Subjects

Abstract

The quantification and characterization of circulating immune cells provide key indicators of human health and disease. To identify the relative effects of environmental and genetic factors on variation in the parameters of innate and adaptive immune cells in homeostatic conditions, we combined standardized flow cytometry of blood leukocytes and genome-wide DNA genotyping of 1,000 healthy, unrelated people of Western European ancestry. We found that smoking, together with age, sex and latent infection with cytomegalovirus, were the main non-genetic factors that affected variation in parameters of human immune cells. Genome-wide association studies of 166 immunophenotypes identified 15 loci that showed enrichment for disease-associated variants. Finally, we demonstrated that the parameters of innate cells were more strongly controlled by genetic variation than were those of adaptive cells, which were driven by mainly environmental exposure. Our data establish a resource that will generate new hypotheses in immunology and highlight the role of innate immunity in susceptibility to common autoimmune diseases.

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Change history

  • Correction 03 May 2018

    In the version of this article initially published, the name of one author was incorrect (James P. Santo). The correct name is James P. Di Santo. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank the Center for Translation Research, Institut Pasteur, and the OMNI Biomarker Development-Flow Cytometry Biomarker group, Genentech, for support. This work benefited from support of the French government’s program ‘Investissement d’Avenir’, managed by the Agence Nationale de la Recherche (reference 10-LABX-69-01). J.B. is a member of the LCCC Linnaeus Center and the ELLIIT Excellence Center at Lund University and is supported by the ELLIIT Excellence Center.

Author information

Author notes

    • Etienne Patin
    • , Milena Hasan
    •  & Jacob Bergstedt

    These authors contributed equally to this work.

  1. These authors contributed equally: Etienne Patin, Milena Hasan and Jacob Bergstedt

  2. These authors jointly directed this work: Lluís Quintana-Murci and Matthew L. Albert.

Affiliations

  1. Unit of Human Evolutionary Genetics, Department of Genomes & Genetics, Institut Pasteur, Paris, France

    • Etienne Patin
    • , Hélène Quach
    • , Barbara Piasecka
    • , Lluís Quintana-Murci
    • , Etienne Patin
    •  & Lluís Quintana-Murci
  2. CNRS UMR 2000, Paris, France

    • Etienne Patin
    • , Hélène Quach
    • , Barbara Piasecka
    • , Lluís Quintana-Murci
    • , Françoise Dromer
    • , Etienne Patin
    • , Anavaj Sakuntabhai
    •  & Lluís Quintana-Murci
  3. Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France

    • Etienne Patin
    • , Vincent Rouilly
    • , Hélène Quach
    • , Barbara Piasecka
    • , Lluís Quintana-Murci
    • , Etienne Patin
    • , Benno Schwikowski
    •  & Lluís Quintana-Murci
  4. Center for Translation Research, Institut Pasteur, Paris, France

    • Milena Hasan
    • , Vincent Rouilly
    • , Valentina Libri
    • , Alejandra Urrutia
    • , Cécile Alanio
    • , Benoît Beitz
    • , Barbara Piasecka
    • , Lars Rogge
    • , James P. Di Santo
    • , Stéphanie Thomas
    • , Darragh Duffy
    • , Matthew L. Albert
    • , James P. Di Santo
    • , Darragh Duffy
    • , Stanislas Pol
    • , Lars Rogge
    • , Marie-Noëlle Ungeheuer
    •  & Matthew L. Albert
  5. Department of Automatic Control, Lund University, Lund, Sweden

    • Jacob Bergstedt
  6. International Group for Data Analysis, Institut Pasteur, Paris, France

    • Jacob Bergstedt
    •  & Magnus Fontes
  7. Laboratory of Dendritic Cell Immunobiology, Department of Immunology, Institut Pasteur, Paris, France

    • Alejandra Urrutia
    • , Cécile Alanio
    • , Stéphanie Thomas
    • , Darragh Duffy
    • , Matthew L. Albert
    • , Darragh Duffy
    •  & Matthew L. Albert
  8. INSERM U1223, Paris, France

    • Alejandra Urrutia
    • , Cécile Alanio
    • , James P. Di Santo
    • , Stéphanie Thomas
    • , Darragh Duffy
    • , Matthew L. Albert
    • , Philippe Bousso
    • , Ana Cumano
    • , James P. Di Santo
    • , Darragh Duffy
    • , Stanislas Pol
    •  & Matthew L. Albert
  9. Department of Cancer Immunology, Genentech, South San Francisco, CA, USA

    • Alejandra Urrutia
    • , Yoong Wearn Lim
    • , Jacques Fellay
    • , Matthew L. Albert
    • , Jacques Fellay
    •  & Matthew L. Albert
  10. School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    • Petar Scepanovic
    • , Christian Hammer
    • , Jacques Fellay
    •  & Jacques Fellay
  11. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    • Petar Scepanovic
    •  & Christian Hammer
  12. Antibodies in Therapy and Pathology, Department of Immunology, Institut Pasteur, Paris, France

    • Friederike Jönsson
    •  & Pierre Bruhns
  13. INSERM U760, Paris, France

    • Friederike Jönsson
    •  & Pierre Bruhns
  14. Department of Human Genetics, Genentech, South San Francisco, CA, USA

    • Julie Hunkapiller
    •  & Claire Leloup
  15. Employee Donation Program, Genentech, South San Francisco, CA, USA

    • Magge Zepeda
  16. Department of Development Sciences, Genentech, South San Francisco, CA, USA

    • Cherie Green
  17. Immunoregulation Unit, Department of Immunology, Institut Pasteur, Paris, France

    • Lars Rogge
    •  & Lars Rogge
  18. INSERM U783, Faculté de Médecine, Site Necker-Enfants Malades, Université Paris Descartes, Paris, France

    • François Huetz
  19. Lymphocyte Population Biology, CNRS URA 1961, Institut Pasteur, Paris, France

    • François Huetz
    • , Andres Alcover
    •  & Antonio Freitas
  20. Center of Clinical Investigations CIC-BT1428 IGR/Curie, Paris, France

    • Isabelle Peguillet
    • , Olivier Lantz
    •  & Olivier Lantz
  21. Department of Biopathology, Institut Curie, Paris, France

    • Isabelle Peguillet
    • , Olivier Lantz
    •  & Olivier Lantz
  22. Equipe Labellisée de la Ligue de Lutte Contre le Cancer, Institut Curie, Paris, France

    • Olivier Lantz
    •  & Olivier Lantz
  23. INSERM/Institut Curie U932, Paris, France

    • Olivier Lantz
    •  & Olivier Lantz
  24. Centre for Mathematical Sciences, Lund University, Lund, Sweden

    • Magnus Fontes
    •  & Kalle Astrom
  25. Innate Immunity Unit, Institut Pasteur, Paris, France

    • James P. Di Santo
    •  & James P. Di Santo
  26. St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, Rockefeller University, New York, NY, USA

    • Laurent Abel
  27. Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France

    • Laurent Abel
  28. Paris Descartes University, Imagine Institute, Paris, France

    • Laurent Abel
  29. INSERM, U1221, Paris, France

    • Andres Alcover
    •  & Caroline Demangel
  30. Laboratory of Dynamics of Immune Responses, Institut Pasteur, Paris, France

    • Philippe Bousso
  31. Unité de Lymphopoïèse, Département d’Immunologie, Institut Pasteur, Paris, France

    • Ana Cumano
  32. Immunobiology of Infection Unit, Institut Pasteur, Paris, France

    • Caroline Demangel
  33. Genome Integrity, Immunity and Cancer Unit, Department of Immunology, Department of Genomes and Genetics, Institut Pasteur, Paris, France

    • Ludovic Deriano
  34. Molecular Mycology Unit, National Reference Center for Invasive Mycoses and Antifungals, Institut Pasteur, Paris, France

    • Françoise Dromer
  35. Microenvironment and Immunity Unit, Institut Pasteur, Paris, France

    • Gérard Eberl
  36. INSERM, U1224, Paris, France

    • Gérard Eberl
  37. Dynamics of Host-Pathogen Interactions Unit, Institut Pasteur, Paris, France

    • Jost Enninga
  38. Direction des Affaires médicales et Santé publique, Institut Pasteur, Paris, France

    • Odile Gelpi
  39. Unit of Biology and Genetics of the bacterial cell wall, Institut Pasteur, Paris, France

    • Ivo Gomperts Boneca
  40. INSERM Groupe Avenir, Paris, France

    • Ivo Gomperts Boneca
  41. Université Paris 13, Equipe de Recherche en Epidémiologie Nutritionnelle, Centre de Recherche en Epidémiologie et Statistiques, INSERM U1153, INRA U1125, CNAM, COMUE Sorbonne Paris Cité, Bobigny, France

    • Serge Hercberg
  42. Département de Santé Publique, Hôpital Avicenne, Bobigny, France

    • Serge Hercberg
  43. Immune Regulation and Vaccinology Unit, Immunology Department, Institut Pasteur, Equipe Labellisée Ligue Contre le Cancer, Paris, France

    • Claude Leclerc
  44. INSERM, U1041, Paris, France

    • Claude Leclerc
  45. Vaccine Research Institute, Créteil, France

    • Hugo Mouquet
  46. Humoral Response to Pathogens Unit, Immunology Department, Institut Pasteur, Paris, France

    • Hugo Mouquet
  47. INSERM, U1222, Paris, France

    • Hugo Mouquet
  48. Unit of Cytokine Signaling, CNRS URA1961, Institut Pasteur, Paris, France

    • Sandra Pellegrini
  49. Liver Department, Université Paris-Descartes, APHP, Hôpital Cochin, Paris, France

    • Stanislas Pol
  50. Functional Genetics of Infectious Diseases Unit, Department of Genomes and Genetics, Institut Pasteur, Paris, France

    • Anavaj Sakuntabhai
  51. Virus and Immunity Unit, Institut Pasteur, Paris, France

    • Olivier Schwartz
  52. CNRS, UMR3569, Paris, France

    • Olivier Schwartz
    •  & Frédéric Tangy
  53. Systems Biology Lab, Institut Pasteur, Paris, France

    • Benno Schwikowski
  54. Center for Innovation and Technological Research, Imagopole-Plateforme D’Imagerie Dynamique, Institut Pasteur, Paris, France

    • Spencer Shorte
  55. Laboratoire d’Immunologie Clinique, Institut Curie, Paris, France

    • Vassili Soumelis
  56. INSERM, U932, Paris, France

    • Vassili Soumelis
  57. Viral Genomics and Vaccination, Institut Pasteur, Paris, France

    • Frédéric Tangy
  58. Equipe Labellisée Ligue Contre le Cancer, Department of Immunology, Hopital Européen Georges Pompidou, Paris, France

    • Eric Tartour
  59. INSERM U970, Université Paris Descartes Sorbonne Paris-Cité, Paris, France

    • Eric Tartour
  60. Institut Universitaire d’Hématologie, Université Paris Diderot, Sorbonne Paris Cité, APHP, Paris, France

    • Antoine Toubert
  61. INSERM, U1160, Paris, France

    • Antoine Toubert
  62. ICAReB platform, Institut Pasteur, Paris, France

    • Marie-Noëlle Ungeheuer

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  1. The Milieu Intérieur Consortium

Contributions

Conceptualization, E.P., L.Q.-M. and M.L.A.; methodology, M.H., V.L., A.U., F.J., B.B., C.L., F.H., L.R., I.P., O.L. and J.P.D.; software, E.P., J.B., V.R., P.S., C.H., B.P. and J.F.; validation, M.H., V.R., F.J., Y.W.L. and M.L.A.; formal analysis, E.P., J.B., V.R., P.S., C.H., C.G., B.P. and J.F.; investigation, E.P., M.H., J.B., V.L., A.U., C.A., F.J., H.Q., M.Z., B.P., C.L., L.R., F.H., O.L., J.P.D. and M.L.A.; data curation, E.P., M.H., J.B., V.R., V.L., A.U., B.P., C.L., L.R., I.P., O.L. and J.P.D.; writing (original draft), E.P., J.B., C.A., D.D. and M.L.A.; writing (review and editing), E.P., M.H., J.B., C.A., L.R., O.L., M.F., J.P.D., J.F., L.Q.-M. and M.L.A.; supervision, E.P., M.H., M.F., J.F., D.D. and M.L.A.; project administration, J.H., S.T. and D.D.; and funding acquisition, M.F., J.F., L.Q.-M. and M.L.A. L.Q.M. and M.L.A. are co-coordinators of the Milieu Intérieur Consortium (more information available at http://www.milieuinterieur.fr/en).

Competing interests

A.U., C.H., Y.W.L., J.H., M.Z., C.G. and M.L.A. are employees of Genentech, a member of The Roche Group.

Corresponding authors

Correspondence to Etienne Patin or Matthew L. Albert.

Integrated supplementary information

  1. Gating strategy for the T cell flow cytometry panel.

  2. Gating strategy for the Treg cell flow cytometry panel.

  3. Gating strategy for the NKT/MAIT cell flow cytometry panel.

  4. Gating strategy for the NK cell flow cytometry panel.

  5. Gating strategy for the lineage cell flow cytometry panel.

  6. Gating strategy for the B cell flow cytometry panel.

  7. Gating strategy for the PMN cell flow cytometry panel.

  8. Gating strategy for the DC flow cytometry panel.

  9. Gating strategy for the TH cell flow cytometry panel.

  10. Gating strategy for the ILC flow cytometry panel.

  11. Supplementary Figure 11 Raw distributions of the 166 immunophenotypes studied in the Milieu Intérieur cohort.

    Distributions are shown before outlier removal, imputation, batch correction and transformation to normality. Transformed distributions are shown in Supplementary Fig. 15.

  12. Supplementary Figure 12 Repeatability of semi-automated flow cytometry measurements.

    Repeatability of (a) absolute cell counts and (b) MFI estimated based on measurements performed at five different time points over five months on the same five individuals. Points represent the average of measurements across replicates of the same individual, and bars 3 times their standard deviation.

  13. Supplementary Figure 13 Reproducibility of semi-automated flow cytometry measurements.

    Comparison of cell counts measured in different flow cytometry panels across the 1,000 Milieu Intérieur donors. Spearman’s ρ correlation coefficients are shown.

  14. Supplementary Figure 14 Batch effects on measured immunophenotypes.

    (a) Effect of the sampling hour on the absolute number of CD16hi NK cells. Whole blood samples were collected from the 1,000 Milieu Intérieur healthy subjects every working day from 8 to 11AM. The sampling hour has a significant effect on different immune cell counts, such as the absolute number of CD16hi NK cells, which were adjusted for this batch effect. (b) Effect of the sampling day on immunophenotypes measured in the Milieu Intérieur cohort. Whole blood samples were collected from the 1,000 Milieu Intérieur healthy subjects every working day from September 2012 to august 2013. Variation in the mean fluorescence intensity (MFI) of several protein markers was observed along the sampling period. All MFIs were corrected for this batch effect using the ComBat nonparametric Bayesian framework. Three examples of uncorrected (left column) and corrected (right columns) values are shown.

  15. Supplementary Figure 15 Transformed distributions of the 166 immunophenotypes studied in the Milieu Intérieur cohort.

    Distributions are shown after outlier removal, imputation, batch correction and transformation to normality. Raw distributions are shown in Supplementary Fig. 11.

  16. Supplementary Figure 16 Correlations among circulating levels of immune cell populations in the Milieu Intérieur cohort.

    Sample correlations were estimated between the residuals of the log-transformed immunophenotypes from a regression model that included non-genetic covariates selected with the stability selection algorithm (described below) on the 39 non-genetic covariates, together with the batch variables. Correlations between -0.3 and 0.3 were set to 0, to improve readability.

  17. Supplementary Figure 17 Age, sex and CMV infection impact protein levels of immune cell markers in 1,000 healthy individuals.

    Significant multiplicative effects of (a) increasing age, (b) female sex and (c) CMV seropositivity on the mean fluorescence intensity (MFI) of protein markers of immune cells of 1,000 healthy individuals, controlling for the two other factors, batch effects and genome-wide significant SNPs The multiplicative effect sizes were estimated in a linear mixed model with a log-transformed response variable, controlling for batch effects and genome-wide significant SNPs, and then transformed to the original data scale. The 99% confidence intervals (99%CIs) were false coverage-adjusted, considering that only 99%CIs of effect sizes with a significant test (adjusted P<0.01) are shown. Adaptive and innate immune cells are represented in grey and black, respectively. These analyses were performed with the mmi R package (Online Methods).

  18. Supplementary Figure 18 Estimated dependency matrix among 39 non-genetic variables analyzed in this study.

    Values are generalized R2 measures from pairwise fitted generalized linear models (Online Methods). Variables listed in rows are response variables and those listed in columns are predictor variables in the models. Categorical variables (i.e., exposure to dust, level of education, income and smoking) were used only as predictors. Details on the non-genetic variables can be found in Supplementary Table 1.

  19. Supplementary Figure 19 Smoking strongly impacts protein levels of immune cell markers in 1,000 healthy individuals.

    (a) Significant multiplicative effects of 39 non-genetic factors on protein levels of immune cell markers (i.e., MFI) in healthy individuals. Colors represent levels of association (i.e., –log10(q-values)) between the 39 non-genetic factors and protein levels of immune cell markers, at a false discovery rate (FDR) < 1%. Except when their effects were specifically measured, immunophenotypes were regressed for age, sex, CMV status, batch effects and genome-wide significant SNPs (Online Methods). (b) Significant effect sizes of active smoking on protein levels of immune cell markers in 1,000 healthy individuals. The multiplicative effect sizes were estimated in a linear mixed model with a log-transformed response variable, controlling for age, sex, CMV status, batch effects and genome-wide significant SNPs, and then transformed to the original data scale. The 99% confidence intervals (99%CIs) were false coverage-adjusted, considering that only 99%CIs of effect sizes with a significant test (adjusted P<0.01) are shown. Adaptive and innate immune cells are represented in grey and black, respectively. Effect sizes in past smokers are shown, for comparison purposes. These analyses were performed with the mmi R package (Online Methods).

  20. Supplementary Figure 20 Genetic relatedness and structure in the Milieu Intérieur cohort.

    (a) Genetic relatedness in the Milieu Intérieur cohort. Pairs of related subjects were detected using an estimate of the kinship coefficient and the proportion of SNPs that are not identical-by-state between all possible pairs of subjects, using KING. (b) Genetic structure of the Milieu Intérieur cohort. Genetic structure was estimated with the Principal Component Analysis (PCA) implemented in EIGENSTRAT. For comparison purposes, the analysis was performed on 261,827 independent SNPs and 1,723 individuals, which include the 1,000 Milieu Intérieur subjects together with 723 individuals from a selection of 36 populations of North Africa, the Near East, western and northern Europe (Online Methods).

  21. Supplementary Figure 21 Local association signals for the 15 genome-wide significant hits associated with immunophenotypes measured in the Milieu Intérieur cohort.

    Each point is a SNP, whose color represents its level of linkage disequilibrium (r2) with the best hit (in purple). Blue lines indicate local recombination rates.

  22. Supplementary Figure 22 Local association signals for the six genome-wide significant hits identified by conditional GWAS of the 15 immunophenotypes showing strong genetic association in the Milieu Intérieur cohort.

    These association signals were identified when conditioning on genotypes of main GWAS signals (Online Methods).

  23. Supplementary Figure 23 Local association signals detected by multi-trait GWAS of immunophenotypes measured in the Milieu Intérieur cohort.

    (a) Local association signals at loci influencing principal components of a PCA of all innate cell immunophenotypes. No suggestive signal (P<5x10-8) was observed for PCs of a PCA of adaptive cell immunophenotypes. (b) Local association signal with the absolute numbers of HLA-DR+ TCM, TEM and TEMRA cells, either CD4+ or CD8+. The six corresponding immunophenotypes were analyzed altogether using a multivariate GWAS (Online Methods).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1-23

  2. Life Sciences Reporting Summary

  3. Supplementary Note

  4. Supplementary Table 1

    Demographic variables included in the study

  5. Supplementary Table 2

    Staining antibodies used for the ten 8-color flow cytometry panels

  6. Supplementary Table 3

    Immunophenotypes measured by standardized flow cytometry in this study

  7. Supplementary Table 4

    Genome-wide signals of association with immunophenotypes in the Milieu Intérieur cohort, with the likely effects of candidate variants on gene expression, protein sequence and human diseases and traits

  8. Supplementary Table 5

    Association signals (-log10(P)) between genome-wide significant variants of loci 1 to 14 and all measured immunophenotypes

  9. Supplementary Table 6

    Other genome-wide significant (P<10-10) or suggestive (P<5x10-8) signals of association with immunophenotypes in the Milieu Intérieur cohort

  10. Supplementary Table 7

    Significant associations of HLA amino-acid positions with candidate immunophenotypes in the Milieu Intérieur cohort

  11. Supplementary Table 8

    Associations of HLA classical alleles with candidate immunophenotypes in the Milieu Intérieur cohort