Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria.

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, implement combinatorial regulation, and 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.

Kinetic FRET Assay to Measure Binding-Induced Conformational Changes of Nucleic Acids.

The interaction of small molecules or proteins with RNA or DNA often involves changes in the nucleic acid (NA) folding and structure. A biophysical characterization of these processes helps us to understand the underlying molecular mechanisms. Here, we propose kinFRET (kinetics Förster resonance energy transfer), a real-time ensemble FRET methodology to measure binding and folding kinetics. With kinFRET, the kinetics of conformational changes of NAs (DNA or RNA) upon analyte binding can be directly followed via a FRET signal using a chip-based biosensor. We demonstrate the utility of this approach with two representative examples. First, we monitored the conformational changes of different formats of an aptamer (MN19) upon interaction with small-molecule analytes. Second, we characterized the binding kinetics of RNA recognition by tandem K homology (KH) domains of the human insulin-like growth factor II mRNA-binding protein 3 (IMP3), which reveals distinct kinetic contributions of the two KH domains. Our data demonstrate that kinFRET is well suited to study the kinetics and conformational changes of NA-analyte interactions.

Experiences with a training DSW knowledge model for early-stage researchers.

Background: Data management is fast becoming an essential part of scientific practice, driven by open science and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst data management plans (DMPs) are clear to data management experts and data stewards, understandings of their purpose and creation are often obscure to the producers of the data, which in academic environments are often PhD students. Methods: Within the RNAct EU Horizon 2020 ITN project, we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with computational or experimental backgrounds, in core data management principles. We collected feedback from the ESRs during this exercise. Results: Here, we introduce our new life-sciences training DMP template for young scientists. We report and discuss our experiences as principal investigators (PIs) and ESRs during this project and address the typical difficulties that are encountered in developing and understanding a DMP. Conclusions: We found that the DS-wizard can also be an appropriate tool for DMP training, to get terminology and concepts across to researchers. A full training in addition requires an upstream step to present basic DMP concepts and a downstream step to publish a dataset in a (public) repository. Overall, the DS-Wizard tool was essential for our DMP training and we hope our efforts can be used in other projects.