Activation of protein kinase C decreases equilibrative nucleobase transporter 1-mediated substrate uptake via phosphorylation of threonine 231
Bibliographic record
Abstract
Protein kinase C (PKC) signalling has been shown to be dysregulated in various cancers including acute lymphoblastic leukemia (ALL). We have previously determined that changes in the expression levels of SLC43A3-encoded equilibrative nucleobase transporter 1 (ENBT1) can significantly alter 6-mercaptopurine (6-MP) toxicity in ALL cells. 6-MP is a common drug used in ALL chemotherapy. Furthermore, it has been reported that activation of PKC by phorbol 12-myristate 13-acetate (PMA) impacts nucleobase uptake via an ENBT1-like transporter in Lilly Laboratories Culture-Porcine Kidney 1 (LLC-PK1) cells. We hypothesized that activation of PKC would also alter ENBT1-mediated uptake of nucleobases in leukemia cell models. Using MOLT-4, SUP-B15, and K562 cells, we incubated the cells with PMA or its inactive isoform 4α-PMA for 30 min and determined changes to ENBT1-mediated substrate uptake. All of the cell lines tested showed decreased ENBT1-mediated substrate uptake when exposed PMA, relative to that observed using 4α-PMA. Pre-incubation with the broad-spectrum PKC inhibitor, Gö6983, reversed the decrease caused by PMA. Finally, to determine the residue responsible for this PKC-mediated effect, we transiently transfected HEK293 cells (which do not express endogenous ENBT1) with wild-type SLC43A3 transcript or constructs mutated to modify the predicted PKC sites in ENBT1. We found that the mutation of threonine 231 to alanine prevents the decrease in ENBT1-mediated uptake following incubation with PMA, suggesting its involvement. This study shows that activation of PKC decreases ENBT1-mediated uptake, suggesting that aberrant activation of PKC in ALL could decrease ENBT1-mediated 6-MP uptake potentially leading to decreased therapeutic efficacy.
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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) | 0.003 | 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".