Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies

Abstract

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

Figure 1:. Overview of sepsis immunobiology and compartmentalisation of immune responses

Health is characterised by constant (re)circulation of the major cellular and humoral components of the immune system via the bloodstream and lymphatic systems, providing surveillance of danger signals. Danger signals that trigger inflammation include PAMPs from pathogens, DAMPs from stress and tissue damage, and HAMPs from disruptions to cellular homoeostasis. Sensors for these signals include PRRs as well as stress sensors expressed on leukocytes and non-leukocyte cells such as epithelial cells and endothelial cells. When danger signals are sensed, inflammation signals, effector signals, homoeostasis signals, and inflammation pathways are activated. Organ dysfunction in sepsis results from altered tissue homoeostasis with minimal tissue damage. In the context of immune responses, all organs have organ-specific cells (eg, neurons, cardiomyocytes, hepatocytes, specialised epithelial cells in the kidney, alveolar epithelial cells in the lung), tissue-resident immune cells, and newly recruited immune cells that can sense and display effector mechanisms that further alter organ milieu and function. Blue boxes provide an overview of immune responses occurring in sepsis, based on fundamental immunological principles. Orange boxes indicate concepts for which there is either a paucity of data or lack of explicit framing in current sepsis immunobiology models; see main text for discussion of these concepts to inform the proposed definition of dysregulated immune responses. Green boxes represent summary information for sepsis immune states; note that only blood-level assessments of immune responses are commonly performed at the bedside. DAMPs=damage-associated molecular patterns. HAMPs=homoeostasis-altering molecular processes. PAMPs=pathogen-associated molecular patterns. PRRs=pattern-recognition receptors.

Figure 2:. Overlap of subphenotypes reported in sepsis

Current sepsis subtyping is often done as a single-domain (clinical data or a single omics approach) focused analysis, which largely ignores the functional interconnections between different biological domains and is unlikely to capture the entire immunological complexity of sepsis biology. Hypothetical molecular and clinical subtypes are shown, with similar subphenotypes overlapping in the figure. Summary descriptors highlight apparent similarities between molecular subphenotypes and between clinical subphenotypes—for example, there are similarities between MARS-2, SRS-1, and inflammopathic molecular subphenotypes and between MARS-3, SRS-2, and adaptive molecular subphenotypes.^– Subphenotypes are described relative to other subphenotypes within the same group. There are numerous challenges with the current approach to subphenotyping. These include (but are not limited to) differences in input data and analytical approaches for dimensionality reduction, limited use of integrated information from two or more biological data domains, and uncertainty around differential biological mechanisms linked to each subphenotype, probabilistic assignment of subphenotypes, unique targetable mechanisms with functional relevance in a subphenotype, relevant surrogate markers or endpoints, treatment response features at a biological level for each subphenotype, and the reproducibility of subphenotypes in multiple independent datasets. There is also uncertainty about the feasibility of implementation globally, including in resource-limited settings. See original studies^,– for more details on the different groups of subtypes, and elsewhere for additional information on the concepts included in the figure. ARDS=acute respiratory distress syndrome. ICU=intensive care unit. IL-6=interleukin 6. MARS=molecular diagnosis and risk stratification of sepsis. MODS=multiorgan dysfunction syndrome. NF-κB=nuclear factor κB. SOFA=Sequential Organ Failure Assessment. SRS=sepsis response signature.

Figure 3:. Reframing of dysregulated immune responses in sepsis to inform potential treatments

The degree of immunopathology in sepsis is related to the magnitude and duration of abnormalities in resistance, disease tolerance, resilience, resolution, and repair mechanisms. If future studies could identify patients with one or more dominant mechanisms that explain the sepsis state, then these mechanisms could be targeted with specific treatments in clinical trials. The proposed treatments are examples and do not represent an exhaustive list. A patient might require more than one treatment based on their dominant mechanism(s). These dominant mechanisms might vary over time when assessed with longitudinal sampling. The dominant mechanism could also differ between blood and one or more tissue compartments and is likely to vary by sepsis subtype. GM-CSF=granulocyte-macrophage colony-stimulating factor. IL=interleukin. JAK=Janus kinase. PD-1=programmed cell death 1. STAT=signal transducer and activator of transcription.

Figure 4:. Identification of biological variations and classification of sepsis using systems immunology

The generation of multidomain (also termed cross-scale) immunology data from patients with infections, sepsis, and acute illnesses is needed because all omics dimensions contribute towards the observed heterogeneity in sepsis. Genotyping gives information on past population selection and genetic drift. Epigenetic changes account for lifetime exposures before sepsis and intergenerational effects. Variations within biological data occur in genomics, epigenomics, transcriptomics, and proteomics data, and at metabolome levels. The generation of protein-coding mRNAs and metabolites are complex processes. When transcription factors and RNA polymerase can access DNA and initiate transcription, protein-coding pre-mRNAs are produced. Subsequent generation of mature mRNA is essential for nuclear export, stability, and translation. Only a portion of such mRNA transcripts (including splice variants) are translated into proteins. Protein levels and biological activity are affected by SNPs in regions of genes coding for amino acids and post-translational modifications. Information flow between these biological domains and combinatorial variations across domains generate heterogenous sepsis clinical phenotypes. Systems immunology refers to the study of interactions within the immune system, their regulatory functions, and the emergent properties of immune responses. Analysis of multidomain data to enable subtyping can be based on dominant mechanisms or on a combination of current knowledge and reframed biology for the enhancement of existing subtypes or discovery of new subtypes, with or without the data-integrative analytical approaches used in systems immunology studies. Subtyping based on reframed sepsis immunobiology could, in turn, be used to inform the development of novel therapeutics for sepsis. Blue boxes represent sources of heterogeneity in sepsis. Orange boxes indicate either proposed new concepts or future research within the roadmap (panel) that incorporates new concepts. Green boxes show the sequence of studies and methods within the proposed roadmap to enable reframing of sepsis immunobiology for translation. CNV=copy number variation. lncRNA=long non-coding RNA. SNP=single-nucleotide polymorphism. *Based on previously reported subphenotypes described in figure 2.