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Record W3001250034 · doi:10.1177/0022034520902452

Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients

2020· article· en· W3001250034 on OpenAlexaboutno aff
Divyashri Baraniya, Monika Naginyte, T. Chen, Jasim M. Albandar, Susan Chialastri, Deirdre DeVine, Philip D. Marsh, Nezar N. Al‐Hebshi

Bibliographic record

VenueJournal of Dental Research · 2020
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial Research
KeywordsSalivaMicrobiomeDysbiosisMicrobiologyOral MicrobiomeBiologyPeriodontitisSpecies richnessBiofilmBacteroidesBacteriaMedicineDentistryBioinformaticsEcologyGeneticsBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.379
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2020
Admission routes1
Has abstractyes

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