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Record W2084915584 · doi:10.1158/1538-7445.am10-1239

Abstract 1239: The transcription factor Kaiso negatively regulates the hypoxia inducible factor-1 alpha

2010· article· en· W2084915584 on OpenAlexaff
Christina C. Pierre, Laura Beatty, Juliet M. Daniel

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTranscription factorHypoxia-inducible factorsCell biologyCarcinogenesisHypoxia (environmental)G alpha subunitHIF1ABiologyHypoxia-Inducible Factor 1Transcription (linguistics)Cellular adaptationCancer researchProtein subunitChemistryGeneGeneticsOxygen

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.007

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.

Opus teacher head0.068
GPT teacher head0.364
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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