Co-chaperone FKBP38 Promotes HERG Trafficking
Bibliographic record
Abstract
The Long QT Syndrome is a cardiac disorder associated with ventricular arrhythmias that can lead to syncope and sudden death. One prominent form of the Long QT syndrome has been linked to mutations in the HERG gene (KCNH2) that encodes the voltage-dependent delayed rectifier potassium channel (I(Kr)). In order to search for HERG-interacting proteins important for HERG maturation and trafficking, we conducted a proteomics screen using myc-tagged HERG transfected into cardiac (HL-1) and non-cardiac (human embryonic kidney 293) cell lines. A partial list of putative HERG-interacting proteins includes several known components of the cytosolic chaperone system, including Hsc70 (70-kDa heat shock cognate protein), Hsp90 (90-kDa heat shock protein), Hdj-2, Hop (Hsp-organizing protein), and Bag-2 (BCL-associated athanogene 2). In addition, two membrane-integrated proteins were identified, calnexin and FKBP38 (38-kDa FK506-binding protein, FKBP8). We show that FKBP38 immunoprecipitates and co-localizes with HERG in our cellular system. Importantly, small interfering RNA knock down of FKBP38 causes a reduction of HERG trafficking, and overexpression of FKBP38 is able to partially rescue the LQT2 trafficking mutant F805C. We propose that FKBP38 is a co-chaperone of HERG and contributes via the Hsc70/Hsp90 chaperone system to the trafficking of wild type and mutant HERG potassium channels.
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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) | 0.002 | 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".