SynBioFromLeukipposInstitute

Toward programmed therapeutics
Advances in synthetic biology are enabling the development of new gene and cell therapies. Kitada et al. review recent successes in areas such as cancer immunotherapy and stem cell therapy, point out the limitations of current approaches, and describe prospects for using synthetic biology to overcome these challenges. Broader adoption of these therapies requires precise, context-specific control over cellular behavior. Gene circuits can be built to give sophisticated control over cellular behaviors so that therapeutic functions can, for example, be programmed to activate in response to disease biomarkers.

Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks.

Synthetic biology has enabled the creation of biological reconfigurable circuits, which perform multiple functions monopolizing a single biological machine; Such a system can switch between different behaviours in response to environmental cues. Previous work has demonstrated switchable dynamical behaviour employing reconfigurable logic gate genetic networks. Here we describe a computational framework for reconfigurable circuits in E.coli using combinations of logic gates, and also propose the biological implementation. The proposed system is an oscillator that can exhibit tunability of frequency and amplitude of oscillations. Further, the frequency of operation can be changed optogenetically. Insilico analysis revealed that two-component light systems, in response to light within a frequency range, can be used for modulating the frequency of the oscillator or stopping the oscillations altogether. Computational modelling reveals that mixing two colonies of E.coli oscillating at different frequencies generates spatial beat patterns. Further, we show that these oscillations more robustly respond to input perturbations compared to the base oscillator, to which the proposed oscillator is a modification. Compared to the base oscillator, the proposed system shows faster synchronization in a colony of cells for a larger region of the parameter space. Additionally, the proposed oscillator also exhibits lesser synchronization error in the transient period after input perturbations. This provides a strong basis for the construction of synthetic reconfigurable circuits in bacteria and other organisms, which can be scaled up to perform functions in the field of time dependent drug delivery with tunable dosages, and sets the stage for further development of circuits with synchronized population level behaviour.

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

Antibiotic resistance represents one of the most challenging global health threats in our society. Antimicrobial peptides (AMPs) represent promising alternatives to conventional antibiotics for the treatment of drug-resistant infections. However, they are limited by their high manufacturing cost. Engineering living organisms represents a promising approach to produce such molecules in an inexpensive manner. Here, we genetically modified the yeast Pichia pastoris to produce the prototypical AMP apidaecin Ia using a fusion protein approach that leverages the beneficial properties (e.g., stability) of human serum albumin. The peptide was successfully isolated from the fusion protein construct, purified, and demonstrated to have bioactivity against Escherichia coli. To demonstrate this approach as a manufacturing solution to AMPs, we scaled-up production in bioreactors to generate high AMP yields. We envision that this system could lead to improved AMP biomanufacturing platforms.

Human induced pluripotent stem cells (hiPSCs) can be used to mass produce surrogates of human tissues, enabling new advances in drug screening, disease modeling, and cell therapy. Recent developments in clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 genome editing technology use homology-directed repair (HDR) to efficiently generate custom hiPSC lines harboring a variety of genomic insertions and deletions. Thus, hiPSCs that encode an endogenous protein fused to a fluorescent reporter protein can be rapidly created by employing CRISPR/Cas9 genome editing, enhancing HDR efficiency and optimizing homology arm length. These fluorescently tagged hiPSCs can be used to visualize protein function and dynamics in real time as cells proliferate and differentiate. Given that nearly any intracellular protein can be fluorescently tagged, this system serves as a powerful tool to facilitate new discoveries across many biological disciplines. In this unit, we present protocols for the design, generation, and monoclonal expansion of genetically customized hiPSCs encoding fluorescently tagged endogenous proteins.

The SynBioHub repository (synbiohub.org) is an open-source software project which facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g. the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.

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.

Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions; and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries.
AVAILABILITY AND IMPLEMENTATION:
The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk.

In this review, we have summarized the research effort into the development of unnatural base pairs beyond standard Watson-Crick (WC) base pairs for synthetic biology. Prior to introducing our research results, we present investigations by four outstanding groups in the field. Their research results demonstrate the importance of shape complementarity and stacking ability as well as hydrogen-bonding (H-bonding) patterns for unnatural base pairs. On the basis of this research background, we developed unnatural base pairs consisting of imidazo[5',4':4.5]pyrido[2,3-d]pyrimidines and 1,8-naphthyridines, i.e., Im : Na pairs. Since Im bases are recognized as ring-expanded purines and Na bases are recognized as ring-expanded pyrimidines, Im : Na pairs are expected to satisfy the criteria of shape complementarity and enhanced stacking ability. In addition, these pairs have four non-canonical H-bonds. Because of these preferable properties, ImNN : NaOO, one of the Im : Na pairs, is recognized as a complementary base pair in not only single nucleotide insertion, but also the PCR.

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the "push-pull-block" strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the Saccharomyces cerevisiae transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2-0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5'-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels and longer genes are associated with lower queuing levels. Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.

Generation of induced pluripotent stem cells typically requires the ectopic expression of transcription factors to reactivate the pluripotency network. However, it remains largely unclear what remodeling events on endogenous chromatin trigger reprogramming toward induced pluripotent stem cells (iPSCs). Toward this end, we employed CRISPR activation to precisely target and remodel endogenous gene loci of Oct4 and Sox2. Interestingly, we found that single-locus targeting of Sox2 was sufficient to remodel and activate Sox2, which was followed by the induction of other pluripotent genes and establishment of the pluripotency network. Simultaneous remodeling of the Oct4 promoter and enhancer also triggered reprogramming. Authentic pluripotent cell lines were established in both cases. Finally, we showed that targeted manipulation of histone acetylation at the Oct4 gene locus could also initiate reprogramming. Our study generated authentic iPSCs with CRISPR activation through precise epigenetic remodeling of endogenous loci and shed light on how targeted chromatin remodeling triggers pluripotency induction.

Within five years, the CRISPR-Cas system has emerged as the dominating tool for genome engineering, while also changing the speed and efficiency of metabolic engineering in conventional (Saccharomyces cerevisiae and Schizosaccharomyces pombe) and non-conventional (Yarrowia lipolytica, Pichia pastoris syn. Komagataella phaffii, Kluyveromyces lactis, Candida albicans and C. glabrata) yeasts.

Especially in S. cerevisiae, an extensive toolbox of advanced CRISPR-related applications has been established, including crisprTFs and gene drives. The comparison of innovative CRISPR-Cas expression strategies in yeasts presented here may also serve as guideline to implement and refine CRISPR-Cas systems for highly efficient genome editing in other eukaryotic organisms.

The SynBioHub repository (synbiohub.org) is an open-source software project which facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g. the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.