bioRxiv - Synthetic Biology最新文献

{"title":"Multiplexing bacteriocin synthesis to kill and prevent antimicrobial resistance","authors":"Alex Quintero-Yanes, Kenny Petit, Hector Rodriguez-Villalobos, Hanne Vande Capelle, Joleen Masschelein, Juan Borrero del Pino, Philippe Gabant","doi":"10.1101/2024.09.06.611659","DOIUrl":"https://doi.org/10.1101/2024.09.06.611659","url":null,"abstract":"Antibiotic resistance represents an emergency for global public health. This calls for using alternative drugs and developing innovative therapies based on a clear understanding of their mechanisms of action and resistance in bacteria. Bacteriocins represent a unique class of natural molecules selectively eliminating bacteria. These secreted proteins exhibit a narrower spectrum of activity compared to conventional broad–spectrum antimicrobials by interacting with specific protein and lipid receptors on bacterial cell envelopes. Despite their diverse molecular structures, the commonality of being genetically encoded makes bacteriocins amenable to synthetic biology design. In using cell–free gene expression (CFE) and continuous-exchange CFE (CECFE), we produced controlled combinations (cocktails) of bacteriocins in single synthesis reactions for the first time. A first set of bacteriocin cocktails comprising both linear and circular proteins allowed the targeting of different bacterial species. Other cocktails were designed to target one bacterial species and considering bacteriocins pathways to cross the cell–envelope. Such combinations demonstrated efficient bacterial eradication and prevention of resistance. We illustrate the effectiveness of these bacteriocin mixtures in eradicating various human pathogenic–multiresistant–isolates. Finally, we highlight their potential as targeted and versatile tools in antimicrobial therapy by testing a combination of bacteriocins for treatment in vivo in the animal model <em>Galleria</em> <em>mellonella</em>.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria","authors":"Cameron T. Roots, Jeffrey E. Barrick","doi":"10.1101/2024.09.05.611466","DOIUrl":"https://doi.org/10.1101/2024.09.05.611466","url":null,"abstract":"Foundational techniques in molecular biology-such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins-rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in <em>E. coli</em> interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational identification of small molecules for increased gene expression by synthetic circuits in mammalian cells","authors":"Mara Pisani, Fabiana Calandra, Antonio Rinaldi, Federica Cella, Fabiana Tedeschi, Iole Boffa, Nicola Brunetti-Pierri, Annamaria Carissimo, Francesco Napolitano, Velia Siciliano","doi":"10.1101/2024.09.05.611507","DOIUrl":"https://doi.org/10.1101/2024.09.05.611507","url":null,"abstract":"Engineering mammalian cells with synthetic circuits is leading the charge in next generation biotherapeutics and industrial biotech innovation. However, applications often depend on the cells' productive capacity, which is limited by the finite cellular resources available. We have previously shown that cells engineered with incoherent feedforward loops (iFFL-cells) operate at higher capacity than those engineered with the open loop (OL). Here, we performed RNA-sequencing on cells expressing the iFFL and utilized DECCODE, an unbiased computational method, to match our data with thousands of drug-induced transcriptional profiles. DECCODE identified compounds that consistently enhance expression of both transiently and stably expressed genetic payloads across various experimental scenarios and cell lines, while also reducing external perturbations on integrated genes. Further, we show that drug treatment enhances the rate of AAV and lentivirus transduction, facilitating the prototyping of genetic devices for gene and cell therapies. Altogether, despite limiting intracellular resources is a pervasive, and strongly cell-dependent problem, we provide a versatile tool for a wide range of biomedical and industrial applications that demand enhanced productivity from engineered cells.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput DNA engineering by mating bacteria","authors":"Takeshi Matsui, Po-Hsiang Hung, Han Mei, Xianan Liu, Fangfei Li, John Collins, Weiyi Li, Darach Miller, Neil Wilson, Esteban Toro, Geoffrey J Taghon, Gavin J Sherlock, Sasha Levy","doi":"10.1101/2024.09.03.611066","DOIUrl":"https://doi.org/10.1101/2024.09.03.611066","url":null,"abstract":"To reduce the operational friction and scale DNA engineering, we report here an <em>in vivo</em> DNA assembly technology platform called SCRIVENER (<strong>S​</strong>equential ​<strong>C​</strong>onjugation and <strong>​R</strong>​ecombination for <strong>I</strong>​n ​<strong>V</strong>​ivo ​<strong>E</strong>​longation of ​<strong>N</strong>​ucleotides with low <strong>​ER</strong>​rors). SCRIVENER combines bacterial conjugation, <em>in vivo</em> DNA cutting, and <em>in vivo</em> homologous recombination to seamlessly stitch blocks of DNA together by mating <em>E. coli</em> in large arrays or pools. This workflow is simpler, cheaper, and higher throughput than current DNA assembly approaches that require DNA to be moved in and out of cells at different procedural steps. We perform over 5,000 assemblies with two to 13 DNA blocks that range from 240 bp to 8 kb and show that SCRIVENER is capable of assembling constructs as long as 23 kb at relatively high throughput and fidelity. Most SCRIVENER errors are deletions between long interspersed repeats. However, SCRIVENER can overcome these errors by enabling assembly and sequence verification at high replication at a nominal additional cost per replicate. We show that SCRIVENER can be used to build combinatorial libraries in arrays or pools, and that DNA blocks onboarded into the platform can be repurposed and reused with any other DNA block in high throughput without a PCR step. Because of these features, DNA engineering with SCRIVENER has the potential to accelerate design-build-test-learn cycles of DNA products.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineered Transcription Factor Binding Arrays for DNA-based Gene Expression Control in Mammalian Cells","authors":"Annalise Zouein, Brittany Lende-Dorn, Kate E Galloway, Tom Ellis, Francesca Ceroni","doi":"10.1101/2024.09.03.610999","DOIUrl":"https://doi.org/10.1101/2024.09.03.610999","url":null,"abstract":"Manipulating gene expression in mammalian cells is critical for cell engineering applications. Here we explore the potential of transcription factor (TF) recognition element arrays as DNA tools for modifying free TF levels in cells and thereby controlling gene expression. We first demonstrate proof-of-concept, showing that Tet TF-binding recognition element (RE) arrays of different lengths can tune gene expression and alter gene circuit performance in a predictable manner. We then open-up the approach to interface with any TF with a known binding site by developing a new method called Cloning Troublesome Repeats in Loops (CTRL) that can assemble plasmids with up to 256 repeats of any RE sequence. Transfection of RE array plasmids assembled by CTRL into mammalian cells show potential to modify host cell gene regulation at longer array sizes by sequestration of the TF of interest. RE array plasmids built using CTRL were demonstrated to target both synthetic and native mammalian TFs, illustrating the ability to use these tools to modulate genetic circuits and instruct cell fate. Together this work advances our ability to assemble repetitive DNA arrays and showcases the use of TF-binding RE arrays as a method for manipulating mammalian gene expression, thus expanding the possibilities for mammalian cell engineering.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding pattern formation rules by integrating mechanistic modeling and deep learning","authors":"Jia Lu, Nan Luo, Sizhe Liu, Kinshuk Sahu, Rohan Maddamsetti, Yasa Baig, Lingchong You","doi":"10.1101/2024.09.02.610872","DOIUrl":"https://doi.org/10.1101/2024.09.02.610872","url":null,"abstract":"Predictive programming of self-organized pattern formation using living cells is challenging in major part due to the difficulty in navigating through the high-dimensional design space effectively. The emergence and characteristics of patterns are highly sensitive to both system and environmental parameters. Often, the optimal conditions able to generate patterns represent a small fraction of the possible design space. Furthermore, the experimental generation and quantification of patterns is typically labor intensive and low throughput, making it impractical to optimize pattern formation solely based on trials and errors. To this end, simulations using a well-formulated mechanistic model can facilitate the identification of optimal experimental conditions for pattern formation. However, even a moderately complex system can make these simulations computationally prohibitive when applied to a large parameter space. In this study, we demonstrate how integrating mechanistic modeling with machine learning can significantly accelerate the exploration of design space for patterning circuits and aid in deriving human-interpretable design rules. We apply this strategy to program self-organized ring patterns in Pseudomonas aeruginosa using a synthetic gene circuit. Our approach involved training a neural network with simulated data to predict pattern formation 10 million times faster than the mechanistic model. This neural network was then used to predict pattern formation across a vast array of parameter combinations, far exceeding the size of the training dataset and what was computationally feasible using the mechanistic model alone. By doing so, we identified many parameter combinations able to generate desirable patterns, which still represent an extremely small fraction of explored parametric space. We next used the mechanistic model to validate top candidates and identify coarse-grained rules for patterning. We experimentally demonstrated the generation and control of patterning guided by the learned rules. Our work highlights the effectiveness in integrating mechanistic modeling and machine learning for rational engineering of complex dynamics in living cells.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning","authors":"Ke Wu, Haohao Liu, Manda Sun, Runze Mao, Yindi Jiang, Eduard J Kerkhoven, Jens Nielsen, Yu Chen, Feiran Li","doi":"10.1101/2024.09.02.610684","DOIUrl":"https://doi.org/10.1101/2024.09.02.610684","url":null,"abstract":"Underground metabolism plays a crucial role in understanding enzyme promiscuity, cellular metabolism, and biological evolution, yet experimental exploration of underground metabolism is often sparse. Even though yeast genome-scale metabolic models have been reconstructed and curated for over 20 years, more than 90% of the yeast metabolome is still not covered by these models. To address this gap, we have developed a workflow based on retrobiosynthesis and deep learning methods to comprehensively explore yeast underground metabolism. We integrated the predicted underground network into the yeast consensus genome-scale model, Yeast8, to reconstruct the yeast metabolic twin model, Yeast-MetaTwin, covering 16,244 metabolites (92% of the total yeast metabolome), 2,057 metabolic genes and 59,914 reactions. We revealed that Km parameters differ between the known and underground network, identified hub molecules connecting the underground network and pinpointed the underground percentages for yeast metabolic pathways. Moreover, the Yeast-MetaTwin can predict the by-products of chemicals produced in yeast, offering valuable insights to guide metabolic engineering designs.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phage-mediated intercellular CRISPRi for biocomputation in bacterial consortia","authors":"Abhinav Pujar, Amit Pathania, Corbin Hopper, Amir Pandi, Matthias Fugger, Thomas Nowak, Manish Kushwaha","doi":"10.1101/2024.09.02.610857","DOIUrl":"https://doi.org/10.1101/2024.09.02.610857","url":null,"abstract":"Coordinated actions of cells in microbial communities and multicellular organisms enable them to perform complex tasks otherwise difficult for single cells. This has inspired biological engineers to build cellular consortia for larger circuits with improved functionalities, while implementing communication systems for coordination among cells. Here, we investigate the signalling dynamics of a phage-mediated synthetic DNA messaging system, and couple it with CRISPR interference to build distributed circuits that perform logic gate operations in multicellular bacterial consortia. We find that growth phases of both sender and receiver cells, as well as resource competition between them, shape communication outcomes. Leveraging the easy programmability of DNA messages, we build 8 orthogonal signals and demonstrate that intercellular CRISPRi (i-CRISPRi) regulates gene expression across cells. Finally, we multiplex the i-CRISPRi system to implement several multicellular logic gates that involve up to 7 cells and take up to 3 inputs simultaneously, with single- and dual-rail encoding: NOT, YES, AND, and AND-AND-NOT. The communication system developed here lays the groundwork for implementing complex biological circuits in engineered bacterial communities, using phage signals for communication.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-guided gene circuit design for engineering genetically stable cell populations in diverse applications","authors":"Kirill Sechkar, Harrison Steel","doi":"10.1101/2024.09.01.610672","DOIUrl":"https://doi.org/10.1101/2024.09.01.610672","url":null,"abstract":"Maintaining engineered cell populations' genetic stability is a key challenge in synthetic biology. Synthetic genetic constructs compete with a host cell's native genes for expression resources, burdening the cell and impairing its growth. This creates a selective pressure favouring mutations which alleviate this growth defect by removing synthetic gene expression. Non-functional mutants thus spread in cell populations, eventually making them lose engineered functions. Past work has attempted to limit mutation spread by coupling synthetic gene expression to survival. However, these approaches are highly context-dependent and must be tailor-made for each particular synthetic gene circuit to be retained. In contrast, we develop and analyse a biomolecular controller which depresses mutant cell growth independently of the mutated synthetic gene's identity. Modelling shows how our design can be deployed alongside various synthetic circuits without any re-engineering of its genetic components, outperforming extant gene-specific mutation spread mitigation strategies. Our controller's performance is evaluated using a novel simulation approach which leverages resource-aware cell modelling to directly link a circuit's design parameters to its population-level behaviour. Our design's adaptability promises to mitigate mutation spread in an expanded range of applications, whilst our analyses provide a blueprint for using resource-aware cell models in circuit design.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolverator: An engineered in cellulo yeast system to drive rapid continuous evolution of proteins","authors":"Asli Azizoglu, Eline Y. Bijman, Joerg Stelling, Roger Brent","doi":"10.1101/2024.09.01.610536","DOIUrl":"https://doi.org/10.1101/2024.09.01.610536","url":null,"abstract":"In vivo continuous directed evolution generates genetic diversity and selects a target phenotype to generate proteins with desired functionality. However, in current systems, the two processes do not operate simultaneously in the same cell, restricting applications such as evolution of eukaryotic protein-ligand binding. Here, we describe Evolverator Saccharomyces cerevisiae cells that combine inducible targeted mutagenesis with engineered gene circuits that link the emergence of a desired function to graded increases in cell proliferation. Poor ligand binding induces targeted mutagenesis and cells with mutations that improve ligand binding overtake the cell population. By combining strain development with mathematical modeling for systems and process design, we evolved ligand specificities of the human estrogen receptor and more effective variants of the bacterial lacI repressor. Previously undescribed mutations affected residues plausibly involved in ligand binding and residues that exert allosteric effects. Evolverator should aid generation of proteins that bind new targets for many applications.","PeriodicalId":501408,"journal":{"name":"bioRxiv - Synthetic Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}