The truncated isoform of the receptor for hyaluronan-mediated motility (RHAMMΔ163) modulates shelterin and telomerase reverse transcriptase transcription affecting telomerase activity
Bibliographic record
Abstract
Introduction The receptor for hyaluronan-mediated motility (RHAMM), a centrosomal protein expressing in multiple isoforms, is implicated in telomerase-independent aging. However, its involvement in telomerase regulation is unproven. This study aims to investigate whether RHAMM correlates with telomerase activity in mammalian cells. Methods Mouse embryonic fibroblasts expressing or lacking full-length RHAMM (RHAMM FL , amino acids 1–794) and the shorter isoform RHAMM Δ163 (amino acids 164–794), were explored to examine the effect of RHAMM isoforms on mRNA expression of telomerase reverse transcriptase (TERT) and selective shelterin proteins regulating telomere maintenance. Results The preliminary findings revealed that RHAMM regulated Tert expression based on its isoforms. RHAMM Δ163 enhanced Tert mRNA expression and promoted telomerase activity by stimulating sirtuin 1 ( Sirt1 ), shelterin proteins Tpp1 , and Pot1a and repressing the telomerase inhibitor Pinx1 levels. In contrast, RHAMM FL did not have significant effect on TERT expression and telomerase activity. Increasing Tert mRNA expression by blocking leucine zipper sequence with function-blocking RHAMM peptide NP-110 in a TERT-deficient mouse model of idiopathic pulmonary fibrosis, alongside suppressing Tpp1 and Pot1a expression in mouse embryonic fibroblasts using ERK1 inhibitor PD98059, highlights the importance of the HATABD domain (amino acids 718–751), which includes leucine zipper and ERK-binding sequences at the C-terminus of mouse RHAMM in regulating telomerase function. Increased telomerase activity raised Hmmr expression, suggesting a potential feedback loop between RHAMM and TERT expression. Discussion Taken together, this report provides the first evidence that RHAMM Δ163 regulates TERT and shelterin expression and telomerase activity in mammalian cells.
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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".