Periodic Reporting for period 1 - ID-DarkMatter-NCD (Unraveling the dark matter of infectious diseases, environmental and genetic factors tipping the balance towards NCDs)
Berichtszeitraum: 2024-01-01 bis 2025-06-30
Overall, the objectives of ID-DarkMatter-NCD have remained consistent throughout the first 18 months and continue to align with those originally outlined in the proposal. The main focus is too still focus on the interplay of infectious diseases (IDs) in triggering immune-related noncommunicable diseases (IR-NCDs).
While it is known that post-COVID-19-condition (PCC) is caused by SARS-CoV-2 infection, for most other 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.
In ID-DarkMatter-NCD, 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, IBD, rheumatoid arthritis, lupus) 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.
While it is known that post-COVID-19-condition (PCC) is caused by SARS-CoV-2 infection, for most other 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.
In ID-DarkMatter-NCD, 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, IBD, rheumatoid arthritis, lupus) 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.
In the first 18 months we have focused on setting up the infrastructure of analyzing the data, as well as preparing MTAs and exchanging samples (as detailed in the deliverable reports filed so far). Due to limited space, we refer readers to the details in the respective reports and will rather focus in the next section on new data.
Since the project is ongoing and samples have only been selected and shipped towards the end of year 1, we are still in the process of analyzing the data. However, we have already worked together in various ways around data analysis approaches exemplified by the collaborative efforts and publications below (there have been more publications, but we focus on these research articles to highlight different aspects we are covering with ID-DarkMatter-NCD):
“T and B cell responses against Epstein–Barr virus in primary sclerosing cholangitis” published in 2025 in the journal Nature Medicine [1]
Partners: UKSH/CAU, UNIBAS, MUW
Primary sclerosing cholangitis (PSC) is related to IBD, but so far no conclusive data on ID triggers had been reported. Building on data generated previously, the DarkMatter partners joined forces in analyzing complementary TCRseq and PhIP-Seq datasets, implicating EBC as a potential trigger. Colleagues from UNIBAS performed critical experimental validations. This project represents a blueprint on how we share data, and use complementary expertise synergistically.
Summary of results:
Primary sclerosing cholangitis (PSC) is an idiopathic, progressive and incurable liver disease. Here, we aimed for systematic analyses of adaptive immune responses in PSC. By profiling the T cell repertoires of 504 individuals with PSC and 904 healthy controls, we identified 1,008 clonotypes associated with PSC. A substantial fraction of these clonotypes was restricted to known PSC human leukocyte antigen susceptibility alleles and known to target Epstein–Barr virus (EBV) epitopes. We further utilized p ha ge -i mm un oprecipitation sequencing to determine antibody epitope repertoires of 120 individuals with PSC and 202 healthy controls, which showed a higher burden of anti-EBV responses in PSC than controls. EBV-specific monoclonal antibodies isolated from B cells in PSC livers corroborated convergent B and T cell responses against EBV. By analyzing electronic health records of >116 million people, we identified an association between infectious mononucleosis and PSC (odds ratio, 12; 95% confidence interval, 6.3–22.9) suggesting a link between EBV and PSC.
“Associations between HLA-II variation and antibody specificity are predicted by antigen properties” published in 2025 in the journal Genome Medicine [2]
Partners: UKSH/CAU, BRC, MUW
This project relates to the HLA analysis, linking functional PhIP-Seq data with genetic HLA data. We use novel approaches to link these datasets, and generate new insights into which types of antigens to focus on.
“Systemic antibody responses against gut microbiota flagellins implicate shared and divergent immune reactivity in Crohn’s disease and chronic fatigue syndrome” published in 2024 in the journal Microbiome [3]
Partners: UMCG, MUW
This project represents the first systematic comparison of two different diseases covered in ID-DarkMatter-NCD. We show that IBD and ME/CFS show some shared by also different antibody responses against certain gut microbiota. This approach, and the comparisons performed, will also be highly relevant to upcoming data of RA, SLE, PCC, and MS and comparing these diseases to each other.
“T and B cell responses against Epstein–Barr virus in primary sclerosing cholangitis” published in 2025 in the journal Nature Medicine [1]
Partners: UKSH/CAU, UNIBAS, MUW
Primary sclerosing cholangitis (PSC) is related to IBD, but so far no conclusive data on ID triggers had been reported. Building on data generated previously, the DarkMatter partners joined forces in analyzing complementary TCRseq and PhIP-Seq datasets, implicating EBC as a potential trigger. Colleagues from UNIBAS performed critical experimental validations. This project represents a blueprint on how we share data, and use complementary expertise synergistically.
Summary of results:
Primary sclerosing cholangitis (PSC) is an idiopathic, progressive and incurable liver disease. Here, we aimed for systematic analyses of adaptive immune responses in PSC. By profiling the T cell repertoires of 504 individuals with PSC and 904 healthy controls, we identified 1,008 clonotypes associated with PSC. A substantial fraction of these clonotypes was restricted to known PSC human leukocyte antigen susceptibility alleles and known to target Epstein–Barr virus (EBV) epitopes. We further utilized p ha ge -i mm un oprecipitation sequencing to determine antibody epitope repertoires of 120 individuals with PSC and 202 healthy controls, which showed a higher burden of anti-EBV responses in PSC than controls. EBV-specific monoclonal antibodies isolated from B cells in PSC livers corroborated convergent B and T cell responses against EBV. By analyzing electronic health records of >116 million people, we identified an association between infectious mononucleosis and PSC (odds ratio, 12; 95% confidence interval, 6.3–22.9) suggesting a link between EBV and PSC.
“Associations between HLA-II variation and antibody specificity are predicted by antigen properties” published in 2025 in the journal Genome Medicine [2]
Partners: UKSH/CAU, BRC, MUW
This project relates to the HLA analysis, linking functional PhIP-Seq data with genetic HLA data. We use novel approaches to link these datasets, and generate new insights into which types of antigens to focus on.
“Systemic antibody responses against gut microbiota flagellins implicate shared and divergent immune reactivity in Crohn’s disease and chronic fatigue syndrome” published in 2024 in the journal Microbiome [3]
Partners: UMCG, MUW
This project represents the first systematic comparison of two different diseases covered in ID-DarkMatter-NCD. We show that IBD and ME/CFS show some shared by also different antibody responses against certain gut microbiota. This approach, and the comparisons performed, will also be highly relevant to upcoming data of RA, SLE, PCC, and MS and comparing these diseases to each other.