Differential Analysis of Longitudinal Methicillin-Resistant Staphylococcus aureus Colonization in Relation to Microbial Shifts in the Nasal Microbiome of Neonatal Piglets.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important human pathogen and often colonizes pigs. To lower the risk of MRSA transmission to humans, a reduction of MRSA prevalence and/or load in pig farms is needed. The nasal microbiome contains commensal species that may protect against MRSA colonization and may be used to develop competitive exclusion strategies. To obtain a comprehensive understanding of the species that compete with MRSA in the developing porcine nasal microbiome, and the moment of MRSA colonization, we analyzed nasal swabs from piglets in two litters. The swabs were taken longitudinally, starting directly after birth until 6 weeks. Both 16S rRNA and gene sequencing data with different phylogenetic resolutions and complementary culture-based and quantitative real-time PCR (qPCR)-based MRSA quantification data were collected. We employed a compositionally aware bioinformatics approach (CoDaSeq + rmcorr) for analysis of longitudinal measurements of the nasal microbiota. The richness and diversity in the developing nasal microbiota increased over time, albeit with a reduction of and , and an increase of . Coabundant groups (CAGs) of species showing strong positive and negative correlation with colonization of MRSA and S. aureus were identified. Combining 16S rRNA and gene sequencing provided greater Staphylococcus species resolution, which is necessary to inform strategies with potential protective effects against MRSA colonization in pigs. The large reservoir of methicillin-resistant Staphylococcus aureus (MRSA) in pig farms imposes a significant zoonotic risk. An effective strategy to reduce MRSA colonization in pig farms is competitive exclusion whereby MRSA colonization can be reduced by the action of competing bacterial species. We complemented 16S rRNA gene sequencing with Staphylococcus-specific gene sequencing to identify species anticorrelating with MRSA colonization. This approach allowed us to elucidate microbiome dynamics and identify species that are negatively and positively associated with MRSA, potentially suggesting a route for its competitive exclusion.