The human skin harbors a diverse community of bacteria, including the Gram-positive, anaerobic bacterium Propionibacterium acnes. P. acnes has historically been linked to the pathogenesis of acne vulgaris, a common skin disease affecting over 80% of all adolescents in the US. To gain insight into potential P. acnes pathogenic mechanisms, we previously sequenced the complete genome of a P. acnes strain HL096PA1 that is highly associated with acne. In this study, we compared its genome to the first published complete genome KPA171202. HL096PA1 harbors a linear plasmid, pIMPLE-HL096PA1. This is the first described P. acnes plasmid. We also observed a five-fold increase of pseudogenes in HL096PA1, several of which encode proteins in carbohydrate transport and metabolism. In addition, our analysis revealed a few island-like genomic regions that are unique to HL096PA1 and a large genomic inversion spanning the ribosomal operons. Together, these findings offer a basis for understanding P. acnes virulent properties, host adaptation mechanisms, and its potential role in acne pathogenesis at the strain level. Furthermore, the plasmid identified in HL096PA1 may potentially provide a new opportunity for P. acnes genetic manipulation and targeted therapy against specific disease-associated strains.
The microbiome plays an important role in human physiology. The composition of the human microbiome has been described at the phylum, class, genus, and species levels, however, it is largely unknown at the strain level. The importance of strain-level differences in microbial communities has been increasingly recognized in understanding disease associations. Current methods for identifying strain populations often require deep metagenomic sequencing and a comprehensive set of reference genomes. In this study, we developed a method, metagenomic multi-locus sequence typing (MG-MLST), to determine strain-level composition in a microbial community by combining high-throughput sequencing with multi-locus sequence typing (MLST). We used a commensal bacterium, Propionibacterium acnes, as an example to test the ability of MG-MLST in identifying the strain composition. Using simulated communities, MG-MLST accurately predicted the strain populations in all samples. We further validated the method using MLST gene amplicon libraries and metagenomic shotgun sequencing data of clinical skin samples. MG-MLST yielded consistent results of the strain composition to those obtained from nearly full-length 16S rRNA clone libraries and metagenomic shotgun sequencing analysis. When comparing strain-level differences between acne and healthy skin microbiomes, we demonstrated that strains of RT2/6 were highly associated with healthy skin, consistent with previous findings. In summary, MG-MLST provides a quantitative analysis of the strain populations in the microbiome with diversity and richness. It can be applied to microbiome studies to reveal strain-level differences between groups, which are critical in many microorganism-related diseases.
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