RNAMolecular BiologyPeer-reviewed journal article
SF3B1K700E rewires splicing of cell-cycle regulators
Mai BakeriD, Eden EngaliD, Aveksha SharmaiD, Mayra Petasny, Shiri Jaffe‐HermaniD, Ophir GeminderiD, Minhua Su, Mercedes Bentata, Adi Gershon, Gillian KayiD, Maayan SaltoniD
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Abstract
Pre-mRNA splicing plays a crucial role in maintaining cellular homeostasis, with strict regulation required for processes such as cell cycle progression. SF3B1, a core component of the spliceosome, has emerged as a key player in alternative splicing regulation and is frequently mutated in cancer. Among these mutations, SF3B1K700E disrupts normal splicing patterns and deregulates cell cycle control. Here we profiled K562 erythroleukaemia cells expressing either wild-type or SF3B1K700E by RNA-seq and uncovered 763 high-confidence splicing alterations enriched for G2/M regulators, including ARPP19, ENSA, STAG2 and ECT2. Notably, increased inclusion of ARPP19 exon 2 produces the ARPP19-long isoform, which sustains PP2A-B55 inhibition and promotes mitotic progression. A core subset of the K700E-linked splicing changes re-appeared after siRNA-mediated SF3B1 depletion in HeLa cells, underscoring a mutation-dependent spliceosomal signature that transcends cell type. Pharmacological inhibition of DYRK1A (INDY) or broad serine/threonine phosphatases (okadaic acid) shifted ARPP19 exon 2 inclusion in the same direction as SF3B1K700E, pointing to a kinase-phosphatase signaling axis that influences these splice events. Functionally, ectopic expression of ARPP19-long accelerated mitotic exit, and high ARPP19-long abundance associated with poorer overall survival in the TCGA-AML cohort. Our findings highlight a connection between SF3B1-dependent splicing, cell cycle progression, and tumorigenesis, offering new insights into the molecular mechanisms underlying cancer-associated splicing dysregulation.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
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