Dysfunctional RNA‐binding protein biology and neurodegeneration in experimental autoimmune encephalomyelitis in female mice
Bibliographic record
Abstract
Abstract Altered stress granule (SG) and RNA‐binding protein (RBP) biology have been shown to contribute to the pathogenesis of several neurodegenerative diseases, yet little is known about their role in multiple sclerosis (MS). Pathological features associated with dysfunctional RBPs include RBP mislocalization from its normal nuclear location to the cytoplasm and the formation of chronic SGs. We tested the hypothesis that altered SG and RBP biology might contribute to the neurodegeneration in experimental autoimmune encephalomyelitis (EAE). C57BL/6 female mice were actively immunized with MOG35‐55 to induce EAE. Spinal cords were examined for mislocalization of the RBPs, heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) and TAR‐DNA binding protein‐43 (TDP‐43), SGs, neurodegeneration (SMI‐32), T cells (CD3), and macrophages (CD68). In contrast to naive mice, mice with EAE showed SG formation (p < 0.0001) and mislocalization of hnRNP A1 (p < 0.05) in neurons of the ventral spinal cord gray matter, which correlated with clinical score (R = 0.8104, p = 0.0253). In these same areas, there was a neuronal loss (p < 0.0001) and increased SMI‐32 immunoreactivity (both markers of neurodegeneration) and increased staining for CD3+ T cells and IFN‐gamma. These findings recapitulate the SG and RBP biology and markers of neurodegeneration in MS tissues and suggest that altered SG and RBP biology contribute to the neurodegeneration in EAE, which might also apply to the pathogenesis of MS.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".