{"title":"Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits","authors":"Shimshon Belkin, Baojun Wang","doi":"10.1111/1751-7915.13955","DOIUrl":null,"url":null,"abstract":"<p>It is tempting to speculate that sixty years ago, when Jacob and Monod presented their model of the <i>lac</i> operon (Jacob and Monod, <span>1961</span>), they already had a glimpse of the future of the <i>lacZ</i> gene, not only as encoding a cleaver of disaccharides, nor as a component in a beautiful and groundbreaking model of gene regulation, but also as a universal reporter of gene activation. Indeed, reporter gene technology rapidly became a basic tool in studying the regulation of gene expression; several decades had to pass, however, before the same approach has led to the first report of a microorganism genetically engineered to perform an accurate, specific and sensitive analysis of an environmental pollutant (King <i>et al</i>., <span>1990</span>). The term ‘whole cell biosensor’ soon entered into use, accompanied by some semantic controversy: purists view the term ‘biosensor’ as a hardware device, in which the biological entity (e.g. enzyme, antibody, oligonucleotide or a live cell) serves as its sensing component (IUPAC, <span>2017</span>); according to this view, a microbial strain, notwithstanding the complexity of its re-engineering, may be called a ‘sensor strain’ or a ‘bioreporter’, but never a ‘biosensor’. Long before this linguistic polemic became an issue, however, a pioneering article from the Sayler group (King <i>et al</i>., <span>1990</span>) described a bioluminescent <i>Pseudomonas</i>-based sensor of naphthalene. This publication was trailed by the first <i>E. coli</i>-based mercury sensor (Selifonova <i>et al.</i>, <span>1993</span>), soon to be followed by numerous others, all sharing the same basic structure: a gene promoter induced by the target compound (directly, or via the removal of a repressor), fused downstream of a reporter gene. The latter could code for a traceable protein (e.g. GFP) or – more often – for an enzyme, the activity of which could be monitored quantitatively in real time (van der Meer and Belkin, <span>2010</span>). When necessary, regulatory elements had to be cloned as well, especially when the gene promoter acting as the sensing element was not native to the host organism. In view of the practically infinite number of gene promoters and regulatory proteins available as candidate sensor elements, the scope of possible sensing targets of such sensors is exceptionally broad. In parallel to the development of microbial sensors of specific compounds, bioreporter strains have also been described for the detection of global sample characteristics such as toxicity or genotoxicity/mutagenicity, parameters of importance for environmental health as well as for chemicals’ safety. The commercial SOS Chromotest (Quillardet <i>et al</i>., <span>1982</span>), the forerunner of this group of assays, was followed by the <i>umu</i>-test (Oda <i>et al</i>., <span>1985</span>). In both cases, the activation of gene promoters from the <i>E. coli</i> SOS repair regulon by DNA damaging agents was chromogenically monitored with <i>lacZ</i> as a reporter gene.</p><p>Looking back over the last 15 years, possibly the most powerful innovator of microbial biosensor design was the coming of age of synthetic biology. While the term has been introduced to the scientific literature over a century ago (Leduc, <span>1910</span>), its meaning has slowly changed over the years. Following the introduction of the Jacob and Monod model, microbial biotechnology horizons opened up with the advent of increasingly more sophisticated molecular tools, including numerous enzymes derived from diverse microorganisms and viruses, harnessed and retrained to perform cutting, pasting and editing tricks. The same horizons practically exploded when thermophilic variants of these enzymes were ingeniously employed in the invention of PCR technology, and turned essentially limitless when genome sequencing was made trivial and bioinformatic data (and tools for its analysis) became freely available to all. These advances have prepared the ground for the invasion of practitioners of additional disciplines into the realm of whole cell sensor design; when engineers, physicists and computer scientists started to practice biology in earnest, things have started to become truly interesting. In <span>2004</span>, van der Meer <i>et al</i>. have claimed that one of the reasons current bioreporters’ performance cannot comply with environmental detection standards is the ‘lack of engineering principles’. More or less at the same time, the ‘Biobricks’ concept has been presented (Knight, <span>2003</span>), aiming to provide ‘a set of standard and reliable engineering mechanisms to remove much of the tedium and surprise during assembly of genetic components into larger systems’. The trend embodied by these two examples paved the ground for engineering school graduates to advise ‘classical’ molecular biologists involved in microbial bioreporter design that the time of simplistic promoter-reporter fusions is over; more complex (and hopefully, more efficient and diverse) molecular sensor circuits can be designed, for both <i>in vivo</i> and <i>in vitro</i> expression, by employing an engineering-like point of view.</p><p>Indeed, synthetic biology adopts engineering principles (e.g. standardization, modularization and modelling) to facilitate complex genetic circuit construction, particularly using ‘Lego-like’ standardized building blocks (Endy, <span>2005</span>). Though the blocks alone do not perform advanced actions, they can generate bespoke coordinated functions when connected. Hence, synthetic biology offers new tools to precisely manipulate cells for achieving customized tasks, using engineered gene circuits of varying scales and complexity. The developments in synthetic biology have permitted both fine-tuning the performance of existing microbial biosensors, and creating new ones with unique functionalities in a more predictable and rapid manner.</p><p>Synthetic microbial biosensors typically comprise three exchangeable modules: an input sensing block, an internal signal processing block, and an output reporting block (Wang and Barahona, <span>2013</span>). In contrast to traditional microbial sensors consisting of a genetic reporter fused to an inducible promoter to control the expression of a detectable output, synthetic biology enables biosensor designs to incorporate additional complex signal processing circuits. Accordingly, the sensing unit triggers more sophisticated actions before activating reporter expression, in order to enhance a sensor’s performance or perform additional functions. Such circuits include toggle switches (Gardner and Cantor, <span>2000</span>), logic gates (Anderson <i>et al</i>., <span>2006</span>; Wang <i>et al</i>., <span>2011</span>), transcriptional amplifiers (Wang and Barahona, <span>2014</span>) and memory circuits (Courbet <i>et al</i>., <span>2015</span>; Riglar <i>et al</i>., <span>2017</span>). Furthermore, microbial sensor cell arrays could be designed to display an easy-to-interpret output pattern corresponding to specific input analyte levels without the use of specialist lab equipment (Wan <i>et al</i>., <span>2019</span>).</p><p>As many early stage microbial biosensors are inadequate to meet practical requirements in detection limit, specificity and output amplitude, various gene circuit-based optimization strategies have recently been developed to improve their sensing performance. In contrast to traditional optimization methods such as random mutagenesis, these synthetic biology-enabled optimization tools are based on rational design, and are thus more predictable and faster to achieve the desired sensing specifications. For example, integrating multiple signal inputs using genetic AND gates have been shown to be effective in increasing microbial sensors’ specificity (Wang <i>et al</i>., <span>2013</span>), and rationally tuning the intracellular levels of the receptor proteins can drastically improve sensors’ detection limits (Wang and Barahona, <span>2015</span>). In addition, a toggle switch (Wu <i>et al</i>., <span>2009</span>) and a post-translational regulation device (Wan <i>et al</i>., <span>2019</span>) have been designed to lower microbial sensors’ background expression and detection limits. Amplification of the transduced sensor signal is another powerful strategy to further boost the sensor’s performance, using strategies such as positive feedback loops (Jia <i>et al</i>., <span>2019</span>) or transcription signal amplifiers (Wan <i>et al</i>., <span>2019</span>).</p><p>Albeit successful proof-of-concept laboratory demonstrations of a number of synthetic microbial sensors, very few have made it into the market. Several barriers remain to be overcome, including an insufficient number of sensory building blocks, poor sensing performance, long-term stability issues, risk of releasing genetically modified microorganisms (GMMs), and lack of practical experience in acceptance by professional stakeholders (Hicks and Bachmann, <span>2020</span>). Nevertheless, synthetic biology has contributed novel strategies to address these limitations. For example, different approaches have been applied to keep biosensor cells alive and active for longer term including freeze-drying of cells, and encapsulating cells within polymers (Bjerketorp <i>et al</i>., <span>2006</span>; Liu <i>et al</i>., <span>2018</span>; Wan <i>et al</i>., <span>2019</span>; Shemer <i>et al</i>., <span>2020</span>). Recent advances demonstrated the potential of harnessing the amazing sensing capabilities of microbes for versatile applications, for example as wearable sensors for biomarker analysis in sweat to achieve non-invasive in situ real-time physiological state monitoring (Liu <i>et al</i>., <span>2018</span>; Nguyen <i>et al</i>., <span>2021</span>), or the standoff detection of buried landmines (Belkin <i>et al</i>., <span>2017</span>). However, biosafety concerns regarding the usage of GMMs remain an issue associated with field and in vivo applications, including potential horizontal gene transfer and disruption of natural ecosystems. Accordingly, different biocontainment strategies have been proposed to mitigate biosafety concerns such as replacing antibiotics resistance with toxin-antitoxin systems (Wright <i>et al</i>., <span>2015</span>), incorporating conditional kill switches (Chan <i>et al</i>., <span>2016</span>) and non-canonical amino acid substitution (Fredens <i>et al</i>., <span>2019</span>). Furthermore, chromosome-free bacterial chassis such as SimCells (Fan <i>et al</i>., <span>2020</span>) can be considered. Notably, cell-free expression systems have become increasingly popular as a new sensor platform, by avoiding biosafety concerns associated with using living cells. Cell-free biosensors lend faster responses, higher sensitivity and an enhanced compatibility to toxic samples (Lopreside <i>et al</i>., <span>2019</span>; Silverman and Karim, <span>2020</span>). Moreover, cell-free extracts comprising genetic sensors could be embedded on paper, providing a portable platform for easy-to-use and cost-effective on-site screening (Pardee <i>et al</i>., <span>2016</span>) or in hydrogels acting as smart stimuli-responsive biomaterials (Whitfield <i>et al</i>., <span>2020</span>).</p><p>The latest developments in synthetic biology enable a fast design-build-test cycle for sensor construction and response optimization, to address the limitations of microbial biosensors. Yet, challenges remain to be addressed both within and beyond the scope of technical developments. For instance, environmental, food and health monitoring necessitate sensor cell exposure to complex samples, and therefore require complex signal processing circuits and likely multiple input modules. Notably, for biomedical applications involving complex media compositions such as tumours, non-specific localization of sensor cells prevents accurate diagnosis and biotherapy. Consequently, engineering microbes for sensing and reporting at designated spatial locations will be critical (Chien <i>et al</i>., <span>2021</span>). Considering that a single microbial cell has a limited capacity in resources, and that large complex circuits tend to burden host cells, cell consortia comprising multiple communicating sensor strains may be used to facilitate multiplex detection and reconfigurability of sensor function (Wang <i>et al</i>., <span>2013</span>; Khatun <i>et al</i>., <span>2018</span>). Innovative designs with fewer time-consuming signal propagation steps such as a transcription-only design, with RNA as the reporter entity, or engineered ligand-responsive fluorescent reporter proteins, could significantly shorten the response time of microbial biosensors, while alternative reporting formats such as direct bioelectronic signal output may lead to increasing seamless interfacing with conventional electronic devices. Further, integrating engineered microbial biosensors into various materials will lead to progarmmable living materials with bespoke functionalities such as self-healing.</p><p>In summary, we have witnessed a new wave of microbial sensors development in the rising era of synthetic biology, and expect this trend to continue and probably grow stronger in the coming decades. While living bioreporters presently face certain restrictions, synthetic biology offers new tools and strategies to accelerate the development, enhance the performance and address the current limitations of microbial biosensors; this will facilitate their future adoption and uptake as promising alternative analytical devices in diverse settings.</p><p>B.W. acknowledges support by the UK Research and Innovation Future Leaders Fellowship [MR/S018875/1], Leverhulme Trust research project grant [RPG-2020-241] and US Office of Naval Research Global grant [N62909-20-1-2036]. S.B. was partially supported by the Minerva Center for Bio-Hybrid Complex Systems.</p><p>None declared.</p>","PeriodicalId":49145,"journal":{"name":"Microbial Biotechnology","volume":"15 1","pages":"103-106"},"PeriodicalIF":4.8000,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sfamjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1751-7915.13955","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbial Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13955","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 5
Abstract
It is tempting to speculate that sixty years ago, when Jacob and Monod presented their model of the lac operon (Jacob and Monod, 1961), they already had a glimpse of the future of the lacZ gene, not only as encoding a cleaver of disaccharides, nor as a component in a beautiful and groundbreaking model of gene regulation, but also as a universal reporter of gene activation. Indeed, reporter gene technology rapidly became a basic tool in studying the regulation of gene expression; several decades had to pass, however, before the same approach has led to the first report of a microorganism genetically engineered to perform an accurate, specific and sensitive analysis of an environmental pollutant (King et al., 1990). The term ‘whole cell biosensor’ soon entered into use, accompanied by some semantic controversy: purists view the term ‘biosensor’ as a hardware device, in which the biological entity (e.g. enzyme, antibody, oligonucleotide or a live cell) serves as its sensing component (IUPAC, 2017); according to this view, a microbial strain, notwithstanding the complexity of its re-engineering, may be called a ‘sensor strain’ or a ‘bioreporter’, but never a ‘biosensor’. Long before this linguistic polemic became an issue, however, a pioneering article from the Sayler group (King et al., 1990) described a bioluminescent Pseudomonas-based sensor of naphthalene. This publication was trailed by the first E. coli-based mercury sensor (Selifonova et al., 1993), soon to be followed by numerous others, all sharing the same basic structure: a gene promoter induced by the target compound (directly, or via the removal of a repressor), fused downstream of a reporter gene. The latter could code for a traceable protein (e.g. GFP) or – more often – for an enzyme, the activity of which could be monitored quantitatively in real time (van der Meer and Belkin, 2010). When necessary, regulatory elements had to be cloned as well, especially when the gene promoter acting as the sensing element was not native to the host organism. In view of the practically infinite number of gene promoters and regulatory proteins available as candidate sensor elements, the scope of possible sensing targets of such sensors is exceptionally broad. In parallel to the development of microbial sensors of specific compounds, bioreporter strains have also been described for the detection of global sample characteristics such as toxicity or genotoxicity/mutagenicity, parameters of importance for environmental health as well as for chemicals’ safety. The commercial SOS Chromotest (Quillardet et al., 1982), the forerunner of this group of assays, was followed by the umu-test (Oda et al., 1985). In both cases, the activation of gene promoters from the E. coli SOS repair regulon by DNA damaging agents was chromogenically monitored with lacZ as a reporter gene.
Looking back over the last 15 years, possibly the most powerful innovator of microbial biosensor design was the coming of age of synthetic biology. While the term has been introduced to the scientific literature over a century ago (Leduc, 1910), its meaning has slowly changed over the years. Following the introduction of the Jacob and Monod model, microbial biotechnology horizons opened up with the advent of increasingly more sophisticated molecular tools, including numerous enzymes derived from diverse microorganisms and viruses, harnessed and retrained to perform cutting, pasting and editing tricks. The same horizons practically exploded when thermophilic variants of these enzymes were ingeniously employed in the invention of PCR technology, and turned essentially limitless when genome sequencing was made trivial and bioinformatic data (and tools for its analysis) became freely available to all. These advances have prepared the ground for the invasion of practitioners of additional disciplines into the realm of whole cell sensor design; when engineers, physicists and computer scientists started to practice biology in earnest, things have started to become truly interesting. In 2004, van der Meer et al. have claimed that one of the reasons current bioreporters’ performance cannot comply with environmental detection standards is the ‘lack of engineering principles’. More or less at the same time, the ‘Biobricks’ concept has been presented (Knight, 2003), aiming to provide ‘a set of standard and reliable engineering mechanisms to remove much of the tedium and surprise during assembly of genetic components into larger systems’. The trend embodied by these two examples paved the ground for engineering school graduates to advise ‘classical’ molecular biologists involved in microbial bioreporter design that the time of simplistic promoter-reporter fusions is over; more complex (and hopefully, more efficient and diverse) molecular sensor circuits can be designed, for both in vivo and in vitro expression, by employing an engineering-like point of view.
Indeed, synthetic biology adopts engineering principles (e.g. standardization, modularization and modelling) to facilitate complex genetic circuit construction, particularly using ‘Lego-like’ standardized building blocks (Endy, 2005). Though the blocks alone do not perform advanced actions, they can generate bespoke coordinated functions when connected. Hence, synthetic biology offers new tools to precisely manipulate cells for achieving customized tasks, using engineered gene circuits of varying scales and complexity. The developments in synthetic biology have permitted both fine-tuning the performance of existing microbial biosensors, and creating new ones with unique functionalities in a more predictable and rapid manner.
Synthetic microbial biosensors typically comprise three exchangeable modules: an input sensing block, an internal signal processing block, and an output reporting block (Wang and Barahona, 2013). In contrast to traditional microbial sensors consisting of a genetic reporter fused to an inducible promoter to control the expression of a detectable output, synthetic biology enables biosensor designs to incorporate additional complex signal processing circuits. Accordingly, the sensing unit triggers more sophisticated actions before activating reporter expression, in order to enhance a sensor’s performance or perform additional functions. Such circuits include toggle switches (Gardner and Cantor, 2000), logic gates (Anderson et al., 2006; Wang et al., 2011), transcriptional amplifiers (Wang and Barahona, 2014) and memory circuits (Courbet et al., 2015; Riglar et al., 2017). Furthermore, microbial sensor cell arrays could be designed to display an easy-to-interpret output pattern corresponding to specific input analyte levels without the use of specialist lab equipment (Wan et al., 2019).
As many early stage microbial biosensors are inadequate to meet practical requirements in detection limit, specificity and output amplitude, various gene circuit-based optimization strategies have recently been developed to improve their sensing performance. In contrast to traditional optimization methods such as random mutagenesis, these synthetic biology-enabled optimization tools are based on rational design, and are thus more predictable and faster to achieve the desired sensing specifications. For example, integrating multiple signal inputs using genetic AND gates have been shown to be effective in increasing microbial sensors’ specificity (Wang et al., 2013), and rationally tuning the intracellular levels of the receptor proteins can drastically improve sensors’ detection limits (Wang and Barahona, 2015). In addition, a toggle switch (Wu et al., 2009) and a post-translational regulation device (Wan et al., 2019) have been designed to lower microbial sensors’ background expression and detection limits. Amplification of the transduced sensor signal is another powerful strategy to further boost the sensor’s performance, using strategies such as positive feedback loops (Jia et al., 2019) or transcription signal amplifiers (Wan et al., 2019).
Albeit successful proof-of-concept laboratory demonstrations of a number of synthetic microbial sensors, very few have made it into the market. Several barriers remain to be overcome, including an insufficient number of sensory building blocks, poor sensing performance, long-term stability issues, risk of releasing genetically modified microorganisms (GMMs), and lack of practical experience in acceptance by professional stakeholders (Hicks and Bachmann, 2020). Nevertheless, synthetic biology has contributed novel strategies to address these limitations. For example, different approaches have been applied to keep biosensor cells alive and active for longer term including freeze-drying of cells, and encapsulating cells within polymers (Bjerketorp et al., 2006; Liu et al., 2018; Wan et al., 2019; Shemer et al., 2020). Recent advances demonstrated the potential of harnessing the amazing sensing capabilities of microbes for versatile applications, for example as wearable sensors for biomarker analysis in sweat to achieve non-invasive in situ real-time physiological state monitoring (Liu et al., 2018; Nguyen et al., 2021), or the standoff detection of buried landmines (Belkin et al., 2017). However, biosafety concerns regarding the usage of GMMs remain an issue associated with field and in vivo applications, including potential horizontal gene transfer and disruption of natural ecosystems. Accordingly, different biocontainment strategies have been proposed to mitigate biosafety concerns such as replacing antibiotics resistance with toxin-antitoxin systems (Wright et al., 2015), incorporating conditional kill switches (Chan et al., 2016) and non-canonical amino acid substitution (Fredens et al., 2019). Furthermore, chromosome-free bacterial chassis such as SimCells (Fan et al., 2020) can be considered. Notably, cell-free expression systems have become increasingly popular as a new sensor platform, by avoiding biosafety concerns associated with using living cells. Cell-free biosensors lend faster responses, higher sensitivity and an enhanced compatibility to toxic samples (Lopreside et al., 2019; Silverman and Karim, 2020). Moreover, cell-free extracts comprising genetic sensors could be embedded on paper, providing a portable platform for easy-to-use and cost-effective on-site screening (Pardee et al., 2016) or in hydrogels acting as smart stimuli-responsive biomaterials (Whitfield et al., 2020).
The latest developments in synthetic biology enable a fast design-build-test cycle for sensor construction and response optimization, to address the limitations of microbial biosensors. Yet, challenges remain to be addressed both within and beyond the scope of technical developments. For instance, environmental, food and health monitoring necessitate sensor cell exposure to complex samples, and therefore require complex signal processing circuits and likely multiple input modules. Notably, for biomedical applications involving complex media compositions such as tumours, non-specific localization of sensor cells prevents accurate diagnosis and biotherapy. Consequently, engineering microbes for sensing and reporting at designated spatial locations will be critical (Chien et al., 2021). Considering that a single microbial cell has a limited capacity in resources, and that large complex circuits tend to burden host cells, cell consortia comprising multiple communicating sensor strains may be used to facilitate multiplex detection and reconfigurability of sensor function (Wang et al., 2013; Khatun et al., 2018). Innovative designs with fewer time-consuming signal propagation steps such as a transcription-only design, with RNA as the reporter entity, or engineered ligand-responsive fluorescent reporter proteins, could significantly shorten the response time of microbial biosensors, while alternative reporting formats such as direct bioelectronic signal output may lead to increasing seamless interfacing with conventional electronic devices. Further, integrating engineered microbial biosensors into various materials will lead to progarmmable living materials with bespoke functionalities such as self-healing.
In summary, we have witnessed a new wave of microbial sensors development in the rising era of synthetic biology, and expect this trend to continue and probably grow stronger in the coming decades. While living bioreporters presently face certain restrictions, synthetic biology offers new tools and strategies to accelerate the development, enhance the performance and address the current limitations of microbial biosensors; this will facilitate their future adoption and uptake as promising alternative analytical devices in diverse settings.
B.W. acknowledges support by the UK Research and Innovation Future Leaders Fellowship [MR/S018875/1], Leverhulme Trust research project grant [RPG-2020-241] and US Office of Naval Research Global grant [N62909-20-1-2036]. S.B. was partially supported by the Minerva Center for Bio-Hybrid Complex Systems.
人们很容易推测,60年前,当Jacob和Monod提出他们的lac操纵子模型(Jacob and Monod, 1961)时,他们已经瞥见了lacZ基因的未来,不仅是编码双糖切割,也不是作为一个美丽的、开创性的基因调控模型的组成部分,而且是基因激活的普遍报告者。事实上,报告基因技术迅速成为研究基因表达调控的基本工具;然而,几十年过去了,同样的方法才导致了第一份关于基因工程微生物对环境污染物进行准确、具体和敏感分析的报告(King et al., 1990)。术语“全细胞生物传感器”很快进入使用,伴随着一些语义争议:纯粹主义者将术语“生物传感器”视为硬件设备,其中生物实体(例如酶,抗体,寡核苷酸或活细胞)作为其传感组件(IUPAC, 2017);根据这一观点,一个微生物菌株,尽管其再工程的复杂性,可能被称为“传感器菌株”或“生物报告者”,但永远不会被称为“生物传感器”。然而,早在这种语言争论成为一个问题之前,Sayler小组的一篇开创性文章(King et al., 1990)就描述了一种基于假单胞菌的生物发光萘传感器。这篇论文发表后,第一个基于大肠杆菌的汞传感器(Selifonova等人,1993年)紧随其后,很快又有许多其他传感器紧随其后,它们都具有相同的基本结构:由目标化合物诱导的基因启动子(直接或通过去除抑制子)融合在报告基因的下游。后者可以编码一种可追踪的蛋白质(例如绿色荧光蛋白),或者更常见的是编码一种酶,其活性可以实时定量监测(van der Meer和Belkin, 2010)。必要时,调控元件也必须克隆,特别是当作为传感元件的基因启动子不是宿主生物的原生元件时。鉴于几乎无限数量的基因启动子和调控蛋白可作为候选传感器元件,这些传感器可能的传感目标范围非常广泛。在开发特定化合物的微生物传感器的同时,还描述了生物报告菌株,用于检测总体样品特征,例如毒性或遗传毒性/诱变性,以及对环境健康和化学品安全至关重要的参数。商业性SOS染色体试验(Quillardet等人,1982年)是这组测定法的先驱,随后是umu试验(Oda等人,1985年)。在这两种情况下,DNA损伤剂对大肠杆菌SOS修复调控基因启动子的激活进行了染色体监测,lacZ作为报告基因。回顾过去的15年,微生物生物传感器设计最强大的创新可能是合成生物学时代的到来。虽然这个术语在一个多世纪前就被引入科学文献(Leduc, 1910),但它的含义多年来慢慢发生了变化。随着雅各布和莫诺德模型的引入,微生物生物技术的视野随着越来越复杂的分子工具的出现而打开,包括来自不同微生物和病毒的许多酶,利用和重新训练来执行剪切,粘贴和编辑技巧。当这些酶的嗜热变异体被巧妙地应用于PCR技术的发明时,同样的视野实际上爆发了,当基因组测序变得微不足道,生物信息学数据(及其分析工具)对所有人免费开放时,这种视野基本上是无限的。这些进步为其他学科的从业者进入全细胞传感器设计领域奠定了基础;当工程师、物理学家和计算机科学家开始认真地实践生物学时,事情开始变得真正有趣起来。2004年,van der Meer等人声称,目前生物记者的表现不符合环境检测标准的原因之一是“缺乏工程原理”。几乎在同一时间,“生物砖”的概念已经提出(Knight, 2003),旨在提供“一套标准和可靠的工程机制,以消除在将遗传成分组装成更大系统期间的许多乏味和意外”。这两个例子所体现的趋势为工程学院毕业生建议参与微生物生物报告器设计的“经典”分子生物学家奠定了基础,即简单的启动子-报告器融合的时代已经结束;通过采用类似工程的观点,可以为体内和体外表达设计更复杂(希望是更高效和多样化)的分子传感器电路。事实上,合成生物学采用了工程原理(例如; 标准化、模块化和建模)以促进复杂的遗传电路构建,特别是使用“乐高”式的标准化构建模块(Endy, 2005)。虽然单独的块不能执行高级操作,但它们可以在连接时生成定制的协调功能。因此,合成生物学提供了新的工具来精确地操纵细胞,以实现定制的任务,使用不同规模和复杂性的工程基因电路。合成生物学的发展使得既可以微调现有微生物生物传感器的性能,又可以以更可预测和快速的方式创造具有独特功能的新传感器。合成微生物生物传感器通常包括三个可交换模块:输入传感块,内部信号处理块和输出报告块(Wang和Barahona, 2013)。与传统的微生物传感器组成的遗传报告融合到一个诱导启动子来控制可检测输出的表达相比,合成生物学使生物传感器设计纳入额外的复杂信号处理电路。因此,传感单元在激活报告表达之前触发更复杂的动作,以增强传感器的性能或执行附加功能。这种电路包括拨动开关(Gardner and Cantor, 2000)、逻辑门(Anderson et al., 2006;Wang et al., 2011),转录放大器(Wang and Barahona, 2014)和记忆电路(Courbet et al., 2015;Riglar et al., 2017)。此外,微生物传感器细胞阵列可以设计成显示易于解释的输出模式,对应于特定的输入分析物水平,而无需使用专业实验室设备(Wan et al., 2019)。由于许多早期的微生物传感器在检测限、特异性和输出振幅等方面都不能满足实际要求,近年来人们开发了各种基于基因电路的优化策略来提高其传感性能。与传统的优化方法(如随机诱变)相比,这些合成生物学优化工具基于合理设计,因此更可预测,更快地实现所需的传感规格。例如,使用遗传与门整合多个信号输入已被证明可有效提高微生物传感器的特异性(Wang et al., 2013),合理调节受体蛋白的细胞内水平可大大提高传感器的检测限(Wang AND Barahona, 2015)。此外,还设计了拨动开关(Wu et al., 2009)和翻译后调节装置(Wan et al., 2019),以降低微生物传感器的背景表达和检测限。利用正反馈回路(Jia et al., 2019)或转录信号放大器(Wan et al., 2019)等策略,放大转导传感器信号是进一步提高传感器性能的另一种强大策略。尽管许多合成微生物传感器的概念验证实验室演示取得了成功,但很少能进入市场。仍有几个障碍有待克服,包括感官构建模块数量不足、传感性能差、长期稳定性问题、释放转基因微生物(GMMs)的风险,以及缺乏专业利益相关者接受的实践经验(Hicks和Bachmann, 2020)。然而,合成生物学提供了新的策略来解决这些限制。例如,已经应用了不同的方法来保持生物传感器细胞的长期存活和活性,包括细胞的冷冻干燥和将细胞包裹在聚合物中(Bjerketorp等人,2006;Liu et al., 2018;Wan等人,2019;Shemer et al., 2020)。最近的进展证明了利用微生物惊人的传感能力进行多种应用的潜力,例如可穿戴传感器用于汗液中的生物标志物分析,以实现非侵入性的实时生理状态监测(Liu et al., 2018;Nguyen et al., 2021),或地埋地雷的对峙探测(Belkin et al., 2017)。然而,转基因作物使用的生物安全问题仍然是与田间和体内应用相关的一个问题,包括潜在的水平基因转移和对自然生态系统的破坏。因此,已经提出了不同的生物控制策略来减轻生物安全问题,例如用毒素-抗毒素系统取代抗生素耐药性(Wright等人,2015年),结合条件终止开关(Chan等人,2016年)和非规范氨基酸替代(Fredens等人,2019年)。此外,可以考虑无染色体细菌底盘,如SimCells (Fan et al., 2020)。 值得注意的是,通过避免与使用活细胞相关的生物安全问题,无细胞表达系统作为一种新的传感器平台越来越受欢迎。无细胞生物传感器提供更快的响应,更高的灵敏度和对有毒样品的增强兼容性(Lopreside等人,2019;Silverman and Karim, 2020)。此外,包含遗传传感器的无细胞提取物可以嵌入纸上,为易于使用和具有成本效益的现场筛选提供便携式平台(Pardee等人,2016)或作为智能刺激响应生物材料的水凝胶(Whitfield等人,2020)。合成生物学的最新发展为传感器的构建和响应优化提供了快速的设计-构建-测试周期,以解决微生物生物传感器的局限性。然而,在技术发展范围之内和之外,仍有挑战有待解决。例如,环境、食品和健康监测需要传感器细胞接触复杂的样品,因此需要复杂的信号处理电路和可能的多个输入模块。值得注意的是,对于涉及复杂介质组成(如肿瘤)的生物医学应用,传感器细胞的非特异性定位妨碍了准确的诊断和生物治疗。因此,在指定的空间位置进行传感和报告的工程微生物将是至关重要的(Chien et al., 2021)。考虑到单个微生物细胞的资源容量有限,而大型复杂电路往往会给宿主细胞带来负担,可以使用包含多个通信传感器菌株的细胞联合体来促进传感器功能的多路检测和可重构性(Wang et al., 2013;Khatun et al., 2018)。具有较少耗时的信号传播步骤的创新设计,如仅转录设计,以RNA作为报告实体,或工程配体响应荧光报告蛋白,可以显着缩短微生物生物传感器的响应时间,而替代报告格式,如直接生物电子信号输出,可能会增加与传统电子设备的无缝接口。此外,将工程微生物生物传感器集成到各种材料中,将导致具有自修复等定制功能的可编程生物材料。总之,在合成生物学的崛起时代,我们见证了微生物传感器的新浪潮,并预计这一趋势将继续下去,并可能在未来几十年变得更加强大。虽然活体生物记者目前面临一定的限制,但合成生物学提供了新的工具和策略来加速发展,提高性能并解决微生物生物传感器当前的局限性;这将促进它们在未来的采用和吸收,作为不同环境中有前途的替代分析设备。感谢英国研究与创新未来领袖奖学金[MR/S018875/1], Leverhulme信托研究项目资助[RPG-2020-241]和美国海军研究办公室全球资助[N62909-20-1-2036]的支持。S.B.得到了密涅瓦生物混合复杂系统中心的部分支持。没有宣布。
期刊介绍:
Microbial Biotechnology publishes papers of original research reporting significant advances in any aspect of microbial applications, including, but not limited to biotechnologies related to: Green chemistry; Primary metabolites; Food, beverages and supplements; Secondary metabolites and natural products; Pharmaceuticals; Diagnostics; Agriculture; Bioenergy; Biomining, including oil recovery and processing; Bioremediation; Biopolymers, biomaterials; Bionanotechnology; Biosurfactants and bioemulsifiers; Compatible solutes and bioprotectants; Biosensors, monitoring systems, quantitative microbial risk assessment; Technology development; Protein engineering; Functional genomics; Metabolic engineering; Metabolic design; Systems analysis, modelling; Process engineering; Biologically-based analytical methods; Microbially-based strategies in public health; Microbially-based strategies to influence global processes