Molecular classification of outcomes from dengue virus -3 infections

The association between diabetes and obesity with Dengue infections

Dengue, an arboviral disease is a global threat to public health as the number of Dengue cases increases through the decades and this trend is predicted to continue. Non-communicable diseases such as diabetes and obesity are also on an upward trend. Moreover, past clinical studies have shown comorbidities worsen the clinical manifestation of especially Severe Dengue. However, discussion regarding the underlying mechanisms regarding the association between these comorbidities and dengue are lacking. The hallmark of Severe Dengue is plasma leakage which is due to several factors including presence of pro-inflammatory cytokines and dysregulation of endothelial barrier protein expression. The key factors of diabetes affecting endothelial functions are Th1 skewed responses and junctional-related proteins expression. Additionally, obesity alters the lipid metabolism and immune response causing increased viral replication and inflammation. The similarity between diabetes and obesity individuals is in having chronic inflammation resulting in endothelial dysfunction. This review outlines the roles of diabetes and obesity in severe dengue and gives some insights into the plausible mechanisms of comorbidities in Severe Dengue.

Pathogenesis and virulence of flavivirus infections

The Flavivirus genus consists of >70 members including several that are considered significant human pathogens. Flaviviruses display a broad spectrum of diseases that can be roughly categorised into two phenotypes - systemic disease involving haemorrhage exemplified by dengue and yellow Fever virus, and neurological complications associated with the likes of West Nile and Zika viruses. Attempts to develop vaccines have been variably successful against some. Besides, mosquito-borne flaviviruses can be vertically transmitted in the arthropods, enabling long term persistence and the possibility of re-emergence. Therefore, developing strategies to combat disease is imperative even if vaccines become available. The cellular interactions of flaviviruses with their human hosts are key to establishing the viral lifecycle on the one hand, and activation of host immunity on the other. The latter should ideally eradicate infection, but often leads to immunopathological and neurological consequences. In this review, we use Dengue and Zika viruses to discuss what we have learned about the cellular and molecular determinants of the viral lifecycle and the accompanying immunopathology, while highlighting current knowledge gaps which need to be addressed in future studies.

Deciphering flavivirus-host interactions using quantitative proteomics

Flaviviruses are a group of important emerging and re-emerging human pathogens that cause worldwide epidemics with thousands of deaths annually. Flaviviruses are small, enveloped, positive-sense, single-stranded RNA viruses that are obligate intracellular pathogens, relying heavily on host cell machinery for productive replication. Proteomic approaches have become an increasingly powerful tool to investigate the mechanisms by which viruses interact with host proteins and manipulate cellular processes to promote infection. Here, we review recent advances in employing quantitative proteomics techniques to improve our understanding of the complex interplay between flaviviruses and host cells. We describe new findings on our understanding of how flaviviruses impact protein-protein interactions, protein-RNA interactions, protein abundance, and post-translational modifications to modulate viral infection.

Severe Dengue Prognosis Using Human Genome Data and Machine Learning

Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.

OBJECTIVE

We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data.

METHODS

One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity single nucleotide polymorphisms (SNPs). Our model uses a support vector machine algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue.

RESULTS

The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively.

CONCLUSION

The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions.

SIGNIFICANCE

Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian based and genetically influenced diseases.

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines

Background

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting.

Methods

Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 – December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated.

Results

Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model.

Conclusion

The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3066-0) contains supplementary material, which is available to authorized users.

Recent advances in the identification of the host factors involved in dengue virus replication

Dengue virus (DENV) belongs to the genus Flavivirus of the family Flaviviridae and it is primarily transmitted via Aedes aegypti and Aedes albopictus mosquitoes. The life cycle of DENV includes attachment, endocytosis, protein translation, RNA synthesis, assembly, egress, and maturation. Recent researches have indicated that a variety of host factors, including cellular proteins and microRNAs, positively or negatively regulate the DENV replication process. This review summarizes the latest findings (from 2014 to 2016) in the identification of the host factors involved in the DENV life cycle and Dengue infection.

1H Nuclear Magnetic Resonance Metabolomics of Plasma Unveils Liver Dysfunction in Dengue Patients

Dengue, due to its global burden, is the most important arthropod-borne flavivirus disease, and early detection lowers fatality rates to below 1%. Since the metabolic resources crucial for viral replication are provided by host cells, detection of changes in the metabolic profile associated with disease pathogenesis could help with the identification of markers of prognostic and diagnostic importance. We applied (1)H nuclear magnetic resonance exploratory metabolomics to study longitudinal changes in plasma metabolites in a cohort in Recife, Brazil. To gain statistical power, we used innovative paired multivariate analyses to discriminate individuals with primary and secondary infection presenting as dengue fever (DF; mild) and dengue hemorrhagic fever (DHF; severe) and subjects with a nonspecific nondengue (ND) illness (ND subjects). Our results showed that a decrease in plasma low-density lipoprotein (LDL) and very-low-density lipoprotein (VLDL) discriminated dengue virus (DENV)-infected subjects from ND subjects, and also, subjects with severe infection even presented a decrease in lipoprotein concentrations compared to the concentrations in subjects with mild infection. These results add to the ongoing discussion that the manipulation of lipid metabolism is crucial for DENV replication and infection. In addition, a decrease in plasma glutamine content was characteristic of DENV infection and disease severity, and an increase in plasma acetate levels discriminated subjects with DF and DHF from ND subjects. Several other metabolites shown to be altered in DENV infection and the implications of these alterations are discussed. We hypothesize that these changes in the plasma metabolome are suggestive of liver dysfunction, could provide insights into the underlying molecular mechanisms of dengue virus pathogenesis, and could help to discriminate individuals at risk of the development of severe infection and predict disease outcome.

IMPORTANCE

Dengue, due to its global burden, is the most important mosquito-borne viral disease. There is no specific treatment for dengue disease, and early detection lowers fatality rates to below 1%. In this study, we observed the effects of dengue virus infection on the profile of small molecules in the blood of patients with mild and severe infection. Variations in the profiles of these small molecules reflected the replication of dengue virus in different tissues and the extent of tissue damage during infection. The results of this study showed that the molecules that changed the most were VLDL, LDL, and amino acids. We propose that these changes reflect liver dysfunction and also that they can be used to discriminate subjects with mild dengue from those with severe dengue.

Genomic approaches for understanding dengue: insights from the virus, vector, and host

The incidence and geographic range of dengue have increased dramatically in recent decades. Climate change, rapid urbanization and increased global travel have facilitated the spread of both efficient mosquito vectors and the four dengue virus serotypes between population centers. At the same time, significant advances in genomics approaches have provided insights into host–pathogen interactions, immunogenetics, and viral evolution in both humans and mosquitoes. Here, we review these advances and the innovative treatment and control strategies that they are inspiring.

Discovery of Candidate Biomarkers

Properly performed, biomarker discovery can lead to effective candidates that can ultimately serve as predictors of disease, medical condition, define therapeutic parameters, and many other applications in medicine. Preferably, biomarkers comprise a panel of indicators, e.g. proteins and/or peptides that can be predictive or diagnostic of the medical condition of interest. Emphasis here is placed on "panel," as single candidates are rarely sufficient to provide the necessary sensitivity and specificity. To develop an effective panel that survives the development process described in Chap. 19 , proper experimental design and attention to important statistical parameters are critical to ensure success. Errors in discovery can lead to an inefficient use of expensive resources, as these may not be uncovered until the latter stages in biomarker development. Hence, accuracy, precision, and an estimate of the power of the proposed analyses are critical in the discovery of the panel of candidate biomarkers by proteomic methods, as is the selection of statistical approaches to refine and appropriately reduce the dataset for subsequent confirmatory assays.