The abundance of the potential pathogen Staphylococcus hominis in the air microbiome in a dental clinic and its susceptibility to far-UVC light.

The dental clinic air microbiome incorporates microbes from the oral cavity and upper respiratory tract (URT). This study aimed to establish a reliable methodology for air sampling in a dental clinic setting and quantify the abundance of culturable mesophilic aerobic bacteria present in these samples using regression modeling. Staphylococcus hominis, a potentially pathogenic bacterium typically found in the human oropharynx and URT, was consistently isolated. S. hominis was the most abundant species of aerobic bacteria (22%-24%) and comprised 60%-80% of all Staphylococcus spp. The study also assessed the susceptibility of S. hominis to 222 nm-far-UVC light in laboratory experiments, which showed an exponential surface inactivation constant of k = 0.475 cm2 /mJ. This constant is a critical parameter for future on-site use of far-UVC light as a technique for reducing pathogenic bacterial load in dental clinics.

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data.

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.