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Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.

International audience; Immunophenotyping by multi-parametric flow cytometry is the cornerstone technology for enumeration and characterization of immune cell populations in health and disease. Standardized procedures are essential to allow for inter-individual comparisons in the context of population based or clinical studies. Herein we report the approach taken by the Milieu Intérieur Consortium, highlighting the standardized and automated procedures used for immunophenotyping of human whole blood samples. We optimized eight-color antibody panels and procedures for staining and lysis of whole blood samples, and implemented pre-analytic steps with a semi-automated workflow using a robotic system. We report on four panels that were designed to enumerate and phenotype major immune cell populations (PMN, T, B, NK cells, monocytes and DC). This work establishes a foundation for defining reference values in healthy donors. Our approach provides robust protocols for affordable, semi-automated eight-color cytometric immunophenotyping that can be used in population-based studies and clinical trial settings.

The project Milieu Intérieur aims to study the genetic and environmental factors that can have a major impact on occurring immunological variance in healthy human population. This characterization requires the use of standardized immunophenotyping technologies for integrating diverse, complex datasets. With this goal in mind, we used an optimized suite of standardized whole-blood stimulation systems to study the human induced immune response in physiological condition and developed a unique standardized protocol to analyze the ARN signatures upon whole-blood stimulation to test the hypothesis that responses to complex stimuli can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, which captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. This provides new strategies for dimension reduction of large datasets and for deconvolution of innate immune responses, applicable for characterizing novel immunomodulatory molecules.In a second related study, we aimed to identify the environmental and genetic factors driving innate and adaptive immune cell parameters in homeostatic conditions. To do so, we combined semi-automated flow cytometric analysis of blood leukocytes and genome-wide DNA genotyping in the 1,000 healthy donors included in the collection. We show that smoking, age, gender and latent cytomegalovirus infection, are main drivers of human variation (i.e. numbers of Treg and MAIT cells). These results demonstrated that innate cell parameters are strongly controlled by genetic factors, whereas adaptive cells are driven by life-long environmental exposures. In addition, to help on the public data mining, we developed interactive R-Shiny application including healthy donor reference values for both studies.All together, these results indicate that we developed powerful tools for human system biology approaches to support personalized medecine., Le projet Milieu Intérieur a pour but d'identifier les facteurs génétiques et environnementaux ayant un impact sur la variabilité immunitaire naturelle. Cette analyse multiparamétrique requière néanmoins d'utiliser des outils standardisés. Afin d'étudier la réponse immune induite, nous avons utilisé un système optimisé prêt à l'emploi de stimulation du sang et développé un protocole unique de quantification de l'ARN afin d'étudier la signature transcriptionnelle en réponse à des immuno-modulateurs. Testant l'hypothèse que la réponse à des composés complexes peut être définie par la signature ARN de cytokines clefs et utilisant une méthode statistique robuste, nous avons identifié 44 gènes capables d'optimiser la capture de la réponse à des stimulations plus complexes aidant à la réduction dimensionnelle pour la décomposition de réponses innées. Dans une seconde étude, l'analyse semi-automatisée par cytomètrie en flux des cellules du sang a été associée à l'analyse épidémiologique et génotypique pour les 1,000 donneurs inclus dans la cohorte. Nous avons observé que le tabac, l'âge, le genre et l'infection latente par le cytomégalovirus sont les facteurs impactant le plus la variabilité immunitaire. Cette étude a montré que les paramètres des cellules innées sont contrôlés par des facteurs génétiques alors que ceux des cellules adaptatives le sont plutôt par des expositions environnementales tout au long de la vie. Des outils interactifs incluant ces données de référence accompagnent ces études. Ces analyses montrent que nous avons développé des outils performants pour une étude intégrative du modèle humain constituant une approche innovante vers une médecine personnalisée.

The project Milieu Intérieur aims to study the genetic and environmental factors that can have a major impact on occurring immunological variance in healthy human population. This characterization requires the use of standardized immunophenotyping technologies for integrating diverse, complex datasets. With this goal in mind, we used an optimized suite of standardized whole-blood stimulation systems to study the human induced immune response in physiological condition and developed a unique standardized protocol to analyze the ARN signatures upon whole-blood stimulation to test the hypothesis that responses to complex stimuli can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, which captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. This provides new strategies for dimension reduction of large datasets and for deconvolution of innate immune responses, applicable for characterizing novel immunomodulatory molecules.In a second related study, we aimed to identify the environmental and genetic factors driving innate and adaptive immune cell parameters in homeostatic conditions. To do so, we combined semi-automated flow cytometric analysis of blood leukocytes and genome-wide DNA genotyping in the 1,000 healthy donors included in the collection. We show that smoking, age, gender and latent cytomegalovirus infection, are main drivers of human variation (i.e. numbers of Treg and MAIT cells). These results demonstrated that innate cell parameters are strongly controlled by genetic factors, whereas adaptive cells are driven by life-long environmental exposures. In addition, to help on the public data mining, we developed interactive R-Shiny application including healthy donor reference values for both studies.All together, these results indicate that we developed powerful tools for human system biology approaches to support personalized medecine.; Le projet Milieu Intérieur a pour but d'identifier les facteurs génétiques et environnementaux ayant un impact sur la variabilité immunitaire naturelle. Cette analyse multiparamétrique requière néanmoins d'utiliser des outils standardisés. Afin d'étudier la réponse immune induite, nous avons utilisé un système optimisé prêt à l'emploi de stimulation du sang et développé un protocole unique de quantification de l'ARN afin d'étudier la signature transcriptionnelle en réponse à des immuno-modulateurs. Testant l'hypothèse que la réponse à des composés complexes peut être définie par la signature ARN de cytokines clefs et utilisant une méthode statistique robuste, nous avons identifié 44 gènes capables d'optimiser la capture de la réponse à des stimulations plus complexes aidant à la réduction dimensionnelle pour la décomposition de réponses innées. Dans une seconde étude, l'analyse semi-automatisée par cytomètrie en flux des cellules du sang a été associée à l'analyse épidémiologique et génotypique pour les 1,000 donneurs inclus dans la cohorte. Nous avons observé que le tabac, l'âge, le genre et l'infection latente par le cytomégalovirus sont les facteurs impactant le plus la variabilité immunitaire. Cette étude a montré que les paramètres des cellules innées sont contrôlés par des facteurs génétiques alors que ceux des cellules adaptatives le sont plutôt par des expositions environnementales tout au long de la vie. Des outils interactifs incluant ces données de référence accompagnent ces études. Ces analyses montrent que nous avons développé des outils performants pour une étude intégrative du modèle humain constituant une approche innovante vers une médecine personnalisée.

Figura como autor también el Milieu Intérieur Consortium Systems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes.