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Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [RNA-seq]

ABSTRACT: The PML-RARA fusion protein is the hallmark driver of Acute Promyelocytic Leukemia (APL) and disrupts retinoic acid signaling, leading to wide-scale gene expression changes and uncontrolled proliferation of myeloid precursor cells. While known to be recruited to binding sites across the genome, its impact on gene regulation and expression is under-explored. Using integrated multi-omics datasets, we characterize the influence of PML-RARA binding on gene expression and regulation in an inducible cell line model and APL patient ex vivo samples. We find that genes whose regulatory elements recruit PML-RARA are not uniformly transcriptionally repressed, as commonly suggested, but also may be upregulated or remain unchanged. We develop a novel, computational machine learning application to deconvolute the complex, local transcription factor binding site environment at PML-RARA bound positions to reveal distinct signatures that modulate how PML-RARA directs the transcriptional response.

ORGANISM(S): Homo sapiens

PROVIDER: GSE173754 | GEO | 2022/12/14

REPOSITORIES: GEO

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Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [Cut&Run]

Project description:The PML-RARA fusion protein is the hallmark driver of Acute Promyelocytic Leukemia (APL) and disrupts retinoic acid signaling, leading to wide-scale gene expression changes and uncontrolled proliferation of myeloid precursor cells. While known to be recruited to binding sites across the genome, its impact on gene regulation and expression is under-explored. Using integrated multi-omics datasets, we characterize the influence of PML-RARA binding on gene expression and regulation in an inducible cell line model and APL patient ex vivo samples. We find that genes whose regulatory elements recruit PML-RARA are not uniformly transcriptionally repressed, as commonly suggested, but also may be upregulated or remain unchanged. We develop a novel, computational machine learning application to deconvolute the complex, local transcription factor binding site environment at PML-RARA bound positions to reveal distinct signatures that modulate how PML-RARA directs the transcriptional response.

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