Shikonin impairs mitochondrial activity to selectively target leukemia cells
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a hematopoietic malignancy that results from the accumulation of undifferentiated myeloid cells in the peripheral blood and bone marrow. Limited therapeutics contribute to unfavorable patient outcomes, highlighting the need for novel therapeutics to improve prognosis. We previously demonstrated that shikonin, a constituent of Lithospermum erythrorhizon, preferentially targets bulk AML cells through inhibition of electron transport chain complex II. In this study, we aim to further characterize the anti-leukemia effects of shikonin in vitro and in vivo. AML cell lines and patient-derived cells were used to assess the cytotoxic effect of shikonin in vitro and in vivo. Respirometry, stable-isotope tracing, flow cytometry, and immunoblotting were used to assess the metabolic changes which precede shikonin-mediated cell death. Shikonin induced cytotoxicity in AML cell lines and patient-derived cells while sparing normal hematopoietic cells through a reactive-oxygen species (ROS) dependent mechanism. Shikonin (2.5 mg/kg) reduced patient-derived AML cell engraftment in mouse bone marrow without toxicity. Mechanistically, it increased mitochondrial ROS, impaired oxidative tricarboxylic acid cycling, and reprogrammed metabolism towards glycolysis. Chronic cellular exposure to shikonin resulted in a unique phenotype characterized by decreased mitochondrial activity and increased glycolysis. Consistent with this, cells with increased glycolytic and antioxidant capacities were less sensitive to shikonin. Together, these results highlight shikonin as a mitochondria-targeting agent and provide further insight into its anti-AML activity.
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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".