Absence of postsynaptic activity on developing neurons alters gene expression profiles without preventing the refinement of inputs
Bibliographic record
Abstract
Abstract Synaptic activity plays several roles as developing neurons make connections with their targets. It acts locally at synapses to influence the expression of genes needed to establish and maintain synaptic contacts. And, downstream it provides the necessary activity to strengthen and refine connections. Many studies have demonstrated how synaptic activity alters synaptic strength and increases synapse numbers. Much less is known, however, about the long-term consequences when a circuit develops without synaptic activity. To address this, we developed a mosaic model of sympathetic ganglia where synaptically-active and synaptically-inactive sympathetic neurons develop side-by-side in vivo. This model allowed us to address two issues. One is the relationship between activity and the refinement of converging inputs; the second is how synaptic activity contributes to a neuron’s gene expression profile. Our results indicate that converging presynaptic inputs to synaptically-silent neurons do not require postsynaptic activity to refine, provided these neurons share targets with synaptically-active neurons. Second, we demonstrate with single-cell RNA sequencing experiments that the expression of many genes by sympathetic neurons is independent of endogenous activity or local signals immediately downstream of excitatory postsynaptic potentials. An exception are genes required for neurotransmitter metabolism: We found that for a large sub-population of sympathetic neurons, synaptic activity increases the expression of adrenergic genes and supresses the expression of cholinergic genes. We conclude that signals generated locally at synapses do not initiate refinement of converging inputs, and that synaptic activity’s influence on a neuron’s gene expression profiles is complex and depends on context.
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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".