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Record W4361246515 · doi:10.1016/j.isci.2023.106525

A genome-wide CRISPR screen maps endogenous regulators of PPARG gene expression in bladder cancer

2023· article· en· W4361246515 on OpenAlexafffund
Davide Tortora, Morgan E. Roberts, Gunjan Kumar, Sudha S. Kotapalli, Elie Ritch, Joshua Scurll, Brian McConeghy, Sunita Sinha, Alexander W. Wyatt, Peter C. Black, Mads Daugaard

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaUrology Care FoundationCanadian Institutes of Health ResearchBristol-Myers Squibb
KeywordsPeroxisome proliferator-activated receptor gammaBiologyCancer researchRegulation of gene expressionCell biologyGene expressionGenePeroxisome proliferator-activated receptorGenetics

Abstract

fetched live from OpenAlex

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor central in the regulation of key cellular processes including cell metabolism, tissue differentiation, and regulation of the immune system. PPARγ is required for normal differentiation of the urothelium and is thought to be an essential driver of the luminal subtype of bladder cancer. However, the molecular components that regulate PPARG gene expression in bladder cancer remain unclear. Here, we developed an endogenous PPARG reporter system in luminal bladder cancer cells and performed genome-wide CRISPR knockout screening to identify bona fide regulators of PPARG gene expression. Functional validation of the dataset confirmed GATA3, SPT6, and the cohesin complex components SMC1A, and RAD21, as permissive upstream positive regulators of PPARG gene expression in luminal bladder cancer. In summary, this work provides a resource and biological insights to aid our understanding of PPARG regulation in bladder cancer.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.022
GPT teacher head0.267
Teacher spread0.245 · 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 teacher head, 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

Citations8
Published2023
Admission routes2
Has abstractyes

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