Quantification of Pseudouridine Levels in Cellular RNA Pools with a Modified HPLC-UV Assay
Bibliographic record
Abstract
Pseudouridine (Ψ) is the most abundant post-transcriptionally modified ribonucleoside. Different Ψ modifications correlate with stress responses and are postulated to coordinate the distinct biological responses to a diverse panel of cellular stresses. With the help of different guide RNAs, the dyskerin complex pseudouridylates ribosomal RNA, small nuclear RNA and selective messenger RNAs. To monitor Ψ levels quantitatively, a previously reported high performance liquid chromatography method coupled with ultraviolet detection (HPLC-UV) was modified to determine total Ψ levels in different cellular RNA fractions. Our method was validated to be accurate and precise within the linear range of 0.06-15.36 pmol/μL and to have absolute Ψ quantification levels as low as 3.07 pmol. Using our optimized HPLC assay, we found that 1.20% and 1.94% of all ribonucleosides in nuclear-enriched RNA and small non-coding RNA pools from the HEK293 cell line, and 1.77% and 0.98% of ribonucleosides in 18S and 28S rRNA isolated from the HeLa cell line, were pseudouridylated. Upon knockdown of dyskerin expression, a consistent and significant reduction in total Ψ levels in nuclear-enriched RNA pools was observed. Our assay provides a fast and accurate quantification method to measure changes in Ψ levels of different RNA pools without sample derivatization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".