A PROXIMITY LIGATION SCREEN IDENTIFIES SNAT2 AS A NOVEL TARGET OF THE MARCH1 E3 UBIQUITIN LIGASE
Bibliographic record
Abstract
ABSTRACT E3 ubiquitin ligases include hundreds of members that can regulate the half-life of other proteins but also modulate their cellular localization and functions. While MARCH1 appears to target principally immune cell components, such as MHC class II molecules and the co-stimulatory molecule CD86, the repertory of its targets remains to be fully documented. Here, we adapted a proximity-dependent biotin identification (BioID)-based screening approach in live HEK293 cells. We transfected a fusion protein consisting of mouse MARCH1 linked to YFP at its N-terminus and to the biotin ligase of Aquifex aeolicus at its C-terminus. Upon transient overexpression in the presence of biotin, we could recover biotinylated proteins that are presumably found within 10 nm of MARCH1. CD98 and CD71, two previously described targets of MARCH1, were identified. Of 16 other biotinylated proteins identified by semi-quantitative mass spectrometry, ten were tested directly by flow cytometry to monitor their expression in the presence or absence of transfected MARCH1. SNAT2 was particularly sensitive to the presence of MARCH1 and was found to be ubiquitinated on western blots. Thus, BioID2 is an effective mean of characterizing the interactome of MARCH1, and the identification of SNAT2 suggests a role of this ubiquitin ligase in cellular metabolism.
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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".