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Bellato M, Frusteri Chiacchiera A, Salibi E, Casanova M, De Marchi D, Castagliuolo I, Cusella De Angelis MG, Magni P, Pasotti L. CRISPR Interference Modules as Low-Burden Logic Inverters in Synthetic Circuits. Front Bioeng Biotechnol 2022; 9:743950. [PMID: 35155399 PMCID: PMC8831695 DOI: 10.3389/fbioe.2021.743950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
CRISPR and CRISPRi systems have revolutionized our biological engineering capabilities by enabling the editing and regulation of virtually any gene, via customization of single guide RNA (sgRNA) sequences. CRISPRi modules can work as programmable logic inverters, in which the dCas9-sgRNA complex represses a target transcriptional unit. They have been successfully used in bacterial synthetic biology to engineer information processing tasks, as an alternative to the traditionally adopted transcriptional regulators. In this work, we investigated and modulated the transfer function of several model systems with specific focus on the cell load caused by the CRISPRi logic inverters. First, an optimal expression cassette for dCas9 was rationally designed to meet the low-burden high-repression trade-off. Then, a circuit collection was studied at varying levels of dCas9 and sgRNAs targeting three different promoters from the popular tet, lac and lux systems, placed at different DNA copy numbers. The CRISPRi NOT gates showed low-burden properties that were exploited to fix a high resource-consuming circuit previously exhibiting a non-functional input-output characteristic, and were also adopted to upgrade a transcriptional regulator-based NOT gate into a 2-input NOR gate. The obtained data demonstrate that CRISPRi-based modules can effectively act as low-burden components in different synthetic circuits for information processing.
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Affiliation(s)
- Massimo Bellato
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Angelica Frusteri Chiacchiera
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | - Elia Salibi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | - Michela Casanova
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | - Davide De Marchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | | | - Maria Gabriella Cusella De Angelis
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | - Lorenzo Pasotti
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- *Correspondence: Lorenzo Pasotti,
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Dwijayanti A, Storch M, Stan GB, Baldwin GS. A modular RNA interference system for multiplexed gene regulation. Nucleic Acids Res 2022; 50:1783-1793. [PMID: 35061908 PMCID: PMC8860615 DOI: 10.1093/nar/gkab1301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
The rational design and realisation of simple-to-use genetic control elements that are modular, orthogonal and robust is essential to the construction of predictable and reliable biological systems of increasing complexity. To this effect, we introduce modular Artificial RNA interference (mARi), a rational, modular and extensible design framework that enables robust, portable and multiplexed post-transcriptional regulation of gene expression in Escherichia coli. The regulatory function of mARi was characterised in a range of relevant genetic contexts, demonstrating its independence from other genetic control elements and the gene of interest, and providing new insight into the design rules of RNA based regulation in E. coli, while a range of cellular contexts also demonstrated it to be independent of growth-phase and strain type. Importantly, the extensibility and orthogonality of mARi enables the simultaneous post-transcriptional regulation of multi-gene systems as both single-gene cassettes and poly-cistronic operons. To facilitate adoption, mARi was designed to be directly integrated into the modular BASIC DNA assembly framework. We anticipate that mARi-based genetic control within an extensible DNA assembly framework will facilitate metabolic engineering, layered genetic control, and advanced genetic circuit applications.
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Affiliation(s)
| | - Marko Storch
- Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK
| | - Guy-Bart Stan
- Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK.,Department of Bioengineering, Bessemer Building, South Kensington Campus, Imperial College London, London SW7 2AZ, UK
| | - Geoff S Baldwin
- Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK.,Department of Life Sciences, Sir Alexander Fleming Building, South Kensington Campus, Imperial College London, London SW7 2AZ, UK
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Pasotti L, Bellato M, Casanova M, Zucca S, Cusella De Angelis MG, Magni P. Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up. J Biol Eng 2017; 11:50. [PMID: 29255481 PMCID: PMC5729246 DOI: 10.1186/s13036-017-0090-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 11/21/2017] [Indexed: 01/09/2023] Open
Abstract
Background The study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden. Methods Here, we analyzed a set of synthetic transcriptional cascades in Escherichia coli. We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively. Results Among the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour. Conclusions Superior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering. Electronic supplementary material The online version of this article (10.1186/s13036-017-0090-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lorenzo Pasotti
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.,Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Massimo Bellato
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.,Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Michela Casanova
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.,Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Susanna Zucca
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.,Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | | | - Paolo Magni
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.,Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
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Pasotti L, Zucca S, Casanova M, Micoli G, Cusella De Angelis MG, Magni P. Fermentation of lactose to ethanol in cheese whey permeate and concentrated permeate by engineered Escherichia coli. BMC Biotechnol 2017; 17:48. [PMID: 28577554 PMCID: PMC5457738 DOI: 10.1186/s12896-017-0369-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 05/22/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Whey permeate is a lactose-rich effluent remaining after protein extraction from milk-resulting cheese whey, an abundant dairy waste. The lactose to ethanol fermentation can complete whey valorization chain by decreasing dairy waste polluting potential, due to its nutritional load, and producing a biofuel from renewable source at the same time. Wild type and engineered microorganisms have been proposed as fermentation biocatalysts. However, they present different drawbacks (e.g., nutritional supplements requirement, high transcriptional demand of recombinant genes, precise oxygen level, and substrate inhibition) which limit the industrial attractiveness of such conversion process. In this work, we aim to engineer a new bacterial biocatalyst, specific for dairy waste fermentation. RESULTS We metabolically engineered eight Escherichia coli strains via a new expression plasmid with the pyruvate-to-ethanol conversion genes, and we carried out the selection of the best strain among the candidates, in terms of growth in permeate, lactose consumption and ethanol formation. We finally showed that the selected engineered microbe (W strain) is able to efficiently ferment permeate and concentrated permeate, without nutritional supplements, in pH-controlled bioreactor. In the conditions tested in this work, the selected biocatalyst could complete the fermentation of permeate and concentrated permeate in about 50 and 85 h on average, producing up to 17 and 40 g/l of ethanol, respectively. CONCLUSIONS To our knowledge, this is the first report showing efficient ethanol production from the lactose contained in whey permeate with engineered E. coli. The selected strain is amenable to further metabolic optimization and represents an advance towards efficient biofuel production from industrial waste stream.
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Affiliation(s)
- Lorenzo 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, 27100, Pavia, Italy
| | - Susanna Zucca
- 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, 27100, Pavia, Italy
| | - Michela 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, 27100, Pavia, Italy
| | - Giuseppina Micoli
- Centro di Ricerche Ambientali, IRCCS Fondazione Salvatore Maugeri, via Salvatore Maugeri 10, 27100, Pavia, Italy
| | | | - Paolo Magni
- 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, 27100, Pavia, Italy.
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Lynch MD. Into new territory: improved microbial synthesis through engineering of the essential metabolic network. Curr Opin Biotechnol 2016; 38:106-11. [DOI: 10.1016/j.copbio.2016.01.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 01/24/2016] [Accepted: 01/26/2016] [Indexed: 01/21/2023]
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Casanova M, Pasotti L, Zucca S, Politi N, Massaiu I, Calvio C, Cusella De Angelis MG, Magni P. A BioBrick™-Compatible Vector for Allelic Replacement Using the XylE Gene as Selection Marker. Biol Proced Online 2016; 18:6. [PMID: 26877712 PMCID: PMC4752771 DOI: 10.1186/s12575-016-0036-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 02/08/2016] [Indexed: 01/10/2023] Open
Abstract
Background Circular plasmid-mediated homologous recombination is commonly used for marker-less allelic replacement, exploiting the endogenous recombination machinery of the host. Common limitations of existing methods include high false positive rates due to mutations in counter-selection genes, and limited applicability to specific strains or growth media. Finally, solutions compatible with physical standards, such as the BioBrick™, are not currently available, although they proved to be successful in the design of other replicative or integrative plasmids. Findings We illustrate pBBknock, a novel BioBrick™-compatible vector for allelic replacement in Escherichia coli. It includes a temperature-sensitive replication origin and enables marker-less genome engineering via two homologous recombination events. Chloramphenicol resistance allows positive selection of clones after the first event, whereas a colorimetric assay based on the xylE gene provides a simple way to screen clones in which the second recombination event occurs. Here we successfully use pBBknock to delete the lactate dehydrogenase gene in E. coli W, a popular host used in metabolic engineering. Conclusions Compared with other plasmid-based solutions, pBBknock has a broader application range, not being limited to specific strains or media. We expect that pBBknock will represent a versatile solution both for practitioners, also among the iGEM competition teams, and for research laboratories that use BioBrick™-based assembly procedures. Electronic supplementary material The online version of this article (doi:10.1186/s12575-016-0036-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Michela Casanova
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Lorenzo Pasotti
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Susanna Zucca
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Nicolò Politi
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Ilaria Massaiu
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | - Cinzia Calvio
- Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
| | | | - Paolo Magni
- Department of Electrical, Computer and Biomedical Engineering, Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, University of Pavia, 27100 Pavia, Italy ; Centre for Health Technologies, University of Pavia, 27100 Pavia, Italy
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