MIR155 Regulation of Ubiquilin1 and Ubiquilin2: Implications in Cellular Protection and Tumorigenesis
Bibliographic record
Abstract
Ubiquilin (UBQLN) proteins are adaptors thought to link ubiquitinated proteins to the proteasome. However, our lab has recently reported a previously unappreciated role for loss of UBQLN in lung cancer progression. In fact, UBQLN genes are lost in over 50% of lung cancer samples examined. However, a reason for the loss of UBQLN has not been proposed, nor has a selective pressure that could lead to deletion of UBQLN been reported. Diesel Exhaust Particles (DEP) are a major concern in the large cities of developing nations and DEP exposed populations are at an increased risk of developing a number of illnesses, including lung cancer. A connection between DEP and UBQLN has never been examined. In the present study, we determined the effect of DEP on lung cell lines and were interested to determine if UBQLN proteins could potentially play a protective role following treatment with DEP. Interestingly, we found that DEP treated cells have increased expression of UBQLN proteins. In fact, over-expression of UBQLN was capable of protecting cells from DEP toxicity. To investigate the mechanism by which DEP leads to increased UBQLN protein levels, we identified and interrogated microRNAs that were predicted to regulate UBQLN mRNA. We found that DEP decreases the oncogenic microRNA, MIR155. Further, we showed that MIR155 regulates the mRNA of UBQLN1 and UBQLN2 in cells, such that increased MIR155 expression increased cell invasion, migration, wound formation and clonogenicity in UBQLN-loss dependent manner. This is the first report of an environmental carcinogen regulating expression of UBQLN proteins. We show that exposure of cells to DEP causes an increase in UBQLN levels and that MIR155 regulates mRNA of UBQLN. Thus, we propose that DEP-induced repression of MIR155 leads to increased UBQLN levels, which in turn may be a selective pressure on lung cells to lose UBQLN1.
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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 | 0.000 | 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".