Nature Structural & Molecular BiologyMolecular BiologyPeer-reviewed journal article
The anti-cancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3' processing machinery
Liang LiuiD, Angela M YuiD, Xiuye WangiD, Lindsey V. SolesiD, Xueyi TengiD, Yi-Ling CheniD, Yoseop YooniD, Kristianna S.K. SarkaniD, Marielle Cárdenas Valdez, Johannes LinderiD, Whitney EnglandiD, Robert C. SpitaleiD, Zhaoxia YuiD, Ivan MarazziiD, Feng QiaoiD, Wei LiiD, Georg SeeligiD, Yongsheng ShiiD
Abstract
JTE-607 is a small molecule compound with anti-inflammation and anti-cancer activities. Upon entering the cell, it is hydrolyzed to Compound 2, which directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-mRNA 3' processing. Although CPSF73 is universally required for mRNA 3' end formation, we have unexpectedly found that Compound 2- mediated inhibition of pre-mRNA 3' processing is sequence-specific and that the sequences flanking the cleavage site (CS) are a major determinant for drug sensitivity. By using massively parallel in vitro assays, we have measured the Compound 2 sensitivities of over 260,000 sequence variants and identified key sequence features that determine drug sensitivity. A machine learning model trained on these data can predict the impact of JTE-607 on poly(A) site (PAS) selection and transcription termination genome-wide. We propose a biochemical model in which CPSF73 and other mRNA 3' processing factors bind to RNA of the CS region in a sequence-specific manner and the affinity of such interaction determines the Compound 2 sensitivity of a PAS. As the Compound 2-resistant CS sequences, characterized by U/A-rich motifs, are prevalent in PASs from yeast to human, the CS region sequence may have more fundamental functions beyond determining drug resistance. Together, our study not only characterized the mechanism of action of a compound with clinical implications, but also revealed a previously unknown and evolutionarily conserved sequence-specificity of the mRNA 3' processing machinery.
- organism: human
- organism: yeast
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: machine learning (classical)
- Citations
- 18
- FWCI
- 1.60
- Field percentile
- top 16%
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