Profile

Unlike some of the other scientists in this zone, I don’t work in a ‘lab’ – though I have lots of colleagues who do! I use genetic data from micro-organisms to understand how infectious diseases spread from one person to another. I mainly work with viruses, such as norovirus (the winter vomiting bug) and hepatitis C, but I also work on some bacteria too (including Campylobacter, which causes stomach bugs).

The protective outer layer of norovirus (left), and Campylobacter jejuni under the microscope (right).

When micro-organisms reproduce to be able to transmit and infect another person, they have to copy their genome (the instructions to build a new cell) – and quite often they make mistakes, just like we would do if we had to make a copy of a very long book! I use these mistakes, known as mutations, to track the order in which they must have jumped from person to person. This makes it sound easy, but genetic data is very big and complicated! Sequence data on the computer looks like this, where each row represents a sequence, and the sequence names are in the left column:

Can you find some mutations, where a column contains two or more different letters?
The different letters in the sequences stand for nucleotides in the DNA – adenine (A), cytosine (C), thymine (T) or guanine (G). The number of letters, known as bases, in the whole sequence depends on the organism. The norovirus genome (part of which is shown in the picture above) is 7,500 bases long, the Campylobacter genome is around 1,500,000 bases long, and we humans have a whopping 6 billion bases in most of our cells! That’s a lot of data that we can make use of – feel free to ask me about it!

I start the day by catching up on the latest science news over a cup of coffee. These might be articles published in academic journals, or from more informal sources, such as New Scientist (a science magazine), Daily Science, or recommendations from Twitter. Though I work in infectious disease, I like to keep up with what else is going on elsewhere. One of the cool things with science is how multi-disciplinary it is – methods and results from something that appears unrelated can also inspire my work. I work with people from over the UK, so I’ll also spend a bit of time emailing, Skyping or phoning them so everyone stays up to date with what’s going on.

My desk, all set up for a morning of reading.
The rest of the day varies according to what I’m working on, and where I am in a project. Near the start of a project, I’ll spend a lot of time just getting the feel for my data – I’ve only recently started working on HIV, so I’ve done a lot of background reading on what we already know about the virus and how it transmits, so I can make sure I model it as close to real life as possible (and not repeat what others have done unnecessarily!). I then write computer programs to help analyse my data – it’d take me forever to do it all by hand! I look at my data in many ways, but often it involves comparing the number of mutations between sequences to draw trees like the one for norovirus strains below (think of it like a family tree, where the shorter the line joining two names, the more related they are). I also run simulations, to see how data would look if it had been obtained from certain situations. This allows me to compare my models with real life, and test how well I’m doing. It’s often quite a challenge to summarise lots of sequence data, so near to the end of a project I spend a lot of time thinking about how best I can show my results clearly, and then writing everything up to publish so that other scientists can make use of my work.

The major strains of norovirus (named as location and year first discovered) – can you see how those closely related are not necessarily closest in age?

A lot of my data comes from hospitals, so I also regularly present my work back to the doctors and nurses, so that they are kept up to date and can use the information to help their patients. For example, if lots of patients had samples sequenced for norovirus, I could say whether they were related or not. If they’re not related, then we know it’s just coincidence, and just to be on the look out for further cases. However, if they are related, we might be able to identify how (shared toilet? in the same ward?) and then put measures in place to help prevent further spread (cleaning? close the ward?).