A genome-wide survey for prion-regulated miRNAs associated with cholesterol homeostasis
Bibliographic record
Abstract
BACKGROUND: Prion diseases are neurodegenerative diseases that are characterized by the conversion of the cellular prion protein (PrPc) into a pathogenic isoform (PrPSc). It is known that neurodegeneration is often accompanied by the disturbance of cholesterol homeostasis. We have recently identified a set of genes that were upregulated after prion infection of N2a neuronal cells (Bach et al., 2009). RESULTS: We have now used ultra-deep sequencing technology to profile all microRNAs (miRNA) that could be associated with this effect in these N2a cells. Using stringent filters and normalization strategies we identified a small set of miRNAs that were up- or downregulated upon prion infection. Using bioinformatic tools we predicted whether the downregulated miRNAs could target mRNAs that have been previously identified to enhance cholesterol synthesis in these cells. Application of this joint profiling approach revealed that nine miRNAs potentially target cholesterol-related genes. Four of those miRNAs are localized in a miRNA-dense cluster on the mouse X-chromosome. Among these, twofold downregulation of mmu-miR-351 and mmu-miR-542-5p was confirmed by qRT-PCR. The same miRNAs were predicted as putative regulators of the sterol regulatory element-binding factor 2 (Srebf2), the low-density lipoprotein receptor (Ldlr) or the IPP isomerase. CONCLUSIONS: The results demonstrate that joined profiling by ultra-deep sequencing is highly valuable to identify candidate miRNAs involved in prion-induced dysregulation of cholesterol homeostasis.
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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 | 0.001 |
| 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".