Significance Identifying the drivers of the interindividual diversity of the human immune system is crucial to understand their consequences on immune-mediated diseases. By examining the transcriptional responses of 1,000 individuals to various microbial challenges, we show that age and sex influence the expression of many immune-related genes, but their effects are overall moderate, whereas genetic factors affect a smaller gene set but with a stronger effect. We identify numerous genetic variants that affect transcriptional variation on infection, many of which are associated with autoimmune or inflammatory disorders. These results enable additional exploration of the role of regulatory variants in the pathogenesis of immune-related diseases and improve our understanding of the respective effects of age, sex, and genetics on immune response variation.

Abstract The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8+ T cells for age and CD4+ T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including a Candida albicans-specific master regulator at the CR1 locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes.

Unraveling the contributions of host and environmental factors to interindividual variability in immune responses is crucial to understand immune pathology (1). Immunological research has largely neglected the concept of interindividual heterogeneity, but there is now growing biomedical interest in studies of the variation of the immune response and its determinants in healthy populations (2)—a strategy known as systems or population immunology (1, 3, 4). Recent cohort-based studies have shed light on how host genetic and nongenetic factors, including environmental variables (e.g., annual seasonality, nutrition, latent infections) and variation of the commensal microbiota, drive the plasticity of immune responses. For example, intrinsic factors, such as age and sex, have an impact on cellular and molecular phenotypes, such as immune cell and protein levels (5⇓⇓⇓⇓⇓⇓–12), and genetic variants also account for a significant fraction of the observed variation of these immune traits (5, 6, 8, 13⇓⇓–16).

In terms of gene expression, immune responses vary markedly between individuals and populations (17⇓⇓⇓⇓–22), but the extent of this variation and its drivers are only beginning to be clarified (1, 3, 23). Recent whole-blood studies have shown that age and sex strongly affect gene expression in the basal state (12, 24, 25). Likewise, genetic variation is an important source of variability in gene expression (20, 26⇓–28). The mapping of expression quantitative trait loci (eQTLs; genetic variants that affect gene expression variation) has become an important approach in translational medicine (29), as regulatory variants are increasingly recognized as contributing to complex disease risk (22, 23, 26⇓–28, 30, 31). eQTLs are particularly informative in studies of immune phenotypes, in which interactions between genetic and environmental factors, such as infection, may be required for phenotypic manifestations (23, 32). In this context, thousands of eQTLs that only appear after immune challenge (i.e., response eQTLs) have been identified over the last years (17⇓–19, 21, 22, 33, 34), establishing putative functional links between expression phenotypes and organismal traits, such as immunity to infection (23, 26, 32). Furthermore, recent data suggest that immune-related response eQTLs play an important role in the genetic architecture of human diseases (35).

Despite the major contribution of systems immunology studies to the increased comprehension of human immune system variation (4), important questions remain to be systematically explored. The investigation of how intrinsic factors impact gene expression variation on infection is missing, yet this is critical to understand the observed inequalities among individuals of different ages and sexes in immune responses and disease risk (36, 37). Furthermore, most studies have focused on isolated cell types treated with single agonists and have not quantified jointly the influence of the genetic and nongenetic drivers of gene expression variation on immune stimulation or infection in a multicellular environment.

In this study, we adopted an integrative approach, combining genetic, transcriptomic, and cytometric data. We generated 7,000 immune transcriptional profiles for whole-blood samples, after stimulation with a wide range of microbes, from 1,000 healthy individuals of European ancestry stratified by age (20–69 y old, 200 per decade) and sex (500 women, 500 men). This balanced experimental design (Fig. S1) provided a unique opportunity to delineate the respective effects of age, sex, and genetic factors and of inherent variation in immune cell populations on the interindividual variability of immune responses to infection. In doing so, our study lays the foundations for future precision medicine clinical strategies that may stratify patient groups based on age, sex, or genetic background.

Discussion Using a systems immunology approach in a 1,000-individual healthy cohort specifically designed for the comprehension of the diversity of the human immune system (55), our study represents a systematic investigation of the respective contributions of age, sex, genetics, and cellular heterogeneity to variation in transcriptional responses to immune activation. We found that the variation of immune cell populations in whole blood was the main driver of interindividual differences in immune responses, accounting for ∼18% of the total variance in gene expression. The effects of age and sex were overall moderate (<5% of the total variance), consistent with reports based on steady-state expression (24, 25, 56), but they were widespread among immune genes and were generally not mediated by immune cell composition. We also found that age effects were more stimulus-specific compared with those of sex. Although future studies with increased power and the inclusion of donors more than 70 y of age may provide a more nuanced view of their respective effects, our results suggest that the microbial specificity of age effects may be driven by environmental exposures that change throughout life, whereas sex effects are more constant. The detected differences of age effects across stimuli echo recent studies, in which immune cell frequencies were found to be more similar between younger than older monozygotic twins (5) and older individuals presented more heterogeneous immunotypes (i.e., cell populations, cell signaling, and antibody responses) than younger donors in unrelated individuals with ages between 8 and 89 y old (11). We show that genetic factors affect fewer genes than age or sex but that their effect sizes are stronger (∼10% of the total variance) (3, 57). Although the contribution of genetic factors to expression variance can reach even higher values for specific genes or pathways, our findings are in accordance with the moderate influence of genetics in shaping the variation of other immune traits, such as cell proportions (5⇓–7, 15). We nevertheless detected local and trans-eQTLs for 43 and 42%, respectively, of the immune genes studied. About 100 genes presented an immune response eQTL, enabling the identification of G × E interactions in the context of infection. We also identified master regulators of immune responses, including the trans-eQTL at TLR1/6/10, which we previously detected in monocytes (19). We now extend the description of this trans-effect to whole-blood responses to E. coli, BCG, and to a lesser extent, S. aureus and SEB, highlighting this locus as a major source of immune response variation to bacterial challenges in Europeans. Likewise, the detected trans-eQTL at the CR1 locus reveals a source of variation related to responses to C. albicans. CR1 is a receptor for the C3b and C4b split products, opsonins formed as a result of complement system activation. Both C. albicans and BCG trigger C3 and C4 cleavage, but only opsonized C. albicans engages CR1 (58). We found that CR1 variation regulated the strength of induced IFNγ responses, downstream signaling pathways (JAK2/STAT1), and subsequent chemokine induction (CXCL10/CXCL9). The trans-eQTL SNP also had a local effect on CR1 expression (Fig. 6A), but this effect could not account for all of the variance of the trans-effect, perhaps reflecting differential temporal regulation as previously observed for the IFNB1 gene (18). CR1 variants have been previously associated with differences in erythrocyte sedimentation rate (54, 59), which increases during fungal infections. This highlights the need for studies evaluating the clinical impact of CR1 variation on susceptibility to fungal infections. Our age- and sex-stratified cohort allowed us to explore if interactions between these intrinsic variables and the numerous genetic factors identified affect immune responses. With the exception of an AGE × SNP interaction for E. coli-induced SPP1 expression, we found no interactions of age or sex with genetic factors, contrary to recent reports (46, 60). This discrepancy may stem from our focus on immune functions and suggests that the effects of genetic variants on immune gene expression are constant across age groups and between sexes. Consistent with this view, previously reported interactions between age, sex, and genetics did not affect immune genes (with the exception of NOD2) (46, 60) or were not identified in whole blood (57). A nonnegligible fraction of immune response variance remains presently unexplained. Hence, the contribution of other determinants requires additional investigation, including the effects of environmental exposures, epigenetic modifications, interactions with the microbiota, or more complex genetic control (57). In this context, our quantification of the effects of genetic factors on immune response variance (i.e., ∼10% on average) should be considered as a conservative estimate, especially if one considers the many regulatory variants with small effect sizes that our analyses cannot detect. Our study also presents some limitations. Gene expression variation was assessed at 560 immune genes, providing a partial view of the impact that nongenetic and genetic factors have on gene expression sensu lato. This choice was based on the robust, highly reproducible gene expression measurements generated by the NanoString arrays in whole blood, avoiding technical variability introduced by amplification steps (38). It was also our strategy to deliberately focus on the expression variation of only immune genes to limit the burden of multiple testing. The use of RNA sequencing will make it possible to extend these analyses to a wider array of genes and layers of transcriptional variability, such as potential differential isoform usage on infection. Furthermore, analyses of other immune system measurements after stimulation (proteins and metabolites after microbial challenges, antibody responses to vaccines, etc.) are necessary to provide a more comprehensive view of the different intermediate phenotypes that constitute the human immune system. Despite these perceived limitations, the use of gene expression variation and its genetic determinants remains a powerful tool for systems immunology studies, as variants affecting gene expression, in particular after immune stimulation (35), are increasingly recognized as contributing to ultimate organismal phenotypes (22, 26, 27, 30, 31). This notion is strongly supported by the cases of G × E interactions detected in this study, as response eQTLs exhibited a stronger enrichment in risk variants for human diseases, such as autoimmune and inflammatory disorders, than eQTLs in the nonstimulated state. Overall, this study provides an assessment of how intrinsic and genetic factors drive interindividual differences in transcriptional responses to bacterial, fungal, and viral challenges. Our dataset is freely accessible via a web-based browser (misage.pasteur.fr/), making it possible to query and visualize the contribution of these factors to immune response variation. It also constitutes a valuable resource for additional exploration in the context of different infection risks or disease outcomes in human populations.

Materials and Methods The cohort consists of 1,000 healthy volunteers (500 men and 500 women) aged 20–69 y old equally distributed across five decades of life who were selected based on stringent inclusion and exclusion criteria (55). The study was approved by the Comité de Protection des Personnes—Ouest 6 and the Agence Nationale de Sécurité du Médicament and is sponsored by the Institut Pasteur (ID-RCB no. 2012-A00238-35). The study protocol was designed and conducted in accordance with the Declaration of Helsinki and good clinical practice as outlined in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) Guidelines for Good Clinical Practice (https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R1_Guideline.pdf), and all subjects gave informed consent. Stimulations were performed on 1 mL whole blood for 22 h using TruCulture tubes (61), and flow cytometry analyses were performed with an eight-color cytometry panel (62). Gene expression was performed using the Human Immunology v2 Gene Expression CodeSet, which contains 594 gene probes that encompass major immune pathways and functions, such as the TLR, Jak-STAT, and MAPK signaling pathways, cytokine–cytokine receptor interactions, apoptosis, and the complement and coagulation cascades. For each gene in each stimulated condition, a paired t test was used to compare expression levels in stimulated and nonstimulated states, controlling for FDR. Seven multiple regression models were built to estimate the effects of age and sex on gene expression. Structural equation modeling (44) was used to investigate the ways in which the different cell populations mediate the effects of age and sex on gene expression. DNA genotyping was performed using the HumanOmniExpress-24 BeadChip and the HumanExome-12 BeadChip (Illumina). After imputation using the 1,000 Genomes Project imputation reference panel (45), a final dataset of 5,265,361 SNPs was obtained. eQTLs mapping was performed with a linear mixed model implemented in GenABEL (63). Interaction effects between variables (genetics, sex, and age) on gene expression were estimated using ProbABEL v.0.4.5 (64). Detailed information about the experimental methods and statistical analyses may be found in SI Materials and Methods.

Acknowledgments We acknowledge Stephanie Thomas for managing the Milieu Intérieur Consortium. This work was supported by the French Government’s Investissement d’Avenir Program, Laboratoire d’Excellence “Milieu Intérieur” Grant ANR-10-LABX-69-01.

Footnotes Author contributions: B.P., D.D., M.L.A., and L.Q.-M. designed research; B.P., A.U., H.Q., C.P., B.C., M.H., B.A., D.G., and J.F. performed research; M.I.C. contributed conceptual and analytic tools; B.P., D.D., E.P., J.B., V.R., C.R.M., M.L.A., and L.Q.-M. analyzed data; and B.P., D.D., E.P., M.L.A., and L.Q.-M. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: The genotype data reported in this paper have been deposited in the European Genome-Phenome Archive (EGA; accession no. EGAS00001002460).

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