Muscleblind-like proteins use modular domains to localize RNAs by riding kinesins and docking to membranes

Ryan P. Hildebrandt, Kathryn R. Moss, Aleksandra Janusz-Kaminska, Luke A. Knudson, Lance T. Denes, Tanvi Saxena, Devi Prasad Boggupalli, Zhuangyue Li, Kun Lin, Gary J. Bassell () and Eric T. Wang ()
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Ryan P. Hildebrandt: Genetics Institute, University of Florida
Kathryn R. Moss: Emory University School of Medicine
Aleksandra Janusz-Kaminska: Emory University School of Medicine
Luke A. Knudson: Emory University School of Medicine
Lance T. Denes: Genetics Institute, University of Florida
Tanvi Saxena: Genetics Institute, University of Florida
Devi Prasad Boggupalli: Genetics Institute, University of Florida
Zhuangyue Li: Genetics Institute, University of Florida
Kun Lin: Emory University School of Medicine
Gary J. Bassell: Emory University School of Medicine
Eric T. Wang: Genetics Institute, University of Florida

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: Abstract RNA binding proteins (RBPs) act as critical facilitators of spatially regulated gene expression. Muscleblind-like (MBNL) proteins, implicated in myotonic dystrophy and cancer, localize RNAs to myoblast membranes and neurites through unknown mechanisms. We find that MBNL forms motile and anchored granules in neurons and myoblasts, and selectively associates with kinesins Kif1bα and Kif1c through its zinc finger (ZnF) domains. Other RBPs with similar ZnFs associate with these kinesins, implicating a motor-RBP specificity code. MBNL and kinesin perturbation leads to widespread mRNA mis-localization, including depletion of Nucleolin transcripts from neurites. Live cell imaging and fractionation reveal that the unstructured carboxy-terminal tail of MBNL1 allows for anchoring at membranes. An approach, termed RBP Module Recruitment and Imaging (RBP-MRI), reconstitutes kinesin- and membrane-recruitment functions using MBNL-MS2 coat protein fusions. Our findings decouple kinesin association, RNA binding, and membrane anchoring functions of MBNL while establishing general strategies for studying multi-functional, modular domains of RBPs.

Date: 2023
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DOI: 10.1038/s41467-023-38923-6

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