Microbes and Infection 17 (2015) 505e516 www.elsevier.com/locate/micinf

Beyond microbial community composition: functional activities of the oral microbiome in health and disease Ana E. Duran-Pinedo a, Jorge Frias-Lopez a,b,* b

a Department of Microbiology, The Forsyth Institute, 245 First Street, Cambridge, MA 02142, USA Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, 188 Longwood Ave, Boston, MA 02115, USA

Received 21 January 2015; revised 23 March 2015; accepted 26 March 2015 Available online 7 April 2015

Abstract The oral microbiome plays a relevant role in the health status of the host and is a key element in a variety of oral and non-oral diseases. Despite advances in our knowledge of changes in microbial composition associated with different health conditions the functional aspects of the oral microbiome that lead to dysbiosis remain for the most part unknown. In this review, we discuss the progress made towards understanding the functional role of the oral microbiome in health and disease and how novel technologies are expanding our knowledge on this subject. © 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Keywords: Microbiome; Oral; Omics; Dysbiosis; Periododontitis; Caries

1. Introduction: role of the human oral microbiome in health and disease It is now common knowledge that the human microbiome plays an important role in the well-being and health status of the human host. A great effort has been placed in recent years on characterizing the different microbial communities colonizing the human body [1]. Among those sites, the oral cavity represents one of the most diverse microbial communities associated with any of the human sites studied [1]. It is a highly complex community with around 700 species identified to be associated with any of the different environments within the oral cavity [2]. To date, it is probably one of the best characterized communities of the human microbiome. The oral cavity includes diverse structures and tissues, such as teeth, gingival sulcus, gingiva, tongue, cheeks, lips and palate, which provide different habitats, growth conditions and

* Corresponding author. 245 First Street, Cambridge, MA 02142, USA. Tel.: þ1 617 892 8576; fax: þ1 617 892 8510. E-mail address: [email protected] (J. Frias-Lopez).

availability of nutrients. It has been shown that microbial profiles differ markedly depending on the intraoral location [2]. The microbiome of saliva is more similar to that of the dorsal and lateral surfaces of the tongue, and the soft tissues communities resemble each other more than the microbiota on the teeth above and below the gingival margin [3]. Hence, the oral microbiome can be seen as a group of diverse microbial biofilms. Microorganisms from the oral cavity are the etiological agents for a number of infectious diseases including caries, periodontitis, endodontic infections, alveolar osteitis and tonsillitis. Additionally, several studies have linked oral diseases to systemic chronic diseases, including cardiovascular disease, stroke, preterm birth, diabetes and pneumonia [4]. Changes in microbiota have been also associated with different types of cancer [5], but the question remains whether those changes are the cause or the consequence of the pathological process. Strong evidence seems to suggest that Porphyromonas gingivalis, may be a driver in the development of orodigestive cancers including oral, intestinal and pancreatic cancers, oral squamous cell carcinoma (OSCC) being the most highly associated with this oral bacterium [6].

http://dx.doi.org/10.1016/j.micinf.2015.03.014 1286-4579/© 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

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Oral bacteria can infect hard and soft tissue. Caries and periodontitis are the most common oral diseases caused by bacterial infection. A report from the US surgeon general on the state of oral health in the USA shows that dental caries is the single most common disease in childhood and that periodontal disease is widespread among adults, with l4% for adults aged 45-54 showing signs of severe periodontal disease that increases to 33% in older adults (ages 65e74) [7]. Despite substantial research, dental caries remains a significant public health issue in children worldwide. Over 50% of children in the United States between the ages of 5 and 9 have at least one cavity or filling, and that number increases to 78% among 17 year old [7]. Dental caries, or tooth decay, is the dissolution of tooth structure by acid produced by oral bacteria as a result of the fermentation of dietary carbohydrates. When the fermentation process is enhanced by excess of ingestion of sugar, the saliva loses its buffering capacity and constant reductions on the pH lead to the erosion of the enamel, cementum and dentin. Under these conditions, the oral microbiota switch in favor of aciduric species such as Streptococcus mutants and lactobacilli, which are the major cariogenic species [8]. Lately, a large number of acid producing species have been caries-associated. Besides S. mutants and lactobacilli, members of the genera Bifidobacterium, Propionibacterium and Scardovia have also been associated with caries [9]. In juxtaposition, some bacteria help maintain homeostasis through ammonia production from arginine and urea. For example, Streptococcus salivarius, one of the major alkali producers in the mouth, expresses urease gene under acidic pH and excess of carbohydrates [10]. Since the metabolic activities of strains within species show high variability, species-level identifications may not represent a great value in understanding the disease process and more information would be retrieved from the functional characterization of the community in vivo. If caries is left untreated, the lesion can progress to endodontic infections where the pulp becomes infected and dies causing pain and inflammation. Microorganisms can also reach the pulp through dentinal tubules when the dentin distance between the border of carious lesion and the pulp is small. Direct pulp exposure after coronal fracture can result in fault on restoration. Salivary contamination can reach the periapical area in canals already obturated and sealed. Additionally, bacteria can reach those areas due to bacteremia, since blood-borne bacteria are attracted to the dental pulp. Endodontic infections have a polymicrobial nature, with anaerobic proteolytic bacteria dominating the microbiota in primary infections [11]. When the infections persist after treatment, enterococci are the most commonly identified species, even though they are present in the mouth at low numbers. Periodontal diseases (gingivitis and periodontitis) are the diseases of the oral soft tissue. Gingivitis is maybe the most common oral disease worldwide with a prevalence in adults over 90% in the United States [12]. Gingivitis is a reversible gingival lesion. It develops in response to the accumulation of dental plaque, the oral microbial biofilm, on the adjacent tooth

surfaces. If oral hygiene practices fail, a mature biofilm is built, the proportion of Gram-negative and anaerobic species increases, and endotoxin and lytic enzymes pass into the gingivae causing irritation and inflammation. The gums are swollen and inflamed with bleeding on probing or spontaneously. Unlike periodontitis, there is no specific group of bacteria associated with gingivitis [13]. Periodontitis is a bacterially induced chronic inflammatory disease of the periodontium, affecting the tissues surrounding and supporting the teeth, including the periodontal ligament. The most recent data from the National Health and Nutrition Examination Survey indicate that the prevalence of chronic periodontitis among adult Americans is over 47%, representing 64.7 million adults [14]. In 1999, the total annual expenditure for treating and preventing periodontal disease in the United States was estimated to be $14.3 billion [15]. Chronic periodontitis is one of the 50 most prevalent disabling health conditions and is the main cause of tooth loss in adults [16]. Despite advances in our knowledge of the causes and risk factors associated with periodontitis, there are no signs of a decline in periodontal disease prevalence. In fact, longer retention of teeth coupled with an aging population might account for future increases in the number of subjects affected by periodontal destruction with important repercussion in the economics of health care [17]. Because less than half of the oral microbiome has been cultured and formally named (17% cultured not yet named or 34% uncultured in vitro, not yet named) unknown organisms playing a key role may have been overlooked [18,19]. The current working model to explain periodontitis progression proposes that changes in the relative abundance of members of the oral microbiome lead to dysbiosis. Further, there is a hostmicrobiome crosstalk which leads to inflammation and bone loss [20]. The late downstream events that activate osteoclasts to resorb alveolar bone are well established in both human and animal models and predominantly involve mechanisms dependent on receptor activator of nuclear factor kB ligand (RANKL) [21]. In this review we present a brief overview of the impact that modern 'omics' approaches have had on our understanding on the role of the oral microbiome in health and disease, and provide directions for future studies that should emphasize, beyond community composition, the functional activities of the oral microbial community. Although viruses, fungi and archaea are natural inhabitants of the oral microbiome [22] a description of their role in health and disease goes beyond the scope of this review and in our discussion we will focus exclusively on the role of oral bacteria. Microbial functionality can be characterized at several different levels. As shown in Fig. 1 we can characterize the community by its composition (DNA, metagenome), by the analysis of transcripts (metatranscriptome: collective RNA from all microorganisms present in an ecosystem), by the analysis of proteins (metaproteome: collective proteins from all microorganisms present in an ecosystem) or by the analysis of the final products resulting from the collective action of the community [23].

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Fig. 1. Schematic representation of the different 'omic' levels in the ecology of microbial communities. Different strategies of study would results in different levels of knowledge when studying microbial communities. DNA based analysis would only give information about the potential activities of the community. Metatrancriptome and metaproteome give functional information while only the metabolome would give information about the final products of microbial acitivity. Adapted from Maron et al. [23].

2. Contribution of culture independent methods and next generation DNA sequencing (NGS) techniques to our understanding of changes in community composition in the oral microbiome in health and disease The study of the oral microbiome composition is as old as the dawn of microscopy as a scientific discipline. Leeuwenhoek's letter from Sept 17, 1683, is the first definitive report on the microorganisms indigenous to humans and the undisputed claim to the discovery of oral flora. As in the rest of the microbiological disciplines, the first characterizations of dental plaque microbiome were performed using culturing methods. Interestingly enough, those pioneering studies already identified the major groups of organisms important in composition of the oral microbiome. Streptoccocus spp. were found to be predominant in early plaque formation, representing more than 50% of all cultivable flora. Additionally they found Neisseria spp. and Veillonella spp. are also present at the initial stages of colonization. As the biofilm matured more Gram-negative filamentous organisms were found, such as Fusobacterium spp [24]. However, with the arrival of culture independent methods applied to the study of the oral microbiome a new era of discovery ensued. Before the development of those methods conducting large-scale studies of the oral microbiome was extremely difficult. 2.1. Microarray based technologies The use of checkerboard DNAeDNA hybridization resulted in major breakthroughs in our understanding of the composition of the oral biofilms in health and disease. The checkerboard DNAeDNA hybridization technique allowed enumeration of large numbers of species in very large numbers of samples. The checkerboard DNAeDNA hybridization technique was first described in 1994 by Socransky and collaborators. Using 40 species-specific DNAeDNA hybridization probes, based on whole genomes, to detect oral bacteria in the subgingival plaque, 5 different complexes were identified based on different levels of association with health and severity of periodontitis [25]. The 'complex' idea

revolutionized the view we had of periodontal diseases. 'Bacterial complexes' were defined based on their level of association with severity of disease. It advanced the idea of a series of organisms working together to cause disease, which it was an oddity in terms of infectious diseases, that in most cases are caused by the action of a single pathogenic organism. The 'red complex', the most highly associated with chronic severe periodontitis is composed by 3 species: P. gingivalis, Tannerella forsythia and Treponema denticola. On the other extreme of the scale, members of the 'yellow complex' (Streptococcus gordonii, Streptococcus intermedius, Streptococcus mitis, Streptococcus oralis and Streptococcus sanguis) and 'purple complex' (Actinomyces odontolyticus and Veillonella parvula) are mainly associated with healthy sites. Checkerboard analysis has been also applied to study the impact of other diseases of the oral microbiome. Studies of caries confirmed results already revealed by culturing methods where Actinomyces spp., Streptococcus mutans, and Lactobacillus spp. were the most abundant organisms in caries lesions and with inverse relationship to beneficial bacterial species, such as Streptococcus parasanguinis, Abiotrophia defectiva, S. mitis, S. oralis, and Streptococcus sanguinis [26]. In another study where checkerboard was used to characterize microbial communities in disease, saliva samples from subjects with oral squamous cell carcinoma revealed high salivary counts of Capnocytophaga gingivalis, Prevotella melaninogenica and S. mitis in disease [27]. Building on the same concept of using microarrays to characterize the oral microbiome, another microarray based platform, the Human Oral Microbe Identification Microarray (HOMIM), includes a panel of more than 400 probes, based on the 16S rRNA genes, to detect simultaneously the 270 most prevalent, cultivated and not yet cultivated oral bacterial species (http://mim.forsyth.org). Thus, it is better suited for the study of the oral microbiome than the old checkerboard platform. HOMIM technology has been applied to study a variety of oral diseases, among them progressive periodontitis, which includes localized aggressive periodontitis (LAP) and generalized aggressive periodontitis (GAP). GAP is characterized by fast progression of the disease being much less common than chronic periodontitis and generally affecting younger

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patients. Presence of Aggregatibacter actinomycetemcomitans, Cardiobacterium hominis, Peptostreptococcaceae, Pseudoramibacter alactolyticus and absence of Fretibacterium spp., Fusobacterium naviforme/Fusobacterium nucleatum ss vincentii, Granulicatella adiacens/Granulicatella elegans were associated with aggressive periodontitis [28]. Changes detected by HOMIM in the subgingival microbiota of patients with refractory periodontitis, in comparison with good responders to treatment, included Bacteroidetes sp., Porphyromonas endodontalis, P. gingivalis, Prevotella spp., T. forsythia, Dialister spp., Selenomonas spp., Catonella morbi, Eubacterium spp., Filifactor alocis, Parvimonas micra, Peptostreptococcus sp. OT113, Fusobacterium sp. OT203, P. alactolyticus, S. intermedius or Streptococcus constellatus, and Shuttlesworthia satelles. In contrast, Capnocytophaga sputigena, C. hominis, Gemella haemolysans, Haemophilus parainfluenzae, Kingella oralis, Lautropia mirabilis, Neisseria elongata, Rothia dentocariosa, Streptococcus australis, and Veillonella spp. were more associated with therapeutic success [29]. In saliva of subjects with periodontitis, eight bacterial taxa, including putative periodontal pathogens as Prevotella micra and F. alocis, and four bacterial clusters were identified statistically more frequently and at higher levels in samples from periodontitis patients than in samples from the control cohort [30]. HOMIM profiles of dental caries show a decrease in diversity in impacted sites and presence of five bacterial taxa in those sites (V. parvula, Veillonella atypica, Megasphaera micronuciformis, Fusobacterium periodontium and Achromobacter xylosoxidans). One bacterial cluster (Leptotrichia spp. clones C3MKM102 and GT018_ot417/462) was less frequently found in the caries group while two bacterial taxa (Solobacterium moorei and S. salivarius) and three bacterial clusters (S. parasanguinis I and II and sp. clone BE024_ot057/ 411/721, S. parasanguinis I and II and Streptoccocus sinensis ot411/721/767, S. salivarius and sp. clone FO042 ot067/755) were present at significantly higher levels. The principal component analysis displayed a marked difference in the bacterial community profiles between groups. Presence of manifested caries was associated with a reduced diversity and an altered salivary bacterial community profile [31]. HOMIM has been also applied to study the effect of certain systemic diseases on the oral microbiome. HOMIM profiles of salivary microbiota in pancreatic cancer patients have shown a significant difference between pancreatic cancer and controls in 16 bacterial species, including Streptococcus spp. (3 species/groups), Prevotella spp. (4 species/groups), Campylobacter spp. (4 species/groups), Granulicatella spp. (2 species), Atopobium sp. (1 species) and Neisseria spp. (2 species) [32]. In a different study where HOMIM was used to study sputum from cystic fibrosis patients there was a prevalence of streptoccoci in a large fraction of those samples, with higher numbers than Pseudomonas aeruginosa, the most common pathogen in this disease. The most prevalent streptococci included S. salivarius, Streptoccocus parasanguis and the Streptococcus milleri group species. These species of Streptococcus may play an important role in increasing the diversity

of the cystic fibrosis lung environment and promoting patient stability [33]. As a reflection of the current tendency to substitute microarray based technologies for Next Generation Sequencing (NGS) technologies, the HOMIM platform has been recently replaced by HOMINGS, a new version that combines next generation sequencing with the refinement of species-level identification that HOMIM provided. HOMINGS is capable to identify close to 600 oral bacterial taxa (http:// homings.forsyth.org/index2.html). While these findings have significantly enhanced our understanding of the oral microbiome, they have also highlighted the likelihood there may be an additional large number of low abundance species that have remained undetected with this standard methodological approach, largely because of the relatively time consuming and laborious nature of the techniques. This issue is now being addressed through the application of deep-sequencing methods (see below), which enable a more comprehensive coverage. In a comparison between 16S rRNA pyrosequencing and HOMIM, certain phylogenetic groups representing a small fraction of the community, such as candidate division SR1, Tenericutes spp. and Synergistetes spp., were not detected with the microarray based technique but they were by 16S rRNA pyrosequencing [34]. 2.2. 16S rRNA cloning and sequencing: expanding our knowledge on oral microbiome diversity An important limitation of microarray based methods is the fixed number of species in the panel that can be detected. Moreover, a certain amount of DNA is needed to detect an organism. Organisms present at low numbers may be missed in the analysis if the amount of DNA representing them is below that threshold. To overcome these limitations sequence analysis of 16S ribosomal RNA (rRNA) was the method of choice. 16S rRNA is universally present in all prokaryotic organisms and using universal primers it is possible to describe the species present in a given sample even if they have not been identified previously. Using 16S rRNA amplification, cloning and Sanger sequencing, B. Paster, F. Dewhirst and colleagues unveiled the complexity of the oral microbiome, showing that it is composed of around 700 species belonging to 13 different phyla [2,19]. Firmicutes, Bacteroidetes and Proteobacteria are the most frequently identified phyla in the clone libraries used to construct the Human Oral Microbiome Project (HOMD) [19]. Among those members of the community identified as oral-associated are non-cultured organisms such as members of the candidate division TM7 and candidate division SR1. Studies of caries using 16S rRNA cloning and sequencing confirmed previous results obtained by culturing and checkerboard methods where S. mutans and Lactobacillus spp. are important members of the diseased community. However, 16S rRNA analysis revealed the presence of other organisms such as R. dentocariosa and an unnamed Propionibacterium sp. as highly associated with caries samples [35].

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Molecular analysis of periodontitis using 16s rRNA clone libraries found new bacterial species associated with disease such as F. alocis, Peptostreptococcus spp., Megasphaera spp. Desulfobulbus spp., Eubacterium saphenum, P. endodontalis, Prevotella denticola, and Cryptobacterium curtum [36], that were missed in the original studies of checkerboard DNAeDNA hybridization due to the lack of a prior information on those organisms. The oral microbiota is also altered in patients with oral squamous cell carcinoma. 16S rRNA clone libraries revealed a shift in bacterial colonization of the oral mucosal tissues where Streptococcus sp. oral taxon 058, Peptostreptococcus stomatis, S. salivarius, S. gordonii, G. haemolysans, Gemella morbillorum, Johnsonella ignava and S. parasanguinis were highly associated with tumor sites [37]. 2.3. NGS analysis of the oral microbiome With the arrival of next-generation sequencing (NGS) technologies, we now have the tools that allow for profiling of the microbiomes and metagenomes at unprecedented depths, impossible to attain with any of the methodologies described above. The first NGS application (454 Life Sciences) was introduced by Roche in the year 2005 based on pyrosequencing, which relies on the detection of pyrophosphate release on nucleotide incorporation rather than chain termination with dideoxynucleotides, as in the classic Sanger sequencing. Since then, a large number of different platforms have been introduced to the market at a fast pace (Illumina, SOLiD, Ion Torrent and PacBio), with the different members of the Illumina family being the most widely used platforms. 454 pyrosequencing was the first platform used in 16S rRNA profiling because it was able to obtain sequences of more than 400 bp length, enough to obtain a good phylogenetic assignment of the reads depending of the hypervariable region used to amplify the 16S rRNA gene. Until recently Illumina sequencing lengths were too short to be used on phylogenetic studies at species-level but sequence-length of Illumina reads have been increasing at a fast pace that now allows to obtain reliable results, at least at genus level. In parallel to the use of high-throughput 16S rRNA sequencing, different bioinformatic pipelines have been developed to handle the amount of data generated by NGS technologies. 454 sequencing of 16S rRNA genes analysis comparing subgingival bacterial communities from periodontally healthy controls and subjects with chronic periodontitis revealed that community diversity is higher in disease than in health and Spirochaetes, Synergistetes and Bacteroidetes are more abundant in diseased individuals, whereas Proteobacteria are found at higher levels in healthy controls [38]. Within the phylum Firmicutes, the class Bacilli is health-associated, whereas the Clostridia, Negativicutes and Erysipelotrichia are associated with disease. 454 sequencing of 16S rRNA genes analysis revealed that the healthy “core microbiome” of the oral cavity is predominantly composed by Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria

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(genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium) [39]. Oral microbiome profiles of caries obtained by 454 sequencing of 16S rRNA genes showing high complexity of the microbiome in disease were Lactobacillus spp., Prevotella spp., Atopobium spp., Olsenella spp. and Actinomyces spp [40]. However, there are disparities between microbial communities localized at acidic versus neutral pH strata. Acidic conditions were associated with low diversity of microbial populations, with Lactobacillus species being prominent. In comparison, the distinctive species of a more diverse flora associated with neutral pH regions of carious lesions included Alloprevotella tanerrae, Leptothrix sp., Sphingomonas sp. and Streptococcus anginosus [41]. Analysis of the oral microbial community in oral squamous cell carcinoma patients assessed by 16S rRNA 454 sequencing showed that Peptostreptococcus spp., Abiotrophia spp., Lactobacillus spp. and Micromonas spp. were considerably more abundant in saliva from disease individuals than in healthy controls [37]. Finally, shotgun metagenomics have also been used to characterize phylogenetic changes in oral communities in health and disease but to a lesser extent than the methods previously described. There is good agreement between 16S rRNA Illumina sequencing and shotgun metagenomics at the genus level with regard to the dominant genera identified in periodontal disease and health. Prevotella and Fusobacterium dominate the disease communities while the healthy communities were dominated by members of the following genera: Streptoccocus, Leptotrichia, Actinomyces, Neisseria, Peptostreptococcus, Fusobacterium and Kingella [42]. 3. Beyond community composition. The functional role of the oral microbiome in health and disease In the previous section we briefly summarized the current understanding of community composition of the oral microbiome under different environmental conditions. Nonetheless, although changes in composition of the oral microbial community in health and disease provides important information regarding potentially key organisms, they provide only limited information on the activity of these organisms in the oral environment. Taxonomic composition of the oral microbiomes between healthy subjects can differ significantly, even though their oral cavity is perfectly healthy [43]. Moreover, studies on the gut microbiome have shown that the metatranscriptome of fecal samples varies significantly among subjects despite the fact that they presented a similar metagenome [44]. This indicates that taxonomic characterization alone is not sufficient to reveal relationships between the microbiome and specific health or disease states. Furthermore, description of the community at the species-level may not be enough to explain what the community is doing that leads to pathogenesis. It is quite common that differences at the strain level are the ones responsible for the virulence of certain organisms. A classic example of this is the case of Escherichia coli where non-

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pathogenic and pathogenic species share only 60% of the total genome content. The variable or 'non-core' genes thus make up more than 90% of the pan-genome and about 80% of a typical genome; some of these variable genes tend to be co-localized on genomic islands, which are strain specific [45]. The complexity of the oral microbiome and the fact that a large fraction of species is non-cultivable have hampered our knowledge of the specific role of each organism in the community. In order to achieve the goal of assigning roles to specific organisms in the transition from health to disease we need approaches that unveil the functional activities of these organisms in situ. A new generation of '-'omics'' techniques (e.g. transcriptomic, proteomic and metabolomic approaches) have begun to facilitate the study of functional activities in the oral microbiome, opening a new door to our understanding of the dynamics of the oral microbial biofilm under different environmental situations. 3.1. Shotgun metagenomics to study the functional diversity in microbial communities Shotgun metagenomics (i.e. community genomics of the whole pool of genes), henceforth referred to as just metagenomics, randomly shears DNA and those short fragments are sequenced revealing genes present in environmental samples. Metagenomics provides insight not only on the phylogenetic composition but also into the genetic potential of complex microbial communities to carry out different functional activities. The use of metagenomic analysis to study functional diversity has a long history in microbial ecology. More recently, this approach has been used to study the functional potential of the human microbiome, including the oral microbiome [1,42]. Using deep sequencing to examine enrichment of metabolic pathways in bacterial communities in healthy and periodontally diseased subjects shows that the disease microbiome is enriched in virulence factors, functions related to drug and metal resistance (mercury, cobaltezincecadmium), and functions related to acquisition of nutrients derived from the degradation of host tissue, and from bacterial cells lysed by the host immune system (e.g. fatty acid metabolism and acetyl-coenzyme A degradation, aromatic amino acid degradation, ferrodoxin oxidation, and energy-coupling factor (ECF) class transporters) [42]. Enrichment in the metagenome of genes involved in virulence factors, amino acid metabolism and glycosaminoglycan and pyrimidine degradation in periodontitis, suggest their potential importance in periodontal pathogenesis, while genes involved in amino acid synthesis and pyrimidine synthesis are present at significantly lower relative abundance compared with healthy group [46]. The metabolic potential of the metagenome in caries showed that samples from diseased individuals tended to cluster together, indicating that a similar set of functions were present in their metagenomes. Interestingly, healthy individuals showed a significant under-representation of genes involved in antibacterial activities like bacteriocins, periplasmic stress response genes like degS, degQ, capsular and

extracellular polysaccharides and bacitracin stress response genes [47]. 3.2. Metatrancriptomic analysis of complex microbial communities Although the metagenome represents a glimpse into the metabolic potential of the microbiome, it does not necessarily provide information about the fraction of the metagenome that is being expressed under different conditions. This kind of information could only be obtained by using methods that assess the synthesis of transcripts, proteins or metabolic products resulting from a specific set of activities. Metatranscriptomic analysis characterizes gene expression profiles of the whole microbial community based on the set of transcripts being synthesized under diverse environmental conditions. Given the complexity of the oral microbiome, it would have not been possible to perform this type of analysis without the arrival of NGS technologies. The parallel development of bioinformatic tools suited to perform analysis of NGS data has been just as vital, given the sheer size of data, which otherwise would have been impossible to analyze using classical alignment algorithms such as BLAST. As in the case of metagenomic studies, these techniques have been widely used in environmental studies [48] but much less on the study of the human microbiome. In order to perform transcriptome analysis of the oral microbiome we have to overcome several technical hurdles, such as the low biomass of the samples and the large amount of rRNA present in prokaryotic cells, which would take up much of the sequencing capacity of NGS methods. The first of these problems has been overcome either by combining samples from different teeth to increase the total biomass [49] or by using RNA linear amplification as an initial step to increase the amount of RNA sequences to a level that could be used [50]. To solve the second problem, strategies to remove rRNA have been devised based on the use of specific probes coupled to magnetic beads which hybridize to the rRNA in the sample. The complex magnetic beads-probe-rRNA is then separated from the sample using a magnet, leaving behind a suspension that contains mRNA free of rRNA [51]. Metatranscriptome analysis is just as good as the number of genomes sequenced and available in the databases. Luckily for us, the oral microbiome has a large representation of sequenced genomes, covering most of the abundant species in the oral cavity [18]. Metatranscriptome analysis has been just recently performed to study oral communities in caries [52], periodontitis [50,53] and during biofilm formation and after meal ingestion [49], giving us new insights into the functional activities of the oral biofilm under different conditions. In caries metatranscriptomes transcripts encoding functions related to monosaccharide and disaccharide metabolism represent a significant portion of the dental biofilm transcriptome (around 15% of total). Transcripts encoding for genes associated with disaccharide metabolism are more prevalent than those encoding for genes associated with monosaccharide metabolism by a factor of 2. Interestingly,

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transcripts encoding for genes of resistance to antibiotics and toxic compounds were also expressed in the oral biofilm, although genes involved in acid stress and bacteriocins represented a small fraction of all transcripts across all subjects [52]. In periodontitis studies, differences in gene expression between periodontally healthy subjects and patients with severe periodontitis showed that the composition of the shotgun metagenome does not reflect the active community represented in the metatranscriptome. Moreover, the phylogenetic profiles based on metatranscriptome analysis were more similar than those based on shotgun metagenome analysis [50]. Interestingly, the vast majority of putative virulence factors up-regulated in disease were expressed by organisms of the oral biofilm that are considered non-pathogenic (Fig. 1), which implies that the community as a whole becomes more virulent during disease [50]. Changes in community-level gene expression based on the Gene Ontology (GO) orthology showed that during disease iron acquisition, oligopeptide transport, lipopolysaccharide synthesis, beta-lactam antibiotic catabolic processes and flagellar synthesis were major activities, while potassium ion transport was associated with healthy sites (Fig. 2) [50]. Moreover, metatranscriptome activities are well conserved in health- and disease-associated communities, while the organisms carrying out these processes varied between communities [53], which could explain why taxonomic composition of the oral microbiomes of healthy individuals differ significantly while maintaining the same healthy status [1,43] (Fig. 3). Differences between phylogenetic composition of the metagenome and the metatranscriptome from the same samples have been also observed when studying dynamics of gene expression in the oral microbiome after meal ingestion and during supragingival biofilm maturation. In general, Actinomyces spp., Corynebacterium spp. and Neisseria spp. were the three most abundant genera in the RNA-based community, whereas Veillonella spp., Streptococcus spp. and Leptotrichia spp. were the most commonly found in the total DNA-based metagenome [49]. Early stages of biofilm formation were characterized by over-expression of KEGG orthology (KO) categories involved in metabolism of carbohydrates, energy, amino acid, cofactor/vitamins, and xenobiotic degradation. Genes over-expressed in a more mature biofilm had a more variable functional profile based on KO categories, including ABC transporters, cell motility, bacterial chemotaxis, flagella assembly and genes involved in DNA repair [49]. 3.3. Metaproteomic analysis of complex microbial communities The following is a summary of a different '-omic' with high potential to study the effect of the oral microbiome in health and disease. The characterization of the pool of transcripts does not necessarily reflect the spectrum of proteins synthesized by the microbial community. To this end, metaproteomics investigate protein expression from complex biological systems allowing identifying and quantifying the

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protein repertoire collectively expressed by microbes colonizing a given environment [23]. Contrary to what happens with mRNA, proteins in general are rather stable, which makes manipulation of the sample easier. Furthermore, they relate directly to the prokaryotic genetic code given the limited posttranslational processing of prokaryotic proteins. However, to this date, the study of the metaproteome is more expensive and technically demanding than the study of the metatranscriptome. Metaproteomics have been used to study profiles of expression in microbial communities in soil and marine environments [54]. Nonetheless, the use of metaproteomics as a method to assess expression profiles of complex microbial communities is limited by the amount of biomass of the samples, which in the case of the oral plaque is rather small. Metatranscriptomic analysis of fecal samples does not pose a problem in relation with the amount of biomass and there have been studies on the gut microbiome that use this approach [55]. Because of the limited amount of biomass of the dental plaque, in the case of the oral microbiome the studies have been limited to laboratory biofilm models [56,57], saliva samples [58,59] and gingival crevicular fluid (GCF) [60,61]. Moreover, proteomic analysis of saliva (as well as metabolomic) has a big potential in identifying possible biomarkers of different diseases, though this topic goes beyond the scope of the present review. In a three species biofilm model composed by P. gingivalis, S. gordonii and F. nucleatum, metaproteomic analysis identified 403 proteins as down-regulated and 89 proteins as upregulated and found that proteins such as HmuR could be necessary for maintaining the structure of the community biofilm [57]. Additionally, changes in the proteome of S. gordonii were specific when growing with F. nucleatum or P. gingivalis or both. F. nucleatum association decreased proteins for the metabolic end products acetate and ethanol but increased lactate, the primary source of acidity from streptococcal cultures. While in the presence of P. gingivalis, there was a reduction in levels of proteins for ethanol and formate but increased proteins for both acetate and lactate production [56]. Metaproteomic analysis of human salivary supernatant from six healthy individuals was capable of identifying peptides from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veillonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction were most represented [58]. Another study on the metatranscriptome of saliva agreed with those results and showed that most peptides were linked to translation (37%), followed by glycolysis (19%), amino acid metabolism (8%), and energy production (8%) [59]. GCF is a local serum exudate of the periodontal tissues and it has been the focus of interest to search for potential

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Metagenome Metatranscriptome

log2 ratio counts Disease/Health

Bacteroidetes oral taxon 274 Scardovia wiggsiae Kingella denitrificans Synergistetes bacterium Atopobium parvulum Leptotrichia hofstadii Prevotella veroralis Treponema denticola Prevotella intermedia Prevotella tannerae Leptotrichia buccalis Porphyromonas gingivalis Streptococcus parasanguinis Dialister microaerophilus Pyramidobacter piscolens Treponema vincentii Campylobacter showae Filifactor alocis Cardiobacterium hominis Porphyromonas sp. oral taxon 279 Peptostreptococcus stomatis Prevotella sp. oral taxon 306 Neisseria flavescens Campylobacter rectus Tannerella forsythia Rothia dentocariosa Leptotrichia goodfellowii Corynebacterium matruchotii Rothia aeria Veillonella atypica Actinomyces sp. oral taxon 448 Eubacterium saburreum Actinomyces sp. oral taxon 170 Aggregatibacter actinomycetemcomitans Acinetobacter baumannii Lactobacillus gasseri Propionibacterium acnes Bifidobacterium dentium Actinomyces sp. oral taxon 180 Actinomyces sp. oral taxon 171 Streptococcus mutans Lactococcus lactis Ralstonia pickettii Burkholderia cepacia Pseudomonas fluorescens Eubacterium saphenum Bordetella pertussis Enterococcus saccharolyticus Achromobacter xylosoxidans Streptococcus anginosus Enterococcus italicus Pseudomonas stutzeri Neisseria lactamica Comamonas testosteroni Bradyrhizobium elkanii Escherichia coli Lachnospiraceae oral taxon 107 Oribacterium sp. oral taxon 108 Peptoniphilus sp. oral taxon 386 Lachnospiraceae oral taxon 082 Peptoniphilus indolicus

Fig. 2. Rank distribution of relative increase in number of hits for the metagenome and metatranscriptome results. The ratio of counts in disease vs health was log2 transformed and plotted according to ranks. Only species with significant differences either in metagenomic or metatranscriptomic counts are presented. The statistical significance was calculated using the non-parametric test implemented in the program NOISeq as described in the methods section. In green species with statistical differences in both metagenome and metatranscriptome. In blue species with statistical differences in metagenomic counts. In orange species with statistical differences in metatranscriptome counts. Red start indicate major periodontal pathogens previously assigned to the 'red-complex'.

biomarkers in different diseases [62]. Because it represents the interface between the subgingival plaque and host epithelial tissue, GCF offers valued information on the hostebacteria interaction activities. Although its volume is limited, especially in healthy individuals, it has been possible to perform

metaproteomic analysis of GCF. Its composition greatly varies between health and periodontal disease. Metaproteomic analysis of GCF in a twenty-one day gingivitis model identified 16 bacterial proteins that did not vary in concentration during the period of study, most of them belonging to Fusobacterium spp.

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Fig. 3. GO enrichment analysis summarized and visualized as a scatter plot using REVIGO. a) Biological processes in disease. b) Biological processes in health. c) Molecular functions in disease. d) Molecular functions in healthy. GO terms are represented by circles and are plotted according to semantic similarities to other GO terms (adjoining circles are most closely related). Circle size is proportional to the frequency of the GO term, while color indicates the log10 p value (red higher, blue lower).

(e.g.: Fusobacterium outer membrane proteins) [61]. Metaproteomic analysis of GCF in periodontitis identified peptides from bacterial, viral, and yeast proteins, whose proportion increased in generalized aggressive periodontitis samples. Among them, herpes virus protein 2, and a single protein from a putative periodontal pathogen, the methylmalonyl-CoA mutase from P. gingivalis, were identified [60].

3.4. Metabolomics of the oral microbiome Metabolomics is the comprehensive identification and quantification of metabolites in biological systems, representing the final products of bacterial metabolism in the community. The analysis of the metabolome in GCF or saliva has the potential of be used to search for biomarkers beyond

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just common oral diseases. Thus, oral metabolomic analysis has been used for the search of biomarkers in different kind of cancers [63,64], including oral cancer [65]. One of the difficulties of using metabolomic analysis to study the metabolic activities of the oral microbiome is that a large number of metabolites could have both human and bacterial origin. Takahashi et al. described the metabolome of supragingival plaque to elucidate the sugar metabolic system, given the importance of sugar metabolism of supragingival plaque microflora on initiation of dental caries [66]. Samples in that study did not contain high levels of metabolites from the host and were mainly representatives of the activity of the oral biofilm. The study revealed that after a glucose rinse to mimic cariogenic conditions, the metabolite profile of supragingival plaque changed markedly. Glucose 6-phosphate, fructose 6-phosphate, fructose 1,6-bisphosphate, dihydroxyacetone phosphate, and pyruvate in the Embden-MeyerhofParnas (EMP) pathway and 6-phosphogluconate, ribulose 5phosphate, and sedoheptulose 7-phosphate in the pentosephosphate pathway, and acetyl CoA were increased [66]. The metabolome of periodontal disease has also been the focus of several studies. The metabolomic profile of GCF from gingivitis and periodontitis samples showed that inosine, hypoxanthine and xanthine, guanosine, and guanine were among the most dramatically elevated metabolites in diseased samples, indicating that purine degradation pathway, a major biochemical source for reactive oxygen species (ROS) production, was significantly accelerated at the diseased sites. Cadaverine, an end product of amino acid degradation which has almost exclusively bacterial origin was up-regulated in disease and an increase of many amino acids, choline, and glycerol- 3-phosphate at the disease sites was likely the result of host tissue degradation [67]. Metabolome profiles of saliva in periodontal disease, showed an elevated rate of degradation of macromolecules, where levels of dipeptide, amino acid, carbohydrate, lipids, and nucleotide metabolites, were altered. Aromatic amino acid metabolites, including p-cresol sulfate and phenol sulfate, associated with bacterial biochemistry were increased in the periodontal population [68]. Degradation of these macromolecules led to a more favorable environment for the periodontal pathogens to thrive in disease. The final goal of the study of all ‘-omics’ in the oral microbiome should be to integrate those results so that we get a global view of the processes involved in health and disease. This field of systems biology has just begun to produce its fruits and to this date we do not have meta-analysis of different 'omics' in the oral microbiome. Nonetheless, some attempts of integration have been performed in other areas of study and new bioinformatic tools are being constantly developed to that end [69].

implication in pathogenic processes. Above we have shown that to understand the role that organisms play in health and disease, we should move towards functional analysis of the communities, rather than focus on microbial composition. In fact, the second phase of the NIH Human Microbiome Project (Integrative Human Microbiome Project, iHMP, http://hmp2. org) will study the interactions between the microbiome and host in longitudinal studies by analyzing host-microbiome activities [70]. To this day, most of the '-omics' studies have been focused on organisms, genes, or pathways rather than on the system as a whole. Although this represents a first step, it overlooks the immense potential of these techniques to analyze the entire ecosystem at the system level. In previous sections we have avoided to mention the role of the host in all disease processes described but an obvious next step in all '-omics' studies of the oral microbiome should include the role of the host in defining health and disease (e.g. Dual RNA-Seq). By the novel nature of the field applied to the study of the oral microbiome, there are still major challenges that have to be overcome. There are technical challenges associated with each of the specific '-omics' approaches described above. For instance, in metatranscriptomic experiments, removal of rRNA is still a major hurdle since, even after treatment of the sample with methods for rRNA depletion, a large fraction of the sequences are rRNA. In metaproteomic analysis, assignment of peptides to specific species, especially in conserved proteins, could be extremely difficult. It is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites in metabolomics experiments. Moreover, sometimes it is extremely difficult to discern whether a metabolite has been produced by the host or by the oral microbiome. In all '-omics', the development of bioinformatic tools to analyze the obtained data represents an essential task that should be addressed if we want to maximize the information obtained from our results. In that regard, a major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different highthroughput '-omics' platforms, which has the potential to revolutionize our understanding of the role that the human microbiome in general, and the oral microbiome in particular, play in our well-being. Acknowledgments We are grateful to Rebecca Misra for reviewing the manuscript and her useful comments. This research was supported in part by the research grants DE021553 and DE021127 of the National Institute of Dental and Craniofacial Research of the National Institutes of Health (NIDCR/NIH).

4. Future directions and challenges References We are living in exciting times regarding the study of the role of the human microbiome in health and disease. We are just beginning to uncover the tip of the iceberg as far as assessing functional activities in the oral microbiome and their

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