Elongation factor 1A1 regulates metabolic substrate preference in mammalian cells
Bibliographic record
Abstract
Eukaryotic elongation factor 1A1 (EEF1A1) is canonically involved in protein synthesis but also has noncanonical functions in diverse cellular processes. Previously, we identified EEF1A1 as a mediator of lipotoxicity and demonstrated that chemical inhibition of EEF1A1 activity reduced mouse liver lipid accumulation. These findings suggested a link between EEF1A1 and metabolism. Therefore, we investigated its role in regulating metabolic substrate preference. EEF1A1-deficient Chinese hamster ovary (2E2) cells displayed reduced media lactate accumulation. These effects were also observed with EEF1A1 knockdown in human hepatocyte-like HepG2 cells and in WT Chinese hamster ovary and HepG2 cells treated with selective EEF1A inhibitors, didemnin B, or plitidepsin. Extracellular flux analyses revealed decreased glycolytic ATP production and increased mitochondrial-to-glycolytic ATP production ratio in 2E2 cells, suggesting a more oxidative metabolic phenotype. Correspondingly, fatty acid oxidation was increased in 2E2 cells. Both 2E2 cells and HepG2 cells treated with didemnin B exhibited increased neutral lipid content, which may be required to support elevated oxidative metabolism. RNA-seq revealed a >90-fold downregulation of a rate-limiting glycolytic enzyme, hexokinase 2, which we confirmed through immunoblotting and enzyme activity assays. Pathway enrichment analysis identified downregulations in TNFA signaling via NFKB and MYC targets. Correspondingly, nuclear abundances of RELB and MYC were reduced in 2E2 cells. Thus, EEF1A1 deficiency may perturb glycolysis by limiting NFKB- and MYC-mediated gene expression, leading to decreased hexokinase expression and activity. This is the first evidence of a role for a translation elongation factor, EEF1A1, in regulating metabolic substrate utilization in mammalian cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".