Effects of the inhibition of miRNA biogenesis in the central ring ganglia of a widely used invertebrate model species, Lymnaea stagnalis
Bibliographic record
Abstract
MicroRNAs (miRNAs) are key regulators of gene expression, shaping neuroplasticity, stress responses, and neuromodulation. In the pond snail Lymnaea stagnalis, inhibition of the miRNA-processing enzyme Dicer with Poly-l-Lysine (PLL) has been shown to impair long-term memory (LTM) formation, yet the molecular pathways affected remain unclear. Here, we examined PLL injection's transcriptional and cellular/neurochemical effects in untrained (i.e., non-associatively conditioned) snails. We focused on genes involved in neuroplasticity (LymGRIN1, LymCREB1), stress response (LymHSP70), and serotonergic/dopaminergic signaling (LymTPH, LymSERT, LymDDC), along with measurements of the levels of serotonin, dopamine, and HSP70 protein. We found that PLL did not alter the expression of memory-related genes in the non-associatively conditioned snails. However, we observed a marked downregulation of LymTPH and LymSERT, which was accompanied by a significant reduction in the level of serotonin. In contrast, the expression of LymDDC and the level of dopamine remained unchanged. Although we detected a significant upregulation in the expression of LymHSP70, indicating a strong stress response, the level of HSP70 protein did not change significantly. These findings suggest that PLL-induced Dicer inhibition primarily disrupts serotonin homeostasis, potentially altering motivation and feeding behaviour rather than directly impairing memory processes. This study highlights the broader role of miRNA pathways in regulating stress resilience and neuromodulation. By linking miRNA activity to serotonergic signaling, our findings suggest that miRNAs influence behavioural states beyond synaptic plasticity, with potential implications for understanding how miRNA dysregulation affects mood, motivation, and cognitive function across species.
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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".