Unravelling_sepsis_heterogeneity_through_trascriptomics__BioAID_Imperial

There is marked heterogeneity in the septic response, which has hindered efforts to understand pathophysiology and develop targeted therapies. Molecular approaches may provide insights into variation in the host response, enabling biomarker development. Recent evidence suggests that transcriptomic sepsis response signatures (SRSs) can define patient subgroups associated with early outcome in sepsis. In this context, using transcriptomic approaches, the co-applicants were the first to identify differential gene signatures, or immune endotypes, correlating with outcome in sepsis, (Davenport E. et al, The Lancet Respiratory medicine, 2016); potentially providing a path to personalised treatment in sepsis. Specifically, this work defined two groups based on transcriptomic profiles: SRS group 1 being more associated with endotoxin tolerance demonstrating an immunosuppressed signature and is associated with higher mortality, whereas, SRS2 is more immune-heightened. However, despite the importance of these findings, previous studies have been underpowered, and limited in depth of data, to resolve these endophenotypes further. In this context, the Bioresource for Adult Infectious Disease (BioAID) offers substantial opportunities in terms of power, depth and breadth of data linked to biological samples of patients presenting acutely with the sepsis syndrome across the UK. BioAID currently holds samples for DNA, RNA, serum and microbial isolates for more than 4,000 individuals (with a planned recruit of 10,000 individuals) and therefore provides a powerful resource for examining the underlying biology and correlates of sepsis syndrome with a view to informing early risk stratification of patients, need for critical care, and treatments. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

• Type: Transcriptome Analysis
• Archiver: European Genome-Phenome Archive (EGA)