Next Article in Journal
The Immune, Inflammatory and Hematological Response in COVID-19 Patients, According to the Severity of the Disease
Previous Article in Journal
PeVL1 Novel Elicitor Protein, from Verticillium lecanii 2, Enhances Systemic Resistance against Rice Leaf Roller (Marasmia ruralis Wlk.) in Rice (Oryza sativa L.)
Previous Article in Special Issue
Virulence Factors of the Periodontal Pathogens: Tools to Evade the Host Immune Response and Promote Carcinogenesis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 11
Issue 2
10.3390/microorganisms11020318
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Contribution of the Human Oral Microbiome to Oral Disease: A Review

by
Austin Gregory Morrison
1,
Soumyadev Sarkar
2,
Shahid Umar
3,
Sonny T. M. Lee
2,4,* and
Sufi Mary Thomas
1,5,6,7,*
1
Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
2
Division of Biology, Kansas State University, Manhattan, KS 66506, USA
3
Department of General Surgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
4
1717 Claflin Road, 136 Ackert Hall, Manhattan, KS 66506, USA
5
Departments of Otolaryngology, University of Kansas Medical Center, Kansas City, KS 66160, USA
6
Departments of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
7
3901 Rainbow Blvd., 4031 Wahl Hall East, MS 3040, Kansas City, KS 66160, USA
*
Authors to whom correspondence should be addressed.
Microorganisms 2023, 11(2), 318; https://doi.org/10.3390/microorganisms11020318
Submission received: 19 December 2022 / Revised: 16 January 2023 / Accepted: 20 January 2023 / Published: 26 January 2023
(This article belongs to the Special Issue Advances in Periodontal Pathogens)
Browse Figure
Versions Notes

Abstract

:
The oral microbiome is an emerging field that has been a topic of discussion since the development of next generation sequencing and the implementation of the human microbiome project. This article reviews the current literature surrounding the oral microbiome, briefly highlighting most recent methods of microbiome characterization including cutting edge omics, databases for the microbiome, and areas with current gaps in knowledge. This article also describes reports on microorganisms contained in the oral microbiome which include viruses, archaea, fungi, and bacteria, and provides an in-depth analysis of their significant roles in tissue homeostasis. Finally, we detail key bacteria involved in oral disease, including oral cancer, and the current research surrounding their role in stimulation of inflammatory cytokines, the role of gingival crevicular fluid in periodontal disease, the creation of a network of interactions between microorganisms, the influence of the planktonic microbiome and cospecies biofilms, and the implications of antibiotic resistance. This paper provides a comprehensive literature analysis while also identifying gaps in knowledge to enable future studies to be conducted.

1. Introduction

The human microbiome is defined as the entire genomic content of the microorganisms inhabiting a particular site of the human body. Nobel prize laureate Joshua Lederberg first coined the term “microbiome” as a community of commensal, symbiotic, and pathogenic microorganisms [1]. It has also been noted that these microorganisms share physical space within the human body [1]. It was initially thought of as common knowledge that bacteria present in the human body outnumbered human cells by 10:1; however, recent studies have shown that a more realistic ratio for bacteria to human cells is 1:1 [2]. The human microbiome exists in different ecological niches. This can include that of the oral cavity, skin surface, intestinal tract, esophagus, lungs, and others; all of which communicate and interact within the molecular microenvironment [3]. The terminology “microbiome” and “microbiota” are often used interchangeably; however, this is inaccurate. The term “microbiome” refers to the collective genomes of the microorganisms residing within a given environment, while the term “microbiota” refers to the wide variety of microorganisms that reside within a given environment. Emerging evidence suggests the importance of having a balanced microbiota to maintain proper digestion, metabolism, and immune response [4]. Of the human microbiome, the most widely studied is the gut microbiota. Digestive diseases that result in inflammation are often due to dysbiosis of the gut microbiota [5]. However, there is emerging data that dysbiosis in other areas of the human microbiome such as the pancreas, oral cavity, sinonasal cavity, and vaginal cavity can also cause disease [6,7]. It has recently been shown that local tumor microbiota influences survival independent of immune therapy, albeit the direct link to the microbiome is not understood, indicating a need for further exploration [8,9]. In this review, we focus on the oral microbiome in order to better understand its effects on periodontal disease.
The human oral microbiome is similarly defined as the entire genomic composition of the population of microbial organisms that resides in the oral cavity. The oral microbiota was first identified by Antony van Leeuwenhoek who observed his own dental plaque and described it as “little living animalcules prettily moving” [10,11]. He was later acknowledged as the father of microbiology, after being credited for discovering both protists and bacteria [12]. Today the human oral microbiome is still readily studied and it is now understood that there are a combination of microorganisms that are heavily colonized within the oral cavity including viruses, protozoa, fungi, archaea, and bacteria [13]. After the colon, the oral microbiota is the second most complex in terms of species [14]. Of these microorganisms, the most widely studied is bacteria. The oral microbiota resides in both saliva and the surface area of the mouth. The bacteriome of the saliva is dominated by the Streptococcus, Prevotella, and Veillonella genera, which comprise 70% of this microbiota [10]. Studies have shown that the neonatal microbiota may have a prenatal origin [15]. Fusobacteria is among some of the most common cultivable microorganisms found, but other pioneer bacterial populations include Streptococcus, Lactobacillus, Actinomyces, Neisseria, and Veillonella [11]. The oral surface cavity is about 214.7 ± 12.9 cm2, and there is no significant difference due to gender in this regard. The teeth, keratinized epithelium, and nonkeratinized epithelium occupy about 20%, 50%, and 30% of the total surface area, respectively [16]. The mouth cavity includes the inner cheeks, hard and soft palates, and tongue, with connections to the pharynx. Each of these surfaces, along with the saliva and gingival crevices, contain their own microenvironments and harbor site-specific microbiota [17]. Next generation DNA sequencing is allowing for a much deeper analysis; however, it may not be enough on its own to make conclusions because of the strong influence of the environment on heterogeneity across samples from both the same individual at different sites of the oral cavity (tongue, palate, teeth, cheeks, floor, etc.) and between individuals [5]. Some of the main considerations in using sequencing strategies to study the oral bacteriome are discussed later. Moreover, there are some biases related to PCR amplification errors and assembly strategies. This approach also requires high sequence coverage that has a direct relation with the high cost associated with this technique [18].
The normal microbiota of the oral cavity is associated with oral pathologies, some of which are involved with periodontal disease. Periodontal disease is caused by the sessile and planktonic microbiota found within the saliva and dental plaque. The migration of these microorganisms leads to inflammation of the gingiva [19]. This inflammation leads to increased flow of gingival crevicular fluid, an inflammatory exudate found in the periodontal pocket between the tooth and the marginal gingiva. This increased flow is thought to contribute to host defense by flushing away bacteria from the periodontal pocket [20]. Even though oral microbes are the foremost cause of periodontitis, other risk factors include tobacco use, osteoporosis, obesity, and diabetes. These risk factors in combination with the oral microbes intensify the disease [21]. Other more generalized diseases that have been correlated with oral disease include rheumatoid arthritis [22], endocarditis [23], bacteremia [23], cardiovascular disease [24], pulmonary disease [25], liver disease [26], and cancer (gastrointestinal, pancreatic, and breast) [27]. Microbial oral biofilms contain a complex microenvironment which protects oral microbes from UV light exposure, dehydration, host immune cells, and killing molecules. The biological and non-biological surface of these biofilms within the oral cavity provide a level of protection for the microbes, making biofilm mediated infection extremely dangerous and difficult to eliminate [28,29]. In recent years, evidence has emerged to show a relationship between periodontal disease and oral cancer. Patients with oral cancer often have inflammation and poor oral hygiene suggesting the possibility that the oral microbiota may play a role in carcinogenesis. Chronic inflammation has been linked to the development of cancer through the pro-inflammatory cytokine release [30]. In addition, the mucosal microenvironment is a specific niche that can genetically influence epithelial cells. In the saliva, poor dental hygiene increases production of carcinogenic acetaldehyde from ethanol [31]. The imbalance of the microbiome has been shown to effect immune cell activation, although the mechanisms in which this occurs are not well understood [32]. Head and neck squamous cell carcinoma (HNSCC) is the most frequently occurring malignancy of oral cavity cancers with an incidence of over 90% of all cancers. There are between 350,000–400,000 new cases each year [33]. With this elevated incidence coupled with an increase in cases each year, the human oral microbiome is an important subject of research.

2. The Human Microbiome Analysis

The human microbiome has now been studied over a span of several decades. Initially, microbiology was almost totally dependent on the ability to isolate a culture and grow that culture exponentially in a sterile environment to study it. The difficulties in this method were vast, in that unknown microbial species had to be identified and then grown on a specific selected medium to promote growth, or they were identified by either their morphological or metabolic characteristics [34]. Thus, the total microbiome composition was skewed to those which were cultivatable and more readily studied. This remained an issue until nucleic acid sequencing technology was first used in the 1980s. This technology, in combination with recombinant DNA technology, allowed for a more comprehensive study of mixed microbial populations [35]. As technology has continued to improve over the last few decades, it has allowed the scientific community to have a more accurate understanding of the diversity of microbial species found within the human body.
Advancements in technology have also led to an increased understanding of the human microbiome. The following two methods are commonly used to study the microbiome: a targeted gene approach and a metagenomics approach. The latter is sometimes referred to as a shotgun approach because of the randomness in which genomic data is acquired [36]. Both of these methods utilize next generation sequencing as their main platform of analysis. The advantages in using a targeted gene approach is that it is cost-effective, the use of this analysis is widely accepted due to extensive research to compare to with popular databases, protocols for bioinformatic analysis have been well established, and host contamination is much more easily resolved. However, a lack of functional information and PCR bias can cause discrepancies in the differentiation of taxa [37]. The advantage in using shotgun metagenomics is that it encompasses all the microbial genomes present, including the host genome. Alternatively, this can also be viewed as a weakness due to its lack of specificity. Other disadvantages include the following: the cost of the computationally vast analysis, lack of well-established protocols, and the lack of consensus on analytical pipelines due to deficiencies in databases to make comparisons [37]. An overview of the processes can be seen in Table 1.
The ribosome is responsible for the translation of RNA into proteins. The 16S RNA is a gene present only in bacteria. It is found at the level of the ribosomes; these are formed of two subunits allowing the translation of RNA into proteins. The targeted gene approach to study the microbiome incorporates the use of the 16S ribosomal RNA gene sequencing for bacteria. Studies using this approach also typically involve using one or a combination of multiple hypervariable regions. Within bacterial 16S RNA, there are nine hypervariable regions (V1–V9). These regions are flanked by conserved regions in most bacteria [43]. This allows for more specificity when determining the phylogenetic assignment of bacteria. Evolution has led to variations in genes over time, which allows a unique fingerprint to be assigned to individual taxonomies and members of the microbial community. Due to its slow evolution, only a marginal number of bacterial genomes include identical 16S rRNA sequences, and this diversity rises with increasing 16S copy numbers [44]. However, during the amplification of each individual gene, a PCR product is sequenced one single time which allows for multiple chances of error to occur [37,45]. This potentiated the need to bundle sequences based on similarity to construct operational taxonomic units (OTUs) by using opensource software like Mothur, and more recently the construction of Amplicon sequence variants (ASVs) by using databases such as Quantitative Insights into Microbial Ecology 2 (QIIME2) which was launched in January of 2019. This imposed the need to make certain assumptions, such as the sequences >95% identity represent the same genus and the sequences >97% identity represent the same family [46]. Taxonomy can be assigned through data analysis by mapping to reference databases such as Silva and EzBioCloud. The classification of bacteria is established phylogenetically. It can be arranged in a hierarchy as follows: Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species [47]. When sequencing fungi, the internal transcribed spacer (ITS) region is utilized. The ITS region exists between 16S and 23S rRNA genes and when this region is targeted for species characterization, it allows for the greatest differentiation and delineation of systematic relationships [48]. The most commonly used database for molecular identification of fungi is UNITE [49].
Shotgun metagenomics consists of an untargeted sequencing of the microbial genomes which contains a wide variety of genetic information that can be useful in the study of microorganisms. Currently, there are two strategies to obtain metagenome-assembled genomes. In the absence of any available reference genomes, the de novo option can be used where the sequence reads are compared against each other to ensure reads overlapping that leads to the generation of longer contiguous sequences (contigs) [50]. Another option is the reference-based assembly approach [51] in which reads are mapped to the reference genomes and is capable of detecting single nucleotide polymorphisms (SNPs), identification of potential functions, etc. [52]. The process for shotgun metagenomics is much more time consuming than that of targeted gene sequencing. At every step of the process, there are multiple considerations in order to ensure reproducibility and integrity of the data. There are currently multiple DNA extraction techniques for metagenomics experiments. Each one of these has its pros and cons and can cause variations in generating the raw reads. There have been reports that when different protocols have been applied it has resulted in variable bacterial taxa distributions [53]. So, it is always advisable to standardize a fixed protocol for a specific type of sample using a pilot study. Whatever the approaches used, there is still a chance of unwanted sequences being generated and proper steps should be taken, especially during sample preparation, to remove unwanted contaminations in order to bring out the targeted sequence [54]. Sometimes, there might be contaminations in the reagents and buffers of the kits themselves and these contaminations have been found to vary with different batches [55]. Previous studies have even reported the presence of microbes in kits, thereby contaminating the populations of study [56]. The use of different batches influencing the metagenomics workflow can be negated by a combination of using proper reagent blanks and ensuring random processing of samples. Thus, it is critical to ensure that the best kit is selected as well as the samples are randomly processed when used in multiple batches for optimum results [57].
Often it is the case that certain segments of the genomes remain underrepresented after sequencing. The primary reasons on which sequencing coverage and quality depends are (a) sequencing depth; (b) sequencing technology; (c) complexity of the genome [58,59]. So, implementing a correct balance between these three parameters leads to a successful execution of the study. There are ways to calculate the sequencing depth for a study based on the taxonomic profile of the community, but there is never a strict rule of thumb [38]. Ideally, the sequencing depth should be decided based on the research question, experimental design, and budget. Illumina’s sequencers ensure a high read quality coupled with the lowest per-based pricing and lead the sequencing market as it is a more reasonable option than PacBio and Oxford Nanopore [60]. However, the PacBio and Nanopore sequencing technologies are ever-evolving and provide a long-read sequencing approach which might be important to specific studies, and thus possess great potential [60].
Once the sequencing is completed, preprocessing of the sequence reads is required before analysis can begin to build the taxonomy profile. This process is filled with multiple experimental and computational approaches which lead to the lack of consensus on specific analytical pipelines [39]. Metagenomics data analysis is an active subject of research and is being updated almost every week as we are far from standardizing the most optimal one [60]. At present, the most accepted workflow typically follows the following steps: (a) initial quality control; (b) using tools such as MetaPhlAn to assign taxonomy to individual reads to generate community taxonomic composition [40]; (c) searching the reads against databases like KEGG can yield functional composition using tools such as MEGAN [41], MG-RAST [42], HUMAnN [61]. Another approach uses Meta_IDBA assemblers-based genome assembly [62], after which CONCOCT [63] can be applied to bin contigs into groups. Then, Anvi’o [64] can be used to perform the binning correction followed by subsequent annotations and functional analysis. In terms of reference-based metagenomic analyses, several software, including MetaCompass is a software package that allows for reference-assisted assembly of metagenomic data of low abundance genomes [65]. This is done by mapping reads against reference data sets, constructing reference-assisted contigs, and then correcting the assemblies by using Pilon [66]. This is limited by the availability of reference genomes and databases used for cross-referencing. For an unambiguous identification of microbial species from sequencing data, the electronic Tree of Life (eTOL) can be used. This allows for the referencing of human tissues across the entire tree of life, including archaea, bacteria, chloroplasts, basal eukaryotes, fungi, and holozoan. The use of eTOL is especially important for viral sequences due to significant homology between human sequences. This can be combatted using a ‘stripping’ method where similar sequences are removed from the viral genome before characterization [67].
A less common method of characterization of members of the human microbiome is metabolomics. Metabolomics is a comprehensive approach where all the metabolites of a sample are included in the analysis [68]. The advantage of metabolomics is that small intermediate molecules can be used to identify metabolites through mass spectrometry coupled with chromatographic separation techniques. A direct measurement of the underlying biochemical activity can provide information to help identify the origin of the metabolite. This provides information on the host metabolism as well [69]. Metabolomics provides a more comprehensive view of cell function, which has been seen as bridging the gap between genotype and phenotype [70]. Another emerging method of analysis is metaproteomics which similarly uses mass spectrometry; however, the main focus is to identify proteins present within the microbiome [37,71]. With metabolomic and proteomic analysis, there is a lack of comprehensive datasets when compared to nucleic acid-based methods regarding microorganisms. Metatranscriptomics is the study of gene expression of microbes within their natural environment. This helps to provide information about the active functions of microbial communities, information that is not discovered from a simple microbe composition. However, with metatranscriptomics, sample collection is destructive and experimental design is critical [72]. The use of transcriptomics and proteomics also only accounts for the biological potential and ignores the cellular activity component. This can be overcome through the use of metabolomics to reflect the enzymatic activity of the cell. Microbial culturomics is another method that has started to gain popularity within the last few years. This method combines the culturing conditions of unknown bacteria with popular identification methods to determine their significance within the human microbiome [73]. While these methods are less common than those mentioned previously, new developments in the field of bioinformatics continue to push the edge on ways to characterize the human microbiome. All methods are highlighted in Table 1.
The Human Microbiome Project (HMP) was a project launched by the NIH in October 2007, shortly after the completion of the Human Genome Project. There was increasing interest in the scientific community to fully sequence the genome of microbes growing within the human body [74]. The NIH defined the goal of the project as “specifically devised and implemented to create a set of data, reagents, or other material whose primary utility will be as a resource for the broad scientific community” [75]. Initially, this project was supposed to be completed within five years. However, after discovering that the correlation between the host phenotype and taxonomic composition of the microbiome was often not sufficient [14], a second phase of the project was initiated. The Integrative HMP was designed to study host–microbiome interactions, including immunity, metabolism, and dynamic molecular activity [76]. Although work is still continuing in this area, one advantageous aspect has been the creation of online databases to expedite characterization of the human microbiome.

3. The Human Oral Microbiome

The human mouth is home to a vast variety of microorganisms that rely on each other to maintain stable conditions. For the most part, these microorganisms live in harmony with one another and the host. In part, this symbiosis is due to the resident commensal flora, including planktonic bacteria, that occupies the hard and mucous surfaces preventing the establishment of exogenous microorganisms. When last updated, the expanded Human Oral Microbiome Database (eHOMD, www.homd.org (accessed on 9 December 2022)) included a total of 772 microbial species present in the human aerodigestive tract [77]. Of those species, 57% are cultivated and named, 13% are cultivated but unnamed, and 30% are uncultivated. A total of 1570 genomes from 475 taxa can be viewed in the genome browser software. The composition of the oral microbiome includes viruses, archaea, fungi, and bacteria. The most common identification tool used for characterizing the oral bacteriome is 16S rRNA amplicon sequencing by targeted gene approach, as listed previously [78]. However, sample collection can be difficult as there is no standardized procedure for collecting oral microbiome samples. Several parameters can vary the collection, including timing of the day, before or after meals, the sample collection tools, and the storage conditions of the samples. Similar to the sample collection process, there is a lack of standardization for the sequencing analysis as well. For these reasons, it can be difficult to make reliable assumptions from the data.
A substantial number of viruses co-exist in the oral cavity, most of which are considered pathogens. Viruses and phages outnumber all other biological entities on earth; however, the virome, that is the viral component of the microbiome, has not been studied well enough to conclude its overall diversity in the mouth [79]. Recent focus has been on the bacteria and its role in the oral microbiome, leaving the effects of viruses in the microbiome vastly understudied [80]. As technological advancements are discovered, new information is brought to light debunking ideas that were once thought of as common knowledge. Human blood was once thought of as aseptic, but it is now known that it contains both viruses and phages [79]. Phage, or bacteriophage, are viruses that infect and replicate within bacteria. This can be done in one of these two ways: the lytic cycle where the phage uses bacterial processes to replicate and then lyses the bacterial cells, or the lysogenic cycle where the phage inserts its DNA into the bacterial chromosome to be replicated. A large number of phages have been shown to be a main component of the oral virome. It has been shown that a majority of virus sequences found within the oral cavity had homology with phages, suggesting a predominant role in lysogeny, as this is one of the two ways previously mentioned for phages to infect bacteria [81]. Hepatitis viruses and HIV are examples of viruses that enter through the oral cavity by traversing the oral mucosa and travel into the upper respiratory tract, causing the immune system to become compromised [79,82,83]. Human papilloma virus (HPV) has been shown to not only cause oral disease, but also to be a dominant microbial factor in HNSCC [84]. Herpes simplex virus has also been shown to cause gingival lesions [85]. With the overwhelming presence of viruses in the oral cavity, it is not surprising that the virome plays a major role in oral disease.
The Archaea are found to play a minor role in the oral microbiome; however, due to the diversity of the community that makes up the oral microbiome, and the lack of understanding of the interrelationships between microorganisms, Archaea should not be considered nonessential at this time. All of the members of the archaeal community found within the oral cavity are considered methanogens, meaning they obtain most of their metabolic energy from the biosynthesis of methane [13]. Archaea are increased in periodontitis subjects with Methanobrevibacter oralis, Methanobacterium curvum/congolense, and Methanosarcina mazeii showing the greatest prevalence [86]. Archea may be contributing to this diseased state through interspecies hydrogen transfer and by favoring the growth of fermenting bacteria. Recently, there has been a correlation identified between methanogens and colorectal cancer [87]. Methanogens possess the ability to transfer heavy metals into highly toxic methylated derivatives. Continued research needs to be performed in order to better understand the interrelationships between Archaea and other microorganisms to determine the more accurate role that these organisms play in oral disease.
The fungi that reside in the microbiome are collectively known as the mycobiome, which is an emerging topic of discussion. During a study of the oral cavity of 20 healthy individuals, the “basal” oral mycobiome was shown to contain 85 genera—74 culturable and 11 non-culturable [88]. More recent studies show over 100 culturable species of fungi in the oral cavity alone [89]. Current topics of research have focused around the fungal species Candida albicans (C. albicans) which is commonly found in the oral mucosa but can rapidly take on pathogenic properties [90]. Some of these properties include stimulating invasive filamentous growth in a hypoxic environment, and maintaining continued growth in an environment with minimal nutrition by inducing alternative metabolic pathways like the glyoxylate cycle, gluconeogenesis, and fatty acid oxidation [91,92]. Other members of the oral microbiome, such as streptococci, have been shown to provide advantageous elements, for instance a carbon source and an adhesion site, for C. albicans to flourish [90]. This may not be so advantageous to the host however, as studies in a rat model have shown that in combination, Streptococcus mutans (S. mutans), a known carcinogen, and C. albicans heightens the development of dental caries [93]. These interactions have shown the possibility of C. albicans acting as a bridge for bacteria, allowing them to adhere to mucosal surfaces [94,95]. Conversely, Streptococcus salivarius has been shown to prevent the adherence of C. albicans to mucosal surfaces, suggesting the potential for use as a probiotic [96]. Mechanisms for defense have developed; human β-defensins mediate this cross-talk and help to maintain a healthy oral mucosal environment [97]. Both an early and late host immune response occurs when C. albicans is present in pathogenic levels. There is early recognition by neutrophils, macrophages, and dendritic cells, but there is also an adaptive antifungal response [98]. The combination of all these factors represents the importance of the mycobiome and its relevance within the oral microbiome.
The most substantial contributor to the oral microbiome, and the most broadly studied, is bacterium. This composition of bacteria is fairly conserved among several factors including age, race, sex, and geographical location. A study of 120 individuals from 12 geographic locations determined that there was no significant geographical variance between individuals’ salivary microbiota [10,13]. There are six major phyla, accounting for 96% of the taxa, that constitute the oral microbiome—Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes, and Fusobacteria. The remaining 4% of the taxa are made up by the phyla Euryarchaeota, Chlamydia, Chloroflexi, SR1, Synergistetes, Tenericutes, and Saccharibacteria [99,100]. While these are all commensal in nature, some of the bacteria from these phyla have also been viewed as pathogens. The transition from commensal to pathological most often depends on the quantity of this microorganism within the biofilms of the oral cavity. Two of the most significant bacteria to harbor pathogenic properties are F. nucleatum and Porphyromonas gingivalis (P. gingivalis) [101]. Dysbiosis was reported in e-cigarette users compared with never smokers or tobacco cigarette smokers. Porphyromonas and Veillonella were higher among vapers [102]. As previously mentioned S. mutans is a known carcinogenic bacterium observed during the formation of dental caries [93]. Several species from the genus Prevotella have been implicated in periodontitis including P. dentalis, P. enoeca, P. fusca, P. melaninogenica, P. denticola, P. intermedia 17, P. intermedia 17-2 [103]. Leptotrichia buccalis (L. buccalis) has been isolated and recovered from patients with varying levels of gingivitis [104]. A majority of the bacteria in the oral microbiome are strict anaerobes, making them more difficult to culture and thus requiring extreme caution when handling and processing samples [90]. These bacteria will be reviewed in further detail in the ensuing section. It is obvious from the extensive characterization of the bacterial taxa in the oral microbiome that they are central players involved in both oral and overall health. Considerable studies need to be conducted in order to identify better avenues to counter the detrimental effects of these bacteria.

4. Key Bacteria Involved in Oral Disease

L. buccalis is a Gram-negative, rod-shaped facultative anaerobic bacterium that is a normal resident of the oral flora. It is placed in the phylum Fusobacteria in the family Leptotrichiaceae. Growth can be visualized by streaking bacteria onto a blood agar plate under anaerobic conditions. Members of the Leptotrichia genus are similar in size and shape so identification is typically performed through 16S rRNA gene sequencing [104]. Leptotrichia survives off the fermentation of carbohydrates, ultimately producing lactic acid as an end product [105]. Studies have shown that L. buccalis, recovered from patients with gingivitis, may play a role in the inflammatory process [104], by regulating expression of pro-inflammatory interleukin (IL)-1β, IL-6, IL-8 and the anti-inflammatory IL-10 [106]. While it is possible that Leptotrichia plays a direct role in the inflammatory process, a vast number of oral bacteria has been implicated in the inflammatory response pathways, so more than likely there is a combination of microorganisms involved. Ultimately, further studies need to be conducted in order to determine the true role that each of the key bacteria plays.
The genus Prevotella and several of its species have been known to cause disease in the oral cavity. Some of the species that are involved include P. dentalis, P. enoeca, P. fusca, P. melaninogenica, P. denticola, P. intermedia 17, P. intermedia 17-2 [103]. Prevotella are gram-negative, anaerobic bacteria that are common members of the oral microbiome. They are placed in the phylum Bacteroidetes in the family Prevotellaceae. Prevotella has also been observed as a commensal in both vaginal microbiota and gut microbiota [107,108]. Prevotella initiates the production of Toll-like receptor (TLR) 2, which subsequently leads to the polarization of Th17 while production of inflammatory cytokines can lead to increased prevalence in periodontal disease [103]. Studies in mice have also shown Prevotella to promote clinical features of human disease. An upregulation of Prevotella and a downregulation of Lactobacillus, a major member of the lactic acid bacteria group, have shown correlation to osteomyelitis in mice. When Prevotella is reduced and Lactobacillus is increased, protection against osteomyelitis has been observed [109]. A continuation of research into Prevotella could show the clinical importance of its participation in oral disease.
Helicobacter pylori (H. pylori) is a Gram-negative, microaerophilic, helical bacterium that is commonly found in the stomach. H. pylori has been implicated in a variety of diseases including duodenal ulcers, gastric ulcers, gastric atrophy, gastritis, and gastric carcinoma [110]. Recently, there has been much controversy surrounding whether H. pylori also frequently co-habitats within the oral cavity or only during disease states [111]. The helical shape of H. pylori is thought to have evolved in order to better help it penetrate the mucosal lining of the stomach [112]. Having once been considered a species of Campylobacter, it is similar in nature to their corkscrew shapes. Once infected, removal of H. pylori from the oral cavity is much more difficult than the eradication from the stomach. Typically, antibacterial therapy is used to remove the bacterium. However, if this is ineffective, H. pylori will reinfect the stomach within a couple of weeks [113]. In addition, studies have found that improved oral health and hygiene are unlikely to increase the efficacy of H. pylori eradication from the stomach [114]. In contrast, long-term dental plaque control has been implicated to help prevent H. plyori-induced gastric disease or re-infection [115], suggesting a need for further research into the relationship of oral health and H. pylori.
S. mutans is a gram-positive, facultatively anaerobic bacterium found within the oral microbiome. It is located within different areas of the oral cavity, but the round bacterium most commonly inhabits the pits and fissures. This known carcinogen has recently been considered an opportunistic commensal of the oral cavity, due to its interactions with certain fungal species. This communication between S. mutans and fungal species allows the microorganisms to exchange chemical signals, known as quorum sensing, which help to promote the formation of oral biofilms [116]. The combination of S. mutans and C. albicans has often been considered the main contributor to dental caries, specifically early childhood caries (ECC) [117]. The bacterial etiology of ECC is complicated due to the colonizing of bacteria while the teeth are forming and being susceptible to demineralization [118]. Cospecies biofilms accumulate more biomass and increase the likelihood for periodontal disease [117]. Dental caries are still considered the most common chronic disease worldwide, and there are currently very few approved precision antibiotic treatments to prevent them. However, recent studies have shown the potential for synthetic molecules paired with antibiotics to target specific domains of the dental caries [119]. Other studies have shown promising results using Lactobacillus salivarius probiotics to reduce pathogenic species and biofilm mass, although future research will aim to look at the long-term effects of method [120]. Other future studies will look to push the edge on how dental caries are controlled on a worldwide basis.
One of the most substantial bacteria involved in periodontal disease is P. gingivalis [121,122]. This bacterium is a Gram-negative, rod-shaped, anaerobe that belongs to the phylum Bacteroidetes and the family Porphyromonadaceae. P. gingivalis is a known pathogen that dwells in the oral cavity and can be visualized on blood agar, forming black colonies. This bacterium thrives off a dysbiotic environment in the oral microbiome. In order to achieve this dysbiosis, P. gingivalis manipulates complement-TLR crosstalk which activates an inflammatory response. The combination of dysbiosis with inflammation leads to a situation in which periodontal disease blooms [123]. Studies have shown that in vitro P. gingivalis can invade human fibroblasts and be able to achieve a level of antibiotic resistance [124]. Most strains of P. gingivalis have been shown to exhibit fimbriae, or long and thin proteinaceous surface appendages which play essential roles in the binding and invasion of host cells [125]. One study declared that fimbriae type II has the ability to bind to α5β1-integrin, which facilitates bacteria to be taken up by phagocytes and dendritic cells further permitting the rearrangement of the actin cytoskeleton [126]. P. gingivalis infection has also been shown to increase the risk of developing oral and digestive cancers, and this bacterium is frequently found in oral squamous cell carcinoma [127]. This bacterium promotes PI3K/Akt signaling which leads to host cell proliferation by reducing apoptosis. Tumorigenesis is further promoted through P. gingivalis-facilitated EMT because of GSK3-beta downregulation, reduced E-cadherin, and increased pro-MMP9. Additionally, this bacterium stimulates tumor growth and metastasis by inhibiting p53 [128]. A combination of these factors makes P. gingivalis a deadly pathogen that causes widespread periodontal disease and increased risk of oral cancer simply by its presence.
Potentially the most influential bacterium implicated in periodontal disease is F. nucleatum [121,129]. This Gram-negative, anaerobic bacterium was once considered an oral commensal but is now more frequently referred to as a pathogen. F. nucleatum can be observed through growth in anaerobic conditions on a blood agar plate. F. nucleatum belongs to the phylum Fusobacteria and the family Fusobacteriaceae. This bacterium is one of the most abundant species in the oral cavity in both healthy and diseased individuals [130]. F. nucleatum was once thought to exist almost exclusively in the oral cavity [131]; however, under disease conditions, it can be found in extra-oral sites and has been implicated in diseases such as inflammatory bowel disease, colorectal cancer, Crohn’s disease, rheumatoid arthritis, meningitis, appendicitis, and adverse pregnancy outcomes [132,133,134,135,136,137]. When this species is found in abundance in the oral cavity, or located in extra-oral sites, it has been known to exhibit pathogenic properties [129]. Studies have shown that F. nucleatum can increase tumor growth by activating the IL-6-STAT3 axis through the signaling of Fusobacterium Adhesin A (FadA) [138]. Figure 1 provides the pathway in which FadA is involved. Other bacteria with known pathogenic roles in periodontal disease, such as genera Leptotrichia, Prevotella, Streptococcus, and Porphyromonas will also be reviewed to highlight the importance of their continued study.
In co-culture, F. nucelatum is considered a secondary colonizer after primary colonizers, such as Streptococcus, and is vital in the structural relationships of the polymicrobial biofilm [139]. F. nucleatum encodes for the fadA gene which is an adhesin that binds to host cells and is required for the binding of normal and cancerous cells, suggesting a potential mechanism for the invasion of anti-tumor immune response [129]. Fap2 is a galactose sensitive adhesin that is involved in cell adhesion and the inflammatory pathway [140]. F. nucleatum provokes a variety of host responses including the stimulation of IL-6, IL-8, and TNF α [141]. Inflammatory responses involved in periodontal disease are activated by the binding of F. nucleatum to natural killer cells [142]. These pathways can be better visualized in the schematic provided in Figure 1. The diseases where F. nucleatum is implicated, have wide variation, from mild reversible forms of gingivitis to generalized aggressive forms of periodontitis [143]. F. nucleatum has been found in a variety of extra oral sites [131], and it is speculated that FadA binding to cadherins on epithelial and endothelial cells is likely the reason for colonization on multiple body sites [129].
Recent evidence has emerged showing the potential for antibiotic resistance via F. nucleatum and Pseudomonas aeruginosa interaction through FadA [144]. Another recent study observed the targeting of TLR4 and specific microRNAs to activate the autophagy pathway and change the chemotherapeutic response, promoting chemoresistance [145]. Further studies have investigated the role of F. nucleatum and P. gingivalis in oral cancer development and progression in mice models. These bacteria are often found together in oral cancer cells triggering the release IL-6, activating the STAT3 pathway. This increase in pSTAT3 and IL-6 is also greater in mice infected with F. nucleatum and P. gingivalis than in non-infected mice [146]. With all of the implications in disease, both major and minor, F. nucleatum will certainly be a topic of future discussion.

5. Conclusions

The oral microbiome is a vast environment that is made up of multiple species of microorganisms that include viruses, archaea, fungi, and bacteria. The development of 16S rRNA gene sequencing has enabled the characterization of the oral bacteriome, providing us with knowledge that would not have been obtainable only a few decades ago. However, this has left us with numerous questions and the curiosity to study not only the individual microorganisms, but also how they interact with one another. The oral microbiome is an environment full of symbiotic relationships, but when pathogens interrupt this relationship and cause dysbiosis, a once healthy situation can become diseased and increase the likelihood of comorbidities. In recent years, science has been able to uncover some of the common properties that these pathogens share, such as the stimulation of inflammatory cytokines. It has also been implicated that certain bacteria can serve as a bridge for other invasive pathogens allowing for easier migration, however more research needs to be conducted to achieve the knowledge of both the reason for this and the mechanism by which this occurs. In this review, we summarized the current literature and highlighted the tools used to characterize the oral microbiome, while also mentioning the discrepancies within those techniques. We also provided a critical summary of key bacteria currently being studied, while suggesting gaps in knowledge for future studies. The study of the oral microbiome is one that has been around for the last few decades, nonetheless it is still very much considered as emerging. More precisely, the role of viruses, fungi and archaea remains poorly studied; it is to be expected that in the years to come, answers to these questions will be provided and knowledge on subjects which have not yet been addressed will be advanced.

Author Contributions

Conceptualization, A.G.M. and S.M.T.; writing—original draft preparation, A.G.M. and S.S.; writing—review and editing, A.G.M., S.U., S.T.M.L. and S.M.T. All authors have read and agreed to the published version of the manuscript.

Funding

National Cancer Institute Cancer Center Support Grant P30 CA168524, and philanthropic donations provided financial support for this review.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

Figures were generated on BioRender.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kilian, M.; Chapple, I.L.; Hannig, M.; Marsh, P.D.; Meuric, V.; Pedersen, A.M.; Tonetti, M.S.; Wade, W.G.; Zaura, E. The oral microbiome-an update for oral healthcare professionals. Br. Dent. J. 2016, 221, 657–666. [Google Scholar] [CrossRef] [PubMed]
  2. Sender, R.; Fuchs, S.; Milo, R. Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans. Cell 2016, 164, 337–340. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Manos, J. The human microbiome in disease and pathology. APMIS 2022, 130, 690–705. [Google Scholar] [CrossRef] [PubMed]
  4. Gomaa, E.Z. Human gut microbiota/microbiome in health and diseases: A review. Antonie Van Leeuwenhoek 2020, 113, 2019–2040. [Google Scholar] [CrossRef] [PubMed]
  5. Yoo, W.; Byndloss, M.X. How to thrive in the inflamed gut. Nat. Microbiol. 2020, 5, 10–11. [Google Scholar] [CrossRef]
  6. Herremans, K.M.; Riner, A.N.; Cameron, M.E.; McKinley, K.L.; Triplett, E.W.; Hughes, S.J.; Trevino, J.G. The oral microbiome, pancreatic cancer and human diversity in the age of precision medicine. Microbiome 2022, 10, 93. [Google Scholar] [CrossRef] [PubMed]
  7. Peng, X.; Cheng, L.; You, Y.; Tang, C.; Ren, B.; Li, Y.; Xu, X.; Zhou, X. Oral microbiota in human systematic diseases. Int. J. Oral Sci. 2022, 14, 14. [Google Scholar] [CrossRef]
  8. Frankel, T.L.; Pasca di Magliano, M. Immune sensing of microbial metabolites: Action at the tumor. Immunity 2022, 55, 192–194. [Google Scholar] [CrossRef]
  9. Hezaveh, K.; Shinde, R.S.; Klotgen, A.; Halaby, M.J.; Lamorte, S.; Ciudad, M.T.; Quevedo, R.; Neufeld, L.; Liu, Z.Q.; Jin, R.; et al. Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity. Immunity 2022, 55, 324–340.e28. [Google Scholar] [CrossRef]
  10. Nasidze, I.; Li, J.; Quinque, D.; Tang, K.; Stoneking, M. Global diversity in the human salivary microbiome. Genome Res. 2009, 19, 636–643. [Google Scholar] [CrossRef] [Green Version]
  11. Deo, P.N.; Deshmukh, R. Oral microbiome: Unveiling the fundamentals. J. Oral Maxillofac. Pathol. 2019, 23, 122–128. [Google Scholar] [CrossRef] [PubMed]
  12. Lane, N. The unseen world: Reflections on Leeuwenhoek (1677) ‘Concerning little animals’. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2015, 370, 20140344. [Google Scholar] [CrossRef] [PubMed]
  13. Wade, W.G. The oral microbiome in health and disease. Pharmacol Res. 2013, 69, 137–143. [Google Scholar] [CrossRef]
  14. Huttenhower, C.; Gevers, D.; Knight, R.; Abubucker, S.; Badger, J.H.; Chinwalla, A.T.; Creasy, H.H.; Earl, A.M.; FitzGerald, M.G.; Fulton, R.S.; et al. Structure, function and diversity of the healthy human microbiome. Nature 2012, 486, 207–214. [Google Scholar] [CrossRef] [Green Version]
  15. Tuominen, H.; Collado, M.C.; Rautava, J.; Syrjanen, S.; Rautava, S. Composition and maternal origin of the neonatal oral cavity microbiota. J. Oral Microbiol. 2019, 11, 1663084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Collins, L.M.; Dawes, C. The surface area of the adult human mouth and thickness of the salivary film covering the teeth and oral mucosa. J. Dent. Res. 1987, 66, 1300–1302. [Google Scholar] [CrossRef] [PubMed]
  17. Xie, H.; Cook, G.S.; Costerton, J.W.; Bruce, G.; Rose, T.M.; Lamont, R.J. Intergeneric communication in dental plaque biofilms. J. Bacteriol. 2000, 182, 7067–7069. [Google Scholar] [CrossRef] [Green Version]
  18. D’Argenio, V. Human microbiome acquisition and bioinformatic challenges in metagenomic studies. Int. J. Mol. Sci. 2018, 19, 383. [Google Scholar] [CrossRef] [Green Version]
  19. Dentino, A.; Lee, S.; Mailhot, J.; Hefti, A.F. Principles of periodontology. Periodontol 2000 2013, 61, 16–53. [Google Scholar] [CrossRef] [Green Version]
  20. Subbarao, K.C.; Nattuthurai, G.S.; Sundararajan, S.K.; Sujith, I.; Joseph, J.; Syedshah, Y.P. Gingival Crevicular Fluid: An Overview. J. Pharm. Bioallied. Sci. 2019, 11, S135–S139. [Google Scholar] [CrossRef]
  21. Genco, R.J.; Borgnakke, W.S. Risk factors for periodontal disease. Periodontol 2000 2013, 62, 59–94. [Google Scholar] [CrossRef] [PubMed]
  22. Zhang, X.; Zhang, D.; Jia, H.; Feng, Q.; Wang, D.; Liang, D.; Wu, X.; Li, J.; Tang, L.; Li, Y.; et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 2015, 21, 895–905. [Google Scholar] [CrossRef] [PubMed]
  23. Dhotre, S.; Jahagirdar, V.; Suryawanshi, N.; Davane, M.; Patil, R.; Nagoba, B. Assessment of periodontitis and its role in viridans streptococcal bacteremia and infective endocarditis. Indian Heart. J. 2018, 70, 225–232. [Google Scholar] [CrossRef]
  24. Ordovas, J.M.; Mooser, V. Metagenomics: The role of the microbiome in cardiovascular diseases. Curr. Opin. Lipidol. 2006, 17, 157–161. [Google Scholar] [CrossRef]
  25. Scannapieco, F.A.; Genco, R.J. Association of periodontal infections with atherosclerotic and pulmonary diseases. J. Periodontal. Res. 1999, 34, 340–345. [Google Scholar] [CrossRef]
  26. Bajaj, J.S.; Betrapally, N.S.; Hylemon, P.B.; Heuman, D.M.; Daita, K.; White, M.B.; Unser, A.; Thacker, L.R.; Sanyal, A.J.; Kang, D.J.; et al. Salivary microbiota reflects changes in gut microbiota in cirrhosis with hepatic encephalopathy. Hepatology 2015, 62, 1260–1271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Meurman, J.H. Oral microbiota and cancer. J. Oral Microbiol. 2010, 2, 5195. [Google Scholar] [CrossRef] [Green Version]
  28. Bianchi, S.; Fantozzi, G.; Bernardi, S.; Antonouli, S.; Continenza, M.A.; Macchiarelli, G. Commercial oral hygiene products and implant collar surfaces: Scanning electron microscopy observations. Can. J. Dent. Hyg. 2020, 54, 26–31. [Google Scholar]
  29. Gholizadeh, P.; Eslami, H.; Yousefi, M.; Asgharzadeh, M.; Kafil, H.S. Role of oral microbiome on oral cancers, a review. Biomed Pharm. 2016, 84, 552–558. [Google Scholar] [CrossRef]
  30. Lamont, R.J.; Koo, H.; Hajishengallis, G. The oral microbiota: Dynamic communities and host interactions. Nat. Rev. Microbiol. 2018, 16, 745–759. [Google Scholar] [CrossRef]
  31. Seitz, H.K.; Becker, P. Alcohol metabolism and cancer risk. Alcohol. Res. Health 2007, 30, 38–41, 44–47. [Google Scholar] [PubMed]
  32. Rajakaruna, G.A.; Negi, M.; Uchida, K.; Sekine, M.; Furukawa, A.; Ito, T.; Kobayashi, D.; Suzuki, Y.; Akashi, T.; Umeda, M.; et al. Localization and density of Porphyromonas gingivalis and Tannerella forsythia in gingival and subgingival granulation tissues affected by chronic or aggressive periodontitis. Sci. Rep. 2018, 8, 9507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin. 2011, 61, 69–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Morgan, X.C.; Huttenhower, C. Chapter 12: Human microbiome analysis. PLoS Comput. Biol. 2012, 8, e1002808. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Olsen, G.J.; Lane, D.J.; Giovannoni, S.J.; Pace, N.R.; Stahl, D.A. Microbial ecology and evolution: A ribosomal RNA approach. Annu. Rev. Microbiol. 1986, 40, 337–365. [Google Scholar] [CrossRef] [PubMed]
  36. Kuczynski, J.; Lauber, C.L.; Walters, W.A.; Parfrey, L.W.; Clemente, J.C.; Gevers, D.; Knight, R. Experimental and analytical tools for studying the human microbiome. Nat. Rev. Genet. 2011, 13, 47–58. [Google Scholar] [CrossRef] [Green Version]
  37. Galloway-Pena, J.; Hanson, B. Tools for Analysis of the Microbiome. Dig. Dis. Sci. 2020, 65, 674–685. [Google Scholar] [CrossRef] [Green Version]
  38. Ni, J.; Yan, Q.; Yu, Y. How much metagenomic sequencing is enough to achieve a given goal? Sci. Rep. 2013, 3, 1968. [Google Scholar] [CrossRef] [Green Version]
  39. Quince, C.; Walker, A.W.; Simpson, J.T.; Loman, N.J.; Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 2017, 35, 833–844. [Google Scholar] [CrossRef] [Green Version]
  40. Segata, N.; Waldron, L.; Ballarini, A.; Narasimhan, V.; Jousson, O.; Huttenhower, C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 2012, 9, 811–814. [Google Scholar] [CrossRef]
  41. Huson, D.H.; Mitra, S.; Ruscheweyh, H.J.; Weber, N.; Schuster, S.C. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011, 21, 1552–1560. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Meyer, F.; Paarmann, D.; D’Souza, M.; Olson, R.; Glass, E.M.; Kubal, M.; Paczian, T.; Rodriguez, A.; Stevens, R.; Wilke, A.; et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 2008, 9, 386. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Chakravorty, S.; Helb, D.; Burday, M.; Connell, N.; Alland, D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 2007, 69, 330–339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Vetrovsky, T.; Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 2013, 8, e57923. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Xue, Z.; Kable, M.E.; Marco, M.L. Impact of DNA Sequencing and Analysis Methods on 16S rRNA Gene Bacterial Community Analysis of Dairy Products. mSphere 2018, 3, e00410-18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Johnson, J.S.; Spakowicz, D.J.; Hong, B.Y.; Petersen, L.M.; Demkowicz, P.; Chen, L.; Leopold, S.R.; Hanson, B.M.; Agresta, H.O.; Gerstein, M.; et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 2019, 10, 5029. [Google Scholar] [CrossRef] [Green Version]
  47. Park, S.C.; Won, S. Evaluation of 16S rRNA Databases for Taxonomic Assignments Using Mock Community. Genom. Inform. 2018, 16, e24. [Google Scholar] [CrossRef] [Green Version]
  48. Man, S.M.; Kaakoush, N.O.; Octavia, S.; Mitchell, H. The internal transcribed spacer region, a new tool for use in species differentiation and delineation of systematic relationships within the Campylobacter genus. Appl. Environ. Microbiol. 2010, 76, 3071–3081. [Google Scholar] [CrossRef] [Green Version]
  49. Nilsson, R.H.; Larsson, K.-H.; Taylor, A.F.S.; Bengtsson-Palme, J.; Jeppesen, T.S.; Schigel, D.; Kennedy, P.; Picard, K.; Glöckner, F.O.; Tedersoo, L.; et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018, 47, D259–D264. [Google Scholar] [CrossRef]
  50. Sutton, T.D.S.; Clooney, A.G.; Ryan, F.J.; Ross, R.P.; Hill, C. Choice of assembly software has a critical impact on virome characterisation. Microbiome 2019, 7, 12. [Google Scholar] [CrossRef] [Green Version]
  51. Stern, J.; Miller, G.; Li, X.; Saxena, D. Virome and bacteriome: Two sides of the same coin. Curr. Opin. Virol. 2019, 37, 37–43. [Google Scholar] [CrossRef]
  52. Breitwieser, F.P.; Lu, J.; Salzberg, S.L. A review of methods and databases for metagenomic classification and assembly. Brief. Bioinform. 2019, 20, 1125–1136. [Google Scholar] [CrossRef] [PubMed]
  53. Wesolowska-Andersen, A.; Bahl, M.I.; Carvalho, V.; Kristiansen, K.; Sicheritz-Pontén, T.; Gupta, R.; Licht, T.R. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2014, 2, 19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Thurber, R.V.; Haynes, M.; Breitbart, M.; Wegley, L.; Rohwer, F. Laboratory procedures to generate viral metagenomes. Nat. Protoc. 2009, 4, 470. [Google Scholar] [CrossRef] [PubMed]
  55. Goodrich, J.K.; Di Rienzi, S.C.; Poole, A.C.; Koren, O.; Walters, W.A.; Caporaso, J.G.; Knight, R.; Ley, R.E. Conducting a microbiome study. Cell 2014, 158, 250–262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Turner, P.; Turner, C.; Jankhot, A.; Helen, N.; Lee, S.J.; Day, N.P.; White, N.J.; Nosten, F.; Goldblatt, D. A longitudinal study of Streptococcus pneumoniae carriage in a cohort of infants and their mothers on the Thailand-Myanmar border. PLoS ONE 2012, 7, e38271. [Google Scholar] [CrossRef] [Green Version]
  57. Salter, S.J.; Cox, M.J.; Turek, E.M.; Calus, S.T.; Cookson, W.O.; Moffatt, M.F.; Turner, P.; Parkhill, J.; Loman, N.J.; Walker, A.W. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014, 12, 87. [Google Scholar] [CrossRef] [Green Version]
  58. Ross, M.G.; Russ, C.; Costello, M.; Hollinger, A.; Lennon, N.J.; Hegarty, R.; Nusbaum, C.; Jaffe, D.B. Characterizing and measuring bias in sequence data. Genome Biol. 2013, 14, R51. [Google Scholar] [CrossRef] [Green Version]
  59. Pérez-Cobas, A.E.; Gomez-Valero, L.; Buchrieser, C. Metagenomic approaches in microbial ecology: An update on whole-genome and marker gene sequencing analyses. Microb. Genom. 2020, 6, e000409. [Google Scholar] [CrossRef]
  60. Mulcahy-O’Grady, H.; Workentine, M.L. The challenge and potential of metagenomics in the clinic. Front. Immunol. 2016, 7, 29. [Google Scholar] [CrossRef] [Green Version]
  61. Abubucker, S.; Segata, N.; Goll, J.; Schubert, A.M.; Izard, J.; Cantarel, B.L.; Rodriguez-Mueller, B.; Zucker, J.; Thiagarajan, M.; Henrissat, B. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 2012, 8, e1002358. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Peng, Y.; Leung, H.C.M.; Yiu, S.M.; Chin, F.Y.L. Meta-IDBA: A de Novo assembler for metagenomic data. In Bioinformatics; Oxford University Press: Oxford, UK, 2011; Volume 27, p. i94. Available online: http://bioinformatics.oxfordjournals.org/ (accessed on 9 December 2022).
  63. Alneberg, J.; Bjarnason, B.S.; De Bruijn, I.; Schirmer, M.; Quick, J.; Ijaz, U.Z.; Lahti, L.; Loman, N.J.; Andersson, A.F.; Quince, C. Binning metagenomic contigs by coverage and composition. Nat. Methods 2014, 11, 1144–1146. [Google Scholar] [CrossRef] [PubMed]
  64. Eren, A.M.; Esen, Ö.C.; Quince, C.; Vineis, J.H.; Morrison, H.G.; Sogin, M.L.; Delmont, T.O. Anvi’o: An advanced analysis and visualization platform for ‘omics data. PeerJ 2015, 3, e1319. [Google Scholar] [CrossRef] [Green Version]
  65. Ayling, M.; Clark, M.D.; Leggett, R.M. New approaches for metagenome assembly with short reads. Brief Bioinform 2020, 21, 584–594. [Google Scholar] [CrossRef] [Green Version]
  66. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, Q.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9, e112963. [Google Scholar] [CrossRef]
  67. Hu, X.; Haas, J.G.; Lathe, R. The electronic tree of life (eToL): A net of long probes to characterize the microbiome from RNA-seq data. BMC Microbiol. 2022, 22, 317. [Google Scholar] [CrossRef]
  68. Aguiar-Pulido, V.; Huang, W.; Suarez-Ulloa, V.; Cickovski, T.; Mathee, K.; Narasimhan, G. Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis. Evol. Bioinform. 2016, 12, 5–16. [Google Scholar] [CrossRef] [Green Version]
  69. Zierer, J.; Jackson, M.A.; Kastenmuller, G.; Mangino, M.; Long, T.; Telenti, A.; Mohney, R.P.; Small, K.S.; Bell, J.T.; Steves, C.J.; et al. The fecal metabolome as a functional readout of the gut microbiome. Nat. Genet. 2018, 50, 790–795. [Google Scholar] [CrossRef]
  70. Roessner, U.; Bowne, J. What is metabolomics all about? Biotechniques 2009, 46, 363–365. [Google Scholar] [CrossRef]
  71. Blakeley-Ruiz, J.A.; Erickson, A.R.; Cantarel, B.L.; Xiong, W.; Adams, R.; Jansson, J.K.; Fraser, C.M.; Hettich, R.L. Metaproteomics reveals persistent and phylum-redundant metabolic functional stability in adult human gut microbiomes of Crohn’s remission patients despite temporal variations in microbial taxa, genomes, and proteomes. Microbiome 2019, 7, 18. [Google Scholar] [CrossRef] [Green Version]
  72. Shakya, M.; Lo, C.C.; Chain, P.S.G. Advances and Challenges in Metatranscriptomic Analysis. Front. Genet. 2019, 10, 904. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Lagier, J.-C.; Dubourg, G.; Million, M.; Cadoret, F.; Bilen, M.; Fenollar, F.; Levasseur, A.; Rolain, J.-M.; Fournier, P.-E.; Raoult, D. Culturing the human microbiota and culturomics. Nat. Rev. Microbiol. 2018, 16, 540–550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Gevers, D.; Knight, R.; Petrosino, J.F.; Huang, K.; McGuire, A.L.; Birren, B.W.; Nelson, K.E.; White, O.; Methe, B.A.; Huttenhower, C. The Human Microbiome Project: A community resource for the healthy human microbiome. PLoS Biol. 2012, 10, e1001377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Proctor, L.M. The Human Microbiome Project in 2011 and beyond. Cell Host Microbe 2011, 10, 287–291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. iHMP Consortium. The Integrative Human Microbiome Project. Nature 2019, 569, 641–648. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Verma, D.; Garg, P.K.; Dubey, A.K. Insights into the human oral microbiome. Arch. Microbiol. 2018, 200, 525–540. [Google Scholar] [CrossRef]
  78. Wang, S.; Song, F.; Wang, Y.; Huang, Y.; Xie, B.; Luo, H. High resolution melting analysis (HRM) based on 16SrRNA as a tool for personal identification with the human oral microbiome. Forensic Sci. Int. Genet. Suppl. Ser. 2019, 7, 161–163. [Google Scholar] [CrossRef]
  79. Abeles, S.R.; Pride, D.T. Molecular bases and role of viruses in the human microbiome. J. Mol. Biol. 2014, 426, 3892–3906. [Google Scholar] [CrossRef]
  80. Baker, J.L.; Bor, B.; Agnello, M.; Shi, W.; He, X. Ecology of the Oral Microbiome: Beyond Bacteria. Trends Microbiol. 2017, 25, 362–374. [Google Scholar] [CrossRef] [Green Version]
  81. Pride, D.T.; Salzman, J.; Haynes, M.; Rohwer, F.; Davis-Long, C.; White, R.A., 3rd; Loomer, P.; Armitage, G.C.; Relman, D.A. Evidence of a robust resident bacteriophage population revealed through analysis of the human salivary virome. ISME J. 2012, 6, 915–926. [Google Scholar] [CrossRef]
  82. Gruffaz, M.; Zhang, T.; Marshall, V.; Goncalves, P.; Ramaswami, R.; Labo, N.; Whitby, D.; Uldrick, T.S.; Yarchoan, R.; Huang, Y.; et al. Signatures of oral microbiome in HIV-infected individuals with oral Kaposi’s sarcoma and cell-associated KSHV DNA. PLoS Pathog. 2020, 16, e1008114. [Google Scholar] [CrossRef] [Green Version]
  83. Wood, L.F.; Chahroudi, A.; Chen, H.L.; Jaspan, H.B.; Sodora, D.L. The oral mucosa immune environment and oral transmission of HIV/SIV. Immunol. Rev. 2013, 254, 34–53. [Google Scholar] [CrossRef] [Green Version]
  84. Hayes, R.B.; Ahn, J.; Fan, X.; Peters, B.A.; Ma, Y.; Yang, L.; Agalliu, I.; Burk, R.D.; Ganly, I.; Purdue, M.P.; et al. Association of Oral Microbiome With Risk for Incident Head and Neck Squamous Cell Cancer. JAMA Oncol. 2018, 4, 358–365. [Google Scholar] [CrossRef]
  85. Gomez, A.; Espinoza, J.L.; Harkins, D.M.; Leong, P.; Saffery, R.; Bockmann, M.; Torralba, M.; Kuelbs, C.; Kodukula, R.; Inman, J.; et al. Host Genetic Control of the Oral Microbiome in Health and Disease. Cell Host Microbe 2017, 22, 269–278. [Google Scholar] [CrossRef] [Green Version]
  86. Matarazzo, F.; Ribeiro, A.C.; Feres, M.; Faveri, M.; Mayer, M.P. Diversity and quantitative analysis of Archaea in aggressive periodontitis and periodontally healthy subjects. J. Clin. Periodontol. 2011, 38, 621–627. [Google Scholar] [CrossRef]
  87. Liu, N.N.; Jiao, N.; Tan, J.C.; Wang, Z.; Wu, D.; Wang, A.J.; Chen, J.; Tao, L.; Zhou, C.; Fang, W.; et al. Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts. Nat. Microbiol. 2022, 7, 238–250. [Google Scholar] [CrossRef]
  88. Ghannoum, M.A.; Jurevic, R.J.; Mukherjee, P.K.; Cui, F.; Sikaroodi, M.; Naqvi, A.; Gillevet, P.M. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 2010, 6, e1000713. [Google Scholar] [CrossRef] [Green Version]
  89. Cheung, M.K.; Chan, J.Y.K.; Wong, M.C.S.; Wong, P.Y.; Lei, P.; Cai, L.; Lan, L.; Ho, W.C.S.; Yeung, A.C.M.; Chan, P.K.S.; et al. Determinants and Interactions of Oral Bacterial and Fungal Microbiota in Healthy Chinese Adults. Microbiol. Spectr. 2022, 10, e0241021. [Google Scholar] [CrossRef]
  90. Sultan, A.S.; Kong, E.F.; Rizk, A.M.; Jabra-Rizk, M.A. The oral microbiome: A Lesson in coexistence. PLoS Pathog. 2018, 14, e1006719. [Google Scholar] [CrossRef] [Green Version]
  91. Burgain, A.; Pic, É.; Markey, L.; Tebbji, F.; Kumamoto, C.A.; Sellam, A. A novel genetic circuitry governing hypoxic metabolic flexibility, commensalism and virulence in the fungal pathogen Candida albicans. PLoS Pathogens. 2019, 15, e1007823. [Google Scholar] [CrossRef] [Green Version]
  92. Vylkova, S.; Lorenz, M.C. Phagosomal neutralization by the fungal pathogen Candida albicans induces macrophage pyroptosis. Infect. Immun. 2017, 85, e00832-16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Metwalli, K.H.; Khan, S.A.; Krom, B.P.; Jabra-Rizk, M.A. Streptococcus mutans, Candida albicans, and the human mouth: A sticky situation. PLoS Pathog. 2013, 9, e1003616. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Krom, B.P.; Kidwai, S.; Ten Cate, J.M. Candida and other fungal species: Forgotten players of healthy oral microbiota. J. Dent. Res. 2014, 93, 445–451. [Google Scholar] [CrossRef]
  95. Morse, D.J.; Wilson, M.J.; Wei, X.; Bradshaw, D.J.; Lewis, M.A.O.; Williams, D.W. Modulation of Candida albicans virulence in in vitro biofilms by oral bacteria. Lett. Appl. Microbiol. 2019, 68, 337–343. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Ishijima, S.A.; Hayama, K.; Burton, J.P.; Reid, G.; Okada, M.; Matsushita, Y.; Abe, S. Effect of Streptococcus salivarius K12 on the in vitro growth of Candida albicans and its protective effect in an oral candidiasis model. Appl. Environ. Microbiol. 2012, 78, 2190–2199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Vila, T.; Rizk, A.M.; Sultan, A.S.; Jabra-Rizk, M.A. The power of saliva: Antimicrobial and beyond. PLoS Pathog. 2019, 15, e1008058. [Google Scholar] [CrossRef]
  98. Verma, A.; Gaffen, S.L.; Swidergall, M. Innate Immunity to Mucosal Candida Infections. J. Fungi 2017, 3, 60. [Google Scholar] [CrossRef] [Green Version]
  99. Benn, A.; Heng, N.; Broadbent, J.; Thomson, W. Studying the human oral microbiome: Challenges and the evolution of solutions. Aust. Dent. J. 2018, 63, 14–24. [Google Scholar] [CrossRef] [Green Version]
  100. Dewhirst, F.E.; Chen, T.; Izard, J.; Paster, B.J.; Tanner, A.C.; Yu, W.H.; Lakshmanan, A.; Wade, W.G. The human oral microbiome. J. Bacteriol. 2010, 192, 5002–5017. [Google Scholar] [CrossRef] [Green Version]
  101. Perera, M.; Al-Hebshi, N.N.; Speicher, D.J.; Perera, I.; Johnson, N.W. Emerging role of bacteria in oral carcinogenesis: A review with special reference to perio-pathogenic bacteria. J. Oral Microbiol. 2016, 8, 32762. [Google Scholar] [CrossRef] [Green Version]
  102. Pushalkar, S.; Paul, B.; Li, Q.; Yang, J.; Vasconcelos, R.; Makwana, S.; Gonzalez, J.M.; Shah, S.; Xie, C.; Janal, M.N.; et al. Electronic Cigarette Aerosol Modulates the Oral Microbiome and Increases Risk of Infection. iScience 2020, 23, 100884. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Ibrahim, M.; Subramanian, A.; Anishetty, S. Comparative pan genome analysis of oral Prevotella species implicated in periodontitis. Funct. Integr. Genom. 2017, 17, 513–536. [Google Scholar] [CrossRef] [PubMed]
  104. Eribe, E.R.K.; Olsen, I. Leptotrichia species in human infections II. J. Oral Microbiol. 2017, 9, 1368848. [Google Scholar] [CrossRef] [Green Version]
  105. Eribe, E.R.; Olsen, I. Leptotrichia species in human infections. Anaerobe 2008, 14, 131–137. [Google Scholar] [CrossRef]
  106. Langfeldt, D.; Neulinger, S.C.; Stiesch, M.; Stumpp, N.; Bang, C.; Schmitz, R.A.; Eberhard, J. Health- and disease-associated species clusters in complex natural biofilms determine the innate immune response in oral epithelial cells during biofilm maturation. FEMS Microbiol. Lett. 2014, 360, 137–143. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Gupta, V.K.; Chaudhari, N.M.; Iskepalli, S.; Dutta, C. Divergences in gene repertoire among the reference Prevotella genomes derived from distinct body sites of human. BMC Genom. 2015, 16, 153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Randis, T.M.; Ratner, A.J. Gardnerella and Prevotella: Co-conspirators in the Pathogenesis of Bacterial Vaginosis. J. Infect. Dis. 2019, 220, 1085–1088. [Google Scholar] [CrossRef]
  109. Lukens, J.R.; Gurung, P.; Vogel, P.; Johnson, G.R.; Carter, R.A.; McGoldrick, D.J.; Bandi, S.R.; Calabrese, C.R.; Vande Walle, L.; Lamkanfi, M.; et al. Dietary modulation of the microbiome affects autoinflammatory disease. Nature 2014, 516, 246–249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  110. de Brito, B.B.; da Silva, F.A.F.; Soares, A.S.; Pereira, V.A.; Santos, M.L.C.; Sampaio, M.M.; Neves, P.H.M.; de Melo, F.F. Pathogenesis and clinical management of Helicobacter pylori gastric infection. World J. Gastroenterol. 2019, 25, 5578–5589. [Google Scholar] [CrossRef]
  111. Yee, J.K.C. Are the view of Helicobacter pylori colonized in the oral cavity an illusion? Exp. Mol. Med. 2017, 49, e397. [Google Scholar] [CrossRef] [Green Version]
  112. Brown, L.M. Helicobacter pylori: Epidemiology and routes of transmission. Epidemiol. Rev. 2000, 22, 283–297. [Google Scholar] [CrossRef]
  113. Loster, B.W.; Majewski, S.W.; Czesnikiewicz-Guzik, M.; Bielanski, W.; Pierzchalski, P.; Konturek, S.J. The relationship between the presence of Helicobacter pylori in the oral cavity and gastric in the stomach. J. Physiol.Pharmacol. 2006, 57 (Suppl. 3), 91–100. [Google Scholar] [PubMed]
  114. Namiot, D.B.; Namiot, Z.; Kemona, A.; Bucki, R.; Gotebiewska, M. Oral health status and oral hygiene practices of patients with peptic ulcer and how these affect Helicobacter pylori eradication from the stomach. Helicobacter 2007, 12, 63–67. [Google Scholar] [CrossRef]
  115. Jia, C.L.; Jiang, G.S.; Li, C.H.; Li, C.R. Effect of dental plaque control on infection of Helicobacter pylori in gastric mucosa. Tex. Dent. J. 2012, 129, 1069–1073. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Regis, W.; Reis, A.; Rocha, F.; Guedes, S.; Maia, D.; Rodrigues, L. The Role of Candida Albicans and Streptococcus Mutans Spent Culture Supernatant in Single and Dual-Species Biofilm. J. Sci. Tech. Res. 2019, 14, 1–3. [Google Scholar]
  117. Falsetta, M.L.; Klein, M.I.; Colonne, P.M.; Scott-Anne, K.; Gregoire, S.; Pai, C.H.; Gonzalez-Begne, M.; Watson, G.; Krysan, D.J.; Bowen, W.H.; et al. Symbiotic relationship between Streptococcus mutans and Candida albicans synergizes virulence of plaque biofilms in vivo. Infect. Immun. 2014, 82, 1968–1981. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Dashper, S.G.; Mitchell, H.L.; Le Cao, K.A.; Carpenter, L.; Gussy, M.G.; Calache, H.; Gladman, S.L.; Bulach, D.M.; Hoffmann, B.; Catmull, D.V.; et al. Temporal development of the oral microbiome and prediction of early childhood caries. Sci. Rep. 2019, 9, 19732. [Google Scholar] [CrossRef] [Green Version]
  119. Baker, J.L.; He, X.; Shi, W. Precision Reengineering of the Oral Microbiome for Caries Management. Adv. Dent. Res. 2019, 30, 34–39. [Google Scholar] [CrossRef]
  120. Krzysciak, W.; Koscielniak, D.; Papiez, M.; Vyhouskaya, P.; Zagorska-Swiezy, K.; Kolodziej, I.; Bystrowska, B.; Jurczak, A. Effect of a Lactobacillus Salivarius Probiotic on a Double-Species Streptococcus Mutans and Candida Albicans Caries Biofilm. Nutrients 2017, 9, 1242. [Google Scholar] [CrossRef] [Green Version]
  121. Surdu, A.E.; Popa, C.; Luchian, I. Identification of bacteria involved in periodontal disease using molecular biology techniques. Rev. Chim. 2017, 68, 2407–2412. [Google Scholar]
  122. Teodorescu, A.C.; Teslaru, S.; Solomon, S.M.; Zetu, L.; Luchian, I.; Sioustis, I.-A.; Martu, M.A.; Vasiliu, B.; Martu, S. Assessment of Bacterial Associations Involved in Periodontal Disease Using Crevicular Fluid. Rev. Chim. 2019, 70, 2145–2149. [Google Scholar] [CrossRef]
  123. Olsen, I.; Lambris, J.D.; Hajishengallis, G. Porphyromonas gingivalis disturbs host-commensal homeostasis by changing complement function. J. Oral Microbiol. 2017, 9, 1340085. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Irshad, M.; van der Reijden, W.A.; Crielaard, W.; Laine, M.L. In vitro invasion and survival of Porphyromonas gingivalis in gingival fibroblasts; role of the capsule. Arch. Immunol. Ther Exp. 2012, 60, 469–476. [Google Scholar] [CrossRef] [PubMed]
  125. How, K.Y.; Song, K.P.; Chan, K.G. Porphyromonas gingivalis: An Overview of Periodontopathic Pathogen below the Gum Line. Front. Microbiol. 2016, 7, 53. [Google Scholar] [CrossRef] [Green Version]
  126. Amano, A. Bacterial adhesins to host components in periodontitis. Periodontol 2000 2010, 52, 12–37. [Google Scholar] [CrossRef]
  127. Xiao, L.; Zhang, Q.; Peng, Y.; Wang, D.; Liu, Y. The effect of periodontal bacteria infection on incidence and prognosis of cancer: A systematic review and meta-analysis. Medicine 2020, 99, e19698. [Google Scholar] [CrossRef] [PubMed]
  128. Olsen, I.; Yilmaz, O. Possible role of Porphyromonas gingivalis in orodigestive cancers. J. Oral Microbiol. 2019, 11, 1563410. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  129. Han, Y.W. Fusobacterium nucleatum: A commensal-turned pathogen. Curr. Opin. Microbiol. 2015, 23, 141–147. [Google Scholar] [CrossRef] [Green Version]
  130. Loozen, G.; Ozcelik, O.; Boon, N.; De Mol, A.; Schoen, C.; Quirynen, M.; Teughels, W. Inter-bacterial correlations in subgingival biofilms: A large-scale survey. J. Clin. Periodontol. 2014, 41, 12167. [Google Scholar] [CrossRef]
  131. Han, Y.W.; Wang, X. Mobile microbiome: Oral bacteria in extra-oral infections and inflammation. J. Dent. Res. 2013, 92, 485–491. [Google Scholar] [CrossRef] [Green Version]
  132. Bashir, A.; Miskeen, A.Y.; Hazari, Y.M.; Asrafuzzaman, S.; Fazili, K.M. Fusobacterium nucleatum, inflammation, and immunity: The fire within human gut. Tumor Biol. 2016, 37, 2805–2810. [Google Scholar] [CrossRef] [PubMed]
  133. Cao, P.; Chen, Y.; Chen, Y.; Su, W.; Zhan, N.; Dong, W. Fusobacterium nucleatum Activates Endoplasmic Reticulum Stress to Promote Crohn’s Disease Development via the Upregulation of CARD3 Expression. Front. Pharmacol. 2020, 11, 106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Corona, P.S.; Lung, M.; Rodriguez-Pardo, D.; Pigrau, C.; Soldado, F.; Amat, C.; Carrera, L. Acute periprosthetic joint infection due to Fusobacterium nucleatum in a non-immunocompromised patient. Failure using a Debridement, Antibiotics + Implant retention approach. Anaerobe 2018, 49, 116–120. [Google Scholar] [CrossRef]
  135. Hintze, T.; Steed, M.; Sievers, E.; Bagwell, J.T.; Selfa, N. Primary meningitis due to Fusobacterium nucleatum successfully treated with ceftriaxone in a healthy adult male. IDCases 2019, 18, e00616. [Google Scholar] [CrossRef] [PubMed]
  136. Hattori, T.; Yuasa, N.; Ikegami, S.; Nishiyama, H.; Takeuchi, E.; Miyake, H.; Kuno, R.; Miyata, K.; Fujino, M.; Minami, M. Culture-based bacterial evaluation of the appendix lumen in patients with and without acute appendicitis. J. Infect. Chemother. 2019, 25, 708–713. [Google Scholar] [CrossRef]
  137. Vander Haar, E.L.; So, J.; Gyamfi-Bannerman, C.; Han, Y.W. Fusobacterium nucleatum and adverse pregnancy outcomes: Epidemiological and mechanistic evidence. Anaerobe 2018, 50, 55–59. [Google Scholar] [CrossRef]
  138. Amer, A.; Galvin, S.; Healy, C.M.; Moran, G.P. The Microbiome of Potentially Malignant Oral Leukoplakia Exhibits Enrichment for Fusobacterium, Leptotrichia, Campylobacter, and Rothia Species. Front Microbiol. 2017, 8, 2391. [Google Scholar] [CrossRef] [Green Version]
  139. Brennan, C.A.; Garrett, W. Fusobacterium nucleatum-symbiont, opportunist and oncobacterium. Nat. Rev. Microbiol. 2019, 17, 156–166. [Google Scholar] [CrossRef]
  140. Coppenhagen-Glazer, S.; Sol, A.; Abed, J.; Naor, R.; Zhang, X.; Han, Y.W.; Bachrach, G. Fap2 of Fusobacterium nucleatum Is a Galactose-Inhibitable Adhesin Involved in Coaggregation, Cell Adhesion, and Preterm Birth. Infect. Immun. 2015, 83, 1104–1113. [Google Scholar] [CrossRef] [Green Version]
  141. Park, S.R.; Kim, D.J.; Han, S.H.; Kang, M.J.; Lee, J.Y.; Jeong, Y.J.; Lee, S.J.; Kim, T.H.; Ahn, S.G.; Yoon, J.H.; et al. Diverse Toll-like receptors mediate cytokine production by Fusobacterium nucleatum and Aggregatibacter actinomycetemcomitans in macrophages. Infect. Immun. 2014, 82, 1914–1920. [Google Scholar] [CrossRef] [Green Version]
  142. Chaushu, S.; Wilensky, A.; Gur, C.; Shapira, L.; Elboim, M.; Halftek, G.; Polak, D.; Achdout, H.; Bachrach, G.; Mandelboim, O. Direct recognition of Fusobacterium nucleatum by the NK cell natural cytotoxicity receptor NKp46 aggravates periodontal disease. PLoS Pathog. 2012, 8, e1002601. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Feng, X.; Zhang, L.; Xu, L.; Meng, H.; Lu, R.; Chen, Z.; Shi, D.; Wang, X. Detection of eight periodontal microorganisms and distribution of Porphyromonas gingivalis fimA genotypes in Chinese patients with aggressive periodontitis. J. Periodontol. 2014, 85, 150–159. [Google Scholar] [CrossRef] [PubMed]
  144. Li, Q.; Tan, L.; Wang, H.; Kou, Y.; Shi, X.; Zhang, S.; Pan, Y. Fusobacterium nucleatum Interaction with Pseudomonas aeruginosa Induces Biofilm-Associated Antibiotic Tolerance via Fusobacterium Adhesin A. ACS Infect. Dis. 2020, 6, 1686–1696. [Google Scholar] [CrossRef] [PubMed]
  145. Yu, T.; Guo, F.; Yu, Y.; Sun, T.; Ma, D.; Han, J.; Qian, Y.; Kryczek, I.; Sun, D.; Nagarsheth, N.; et al. Fusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy. Cell 2017, 170, 548–563.e16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Binder Gallimidi, A.; Fischman, S.; Revach, B.; Bulvik, R.; Maliutina, A.; Rubinstein, A.M.; Nussbaum, G.; Elkin, M. Periodontal pathogens Porphyromonas gingivalis and Fusobacterium nucleatum promote tumor progression in an oral-specific chemical carcinogenesis model. Oncotarget 2015, 6, 22613–22623. [Google Scholar] [CrossRef] [Green Version]
Microorganisms 11 00318 g001 550
Figure 1. The role of Fusobacterium nucleatum in the inflammation and ultimately the increase in cell proliferation of host cells. Both FadA and Fap2 are encoded by F. nucleatum, and their individual pathways are also shown [129]. FadA utilizes the E-cadherin complex while Fap2 adheres to the galactose sensitive pathway through the use of N-acetylgalactosamine (Gal/NAc) [131]. F. nucleatum provokes a variety of host responses including the stimulation of IL-6, IL-8, IL-17, and TNF α [138]. During a healthy state, this inflammation leads to wound repair and healing. However, during a diseased state, this can lead to DNA damage and cancer.
Figure 1. The role of Fusobacterium nucleatum in the inflammation and ultimately the increase in cell proliferation of host cells. Both FadA and Fap2 are encoded by F. nucleatum, and their individual pathways are also shown [129]. FadA utilizes the E-cadherin complex while Fap2 adheres to the galactose sensitive pathway through the use of N-acetylgalactosamine (Gal/NAc) [131]. F. nucleatum provokes a variety of host responses including the stimulation of IL-6, IL-8, IL-17, and TNF α [138]. During a healthy state, this inflammation leads to wound repair and healing. However, during a diseased state, this can lead to DNA damage and cancer.
Microorganisms 11 00318 g001
Table
Table 1. A comprehensive description of the main microbiome characterization methods and applications.
Table 1. A comprehensive description of the main microbiome characterization methods and applications.
Characterization Method Description References
Targeted Gene Analysis Analysis involving the use of one or a couple hypervariable regions. This is the most common analysis and is best used for highly specific sequencing. [26,27,28]
Shotgun Metagenomics A widespread, untargeted approach that incorporates a wide variety of genetic information. This analysis is best used in the absence of reference genome. [33,34,35]
Metabolomics A comprehensive analysis which utilizes mass spectrometry for characterization. This is best used to gain information on the role of small metabolites in cell function. [38,39]
Metraproteomics Analysis using mass spectrometry to provide information on macromolecules. This should be used when inquiring about protein interaction with the system. [26,40]
Metratransciptomics An approach which utilizes microbes within their natural environment to provide additional information on the overall function of the community [41]
Microbial Culturomics A method used in conjunction with other characterization methods to provide information of unknown species through their culturing methods. Experimental design is critical. [42]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Morrison, A.G.; Sarkar, S.; Umar, S.; Lee, S.T.M.; Thomas, S.M. The Contribution of the Human Oral Microbiome to Oral Disease: A Review. Microorganisms 2023, 11, 318. https://doi.org/10.3390/microorganisms11020318

AMA Style

Morrison AG, Sarkar S, Umar S, Lee STM, Thomas SM. The Contribution of the Human Oral Microbiome to Oral Disease: A Review. Microorganisms. 2023; 11(2):318. https://doi.org/10.3390/microorganisms11020318

Chicago/Turabian Style

Morrison, Austin Gregory, Soumyadev Sarkar, Shahid Umar, Sonny T. M. Lee, and Sufi Mary Thomas. 2023. "The Contribution of the Human Oral Microbiome to Oral Disease: A Review" Microorganisms 11, no. 2: 318. https://doi.org/10.3390/microorganisms11020318

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop