Dataset Information

A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children.

ABSTRACT:

Background

Severe dengue with severe plasma leakage (SD-SPL) is the most frequent of dengue severe form. Plasma biomarkers for early predictive diagnosis of SD-SPL are required in the primary clinics for the prevention of dengue death.

Methodology

Among 63 confirmed dengue pediatric patients recruited, hospital based longitudinal study detected six SD-SPL and ten dengue with warning sign (DWS). To identify the specific proteins increased or decreased in the SD-SPL plasma obtained 6-48 hours before the shock compared with the DWS, the isobaric tags for relative and absolute quantification (iTRAQ) technology was performed using four patients each group. Validation was undertaken in 6 SD-SPL and 10 DWS patients.

Principal findings

Nineteen plasma proteins exhibited significantly different relative concentrations (p<0.05), with five over-expressed and fourteen under-expressed in SD-SPL compared with DWS. The individual protein was classified to either blood coagulation, vascular regulation, cellular transport-related processes or immune response. The immunoblot quantification showed angiotensinogen and antithrombin III significantly increased in SD-SPL whole plasma of early stage compared with DWS subjects. Even using this small number of samples, antithrombin III predicted SD-SPL before shock occurrence with accuracy.

Conclusion

Proteins identified here may serve as candidate predictive markers to diagnose SD-SPL for timely clinical management. Since the number of subjects are small, so further studies are needed to confirm all these biomarkers.

SUBMITTER: Nhi DM

PROVIDER: S-EPMC4764501 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children.

Nhi Dang My DM Huy Nguyen Tien NT Ohyama Kaname K Kimura Daisuke D Lan Nguyen Thi Phuong NT Uchida Leo L Thuong Nguyen Van NV Nhon Cao Thi My CT Phuc Le Hong le H Mai Nguyen Thi NT Mizukami Shusaku S Bao Lam Quoc LQ Doan Nguyen Ngoc NN Binh Nguyen Van Thanh NV Quang Luong Chan LC Karbwang Juntra J Yui Katsuyuki K Morita Kouichi K Huong Vu Thi Que VT Hirayama Kenji K

PLoS neglected tropical diseases 20160219 2

<h4>Background</h4>Severe dengue with severe plasma leakage (SD-SPL) is the most frequent of dengue severe form. Plasma biomarkers for early predictive diagnosis of SD-SPL are required in the primary clinics for the prevention of dengue death.<h4>Methodology</h4>Among 63 confirmed dengue pediatric patients recruited, hospital based longitudinal study detected six SD-SPL and ten dengue with warning sign (DWS). To identify the specific proteins increased or decreased in the SD-SPL plasma obtained ...[more]

PMID: 26895439

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Project description:The relationship between repetitive mild traumatic brain injury (r-mTBI) and Alzheimer's disease (AD) is well-recognized. However, the precise nature of how r-mTBI leads to or precipitates AD pathogenesis is currently not understood. Plasma biomarkers potentially provide non-invasive tools for detecting neurological changes in the brain, and can reveal overlaps between long-term consequences of r-mTBI and AD. In this study we address this by generating time-dependent molecular profiles of response to r-mTBI and AD pathogenesis in mouse models using unbiased proteomic analyses. To model AD, we used the well-validated hTau and PSAPP(APP/PS1) mouse models that develop age-related tau and amyloid pathological features, respectively, and our well-established model of r-mTBI in C57BL/6 mice. Plasma were collected at different ages (3, 9, and 15 months-old for hTau and PSAPP mice), encompassing pre-, peri- and post-"onset" of the cognitive and neuropathological phenotypes, or at different timepoints after r-mTBI (24 h, 3, 6, 9, and 12 months post-injury). Liquid chromatography/mass spectrometry (LC-MS) approaches coupled with Tandem Mass Tag labeling technology were applied to develop molecular profiles of protein species that were significantly differentially expressed as a consequence of mTBI or AD. Mixed model ANOVA after Benjamini-Hochberg correction, and a stringent cut-off identified 31 proteins significantly changing in r-mTBI groups over time and, when compared with changes over time in sham mice, 13 of these were unique to the injured mice. The canonical pathways predicted to be modulated by these changes were LXR/RXR activation, production of nitric oxide and reactive oxygen species and complement systems. We identified 18 proteins significantly changing in PSAPP mice and 19 proteins in hTau mice compared to their wild-type littermates with aging. Six proteins were found to be significantly regulated in all three models, i.e., r-mTBI, hTau, and PSAPP mice compared to their controls. The top canonical pathways coincidently changing in all three models were LXR/RXR activation, and production of nitric oxide and reactive oxygen species. This work suggests potential biomarkers for TBI and AD pathogenesis and for the overlap between these two, and warrant targeted investigation in human populations. Data are available via ProteomeXchange with identifier PXD010664.

Project description:BackgroundDengue viral infection is an acute infection that has the potential to have severe complications as its major sequela. Currently, there is no routine laboratory biomarker with which to predict the severity of dengue infection or monitor the effectiveness of standard management. Hence, this meta-analysis compared biomarker levels between dengue fever (DF) and severe dengue infections (SDI) to identify potential biomarkers for SDI.MethodsData concerning levels of cytokines, chemokines, and other potential biomarkers of DF, dengue hemorrhagic fever, dengue shock syndrome, and severe dengue were obtained for patients of all ages and populations using the Scopus, PubMed, and Ovid search engines. The keywords "(IL1* or IL-1*) AND (dengue*)" were used and the same process was repeated for other potential biomarkers, according to Medical Subject Headings terms suggested by PubMed and Ovid. Meta-analysis of the mean difference in plasma or serum level of biomarkers between DF and SDI patients was performed, separated by different periods of time (days) since fever onset. Subgroup analyses comparing biomarker levels of healthy plasma and sera controls, biomarker levels of primary and secondary infection samples were also performed, as well as analyses of different levels of severity and biomarker levels upon infection by different dengue serotypes.ResultsFifty-six studies of 53 biomarkers from 3,739 dengue cases (2,021 DF and 1,728 SDI) were included in this meta-analysis. Results showed that RANTES, IL-7, IL-8, IL-10, IL-18, TGF-b, and VEGFR2 levels were significantly different between DF and SDI. IL-8, IL-10, and IL-18 levels increased during SDI (95% CI, 18.1-253.2 pg/mL, 3-13 studies, n = 177-1,909, I2 = 98.86%-99.75%). In contrast, RANTES, IL-7, TGF-b, and VEGFR2 showed a decrease in levels during SDI (95% CI, -3238.7 to -3.2 pg/mL, 1-3 studies, n = 95-418, I2 = 97.59%-99.99%). Levels of these biomarkers were also found to correlate with the severity of the dengue infection, in comparison to healthy controls. Furthermore, the results showed that IL-7, IL-8, IL-10, TGF-b, and VEGFR2 display peak differences between DF and SDI during or before the critical phase (day 4-5) of SDI.DiscussionThis meta-analysis suggests that IL-7, IL-8, IL-10, TGF-b, and VEGFR2 may be used as potential early laboratory biomarkers in the diagnosis of SDI. This can be used to predict the severity of dengue infection and to monitor the effectiveness of treatment. Nevertheless, methodological and reporting limitations must be overcome in future research to minimize variables that affect the results and to confirm the findings.

Dataset Information

A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children.

Background

Methodology

Principal findings

Conclusion

Publications

A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children.

Similar Datasets

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