Abstract Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Bottom-up approaches in creating artificial cells that can mimic natural cells have significant implications for both basic research and translational application. Among various artificial cell models, liposome is one of the most sophisticated systems. By encapsulating proteins and associated biomolecules, they can functionally reconstitute foundational features of biological cells, such as the ability to divide, communicate, and undergo shape deformation. Yet constructing liposome artificial cells from the genetic level, which is central to generate self-sustained systems remains highly challenging. Indeed, many studies have successfully established the expression of gene-coded proteins inside liposomes. Further, recent endeavors to build a direct integration of gene-expressed proteins for reconstituting molecular functions and phenotypes in liposomes have also significantly increased. Thus, this review presents the development of liposome-based artificial cells to demonstrate the process of gene-expressed proteins and their reconstitution to perform desired molecular and cell-like functions. The molecular and cellular phenotypes discussed here include the self-production of membrane phospholipids, division, shape deformation, self-DNA/RNA replication, fusion, and intercellular communication. Together, this review gives a comprehensive overview of gene-expressing liposomes that can stimulate further research of this technology and achieve artificial cells with superior properties in the future.

Abstract Cell-free expression systems provide a suite of tools that are used in applications from sensing to biomanufacturing. One of these applications is genetic circuit prototyping, where the lack of cloning is required and a high degree of control over reaction components and conditions enables rapid testing of design candidates. Many studies have shown utility in the approach for characterizing genetic regulation elements, simple genetic circuit motifs, protein variants or metabolic pathways. However, variability in cell-free expression systems is a known challenge, whether between individuals, laboratories, instruments, or batches of materials. While the issue of variability has begun to be quantified and explored, little effort has been put into understanding the implications of this variability. For genetic circuit prototyping, it is unclear when and how significantly variability in reaction activity will impact qualitative assessments of genetic components, e.g. relative activity between promoters. Here, we explore this question by assessing DNA titrations of seven genetic circuits of increasing complexity using reaction conditions that ostensibly follow the same protocol but vary by person, instrument and material batch. Although the raw activities vary widely between the conditions, by normalizing within each circuit across conditions, reasonably consistent qualitative performance emerges for the simpler circuits. For the most complex case involving expression of three proteins, we observe a departure from this qualitative consistency, offering a provisional cautionary line where normal variability may disrupt reliable reuse of prototyping results. Our results also suggest that a previously described closed loop controller circuit may help to mitigate such variability, encouraging further work to design systems that are robust to variability. Graphical Abstract

Gene-editing technologies, including the widespread usage of CRISPR endonucleases, have the potential for clinical treatments of various human diseases. Due to the rapid mutations of SARS-CoV-2, specific and effective prevention and treatment by CRISPR toolkits for coronavirus disease 2019 (COVID-19) are urgently needed to control the current pandemic spread. Here, we designed Type III CRISPR endonuclease antivirals for coronaviruses (TEAR-CoV) as a therapeutic to combat SARS-CoV-2 infection. We provided a proof of principle demonstration that TEAR-CoV-based RNA engineering approach leads to RNA-guided transcript degradation both in vitro and in eukaryotic cells, which could be used to broadly target RNA viruses. We report that TEAR-CoV not only cleaves SARS-CoV-2 genome and mRNA transcripts, but also degrades live influenza A virus (IAV), impeding viral replication in cells and in mice. Moreover, bioinformatics screening of gRNAs along RNA sequences reveals that a group of five gRNAs (hCoV-gRNAs) could potentially target 99.98% of human coronaviruses. TEAR-CoV also exerted specific targeting and cleavage of common human coronaviruses. The fast design and broad targeting of TEAR-CoV may represent a versatile antiviral approach for SARS-CoV-2 or potentially other emerging human coronaviruses.

In the fight against antimicrobial resistance, bacteriophages are a promising alternative to antibiotics. However, due to their narrow spectra, phage therapy requires the careful matching between the host and bacteriophage to be effective. Despite our best efforts, nature remains as the only source of novel phage specificity. Directed evolution can potentially open an avenue for engineering phage specificity and improving qualities of phages that are not strongly selected for in their natural environments but are important for therapeutic applications. In this work, we present a strategy that generates large libraries of replication-competent phage variants directly from synthetic DNA fragments, with no restriction on their host specificity. Using the T7 bacteriophage as a proof-of-concept, we created a large library of tail fiber mutants with at least 107 unique variants. From this library, we identified mutants that have broadened specificity as evidenced by their novel lytic activity against Yersinia enterocolitica, a strain that the wildtype T7 was unable to lyse. Using the same concept, mutants with improved lytic efficiency and characteristics, such as lytic condition tolerance and resistance suppression, were also identified. However, the observed limitations in altering host specificity by tail fiber mutagenesis suggest that other bottlenecks could be of equal or even greater importance.