CRISPR-Based Approaches for Gene Regulation in Non-Model Bacteria

Abstract

CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) have become ubiquitous approaches to control gene expression in bacteria due to their simple design and effectiveness. By regulating transcription of a target gene(s), CRISPRi/a can dynamically engineer cellular metabolism, implement transcriptional regulation circuitry, or elucidate genotype-phenotype relationships from smaller targeted libraries up to whole genome-wide libraries. While CRISPRi/a has been primarily established in the model bacteria Escherichia coli and Bacillus subtilis, a growing numbering of studies have demonstrated the extension of these tools to other species of bacteria (here broadly referred to as non-model bacteria). In this mini-review, we discuss the challenges that contribute to the slower creation of CRISPRi/a tools in diverse, non-model bacteria and summarize the current state of these approaches across bacterial phyla. We find that despite the potential difficulties in establishing novel CRISPRi/a in non-model microbes, over 190 recent examples across eight bacterial phyla have been reported in the literature. Most studies have focused on tool development or used these CRISPRi/a approaches to interrogate gene function, with fewer examples applying CRISPRi/a gene regulation for metabolic engineering or high-throughput screens and selections. To date, most CRISPRi/a reports have been developed for common strains of non-model bacterial species, suggesting barriers remain to establish these genetic tools in undomesticated bacteria. More efficient and generalizable methods will help realize the immense potential of programmable CRISPR-based transcriptional control in diverse bacteria.

Introduction

Since the development of CRISPR interference (CRISPRi) (Qi et al., 2013) and CRISPR activation (CRISPRa) (Bikard et al., 2013) in 2013, they have become efficient and prevalent tools for transcriptional regulation in bacteria. CRISPR-Cas originates as a form of prokaryotic immunity, with systems comprising one or more CRISPR-associated (Cas) proteins and a short guide RNA (gRNA) that complex together to target and cleave foreign DNA or RNA molecules, such as viruses (Nussenzweig and Marraffini, 2020). The gRNA leads the complex to target sequence via complementarity between the protospacer sequence of the gRNA and the target site on the DNA/RNA molecule. Various mechanisms exist to prevent cleavage of chromosomal DNA, which most often involves a protospacer adjacent motif (PAM) or equivalent next to the target site that is not present in the CRISPR arrays on the chromosome (Jackson et al., 2017).

Researchers developed CRISPRi technology by deactivating the nuclease activity of select Cas enzymes to create mutant dCas proteins that bind, but do not cleave, the DNA target (Qi et al., 2013). Most CRISPRi systems repress a gene’s expression through steric inhibition of RNA polymerase binding or extension (Qi et al., 2013), although some repress gene expression through RNA cleavage (Zhang K. et al., 2020; Rahman et al., 2021). Gene repression over 100-fold has been reported for several diverse CRISPRi tools and can approach near knockout levels of gene expression (Qi et al., 2013; Miao et al., 2019). Targeting a different sequence is easily achieved by changing the short protospacer sequence on the gRNA to bind a location within the promoter, untranslated region, or coding sequence of the target gene based on simple design rules (Qi et al., 2013; Zetsche et al., 2015; Zhang et al., 2017). Additionally, multiplexed gene repression can be achieved by simply expressing multiple gRNA within a cell (Qi et al., 2013; Zhang et al., 2017).

Shortly after the development of CRISPRi, researchers developed CRISPRa for bacterial transcriptional activation by combining the dCas protein with a transcriptional activator that recruits transcription machinery to the target gene’s promoter to increase gene expression (Bikard et al., 2013). The specific mechanism for transcriptional activation depends on the activator, which can be incorporated by directly fusing a transcriptional activator domain to the dCas protein (Bikard et al., 2013; Ho et al., 2020; Schilling et al., 2020), incorporating RNA scaffolds into the gRNA sequence to recruit activator domains to the dCas complex (Dong et al., 2018; Liu Y. et al., 2019; Fontana et al., 2020a), or using non-covalent protein-protein interaction domains to complex the transcriptional activator and dCas protein (Villegas Kcam et al., 2021; Villegas Kcam et al., 2022). Unlike CRISPRi, however, CRISPRa has complex design rules that often strongly depend on the CRISPRa technology (i.e., type of activation domain and approach to couple the activation domain and dCas complex) as wells as several other factors (Liu Y. et al., 2019; Fontana et al., 2020a; Ho et al., 2020; Villegas Kcam et al., 2021). These other considerations include the basal expression of the target gene and the location of the binding site for the CRISPRa complex, where activation is typically achieved in a narrow range upstream of the target gene’s promoter and the activation strength fluctuates sharply as the nucleotide position shifts. Combined with the PAM requirement for DNA binding, these requirements greatly restrict the available DNA target sites for effective gene activation, especially for endogenous genes. Due to these relatively stringent design rules for gene activation and often low (<10-fold) activation levels compared to CRISPRi repression (Bikard et al., 2013; Liu W. et al., 2019; Fontana et al., 2020a; Villegas Kcam et al., 2021), CRISPRa development has been slower in bacteria than in eukaryotes (Kampmann, 2018; Fontana et al., 2020b). Despite the current limitations of CRISPRa, however, the simplicity and inherent properties of CRISPRi/a gene regulation can provide strong transcriptional control of multiple genes simultaneously, making these approaches often easier and faster than traditional methods and allowing for dynamic transcriptional control.

CRISPR systems are classified into a variety of classes, types, subtypes, and variants, each with unique genes and properties (Koonin et al., 2017; Makarova et al., 2020). Many systems have been engineered to create effective CRISPRi/a tools. The first and most common tool is derived from the Type II Cas9 system, which comprises a single deactivated Cas9 (dCas9) protein and two small RNAs that create the gRNA (Qi et al., 2013). These two RNAs can be combined into a synthetic single guide RNA (sgRNA) for easier synthesis, but each sgRNA requires an independent promoter for expression (Jiang et al., 2015). Although many different dCas9 variants exist, the Streptococcus pyogenes dCas9 (Sp dCas9) system is the most common due to its short PAM sequence and strong transcription regulation abilities. Recently, tools derived from the Type V Cas12a (formerly Cpf1) system have been developed, which uses a single deactivated Cas12a (dCas12a) protein and one gRNA (Zetsche et al., 2015; Kim et al., 2017; Zhang et al., 2017; Miao et al., 2019). Unlike dCas9, dCas12a can process its gRNA from CRISPR arrays, providing easier multiplexed regulation (Fonfara et al., 2016; Zhang et al., 2017). Additionally, several studies have suggested that dCas12a variants are less toxic than dCas9 variants across different bacterial phyla (Liu W. et al., 2019; Knoot et al., 2020; Kuo et al., 2020), making them an attractive alternative to dCas9. The most common dCas12a variants used in bacteria are derived from Francisella tularensis subsp. novicida (Fn dCas12a) and Acidaminococcus sp. BV3L6 (As dCas12a). Several Type I CRISPRi/a tools have been designed, but due to the large number of genes in these systems, most tools are implemented by reprogramming the host species’ endogenous CRISPR system for gene repression (Luo et al., 2015; Xu et al., 2021; Villegas Kcam et al., 2022). Only a handful of CRISPRi tools from other systems have been reported for transcriptional regulation in bacteria, likely due to the novelty of the system (Rahman et al., 2021) or high cellular toxicity observed upon expression (Zhang K. et al., 2020).

Despite the unique traits and relevance of a vast diversity of bacteria, CRISPRi/a tools have been primarily developed in the model bacteria Escherichia coli and Bacillus subtilis. Yet, non-model bacteria (a broad definition of non-model, excluding E. coli and B. subtilis, is used here) offer great promise in research and industry spanning a wide range of medical, environmental, and biomanufacturing applications. For example, Streptomyces, Sorangium, and Photorhabdus spp. naturally produce bioactive secondary metabolites, such as antibiotics, and contain silent biosynthetic gene clusters with unknown and potentially useful products (Ye et al., 2019; Tian et al., 2020; Ke et al., 2021). Additionally, Rhodococcus and Corynebacterium spp. can produce valuable chemicals from cheap and simple feedstock and are tolerant to harsh conditions, making them ideal cell factories (Cleto et al., 2016; DeLorenzo et al., 2018). However, several conditions must be reached to successfully establish efficient CRISPRi/a tools in a non-model bacterium. In this mini-review, we detail these criteria, emphasizing the importance of characterized genetic parts to tightly control the expression of CRISPRi/a systems to limit potential toxicity while providing sufficient expression for effective transcriptional control. We demonstrate that despite the potential difficulties in creating these tools in non-model bacteria, they have been established across eight different bacterial phyla and have been used for a variety of applications, including high-throughput genome-wide selections. Finally, we highlight the current challenges to developing CRISPRi/a tools in non-model bacteria and novel species, which suggest directions for future progress.

Requirements and Challenges to Establish CRISPRi/a in Non-Model Bacteria

Several criteria must be met to successfully establish an effective CRISPRi/a tool in a non-model species or strain. First, the conditions for culturing, maintaining, and genetically manipulating the strain (often referred to as strain “domestication”) must be determined. For a phylogenetically similar strain to a previously established model bacteria, such as many Bacillus species (Zhan et al., 2020) and Enterobacteriaceae (Ho et al., 2020), suitable culture conditions may be similar to those previously determined. For novel or fastidious species, however, trial and error and patience may be required to determine appropriate culture conditions for growth and genetic manipulation, such as the obligate intracellular pathogen Chlamydia trachomatis (Ouellette, 2018). Additionally, introducing foreign DNA is often challenging for a non-model bacterium, as many are genetically recalcitrant, especially pathogens (Fernandes et al., 2021b) and novel strains (Zhao et al., 2020; Jin et al., 2022), and establishing a sufficient genetic transformation method can require significant effort. Additionally, care must be taken when introducing synthetic DNA to circumvent the bacterial host’s native immunity that may degrade foreign DNA, including restriction-modification and CRISPR systems (Marraffini and Sontheimer, 2008; Jin et al., 2022), such as by mimicking the recipient strain’s methylation patterns (Monk et al., 2015; Zhao et al., 2020). More discussion on the isolation and domestication of non-model bacteria can be found in other reviews (Vartoukian et al., 2010; Lewis et al., 2021; Riley and Guss, 2021).

Next, reliable genetic parts for the non-model bacterium are required to be able to express and tightly control the CRISPRi/a tool, including promoters, ribosome binding sites, terminators, and expression or integration vectors. For many non-model bacteria, especially novel species, these genetic part libraries are unavailable, and so, the necessary genetic parts must be created and characterized. In some cases, established genetic parts may be transferable from a model bacterium to a related species, such as promoters between Gram-positive bacteria (Liew et al., 2010). However, genetic parts often do not function equivalently between bacterial species or even strains (Tong et al., 2015; Leonard et al., 2018). Each CRISPRi/a component should be expressed using unique genetic parts to prevent repeated DNA sequences. Since dCas protein expression can elicit cytotoxicity, high strength promoters used for overexpression may not be optimal. If existing genetic parts are insufficient for a new bacterial species, identifying genetic regulatory elements from the endogenous genome provides an alternative to synthetic DNA design strategies (Fernandes et al., 2019). Libraries of genetic parts and inducible promoters are excellent tools to tune the expression of CRISPRi/a systems, and several studies have established such toolboxes in non-model bacteria to facilitate the development of genetic tools such as CRISPRi/a (Mimee et al., 2015; Leonard et al., 2018; Shin et al., 2019; Teh et al., 2019; Liow et al., 2020). These libraries and tunable parts are especially important to control the expression of the CRISPRi/a tool to minimize potential cellular toxicity and to precisely control transcriptional regulation (Qu et al., 2019; Bosch et al., 2021; Shabestary et al., 2021).

In the design of a synthetic CRISPRi/a system for a bacterium, consideration should be given to prevent interference with endogenous CRISPR systems and/or anti-CRISPR genes harbored on the strain’s genome. If the foreign and native CRISPR-Cas types are too similar, the introduction of the synthetic gRNA may induce cleavage of the host bacterium’s genome (via the catalytically active endogenous Cas enzyme) and can cause cell death in a DNA repair-deficient strain or undesired mutations if the strain has appropriate DNA repair pathways. This can be avoided by choosing a CRISPRi/a tool that does not share significant homology to any endogenous CRISPR-Cas. Native CRISPR-Cas systems can be predicted from the sequenced genome or proteome using computer software (Couvin et al., 2018; Chai et al., 2019), aiding in CRISPRi/a tool selection for novel strains. Alternatively, the native system can be engineered to create a CRISPRi/a tool via genetic manipulation, such as the deletion of the native cas2/3 or cas3 gene responsible for cleavage in Type I-F systems (Zheng et al., 2019; Qin et al., 2021; Xu et al., 2021) or mutating the native cas9 sequence for Type II systems (Shields et al., 2020; Dammann et al., 2021). Anti-CRISPR proteins, which inhibit CRISPR systems through a variety of mechanisms (Pawluk et al., 2018), may require deletion or disruption before a heterologous CRISPRi/a tool can be expressed (Xu et al., 2021). Online tools and databases are available to predict and describe anti-CRISPR proteins from protein sequences to help select an appropriate CRISPRi/a system (Wang et al., 2020; Wang et al., 2021a).

Finally, the CRISPRi/a components should be expressed at a level that provides adequate transcriptional repression or activation for the given application without significant cellular toxicity. Many studies have reported CRISPRi toxicity for diverse bacteria, while little is known about CRISPRa toxicity due to limited reports in the literature. These observed forms of toxicity include changes in cell morphology (Cho et al., 2018; Ouellette et al., 2021) and slower growth or complete growth inhibition (Rock et al., 2017; Yu et al., 2018; Wurihan et al., 2019; Zhang K. et al., 2020; Brito et al., 2020). To prevent toxicity, one can use a less toxic CRISPRi/a system for the host species (Rock et al., 2017; Zhao et al., 2019), or reduce the expression of the components by substituting genetic parts (Qu et al., 2019). Expansive libraries of genetic parts, including inducible and constitutive promoters, ribosome binding sites, and protein degradation tags, can be used to tune gene expression and characteristics of the CRISPRi/a tool (Depardieu and Bikard, 2020; Fleck and Grundner, 2021; Ouellette et al., 2021). However, the components cannot be expressed so low that it cannot effectively repress or activate the target gene(s), especially during multiplexed gene regulation that relies on a shared dCas protein pool for multiple gRNAs (Zhang and Voigt, 2018; Zhao et al., 2018). A careful balance is required to express the CRISPRi/a components.

Current CRISPRi/a Tools for Non-Model Bacteria

Different CRISPRi/a tools have been established in a range of bacteria that span many phyla and have been used for a variety of applications, as summarized here (Table 1). Overwhelmingly, these studies have utilized the Sp dCas9 CRISPRi system. More detailed information for each study can be found in Supplementary Table S1 (Supplementary Data Sheet 1).

Actinomycetota

CRISPRi has been well established in a wide range of Actinobacteria, including Mycobacteria, Streptomyces, and Corynebacterium, and has been used for metabolic engineering and the elucidation of gene functions in both small studies and genome-wide screens (Table 1). Additionally, several CRISPRi tools are commonly used in Mycobacteria (Choudhary et al., 2015; Rock et al., 2017; Agarwal, 2020; Fleck and Grundner, 2021) and Streptomycetes (Tong et al., 2015; Li et al., 2018; Zhao et al., 2018). CRISPRa has also been recently establish in Corynebacterium (Liu W. et al., 2019) and Streptomycetes (Ameruoso et al., 2021).

Cyanobacteria

CRISPRi/a is especially useful in cyanobacteria due to their polyploidal genomes (Kirtania et al., 2019). CRISPRi is relatively well-established in a wide range of cyanobacterial species, including those of research and industrial significance, and has been used for metabolic engineering, transcriptional regulatory networks, and the study of gene functions in small studies and a genome-wide screen/selection (Table 1). Many CRISPRi tools are available in cyanobacteria, each with their own characteristics (Gordon et al., 2016; Yao et al., 2016; Liu D. et al., 2020; Choi and Woo, 2020). CRISPRa has not yet been reported in cyanobacteria.

Firmicutes

CRISPRi is well-established in a wide range of Firmicutes, including Bacilli, Clostridia, Staphylococci, and Streptococci (Table 1). CRISPRi tools have been developed and used for metabolic engineering, elucidation of gene functions, and genome-wide screens and selections. CRISPRa has been reported in Bacillus amyloliquefaciens and Paenibacillus polymyxa in tool development work and some metabolic engineering applications (Schilling et al., 2020; Zhao et al., 2020).

Proteobacteria

CRISPRi and CRISPRa are well established in a wide variety of Proteobacteria, including Klebsiella, Salmonella, Pseudomonas, and Vibrio (Table 1). These tools have been developed and used for metabolic engineering, synthetic transcriptional regulatory networks, and mapping gene function using small gRNA sets and genome-wide screens and selections. Reports of CRISPRi are far more common than CRISPRa.

Other Bacterial Phyla

CRISPRi has also been reported in the phyla Chlamydiae, Tenericutes, Spirochaetes, and Bacteroidetes (Table 1). Although these reports have primarily been for tool development, some have used CRISPRi to investigate gene function (Fernandes et al., 2021b; Brockett et al., 2021) or create synthetic genetic circuits (Mimee et al., 2015; Taketani et al., 2020).

Applications of CRISPRi/a in Non-Model Bacteria

CRISPRi/a tools can be used for a variety of applications in non-model bacteria (Figure 1). The most common application is mapping a gene’s function by altering its gene expression and assaying cellular phenotypic change under some applied selective condition (Figure 1A). CRISPRi is particularly useful for investigating essential genes because its repression can be titrated to prevent full knockdown and cell death (Knoot et al., 2020; Bosch et al., 2021). Additionally, epistatic effects of multiple genes can easily be investigation by simply expressing multiple gRNA within the same cell (Ellis et al., 2021; McNeil et al., 2022). Although not as common as CRISPRi due to stricter design rules (Fontana et al., 2020a), CRISPRa can be used to induce expression of silent genes to investigate their functions and products, including entire silent biosynthetic gene clusters (Ke et al., 2021). Combined, these are the most common use of CRISPRi/a tools in non-model bacteria, with 80 reports across six phyla (Table 1) (Behler et al., 2018; Stamsås et al., 2018; Ke et al., 2021). The recent development of Mobile-CRISPRi (Peters et al., 2019), CRAGE-CRISPR (Ke et al., 2021), and a workflow for introducing genetic manipulation tools into non-model gut bacteria (Jin et al., 2022) will facilitate the expansion of CRISPRi/a tools into new species and strains, including recalcitrate pathogens and novel species without sequenced genomes.

FIGURE 1

Additionally, CRISPRi/a can be used to control transcription regulatory networks, such as genetic circuits, by designing and expressing gRNA to regulate the output promoter for each logic gate or node (Figure 1B). CRISPRi/a is especially effective for controlling complex synthetic transcription regulatory networks as the gRNA can be designed to target nearly any arbitrary sequence with an appropriate PAM (or equivalent) sequence (Taketani et al., 2020; Ellis et al., 2021). CRISPRi/a circuits can be fully synthetic and auxiliary to the native genetic regulatory networks, such as a heterologous sensor or multi-input circuit that senses and responds to external inputs in complex environments (Mimee et al., 2015; Taketani et al., 2020). Alternatively, CRISPRi/a can be interfaced with native gene regulatory systems to control the host’s metabolism in response to external stimuli, such as cell density, through either heterologous (Liu Y. et al., 2020) or even indigenous sensor systems (Tian et al., 2020). However, caution must be taken to prevent the expression of too many gRNA at once since they compete over the limited dCas protein resource and, thus, can decrease the repression of target genes (Del Vecchio et al., 2008; Li et al., 2018; Zhang and Voigt, 2018). Synthetic CRISPRi/a regulatory networks are rare in non-model bacteria, having been reported in only seven studies across four phyla, and primarily incorporate CRISPRi (Table 1). However, a single CRISPRa genetic circuit in Salmonella has been reported (Bhokisham et al., 2020).

CRISPRi/a tools have also been used to redirect carbon and energy flow for metabolic engineering in non-model bacteria (Figure 1C). CRISPRi is often used to repress a native gene(s), including essential genes, to redirect carbon flux towards a desired product (Wang et al., 2017; Shabestary et al., 2018) or bioactive molecule (Yu et al., 2018; Liu et al., 2021b). CRISPRa can be used to activate the desired metabolic pathway to increase biosynthesis of the desired product, such as an anti-cancer drug in a weakly-expressed biosynthetic gene cluster (Peng et al., 2018; Ye et al., 2019). In most examples, the CRISPRi/a components are constitutively expressed, yet some studies employ dynamic metabolic engineering strategies by utilizing inducible systems and/or genetically encoded biosensors to switch between cell growth and product biosynthesis states to improve production (Liu Y. et al., 2020; Tian et al., 2020; Shabestary et al., 2021). These tools can be used to tune endogenous metabolism and/or heterologous metabolic pathways (Peng et al., 2018; Banerjee et al., 2020). CRISPRi/a tools are most often combined with other metabolic engineering techniques, such as the deletion, overexpression, or mutation of select genes and optimization of medium, to further increase titers of the desired product (Park et al., 2019; Dietsch et al., 2021; Kozaeva et al., 2021).

Large-scale CRISPRi screens and selections have been developed to investigate genotype-phenotype relationships through gRNA fitness (Figure 1D). These assays can use small, targeted libraries, such as essential genes or genes in a metabolic pathway (Shields et al., 2020; Göttl et al., 2021), or large genome-wide libraries targeting nearly all genes in the bacterial genome (Lee et al., 2019; Jiang et al., 2020). Additionally, CRISPRi libraries can be constructed in two major forms—pooled libraries, where cells containing different gRNA are mixed during library construction (Bosch et al., 2021; Rahman et al., 2021), a strategy known as multiplexing, or arrayed libraries where different gRNA designs are constructed individually in different clonal populations, typically arrayed in microtiter plates (Liu et al., 2017; Göttl et al., 2021). Pooled competitive selections are more common due to the ease of DNA construction and analysis of large, genome-scale gRNA libraries with >10,000 designs by next-generation sequencing (Lee et al., 2019; Bosch et al., 2021). However, because all cells directly compete in pooled competitive growth assays, “cheaters” may arise that take advantage of different strain interactions, so the results of any individual gRNA design should be verified in isolation (Yao et al., 2020; Liu X. et al., 2021). Additionally, the results from these pooled CRISPRi screens or selections are specific to the gRNA design and not the target gene since confounding effects (i.e., off-target effects) could produce false positives or negatives, so careful design of gRNA libraries is vital (Cui et al., 2018; Wang T. et al., 2018). Genome-wide CRISPRi screens or selections are relatively uncommon (Table 1). While not demonstrated to date, genome-wide bacterial CRISPRa is theoretically possible, provided the design rules for activation are met (Fontana et al., 2020a).

Conclusion and Perspectives

CRISPRi has been established in non-model bacteria across eight phyla and applied from small, single gene functional studies to large genome-wide screens. The creation of new tools and protocols for introducing CRISPRi/a into non-model bacteria will facilitate the continuation of this rapid expansion. Several novel and exciting CRISPRa tools with greater activation and unique characteristics have been developed recently in both model and non-model bacteria, yet there remains a need for stronger and more versatile bacterial CRISPRa tools, especially for the activation of native genes. These bacterial CRISPRa tools have lagged behind the development of both eukaryotic CRISPRa tools and bacterial CRISPRi tools. However, the recent development of several new CRISPRa systems with less stringent design rules and higher levels of activation (>10-fold) shows great promise for effective, tailored gene activation in bacteria (Liu Y. et al., 2019; Fontana et al., 2020a; Ho et al., 2020; Villegas Kcam et al., 2021). These CRISPRa technologies were created using directed evolution and thorough tool design. Further improvements could be achieved by creating CRISPRa tools from CRISPR systems with more relaxed PAM requirements, directed evolution of CRISPRa components (activator domain, gRNA scaffold(s), and dCas protein) for greater activation, and high-throughput screening of gRNAs and promoters to uncover additional nuanced design rules for a given tool. CRISPRa has the potential to become a more effective and widely used tool for programmable gene activation in both model and non-model bacteria for a variety of industrial and research applications, such as metabolic engineering and elucidation of gene function. While many CRISPRi/a approaches in non-model bacteria have been established using genetic parts that are not well-defined or characterized, the creation of comprehensive genetic part toolboxes for these strains, which are vital for the rational design and precise control of CRISPRi/a tools, will accelerate further development and optimization of the tools. Finally, CRISPRi/a approaches have primarily been developed for more genetically tractable strains of non-model bacteria. There is a need for efficient workflows to domesticate and introduce CRISPRi/a tools to novel bacterial species and strains. Despite these current challenges, CRISPRi/a technology remains a versatile approach for programmable transcriptional regulation in non-model bacteria.

References

• 1

  AfoninaI.OngJ.ChuaJ.LuT.KlineK. A. (2020). Multiplex CRISPRi System Enables the Study of Stage-specific Biofilm Genetic Requirements in Enterococcus faecalis. mBio11. 10.1128/mBio.01101-20

  • CrossRef
  • Google Scholar

• 2

  AgarwalN. (2020). Construction of a Novel CRISPRi-Based Tool for Silencing of Multiple Genes in Mycobacterium tuberculosis. Plasmid110, 102515. 10.1016/j.plasmid.2020.102515

  • CrossRef
  • Google Scholar

• 3

  AmeruosoA.Villegas KcamM. C.CohenK. P.ChappellJ. (2021). Activating Natural Product Synthesis Using CRISPR Interference and Activation Systems in Streptomyces. bioRxiv. [Preprint]. 10.1101/2021.10.28.466254

  • CrossRef
  • Google Scholar

• 4

  BaiJ.DaiY.FarinhaA.TangA. Y.SyalS.Vargas-CuebasG.et al (2021). Essential Gene Analysis in Acinetobacter Baumannii by High-Density Transposon Mutagenesis and CRISPR Interference. J. Bacteriol.203, 1. 10.1128/JB.00565-20

  • CrossRef
  • Google Scholar

• 5

  BanerjeeD.EngT.LauA. K.SasakiY.WangB.ChenY.et al (2020). Genome-scale Metabolic Rewiring Improves Titers Rates and Yields of the Non-native Product Indigoidine at Scale. Nat. Commun.11, 5385. 10.1038/s41467-020-19171-4

  • CrossRef
  • Google Scholar

• 6

  BantaA. B.EnrightA. L.SilettiC.PetersJ. M. (2020). A High-Efficacy CRISPR Interference System for Gene Function Discovery in Zymomonas Mobilis. Appl. Environ. Microbiol.86, 1. 10.1128/AEM.01621-20

  • CrossRef
  • Google Scholar

• 7

  BaranowskiC.WelshM. A.ShamL.-T.EskandarianH. A.LimH. C.KieserK. J.et al (2018). Maturing Mycobacterium Smegmatis Peptidoglycan Requires Non-canonical Crosslinks to Maintain Shape. eLife7, e37516. 10.7554/eLife.37516

  • CrossRef
  • Google Scholar

• 8

  BatianisC.KozaevaE.DamalasS. G.Martín‐PascualM.VolkeD. C.NikelP. I.et al (2020). An Expanded CRISPRi Toolbox for Tunable Control of Gene Expression inPseudomonas Putida. Microb. Biotechnol.13, 368–385. 10.1111/1751-7915.13533

  • CrossRef
  • Google Scholar

• 9

  BehleA.DietschM.GoldschmidtL.MurugathasW.BrandtD.BuscheT.et al (2021). Uncoupling of the Diurnal Growth Program by Artificial Genome Relaxation in Synechocystis Sp. PCC 6803. bioRxiv. [Preprint], 453758. 10.1101/2021.07.26.453758

  • CrossRef
  • Google Scholar

• 10

  BehlerJ.SharmaK.ReimannV.WildeA.UrlaubH.HessW. R. (2018). The Host-Encoded RNase E Endonuclease as the crRNA Maturation Enzyme in a CRISPR-Cas Subtype III-Bv System. Nat. Microbiol.3, 367–377. 10.1038/s41564-017-0103-5

  • CrossRef
  • Google Scholar

• 11

  BerlecA.ŠkrlecK.KocjanJ.OlenicM.ŠtrukeljB. (2018). Single Plasmid Systems for Inducible Dual Protein Expression and for CRISPR-Cas9/CRISPRi Gene Regulation in Lactic Acid Bacterium Lactococcus Lactis. Sci. Rep.8, 1009. 10.1038/s41598-018-19402-1

  • CrossRef
  • Google Scholar

• 12

  BhokishamN.VanArsdaleE.StephensK. T.HaukP.PayneG. F.BentleyW. E. (2020). A Redox-Based Electrogenetic CRISPR System to Connect with and Control Biological Information Networks. Nat. Commun.11, 2427. 10.1038/s41467-020-16249-x

  • CrossRef
  • Google Scholar

• 13

  BikardD.JiangW.SamaiP.HochschildA.ZhangF.MarraffiniL. A. (2013). Programmable Repression and Activation of Bacterial Gene Expression Using an Engineered CRISPR-Cas System. Nucleic Acids Res.41, 7429–7437. 10.1093/nar/gkt520

  • CrossRef
  • Google Scholar

• 14

  BoschB.DeJesusM. A.PoultonN. C.ZhangW.EngelhartC. A.ZaveriA.et al (2021). Genome-wide Gene Expression Tuning Reveals Diverse Vulnerabilities of M. tuberculosis. Cell184, 4579–4592. e24. 10.1016/j.cell.2021.06.033

  • CrossRef
  • Google Scholar

• 15

  BritoL. F.SchultenkämperK.PassagliaL. M. P.WendischV. F. (2020). CRISPR Interference-Based Gene Repression in the Plant Growth Promoter Paenibacillus Sonchi Genomovar Riograndensis SBR5. Appl. Microbiol. Biotechnol.104, 5095–5106. 10.1007/s00253-020-10571-6

  • CrossRef
  • Google Scholar

• 16

  BrockettM. R.LeeJ.CoxJ. V.LiechtiG. W.OuelletteS. P. (2021). A Dynamic, Ring-Forming Bactofilin Critical for Maintaining Cell Size in the Obligate Intracellular Bacterium Chlamydia trachomatis. Infect. Immun.89, 1. 10.1128/IAI.00203-21

  • CrossRef
  • Google Scholar

• 17

  BruderM. R.PyneM. E.Moo-YoungM.ChungD. A.ChouC. P. (2016). Extending CRISPR-Cas9 Technology from Genome Editing to Transcriptional Engineering in the Genus Clostridium. Appl. Environ. Microbiol.82, 6109–6119. 10.1128/AEM.02128-16

  • CrossRef
  • Google Scholar

• 18

  BrzostekA.PłocińskiP.MiniasA.CiszewskaA.GąsiorF.PawełczykJ.et al (2021). Dissecting the RecA-(In)dependent Response to Mitomycin C in Mycobacterium tuberculosis Using Transcriptional Profiling and Proteomics Analyses. Cells10, 1168. 10.3390/cells10051168

  • CrossRef
  • Google Scholar

• 19

  CaoY.LiX.LiF.SongH. (2017). CRISPRi-sRNA: Transcriptional-Translational Regulation of Extracellular Electron Transfer in Shewanella Oneidensis. ACS Synth. Biol.6, 1679–1690. 10.1021/acssynbio.6b00374

  • CrossRef
  • Google Scholar

• 20

  CaroF.PlaceN. M.MekalanosJ. J. (2019). Analysis of Lipoprotein Transport Depletion in Vibrio cholerae Using CRISPRi. Proc. Natl. Acad. Sci. U.S.A.116, 17013–17022. 10.1073/pnas.1906158116

  • CrossRef
  • Google Scholar

• 21

  ChaiG.YuM.JiangL.DuanY.HuangJ. (2019). HMMCAS: A Web Tool for the Identification and Domain Annotations of CAS Proteins. IEEE/ACM Trans. Comput. Biol. Bioinf.16, 1313–1315. 10.1109/TCBB.2017.2665542

  • CrossRef
  • Google Scholar

• 22

  ChenW.ZhangY.YeoW.-S.BaeT.JiQ. (2017). Rapid and Efficient Genome Editing in Staphylococcus aureus by Using an Engineered CRISPR/Cas9 System. J. Am. Chem. Soc.139, 3790–3795. 10.1021/jacs.6b13317

  • CrossRef
  • Google Scholar

• 23

  CheungC.-Y.McNeilM. B.CookG. M. (2021). Utilization of CRISPR Interference to Investigate the Contribution of Genes to Pathogenesis in a Macrophage Model of Mycobacterium tuberculosis Infection. J. Antimicrob. Chemother.77, 615–619. 10.1093/jac/dkab437

  • CrossRef
  • Google Scholar

• 24

  ChoS.ChoeD.LeeE.KimS. C.PalssonB.ChoB.-K. (2018). High-Level dCas9 Expression Induces Abnormal Cell Morphology in Escherichia coli. ACS Synth. Biol.7, 1085–1094. 10.1021/acssynbio.7b00462

  • CrossRef
  • Google Scholar

• 25

  ChoiS. Y.WooH. M. (2020). CRISPRi-dCas12a: A dCas12a-Mediated CRISPR Interference for Repression of Multiple Genes and Metabolic Engineering in Cyanobacteria. ACS Synth. Biol.9, 2351–2361. 10.1021/acssynbio.0c00091

  • CrossRef
  • Google Scholar

• 26

  ChoudharyE.SharmaR.KumarY.AgarwalN. (2019). Conditional Silencing by CRISPRi Reveals the Role of DNA Gyrase in Formation of Drug-Tolerant Persister Population in Mycobacterium tuberculosis. Front. Cell Infect. Microbiol.9, 70. 10.3389/fcimb.2019.00070

  • CrossRef
  • Google Scholar

• 27

  ChoudharyE.ThakurP.PareekM.AgarwalN. (2015). Gene Silencing by CRISPR Interference in Mycobacteria. Nat. Commun.6, 6267. 10.1038/ncomms7267

  • CrossRef
  • Google Scholar

• 28

  CletoS.JensenJ. V.WendischV. F.LuT. K. (2016). Corynebacterium Glutamicum Metabolic Engineering with CRISPR Interference (CRISPRi). ACS Synth. Biol.5, 375–385. 10.1021/acssynbio.5b00216

  • CrossRef
  • Google Scholar

• 29

  ColquhounJ. M.FarokhyfarM.HutchesonA. R.AndersonA.BethelC. R.BonomoR. A.et al (2021). OXA-23 β-Lactamase Overexpression in Acinetobacter Baumannii Drives Physiological Changes Resulting in New Genetic Vulnerabilities. mBio12. 10.1128/mBio.03137-21

  • CrossRef
  • Google Scholar

• 30

  CouvinD.BernheimA.Toffano-NiocheC.TouchonM.MichalikJ.NéronB.et al (2018). CRISPRCasFinder, an Update of CRISRFinder, Includes a Portable Version, Enhanced Performance and Integrates Search for Cas Proteins. Nucleic Acids Res.46, W246–W251. 10.1093/nar/gky425

  • CrossRef
  • Google Scholar

• 31

  CuiL.VigourouxA.RoussetF.VaretH.KhannaV.BikardD. (2018). A CRISPRi Screen in E. coli Reveals Sequence-specific Toxicity of dCas9. Nat. Commun.9, 1912. 10.1038/s41467-018-04209-5

  • CrossRef
  • Google Scholar

• 32

  CzajkaJ. J.BanerjeeD.EngT.MenasalvasJ.YanC.MunozN. M.et al (2021). Optimizing a High Performing Multiplex-CRISPRi P. Putida Strain with Integrated Metabolomics and 13C-Metabolic Flux Analyses. bioRxiv. [Preprint], 473729. 10.1101/2021.12.23.473729

  • CrossRef
  • Google Scholar

• 33

  DaiY.PinedoV.TangA. Y.CavaF.GeisingerE. (2021). A New Class of Cell Wall-Recycling L , D -Carboxypeptidase Determines β-Lactam Susceptibility and Morphogenesis in Acinetobacter Baumannii. mBio12. 10.1128/mBio.02786-21

  • CrossRef
  • Google Scholar

• 34

  DammannA. N.ChambyA. B.CatomerisA. J.DavidsonK. M.TettelinH.van PijkerenJ.-P.et al (2021). Genome-Wide Fitness Analysis of Group B Streptococcus in Human Amniotic Fluid Reveals a Transcription Factor that Controls Multiple Virulence Traits. PLoS Pathog.17, e1009116. 10.1371/journal.ppat.1009116

  • CrossRef
  • Google Scholar

• 35

  de BakkerV.LiuX.BravoA. M.VeeningJ.-W. (2022). CRISPRi-seq for Genome-wide Fitness Quantification in Bacteria. Nat. Protoc.17, 252–281. 10.1038/s41596-021-00639-6

  • CrossRef
  • Google Scholar

• 36

  de WetT. J.WinklerK. R.MhlangaM.MizrahiV.WarnerD. F. (2020). Arrayed CRISPRi and Quantitative Imaging Describe the Morphotypic Landscape of Essential Mycobacterial Genes. eLife9, e60083. 10.7554/eLife.60083

  • CrossRef
  • Google Scholar

• 37

  Del VecchioD.NinfaA. J.SontagE. D. (2008). Modular Cell Biology: Retroactivity and Insulation. Mol. Syst. Biol.4, 161. 10.1038/msb4100204

  • CrossRef
  • Google Scholar

• 38

  DeLorenzoD. M.DiaoJ.CarrR.HuY.MoonT. S. (2021). An Improved CRISPR Interference Tool to Engineer Rhodococcus Opacus. ACS Synth. Biol.10, 786–798. 10.1021/acssynbio.0c00591

  • CrossRef
  • Google Scholar

• 39

  DeLorenzoD. M.RottinghausA. G.HensonW. R.MoonT. S. (2018). Molecular Toolkit for Gene Expression Control and Genome Modification in Rhodococcus Opacus PD630. ACS Synth. Biol.7, 727–738. 10.1021/acssynbio.7b00416

  • CrossRef
  • Google Scholar

• 40

  DepardieuF.BikardD. (2020). Gene Silencing with CRISPRi in Bacteria and Optimization of dCas9 Expression Levels. Methods172, 61–75. 10.1016/j.ymeth.2019.07.024

  • CrossRef
  • Google Scholar

• 41

  DewachterL.LiuX.DénéréazJ.de BakkerV.CostaC.BaldryM.et al (2021). Amoxicillin-resistant Streptococcus Pneumoniae Can Be Resensitized by Targeting the Mevalonate Pathway as Indicated by sCRilecs-Seq. bioRxiv. [Preprint], 460059. 10.1101/2021.09.13.460059

  • CrossRef
  • Google Scholar

• 42

  DietschM.BehleA.WesthoffP.AxmannI. M. (2021). Metabolic Engineering of Synechocystis Sp. PCC 6803 for the Photoproduction of the Sesquiterpene Valencene. Metab. Eng. Commun.13, e00178. 10.1016/j.mec.2021.e00178

  • CrossRef
  • Google Scholar

• 43

  DomenechA.SlagerJ.VeeningJ.-W. (2018). Antibiotic-Induced Cell Chaining Triggers Pneumococcal Competence by Reshaping Quorum Sensing to Autocrine-like Signaling. Cell Rep.25, 2390–2400. e3. 10.1016/j.celrep.2018.11.007

  • CrossRef
  • Google Scholar

• 44

  DongC.FontanaJ.PatelA.CarothersJ. M.ZalatanJ. G. (2018). Synthetic CRISPR-Cas Gene Activators for Transcriptional Reprogramming in Bacteria. Nat. Commun.9, 2489. 10.1038/s41467-018-04901-6

  • CrossRef
  • Google Scholar

• 45

  DongX.JinY.MingD.LiB.DongH.WangL.et al (2017). CRISPR/dCas9-mediated Inhibition of Gene Expression in Staphylococcus aureus. J. Microbiol. Methods139, 79–86. 10.1016/j.mimet.2017.05.008

  • CrossRef
  • Google Scholar

• 46

  DuttaA. K.ChoudharyE.WangX.ZáhorszkaM.ForbakM.LohnerP.et al (2019). Trehalose Conjugation Enhances Toxicity of Photosensitizers against Mycobacteria. ACS Cent. Sci.5, 644–650. 10.1021/acscentsci.8b00962

  • CrossRef
  • Google Scholar

• 47

  EllisN. A.KimB.TungJ.MachnerM. P. (2021). A Multiplex CRISPR Interference Tool for Virulence Gene Interrogation in Legionella pneumophila. Commun. Biol.4, 1–13. 10.1038/s42003-021-01672-7

  • CrossRef
  • Google Scholar

• 48

  FacklerN.HeffernanJ.JuminagaA.DoserD.NagarajuS.Gonzalez-GarciaR. A.et al (2021). Transcriptional Control of Clostridium Autoethanogenum Using CRISPRi. Synth. Biol.6, ysab008. 10.1093/synbio/ysab008

  • CrossRef
  • Google Scholar

• 49

  FaulknerV.CoxA. A.GohS.van BohemenA.GibsonA. J.LiebsterO.et al (2020). Re-sensitization of Mycobacterium Smegmatis to Rifampicin Using CRISPR Interference Demonstrates its Utility for the Study of Non-essential Drug Resistance Traits. Front. Microbiol.11, 619427. 10.3389/fmicb.2020.619427

  • CrossRef
  • Google Scholar

• 50

  FernandesL. G. V.GuamanL. P.VasconcellosS. A.HeinemannM. B.PicardeauM.NascimentoA. L. T. O. (2019). Gene Silencing Based on RNA-Guided Catalytically Inactive Cas9 (dCas9): a New Tool for Genetic Engineering in Leptospira. Sci. Rep.9, 1839. 10.1038/s41598-018-37949-x

  • CrossRef
  • Google Scholar

• 51

  FernandesL. G. V.HornsbyR. L.NascimentoA. L. T. O.NallyJ. E. (2021a). Application of CRISPR Interference (CRISPRi) for Gene Silencing in Pathogenic Species of Leptospira. JoVE174, e62631. 10.3791/62631

  • CrossRef
  • Google Scholar

• 52

  FernandesL. G. V.HornsbyR. L.NascimentoA. L. T. O.NallyJ. E. (2021b). Genetic Manipulation of Pathogenic Leptospira: CRISPR Interference (CRISPRi)-Mediated Gene Silencing and Rapid Mutant Recovery at 37 °C. Sci. Rep.11, 1768. 10.1038/s41598-021-81400-7

  • CrossRef
  • Google Scholar

• 53

  FleckN.GrundnerC. (2021). A Cas12a-Based CRISPR Interference System for Multigene Regulation in Mycobacteria. J. Biol. Chem.297, 100990. 10.1016/j.jbc.2021.100990

  • CrossRef
  • Google Scholar

• 54

  FonfaraI.RichterH.BratovičM.Le RhunA.CharpentierE. (2016). The CRISPR-Associated DNA-Cleaving Enzyme Cpf1 Also Processes Precursor CRISPR RNA. Nature532, 517–521. 10.1038/nature17945

  • CrossRef
  • Google Scholar

• 55

  FontanaJ.DongC.KiattiseweeC.ChavaliV. P.TickmanB. I.CarothersJ. M.et al (2020a). Effective CRISPRa-Mediated Control of Gene Expression in Bacteria Must Overcome Strict Target Site Requirements. Nat. Commun.11, 1618. 10.1038/s41467-020-15454-y

  • CrossRef
  • Google Scholar

• 56

  FontanaJ.Sparkman-YagerD.ZalatanJ. G.CarothersJ. M. (2020b). Challenges and Opportunities with CRISPR Activation in Bacteria for Data-Driven Metabolic Engineering. Curr. Opin. Biotechnol.64, 190–198. 10.1016/j.copbio.2020.04.005

  • CrossRef
  • Google Scholar

• 57

  GallayC.SanselicioS.AndersonM. E.SohY. M.LiuX.StamsåsG. A.et al (2021). CcrZ Is a Pneumococcal Spatiotemporal Cell Cycle Regulator that Interacts with FtsZ and Controls DNA Replication by Modulating the Activity of DnaA. Nat. Microbiol.6, 1175–1187. 10.1038/s41564-021-00949-1

  • CrossRef
  • Google Scholar

• 58

  GangulyJ.Martin‐PascualM.KranenburgR. (2020). CRISPR Interference (CRISPRi) as Transcriptional Repression Tool for Hungateiclostridium Thermocellum DSM 1313. Microb. Biotechnol.13, 339–349. 10.1111/1751-7915.13516

  • CrossRef
  • Google Scholar

• 59

  GaniZ.BoradiaV. M.KumarA.PatidarA.TalukdarS.ChoudharyE.et al (2021). Mycobacterium tuberculosis Glyceraldehyde‐3‐phosphate Dehydrogenase Plays a Dual Role-As an Adhesin and as a Receptor for Plasmin(ogen). Cell. Microbiol.23, e13311. 10.1111/cmi.13311

  • CrossRef
  • Google Scholar

• 60

  GauttamR.MukhopadhyayA.SimmonsB. A.SingerS. W. (2021). Development of Dual‐inducible Duet‐expression Vectors for Tunable Gene Expression Control and CRISPR Interference‐based Gene Repression in Pseudomonas Putida KT2440. Microb. Biotechnol.14, 2659–2678. 10.1111/1751-7915.13832

  • CrossRef
  • Google Scholar

• 61

  GauttamR.SeiboldG. M.MuellerP.WeilT.WeißT.HandrickR.et al (2019). A Simple Dual-Inducible CRISPR Interference System for Multiple Gene Targeting in Corynebacterium Glutamicum. Plasmid103, 25–35. 10.1016/j.plasmid.2019.04.001

  • CrossRef
  • Google Scholar

• 62

  GelinM.PaolettiJ.NahoriM.-A.HuteauV.LeseigneurC.JouvionG.et al (2020). From Substrate to Fragments to Inhibitor Active In Vivo against Staphylococcus aureus. ACS Infect. Dis.6, 422–435. 10.1021/acsinfecdis.9b00368

  • CrossRef
  • Google Scholar

• 63

  GengP.LeonardS. P.MishlerD. M.BarrickJ. E. (2019). Synthetic Genome Defenses against Selfish DNA Elements Stabilize Engineered Bacteria against Evolutionary Failure. ACS Synth. Biol.8, 521–531. 10.1021/acssynbio.8b00426

  • CrossRef
  • Google Scholar

• 64

  GibsonA. J.PassmoreI. J.FaulknerV.XiaD.NobeliI.StiensJ.et al (2021). Probing Differences in Gene Essentiality between the Human and Animal Adapted Lineages of the Mycobacterium tuberculosis Complex Using TnSeq. Front. Vet. Sci.8, 760717. 10.3389/fvets.2021.760717

  • CrossRef
  • Google Scholar

• 65

  GordonG. C.KoroshT. C.CameronJ. C.MarkleyA. L.BegemannM. B.PflegerB. F. (2016). CRISPR Interference as a Titratable, Trans-acting Regulatory Tool for Metabolic Engineering in the Cyanobacterium Synechococcus Sp. Strain PCC 7002. Metab. Eng.38, 170–179. 10.1016/j.ymben.2016.07.007

  • CrossRef
  • Google Scholar

• 66

  GöttlV. L.SchmittI.BraunK.Peters-WendischP.WendischV. F.HenkeN. A. (2021). CRISPRi-Library-Guided Target Identification for Engineering Carotenoid Production by Corynebacterium Glutamicum. Microorganisms9, 670. 10.3390/microorganisms9040670

  • CrossRef
  • Google Scholar

• 67

  GuzzoM.CastroL. K.ReischC. R.GuoM. S.LaubM. T. (2020). A CRISPR Interference System for Efficient and Rapid Gene Knockdown in Caulobacter crescentus. mBio11. 10.1128/mBio.02415-19

  • CrossRef
  • Google Scholar

• 68

  HigoA.EhiraS. (2019). Spatiotemporal Gene Repression System in the Heterocyst-Forming Multicellular Cyanobacterium Anabaena Sp. PCC 7120. ACS Synth. Biol.8, 641–646. 10.1021/acssynbio.8b00496

  • CrossRef
  • Google Scholar

• 69

  HigoA.IsuA.FukayaY.EhiraS.HisaboriT. (2018). Application of CRISPR Interference for Metabolic Engineering of the Heterocyst-Forming Multicellular Cyanobacterium Anabaena Sp. PCC 7120. Plant Cell Physiology59, 119–127. 10.1093/pcp/pcx166

  • CrossRef
  • Google Scholar

• 70

  HigoA.NishiyamaE.NakamuraK.HiharaY.EhiraS. (2019). cyAbrB Transcriptional Regulators as Safety Devices to Inhibit Heterocyst Differentiation in Anabaena Sp. Strain PCC 7120. J. Bacteriol.201, 1. 10.1128/JB.00244-19

  • CrossRef
  • Google Scholar

• 71

  HoH. I.FangJ. R.CheungJ.WangH. H. (2020). Programmable CRISPR‐Cas Transcriptional Activation in Bacteria. Mol. Syst. Biol.16, e9427. 10.15252/msb.20199427

  • CrossRef
  • Google Scholar

• 72

  HoganA. M.RahmanA. S. M. Z.LightlyT. J.CardonaS. T. (2019). A Broad-Host-Range CRISPRi Toolkit for Silencing Gene Expression in Burkholderia. ACS Synth. Biol.8, 2372–2384. 10.1021/acssynbio.9b00232

  • CrossRef
  • Google Scholar

• 73

  HuangC.-H.ShenC. R.LiH.SungL.-Y.WuM.-Y.HuY.-C. (2016). CRISPR Interference (CRISPRi) for Gene Regulation and Succinate Production in Cyanobacterium S. Elongatus PCC 7942. Microb. Cell Fact.15, 196. 10.1186/s12934-016-0595-3

  • CrossRef
  • Google Scholar

• 74

  HuangJ.ChenJ.WangY.ShiT.NiX.PuW.et al (2021). Development of a Hyperosmotic Stress Inducible Gene Expression System by Engineering the MtrA/MtrB-dependent NCgl1418 Promoter in Corynebacterium Glutamicum. Front. Microbiol.12. 10.3389/fmicb.2021.718511

  • CrossRef
  • Google Scholar

• 75

  HuangL. H.LiuQ. J.SunX. W.LiX. J.LiuM.JiaS. R.et al (2020). Tailoring Bacterial Cellulose Structure through CRISPR Interference‐mediated Downregulation of galU in Komagataeibacter Xylinus CGMCC 2955. Biotechnol. Bioeng.117, 2165–2176. 10.1002/bit.27351

  • CrossRef
  • Google Scholar

• 76

  IrnovI.WangZ.JannettyN. D.BustamanteJ. A.RheeK. Y.Jacobs-WagnerC. (2017). Crosstalk between the Tricarboxylic Acid Cycle and Peptidoglycan Synthesis in Caulobacter crescentus through the Homeostatic Control of α-ketoglutarate. PLoS Genet.13, e1006978. 10.1371/journal.pgen.1006978

  • CrossRef
  • Google Scholar

• 77

  JacksonS. A.McKenzieR. E.FagerlundR. D.KieperS. N.FineranP. C.BrounsS. J. J. (2017). CRISPR-cas: Adapting to Change. Science356, eaal5056. 10.1126/science.aal5056

  • CrossRef
  • Google Scholar

• 78

  JiangF.ZhouK.MaL.GresselS.DoudnaJ. A. (2015). A Cas9-Guide RNA Complex Preorganized for Target DNA Recognition. Science348, 1477–1481. 10.1126/science.aab1452

  • CrossRef
  • Google Scholar

• 79

  JiangW.OikonomouP.TavazoieS. (2020). Comprehensive Genome-wide Perturbations via CRISPR Adaptation Reveal Complex Genetics of Antibiotic Sensitivity. Cell180, 1002–1017. e31. 10.1016/j.cell.2020.02.007

  • CrossRef
  • Google Scholar

• 80

  JinW.-B.LiT.-T.HuoD.QuS.LiX. V.ArifuzzamanM.et al (2022). Genetic Manipulation of Gut Microbes Enables Single-Gene Interrogation in a Complex Microbiome. Cell185, 547–562. e22. 10.1016/j.cell.2021.12.035

  • CrossRef
  • Google Scholar

• 81

  JuddJ. A.CanestrariJ.ClarkR.JosephA.LapierreP.Lasek-NesselquistE.et al (2021). A Mycobacterial Systems Resource for the Research Community. mBio12. 10.1128/mBio.02401-20

  • CrossRef
  • Google Scholar

• 82

  KaczmarzykD.CengicI.YaoL.HudsonE. P. (2018). Diversion of the Long-Chain Acyl-ACP Pool in Synechocystis to Fatty Alcohols through CRISPRi Repression of the Essential Phosphate Acyltransferase PlsX. Metab. Eng.45, 59–66. 10.1016/j.ymben.2017.11.014

  • CrossRef
  • Google Scholar

• 83

  KampmannM. (2018). CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine. ACS Chem. Biol.13, 406–416. 10.1021/acschembio.7b00657

  • CrossRef
  • Google Scholar

• 84

  KeJ.RobinsonD.WuZ.-Y.KuftinA.LouieK.KosinaS.et al (2022). CRAGE-CRISPR Facilitates Rapid Activation of Secondary Metabolite Biosynthetic Gene Clusters in Bacteria. Cell Chem. Biol.29, 696–710. 10.1016/j.chembiol.2021.08.009

  • CrossRef
  • Google Scholar

• 85

  KiattiseweeC.DongC.FontanaJ.SugiantoW.Peralta-YahyaP.CarothersJ. M.et al (2021). Portable Bacterial CRISPR Transcriptional Activation Enables Metabolic Engineering in Pseudomonas Putida. Metab. Eng.66, 283–295. 10.1016/j.ymben.2021.04.002

  • CrossRef
  • Google Scholar

• 86

  KimS. K.KimH.AhnW.-C.ParkK.-H.WooE.-J.LeeD.-H.et al (2017). Efficient Transcriptional Gene Repression by Type V-A CRISPR-Cpf1 from Eubacterium Eligens. ACS Synth. Biol.6, 1273–1282. 10.1021/acssynbio.6b00368

  • CrossRef
  • Google Scholar

• 87

  KimS. K.YoonP. K.KimS. J.WooS. G.RhaE.LeeH.et al (2020). CRISPR Interference‐mediated Gene Regulation in Pseudomonas Putida KT 2440. Microb. Biotechnol.13, 210–221. 10.1111/1751-7915.13382

  • CrossRef
  • Google Scholar

• 88

  KirtaniaP.HódiB.MallickI.VassI. Z.FehérT.VassI.et al (2019). A Single Plasmid Based CRISPR Interference in Synechocystis 6803 - A Proof of Concept. PLOS ONE14, e0225375. 10.1371/journal.pone.0225375

  • CrossRef
  • Google Scholar

• 89

  KnoopsA.Vande CapelleF.FontaineL.VerhaegenM.MignoletJ.GoffinP.et al (2022). The CovRS Environmental Sensor Directly Controls the ComRS Signaling System to Orchestrate Competence Bimodality in Salivarius Streptococci. mBio13. 10.1128/mbio.03125-21

  • CrossRef
  • Google Scholar

• 90

  KnootC. J.BiswasS.PakrasiH. B. (2020). Tunable Repression of Key Photosynthetic Processes Using Cas12a CRISPR Interference in the Fast-Growing Cyanobacterium Synechococcus Sp. UTEX 2973. ACS Synth. Biol.9, 132–143. 10.1021/acssynbio.9b00417

  • CrossRef
  • Google Scholar

• 91

  KooninE. V.MakarovaK. S.ZhangF. (2017). Diversity, Classification and Evolution of CRISPR-Cas Systems. Curr. Opin. Microbiol.37, 67–78. 10.1016/j.mib.2017.05.008

  • CrossRef
  • Google Scholar

• 92

  KozaevaE.VolkovaS.MatosM. R. A.MezzinaM. P.WulffT.VolkeD. C.et al (2021). Model-guided Dynamic Control of Essential Metabolic Nodes Boosts Acetyl-Coenzyme A-dependent Bioproduction in Rewired Pseudomonas Putida. Metab. Eng.67, 373–386. 10.1016/j.ymben.2021.07.014

  • CrossRef
  • Google Scholar

• 93

  KuoJ.YuanR.SánchezC.PaulssonJ.SilverP. A. (2020). Toward a Translationally Independent RNA-Based Synthetic Oscillator Using Deactivated CRISPR-Cas. Nucleic Acids Res.48, 8165–8177. 10.1093/nar/gkaa557

  • CrossRef
  • Google Scholar

• 94

  LandetaC.McPartlandL.TranN. Q.MeehanB. M.ZhangY.TanweerZ.et al (2019). Inhibition ofPseudomonas aeruginosaandMycobacterium Tuberculosisdisulfide Bond Forming Enzymes. Mol. Microbiol.111, 918–937. 10.1111/mmi.14185

  • CrossRef
  • Google Scholar

• 95

  LeeH. H.OstrovN.WongB. G.GoldM. A.KhalilA. S.ChurchG. M. (2019). Functional Genomics of the Rapidly Replicating Bacterium Vibrio Natriegens by CRISPRi. Nat. Microbiol.4, 1105–1113. 10.1038/s41564-019-0423-8

  • CrossRef
  • Google Scholar

• 96

  LeeM.WooH. M. (2020). A Logic NAND Gate for Controlling Gene Expression in a Circadian Rhythm in Cyanobacteria. ACS Synth. Biol.9, 3210–3216. 10.1021/acssynbio.0c00455

  • CrossRef
  • Google Scholar

• 97

  LeeS. S.ShinH.JoS.LeeS.-M.UmY.WooH. M. (2018). Rapid Identification of Unknown Carboxyl Esterase Activity in Corynebacterium Glutamicum Using RNA-Guided CRISPR Interference. Enzyme Microb. Technol.114, 63–68. 10.1016/j.enzmictec.2018.04.004

  • CrossRef
  • Google Scholar

• 98

  LeonardS. P.PerutkaJ.PowellJ. E.GengP.RichhartD. D.ByromM.et al (2018). Genetic Engineering of Bee Gut Microbiome Bacteria with a Toolkit for Modular Assembly of Broad-Host-Range Plasmids. ACS Synth. Biol.7, 1279–1290. 10.1021/acssynbio.7b00399

  • CrossRef
  • Google Scholar

• 99

  LewisW. H.TahonG.GeesinkP.SousaD. Z.EttemaT. J. G. (2021). Innovations to Culturing the Uncultured Microbial Majority. Nat. Rev. Microbiol.19, 225–240. 10.1038/s41579-020-00458-8

  • CrossRef
  • Google Scholar

• 100

  LiJ.TangQ.LiY.FanY.-Y.LiF.-H.WuJ.-H.et al (2020a). Rediverting Electron Flux with an Engineered CRISPR-ddAsCpf1 System to Enhance the Pollutant Degradation Capacity of Shewanella Oneidensis. Environ. Sci. Technol.54, 3599–3608. 10.1021/acs.est.9b06378

  • CrossRef
  • Google Scholar

• 101

  LiJ.YeB.-C. (2021). Metabolic Engineering of Pseudomonas Putida KT2440 for High-Yield Production of Protocatechuic Acid. Bioresour. Technol.319, 124239. 10.1016/j.biortech.2020.124239

  • CrossRef
  • Google Scholar

• 102

  LiL.WeiK.ZhengG.LiuX.ChenS.JiangW.et al (2018). CRISPR-Cpf1-Assisted Multiplex Genome Editing and Transcriptional Repression in Streptomyces. Appl. Environ. Microbiol.84, 1. 10.1128/AEM.00827-18

  • CrossRef
  • Google Scholar

• 103

  LiM.ChenJ.WangY.LiuJ.HuangJ.ChenN.et al (2020b). Efficient Multiplex Gene Repression by CRISPR-dCpf1 in Corynebacterium Glutamicum. Front. Bioeng. Biotechnol.8, 357. 10.3389/fbioe.2020.00357

  • CrossRef
  • Google Scholar

• 104

  LiQ.ChenJ.MintonN. P.ZhangY.WenZ.LiuJ.et al (2016). CRISPR-based Genome Editing and Expression Control Systems inClostridium acetobutylicumandClostridium Beijerinckii. Biotechnol. J.11, 961–972. 10.1002/biot.201600053

  • CrossRef
  • Google Scholar

• 105

  LiZ.LiuJ. Z. (2017). Transcriptomic Changes in Response to Putrescine Production in Metabolically Engineered Corynebacterium Glutamicum. Front. Microbiol.8, 1987. 10.3389/fmicb.2017.01987

  • CrossRef
  • Google Scholar

• 106

  LiewA. T. F.TheisT.JensenS. O.Garcia-LaraJ.FosterS. J.FirthN.et al (2011). A Simple Plasmid-Based System that Allows Rapid Generation of Tightly Controlled Gene Expression in Staphylococcus aureus. Microbiology157, 666–676. 10.1099/mic.0.045146-0

  • CrossRef
  • Google Scholar

• 107

  LiowL. T.GoM. K.ChangM. W.YewW. S. (2020). Toolkit Development for Cyanogenic and Gold Biorecovery Chassis Chromobacterium Violaceum. ACS Synth. Biol.9, 953–961. 10.1021/acssynbio.0c00064

  • CrossRef
  • Google Scholar

• 108

  LiuD.JohnsonV. M.PakrasiH. B. (2020a). A Reversibly Induced CRISPRi System Targeting Photosystem II in the Cyanobacterium Synechocystis Sp. PCC 6803. ACS Synth. Biol.9, 1441–1449. 10.1021/acssynbio.0c00106

  • CrossRef
  • Google Scholar

• 109

  LiuW.TangD.WangH.LianJ.HuangL.XuZ. (2019a). Combined Genome Editing and Transcriptional Repression for Metabolic Pathway Engineering in Corynebacterium Glutamicum Using a Catalytically Active Cas12a. Appl. Microbiol. Biotechnol.103, 8911–8922. 10.1007/s00253-019-10118-4

  • CrossRef
  • Google Scholar

• 110

  LiuX.GallayC.KjosM.DomenechA.SlagerJ.KesselS. P.et al (2017). High‐throughput CRISPRi Phenotyping Identifies New Essential Genes in Streptococcus Pneumoniae. Mol. Syst. Biol.13, 931. 10.15252/msb.20167449

  • CrossRef
  • Google Scholar

• 111

  LiuX.KimmeyJ. M.MatarazzoL.de BakkerV.Van MaeleL.SirardJ.-C.et al (2021a). Exploration of Bacterial Bottlenecks and Streptococcus Pneumoniae Pathogenesis by CRISPRi-Seq. Cell Host Microbe29, 107–120. e6. 10.1016/j.chom.2020.10.001

  • CrossRef
  • Google Scholar

• 112

  LiuY.ChenJ.CrisanteD.Jaramillo LopezJ. M.MahadevanR. (2020b). Dynamic Cell Programming with Quorum Sensing-Controlled CRISPRi Circuit. ACS Synth. Biol.9, 1284–1291. 10.1021/acssynbio.0c00148

  • CrossRef
  • Google Scholar

• 113

  LiuY.KhanS.WuP.LiB.LiuL.NiJ.et al (2021b). Uncovering and Engineering a Mini-Regulatory Network of the TetR-Family Regulator SACE_0303 for Yield Improvement of Erythromycin in Saccharopolyspora Erythraea. Front. Bioeng. Biotechnol.9. 10.3389/fbioe.2021.692901

  • CrossRef
  • Google Scholar

• 114

  LiuY.WanX.WangB. (2019b). Engineered CRISPRa Enables Programmable Eukaryote-like Gene Activation in Bacteria. Nat. Commun.10, 3693. 10.1038/s41467-019-11479-0

  • CrossRef
  • Google Scholar

• 115

  LiuY.WangH.LiS.ZhangY.ChengX.XiangW.et al (2021c). Engineering of Primary Metabolic Pathways for Titer Improvement of Milbemycins in Streptomyces Bingchenggensis. Appl. Microbiol. Biotechnol.105, 1875–1887. 10.1007/s00253-021-11164-7

  • CrossRef
  • Google Scholar

• 116

  LungeA.GuptaR.ChoudharyE.AgarwalN. (2020). The Unfoldase ClpC1 of Mycobacterium tuberculosis Regulates the Expression of a Distinct Subset of Proteins Having Intrinsically Disordered Termini. J. Biol. Chem.295, 9455–9473. 10.1074/jbc.RA120.013456

  • CrossRef
  • Google Scholar

• 117

  LuoM. L.MullisA. S.LeenayR. T.BeiselC. L. (2015). Repurposing Endogenous Type I CRISPR-Cas Systems for Programmable Gene Repression. Nucleic Acids Res.43, 674–681. 10.1093/nar/gku971

  • CrossRef
  • Google Scholar

• 118

  MaiJ.RaoC.WattJ.SunX.LinC.ZhangL.et al (2019). Mycobacterium tuberculosis 6C sRNA Binds Multiple mRNA Targets via C-Rich Loops Independent of RNA Chaperones. Nucleic Acids Res.47, 4292–4307. 10.1093/nar/gkz149

  • CrossRef
  • Google Scholar

• 119

  MakarovaK. S.WolfY. I.IranzoJ.ShmakovS. A.AlkhnbashiO. S.BrounsS. J. J.et al (2020). Evolutionary Classification of CRISPR-Cas Systems: a Burst of Class 2 and Derived Variants. Nat. Rev. Microbiol.18, 67–83. 10.1038/s41579-019-0299-x

  • CrossRef
  • Google Scholar

• 120

  MariscalA. M.KakizawaS.HsuJ. Y.TanakaK.González-GonzálezL.BrotoA.et al (2018). Tuning Gene Activity by Inducible and Targeted Regulation of Gene Expression in Minimal Bacterial Cells. ACS Synth. Biol.7, 1538–1552. 10.1021/acssynbio.8b00028

  • CrossRef
  • Google Scholar

• 121

  MårliM. T. (2020). Using CRISPR Interference to Study Novel Biofilm-Associated Genes in Staphylococcus aureus. Available at: https://nmbu.brage.unit.no/nmbu-xmlui/handle/11250/2682085 (Accessed January 28, 2022).

  • Google Scholar

• 122

  MarraffiniL. A.SontheimerE. J. (2008). CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA. Science322, 1843–1845. 10.1126/science.1165771

  • CrossRef
  • Google Scholar

• 123

  MarreddyR. K. R.WuX.SapkotaM.PriorA. M.JonesJ. A.SunD.et al (2019). The Fatty Acid Synthesis Protein Enoyl-ACP Reductase II (FabK) Is a Target for Narrow-Spectrum Antibacterials for Clostridium difficile Infection. ACS Infect. Dis.5, 208–217. 10.1021/acsinfecdis.8b00205

  • CrossRef
  • Google Scholar

• 124

  McNeilM. B.CookG. M. (2019). Utilization of CRISPR Interference to Validate MmpL3 as a Drug Target in Mycobacterium tuberculosis. Antimicrob. Agents Chemother.63, 1. 10.1128/AAC.00629-19

  • CrossRef
  • Google Scholar

• 125

  McNeilM. B.KeighleyL. M.CookJ. R.CheungC. Y.CookG. M. (2021). CRISPR Interference Identifies Vulnerable Cellular Pathways with Bactericidal Phenotypes in Mycobacterium tuberculosis. Mol. Microbiol.116, 1033–1043. 10.1111/mmi.14790

  • CrossRef
  • Google Scholar

• 126

  McNeilM. B.RyburnH. W. K.HaroldL. K.TiradosJ. F.CookG. M. (2020). Transcriptional Inhibition of the F 1 F 0 -Type ATP Synthase Has Bactericidal Consequences on the Viability of Mycobacteria. Antimicrob. Agents Chemother.64, 1. 10.1128/AAC.00492-20

  • CrossRef
  • Google Scholar

• 127

  McNeilM. B.RyburnH. W.TiradosJ.CheungC.-Y.CookG. M. (2022). Multiplexed Transcriptional Repression Identifies a Network of Bactericidal Interactions between Mycobacterial Respiratory Complexes. iScience25, 103573. 10.1016/j.isci.2021.103573

  • CrossRef
  • Google Scholar

• 128

  MiaoC.ZhaoH.QianL.LouC. (2019). Systematically Investigating the Key Features of the DNase Deactivated Cpf1 for Tunable Transcription Regulation in Prokaryotic Cells. Synthetic Syst. Biotechnol.4, 1–9. 10.1016/j.synbio.2018.11.002

  • CrossRef
  • Google Scholar

• 129

  MimeeM.TuckerA. C.VoigtC. A.LuT. K. (2015). Programming a Human Commensal Bacterium, Bacteroides Thetaiotaomicron, to Sense and Respond to Stimuli in the Murine Gut Microbiota. Cell Syst.1, 62–71. 10.1016/j.cels.2015.06.001

  • CrossRef
  • Google Scholar

• 130

  MoX.-H.ZhangH.WangT.-M.ZhangC.ZhangC.XingX.-H.et al (2020). Establishment of CRISPR Interference in Methylorubrum Extorquens and Application of Rapidly Mining a New Phytoene Desaturase Involved in Carotenoid Biosynthesis. Appl. Microbiol. Biotechnol.104, 4515–4532. 10.1007/s00253-020-10543-w

  • CrossRef
  • Google Scholar

• 131

  MonkI. R.TreeJ. J.HowdenB. P.StinearT. P.FosterT. J. (2015). Complete Bypass of Restriction Systems for Major Staphylococcus aureus Lineages. mBio6. 10.1128/mBio.00308-15

  • CrossRef
  • Google Scholar

• 132

  MougiakosI.MohanrajuP.BosmaE. F.VrouweV.Finger BouM.NaduthodiM. I. S.et al (2017). Characterizing a Thermostable Cas9 for Bacterial Genome Editing and Silencing. Nat. Commun.8, 1647. 10.1038/s41467-017-01591-4

  • CrossRef
  • Google Scholar

• 133

  MühU.PannulloA. G.WeissD. S.EllermeierC. D. (2019). A Xylose-Inducible Expression System and a CRISPR Interference Plasmid for Targeted Knockdown of Gene Expression in Clostridioides Difficile. J. Bacteriol.201, 1. 10.1128/JB.00711-18

  • CrossRef
  • Google Scholar

• 134

  MyrbråtenI. S.StamsåsG. A.ChanH.AngelesD. M.KnutsenT. M.SalehianZ.et al (2021). SmdA Is a Novel Cell Morphology Determinant in Staphylococcus aureus. bioRxiv. [Preprint], 469651. 10.1101/2021.11.23.469651

  • CrossRef
  • Google Scholar

• 135

  MyrbråtenI. S.WiullK.SalehianZ.HåvarsteinL. S.StraumeD.MathiesenG.et al (2019). CRISPR Interference for Rapid Knockdown of Essential Cell Cycle Genes in Lactobacillus Plantarum. mSphere4. 10.1128/mSphere.00007-19

  • CrossRef
  • Google Scholar

• 136

  NadolinskaiaN. I.ZamakhaevM. V.ShumkovM. S.ArmianinovaD. K.KarpovD. S.GoncharenkoA. V. (2021). CRISPR Interference of Adenylate Cyclases from Mycobacterium tuberculosis. Appl. Biochem. Microbiol.57, 421–425. 10.1134/S0003683821040128

  • CrossRef
  • Google Scholar

• 137

  Noirot-GrosM.-F.ForresterS.MalatoG.LarsenP. E.NoirotP. (2019). CRISPR Interference to Interrogate Genes that Control Biofilm Formation in Pseudomonas Fluorescens. Sci. Rep.9, 15954. 10.1038/s41598-019-52400-5

  • CrossRef
  • Google Scholar

• 138

  NussenzweigP. M.MarraffiniL. A. (2020). Molecular Mechanisms of CRISPR-Cas Immunity in Bacteria. Annu. Rev. Genet.54, 93–120. 10.1146/annurev-genet-022120-112523

  • CrossRef
  • Google Scholar

• 139

  OuelletteS. P. (2018). Feasibility of a Conditional Knockout System for Chlamydia Based on CRISPR Interference. Front. Cell Infect. Microbiol.8, 59. 10.3389/fcimb.2018.00059

  • CrossRef
  • Google Scholar

• 140

  OuelletteS. P.BlayE. A.HatchN. D.Fisher-MarvinL. A. (2021). CRISPR Interference to Inducibly Repress Gene Expression in Chlamydia trachomatis. Infect. Immun.89, 1. 10.1128/IAI.00108-21

  • CrossRef
  • Google Scholar

• 141

  ParkJ.ShinH.LeeS.-M.UmY.WooH. M. (2018). RNA-guided Single/double Gene Repressions in Corynebacterium Glutamicum Using an Efficient CRISPR Interference and its Application to Industrial Strain. Microb. Cell Fact.17, 4. 10.1186/s12934-017-0843-1

  • CrossRef
  • Google Scholar

• 142

  ParkJ.YuB. J.ChoiJ.-i.WooH. M. (2019). Heterologous Production of Squalene from Glucose in Engineered Corynebacterium Glutamicum Using Multiplex CRISPR Interference and High-Throughput Fermentation. J. Agric. Food Chem.67, 308–319. 10.1021/acs.jafc.8b05818

  • CrossRef
  • Google Scholar

• 143

  PawlukA.DavidsonA. R.MaxwellK. L. (2018). Anti-CRISPR: Discovery, Mechanism and Function. Nat. Rev. Microbiol.16, 12–17. 10.1038/nrmicro.2017.120

  • CrossRef
  • Google Scholar

• 144

  PengR.WangY.FengW.-w.YueX.-j.ChenJ.-h.HuX.-z.et al (2018). CRISPR/dCas9-mediated Transcriptional Improvement of the Biosynthetic Gene Cluster for the Epothilone Production in Myxococcus Xanthus. Microb. Cell Fact.17, 15. 10.1186/s12934-018-0867-1

  • CrossRef
  • Google Scholar

• 145

  PetersJ. M.KooB.-M.PatinoR.HeusslerG. E.HearneC. C.QuJ.et al (2019). Enabling Genetic Analysis of Diverse Bacteria with Mobile-CRISPRi. Nat. Microbiol.4, 244–250. 10.1038/s41564-018-0327-z

  • CrossRef
  • Google Scholar

• 146

  QiL. S.LarsonM. H.GilbertL. A.DoudnaJ. A.WeissmanJ. S.ArkinA. P.et al (2013). Repurposing CRISPR as an RNA-Guided Platform for Sequence-specific Control of Gene Expression. Cell152, 1173–1183. 10.1016/j.cell.2013.02.022

  • CrossRef
  • Google Scholar

• 147

  QinZ.YangY.YuS.LiuL.ChenY.ChenJ.et al (2021). Repurposing the Endogenous Type I-E CRISPR/Cas System for Gene Repression in Gluconobacter Oxydans WSH-003. ACS Synth. Biol.10, 84–93. 10.1021/acssynbio.0c00456

  • CrossRef
  • Google Scholar

• 148

  QuJ.PrasadN. K.YuM. A.ChenS.LydenA.HerreraN.et al (2019). Modulating Pathogenesis with Mobile-CRISPRi. J. Bacteriol.201. 10.1128/JB.00304-19

  • CrossRef
  • Google Scholar

• 149

  Quiñones-GarciaS.GilmanR. H.SheenP.ZimicM. (2021). Silencing of an Efflux Pump Coding Gene Decreases the Efflux Rate of Pyrazinoic Acid in Mycobacterium Smegmatis. bioRxiv. [Preprint], 466536. 10.1101/2021.10.29.466536

  • CrossRef
  • Google Scholar

• 150

  RahmanK.JamalM.ChenX.ZhouW.YangB.ZouY.et al (2021). Reprogramming Mycobacterium tuberculosis CRISPR System for Gene Editing and Genome-wide RNA Interference Screening. Genomics, Proteomics Bioinforma.2021, 1. 10.1016/j.gpb.2021.01.008

  • CrossRef
  • Google Scholar

• 151

  RandallS. E.MartiniM. C.ZhouY.JoubranS. R.ShellS. S. (2020). MamA Essentiality in Mycobacterium Smegmatis Is Explained by the Presence of an Apparent Cognate Restriction Endonuclease. BMC Res. Notes13, 462. 10.1186/s13104-020-05302-z

  • CrossRef
  • Google Scholar

• 152

  RathD.AmlingerL.HoekzemaM.DevulapallyP. R.LundgrenM. (2015). Efficient Programmable Gene Silencing by Cascade. Nucleic Acids Res.43, 237–246. 10.1093/nar/gku1257

  • CrossRef
  • Google Scholar

• 153

  RileyL. A.GussA. M. (2021). Approaches to Genetic Tool Development for Rapid Domestication of Non-model Microorganisms. Biotechnol. Biofuels14, 30. 10.1186/s13068-020-01872-z

  • CrossRef
  • Google Scholar

• 154

  RockJ. M.HopkinsF. F.ChavezA.DialloM.ChaseM. R.GerrickE. R.et al (2017). Programmable Transcriptional Repression in Mycobacteria Using an Orthogonal CRISPR Interference Platform. Nat. Microbiol.2, 1–9. 10.1038/nmicrobiol.2016.274

  • CrossRef
  • Google Scholar

• 155

  SantosM.PachecoC. C.YaoL.HudsonE. P.TamagniniP. (2021). CRISPRi as a Tool to Repress Multiple Copies of Extracellular Polymeric Substances (EPS)-Related Genes in the Cyanobacterium Synechocystis Sp. PCC 6803. Life11, 1198. 10.3390/life11111198

  • CrossRef
  • Google Scholar

• 156

  Sato’oY.HisatsuneJ.YuL.SakumaT.YamamotoT.SugaiM. (2018). Tailor-made Gene Silencing of Staphylococcus aureus Clinical Isolates by CRISPR Interference. PLOS ONE13, e0185987. 10.1371/journal.pone.0185987

  • CrossRef
  • Google Scholar

• 157

  SavkováK.HuszárS.BaráthP.PakanováZ.KozmonS.VancováM.et al (2021). An ABC Transporter Wzm-Wzt Catalyzes Translocation of Lipid-Linked Galactan across the Plasma Membrane in Mycobacteria. Proc. Natl. Acad. Sci. U.S.A.118. 10.1073/pnas.2023663118

  • CrossRef
  • Google Scholar

• 158

  SchillingC.KoffasM. A. G.SieberV.SchmidJ. (2020). Novel Prokaryotic CRISPR-Cas12a-Based Tool for Programmable Transcriptional Activation and Repression. ACS Synth. Biol.9, 3353–3363. 10.1021/acssynbio.0c00424

  • CrossRef
  • Google Scholar

• 159

  SchultenkämperK.BritoL. F.LópezM. G.BrautasetT.WendischV. F. (2019). Establishment and Application of CRISPR Interference to Affect Sporulation, Hydrogen Peroxide Detoxification, and Mannitol Catabolism in the Methylotrophic Thermophile Bacillus Methanolicus. Appl. Microbiol. Biotechnol.103, 5879–5889. 10.1007/s00253-019-09907-8

  • CrossRef
  • Google Scholar

• 160

  SchultenkämperK.GütleD. D.LópezM. G.KellerL. B.ZhangL.EinsleO.et al (2021). Interrogating the Role of the Two Distinct Fructose-Bisphosphate Aldolases of Bacillus Methanolicus by Site-Directed Mutagenesis of Key Amino Acids and Gene Repression by CRISPR Interference. Front. Microbiol.12.

  • Google Scholar

• 161

  ShaY.QiuY.ZhuY.SunT.LuoZ.GaoJ.et al (2020). CRISPRi-Based Dynamic Regulation of Hydrolase for the Synthesis of Poly-γ-Glutamic Acid with Variable Molecular Weights. ACS Synth. Biol.9, 2450–2459. 10.1021/acssynbio.0c00207

  • CrossRef
  • Google Scholar

• 162

  ShabestaryK.AnfeltJ.LjungqvistE.JahnM.YaoL.HudsonE. P. (2018). Targeted Repression of Essential Genes to Arrest Growth and Increase Carbon Partitioning and Biofuel Titers in Cyanobacteria. ACS Synth. Biol.7, 1669–1675. 10.1021/acssynbio.8b00056

  • CrossRef
  • Google Scholar

• 163

  ShabestaryK.HernándezH. P.MiaoR.LjungqvistE.HallmanO.SporreE.et al (2021). Cycling between Growth and Production Phases Increases Cyanobacteria Bioproduction of Lactate. Metab. Eng.68, 131–141. 10.1016/j.ymben.2021.09.010

  • CrossRef
  • Google Scholar

• 164

  ShieldsR. C.WalkerA. R.MaricicN.ChakrabortyB.UnderhillS. A. M.BurneR. A. (2020). Repurposing the Streptococcus Mutans CRISPR-Cas9 System to Understand Essential Gene Function. PLoS Pathog.16, e1008344. 10.1371/journal.ppat.1008344

  • CrossRef
  • Google Scholar

• 165

  ShinJ.KangS.SongY.JinS.LeeJ. S.LeeJ.-K.et al (2019). Genome Engineering of Eubacterium Limosum Using Expanded Genetic Tools and the CRISPR-Cas9 System. ACS Synth. Biol.8, 2059–2068. 10.1021/acssynbio.9b00150

  • CrossRef
  • Google Scholar

• 166

  SinghA. K.CaretteX.PotluriL.-P.SharpJ. D.XuR.PrisicS.et al (2016). Investigating Essential Gene Function inMycobacterium Tuberculosisusing an Efficient CRISPR Interference System. Nucleic Acids Res.44, e143. 10.1093/nar/gkw625

  • CrossRef
  • Google Scholar

• 167

  SinghK. H.JhaB.DwivedyA.ChoudharyE.NA. G.AshrafA.et al (2017). Characterization of a Secretory Hydrolase from Mycobacterium tuberculosis Sheds Critical Insight into Host Lipid Utilization by M. tuberculosis. J. Biol. Chem.292, 11326–11335. 10.1074/jbc.M117.794297

  • CrossRef
  • Google Scholar

• 168

  SonJ.JangS. H.ChaJ. W.JeongK. J. (2020). Development of CRISPR Interference (CRISPRi) Platform for Metabolic Engineering of Leuconostoc Citreum and its Application for Engineering Riboflavin Biosynthesis. Ijms21, 5614. 10.3390/ijms21165614

  • CrossRef
  • Google Scholar

• 169

  SpotoM.GuanC.FlemingE.OhJ. (2020). A Universal, Genomewide GuideFinder for CRISPR/Cas9 Targeting in Microbial Genomes. mSphere5. 10.1128/mSphere.00086-20

  • CrossRef
  • Google Scholar

• 170

  SpotoM.Riera PumaJ. P.FlemingE.GuanC.NzutchiY. O.KimD.et al (2021). Large-scale CRISPRi and Transcriptomics of Staphylococcus Epidermidis Identify Genetic Factors Implicated in Commensal-Pathogen Lifestyle Versatility. bioRxiv. [Preprint]. 10.1101/2021.04.29.442003

  • CrossRef
  • Google Scholar

• 171

  StamsåsG. A.MyrbråtenI. S.StraumeD.SalehianZ.VeeningJ. W.HåvarsteinL. S.et al (2018). CozEa and CozEb Play Overlapping and Essential Roles in Controlling Cell Division in Staphylococcus aureus. Mol. Microbiol.109, 615–632. 10.1111/mmi.13999

  • CrossRef
  • Google Scholar

• 172

  StolleA.-S.MeaderB. T.ToskaJ.MekalanosJ. J. (2021). Endogenous Membrane Stress Induces T6SS Activity in Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. U.S.A.118. 10.1073/pnas.2018365118

  • CrossRef
  • Google Scholar

• 173

  SunJ.WangQ.JiangY.WenZ.YangL.WuJ.et al (2018). Genome Editing and Transcriptional Repression in Pseudomonas Putida KT2440 via the Type II CRISPR System. Microb. Cell Fact.17, 41. 10.1186/s12934-018-0887-x

  • CrossRef
  • Google Scholar

• 174

  TakacsC. N.ScottM.ChangY.KloosZ. A.IrnovI.RosaP. A.et al (2021). A CRISPR Interference Platform for Selective Downregulation of Gene Expression in Borrelia Burgdorferi. Appl. Environ. Microbiol.87, 1. 10.1128/AEM.02519-20

  • CrossRef
  • Google Scholar

• 175

  TaketaniM.ZhangJ.ZhangS.TriassiA. J.HuangY.-J.GriffithL. G.et al (2020). Genetic Circuit Design Automation for the Gut Resident Species Bacteroides Thetaiotaomicron. Nat. Biotechnol.38, 962–969. 10.1038/s41587-020-0468-5

  • CrossRef
  • Google Scholar

• 176

  TanS. Z.ReischC. R.PratherK. L. J. (2018). A Robust CRISPR Interference Gene Repression System in Pseudomonas. J. Bacteriol.200, 1. 10.1128/JB.00575-17

  • CrossRef
  • Google Scholar

• 177

  TaoW.LvL.ChenG.-Q. (2017). Engineering Halomonas Species TD01 for Enhanced Polyhydroxyalkanoates Synthesis via CRISPRi. Microb. Cell Fact.16, 48. 10.1186/s12934-017-0655-3

  • CrossRef
  • Google Scholar

• 178

  TehM. Y.OoiK. H.Danny TeoS. X.Bin MansoorM. E.Shaun LimW. Z.TanM. H. (2019). An Expanded Synthetic Biology Toolkit for Gene Expression Control in Acetobacteraceae. ACS Synth. Biol.8, 708–723. 10.1021/acssynbio.8b00168

  • CrossRef
  • Google Scholar

• 179

  ThakurP.GantasalaN. P.ChoudharyE.SinghN.AbdinM. Z.AgarwalN. (2016). The Preprotein Translocase YidC Controls Respiratory Metabolism in Mycobacterium tuberculosis. Sci. Rep.6, 24998. 10.1038/srep24998

  • CrossRef
  • Google Scholar

• 180

  TianJ.YangG.GuY.SunX.LuY.JiangW. (2020). Developing an Endogenous Quorum-sensing Based CRISPRi Circuit for Autonomous and Tunable Dynamic Regulation of Multiple Targets in Streptomyces. Nucleic Acids Res.48, 8188–8202. 10.1093/nar/gkaa602

  • CrossRef
  • Google Scholar

• 181

  TongY.CharusantiP.ZhangL.WeberT.LeeS. Y. (2015). CRISPR-Cas9 Based Engineering of Actinomycetal Genomes. ACS Synth. Biol.4, 1020–1029. 10.1021/acssynbio.5b00038

  • CrossRef
  • Google Scholar

• 182

  TongY.WhitfordC. M.BlinK.JørgensenT. S.WeberT.LeeS. Y. (2020). CRISPR-Cas9, CRISPRi and CRISPR-BEST-Mediated Genetic Manipulation in Streptomycetes. Nat. Protoc.15, 2470–2502. 10.1038/s41596-020-0339-z

  • CrossRef
  • Google Scholar

• 183

  UlteeE.van der AartL. T.ZhangL.van DisselD.DiebolderC. A.van WezelG. P.et al (2020). Teichoic Acids Anchor Distinct Cell Wall Lamellae in an Apically Growing Bacterium. Commun. Biol.3, 1–9. 10.1038/s42003-020-1038-6

  • CrossRef
  • Google Scholar

• 184

  VartoukianS. R.PalmerR. M.WadeW. G. (2010). Strategies for Culture of 'unculturable' Bacteria. FEMS Microbiol. Lett.309, no. 10.1111/j.1574-6968.2010.02000.x

  • CrossRef
  • Google Scholar

• 185

  Villegas KcamM. C.TsongA. J.ChappellJ. (2022). Uncovering the Distinct Properties of a Bacterial Type I-E CRISPR Activation System. ACS Synth. Biol.11, 1000–1003. 10.1021/acssynbio.1c00496

  • CrossRef
  • Google Scholar

• 186

  Villegas KcamM. C.TsongA. J.ChappellJ. (2021). Rational Engineering of a Modular Bacterial CRISPR-Cas Activation Platform with Expanded Target Range. Nucleic Acids Res.49, 4793–4802. 10.1093/nar/gkab211

  • CrossRef
  • Google Scholar

• 187

  WangJ.DaiW.LiJ.LiQ.XieR.ZhangY.et al (2021a). AcrHub: an Integrative Hub for Investigating, Predicting and Mapping Anti-CRISPR Proteins. Nucleic Acids Res.49, D630–D638. 10.1093/nar/gkaa951

  • CrossRef
  • Google Scholar

• 188

  WangJ.DaiW.LiJ.XieR.DunstanR. A.StubenrauchC.et al (2020). PaCRISPR: a Server for Predicting and Visualizing Anti-CRISPR Proteins. Nucleic Acids Res.48, W348–W357. 10.1093/nar/gkaa432

  • CrossRef
  • Google Scholar

• 189

  WangJ.ZhaoP.LiY.XuL.TianP. (2018a). Engineering CRISPR Interference System in Klebsiella pneumoniae for Attenuating Lactic Acid Synthesis. Microb. Cell Fact.17, 56. 10.1186/s12934-018-0903-1

  • CrossRef
  • Google Scholar

• 190

  WangK.NicholaouM. (2017). Suppression of Antimicrobial Resistance in MRSA Using CRISPR-dCas9. Clin. Lab. Sci.30, 207–213. 10.29074/ascls.30.4.207

  • CrossRef
  • Google Scholar

• 191

  WangM.LiuL.FanL.TanT. (2017). CRISPRi Based System for Enhancing 1-butanol Production in Engineered Klebsiella pneumoniae. Process Biochem.56, 139–146. 10.1016/j.procbio.2017.02.013

  • CrossRef
  • Google Scholar

• 192

  WangT.GuanC.GuoJ.LiuB.WuY.XieZ.et al (2018b). Pooled CRISPR Interference Screening Enables Genome-Scale Functional Genomics Study in Bacteria with Superior Performance. Nat. Commun.9, 2475. 10.1038/s41467-018-04899-x

  • CrossRef
  • Google Scholar

• 193

  WangT.WangM.ZhangQ.CaoS.LiX.QiZ.et al (2019). Reversible Gene Expression Control in Yersinia pestis by Using an Optimized CRISPR Interference System. Appl. Environ. Microbiol.85, 1. 10.1128/AEM.00097-19

  • CrossRef
  • Google Scholar

• 194

  WangW.SunB. (2021). VraCP Regulates Cell Wall Metabolism and Antibiotic Resistance in Vancomycin-Intermediate Staphylococcus aureus Strain Mu50. J. Antimicrob. Chemother.76, 1712–1723. 10.1093/jac/dkab113

  • CrossRef
  • Google Scholar

• 195

  WangX.FuY.WangM.NiuG. (2021b). Synthetic Cellobiose-Inducible Regulatory Systems Allow Tight and Dynamic Controls of Gene Expression in Streptomyces. ACS Synth. Biol.10, 1956–1965. 10.1021/acssynbio.1c00152

  • CrossRef
  • Google Scholar

• 196

  WangY.YueX.YuanS.HongY.HuW.LiY. (2021c). Internal Promoters and Their Effects on the Transcription of Operon Genes for Epothilone Production in Myxococcus Xanthus. Front. Bioeng. Biotechnol.9. 10.3389/fbioe.2021.758561

  • CrossRef
  • Google Scholar

• 197

  WangY.ZhangZ.-T.SeoS.-O.LynnP.LuT.JinY.-S.et al (2016). Gene Transcription Repression inClostridium Beijerinckiiusing CRISPR-dCas9. Biotechnol. Bioeng.113, 2739–2743. 10.1002/bit.26020

  • CrossRef
  • Google Scholar

• 198

  WenZ.MintonN. P.ZhangY.LiQ.LiuJ.JiangY.et al (2017). Enhanced Solvent Production by Metabolic Engineering of a Twin-Clostridial Consortium. Metab. Eng.39, 38–48. 10.1016/j.ymben.2016.10.013

  • CrossRef
  • Google Scholar

• 199

  WernerJ. N.ShiH.HsinJ.HuangK. C.GitaiZ.KleinE. A. (2020). AimB Is a Small Protein Regulator of Cell Size and MreB Assembly. Biophysical J.119, 593–604. 10.1016/j.bpj.2020.04.029

  • CrossRef
  • Google Scholar

• 200

  WilesT. J.SchlomannB. H.WallE. S.BetancourtR.ParthasarathyR.GuilleminK. (2020). Swimming Motility of a Gut Bacterial Symbiont Promotes Resistance to Intestinal Expulsion and Enhances Inflammation. PLoS Biol.18, e3000661. 10.1371/journal.pbio.3000661

  • CrossRef
  • Google Scholar

• 201

  Williams McMackinE. A.MarsdenA. E.YahrT. L. (2019). H-NS Family Members MvaT and MvaU Regulate the Pseudomonas aeruginosa Type III Secretion System. J. Bacteriol.201, 1. 10.1128/JB.00054-19

  • CrossRef
  • Google Scholar

• 202

  WoolstonB. M.EmersonD. F.CurrieD. H.StephanopoulosG. (2018). Rediverting Carbon Flux in Clostridium Ljungdahlii Using CRISPR Interference (CRISPRi). Metab. Eng.48, 243–253. 10.1016/j.ymben.2018.06.006

  • CrossRef
  • Google Scholar

• 203

  WuJ.ChengZ.-H.MinD.ChengL.HeR.-L.LiuD.-F.et al (2020). CRISPRi System as an Efficient, Simple Platform for Rapid Identification of Genes Involved in Pollutant Transformation by Aeromonas Hydrophila. Environ. Sci. Technol.54, 3306–3315. 10.1021/acs.est.9b07191

  • CrossRef
  • Google Scholar

• 204

  WuX.ZhaJ.KoffasM. A. G.DordickJ. S. (2019). ReducingStaphylococcus Aureusresistance to Lysostaphin Using CRISPR‐dCas9. Biotechnol. Bioeng.116, 3149–3159. 10.1002/bit.27143

  • CrossRef
  • Google Scholar

• 205

  WurihanW.HuangY.WeberA. M.WuX.FanH. (2019). Nonspecific Toxicities of Streptococcus Pyogenes and Staphylococcus aureus dCas9 in Chlamydia trachomatis. Pathogens Dis.77, ftaa005. 10.1093/femspd/ftaa005

  • CrossRef
  • Google Scholar

• 206

  XiangL.QiF.JiangL.TanJ.DengC.WeiZ.et al (2020). CRISPR‐dCas9‐mediated Knockdown of prtR , an Essential Gene in Pseudomonas aeruginosa. Lett. Appl. Microbiol.71, 386–393. 10.1111/lam.13337

  • CrossRef
  • Google Scholar

• 207

  XiaoJ.JiaH.PanL.LiZ.LvL.DuB.et al (2019). Application of the CRISPRi System to Repress sepF Expression in Mycobacterium Smegmatis. Infect. Genet. Evol.72, 183–190. 10.1016/j.meegid.2018.06.033

  • CrossRef
  • Google Scholar

• 208

  XiongZ.-Q.WeiY.-Y.KongL.-H.SongX.YiH.-X.AiL.-Z. (2020). Short Communication: An Inducible CRISPR/dCas9 Gene Repression System in Lactococcus Lactis. J. Dairy Sci.103, 161–165. 10.3168/jds.2019-17346

  • CrossRef
  • Google Scholar

• 209

  XuZ.LiY.CaoH.SiM.ZhangG.WooP. C. Y.et al (2021). A Transferrable and Integrative Type I-F Cascade for Heterologous Genome Editing and Transcription Modulation. Nucleic Acids Res.49, e94. 10.1093/nar/gkab521

  • CrossRef
  • Google Scholar

• 210

  YamadaS.SuzukiY.KouzumaA.WatanabeK. (2022). Development of a CRISPR Interference System for Selective Gene Knockdown in Acidithiobacillus Ferrooxidans. J. Biosci. Bioeng.133, 105–109. 10.1016/j.jbiosc.2021.10.012

  • CrossRef
  • Google Scholar

• 211

  YanY.-S.YangY.-Q.ZouL.-S.ZhangL.XiaH.-Y. (2022). MilR3, a Unique SARP Family Pleiotropic Regulator in Streptomyces Bingchenggensis. Europepmc. [Preprint]. 10.21203/rs.3.rs-1248187/v1

  • CrossRef
  • Google Scholar

• 212

  YaoL.CengicI.AnfeltJ.HudsonE. P. (2016). Multiple Gene Repression in Cyanobacteria Using CRISPRi. ACS Synth. Biol.5, 207–212. 10.1021/acssynbio.5b00264

  • CrossRef
  • Google Scholar

• 213

  YaoL.ShabestaryK.BjörkS. M.Asplund-SamuelssonJ.JoenssonH. N.JahnM.et al (2020). Pooled CRISPRi Screening of the Cyanobacterium Synechocystis Sp PCC 6803 for Enhanced Industrial Phenotypes. Nat. Commun.11, 1666. 10.1038/s41467-020-15491-7

  • CrossRef
  • Google Scholar

• 214

  YeW.LiuT.ZhuM.ZhangW.HuangZ.LiS.et al (2019). An Easy and Efficient Strategy for the Enhancement of Epothilone Production Mediated by TALE-TF and CRISPR/dcas9 Systems in Sorangium Cellulosum. Front. Bioeng. Biotechnol.7, 334. 10.3389/fbioe.2019.00334

  • CrossRef
  • Google Scholar

• 215

  YiY.-C.NgI.-S. (2021). Redirection of Metabolic Flux in Shewanella Oneidensis MR-1 by CRISPRi and Modular Design for 5-aminolevulinic Acid Production. Bioresour. Bioprocess.8, 13. 10.1186/s40643-021-00366-6

  • CrossRef
  • Google Scholar

• 216

  YoonJ.WooH. M. (2018). CRISPR Interference-Mediated Metabolic Engineering ofCorynebacterium Glutamicumfor Homo-Butyrate Production. Biotechnol. Bioeng.115, 2067–2074. 10.1002/bit.26720

  • CrossRef
  • Google Scholar

• 217

  YuL.SuW.FeyP. D.LiuF.DuL. (2018). Yield Improvement of the Anti-MRSA Antibiotics WAP-8294A by CRISPR/dCas9 Combined with Refactoring Self-Protection Genes inLysobacter enzymogenesOH11. ACS Synth. Biol.7, 258–266. 10.1021/acssynbio.7b00293

  • CrossRef
  • Google Scholar

• 218

  YunusI. S.AnfeltJ.SporreE.MiaoR.HudsonE. P.JonesP. R. (2022). Synthetic Metabolic Pathways for Conversion of CO2 into Secreted Short-To Medium-Chain Hydrocarbons Using Cyanobacteria. Metab. Eng.72, 14–23. 10.1016/j.ymben.2022.01.017

  • CrossRef
  • Google Scholar

• 219

  ZetscheB.GootenbergJ. S.AbudayyehO. O.SlaymakerI. M.MakarovaK. S.EssletzbichlerP.et al (2015). Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System. Cell163, 759–771. 10.1016/j.cell.2015.09.038

  • CrossRef
  • Google Scholar

• 220

  ZhanY.XuY.ZhengP.HeM.SunS.WangD.et al (2020). Establishment and Application of Multiplexed CRISPR Interference System in Bacillus Licheniformis. Appl. Microbiol. Biotechnol.104, 391–403. 10.1007/s00253-019-10230-5

  • CrossRef
  • Google Scholar

• 221

  ZhangB.LiuZ.-Q.LiuC.ZhengY.-G. (2016). Application of CRISPRi in Corynebacterium Glutamicum for Shikimic Acid Production. Biotechnol. Lett.38, 2153–2161. 10.1007/s10529-016-2207-z

  • CrossRef
  • Google Scholar

• 222

  ZhangK.ZhangZ.KangJ.ChenJ.LiuJ.GaoN.et al (2020a). CRISPR/Cas13d-Mediated Microbial RNA Knockdown. Front. Bioeng. Biotechnol.8, 856. 10.3389/fbioe.2020.00856

  • CrossRef
  • Google Scholar

• 223

  ZhangL.RamijanK.CarriónV. J.van der AartL. T.WillemseJ.van WezelG. P.et al (2021). An Alternative and Conserved Cell Wall Enzyme that Can Substitute for the Lipid II Synthase MurG. mBio12. 10.1128/mBio.03381-20

  • CrossRef
  • Google Scholar

• 224

  ZhangL.WillemseJ.YagüeP.de WaalE.ClaessenD.van WezelG. P. (2020b). Branching of Sporogenic Aerial Hyphae in sflA and sflB Mutants of Streptomyces Coelicolor Correlates to Ectopic Localization of DivIVA and FtsZ in Time and Space. bioRxiv. [Preprint], 424426. 10.1101/2020.12.26.424426

  • CrossRef
  • Google Scholar

• 225

  ZhangS.VoigtC. A. (2018). Engineered dCas9 with Reduced Toxicity in Bacteria: Implications for Genetic Circuit Design. Nucleic Acids Res.46, 11115–11125. 10.1093/nar/gky884

  • CrossRef
  • Google Scholar

• 226

  ZhangX.WangJ.ChengQ.ZhengX.ZhaoG.WangJ. (2017). Multiplex Gene Regulation by CRISPR-ddCpf1. Cell Discov.3, 1–9. 10.1038/celldisc.2017.18

  • CrossRef
  • Google Scholar

• 227

  ZhangY.YuanJ. (2021). CRISPR/Cas12a‐mediated Genome Engineering in the Photosynthetic Bacterium Rhodobacter Capsulatus. Microb. Biotechnol.14, 2700–2710. 10.1111/1751-7915.13805

  • CrossRef
  • Google Scholar

• 228

  ZhaoC.ShuX.SunB. (2017). Construction of a Gene Knockdown System Based on Catalytically Inactive (“Dead”) Cas9 (dCas9) in Staphylococcus aureus. Appl. Environ. Microbiol.83, 1. 10.1128/AEM.00291-17

  • CrossRef
  • Google Scholar

• 229

  ZhaoR.LiuY.ZhangH.ChaiC.WangJ.JiangW.et al (2019). CRISPR-Cas12a-Mediated Gene Deletion and Regulation in Clostridium Ljungdahlii and its Application in Carbon Flux Redirection in Synthesis Gas Fermentation. ACS Synth. Biol.8, 2270–2279. 10.1021/acssynbio.9b00033

  • CrossRef
  • Google Scholar

• 230

  ZhaoX.ZhengH.ZhenJ.ShuW.YangS.XuJ.et al (2020). Multiplex Genetic Engineering Improves Endogenous Expression of Mesophilic α-amylase Gene in a Wild Strain Bacillus Amyloliquefaciens 205. Int. J. Biol. Macromol.165, 609–618. 10.1016/j.ijbiomac.2020.09.210

  • CrossRef
  • Google Scholar

• 231

  ZhaoY.LiL.ZhengG.JiangW.DengZ.WangZ.et al (2018). CRISPR/dCas9-Mediated Multiplex Gene Repression inStreptomyces. Biotechnol. J.13, 1800121. 10.1002/biot.201800121

  • CrossRef
  • Google Scholar

• 232

  ZhengY.HanJ.WangB.HuX.LiR.ShenW.et al (2019). Characterization and Repurposing of the Endogenous Type I-F CRISPR-Cas System of Zymomonas Mobilis for Genome Engineering. Nucleic Acids Res.47, 11461–11475. 10.1093/nar/gkz940

  • CrossRef
  • Google Scholar