INTRODUCTION
Otitis media (OM) is associated with over 10 million office visits in the United States
each year and is the leading diagnosis for the prescription of antibiotics for children.
OM arises in the complex microbial community of the upper respiratory tract. The bacteria
most often associated with OM are Streptococcus pneumoniae, Haemophilus influenzae,
and Moraxella catarrhalis (1). S. pneumoniae asymptomatically colonizes up to 50%
of children (2–7). Colonization of the upper respiratory tract is the first step in
infection; even transient colonization provides an opportunity for S. pneumoniae to
invade the middle ear space.
Shifts in the composition of microbial communities of the upper respiratory tract
are associated with age, vaccination, antibiotic use, and upper respiratory tract
infection (7–12). Bacterial interactions can impact microbial community composition
as well as disease incidence (13–15). Studies of children’s upper respiratory tract
microbial flora often rely on culture to identify taxa of interest (16–19). In our
bodies, bacterial cells outnumber human cells (20, 21), and a majority of the bacteria
that colonize and infect humans are not routinely cultured or cannot be cultured (22).
Therefore, it is likely that the members of the microbial flora that influence S. pneumoniae
colonization and subsequent OM are not limited to taxa routinely identified by culture.
The use of 16S rRNA full- and partial-gene sequencing provides an alternative to culture-based
methods (23–29). The 16S rRNA gene sequence contains both conserved and hypervariable
regions, which can be used for taxonomic classification (30). Hypervariable regions
V1 and V2 have been shown to provide a high level of resolution in species discrimination,
including identification of the most common OM pathogens (31).
Currently, our knowledge of the nasal microbial flora and its association with OM
is limited. In this cross-sectional study of children aged 6 months to 6 years, we
describe the complex microbial communities present during upper respiratory tract
infection, when children are most susceptible to developing OM (8). We also examine
associations among members of microbial communities, S. pneumoniae colonization, and
OM.
RESULTS
Study population.
Of 108 children, 20 (18.5%) were 6 to 12 months of age, 29 (26.9%) were 12 to 24 months,
19 (17.6%) were 24 to 36 months, and 40 (37.0%) were over 36 months. A total of 60
(55.6%) children were female, 75 (69.4%) were African-American, and 74 (68.5%) lived
in households with other children. Forty-seven (43.5%) of the children were culture
positive for S. pneumoniae. Nearly one-fourth (23.2%) of the children were diagnosed
with OM. S. pneumoniae was isolated by culture in 15 (60%) of the OM cases. Most children
(n = 95; 88.0%) had received the appropriate number of heptavalent pneumococcal conjugate
vaccinations for their age. Logistic regression was used to assess the association
between OM and demographic risk factors, including age, race, and gender. No significant
associations were found, perhaps because all study subjects presented with upper respiratory
tract infection, a major OM risk factor (8).
Sequence diversity.
A mean of 1,087.5 sequences per sample (standard deviation [SD], 496.01) was included
in the analyses (114,397 total sequences). The average sequence length was 234 bp.
The mean Shannon diversity and evenness indices for all samples were 3.0 (SD, 0.83)
and 0.62 (SD, 0.12), respectively. Both indices were significantly lower in S. pneumoniae
culture-positive samples than in culture-negative samples (2.66 versus 3.25, P = 0.0002,
and 0.58 versus 0.66, P = 0.002, respectively). Of 108 children, 7 (6.5%) had used
antibiotics within the past 7 days, 15 (13.9%) within the past 14 days, 17 (15.7%)
within the past 21 days, and 32 (29.6%) within the past 6 months. Separate analyses
of variance (ANOVAs) examined the relationship between the Shannon diversity and evenness
indices, pneumococcal colonization, and antibiotic use and the interaction between
them. Pneumococcal colonization had a significant effect on diversity (F test, P =
0.0002 for Shannon diversity, P = 0.002 for evenness), but antibiotic use (within
the past 7 days, 14 days, 21 days, or 6 months) did not. There was no significant
interaction between pneumococcal colonization and antibiotic use. This suggests that
antibiotic use within the past 6 months does not confound the relationship between
diversity indices and pneumococcal colonization. The Shannon diversity and evenness
indices were not significantly different by OM diagnosis (t test, P = 0.6 and 0.64,
respectively).
Using the Ribosomal Database Project (RDP) Classifier tool, pyrosequencing provided
sufficient information to classify 68.5% of the sequences at the genus and subgenus
levels with 90% confidence, 25.7% at the family and subfamily levels, 1.8% at the
order and suborder levels, 0.6% at the class and subclass levels, and 3.4% at the
phylum level. We identified 389 operational taxonomic units (OTUs). OTU proportions
of ≥1% in the overall population (n = 16) are listed in Table 1. OTU proportions were
compared by S. pneumoniae colonization (Fig. 1A) and by OM diagnosis (Fig. 1B). The
most frequent OTU proportions included potential OM pathogens Streptococcus, Moraxella,
and Haemophilus. In the S. pneumoniae-colonized group, we observed fewer “rare” bacteria
than in the S. pneumoniae-negative group, as evidenced by the difference in the “all
other bacteria” bar, suggesting lower levels of overall diversity in the S. pneumoniae-colonized
group (Fig. 1A). The OTU proportion of potential OM pathogens Streptococcus (23.1%)
and Haemophilus (10.2%) was greater in the OM group than in the OM-absent group (16.3%
and 3.0%, respectively).
FIG 1
Comparison of the most frequent operational taxonomic unit (OTU) distributions grouped
by S. pneumoniae colonization (determined by culture) (A) and by otitis media (OM)
diagnosis (B). Percentages of total sequences per nasal microbial community, i.e.,
per child, that each OTU represented are shown.
TABLE 1
Most frequent nasal swab OTUs
a
OTU
Frequency (%)
b
Unclassified Moraxellaceae
19.00
Streptococcus
17.86
Corynebacterium
7.04
Moraxella
6.46
Haemophilus
4.66
Unclassified Pasteurellaceae
4.09
Staphylococcus
3.84
Acinetobacter
3.44
Dolosigranulum
3.21
Propionibacterium
3.13
Unclassified Proteobacteria
2.59
Lactococcus
2.58
Neisseria
1.45
Actinomyces
1.24
Rothia
1.13
Veillonella
1.05
a
Frequency of ≥1%.
b
Percentage of total sequences per nasal microbial community, i.e., per child.
PCA.
OTUs representing ≥1% of the microbial flora were used for principal component analysis
(PCA), which grouped microbial community members into independent factors. The four
resulting PCA factors (factors A to D) represent linear relationships among correlated
taxa. Factor A includes Corynebacterium, Dolosigranulum, and Streptococcus (negatively
correlated); factor B includes Lactococcus, Propionibacterium, unclassified Proteobacteria,
and Staphylococcus; factor C includes unclassified Pasteurellaceae, Moraxella, and
Haemophilus; and factor D includes Veillonella, Neisseria, Rothia, and Actinomyces.
Logistic regression analysis was used to examine associations between PCA factors
and S. pneumoniae colonization or OM diagnosis. Two of the four factors were significantly
associated with a decreased risk of S. pneumoniae colonization (odds ratio [OR] [95% confidence
interval {CI}]), as follows: factor A (0.55 [0.35, 0.86]) and factor B (0.34 [0.20,
0.59]) (Table 2). These same two factors were also associated with a decreased risk
of OM (OR [95% CI]), as follows: factor A (0.54 [0.30, 0.96]) and factor B (0.44 [0.21,
0.91]) (Table 3). Factors A and B include bacteria that potentially are interfering
members of the normal flora (Tables 2 and 3). Furthermore, the presence of Corynebacterium,
Dolosigranulum, and Lactococcus was negatively correlated with the genus Streptococcus,
with r values of −0.25 (P = 0.01), −0.26 (P = 0.006), and −0.19 (P = 0.05), respectively.
Two other factors were significantly associated with an augmented risk for OM (OR
[95% CI]), as follows: factor C (1.73 [1.10, 2.73]) and factor D (2.24 [1.26, 3.97])
(Table 3). Factor C includes Haemophilus and unclassified Pasteurellaceae, and factor
D includes Veillonella, Rothia, Actinomyces, and Neisseria (Tables 2 and 3).
TABLE 2
Associations between S. pneumoniae colonization determined by culture and factors
obtained by PCA as well as individual taxa within factors
Factor
OR (95% CI)
b
PCA factor
Component
Factor A
0.55 (0.35, 0.86)
Corynebacterium
0.99 (0.95, 1.03)
Dolosigranulum
0.99 (0.91, 1.06)
Streptococcus
a
1.06 (1.03, 1.09)
Factor B
0.34 (0.20, 0.56)
Lactococcus
0.73 (0.61, 0.89)
Propionibacterium
0.89 (0.79, 1.00)
Unclassified Proteobacteria
0.94 (0.88, 1.02)
Staphylococcus
0.98 (0.93, 1.02)
Factor C
0.94 (0.64, 1.39)
Unclassified Pasteurellaceae
1.08 (1.00, 1.16)
Moraxella
a
1.03 (0.99, 1.07)
Haemophilus
1.02 (0.98, 1.05)
Factor D
0.61 (0.37, 1.02)
Veillonella
0.98 (0.83, 1.15)
Neisseria
0.85 (0.70, 1.04)
Rothia
0.82 (0.62, 1.08)
Actinomyces
0.77 (0.58, 1.02)
a
The OTU was negatively correlated with the factor; factor loadings were otherwise
positive.
b
OR and 95% CI values were obtained by separate logistic regression analyses of each
factor and each taxa within each factor.
TABLE 3
Associations between OM diagnosis and factors obtained by PCA as well as individual
taxa within factors
Factor
OR (95% CI)
b
PCA factor
Component
Factor A
0.54 (0.30, 0.96)
Corynebacterium
0.90 (0.81, 0.99)
Dolosigranulum
0.91 (0.80, 1.04)
Streptococcus
a
1.02 (1.00, 1.05)
Factor B
0.44 (0.21, 0.91)
Staphylococcus
0.86 (0.71, 1.06)
Lactococcus
0.92 (0.78, 1.09)
Unclassified Proteobacteria
0.93 (0.83, 1.05)
Propionibacterium
0.97 (0.87, 1.07)
Factor C
1.73 (1.10, 2.73)
Unclassified Pasteurellaceae
1.08 (1.01, 1.15)
Haemophilus
1.04 (1.00, 1.08)
Moraxella
a
0.99 (0.96, 1.06)
Factor D
2.24 (1.26, 3.97)
Veillonella
1.37 (1.07, 1.76)
Rothia
1.35 (1.03, 1.77)
Actinomyces
1.34 (1.07, 1.68)
Neisseria
1.13 (0.99, 1.29)
a
The OTU was negatively correlated with the factor; factor loadings were otherwise
positive.
b
OR and 95% CI values were obtained by separate logistic regression analyses of each
factor and each taxa within each factor.
As factor D taxa are not generally recognized as OM pathogens, we explored additional
associations and found that the OTU proportion of each factor D taxon (Veillonella,
Rothia, Actinomyces, and Neisseria) in a sample was significantly higher in swabs
obtained from children with OM (t test, P = 0.001, 0.02, 0.002, and 0.03, respectively).
In addition, samples obtained from children had significantly higher factor D scores
if antibiotics had been prescribed within the past 21 days or the past 6 months (t test,
P = 0.02 or 0.05, respectively).
DISCUSSION
We describe nasal microbial communities in children with upper respiratory tract infection
with and without concurrent OM. We observed lower microbial diversity when S. pneumoniae
is present. Using principal component analysis to group microbial community members
into factors revealed taxa associated with a decreased risk for S. pneumoniae colonization
and OM. These taxa may be useful for the development of multispecies probiotics for
OM. We also identified taxa associated with an increased risk for OM. Further study
is warranted to define their role in OM pathogenesis.
Vaccine-driven immunological pressures and increasing antibiotic resistance may result
in shifts in the upper respiratory tract microbial community that affect the distribution
and pathogenic potential of OM-associated bacteria. We used complete-linkage clustering
in our diversity calculations through the RDP pyrosequencing pipeline. Average-linkage
clustering may yield more accurate estimates of diversity with regard to identifying
rare microbiota in short amplicon (60-bp) data sets (32, 33). However, Quince et al.
found that diversity inflation was mitigated in data sets of longer sequences (250 bp),
and our sequences are of similar lengths (average, 234 bp) (33).
In culture-positive S. pneumoniae samples, diversity and evenness indices were lower
than those in culture-negative samples. Children with OM have decreased levels of
healthy normal flora bacteria like viridans group streptococci and diphtheroids, while
the OTU proportion of OM pathogens increases by 2- to 3-fold (34). The association
between diversity indices and OM was not significant here. However, all children in
the study were seen for a sick visit, which in this population may have attenuated
the differences in microbial diversity by OM status.
The two factors associated with a reduced risk of OM were dominated by the genera
Corynebacterium, Dolosigranulum, Propionibacterium, Lactococcus, and Staphylococcus.
Together, these two factors (factors A and B) (Tables 2 and 3) identify potential
protective members of the normal flora that may interfere with OM pathogens. Streptococcus
loaded negatively on factor A and is negatively correlated with both Corynebacterium
and Dolosigranulum, suggesting a competitive interaction between this potential OM
pathogen and these two genera. Lemon et al. also observed negative correlations between
members of the normal flora such as the Corynebacteriaceae and Propionibacteriaceae
families and the Staphylococcaceae family, which includes potential OM pathogens,
in nasal samples obtained from healthy adults by PhyloChip analysis (35).
Samples obtained from children with high factor C loading are characterized by OM
diagnosis as well as by the presence of Haemophilus and the absence of Moraxella.
This is consistent with previous literature linking Haemophilus with OM (1). In addition,
H. influenzae has been negatively correlated with M. catarrhalis during upper respiratory
tract infection (36). It is important to note that as sequence data could not classify
at the species level, factor C likely contains other species of Moraxella in addition
to the OM pathogen M. catarrhalis.
Several mechanisms may account for the association between factor D and an increased
risk of OM. Although the bacteria associated with factor D are not normally recognized
as OM pathogens, they may be important to the causal pathway. Higher levels of colonization
by anaerobes such as Actinomyces and Veillonella have been identified during acute
OM in children up to 2 years of age (37). Neisseria has also been isolated from children
with chronic OM (38). In this study, the OTU proportion of Neisseria was significantly
higher in swabs obtained from children with OM. Previous studies show that bacterial
species can impact the colonization and pathogenicity of other species (39, 40). Therefore,
individual species or even specific strains, which cannot cause disease themselves,
can interact in a synergistic fashion when in the presence of other coinfecting species.
Sibley et al. also found a synergistic effect on pathogenicity between chronic pulmonary
infection pathogen Pseudomonas aeruginosa and both Actinomyces and Rothia spp. in
the Drosophila model of polymicrobial infections (41). Factor D taxa (Veillonella,
Neisseria, Rothia, and Actinomyces) may or may not be pathogenic themselves but may
be able to increase the pathogenicity of OM pathogens. Further study is needed to
better elucidate the associations among these taxa, known OM pathogens, and the development
of OM.
Antibiotic resistance may also play a role in the observed association between factor
D taxa and OM. Samples in this study obtained from children who were prescribed antibiotics
had significantly higher factor D scores. Both Neisseria and Veillonella have been
shown to have widespread antibiotic resistance (42–44). Fluoroquinolone resistance
has emerged recently in Neisseria (44), and Veillonella spp. are among the most prevalent
tetracycline-resistant bacteria in the oral cavity (42). The survival of Streptococcus
mutans increases during antibiotic treatment when cocultured with Veillonella parvula
in a biofilm (45). As a result of repeated OM-related courses of antibiotics, children
with a history of recurrent OM certainly have the potential to develop a more resistant
microbial flora.
One limitation of our study is that data on viral pathogens, although important in
OM, were not collected. In addition, as this was a cross-sectional study, we could
not characterize within-subject variability. All children presented with upper respiratory
tract infection symptoms. Additional studies comparing healthy and sick children are
necessary. The Roche/454 Life Sciences pyrosequencing platform is one of several methods
available for high-throughput sequencing and circumvents the need for creating clone
libraries. However, a limitation of the short-read sequencing technology is that it
does not provide sufficient sequence data for the classification of species. Thus,
the majority of sequences could be classified only at the genus level. In addition,
due to potential PCR and sequencing biases, the relationship between the number of
sequences per OTU and the bacteria in the population may not be linear.
The present study is based on nasal sampling, which has the advantage of being easier
and is more comfortable for children than nasopharyngeal sampling (46). Other studies
have used data obtained from nasopharyngeal sampling to characterize pneumococcal
colonization patterns in children (6, 47). However, Rapola et al. have shown that
the rates of OM pathogens S. pneumoniae and H. influenzae isolation do not differ
by site (48). Although regions of the upper respiratory tract harbor distinct microbial
communities, they are physically connected, and thus, a high level of overlap is observed
between sites (35).
Our culture-independent classification of bacteria present in children’s nasal mucosa
during upper respiratory tract infection contributes to the understanding of nasal
microbial communities, development of OM, and polymicrobial interactions. Increased
understanding of complex microbial communities will advance the development of efficacious
prevention and treatment protocols for OM, including probiotic therapies that target
high-risk individuals, which in turn can reduce the risk of permanent damage caused
by OM.
MATERIALS AND METHODS
Study design and participants.
Nasal swabs were collected as part of a cross-sectional study of Philadelphia, PA,
children presenting with upper respiratory tract infection symptoms at one of six
primary care clinics within the Pediatric Research Consortium of the Children’s Hospital
of Philadelphia during the 2008-2009 winter respiratory virus season. Samples obtained
for this study (n = 108) were randomly selected from swabs collected between 9 December
2008 and 2 January 2009 from participants aged 6 months to 6 years (n = 237). Demographic
data collected from each participant through a case report form at the clinic visit
included age, gender, race, ethnicity, and number of children in household. Clinical
data, including diagnosis at visit, comorbidities, vaccination history, and recent
antibiotic use, were obtained from electronic medical record extraction, including
International Classification of Diseases, 9th Edition (ICD-9) codes. Trained research
assistants obtained informed consent for participation in the study during the clinic
visit. The Institutional Review Board (IRB) of the University of Pennsylvania approved
the study protocol.
Nasal swab collection and processing.
Anterior nasal swabs (49) were collected from each child by inserting a rayon-tipped
swab moistened with saline approximately 1 cm into the anterior nares. Swabs were
placed in STGG (skim milk, Oxoid tryptone soya broth, glucose, and glycerol) transport
media until processed (19). Pneumococcal colonization was determined by standard microbiological
methods. DNA was extracted using the QIAamp DNA minikit (Qiagen, Valencia, CA) protocol
for extraction of Gram-positive bacteria from nasal swabs, with one modification:
DNA was eluted into 50 µl.
Roche/454 Life Sciences pyrosequencing of a 16S rRNA gene fragment targeting hypervariable
regions V1 and V2 was used to describe the taxa of bacteria colonizing the nasal mucosa.
16S rRNA gene amplification was performed as described (23). The forward primer sequence
included the Roche/454 Life Sciences adaptor primer B, TC-linker, and the 27F conserved
bacterial 16S rRNA forward primer: 5′ GCCTTGCCAGCCCGCTCAGTCAGAGTTTGATCCTGGCTCAG 3′
(23). The reverse primers carried the Roche/454 Life Sciences adaptor primer A, a
unique eight-base barcode (denoted here as 8 N), CA-linker, and the 338R conserved
bacterial primer: 5′ GCCTCCCTCGCGCCATCAGNNNNNNNNCACTGCTGCCTCCCGTAGGAGT 3′ (23). PCR
reactions were done in triplicate, and products were cleaned by QIAquick PCR purification
(Qiagen). Samples were pooled in equimolar amounts and submitted for pyrosequencing.
Pyrosequencing results analysis.
Amplicons were sequenced at the Yale University Center for Genomics and Proteomics
using the SLR70 Roche/454 Life Sciences pyrosequencing platform. The sequences were
first searched for the linker, primers, and their reverse complements using in-house
programs (http://graphics.med.yale.edu/trim/). Two errors were allowed, and the identified
primer sequences were trimmed from each sequence read. Sequence reads that did not
contain the 5′-end primer were removed from the data set. The same program was also
used for barcode identification. Barcodes were identified within the first 15 bases
of the reads, with one error allowed. Sequence reads were binned into separate FASTA
files based on the individual barcodes, and the barcode sequences were trimmed. Current
methods for chimera detection are designed for longer sequences (~500 bp or longer),
such as ChimeraSlayer and Bellerophon (50, 51; http://microbiomeutil.sourceforge.net/).
In an effort to minimize the number of chimeras, we set strict alignment criteria;
all sequences had to align to at least 100 bp of the 16S rRNA gene, and any sequence
aligning outside the 27 and/or 338 positions of the 16S rRNA gene was discarded.
Sequences were analyzed using tools available from the Ribosomal Database Project
(RDP). Individual sequences were aligned using the Aligner tool. Aligned sequence
files for each child were processed by complete-linkage clustering using distance
criteria. We used a maximum cluster distance cutoff of 3% (97% identity). These data
were used to calculate the Shannon diversity and evenness indices (25, 26, 52, 53).
Shannon’s index measures the degree of nonredundancy within a microbial community,
and the evenness index measures the distribution of species or groups within a sample.
Sequences were classified at 90% by the RDP Classifier tool (52). The RDP website
recommends using a 50% bootstrap value for short pyrosequences to increase the number
of sequences classified (54). Claesson et al. (54) and Wang et al. (52) note that
the proportion of sequences classified correctly increases as the bootstrap cutoff
is increased. Therefore, we selected a 90% cutoff in order to classify the sequences
with greater accuracy. The sequence data could not be classified at the species level;
OTUs (operational taxonomic units) were defined by grouping together all sequences
belonging to the same genus. Sequences that were unclassified at the genus level were
classified and grouped at the next lowest taxonomic level. In total, there were 389
OTUs. We normalized the data by calculating the OTU proportion—the proportion of total
sequences per nasal microbial community, i.e., per child, that each OTU represented.
These data were used for principal component analysis (PCA) and all subsequent analyses.
Data analysis.
Associations among community diversity indices, the presence of S. pneumoniae by culture,
OM diagnosis, and antibiotic use within the past 7 days, 14 days, 21 days, or 6 months
were explored using Student’s t test, Fisher’s exact chi-square test, and analysis
of variance (ANOVA). PCA was used to group microbial community members into four factors
representing linear relationships among selected component taxa (SAS 9.1.3; SAS Institute,
Cary, NC). OTUs that were more frequent than 1% of the microbial community were included
as component taxa (n = 16) in the PCA. An eigenvalue of 1 and an orthogonal rotation
were specified for PCA. PCA factor components had significant loadings of at least
±0.4. Associations among PCA factors, PCA factor scores, and the presence of S. pneumoniae
by culture and OM diagnosis were explored using Student’s t test, correlation, and
logistic regression.