Extraction of High-Quality RNA from S. aureus Internalized by Endothelial Cells

GND
1237239761
Affiliation
Institute for Medical Microbiology, Jena University Hospital, 07747 Jena, Germany
Maurer, Michelle;
Affiliation
Department of Respiratory Medicine, Medizinische Hochschule Hannover, 30625 Hannover, Germany;(T.E.K.);(H.S.)
Klassert, Tilman E.;
GND
1182580505
Affiliation
Institute for Medical Microbiology, Jena University Hospital, 07747 Jena, Germany
Löffler, Bettina;
Affiliation
Department of Respiratory Medicine, Medizinische Hochschule Hannover, 30625 Hannover, Germany;(T.E.K.);(H.S.)
Slevogt, Hortense;
GND
1137342919
ORCID
0000-0002-9328-5302
Affiliation
Institute for Medical Microbiology, Jena University Hospital, 07747 Jena, Germany
Tuchscherr, Lorena

Staphylococcus aureus evades antibiotic therapy and antimicrobial defenses by entering human host cells. Bacterial transcriptomic analysis represents an invaluable tool to unravel the complex interplay between host and pathogen. Therefore, the extraction of high-quality RNA from intracellular S. aureus lays the foundation to acquire meaningful gene expression data. In this study, we present a novel and straightforward strategy to isolate RNA from internalized S. aureus after 90 min, 24 h, and 48 h postinfection. Real-time PCR data were obtained for the target genes agrA and fnba , which play major roles during infection. The commonly used reference genes gyrB , aroE , tmRNA , gmk , and hu were analyzed under different conditions: bacteria from culture (condition I), intracellular bacteria (condition II), and across both conditions I and II. The most stable reference genes were used for the normalization of agrA and fnbA . Delta C q (quantification cycle) values had a relatively low variability and thus demonstrated the high quality of the extracted RNA from intracellular S. aureus during the early phase of infection. The established protocol allows the extraction and purification of intracellular staphylococcal RNA while minimizing the amount of host RNA in the sample. This approach can leverage reproducible gene expression data to study host–pathogen interactions.

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