1
|
Tica J, Chen H, Luo S, Chen M, Isalan M. Engineering Tunable, Low Latency Spatial Computation with Dual Input Quorum Sensing Promoters. ACS Synth Biol 2024; 13:1750-1761. [PMID: 38781598 PMCID: PMC11197083 DOI: 10.1021/acssynbio.4c00068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/10/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Quorum sensing signals have evolved for population-level signaling in bacterial communities and are versatile tools for engineering cell-cell signaling in synthetic biology projects. Here, we characterize the spatial diffusion of a palette of quorum sensing signals and find that their diffusion in agar can be predicted from their molecular weight with a simple power law. We also engineer novel dual- and multi-input promoters that respond to quorum-sensing diffusive signals for use in engineered genetic systems. We engineer a promoter scaffold that can be adapted for activation and repression by multiple diffusers simultaneously. Lastly, we combine the knowledge on diffusion dynamics with the novel genetic components to build a new generation of spatial, stripe-forming systems with a simplified design, improved robustness, tuneability, and response time.
Collapse
Affiliation(s)
- Jure Tica
- Department
of Life Sciences, Imperial College London, London SW7 2AZ, U.K.
| | - Haobin Chen
- Department
of Life Sciences, Imperial College London, London SW7 2AZ, U.K.
| | - Shulei Luo
- Department
of Life Sciences, Imperial College London, London SW7 2AZ, U.K.
| | - Manman Chen
- Department
of Life Sciences, Imperial College London, London SW7 2AZ, U.K.
| | - Mark Isalan
- Department
of Life Sciences, Imperial College London, London SW7 2AZ, U.K.
- Imperial
College Centre for Synthetic Biology, Imperial
College London, London SW7 2AZ, U.K.
| |
Collapse
|
2
|
Sechkar K, Steel H, Perrino G, Stan GB. A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits. Nat Commun 2024; 15:1981. [PMID: 38438391 PMCID: PMC10912777 DOI: 10.1038/s41467-024-46410-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 02/27/2024] [Indexed: 03/06/2024] Open
Abstract
Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.
Collapse
Affiliation(s)
- Kirill Sechkar
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Harrison Steel
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Giansimone Perrino
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| |
Collapse
|
3
|
Cao Z, Liu Z, Mao X. Application of Quorum Sensing in Metabolic Engineering. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:5062-5074. [PMID: 36967589 DOI: 10.1021/acs.jafc.3c00176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Metabolic engineering is widely utilized in the food and other fields and has the benefits of low-cost substrates, eco-friendly fermentation processes, and efficient substrate synthesis. Microbial synthesis by metabolic engineering requires maintaining the productive capacity of the microorganism. Moreover, economic reasons limit the use of inducers in the exogenous synthesis pathway. Most unicellular microorganisms can interact by emitting signaling molecules; this mechanism, known as quorum sensing (QS), is an autoinduced system of microorganisms. With the deepening research on QS systems of different microorganisms, its components are widely used to regulate the metabolic synthesis of microorganisms as a dynamic regulatory system. In this Review, we described the typical bacterial QS mechanisms. Then, we summarized various regulatory strategies for QS and their applications to metabolic engineering. Finally, we underlined the potential for QS modularity in future metabolic engineering and suggested stimulating research on fungal QS systems.
Collapse
Affiliation(s)
- Zhuoning Cao
- Qingdao Key Laboratory of Food Biotechnology, College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
- Key Laboratory of Biological Processing of Aquatic Products, China National Light Industry, Qingdao 266404, China
| | - Zhen Liu
- Qingdao Key Laboratory of Food Biotechnology, College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
- Key Laboratory of Biological Processing of Aquatic Products, China National Light Industry, Qingdao 266404, China
| | - Xiangzhao Mao
- Qingdao Key Laboratory of Food Biotechnology, College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
- Laboratory for Marine Drugs and Bioproducts of Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
- Key Laboratory of Biological Processing of Aquatic Products, China National Light Industry, Qingdao 266404, China
| |
Collapse
|
4
|
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: 3] [Impact Index Per Article: 1.5] [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.
Collapse
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,
| |
Collapse
|
5
|
Casas A, Bultelle M, Motraghi C, Kitney R. Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology. Front Bioeng Biotechnol 2022; 9:785131. [PMID: 35083201 PMCID: PMC8784771 DOI: 10.3389/fbioe.2021.785131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022] Open
Abstract
We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).
Collapse
Affiliation(s)
- Alexis Casas
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Matthieu Bultelle
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Charles Motraghi
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Richard Kitney
- Department of Bioengineering, Imperial College London, London, United Kingdom
| |
Collapse
|
6
|
Jin L, Nawab S, Xia M, Ma X, Huo Y. Context-dependency of synthetic minimal promoters in driving gene expression: a case study. Microb Biotechnol 2019; 12:1476-1486. [PMID: 31578818 PMCID: PMC6801132 DOI: 10.1111/1751-7915.13489] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 09/09/2019] [Indexed: 12/13/2022] Open
Abstract
Synthetic promoters are considered ideal candidates in driving robust gene expression. Most of the available synthetic promoters are minimal promoters, for which the upstream sequence of the 5' end of the core region is usually excluded. Although the upstream sequence has been shown to mediate transcription of natural promoters, its impact on synthetic promoters has not been widely studied. Here, a library of chromosomal DNA fragments is randomly fused with the 5' end of the J23119 synthetic promoter, and the transcriptional performance of the promoter is evaluated through β-galactosidase assay, fluorescence intensity and chemical biosynthesis. Results show that changes in the upstream sequence can induce significant variation in the promoter strength of up to 5.8-fold. The effect is independent of the length of the insertions and the number of potential transcription factor binding sites. Several DNA fragments that are able to enhance the transcription of both the natural and the synthetic promoters are identified. This study indicates that the synthetic minimal promoters are susceptible to the surrounding sequence context. Therefore, the upstream sequence should be treated as an indispensable component in the design and application of synthetic promoters, or as an independent genetic part for the fine-tuning of gene expression.
Collapse
Affiliation(s)
- Liyuan Jin
- Key Laboratory of Molecular Medicine and BiotherapySchool of Life ScienceBeijing Institute of Technology5 South Zhongguancun Street, Haidian DistrictBeijing100081China
| | - Said Nawab
- Key Laboratory of Molecular Medicine and BiotherapySchool of Life ScienceBeijing Institute of Technology5 South Zhongguancun Street, Haidian DistrictBeijing100081China
| | - Mengli Xia
- Key Laboratory of Molecular Medicine and BiotherapySchool of Life ScienceBeijing Institute of Technology5 South Zhongguancun Street, Haidian DistrictBeijing100081China
| | - Xiaoyan Ma
- Key Laboratory of Molecular Medicine and BiotherapySchool of Life ScienceBeijing Institute of Technology5 South Zhongguancun Street, Haidian DistrictBeijing100081China
| | - Yi‐Xin Huo
- Key Laboratory of Molecular Medicine and BiotherapySchool of Life ScienceBeijing Institute of Technology5 South Zhongguancun Street, Haidian DistrictBeijing100081China
- UCLA Institute for Technology Advancement (Suzhou)10 Yueliangwan Road, Suzhou Industrial ParkSuzhou215123China
| |
Collapse
|
7
|
Modular Diversity of the BLUF Proteins and Their Potential for the Development of Diverse Optogenetic Tools. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9183924] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Organisms can respond to varying light conditions using a wide range of sensory photoreceptors. These photoreceptors can be standalone proteins or represent a module in multidomain proteins, where one or more modules sense light as an input signal which is converted into an output response via structural rearrangements in these receptors. The output signals are utilized downstream by effector proteins or multiprotein clusters to modulate their activity, which could further affect specific interactions, gene regulation or enzymatic catalysis. The blue-light using flavin (BLUF) photosensory module is an autonomous unit that is naturally distributed among functionally distinct proteins. In this study, we identified 34 BLUF photoreceptors of prokaryotic and eukaryotic origin from available bioinformatics sequence databases. Interestingly, our analysis shows diverse BLUF-effector arrangements with a functional association that was previously unknown or thought to be rare among the BLUF class of sensory proteins, such as endonucleases, tet repressor family (tetR), regulators of G-protein signaling, GAL4 transcription family and several other previously unidentified effectors, such as RhoGEF, Phosphatidyl-Ethanolamine Binding protein (PBP), ankyrin and leucine-rich repeats. Interaction studies and the indexing of BLUF domains further show the diversity of BLUF-effector combinations. These diverse modular architectures highlight how the organism’s behaviour, cellular processes, and distinct cellular outputs are regulated by integrating BLUF sensing modules in combination with a plethora of diverse signatures. Our analysis highlights the modular diversity of BLUF containing proteins and opens the possibility of creating a rational design of novel functional chimeras using a BLUF architecture with relevant cellular effectors. Thus, the BLUF domain could be a potential candidate for the development of powerful novel optogenetic tools for its application in modulating diverse cell signaling.
Collapse
|
8
|
Forsberg KJ, Bhatt IV, Schmidtke DT, Javanmardi K, Dillard KE, Stoddard BL, Finkelstein IJ, Kaiser BK, Malik HS. Functional metagenomics-guided discovery of potent Cas9 inhibitors in the human microbiome. eLife 2019; 8:e46540. [PMID: 31502535 PMCID: PMC6739867 DOI: 10.7554/elife.46540] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 08/09/2019] [Indexed: 12/12/2022] Open
Abstract
CRISPR-Cas systems protect bacteria and archaea from phages and other mobile genetic elements, which use small anti-CRISPR (Acr) proteins to overcome CRISPR-Cas immunity. Because Acrs are challenging to identify, their natural diversity and impact on microbial ecosystems are underappreciated. To overcome this discovery bottleneck, we developed a high-throughput functional selection to isolate ten DNA fragments from human oral and fecal metagenomes that inhibit Streptococcus pyogenes Cas9 (SpyCas9) in Escherichia coli. The most potent Acr from this set, AcrIIA11, was recovered from a Lachnospiraceae phage. We found that AcrIIA11 inhibits SpyCas9 in bacteria and in human cells. AcrIIA11 homologs are distributed across diverse bacteria; many distantly-related homologs inhibit both SpyCas9 and a divergent Cas9 from Treponema denticola. We find that AcrIIA11 antagonizes SpyCas9 using a different mechanism than other previously characterized Type II-A Acrs. Our study highlights the power of functional selection to uncover widespread Cas9 inhibitors within diverse microbiomes.
Collapse
Affiliation(s)
- Kevin J Forsberg
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Ishan V Bhatt
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Danica T Schmidtke
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Kamyab Javanmardi
- Department of Molecular Biosciences and Institute of Cellular and Molecular BiologyUniversity of Texas at AustinAustinUnited States
| | - Kaylee E Dillard
- Department of Molecular Biosciences and Institute of Cellular and Molecular BiologyUniversity of Texas at AustinAustinUnited States
| | - Barry L Stoddard
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Ilya J Finkelstein
- Department of Molecular Biosciences and Institute of Cellular and Molecular BiologyUniversity of Texas at AustinAustinUnited States
- Center for Systems Biology and Synthetic BiologyUniversity of Texas at AustinAustinUnited States
| | - Brett K Kaiser
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of BiologySeattle UniversitySeattleUnited States
| | - Harmit S Malik
- Division of Basic SciencesFred Hutchinson Cancer Research CenterSeattleUnited States
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research CenterSeattleUnited States
| |
Collapse
|
9
|
Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load. Processes (Basel) 2019. [DOI: 10.3390/pr7030119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details.
Collapse
|
10
|
Pasotti L, Bellato M, Politi N, Casanova M, Zucca S, Cusella De Angelis MG, Magni P. A Synthetic Close-Loop Controller Circuit for the Regulation of an Extracellular Molecule by Engineered Bacteria. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:248-258. [PMID: 30489274 DOI: 10.1109/tbcas.2018.2883350] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Feedback control is ubiquitous in biological systems. It can also play a crucial role in the design of synthetic circuits implementing novel functions in living systems, to achieve self-regulation of gene expression, noise reduction, rise time decrease, or adaptive pathway control. Despite in vitro, in vivo, and ex vivo implementations have been successfully reported, the design of biological close-loop systems with quantitatively predictable behavior is still a major challenge. In this work, we tested a model-based bottom-up design of a synthetic close-loop controller in engineered Escherichia coli, aimed to automatically regulate the concentration of an extracellular molecule, N-(3-oxohexanoyl)-L-homoserine lactone (HSL), by rewiring the elements of heterologous quorum sensing/quenching networks. The synthetic controller was successfully constructed and experimentally validated. Relying on mathematical model and experimental characterization of individual regulatory parts and enzymes, we evaluated the predictability of the interconnected system behavior in vivo. The culture was able to reach an HSL steady-state level of 72 nM, accurately predicted by the model, and showed superior capabilities in terms of robustness against cell density variation and disturbance rejection, compared with a corresponding open-loop circuit. This engineering-inspired design approach may be adopted for the implementation of other close-loop circuits for different applications and contribute to decreasing trial-and-error steps.
Collapse
|
11
|
Massaiu I, Pasotti L, Sonnenschein N, Rama E, Cavaletti M, Magni P, Calvio C, Herrgård MJ. Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains. Microb Cell Fact 2019; 18:3. [PMID: 30626384 PMCID: PMC6325765 DOI: 10.1186/s12934-018-1052-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 12/29/2018] [Indexed: 12/15/2022] Open
Abstract
Background Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design. Results Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain. Conclusions This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.
Collapse
Affiliation(s)
- Ilaria Massaiu
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. 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 Pasotti
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. 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
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Erlinda Rama
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Matteo Cavaletti
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Paolo Magni
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. 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
| | - Cinzia Calvio
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| |
Collapse
|
12
|
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: 6] [Impact Index Per Article: 0.9] [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.
Collapse
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
| |
Collapse
|
13
|
Engstrom MD, Pfleger BF. Transcription control engineering and applications in synthetic biology. Synth Syst Biotechnol 2017; 2:176-191. [PMID: 29318198 PMCID: PMC5655343 DOI: 10.1016/j.synbio.2017.09.003] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 09/26/2017] [Accepted: 09/26/2017] [Indexed: 12/18/2022] Open
Abstract
In synthetic biology, researchers assemble biological components in new ways to produce systems with practical applications. One of these practical applications is control of the flow of genetic information (from nucleic acid to protein), a.k.a. gene regulation. Regulation is critical for optimizing protein (and therefore activity) levels and the subsequent levels of metabolites and other cellular properties. The central dogma of molecular biology posits that information flow commences with transcription, and accordingly, regulatory tools targeting transcription have received the most attention in synthetic biology. In this mini-review, we highlight many past successes and summarize the lessons learned in developing tools for controlling transcription. In particular, we focus on engineering studies where promoters and transcription terminators (cis-factors) were directly engineered and/or isolated from DNA libraries. We also review several well-characterized transcription regulators (trans-factors), giving examples of how cis- and trans-acting factors have been combined to create digital and analogue switches for regulating transcription in response to various signals. Last, we provide examples of how engineered transcription control systems have been used in metabolic engineering and more complicated genetic circuits. While most of our mini-review focuses on the well-characterized bacterium Escherichia coli, we also provide several examples of the use of transcription control engineering in non-model organisms. Similar approaches have been applied outside the bacterial kingdom indicating that the lessons learned from bacterial studies may be generalized for other organisms.
Collapse
Affiliation(s)
- Michael D. Engstrom
- Genetics-Biotechnology Center, University of Wisconsin-Madison School of Medicine and Public Health, USA
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison College of Engineering, USA
| | - Brian F. Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison College of Engineering, USA
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, USA
| |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Avcilar-Kucukgoze I, Ignatova Z. Rewiring host activities for synthetic circuit production: a translation view. Biotechnol Lett 2016; 39:25-31. [DOI: 10.1007/s10529-016-2229-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 09/30/2016] [Indexed: 11/30/2022]
|
16
|
Experimental measurements and mathematical modeling of biological noise arising from transcriptional and translational regulation of basic synthetic gene circuits. J Theor Biol 2016; 395:153-160. [DOI: 10.1016/j.jtbi.2016.02.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 01/28/2016] [Accepted: 02/03/2016] [Indexed: 01/08/2023]
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Massaiu I, Pasotti L, Casanova M, Politi N, Zucca S, Cusella De Angelis MG, Magni P. Quantification of the gene silencing performances of rationally-designed synthetic small RNAs. SYSTEMS AND SYNTHETIC BIOLOGY 2015; 9:107-23. [PMID: 26279705 DOI: 10.1007/s11693-015-9177-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 06/12/2015] [Accepted: 07/27/2015] [Indexed: 11/30/2022]
Abstract
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.
Collapse
Affiliation(s)
- Ilaria 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 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 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 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 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, via Ferrata 5, 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, via Ferrata 5, 27100 Pavia, Italy
| |
Collapse
|