A non-canonical role for glutamate decarboxylase 1 in cancer cell amino acid homeostasis, independent of the GABA shunt
Bibliographic record
Abstract
Glutamate decarboxylase 1 (GAD1) is best known for its role in producing the neurotransmitter γ-amino butyric acid (GABA) as part of the “GABA shunt” metabolic pathway, an alternative mechanism of glutamine anaplerosis for TCA cycle metabolism (Yogeeswari et al., 2005). However, understanding of the metabolic function of GAD1 in non-neuronal tissues has remained limited. Here, we show that GAD1 supports cancer cell proliferation independent of the GABA shunt. Despite its elevated expression in lung cancer tissue, GAD1 is not engaged in the GABA shunt in proliferating non-small cell lung cancer (NSCLC) cells, but rather is required for regulating amino acid homeostasis. Silencing GAD1 promotes a broad deficiency in amino acid uptake, leading to reduced glutamine-dependent TCA cycle metabolism and defects in serum- and amino acid-stimulated mTORC1 activation. Mechanistically, GAD1 regulates amino acid uptake through ATF4-dependent amino acid transporter expression including SLC7A5 (LAT1), an amino acid transporter required for branched chain amino acid (BCAA) uptake. Overexpression of LAT1 rescues the proliferative and mTORC1 signalling defects of GAD1-deficient tumor cells. Our results, therefore, define a non-canonical role for GAD1, independent of its characterised role in GABA metabolism, whereby GAD1 regulates amino acid homeostasis to maintain tumor cell proliferation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".