Image-based screens identify regulators of endogenous Dvl2 biomolecular condensates
Schubert, A.; Heigwer, F.; Voloshanenko, O.; Scheeder, C.; Kranz, D.; Miersch, T.; Schmitt, B.; Kuhse, M.; Ragaller, F.; Winkler, N.; Gimenes, D.; Ordonez-Rueda, D.; Schwarz, J.; Stein, F.; Jaeger, D.; Engel, U.; Boutros, M.
Show abstract
Dishevelled (Dvl) proteins are essential transducers in Wnt signaling pathways, which have been implicated in development, stem cell maintenance, and human diseases such as cancer. Several studies have shown that Dvl proteins form dynamic biomolecular condensates. However, how cellular signals and cell states influence the formation of biomolecular condensates remains poorly understood. Here, we analyzed cells with endogenous Dvl2 condensates using image-based cell sorting in combination with phosphoproteomics and identified protein enrichment for Wnt/PCP signaling and the G2/M cell cycle transition. We then performed an image-based high-throughput screen to identify small molecule kinase inhibitors that affect Dvl2 liquid-liquid phase separation. Strikingly, CK1{delta}/{varepsilon} inhibition blocked Dvl2 condensate formation. Its effect on Wnt signaling was modulated in genetic epistasis experiments with loss-of-function alleles of APC, Axin1, and MCC. Our study highlights the interplay between post-translational modifications and condensate dynamics, opening new avenues for research on their role in cellular signaling and disease intervention.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The PMA Phorbol Ester Tumor Promoter Increases Canonical Wnt Signaling Via Macropinocytosis 96%
- Quantitative live-cell imaging and computational modelling yield novel insight into endogenous WNT/CTNNB1 signaling dynamics 95%
- Limited Dishevelled/Axin oligomerization determines efficiency of Wnt/β-catenin signal transduction 94%
Similar papers in this journal
"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.