bioRxivCancer BiologyPreprint
Epigenetic silencing of transposable elements by IRF2BP2 is a selective dependency of myeloid leukemia
Jingru XuiD, Jessica WhittleiD, Liam ClayfieldiD, Michael Lie‐A‐LingiD, Eve LeddyiD, Duncan L. SmithiD, Robert SellersiD, Özgen DeniziD, Georges LacaudiD
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Abstract
Acute myeloid leukemia (AML) is a heterogeneous malignancy with limited curative treatment options. Transposable elements (TEs) are now recognized as key regulators of genome function, with aberrant activation implicated in cancer. However, their tumor-type-specific roles remain poorly characterized. Using single-cell Perturb-seq, we systematically screened for chromatin-associated regulators in primary AML patient cells to uncover dependencies required for leukemia cell viability. Our screen identified IRF2BP2 as an AML-selective dependency, functioning as a repressor of TE expression. Loss of IRF2BP2 induced differentiation, apoptosis, and impaired leukemic cell fitness, phenotypes linked to transcriptional activation of TEs, particularly evolutionarily young human endogenous retrovirus K (HERV-K). Mechanistically, IRF2BP2 cooperates with TRIM28 and DNMT1 to epigenetically silence TE expression. CRISPR-mediated activation of HERV-K/LTR5_Hs recapitulated the phenotypic effects of IRF2BP2 loss, while targeted re-silencing of HERV-K/LTR5_Hs partially rescued the effects, establishing a causal link between TE regulation and AML maintenance. Our findings highlight tumor-suppressive functions of TEs in leukemia and reveal IRF2BP2 as a key regulator of TE silencing in AML. Targeting the epigenetic machinery governing TE repression may represent a promising therapeutic avenue for differentiation-inducing and immunomodulatory strategies in AML.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: experimental assay
- approach: deep learning
- approach: statistical model
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