Neuronal plasticity: cell-based strategy for target identification and validation
Bibliographic record
Abstract
Abstract While growing neurites are relatively plastic during development, their plasticity levels drop rapidly as neurons mature and become integrated into neuronal networks. As a consequence, the central nervous system ability to reorganize itself in response to injury or disease is insufficient. One of the main limitations for the design of therapeutic strategies to enhance neurite sprouting following neurological diseases is our poor understanding of the mechanisms underlying neurite structural plasticity. To overcome this limitation, we have implemented a strategy to identify, characterize and validate the most therapeutically relevant drug targets to modulate neuronal plasticity. This strategy is based on the hypothesis that the molecular regulation of the neurite shaft (controlling its ability to sprout) shares similarities with other cell systems. In an initial selection step (1), candidate targets are identified from the literature based on predetermined criteria (e.g. involvement in cell migration and growth cone collapse). (2) Compounds acting on those targets are tested in a neuronal cell-based assay for their effects on neurite sprouting as well as for early detection of undesired effects. (3) The selected targets are further analyzed during a verification step where their subcellular expression and activity is assessed. The regulation of the activity of the potential targets by branching factors is also determined at this point. (4) Once the biological relevance has been established, the signalling pathway in which the potential targets operates within neurons is mapped using activity reporters to uncover additional potential targets. This candidate approach, combined with the use of primary neurons, is designed to increase the probability of identifying suitable targets at the same time that it minimizes costs and time to validation. By using this strategy, we have been able to identify a set of proteins controlling neuronal sprouting together with an unsuspected mechanism of regulation of structural plasticity in neurons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".