U6 snRNA m6A modification is required for accurate and efficient splicing of C. elegans and human pre-mRNAs.

pre-mRNA splicing is a critical feature of eukaryotic gene expression. Both cis- and trans-splicing rely on accurately recognising splice site sequences by spliceosomal U snRNAs and associated proteins. Spliceosomal snRNAs carry multiple RNA modifications with the potential to affect different stages of pre-mRNA splicing. Here, we show that the conserved U6 snRNA m6A methyltransferase METT-10 is required for accurate and efficient cis- and trans-splicing of C. elegans pre-mRNAs. The absence of METT-10 in C. elegans and METTL16 in humans primarily leads to alternative splicing at 5' splice sites with an adenosine at +4 position. In addition, METT-10 is required for splicing of weak 3' cis- and trans-splice sites. We identified a significant overlap between METT-10 and the conserved splicing factor SNRNP27K in regulating 5' splice sites with +4A. Finally, we show that editing endogenous 5' splice site +4A positions to +4U restores splicing to wild-type positions in a mett-10 mutant background, supporting a direct role for U6 snRNA m6A modification in 5' splice site recognition. We conclude that the U6 snRNA m6A modification is important for accurate and efficient pre-mRNA splicing.

U6 snRNA m6A modification is required for accurate and efficient cis- and trans-splicing of C. elegans mRNAs.

pre-mRNA splicing is a critical feature of eukaryotic gene expression. Many eukaryotes use cis-splicing to remove intronic sequences from pre-mRNAs. In addition to cis-splicing, many organisms use trans-splicing to replace the 5' ends of mRNAs with a non-coding spliced-leader RNA. Both cis- and trans-splicing rely on accurately recognising splice site sequences by spliceosomal U snRNAs and associated proteins. Spliceosomal snRNAs carry multiple RNA modifications with the potential to affect different stages of pre-mRNA splicing. Here, we show that m6A modification of U6 snRNA A43 by the RNA methyltransferase METT-10 is required for accurate and efficient cis- and trans-splicing of C. elegans pre-mRNAs. The absence of U6 snRNA m6A modification primarily leads to alternative splicing at 5' splice sites. Furthermore, weaker 5' splice site recognition by the unmodified U6 snRNA A43 affects splicing at 3' splice sites. U6 snRNA m6A43 and the splicing factor SNRNP27K function to recognise an overlapping set of 5' splice sites with an adenosine at +4 position. Finally, we show that U6 snRNA m6A43 is required for efficient SL trans-splicing at weak 3' trans-splice sites. We conclude that the U6 snRNA m6A modification is important for accurate and efficient cis- and trans-splicing in C. elegans.

Genomic basis of European ash tree resistance to ash dieback fungus.

Populations of European ash trees (Fraxinus excelsior) are being devastated by the invasive alien fungus Hymenoscyphus fraxineus, which causes ash dieback. We sequenced whole genomic DNA from 1,250 ash trees in 31 DNA pools, each pool containing trees with the same ash dieback damage status in a screening trial and from the same seed-source zone. A genome-wide association study identified 3,149 single nucleotide polymorphisms (SNPs) associated with low versus high ash dieback damage. Sixty-one of the 192 most significant SNPs were in, or close to, genes with putative homologues already known to be involved in pathogen responses in other plant species. We also used the pooled sequence data to train a genomic prediction model, cross-validated using individual whole genome sequence data generated for 75 healthy and 75 damaged trees from a single seed source. The model's genomic estimated breeding values (GEBVs) allocated these 150 trees to their observed health statuses with 67% accuracy using 10,000 SNPs. Using the top 20% of GEBVs from just 200 SNPs, we could predict observed tree health with over 90% accuracy. We infer that ash dieback resistance in F. excelsior is a polygenic trait that should respond well to both natural selection and breeding, which could be accelerated using genomic prediction.