Deep brain stimulation of the subthalamic nucleus preferentially alters the translational profile of striatopallidal neurons in an animal model of Parkinson's disease
Bibliographic record
Abstract
Deep brain stimulation targeting the subthalamic nucleus (STN-DBS) is an effective surgical treatment for the motor symptoms of Parkinson's disease (PD), the precise neuronal mechanisms of which both at molecular and network levels remain a topic of debate. Here we employ two transgenic mouse lines, combining translating ribosomal affinity purification (TRAP) with bacterial artificial chromosome expression (Bac), to selectively identify changes in translational gene expression in either Drd1a-expressing striatonigral or Drd2-expressing striatopallidal medium spiny neurons (MSNs) of the striatum following STN-DBS. 6-hydroxydopamine lesioned mice received either 5 days stimulation via a DBS electrode implanted in the ipsilateral STN or 5 days sham treatment (no stimulation). Striatal polyribosomal RNA was selectively purified from either Drd2 or Drd1a MSNs using the TRAP method and gene expression profiling performed. We identified eight significantly altered genes in Drd2 MSNs (Vps33b, Ppp1r3c, Mapk4, Sorcs2, Neto1, Abca1, Penk1, and Gapdh) and two overlapping genes in Drd1a MSNs (Penk1 and Ppp1r3c) implicated in the molecular mechanisms of STN-DBS. A detailed functional analysis, using a further 728 probes implicated in STN-DBS, suggested an increased ability to receive excitation (mediated by increased dendritic spines, increased calcium influx and enhanced excitatory post synaptic potentials) accompanied by processes that would hamper the initiation of action potentials, transport of neurotransmitters from soma to axon terminals and vesicular release in Drd2-expressing MSNs. Finally, changes in expression of several genes involved in apoptosis as well as cholesterol and fatty acid metabolism were also identified. This increased understanding of the molecular mechanisms induced by STN-DBS may reveal novel targets for future non-surgical therapies for PD.
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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) | 0.001 | 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".