The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease.


Marcio Luis Acencio, Marek Ostaszewski, Alexander Mazein, Philip Rosenstiel, Konrad Aden, Neha Mishra, Vibeke Andersen, Prodromos Sidiropoulos, Aggelos Banos, Anastasia Filia, Souad Rahmouni, Axel Finckh, Wei Gu, Reinhard Schneider, Venkata Satagopam

Year of publication



Front Immunol







Impact factor



Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (, a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.