CAT‐1‐mediated arginine uptake and regulation of nitric oxide synthases for the survival of human breast cancer cell lines
Bibliographic record
Abstract
Growth of the human MCF-7 breast cancer cell line is highly dependent on L-arginine. We have reported that L-arginine, released from extracellular substrates by prolactin (PRL)- and 17β-estradiol (E2)-induced carboxypeptidase-D in the cell membrane, promotes nitric oxide (NO) production for MCF-7 cell survival. Arginine uptake is mediated by members of the cationic amino acid transporter (CAT) family and may coincide with induction of nitric oxide synthase (NOS) for the production of NO. The present study investigated the CAT isoforms and PRL/E2 regulation of CAT and NOS in breast cancer cell lines. Using RT-PCR analysis, CAT-1, CAT-2A, and CAT-2B transcripts were detected in MCF-7, T47D, and MDA-MB-231 cells. The CAT-4 transcript was detected in MDA-MB-231 only. CAT-3 was not detected in any of these cells. PRL and E2 did not significantly alter levels of CAT-1 mRNA and protein, nor CAT-2A and CAT-2B mRNAs in MCF-7 and T47D cells. PRL and E2 also had no effect on the overall uptake of L-[2,3,4,5-H(3)] arginine into these cells. However, confocal immunofluorescent microscopy showed that PRL and E2 upregulated eNOS and iNOS proteins, which distributed in the cytoplasm and/or nucleus of MCF-7 cells. Knockdown of CAT-1 gene expression using small interfering RNA significantly decreased L-[2,3,4,5-H(3)]-arginine uptake, decreased viability and increased apoptosis of MCF-7 and T47D cells. In summary, several CAT isoforms are expressed in breast cancer cells. The CAT-1 isoform plays a role in arginine uptake and, together with PRL/E2-induced NOS, contribute to NO production for the survival of MCF-7 and T47D 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 | 0.000 | 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".