Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements

Fontana, Jason; Dong, Chen; Kiattisewee, Cholpisit; Chavali, Venkata P.; Tickman, Benjamin I.; Carothers, James M.; Zalatan, Jesse G.

doi:10.1038/s41467-020-15454-y

Download PDF

Article
Open access
Published: 01 April 2020

Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements

Nature Communications volume 11, Article number: 1618 (2020) Cite this article

15k Accesses
67 Citations
6 Altmetric
Metrics details

Subjects

Abstract

In bacterial systems, CRISPR-Cas transcriptional activation (CRISPRa) has the potential to dramatically expand our ability to regulate gene expression, but we lack predictive rules for designing effective gRNA target sites. Here, we identify multiple features of bacterial promoters that impose stringent requirements on CRISPRa target sites. Notably, we observe narrow, 2–4 base windows of effective sites with a periodicity corresponding to one helical turn of DNA, spanning ~40 bases and centered ~80 bases upstream of the TSS. However, we also identify two features suggesting the potential for broad scope: CRISPRa is effective at a broad range of σ⁷⁰-family promoters, and an expanded PAM dCas9 allows the activation of promoters that cannot be activated by S. pyogenes dCas9. These results provide a roadmap for future engineering efforts to further expand and generalize the scope of bacterial CRISPRa.

Allostery through DNA drives phenotype switching

Article Open access 20 May 2021

Gabriel Rosenblum, Nadav Elad, … Hagen Hofmann

Engineered CRISPRa enables programmable eukaryote-like gene activation in bacteria

Article Open access 26 August 2019

Yang Liu, Xinyi Wan & Baojun Wang

Targeted transcriptional modulation with type I CRISPR–Cas systems in human cells

Article 23 September 2019

Adrian Pickar-Oliver, Joshua B. Black, … Charles A. Gersbach

Introduction

Developing tools to activate the expression of arbitrary genes has been transformative for biotechnology and biological research¹. In metabolic engineering, regulating the timing and levels of the expression of complex multi-gene pathways is critical for reducing cellular burden and improving production of valuable metabolites². To enable these goals, we recently developed a CRISPR-Cas transcriptional activation (CRISPRa) system that is effective in Escherichia coli. Our system can be combined with CRISPRi gene repression to programmably target multiple genes for simultaneous activation and repression³. Although our CRISPRa system can be used with heterologous genes, an outstanding challenge is to understand the rules that define effective target sites at arbitrary promoters in the genome.

To programmably downregulate target genes, we use nuclease defective Cas9 (dCas9) with a guide RNA (gRNA) that specifies a target site on the DNA. Targeting this complex to a promoter or an open reading frame (ORF) results in gene repression (CRISPRi)⁴. To enable simultaneous activation, we use modified guide RNAs, termed scaffold RNAs (scRNAs), that include a 3’ MS2 hairpin to recruit a transcriptional activator fused to the MS2 coat protein (MCP)³. We can express multiple gRNAs and scRNAs to inhibit and activate genes simultaneously; gRNAs targeted to a promoter or ORF result in CRISPRi and scRNAs targeted to an appropriate site upstream of a minimal promoter result in CRISPRa.

We demonstrate here that the rules for targeting CRISPRa to effective sites in E. coli are surprisingly stringent. In prior work, we found that CRISPRa in E. coli was effective at target sites located in a narrow 40 base window between 60 and 100 bases upstream of the transcriptional start site (TSS)³. Here, we show that multiple factors combine to make the requirements for effective sites even stricter. We demonstrate that the basal promoter strength of the target gene and the sequence composition between the target site and the minimal promoter can have marked effects on gene activation. Further, by scanning the 40 base window at single base resolution, we find sharp peaks of activity and broad regions of inactivity that occur in a periodic 10–11 base pattern, corresponding to one helical turn along the DNA target. The observation that only a few precisely positioned target sites upstream of the TSS are effective for CRISPRa poses a significant challenge, as many genes will likely lack an NGG PAM sequence at exactly the right position necessary for Streptococcus pyogenes dCas9. These stringent requirements may explain why CRISPRa and other tools for gene activation in bacteria have lagged far behind comparable tools in eukaryotic systems, where such strict target site requirements are absent⁵.

Although the requirements for bacterial CRISPRa target sites pose challenges, our data also demonstrate CRISPRa has the potential to be effective at a broad range of target genes. In addition to σ⁷⁰-dependent genes, CRISPRa can activate expression from genes that use the σ⁷⁰ family members σ³⁸, σ³², and σ²⁴. We further demonstrate that the strict requirement for a precisely positioned PAM site can be partially overcome using a re-engineered dCas9 protein that targets an expanded set of PAM sequences⁶. Recently, some of the rules that we describe here were independently reported for an alternative bacterial CRISPRa system that can target genes regulated by σ⁵⁴ promoters⁷. Our results demonstrate that this behavior applies to a much broader range of σ⁷⁰ family promoters, which cover the majority of the E. coli genome⁸. The availability of these complementary systems should further extend the scope of bacterial CRISPRa. More broadly, by systematically defining the rules for effective CRISPRa sites, we identify strategies for improving and generalizing synthetic gene regulation in bacteria.

Results

A SoxS mutant reduces off-target activation

Ideally, a synthetic transcriptional activator should only activate its programmed target genes. The activation domain for our CRISPRa system is SoxS, a native E. coli transcription factor that directly binds DNA and activates endogenous gene targets as part of a stress response program³. We previously demonstrated that point mutations in the SoxS DNA-binding site can reduce activation of endogenous SoxS targets while maintaining CRISPRa activity at a heterologous reporter gene. However, the most effective single point mutants, R93A and S101A, did not completely abolish activity at endogenous targets. To further minimize off-target SoxS activity, we tested a double mutant SoxS(R93A/S101A). This double mutant SoxS retained full CRISPRa activity and showed a reduction in endogenous SoxS-dependent gene expression to levels indistinguishable from background (Fig. 1). Thus, SoxS(R93A/S101A) is an effective modular transcriptional effector that can activate gene expression only when recruited to a target gene via the CRISPR–Cas complex.

**Fig. 1: A SoxS double mutant maintains CRISPRa activity and does not activate endogenous SoxS targets.**

A distance metric for target sites is not effective

To determine whether we could predictably activate endogenous genes with CRISPRa, we selected three candidate genes with appropriately positioned PAM sites upstream of the TSS. Previously, we demonstrated that CRISPRa can activate heterologous promoters up to 50-fold with target sites positioned within a 40 base window between 60 and 100 bases upstream of the TSS³. We therefore targeted the CRISPR–Cas complex to the same window upstream of the candidate target genes. First, we targeted the aroK-aroB operon, which expresses enzymes involved in aromatic amino-acid biosynthesis, whose programmed overexpression could be useful for bioproduction⁹. Targeting the CRISPR–Cas complex to two sites within the optimal 40 base window resulted in no statistically significant increases in gene expression. Further, sites inside and outside of the 40 base window gave similar effects (Fig. 2a). Next, we targeted cysK, an enzyme involved in cysteine biosynthesis¹⁰. Similar to what we observed with aroK-aroB, targeting three sites within the 40 base window resulted in no statistically significant increases in gene expression (Fig. 2b). Finally, we targeted ldhA, an enzyme involved in mixed acid fermentation¹¹. We selected eight sites and observed no apparent relationship between the position of the target site and ldhA expression (Supplementary Fig. 1). Together, these results suggest that endogenous genes cannot be activated simply by targeting the CRISPR–Cas complex to sites positioned between 60 and 100 bases upstream of the TSS.

**Fig. 2: A simple distance metric does not predict CRISPRa activity.**

There are several possible explanations for our inability to activate endogenous bacterial genes with CRISPRa. First, we originally demonstrated CRISPRa using a relatively weak synthetic promoter. The basal levels of expression of endogenous genes vary significantly¹², and it may be difficult to increase the transcription of genes that are already strongly expressed¹³. In addition, some endogenous target genes might require an alternative sigma factor. Our original reporter gene is controlled by the σ⁷⁰ housekeeping sigma factor, and we do not know if our CRISPRa system is effective at gene targets that use alternative sigma factors. Another possibility is that native transcriptional regulator binding sites near endogenous gene promoters could disrupt CRISPRa. Finally, the optimal distance window metric that we previously identified may have been oversimplified. We initially identified the optimal window from an experiment with target sites spaced 10 bases apart, which may not be sufficient to generalize to any site within the 40 base window. To systematically explore these possibilities, we proceeded to test the efficacy of CRISPRa with a new set of synthetic promoters engineered with variable basal expression levels, alternative sigma factors, variable regulator binding sites, and variable scRNA target site positions.

CRISPRa is sensitive to promoter strength

To evaluate whether the intrinsic strength of the promoter affects CRISPRa, we tested activation on a set of fluorescent reporter genes with minimal promoters spanning a 200-fold range in basal expression level (http://parts.igem.org) (Fig. 3a). We observed the most effective gene activation with a moderately weak J23117 promoter. With the weakest promoters, we could not detect any activation, even though their basal expression levels were only twofold weaker than the J23117 promoter. With stronger promoters, we observed progressively smaller CRISPRa-mediated activation of gene expression; the basal expression level increased, whereas the maximal, CRISPRa-induced expression remained roughly constant. These results indicate that the bacterial CRISPRa activity varies considerably with promoter strength, similar to effects observed in eukaryotic systems^14,15. Thus, when targeting arbitrary endogenous genes, the level of activation that can be achieved may depend on the basal level of expression of its promoter.

**Fig. 3: CRISPRa is sensitive to promoter identity and local sequence.**

CRISPRa is effective with alternative sigma factors

Bacterial transcription is initiated by a sigma factor binding to the minimal promoter and the RNA polymerase holoenzyme¹⁶.The SoxS activator binds directly to the α subunit of RNA polymerase¹⁷, which suggests that our CRISPRa system could be compatible with genes that are controlled by non-housekeeping sigma factors. To investigate this possibility, we built synthetic promoters regulated by σ³⁸ (RpoS), σ³² (RpoH), σ²⁴ (RpoE), and σ⁵⁴ (RpoN) to compare with our original housekeeping σ⁷⁰ (RpoD) promoter (Fig. 3b)^18,19,20,21. CRISPRa was able to activate reporter gene expression when we targeted σ³⁸, σ³², and σ²⁴-dependent promoters; these σ factors are all members of the σ⁷⁰ family. CRISPRa was not active on the σ⁵⁴ promoter, possibly because σ⁵⁴ initiates gene expression using a distinct mechanism that requires additional cis-regulatory elements¹⁶. These results suggest that CRISPRa can activate promoters regulated by non-housekeeping sigma factors such as σ³⁸, σ³², and σ²⁴, and likely other members of the homologous σ⁷⁰ family.

A recent paper described an alternative CRISPRa system that is capable of activating σ⁵⁴-dependent genes⁷, which comprise a small fraction of the genome⁸ (Supplementary Fig. 2). The availability of multiple, complementary CRISPRa systems should further extend the scope of bacterial CRISPRa. Both systems effectively activate expression from synthetic and heterologous promoters, and each system has the potential to target a different, non-overlapping set of endogenous genes.

CRISPRa is sensitive to intervening sequence composition

To determine whether the sequence composition between the target site and the −35 site affects CRISPRa, we constructed a promoter library with randomized sequences in this intervening region. We analyzed single colonies from this library and observed gene activation with a broad distribution over a 27-fold range (Fig. 3c). Although most variant sequences can still be activated (more than twofold) with CRISPRa, the large variation in activity was unexpected because each reporter gene was driven by the same minimal promoter and contained the same scRNA target site. One possible interpretation of this result is that these randomized intervening sequences contain binding sites for endogenous transcriptional regulators; there is evidence that binding sites can emerge with relatively high frequency from random sequences²². These sites could potentially affect CRISPRa by directly blocking access to a scRNA target site, by blocking RNA polymerase binding, or by interfering with the ability of a CRISPRa effector protein to engage with RNA polymerase.

To directly test the hypothesis that a bound transcriptional effector can disrupt CRISPRa, we introduced a binding site for the transcriptional repressor TetR upstream of the −35 region²³. The presence of a bound TetR significantly disrupted CRISPRa-mediated gene activation. Further, adding anhydrotetracycline (aTc), which releases TetR from the DNA, restored CRISPRa activity to the levels observed when TetR was not present (Fig. 3d). Because endogenous genes contain binding sites for a variety of transcriptional activators and repressors upstream of the minimal promoter^24,25, this effect could be contributing to the inconsistent and variable effects we observed when targeting endogenous genes for CRISPRa (Fig. 2).

To determine whether transcription factor-binding sites appear in the library of randomized intervening sequences, we sequenced 29 variants spanning the full range of observed activation levels (Supplementary Table 6). Only five intervening sequences contained exact matches to a known consensus transcription factor-binding motif. However, all sequences contained at least one match within a single base of a known motif, and it is well established that DNA-binding proteins can recognize sites that deviate from the consesus²⁶. There was no significant correlation between gene activation by CRISPRa and the number of these motifs (Spearman rank order correlation r_s = 0.29, p = 0.11, Supplementary Fig. 3A), but we note that it is not known which of these motifs actually bind endogenous transcription factors. We did find that intervening sequences that give more effective CRISPRa tend to be more GC-rich, though we do not yet understand the basis for this trend (r_s = 0.42, p = 0.02, Supplementary Fig. 3B & C). Nonetheless, these experiments indicate that the composition of the intervening sequence between the CRISPR–Cas complex and the minimal promoter is an important factor determining the level of CRISPRa.

CRISPRa is sharply dependent on single base shifts

Our original hypothesis that optimal target sites are located −60 to −100 bases upstream of the TSS was based on an experiment with scRNA sites spaced every 10 bases³. To further test this hypothesis, we targeted the CRISPRa complex to a window from −61 to −113 at single base resolution. We used a reporter gene with five scRNA sites located at −61, −71, −81, −91, and −101 relative to the TSS, and we inserted 1–12 bases upstream of the −35 site to generate a set of reporter genes that allowed the CRISPRa complex to target every possible distance in the optimal targeting window. Using this reporter gene set, we found that shifting the target site by 1–3 bases caused significant decreases in activation (Fig. 4a). Shifting the target site further by 4–9 bases decreased expression to levels nearly indistinguishable from background. At 10–11 base shifts, corresponding to one full turn of a DNA helix, gene expression increased again. This periodic positional dependence of CRISPRa extended over the entire −60 to −100 window, with the strongest peaks centered at −81 and −91 and smaller peaks centered at −102 and −70. There is no recovery of activity when the site at −101 is shifted to −111, outside of the −60 to −100 window. This sharp periodic relationship suggests that the criteria for effective target sites are quite stringent, and that both distance and relative periodicity to the TSS are critical factors.

**Fig. 4: CRISPRa is sensitive to the precise position of the scRNA target.**

Notably, the distance to the TSS is not the sole determining factor for CRISPRa-mediated expression level. Sites that overlap at the same distance, such as the original −81 site and the −71 site shifted by 10, do not give the same gene expression output (Fig. 4a). These discrepancies could arise from intrinsic differences in the activity of the 20 base scRNA target sequence (Supplementary Fig. 4) or from the effect of different intervening sequence composition between the scRNA target site and the minimal promoter (Fig. 3).

Because we demonstrated that sequence composition can have unexpected effects on CRISPRa (Fig. 3), we tested whether the periodicity of CRISPRa was similar in different sequence contexts. We obtained comparable periodic phase dependence when different nucleotide sequences were used to shift the scRNA target site, and when the bases were inserted at a different location in the promoter (Supplementary Fig. 5A). Similar results were also obtained when we performed the base shift experiment with a reporter that had a different 5’ upstream sequence (Supplementary Fig. 5B) or where the minimal BBa_J23117 promoter was replaced by endogenous aroK promoter (Supplementary Fig. 5C). Further, the sharp positioning dependence was observed when targeting the template or non-template strand of the reporter (Supplementary Fig. 5D). Finally, one possible confounding effect could arise if the basal expression level of the reporter gene changes when bases are inserted, which can affect the efficacy of CRISPRa (Fig. 3a). However, we observed that basal expression from the original reporter and the +5 base shifted reporter were indistinguishable (Supplementary Fig. 5E). Together, these experiments confirm that bacterial CRISPRa is sensitive to periodicity in multiple different sequence contexts.

In the experiments described above, comparisons between single base shifted scRNA sites were performed with different reporter gene constructs, each with a differing number of inserted bases. To test the positional dependence of CRISPRa at single base resolution in a single reporter construct, we designed an alternative reporter gene with 6 adjacent scRNA target sites between −81 and −86. We again observed sharp drops in gene expression when targeting sites one or more bases away from the optimal site at −81 (Supplementary Fig. 5F).

The finding that CRISPRa displays the same ~10 base periodicity as the DNA helix suggests that the angular phase of the CRISPRa complex relative to the minimal promoter is critical for effective activation. Our bacterial CRISPRa system requires a direct interaction between the SoxS activation domain and RNA polymerase³, and this interaction appears to be highly sensitive to both the distance and relative phase of the target site to the minimal promoter. The sharp phase dependence of CRISPRa may be a general feature of transcriptional regulation in E. coli. The native SoxS protein and other transcription factors such as CAP and LacI have restrictive positioning requirements that correspond to DNA periodicity^{27,28,29,30,31,32,33,34}; we confirmed this result with an endogenous SoxS reporter (Supplementary Fig. 6). In practice, this periodic behavior means that effective target sites must be located at one of the narrow peaks of activation within the optimal distance range. These stringent requirements suggest that targeting endogenous genes will be extremely challenging. There is ~1 PAM site every 10 bases in the regions upstream of endogenous promoters in E. coli (Supplementary Fig. 7A & B), and the likelihood that a PAM site will be located at the appropriate phase within a 10 base window is low (Supplementary Fig. 7C).

Tuning structure to expand target site range is ineffective

If rotating the CRISPRa complex out of phase along the DNA prevents SoxS from interacting with RNA polymerase, then a longer amino-acid linker to SoxS might allow effective CRISPRa at more scRNA sites. To test this possibility, we extended the linker between MCP and SoxS from five amino acids (aa) to 10 or 20 aa, but even with these longer linkers we observed the same sharp dependence on the target site position as with the original 5 aa linker (Fig. 4b). We obtained similar results using a linker with a different amino-acid composition (Supplementary Fig. 8A).

Another potential approach to expand the range of effective CRISPRa sites would be to change the spatial position of the MCP-SoxS protein by altering the position of the MS2 hairpin that binds MCP. We therefore tested multiple alternative scRNA designs that present the MS2 hairpin at different locations. Extending the MS2 stem by 2, 5, 10, and 20 bp resulted in progressively lower CRISPRa activity, but no change in the position of the target sites that were most effective (Supplementary Fig. 8B). Similarly, no changes were observed with alternative scRNA designs with one or two MS2 hairpins presented from different locations within the scRNA structure (Supplementary Fig. 8C).

Finally, we assessed whether any alternative activation domains could produce a different phase dependent behavior. Previously, these constructs all produced weaker activation than SoxS³, perhaps because they have each distinct optimal target site positions. We tested MCP fused to TetD, αNTD, lambda cII, and RpoZ³, and dCas9 fused to RpoZ³⁵; however, none of these constructs produced gene activation at any site that was not already effective with SoxS (Supplementary Fig. 9).

Although endogenous bacterial transcription factors exhibit a sharp periodic dependence on distance^{27,28,29,30,31,32,33,34}, it remains surprising that no structural modifications of the CRISPRa complex produced any changes in the phase dependence. If SoxS is simply tethered to the CRISPRa complex by a flexible linker, we would have expected the peak of effective CRISPRa sites to broaden with longer linkers. The failure of this prediction suggests that our understanding of the CRISPR–Cas complex and its interactions with bacterial transcriptional machinery is fundamentally incomplete, or that the linker tethering SoxS to the CRISPRa complex is not truly flexible. Practically, it means that we still lack a way to expand the range of effective CRISPRa target sites.

A dCas9 variant expands the range of targetable sites

Because there is a limited number of genes with an appropriate NGG PAM site at precisely the optimal position upstream of the promoter (Supplementary Fig. 7C), we attempted to expand the scope of targetable PAM sites for CRISPRa. We used a recently characterized dCas9 variant, dxCas9(3.7), that has improved activity at a variety of non-NGG PAM sites including NGN, GAA, GAT, and CAA⁶. We generated reporter plasmids by replacing AGG PAM sites with alternative PAM sequences and delivered a CRISPRa system with dxCas9(3.7) to target these reporters. dxCas9(3.7) maintained the ability to target the AGG PAM and showed significantly increased levels of activation at alternative PAM sites compared to dCas9 (Fig. 5a). Activation levels varied with different PAM sites and correlated well with dxCas9(3.7) activity previously reported in human cells (Supplementary Fig. 10A)⁶. dxCas9(3.7) showed similar distance and phase dependent target site preferences as dCas9 (Supplementary Fig. 10B & C), but its expanded PAM scope makes it more likely that an arbitrary gene will have a targetable PAM site at an effective position. Bioinformatic analysis of the sequences between transcriptional units in E. coli revealed that there are on average 6.4 times more dxCas9(3.7)-compatible PAM sites than NGG PAM sites (Supplementary Fig. 10D). Accounting for the fact that dCas9 has some activity at non-NGG sites⁶ (Fig. 5a), there are still on average ~2.2-fold more dxCas9(3.7)-compatible PAM sites than dCas9-compatible PAM sites (Supplementary Fig. 10D).

**Fig. 5: dxCas9(3.7) expands the range of targetable scRNA target sites by recognizing alternative PAMs.**

To demonstrate the utility of dxCas9(3.7) for CRISPRa at sites inaccessible to dCas9, we constructed a reporter plasmid that contains an AGG PAM site at the original position with maximum CRISPRa activity and an AGT PAM five bases downstream. Using this reporter, we observe that both dCas9 and dxCas9(3.7) are effective for CRISPRa at the optimally positioned NGG PAM site, but neither is capable of activating the AGT PAM site, which is five bases out of phase from the optimal site (Fig. 5b). We then inserted five bases into the reporter to shift the AGT PAM site into the peak activation range. With this reporter, neither dCas9 nor dxCas9(3.7) can activate the NGG PAM site, which is now out of phase. dxCas9(3.7) was now able to effectively activate the AGT PAM site, and dCas9 was ineffective at this site (Fig. 5b). This result confirms that dxCas9(3.7) is able to activate optimally positioned target sites that are inaccessible to dCas9. We expect that this behavior will be effective at many σ⁷⁰-family promoters (Fig. 3b), and a recent report demonstrated a similar behavior of dxCas9(3.7) at σ⁵⁴-dependent promoters⁷.

Defined rules enable endogenous gene activation

Our systematic characterization of the requirements for effective CRISPRa in E. coli demonstrates that candidate genes must have a targetable PAM site located at one of the sharp peaks of activity upstream of the TSS. In hindsight, the scRNA sites at endogenous genes that we initially targeted in Fig. 2 did note meet this criterion. To determine whether the revised rules would enable activation of endogenous E. coli genes, we surveyed the genome for candidate genes with appropriately positioned, dxCas9(3.7)-compatible PAM sites (Supplementary Methods) (Supplementary Fig. 7C). We selected candidates with multiple potentially effective PAM sites and further narrowed the pool based on two additional criteria: (1) genes should not be too highly expressed (Fig. 3a) and (2) genes should be regulated by σ⁷⁰, which is the sigma factor that regulates most genes⁸ (Fig. 3b). Ideally, we would also exclude genes with tightly bound transcriptional regulators in the promoter region (Fig. 3d), but this information is not readily available. We chose six genes that could be tested using reporter strains from the E. coli promoter collection³⁶ and targeted two PAM sites for each gene.

We first examined the yajG gene, which had two plausible target sites, one of which was only compatible with dxCas9(3.7). We also included an additional site predicted to be out of phase and ineffective for CRISPRa. We observed significant, ~4–6-fold gene activation for the two sites located at the predicted peak of activity at −80/−81, and no activation at the out of phase site at −87 (Fig. 6a). The site at −81 is inaccessible to dCas9, and we only observed activation with dxCas9(3.7). We proceeded to test an additional five genes with partial success. We observed significant activation at poxB (~10-fold) and uxuR (approximately twofold) (Fig. 6b). We validated these results by performing RT-qPCR on the endogenous yajG and poxB loci. Targeting CRISPRa to these genes resulted in increases in RNA levels (Supplementary Fig. 11). Targeting CRISPRa to araE produced a statistically significant difference in expression, but the activation measured was modest (1.13-fold). For the remaining two candidate genes, ansB was modestly repressed at one of the target sites and we did not observe a statistically significant difference in expression at ppiD. Similarly, one of the ldhA sites that we targeted in initial experiments (Supplementary Fig. 1) was at a predicted optimal site at −91 and failed to give substantial activation. Thus, of seven endogenous genes tested with target sites that we predict should be effective (the six genes from Fig. 6b and ldhA from Supplementary Fig. 1), we were able to activate three genes with more than twofold increases in gene expression.

**Fig. 6: Predictive rules enable endogenous activation.**

Although any success at endogenous gene activation is encouraging, significant challenges remain for predictable CRISPRa in bacteria. Our results suggest that even with a precise distance metric for effective target sites, some genes will not be predictably activated. There are several possible explanations: (1) tightly bound negative regulators could interfere with CRISPRa (Fig. 3d), and (2) small errors in TSS annotation could lead to inaccurate predictions for effective sites, given that 1–2 base shifts can have dramatic effects on CRISPRa (Fig. 4), and (3) intrinsic differences in the activity of the 20 base scRNA target sequence (Supplementary Fig. 4).

Discussion

Bacterial CRISPRa is sensitive to a number of factors, including (i) the strength of the target promoter, (ii) the sigma factor regulating the promoter, (iii) the sequence composition immediately upstream of the minimal promoter, (iv) the composition of the scRNA target sequence, (v) the position of the scRNA target site with respect to the TSS at single base resolution. Some of these factors, such as promoter strength and scRNA target sequence composition, are also relevant in eukaryotic systems^13,15,37,38. Other factors are plausible given our understanding of bacterial transcription. Sigma factor levels are regulated to control gene expression in response to cell state and external signals¹⁶, so it is reasonable that we observed variable levels of activation from promoters with alternative sigma factors. Many bacterial genes are controlled by negative regulators³⁹, and different sequences upstream of the minimal promoter could be recruiting repressors.

The most unexpected property that we observed with bacterial CRISPRa was its sharp, periodic dependence on-target site position. This behavior is quite distinct from CRISPRa in eukaryotes, where a broad range of sites upstream of the TSS are effective⁴⁰, possibly because eukaryotic activators typically recruit transcription factors and chromatin modifying machinery rather than directly recruiting RNA polymerase. There is precedent for bacterial transcriptional activators that are sensitive to target site periodicity^{27,28,29,30,31,32,33,34}, but the marked changes in activity with only single base shifts is surprising. Moreover, it is puzzling that we were unable to predictably alter or broaden the range of sites that are effective. Our models for how activators interact with bacterial transcription machinery may be incomplete. It will likely be productive to continue screening for activity at out-of-phase target sites using additional systematic modifications to the CRISPRa complex structure, alternative CRISPR–Cas systems, and additional candidate transcriptional activation domains.

Despite the challenges described above for identifying effective CRISPRa sites in E. coli, our systematic characterization provides a framework for immediate practical applications and a path for future improvements. We now have a clear understanding of the criteria needed to design synthetic promoters that can be regulated by CRISPRa, which will enable the construction of complex, tunable synthetic multi-gene circuits. To extend the scope of CRISPRa to endogenous target genes, expanded PAM variants like dxCas9(3.7)⁶, or orthologous dCas9 proteins with alternate PAM specificities^41,42 will open more DNA sites for targeting, increasing the likelihood of finding a targetable site at an optimal position relative to the TSS. These strategies lay the groundwork for more widespread use of bacterial CRISPRa in basic research and practical applications including functional genomics screens, metabolic engineering, and synthetic microbial communities.

Methods

Bacterial strain construction and manipulation

Plasmids were cloned using standard molecular biology protocols. Bacterial strains with sfGFP or mRFP1 reporter strains are described in Supplementary Table 1. The CRISPRa system used for each figure panel is described in Supplementary Table 2. Guide RNA target sequences are described in Supplementary Table 3. Plasmid containing the reporter genes and the CRISPR components are described in Supplementary Table 4. S. pyogenes dCas9 (Sp-dCas9) or dxCas9(3.7) were expressed from the endogenous Sp.pCas9 promoter in a p15A vector. MCP-SoxS containing wild-type and mutant SoxS were expressed using the BBa_J23107 promoter (http://parts.igem.org) in the same plasmid with dCas9. The scRNAs were expressed using the BBa_J23119 promoter, either in the same plasmid with the dCas9 protein and the activation domain or in a separate ColE1 plasmid. The scRNA.b1 or scRNA.b2 designs, where the endogenous tracr terminator hairpin upstream of MS2 was removed³, were used in all experiments except otherwise noted. The zwfp-lacZ and fumCp-lacZ reporter plasmids were generated in a previous study³. mRFP1 and sfGFP reporters were expressed from the weak BBa_J23117 minimal promoter (http://parts.igem.org) in a low-copy pSC101** vector. Variant versions of reporter genes are described in the Supplementary Methods. Plasmid libraries containing N26 sequences between the scRNA target site and BBa_J23117 minimal promoter were constructed by PCR amplification using mixed bases oligos (IDT). The dxCas9(3.7)-VPR plasmid was a gift from David Liu (Addgene #108383)⁶.

Flow cytometry

Single colonies from LB plates were inoculated in 500 μL EZ-RDM (Teknova) supplemented with appropriate antibiotics and grown in 96-deep-well plates at 37 °C and shaking. Cultures were grown overnight at 37 °C and shaking and then diluted in 1:50 in Dulbecco’s phosphate-buffered saline and analyzed on a MACSQuant VYB flow cytometer with the MACSQuantify 2.8 software (Miltenyi Biotec). A side scatter threshold trigger (SSC-H) was applied to enrich for single cells until 10000 events were collected. The FlowJo 10.0.7 software was used to apply a narrow gate along the diagonal line on the SSC-H vs SSC-A plot was selected to exclude the events where multiple cells were grouped together. Within the selected population, events that appeared on the edges of the FSC-A vs. SSC-A plot and the fluorescence histogram were excluded.

Plate reader experiments

Single colonies from LB plates were inoculated in 500 μL EZ-RDM (Teknova) supplemented with appropriate antibiotics and grown in 96-deep-well plates at 37 °C and shaking overnight. For experiments with the E. coli promoter collection³⁶ the activation domain was placed under the control of a tet-inducible promoter. Attempts to use constitutive CRISPRa were unsuccessful due to plasmid instability, possibly because of toxicity arising from increased expression of the target genes. Single colonies from LB plates were inoculated in 500 μL EZ-RDM supplemented with appropriate antibiotics and 400 nM anhydrotetracycline (aTc) and grown in 96-deep-well plates at 37 °C and shaking overnight. 150 μL of the overnight culture were transferred into a flat, clear-bottomed black 96-well plate and the OD₆₀₀ and fluorescence were measured in a Biotek Synergy HTX plate reader and analyzed using the BioTek Gen5 2.07.17 software. For mRFP1 detection, the excitation wavelength was 540 nm and emission wavelength was 600 nm. For sfGFP detection, the excitation wavelength was 485 nm and emission wavelength was 528 nm.

Quantitative RT-PCR

Single colonies from LB plates were inoculated in 5 mL LB containing appropriate antibiotics and grown overnight at 37 °C and shaking. Overnight cultures were diluted 1:100 into 5 mL EZ-RDM supplemented with appropriate antibiotics and grown at 37 °C and shaking until an OD₆₀₀ of 0.5 (using 150 μL of culture in a 96-well plate) was reached. For the experiments targeting yajG and poxB, the activation domain was placed under the control of a tet-inducible promoter and cultures in EZ-RDM were supplemented with 400 nM aTc. Cultures were pelleted and total RNA was extracted using the Aurum Total RNA Mini Kit (Bio-rad). Reverse transcription reactions were performed from 1 μg RNA in 20 μL reactions using iScript reverse transcriptase (Bio-Rad). qPCR reactions were prepared in triplicate in a final volume of 10 μL using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), 0.5–5 ng of cDNA and 400 nm primers. The reaction was performed in a CFX Connect (Bio-Rad) with a 58 °C annealing temperature and 30 s extension time. A list of the qPCR primer sequences is provided in Supplementary Table 5. Expression levels for each gene were calculated in the Bio-Rad CFX Maestro 4.0.23225.0418 software by normalizing to the 16S rRNA gene and relative to a negative control carrying an off-target scRNA using the ΔΔCT method⁴³.

Statistics and reproducibility

Statistical significance was calculated using two-tailed unpaired Welch’s t tests. To ensure reproducibility, experiments were performed using n = 3 biologically independent samples, unless otherwise noted.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data supporting the findings of this work are available within the paper and its Supplementary Information files. A reporting summary for this article is available as a Supplementary Information file. The data sets generated and analyzed during the current study are available from the corresponding author upon request. The source data underlying Figs. 1b and 2–6, as well as Supplementary Figs. 1, 2, 3A–B, and 4–11 are provided as a Source Data file.

Code availability

Custom Python code to generate the DNA sequences between transcriptional units in E. coli and analyze the density of PAM sites in these sequences (detailed in the Supplementary Methods) is available on GitHub (https://github.com/carothersresearch/Fontana-Dong_2020_NatComm).

References

Brophy, J. A. N. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).
CAS PubMed PubMed Central Google Scholar
Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).
Article CAS PubMed Google Scholar
Dong, C., Fontana, J., Patel, A., Carothers, J. M. & Zalatan, J. G. Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria. Nat. Commun. 9, 2489 (2018).
Article ADS PubMed PubMed Central CAS Google Scholar
Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
Article CAS PubMed PubMed Central Google Scholar
Wang, H., La Russa, M. & Qi, L. S. CRISPR/Cas9 in genome gditing and beyond. Annu. Rev. Biochem. 85, 227–264 (2016).
Article CAS PubMed Google Scholar
Hu, J. H. et al. Evolved Cas9 variants with broad PAM compatibility and high DNA specificity. Nature 556, 57–63 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Yang Liu, B. W. & Wan, X. Engineered CRISPRa enables programmable eukaryote-like gene activation in bacteria. Nat. Commun. 10, 3693 (2019).
Article ADS PubMed CAS Google Scholar
Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 45, D543–550 (2017).
Article CAS PubMed Google Scholar
Rodriguez, A. et al. Engineering Escherichia coli to overproduce aromatic amino acids and derived compounds. Micro. Cell Fact. 13, 126 (2014).
Google Scholar
Byrne, C. R., N., M. K., Monroe, R. S. & Ward, K. A. DNA sequences of the cysK regions of Salmonella typhimurium and Escherichia coli and linkage of the cysK regions to ptsH. J. Bacteriol. 170, 3150–3157 (1988).
Article CAS PubMed PubMed Central Google Scholar
Jiang, G. R. & Sonia Nikolova, D. P. C. Regulation of the ldhA gene, encoding the fermentative lactate dehydrogenase of Escherichia coli. Microbiology 147, 2437–2446 (2001).
Article CAS PubMed Google Scholar
Silander, O. K. et al. A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli. PLoS Genet. 8, 5 (2012).
Article Google Scholar
Chavez, A. et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).
Article CAS PubMed PubMed Central Google Scholar
Alejandro Chavez, G. C. et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016).
Article PubMed PubMed Central CAS Google Scholar
Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).
Article ADS CAS PubMed Google Scholar
Gruber, T. M. & Gross, C. A. Multiple sigma subunits and the partitioning of bacterial transcription space. Annu. Rev. Microbiol. 57, 441–466 (2003).
Article CAS PubMed Google Scholar
Shah, I. M. & R., E. W. Novel protein–protein interaction between Escherichia coli SoxS and the DNA binding determinant of the RNA polymerase α subunit: SoxS functions as a co-sigma factor and redeploys RNA polymerase from UP-element-containing promoters to SoxS-dependent promoters during oxidative stress. J. Mol. Biol. 343, 513–532 (2004).
Article CAS PubMed Google Scholar
Gort, A. S., J., A. I. & Ferber, D. M. The regulation and role of the periplasmic copper, zinc superoxide dismutase of Escherichia coli. Mol. Microbiol. 32, 179–191 (1999).
Article CAS PubMed Google Scholar
Gen Nonaka, V. A. R., Blankschien, M., Herman, C. & Gross, C. A. Regulon and promoter analysis of the E. coli heat-shock factor, σ³², reveals a multifaceted cellular response to heat stress. Genes Dev. 20, 1776–1789 (2006).
Article PubMed PubMed Central CAS Google Scholar
Rhodius, V. A., Won Chul Suh, C. A. G., Nonaka, G. & West, J. Conserved and variable functions of the σ^E stress response in related genomes. PLoS Biol. 4, e2 (2006).
Article PubMed CAS Google Scholar
Zhe‐Xian, T., Y., W., Quan‐Sheng, L., Buck, M. & Kolb, A. The CRP–cAMP complex and downregulation of the glnAp2 promoter provides a novel regulatory linkage between carbon metabolism and nitrogen assimilation in Escherichia coli. Mol. Microbiol. 41, 911–924 (2001).
Google Scholar
Yona, A. H., G., J. & Alm, E. J. Random sequences rapidly evolve into de novo promoters. Nat. Commun. 9, 1530 (2018).
Article ADS PubMed PubMed Central CAS Google Scholar
Lee, T. et al. BglBrick vectors and datasheets: a synthetic biology platform for gene expression. J. Biol. Eng. 5, 12 (2011).
Article CAS PubMed PubMed Central Google Scholar
Mendoza-Vargas, A. et al. Genome-wide identification of transcription start sites, promoters and transcription factor binding sites in E. coli. PLoS ONE 4, e7526 (2009).
Article ADS PubMed PubMed Central CAS Google Scholar
Georgina Lloyd, S. B. & Paolo, L. Activation and repression of transcription initiation in bacteria. Essays Biochem. 37, 17–31 (2001).
Article Google Scholar
Schneider, T. D. 70% efficiency of bistate molecular machines explained by information theory, high dimensional geometry and evolutionary convergence. Nucleic Acids Res. 38, 5995–6006 (2010).
Article CAS PubMed PubMed Central Google Scholar
Wood, T. I. et al. Interdependence of the position and orientation of SoxS binding sites in the transcriptional activation of the class I subset of Escherichia coli superoxide‐inducible promoters. Mol. Microbiol. 34, 414–430 (1999).
Article CAS PubMed Google Scholar
Zhou, Y., W., Y.-P., Kolb, A. & Busby, S. J. W. Spacing requirements for Class I transcription activation in bacteria are set by promoter elements. Nucleic Acids Res. 42, 9209–9216 (2014).
Article CAS PubMed PubMed Central Google Scholar
Johannes Müller, B. M.-H., Barker, A. & Oehler, S. Dimeric lac repressors exhibit phase-dependent co-operativity. J. Mol. Biol. 284, 851–857 (1998).
Article Google Scholar
Straney, D. C., Straney, S. B. & Crothers, D. M. Synergy between Escherichia coli CAP protein and RNA polymerase in the lac promoter open complex. J. Mol. Biol. 206, 41–57 (1989).
Article CAS PubMed Google Scholar
Gaston, K., Bell, A., Kolb, A., Buc, H. & Busby, S. Stringent spacing requirements for transcription activation by CRP. Cell 62, 733–743 (1990).
Article CAS PubMed Google Scholar
Ushida, C. & Aiba, H. Helical phase dependent action of CRP: effect of the distance between the CRP site and the −35 region on promoter activity. Nucleic Acids Res. 18, 6325–6330 (1990).
Article CAS PubMed PubMed Central Google Scholar
Müller, J., Oehler, S. & Müller-Hill, B. Repression of lac Promoter as a function of distance, phase and quality of an auxiliary lac operator. J. Mol. Biol. 257, 21–29 (1996).
Article PubMed Google Scholar
Martin, R. G., Gillette, W. K., Rhee, S. & Rosner, J. L. Structural requirements for marbox function in transcriptional activation of mar/sox/rob regulon promoters in Escherichia coli: sequence, orientation and spatial relationship to the core promoter. Mol. Microbiol. 34, 431–441 (1999).
Article CAS PubMed Google Scholar
Bikard, D. et al. Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. Nucleic Acids Res. 41, 7429–7437 (2013).
Article CAS PubMed PubMed Central Google Scholar
Zaslaver, A. et al. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat. Methods 3, 623–628 (2006).
Article CAS PubMed Google Scholar
Kiani, S. et al. CRISPR transcriptional repression devices and layered circuits in mammalian cells. Nat. Methods 11, 723–726 (2014).
Article CAS PubMed PubMed Central Google Scholar
Gander, M. W., Vrana, J. D., Voje, W. E., Carothers, J. M. & Klavins, E. Digital logic circuits in yeast with CRISPR-dCas9 NOR gates. Nat. Commun. 8, 15459 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Rojo, F. Repression of transcription initiation in bacteria. J. Bacteriol. 181, 2987–91 (1999).
Article CAS PubMed PubMed Central Google Scholar
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Article CAS PubMed PubMed Central Google Scholar
Leenay, R. T. & B., C. L. Deciphering, communicating, and engineering the CRISPR PAM. J. Mol. Biol. 429, 177–191 (2017).
Article CAS PubMed Google Scholar
Shmakov, S. et al. Diversity and evolution of class 2 CRISPR-Cas systems. Nat. Rev. Microbiol. 15, 169–182 (2017).
Article CAS PubMed PubMed Central Google Scholar
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2^−ΔΔCT method. Methods 25, 402–408 (2001).
Article CAS PubMed Google Scholar
Griffith, K. L. & Wolf, R. E. A comprehensive alanine scanning mutagenesis of the Escherichia coli transcriptional activator SoxS: Identifying amino acids important for DNA binding and transcription activation. J. Mol. Biol. 322, 237–257 (2002).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

The dxCas9(3.7)-VPR plasmid was a gift from David Liu (Addgene #108383). We thank Mary Lidstrom, Joanne Wong, Semira Beraki, and members of the Zalatan and Carothers groups for technical assistance, advice, and helpful discussions. This work was supported by NSF Award 1817623 (J.M.C, J.G.Z.) and NSF Award 1844152 (J.M.C.).

Author information

These authors contributed equally: Jason Fontana, Chen Dong.

Authors and Affiliations

Molecular Engineering & Sciences Institute, University of Washington, Seattle, 98195, WA, USA
Jason Fontana, Cholpisit Kiattisewee, Benjamin I. Tickman & James M. Carothers
Department of Chemistry, University of Washington, Seattle, 98195, WA, USA
Chen Dong & Jesse G. Zalatan
Department of Chemical Engineering, University of Washington, Seattle, 98195, WA, USA
Venkata P. Chavali, James M. Carothers & Jesse G. Zalatan
Center for Synthetic Biology, University of Washington, Seattle, 98195, WA, USA
James M. Carothers & Jesse G. Zalatan

Authors

Jason Fontana
View author publications
You can also search for this author in PubMed Google Scholar
Chen Dong
View author publications
You can also search for this author in PubMed Google Scholar
Cholpisit Kiattisewee
View author publications
You can also search for this author in PubMed Google Scholar
Venkata P. Chavali
View author publications
You can also search for this author in PubMed Google Scholar
Benjamin I. Tickman
View author publications
You can also search for this author in PubMed Google Scholar
James M. Carothers
View author publications
You can also search for this author in PubMed Google Scholar
Jesse G. Zalatan
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

J.F., C.D., C.K., B.I.T., J.M.C., and J.G.Z. designed experiments and analyzed data. J.F., C.D., C.K., V.P.C., and B.I.T. performed experiments. J.F., C.D., J.M.C., and J.G.Z. wrote the manuscript.

Corresponding authors

Correspondence to James M. Carothers or Jesse G. Zalatan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Reporting Summary

Peer Review File

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Fontana, J., Dong, C., Kiattisewee, C. et al. Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements. Nat Commun 11, 1618 (2020). https://doi.org/10.1038/s41467-020-15454-y

Download citation

Received: 20 September 2019
Accepted: 04 March 2020
Published: 01 April 2020
DOI: https://doi.org/10.1038/s41467-020-15454-y

This article is cited by

Increasing the robustness of Escherichia coli for aromatic chemicals production through transcription factor engineering
- Xiao-Ling Zhou
- Meng-Sang Zhang
- Jian-Zhong Liu
Advanced Biotechnology (2024)
CRISPR-Combo–mediated orthogonal genome editing and transcriptional activation for plant breeding
- Changtian Pan
- Yiping Qi
Nature Protocols (2023)
Integrase-mediated differentiation circuits improve evolutionary stability of burdensome and toxic functions in E. coli
- Rory L. Williams
- Richard M. Murray
Nature Communications (2022)
Engineered dual selection for directed evolution of SpCas9 PAM specificity
- Gregory W. Goldberg
- Jeffrey M. Spencer
- Marcus B. Noyes
Nature Communications (2021)
Genome-scale target identification in Escherichia coli for high-titer production of free fatty acids
- Lixia Fang
- Jie Fan
- Hao Song
Nature Communications (2021)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.