Author response: Translational control by eIF2α phosphorylation regulates vulnerability to the synaptic and behavioral effects of cocaine
Bibliographic record
Abstract
Drug addiction a is major mental health problem that presents a huge financial, social and legal burden worldwide. Adolescents are notoriously prone to drug abuse and addicts typically begin using drugs at a young age. However, an explanation for why young people are particularly vulnerable to the effects of addictive substances remains elusive. Addictive drugs change how the brain works, in particular by strengthening the connections (synapses) between brain cells (neurons) and making it easier for neurons to communicate with each other. Such strengthening of synaptic connections, which can be observed when the activity of the neurons is recorded with microelectrodes, relies on new proteins being made in the brain. Since adolescents have a greater capacity than adults to make new proteins, Huang et al. hypothesized that changes in synaptic strength might occur more easily in the brain of adolescents, explaining why they are more likely to become addicted to drugs than adults. A protein called eIF2α plays a key role in regulating the production of new proteins. Huang et al. discovered that reduced eIF2α activity accounts for why adolescents are particularly vulnerable to the synaptic and behavioral effects of cocaine. Giving adolescent mice a low dose of cocaine reduced the activity of eIF2α, caused an increase in the strength of synaptic connections in a part of the brain that processes pleasurable feelings, and promoted drug-reinforced behavior. This did not occur in adult mice. Reducing the activity of eIF2α using either genetics or pharmacological methods caused adult mice to become as vulnerable as adolescents to cocaine-induced changes in synaptic strength and addiction-related behavior. Conversely, increasing the activity of eIF2α made adolescent mice more resistant to cocaine’s effects; in other words, adolescents responded to cocaine more like adults. Huang et al. also found that other drugs of abuse, including alcohol, methamphetamine and nicotine, all reduce eIF2α activity, suggesting that eIF2α is a common target of different drugs of abuse. In a related study, Placzek et al. investigated the role of eIF2α in nicotine addiction in mice and humans. These findings raise several intriguing questions. How do cocaine and other drugs of abuse reduce eIF2α activity? Could variations in the activity of eIF2α or other components of the eIF2α pathway in the brain explain why some people are more likely to abuse drugs? Finally, could compounds that regulate the activity of eIF2α be useful for treating addiction?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.268 | 0.084 |
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".