bioRxivMolecular BiologyPreprint
The splicing paralogues SNRPB and SNRPN control differential metabolic states.
Feyza Polat HaasiD, Ana VillalbaiD, Polina RusinaiD, Anusha B. GopalaniD, Hector Fritz, Azamat AkhmetkaliyeviD, Frank RühleiD, Anna EinsiedeliD, Anna SzczepińskaiD, Fridolin KielischiD, Jia-Xuan CheniD, Susanne NguyeniD, Thierry SchmidliniD, Simon HippenmeyeriD, Maria Felicia BasilicataiD, Claudia Isabelle Keller ValsecchiiD
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.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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