Alternative splicing generates a Ribosomal Protein S24 isoform induced by neuroinflammation and neurodegeneration
Bibliographic record
Abstract
Abstract Neuroinflammation, particularly that involving reactive microglia, the brain’s resident immune cells, is implicated in the pathogenesis of major neurodegenerative diseases. However, early markers of this process are in high demand. Multiple studies have reported changes in ribosomal protein (RP) expression during neurodegeneration, but the significance of these changes remains unclear. Ribosomes are evolutionarily conserved protein synthesizing machines, and although commonly viewed as invariant, accumulating evidence suggest functional ribosome specialization through variation in their protein composition. By analyzing cell type-specific translating mRNAs from mouse brains, we identify distinct RP expression patterns between neurons, astrocytes, and microglia, including neuron-specific RPs, Rpl13a and Rps10 . We also observed complex expression relationships between RP paralogs and their canonical counterparts, suggesting regulated mechanisms for generating heterogeneous ribosomes. Analysis across brain regions revealed that Rplp0 and Rpl13a , commonly used normalization references, show heterogeneous expression, raising important methodological considerations for gene expression studies. Importantly, we show that Rps24 , an essential ribosome component that undergoes alternative splicing to produce protein variants with different C-termini, exhibits striking cell type-specific isoform expression in brain. The Rps24c isoform is predominantly expressed in microglia and is increased by neuroinflammation caused by aging, neurodegeneration, or inflammatory chemicals. We verify increased expression of S24-PKE, the protein variant encoded by Rps24c , in brains with Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, and relevant mouse models, using isoform-specific antibodies. These findings establish heterogeneous RP expression as a feature of brain cell types and identify Rps24c /S24-PKE as a novel marker for neuroinflammation and neurodegeneration.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".