Retinoid X receptor α downregulation is required for tail and caudal spinal cord regeneration in the adult newt
Bibliographic record
Abstract
Some adult vertebrate species, such as newts, axolotls and zebrafish, have the ability to regenerate their central nervous system (CNS). However, the factors that establish a permissive CNS environment for correct morphological and functional regeneration in these species are not well understood. Recent evidence supports a role for retinoid signaling in the intrinsic ability of neurons, in these regeneration-competent species, to regrow after CNS injury. Previously, we demonstrated that a specific retinoic acid receptor (RAR) subtype, RARβ, mediates the effects of endogenous retinoic acid (RA) on neuronal growth and guidance in the adult newt CNS after injury. Here, we now examine the expression of the retinoid X receptor RXRα (a potential heterodimeric transcriptional regulator with RARβ), in newt tail and spinal cord regeneration. We show that at 21 days post-amputation (dpa), RXRα is expressed at temporally distinct periods and in non-overlapping spatial domains compared to RARβ. Whereas RARβ protein levels increase, RXRα proteins level decrease by 21 dpa. A selective agonist for RXR, SR11237, prevents both this downregulation of RXRα and upregulation of RARβ and inhibits tail and caudal spinal cord regeneration. Moreover, treatment with a selective antagonist for RARβ, LE135, inhibits regeneration with the same morphological consequences as treatment with SR11237. Interestingly, LE135 treatment also inhibits the normal downregulation of RXRα in tail and spinal cord tissues at 21 dpa. These results reveal a previously unidentified, indirect regulatory feedback loop between these two receptor subtypes in regulating the regeneration of tail and spinal cord tissues in this regeneration-competent newt.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".