Defective structural RNA processing in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Surveillance of integrity of the basic elements of the cell including DNA, RNA, and proteins is a critical element of cellular physiology. Mechanisms of surveillance of DNA and protein integrity are well understood. Surveillance of structural RNAs making up the vast majority of RNA in a cell is less well understood. Here, we sought to explore integrity of processing of structural RNAs in relapsing remitting multiple sclerosis (RRMS) and other inflammatory diseases. RESULTS: We employed mononuclear cells obtained from subjects with RRMS and cell lines. We used quantitative-PCR and whole genome RNA sequencing to define defects in structural RNA surveillance and siRNAs to deplete target proteins. We report profound defects in surveillance of structural RNAs in RRMS exemplified by elevated levels of poly(A) + Y1-RNA, poly(A) + 18S rRNA and 28S rRNAs, elevated levels of misprocessed 18S and 28S rRNAs and levels of the U-class of small nuclear RNAs. Multiple sclerosis is also associated with genome-wide defects in mRNA splicing. Ro60 and La proteins, which exist in ribonucleoprotein particles and play different roles in quality control of structural RNAs, are also deficient in RRMS. In cell lines, silencing of the genes encoding Ro60 and La proteins gives rise to these same defects in surveillance of structural RNAs. CONCLUSIONS: Our results establish that profound defects in structural RNA surveillance exist in RRMS and establish a causal link between Ro60 and La proteins and integrity of structural RNAs.
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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 | 0.001 | 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".