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. 2020 Jan 30;99(6):695–702. doi: 10.1177/0022034520902452

Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients

D Baraniya 1, M Naginyte 2, T Chen 3, JM Albandar 4, SM Chialastri 4, DA Devine 2, PD Marsh 2, NN Al-hebshi 1,✉
Editor: W Shi
PMCID: PMC7243421  PMID: 31999932

Abstract

Screening for microbiome modulators requires availability of a high-throughput in vitro model that replicates subgingival dysbiosis and normobiosis, with a tool to measure microbial dysbiosis. Here, we tested various formulations to grow health- and periodontitis-associated subgingival microbiomes in parallel, and we describe a new subgingival dysbiosis index. Subgingival plaque samples pooled from 5 healthy subjects and, separately, 5 subjects with periodontitis were used to inoculate a Calgary Biofilm Device containing saliva-conditioned, hydroxyapatite-coated pegs. Microbiomes were grown for 7 d on either nutrient-rich media—including a modification of SHI medium, brain-heart infusion (BHI) supplemented with hemin and vitamin K, and a blend of SHI and BHI, each at 3 sucrose concentrations (0%, 0.05% and 0.1%)—or nutrient-limited media (saliva with 5%, 10%, or 20% inactivated human serum). The microbiomes were assessed for biomass, viability, and 16S rRNA profiles. In addition to richness and diversity, a dysbiosis index was calculated as the ratio of the sum of relative abundances of disease-associated species to that of health-associated species. The supplemented BHI and blend of SHI and BHI resulted in the highest biomass, whereas saliva-serum maximized viability. Distinct groups of bacteria were enriched in the different media. Regardless of medium type, the periodontitis-derived microbiomes showed higher species richness and alpha diversity and clustered with their inoculum separate from the health-derived microbiomes. Microbiomes grown in saliva-serum showed the highest species richness and the highest similarity to the clinical inocula in both health and disease. However, inclusion of serum reduced alpha diversity and increased dysbiosis in healthy microbiomes in a dose-dependent manner, mainly due to overenrichment of Porphyromonas species. The modification of SHI stood second in terms of species richness and diversity but resulted in low biomass and viability and significantly worsened dysbiosis in the periodontitis-derived microbiomes. Overall, saliva with 5% human serum was optimal for replicating subgingival microbiomes from health and disease.

Keywords: biofilm, dysbiosis, high-throughput nucleotide sequencing, microbiota, periodontitis, in vitro techniques

Introduction

An emerging strategy for the prevention and adjunctive treatment of periodontitis is to selectively target keystone pathogens and/or stimulate growth of commensals to reverse microbial dysbiosis (or reestablish normobiosis) by using microbiome modulators, such as prebiotics or probiotics. Prebiotics and probiotics have been extensively studied within the context of gut heath (Gareau et al. 2010; Holscher 2017); however, their applicability, particularly prebiotics, to periodontal health—and oral health generally—has been minimally explored. A major obstacle has been the lack of a reliable in vitro tool for the screening of banks of potential modulators to identify those with promising activities before testing them further in animals and eventually humans. Recently, Slomka et al. (2017) used a dual-species biofilm model to screen a panel of 704 nutritional compounds against 16 oral bacterial species for prebiotic activity. However, dual-species biofilms are far from being representative of complex oral microbial communities.

Classically, dental biofilm models for the screening of antibacterials or mouthwashes have included a limited number of oral species (Guggenheim et al. 2001; Ammann et al. 2013), while more complex biofilms have been produced from clinical inocula in constant depth fermenters or CDC Biofilm Reactors (Hope and Wilson 2006; Rudney et al. 2012). Although the latter systems replicate the dynamic conditions of the mouth, they are complex and costly, have low throughput, and are more suited for studying biofilm development and structure (Darrene and Cecile 2016). More recently, oral microbiome models have been successfully generated from pooled saliva samples in cheaper, high-throughput devices, such as microtiter plates or the Calgary Biofilm Device (Tian et al. 2010; Edlund et al. 2013; Kistler et al. 2015; Kolderman et al. 2015). While these models are static, the biofilms generated capture a great deal of the species and functional diversity of the original samples. Furthermore, when subgingival plaque samples from healthy patients and those with periodontitis were used as inocula, biofilms were generated with a close similarity to the clinical inocula, with clear distinction between the health- and periodontitis-derived microbiomes (Walker and Sedlacek 2007; Velsko and Shaddox 2018). However, none of these models have been designed for studying microbiome modulation.

The current study is one in a series aimed to establish a high-throughput, reproducible in vitro subgingival microbiome model specifically optimized for the testing of microbiome modulators. Here, the objective was to identify the optimal medium for replicating subgingival normobiosis and dysbiosis in vitro and to describe a new subgingival dysbiosis index (SDI) that can be used to quantitatively assess dysbiosis and microbiome modulation.

Materials and Methods

Clinical Inocula and Saliva

Subgingival dental plaque samples were collected from 5 patients with untreated moderate to severe periodontitis (defined as having at least 1 tooth per quadrant with bleeding on probing, pocket depth ≥5 mm, and attachment loss ≥4 mm) and 5 periodontally healthy controls (defined as having no more than slight gingivitis and no probing pocket depth or attachment loss ≥3 mm) with no history of periodontitis. Subgingival plaque was sampled by inserting a size 40 paper point to the base of gingival sulcus/pocket for 30 s. Samples were obtained from the deepest pocket in each quadrant in the patients with periodontitis and from the buccal gingival sulcus of first molars in the healthy subjects. The samples from each subject were pooled in 1-mL reduced transport fluid (Hoover and Newbrun 1977) and placed on ice for use on the same day.

Separately, unstimulated saliva samples (5 to 10 mL) were collected from each of 10 dentally healthy volunteers (distinct from the 5 healthy controls) and centrifuged at 5,000 rpm for 15 min. The supernatants were pooled, treated with dithiothreitol (2.5mM final concentration) for 10 min, mixed with equal volume of phosphate-buffered saline (PBS), filter sterilized, and stored at −20 °C.

The study was approved by Temple University’s Institutional Review Board (protocol 25586).

Growth Media

Nutrient-rich and nutrient-limited media were used to grow the microbiomes. The former included the following:

  • sBHI: brain-heart infusion broth (Difco, Becton Dickinson) supplemented with hemin (5 mgL−1), vitamin K (0.5 mgL−1), and mucin (1 mgL−1)

  • mSHI: a modification of SHI medium (Tian et al. 2010) in which potassium chloride was replaced by PBS for buffering

  • BSHI: a blend of mSHI and sBHI prepared by adding the nonredundant components from both media

Each medium was tested at 3 sucrose concentrations (0%, 0.05%, and 0.1% w/v). The nutrient-limited media comprised sterile saliva (prepared as described earlier) containing 5%, 10%, or 20% (v/v) heat-inactivated serum (Sigma Aldrich). In all, a total of 12 media were compared (for detailed composition, see Appendix Tables 1 and 2).

Growing the Microbiomes

Subgingival plaque samples were briefly vortexed and pooled separately for the patients with periodontitis and healthy controls to make 2 inocula. Periodontitis- and health-derived microbiomes were grown in triplicate in each medium on a Calgary Biofilm Device (Ceri et al. 1999) with hydroxyapatite-coated pegs (Innovotech) at 37 °C in an anaerobic chamber (10% hydrogen, 10% carbon dioxide, and 80% nitrogen). The plate layout is shown in Appendix Figure 1. Outer wells were not used, to prevent evaporation. The different microbiomes were separated from each other and from the negative control by empty wells filled with 180-μL PBS to avoid well-to-well contamination. The pegs were preconditioned by immersion in sterile human saliva for 16 h prior to inoculation. Experimental wells contained 170 μL of growth medium and 10 μL of the pooled clinical sample (sterile PBS for negative control wells). The plate was incubated for 7 d, with media replenished on days 2, 4, and 6. Microbiomes (for each inoculum and medium type) were generated in 2 sets: one was used immediately for measurement of viability and the other for extraction of DNA. A portion of each pooled clinical sample was kept aside for microbiome analysis.

Measurement of Biomass and Viability

The pegs with microbiomes were washed 3 times with PBS to remove planktonic bacteria. Biomass was measured in terms of DNA yield (ng/microbiome). For DNA extraction, pegs were snapped off, and each was placed in an Eppendorf tube with 180-µL PBS containing 18-µL MetaPolyzyme (Sigma) and incubated at 35 °C for 4 h. The digests were then used for DNA extraction with the Purelink Genomic Kit (Life Technologies), following the manufacturer’s instructions. DNA from the clinical samples was extracted similarly. To account for the extracellular DNA possibly present in saliva (Okshevsky and Meyer 2015), a saliva-serum medium-only control was included. DNA was quantified by a Qubit 2.0 Fluorimeter (Life Technologies) before being stored at −80 °C.

Viability of the microbiomes was directly assessed on the pegs (i.e., without harvesting the bacteria) with an ATP assay (BacTiter-Glo; Promega) according to the manufacturer’s modified protocol for biofilms (https://www.promega.com/-/media/files/resources/promega-notes/99/use-of-the-bactiter-glo-microbial-cell-viability-assay-to-study-bacterial-attachment.pdf?la=en). Luminescence signal was recorded on a Synergy HTX multimode microplate reader (Biotek) and normalized to biomass.

16S Sequencing and Bioinformatic Analysis

16S rRNA gene library preparation and sequencing were performed at the Australian Center for Ecogenomics as described previously (Al-Hebshi, Alharbi, et al. 2017). Briefly, the degenerate primers 27FYM (Frank et al. 2008) and 519R (Lane et al. 1985) were used to amplify the V1-3 region with standard polymerase chain reaction (PCR) conditions. The resultant PCR amplicons (~520 bp) were purified, indexed with unique 8-base barcodes in a second PCR, pooled in equimolar concentrations, and sequenced by employing v3 chemistry (2 × 300 bp) on a MiSeq platform (Illumina) at 30,000 reads per sample. No detectable extracellular DNA was found in the saliva-serum medium-only control (with the Qubit dsDNA High Sensitivity Kit), and so it was not sequenced.

Preprocessing of data (primer trimming, merging of reads, quality filtration, alignment, and chimera removal) was done as described previously (Al-Hebshi, Nasher, et al. 2017). The high-quality merged reads were classified to the species level with our BLASTn-based algorithm (Al-Hebshi et al. 2015; Al-Hebshi, Nasher, et al. 2017). The QIIME 1.9.1 software package (Quantitative Insights into Microbial Ecology; Caporaso et al. 2010) integrated into our analysis pipeline was used for downstream analysis, including subsampling; generation of taxonomy plots/tables and rarefaction curves; and calculation of species richness, coverage, alpha diversity indices, and beta diversity distance matrices. Principal component analysis was used to visualize the distances between the microbiomes. Linear discriminant analysis effect size (Segata et al. 2011) was used to detect taxa enriched by the different media, adjusting for multiple comparison with the Benjamini-Hochberg method. To quantitatively assess the similarity of the generated microbiomes to the clinical inocula, a similarity index was calculated as 1- abundance-weighted Jaccard distance from the clinical inoculum.

Subgingival Dysbiosis Index

An SDI, inspired from the dysbiosis index described by Greves et al. (2014) for Crohn’s disease, was calculated for each generated microbiome as follows:

SDI = Σ relative abundances of periodontitis-associated species / Σ relative abundances of health-associated species,

where periodontitis-associated species are all of the species that were more abundant in the periodontitis clinical inoculum as compared with the healthy clinical inoculum and vice versa (Appendix Data Set 1).

Results

Raw data are available from the Sequence Read Archive (PRJNA579567). Summary and detailed sequencing and data preprocessing statistics are provided in Appendix Data Set 2.

Biomass and Viability

Figure 1 shows the biomass and viability of the microbiomes generated in the different media. sBHI and BSHI resulted in the highest biomass, with the periodontitis-derived microbiomes having a significantly higher biomass than the health-derived ones, regardless of sucrose concentration (mean ± SD: 1,712 ± 416 ng vs. 1,055 ± 389 ng for BSHI; 1,438 ± 113 ng vs. 771 ± 128 ng for sBHI). However, the viability of periodontitis-derived microbiomes was very low in both media. The microbiomes grown in mSHI had the lowest biomass (144 ± 74 ng and 421 ± 185 ng for the disease- and health-derived microbiomes, respectively), and both also displayed low viability. The addition of sucrose to the 3 media did not have a consistent effect. For example, including sucrose at 0.1% in sBHI and BSHI significantly enhanced viability of the health-derived microbiomes but not the periodontitis-associated microbiomes; it also reduced biomass of both types of microbiomes grown in BSHI but only health-associated microbiomes grown in sBHI, while it tended to increase it in mSHI-grown microbiomes. The saliva-serum media generated microbiomes with intermediate biomass (461 ± 286 ng and 453 ± 145 ng for the disease- and health-derived microbiomes, respectively) but also the highest viability, especially in the periodontitis-derived microbiomes (on average, ~4 to 10 times higher than other media). Increasing the serum concentration to 10% (v/v) improved the biomass and viability of the microbiomes; at 20% (v/v) serum, biomass further increased, but viability was adversely affected.

Figure 1.

Figure 1.

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Biomass and viability of the microbiomes grown in the different media. Biomass was measured as the yield of DNA in nanograms extracted from the microbiomes. Viability was assessed with a luminescence ATP assay, normalizing the relative luminescence signal to biomass. Values are presented as box plots. For media types, see Growth Media section. Plots were generated with ggplot R package.

Microbiological Profiles by General Medium Type

All phyla, 71 of 74 genera, and 224 of 231 species present in the clinical inocula were also detected in at least 1 of the in vitro microbiome subgroups (Appendix Data Sets 2–4). Figure 2 presents the average relative abundances of phyla and top genera (accounting for ~80% of the sequences) in the microbiomes generated and the clinical inocula. Major phyla identified in the inocula and grown microbiomes were Firmicutes, Fusobacteria, and Bacteroidetes. All media significantly enriched Firmicutes but hardly supported the growth of Proteobacteria. At the genus level, Haemophilus, Leptotrichia, Aggregatibacter, Capnocytophaga, Fretibacterium, and Mycoplasma were among the major genera present in the clinical inocula but were found in low abundance (or absent) in the in vitro microbiomes. Conversely, Parvimonas, Mogibacterium, Oribacterium, Atopobium, Dialister, Eggerthia, and Peptostreptococcus were overrepresented genera in the microbiomes as compared with the inocula, irrespective of the medium used. Interestingly, 25 species (6 genera)—including Mogibacterium neglectum, Mogibacterium pumilum, Alloscardovia omnicolens, Acidaminococcus sp. str. D21, and several potentially novel operational taxonomic units—were not detected in the clinical inocula but were present in the derived microbiomes (Appendix Data Set 5). The microbial profiles were highly reproducible between the replicates (Appendix Fig. 2).

Figure 2.

Figure 2.

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Microbiological profiles. The relative abundances of phyla (upper panel) and major genera (lower panel) identified in the clinical inocula and microbiomes grown in the different media (data presented for the 4 general media types; see Growth Media section).

Different bacteria were enriched in the various media as revealed by linear discriminant analysis effect size analysis (Appendix Fig. 3). Saliva-serum enriched Bacteroidetes (genera Porphyromonas and Alloprevotella) and Spirochaetes (Genus Treponema), whereas sBHI enriched Firmicutes (genera Veillonella and Peptostreptococcus) and Saccharibacteria. mSHI enhanced the growth of genera Fusobacterium, Streptococcus, and Tannerella, and BSHI favored the genus Prevotella. The most pronounced enrichment was that of Porphyromonas gingivalis in the health-derived microbiomes grown in saliva-serum (relative abundance of ~30% vs. 5.8% in the clinical inoculum and <0.1% in microbiomes grown in other media). Similarly, Pyramidobacter piscolens was significantly enriched in the periodontitis-derived microbiomes grown in sBHI, reaching a relative abundance of 11.0% as compared with 1.7% in the periodontitis inoculum.

Species Richness and Alpha and Beta Diversity

The species richness and alpha diversity indices are presented in Figure 3. Saliva-serum was associated with the highest observed and expected (Chao) species richness, especially in the periodontitis-derived microbiomes, supporting growth of up to 160 species from the clinical inocula; however, a higher serum concentration (especially at 20%) was associated with lower species richness. mSHI generated a richness in the health-derived microbiomes comparable to that of microbiomes grown in saliva-serum (125 to 140 observed species). Growth of microbiomes in saliva-serum, however, resulted in a significant drop in Shannon’s and Simpson’s indices, particularly in the health-derived microbiomes, in which the reduction was serum concentration dependent. Overall, BSHI resulted in the lowest species richness and alpha diversity. Including sucrose in BSHI, mSHI, or sBHI increased species richness but did not have a consistent effect on alpha diversity.

Figure 3.

Figure 3.

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Species richness and alpha diversity. Taxonomic profiles were rarified and used to calculate observed richness, expected richness (Chao index), and alpha diversity indices (Shannon’s and Simpson’s) for each of the clinical inocula and microbiomes grown in the different media, employing standard QIIME scripts. Values are presented as box plots. For media types, see Growth Media section. Plots were generated with ggplot R package.

The results of clustering of the microbiomes and clinical inocula by principal component analysis are shown in Figure 4A and B. Regardless of the medium used, the periodontitis-derived microbiomes clustered with the periodontitis inoculum separately from the health-derived microbiomes, accounting for the variation along principal coordinate 1 (44.55%). Differences by medium type accounted for variation along principal coordinate 2 (31.15%). The microbiomes grown in saliva-serum clustered closest to the clinical inocula, followed by those generated in mSHI. To better visualize the similarity of the microbiomes to their clinical inocula by specific medium type, the similarity index was calculated and plotted as presented in Figure 4C. In health- and periodontitis-derived microbiomes, saliva with 5% serum resulted in the highest similarity to the clinical inocula. Increasing serum concentration reduced similarity, but the microbiomes generated still had a greater similarity than those grown in mSHI. Including sucrose in mSHI enhanced similarity but only for the health-derived microbiomes. BSHI and sBHI generated microbiomes with the least similarity to the clinical inocula.

Figure 4.

Figure 4.

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Beta diversity analysis. Distances between the microbiomes were calculated per the abundance-weighted Jaccard index employing standard QIIME scripts. The microbiomes were clustered with principal coordinate (PC) analysis by (A) clinical inoculum and (B) general medium type. The clinical inocula are represented by rhomboid icons. (C) The similarity of the microbiomes to the clinical inocula from which they were grown, calculated for each microbiome as 1 abundance-weighted Jaccard distance from the clinical inoculum. Values are presented as box plots. For media types, see Growth Media section. The figures were created with QIIME (A and B) and ggplot R package (C).

Dysbiosis

All media replicated the normobiotic and dysbiotic states of the clinical inocula to some degree (Fig. 5), although there were significant differences among them. sBHI and BSHI media generated health-derived microbiomes with an SDI very close to that of the clinical sample, but they were associated with a significant drop in dysbiosis of the periodontitis-derived microbiomes, resulting in a difference in SDI of 0.9 to 1.4 between the normobiotic and corresponding dysbiotic microbiomes (vs. a difference of 1.84 between the clinical inocula). Growth in mSHI resulted in an extreme difference in SDI of 2.4 to 3.2 by lowering dysbiosis in the health-derived microbiomes relative to the healthy clinical inoculum and significantly worsening it in the periodontitis-derived microbiomes. Saliva with 5% serum generated periodontitis-derived microbiomes with the closest SDI to that of the periodontitis sample but worsened dysbiosis in the health-derived microbiomes; nevertheless, it nearly replicated the difference in SDI between the clinical inocula (1.80 vs. 1.84). Higher serum concentrations increased dysbiosis in both microbiome types and resulted in an SDI difference of 2.2 and 1.6 for 10% and 20% serum, respectively.

Figure 5.

Figure 5.

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The level of dysbiosis in the clinical inocula and microbiomes grown in the different media as assessed by a subgingival dysbiosis index (SDI) calculated as follows: total abundance of all species increased in the periodontitis inoculum / total abundance of all species increased in the healthy inoculum. Higher values indicate a greater level of dysbiosis. For media types, see Growth Media section.

Discussion

In this study, we tested various media to replicate health- and periodontitis-associated subgingival microbiomes. Brain-heart infusion broth supplemented with hemin, mucin, and vitamin K has been shown to support growth of a diverse microbial community, including periodontal pathogens, from saliva inocula (Kistler et al. 2015). The original SHI with 0.5% sucrose has been primarily used to replicate microbiomes associated with dental caries (Edlund et al. 2013); however, lowering the sucrose concentration (0.1%) had resulted in higher proportions of subgingival species, which is why we evaluated it and compared different sucrose concentrations. The combined medium (BSHI) was developed on the assumption that it could maximize the number of species in the microbiomes. Saliva-serum was included as a nutrient-limited medium that simulates nutritional conditions in the gingival crevice/pocket. More details about the choice of methods and their limitations are provided in the Appendix Discussion.

Regardless of the medium, the health- and periodontitis-derived microbiomes clustered separately, consistent with previous work (Fernandez et al. 2017; Velsko and Shaddox 2018), indicating that the final composition of the generated microbiomes is largely dictated by that of the inocula. All media also replicated normobiosis and dysbiosis to some degree, demonstrating the overall validity of the model. Nevertheless, there were differences among the media worth highlighting, such as the relationship between biomass and viability. sBHI and BSHI resulted in high biomass but low viability, suggesting that they provide an early boost to the growth of bacteria such that a significant proportion of the microbiome enters the stationary and decline phases of growth before the medium is replenished. This may also explain the lower species richness observed in these media, as slowly growing species were probably outcompeted. Perhaps, growth rates in the less rich saliva media were lower and population growth was more balanced, resulting in a better relationship between final biomass and viability. This might explain why saliva-serum resulted in the highest species richness and, hence, greater similarity to the clinical inocula. mSHI was an outlier in that it resulted in low biomass and viability, especially in the periodontitis-derived microbiomes, which warrants further investigation.

The dysbiosis index provided a valuable layer of information in addition to the standard microbiome metrics and reflected another important difference among the tested media. As compared with the clinical inocula, sBHI and BSHI narrowed the difference in SDI between the health- and periodontitis-derived microbiome, while mSHI widened it. Yet, saliva-serum (especially at 5% serum) nearly replicated the difference between the clinical inocula, despite overenrichment of P. gingivalis in the health-derived microbiomes, indicating that normobiosis/dysbiosis is a microbial community feature not dependent on a single species. In other words, the high abundance of P. gingivalis was accompanied by decreases in the abundance of other periodontitis-associated species and maintenance of commensal/health-associated species—hence, the low SDI and a difference between healthy and diseased microbiomes similar to that seen between the inocula. Nevertheless, overenrichment of P. gingivalis remains noteworthy since we cannot exclude if such a microbiome (high P. gingivalis/low SDI) would still be pathogenic. Since serum is probably enriching P. gingivalis (Cieplik et al. 2019; Naginyte et al. 2019), lowering its concentration may overcome this limitation. Another caveat worth mentioning is that the SDI was calculated per the differences between the clinical inocula, which limits its validity to the experimental run. We are currently developing a more generic index based on the relative abundances of a predefined set of health- and periodontitis-associated species that can be compared across experimental runs and even for assessment of clinical samples.

In conclusion, we describe here a model system and a novel dysbiosis index that could form the basis of a high-throughput model for screening microbiome modulators. Overall, saliva-serum is probably the optimal medium for modeling the subgingival microbiome by maximizing species diversity and maintaining viability while replicating normobiosis/dysbiosis.

Author Contributions

D. Baraniya, contributed to design, data acquisition, and analysis, critically revised the manuscript; M. Naginyte, contributed to design and data acquisition, critically revised the manuscript; T. Chen, contributed to data analysis, critically revised the manuscript; J.M. Albandar, S.M. Chialastri, contributed to data acquisition, critically revised the manuscript; D.A. Devine, P.D. Marsh, contributed to design and data interpretation, critically revised the manuscript; N.N. Al-hebshi, contributed to conception, design, data analysis, and interpretation, drafted and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Supplemental Material

DS_10.1177_0022034520902452 – Supplemental material for Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients
Click here for additional data file. (1.1MB, pdf)

Supplemental material, DS_10.1177_0022034520902452 for Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients by D. Baraniya, M. Naginyte, T. Chen, J.M. Albandar, S.M. Chialastri, D.A. Devine, P.D. Marsh and N.N. Al-hebshi in Journal of Dental Research

Footnotes

This study was supported by the National Institute of Dental and Craniofacial Research (grant 1R03DE028379-01A1).

The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

A supplemental appendix to this article is available online.

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Associated Data

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Supplementary Materials

DS_10.1177_0022034520902452 – Supplemental material for Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients
Click here for additional data file. (1.1MB, pdf)

Supplemental material, DS_10.1177_0022034520902452 for Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients by D. Baraniya, M. Naginyte, T. Chen, J.M. Albandar, S.M. Chialastri, D.A. Devine, P.D. Marsh and N.N. Al-hebshi in Journal of Dental Research

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