Back

RpS12-mediated induction of the Xrp1short isoform links ribosomal protein mutations to cell competition

Potiri, M.; Tsakiri, E.; Kontogiannidi, K.; Loizou, M.; Skoulakis, E. M. C.; Samiotaki, M.; Kafasla, P.; Kiparaki, M.

2025-06-15 developmental biology
10.1101/2025.06.15.659587 bioRxiv
Show abstract

Cell competition, a universal yet enigmatic phenomenon, eliminates less-fit cells via interactions with their neighbors. It was originally described in Drosophila mosaics, where heterozygous ribosomal protein (Rp+/-) mutant cells are eliminated by wild-type neighbors. The transcription factor Xrp1 mediates most of the Rp+/- -associated phenotypes, including reduced competitiveness and translation. Although RpS12 is required for Xrp1 induction in Rp+/- cells, the mechanism remained unresolved. We demonstrate that RpS12, via alternative splicing, induces the Xrp1 short (Xrp1short) isoform expression in Rp+/- cells, which is both necessary and sufficient for their elimination. Strikingly, RpS12 overexpression in wild-type cells is sufficient to induce Xrp1short expression and confer a "loser" phenotype. While Xrp1long isoform is not required in Rp+/- cells, expression of either Xrp1 isoform is sufficient to promote the loser status in wild-type cells. We further identify Syncrip, an RNA-binding protein reduced in Rp+/- cells, as a critical Xrp1 suppressor; its depletion in wild-type cells activates Xrp1-dependent competition. Our findings establish RpS12s specialized function in Xrp1short promotion, not proteotoxic stress, as the primary driver in Rp+/- cells, providing new perspectives that challenge and refine prevailing models. Our work contributes in long-standing questions about ribosomal protein-linked fitness surveillance and provides insights into ribosomopathy pathologies.

Published in Cell Reports (predicted rank #6) · training set

Matching journals

The top 2 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.