Across the world, clinicians and scientists have volunteered to make their skills and resources available to the COVID-19 response. Here's what we've been doing to contribute to this global effort.
We have been preparing for a global pandemic for many years and worked with the WHO on influenza, MERS and Ebola. Since January 2020 we have focused all of our activities on coronavirus research. The whole lab have got behind this effort with extraordinary enthusiasm, redeploying to do whatever was needed. We're all contributing where we can:
- Leading the GenOMICC study, which has discovered genetic associations with severe Covid-19 that may lead directly to new therapies.
- Leading the ISARIC 4C consortium (together with co-leads Calum Semple and Peter Openshaw), which has provided a comprehensive description of the new disease, defining the importance of obesity, diabetes, and age; produced a widely-used prognostic tool (the 4C score); and provided essential weekly updates to government (SAGE).
- Contributing to the steering committee, design and recruitment to the RECOVERY trial, which found the first effective treatment for Covid-19, dexamethasone and has convincingly excluded two others (hydroxycholoroquine and lopinavir/ritonavir)
- Helping to establish the RECOVERY Respiratory Support trial to test different modes of ventilatory support
- Leading global harmonisation in observational outbreak research (the ISARIC CCP)
- Contributing to WHO Panels (Clinical Management; Research Prioritisation)
- Providing advice on clinical management, trials and supportive care to UK Government (NERVTAG & MHRA)
- Caring for patients with Covid-19 in intensive care
Our existing research has mostly been paused, but some essential elements continue under the leadership of Sara Clohisey, who has essentially stepped into my role running the lab while I'm working flat-out on COVID-19. More information on our research programme in peacetime can be found below on this site.
Translational genomics in critical illness
We are trying to understand the mechanisms that make people desperately sick in sepsis, influenza and emerging infections, so that we can find ways to help them survive and recover. Since there is biological variation in the host response to injury, and some of this variation is genetic, we can use this genetic variation to find new treatments.
Combining signals by meta-analysis
We develop and use computational methods to combine data from different sources. The ultimate aim is to use these signals to lead us to biological processes that might be amenable to treatment. We influenza as a model for the broader host response to critical injury.
We develop and apply computational tools, and use in vitro and in vivo genome editing with CRISPR to generate and test hypotheses.
Read a summary of our approach in this perspective article:
Science (New York, N.Y.): 2014;344: 807-8. doi:10.1126/science.1255074
Combining CRISPR screen with diverse experimental sources: meta-analysis by information content (MAIC)
Using genome-wide CRISPR knockdown in human cells, we found 121 host genes that the flu virus needs to replicate itself. About half of them are new discoveries, and might make good therapeutic targets (https://www.nature.com/articles/s41467-019-13965-x). This is important because we've argued for a while that if we design drugs to target these host genes/proteins, it will be harder for the virus to evolve resistance to our treatments (http://dx.doi.org/10.1126/science.1255074).
We found a lot of new genes, but we also validated many findings from previous groups. So we've taken the concept of meta-analysis - well established in clinical research - into basic science.We developed an algorithm called meta-analysis by information content (MAIC) to integrate our new findings with many previous studies of host genes required by flu.
Code to run the MAIC algorithm can be downloaded from github.com/baillielab/maic.Li B et al. Nature Communications 11:164(2020)
Combining genetic signals with cellular transcriptomics: coexpression of GWAS hits
Our post-GWAS analysis method (network density analysis; NDA) reveals new biological features of numerous disease states and traits. It works by examining a coexpression network of transcription start sites (discovered in FANTOM5). We find that transcripts containing GWAS hits for a given trait tend to fall into more dense groupings in the coexpression network than randomly-selected transcripts.
NDA demonstrates that GWAS hits for a given disease tend to be near promoter/enhancer elements with similar expression profiles, which enables us to find more hits, fine map probable causative SNPs, and implicate cell types in pathogenesis. Surprisingly, for some diseases, the underlying variants fall into distinct functional groups, suggesting either dual mechanisms of disease, or distinct disease endotypes.Baillie JK et al. Shared Activity Patterns Arising at Genetic Susceptibility Loci Reveal Underlying Genomic and Cellular Architecture of Human Disease.PLOS Computational Biology 14, no. 3 (March 1, 2018): e1005934. PMC5849332.
Combining biological hypotheses with genetic signals: gene set hypothesis testing
The lack of specific hypotheses in traditional genome-wide association studies has a disadvantage - it greatly limits the statistical power, ensuring that only the strongest signals can be detected, even with very large numbers of subjects.
Gene set hypothesis testing (GSHT) enables investigators to take a biological hypothesis and test it using GWAS data. For example, in our paper, we tested the hypothesis that failure to downregulate inflammatory signals during macrophage maturation is a key mechanism in the pathogenesis of Crohn's disease. We defined search criteria to find inflammatory genes that are downregulated during macrophage differentiation, and then empirically tested the hypothesis that these genes are likely to be associated with Crohn's disease.Baillie, J. K. et al. PLOS Genetics 13, e1006641 (Mar. 6, 2017).
Testing guilt-by-association: FANTOM5
In the FANTOM5 consortium we showed that, with a sufficiently rich expression dataset, it is possible to infer functional relationships between genes (and other transcribed elements in the genome, such as enhancers and lncRNAs) by grouping them according to similarities in expression. We proved this using known pathways, but the real value of this finding is that it enables us to discover relationships between genomic regions about which almost nothing is already known.Forrest*, Kawaji*, Rehli*, Baillie*, et al. A promoter-level mammalian expression atlas. Nature: 2014;507: 462-70. PMC4529748
Throughout the history of medicine, progress has been made by recognizing patterns of disease, or syndromes. When new technologies are invented, they can reveal observable characteristics that have close relationships to disease trajectories, outcomes, and most importantly of all, response to therapy. Genomics and transcriptomics have these properties, but raise an additional challenge: the scale of the data is such that detecting relevant signals is a substantial computational and mathematical challenge.Scicluna BP , Baillie JK. The Search for Efficacious New Therapies in Sepsis Needs to Embrace Heterogeneity. American Journal of Respiratory and Critical Care Medicine: 2018;doi:10.1164/rccm.201811-2148ED
Russell, C. D. & Baillie, J. K. Treatable traits and therapeutic targets: Goals for systems biology in infectious disease. Current Opinion in Systems Biology 2, 139–145 (Apr. 2017).
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Horby P , Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, Staplin N, Brightling C, Ustianowski A, Elmahi E, ..., Baillie JK, Haynes R, Landray MJ. Dexamethasone in Hospitalized Patients with Covid-19. The New England Journal Of Medicine (2021); 384: 693-704. PMC7383595
Gupta RK , Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, Abubakar I, Lipman M, Quartagno M, Pius R, ..., Baillie JK, Semple MG, Noursadeghi M. Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. The Lancet. Respiratory Medicine (2021); 9: 349-359. PMC7832571
Parkinson N , Rodgers N, Head Fourman M, Wang B, Zechner M, Swets MC, Millar JE, Law A, Russell CD, Baillie JK, Clohisey S. Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19. Scientific Reports (2020); 10: 22303. PMC7749145
Neyton LPA , Zheng X, Skouras C, Doeschl-Wilson A, Gutmann MU, Uings I, Rao FV, Nicolas A, Marshall C, Wilson L, Baillie JK, Mole DJ. Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans. Annals Of Surgery (2020); doi:10.1097/SLA.0000000000003974
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Knight SR , Ho A, Pius R, Buchan I, Carson G, Drake TM, Dunning J, Fairfield CJ, Gamble C, Green CA, ..., Baillie JK, Semple MG, Docherty AB, Harrison EM. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. Bmj (Clinical Research Ed.) (2020); 370: m3339. PMC7116472
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We are very grateful to recieve funding from the following sources: Wellcome Trust, BBSRC, Sepsis Research (FEAT), Intensive Care Society, MRC.