Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding
Liu, Y.; Reisbitzer, A.; Doresic, D.; Hasenauer, J.; Krauss, S.; Tchumatchenko, T.
Show abstract
RNA-binding proteins (RBP) are important regulators of RNA metabolism. In neurode-generative disorders such as Huntingtons Disease (HD), disrupted RBP-RNA interactions contribute to neuronal dysfunction. One such RBP, Midline 1 (MID1), has been shown to aberrantly associate with mutant huntingtin (Htt) RNA, enhancing its translation, yet the mechanism driving this effect remains unknown. Here, we develop a computational model to understand the role of MID1. Based on previously published data, our model predicts that MID1 increases the stability of the Htt RNA. We experimentally validate this prediction, showing that overexpression of MID1 significantly prolongs the half-life of mutant Htt RNA. Furthermore, we evaluate model refinements, including clustering of MID1-bound RNA, which allow capturing all key observations in the data. Together, we provide a data-driven framework that underlines the importance of RBP-RNA interaction in post-transcriptional regulation. This framework also shows how individual molecular reactions jointly determine RNA stability and protein levels in HD.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments 96%
- Toward Single-Cell Control: Noise-Robust Perfect Adaptation in Biomolecular Systems 95%
- Characterization, modelling and mitigation of gene expression burden in mammalian cells 95%
Similar papers in this journal
- Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle 95%
- Nearly maximal information gain due to time integration in central dogma reactions 94%
- Spatially coordinated collective phosphorylation filters spatiotemporal noises for precise circadian timekeeping 94%
"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.