MicroRNA-16 targets mRNA involved in neurite extension and branching in hippocampal neurons during presymptomatic prion disease
Bibliographic record
Abstract
The mechanisms that lead to neuronal death in neurodegenerative diseases are poorly understood. Prion diseases, like many more common disorders such as Alzheimer's and Parkinson's diseases, are characterized by the progressive accumulation of misfolded disease-specific proteins. The earliest changes observed in brain tissue include a reduction in synaptic number and retraction of dendritic spines, followed by reduced length and branching of neurites. These pathologies are observable during presymptomatic stages of disease and are accompanied by altered expression of transcripts that include miRNAs. Here we report that miR-16 localized within hippocampal CA1 neurons is increased during early prion disease. Modulating miR-16 expression in mature murine hippocampal neurons by expression from a lentivirus, thus mimicking the modest increase seen in vivo, was found to induce neurodegeneration. This was characterized by retraction of neurites and reduced branching. We performed immunoprecipitation of the miR-16 enriched RISC complex, and identified associated transcripts from the co-immunoprecipitated RNA (Ago2 RIP-Chip). These transcripts were enriched with predicted binding sites for miR-16, including the validated miR-16 targets APP and BCL2, as well as numerous novel targets. In particular, genes within the neurotrophin receptor mediated MAPK/ERK pathway were potentially regulated by miR-16; including TrkB (NTRK2), MEK1 (MAP2K1) and c-Raf (RAF). Increased miR-16 expression in neurons during presymptomatic prion disease and reduction in proteins involved in MAPK/ERK signaling represents a possible mechanism by which neurite length and branching are decreased during early stages of disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".