Periodic Reporting for period 4 - ImmunAID (Immunome project consortium for AutoInflammatory Disorders)
Berichtszeitraum: 2022-11-01 bis 2024-04-30
The rare systemic autoinflammatory diseases (SAID) constitute a set of diseases that can be hereditary, with no specific symptoms (fevers, rash, joint pain ...) and which have phenotypic similarities. These diseases can be divided into two groups: autoinflammatory monogenic diseases for which mutations have been identified, and genetically undiagnosed diseases for which no genetic mutation has been identified and for which the diagnosis is based on the elimination of any other cause of disease.
At present, the causes and mechanisms of these diseases are poorly understood and their diagnosis is difficult and time-consuming, often leading to misdiagnosis. On average, up to 5 inadequate or inefficient treatments are prescribed to each patient with SAID-related conditions before a correct diagnosis is made and initiation of adequate immunomodulating drugs. Through parallel analyses of samples from 1616 patients with monogenic or genetically undiagnosed SAID, parents of patients or negative controls, collected from all over Europe, the ImmunAID consortium project will generate a unique and comprehensive set of data based on multi-omic unbiased analysis (genomics, transcriptomics, proteomics, microbiomics), and functional tests of the innate immune system based on hypotheses exploring the inflammasome, the resolution of inflammation and cytokines. Overall, ImmunAID will unravel the spectrum of SAID and propose a new classification based on omics and pathogenesis, combined with a clinical decision algorithm that can be implemented in daily practice
ImmunAID consortium includes a strong clinical network, together with academic research labs and small SME with proven track records in a broad range of analytical techniques and bioinformatic teams expert in artificial intelligence. Professional management is supporting the project implementation.
At present, the causes and mechanisms of these diseases are poorly understood and their diagnosis is difficult and time-consuming, often leading to misdiagnosis. On average, up to 5 inadequate or inefficient treatments are prescribed to each patient with SAID-related conditions before a correct diagnosis is made and initiation of adequate immunomodulating drugs. Through parallel analyses of samples from 1616 patients with monogenic or genetically undiagnosed SAID, parents of patients or negative controls, collected from all over Europe, the ImmunAID consortium project will generate a unique and comprehensive set of data based on multi-omic unbiased analysis (genomics, transcriptomics, proteomics, microbiomics), and functional tests of the innate immune system based on hypotheses exploring the inflammasome, the resolution of inflammation and cytokines. Overall, ImmunAID will unravel the spectrum of SAID and propose a new classification based on omics and pathogenesis, combined with a clinical decision algorithm that can be implemented in daily practice
ImmunAID consortium includes a strong clinical network, together with academic research labs and small SME with proven track records in a broad range of analytical techniques and bioinformatic teams expert in artificial intelligence. Professional management is supporting the project implementation.
The recruitment of ImmunAID cohort of SAID patients is performed under the work-package (WP) 1. Ethical approvals were obtained in 11 countries, leading to the involvement of 34 centres actively recruiting. 390 SAID patients, 56 trios (one SAID patient and his/her 2 parents) and 73 healthy controls were recruited. Alll samples were transferred to the 14 research labs through the 2 biobanks.
All OMIC analysis (WP2) have been run and the analysis of the data is ongoing. The first hints of interesting biomarker candidates have emerged and cross-analysis with other modalities is underway. Further refinements are being implemented to validate the findings, hopefully leading to robust markers able to classify the SAID diseases, and improve the diagnostics and treatment of SAID patients.
WP3 is dedicated to studying the inflammasomes in order to identify the dysregulatory mechanisms leading to their abnormally intense and prolonged activation in SAID. The work to validate the assay tracking the activation of inflammasome was continued, together with the role of sex hormones. The role of anti-cytokine autoantibodies was also evaluated. Functional studies to test the pathogenicity of candidate genes identified within WP2 were also initiated.
WP4 is dedicated to the exploration of the role played by inflammation resolution mediators in the autoflammatory process. The analysis of patient samples was continued, and data was integrated to enhance understanding of the regulation of inflammation resolution. Different SAIDs were compared and the phases of disease exacerbation or remission (visit A or B) within a specific SAID were looked at. Further work focused on deciphering the role of neutrophils and monocytes at the molecular level in the observed defective Lipid Mediators metabolic pathways.
WP5 encompasses all the functional analysis of the cytokines and their effectors of patients. The analysis of all samples from the cohort was finalised, generating new knowledge on the role of cytokine network as well as other cell population of interest. The studies included soluble factor profiling (cytokine, chemokine, adipokine levels, tryptophan metabolism, signatures of S100-alarmins and pro-/anti-inflammatory mediators), deep-phenotyping of protein structures and modifications (structural and/or post-translational modifications, modifying enzymes) and specific immune cell profiling (NK cells, deep-profiling of PBMC subpopulations) in SAIDs.
A series of data analysis and data integration strategies and algorithms have been developed and used in WP6. Both supervised and unsupervised approaches were applied, based on single data modalities at first (i.e RNAseq-based model, proteomics-based model), and subsequently on multi-omics datasets. We were able to identify signatures specific of SAID as compared with healthy volunteers (pan-SAID signature), as well as signatures and mechanisms underlying specific SAID subgroups, notably Still’s disease, Behcet disease and recurrent pericarditis. We also identified signatures and biomarkers of Fever of Unknown Origin (FUO) as well as classification of existing SAID in novel subgroups.
Up to know, ImmunAID has generated 43 peer-reviewed scientific articles.
All OMIC analysis (WP2) have been run and the analysis of the data is ongoing. The first hints of interesting biomarker candidates have emerged and cross-analysis with other modalities is underway. Further refinements are being implemented to validate the findings, hopefully leading to robust markers able to classify the SAID diseases, and improve the diagnostics and treatment of SAID patients.
WP3 is dedicated to studying the inflammasomes in order to identify the dysregulatory mechanisms leading to their abnormally intense and prolonged activation in SAID. The work to validate the assay tracking the activation of inflammasome was continued, together with the role of sex hormones. The role of anti-cytokine autoantibodies was also evaluated. Functional studies to test the pathogenicity of candidate genes identified within WP2 were also initiated.
WP4 is dedicated to the exploration of the role played by inflammation resolution mediators in the autoflammatory process. The analysis of patient samples was continued, and data was integrated to enhance understanding of the regulation of inflammation resolution. Different SAIDs were compared and the phases of disease exacerbation or remission (visit A or B) within a specific SAID were looked at. Further work focused on deciphering the role of neutrophils and monocytes at the molecular level in the observed defective Lipid Mediators metabolic pathways.
WP5 encompasses all the functional analysis of the cytokines and their effectors of patients. The analysis of all samples from the cohort was finalised, generating new knowledge on the role of cytokine network as well as other cell population of interest. The studies included soluble factor profiling (cytokine, chemokine, adipokine levels, tryptophan metabolism, signatures of S100-alarmins and pro-/anti-inflammatory mediators), deep-phenotyping of protein structures and modifications (structural and/or post-translational modifications, modifying enzymes) and specific immune cell profiling (NK cells, deep-profiling of PBMC subpopulations) in SAIDs.
A series of data analysis and data integration strategies and algorithms have been developed and used in WP6. Both supervised and unsupervised approaches were applied, based on single data modalities at first (i.e RNAseq-based model, proteomics-based model), and subsequently on multi-omics datasets. We were able to identify signatures specific of SAID as compared with healthy volunteers (pan-SAID signature), as well as signatures and mechanisms underlying specific SAID subgroups, notably Still’s disease, Behcet disease and recurrent pericarditis. We also identified signatures and biomarkers of Fever of Unknown Origin (FUO) as well as classification of existing SAID in novel subgroups.
Up to know, ImmunAID has generated 43 peer-reviewed scientific articles.
No prior study has generated such a large and comprehensive set of omics data of diverse nature. ImmunAID major competitive advantage, and extent beyond the state-of-the-art will lie in its ability to generate such a large collection of data, from a large and heterogeneous SAID patient cohorts, with high standards for sample and data management. ImmunAID originality will also stand in the expertise in data analysis, and ability to integrate such complex datasets into biologically and clinically meaningful signatures.
Thanks to the ImmunAID project results, it will be possible to develop an innovative classification rather pathogenic pathway-based than phenotype-based. In parallel, through ImmunAID’s unbiased omics strategies, we may uncover original signatures spanning biological areas not currently associated to disease physiopathology. The combined application of these two approaches should maximize the efficiency of the discovery process, and provide the most comprehensive set of SAID-associated biomarkers, with strong links to at least one of three disease-associated areas: physiopathology, prognosis/severity, and treatment.
Thanks to the ImmunAID project results, it will be possible to develop an innovative classification rather pathogenic pathway-based than phenotype-based. In parallel, through ImmunAID’s unbiased omics strategies, we may uncover original signatures spanning biological areas not currently associated to disease physiopathology. The combined application of these two approaches should maximize the efficiency of the discovery process, and provide the most comprehensive set of SAID-associated biomarkers, with strong links to at least one of three disease-associated areas: physiopathology, prognosis/severity, and treatment.