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Transmission of trained immunity and heterologous resistance to infections across generations

Nature Immunology volume 22, pages 1382–1390 (2021)Cite this article

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An Author Correction to this article was published on 18 January 2023

Matters Arising to this article was published on 20 January 2022

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Abstract

Intergenerational inheritance of immune traits linked to epigenetic modifications has been demonstrated in plants and invertebrates. Here we provide evidence for transmission of trained immunity across generations to murine progeny that survived a sublethal systemic infection with Candida albicans or a zymosan challenge. The progeny of trained mice exhibited cellular, developmental, transcriptional and epigenetic changes associated with the bone marrow-resident myeloid effector and progenitor cell compartment. Moreover, the progeny of trained mice showed enhanced responsiveness to endotoxin challenge, alongside improved protection against systemic heterologous Escherichia coli and Listeria monocytogenes infections. Sperm DNA of parental male mice intravenously infected with the fungus C. albicans showed DNA methylation differences linked to immune gene loci. These results provide evidence for inheritance of trained immunity in mammals, enhancing protection against infections.

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Fig. 1: Infection of male mice increases the resistance of offspring to infections.
Fig. 2: Infection of male mice increases progeny responsiveness.
Fig. 3: Training of male mice induces cellular changes in the bone marrow of offspring.
Fig. 4: Infection induces transcriptional changes in bone marrow progenitors of F1 control and F1 exposed offspring.
Fig. 5: Infection with C. albicans induces changes in the DNA methylation landscape of sperm.

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Data availability

Data from the RNA-seq, ATAC-seq and sperm DNA methylation (RRBS) experiments have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE130327. Raw sequencing data and sample annotations are available from the GEO under accession number GSE130327. The mouse reference genome (mm10, build GRCm38) was accessed from the UCSC genome browser (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/). Blacklisted genomic regions were downloaded from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/mm10-mouse/mm10.blacklist.bed.gz. Prebuilt gene sets for GSEA analysis were downloaded from the GO2MSIG (http://www.bioinformatics.org/go2msig/). The ATAC-seq peaks were annotated using annotatePeaks.pl (http://homer.ucsd.edu/homer/ngs/annotation.html), Reference SNPs and indels from the dbSNP were obtained from the NCBI (http://www.ncbi.nlm.nih.gov/SNP). Repeats were downloaded from Repbase (https://www.girinst.org/repbase/). Differentially methylated regions were annotated using the GENCODE gene model GRCm38.p6 release M20 (https://www.gencodegenes.org/mouse/release_M20.html). Data supporting the findings of this study are available within the article and supplementary information (Supplementary Tables 1–6). No restrictions on data availability apply. Correspondence and requests for materials should be addressed to M.G.N. (Mihai.Netea@radboudumc.nl). Source data are provided with this paper.

Code availability

The raw sequencing data within this study were processed using established software packages with indicated parameters and published datasets as stated in detail in the Methods and Reporting Summary. The code scripts used to analyze these data will be shared upon reasonable request.

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Acknowledgements

M.G.N. was supported by a European Research Council advanced grant (no. 833247) and a Spinoza grant of the Netherlands Organization for Scientific Research. E.J.G.-B. is funded by the Hellenic Institute for the Study of Sepsis. T.R. was supported by the Swiss National Science Foundation (no. 310030_173123) and grants from Fondation Carigest/Promex Stiftung für die Forschung and Fondation de Recherche en Biochimie. J.D.-A. is supported by the Netherlands Organization for Scientific Research (VENI grant no. 09150161910024). G.R. is funded by the Horizon 2020 Marie Skłodowska-Curie Action-European Sepsis Academy-Innovative Training Network (no. 676129). A.S. holds an Emmy Noether fellowship of the Deutsche Forschungsgemeinschaft (DFG) (no. SCHL2116/1-1). M.G.N., A.S. and J.L.S. are funded by the DFG under Germany’s Excellence Strategy EXC2151 390873048. J.W. and K.L. are funded by the DFG within the SFB 1309 (Chemical Biology of Epigenetic Modifications). M.B. is a member of the excellence cluster ImmunoSensation2. We thank K. Nordström for bioinformatics support.

Author information

Author notes

  1. These authors contributed equally: Natalie Katzmarski, Jorge Domínguez-Andrés, Branko Cirovic, Thierry Roger, Evangelos J. Giamarellos-Bourboulis, Andreas Schlitzer, Mihai G. Netea.

Authors and Affiliations

  1. Quantitative Systems Biology, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany

    Natalie Katzmarski, Branko Cirovic & Andreas Schlitzer

  2. Department of Internal Medicine and Radboud Center for Infectious diseases, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

    Jorge Domínguez-Andrés, Jos W. M. van der Meer, Leo A. B. Joosten & Mihai G. Netea

  3. Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands

    Jorge Domínguez-Andrés, Leo A. B. Joosten & Mihai G. Netea

  4. Department 4th of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece

    Georgios Renieris & Evangelos J. Giamarellos-Bourboulis

  5. Infectious Diseases Service, Department of Medicine, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland

    Eleonora Ciarlo, Didier Le Roy & Thierry Roger

  6. Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany

    Konstantin Lepikhov, Kathrin Kattler, Gilles Gasparoni & Jörn Walter

  7. Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany

    Kristian Händler, Heidi Theis & Marc Beyer

  8. Molecular Immunology in Neurodegeneration, German Center for Neurodegenerative Diseases, Bonn, Germany

    Marc Beyer & Joachim L. Schultze

  9. Department for Genomics & Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany

    Joachim L. Schultze

  10. Department for Immunology & Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany

    Mihai G. Netea

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Contributions

N.K., J.D.-A., B.C., G.G., G.R., E.C., D.L.R., T.R., K.L., K.K., K.H., M.B. and H.T. designed and performed the experiments and analyzed the data. N.K. performed the cytokine measurements. N.K., B.C. and E.C. performed the flow cytometry. B.C. processed the RNA-seq and ATAC-seq experiments, pathway analysis and performed the colony assessment under the supervision of A.S. J.D.-A. performed the cytokine measurements and trained the immunity experiments under the supervision of M.G.N. G.R. designed and performed the experiments with mice and the infection assays under the supervision of E.J.G.-B. T.R. designed and E.C. performed the listeriosis model. K.L. and K.K. isolated the mouse sperm and performed the DNA methylation analysis with G.G. J.W., J.W.M.v.d.M., L.A.B.J., M.B. and J.L.S. provided guidance and advice. J.W. supervised the sperm and DNA methylation analysis. T.R., E.J.G.-B., A.S. and M.G.N. conceived the study and oversaw the research program. J.D.-A. wrote the first draft of the manuscript with all authors contributing to the writing and providing feedback.

Corresponding author

Correspondence to Jorge Domínguez-Andrés.

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Competing interests

E.J.G.-B. has received honoraria (paid to the University of Athens) from AbbVie, Abbott, Biotest, Brahms, InflaRx, the Medicines Company, MSD and XBiotech. He has received independent educational grants from AbbVie, Abbott, Astellas Pharma, Axis Shield, bioMérieux, InflaRx, the Medicines Company and XBiotech. M.G.N. is a scientific founder of TTxD.

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Peer review information Nature Immunology thanks Søren Paludan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Fungal burden in F1-control and F1-exposed mice.

(a) Fungal burden assessed in F1-control and F1-exposed mice in liver and kidney 3 and 7 days after infection with C. albicans, n = 5 per group. (b) Myeloperoxidase activity assessed in F1-control and F1-exposed mice in liver and kidney 3 and 7 days after infection with C. albicans, n = 5 per group. Statistical significance was calculated by two-tailed Mann Whitney U test. P values are depicted on the figures; ns, not significant.

Source data

Extended Data Fig. 2 No protective effects are seen in the F3-exposed offspring.

Bacterial burden assessed in F3-control and F3-exposed mice in liver, kidney and spleen 3 days after i.v. infection with E. coli. n = 5 per group; Mann Whitney U test. P values are depicted on the figures.

Source data

Extended Data Fig. 3 Cell counts in blood after infection of F1 mice.

Leukocyte subpopulations in blood collected just before (day 0), 2 and 3 days post-infection with L. monocytogenes (2 ×104 CFU i.v.) in F1-control and F1-exposed mice (n = 8). Statistical significance was calculated by two-tailed Mann Whitney U test. P values are depicted on the figures; ns, not significant.

Source data

Extended Data Fig. 4 Responses in the female progeny. Trained parents: females; progeny analyzed: F1 females.

(a) Survival of F1-control and F1-exposed mice after infection with L. monocytogenes. Data are presented as a Kaplan-Maier plot with a log rank test used to compare susceptibility between the two groups, n = 16 per group. (b) Percent of initial weight of mice 48 h after infection, n = 16 per group. (c, d) Bacteria in blood collected 48 (c) and 72 h after infection (d), n = 16 per group. P values are depicted on the figures, Mann-Whitney U test, unless otherwise stated. (e) Leukocyte subpopulations in blood collected just before (day 0), 2 and 3 days post-infection with L. monocytogenes in F1-control and F1-exposed mice, n = 16 per group. Statistical significance was calculated by two-tailed Mann Whitney U test. P values are depicted on the figures; ns, not significant.

Source data

Extended Data Fig. 5 Responses in the male progeny. Trained parents: females; progeny analyzed: F1 males.

(a) Survival of F1-control and F1-exposed mice after infection with L. monocytogenes. Data are presented as a Kaplan-Maier plot with a log rank test used to compare susceptibility between the two groups. n = 6 for F1-control, 8 for F1-exposed. (b) Percent of initial weight of mice 48 h after infection, n = 6 for F1-control, 8 for F1-exposed (c, d) Bacteria in blood collected 48 (c) and 72 h after infection (d), n = 6 for F1-control, 8 for F1-exposed. P values are depicted on the figures, Mann-Whitney U test, unless otherwise stated. (e) Leukocyte subpopulations in blood collected just before (day 0), 2 and 3 days post-infection with L. monocytogenes in F1-control and F1-exposed mice, n = 6 for F1-control, 8 for F1-exposed. Statistical significance was calculated by two-tailed Mann Whitney U test. P values are depicted on the figures; ns, not significant.

Source data

Extended Data Fig. 6 Tlr1-/-, Tlr2-/-, and Tlr6-/- mice are fully trainable by zymosan and fully resistant to infection with L. monocytogenes.

Survival of wild type, Tlr1-/-, Tlr2-/-, and Tlr6-/- mice female mice trained with zymosan before i.v. challenge with 1.1 ×105 CFU L. monocytogenes. Data are presented as a Kaplan-Maier plot with a log rank test used to compare susceptibility between the two groups. Number of mice per group and p value are depicted in the figure.

Source data

Extended Data Fig. 7 Phenotyping strategy.

(a) Manual gating strategy to phenotype BM myeloid lineages and progenitors using flow cytometry. GMP, cMoP and Ly6chigh monocytes (Mono) were sorted for RNA-seq or ATAC-seq. (b) Quantification of cell populations in the progeny of F1-control or F1-exposed mice. F1-control, n = 17, F1-exposed n = 13, Statistical significance was calculated by two-tailed unpaired t-test; ns, not significant.

Source data

Extended Data Fig. 8 Infection induces epigenetic changes in bone marrow progenitors of the F1-control and F1-exposed offspring.

(a) General distribution of all identified peaks by ATAC-seq relative to the distance to the closest gene transcription start site (TSS). (b) Annotation of all identified peaks according to the genomic location. UTR, untranslated region. (c) Hierarchical clustering and heatmap of all differentially accessible (DA) ATAC-seq peaks of sorted GMPs from F1-control and F1-exposed offspring. (1460 opening, 1402 closing regions). (d) Heatmap and hierarchical clustering of the subfraction of DA ATAC-seq peaks located within gene promoter regions (176 opening, 177 closing regions). F1-control/F1-exposed, n = 13 per group. See Supplementary Table 6 for the full list of differentially accessible regions.

Extended Data Fig. 9 DNA methylation landscape of sperm.

(a) Methylation density plot of global methylation levels. (b) Genomic annotation of DMRs. (c) Methylation levels at repetitive elements. (d) KEGG pathways enriched for hypomethylated and hypermethylated DMRs. n = 10 per group.

Supplementary information

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Katzmarski, N., Domínguez-Andrés, J., Cirovic, B. et al. Transmission of trained immunity and heterologous resistance to infections across generations. Nat Immunol 22, 1382–1390 (2021). https://doi.org/10.1038/s41590-021-01052-7

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