Identification and characterization of interactions between abscisic acid and human heat shock protein 70 family members
Bibliographic record
Abstract
Abscisic acid (ABA) is a stress-inducible plant hormone comprising an inevitable component of the human diet. Recently, stress-induced accumulation of autocrine ABA was shown in humans, as well as ABA-mediated modulation of a number of disease-associated systems. Now, the application of a chemical proteomics approach to gain further insight into ABA mechanisms of action in mammalian cells is reported. An ABA mimetic photoaffinity probe was applied to intact mammalian insulinoma and embryonic cells, leading to the identification of heat shock protein 70 (HSP70) family members, (including GRP78 and HSP70-2) as putative human ABA-binding proteins. In vitro characterization of the ABA-HSP70 interactions yielded K(d)s in the 20-60 µM range, which decreased several fold in the presence of co-chaperone. However, ABA was found to have only variable- and co-chaperone-independent effects on the ATPase activity of these proteins. The potential implications of these ABA-HSP70 interactions are discussed with respect to the intracellular protein folding and extracellular receptor-like activities of these stress-inducible proteins. While mechanistic and functional relevance remain enigmatic, we conclude that ABA can bind to human HSP70 family members with physiologically relevant affinities and in a co-chaperone-dependent manner.
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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 | 0.001 |
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".