Search

Staphylococcus epidermidis is part of the commensal microbiota of the skin and mucous membranes, though it can also act as a pathogen in certain scenarios, causing a range of infections, including periprosthetic joint infection (PJI). Transcriptomic profiling may provide insights into mechanisms by which S. epidermidis adapts while in a pathogenic compared to a commensal state. Here, a total RNA-sequencing approach was used to profile and compare the transcriptomes of 19 paired PJI-associated S. epidermidis samples from an in vivo clinical source and grown in in vitro laboratory culture. Genomic comparison of PJI-associated and publicly available commensal-state isolates were also compared. Of the 1919 total transcripts found, 145 were from differentially expressed genes (DEGs) when comparing in vivo or in vitro samples. Forty-two transcripts were upregulated and 103 downregulated in in vivo samples. Of note, metal sequestration-associated genes, specifically those related to staphylopine activity (cntA, cntK, cntL, and cntM), were upregulated in a subset of clinical in vivo compared to laboratory grown in vitro samples. About 70% of the total transcripts and almost 50% of the DEGs identified have not yet been annotated. There were no significant genomic differences between known commensal and PJI-associated S. epidermidis isolates, suggesting that differential genomics may not play a role in S. epidermidis pathogenicity. In conclusion, this study provides insights into phenotypic alterations employed by S epidermidis to adapt to infective and non-infected microenvironments, potentially informing future therapeutic targets for related infections.

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.