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Record W2131718574 · doi:10.1371/journal.pone.0000690

The Tricarboxylic Acid Cycle, an Ancient Metabolic Network with a Novel Twist

2007· article· en· W2131718574 on OpenAlexafffund
Ryan J. Mailloux, Robin Bériault, Joseph Lemire, Ranji Singh, Daniel Chénier, Robert Hamel, Vasu D. Appanna

Bibliographic record

VenuePLoS ONE · 2007
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsLaurentian University
FundersIndustry Canada
KeywordsCitric acid cycleIsocitrate dehydrogenaseOxidative phosphorylationNAD+ kinaseBiochemistryTricarboxylic acidDehydrogenaseMetabolic pathwayBiologyOxidative stressReactive oxygen speciesMetabolismCell biologyEnzyme

Abstract

fetched live from OpenAlex

The tricarboxylic acid (TCA) cycle is an essential metabolic network in all oxidative organisms and provides precursors for anabolic processes and reducing factors (NADH and FADH(2)) that drive the generation of energy. Here, we show that this metabolic network is also an integral part of the oxidative defence machinery in living organisms and alpha-ketoglutarate (KG) is a key participant in the detoxification of reactive oxygen species (ROS). Its utilization as an anti-oxidant can effectively diminish ROS and curtail the formation of NADH, a situation that further impedes the release of ROS via oxidative phosphorylation. Thus, the increased production of KG mediated by NADP-dependent isocitrate dehydrogenase (NADP-ICDH) and its decreased utilization via the TCA cycle confer a unique strategy to modulate the cellular redox environment. Activities of alpha-ketoglutarate dehydrogenase (KGDH), NAD-dependent isocitrate dehydrogenase (NAD-ICDH), and succinate dehydrogenase (SDH) were sharply diminished in the cellular systems exposed to conditions conducive to oxidative stress. These findings uncover an intricate link between TCA cycle and ROS homeostasis and may help explain the ineffective TCA cycle that characterizes various pathological conditions and ageing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.279
Teacher spread0.234 · 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.

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

Citations323
Published2007
Admission routes2
Has abstractyes

Explore more

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