We also observed a gradient in T1D autoantibody seropositivity within the cohort with higher prevalence of T1D autoantibodies in Finland. The number of infants that tested seropositive for one or more T1D-associated autoantibodies was 16 for Finland, 14 for Estonia, and 4 for Russia. Other studies in an older population (7–15 years of age) have shown that children in Russian Karelia display a substantially higher microbial exposure than their Finnish peers, as denoted by higher prevalence of antibodies against Helicobacter pylori (15-fold), Toxoplasma gondii (5-fold), and hepatitis A virus (12-fold) (). This increased pathogen exposure in older children suggests an overall higher exposure rate to diverse microorganisms, possibly due to higher hygiene levels in urban Finland.

A subcohort of 74 infants from each country was selected on the basis of similar histocompatibility leukocyte antigen (HLA) risk class distribution and matching gender ( Figures 1 A and 1B; Table S1 ). For each infant, 3 years of monthly stool samples and questionnaires regarding breastfeeding, diet, allergies, infections, family history, use of drugs, clinical examinations, and laboratory assays were collected. In accordance with the recruitment criteria for the DIABIMMUNE cohort, all subjects had increased HLA-conferred susceptibility to autoimmunity ( Figure 1 B) (). Although these children were only followed until 3 years of age and it was therefore unlikely to see indications of allergic disease or autoimmunity, laboratory examinations revealed a high prevalence of allergen-specific sensitization and seropositivity for T1D-associated antibodies in Finnish and Estonian infants ( Figure 1 B, bottom).

The diversity of the microbiota within individual samples (alpha diversity) increased with age ( Figure S3 A) as the microbiota developed toward an adult composition (). However, Russians displayed a significantly less diverse microbiota compared to Finns and Estonians during the first year ( Figure S3 B). This difference could be explained by the 2-fold overrepresentation of the phylum Actinobacteria and the genus Bifidobacterium in the Russians for that time period ( Figures 2 C and 2D). Lastly, we also uncovered differences in stability within taxa between the countries. These differences were particularly evident when comparing samples collected during early and late time windows ( Figures S3 C and S3D). Russians had an overall more plastic microbiota during the first 3 years of life, with the exception of the most dominant genus Bifidobacterium in the early time window. In contrast, the phylum Bacteroidetes and the genus Bacteroides were more stable in Finns and Estonians throughout the entire observation period. Taken together, we uncovered strong global differences between the Russian versus Finnish and Estonian microbiota, with the largest differences occurring in the first year and dissipating during the second and third years.

(E) Comparison between the absolute abundance measurements via qPCR and predictions of absolute abundances given the metagenomics data and total bacterial mass (estimated using universal 16S primers). Pearson correlation r = 0.79 for B. dorei and r = 0.76 for E. coli. x axis was computed by taking the product of relative abundance (given by the metagenomics data) and total bacterial load estimated using universal 16S primers. Both axes are on a log-scale.

(D) An illustration of taxa-specific deviations from the power law fits given the model in (C). The heatmap shows the country-specific deviations from the model (i.e., more stable or plastic behavior in a given taxon) in two time windows (see C); asterisk denotes q-value < 0.01 (p values for the fixed effect terms given by a bayesglm model after correcting for multiple testing with Benjamini & Hochberg adjustment).

(C) Power law model used for modeling stability of the infant gut microbiota (see the Supplemental Experimental Procedures ). The month of the earlier sample is shown on the vertical axis, and the time between the later and earlier sample is shown on the horizontal axis.

(B) Comparison of residuals of the sigmoid function shown in (A). The residuals of the fit were compared using a Bayesian linear model by fitting a model with country as a factorial fixed effect, with the first-year samples and samples collected after the first year analyzed separately. Height of each bar represents the fixed effect coefficient per country; errors bars show SD (68% credible intervals) of these coefficients. Positive numbers indicate higher diversity and negative numbers indicate lower diversity. n.s., not significant; ∗∗∗ p < 0.001, Bayesian linear model.

To generate an overview of the composition of the gut microbiota throughout the first three years of life, we sequenced the V4 region of the 16S rRNA gene from 1,584 samples ( Figure S1 ) and observed several strong high-level trends within this cohort. Principal coordinate plots ( Figure 2 A) showed that, besides age, country was the major source of variation, particularly during the first year of life. To further confirm separability between countries, we trained a set of random forest classifiers using genus-level data from samples collected between 170 and 260 days of age. The classifier was able to predict country with high-classification accuracy (area under the curve [AUC] = 0.944 for Finns versus Russians) ( Figure 2 B). Classification was least accurate between Finns and Estonians (AUC = 0.546), suggesting that early microbial profiles were fairly homogenous in these two countries. Differences between the Russian samples compared to Finnish and Estonian samples were already evident at phylum-level composition ( Figure 2 C), represented by two distinct hallmarks. First, Finnish and Estonian children had higher levels of Bacteroidetes throughout the 3-year period (false discovery rate [FDR] corrected p = 5.4 × 10; see the Supplemental Experimental Procedures ). Second, Russians had higher levels of the phylum Actinobacteria during the first year of life (FDR corrected p = 0.014). The latter difference dissipated over time and was no longer significant after two years of age. The abundance of the phylum Bacteroidetes correlated with serum insulin autoantibody (IAA) levels, both within Finland (p = 0.017) and cohort-wide (p = 0.0020; Figure S2 Supplemental Experimental Procedures ). We conducted extensive testing of associations between the metadata and taxonomic groups using MaAsLin, a linear modeling tool adapted for microbial community data (). Hence, all reported country-level differences were corrected for all major confounding effects, such as birth mode, breastfeeding and other dietary factors, antibiotics use, and age (see the Supplemental Experimental Procedures ). Table 1 highlights selected associations between the metadata and microbiota. A comprehensive list of results, including taxonomic differences between countries, taxonomic alterations associated with allergen-specific immunoglobulin E (IgE), and microbial changes associated with other collected metadata, can be accessed at http://pubs.broadinstitute.org/diabimmune

The table shows microbial taxa that are associated with T1D autoantibody seropositivity, caesarean section, intake of antibiotics, breastfeeding, and other dietary compounds. The left column shows taxa that are increased, and the right column shows taxa that are decreased in each association. All findings are FDR-corrected; p < 0.1, ∗ p < 0.01, ∗∗ p < 0.001. p, phylum; c, class; o, order; f, family; g, genus.

(D) Estimate of the ratio between LPS derived from Bacteroides species in (C) and LPS derived from E. coli and Klebsiella oxytoca (WGS data): p = 0.0021 (Finns), p = 0.0004 (all).

(A–D) Each panel in this figure is comprised of four plots, one plot per column: first and third columns show how the correlations between IAA and microbial features are confounded by the age of stool sample collection within Finns and the whole cohort, respectively; measured microbial correlates on the y axis are plotted with respect to age at stool sample collection. Black dashed lines show the longitudinal trend (linear fit with respect to age); the size and color of the dots show the measured IAA levels for the corresponding serum sample (corresponding serum samples were collected 1-18 months after the stool sample). The significance of the correlation between IAA and microbial correlates was estimated using a mixed effect linear model with microbial correlate as a target variable, log 10 of IAA levels, age at collection (stool sample), and mode of delivery as fixed effects, and subject ID as a random effect. The following p values were obtained from the model. Second and fourth columns show the model residuals after effects of age, mode of delivery, and subject ID have been corrected from the data.

(E and F) Strain-level diversity (E) and stability (F) in Bacteroides and Bifidobacterium species. Diversity and stability distributions for Bifidobacterium species are significantly different between the Finnish and Russian populations (two-sample Kolmogorov-Smirnov test; p = 5.0 × 10 −4 and p = 1.5 × 10 −6 , respectively).

(D) Genus-level (darker colors) and species-level (lighter colors) bootstrapped mean log 2 fold changes and their SD between Finnish and Russian gut microbiota during the first year and after.

(C) Average phylum-level composition of DIABIMMUNE samples during the first 2 years of age.

(B) ROC curves for pairwise random forest classifiers predicting country based on 16S genus data using samples collected between 170 and 260 days of age.

Stool samples sequenced using 16S (red) or whole-genome shotgun sequencing (green); samples sequenced using both techniques are shown in blue. Rows in the panel represent subjects.

Next, we analyzed the metagenomics data at the strain level using ConStrains, a recently developed strain haplotyping tool, and evaluated the diversity and stability of the infant microbiota (). In 60% of all strain profiles, communities were composed of species with a single dominant strain (>90% within-species abundance), as reflected in low within-species, within-sample haplotype diversity scores ( Figure S4 A). However, species in some genera, such as Faecalibacterium and Veillonella, had bimodal haplotype diversity distributions, indicative of more complex strain compositions. Moreover, strain diversity had a tendency to increase with age ( Figure S4 B). Analysis of the strain stability over time revealed that species tended to either (1) remain stable, maintaining their single dominant strain, or (2) experience a strain “sweep,” in which the original dominant strain was replaced by a new dominant strain ( Figure S4 C). We observed an inverse correlation between the longitudinal distance of the samples and the corresponding strain stability ( Figure S4 D). When comparing strain stability with average diversity of the compared samples, we saw an inverse correlation, indicating that less diverse strain profiles (i.e., single dominant strain behavior) tended to be more stable compared to more diverse strain profiles ( Figure S4 E). Within the genera of interest, we observed that Bifidobacterium species failed to establish stable single-strain communities in Finnish children, as shown by a more evenly distributed strain diversity and stability compared to Russians ( Figures 2 E and 2F). In contrast, Bacteroides species (when present) tended to establish stable, single-strain compositions in both Finns and Russians ( Figures 2 E and 2F).

(E) Relationship between diversity and stability: within-species stability plotted against the mean of the within-species diversities of the two samples in the given stability observation. An inverse correlation was observed, r = −0.107, p = 7.7e-4.

(D) Observations of within-species strain stability plotted against the time difference between the compared samples. Stability is correlated with the time difference, r = −0.124, p = 9.5e-5.

(C) The distributions of within-species strain stability stratified by genera (shown for genera with more than 20 observations).

(A) The distributions of within-species strain diversity stratified by genera (shown for genera with more than 20 observations).

To obtain a more complete and higher resolution taxonomic view of the infants’ microbiome, we performed deep whole-genome shotgun metagenomic sequencing on a representative subset of 785 samples ( Figure S1 ). We first investigated the metagenomic reads for their detailed taxonomic composition down to the species level using MetaPhlAn (v.2.2) (Metagenomic Phylogenetic Analysis) () and observed multiple differentially abundant species in the Bacteroides and Bifidobacterium genera between Finland and Russia ( Figure 2 D). Notably, B. dorei, which has been previously associated with T1D pathogenesis (), was the Bacteroides species with the largest fold change between Finns and Russians. We confirmed the validity of the metagenomics data by running qPCR on DNA from 85 stool samples. Interpolated absolute abundances of B. dorei and E. coli species were in good agreement with absolute abundances predicted by the metagenomics data when total bacterial mass was estimated using universal 16S primers ( Figure S3 E; Supplemental Experimental Procedures ).

Most significantly, we found that GO terms related to LPS functions, LPS biosynthetic process (GO: 0009103), and lipid A biosynthetic process (GO: 0009245) showed a striking difference in abundance between countries ( Figures 3 A and 3C), indicating that microbial communities in Finnish and Estonian subjects produced more LPS. This molecule is of particular interest because it elicits a strong immune response in mammalian cells (). When deconvoluting the species contributing to biosynthesis of lipid A, the subunit responsible for the immunostimulatory properties of LPS, we made two key observations. In all three countries, E. coli was a major contributor to lipid A biosynthesis, but in Finland and Estonia a number of other bacterial species contributed to lipid A biosynthesis potential, many of which belong to the genus Bacteroides ( Figures 3 D and 3E). LPS subtypes derived from Bacteroides species have been shown to exhibit lower endotoxicity relative to LPS isolated from other enteric bacteria (). This finding prompted us to examine whether there was a difference in immunogenicity of the LPS subtypes derived from the predominant species of the three populations.

Chemical composition, serological reactivity and endotoxicity of lipopolysaccharides extracted in different ways from Bacteroides fragilis, Bacteroides melaninogenicus and Bacteroides oralis.

Glycolytic functions were differentially abundant between the two populations ( Figure 3 A), leading us to computationally investigate differences in milk oligosaccharide metabolism. The gut microbiome composition within the first year is largely shaped by milk, the sole nutrient source available to infants, whether from breast- or bottle-feeding (reviewed in). The Bifidobacterium and Bacteroides genera are the two main groups of human milk oligosaccharide (HMO)-metabolizing bacteria (). Within Bifidobacterium, B. bifidum and B. longum (predominant in Russians) are capable of metabolizing HMOs, whereas B. breve (present in Finns) is incapable of metabolizing intact HMOs, though it readily utilizes monosaccharides liberated from HMOs (). This observation led us to hypothesize that HMO metabolism in Finnish and Estonian children is performed by Bacteroides species, whereas it is performed by B. bifidum and B. longum in Russians. Indeed, by analyzing the taxonomic origin of genes belonging to a bona fide HMO gene cluster (), we showed that although the average abundance of HMO utilization genes is approximately equal across the three countries (mean ± SD in RPKM: Finland 460 ± 372, Estonia 462 ± 331, Russia 504 ± 469), most of the genes are conferred by Bifidobacterium in Russians and Bacteroides in Finns and Estonians ( Figures 3 B and S5 C). We note that the higher abundance of B. bifidum in Russians is not a result of increased breastfeeding; Finnish infants were breastfed for a longer period than Russians, on average (mean ± SD breastfeeding/days: Finland 268 ± 149, Estonia 307 ± 217, Russia 199 ± 165).

To survey the functional and metabolic consequences of the taxonomic differences between countries, we next analyzed the metagenomic sequences for their genomic functional potential using HUMAnN2 () and linked quantified gene abundances (reads per kilobase per million reads [RPKMs]) to gene ontology (GO) terms. As observed for microbial diversity, functional diversity of the microbiome also started with a less complex composition in Russians but developed to reach greater diversity by the end of the 3-year period ( Figure S5 A). We identified multiple GO categories with significantly different abundances between Finns and Russians in both the early (first year) and late (after first year) time windows ( Figure 3 A). For instance, siderophore-related functions, which include iron scavenging, as well as virulence-related functionalities, were higher in Finnish infants, possibly reflecting an increase in pathobiont organisms in Finland. A comprehensive list of differential categories between the two countries is shown in Figure S5 B and Table S2

(D and E) Mean relative abundances of 15 species with the largest contributions to the lipid A biosynthesis signal (D) and their relative contributions (E) to the signal in all samples within each country.

(B) Mean human milk oligosaccharide utilization gene abundance across the three countries within the first year of life, stratified by taxonomic origin of each gene (“conserved” genes were too highly conserved to confidently assign to a unique genus).

(A) Bootstrapped mean log 2 fold changes and their SD in the functional categories with the largest differences between Finnish and Russian children.

(C) Human milk oligosaccharide utilization gene abundance across the three countries within the first year of life, stratified by taxonomic origin of each gene. The samples are ordered by country (color in the bottom panel) and age of collection within countries (left = earliest samples), and both panels have same ordering. These data are averaged in Figure 3 B.

(B) GO categories that are differentially abundant between Finnish and Russian infants during the first year or after the first year. In the majority of categories, the difference is larger during the first year. Boxplots show median and interquartile range of the data.

(A) The diversity of functional potential of the microbiome per sample as measured by Shannon diversity of the distribution of GO categories.

Contrasting Immunogenicity of LPS Subtypes

Figure S6 LPS Structural and Functional Features across B. dorei Isolates, Related to Figure 4 Show full caption (A) MALDI-TOF MS analysis of lipid A purified from crude biomass of type strain (DSMZ) and four independent clinical isolates of B. dorei. (B) SDS-PAGE gel stained with LPS-specific stain (Pro-Q Emerald) revealed a similar O-antigen staining pattern across 6 independent clinical isolates and the type strain of B. dorei. E. coli LPS was used as a positive staining control. In order to ensure that LPS from our B. dorei type strain was representative of clinical samples, we isolated B. dorei strains from stool samples of six healthy donors for comparative LPS structural analysis. These data revealed identical LPS structural features across all B. dorei isolates ( Figures S6 A and S6B). Thus, our findings regarding B. dorei LPS structure and function are likely to be recapitulated in patients.

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et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Figure 5 Immunostimulatory Properties of LPS from Different Bacterial Strains Show full caption (A) Mean cytokine production in PBMCs stimulated with the indicated doses of LPS as assessed by cytokine bead array. (B) Mean cytokine production in monocyte-derived dendritic cells stimulated with indicated doses of LPS. (C) Reporter cells expressing human TLR4 were stimulated with LPS from indicated bacterial strains for 6 hr, and NF-κB activity was measured by luciferase activity. Activity is expressed as the percent of maximum luciferase signal. (D and E) Inhibition of E. coli LPS-induced PBMC (D) or monocyte-derived dendritic cells (E) cytokine production by additional doses of LPS from B. dorei. Inhibition of cytokine production is shown in comparison to stimulation with E. coli LPS alone. (F) Induction of endotoxin tolerance by LPS from E. coli or B. dorei in primary human monocytes as assessed by cytokine bead array. Bars show TNFα concentration in monocyte supernatants after 24-hr restimulation with zymosan as assessed by cytokine bead array. (G) Inhibition of E. coli-driven endotoxin tolerance induction in human monocytes by B. dorei LPS. (H) Impact of E. coli- or B. dorei-derived LPS exposure on diabetes incidence in NOD mice. Mice were injected i.p. once a week (arrows) with LPS from E. coli (n = 9 mice) or B. dorei (n = 12 mice). Blood glucose was monitored weekly. (I) Induction of endotoxin tolerance in NOD mice. The mice (n = 5 per group) were injected i.p. with LPS purified from E. coli or B. dorei. The splenocytes were isolated after 24 hr and restimulated in vitro with zymosan. Bars show TNFα concentration assessed by cytokine bead array after 24 hr. In vitro data are representative of three or more independent experiments and are presented as the mean (and SD) of triplicate assessments. ∗p < 0.05, ∗∗p < 0.005 by Student’s t test compared to E. coli stimulation (D and E), E. coli LPS treatment alone (F and G), or PBS treatment (I) or by ANOVA for diabetes incidence (H). See also Figure S7 Figure S7 Stimulation of Primary Immune Cells and TLR4 Reporter Cells by LPS from Different Bacterial Strains, Related to Figure 5 Show full caption LPS isolated from individual bacterial strains was used to stimulate human PBMCs, human monocyte-derived dendritic cells, or HEK293 NFκB-luciferase reporter cells expressing human TLR4. (A and B) Human PBMCs (A) or monocyte-derived dendritic cells (B) were stimulated for 16-18 hr with LPS from indicated bacterial strains, after which supernatants were collected and analyzed by cytokine bead array. Results are expressed as mean concentration and SD of triplicate assessments. Data are representative of three independent experiments. (C) Reporter cells expressing human TLR4 were stimulated with LPS from indicated bacterial strains for 6 hr and NFκB activity was measured by luciferase activity. Activity is expressed as percent of maximum luciferase signal. Data are represented as mean and SD of triplicate assessments. (D) Inhibition by B. dorei LPS of NFκB activation by 1 ng/ml E. coli LPS in hTLR4 reporter cells. Data are represented as mean and SD of triplicate assessments. ∗p < 0.05, ∗∗∗p < 0.0005 by Student’s t test compared to E. coli stimulation. (E) Human primary PBMCs (3 donors) and CBMCs (4 donors) were stimulated in the presence of increasing doses of E. coli- or B. dorei-derived LPS. Supernatants were collected after 24 hr of culture, and TNFα (upper) and IL1β (middle) concentrations were assessed by CBA. Data are shown as mean and SD of triplicate assessments. (F) PBMCs and CBMCs were stimulated with 1 ng/ml E. coli LPS and increasing doses of B. dorei LPS. Supernatants were collected at 24 hr, and TNFα and IL1β concentrations were assessed by CBA. Data are shown as mean and SD of triplicate assessments. All donors were Caucasians. Cells were procured as frozen isolated mononuclear cells from commercial vendors. (G) HEK293 NFκB-reporter cells expressing hTLR4, hMD2, and hCD14 were stimulated with increasing doses of LPS isolated from B. thetaiotaomicron WT (1′-mono-phosphorylated) or lacking LpxF (1,4-bis-phosphorylated). Luciferase activity was measured after 6 hr. (H) hTLR4-expressing NFκB reporter cells were stimulated with E. coli LPS alone or in combination with LPS from the indicated organisms. Luciferase activity was measured after 6 hr. Data are represented as mean and SD of triplicate assessments. (Note: Bacteroides spp. = both B. thetaiotaomicron and B. dorei.) ∗∗p < 0.005, n.s., not significant, ANOVA with Holm-Sidak multiple comparison post-test for panels G and H. Extensive lipid A structure-function studies have shown that the number of acyl chains is a strong determinant of immune activation by LPS () and that penta- and tetra-acylated lipid A structures elicit reduced TLR4 responses (). In order to understand the consequences of the structural differences between the LPS subtypes, we assessed the immunogenicity of LPS derived from the bacterial species contributing to the LPS load in our samples (see Figure 3 E). Of the 15 strongest contributors, we were able to purify LPS from 11 type strains listed in Table S3 . We first used the LPS purified from these strains to stimulate primary human peripheral blood mononuclear cells (PBMCs), which contain LPS-responsive cell types similar to those present in the gut and are thus a common proxy for mucosal leukocytes (). LPS derived from E. coli produced a strong response as measured by the production of the necrosis factor κB (NF-κB)-dependent cytokines interleukin-10 (IL-10), tumor necrosis factor alpha (TNFα), IL-1β, and IL-6 in primary PBMCs ( Figures 5 A and S7 A), whereas LPS derived from B. dorei failed to elicit any response regardless of the dose. Notably, LPS derived from all analyzed members of the phylum Bacteroidetes (Bacteroides species and Prevotella copri) also showed a severely impaired capacity to elicit the production of these inflammatory cytokines. We then measured cytokine production in human monocyte-derived dendritic cells after stimulation with LPS from these same strains and obtained similar results ( Figures 5 B and S7 B). Consistent with assays in primary cells, E. coli-derived LPS elicited high levels of luciferase activity in TLR4-NF-κB reporter cells, whereas Bacteroides species failed to induce an inflammatory signal in these cells ( Figures 5 C and S7 C).

Our metagenomics analyses revealed that E. coli and B. dorei LPS often co-occur in the gut of Finnish and Estonian infants. In order to study possible interactions between these LPS subtypes, we used a base dose of E. coli LPS, while co-treating human primary immune cells with B. dorei LPS at increasing ratios. We then measured changes in the production of inflammatory cytokines with respect to baseline E. coli LPS stimulation. Cytokine production was inhibited by B. dorei LPS in primary human PBMCs ( Figure 5 D) and in monocyte-derived dendritic cells ( Figure 5 E). Notably, we observed maximal inhibition in PBMCs at a ratio of 10:1 B. dorei:E. coli LPS, corresponding to the computational prediction of the ratio between inhibitory and stimulatory LPS typical for IAA-seropositive infants ( Figure S2 ). Similar to cytokine production in PBMCs, NF-κB-luciferase activity was inhibited by B. dorei LPS in a dose-dependent manner ( Figure S7 D). We also obtained similar results when examining cord blood mononuclear cells ( Figures S7 E and S7F), suggesting that our observations reflect the reaction of the naive immune system of infants. Our results show that B. dorei LPS acts as an inhibitor of immune stimulation by E. coli-derived LPS, with a potency that is concordant with ratios of the LPS subtypes observed in vivo.

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Lopez-Collazo E. Endotoxin tolerance: new mechanisms, molecules and clinical significance. Stimulation of immune cells with LPS induces a temporary refractory state to a repeated immune challenge, a phenomenon known as endotoxin tolerance (). This mechanism of immunosuppression was originally described in sepsis, but is thought to underlie multiple other physiological contexts of innate immune unresponsiveness, such as the immune protective effect conferred by microbial exposure suggested by the hygiene hypothesis (). We assessed the potency of E. coli and B. dorei LPS subtypes to induce endotoxin tolerance in primary human monocytes. Initial exposure to E. coli LPS prevented TNFα production after restimulation at all conditioning doses tested ( Figure 5 F). In contrast, B. dorei LPS conditioning did not abrogate cytokine production in these cells even at the highest concentrations, corresponding to a potency at least four orders of magnitude lower than E. coli LPS. Hence, the LPS produced by B. dorei failed to induce protective endotoxin tolerance. Finally, the addition of B. dorei LPS to E. coli LPS during the endotoxin tolerance induction phase prevented the establishment of endotoxin tolerance by E. coli LPS in a dose-dependent manner ( Figure 5 G), suggesting that the presence of B. dorei in the infant gut could prevent the establishment of protective immune tolerance by E. coli LPS.