WP2 focuses on uncovering how inflammatory skin diseases can progress into multiorgan conditions. By combining clinical data, cutting-edge molecular analyses (multi-omics), and computational modelling, we’re building a deeper understanding of disease mechanisms. This knowledge will help shape more effective, personalised treatments for patients.

By integrating multi-omics research with clinical expertise, WP2 ensures that discoveries translate directly into patient care. Predictive models will be continuously refined and tested against real-world patient outcomes, ensuring that:

• Diagnoses become more precise
• Treatments are tailored to the individual
• Patients experience better long-term health and quality of life

This work builds on a unique patient cohort treated at Aarhus University Hospital, where we have access to extensive clinical data and biological samples.

These include:

• Skin biopsies (processed for detailed spatial and molecular analyses).
• Stool samples (to study the intestinal microbiome).
• Serum samples (for systemic biomarkers).

Planned for collection in new cohorts:

• Peripheral blood mononuclear cells – PBMCs.
• Synovial fluid (from patients with psoriatic arthritis).

Using advanced technologies such as spatial transcriptomics and proteomics on skin biopsies, along with single-cell RNA sequencing, single-cell profiling, metagenomic sequencing, and metabolomics, we can capture molecular markers of disease activity at multiple levels. This data is then combined with clinical and histological information, creating a holistic view of how inflammatory diseases develop and spread across organ systems.

• Build an integrated systems medicine infrastructure that combines clinical, biological, and sociodemographic data
• Use data from WP1’s patient cohort and recall visits to strengthen insights
• Develop computational models that predict how diseases evolve and transition between organ systems
• Study the gut microbiome and its role in systemic inflammatory diseases

All computational modelling runs on the GenomeDK infrastructure, where we map co-expression networks onto known protein-protein interaction pathways. This allows us to:

• Predict relationships between different diseases
• Identify regulatory pathways that could be targeted with therapies (e.g., protein kinase modulators)
• Explore how changes in the intestinal microbiome influence disease progression

For example, stool samples from psoriasis patients who also have conditions such as spondyloarthritis or inflammatory bowel disease will undergo metagenomic and metabolomic analyses. This helps us understand how the microbiome interacts with systemic inflammation and identify new strategies for microbiome-based therapies.

• A systems medicine database combining clinical, molecular, and environmental data
• Predictive models that map disease networks and systemic pathways
• A detailed characterisation of microbiome changes in multiorgan inflammatory diseases
• A validated framework for personalised treatment strategies