Difference between revisions of "Team:NPU-China/Design"

 
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         <img class="img-responsive" src="https://static.igem.org/mediawiki/2017/4/46/%E9%A2%98%E7%9B%AE%E5%B0%8F%E9%80%9A%E6%A0%8Fdesign.jpg">
 
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                     <h2 style="text-align:center">Introduction</h2>
                        <li class="active">
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                    <h4>The essence of biochemical synthesis is the catalytic reaction with enzyme as the catalyst. Creating
                            <a href="#service-one" data-toggle="tab">
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                        new biochemical reactions is an important research direction of synthetic biology.
                                <h2>Metabolic flow modeling</h2>
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                        <br><br> ceaS2, whose full name is N2-(2-carboxyethyl)arginine synthase2, is a kind of enzyme in Streptomyces
                            </a>
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                        clavuligerus. The mentor of our team, Jiang Huifeng, has confirmed the new functions of ceaS2 with the help of TPP (Thiamine pyrophosphate) and magnesium ions. ceaS2 enzyme can catalyze the production of acrylic acid with DHAP (dihydroxy acetone phosphate) or G3P (glyceraldehyde 3-phosphate) as substrate.
                        </li>
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                        <br><br> Cell factory of acrylic acid (GAACF) 1.0:
                        <li class="">
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                        <br><br> DHAP and G3P are the central metabolic secondary products which can be easily found in various organisms.
                            <a href="#service-two" data-toggle="tab">
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                        They are the carbon flow nodes that must be passed in the glycerol metabolic pathway in most organisms.
                                <h2>AEMD</h2>
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                      ceaS2 enzyme being the core part, it is possible to create a new pathway to synthesize acrylic acid based on glycerol metabolic pathway in organisms and construct a cell factory with a high yield of acrylic acid.  
                            </a>
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                            <h2 style="text-align:center">Introduction</h2>
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                            <h4>This year our project is the introduction of acrylic synthetic routes in Escherichia coli or
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                                Saccharomyces cerevisiae to produce acrylic acid.</h4>
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                            <br>
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                            <div class="col-md-12">
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                                <div class="col-md-6">
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                                    <img src="https://static.igem.org/mediawiki/2017/2/21/%E5%A4%A7%E8%82%A0%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2.png" class="img-responsive">
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                                    <h5 style="text-align:center"> Primitive metabolic path map in E.Coli</h5>
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                                </div>
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                                <div class="col-md-6">
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                                    <img src="https://static.igem.org/mediawiki/2017/7/7b/%E9%85%B5%E6%AF%8D%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2%E5%9B%BE.png" class="img-responsive">
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                                    <h5 style="text-align:center"> Primitive metabolic path map in S.Cerevisiae</h5>
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                                </div>
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                            </div>
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                            <h4>
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                                <br> We have a rational new design and transformation of the core enzyme ceaS2, at the same time,
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                                we also want to be optimized to improve the acrylic acid production in the metabolic flow.
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                                <br> We know that for Escherichia coli, the carbon flow rate of its original glycerol metabolic
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                                pathway may not be sufficient, and if the new glycerol metabolic pathway can be used to increase
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                                the carbon flow of DHAP or G3P, the substrate of the core enzyme ceaS2 can be increased Concentration
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                                to increase acrylic acid production.
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                                <br> Therefore, through the literature review, we found two enzymes which can achieve efficient
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                                conversion of glycerol to generate DHAP the same way.
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                                <br> In our new approach, Glycerol dehydrogenase (Gly DH) is capable of efficiently converting
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                                glycerol to 1,3-Dihydroxyacetone (DHA) and then phosphorylates DHA to DHAP via Dihydroxyacetone
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                                kinase (DAK).
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                            </h4>
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                            <br>
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                                    <h5 style="text-align:center"> New route map in E.Coli</h5>
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                                </div>
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                                    <img src="https://static.igem.org/mediawiki/2017/b/b0/%E9%85%B5%E6%AF%8D%E8%B7%AF%E5%BE%84%E5%9B%BE.png" class="img-responsive">
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                                    <h5 style="text-align:center"> New route map S.Cerevisiae</h5>
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                                </div>
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                            </div>
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                            <h4>
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                                <br> Before the implementation of the formal experiment, we need to model it to analyze the impact
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                                of the introduction of new routes on the original metabolic flow, especially the two intermediates
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                                of DHAP or G3P. Specifically, we care about the following two issues:
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                                <br> 1. Has the DHAP or G3P's carbon flow improved after the introduction of new metabolic pathways?
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                                Is it compared to the previous increase in production?
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                                <br> 2. The introduction of new pathways after the entire metabolic pathway is stable and robust.
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                                How is it?
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                                <br> In order to answer these two questions, we established a carbon metabolic flow model.
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                            </h4>
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                            <h4>The overall workflow is as follows:</h4>
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                            <h2 style="text-align:center">Parameter estimation</h2>
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                            <h4>There are many parameters to be determined in the model. Most of these kinetic parameters can
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                                be found in the literature or in the database, but at the same time, there are some kinetic
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                                parameters of the enzyme we are looking for. Its organic matter, or the temperature and ph
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                                of the enzyme are different. Therefore, we need to re-estimate this part of the parameters.
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                                <br>
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                                <br> In the process data link, we cited the method using the data point weighting of University
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                                of Manchester in year 2016 . The weighting of the samples is as follows:
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                                <br> 1. When the sample PH is the same, the sample is weighted by 4 .2 when they are close. 1
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                                when they differ much.
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                                <br> 2. When the sample temperature is the same, the pH is the same.
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                                <br> 3. When the samples are from the same species , the weight of the sample is 4.When they
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                                are the non-identical species and are the prokaryotes,or the corresponding species mutated
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                                to the corresponding species, the weight is 2. When they are the non-identical species and
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                                are the eukaryotes, the weight is 1.
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                                <br> 4. Try to delete the missing data. If there are some essential samples of the temperature
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                                and PH missing, then the corresponding weight is 2.
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                                <br>
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                                <br> 1.kernel density estimate 2. Gaussian mixed model.
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                                <br>
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                                <br> The fourth point reflects our point of view of Bayesian. In the absence of prior knowledge
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                                of the case, we take as much as possible the weight of neutrality.
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                                <br> Based on the points above, we get a new parametric vector after weighting.In our model,
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                                we do the fitting of the probability density according to the two methods of the parameter
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                                vector: 1.kernel density estimatebsp;2. Gaussian mixed model.
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                                <div align="center">
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                                    <img src="https://static.igem.org/mediawiki/2017/3/32/Model-4.png" class="img-responsive" width="60%" height="60%">
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                                </div>
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                                <br>
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                            </h4>
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                            <br>
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                            <br>
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                            <br>
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                            <h2 style="text-align:center">The basic workflow of parameter estimation:</h2>
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                            <h4>The Gaussian mixture model can be approximated to any real probability distribution in theory.
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                                The EM algorithm is used to estimate the parameters required for the model. And we use the
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                                Gaussian mixture model to estimate the probability density of the possible distribution of
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                                parameters.
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                                <div align="center">
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                        <br><br> First, we took E. coli BL21 (DE3) as the chassis cells and constructed engineering bacteria carrying
                                    <img src="https://static.igem.org/mediawiki/2017/f/f7/Model-5.png" class="img-responsive" width="60%" height="60%">
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                        the gene of ceaS2 enzyme with pET-28a plasmid as the vector. We constructed a new pathway to synthesize
                                </div>
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                        acrylic acid from any carbon source by transforming ceaS2 directly into the chassis cells. This new
                                <div align="center">
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                        approach is the shortest compared to other pathways. Take the glycerol metabolic pathway of E. coli
                                    <img src="https://static.igem.org/mediawiki/2017/b/b2/Model-6.1.png" class="img-responsive" width="60%" height="60%">
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                        as an example, we only need three enzymes to achieve the synthesis of acrylic acid from glycerol.
                                </div>
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                        So this pathway has stronger malleability and broader development prospects.
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                        <img src="https://static.igem.org/mediawiki/2017/7/7b/NPU-newE.png" class="img-responsive">
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                        <h4> </h4>
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                    </div>
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                    <div class="col-md-6">
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                        <img src="https://static.igem.org/mediawiki/2017/2/21/%E5%A4%A7%E8%82%A0%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2.png" class="img-responsive">
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                        <h4> </h4>
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                    </div>
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<div class="col-md-3">
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                        <img src="https://static.igem.org/mediawiki/2017/4/44/%28%E5%B0%8F%29%E5%A4%A7%E8%82%A03_pETDuet-NOX-CAT_7451.png" class="img-responsive">
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                        <h4> </h4>
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                    </div>
 +
                </div>
  
                                <br> After making the probability distribution, we select Bin randomly, which meet the conditions
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                                of width = len / 10.And we select the most possible bin based on the CDF, and estimate the
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                                corresponding parameters when the bin reach average value.
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                                <div align="center">
 
                                    <img src="https://static.igem.org/mediawiki/2017/e/e5/Model-6.2.png" class="img-responsive" width="20%" height="20%">
 
                                </div>
 
  
  
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 +
                       
 +
                       
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                        <br>Through the whole cell catalysis and HPLC (High Performance Liquid Chromatography),
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                        the results show that the engineering bacteria can use glycerol as carbon source to produce acrylic
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                        acid. However, the yield of the cell factory 1.0 is not high, only about 1mg / L.
  
                                <br> Finally, we get the estimated parameter values, as well as the corresponding parameters
 
                                of the original PDF. The specific form and parameter values are as follows:
 
                                <br> The reaction path of the original pathway is Gly to Gly-3-p and then to DAHP
 
                                <div align="center">
 
                                    <img src="https://static.igem.org/mediawiki/2017/d/da/Model-7.png" class="img-responsive" width="40%" height="40%">
 
                                </div>
 
  
 +
                        <br>
  
                                <br> The original pathway belongs to the reaction of a single channel, and there is a random
 
                                bibi reaction and an irreversible Mickey equation reaction. The reaction involves two enzymes
 
                                paticipating - glpk and glpD. We assume that the reaction concentration of these two enzymes
 
                                is 0.01 mM, assuming that the initial [Gly] concentration is 10 mM, the initial concentration
 
                                of ATP 10 mM, Gly-3 The concentration of -p 0 mM and the concentration of DHAP 0 mM at the
 
                                same time.
 
                                <br>
 
                                <br> In this reaction, we make the following assumptions about our model:
 
                                <br> 1. The ATP of the E.coil system is given externally completely, assuming that the culture
 
                                conditions given externally are sufficient and ATP maintains a stable constant.
 
                                <br> 2. Assume that the substrate involved in the reaction does not participate in other reactions.
 
                                <br> In order to determine the yield of the target product, we chose to observe the efficiency
 
                                of the DHAP yield estimation system in view of the lack of basic Deas2 enzyme data.
 
                                <br>
 
                            </h4>
 
  
                            <div align="center">
 
                                <img src="https://static.igem.org/mediawiki/2017/9/95/Model-8.png" class="img-responsive" width="60%" height="60%">
 
                            </div>
 
  
 +
                        <br> It is known that acrylic acid can not be metabolized in the cell, so we analyzed the possible reasons
 +
                        as the following:
 +
                        <br> 1. The activity and the catalytic efficiency of wild type ceaS2 is low.
 +
                        <br> 2. The low carbon flow rate of glycerol metabolic pathway in E. coli leads to the low concentration
 +
                        of DHAP and G3P.
 +
                        <br> 3. Acrylic acid is toxic to the chassis cells.
 +
                        <br> 4. The reaction conditions such as carbon source, pH, temperature and reaction time are not suitable.
 +
                        <br> Based on the analyzing results, we have made improvements and built a new cell factory.
 +
                        <br>
 +
                        <br> Cell factory of acrylic acid (GAACF) 2.0:
 +
                        <br> We built a new cell factory of acrylic acid through the four part: CO-PART, SYSTEM, PATHWAY, PRODUCTION!
 +
                        <br>
 +
                    </h4>
 +
        <div class="container" style="padding-top:50px">
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                    <div id="COREPART" style="padding-top:50px;margin-top:-50px;">
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                        <h2 style="text-align:center">Core Part</h2>
 +
                        <h4>Acrylic acid is a byproduct of ceaS2 enzyme, the catalytic effect of wild type ceaS2 enzyme is very
 +
                            weak, and acrylic acid production is only 1mg / L. So it is necessary to improve the catalytic
 +
                            effect of this core factor, ceaS2 enzyme.
 +
                            <br><br> The gene of ceaS2 enzyme consists of 1719 deoxynucleotides and the protein sequence consists
 +
                            of 573 amino acids. We need to use bioinformatics to analyze and simulate, in order to help us
 +
                            decide the correct proposal.
 +
                            <br>
 +
<center><img src="https://static.igem.org/mediawiki/2017/a/ac/Ceas2.png" class="img-responsive"></center>
 +
<br> We constructed ceaS2 enzyme mutants using the AEMD (Auto Enzyme Mutation Design) platform. We
 +
                            constructed the ceaS2 wild-type sequence on pET-28a plasmid. We used pET-28a-ceaS2 plasmid as
 +
                            a template to create point mutation, and then transformed the plasmid into BL21. Then, we did
 +
                            the whole cell catalysis to get the products. Finally, we screened for ceaS2 mutants with high
 +
                            catalytic efficiency by HPLC (High Performance Liquid Chromatography) and
 +
                            HTS (High throughput screening) .
 +
                            <br>
 +
                        </h4>
 +
                    </div>
 +
        <center><img src="https://static.igem.org/mediawiki/2017/b/bf/5A_ceaS2.gif"></center>
  
                            <h4>
 
                                <br> We can observe that the DHAP stops growing close to 50 minutes.
 
                                <br>
 
                                <br> Then, we need to test the carbon pathway through the modified pathway, And add a metabolic
 
                                pathway enzyme-catalyzed by GlyDH enzyme and DAK in the original path, while the need for
 
                                NOX enzyme and CAT enzyme from the role of NAD + supplement, resulting in DHA, and finally
 
                                Phosphorylation produces DHAP.
 
                                <br> metabolic flow after the transformation of the reaction model. According to the actual situation
 
                                of the reaction, we make the following assumptions:
 
                                <br> 1. In the reaction , due to the process of hydrogen peroxide to the production of O2, that
 
                                is, the process of generating acceptor, is faster, we will regard the reaction of NOX enzyme
 
                                NADH catalytic as an ordinary Michael's equation, rather than ordered sequence reaction.
 
                                <br> 2. Random pairs of sequence reactions and ordered sequence reaction equations are identical.
 
                                So we substrate which is identified as the [A] substrate depending on the integrity of the
 
                                data.
 
                                <br>
 
                            </h4>
 
                            <h4 style="text-align:center">Metabolic pathways after transformation</h4>
 
                            <div align="center">
 
                                <img src="https://static.igem.org/mediawiki/2017/3/33/Model-10.png" class="img-responsive" width="60%" height="60%">
 
                            </div>
 
  
 +
        <div class="container" style="padding-top:50px">
  
                            <h4>
+
                    <div id="Pathway" style="padding-top:50px;margin-top:-50px;">
                                <br> We can see that the rate of DHAP is faster after changing the metabolic pathway, which means
+
                        <h2 style="text-align:center">Pathway</h2>
                                that the higher the output per unit time after being put in use, the sooner the reaction
+
                        <h4>The carbon flow rate of the glycerol metabolic pathway is low. In order to solve the problem, we
                                is done. Compared to the pre-improved pathway ,the reaction finishes roughly five minutes
+
                            need reconstruction and optimization of the original metabolic pathway.
                                ahead.
+
                            <br>
                                <br> Sensitive analysis
+
                            <br> RE-Construction:We designed the GDC (GlyDH-DAK-ceaS2) pathway to produce acrylic acid from glycerol.
                                <br> In the previous pathway study, we noted that the Kcat values of glpK and GlyDH enzymes are
+
                            In this pathway, GlyDH(Glycerol dehydrogenase) can efficiently convert Glycerol into DHA(1,3-Dihydroxyacetone).
                                unknown (we do not have a large deviation in the absence of a sample expressed in E.coil).
+
                            Then DAK (Dihydroxyacetone kinase) converts DHA into DHAP. Finally, ceaS2 converts DHAP into
                                <br> The Kcat value of the reaction of propanal is the Kcat value of the reaction of Gly and
+
                            acrylic acid. In addition, because GlyDH depends on NAD+, we added two reduction models, NOX
                                GlyDH. It is also assumed that the K63 ratio of the two enzymes is 2: 1.
+
                            (NADH dehydrogenase )and CAT(Catalase), to the pathway, with the purpose of providing the required
                                <br> We often use Kcat / Km to describe the catalytic efficiency of different enzymes for the
+
                            reduction force for GLY DH through the two layers of substrate level cycle. At last, we construct
                                same substrate, and as a result of our experiments, GlyDH exhibited higher catalytic efficiency
+
                            a new pathway for acrylic acid synthesis- GNCDC(GlyDH-NOX-CAT-DAK-ceaS2)
                                than glpK. Thus we assume that the GlyDH enzyme and the glpK enzyme satisfy the following
+
                                relationship:
+
                                <div align="center">
+
                                    <img src="https://static.igem.org/mediawiki/2017/5/50/Model-11.1.png" class="img-responsive" width="30%" height="30%">
+
                                </div>
+
  
  
                                <br> GlyDH enzyme activity and the Km value for gly and the Km value of the glpk enzyme to gly
+
<br>
                                have been known in previous studies. Next we adjust the alpha coefficient to study the effect
+
<center><img src="https://static.igem.org/mediawiki/2017/1/10/%E5%A4%A7%E8%82%A0%E8%B7%AF%E5%BE%84%E5%9B%BE.png" class="img-responsive"></center>
                                of different ratios on the overall metabolic flow.
+
                                <div align="center">
+
                                    <img src="https://static.igem.org/mediawiki/2017/9/9c/Model-11.2.png" class="img-responsive" width="60%" height="60%">
+
                                </div>
+
  
                            </h4>
 
                            <br>
 
                            <h4 style="text-align:center">KATA Sensitivity Test before Modification</h4>
 
                            <div align="center">
 
                                <img src="https://static.igem.org/mediawiki/2017/8/89/Model-12.1.png" class="img-responsive" width="60%" height="60%">
 
                            </div>
 
  
  
                             <br>
+
                             <br><br> The genes of GlyDH and DAK were constructed on two MCS (multiple cloning sites) on the backbone
                            <h4 style="text-align:center">KATA Sensitivity Test after Modification</h4>
+
                             of pCDFDuet-1 plasmid. NOX and CAT were constructed on two MCSs on the backbone of pETDuet-1
                             <div align="center">
+
                             plasmid.
                                <img src="https://static.igem.org/mediawiki/2017/e/e9/Model-12.2.png" class="img-responsive" width="60%" height="60%">
+
                             </div>
+
  
 +
<div class="col-md-12" style="padding-top:30px">
 +
                    <div class="col-md-4">
 +
                        <img src="https://static.igem.org/mediawiki/2017/5/5c/%E5%A4%A7%E8%82%A01_pCDFDuet-gld-DAK_6550.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                    <div class="col-md-4">
 +
                        <img src="https://static.igem.org/mediawiki/2017/c/c9/%E5%A4%A7%E8%82%A02_pET-28a-ceas2_7015.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                    <div class="col-md-4">
 +
                        <img src="https://static.igem.org/mediawiki/2017/7/70/%E5%A4%A7%E8%82%A03_pETDuet-NOX-CAT_7451.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                </div>
 +
                         
 +
                    </div>
  
                            <h4>
+
        <div class="container" style="padding-top:50px">
                                <br> We show the highest ratio (1000) and the lowest ratio (0.001) in yellow and blue lines respectively.
+
                                The pre-transformation pathway is most sensitive to the change of Kcat in glpK enzyme, and
+
                                the metabolic pathway of the target substrate is transformed with the change of α Rate is
+
                                always higher than the pre-transformation pathway, even when the glpK Kcat / Km value is
+
                                100 times the GlyDH, the reason may be DAK enzyme catalytic efficiency’s higher than glpD.
+
                                <br> As we can see, the previous reaction is dependent on ATP, and in the previous hypothesis,
+
                                we make ATP stable in the constant .In order to analyze the ATP concentration changes on
+
                                the impact of glycerol conversion, we add a Standard deviation of 0.05, mean of the normal
+
                                distribution of variables to disturb the timing of the concentration of ATP in ODE.
+
                                <br> For the sake of us to observe the significant results, we assume that the initial concentration
+
                                of ATP is 0 and is always greater than zero.
+
                                <br> And the change curve of the concentration in 60min is shown below:
+
                            </h4>
+
                            <br>
+
                            <br>
+
                            <h4 style="text-align:center">Before transformation</h4>
+
  
 +
                    <div id="Syetem" style="padding-top:50px;margin-top:-50px;">
 +
                        <h2 style="text-align:center">System</h2>
 +
                        <h4>The choice of the chassis organism is vital to the efficiency of the cell factory. Acrylic acid may
 +
                            do damage to the cell membrane. So we need to choose an organism which has high tolerance of
 +
                            acrylic acid. Escherichia coli and Saccharomyces cerevisiae are two model organisms which can
 +
                            be easily modified in the prokaryotic and eukaryotic.
 +
                            <br><br> Therefore, in the choice of the chassis organism, we tested two organisms, E. coli MG1655 and
 +
                            Saccharomyces cerevisiae BY4741. BY4741 has a great ability to metabolize glycerol. According
 +
                            to GAACF1.0, we used the YCPlac33 plasmid with LEU defect screening marker as the backbone and
 +
                            used the pTDH3 constitutive promoter and tPFK1 constitutive terminator to construct ceaS2 plasmid.<br>
  
                            <div align="center">
 
                                <img src="https://static.igem.org/mediawiki/2017/9/91/Model-14.1.png" class="img-responsive" width="60%" height="60%">
 
                            </div>
 
                            <br>
 
                            <h4 style="text-align:center">After trransformation</h4>
 
  
                            <div align="center">
 
                                <img src="https://static.igem.org/mediawiki/2017/d/d8/Model-14.2.png" class="img-responsive" width="60%" height="60%">
 
                            </div>
 
                            <h4>
 
                                <br> We found that the random change of ATP concentration had a significant effect on the pathway
 
                                after transformation, and the rate of DHAP synthesis was lower than that before transformation.
 
                                <br> But when we adjust the standard deviation of the normal distribution random variable to
 
                                0.05, the result is shown below.
 
                                <div align="center">
 
                                    <img src="https://static.igem.org/mediawiki/2017/f/f2/Model-15.1.png" class="img-responsive" width="60%" height="60%">
 
                                </div>
 
  
                                <div align="center">
 
                                    <img src="https://static.igem.org/mediawiki/2017/c/ce/Model-15.2.png" class="img-responsive" width="60%" height="60%">
 
                                </div>
 
  
 +
<div class="col-md-12" style="padding-top:30px">
 +
                    <div class="col-md-3">
 +
                        <img src="https://static.igem.org/mediawiki/2017/5/50/NPU-newSC.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                    <div class="col-md-6">
 +
                        <img src="https://static.igem.org/mediawiki/2017/7/7b/%E9%85%B5%E6%AF%8D%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2%E5%9B%BE.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
<div class="col-md-3">
 +
                        <img src="https://static.igem.org/mediawiki/2017/4/47/%E9%85%B5%E6%AF%8D1_Y33-Leu-ceas2_9033.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                </div>
  
                                <br> Thus, we found that even if ATP had a greater perturbation, the overall level was relatively
+
                            <br> We confirmed the proposal can make S.cerevisiae produce acrylic acid, but the
                                high in 0-60 min compared to the previous standard deviation of 0.02. While the transformation
+
                            yield is low, so we decided to optimize it.
                                of the metabolic pathway also reflects a more stable curve of change. At this point the concentration
+
                            <br> First, according to GNCDC(GlyDH-NOX-CAT-DAK-ceaS2) in E.coli, we added NOX to the pathway(the
                                of DHAP is not significantly affected by changes in ATP concentration.
+
                            CAT enzyme is active in S.cerevisiae). So we designed a pathway, GNDC(GlyDH-NOX -DAK-ceaS2),
                                <br> Thus, in actual production, we only need to keep the ATP concentration at a slightly higher
+
                            for S.cerevisiae.
                                level, not only to ensure the production of the target product, but to increase the stability
+
                                of the system as well.
+
                                <br>
+
                            </h4>
+
  
                         </div>
+
<div class="col-md-12" style="padding-top:30px">
                         <div class="tab-pane fade" id="service-two">
+
                    <div class="col-md-3">
 +
                         <img src="https://static.igem.org/mediawiki/2017/2/23/%E9%85%B5%E6%AF%8D3_Y33-URA-gld-DAK_10763.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                    <div class="col-md-6">
 +
                         <img src="https://static.igem.org/mediawiki/2017/b/b0/%E9%85%B5%E6%AF%8D%E8%B7%AF%E5%BE%84%E5%9B%BE.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
<div class="col-md-3">
 +
                        <img src="https://static.igem.org/mediawiki/2017/2/25/%E9%85%B5%E6%AF%8D2_Y33-leu-ceas2-NOX_10513.png" class="img-responsive">
 +
                        <h4> </h4>
 +
                    </div>
 +
                </div>
  
                             <h3 style="text-align: center;">Abstract</h3>
+
                             <br><br> The genes of GlyDH and DAK were constructed on the backbone of YCPlac33 plasmid with
 +
                            URA marker. We used the ADH1 promoter and tGPD1 terminator for GlyDH, the PGK1 promoter and the
 +
                            tPFK1 terminator for DAK. NOX and ceaS2 were constructed on the backbone of the other YCPlac33
 +
                            plasmid. We replaced URA marker with Leu marker to screen for two plasmids easily. We used the
 +
                            TEF2 promoter and tRPS2 terminator for GlyDH, the same promoter and terminator as the original
 +
                            pathway for ceaS2.
 
                             <br>
 
                             <br>
                            <h4>Engineering for the desired enzyme catalytic properties plays an important role in the biosynthesis
+
                        </h4>
                                of bulk chemicals and natural products. However, it is a time-consuming task to improve enzyme
+
                                catalysis by traditional random mutagenesis. And the utility of rational design based on
+
                                protein structure often was limited by the lack of protein structure for target enzymes and
+
                                professional backgrounds of bioinformatics.
+
                                <br>
+
                                <br>
+
                            </h4>
+
                            <h3 style="text-align: center;">Introduction</h3>
+
                            <h4>
+
                                <br> Enzyme engineering has been extensively used to optimize biocatalysts in industrial biotechnology
+
                                since most of enzymes in nature prefer to organisms adaptation but not industrial production
+
                                (Alvizo, et al., 2014; Ma, et al., 2009; Savile, et al., 2010). Traditionally, optimized
+
                                enzymes were obtained by random site-directed or saturated mutagenesis such as Error Prone
+
                                PCR, DNA shuffling and so on (Kabumoto, et al., 2009; Qi, et al., 2009; Reetz and Carballeira,
+
                                2007; Yep, et al., 2008). Due to the immense possibility of sequence mutation at amino acids
+
                                level, it is a time-consuming and low efficiency task to obtain a high efficient biocatalyst
+
                                by random mutation.
+
                                <br> With the availability of an increasing number of protein structural and biochemical data,
+
                                rational design of enzymatic mutation has become more and more popular (Bloom, et al., 2005;
+
                                Chica, et al., 2005; Kiss, et al., 2013; Li, et al., 2012; Steiner and Schwab, 2012). Many
+
                                strategies have been used to obtain evolutionary information, catalytic sites and substrate
+
                                channels by integrating sequence and structural features of enzymes. Previous studies have
+
                                developed many effective computational tools for enzyme engineering, such as the enzyme design
+
                                software Rosetta (Leaver-Fay, et al., 2011) and stability design software Foldx (Van, et
+
                                al., 2011) and so on (Table S2). However, most of them only focus on one feature, like the
+
                                thermo-stability based on the known PDB structure, and often request professional backgrounds
+
                                in protein structure, biochemistry, bioinformatics and so on.
+
                                <br>
+
                                <br>
+
                            </h4>
+
                            <h3 style="text-align: center;">What is AEMD?</h3>
+
                            <h4>
+
                                <br> AEMD is a web-based pipeline, which integrates several approaches together for enzyme stability,
+
                                selectivity and activity engineering. This pipeline can generate comprehensive reports, which
+
                                include the recommended mutation for improving enzyme catalytic property. Specifically, users
+
                                can get the recommended mutation only inputting sequence information of target enzymes, which
+
                                is very useful in the situation without professional knowledge and the known protein structure,
+
                                since AEMD contains a functional module that can automatically predict structure of the target
+
                                enzyme based on the known structures in Protein Data Bank (PDB).
+
                                <br> AEMD-Web provides a web interface, enabling users to conveniently predict mutants which
+
                                could improve the stability, selectivity and activity of enzymes. Users can obtain the suggestion
+
                                of mutations for almost all enzyme even without protein structure. In the future, we will
+
                                construct a comprehensive enzymatic mutant database and integrate new computing technology,
+
                                to improve the efficiency of enzyme engineering in industrial biotechnology.
+
                                <br>Fig.1 Workflow of the Stability analysis (A), Selectivity analysis (B) and Activity analysis
+
                                (C). The blue color rectangle blocks represent the inputs of sequence or PDB file, and the
+
                                output of recommended mutation sites. The green and gray color rectangle blocks represent
+
                                the evolution- and energy-based analysis process, respectively. The yellow color diamond
+
                                blocks represent the use of other softwares and approaches. The processes were shown in Supplementary
+
                                methods 【click here】in more detail. AEMD is freely available for non-commercial use at www.AEMD.tech:8181.
+
                                <br>
+
                                <br>
+
                            </h4>
+
                            <h3 style="text-align: center;">Process</h3>
+
                            <h4>
+
                                <br> AEMD is a web-based pipeline, which integrates several approaches together for enzyme stability,
+
                                selectivity and activity engineering. This pipeline can generate comprehensive reports, which
+
                                include the recommended mutation for improving enzyme catalytic property. Specifically, users
+
                                can get the recommended mutation only inputting sequence information of target enzymes, which
+
                                is very useful in the situation without professional knowledge and the known protein structure,
+
                                since AEMD contains a functional module that can automatically predict structure of the target
+
                                enzyme based on the known structures in Protein Data Bank (PDB).
+
                                <br> AEMD-Web provides a web interface, enabling users to conveniently predict mutants which
+
                                could improve the stability, selectivity and activity of enzymes. Users can obtain the suggestion
+
                                of mutations for almost all enzyme even without protein structure. In the future, we will
+
                                construct a comprehensive enzymatic mutant database and integrate new computing technology,
+
                                to improve the efficiency of enzyme engineering in industrial biotechnology.
+
                                <br>Fig.1 Workflow of the Stability analysis (A), Selectivity analysis (B) and Activity analysis
+
                                (C). The blue color rectangle blocks represent the inputs of sequence or PDB file, and the
+
                                output of recommended mutation sites. The green and gray color rectangle blocks represent
+
                                the evolution- and energy-based analysis process, respectively. The yellow color diamond
+
                                blocks represent the use of other softwares and approaches. The processes were shown in Supplementary
+
                                methods 【click here】in more detail. AEMD is freely available for non-commercial use at www.AEMD.tech:8181.
+
                                <br>
+
                                <br>
+
                            </h4>
+
                        </div>
+
 
+
 
                     </div>
 
                     </div>
  
 +
        <div class="container" style="padding-top:50px">
  
 +
                    <div id="Production" style="padding-top:50px;margin-top:-50px;">
 +
                        <h2 style="text-align:center">Production</h2>
 +
                        <h4>To make the engineering bacteria produce acrylic acid, it takes two stages. First, bacteria must
 +
                            grow and express the enzyme, then use carbon source to synthesize acrylic acid. To screen for
 +
                            engineering bacteria, it is a waste of time and reagents to use the traditional fermentation
 +
                            method. We used whole cell catalysis to carry out the reaction for acrylic acid production
 +
                            <br><br> After the enzyme is expressed, the bacteria solution will be centrifuged and concentrated 10
 +
                            times with buffer before the reaction. Therefore, we optimized the reaction process, selected
 +
                            the carbon source, Buffer, temperature, pH, reaction time and other conditions to optimize the
 +
                            production process of the cell factory.
 +
                            <br>
 +
                        </h4>
 +
                        <center><img src="https://static.igem.org/mediawiki/2017/2/2a/%E7%AD%9B%E9%80%89%E7%BB%84%E5%90%88%E8%A1%A8.png" class="img-responsive"></center>
 +
                        <br>
 +
                        <h3>PS. We also made Hardware
 +
                            <a href="https://2017.igem.org/Team:NPU-China/Hardware">(Click Here)</a> to simulate the industrial production process of acrylic acid!</h3>
 +
                    </div>
  
 
                 </div>
 
                 </div>
 +
 
             </div>
 
             </div>
 
             <!-- Blog Post Row -->
 
             <!-- Blog Post Row -->

Latest revision as of 19:35, 1 November 2017

Introduction

The essence of biochemical synthesis is the catalytic reaction with enzyme as the catalyst. Creating new biochemical reactions is an important research direction of synthetic biology.

ceaS2, whose full name is N2-(2-carboxyethyl)arginine synthase2, is a kind of enzyme in Streptomyces clavuligerus. The mentor of our team, Jiang Huifeng, has confirmed the new functions of ceaS2 with the help of TPP (Thiamine pyrophosphate) and magnesium ions. ceaS2 enzyme can catalyze the production of acrylic acid with DHAP (dihydroxy acetone phosphate) or G3P (glyceraldehyde 3-phosphate) as substrate.

Cell factory of acrylic acid (GAACF) 1.0:

DHAP and G3P are the central metabolic secondary products which can be easily found in various organisms. They are the carbon flow nodes that must be passed in the glycerol metabolic pathway in most organisms. ceaS2 enzyme being the core part, it is possible to create a new pathway to synthesize acrylic acid based on glycerol metabolic pathway in organisms and construct a cell factory with a high yield of acrylic acid.

First, we took E. coli BL21 (DE3) as the chassis cells and constructed engineering bacteria carrying the gene of ceaS2 enzyme with pET-28a plasmid as the vector. We constructed a new pathway to synthesize acrylic acid from any carbon source by transforming ceaS2 directly into the chassis cells. This new approach is the shortest compared to other pathways. Take the glycerol metabolic pathway of E. coli as an example, we only need three enzymes to achieve the synthesis of acrylic acid from glycerol. So this pathway has stronger malleability and broader development prospects.


Through the whole cell catalysis and HPLC (High Performance Liquid Chromatography), the results show that the engineering bacteria can use glycerol as carbon source to produce acrylic acid. However, the yield of the cell factory 1.0 is not high, only about 1mg / L.

It is known that acrylic acid can not be metabolized in the cell, so we analyzed the possible reasons as the following:
1. The activity and the catalytic efficiency of wild type ceaS2 is low.
2. The low carbon flow rate of glycerol metabolic pathway in E. coli leads to the low concentration of DHAP and G3P.
3. Acrylic acid is toxic to the chassis cells.
4. The reaction conditions such as carbon source, pH, temperature and reaction time are not suitable.
Based on the analyzing results, we have made improvements and built a new cell factory.

Cell factory of acrylic acid (GAACF) 2.0:
We built a new cell factory of acrylic acid through the four part: CO-PART, SYSTEM, PATHWAY, PRODUCTION!

Core Part

Acrylic acid is a byproduct of ceaS2 enzyme, the catalytic effect of wild type ceaS2 enzyme is very weak, and acrylic acid production is only 1mg / L. So it is necessary to improve the catalytic effect of this core factor, ceaS2 enzyme.

The gene of ceaS2 enzyme consists of 1719 deoxynucleotides and the protein sequence consists of 573 amino acids. We need to use bioinformatics to analyze and simulate, in order to help us decide the correct proposal.

We constructed ceaS2 enzyme mutants using the AEMD (Auto Enzyme Mutation Design) platform. We constructed the ceaS2 wild-type sequence on pET-28a plasmid. We used pET-28a-ceaS2 plasmid as a template to create point mutation, and then transformed the plasmid into BL21. Then, we did the whole cell catalysis to get the products. Finally, we screened for ceaS2 mutants with high catalytic efficiency by HPLC (High Performance Liquid Chromatography) and HTS (High throughput screening) .

Pathway

The carbon flow rate of the glycerol metabolic pathway is low. In order to solve the problem, we need reconstruction and optimization of the original metabolic pathway.

RE-Construction:We designed the GDC (GlyDH-DAK-ceaS2) pathway to produce acrylic acid from glycerol. In this pathway, GlyDH(Glycerol dehydrogenase) can efficiently convert Glycerol into DHA(1,3-Dihydroxyacetone). Then DAK (Dihydroxyacetone kinase) converts DHA into DHAP. Finally, ceaS2 converts DHAP into acrylic acid. In addition, because GlyDH depends on NAD+, we added two reduction models, NOX (NADH dehydrogenase )and CAT(Catalase), to the pathway, with the purpose of providing the required reduction force for GLY DH through the two layers of substrate level cycle. At last, we construct a new pathway for acrylic acid synthesis- GNCDC(GlyDH-NOX-CAT-DAK-ceaS2)


The genes of GlyDH and DAK were constructed on two MCS (multiple cloning sites) on the backbone of pCDFDuet-1 plasmid. NOX and CAT were constructed on two MCSs on the backbone of pETDuet-1 plasmid.

System

The choice of the chassis organism is vital to the efficiency of the cell factory. Acrylic acid may do damage to the cell membrane. So we need to choose an organism which has high tolerance of acrylic acid. Escherichia coli and Saccharomyces cerevisiae are two model organisms which can be easily modified in the prokaryotic and eukaryotic.

Therefore, in the choice of the chassis organism, we tested two organisms, E. coli MG1655 and Saccharomyces cerevisiae BY4741. BY4741 has a great ability to metabolize glycerol. According to GAACF1.0, we used the YCPlac33 plasmid with LEU defect screening marker as the backbone and used the pTDH3 constitutive promoter and tPFK1 constitutive terminator to construct ceaS2 plasmid.


We confirmed the proposal can make S.cerevisiae produce acrylic acid, but the yield is low, so we decided to optimize it.
First, according to GNCDC(GlyDH-NOX-CAT-DAK-ceaS2) in E.coli, we added NOX to the pathway(the CAT enzyme is active in S.cerevisiae). So we designed a pathway, GNDC(GlyDH-NOX -DAK-ceaS2), for S.cerevisiae.



The genes of GlyDH and DAK were constructed on the backbone of YCPlac33 plasmid with URA marker. We used the ADH1 promoter and tGPD1 terminator for GlyDH, the PGK1 promoter and the tPFK1 terminator for DAK. NOX and ceaS2 were constructed on the backbone of the other YCPlac33 plasmid. We replaced URA marker with Leu marker to screen for two plasmids easily. We used the TEF2 promoter and tRPS2 terminator for GlyDH, the same promoter and terminator as the original pathway for ceaS2.

Production

To make the engineering bacteria produce acrylic acid, it takes two stages. First, bacteria must grow and express the enzyme, then use carbon source to synthesize acrylic acid. To screen for engineering bacteria, it is a waste of time and reagents to use the traditional fermentation method. We used whole cell catalysis to carry out the reaction for acrylic acid production

After the enzyme is expressed, the bacteria solution will be centrifuged and concentrated 10 times with buffer before the reaction. Therefore, we optimized the reaction process, selected the carbon source, Buffer, temperature, pH, reaction time and other conditions to optimize the production process of the cell factory.


PS. We also made Hardware (Click Here) to simulate the industrial production process of acrylic acid!