Impact of sleep on the microbiome of oral biofilms

Maki Sotozono; Nanako Kuriki; Yoko Asahi; Yuichiro Noiri; Mikako Hayashi; Daisuke Motooka; Shota Nakamura; Mikiyo Yamaguchi; Tetsuya Iida; Shigeyuki Ebisu

doi:10.1371/journal.pone.0259850

. 2021 Dec 9;16(12):e0259850. doi: 10.1371/journal.pone.0259850

Impact of sleep on the microbiome of oral biofilms

Maki Sotozono ¹, Nanako Kuriki ², Yoko Asahi ^2,^*, Yuichiro Noiri ¹, Mikako Hayashi ², Daisuke Motooka ³, Shota Nakamura ³, Mikiyo Yamaguchi ², Tetsuya Iida ³, Shigeyuki Ebisu ²

Editor: Yiping Han⁴

PMCID: PMC8659294 PMID: 34882696

Abstract

Dysbiosis of the oral microbiome is associated with diseases such as periodontitis and dental caries. Because the bacterial counts in saliva increase markedly during sleep, it is broadly accepted that the mouth should be cleaned before sleep to help prevent these diseases. However, this practice does not consider oral biofilms, including the dental biofilm. This study aimed to investigate sleep-related changes in the microbiome of oral biofilms by using 16S rRNA gene sequence analysis. Two experimental schedules—post-sleep and pre-sleep biofilm collection—were applied to 10 healthy subjects. Subjects had their teeth and oral mucosa professionally cleaned 7 days and 24 h before sample collection. Samples were collected from several locations in the oral cavity: the buccal mucosa, hard palate, tongue dorsum, gingival mucosa, tooth surface, and saliva. Prevotella and Corynebacterium had higher relative abundance on awakening than before sleep in all locations of the oral cavity, whereas fluctuations in Rothia levels differed depending on location. The microbiome in different locations in the oral cavity is affected by sleep, and changes in the microbiome composition depend on characteristics of the surfaces on which oral biofilms form.

Introduction

The Human Microbiome Project (2007–2017) revealed that an enormous number of bacteria inhabit the human body, forming indigenous microbiomes in each habitat, including the gut, oral cavity, skin, vagina, and airways. In the Human Microbiome Project, 16S rRNA gene sequence analysis was performed to investigate the microbiomes of different parts of the body [1]. More than 700 species of bacteria are present in various locations in the oral cavity, including the tooth surface, tongue, soft and hard palates, gingival mucosa, and buccal mucosa [2, 3]. These oral biofilms have different microbiome compositions in different locations [4]. The oral microbiome is one of the most diverse human microbiomes. Studies have reported characteristics of the oral microbiome and intra- and interindividual variations; indeed, the microbial diversity in saliva and dental biofilms is widely different among individuals and is affected by the behavior of the host, such as oral self-care [5]. Many other factors also influence the oral microbiome, including salivary enzymes, pH, host immunity, and antibacterial agents [6].

The dental biofilm is closely associated with dental caries and periodontitis, which are chronic infectious diseases worldwide [7]. Dental caries are related to low pH on the tooth surface caused by acid produced from carbohydrates by oral bacteria [8–10]. Periodontitis is strongly associated with bacteria including Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia [11]. An imbalance in the oral microbial community (i.e., dysbiosis) was found to be associated with oral infections [12, 13], just as some intestinal diseases are caused by gut dysbiosis. Acidogenic and acid-tolerant bacteria are involved in dental caries, while strictly anaerobic proteolytic and alkaliphilic bacteria are involved in periodontitis [14–17].

In addition to these diseases associated with the dental biofilm, halitosis has been associated with the tongue-coating biofilm and its metabolites in many studies [18]. The tongue-coating biofilm produces volatile sulfur compounds [18–20], such as methyl mercaptan, hydrogen sulfide, and dimethyl sulfide [21–24].

Salivary flow during sleep is decreased compared with the waking hours [25]. The number of bacteria in saliva is highest upon awakening because numbers increase rapidly during sleep [26]. It is generally thought that production of volatile sulfur compounds is highest in the morning [27] because of the increased bacterial number in saliva [26]. Therefore, it is usually recommended to perform oral self-care such as brushing teeth before sleeping, to help prevent oral disorders. In general, clinicians and patients consider that oral care before sleep is important. However, this idea is based only on the bacterial number in saliva and biofilm metabolites, and does not consider the role of the microbiome composition. Therefore, we previously investigated the effect of sleep on the characteristics of dental biofilms formed in an in situ model [28]. We found that the number of biofilm-forming bacteria did not change significantly before and after sleep, but genera associated with periodontitis (i.e., Prevotella and Fusobacterium) were relatively more abundant on awakening than during the day [28].

The salivary microbiome reportedly has circadian oscillation [29]; however, the relationship between oral biofilms and the circadian rhythm has not been clarified. Moreover, the effect of sleep on the microbiome of oral biofilms has not been sufficiently investigated because it is difficult to perform such experiments.

We hypothesized that the microbiome of oral biofilms is affected by sleep, as was true for the experimental dental biofilm. Therefore, in this study, we used 16S rRNA gene sequence analysis to investigate changes in the microbiome of biofilms in various oral locations before and after sleep to determine those changes associated with sleep.

Materials and methods

Selection of study subjects

Ten healthy volunteers (six men and four women, 27–32 years-of-age) were recruited from the students and staff of Osaka University Graduate School of Dentistry. We defined healthy subjects as previously reported [30]. Written informed consent was obtained from all subjects. No clinical signs of gingivitis, periodontitis, or caries were detected and no systemic disease was observed in any of the subjects. For each participant, we recorded the total number of decayed, missing, or filled teeth as an index of dental caries, and the Community Periodontal Index as an index of periodontal disease. Table 1 shows information on subject characteristics. Subjects abstained from antibiotics 3 months before this study. The study design was reviewed and approved by the Ethics Committee of the Osaka University Graduate School of Dentistry (H30-E42) and conducted according to the guidelines of the Declaration of Helsinki.

Table 1. Characteristics of subjects.

Subject number	Sex	Age (years)	DMF
1	F	31	14
2	M	31	0
3	F	29	12
4	F	31	2
5	M	33	3
6	M	28	1
7	M	28	4
8	M	33	7
9	M	32	0
10	M	29	2

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F, female; M, male. Dental caries were quantified as the total number of teeth that were decayed, missing, or filled (DMF). The Community Periodontal Index (CPI) was used as an index of periodontal disease. No clinical signs of caries, gingivitis, or periodontitis were detected, and no systemic disease was observed in any of the subjects.

Sample collection and DNA extraction

The experimental schedule is shown in Fig 1. In this study, all 10 subjects participated in two experimental schedules (post- and pre-sleep sample collection) with a minimum of 2 weeks between the two schedules. In both schedules, subjects had their teeth and oral mucosa cleaned twice by a specialist, the first cleaning 7 days before sample collection and the second cleaning 24 h before sample collection. Biofilm samples were collected at 08:00 in the post-sleep schedule, and at 00:00 (midnight) in the pre-sleep schedule. The subjects avoided oral self-care for the 24 h between the second professional cleaning and sample collection. All subjects slept for 8 h, from 00:00 to 08:00.

Schematic of schedules and timing of sample collection. Subjects had their teeth and oral mucosa professionally cleaned 7 days before sample collection in both schedules. Subjects had their teeth and oral mucosa professionally cleaned again at 08:00 in the post-sleep schedule and at 00:00 (midnight) in the pre-sleep schedule (indicated by black arrows) and were instructed not to perform oral self-care after this cleaning. Biofilm samples were collected 24 h after this second professional cleaning (indicated by arrowheads). All subjects slept for 8 h (from 00:00 to 08:00) in both schedules.

Biofilm samples were collected in accordance with the methods of the Manual of Procedures for the Human Microbiome Project, with partial modification [1, 31]. In brief, biofilm samples were collected from the buccal mucosa, hard palate, tongue dorsum, and gingival mucosa with an Isohelix swab (Sci Trove, Kent, United Kingdom). Subgingival and supragingival dental biofilm samples were collected from the maxillary right first molar, mandibular left first molar, maxillary right central incisor, mandibular left central incisor, maxillary left first premolar, and mandibular right first premolar with Gracey curettes. Unstimulated saliva was collected. Collected biofilm and saliva samples were immediately processed for DNA extraction with a DNeasy^® PowerSoil^® DNA Isolation Kit (QIAGEN, Hilden, Germany).

16S rRNA sequence analysis

The V1–V2 region of bacterial 16S rRNA genes was amplified using the primer set 27F (AGR GTT TGATCMTGG CTC AG [32, 33]) and 338R (TGC TGC CTC CCG TAG GAG T [34]). The Illumina library was prepared by the tailed PCR method in accordance with the Illumina 16S Metagenomic Sequencing Library Preparation Guide. Sequencing (251-bp paired-end) was performed using MiSeq Reagent Kit v2 (500 cycles) and a MiSeq instrument (Illumina Inc.). The sequences were processed and clustered into operational taxonomic units (OTUs) with a 97% similarity cutoff by using the Greengenes database (v. 13.8) [35]. The results of sequences were analyzed by using the Quantitative Insights into Microbial Ecology pipeline (v. 1.9.1) [36].

The 16S rRNA amplicon sequencing data from this study was deposited in the DNA Data Bank of Japan (DDBJ) with accession number DRA011991.

Statistical analysis

Non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) were performed with R software v. 3.6.1 (R Core Team, Vienna, Austria) and the vegan package. In PERMANOVA, P < 0.05 was considered a statistically significant difference between experimental schedules. The Wilcoxon signed rank test was used to evaluate alpha diversity and the relative abundance of each genus; P < 0.05 was considered a statistically significant difference between schedules. The Friedman test was used to evaluate beta diversity; P < 0.05 was considered a statistically significant difference between sites. Statistical analysis was performed and graphical outputs were prepared using IBM SPSS Statistics (v. 22.0, IBM SPSS Inc., Endicott, New York).

Results

Profiles of microbiome at each location in the oral cavity

The total number of reads was 10,169,418, and the average read count in samples was 72,639. Alpha diversity (Chao1 and Shannon indexes) are shown in Fig 2A, and beta diversity (UniFrac distances) in Fig 2B. There was a significant difference in the Chao1 index between the post-sleeping and pre-sleeping schedules at the buccal mucosa and gingival mucosa (buccal mucosa P = 0.022, gingival mucosa P = 0.037). There was also a significant difference in the Shannon index at the buccal mucosa (P = 0.007). No significant difference in intraindividual diversity was observed between the post-sleeping and pre-sleeping schedules for any oral location by unweighted UniFrac distance analysis. However, the weighted UniFrac distance of the supragingival dental biofilm between the two schedules was significantly higher than those for the buccal mucosa, hard palate, and gingival mucosa.

Fig 2 — The Chao1 index and Shannon index (A) are shown to indicate alpha diversity, and unweighted and weighted UniFrac distances between the two schedules (B) are shown to indicate intraindividual beta diversity. Asterisks indicate significant differences and circles represent outliers.

To investigate the variability of the microbiome collected from each oral site and in the morning versus at night, NMDS based on the Bray–Curtis distance and PERMANOVA were performed. Samples collected from different oral sites (buccal mucosa, hard palate, tongue dorsum, gingival mucosa, subgingival dental biofilm, supragingival dental biofilm, and saliva) showed statistically significant differences in composition (Fig 3, PERMANOVA, P = 0.001). The microbiome compositions of the buccal mucosa, hard palate, and gingival mucosa resembled each other. That of the tongue dorsum was similar to that of saliva.

Symbol shapes indicate the oral sampling location, and the color indicates the sample collection schedule. The number in parentheses indicates the P-value in PERMANOVA.

In addition, the post-sleep tongue dorsum microbiome was significantly different from the pre-sleep tongue dorsum microbiome (Fig 4; PERMANOVA P = 0.046). No significant difference was observed between the post-sleep and pre-sleep schedules in the microbial composition of the buccal mucosa, hard palate, gingival mucosa, subgingival dental biofilm, supragingival dental biofilm, or saliva (Fig 4; PERMANOVA: buccal mucosa, P = 0.097; hard palate, P = 0.246; gingival mucosa, P = 0.68; subgingival dental biofilm, P = 0.754; supragingival dental biofilm, P = 0.206; saliva, P = 0.081). However, there were significant differences in the composition of the overall microbiome samples from all locations and the two schedules (PERMANOVA, P = 0.007).

Bacterial taxa at the phylum level

The bacterial composition of the biofilms at the phylum level is shown in Fig 5 and S1 Table. Biofilm-forming bacteria at oral sites belonged to five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria. Actinobacteria were present at lower levels on the buccal mucosa than at other sites. Firmicutes accounted for about 50% of bacteria on the buccal mucosa, hard palate, and gingival mucosa (relative abundance of Firmicutes at each site in the morning and at night, respectively: buccal mucosa, 50.7% and 60.9%; hard palate, 52.5% and 58.9%; gingival mucosa, 54.2% and 55.1%). In contrast, Firmicutes accounted for 15% to 30% of bacteria in subgingival and supragingival dental biofilms, in saliva, and on the tongue dorsum (subgingival dental biofilm, 16.3% and 14.7% in the morning and at night, respectively; supragingival dental biofilm, 19.3% and 23.5%; saliva, 29.6% and 32.0%; tongue dorsum, 27.5% and 30.3%).

Bacterial taxa at the genus level

The bacterial composition of the biofilms at the genus level is shown in Fig 6. Corynebacterium and Capnocytophaga were relatively more abundant in the subgingival and supragingival dental biofilms than at other sites.

Genera with significant differences between morning and night are shown in Fig 7. The relative abundance of Prevotella on the buccal mucosa, hard palate, tongue dorsum, and in saliva was significantly higher in the morning than at night (Wilcoxon rank sum test: buccal mucosa, P = 0.013; hard palate, P = 0.028; tongue dorsum, P = 0.013; saliva, P = 0.011). Corynebacterium was more abundant on the buccal mucosa, hard palate, gingival mucosa, and supragingival dental biofilm in the morning than at night (Wilcoxon rank sum test: buccal mucosa, P = 0.015; hard palate, P = 0.013; gingival mucosa, P = 0.031; supragingival dental biofilm, P = 0.022). The relative abundance of Streptococcus on the buccal mucosa was significantly higher at night than in the morning (Wilcoxon rank sum test, P = 0.005). The relative abundance of Rothia on the gingival mucosa was significantly higher in the morning than at night (Wilcoxon rank sum test, P = 0.011); in contrast, the relative abundance of Rothia in saliva and on the tongue dorsum was significantly lower in the morning than at night (Wilcoxon rank sum test: saliva, P = 0.007; tongue dorsum, P = 0.022). The effect of sleep on the relative abundance of bacterial taxa differed depending on the oral site and bacterial taxon.

Discussion

This study was performed to investigate how sleep affects the microbiome diversity of oral biofilms. The effect of sleep on the microbiome of oral biofilms was investigated in detail using 16S rRNA gene sequence analysis. Takayasu et al. found circadian oscillation patterns in some genera and bacterial phenotypes of the salivary microbiome, with the relative abundance of Prevotella increasing between 04:00 and 12:00, that of Gemella and Streptococcus increasing between 16:00 and 00:00, that of Gram-positive species increasing between 16:00 and 04:00, and that of Gram-negative species increasing between 04:00 and 16:00 [29]. However, there is little information about circadian changes in other parts of the oral microbiome, including in the dental biofilms and tongue-coating biofilm. Therefore, we investigated microbial differences in the microbiome of oral biofilms before and after sleep.

To research the impact of sleep on biofilms, it seems reasonable to collect samples before and after sleep. However, if samples are simply taken at a series of timepoints, the biofilm age will be different when sampled before and after sleep, making it difficult to accurately assess the effects of sleep. Therefore, schedules were applied in this study to allow us to obtain samples of the same age.

It has been reported that the microbiomes of oral biofilms that form on the oral mucosa differ from those on the tooth surface, and that almost all bacteria present in oral biofilms belong to five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria [37, 38]. The results of the present study are consistent with those of previous reports. Corynebacterium and Capnocytophaga were predominant in the supragingival and subgingival dental biofilms, as reported previously [3]. Using Human Microbiome Project 16S rRNA gene sequencing data, Eren et al. reported that the microbiomes of different oral sites are distinct from each other, and, in particular, the dental biofilm is very different from that of other locations, such as saliva, buccal mucosa, tongue dorsum, and gingival mucosa [39]. Segata et al. investigated the bacterial composition of 10 sites in the digestive tracts of healthy subjects, including seven oral sites, and showed that the microbiomes of the 10 sites divided into four groups [40]; the microbial composition was similar between the tongue dorsum and saliva. Somineni et al. compared the oral microbiome in individuals with inflammatory bowel disease and healthy controls, and reported that the bacterial composition of the buccal mucosa was clearly separate from that of the tongue dorsum with or without disease [41]. Similarly, our results showed significant differences in the microbial composition at different oral sites. Samples from the tongue dorsum resembled those from saliva, but were different from those from buccal mucosa.

In this study, biofilms collected before and after sleep were compared. Regarding alpha diversity, the Chao1 index in the post-sleep schedule was significantly higher than that in the pre-sleep schedule for samples from the buccal mucosa and gingival mucosa, and the Shannon index in the post-sleep schedule was significantly higher than that in the pre-sleep schedule in samples from buccal mucosa. Some bacteria were observed only in the post-sleeping schedule, however, their relative abundance was very low; they may have been present but below the limit of detection in the pre-sleeping schedule. Another possibility is that bacteria released from other oral sites temporarily adhered to the sample sites because of a decrease in self-cleaning action at night, i.e., decrease of salivary flow, mechanical cleaning (such as eating), and mucosal movement during sleep.

Considering beta diversity, no significant difference was observed between locations in the oral cavity in unweighted UniFrac distance analysis; however, a significant difference was observed between the supragingival dental biofilm and the buccal mucosa, hard palate, and gingival mucosa in weighted UniFrac distance analysis (Fig 2B). UniFrac distance in Fig 2B shows the difference between the pre-sleeping schedule and the post-sleeping schedule. These results suggest that the microbiome composition, rather than the microbial (taxonomic) members of the various communities, are affected by sleeping. Of the seven locations in the oral cavity that we sampled, supragingival dental biofilms had the largest changes in microbiome composition before and after sleep, and are presumed to be susceptible to environmental change.

PERMANOVA showed significant differences before compared with after sleep only in the microbiota of tongue-coating biofilms. However, at the genus level, significant time-related differences were observed not only in the tongue-coating biofilm but also in the biofilm microbiomes at other locations in the oral cavity. It is possible that differences in the relative abundance of each genus between the post-sleep and pre-sleep schedule were too small to be detected with PERMANOVA. Prevotella and Corynebacterium were relatively more abundant in the post-sleep schedule than in the pre-sleep schedule in all locations in the oral cavity. A high abundance of these genera in the post-sleep schedule was observed in our previous study that used an in situ dental biofilm model [28]. These common tendencies among locations in the oral cavity are thought to result from environmental factors, including host immunity, saliva pH, and enzymes, which reportedly affect the oral microbiome [6]. In particular, saliva plays a crucial role in host defense [42] and contains various antibacterial agents, such as cystatins, histatins, lactoferrin, lysozyme, mucins, statherin, and immunoglobulins [especially secretory immunoglobulin A (sIgA)]. Sarkar et al. explored the relationship between the salivary microbiome and cytokines [interleukin (IL)-1β, IL-6 and IL-8] in saliva in healthy subjects over 24 h. They reported that cytokine concentrations were highest at the time of waking, and the relative abundance of Prevotella was associated with IL-1β and IL-8 concentrations (most significantly with IL-1β) [43]. The salivary microbiome is thought to be formed by bacteria attached to other oral sites, especially the tongue dorsum [38, 39], so microbial changes on the tongue dorsum and saliva may be similar.

Several factors change in the oral environment during sleep. Concentrations of sIgA have been shown to peak during sleep [44]. The amount of glucose and protein contained in saliva during sleep is lower than that during awakening [25, 45]. The pH is high during the day (pH = 7.7) and slowly decreases during sleep (to pH 6.6). There is also a significant difference in the intraoral temperature: 33.9°C when awake, and 35.9°C during sleep [46]. Other factors that affect the oral microbiota may also change with periodicity. Further research is needed to clarify such interactions.

Using an in situ dental biofilm model, we investigated the chronological changes in an experimental dental biofilm, and revealed that the microbial composition changed with increasing bacterial counts [47]. We have also previously reported that the amount of dental biofilm was not changed pre- versus post-sleeping, while the dental biofilm microbiome changed [28]. In the present study, we could not estimate whether the amount of biofilm was changed by sleeping because the samples were collected by swabbing; the difference in the oral microbiome composition during sleep is considered not to be due to a change in microbial amount.

In contrast to Prevotella and Corynebacterium, which showed similar time-related changes at different oral biofilm locations, the abundance of Rothia fluctuated differently in different locations. The relative abundance of Rothia on the gingival mucosa was significantly higher on awakening, whereas levels in the saliva and on the tongue dorsum were significantly lower on awakening than before sleep. Changes in the microbiome may depend on characteristics of the surface on which the biofilm forms and on responses of the biofilm to environmental change in different locations. However, this is only inference because there remains a lack of information about environmental changes in different locations in the oral cavity during sleep. Greater knowledge about differences in the biofilm microbiome in various locations in the oral cavity and how these changes are affected by host behaviors, such as sleep, may contribute to the development of effective methods to control oral biofilms and associated diseases.

Acidic environments caused by oral bacteria lead to dental caries [8, 9]; especially on the surface of enamel, the pH drops significantly after exposure to carbohydrate [10]. The bacteria reported to produce acid in the oral cavity include members of the Streptococcus [48], Veillonella [49], and Lactobacillus [50]. Although Streptococcus and Veillonella were detected in this study, there was no significant difference in their levels in the dental biofilm before and after sleep.

Periodontitis is associated with obligate anaerobes in the dental biofilm [51]. In this work, a significantly higher relative abundance of Corynebacterium was seen in biofilms of the buccal mucosa, hard palate, gingival mucosa and in the supragingival dental biofilm on awakening compared with those before sleep. C. matruchotii contributes to dental biofilm mineralization, which leads to dental calculus formation [52]. Dental calculus promotes bacterial accumulation on tooth surfaces because of its rough surface, and is a risk factor for periodontitis. Ritz et al. examined chronological changes in the microbial composition of supragingival biofilms and showed that Corynebacterium increases up to 5 days, and its relative abundance is 1% after 3 days [53]. Corynebacterium is considered to play a central role in supragingival biofilm development; it is absent from the early microbiota and seems to bind to early colonizers [54]; late colonizers then bind to the Corynebacterium. Our study revealed that the relative abundance of Corynebacterium in the supragingival dental biofilm was significantly higher in the post-sleep schedule (9.3%) than in the pre-sleep schedule (4.2%); the same tendency was observed in the subgingival dental biofilm. Therefore, the dental biofilm may have higher potential for late-colonizing bacteria to settle on awakening than at other times of the day.

Halitosis is reportedly associated with tongue-coating biofilms [18]. In this study, Prevotella had higher relative abundance on the buccal mucosa, saliva, hard palate, and tongue dorsum in the post-sleep schedule than in the pre-sleep schedule. Some Prevotella species are related to a high concentration of methyl mercaptan [55], which is a main cause of halitosis [56]. Moreover, salivary flow and swallowing play an important role in clearance of oral bacteria and balancing the oral microbiome. [43]. Because salivary flow and swallowing decrease during sleep [25], halitosis is often observed in the morning [57]. From our findings, we consider that in addition to the low salivary flow, changes in the microbiome of the tongue dorsum and saliva may also be associated with halitosis.

There are some limitations to this study. The various sleep-related changes in the microbiomes of different locations may have resulted from differences in the characteristics of the surfaces on which the biofilms form and the responses of the biofilms to environmental changes. However, the effect of sleep on the oral environment remains unclear and is a challenge for future study. In addition, only healthy subjects without periodontitis or dental caries were investigated in this research. We found little change in the relative abundance of different genera in this study, perhaps because healthy subjects were sampled. In future study, a similar investigation into the oral microbiome of patients with oral disease will contribute to establishing effective oral care and prevention of oral disease.

In conclusion, the microbiome at different locations in the oral cavity is affected by sleep and the changes depend on the bacterial genera and the characteristics of the surface on which the oral biofilms form. The findings of this study will be useful in establishing evidence-based methods for improving oral care.

Supporting information

S1 Table. Relative abundance of bacterial taxa in oral biofilms at the phylum level.

Abbreviations: BM, buccal mucosa; HP, hard palate; GM, gingival mucosa; SUB, subgingival dental biofilm; SUP, supragingival dental biofilm; SV, saliva; TD, tongue dorsum; Post, post-sleeping schedule; Pre, pre-sleeping schedule.

(DOCX)

Click here for additional data file.^{(18KB, docx)}

Data Availability

The 16S rRNA sequencing data is available from DDBJ under the accession number DRA011991.

Funding Statement

This study was supported by JSPS KAKENHI under Grant #17H04384 (SE), #20K23104 (MS) and SECOM Science and Technology Foundation (MH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012; 486: 215–221. doi: 10.1038/nature11209 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Kroes I, Lepp PW, Relman DA. Bacterial diversity within the human subgingival crevice. Proc Natl Acad Sci U S A. 1999; 96: 14547–14552. doi: 10.1073/pnas.96.25.14547 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005; 43: 5721–5732. doi: 10.1128/JCM.43.11.5721-5732.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2013; 486: 207–214. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Hall MW, Singh N, Ng KF, Lam DK, Goldberg MB, Tenenbaum HC, et al. Inter-personal diversity and temporal dynamics of dental, tongue, and salivary microbiota in the healthy oral cavity. NPJ Biofilms Microbiomes. 2017; 3: 2. doi: 10.1038/s41522-016-0011-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Stewart PS, Costerton JW. Antibiotic resistance of bacteria in biofilms. Lancet. 2001; 358: 135–138. doi: 10.1016/s0140-6736(01)05321-1 [DOI] [PubMed] [Google Scholar]
7.Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR, et al. Oral diseases: a global public health challenge. Lancet. 2019;394: 249–260. doi: 10.1016/S0140-6736(19)31146-8 [DOI] [PubMed] [Google Scholar]
8.Williams JL. A contribution to the study of pathology of enamel. Dental Cosmos. 1897; 39: 269–301. [Google Scholar]
9.Black GV. A work on operative dentistry. 6th ed. Medico-Dental Publishing Company; 1936. [Google Scholar]
10.Stephan RM, Miller BF. A quantitative method for evaluating physical and chemical agents which modify production of acids in bacterial plaques on human teeth. J Dent Res. 1943; 22: 45–51. [Google Scholar]
11.Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL Jr. Microbial complexes in subgingival plaque. J Clin Periodontol. 1998; 25: 134–144. doi: 10.1111/j.1600-051x.1998.tb02419.x [DOI] [PubMed] [Google Scholar]
12.Simón-Soro A, Mira A. Solving the etiology of dental caries. Trends Microbiol. 2015; 23: 76–82. doi: 10.1016/j.tim.2014.10.010 [DOI] [PubMed] [Google Scholar]
13.Hajishengallis G, Lamont RJ. Beyond the red complex and into more complexity: the polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. Mol Oral Microbiol. 2012; 27: 409–419. doi: 10.1111/j.2041-1014.2012.00663.x [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Prakash S, Rodes L, Coussa-Charley M, Tomaro-Duchesneau C. Gut microbiota: next frontier in understanding human health and development of biotherapeutics. Biologics. 2011; 5: 71–86. doi: 10.2147/BTT.S19099 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Cani PD. Gut microbiota: changes in gut microbes and host metabolism: squaring the circle? Nat Rev Gastroenterol Hepatol. 2016; 13: 563–564. doi: 10.1038/nrgastro.2016.135 [DOI] [PubMed] [Google Scholar]
16.Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N Engl J Med. 2016; 375: 2369–2379. doi: 10.1056/NEJMra1600266 [DOI] [PubMed] [Google Scholar]
17.Rosier BT, Marsh PD, Mira A. Resilience of the oral microbiota in health: Mechanisms that prevent dysbiosis. J Dent Res. 2018; 97: 371–380. doi: 10.1177/0022034517742139 [DOI] [PubMed] [Google Scholar]
18.De Boever EH, Loesche WJ. Assessing the contribution of anaerobic microflora of the tongue to oral malodor. J Am Dent Assoc. 1995;126: 1384–1393. doi: 10.14219/jada.archive.1995.0049 [DOI] [PubMed] [Google Scholar]
19.Bosy A, Kulkarni GV, Rosenberg M, McCulloch CAG. Relationship of oral malodor to periodontitis: evidence of independence in discrete subpopulations. J Periodontol. 1994; 65: 37–46. doi: 10.1902/jop.1994.65.1.37 [DOI] [PubMed] [Google Scholar]
20.Yaegaki K, Sanada K. Effects of a two-phase oil-water mouthwash on halitosis. Clin Prev Dent. 1992; 14: 5–9. [PubMed] [Google Scholar]
21.Jenkins GN. Physiology and biochemistry of the mouth. In: Jenkins GNeditor. Sensations arising in the mouth. Blackwell Scientific Publications; 1978. pp. 542–570. [Google Scholar]
22.Kleinberg I, Westbay G. Oral malodor. Crit Rev Oral Biol Med. 1990; 1: 247–259. doi: 10.1177/10454411900010040401 [DOI] [PubMed] [Google Scholar]
23.Tonzetich J. Direct gas chromatographic analysis of sulphur compounds in mouth air in man. Arch Oral Biol. 1971; 16: 587–597. doi: 10.1016/0003-9969(71)90062-8 [DOI] [PubMed] [Google Scholar]
24.Tonzetich J. Production and origin of malodor: a review of mechanisms and methods of analysis. J Periodontol. 1977; 48: 13–20. doi: 10.1902/jop.1977.48.1.13 [DOI] [PubMed] [Google Scholar]
25.Dawes C. Circadian rhythms in human salivary flow rate and composition. J Physiol. 1972; 220: 529–545. doi: 10.1113/jphysiol.1972.sp009721 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Nolte WA. Oral Microbiology: with basic microbiology and immunology. 4th ed.; Mosby, 1982. pp.198–201. [Google Scholar]
27.Sharma NC, Galustians HJ, Qaqish J. The clinical effectiveness of a dentifrice containing triclosan and a copolymer for controlling breath odor measured organoleptically twelve hours after toothbrushing. J Clin Dent. 1999; 10: 131–134. [PubMed] [Google Scholar]
28.Sotozono M, Kuriki N, Asahi Y, Noiri Y, Hayashi M, Motooka D, et al. Impacts of sleep on the characteristics of dental biofilm. Sci Rep. 2021; 11: 138. doi: 10.1038/s41598-020-80541-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Takayasu L, Suda W, Takanashi K, Iioka E, Kurokawa R, Shindo C, et al. Circadian oscillations of microbial and functional composition in the human salivary microbiome. DNA Res. 2017; 24: 261–270. doi: 10.1093/dnares/dsx001 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Aagaard K, Petrosino J, Keitel W, Watson M, Katancik J, Garcia N, et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 2013; 27: 1012–1022. doi: 10.1096/fj.12-220806 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.McInnes P, Cutting M. Manual of procedures for human microbiome project—Core microbiome sampling protocol A HMP Protocol # 07–001 Version Number: 12.0 29. Available online: https://www.hmpdacc.org/hmp/resources/
32.Lane DJ. 16S/23S rRNA Sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic Acid Techniques in Bacterial Systematics. John Wiley & Sons. Press; 1991. pp. 115–175. [Google Scholar]
33.Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991; 173: 697–703. doi: 10.1128/jb.173.2.697-703.1991 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microbiol. 1996; 62: 625–630. doi: 10.1128/aem.62.2.625-630.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006; 72: 5069–5072. doi: 10.1128/AEM.03006-05 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7: 335–336. doi: 10.1038/nmeth.f.303 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Zaura E, Keijser BFJ, Huse SM, Crielaard W. Defining the healthy “core microbiome” of oral microbial communities. BMC Microciol. 2009; 9: 259. doi: 10.1186/1471-2180-9-259 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Takeshita T, Kageyama S, Furuta M, Tsuboi H, Takeuchi K, Shibata Y, et al. Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study. Sci Rep. 2016; 6: 22164. doi: 10.1038/srep22164 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Eren AM, Borisy GG, Huse SM, Mark Welch JL. Oligotyping analysis of the human oral microbiome. Proc Natl Acad Sci U S A. 2014; 111: E2875–2884. doi: 10.1073/pnas.1409644111 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Segata N, Haake SK, Mannon P, Lemon KP, Waldron L, Gevers D, et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012; 13: R42. doi: 10.1186/gb-2012-13-6-r42 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Somineni HK, Weitzner JH, Venkateswaran S, Dodd A, Prince J, Karikaran A et al. Site- and taxa-specific disease-associated oral microbial structures distinguish inflammatory bowel diseases. Inflamm Bowel Dis. 2021. May 14; izab082. doi: 10.1093/ibd/izab082 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Pedersen AML, Belstrøm D. The role of natural salivary defences in maintaining a healthy oral microbiota. J Dent. 2019; 80: S3–S12. doi: 10.1016/j.jdent.2018.08.010 [DOI] [PubMed] [Google Scholar]
43.Sarkar A, Kuehl MN, Alman AC, Burkhardt BR. Linking the oral microbiome and salivary cytokine abundance to circadian osillations. Sci Rep. 2021; 11: 2658. doi: 10.1038/s41598-021-81420-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Wada M, Orihara K, Kamagata M, Hama K, Sasaki H, Haraguchi A, et al. Circadian clock-dependent increase in salivary IgA secretion modulated by sympathetic receptor activation in mice. Sci Rep. 18; 7: 8802. doi: 10.1038/s41598-017-09438-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Sashikumar R, Kannan R. Salivary glucose levels and oral candidal carriage in type II diabetics. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2010; 109: 706–711. doi: 10.1016/j.tripleo.2009.12.042 [DOI] [PubMed] [Google Scholar]
46.Choi JE, Loke C, Waddell JN, Lyons KM, Kieser JA, Farella M. Continuous measurement of intra-oral pH and temperature: development, validation of an appliance and a pilot study. J Oral Rehabil. 2015; 42: 563–570. doi: 10.1111/joor.12294 [DOI] [PubMed] [Google Scholar]
47.Wake N, Asahi Y, Noiri Y, Hayashi M, Motooka D, Nakamura S et al. Temporal dynamics of bacterial microbiota in the human oral cavity determined using an in situ model of dental biofilms. NPJ Biofilms Microbiomes. 2016; 2: 16018. doi: 10.1038/npjbiofilms.2016.18 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Loesche WJ. Role of Streptococcus mutans in human dental decay. Microbiol Rev. 1986; 50: 353–380. doi: 10.1128/mr.50.4.353-380.1986 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Widyarman S, Theodorea CF. Effect of reuterin on dual-species biofilm in vitro of streptococcus mutans and veillonella parvula. J Int Dent Med Res. 2019; 12: 77–83. [Google Scholar]
50.Eşian D, Man A, Burlibasa L, Burlibasa M, Perieanu MV, Bică C. Salivary level of Streptococcus mutans and Lactobacillus spp. related to a high a risk of caries disease. Rom. Biotechnol. Lett. 2017; 22: 12496–12503. [Google Scholar]
51.Marsh PD, Moter A, Devine DA. Dental plaque biofilms: communities, conflict and control. Periodontol 2000. 2011; 55: 16–35. doi: 10.1111/j.1600-0757.2009.00339.x [DOI] [PubMed] [Google Scholar]
52.Ooi SW, Smillie AC, Kardos TB, Shepherd MG. Intracellular mineralization of Bacterionema matruchotii. Can J Microbiol. 1981; 27: 267–270. doi: 10.1139/m81-042 [DOI] [PubMed] [Google Scholar]
53.Ritz HL. Microbial population shifts in developing human dental plaque. Arch Oral Biol. 1967; 12: 1561–1568. doi: 10.1016/0003-9969(67)90190-2 [DOI] [PubMed] [Google Scholar]
54.Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci U S A. 2016; 113: E791–800. doi: 10.1073/pnas.1522149113 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Takeshita T, Suzuki N, Nakano Y, Yasui M, Yoneda M, Shimazaki Y, et al. Discrimination of the oral microbiota associated with high hydrogen sulfide and methyl mercaptan production. Sci Rep. 2012; 2: 215. doi: 10.1038/srep00215 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Spielman AI, Bivona P, Rifkin BR. Halitosis. A common oral problem. N Y State Dent J. 1996; 62: 36–42. [PubMed] [Google Scholar]
57.Fukui Y, Yaegaki K, Murata T, Sato T, Tanaka T, Imai T, et al. Diurnal changes in oral malodour among dental‐office workers. Int Dent J. 2008; 58: 159–166. doi: 10.1111/j.1875-595x.2008.tb00192.x [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0259850.r001

Decision Letter 0

Yiping Han

29 Jul 2021

PONE-D-21-11990

Impact of Sleep on the Microbiome of Oral Biofilms

PLOS ONE

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Reviewer #2: Yes

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Reviewer #1: Yes

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Reviewer #1: In this study titled “Impact of Sleep on the Microbiome of Oral Biofilms), the authors investigated the microbiome composition of multiple intra-oral sites in healthy subjects before and after sleep using 16s sequencing analysis. Of the 7 sites investigated, only tongue dorsum showed significant, albeit weak (P=0.046), shift in microbiome composition between pre- and post-sleep. A few genera were detected displaying significant difference in relative abundance between pre- and post-sleep at specific locations.

The reviewer likes the two-schedule setup for sample collection used in this study, which allows for more specific look at the impact of sleep on the pre-existing, mature biofilm. However, there are some issues with the experimental design, the limited information provided by the study, as well as the data interpretation that make the reviewer less enthusiastic about this study.

Main comments:

1) Experimental design: Lack of the justification for the # of subject recruited. Will the # of subjects used provide enough statistical power to allow for detection of significant change between pre- and post-sleep?

2) The new knowledge came out of this study is very limited.

Other than mainly confirming published findings, the study offers very little new knowledge. Even for a few genera detected which display significant difference in their relative abundance between pre- and post-sleep at specific locations, they were presented as “inventory lists” with no in-depth discussion/explanation as to “why” some of the difference was observed.

3) within oral microbiome, there are species level diversity with diverse physiology and pathogenesis potentials within many bacterial genera, such as Streptococci. it is worthwhile to investigate microbiome composition at species level which could potentially reveal new information related to the impact of sleep.

4) Other than microbial composition, any change in microbial “load” (absolute total abundance) pre- and post-sleep?

5) The manuscript could benefit from language editing from a native English speaker.

Other comments:

1. Page 4, line 58: change “sucrose” to “carbohydrates”

2. Page 10, line 151-153. Need more references.

3. Page 12, line 171-173. Tongue data comparison is NOT in Fig.2

4. Page 17, line 252-253. Please clarify. Assuming subject performs self-cleaning twice a day (at 8:00 and 24:00), then, biofilm collected after sleep would have less time to grow (8hrs) than biofilm collected before sleep (16hr)

5. Page 21, line 311-313. The data presented (abundance shift in one genus) is not strong enough to draw such a conclusion. Same applies to Page 12, line 321-322.

Reviewer #2: General comments:

This manuscript is focused on the investigation of the microbiome of oral biofilms affected and how it is affected by sleep. Research questions are well defined, relevant, meaningful, and original. This research fills an identified knowledge gap. The method section requires more information about the sample collection and data analyses. The results are solid (but limited) and statistical analyses are robust. The results focused on comparing the relative abundance of OTUs between different experimental schedules. The discussion should attempt explaining why the variation of the microbiome was observed between the pre-sleep and the post-sleep schedules by answering question such as “Is this variation only due to the changes in microbial abundance or due to the changes in microbial species?” An improved explanation or discussion about the relationship between sleep and environmental factors modification is warrentied.

Specific Comments:

Abstract:

L38: “changes”. Consider “changes in the microbiome composition”.

Introduction:

L44: “In that project”. It is vague. Consider using “In Human Microbiome Project or In HMP”.

L48: “microbiome construction”. Based on the context, “microbiome composition/microbial assemblages/microbial composition” is more appropriate here.

L61: Dysbiosis is not a new concept is oral biofilm

L81: “in situ”. Italic font “in situ”

Methods:

L119-120: “once 7 days before sample collection and once 24 hours before sample collection.” “once” is vague here. Consider revising “the first cleaning 7 days before sample collection and the second cleaning 24 hours before sample collection.”

L148: “The Illumina library…..”. Which Illumina kit did authors use to prepare the library? What is the sequencing method? Single or paired end sequencing? The length of sequencing? (150bp, 250bp or 300bp), the total number of reads

L151-152: “operative taxonomic units”. Consider revising to “operative taxonomic units (OTUs)”

L151-152: The version of the Green Genes database is missing.

L152-153 : The version of software QIIME is missing. It is not clear if the authors checked the chimeras or remove any singletons. When authors analyze the data, it is not clear whether they rarefy the OTU table.

L157: The version of R software is needed.

At the end of the Materials and Methods, authors need an separate paragraph, which gives the information of NCBI SRA or similar database submission.

Results

It is suggested that the authors also examining the microbial diversity change between different experiment schedules or different oral locations. This can be done by calculating phylogenetic diversity in QIIME. By doing this, we will know whether the microbial composition is significantly different across experimental schedules. For example, are there any bacteria only found in the pre-sleep schedule but not in the post-sleep schedule. It would be interesting to investigate the diversity related to phylogenetics. I suggest authors checking phylogenetic diversity (e.g., UniFrac).

Figure 2. Panel B does not bring any information. Square and triangles instead of removing the color coding of the body sites would be beneficial

L164: Authors only reported beta diversity. It is not clear about the alpha diversity. For example, how many OTUs or species were found in the pre-sleep schedule and in the post-sleep schedule, respectively.

L180: “microbiome”. Consider revising to “microbial composition or microbiome composition”.

L194-195: “five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria. Actinobacteria were present………”. The relative abundance (%) of each phylum could be reported in supplemental data (only the phylum Firmicutes has been reported so far).

Discussion

The authors reference the HMP work but fail to do a comparison to articles were multisite analysis were performed [Segata et al Genome Biol 2012] [Eren PNAS 2014] [Somineni Infl Bowel Dis 2021]

L240: “affects the microbiome”. It should be more specific here. For example, “affects the microbiome abundance”, “affect the microbiome diversity”, etc.

L274-276: Please see my comments above. Can authors do a further discussion about whether environmental factors would contribute to the changes in the microbiome in this study?

L321-322: No supporting data for this statement.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Dec 9;16(12):e0259850. doi: 10.1371/journal.pone.0259850.r002

Author response to Decision Letter 0

8 Sep 2021

Responses to Academic Editor

We appreciate the time and effort you have dedicated to providing insightful feedback on ways to strengthen our paper.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have checked PLOS ONE’s style requirements, and adapted our manuscript to them.

Response: The 16S rRNA gene sequencing data have been uploaded to the DNA Data Bank of Japan (DDBJ), and the accession number (DRA011991) has been included in our cover letter and the revised manuscript (page 9, lines 140–141).

3. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

- https://www.nature.com/articles/s41598-020-80541-5

Response: We have rephrased text that was duplicated from our previous publication, except in the Methods section (indicated by blue highlights in the revised manuscript).

Responses to Reviewer #1

We greatly appreciate the Reviewer’s insightful comments, which have aided us in significantly improving our paper.

Responses to main comments:

1. Experimental design: Lack of the justification for the # of subject recruited. Will the # of subjects used provide enough statistical power to allow for detection of significant change between pre- and post-sleep?

Response: Although there was some variation among individuals, a similar tendency was observed among all 10 of our participants. Additionally, the sample size in similar dental biofilm studies was the same as the sample size in our study (e.g., Sci. Rep. 2021; 11: 138. doi: 10.1038/s41598-020-80541-5, npj Biofilms Microbiomes 2016; 10: 2:16018, Sci. Rep. 2015; 5: 8136). Thus, while a larger sample size would be better, we believe that the sample size of our study is sufficient to support our conclusions.

2. The new knowledge came out of this study is very limited.

Response: This study did not compare healthy and diseased subjects, but was a study of diurnal variation in healthy subjects, so the difference in microbiota was expected to be small. Though the changes in the oral environment during sleep and the association between the oral bacteria and the oral environment have not yet been fully elucidated, we have added the following discussion:

“Sarkar et al. explored the relationship between the salivary microbiome and cytokines [interleukin (IL)-1β, IL-6 and IL-8] in saliva in healthy subjects over 24 h. They reported that cytokine concentrations were highest at the time of waking, and the relative abundance of Prevotella was associated with IL-1β and IL-8 concentrations (most significantly with IL-1β) [44]. The salivary microbiome is thought to be formed by bacteria attached to other oral sites, especially the tongue dorsum [38, 39], so microbial changes on the tongue dorsum and saliva may be similar.

Several factors change in the oral environment during sleep. Concentrations of sIgA have been shown to peak during sleep [45]. The amount of glucose and protein contained in saliva during sleep is lower than that during awakening [46, 47]. The pH is high during the day (pH = 7.7) and slowly decreases during sleep (to pH 6.6). There is also a significant difference in the intraoral temperature: 33.9 °C when awake, and 35.9 °C during sleep [48]. Other factors that affect the oral microbiota may also change with periodicity. Further research is needed to clarify such interactions.” (page20-21, line319-332)

3. Within oral microbiome, there are species level diversity with diverse physiology and pathogenesis potentials within many bacterial genera, such as Streptococci. It is worthwhile to investigate microbiome composition at species level which could potentially reveal new information related to the impact of sleep.

Response: The sequencing in this study was conducted using the MiSeq system, from which we obtained sequences of 16S rRNA gene V1–V2 regions (about 300 bp). As the Reviewer points out, it would be valuable to investigate microbial composition at the species level to enable more detailed consideration. However, species-level analysis is difficult (having low classification rate and accuracy) using such partial 16S rRNA gene sequences. Full-length 16S rRNA gene sequencing, which would enable species-level analysis, is possible using PacBio or Oxford Nanopore technology, but the sequence error rates are higher than in MiSeq. Shotgun metagenomic analysis is another method of analysis at the species level, but it is time-consuming and expensive. Based on the data we obtained here, we chose the more reliable genus level for analysis. Nevertheless, analysis at the species level is a prospect for future studies.

4. Other than microbial composition, any change in microbial “load” (absolute total abundance) pre- and post-sleep?

Response: In this study, oral biofilm samples from various places in the oral cavity were collected by swabbing. So, it is difficult to estimate the amount of biofilm per unit. However, using an in situ dental biofilm model, which was quantitatively evaluated, we previously reported that the amount of dental biofilm (the number of bacteria and the biovolume) was not affected by sleep (Sci. Rep. 2021; 11: 138). We added the following text in the Discussion of the revised manuscript:

“Using an in situ dental biofilm model, we investigated the chronological changes in an experimental dental biofilm, and revealed that the microbial composition changed with increasing bacterial counts [49]. We have also previously reported that the amount of dental biofilm was not changed pre- versus post-sleeping, while the dental biofilm microbiome changed [28]. In the present study, we could not estimate whether the amount of biofilm was changed by sleeping because the samples were collected by swabbing.” (page 21, line 333-338)

5. The manuscript could benefit from language editing from a native English speaker.

Response: We have had the English and grammar in the manuscript checked by a native English speaker from a professional editing company.

Responses to other comments:

1. Page 4, line 58: change “sucrose” to “carbohydrates”

Response: In accordance with the Reviewer’s suggestion, we have changed “sucrose” to “carbohydrates” in the revised manuscript (page 3, line 48).

2. Page 10, line 151-153. Need more references.

Responses: We have added references, as follows:

“The sequences were processed and clustered into operational taxonomic units (OTUs) with a 97% similarity cutoff by using the Greengenes database (v. 13.8) [35]. The results of sequences were analyzed by using the Quantitative Insights into Microbial Ecology pipeline (v. 1.9.1) [36].” (page 9, line 138-139)

“[35] DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006; 72: 5069–5072.

[36] Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7: 335–336.” (page 63, line 476-481)

3. Page 12, line 171-173. Tongue data comparison is NOT in Fig.2.

Responses: “Tongue” in line 171–173 meant the same as “tongue dorsum” in Fig. 2. The words “tongue” and “tongue dorsum” that were used to mean the sample taken from the tongue were mixed in the manuscript and the figure, and that caused confusion, for which we apologize. The expression for the tongue sample has been unified to “tongue dorsum” throughout the revised manuscript (page 8, line 121; page 12, lines 175, 185-186; page 14, lines 211, 213; page 15, lines 229, 231, 239, 241; page 18, lines 275; page 22, lines 342; page 24, line 375).

Responses: As described in the Methods, in this study, the subjects received professional oral care twice, 7 d and 24 h before sampling. After professional care 24 h before sampling, they avoided oral self-care until after sample collection. For example, in the post-sleeping schedule, the subjects had their mouths cleaned at 08:00 and they avoided tooth brushing until sample collection (08:00 the next day). In the post-sleeping schedule, they had their mouths cleaned at 00:00 and avoided oral self-care until sample collection, which was performed the next day at 00:00. We have added the following text to the Discussion:

“However, if samples are simply taken at a series of timepoints, the biofilm age will be different when sampled before and after sleep, making it difficult to accurately assess the effects of sleep.” (page 17, line 262-264)

5. Page 21, line 311-313. The data presented (abundance shift in one genus) is not strong enough to draw such a conclusion. Same applies to Page 12, line 321-322.

Responses: As the Reviewer points out, it was an overestimate to discuss that a relative abundance shift in one genus may lead to higher periodontal disease-related pathogenicity. However, recent structural analysis reports that Corynebacterium is the cornerstone of dental biofilm development. Corynebacterium seems to bind to the early colonizers, and then late colonizers bind to Corynebacterium. Therefore, we have revised the manuscript as follows:

“Corynebacterium is considered to play a central role in supragingival biofilm development; it is absent from the early microbiota and seems to bind to early colonizers [56]; late colonizers then bind to the Corynebacterium.…Therefore, the dental biofilm may have higher potential for late-colonizing bacteria to settle on awakening than at other times of the day.” (page21, line 365-368, 371-372)

Additionally, we overstated the relationship with halitosis. We have revised the manuscript as follows:

“From our findings, we consider that in addition to the low salivary flow, changes in the microbiome of the tongue dorsum and saliva may also be associated with halitosis.” (page 24, line 380-381)

Responses to Reviewer #2

We thank the Reviewer for the very helpful comments, which have aided us in significantly improving our paper.

Responses to General Comments:

Responses: We have improved the Discussion with reference to the Reviewer’s question.

Responses to Specific Comments:

Abstract:

1. L38: “changes”. Consider “changes in the microbiome composition”.

Responses: In accordance with Reviewer’s suggestion, we have changed “changes” to “changes in the microbiome composition” (page 2, line 30).

Introduction:

2. L44: “In that project”. It is vague. Consider using “In Human Microbiome Project or In HMP”.

Responses: We have changed “In that project” to “In the Human Microbiome Project” (page 3, line 35-36).

3. L48: “microbiome construction”. Based on the context, “microbiome composition/microbial assemblages/microbial composition” is more appropriate here.

Responses: We have changed “microbiome construction” to “microbiome composition” (page 3, line 40).

4. L61: Dysbiosis is not a new concept is oral biofilm.

Responses: We have removed “recently” from the revised manuscript (page 4, line 50-51).

5. L81: “in situ”. Italic font “in situ”.

Responses: We have put “in situ” in italics (page 5, line 68).

Methods:

6. L119-120: “once 7 days before sample collection and once 24 hours before sample collection.” “once” is vague here. Consider revising “the first cleaning 7 days before sample collection and the second cleaning 24 hours before sample collection.”

Responses: Thank you for this helpful suggestion. We have revised “once 7 days before sample collection and once 24 hours before sample collection” to “the first cleaning 7 days before sample collection and the second cleaning 24 h before sample collection” (page 7, line 104-105).

7. L148: “The Illumina library…..”. Which Illumina kit did authors use to prepare the library? What is the sequencing method? Single or paired end sequencing? The length of sequencing? (150bp, 250bp or 300bp), the total number of reads.

Responses: The Illumina kit used was the MiSeq Reagent Kit v2 (500 cycles) and we sequenced in a 251-bp paired-end run. We have added the sentence below:

“Sequencing (251-bp paired-end) was performed using MiSeq Reagent Kit v2 (500 cycles) and a MiSeq instrument (Illumina Inc.)” (page9, line 135)

The total number of reads was 10,169,418. We have revised the manuscript as follows:

“The total number of reads was 10,169, 418, and the average read count was 72,639.” (page 11, line 154-155)

8. L151-152: “operative taxonomic units”. Consider revising to “operative taxonomic units (OTUs)”.

Responses: We have added “(OTUs)” just after “operative taxonomic units” (page 9, line 137).

9. L151-152: The version of the Green Genes database is missing.

Responses: We used Greengenes database ver. 13.8; this has been added to the revised manuscript (page 9, line 138).

10. L152-153: The version of software QIIME is missing. It is not clear if the authors checked the chimeras or remove any singletons. When authors analyze the data, it is not clear whether they rarefy the OTU table.

Responses: The version of QIIME used (v. 1.9.1) has been added to the revised manuscript (page 9, line 139).

Chimera sequences and singletons were not removed. We checked the alpha rarefaction, and analyzed the data at 20,000 reads.

11. L157: The version of R software is needed.

Responses: The version of R software used (v. 3.6.1) has been added to the revised manuscript (page 10, line 145).

12. At the end of the Materials and Methods, authors need a separate paragraph, which gives the information of NCBI SRA or similar database submission.

Responses: The 16S rRNA sequencing data were deposited in the DNA Data Bank of Japan (DDBJ) under accession number DRA011991. We have added the following sentence:

“The 16S rRNA amplicon sequencing data from this study was deposited in the DNA Data Bank of Japan (DDBJ) with accession number DRA011991.” (page 9, line 140-141)

Results:

13. It is suggested that the authors also examining the microbial diversity change between different experiment schedules or different oral locations. This can be done by calculating phylogenetic diversity in QIIME. By doing this, we will know whether the microbial composition is significantly different across experimental schedules. For example, are there any bacteria only found in the pre-sleep schedule but not in the post-sleep schedule. It would be interesting to investigate the diversity related to phylogenetics. I suggest authors checking phylogenetic diversity (e.g., UniFrac).

Responses: Thank you for this helpful suggestion. Unweighted and weighted UniFrac distance was checked as beta diversity. The results and discussion were added to the revised manuscript:

“No significant difference in intraindividual diversity was observed between the post-sleeping and pre-sleeping schedules for any oral location by unweighted UniFrac distance analysis. However, the weighted UniFrac distance of the supragingival dental biofilm between the two schedules was significantly higher than those for the buccal mucosa, hard palate, and gingival mucosa.” (page11, line 159-164)

“Regarding alpha diversity, the Chao1 index in the post-sleep schedule was significantly higher than that in the pre-sleep schedule for samples from the buccal mucosa and gingival mucosa, and the Shannon index in the post-sleep schedule was significantly higher than that in the pre-sleep schedule in samples from buccal mucosa. Some bacteria were observed only in the post-sleeping schedule, however, their relative abundance was very low; they may have been present but below the limit of detection in the pre-sleeping schedule. Another possibility is that bacteria released from other oral sites temporarily adhered to the sample sites because of a decrease in self-cleaning action at night, i.e., decrease of salivary flow, mechanical cleaning (such as eating), and mucosal movement during sleep.

Considering beta diversity, no significant difference was observed between locations in the oral cavity in unweighted UniFrac distance analysis; however, a significant difference was observed between the supragingival dental biofilm and the buccal mucosa, hard palate, and gingival mucosa in weighted UniFrac distance analysis (Fig. 2B). UniFrac distance in Fig. 2B shows the difference between the pre-sleeping schedule and the post-sleeping schedule. These results suggest that the microbiome composition, rather than the microbial (taxonomic) members of the various communities, are affected by sleeping. Of the seven locations in the oral cavity that we sampled, supragingival dental biofilms had the largest changes in microbiome composition before and after sleep, and are presumed to be susceptible to environmental change.” (page19-20, line285-304)

14. Figure 2. Panel B does not bring any information. Square and triangles instead of removing the color coding of the body sites would be beneficial.

Responses: In accordance with Reviewer’s helpful suggestion, we have changed the Figure using both colors and shapes. To match the following figures (post-sleeping schedule, red; pre-sleeping schedule, blue), the schedule is shown in colors and the sample sites are shown using shapes.

15. L164: Authors only reported beta diversity. It is not clear about the alpha diversity. For example, how many OTUs or species were found in the pre-sleep schedule and in the post-sleep schedule, respectively.

Responses: Data on alpha diversity were investigated. The results were added to the revised manuscript (page 12, line 171–177, Fig 2A):

“Alpha diversity (Chao1 and Shannon indexes) are shown in Fig 2A, and beta diversity (UniFrac distances) in Fig 2B. There was a significant difference in the Chao1 index between the post-sleeping and pre-sleeping schedules at the buccal mucosa and gingival mucosa (buccal mucosa P = 0.022, gingival mucosa P = 0.037). There was also a significant difference in the Shannon index at the buccal mucosa (P = 0.007).” (page 11, line 155-159)

There was also a significant difference in the species between the post-sleeping and pre-sleeping schedules at the buccal mucosa and gingival mucosa.

16: L180: “microbiome”. Consider revising to “microbial composition or microbiome composition”.

Responses: We revised “microbiome” to “microbial composition” (page 13, line 188).

17. L194-195: “five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria. Actinobacteria were present………”. The relative abundance (%) of each phylum could be reported in supplemental data (only the phylum Firmicutes has been reported so far).

Responses: Per the Reviewer’s suggestion, we have added the relative abundance (%) of each of the five phyla as supplemental data (Supporting Information, S1 Table).

Discussion:

18. The authors reference the HMP work but fail to do a comparison to articles were multisite analysis were performed [Segata et al Genome Biol 2012] [Eren PNAS 2014] [Somineni Infl Bowel Dis 2021].

Responses: We have compared our data with the articles that were suggested by the Reviewer, and added discussion to the revised manuscript:

“Using Human Microbiome Project 16S rRNA gene sequencing data, Eren et al. reported that the microbiomes of different oral sites are distinct from each other, and, in particular, the dental biofilm is very different from that of other locations, such as saliva, buccal mucosa, tongue dorsum, and gingival mucosa [39]. Segata et al. investigated the bacterial composition of 10 sites in the digestive tracts of healthy subjects, including seven oral sites, and showed that the microbiomes of the 10 sites divided into four groups [40]; the microbial composition was similar between the tongue dorsum and saliva. Somineni et al. compared the oral microbiome in individuals with inflammatory bowel disease and healthy controls, and reported that the bacterial composition of the buccal mucosa was clearly separate from that of the tongue dorsum with or without disease [41]. Similarly, our results showed significant differences in the microbial composition at different oral sites. Samples from the tongue dorsum resembled those from saliva, but were different from those from buccal mucosa.” (page 18, line 271-284)

19. L240: “affects the microbiome”. It should be more specific here. For example, “affects the microbiome abundance”, “affect the microbiome diversity”, etc.

Responses: In accordance with the Reviewer’s suggestion, we revised “affects the microbiome” to “affects the microbiome diversity” (page 17, line 250).

20. L274-276: Please see my comments above. Can authors do a further discussion about whether environmental factors would contribute to the changes in the microbiome in this study?

Responses: How sleep changes the oral environment, and the relationship between changes in the oral environment and the oral microbiome, have not yet been fully elucidated. However, we considered as much as possible and added the following text to the Discussion:

21. L321-322: No supporting data for this statement.

Responses: As the Reviewer suggested, the discussion about halitosis was exaggerated, we have revised the manuscript, as follows:

“From our findings, we consider that in addition to the low salivary flow, changes in the microbiome of the tongue dorsum and saliva may also be associated with halitosis” (page 24, line 380-381)

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PLoS One. doi: 10.1371/journal.pone.0259850.r003

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Impact of sleep on the microbiome of oral biofilms

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1 Dec 2021

PONE-D-21-11990R1

Impact of sleep on the microbiome of oral biofilms

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Supplementary Materials

S1 Table. Relative abundance of bacterial taxa in oral biofilms at the phylum level.

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Data Availability Statement

The 16S rRNA sequencing data is available from DDBJ under the accession number DRA011991.

[pone.0259850.ref001] 1.Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012; 486: 215–221. doi: 10.1038/nature11209 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref002] 2.Kroes I, Lepp PW, Relman DA. Bacterial diversity within the human subgingival crevice. Proc Natl Acad Sci U S A. 1999; 96: 14547–14552. doi: 10.1073/pnas.96.25.14547 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref003] 3.Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005; 43: 5721–5732. doi: 10.1128/JCM.43.11.5721-5732.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref004] 4.The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2013; 486: 207–214. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref005] 5.Hall MW, Singh N, Ng KF, Lam DK, Goldberg MB, Tenenbaum HC, et al. Inter-personal diversity and temporal dynamics of dental, tongue, and salivary microbiota in the healthy oral cavity. NPJ Biofilms Microbiomes. 2017; 3: 2. doi: 10.1038/s41522-016-0011-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref006] 6.Stewart PS, Costerton JW. Antibiotic resistance of bacteria in biofilms. Lancet. 2001; 358: 135–138. doi: 10.1016/s0140-6736(01)05321-1 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref007] 7.Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR, et al. Oral diseases: a global public health challenge. Lancet. 2019;394: 249–260. doi: 10.1016/S0140-6736(19)31146-8 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref008] 8.Williams JL. A contribution to the study of pathology of enamel. Dental Cosmos. 1897; 39: 269–301. [Google Scholar]

[pone.0259850.ref009] 9.Black GV. A work on operative dentistry. 6th ed. Medico-Dental Publishing Company; 1936. [Google Scholar]

[pone.0259850.ref010] 10.Stephan RM, Miller BF. A quantitative method for evaluating physical and chemical agents which modify production of acids in bacterial plaques on human teeth. J Dent Res. 1943; 22: 45–51. [Google Scholar]

[pone.0259850.ref011] 11.Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL Jr. Microbial complexes in subgingival plaque. J Clin Periodontol. 1998; 25: 134–144. doi: 10.1111/j.1600-051x.1998.tb02419.x [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref012] 12.Simón-Soro A, Mira A. Solving the etiology of dental caries. Trends Microbiol. 2015; 23: 76–82. doi: 10.1016/j.tim.2014.10.010 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref013] 13.Hajishengallis G, Lamont RJ. Beyond the red complex and into more complexity: the polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. Mol Oral Microbiol. 2012; 27: 409–419. doi: 10.1111/j.2041-1014.2012.00663.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref014] 14.Prakash S, Rodes L, Coussa-Charley M, Tomaro-Duchesneau C. Gut microbiota: next frontier in understanding human health and development of biotherapeutics. Biologics. 2011; 5: 71–86. doi: 10.2147/BTT.S19099 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref015] 15.Cani PD. Gut microbiota: changes in gut microbes and host metabolism: squaring the circle? Nat Rev Gastroenterol Hepatol. 2016; 13: 563–564. doi: 10.1038/nrgastro.2016.135 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref016] 16.Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N Engl J Med. 2016; 375: 2369–2379. doi: 10.1056/NEJMra1600266 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref017] 17.Rosier BT, Marsh PD, Mira A. Resilience of the oral microbiota in health: Mechanisms that prevent dysbiosis. J Dent Res. 2018; 97: 371–380. doi: 10.1177/0022034517742139 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref018] 18.De Boever EH, Loesche WJ. Assessing the contribution of anaerobic microflora of the tongue to oral malodor. J Am Dent Assoc. 1995;126: 1384–1393. doi: 10.14219/jada.archive.1995.0049 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref019] 19.Bosy A, Kulkarni GV, Rosenberg M, McCulloch CAG. Relationship of oral malodor to periodontitis: evidence of independence in discrete subpopulations. J Periodontol. 1994; 65: 37–46. doi: 10.1902/jop.1994.65.1.37 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref020] 20.Yaegaki K, Sanada K. Effects of a two-phase oil-water mouthwash on halitosis. Clin Prev Dent. 1992; 14: 5–9. [PubMed] [Google Scholar]

[pone.0259850.ref021] 21.Jenkins GN. Physiology and biochemistry of the mouth. In: Jenkins GNeditor. Sensations arising in the mouth. Blackwell Scientific Publications; 1978. pp. 542–570. [Google Scholar]

[pone.0259850.ref022] 22.Kleinberg I, Westbay G. Oral malodor. Crit Rev Oral Biol Med. 1990; 1: 247–259. doi: 10.1177/10454411900010040401 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref023] 23.Tonzetich J. Direct gas chromatographic analysis of sulphur compounds in mouth air in man. Arch Oral Biol. 1971; 16: 587–597. doi: 10.1016/0003-9969(71)90062-8 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref024] 24.Tonzetich J. Production and origin of malodor: a review of mechanisms and methods of analysis. J Periodontol. 1977; 48: 13–20. doi: 10.1902/jop.1977.48.1.13 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref025] 25.Dawes C. Circadian rhythms in human salivary flow rate and composition. J Physiol. 1972; 220: 529–545. doi: 10.1113/jphysiol.1972.sp009721 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref026] 26.Nolte WA. Oral Microbiology: with basic microbiology and immunology. 4th ed.; Mosby, 1982. pp.198–201. [Google Scholar]

[pone.0259850.ref027] 27.Sharma NC, Galustians HJ, Qaqish J. The clinical effectiveness of a dentifrice containing triclosan and a copolymer for controlling breath odor measured organoleptically twelve hours after toothbrushing. J Clin Dent. 1999; 10: 131–134. [PubMed] [Google Scholar]

[pone.0259850.ref028] 28.Sotozono M, Kuriki N, Asahi Y, Noiri Y, Hayashi M, Motooka D, et al. Impacts of sleep on the characteristics of dental biofilm. Sci Rep. 2021; 11: 138. doi: 10.1038/s41598-020-80541-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref029] 29.Takayasu L, Suda W, Takanashi K, Iioka E, Kurokawa R, Shindo C, et al. Circadian oscillations of microbial and functional composition in the human salivary microbiome. DNA Res. 2017; 24: 261–270. doi: 10.1093/dnares/dsx001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref030] 30.Aagaard K, Petrosino J, Keitel W, Watson M, Katancik J, Garcia N, et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 2013; 27: 1012–1022. doi: 10.1096/fj.12-220806 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref031] 31.McInnes P, Cutting M. Manual of procedures for human microbiome project—Core microbiome sampling protocol A HMP Protocol # 07–001 Version Number: 12.0 29. Available online: https://www.hmpdacc.org/hmp/resources/

[pone.0259850.ref032] 32.Lane DJ. 16S/23S rRNA Sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic Acid Techniques in Bacterial Systematics. John Wiley & Sons. Press; 1991. pp. 115–175. [Google Scholar]

[pone.0259850.ref033] 33.Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991; 173: 697–703. doi: 10.1128/jb.173.2.697-703.1991 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref034] 34.Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microbiol. 1996; 62: 625–630. doi: 10.1128/aem.62.2.625-630.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref035] 35.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006; 72: 5069–5072. doi: 10.1128/AEM.03006-05 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref036] 36.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7: 335–336. doi: 10.1038/nmeth.f.303 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref037] 37.Zaura E, Keijser BFJ, Huse SM, Crielaard W. Defining the healthy “core microbiome” of oral microbial communities. BMC Microciol. 2009; 9: 259. doi: 10.1186/1471-2180-9-259 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref038] 38.Takeshita T, Kageyama S, Furuta M, Tsuboi H, Takeuchi K, Shibata Y, et al. Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study. Sci Rep. 2016; 6: 22164. doi: 10.1038/srep22164 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref039] 39.Eren AM, Borisy GG, Huse SM, Mark Welch JL. Oligotyping analysis of the human oral microbiome. Proc Natl Acad Sci U S A. 2014; 111: E2875–2884. doi: 10.1073/pnas.1409644111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref040] 40.Segata N, Haake SK, Mannon P, Lemon KP, Waldron L, Gevers D, et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012; 13: R42. doi: 10.1186/gb-2012-13-6-r42 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref041] 41.Somineni HK, Weitzner JH, Venkateswaran S, Dodd A, Prince J, Karikaran A et al. Site- and taxa-specific disease-associated oral microbial structures distinguish inflammatory bowel diseases. Inflamm Bowel Dis. 2021. May 14; izab082. doi: 10.1093/ibd/izab082 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref042] 42.Pedersen AML, Belstrøm D. The role of natural salivary defences in maintaining a healthy oral microbiota. J Dent. 2019; 80: S3–S12. doi: 10.1016/j.jdent.2018.08.010 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref043] 43.Sarkar A, Kuehl MN, Alman AC, Burkhardt BR. Linking the oral microbiome and salivary cytokine abundance to circadian osillations. Sci Rep. 2021; 11: 2658. doi: 10.1038/s41598-021-81420-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref044] 44.Wada M, Orihara K, Kamagata M, Hama K, Sasaki H, Haraguchi A, et al. Circadian clock-dependent increase in salivary IgA secretion modulated by sympathetic receptor activation in mice. Sci Rep. 18; 7: 8802. doi: 10.1038/s41598-017-09438-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref045] 45.Sashikumar R, Kannan R. Salivary glucose levels and oral candidal carriage in type II diabetics. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2010; 109: 706–711. doi: 10.1016/j.tripleo.2009.12.042 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref046] 46.Choi JE, Loke C, Waddell JN, Lyons KM, Kieser JA, Farella M. Continuous measurement of intra-oral pH and temperature: development, validation of an appliance and a pilot study. J Oral Rehabil. 2015; 42: 563–570. doi: 10.1111/joor.12294 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref047] 47.Wake N, Asahi Y, Noiri Y, Hayashi M, Motooka D, Nakamura S et al. Temporal dynamics of bacterial microbiota in the human oral cavity determined using an in situ model of dental biofilms. NPJ Biofilms Microbiomes. 2016; 2: 16018. doi: 10.1038/npjbiofilms.2016.18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref048] 48.Loesche WJ. Role of Streptococcus mutans in human dental decay. Microbiol Rev. 1986; 50: 353–380. doi: 10.1128/mr.50.4.353-380.1986 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref049] 49.Widyarman S, Theodorea CF. Effect of reuterin on dual-species biofilm in vitro of streptococcus mutans and veillonella parvula. J Int Dent Med Res. 2019; 12: 77–83. [Google Scholar]

[pone.0259850.ref050] 50.Eşian D, Man A, Burlibasa L, Burlibasa M, Perieanu MV, Bică C. Salivary level of Streptococcus mutans and Lactobacillus spp. related to a high a risk of caries disease. Rom. Biotechnol. Lett. 2017; 22: 12496–12503. [Google Scholar]

[pone.0259850.ref051] 51.Marsh PD, Moter A, Devine DA. Dental plaque biofilms: communities, conflict and control. Periodontol 2000. 2011; 55: 16–35. doi: 10.1111/j.1600-0757.2009.00339.x [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref052] 52.Ooi SW, Smillie AC, Kardos TB, Shepherd MG. Intracellular mineralization of Bacterionema matruchotii. Can J Microbiol. 1981; 27: 267–270. doi: 10.1139/m81-042 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref053] 53.Ritz HL. Microbial population shifts in developing human dental plaque. Arch Oral Biol. 1967; 12: 1561–1568. doi: 10.1016/0003-9969(67)90190-2 [DOI] [PubMed] [Google Scholar]

[pone.0259850.ref054] 54.Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci U S A. 2016; 113: E791–800. doi: 10.1073/pnas.1522149113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref055] 55.Takeshita T, Suzuki N, Nakano Y, Yasui M, Yoneda M, Shimazaki Y, et al. Discrimination of the oral microbiota associated with high hydrogen sulfide and methyl mercaptan production. Sci Rep. 2012; 2: 215. doi: 10.1038/srep00215 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0259850.ref056] 56.Spielman AI, Bivona P, Rifkin BR. Halitosis. A common oral problem. N Y State Dent J. 1996; 62: 36–42. [PubMed] [Google Scholar]

[pone.0259850.ref057] 57.Fukui Y, Yaegaki K, Murata T, Sato T, Tanaka T, Imai T, et al. Diurnal changes in oral malodour among dental‐office workers. Int Dent J. 2008; 58: 159–166. doi: 10.1111/j.1875-595x.2008.tb00192.x [DOI] [PubMed] [Google Scholar]

Subject number	Sex	Age (years)	DMF
1	F	31	14
2	M	31	0
3	F	29	12
4	F	31	2
5	M	33	3
6	M	28	1
7	M	28	4
8	M	33	7
9	M	32	0
10	M	29	2

Subject number	Sex	Age (years)	DMF
1	F	31	14
2	M	31	0
3	F	29	12
4	F	31	2
5	M	33	3
6	M	28	1
7	M	28	4
8	M	33	7
9	M	32	0
10	M	29	2

PERMALINK

Impact of sleep on the microbiome of oral biofilms

Maki Sotozono

Nanako Kuriki

Yoko Asahi

Yuichiro Noiri

Mikako Hayashi

Daisuke Motooka

Shota Nakamura

Mikiyo Yamaguchi

Tetsuya Iida

Shigeyuki Ebisu

Roles

Abstract

Introduction

Materials and methods

Selection of study subjects

Table 1. Characteristics of subjects.

Sample collection and DNA extraction

Fig 1. Experimental schedules.

16S rRNA sequence analysis

Statistical analysis

Results

Profiles of microbiome at each location in the oral cavity

Fig 2. Alpha and beta diversity of microbiomes collected from the oral cavity.

Fig 3. Microbial profiles of all samples.

Fig 4. Microbial profiles of samples at each location.

Bacterial taxa at the phylum level

Fig 5. Relative abundance of bacterial taxa in oral biofilms at the phylum level.

Bacterial taxa at the genus level

Fig 6. Relative abundance of bacterial taxa in oral biofilms at the genus level.

Fig 7. Genera with significantly different relative abundances between post-sleep and pre-sleep schedules.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Yiping Han

Roles

Author response to Decision Letter 0

Decision Letter 1

Yiping Han

Roles

Acceptance letter

Yiping Han

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Subject number	Sex	Age (years)	DMF
1	F	31	14
2	M	31	0
3	F	29	12
4	F	31	2
5	M	33	3
6	M	28	1
7	M	28	4
8	M	33	7
9	M	32	0
10	M	29	2