Examining the relationship between maternal body size, gestational glucose tolerance status, mode of delivery and ethnicity on mother’s milk microbiota at three months post-partum

doi:10.21203/rs.3.rs-17184/v1

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Research article

Examining the relationship between maternal body size, gestational glucose tolerance status, mode of delivery and ethnicity on mother’s milk microbiota at three months post-partum

https://doi.org/10.21203/rs.3.rs-17184/v1

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published 20 Jul, 2020

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Background: Few studies have examined how maternal body mass index (BMI), mode of delivery and ethnicity affect the microbial composition of human milk and none have examined associations with maternal metabolic status. Given the high prevalence of maternal adiposity and impaired glucose metabolism, and the importance of human milk in the colonization of the infant gut, we systematically investigated the associations between these maternal factors and milk microbial composition and functionality.

Methods: Women ≥20 years were recruited during pregnancy and milk samples were collected at 3 months post-partum (NCT01405547). Demographic data, weight, height, and a 3-hour oral glucose tolerance test were conducted at 30 (95% CI: 25-33) weeks gestation. Metagenomic DNA extraction and 16S ribosomal RNA gene sequencing of the V4 hypervariable region (Illumina MiSeq) was carried out on 113 milk samples.

Results: Multivariable linear regression analyses demonstrated no significant associations between maternal characteristics (maternal BMI [pre-pregnancy, 3 months post-partum], glucose tolerance, mode of delivery and ethnicity) and microbiota alpha-diversity; however, pre-pregnancy BMI was associated with human milk beta-diversity (Bray-Curtis p=0.040). Women with a pre-pregnancy BMI >30 kg/m2 (obese) had a greater incidence of Bacteroidetes (incidence rate ratio [IRR]: 3.70 [95% CI: 1.61-8.48]) and a reduced incidence of Proteobacteria (0.62 [0.43-0.90]), compared to overweight women (BMI 25.0-29.9 kg/m2) as assessed by multivariable Poisson regression. Increased incidence of Gemella was observed among overweight (versus healthy) mothers with gestational diabetes (5.96 [1.85-19.21]) and obese (versus healthy) mothers with impaired glucose tolerance (4.04 [1.63-10.01]). An increased incidence of Brevundimonas (16.70 [5.99-46.57]) was found in the milk of women who underwent an unscheduled C-section versus vaginal delivery. Lastly, functional gene inference demonstrated that obesity was associated with increased abundance of genes encoding for the biosynthesis of secondary metabolites in milk (coefficient=0.00028, p=0.0070).

Conclusions: Mother’s milk has a diverse microbiota of which its diversity and differential abundance appear associated with maternal body size, glucose tolerance status, mode of delivery, and ethnicity. Further research is warranted to determine whether this variability in the milk microbiota impacts colonization of the infant gut.

Applied & Industrial Microbiology

Nutrition & Dietetics

Mother’s milk

microbiota

body mass index

gestational diabetes

impaired glucose tolerance

mode of delivery

vaginal delivery

Caesarean delivery

ethnicity

microbiome

Breastfeeding is the recommended method of feeding for all infants irrespective of whether the country of origin is low, middle, or high-income (1). Mother’s milk is a rich source of nutrients and bioactive components, such as the antimicrobial proteins lactoferrin and lysozyme, and contains an array of oligosaccharides which serve as a source of prebiotics (2–5). It is now well accepted that mother’s milk contains a rich supply of bacteria (~ 10⁶ bacterial cells/mL), which are believed to play an important role in postnatal colonization of the infant’s gastrointestinal tract (gut) (6–13). Microbial composition of the gut, in turn, is associated with maturation of an infant’s gut and immune system, and aberrant gut microbial compositions have been linked to a number of short- and long-term health outcomes including diarrhea, respiratory tract infection, asthma, inflammatory bowel disease, obesity, and metabolic syndrome (14–18).

There is a limited understanding of the stability of the human milk microbiota in the face of environmental influences. Despite the high prevalence and known impact on other milk constituents, no study we are aware of has examined the association between maternal glucose tolerance status, either gestational diabetes or impaired glucose tolerance, and the milk microbiota. Only a few studies to date have cross-sectionally examined other maternal factors, such as maternal body size and mode of delivery, on the microbiota composition of mature mother’s milk (collected from 1-week to 6-months post-partum) (19–23). Moreover, the findings from the few available studies are inconsistent likely due, in part, to the small number of study participants, and differing methods of milk collection and analysis (Supplementary Table 1). Of the limited studies conducted, maternal BMI and mode of delivery have been associated with the mature milk microbiota; however, many of these reports relied on small cohorts and thus their findings require replication in larger scale studies (19–23). Further, most studies were unable to employ multivariable statistical modelling to adjust for multiple potential maternal factors of interest simultaneously. Lastly, none of these studies carried out functional inference analyses to determine if maternal body size and mode of delivery also perturb the milk microbiome’s potential metabolic activities or functional capabilities. It is vital to determine if maternal metabolic (BMI, glucose tolerance status), obstetrical (mode of delivery), and demographic (ethnicity) factors impact the mother’s milk microbiota and metagenome due to the role they play in the colonization of the infant gut and the importance of a healthy gut microbiome in human health.

Therefore, we set out to fill these knowledge gaps in the field. We collected a large cohort of mother’s milk samples from women prospectively enrolled in a study to investigate the impact of metabolic abnormalities and maternal nutrition in pregnancy on human milk (NCT01405547). The objective of this current study was to investigate the associations between maternal pre-pregnancy BMI (healthy, overweight, or obese), 3-month post-partum BMI, maternal glucose tolerance status in late pregnancy (gestational diabetes mellitus [GDM], impaired glucose tolerance [IGT], or normoglycemia), mode of delivery (vaginal delivery, unscheduled Caesarean delivery [C-section], or scheduled C-section), and ethnicity (white, Asian, or other) on the microbial community composition and functional capabilities of mother’s milk at three months post-partum. This research will help to elucidate the modulatory potential of mother’s milk microbiota in the face of physiological perturbations.

Participant Description

Milk samples were collected at 3 ± 1-month post-partum (mean ± standard deviation) (n = 113). Fifty-six (49.6%) mothers fed their infants their own milk exclusively at the time of milk collection, and 61 (53.9%) samples were from a complete breast expression. The mean (± standard deviation) age of the mothers was 34.2 ± 4.2 years (Table 1) with a pre-pregnancy BMI (kg/m²) of 24.3 ± 4.6, which is within a healthy BMI range (18.5–24.9 kg/m²). A modified oral glucose tolerance test (OGTT) administered at 30 weeks’ gestation (95% CI: 25–33 weeks) revealed that 24 (21.2%) women had GDM, 20 (17.7%) had IGT, and 69 (61.1%) had healthy glucose metabolism (normoglycemic). Among women with a healthy pre-pregnancy BMI, 12 and 16 had IGT and GDM, respectively. Among women with an overweight pre-pregnancy BMI, 5 had IGT and 5 had GDM (10 total). Lastly, among women with an obese pre-pregnancy BMI 3 had IGT and 3 had GDM (6 total; Supplementary Table 2). Sixty-four (56.6%) mothers delivered their infants vaginally, compared to the 49 (43.4%) who underwent a C-section. Following 16S rRNA gene sequencing, rarefying, and filtering, the bacterial taxa from 109 milk samples were included in the analyses. The total counts per sample were rarefied to 20,000 reads prior to calculating diversity metrics.

Overall microbial composition of mother’s milk

Twenty-six unique phyla-level and 292 unique genus-level taxa were identified (Figs. 1 and 2). Proteobacteria and Firmicutes were the most abundant phyla at 58.6 ± 27.3% and 35.6 ± 26.3%, respectively, followed by Actinobacteria (4.1 ± 4.7%), Bacteroidetes (1.4 ± 2.7%), and Fusobacteria (0.1 ± 0.3%). Pseudomonas (43.4 ± 26.0%) and Streptococcus (30.6 ± 25.3%) were the predominant genera across all samples, followed by smaller abundances of Staphylococcus (6.2 ± 11.5%), Acinetobacter (3.5 ± 7.4%), Veillonella (3.2 ± 7.2%), Gemella (1.9 ± 3.3%), Corynebacterium (1.6 ± 5.5%), Rothia (1.3 ± 2.4%), Aeromonas (0.6 ± 6.2%), and Brevundimonas (0.6 ± 5.7%). Plotting the relative abundances of taxa at the phylum (Fig. 1) and genus taxonomic levels (Fig. 2) reveals obvious inter-individual variability in the microbial composition of milk.

Associations between maternal body size, glucose tolerance status, mode of delivery, ethnicity and the milk microbiota

The Chao1 and Shannon indices were used to assess alpha diversity (richness and evenness, respectively) within each mother’s milk sample. No statistically significant associations were found between maternal characteristics (maternal glucose tolerance status, mode of delivery, pre-pregnancy BMI, 3-month post-partum BMI, ethnicity), and milk microbiota richness or evenness using multivariable linear regression analyses (Fig. 3A-E; complete results in Supplementary Table 3).

To further investigate associations between maternal characteristics and microbial composition, the beta-diversity of the microbiota in milk samples was assessed by principal coordinate analysis (PCoA) using the weighted UniFrac distance metric and the Bray-Curtis index of dissimilarity (Supplementary Figs. 1, 2; Supplementary Table 4 ) (24). No obvious clustering or separation based on maternal characteristics was observed; however, small but statistically significant associations between maternal pre-pregnancy BMI and beta-diversity clustering were identified (Bray-Curtis R² = 0.037, p = 0.044). No other statistically significant associations were found for the other maternal characteristics and beta-diversity.

We assessed whether taxa abundance was associated with maternal characteristics by using multivariable Poisson regressions and accounting for multiple comparisons (Tables 2, 3, 4, 5; Supplementary Tables 5, 6). At least one maternal characteristic was associated with the differential abundance of Proteobacteria, Bacteroidetes, and Actinobacteria at the phylum level, and Staphylococcus, Veillonella, Gemella, Corynebacterium, and Brevundimonas at the genus level.

Association between maternal body size and the milk microbiota

Pre-pregnancy BMI (i.e., healthy, overweight, obese) was found to be most consistently associated with differentially abundant taxa after controlling for relevant maternal characteristics. Mothers categorized as obese pre-pregnancy displayed a lower incidence of Proteobacteria (incidence rate ratio [IRR]: 0.62 [95% CI: 0.43–0.90]) in their milk as compared to mothers who were overweight. Conversely, mothers who were overweight presented with an increased incidence of Proteobacteria in their milk, compared to healthy weight mothers (1.23 [1.00-1.50]). Mothers defined as obese pre-pregnancy had a greater incidence of Bacteroidetes in their milk as compared to overweight (3.70 [1.61–8.48]) or had a healthy BMI (2.56 [1.27–5.17]) mothers. When examining 3-month post-partum BMI, Actinobacteria incidence was greater in obese women versus both overweight (2.34 [1.38–3.98]) and healthy weight mothers (2.02 [1.18–3.46]).

At the genus-level, women who were obese pre-pregnancy displayed a higher incidence of Staphylococcus as compared to overweight (2.50 [1.09–5.72]) or healthy weight mothers (3.15 [1.47–6.08]) (Table 2). Mothers who were obese pre-pregnancy also displayed a greater incidence of Corynebacterium in their milk versus both overweight (5.13 [1.79–14.70]) and healthy weight (4.98 [2.11–11.74]) mothers. This same relationship with Corynebacterium was seen in mothers who were obese at 3-months post-partum compared to overweight (4.84 [2.19–10.72]) and healthy weight (7.77 [2.95–20.43]) mothers. An increased incidence of Brevundimonas was also observed in mothers who were overweight pre-pregnancy vs healthy weight (8.72 [3.24–23.48]). At 3-months post-partum, women who were obese also displayed a greater incidence of Brevundimonas versus both overweight (8.89 [2.29–34.57]) and healthy (9.56 [2.17–42.22]) mothers (Table 2).

Association between maternal glucose tolerance status and the milk microbiota

When examining an interaction between BMI and maternal glucose tolerance status, Gemella showed an increased incidence among overweight (versus healthy) mothers with gestational diabetes (5.96 [1.85–19.21]). In addition, Gemella was increased in obese mothers with impaired glucose tolerance versus overweight (11.42 [1.49–87.67]) and versus healthy (4.04 [1.63–10.01]) mothers with impaired glucose tolerance (Table 2).

Association between mode of delivery and the milk microbiota

Associations between mode of delivery and the differential abundance of select taxa at the phylum and genus level were found for both pre-pregnancy and post-partum BMI models (Table 2). A greater incidence of Brevundimonas was observed in mothers who underwent an unscheduled C-section versus a vaginal delivery from both the pre-pregnancy BMI model (16.70 [5.99–46.57]) and 3-month post-partum BMI model (13.01 [4.01–42.20]). Conversely, a reduced incidence of Brevundimonas was observed in mothers who underwent a scheduled C-section versus an unscheduled C-section from both the pre-pregnancy BMI model (0.070 [0.011–0.46]) and 3-month post-partum BMI model (0.08 [0.013–0.62]).

Association between maternal ethnicity and the milk microbiota

Lastly, ethnicity (white, Asian, other) was associated with the differential abundance of Corynebacterium and Brevundimonas (Table 3). White mothers had a reduced incidence of both Corynebacterium (0.27 [0.12–0.59]) and Brevundimonas (0.084 [0.015–0.46]) when compared to ‘other’ mothers and Asian mothers, respectively; Asian mothers also had a reduced incidence of Corynebacterium when compared to ‘other’ mothers (0.17 [0.049–0.63]).

Association between maternal body size, glucose tolerance status, mode of delivery, ethnicity and functional gene expression of the milk microbiota

We carried out functional inference analyses using Piphillin to assess if there were any differences in functional capabilities of the milk microbiota based on the maternal clinical data. In contrast with the bacterial taxonomic results, the relative abundance of the 20 top KEGG pathways across all milk samples was fairly consistent (Supplementary Fig. 3). We analyzed the association between maternal clinical data and KEGG ortholog beta-diversity as well as examined the association between maternal clinical parameters and differentially-expressed KEGG pathways (Supplementary Tables 7–9). No significant results were found when examining metadata and KEGG ortholog beta-diversity (Supplementary Table 7); however, one statistically significant differentially-expressed pathway was observed (Supplementary Table 8, Supplementary Fig. 4). BMI, specifically the obese sub-category, was shown to be significantly associated with enrichment of the KEGG category “Biosynthesis of secondary metabolites” (Coefficient 0.00024, p = 0.0079). (Supplementary Fig. 4).

Our results suggest that maternal factors, and most consistently maternal pre-pregnancy BMI, are associated with the microbial composition of mother’s milk. This is the first study to include maternal glucose tolerance status in the investigations of the association between maternal body size and the milk microbiota (Supplementary Table 1). Gestational diabetes, a well-known risk factor of maternal adiposity, is associated with a number of negative health outcomes, including increased risk of type 2 diabetes and metabolic syndrome in the mother, as well as large for gestational age, congenital malformations and hypoglycemia in the infant (25–28). These changes in infant health may be partially mediated by microbes transmitted from mother to infant, from both the birthing process as well as during direct breastfeeding. For this reason, it is imperative to study the relationship between these factors in order to best mitigate these maternal and infant outcomes as well as distinguish the role of body size versus glucose tolerance status on the milk microbiota and, hence, the infant.

According to a recent systematic review and dose-response meta-analysis, the risk of GDM increases by 4% for every unit increase in BMI; thus, to investigate BMI without also adjusting for GDM could lead to erroneous results (28). Our results suggest that these associations extend beyond the gut microbiota and that alterations in glucose tolerance status may also perturb the composition (Tables 2, 4) of mother’s milk microbiota. Previous studies have reported associations between both type 2 diabetes and insulin resistance and the gut microbiota composition (29). Potential mechanisms of action linking impaired glucose tolerance and the gut microbial community include lipopolysaccharide-triggered inflammation, impairment of GLP-1 and GLP-2 via bacterial production of short chain fatty acids, insulin resistance triggered by bacterial synthesis and absorption of branch-chained amino acids, or the metabolism of bile acids by bacteria and their organ-specific effects (29). It is unclear how impaired glucose tolerance may modulate the bacteria in human milk and the interplay present with maternal body size.

We did not find any differences in alpha diversity based on our maternal characteristics; however, we did find statistically significant differences in beta-diversity, with mother’s milk microbiota separating, or non-randomly clustering, based on pre-pregnancy BMI even after adjustment for other covariates (Supplementary Table 4, Supplementary Figs. 1 and 2). The human gut microbiota has been reported to cluster as a function of body size, but this has not yet been reported for the human milk microbiota (30). Our results demonstrating an association between maternal body size and microbial composition is consistent with other smaller scale studies on human milk microbiotas (Supplementary Table 1). Cabrera-Rubio et al. (2012) examined the association between maternal body size and the differential abundance of mother’s milk genera in a study of healthy Finnish women (n = 18) (20). They reported an increase in Staphylococcus in mother’s milk collected from obese women, which mirrors the findings in our study (Table 4).

Mode of delivery was also associated with changes in mother’s milk microbiota at both the phylum and genus levels (Table 2, 4). For example, we observed greater differential abundance of Staphylococcus in mother’s milk from women who underwent a (scheduled) C-section versus vaginal delivery (Table 4). Our results are similar to that reported by Cabrera-Rubio et al. (2012, 2016). These two small cross-sectional cohorts of healthy Finnish women (n = 18, 10) showed a non-statistically significant increase in Staphylococcus in milk observed among women who delivered their infant via a scheduled C-section versus a vaginal delivery (Table 4) (19, 20). The proposed mechanism whereby mode of delivery alters the milk microbiota is via the infant oral cavity, which is colonized during either vaginal delivery or C-section; from here, retrograde inoculation of bacteria can occur from the infant’s oral cavity into the mammary gland via the suckling process with direct breastfeeding (31–34).

The results of our multi-ethnic cohort revealed associations between ethnicity and specific bacterial taxa. Ethnicity and/or geographic location have been shown to be factors in determining various microbiomes of the body including the gut, oral cavity, respiratory tract, skin, and urogenital tract (35). Ethnicity and geographic location typically come with an overlay of dietary variation, making the impact of each variable challenging to separate. Only a few studies to date have assessed associations between ethnicity and the milk microbiota; however, the ethnic/geographic groups differed from our study as they generally examined Europe, Africa and the United States, making it challenging to compare findings (Tables 3, 5; Supplementary Tables 5, 6) (12,21,36,37).

We used a functional inference approach to characterize the microbial genetic potential in human milk. In agreement with what has been reported for other human-associated microbiotas, the functional capacity of the human milk microbiota is more stable than its taxonomic composition (38). We then assessed whether there were specific pathways that were associated with maternal characteristics and found that maternal BMI, specifically the obese sub-category, was significantly associated with an increase in the “Biosynthesis of secondary metabolites” KEGG pathway. Microbes produce secondary metabolites, which are small, bioactive molecules, not necessary for growth or development but are instead involved in microbe-host or microbe-microbe interactions (39,40). Indeed, many of the genes in the biosynthesis of secondary metabolites pathway encode for the biosynthesis of antibiotics (41). Increased production of these secondary metabolites could impact the overall microbial composition/function in these feeding infants. These potential alterations could represent a mechanism by which maternal BMI impacts infant health over both the short- and long-term and warrants future investigation.

Human milk is considered a low biomass sample and, for this reason, may be more affected by sample processing than higher biomass samples, such as stool. To address this concern, we used PCoA plots to visualize clustering, or lack thereof, of our milk samples and negative controls (Supplementary Fig. 5). Our negative controls were seen to cluster away from the samples, suggesting that our results do not arise from technical contaminates, which was confirmed using Adonis analyses to statistically corroborate that our samples clustered away from the negative controls (Weighted UniFrac R² = 0.07, p = 0.0001; Bray-Curtis R² = 0.10, p = 0.0001, Supplementary Fig. 5).

Strengths of the current study include its relatively large sample size, the diverse ethnicity of women included, clinical examination via an OGTT, and enrichment of the cohort with women of varying body size who had abnormal glucose tolerance status. These strengths allowed for a more fulsome investigation using multivariable statistics to determine how each maternal factor is independently associated with the milk microbiome. Limitations of the current study include a lack of disinfection of the mother’s breast, peri-areolar region, and/or nipple prior to milk sampling, and single time-point sampling. The microbes identified in the milk from the present study likely include bacteria from the skin microbiota. Practically, however, mothers do not disinfect their breast prior to pumping and storing milk for their infant, nor do they disinfect prior to breastfeeding. Therefore, the mother’s milk microbiota as collected in the current study is likely a more accurate depiction of what the infant would receive. Secondly, we could not adjust for all variables of interest to due sample size constraints, so we are likely missing additional determinants of both the milk microbiota and functional capabilities. Lastly, our study is limited by its cross-sectional design and thus we cannot assess how the milk microbiome changes over time. It is possible that the associations we identified between maternal factors and microbial composition in milk are not transitory and change across the course of lactation.

Our study found that mother’s milk has a highly personalized microbiota with high inter-individual variability. Expressed mother’s milk at both the phylum and genus levels appear to be related to maternal metabolic and obstetrical factors. Surprisingly, glucose tolerance status was significantly associated with fewer microbiota parameters than anticipated. Most consistently, maternal pre-pregnancy BMI, despite glucose tolerance status, was associated with the differential abundance of various taxa in mother’s milk and potentially the production of bacterial secondary metabolites as well. To understand the clinical significance of these findings, future research should explore the impact of differences in the microbial composition of mother’s milk on colonization of the infant gut microbiome and infant health.

Study participants and design

To address the research objectives of this study, we used maternal metabolic and obstetrical health data and bio-banked human milk samples available from a previously conducted prospective cohort study (ClinicalTrials.gov Identifier: NCT01405547); a detailed description of the study protocol has been previously published (42). Pregnant women (n = 216) were recruited from outpatient clinics at Mount Sinai Hospital in Toronto, Canada and completed a 3-hour 100 g OGTT between March 2009 and July 2010. In total, 117 women donated a milk sample at 3 months post-partum, with 113 samples available for this study (Supplementary Fig. 6). Women were eligible for inclusion in the original study if they were ≥ 20 years of age and had an intention to breastfeed. Exclusion criteria included pre-existing diabetes diagnosis, current use of insulin, or completion of an OGTT prior to recruitment (43). By design, mothers were recruited from clinics which follow higher risk pregnancies with a greater risk of either GDM or IGT diagnosis. Written informed consent was obtained from all women and the study protocol was approved by the Mount Sinai Hospital Human Research Ethics Board (42).

Collection of Demographic, Anthropometric and Metabolic Data

During the first study visit, which occurred in late pregnancy (30 weeks [95% CI: 25–33 weeks]), demographic and anthropometric data were collected (e.g. age, ethnicity, weight, height); mothers were asked to recall their pre-pregnancy weight. All pregnant women in Canada are screened for GDM by way of a 50 g glucose challenge test (GCT). If the plasma glucose concentration at 1-hour post-glucose load is ≥7.8 mmol/L, the patient is then referred for a diagnostic OGTT. Contrary to standard obstetrical practice, all women completed a 3-hour 100 g OGTT in the current study during their first study visit regardless of whether or not they completed a GCT. The OGTT involved having blood samples drawn at fasting, 30, 60, 90, 120- and 180-minutes post-glucose load. Women were then diagnosed with either GDM, IGT, or as normoglycemic based on the following glycemic thresholds: 1) GDM diagnosis = 2 or more of the following: fasting blood glucose ≥ 5.8 mmol/L, 1-hour blood glucose ≥ 10.6 mmol/L, 2-hour blood glucose ≥ 9.2 mmol/L, or 3-hour blood glucose ≥ 8.1 mmol/L, or 2) IGT diagnosis would exceed only one of the previous thresholds, or 3) normoglycemic = normal OGTT (42).

Mother’s milk collection, processing and amplification

At the three-month post-partum research visit, mothers were asked to pump a complete breast expression of milk using a double electric breast pump (Medela Inc., Illinois, USA) with a sterile pumping kit. Mothers were instructed not to pump or breastfeed their infant for 2 hours before the study visit. Samples of whole human milk were then divided into aliquots and stored at -80 °C until the time of analyses.

DNA was extracted from human milk using the NucleoSpin Food DNA Isolation Kit (Macherey-Nagel, Pennsylvania, USA) according to manufacturer’s instructions with modifications as we have described previously (44). Due to the small concentration of DNA in human milk, an elution buffer volume of 30 µL, instead of the recommended 100 µL, was used to ensure adequate DNA concentrations for downstream PCR.

PCR amplification of the V4 hypervariable region was performed using the forward primer (515F) 5’AATGATACGGCGACCACCGAGATCTACACTATGGTA ATTGTGTGCCAGCMGCCGCGGTAA and reverse primer (806R) 5’CAAGCAGA AGACGGCATACGAGATA GTCAGTCAGCCGGACTACHVGGGTWTCTAAT (45). PCR reactions were set up following the manufacturer’s recommendations (Roche) including 12.5 µL of KAPA2G Robust HotStart ReadyMix, 1.5 µL of 10 µM forward and 1.5 µL of 10 µM reverse primer, 3.5 µL of sterile water and 6 µL of DNA. Amplification of the V4 hypervariable region of the 16S rRNA gene involved 28 cycles of PCR: 95 °C for 3 minutes, 25–30 cycles of 95 °C for 15 seconds, 50 °C for 15 seconds and 72 °C for 15 seconds, followed by a 5 minute 72 °C extension (different numbers of cycles between PCR runs were adjusted for statistically). All amplifications were completed in triplicate and all amplicons were run on a 1% TBE agarose gel to ensure accurate amplification (amplicon size ~ 390 bp). A negative control without template DNA and a positive control with DNA from a known bacterial species (Pseudomonas aeruginosa) were also included to confirm the amplification quality. Bands of the same size and intensity were pooled and quantified to create the pooled sequence library. Purification of the pooled library was completed with AMPure XP beads (0.8X volume of beads to 1X volume of library DNA) following the manufacturer’s protocol. The purified library was quantified using the Qubit High Sensitivity DNA Kit (Thermo Fisher). The quantified library was loaded on an Illumina MiSeq and sequenced using the MiSeq-V2-300 cycle chemistry to generate 150 PE reads.

Bioinformatics analyses

The raw paired end sequences from the MiSeq instrument have been deposited to the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under accession number PRJNA516669. The UPARSE pipeline (USEARCH) was used for sequence analysis. Raw paired end sequences were assembled (-fastq_mergepairs; -fastq_merge_maxee = 1.0), filtered (-fastq_filter; -fastq_maxee = 0.5) and sequences shorter than 225 base pairs were removed (-fastq_filter; -fastq_minlen 225) (46). Sequences were then de-replicated and sorted using USEARCH (-derep_full; -sortybysize). Chimeric sequences in the OTUs were detected and removed using the Ribosomal Database Project (RDP) 16S gold database (USEARCH), while ensuring the number of false positive chimeras detected was minimized (47). Sequences were then grouped together into Operational Taxonomic Units (OTUs) at 97% similarity (-usearch_global). Taxonomy was assigned to these OTUs (RDP 16S gold database) (-utax) and OTU fasta sequences were aligned using PyNast via a QIIME python script (align_seqs.py). A phylogenetic tree was assembled using the FastTree QIIME python script (make_phylogeny.py) (48).

Data analysis and statistics

The phyloseq package (1.25.2) in R (version 3.4.1) was used to analyze microbiota composition (49). OTUs that only appeared once or twice (singletons and doubletons) were removed and all OTUs were rarefied to 20,000 reads prior to calculating relative abundances at different taxonomic levels, alpha diversities and beta diversities using phyloseq.

Statistically significant differences between the alpha diversities (Chao1/Shannon indices determined in R) and maternal metabolic or obstetrical characteristics were determined using multivariable linear regression models (PROC MIXED) in SAS version 9.4. Independent variables included in the models were: maternal BMI (healthy = 18.5–24.9 kg/m², overweight = 25-29.9 kg/m², obese = > 30 kg/m²), maternal glucose tolerance status (GDM, IGT, normoglycemic), mode of delivery (vaginal, unscheduled C-section, scheduled C-section), DNA extraction batch, and PCR sequencing batch. Separate statistical models were built using pre-pregnancy and 3-month post-partum BMI as covariates, due to concerns about collinearity. An interaction term between BMI and maternal glucose tolerance status was also tested in each model and removed if it was non-significant. Due to our sample size and the number of covariates we wished to test, separate models for ethnicity (white, Asian, other) were run that adjusted for DNA extraction and PCR sequencing batches, but no other covariates. Multicollinearity was assessed between independent variables in all models, using a variance inflation cut-off of > 5. The significance level was set at p < 0.05. Of note, 6 mothers with a pre-pregnancy BMI between 18.0-18.4 kg/m² were placed in the “healthy BMI” group for all analyses.

Beta diversities and principal coordinate analysis (PCoA) were also ascertained in phyloseq and statistical significance based on maternal characteristics was determined using the adonis function in vegan (version 2.5-3) (24). Adonis assesses the amount of variation explained by each metadata variable, such as maternal BMI or glucose tolerance status; all variables were run individually and together in adonis to adjust for one another. The interaction term between BMI and glucose tolerance status was also tested and removed if non-significant. Four patient’s samples were missing the post-partum BMI data and thus we used their pre-pregnancy BMI for the post-partum analyses. Again, the significance level was set at p < 0.05.

Multivariable Poisson regression models (PROC GENMOD) were run in SAS version 9.4 to assess differential abundance at the phylum and genus levels based on maternal characteristics. The Benjamini-Yekutieli cut point approach was used to account for multiple testing. A p ≤ 0.022 at the phylum level (5 tests) and p ≤ 0.017 (10 tests) at the genus level were considered statistically significant for the overall group effect. If the overall group-adjusted p-value was significant, pairwise comparisons were conducted and a pairwise p < 0.05 was considered statistically significant.

Piphillin: Functional analysis of human milk microbiota

Piphillin, a metagenomics inference tool, was used to infer functional capabilities in milk samples (https://piphillin.secondgenome.com/) (50). In this study, the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/) was used as a reference database to retrieve gene copy numbers and create a gene feature table from the 16S rRNA sequence data. Statistically significant associations between maternal characteristics and KEGG pathways where assessed in two ways: first, examining the association between maternal characteristics and KEGG orthologs beta-diversity using Adonis in R (p < 0.05), and second, investigating metadata associated with differentially-expressed functional pathways using MaAsLin2 in R (p < 0.1).

BMI Body mass index

GDM Gestational diabetes mellitus

IGT Impaired glucose tolerance

Caesarean delivery C-section

OGTT Oral glucose tolerance test

rRNA Ribosomal RNA

PCoA Principal coordinates analysis

OTU Operational taxonomic unit

Ethics Approval and Consent to participate: The study protocol was approved by the Mount Sinai Hospital Research Ethics Board.

Consent for publication: N/A

Availability of data and materials

The dataset supporting the conclusions of this article is available in the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under accession number PRJNA516669.

Competing Interests

None to declare.

Funding

CIHR MOP 125997; CDA Operating Grant #OG-3-09-2393.

Author’s contributions: SHL, AJH, BZ, and DLO designed the prospective cohort study, SHL coordinated data and milk collection. LLN and DLO designed the present study and LLN, JB, JKC, and PWW worked out laboratory methods and conducted the 16S rRNA sequencing. JKC and PWW performed the bioinformatics, and LLN, JB, MRA, and AK performed the data analysis and statistics. LLN wrote the first draft of the paper. All authors provided important critical review and DLO had responsibility for the final manuscript.

Acknowledgements: We would like to thank Michael Jory for his assistance in setting up bioinformatic software in our laboratory

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Table 1. Baseline characteristics of mothers
Baseline variables	n=113
Mean age (y), mean ± SD	34.2 ± 4.2
Ethnicity, No. (%)
White	64 (56.6%)
Asian (Chinese, Korean, Japanese, Filipino)	27 (23.9%)
Other (South Asian, black, other)	22 (19.5%)
Pre-pregnancy BMI* (kg/m²),
Mean ± SD	24.3 ± 4.6
Obese (>30kg/m²), No. (%)	11 (9.7%)
Overweight (25-29.9kg/m²), No (%)	30 (26.5%)
Healthy (18.5-24.9kg/m²), No (%)	72 (63.7%)
3-month post-partum BMI* (kg/m²),
Mean ± SD	26.4 ± 5.2
Obese (>30kg/m²), No. (%)	17 (15.0%)
Overweight (25-29.9kg/m²), No. (%)	46 (40.7%)
Healthy (18.5-24.9kg/m²), No. (%)	50 (44.2%)
Glucose tolerance status, No. (%)
Gestational diabetes mellitus	24 (21.2%)
Impaired glucose tolerance	20 (17.7%)
Normoglycemic	69 (61.1%)
Mode of delivery, No. (%)
Vaginal	64 (56.6%)
Scheduled Caesarean section	21 (18.6%)
Unscheduled Caesarean section	28 (24.8%)
*BMI= body mass index

Table 2. Differential abundance of top 5 phyla and top 10 genera based on maternal BMI.
Taxa	Group effect p-value	Pairwise comparison	IRR	95% CI	Pairwise comparison p-value
Pre-pregnancy BMI
Phylum
Proteobacteria	0.020	Obese vs overweight	0.62	0.43-0.90	0.0012
		Overweight vs healthy	1.23	1.00-1.50	0.045
Bacteroidetes	0.0051	Obese vs overweight	3.70	1.61-8.48	0.002
		Obese vs healthy	2.56	1.27-5.17	0.0086
Genus
Staphylococcus	0.011	Obese vs overweight	2.50	1.09-5.72	0.031
		Obese vs healthy Overweight vs healthy	3.15 5.96	1.47-6.80 1.85-19.21	0.0032 0.0028
Corynebacterium	0.00030	Obese vs overweight	5.13	1.79-14.70	0.0023
		Obese vs healthy	4.98	2.11-11.74	0.0002
Brevundimonas	<0.0001 <0.0001	Overweight vs healthy Unscheduled C-section vs vaginal Scheduled C-section vs unscheduled C-section	8.72 16.70 0.07	3.24-23.48 5.99-46.57 0.011-0.46	<0.0001 <0.0001 0.0053
3-month post-partum BMI
Phylum
Actinobacteria	0.0058	Obese vs overweight	2.34	1.38-3.98	0.0017
		Obese vs healthy	2.02	1.18-3.46	0.010
Genus
Corynebacterium	<0.0001	Obese vs overweight	4.84	2.19-10.72	0.0001
		Obese vs healthy	7.77	2.95-20.43	<0.0001
Brevundimonas	<0.0001 0.00050	Obese vs overweight Obese vs healthy Unscheduled C-section vs vaginal Scheduled C-section vs unscheduled C-section	8.89 9.56 13.01 0.08	2.29-34.57 2.17-42.22 4.01-42.20 0.013-0.62	0.0016 0.0029 <0.0001 0.015
Separate Poisson regression models were run for pre-pregnancy BMI and 3-month post-partum BMI, while adjusting for maternal glucose tolerance status, mode of delivery, DNA extraction batch, and PCR sequencing batch. Statistically significant main group effect findings shown only (group effect: p≤0.022 for phylum, p≤0.017 for genus; pairwise comparison: p<0.05). Abbreviations: confidence interval, CI; incidence rate ratio, IRR.

   <table border="1" cellpadding="0" cellspacing="0" width="784">
      <tbody>
        <tr>
          <td colspan="6" valign="top" width="100%">
            <p><strong>Table 3</strong>. Differential abundance of top 5 phyla and top 10 genera based on ethnicity.</p>
            <p>&nbsp;</p>
          </td>
        </tr>
        <tr>
          <td valign="top" width="24.713375796178344%">
            <p><strong>Taxa</strong></p>
          </td>
          <td valign="top" width="13.885350318471337%">
            <p><strong>Group effect&nbsp;</strong></p>
            <p><strong><em>p</em></strong><strong>-value &nbsp;</strong></p>
          </td>
          <td valign="top" width="21.273885350318473%">
            <p><strong>Pairwise comparison</strong></p>
          </td>
          <td valign="top" width="7.388535031847134%">
            <p><strong>&nbsp;IRR</strong></p>
          </td>
          <td valign="top" width="11.719745222929935%">
            <p><strong>95% CI</strong></p>
          </td>
          <td valign="top" width="21.019108280254777%">
            <p><strong>Pairwise comparison&nbsp;</strong></p>
            <p><strong><em>p</em></strong><strong>-value</strong></p>
          </td>
        </tr>
        <tr>
          <td colspan="6" valign="top" width="100%">
            <p><strong>&nbsp;</strong></p>
          </td>
        </tr>
        <tr>
          <td valign="top" width="24.713375796178344%">
            <p><strong>Phylum</strong></p>
          </td>
          <td valign="top" width="13.885350318471337%">
            <p>&nbsp;</p>
          </td>
          <td valign="top" width="21.273885350318473%">
            <p>&nbsp;</p>
          </td>
          <td valign="top" width="7.388535031847134%">
            <p>&nbsp;</p>
          </td>
          <td valign="top" width="11.719745222929935%">
            <p>&nbsp;</p>
          </td>
          <td valign="top" width="21.019108280254777%">
            <p>&nbsp;</p>
          </td>
        </tr>
        <tr>
          <td valign="top" width="24.713375796178344%">
            <p><em>Corynebacterium</em></p>
          </td>
          <td valign="top" width="13.885350318471337%">
            <p>0.0008</p>
            <p>&nbsp;</p>
          </td>
          <td valign="top" width="21.273885350318473%">
            <p>White vs other</p>
            <p>Asian vs other</p>
          </td>
          <td valign="top" width="7.388535031847134%">
            <p>&nbsp; 0.27</p>
            <p>&nbsp; 0.17</p>
            <p>&nbsp;&nbsp;</p>
          </td>
          <td valign="top" width="11.719745222929935%">
            <p>&nbsp; 0.12-0.59</p>
            <p>&nbsp; 0.049-0.63</p>
          </td>
          <td valign="top" width="21.019108280254777%">
            <p>&nbsp; 0.0010</p>
            <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0075</p>
            <p>&nbsp;</p>
          </td>
        </tr>
        <tr>
          <td valign="top" width="24.713375796178344%">
            <p><em>Brevundimonas</em>&nbsp;</p>
          </td>
          <td valign="top" width="13.885350318471337%">
            <p>0.0051</p>
          </td>
          <td valign="top" width="21.273885350318473%">
            <p>White vs Asian</p>
          </td>
          <td valign="top" width="7.388535031847134%">
            <p>&nbsp; 0.084</p>
          </td>
          <td valign="top" width="11.719745222929935%">
            <p>0.015-0.46</p>
          </td>
          <td valign="top" width="21.019108280254777%">
            <p>0.0042</p>
            <p>&nbsp;</p>
          </td>
        </tr>
        <tr>
          <td colspan="6" valign="top" width="100%">
            <p>Ethnicity was investigated for all taxa and models were adjusted for DNA extraction and PCR sequencing batch effects. Statistically significant findings shown only (group effect: p≤0.022 for phylum, p≤0.017 for genus; pairwise comparison: p&lt;0.05). Abbreviations: confidence interval, CI; incidence rate ratio, IRR.</p>
          </td>
        </tr>
      </tbody>
    </table>

Table 4. Differential abundance of top 5 phyla and top 10 genera showing results with non-significant group effect p-values but significant pairwise comparison p-values.
Taxa	Group effect p-value	Pairwise comparison	IRR	95% CI	Pairwise comparison p-value
Pre-pregnancy BMI
Phylum
Firmicutes	0.060	Obese vs overweight	1.76	1.06-2.94	0.030
Actinobacteria	0.039	Obese vs overweight	2.23	1.19-4.17	0.012
Bacteroidetes	0.070 0.066	Obese vs healthy Gestational diabetes vs normoglycemia Scheduled C-section vs vaginal	1.76 0.34 2.16	1.01-3.04 0.14-0.85 1.12-4.14	0.040 0.021 0.021
Genus
Pseudomonas	0.055	Obese vs overweight	0.63	0.41-0.96	0.032
Streptococcus Staphylococcus Veillonella	0.083 0.060 0.029	Overweight vs healthy Scheduled C-section vs vaginal Obese vs overweight Obese vs healthy	0.59 2.43 3.88 2.91	0.37-0.95 1.13-5.21 1.25-12.11 1.18-7.14	0.029 0.022 0.019 0.020
3-month post-partum BMI
Phylum
Bacteroidetes	0.10	Scheduled C-section vs vaginal	2.16	1.05-4.44	0.037
Genus
Staphylococcus	0.029	Obese vs overweight	2.59	1.25-5.38	0.011
	0.023	Scheduled C-section vs vaginal	2.62	1.29-5.35	0.0079
Separate Poisson regression models were run for pre-pregnancy BMI and 3-month post-partum BMI, while adjusting for maternal glucose tolerance status, mode of delivery, DNA extraction batch, and PCR sequencing batch. All models were run testing for interaction terms between BMI and maternal glucose tolerance status. Statistically significant findings shown only (group effect: p≤0.022 for phylum, p≤0.017 for genus; pairwise comparison: p<0.05). Abbreviations: confidence interval, CI; incidence rate ratio, IRR.

Table 5. Differential abundance of top 5 phyla and top 10 genera based on ethnicity showing results with non-significant group effect p-values but significant pairwise comparison p-values.
Taxa	Group effect p-value	Pairwise comparison	IRR	95% CI	Pairwise comparison p-value

Phylum
Gemella	0.073	White vs Asian	0.45	0.22-0.95	0.037
Aeromonas	0.022	Asian vs other	0.020	0.0007-0.59	0.023
Ethnicity was investigated for all taxa and models were adjusted for DNA extraction and PCR sequencing batch effects. Statistically significant findings shown only (group effect: p≤0.022 for phylum, p≤0.017 for genus; pairwise comparison: p<0.05). Abbreviations: confidence interval, CI; incidence rate ratio, IRR.

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Examining the relationship between maternal body size, gestational glucose tolerance status, mode of delivery and ethnicity on mother’s milk microbiota at three months post-partum

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Journal Publication

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Abstract

Figures

Background

Results

Participant Description

Overall microbial composition of mother’s milk

Associations between maternal body size, glucose tolerance status, mode of delivery, ethnicity and the milk microbiota

Association between maternal body size and the milk microbiota

Association between maternal glucose tolerance status and the milk microbiota

Association between mode of delivery and the milk microbiota

Association between maternal ethnicity and the milk microbiota

Association between maternal body size, glucose tolerance status, mode of delivery, ethnicity and functional gene expression of the milk microbiota

Discussion

Conclusions

Methods

Study participants and design

Collection of Demographic, Anthropometric and Metabolic Data

Mother’s milk collection, processing and amplification

Bioinformatics analyses

Data analysis and statistics

Piphillin: Functional analysis of human milk microbiota

List Of Abbreviations

Declarations

References

Tables

Supplementary Files

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