Difference between revisions of "Team:Shenzhen SFLS/Model"

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<h1> Modeling</h1>
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
 
  
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<h3> Gold Medal Criterion #3</h3>
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To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.
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Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.
 
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<h3>Best Model Special Prize</h3>
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        <h1>Modeling</h1>
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To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
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You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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      <a class="taoba" href="#index_1" title="1">Introduction</a>
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      <a class="taoba" href="#index_2" title="2">Methods</a>
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      <a class="taoba" href="#index_3" title="3">Results</a>
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      <a class="taoba" href="#index_4" title="3">Conclusion</a>
  
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<h5> Inspiration </h5>
 
<p>
 
Here are a few examples from previous teams:
 
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<ul>
 
<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
 
<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
 
<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
 
<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
 
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          <h5 id="index_1">INTRODUCTION</h5>
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<p>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.</p>
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          <h5 id="index_2">Methods</h5>
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          <p>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. </p>
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          <p>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:</p>
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            <img src="https://static.igem.org/mediawiki/2017/3/39/Sfls-demonstrate-sequencing.PNG" width="700" alt="demonstrate-sequencing">
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          <p>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.</p>
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          <p>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.  </p>
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            <img src="https://static.igem.org/mediawiki/2017/2/23/Demonstrate-sequencing-ATCG.PNG" width="700" alt="demonstrate-sequencing-ATCG">
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          <p>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(https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM =blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome); 3) CFD score is greater than 5%.</p>
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          <h5 id="index_3">RESULTS</h5>
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          <p>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). As reported (3) that ‘NGG’ PAM has much higher efficiency of cleavage than ‘NAG’ (Fig.5), the off-target probability of Seq 2, 3, 4, 5, 6 and 7 may be higher than it scores.</p>
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            <img src="https://static.igem.org/mediawiki/2017/2/23/Demonstrate-sequencing-ATCG.PNG" width="700" alt="demonstrate-sequencing-ATCG">
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            <img src="https://static.igem.org/mediawiki/2017/2/23/Demonstrate-sequencing-ATCG.PNG" width="700" alt="demonstrate-sequencing-ATCG">
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          <h5 id="index_4">CONCLUSION</h5>
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          <p>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.</p>
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Revision as of 16:19, 29 October 2017

Team:Shenzhen SFLS/Demonstrate - 2017.igem.org

Team:Shenzhen SFLS/Demonstrate - 2017.igem.org

Modeling

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.

demonstrate_fig.1


demonstrate_fig.2





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:

demonstrate-sequencing


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.

demonstrate-sequencing-ATCG


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.

demonstrate-sequencing-ATCG


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(https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM =blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome); 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). As reported (3) that ‘NGG’ PAM has much higher efficiency of cleavage than ‘NAG’ (Fig.5), the off-target probability of Seq 2, 3, 4, 5, 6 and 7 may be higher than it scores.

demonstrate-sequencing-ATCG


demonstrate-sequencing-ATCG





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.