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Transmission of trained immunity and heterologous resistance to infections across generations - Nature Immunology
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. You have full access to this article via your institution. Download PDF Download PDF Main . Transgenerational inheritance of traits acquired during the life of an organism was originally proposed by Lamarck as an evolutionary mechanism of adaptation and species generation but was later discarded in favor of the evolutionary model proposed by Darwin and Wallace 1 . However, an increasing number of studies have shown that transient environmental stimuli can trigger short- or long-term inheritance of phenotypic traits, most likely transmitted through epigenetic mechanisms. For example, genes controlling dorsoventral asymmetry in the plant Linaria vulgaris 2 or systemic acquired resistance in plants show epigenetic transmission to offspring 3 . In Caenorhabditis elegans , transgenerational mechanisms were either linked to alteration of the histone landscape or modifications of the RNA interference pathway, leading to improved fitness of the offspring after challenge with pathogenic bacteria 4 . Transgenerational inheritance of traits is also observed in mammals and a role for heritable epigenetic modifications has been proposed for a variety of phenotypic adaptations linked to metabolism 5 , appearance 6 , sensory perception 7 or resistance to toxins 8 . Since infections are among the strongest factors impacting survival, it is conceivable that nongenetic transgenerational inheritance of traits evolved as an additional host defense adaptation in mammals, similarly to plants and invertebrates. Therefore, we hypothesized that mammalian host defense experiences against infections may be propagated to the progeny by nongenetic intergenerational and transgenerational mechanisms. In this study, we describe that the progeny of mice that survive a fungal infection, or stimulated with the fungal-derived ligand zymosan, showed enhanced responsiveness to endotoxin challenge, alongside improved protection against systemic bacterial infections with E. coli and L. monocytogenes . These functional changes are based on cellular, developmental, transcriptional and epigenetic changes associated with the bone marrow-resident myeloid cell compartment. Results . Infection of male mice increases the resistance and responsiveness of the offspring . To test this hypothesis, we infected six-week-old mice with a systemic sublethal dose of C. albicans , which is spontaneously cleared by the murine immune system but induces strong inflammation 9 . To avoid the influence of the estrous cycle or possible effects of the infection on the reproductive capacity or uterine environment of the animals, we employed only male mice in the first set of experiments. Nonlethal systemic candidiasis induces protection against bacterial reinfection through epigenetic rewiring of myeloid cells, a process termed trained immunity 10 . One month after complete clearance of the infection, ‘control’ (mock-infected with PBS, F0 control) and ‘exposed’ (infected with C. albicans and later recovered, F0 exposed) mice were mated with healthy noninfected females, generating the first offspring of these mice, the control (F1 control) and exposed lineages (F1 exposed), respectively. These independent progenies were subsequently subjected to a heterologous systemic infection with the Gram-negative bacterium E. coli (Fig. 1a ). We compared the bacterial burden in the liver, kidney and lung of the mice 3?d after infection. F1 exposed mice showed a reduction of the bacterial burden in the liver and lung (Fig. 1b ). We subsequently assessed the functional differences in the response to an E. coli challenge. Challenged F1 exposed mice showed an increased recruitment of cells to the site of infection in the peritoneal cavity (Fig. 1c ) and higher concentrations of pro-inflammatory cytokines 4?h after infection (Fig. 1d ). This was accompanied by enhanced microbicidal function measured by myeloperoxidase (MPO) activity in the kidney 3?d after systemic E. coli infection (Fig. 1e ). Fig. 1: Infection of male mice increases the resistance of offspring to infections. a , Scheme of the experimental procedures followed in the study. b , Bacterial burden assessed in F1 control and F1 exposed mice in the liver, kidney and lung 3?d after intravenous infection with E. coli , n ?=?5 mice per group. c , Count of peritoneal cells recruited to the peritoneal cavity in F1 control and F1 exposed mice 4?h after intraperitoneal E. coli infection, n ?=?5 mice per group. d , Concentrations of IL-1β and IL-6 produced by peritoneal macrophages from F1 control and F1 exposed animals 4?h after intraperitoneal E. coli infection, n ?=?5 mice per group. e , MPO activity in F1 control and F1 exposed mice in the liver, kidney and lung 3?d after intravenous infection with E. coli , n ?=?5 mice per group. f , CFUs in the bone marrow of the progeny of F1 control and F1 exposed mice 7?d after in vivo intravenous E. coli infection, n ?=?5 mice per group. g , Relative proportions of granulocyte, macrophage or GMP colonies in the bone marrow of F1 control and F1 exposed mice 7?d after in vivo intravenous E. coli infection. Total number of colonies quantified per condition, n ?=?5 mice per group. h , Percentages of GMPs and cMoPs present in the bone marrow of F1 control and F1 exposed mice 3?d after intravenous C. albicans infection, n ?=?5 mice per group. i , Bacterial burden assessed in F2 control and F2 exposed mice in the liver, kidney and spleen 3?d after intravenous infection with E. coli , n ?=?10 mice 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 Full size image Furthermore, subjecting the bone marrow of F1 control and F1 exposed animals 7?d after E. coli infection to a colony formation assay revealed a higher number of myeloid lineage-associated colonies developing from the bone marrow of F1 exposed mice compared to the bone marrow of F1 control mice (Fig. 1f,g ). Infection of F1 exposed mice with C. albicans induced an increase of bone marrow granulocyte monocyte progenitors (GMPs) and common monocyte progenitors (cMoPs) compared to F1 control mice in the bone marrow 3?d after infection (Fig. 1h ). Under these conditions, fungal burden was unchanged, while partial increase in MPO activity could be detected (Extended Data Fig. 1 ). Up to this point, our results demonstrated the existence of generational inheritance of enhanced infectious protection in a setting of direct F0 male germline exposure to a pathogen, influencing the subsequent F1 generation, whereas transgenerational inheritance involves the transmission of a trait without exposure of the prior generation 11 . To assess the persistence of the increased responsiveness against infections across generations, we generated F2 and F3 control and F2 and F3 C. albicans -exposed mice. To generate F2 and F3 mice, healthy noninfected F1 control and F1 exposed mice (or F2 control and F2 exposed, respectively) never exposed to pathogenic agents were mated with healthy noninfected females. The progenies of these crosses were infected systemically with E. coli . We detected a lower bacterial burden in F2 exposed versus F2 control mice, showing a persistence of nongenetic transgenerational effects in the F2 generation (Fig. 1i ). In contrast, no protective effects were seen in the F3 exposed offspring (Extended Data Fig. 2 ). These results suggest a nongenetic transgenerational transmission of trained immunity from F1 to F2 mice. To shed light on the potential transmission of enhanced protection against E. coli to the progeny, we used the prototypic systemic lipopolysaccharide (LPS)-induced inflammation model. We assessed serum concentrations of tumor necrosis-α (TNF-α) and interferon-γ (IFN-γ) 90?min and 4?h after systemic challenge of F1 control and F1 exposed mice with LPS (Fig. 2a ). F1 exposed mice presented with higher concentrations of the pro-inflammatory cytokines TNF-α and IFN-γ 90?min and 4?h postchallenge, respectively (Fig. 2b ). These data further demonstrate the transmission of trained immunity in F1 exposed mice. Fig. 2: Infection of male mice increases progeny responsiveness. a , Scheme of the experimental procedure followed in b . b , Circulating TNF-α and IFN-γ concentrations in the blood of the progeny of control (F1 control) and exposed (F1 exposed) animals restimulated with LPS. n ?=?5 mice per group. c , Scheme of the experimental procedure followed in d – g . d , Survival of F1 control and F1 exposed mice after infection with 10 4 L. monocytogenes intravenously. Data are presented as a Kaplan–Maier plot with a log rank test used to compare susceptibility between the two groups. n ?=?16 mice per group. e , Percentage of initial weight of mice 48?h after infection, n ?=?16 mice per group. f , g , Bacteria in blood collected 48 ( f ) and 72?h ( g ) after infection, n ?=?16 mice per group. Statistical significance was calculated by two-tailed Mann–Whitney U -test unless otherwise stated. P values are depicted on the figures. Source data Full size image To test the robustness of the concept of transgenerational transmission of resistance to infection, we assessed the responses of F1 exposed and F1 control mice to another unrelated pathogen in a second independent laboratory (Lausanne University Hospital). Naive male mice were challenged with zymosan A from Saccharomyces cerevisiae or PBS to induce trained immunity 12 and subsequently bred with naive female mice to produce F1 exposed or F1 control progeny. Next, 8–9-week-old F1 exposed or F1 control mice were challenged with the Gram-positive bacterium L. monocytogenes (Fig. 2c ). F1 exposed mice showed increased survival, decreased weight loss and lower bacterial burden than F1 control mice (Fig. 2d–g ). Two days after infection, F1 exposed mice displayed higher absolute numbers of neutrophils and inflammatory Ly6C hi monocytes in the blood, suggesting an increased mobilization or development of myeloid cells (Extended Data Fig. 3 ). To test the influence of sex on the inheritance of increased resistance to infection, we challenged female F0 mice with zymosan A and subsequently investigated the responses to L. monocytogenes in 8–9-week-old F1 exposed versus F1 control progeny. Female F1 exposed mice showed increased survival, lower bacterial burden and higher absolute numbers of neutrophils and inflammatory monocytes in the blood (female progeny, Extended Data Fig. 4 ; male progeny, Extended Data Fig. 5 ). We considered employing Toll-like receptor (TLR) knockout mice models to study the influence of different pathways on these mechanisms. However, normal Tlr1 ?/? , Tlr2 ?/? and Tlr6 ?/? mice respond normally to the induction of trained immunity with zymosan, which induces resistance to the subsequent infection with L. monocytogenes (Extended Data Fig. 6 ). Therefore, we expected the progeny of these mice to be protected accordingly and behave like wild-type (WT) mice. Altogether, our results demonstrate the protective effects of a nonlethal infection with live fungi or a stimulation with a fungal-derived compound against two types of heterologous bacterial challenge of different nature that were transmitted across generations independent of sex. Overall, these results support the existence of transgenerational transmission of protective trained immunity. Infection of male mice induces cellular changes in the myeloid cell compartment of their offspring . Adaptation of the bone marrow-resident myeloid cell progenitor repertoire is crucial for the induction of trained immunity 13 . Accordingly, we assessed the abundance and phenotype of myeloid cell progenitors and mature myeloid cell subsets in the bone marrow and blood of steady state F1 control or F1 exposed mice using flow cytometry (Fig. 3a,b and Extended Data Fig. 7a,b ). Monocyte subsets can be divided into different subgroups by their expression level of different surface markers, for example, Ly6C and major histocompatibility complex (MHC) class II. We examined the population of monocytes in the bone marrow and separated the more ‘inflammatory’ Ly6C hi monocytes from the ‘patrolling’ Ly6C lo monocytes. This analysis revealed a reduction in bone marrow Ly6C lo monocytes in F1 exposed mice. Furthermore, we observed an upregulation of MHC class II expression on bone marrow cMoPs in F1 exposed mice, indicating a more activated cellular status (Fig. 3a ). Fig. 3: Training of male mice induces cellular changes in the bone marrow of offspring. a , Quantification of Ly6C hi monocytes, Ly6C lo monocytes and cMoPs in the bone marrow of the progeny of control (F1 control) and exposed (F1 exposed) mice. F1 control n ?=?17 mice, F1 exposed n ?=?13 mice, unpaired t -test. MFI, mean fluorescence intensity. b , Percentages of CD45 + cells, Ly6C hi monocytes, Ly6C lo monocytes and neutrophils in the blood of the progeny. n ?=?22 mice for F1 control group, n ?=?18 mice for F1 exposed group. Statistical significance was calculated by two-tailed unpaired t -test. P values are depicted on the figures. Source data Full size image Infection of male mice induces transcriptional and epigenetic changes in bone marrow progenitors . To understand these changes at the transcriptional level, we performed RNA sequencing (RNA-seq) analysis of bone marrow GMP, cMoPs and bone marrow Ly6C hi monocytes and compared the transcriptional profiles of F1 control and F1 exposed mice during homeostasis (Supplementary Tables 1 and 2 ). Differential expression analysis indicated that F1 exposed cMoPs upregulated genes involved in immune function, such as Rnd2 , related to extracellular signal-regulated kinase signaling, 14 Ssc4d , a scavenger receptor involved in the development and regulation of innate and adaptive immunity, 15 Tnfsf13 ( APRIL ), a central modulator of lymphocytic responses, 16 Slamf9 , which encodes a member of the signaling lymphocytic activation molecule family 17 and Maob , which is involved in mitochondrial metabolism 18 (Fig. 4a ). Furthermore, comparing the transcriptomes of bone marrow Ly6C hi monocytes from F1 control and F1 exposed mice revealed important differences in the expression of genes associated with acute immune responses, such as Mmp2 (ref. 19 ), Il21r 20 , Il18rap 21 , Rab7b 22 and the metabolic-related genes Tmem25 (ref. 23 ) and Grhpr 24 , indicating an immune-related priming of F1 exposed bone marrow Ly6C hi monocytes during homeostasis (Fig. 4b ). These results were also in line with the observed upregulation of MHC class II on bone marrow cMoPs in F1 exposed mice. Fig. 4: Infection induces transcriptional changes in bone marrow progenitors of F1 control and F1 exposed offspring. a , b , Heatmaps of DEGs for F1 exposed versus F1 control in cMoPs ( a ) and Ly6C hi monocytes ( b ). c , d , GSEA of ranked gene fold changes comparing F1 exposed versus F1 control testing myeloid pathways gene sets in cMoPs ( c ) and inflammatory pathways gene sets in monocytes ( d ). NES, normalized enrichment score. Pathways with P ?exposed bone marrow cMoPs (Fig. 4c and Supplementary Table 3 ) and an enrichment for acute inflammatory response pathways in homeostatic F1 exposed bone marrow Ly6C hi monocytes (Fig. 4d ). Investigation of the developmental progression of these pathways across GMPs, cMoPs and Ly6C hi monocytes showed a decrease of the pathway ‘negative regulation of myeloid leukocyte differentiation’ in the transition from GMPs to cMoPs overall, indicating enhanced myeloid cell differentiation toward monocyte lineage-committed progenitors of F1 exposed mice (Fig. 4e ). On the other hand, the pathway ‘regulation of acute inflammatory response’ increased from the cMoP stage onward, further supporting the notion of enhanced inflammatory priming within the monocyte lineage (Fig. 4f ). In line with this, focusing on the fraction of pathway genes contributing most to the enriched or decreased signatures identified by GSEA (leading-edge genes) highlighted a number of genes related to key immune functions, such as Gata2 , Il4 or Mafb in F1 exposed cMoPs (Fig. 4g ), whereas F1 exposed Ly6C hi monocytes showed an upregulation of genes related to pro-inflammatory pathways, such as Fcer1a 25 , Cd59 (ref. 26 ) or IL1b 27 (Fig. 4h ). Overall, we observed an alteration on the myeloid cell compartment and associated transcriptional changes in myeloid progenitors in the bone marrow. To investigate the underlying molecular basis of the observed acute inflammatory priming in F1 exposed cMoPs and Ly6C hi monocytes, we analyzed the DNA accessibility profiles of F1 control and F1 exposed GMPs using the assay for transposase accessible chromatin by sequencing (ATAC-seq) (Extended Data Fig. 8a–c and Supplementary Tables 4 and 5 ). ATAC-seq analysis of F1 control versus F1 exposed GMPs showed 2,862 differentially accessible peaks across all annotated genomic regions and 353 differentially accessible peaks within gene promoter regions, indicating an important remodeling at the level of DNA accessibility in F1 exposed mice (Extended Data Fig. 8c,d and Supplementary Table 6 ). Gene-level analysis of the observed changes in DNA accessibility between F1 control and F1 exposed mice revealed that genomic regions closest to genes involved in the regulation of myeloid cell development and activation, such as Nfatc2 (ref. 28 ), Daxx 29 , Runx3 (ref. 30 ) and Klf4 (ref. 31 ) were more accessible in F1 exposed than in F1 control mice. This suggests the alteration of DNA accessibility at the GMP stage as a possible mode for the induction of acute inflammatory priming in F1 exposed GMP-derived cMoPs and Ly6C hi monocytes (Fig. 4i ) at the transcriptional level. Of note, differentially accessible regions at the promoter sites of GMPs more efficiently stratified F1 control versus F1 exposed animals according to hierarchical clustering compared to clustering using the total set of differentially accessible regions. Taken together, transcriptomic and DNA accessibility data suggest that the basis of the enhanced innate immune response observed in F1 exposed mice is mediated by the establishment of an inflammatory epigenetic priming toward a more efficient innate immune response to subsequent challenges, as previously described for trained immunity 10 . Fungal infection of male mice induces changes in the DNA methylation landscape of their sperm . Next, we set out to unravel the potential epigenetic mechanisms linked to the transmission of the observed trained immunity trait from the infected fathers to the F1 exposed generation. Epigenetic alterations in sperm can affect the phenotypic characteristics of the following generations 7 , 32 . A previous study reported changes in sperm DNA methylation from male mice exposed to hypothermia, suggesting a link to the transmission of physiological alterations observed in the offspring 33 . Following a similar approach, we compared the DNA methylomes of 10 isogenic control males and 10 infected and recovered males using a comprehensive reduced representation bisulfite sequencing (RRBS) approach covering 4 million CpGs (approximately 18% of all CpGs). At a genome-wide scale, the levels of sperm CpG methylation of infected and recovered animals were indistinguishable (Extended Data Fig. 9a ). However, using strict filtering conditions (>3 CpGs in 1,000-base pair (bp) tiles, false discovery rate (FDR) adjusted P ?5%) and considering stochastic intersample variability (differential variability P ?Extended Data Fig. 9b ). In addition, we found differences at the subclasses of long terminal repeats of retrotransposable elements (Extended Data Fig. 9c ). These findings indicate a clear treatment-dependent difference in germline transmission of gene-associated DNA methylation, suggesting a connection to the transgenerational inheritance of the immune reactivity phenotype. Along this line, we identified DMRs in sperm at transcription factor genes known to be important for myeloid cell regulation such as Nfatc2 , Foxp4 , Tcf4 , Stat2 , Itga1 and Rora 28 , 34 , 35 (Fig. 5b ) and an enrichment of hypomethylated DMRs linked to the pathway ‘aldosterone synthesis and secretion’ (Extended Data Fig. 9d ). Remarkably, we detected differences between the sperm DNA methylomes of isogenic control males and infected and recovered males. Fig. 5: Infection with C. albicans induces changes in the DNA methylation landscape of sperm. a , Volcano plot displaying the methylation difference of 500-bp tiles versus associated –log 10 FDR-adjusted P . P values were calculated by logistic regression and adjusted for multiple testing (FDR) using a sliding linear model as outlined as default in the methylKit package. Regions hypermethylated in the infected group (methylation difference >5%, FDR?5%, FDR?further analysis. n ?=?10 mice per group. b , Scaled heatmap of significant DMRs clustered by Manhattan distance with annotation of DMR-associated genes of interest. Control samples are annotated in blue, infected samples in orange. n ?=?10 mice per group. Full size image Discussion . Taken together, this study provides evidence that a nonlethal challenge in mammals can induce increased heterologous resistance to infections in the next generation. This effect is accompanied by functional, transcriptional and epigenetic changes inducing a poised acute inflammatory state, thereby enabling a more potent innate immune response in the face of infection, which is reminiscent of trained immunity. Several studies described that trained immunity is mediated at the level of hematopoietic progenitors and specifically involves adaptations toward enhanced myelopoiesis 12 , 13 . Our findings demonstrate that the increased responsiveness of myeloid cells can be passed on to the following generation causing transcriptional and epigenetic changes in key inflammatory pathways in myeloid cells, by inhibition of the expression of anti-inflammatory factors in cMoPs and increase of the acute response pathways in Ly6C hi monocytes. The transgenerational transmission of traits related to immune-mediated resistance has been described previously in plants and invertebrate animals, such as flies, beetles and worms 36 , 37 , 38 . Specifically, the offspring of Indian meal moths ( Plodia interpunctella ) exposed to low doses of P. interpunctella granulosis virus were subsequently less susceptible to viral challenge 38 . In honeybees, the offspring of queens previously exposed to the bacterium Paenibacillus larvae had lower mortality rates via enhanced differentiation of prohemocytes to hemocytes 37 , resembling a somewhat similar mechanism as described in our study, namely the increased activity of myeloid progenitors observed in the bone marrow of F1 exposed mice. Scallop eggs from mothers exposed to heat-killed Vibrio anguillarum exhibited increased agglutination and microbicidal effect against Gram-negative bacteria ( E. coli ) and fungi ( Pichia pastoris ) 39 . A recent study showed a nongenetic, nonepigenetic, nonmicrobial mode of transmission of regulatory T cell proportions in different mouse strains 40 , whereas another study showed the transgenerational transmission of behavioral alterations after prenatal exposure to the double-stranded RNA synthetic analog poly(I:C) 41 . These authors focused on the behavioral phenotype, although it is likely that these mice also presented heritable immune alterations, as reported in this study. Taken together these studies indicate that the multigenerational transmission of traits is governed by a diverse set of mechanisms that can differ greatly between members of the same species. One important aspect to point out is that the differences in immune responses transmitted across generations are expected to be smaller than differences seen after acute perturbation elicited by infection models in knockout mouse models or after the use of immune modulators. Despite this, they have large consequences during the long-time intervals at work. One example of the importance of subtle evolutionary changes is the persistence of lactase that spread through populations during the last 6,000 years: while at the individual level the differences in nutrition and fertility between individuals drinking milk or not as adults are quite small, at evolutionary scales these changes are major 42 . The long-term impact of transgenerational transmission of resistance to infection might have similar relevant large effects at the population level. The observations reported in this study are in line with a recent study that suggested that TNF, one of the most important pro-inflammatory cytokines, can intergenerationally transmit chromatin changes through ATF7 (ref. 43 ). Intergenerational immune inheritance after the nonlethal C. albicans infection employed in this study showed increases in the expression of cytokines associated with trained immunity, such as interleukin-1β (IL-1β), IL-6 and TNF. Indeed, the hypothesis that trained immunity is responsible for intergenerational transmission of protection against infections was supported by a recent study in shrimps showing increased resistance of the progenies of Vibrio -exposed ancestors for three successive generations 44 . The exact molecular mechanisms underlying the inheritance of immune modulation is to be elucidated. Our results show both the intergenerational and transgenerational transmission of resistance to infections. Intergenerational effects involve the direct exposure of the F0 and their germline to the stimulus. In this sense, the challenge of mice with fungal-derived stimuli gave rise to an F1 generation with increased antimicrobial resistance 11 . Transgenerational effects refer to the transmission of a trait in the lack of direct exposure 11 . In this regard, we report that in this study the F2 of the exposed lineage, which were never in contact with the original stimuli, retained the increased resistance to a bacterial infection. Our results indicate that in mice, transmission of resistance to infection to their offspring is associated with changes in the methylation landscape of the parents’ sperm. How these changes affect epigenetic and/or transcriptional processes in myeloid progenitor and effector cells within the offspring is unclear. The observed range of DMR variation among individual sperm samples in treated and recovered males suggests that the epigenetic inheritance effect cannot be reduced to a single gene-specific change. Our data rather suggest that multiple but regular changes in individual sperm samples may exert a ‘coordinated’ multigene-driven influence on the epigenetic resetting and programming events occurring during early development and thus affect myeloid precursor development. Such a scenario would be compatible with the observation that alterations found in the sperm of individual donors and open chromatin signatures in myeloid effector cells in F1 offspring are not necessarily at identical genes and locations but overall exert a similar priming phenotype. Comparable phenomena have been described for individual epigenetic inheritance at repetitive elements 45 , 46 . Genome-wide DNA methylation approaches of early embryonic stages and/or early fetal hematopoietic cells, for example, by RRBS may further unravel the molecular basis for the transgenerational inheritance of trained immunity. One strength of our study is that the intergenerational transmission of trained immunity was observed in two independent laboratories and three different models. The heterogeneity of these families of pathogens (Gram-positive, Gram-negative bacteria, fungi, stimuli derived from bacteria and fungi) suggest the existence of the transmission of a general ‘infection alertness’ rather than a pathogen-specific effect. The immunological effects of trained immunity last at least three months and up to one year, although heterologous protection against infections can last for up to five years 47 . Increased resistance to infection was maintained in F2 but lost in the subsequent F3 generation. The exact duration of the intergenerational and transgenerational effects should be clarified in further studies. The developmental stage of exposure and age of mice also impacted the comparison. The immune system of adult mice was less responsive than that of young adults to the inflammatory stimuli used in the current study 48 . Additional open questions are represented by the challenges to demonstrate the exact molecular mechanism responsible for these effects, including the relationships between gametes and soma to establish, transmit and maintain nongenetic modifications in the lineage of exposed individuals or the influence of these mechanisms on aging and inflammation (also known as ‘inflammaging’), which should be investigated in future studies. In conclusion, the results presented in this study challenge the paradigm that innate immune system-mediated adaptation to infections cannot be inherited. Our data suggest the existence of Lamarckian mechanisms modulating immunological traits over generations that may cause evolutionary advantages. This concept implies that the genetic evolution of immunological fitness can be aided by rapid epigenetic adaptation during infectious pandemics in a population, resulting in short-term induction of partial protection during instances of extremely high evolutionary pressure. However, more studies are needed to investigate these processes and the mechanisms mediating them at the immunological and molecular levels. Moreover, additional research is warranted to study the potential implications of intergenerational transmission of immune traits for the pathogenesis of other immune-mediated diseases where trained immunity plays an important role, such as atherosclerosis, diabetes or cancer. Methods . Ethics statement . Our research complies with all relevant ethical regulations. The animal studies were approved by the Greek veterinary directorate (protocol no. 7467 on 24 December 2013) and the Division des Affaires Vétérinaires, Direction Générale de l’Agriculture, de la Viticulture et des Affaires Vétérinaires, état de Vaud (Epalinges, Switzerland; authorization no. 877.9). Experiments were performed according to Greek, Swiss and Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. Flow cytometry . Mice were euthanized by injection of ketamine and xylazine. Blood was collected by cardiac puncture of the inferior vena cava. Bone marrow was flushed from the femur and tibia of one leg. Samples were washed with FACS buffer (0.5% BSA, 2?mM of EDTA, PBS), resuspended and incubated with the respective antibody mixture for 1?h (bone marrow cells) or 35?min (blood cells) at 4?°C in the dark. To reduce nonspecific antibody binding TruStain FcX antibody (1:200 dilution; BioLegend) was used. To exclude lymphoid cell, neutrophil and erythroid lineages in the bone marrow, we used antibodies directed against CD3e, CD19, NK1.1, B220, Ly6g, Ter119 and CD3e, CD19, NK1.1, B220 and Ter119 in all other panels. Cells were washed; where appropriate, cells were further incubated with streptavidin conjugates for a further 15?min. After washing, red blood cells were lysed with red blood cell lysis buffer (BioLegend) followed by another washing step. Cells were resuspended in FACS buffer and incubated with the Live/Dead marker DRAQ7 (1:1,000 dilution; BioLegend) for 5?min at 4?°C in the dark. Cells were resuspended in FACS buffer. Samples were acquired using BD FACS Aria III and LSRII (BD Biosciences) and the FACS Diva software version 8.0.1 (BD Biosciences). Data were analyzed using FlowJo 10.7.1 (FlowJo LLC). For RNA isolation from bone marrow cells, cells were first purified by flow cytometry into cooled 1.5-ml reaction tubes containing FACS buffer. Cells were pelleted at 2,200?r.p.m. for 5?min at 4?°C and vigorously resuspended in 500?μl of QIAzol. Tubes were stored at ?80?°C until further processing. The antibodies used were (sourced from BioLegend unless stated otherwise): CD3 APC/Cy7 (1:200 dilution; clone 145-2C11; catalog no. 100222); CD19 APC/Cy7 (1:200 dilution; clone 6D5; catalog no. 115530); TER-119 APC/Cy7 (1:200 dilution; clone TER-119; catalog no. 116223); NK-1.1 APC/Cy7 (1:200 dilution; clone PK136; catalog no. 108724); CD45R (B220) APC/Cy7 (1:200 dilution; clone RA3-6B2; catalog no. 103224); MHC class II (I-A/I-E) BrilliantViolet510 (1:200 dilution; clone M5/114.15.2; catalog no. 107635); CD11b BrilliantViolet421 (1:200 dilution; clone M1/70; catalog no. 101236); CD11c PerCP-Cyanine5.5 (1:100 dilution; clone N418; catalog no. 117328); CD24 PE (1:500 dilution; clone M1/69; catalog no. 101808); MERTK PE-Cyanine7 (1:100 dilution; clone DS5MMER; catalog no. 25-5751-82; eBioscience); CD115 (CSF-1R) PE/Dazzle 594 (1:100 dilution; clone AFS98; catalog no. 135528); Ly-6G APC (1:400 dilution; clone 1A8; catalog no. 127614); Ly-6C Brilliant Violet 605 (1:200 dilution; clone HK1.4; catalog no. 128036); CD45 FITC (1:200 dilution; clone I3/2.3; catalog no. 147710); TruStain CD16/32 (1:200 dilution; clone 93; catalog no. 101320;); Ly-6G APC/Cy7 (1:400 dilution; clone 1A8; catalog no. 127624); Ly-6A/E(Sca-1) PerCP-Cyanine5.5 (1:200 dilution; clone D7; catalog no. 45-5981-82; eBioscience); CD16/32 APC (1:100 dilution; clone 93; catalog no. 101325); CD135 Biotin (1:100 dilution; clone A2F10; catalog no. 135308); CD117 (c-Kit) PE/Cyanine7 (1:100 dilution; clone 2B8; catalog no. 105814); CD34 eFluor 450 (1:100 dilution; clone RAM34; catalog no. 48-0341-82; eBioscience); CD131 PE (1:100 dilution; clone JORO 50; catalog no. 559920; BD Biosciences); Brilliant Violet 785 Streptavidin (1:200 dilution; catalog no. 405249); Live/Dead DRAQ7 far-red (1:200 dilution; catalog no. 424001). Colony formation assay . Colony formation assays were performed in M3434 medium according to the manufacturer’s guidelines (STEMCELL Technologies). Briefly, frozen bone marrow samples were thawed, washed in Roswell Park Memorial Institute (RPMI) 1640 medium and counted. Twenty thousand cells (trypan blue-negative) were plated in the 35-mm dish format in duplicates per sample and incubated for 14?d at 37?°C, 5% CO 2 and humidified conditions. Colonies were evaluated with the Nikon Eclipse TS100 microscope (Nikon Instruments). Colonies were classified into colony-forming unit (CFU)-G (G), CFU-M (M) and CFU-GM (GMP) according to the manufacturer’s guidelines. Apoptotic colonies were designated as ‘undefined’. Preparation of complementary DNA libraries for transcriptome analysis . FACS-enriched cell populations (5,000 cells per population) were resuspended in QIAzol (QIAGEN) and stored at ?80?°C. Total RNA was purified using the miRNeasy Micro Kit (QIAGEN) and evaluated using the TapeStation 2200 system and high-sensitivity reagents (Agilent Technologies). Libraries were generated following the SMART-Seq2 protocol 57. After library quantification using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific) and size estimation on the TapeStation 2200 High-Sensitivity D500 assay system (Agilent Technologies), samples were pooled and sequenced on a NextSeq500 using High Output v.2 chemistry (SR 75?bp). Transcriptome analysis . Raw sequencing data were demultiplexed using blc2fastq2 v.2.20. After quality check with MultiQC v.1.5, reads were aligned to the mm10 mouse reference transcriptome from the UCSC Genome Browser (build GRCm38; https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/ ). Transcript abundance was assessed with kallisto v.0.440 with default parameters. For downstream processing, data were imported into R v.3.5.1 and filtered to remove those transcripts not corresponding to HUGO Gene Nomenclature Committee gene symbols, read sum lower than ten across all samples or matching antisense, long intergenic non-protein coding (LINC) or pseudogenes. The DESeq2 package v.1.20.0 served as foundation for the core analysis. To identify differentially expressed genes (DEGs), a threshold of an adjusted P ?ATAC-seq . Fresh FACS-enriched GMPs (50,000 per population) were washed in cold PBS and subsequently lysed in cold lysis buffer (10?mM of NaCl, 3?mM of MgCl 2 , 0.1% IGEPAL, 10?mM of Tris HCl, pH 7.4). Pellets were resuspended in 20??l of reaction buffer (10?mM of TAPS-NaOH (pH 8.5), 5?mM of MgCl 2 , 10% dimethylformamide) in the presence of primer-loaded transposase Tn5 for 30?min at 37?°C. After purification with the MinElute PCR Purification Kit (QIAGEN), samples were eluted in 10??l of H 2 O and frozen until further processing. Tagmented libraries were quantified with the KAPA Library Quantification Kits (Kapa Biosystems). Sequencing was performed PE 2?×?50 bases on a HiSeq 1500 instrument using TruSeq SBS v3-HS chemistry; data were demultiplexed using bcl2fastq2 v.2.20. Raw data were aligned to the mouse mm10 index with Bowtie1 v.1.1.1 and adapter offset and duplicates were removed with SAMtools v1.3 and Picard v.1.134. Peaks indicating open chromatin regions were called using MACS2 v.2.1.0.20140616 and blacklisted regions were removed ( http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/mm10-mouse/mm10.blacklist.bed.gz ). Annotation was performed using HOMER v.4.10, applying annotatePeaks.pl ( http://homer.ucsd.edu/homer/ngs/annotation.html ). Further data processing was performed with DESeq2 v.1.20.0. Differentially accessible peaks were defined as baseMean >5 and P ?TNF-α in serum and cell culture supernatants was measured using murine IL-1β, IL-6 and TNF-α commercial ELISA kits (R&D Systems) according to the manufacturer’s instructions. Ex vivo stimulation assays . Peritoneal lavage was performed directly after abdominal incision using 2?ml of PBS. Peritoneal washings were then centrifuged at 1,700?r.p.m. for 7?min and the pellet was collected and added in 1?ml of RPMI 1640 (Biochrom) containing 100?U?ml ?1 of penicillin G and 0.1?mg?ml ?1 streptomycin. After counting, macrophages were incubated for 24?h at 37?°C in 5% CO 2 at a density of 5?×?10 6 cells?ml ?1 with or without 100?ng?ml ?1 LPS of E. coli O55:B5. Plates were centrifuged after 24 or 5?d of stimulation and cell supernatants were collected for cytokine assessment. Killing assays . C. albicans yeast or E. coli were exposed to trained cells for 2?h in a 2:1 pathogen:monocyte ratio. Monocytes were then lysed with 0.5% Triton X-100 and the number of surviving yeast cells or bacteria were assessed on Sabouraud or blood agar plates, respectively. Killing activity was expressed as the percentage of yeast or bacteria surviving in the presence of monocytes compared to yeast or bacteria surviving in the absence of monocytes. In vivo models of infection . Mice were housed in cages under constant temperature (21?°C) and humidity with a constant 12-h light–dark cycle. Animals had ad libitum access to food and water. Analgesia was achieved by the subcutaneous administration of 5?mg?kg ?1 of meloxicam. The animal studies were approved by the Greek veterinary directorate under the protocol no. 7467 on 24 December 2013. C. albicans UC820 was grown on Sabouraud plates at 30?°C. C. albicans cells were centrifuged, washed in PBS and counted using a hematocytometer. Fifteen 6-week-old C57BL/6J male mice from the Pasteur Institute of Athens (ΕΛ 25 ΒΙΟ 011) (F0 control) were infected with a sublethal intravenous dose of 1?×?10 4 C. albicans through a lateral tail vein. One month later, ‘control’ (mock-infected with PBS, F0 control) and ‘exposed’ ( Candida- infected and recovered, F0 exposed) mice were mated with healthy noninfected females at a ratio of 1:3. The first generation offspring of the trained mice (exposure lineage; F1 exposed) and the first generation offspring of the resting mice (control lineage; F1 control) were used for further experiments. To avoid the influence of the estrous cycle or possible effects of the infection on the reproductive capacity or uterine environment of the animals, we employed only male mice in these experiments. To generate the second and third generation offspring of the control (F2 control) and exposed (F2 exposed) mice, healthy noninfected mice from F1 control and F1 exposed (or F2 control and F2 exposed, respectively) never exposed to pathogenic agents were mated with healthy noninfected females from F0 at a ratio of 1:3. Six-week-old male mice with an average weight of 25?g were studied. Sample size calculations were performed with the PS Power and Sample Size calculator (Vanderbilt Biostatistics) using the chi-squared method to compare mortality between the two main groups, under the assumption that there would be a difference of 60% in mortality, with a power of 80% and a type α error of 0.05. Under this assumption, calculations concerning all other groups were made. F0 mice were randomized, using a randomization table, into two groups (trained/control). The F1 offspring from each group were once again randomized in subgroups using a randomization table. Twenty mice from each group were infected by an intravenous injection of 1?×?10 7 of C. albicans via a lateral tail vein or an intraperitoneal injection of 1?×?10 5 CFUs of E. coli (clinical isolate). To assess fungal burden, organs were aseptically removed through an abdominal incision, weighed and homogenized in PBS. Fungal burden was determined by plating organ homogenates in serial dilutions on yeast extract peptone dextrose plates. CFUs were counted after growth for 48?h at 30?°C. For the LPS in vivo assays, 10??g of LPS (from E. coli serotype O55:B5; Sigma-Aldrich) were injected intraperitoneally. Analgesia was achieved by subcutaneous administration of 5?mg?kg ?1 of meloxicam. In 10 mice per group, survival was recorded for 30?d. Five mice from each group were killed at specified time points. The numbering of mice was performed via tail marking. Mice were killed by the administration of ketamine (300?mg?kg ?1 ) and xylazine (30?mg?kg ?1 ) intraperitoneally followed by cervical dislocation. L. monocytogenes 10403S was grown in brain heart infusion broth (Oxoid), washed in 0.5% NaCl and adjusted to 10 7 CFU?ml ?1 . Animal experiments were approved by the Division des Affaires Vétérinaires, Direction Générale de l’Agriculture, de la Viticulture et des Affaires Vétérinaires, état de Vaud under authorization no. 877.9 and performed according to Swiss and ARRIVE guidelines. Mice were housed under specific pathogen-free conditions in the animal facility of the Centre des Laboratoires d’Epalinges (license no. VD-H04) at 22?°C with 70% humidity in ambient air and 14-h light/10-h dark cycles. Male and female C57BL/6J mice were randomized and injected with either PBS (control) or zymosan (trained). For each combination (trained male?×?control female, trained female?×?control male, control male?×?control female), three breeding cages were set up, each containing two females and one male. Male or female offspring were tested randomly. Mice were free of mouse hepatitis and norovirus. C57BL/6J male mice (Charles River Laboratories) were injected intraperitoneally with 1?mg of zymosan from S. cerevisiae (catalog no. Z4250; Sigma-Aldrich) to induce trained immunity 12 . Control and trained males or females were crossed with naive C57BL/6J female or male mice, respectively to obtain F1 exposed mice. Eight–nine-week-old F1 female and male mice were challenged intravenously with 2?×?10 4 and 1.6?×?10 5 CFUs of L. monocytogenes 10403S, respectively. WT and Tlr1 ?/? , Tlr2 ?/? and Tlr6 ?/? C57BL/6J female mice were trained with zymosan (1?mg intraperitoneally) 7 and 3?d before intravenous challenge with 1.1?×?10 5 CFUs of L. monocytogenes . Body weight loss, severity score and survival were registered at least once daily 49 . Blood was collected from the submandibular vein in EDTA-coated tubes (Microvette CB 300 K2E; SARSTEDT) before and 2–3?d after infection to quantify bacteria by plating serial dilutions of blood on Columbia III agar plates with 5% sheep blood (BD Biosciences) and leukocytes by flow cytometry. For flow cytometry, 20??l of blood were incubated for 30?min in the dark at room temperature with 50??l of anti-mouse CD3e (phycoerythrin, clone 145-2C11; catalog no. 12-0031; eBioscience), CD11b (APC eFluor 780, clone M1/70; catalog no. 47-0112-82; eBioscience), Ly-6C (PerCp-Cyanine 5.5, clone HK1.4; catalog no. 45-5932-82; eBioscience), Ly-6G (BV605, clone 1A8, catalog no. 563005; BD Biosciences), CD19 (PE-Cyanine7, clone 1D3; catalog no. 25-0193; eBioscience) and CD45 (FITC, clone 30-F11; catalog no. 11-0451; eBioscience) antibodies diluted in PBS containing 0.5% BSA (Sigma-Aldrich). Cell viability was assessed using the Fixable Viability Dye eFluor 450 (catalog no. 65-0863-14; Thermo Fisher Scientific). The reaction mixture was diluted into 500??l of red blood cell lysis buffer (0.65?M of ammonium chloride, 10?mM of sodium bicarbonate, 0.1?mM of EDTA, pH 7.4). After 10?min, cells were washed and samples were acquired using an Attune NxT Flow Cytometer (Thermo Fisher Scientific). Data collection and analysis were not performed blind to the conditions of the experiments. However, samples were given codes that only 1 out of the 4–6 experimenters knew; 2–3 experimenters recorded weight, bacteremia and survival, while up to 6 experimenters performed the flow cytometry analyses. Pseudo-blinding was performed for the FACS sample acquisition, analysis and cell sorting. No animal was excluded from the survival and weight analyses. Two data points are missing for bacteremia and flow cytometry since no blood or not enough blood could be drawn from the mice. Group size was predefined based on the maximum size of each group and the availability of mice of the sex of interest. Flow cytometry data were analyzed using the FlowJo_V10_CL software. The gating strategy has been described in Ciarlo et al. 12 . Mouse sperm genomic DNA preparation . Males were killed by cervical dislocation and the cauda epididymides were dissected. Mature and motile sperm were allowed to swim out of the cauda epididymis, which was cut through with fine scissors into 500??l of prewarmed M2 medium (Sigma-Aldrich). Free-swimming sperm was pelleted by centrifugation on a benchtop centrifuge (1?min at 6,000?r.p.m.) and snap-frozen in liquid nitrogen. The frozen sperm samples were thawed and resuspended in 1?ml of PBS. The freezing-thawing-washing procedure was repeated once again to eliminate non-sperm cells from the preparation. Finally, the washed sperm cells were resuspended in 500??l of lysis buffer (10?mM of Tris Cl, pH 8.0, 25?mM of EDTA, 75?mM of NaCl, 0.5% SDS and 10?mM of dithiothreitol); proteinase K was added to 200??g?ml ?1 and the mixture was incubated at 55?°C overnight. The genomic DNA from the lysed cells was further purified by phenol/chloroform extraction, precipitated with ethanol and dissolved in TE buffer. DNA methylation analysis by RRBS . For RRBS library preparation, at least 180?ng of genomic sperm DNA were digested overnight with 50?U of HaeIII (New England Biolabs), followed by enzyme inactivation at 80?°C. A-tailing was achieved with 5?U of Klenow exo- (New England Biolabs); indexed TruSeq adapters (Illumina) were ligated at 16?°C overnight with 2,000?U of T4 Ligase (New England Biolabs). After bisulfite conversion using the EZ DNA Methylation-Gold Kit (Zymo Research), libraries were amplified by 15 cycles of PCR with 2.5?U of HotStar Taq Polymerase (QIAGEN) and purified with 0.8× Agencourt Ampure XP beads (Beckman Coulter). After quantification with the Qubit dsDNA HS Assay Kit and quality control on a 2100 Bioanalyzer (Agilent Technologies), libraries were sequenced on a HiSeq 2500 system (Illumina) employing the TruSeq SBS Kit v3 – HS Chemistry in a 100-bp single read run. DNA methylation analysis . Raw FASTQ files were quality-controlled with FastQC ( http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/ ) and subsequently processed using the DEEP pipeline for RRBS. Reads were first trimmed with the cutadapt wrapper Trim Galore! and then mapped with the Burrows–Wheeler Aligner wrapper MethylCtools. Alignments were manipulated and filtered with Picard Tools (Broad Institute) and SAMtools. After realignment and recalibration, methylation calls were made with Bis-SNP. Reference SNPs and indels from the Single Nucleotide Polymorphism Database (dbSNP) v.138 ( http://www.ncbi.nlm.nih.gov/SNP ) were used in this process. In parallel, the alignments were evaluated with epiRepeatR ( https://github.com/MPIIComputationalEpigenetics/epiRepeatR ) as outlined in the vignette with Mus musculus only repeats from Repbase. For differential methylation analysis using methylKit v.1.3.1, all analyzed CpGs were filtered for coverage ≥5× and presence in at least 6 replicates per group. DMRs were detected in 1,000-bp tiles with at least 3 CpGs, a maximum FDR-adjusted P value of 0.01 and minimal methylation difference of 5%. Additionally, to assess DMR robustness, bootstrapping was performed and DMRs with high variance (differential variability P ?>?0.01) within a group were not considered for further analysis. Resulting DMRs were annotated to the closest genes and genomic features (promoter, exon, intron or intergenic) using GENCODE gene models (M20) with the help of bedtools v.2.20.1. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed with DAVID. Statistical analysis . Paired samples across time points were compared using the Wilcoxon signed-rank test. Comparisons between groups were performed using the Mann–Whitney U -test. In figures with two-tailed unpaired t -tests, data normality was tested using the Kolmogorov–Smirnov test (alpha level?=?0.05). Survival curves are presented as a Kaplan–Maier plots with a log rank test used to compare susceptibility between groups. Raw sequencing data were demultiplexed using blc2fastq2 v.2.20. After quality checking with MultiQC v.1.5, reads were aligned to the mm10 mouse reference transcriptome from UCSC. Transcript abundance was assessed using kallisto v.0.440 with default parameters. For downstream processing, data were imported into R v.3.5.1 and filtered to remove those transcripts not corresponding to HUGO Gene Nomenclature Committee gene symbols, read sum lower than ten across all samples or matching antisense, LINC or pseudogenes. DESeq2 v.1.20.0 served as the foundation for the core analysis. To identify DEGs, a threshold of an adjusted P ?research design is available in the Nature Research Reporting Summary linked to this article. 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 ). 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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 Sk?odowska-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 ImmunoSensation 2 . We thank K. Nordstr?m for bioinformatics support. Author information . Author notes These authors contributed equally: Natalie Katzmarski, Jorge Domínguez-Andrés, Branko Cirovic, Thierry Roger, Evangelos J. Giamarellos-Bourboulis, Andreas Schlitzer, Mihai G. Netea. Affiliations . Quantitative Systems Biology, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany Natalie Katzmarski,?Branko Cirovic?&?Andreas Schlitzer 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 Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands Jorge Domínguez-Andrés,?Leo A. B. Joosten?&?Mihai G. Netea Department 4th of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece Georgios Renieris?&?Evangelos J. Giamarellos-Bourboulis Infectious Diseases Service, Department of Medicine, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland Eleonora Ciarlo,?Didier Le Roy?&?Thierry Roger Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany Konstantin Lepikhov,?Kathrin Kattler,?Gilles Gasparoni?&?J?rn Walter Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany Kristian H?ndler,?Heidi Theis?&?Marc Beyer Molecular Immunology in Neurodegeneration, German Center for Neurodegenerative Diseases, Bonn, Germany Marc Beyer?&?Joachim L. Schultze Department for Genomics & Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany Joachim L. Schultze Department for Immunology & Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany Mihai G. Netea Authors Natalie Katzmarski View author publications You can also search for this author in PubMed ? Google Scholar Jorge Domínguez-Andrés View author publications You can also search for this author in PubMed ? Google Scholar Branko Cirovic View author publications You can also search for this author in PubMed ? Google Scholar Georgios Renieris View author publications You can also search for this author in PubMed ? Google Scholar Eleonora Ciarlo View author publications You can also search for this author in PubMed ? Google Scholar Didier Le Roy View author publications You can also search for this author in PubMed ? Google Scholar Konstantin Lepikhov View author publications You can also search for this author in PubMed ? Google Scholar Kathrin Kattler View author publications You can also search for this author in PubMed ? Google Scholar Gilles Gasparoni View author publications You can also search for this author in PubMed ? 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Google Scholar Andreas Schlitzer View author publications You can also search for this author in PubMed ? Google Scholar Mihai G. Netea View author publications You can also search for this author in PubMed ? Google Scholar 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 . Ethics declarations . 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. Additional information . Peer review information Nature Immunology thanks S?ren 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 . Reporting Summary . Peer Review Information . Supplementary Tables . Supplementary Tables 1–7 Source data . Source Data Fig. 1 . Statistical source data (Excel) for Fig. 1. Source Data Fig. 2 . Statistical source data (Excel) for Fig. 2. Source Data Fig. 3 . Statistical source data (Excel) for Fig. 3. Source Data Extended Data Fig. 1 . Statistical source data (Excel) for Extended Data Fig. 1. Source Data Extended Data Fig. 2 . Statistical source data (Excel) for Extended Data Fig. 2. Source Data Extended Data Fig. 3 . Statistical source data (Excel) for Extended Data Fig. 3. Source Data Extended Data Fig. 4 . Statistical source data (Excel) for Extended Data Fig. 4 Source Data Extended Data Fig. 5 . Statistical source data (Excel) for Extended Data Fig. 5. Source Data Extended Data Fig. 6 . Statistical source data (Excel) for Extended Data Fig. 6. Source Data Extended Data Fig. 7 . Statistical source data (Excel) for Extended Data Fig. 7. Rights and permissions . Reprints and Permissions About this article . Cite this article . Katzmarski, N., Domínguez-Andrés, J., Cirovic, B. et al. Transmission of trained immunity and heterologous resistance to infections across generations. Nat Immunol (2021). https://doi.org/10.1038/s41590-021-01052-7 Download citation Received : 03 September 2019 Accepted : 16 September 2021 Published : 18 October 2021 DOI : https://doi.org/10.1038/s41590-021-01052-7 Share this article . Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative .
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