The aim of present study was to describe the genetic pathways activated during the community acquired bacterial meningitis (BM) by using genome-wide RNA expression profiling combined with functional annotation of transcriptional changes. We included 21 patients with BM hospitalized in 2008. The control group consisted of 18 healthy subjects. The RNA was extracted from whole blood, globin mRNA was depleted and gene expression profiling was performed with GeneChip Human Gene 1.0 ST Arrays enabling the analysis of 28,869 genes. Gene expression profile data were analyzed using Bioconductor packages and Bayesian modeling. Functional annotation of the enriched gene sets was used to define changed genetic networks. We also analyzed if the gene expression profile depends on the clinical course and outcome. In order to verify the genechip results, we chose ten functionally relevant genes with high statistical significance (CD177, IL1R2, IL18R1, IL18RAP, OLFM4, TLR5, CPA3, FCER1A, IL5RA, IL7R) and performed quantitative real-time (qRT) PCR.We identified the significant differences at p values of <0.05 in 8569 genes and after False Discovery Rate (FDR) correction, total of 5500 genes remained significant at p value of <0.01. Quantitative RT-PCR confirmed differential expression for selected genes. Functional annotation and network analysis indicated that most of the genes were related to activation of humoral and cellular immune responses (enrichment score 43). Those changes were found in adults and in children with BM compared to the healthy controls. Gene expression profile didn’t depend on the clinical outcome, but there was very strong influence by the type of the pathogen. This study demonstrates a strong functional genomic evidence of the over-active immune response during bacterial meningitis. This hyperactive response possibly explains the complicated clinical course of this disease.

22 bacterial meningitis patients and 18 healthy controls

Bacterial meningitis (2021 ICD-10-CM code* = G00.9)

Images were processed using the Affymetrix Microarray Suite 5.0, cel files were normalized with RMA and further analyzed using Bioconductor software based on R language.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40586