Hypoxia upregulates Malat1 expression through a CaMKK/AMPK/HIF-1α axis
Bibliographic record
Abstract
Increased expression levels of the long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (Malat1) have been associated with enhanced proliferation and metastasis of several cancer cell types. Hypoxia, a hallmark characteristic of solid tumors, has been linked to an increase in the activity of the ATP-generating AMPK protein. Since Malat1 was recently shown to be upregulated during hypoxia, the objective of this study was to determine the contribution of AMPK in the mechanistic pathways regulating Malat1 expression in low oxygen conditions. Compared to those cultured in 21% O2 conditions, HeLa cells incubated in 1.5% O2 expressed more Malat1 transcripts. This observation was mimicked in HEK293T cells using a synthetic reporter construct containing 5.6 kb of the human Malat1 promoter, suggesting that hypoxia directly impacted Malat1 gene transcription. Interestingly, pharmacological stimulation of AMPK increased Malat1 promoter transactivation in 21% O2 conditions, whereas inhibition of either AMPK or its upstream activator CaMKK completely abolished the augmentation of Malat1 under hypoxia. Pharmacological modulation of LKB1, another major regulator of AMPK, had no impact on Malat1 promoter transactivation, suggesting that calcium inputs are important in the control of Malat1 expression by AMPK. Overexpression of hypoxia-inducible factor-1α (HIF-1α) increased Malat1 expression in 21% O2 conditions, whereas pharmacological inhibition of HIF-1α blocked the impact of hypoxia on the Malat1 promoter. Taken together, these findings strongly suggest that Malat1 expression is regulated in hypoxic conditions by a CaMKK/AMPK/HIF-1α axis. More research is needed in physiological settings to test the clinical relevance of this pathway.
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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) | 0.002 | 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".