Transcriptome Mining to Identify Molecular Markers for the Diagnosis of Staphylococcus epidermidis Bloodstream Infections

Abstract

Bloodstream infections caused by Staphylococcus epidermidis are often misdiagnosed since no diagnostic marker found so far can unequivocally discriminate “true” infection from sample contamination. While attempts have been made to find genomic and/or phenotypic differences between invasive and commensal isolates, possible changes in the transcriptome of these isolates under in vivo-mimicking conditions have not been investigated. Herein, we characterized the transcriptome, by RNA sequencing, of three clinical and three commensal isolates after 2 h of exposure to whole human blood. Bioinformatics analysis was used to rank the genes with the highest potential to distinguish invasive from commensal isolates and among the ten genes identified as candidates, the gene SERP2441 showed the highest potential. A collection of 56 clinical and commensal isolates was then used to validate, by quantitative PCR, the discriminative power of the selected genes. A significant variation was observed among isolates, and the discriminative power of the selected genes was lost, undermining their potential use as markers. Nevertheless, future studies should include an RNA sequencing characterization of a larger collection of isolates, as well as a wider range of conditions to increase the chances of finding further candidate markers for the diagnosis of bloodstream infections caused by S. epidermidis.