Using intracellular metabolic profiling to identify novel biomarkers of cisplatin-induced acute kidney injury in NRK-52E cells
Bibliographic record
Abstract
The aim of this study was to investigate changes in the intracellular metabolism resulting from cisplatin (CDDP)-induced nephrotoxicity in normal kidney tubular epithelial NRK-52E cells. Cytotoxicity, cell cycle analysis, and apoptotic cell death were all evaluated in NRK-52E cells treated with CDDP. Subsequently, proton nuclear magnetic resonance (1H-NMR) spectroscopy was used to investigate cellular metabolic profiles. CDDP-induced nephrotoxicity was determined in vivo model. Cytotoxicity in the NRK-52E cells significantly rose following treatment with CDDP and these increases were found to be concentration-dependent. Both p53 and Bax protein expression was increased in CDDP-treated NRK-52E cells, correlating with enhanced cellular apoptosis. In addition, a number of metabolites were altered in both media and cell lysates in these cells. In cell lysates, citrate, creatinine, and acetate levels were dramatically reduced following treatment with 20 µM CDDP concentrations, while glutamate level was elevated. Lactate and acetate levels were significantly increased in culture media but citrate concentrations were reduced following high 20 µM CDDP concentrations incubation. In addition, excretion of clusterin, calbindin, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), selenium binding protein 1 (SBP1), and pyruvate kinase M2 (PKM2) into the culture media was significantly increased in CDDP-treated cells while expression of acetyl CoA synthetase 1 (AceCS1) was markedly reduced in these cells. These findings suggest that acetate-dependent metabolic pathway may be a reliable and useful biomarker for detecting CDDP-induced nephrotoxicity. Taken together, data demonstrate that the discovery of novel biomarkers by metabolite profiling in target cells may contribute to the detection of nephrotoxicity and new drug development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".