eLifeSynthetic BiologyPeer-reviewed journal article
Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria
Roswitha DolcemascoloiD, María Heras‐Hernández, Lucas GoiriziD, Roser Montagud‐MartíneziD, Alejandro Requena-MenéndeziD, Raúl RuiziD, Anna Pérez‐RàfolsiD, R. Anahí Higuera-RodrígueziD, Guillermo Pérez-RoperoiD, Wim VrankeniD, Tommaso MartelliiD, W. KaiseriD, Jos BuijsiD, Guillermo RodrigoiD
Abstract
The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, to implement combinatorial regulation, and to reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.
- organism: E. coli
- assay: proteomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
- FWCI
- 0.27
- Field percentile
- 45th percentile
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