Metabolomics analysis of SNAT2-deficient cells: Implications for the discovery of selective small-molecule inhibitors of an amino acid transporter
Bibliographic record
Abstract
Amino acid uptake by the solute carrier family of transporter proteins is critical to support cell metabolism, and inhibition of transporter activity represents a tractable strategy to restrict nutrient availability to cancer cells. A small-molecule inhibitor of the sodium-coupled neutral amino acid transporter 2 (SNAT2), 3-(N-methyl(4-methylphenyl)sulfonamido)-N-(2-trifluoromethylbenzyl)thiophene-2-carboxamide (MMTC/57E), was recently identified and shown to inhibit cell proliferation when combined with glucose transport inhibitors in breast and pancreatic cancer cell lines. In this study, we use mass spectrometry and a model competitive substrate inhibitor, α-(methylamino)-isobutyric acid (MeAIB), to establish cell-based SNAT2 activity assays and validate target engagement of MMTC/57E. We show that cellular uptake of MeAIB is dependent on SNAT2 or the closely related SNAT1 and is inhibited by the endogenous substrate l-alanine in a dose-dependent manner. We show that SNAT2-KO cells or cells treated with MeAIB exhibit a similar metabolomic signature associated with defects in amino acid availability and other metabolites. Applying these assays, we fail to observe that MMTC/57E inhibits SNAT2 activity. MMTC/57E exhibits poor aqueous solubility that hinders its use as a tool SNAT2 inhibitor. Our results highlight the challenges associated with identifying and validating transporter inhibitors and report robust assays that may be used to identify and evaluate SNAT2 inhibitors in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".