Dr. Ben Steil presents this Lightning Talk at SynBioBeta 2023 in the Reading, Writing, and Editing DNA category.
This webinar features the contributions of Strateos and Daicel Arbor Biosciences to the fields of protein engineering and drug discovery.
Abstract Certain CRISPR-Cas elements integrate into Tn7-like transposons, forming CRISPR-associated transposon (CAST) systems. How the activity of these systems is controlled in situ has remained largely unknown. Here we characterize the MerR-type transcriptional regulator Alr3614 that is encoded by one of the CAST (AnCAST) system genes in the genome of cyanobacterium Anabaena sp. PCC 7120. We identify a number of Alr3614 homologs across cyanobacteria and suggest naming these regulators CvkR for Cas V-K repressors. Alr3614/CvkR is translated from leaderless mRNA and represses the AnCAST core modules cas12k and tnsB directly, and indirectly the abundance of the tracr-CRISPR RNA. We identify a widely conserved CvkR binding motif 5’-AnnACATnATGTnnT-3’. Crystal structure of CvkR at 1.6 Å resolution reveals that it comprises distinct dimerization and potential effector-binding domains and that it assembles into a homodimer, representing a discrete structural subfamily of MerR regulators. CvkR repressors are at the core of a widely conserved regulatory mechanism that controls type V-K CAST systems.
In this webinar, we present a protein synthesis workflow leveraging myTXTL® with high-fidelity dsDNA gBlocks™ Gene Fragments from IDT.
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.
myTXTL can produce a range of biocatalysts with diverse functions, sizes, and cofactor requirements much faster than traditional in vivo expression.
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.
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