Metabolic Effects of the Cancer Metastasis Modulator MEMO1
Bibliographic record
Abstract
Background/Objectives: Cancer cells often display altered energy metabolism. In particular, expression levels and activity of the tricarboxylic acid cycle (TCA cycle) enzymes may change in cancer, and dysregulation of the TCA cycle is a frequent hallmark of cancer cell metabolism. MEMO1, a modulator of cancer metastasis, has been shown to bind iron and regulate iron homeostasis in the cells. MEMO1 knockout changed mitochondrial morphology and iron content in breast cancer cells. Our previous genome-wide analysis of MEMO1 genetic interactions across multiple cancer cell lines revealed that gene sets involved in mitochondrial respiration and the TCA cycle are enriched among the gain-of-function interaction partners of MEMO1. Based on these findings, we measured the TCA cycle metabolite levels in breast cancer cells with varying levels of MEMO1 expression. Methods: ShRNA knockdown assay was performed to test essentiality of key TCA cycle enzymes. TCA metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in MDA-MB-231 (high MEMO1), M67-2 (MEMO1 knockdown), and M67-9 (MEMO1 knockout) cells under iron-depleted, basal iron, and iron-supplemented conditions. Results:ACO2 and OGDH knockdowns inhibit cell proliferation, indicating an essential role of the TCA cycle in MDA-MB-231 metabolism. α-Ketoglutarate and citrate levels exhibited an inverse relationship with MEMO1 expression, increasing significantly in MEMO1 knockout cells regardless of iron availability. In contrast, fumarate, malate, and glutamate levels were elevated in MEMO1 knockout cells specifically under low iron conditions, suggesting an iron-dependent effect. Conclusions: Overall, our results indicate that MEMO1 plays a role in regulating the TCA in cancer cells in an iron-dependent manner.
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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 | 0.000 | 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 | 0.001 | 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".