Simulating phase variation in Neisseria

This is an exercise aimed at helping you visualise phase variation, as well as consider some of the consequences of phase variation in Neisseria meningitidis. Before starting this tutorial, you should read up on N. meningitidis on the pathogens and disease page as well as the phase variation page. This tutorial will also require you to search the literature to derive functional information on the genes in question to help you draw the link between expression and disease.

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

Introduction to N. meningitidis and the tutorial

Neisseria meninigitidis is a Gram-negative coccus and is the major causative agent of bacterial meningitis worldwide. N. meninigitidis colonizes the nasopharynx of many healthy individuals (the actual percentage fluctuates between populations) but only causes meningicoccal disease is rare cases. The ability of N. meningitidis to infect us and cause disease is due to several key virulence determinants, many of which are phase variable.

This tutorial aims to help us understand the dynamics of phase variation and also the role of phase variation in the progression meningococcal 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.
Genes in the simulator-
  • Gene 1- encodes Opa
  • Gene 2- encodes HpuA
  • Gene 3- encodes HmbR
*Note- you should search the literature for the functions of the genes below to make the link between their expression and disease.

There must be at least 30 cells with the 111 genotype to causes meningitis

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 on the ‘Very High’ setting a few times, making a note of what the final population looked like each time.


• 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 we’ll look at how different mutation rates effect the final population.

Run the simulator at a few different mutation rates and jot down how the final population differs.


• Which genotypes are capable of iron acquisition and how does varying the mutation rate affect the proportion of cells capable of this?
• How does varying the mutation rate affect the diversity of the final population?
• What course of mutation would most likely lead to disease (assuming each protein is highly immunogenic)

Comments on exercise 2 (opens in new window)

Exercise 3

Here we’ll look at how different patterns of mutation effect the final population.

Run the simulator a few times and have a look at what happens to cells that mutate early, and later on.


• 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 with the disease causing genotypes. Which cells do they most often come from? is this always the case ?                            • Run the model at 'very high' five times. How many times were the populations able to cause disease?

Comments on exercise 3 (opens in new window)

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?
• 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 Neisseria meningitidis as phase variation of its surface antigens can help the bacteria to evade the immune response.

 

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