Research

As inflammation lies at the heart of a wide variety of common diseases, a unifying approach is required to produce mechanistic knowledge of inflammation per se - to subsequently be able to properly deal with dynamic and longitudinal aspects of inflammation in diseases.

The research focus of X-HiDE is to refine and combine mechanistic models based on the concept of inflammatory phenotypes, with the goal to make the phenotypes applicable to clinical data and to enhance the understanding of inflammation both across and within different diseases.

X-HiDE’s research stands on three legs: Molecular and cellular focus areas, Model diseases, and Systems Biology modelling.

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Molecular and cellular focus areas

  • Interleukin-6 (IL-6) signalling: IL-6 is a central inflammation signalling molecule, a cytokine, produced by a variety of cells, both immune and non-immune cells. IL-6 is the major regulator of acute phase proteins and is rapidly increased during inflammatory conditions but is also a potent regulator of anti-inflammatory and homeostatic functions. Understanding this dual role is key to understanding regulation of inflammation across diseases.
  • Inflammasome activation: Inflammasomes are multiprotein complexes that are central to innate immune responses with production of key pro-inflammatory cytokines that drive inflammation. Dysregulation of the inflammasome mechanisms, often generating a too powerful inflammatory response, is linked to several diseases. X-HiDE studies inflammasome regulation across (model) diseases, facilitating the translation of knowledge between diseases regarding inflammasome regulation and disease-linked contributions of such mechanisms.
  • Monocyte/Macrophage responses: Monocytes/macrophages are immune cells involved in pathogen recognition, inflammation mediation, and tissue repair. X-HiDE will focus on how these cells contribute to inflammatory processes in different diseases and how their behaviour changes in response to various inflammatory stimuli. In particular, two distinct monocyte response-phenotypes; hyper-inflammatory and immunosuppressed, are key to understanding inflammatory diseases.

Model diseases

  • Sepsis: Sepsis is a dysregulated host immune response following an infection and is associated with both acute inflammation and immunosuppression. Sepsis patients are highly heterogeneous, underscoring the idea that identifying distinct phenotypes would also enable us to better identify sepsis, predict outcome, and response to treatment i.e. enabling precision medicine in sepsis.
  • Systemic Lupus Erythematosus (SLE): SLE is an autoimmune disease primarily affecting women, characterized by unpredictable flares that cause irreversible organ damage and significantly impair quality of life. SLE is associated with both acute inflammation and low-grade long-term inflammation due to its complex and variable disease course.
  • Cardiovascular Disease (CVD): CVD is the most common cause of death globally. Most CVD is caused by atherosclerosis, which is a lipid-driven inflammatory disease affecting large and medium-sized arteries. CVD is associated with chronic low-grade inflammation that can lead to acute inflammatory events, such as myocardial infarction or stroke.
  • Particle-induced Lung Disease (LD): Particle exposure can lead to lung disease, including Pneumoconiosis, COPD, and asthma, but also contribute to CVD, and is one of the leading risk factors of disease and mortality worldwide, with millions of deaths due to exposure yearly. LD is associated with chronic local inflammation, which can contribute to systemic low-grade inflammation affecting the entire body.

Systems Biology modelling

  • Unified model on inflammation: The goal of X-HiDE is a model that can explain inflammatory phenotypes across diseases and patients. This requires both sufficient detail – to account for mechanisms and how they differ – and scope – to account for the context that determines the effect of signals, as e.g. IL-6.
  • Knowledge models: Knowledge models are the most recent incarnation of hypothesis driven. As hypotheses become more complex and/or quantitative, it is imperative to leverage machine reasoning. By formalising mechanistic knowledge into computational models, we enable this augmented reasoning.
  • Hybrid models: The sheers scope of the processes controlling inflammation – combined with sparsity in data and knowledge in (parts of) those processes – makes it very challenging to build a comprehensively detailed knowledge model of those processes. To address this, X-HiDE will develop and deploy hybrid models, using different resolutions as well as both knowledge (white box) and statistical (black box) models.
  • Digital twins: Digital twins are computer representations of real objects. If we achieve digital twins of cells or patients, then those can be used to predict the effects of e.g. different treatments.
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Ongoing projects in X-HiDE

Seed Money Projects

X-HiDEs Pre-clinical and Clinical collaborative inflammation project seeds


X-HiDEs Clinical inflammation project seeds

  • Ioannis Parodis “Precision medicine and tertiary prevention in Systemic Lupus Erythematosus (PREMISE)”
  • Kaya Tuerxun “M-MDSCs response profile in sepsis patients and their relationship with known features of immunosuppressive monocytes in sepsis”

X-HiDEs Experimental and mathematical model project seeds

  • Aidan McGlinchey “Set up of a 3D organoid lab and subsequent studies of human inflammaging” 
  • Alexander Hedbrant “Primary monocyte derived macrophages as a model to study chronic low-grade inflammation”
  • Liza Ljungberg “Interleukin-6 classic trans-signaling in human monocytes – molecular mechanisms and functional consequences”
  • Mikael Ivarsson “Bear winter serum and effects on inflammatory/innate pathways in fibroblasts”
  • Niloofar Nickaeen “b-glucan stimulation data for modeling trained immunity in human monocytes”

X-HiDEs Academic collaboration partner seeds

  • InflaMed “miniWAEVE - define the preliminary links between indoor particle exposure and its impact on human health”
  • Rostock University Systems Biology and Bioinformatics – To be established
  • CEMIR – To be established