Quantification of the gene silencing performances of rationally-designed synthetic small RNAs.
Small RNAs (sRNAs) are genetic tools for the efficient and specific tuning of target genes expression in bacteria. Inspired by naturally occurring sRNAs, recent works proposed the use of artificial sRNAs in synthetic biology for predictable repression of the desired genes. Their potential was demonstrated in several application fields, such as metabolic engineering and bacterial physiology studies. Guidelines for the rational design of novel sRNAs have been recently proposed. According to these guidelines, in this work synthetic sRNAs were designed, constructed and quantitatively characterized in Escherichia coli. An sRNA targeting the reporter gene RFP was tested by measuring the specific gene silencing when RFP was expressed at different transcription levels, under the control of different promoters, in different strains, and in single-gene or operon architecture. The sRNA level was tuned by using plasmids maintained at different copy numbers. Results demonstrated that RFP silencing worked as expected in an sRNA and mRNA expression-dependent fashion. A mathematical model was used to support sRNA characterization and to estimate an efficiency-related parameter that can be used to compare the performance of the designed sRNA. Gene silencing was also successful when RFP was placed in a two-gene synthetic operon, while the non-target gene (GFP) in the operon was not considerably affected. Finally, silencing was evaluated for another designed sRNA targeting the endogenous lactate dehydrogenase gene. The quantitative study performed in this work elucidated interesting performance-related and context-dependent features of synthetic sRNAs that will strongly support predictable gene silencing in disparate basic or applied research studies.
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Ilaria
I
Massaiu
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Lorenzo
L
Pasotti
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Michela
M
Casanova
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Nicolò
N
Politi
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Susanna
S
Zucca
Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Maria Gabriella
MG
Cusella De Angelis
Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, via Ferrata 5, 27100 Pavia, Italy.
Paolo
P
Magni
Lactate dehydrogenase; Mathematical modelling; Operon; Quantitative characterization; Small RNA; Synthetic biology