When considering healthy peri-implant environments, bacterial colonization of the implant surface tends to be like healthy surrounding periodontal sites, with lower diversity (Heitz-Mayfield and Salvi 2018). However, it evolves toward the establishment of organized biofilms within the next two weeks (Belibasakis et al. 2015). Despite the fact that salivary pellicles adsorbed to implant surfaces promote the adhesion of microbes, its molecular features (chemical composition) and the immunological microenvironment determine the microbial colonization process (Dhir 2013). In the present study, certain alpha diversity metrics (Observed taxa, Chao index, Fisher’s alpha index and Simpson’s indices) have revealed significant differences between crevicular fluid samples (ToCF and/or TACF) and transepithelial abutments (TA). Differential statistical significance between diversity metrics could be explained by the varying influence of richness, evenness and dominance in each index. Considering that TA samples, as opposed to crevicular fluid samples, account not only free-living bacteria around transepithelial abutments, but also most biofilm forming surface adhered bacteria, these results suggest that tooth surface could harbor a richer microbiota than TA surface. These results – indicating a lower diversity in peri-implant microbiota - are in agreement with those of Dabdoub et al. (2013) and Payne et al. (2017). Nevertheless, bacterial diversity in peri-implantitis sites is usually higher than that of healthy periodontal sites (Zheng et al. 2015; Sousa et al. 2022), but lower than that of periodontitis environments (Belibasakis and Manoil 2020).
According to the consulted bibliography, differentially abundant taxa related to healthy sites include mainly gram-positive cocci and non-motile bacilli, with higher frequencies of lactic acid bacteria (Lactobacillales and Bifidobacterium) (Hashimoto et al. 2022), Lactococcus (Butera et al. 2022), Haemophilus (Sanz-Martín et al. 2017), Veillonella (Tamura et al. 2013; Shiba et al. 2016; Sanz-Martín et al. 2017), Streptococcus (Tamura et al. 2013; Sanz-Martín et al. 2017), Neisseria (Sanz-Martín et al. 2017), Rothia (Sanz-Martín et al. 2017), Prevotella (Tamura et al. 2013; Gao et al. 2018), Actinomyces (Kumar et al. 2012; Tamura et al. 2013; Dinsdag et al. 2016), Leptotrichia (Kumar et al. 2012; Butera et al. 2022), Gemella spp. (Shiba et al. 2016), Vibrio (Gao et al. 2018), Brevundimonas (Zhang et al. 2015; Butera et al. 2022), Pseudomonas (Zhang et al. 2015), Oribacterium spp., Selenomonas spp. and Cardiobacterium spp. (Dabdoub et al. 2013), Staphylococcus (Sousa et al. 2017), Granulicatella adjacens, Veillonella dispar, Actinomyces meyeri and Streptococcus mitis (da Silva et al. 2014), Propionibacterium acnes (Zheng et al. 2015), Acinetobacter, Paracoccus and Moraxella (Griffen et al. 2012), Dialister (Kotsakis and Olmedo 2021), Abiotrophia defectiva (Tsigarida et al. 2015), Microbacterium (Sun et al. 2022), Corynebacterium (Dinsdag et al. 2016; Sousa et al. 2017), Novosphingobium capsulatum (Esparbés et al. 2021), Propionibacter, Lautropia, Chitinophagaceae, Brevundimonas nasdae, Delfitia acidivorans, Rothia aeria, Anaerofilum pentosovorans, Anaerofilum agile, Pseudoramibacter alactolyticus and Porphyromonas HMT-277/278 (Butera et al. 2022). Moreover, Kroeger et al. (2018) found higher abundances of Lautropia mirabilis, Rhodobacteriaceae or Bergeyella in shallow peri-implant pockets.
On the other hand, most published research findings confirm that peri-implantitis associated bacteria consist mainly of gram-negative motile anaerobic periopathogens and opportunistic bacteria, including higher abundancies of Porphyromonas gingivalis (Hultin et al. 2001; Leonhardt et al. 2003; Shibli et al. 2008; Tamura et al. 2013; Persson and Renvert 2014; Jakobi et al. 2015; Stokman et al. 2016; Sanz-Martín et al. 2017; Belkacemi et al. 2018; Al-Ahmad et al. 2018; Yu et al. 2019; Costa et al. 2019), Aggregatibacter actinomycetemcomitans (Hultin et al. 2001; Leonhardt et al. 2003; Yu et al. 2019), Prevotella intermedia (Hultin et al. 2001; Mombelli et al. 2001; Leonhardt et al. 2003; Tamura et al. 2013; Jakobi et al. 2015), Prevotella nigrescens (Mombelli et al. 2001; Leonhardt et al. 2003), Treponema denticola (Hultin et al. 2001; Shibli et al. 2008; Canullo et al. 2015; Pérez-Chaparro et al. 2016; Costa et al. 2019), Tannerella forsythia (Hultin et al. 2001; Mombelli et al. 2001; Shibli et al. 2008; Persson and Renvert 2014; Al-Ahmad et al. 2018; Belkacemi et al. 2018; Yu et al. 2019), Fusobacterium nucleatum (da Silva et al. 2013; Tamura et al. 2013; Jakobi et al. 2015; Al-Ahmad et al. 2018; Belkacemi et al. 2018; Costa et al. 2019), Parvimonas micra (Koyanagi et al. 2010; Tamura et al. 2013; Canullo et al. 2015), Eubacterium (Kumar et al. 2012; Tamura et al. 2013), Butyrivibrio (Dabdoub et al. 2013; Kumar et al. 2012), Filifactor alocis (Tamura et al. 2013; Sanz-Martín et al. 2017), Pseudoramibacter (Korsch et al. 2021), Desulfobulbus (Camelo-Castillo et al. 2015; Dingsdag et al. 2016), Streptococcus (da Silva et al. 2013; Tamura et al. 2013; Jakobi et al. 2015), Exiguobacterium (Chen et al. 2018), Streptococcus mutans and Peptococcus (Kumar et al. 2012; Dabdoub et al. 2013). Koyanagi et al. (2010) reported that Chloroflexi, Tenericutes, Synergistetes, Peptostreptococcus stomatis and Solobacterium moorei were detected only in peri-implantitis sites.
Peri-implantitis has been also associated with higher relative frequencies of Eikenella corrodens (Canullo et al. 2015), Streptococcus intermedius, Streptococcus mitis, Haemophilus influenzae and Treponema socranskii (Persson and Renvert 2014), Campyblobacter gracilis, Dialister invisus, Eubacterium infirmum and Mitsuokella (da Silva et al. 2013), Campylobacter rectus (Cortelli et al. 2013; Canullo et al. 2015), Fusobacterium (Mombelli et al. 2001; Kumar et al. 2012), Slackia exigua, Parascardovia denticolens and Centipeda periodontii (Tamura et al. 2013), Mycoplasma and Treponema (Kumar et al. 2012), Streptococcus non-mutans (Dabdoub et al. 2013), Neisseria, Kingella, Enterococcus, Fretibacterium and Bacillus (Jakobi et al. 2015), Propionibacterium, Paludibacter, Staphylococcus, Filifactor and Mogibacterium (Sousa et al. 2017), Treponema maltophilum (Sanz-Martín et al. 2017), Olsenella and Sphingomonas (Murayama et al. 2014), Veillonella (Kumar et al. 2012; Daubert et al. 2018), Treponema (Kroeger et al. 2018; Yu et al. 2019), Prevotella tannerae (Ebadian et al. 2012), Actinomyces (Dabdoub et al. 2013), Fretibacterium fastidiosum (Sanz-Martín et al. 2017), Pseudoramibacter alactolyticus (Koyanagi et al. 2010; Dabdoub et al. 2013), Campylobacter (Kumar et al. 2012) and Staphylococcus aureus (Persson and Renvert 2014), Stenotrophomonas, Leuconostoc, Faecalibacterium prausnitzii, Haemophilus parainfluenzae, Prevotella copri, Bacteroides vulgatus and Bacteroides stercoris (Pallos et al. 2022). Furthermore, the following taxa are also strongly associated with subgingival plaque of periodontitis: Alloprevotella, Phocaeicola, Johnsonella and Mycoplasma (Chen et al. 2018).
Oral micro-habitats, including teeth, transepithelial abutments and crevicular fluid, provide unique biological niches that harbors specific bacterial communities. Compositional variation in oral microbiota related to sampling origin has been highlighted by multiple studies, including crevicular fluid around dental prostheses that were fabricated by various biomaterials and fabrication techniques (Blank et al. 2021; Heboyan et al. 2021), supra and subgingival biofilm (Shibli et al. 2008; Yu et al. 2019) and different peri-implantitis lesions (Kroeger et al. 2018; Polymeri et al. 2022). Moreover, dental intervention-related perturbations lead to significant environmental changes at the microscale level, including surface topology, chemistry and immunological response, resulting in dysbiosis and structural disruption of the oral microbiota. Compositional variation was higher between bacterial communities from transepithelial abutments (TA) and periodontal crevicular fluid (ToCF). In this sense, a significant reduction in certain peri-implant or periodontal health related taxa was observed in TA when compared to ToCF (Bacilli, Actinomycetia and Lactococcus), while several of those related with oral dysbiosis showed increased relative frequencies (Synergistetes, Peptostreptococcus, Slackia, Atopobium and Mogibacterium).
Considering higher taxonomic ranks, higher abundances of certain taxa after implant insertion (Bacteroidota, Synergistetes and Coriobacteriaceae) and lower frequencies of Actinomycetia, Bacillaceae, Cellulomonadaceae and Enterococcaceae were identified in TA samples. In this context, Heyman et al. (2020) reported analogous population trends for Bacteroidota and Coriobacteriaceae after implant placement using a murine model. On the other hand, certain peri-implant and periodontal disease related taxa, including Mogibacterium, Slackia, Peptostreptococcus, Atopobium, Slackia exigua and Peptostreptococcus stomatis showed increased abundances in transepithelial abutments. Similar results were obtained after comparing crevicular fluid from transepithelial abutments (TACF) with transepithelial abutments (TA), with lower frequencies of several opportunistic commensal enterococci, Cellulomonas, Lactococcus and Actinomyces naturae in TA samples. When compared with crevicular fluid samples extracted transepithelial abutments, TA were also depleted in other taxa, including Bacilli, Paenibacillus, Exiguobacterium, Microbacterium and Corynebacterium. These results do not agree with those of Schaumann et al. (2014), who concluded that peri-implant and periodontal tissues share a similar biofilm composition at the genus level.
Dental intervention-related perturbations lead to significant environmental changes at the microscale level, including surface topology and chemistry, resulting in dysbiosis and structural disruption of the oral microbiota. Abundance-based associations also revealed differential correlation across sampling methods, particularly when TA was compared to ToCF. More specifically, differential associations affecting mainly peri-implant health and peri-implantitis related taxa (Bifidobacterium, Haemophilus, Campylobacter, Peptostreptococcus, Porphyromonas, Treponema, Selenomonas, Fusobacterium, Rothia and Prevotella) were detected after performing pairwise comparisons. Moreover, significant variations regarding hub OTUs were identified between ToCF and TA. These findings suggest that both peri-implant and crevicular fluid bacterial communities are characterized by a particular distinctive associational footprint as opposed to that of the adjacent teeth or transepithelial abutments and also that environmental gradients and complex ecological interactions contribute to variations in ecosystem structure and function.
According to the results found in the present study, the abundances of most peri-implantitis or periodontal disease related taxa were higher in TA samples, whereas those of peri-implant health associated bacteria were higher in ToCF or TACF. Thus, current data suggest that implant micro-habitats are usually characterized by a dysbisotic shift, enriched in Synergistetes, Bacteroidota, Atopobium, Slackia, Peptostreptococcus, Mogibacterium and Slackia exigua, and depleted in Bacillus, Paenibacillus, Enterococcus, Lactococcus, Microbacterium, Corynebacterium and Actinomyces naturae. Differential abundance patterns between crevicular fluid from transepithelial abutments and transepithelial abutments, not detected when comparing crevicular fluid samples from different origins, reflect the particularities of specific oral niches (Li et al. 2022). Furthermore, sampling method was a significant source of bacterial community dissimilarity. In this sense, significant differences were identified at the community level between TA and ToCF and also between TACF and TA, but not between ToCF and TACF. With respect to network properties, sampling procedures did not significantly differ in terms of global network properties or centrality measures. However, variations in hub OTUs were identified between ToCF and TA. By contrast, TACF and TA shared the most relevant hub taxa at both the OTU and the genus level.
As stated by Berry and Widder (2014), hub taxa are not necessarily keystones in the microbial community, but ecologically relevant hub taxa are likely to be. Thus, different keystone taxa were also identified in bacterial communities from different sampling origins. Considering node degree and betweenness, Streptococcus oligofermentans, Actinomyces odontolyticus, Bacteroides heparinolyticus, Acidaminococcus intestini, Moryella indoligenes, Leptotrichia wadei, Haloactinobacterium, Porphyromonas, Sphingobacterium, Shuttleworthia, Moraxella and Propionibacterium have a pivotal role in TACF samples, whereas Rothia aeria, Streptococcus sobrinus, Moryella indoligenes, Actinobaculum massiliense, Prevotella paludivivens, Megasphaera sueciensis, Bacillus, Alloprevotella, Campylobacter, Pontibacillus, Eikenella and Parascardovia showed a higher influence in TA samples. On the other hand, Prevotella multisaccharivorax, Streptococcus dentapri, Peptococcus niger, Porphyromonas circumdentaria, Mycoplasma salivarium, Streptococcus gallinaceus, Butyrivibrio, Flavobacterium, Leptotrichia, Paracoccus, Pasteurella and Sphingomonas were identified as drivers of microbiome structure and functioning in ToCF samples. With the exception of Flavobacterium (ToCF), Sphingobacterium (TACF), Rothia aeria (TA), Moraxella (TACF), Sphingomonas (ToCF) and Rothia mucilaginosa (TA), most keystone taxa were obligate (eg. Bacteroides heparinolyticus, Moryella indoligenes, Acidaminococcus intestine, Peptococcus niger, Megasphaera sueciensis, Shuttleworthia, Campylobacter, Butyrivibrio, Actinomyces, Propionibacterium) or facultative anaerobes (eg. Streptococcus spp., Parascardovia, Alloprevotella, Bacillus, Paracoccus, Leptotrichia, Eikenella, Porphyromonas, Pasteurella, Mycoplasma salivarium or Actinobaculum massiliense).
According to these findings, it should be highlighted that bacterial community assessment via mNGS could be considered a promising strategy for peri-implant and periodontal health surveillance and early peri-implant disease diagnosis. Despite the fact that, regardless of sampling approach, association network properties and centrality measures from transepithelial bacterial communities were similar, significant differences were detected in terms of hub taxa, alpha and beta diversity and individual taxa abundance, including some peri-implant and periodontal health related taxa. As a result, it can be concluded that the choice of sampling strategy can deeply affect the results of oral microbiota profiling.