Translational genomics in critical illness
We use computational biology and genomics to understand the mechanisms that make people desperately sick in intensive care, so that we can find ways to help them survive and recover. Our focus is sepsis, viral lung infections, and the impact of a shortage of oxygen. It’s quite old now, but the broad approach we take is summarised in this perspective article: Baillie JK, Science, 2014.
Work with us
We’ve been very lucky to make several contributions that have already improved medical practice. That is primarily because we’ve had some extremely capable and motivated people in the lab over the years. Many of them have moved on to greater things so, of course, we always need to find more. This website is aimed at people who might want to join us. If you’re excited about any of our work, even if we don’t have a post open at the moment, we want to hear from you.
You can get an idea of the kind of work we’ve done, and where it’s going, from the project summaries below.
Genome-wide unbiased annotation of regulatory elements Nature, 2014
Systems biology
Computational biology is the main focus of the lab. Put simply, we’re trying to explain the molecular mechanisms underlying diseases in patients. We run a large-scale programme to discovery molecular consequences of genetic variation (molQTL). In order to test our predictions, we have a longstanding programme using genome editing in human and porcine cells and tissues to validate potential future drug targets.
Host Genomics
Host genetics provides the foundation for causal inference in our work. Since 2015 we have run the GenOMICC study to discover new human genetic associations with susceptibility to, and outcome from, critical illness. In 2020, only 5 months after the first Covid-19 patient recruited, we reported a functional genomic analysis in the GenOMICC that suggested a specific drug, baricitinib, would be an effective treatment for critical Covid-19. We went on to show that the treatment was effective in a clinical trial - to our knowledge, the first time this genetics-to-drug-treatment journey has been completed in any infectious disease or critical illness.
Interpretation of molecular mechanisms explaining genetic associations in Covid-19. Nature, 2023
Chord diagram showing weighted contributions of each data source about genes implicated in Covid-19 (Nature 2020)
MAIC (Meta-Analysis by Information Content)
We have developed a new computational approach to do something that is intuitive to biologists, but difficult for computers: to integrate data from diverse sources, weight it according to quality and relevance, and combine it. Our meta-analysis by infomation content (MAIC) algorithm does this, and opens up a range of opportunities across the field of genomics and biology.
You can learn more about the algorithm, and how to use it, at our MAIC project page
Hypoxia
Our theme of hypoxia research follows on from my early career work in high altitude medicine. In 2000 I set up a charity, Apex (altitude physiology expeditions), and organised a series of research expeditions. You can read about the past expeditions, and future ones, at the Apex website. Today we focus on physiological modelling and functional genomics.
Our mathematical models of gas exchange are used for teaching all over the world, and were the foundation for our development of the S/F94 clincial endpoint.
Helium-dilution measurement of lung volumes at Chacaltaya laboratory, Bolivia.
Large scale projects
We run several large-scale studies, which you can read about more at these sites: