The Oral and Gut Bacterial Microbiomes: Similarities, Differences, and Connections

Abstract

Background: The oral cavity is associated with local and systemic diseases, although oral samples are not as commonly studied as fecal samples in microbiome research. There is a gap in understanding between the similarities and differences in oral and gut microbiomes and how they may influence each other. Methods: A scoping literature review was conducted comparing oral and gut microbiome communities in healthy humans. Results: Ten manuscripts met inclusion criteria and were examined. The oral microbiome sites demonstrated great variance in differential bacterial abundance and the oral microbiome had higher alpha diversity as compared to the gut microbiome. Studies using 16S rRNA sequencing analysis resulted in overall community differences between the oral and gut microbiomes when beta diversity was analyzed. Shotgun metagenomics sequencing increased taxonomic resolution to strain level (intraspecies) and demonstrated a greater percentage of shared taxonomy and oral bacterial translocation to the gut microbiome community. Discussion: The oral and gut microbiome bacterial communities may be more similar than earlier research has suggested, when species strain is analyzed through shotgun metagenomics sequencing. The association between oral health and systemic diseases has been widely reported but many mechanisms underlying this relationship are unknown. Although future research is needed, the oral microbiome may be a novel interventional target through its downstream effects on the gut microbiome. As nurse scientists are experts in symptom characterization and phenotyping of patients, they are also well posed to lead research on the connection of the oral microbiome to the gut microbiome in health and disease.

The oral cavity and the gut are two important gastrointestinal tract structures that are primarily responsible for energy intake and metabolic processing necessary for human survival (Greenwood-Van Meerveld et al., 2017). The human microbiome is defined as the group of bacteria, archaea, viruses and fungi that populate and coexist with humans and studying this community is crucial to understanding the complex factors that contribute to multifactorial health and disease (Gilbert et al., 2018). Many studies in the microbiome literature refer to dysbiosis as an alteration or interruption in the bacterial community of a specific microbiome (Belizário et al., 2018; Maruvada et al., 2017; Weiss & Hennet, 2017), although determining the differences between physiologic variation and true dysbiosis is difficult without having a clear understanding of a microbiome’s standard composition (Lloyd-Price et al., 2017).

Nurse scientists have a long history in examining the oral environment as a way to improve care (Ames et al., 2011; Feider et al., 2010; Munro & Baker, 2018; Prendergast et al., 2009). Focusing on the study of the oral microbiome is the next step in this science as oral cavity health status is linked to systemic outcomes (Acharya et al., 2017; Ames et al., 2019; Chapple et al., 2017; Farquhar et al., 2017; Lockhart et al., 2012). Nevertheless, the majority of studies exploring the human microbiome have collected fecal samples aimed at analyzing the gut microbiome as a sole measure. This poses an opportunity for nurse scientists to use the oral microbiome as a tool to understand the multifaceted relationships between the environment and human health and disease using a holistic patient care approach.

The Human Microbiome Project (HMP) was a carefully designed study that sampled 15 body sites in men and 18 sites in women, including nine oral samples (Figure 1) and one fecal sample for microbiome analysis in over 200 healthy volunteers from the United States (Human Microbiome Project Consortium, 2012). Nevertheless, it was noted that most variance in microbiome diversity across individuals was not explained by the covariates that were measured in the HMP study cohort (Lloyd-Price et al., 2017). For example, transit time, diet characteristics and many pharmaceutical treatments influence the microbiome across individuals (Falony et al., 2016; Vandeputte et al., 2016). These covariates were not captured in detail with the HMP due to the large-scale nature of the research, and questions remain as to whether the microbiome is dynamic across and within individuals or as to whether there were confounders to the analysis that were not initially measured. As nurses have expertise with rigorous and detailed phenotyping and direct contact with individuals (Alexander et al., 2016; Cashion et al., 2016; Joseph et al., 2016; Wright et al., 2016), we are well positioned to study this gap.

Figure 1.

Oropharyngeal sites collected from the Human Microbiome Project. Nine oropharyngeal sites were sampled from the Human Microbiome Project.

Arguably, two of the most functionally active microbiomes affecting human health, the oral and the gut, require much more detailed study especially of their interrelationships (Kodukula et al., 2017). While there has been extensive research on the focus of the human microbiome (human AND microbiome yielded 36,847 results on PubMed), there is still a large gap in research focusing on understanding the similarities and differences between the oral and gut microbiome communities, and ultimately, the functional implications of these associations. Despite being linked anatomically, the current thought is that the oral and gut microbiomes are inhabited by diverse bacterial communities that perform divergent functions (Segata et al., 2012). However, studies that performed oral interventions and subsequently studied the gut microbiome provide evidence that these two biomes may not only be more related, but that alterations in oral bacterial communities by beneficial (periodontal treatment) or detrimental (oral administration of a dental pathogen Porphyromonas gingivalis) interventions may be linked to downstream gut microbiome changes (Bajaj et al., 2018; Olsen & Yamazaki, 2019). The aim of this review is to evaluate and synthesize studies that analyzed both the oral and gut microbiome bacterial communities. We present results investigating the bacterial composition of the oral and gut microbiomes, how they are similar and divergent, and if the oral microbiome community and the bacterial community in the gut microbiome are interrelated.

Method

A scoping review methodology was used to synthesize the currently available literature on studies comparing the oral and gut microbiome communities in healthy humans. This method was selected in order to characterize the nature and scope of currently available research literature, and focused more on the research results versus the study design and methodology used to obtain them (Grant & Booth, 2009).

Systematic searches were carried out across four databases: PubMed, Web of Science, Scopus, and Embase. There was no restriction applied for year of publication and language; however, if the free translation of foreign language articles was not available, the study was excluded from further analysis. Only studies analyzing human populations were selected using filters in all databases. The final search strategies were selected by the review team with the help of a librarian after a series of pilot searches. Records that contained the following three components in the title and abstract were targeted: 1) study of the microbiome, using the search terms “microbiome” “microbiomes” “microbiota” “microbe” “microbes” “microbial”; 2) presence in the gut using the terms “gut” “gastrointestinal microbiome” “feces” “stool”; and 3) presence in oral cavity using search terms “mouth” “tongue” “oral cavity” and “saliva.”

After removal of duplicates, one of the authors (NK) screened the titles of all records for eligibility. The criteria for title screening was to exclude any article that did not study both oral AND gut microbiomes and their relationship. Following that, two authors (NA & NK) independently screened the abstracts of the records for eligibility. Disagreements were resolved by discussion of the review team. Full texts of the remaining articles were further assessed by three authors (NA, NK & KM) for eligibility. Studies were included if they contained a comparison between the oral and the gut microbiomes and/or presented observational assessment of oral and gut microbiome communities. Records were excluded if: 1) the study did not use next generation sequencing; 2) was not a research study (reviews, letters or editorials) or not a peer reviewed publication (conference paper or abstract); 3) was not available in English; and 4) comparison of the oral and gut microbiome communities were not assessed as one of the aims of the study. Finally, interventional studies where pharmacologic or bacterial manipulation of the microbiome communities was performed were additionally excluded. To ensure that all available research was included, references of the articles were reviewed for any additional relevant study based on the inclusion criteria. A final screen of all studies was performed by all authors to verify all reviewed studies met inclusion criteria.

All articles that met inclusion criteria were evaluated and the following information was extracted: study aim/objective, groups or diseases studied, microbiome sites analyzed, analysis methods, shotgun vs. 16S ribosomal ribonucleic acid (rRNA) gene sequencing, region of 16S gene amplified, and main findings. Table 1 provides an overview of all included manuscripts.

Table 1.

Description of Studies Comparing Oral and Gut Microbiome Communities.

Author Year	Study Aim	Specimen Collection	HMP Yes/No	Subject Age	Shotgun vs 16S	Region	Extraction	Database
Costello et al. (2009)	To understand human microbiota in healthy adults including how bacterial diversity is partitioned across body habitats, people & time	N = 9 adults (6 M, 3 F) Sampled × 2 days (two oral sites [dorsal tongue, oral cavity rinse] & stool) Repeated 3 months later over 2 days	No	Range 30–35	16S—Roche 454	V2	MoBio PowerSoil	Greengenes
Ding & Schloss (2014)	Partitioned HMP data into community types for each body site sampled using DMM models to map enterotypes	N = 300 (50% F). HMP Sampling Protocol.	Yes	Median 25 (range 18–40)	16S—Roche 454	V3-V5	MoBio PowerSoil	SILVA
Huse et al. (2012)	Identified a set of core OTUs across individuals & body sites	N = 229 (50% F) HMP Sampling Protocol.	Yes	Median 25 (range 18–40)	16S—Roche 454	V1-V3 & V3-V5	MoBio PowerSoil	SILVA
Iwauchi et al. (2019)	Study if transmission of oral bacteria to the GI tract is more prevalent in the elderly vs. adults	N = 29 elderly, 30 adults. 1 time point. Samples = two oral (one plaque, one tongue), fecal	No	Elderly (80.2 ± 9.1) adults (35.9 ± 5.0)	16S—Illumina MiSeq	V3-V4	Custom protocol	Greengenes
Li et al. (2012)	Investigate microbiome diversity across body habitats & individuals	N = 229 (50% F) HMP Sampling Protocol.	Yes	Median 25 (range 18–40)	16S—Roche 454	V3-V5	MoBio PowerSoil	SILVA
Lloyd-Price et al. (2017)	To further knowledge of baseline human microbial diversity across body sites	N = 265 (50% F) HMP Sampling Protocol.	Yes	Median 25 (range 18–40)	16S—Roche 454	V3-V5	MoBio PowerSoil	SILVA
Schmidt et al. (2019)	To test if microbial transmission along the GI tract (oral to gut) is more common that previously appreciated	Collected data merged with publicly available data. Control samples total = 340 stool (114 M, 225 F), 259 oral (93M, 166F). 3 time points.	No	Mean 48.72 ± 11.46	Shotgun—Illumina HiSeq & NextSeq	N/A	GNOME DNA (MP Biomedical; Allprep Kit (Qiagen); MoBio PowerSoil	NGless & ETE3 toolkit
Segata et al. (2012)	Compare the composition, relative abundance, phylogenetic & metabolic potential of the bacterial populations inhabiting sites along the digestive tract	N= 229 (50% F) HMP Sampling Protocol. Shotgun sequencing in 98 subjects.	Yes	Median 25 (range 18–40)	16S—Roche 454; Shotgun—Illumina Gaiix	V3-V5	MoBio PowerSoil	SILVA
Stearns et al. (2011)	To address the changing microbial communities along the GI tract & identify relevant bacterial communities	N = 4 (2F, 2M). Samples: mouth plaque (supra-gingival & sub-gingival, tongue), stomach (antrum & body), duodenum, colon (transverse & descending), rectum & stool	No	Not listed	16S—Illumina	V3	MoBio PowerSoil—bead beating & heating steps added	PyNAST aligner in QIIME v 1.2.0 aligned to Greengenes
Vasapolli et al. (2019)	To characterize the transcriptionally active bacteria (i.e., alive & capable of reproducing) in saliva, stomach (corpus, antrum), duodenum, terminal ileum, ascending/descending colon & feces in GI tract	N = 21 (50% F). Samples collected during upper & lower GI tract endoscopy. Tissue samples from biopsies	No	Mean 59 ± 12.3 years	16S—Illumina MiSeq	V1-V2	RNA extracted with RNeasy kit (Qiagen)	Aligned with MOTHUR using the SILVA ref. database

Note. M = male; F = female; DMM = Dirichlet multinomial mixture; HMP = Human Microbiome Project. HMP Sampling Protocol = seven samples from mouth (buccal mucosa, keratinized gingiva, hard palate, saliva, tongue, two surfaces along tooth), two oropharyngeal sites (throat and palatine tonsils), stool.

Results

Literature Search

Database searches returned 1,344 results whereas after duplicate removal 803 records were retained for title screening (Figure 2). Sixty-two studies were kept after title screening out of which 29 met the criteria for full text review. Of the excluded manuscripts, 25 studies did not use the required sequencing methods or did not assess oral and gut microbiome. Six were review articles/conference abstracts and two articles were in a foreign language. Three additional articles were found through reference searches.

Figure 2.

Flow diagram of literature search and study selection.

After full text review by all authors, 10 studies of healthy individuals were selected for final synthesis. Five studies used data from the HMP (Ding & Schloss, 2014; Huse et al., 2012; Li et al., 2012; Lloyd-Price et al., 2017; Segata et al., 2012). The remaining investigators collected oral and fecal samples for microbiome analysis (Costello et al., 2009; Iwauchi et al., 2019; Stearns et al., 2011; Vasapolli et al., 2019), while one study involved the collection of new samples for combined analysis with samples obtained from publicly available datasets (Schmidt et al., 2019). Most of the work (seven studies) used 16S rRNA gene sequencing as a solo analysis measure (Costello et al., 2009; Ding & Schloss, 2014; Huse et al., 2012; Iwauchi et al., 2019; Li et al., 2012; Vasapolli et al., 2019), while one study used shotgun metagenomics sequencing only (Schmidt et al., 2019), and two used both 16S rRNA and shotgun metagenomics sequencing (Lloyd-Price et al., 2017; Segata et al., 2012).

16S rRNA Amplicon Sequencing Analysis Data

Oral Bacterial Community Structure is Dependent on Site Sampled

The oral microbiome is an important site in microbiome analysis because it is both exposed to the external environment and is connected to the gut microbiome through the gastrointestinal tract. Unlike the gut microbiome/fecal sampling sites, there are multiple sites within the mouth that house different bacterial communities that can be sampled by minimally invasive measures. Across the studies, samples collected to analyze the oral microbiome ranged from saliva only (Schmidt et al., 2019; Vasapolli et al., 2019) to nine sites in the mouth and throat, including saliva, in the HMP studies (Ding & Schloss, 2014; Huse et al., 2012; Li et al., 2012; Lloyd-Price et al., 2017; Segata et al., 2012). Prior to HMP, some of the earlier investigators sampled from one to three oral sites but combined all of the results together when reporting on the oral microbiome (Costello et al., 2009; Stearns et al., 2011). The investigators designing the HMP sampling protocol likely had insight to the variability of samples in the oral cavity and throat, as there were nine oropharyngeal samples collected and separately analyzed as part of the study procedures (Figure 1; Human Microbiome Project Consortium, 2012).

Using the HMP data, Segata et al. (2012) stratified the oropharyngeal samples into three distinct categories of microbiome community types based on the characteristics and abundance patterns of the bacterial taxa housed in these communities in order to understand which sites had bacterial communities that were the most similar. Conversely, Ding and Schloss (2014) used Dirichlet multinomial mixture models to cluster the same HMP data and identify “community-types” per site sampled. These community-types were used to test associations between bacterial communities both within the same sampling site across individuals and within the same individual across body sampling sites (Ding & Schloss, 2014). Using the three oral microbiome groups described above, Segata et al. (2012) presented differences in relative abundance patterns across all bacterial phyla highlighting the difference in bacterial community characteristics despite the close proximity in location (Figure 3).

Figure 3.

Average relative abundance of bacterial phyla by oral sites. Data from Segata et al. (2012) used to create figure. Oral groups stratified into groups by Segata et al. (2012) based on taxa similarity. Numbers are expressed as relative abundance (%) at the taxonomic level of phylum.

Community metrics, such as alpha diversity and community richness estimates, varied substantially based on the oral site sampled. Alpha diversity of microbiome samples is used to describe the bacterial richness (number of taxa) and evenness (representation of the various taxa in a sample) across individual microbiome samples (Maki et al., 2019). Subgingival plaque and supragingival plaque were the most diverse oral samples (Supplemental Figure 1, Supplemental Table 1), of the nine samples collected from the HMP, when Shannon entropy, inverse Simpson and richness (the absolute number of species counted) estimates were all considered (Huse et al., 2012; Li et al., 2012; Segata et al., 2012). The buccal mucosa had the lowest alpha diversity when calculated by inverse Simpson (Segata et al., 2012), but in the median of oral samples according to richness estimates (Huse et al., 2012). Conversely, the hard palate had the lowest richness estimate (Huse et al., 2012), but had higher alpha diversity when inverse Simpson and Shannon indices were considered (Li et al., 2012; Segata et al., 2012).

Alpha Diversity Is Higher in Oral Samples Versus Fecal When Shannon or Simpson Indices are Used

In the studies reviewed here, alpha diversity was analyzed by bacterial species richness, Shannon diversity index, and inverse Simpson diversity index (Table 2). Despite the variance in alpha diversity across oral sites, in the studies that used the inverse Simpson index or the Shannon diversity index (Li et al., 2012; Segata et al., 2012; Stearns et al., 2011; Vasapolli et al., 2019), the alpha diversity of microbiome samples was much higher in the oral samples, as compared to the stool samples (Supplemental Figure 1, Supplemental Table 1). The average alpha diversity of stool samples was similar to the lower diversity ranges in oral sample sites (buccal mucosa and keratinized gingiva) when inverse Simpson index or the Shannon diversity index were used (Li et al., 2012; Segata et al., 2012).

Table 2.

Alpha and Beta Diversity Metrics Used for Oral and Gut Comparisons.

Measure	Description	Studies
Alpha Diversity
Bacterial species richness	Measure of the absolute number of different bacterial species or taxa (Clarke et al., 2014)	Huse et al. (2012)
Shannon diversity index	Diversity index that considers both species richness (or total number of species/taxa present) & the evenness or how equally these taxa are distributed in the sample (Clarke et al., 2014)	Li et al. (2012); Stearns et al. (2011); Vasapolli et al. (2019)
Simpson or Inverse Simpson diversity index	Simpson diversity index (λ) is calculated considering both the number of individual species/taxa present the probability that any two individual taxa, chosen at random, are from the same species. The inverse Simpson is when the reciprocal of Simpson’s dominance λ is calculated (Magurran, 2004; Simpson, 1949)	Segata et al. (2012); Vasapolli et al. (2019)
Beta Diversity
Unweighted UniFrac	Distance metric for comparing biologic communities. Incorporates information about the phylogenetic distance between taxa. Considered a qualitative measure as Unweighted UniFrac only considers presence or absence of taxa (Lozupone et al., 2007)	Costello et al. (2009); Iwauchi et al. (2019); Stearns et al. (2011)
Weighted UniFrac	Distance metric for comparing biologic communities. Incorporates information about the phylogenetic distance between taxa. Considered a quantitative measure as Weighted UniFrac has a correction factor for taxa relative abundance (Lozupone et al., 2007)
Bray-Curtis Distance	Bray-Curtis distances can use either similarity or dissimilarity (1-similiarity measure) values to highlight the similarity or difference in ecological communities. When interpreting Bray-Curtis dissimilarity values, a result of 0 means the microbiome communities share are identical where a value of 1 indicates the communities are significantly different both in presence/absence of bacterial taxa & the relative abundance of those taxa (Bray & Curtis, 1957; Clarke et al., 2014).	Schmidt et al. (2019); Segata et al. (2012); Vasapolli et al. (2019)

Similarity of Oral and Gut Microbiome Communities by Beta Diversity Is Dependent on Analysis Method

Beta diversity of microbiome samples is defined as between-sample differences in taxonomic composition (Clarke et al., 2014; Maki et al., 2019), and these between-sample differences can be investigated across two sample sites, ecological communities, or populations. In order to analyze and investigate beta diversity, the entire taxonomic community is reduced to a smaller number of components in order to calculate distances between samples (Jost, 2007). The two beta diversity analysis methods used to investigate between-sample differences were the Bray-Curtis measure (Bray & Curtis, 1957) and the UniFrac distance measure (both weighted and unweighted, Table 2; Lozupone et al., 2007). Principal Coordinates Analysis (PCoA) plots were subsequently used to visualize the matrix of distance measures (Bray-Curtis or UniFrac) in order to interpret the clustering and proximity of microbiome samples from different communities to each other (i.e., oral and gut). According to studies using the Bray-Curtis distance metric (Lloyd-Price et al., 2017; Schmidt et al., 2019; Segata et al., 2012; Vasapolli et al., 2019), the oral and gut communities were very different from each other based on their overall community structure evidenced by the discrete separation between oral and gut communities in the PCoA plots (Figure 4 for an exemplar). This suggests the two microbiomes are unrelated and do not share bacterial taxa or community characteristics. Analysis of similarity testing (ANOSIM) was additionally used in two studies to statistically evaluate if samples within categories (within same individuals over time) were more similar than samples in different categories (i.e., oral versus gut sites, Segata et al., 2012; Vasapolli et al., 2019). These studies reported the difference in community structure using Bray-Curtis distance calculations and visualization techniques. Segata et al. (2012) and Vasapolli et al. (2019) concluded differences in oral and gut communities to be statistically significant using ANOSIM testing (p < 10⁻²⁰ and p = 0.0001 between oral and gut sample site groups, respectively).

Figure 4.

Principal Coordinates Analysis plot using Bray–Curtis distances among all microbes at the species level. Principal Coordinates Analysis plot using Bray-Curtis distances for all taxa encompassing HMP1-II at the species level. PCo = Principal Coordinates Analysis. Figure adapted from Lloyd-Price et al. (2017).

UniFrac is another distance metric used for comparing microbiome communities and differs from Bray-Curtis distance in that it also integrates taxonomic information in its calculation where unweighted UniFrac counts presence/absence of taxa and weighted UniFrac has a correction factor for taxonomic relative abundance (Table 2 for an expanded definition). Studies that used UniFrac to assess oral/gut microbial community differences reported clear separation of discrete clusters by body habitat site on PCoA plots when unweighted UniFrac was used (Costello et al., 2009; Iwauchi et al., 2019; Stearns et al., 2011). Interestingly, when weighted UniFrac was used as the community distance metric, not only did the oral and gut microbial communities appear closer in space on the PCoA plots, but the percentage of variation in the data was better represented in the first two PCs (Costello et al., 2009; Iwauchi et al., 2019; Stearns et al., 2011).

Instead of performing beta diversity metrics such as Bray-Curtis distances or UniFrac, Ding and Schloss (2014) used Dirichlet multinomial mixture models to stratify the oral and fecal microbiome samples into community types and tested associations across body sites using the Fisher exact test. Their work paralleled other manuscripts that the strongest associations are within the same body region (Costello et al., 2009; Schmidt et al., 2019), but suggested there were also similarities between the stool and oral cavity sampling site bacterial communities with the saliva sampling site having the strongest oral-gut association (Ding & Schloss, 2014).

Taxonomic Signatures are Present for the Oral and Gut Microbiome Communities at the Phylum Level

Gut microbiome phylum level data

Three studies of healthy controls reported comprehensive descriptions of bacteria at the level of phylum (Figure 5) between oral and fecal sampling sites (Costello et al., 2009; Segata et al., 2012; Stearns et al., 2011). Bacteroidetes and Firmicutes were the prominent phyla of the gut microbiome and the remaining bacteria represented less than 10% of the total relative abundance of bacterial phyla. In fecal samples, Bacteroidetes were dominant with average relative abundances of 50%–60% (Costello et al., 2009; Stearns et al., 2011) up to 65.2% (Segata et al., 2012), and Firmicutes were reported at relative abundances of 30%–35% (Costello et al., 2009; Segata et al., 2012; Stearns et al., 2011). Interestingly, Bacteroidetes species were found to be highly variable across individuals in the fecal samples (Lloyd-Price et al., 2017). Proteobacteria ranged from 3% to 5%, while Verrucomicrobia, Lentisphaerae, Actinobacteria, and Fusobacteria were less common bacteria and generally occupied less than 1% of fecal samples if they were present (Costello et al., 2009; Segata et al., 2012; Stearns et al., 2011).

Figure 5.

Taxonomic classification of bacteria from phylum to strain level.

Oral microbiome phylum level data

In oral samples, there was a greater number of sequences derived from bacterial taxa at the phylum level (compared to fecal samples), but smaller relative abundance levels were present across these taxa. All oral samples were defined by Firmicutes bacteria as the most abundant taxa, unlike fecal samples that contained the highest levels of Bacteroidetes. The average relative abundance of Firmicutes bacteria was 40%–43% in tongue and saliva oral samples (Costello et al., 2009; Segata et al., 2012). Bacteroidetes and Proteobacteria relative abundance levels followed Firmicutes and were present at similar relative abundances of approximately 20% in supragingival and subgingival plaque, tongue and saliva oral samples (Costello et al., 2009; Segata et al., 2012; Stearns et al., 2011). Actinobacteria and Fusobacteria occupied 5%–10% of samples, followed by phyla not seen in gut/fecal samples including TM7, SE1, Spirochaetes and Acidobacteria (Costello et al., 2009; Segata et al., 2012; Stearns et al., 2011).

There are both Differences and Similarities Between the Oral and Gut Microbiomes at the Genus Level

Gut microbiome genus-level taxonomic signatures

At the genus level, fecal samples had the highest relative abundance of Bacteroides (Ding & Schloss, 2014; Huse et al., 2012; Iwauchi et al., 2019; Segata et al., 2012). The gut microbiome bacterial community were also defined by high relative abundances of Alistipes, Akkermansia, Anaerotruncus, Blautia, Faecalibacterium, Odoribacter, Parabacteroides, Roseburia, and Sutterella at the genus level (Costello et al., 2009; Schmidt et al., 2019; Segata et al., 2012; Vasapolli et al., 2019). Bacterial community types in the gut microbiome were found to be more variable across individuals, but more stable within individuals over time, as compared to the oral microbiome (i.e., consistent values over repeated measures; Costello et al., 2009; Ding & Schloss, 2014).

Oral microbiome genus-level taxonomic signatures

Buccal mucosa, gingiva and hard palate oral samples were defined by a very high relative abundance (47.32%) of Streptococcus genera (Segata et al., 2012). Other genera that had higher relative abundance values in saliva, tongue, tonsils, and throat samples were Actinomyces, Fusobacterium, Leptotrichia, Streptococcus, Pasteurella, Neisseria, and Veillonella (Costello et al., 2009; Ding & Schloss, 2014; Iwauchi et al., 2019; Segata et al., 2012), and the oral cavity was reported to have a greater set of “core” genera that were consistently sequenced across all individual subjects (Huse et al., 2012). Longitudinal oral samples were measured in a subset of patients and there were conflicting results about the stability of the microbial taxa within individuals. Some authors reported that species in the oral microbiome samples had more temporal variability and dynamics, especially when compared to the gut microbiome (Ding & Schloss, 2014; Lloyd-Price et al., 2017), while others stated the oral microbiome was relatively stable over time (Schmidt et al., 2019).

Overlapping genus-level taxa between oral and gut microbiomes

There were several bacteria annotated at the genus level that were identified in both the oral and gut microbiome samples (Figure 6). Although multiple bacterial taxa were present in both oral and gut microbiome communities, the relative abundance levels of the bacteria were significantly different between sites. For example, Streptococcus had a relative abundance of 47.32% in oral group 1 (buccal mucosa, keratinized gingiva, and hard palate) and abundance of 20.36% in oral group 2 (throat, palatine tonsils, tongue dorsum, saliva), compared to a relative abundance of 0.07% in stool (Segata et al., 2012). Conversely, Bacteroides had a relative abundance of 47.82% in stool samples, compared with 0.23% abundance in oral group 1 and 0.15% abundance in oral group 2 (Segata et al., 2012). A notable exception to this pattern was Prevotella, which had the closest relative abundance levels between oral and gut microbiome sites (Costello et al., 2009; Ding & Schloss, 2014; Iwauchi et al., 2019; Segata et al., 2012). Prevotella relative abundance levels were 3.66% in oral group 1, 11.56% in oral group 2 and 3.16% in fecal samples (Segata et al., 2012).

Figure 6.

Genus-level taxa shared between the oral and gut microbiome communities. Data from Segata et al. (2012) used to create figure. There were several bacteria, at the genus level, that were present in both the oral and gut microbiomes, although relative abundance levels were significantly different. Oral 1: buccal mucosa, keratinized gingiva, and hard palate. Oral 2: throat, palatine tonsils, tongue dorsum and saliva. The shared taxa, at the genus level, were included if they were detected in both oral (groups 1 and 2) and gut microbiome samples at any level, and the relative abundance of taxa ranged from of 0.001%–47.82% across sites. Prevotella (orange boxes) had the highest overlap and most similar relative abundance across oral and gut sampling sites.

Shotgun Metagenomics Sequencing Analysis Data

Oral and Gut Microbiome Communities are Similar at Strain Taxonomic Level

Most of the studies analyzed used 16S rRNA gene sequencing to quantify bacteria to the level of genus or species (Table 1), but 2 studies used shotgun metagenomics to calculate microbial populations to the level of bacterial strain (Lloyd-Price et al., 2017; Schmidt et al., 2019). At the species level, 40% of the total identified species were present in both the oral (saliva) and gut communities but the relative abundances in the shared bacterial species varied greatly between the two communities (Schmidt et al., 2019). These findings were consistent with the shared oral and gut microbiome taxa analysis performed at the genus level by Segata et al. (2012). In order to study the microbiome communities at a higher resolution than bacterial species, Schmidt et al. (2019) profiled microbial single nucleotide variants as a proxy for bacterial strain and calculated oral-fecal transmission scores to quantify the similarity between oral and fecal single nucleotide variant profile across individuals. Out of the 125 species quantified in both the oral and gut microbiome, 74 species showed high scores for oral-fecal transmission within individual participants (Schmidt et al., 2019). Smaller genomes and oxygen tolerant species were more likely to be transmitted than larger genomes and anaerobes, and transmission was hypothesized to occur by passive microbial transmission facilitated by the movement of saliva through the gastrointestinal tract (Schmidt et al., 2019). Similarly, Lloyd-Price et al. (2017) observed core metabolic pathways that were ubiquitous in oral and fecal samples and annotated these core pathways to multiple genera in both oral and gut microbiome sampling sites. Paralleling bacterial taxon longitudinal stability, observed metabolic pathway genes were slow to change over time in the gut microbiome, while pathway genes were greatly variable in the oral microbiome sampling sites with repeated measures (Lloyd-Price et al., 2017).

Discussion

The main findings from this review are that ecological characteristics vary significantly across oral sampling sites (Costello et al., 2009; Segata et al., 2012), the oral microbiome has a more diverse and dynamic bacterial community as compared to the gut microbiome (Li et al., 2012; Stearns et al., 2011; Vasapolli et al., 2019), and the taxonomic characteristics of the oral and gut microbiome may be more similar than previously realized, when analyzed at the strain level (Schmidt et al., 2019; Segata et al., 2012). Additionally, strain-level analysis provided evidence that these oral-gut bacterial community similarities resulted from passive translocation of the oral microbiome bacteria to the gut microbiome through saliva (Schmidt et al., 2019). This suggests the oral microbiome may be a novel interventional target for manipulating the gut microbiome through the oral microbiome’s downstream influence.

The results from these manuscripts provide guidance for best practices when using oral specimens to study the oral microbiome. In research planning, the physiologic influence of the taxa or bacterial population of interest should be considered when planning the oral sampling site(s) based on the research question or study hypotheses. The site used for oral microbiome evaluation should be specified (i.e., buccal mucosa, tongue dorsum, saliva) and listed in research protocols and publications, even if samples are collectively reported as “oral microbiome” analysis. Additionally, if multiple discrete oral sites are sampled in oral microbiome analysis, investigators should avoid grouping oral sites that do not have similar bacterial communities when reporting oral microbiome results, as the variance across oral sample sites may be lost if all relative abundance values are merged (Figure 3). Oral mouthwash samples have been shown to yield similar counts of bacterial genomic DNA, as compared to saliva samples, and may be an alternative technique for oral microbiome analysis (Fan et al., 2018). Finally, saliva may be a particularly clinically informative sampling specimen for oral microbiome analysis. Schmidt et al. (2019) and Segata et al. (2012) hypothesized that saliva is a key driver of oral-gut microbiome transmission through its influence on internal pH, nutrient availability, and potential as a vector of passive microbial transmission. Therefore, if the number of oral samples to be collected is limited, saliva collection should be considered as one of the initial sampling techniques for oral microbiome analysis.

With one exception (Segata et al., 2012), all studies that compared the oral and gut microbiome bacterial populations used 16S rRNA amplicon sequencing as a solo sequencing technology for their microbiome analysis until 2017. In the species and genus-level analyses resulting from 16S rRNA amplicon sequencing analysis, the research results suggested that the oral and gut microbiomes had very different bacterial communities. This was represented both in the different relative abundance levels of many bacteria and PCoA plots using Bray-Curtis or unweighted UniFrac distance metrics showing strong separation when oral and gut microbiome communities were compared (Iwauchi et al., 2019; Segata et al., 2012; Vasapolli et al., 2019). Weighted UniFrac, which accounts for the relative abundance of features in a microbiome sample, may be a more realistic picture of the microbiome community composition versus the presence/absence data used in unweighted UniFrac (Lozupone et al., 2007). Nevertheless, the taxonomic assumptions used in UniFrac analyses are not favored by all microbiome researchers and Bray-Curtis distances are used when composition and abundance data are needed without the influence of taxonomy. Despite the differences seen in bacterial communities with Bray-Curtis and UniFrac, earlier researchers hypothesized the oral and gut microbiome communities were related. Ding and Schloss (2014) noted oral and gut bacterial enterotypes were correlated with each other, and Lloyd-Price et al. (2017) observed core metabolic pathways that were ubiquitous in oral and fecal samples suggesting shared bacterial communities.

When shotgun metagenomics sequencing was used, a greater resolution to species strain was achieved providing a deeper understanding of the oral-gut relationship. Interpretations made from analyses using 16S rRNA gene sequencing techniques were quite different and illustrated two biomes as distinct bacterial communities between the oral and gut. The caveat to this interpretation was that most analyses using 16S rRNA gene sequencing analysis can only reliably categorize taxa to the genus level, and species and strain are not usually reported. Previous studies (Asnicar et al., 2017; Li et al., 2016) have concluded that strain level taxa resolution is likely required to accurately quantify microbial transmission and to understand whether suspected bacterial colonization in a new environment (i.e., fecal microbiota transplant or bacterial transmission from mother to infant) is a unique event or a standard physiologic process. Therefore, more studies using shotgun metagenomics sequencing to quantify taxa at the strain level are needed to replicate the findings by Schmidt et al. (2019), but the results suggest that a majority of the shared species between the oral and gut microbiome communities are in fact, supplied to the gut by passive transmission from the mouth through the gastrointestinal tract.

In the review of manuscripts comparing and contrasting the oral and gut microbiomes, it was noted that there is not one universal standard used to process and analyze microbiome samples (Table 1). This makes it extremely difficult to compare study findings, and some scientists believe that is impossible to merge bacterial relative abundance findings from multiple analyses due to bias inherent to various analysis technologies lacking consistency across microbiome research studies (Gohl et al., 2016; McLaren et al., 2019). For example, different DNA extraction kits were used, and most of the studies employed different sequencing technologies (16S rRNA gene sequencing versus shotgun metagenomics sequencing) and sequencing platforms (i.e., Roche versus Illumina). In the studies that used 16S rRNA gene sequencing, there were different hypervariable (V) regions amplified in the 16S rRNA gene and also a variety of databases were used to compare the amplified DNA sequences for taxonomic classification. These are just some factors that can bias the results of the bacterial abundance and downstream analyses (Maki et al., 2019).

Another issue in microbiome research reporting that was noted was many studies selectively performed microbiome analysis tailored to their hypothesis or study design. For example, because many of the manuscripts highlighted the differences in the oral and gut microbiome communities (Costello et al., 2009; Ding & Schloss, 2014; Iwauchi et al., 2019; Vasapolli et al., 2019), there were only two manuscripts that presented information about shared taxa between the oral and gut microbiomes (Schmidt et al., 2019; Segata et al., 2012). This highlights the importance of researchers uploading their microbiome data and taxonomic count tables to their manuscripts, so new analyses can be performed as the state of the science and understanding evolves. Finally, Vasapolli et al. (2019) amplified and sequenced RNA instead of DNA to selectively identify transcriptionally active bacteria, because they were concerned that the DNA from dead bacterial cells would be amplified in addition to alive cells, potentially giving physiologic significance to quiescent bacterial cells. Despite selectively amplifying RNA, the alpha and beta diversity analysis results were consistent with studies that used DNA amplification of the 16S rRNA gene (Li et al., 2012; Segata et al., 2012; Stearns et al., 2011; Vasapolli et al., 2019). Like strain analysis for oral and gut microbiome similarity, further studies on the utility of RNA amplification versus DNA amplification for microbiome community analysis are needed. There has been some work in an attempt to create standards of reporting and analysis to improve comparison across studies (Gonzalez et al., 2018; Sinha et al., 2017; Yilmaz et al., 2011), but uptake and implementation is far from universal. Groups such as the International Metagenomics and Microbiome Standards Alliance (https://microbialstandards.org/) are working to enforce and apply these standards in routine research practice and will continue to work to identify areas of measurement ambiguity to increase reporting consistency.

It is inevitable that technology will advance to provide researchers the ability to analyze microbiome specimens routinely at the species or strain level and this greater resolution has the potential to change the field. The goal of microbiome research is to obtain the best taxonomic resolution with the analyses used, and recently it has become possible to obtain both species and strain level data from specimens using 16S rRNA gene sequencing (Johnson et al., 2019). Although the full 16S gene must be sequenced for species and strain resolution, advances in sequencing platforms and reductions in costs facilitate sequencing of longer reads in lieu of a single hypervariable region such as V4 (Johnson et al., 2019). Previously, the only studies that could obtain strain level data were ones that used shotgun metagenomics sequencing for their analysis (Kalan et al., 2019; Schmidt et al., 2019; Yassour et al., 2018), and therefore this represents an immense advance in the field of microbiome research. Whether field advances provide easier more cost-effective whole genome sequencing (Ranjan et al., 2016) or allow higher taxonomic resolution and more information to come from 16S rRNA gene sequencing (Johnson et al., 2019), we hypothesize more information will be elucidated about the oral-gut microbiome connections. The similarity in oral and gut microbiome communities realized with strain level analysis seen in the study by Schmidt et al. (2019) has perhaps given us a glimpse of the future but awaits further validation at this time.

Conclusion

The oral and gut microbiomes are dynamic microbial communities that are of interest to nurse scientists as they noninvasively provide information related to disease and may be targets for future therapeutic interventions. Before defining the microbiome as dysbiotic, or unhealthy, a full understanding of the similarities, differences and interrelatedness between the oral and gut microbiomes is needed. At the phylum and genus level, oral and gut microbiome bacterial communities have unique taxonomic signatures, and although there are shared taxa at the genus level, relative abundances are significantly different. Nevertheless, when shotgun metagenomics sequencing was used to characterize bacterial taxa to the strain level, oral and gut community similarities were identified and even some evidence of oral to gut transmission was found. Although future studies are needed to replicate research results at the strain level, the oral microbiome is proving to be a novel and accessible target for bacterial biomarker characterization. As nurse scientists continue to incorporate the oral and gut microbiomes into their research, they will capitalize on its potential to provide early intervention for pathogenic oral-gut microbiome transmission to prevent future systemic disease in humans.