Prediction of Off-target Effect

INTRODUCTION

Since the CRISPR/Cas9 system wasfirstly used in genetic engineering, the researches on its off-target effects have never stopped. The methods of Hsu-Zhang scoring (1) and CCTop (2) are two widely used algorithms for designing a single guide RNA (sgRNA) sequence and finding potential off-target locus. Last year, a new algorithm named CFD (Cutting Frequency Determination) scoring method was developed to evaluate potential off-target sits with 240 parameters (Fig. 1) (3). All of these three methods (Hsu-Zhang scoring, CCTop, and CFD) take into consideration different weight coefficients of different mismatch position, however, only CFD scoring method considers mismatch types as a factor as well.

Methods

We chose to use CFD scoring method instead of Hsu-Zhang scoring or CCTop for the following reasons: Firstly, it is reported that the CFD method has higher Pearson correlation (3), compared with Hsu-Zhang scoring method and CCTop, especially when the number of mismatched bases is large; Secondly, Computing the scores by using CFD method is much easier than the other two methods.

In order to obtain the CFD score of a certain DNA locus, we multiply all the scores of single base mismatch together. If the DNA loci and sgRNA has mismatched bases at position α, β, γ… with mismatch type rA-dC, rC-dC, rU-dT…(Fig. 1B), its CFD score is calculated as:

It is reported that about 60% of melanomas contain a mutation in the v-raf murine sarcoma viral oncogene homolog B (BRAF), and V600E (1799T> A) variation (Fig. 2) in BRAF is the main type of mutations in the cancer tissues, which plays a critical role in carcinogenesis of melanoma.

Fig. 2 The graphic of BRAF 1799T>A (V600E) mutation

In our project, we try to disrupt the mutant BRAF in the two melanoma cell lines (A375 and G361) by CRISPR/Cas9 technology. A typical PAM is ‘NGG’. However, we didn’t find it. It has been reported that alternative PAM sequence ‘NAG’ has rather high cleavage efficiency while ‘NTG’ shows no tendency of cutting (3). To meet the goal of specifical cleavage, we arranged the mutant base on the three PAM bases as shown in Fig 3.

Fig.3 The sgRNA for targeting mutant BRAf in melanoma cells

After setting the sgRNA sequence, we searched for the potential off-target locus. The potential off-target locus must meet the following conditions: 1) Having a PAM sequence (‘NGG’, ‘NAG’, ‘NCG’, or ‘NGA’) at 3’ end; 2) Base identities are more than 13 identified by MegaBLAST; 3) CFD score is greater than 5%.

RESULTS

Using Megablast, we find that over 500 alignments have potential off-target effects, and 62 of them have a PAM sequence (‘NGG’, ‘NAG’, ‘NCG’, or ‘NGA’). Seven of them are scored higher than 5% (Fig. 4). Since ‘NGG’ PAM has much higher efficiency of cleavage than ‘NAG’ (Fig.5)(3), the off-target probability of Seq 2, 3, 4, 5, 6 and 7 may be higher than its scores.

Fig.4 Potential off-target sites of our sgRNA. PAM is marked in red. Compared with the sgRNA, the different bases are marked in yellow.

Conclusion

Our sgRNA sequence has high cleavage efficiency on the mutated BRAF gene, as well as a high risk of off-target effect. To avoid the off-target effect, we designed an artificial microRNA complementary to SAMMSON gene, which is specifically expressed in human melanomas. The CRISPR/CAS9 system is only activated in cancer cells, no any effects on normal cells.

Drug Designing

Introduction

At first, we intended to use liposome as genetic vector. However, experiment data shows that liposome (has blank plasmid in it) has a high cytotoxic effect. When the liposome concentration is 4μg/mL, the number of cells died from cytotoxic effect is even greater than the number of survival cells (G361). Because the malignant degree of A375 is higher than G361, cytotoxic effect seems much weaker in A375.

Therefore, we planned to use lentivirus as gene vector, which is reported to have lower cytotoxic effect and higher transduction efficiency than liposome vector. Lentiviruses enter into cells when transducing, while most kinds of viruses bind to cellular membrane and inject their genetic material into cells.

As the picture shown, the lentiviruses behave two ways in vivo: Diffusion and reaction (entering cells). The concentration of drug is a function of time (since injection of drug) and position (the distance to drug injection site).

Diffusion part

we assume that lentiviruses diffusing in extracellular substance is unsteady-state diffusion, which follows Fick’s Second Law:

C_diff is the concentration of drug which participates diffusion ,C is the concentration of drug at certain time and position. t is time, D is the diffusion coefficient, ∇C is the concentration gradient of drug in vivo.

In order to reduce the quantity of calculation, we consider the diffusion coefficient D is a constant, which does not change over time or space. Thus, the equation can be written as:

Δ is the Laplace operator.

Reaction part

We assume that at each moment, the number of lentiviruses entering into cells is proportional to the concentration of lentivirus at which time and position.

C_reac is the concentration of drug which participates the reaction,C is the concentration of drug at certain time and position, k is a constant.

Combining the two terms together, we have:

The partial differential equation has the following boundary conditions:

I means that at the moment of injection, the drug has not diffused or entered into cells, so the concentration at the injection site is the initial drug concentration C⁰.

II means that when drug gets to the border of the dermis and subcutaneous tissue (where the distance to injection site is χ_c), it will be immediately taken away through the complex network of blood vessels.

Future work

Due to the limited time, we could not realize our genetic circuit into drugs. We also failed to find data from the existing articles with which the values of parameters can be perfectly determined.

After determining the parameters, we can know the concentration at any time and position, when the initial concentration is set.

References