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Record W4307451010 · doi:10.1101/2022.10.18.512735

Inhibition of GCN5 decreases skeletal muscle fat metabolism during high fat diet feeding

2022· preprint· en· W4307451010 on OpenAlexafffund
Alex E. Green, Brayden L. Perras, Hongbo Zhang, Elena Katsyuba, Alaa Haboush, Kwadjo M. Nyarko, Dheeraj K. Pandey, Abolfazl Nik-Akhtar, Dongryeol Ryu, Johan Auwerx, Keir J. Menzies

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaÉcole Polytechnique Fédérale de LausanneUniversity of Ottawa
KeywordsSkeletal muscleMyogenesisInternal medicineEndocrinologyBiologyPCAFCitrate synthaseMetabolismMitochondrionCell biologyBiochemistryGeneEnzymeTranscription factor

Abstract

fetched live from OpenAlex

Abstract Introduction GCN5 ( Kat2a ) is a lysine acetyl transferase capable of acetylating and inhibiting PGC-1α activity. As such, it is described as a negative regulator of PGC-1α and subsequently restricts mitochondrial content. However, elimination of GCN5 in skeletal muscle does not increase mitochondrial content or alter lipid metabolism under normal metabolic conditions. GCN5 levels increase with high-fat diet (HFD) feeding in rodents. Additionally, the GCN5 homolog, PCAF, has previously been shown to also acetylate and inhibit PGC-1α and therefore may possibly compensate for loss of GCN5. Objective The objective of this study was to examine if with HFD feeding that elimination of GCN5 ( Kat2a gene) from skeletal muscle would elicit improvements in mitochondrial and metabolic markers. Methods Skeletal muscle specific GCN5 knockouts ( Gcn5 skm-/- ) were fed an HFD. Body composition, cardio-metabolic and physical fitness outcomes were monitored. Additionally, cultured myotubes were treated with a pan-GCN5/PCAF inhibitor and examined for changes in mitochondrial markers. Results Elimination of skeletal muscle GCN5 did not alter body composition, tissue masses, energy intake, or energy expenditure measurements of mice fed an HFD. Furthermore, whole body glucose homeostasis and cardiac measurements were not altered. There were few differences in lipid metabolism genes, relatively more glucose oxidation versus Gcn5 skm+/+ (wildtype) mice, and a reduction in Pdk4 expression. Exercise capacity and mitochondrial content levels were not altered in Gcn5 skm-/- mice. Further, elimination of GCN5 in skeletal muscle increased Kat2b (PCAF) mRNA expression; however, inhibition of GCN5/PCAF bromodomains in cultured myotubes did not increase oxidative metabolism genes and decreased expression of some mitochondrial genes and Pdk4 mRNA. Conclusions Neither elimination of GCN5, nor simultaneous inhibition of GCN5 and its homolog PCAF improved skeletal muscle mitochondrial content under normal or HFD-fed conditions. Despite this, GCN5 may play a role in regulating macronutrient preference by regulating Pdk4 content. Thus, HFD/macronutrient excess revealed novel roles of GCN5 in skeletal muscle. Highlights – Skeletal muscle specific elimination of Gcn5/Kat2a decreases fat oxidation without 1) preventing high-fat diet induced weight gain, 2) improving whole body glucose handling, or 3) improving skeletal muscle mitochondrial content. – Inhibition of the GCN5 and PCAF bromodomains and Gcn5 ablation decreases expression of Pdk4 . – Expression of Kat2b increases with Gcn5 elimination in skeletal muscle. – Inhibition of the GCN5 and PCAF bromodomains do not result in increased skeletal muscle mitochondrial content.

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 score0.002
Threshold uncertainty score0.008

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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