Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges

Barbara Piasecka, Darragh Duffy, Alejandra Urrutia, Hélène Quach, Etienne Patin, Céline Posseme, Jacob Bergstedt, Bruno Charbit, Vincent Rouilly, Cameron R MacPherson, Milena Hasan, Benoit Albaud, David Gentien, Jacques Fellay, Matthew L Albert, Lluis Quintana-Murci, Milieu Intérieur Consortium, Laurent Abel, Andres Alcover, Hugues Aschard, Kalle Aström, Philippe Bousso, Pierre Bruhns, Ana Cumano, Darragh Duffy, Caroline Demangel, Ludovic Deriano, James Di Santo, Françoise Dromer, Gérard Eberl, Jost Enninga, Jacques Fellay, Magnus Fontes, Antonio Freitas, Odile Gelpi, Ivo Gomperts-Boneca, Serge Hercberg, Olivier Lantz, Claude Leclerc, Hugo Mouquet, Etienne Patin, Sandra Pellegrini, Stanislas Pol, Antonio Raussel, Lars Rogge, Anavaj Sakuntabhai, Olivier Schwartz, Benno Schwikowski, Spencer Shorte, Vassili Soumelis, Frédéric Tangy, Eric Tartour, Antoine Toubert, Marie-Noëlle Ungeheuer, Lluis Quintana-Murci, Matthew L Albert, Barbara Piasecka, Darragh Duffy, Alejandra Urrutia, Hélène Quach, Etienne Patin, Céline Posseme, Jacob Bergstedt, Bruno Charbit, Vincent Rouilly, Cameron R MacPherson, Milena Hasan, Benoit Albaud, David Gentien, Jacques Fellay, Matthew L Albert, Lluis Quintana-Murci, Milieu Intérieur Consortium, Laurent Abel, Andres Alcover, Hugues Aschard, Kalle Aström, Philippe Bousso, Pierre Bruhns, Ana Cumano, Darragh Duffy, Caroline Demangel, Ludovic Deriano, James Di Santo, Françoise Dromer, Gérard Eberl, Jost Enninga, Jacques Fellay, Magnus Fontes, Antonio Freitas, Odile Gelpi, Ivo Gomperts-Boneca, Serge Hercberg, Olivier Lantz, Claude Leclerc, Hugo Mouquet, Etienne Patin, Sandra Pellegrini, Stanislas Pol, Antonio Raussel, Lars Rogge, Anavaj Sakuntabhai, Olivier Schwartz, Benno Schwikowski, Spencer Shorte, Vassili Soumelis, Frédéric Tangy, Eric Tartour, Antoine Toubert, Marie-Noëlle Ungeheuer, Lluis Quintana-Murci, Matthew L Albert

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.

Keywords: age; gene expression; genetics; human immune variation; sex.

Conflict of interest statement

The authors declare no conflict of interest.

Copyright © 2018 the Author(s). Published by PNAS.

Figures

Fig. 1.
Fig. 1.
Distinct transcriptional responses to bacterial, viral, and fungal infections. (A) Number of genes presenting differential expression on immune stimulation. (B) Number of genes presenting common patterns of expression changes across stimulation conditions. Only expression patterns common to at least five genes are presented. (C) Principal component analysis of immune gene expression profiles in the nonstimulated state and on immune simulation. NS, nonstimulated control.
Fig. 2.
Fig. 2.
Effects of age and sex on the variation of gene expression. (A) Number of genes presenting age-dependent expression changes, as detected by linear regression and ANOVA, in the absence of stimulation and after stimulation. (B) Expression patterns of IFNA2 and FCGRT in response to IAV stimulation across five decades of life. (C) Specificity of age effects on gene expression across conditions. Numbers in the circle sectors (1–6) denote the numbers of stimuli for which the expression of the corresponding genes was age-dependent. The IAV condition was not considered, as it presented a nonlinear association with age. (D) Age-specific (20–69 y) expression of IL13 and IL4R for each stimuli. A significant age association was observed in response to SEB stimulation. (E) Specificity of the effect of sex on gene expression across conditions. Numbers in the circle sectors (1–7) denote the numbers of stimuli for which the expression of the corresponding genes was sex-dependent. (F) Expression differences between men and women for CD14 and ICOS, common to all conditions. Gene expression is presented as normalized gene counts. The legend with color-coding applies to C–F. F, female; M, male; NS, nonstimulated control.
Fig. 3.
Fig. 3.
Structural equation models. (A) Mediation of the effects of age and sex on gene expression by blood cell composition in the nonstimulated control. (B) Fraction of genes displaying expression directly or indirectly affected by age across stimulation conditions. (C) Fraction of genes displaying expression directly or indirectly affected by sex across conditions. NS, nonstimulated control.
Fig. 4.
Fig. 4.
Local and trans-genetic factors associated with gene expression variation. The genomic position of the regulatory SNP is presented on the x axis, whereas that of the gene for which expression variation is associated with the regulatory variant is presented on the y axis. The numbers along the x and y axes are the chromosome numbers. Circles on the diagonals represent genes with expression patterns regulated by local eQTLs, whereas off-diagonal circles correspond to genes with expression patterns regulated in trans. On each panel, the eQTLs detected in the absence of stimulation were plotted first and were then overlaid with eQTLs detected in the corresponding stimulation conditions. Labels are shown only for genes regulated by local eQTLs with a P value <10−32. For trans-eQTLs regulating multiple genes, only the top 30 most significant genes are highlighted. Colored labels indicate genes with significant eQTLs only after stimulation; gray labels indicate the genes with significant eQTLs in both the presence and absence of stimulation. The exact position and rs numbers of both local and trans-associated SNPs are provided in Datasets S8–S21.
Fig. 5.
Fig. 5.
Stimulus specificity of immune response eQTLs. (A) Specificity of local eQTLs across stimulation conditions. Numbers in the circle sectors (1–7) denote the numbers of stimuli for which the expression of the corresponding genes was associated with a nearby genetic variant. (B) Cases of CTSC and IFIT2 presenting local eQTLs across all seven conditions. The eQTL effect at CTSC differed between nonstimulated and stimulated conditions. (C) Cases of TRAF4, IL7R, and TLR3 presenting local response eQTLs specific to E. coli, SEB, and IAV stimulations, respectively, highlighting G × E interactions. Gene expression is presented as normalized gene counts. MAF, minor allele frequency; NS, nonstimulated control.
Fig. 6.
Fig. 6.
Stimulus-specific trans-acting eQTL at the CR1 locus. (A) Local eQTL at CR1 acting specifically in response to C. albicans stimulation. (B) rs12567990 was significantly associated, in trans, with the expression of IFNG and CLEC5A only after C. albicans stimulation. Gene expression is presented as normalized gene counts. (C) Network of the 34 genes significantly trans-regulated by the CR1 locus (P < 1.7 × 10−11). The size of the nodes is proportional to −log10(p) of the association between rs12567990 and gene expression. Colors indicate the direction of the change in expression associated with the C allele (frequency = 0.81). NS, nonstimulated control.
Fig. 7.
Fig. 7.
Expression variance explained by age, sex, genetics, and blood cell composition. (A) Mean variance, across genes, explained by age, sex, genetics, and the proportions of CD45+ cell populations in the absence of stimulation and in the six stimulation conditions. The sizes of the circles correspond to the number of genes affected by each factor in each condition. (B) Proportion of the expression variance explained by age, sex, genetics, and proportions of CD45+ cells for all genes expressed in response to E. coli stimulation. (C) Proportion of the expression variance explained by the different intrinsic and heritable factors for the genes of the type 1 IFN and TLR-MyD88 pathways. The order of stimuli on the x axis is the same as on A. The legend with color-coding applies to A–C. NS, nonstimulated control.

References

    1. Brodin P, Davis MM. Human immune system variation. Nat Rev Immunol. 2017;17:21–29.
    1. De Jager PL, et al. ImmVar project: Insights and design considerations for future studies of “healthy” immune variation. Semin Immunol. 2015;27:51–57.
    1. Liston A, Carr EJ, Linterman MA. Shaping variation in the human immune system. Trends Immunol. 2016;37:637–646.
    1. Davis MM, Tato CM, Furman D. Systems immunology: Just getting started. Nat Immunol. 2017;18:725–732.
    1. Brodin P, et al. Variation in the human immune system is largely driven by non-heritable influences. Cell. 2015;160:37–47.
    1. Orrù V, et al. Genetic variants regulating immune cell levels in health and disease. Cell. 2013;155:242–256.
    1. Carr EJ, et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat Immunol. 2016;17:461–468.
    1. Ter Horst R, et al. Host and environmental factors influencing individual human cytokine responses. Cell. 2016;167:1111–1124.e13.
    1. Shen-Orr SS, et al. Defective signaling in the JAK-STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans. Cell Syst. 2016;3:374–384.e4.
    1. Furman D, et al. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc Natl Acad Sci USA. 2014;111:869–874.
    1. Kaczorowski KJ, et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc Natl Acad Sci USA. 2017;114:E6097–E6106.
    1. Blazkova J, et al. Multicenter systems analysis of human blood reveals immature neutrophils in males and during pregnancy. J Immunol. 2017;198:2479–2488.
    1. Li Y, et al. A functional genomics approach to understand variation in cytokine production in humans. Cell. 2016;167:1099–1110.e14.
    1. Li Y, et al. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat Med. 2016;22:952–960.
    1. Roederer M, et al. The genetic architecture of the human immune system: A bioresource for autoimmunity and disease pathogenesis. Cell. 2015;161:387–403.
    1. Aguirre-Gamboa R, et al. Differential effects of environmental and genetic factors on T and B cell immune traits. Cell Rep. 2016;17:2474–2487.
    1. Lee MN, et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science. 2014;343:1246980.
    1. Fairfax BP, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science. 2014;343:1246949.
    1. Quach H, et al. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell. 2016;167:643–656.e17.
    1. Raj T, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344:519–523.
    1. Nedelec Y, et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell. 2016:167, 657–669.e21.
    1. Kim-Hellmuth S, et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat Commun. 2017;8:266.
    1. Fairfax BP, Knight JC. Genetics of gene expression in immunity to infection. Curr Opin Immunol. 2014;30:63–71.
    1. Peters MJ, et al. NABEC/UKBEC Consortium The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6:8570.
    1. Jansen R, et al. Sex differences in the human peripheral blood transcriptome. BMC Genomics. 2014;15:33.
    1. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nat Rev Genet. 2015;16:197–212.
    1. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res. 2012;22:1748–1759.
    1. Raj T, et al. Common risk alleles for inflammatory diseases are targets of recent positive selection. Am J Hum Genet. 2013;92:517–529.
    1. Gibson G, Powell JE, Marigorta UM. Expression quantitative trait locus analysis for translational medicine. Genome Med. 2015;7:60.
    1. Pickrell JK. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am J Hum Genet. 2014;94:559–573.
    1. Li YI, et al. RNA splicing is a primary link between genetic variation and disease. Science. 2016;352:600–604.
    1. Quach H, Quintana-Murci L. Living in an adaptive world: Genomic dissection of the genus Homo and its immune response. J Exp Med. 2017;214:877–894.
    1. Barreiro LB, et al. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. Proc Natl Acad Sci USA. 2012;109:1204–1209.
    1. Çalışkan M, Baker SW, Gilad Y, Ober C. Host genetic variation influences gene expression response to rhinovirus infection. PLoS Genet. 2015;11:e1005111.
    1. Alasoo K, et al. 2017. Shared genetic effects on chromatin and gene expression reveal widespread enhancer priming in immune response. bioRxiv:10.1101/102392.
    1. Klein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol. 2016;16:626–638.
    1. vom Steeg LG, Klein SL. SeXX matters in infectious disease pathogenesis. PLoS Pathog. 2016;12:e1005374.
    1. Urrutia A, et al. Milieu Intérieur Consortium Standardized whole-blood transcriptional profiling enables the deconvolution of complex induced immune responses. Cell Rep. 2016;16:2777–2791.
    1. Viñuela A, Snoek LB, Riksen JA, Kammenga JE. Genome-wide gene expression regulation as a function of genotype and age in C. elegans. Genome Res. 2010;20:929–937.
    1. Somel M, Khaitovich P, Bahn S, Pääbo S, Lachmann M. Gene expression becomes heterogeneous with age. Curr Biol. 2006;16:R359–R360.
    1. Bahar R, et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature. 2006;441:1011–1014.
    1. Sobolev O, et al. Adjuvanted influenza-H1N1 vaccination reveals lymphoid signatures of age-dependent early responses and of clinical adverse events. Nat Immunol. 2016;17:204–213.
    1. Melzer S, et al. Reference intervals for leukocyte subsets in adults: Results from a population-based study using 10-color flow cytometry. Cytometry B Clin Cytom. 2015;88:270–281.
    1. Gunzler D, Chen T, Wu P, Zhang H. Introduction to mediation analysis with structural equation modeling. Shanghai Jingshen Yixue. 2013;25:390–394.
    1. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.
    1. Yao C, et al. Sex- and age-interacting eQTLs in human complex diseases. Hum Mol Genet. 2014;23:1947–1956.
    1. Dimas AS, et al. MuTHER Consortium Sex-biased genetic effects on gene regulation in humans. Genome Res. 2012;22:2368–2375.
    1. Zhang FR, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361:2609–2618.
    1. Liu H, et al. Discovery of six new susceptibility loci and analysis of pleiotropic effects in leprosy. Nat Genet. 2015;47:267–271.
    1. Naranbhai V, et al. Genomic modulators of gene expression in human neutrophils. Nat Commun. 2015;6:7545.
    1. Liu JZ, et al. International Multiple Sclerosis Genetics Consortium; International IBD Genetics Consortium Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47:979–986.
    1. Bentham J, et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet. 2015;47:1457–1464.
    1. Hinds DA, et al. A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci. Nat Genet. 2013;45:907–911.
    1. Naitza S, et al. A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet. 2012;8:e1002480.
    1. Thomas S, et al. Milieu Intérieur Consortium The Milieu Intérieur study–An integrative approach for study of human immunological variance. Clin Immunol. 2015;157:277–293.
    1. Whiting CC, et al. Large-scale and comprehensive immune profiling and functional analysis of normal human aging. PLoS One. 2015;10:e0133627.
    1. Vinuela A, et al. 2016. Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. bioRxiv:10.1101/063883.
    1. Kozel TR, Brown RR, Pfrommer GS. Activation and binding of C3 by Candida albicans. Infect Immun. 1987;55:1890–1894.
    1. Kullo IJ, et al. Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate. Am J Hum Genet. 2011;89:131–138.
    1. Kukurba KR, et al. Impact of the X chromosome and sex on regulatory variation. Genome Res. 2016;26:768–777.
    1. Duffy D, et al. Milieu Intérieur Consortium Functional analysis via standardized whole-blood stimulation systems defines the boundaries of a healthy immune response to complex stimuli. Immunity. 2014;40:436–450.
    1. Hasan M, et al. Milieu Intérieur Consortium Semi-automated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping. Clin Immunol. 2015;157:261–276.
    1. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: An R library for genome-wide association analysis. Bioinformatics. 2007;23:1294–1296.
    1. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics. 2010;11:134.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj