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Early-life viral infections are associated with disadvantageous immune and microbiota profiles and recurrent respiratory infections

Abstract

The respiratory tract is populated by a specialized microbial ecosystem, which is seeded during and directly following birth. Perturbed development of the respiratory microbial community in early-life has been associated with higher susceptibility to respiratory tract infections (RTIs). Given a consistent gap in time between first signs of aberrant microbial maturation and the observation of the first RTIs, we hypothesized that early-life host–microbe cross-talk plays a role in this process. We therefore investigated viral presence, gene expression profiles and nasopharyngeal microbiota from birth until 12 months of age in 114 healthy infants. We show that the strongest dynamics in gene expression profiles occurred within the first days of life, mostly involving Toll-like receptor (TLR) and inflammasome signalling. These gene expression dynamics coincided with rapid microbial niche differentiation. Early asymptomatic viral infection co-occurred with stronger interferon activity, which was related to specific microbiota dynamics following, including early enrichment of Moraxella and Haemophilus spp. These microbial trajectories were in turn related to a higher number of subsequent (viral) RTIs over the first year of life. Using a multi-omic approach, we found evidence for species-specific host–microbe interactions related to consecutive susceptibility to RTIs. Although further work will be needed to confirm causality of our findings, together these data indicate that early-life viral encounters could impact subsequent host–microbe cross-talk, which is linked to later-life infections.

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Fig. 1: Differentially expressed genes (DEGs) and gene module activity in the respiratory mucosa over time.
Fig. 2: Module activity driven by first viral infection.
Fig. 3: Gene expression and module activity in relation to RTI susceptibility.
Fig. 4: Early-life nasopharyngeal microbiota development.
Fig. 5: Microbiota development and associations with cumulative module M3 activity.
Fig. 6: HAllA results.

Data availability

Both microbiota and gene expression data, including minimal patient metadata, have been deposited at the National Centre for Biotechnology Information GenBank database (accession no. PRJNA740120 and GSE152951, respectively). Full patient metadata are available upon request. Source data are provided for each Figure and Extended Data Figure.

Taxonomic annotations were based on the Silva database (v138.2). For gene set enrichment analyses, gene sets from the Reactome Pathways Database (https://reactome.org/download-data; downloaded 23 March 2020)61 and the Gene Ontology (GO) database (GO.db R package; release 10 July 2019) were used62.

To deconvolute microarray data, we used the ‘single-cell atlas of the airway epithelium’ dataset

(https://www.genomique.eu/cellbrowser/HCA/; hg19 genes annotation; downloaded 17 September 2021). Source data are provided with this paper.

Code availability

Code used to process and analyse the data is available at https://gitlab.com/wsteenhu/MUIS_trx/. A release version of the code has been archived in a Zenodo repository (https://doi.org/10.5281/zenodo.5736115).

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Acknowledgements

We thank all volunteers who participated in this study; A. A. T. M. Bosch and all members of the Spaarne Gasthuis Academy research team for their dedication and practical support with participant enrolment and sample collection; and M. Clerc for her support with microarray sample preparation. This work was supported in part by the Netherlands Organisation for Scientific research (NWO-VIDI; grant 91715359) and CSO/NRS through a Scottish Senior Clinical Fellowship award (SCAF/16/03).

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Authors

Contributions

D.B., M.A.v.H. and E.A.M.S. designed the experiments and wrote the study protocols. D.B., M.A.v.H., P.C.M.d.G. and E.A.M.S. were responsible for (supervision of) participant enrolment, sample and data collection. R.H., K.A. and M.L.J.N.C. were responsible for laboratory processing of samples. W.A.A.d.S.P., D.B. and R.L.W. performed bioinformatic processing and W.A.A.d.S.P., E.M.d.K. and D.B. ran statistical analyses. W.A.A.d.S.P., E.M.d.K. and D.B. wrote the paper. All authors contributed to interpretation of the results, critically revised the manuscript for important intellectual content, and approved the final manuscript.

Corresponding author

Correspondence to Debby Bogaert.

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Nature Microbiology thanks Leopoldo Segal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Between-dataset correlations and shared host and environmental determinants.

a, Mantel tests quantifying the variance explained (square of Mantel r statistic) between each pair of datasets (percentages/blue shades). The Mantel test is a test of correlation between pairs of dissimilarity matrices derived from the original microbiota, gene expression or viral data. Mantel statistics were calculated between subjects (interindividual) or within subjects over time (intraindividual; see Methods). b, PERMANOVA results showing the association between host and environmental variables and each dataset (that is microbiota, gene expression and viral data). Asterisks denote Benjamini-Hochberg (BH)-corrected statistical significance (correction per dataset; *, q ≤ 0.05; **, q ≤ 0.01; ***, q ≤ 0.001). The variance explained is estimated for each variable independently (percentages/blue shades). ‘All’ refers to a test including all metadata. For each column, the total n for each dataset is shown in square brackets. ‘full’ indicates the maximum samples available for that dataset. ‘matched’ refers to the set of paired samples between microbiota and gene-expression data. For each variable tested, the number of degrees of freedom is given in square brackets. Significance of both Mantel and PERMANOVA-tests was based on 1,000 permutations.

Source data

Extended Data Fig. 2 Viral detection rates of a panel of respiratory viruses.

Viruses could be detected from directly after birth and on.

Source data

Extended Data Fig. 3 Timing of first viral detection and age at which parents first report respiratory symptoms.

Time lines of all subjects showing the temporal relationship between first viral detection (red points) and the time frame within which parents first reported respiratory symptoms (black horizontal lines). At each regular visit, parents were asked whether their child had been suffering from RTI symptoms since the last regular visit. We therefore defined the preceding regular visit as ‘start’ and the current visit as ‘end’ of the time frame within which RTI complaints had first occurred. Other sampling moments for each individual are shown in grey. We stratified the individual time lines by timing of first viral presence vs first report of RTI symptoms, with first viral detection preceding first report of symptoms in n=59 infants, viral detection co-occurring with the end of the first RTI episode in n=26 infants and the first viral infection reported after the first RTI episode in n=25 infants. 4/114 infants are not included in this overview as we did not detect a viral infection and/or parents did not report any RTI complaints.

Source data

Extended Data Fig. 4 Dendrogram visualizing an average linkage hierarchical clustering of samples based on the Bray–Curtis dissimilarity matrix.

The length of the branches of the tree structure corresponds with the similarities between samples (n=1,156). Adjacent to the branch ends information on 1) initial clustering, 2) subclustering of a large cluster characterized by Corynebacterium/Dolosigranulum/Moraxella (n=587 samples) and 3) (supervised) stratification of the resulting CDG5 cluster into Moraxella (2)-enriched (CDG5/MOR2) and -depleted (CDG5) subclusters is depicted. Combined, these steps result in the 11 (final) clusters as shown by colour-coded horizontal panels. Clusters are named after the most discriminative Amplicon Sequence Variants (ASVs) within those clusters. Gray panels indicate samples not grouped into clusters consisting of 10 or more samples. A heatmap shows the relative abundance of the 20 highest-ranked ASVs based on mean relative abundance across all samples. Repeated samples from individuals were included in this clustering analysis to optimize cluster identification and increase comparability across time points.

Source data

Extended Data Fig. 5 Microbiota transitions related to cumulative module M1 activity.

Kaplan-Meier curves depicting cumulative module M1 activity in relation to age at which a given infant first transitioned into a Corynebacterium/Dolosigranulum (CDG5), Corynebacterium (5)/Dolosigranulum/Moraxella (2) (CDG5/MOR2), Haemophilus (HAE) or Moraxella (2) (MOR2) cluster. Cumulative events are shown on the y-axis. P values shown are based on logrank tests. AUC-values were classified as ‘low’ (below median) or ‘high’ (above median) compared to all other subjects with M1 AUC-values over that interval (see Fig. 5b).

Source data

Extended Data Fig. 6 Relationship between presence and abundance of S. pneumoniae (lytA) and members of the Streptococcus genus.

a, Boxplots depicting the relationship between presence/absence of S. pneumoniae based on lytA-qPCR results (CT <40 cycles) and centre logratio (clr) transformed relative abundance of several members of the Streptococcus genus. Only streptococci present in at least 100 samples were shown. P values were based on mixed linear effects models with pneumococcal presence/absence based on lytA-qPCR as predictor and subject as random intercept. These results indicate that pneumococcal presence is strongly associated with Streptococcus (13) abundance. Box plots represent the 25th and 75th percentiles (lower and upper boundaries of boxes, respectively), the median (middle horizontal line), and measurements that fall within 1.5 times the interquartile range (IQR; distance between 25th and 75th percentiles; whiskers). b, Correlation plot showing the relationship between S. pneumoniae abundance based on lytA-qPCR results (CT-values) and centre logratio (clr) transformed relative abundance of Streptococcus (13). P values and repeated measures correlation coefficients (r) are based on the ‘rmcorr’-R package. P values were calculated including all data, as well as only data from samples in which S. pneumoniae and Streptococcus (13) were detected (presence defined as CT <40 cycles and clr transformed relative abundance> 4, respectively; dotted lines). The shaded area surrounding the correlation line represents the 95% confidence interval.

Source data

Extended Data Fig. 7 Relative abundance Z-score of microbiota members over time.

Z-scores were calculated by subtracting the mean and subdividing by the standard deviation across all samples for each given Amplicon Sequence Variant (ASV). Similar maturation patterns of Corynebacterium (5)/Dolosigranulum pigrum (7), and Moraxella (2)/Haemophilus (12)/Streptococcus (13) were observed, suggesting that differences in genes associated with these ASVs are not explained by a residual effect of age.

Source data

Extended Data Fig. 8 HAllA-associated microbiota in relation to RTI susceptibility.

Association between Corynebacterium (5), Dolosigranulum pigrum (7), Moraxella (2), Haemophilus (12) and Streptococcus (13) abundance and the number of mild RTIs over the first year of life. Colored bars indicate the time window within which a significant difference between groups (3–4 and 5–7 vs 0–2 RTIs) was detected. Bar height correlates with effect size (‘Area’). Values depicted in/on top of bars are q-values. Associations with q-value ≤0.1 are depicted (see Supplementary Table 8).

Source data

Extended Data Fig. 9 Flowchart.

Overview of all statistical analyses used. Both analyses and the figures/tables where results of these analyses can be found are shown. Links between analyses/nested analyses are depicted using arrows. Asterisks denote those analyses that were data-driven. ORA, overrepresentation analysis.

Extended Data Fig. 10 Sequencing depth and rarefaction curves.

a, Raincloud plot indicating the distribution of the number of reads per sample. Only samples with a read count of ≥3,000 reads (after decontamination/before filtering rare taxa) were included. The distribution of read counts was approximately log-normal (n=1,156 samples; median 23,938 reads, range 3,184–190,874 reads). b, Rarefaction curves for samples with <15,000 reads (n=175 samples). For this subset of samples with lower numbers of reads, we find that rarefaction curves generally saturate ~3,000 reads (dotted line), indicating samples were sequenced sufficiently deep to capture the microbial diversity.

Source data

Supplementary information

Reporting Summary

Peer Review File

41564_2021_1043_MOESM3_ESM.xlsx

Supplementary Tables 1–8 Supplementary Tables 18.

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de Steenhuijsen Piters, W.A.A., Watson, R.L., de Koff, E.M. et al. Early-life viral infections are associated with disadvantageous immune and microbiota profiles and recurrent respiratory infections. Nat Microbiol 7, 224–237 (2022). https://doi.org/10.1038/s41564-021-01043-2

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