- Research
- Open access
- Published:
Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome
Genome Biology volume 21, Article number: 301 (2020)
Abstract
Background
Staphylococcus aureus is a leading cause of healthcare- and community-associated infections and can be difficult to treat due to antimicrobial resistance. About 30% of individuals carry S. aureus asymptomatically in their nares, a risk factor for later infection, and interactions with other species in the nasal microbiome likely modulate its carriage. It is thus important to identify ecological or functional genetic elements within the maternal or infant nasal microbiomes that influence S. aureus acquisition and retention in early life.
Results
We recruited 36 mother-infant pairs and profiled a subset of monthly longitudinal nasal samples from the first year after birth using shotgun metagenomic sequencing. The infant nasal microbiome is highly variable, particularly within the first 2 months. It is weakly influenced by maternal nasal microbiome composition, but primarily shaped by developmental and external factors, such as daycare. Infants display distinctive patterns of S. aureus carriage, positively associated with Acinetobacter species, Streptococcus parasanguinis, Streptococcus salivarius, and Veillonella species and inversely associated with maternal Dolosigranulum pigrum. Furthermore, we identify a gene family, likely acting as a taxonomic marker for an unclassified species, that is significantly anti-correlated with S. aureus in infants and mothers. In gene content-based strain profiling, infant S. aureus strains are more similar to maternal strains.
Conclusions
This improved understanding of S. aureus colonization is an important first step toward the development of novel, ecological therapies for controlling S. aureus carriage.
Background
Staphylococcus aureus is a leading cause of healthcare-associated infections among infants [1] and adults [2], and it is the most common cause of hospital- and community-acquired skin and soft tissue infections (SSTIs) [3]. Antibiotic resistance, in particular, to β-lactams (e.g., methicillin-resistant S. aureus (MRSA)), makes the treatment of these infections difficult and expensive [4]. S. aureus is responsible for approximately 119,000 bloodstream infections and 20,000 deaths per year in the USA alone [5]. Nasal colonization by S. aureus increases the risk of SSTIs [6] and is a risk factor for bacteremia, as 80% of bloodstream infections are caused by the same strain found in the individual’s nose [7,8,9,10]. Additionally, colonization allows for transmission to new hosts and selection for new, possibly detrimental microbial traits, such as antibiotic resistance [11]. At any one time, approximately 30% of individuals carry this species asymptomatically in their nares [12], and over time, individuals display three carriage patterns: persistent (~ 20%), transient (~ 60%), and never (~ 20%) carriers [13,14,15,16]. It is unknown what causes these differences between individuals [13,14,15,16], and research in infants is especially scarce [17].
Previous studies suggest that both S. aureus carriage and nasal microbiome composition are primarily determined by environmental rather than host genetic factors [18, 19]. Co-occurring nasal species can provide resistance to S. aureus colonization through competition for nutrients and binding sites [20, 21], such as Corynebacterium strain Co304 which is believed to outcompete S. aureus at binding nasal mucin [21], or through the induction of a host immune response [22,23,24], such as the targeting of S. aureus by a cross-reactive antibody triggered against Streptococcus pneumoniae [23]. Additionally, some nasal species produce antimicrobial molecules that are directly toxic to S. aureus [25,26,27,28,29,30], such as hydrogen peroxide produced by S. pneumoniae [29] or the peptide antibiotic lugdunin produced by Staphylococcus lugdunensis [30]. While laboratory studies have identified these mechanisms, it is not clear how they translate into the observed population-level trends in S. aureus carriage. For instance, although lugdunin is highly effective at killing S. aureus and the risk of S. aureus colonization is sixfold-lower in those colonized with S. lugdunensis, the population prevalence of S. lugdunensis (estimated at 9% [30, 31] or 26% [32]) is too low to explain why 80% of individuals only temporarily or never acquire S. aureus [30, 33]. Similarly, some Staphylococcus epidermidis isolates produce lantibiotics, and many produce an extracellular serine protease Esp that inhibits S. aureus colonization [34, 35], but S. epidermidis also does not consistently predict S. aureus absence [19, 32, 35, 36]. Understanding nasal microbial community structure, ecology, metabolism, and signaling thus holds promise for elucidating patterns of S. aureus carriage, but many complementary and competing mechanisms likely contribute to its ultimate colonization pattern [33]. Human population studies using shotgun metagenomic measures that are both detailed and comprehensive are thus needed to characterize nasal microbial communities with a high degree of phylogenetic and functional resolution.
Understanding the relationship between the nasal microbiome and S. aureus carriage in early life is especially important because the infant microbiome across the body undergoes dramatic shifts in the first year [37, 38], and infants have also been shown to acquire and lose S. aureus in the nares during this time [39, 40]. Prior studies of S. aureus carriage have mainly been performed in adults and are not necessarily generalizable to infants, as the infant microbiome differs greatly from that of adults during this early period of rapid development [41, 42]. Infant nasal microbiome colonization may be influenced by many of the same environmental factors as the gut microbiome (e.g., delivery mode, feeding method, antibiotic usage, and siblings [42,43,44]), but it has been substantially less studied than has the gut [11, 43, 45]. Particularly with the goal of mitigating later S. aureus infection risk, additional longitudinal studies in this area can determine whether microbiome changes causally precede S. aureus acquisition or loss and shed light on the development of the infant nasal microbiome, which is also broadly involved in later asthma, allergies, and upper respiratory tract infections [44, 46,47,48].
In this study, we aimed to identify intra-community interactions involved in the modulation of S. aureus carriage in vivo and to characterize the development of the early infant nasal microbiome. We examined nasal microbiome development in 36 mother-infant pairs over the first year after birth, combining shotgun metagenomic profiling of longitudinal samples with monthly S. aureus culture-based testing. Both methods generally agreed in their prevalence of S. aureus carriage (per sample, 43.8% by culture vs. 38% by sequencing) and were used to define subpopulations of early-, late-, and non-S. aureus acquirers. In gene content-based strain profiling, infants carried strains of S. aureus that were more similar to maternal strains compared to unrelated mothers. Furthermore, we identified a collection of species associated with patterns of S. aureus carriage among infants, as well as a possible uncharacterized taxon significantly anticorrelated with S. aureus in infants and mothers. With the CDC reporting slowed progress on methicillin-resistant and methicillin-susceptible S. aureus infection reduction in hospitals and communities [5], understanding such dynamics of S. aureus colonization in the nasal microbiome is an important first step toward the development of safe and mechanistically understood ecological therapies for modulating S. aureus carriage.
Results
Patterns of S. aureus carriage in a mother-infant cohort
We collected monthly longitudinal nasal swab samples from a total of 36 mother-infant pairs recruited through Sheba Medical Center as part of a larger cohort study on S. aureus [17], as well as S. aureus-negative mothers enrolled specifically for this study (the “Methods” section). A subset of samples was selected for microbial community profiling by culture testing a total of 856 samples from mothers and infants, both throughout the first year after birth. Samples were selected for shotgun metagenomics to include early time points (i.e., soon after delivery) for all subjects, later time points (as representatives) from selected mothers, and time points immediately preceding and following changes in S. aureus carriage status as determined by culture test in infants (as well as additional, typically later, representative infant time points in order to assess developmental changes). This resulted in a total of 284 samples profiled metagenomically, with 208 samples passing sample quality control procedures (Fig. 1, the “Methods” section).
Longitudinal shotgun metagenomics of the nasal microbiome and Staphylococcus aureus carriage in 36 mother-infant pairs. Nasal swabs were taken from 36 mother-infant pairs at birth and monthly for the first year after birth. Culture testing for S. aureus was performed on all samples to assess carriage. Culture results, red for S. aureus positive and blue for S. aureus negative, are shown as the icon fill color. A subset of 208 samples was profiled with shotgun metagenomic sequencing and retained after quality control (the “Methods” section) for further analyses. Sequencing is shown as performed only for samples that passed QC procedures. Square icons indicate samples where sequencing was performed, and the outline color, red for S. aureus positive and blue for S. aureus negative, indicates whether S. aureus was identified by sequencing. Infants displayed three unique patterns of S. aureus carriage highlighted here and used for sequenced sample prioritization: “Early” (acquisition by month one), “Late” (acquisition after month one, also typically transient), and “Never” (no acquisition during the study period)
During the first year of life, infants displayed distinctive S. aureus carriage phenotypes. Based on culture, 20 out of 36 infants (56%) acquired S. aureus for the first time at or before month 1 (“early” acquisition), and three of these infants displayed persistent colonization, defined as being positive for at least two thirds of all time points and for at least half of time points between months 6 and 12. Of those remaining, eight infants (22%) never acquired S. aureus over the study period (“never” acquisition), and eight infants (22%) acquired S. aureus after month 1 but were never persistently colonized (“late” acquisition). Among retained samples, we sequenced 71, 43, and 30 samples from early, late, and never acquirer infants, respectively, as well as 13 samples from persistently colonized infants and 64 samples from mothers (Fig. 1).
Detection of S. aureus by culture and sequencing showed moderate agreement; as expected, samples that were positive for S. aureus by culture had a higher relative abundance of S. aureus by sequencing compared to culture-negative samples (mean 13.53% vs. 1.09%; median 0.45% vs. 0%; Wilcoxon p < 10−7 across all 208 filtered samples). Since both culture and sequencing have low rates of false positives, we believe disagreement between the two tests arises from the combination of a true positive from one test and a false negative from the other, although the mechanisms causing false negatives differ between culture and sequencing. Previous work found that the sensitivity of culture is highly positively correlated with S. aureus absolute abundance, which suggests that culture-based methods alone may miss a significant fraction of S. aureus carriage [19], in part due to idiosyncratic mechanisms such as viable but non-culturable biofilm formation [49]. Conversely, any sufficiently rare microbe—including S. aureus—can be missed by sequencing of insufficient depth (which in sites such as the nares includes human nucleotides; Additional file 1: Fig. S1). Due to these likely false negatives from both assays, our subsequent analyses included a composite variable for S. aureus positivity by culture or sequencing.
Infant nasal microbiome composition matures over the first year but remains distinct from that of the mothers
Individual infants’ species composition rapidly diverged from that observed at birth and, over time, was increasingly dissimilar to this early composition (Fig. 2). Controlling for subject, time was significantly associated with infant microbiome composition (PERMANOVA on Bray-Curtis dissimilarity, p = 0.001). The rate of change in species composition declined over time and infants became slightly more self-conserved, consistent with stabilization toward a more mature nasal microbiome and somewhat similar to early life gut microbiome development [50] (Fig. 2). However, infant composition remained significantly different from maternal composition across all months except month eight (PERMANOVA on Bray-Curtis dissimilarity, p < 0.05), indicating that by the end of the first year, infants still did not have a fully adult nasal microbiome. These findings are consistent with those for the infant skin and gut microbiomes, which are compositionally unstable relative to adults [41, 51] and increase in alpha diversity [41, 51] over the first year, although reported alpha diversity trends in the nasal microbiome of healthy infants are inconsistent [44, 46]. In line with previous research that proposes that nasal microbiome development likely continues after year 1 [42], we find that infants are still significantly different from adults at the latest time points.
Development of infant nasal microbiome composition over the first year of life. a Infant nasal microbiome development showed general ecological patterns similar to those of other body sites. Alpha diversity increased over time in infants, although confidence was lower at later time points due to fewer samples; it decreased slightly at later time points (L) in mothers. Mothers were sequenced at birth, month 1, and one later time point (matched to a later time point for their infant, making this not the same time point for all mothers), which is labeled as “L.” b Mean Bray-Curtis dissimilarity per time point comparing (1) infants to themselves at birth, (2) infants to themselves at their most recent preceding time point, (3) infants to related and unrelated mothers, and (4) infants to other infants with the same or different S. aureus status at the same time point. In (1), the Bray-Curtis dissimilarity at birth is equal to 0 since we are comparing identical compositions and is labeled as “NA.” The use of the most recently recorded preceding time point for (2) is conservative because we have less frequent sampling at later time points but still find that infants are more self-conserved at these times. S. aureus status as positivity by either sequencing or culture is shown here, but there were no group differences if S. aureus was defined using other phenotypes. In both graphs, time points with fewer than 5 data points are not displayed (see Additional file 2 for sample sizes), and diversity measures are calculated on average subject taxonomic composition for the mothers (due to the limited sample numbers)
Infants were significantly more similar to their own mothers than unrelated mothers at month 1, but not at other time points (Wilcoxon-Mann-Whitney test, p = 0.0026); this trend was generally true of early months, although it did not otherwise reach significance (Fig. 2). This supports the hypothesis that maternal influence on nasal microbiome composition mainly occurs at the earliest time points after birth. It is also consistent with studies on the infant gut microbiome, which find greater similarity between vaginally delivered infants and their mothers shortly after birth, but that this similarity diminishes afterwards, often quickly, based on environmental exposures [50, 52,53,54]. Lastly, the composition of the infant microbiome over time was not related to infant S. aureus status for the S. aureus phenotypes tested (PERMANOVA on Bray-Curtis dissimilarity, p ≥ 0.05) (Fig. 2). This is reassuring given that omnibus testing on the full microbiome captures large differences in composition between samples, which likely would have been identified in earlier (e.g., culture-based) studies if grossly responsible for S. aureus phenotypes.
Species-level and functional drivers of infant S. aureus phenotypes
To identify if individual microbial community features correlated with S. aureus status, we fit linear mixed models for the association between S. aureus phenotype and microbial features (taxonomic and functional) for infants and mothers separately, as well as for the association between infant phenotypes and the related mother’s microbial features (Additional file 3). Different measurements of S. aureus phenotype were utilized in our models to fully capture carriage patterns (the “Methods” section), such as the “ever” acquisition of S. aureus over the study period, or positivity at a single time point by sequencing, culture, or either. We found that, of the covariates tested, daycare attendance in the preceding month produced the most significant changes in the infant microbiome (Fig. 3). More specifically, daycare attendance was associated with increases in the relative abundance of the pathogens Moraxella catarrhalis and Haemophilus influenzae (Fig. 3), which are also reported in the literature [44, 55, 56], and linked to the development of wheezing, asthma, and respiratory infection in infants [55, 57,58,59,60].
Significant associations between nasal microbiome taxonomic and functional composition and subject phenotypes in feature-wise testing. Significant associations (q < 0.25) between individual taxonomic and functional features and phenotypic covariates using a MaAsLin multivariable linear model. a Of note, first attendance at daycare (during the preceding month) was an extremely strong determinant of initial microbiome colonization by several taxa (M. catarrhalis, H. influenzae), and the nasal microbiome exhibited a much weaker time dependence than does the gut during infant development (i.e., few taxa were consistently temporally variable between months 0 and 12). The presence of a number of oral-associated species (i.e., Streptococcus and Veillonella species) was a mild correlate of S. aureus gain. S. aureus relative abundance was included as a positive control. b After applying a species dominance filter, we found the association of many gene families (grouped by Enzyme Commission (EC) number) in infants with daycare attendance, while many functions in mothers were associated with time and infant “ever” acquisition of S. aureus
In the taxonomic subset of these linear models, abundances of infant Acinetobacter species, Streptococcus parasanguinis, Streptococcus salivarius, and Veillonella species were weakly positively associated with S. aureus acquisition by the next month’s sample (FDR q < 0.25, Fig. 3). These trends tended to be driven by acquisitions occurring at later time points; the presence of any of the four species was more frequently observed during acquisition events occurring after the first month (Fisher’s exact test, p = 0.0805). The nasal microbiome may thus play a larger role in S. aureus carriage patterns as infants get older, which is concordant with a diminishing maternal and increasing environmental influence over time. However, we were limited in our cohort in the number of acquisition events occurring after the first month, causing the significance of these associations—except for S. parasanguinis—to be somewhat dependent on the model specification (i.e., not always significant during robustness assessment, the “Methods” section, Additional file 1: Fig. S7). Although this finding is exploratory in nature, we recommend that future studies account for early and late acquisition events differently in order to ensure sufficient and balanced sample sizes.
Notably, there was no overlap in the predictors of S. aureus in infants and mothers, which is consistent with the significant taxonomic differences between the two. However, the maternal abundance of the commensal species Dolosigranulum pigrum was inversely associated with infant “ever” acquisition of S. aureus. A number of epidemiological studies have reported an inverse association between D. pigrum and S. aureus in adults [19, 36, 61, 62], as well as infants [63], although the potential mechanisms or causality of the relationship is unknown.
In our results, the two Streptococcus species positively associated with S. aureus gain in infants were both inversely associated with breastfeeding in the previous month (FDR q < 0.25). These findings are consistent with previous research on the infant nasopharyngeal microbiome at 6 weeks of age, which reported inverse relationships between breastfeeding and Streptococcus OTUs [64], as well as, interestingly, inverse associations for breastfeeding with Veillonella species and S. aureus [64]. Breastfeeding has been identified as a risk factor for S. aureus nasal carriage in infants [65] due to the presence of S. aureus on maternal skin and in breast milk [43] but was not associated with S. aureus carriage in this cohort [17]. This absence of an effect may occur due to the competing pressures of increased exposure to maternal S. aureus during breastfeeding with protective ecological changes to the infant microbiome promoted by breastfeeding.
We next assessed microbial functions (specifically gene families grouped by Enzyme Commission (EC) number) that were contributed by diverse members of the nasal community. To avoid gene families that recapitulated individual taxa, we removed functional features for which a single species contributed more than half the feature abundance in more than half of samples containing that feature. Among the infants, 81 ECs (of 443 total retained in infants) were associated with daycare, demonstrating the strong functional consequences of the corresponding structural rearrangement subsequent to daycare attendance. Fifty-six ECs (of 423 total in mothers) were associated with time in the mothers, indicating that the maternal nasal microbiome does change, if subtly, in the year after birth. Overall, few functions in the infant and maternal microbiomes were associated with infant S. aureus phenotypes: four to five ECs each (of 160 total) in the infants were associated with infant S. aureus positivity by sequencing, S. aureus gain, and persistent carriage. The only EC to be associated with a S. aureus phenotype in infants and mothers was EC 1.6.5.5, nominally annotated as a NADPH:quinone reductase, which was inversely associated with S. aureus positivity by sequencing in both populations; this feature is examined in detail below. There were 50 ECs (of 181 total) in the mothers significantly associated with infant “ever” acquisition of S. aureus over the study period, 44 of which were inverse associations. As was subsequently found to be linked with EC 1.6.5.5, further investigation showed that this signal was mainly driven by the maternal D. pigrum association with infant “ever” acquisition (Additional file 1: Fig. S2).
To further explore this and overall relationships between S. aureus and the nasal microbiome, we constructed random forest models to predict infant S. aureus phenotype using the taxonomic and functional composition of the infant and matched maternal microbiomes. Discriminative (e.g., random forest) rather than generative (e.g., linear) models can be used to identify a subset of features that most strongly differentiate populations with different phenotypes. In random forest models, prediction of infant S. aureus phenotypes using infant and maternal taxonomic profiles did not perform significantly better than chance. However, prediction accuracy improved slightly when using functional profiles; the prediction of S. aureus by sequencing using infant functions had an AUC of 0.748 with 95% CI [0.602, 0.895], while the prediction of infant “ever” acquisition of S. aureus using maternal functions had an AUC of 0.748 with 95% CI [0.538, 0.959].
To understand why prediction improved when using functions (compared to species), we pinpointed an uncharacterized protein that proved to be associated with D. pigrum. The functional feature most responsible for this improvement was gene family UniRef90 X5NU12, a small 42-amino acid sequence to which taxonomy was not assigned by our analysis. However, its abundance was moderately correlated with the relative abundance of numerous other genes contributed by D. pigrum (1358 genes with a Spearman’s rho 0.35–0.44, Additional file 1: Fig. S3). This gene family was of major interest because it was present in about half (96 of 208) of samples and made up the majority (92%) of all sequences assigned (likely incorrectly) to EC 1.6.5.5 (NADPH:quinone reductases). Thus, the gene family summarized as EC 1.6.5.5 was the primary random forest predictor of infant S. aureus status by sequencing (Additional file 1: Fig. S4) and was significantly inversely associated with S. aureus positivity by sequencing in both infants and mothers in linear modeling (Additional file 1: Fig. S5). This finding was not limited to our dataset—in healthy adults in the second phase Human Microbiome Project (HMP1-II) [66], UniRef90 X5NU12 was present in about half of anterior nares samples (131 of 251), and its relative abundance was significantly inversely correlated with S. aureus presence by sequencing (Wilcoxon p < 10−5 across 251 samples). A BLAST [67] search of reads mapping to this gene family revealed many significant alignments with 16S rRNA gene sequences nominally assigned to species as diverse as Streptococcus, Staphylococcus, Lactobacillus, Cutibacterium, and Bifidobacterium. Given these diverse results, closer inspection of the UniRef90 X5NU12 family suggests that it may be a misannotated fragment of the highly conserved 16S rRNA gene, with sequences matching these reads from previous skin and nasal studies potentially misannotated to a variety of closely related taxa (including D. pigrum). Thus, UniRef90 X5NU12 may be acting as a taxonomic marker for a closely related, uncharacterized species co-occurring or sharing genetic similarity with D. pigrum and competing with S. aureus. Meanwhile, many of the maternal ECs associated with infant S. aureus “ever” acquisition in the linear models contributed somewhat to the predictive power of the random forest for this variable, but none were highly dominant predictors like UniRef90 X5NU12 (Additional file 1: Fig. S6).
Random forest classifiers using infant species-level taxonomic and functional profiles were also able to successfully predict the presence/absence of a number of other taxa in the infant microbiome, including several potential pathogens and important commensals (Fig. 4). In total, 11 out of 13 of these models had AUCs that were significantly above 0.5 when using species as predictors (Fig. 4), and 9 out of 13 models had AUCs significantly above 0.5 when using functions (Fig. 4, bootstrap 95% CI > 0.5). Overall, the maternal microbiome was not predictive of the infant microbiome, with only one of the models achieving significance. This is consistent with our earlier analysis (i.e., Figure 2), as the random forest models are built using time-matched mother-infant samples from all time points, and mothers and their infants were significantly more similar at only one of 13 time points (and even then were still quite different from each other). Many species in the nasal microbiome appear to be interdependent, but in complex ways, thereby allowing moderate prediction by random forest models; however, S. aureus itself appears to be a more isolated member of the ecological community of the nose.
Ability of infant or maternal microbiome profiles to predict infant microbiome membership. Both infant and maternal a species-level relative abundance profiles and b functional profiles (ECs) were used as predictors in random forests to infer (1) the presence/absence of other individual species by sequencing, (2) subject-varying S. aureus variables, and (3) subject-fixed S. aureus culture phenotypes in infants. When predicting properties of a given species (presence/absence or any S. aureus-derived property), we a removed the species from the abundance table or b removed any ECs contributed to by the species and renormalized all samples to 100% prior to model fitting to avoid circularity. The infant microbiome exhibited reasonably strong within-ecosystem cohesion (i.e., predictability) but essentially none from the maternal to infant microbiome; conversely, only certain S. aureus carriage phenotypes were well predicted by this model
Gene-level strain profiles reveal similarity between mother and infant S. aureus
To finally explore strain-level trends in S. aureus acquisition and carriage and put these in context relative to other common nasal species, we compared species-level gene content (i.e., strain-specific accessory genome carriage) across samples (Fig. 5). Given the limited sequencing depth available after human nucleotide depletion, strains were characterized based on the presence/absence of gene families in each species’ reference-based pan-genome [68, 69] rather than by calling individual nucleotide variants. Per species, we calculated the average Jaccard distance between accessory gene profiles in different subjects to quantify genetic diversity for that species in our population. We also compared the average Jaccard distance between sample pairs matched on a metadata variable of interest (e.g., mother-infant pair) to that between discordant sample pairs. For example, one test (Fig. 5b) considered whether infants were ever positive for S. aureus over the study period and compared the strain similarities (averaged over subject pairs) of concordant samples (i.e., both infants “ever” or “never” acquired S. aureus) compared to discordant samples (i.e., one infant “ever” acquired and one “never” acquired S. aureus).
Gene content-based strain profiling for S. aureus and other common nasal species. To perform gene content-based strain profiling for each species, pairwise Jaccard distances were calculated between samples on accessory gene presence/absence and averaged over same-subject pairs. Species/distance metric combinations with fewer than three subject pairs formed from six fully unique subjects are not displayed (see Additional file 2 for sample sizes). Significance testing was performed with a one-sided Wilcoxon-Mann-Whitney test
Of the eight species with sufficient coverage to profile strains, the overall population mean genetic diversity/distance was highest in S. epidermidis and S. aureus and lowest in Corynebacterium propinquum and Corynebacterium pseudodiphtheriticum (Fig. 5a). For many of the species, gene content was more similar within an infant over time than compared to other infants (Fig. 5e), indicating stability of the strains over time (mirroring the behavior of the adult gut [66, 70, 71]), and this reached significance for C. accolens and C. pseudodiphtheriticum. Interestingly, individuals with the same infant/mother status (i.e., both mothers or both infants) had more similar gene content for C. pseudodiphtheriticum, D. pigrum, and S. epidermidis than individuals with differing infant/mother status (Fig. 5f), suggesting that these species may have strains adapted to microbiome maturity or be transmitted within age groups.
Infants carried strains of S. aureus that were somewhat more similar to those of their own mothers than to unrelated mothers (Fig. 5d) or other infants (p = 0.0520). This is consistent with the findings from the parent cohort study that infants often carried maternal strains [17], although we use different methods for defining similarity and, due to undersampling in our data and the high specificity of our accessory genome barcoding, infants rarely had identical strains to their mother. Interestingly, this similarity of the infant strain to the maternal strain was more pronounced in S. aureus compared to other species. Furthermore, infants who were positive for S. aureus by culture and were concordant on loss status (i.e., either both lost or did not lose S. aureus) had S. aureus with more similar gene content compared to individuals discordant on loss status (p = 0.0359) (Fig. 5c), suggesting some strains may be more ecologically stable, i.e., more difficult to lose once acquired. Strain-level variation in other species may also be related to S. aureus status; for instance, we found that infants with the same S. aureus “ever” acquisition status had very significantly more similar strains of M. catarrhalis (p < 10−4) (Fig. 5b).
Discussion
In this study, we performed the first shotgun metagenomic analysis of the infant nasal microbiome, with a particular focus on S. aureus acquisition and retention. Using multiple measures of S. aureus status and maternally paired, longitudinal microbiome data, we sought to identify microbiome determinants temporally preceding changes in infant S. aureus status and to investigate the influence of developmental time and the maternal microbiome on the early infant microbiome. The infant nasal microbiome was highly variable over the first year and was primarily shaped by developmental and external factors (e.g., environmental exposures such as daycare). Using both linear and random forest models, as well as replication in healthy adults in the HMP1-II dataset, we found evidence of a gene family, possibly representative of an uncharacterized taxon, that was associated with D. pigrum and significantly anticorrelated S. aureus.
Generally, the taxonomic composition of our samples matched that seen in previous studies [42, 72] with Moraxella, Streptococcus, Haemophilus, Staphylococcus, Corynebacterium, and Dolosigranulum making up 71.78% of the total abundance of infant samples. In contrast to some previous studies, we identified Cutibacterium (previously Propionibacterium) as a major genus dominating the infant nasal microbiome in the first year of life. This difference is likely due to previous studies often sequencing hypervariable region 4 of the 16S ribosomal RNA (rRNA) gene, which has been shown to severely underestimate Propionibacterium abundance [73]. After Dolosigranulum pigrum (30.60%), Cutibacterium acnes had the second highest mean abundance among infants (18.44%). In a linear model, birth time points were significantly (q < 0.25) associated with C. acnes and Propionibacteriaceae unclassified (possibly C. acnes that could not be identified due to low sequencing read counts), which together made up 60.13% of the relative abundance of samples at birth. D. pigrum tended to dominate subsequently, and later (non-birth) time points were associated with significant elevations of D. pigrum, C. pseudodiphtheriticum, M. catarrhalis, and S. tigurinus (q < 0.25). Previous research has suggested that interaction with C. acnes can increase S. aureus virulence [74], helping S. aureus to invade the human host [74]. Similarly, the C. acnes-produced small molecule coproporphyrin III can promote S. aureus biofilm formation, potentially improving its survival in challenging environments [75]. In our study, just over half of infants gained S. aureus at or before month 1; in contrast to more complex D. pigrum-dominated microbiome compositions seen later in the first year, this low-diversity C. acnes dominated nasal microbiome at birth may fail to prevent or even promote S. aureus acquisition.
Our findings on infant nasal microbiome development further elucidate the results from the parent cohort study, which tracked S. aureus phenotypes using cultured isolates and defined strains by pulsed-field gel electrophoresis (PFGE) and spa typing (rather than sequencing) [17]. In the full cohort, it was found that 61.8% (n = 76) of the infants carried S. aureus at month 1, with the majority (72%, n = 52) carrying the same strain as the mother by PFGE/spa [17]. Our results suggest the mother is an important influence on the infant nasal microbiome at and before month 1, which could allow for the sharing of both commensal nasal species, as well as S. aureus and other pathogens. Our strain definition via sequencing also suggests that infant strains are often genomically similar, but not identical, to the most abundant strain in the mother. In the larger cohort, we found that early acquisition (by month 1) of S. aureus resulted in a longer length of carriage [17]. Here, in the early months, full metagenomic profiling made it clear that the nasal microbiome is undeveloped with low species alpha diversity and high C. acnes, which may allow S. aureus transmitted at this time to gain a strong foothold.
Although S. aureus transmitted by the mother may be present very early in life, the infant nasal microbiome and immune system undergo major changes throughout the first year of life that likely act to exclude S. aureus, resulting in declining rates of carriage in the year after birth. Consistent with studies of the gut microbiome, in which maternal influence after birth on vaginally delivered infants is quite strong for a short period of time, but then quickly fades [50, 52,53,54], we found that infants were not more similar to their own mother at later time points and that their taxonomic profiles overall rapidly diverged from the earliest time points. In contrast, environmental factors, especially daycare, strongly shaped the nasal microbiome in the first year, as it was associated with the acquisition of new species, and possible strain replacement for existing species.
Daycare attendance in particular was one of the largest effects in our study and the parent study, both on S. aureus specifically and the nasal microbiome generally. In the larger cohort, daycare was significantly negatively associated with persistent S. aureus carriage [17], and, in our subset, none of the three persistent carriers attended daycare at any point. Consistent with previous research [44, 55, 56], daycare attendance was marked by a large increase in the relative abundance of M. catarrhalis (Fig. 3). The likely inverse relationship between M. catarrhalis and S. aureus [76,77,78] and the acquisition of M. catarrhalis at daycare then explain why daycare attendance may reduce persistent carriage. Surprisingly, we also found that the three persistent carriers had higher M. catarrhalis relative abundance than other daycare non-attendees (Fig. 3). While this is not inherently contradictory, since none of these associations must be transitive per se, it may be explained by external confounding factors such as contact with siblings, since the presence of siblings is a risk factor for both M. catarrhalis [44, 79] and S. aureus colonization [65], or by random chance given the small sample size. However, there may also be a more interesting biological explanation, such as combinatorial interactions with additional species like H. influenzae, which is also acquired with daycare attendance, or strain differences in M. catarrhalis, as we did find a highly significant association of M. catarrhalis strain type with S. aureus “ever” acquisition (Fig. 5). Similar to Corynebacterium species, in which studies have reported inconsistent associations with S. aureus due to the possible complexity of these interactions [11], further in vitro work exploring the circumstances under which M. catarrhalis inhibits S. aureus would be valuable.
One of the other most striking observations in our data was the presence of a gene family, correlated with D. pigrum genomic content, that was strongly co-exclusionary with S. aureus carriage. Due to its similarity with 16S rRNA gene sequences, this gene family likely represents a species not closely homologous to current reference isolates that competes with S. aureus. Its correlation with D. pigrum in our dataset suggests that it may either occupy a related phylogenetic space or that these two species may act as co-colonizers. Furthermore, previous epidemiological studies have reported inverse associations between D. pigrum and S. aureus in adults [19, 36, 61, 62] and infants [63], and identified diverse biosynthetic gene clusters in D. pigrum believed to be involved in its in vitro inhibition of S. aureus [62]. In one study of hospitalized neonates, cases (those acquiring S. aureus) had lower D. pigrum relative abundance compared to matched controls 7 days prior to S. aureus acquisition [63], and a recent study found that 10 of 10 diverse D. pigrum isolates inhibited S. aureus growth in vitro [62]. The mechanisms through which D. pigrum inhibited S. aureus in this experiment were not fully clear, but one hypothesis explored by the authors was an enrichment of biosynthetic gene clusters (BGCs) in D. pigrum, with strains containing both a lanthipeptide BGC and a bacteriocin inducing the greatest inhibition [62]. Our finding of the inverse association between maternal D. pigrum and infant “ever” acquisition of S. aureus, however, is the first to suggest that D. pigrum is one pathway through which the maternal microbiome influences infant S. aureus carriage. From our results alone, it is not clear whether D. pigrum prevents the acquisition of maternal S. aureus that could be passed to the infant, or whether maternal D. pigrum is passed to the infant where it is protective against S. aureus acquisition, and in either case, whether D. pigrum itself or an uncharacterized relative is responsible for the additional sequences co-exclusionary with S. aureus.
Due to the cost of metagenomic sequencing and the complexity of S. aureus carriage, our study has some limitations. Firstly, our data is collected monthly, which may be too coarse a temporal resolution for understanding the full relationship between S. aureus status and the rapidly developing infant microbiome. Secondarily, we are limited by the size of our sample, and this is ultimately an exploratory analysis whose findings should be tested in larger cohorts. In particular, this creates challenges if early and late S. aureus acquisition events indeed are driven by different influences (e.g., maternal S. aureus carriage vs. the establishing infant nasal microbiome), as this creates a mixed signal with an already limited sample size and is worthwhile to consider for future study design. Additionally, using metagenomics for skin habitats is challenging due to the high degree of human contamination, and we were not able to perform nucleotide-level strain analysis given the relatively low sample read depths. Lastly, S. aureus carriage is a complex phenomenon, with ecological and microbiological variation undoubtedly contributed by unmeasured factors (e.g., immune development, contact with other caregivers and relatives, diet), and therefore, microbiome contributions need to be large to be detectable. Particularly, during highly dynamic infant colonization, a wide range of environmental influences (in addition to direct maternal effects) are likely to influence microbiome composition. These effects are further confounded since the mother’s nasal (and other) microbiome composition may in turn be influenced by some of these same factors due to shared genetics, cohabitation, lifestyle factors, and/or transmission of strains with other children, all of which have been demonstrated for the gut microbiome [52, 80,81,82,83,84,85,86]. Through an integrative microbiome epidemiology study design, we have provided new insights into the development of the infant nasal microbiome and its role in S. aureus carriage that suggest further targets of investigation for eventual ecological therapies to control S. aureus carriage.
Conclusions
In this study, we characterized the development of the infant nasal microbiome, which was highly variable, weakly influenced by the maternal nasal microbiome composition, and strongly shaped by daycare attendance. Mothers thus represented a sporadic early source for S. aureus transmission to the naive infant microbiome, but microbiome determinants became more important later on. We gained a better understanding of the role of the microbiome in S. aureus carriage through the identification of a specific protein family that was highly predictive of infant S. aureus status, significantly anticorrelated with S. aureus positivity in both infants and mothers, and which ecologically interacts with the commensal species D. pigrum. We determined that this (misannotated) protein family was a non-protein-coding sequence acting as a phylogenetic marker of a likely novel bacterial species. An inverse relationship between D. pigrum and S. aureus has been a main result of multiple prior 16S rRNA amplicon studies; however, using metagenomic sequencing, we were able to differentiate this novel species from D. pigrum (which it often co-occurs with) and have shown that it is more predictive of S. aureus presence than is D. pigrum itself (with our result thus representing a likely mechanistic “driver” rather than previously identified “passengers”). Furthermore, this novel taxon was sufficiently prevalent in adults and infants to drive widespread patterns of S. aureus carriage. These findings were not limited to our cohort: a similar prevalence and a strong anti-correlation with S. aureus were also found for this novel clade in geographically distinct adults from the Human Microbiome Project. Our study provides an improved understanding of how the infant nasal microbiome develops in early life, and how it can act to promote or exclude S. aureus colonization.
Methods
Cohort and experimental design
Subjects consisted of 36 mother-infant pairs recruited through Sheba Medical Center as part of a larger cohort study on S. aureus [17], as well as S. aureus-negative mothers enrolled specifically for this study. More specifically, subjects were recruited from among pregnant women who attended the Sheba Medical Center obstetrics monitoring unit between 2013 and 2016, were at least 34 weeks pregnant, visited the monitoring unit during screening hours (held for three hours per week), and provided consent to participate. Exclusion criteria included delivery prior to 34 weeks of pregnancy and failure to provide consent to participate. Nasal swabs and subject covariates were collected from mothers and infants within 48 h of birth and then monthly for the first year after birth. At each time point, two swabs (one for S. aureus culture testing and one for DNA extraction and sequencing) were collected from both nostrils. The covariates included infant sex, maternal age, delivery mode, daycare attendance in the past month, antibiotic use in the past month, and breastfeeding in the past month (Additional file 4). Culture testing for S. aureus (detailed below) was performed for all non-missing samples (n = 856), and 33.2% of samples (n = 284) were sequenced with shotgun metagenomic sequencing (also below, and see Fig. 1). As much as possible, shotgun-sequenced samples were selected to target birth and the first month for all subjects, S. aureus gain and loss events in infants, and extended periods of S. aureus carriage or non-carriage in infants.
S. aureus culture testing
Nasal screening was performed using a cotton-tipped swab placed in Amies transport media (Copan innovation, Brescia, Italy). Swabs were streaked on CHROMagar S. aureus plates (HiLabs, Rehovot, Israel) within 24 h and incubated for 24–48 h at 35 °C. Catalase and Staphylase (PASTOREX® STAPH-PLUS, BioRad, Marnes-la-Coquette, France) were performed on suspected colonies to conclusively identify them as S. aureus.
DNA extraction and sequencing
DNA extraction was performed with the QIAGEN DNeasy PowerLyzer PowerSoil Kit with a bead-beating protocol. After extraction, DNA concentration was measured by NanoDrop and frozen at − 20 °C until being shipped to the USA for sequencing. Prior to starting the project, the DNA extraction method was validated by performing extractions on double-distilled water (DDW) samples and confirming that no DNA concentration was measured by NanoDrop and that the displayed band was a true band and not due to contamination. Negative controls were not additionally sequenced since specimen DNA concentrations were measured after extraction to be reliably high and differentiated from potential background contaminants (due to the otherwise substantial added cost).
Whole-genome fragment libraries were prepared as follows at the Broad Institute [87]. Metagenomic DNA samples were quantified by Quant-iT PicoGreen dsDNA Assay (Life Technologies) and normalized to a concentration of 50 pg/μL. Illumina sequencing libraries were prepared from 100 to 250 pg of DNA using the Nextera XT DNA Library Preparation kit (Illumina) according to the manufacturer’s recommended protocol, with reaction volumes scaled accordingly. Prior to sequencing, libraries were pooled by collecting equal volumes (200 nl) of each library from batches of 96 samples. Insert sizes and concentrations for each pooled library were determined using an Agilent Bioanalyzer DNA 1000 kit (Agilent Technologies). Libraries were sequenced on HiSeq 2 × 101 to yield ~ 10 million paired-end reads. Post-sequencing de-multiplexing and generation of BAM and Fastq files were generated using the Picard suite.
S. aureus phenotype definitions
The S. aureus phenotype of a sample was defined using multiple measures to capture different dimensions of S. aureus carriage (Additional file 5). Subject-fixed S. aureus phenotypes were defined strictly by the S. aureus culture data and included S. aureus early acquisition, late acquisition, “ever” acquisition, persistent carriage, the percent of positive time points, and the time point of first S. aureus acquisition. S. aureus early acquisition applied only to infants, and was defined as first acquisition of S. aureus by or at the first month. S. aureus late acquisition also applied only to infants and was defined as first acquisition of S. aureus after month 1. If culture results for birth were missing, early and late acquisition phenotypes were calculated using month 1 data only. S. aureus “ever” acquisition for both mothers and infants was defined as ever being positive for S. aureus by culture over the study period. To match definitions in the larger cohort study [17], infant persistent carriage was defined as being positive for at least two thirds of non-missing time points and being positive for at least half of non-missing time points between months 6 and 12. For mothers, persistent carriage was defined as being positive for at least two thirds of non-missing time points. For infants and mothers, the percent of positive time points was defined as the number of positive time points divided by the number of non-missing time points for the subject over the study period. For infants, the time point of first S. aureus acquisition was defined as the first time point for which the subject tested positive for S. aureus or NA for subjects who never tested positive.
Subject-varying S. aureus phenotypes included positivity by culture or sequencing or either, gain of S. aureus by the next month among culture-negative samples, and loss of S. aureus by the next month among culture-positive samples. Positivity by sequencing was defined as having a non-zero S. aureus relative abundance. Positivity by either was defined as being positive by culture or sequencing or both. Due to the false-negative rate from culture, the culture data was smoothed according to the following procedure: (1) negative or missing data points that were preceded and followed by positive data points were treated as positive, and (2) missing data points that were preceded and followed by negative data points were treated as negative. Using the smoothed culture dataset, S. aureus gain by the next month was defined as changing from negative by culture at month t to positive by culture at month t + 1. S. aureus loss by the next month was defined as changing from positive by culture at month t to negative by culture at both months t + 1 and t + 2. Figure 1 shows the raw, unsmoothed data.
Data processing and quality control
Three sequenced samples failed our sample tracking quality control and were excluded from further analysis. Samples were retained in the analysis if they contained more than 5 × 104 sequenced read pairs (mean 9.47 × 105 read pairs per sample) and were not taxonomically 100% unclassified (detailed below). This cut-point was selected a priori based on approximately 1× coverage of an Escherichia coli-equivalent genome size. Of the 284 sequenced samples, a total of 208 samples (144 from infants and 64 from mothers) were retained for further analyses.
Taxonomic and functional profiles were generated using the bioBakery meta’omics workflow v0.9.0 [88]. Briefly, reads mapping to the human genome were first filtered out using KneadData v0.7.0 with default parameters. Taxonomic profiles of shotgun metagenomes were generated using MetaPhlAn2 v2.6.0, and functional profiling was performed by HUMAnN2 v0.11.0. In taxonomic profiles, relative abundances were given at the species level or, if unidentified, at the genus level or family level. Phage abundance was originally included in the taxonomic profiles, but due to the lack of any significant associations (q < 0.25) in linear models, phage abundance was normalized out and subsequent analyses focused purely on non-viral taxa. In functional profiles, per-sample gene abundances were grouped by Enzyme Commission (EC) number using the HUMAnN2 utility script.
Due to the presence of quality-controlled DNA that did not map, we treated the unmapped sample mass like a microbial feature (i.e., rescaled the taxonomic profiles by the percent mapped reads and included a feature for the percent unmapped reads) and assessed the robustness of our results using these new profiles. The percent of unmapped reads was strongly correlated with human contamination (Spearman rho = 0.69, p < 10−15 across all 208 filtered samples) and did not change our main findings from linear models (Fig. 3, Additional file 1: Fig. S7), other than reducing power (due to noise introduction and re-removal) and causing some weaker associations to drop out. We concluded that much of this unmapped sample mass represented mainly cryptic human contamination and that our initial approach of focusing purely on mapped reads and adjusting linear models for the proportion of human contamination was robust.
Statistical methods for assessing infant developmental trends
Alpha and beta diversities were calculated using the Simpson index and Bray-Curtis dissimilarity, respectively, in the vegan R package v2.5.4. For mothers, beta diversity was calculated using average per-subject taxonomic composition (due to limited sample sizes). In comparisons between infants with different S. aureus statuses, S. aureus relative abundance was normalized out prior to the calculation of diversity metrics. Significance testing was performed using a one-sided Wilcoxon-Mann-Whitney test. Any time points with fewer than five data points were not included in this analysis (see Additional file 2 for sample sizes). PERMANOVA was performed using the adonis function within the vegan R package. We tested the following variables using blocked PERMANOVA [87]: S. aureus early acquisition, late acquisition, “ever” acquisition, and persistent carriage. Within-subject permutations were used to test the effect of time (months). We used repeated cross-sectional PERMANOVAs to test: S. aureus positivity by culture, sequencing or either, and infant vs. mother status.
Feature filtering
Prior to the creation of linear and random forest models, we filtered taxonomic and functional features to retain analyzable subsets of these features passing quality control. All filtering was applied separately to infant and mother samples due to the differences in microbiome composition, and all cut-points for filtering were selected a priori based on best practices from similar studies. First, all feature abundances below 0.1% (taxonomy) or 0.05% (function) abundance in more than 95% of samples were aggregated to a single “other” category to avoid testing features that were too uncommon to possibly detect an effect. Next, unless otherwise noted, microbiome features trivially related to the outcome variable of interest were dropped and the remaining per-sample abundance was renormalized to 100%. For example, S. aureus abundance was removed from infant taxonomic profiles in this way prior to the prediction of infant S. aureus-related variables in the random forest models. ECs were considered related to the outcome of interest if they contained a stratification for the outcome species in any infant or mother sample. Lastly, we applied a filter to remove functions that were primarily contributed by a single species to avoid re-analyzing functions that essentially recapitulated the genomic abundances of a single dominant carrying species. Specifically, if a single species (excluding unclassified) contributed more than 50% of a functional feature’s abundance in more than 50% of samples containing that feature, it was aggregated to an “other” category as above. Functions contributed by unclassified species were identified through the HUMAnN2 translated search (but not MetaPhlAn2) and were retained because they were not previously captured in our taxonomic analysis.
Linear models
Linear mixed effects models to identify significant associations between microbiome features and covariates were created in MaAsLin2 v0.2.2. Briefly, log-transformed taxonomic and functional feature abundances were modeled as outcomes of a function of covariates, including a random effect for the subject and fixed effects for the S. aureus phenotype, time point (months), proportion of human contamination (defined as one minus the proportion of reads after vs. before human depletion), and total read count. We created models combining covariates and outcome features for (1) infant features and infant metadata, (2) mother features and infant metadata, and (3) mother features and mother metadata. Therefore, each class of model was defined by the choice of microbiome feature type (taxonomic or functional), S. aureus phenotype, and the three feature/metadata configurations listed above (Fig. 3). Within each unique combination of these levels, FDR correction was applied per variable using Benjamini-Hochberg, and the results with q < 0.25 are displayed (Fig. 3).
Models were fit for both subject-fixed and subject-varying S. aureus phenotypes. For subject-fixed phenotypes, such as “ever” acquisition of S. aureus, all samples were given the subjects’ phenotype value and included in the analysis. For subject-varying S. aureus phenotypes, such as sample positivity by sequencing, each sample with a non-missing value for the phenotype variable was included in the analysis. For S. aureus gain and loss, the analysis was subset to samples that were negative or positive for S. aureus by culture, respectively.
Other recorded exposures or environmental factors, such as antibiotic usage or delivery mode, were also evaluated as covariates potentially contributing to the relationship between microbiome features and the S. aureus phenotype. For mothers and infants separately, we included covariates in the model when (1) the covariate was significantly (q < 0.25) associated with at least one species in a univariate prescreen, and (2) the covariate was at present in at least 10% of samples (including those that were missing covariate data). Furthermore, models for maternal microbiome features were not adjusted for variables considered primarily related to infants, such as breastfeeding and daycare. These criteria were decided upon a priori. The rationale for (2) was that these covariates were not able to be tested in our dataset because they were too rare to be individually significant. Based on these criteria, models for infant microbiome features were also adjusted for breastfeeding and daycare in the preceding month, while models for maternal microbiome features were not adjusted for any additional covariates. The displayed results (Fig. 3) for non-S. aureus metadata are from a model containing all covariates except a S. aureus phenotype.
Random forest models
Random forest classifiers were created using the randomForest R package v4.6.14. Across models, the predictors consisted of filtered infant and maternal taxonomic and functional features. The binary response variable was defined as the infant S. aureus phenotype or the presence/absence of a species in the infant nasal microbiome by sequencing (the species tested were those that passed the prevalence/abundance filter). We did not study response variables for the mothers due to the lower sample size. As described above, features were normalized out of the predictor dataset if they were related to the outcome variable in models constructed with infant predictors/infant outcomes.
The randomForest function was run with default parameters, with the exception of downsampling to the size of the smaller class to handle the unbalancedness in the data. To conservatively address subject-level effects, cross-validation was performed per subject: iterating through each subject, a test dataset containing all samples from the same subject was created, the model was trained on data from all other subjects, and then the outcome for the excluded individual was predicted. To find 95% confidence intervals, the standard error was calculated through per-subject bootstrapping, with 1000 bootstrap samples used. Random forest models were only run if they had at least 20 samples in each class (i.e., a sufficient size for meaningful cross-validation) (see Additional file 6 for sample sizes).
Strain-level profiling
Given the limited sequencing depth available after human nucleotide depletion, strain profiles were generated using PanPhlAn v1.2.2, which characterizes strains based on the presence/absence across gene families from across each species’ reference-based pan-genome [68].
To create Fig. 5, per-species gene presence/absence matrices were first filtered to remove genes present in less than 5% of samples for which PanPhlAn was run. The vegdist function in the vegan R package v2.5.4 was used to calculate pairwise Jaccard distances between samples based on a metadata variable of interest. For each comparison in Fig. 5, pairwise distances were averaged over same subject pairs to avoid undue influence by subjects with more sequenced time points. Comparisons were displayed if they had data from at least three unique pairs consisting of six unique subjects. This was to avoid bias that could occur if many pairs were created with the same subject and this subject happened to have unusual data. Significance testing was performed using a one-sided Wilcoxon-Mann-Whitney test. Due to low sample read depths, underdetection of genes is likely; however, the underdetection should not be differential with respect to the metadata variable of interest, and therefore, the significance testing is unbiased.
Availability of data and materials
The metagenomic sequences generated and analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) repository as BioProject PRJNA610982 [89]. The subject metadata and the processed taxonomic and functional datasets generated and analyzed during the current study are included in this published article (Additional files 7, 8, and 9, respectively). All software used in this study is free and open source.
References
Tong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG Jr. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev. 2015;28(3):603–61.
Magill SS, O’Leary E, Janelle SJ, Thompson DL, Dumyati G, Nadle J, et al. Changes in prevalence of health care-associated infections in U.S. hospitals. N Engl J Med. 2018;379(18):1732–44.
Olaniyi R, Pozzi C, Grimaldi L, Bagnoli F. Staphylococcus aureus-associated skin and soft tissue infections: anatomical localization, epidemiology, therapy and potential prophylaxis. Curr Top Microbiol Immunol. 2017;409:199–227.
Klevens RM, Edwards JR, Gaynes RP. The impact of antimicrobial-resistant, health care-associated infections on mortality in the United States. Clin Infect Dis. 2008;47(7):927–30.
Kourtis AP, Hatfield K, Baggs J, Mu Y, See I, Epson E, et al. Vital signs: epidemiology and recent trends in methicillin-resistant and in methicillin-susceptible Staphylococcus aureus bloodstream infections - United States. MMWR Morb Mortal Wkly Rep. 2019;68(9):214–9.
Johnson RC, Ellis MW, Lanier JB, Schlett CD, Cui T, Merrell DS. Correlation between nasal microbiome composition and remote purulent skin and soft tissue infections. Infect Immun. 2015;83(2):802–11.
Luzar MA, Coles GA, Faller B, Slingeneyer A, Dah GD, Briat C, et al. Staphylococcus aureus nasal carriage and infection in patients on continuous ambulatory peritoneal dialysis. N Engl J Med. 1990;322(8):505–9.
Kluytmans JA, Mouton JW, Ijzerman EP, Vandenbroucke-Grauls CM, Maat AW, Wagenvoort JH, et al. Nasal carriage of Staphylococcus aureus as a major risk factor for wound infections after cardiac surgery. J Infect Dis. 1995;171(1):216–9.
von Eiff C, Becker K, Machka K, Stammer H, Peters G. Nasal carriage as a source of Staphylococcus aureus bacteremia. Study Group N Engl J Med. 2001;344(1):11–6.
Wertheim HFL, Vos MC, Ott A, van Belkum A, Voss A, Kluytmans JAJW, et al. Risk and outcome of nosocomial Staphylococcus aureus bacteraemia in nasal carriers versus non-carriers. Lancet. 2004;364(9435):703–5.
Brugger SD, Bomar L, Lemon KP. Commensal-pathogen interactions along the human nasal passages. PLoS Pathog. 2016;12(7):e1005633.
Gorwitz RJ, Kruszon-Moran D, McAllister SK, McQuillan G, McDougal LK, Fosheim GE, et al. Changes in the prevalence of nasal colonization with Staphylococcus aureus in the United States, 2001-2004. J Infect Dis. 2008;197(9):1226–34.
Williams RE. Healthy carriage of Staphylococcus aureus: its prevalence and importance. Bacteriol Rev. 1963;27:56–71.
Eriksen NH, Espersen F, Rosdahl VT, Jensen K. Carriage of Staphylococcus aureus among 104 healthy persons during a 19-month period. Epidemiol Infect. 1995;115(1):51–60.
Hu L, Umeda A, Kondo S, Amako K. Typing of Staphylococcus aureus colonising human nasal carriers by pulsed-field gel electrophoresis. J Med Microbiol. 1995;42(2):127–32.
Kluytmans J, van Belkum A, Verbrugh H. Nasal carriage of Staphylococcus aureus: epidemiology, underlying mechanisms, and associated risks. Clin Microbiol Rev. 1997;10(3):505–20.
Reiss-Mandel A, Rubin C, Maayan-Mezger A, Novikov I, Jaber H, Dolitzky M, et al. Patterns and predictors of Staphylococcus aureus carriage during the first year of life: a longitudinal study. J Clin Microbiol. 2019;57(9). https://doi.org/10.1128/JCM.00282-19.
Andersen PS, Pedersen JK, Fode P, Skov RL, Fowler VG Jr, Stegger M, et al. Influence of host genetics and environment on nasal carriage of staphylococcus aureus in danish middle-aged and elderly twins. J Infect Dis. 2012;206(8):1178–84.
Liu CM, Price LB, Hungate BA, Abraham AG, Larsen LA, Christensen K, et al. Staphylococcus aureus and the ecology of the nasal microbiome. Sci Adv. 2015;1(5):e1400216.
Prince T, McBain AJ, O’Neill CA. Lactobacillus reuteri protects epidermal keratinocytes from Staphylococcus aureus-induced cell death by competitive exclusion. Appl Environ Microbiol. 2012;78(15):5119–26.
Uehara Y, Nakama H, Agematsu K, Uchida M, Kawakami Y, Abdul Fattah AS, et al. Bacterial interference among nasal inhabitants: eradication of Staphylococcus aureus from nasal cavities by artificial implantation of Corynebacterium sp. J Hosp Infect. 2000;44(2):127–33.
Lai Y, Cogen AL, Radek KA, Park HJ, Macleod DT, Leichtle A, et al. Activation of TLR2 by a small molecule produced by Staphylococcus epidermidis increases antimicrobial defense against bacterial skin infections. J Invest Dermatol. 2010;130(9):2211–21.
Lijek RS, Luque SL, Liu Q, Parker D, Bae T, Weiser JN. Protection from the acquisition of Staphylococcus aureus nasal carriage by cross-reactive antibody to a pneumococcal dehydrogenase. Proc Natl Acad Sci U S A. 2012;109(34):13823–8.
Wanke I, Steffen H, Christ C, Krismer B, Götz F, Peschel A, et al. Skin commensals amplify the innate immune response to pathogens by activation of distinct signaling pathways. J Invest Dermatol. 2011;131(2):382–90.
Cogen AL, Yamasaki K, Sanchez KM, Dorschner RA, Lai Y, MacLeod DT, et al. Selective antimicrobial action is provided by phenol-soluble modulins derived from Staphylococcus epidermidis, a normal resident of the skin. J Invest Dermatol. 2010;130(1):192–200.
Gonzalez DJ, Haste NM, Hollands A, Fleming TC, Hamby M, Pogliano K, et al. Microbial competition between Bacillus subtilis and Staphylococcus aureus monitored by imaging mass spectrometry. Microbiology. 2011;157(Pt 9):2485–92.
Iwase T, Uehara Y, Shinji H, Tajima A, Seo H, Takada K, et al. Staphylococcus epidermidis Esp inhibits Staphylococcus aureus biofilm formation and nasal colonization. Nature. 2010;465(7296):346–9.
Shu M, Wang Y, Yu J, Kuo S, Coda A, Jiang Y, et al. Fermentation of Propionibacterium acnes, a commensal bacterium in the human skin microbiome, as skin probiotics against methicillin-resistant Staphylococcus aureus. Plos One. 2013;8(2):e55380.
Regev-Yochay G, Trzcinski K, Thompson CM, Malley R, Lipsitch M. Interference between Streptococcus pneumoniae and Staphylococcus aureus: in vitro hydrogen peroxide-mediated killing by Streptococcus pneumoniae. J Bacteriol. 2006;188(13):4996–5001.
Zipperer A, Konnerth MC, Laux C, Berscheid A, Janek D, Weidenmaier C, et al. Human commensals producing a novel antibiotic impair pathogen colonization. Nature. 2016;535(7613):511–6.
Bieber L, Kahlmeter G. Staphylococcus lugdunensis in several niches of the normal skin flora. Clin Microbiol Infect. 2010;16(4):385–8.
Kaspar U, Kriegeskorte A, Schubert T, Peters G, Rudack C, Pieper DH, et al. The culturome of the human nose habitats reveals individual bacterial fingerprint patterns. Environ Microbiol. 2016;18(7):2130–42.
Krismer B, Weidenmaier C, Zipperer A, Peschel A. The commensal lifestyle of Staphylococcus aureus and its interactions with the nasal microbiota. Nat Rev Microbiol. 2017;15(11):675–87.
Laux C, Peschel A, Krismer B. Staphylococcus aureus colonization of the human nose and interaction with other microbiome members. Microbiol Spectr. 2019;7(2). https://doi.org/10.1128/microbiolspec. GPP3-0029-2018.
Fredheim EGA, Flægstad T, Askarian F, Klingenberg C. Colonisation and interaction between S. epidermidis and S. aureus in the nose and throat of healthy adolescents. Eur J Clin Microbiol Infect Dis. 2015;34(1):123–9.
Yan M, Pamp SJ, Fukuyama J, Hwang PH, Cho D-Y, Holmes S, et al. Nasal microenvironments and interspecific interactions influence nasal microbiota complexity and S. aureus carriage. Cell Host Microbe. 2013;14(6):631–40.
Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, et al. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci U S A. 2011;108(Suppl 1):4578–85.
Maynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal microbiota and immune system. Nature. 2012;489(7415):231–41.
Leshem E, Maayan-Metzger A, Rahav G, Dolitzki M, Kuint J, Roytman Y, et al. Transmission of Staphylococcus aureus from mothers to newborns. Pediatr Infect Dis J. 2012;31(4):360–3.
Lebon A, Labout JAM, Verbrugh HA, Jaddoe VWV, Hofman A, van Wamel W, et al. Dynamics and determinants of Staphylococcus aureus carriage in infancy: the generation R study. J Clin Microbiol. 2008;46(10):3517–21.
Capone KA, Dowd SE, Stamatas GN, Nikolovski J. Diversity of the human skin microbiome early in life. J Invest Dermatol. 2011;131(10):2026–32.
Bomar L, Brugger SD, Lemon KP. Bacterial microbiota of the nasal passages across the span of human life. Curr Opin Microbiol. 2018;41:8–14.
Rawls M, Ellis AK. The microbiome of the nose. Ann Allergy Asthma Immunol. 2019;122(1):17–24.
Ta LDH, Yap GC, Tay CJX, Lim ASM, Huang C-H, Chu CW, et al. Establishment of the nasal microbiota in the first 18 months of life: correlation with early-onset rhinitis and wheezing. J Allergy Clin Immunol. 2018;142(1):86–95.
Peterson SW, Knox NC, Golding GR, Tyler SD, Tyler AD, Mabon P, et al. A study of the infant nasal microbiome development over the first year of life and in relation to their primary adult caregivers using cpn60 universal target (UT) as a phylogenetic marker. PLoS One. 2016;11(3):e0152493.
Mika M, Mack I, Korten I, Qi W, Aebi S, Frey U, et al. Dynamics of the nasal microbiota in infancy: a prospective cohort study. J Allergy Clin Immunol. 2015;135(4):905–12.e11.
Proctor DM, Relman DA. The landscape ecology and microbiota of the human nose, mouth, and throat. Cell Host Microbe. 2017;21(4):421–32.
Shilts MH, Rosas-Salazar C, Tovchigrechko A, Larkin EK, Torralba M, Akopov A, et al. Minimally invasive sampling method identifies differences in taxonomic richness of nasal microbiomes in young infants associated with mode of delivery. Microb Ecol. 2016;71(1):233–42.
Li L, Mendis N, Trigui H, Oliver JD, Faucher SP. The importance of the viable but non-culturable state in human bacterial pathogens. Front Microbiol. 2014;5:258.
Vatanen T, Franzosa EA, Schwager R, Tripathi S, Arthur TD, Vehik K, et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature. 2018;562(7728):589–94.
Milani C, Duranti S, Bottacini F, Casey E, Turroni F, Mahony J, et al. The first microbial colonizers of the human gut: composition, activities, and health implications of the infant gut microbiota. Microbiol Mol Biol Rev. 2017;81(4). https://doi.org/10.1128/MMBR.00036-17.
Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24(1):133–45.e5.
Vatanen T, Plichta DR, Somani J, Münch PC, Arthur TD, Hall AB, et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat Microbiol. 2019;4(3):470–9.
Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A. 2010;107(26):11971–5.
Teo SM, Mok D, Pham K, Kusel M, Serralha M, Troy N, et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe. 2015;17(5):704–15.
Sá-Leão R, Nunes S, Brito-Avô A, Alves CR, Carriço JA, Saldanha J, et al. High rates of transmission of and colonization by Streptococcus pneumoniae and Haemophilus influenzae within a day care center revealed in a longitudinal study. J Clin Microbiol. 2008;46(1):225–34.
Mansbach JM, Hasegawa K, Henke DM, Ajami NJ, Petrosino JF, Shaw CA, et al. Respiratory syncytial virus and rhinovirus severe bronchiolitis are associated with distinct nasopharyngeal microbiota. J Allergy Clin Immunol. 2016;137(6):1909–13.e4.
Bisgaard H, Hermansen MN, Buchvald F, Loland L, Halkjaer LB, Bønnelykke K, et al. Childhood asthma after bacterial colonization of the airway in neonates. N Engl J Med. 2007;357(15):1487–95.
von Linstow M-L, Schønning K, Hoegh AM, Sevelsted A, Vissing NH, Bisgaard H. Neonatal airway colonization is associated with troublesome lung symptoms in infants. Am J Respir Crit Care Med. 2013;188(8):1041–2.
Ederveen THA, Ferwerda G, Ahout IM, Vissers M, de Groot R, Boekhorst J, et al. Haemophilus is overrepresented in the nasopharynx of infants hospitalized with RSV infection and associated with increased viral load and enhanced mucosal CXCL8 responses. Microbiome. 2018;6(1):10.
Escapa IF, Chen T, Huang Y, Gajare P, Dewhirst FE, Lemon KP. New insights into human nostril microbiome from the Expanded Human Oral Microbiome Database (eHOMD): a resource for the microbiome of the human aerodigestive tract. mSystems. 2018;3(6). https://doi.org/10.1128/mSystems.00187-18.
Brugger SD, Eslami SM, Pettigrew MM, Escapa IF, Henke MM, Kong Y, et al. Dolosigranulum pigrum cooperation and competition in human nasal microbiota. bioRxiv. 2019:678698 Available from: https://www.biorxiv.org/content/10.1101/678698v1. [cited 2019 Aug 29].
Khamash DF, Mongodin EF, White JR, Voskertchian A, Hittle L, Colantuoni E, et al. The association between the developing nasal microbiota of hospitalized neonates and Staphylococcus aureus colonization. Open Forum Infect Dis. 2019; Available from: https://academic.oup.com/ofid/advance-article/doi/10.1093/ofid/ofz062/5315771?searchresult=1. [cited 2019 Feb 11].
Biesbroek G, Bosch AATM, Wang X, Keijser BJF, Veenhoven RH, Sanders EAM, et al. The impact of breastfeeding on nasopharyngeal microbial communities in infants. Am J Respir Crit Care Med. 2014;190(3):298–308.
Peacock SJ, Justice A, Griffiths D, de Silva GDI, Kantzanou MN, Crook D, et al. Determinants of acquisition and carriage of Staphylococcus aureus in infancy. J Clin Microbiol. 2003;41(12):5718–25.
Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature. 2017;550(7674):61–6.
Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000;7(1–2):203–14.
Scholz M, Ward DV, Pasolli E, Tolio T, Zolfo M, Asnicar F, et al. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat Methods. 2016;13(5):435–8.
Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018;15(11):962–8.
Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 2017;27(4):626–38.
Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486(7402):215–21.
Kumpitsch C, Koskinen K, Schöpf V, Moissl-Eichinger C. The microbiome of the upper respiratory tract in health and disease. BMC Biol. 2019;17(1):87.
Meisel JS, Hannigan GD, Tyldsley AS, SanMiguel AJ, Hodkinson BP, Zheng Q, et al. Skin microbiome surveys are strongly influenced by experimental design. J Invest Dermatol. 2016;136(5):947–56.
Lo C-W, Lai Y-K, Liu Y-T, Gallo RL, Huang C-M. Staphylococcus aureus hijacks a skin commensal to intensify its virulence: immunization targeting β-hemolysin and CAMP factor. J Invest Dermatol. 2011;131(2):401–9.
Wollenberg MS, Claesen J, Escapa IF, Aldridge KL, Fischbach MA, Lemon KP. Propionibacterium-produced coproporphyrin III induces Staphylococcus aureus aggregation and biofilm formation. MBio. 2014;5(4):e01286–14.
Pan H, Cui B, Huang Y, Yang J, Ba-Thein W. Nasal carriage of common bacterial pathogens among healthy kindergarten children in Chaoshan region, southern China: a cross-sectional study. BMC Pediatr. 2016;16(1):161.
Dunne EM, Murad C, Sudigdoadi S, Fadlyana E, Tarigan R, Indriyani SAK, et al. Carriage of Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus in Indonesian children: a cross-sectional study. Plos One. 2018;13(4):e0195098.
Bae S, Yu J-Y, Lee K, Lee S, Park B, Kang Y. Nasal colonization by four potential respiratory bacteria in healthy children attending kindergarten or elementary school in Seoul, Korea. J Med Microbiol. 2012;61(Pt 5):678–85.
Verhaegh SJC, Lebon A, Saarloos JA, Verbrugh HA, Jaddoe VWV, Hofman A, et al. Determinants of Moraxella catarrhalis colonization in healthy Dutch children during the first 14 months of life. Clin Microbiol Infect. 2010;16(7):992–7.
Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458.
Brito IL, Gurry T, Zhao S, Huang K, Young SK, Shea TP, et al. Transmission of human-associated microbiota along family and social networks. Nat Microbiol. 2019;4(6):964–71.
Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, et al. Human genetics shape the gut microbiome. Cell. 2014;159(4):789–99.
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486(7402):222–7.
Browne HP, Neville BA, Forster SC, Lawley TD. Transmission of the gut microbiota: spreading of health. Nat Rev Microbiol. 2017;15(9):531–43.
Yassour M, Jason E, Hogstrom LJ, Arthur TD, Tripathi S, Siljander H, et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe. 2018;24(1):146–54.e4.
Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17(6):852.
Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655–62.
McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, et al. bioBakery: a meta’omic analysis environment. Bioinformatics. 2018;34(7):1235–7.
Accorsi EK, Franzosa EA, Hsu T, Cordy RJ, Maayan-Metzger A, Jaber H, et al. Determinants of S. aureus carriage in the developing infant nasal microbiome. BioProject PRJNA610982. NCBI Sequence Read Archive. 2020. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA610982/.
Acknowledgements
The authors express their gratitude to all participants for generously providing their time and samples.
Peer review information
Kevin Pang was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Review history
The review history is available as Additional file 10.
Funding
This research was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (grants R21AI112991 to CH and T32AI007535 to EA), the Chief Scientist, Ministry of Health, Israel (grant 3-00000-5622 to GRY), and the Israel Science Foundation (grants 1590/09 and 1658/15 to GRY). The funding bodies had no role in the design of the study; collection, analysis, and interpretation of the data; writing of the manuscript.
Author information
Authors and Affiliations
Contributions
EKA, EAF, TH, RJC, ML, GRY, and CH designed the research. TH, AMM, HJ, ARM, and CD generated the data. EKA, EAF, and MK analyzed the data. EAF, ML, GRY, and CH provided project oversight. EKA, EAF, ML, GRY, and CH wrote the paper. All authors approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Human patient research was approved by the Sheba Medical Center Institutional Review Board (protocol number: 5534-08-SMC). Each woman gave written informed consent for her and her newborn’s participation, and the other parent gave a non-written approval. Data analysis activities were conducted at the Harvard T.H. Chan School of Public Health and approved by the Harvard T.H. Chan School of Public Health Institutional Review Board (protocol number: IRB13-1664). All experimental methods were in compliance with the Helsinki Declaration.
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1:
All supplementary figures.
Additional file 2:
Table S1. Sample sizes for boxplots.
Additional file 3:
Table S2. Full linear model results for Fig. 3.
Additional file 4:
Summary of metadata for study population and samples collected for S. aureus microbiome profiling.
Additional file 5: Table S4
. Summary of S. aureus variables for study population and samples collected for S. aureus microbiome profiling.
Additional file 6: Table S5
. Sample sizes for random forest models.
Additional file 7: Table S6
. Subject metadata.
Additional file 8: Table S7
. MetaPhlAn2 taxonomic profiles.
Additional file 9: Table S8
. HUMAnN2 functional profiles.
Additional file 10:
Review history.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Accorsi, E.K., Franzosa, E.A., Hsu, T. et al. Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome. Genome Biol 21, 301 (2020). https://doi.org/10.1186/s13059-020-02209-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s13059-020-02209-7