Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans

Abstract

According to the hygiene hypothesis, the increasing incidence of autoimmune diseases in western countries may be explained by changes in early microbial exposure, leading to altered immune maturation. We followed gut microbiome development from birth until age three in 222 infants in Northern Europe, where early-onset autoimmune diseases are common in Finland and Estonia but are less prevalent in Russia. We found that Bacteroides species are lowly abundant in Russians but dominate in Finnish and Estonian infants. Therefore, their lipopolysaccharide (LPS) exposures arose primarily from Bacteroides rather than from Escherichia coli, which is a potent innate immune activator. We show that Bacteroides LPS is structurally distinct from E. coli LPS and inhibits innate immune signaling and endotoxin tolerance; furthermore, unlike LPS from E. coli, B. dorei LPS does not decrease incidence of autoimmune diabetes in non-obese diabetic mice. Early colonization by immunologically silencing microbiota may thus preclude aspects of immune education.

Figure 1. DIABIMMUNE Cohort

(A) Locations of cities and countries where DIABIMMUNE infants were screened and samples were collected. (B) Selected within-cohort statistics and stool sample collection schedule (monthly stool sampling until three years of age). Numbers next to stool samples reflect the number of samples collected in 6-month time windows. Within-cohort distribution of HLA conferred risk for autoimmunity is shown (see Table S1 for corresponding HLA allele identities), as well as prevalence of T1D-associated autoantibody seropositivity, egg allergy, and milk allergy at year 2. For T1D autoantibody seropositivity, n = 291 serum samples from 73 infants for Finns, n = 235 serum samples from 72 infants for Estonians, and n = 118 serum samples from 54 infants for Russians. For egg allergy, n = 72 for Finns, n = 51 for Estonians, and n = 24 for Russians. For milk allergy, n = 72 for Finns, n = 46 for Estonians, and n = 24 for Russians. (C) Analysis workflow highlighting important steps in metagenomic data analysis and mechanistic experiments. See also Figure S1 and Table S1.

Figure 2. Differences in Microbial Ecology between Countries in Early Infancy

(A) Principal coordinate analysis plots of DIABIMMUNE 16S samples, colored by country (top) and age (bottom). Each circle represents an individual stool sample (n = 1,584). (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. (C) Average phylum-level composition of DIABIMMUNE samples during the first two years of age. (D) Genus-level (darker colors) and species-level (lighter colors) bootstrapped mean log₂ fold changes and their standard deviations between Finnish and Russian gut microbiota during the first year and after. (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). See also Figures S2, S3, and S4.

Figure 3. Functional Differences, HMO Utilization, and Lipid A Biosynthesis

(A) Bootstrapped mean log₂ fold changes and their standard deviations in the functional categories with the largest differences between Finnish and Russian children. (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). (C) Lipid A biosynthesis pathway normalized read counts (RPKM) per sample (n = 785) and a linear fit per country. (D and E) Mean relative abundances of 15 species with the largest contributions to lipid A biosynthesis signal (D), and their relative contributions (E) to the signal in all samples within each country. See also Figure S5 and Table S2.

Figure 4. Structures of LPS Molecules and Impact on Tolerogenic Function

MALDI-TOF MS analysis of lipid A purified from E. coli (A) and B. dorei (B). Representative structures are shown as insets with predicted exact mass. See also Figure S6.

Figure 5. Immunostimulatory Properties of LPS from Different Bacterial Strains

(A) Mean cytokine production in PBMCs stimulated with 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 h and NFκB activity was measured by luciferase activity. Activity is expressed as 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 the cytokine production is expressed as measured upon 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 upon 24 h 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 h and restimulated in vitro with zymosan. Bars show TNFα concentration assessed by cytokine bead array after 24 h. In vitro data are representative of three or more independent experiments and are presented as 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 and Table S3.

Figure 6. Differences in HMO-Utilizing Bacteria Provide a Route to Differences in Immune Education

Human milk oligosaccharides can be metabolized by different prevalent microbes in Russia (primarily Bifidobacterium species) versus Finland and Estonia (primarily Bacteroides species). Potentially as a result of these population differences, Bacteroides-derived lipopolysaccharide (LPS) constitutes the major portion of LPS produced by microbes in Finnish and Estonian infants, whereas LPS in Russian infants is mostly derived from E. coli. Bacteroides-derived LPS is of an immunoinhibitory subtype, thus leading to differential immune education by means of endotoxin tolerance or other routes.