affiliations: · web view2020. 1. 9. · word count main text: 3,983 / 3,500. word count...
TRANSCRIPT
Page 1 of 58
Infant RSV prophylaxis and nasopharyngeal microbiota until six years of life: a sub-analysis of a randomized controlled trial
Wing Ho Mana,b,c, Nienke M. Scheltemaa, Melanie Clerc d, Marlies A. van Houtenb, Elisabeth E.
Nibbelkea, Niek B. Achtena, Kayleigh Arpa, Elisabeth A.M. Sandersa, Louis J. Bonta, Debby Bogaerta,d
Affiliations:a Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children’s
Hospital/University Medical Center Utrecht, Utrecht, The Netherlands;
b Spaarne Gasthuis Academy, Hoofddorp and Haarlem, The Netherlands;
c Department of Pediatrics, Leiden University Medical Center, Leiden, The Netherlands;
d Medical Research Council/University of Edinburgh Centre for Inflammation Research, Queen's
Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom.
Preferred degree (one only):
W H Man MD, N M Scheltema MD, M Clerc PhD, M A van Houten MD, E E Nibbelke MSc, N B
Achten MD, K Arp BASc, Prof E A M Sanders MD, Prof L J Bont MD, Prof D Bogaert MD
Correspondence to:
D. Bogaert, MD, PhD
Medical Research Council/University of Edinburgh Centre for Inflammation Research
Queen's Medical Research Institute, University of Edinburgh
47 Little France Crescent
EH16 4TJ, Edinburgh, United Kingdom
Email: [email protected]
Tel: +44 131 2426582
Author contributions
N.M.S, D.B. and L.J.B. designed the study. N.M.S., E.E.N. and N.B.A. collected data. K.A. was
responsible for the execution and quality control of the laboratory work. W.H.M., M.C., and D.B.
analyzed and interpreted data. W.H.M., M.A. van H., E.A.M.S, L.J.B. and D.B. wrote the paper. All
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Page 2 of 58
authors significantly contributed to interpreting the results, critically revised the manuscript for
important intellectual content, and approved the final manuscript.
Word count main text:
3,983 / 3,500.
Word count abstract:
375 / 250.
2
27
28
29
30
31
32
Page 3 of 58
Summary
Background Respiratory syncytial virus (RSV) infection during infancy is suggested to cause long-
term wheeze. In turn, wheeze has been associated with bacterial dysbiosis of the respiratory tract. We
investigated the effects of RSV prophylaxis by palivizumab during infancy in otherwise healthy
preterms on respiratory microbiota composition at one year and six years of age in participants of the
randomized, placebo-controlled MAKI trial.
Methods 429 infants born between 32-35 weeks of gestation randomly received palivizumab or
placebo during the RSV season of their first year of life. The trial is registered in the ISRCTN registry,
number ISRCTN73641710. In total, 395/429 (92%) children were followed for clinical symptoms
until six years of age. For this sub-analysis, the aim was to assess the impact of palivizumab during
infancy on the respiratory microbiota composition of the available samples at age one and six years.
We obtained nasopharyngeal swabs at age 12 months from 170/429 (40%) children and analyzed
145/170 of these by 16S-rRNA sequencing. At age six, 349 nasopharyngeal swabs were obtained of
which 349/395 (88%) were analyzed by 16S-rRNA sequencing. At age six, also lung function
(including reversible airway obstruction) was determined.
Findings The overall microbiota composition was significantly different (p=0·0185, R2 1.2%)
between the palivizumab and placebo group at 12 months of life, but not significant at 6 years of life
(p=0·0575, R2 0.7%). At 12 months of life, a significant lower abundance of the Staphylococcus-
dominated cluster, and increased abundance of biomarker species such as Klebsiella and a diverse set
of oral taxa including Streptococcus spp. was observed in children who had received palivizumab
early in life, whereas at age six years, a significant increased abundance of Haemophilus spp. and
lower abundance of Moraxella and Neisseriaceae spp. was observed in the prophylaxis group.
Absence of PCR-confirmed RSV infection in the first year of life was also significantly associated
with a higher abundance of Haemophilus spp. at age 6 years and a significantly lower abundance of
Moraxella and Neisseriaceae. Reversible airway obstruction (RAO) at age six was also positively
associated with Haemophilus abundance and negatively associated with the abundance of health-
associated taxa such as Moraxella, Corynebacterium, Dolosigranulum and Staphylococcus, even after
correction for RSV immunoprophylaxis (all: p < 0.05). Additionally, RAO was associated with a
significant increase in Streptococcus pneumoniae abundance.
3
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
Page 4 of 58
Interpretation Palivizumab in infancy in otherwise healthy preterm infants is associated with
persistent effects on the abundance of specific potentially pathogenic microbial taxa in the respiratory
tract. Several of the palivizumab-associated biomarker species were associated with reversible airway
obstruction at age six. Together, our results warrant further studies to shed light on the long-term
ecological effects and health consequences of palivizumab in infancy.
Funding This study was funded by MedImmune (grant ESR-14-10006)
4
62
63
64
65
66
67
68
Page 5 of 58
Research in context
Evidence before this study
Infant respiratory syncytial virus (RSV) infection has been associated with wheeze and asthma in later
childhood, but the pathophysiological mechanism is unclear. We hypothesized that the respiratory
microbiota plays an important role since wheeze has been associated with dysbiosis in the respiratory
tract. We searched PubMed for clinical trials published up to April 8 th, 2018, using the search terms
‘(Child[mh] OR Infant[mh]) AND ("Respiratory Syncytial Viruses"[mh] OR "Respiratory Syncytial
Virus Infections"[mh] OR "Respiratory Syncytial Virus"[tiab] OR palivizumab[mh]) AND
(microbiota[tiab] OR microbiome[tiab] OR “RNA, Ribosomal, 16S”[mh]) NOT Review[pt]’. Of the
eight records retrieved, three observational studies showed that the respiratory microbiota composition
of children during RSV infection is distinct from that of healthy children with especially an
overrepresentation of Haemophilus and Streptococcus spp. in RSV infected children. None of the
studies, however, investigated the impact of early life RSV prophylaxis on the respiratory microbiota
later in life.
Added value of this study
In this single-blind, randomized, placebo-controlled trial, we demonstrate that early life RSV
prophylaxis (palivizumab) in otherwise healthy pre-term infants impacts the respiratory microbiota
composition at age one and six. At age one this was mainly associated with reduced abundance of
Staphylococcus spp., and an overrepresentation of bacteria routinely classified as ‘oral flora’, whereas
at age six this was mainly associated with increased abundance of Haemophilus spp. and reduced
abundance of Moraxella and Neisseriaceae spp. Alternative analysis comparing microbiota following
PCR-confirmed RSV versus no proven RSV showed corroborating results. Furthermore, reversible
airway obstruction at age six, as a marker for asthma, was, among others, positively associated with
Haemophilus and multiple Streptococcus spp. and negatively associated with Moraxella spp.,
Neisseria spp. and the health-associated Corynebacterium and Dolosigranulum spp. independent of
intervention. Together, our findings suggest that palivizumab in early life in otherwise healthy
preterms has long-term ecology-mediated health consequences.
5
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
Page 6 of 58
Implications of all the available evidence
To date, no randomized clinical trial using palivizumab has been conducted to assess the direct link
between RSV infection during infancy in otherwise healthy pre-term infants and subsequent
respiratory microbiota alterations at school age. Our study findings substantially extend the current
knowledge on the relationship between early life RSV infection, and respiratory microbiota
development, with a potential link to asthma in later childhood. Our findings endorse studies to
unravel the mechanistical links between early life viral infections, and potential respiratory
microbiota-mediated effects on asthma. This will not only improve our understanding, but potentially
also inform on the potential benefits and ecological risks of future RSV preventive interventions.
6
96
97
98
99
100
101
102
103
104
Page 7 of 58
Introduction
Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory tract infection
(LRTI) worldwide in children younger than five years and remains a major cause of mortality in
developing countries.1 Severe RSV-related LRTI during infancy has been strongly associated with
asthma inception.2
The pathophysiological mechanism underlying the association between RSV infection and asthma
inception remains to be elucidated, but cumulating evidence alludes to the importance of the
respiratory microbiota as a possible mediator. For example, early life microbial colonization with
Corynebacterium and Dolosigranulum spp. have been associated with lower susceptibility to upper
and lower respiratory infections, wheeze and asthma in infants, toddlers and young children, whereas
early life Haemophilus, Streptococcus, and Moraxella spp. colonization have been associated with the
reciprocal.3–5 We and others demonstrated that these latter bacterial species are also overrepresented
during symptomatic RSV in early life, and that Haemophilus and Streptococcus spp. are associated
with immunomodulation during RSV disease and severity of RSV symptoms.6–9 In addition, a higher
abundance of Haemophilus and Streptococcus spp. appear to be associated with wheezing illness,8,10,11
whereas Moraxella spp. colonization has shown ambiguous associations with health and disease.12,13 In
all, data suggest that susceptibility to and severity of RSV infection is related to the bacterial ecology
in the respiratory tract during the acute stage of disease. We here hypothesize that RSV infection may
also skew the composition of the respiratory tract microbiota in the long term towards profiles
associated with asthma.
We previously reported that RSV prophylaxis by the monoclonal antibody palivizumab (MedImmune,
Gaithersburg, USA) substantially reduced the incidence of RSV infection in the first year of life with
coinciding reduction in recurrent wheeze in otherwise healthy preterm infants (MAKI trial). 14
However, in a recent single, assessor-blind follow-up study of this randomized, placebo-controlled
trial, no major impact of palivizumab on asthma or lung function at six years of age was observed
although there was a decrease in parent-reported asthma symptoms.15
We here investigated nasopharyngeal microbiota at age one and six years of participant of the MAKI
follow-up study to explore the link between palivizumab during infancy and subsequent respiratory
microbiota alterations at school age. We additionally studied the association of respiratory microbiota
7
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
Page 8 of 58
with reversible airway obstruction at age six, to assess how the variations in respiratory microbiota
associated with palivizumab are associated with respiratory health.
Methods
Study design and participants
In the MAKI trial 429 otherwise healthy preterm infants, born between 32 weeks and 1 day, and 35
weeks and 6 days of gestation, were enrolled between 2008 and 2010.14 Infants were younger than six
months of age at the start of the RSV season and were randomly assigned in a 1:1 ratio to receive
either palivizumab or placebo. Randomization was stratified according to gestational age and masking
was secured by an independent pharmacist who had generated a permuted-block randomization list.
To investigate early life viral infections, parents were instructed to take a nasopharyngeal swab in all
instances of upper or lower respiratory tract symptoms lasting more than one day until the first
birthday of their infant.14 These samples were stored for further analyses of RSV and other viral
infections.
The study team had been re-blinded after the first-year analysis. Parents provided separate written
informed consent for their child to participate in the follow-up study. The randomization code was
kept by an independent physician until the six-year follow-up was completed. Parents who had been
unblinded were instructed not to reveal treatment allocation to the researchers at follow-up. Details
about the design, definitions and protocol of the primary study and the follow-up study have been
previously described (ISRCTN73641710).14,15
The protocol was approved by the institutional review board at the University Medical Center Utrecht.
Written informed parental consent was obtained from all participants.
Procedures
At age one year transnasal nasopharyngeal swabs were obtained according to WHO standard
procedures from the last 40% of enrolled participants (figure 1, see also appendix).16 At age 6 years,
transnasal nasopharyngeal swabs were obtained from all participating children and reversible airway
obstruction was assessed, expressed as change in percentage predicted FEV0·5 after administration of
salbutamol, was measured as previously described.15
Bacterial DNA was isolated and quantified from the 170 available samples at age 12 months and all
available samples at age six (figure 1).17 In addition, we included a random subset of (first) respiratory
8
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
Page 9 of 58
infection samples from 81 children obtained in the first year of life. Only samples that fulfilled our
quality control standards, having DNA levels of ≥0·3 pg/µl above negative controls, were included for
further analysis. Amplification of the V4 hypervariable region of the 16S rRNA gene was performed
using barcoded universal primer pair 533F/806R. Amplicons were quantified by PicoGreen
(Thermofisher) and pooled in equimolar amounts. Amplicon pools of samples and controls were
sequenced using the Illumina MiSeq platform (San Diego, CA, USA) and processed in our
bioinformatics pipeline as previously described.5 Analysis included binning of operational taxonomic
units (OTUs) at 97% sequence identity (VSEARCH). The appendix provides detailed information
about each step in our bioinformatic analysis, including the trimming (Sickle), error correction
(BayesHammer), merging (PANDAseq), demultiplexing (QIIME), chimera removal of sequences
(UCHIME), the subsequent picking, annotation, and filtering of OTUs (SILVA), and the identification
and removal of potential contaminants (Decontam). To avoid OTUs with identical annotations, we
refer to OTUs using their taxonomical annotations combined with a rank number based on the
abundance of each given OTU. Sequence reads were submitted to the National Center for
Biotechnology Information Sequence Read Archive (accession number SRP141698).
In addition, identification of Streptococcus pneumoniae was done by qPCR targeting the autolysin
(lytA) gene.18
Assessments
Our predefined primary aim was to assess the impact of palivizumab during infancy on the respiratory
microbiota composition at age one and six years. Predefined secondary aims were assessment of the
impact of proven RSV infection in the first year of life on the respiratory microbiota composition at
age one and six years. Proven RSV infection was defined as having a respiratory infection with a
PCR-detected RSV, regardless of viral codetection and regardless of palivizumab. Additionally, in a
posthoc analysis, we excluded the children in the palivizumabarm who developed an RSV infection in
the first year of life in the comparisons of microbiota across treatment groups, as microbiota profiles
identified in these children may be more related to RSV infection than the lack of effect of
palivizumab. We also cross-sectionally studied the relationship between the respiratory microbiota and
reversible airway obstruction at age six.
9
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
Page 10 of 58
Statistical analysis
Data analysis was performed in R v3·4 within Rstudio v1·0.19 For all assessments, we consecutively
analyzed the effects on overall microbial community structure (beta diversity) and the relationships on
cluster level and individual bacterial taxon level. A p-value of less than 0·05 or a Benjamini-Hochberg
adjusted q-value less than 0·10 was considered statistically significant.
Beta diversity analysis
Nonmetric multidimensional scaling (NMDS) plots were used to visualize differences of total
microbiota communities. Statistical significance between treatment groups was calculated by adonis
(vegan, 1,999 permutations).
Cluster analysis
Unsupervised average linkage hierarchical clustering was performed as described previously.9
Random forests (RF) classifier analyses using VSURF and normalized relative abundance analysis
were performed to determine biomarker species that most discriminate between clusters, as described
previously.20,21 A Chi-square test was used to test for the association between clusters and treatment
groups. A two-sided Wilcoxon rank-sum test was used to test for the association between clusters and
reversible airway obstruction.
Individual bacterial taxon analysis
To identify specific microbial taxa associated with variables of interest, we used either
metagenomeSeq for discrete variables (i.e. palivizumab group) or RF regression analysis for
continuous variables (i.e. reversible airway obstruction). For metagenomeSeq we filtered on the 100
most abundant taxa and used a maximum of 100 iterations.22 We performed sparse RF regression
analysis using a 10-fold cross-validated VSURF procedure. Taxa that were selected at least 20% of the
time during the interpretation step, were deemed important. Variable importance was assessed by 100
RF iterations generating 10,000 trees and the association of these variables with reversible airway
obstruction was crudely estimated post-hoc using Pearson’s correlation.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation,
or writing of the report. WHM and DB had full access to all data in the study and the corresponding
author had final responsibility for the decision to submit for publication.
10
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
Page 11 of 58
11
220
Page 12 of 58
Results
Characterization of nasopharyngeal microbiota
429 otherwise healthy preterm infants were recruited to the original study between April and
December of each year between 2008 and 2010, and assigned to receive either palivizumab (n=214) or
placebo (n=215). Nasopharyngeal samples were obtained of 170/429 (the last 40%) of participants of
the original study at age one year, of which 145/170 (85%) generated sufficient and high-quality
material for further analyses (figure 1). From the 395 participants who had agreed with follow-up until
6 years of life, a nasopharyngeal sample was obtained from 349 children (88%); 342/349 (98%) of
these samples generated sufficient and high-quality material for further analyses (baseline
characteristics in table 1).
In addition, a random subset of first respiratory infection samples from 81 children obtained by the
parents during the first year of life (median [IQR] age 6·3 [4·1-8·3] months), i.e. during palivizumab
prophylaxis, were processed, from which 66/81 (81%) generated sufficient and high-quality material
for further analyses. Data regarding the nasopharyngeal microbiota composition of these samples,
including the viral detection in the first year of life, are detailed in the appendix.
Microbiota characterization across the three sample sets showed a strong age-related development of
respiratory microbiota over time, with initially a predominance of Enterococcus, Chryseobacterium,
Rothia, Brevundimonas, and oral Streptococci in the first months of life, via emerging abundance of
Staphylococcus, Moraxella, Klebsiella, Serratia and Enterococcus spp. at age 12 months, towards
predominance of Haemophilus, Moraxella, S. pyogenes, Corynebacterium and Dolosigranulum at 6
years (n=118 paired samples, see supplemental data in appendix).
Palivizumab treatment during infancy is associated with the microbiota composition at age one
At 12 months of life, the microbiota profile shows high interindividual variation, and is represented by
similar clusters such as Staphylococcus, Klebsiella, Moraxella, S. pneumoniae & Rothia, and
emergence of clusters dominated by Chryseobacterium, Brevundimonas and Enterococcus spp.
(supplementary figure 1b). Interestingly, we observed a significant difference in overall microbial
community structure between the palivizumab and the placebo group (R2=1·3%, p=0·0185; figure
2a). On cluster level, the Staphylococcus-dominated profile was positively associated with the placebo
group (chi-square, p=0·00394; OR 0·28, 95% CI 0·11-0·68), with a posteriori plotting of all
12
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
Page 13 of 58
biomarker species in the NMDS ordination further underlining the validity of this association (figure
2a). On individual taxon level, we confirmed that, although not statistically significant,
Staphylococcus was more abundant in the placebo group compared to palivizumab (metagenomeSeq,
log2 fold change 1·4, q=0·105, figure 3a). We further observed a significantly higher abundance of a
range of gram negative environmental and oral bacteria in the palivizumab group including
Chryseobacterium, Sphingobacterium, Ochrobacterium and Brevundimonas spp. ranging between 3
and 8 Log2 fold (8 to 256 fold) higher abundances compared to the control group, and some more
modest effects on several other gram-negative spp. including Klebsiella and gram positive bacteria
like Dolosigranulum pigrum, Lactobacillus spp., Streptococcus spp. whereas Leuconostoc, a gram
positive lactic acid producing bacterium, was overrepresented in the placebo group.
The effect of palivizumab on the microbiota composition persists up to age six years
When evaluated at six years of age, we still observed a small though non-significant difference in
overall microbial community structure between the otherwise healthy preterm infants who were
treated with palivizumab and those who received placebo (R2=0·6%, p=0·0575; figure 2b).
Especially, at age six years the Haemophilus-dominated profile was strongly associated with the
palivizumab group (chi-square, p=0·02960; OR 1·88, 95% CI 1·06-3·33). On individual bacterial
taxon level, again the abundance of Haemophilus spp. as well as S. pyogenes were positively
associated with the palivizumab group with effect sizes ranging from 0·4-1·7 log2 fold (=1·3-3·2 fold;
figure 3b), whereas Moraxella, Corynebacterium and Neisseriaceae spp. were negatively associated
with palivizumab in the first year of life. A posteriori plotting of all biomarker species in the NMDS
ordination further supports the validity of the above associations (figure 2b).
Since palivizumab is not 100% effective in protecting against RSV infections (relative risk reduction
67%)14, several samples in the analyses at age one and 6 years of life were from children with a proven
RSV infection despite having received palivizumab. To rule out a potential confounding effect of
these ‘therapy failures’, we repeated above analyses, excluding the samples from children with proven
RSV infections from the palivizumab arm. This posthoc analyses had no effect on the result at age one
(R2=1·3%, p=0·0285; appendix) but even slightly increased the effect size at age 6 years (R 2 0·7%),
making the difference between randomization groups significant (p=0·0425; appendix). These results
suggest a true effect of RSV infection (and the reversed, i.e. prevention thereof) on microbiota
development.
13
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
Page 14 of 58
Finally, we also tested the effect of palivizumab on stability of microbiota development over time, and
observed significantly less stable microbiota development (i.e. higher Bray-Curtis distance between
consecutive samples) in children who had received palivizumab compared to the placebo group (for
details: see supplemental information appendix).
Infant RSV infection has similar effects compared to placebo treatment
To further understand whether above findings could be directly attributed to (prevention of) RSV
infections themselves or by an indirect ecological effect of palivizumab, we compared the overall
microbiota composition between infants with RSV infection who received palivizumab versus infants
with RSV infection who received placebo: acknowledging the limited power of these analyses, we
found no difference at year one (adonis, n=14, 5/14 had received palivizumab and 9/14 had received
placebo, p=0·3615) nor at year 6 (adonis, n=34, 8/34 had received palivizumab and 26/34 placebo,
p=0·6615) between those children. We also performed a posthoc stratified analysis comparing the
microbiota of cases with PCR-confirmed versus cases with no proven RSV infections: At 12 months
of age, we had samples from 14 children with a history of a PCR-confirmed infection and 132 children
with no history of a proven RSV infection. Acknowledging the limited power of this analysis, we
found no significant difference in overall microbial community structure between children 12 months
of age with and without a history of RSV infection (R2=0·5%, p=0·732; supplementary figure 2a).
At six years of life, we had 34 samples of children with a history of a proven RSV infection, whereas
308 children had no history of a proven RSV infection in the first year of life. We did not find a
significant difference in overall microbial community structure between children at age six with or
without a history of proven RSV infection (R2=0·5%, p=0·082), though the trend in microbiota
deviation in the children without a proven RSV infection was oriented towards a more Haemophilus-
dominated community (supplementary figure 2b). On species level, absence of PCR-confirmed RSV
infection in the first year of life was significantly associated with a higher abundance of Haemophilus
spp. at age 6 years and a significantly lower abundance of Moraxella and Neisseriaceae spp.
(metagenomeSeq, q<0·10, supplementary figure 3), in line with the effect of palivizumab.
Microbiota variation in relation to reversible airway obstruction
Next, we tested the cross-sectional associations between microbiota composition at age six and
reversible airway obstruction at age six. Reversible airway obstruction was significantly associated
with overall respiratory microbial composition (adonis, R2=0·8%, p=0·0335). On individual taxon
14
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
Page 15 of 58
level, cross-validated random forests analysis confirmed the negative association between M.
catarrhalis/nonliquefaciens, Corynebacterium propinquum, Dolosigranulum pigrum, Kocuria,
Granulicatella, Pantoea agglomerans, and Roseomonas with reversible airway obstruction. In
contrast, several Haemophilus spp. were positively related with reversible airway obstruction, as well
as S. pneumoniae, Cupriavidus and a low-abundant Corynebacterium (figure 4). Correcting for the
use of palivizumab in the first year of life did not change the results.
15
311
312
313
314
315
316
Page 16 of 58
Discussion
In this single, assessor-blind, randomized, placebo-controlled trial, we demonstrate that palivizumab
during infancy in otherwise healthy preterms is linked with significant changes in respiratory
microbiota composition at one year and six years following the intervention.
Surprisingly, the nasopharyngeal microbial community at year one was still dominated by
Staphylococcus, a profile that has been associated extensively with a respiratory community of
younger infants.3,5,12,23 Only at 6 years of life we observed the ‘classical’ distribution of microbial
profiles as previously observed in younger children 1-2 years of life, including Moraxella,
Streptococcus, Haemophilus and Corynebacterium plus or minus Dolosigranulum-dominated profiles.
In contrast to the children in the previous studies however, we studied preterm born children. A recent
study also demonstrated that the maturation of the gut microbiota of preterm born infants lags behind
that of full term born infants and has not caught up yet at four years of age. 24 This might explain our
findings for the respiratory microbiota as well, and might also in part influence the observed
microbiota differences between the palivizumab and placebo arms of this study.
In children that had received palivizumab in their first year of life, Staphylococcus was less present
and abundant at age 12 months, and their microbial community was less stable over time, suggesting
that palivizumab might accelerate the maturation of the nasopharyngeal microbiota. Whether this is
caused by the prevention of RSV infection or may be related to infections by other viruses remains to
be elucidated. Also, it is unclear whether this faster maturation is beneficial to respiratory health or not
but a previous study suggested that an expedited maturation of the respiratory microbiota early in life
is related with increased susceptibility to respiratory disease later on.5
At six years, we find a higher abundance of Haemophilus spp. and a lower abundance of Moraxella
spp. in children who received palivizumab compared to those who received placebo. Similar effects
were found when comparing children without versus with PCR-confirmed RSV infection in the first
year of life, which is in line with the previous findings that accelerated microbiota maturation, and
consequently reduced stability is associated with increased abundance of non-typeable H.
influenzae.5,12
Although our study design does not allow us to fully unravel the underlying mechanisms, our data
suggest that RSV infection in otherwise healthy preterm infants may have long-term beneficial
ecological effects with reduction of Haemophilus spp. This effect could for example be mediated by
16
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
Page 17 of 58
the induction of local antiviral responses in the airway epithelium25 or by inducing an adaptive
immune response to the co-colonizing bacterial potential pathogens at time of RSV infection.
Reversible airway obstruction, a core measure for characterizing asthma,26 was positively associated
with microbiota community composition, with palivizumab-associated microbes like Haemophilus
spp. positively and Moraxella and Neisseria spp. and health-associated Corynebacterium and
Dolosigranulum spp. being negatively correlated with reversible airway obstruction. Additionally, we
demonstrated that reversible airway obstruction was positively associated with bacterial species that
were previously reported to be associated with asthma, i.e. Streptococcus spp., including S.
pneumoniae, and gram-negative oral bacteria,11,27 and negatively correlated with presumed
commensals of the nasopharyngeal niche, i.e. Corynebacterium, Dolosigranulum and
Staphylococcus.28,29 In all, we previously showed that palivizumab affects the spectrum of viral
infections in infancy, and prevents wheezing in early life. However, on the long term these effects
seem to diminish, which may in part be explained by long-term ecological effects, including
enrichment of more pro-inflammatory bacterial species like Haemophilus and a reduction in potential
beneficial species.
Several limitations of our study should be recognized. First, our cohort of children that were treated
with palivizumab still contained several children (n=8) that had a symptomatic RSV infection in the
first year of life.14 In addition, we probably underestimated the true incidence of RSV infections in our
analyses comparing children with and without PCR-confirmed RSV infections, because these were
based on voluntary parental swab collection.14 Both phenomena may likely have led to an
underestimation of the true impact of proven RSV infection in infancy on respiratory microbiota later
in life, especially with regard to mild RSV infections. The fact that we find very similar results when
comparing children with and without palivizumab, with children without and with PCR-confirmed
RSV infection, however, supports the validity of our results; i.e. at age one, Staphylococcus is
overrepresented in both the placebo group and the children with proven RSV infection, and at age six,
Haemophilus is overrepresented and Moraxella is underrepresented in both the palivizumab group and
the children without proven RSV infection. Second, our study was primarily designed to study the
effects of early life RSV infection, but not of other viral infections on respiratory microbiota
composition at age six, whereas our results now indicate it might be important to study the potential
impact of all viral infections early in life on the respiratory ecosystem. Our data suggests that the
impact of RSV immunoprophylaxis on long-term respiratory microbiota composition is at least in part
17
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
Page 18 of 58
due to prevention of RSV infection since absence of PCR-confirmed RSV infection in the first year of
life was associated with individual bacterial taxa in a similar way to having received palivizumab. It is,
however, possible that these associations are mediated by other non-RSV viral infections, which as
reported previously, are increased observed in children receiving RSV prophylxis.30,31 Third, we lacked
power to analyze the effect of age and timing of viral infections, which might be extremely important
for long-term ecological and health effects. Fourth, our study only included three sampling moments
of respiratory microbiota, whereas we and others have demonstrated the value of frequent sampling in
studying long-term respiratory health and disease.3,4,28 Even though potential differences in baseline
microbiota between randomization groups cannot be ruled out, the fact that microbiota composition
during the first infections in early life were highly similar between groups, baseline differences
become highly unlikely. Fifth, our study sample was drawn from preterm born children between 32
and 35 weeks’ gestational age and is therefore not representative of the general population. A
comparator with term born babies might therefore be warranted for future studies. Sixth, it is highly
likely that all children, including those who received palivizumab, were infected with RSV at some
point beyond the first year of life. Our study design cannot ascertain this effect on the microbiota
composition at age six. It is, however, presumed that changes in respiratory microbiota composition in
early life -during the so-called “window of opportunity”- have more impact on respiratory health later
in life than microbiota changes later in life.28 Finally, 16S-rRNA sequencing can only examine the
bacterial microbiota, but not the viral or fungal microbiota, while an increasing body of evidence
suggests the importance of the respiratory virome and mycobiome in respiratory health and disease.28
This should be taking into consideration with new studies.
Nevertheless, our results suggest that albeit generating a major direct health benefit by prevention of
RSV infections, in early life, in this cohort of otherwise healthy preterm infants palivizumab seems to
affect the respiratory microbiota composition at age one and age six. At age six, palivizumab is
accompanied by the potentially unfavorable overrepresentation of Haemophilus spp. that, in turn, are
independent from the intervention, associated with reversible airway obstruction in our cohort. At the
least, our study findings suggest that viral infections in early life have an important role in shaping the
respiratory ecosystem long-term, possibly as a result of immune modulation during the essential phase
of early life immune maturation.32 These data may nuance the discussion regarding the effects of
universal prevention of RSV infection33, and provide a premise for further studies on early life
interactions between respiratory viruses, microbiota, and the host immune system, and their potential
18
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
Page 19 of 58
long-term consequences on the human ecosystem as well as asthma development. This is of critical
importance, especially in our population of otherwise healthy preterm children, as asthma is still one
of the leading and increasing causes of substantial disability in this group of children.
19
409
410
411
Page 20 of 58
Acknowledgements
The authors are indebted to all the participating children and their families for their commitment and
participation. We thank all members of the MAKI research team, including the staff of the pediatric
lung function laboratory, and the laboratory staff. We are grateful to Hicham el Madkouri for his
primary exploration of the data. This work was supported in part by the Netherlands Organisation for
Scientific Research (NWO-VIDI; grant 91715359) and CSO/NRS Scottish Senior Clinical Fellowship
award (SCAF/16/03).
Declaration of interests
E.A.M.S. declares to have received unrestricted research support from Pfizer, grant support for
vaccine studies from Pfizer and GSK. L.J.B. reports grants from AbbVie during the conduct of the
study and grants from MedImmune, Janssen, MeMed, and the Bill & Melinda Gates Foundation. D.B.
declares to have received unrestricted fees paid to the institution for advisory work for Friesland
Campina and well as research support from Nutricia. No other authors reported financial disclosures.
None of the other authors report competing interests.
20
412
413
414
415
416
417
418
419
420
421
422
423
424
425
Page 21 of 58
References
1 Shi T, McAllister DA, O’Brien KL, et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. Lancet 2017; 390: 946–58.
2 Feldman AS, He Y, Moore ML, Hershenson MB, Hartert T V. Toward Primary Prevention of Asthma. Reviewing the Evidence for Early-Life Respiratory Viral Infections as Modifiable Risk Factors to Prevent Childhood Asthma. Am J Respir Crit Care Med 2015; 191: 34–44.
3 Teo SM, Mok D, Pham K, et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe 2015; 17: 704–15.
4 Bisgaard H, Hermansen MN, Buchvald F, et al. Childhood Asthma after Bacterial Colonization of the Airway in Neonates. N Engl J Med 2007; 357: 1487–95.
5 Bosch AATM, Piters WAA de S, van Houten MA, et al. Maturation of the Infant Respiratory Microbiota, Environmental Drivers, and Health Consequences: A Prospective Cohort Study. Am J Respir Crit Care Med 2017; 196: 1582–90.
6 Hyde ER, Petrosino JF, Piedra PA, Camargo CA, Espinola JA, Mansbach JM. Nasopharyngeal Proteobacteria are associated with viral etiology and acute wheezing in children with severe bronchiolitis. J Allergy Clin Immunol 2014; 133: 1220–2.
7 Mansbach JM, Hasegawa K, Henke DM, et al. Respiratory syncytial virus and rhinovirus severe bronchiolitis are associated with distinct nasopharyngeal microbiota. J Allergy Clin Immunol 2016; 137: 1909–1913.e4.
8 Rosas-Salazar C, Shilts MH, Tovchigrechko A, et al. Nasopharyngeal microbiome in respiratory syncytial virus resembles profile associated with increased childhood asthma risk. Am. J. Respir. Crit. Care Med. 2016; 193: 1180–3.
9 De Steenhuijsen Piters WAA, Heinonen S, Hasrat R, et al. Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. Am J Respir Crit Care Med 2016; 194: 1104–15.
10 Huang YJ, Boushey HA. The microbiome in asthma. J Allergy Clin Immunol 2015; 135: 25–30.
11 Lynch JP, Sikder MAA, Curren BF, et al. The influence of the microbiome on early-life severe viral lower respiratory infections and asthma-Food for thought? Front. Immunol. 2017; 8: 156.
12 Biesbroek G, Tsivtsivadze E, Sanders EAM, et al. Early Respiratory Microbiota Composition Determines Bacterial Succession Patterns and Respiratory Health in Children. Am J Respir Crit Care Med 2014; 190: 1283–92.
13 Sze M a., Dimitriu P a., Hayashi S, et al. The lung tissue microbiome in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012; 185: 1073–80.
14 Blanken MO, Rovers MM, Molenaar JM, et al. Respiratory Syncytial Virus and Recurrent Wheeze in Healthy Preterm Infants. N Engl J Med 2013; 368: 1791–9.
15 Scheltema NM, Nibbelke EE, Pouw J, et al. Respiratory syncytial virus prevention and asthma in healthy preterm infants: a randomised controlled trial. Lancet Respir Med 2018; 6: 257–64.
16 O’Brien KL, Nohynek H, World Health Organization Pneumococcal Vaccine Trials Carriage Working Group. Report from a WHO Working Group: standard method for detecting upper respiratory carriage of Streptococcus pneumoniae. Pediatr Infect Dis J 2003; 22: e1-11.
17 Prevaes SMPJ, de Winter-de Groot KM, Janssens HM, et al. Development of the Nasopharyngeal Microbiota in Infants with Cystic Fibrosis. Am J Respir Crit Care Med 2016; 193: 504–15.
18 Carvalho M da GS, Tondella ML, McCaustland K, et al. Evaluation and improvement of real-time PCR assays targeting lytA, ply, and psaA genes for detection of pneumococcal DNA. J Clin Microbiol 2007; 45: 2460–6.
19 Team RC. R: A Language and Environment for Statistical Computing. 2015. https://www.r-project.org/ (accessed April 8, 2017).
21
426
427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476
Page 22 of 58
20 Man WH, Clerc M, de Steenhuijsen Piters WAA, et al. Loss of Microbial Topography between Oral and Nasopharyngeal Microbiota and Development of Respiratory Infections Early in Life. Am J Respir Crit Care Med 2019; 200: 760–70.
21 Man WH, van Houten MA, Mérelle ME, et al. Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study. Lancet Respir Med 2019; 7: 417–26.
22 Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods 2013; 10: 1200–2.
23 Salter SJ, Turner C, Watthanaworawit W, et al. A longitudinal study of the infant nasopharyngeal microbiota: The effects of age, illness and antibiotic use in a cohort of South East Asian children. PLoS Negl Trop Dis 2017; 11: e0005975.
24 Fouhy F, Watkins C, Hill CJ, et al. Perinatal factors affect the gut microbiota up to four years after birth. Nat Commun 2019; 10: 1517.
25 Chen Y, Hamati E, Lee P-K, et al. Rhinovirus induces airway epithelial gene expression through double-stranded RNA and IFN-dependent pathways. Am J Respir Cell Mol Biol 2006; 34: 192–203.
26 Tepper RS, Wise RS, Covar R, et al. Asthma outcomes: pulmonary physiology. J Allergy Clin Immunol 2012; 129: S65-87.
27 Huang YJ, Nelson CE, Brodie EL, et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J Allergy Clin Immunol 2011; 127: 372-381.e1-3.
28 Man WH, de Steenhuijsen Piters WAA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol 2017; 15: 259–70.
29 Liu G, Tang CM, Exley RM. Non-pathogenic Neisseria: members of an abundant, multi-habitat, diverse genus. Microbiology 2015; 161: 1297–312.
30 Achten NB, Wu P, Bont L, et al. Interference Between Respiratory Syncytial Virus and Human Rhinovirus Infection in Infancy. J Infect Dis 2017; 215: 1102–6.
31 Blanken MO, Korsten K, Achten NB, et al. Population-Attributable Risk of Risk Factors for Recurrent Wheezing in Moderate Preterm Infants During the First Year of Life. Paediatr Perinat Epidemiol 2016; 30: 376–85.
32 Lambrecht BN, Hammad H. The immunology of the allergy epidemic and the hygiene hypothesis. Nat Immunol 2017; 18: 1076–83.
33 Hayden FG, Herrington DT, Coats TL, et al. Efficacy and Safety of Oral Pleconaril for Treatment of Colds Due to Picornaviruses in Adults: Results of 2 Double-Blind, Randomized, Placebo-Controlled Trials. Clin Infect Dis 2003; 36: 1523–32.
34 Clarke KR. Non‐parametric multivariate analyses of changes in community structure. Aust J Ecol 1993; 18: 117–43.
35 Joshi N, Fass J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. 2011. https://github.com/najoshi/sickle (accessed Dec 31, 2018).
36 Nikolenko SI, Korobeynikov AI, Alekseyev MA. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 2013; 14: S7.
37 Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 2012; 13: 31.
38 Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7: 335–6.
39 Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011; 27: 2194–200.
40 Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016; 4: e2584.
41 Westcott SL, Schloss PD. De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units. PeerJ 2015; 3: e1487.
42 Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2012; 41: D590–6.
43 Subramanian S, Huq S, Yatsunenko T, et al. Persistent gut microbiota immaturity in
22
477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530
Page 23 of 58
malnourished Bangladeshi children. Nature 2014; 510: 417.
Tables and figures
Table 1. Baseline characteristics
Demographic characteristics of the children analyzed on year 1 (A) and year 6 (B). Baseline characteristics at randomization have been published and are available at NEJM.org.14
A
Placebo Palivizumab
n 66 79
Male (%) 34 (51·5) 51 (64·6)
Height in cm (median [IQR]) 116 [111, 119] 116 [113, 120]
Weight in kg (median [IQR]) 20·1 [18·6, 21·7] 20·4 [18·8, 21·8]
Any wheezing year 1 (%) 30 (46·9) 25 (31·6)
Recurrent wheezing year 1(%) 11 (16·7) 9 (11·4)
Pets (%) 31 (49·2) 35 (44·3)
Breastfeeding (%) 45 (75·0) 48 (67·6)
Maternal smoking during pregnancy (%) 10 (16·1) 16 (20·8)
Parental atopy (%) 35 (54·7) 53 (69·7)
Atopy Mother (%) 24 (38·1) 34 (43·0)
Asthma Mother (%) 12 (18·8) 9 (11·4)
Hay fever Mother (%) 15 (23·8) 19 (24·4)
Eczema Mother (%) 12 (18·8) 18 (22·8)
23
531532
533
534
535
536537
538
Page 24 of 58
Atopy Father (%) 18 (29·0) 31 (41·3)
Asthma Father (%) 6 ( 9·7) 10 (13·0)
Hay fever Father (%) 9 (14·5) 20 (26·0)
Eczema Father (%) 6 ( 9·5) 11 (14·7)
Parental smoking (%) 26 (39·4) 31 (39·2)
Smoking Mother (%) 11 (18·0) 13 (16·5)
Smoking Father (%) 18 (28·6) 26 (34·7)
24
539
540
Page 25 of 58
B
Placebo Palivizumab
n 166 176
Male (%) 81 (48·8) 109 (61·9)
Height in cm (median [IQR]) 116 [113, 119] 116 [113, 120]
Weight in kg (median [IQR]) 20·4 [18·9, 22·3] 20·9 [19·1, 22·6]
Any wheezing year 1 (%) 85 (51·5) 61 (35·1)
Recurrent wheezing year 1(%) 40 (24·2) 23 (13·1)
Pets (%) 79 (48·8) 80 (45·5)
Breastfeeding (%) 121 (74·7) 117 (72·2)
Maternal smoking during pregnancy (%) 23 (14·6) 24 (14·0)
Parental atopy (%) 98 (60·5) 110 (63·6)
Atopy Mother (%) 55 (34·2) 73 (41·5)
Asthma Mother (%) 17 (10·4) 18 (10·2)
Hay fever Mother (%) 33 (20·5) 46 (26·1)
Eczema Mother (%) 25 (15·3) 40 (22·7)
Atopy Father (%) 62 (38·5) 65 (37·8)
Asthma Father (%) 17 (10·7) 25 (14·4)
Hay fever Father (%) 41 (25·3) 37 (21·3)
Eczema Father (%) 19 (11·7) 26 (15·1)
Parental smoking (%) 57 (34·3) 61 (34·7)
Smoking Mother (%) 23 (14·6) 24 (13·8)
25
541
Page 26 of 58
Smoking Father (%) 43 (26·9) 47 (27·8)
26
542
12 months NP swab
Availablen = 170
Participantsprimary study
n = 429
Participantsfollow-up study
n = 395
6 yearsNP swab
Availablen = 349
High quality DNAn = 145
High quality DNAn = 342
Palivizumabn = 79
Placebon = 66
Palivizumabn = 176
Placebon = 166
Primary analysis
Secondary analysis
16S sequencingn = 170
16S sequencingn = 349
Palivizumabn = 74#
Placebon = 66
No RSVn = 132
Proven RSV
n = 14
Palivizumabn = 168#
Placebon = 166
No RSVn = 342
Proven RSV
n = 34
Combinedn = 274
6 yearsSpirometry
Availablen = 290
Palivizumabn = 137
Placebon = 137
Page 27 of 58
Figure 1. Flow diagram for samples analyzed.
NP = nasopharynx. # We excluded 5/79 and 8/176 children from the palivizumab group in our posthoc
analyses of the children at age 12 months and 6 years, respectively, as they developed an RSV
infection during their first year of life. Including these samples yielded similar results (appendix).
27
543
544
545
546
547
548
Page 28 of 58
Figure 2. Microbiota profiles at age 12 months and 6 years.
(A) Three-dimensional NMDS plots depicting the individual nasopharyngeal microbiota composition
at year one (data points, n=145) colored by treatment group: placebo (brown, n=66) and palivizumab
(dark green, n=79). The difference in total microbiota composition in both groups is significant
(adonis, R2=1·3%, p=0·0185).
(B) Three-dimensional NMDS plots depicting the individual nasopharyngeal microbiota composition
at year 6 (data points, n=342) colored by treatment group: placebo (brown, n=166) and palivizumab
(dark green, n=176). The difference in total microbiota composition in both groups is smaller
compared to 12 months (R2 0·6% versus 1·3%), though still not significant (adonis, R2=0·6%,
p=0·0575). Ellipses represent the standard deviation of all points within a cohort. The stress-value
using the first two dimensions was 0·25, whereas this dropped to 0·18 when using three dimensions.
Because a stress of <0·2 indicates a reasonable interpretability,34 we decided to depicts the samples
across these three dimensions (NMDS1-NMDS3). The figures also depict the biomarkers species
(determined by random forests analysis on hierarchical clustering results) colored by phylum (Green
diamonds = Proteobacteria, orange triangles = Firmicutes, purple squares = Actinobacteria, pink
circles = Bacteroides). To avoid OTUs with identical annotations, we refer to OTUs using their
taxonomical annotations combined with a rank number based on the abundance of each given OTU.
28
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
Page 29 of 58
A
B
29
567
Page 30 of 58
Figure 3. Differential abundance of bacterial taxa between treatment groups.
Effect sizes are depicted for the 20 most differentially abundant taxa in either the palivizumab group (right) or placebo group (left). Log2 fold changes (including 95% confidence intervals) were obtained by metagenomeSeq analysis and corrected for multiple comparisons (Benjamini-Hochberg). (A) depicts the results for samples (n = 145) obtained at 12 months of life, whereas (B) depicts the results for samples obtained 6 years of life (n = 342). To avoid OTUs with identical annotations, we refer to OTUs using their taxonomical annotations combined with a rank number based on the abundance of each given OTU.
A
B
30
568
569570571572573574575
Page 31 of 58
31
576577
Page 32 of 58
Figure 4. Sparse random forests models associating microbial species with reversible airway obstruction.
Twelve taxa were associated with reversible airway obstruction as selected by a cross-validated
random forests analysis using all samples (n=274). Taxa are ranked in descending order based on their
importance to the accuracy of the model. Variable importance was estimated by calculating the mean
increase in node purity after randomly permuting the values of each given variable (mean ± standard
deviation, 100 replicates). A higher value increase in node purity represents a higher variable
importance. The direction of the associations was estimated post-hoc using Pearson’s correlation (red
= positive association reversibility; blue = negative association with reversibility). To avoid OTUs
with identical annotations, we refer to OTUs using their taxonomical annotations combined with a
rank number based on the abundance of each given OTU. Whether children had received RSV
prophylaxis versus placebo in the first year is not significantly contributing to the model (gray).
32
578579
580
581
582
583
584
585
586
587
588
589
590
Page 33 of 58
Supplementary appendix
Sampling at 12 months
Only during the last year of recruitment, we sampled children at 12 months. We saw no differences in
baseline characteristics between these last 40% children and the first 60% children of which we did
not have a 12-month sample (p>0·20 for all characteristics).
Additional methods for bioinformatics analysis
Raw sequences were trimmed using an adaptive, window-based trimming algorithm (Sickle, Q>20,
length threshold of 150 nucleotides).35 We aimed to further reduce the number of sequence errors in
the reads by applying an error correction algorithm (BayesHammer, SPAdes genome assembler
toolkit).36 Forward and reverse reads were then assembled into contigs using PANDAseq.37 Merged
reads were demultiplexed using QIIME (v1·9).38 After removal of singleton sequences, we removed
chimeras using both de novo and reference (against Gold database) chimera identification (UCHIME
algorithm in VSEARCH).39,40 VSEARCH abundance-based greedy clustering was used to pick OTUs
at a 97% identity threshold.41 Taxonomic annotation was executed using the RDP-II naïve Bayesian
classifier on SILVA v119 training set.42 Taxonomic assignment was validated by blasting against the
NCBI database, using a 100% identity cut-off. We generated an abundance-filtered dataset by
including only those OTUs that were present at or above a confident level of detection (0·1% relative
abundance) in at least two samples.43 In addition, to ensure our data was of the highest quality, we
identified and removed 58 potential contaminants using the Decontam R-package, as previously
described (supplementary figure 4).21 Also, we only kept samples that contained at least 9,000 reads.
To avoid OTUs with identical annotations, we refer to OTUs using their taxonomical annotations
combined with a rank number based on the abundance of each given OTU. In case when an OTU
could not be confidently annotated as either of two species, both species are indicated and separated
by a solidus. The raw OTU-counts table was used for calculations of alpha-diversity (local diversity)
and analyses using the metagenomeSeq package.22 The OTU-proportions table was used for all other
downstream analyses, including hierarchical clustering and random forests modelling. Beta-diversity
was assessed using the Bray-Curtis dissimilarity metric.
Sequence reads were submitted to the National Center for Biotechnology Information Sequence Read
Archive (accession number SRP141698).
33
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
Page 34 of 58
Quality control of 16S-rRNA gene amplicon sequencing
In total, 24 DNA isolation and PCR blanks were sequenced along with the study samples. All blanks
yielded <4,500 reads and the number of reads was more than one order of magnitude lower compared
to that of the samples (median 551 vs 37,529 reads; supplementary figure 5a). Hierarchical
clustering also clearly separated the blanks from samples (supplementary figure 5b). These results
robustly indicate that our strict sequencing protocol resulted in no apparent contamination.
Characterization of nasopharyngeal microbiota
Across all analyzed samples, 19,516,175 reads in total (mean 36,412 ± 19,513 reads/sample) were
retained for downstream analyses and these were binned into 309 operational taxonomic units (OTUs),
further referred to as bacterial taxa. For all samples the Good’s estimator of coverage was above
99·9%. The taxon annotated as “Streptococcus (6)” had a strong correlation with lytA qPCR Ct-values,
confirming its origin Streptococcus pneumoniae (Spearman’s rho -0·81, p<0·001). The dominant
phyla were Proteobacteria (47·5%), Firmicutes (34·8%), and Actinobacteria (15·9%). Hierarchical
clustering showed the presence of 15 distinct microbiota profiles (supplementary figure 1b) driven
by Moraxella catarrhalis/nonliquefaciens (28·3%), Staphylococcus (16·2%), Corynebacterium
propinquum & Dolosigranulum pigrum (14·4%), Haemophilus (11·2% of samples), Streptococcus
pneumoniae & Rothia (5·2%), Streptococcus pneumoniae (3·1%), Moraxella lincolnii (2·2%),
Moraxella osloensis (2·0%), Streptococcus salivarius (1·8%), Klebsiella (1·6%), Streptococcus
pyogenes (1·6%), Enterococcus faecium (1·3%), Chryseobacterium (1·1%), Moraxella lacunata
(1·1%), and Brevundimonas (0·9%).
Viruses and microbiota composition in the first year of life
In the subset of RTI samples, we detected a virus in 58/66 (87·9%) samples. RSV was detected in 1/66
of the subset of children (1·5%). The most common other virus was HRV, detected in 48/66 (72·7%)
of the children, followed by adenovirus (11/66 [16·7%]), and coronavirus (9/66 [13·6%];
supplementary table 1). The nasopharyngeal microbiota composition during those respiratory
infections was typified by microbiota dominated of Staphylococcus, followed by a.o. Klebsiella, S.
pneumoniae, Rothia and Moraxella spp. (supplementary figure 1a): microbiota did not differ
between the RSV prophylaxis group and the placebo group (adonis, R2 0·6%, p=0·981).
34
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
Page 35 of 58
Viruses in the first year of life
From the analyzed samples at 12 months, a total of 80/145 (55·1%) otherwise healthy preterm born
children experienced a PCR-confirmed viral infection in the first year of life. RSV was detected in
14/145 children 9·6%). The most common other virus was HRV, detected in 66/145 (45·5%) of the
children, followed by adenovirus (22/145 [15·2%]), and bocavirus (18/145 [12·4%]; supplementary
table 2a). There was a similar distribution of early life viral infection within our subset of samples at
age 12 months.
From the analyzed samples at 6 years, a total of 182/342 (53·2%) otherwise healthy preterm born
children experienced a PCR-confirmed viral infection in the first year of life. RSV was detected in
34/342 children 9·9%) and was detected significantly more in the placebo group compared to the RSV
prophylaxis group (8/166 [4·5%] and 26/176 [15·7%], respectively, p=0·001; supplementary table
2b). The most common other virus was HRV, detected in 158/342 (46·2%) of the children. HRV was
detected significantly more in the RSV prophylaxis group compared to the placebo group (87/176
[49·4%] and 71/166 [42·8%], respectively, p=0·027). The other viruses were detected equally across
treatment groups in the first year of life in the subset of the samples analyzed at year six.
Relationship between microbiota composition at age one and six
We confirmed that microbiota maturation continues from age 12 months to age 6 years with
significant differences in microbiota community compositions between both age groups (R 2=9·5%,
p<0·0001). Especially the biomarker taxa E. faecium, M. osloensis, Chryseobacterium, Rothia,
Brevundimonas, and S. salivarius diminished over time, whereas Haemophilus spp., M.
catarrhalis/nonliquefaciens, C. propinquum, D. pigrum, S. pyogenes, and M. lacunata increased
significantly with age (supplementary figure 7). Interestingly, although most children had a different
microbiota profile at age 6 years compared to age 12 months (n=118 paired samples, supplementary
figure 8), we still saw that there was a higher but not significant correlation between paired microbiota
of the same child at 12 months and 6 year of life when compared to unpaired samples (median Bray-
Curtis similarity 0·101 and 0·044, respectively; p=0.056; supplementary figure 9a) suggesting the
existence of a modest intra-individual core microbiome. This was strengthened by the fact that more
stable microbiota development between year 1 and year 6 samples was associated with the presence of
Moraxella catarrhalis/nonliquefaciens and Corynebacterium propinquum & Dolosigranulum pigrum-
dominated clusters (median Bray-Curtis similarity 0·455 and 0·348, respectively, versus 0·001-0·191
for all other clusters; supplementary figure 9b), corroborating previous studies suggesting the
35
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
Page 36 of 58
presence of these microbiota profiles in early life are associated with stable and resilient microbiota
development over time.
Interestingly, when stratifying for treatment groups, the existence of a core microbiome was only
evident for the placebo group (median Bray-Curtis similarity 0·126 and 0·039, for intra-individual
versus inter-individual concordance, respectively; p=0·03583; supplementary figure 9a) but not for
the palivizumab group (median Bray-Curtis similarity 0·054 and 0·045, respectively; p=0·475;
supplementary figure 9a), suggesting that children receiving placebo had more stable microbiota
development compared to the palivizumab group.
Findings excluding children within the palivizumab group who developed an RSV infection during their first year of life
We excluded 5/79 and 8/176 children from the palivizumab group in a posthoc analyses of the
children at age 12 months and 6 years, respectively, as they developed an RSV infection during their
first year of life. At 12 months of life, we observed a significant difference in overall microbial
community structure between the palivizumab and the placebo group (R2=1·3%, p=0·0285). On
cluster level, the Staphylococcus-dominated profile was positively associated with the placebo group
(chi-square, p=0·00381; OR 0·27, 95% CI 0·10-0·67). On individual taxon level, Staphylococcus
abundance was not different in the placebo group compared to palivizumab (metagenomeSeq, log2 fold
change 1·4, q=0·122, figure 10a). We further observed a significantly higher abundance of
Helcococcus, Dolosigranulum pigrum, Lactobacillus spp., Streptococcus spp. and a range of gram-
negative spp. including Klebsiella and several oral bacteria in the palivizumab group.
When evaluated at six years of age, we still observed a small though not significant difference in
overall microbial community structure between the otherwise healthy preterm infants who were
treated with palivizumab and those who were treated with placebo (R2=0·7%, p=0·0425). On cluster
level, the Haemophilus-dominated profile at age six years was positively associated with the
palivizumab group (chi-square, p=0·02571; OR 1·91, 95% CI 1·08-3·41). On individual bacterial
taxon level, Haemophilus spp. as well as S. pyogenes were positively associated with the palivizumab
group (figure 10b), whereas Moraxella, Corynebacterium and Neisseriaceae spp. were negatively
associated with palivizumab in the first year of life.
36
679
680
681
682
683
684
685
686
687688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
Page 37 of 58
Supplementary table 1. Prevalence of respiratory virus detection in the 66 RTI samples analyzed in this study.
None of the differences between treatment groups were statistically significant (Chi-square all p>0·4).
All RTI samples(n=66)
Placebo(n=33)
Palivizumab(n=33)
Virus n % n % n %
Any virus 58 87·9 28 84·8 30 90·9
Multiple viruses 20 30·3 8 24·2 12 36·4
Respiratory syncytial virus (RSV) 1 1·5 1 3·0 0 0
RSV with any other virus 1 1·5 1 3·0 0 0
Human rhinovirus 48 72·7 23 69·7 25 75·8
Adenovirus 11 16·7 4 12·1 7 21·2
Bocavirus 7 10·6 2 6·1 5 15·2
Coronavirus 9 13·6 3 9·1 6 18·2
Parainfluenza 6 9·1 3 9·1 3 9·1
Human metapneumovirus 2 3·0 1 3·0 1 3·0
Influenza 0 0 0 0 0 0
37
708709
710
711
Page 38 of 58
Supplementary table 2. Prevalence of respiratory virus detection in the first year of life.
The total number of PCR detected viral infections in the first year of life of the subset of samples that we analyzed at age 12 months (A) and 6 years of life (B).
A12 mo samples
(n=145)Placebo(n=66)
Palivizumab(n=79)
Virus n % n % n % P
Any virus 79 54·5 38 57·6 41 51·9 0·93
Multiple viruses 47 32·4 21 31·8 26 32·9 0·53
Respiratory syncytial virus (RSV) 14 9·6 9 13·6 5 6·3 0·24
RSV with any other virus 9 6·2 5 7·6 4 5·1 0·75
Human rhinovirus 66 45·5 28 42·4 38 48·1 0·08
Adenovirus 22 15·2 9 13·6 13 16·5 0·81
Bocavirus 18 12·4 9 13·6 9 11·4 0·88
Coronavirus 17 11·7 7 10·6 10 12·7 0·90
Parainfluenza 16 11·0 4 6·1 12 15·2 0·14
Human metapneumovirus 6 4·1 4 6·1 2 2·5 0·43
Influenza 3 2·1 2 3·0 1 1·3 0·61
B6 year samples
(n=342)Placebo(n=166)
Palivizumab(n=176)
Virus n % n % n % P
Any virus 182 53·2 90 54·2 92 52·3 1·000
Multiple viruses 113 33·0 50 30·1 63 35·8 0·127
Respiratory syncytial virus (RSV) 34 9·9 26 15·7 8 4·5 0·001
RSV with any other virus 22 6·4 15 9·0 7 4·0 0·081
38
712
713714
715
716
Page 39 of 58
Human rhinovirus 158 46·2 71 42·8 87 49·4 0·027
Adenovirus 60 17·5 22 13·3 38 21·6 0·063
Bocavirus 52 15·2 24 14·5 28 15·9 0·842
Coronavirus 44 12·9 16 9·6 28 15·9 0·122
Parainfluenza 30 8·8 10 6·0 20 11·4 0·124
Human metapneumovirus 13 3·8 7 4·2 6 3·4 0·906
Influenza 7 2·0 6 3·6 1 0·6 0·066
39
717
Page 40 of 58
Supplementary figure 1. Microbiota profiles during early respiratory infections, at and age 12 months and 6 years.
(A) Bar chart showing average profiles of respiratory samples obtained during early respiratory infections in the first year of life (n=66), and during routine sampling at age 12 months (n=145) and 6 years (n=342). The legend shows the biomarker taxa over time.(B) Hierarchical clustering of all study samples identified 15 profiles. Biomarker species defined by random forests analysis for these 15 profiles identified by hierarchical clustering were from left to right: Chryseobacterium, Klebsiella, Enterococcus faecium, Moraxella osloensis, Streptococcus salivarius, Staphylococcus, Moraxella lincolnii, Streptococcus pneumoniae, Rothia & Streptococcus pneumoniae, Streptococcus pyogenes, Haemophilus, Moraxella lacunata, Corynebacterium propinquum & Dolosigranulum pigrum, M. catarrhalis/nonliquefaciens and Brevundimonas. The figure visualizes from top to bottom the clustering dendrogram, including information on the treatment allocation, sample type, and a bar chart of the relative abundance for each of the biomarker species in the individual samples. A
40
718719
720721722723724725726727728729730731732
733
734
Page 41 of 58
B
41
Page 42 of 58
Supplementary figure 2. Three-dimensional NMDS plot.
(A) NMDS plots depicting the individual nasopharyngeal microbiota composition at year one (data points, n=145) colored by early life RSV infection: with RSV (purple, n=14) and without RSV (orange, n=132).(B) NMDS biplots depicting the individual nasopharyngeal microbiota composition at year six (data points, n=342) colored by early life RSV infection: with RSV (purple, n=34) and without RSV (orange, n=308). Ellipses represent the standard deviation of all points within a cohort. The stress-value using the first two dimensions was 0·25, whereas this dropped to 0·18 when using three dimensions. Because a stress of <0·2 indicates a reasonable interpretability, 34 we decided to depicts the samples across these three dimensions (NMDS1-NMDS3). The figures also depict the biomarkers species (determined by random forests analysis on hierarchical clustering results) colored by phylum (Green diamonds = Proteobacteria, orange triangles = Firmicutes, purple squares = Actinobacteria, pink circles = Bacteroides).
A
42
735
736737738739740741742743744745746747
Page 43 of 58
B
43
748
Page 44 of 58
Supplementary figure 3. Differential abundance of bacterial taxa between children without and with proven RSV infection.
An analysis similar to that of figure 2b was performed stratifying the cohort at age 6 years alternatively, i.e. the associations between the 20 most differentially abundant taxa and either children with (n=34, left) and without proven RSV (n=308, right) infection early in life. Log 2 fold changes (including 95% confidence intervals) were obtained by metagenomeSeq analysis and corrected for multiple comparisons (Benjamini-Hochberg).
44
749
750751
752753754755756
757
Page 45 of 58
Supplementary figure 4. Fifty-eight OTUs were identified as contaminants.
The frequency of each OTU is depicted as a function of the bacterial biomass. The dashed black line shows the model of a noncontaminant sequence feature for which frequency is expected to be independent of the input DNA concentration. The red line shows the model of a contaminant sequence feature, for which frequency is expected to be inversely proportional to input DNA concentration, as contaminating DNA will make up a larger fraction of the total DNA in samples with very little total DNA.
45
758
759760761762763764
Page 46 of 58
46765
Page 47 of 58
Supplementary figure 5. Samples are distinct from blanks.
(A) The number of sequences in the DNA isolation blanks and PCR blanks (grey) were an order of magnitude lower compared to the samples of children who received either placebo (brown) or palivizumab (green). (B) visualizes the hierarchical clustering dendrogram, which clearly separates the blanks (red) from the samples (grey).
A
47
766
767768769770771
Page 48 of 58
B
Supplementary figure 7. Association of the biomarker taxa with age.
Associations between the biomarker taxa and either year one (left) or year six (right). Log 2 fold changes (including 95% confidence intervals) were obtained by metagenomeSeq analysis and corrected for multiple comparisons (Benjamini-Hochberg).
48
772
773774775
Page 49 of 58
49
776
Page 50 of 58
Supplementary figure 8. Individual microbial developmental trajectories between age one and six.
(A) Visualization of the relation between the overall microbiota profile of the same participant over time as a parallel alluvial diagram. The alluvial diagram depicts the direct links between the microbiota profile at age one (left) and at age six (right). Green lines represent participants that have the same profile at both ages (n=9/118 [7·6%]) and brown lines represent participants that have different profiles at both ages (n=109/118 [92·4%]). Participants with a similar profile at both ages were distributed evenly across treatment groups. (B) In the placebo group, 5/53 (9.4%) participants had the same profile at both ages 48/53 (90·6%) participants had different profiles at both ages. (C) In the palivizumab group, 4/65 (6·2%) participants had the same profile at both ages 61/65 (93·8%) participants had different profiles at both ages.
50
777778
779780781782783784785786787
Page 51 of 58
A
51
Page 52 of 58
B
C
52
788
Page 53 of 58
Supplementary figure 9. Similarity in total microbiota composition between year one and six.
(A) Boxplots depicting the Bray-Curtis similarities between the microbiota composition at the age of one and six in paired samples of the same child (Within child) or in unpaired samples (Between children). (B) Boxplots depicting the Bray-Curtis similarities between the microbiota composition at the age of one and six in paired samples of the same child stratified for the microbial clusters determined in supplementary figure 1.
A
B
53
789
790791792793794795796
Page 54 of 58
54
797
798
Page 55 of 58
Supplementary 10. Differential abundance of bacterial taxa between treatment groups.
We repeated an identical analysis as in Figure 2, but including the samples of children within the palivizumab group who still developed an RSV infection during their first year of life. Associations between the 20 most differentially abundant taxa and either the palivizumab group (right) or placebo group (left). Log2 fold changes (including 95% confidence intervals) were obtained by metagenomeSeq analysis and corrected for multiple comparisons (Benjamini-Hochberg). (A) depicts the results for samples obtained at 12 months of life, whereas (B) depicts the results for 6 years of life. To avoid OTUs with identical annotations, we refer to OTUs using their taxonomical annotations combined with a rank number based on the abundance of each given OTU.
A
B
55
799
800801802803804805806807
Page 56 of 58
56
808
Page 57 of 58
Supplementary references
1 Shi T, McAllister DA, O’Brien KL, et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. Lancet 2017; 390: 946–58.
2 Feldman AS, He Y, Moore ML, Hershenson MB, Hartert T V. Toward Primary Prevention of Asthma. Reviewing the Evidence for Early-Life Respiratory Viral Infections as Modifiable Risk Factors to Prevent Childhood Asthma. Am J Respir Crit Care Med 2015; 191: 34–44.
3 Teo SM, Mok D, Pham K, et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe 2015; 17: 704–15.
4 Bisgaard H, Hermansen MN, Buchvald F, et al. Childhood Asthma after Bacterial Colonization of the Airway in Neonates. N Engl J Med 2007; 357: 1487–95.
5 Bosch AATM, Piters WAA de S, van Houten MA, et al. Maturation of the Infant Respiratory Microbiota, Environmental Drivers, and Health Consequences: A Prospective Cohort Study. Am J Respir Crit Care Med 2017; 196: 1582–90.
6 Hyde ER, Petrosino JF, Piedra PA, Camargo CA, Espinola JA, Mansbach JM. Nasopharyngeal Proteobacteria are associated with viral etiology and acute wheezing in children with severe bronchiolitis. J Allergy Clin Immunol 2014; 133: 1220–2.
7 Mansbach JM, Hasegawa K, Henke DM, et al. Respiratory syncytial virus and rhinovirus severe bronchiolitis are associated with distinct nasopharyngeal microbiota. J Allergy Clin Immunol 2016; 137: 1909–1913.e4.
8 Rosas-Salazar C, Shilts MH, Tovchigrechko A, et al. Nasopharyngeal microbiome in respiratory syncytial virus resembles profile associated with increased childhood asthma risk. Am. J. Respir. Crit. Care Med. 2016; 193: 1180–3.
9 De Steenhuijsen Piters WAA, Heinonen S, Hasrat R, et al. Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. Am J Respir Crit Care Med 2016; 194: 1104–15.
10 Huang YJ, Boushey HA. The microbiome in asthma. J Allergy Clin Immunol 2015; 135: 25–30.
11 Lynch JP, Sikder MAA, Curren BF, et al. The influence of the microbiome on early-life severe viral lower respiratory infections and asthma-Food for thought? Front. Immunol. 2017; 8: 156.
12 Biesbroek G, Tsivtsivadze E, Sanders EAM, et al. Early Respiratory Microbiota Composition Determines Bacterial Succession Patterns and Respiratory Health in Children. Am J Respir Crit Care Med 2014; 190: 1283–92.
13 Sze M a., Dimitriu P a., Hayashi S, et al. The lung tissue microbiome in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012; 185: 1073–80.
14 Blanken MO, Rovers MM, Molenaar JM, et al. Respiratory Syncytial Virus and Recurrent Wheeze in Healthy Preterm Infants. N Engl J Med 2013; 368: 1791–9.
15 Scheltema NM, Nibbelke EE, Pouw J, et al. Respiratory syncytial virus prevention and asthma in healthy preterm infants: a randomised controlled trial. Lancet Respir Med 2018; 6: 257–64.
16 O’Brien KL, Nohynek H, World Health Organization Pneumococcal Vaccine Trials Carriage Working Group. Report from a WHO Working Group: standard method for detecting upper respiratory carriage of Streptococcus pneumoniae. Pediatr Infect Dis J 2003; 22: e1-11.
17 Prevaes SMPJ, de Winter-de Groot KM, Janssens HM, et al. Development of the Nasopharyngeal Microbiota in Infants with Cystic Fibrosis. Am J Respir Crit Care Med 2016; 193: 504–15.
18 Carvalho M da GS, Tondella ML, McCaustland K, et al. Evaluation and improvement of real-time PCR assays targeting lytA, ply, and psaA genes for detection of pneumococcal DNA. J Clin Microbiol 2007; 45: 2460–6.
19 Team RC. R: A Language and Environment for Statistical Computing. 2015. https://www.r-project.org/ (accessed April 8, 2017).
20 Man WH, Clerc M, de Steenhuijsen Piters WAA, et al. Loss of Microbial Topography between Oral and Nasopharyngeal Microbiota and Development of Respiratory Infections Early in Life. Am J Respir Crit Care Med 2019; 200: 760–70.
57
809
810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855856857858859860861862
Page 58 of 58
21 Man WH, van Houten MA, Mérelle ME, et al. Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study. Lancet Respir Med 2019; 7: 417–26.
22 Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods 2013; 10: 1200–2.
23 Salter SJ, Turner C, Watthanaworawit W, et al. A longitudinal study of the infant nasopharyngeal microbiota: The effects of age, illness and antibiotic use in a cohort of South East Asian children. PLoS Negl Trop Dis 2017; 11: e0005975.
24 Fouhy F, Watkins C, Hill CJ, et al. Perinatal factors affect the gut microbiota up to four years after birth. Nat Commun 2019; 10: 1517.
25 Chen Y, Hamati E, Lee P-K, et al. Rhinovirus induces airway epithelial gene expression through double-stranded RNA and IFN-dependent pathways. Am J Respir Cell Mol Biol 2006; 34: 192–203.
26 Tepper RS, Wise RS, Covar R, et al. Asthma outcomes: pulmonary physiology. J Allergy Clin Immunol 2012; 129: S65-87.
27 Huang YJ, Nelson CE, Brodie EL, et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J Allergy Clin Immunol 2011; 127: 372-381.e1-3.
28 Man WH, de Steenhuijsen Piters WAA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol 2017; 15: 259–70.
29 Liu G, Tang CM, Exley RM. Non-pathogenic Neisseria: members of an abundant, multi-habitat, diverse genus. Microbiology 2015; 161: 1297–312.
30 Achten NB, Wu P, Bont L, et al. Interference Between Respiratory Syncytial Virus and Human Rhinovirus Infection in Infancy. J Infect Dis 2017; 215: 1102–6.
31 Blanken MO, Korsten K, Achten NB, et al. Population-Attributable Risk of Risk Factors for Recurrent Wheezing in Moderate Preterm Infants During the First Year of Life. Paediatr Perinat Epidemiol 2016; 30: 376–85.
32 Lambrecht BN, Hammad H. The immunology of the allergy epidemic and the hygiene hypothesis. Nat Immunol 2017; 18: 1076–83.
33 Hayden FG, Herrington DT, Coats TL, et al. Efficacy and Safety of Oral Pleconaril for Treatment of Colds Due to Picornaviruses in Adults: Results of 2 Double-Blind, Randomized, Placebo-Controlled Trials. Clin Infect Dis 2003; 36: 1523–32.
34 Clarke KR. Non‐parametric multivariate analyses of changes in community structure. Aust J Ecol 1993; 18: 117–43.
35 Joshi N, Fass J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. 2011. https://github.com/najoshi/sickle (accessed Dec 31, 2018).
36 Nikolenko SI, Korobeynikov AI, Alekseyev MA. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 2013; 14: S7.
37 Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 2012; 13: 31.
38 Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7: 335–6.
39 Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011; 27: 2194–200.
40 Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016; 4: e2584.
41 Westcott SL, Schloss PD. De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units. PeerJ 2015; 3: e1487.
42 Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2012; 41: D590–6.
43 Subramanian S, Huq S, Yatsunenko T, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 2014; 510: 417.
58
863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895896897898899900901902903904905906907908909910911912913914915