Synthetic Biology underpins advances in the bioeconomy
Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology.
As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.
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Journal articleGraham N, Junghans C, Downes R, et al., 2020,
SARS-CoV-2 infection, clinical features and outcome of COVID-19 in United Kingdom nursing homes, Journal of Infection, Vol: 81, Pages: 411-419, ISSN: 0163-4453
OBJECTIVES: To understand SARS-Co-V-2 infection and transmission in UK nursing homes in order to develop preventive strategies for protecting the frail elderly residents. METHODS: An outbreak investigation involving 394 residents and 70 staff, was carried out in 4 nursing homes affected by COVID-19 outbreaks in central London. Two point-prevalence surveys were performed one week apart where residents underwent SARS-CoV-2 testing and had relevant symptoms documented. Asymptomatic staff from three of the four homes were also offered SARS-CoV-2 testing. RESULTS: Overall, 26% (95% CI 22 to 31) of residents died over the two-month period. All-cause mortality increased by 203% (95% CI 70 to 336) compared with previous years. Systematic testing identified 40% (95% CI 35 to 46) of residents as positive for SARS-CoV-2, and of these 43% (95% CI 34 to 52) were asymptomatic and 18% (95% CI 11 to 24) had only atypical symptoms; 4% (95% CI -1 to 9) of asymptomatic staff also tested positive. CONCLUSIONS: The SARS-CoV-2 outbreak in four UK nursing homes was associated with very high infection and mortality rates. Many residents developed either atypical or no discernible symptoms. A number of asymptomatic staff members also tested positive, suggesting a role for regular screening of both residents and staff in mitigating future outbreaks.
Journal articleGreenig M, Melville A, Huntley D, et al., 2020,
Cross-sectional transcriptional analysis of the ageing murine heart, Frontiers in Molecular Biosciences, Vol: 7, Pages: 1-14, ISSN: 2296-889X
Cardiovascular disease accounts for millions of deaths each year and is currently the leading cause of mortality worldwide. The ageing process is clearly linked to cardiovascular disease, however, the exact relationship between ageing and heart function is not fully understood. Furthermore, a holistic view of cardiac ageing, linking features of early life development to changes observed in old age, has not been synthesized. Here, we re-purpose RNA-sequencing data previously-collected by our group, investigating gene expression differences between wild-type mice of different age groups that represent key developmental milestones in the murine lifespan. DESeq2’s generalized linear model was applied with two hypothesis6testing approaches to identify differentially-expressed (DE) genes, both between pairs of age groups and across mice of all ages. Pairwise comparisons identified genes associated with specific age transitions, while comparisons across all age groups identified a large set of genes associated with the ageing process more broadly. An unsupervised machine learning approach was then applied to extract common expression patterns from this set of age-associated genes. Sets of genes with both linear and non-linear expression trajectories were identified, suggesting that ageing not only involves the activation of gene expression programs unique to different age groups, but also the re-activation of gene expression programs from earlier ages. Overall, we present a comprehensive transcriptomic analysis of cardiac gene expression patterns across the entirety of the murine lifespan.
Journal articleMeccariello A, Krsticevic F, Colonna R, et al., 2020,
Engineered sex distortion in the global agricultural pest<i>Ceratitis capitata</i>
<jats:title>Abstract</jats:title><jats:p>Genetic sex ratio distorters have potential for the area-wide control of harmful insect populations. Endonucleases targeting the X-chromosome and whose activity is restricted to male gametogenesis have recently been pioneered as a means to engineer such traits. Here we enabled endogenous CRISPR/Cas9 and CRISPR/Cas12a activity during spermatogenesis of the Mediterranean fruit fly<jats:italic>Ceratitis capitata</jats:italic>, a worldwide agricultural pest of extensive economic significance. In the absence of a chromosome-level assembly, we analysed long and short-read genome sequencing data from males and females to identify two clusters of abundant and X-chromosome specific sequence repeats. When targeted by gRNAs in conjunction with Cas9 they yielded a significant and consistent distortion of the sex ratio in independent transgenic strains and a combination of distorters induced a strong bias towards males (~80%). Our results demonstrate the design of sex distorters in a non-model organism and suggest that strains with characteristics suitable for field application could be developed for a range of medically or agriculturally relevant insect species.</jats:p>
Journal articleSelvaraj P, Wenger EA, Bridenbecker D, et al., 2020,
Vector genetics, insecticide resistance and gene drives: An agent-based modeling approach to evaluate malaria transmission and elimination, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
- Author Web Link
- Citations: 10
Journal articleGarcia LDA, Jones PR, 2020,
In silicoco-factor balance estimation using constraint-based modelling informs metabolic engineering inEscherichia coli, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
- Author Web Link
- Citations: 6
Journal articleJi X-J, Ledesma-Amaro R, 2020,
Microbial lipid biotechnology to produce polyunsaturated fatty acids., Trends in Biotechnology, Vol: 38, Pages: 832-834, ISSN: 0167-7799
Lipids rich in polyunsaturated fatty acids are important nutrients. They are traditionally extracted from animals and plants but alternatively can be obtained from microbes through microbial lipid biotechnology. To make this process more economical, apart from strain engineering, the next frontier is through bioprocess and downstream innovation.
Journal articlePerin G, Fletcher T, Sagi-Kiss V, et al., 2020,
Calm on the surface, dynamic on the inside. Molecular homeostasis in response to regulatory and metabolic perturbation of<i>Anabaena</i>sp. PCC 7120 nitrogen metabolism
<jats:title>Abstract</jats:title><jats:p>Nitrogen is a key macro-nutrient required for the metabolism and growth of biological systems. Although multiple nitrogen sources can serve this purpose, they are all converted into ammonium/ammonia as a first step of assimilation. It is thus reasonable to expect that molecular parts involved in the transport of ammonium/ammonia across biological membranes (i.e. catalysed by AMT transporters) connect with the regulation of both nitrogen and central carbon metabolism. In order to test this hypothesis, we applied both (1) genetic (i.e. Δ<jats:italic>amt</jats:italic>mutation) and (2) environmental treatments to a target biological system, the cyanobacterium Anabaena sp. PCC 7120. Cyanobacteria have a key role in the global nitrogen cycle and thus represent a useful model system. The aim was to both (1) perturb sensing and low-affinity uptake of ammonium/ammonia and (2) induce multiple inner N states, followed by targeted quantification of key proteins, metabolites and enzyme activities, with experiments intentionally designed over a longer time-scale than the available studies in literature. We observed that the absence of AMT transporters triggered a substantial response at a whole-system level, affecting enzyme activities and the quantity of both proteins and metabolites, spanning both N and C metabolism. Moreover, the absence of AMT transporters left a molecular fingerprint indicating N-deficiency even under N replete conditions (i.e. greater GS activity, lower 2-OG content and faster nitrogenase activation upon N deprivation). Contrasting with all of the above dynamic adaptations was the striking near-complete lack of any externally measurable phenotype (i.e. growth, photosynthesis, pigments, metabolites). We thus conclude that this species evolved a highly robust and adaptable molecular network to maintain homeostasis, resulting in substantial internal but minimal external perturbations.
Journal articleKis Z, Kontoravdi K, Dey AK, et al., 2020,
Rapid development and deployment of high-volumevaccines for pandemic response, Journal of Advanced Manufacturing and Processing, Vol: 2, Pages: 1-10, ISSN: 2637-403X
Overcoming pandemics, such as the current Covid‐19 outbreak, requires the manufacture of several billion doses of vaccines within months. This is an extremely challenging task given the constraints in small‐scale manufacturing for clinical trials, clinical testing timelines involving multiple phases and large‐scale drug substance and drug product manufacturing. To tackle these challenges, regulatory processes are fast‐tracked, and rapid‐response manufacturing platform technologies are used. Here, we evaluate the current progress, challenges ahead and potential solutions for providing vaccines for pandemic response at an unprecedented scale and rate. Emerging rapid‐response vaccine platform technologies, especially RNA platforms, offer a high productivity estimated at over 1 billion doses per year with a small manufacturing footprint and low capital cost facilities. The self‐amplifying RNA (saRNA) drug product cost is estimated at below 1 USD/dose. These manufacturing processes and facilities can be decentralized to facilitate production, distribution, but also raw material supply. The RNA platform technology can be complemented by an a priori Quality by Design analysis aided by computational modeling in order to assure product quality and further speed up the regulatory approval processes when these platforms are used for epidemic or pandemic response in the future.
Journal articleStorch M, Haines MC, Baldwin GS, 2020,
DNA-BOT: a low-cost, automated DNA assembly platform for synthetic biology, Synthetic Biology, Vol: 5, Pages: ysaa010-ysaa010, ISSN: 2397-7000
Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process is now receiving serious attention. Automation will enable larger builds using less researcher time, while increasing the accessible design space. However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT). For this purpose, we developed an open-source software package and demonstrated the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, evaluating the promoter, ribosome binding site and gene order design space for a three-gene operon. All 88 constructs were assembled with high accuracy, at a consumables cost of $1.50-$5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT, making it accessible for most labs and democratizing automated DNA assembly.
Journal articleIrmisch P, Ouldridge TE, Seidel R, 2020,
Modelling DNA-strand displacement reactions in the presence of base-pair mismatches, Journal of the American Chemical Society, Vol: 142, Pages: 11451-11463, ISSN: 0002-7863
Toehold-mediated strand displacement is the most abundantly used method to achieve dynamic switching in DNA-based nanotechnology. An ‘invader’ strand binds to the ‘toehold’ overhang of a target strand and replaces a target-bound ’incumbent’ strand. Hereby, complementarity of the invader to the single-stranded toehold provides the energetic bias of the reaction. Despite the widespread use of strand displacement reactions for realizing dynamic DNA nanostructures, variants on the basic motif have not been completely characterized. Here we introduce a simple thermodynamic model, which is capable of quantitatively describing the kinetics of strand displacement reactions in the presence of mismatches, using a minimal set of parameters. Furthermore, our model highlights that base pair fraying and internal loop formation are important mechanisms when involving mismatches in the displacement process. Our model should provide a helpful tool for the rational design of strand-displacement reaction networks.
Journal articleXu X, Liu Y, Du G, et al., 2020,
Microbial chassis development for natural product biosynthesis, Trends in Biotechnology, Vol: 38, Pages: 779-796, ISSN: 0167-7799
Engineering microbial cells to efficiently synthesize high-value-added natural products has received increasing attention in recent years. In this review, we describe the pipeline to build chassis cells for natural product production. First, we discuss recently developed genome mining strategies for identifying and designing biosynthetic modules and compare the characteristics of different host microbes. Then, we summarize state-of-the-art systems metabolic engineering tools for reconstructing and fine-tuning biosynthetic pathways and transport mechanisms. Finally, we discuss the future prospects of building next-generation chassis cells for the production of natural products. This review provides theoretical guidance for the rational design and construction of microbial strains to produce natural products.
Journal articleWen Z, Ledesma-Amaro R, Lu M, et al., 2020,
Combined evolutionary engineering and genetic manipulation improve low pH tolerance and butanol production in a synthetic microbial Clostridium community, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 117, Pages: 2008-2022, ISSN: 0006-3592
- Author Web Link
- Citations: 17
Journal articlePrabhu AA, Ledesma-Amaro R, Lin CSK, et al., 2020,
Bioproduction of succinic acid from xylose by engineered Yarrowia lipolytica without pH control, Biotechnology for Biofuels, Vol: 13, ISSN: 1754-6834
BackgroundXylose is the most prevalent sugar available in hemicellulose fraction of lignocellulosic biomass (LCB) and of great interest for the green economy. Unfortunately, most of the cell factories cannot inherently metabolize xylose as sole carbon source. Yarrowia lipolytica is a non-conventional yeast that produces industrially important metabolites. The yeast is able to metabolize a large variety of substrates including both hydrophilic and hydrophobic carbon sources. However, Y. lipolytica lacks effective metabolic pathway for xylose uptake and only scarce information is available on utilization of xylose. For the economica feasibility of LCB-based biorefineries, effective utilization of both pentose and hexose sugars is obligatory.ResultsIn the present study, succinic acid (SA) production from xylose by Y. lipolytica was examined. To this end, Y. lipolytica PSA02004 strain was engineered by overexpressing pentose pathway cassette comprising xylose reductase (XR), xylitol dehydrogenase (XDH) and xylulose kinase (XK) gene. The recombinant strain exhibited a robust growth on xylose as sole carbon source and produced substantial amount of SA. The inhibition of cell growth and SA formation was observed above 60 g/L xylose concentration. The batch cultivation of the recombinant strain in a bioreactor resulted in a maximum biomass concentration of 7.3 g/L and SA titer of 11.2 g/L with the yield of 0.19 g/g. Similar results in terms of cell growth and SA production were obtained with xylose-rich hydrolysate derived from sugarcane bagasse. The fed-batch fermentation yielded biomass concentration of 11.8 g/L (OD600: 56.1) and SA titer of 22.3 g/L with a gradual decrease in pH below 4.0. Acetic acid was obtained as a main by-product in all the fermentations.ConclusionThe recombinant strain displayed potential for bioconversion of xylose to SA. Further, this study provided a new insight on conversion of lignocellulosic biomass into value-added products. To the best of o
Journal articleBroedel A, Rodrigues R, Jaramillo A, et al., 2020,
Accelerated evolution of a minimal 63-amino acid dual transcription factor, Science Advances, Vol: 6, Pages: 1-9, ISSN: 2375-2548
Transcription factors control gene expression in all life. This raises the question of what is the smallest protein that can support such activity. In nature, Cro from bacteriophage λ is one of the smallest known repressors (66 amino acids; a.a.) and activators are typically much larger (e.g. λ cI, 237 a.a.). Indeed, previous efforts to engineer a minimal activator from λ Cro resulted in no activity in vivo, in cells. In this study, we show that directed evolution results in a new Cro activator-repressor that functions as efficiently as λ cI, in vivo. To achieve this, we develop Phagemid-Assisted Continuous Evolution: PACEmid. We find that a peptide as small as 63 a.a. functions efficiently as an activator and/or repressor. To our knowledge, this is the smallest protein activator that enables polymerase recruitment, highlighting the capacity of transcription factors to evolve from very short peptide sequences.
Journal articlePrabhu AA, Thomas DJ, Ledesma-Amaro R, et al., 2020,
Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica, MICROBIAL CELL FACTORIES, Vol: 19
- Author Web Link
- Open Access Link
- Citations: 21
Journal articleBaig H, Fontanarrosa P, Kulkarni V, et al., 2020,
Synthetic biology open language visual (SBOL visual) version 2.2, Journal of Integrative Bioinformatics, Vol: 17, Pages: 1-85, ISSN: 1613-4516
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.
Journal articleKotidis P, Kontoravdi K, 2020,
Harnessing the potential of artificial neural networks for predicting protein glycosylation, Metabolic Engineering Communications, Vol: 10, ISSN: 2214-0301
Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
Journal articleTica J, Zhu T, Isalan M, 2020,
Dynamical model fitting to a synthetic positive feedback circuit in E. coli, Engineering Biology, Vol: 4, Pages: 25-31, ISSN: 2398-6182
Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single-equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time-series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.
Journal articleOuldridge T, Turberfield A, Mullor Ruiz I, et al., 2020,
Design of hidden thermodynamic driving for non-equilibrium systems via mismatch elimination during DNA strand displacement, Nature Communications, Vol: 11, ISSN: 2041-1723
Recent years have seen great advances in the development of synthetic self-assembling molecular systems. Designing out-of-equilibrium architectures, however, requires a more subtle control over the thermodynamics and kinetics of reactions. We propose a mechanism for enhancing the thermodynamic drive of DNA strand-displacement reactions whilst barely perturbing forward reaction rates: the introduction of mismatches within the initial duplex. Through a combination of experiment and simulation, we demonstrate that displacement rates are strongly sensitive to mismatch location and can be tuned by rational design. By placing mismatches away from duplex ends, the thermodynamic drive for a strand-displacement reaction can be varied without significantly affecting the forward reaction rate. This hidden thermodynamic driving motif is ideal for the engineering of non-equilibrium systems that rely on catalytic control and must be robust to leak reactions.
Journal articleArpino JAJ, Polizzi KM, 2020,
A modular method for directing protein self-assembly, ACS Synthetic Biology, Vol: 9, Pages: 993-1002, ISSN: 2161-5063
Proteins are versatile macromolecules with diverse structure, charge, and function. They are ideal building blocks for biomaterials for drug delivery, biosensing, or tissue engineering applications. Simultaneously, the need to develop green alternatives to chemical processes has led to renewed interest in multienzyme biocatalytic routes to fine, specialty, and commodity chemicals. Therefore, a method to reliably assemble protein complexes using protein-protein interactions would facilitate the rapid production of new materials. Here we show a method for modular assembly of protein materials using a supercharged protein as a scaffolding "hub" onto which target proteins bearing oppositely charged domains have been self-assembled. The physical properties of the material can be tuned through blending and heating and disassembly triggered using changes in pH or salt concentration. The system can be extended to the synthesis of living materials. Our modular method can be used to reliably direct the self-assembly of proteins using small charged tag domains that can be easily encoded in a fusion protein.
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