Dr Alicia Oshlack, bioinformatician

Dr Alicia Oshlack

Bioinformatician

Alicia Oshlack was born in Roleystone, Perth in 1975. Oshlack graduated dux from Warrnambool College in 1993. She then went on to complete a Bachelor of Science (Hons) (1994-98) from the University of Melbourne, majoring in physics. Oshlack remained at the University of Melbourne for her PhD studies, which she completed on the topic of the central structure of radio quasars (1999-2003). Dr Oshlack continued in the Astrophysics Group at the University of Melbourne until later that year when she moved from using mathematics to look at the heavens to using mathematics to look at genetics. She made this transition at the Walter and Eliza Hall Institute where she worked as a research officer (2003-07) and then senior research officer (2007-11) in the Bioinformatics Division. Dr Oshlack has since moved to the Murdoch Childrens Research Institute in Melbourne where she is the head of the bioinformatics research group.

Teachers' notes to accompany this transcript.
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Interviewed by Dr Cecily Oakley in 2011.

Contents


Introduction

Hello. My name is Cecily Oakley and I am here at the Australian Academy of Science to talk to Dr Alicia Oshlack about her life in science. Welcome, Alicia, and congratulations on your Ruth Stephens Gani medal.

Roleystone to Warrnambool

Where and when you were born?

I was born in 1975 in a suburb of Perth called Roleystone, which I left when I was 10 days old. So I don’t have much recollection of that. I travelled around quite a lot as a child.

Your parents were involved in a travelling circus?

Pretty much. They didn’t have a firm base when I was a child until I was about eight years old. We moved to Warrnambool on the coast of Victoria. I spent all my schooling in Warrnambool, until I went to university.

When did you first get interested in science?

I was always sort of interested in science, but I never thought that I would be a scientist.

What made you choose a university degree in science?

Science was always my forte. So at school I took lots of science, mathematical subjects in particular. I left school not knowing what I wanted to do as a career. I thought, ‘I’ll do a general science course and then maybe that will give me time to decide what it is I want to pursue later on.’

You got dux of your high school. Did that come easily or was that something that you had to work hard for?

Obviously you to have put in work. It was a surprise to me that I came out with the top score in my school. But it wasn’t the major award that the school gave out. They had an award called an all-rounder award, which in my school was the most prestigious one. I just got the academic achievement one. I knew I would do well, but I was surprised that I won the academic achievement award. Yes, I worked hard, but I wouldn’t have considered that I would have been the hardest worker in the school.

Bright black holes

Then you went to university and you studied physics. Were there any teachers or role models that inspired you?

Yes, there were several. I formed some good relationships with some of the lecturers in a variety of subjects. One in particular was my lecturer in astronomy, Professor Rachel Webster. She was very supportive of me throughout my undergraduate degree and I went on to do my honours project and my PhD with her.

For your PhD thesis, you studied quasars. Perhaps you should explain to us: what is a quasar?

The name ‘quasar’ came about as ‘quasi-stellar’. ‘Stellar’ means star and ‘quasi’ means that it looks like a star but it is not really a star. Stars look like very small points of light in the sky. These aren’t really stars. They are the centres of galaxies. But, because they are so distant, they just look like a little point of light. So they are called quasi-stellar objects or quasars. They are basically the brightest things that we can see in the universe. So they are the most distant thing that we can still see from Earth.

What aspects of quasars were you researching?

The thing that makes quasars look so bright is that they have a big black hole in the middle. This has the potential to create a lot of energy by matter falling onto the black hole. Because black holes are very, very, very heavy, they attract lots of matter from the surrounding area. That starts to fall in on the black hole and then it starts to rotate. As it rotates, it gets very, very, very hot and very, very, very bright. That is how they get so bright and that is how we can see them from such large distances.

So they are very, very bright, despite the fact that they have this black hole?

In the middle, yes. It is not the actual black hole itself that is shining. It is the matter that is around the black hole but very close, which is getting a lot of energy from the gravitational pull of that black hole.

What sorts of experiments did you do?

What astronomers really do is use a telescope and look at these objects as much as they can. In astronomy we just don’t look in the optical, like what we can see with our eye. We look in other regions of the electromagnetic spectrum. We look at whether there are x-rays or radio waves coming from these objects, or all different types of radiation. We use all those observations to try to build up a picture of how we think this object works.

Part of my PhD was to say, ‘I think it works like this’. And then build a computer simulation to see if how I think it works matches to what we are observing with a telescope. If the two match, then you can conclude that the computer simulation is correct.

So you were doing the sort of theoretical physics side. Were you also making the observations?

Yes. I did a combination of the two, which is what I quite like. I like dealing with the real data that is coming out of the telescope and really trying to understand that data by using a computational technique.

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Star gazing to gene mining

After you had finished your PhD, you continued in astrophysics in your postdoc, but then you changed to bioinformatics. That is quite a big jump in fields. How did that come about?

Even though astronomy is a very interesting field, I always knew that it didn’t quite capture my interest fully. So, when I was coming to the end of my PhD, I was looking around for other different fields. These things happen in unpredictable ways. I met another PhD student who was working in the field of bioinformatics – which I had never heard of before. She said to me, ‘We have a postdoc position,’ and I said, ‘Could somebody with my skills and experience do that sort of role?’ and she said, ‘Yes. I think you should come in for the interview.’ So I went to the interview and they gave me the job.

I was still quite hesitant, because I didn’t know much about bioinformatics at that stage. They offered me a two­year position, which I accepted, at the very prestigious Walter and Eliza Hall Institute. I thought, ‘I’ll give myself those two years and, after that time, I’ll reassess the situation and see whether this is something that I really want to do.’ It probably took me about a year and, after a year, I thought, ‘This is great. I love this field.’ It was as though this whole new world of biology had opened up to me. I had never come across biology. I never did biology in high school or university, but I found that whole field very inspiring. It definitely sucked me in and that’s where I am today.

Bioinformatics? Microarrays? and other big words

You have been in the field of bioinformatics for a while now. Perhaps you could explain for us what it is.

It is a word that encompasses computational, statistical or mathematical descriptions of biology. It is using biological data and assessing biological data using those quantitative analysis procedures. So I feel that my role is really at the interface between data generation and understanding biology. There are lots of technologies today which can generate vast amounts of biological data. But we really want to understand biology. We want to understand diseases, we want to understand how medicines work and we want to understand evolution, amongst many, many biological questions. But to do that we need to be able to use these massive amounts of data. My role in bioinformatics is to use my skills to connect the two. So I need to be standing at the interface between the data and the biology.

In your postdoc, after you changed from astronomy to biology, you worked in microarrays. Perhaps you could explain for us how microarrays work and what kind of data they generate.

A microarray is a technology which is used to measure gene expression. In every cell in your body you have a copy of your DNA. However, different cells in your body are doing different things. So your skin cell is very different from your eye cell, which is very different from your heart or your liver cells. Your different cells have got lots of different functions, yet they all contain the same DNA. So what is the difference between the cells? It is how these genes in the DNA are expressed. It is how the genes function. What microarrays can do is tell you how much of each gene is functioning at any given moment. A microarray will do this not just one gene at a time but all the genes in a genome simultaneously. There are around 20,000 genes in the human genome, so it is possible to look at 20,000 different genes all at once and see how much they are functioning. How they are 'expressing' – which means what the levels of that gene are, in that cell, at a particular time.

You are looking at the expression of potentially thousands of different genes in a cell at any one time. How do you then make sense of all of that information?

You have to design an experiment very carefully in order to look at a question that you are really interested in. As an example, you might say, ‘Are genes in cancer cells functioning in the same way as genes in a normal cell?’ Say you are looking at liver cancer – you want to know whether a liver cell is functioning in the same way as a liver cancer cell. So we look at all the genes in a normal liver cell and we compare them to all the genes in a liver cancer cell. We use lots of statistical techniques that we have developed to look at the expression level changes between the things that we are interested in. Basically you have to design an experiment with a specific question in mind to be able to then analyse all this data in a meaningful way.

Man vs Ape

You were awarded the 2011 Ruth Stephens Gani Medal from the Australian Academy of Science for your work in human genetics. Specifically for looking at human evolution, which you did by comparing us to apes. Could you talk to us a little about what you found were the differences between humans and chimpanzees?

Again we were looking at gene expression. We have done a few different experiments, but the initial one was looking in the livers of four primate species. These were humans and then our closest evolutionary relative, which is a chimpanzee. We also used orang-utans and rhesus macaque, which is a smaller monkey. What we wanted to do was to compare the expression level of the genes, but we didn’t want to have the differences in the genome confusing our results. So for the first time we were able to look at differences in gene expression independently of differences in the genome.

To do this, with some collaborators at the University of Chicago, we built a custom microarray and we did a very large experiment to look at this specific question. We generated 80 different microarrays for this first experiment. We looked at lots of individual livers from humans and from the other three species. We were able to identify specific genes which were evolving under natural selection. We could then identify the genes and look at which genes were specifically changed in humans. That is which genes are relevant specifically to humans compared to our very close evolutionary relatives.

Why did you choose the liver?

Liver is a very homogeneous tissue. All the liver cells in a liver look pretty much exactly the same, so you don’t get much contamination from other tissues in the liver. That was probably the main reason. Although, in our next experiment we looked at other tissues, we looked at heart and kidney as well. We have also looked at blood because that is one of the easiest things to access from other primates. It is very difficult to get tissues from primates – and from people. You can’t just go and take out a liver. So you have to wait a long time. You have set up a collaboration with the zoo, so that if an orang-utan dies of other unrelated causes, they can send you some of the tissues. It takes a very long time to build up these tissue banks to be able to do these kinds of experiments.

What differences did you find in the expression between humans and chimpanzees? Did you find out what makes humans so different and special?

One thing that we found was that many of the genes that have been under selective pressure or that are specific to humans were involved in metabolic pathways. What this means is that these genes are involved in making energy from our food. This makes sense because we are looking at livers. This is consistent with the idea that many of our human adaptations come from changes that have occurred in our diets compared to other primate species. Humans consume cooked food and other primates do not. We also consume a lot more meat than other primates. So there have been significant changes in our diet and, therefore, we are finding significant changes in the genes that control how we use this food.

So our diets are driving the evolution of our liver and, I guess, other digestive organs?

Yes, I think so. And that makes sense because these foods are selective pressures. If we start eating different foods, then doesn’t it give us an advantage to be able to digest them? If that is the case, then that is what we really mean by fitness and evolution.

Collaborations to interpret the flood of data

High-throughput sequencing is a bit of a mouthful. What does it mean?

High-throughput sequencing is telling us what the DNA code is, one individual base at a time. We can look at the DNA sequence through high-throughput sequencing. We can also look at gene expression by looking at RNA. RNA is a very similar molecule to DNA. Really these high-throughput sequencing technologies have only come about in the last five years. So it is a very exciting field and it is revolutionising human genetics, in my opinion.

The data that is available now was unimaginable even five years ago. For example, about 10 years ago, they sequenced the human genome, and this was a massive project. Actually two human genomes were done. One was a public effort and one was a private effort. The private effort was much cheaper. It only cost $300 million. Now you can sequence a human genome for less than $5,000. The changes are massive. The public effort of the original human genome took over 10 years to complete. It now takes about a week. So you can see that the amount of data that we can generate with these new technologies is moving at an exponential rate. Our understanding of the genome is also moving on to unprecedented levels.

You mentioned earlier one of your collaborators. Do you still collaborate? Do you have other people that you work with in your experiments?

I have many collaborations. In bioinformatics, because we sit at the interface of these two areas, all our work is collaborative. I did at least six different projects with my collaborator at the University of Chicago, Yoav Gilad. We are not currently working on anything but we are still quite good friends. We keep up to date with what each of us is doing. I have some more local collaborations doing very different work – medical research and other projects.

Would you like to comment on those medical applications?

One example of a project with a medical application is work on a specific type of epilepsy. It is very rare but a horrible disease if you get it. In collaboration with some other bioinformaticians and with some epilepsy specialists, we were able to isolate a couple of families that gets this specific type of epilepsy. Using these families, we were able to find the specific gene that caused that epilepsy.

That is very exciting work.

It was.

Changes challenges children

Just before the interview, you told me that you changed jobs recently. Have you changed fields again? Are you moving to chemistry now?

No. I am still in bioinformatics. I have just set up my own lab at a different research institute. Now I am working at the Murdoch Children’s Research Institute, which is within the Royal Children’s Hospital. So I suppose the application of my work is going to be more on child health than it has been previously. But, yes, it is a very exciting change for me.

Where do you see yourself in 10 years time?

I will definitely still be in the same field. I don’t plan on ever leaving bioinformatics! I love it. I plan to be at the Murdoch for at least the next five years. But I am also open to where things take me. I do not think we can plan too far ahead in our lives, because you just never know what opportunities are going to come up.

Do you have any interests outside of science?

Yes, I have lots of interests, but I suppose my main occupation outside of science is my family. I have two young children, ages four and 1½, a boy and a girl. A lot of my time is spent playing with them, entertaining them, taking them around to visit their friends and that sort of thing. They are a lot of fun. I do that with my husband.

How do you manage to balance your work and your family?

I think that is the trickiest thing I have had to deal with so far. That balance between having young children and having my career. They’ve really coincided at the same time. It is a tricky balance but definitely not impossible to do. It takes an effort and it’s tricky at times. But it’s very rewarding on both fronts.

It is good to hear that it is possible.

Yes. I think people need to hear that. I think people need to hear that it is definitely possible because it is a very daunting prospect for a lot of people. A lot of women in science think, ‘How am I ever going to have my career, if I want to have children and I want to have a strong family life?’ but it is definitely possible.

Do you think that is one of the major challenges for women in science?

I do. I think that is the major challenge. There are other challenges, but I think a lot of those are being addressed with very good initiatives that are coming about to support women in doing science. But, personally, I think having children and having a science career is the most difficult thing to address.

Finally, what skills do you think you need in science today?

I think you need a breadth of skills. I think science is becoming more multidisciplinary and you need the ability to collaborate. You need to like working with other people. Science is no longer a career where you can lock yourself away in your room and do your little experiments and come out with some good results. These days you have to be able to work in a team, collaborate with people and be interested in different areas of science. If you can bring that different knowledge together, I think you do well in science.

Thank you so much for agreeing to participate in the Interviews program. It was a delight to speak to you.

Thank you.

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