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DNA Methylation Underlies the Long‐Term Association Between Periodontitis and Atherosclerotic Cardiovascular Disease

2022· article· en· W4225424835 on OpenAlexaff
Maria Febbraio, Mohamed A. Omar

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPeriodontitisMedicineImmunologyDNA methylationPorphyromonas gingivalisPeriodontal pathogenInternal medicineBiologyGeneticsGene

Abstract

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Periodontitis, one of the most common inflammatory conditions, and the leading cause of adult tooth loss, has been identified as an independent risk factor for cardiovascular disease (CVD). Long‐term studies in edentulous patients with a history of periodontitis showed persistence of increased CVD risk for years after edentulism, suggesting that clinical elimination of disease is not sufficient in decreasing periodontitis‐induced CVD risk. This is similar to other CVD risk factors, such as smoking and diabetes. We hypothesized that periodontitis induces epigenetic changes in hematopoietic stem cells in the bone marrow (BM), and such changes persist after clinical elimination of the disease and underlie the induced‐CVD risk. To simulate clinical elimination of periodontitis and persistence of the hypothesized epigenetic reprogramming, we used a BM transplant approach. Using the low density lipoprotein receptor ( LDLR ) KO atherosclerosis mouse model, BM donor mice were fed a high fat diet (HFD) to induce atherosclerosis, and orally inoculated with Porphyromonas gingivalis ( Pg ), a keystone periodontal pathogen, to induce periodontitis; a second group was sham inoculated. (We previously showed that HFD‐fed, Pg inoculated mice developed more atherosclerosis than sham‐inoculated). Naïve LDLR KO mice were irradiated and transplanted with BM from one of the 2 donor groups. Recipients of BM from Pg ‐inoculated donors developed significantly more atherosclerosis, accompanied by more pro‐inflammatory plasma and macrophage cytokine profiles. Using whole genome bisulfite sequencing, 375 differentially methylated regions (DMR), and a global hypomethylation in recipients of BM from Pg ‐inoculated donors, were identified. Some DMRs pointed to involvement of enzymes with major roles in methylation and demethylation. In recipients of BM from Pg ‐inoculated donors, methionine adenosyl transferase (MAT), which catalyzes the conversion of methionine to S‐denosylmethionine (SAM), the universal methyl‐donor, was hypermethylated. The gene for S‐adenosylhomocysteine hydrolase, which catalyzes the reversible conversion of the potent methylation inhibitor, S‐adenosylhomocysteine (SAH), to homocysteine and adenosine, was also hypermethylated. In contrast, the de‐methylation enzyme, Ten‐Eleven Translocase (TET) 2, was hypomethylated. In validation assays, we found a significant increase in activity of TET2 and a decrease in activity of DNA methyltransferases. Plasma SAH levels were significantly higher and the SAM to SAH ratio was decreased, both of which have been associated with CVD. Homocysteine, is at the intersection of the methionine cycle and the transsulphuration pathway, where it can either be re‐methylated to methionine and go through the methionine cycle, or it can synthesize cysteine through the transsulphuration pathway. Glutathione, a critical antioxidant, is the end‐product of the transsulphuration pathway. In conditions of oxidative stress, the transsulphuration pathway is favored over the methionine cycle. Periodontitis is associated with oxidative stress. These data suggest a paradigm shifting mechanism linking epigenetic changes in gene methylation with the long term association between periodontitis and atherosclerotic CVD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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