Dataset Information

Interaction of Cutibacterium (?formerly Propionibacterium) acnes with bone cells: a step toward understanding bone and joint infection development.

ABSTRACT: Cutibacterium acnes (formerly Propionibacterium acnes) is recognized as a pathogen in foreign-body infections (arthroplasty or spinal instrumentation). To date, the direct impact of C. acnes on bone cells has never been explored. The clade of 11 C. acnes clinical isolates was determined by MLST. Human osteoblasts and osteoclasts were infected by live C. acnes. The whole genome sequence of six isolates of this collection was analyzed. CC36 C. acnes strains were significantly less internalized by osteoblasts and osteoclasts than CC18 and CC28 C. acnes strains (p???0.05). The CC18 C. acnes ATCC6919 isolate could survive intracellularly for at least 96?hours. C. acnes significantly decreased the resorption ability of osteoclasts with a major impact by the CC36 strain (p???0.05). Genome analysis revealed 27 genes possibly linked to these phenotypic behaviors. We showed a direct impact of C. acnes on bone cells, providing new explanations about the development of C. acnes foreign-body infections.

SUBMITTER: Aubin GG

PROVIDER: S-EPMC5317161 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interaction of Cutibacterium ( formerly Propionibacterium) acnes with bone cells: a step toward understanding bone and joint infection development.

Aubin Guillaume Ghislain GG Baud'huin Marc M Lavigne Jean-Philippe JP Brion Régis R Gouin François F Lepelletier Didier D Jacqueline Cédric C Heymann Dominique D Asehnoune Karim K Corvec Stéphane S

Scientific reports 20170220

Cutibacterium acnes (formerly Propionibacterium acnes) is recognized as a pathogen in foreign-body infections (arthroplasty or spinal instrumentation). To date, the direct impact of C. acnes on bone cells has never been explored. The clade of 11 C. acnes clinical isolates was determined by MLST. Human osteoblasts and osteoclasts were infected by live C. acnes. The whole genome sequence of six isolates of this collection was analyzed. CC36 C. acnes strains were significantly less internalized by ...[more]

PMID: 28218305

Similar Datasets

Project description:The human skin surface harbors huge numbers of microbes. The skin microbiota interacts with its host and forms a skin microbiome profile that is specific for each individual. It has been reported that the skin microbiota that is left on an individual's possessions can act as a sort of "fingerprint" and be used for owner identification. However, this approach needs to be improved to take into account any long-term instability of skin microbiota and contamination from nonspecific bacteria. Here, we took advantage of single-nucleotide polymorphisms (SNPs) in the 16S-encoding rRNA gene of Cutibacterium acnes, the most common and abundant bacterium on human skin, to perform owner identification. We first developed a high-throughput genotyping method based on next-generation sequencing to characterize the SNPs of the C. acnes 16S rRNA gene and found that the genotype composition of C. acnes 16S rRNA is individual specific. Owner identification accuracy of around 90% based on random forest machine learning was achieved by using a combination of C. acnes 16S rRNA genotype and skin microbiome profile data. Furthermore, our study showed that the C. acnes 16S rRNA genotype remained more stable over time than the skin microbiome profile. This characteristic of C. acnes was further confirmed by the analysis of publicly available human skin metagenome data. Our approach, with its high precision, good reproducibility, and low costs, thus provides new possibilities in the field of microbiome-based owner identification and forensics in general.IMPORTANCECutibacterium acnes is the most common and abundant bacterial species on human skin, and the gene that encodes its 16S rRNA has multiple single-nucleotide polymorphisms. In this study, we developed a method to efficiently determine the C. acnes 16S rRNA genotype composition from microbial samples taken from the hands of participants and from their possessions. Using the C. acnes 16S rRNA genotype composition, we could predict the owner of a possession with around 90% accuracy when the 16S rRNA gene-based microbiome profile was included. We also showed that the C. acnes 16S rRNA genotype composition was more stable over time than the skin microbiome profile and thus is more suitable for owner identification.

Dataset Information

Interaction of Cutibacterium (?formerly Propionibacterium) acnes with bone cells: a step toward understanding bone and joint infection development.

Publications

Interaction of Cutibacterium ( formerly Propionibacterium) acnes with bone cells: a step toward understanding bone and joint infection development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets