Chapter III: Modeling Dynamic Transcriptional CRISPRi Circuits
3.5 Engineering Requirements of CRISPRi
Parameter Sensitivity (normalized) Description
rA 0.848310 Fold change of activation
Ctot 0.716537 Total steady-state dCas pool
α1 0.709052 Transcription rate of g1
Ptot -0.690079 Target promoter concentration αx -0.679508 Transcription rate of other gRNAs
β -0.029544 Kδ
1,δ=(degradation plus dilution) rate for gRNAs K 0.026457 Association constant for dCas:gRNA complex
binding to promoter.
Table 3.4: Sensitivity of fold change overperformance to each parameter.
Parameter Sensitivity (normalized) Description
rA 1.079925 Fold change of activation Ctot -0.335800 Total steady-state dCas pool
α1 -0.267926 Transcription rate of g1
β 0.267926 Kδ
1,δ=(degradation plus dilution) rate for gRNAs Ptot 0.207065 Target promoter concentration
K -0.128736 Association constant for dCas:gRNA complex binding to promoter.
αx -0.000000 Transcription rate of other gRNAs Table 3.5: Sensitivity of scaling overperformance to each parameter.
αxchanges the concentration of competing guide RNA in the same way that increas- ingNdoes, while changingα1is equivalent to changing bothαxandCtot. Therefore, changes to these two variables are roughly equivalent to shifting the performance curves shown in Figure 1 along the "# Competing Guides" axis, albeit in a nonlinear way.
Fold change overperformance is relatively unaffected by K and β, which both incorporate binding constants and are therefore parameters of particularly high uncertainty.
In contrast, scaling overperformance is fairly sensitive to β, though only somewhat more so than to Ptot (and less than toCtot, which we can see from Figure 3.11 is still not particularly large (remember that overperformance is a relative measure of the effectiveness of CRISPRa vs. CRISPRi). We are therefore less confident in our ability to quantitatively predict scaling overperformance than fold change overperformance, but we believe the overall trend that CRISPRa performs better than CRISPRi under conditions of high gRNA competition will hold.
Sections 3.3 and 3.4 do provide a few key lessons:
• Active degradation of dCas can improve circuit performance, and may be necessary.
• Almost any amount of transcriptional leak can be quite destructive to CRISPRi circuits.
• The speed at which dCas:gRNA complexes bind to DNA is important for circuit function, and implementing CRISPRi circuits in vivo may require increasing this binding rate.
• CRISPRi circuits should fall off in performance with scale faster than CRISPRa circuits.
Each of these lessons is accompanied by an engineering requirement. Some possible strategies for fulfilling these requirements are outlined below.
Degradation of dCas
E. coli can actively degrade proteins using the ClpXP system, which uses the proteases ClpXP and ClpXA to selectively degrade proteins bearing ssrA, a small C-terminal peptide tag. The native ClpXP system degrades at least 400 times faster than the rate of cell dilution, which is far too fast for any of the circuits outlined in Section 3.3 (Farrell, Grossman, and Sauer, 2005). However, targeted mutations to the ssrA tag have been used to tune degradation rates to anywhere between 2 and 100 times the rate of dilution, which falls solidly into the target degradation range outlined in Figure 3.7 (Landry, Stöckel, and Pakrasi, 2013). Alternatively, dCas could be degraded using themf-Lon protease system, which is similar to the ClpXP system in function and tunability but is orthogonal to any system inE. coli(Gur and Sauer, 2008; Cameron and J. Collins, 2014).
Improving fold-repression
The CRISPRi repressors reported in the literature typically repress with strengths between 10x and 100x. Moreover, the strongest CRISPRi repressors are typically targeted inside the target promoter, which limits their design space severely. Simula- tions suggest that leaks reported for elongation-blocking CRISPRi will break toggle switch and oscillator circuits unless degradation rates are extremely well-tuned. En- gineering high fold-change in a promoter may be challenging, but several simple strategies are possible for improving repressor performance.
However, any “transcriptional leak” of the kind observed in any repression exper- iment can be explained as a consequence of slow DNA binding or weak binding equilibrium, rather than proper leak from bound promoters; in fact, real-world
“leak” is likely to be a combination of the two. Our models predict that CRISPRla- tors without dCas degradation should only function if proper leak from dCas-bound promoters is extremely low.
Real-world CRISPRlators, though not particularly robust, do oscillate, suggesting that a consequence of our model would be that proper leak in CRISPRi systems is much smaller than leak from un-bound targets. If true, this suggests that efforts to reduce CRISPRi leak should be targeted at improving binding speed, rather than binding strength.
Faster dCas:DNA binding
As previously mentioned, the results shown in this report assume “best-case” binding rate of dCas:gRNA complexes to their target DNAs, with binding rates taken from in vitro association rate measurement. In live E. coli, dCas binding to DNA is much slower, with a single dCas complex estimated to require a few hours to bind to its target (Jones et al., 2017). A likely explanation for Cas9’s slow binding time is that dCas spends most of its time transiently bound to off-target PAM sites in the genome. The dCas protein can temporarily bind to any double-stranded DNA site with a correct PAM sequence. When it does, it briefly opens the DNA helix to “check” whether its associated guide RNA matches the sequence immediately adjacent to the PAM. Each non-target PAM present in the cell slows the rate of correct binding by acting as a low-affinity “decoy” binding site, which slows binding to the target promoter. TheS. pyogenesCas9 (by far the most widely-used Cas variant, and the one used exclusively in this report) uses the PAM "NGG", which can be expected to appear roughly 750,000 times in the (approximately diploid) genome of a growing E. colicell. This represents substantial barrier to correct target identification.
There are several possible solutions to the problem of slow dCas:DNA binding in vivo. The most straightforward, at least conceptually, would be to move out of cells entirely and construct circuits exclusively in TX-TL or another cell-free system.
Since cell-free systems do not have DNA replication or dilution to remove dCas from DNA, this would have to be done either in a microfluidic device capable of manually diluting a running reaction (see (Niederholtmeyer et al., 2015)) or using degradation-tagged dCas proteins.
Another simple way to speed up dCas:DNA binding rates would be to simply increase the concentration of either dCas (by increasing the baseline production of dCas) or target (by either genomically integrating multiple copies of the CRISPRi circuit or by expressing the circuit off of a plasmid). Simulations so far suggest that, all other things being equal, increasing the rate of dCas production has the effect of narrowing the window of acceptable dCas degradation rates. More simulation will be required to determine the feasibility of either of these two interventions.
Another possible solution would be to use a dCas (or another programmable binding protein) with a different, more complex PAM sequence. For example, theTreponema denticolaCas9 (TD-Cas) uses the PAM NAAAAC. After accounting for nonspecific PAM recognition, TD-Cas PAM sites ought to occur between 64 and 1024 times less frequently than S. pyogenesCas PAM sites, with a corresponding increase in binding rate (Esvelt et al., 2013). If the other kinetics of TD-dCas are similar to those ofS. pyogenes dCas, then we should expect TD-dCas binding to DNA to be
only perhaps twice as slow as in vitro dCas, which should be quite manageable.
Use CRISPRa where possible
There are good reasons to use CRISPRi instead of CRISPRa when building bacterial biocircuits, at least in the near future. CRISPRi requires fewer moving parts, produces larger best-case fold changes (and unquestionably higher fold changes in single-gRNA systems), and is less likely to drastically interfere with host genetic expression, at least inE. coli, as current-generation prokaryotic CRISPRa activators use modified versions ofE. coliactivators that upregulate some native promoters.
Repression is arguably also a more useful tool than activation. In particular, several simple, classic genetic circuits rely exclusively on repression (e.g., the repressilator (Elowitz and Leibler, 2000), the two-gene genetic toggle switch (Gardner, Cantor, and J. J. Collins, 2000), and NOR-gates (Gander et al., 2017)).
Finally, CRISPRa is more difficult to use on natural genomic targets, as it requires a PAM within a∼10 bp window of an ideal position upstream of the target promoter’s -35 box. For some genes, this target simply does not exist.
Nevertheless, CRISPRa has a distinct advantage over CRISPRi—it should be sig- nificantly less impacted by bottlenecking of core dCas9. CRISPRi effectiveness is predicted to drop precipitously for systems above about a dozen gRNAs, while CRISPRa should still function in the presence of dozens-to-hundreds of competing gRNAs.
As CRISPR-based synthetic circuits grow in scale and complexity past about a dozen components, we anticipate that the usefulness of CRISPRi will drop off precipitously, while CRISPRa should still function even in the presence of dozens of competing gRNAs (at least, for circuits with low target concentrations). We urge anyone who dreams of building large CRISPR-based biocircuits to consider using CRISPRa.