Dermal fibroblast transcriptome indicates contribution of WNT signaling pathways in the pathogenesis of Apert syndrome
Bibliographic record
Abstract
Çetinkaya A, Taşkıran E, Soyer T, Şimşek-Kiper PÖ, Utine GE, Tunçbilek G, Boduroğlu K, Alikaşifoğlu M. Dermal fibroblast transcriptome indicates contribution of WNT signaling pathways in the pathogenesis of Apert syndrome. Turk J Pediatr 2017; 59: 619-624. Cranial sutures are unossified connective tissue structures between the cranial bones, which allow expansion of these bones during development. Premature ossification of these structures is called craniosynostosis. Apert syndrome is a well-defined genetic syndrome, which is characterized by craniosynostosis and arises as a result of two missense mutations in Fibroblast Growth Factor Receptor, type 2 gene (FGFR2). In this study, differentially expressed genes in dermal fibroblasts from individuals with Apert syndrome and controls were investigated to identify important pathways in the pathogenesis of Apert syndrome. For this purpose, primary skin fibroblast cultures obtained from 3 individuals with Apert syndrome and 3 controls without craniosynostosis were compared by transcriptome microarray, GeneChip Human Genome U133 Plus 2.0. As a result, 181 genes were shown to be differentially expressed between experimental groups. Among these, 10 genes, which significantly differ in Apert syndrome fibroblasts compared to controls, were shown to be involved in a common interaction network and have common Gene ontology (GO) biological processes terms. COL11A1, COMP, CPXM2, ITGA8, MGF and TNC are differentially expressed genes that have GO terms associated with extracellular matrix (ECM) organization, while FRZB, SFRP2 and WNT2 are involved in WNT signaling pathway. Reorganization of ECM and changes in WNT signaling pathway show that Apert syndrome primary fibroblast cultures may have an increased potential for bone differentiation. The results of this study support craniosynostosis in Apert syndrome may be the result of fast and early differentiation of connective tissue along the sutures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".