Pub Date : 2024-09-07DOI: 10.1101/2024.09.06.611659
Alex Quintero-Yanes, Kenny Petit, Hector Rodriguez-Villalobos, Hanne Vande Capelle, Joleen Masschelein, Juan Borrero del Pino, Philippe Gabant
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 Galleriamellonella.
{"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":"74 1","pages":""},"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}
Pub Date : 2024-09-05DOI: 10.1101/2024.09.05.611466
Cameron T. Roots, Jeffrey E. Barrick
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 E. coli 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.
分子生物学的基础技术--如克隆基因、标记生物分子以进行纯化或鉴定以及过表达重组蛋白--依赖于将非本地或合成 DNA 序列引入生物体。这些序列在新的环境中可能会以非预期的方式被转录和翻译机器识别。事实证明，有时导致的隐性基因表达会产生遗传不稳定性并掩盖实验信号。目前已开发出一些计算工具来预测单个类型的基因表达元素，但研究人员很难将这些工具的集体输出结果与上下文联系起来。在这里，我们将介绍 CryptKeeper，它是一种可视化细菌基因表达信号预测并估算 DNA 序列可能产生的翻译负担的软件管道。我们研究了几个已发表的例子，其中大肠杆菌中的隐性基因表达干扰了实验。CryptKeeper 能准确预测真核病毒感染克隆和导致基因不稳定的单个蛋白质中不需要的基因表达。CryptKeeper 还能识别导致蛋白质纯化受阻的截断基因表达元件。在反向遗传学和合成生物学工作流程中使用 CryptKeeper 进行负设计，有助于减轻克隆挑战，避免因无意的基因表达而导致无法解释的失败和并发症。
{"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":"45 1","pages":""},"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}
Pub Date : 2024-09-05DOI: 10.1101/2024.09.05.611507
Mara Pisani, Fabiana Calandra, Antonio Rinaldi, Federica Cella, Fabiana Tedeschi, Iole Boffa, Nicola Brunetti-Pierri, Annamaria Carissimo, Francesco Napolitano, Velia Siciliano
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.
{"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":"16 1","pages":""},"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}
Pub Date : 2024-09-03DOI: 10.1101/2024.09.03.611066
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
To reduce the operational friction and scale DNA engineering, we report here an in vivo DNA assembly technology platform called SCRIVENER (Sequential Conjugation and Recombination for In Vivo Elongation of Nucleotides with low ERrors). SCRIVENER combines bacterial conjugation, in vivo DNA cutting, and in vivo homologous recombination to seamlessly stitch blocks of DNA together by mating E. coli 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.
为了减少操作摩擦并扩大 DNA 工程规模，我们在此报告了一种名为 SCRIVENER（低ERrors 核苷酸体内延伸的顺序共轭和重组）的体内 DNA 组装技术平台。SCRIVENER 结合了细菌共轭、体内 DNA 切割和体内同源重组技术，通过大肠杆菌交配将 DNA 块无缝地拼接在一起，形成大型阵列或集合。与目前需要在不同程序步骤中将 DNA 移入和移出细胞的 DNA 组装方法相比，这种工作流程更简单、更便宜、吞吐量更高。我们用 240 bp 到 8 kb 不等的 2 到 13 个 DNA 块进行了 5,000 多次组装，结果表明 SCRIVENER 能够以相对较高的吞吐量和保真度组装长达 23 kb 的构建体。大多数 SCRIVENER 错误都是长穿插重复序列之间的缺失。不过，SCRIVENER 可以克服这些错误，只需象征性地增加每次复制的成本，就能在高复制条件下进行组装和序列验证。我们的研究表明，SCRIVENER 可用于构建阵列或集合的组合文库，而且该平台上的 DNA 块可与其他任何 DNA 块进行高通量的重复使用，而无需 PCR 步骤。由于这些特点，使用 SCRIVENER 进行 DNA 工程有可能加快 DNA 产品的设计-构建-测试-学习周期。
{"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":"3 1","pages":""},"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}
Pub Date : 2024-09-03DOI: 10.1101/2024.09.03.610999
Annalise Zouein, Brittany Lende-Dorn, Kate E Galloway, Tom Ellis, Francesca Ceroni
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.
控制哺乳动物细胞中的基因表达对细胞工程应用至关重要。在这里，我们探索了转录因子（TF）识别元件阵列作为 DNA 工具的潜力，它可以改变细胞中游离 TF 的水平，从而控制基因表达。我们首先进行了概念验证，证明不同长度的 Tet TF 结合识别元件（RE）阵列能以可预测的方式调节基因表达和改变基因回路性能。然后，我们通过开发一种名为 "在环路中克隆麻烦重复序列"（CTRL）的新方法，将该方法扩展到与任何具有已知结合位点的 TF 连接，这种方法可以组装具有多达 256 个重复的任何 RE 序列的质粒。将通过 CTRL 组装的 RE 阵列质粒转染到哺乳动物细胞中显示出了通过固着感兴趣的 TF 来改变宿主细胞基因调控的潜力。利用 CTRL 构建的 RE 阵列质粒被证明可以靶向合成的和原生的哺乳动物 TF，这说明了利用这些工具调节遗传回路和指导细胞命运的能力。这项工作共同提高了我们组装重复 DNA 阵列的能力，并展示了使用 TF 结合 RE 阵列作为操纵哺乳动物基因表达的方法，从而拓展了哺乳动物细胞工程的可能性。
{"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":"10 1","pages":""},"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}
Pub Date : 2024-09-02DOI: 10.1101/2024.09.02.610872
Jia Lu, Nan Luo, Sizhe Liu, Kinshuk Sahu, Rohan Maddamsetti, Yasa Baig, Lingchong You
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.
{"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":"7 1","pages":""},"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}
Pub Date : 2024-09-02DOI: 10.1101/2024.09.02.610684
Ke Wu, Haohao Liu, Manda Sun, Runze Mao, Yindi Jiang, Eduard J Kerkhoven, Jens Nielsen, Yu Chen, Feiran Li
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.
{"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":"34 1","pages":""},"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}
Pub Date : 2024-09-02DOI: 10.1101/2024.09.02.610857
Abhinav Pujar, Amit Pathania, Corbin Hopper, Amir Pandi, Matthias Fugger, Thomas Nowak, Manish Kushwaha
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.
在微生物群落和多细胞生物体中，细胞之间的协调行动使它们能够执行复杂的任务，否则单个细胞很难完成这些任务。这启发了生物工程师建立细胞联合体，形成具有更多功能的更大电路，同时实现细胞间的通信系统协调。在这里，我们研究了噬菌体介导的合成 DNA 信息传递系统的信号动态，并将其与 CRISPR 干扰结合起来，构建了在多细胞细菌联盟中执行逻辑门操作的分布式电路。我们发现，发送方和接收方细胞的生长阶段以及它们之间的资源竞争都会影响通信结果。利用 DNA 信息易于编程的特点，我们建立了 8 个正交信号，并证明了细胞间 CRISPRi（i-CRISPRi）可以调节跨细胞的基因表达。最后，我们将 i-CRISPRi 系统复用，实现了多个多细胞逻辑门，其中涉及多达 7 个细胞，并同时接受多达 3 个输入，具有单轨和双轨编码：NOT、YES、AND 和 AND-AND-NOT。这里开发的通信系统为在工程细菌群落中利用噬菌体信号进行通信，实现复杂的生物电路奠定了基础。
{"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":"58 1","pages":""},"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}
Pub Date : 2024-09-01DOI: 10.1101/2024.09.01.610672
Kirill Sechkar, Harrison Steel
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.
{"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":"10 1","pages":""},"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}
Pub Date : 2024-09-01DOI: 10.1101/2024.09.01.610536
Asli Azizoglu, Eline Y. Bijman, Joerg Stelling, Roger Brent
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.
{"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":"62 1","pages":""},"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}
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