Computing the Pathways Behind Genes and Proteins

Interview with research group leader Dr. Sarah Teichmann

30.9.2014 | Sarah Teichmann and her team at the European Bioinformatics Institute (EMBL-EBI) in Cambridge use computational techniques to explore regulation of gene transcription networks and interactions between proteins.
AcademiaNet: You started out your career as an undergraduate studying biochemistry at the University of Cambridge, where broadly speaking you've continued to work since. What was it in those early years that made you interested in protein structures and gene expression?

Dr. Sarah Teichmann: Gene regulation is something so fundamental, we're all interested in it at some level. If you define it broadly, gene expression determines the difference between cell types: muscles, neurons and so on. So it's a really fundamental thing to biology.

When I was an undergraduate, I did an internship at the German Cancer Research Centre in Heidelberg and tagged along to a conference at the European Molecular Biology Laboratory EMBL in Heidelberg on regulation of transcription. That made it click for me that protein structure is really important. Then I went on to do my undergrad project in nuclear magnetic resonance (NMR) spectroscopy, so more structural biology. But I was working in the computational side of NMR, doing simulations on different NMR labeling patterns. So that got me into computational biology.
Dr. Sarah Teichmann
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Dr. Sarah Teichmann | is a group leader at the European Bioinformatics Institute in Cambridge: in the midst of the mainframe computers she uses for bulk data processing.

In your current post as research group leader at the European Bioinformatics Institute in Cambridge, you study several different areas of cell biology. One of these is the pathways that proteins follow to assemble together into complexes. Does each protein type have its own pathway, or are there some common pathways that all proteins use?

If you think about how proteins fold, there are different fold shapes, or topologies. What we have shown is that there are likewise different topologies for how proteins interact with each other, and there are different contact patterns for how protein subunits are arranged relative to each other. We have tracked how those topologies have evolved and how they are related to assembly pathways. We had a paper in Nature in 2008 where we showed that the assembly pathways are the same as the evolutionary pathways.

Also last year we had a paper in Cell where we showed that the assembly pathways for protein complexes are conserved in families of heteromers - protein complexes with two or more different polypeptides. So the pathways represent more than just the different surface areas of the two subunits when they contact each other. Most importantly, we showed in a general sense that proteins aren't just bumbling into each other randomly: there are stereotypical and evolutionarily conserved pathways that they follow in order to assemble into complexes in an ordered manner.

As well as the work you're doing at EMBL-EBI, you're also working on a big project at the Sanger Institute using high-throughput methods to look at genomes in single cells, rather than in groups of cells. Why is it important to look at single cells versus large groups of cells?

We started working on T cells using traditional 'bulk' transcriptomics: sequencing RNA using about 50,000 or 100,000 cells. What we realized is that we were getting an average of those 100,000 cells – but what does an average tell you? There could be different subtypes of cells within that sample, each doing something different, and we need to explore that.

If you profile the RNA in individual cells, you're getting a really unbiased picture about all the different subtypes within the sample. And you can get information about stochastic gene expression and noise within the cell population, which is important for gene regulation from a systems biology point of view. So there's a whole new set of insights or information that we can mine from that kind of data that we can't get from the traditional bulk data.

What is it that you're looking at in particular in how T cells work?

When T helper cells are presented with an antigen, they can differentiate to a number of different subtypes: TH1, TH2 and so on. And also into regulator cells: either effector cells, which activate the system, or cells that suppress the immune system. The main thrust of our work is to understand how the regulation in that compartment works.

That's taken us on a journey where we've also discovered things like a steroid-producing T cell. That was a surprise. Although steroids are used as drugs to suppress the immune system, the actual biosynthetic organs are thought to be the adrenal glands and the gonads and so on. No one thought that there were actually immune cells that made steroids themselves de novo.
Single-cell transcriptomics and single-cell genomics, they're what we're super excited about. They're going to be a real game changer I think, right across biology.

You have mentored a lot of PhD and postdoctoral students during your career. Is supporting up-and-coming scientists something that is important to you?

Yes, absolutely. I think that is really one of the big draws of this job to me. Interacting with young people who are talented and smart and nice, ideally all three. And hardworking as well. I've been really fortunate to have a lot of great people go through my lab.

Speaking of people in your lab, one more unusual person that you have had working with you is a composer, who put together a piece for string quartet called 'Hearing Your Genes Evolve'. How did this collaboration come about?

Dr. Deirdre Gribbin was a visiting fellow in the creative arts at Trinity College, where I'm a fellow. We met in the photocopier room initially and started chatting, and we became friends. About six or seven years ago she had a son with Down's syndrome and started getting interested in the human genome and human genetics and biology and so on. She talked to me a lot about that, trying to come to grips with it. Encoding it in music was one of her ways of understanding it and also communicating it and making genomics accessible to other people, which I think is brilliant.

What struck me about this project was how opaque or difficult to understand genomics is for other people. We scientists are in this little world where we work with this stuff every day and it's our bread and butter. A lot of the public, even our friends and family, don't actually have a really good understanding of genomics. And that needs to change in our society because genetics becoming so central to healthcare.

Collaborations with people from other fields like arts and humanities forces us to figure out ways of explaining things that are generally accessible and not get trapped in the lingo of how we scientists speak to each other.

Dear Dr. Teichmann, thank you very much for this interesting interview.

Interview: Helen Jaques   (© AcademiaNet)

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