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N 6 -Methyladenosine mRNA modification regulates transcripts stability associated with cotton fiber elongation.

Kun XingZhao LiuLe LiuJie ZhangGhulam QanmberYe WangLisen LiuYu GuChangsheng ZhangShuaijie LiYan ZhangZuoren Yang
Published in: The Plant journal : for cell and molecular biology (2023)
N 6 -Methyladenosine (m 6 A) is the most abundant methylation modification in eukaryotic mRNA. The discovery of the dynamic and reversible regulatory mechanism of m 6 A has greatly promoted the development of m 6 A-led epitranscriptomics. However, the characterization of m 6 A in cotton fiber is still unknown. Here, we reveal the potential link between m 6 A modification and cotton fiber elongation by parallel m 6 A-immunoprecipitation-sequencing (m 6 A-seq) and RNA-seq analysis of fibers from the short fiber mutants Ligonliness-2 (Li 2 ) and wild-type (WT). This study demonstrated a higher level of m 6 A in the Li 2 mutant, with the enrichment of m 6 A modifications in the stop codon, 3'-untranslated region and coding sequence regions than in WT cotton. In the correlation analysis between genes containing differential m 6 A modifications and differentially expressed genes, we identified several genes that could potentially regulate fiber elongation, including cytoskeleton, microtubule binding, cell wall and transcription factors (TFs). We further confirmed that the methylation of m 6 A affected the mRNA stability of these fiber elongation-related genes including the TF GhMYB44, which showed the highest expression level in the RNA-seq data and m 6 A methylation in the m 6 A-seq data. Next, the overexpression of GhMYB44 reduces fiber elongation, whereas the silencing of GhMYB44 produces longer fibers. In summary, these results uncover that m 6 A methylation regulated the expression of genes related to fiber development by affecting mRNA's stability, ultimately affecting cotton fiber elongation.
Keyphrases
  • rna seq
  • genome wide
  • single cell
  • transcription factor
  • dna methylation
  • binding protein
  • wild type
  • high throughput
  • cell proliferation
  • deep learning
  • dna binding
  • climate change
  • genome wide identification