ID-DarkMatter-NCD | Unraveling the dark matter of infectious diseases, environmental and genetic factors tipping the balance towards NCDs

Summary
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.
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.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101136582
Start date: 01-01-2024
End date: 31-12-2028
Total budget - Public funding: 7 185 870,00 Euro - 7 185 361,00 Euro
Cordis data

Original description

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.
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.

Status

SIGNED

Call topic

HORIZON-HLTH-2023-DISEASE-03-07

Update Date

12-03-2024
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Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.1 Health
HORIZON.2.1.0 Cross-cutting call topics
HORIZON-HLTH-2023-DISEASE-03
HORIZON-HLTH-2023-DISEASE-03-07 Relationship between infections and non-communicable diseases
HORIZON.2.1.3 Non-Communicable and Rare Diseases
HORIZON-HLTH-2023-DISEASE-03
HORIZON-HLTH-2023-DISEASE-03-07 Relationship between infections and non-communicable diseases
HORIZON.2.1.4 Infectious Diseases, including poverty-related and neglected diseases
HORIZON-HLTH-2023-DISEASE-03
HORIZON-HLTH-2023-DISEASE-03-07 Relationship between infections and non-communicable diseases