Identification of novel candidate genes involved in mineralization of dental enamel by genome‐wide transcript profiling
Bibliographic record
Abstract
The gene repertoire regulating vertebrate biomineralization is poorly understood. Dental enamel, the most highly mineralized tissue in mammals, differs from other calcifying systems in that the formative cells (ameloblasts) lack remodeling activity and largely degrade and resorb the initial extracellular matrix. Enamel mineralization requires that ameloblasts undergo a profound functional switch from matrix-secreting to maturational (calcium transport, protein resorption) roles as mineralization progresses. During the maturation stage, extracellular pH decreases markedly, placing high demands on ameloblasts to regulate acidic environments present around the growing hydroxyapatite crystals. To identify the genetic events driving enamel mineralization, we conducted genome-wide transcript profiling of the developing enamel organ from rat incisors and highlight over 300 genes differentially expressed during maturation. Using multiple bioinformatics analyses, we identified groups of maturation-associated genes whose functions are linked to key mineralization processes including pH regulation, calcium handling, and matrix turnover. Subsequent qPCR and Western blot analyses revealed that a number of solute carrier (SLC) gene family members were up-regulated during maturation, including the novel protein Slc24a4 involved in calcium handling as well as other proteins of similar function (Stim1). By providing the first global overview of the cellular machinery required for enamel maturation, this study provide a strong foundation for improving basic understanding of biomineralization and its practical applications in healthcare.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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.
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".