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Linking the Oral Microbiome and Salivary Cytokine Abundance to Circadian Oscillations Anujit Sarkar1, Melanie N

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Linking the Oral Microbiome and Salivary Cytokine Abundance to Circadian Oscillations Anujit Sarkar1, Melanie N www.nature.com/scientificreports OPEN Linking the oral microbiome and salivary cytokine abundance to circadian oscillations Anujit Sarkar1, Melanie N. Kuehl2,3*, Amy C. Alman1 & Brant R. Burkhardt2* Saliva has immense potential as a diagnostic fuid for identifcation and monitoring of several systemic diseases. Composition of the microbiome and infammation has been associated and refective of oral and overall health. In addition, the relative ease of collection of saliva further strengthens large- scale diagnostic purposes. However, the future clinical utility of saliva cannot be fully determined without a detailed examination of daily fuctuations that may occur within the oral microbiome and infammation due to circadian rhythm. In this study, we explored the association between the salivary microbiome and the concentration of IL-1β, IL-6 and IL-8 in the saliva of 12 healthy adults over a period of 24 h by studying the 16S rRNA gene followed by negative binomial mixed model regression analysis. To determine the periodicity and oscillation patterns of both the oral microbiome and infammation (represented by the cytokine levels), two of the twelve subjects were studied for three consecutive days. Our results indicate that the Operational Taxonomic Units (OTUs) belonging to Prevotella, SR1 and Ruminococcaceae are signifcantly associated to IL-1β while Prevotella and Granulicatella were associated with IL-8. Our fndings have also revealed a periodicity of both the oral microbiome (OTUs) and infammation (cytokine levels) with identifable patterns between IL-1β and Prevotella, and IL-6 with Prevotella, Neisseria and Porphyromonas. We believe that this study represents the frst measure and demonstration of simultaneous periodic fuctuations of cytokine levels and specifc populations of the oral microbiome. Saliva is a biofuid with much promise for identifcation of numerous disease biomarkers 1,2. Te use of saliva as a bio-reservoir for this purpose is also highly pragmatic. Te relative ease of rapid collection with non-invasive accessibility and generous sample size (typically 1 ml) is extremely advantageous within a clinical setting espe- cially if coupled to routine medical visits. Furthermore, the quantity of information that can be obtained from a saliva sample and utilized for analytical purposes is extensive. In examination of the saliva, large scale “omic” approaches, termed “salivaomics”, can be employed such as lipidomics, proteomics and metabolomics 3–5. Single analyte determination can also be performed examining markers of infammation, periodontal disease, and hor- mones such as cortisol 6,7. Taken together, the relative abundance of measurable biomarkers in saliva has shown promise in the therapeutic identifcation of numerous diseases ranging from cancer 8–10, Sjögren’s syndrome10, type 1 and 2 diabetes mellitus 6,11, psychiatric disorders 12, cardiovascular disease 13 and many others (reviewed in 1). In addition to the numerous proteins and metabolites present in the saliva, there is also an abundant amount of nucleic acids including DNA, mRNA and microRNA 14,15. Approximately 30% of the DNA present in the saliva is of non-human origin and is from the oral microbiome. Te composition of the oral microbiome is an extremely complex niche with estimates of up to 100 billion bacteria with 700 predominant taxa 16. Te oral microbiome composition can be impacted by numerous factors such as periodontal disease, diet or smoking 17,18. Te relationship between the oral microbiome and local and systemic diseases has been demonstrated and is still being actively investigated across various disciplines 19. Various studies have indicated that the composition of the microbiome are strongly associated with changes in periodontal status and can be an indicator of systemic diseases20,21. In addition, the composition of the oral microbiome is believed to afect overall infammation and immunology. For example, periodontal disease has been associated with cardiovascular disease, possibly due to the induction of chronic infammation22. Tere is increasing evidence that specifc oral bacterial species and particularly periodontal pathogens have an etiological role in atherogenesis which is associated with a height- ened state of oral and systemic infammation 23,24. Te oral microbiome and infammation have been associated 1College of Public Health, University of South Florida, Tampa, FL 33612, USA. 2Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, Tampa, FL 33620, USA. 3Present address: IPS Labs, 1 Harvard Way, Hillsborough Township, NJ 08844, USA. *email: [email protected]; bburkhardt@ usf.edu Scientifc Reports | (2021) 11:2658 | https://doi.org/10.1038/s41598-021-81420-3 1 Vol.:(0123456789) www.nature.com/scientificreports/ Characteristics Age (years)* 26.5 (± 5.2) Male (n)† 5 (41.7) Race/ethnicity (n)† White 10 (83.3) Hispanic 2 (16.7) BMI (kg/m2)* 23.1 (± 3.5) BMI category (n)† Underweight (< 18.5 kg/m2)† 1 (8.3) Normal (18.5– < 25 kg/m2)† 6 (50) Overweight (25– < 30 kg/m2)† 5 (41.7) Obese (≥ 30 kg/m2)† 0 (0) Current smoker (n; cotinine > 10 ng/ml) 0 Amount of sleep prior to collection (h)* 7.1 ± 0.79 Average wake time* 7:47 A.M. ± 1.3 Average time to bed* 12:17 A.M. ± 1.3 Table 1. Subject characteristics (n = 12). *Data presented as mean ± SD. † Data presented as number (%). with numerous other diseases as well, such as cancer, Alzheimer’s, metabolic syndrome and mental illness 25. Terefore, both the oral microbiome and oral infammation could provide useful insights that may eventually lead to salivary diagnostics for the prediction and identifcation of various diseases. Saliva’s use as a diagnostic fuid has been hindered due to a lack of understanding of biomolecules within saliva and their relevance to disease etiology, combined with the diurnal/circadian variations of these biomolecules 1. If saliva is to be utilized as a non-invasive source of biomarkers indicating systemic disease, biological variation in association with diurnal rhythms must be considered and evaluated. At present, there have been limited studies examining the circadian oscillations of the human salivary microbiome and with conficting conclusions 26,27. Most studies examining diurnal microbiome variation have been performed evaluating the gut28–31. Similarly, limited studies have been conducted examining circadian rhythm of oral infammation as determined by meas- urement of various cytokines 32. Altogether, similar studies have examined diurnal variation within both the oral microbiome and various infammatory biomarkers separately but none to our knowledge have examined these simultaneously and longitudinally within the same individuals. To examine oral infammatory and microbiome diurnal rhythms, we examined longitudinal saliva sam- ples collected from 12 healthy adults and measured a panel of cytokines by multiplexing analysis and the oral microbiome via 16S rRNA analysis across both a 24-h time period and across 72 h for 2 individuals. Following determination of cytokine concentrations and bacterial populations, we examined infammatory periodicity by performing a normalization method on the measured cytokines and the association between the cytokines and specifc bacterial populations. We also tested the association between cytokine levels and OTUs from oral microbiome using a negative binomial mixed model regression to account for both the repeated measures taken on the same individuals over time and the over-dispersion commonly found in microbiome count data. Results Participant characteristics. Participant characteristics are displayed in Table 1. Te mean participant age was 26.5 (± 5.2) years. Te majority were female (58.3%) and White, non-Hispanic (83.3%). Average BMI was 23.1 (± 3.5) and most participants (11 out of 12) were in the normal (n = 6) to overweight (n = 5) BMI category. Te average amount of sleep prior to frst collection was 7.1 ± 0.79 h. All participants followed a typical sleep/ wake cycle of daytime activity followed by nighttime sleep. Te average wake time was 7:47 A.M. ± 1.3 S.D. and average time to bed was 12:17 A.M. ± 1.3 S.D. with a typical time of wake activity of 16 h Detailed listing of col- lection times is supplied in Supplementary Table S1. Salivary cotinine levels were also determined and found to be below 10 ng/ml for all participants. In addition, participants completed an oral health questionnaire inquiring about gingival condition and if loose tooth was present. None reported a loose tooth and 9 out of 12 reported gingival condition in the excellent, very good or good category with 3 reporting fair condition. Alpha diversity and diurnal variation in composition of the salivary microbiome. Alpha diver- sity analysis was performed to measure the overall bacterial richness and composition within the salivary micro- biome. Initial run metrics of the 16S Ilumina MiSeq analysis identifed 223 operational taxonomic units (OTUs) with 7033 average reads per sample (data not shown). Observed OTUs were evaluated along with Shannon Diversity Index (SDI) and Chao1 analysis (Fig. 1) in relation to time of sample collection across the 12 subjects with samples collected at 6 time points (at waking, afer 30 min., 3 h., 6 h., 9 h. and just prior to sleep) over a single 24 h period. Te bacterial richness, diversity or abundance of the oral microbiome did not signifcantly difer within the samples among individuals either at matched time of collection or across time of collections (Fig. 1). In addition, no
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