Standardization of regulatory nodes for engineering heterologous gene expression: a feasibility study.
Pablo Iván NikelIlaria BenedettiNicolas Thilo WirthVictor de LorenzoBelén CallesPublished in: Microbial biotechnology (2022)
The potential of LacI/P trc , XylS/P m , AlkS/P alkB , CprK/P DB3 and ChnR/P chnB regulatory nodes, recruited from both Gram-negative and Gram-positive bacteria, as the source of parts for formatting expression cargoes following the Standard European Vector Architecture (SEVA) has been examined. The five expression devices, which cover most known regulatory configurations in bacteria were assembled within exactly the same plasmid backbone and bearing the different functional segments arrayed in an invariable DNA scaffold. Their performance was then analysed in an Escherichia coli strain of reference through the readout of a fluorescence reporter gene that contained strictly identical translation signal elements. This approach allowed us to describe and compare the cognate expression systems with quantitative detail. The constructs under scrutiny diverged considerably in their capacity, expression noise, inducibility and ON/OFF ratios. Inspection of such a variance exposed the different constraints that rule the optimal arrangement of functional DNA segments in each case. The data highlighted also the ease of standardizing inducer-responsive devices subject to transcriptional activation as compared to counterparts based on repressors. The study resulted in a defined collection of formatted expression cargoes lacking any cross talk while offering a panoply of choices to potential users and help interoperability of the specific constructs.
Keyphrases
- poor prognosis
- escherichia coli
- gene expression
- gram negative
- transcription factor
- multidrug resistant
- early stage
- machine learning
- electronic health record
- climate change
- risk assessment
- cystic fibrosis
- genome wide
- lymph node
- copy number
- circulating tumor cells
- drug delivery
- deep learning
- radiation therapy
- artificial intelligence
- big data
- nucleic acid