Back

The splicing paralogues SNRPB and SNRPN control differential metabolic states.

Polat Haas, F.; Villalba, A.; Rusina, P.; Gopalan, A.; Fritz, H.; Akhmetkaliyev, A.; Ruehle, F.; Einsiedel, A.; Szczepinska, A.; Kielisch, F.; Chen, J.-X.; Nguyen, S.; Schmidlin, T.; Hippenmeyer, S.; Bailicata, M. F.; Keller Valsecchi, C. I.

2026-02-11 molecular biology
10.64898/2026.02.11.705284 bioRxiv
Show abstract

Gene duplication underlies evolutionary innovation, yet many paralogues remain highly similar, raising questions about their functional divergence and physiological relevance. The spliceosomal Sm core protein SNRPB and its mammalian-specific paralogue SNRPN share over 90% sequence identity, but their distinct expression patterns - SNRPB being ubiquitous and SNRPN confined to the brain - suggest specialized functions. Why mammals have two different spliceosomes has remained obscure. Here, we generated isogenic human cell lines expressing ectopically either SNRPB or SNRPN exclusively and found that SNRPN stabilizes transcripts involved in energy metabolism and mitochondrial function, leading to increased mitochondrial abundance and oxygen consumption. Despite similar spliceosomal interactomes, SNRPN more strongly associates with the PRMT5 methylosome complex and exhibits dynamic arginine methylation in its C-terminal region that is sensitive to translation inhibition and amino acid availability. The SNRPN-dependent transcriptome responds to translation inhibition by stabilizing long, intron-rich genes involved in amino acid and energy metabolism. Our findings reveal a nutrient-sensitive, methylation-dependent mechanism that differentiates the two paralogues. This suggests that SNRPN functions as a metabolic-specialized spliceosomal subunit thereby providing tissue-specific adaptation of RNA processing in mammals.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.