Health monitoring (HM) of laboratory rodents is essential for quality assurance in animal facilities. Besides pathogens, opportunists and individual study confounders, there is profound evidence, that also commensals and the microbiota composition influences research data. Consequently, microbiome characterization should be included in HM strategies to increase the scientific validity of projects. However, these analyses are, until now, not part of routine programs. Therefore, the aim of this project is to establish an innovative HM methodology, which involves the metagenome analyses of the exhaust air dust (EAD) of individually ventilated cage systems.
To this purpose, 23 EAD-filters were analyzed by quantitative Real Time Polymerase Chain Reaction (qPCR) and Next-Generation-Sequencing (NGS) and the diagnostic sensitivity and specificity were calculated. Filters spiked with defined bacterial suspensions as well as EAD field samples from existing colonies were used as positive and negative controls. Furthermore, a full microbiome profiling was performed comparing EAD-filters and feces as a sample matrix.
Test accuracy of NGS was comparable to the qPCR used as a gold standard. While most agents were successfully detected based on the relative number of read counts, precise diagnostics of Staphylococcus aureus and Klebsiella oxytoca required using specific genome coverage ratios as thresholds to increase the diagnostic specificity.
As expected, microbiome analyses of EAD-filters and feces show distinct alpha-diversity of bacterial communities, whereas both sample matrices were suitable to detect differences in beta-diversity between separate units.
Altogether, the establishment of this innovative HM approach contributes to the 6R concept, which addresses besides animal welfare also the scientific validity of research.