Periodic Reporting for period 3 - ImmunAID (Immunome project consortium for AutoInflammatory Disorders)
Periodo di rendicontazione: 2021-05-01 al 2022-10-31
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 opening of 35 recruiting centres, and 345 participants were recruited. Two batches of biological samples (3 since project start) were transferred to the 14 research labs through the 2 biobanks.
OMIC analysis (WP2) were run together with further some fine-tuning of the techniques. The processing of most of the ImmunAID samples from batches 1 to 3 has been completed using different multi-OMIC and biological approaches. Most of the samples (>90%) passed the quality control checks with a high success rate. All partners also performed bioinformatic preprocessing of raw data and initiated the first statistical analyses to evaluate batch effects and correct for biases, presence of missing values, have normalised the datasets and have developed disease classification models. The first hints of interesting biomarker candidates have emerged. More samples are now necessary to validate these findings, and to allow us to assess the robustness of statistics used for molecularly classifying 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. An efficient assay to determine ASC specks by flow cytometry has been developed. In addition, the conditions to study expression and regulation of the inflammasome decoy proteins have been optimized. Functional work is ongoing. A new stimulus was discovered allowing to identify a specific dysfunction of cytokine production and cell death process in SAID patients with specific MEFV mutations. The single cell death assay is validated and ready to be implemented on specific patients/pathways following results from Omics data.
WP4 is dedicated to the exploration of the role played by inflammation resolution mediators in the autoflammatory process. Initial lipidomic analysis of first 200 SAID patient samples were performed. The analysis was further deepened at the gene level, and promising results are emerging.
WP5 encompasses all the functional analysis of the cytokines and their effectors of patients. Progress in the understanding of neutrophil activation and biology in Behcet’s Disease (BD) has been made. 59 different cytokines and chemokines have been measured from 156 serum samples. The role of S100 proteins, free IL-18 and IL-18BP are still under investigation. In addition, the three-dimensional structure of the human IL-18:IL-18BP complex to 1.8 Å resolution using X-ray crystallography has been determined, thereby mapping out in great detail how IL-18BP serves its role as a critical decoy soluble receptor for IL-18. Complementary to multiplex data generated by the consortium, chemokine serum levels in ImmunAID patients have been quantified by ELISA, together with other cytokines and effectors, alone or in complexes; at present most samples of received batches have been analysed. In order to detect a specific immune signature for each enrolled patient and every studied autoinflammatory disease, an unsupervised analysis method (automated flow cytometry acquisition, analysis and unsupervised cluster analysis) has been established, and preliminary data are available. Finally, more than 50 markers related to NK cell activation and biology were analysed from 48 serum specimens of SAID patients.
Data management, analysis and integration are carried out in WP6. The eCRF was modified to reflect the changes made to the clinical protocol. A visualisation tool Gersimi was developed and the first OMICs datasets that were previously normalised by WP2 started to be entered. The first produced OMICs datasets were quality-checked by applying the SOPs for quality controls. In close collaboration with WP1, the quality of the clinical datasets was assessed before making them available for further analyses. The classification of the first complete SAID sub-cohort (Adult-onset Still’s disease) was started based on the transcriptomic data.
Up to know, ImmunAID has generated 31 peer-reviewed scientific articles, and a communication strategy is being implemented with a current focus on supporting patient recruitment activities.
OMIC analysis (WP2) were run together with further some fine-tuning of the techniques. The processing of most of the ImmunAID samples from batches 1 to 3 has been completed using different multi-OMIC and biological approaches. Most of the samples (>90%) passed the quality control checks with a high success rate. All partners also performed bioinformatic preprocessing of raw data and initiated the first statistical analyses to evaluate batch effects and correct for biases, presence of missing values, have normalised the datasets and have developed disease classification models. The first hints of interesting biomarker candidates have emerged. More samples are now necessary to validate these findings, and to allow us to assess the robustness of statistics used for molecularly classifying 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. An efficient assay to determine ASC specks by flow cytometry has been developed. In addition, the conditions to study expression and regulation of the inflammasome decoy proteins have been optimized. Functional work is ongoing. A new stimulus was discovered allowing to identify a specific dysfunction of cytokine production and cell death process in SAID patients with specific MEFV mutations. The single cell death assay is validated and ready to be implemented on specific patients/pathways following results from Omics data.
WP4 is dedicated to the exploration of the role played by inflammation resolution mediators in the autoflammatory process. Initial lipidomic analysis of first 200 SAID patient samples were performed. The analysis was further deepened at the gene level, and promising results are emerging.
WP5 encompasses all the functional analysis of the cytokines and their effectors of patients. Progress in the understanding of neutrophil activation and biology in Behcet’s Disease (BD) has been made. 59 different cytokines and chemokines have been measured from 156 serum samples. The role of S100 proteins, free IL-18 and IL-18BP are still under investigation. In addition, the three-dimensional structure of the human IL-18:IL-18BP complex to 1.8 Å resolution using X-ray crystallography has been determined, thereby mapping out in great detail how IL-18BP serves its role as a critical decoy soluble receptor for IL-18. Complementary to multiplex data generated by the consortium, chemokine serum levels in ImmunAID patients have been quantified by ELISA, together with other cytokines and effectors, alone or in complexes; at present most samples of received batches have been analysed. In order to detect a specific immune signature for each enrolled patient and every studied autoinflammatory disease, an unsupervised analysis method (automated flow cytometry acquisition, analysis and unsupervised cluster analysis) has been established, and preliminary data are available. Finally, more than 50 markers related to NK cell activation and biology were analysed from 48 serum specimens of SAID patients.
Data management, analysis and integration are carried out in WP6. The eCRF was modified to reflect the changes made to the clinical protocol. A visualisation tool Gersimi was developed and the first OMICs datasets that were previously normalised by WP2 started to be entered. The first produced OMICs datasets were quality-checked by applying the SOPs for quality controls. In close collaboration with WP1, the quality of the clinical datasets was assessed before making them available for further analyses. The classification of the first complete SAID sub-cohort (Adult-onset Still’s disease) was started based on the transcriptomic data.
Up to know, ImmunAID has generated 31 peer-reviewed scientific articles, and a communication strategy is being implemented with a current focus on supporting patient recruitment activities.
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.