A connectivity mapping approach predicted acetylsalicylic acid (aspirin) to induce osteo/odontogenic differentiation of dental pulp cells
Bibliographic record
Abstract
Abstract Aim To use connectivity mapping, a bioinformatics approach, to identify compounds that could induce odontogenic differentiation of dental pulp cells (DPCs) and to experimentally validate this effect. A subsidiary aim was to investigate the anti‐inflammatory effect of any identified compound. Methodology The Gene Expression Omnibus (GEO) database was searched for microarray data sets assessing odontogenic differentiation of human DPCs. An odontogenic gene expression signature was generated by differential expression analysis. The statistical significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. DPCs were treated with the compound identified, and osteo/odontogenic differentiation was assessed by Alizarin red staining, alkaline phosphatase activity and expression of osteo/odontogenic genes ALPL, RUNX2, COL1A1, DSPP, DMP1 and SPP1 by RT‐PCR. The anti‐inflammatory effect of the compound was assessed using an ex vivo pulpitis model, and cytokine levels were measured with multiplex assay. Means were compared using the t‐test or ANOVA followed by a Bonferroni post hoc test with the level of significance set at P ≤ 0.05. Results The GEO database search identified a specific gene expression signature for osteo/odontogenic differentiation. Analysis using ssCMap found that acetylsalicylic acid [(ASA)/aspirin] was the drug with the strongest correlation with that gene signature. The treatment of DPCs with 0.05 mmol L−1 ASA showed increased alkaline phosphatase activity (P < 0.001), mineralization (P < 0.05), and increased the expression of the osteo/odontogenic genes, DMP1 and DSPP (P < 0.05). Low concentration (0.05 mmol L−1) ASA reduced inflammatory cytokines IL‐6 (P < 0.001), CCL21 (P < 0.05) and MMP‐9 (P < 0.05) in an ex vivo pulpitis model. Conclusions Connectivity mapping, a web‐based informatics method, was successfully used to identify aspirin as a candidate drug that could modulate the differentiation of DPCs. Aspirin was shown to induce odontogenic differentiation in DPCs in vitro and this, together with its anti‐inflammatory effects, makes it a potential candidate for vital pulp therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".