bioRxivMolecular BiologyPreprint
NAT10 supports CAG repeat-associated mRNA stabilization and is associated with hepatic metabolic reprogramming during spaceflight
Ryota KurimotoiD, Tomoaki TanakaiD, Izumi OhnoiD, Tomoki ChibaiD, Takahide MatsushimaiD, Yutaro UchidaiD, Yuki NaitoiD, Hiroshi AsaharaiD
Abstract
The regulatory factors underlying posttranscriptional adaptation to spaceflight are incompletely understood. We identified NAT10, an RNA acetyltransferase, among RNA modification enzymes significantly upregulated in spaceflight-exposed mouse liver. Transcriptome-wide correlation analysis linked NAT10 expression to an RNA-metabolism module enriched for CAG repeat density in coding sequences. Actinomycin D chase assays showed that NAT10 knockdown destabilized CAG repeat-containing transcripts, with DDX17 serving as the primary validated target; DCP1A showed a directionally consistent pattern. Wild-type NAT10 re-expression fully rescued DDX17 mRNA stability, the acetyltransferase-dead G641E mutant rescued mRNA stability to a comparable extent, and the K290A helicase-domain mutant failed to rescue, consistent with a requirement for helicase domain integrity, whereas acetyltransferase activity was not strictly required under these experimental conditions. RNA immunoprecipitation further supported preferential NAT10 association with CAG repeat-containing mRNAs, with reduced association after single-site CAGCAG deletion in DDX17 and DCP1A reporters. Gene set enrichment analysis associated NAT10 upregulation in spaceflight liver with suppressed fatty acid beta-oxidation and induced cholesterol biosynthesis; NAT10 depletion in hepatocellular carcinoma cells selectively opposed the cholesterol-associated, but not the broader metabolic, component of this signature. These findings support a model linking NAT10 to CAG repeat-associated mRNA stabilization in the context of hepatic metabolic and posttranscriptional adaptation to spaceflight.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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