<i>CREB3</i> gain of function variants protect against ALS
Bibliographic record
Abstract
Abstract Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly evolving neurodegenerative disease that arises from the loss of glutamatergic corticospinal neurons (CSN) and cholinergic motoneurons (MN). The disease is mostly sporadic, but genetics is expected to highly contribute to disease onset and progression. Genome wide association studies identified a few genetic disease modifiers, mostly associated with a negative outcome, and demonstrated that ALS is primarily a disease of excitatory glutamatergic neurons. Here, we reasoned that at least a subpart of genetic disease modifiers may directly modulate the molecular pathways selectively activated in vulnerable neurons as the disease progresses, and concentrated on CSN for their selective vulnerability and glutamatergic identity. We implemented comparative cross-species transcriptomics using snRNAseq data from postmortem motor cortex of ALS patients and controls, and longitudinal RNAseq data from anatomically defined CSN purified from the Sod1 G86R mouse model of ALS. We report that disease vulnerable neuronal populations undergo ER stress and altered mRNA translation, and identify the transcription factor CREB3 and its regulatory network as a resilience marker of neuronal dysfunction in ALS. Using genetic and epidemiologic analyses we further identify the rare variant CREB3 R119G (rs11538707) as a new disease modifier in ALS. Through gain of function, CREB3 R119G decreases both the risk of developing ALS and the progression rate of ALS patients. This study reveals novel genetic variants that protect against ALS and highlights the benefice of combining transcriptomics and genetics to identify new disease modifiers and therapeutic targets.
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 | 0.001 |
| 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) | 0.006 | 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".