1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Bioinformatics investigation of human genes expression to bacterial meningitis pathogenesis
Paper ID : 1241-ICB10
Authors:
Hossein Sahragard *1, mahlagha cheraghi1, amirreza Hooshmand2, behin omidi1
1Department of Biology, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Cell and Molecular Sciences, Faculty of Advanced Science & Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
Abstract:
Introduction

The most common bacteria that cause bacterial meningitis are Neisseria meningitis, Streptococcus pneumonia, Listeria monocytogenes, Haemophilus influenzae, and Escherichia coli. Meningitis, pneumonia, and sepsis are all life-threatening symptoms of Streptococcus pneumonia infection, which is one of the most common bacterial causes of morbidity and mortality worldwide. (1, 2)Bacteria enter the CSF in the subarachnoid space via crossing the blood–CNS barrier (either the blood-brain barrier through the brain parenchymal microvasculature or the blood–CSF barrier through the choroid plexus or the pial or arachnoidal microvasculature) or from a nearby infection site. (3) The goal of this study was to figure out which genomic pathways and hub genes were turned on during bacterial meningitis.

Method and material

GSE40586, a microarray dataset, was obtained from the Gene Expression Omnibus (GEO) There are 22 bacterial meningitis samples and 18 control samples in this collection. The transcriptome analysis console (TAC) was used then utilized to normalize and assess the genes with differential expression (DEGs). DEGs between normal and BM samples were chosen based on adjusted p-value (FDR) -< 0.05 and |log2 FC|>-2. Protein-protein interaction (PPI) and visualization were created using String, Cytoscape, and Gephi, respectively.
Results

DEGs were obtained for 624 genes (316 upregulated, 308 downregulate). Our research identified five hub genes, namely, IL CD4 (T-cell surface glycoprotein cd4), CD8A (T-cell surface glycoprotein cd8 alpha chain), KCNA3 (potassium voltage-gated channel subfamily A member), ITGAM (integrin alpha-M), and LCK (Tyrosine-protein kinase Lck). Furthermore, the results of the KEGG pathway analysis revealed that these genes were enriched in significant pathways Th1 and Th2 cell differentiation, T cell receptor signaling pathway, and Hematopoietic cell lineage.

Conclusions
In conclusion, the findings of this study may aid in the development of new targets for medication discovery and therapy of bacterial effects
Keywords:
Keywords Systems biology, Bioinformatics, Gene network analysis, biomarker
Status : Paper Accepted (Poster Presentation)