A mitochondrial recruitment assay to characterize a family of DENN‐domain proteins, Rab GDP/GTP exchange factors.
Bibliographic record
Abstract
Intracellular membrane trafficking controls the levels, localization and functional activity of a myriad of proteins and is thus critical for cellular function. Rab GTPases are molecular switches that regulate membrane trafficking by toggling between GTP‐bound (active; membrane‐bound) and GDP‐bound (inactive; cytosolic) states. These GTPases are controlled largely by guanine nucleotide exchange factors (GEFs) that mediate the exchange of GDP for GTP. Mammalian cells possess 61 Rabs but many of their GEFs are unknown. One of the largest family of Rab GEFs contain an evolutionarily conserved protein module called the DENN domain. There are 18 DENN domain proteins but many of their corresponding Rab GTPase partner(s) are either not known or are controversial. This is due to the fact that all studies concerning DENN domains have used biochemical and in‐vitro assays, which do not reflect physiological conditions. Here we describe a cell‐based GEF assay as a method to identify GTPase substrates of DENN domain proteins. By overexpressing DENN domains fused to a mitochondrial targeting sequence we can drive the recruitment of physiological GTPase substrates to the mitochondrial membrane, which can then be detected by immunofluorescence analysis. We validated our assay with a known DENN/Rab pair, DENND1A and Rab35. Using a panel of GFP‐tagged Rabs we have resolved controversies over previously reported DENN/Rab pairs and have identified new Rab substrates for DENN domains. Identifying the full complement of DENN/Rab pairs will provide new insights into membrane trafficking and cellular function. Support or Funding Information 1) Canada First Research Excellence Fund, awarded to McGill University for Healthy Brains for Healthy Lives. 2) ALS Canada
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".