma leakage [55]. The utility of these and other endothelial activation factors as prognostic indicators thus remains to be determined.
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Metabolic and Apoptotic Candidate Biomarkers of Dengue Severity A metabolomics screen of serum samples from dengue patients in the early febrile period via liquid chromatography mass spectrometry (LC-MS) has recently identified 20 metabolites that are differentially enriched in patients with DHF relative to those with uncomplicated dengue [56]. Among these 20 factors are two products of tryptophan metabolism: serotonin, which is involved in platelet aggregation and the immunomodulator kynurenine. Serotonin alone provided a good prognostic power with an AUCROC of 0.8 and when combined with IFN-γ, this further improved to 0.92 with a sensitivity of 77.8% and a specificity of 95.8% [56]. This combination of serum factors may thus represent a potentially clinically usable marker to predict progression to severe disease, yet its generalizability to other cohorts remains to be determined. Another candidate predictive biomarker for SD is circulating plasma cell-free DNA (cfDNA), which is being used as a biomarker in cancer [57]. cfDNA is composed of double-stranded DNA fragments that are released into the extracellular fluid by various cells upon their death. Since apoptosis is thought to play a role in dengue infection, it has been hypothesized that cfDNA levels may be an early indicator of disease severity [58]. In a study of 61 patients, the level of cfDNA was higher in eight patients who progressed to SD than those with an uncomplicated course, demonstrating some promise. Yet, the predictive power was somewhat limited with a sensitivity of 87.5%, specificity of 54.7%, and an AUCROC of 0.72. Additional studies are therefore needed to further validate the prognostic value of cfDNA [59].
The Utility of Having a Prognostic Assay for Dengue A validated biomarker predictive of SD with high sensitivity and specificity that incorporates the clinical heterogeneity of DENV infection could potentially be used as a molecular prognostic tool. Such an assay will enable us to appropriately triage infected individuals and better define their level of care. Since timely administration of supportive care to patients has been shown to improve clinical outcomes [13], early identification of high-risk individuals has the potential to reduce morbidity and mortality. Moreover, effective triage of patients will facilitate the conservation of resources for those most at need, which is particularly critical in the setting of dengue outbreaks. Identification of patients at high risk to progress to SD – the group of patients that is more likely to benefit from antiviral treatment – can also guide patient selection and endpoint measurements in clinical trials aimed at evaluating emerging anti-DENV drugs, such as those our laboratory and others have been developing [60,61]. Once antiviral agents are approved, such an assay can guide treatment decisions.
Translational Challenges Translating biomarkers of dengue severity into a prognostic assay presents multiple challenges. One such challenge is related to the definition of SD. While the 1997 WHO criteria define SD specifically as DHF/DSS [14], a condition resulting from vascular leak [9], the 2009 WHO criteria intentionally define SD more broadly and include organ damage as one criterion [4]. Given the variable presentations of SD, it is important to define disease severity consistently [62] and ideally analyze the data based on the two WHO classification methods, as reported [37,63]. Moreover, since these presentations may represent distinct syndromes, it is important to validate predictive biomarkers in all the SD categories including the less common cases of SD caused by mechanisms that may not be linked to vascular leak [15]. Also challenging is the need to validate candidate biomarkers broadly. Since disease severity appears to vary with genetic background [7], it is critical to validate host-based biomarkers in independent cohorts representing diverse populations. Furthermore, given the altered host responses to DENV documented in children and pregnant women, validation of host-based biomarkers has to include these specific populations [64–66]. 6
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Additional challenges involve the availability of a suitable technology to facilitate the development of a prognostic assay for use in resource-limited environments. The ideal setting for such an assay is an emergency room or a clinic, where patients who present with a dengue compatible illness and are diagnosed with dengue via a rapid test, qualify for prognostic testing. In order to guide clinical management, such an assay thus has to have a fast turnaround time (1–2 h) and a sample-to-answer workflow (Table 1). Ideally, to avoid delays due to repeat sampling, the assay should utilize the initial diagnostic sample or simultaneously both establish the diagnosis of DENV infection and assess the risk for progression to SD. It also has to enable testing individual samples rather than relying on sample batching. Importantly, such an assay has to be affordable and relatively easy to deploy in the developing world. Additional requirements are relevant to specific biomarkers. For example, translation of the 20-gene set predictive of SD requires a platform capable of quantitatively measuring the level of 20 genes in whole blood samples and ideally incorporating an integral, rapid RNA extraction step (Table 1). Various platforms, such as the Biomark (Fluidigm) and nCounter (Nanostring) represent useful research tools that meet many of these criteria [37,67,68]. Nevertheless, their current format is not designed for the development of a sample-to-answer assay. A few platforms have been developed that enable multiplexing of a large number of genes based on quantitative reverse transcription (qRT)-PCR within just a few hours, such as the BioFire FilmArray, QIAstat-Dx, and Biomeme [69–71]. While these technologies are emerging as promising diagnostic tools in various clinical syndromes in the developed world [72], the high manufacturing cost currently challenges their use in more resource-limited settings. Public–private partnerships such as those facilitating the use of Cepheid’s GeneXpert platform for various infectious disease indications in the developing world [73,74] provide examples for how such technologies can be implemented in resource-limited setting until they become affordable. Similarly, while a few technologies have been developed recently for quantitative multiplex measurement of antibodies [75,76], their capability to support the development of a sample-to-answer assay format in a clinical setting needs be established. Measuring the level of cell type-specific candidate biomarkers as those discovered via single-cell transcriptomics will require combining technologies for isolation of specific cell types (e.g., monocytes or B cells), such as magnetic beads, and RT-PCR or using technologies that integrate both functions [77]. Quantitating serum biomarkers by conventional ELISA may also be problematic, as it is time consuming and requires specialized laboratory equipment and expertise to carry out. Lastly, studying blood biomarkers via LC-MS can have a fast turnaround but relies on having access to the device, which may not be feasible in some settings.
Outstanding Questions What is the predictive power of some of these biomarkers in large, heterogeneous patient populations with different ages, host genetic factors, virus strains, and prior exposures? What is the optimal cutoff for distinguishing a severe from uncompli -cated dengue course with these biomarkers? Would combining various biomarkers increase the sensitivity and/or specificity to predict progression to SD relative to individual biomarkers? Are current technologies capable to support the development of a sample-to-answer assay format for predicting progression to severe dengue in resource-limited clinical settings? Would the utilization of sample-toanswer prognostic assays for dengue progression improve patient care and allocation of health care resources? What are the roles of these biomarkers in the pathogenesis of SD?
Concluding Remarks and Future Perspectives Taken together, several candidate biomarkers that are predictive of progression to SD early in the disease course have been identified recently, representing an important breakthrough in the field. Some of these biomarkers are currently undergoing further validation in larger prospective cohorts. While several important challenges have to be addressed (see Outstanding Questions), biomarkers found to be generalizable across independent cohorts could be potentially utilized for the development of the first prognostic assay for use in dengue endemic countries. Such an assay has the potential to both improve patient care and allocate healthcare resources for dengue more effectively. Acknowledgments This research was supported by a Catalyst Award from the Dr Ralph and Marian Falk Medical Research Trust and by grants from Department of Defense (DoD)/ Congressionally Directed Medical Research Programs (CDMRP) (W81XWH-16-1-0691) and Defense Threat Reduction Agency (DTRA) (HDTRA11810039) to S.E. M.R. was supported by the Stanford Advanced Residency Training at Stanford (ARTS) Fellowship Program. The authors acknowledge all the contributions in the field that could not be included in this review.
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51. Lee, Y.H. et al. (2016) Markers of dengue severity: a systematic review of cytokines and chemokines. J. Gen. Virol. 97, 3103–3119 52. Keawvichit, R. et al. (2018) Differences in activation and tissue homing markers of natural killer cell subsets during acute dengue infection. Immunology 153, 455–465 53. Green, S. et al. (1999) Early CD69 expression on peripheral blood lymphocytes from children with dengue hemorrhagic fever. J. Infect. Dis. 180, 1429–1435 54. Conroy, A.L. et al. (2015) Host biomarkers are associated with progression to dengue haemorrhagic fever: a nested casecontrol study. Int. J. Infect. Dis. 40, 45–53 55. Yacoub, S. et al. (2016) Association of microvascular function and endothelial biomarkers with clinical outcome in dengue: an observational study. J. Infect. Dis. 214, 697–706 56. Cui, L. et al. (2016) Serum metabolomics reveals serotonin as a predictor of severe dengue in the early phase of dengue fever. PLoS Negl. Trop. Dis. 10, e0004607 57. Yamamoto, Y. et al. (2018) Increased level and fragmentation of plasma circulating cell-free DNA are diagnostic and prognostic markers for renal cell carcinoma. Oncotarget 9, 20467–20475 58. Jaiyen, Y. et al. (2009) Characteristics of dengue virus-infected peripheral blood mononuclear cell death that correlates with the severity of illness. Microbiol. Immunol. 53, 442–450 59. Phuong, N.T.N. et al. (2019) Plasma cell-free DNA: a potential biomarker for early prediction of severe dengue. Ann. Clin. Microbiol. Antimicrob. 18, 10 60. Bekerman, E. et al. (2017) Anticancer kinase inhibitors impair intracellular viral trafficking and exert broad-spectrum antiviral effects. J. Clin. Invest. 127, 1338–1352 61. Lim, S.P. et al. (2013) Ten years of dengue drug discovery: progress and prospects. Antivir. Res. 100, 500–519 62. Morra, M.E. et al. (2018) Definitions for warning signs and signs of severe dengue according to the WHO 2009 classification: systematic review of literature. Rev. Med. Virol. 28, e1979 63. Bodinayake, C.K. et al. (2018) Evaluation of the WHO 2009 classification for diagnosis of acute dengue in a large cohort of adults and children in Sri Lanka during a dengue-1 epidemic. PLoS Negl. Trop. Dis. 12, e0006258 64. Useche, Y.M. et al. (2018) Single-nucleotide polymorphisms in NOD1, RIPK2, MICB, PLCE1, TNF, and IKBKE genes associated with symptomatic dengue in children from Colombia. Viral Immunol. 31, 613–623
65. Toro, J.F. et al. (2016) Total and envelope protein-specific antibody-secreting cell response in pediatric dengue is highly modulated by age and subsequent infections. PLoS One 11, e0161795 66. Zhang, H. et al. (2017) Roles of interferons in pregnant women with dengue infection: protective or dangerous factors. Can. J. Infect. Dis. Med. Microbiol. 2017, 1671607 67. Sanchez-Freire, V. et al. (2012) Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns. Nat. Protocols 7, 829–838 68. Brumbaugh, C.D. et al. (2011) NanoStriDE: normalization and differential expression analysis of NanoString nCounter data. BMC Bioinformatics 12, 479 69. Popowitch, E.B. et al. (2013) Comparison of the Biofire FilmArray RP, Genmark eSensor RVP, Luminex xTAG RVPv1, and Luminex xTAG RVP fast multiplex assays for detection of respiratory viruses. J. Clin. Microbiol. 51, 1528–1533 70. Myrick, J.T. et al. (2019) Integrated extreme real-time PCR and high-speed melting analysis in 52 to 87 seconds. Clin. Chem. 65, 263–271 71. Russell, J.A. et al. (2018) Unbiased strain-typing of arbovirus directly from mosquitoes using nanopore sequencing: a fieldforward biosurveillance protocol. Sci. Rep. 8, 5417 72. Leber, A.L. et al. (2016) Multicenter evaluation of BioFire FilmArray meningitis/encephalitis panel for detection of bacteria, viruses, and yeast in cerebrospinal fluid specimens. J. Clin. Microbiol. 54, 2251–2261 73. Raftery, P. et al. (2018) Establishing Ebola virus disease (EVD) diagnostics using GeneXpert technology at a mobile laboratory in Liberia: impact on outbreak response, case management and laboratory systems strengthening. PLoS Negl. Trop. Dis. 12, e0006135 74. World Health Organization (2012) Public–Private Partnership Announces Immediate 40 Percent Cost Reduction for Rapid TB Test, World Health Organization 75. Zhang, B. et al. (2017) Diagnosis of Zika virus infection on a nanotechnology platform. Nat. Med. 23, 548–550 76. Reed, E.F. et al. (2013) Comprehensive assessment and standardization of solid phase multiplex-bead arrays for the detection of antibodies to HLA. Am. J. Transplant. 13, 1859–1870 77. Rival, A. et al. (2014) An EWOD-based microfluidic chip for single-cell isolation, mRNA purification and subsequent multiplex qPCR. Lab Chip 14, 3739–3749
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Review
Towards Predicting Progression to Severe Dengue Makeda Robinson1,2 and Shirit Einav1,2,* There is an urgent need for prognostic assays to predict progression to severe dengue infection, which is a major global threat. While the majority of symptomatic dengue patients experience an acute febrile illness, 5–20% progress to severe infection associated with significant morbidity and mortality. Early monitoring and administration of supportive care reduce mortality and clinically usable biomarkers to predict severe dengue are needed. Here, we review recent discoveries of gene sets, anti-dengue antibody properties, and inflammatory markers with potential utility as predictors of disease progression. Upon larger scale validation and development of affordable sample-to-answer technologies, some of these biomarkers may be utilized to develop the first prognostic assay for improving patient care and allocating healthcare resources more effectively in dengue endemic countries.
Highlights
The Urgent Need for Predictive Biomarkers
Recently discovered anti-dengue antibody properties, inflammatory, apoptotic and metabolic markers, some of which are generalizable across cohorts, also have potential utility as predictors of dengue disease progression.
Until recently there have been no clinically usable biomarkers to accurately predict which patients will progress to severe dengue, a major global threat. A 20-gene set that is strongly associated with the progression to severe dengue and represents a predictive signature that is generalizable across ages, host genetic factors, virus strains, and sample types was recently discovered using a multicohort analysis framework that integrates biologically heterogeneous data sets.
Dengue is the most common human arboviral disease worldwide, with an estimated 400 million infections occurring annually in over 100 countries and a growing distribution in the developed world due to climate changes and urbanization [1–3]. The four dengue virus serotypes (DENV 1–4) are transmitted via a mosquito vector. The majority of symptomatic individuals present with acute dengue fever, yet a fraction (5–20%) of these patients progresses to severe dengue (SD), which can manifest by bleeding, plasma leakage, shock, organ failure, and sometimes death [4,5].
Some of these biomarkers may be utilized to develop the first prognostic assay for improving patient care and allocating healthcare resources more effectively in dengue endemic countries.
Dengue severity has been shown to be determined by host genetic factors, the specific viral serotype, and the level of viremia [6,7]. The greatest risk factor for SD, however, is the immunological status of the patient. Specifically, the presence of preexisting, nonneutralizing anti-DENV antibodies at a specific titer range can predispose individuals to antibody-dependent enhancement (ADE) upon secondary infection with a heterologous DENV serotype [8–11]. Aberrant activation of crossreactive T cells may also play a role [10]. The complex interplay of protective and enhancing components of the human immune response to DENV infection has challenged the vaccine development effort [12] and has hampered the understanding of mechanisms that underlie progression to severe disease and hence the discovery of predictive biomarkers. Early admission to an inpatient facility and timely administration of supportive care have been shown to improve clinical outcomes and reduce mortality in patients with SD [13]. The World Health Organization (WHO) has therefore defined a set of criteria to classify dengue infection based on its severity. The currently used (2009) criteria classify patients into uncomplicated dengue, dengue with warning signs and SD, whereas the former (1997) criteria define dengue fever (DF) and two forms of severe dengue: dengue hemorrhagic fever (DHF) and/or dengue shock syndrome (DSS) [4,14,15]. The implementation of the currently utilized warning signs to identify patients at risk of progressing to SD has improved the sensitivity in capturing patients at risk, but has led to a substantial increase in the number of patients admitted to hospitals, resulting Trends in Microbiology, Month 2019, Vol. xx, No. xx
1
Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA 2 Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
*Correspondence: [email protected] (S. Einav).
https://doi.org/10.1016/j.tim.2019.12.003 © 2019 Elsevier Ltd. All rights reserved.
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in ineffective resource allocation [16]. Moreover, although improved, the sensitivity of the utilized warning signs, which are based on clinical parameters that often develop late during the course of disease, is still limited leading to continued morbidity and mortality [17,18]. There is thus an urgent need for novel biomarkers to predict progression to SD. Here, we review prior efforts to identify such predictive biomarkers and highlight several recent discoveries of candidate clinically usable biomarkers to predict the development of severe complications associated with dengue infection. The potential translational impact of these biomarkers and current challenges in the development of sample-to-answer assays for use in resource-limited settings are also discussed (Figure 1 and Table 1, Key Table).
Transcriptomic Signatures for Predicting Progression to SD Prior efforts to identify molecular biomarkers for dengue severity have focused primarily on bulk transcriptomics; that is, measuring gene expression in bulk RNA samples extracted from human blood and peripheral blood mononuclear cells (PBMCs) [19–29] or from primary cells infected with DENV [30]. These studies identified alterations in either the timing and/or magnitude of gene transcript abundance that were associated with dengue severity, yet had several limitations. First, the majority of these studies identified genes whose altered expression is associated with, but does not precede, the onset of SD and therefore cannot be used as prognostic biomarkers. Second, these studies did not yield parsimonious gene sets to enable their translation into a prognostic assay. Third, being single-cohort transcriptomics studies, none of the generated gene sets has yet been shown to be generalizable across independent cohorts.
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Figure 1. Schematic of the Various Categories of Candidate Biomarkers Showing Potential Promise for Prediction of Progression to Severe Dengue (SD). 2
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Key Table
Table 1. Classes and Examples of Candidate Predictive Biomarkers of SD and Their Translational Potential Category
Transcriptomic
Anti-DENV antibody properties
Other inflammatory
Endothelial activation
Candidate biomarker
Generalizability across cohorts
Timing of testing
Sample
Discovery platform
Potential sample-toanswer platform
Refs
20-gene set
Yes
Upon infection
Whole blood
Multiplexed qRT-PCR
Multiplexed qRT-PCR
[37]
MX2
NDa
Upon infection
B cells
viscRNA-Seq
qRT-PCR
[41]
IFIT1
ND
Upon infection
Monocytes
viscRNA-Seq
qRT-PCR
[41]
CD163
ND
Upon infection
Monocytes
viscRNA-Seq
qRT-PCR
[41]
Serum
Indirect (i) ELISA
Multiplexed ELISA
[9,11]
Anti-DENV antibody titer
Yes
Preinfection
Afucosylated IgG1
ND
Before or upon infection
Serum
LC-MS
?
[8]
IgG1/IgG2 ratio
ND
Before or upon infection
Serum
LC-MS
ELISA
[8]
Chymase
Yes
Upon infection
Serum
ELISA
ELISA
[46,48–50]
IL-10
Yes
Upon infection
Serum
Immunoassay
ELISA
[51]
IFN-γ
Yes
Upon infection
Serum
Immunoassay
ELISA
[51]
Endoglin
ND
Upon infection
Serum
ELISA
ELISA
[54]
CXCL10
ND
Upon infection
Serum
ELISA
ELISA
[54]
ICAM-1
ND
Upon infection
Serum
ELISA
ELISA
[54]
VCAM-1
ND
Upon infection
Serum
ELISA
ELISA
[55]
Angiopoetin-2
ND
Upon infection
Serum
ELISA
ELISA
[55]
Serotonin
ND
Upon infection
Serum
LC-MS
LC-MS
[56]
Kynurenine
ND
Upon infection
Serum
LC-MS
LC-MS
[56]
Plasma
Fluorescent assay
Fluorescent assay
[59]
b
Metabolic
Apoptotic a b
cfDNA
ND
Upon infection
Key: ND, not determined; NA, not available; ?, no known available sample-to-answer platform. Predictive utility of pre-existing antibodies measured upon infection remains to be determined.
To overcome these limitations, a multicohort analysis framework was recently used to integrate biologically heterogeneous data sets to identify robust host gene signatures that are generalizable and prospectively validated. This framework was previously used to identify discrete diagnostic or prognostic gene sets in sepsis, viral infections, active tuberculosis, organ transplant, vaccination, and systemic sclerosis [31–36]. To identify a set of conserved genes that is predictive of SD and generalizable across cohorts, this integrated multicohort analysis framework was used to analyze blood samples of dengue patients from seven publicly accessible gene expression datasets (446 samples, five countries) [37]. A 20-gene set was identified to predict progression to SD [37]. The predictive power of this 20-gene set was then validated in three retrospective independent publicly available dengue datasets (84 samples, three countries) and achieved an area under the receiver operating characteristic curve (AUCROC) of 0.80 [95% confidence interval (CI) 0.68–0.88]. This 20-gene set was also validated in a prospective Colombia cohort (34 patients), with an AUCROC of 0.89 (95% CI 0.81–0.97). To avoid misclassification of SD patients, the test sensitivity was set to 100% and calculated a specificity of 76% for the gene set to separate uncomplicated from SD [37]. This 20-gene set is strongly associated with the progression to SD and represents a predictive signature that is generalizable across ages, host genetic factors, virus
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strains, and sample types. While further prospective validation is ongoing, this 20-gene set has potential implications for the development of a host response-based dengue prognostic assay. Another novel transcriptomics approach that has been applied for the discovery of candidate predictive biomarkers of SD is coupling single-cell transcriptomics with fluorescence activated cell sorting (FACS). This approach was designed to overcome a different limitation of standard bulk transcriptomics: its limited ability to capture tissue heterogeneity. Specifically, averaging the signal over various cell populations is confounded by changes both in the abundance of cell types (e.g., B and T lymphocytes) and cell states (activation of SD specific genes) [38,39]. A novel virus-inclusive single-cell RNA sequencing (viscRNA-Seq) approach was developed to probe the host transcriptome together with intracellular viral RNA (vRNA) at the single-cell level [40]. Applying viscRNA-Seq to monitor DENV and Zika virus infections in cultured human hepatoma (Huh7) cells revealed extreme heterogeneity in the level of virus abundance, and enabled identification of host factors required for infection with one or both viruses that correlated with intracellular viral abundance [40]. More recently, FACS technology was combined with viscRNA-Seq to study the molecular signatures preceding the development of SD infection and identify cells with vRNA in PBMC samples derived from human dengue patients [41]. The use of antibodies against surface proteins via FACS enabled enrichment for specific cell populations and high-resolution screening of the entire human transcriptome for changes in gene expression at a single-cell level. Analyzing blood samples obtained prior to the progression to SD combined with sampling a wide range of cell types and activation states via the single-cell resolution of viscRNA-Seq provided a unique opportunity to discover candidate biomarkers of disease progression. For example, the expression of MX2, one of only four interferon (IFN)-induced genes previously shown to be induced in an IFN-regulatory factor (IRF)3 and IRF7 independent manner in DENV-infected mice [42], is greatly upregulated in naïve B cells before the development of SD [41]. Similarly, the expression of IFN-induced protein with tetratricopeptide repeats (IFIT)1 and CD163, previously shown to contribute to the pathogenesis of SD [43,44], within CD14+ CD16+ monocytes is greatly induced [41]. The predictive power of these cell-type-specific markers to identify patients at risk for SD is high (AUCROC ≥0.95), yet given the small number of subjects analyzed and the female predominance in this study, it warrants further validation in larger, gender-balanced cohorts. Nevertheless, these findings underscore the utility of the FACS-assisted viscRNA-Seq approach to identify candidate prognostic biomarkers for dengue.
Anti-DENV Antibodies as Candidate Predictive Biomarkers Two independent cohort studies in dengue endemic regions have recently identified preinfection levels of anti-DENV antibodies as an important risk factor for disease severity [9,11]. In a Nicaraguan cohort, patients with intermediate titers of preexisting anti-DENV antibodies (1:21–1:80) were found to have the greatest risk for SD, whereas patients with low or high preexisting antibody titers had a low risk for severe infection [9]. Preexisting anti-DENV antibody levels were also found to correlate with disease severity in a cohort of dengue patients in Thailand, albeit low titers (≤1:40) were associated with severe disease and higher titers (N1:40) were protective in this cohort [11]. These findings suggest that the preexisting DENV antibody level may be used as a candidate biomarker for disease severity. Yet, the clinical utility of this approach may be challenged by the need for preinfection samples, unless comparable findings are observed in samples obtained upon infection. Moreover, since the precise titer cutoffs appear to vary with the genetic background and the specific assay utilized, these will have to be determined in distinct populations and laboratories. Another recent finding that further addresses the question why only a subset of patients previously exposed to dengue develop severe disease is the discovery that some individuals respond 4
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to DENV infection by producing IgGs with higher affinity for FcγRIIIA [45]. More specifically, it was shown that patients who develop DHF/DSS have anti-DENV IgG antibodies with an elevated level of afucosylated Fc and increased IgG1/IgG2 ratio relative to patients with DF [45]. These findings correlated with the presence of thrombocytopenia, a criterion of DHF diagnosis, and the lowest platelet count recorded for each patient during hospitalization, attesting for the role of these enhanced affinity anti-DENV IgG antibodies in mediating disease enhancement [45]. While the predictive power of these determinants and their generalizability across cohorts remain to be studied, measurement of the fraction of afucosylated Fc anti-DENV IgG antibodies and/or the IgG1/IgG2 ratio may provide additional means to identify DENV exposed patients at risk for SD prior to or during a subsequent infection.
Other Inflammatory and Endothelial Activation Factors as Candidate Biomarkers Chymase, a serine protease and angiotensin-converting enzyme released from mast cells upon their activation by anti-DENV antibodies, was also proposed as a candidate biomarker for dengue severity [46,47]. In a Sri Lankan cohort, the serum chymase level at presentation demonstrated a prognostic potential with 1.32 (95% CI 1.21–1.44) fold higher odds for developing DHF than DF per unit increase in serum chymase level [48]. Similarly, in Vietnamese and Singaporean cohorts, chymase serum levels were higher at the time of presentation in patients who progressed to DHF/DSS than those with uncomplicated dengue [46,49]. Notably, even though the chymase level during acute dengue is altered by certain comorbidities, such as diabetes and respiratory and cardiovascular diseases, its predictive power is maintained with an AUCROC ranging from 0.6–0.77 in adults to 0.84–0.93 in children [48]. In contrast, in another independent study in Vietnam, no differences in chymase level were measured between various dengue categories during the early stage of illness, but rather only in later stages [50]. Accordingly, the utility of chymase as a predictive biomarker and its generalizability to other cohorts remain to be determined. Since dengue pathogenesis is largely linked to aberrant immune responses to the virus, other circulating immunomodulating proteins and cell surface markers have been widely studied as candidate predictive biomarkers. A comprehensive review that evaluated the relevant collective data generated in numerous (N700) papers revealed elevations of interleukin (IL)-10 and IFN-γ early in the course of disease as potential predictive biomarkers [51]. However, significant heterogeneity in methodologies, patient cohorts, and protein or cell levels raise questions about the utility of these factors as prognostic markers [52]. Similarly, large heterogeneity within cohorts presents challenges in the interpretation of data concerning alterations in cellular abundance such as in NK and T cell populations and expression level of cell surface markers in patients who progress to SD [52,53]. Since an important clinical feature of SD is an increase in vascular permeability, identification of biomarkers indicative of endothelial activation may provide another potential avenue to identify patients at risk to progress to SD. Indeed, a prospective cohort study of dengue in Colombia that measured the level of 19 serum biomarkers by ELISA revealed elevated levels of soluble intercellular adhesion molecule (ICAM)-1, endoglin and chemokine CXC ligand (CXCL)10/IFN-γ-induced protein 10 in subjects who subsequently developed DHF/DSS [54]. Elevated soluble ICAM-1 at presentation was independently associated with progression to DHF/DSS [54]. However, while the levels of the other known endothelial activation markers vascular cell adhesion molecule (VCAM)-1 and angiopoetin-2 were elevated during acute dengue, they did not significantly correlate with the degree of plasma leakage [55]. The utility of these and other endothelial activation factors as prognostic indicators thus remains to be determined.
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Metabolic and Apoptotic Candidate Biomarkers of Dengue Severity A metabolomics screen of serum samples from dengue patients in the early febrile period via liquid chromatography mass spectrometry (LC-MS) has recently identified 20 metabolites that are differentially enriched in patients with DHF relative to those with uncomplicated dengue [56]. Among these 20 factors are two products of tryptophan metabolism: serotonin, which is involved in platelet aggregation and the immunomodulator kynurenine. Serotonin alone provided a good prognostic power with an AUCROC of 0.8 and when combined with IFN-γ, this further improved to 0.92 with a sensitivity of 77.8% and a specificity of 95.8% [56]. This combination of serum factors may thus represent a potentially clinically usable marker to predict progression to severe disease, yet its generalizability to other cohorts remains to be determined. Another candidate predictive biomarker for SD is circulating plasma cell-free DNA (cfDNA), which is being used as a biomarker in cancer [57]. cfDNA is composed of double-stranded DNA fragments that are released into the extracellular fluid by various cells upon their death. Since apoptosis is thought to play a role in dengue infection, it has been hypothesized that cfDNA levels may be an early indicator of disease severity [58]. In a study of 61 patients, the level of cfDNA was higher in eight patients who progressed to SD than those with an uncomplicated course, demonstrating some promise. Yet, the predictive power was somewhat limited with a sensitivity of 87.5%, specificity of 54.7%, and an AUCROC of 0.72. Additional studies are therefore needed to further validate the prognostic value of cfDNA [59].
The Utility of Having a Prognostic Assay for Dengue A validated biomarker predictive of SD with high sensitivity and specificity that incorporates the clinical heterogeneity of DENV infection could potentially be used as a molecular prognostic tool. Such an assay will enable us to appropriately triage infected individuals and better define their level of care. Since timely administration of supportive care to patients has been shown to improve clinical outcomes [13], early identification of high-risk individuals has the potential to reduce morbidity and mortality. Moreover, effective triage of patients will facilitate the conservation of resources for those most at need, which is particularly critical in the setting of dengue outbreaks. Identification of patients at high risk to progress to SD – the group of patients that is more likely to benefit from antiviral treatment – can also guide patient selection and endpoint measurements in clinical trials aimed at evaluating emerging anti-DENV drugs, such as those our laboratory and others have been developing [60,61]. Once antiviral agents are approved, such an assay can guide treatment decisions.
Translational Challenges Translating biomarkers of dengue severity into a prognostic assay presents multiple challenges. One such challenge is related to the definition of SD. While the 1997 WHO criteria define SD specifically as DHF/DSS [14], a condition resulting from vascular leak [9], the 2009 WHO criteria intentionally define SD more broadly and include organ damage as one criterion [4]. Given the variable presentations of SD, it is important to define disease severity consistently [62] and ideally analyze the data based on the two WHO classification methods, as reported [37,63]. Moreover, since these presentations may represent distinct syndromes, it is important to validate predictive biomarkers in all the SD categories including the less common cases of SD caused by mechanisms that may not be linked to vascular leak [15]. Also challenging is the need to validate candidate biomarkers broadly. Since disease severity appears to vary with genetic background [7], it is critical to validate host-based biomarkers in independent cohorts representing diverse populations. Furthermore, given the altered host responses to DENV documented in children and pregnant women, validation of host-based biomarkers has to include these specific populations [64–66]. 6
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Additional challenges involve the availability of a suitable technology to facilitate the development of a prognostic assay for use in resource-limited environments. The ideal setting for such an assay is an emergency room or a clinic, where patients who present with a dengue compatible illness and are diagnosed with dengue via a rapid test, qualify for prognostic testing. In order to guide clinical management, such an assay thus has to have a fast turnaround time (1–2 h) and a sample-to-answer workflow (Table 1). Ideally, to avoid delays due to repeat sampling, the assay should utilize the initial diagnostic sample or simultaneously both establish the diagnosis of DENV infection and assess the risk for progression to SD. It also has to enable testing individual samples rather than relying on sample batching. Importantly, such an assay has to be affordable and relatively easy to deploy in the developing world. Additional requirements are relevant to specific biomarkers. For example, translation of the 20-gene set predictive of SD requires a platform capable of quantitatively measuring the level of 20 genes in whole blood samples and ideally incorporating an integral, rapid RNA extraction step (Table 1). Various platforms, such as the Biomark (Fluidigm) and nCounter (Nanostring) represent useful research tools that meet many of these criteria [37,67,68]. Nevertheless, their current format is not designed for the development of a sample-to-answer assay. A few platforms have been developed that enable multiplexing of a large number of genes based on quantitative reverse transcription (qRT)-PCR within just a few hours, such as the BioFire FilmArray, QIAstat-Dx, and Biomeme [69–71]. While these technologies are emerging as promising diagnostic tools in various clinical syndromes in the developed world [72], the high manufacturing cost currently challenges their use in more resource-limited settings. Public–private partnerships such as those facilitating the use of Cepheid’s GeneXpert platform for various infectious disease indications in the developing world [73,74] provide examples for how such technologies can be implemented in resource-limited setting until they become affordable. Similarly, while a few technologies have been developed recently for quantitative multiplex measurement of antibodies [75,76], their capability to support the development of a sample-to-answer assay format in a clinical setting needs be established. Measuring the level of cell type-specific candidate biomarkers as those discovered via single-cell transcriptomics will require combining technologies for isolation of specific cell types (e.g., monocytes or B cells), such as magnetic beads, and RT-PCR or using technologies that integrate both functions [77]. Quantitating serum biomarkers by conventional ELISA may also be problematic, as it is time consuming and requires specialized laboratory equipment and expertise to carry out. Lastly, studying blood biomarkers via LC-MS can have a fast turnaround but relies on having access to the device, which may not be feasible in some settings.
Outstanding Questions What is the predictive power of some of these biomarkers in large, heterogeneous patient populations with different ages, host genetic factors, virus strains, and prior exposures? What is the optimal cutoff for distinguishing a severe from uncompli -cated dengue course with these biomarkers? Would combining various biomarkers increase the sensitivity and/or specificity to predict progression to SD relative to individual biomarkers? Are current technologies capable to support the development of a sample-to-answer assay format for predicting progression to severe dengue in resource-limited clinical settings? Would the utilization of sample-toanswer prognostic assays for dengue progression improve patient care and allocation of health care resources? What are the roles of these biomarkers in the pathogenesis of SD?
Concluding Remarks and Future Perspectives Taken together, several candidate biomarkers that are predictive of progression to SD early in the disease course have been identified recently, representing an important breakthrough in the field. Some of these biomarkers are currently undergoing further validation in larger prospective cohorts. While several important challenges have to be addressed (see Outstanding Questions), biomarkers found to be generalizable across independent cohorts could be potentially utilized for the development of the first prognostic assay for use in dengue endemic countries. Such an assay has the potential to both improve patient care and allocate healthcare resources for dengue more effectively. Acknowledgments This research was supported by a Catalyst Award from the Dr Ralph and Marian Falk Medical Research Trust and by grants from Department of Defense (DoD)/ Congressionally Directed Medical Research Programs (CDMRP) (W81XWH-16-1-0691) and Defense Threat Reduction Agency (DTRA) (HDTRA11810039) to S.E. M.R. was supported by the Stanford Advanced Residency Training at Stanford (ARTS) Fellowship Program. The authors acknowledge all the contributions in the field that could not be included in this review.
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