IL-10 and socs3 Are Predictive Biomarkers of Dengue Hemorrhagic Fever.
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ABSTRACT:
SUBMITTER: Flores-Mendoza LK
PROVIDER: S-EPMC5554562 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:Dengue serotype 3 viruses were isolated from patients in Brazil from 2002 through 2004. On the basis of phylogenetic analyses, these isolates were assigned genotype 1. This genotype had never been reported in South America before. Its appearance indicates a major risk factor for dengue epidemics and severe disease.
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Project description:BackgroundGenetic risk factors for dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) and dengue fever (DF) are limited, in particular there are sparse data on genetic risk across diverse populations.MethodsWe conducted a genome-wide association study (GWAS) in a derivation and validation sample of 7, 460 participants of Latin American, South Asian, and South East Asian ancestries. We then developed a weighted polygenic risk score (PRS) for each participant in each of the validation cohorts of the three ancestries to predict the risk of DHF/DSS compared to DF, DHF/DSS compared to controls, and, DF compared to controls.FindingsThe risk of DHF/DSS was significantly increased, odds ratio [OR] 1.84 (95%CI 1.47 to 2.31) (195 SNPs), compared to DF, fourth PRS quartile versus first quartile, in the validation cohort. The risk of DHF/DSS compared to controls was increased (OR=3.94; 95% CI 2.84 to 5.45) (278 SNPs), as was the risk of DF compared to controls (OR=1.97; 95%CI 1.63 to 2.39) (251 SNPs). Risk increased in a dose-dependent manner with increase in quartiles of PRS across comparisons. Significant associations persisted for PRS built within ancestries and applied to the same or different ancestries as well as for PRS built for one outcome (DHF/DSS or DF) and applied to the other.InterpretationThere is a strong genetic effect that predisposes to risk of DHF/DSS and DF. The genetic risk for DHF/DSS is higher than that for DF when compared to controls, and this effect persists across multiple ancestries.
| S-EPMC6940652 | biostudies-literature
Project description:BackgroundMost patients with dengue experience mild disease, dengue fever (DF), while few develop the life-threatening diseases dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). No laboratory tests predict DHF or DSS. We evaluated whether the serum chymase level can predict DHF or DSS in adult and pediatric patients and the influence of preexisting conditions (PECs) on chymase levels.MethodsSerum chymase levels were measured in patients presenting with undifferentiated fever to hospitals in Colombo District, Sri Lanka. The value of serum the chymase concentration and clinical signs and symptoms as predictors of DHF and/or DSS was evaluated by multivariate analysis. We assessed the influence of age, PECs, and day after fever onset on the robustness of the chymase level as a biomarker for DHF and/or DSS.ResultsAn elevated chymase level in acute phase blood samples was highly indicative of later diagnosis of DHF or DSS for pediatric and adult patients with dengue. No recorded PECs prevented an increase in the chymase level during DHF. However, certain PECs (obesity and cardiac or lung-associated diseases) resulted in a concomitant increase in chymase levels among adult patients with DHF.ConclusionsThese results show that patients with acute dengue who present with high levels of serum chymase consistently are at greater risk of DHF. The chymase level is a robust prognostic biomarker of severe dengue for adult and pediatric patients.
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Project description:Dengue fever (DF) and dengue hemorrhagic fever (DHF) are recurrent diseases that are widespread in the tropics. Here, we identified candidate genes associated with these diseases by performing integrated analyses of DF (GSE51808) and DHF (GSE18090) microarray datasets in the Gene Expression Omnibus (GEO). In all, we identified 7635 differentially expressed genes (DEGs) in DF and 8147 DEGs in DHF as compared to healthy controls (P < 0.05). In addition, we discovered 215 differentially expressed long non-coding RNAs (DElncRNAs) in DF and 225 DElncRNAs in DHF. There were 1256 common DEGs and eight common DElncRNAs in DHF vs DF, DHF vs normal control, and DF vs normal control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that signal transduction (false discovery rate = 8.33E-10), 'toxoplasmosis', and 'protein processing in endoplasmic reticulum' were significantly enriched pathways for common DEGs. We conclude that the MAGED1,STAT1, and IL12A genes may play crucial roles in DF and DHF, and suggest that our findings may facilitate the identification of biomarkers and the development of new drug design strategies for DF and DHF treatment.
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Project description:To identify genes associated with the clinical presentation of dengue, 50 cases of probable or possible dengue hemorrhagic fever (DHF), 236 dengue fever (DF), and 236 asymptomatic infections were genotyped for 593 single-nucleotide polymorphisms (SNPs) in 56 genes across the type 1 interferon (IFN) response pathway as well as other important candidate genes. By single locus analysis comparing DHF with DF, 11 of the 51 markers with P<0.05 were in the JAK1 gene. Five markers were significantly associated by false discovery rate criteria (q<0.20 when P<6 × 10(-4)). The JAK1 SNPs showed differential distribution by ethnicity and ancestry consistent with epidemiologic observations in the Americas. The association remained significant after controlling for ancestry and income. No association was observed with markers in the gene encoding CD209 (DC-SIGN). An association between DHF and JAK1 polymorphisms is in agreement with expression profiles showing generalized decreased type 1 IFN-stimulated gene expression in these patients.
| S-EPMC2950898 | biostudies-literature
Project description:BACKGROUND: Dengue virus (DENV) infection is the most common arthropod-borne viral disease in man and there are approximately 100 million infections annually. Despite the global burden of DENV infections many important questions regarding DENV pathogenesis remain unaddressed due to the lack of appropriate animal models of infection and disease. A major problem is the fact that no non-human species naturally develop disease similar to human dengue fever (DF) or dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Apart from other risk factors for severe dengue such as host genetics and secondary infection with a heterologous DENV, virus virulence is a risk factor that is not well characterized. RESULTS: Three clinical DENV-1 isolates from Cambodian patients experiencing the various forms of dengue disease (DF, DHF, and DSS) were inoculated in BALB/c mice at three different concentrations. The DENV-1 isolates had different organ and cell tropism and replication kinetics. The DENV-1 isolate from a DSS patient infected the largest number of mice and was primarily neurotropic. In contrast, the DENV-1 isolates from milder clinical dengue cases infected predominantly lungs and liver, and to a lesser extent brain. In addition, infection with the DENV isolate derived from a DSS patient persisted for more than two weeks in a majority of mice compared to the other DENV-1 isolates that peaked during the first week. CONCLUSIONS: These results confirm the in vitro findings of the same DENV-1 isolates, that showed that the isolate derived from a DSS patient can be distinguished based on phenotypic characteristics that differ from the isolates derived from a DF and DHF case 1. We observed in this study that the DSS virus isolate persist longer in vivo with extensive neuroinvasion in contrast to the other DENV-1 isolates originating in milder human cases. Genomic characterization of the three clinical isolates identified six amino acid substitutions unique for the DSS isolates that were located both in structural genes (M and E) and in non-structural genes (NS1, NS3, and NS5). The characterization of these clinically distinct DENV-1 isolates highlight that DENVs within the same genotype may have different in vivo phenotypes. HIGHLIGHTS: • Clinical DENV-1 isolates have different organ tropism in BALB/c mice.• The isolate from a DSS patient is primarily neurotropic compared to the other isolates.• The DENV-1 isolates have different in vivo replication kinetics.• The isolate from a DSS patient persists longer compared to the other isolates.• These phenotypic differences confirm our earlier in vitro findings with the same DENV-1 isolates. Thus, DENVs within the same serotype and genotype may differ enough to affect clinical conditions in vivo.
| S-EPMC3170302 | biostudies-literature
Project description:BACKGROUND: Dengue is the most prevalent mosquito-borne virus, and potentially fatal dengue hemorrhagic fever (DHF) occurs mainly in secondary infections. It recently was hypothesized that, due to the presence of cross-immunity, the relationship between the incidence of DHF and transmission intensity may be negative at areas of intense transmission. We tested this hypothesis empirically, using vector abundance as a surrogate of transmission intensity. METHODOLOGY/PRINCIPAL FINDINGS: House Index (HI), which is defined as the percentage of households infested with vector larvae/pupae, was obtained from surveys conducted on one million houses in Thailand, between 2002 and 2004. First, the utility of HI as a surrogate of transmission intensity was confirmed because HI was correlated negatively with mean age of DHF in the population. Next, the relationship between DHF incidence and HI was investigated. DHF incidence increased only up to an HI of about 30, but declined thereafter. Reduction of HI from the currently maximal level to 30 would increase the incidence by more than 40%. Simulations, which implemented a recently proposed model for cross-immunity, generated results that resembled actual epidemiological data. It was predicted that cross-immunity generates a wide variation in incidence, thereby obscuring the relationship between incidence and transmission intensity. The relationship would become obvious only if data collected over a long duration (e.g., >10 years) was averaged. CONCLUSION: The negative relationship between DHF incidence and dengue transmission intensity implies that in regions of intense transmission, insufficient reduction of vector abundance may increase long-term DHF incidence. Further studies of a duration much longer than the present study, are warranted.
| S-EPMC2442222 | biostudies-other
Project description:BackgroundCreating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.MethodsThis scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.ResultsA variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.ConclusionsDHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.
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Project description: Not available
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Project description:Dengue hemorrhagic fever (DHF) is a potentially fatal manifestation of an infection with the mosquito-borne dengue virus. Because of the social and economic costs of DHF, many countries in Asia and South America have initiated public health measures aimed at vector control. Despite these measures, DHF incidence rates do not appear to be declining. The effectiveness of vector control in reducing dengue transmissibility has thereby been questioned. Here, we revisit this conclusion using epidemiological data from Thailand. We first show, with age incidence data, that dengue transmission rates have fallen since 1981; surprisingly, however, these declines are not associated with decreases in DHF incidence. Instead, district-level analyses indicate a nonmonotonic relationship between the basic reproductive number R0 and DHF incidence. To understand this relationship, we formulated three mathematical models, which differ in their assumptions of transient between-serotype cross-protection. Unlike the first two models, the previously unconsidered third model with clinical cross-protection can reproduce this nonmonotonic relationship. Simulation of this model with nonstationary R0 reproduces several previously unexplained patterns of dengue dynamics, including a transition from a approximately 2-year cycle to a approximately 4-year cycle and a transient trough in DHF incidence in provinces with rapid R0 declines. These results imply that DHF incidence can be effectively controlled with a sufficiently large reduction in R0 but that moderate reductions may be counterproductive. More broadly, these results show that assuming parameter stationarity in systems with approximate stationarity in disease incidence is unjustified and may result in missed opportunities to understand the drivers of disease variability.
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