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.

The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, implement combinatorial regulation, and reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.

Plant synthetic biology research requires diverse bioparts that facilitate the redesign and construction of new-to-nature biological devices or systems in plants. Limited by few well-characterized bioparts for plant chassis, the development of plant synthetic biology lags behind that of its microbial counterpart. Here, we constructed a web-based Plant Synthetic BioDatabase (PSBD), which currently categorizes 1677 catalytic bioparts and 384 regulatory elements and provides information on 309 species and 850 chemicals. Online bioinformatics tools including local BLAST, chem similarity, phylogenetic analysis, and visual strength are provided to assist with the rational design of genetic circuits for manipulation of gene expression in planta. We demonstrated the utility of the PSBD by functionally characterizing taxadiene synthase 2 and its quantitative regulation in tobacco leaves. More powerful synthetic devices were then assembled to amplify the transcriptional signals, enabling enhanced expression of flavivirus non-structure 1 proteins in plants. The PSBD is expected to be an integrative and user-centered platform that provides a one-stop service for diverse applications in plant synthetic biology research.

The field of synthetic biology emerged a few decades ago, following some key works of researchers in the USA, Europe, and the Far East. It reached Israel through academia and a few years later it finally got the attention of industry, venture capitals, and government authorities, especially the Israeli Innovation Authority, hoping to encourage entrepreneurs to establish startups in this field. Here we provide an overview of the activity of the field of synthetic biology in Israel, including historical notes, current strategy, prospects and developments, and further insight that are relevant to any stakeholders in the synthetic biology field.

In this study, we designed a novel genetic circuit sensitive to Cd2+, Zn2+ and Pb2+ by mimicking the CadA/CadR operon system mediated heavy metal homeostasis mechanism of Pseudomonas aeruginosa. The regular DNA motifs on natural operon were reconfigured and coupled with the enhanced Green Fluorescent Protein (eGFP) reporter to develop a novel basic NOT type logic gate CadA/CadR-eGFP to respond metal ions mentioned above. A Genetically Engineered Microbial (GEM)-based biosensor (E.coli-BL21:pJET1.2-CadA/CadR-eGFP) was developed by cloning the chemically synthesised CadA/CadR-eGFP gene circuit into pJET1.2-plasmid and transforming into Escherichia coli (E. coli)-BL21 bacterial cells.
The GEM-based biosensor cells indicated the reporter gene expression in the presence of Cd2+, Zn2+ and Pb2+ either singly or in combination. Further, the same biosensor cells calibrated for fluorescent intensity against heavy metal concentration generated linear graphs for Cd2+, Zn2+ and Pb2+ with the R2 values of 0.9809, 0.9761 and 0.9758, respectively as compared to non-specific metals, Fe3+ (0.0373), AsO43- (0.3825) and Ni2+ (0.8498) making our biosensor suitable for the detection of low concentration of the former metal ions in the range of 1-6 ppb. Furthermore, the GEM based biosensor cells were growing naturally within the concentration range of heavy metals, at 37 °C and optimum pH = 7.0 in the medium, resembling the characteristics of wildtype E.coli.
Finally, the novel GEM based biosensor cells developed in this study can be applied for detection of targeted heavy metals in low concentration ranges (1-6 ppb) at normal bacterial physiological conditions.

Although cell-free protein expression has been widely used for the synthesis of single proteins, cell-free synthetic biology has rapidly expanded to new, more complex applications. One such application is the prototyping or implementation of complex genetic networks involving the expression of multiple proteins at precise ratios, often from different plasmids. However, expression of multiple proteins from multiple plasmids may inadvertently result in unexpected, off-target changes to the levels of the proteins being expressed, a phenomenon termed plasmid crosstalk. Here, we show that the effects of plasmid crosstalk─even at the qualitative level of increases vs decreases in protein expression─depend on the concentration of plasmids in the reaction and the type of transcriptional machinery involved in the expression. This crosstalk can have a significant impact on genetic circuitry function and even interpretation of simple experimental results and thus should be taken into consideration during the development of cell-free applications.

The early clinical success and subsequent US Food and Drug Administration approval of chimeric antigen receptor (CAR)-T cell therapy for leukemia and lymphoma affirm that engineered T cells can be a powerful treatment for hematologic malignancies. Yet this success has not been replicated in solid tumors. Numerous challenges emerged from clinical experience and well-controlled preclinical animal models must be met to enable safe and efficacious CAR-T cell therapy in solid tumors. Here, we review recent advances in bioengineering strategies developed to enhance CAR-T cell therapy in solid tumors, focusing on targeted single-gene perturbation, genetic circuits design, cytokine engineering, and interactive biomaterials. These bioengineering approaches present a unique set of tools that synergize with CAR-T cells to overcome obstacles in solid tumors and achieve robust and long-lasting therapeutic efficacy.

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.

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Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.