Carnitine/xenobiotics transporters in the human mammary gland epithelia, MCF12A
Bibliographic record
Abstract
The barrier function of the human mammary gland collapses if challenged with cationic drugs, causing their accumulation in milk. However, underlying molecular mechanisms are not well understood. To gain insight into the mechanism, we characterized transport of organic cations in the MCF12A human mammary gland epithelial cells, using carnitine and tetraethylammonium (TEA) as representative nutrient and xenobiotics probes, respectively. Our results show that the mammary gland cells express mRNA and proteins of human (h) novel organic cation transporters (OCTN) 1 and hOCTN2 (a Na+-dependent carnitine carrier with Na+-independent xenobiotics transport function), which belong to the solute carrier superfamily (SLC) of transporters. Other SLC OCTs such as hOCT1 and extraneuronal monoamine transporter (EMT)/hOCT3 are also expressed at mRNA levels, but hOCT2 was undetectable. We further showed mRNA expression of ATB0+ (an amino acid transporter with a Na+/Cl(-)-dependent carnitine transport activity), and Fly-like putative transporter 2/OCT6 (a splice variant of carnitine transporter 2: a testis-specific Na+-dependent carnitine transporter). TEA uptake was pH dependent. Carnitine uptake was dependent on Na+, and partly on Cl-, compatible with hOCTN2 and ATB0+ function. Modeling analyses predicted multiplicity of the uptake mechanisms with the high-affinity systems characterized by K(m) of 5.1 microM for carnitine and 1.6 mM for TEA, apparently similar to the reported hOCTN2 parameter for carnitine, and that of EMT/hOCT3 for TEA. Verapamil, cimetidine, carbamazepine, quinidine, and desipramine inhibited the carnitine uptake but required supratherapeutic concentrations, suggesting robustness of the carnitine uptake systems against xenobiotic challenge. Our findings suggest functional roles of a network of multiple SLC organic cation/nutrient transporters in human mammary gland drug transfer.
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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 | 0.000 |
| 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".