bioRxivBiochemistryPreprint
5-Azacytidine incorporation into mRNAs disrupts translation and induces ribosome collisions
Alexis B. RobersoniD, James MarksiD, Ruby Pitts, Bavavarshini Tamilselvam, Brian C. GriebiD, William P. TanseyiD, Sezen MeydaniD
Abstract
5-Azacytidine (5-AzaC) is a cytidine analog and is widely used to treat myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Although its therapeutic activity is primarily attributed to hypomethylation resulting from DNA incorporation, the majority of 5-AzaC is incorporated into RNA. However, the functional consequences of 5-AzaC incorporation into RNA have been unknown. Here, we show that 5-AzaC treatment of cells leads to inhibition of protein synthesis. Ribo-seq, Disome-seq, and RNA-seq in cells treated with 5-AzaC exhibit a time-dependent C-to-G transversion signature in mRNAs within 2 h of treatment. These transversion events are enriched within footprint positions corresponding to the A-site of monosomes or leading stalled ribosome in a disome complex. Consistently, ribosome and disome footprints are accumulated at sites with C-rich codons in the A-site, specifically with the codons containing a C in the second position. 5-AzaC activates the integrated stress response (ISR) and the ribotoxic stress response (RSR) in a GCN2- and ZAK-dependent manner, consistent with disome-mediated signaling. Furthermore, loss of the Ribosome Quality Control (RQC) factor, ZNF598, sensitizes cells to 5-AzaC. Collectively, our results support a model where 5-AzaC is rapidly incorporated into mRNAs, disrupts decoding, and triggers disome-mediated signaling pathways, which contribute to its cytotoxicity. These findings suggest that translation disruption represents an additional layer of 5-AzaCs mechanism of action, alongside its known DNA-mediated effects.
- organism: human
- assay: Ribo-seq
- assay: bulk RNA-seq
- research focus: cell signaling & trafficking
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 1
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