Synthetic gene circuits allow us to govern cell behavior in a programmable manner, which is central to almost any application aiming to harness engineered living cells for user-defined tasks. Transcription factors (TFs) constitute the ‘classic’ tool for synthetic circuit construction but some of their inherent constraints, such as insufficient modularity, orthogonality and programmability, limit progress in such forward-engineering endeavors. Here we review how CRISPR (clustered regularly interspaced short palindromic repeats) technology offers new and powerful possibilities for synthetic circuit design. CRISPR systems offer superior characteristics over TFs in many aspects relevant to a modular, predictable and standardized circuit design. Thus, the choice of CRISPR technology as a framework for synthetic circuit design constitutes a valid alternative to complement or replace TFs in synthetic circuits and promises the realization of more ambitious designs.

Introduction

The human species embraces engineering endeavors in every aspect of life, and biology is no exception. Synthetic biology is a promising multidisciplinary field that uses concepts from engineering (such as abstraction, standardization and modularity) to design and create, rather than to only observe and perturb, biological systems. The interface between biology and engineering is successfully growing in areas as varied as metabolic engineering [1], biocomputing [2], cell-based therapeutics [3], biosensors [4], biomaterials [5], tissue engineering and artificial patterning [6,7], genetic re-coding [8], or synthetic/minimal life [9]. One of the fundamental goals of synthetic biology is to create gene circuits that allow for precise spatiotemporal control of gene expression [10]. In nature, gene circuits allow living organisms to respond to intra- or extracellular cues or conditions. Synthetic gene circuits provide researchers with similar programming capabilities and constitute key elements for the success of most engineering efforts in the areas of research listed above. Moreover, the construction and analysis of synthetic circuits that are simplified and controllable versions of natural ones revealed to be a powerful approach for understanding natural gene networks, which often are not amenable to study for complexity or essentiality reasons. Examples of successful efforts to construct synthetic circuits include logic gates [11], bistable switches [12,13], memory devices [14,15], signal-dependent and -independent patterning circuits [16–18], counters [19], light-pattern- and edge-detectors [20,21], intercellular communication circuits [22,23], or molecular clocks [24–26].

Most of the synthetic circuits developed so far employ protein regulators (i.e. transcription factors, TFs) to control gene expression. While protein-based regulators generally offer robust behaviors, they suffer from inherent constraints that limit their applicability from an engineering perspective, such as insufficient modularity, orthogonality and programmability. The use of CRISPR-based circuits promises to bypass many of those limitations. Here we provide an overview of the ever-expanding CRISPR technology for synthetic circuit construction, its possibilities and limitations, and the successful endeavors that have produced CRISPR-based synthetic circuits.

CRISPR mechanism and applications

CRISPR refers to a defense mechanism that is employed by bacteria and archaea to inactivate exogenous nucleic acids, such as plasmids or phage genomes [27]. The basic mechanism of CRISPR immunity is simple. When an exogenous nucleic acid invades the cell, its identity is recorded in a particular cluster of the host genome (the CRISPR array) in the form of short stretches of the invader sequence. This cluster is transcribed and processed into short RNA molecules able to recognize invading sequences and guide the defensive response against them. Thus, during subsequent attacks, specific endonucleases associated to such ‘guide' RNAs will bind to and cleave the invading nucleic acid. The exogenous DNA (or RNA) is identified through a combination of two signals: a stretch (the ‘protospacer') with perfect complementarity to the 5′ region (the ‘spacer') of the guide RNA, and an adjacent short motif (named the protospacer adjacent motif, PAM) recognized by the endonuclease. Some CRISPR types actually utilize two RNA molecules (the CRISPR RNA, cRNA; and the trans-activating CRISPR RNA, tracrRNA) complexed to the nuclease, but chimeric single guide RNAs (sgRNAs) have been engineered for enhanced versatility and straightforward design. For simplicity, here we generically refer to guide RNA (gRNA) throughout the text.

The most immediate application of CRISPR is as highly programmable and specific molecular scissors [28]. By modifying the short (commonly 20 nt) spacer sequence of the gRNA, the system can be easily targeted to virtually any DNA sequence of interest. The Streptococcus pyogenes CRISPR associated protein 9 (Cas9, the most studied CRISPR nuclease) [29] has largely transformed the field of gene editing. Cas9-mediated cleavage enables gene editing through double-strand breaks (DSBs) and subsequent cellular repair [30], and even user-defined modifications when ‘donor’ template sequences are provided [31,32]. Moreover, nuclease-null (the catalytically ‘dead’ dCas9) or nickase (nCas9) variants of the CRISPR nucleases have also been employed, often fused to additional ‘effector’ domains, to perform precise (single-base) editing of genomic DNA without the need for donor templates or DSBs [33–35].

dCas9 and equivalents, alone or in combination with effector domains, have been extensively used for applications requiring cleavage-free specific DNA binding. These include gene expression control (dubbed CRISPRa and CRISPRi, for CRISPR activation and interference, respectively) [36,37], loss-of-function (via CRISPRi) or gain-of-function (using CRISPRa) screens [38,39], gene circuit construction [40,41], epigenome engineering [42–44], CRISPR-ChIP (chromatin immunoprecipitation) [45,46] and genome visualization [47,48]. Likewise, other CRISPR proteins with alternative functions offer attractive characteristics and have been harnessed for varied applications, such as polycistronic mRNA cleavage [49–51], molecular recording [52,53], post-transcriptional regulation [54], or highly sensitive and specific DNA and RNA detection – including detection of human viruses such as Dengue, Zika and SARS-CoV-2 coronavirus [55,56].

Why CRISPR circuits

One of the applications where the use of CRISPR tools holds a vast potential is the construction of synthetic gene circuits. Since the publication of the first artificial gene circuits in the year 2000 [12,24], most synthetic circuits have made use of (natural, mutant or synthetic) TFs as a means to regulate gene expression. Generally, TFs offer important characteristics for gene circuit function such as a high dynamic range (i.e. difference between OFF and ON states) and frequently also an ultrasensitive behavior (i.e. a switch-like activation/repression), but they are far from ideal from an engineering point of view.

TFs are difficult to engineer: their specificity relies on protein-DNA binding, an interface that is immensely more difficult to predict, model and design than DNA-DNA or RNA-DNA binding. Despite notorious efforts to develop modular TFs – e.g. transcription activator-like effector (TALE) TFs [57] or zinc finger TFs [58] – their designability still remains limited [59,60]. Another problem relates to the available choice of TFs: because the number of well-characterized TFs is still rather modest, synthetic biologist often use TFs from the same superfamilies. However, this often results in a limited orthogonality, i.e TFs binding to each other's binding sites, albeit with lower affinity – this is particularly troublesome when building large circuits. Despite notable exceptions [20,61], large circuits are also likely to suffer from ‘incremental burden’: as the number of TFs increases, the host cell needs to cope with additional transcription and, more importantly, translation, the latter being a highly demanding process that can notoriously impact the host metabolism. Moreover, sequences encoding TFs are relatively long – for instance, the median length of proteins involved in transcription is 240 aa in bacteria and 444 aa in eukaryotes, which corresponds to 720 nt-long and 1332 nt-long genes, respectively [62]. Longer encoding sequences result in higher synthesis costs and more challenging cloning and delivery to the host cell.

In CRISPR circuits, the binding of the effector protein (nearly always dCas9 or a derivative) to the DNA is mediated by an associated RNA molecule, the gRNA, and this effectively circumvents many of the intrinsic limitations of TFs. CRISPR-mediated gene expression control is extremely programmable and easy to design – the modification of the 20 nt spacer sequence of the gRNA suffices to direct the activity of dCas9 to a new target. For the same reason, CRISPR orthogonality is virtually unlimited, with a theoretical upper limit of 420 possible sequences of 20 nt. Although this number is practically unrealistic, because highly similar sequences are likely to exhibit some degree of crosstalk, the pool of potentially orthogonal gRNAs is way larger than the number of gRNAs that a researcher would possibly be able to use in a rationally-designed circuit. The ribonucleic nature of the gRNAs allows for straightforward and reliable in silico prediction of their structure and behavior in different environments, which facilitates the extension of the gRNAs with functional modules conferring new functions, such as ligand-binding RNA aptamers [63,64]. Besides, gRNA sequences are short (typically ∼100 nt for the most popular gRNA), which positively impacts design, synthesis economy, cloning procedures and delivery to the host cell.

Despite existing reports on (d)Cas9 toxicity [65–69], which has been proposed to be sequence-specific [70], the incremental burden of CRISPR circuits is generally low. Given a basal expression of dCas9, the extension of a circuit through the addition of extra regulatory interactions does not pose an insurmountable challenge on the host metabolism, since only transcription of the gRNAs (but not translation) is required [69]. Indeed, this low incremental burden facilitated in 2017 the construction of the largest synthetic circuit hitherto [71]. The production of the relatively large dCas9 might, in some instance, challenge the host metabolism, especially when high dCas9 levels are required or when (small) fitness variations introduce a high penalty – e.g in a poor medium or in a competition assay. Yet, with a steadily increasing number of available CRISPR systems, such considerations may soon become obsolete – for instance, a hypercompact CasΦ has been recently discovered that is about half the size of Cas9 [72]. Alternatively, novel resource-effective designs may help mitigate the potential load on the host metabolism, for example by engineering self-adjustable dCas9 expression systems [73].

Gene expression control using CRISPR

The general requirement for the use of CRISPR technology in gene circuits is the ability to regulate gene expression. The strategies for transcriptional activation and repression through CRISPR tools are known as CRISPRa (activation) and CRISPRi (interference), respectively (Table 1 and Figure 1A). In bacteria, the mechanism for CRISPRi is rather simple: repression can be achieved by directing a dead CRISPR nuclease (most commonly dCas9) to a transcriptional promoter or a downstream coding sequence (CDS) (Figure 1A) [37,74]. Promoter targeting is more effective because it blocks the binding of the RNA polymerase (RNAP) to the DNA, thereby preventing transcription initiation. Targeting the CDS works as a roadblock for transcriptional elongation, and its efficiency decreases with increasing distance between the binding site and the promoter. In yeast, dCas9 is an efficient transcriptional repressor both alone and in combination with the Mxi1 silencing domain [36,75]. In mammalian cells, dCas9 alone is not an efficient repressor [37], and needs to be fused to a repressive domain such as KRAB (Krüppel-associated box) (Figure 1A) [36,75].

Gene expression control through CRISPRi and CRISPRa, and strategies to modulate their activity.

Figure 1.
Gene expression control through CRISPRi and CRISPRa, and strategies to modulate their activity.

(A) Mechanisms of CRISPRi/a-based gene expression control. The activity of ‘helper’ effector domains is required except for bacterial and yeast CRISPRi, where dCas9 alone can efficiently repress gene expression by interfering with transcription initiation or elongation. These effector domains can be either fused to dCas9 or recruited through an additional RNA-recognizing domain that binds to a specific extension of the gRNA. (B) Strategies to modulate CRISPRi/a activity. CRISPR gene control has proved a versatile platform that tolerates numerous engineering extensions. Due to its dual protein-RNA nature, the activity of the dCas9-gRNA complex can be modulated at the level of the protein (dCas9), at the level of the gRNA, or at the interface between the two. Note that for simplicity this panel depicts dCas9 alone, but many of these tuning strategies actually also work with dCas9 fused to effectors.

Figure 1.
Gene expression control through CRISPRi and CRISPRa, and strategies to modulate their activity.

(A) Mechanisms of CRISPRi/a-based gene expression control. The activity of ‘helper’ effector domains is required except for bacterial and yeast CRISPRi, where dCas9 alone can efficiently repress gene expression by interfering with transcription initiation or elongation. These effector domains can be either fused to dCas9 or recruited through an additional RNA-recognizing domain that binds to a specific extension of the gRNA. (B) Strategies to modulate CRISPRi/a activity. CRISPR gene control has proved a versatile platform that tolerates numerous engineering extensions. Due to its dual protein-RNA nature, the activity of the dCas9-gRNA complex can be modulated at the level of the protein (dCas9), at the level of the gRNA, or at the interface between the two. Note that for simplicity this panel depicts dCas9 alone, but many of these tuning strategies actually also work with dCas9 fused to effectors.

Table 1
The CRISPR toolbox for gene expression control
Cas proteinFusionFunctionCellular contextReference(s)
dCas9 CRISPRi E. coli and other bacteria, yeast [36,74,75,84
dCas9 RNAP omega subunit CRISPRa E. coli [74
dCas9 MS2 aptamer (in gRNA) that recruits MCP-SoxS CRISPRa E. coli [77,78
dCas9 BoxB aptamer (in gRNA) that recruits λN22plus-PspFΔHTH CRISPRa E. coli [79
dCas9 AsiA CRISPRa E. coli [76
dCas9 KRAB CRISPRi Yeast, human cells [36,75,85–87
dCas9 Mxi1 CRISPRi Yeast [36
dCas9 SRDX CRISPRi Arabidopsis [88
dCas9 VP64 CRISPRa Yeast, human cells, Arabidopsis, tobacco [36,75,80,88,89
dCas9 p65 CRISPRa Human cells [36
dCas9 SunTag-VP64 CRISPRa Human cells [81,85
dCas9 VPR (VP64-p65-Rta) CRISPRa Yeast, fruit fly, mouse and human cells [82
dCas9 SAM: dCas9-VP64 & MS2 aptamer (in gRNA) that recruits MCP-p65-Hsf1 CRISPRa Mouse and human cells [39
dCas9 TV (tandem TAL and VP64) CRISPRa Arabidopsis, rice [90
dCas9 VP64 & MS2 aptamer (in gRNA) that recruits MCP-VP64 CRISPRa Arabidopsis, rice [91
dCas9 MS2, PP7 or Com aptamer (in gRNA) that recruits MCP, PCP or Com fused to VP64 or KRAB CRISPRa/i Yeast, human cells [83
dCas9 p300 Histone acetylation Human cells [44
dCas9 HDAC3 Histone deacetylation Mouse cells [92
dCas9 PRDM9 Histone methylation Human cells [93
dCas9 DOT1L Histone methylation Human cells [93
dCas9 LSD1 Histone demethylation Mouse cells [94
dCas9 DNMT3a DNA methylation Mouse and human cells [42,95
dCas9 TET1 DNA demethylation Mouse cells [95
dCas9 TET1 & MS2 aptamer (in gRNA) that recruits MCP-TET1 DNA demethylation Human cells [43
dCas12 CRISPRi E. coli, human cells [96–98
dCas12 KRAB CRISPRi Human cells [98
dCas12 SRDX CRISPRi Arabidopsis [99
dCas12 VP64 CRISPRa Human cells [98
dCas12 p65 CRISPRa Human cells [100
dCas12 VPR (VP64-p65-Rta) CRISPRa Human cells [98
Cas13 RNA transcript knockdown E. coli, rice and human cells [101
Cas proteinFusionFunctionCellular contextReference(s)
dCas9 CRISPRi E. coli and other bacteria, yeast [36,74,75,84
dCas9 RNAP omega subunit CRISPRa E. coli [74
dCas9 MS2 aptamer (in gRNA) that recruits MCP-SoxS CRISPRa E. coli [77,78
dCas9 BoxB aptamer (in gRNA) that recruits λN22plus-PspFΔHTH CRISPRa E. coli [79
dCas9 AsiA CRISPRa E. coli [76
dCas9 KRAB CRISPRi Yeast, human cells [36,75,85–87
dCas9 Mxi1 CRISPRi Yeast [36
dCas9 SRDX CRISPRi Arabidopsis [88
dCas9 VP64 CRISPRa Yeast, human cells, Arabidopsis, tobacco [36,75,80,88,89
dCas9 p65 CRISPRa Human cells [36
dCas9 SunTag-VP64 CRISPRa Human cells [81,85
dCas9 VPR (VP64-p65-Rta) CRISPRa Yeast, fruit fly, mouse and human cells [82
dCas9 SAM: dCas9-VP64 & MS2 aptamer (in gRNA) that recruits MCP-p65-Hsf1 CRISPRa Mouse and human cells [39
dCas9 TV (tandem TAL and VP64) CRISPRa Arabidopsis, rice [90
dCas9 VP64 & MS2 aptamer (in gRNA) that recruits MCP-VP64 CRISPRa Arabidopsis, rice [91
dCas9 MS2, PP7 or Com aptamer (in gRNA) that recruits MCP, PCP or Com fused to VP64 or KRAB CRISPRa/i Yeast, human cells [83
dCas9 p300 Histone acetylation Human cells [44
dCas9 HDAC3 Histone deacetylation Mouse cells [92
dCas9 PRDM9 Histone methylation Human cells [93
dCas9 DOT1L Histone methylation Human cells [93
dCas9 LSD1 Histone demethylation Mouse cells [94
dCas9 DNMT3a DNA methylation Mouse and human cells [42,95
dCas9 TET1 DNA demethylation Mouse cells [95
dCas9 TET1 & MS2 aptamer (in gRNA) that recruits MCP-TET1 DNA demethylation Human cells [43
dCas12 CRISPRi E. coli, human cells [96–98
dCas12 KRAB CRISPRi Human cells [98
dCas12 SRDX CRISPRi Arabidopsis [99
dCas12 VP64 CRISPRa Human cells [98
dCas12 p65 CRISPRa Human cells [100
dCas12 VPR (VP64-p65-Rta) CRISPRa Human cells [98
Cas13 RNA transcript knockdown E. coli, rice and human cells [101

CRISPRa requires activator domains both in eukaryotes and prokaryotes (Table 1 and Figure 1A). In bacteria, dCas9 has been fused to the RNAP omega subunit [74] or to the phage activator AsiA [76]. Alternatively, the gRNA has been extended with protein-recruiting RNA scaffolds (Figure 1A), such as the MS2 hairpin that recruits a MCP (MS2 coat protein)-SoxS activator fusion protein [77,78], or the BoxB aptamers that bind to the λN22plus peptide fused to the PspF activation domain to promote gene expression from σ54 promoters [79]. Likewise, in eukaryotic systems the activator domain works as a direct dCas9 fusion (for example the VP64 domain, the tripartite VPR, or the repetitive SunTag) [75,80–82], indirectly as a fusion to an RNA-recognizing peptide (like the MS2-MCP pair) [83], or by combining these two approaches (e.g. the synergistic activation mediator, SAM) [39]. Table 1 summarizes the different CRISPR proteins and their fusions used for CRISPRi, CRISPRa and epigenome-modifying functions.

Possibilities and limitations

A key feature of CRISPR gene control for building complex circuits is multiplexing, i.e. the ability to regulate multiple targets at the same time [102]. CRISPR-based regulation is intrinsically modular: the target is chosen by a gRNA, while the action on that target is mediated by the associated ‘effector’ protein (e.g. dCas9). Therefore, the same effector can be directed to multiple targets simply by using different gRNAs, which allows to simultaneously control tens of genes [103,104] and even to activate some genes while repressing others [83,105]. Of course, orthogonality is a prerequisite for multiplexing; the design of orthogonal gRNA spacers or the use of orthogonal CRISPR systems from different species [106] allows to easily avoid cross-reactivity.

One of the downsides of multiplexing is retroactivity, i.e. a decrease in performance of CRISPR-mediated regulation with increasing numbers of gRNAs due to competition for a common pool of dCas9 molecules [107–109]. The pool of dCas9 may be regulated to limit retroactivity: recently, optimization of dCas9 expression levels enabled efficient repression of up to 22 gRNA targets simultaneously [104]. This should be sufficient for most synthetic circuits, especially since often not all gRNA are expressed at the same time but rather regulate each other in a concerted manner [69,109].

When trying to express multiple gRNAs, instead of expressing them from separate promoters, strategies exist to combine them within a single transcriptional unit and to process the transcribed mRNA molecule into independent gRNAs. This simplifies the design, reduces the probability of unintended recombination due to repetitive promoter/terminator sequences, and allows for combined control of gRNA expression. Unintended recombination may also result from the repetition of gRNAs sharing the same nuclease-binding scaffold. To address this issue, a recent work developed non-repetitive gRNA scaffolds that enabled the simultaneous expression of more than 20 gRNAs [104]. In some natural CRISPR systems, the effector nuclease also works as a sequence-specific endoribonuclease (e.g. Cas12a), which enables the use of multi-gRNA constructs without additional RNA processing tools [103]. Some other CRISPR systems employ accessory proteins to cleave the multi-gRNA transcript – for instance Csy4 [110], which has been used as a gRNA-processing tool in (d)Cas9-based setups [40,50,51]. Alternatively, self-cleaving ribozymes have also been used to process multi-gRNA transcripts [40].

An important consideration when working with CRISPR is the target specificity. Unintended off-target activity has been extensively reported [111,112], and constitutes one of the main limitations of CRISPR technology, especially for applications where exquisite precision is needed, like gene editing for human therapeutics. The lack of specificity is more pernicious in eukaryotes because larger genomes increase the number of potential unintended binding sites. There is consequently a major interest in developing CRISPR tools with improved specificity, and numerous successful approaches have been reported [35,49,113–116]. Interestingly, CRISPRi and CRISPRa show reduced off-target levels compared to CRISPR-mediated DNA cleavage [36,39], possibly due to the requirement for binding in the proximity of a promoter in the former [117], which makes it less likely for an off-target binding to be deleterious. The off-target activity is likely a major contributor of (d)Cas9 toxicity, and approaches that increase binding specificity can concomitantly reduce toxicity [108].

Regardless of the RNA-guided nature, CRISPR systems do not display fast dynamics. In fact, CRISPR target search requires a constant DNA interrogation by (d)Cas9 in search for PAM sites, which are very abundant across the genome (e.g. the dCas9 PAM NGG is present every 8 bp on average in the E. coli genome), and then unwinds the adjacent DNA and assesses gRNA complementarity [118]. Studies in E. coli and Lactococcus lactis suggest that a single fluorescently labelled dCas9 molecule requires few hours (∼4–6 h) to find a single target within the cell, which means that dCas9 binds for ∼20–30 ms to each potential target [119,120]. For comparison, a single fluorescently labelled LacI dimer in E. coli needs less than 5 min to find a single target, and spends <5 ms (the detection limit) bound to each potential target [121]. In real-world applications such as synthetic circuits, the amount of intracellular dCas9 is well above one molecule, which effectively reduces the target search time to few minutes [119,120]. Once bound to its target, the dissociation rate of dCas9 is low, with DNA replication being the major driver of unbinding [119].

One of the prerequisites for CRISPR-based gene regulation to replace TFs in gene circuits is to achieve a sufficiently high dynamic range. While the dynamic range of TFs is generally high, that of CRISPRi and CRISPRa systems seems to depend to a broad extent on the particular implementation. With a steadily growing number of available CRISPR-based gene activation and repression strategies, the dynamic range is not a major limitation anymore for most CRISPR circuit applications in both prokaryotes and eukaryotes. Only CRISPRa in bacteria is lagging behind, possibly hampered by the limited number of gene expression activation tools in bacteria, but recent studies reported up to 1000-fold activation [74,76,77,79].

Another potential limitation of CRISPR circuits is the apparent lack of non-linearity [41,122,123]. In TFs, the cooperative activity of multimers or the presence of multiple binding sites commonly results in a non-linear, ultrasensitive behavior characterized by a sigmoidal dose-response with a steep output increase for a narrow input range. Since dCas9 works as a monomer and associates to a single binding site, its dose-response was thought to be linear, which would greatly limit the broad applicability of CRISPR tools for synthetic circuit construction [41,122,123]. Indeed, a lack of non-linearity poses major challenges for building important synthetic networks such as oscillating or multistable circuits, which require non-linear gene expression control to operate [124–127]. However, recent results demonstrate that it is actually possible to build CRISPR-based multistable and dynamic circuits such as a toggle switch and an oscillator (see ‘CRISPR-based synthetic circuits' section for details) [69,128,129].

Modulating CRISPRa/i activity

A central aspect of any circuit design is how to precisely modulate gene expression. Many TFs can be externally regulated by simple actions such as adding a small molecule to the medium, changing the temperature or the pH, or exposing cells to a light of a given wavelength. Unfortunately, no such regulation of nuclease activity has been found in native CRISPR systems. The simplest way to regulate CRISPR activity is to express either the gRNAs, the effector, or both, from an inducible promoter (Figure 1B). Yet, more sophisticated approaches for conditional CRISPR regulation have been developed (Figure 1B). In riboswitch-like gRNAs, for example, an RNA aptamer appended to the gRNA sequence undergoes conformational changes upon ligand binding that disrupt or restore the native gRNA structure, thereby abolishing [130] or enabling [63,130] function. Similarly, strand displacement reactions allow for disruption or reconstitution of a functional gRNA only in the presence of a trigger RNA molecule [131–134]. The availability of the gRNA can also be modulated by incorporating protein- or oligonucleotide-controlled RNA-cleaving units [135] or a ligand-responsive self-cleaving catalytic RNA (aptazyme) into the gRNA (Figure 1B) [64]. Additionally, inducible heterodimerization domains allow for colocalization of regulatory domains and DNA-bound gRNA-dCas9 complexes only in the presence of the inducer [105].

The strengths of CRISPR activation and repression can also be modulated by partially disrupting the DNA-gRNA or the gRNA-effector interactions (Figure 1B). For instance, truncations of the 20 nt spacer or mismatches between the spacer and its target reduce the binding strength and the subsequent regulation efficiency in a reasonably predictable manner. Moreover, short 5′ truncations of the gRNAs have interesting (and somehow unexpected) effects: 17–18 nt spacers result in reduced off-target activity without losing on-target efficacy [113], and even shorter spacers (15 nt) prevent DNA cleavage but still allow DNA binding and transcriptional repression, even when using catalytically-active Cas nucleases [103]. The magnitude of the regulation can also be tuned down by introducing mismatches in the gRNA scaffold, which presumably decreases gRNA-dCas9 affinity [79]. Other strategies for CRISPR activity modulation include the use of ligand-stabilized degron domains [136], viral anti-CRISPR proteins [137], light-induced CRISPR proteins [138,139] and antisense RNA molecules that bind and sequester gRNAs (anti-gRNAs) (Figure 1B) [67].

CRISPR-based synthetic circuits

By definition, a circuit implies connecting components together into a larger, higher-level design. In that sense, individual circuit components should ideally feature two important properties: the amenability to form layered regulatory cascades; and the capacity to integrate multiple inputs. The former implies that regulatory interactions can be connected to each other without excessive loss of signal. The latter is key for implementing multi-input logic gates and for building branched networks.

A number of CRISPR circuits have been built so far in bacteria, yeast and mammalian cells (Figure 2), and many of these constitute experimental demonstrations of efficient layering and signal integration provided by CRISPR tools. Both CRISPRa and CRISPRi can be layered in series to form linear cascades. In CRISPRa cascades, layered activation results in signal transmission with no sign change [40,79]. On the other hand, layered CRISPRi provides sign inversions with every new layer added: a simple repression will convert a high signal into a low one, but two serial repressions (a ‘double-inverter’) will generate a high signal again, and so on [41,50,69,71,122,140–142]. So far, up to seven CRISPRi layers have been successfully connected into a repression cascade (Figure 2B) [71].

CRISPR-based synthetic gene circuits.

Figure 2.
CRISPR-based synthetic gene circuits.

(A) Examples of CRISPR-based logic gates. Left: AND gate based on ligand-induced heterodimerization domains implemented in human cells [105]. Only in the presence of the two ligands can the activator domain be recruited to the DNA-bound dCas9 and activate GFP expression. Middle: NOR gate constructed in E. coli using gRNAs expressed from inducible promoters [41]. Both gRNAs bind to and repress RFP, and thus only in the absence of both inducers can RFP be expressed. Right: NIMPLY gate in human cells mediated by KRAB-fused dCas9 [142]. YFP expression is enabled when one of the input gRNAs (but not the other) is present. (B) Seven-layer repression cascade built in yeast by using dCas9-Mxi1 [71]. Each new layer inverts the ‘sign’ (i.e. 1 or 0, high or low) of the downstream layers and of the final output. (C) Pulse-generating IFFL in human cells by leveraging dCas9-VPR inactivation by anti-CRISPR proteins [137]. The result is a temporal peak of gene expression. (D) Stripe-forming IFFL in E. coli [69]. In the presence of a gradient of arabinose (Ara, the input inducer), the dCas9-governed circuit forms a peak (stripe) of gene expression at intermediate Ara concentrations. Genetically identical bacteria carrying this circuit form a 3-color pattern in an agar plate when subjected to an Ara gradient. (E) dCas9-controlled toggle switch in E. coli [69]. The circuit can be toggled between the two stable states (green and non-green) through the addition of inducers to the medium: after inducing one of the states, the cells remain in that state even in the absence of any inducer, and the same is true for the opposite state. (F) The CRISPRlator, the first CRISPR oscillator [69]. A dCas9-mediated closed-ring repression topology generates periodic oscillations in E. coli cells.

Figure 2.
CRISPR-based synthetic gene circuits.

(A) Examples of CRISPR-based logic gates. Left: AND gate based on ligand-induced heterodimerization domains implemented in human cells [105]. Only in the presence of the two ligands can the activator domain be recruited to the DNA-bound dCas9 and activate GFP expression. Middle: NOR gate constructed in E. coli using gRNAs expressed from inducible promoters [41]. Both gRNAs bind to and repress RFP, and thus only in the absence of both inducers can RFP be expressed. Right: NIMPLY gate in human cells mediated by KRAB-fused dCas9 [142]. YFP expression is enabled when one of the input gRNAs (but not the other) is present. (B) Seven-layer repression cascade built in yeast by using dCas9-Mxi1 [71]. Each new layer inverts the ‘sign’ (i.e. 1 or 0, high or low) of the downstream layers and of the final output. (C) Pulse-generating IFFL in human cells by leveraging dCas9-VPR inactivation by anti-CRISPR proteins [137]. The result is a temporal peak of gene expression. (D) Stripe-forming IFFL in E. coli [69]. In the presence of a gradient of arabinose (Ara, the input inducer), the dCas9-governed circuit forms a peak (stripe) of gene expression at intermediate Ara concentrations. Genetically identical bacteria carrying this circuit form a 3-color pattern in an agar plate when subjected to an Ara gradient. (E) dCas9-controlled toggle switch in E. coli [69]. The circuit can be toggled between the two stable states (green and non-green) through the addition of inducers to the medium: after inducing one of the states, the cells remain in that state even in the absence of any inducer, and the same is true for the opposite state. (F) The CRISPRlator, the first CRISPR oscillator [69]. A dCas9-mediated closed-ring repression topology generates periodic oscillations in E. coli cells.

Signal integration is a necessary requirement for building most logic gates. Simple activation by CRISPRa and repression by CRISPRi constitute 1-input Buffer (passes the input unchanged to the output) and NOT gates, respectively, but gates with more complex functions generally require at least 2 inputs. CRISPR gene regulation has been employed by different groups to build Buffer, NOT, AND, OR, NAND, NOR, XOR, XNOR, and NIMPLY gates (Figure 2A) [41,63,71,105,131,142,143] – some even in non-model organisms [144]. Since CRISPR gene regulation is essentially a two-component system (gRNA + effector), AND and NOR gates can be easily constructed simply by devising designs in which the two components are produced separately and both are required for function, i.e. gRNA + activating effector = AND gate, while gRNA + repressing effector = NOR gate. Alternatively, more elaborated mechanisms can be envisioned (Figure 2A). As repression interactions are sufficient to construct all types of logic gates, CRISPRi suffices and has been used to build all types of logic gates [41,71,142]. Despite the apparent simplicity of logic gates, they constitute powerful cell-programming tools with great potential. For example, CRISPR AND gates have been employed to selectively identify human bladder cancer cells and enable cancer-specific antitumoral activity [143].

CRISPR regulation has also been harnessed for modulating host physiology and biosynthetic pathways. CRISPRa and CRISPRi have proved suitable for tuning or redirecting fluxes in the endogenous E. coli maltose utilization pathway [41], in the heterologous violacein pathway [79,145], and in endogenous cell proliferation and cell migration oncogenic signaling networks [63]; also for preventing metabolic burden [146], for controlling bacterial replication [147], or for triggering DNA looping [148] or cell filamentation [149]. Furthermore, CRISPR-based synthetic circuits have also been used to produce temporal or spatial behaviors. For instance, combining CRISPRa or CRISPRi with additional regulatory approaches (such as anti-CRISPR proteins or small transcription activating RNAs, STARs) has enabled the construction of pulse-generating incoherent feed-forward loops (IFFL) (Figure 2C) [137,150]. In response to a spatial concentration gradient of an inducer molecule IFFLs can also produce a spatial change in gene expression, the so-called stripe pattern or band-pass filter. Such a CRISPR-based IFFL has been built to govern the 3-domain patterning of a bacterial population (Figure 2D) [69]. The high orthogonality and low incremental burden of CRISPR circuits facilitates the co-existence of such stripe-forming IFFL with additional circuits within the same cellular environment without cross-interference, including a second stripe-forming IFFL and a double-inverter [69].

As already mentioned, a potential limitation of CRISPR circuit is the apparent lack of non-linearity [151]. Without non-linearity, circuits with relevant behaviors such as bistable (toggle) switches and oscillators cannot be achieved. The linear dose-response of CRISPR gene regulation has been both modelled [123] and experimentally observed [41], thereby supporting the aforementioned constraint. However, a recent study demonstrated the use of CRISPRi to construct circuits with behaviors requiring non-linearity, including a CRISPRi bistable toggle switch (Figure 2E) and a CRISPRi oscillator (Figure 2F), named CRISPRlator [69]. The CRISPRlator displayed robust inheritance of the oscillatory state across cell divisions, resulting in synchronous long-term oscillations of a cell-population in a microfluidic chamber.

Further confirmation of dynamic CRISPR circuits comes from a subsequent study showing an alternative dCas9 oscillator as well as a dCas12a-based version [128]. In this setup, the dCas12a-based circuit displayed more regular oscillations than the one relying on dCas9 [128]. Additionally, a new preprint explored a hybrid oscillator in which one of the three initial TFs of the repressilator is replaced by a dCas9-gRNA complex [129]. In this study, decoy binding sites for the dCas9-gRNA complex have been suggested to play a role in generating the required non-linearity in the absence of cooperativity. Interestingly, other successful designs lack such specifically designed decoy binding sites [69,128]. Instead, mathematical modeling suggests that the repeated weak binding of dCas9 to the numerous PAM sites during its target search could provide the non-linearity required for these circuits to work [69]. Nonetheless, the conditions that determine the linear [41] versus non-linear [69,128,129] behavior of CRISPR gene regulation remain yet to be uncovered.

Final remarks

CRISPR gene regulation offers an easy design, a notable dynamic range, and a good compatibility with other transcriptional regulation approaches [40,67,150,152]. The exceptional orthogonality and the low incremental burden enable the construction of remarkably large circuits or combinations of them [69,71]. Despite these many advantages of CRISPR tools, relatively few synthetic CRISPR circuits have been built so far. We anticipate that recent substantial contributions such as novel CRISPRa/i approaches or circuits displaying non-linear behaviors will boost the use of CRISPR for synthetic circuit construction in the following years.

Perspectives

  • Synthetic gene circuits allow us to program cells in a reliable and user-defined manner for a myriad of applications ranging from engineered cell-based therapeutics to ‘smart’ crops or sustainable bioproduction. CRISPR-based gene expression control holds great potential over ‘classical’ transcription factor (TF)-based designs for synthetic circuit construction due to favorable characteristics such as high programmability, modularity and orthogonality.

  • Relatively few CRISPR-based gene circuits have been built so far, and their full potential has not yet been unleashed. Present CRISPR circuits include logic gates, cascades, bistable switches and temporal and spatial pattern generators, showing that TF-equivalent (or even improved) behaviors are possible.

  • Recent advances such as improved and modulable CRISPR gene regulation or CRISPR circuits capable of non-linear behaviors have provided the field with new impulses. We anticipate that CRISPR circuits will enable the construction of complex and large synthetic circuits that would not be achievable with TF-based designs.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was funded by the Swiss National Science Foundation grant 31003A_175608.

Author Contributions

J.S.M. and Y.S. conceived the article and wrote the manuscript. J.S.M. prepared the figures. All authors have given approval to the final version of the manuscript.

Acknowledgements

We thank Içvara Barbier and Florian Baier for feedback on the manuscript.

Abbreviations

     
  • aa

    amino acid

  •  
  • ABA

    abscisic acid

  •  
  • AHL

    N-acyl homoserine lactone

  •  
  • Ara

    arabinose

  •  
  • AsiA

    10 kDa anti-sigma factor

  •  
  • Cas9

    CRISPR associated protein 9

  •  
  • CDS

    coding sequence

  •  
  • ChIP

    chromatin immunoprecipitation

  •  
  • CRISPR

    clustered regularly interspaced short palindromic repeats

  •  
  • CRISPRa

    CRISPR activation

  •  
  • CRISPRi

    CRISPR interference

  •  
  • CRISPRlator

    CRISPR oscillator

  •  
  • cRNA

    CRISPR RNA

  •  
  • Csy4

    CRISPR-associated endonuclease Cas6/Csy4

  •  
  • DAPG

    2,4-Diacetylphloroglucinol

  •  
  • dCas9

    catalytically dead Cas9

  •  
  • DNMT3a

    DNA (cytosine-5)-methyltransferase 3A

  •  
  • DOT1L

    DOT1 like histone lysine methyltransferase

  •  
  • DSB

    double-strand break

  •  
  • GA

    gibberellin

  •  
  • GFP

    green fluorescent protein

  •  
  • gRNA

    guide RNA

  •  
  • h

    hour

  •  
  • HDAC3

    histone deacetylase 3

  •  
  • IFFL

    incoherent feed-forward loop

  •  
  • KRAB

    Krüppel-associated box

  •  
  • LacI

    lac repressor

  •  
  • LSD1

    lysine-specific histone demethylase 1

  •  
  • MCP

    MS2 coat protein

  •  
  • min

    minute

  •  
  • mKO2

    mKusabira-Orange 2

  •  
  • ms

    millisecond

  •  
  • Mxi1

    MAX interacting protein 1

  •  
  • nCas9

    nickase Cas9

  •  
  • nt

    nucleotide

  •  
  • PAM

    protospacer adjacent motif

  •  
  • PRDM9

    PR/SET domain 9

  •  
  • PspF

    Psp operon transcriptional activator

  •  
  • RFP

    red fluorescent protein

  •  
  • RNAP

    RNA polymerase

  •  
  • RNase

    ribonuclease

  •  
  • SAM

    synergistic activation mediator

  •  
  • SARS-CoV-2

    severe acute respiratory syndrome coronavirus 2

  •  
  • sgRNA

    single guide RNA

  •  
  • SRDX

    EAR repression domain

  •  
  • STAR

    small transcription activating RNA

  •  
  • TAL

    TALE transcription activation domain

  •  
  • TALE

    transcription activator-like effector

  •  
  • TET1

    Tet methylcytosine dioxygenase 1

  •  
  • TF

    transcription factor

  •  
  • tracrRNA

    trans-activating CRISPR RNA

  •  
  • VP64

    four tandem copies of herpes simplex viral protein 16

  •  
  • VPR

    VP64-p65-Rta tripartite activator

  •  
  • YFP

    yellow fluorescent protein

References

References
1
Nielsen
,
J.
and
Keasling
,
J.D.
(
2016
)
Engineering cellular metabolism
.
Cell
164
,
1185
1197
2
Grozinger
,
L.
,
Amos
,
M.
,
Gorochowski
,
T.E.
,
Carbonell
,
P.
,
Oyarzún
,
D.A.
,
Stoof
,
R.
et al (
2019
)
Pathways to cellular supremacy in biocomputing
.
Nat. Commun.
10
,
5250
3
Kitada
,
T.
,
DiAndreth
,
B.
,
Teague
,
B.
and
Weiss
,
R.
(
2018
)
Programming gene and engineered-cell therapies with synthetic biology
.
Science
359
,
eaad1067
4
Hicks
,
M.
,
Bachmann
,
T.T.
and
Wang
,
B.
(
2020
)
Synthetic biology enables programmable cell-based biosensors
.
ChemPhysChem
21
,
132
144
5
Le Feuvre
,
R.A.
and
Scrutton
,
N.S.
(
2018
)
A living foundry for synthetic biological materials: a synthetic biology roadmap to new advanced materials
.
Synth. Syst. Biotechnol.
3
,
105
112
6
Davies
,
J.A.
and
Cachat
,
E.
(
2016
)
Synthetic biology meets tissue engineering
.
Biochem. Soc. Trans.
44
,
696
701
7
Santos-Moreno
,
J.
and
Schaerli
,
Y.
(
2019
)
Using synthetic biology to engineer spatial patterns
.
Adv. Biosyst.
3
,
1800280
8
Santos-Moreno
,
J.
and
Schaerli
,
Y.
(
2020
)
Changing the biological Rosetta stone: the (commercial) potential of recoded microbes
.
Microb. Biotechnol.
13
,
11
13
9
Lachance
,
J.-C.
,
Rodrigue
,
S.
and
Palsson
,
B.O.
(
2019
)
Minimal cells, maximal knowledge
.
eLife
8
,
e45379
10
Xie
,
M.Q.
and
Fussenegger
,
M.
(
2018
)
Designing cell function: assembly of synthetic gene circuits for cell biology applications
.
Nat. Rev. Mol. Cell Biol.
19
,
507
525
11
Daniel
,
R.
,
Rubens
,
J.R.
,
Sarpeshkar
,
R.
and
Lu
,
T.K.
(
2013
)
Synthetic analog computation in living cells
.
Nature
497
,
619
623
12
Gardner
,
T.S.
,
Cantor
,
C.R.
and
Collins
,
J.J.
(
2000
)
Construction of a genetic toggle switch in Escherichia coli
.
Nature
403
,
339
13
Barbier
,
I.
,
Carrasco
,
R.P.
and
Schaerli
,
Y.
(
2020
)
Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch
.
Mol. Syst. Biol.
16
,
e9361
14
Farzadfard
,
F.
and
Lu
,
T.K.
(
2014
)
Genomically encoded analog memory with precise in vivo DNA writing in living cell populations
.
Science
346
,
1256272
15
Bonnet
,
J.
,
Subsoontorn
,
P.
and
Endy
,
D.
(
2012
)
Rewritable digital data storage in live cells via engineered control of recombination directionality
.
Proc. Natl Acad. Sci. U.S.A.
109
,
8884
16
Schaerli
,
Y.
,
Munteanu
,
A.
,
Gili
,
M.
,
Cotterell
,
J.
,
Sharpe
,
J.
and
Isalan
,
M.
(
2014
)
A unified design space of synthetic stripe-forming networks
.
Nat. Commun.
5
,
4905
17
Liu
,
C.
,
Fu
,
X.
,
Liu
,
L.
,
Ren
,
X.
,
Chau
,
C.K.
,
Li
,
S.
et al (
2011
)
Sequential establishment of stripe patterns in an expanding cell population
.
Science
334
,
238
241
18
Toda
,
S.
,
Blauch
,
L.R.
,
Tang
,
S.K.Y.
,
Morsut
,
L.
and
Lim
,
W.A.
(
2018
)
Programming self-organizing multicellular structures with synthetic cell-cell signaling
.
Science
361
,
156
162
19
Friedland
,
A.E.
,
Lu
,
T.K.
,
Wang
,
X.
,
Shi
,
D.
,
Church
,
G.
and
Collins
,
J.J.
(
2009
)
Synthetic gene networks that count
.
Science
324
,
1199
20
Fernandez-Rodriguez
,
J.
,
Moser
,
F.
,
Song
,
M.
and
Voigt
,
C.A.
(
2017
)
Engineering RGB color vision into Escherichia coli
.
Nat. Chem. Biol.
13
,
706
708
21
Tabor
,
J.J.
,
Salis
,
H.M.
,
Simpson
,
Z.B.
,
Chevalier
,
A.A.
,
Levskaya
,
A.
,
Marcotte
,
E.M.
et al (
2009
)
A synthetic genetic edge detection program
.
Cell
137
,
1272
1281
22
You
,
L.
,
Cox
,
R.S.
,
Weiss
,
R.
and
Arnold
,
F.H.
(
2004
)
Programmed population control by cell–cell communication and regulated killing
.
Nature
428
,
868
871
23
Morsut
,
L.
,
Roybal
,
K.T.
,
Xiong
,
X.
,
Gordley
,
R.M.
,
Coyle
,
S.M.
,
Thomson
,
M.
et al (
2016
)
Engineering customized cell sensing and response behaviors using synthetic Notch receptors
.
Cell
164
,
780
791
24
Elowitz
,
M.B.
and
Leibler
,
S.
(
2000
)
A synthetic oscillatory network of transcriptional regulators
.
Nature
403
,
335
338
25
Stricker
,
J.
,
Cookson
,
S.
,
Bennett
,
M.R.
,
Mather
,
W.H.
,
Tsimring
,
L.S.
and
Hasty
,
J.
(
2008
)
A fast, robust and tunable synthetic gene oscillator
.
Nature
456
,
516
519
26
Potvin-Trottier
,
L.
,
Lord
,
N.D.
,
Vinnicombe
,
G.
and
Paulsson
,
J.
(
2016
)
Synchronous long-term oscillations in a synthetic gene circuit
.
Nature
538
,
514
517
27
Sorek
,
R.
,
Lawrence
,
C.M.
and
Wiedenheft
,
B.
(
2013
)
CRISPR-mediated adaptive immune systems in bacteria and archaea
.
Annu. Rev. Biochem.
82
,
237
266
28
Doudna
,
J.A.
and
Charpentier
,
E.
(
2014
)
Genome editing: the new frontier of genome engineering with CRISPR-Cas9
.
Science
346
,
1258096
29
Jinek
,
M.
,
Chylinski
,
K.
,
Fonfara
,
I.
,
Hauer
,
M.
,
Doudna
,
J.A.
and
Charpentier
,
E.
(
2012
)
A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial immunity
.
Science
337
,
816
821
30
Jinek
,
M.
,
East
,
A.
,
Cheng
,
A.
,
Lin
,
S.
,
Ma
,
E.
and
Doudna
,
J.
(
2013
)
RNA-programmed genome editing in human cells
.
eLife
2
,
e00471
31
Cong
,
L.
,
Ran
,
F.A.
,
Cox
,
D.
,
Lin
,
S.
,
Barretto
,
R.
,
Habib
,
N.
et al (
2013
)
Multiplex genome engineering using CRISPR/Cas systems
.
Science
339
,
819
32
Mali
,
P.
,
Yang
,
L.
,
Esvelt
,
K.M.
,
Aach
,
J.
,
Guell
,
M.
,
DiCarlo
,
J.E.
et al (
2013
)
RNA-guided human genome engineering via Cas9
.
Science
339
,
823
33
Komor
,
A.C.
,
Kim
,
Y.B.
,
Packer
,
M.S.
,
Zuris
,
J.A.
and
Liu
,
D.R.
(
2016
)
Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage
.
Nature
533
,
420
424
34
Gaudelli
,
N.M.
,
Komor
,
A.C.
,
Rees
,
H.A.
,
Packer
,
M.S.
,
Badran
,
A.H.
,
Bryson
,
D.I.
et al (
2017
)
Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage
.
Nature
551
,
464
471
35
Anzalone
,
A.V.
,
Randolph
,
P.B.
,
Davis
,
J.R.
,
Sousa
,
A.A.
,
Koblan
,
L.W.
,
Levy
,
J.M.
et al (
2019
)
Search-and-replace genome editing without double-strand breaks or donor DNA
.
Nature
576
,
149
157
36
Gilbert
,
L.A.
,
Larson
,
M.H.
,
Morsut
,
L.
,
Liu
,
Z.
,
Brar
,
G.A.
,
Torres
,
S.E.
et al (
2013
)
CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes
.
Cell
154
,
442
451
37
Qi
,
L.S.
,
Larson
,
M.H.
,
Gilbert
,
L.A.
,
Doudna
,
J.A.
,
Weissman
,
J.S.
,
Arkin
,
A.P.
et al (
2013
)
Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression
.
Cell
152
,
1173
1183
38
Liu
,
X.
,
Gallay
,
C.
,
Kjos
,
M.
,
Domenech
,
A.
,
Slager
,
J.
,
van Kessel
,
S.P.
et al (
2017
)
High-throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae
.
Mol. Syst. Biol.
13
,
931
39
Konermann
,
S.
,
Brigham
,
M.D.
,
Trevino
,
A.E.
,
Joung
,
J.
,
Abudayyeh
,
O.O.
,
Barcena
,
C.
et al (
2015
)
Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex
.
Nature
517
,
583
588
40
Nissim
,
L.
,
Perli
,
S.D.
,
Fridkin
,
A.
,
Perez-Pinera
,
P.
and
Lu
,
T.K.
(
2014
)
Multiplexed and programmable regulation of gene networks with an integrated RNA and CRISPR/Cas toolkit in human cells
.
Mol. Cell
54
,
698
710
41
Nielsen
,
A.A.
and
Voigt
,
C.A.
(
2014
)
Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks
.
Mol. Syst. Biol.
10
,
763
42
Vojta
,
A.
,
Dobrinić
,
P.
,
Tadić
,
V.
,
Bočkor
,
L.
,
Korać
,
P.
,
Julg
,
B.
et al (
2016
)
Repurposing the CRISPR-Cas9 system for targeted DNA methylation
.
Nucleic Acids Res.
44
,
5615
5628
43
Xu
,
X.
,
Tao
,
Y.
,
Gao
,
X.
,
Zhang
,
L.
,
Li
,
X.
,
Zou
,
W.
et al (
2016
)
A CRISPR-based approach for targeted DNA demethylation
.
Cell Discov.
2
,
16009
44
Hilton
,
I.B.
,
D'Ippolito
,
A.M.
,
Vockley
,
C.M.
,
Thakore
,
P.I.
,
Crawford
,
G.E.
,
Reddy
,
T.E.
et al (
2015
)
Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers
.
Nat. Biotechnol.
33
,
510
517
45
Fujita
,
T.
and
Fujii
,
H.
(
2013
)
Efficient isolation of specific genomic regions and identification of associated proteins by engineered DNA-binding molecule-mediated chromatin immunoprecipitation (enChIP) using CRISPR
.
Biochem. Biophys. Res. Commun.
439
,
132
136
46
Liu
,
X.
,
Zhang
,
Y.
,
Chen
,
Y.
,
Li
,
M.
,
Zhou
,
F.
,
Li
,
K.
et al (
2017
)
In situ capture of chromatin interactions by biotinylated dCas9
.
Cell
170
,
1028
1043.e1019
47
Chen
,
B.
,
Gilbert
,
L.A.
,
Cimini
,
B.A.
,
Schnitzbauer
,
J.
,
Zhang
,
W.
,
Li
,
G.-W.
et al (
2013
)
Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system
.
Cell
155
,
1479
1491
48
Ma
,
H.
,
Tu
,
L.-C.
,
Naseri
,
A.
,
Huisman
,
M.
,
Zhang
,
S.
,
Grunwald
,
D.
et al (
2016
)
Multiplexed labeling of genomic loci with dCas9 and engineered sgRNAs using CRISPRainbow
.
Nat. Biotechnol.
34
,
528
530
49
Tsai
,
S.Q.
,
Wyvekens
,
N.
,
Khayter
,
C.
,
Foden
,
J.A.
,
Thapar
,
V.
,
Reyon
,
D.
et al (
2014
)
Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing
.
Nat. Biotechnol.
32
,
569
576
50
Santos-Moreno
,
J.
and
Schaerli
,
Y.
(
2019
)
A framework for the modular and combinatorial assembly of synthetic gene circuits
.
ACS Synth. Biol.
8
,
1691
1697
51
McCarty
,
N.S.
,
Shaw
,
W.M.
,
Ellis
,
T.
and
Ledesma-Amaro
,
R.
(
2019
)
Rapid assembly of gRNA arrays via modular cloning in yeast
.
ACS Synth. Biol.
52
McKenna
,
A.
,
Findlay
,
G.M.
,
Gagnon
,
J.A.
,
Horwitz
,
M.S.
,
Schier
,
A.F.
and
Shendure
,
J.
(
2016
)
Whole-organism lineage tracing by combinatorial and cumulative genome editing
.
Science
353
,
aaf7907
53
Shipman
,
S.L.
,
Nivala
,
J.
,
Macklis
,
J.D.
and
Church
,
G.M.
(
2017
)
CRISPR–cas encoding of a digital movie into the genomes of a population of living bacteria
.
Nature
547
,
345
349
54
DiAndreth
,
B.
,
Wauford
,
N.
,
Hu
,
E.
,
Palacios
,
S.
and
Weiss
,
R.
(
2019
)
PERSIST: a programmable RNA regulation platform using CRISPR endoRNases
.
bioRxiv
55
Gootenberg
,
J.S.
,
Abudayyeh
,
O.O.
,
Kellner
,
M.J.
,
Joung
,
J.
,
Collins
,
J.J.
and
Zhang
,
F.
(
2018
)
Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6
.
Science
360
,
439
56
Broughton
,
J.P.
,
Deng
,
X.
,
Yu
,
G.
,
Fasching
,
C.L.
,
Servellita
,
V.
,
Singh
,
J.
et al (
2020
)
CRISPR–Cas12-based detection of SARS-CoV-2
.
Nat. Biotechnol.
38
,
870
874
57
Zhang
,
F.
,
Cong
,
L.
,
Lodato
,
S.
,
Kosuri
,
S.
,
Church
,
G.M.
and
Arlotta
,
P.
(
2011
)
Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription
.
Nat. Biotechnol.
29
,
149
153
58
Swank
,
Z.
,
Laohakunakorn
,
N.
and
Maerkl
,
S.J.
(
2019
)
Cell-free gene-regulatory network engineering with synthetic transcription factors
.
Proc. Natl Acad. Sci. U.S.A.
116
,
5892
59
Waryah
,
C.B.
,
Moses
,
C.
,
Arooj
,
M.
and
Blancafort
,
P.
(
2018
) Zinc Fingers, TALEs, and CRISPR Systems: A Comparison of Tools for Epigenome Editing. In
Epigenome Editing: Methods and Protocols
(
Jeltsch
,
A.
and
Rots
,
M.G.
, eds), pp.
19
63
,
Springer New York
,
New York, NY
60
Piatek
,
A.
and
Mahfouz
,
M.M.
(
2017
)
Targeted genome regulation via synthetic programmable transcriptional regulators
.
Crit. Rev. Biotechnol.
37
,
429
440
61
Nielsen
,
A.A.
,
Der
,
B.S.
,
Shin
,
J.
,
Vaidyanathan
,
P.
,
Paralanov
,
V.
,
Strychalski
,
E.A.
et al (
2016
)
Genetic circuit design automation
.
Science
352
,
aac7341
62
Brocchieri
,
L.
and
Karlin
,
S.
(
2005
)
Protein length in eukaryotic and prokaryotic proteomes
.
Nucleic Acids Res.
33
,
3390
3400
63
Liu
,
Y.
,
Zhan
,
Y.
,
Chen
,
Z.
,
He
,
A.
,
Li
,
J.
,
Wu
,
H.
et al (
2016
)
Directing cellular information flow via CRISPR signal conductors
.
Nat. Methods
13
,
938
944
64
Tang
,
W.
,
Hu
,
J.H.
and
Liu
,
D.R.
(
2017
)
Aptazyme-embedded guide RNAs enable ligand-responsive genome editing and transcriptional activation
.
Nat. Commun.
8
,
15939
65
Rock
,
J.M.
,
Hopkins
,
F.F.
,
Chavez
,
A.
,
Diallo
,
M.
,
Chase
,
M.R.
,
Gerrick
,
E.R.
et al (
2017
)
Programmable transcriptional repression in mycobacteria using an orthogonal CRISPR interference platform
.
Nat. Microbiol.
2
,
16274
66
Cho
,
S.
,
Choe
,
D.
,
Lee
,
E.
,
Kim
,
S.C.
,
Palsson
,
B.
and
Cho
,
B.-K.
(
2018
)
High-Level dCas9 expression induces abnormal cell morphology in Escherichia coli
.
ACS Synth. Biol.
7
,
1085
1094
67
Lee
,
Y.J.
,
Hoynes-O'Connor
,
A.
,
Leong
,
M.C.
and
Moon
,
T.S.
(
2016
)
Programmable control of bacterial gene expression with the combined CRISPR and antisense RNA system
.
Nucleic Acids Res.
44
,
2462
2473
68
Morgens
,
D.W.
,
Wainberg
,
M.
,
Boyle
,
E.A.
,
Ursu
,
O.
,
Araya
,
C.L.
,
Tsui
,
C.K.
et al (
2017
)
Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens
.
Nat. Commun.
8
,
15178
69
Santos-Moreno
,
J.
,
Tasiudi
,
E.
,
Stelling
,
J.
and
Schaerli
,
Y.
(
2020
)
Multistable and dynamic CRISPRi-based synthetic circuits
.
Nat. Commun.
11
,
2746
70
Cui
,
L.
,
Vigouroux
,
A.
,
Rousset
,
F.
,
Varet
,
H.
,
Khanna
,
V.
and
Bikard
,
D.
(
2018
)
A CRISPRi screen in E. coli reveals sequence-specific toxicity of dCas9
.
Nat. Commun.
9
,
1912
71
Gander
,
M.W.
,
Vrana
,
J.D.
,
Voje
,
W.E.
,
Carothers
,
J.M.
and
Klavins
,
E.
(
2017
)
Digital logic circuits in yeast with CRISPR-dCas9 NOR gates
.
Nat. Commun.
8
,
15459
72
Pausch
,
P.
,
Al-Shayeb
,
B.
,
Bisom-Rapp
,
E.
,
Tsuchida
,
C.A.
,
Li
,
Z.
,
Cress
,
B.F.
et al (
2020
)
CRISPR-CasΦ from huge phages is a hypercompact genome editor
.
Science
369
,
333
73
Huang
,
H.-H.
,
Bellato
,
M.
,
Qian
,
Y.
,
Cárdenas
,
P.
,
Pasotti
,
L.
,
Magni
,
P.
et al (
2020
)
dCas9 regulator to neutralize competition in CRISPRi circuits
.
bioRxiv
74
Bikard
,
D.
,
Jiang
,
W.
,
Samai
,
P.
,
Hochschild
,
A.
,
Zhang
,
F.
and
Marraffini
,
L.A.
(
2013
)
Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system
.
Nucleic Acids Res.
41
,
7429
7437
75
Farzadfard
,
F.
,
Perli
,
S.D.
and
Lu
,
T.K.
(
2013
)
Tunable and multifunctional eukaryotic transcription factors based on CRISPR/Cas
.
ACS Synth. Biol.
2
,
604
613
76
Ho
,
H.-I.
,
Fang
,
J.
,
Cheung
,
J.
and
Wang
,
H.H.
(
2020
)
Programmable CRISPR-Cas transcriptional activation in bacteria
.
Mol. Syst. Biol.
16
,
e9427
77
Dong
,
C.
,
Fontana
,
J.
,
Patel
,
A.
,
Carothers
,
J.M.
and
Zalatan
,
J.G.
(
2018
)
Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria
.
Nat. Commun.
9
,
2489
78
Fontana
,
J.
,
Dong
,
C.
,
Kiattisewee
,
C.
,
Chavali
,
V.P.
,
Tickman
,
B.I.
,
Carothers
,
J.M.
et al (
2020
)
Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements
.
Nat. Commun.
11
,
1618
79
Liu
,
Y.
,
Wan
,
X.
and
Wang
,
B.
(
2019
)
Engineered CRISPRa enables programmable eukaryote-like gene activation in bacteria
.
Nat. Commun.
10
,
3693
80
Perez-Pinera
,
P.
,
Kocak
,
D.D.
,
Vockley
,
C.M.
,
Adler
,
A.F.
,
Kabadi
,
A.M.
,
Polstein
,
L.R.
et al (
2013
)
RNA-guided gene activation by CRISPR-Cas9–based transcription factors
.
Nat. Methods
10
,
973
976
81
Tanenbaum
,
M.E.
,
Gilbert
,
L.A.
,
Qi
,
L.S.
,
Weissman
,
J.S.
and
Vale
,
R.D.
(
2014
)
A protein-tagging system for signal amplification in gene expression and fluorescence imaging
.
Cell
159
,
635
646
82
Chavez
,
A.
,
Scheiman
,
J.
,
Vora
,
S.
,
Pruitt
,
B.W.
,
Tuttle
,
M.
,
P R Iyer
,
E.
et al (
2015
)
Highly efficient Cas9-mediated transcriptional programming
.
Nat. Methods
12
,
326
328
83
Zalatan
,
J.G.
,
Lee
,
M.E.
,
Almeida
,
R.
,
Gilbert
,
L.A.
,
Whitehead
,
E.H.
,
La Russa
,
M.
et al (
2015
)
Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds
.
Cell
160
,
339
350
84
Qi
,
L.
,
Haurwitz
,
R.E.
,
Shao
,
W.
,
Doudna
,
J.A.
and
Arkin
,
A.P.
(
2012
)
RNA processing enables predictable programming of gene expression
.
Nat. Biotechnol.
30
,
1002
1006
85
Gilbert
,
L.A.
,
Horlbeck
,
M.A.
,
Adamson
,
B.
,
Villalta
,
J.E.
,
Chen
,
Y.
,
Whitehead
,
E.H.
et al (
2014
)
Genome-scale CRISPR-mediated control of gene repression and activation
.
Cell
159
,
647
661
86
Thakore
,
P.I.
,
D'Ippolito
,
A.M.
,
Song
,
L.
,
Safi
,
A.
,
Shivakumar
,
N.K.
,
Kabadi
,
A.M.
et al (
2015
)
Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements
.
Nat. Methods
12
,
1143
1149
87
Mandegar
,
M.A.
,
Huebsch
,
N.
,
Frolov
,
E.B.
,
Shin
,
E.
,
Truong
,
A.
,
Olvera
,
M.P.
et al (
2016
)
CRISPR interference efficiently induces specific and reversible gene silencing in human iPSCs
.
Cell Stem Cell
18
,
541
553
88
Lowder
,
L.G.
,
Zhang
,
D.
,
Baltes
,
N.J.
,
Paul
,
J.W.
,
Tang
,
X.
,
Zheng
,
X.
et al (
2015
)
A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation
.
Plant Physiol.
169
,
971
89
Maeder
,
M.L.
,
Linder
,
S.J.
,
Cascio
,
V.M.
,
Fu
,
Y.
,
Ho
,
Q.H.
and
Joung
,
J.K.
(
2013
)
CRISPR RNA–guided activation of endogenous human genes
.
Nat. Methods
10
,
977
979
90
Li
,
Z.
,
Zhang
,
D.
,
Xiong
,
X.
,
Yan
,
B.
,
Xie
,
W.
,
Sheen
,
J.
et al (
2017
)
A potent Cas9-derived gene activator for plant and mammalian cells
.
Nat. Plants
3
,
930
936
91
Lowder
,
L.G.
,
Zhou
,
J.
,
Zhang
,
Y.
,
Malzahn
,
A.
,
Zhong
,
Z.
,
Hsieh
,
T.-F.
et al (
2018
)
Robust transcriptional activation in plants using multiplexed CRISPR-Act2.0 and mTALE-Act systems
.
Mol. Plant
11
,
245
256
92
Kwon
,
D.Y.
,
Zhao
,
Y.-T.
,
Lamonica
,
J.M.
and
Zhou
,
Z.
(
2017
)
Locus-specific histone deacetylation using a synthetic CRISPR-Cas9-based HDAC
.
Nat. Commun.
8
,
15315
93
Cano-Rodriguez
,
D.
,
Gjaltema
,
R.A.F.
,
Jilderda
,
L.J.
,
Jellema
,
P.
,
Dokter-Fokkens
,
J.
,
Ruiters
,
M.H.J.
et al (
2016
)
Writing of H3K4Me3 overcomes epigenetic silencing in a sustained but context-dependent manner
.
Nat. Commun.
7
,
12284
94
Kearns
,
N.A.
,
Pham
,
H.
,
Tabak
,
B.
,
Genga
,
R.M.
,
Silverstein
,
N.J.
,
Garber
,
M.
et al (
2015
)
Functional annotation of native enhancers with a Cas9–histone demethylase fusion
.
Nat. Methods
12
,
401
403
95
Liu
,
X.S.
,
Wu
,
H.
,
Ji
,
X.
,
Stelzer
,
Y.
,
Wu
,
X.
,
Czauderna
,
S.
et al (
2016
)
Editing DNA methylation in the mammalian genome
.
Cell
167
,
233
247.e217
96
Kim
,
S.K.
,
Kim
,
S.H.
,
Subhadra
,
B.
,
Woo
,
S.-G.
,
Rha
,
E.
,
Kim
,
S.-W.
et al (
2018
)
A genetically encoded biosensor for monitoring isoprene production in engineered Escherichia coli
.
ACS Synth. Biol.
7
,
2379
2390
97
Miao
,
C.
,
Zhao
,
H.
,
Qian
,
L.
and
Lou
,
C.
(
2019
)
Systematically investigating the key features of the DNase deactivated Cpf1 for tunable transcription regulation in prokaryotic cells
.
Synth. Syst. Biotechnol.
4
,
1
9
98
Liu
,
Y.
,
Han
,
J.
,
Chen
,
Z.
,
Wu
,
H.
,
Dong
,
H.
and
Nie
,
G.
(
2017
)
Engineering cell signaling using tunable CRISPR–Cpf1-based transcription factors
.
Nat. Commun.
8
,
2095
99
Tang
,
X.
,
Lowder
,
L.G.
,
Zhang
,
T.
,
Malzahn
,
A.A.
,
Zheng
,
X.
,
Voytas
,
D.F.
et al (
2017
)
A CRISPR–Cpf1 system for efficient genome editing and transcriptional repression in plants
.
Nat. Plants
3
,
17018
100
Tak
,
Y.E.
,
Kleinstiver
,
B.P.
,
Nuñez
,
J.K.
,
Hsu
,
J.Y.
,
Horng
,
J.E.
,
Gong
,
J.
et al (
2017
)
Inducible and multiplex gene regulation using CRISPR–Cpf1-based transcription factors
.
Nat. Methods
14
,
1163
1166
101
Abudayyeh
,
O.O.
,
Gootenberg
,
J.S.
,
Essletzbichler
,
P.
,
Han
,
S.
,
Joung
,
J.
,
Belanto
,
J.J.
et al (
2017
)
RNA targeting with CRISPR–Cas13
.
Nature
550
,
280
284
102
McCarty
,
N.S.
,
Graham
,
A.E.
,
Studená
,
L.
and
Ledesma-Amaro
,
R.
(
2020
)
Multiplexed CRISPR technologies for gene editing and transcriptional regulation
.
Nat. Commun.
11
,
1281
103
Campa
,
C.C.
,
Weisbach
,
N.R.
,
Santinha
,
A.J.
,
Incarnato
,
D.
and
Platt
,
R.J.
(
2019
)
Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts
.
Nat. Methods
16
,
887
893
104
Reis
,
A.C.
,
Halper
,
S.M.
,
Vezeau
,
G.E.
,
Cetnar
,
D.P.
,
Hossain
,
A.
,
Clauer
,
P.R.
et al (
2019
)
Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays
.
Nat. Biotechnol.
37
,
1294
1301
105
Gao
,
Y.C.
,
Xiong
,
X.
,
Wong
,
S.
,
Charles
,
E.J.
,
Lim
,
W.A.
and
Qi
,
L.S.
(
2016
)
Complex transcriptional modulation with orthogonal and inducible dCas9 regulators
.
Nat. Methods
13
,
1043
1049
106
Esvelt
,
K.M.
,
Mali
,
P.
,
Braff
,
J.L.
,
Moosburner
,
M.
,
Yaung
,
S.J.
and
Church
,
G.M.
(
2013
)
Orthogonal Cas9 proteins for RNA-guided gene regulation and editing
.
Nat. Methods
10
,
1116
1121
107
Qian
,
Y.
,
Huang
,
H.-H.
,
Jiménez
,
J.I.
and
Del Vecchio
,
D.
(
2017
)
Resource competition shapes the response of genetic circuits
.
ACS Synth. Biol.
6
,
1263
1272
108
Zhang
,
S.
and
Voigt
,
C.A.
(
2018
)
Engineered dCas9 with reduced toxicity in bacteria: implications for genetic circuit design
.
Nucleic Acids Res.
46
,
11115
11125
109
Chen
,
P.-Y.
,
Qian
,
Y.
and
Vecchio
,
D.D.
(
2018
)
A model for resource competition in CRISPR-mediated gene repression
.
bioRxiv
110
Haurwitz
,
R.E.
,
Jinek
,
M.
,
Wiedenheft
,
B.
,
Zhou
,
K.
and
Doudna
,
J.A.
(
2010
)
Sequence- and structure-specific RNA processing by a CRISPR endonuclease
.
Science
329
,
1355
1358
111
Fu
,
Y.
,
Foden
,
J.A.
,
Khayter
,
C.
,
Maeder
,
M.L.
,
Reyon
,
D.
,
Joung
,
J.K.
et al (
2013
)
High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells
.
Nat. Biotechnol.
31
,
822
826
112
Pattanayak
,
V.
,
Lin
,
S.
,
Guilinger
,
J.P.
,
Ma
,
E.
,
Doudna
,
J.A.
and
Liu
,
D.R.
(
2013
)
High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity
.
Nat. Biotechnol.
31
,
839
843
113
Fu
,
Y.
,
Sander
,
J.D.
,
Reyon
,
D.
,
Cascio
,
V.M.
and
Joung
,
J.K.
(
2014
)
Improving CRISPR-Cas nuclease specificity using truncated guide RNAs
.
Nat. Biotechnol.
32
,
279
284
114
Ran
,
F.A.
,
Hsu
,
P.D.
,
Lin
,
C.-Y.
,
Gootenberg
,
J.S.
,
Konermann
,
S.
,
Trevino
,
A.E.
et al (
2013
)
Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity
.
Cell
154
,
1380
1389
115
Kleinstiver
,
B.P.
,
Prew
,
M.S.
,
Tsai
,
S.Q.
,
Topkar
,
V.V.
,
Nguyen
,
N.T.
,
Zheng
,
Z.
et al (
2015
)
Engineered CRISPR-Cas9 nucleases with altered PAM specificities
.
Nature
523
,
481
485
116
Kleinstiver
,
B.P.
,
Pattanayak
,
V.
,
Prew
,
M.S.
,
Tsai
,
S.Q.
,
Nguyen
,
N.T.
,
Zheng
,
Z.
et al (
2016
)
High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects
.
Nature
529
,
490
495
117
Jusiak
,
B.
,
Cleto
,
S.
,
Perez-Pinera
,
P.
and
Lu
,
T.K.
(
2016
)
Engineering synthetic gene circuits in living cells with CRISPR technology
.
Trends Biotechnol.
34
,
535
547
118
Vigouroux
,
A.
and
Bikard
,
D.
(
2020
)
CRISPR tools to control gene expression in bacteria
.
Microbiol. Mol. Biol. Rev.
84
,
e00077-00019
119
Jones
,
D.L.
,
Leroy
,
P.
,
Unoson
,
C.
,
Fange
,
D.
,
Curic
,
V.
,
Lawson
,
M.J.
et al (
2017
)
Kinetics of dCas9 target search in Escherichia coli
.
Science
357
,
1420
1423
120
Martens
,
K.J.A.
,
van Beljouw
,
S.P.B.
,
van der Els
,
S.
,
Vink
,
J.N.A.
,
Baas
,
S.
,
Vogelaar
,
G.A.
et al (
2019
)
Visualisation of dCas9 target search in vivo using an open-microscopy framework
.
Nat. Commun.
10
,
3552
121
Elf
,
J.
,
Li
,
G.-W.
and
Xie
,
X.S.
(
2007
)
Probing transcription factor dynamics at the single-molecule level in a living cell
.
Science
316
,
1191
122
Menn
,
D.J.
,
Pradhan
,
S.
,
Kiani
,
S.
and
Wang
,
X.
(
2018
)
Fluorescent guide RNAs facilitate development of layered Pol II-driven CRISPR circuits
.
ACS Synth. Biol.
7
,
1929
1936
123
Clamons
,
S.E.
and
Murray
,
R.M.
(
2017
)
Modeling dynamic transcriptional circuits with CRISPRi
.
bioRxiv
124
Cherry
,
J.L.
and
Adler
,
F.R.
(
2000
)
How to make a biological switch
.
J. Theor. Biol.
203
,
117
133
125
Ferrell
,
J.E.
(
2002
)
Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability
.
Curr. Opin. Cell Biol.
14
,
140
148
126
Novák
,
B.
and
Tyson
,
J.J.
(
2008
)
Design principles of biochemical oscillators
.
Nat. Rev. Mol. Cell Biol.
9
,
981
991
127
Cao
,
Y.X.L.
,
Lopatkin
,
A.
and
You
,
L.C.
(
2016
)
Elements of biological oscillations in time and space
.
Nat. Struct. Mol. Biol.
23
,
1030
1034
128
Kuo
,
J.
,
Yuan
,
R.
,
Sánchez
,
C.
,
Paulsson
,
J.
and
Silver
,
P.A.
(
2020
)
Towards a translationally-independent RNA-based synthetic oscillator using deactivated CRISPR-Cas
.
Nucleic Acids Res.
48
,
8165
8177
129
Henningsen
,
J.
,
Schwarz-Schilling
,
M.
,
Leibl
,
A.
,
Guttierez
,
J.A.M.
,
Sagredo
,
S.
and
Simmel
,
F.C.
(
2020
)
Single cell characterization of a synthetic bacterial clock with a hybrid feedback loop containing dCas9-sgRNA
.
bioRxiv
130
Kundert
,
K.
,
Lucas
,
J.E.
,
Watters
,
K.E.
,
Fellmann
,
C.
,
Ng
,
A.H.
,
Heineike
,
B.M.
et al (
2019
)
Controlling CRISPR-Cas9 with ligand-activated and ligand-deactivated sgRNAs
.
Nat. Commun.
10
,
2127
131
Oesinghaus
,
L.
and
Simmel
,
F.C.
(
2019
)
Switching the activity of Cas12a using guide RNA strand displacement circuits
.
Nat. Commun.
10
,
2092
132
Hanewich-Hollatz
,
M.H.
,
Chen
,
Z.
,
Hochrein
,
L.M.
,
Huang
,
J.
and
Pierce
,
N.A.
(
2019
)
Conditional guide RNAs: programmable conditional regulation of CRISPR/Cas function in bacterial and mammalian cells via dynamic RNA nanotechnology
.
ACS Cent.l Sci.
5
,
1241
1249
133
Jin
,
M.
,
Garreau de Loubresse
,
N.
,
Kim
,
Y.
,
Kim
,
J.
and
Yin
,
P.
(
2019
)
Programmable CRISPR-Cas repression, activation, and computation with sequence-independent targets and triggers
.
ACS Synth. Biol.
8
,
1583
1589
134
Siu
,
K.-H.
and
Chen
,
W.
(
2019
)
Riboregulated toehold-gated gRNA for programmable CRISPR–Cas9 function
.
Nat. Chem. Biol.
15
,
217
220
135
Ferry
,
Q.R.V.
,
Lyutova
,
R.
and
Fulga
,
T.A.
(
2017
)
Rational design of inducible CRISPR guide RNAs for de novo assembly of transcriptional programs
.
Nat. Commun.
8
,
14633
136
Maji
,
B.
,
Moore
,
C.L.
,
Zetsche
,
B.
,
Volz
,
S.E.
,
Zhang
,
F.
,
Shoulders
,
M.D.
et al (
2017
)
Multidimensional chemical control of CRISPR–Cas9
.
Nat. Chem. Biol.
13
,
9
11
137
Nakamura
,
M.
,
Srinivasan
,
P.
,
Chavez
,
M.
,
Carter
,
M.A.
,
Dominguez
,
A.A.
,
La Russa
,
M.
et al (
2019
)
Anti-CRISPR-mediated control of gene editing and synthetic circuits in eukaryotic cells
.
Nat. Commun.
10
,
194
138
Nihongaki
,
Y.
,
Kawano
,
F.
,
Nakajima
,
T.
and
Sato
,
M.
(
2015
)
Photoactivatable CRISPR-Cas9 for optogenetic genome editing
.
Nat. Biotechnol.
33
,
755
760
139
Bubeck
,
F.
,
Hoffmann
,
M.D.
,
Harteveld
,
Z.
,
Aschenbrenner
,
S.
,
Bietz
,
A.
,
Waldhauer
,
M.C.
et al (
2018
)
Engineered anti-CRISPR proteins for optogenetic control of CRISPR–Cas9
.
Nat. Methods
15
,
924
927
140
Kiani
,
S.
,
Beal
,
J.
,
Ebrahimkhani
,
M.R.
,
Huh
,
J.
,
Hall
,
R.N.
,
Xie
,
Z.
et al (
2014
)
CRISPR transcriptional repression devices and layered circuits in mammalian cells
.
Nat. Methods
11
,
723
U155
141
Didovyk
,
A.
,
Borek
,
B.
,
Hasty
,
J.
and
Tsimring
,
L.
(
2016
)
Orthogonal modular gene repression in Escherichia coli using engineered CRISPR/Cas9
.
ACS Synth. Biol.
5
,
81
88
142
Kim
,
H.
,
Bojar
,
D.
and
Fussenegger
,
M.
(
2019
)
A CRISPR/Cas9-based central processing unit to program complex logic computation in human cells
.
Proc. Natl Acad. Sci. U.S.A.
116
,
7214
7219
143
Liu
,
Y.C.
,
Zeng
,
Y.Y.
,
Liu
,
L.
,
Zhuang
,
C.L.
,
Fu
,
X.
,
Huang
,
W.R.
et al (
2014
)
Synthesizing AND gate genetic circuits based on CRISPR-Cas9 for identification of bladder cancer cells
.
Nat. Commun.
5
,
5393
144
Taketani
,
M.
,
Zhang
,
J.
,
Zhang
,
S.
,
Triassi
,
A.J.
,
Huang
,
Y.-J.
,
Griffith
,
L.G.
et al (
2020
)
Genetic circuit design automation for the gut resident species Bacteroides thetaiotaomicron
.
Nat. Biotechnol.
38
,
962
969
145
Cress
,
B.F.
,
Jones
,
J.A.
,
Kim
,
D.C.
,
Leitz
,
Q.D.
,
Englaender
,
J.A.
,
Collins
,
S.M.
et al (
2016
)
Rapid generation of CRISPR/dCas9-regulated, orthogonally repressible hybrid T7-lac promoters for modular, tuneable control of metabolic pathway fluxes in Escherichia coli
.
Nucleic Acids Res.
44
,
4472
4485
146
Ceroni
,
F.
,
Boo
,
A.
,
Furini
,
S.
,
Gorochowski
,
T.E.
,
Borkowski
,
O.
,
Ladak
,
Y.N.
et al (
2018
)
Burden-driven feedback control of gene expression
.
Nat. Methods
15
,
387
393
147
Wiktor
,
J.
,
Lesterlin
,
C.
,
Sherratt
,
D.J.
and
Dekker
,
C.
(
2016
)
CRISPR-mediated control of the bacterial initiation of replication
.
Nucleic Acids Res.
44
,
3801
3810
148
Hao
,
N.
,
Shearwin
,
K.E.
and
Dodd
,
I.B.
(
2017
)
Programmable DNA looping using engineered bivalent dCas9 complexes
.
Nat. Commun.
8
,
1628
149
Mückl
,
A.
,
Schwarz-Schilling
,
M.
,
Fischer
,
K.
and
Simmel
,
F.C.
(
2018
)
Filamentation and restoration of normal growth in Escherichia coli using a combined CRISPRi sgRNA/antisense RNA approach
.
PLoS ONE
13
,
e0198058
150
Chappell
,
J.
,
Westbrook
,
A.
,
Verosloff
,
M.
and
Lucks
,
J.B.
(
2017
)
Computational design of small transcription activating RNAs for versatile and dynamic gene regulation
.
Nat. Commun.
8
,
1051
151
Bradley
,
R.W.
,
Buck
,
M.
and
Wang
,
B.
(
2016
)
Recognizing and engineering digital-like logic gates and switches in gene regulatory networks
.
Curr. Opin. Microbiol.
33
,
74
82
152
Westbrook
,
A.
,
Tang
,
X.
,
Marshall
,
R.
,
Maxwell
,
C.S.
,
Chappell
,
J.
,
Agrawal
,
D.K.
et al (
2019
)
Distinct timescales of RNA regulators enable the construction of a genetic pulse generator
.
Biotechnol. Bioeng.
116
,
1139
1151
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).