MétaCan
Menu
← Back to cohort
Record W4206534262 · doi:10.1101/2022.01.13.476189

Dissecting the interactions of PINK1 with the TOM complex in depolarized mitochondria

2022· preprint· en· W4206534262 on OpenAlexaff
Klaudia K. Maruszczak, Martin Jung, Shafqat Rasool, Jean‐François Trempe, Doron Rapaport

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsMcGill University
FundersDeutsche Forschungsgemeinschaft
KeywordsPINK1MitophagyMitochondrionCell biologyOrganelleChemistryBiologyAutophagyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Mitochondria dysfunction is involved in the pathomechanism of many illnesses including Parkinson’s disease. PINK1, which is mutated in some cases of familiar Parkinsonism, is a key component in the degradation of damaged mitochondria by mitophagy. The accumulation of PINK1 on the mitochondrial outer membrane (MOM) of compromised organelles is crucial for the induction of mitophagy, but the molecular mechanism of this process is still unresolved. Here, we investigate the association of PINK1 with the TOM complex. We demonstrate that PINK1 heavily relies on the import receptor TOM70 for its association with mitochondria and directly interacts with this receptor. The structural protein TOM7 appears to play only a moderate role in PINK1 association with the TOM complex, probably due to its role in stabilizing this complex. PINK1 requires the TOM40 pore lumen for its stable interaction with the TOM complex and apparently remains there during its further association with the MOM. Overall, this study provides new insights on the role of the individual TOM subunits in the association of PINK1 with the MOM of depolarized mitochondria.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAutophagy in Disease and Therapy→French-language works237,207→