Non-structural protein 1 (NS1) of dengue virus detection correlates with severity in primary but not in secondary dengue infection

Immune responses and severe dengue: what have we learned?

PURPOSE OF REVIEW

With the marked rise in dengue globally, developing well tolerated and effective vaccines and therapeutics is becoming more important. Here we discuss the recent developments in the understanding of immune mechanisms that lead to severe dengue and the learnings from the past, that can help us to find therapeutic targets, prognostic markers, and vaccines to prevent development of severe disease.

RECENT FINDINGS

The extent and duration of viraemia often appears to be associated with clinical disease severity but with some variability. However, there also appear to be significant differences in the kinetics of viraemia and nonstructural protein 1 (NS1) antigenemia and pathogenicity between different serotypes and genotypes of the DENV. These differences may have significant implications for development of treatments and in inducing robust immunity through dengue vaccines. Although generally higher levels of neutralizing antibodies are thought to protect against infection and severe disease, there have been exceptions and the specificity, breadth and functionality of the antibody responses are likely to be important.

SUMMARY

Although there have been many advances in our understanding of dengue pathogenesis, viral and host factors associated with occurrence of severe dengue, vascular leak and the immune correlates of protection remain poorly understood.

Performance of Fujifilm Dengue NS1 Antigen Rapid Diagnosis Kit Compared to Quantitative Real-Time Polymerase Chain Reaction

Dengue is a viral infection caused by the dengue virus (DENV), transmitted to humans through the bite of infected Aedes mosquitoes. About half of the world's population is now at risk of dengue, which represents a global public health concern, especially in tropical and subtropical countries. Early detection of the viral infection is crucial to manage the disease; hence, effective rapid diagnostic tests are essential. In this study, we evaluated the performance between the new Fujifilm Dengue non-structural antigen diagnosis kit (FF NS1 kit) and the SD Bioline NS1 antigen test kit (SD NS1 kit) against the quantitative real-time polymerase chain reaction (qRT-PCR) assays. The 140 acute serum samples collected from the Yangon General Hospital and Yangon Children's Hospital, Myanmar, from 2017 to 2019 were characterised by the three assays. With the qRT-PCR as the standard, the FF NS1 kit and the SD NS1 kit exhibited sensitivity of 94.3% and 88.6%, respectively, and specificity of 100% in both kits. Moreover, the positivity rates of the FF NS1 kit and the SD NS1 kit were 97.5% and 95% in primary infection and 90% and 80% in secondary infection, respectively. Our overall results suggest that the FF NS1 kit is reliable and accurate for detecting DENV infection.

Non-structural protein 1 and hematology parameters as predictors of dengue virus infection severity in Indonesia

Dengue virus infection (DVI) remains a significant health challenge, and diagnosis must still be considered. Non-structural protein 1 (NS1) is a potential marker of the dengue virus that can help diagnose DVI. The study aimed to assess the role of NS1 as a predictor of the severity of DVI. We utilized Dengue PCR-confirmed samples and employed semi-quantitative NS1Ag ELISA for NS1 examination, adhering to the World Health Organization South-East Asia Region (WHO-SEARO) 2011 criteria for DVI. We included DVI patients from Indonesia aged 1-65 years. Secondary infections had more severe clinical conditions than primary infections. Leukocyte and platelet levels had a more significant effect on NS1 positivity (6.19 (1.9-30.2); p<0.001; 190 (11-417); p=0.015; respectively). Multivariate analysis revealed leukocytes as a more significant predictor of NS1 values than platelets, with an odds ratio of 5.38 contributing to 30.5% of the NS1 value variation. The NS1 value could distinguish undifferentiated fever and dengue fever in the children group with a sensitivity of 76.0% and specificity of 87.5% (p=0.015). The number of NS1(-) in the severe dengue hemorrhagic fever (DHF) group was higher than NS1(+). DENV-4 type and primary infection were dominant in this study, although they did not significantly differ from the NS1 value. NS1 value can be used as a predictor to determine the severity of DVI in children but not in the adult group. The levels of leukocytes and platelets influenced the NS1 value.

Acute-phase Serum Cytokine Levels and Correlation with Clinical Outcomes in Children and Adults with Primary and Secondary Dengue Virus Infection in Myanmar between 2017 and 2019

The dengue virus (DENV) has been endemic in Myanmar since 1970, causing outbreaks every 2–3 years. DENV infection symptoms range from mild fever to lethal hemorrhage. Clinical biomarkers must be identified to facilitate patient risk stratification in the early stages of infection. We analyzed 45 cytokines and other factors in serum samples from the acute phase of DENV infection (within 3–5 days of symptom onset) from 167 patients in Yangon, Myanmar, between 2017 and 2019. All of the patients tested positive for serum DENV nonstructural protein 1 antigen (NS1 Ag); 78.4% and 62.9% were positive for immunoglobulin M (IgM) and G (IgG), respectively; and 18.0%, 19.8%, and 11.9% tested positive for serotypes 1, 3, and 4, respectively. Although the DENV-4 viral load was significantly higher than those of DENV-1 or DENV-3, disease severity was not associated with viral load or serotype. Significant correlations were identified between disease severity and CCL5, SCF, PDGF-BB, IL-10, and TNF-α levels; between NS1 Ag and SCF, CCL5, IFN-α, IL-1α, and IL-22 levels; between thrombocytopenia and IL-2, TNF-α, VEGF-D, and IL-6 levels; and between primary or secondary infection and IL-2, IL-6, IL-31, IL-12p70, and MIP-1β levels. These circulating factors may represent leading signatures in acute DENV infections, reflecting the clinical outcomes in the dengue endemic region, Myanmar.

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.

Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care

The dengue virus (DENV) is a vector-borne flavivirus that infects around 390 million individuals each year with 2.5 billion being in danger. Having access to testing is paramount in preventing future infections and receiving adequate treatment. Currently, there are numerous conventional methods for DENV testing, such as NS1 based antigen testing, IgM/IgG antibody testing, and Polymerase Chain Reaction (PCR). In addition, novel methods are emerging that can cut both cost and time. Such methods can be effective in rural and low-income areas throughout the world. In this paper, we discuss the structural evolution of the virus followed by a comprehensive review of current dengue detection strategies and methods that are being developed or commercialized. We also discuss the state of art biosensing technologies, evaluated their performance and outline strategies to address challenges posed by the disease. Further, we outline future guidelines for the improved usage of diagnostic tools during recurrence or future outbreaks of DENV.