Team:Tongji China/tags/v0.2/json/result.json
{
"header": "Contents
- 1 RESULT
- 1.1 Overview
- 1.2 Indigo production pathway
- 1.3 trpR & bglA knock-out (KO) experiment
- 1.4 Shikimate pathway-related plasmids
- 1.5 Recovery of high-tryptophan-production E. coli and bglA gene verification
- 1.6 Model
- 1.7 <button class=\"btn btn-link\" type=\"button\" data-toggle=\"collapse\" data-target=\"#collapseOne\" aria-expanded=\"false\" aria-controls=\"collapseOne\"> Final Dataset </button>
RESULT
", "content": "Overview
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Indigo production pathway was validated successfully
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trpR ＆ bglA were knocked-out efficiently in E. coli BL21(DE3)
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Four Shikimate pathway-related plasmids were constructed
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High-tryptophan-production E. coli was recovered
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Indigo production pathway
\nWe first verified indigo production pathways with importing integral pET-28a(+)-bFMO plasmid to E. coli BL21(DE3). In this pathway, the main function of bacterial flavin-dependent monooxygenase (bFMO) is to oxidize indole to indoxyl, indigo is then obtained through an spontaneous oxidation pathway.
\nFollowed by the results of this pathway verification. In the left photo, the right is control group without pET-28a(+)-bFMO plasmid, the left is the experimental group which is imported bFMO plasmid. We saw the dark blue product finally which is the INDIGO that was indeed what we expected!
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\" />trpR & bglA knock-out (KO) experiment
\nAccording to our design, we need to knock-out trpR and bglA gene of E.coli BL21(DE3). We applied the pRED system. We constructed the linear fragment, transferred it into E. coli by electroporation, and then induce the expression of pRED protein. pRED homologous recombined the target segment with our linear fragment, so as to knock out the target gene. Ampicillin and Apramycin were used as markers for our screening.
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\" />We achieved the single knockout of trpR and bglA gene, and carried out the preliminary verification by the way of bacterial solution PCR. We need to sequence the bacteria to further verify the knockout results.
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We have constructed 4 plasmids: aroGfbr-pE1a, trpEfbr-pA1c, trpEfbr-aroL- pA1c, aroGfbr-aroL-trpEfbr-pS8k, and transferred them into ΔtrpR E. coil respectively. The corresponding recombinant strain can be seen growing on the screening plate.
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\" />For further verification, we performed colony PCR on the recombinant strain and got positive results of four plasmids. Then we sequenced these plasmids. The recombination of four plasmids has been preliminarily verified. Among them, the sequence of aroGfbr-pE1a and trpEfbr-pA1c was right according to the sequencing result. However, we didn’t make it to sequence the other two plasmids because of the time restriction. More experiments also need to be done to further verify the Shikimic acid pathway.
\nRecovery of high-tryptophan-production E. coli and bglA gene verification
\nWe got a high-tryptophan-production E. coli strain. The color of this strain was bright yellow which is different from which of the other kinds of Escherichia coli strain.
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- \n<img class=\"img-fluid rounded shadow-lg\" src=\"\n
\" />(a) In the left plate was Escherichia coli strain for high-efficiency tryptophan production whose color was bright yellow. It was different from the other strains we used, such as TOP10 strain.
\n \n - <img class=\"img-fluid rounded shadow-lg\" style=\"width: 300px;\" src=\"
\" /> <img class=\"img-fluid rounded shadow-lg\" style=\"width: 200px;\" src=\"
\" />\n(b) After got a clone we cultured it in a tube. The high-tryptophan-production E. coli strain was bright yellow after centrifugation.
\nSince the genetic background of this strain was not clear, we did PCR amplification with bacteria solution and primer for bglA KO. From agarose gel data, we could know that we could probably did bglA KO in this strain in our following experiment. The exact sequence about bglA needs to be verified by sequencing.
\n \n - \n<img class=\"img-fluid rounded shadow-lg\" style=\"width: 300px;\" src=\"\n
\" />(c) E. is a high-tryptophan-production E. coli strain. The band showed that the genetic background about bglA of Escherichia coli strain for high-efficiency tryptophan production was possibly the same with BL21(DE3) which we are using now.
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Model
\nWe tried to find the best culturing condition for bFMO, and we built a model to predict the influence of three variables. Besides, our model helped simplified our workflow of indigo quantification.
\nWe set up different experimental groups, limiting the variables as time, temperature, tryptophan concentration and culture volume. We found the appropriate range of values through experiments and looked for effective combinations.
\nProcess: Determine the appropriate range of variables -- Variable correlation analysis -- Input the highly correlated experimental variables into the neural network as three-dimensional variables, and build the model based on genetic algorithm.
\nVoulume-Temperature response surface analysis
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\" width=\"300px\" />The three-dimensional diagram of response surface analysis shows the relationship between indigo and temperature (℃) and volume (mL).
\nAt this time, the concentration of tryptophan is 2.55 g/L
\nTemperature-Concentration response surface analysis
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\" width=\"300px\" />The relation between indigo and tryptophan concentration temperature, at which time the volume was 50 mL
\nConcentration-volume response surface analysis
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\" width=\"300px\" />The relationship between indigo concentration and volume tryptophan concentration, and the temperature was 33.5 ℃
\nTemperature is the most critical factor affecting the product concentration, and the product concentration curve with temperature is almost the same as the curve of enzymatic reaction, with an optimal temperature. The product concentration curve with volume is also related to other variables, and there is no obvious single factor relationship. The concentration of tryptophan was negatively correlated with the product.
\nResponse surface analysis can be used to intuitively express the strength of correlation between product concentration and each variable, and the approximate relationship between the data can be analyzed qualitatively. Through the analysis above, we have determined that all three variables have a strong correlation with product concentration, which can be input into the neural network as three-dimensional variables for learning.
\nAccording to the established model, the scatter diagram of the predicted value and the real value of each set of data is made, and then the residual of all data sets is combined (the deviation degree of red and blue points is shown in the image) :
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\" width=\"300px\" />Scatter plots of predicted sample values and real sample values after neural network training, blue represents real values and red represents predicted values
\nAfter several iterations, the accuracy is stable at 89%, which proves that the model is reliable.
\nOn the basis of the reliable model, the optimal culture condition obtained by genetic algorithm is:Temperature: 31℃; Culture volume: 50 mL; Tryptophan concentration: 2.5g /L.
\nIn the results, the optimal temperature of 31℃ is within the optimal temperature range of bacterial growth. Similar results can be obtained through response surface analysis. Similarly, the increase of bacterial liquid volume will lead to the increase of yield (within the range of our experiment).
\nWe selected the optimized result of neural network genetic algorithm 2.5g /L as the best result for experimental test.
\nThe following table is the data statistics of the five groups to verify the optimal condition:
\nTable experimental validation of the optimized condition
\nShaker serial number
\n| \n Shaker serial number \n | \n\n 1 \n | \n\n 2 \n | \n3 | \n\n 4 \n | \n\n 5 \n | \n
|---|---|---|---|---|---|
| \n Indigo yield under initial conditions \n | \n\n 230.2053571 \n | \n\n 222.2946429 \n | \n217.1339286 | \n\n 241.0446429 \n | \n\n 251.1160714 \n | \n
| \n Error \n | \n\n -22.892742900000002, \n | \n\n -30.803457099999974, \n | \n-35.9641714, | \n\n -12.053457099999974, \n | \n\n -1.9820285999999783 \n | \n
The error of the red data is large, which just exceeds the confidence range obtained before. Because the other four groups of data are in good agreement, it can be considered as the result of experimental error.
\n<button class=\"btn btn-link\" type=\"button\" data-toggle=\"collapse\" data-target=\"#collapseOne\" aria-expanded=\"false\" aria-controls=\"collapseOne\"> Final Dataset </button>
\n<code> temp/¡ævolume/mltrp g/L indigo mg/L\n 37\t50\t0\t152.8660685\n 37\t50\t3\t213.4017821\n 37\t50\t6\t186.0803572\n 37\t50\t9\t156.616068\n 37\t50\t12\t144.2946411\n 37\t50\t15\t144.8303553\n 37\t3\t0\t120.1875\n 37\t3\t0.5\t191.9732143\n 37\t3\t1\t168.4910714\n 37\t3\t1.5\t199.2053571\n 37\t3\t2\t203.0446429\n 37\t3\t2.5\t189.3839285\n 37\t3\t3\t194.3839286\n 37\t5\t0.5\t90.04821426\n 37\t5\t1\t84.04821425\n 37\t5\t1.5\t92.56607143\n 37\t5\t2\t105.9053571\n 37\t5\t2.5\t101.6196428\n 37\t5\t3\t94.54821432\n 37\t5\t5\t114.4767857\n 37\t5\t15\t82.24107144\n 30\t5\t3\t255.9055804\n 34\t3\t3\t115.2232096\n 28\t10\t3.5\t56.52678571\n 26\t10\t3.5\t79.11607144\n 28\t10\t3.5\t58.22321429\n 26\t10\t3.5\t81.97321426\n 37\t5\t3\t96.10178569\n 30\t5\t3\t239.9844494\n 30\t5\t3\t239.9844494\n 27\t5\t3\t86.16964283\n 37\t5\t0\t99.11607146\n 37\t3\t0\t102.2410715\n 37\t3\t0.5\t175.0089286\n 37\t3\t1\t121.1696428\n 37\t3\t1.5\t110.8125\n 37\t3\t2\t147.3303571\n 37\t3\t3\t215.3660714\n 37\t5\t1.5\t81.61607142\n 37\t5\t2\t141.7946429\n 37\t5\t2.5\t143.6696429\n 37\t5\t3\t126.2589285\n 34\t3\t3\t163.0446429\n 34\t10\t3.5\t103.2232143\n 34\t10\t3.5\t101.9732143\n 30\t10\t3.5\t193.9375\n 31\t10\t0.5\t118.6696429\n 31\t10\t1\t121.7053571\n 31\t10\t3.5\t187.8660714\n 31\t10\t6\t120.3660714\n 31\t10\t8\t109.1160714\n 31\t10\t0.5\t102.6875\n 31\t10\t1\t97.95535716\n 31\t10\t3.5\t164.4732143\n 31\t10\t6\t117.5982143\n 31\t10\t8\t103.8482143\n 31\t3\t3.5\t120.7232143\n 31\t5\t3.5\t139.7410715\n 31\t50\t3.5\t235.9017857\n 31\t50\t2.5\t230.2053571\n 31\t50\t2.5\t222.2946429\n 31\t50\t2.5\t217.1339286\n 31\t50\t2.5\t241.0446429\n 31\t50\t2.5\t251.1160714\n </code>\n
See our page of <a href=\"https://2019.igem.org/Team:Tongji_China/Model\">model</a> to learn the details.
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