From: Sepsis: deriving biological meaning and clinical applications from high-dimensional data
Genomics | A number of genetic variants have been strongly linked to sepsis susceptibility and survival. The downstream effects of these variants are beginning to be uncovered |
Epigenomics | The epigenetic regulation of gene transcription is an emerging field of research in sepsis. First results show methylation of a large proportion of genes involved in the immunological response, which relates to clinical features like disease severity |
Transcriptomics | Several diagnostic gene sets have been identified that can discriminate between types of inflammation. Transcriptome-based clustering can delineate pathophysiologically and prognostically relevant endotypes. In the near future, such tools could potentially guide personalized clinical therapy or the design of sepsis trials aimed at specific patient groups |
Proteomics | Plasma proteomics revealed profiles related to clinical outcome, and perturbed energy metabolism pathways in patients with sepsis. Proteomics in specific cell subsets could pinpoint these alterations, possibly yielding targets for cell metabolism modulation |
Lipidomics & metabolomics | Lipid- and metabolite signatures in plasma have been correlated with clinical outcomes in patients with sepsis. Cellular lipidomics and metabolomics could provide insight into structural changes and metabolic reprogramming of cells during infection |
Microbiomics | Sepsis and antimicrobial therapy are associated with a disrupted gut microbiome, which has been linked to secondary infections and hospital readmissions. Next steps include identifying causal mechanisms and developing therapies aimed at restoring the healthy microbiome |
Multi-omics | Simultaneously analyzing multiple molecular layers holds great potential for improving our understanding of sepsis pathophysiology. For inter-study comparability, transparency of the bioinformatic process must be a focal point |