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. 2019 Sep 17;85(19):e01105-19. doi: 10.1128/AEM.01105-19

Fecal Microbiotas of Indonesian and New Zealand Children Differ in Complexity and Bifidobacterial Taxa during the First Year of Life

Blair Lawley a, Anna Otal a, Kit Moloney-Geany a, Aly Diana b, Lisa Houghton b,c, Anne-Louise M Heath b,c, Rachael W Taylor c,d, Gerald W Tannock a,c,e,✉
Editor: Christopher A Elkinsf
PMCID: PMC6752005  PMID: 31375480

This study addresses the microbiology of a natural ecosystem (the infant bowel) for children in a rural setting in Indonesia and in an urban environment in New Zealand. Analysis of DNA sequences generated from the microbial community (microbiota) in the feces of the infants during the first year of life showed marked differences in the composition and complexity of the bacterial collections. The differences were most likely due to differences in the prevalence and duration of breastfeeding of infants in the two countries. These kinds of studies are essential for developing concepts of microbial ecology related to the influence of nutrition and environment on the development of the gut microbiota and for determining the long-term effects of microbiological events in early life on human health and well-being.

KEYWORDS: bifidobacteria, gut, infants, microbiota

ABSTRACT

The biological succession that occurs during the first year of life in the gut of infants in Western countries is broadly predictable in terms of the increasing complexity of the composition of microbiotas. Less information is available about microbiotas in Asian countries, where environmental, nutritional, and cultural influences may differentially affect the composition and development of the microbial community. We compared the fecal microbiotas of Indonesian (n = 204) and New Zealand (NZ) (n = 74) infants 6 to 7 months and 12 months of age. Comparisons were made by analysis of 16S rRNA gene sequences and derivation of community diversity metrics, relative abundances of bacterial families, enterotypes, and cooccurrence correlation networks. Abundances of Bifidobacterium longum subsp. infantis and B. longum subsp. longum were determined by quantitative PCR. All observations supported the view that the Indonesian and NZ infant microbiotas developed in complexity over time, but the changes were much greater for NZ infants. B. longum subsp. infantis dominated the microbiotas of Indonesian children, whereas B. longum subsp. longum was dominant in NZ children. Network analysis showed that the niche model (in which trophic adaptation results in preferential colonization) of the assemblage of microbiotas was supported in Indonesian infants, whereas the neutral (stochastic) model was supported by the development of the microbiotas of NZ infants. The results of the study show that the development of the fecal microbiota is not the same for infants in all countries, and they point to the necessity of obtaining a better understanding of the factors that control the colonization of the gut in early life.

IMPORTANCE This study addresses the microbiology of a natural ecosystem (the infant bowel) for children in a rural setting in Indonesia and in an urban environment in New Zealand. Analysis of DNA sequences generated from the microbial community (microbiota) in the feces of the infants during the first year of life showed marked differences in the composition and complexity of the bacterial collections. The differences were most likely due to differences in the prevalence and duration of breastfeeding of infants in the two countries. These kinds of studies are essential for developing concepts of microbial ecology related to the influence of nutrition and environment on the development of the gut microbiota and for determining the long-term effects of microbiological events in early life on human health and well-being.

INTRODUCTION

The transition of an infant from a mainly milk diet to the introduction of solid foods (complementary feeding period) is accompanied by transformation of the composition of the gut microbiota (1–8). In Western countries, it is recommended that complementary feeding start at around 6 months of age and that infants eat mainly “family” foods by 1 year (9–11). In addition to diet, environmental (water, sanitation, hygiene, and air pollution) and lifestyle factors differ among countries and their populations. These differences may influence the gut microbiota during its development toward a microbial community more similar to that of adults (12). In general, in studies of Western infants, members of the genus Bifidobacterium that utilize lactose (and human milk oligosaccharides [HMOs] for some species) for growth are abundant in the fecal microbiota of exclusively milk-fed infants. Bacterial species capable of degrading and fermenting plant polysaccharides and their components (Bacteroidaceae, Lachnospiraceae, and Ruminococcaceae) are dominant by the end of the complementary feeding period (1–8).

Knowledge of the development of the gut microbiota in early life is important because deviations from the norm (dysbiosis) may be associated with adverse conditions later in life (13–15). Defining the normal situation for infants in different countries thus has value as concepts of the involvement of the gut microbiota in the etiology of specific diseases and conditions develop (16).

Descriptions of the fecal microbiota commonly rely on culture-independent analyses that use 16S rRNA gene sequences amplified by PCR from bulk DNA extracted from feces. Analysis of these sequences confidently provides taxonomic information about the composition of the microbiota with respect to bacterial families and genera and sometimes species (17). However, differentiation of some species, as in the case of some Bifidobacterium taxa, is difficult using 16S rRNA gene sequences. This difficulty results, in particular, in a paucity of reports of bifidobacterial subspecies distributions in human infants on a global scale (18–20). Knowledge of subspecies occurrence and abundance would be useful for developing concepts of multispecies ecology of bifidobacteria in the infant bowel during development of the microbiota. Fortunately, quantitative PCR (qPCR) provides a quick analytical method by which to accurately ascertain the abundances of bifidobacterial species and subspecies in the microbiota (21, 22).

The purpose of our study was to compare the compositions of fecal microbiotas for infants in Indonesia and New Zealand (NZ) during the complementary feeding period, using 16S rRNA gene sequences obtained by next-generation sequencing and qPCR targeting bifidobacterial species and subspecies. The Indonesian infants were from rural villages, whereas the NZ children were urban and predominantly NZ European (23–25). We found that the composition of the microbiota changed radically in NZ children during the first 12 months of life, whereas bifidobacteria, particularly Bifidobacterium longum subsp. infantis, remained at high levels in Indonesian infants during this time.

RESULTS

Comparison of the development of fecal microbiotas of Indonesian and NZ infants based on 16S rRNA gene sequences.

Alpha diversity metrics (observed amplicon sequence variants [ASVs], Faith’s phylogenetic diversity, Pielou’s evenness, and Shannon’s index) showed that the variety of bacteria in microbiotas in both cohorts increased between 6 to 7 months of age and 12 months of age, but they also indicated that community diversity was less in 12-month Indonesian microbiotas than in NZ microbiotas (observed ASVs, Pielou’s evenness, and Shannon’s index). Faith’s phylogenetic diversity showed that the microbiotas of infants at 6 to 7 months of age and 12 months of age were phylogenetically similar between cohorts, presumably due to the predominance of Bifidobacteriaceae in both groups (Fig. 1). Permutational multivariate analysis of variance (PERMANOVA) comparisons and a test for homogeneity of multivariate dispersion (PERMDISP), using four beta diversity metrics (unweighted UniFrac, weighted UniFrac, Jacard similarity matrix, and Bray-Curtis dissimilarity), supported the view that compositions of microbiotas differed between the cohorts at both 6 to 7 months and 12 months of age (Table 1; also see Fig. S1 in the supplemental material).

FIG 1.

FIG 1

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Alpha diversity box plots (Tukey method; boxes are from the 25th percentile to the 75th percentile with a line at the median, whiskers are 1.5 times the interquartile range, and outliers are shown as points), with samples grouped by cohort and time point. Metrics applied are observed ASVs (A), Faith’s phylogenetic diversity (PD) (B), Pielou’s evenness (C), and the Shannon index (D). Comparisons between cohorts at 6 to 7 months and 12 months and within cohorts, comparing 6 to 7 months and 12 months, performed with the Mann-Whitney test, are shown for each metric. ns, not significant.

TABLE 1.

PERMANOVA and PERMDISP analyses comparing cohort and time groups with four beta diversity metrics

Metric Group 1 Group 2 No. of samples PERMANOVA
 PERMDISP
Pseudo-F P q F P q
Unweighted UniFrac NZ, 12 mo Indonesia, 12 mo 209 16.716 0.001 0.001 0.017 0.882 0.938
Unweighted UniFrac NZ, 7 mo Indonesia, 6 mo 231 9.828 0.001 0.001 3.501 0.061 0.076
Weighted UniFrac NZ, 12 mo Indonesia, 12 mo 209 57.53 0.001 0.0011 27.935 0.001 0.002
Weighted UniFrac NZ, 7 mo Indonesia, 6 mo 231 17.015 0.001 0.0011 13.172 0.004 0.007
Jaccard NZ, 12 mo Indonesia, 12 mo 209 13.234 0.001 0.001 3.276 0.104 0.208
Jaccard NZ, 7 mo Indonesia, 6 mo 231 9.81 0.001 0.001 0.268 0.635 0.706
Bray-Curtis NZ, 12 mo Indonesia, 12 mo 209 35.777 0.001 0.0011 80.536 0.001 0.002
Bray-Curtis NZ, 7 mo Indonesia, 6 mo 231 13.568 0.001 0.0011 22.331 0.001 0.002
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To investigate differences in taxa constituting the infant microbiotas, the relative abundances of the 13 predominant bacterial families that constituted ∼95.5% of the microbiotas were compared. Bifidobacteriaceae declined in abundance in the microbiotas of NZ and Indonesian infants between 6 to 7 months of age and 12 months of age, whereas Lachnospiraceae and Ruminococcaceae increased in abundance (Fig. 2A to F). The development of the Lachnospiraceae population was especially marked in NZ infants, with concomitant decreases in Bifidobacteriaceae and Enterobacteriaceae abundances. Levels of Bacteroidaceae remained constant between cohorts and times. Changes in Indonesian infant microbiotas were much more modest between 6 and 12 months of age.

FIG 2.

FIG 2

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Relative abundances of the 13 predominant families, with samples grouped according to cohort and time (A), and box plots (Tukey method; boxes are from the 25th percentile to the 75th percentile with a line at the median, whiskers are 1.5 times the interquartile range, and outliers are shown as points) showing the relative abundances of Bifidobacteriaceae (B), Bacteroidaceae (C), Lachnospiraceae (D), Ruminococcaceae (E), and Enterobacteriaceae (F) in each cohort. Groups were compared using Mann-Whitney nonparametric tests. ns, not significant.

Taxonomic assignment based on the V4 region of 16S rRNA gene sequences does not allow discrimination between Bifidobacterium longum subspecies and Bifidobacterium breve. Nevertheless, the results showed that B. longum and B. breve were likely to be the most common bifidobacterial taxa in the microbiotas of both NZ and Indonesian infants (Fig. 3).

FIG 3.

FIG 3

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Relative abundances of bifidobacterial species, as measured by 16S rRNA gene amplicon sequencing, with samples grouped according to cohort and time. Where ASV sequences could not be assigned to a single species, all possible identities are named. Columns represent mean relative abundances.

Enterotypes detected in the fecal microbiotas of Indonesian and NZ infants.

We used the Calinski-Harabasz index to search for robust clusters of taxa (enterotypes [26]) that characterized the microbiotas of the infants at 6 to 7 months of age and 12 months of age. It was predicted that two enterotypes would be optimally present in 6- to 7-month microbiotas and three enterotypes at 12 months when data were amalgamated for the respective time points. Between-class analysis (BCA) allowed the visual clustering of the enterotypes to be shown (Fig. 4). Using Statistical Analysis of Taxonomic and Functional Profiles (STAMP) software (27), it was determined that, with clustering into two enterotypes at 6 to 7 months, samples were dominated by an enterotype defined by B. longum/B. breve regardless of the country of origin. A second enterotype, defined by Ruminococcus gnavus, Bacteroides fragilis, and Clostridium ramosum, was observed with greater prevalence in the NZ cohort (Fig. 5A).

FIG 4.

FIG 4

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BCA plots, showing clustering of samples from combined cohort data into two enterotypes at 6 to 7 months of age (A) and three enterotypes at 12 months of age (B).

FIG 5.

FIG 5

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Description of enterotypes predicted for infants at 6 to 7 months of age (A) and 12 months of age (B). Each panel contains the STAMP output (extended error bar plot) depicting species-level features within each enterotype with significant differential abundance (Welch’s t test with Benjamini-Hochberg false discovery rate), compared to all other enterotypes, a table reporting the number of individuals from each cohort within each enterotype, and pie charts showing the proportion of individuals from each cohort associated with each enterotype.

Microbiotas at 12 months showed one enterotype dominated by B. longum/B. breve, a second enterotype defined by B. longum/B. breve, R. gnavus, and B. fragilis, and a third enterotype defined by Bifidobacterium catenulatum, Faecalibacterium prausnitzii, and Blautia wexlerae. In this case, the Indonesian cohort microbiotas were dominated by the B. longum/B. breve-containing enterotypes, whereas the NZ infant microbiotas were dominated by the third enterotype, defined by B. catenulatum, F. prausnitzii, and B. wexlerae (Fig. 5B).

In summary, both Indonesian and NZ microbiotas were characterized by B. longum/B. breve populations at 6 to 7 months. Indonesian microbiotas remained characterized by B. longum/B. breve populations at 12 months but the NZ microbiotas at this age were especially characterized by an enterotype in which these bacterial species did not predominate (Fig. 5A and B).

Cooccurrence networks in the fecal microbiotas of Indonesian and NZ infants.

Construction of microbial (correlation) networks from sequencing data may facilitate understanding community structures. The networks show individual microbes (ASVs or features) as nodes (hub species) and species cooccurrence or mutual exclusion as feature-feature pairs (edges); an edge may imply a biological or biochemical relationship between features. Microbes that benefit each another may be positively correlated, whereas microbes that compete for the same niche may be negatively correlated (28, 29).

Correlation networks constructed from our data underlined the different development of microbiotas of Indonesian infants, relative to those of NZ infants. Indonesian networks displayed an increase in node numbers between 6 and 12 months of age (28 versus 46 nodes) and a 1.5-fold increase in edge numbers (48 versus 74 edges). There was also an increase in the proportion of positive correlations at 12 months (67% at 6 months and 81% at 12 months) (Fig. 6A and B). In contrast, the NZ 12-month network was much more complex, with a greater number of nodes being observed at 12 months than at 7 months (89 and 37 nodes, respectively). There was a 9.5-fold increase in edge numbers (81 edges at 6 months versus 770 edges at 12 months). The two time points showed similar proportions of positive correlations (64% at 7 months and 60% at 12 months). However, 40% of correlations were negative (mutual exclusion edges) at 12 months, in contrast to the Indonesian network, in which 19% were negative. In summary, Indonesian microbiotas had similar numbers of negative correlations between nodes at 6 and 12 months (16 and 14 mutual exclusion edges, respectively), whereas nodes in 12-month NZ microbiotas were more negatively correlated than those at 7 months (306 and 29 mutual exclusion edges, respectively).

FIG 6.

FIG 6

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CoNet-derived networks for Indonesian infants at 6 months of age (A), Indonesian infants at 12 months of age (B), NZ infants at 7 months of age (C), and NZ infants at 12 months of age (D). Cooccurrence edges (positive correlations) are shown in green, while mutual exclusion edges (negative correlations) are shown in red. Edge weight is determined by the q value assigned to the relationship between nodes. Nodes are colored by family and weighted by abundance.

Bifidobacterial subspecies abundances measured by qPCR in the fecal microbiotas of Indonesian and NZ infants.

We determined the abundances of B. breve, B. bifidum, B. longum subsp. infantis, and B. longum subsp. longum because they are common members of fecal microbiotas in early life (18, 30–37). They have distinctly different biochemical capacities with regard to the utilization of HMOs and other carbohydrates (22, 38, 39). Differentiation of the B. longum subspecies and B. longum/B. breve is difficult with standard 16S RNA gene sequence comparisons, necessitating the use of a qPCR assay (21, 22) to determine their proportions in the microbiota. While the four taxa that we enumerated were present in all microbiotas, the Indonesian bifidobacterial populations at both 6 and 12 months were dominated by B. longum subsp. infantis. The NZ bifidobacterial populations at 7 and 12 months were composed mainly of B. longum subsp. longum. These striking results further differentiated the fecal microbiotas of Indonesian and NZ children (Fig. 7).

FIG 7.

FIG 7

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Relative abundance of four bifidobacterial species/subspecies measured by qPCR. Values are the mean percentage of total community 16S rRNA gene target. Error bars show 95% confidence intervals.

DISCUSSION

Comparison of the development of the fecal microbiota of Indonesian and NZ infants during the first year of life showed clear differences in the composition and complexity of the bacterial communities. Enterotypes dominated by B. longum/B. breve (in fact, B. longum subsp. infantis, as shown by qPCR) were common in Indonesian infants at both 6 and 12 months of age, whereas a third enterotype was common in the NZ infant microbiotas at 12 months. This enterotype was not dominated by B. longum/B. breve.

The processes that underpin the assembly of communities in natural systems are of much interest to ecologists. Two contrasting models to explain the development of diversity in communities have been proposed, namely, the niche selection and neutral theories of ecology (40–43). The dynamics of the niche selection model, it is proposed, are dominated by resource selection, with species of particular trophic adaptations being preferential colonizers of the habitat. Sharing of the habitat’s resources might result in interactions of mutual benefit that would result in the development of a stable community (44–46). In contrast, the dynamics of the neutral model are stochastic, implying that species that assemble are functionally equivalent; therefore, competition will be a feature of community assembly.

The gut microbiotas of Indonesian children favor the niche model, because cooccurrence networks at 6 and 12 months have similar numbers of nodes and most of the correlations between nodes are positive (copresence edges). These findings suggest a relatively stable environment with direct reliance on environmental resources by bacteria with special trophic adaptations that permit cohabitation (for example, differential utilization of HMOs for growth [22, 38, 44]). In contrast, NZ microbiotas at 12 months were competitive (large proportions of mutual exclusion edges), with much more complexity (more nodes). These findings suggest that acquisition of bacterial species was more random (neutral theory of assemblage) and involved an assortment of competing bacteria with similar trophic requirements (for example, utilization of dietary fiber).

Niche selection with HMOs is highly likely to explain the situation in Indonesian infants. HMOs and their components are used by B. longum subsp. infantis, following transport into the cell, for growth. B. longum subsp. infantis has a genome that contains a large gene cluster that is specifically involved in HMO utilization (39). In contrast, B. longum subsp. longum has limited capacity to utilize HMO components (22). The extent of breast milk feeding is different in Indonesia and NZ. While initial breast milk feeding uptake is high in both countries (∼100%), rates fall to national averages of 65% of infants by 6 months after birth in NZ and 36% by 12 months of age, whereas rates remain high (national average of 84% at 12 months of age) in Indonesia (47, 48). Cohort-specific values were greater for both groups in our study, but breast milk feeding was still much more common among Indonesian infants 12 months of age (NZ, 64%; Indonesia, 98%). Breastfeeding after 6 months of age refers to continued breastfeeding and not exclusive breastfeeding, rates of which are substantially lower by 6 months of age in both countries because complementary feeding needs to start by this age.

The composition of breast milk has been reported to differ between mothers in different countries and even between mothers in different locations in the same country (49–55). Comparisons of HMO contents and other biochemical features of Indonesian and NZ breast milks have not been made, which may be a topic worth investigating further. Additionally, volumes of milk consumed daily by infants in different settings would be useful information in considering the relationship between microbiotas and infant nutrition.

Evidence from studies of the fecal microbiotas of twins and of cohabiting adults and children indicate the potential importance of shared environments and similar diets in the assembly and maintenance of the microbiota of the human body (56–58). Shared environments provided by communal living, such as in rural Indonesia, might result in greater ease of dispersal (horizontal transmission) of gut commensals such as B. longum subsp. infantis among the infant population. Dispersal theory (59–62) has been advocated to explain the differing diversity, as well as greater similarity, of microbiotas in nonindustrialized populations (for example, rural Papua-New Guinea [12]), relative to Western microbiotas. Easier dispersal of gut microbes in a rural village setting might favor the acquisition of B. longum subsp. infantis, whereas Western methods of sanitation, water treatment, and hygiene might be inimical to this organism. Although B. longum subsp. infantis was detectable in the microbiotas of some NZ infants, it did not dominate the bacterial community, as was the case for Indonesian children. Thus, B. longum subsp. infantis is not an extinct lineage in NZ microbiotas but is most likely to have been influenced by lack of HMO enrichment in the infant gut due to breastfeeding practices. At least in the case of adults, studies of fecal microbiotas in industrialized countries have not shown major differences between nationalities (such as in Japan, Italy, Spain, Denmark, France, and the United States), indicating that genetic factors probably have only minor influences on the composition of microbiotas (26).

Overall, the transition from infant to more adult-like microbiotas during the first year of life was more pronounced in NZ infants than in Indonesian infants. Bifidobacteria are common in infants from both countries at 6 to 7 months of age. However, NZ infants have complex microbiotas, dominated by an enterotype different than that predominating in Indonesian infants at 12 months of age. The outcomes of the study show that the development of the fecal microbiota is not the same for infants in all countries and point to the necessity of obtaining a better understanding of the factors that control the colonization of the gut in early life. Such knowledge is important because early life influences due to the microbiota may have lifelong consequences; this concept was encapsulated by Rene Dubos and colleagues as “biological Freudianism” (63) and is supported by more recent studies of influences of the microbiota on infant health and well-being (64–66).

MATERIALS AND METHODS

Indonesian and NZ infants.

The members of the Indonesian cohort were from the Tanjungsari, Sukasari, and Pamulihan subdistricts of the Sumedang district, West Java, Indonesia. The majority of the infants were of Sundanese ethnicity. A cohort of breast-milk-fed infants was enrolled at 6 months of age after random selection from 30 villages in the three subdistricts, using local birth registry data. Metadata related to these infants and their parents have been published previously (24, 25). Fecal samples were obtained from 158 infants at 6 months of age and 147 infants at 12 months of age. Fecal samples for both time points were available for 101 individuals. Fifty-seven individuals provided samples at 6 months only, while 46 individuals provided samples at 12 months only.

The NZ cohort has been described previously (8) and comprised a subset of infants enrolled in the Baby-Led Introduction to SolidS (BLISS) randomized controlled trial. The design of this trial and metadata for the infants and mothers have been published previously (23). Fecal microbiota data from BLISS and non-BLISS infants were amalgamated because there were no differences in the relative abundances of the most abundant families (>1% relative abundance [8]). Fecal samples were obtained from these infants at 7 months of age (73 infants) and at 12 months of age (68 infants). Fecal samples for both time points were available for 67 individuals. Six individuals provided samples at 7 months only, while 1 individual provided a sample at 12 months only. Complementary feeding practices follow WHO recommendations in NZ and Indonesia (some solid foods are included in the diet from 6 months of age).

Ethics approval for these studies was obtained from the Human Ethics Committees of Padjadjaran University, Indonesia, and the University of Otago, New Zealand (Indonesian study), and from the Lower South Regional Ethics Committee of New Zealand and the University of Otago Human Ethics Committee (NZ study). Written informed consent to participate in the studies was given by the parents or primary guardians of the infants. Participants were free to withdraw from the study at any time.

Fecal DNA extraction, 16S rRNA gene sequencing, and sequence analysis.

DNA was extracted from 250 mg feces according to the kit protocol provided by the manufacturer (PowerSoil DNA isolation kit, product no. 12855-100; Mo Bio). Amplification of the 16S rRNA gene V4 region, library preparation, and sequencing were carried out at Argonne National Laboratories (University of Chicago), using paired-end reads (2 by 250 bp), on an Illumina MiSeq instrument. Initial quality control and read pairing was carried out using QIIME2 v2018.8 (17). Sequence error correction and generation of ASVs were achieved using DADA2 (67). Taxonomic classifications were made using the q2-feature-classifier plugin (68) and the SILVA v123 database (69). A summary of sequence outputs is available in Table S1 in the supplemental material.

The composition of microbiotas was described by using four alpha diversity measures, namely, the number of ASVs (a proxy for observed species), phylogenetic diversity, Pielou’s evenness, and the Shannon index. Therefore, two indices (observed species and phylogenetic diversity) described microbial richness alone (i.e., number of species), one index (Pielou’s evenness) described community evenness (i.e., the equality of distribution of the species’ frequencies), and one index (the Shannon index) described richness and evenness. The feature table was rarefied to the minimum sample count (10,500 sequences) for calculation of the alpha diversity measures. Relative abundance at the family level was calculated by collapsing the raw ASV feature table based on seven-level taxonomic strings obtained from the SILVA v123 database.

Beta diversity metrics (Bray-Curtis index, unweighted UniFrac, weighted UniFrac, and Jaccard distance) were applied by using the QIIME2 v2018.8 command line interface and the core-metrics-phylogenetic plugin, with a sampling depth of 10,500 sequences. Group significance for each metric was measured with PERMANOVA (70), and group dispersion was measured with PERMDISP (71).

Enterotypes were predicted in R by using the approach described by Arumugam et al. (26) and following the tutorial provided by EMBL (https://enterotype.embl.de/). Input tables were filtered to contain data from either the 6- to 7-month samples or the 12-months samples, in order to concentrate on enterotype clusters at distinct time points. Differential abundance testing to determine which species were driving enterotypes was carried out on the full ASV feature table, modified for input to STAMP (27) and filtered within STAMP to focus on time points. Each enterotype was compared to all other samples using Welch’s t test with the Benjamini-Hochberg false discovery rate multiple-test correction.

Bacterial interaction networks were generated by using the CoNet v1.1.1-beta plugin (72) and following recommendations provided in the CoNet tutorial (http://psbweb05.psb.ugent.be/conet/microbialnetworks/conet_new.php), including the following methods: Pearson correlation, Spearman correlation, Bray-Curtis dissimilarity, Kullback-Leibler dissimilarity, and mutual information. Networks were visualized using Cytoscape v3.6.1. Input feature tables were filtered to contain cohort- and time-specific samples, and features found in <25% of samples were removed.

Quantitative PCR of bifidobacterial species and subspecies.

A previously described method was used for 16S rRNA gene-based quantitative differentiation of B. longum subsp. longum and B. longum subsp. infantis (22). For quantitation of B. breve and Bifidobacterium bifidum, primers described by Matsuki et al. (73) were used at a final concentration of 400 nM. Primers for universal detection of 16S rRNA gene targets (74) were used at a final concentration of 300 nM. All reactions were carried out on a Life Technologies ViiA 7 real-time PCR system, in MicroAmp Fast Optical 384-well plates with optical adhesive film (Applied Biosystems, Carlsbad, CA), using 10-μl volumes containing 1× PerfeCTa SYBR FastMix (Quantabio, Beverly, MA, USA), primers, and approximately 0.2 ng of template DNA. The thermocycling profile consisted of initial activation of the polymerase at 95°C for 30 s, followed by 40 cycles of 95°C for 1 s and 62°C for 20 s. Fluorescence levels were measured after the 62°C annealing/extension step. A melting curve was generated to analyze product specificity. Genomic DNA from B. longum subsp. longum (ATCC 15707T), B. longum subsp. infantis (DSM 20088T), B. breve (ATCC 15700T), and B. bifidum (DSM 20456T) was used for control reactions and for generation of standard curves. The standard DNA was quantified spectrophotometrically using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and was diluted in 5-fold steps from 5 × 106 to 3.2 × 102 genomes/reaction, calculated using target gene copies per genome obtained from genome sequence information (NCBI). All reactions were carried out in duplicate and were run twice on separate plates. No-template controls were included on each plate. Statistical analyses of data used GraphPad Prism 7 (GraphPad Software Inc., La Jolla, CA).

Accession number(s).

The DNA sequence data have been deposited in the NCBI database under BioProject accession number PRJNA419227 (for the NZ infants) and BioProject accession number PRJNA528344 (for the Indonesian infants).

Supplementary Material

Supplemental file 1
AEM.01105-19-s0001.pdf (1.4MB, pdf)

ACKNOWLEDGMENTS

The Indonesian study was supported by a grant from Meat and Livestock Australia and the University of Otago; the BLISS study was supported by Lottery Health Research, Meat and Livestock Australia, the Karitane Products Society, Perpetual Trustees, the New Zealand Women’s Institute, and the University of Otago. Supplementary support was provided by the Riddet CORE. K.M.-G. was supported by a summer student scholarship provided by Microbiome Otago.

Footnotes

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.01105-19.

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