Research themes

In 1999 I started planning to conduct high altitude research in order to better understand the physiology of responses to hypoxia, both in travellers to altitude and in critically ill patients at sea level. Together with several of my university classmates, most notably Roger Thompson, I founded a charity Apex (altitude physiology expeditions), which has organised student-led research expeditions to high altitude every few years ever since.

My first grant as a chief investigator was for the GenISIS (Genetics of Influenza Susceptibility in Scotland) study.

I led the host genetics work for the Wellcome Trust MOSAIC grant (CI: Peter Openshaw), which funded us to extend the GenISIS study by including influenza patients in London and Liverpool.

PLoS ONE March 2012

We completed a systematic review of host susceptibility genes in influenza.

Nature March 2012

We discovered the first human gene associated with susceptibility to severe influenza

New England Journal of Medicine January 2013

We propose host-targeted therapy in influenza

Development of MAIC was funded in 2013 by a Wellcome-Beit Prize Intermediate Clinical Fellowship (103258/Z/13/Z to K Baillie). This diagram, from the grant application, shows the design for the algorithm. I wrote the code for version 1 of the MAIC package during the first year of the fellowship.

PLoS ONE December 2013

We use a clustering methodology developed for use in transcriptomics, to reveal striking stratification within acute mountain sickness. Our report in arxiv (before biorxiv and medrxiv existed!) showed for the first time that acute mountain sickness is two distinct syndromes that had previously been conflated. This study used data from the first three Apex high altitude research expeditions in 2001, 2003, and 2011.

The Lancet Infectious Diseases January 2014

I lead a global consensus process on research response to outbreaks, and write the ISARIC/WHO Clinical Characterisation Protocol.

Nature March 2014

I demostrate the potential of clustering methodologies to identify genes implicated in biological pathways, including viral response. [@fantom5_2014]

Science May 2014

This article describes the fundamental approach that we still take in the lab today: using genetics and functional genomics to find host-directed therapies to promote survival in life-threatening infectious disease.

The GenOMICC study recieved its first funding from a Wellcome-Beit Prize fellowship.

GenOMICC recieved additional funding from the UK Intensive Care Society.

Scientific Reports November 2015

We systematically review genes implicated in respiratory viral disease.

Current Opinion in Systems Biology April 2017

In this review article, we set out the unmet to idenfity treatable traits in critically ill hosts, define relevant terms, and describe systems biology approaches to enable detection of stratification signals in broad clinical syndromes such as sepsis.

High Altitude Medicine \& Biology March 2018

We redefine acute mountain sickness based on our previous clustering work.

PLOS Computational Biology March 2018

For more information see baillielab.net/coexpression

In 2018, Andy Law completely refactored the codebase for MAIC, creating a standards-compliant, modular software tool which we shared on the github MAIC repository.

We had a step-change in the GenOMICC when it was supported to expand rapidly by generous commitment from Sepsis Research (Fiona Elizabeth Agnew Trust).

bioRxiv June 2019

For more information see baillielab.net/capsnatching

Intensive Care Medicine Experimental August 2019

effective shunt

Critical Care September 2019

We review host factors implicated in influenza pathogenesis

Lancet (London, England) January 2020

We didn’t contribute directly to this paper, but it is a fantastic demostration of the effect our decade of work preparing by establishing open source clinical characterisation tool. Our ISARIC CCP was used in this, the first description of novel coronavirus disease in Wuhan.

Nature Communications January 2020

In this paper we reported the design and initial evaluation of the MAIC algorithm, and results of the first genome-wide CRISPR knockout screen of host factors required for replication of influenza virus. The paper was accepted 4 years after I travelled to the Broad Institute of Harvard and MIT to perform a CRISPR screen, together with Bo Li in Nir Hacohen’s lab, because individual validation of the hits was technically challenging. The compeletely new dataset from the CRISPR screen provided a perfect test for the MAIC algorithm. We report a meta-analysis of published work, together with our new screen, in the most comprehensive assessment of host:pathogen interactions with influenza virus to date.

The Lancet February 2020

In this short review, we summarise clinical evidence for steroid use in patients with viral lung disease - on balance, suggesting harm overall - and call for trials of steroids in SARS. At the time we thought that although steroids may help some patients, there were likely to harm others. In fact, this is exactly what we subsequently showed in the RECOVERY trial.

BMJ May 2020

We recruit the first UK patients into the CCP and characterise the new disease.

Annals of Surgery June 2020

We discover that stratification signals are common across different acute illnesses (ARDS and Pancreatitis) For more information see baillielab.net/pancreatitis

New England Journal of Medicine July 2020

We contributed to the setup and delivery of the RECOVERY trial, showing that steroids reduce mortality for a subset of patients with Covid-19.

Nature September 2020

GenOMICC r1: within 5 months of the first case, we find therapeutic targets, including TYK2, and propose baricitinib. [@pairo-castineirageneticmechanismscritical2021]

Scientific Reports December 2020

Throughout the Covid-19 pandemic, the whole lab came together to systematically review the evidence implicating each human protein-coding gene in host:viral interactions and disease pathogenesis.

Whole Genome Sequencing in GenOMICC was funded in partnership with Genomics England.

As a direct consequence of the results of GenOMICC, after discussion with SAGE and UK CTAP, baricitinib is included in RECOVERY trial. We chose to add it in addition to steroid treatment and another monoclonal antibody, tocilizumab, which we had already shown to be effective. This was a big risk but, emboldened by the genetic data, we thought it was worth taking.

Science Immunology March 2021

Click here to view fullscreen network.

The Lancet May 2021

We demonstrate in RECOVERY that tocilizumab reduces mortality from severe Covid-19.

PLOS Computational Biology August 2021

We report an additional two variants associated with critical Covid-19, discovered in collaboration with Broad Institute

Nature July 2022

GenOMICC r2: We discovered multiple new therapeutic targets in Covid-19

Nature July 2022

GenOMICC r3: We discovered multiple new genes associated with critical Covid-19.

The Lancet July 2022

In the first-ever direct translation from host genetics to effective drug therapy in infectious disease, we showed in the RECOVERY trial that baricitinib reduces mortality in severe Covid-19.

Bioinformatics November 2022

Starting in 2018, Bo Wang performed a systematic comparison of MAIC against other methods for aggregating experimental results. He found that MAIC performs better than other approaches in most circumstances encountered in genomics and biology. The key factor for deciding on an algorithm is the heterogeneity in information quality between input lists. Because MAIC quantifies this heterogeneity, we were able to modify MAIC to tell users if another algorithm will perform better for their datasets.

We released MAIC as a python package which you can install using this command: pip install pymaic.

Nature Communications November 2023

We report a new non-invasive measure of lung oxygenation function (S/F94), evaluate it using physiological modelling data, and quantify the improvement in statistical power from using S/F94 in a clinical trial. We created a completely synthetic dataset that retains the properties of our >300,000 patient ISARIC4C cohort, and built a trial outcomes assessment tool.

altitude model

Click here to play with the phsyiological model.

We created the ISARIC4C score

David Farr 2018

MERS review [@arabimiddleeastrespiratory2017]