3-C-357 - RNA binding of heterogeneous nuclear ribonucleoprotein A1 is dysregulated in a mouse model of Multiple Sclerosis
Bibliographic record
Abstract
Authors: Patricia Thibault¹, Hannah Salapa¹, Michael Levin¹ ¹University of Saskatchewan Abstract: Neurodegeneration (NDG), hallmarked by neuronal cell body and axonal loss, occurs in progressive Multiple Sclerosis (MS) in the absence of overt relapses and inflammatory episodes. Heterogeneous nuclear ribonucleoprotein A1 (A1) is an RNA binding protein that is key in cellular RNA metabolism, but is mislocalized from nuclei to cytoplasm in the neurons from brains of people with progressive MS, and from spinal cords of mice with experimental autoimmune encephalomyelitis (EAE), a model for MS. We hypothesized that this mislocalization impacts the A1 RNA binding profile to promote NDG. To test this, we compared the A1 RNA binding profile from the spinal cords of naïve mice to those of mice with EAE, using crosslinking-immunoprecipitation/RNA sequencing (CLIPseq). We confirmed that the global RNA transcriptome from EAE mouse spinal cords is distinct from that of naïve mice, and further found that severity of EAE disease resulted in a greater alteration in the global transcriptome, with a number of differentially-expressed genes involved in neurodegeneration. We have also generated a first-of-its-kind profile of homeostatic A1 RNA targets in spinal cord. Further, the A1 RNA binding profile is drastically dysregulated in EAE mouse spinal cords, losing interaction with ~40% of homeostatically bound RNAs and instead enriching for non-coding RNAs. We are in the process of mapping the A1 binding "footprint" on these RNAs to provide insight into its specificity. Together, these data will form the foundation of our understanding of the role A1 dysfunction plays in NDG in MS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".