Exosomal protein biomarkers for early prediction for dengue severity in pediatric population using quantitative proteomics approach

Background: Globally, the incidence of pediatric dengue has been increasing on large scale, resulting in high number of morbidity and deaths. In 2009, WHO classified dengue into three different categories i.e. dengue without warning sign, dengue with warning sign and severe dengue. The issue with the current classification is that classification is not specific for pediatric population and also the classification is so complex that sometimes it is not possible for clinicians to classify the patient dengue category on the basis of symptoms of the patient, which results in improper diagnosis and management of the dengue cases.

Material and Methods

In this study, exosomal proteomics study was performed using LC/MS technique. The study population included 48 samples (12 samples from each dengue category and 12 samples from healthy individuals) for discovery phase and 200 samples (50 samples from each dengue category and 50 samples from healthy individuals) for verification phase. Exosomes were isolated from plasma of dengue patients as were from healthy individuals for control and exosomes were characterized by using different characterization techniques such as western blotting, Nanoparticle tracking analysis, dynamic light scattering and flow cytometry. After characterization quantitative proteomic study was performed.

Results

Comparative data analysis was performed by combing the Mass-spectrometric data with different machine learning algorithms and analysis revealed the identification of 529 protein groups. Statistical approach such as student, limma, Anova were applies to identify the differentially altered significant proteins which identified 96 significant proteins. Further analysis found that 26 proteins were following the trend of sequential upregulation or downregulation in all the three defined categories of dengue with respect to control samples. Out of these 26 proteins, 12 number of proteins were found to be upregulated and 14 Number of proteins were found to be downregulated. 6 proteins were carried out for verification phase on the basis of In-silico analysis which included Gene ontology analysis and KEGG pathway analysis. On the basis of verification phase results and correlation of these results with clinical details of the patients,3 proteins are proposed as potential biomarkers for early prediction of dengue severity in pediatric population.

Conclusion

The utilization of exosomes for the diagnosis of various infectious disease has gained

significant attention due to its potential for early detection, disease progression tracking, non-invasiveness, and cost-effectiveness. After further verification of these outcomes by different approaches these markers could be potentially used for early prediction of dengue severity which could allow for reduction in morbidity and mortality of pediatric dengue patients. Also, this study could help in proper classification of Dengue cases on the basis of these potential proteomic biomarkers.