The ataxin-2 protein is required in kenyon cells for RNP-granule assembly and appetitive long-term memory formation
Bibliographic record
Abstract
Ribonucleoprotein granules (mRNP granules) are thought to contribute to the control of neuronal mRNA translation required for consolidation of long-term memories. Consistent with this, the function of Ataxin-2 in mRNA granule assembly has been shown to be required for long-term olfactory habituation (LTH) in Drosophila, a form of non-associative memory. Knockdown of Ataxin-2 in either local interneurons (LNs) or projection neurons (PNs) of the insect antennal lobe disrupts LTH while leaving short-term habituation intact, leading to a model in which Ataxin-dependent translational control is required in both presynaptic and postsynaptic elements of the LN-PN synapse, whose potentiation has been causally linked to LTH. Here we use novel and established methods for cell-type specific perturbation to ask: (a) whether Ataxin-2 controls mRNA granule assembly in cell types beyond the few that have been examined; and (b) whether it functions not only in LTH, but also for long-term olfactory associative memory (LTM). We show that Ataxin-2 controls mRNP granule assembly in additional neuronal types, namely Kenyon Cells (KCs) that encode associative memory, as well as more broadly in non-neuronal cells, e.g. in nurse cells in the egg chamber. Furthermore, selective knockdown of Atx2 in α/β and α’/β’ KCs blocks appetitive long-term but not short-term associative memories. Taken together these observations support a hypothesis that Ataxin-2 dependent translational control is widely required across different mnemonic circuits for consolidation of respective forms of long-term memories.
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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 | 0.001 |
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".