Cell cycle regulation of mitochondrial protein import revealed by genome-scale pooled bimolecular fluorescence complementation screening
Bibliographic record
Abstract
ABSTRACT A central focus of systems biology is the functional mapping of protein-protein interactions under physiological conditions. Here we describe MaGiCaL-BiFC, a lentivirus-based bimolecular fluorescence protein-fragment complementation approach for the high-throughput, genome-scale identification of protein-protein interactions in mammalian cells. After developing and validating this methodology using known protein-protein interaction pairs, we constructed genome-scale pooled BiFC libraries using the human ORFeome cDNA collection. These pooled libraries, containing ∼ 12,000 unique human cDNAs, were used to screen for candidate interaction partners of the mitochondrial transmembrane protein TOMM22. Following infection of cells with the TOMM22 bait and the pooled cDNA libraries, cells harboring candidate TOMM22 interacting proteins were isolated from the cell pool via fluorescence activated cell sorting, and identified via microarray analysis. This approach identified several known interaction partners of TOMM22, as well as novel physical and functional partners that link the mitochondrial network to proteins involved in diverse cellular processes. Notably, protein kinase CK2 was identified as a novel physical interaction partner of human TOMM22. We found that this association occurs preferentially during mitosis and involves direct phosphorylation of TOMM22, an event that may lead to attenuation of mitochondrial protein import. Together, this data contributes to the growing body of evidence suggesting eloquent coordination between cell cycle progression and mitochondrial physiology. Importantly, through high-throughput screening and focused validation, our study demonstrates the power of the MaGiCaL-BiFC approach to uncover novel functional protein-protein interactions, including those involving proteins with membrane-spanning domains, or of a transient nature, all within their native cellular environment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".