Tutorial- Simulating Phase Variation in Campylobacter

This is an exercise aimed at helping you visualise phase variation, as well as consider some of the consequences of phase variation in Campylobacter jejuni. Before starting this tutorial, you should read up on C. jejuni on the pathogens and disease page as well as the phase variation page.

Note: you can download the simulator and activity work sheets from the 'all infection resources' page.

Introduction to Campylobacter jejuni

Campylobacter jejuni is a Gram-negative, spiral shaped bacterium. C. jejuni is found colonising the guts of poultry, where it lives commensally and very rarely causes disease. The bacteria's relationship with poultry is particularly worrisome for humans however, as eating uncooked poultry allows C. jejuni into our guts where it can cause a disease called gastroenteritis. In rare cases (>1 in 5000) gastroenteritis can progress to a condition known as Guillain–Barré syndrome (GBS), a flaccid, full body paralysis that is often fatal. Many of the genes involved in C. jejuni' s ability to cause disease undergo phase variation.

The aim of this activity is to understand the nature of phase variation, and how this generation of diversity links to C. jejuni' s ability to cause disease.

Introduction to the simulator

The simulator begins with a single cell that then divides into two. These cells then divide again to produce four cells, and again to produce eight and so on for ten generations.
Each time a cell divides there is a small chance that it will mutate. The simulator models each cell as having three phase variable genes that can either be in an ON or OFF state. Each gene has a small chance of switching state at every division. In order to allow you to visualise the changes in genotype, each genotype is indicated in a different colour (as shown on the right of the simulator).
Each genotype is also indicated as a three digit code. The three digits correspond to the three genes and show a 1 for ON and a 0 for OFF so a code of 000 means all three are OFF, 010, means the second gene is ON and the others are OFF and 110 means the first and second genes are ON but the third is OFF and so on. The panel on the right of the simulator will give you running totals on the number of each genotype present.
To start the simulator you simply need to select one of the four mutation rate options. You can pause the simulator at any time by clicking the pause button in the bottom left; and unpause by clicking it again. The simulator itself will pause to draw you attention to certain events.
After the simulation is complete you can move through the timeline of the simulator by dragging the slider at the bottom back and forth. Clicking the 'restart' button will take you back to the mutation rate selection screen and allow you to start a new simulation.
Functions of genes in the simulator-
  • Gene 1- encodes flaA
  • Gene 2- encodes capA
  • Gene 3- encodes cj0045C
*Note- you should search the literature for the functions of the genes below to make the link between their expression and disease.

1.) The 110 genotype is required for invasion of the host. 2.) There must be at least 30 cells with the 111 genotype to trigger Guilain-Barre syndrome (GBS).

Simulator

 

 

 

Note: many browsers require you to enable Java to use the simulator. Failing this you can download the simulator file and accompanying activity from the 'all infection resources' page.

Exercises

Exercise 1

Run the simulator in the 'high mutation rate' setting. Once it has completed, look at the panel on the right hand side that lists the number of bacteria with each genotype. Make a note of how many have the 110 genotype (capable of host invasion) and 111 genotype (which can potentially trigger GBS) and the number that have the original (000) genotype.
Now click 'restart' and then run the simulator in the 'high mutation rate' setting again and compare the results you get from this run to the results you got in the first run?


• Did you get the same results on both runs?
• Why do you think this is?

Comments on exercise 1 (opens in new window)

Exercise 2

Now you will explore how changing the mutation rate alters the final population. You will need to run the simulator at different mutation rates and keep notes on the differences you observe in order to answer the following questions. Write down your answers before looking at the answers page.


• Why are cells with the 110 genotype capable of host invasion and how does varying the mutation rate affect the proportion of cells capable of host invasion and triggering GBS?
• How does varying the mutation rate affect the diversity of the final population?
•Given what we know about the process of infection and immunity, what sequence of mutations would most likely lead to disease?

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Exercise 3

In this exercise you will need to investigate the pattern of mutations closely and consider how the final result is built up by individual mutations. Again you may need to run the simulator multiple times and you should write down your answers before looking at the answers page.


• Do mutations that occur early in the simulation have more or less effect on the population structure than mutations that occur later?
• Look at the populations that the GBS triggering genotypes emerge from. What do you notice about them? Is this always the case?
• Run the model at 'very high' 5 times. How many times were the populations able to cause disease? What is the significance of this?

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Exercise 4

In this final exercise, you need to think about how realistic the simulator is, and why you would or wouldn't want to use such a tool to investigate the behaviour of phase variation. Again, try to write down your own answers before looking at the answers page.


• How realistic do you think the simulator is?
• How important do you think realism is when using a simulator to understand a system?
• What advantages do you think using a simulation can have over real experiments?

Comments on exercise 4 (opens in new window)

Take home messages from the tutorial

Phase variation is a mechanisms for bacteria to mutate and adapt to rapidly changing environments. This process is entirely random and does not occur in response to the environment. Instead, it maximises the chance that some cells in the population will have a favourable phenotype and can go on to cause disease. This is particularly useful for Campylobacter as phase variation of its surface antigens can help the bacteria to both thrive in the guts of poultry and also evade the human immune system and cause disease.

 

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