The main objective of this study is to generate diagnosis and therapeutic-decision tools through the identification of molecular causes of PIDs with autoimmunity/inflammation and the variability in disease outcome at the transcriptional level using a combination of omics signatures (transcriptomics, epigenomics, proteomics, metagenomics, metabolomics and lipidomics).
Primary Immune deficiencies (PIDs) are a group of monogenic diseases related to developmental or functional dysfunction of one or several immune cell types. Individually there are rare entities, but collectively they group several thousands of patients. Approximately 500 000 patients suffer from PIDs worldwide, making their management a true health-care concern. According to European Society for Immunodeficiencies (ESID), the age group in which PIDs is most frequently diagnosed is under 19 years of age (62%)1. PIDs are causing susceptibility to severe and life-threatening infections by common pathogens, but they also predispose to cancer and can initially manifest as autoimmune and inflammatory diseases. Multiple mechanisms underlie the development of autoimmunity/inflammation in PIDs. Moreover, their development can be influenced by the composition of the microbiota, which shapes host metabolic and immune functions and can be modified by many environmental factors. In the last two decades a particular emphasis was given to the elucidation of the genomic mutations causing PIDs. This led to a burst of genetic diagnosis as the numbers of known monogenic causes of PIDs rose from around 200 in 2010 to more than 310 in 2017. These genomic approaches revealed that: 1) a given monogenic defect can lead to very dissimilar clinical presentations, disproving the initial concept that a monogenic defect is associated with specific clinical manifestations ; and 2) the number of cases of autosomal dominant genetic deficiencies has increased, with sometimes a partial clinical penetrance so that some relatives carrying the causal genetic variant remain asymptomatic. Hence, onset and presentation of autoimmune and inflammatory diseases in PIDs is highly unpredictable. PIDs with autoimmunity/inflammation usually require life-long symptomatic treatments including broad immunosuppression or immunotherapies. On the long term, such treatments can have important side effects or poor efficacy and they result in high burden cost. It is therefore crucial to diagnose PIDs as early as possible in order to select the most efficient therapy based not only on clinical features as it is nowadays, but to include the underlying molecular cause of immune dysregulation. The central goal of this project is to explain the very variable outcome of monogenic autoimmune and inflammatory diseases and to define predictive biomarkers in order to stratify patients and to optimize therapeutic choices.
Study Type
OBSERVATIONAL
Enrollment
500
Blood, Urine and Stool samples will be collected from the participants.
hôpital Necker Enfants Malades
Paris, France
RECRUITINGGenerate a diagnosis and therapeutic-decision tools
Identification of molecular causes of PIDs with autoimmunity/inflammation and the variability in disease outcome at the transcriptional level using a combination of omics signatures (transcriptomic, epigenomics, proteomic, metagenomic, metabolomics and lipidomics).
Time frame: 5 years
1- Development of an atlas of molecular interactions leading to autoimmunity and inflammation
Research tool integrating transcriptomic, proteomic, epigenetic, metabolomic and lipidomics data of pediatric affected patients as well as healthy pediatric individuals This atlas will be available widely to the public and private research community.
Time frame: 5 years
2 - To develop an artificial intelligent online application
The decision support tool will be interoperable with any clinical information system and will be clinically validated.
Time frame: 5 years
3- Ancillary study (pilot study)
i. Validation of candidate biomarkers associated to diagnosis and prognosis of Juvenile Idiopathic Arthritis patients ; ii. Definition of a specific clinical outcome and early quantification of the performance of a diagnostic decision tool based on omics signatures (proof of concept)
Time frame: 5 years
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