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Time since onset of disease and individual clinical markers associate with transcriptional changes in uncomplicated dengue.

van de Weg CA, van den Ham HJ, Bijl MA, Anfasa F, Zaaraoui-Boutahar F, Dewi BE, Nainggolan L, van IJcken WF, Osterhaus AD, Martina BE, van Gorp EC, Andeweg AC - PLoS Negl Trop Dis (2015)

Bottom Line: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis.Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression.Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Viroscience, Erasmus Medical Center, Rotterdam, The Netherlands.

ABSTRACT

Background: Dengue virus (DENV) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system. The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters.

Methodology/principle findings: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis. We show that time since onset of disease, but not diagnosis, has a large impact on the blood transcriptome of patients with non-severe dengue. Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression. Network analysis however, indicated that the clinical markers platelet count, fibrinogen, albumin, IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response. Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Conclusions/significance: Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue. The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue. However, we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection. The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue.

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Related in: MedlinePlus

Dengue co-expression network analysis.Significant correlations between gene modules (y-axis) and clinical parameters (x-axis) are depicted in a red-to-green colour scale. Upper number is pearson correlation coefficient, lower number the level of significance (p-value). All gene modules are annotated with gene enrichment categories; clinical parameters are grouped by symptom type.
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pntd.0003522.g005: Dengue co-expression network analysis.Significant correlations between gene modules (y-axis) and clinical parameters (x-axis) are depicted in a red-to-green colour scale. Upper number is pearson correlation coefficient, lower number the level of significance (p-value). All gene modules are annotated with gene enrichment categories; clinical parameters are grouped by symptom type.

Mentions: Next, we investigated the association between the identified gene modules and clinical parameters. To this end, we used weighted gene correlation network analysis (WGCNA) that organizes genes into 25 modules that are subsequently correlated to 18 clinical parameters (Fig. 5, S3 Information). This analysis confirms that time since admission has a strong effect on the transcriptome of dengue patients and that immune-related genes dominate the early response. Significant associations between gene modules and the clinical parameters platelet count, fibrinogen level, albumin level and volume of IV fluid per day were found. Most modules that displayed a positive correlation with time after admission also did so with the quantity of IV fluid and the liver enzyme SGOT. These same modules displayed a negative association with the platelet count and levels of fibrinogen and albumin. Platelets, albumin and fibrinogen are all part of the blood compartment in which dengue targets monocytic cells to replicate [22]. The pro-inflammatory environment due to DENV replication probably affects the expression of these markers. This may explain the association of these markers with these gene modules.


Time since onset of disease and individual clinical markers associate with transcriptional changes in uncomplicated dengue.

van de Weg CA, van den Ham HJ, Bijl MA, Anfasa F, Zaaraoui-Boutahar F, Dewi BE, Nainggolan L, van IJcken WF, Osterhaus AD, Martina BE, van Gorp EC, Andeweg AC - PLoS Negl Trop Dis (2015)

Dengue co-expression network analysis.Significant correlations between gene modules (y-axis) and clinical parameters (x-axis) are depicted in a red-to-green colour scale. Upper number is pearson correlation coefficient, lower number the level of significance (p-value). All gene modules are annotated with gene enrichment categories; clinical parameters are grouped by symptom type.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358925&req=5

pntd.0003522.g005: Dengue co-expression network analysis.Significant correlations between gene modules (y-axis) and clinical parameters (x-axis) are depicted in a red-to-green colour scale. Upper number is pearson correlation coefficient, lower number the level of significance (p-value). All gene modules are annotated with gene enrichment categories; clinical parameters are grouped by symptom type.
Mentions: Next, we investigated the association between the identified gene modules and clinical parameters. To this end, we used weighted gene correlation network analysis (WGCNA) that organizes genes into 25 modules that are subsequently correlated to 18 clinical parameters (Fig. 5, S3 Information). This analysis confirms that time since admission has a strong effect on the transcriptome of dengue patients and that immune-related genes dominate the early response. Significant associations between gene modules and the clinical parameters platelet count, fibrinogen level, albumin level and volume of IV fluid per day were found. Most modules that displayed a positive correlation with time after admission also did so with the quantity of IV fluid and the liver enzyme SGOT. These same modules displayed a negative association with the platelet count and levels of fibrinogen and albumin. Platelets, albumin and fibrinogen are all part of the blood compartment in which dengue targets monocytic cells to replicate [22]. The pro-inflammatory environment due to DENV replication probably affects the expression of these markers. This may explain the association of these markers with these gene modules.

Bottom Line: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis.Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression.Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Viroscience, Erasmus Medical Center, Rotterdam, The Netherlands.

ABSTRACT

Background: Dengue virus (DENV) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system. The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters.

Methodology/principle findings: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis. We show that time since onset of disease, but not diagnosis, has a large impact on the blood transcriptome of patients with non-severe dengue. Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression. Network analysis however, indicated that the clinical markers platelet count, fibrinogen, albumin, IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response. Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Conclusions/significance: Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue. The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue. However, we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection. The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue.

Show MeSH
Related in: MedlinePlus