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Record W4285395213 · doi:10.1101/2022.07.12.499729

The CoQ biosynthetic di-iron carboxylate hydroxylase COQ7 is inhibited by in vivo metalation with manganese but remains function by metalation with cobalt

2022· preprint· en· W4285395213 on OpenAlexafffund
Ying Wang, Siegfried Hekimi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCoenzyme Q10 studies and effects
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsManganeseCobaltChemistryMetalationEnzymeActive siteBiochemistryStereochemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT COQ7 is a mitochondrial hydroxylase that catalyzes the penultimate step of the biosynthesis of coenzyme Q (CoQ; ubiquinone). CoQ is an obligate component of the mitochondrial electron transport chain and an antioxidant. CoQ deficiencies due to mutations in CoQ biosynthetic enzymes are severe genetic disorders often manifesting as mitochondrial disease syndrome. COQ7 is part of the relatively rare class of di-iron carboxylate enzymes, which carry out a wide range of reactions. In a previous study we described how COQ7 activity is inhibited in mammalian cells after treatment with iron chelating agents. Here, we report that manganese exposure of mouse cells leads to decreased COQ7 activity and resulting CoQ deficiency, which might participate in manganese toxicity. We find that the presence of cobalt can interfere with the inhibition of COQ7 by manganese. We present evidence that both manganese inhibition and cobalt interference are the result of metal exchange at the di-iron active site of COQ7. We present findings that suggest that 1) cobalt has greater affinity for the active site of COQ7 than both iron and manganese and, 2) that iron replacement by cobalt at the active site preserves catalytic activity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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 teacher head, not a consensus.

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 routes2
Has abstractyes

Explore more

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