Towards Predicting Progression to Severe Dengue

The Development of New Primer Sets for the Amplification and Sequencing of the Envelope Gene of All Dengue Virus Serotypes

Dengue virus (DENV) poses a significant threat to global health, infecting approximately 390 million people annually. This virus comprises four serotypes capable of causing severe disease. Genetic analyses are crucial for understanding the epidemiology, evolution, and spread of DENV. Although previous studies have focused on the envelope protein-coding (E) gene, only a few primers can efficiently detect and amplify the viral genes from multiple endemic countries simultaneously. In this study, we designed degenerate primer pairs for each DENV serotype to amplify and sequence the entire E gene, using globally representative sequences for each serotype. These primers were validated using DENV isolates from various Asian countries and demonstrated broad-spectrum detection capabilities and high-quality sequences. The primers provide effective tools for genetic analysis in the regions affected by dengue, aiding strain identification and epidemiological studies during outbreaks.

Characterization of soluble TLR2 and CD14 levels during acute dengue virus infection

Dengue virus infection results in a broad spectrum of diseases ranging from mild dengue fever (DF) to severe dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Hitherto, there is no consensus biomarker for the prediction of severe dengue disease in patients. Yet, early identification of patients who progress to severe dengue is pivotal for better clinical management. We have recently reported that an increased frequency of classical (CD14 ⁺⁺CD16^-) monocytes with sustained high TLR2 expression in acutely infected dengue patients correlates with severe dengue development. Here, we hypothesized that the relatively lower TLR2 and CD14 expression in mild dengue patients is due to the shedding of their soluble forms (sTLR2 and sCD14) and that these could be used as indicators of disease progression. Therefore, using commercial sandwich ELISAs, we evaluated the release of sTLR2 and sCD14 by peripheral blood mononuclear cells (PBMCs) in response to in vitro dengue virus (DENV) infection and assessed their levels in acute-phase plasma of 109 dengue patients. We show that while both sTLR2 and sCD14 are released by PBMCs in response to DENV infection in vitro, their co-circulation in an acute phase of the disease is not always apparent. In fact, sTLR2 was found only in 20% of patients irrespective of disease status. In contrast, sCD14 levels were detected in all patients and were significantly elevated in DF patients when compared to DHF patients and age-matched healthy donors. Altogether, our results suggest that sCD14 may help in identifying patients at risk of severe dengue at hospital admittance.

Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue

BACKGROUND

Dengue is the most common vector-borne viral disease worldwide. Most cases are mild, but some evolve into severe dengue (SD), with high lethality. Therefore, it is important to identify biomarkers of severe disease to improve outcomes and judiciously utilize resources.

METHODS/PRINCIPAL FINDINGS

One hundred forty-five confirmed dengue cases (median age, 42; range <1-91 years), enrolled from February 2018 to March 2020, were selected from an ongoing study of suspected arboviral infections in metropolitan Asunción, Paraguay. Cases included dengue virus types 1, 2, and 4, and severity was categorized according to the 2009 World Health Organization guidelines. Testing for anti-dengue virus IgM and IgG and serum biomarkers (lipopolysaccharide binding protein and chymase) was performed on acute-phase sera in plate-based ELISAs; in addition, a multiplex ELISA platform was used to measure anti-dengue virus and anti-Zika virus IgM and IgG. Complete blood counts and chemistries were performed at the discretion of the care team. Age, gender, and pre-existing comorbidities were associated with SD vs. dengue with/without warning signs in logistic regression with odds ratios (ORs) of 1.07 (per year; 95% confidence interval, 1.03, 1.11), 0.20 (female; 0.05,0.77), and 2.09 (presence; 1.26, 3.48) respectively. In binary logistic regression, for every unit increase in anti-DENV IgG in the multiplex platform, odds of SD increased by 2.54 (1.19-5.42). Platelet count, lymphocyte percent, and elevated chymase were associated with SD in a combined logistic regression model with ORs of 0.99 (1,000/μL; 0.98,0.999), 0.92 (%; 0.86,0.98), and 1.17 (mg/mL; 1.03,1.33) respectively.

CONCLUSIONS

Multiple, readily available factors were associated with SD in this population. These findings will aid in the early detection of potentially severe dengue cases and inform the development of new prognostics for use in acute-phase and serial samples from dengue cases.

World Dengue Day: A call for action

Commemorating the 2021 ASEAN Dengue Day and advocacy for World Dengue Day, the International Society for Neglected Tropical Diseases (ISNTD) and Asian Dengue Voice and Action (ADVA) Group jointly hosted the ISNTD-ADVA World Dengue Day Forum–Cross Sector Synergies in June 2021. The forum aimed to achieve international and multisectoral coordination to consolidate global dengue control and prevention efforts, share best practices and resources, and improve global preparedness. The forum featured experts around the world who shared their insight, research experience, and strategies to tackle the growing threat of dengue. Over 2,000 healthcare care professionals, researchers, epidemiologists, and policy makers from 59 countries attended the forum, highlighting the urgency for integrated, multisectoral collaboration between health, environment, education, and policy to continue the march against dengue. Sustained vector control, environmental management, surveillance improved case management, continuous vaccine advocacy and research, capacity building, political commitment, and community engagement are crucial components of dengue control. A coordinated strategy based on science, transparency, timely and credible communication, and understanding of human behavior is needed to overcome vaccine hesitancy, a major health risk further magnified by the COVID-19 pandemic. The forum announced a strong call to action to establish World Dengue Day to improve global awareness, share best practices, and prioritize preparedness in the fight against dengue.

The growing threat of dengue poses a significant public health burden endangering the well-being and socioeconomic development of many countries across the world. The International Society for Neglected Tropical Diseases (ISNTD) and Asian Dengue Voice and Action (ADVA) group brought together the collaborative efforts of healthcare care professionals, researchers, epidemiologists, environmentalists, and policy makers to coordinate international dengue control strategy. A strong call to action to establish a World Dengue Day was announced to improve global awareness, share best practices, and prioritize preparedness in the fight against dengue. The World Dengue Day highlighted the need for all governments, healthcare professionals, civil societies, public and private sectors, schools and universities, and citizens in dengue-endemic countries to form a strong coalition to encourage and accelerate a collective dengue control response.

An 8-gene machine learning model improves clinical prediction of severe dengue progression

BACKGROUND

Each year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.

METHODS

We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.

RESULTS

We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.

CONCLUSIONS

The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning

Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.

Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes

Background

Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of severe/moderate dengue (S/MD).

Methods

We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included.

Results

On days 1-3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults.

Conclusions

Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients.

Funding

This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation.

In vitro Studies and Clinical Observations Imply a Synergistic Effect Between Epstein-Barr Virus and Dengue Virus Infection

Dengue virus (DENV) infection can lead to a complex spectrum of clinical outcomes, ranging from asymptomatic infection to life-threatening severe dengue. The reasons for thus drastically varying manifestations of the disease remain an enigma. Herein, we reported an original discovery of the synergistic effect between preexisting Epstein-Barr virus (EBV) infection and DENV superinfection in vitro and of a strong correlation of these two viruses in the clinical samples from dengue patients. We showed that (I) DENV-2 infection of an EBV-positive cell line (EBV + Akata cell) reactivated EBV, and it could be blocked by wortmannin treatment. (II) Examination of human peripheral blood mononuclear cell (PBMC) samples from dengue patients revealed significantly elevated cell-associated EBV DNA copy number at the time of hospitalization vs. at the time of disease recovery in most individuals. (III) EBV infection promoted DENV propagation in both EBV-hosting B cells and indirectly in THP-1 cells, supported by the following evidence: (A) EBV + Akata cells were more permissive to DENV-2 infection compared with Akata cells harboring no EBV virus (EBV- Akata cells). (B) Low-molecular weight fraction secreted from EBV + Akata cells could enhance DENV-2 propagation in monocytic THP-1 cells. (C) While reactivation of EBV in EBV + Akata cells further increased DENV-2 yield from this cell line, pharmacological inhibition of EBV replication by acyclovir had the opposite effect. To our knowledge, this is the first investigation demonstrating a positive correlation between EBV and DENV in vitro and in human biospecimens.