P1‐186: Deregulation of miRNAs suggest the activation of a neuronal protective mechanism during preclinical stages of prion disease
Bibliographic record
Abstract
Prion diseases are caused by the conversion of the normal prion protein (PrP) to the infectious form (PrP) and with time, the accumulation of PrP leads to disease. All prion diseases exhibit three phenotypic changes as a result of PrP buildup: neuronal dysfunction and loss, plaque deposition and spongiform formation. Nevertheless, it remains unclear how PrP causes the observed neuronal dysfunction. Investigating molecular changes early in disease, when PrP begins to accumulate, may help identify these disease-related pathways. MicroRNAs (miRNAs) are a major class of post-transcription gene regulators that function in two main capacities: as rheostats that fine tune a few gene targets whose levels are critically important for function or as molecular switches by regulating vital transcription factors which govern entire pathways. Not surprising, deregulation of miRNAs has been implicated in numerous neurodegenerative disorders including Alzheimer's and prion diseases. Understanding the miRNA expression profiles in early prion disease may elucidate mechanisms of prion-induced neuronal death. Scrapie infected and control mouse hippocampal CA1 regions were removed using laser capture microdissection at six different days post inoculation: 40, 70, 90, 110, 130, and ∼160 (end-point). RNA was extracted and samples screened for miRNA expression levels using TaqMan low density arrays, validated using individual real-time PCR assays and in situ hybridization. miRNA target prediction programs were employed to curate a list of potential targets which was further refined using mRNA microarray data. Functional screens of candidate miRNAs were initiated on mouse post-mitotic primary cells to test effects of these miRNAs on neuron morphology. We found numerous miRNAs to exhibit diverse temporal expression patterns during prion disease where many were either up-regulated early (ie. miR-132) or late (ie. miR-146a). We further confirmed the expression of select miRNAs using in situ hybridization. The function for many of these early miRNAs has been implicated in dendrite formation and neuronal survival. Currently, we are characterizing the function of the remaining miRNAs on neuron morphology. Deregulation of miRNAs during early prion disease suggests that neuronal-protective mechanisms become activated during pre-clinical stages of disease. With time, this protection becomes exhausted and disease symptoms are observed.
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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 | 0.000 | 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 | 0.001 |
| 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".