Translational genomics in sepsis
We are trying to understand the mechanisms that make people desperately sick in sepsis, so that we can find new treatments. This is our approach:
- there is biological variation in the host response to injury
- some of this variation is genetic
- we can use this genetic variation to find new treatments
We believe that a functional genomics approach can lead us to biological processes that might be amenable to treatment. Much of our work focuses on influenza as a model for the host response to injury.
We develop and apply computational tools, and use in vitro and in vivo models to generate and test hypotheses. We are based in the Roslin Institute, University of Edinburgh and Intensive Care Unit, Royal Infirmary Edinburgh.
Read a summary of our approach in this perspective article:Baillie JK. Translational genomics: targeting the host immune response to fight infection. Science (New York, N.Y.): 2014;344: 807-8. doi:10.1126/science.1255074
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.
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).
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.
Bretherick AD , Canela-Xandri O, Joshi PK, Clark DW, Rawlik K, Boutin TS, Zeng Y, Amador C, Navarro P, Rudan I, ..., Ponting*, Chris P., Baillie*, J Kenneth, Haley*, Chris. Proteome-by-Phenome Mendelian Randomisation Detects 38 Proteins with Causal Roles in Human Diseases and Traits bioRxiv: 2019; 631747. doi:10/gf2zwm
Clohisey S , Parkinson N, Wang B, Bertin N, Wise H, Tomoiu A, Consortium F, Summers KM, Carninci P, Forrest AA, ..., Baillie JK. Comprehensive Characterisation of Molecular Host-Pathogen Interactions in Influenza A Virus-Infected Human Macrophages bioRxiv: 2019; 670919. doi:10/gf5dvf
Neyton L , Zheng X, Skouras C, Wilson AB, Gutmann MU, Yau C, Uings IJ, Rao FV, Nicolas A, Marshall C, ..., Baillie*, J Kenneth, Mole*, Damian James. Multiomic Definition of Generalizable Endotypes in Human Acute Pancreatitis. bioRxiv: 2019; 539569. doi:10/gf2zwn
Baillie JK , Bretherick A, Haley CS, Clohisey S, Gray A, Neyton LPA, Barrett J, Stahl EA, Tenesa A, Andersson R, Brown JB, ..., Hume DA. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease. PLoS Computational Biology: 2018; 14: e1005934. PMC5849332
Roach RC , Hackett PH, Oelz O, Bärtsch P, Luks AM, MacInnis MJ, Baillie JK. The 2018 Lake Louise Acute Mountain Sickness Score. High Altitude Medicine and Biology: 2018; 19: 4-6. PMC6191821
Baillie JK , Arner E, Daub C, De Hoon M, Itoh M, Kawaji H, Lassmann T, Carninci P, Forrest ARR, Hayashizaki Y, Faulkner GJ, ..., Hume DA. Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease. PLoS Genetics: 2017; 13: e1006641. PMC5358891
Arabi YM , Balkhy HH, Hayden FG, Bouchama A, Luke T, Baillie JK, Al-Omari A, Hajeer AH, Senga M, Denison MR, Nguyen-Van-Tam JS, ..., Fowler RA. Middle East Respiratory Syndrome. The New England Journal of Medicine: 2017; 376: 584-594. PMC5362064
Grabert K , Michoel T, Karavolos MH, Clohisey S, Baillie JK, Stevens MP, Freeman TC, Summers KM, McColl BW. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nature Neuroscience: 2016; 19: 504-16. PMC4768346
Arner E , Daub CO, Vitting-Seerup K, Andersson R, Lilje B, Drabløs F, Lennartsson A, Rönnerblad M, Hrydziuszko O, Vitezic M, Freeman TC, Alhendi AMN, Arner P, Axton R, Baillie JK, ..., Hayashizaki Y. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science (New York, N.Y.): 2015; 347: 1010-4. PMC4681433
Hall DP , MacCormick IJC, Phythian-Adams AT, Rzechorzek NM, Hope-Jones D, Cosens S, Jackson S, Bates MGD, Collier DJ, Hume DA, ..., Baillie JK. Network analysis reveals distinct clinical syndromes underlying acute mountain sickness. PloS One: 2014; 9: e81229. PMC3898916
Forrest ARR*, Kawaji H*, Rehli M*, Baillie JK* , de Hoon MJL, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M, Itoh M, Andersson R, Mungall CJ, Meehan TF, Schmeier S, ..., Hayashizaki Y. A promoter-level mammalian expression atlas. Nature: 2014; 507: 462-70. PMC4529748
Dunning JW , Merson L, Rohde GGU, Gao Z, Semple MG, Tran D, Gordon A, Olliaro PL, Khoo SH, Bruzzone R, ..., Baillie JK. Open source clinical science for emerging infections. The Lancet. Infectious Diseases: 2014; 14: 8-9. doi:10.1016/S1473-3099(13)70327-X
Baillie JK . Translational genomics. Targeting the host immune response to fight infection. Science (New York, N.Y.): 2014; 344: 807-8. doi:10.1126/science.1255074
Andersson R , Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, Chen Y, Zhao X, Schmidl C, Suzuki T, ..., Baillie JK, Ishizu Y, Shimizu Y, Furuhata E, Maeda S, Negishi Y, Mungall CJ, Meehan TF, Lassmann T, Itoh M, Kawaji H, Kondo N, Kawai J, Lennartsson A, Daub CO, Heutink P, Hume DA, Jensen TH, Suzuki H, Hayashizaki Y, Müller F, Forrest ARR, Carninci P, Rehli M, Sandelin A. An atlas of active enhancers across human cell types and tissues. Nature: 2014; 507: 455-461. PMC5215096
Shukla R , Upton KR, Muñoz-Lopez M, Gerhardt DJ, Fisher ME, Nguyen T, Brennan PM, Baillie JK, Collino A, Ghisletti S, Sinha S, ..., Faulkner GJ. Endogenous retrotransposition activates oncogenic pathways in hepatocellular carcinoma. Cell: 2013; 153: 101-11. PMC3898742
Baillie JK , Digard P. Influenza–time to target the host? The New England Journal of Medicine: 2013; 369: 191-3. doi:10.1056/NEJMcibr1304414
Everitt AR , Clare S, Pertel T, John SP, Wash RS, Smith SE, Chin CR, Feeley EM, Sims JS, Adams DJ, ..., Baillie JK, Walsh TS, Hume DA, Palotie A, Xue Y, Colonna V, Tyler-Smith C, Dunning J, Gordon SB, Smyth RL, Openshaw PJ, Dougan G, Brass AL, Kellam P. IFITM3 restricts the morbidity and mortality associated with influenza. Nature: 2012; 484: 519-23. PMC3648786
Baillie JK , Barnett MW, Upton KR, Gerhardt DJ, Richmond TA, De Sapio F, Brennan PM, Rizzu P, Smith S, Fell M, Talbot RT, ..., Faulkner GJ. Somatic retrotransposition alters the genetic landscape of the human brain. Nature: 2011; 479: 534-7. PMC3224101
We are very grateful to recieve funding from the following sources: Wellcome Trust, BBSRC, Sepsis Research (FEAT), Intensive Care Society, MRC.