Background Previously we reported the complex nature of the transcriptome in Meningococcal sepsis (MenS) limiting the detection of a representative biomarker from a single time point for a dynamic disease process . In this study, alleviating that limitation, we utilised network methodology to the transcriptome of MenS patients obtained from multiple time points during disease progression.
Methods We applied weighted gene co expression network analysis (WGCNA)  on normalized expression data of 5 children (P1-P5) with MenS (4 confirmed and 1 clinically assumed) who had blood samples taken at admission (designated 0 hours), 4, 8, 12 and 48 hours (T1-T5). The patients had no previous, comorbidities. We extracted RNA from blood and then the samples were checked by capillary electrophoresis and spectrophotometry for quality control purposes. We conducted microarray (Human Gene 1.0 ST Arrays with 33,297 probes) gene expression experiments to capture the transcriptome profile at each time point.
Results Our initial differential gene analysis between all-time points against T1 showed significant (p < 1.0 × 10-7) up regulation of protein binding and immune response activities. Cluster map was compiled (figure 1) and then this was matched to trait data (figure 2). (Next, applying WGCNA analysis we identified 18 cluster (or modules) that have distinct topological characteristics (sizes ranging from 34–14941 to genes). For each module, we defined a quantitative measure of module membership as the correlation of the module eigengene with the gene expression profile. Modules were phenotypically stratified based on weight, age, mortality, and organ dysfunction. A heatmap plot was generated of the adjacencies in the eigengene network (figure 3). Genes from the significant modules showing high module membership were filtered and selected (p.MM ≤ 0.05). Number of Genes related to trait included 740 (mortality) 2151 (weight) and 1616 (age). Weight was correlated high (r 0.53 and p value 0.003) with the purple module (MEpurple) to weight (figure 4).
Conclusions Our analysis shows that using the time series transcriptome in MenS, novel associations can be identified that could influence future treatment options for improved outcomes.
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