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.

The design-build-test cycle has been rapidly accelerated by using in vitro protein expression systems, also known as cell-free protein expression.

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

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against type VI-B CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and subtypes, including over 7,000 putative type IV and VI Acrs not predicted by other algorithms. By performing a cell-free screen for Acr hits against type VI-B systems, we identified a potent inhibitor of Cas13b nucleases we named AcrVIB1. AcrVIB1 blocks Cas13b-mediated defense against a targeted plasmid and lytic phage, and its inhibitory function principally occurs upstream of ribonucleoprotein complex formation. Overall, our work helps expand the known Acr universe, aiding our understanding of the bacteria-phage arms race and the use of Acrs to control CRISPR technologies.

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.

Protocol for preparation of chemo-competent KL740
cells for propagation of P70a vectors

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.

Cell-free transcription-translation (TXTL) systems produce RNAs and proteins from added DNA. By coupling their production to a biochemical assay, these biomolecules can be rapidly and scalably characterized without the need for purification or cell culturing. Here, we describe how TXTL can be applied to characterize Cas13 nucleases from Type VI CRISPR-Cas systems. These nucleases employ guide RNAs to recognize complementary RNA targets, leading to the nonspecific collateral cleavage of nearby RNAs. In turn, RNA targeting by Cas13 has been exploited for numerous applications, including in vitro diagnostics, programmable gene silencing in eukaryotes, and sequence-specific antimicrobials. As part of the described method, we detail how to set up TXTL assays to measure on-target and collateral RNA cleavage by Cas13 as well as how to assay for putative anti-CRISPR proteins. Overall, the method should be useful for the characterization of Type VI CRISPR-Cas systems and their use in ranging applications.

Cellular lysates capable of transcription and translation have become valuable tools for prototyping genetic circuits, screening engineered functional parts, and producing biological components. Here we report that lysates derived from Yersinia pestis CO92− are functional and can utilize both the E. coli σ70 and the bacteriophage T7 promoter systems to produce green fluorescent protein (GFP). Because of the natural lifestyle of Y. pestis, lysates were produced from cultures grown at 21 °C, 26 °C, and 37 °C to mimic the infection cycle. Regardless of the promoter system the GFP production from 37 °C was the most productive and the 26 °C lysate was the least. When reactions are initiated with 5 nM of DNA, the GFP output of the 37 °C lysate is comparable with the productivity of other non-E. coli systems. The data we present demonstrate that, without genetic modification to enhance productivity, cell-free extracts from Y. pestis are functional and dependent on the temperature at which the bacterium was grown.

Many bacterial mechanisms for highly specific and sensitive detection of heavy metals and other hazards have been reengineered to serve as sensors. In some cases, these sensors have been implemented in cell-free expression systems, enabling easier design optimization and deployment in low-resource settings through lyophilization. Here, we apply the advantages of cell-free expression systems to optimize sensors based on three separate bacterial response mechanisms for arsenic, cadmium, and mercury. We achieved detection limits below the World Health Organization-recommended levels for arsenic and mercury and below the short-term US Military Exposure Guideline levels for all three. The optimization of each sensor was approached differently, leading to observations useful for the development of future sensors: (1) there can be a strong dependence of specificity on the particular cell-free expression system used, (2) tuning of relative concentrations of the sensing and reporter elements improves sensitivity, and (3) sensor performance can vary significantly with linear vs plasmid DNA. In addition, we show that simply combining DNA for the three sensors into a single reaction enables detection of each target heavy metal without any further optimization. This combined approach could lead to sensors that detect a range of hazards at once, such as a panel of water contaminants or all known variants of a target virus. For low-resource settings, such “all-hazard” sensors in a cheap, easy-to-use format could have high utility.