Abstract 1239: The transcription factor Kaiso negatively regulates the hypoxia inducible factor-1 alpha
Bibliographic record
Abstract
Abstract Introduction: Hypoxia (reduced oxygen levels) is a common characteristic of solid tumors, and is strongly correlated with poor prognosis and resistance to treatment. In response to hypoxia, cells initiate a cascade of transcriptional events regulated by the Hypoxia Inducible Factor 1 (HIF-1) heterodimer. During hypoxia, the oxygen sensitive HIF-1α subunit is stabilized and translocates to the nucleus where it interacts with the HIF-1β subunit to form a functional HIF-1 transcription factor that regulates genes to facilitate cellular adaptation to hypoxia. To date, while the mechanisms governing HIF-1α stabilization and function have been well studied, those governing HIF-1α gene expression are not fully understood. Recent studies have revealed that the transcription factor Kaiso, a member of the POZ-ZF family of transcription factors implicated in tumorigenesis, is aberrantly expressed and mislocalized at the hypoxic core of various tumors. Mounting evidence suggests that, like other POZ-ZF proteins, Kaiso has a role in tumorigenesis. More importantly, the HIF-1α promoter contains several copies of the consensus Kaiso Binding Site (KBS) suggesting that HIF-1α may be a Kaiso target gene. Since HIF-1α is a key regulator of many hypoxia-induced pathways, and Kaiso is aberrantly expressed in hypoxia, some feed-back mechanism may exist between Kaiso and HIF-1α. These studies seek to elucidate the relationship between Kaiso and HIF-1α during the adaptive response to hypoxia. Methods: Western blot analysis was performed on whole cell lysates harvested from cultured cells that were incubated in hypoxia for varying time periods to compare Kaiso expression levels in these cells. Electrophoretic Mobility Shift Assays (EMSAs) were used to determine the specificity of Kaiso binding to the HIF-1α promoter. To assess the effect of Kaiso on HIF-1α transcription, promoter-reporter assays were performed in cultured cells using a HIF-1α promoter-luciferase construct and a Kaiso expression plasmid. Results: During hypoxia, Kaiso protein levels steadily decline and are significantly reduced after 24 hours in the MCF7 tumor epithelial cell line. Interestingly, the decrease in Kaiso protein levels is less apparent in the non-tumor MCF10A breast cell line. Kaiso binds 3 of the 6 putative Kaiso binding sites as well as a CpG rich region in the HIF-1α promoter in a methylation-dependent manner. Furthermore, Kaiso represses transcription of the HIF-1α promoter in MCF7, MCF10A and HCT116 cell lines. Conclusion: These results indicate that Kaiso negatively regulates HIF-1α and thus implicates HIF-1α as a Kaiso target gene. The fluctuation of Kaiso expression during hypoxia suggests that HIF-1 may also regulate Kaiso and that a feedback mechanism may exist between Kaiso and HIF-1α. Ongoing studies are being performed to elucidate the functional interaction between HIF-1α and Kaiso. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1239.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".