Identification of Thioredoxin1 interacting proteins in neuronal cytoskeletal organization during autophagy
Bibliographic record
Abstract
Abstract Thioredoxin1 (Trx1) is a major cytoplasmic thiol oxidoreductase protein involved in redox signaling. This function is rendered by a rapid electron transfer reaction during which Trx1 reduces its substrate and itself becomes oxidized. In this reaction, Trx1 forms a transient disulfide bond with the substrate which is unstable and therefore identification of Trx1 substrates is technically challenging. This process maintains the cellular proteins in a balanced redox state and ensures cellular homeostasis. Trx1 levels are reduced in some neurodegenerative diseases; therefore, understanding the interactions between Trx1 and its substrates in neurons could have significant therapeutic implications. We utilized a transgenic mouse model expressing a Flag-tagged mutant form of Trx1 that can form stable disulfide bonds with its substrates allowing identification of the Trx1 interacting proteins. The involvement of Trx1 has been suggested in autophagy, we aimed to investigate Trx1 substrate after pharmacologic induction of autophagy in primary hippocampal neurons. Treatment of primary neurons by rapamycin, a standard autophagy inducer, caused significant reduction of neurite outgrowth and alterations in the cytoskeleton. Through immunoprecipitation and mass spectrometry, we have identified 77 Trx1 interacting proteins which were associated with a wide range of cellular functions including a major impact on cytoskeletal organization. The results were confirmed in Trx1 knocked-down cells and in nucleofected primary neurons. Our study suggests a novel role for Trx1 in regulation of neuronal cytoskeleton organization, marking the first investigation of Trx1-interacting proteins in primary neurons and confirming the multifaceted role of Trx1 in physiological and pathological states.
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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 | 0.000 |
| 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".