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Record W2972912103 · doi:10.1101/770669

Cell cycle regulation of mitochondrial protein import revealed by genome-scale pooled bimolecular fluorescence complementation screening

2019· preprint· en· W2972912103 on OpenAlexafffund
Kim Blakely, Patricia Mero, Roland Arnold, Ayesha Saleem, Christine Misquitta, Dahlia Kasimer, Sachin Kumar, Andrea C. Uetrecht, Kevin A. Brown, Alessandro Datti, David A. Hood, Philip Kim, Jason Moffat

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsLunenfeld-Tanenbaum Research InstituteYork UniversityUniversity of Toronto
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsBimolecular fluorescence complementationBiologyProtein–protein interactionCell biologyComplementary DNAcDNA libraryTransmembrane proteinComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.214
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes2
Has abstractyes

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