Axon guidance genes are regulated by <scp>TDP</scp> ‐43 and <scp>RGNEF</scp> through long‐intron removal
Bibliographic record
Abstract
Rho guanine nucleotide exchange factor (RGNEF) is a guanine nucleotide exchange factor (GEF) mainly involved in regulating the activity of Rho-family GTPases. It is a bi-functional protein, acting both as a guanine exchange factor and as an RNA-binding protein. RGNEF is known to act as a destabilizing factor of neurofilament light chain RNA (NEFL) and it could potentially contribute to their sequestration in nuclear cytoplasmic inclusions. Most importantly, RGNEF inclusions in the spinal motor neurons of ALS patients have been shown to co-localize with inclusions of TDP-43, the major well-known RNA-binding protein aggregating in the brain and spinal cord of human patients. Therefore, it can be hypothesized that loss-of-function of both proteins following aggregation may contribute to motor neuron death/survival in ALS patients. To further characterize their relationship, we have compared the transcriptomic profiles of neuronal cells depleted of TDP-43 and RGNEF and show that these two factors predominantly act in an antagonistic manner when regulating the expression of axon guidance genes. From a mechanistic point of view, our experiments show that the effect of these genes on the processivity of long introns can explain their mode of action. Taken together, our results show that loss-of-function of factors co-aggregating with TDP-43 can potentially affect the expression of commonly regulated neuronal genes in a very significant manner, potentially acting as disease modifiers. This finding further highlights that neurodegenerative processes at the RNA level are the result of combinatorial interactions between different RNA-binding factors that can be co-aggregated in neuronal cells. A deeper understanding of these complex scenarios may lead to a better understanding of pathogenic mechanisms occurring in patients, where more than one specific protein may be aggregating in their neurons.
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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".