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. 2018 Jan 16;115(3):E488-E497.
doi: 10.1073/pnas.1714765115. Epub 2017 Dec 27.

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

Barbara Piasecka  1   2 , Darragh Duffy  2   3   4 , Alejandra Urrutia  3   4   5 , Hélène Quach  1   6   7 , Etienne Patin  1   6   7 , Céline Posseme  2 , Jacob Bergstedt  8   9 , Bruno Charbit  2 , Vincent Rouilly  2 , Cameron R MacPherson  2 , Milena Hasan  2 , Benoit Albaud  10 , David Gentien  10 , Jacques Fellay  11   12 , Matthew L Albert  2   3   4   5 , Lluis Quintana-Murci  13   6   7 ; Milieu Intérieur Consortium
Collaborators, Affiliations

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

Barbara Piasecka et al. Proc Natl Acad Sci U S A. .

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 VSports手机版. 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. V体育安卓版.

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Conflict of interest statement

The authors declare no conflict of interest.

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.
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