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. Author manuscript; available in PMC: 2022 Jun 24.
Published in final edited form as: Allergy. 2021 May 14;76(11):3489–3503. doi: 10.1111/all.14877

Infant Gut Microbiome Is Enriched with Bifidobacterium longum ssp. infantis in Old Order Mennonites with Traditional Farming Lifestyle

Antti E Seppo 1,#, Kevin Bu 2,#, Madina Jumabaeva 1, Juilee Thakar 3, Rakin A Choudhury 3, Chloe Yonemitsu 4, Lars Bode 4,5, Camille A Martina 6, Maria Allen 7, Sabrina Tamburini 2, Enrica Piras 2, David S Wallach 2, R John Looney 7, Jose C Clemente 2,^, Kirsi M Järvinen 1,8,^,*
PMCID: PMC9230048  NIHMSID: NIHMS1817128  PMID: 33905556

Abstract

Background:

Growing up on traditional, single-family farms is associated with protection against asthma in school-age, but the mechanisms against early manifestations of atopic disease are largely unknown. We sought determine the gut microbiome and metabolome composition in rural Old Order Mennonite (OOM) infants at low risk and Rochester, NY urban/suburban infants at high risk for atopic diseases.

Methods:

In a cohort of 65 OOM and 39 Rochester mother-infant pairs, 101 infant stool and 61 human milk samples were assessed by 16S rRNA gene sequencing for microbiome composition and qPCR to quantify Bifidobacterium spp. and B. longum ssp. infantis (B. infantis), a consumer of human milk oligosaccharides (HMOs). Fatty acids (FAs) were analyzed in 34 stool and human 24 milk samples. Diagnoses and symptoms of atopic diseases by 3 years of age were assessed by telephone.

Results:

At a median age of 2 months, stool was enriched with Bifidobacteriaceae, Clostridiaceae and Aerococcaceae in the OOM compared to Rochester infants. B. infantis was more abundant (p<0.001) and prevalent, detected in 70% of OOM compared to 21% of Rochester infants (p<0.001). Stool colonized with B. infantis had higher levels of lactate and several medium- to long/odd-chain FAs. In contrast, human milk was enriched with a distinct set of FAs including butyrate. Atopic diseases were reported in 6.5% of OOM and 35% of Rochester children (p<0.001).

Conclusion:

A high rate of B. infantis colonization, similar to that seen in developing countries, is found in the OOM at low risk for atopic diseases.

Keywords: allergy, Bifidobacterium, farming lifestyle, human milk, microbiome

INTRODUCTION

Asthma and allergies have increased dramatically over recent decades of industrialization and urbanization. Genetic factors are unlikely to explain the rapid increase in allergic diseases in affluent countries throughout the world1. A large body of data from Europe2 and North America among the Amish, Old Order Mennonites (OOM) and Hutterites35 suggest that living on farms is associated with a significant reduction in the risk of asthma in older children and adults. Factors associated with this “farm-life effect” include consumption of unpasteurized farm milk and exposure to farm animals and stables2, evident in traditional one-family farms, but not communal farms4. Greater microbial diversity in house dust was associated with living on farms and negatively associated with asthma6. In addition to increased regulatory T cells (Tregs) in farm children7, the innate immune system also appears to be the target of these exposures4,8. More recently the Amish and OOM children have also been reported to be protected from food allergies9,10, which often precede the development of respiratory allergies and asthma, collectively constituting ‘the atopic march”11.

Increasing evidence indicates that gut dysbiosis, especially in early life, precedes the development of multisensitized atopic disease12, atopic dermatitis13, food allergy14,15, asthma16,17, but also autoimmune diseases such as type I diabetes18. Bifidobacteria dominate throughout the first year of life, while diversity and composition of the infant gut microbiome develops in the first 2 years of life19,20. Facultative anaerobic bacteria such as Staphylococcus, Streptococcus, Lactobacillus, and Escherichia initially colonize the colon, followed by strict anaerobes that come to predominate during the first few months of life21. Human milk selects for an intestinal microbiome dominated by bifidobacteria, in large part due to human milk oligosaccharides (HMOs)22. These complex glycans are non-digestible by humans, but provide the main substrate for the infant’s gut microbiota, particularly for bifidobacteria and some Bacteroides23,24. After cessation of breastfeeding and introduction of complementary foods, the dominant microbes of breastfed infants (bifidobacteria, lactobacilli, Enterobacteriaceae) are surpassed by Clostridium and Bacteroides species25.

Since the beginning of the 20th century, more variable degrees of colonization with Bifidobacterium, even in exclusively breastfed infants have been reported26, with higher levels seen in anthroposophic families27 and loss of Bifidobacterium has been associated with development of atopic disease and autoimmunity12,18. Irrespective of the feeding mode (human milk vs. cow’s milk-based formula), Bifidobacterium breve, B. longum and B. bifidum are the most commonly detected bifidobacterial species28. In breastfed infants, Bifidobacterium longum subspecies infantis (from here on B. infantis for short) can dominate the gut microbiome, because it is especially well-adapted to use HMOs as a carbon source29. Interestingly, the rate of B. infantis colonization is very low in the United States30 and other Western countries such as Australia, Belgium and Finland28,31,32 compared to very high rates of colonization in Gambia and Bangladesh33, 34. This mirrors the rates of autoimmune and allergic diseases.

Studies addressing the development of the early infant gut microbiome in farmlife communities are few17. In this pilot study, we sought to assess the differences in the development of infant gut microbiome, with a focus on Bifidobacterium, in two populations with different lifestyles: Rochester infants at high risk for allergic diseases and the Old Order Mennonites (OOM) of the Finger Lakes region in New York, with agrarian, traditional lifestyle, long periods of breastfeeding and with a low risk of asthma and allergic diseases5,10. We show that the OOM have a low rate of early childhood atopic diseases and their stool microbiome is enriched with Bifidobacteriaceae, especially B. infantis, accompanied by a marked change in fecal fatty acid composition.

METHODS

Study population

For this pilot study, we recruited a cohort of mother-infant pairs both pre- and post-natally. The low-risk OOM of the Finger Lakes region in New York State reside predominately in Penn Yan 65 miles southeast of Rochester, and were recruited by a nurse midwife among prenatal visits in her clinic. Rochester urban and suburban mothers were recruited from the University of Rochester Medical Center utilizing posted fliers. The OOM are a branch of the Mennonite Protestant tradition and of Swiss-German ancestry. Some lifestyle attributes include growing up on a farm, having large families and home births, being in contact with numerous pets and farm animals, consuming raw, unpasteurized farm milk, exhibiting a low rate of antibiotic utilization, and using horse and buggy for transportation. Only infants who were born full-term (>36 weeks of gestation), birth weight >2000g and generally healthy, whose mothers did not have chronic infection, inflammatory diseases or known immunodeficiency or metabolic diseases were included. Our protocol was approved by the Institutional Review Board of the University of Rochester Medical Center (RSRB52971) and all subjects provided written consent prior to enrollment to the study.

Altogether, 65 OOM and 39 Rochester dyads were recruited pre- or postnatally. Between 2 weeks and 6 months of age, stool samples were received from 48 OOM infants and 30 Rochester infants, 30 of whom provided samples at two time points. Humna milk was received from 38 OOM and 23 Rochester mothers. See Figure E1 in the Online Repository for the study schematic of events. Mothers’ own milk and infants’ stool samples were collected. The median age at collection of infant fecal samples was 60 (IQR 19.3, range 14–198) days (with 63 collected at <100 days and 38 >100 days), and 57 (IQR 26, range 27–114) days for human milk samples. Among the OOM infants, all were exclusively breastfed at the time of stool sample; among the Rochester infants two were breastfed and the rest were exclusively breastfed. Preschool-aged sibling stool samples were available from 8 children.

Early life exposures

At baseline, we collected data on maternal race and ethnicity, BMI, use of antibiotics during pregnancy, history of allergic diseases (asthma, eczema, allergic rhinitis and food allergy), exposure to indoor and outdoor animals, use of raw milk, and infant race and ethnicity, sex, number of older siblings, mode and place of birth, use of antibiotics and breastfeeding. These questionnaires show OOM mothers have significantly lower rates of self-reported asthma and allergic diseases, and higher rates of exposure to farm animals; OOM infants have higher rates of vaginal birth and lower rates of peri- and postnatal antibiotic use (Table 1).

Table 1.

Demographics of study subjects in the Rochester and Old Order Mennonite (OOM) cohorts.

Rochester OOM p-value
Maternal Characteristics (n=31) (n=60)
Age 31.9 (31.9, 32.0) 27.8 (27.8, 27.8) p<0.001
Caucasian, non-hispanic 24 (77%) 60 (100%) p<0.001
Body Mass Index (kg/m2) 26.1 (26.0, 26.1) 24.8 (24.8, 24.9) p=0.41
Asthma current (self-report) 4 (12.9%) 3 (5.0%) p=0.18
Asthma ever (self-report, includes childhood) 8 (19.5%) 5 (8.3%) p=0.048
Hay fever (self-report) 14 (45.2%) 10 (16.7%) p=0.004
Eczema (self-report) 5 (16.1%) 1 (1.7%) p=0.008
Food allergy (self-report) 7 (22.6%) 1 (1.7%) p=0.001
Rural home 0 (0.0%) 56 (93.3%) p<0.001
Unpasteurized milk consumption 0 (0.0%) 53 (88.3%) p<0.001
Contact to cat 8 (25.8%) 24 (40.0%) p=0.18
Contact to dog 12 (38.7%) 45 (75.0%) p=0.001
Contact to horse 3 (9.7%) 57 (95.0%) p<0.001
Contact to cow 0 (0.0%) 41 (68.3%) p<0.001
Contact to pig 0 (0.0%) 6 (10.0%) P=0.09
Contact to poultry 0 (0.0%) 38 (63.3%) p<0.001
Infant Characteristics (n=30) (n=48)
Caucasian, non-hispanic 22 (73%) 48 (100% p<0.001
Vaginal delivery 24 (80.0%) 47 (97.9%) p=0.007
Prenatal antibiotics 8 (26.0%) 2 (4.2%) p=0.004
Infant antibiotics 5 (16.7%) 1 (2.8%) p=0.019

Data shown include only those subjects where a full data set of demographics and lifestyle questionnaire data is available. Data missing or incomplete for 8 Rochester and 5 OOM families. 95% Confidence intervals are shown in parentheses. For all data besides BMI and age, the Chi-square test of independence was used in order to determine statistical significance. For BMI and age, the Student’s T-test was used. P-values in bold represent significant values where p<0.05.

indicates data collected at the time of stool sampling.

Follow-up of children for atopic symptoms

Presence of possible/likely atopic disease in the first three years of life was determined through a blinded telephone followup for allergic symptoms by a pediatric allergist (KJ). We queried physician-diagnosis of atopic dermatitis, allergic rhinitis, food allergy and asthma, or symptoms consistent with atopic disease including chronic/remitting pruritic rash in a distribution age-typical for atopic eczema that had been treated by steroidal treatments; chronic or recurring symptoms of rhinorrhea/congestion/sneezing treated with antihistamines; symptoms suggestive of IgE-mediated food allergy (itching/swelling of lips/mouth/throat, urticaria or severe vomiting after 2h of ingestion of a specific food) and allergic proctocolitis; and recurrent wheezing episodes. Few intermittent wheezing episodes associated with viral infections alone were not determined indicative of asthma due to the young age, whereas recurrent exercise-induced symptoms and persistent wheezing treated with inhaled corticosteroids were labeled as “recurrent wheezing/asthma”.

Sample collection and processing

Stool was collected from clean, unbleached diapers by mothers wearing gloves, immediately transferred in sterile collection cups. Human milk samples were collected in the morning, in UV irradiated, sterile manual breast pumps (Harmony Breastpump, Medela) or manual expression, wearing gloves. Foremilk was collected after cleaning the breast with Castille soap. All the samples were frozen at −20°C immediately upon collection until transferred to −80°C within four weeks for storage.

Microbiome analysis

DNA extraction, amplification and library construction.

Genomic DNA from stool and human milk samples was extracted using phenol-chloroform technique, see Methods in Online Repository. Milk extraction was done using pelleted bacteria combined with fat layer in order to capture bacterial species that associate with fat globules and do not pellet during centrifugation. Because bacterial load in human milk is significantly lower than that found in stool, there is greater potential for contamination. We have pioneered experimental techniques to reduce contamination in low-biomass samples35; these include avoidance of DNA extraction spin columns36, adhering to a strict aseptic technique, and usage of UV-treated and contamination-tested plasticware and reagents. As negative controls, DNA was extracted from Tris-EDTA and PBS in order to identify putative bacterial contaminants. The V4 region of the 16S rRNA DNA gene was amplified in triplicate from all samples, pooled and sequenced on the Illumina MiSeq platform (paired-end 250bp) following standard protocols37,38, and data was analysed as we have previously described38. See Methods in the Online Repository for more details.

Bifidobacteria qPCR and analysis

We further characterized infant stool and human milk samples collected from OOM and ROC cohorts utilizing Bifidobacterium-specific primer set targeting xylulose-5-phosphate/fructose-6-phosphate phosphoketolase bifidobacterial gene (xfp)39 in probe-based qPCR analysis. B. longum ssp. infantis-specific primer set targeting Blon_0915 region was used in probe-based qPCR analysis40. Given that all bacteria have one or more copies of 16S ribosomal RNA gene, universal primers that anneal to the conserved regions of 16S rRNA sequence was used to quantify total number of 16S copies using interchelator-based qPCR. In calculating abundances based on qPCR data it was assumed that overall microbiome copy number was 5, and thus the abundance is expressed as [species specific gene] / ([16S copies] / 5). For probe sequences and cycling parameters see Table E12 in the Online Repository. Pairwise comparisons using a Wilcoxon sign-ranked test were performed. To assess trajectory over time, this analysis was enriched with a second stool sample >100-days-old from 16 OOM infants and 5 Rochester infants for which two samples available. The B. infantis level was assessed by logistic regression with following co-variates: maternal atopy, delivery mode, cat and dog exposure, infant gender, maternal and infant antibiotics. The binarized B. infantis levels were used as a dependent variable and the regression was performed using glm41 from stats v3.6.1 in R.

HMO composition and analysis

HMO profile was measured by HPLC in the Bode lab as published42. We measured the absolute concentrations of 19 most abundant HMOs including type 1 and type 2 chains, branching, all types of fucosylation (α1–2, α1–3 and α1–4), which will allow determination of the mother’s Secretor and Lewis status, as well as all types of sialylation (α2–3 and terminal and internal α2–6). Pairwise comparisons and regression analysis using generalized linear model and log10 transformed data were performed as described above.

Stool and human milk metabolites and analysis

Stool and human milk samples were analyzed for the concentrations of a comprehensive panel of 69 fatty acids (FAs) ranging in chain length from 2 to 26 (See Table E3). The analysis was carried out at the Metabolic Invention Center (TMIC) at the University of Victoria, Alberta, Canada using a standards based quantitation of 3-aminophenyl derivatized FAs on UHPLC-MS/MS instrument43 as described in Supplemental Methods. The data was manually annotated to Human Metabolome Database (HMDB) database ids. Pairwise comparisons using all the data and data collected <100 days of age (due to possible age effect) and regression analysis using generalized linear model were performed as described above.

RESULTS

OOM stool microbiome is differentialy enriched from Rochester infants

Principal coordinate analysis shows that the gut microbiome of OOM and Rochester infants, collected at a median age of 2 months are distinguishable from each other (p=0.001, Fig. 1A), despite similar levels of alpha diversity (Fig. 1B). ROC curve analysis based on a Random Forest classifier demonstrates that microbiome composition alone has predictive power to distinguish OOM and Rochester populations, with an area under the curve (AUC) of 0.764 (Fig. 1C). Taxonomic summaries identified a clear enrichment of Bifidobacteriaceae and, at lower abundance, Clostridiceae in OOM infants (Fig. 1D). Within the Bifidobacterium clade, we observed Bifidobacterium longum was the dominant species and was significantly enriched OOM compared to Rochester infants (t-test, p=0.0001) (Fig 1E). Differential enrichment analysis further confirmed these findings, and identified Bifidobacteriaceae, Clostridium and Aerococcaceae as enriched in OOM, whereas Rochester gut microbiome was enriched in, among others, Enterobacteriaceae and Bacteroideceae (Fig 1F).

Figure 1: Infant stool microbiome composition measured by 16S rRNA gene sequencing.

Figure 1:

A. Principal coordinate analysis plot based on unweighted UniFrac distances of stool samples from OOM and Rochester (ROC) infants. Group are significantly different based on PERMANOVA test (p=0.02). B. Faith’s phylogenetic diversity of OOM and ROC infants. ROC has higher diversity than OOM, although differences are not significant. C. Receiver operating characteristic curve constructed from a random forest classifier trained on microbiome data to distinguish groups. The classifier used 1,000 trees and leave-one-out error. Area under the curve is 0.764. D. Taxa summary plots at the family level for OOM and ROC samples. E. Relative abundance of Bifidobacterium taxa found in OOM and ROC infants. Bifidobacterium longum is significantly enriched in OOM (t-test, p=0.0001) F. (left) LEfSe dendrogram indicating areas of the phylogeny enriched in OOM or ROC samples. (right) List of significant differential taxa found by LEfSe in OOM and ROC populations. Higher LDA (linear discriminant analysis) scores indicate features more characteristic of a group. Bifidobacterium (arrow) is the top feature in OOM infants.

Bacterial co-occurrence analysis of OOM (Fig 2A) and Rochester infants (Fig 2B) showed a higher clustering (0.612 vs 0.578) and density (0.597 vs 0.563) in OOM gut microbiome, suggesting a more robust network configuration in OOM and higher levels of ecological disruption in Rochester infants. Correlation strengths, however, were stronger in Rochester compared to OOM infants (Fig 2C; p<0.0001, unpaired t-test). We further investigated the role of Bifidobacterium in each of the networks and observed that, while this taxa is among the most central nodes in the OOM network, it has a notably reduced centrality in the Rochester network (Fig 2D), suggesting that Bifidobacterium plays a more fundamental role in OOM infants.

Figure 2. Bacterial co-occurrence analysis of infant stool.

Figure 2.

A, B. Co-occurrence networks of OOM (A) and Rochester (ROC) (B) infant stool samples. Co-occurrence was estimated using SparCC, and the network visualized using Cytoscape with edge-weighted spring embedded layout. Nodes represent bacterial genera, with the size of the node proportional to its mean relative abundance. Edges between nodes indicate significant correlations between bacteria, with shorter lines representing stronger correlations and vice versa. Positive correlations are shown in red, negative correlations in blue. Table presents basic network summary statistics (clustering coefficient, density) and statistics for Bifidobacterium (closeness, betweenness, centrality) in each population. C. Histogram of network correlation strengths for OOM (red) and ROC (green) infants. D. Distribution of closeness centrality for all nodes in the OOM (red) and ROC (green) infants co-occurrence network. Dashed lines indicate the closeness centrality of Bifidobacterium in each population.

Infant antibiotics and C-section delivery were negatively associated with Bifidobacteria abundance (See Fig E2A). To determine the impact of more variables associated with lifestyle, we regressed predictors (age of mother, infant gender, maternal atopy, BMI, delivery mode, maternal and infant antibiotic usage, number of older siblings, presence of cat and/or dog, and cohort arm) against alpha diversity, beta diversity (first two principal coordinates), the Bifidobacterium genus, and Bifidobacterium longum. Alpha and beta diversity, and the Bifidobacterium genus had no significant predictors. However, when using the abundance of Bifidobacterium longum as an outcome, maternal atopy was significant (estimate=0.278, p<0.05), suggesting that controlling for these other factors, maternal atopy is positively associated with Bifidobacterium longum abundance.

Bifidobacteria populations differ between OOM and Rochester stool.

Bifidobacterium-specific qPCR confirmed that OOM are colonized with Bifidobacterium to a larger extent than Rochester infants (p<0.001 at <100-days-old) (Fig 3A). B. longum includes Bifidobacterium longum ssp. infantis (i.e. B. infantis for short), and its 16S rRNA gene sequence is more than 97% similar (our threshold for OTU picking) to other B. longum biotypes44. Using B. infantis-specific qPCR, there was an even more stricking difference in the relative abundance of B. infantis, between OOM and Rochester infants (p<0.001 <100-days-old Fig 3A). Overall, among the available samples, B. infantis was detected in 31/44 (70%) OOM and 5/24 (21%) Rochester infants (p<0.001). Infant antibiotics and C-section delivery were negatively associated with B. infantis abundance (See Fig E2B). In a covariate logistic regression, including maternal atopy, delivery mode, cat and dog exposure, infant gender, maternal and infant antibiotics, B. infantis is positively associated with BMI (p<0.05) and number of older siblings (p<0.05), but only when using data from <100 day-old samples. Total Bifidobacterium is positively correlated with B. infantis in those whom have B. infantis present in their stool samples (Fig 3B). In summary, in comparison to infants from nearby Rochester, NY, the colonization rates and levels B. infantis are higher in OOM infants. Colonization with B. infantis is associated with increased number of older siblings.

Figure 3. Bifidobacteria and B. longum ssp. infantis (B. infantis) abundance in infant stool.

Figure 3.

A. B. infantis and total Bifidobacteria abundance in infant stool samples from the OOM and Rochester (ROC) community collected at <100 days and >100 days of life. B. Correlation between the relative abundance of B. infantis and total Bifidobacteria. A linear model was fitted to B. infantis positive samples only and is shown as blue line. C. B. infantis abundance as function of infant age at the time of collection. A linear model was fitted separately to OOM and ROC data and is shown with colored lines. In B and C grey shading indicates 95% CI of the linear model. Bifidobacteria and B. infantis-specific primer sets were used in qPCR analysis. Data includes one sample (<100-days-old) from 28 OOM and 19 Rochester infants and serial samples (one <100-days-old and one >100-days-old) from 16 OOM and 5 Rochester infants to compare trajectory over time. Abundance was calculated using an estimated 16S rRNA gene copy number of 5. This approximation leads in some cases to abundance > 1. This may be an indication of high abundance of a bacterial strain with 16S rRNA gene copy number that is substantially less than 5 leading to an overestimation.

Correlation of HMOs with infant stool microbiome

Although OOM mothers had higher levels of lacto-N-neotetraose (LNnT) than Rochester mothers in their milk (p=0.01), the levels of HMOs were not significantly different between those with or without B. infantis in their stool samples (See Fig E3).

Stool metabolites

Targeted assessment of infant stool metabolome focused on FAs shows that the levels of several short- to long-chain FAs were significantly higher in samples taken <100 days of age that were colonized with B. infantis, including lactic acid (p<0.05), pentadecanoic acid (p<0.05), palmitic acid (p<0.01), heptadecanoic acid (p<0.01), and stearic acid (p<0.01), nonadecanoic acid (p<0.05), arachidic acid (p<0.05), behenic acid (p=0.01), tricosanoic acid (p<0.05) and hexacosanoic acid (p<0.05) (Fig 4) when compared to samples with no B. infantis. Among these, the differences remained significant for lactic acid, pentadecanoic acid, palmitic acid and stearic acid when samples collected at >100 days of age were included. Conversely, propionic acid (p<0.05), methylmalonic acid (p<0.05) and isocaproic acid (p<0.01) were lower in stool samples in which B. infantis was present. Among these, the differences remained significant for propionic acid and methylmalonic acid when all the samples were included.

Figure 4. Infant stool metabolites.

Figure 4.

Concentrations of all infant stool fatty acids that were significantly different between infants <100 days old with either detectable or undetectable B. infantis. * indicates those fatty acids that were also statistically significantly different when using all the samples collected in the first 180 days of life. Fatty acids were quantitated by UHPLC-MS/MS.

Human milk microbiome

We next aimed to see how human milk microbiome differs between cohorts and contributes to infant microbiome. Principal coordinate analysis based on unweighted UniFrac distances shows that OOM and Rochester human milk microbiome are again distinguishable from each other (p<0.05, Fig 5A). OOM and Rochester human milk showed similar levels of alpha diversity (Fig 5B). ROC curve analysis based on a Random Forest classifier indicates that although the human milk microbiome can distinguish OOM and Rochester populations (AUC 0.685, Fig 5C), it has less predictive power than the stool microbiome (AUC 0.764). Streptococcaceae and Staphylococcaceae are the dominant taxa at the family level in both OOM and Rochester milk (Fig 5D). While Actinobacteria, Carnobacteriaceae, Rhodobacteriaceae and Clostridiaceae are differentially enriched in OOM, Lactobacillaceae are increased in the Rochester milk (Fig 5E).

Figure 5. Human milk microbiome composition measured by 16S rRNA gene sequencing.

Figure 5.

A. Principal coordinate analysis plot based on unweighted UniFrac distances of human milk samples from OOM and Rochester (ROC) infants. Group are significantly different based on PERMANOVA test (p=0.02). B. Faith’s phylogenetic diversity of OOM and ROC infants. OOM has higher diversity (8.16 ± 2.18) than OOM (7.19 ± 2.02), although differences are not significant (t-test, p=0.09). C. Receiver operating characteristic curve constructed from a random forest classifier trained on microbiome data to distinguish groups. The classifier used 1,000 trees and leave-one-out error. Area under the curve is 0.685. D. Taxa summary plots at the family level for OOM and ROC samples. E. List of top differential bacterial features found by LEfSe in OOM and ROC populations (top) and LEfSe dendrogram indicating areas of the phylogeny enriched in either group. F. Ternary diagrams showing results of SourceTracker for OOM and ROC human milk samples. Corners of the diagram indicate potential sources (sibling stool, mother’s own milk, unknown) of infant gut’s microbiome, with each point representing a stool sample from an infant. Colored areas depict density contours based on the number of samples present.

We then investigated what is the most probable origin of infant gut microbiome using SourceTracker, a Bayesian method that estimates the likelihood of a sample having originated from one of several sources45. Using three likely sources (sibling stool, maternal human milk, or “unknown” for other miscellaneous sources), we observed that both OOM and Rochester gut microbiome has most likely originated from gut-like sources (mean probability in OOM: 0.92±0.16; Rochester: 0.94±0.06), with human milk (OOM: 0.02±0.04; Rochester: 0.01±0.01) or other sources (OOM: 0.05±0.13; Rochester: 0.06±0.06) contribute only minimally (Fig 5F). We further examined the correlation between human milk and infant gut microbiome using correlation analysis. Bifidobacteria, the most abundant taxa in infant stool samples, was not significantly correlated with Bifidobacteria abundance in milk samples, and it was only negatively correlated with abundance of Veillonella (r=−0.53, p<0.01). Finally, B. infantis was only detected by qPCR in two human milk samples at a low level (data not shown).

Human milk metabolites

To see whether infant stool metabolites could originate from their mother’s milk, we assayed human milk targeted FAs. In human milk, FAs that were enriched in those mothers’ milk whose infants showed presence of B. infantis in their stool were different FAs than those seen in the infant stools (Fig 6). These included short- to medium chain FAs butyric acid (p<0.01), valeric acid (p<0.01), caproic acid (p<0.01), heptanoic acid (p<0.01), caprylic acid (p<0.05), pelargonic acid (p<0.05) and capric acid (p<0.05) in samples <100 days of age. Among them, the first five remained significant even after samples from >100 days were included.

Figure 6. Human milk metabolites.

Figure 6.

Concentrations of all human milk fatty acids that were significantly different in mothers of infants <100 days old with either detectable or undetectable fecal B. infantis. * indicates those fatty acids that were also statistically significantly different when using all the samples collected in the first 180 days of life. Fatty acids were quantitated by UHPLC-MS/MS. OOM, Old Order Mennonite; ROC, Rochester.

Microbiome and atopic outcomes

Follow-up phone calls to the mothers to assess physician-diagnosis and/or symptoms of atopic diseases by 3 years of age were successful in 87% of Rochester and 95% of the OOM families. Symptoms consistent with atopic diseases were reported in four OOM (6.5%) and 12 (35%) Rochester children (p<0.001), and there was specifically significantly less atopic eczema and food allergy in the OOM (Table 2). There was no significant association between atopic diseases and presence of B. infantis (p=0.08, Fisher exact test).

Table 2.

Likely/possible allergic outcomes of children at 3 years of age in the Rochester and Old Order Mennonite (OOM) arms.

Rochester (n=34) OOM (n=62) p-value
Any allergic outcome 12 (35%) 4 (6.5%) p<0.001
Asthma/recurrent wheeze 4 (12%) 2 (3%) p=0.18
Atopic eczema 9 (26%) 1 (1.6%) p<0.001
Food allergy 3 (9%) 0 (0%) p=0.04
Allergic rhinitis 5 (15%) 2 (3%) p=0.09

Data shown include only those subjects in whom telephone follow-up was available. Data missing for 5 Rochester and 3 OOM families. Presence of possible/likely allergic disease in the child’s lifetime was defined through a telephone survey by an allergist inquiring about physician diagnosis or symptoms consistent with atopic disease.

Those with only intermittent wheezing associated with upper respiratory infections are not included.

includes presentation of hives and positive skin prick test with cow’s milk and tree nuts (n=1) and egg (n=1), and allergic proctocolitis (n=1), all physician-diagnosed.

We further examined whether gut and milk microbiome composition was associated with atopic outcomes. Alpha diversity in gut microbiome was not significantly enriched in atopic compared to non-atopic infants. We did not observe a significant difference in milk microbiome alpha diversity or beta diversity with respect to atopic outcomes. We however observed a differential enrichment of Bacteroides, Lactobacillus and Eggerthella in atopic infants, even when controlling for population and presence/absence of B. infantis (Fig E4).

DISCUSSION

Populations with tradional lifestyles have enriched microbial diversity that is gradually lost as they adopt modern practices38. While previous studies have generally focused in characterizing the microbiome of adults in those populations, it is unclear whether these differences already exist in early life which could explain the surge of allergic, autoinflammatory and metabolic diseases in developed countries, as the microbiome plays a critical role in health and the development of the immune system46. Results of this study suggest that, in comparison to infants in urban and suburban Rochester, OOM infants from a closeby farming community with a lifestyle dating back 100 years and low rate of allergic diseases have more frequent colonization and higher abundance of Bifidobacteriaceae (Fig 1) and especially B. infantis in their stool microbiome (Fig 3). Further, Bifidobacterium was more central in the bacterial network of OOM (Fig 2), suggesting it plays an important ecological role not observed in Rochester infants. Presence of B. infantis was associated with specific infant stool metabolome profile with an increase in lactic acid and several medium- and long-chain FAs (Fig 4). Human milk was not the source of these metabolites nor B. infantis (Fig 56).

Our results are consistent with a previous study that showed a lower relative abundance of Bifidobacterium at 3 months of age in those with the highest risk for multisensitized atopy at 2 years of life12, and expand on the type of Bifidobacterium that may be beneficial in prevention of early manifestations of atopic diseases, such as atopic dermatitis and food allergy. These atopic diseases predispose to respiratory allergies and asthma, but not all asthmatics follow this “atopic march”11. Two other reports on the role of infant gut microbiome did not identify a protective role for Bifidobacterium in school-aged asthmatics16,17, although the estimated microbiome age (EMA) in 12-month-old infants was associated with previous farm exposure17; however these studies did not assess earlier manifestations of atopic disease that have a different phenotype and pathophysiology. Specific bifidobacterial species such as B. infantis were not assessed in any of these prior studies.

Due to the presence of HMOs, breastfeeding supports an infant-like microbiome, dominated by Bifidobacteria, until weaning47. However, there is variation in the ability of different species of Bifidobacterium to consume HMOs30. Some sub-species, such as B. infantis, are capable of consuming almost all HMOs, while others such as Bifidobacterium longum subsp. longum consume only a small subset of HMOs and are better adapted to the consumption of plant oligosaccharides24,48. In our cohort, the OOM mothers had a higher level of LNnT, one of the HMOs which is known to support Bifidobacteria growth, possibly due to dietary or probiotic effects of their diet49. However, we did not find positive associations between this HMO or other HMOs and B. infantis (Fig E2). We conclude that, in breastfed infants, the HMO composition does not appear to define presence or absence of B. infantis, which is known to grow on multiple HMOs. However, B. infantis colonization can be modulated by other factors, such as exposure to sources of B. infantis and usage of antibiotics, which can negatively impact bifidobacteria in the first week of life50.

Among infants born vaginally, several Bifidobacterium strains are transmitted from the mother and colonize the infant shortly after birth31,51. We assessed the potential sources of infant gut microbiome using human milk and siblings stool samples (Fig 5). While our results show that Streptococcaceae and Staphylococcaceae are the dominant taxa at family level, and many taxa are differentially enriched between in OOM and Rochester milk, human milk does not appear a significant direct source of the infant gut microbiome utilizing SourceTracker analysis (Fig 5F). This is in disagreement with two prior studies52,53, although there are differences in the cohorts. 16S sequencing identified B. longum with low abundance in human milk whereas B. infantis-specific qPCR hardly detected this subspecies. B. infantis was positively associated with the number of older siblings, suggesting the more numerous older siblings as a possible source. This could partly explain the extinction of B. infantis in modern families with fewer children. In contrast, the family sizes of the OOM are large (Table 1) and children are born with shorter birth intervals. It is tempting to speculate that the original observation by Strachan et al.54, that having several older siblings was protective against hayfever and atopic dermatitis could be related to B. infantis. Alternatively, B. infantis has also been detected in adult ileal resections55 and transmission from parental sources is also plausible.

It has been shown that an early gut microbiota including Bifidobacterium may promote B cell maturation56 and saliva IgA production57. B. infantis has also been shown to be anti-inflammatory as evidenced by its capacity to attenuate proinflammatory responses in intestinal epithelial cells and induce regulatory cytokine production by dendritic cells58. B. infantis derived metabolite, indole-3-lactic acid, has been shown to reduce inflammatory cytokine production by epithelial cells after inflammatory stimulus.59 In an animal model, B. infantis was recently shown to attenuate allergic inflammation60, and a closely related species B. longum to ameliorate food allergy, which was mediated by a decrease in mast cell numbers.61 In infants, B. infantis was associated with enhanced CD4 T-cell responses and vaccine responses34. In adults, B. infantis has been implicated in induction of generation of Tregs62. Recent work on infants supplemented with one strain of B. infantis, EVC001, identified an increase in stool levels of short-chain fatty acids (SCFAs) lactate and acetate and a decrease in fecal endotoxin40 as well as calprotectin and proinflammatory cytokines63. Recently, lack of Bifidobacterium was associated with elevated markers of intestinal inflammation and immune dysregulation; EVC001 supplementation silenced Th2 and Th17 immune responses64.

Members of the microbiota can influence host immune pathways through signals initiated by both direct contact with microbial surface antigens and by indirect effects of microbe-derived metabolites. We found several metabolites associated with presence of B. infantis in infant stool, including increased SCFA lactate (Fig 4), which is consistent with prior research on B. infantis40. SCFAs influence infant health in a variety of ways, and recent studies have focused on butyrate, which is anti-inflammatory in the context of allergic disease65. Dietary supplementation with butyrate was found to protect against asthma in an animal model66, as well as to induce generation of Tregs67, as was butyrate produced by commensal bacteria68. Dietary fiber and butyrate have also been shown to protect against peanut allergy in an animal model69. In a rural lifestyle study, low fecal butyrate levels and bacteria that predict butyrate production were associated with school-age asthma17. Although B. infantis does not directly produce butyrate, it may play an important role in facilitating the production of butyrate by other commensals, such as Clostridiales, given the large amounts of lactic and acetic acid (and formate) made by B. infantis, which are co-substrates for the butyrate production by commensals. Interestingly, butyrate was enriched in the breast milk consumed by infants colonized by B. infantis, but the causality is not clear. Further work in our laboratory will assess the origin of human milk FAs. Besides lactate, we also found several medium and long-chain FAs elevated in B. infantis-colonized stool, among which especially the odd-chain FAs (OCFA) (pentadecanoic, heptadecanoic and nonadecanoic acids) are likely produced by gut microbiota, and their high levels within plasma phospholipids have been associated with protection against type 2 diabetes and cardiovascular disease70.

In our study, in addition to enrichment of Bifidobacteriaceae in the OOM, other differences in infant gut microbiome composition were also noted. We observed a significant enrichment in Enterobacteriaceae in Rochester infants, which contain many well-known pathogenic organisms previously associated with lack of resolution of milk allergies71. OOM stools were also enriched with taxa within the Clostridiaceae family and Clostridium genus compared to Rochester infants. Animal models of food allergy highlight a protective role for consortia of clostridia in induction of Tregs67 via induction of IL-22 production by innate lymphoid cells72 and MyD88/RORγt pathway73. Clostridia were also enriched in the stool of infants with milk allergy who outgrew their allergy by age 8 years, compared to those whose allergy did not71, and they were underrepresented in those who later developed food sensitization15. Finally, we noted an enrichment of specific bacteria associated with atopic outcome, even when accounting for the presence or absence of B. infantis. Atopic infants had an enrichment in the abundance of Bacteroides, Lactobacillus and Eggerthella. Some Bacteroides species have previously been reported as associated with atopic disease74, although contradictory findings have also been reported13,14 and low prevalence has been associated with C-section births75,76. While Lactobacillus is generally considered a beneficial microbe, it is possible that higher abundance of this bacteria in the infant gut leads to a lower capability to ferment HMOs77. Eggerthella has been associated with immune disorders such as rheumatoid arthritis78. A previous study has reported lower alpha diversity in milk received by infants with allergic outcomes compared to non-allergic79. We only detected a similar trend, which could be due to the smaller cohort size and shorter followup.

The strengths of our study include access to a unique population with a traditional agrain lifestyle that also have modern commodities of homelife such as central heating, electricity, kitchen appliances and mechanized farm equipment, thus making findings more generalizable than those of the Amish. Although 16S rRNA gene sequencing cannot identify bacterial species, we utilized qPCR data to overcome this limitation. Limitations include the cross-sectional nature of this study with a range in age of sampling. Follow-up on development of allergic diseases was limited to self-reporting of physician-diagnosis and/or a retrospective query of allergic symptoms performed by a pediatric allergist. The latter was implemented to mitigate the possible bias due to lower health care utilization by the OOM. Our findings are preliminary and OOM lifestyle includes many other aspects that may be protective. A larger prospective infant cohort study is needed, including serial sampling and in-person allergy evaluations,skin testing and specific IgE to overcome these shortcomings.

Results from this pilot study demonstrate that the microbiome of OOM infants is enriched in B. infantis compared to Rochester infants. Access to a North American community, such as the OOM at low risk for allergies provides an unpredescented opportunity to assess the B. infantis function in infants while searching for novel microbiome and lifestyle approaches to address increase in allergic and autoimmune diseases80.

Supplementary Material

Supplementary Material

Aknowledgements:

We would like to thank Mary Ann Martin and the late Joyce Wade, CNM for their guidance and assistance in recruitment efforts within the Old Order Mennonite Community, and the participating families without whom this research would not be possible.

Funding:

The project described was supported by Grant Number R21 TR002516 from the National Center for Advancing Translational Sciences, Grant Number U01 AI131344 from the National Institute for Allergy and Infectious Diseases, Founders’ Distinguished Professorship in Pediatric Allergy, by Pilot Award from University of Rochester Clinical and Translational Science Institute and Environmental Health Sciences Center Pilot Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Potential conflict of disclosure statement:

Dr Seppo reports grants from NIH, during the conduct of the study. Mr. Bu has nothing to disclose. Ms. Jumabaeva has nothing to disclose. Dr. Thakar reports grants from NIH, during the conduct of the study. Mr. Choudhury has nothing to disclose. Ms Yonemitsu has nothing to disclose. L. Bode reports grants from NIH, during the conduct of the study. Dr. Martina has nothing to disclose. Ms. Allen has nothing to disclose. Dr. Tamburini reports grants from NIH, during the conduct of the study. Ms Piras has nothing to disclose. Mr. Wallach has nothing to disclose. Dr Looney reports grants from NIH during the conduct of the study. Dr Clemente reports grants from NIH, during the conduct of the study. Dr. Jarvinen reports grants from NIH, during the conduct of the study.

ABBREVIATIONS

AUC

area under curve

BMI

body mass index

FA

fatty acid

qPCR

quantitative PCR

HMO

human milk oligosaccaharide

IQR

interquartile range

LefSe

linear discriminant analysis effect size

LNnT

lacto-N-neotetraose

OCFA

odd-chain fatty acids

OOM

Old Order Mennonite

OUT

operational taxonomic unit

PERMANOVA

permutational multivariate ANOVA

rRNA

ribosomal RNA

SCFA

short-chain fatty acid

Treg

regulatory T cell

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