Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease

Genetic Determinants of Phosphate Response in Drosophila

Phosphate is required for many important cellular processes and having too little phosphate or too much can cause disease and reduce life span in humans. However, the mechanisms underlying homeostatic control of extracellular phosphate levels and cellular effects of phosphate are poorly understood. Here, we establish Drosophila melanogaster as a model system for the study of phosphate effects. We found that Drosophila larval development depends on the availability of phosphate in the medium. Conversely, life span is reduced when adult flies are cultured on high phosphate medium or when hemolymph phosphate is increased in flies with impaired Malpighian tubules. In addition, RNAi-mediated inhibition of MAPK-signaling by knockdown of Ras85D, phl/D-Raf or Dsor1/MEK affects larval development, adult life span and hemolymph phosphate, suggesting that some in vivo effects involve activation of this signaling pathway by phosphate. To identify novel genetic determinants of phosphate responses, we used Drosophila hemocyte-like cultured cells (S2R+) to perform a genome-wide RNAi screen using MAPK activation as the readout. We identified a number of candidate genes potentially important for the cellular response to phosphate. Evaluation of 51 genes in live flies revealed some that affect larval development, adult life span and hemolymph phosphate levels

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

A schematic map depicting the interactions of the 12 proteins/genes studied herein, known or indicated to be relevant to the viral innate immune response pathway, including for dengue.

<p>A schematic map depicting the interactions of the 12 proteins/genes studied herein, known or indicated to be relevant to the viral innate immune response pathway, including for dengue.</p

SVM optimization.

<p>Optimization of the parameters <i>C</i> and γ of the SVM kernel RBF: only <i>C</i> values of 0.01, 0.10, 1.0, 10.0 and 100.0, and γ value of 1.0 are shown.</p

Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value = 1.0 and <i>C</i> value = 10) was utilized for model building.

<p>Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value = 1.0 and <i>C</i> value = 10) was utilized for model building.</p

Heatmap for gene expression data of the 12 genes (columns) studied from the 28 patients (rows).

<p>The first 15 are DF patients, while the rest are DHF patients. The DF/ND and DHF/ND gene expression values from qPCR were used to create the heatmap. The colour shades are associated with the values in the cells: green for ratio of DF/ND and DHF/ND of <1 (down-regulated) and red for DF/ND and DHF/ND ratio of > = 1 (up-regulated). The gene expression data for IFN-β of one of the patients (23) was not available and therefore the vector attributes of this gene for the patient were represented as blank.</p