Our Objective

While it is known that post-COVID-19-condition (PCC) is caused by SARS-CoV-2 infection, for most other immune-related noncommunicable diseases (IR-NCDs), no such infectious disease (ID) triggers have been identified (yet). Many IDs exist that could potentially cause IR-NCDs, however these microbes have large genomes encoding many antigens possibly associated with IR-NCDs. Given that it is challenging to measure all these 100,000s of structures in parallel, they represent the dark matter of ID-immune interactions.

Furthermore, exposure to an ID alone typically does not trigger development of an IR-NCD: For example only a subset of patients infected with SARS-CoV-2 develop PCC. So, genetic- and environmental aspects also affect the onset of IR-NCDs, but the exact factors are unknown for most IR-NCDs.

Here, we aim to 1.) identify IDs triggering IR-NCDs by screening for antibody responses against 600,000 ID antigens, and 2.) to disentangle environmental and genetic factors affecting the transition from IDs to IR-NCDs. We will combine novel multi-omics approaches and technologies for personalized genotyping of HLA and adaptive immune receptor genes to deeply profile 6,000 patients of six IR-NCDs (PCC, multiple sclerosis, ME/CFS, rheumatoid arthritis, lupus, IBD) to identify novel biomarkers and disease mechanisms.

© Harry Sokol, APHP/SU

This project will represent the largest and most deeply profiled systematic study of multiple IR-NCDs with layered datasets allowing for comparative analyses yielding insights into shared mechanisms and potential differences in the role of IDs between IR-NCDs. Building on associations identified from population scale and clinical cohorts, we will demonstrate causality in gnotobiotic mouse models, and leverage machine learning (ML) algorithms to predict disease progression and response to treatment. The combination of novel assays with ML represents a broadly applicable pipeline that can be used for studying the interplay of any other IDs/ IR-NCDs.