iGEM 2020 — Team Austin_UTexas

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# Team:Austin UTexas/Notebook

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# Notebook

Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.

### What should this page have?

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

### Inspiration

You can see what others teams have done to organize their notes:

* [2018 Munich](https://2018.igem.org/Team%3AMunich/Notebook)
* [2014 ATOMS-Turkiye](https://2014.igem.org/Team%3AATOMS-Turkiye/Notebook)
* [2014 Tec Monterrey](https://2014.igem.org/Team%3ATec-Monterrey/ITESM14_project.html#tab_notebook)
* [2014 Kyoto](https://2014.igem.org/Team%3AKyoto/Notebook/Magnetosome_Formation#title)
* [2014 Cornell](https://2014.igem.org/Team%3ACornell/notebook)

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# Team:Austin UTexas/Engineering

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# Engineering Success

    Our engineering design cycle can be divided into two iterations: one that is based on computational work and simulations and another that involves wet lab work.

### Computational:

**Our main goal is to be able to successfully create mutations in the T7 bacteriophage genome computationally, simulate our mutant genomes through Pinetree, and visualize our data using RStudio, to ultimately engineer a bacteriophage with maximized GFP production and minimized lysis time.**

#### Summary of Computational Engineering Design Cycle:

![](https://static.igem.org/mediawiki/2020/d/d1/T--Austin_UTexas--computation_image.jpg)The computational part of our project involved researching topics related to lysis time, burst size, conditions for lysis, gene deletions in the T7 genome, and the logistics of inserting GFP and producing a detectable amount of GFP. Then we designed various versions of the T7 genome with one gene deletion for all the possible gene deletions. We used Pinetree to determine the protein production of these mutant genomes compared to the wild type genome and graphed them using RStudio. We were able to analyze these graphs to determine which edits had the most potential to be combined to create the most efficient and effective phage. After deciding on the edits of interest, we broke into groups for further genome designing, testing, and analyzing for those specific edits. These included edits associated with lysis time, GFP production, RNAse, and the lysozyme mutant. After obtaining data from each of those groups, we were able to combine the most beneficial gene edits, in particular the GFP and lysis mutants, to create possible final genomes, which we worked with TU Delft's iGEM team to test with simulations of our phages' infections entire populations of *E. coli* (*Escherichia coli*) rather than just one cell.

**The following is a more stepwise description of the "Summary of Computational Engineering Design Cycle" section above:**

#### Research:

![](https://static.igem.org/mediawiki/2020/e/e9/T--Austin_UTexas--Research.jpg)

T7 bacteriophage is a widely studied model organism, so there was plenty of prior research on its genome structure , lysis behavior, burst size, and non-essential genes. We also researched topics such as how much GFP should be produced to be detectable, and what conditions affect lysis (pH, temperature, etc.) in order to get a better baseline for our expected results.

#### Imagine:

![](https://static.igem.org/mediawiki/2020/2/2a/T--Austin_UTexas--Imagine.jpg)

Using the early research that we compiled, our team determined the purpose for our engineered phage: *E. coli* detection in water. We envisioned a fast-acting colorimetric reporter-based mechanism that would allow users to quickly and accurately assess water samples for *E. Coli* through the infection of T7 and release of GFP at cell lysis.

#### Design:

![](https://static.igem.org/mediawiki/2020/d/d5/T--Austin_UTexas--Design.jpg)Using a systematic approach we designed versions of the T7 bacteriophage genome with each individual gene deleted in order to determine the importance of each gene in regards to lysis time and burst size for the purpose of determining which genes to delete and which genes must be maintained. Additionally, we tested various mutants with GFP and the holin gene inserted into different genomic loci in order to determine the locations of highest expression and fastest lysis within the genome. We developed calculators for lysis time and burst size in order to test our constructs.

#### Build:

![](https://static.igem.org/mediawiki/2020/d/dd/T--Austin_UTexas--Build.jpg) To "build" the T7 bacteriophage genomes designed in the previous step in a way that could be tested, we made edits to the T7 bacteriophage genome code using Breseq, an application used to apply mutations to a GenBank file that could be run through Pinetree (click [here](https://2020.igem.org/Team%3AAustin_UTexas/Experiments)  for more details).

#### Test:

![](https://static.igem.org/mediawiki/2020/1/1b/T--Austin_UTexas--Test.jpg) We tested each of the T7 bacteriophage genomes using the Pinetree simulator to determine the effect of each mutant on gene expression trends throughout the genome. Furthermore, we used Pinetree to simulate infections with each of mutants to determine and the amount of GFP produced and holin produced. We were able to collect large samples of data quickly by using the Texas Advanced Computing Center(TACC),to run large numbers of Pinetree simulations of each mutation quickly, rather than running each simulation one at a time on our own computers.

#### Learn:

![](https://static.igem.org/mediawiki/2020/f/fa/T--Austin_UTexas--Learn.jpg) For each of the mutated genomes, we can visualize and compare the wild type and mutant protein production, as well as burst size and lysis time, using a set of R scripts that we designed. Once we obtained the data from Pinetree for each of the mutants, we used these scripts to determine which mutant have the potential to yield optimal lysis time, burst size, and GFP production. The analysis and graphs generated from R helped us determine the maximum GFP expression, lowest lysis time, as well as changes in burst size due to different mutations. Furthermore, we have a better understanding of which genes can be safely deleted as well as the overall expression trends of the T7 genome. We evaluated these results to determine which mutations are most ideal for our goals. Additionally, we now have a better idea for future research and experiments

#### Improve:

![](https://static.igem.org/mediawiki/2020/9/9a/T--Austin_UTexas--Improve.jpg) After researching about the various T7 genes and their functions, and modeling our mutants, we were able to refine our investigations to those specific genes. We used the data that we collected and analyzed in the "Test" and "Learn" steps and used it to determine what areas needed to be further researched, what genomic edits needed to be created and tested, and what areas of code needed to be fixed for bugs and errors, and how Pinetree can be improved. We also determined what parts of the T7 genome were optimal insertion sites for GFP during the mutations we ran, and incorporated these sites into our final model. We also updated our lysis time calculator after initial experiments to determine the protein expression at the lysis time for each individual simulation, rather than using an average lysis time for each simulation and then determining the protein count at that point. We reran some simulations using this updated calculator and ran some simulations for other replacements and insertions. We then began the engineering cycle again, by researching topics for information needed for the next steps of the project to be continued. After going through this cycle with all the mutations we created, we were able to obtain lysis time and burst size graphs for all of our tested mutations and determine what mutated genomes we want to test in wet lab.

**In conclusion, our team was able to design an engineered T7 bacteriophage with optimized GFP and lysis time through our design, modeling, and analysis, and we are now ready to assess our construct in lab. This designed T7 bacteriophage can be seen below in Figure 1.**

![](https://static.igem.org/mediawiki/2020/b/b4/T--Austin_Utexas--FinalT7Genome.png)

**Figure 1.** Final T7 Bacteriophage Design. The final design for our Mutant T7 Bacteriophage contains a GFP gene and Holin gene between genes 9 and 10 of the wildtype T7 Bacteriophage genome. The Holin gene was derived from the T7 Bacteriophage itself, notated as Gene 17.5 based on its original location. The parts of the genome that were not altered are slightly faded in order to highlight the team's alterations. Figure made by team member Harsh Madaik.

### Wet Lab:

#### Summary of Wet Lab Engineering Design Cycle:

After determining which genetic edits have the optimal lysis time, we will continue with the engineering design cycle. We'll begin by researching the most effective methods for implementing our genetic edits for T7 bacteriophage. We will then design the new bacteriophage based on the data that we collected from Pinetree and the ideas that we found during the research stage. We will use this design to build the new engineered strain of T7 bacteriophage and test it by determining its effectiveness and sensitivity at determining whether or not  *E. coli*  is in a water sample. Once the testing is completed, we will examine the data obtained and find new ways to improve the bacteriophage and procedure to create the ideal bioassay. Specifically, we are using yeast assembly to create our mutated phage genomes and phage titering to measure the amount of phage made in infections. Flow cytometry will be used to measure GFP production of our mutated genomes. What we learn in the computational engineering cycle will determine what mutations we test in the wet lab.

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# Team:Austin UTexas/Experiments

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# Experiments

### PineTree

![](https://static.igem.org/mediawiki/2020/4/4c/T--Austin_UTexas--Pinetree_logo.png) Our project has, from the start to finish, relied on the ability to simulate the infection of T7 Bacteriophage. PineTree, a stochastic gene expression simulator, has been previously optimized to explore T7 gene expression, and thus allows us to model T7 infection and the ability to draw conclusions based on the overall gene expression. To run simulations, genebank files were created and run by PineTree at a specified time limit to generate a gene expression profile. These profiles were then analyzed and used to generate conclusions on the properties, lysis time, burst size, and GFP expression, of wildtype T7 bacteriophage and mutant strands that we created.

### Using Breseq to Generate T7 Bacteriophage Mutants

![](https://static.igem.org/mediawiki/2020/4/4c/T--Austin_UTexas--Compnewtfig.jpg) Breseq is an application used to apply mutations to the wildtype T7 bacteriophage genome via its *gdtools* toolkit. To engineer T7 bacteriophage, we created a gd file for each mutant that specifies what mutations we want to apply (insertion, deletion, replacements, or any combination of the three) to the T7 genome. The gd file is used by Breseq to output the mutated genome sequence in the form of a genbank file. This genbank file is then run by Pinetree to generate output (protein count, ribosome density, etc.) at each time point in the simulation. When creating these mutations, we followed a set of guidelines as follows:

* When removing genes, we decided to delete regions associated with each gene's CDS.
* For genes containing overlaps downstream of the CDS, we ended the deletion 30 bp upstream of the start of the overlapping gene.
* For genes with upstream overlaps, we started the deletion at the end of the previous gene.

Using these guidelines, we created several T7 bacteriophage genomes varying in the placement of holin and GFP genes. These genomes, encoded in their respective genebank files, were then simulated through Pinetree to gather their respective gene expression profile, which was subsequently analyzed for GFP expression and used to calculate a lysis time and burst size.

### Speeding Up Data Collection through the Texas Advanced Computing Center

TACC (Texas Advanced Computing Center) is a supercomputer complex that provides computing resources and services to researchers across the United States. Within our project, we used TACC's computing services to run multiple simulations, at different seeds, of the single designed T7 Mutant. This allowed us to generate more data within a shorter period, rather than running each simulation individually.

### Analyzing Simulations

After the Gene Expression profiles of our mutant T7 Bacteriophages were generated by Pinetree, we sought to analyze how our mutations affected the Phage's lysis time, burst size (progeny production), and GFP expressing capabilities. Analyzing the GFP expression of the T7 Mutant was quite simple as Pinetree directly outputs the quantity of GFP made during the infection process. However, to analyze lysis time and burst size, we had to design our own calculators that would interpret the data generated in the gene expression profile. Specifically, we used R studio to create and calibrate our lysis time/burst size calculators. To create our burst size calculator, we first determined the amount of each virion protein needed to make a complete virion. Based on this information, we made a function that inputs the output tsv file of Pinetree (which represents one infection within a cell) and calculates the minimum number of complete virions (meaning that the virion has all of the proteins it is supposed to contain). The burst size calculator also takes into account the lysis time, which determines where in the infection the cell should lyse, releasing phage progeny. To calibrate the lysis time calculator, we first determined the amount of holins and lysins at 1320 seconds for the wild type (which is the estimated lysis time for the wild type T7 in the Pinetree simulation). This determines how much holin and lysins are needed for an E.coli cell to lyse. Then, in a similar fashion to the burst size calculator, each mutant’s lysis time is the point at which holins and lysins get to this determined level within the PineTree simulation. Thus, by inputting a gene expression profile, from Pinetree, within these calculators we successfully derived the lysis time and burst size for respective runs

![](https://static.igem.org/mediawiki/2020/c/cb/T--Austin_UTexas--Calculator_examples.jpg)

**Figure 1.** An example of our Burst Size Calculator (Left) and Lysis Time Calculator (Right).

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# Team:Austin UTexas/Results

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# Results

Over the course of our project we collected expression data from hundreds of PineTree simulations. Here we display and discuss some of our most important findings.

### Average Protein Expression of T7 Bacteriophage at Lysis Time

We began by finding the average protein expression levels for each gene in the wild-type T7 genome. This provided us with a baseline to compare the efficacy of our engineered phage to. According to our results, gene 10A, responsible for coding the phage's major capsid protein, has the highest level of expression in the genome. We believe this is due to the combination of overlapping polycistronic transcripts and strong promoters directly upstream of it.

![](https://static.igem.org/mediawiki/2020/5/51/T--Austin_Utexas--T7-WT-Plot.png)

**Figure 1.** Simulated protein abundances expressed by the wild-type T7 bacteriophage genome at lysis. Error bars represent 5th and 95th quantiles for each gene. Note: Class 1 genes (shown in pink) are present in this simulation but may be difficult to see due to their low expression levels.

### Testing Specific Gene Deletions

Our first investigation involved sequentially deleting each gene in the T7 bacteriophage genome in order to determine how their absences affected gene expression. We found that none of the average expression levels in genomes with deleted genes were significantly higher than the wild type. Here we display the results for a deletion of gene 2, which codes for a small protein (gp2) that binds and deactivates the native *E. coli* RNA polymerase(RNAP). We expected the deletion of gene 2 to upregulate the expression of Class 1 genes, which are transcribed by the *E. coli* RNAP, however, the change we saw was not significantly higher. It is possible that because the *E. coli* RNAP is so much slower than the T7 RNAP, the stochastic effect of reducing its transcription is not that noticeable. This does illustrate one of the major limitations of our model, however. It is only programmed to recognize certain interactions (such as gp2 binding *E. coli* RNAP) that directly affect translation and transcription. This means that the effects of other interactions, such as the host's restriction enzymes degrading the genome, are not modeled. Thus, it is highly likely that the lack of deactivation of the host RNAP by gp2 would have a much more negative impact on T7's growth in a biological setting, something that we can investigate once we begin lab testing in phase 2.

![](https://static.igem.org/mediawiki/2020/e/e8/T--Austin_Utexas--T7-Del-gp2-Plot.png)

**Figure 2.** Simulated protein abundances expressed by T7 bacteriophage genome at lysis with gene 2 deleted. Error bars represent 5th and 95th quantiles for each gene. Note: Class 1 genes (shown in pink) are present in this simulation but may be difficult to see due to their low expression levels.

### Inserting GFP into the T7 Genome

In addition to deletions, we tested how GFP would be expressed when inserted into various locations within the T7 genome. The mutants include the replacement of nonessential genes 1.4, 1.5, and 1.6 as well as 4.3, 4.5, and 4.7 with GFP and the insertion of GFP before and after gene 10A. Among these mutants, we found that the point of highest expression was insertion of GFP just before gene 10A. This made sense, as it allowed the GFP gene to take advantage of the multiple strong promoters that regulated 10A without having to wait for 10A (a relatively large gene) to be transcribed before it. During the course of these experiments, we found the expression level of our inserted gene correlated very strongly with those of nearby genes. This reinforced the idea that T7's genomic structure, especially the location and multiplicity of promoters, heavily dictates gene expression levels within our model. Furthermore, we saw that the replacement of individual nonessential genes with GFP did not have any significant effect on expression in the rest of the genome, showing that there may be a potential for future engineering of genes into those locations. In addition, we saw no major changes in burst size and lysis time for the GFP mutants compared to the wild type, showing evidence the mutations don't have a significant effect on phage fitness.

![](https://static.igem.org/mediawiki/2020/d/dd/T--Austin_UTexas--gfp_before_10A_plot.png)

**Figure 3.** Simulated protein abundances expressed by T7 bacteriophage genome at lysis with sfGFP inserted before gene 10A. Error bars represent 5th and 95th quantiles for each gene. Note: Class 1 genes (shown in pink) are present in this simulation but may be difficult to see due to their low expression levels.

### Moving the Holin Gene Upstream to Reduce Lysis Time

One of the primary goals of our project was to reduce the lysis time of T7 bacteriophage in order to make it more useful as a reporter for contaminated water sources. We decided to test how moving gene 17.5, which codes for holin, a protein that is responsible for determining how fast the cell lyses during an infection, affects the time point at which our simulated cells lysed. We developed a calculator that determined lysis time from our simulation data by finding the time point where holin abundance reached the amount when lysis occurs in the wild type genome (12341 proteins). We found that moving the holin gene before gene 10A(as the point of highest expression) reduced lysis time by 40% compared to wild type, resulting in the greatest decrease out of all the other lysis mutants tested. One of the consequences of reducing the lysis time so drastically was that the abundances of all protein products at lysis was about a fourth of what they were for the wild-type. This is also reflected in a 62% decrease of burst size. The consequences of decreasing lysis time is a fact that we had to take into account when designing our final genome.

![](https://static.igem.org/mediawiki/2020/d/d7/T--Austin_UTexas--holin_before_10A_plot.png)

**Figure 3.** Simulated protein abundances expressed by T7 bacteriophage genome at lysis with the holin gene moved before gene 10A. Error bars represent 5th and 95th quantiles for each gene. Note: Class 1 genes (shown in pink) are present in this simulation but may be difficult to see due to their low expression levels.

![](https://static.igem.org/mediawiki/2020/f/f5/T--Austin_UTexas--WT_lysis_histo.png)

**Figure 4.** Histogram of lysis times calculated from the wild-type T7 genome. The number of cells that "did not lyse" represents the number of simulations where holin abundance did not reach lysis point before the simulation ended.

![](https://static.igem.org/mediawiki/2020/8/8e/T--Austin_UTexas--holin_before_10-DLT.png)

**Figure 5.** Histogram of lysis times calculated from the T7 genome with the holin gene moved before gene 10A. The number of cells that "did not lyse" represents the number of simulations where holin abundance did not reach lysis point before the simulation ended.

### Testing Final Genome Design

After testing multiple constructs, we determined that the optimal design for our PhastPhage genome consisted of inserting GFP before gene 10A and moving the holin gene between the two, such that the mutated region consists of GFP -> Holin -> 10A. We found that having the holin gene before GFP in this sequence only marginally decreased lysis time while greatly reducing the abundance of GFP at lysis. Likewise, putting 10A before both genes reduced their respective effects. This design yielded optimal results for reducing lysis time and increasing GFP expression, making it the prime candidate for building and testing in lab during phase 2 of our project. We also tested the this design with the nonessential genes 1.4, 1.5, 1.6, 4.3, 4.5, 4.7 all deleted, and found that there was not significant difference in GFP and lysis time compared to our final genome.

![](https://static.igem.org/mediawiki/2020/e/e5/T--Austin_UTexas--gfp-holin-10_plot.png)

**Figure 6.** Simulated protein abundances expressed by T7 bacteriophage genome at lysis with sfGFP inserted before gene 10A and holin gene moved before 10A and after GFP. Error bars represent 5th and 95th quantiles for each gene. Note: Class 1 genes (shown in pink) are present in this simulation but may be difficult to see due to their low expression levels.