Prolonged mask wearing does not alter the oral microbiome, salivary flow rate or gingival health status - A pilot study.

The COVID-19 pandemic has resulted in the widespread use of N95 respirators and surgical masks, with anecdotal reports among healthcare providers and the public of xerostomia, halitosis, and gingivitis, a consortium of symptoms colloquially termed "mask mouth". However, this has not been scientifically verified. The aim of this study was to assess changes in salivary flow rate, gingival health status and oral microbiome associated with prolonged mask use. A total of 25 dental students (mean age = 26.36 ± 1.58) were included in the study and evaluated at three time points: T1, at the end of at least 2 months of full-day mask wear (7.26 ± 1.56 hours/day); T2, at the end of a period of minimal mask use (1.13 ± 1.13 hours/day); and T3, at the end of 2-3 weeks of resuming full-day mask wear (6.93 ± 1.80 hours/day). Unstimulated whole saliva (UWS) flow rate, xerostomia (on a quantitative scale of 10), gingival index (GI) and plaque index (PI) were assessed at each time point. The salivary microbiome was characterized using 16S rRNA gene sequencing. Overall, UWS flow rates were normal (mean of 0.679 ml/min) and xerostomia, PI and GI scores were low (Mean of 3.11, 0.33 and 0.69, respectively) with no significant differences as a result of prolonged mask wearing. Similarly, there were no significant microbial changes at a false discovery rate (FDR) ≤ 0.05. However, some trends were identified using a nominal p-value cut-off of ≤ 0.01, namely Gemella sanguinis, Streptococcus sp. Oral taxon 066 and Oral taxon 058 were associated with prolonged mask wear. Trends were also seen by gender, race and age, for example an increase in P. gingivalis and P. intermedia with age. In conclusion, we found no evidence that prolonged mask wear adversely affects oral health. The findings support that the oral microbiome of healthy individuals is resilient.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.