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Record W4415252432 · doi:10.1021/acschemneuro.5c00628

Region-Specific Quantification of 2-Hydroxyglutarate Enantiomers in Murine Brain during Mitochondrial Complex I Deficiency

2025· article· en· W4415252432 on OpenAlexaff
Richard McCain, Gerardo G. Piroli, Holland Smith, Aman S. Sumal, Paul A. Grimsrud, William E. Cotham, Michael D. Walla, Norma Frizzell

Bibliographic record

VenueACS Chemical Neuroscience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersNational Institute of Neurological Disorders and StrokeUniversity of South CarolinaNational Science Foundation
KeywordsEnantiomerCitric acid cycleMitochondrionMetabolomeTricarboxylic acidMetabolismBioenergeticsProteome

Abstract

fetched live from OpenAlex

The Ndufs4 –/– mouse is a model of mitochondrial Complex I deficiency that contributes to altered production of the tricarboxylic acid cycle metabolites. We hypothesized that l -2-hydroxyglutarate ( l -2-HG) levels would be elevated in the pathologically affected regions of the Ndufs4 –/– mouse brain in parallel with metabolic acidosis. We employed a stable isotope dilution method for the concurrent quantification of l -lactate and the distinct 2-HG enantiomers in isolated mouse brain regions. While lactate levels were elevated, as expected in the Ndufs4 –/– brain, the levels of l -2-HG and the enantiomer d -2-HG were markedly reduced in a region-specific manner, and this decrease was also reproduced in the Ndufs4 –/– serum. The specific and reproducible decreases in 2-HG quantified in Complex I deficiency may have utility as a unique disease biomarker. Quantitative analysis of the mitochondrial proteome of the Ndufs4 –/– mouse brainstem indicated an increased abundance of l -2-HG dehydrogenase, suggesting that 2-HG enantiomers are metabolized in the Ndufs4 –/– mouse yielding FADH 2 to alleviate the bioenergetic deficit.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.271
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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