Sepsis Transcriptomic Trajectory and Phenotypic Correlation Using Time Series Gene Expression from Childhood Meningococcal Infection

Sepsis remains a leading cause of in-hospital morbidity and mortality. In the acute phase sepsis is described as a dysregulated process. The objective was to track temporal transcriptomic changes in infants with meningococcal disease (MSS). Therefore, two datasets underwent secondary analysis using temporal transcriptomic data. Methods applied to analysis included Principal Component Analysis (PCA), Transcript Time Course Analysis (TTCA) and Gene Set Expression Analysis (GSEA). Gene expression clustering algorithm for both datasets suggested three phases from the time of admission, and PCA plots indicated a gene-expression trajectory for both datasets. The data set from Kwan et al., showed that 410 genes differentiated survivors from the non-survivor, which included various cytokine, TNF, and apoptosis-associated pathways (normalized expression scores = -1.60, p = 0.02, and q = 0.15). Additionally, GSEA demonstrated gene sets significantly associated with disease severity. The genes for the cytokines CLC, HFE, HLA-F, NLRP3, and TNFRSF1B were significant. The Gene-sets elicited by GSEA in the Kwan and Emonts dataset had a high degree of molecular signature crossover (89.2% overlap), although a comparison of gene-expression trajectories across both datasets was difficult due to differing sample timing. Transcriptomic analysis demonstrated temporal trajectories in MSS-related gene expression. Larger studies using transcriptome expression are required could enhance the understanding of sepsis pathogenesis, improving prognostication and enable the development of precision therapies.