Difference between revisions of "Team:Tongji China/Demonstrate"

Revision as of 12:12, 16 October 2018

Programme

Wet Lab

Experiment

Plasmid Construction

Achievements

Successfully conduct 2 plasmids containing positive control antigen DNA.
  Successfully conduct 4 plasmids containing antigen DNA according to our filteration.

Introduction

  In order to let the P. aeruginosa inject the antigens into the antigen presenting cells (APCs), we first need to add the antigens into the T3SS plasmid. Escherichia-Pseudomonas shuttle expression plasmid pExoS54F (shows in Figure 1), which encodes the T3SS effector ExoS promoter with N-terminal ExoS1–54 signal sequence, followed by a FLAG tag and a multiple cloning site (MCS). The pExoS54F plasmid contains two promoter region which can be activated simultaneously by ExsA binding to their common promoter region. PexoS is the promoter region which originally belongs to the toxin gene ExoS and the wild type P. aeruginosa inject the toxin ExoS into the host cell through the T3SS. The P. aeruginosa strain we use has knocked out the ExoS gene so we utilize its promoter and its N-terminal ExoS1–54 signal sequence which act as a T3SS secretion signal to let the T3SS secret proteins of interest. SpcS is a kind of T3SS chaperone and help the proteins of interest to enter the T3SS secretion channel.
 

Figure1 here

  The proteins contained in the pExoS54F are actually not all the proteins that function in the T3SS protein delivery. There are approximately 40 proteins that regulate the secretion of T3SS effector proteins and many of them are encoded in the P. aeruginosa genome. The protein ExsE, ExsC, ExsD and ExsA are four cytoplasmic proteins (shows in the Figure 2) that control the coupling of transcription and secretion. ExsA is a DNA-binding protein required for transcriptional activation of the entire T3SS. The second regulatory protein, ExsD, functions as anti-activator by directly binding to ExsA. ExsC functions as an anti-anti-activator by directly binding to and inhibiting ExsD. ExsE functions as a direct inhibitor of ExsC and provide an initiating signal for the whole process. Figure 2 shows the situation when the T3SS secretion is inhibit because the direct activator ExsA is inhibited by the binding ExsD.  

Figure2 here

Overview

  1.Sequence Synthesis
  2.Plasmid Restricted Digestion
  3.Ligation & Transformation

We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of the affinity of each mutation site and each allele type.
The heat map below shows the sum of the affinity of each allele type and each mutation.

Figure.Model.1 Heatmap of the affinity of each allele type and each mutation.

From the heatmap above, we could know that the mutation sites at the bottom of the heatmap have big affinity amount, and some mutation sites at the middle show small sum of affinity. Considering the "affinity" presents the amount of peptides binding to a certain amount of MHC-I molicule, lower "affinity" means stronger binding to the MHC-I. So in that heatmap, if cell color is close to yellow, the mutation site with the allele of that cell may product MHC strong binding peptides. On the contrary if cell color is close to blue, the mutation site with the allele of that cell probabily can not product MHC-I binding peptides.
If a colorectal cancer patient is detected to have an immune response to our medicine, we can predict which mutation is playing the strongest role in cancer using this model. If a patient's mutation sites are already known, this model can also help predict which site can be the best one for peptide making for this certain patient which contributes to the individual therapy.

affinity_conseqeunce.csv Click to download the consequence file

@@ Line 25: / Line 25: @@
                  <br><br>
 <p class="littletitle">Achievements</p>
-                 We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of the affinity of each mutation site and each allele type.<br>
+                 Successfully conduct 2 plasmids containing positive control antigen DNA.<br>
-Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after a large number of trials.<br>Bayes' theorem is a fundamental theorem in Bayesian statistics, as it is used by Bayesian methods to update probabilities, which are degrees of belief, after obtaining new data. Given two events A and B, the conditional probability of A given that B is true is expressed as follows:<br>
+		Successfully conduct 4 plasmids containing antigen DNA according to our filteration.<br>
-                <p style="text-align:center"><img src="https://static.igem.org/mediawiki/2018/8/82/T--Tongji_China--picture-drylab-model-1.png" width="25%" height="25%"></p>
+<p class="littletitle">Introduction</p>
-In the above equation, <I>A</I> usually represents a proposition (such as the statement that a coin lands on heads fifty percent of the time) and <I>B</I> represents the evidence, or new data that is to be considered (such as the result of a series of coin flips). <I>P(A)</I> is the prior probability of <I>A</I> which expresses one's beliefs about <I>A</I> before evidence is considered. The prior probability may also quantify prior knowledge or information about <I>A</I>. <I>P(B|A)</I> is the likelihood function, which can be interpreted as the probability of the evidence <I>B</I> given that <I>A</I> is true. The likelihood quantifies the extent to which the evidence <I>B</I> supports the proposition <I>A</I>. <I>P(A|B)</I> is the posterior probability, the probability of the proposition B into account. Essentially, Bayes' theorem updates one's prior beliefs <I>P(A)</I> after considering the new evidence <I>B</I>.<br>
+		In order to let the P. aeruginosa inject the antigens into the antigen presenting cells (APCs), we first need to add the antigens into the T3SS plasmid. Escherichia-Pseudomonas shuttle expression plasmid pExoS54F (shows in Figure 1), which encodes the T3SS effector ExoS promoter with N-terminal ExoS1–54 signal sequence, followed by a FLAG tag and a multiple cloning site (MCS). The pExoS54F plasmid contains two promoter region which can be activated simultaneously by ExsA binding to their common promoter region. PexoS is the promoter region which originally belongs to the toxin gene ExoS and the wild type P. aeruginosa inject the toxin ExoS into the host cell through the T3SS. The P. aeruginosa strain we use has knocked out the ExoS gene so we utilize its promoter and its N-terminal ExoS1–54 signal sequence which act as a T3SS secretion signal to let the T3SS secret proteins of interest. SpcS is a kind of T3SS chaperone and help the proteins of interest to enter the T3SS secretion channel.<br>
-The probability of the evidence <I>P(B)</I> can be calculated using the law of total probability. If <I>{A1, A2, …, An}</I> is a partition of the sample space, which is the set of all outcomes of an experiment, then,<br>
+		<br><br>Figure1 here<br><br>
-                <p style="text-align:center"><img src="https://static.igem.org/mediawiki/2018/b/b8/T--Tongji_China--picture-drylab-model-2.png" width="90%" height="90%"></p>
+		The proteins contained in the pExoS54F are actually not all the proteins that function in the T3SS protein delivery. There are approximately 40 proteins that regulate the secretion of T3SS effector proteins and many of them are encoded in the P. aeruginosa genome. The protein ExsE, ExsC, ExsD and ExsA are four cytoplasmic proteins (shows in the Figure 2) that control the coupling of transcription and secretion. ExsA is a DNA-binding protein required for transcriptional activation of the entire T3SS. The second regulatory protein, ExsD, functions as anti-activator by directly binding to ExsA. ExsC functions as an anti-anti-activator by directly binding to and inhibiting ExsD.  ExsE functions as a direct inhibitor of ExsC and provide an initiating signal for the whole process. Figure 2 shows the situation when the T3SS secretion is inhibit because the direct activator ExsA is inhibited by the binding ExsD.
-The formulation of statistical models using Bayesian statistics has the identifying feature of requiring the specification of prior distributions for any unknown parameters. Indeed, parameters of prior distributions may themselves have prior distributions, leading to Bayesian hierarchical modeling, or may be interrelated, leading to Bayesian networks.<br><br>
+		<br><br>Figure2 here<br><br>
-<p class="littletitle">Phase 2. Prediction of mutations most likely to bind MHC I</p>
+<p class="littletitle">Overview</p>
+		1.Sequence Synthesis<br>
+		2.Plasmid Restricted Digestion<br>
+		3.Ligation & Transformation<br><br>
 We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of the affinity of each mutation site and each allele type. <br>The heat map below shows the sum of the affinity of each allele type and each mutation.<br>
-                <p style="text-align:center"><img src="https://static.igem.org/mediawiki/2018/9/9f/T--Tongji_China--picture-drylab-model-4.png" width="20%" height="20%" ><img src="https://static.igem.org/mediawiki/2018/e/e4/T--Tongji_China--picture-drylab-model-3.png" width="70%" height="70%" ></p>
 			<div class="instructionOfPicture">
 				Figure.Model.1 Heatmap of the affinity of each allele type and each mutation.