A genetic toolkit and gene switches to limit Mycoplasma growth for a synthetic vaccine chassis

Mycoplasmas have exceptionally streamlined genomes and are strongly adapted to their many hosts, which provide them with essential nutrients. Owing to their relative genomic simplicity, Mycoplasmas have been used for the development of chassis to deploy tailored vaccines. However, the dearth of robust and precise toolkits for genomic manipulation and tight regulation has hindered any substantial advance. Herein we describe the construction of a robust genetic toolkit for M. pneumoniae, and its successful deployment to engineer synthetic gene switches that control and limit Mycoplasma growth, for biosafety containment applications. We found these synthetic gene circuits to be stable and robust in the long-term, in the context of a minimal cell. With this work, we lay a foundation to develop viable and robust biosafety systems to exploit a synthetic Mycoplasma chassis for live attenuated vaccines or even for live vectors for biotherapeutics.

Synthetic neuromorphic computing in living cells

Biological regulatory networks in cells and neuronal networks employ complex circuit topologies with highly interconnected nodes to perform sophisticated information processing. Despite the complexity of neuronal networks, their information processing and computational capabilities can be recapitulated using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Here, we argue that analogous to their revolutionary impact on computing, neuro-inspired models can similarly transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and can be readily reconfigurable for new tasks. We introduce neuromorphic design for synthetic gene circuits by first defining the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in the cellular milieu. Working in Escherichia coli cells, we experimentally demonstrated logarithmic scale analog multiplication using a single perceptgene. We modified perceptgene parameters (weights and biases) to create devices that compute a log-transformed negative rectifier encoding the minimum operation, log-transformed positive rectifier encoding the maximum operation, and log-transformed average of analog inputs. We then created multi-layer perceptgene circuits that compute a majority function, perform analog-to-digital conversion, and implement a ternary switch. Experimental and theoretical analysis showed that our approach enables circuit optimization via artificial intelligence algorithms such as gradient descent and backpropagation. Realizing neural-like computing in the noisy resource-limited environments of individual cells marks an important step towards synthetic biological systems that can be engineered effectively via supervised ANN optimization algorithms.

Differentially optimized cell-free buffer enables robust expression from unprotected linear DNA in exonuclease-deficient extracts

ABSTRACTThe use of linear DNA templates in cell-free systems promises to accelerate the prototyping and engineering of synthetic gene circuits. A key challenge is that linear templates are rapidly degraded by exonucleases present in cell extracts. Current approaches tackle the problem by adding exonuclease inhibitors and DNA-binding proteins to protect the linear DNA, requiring additional time- and resource-intensive steps. Here, we delete the recBCD exonuclease gene cluster from the Escherichia coli BL21 genome. We show that the resulting cell-free systems, with buffers optimized specifically for linear DNA, enable near-plasmid levels of expression from σ70 promoters in linear DNA templates without employing additional protection strategies. When using linear or plasmid DNA templates at the buffer calibration step, we found that the optimal potassium glutamate concentrations obtained when using linear DNA were consistently lower than those obtained when using plasmid DNA for the same extract. We demonstrate the robustness of the exonuclease deficient extracts across seven different batches and a wide range of experimental conditions. Finally, we illustrate the use of the ΔrecBCD extracts for two applications: toehold switch characterization and enzyme screening. Our work provides a simple, efficient, and costeffective solution for using linear DNA templates in cell-free systems and highlights the importance of specifically tailoring buffer composition for the final experimental setup. Our data also suggest that similar exonuclease deletion strategies can be applied to other species suitable for cell-free synthetic biology.

A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins

ABSTRACTDeep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of Streptococcus pyogenes Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and in vitro affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.

Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research

Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers. Mathematical modeling guides the rational design of synthetic gene circuits. These systems are then assembled into unified constructs from existing and/or modified genetic components from a range of organisms. In this chapter, we describe modeling tools for the design and characterization of chemical- and light-inducible synthetic gene circuits in different organisms and highlight how synthetic gene circuits are advancing biomedical research. Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.

A programmable probiotic encapsulation system enhances therapeutic delivery in vivo

Recent advances in therapeutic modulation of human microbiota have driven new efforts to engineer living microbial medicines using synthetic biology. However, a long-standing challenge for live bacterial therapies is balancing the high dose required to achieve robust efficacy with the potential for sepsis. Here, we developed a genetically encoded microbial encapsulation system with tunable and dynamic expression of surface capsular polysaccharides to enhance therapeutic delivery. Following a synthetic small RNA knockdown screen of the capsular biosynthesis pathway, we constructed synthetic gene circuits that regulate bacterial encapsulation based on sensing the levels of environmental inducer, bacterial density, and blood pH. The induced encapsulation system enabled tunable immunogenicity and survivability of the probiotic Escherichia coli, resulting in increased maximum tolerated dose and enhanced efficacy in murine cancer models. Furthermore, triggering in situ encapsulation was found to increase microbial translocation between mouse tumors, leading to efficacy in distal tumors. The programmable encapsulation system demonstrates a new approach to control microbial therapeutic profiles in vivo using synthetic biology.

Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals

Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or leaky biological signals. Here we present a workflow to quantitatively define digitizer performance and predict responses to different input signals. Using a combination of signal-to-noise ratio (SNR), area under a receiver operating characteristic curve (AUC), and fold change (FC), we evaluate three small-molecule inducible digitizer designs demonstrating FC up to 508x and SNR up to 3.77 dB. To study their behavior further and improve modularity, we develop a mixed phenotypic/mechanistic model capable of predicting digitizer configurations that amplify a synNotch cell-to-cell communication signal (Δ SNR up to 2.8 dB). We hope the metrics and modeling approaches here will facilitate incorporation of these digitizers into other systems while providing an improved workflow for gene circuit characterization.