Elucidating the effects of miR-17˜92 on the metabolic program of B-cell lymphoma
Bibliographic record
Abstract
Dysregulation of microRNAs is commonly observed in cancer cells.The polycistronic microRNA cluster, miR-17~92, is a known oncogene.Elevated expression of this miRNA cluster correlates with tumor aggressiveness in Eµ-Myc lymphomas.Our lab has identified Stk11 (which encodes the tumour suppressor gene LKB1) as a target of the miR-17 seed family that alters the metabolism of lymphoma cells.However, the mechanism by which the entire miR-17~92 cluster promotes cancer has yet to be fully elucidated.We hypothesized that miR-17~92 can promote cancer by altering the metabolism of tumour cells by modulating LKB1mediated suppression of mTOR signaling.To test this hypothesis, I used isogenic Eµ-Myc driven B-cell lymphoma cell lines overexpressing miR-17~92, which mimics the amplification of the miR-17~92 gene often observed in human cancer.In my work I showed that overexpression of miR-17~92 induces a bioenergetic shift in lymphomas, marked by an increase in OXPHOS, glycolysis, overall ATP production and mitochondrial DNA content.Lymphoma cells overexpressing miR-17~92 showed upregulation of downstream targets of the mTOR pathway, which modulates the translation of several metabolic genes and could account for the metabolic reprogramming observed.I observed that while there was no difference in transcriptional expression of some anabolic genes in mir-17~92 overexpressing cells, miR-17~92 resulted in increased protein expression of anabolic pathways like fatty acid synthesis and serine biosynthesis.Thus, polysome profiling was done to assess the levels of translation in these lymphomas.I observed enhanced translation of mRNAs from these pathways in miR-17~92 overexpressing cells.The serine biosynthesis pathway is a key metabolic pathway for rapidly dividing cells that provides building blocks for protein, nucleotide and lipid synthesis.I show that shRNA-mediated silencing of Phgdh, the first enzyme of the serine biosynthesis pathway, in lymphomas significantly decreases the growth of lymphoma cells overexpressing
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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".