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Integrative microbiomics in bronchiectasis exacerbations

Nature Medicine volume 27, pages 688–699 (2021)Cite this article

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Abstract

Bronchiectasis, a progressive chronic airway disease, is characterized by microbial colonization and infection. We present an approach to the multi-biome that integrates bacterial, viral and fungal communities in bronchiectasis through weighted similarity network fusion (https://integrative-microbiomics.ntu.edu.sg). Patients at greatest risk of exacerbation have less complex microbial co-occurrence networks, reduced diversity and a higher degree of antagonistic interactions in their airway microbiome. Furthermore, longitudinal interactome dynamics reveals microbial antagonism during exacerbation, which resolves following treatment in an otherwise stable multi-biome. Assessment of the Pseudomonas interactome shows that interaction networks, rather than abundance alone, are associated with exacerbation risk, and that incorporation of microbial interaction data improves clinical prediction models. Shotgun metagenomic sequencing of an independent cohort validated the multi-biome interactions detected in targeted analysis and confirmed the association with exacerbation. Integrative microbiomics captures microbial interactions to determine exacerbation risk, which cannot be appreciated by the study of a single microbial group. Antibiotic strategies probably target the interaction networks rather than individual microbes, providing a fresh approach to the understanding of respiratory infection.

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Fig. 1: Integration of multi-biome data through WSNF.
Fig. 2: Co-occurrence analysis of the multi-biome network in high-frequency exacerbators.
Fig. 3: Network characterization of busy, critical and influential microbes (nodes) in patients with frequent exacerbations.
Fig. 4: Longitudinal analysis of the bronchiectasis multi-biome during exacerbations.
Fig. 5: Integrative microbiomics of the multi-biome showing the core and ancillary microbial networks in bronchiectasis exacerbations.
Fig. 6: Metagenomic analysis of gene function and microbial interactions associated with exacerbation.

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Data availability

All of the sequence data described in this study have been uploaded to the NCBI SRA under project accession PRJNA590225. Publicly available taxonomic and functional databases are referenced by short-read sequence classification tools used in this study as further described in the Nature Research Reporting Summary. Other associated data, including bacterial, fungal and viral profiles for all of the patients, as well as patient clinical attributes, are available at https://github.com/translational-respiratory-lab/The_Interactome/tree/master/Data.

Code availability

All code required for generation of the presented results, with accompanying documentation, are available at the study’s online code repository (https://github.com/translational-respiratory-lab/The_Interactome).

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Acknowledgements

This research is supported by the Singapore Ministry of Health’s National Medical Research Council under its Transition Award (NMRC/TA/0048/2016 to S.H.C.); the Clinician-Scientist Individual Research Grant (MOH-000141 to S.H.C.); NTU Integrated Medical, Biological and Environmental Life Sciences (NIMBELS) (NIM/03/2018 to S.H.C.); the British Lung Foundation through the GSK/British Lung Foundation Chair of Respiratory Research (to J.D.C.) and the Scottish Government Chief Scientist Office (SCAF/17/03). K.T.-A. gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1.

Author information

Author notes

  1. These authors contributed equally: Micheál Mac Aogáin, Jayanth Kumar Narayana.

Authors and Affiliations

  1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Micheál Mac Aogáin, Jayanth Kumar Narayana, Pei Yee Tiew, Nur A’tikah Binte Mohamed Ali, Valerie Fei Lee Yong, Tavleen Kaur Jaggi, Kai Xian Thng, Akina Tsang, Fransiskus Xaverius Ivan & Sanjay H. Chotirmall

  2. Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore

    Pei Yee Tiew, Thun How Ong & Mariko Siyue Koh

  3. Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore

    Albert Yick Hou Lim & John Arputhan Abisheganaden

  4. School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK

    Holly R. Keir, Alison J. Dicker, Yan Hui Giam & James D. Chalmers

  5. Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

    Mau Ern Poh

  6. Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Martina Oriano, Stefano Aliberti & Francesco Blasi

  7. Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy

    Martina Oriano, Stefano Aliberti & Francesco Blasi

  8. Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore

    Teck Boon Low & Augustine Tee

  9. Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia

    Brian Oliver

  10. School of Life Sciences, University of Technology Sydney, Sydney, New South Wales, Australia

    Brian Oliver

  11. Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Krasimira Tsaneva-Atanasova

Authors
  1. Micheál Mac Aogáin
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Contributions

M.M.A. and J.K.N. designed and performed the experiments, carried out data analysis and interpretation, statistical analysis, and wrote the manuscript. P.Y.T., A.Y.H.L, M.E.P., T.B.L., T.H.O, A. Tee, M.S.K. and J.A.A. carried out patient recruitment and collected clinical data and specimens for the Asian patients. P.Y.T., N.A.B.M.A., K.X.T., A. Tsang, Y.H.G., V.F.L.Y. and T.K.J. performed the experiments. F.X.I. provided bioinformatic support. H.R.K., A.J.D. and J.D.C. designed the overall study, interpreted the data, carried out patient recruitment and collected clinical data and specimens for the European patients and the longitudinal cohort (Scotland), and procured funding. M.O., F.B. and S.A. carried out patient recruitment and collected the clinical data and specimens for the European patients (Italy). B.O., K.T.-A., J.D.C. and S.H.C. made intellectual contributions. K.T.-A. carried out data analysis. S.H.C. conceived and designed the overall study and experiments, carried out data analysis and interpretation, statistical analysis, wrote the manuscript, and procured funding.

Corresponding author

Correspondence to Sanjay H. Chotirmall.

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Competing interests

J.D.C. has received research grants from GlaxoSmithKline, Boehringer-Ingelheim, AstraZeneca, Gilead Sciences, Grifols and Insmed, and has received personal fees from GlaxoSmithKline, Boehringer-Ingelheim, AstraZeneca, Chiesi, Grifols, Napp, Novartis, Insmed and Zambon, all outside of the submitted work. S.A. has received personal fees from Bayer Healthcare, Grifols, AstraZeneca and Zambon, and research grants and personal fees from Chiesi, Insmed, GlaxoSmithKline, Menarini, ZetaCube Srl and Fisher & Paykel, all outside of the submitted work. F.B. has received research grants and personal fees from AstraZeneca, Bayer, Chiesi, GlaxoSmithKline, Guidotti, Grifols, Insmed, Menarini, Novartis, Pfizer and Zambon, all outside of the submitted work. All other authors have no competing interests.

Additional information

Peer review information Nature Medicine thanks Grant Waterer, Brian Keller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Sub-analysis of discrete bacterial, viral and fungal profiles in stable-state bronchiectasis.

a, A Principal Coordinate Analysis (PCoA) of bacterial microbiome based on Bray-Curtis dissimilarity illustrates three patient clusters. Colour indicates groupings defined by spectral clustering analysis. b, The bacterial community profile of each identified cluster is illustrated as bar plots of average relative abundance for the top 20 identified genera; cluster 1: Pseudomonas-dominant (green), cluster 2: Streptococcus-dominant (blue), cluster 3: Haemophilus-dominant (red). Comparison of alpha diversity and clinical features among identified patient clusters (cluster 1; n = 23, cluster 2; n = 125, cluster 3, n = 69), derived from n = 217 biologically independent samples are illustrated (c) alpha diversity (Shannon diversity index, p = 2.9 × 109), (d) median number of exacerbations in the preceding year (p = 2.1 × 104), (e) breathlessness score (MMRC, p = 3.6 × 103), (f) lung function (as FEV1 % predicted, p = 1.8 × 10−1) and (g) bronchiectasis severity index (p = 3.9 × 10−3) for patients in each cluster. MMRC = modified medical research council, BSI = bronchiectasis severity index; FEV1% = forced expiratory volume in the 1st second (% predicted). Box plots reflect median and IQRs with whiskers bounding non-outlier values. Throughout, significance levels are indicated as follows: ns: non-significant; *p < 0.05; **p < 0.01; ***p < 0.001 (Kruskal-wallis with post-hoc Dunn test for multiple comparison). h, Viral community profiling in bronchiectasis, assessed by a 17-virus qPCR panel illustrates high viral load (in the stable disease state) compared to a non-diseased (healthy) state. Box and whisker plot illustrating overall viral load, expressed as viral genome copies/g sputum, quantified by qPCR in virus-positive sputum samples from non-diseased (healthy) individuals (n = 30) and patients with bronchiectasis (n = 217). i, A range of viruses and highest prevalence of human parainfluenza virus 3. Prevalence is expressed as percentage of patients demonstrating PCR-positivity originating from either Singapore-Kuala Lumpur (SG-KL, n = 120, red bars) or Dundee (DD, n = 97, blue bars). j, PCoA plot illustrating differences between bacteriome profiles from our stable state bronchiectasis cohort (grey, n = 217) compared to non-diseased (healthy) controls that include young (<40 years of age, green, n = 20) and older (>60 years of age, blue, n = 20) subjects. k, PCoA plot illustrating differences between mycobiome profiles from our stable state bronchiectasis cohort (grey, n = 217) compared to non-diseased (healthy) controls that include young (<40 years of age, green, n = 20) and older (>60 years of age, blue, n = 20) subjects.

Extended Data Fig. 2 Un-weighted SNF analysis of multi-biome data provides inferior resolution of clinically relevant bronchiectasis patient clusters.

a, A heatmap illustrating pairwise patient similarity scores (range; 0–0.5, blue - red) assessed by spectral clustering. Three distinct patient clusters are illustrated by purple, red and orange bars above the heatmap respectively. Clusters are colour-coded as (1) purple; (2) red and (3) orange. b, Linear discriminant effect size (LEfSe) analysis of the observed clusters illustrating taxa that discriminate between the multi-biome profiles of each group. A bar plot details each of the identified discriminant taxa ranked by effect size. Discriminant taxa with a log-transformed effect size of >3 are presented (n = 5). Prefixes indicate whether taxa are bacterial (B), fungal (F) or viral (V). Comparison of alpha diversity and clinical features among identified patient clusters (cluster 1; n = 115, cluster 2; n = 88, cluster 3, n = 14) according to integrated multi-biome profiles, derived from n = 217 biologically independent samples are illustrated; (c) alpha diversity (Shannon diversity index, p = 3.17 × 10−6), (d) number of exacerbations in the preceding year, p = 4.0 × 10−2), (e) breathlessness score (MMRC, p = 3.0 × 10−3), (f) lung function (as FEV1 % predicted, p = 2.5 × 10−1) and (g) bronchiectasis severity index (BSI, p = 7.6 × 10−1) for patients in each cluster. Box plots reflect median and IQRs with whiskers bounding non-outlier values. Significance levels for observed between-group differences are indicated as follows: ns: non-significant; *p < 0.05; **p < 0.01; ***p < 0.001 (Kruskal-wallis with post-hoc Dunn test for multiple comparison).

Extended Data Fig. 3 Sub-analysis of chronic rhinosinusitis (CRS) and antibiotic treatment effects on the microbiome.

a, Pie charts displaying microbiome profiles for bacteria, fungi and viruses for CRS + and CRS – patients with microbial identity indicated in the right-hand colour legend (n = 217 biologically independent samples). b, Linear discriminant effect size (LEfSe) analysis of patients with and without CRS illustrating taxa that discriminate between the multi-biome profiles of each group. A bar plot details each of the identified discriminant taxa ranked by effect size. Discriminant taxa with a log-transformed effect size of >2 are presented (n = 43). Prefixes indicate whether identified taxa are bacterial (B), fungal (F) or viral (V – none detected). Black circles indicated taxa previously established to be associated with CRS status. c, Box plots reflect median and IQRs with whiskers bounding non-outlier values and illustrate diversity metrics for each of the following groups; no inhaled corticosteroids (ICS) or antibiotic use (No ICS or abx), ICS only (ICS only), antibiotics only (abx only), or both (ICS + abx) indicating Shannon Diversity Index and (d) Berger-Parker Index. ns = not significant (Kruskal-wallis with post-hoc Dunn test for multiple comparison). e, Pie charts displaying microbiome profiles for bacteria, fungi and viruses for the same patient groups with microbial identity indicated in the right-hand colour legend.

Extended Data Fig. 4 Co-occurrence networks and prevalence of observed keystone taxa identified in the low (LEF) and high (HEF) exacerbation frequency clusters.

The interaction profiles of key taxa with other microbes within their network is illustrated for the (a–d) low-exacerbation frequency and (e–h) high-exacerbation frequency clusters respectively. Bacteria and fungi with established pathogenic potential including Streptococcus, Haemophilus, Candida and Cryptococcus are illustrated. Though common to both clusters, these ‘busy’, ‘critical’ and ‘influential’ microbes exhibited marked differences in their interaction networks when assessed individually thereby suggesting that microbial interactions, rather than mere presence or absence of a given microbe, may have a direct bearing on the clinical status of patients observed in the high frequency exacerbation cluster. Positive and negative interactions (expressed as edge weights) are colour-coded for illustration as indicated in the figure legend. The prevalence of microbial taxa (detected at >1% relative abundance) among each group is represented by horizontal bars for LEF (Blue) and HEF (purple) clusters. (i), Bacteria, (j) Fungi, (k) Viruses.

Extended Data Fig. 5 Supervised, non-SNF classification of the microbial interactome based on known exacerbation frequencies.

Network visualization of key microbial taxa in (a) non-frequent (<3 exacerbations/year) and (b) frequent (3 or more exacerbations/yr) are illustrated. Coloured circles represent microbes and grey lines their associated interactions within the network. Taxa present at >1% relative abundance in at least 5% of the patient cohort are included. Circle size (degree) reflects the number of direct interactions for a given microbe (termed ‘busy’). Circle outline thickness represents the calculated stress centrality for each microbe termed ‘critical’ while circle colour depth reflects betweenness centrality or the ‘influence’ of the microbe within the network. A table to the right of each network illustrates details the composition of busy (degree), critical (stress centrality) and influential (betweenness centrality) microbes observed in non-frequent vs frequent exacerbators. Additional microbes present in the top taxa compared to SNF-based analysis (Fig. 3a,b) are highlighted by bold typeface.

Extended Data Fig. 6 Additional diversity indices indicating comparable ecological diversity across longitudinal analysis of the integrated multi-biome during bronchiectasis exacerbations.

Box plots reflect median and IQRs with whiskers bounding non-outlier values and illustrate (a) Simpson (b) Berger-Parker and (c) Community dominance index1 for combined bacterial, fungal and viral communities assessed longitudinally in n = 17 bronchiectasis patients at baseline (pre-exacerbation) (‘B’), during an established pulmonary exacerbation (‘E’) and then post-exacerbation (‘P’) following completion of antibiotic therapy. Dotted lines indicate the longitudinal pattern of each individual patient (n = 17). Boxplots are coloured according to their respective longitudinal time-point and timepoints indicated by colour: blue = baseline, red = exacerbation, green = post-exacerbation. ns; not significant (Kruskal-Wallis).

Extended Data Fig. 7 Comparison of culture data vs microbiome profiles and assessment of the interactome as a predictor of ‘time to next exacerbation’ in longitudinal samples.

a, For each patient (P1-17, n = 17 biologically independent patient samples) three stacked barplots indicate the derived 16S rRNA profiles observed at each timepoint (n = 3) from baseline to exacerbation and post-exacerbation (from left to right). Above each set of barplots are the indicated corresponding results from microbiological culture and the antibiotics used to treat the respective exacerbation (AMX – amoxicillin, AMC – amoxicillin-clavulanic acid, CAZ – ceftazidime, CIP – ciprofloxacin, DOX – doxycycline, MEM – meropenem, TZP – piperacillin-tazobactam). *Asterisks indicate the co-isolation of P. aeruginosa and H. influenzae observed in two patients. A sub-analysis of microbiome diversity in patients suffering a subsequent exacerbation in the 12-week period following the initial occurrence versus those who remained exacerbation free for >12 weeks post-exacerbation is illustrated. b, Shannon Diversity Index at baseline (pre-exacerbation), during initial pulmonary exacerbation and post-exacerbation in those experiencing a second exacerbation in <12 weeks (red, n = 9) and >12 weeks (blue, n = 8). c, Berger-Parker index at baseline (pre-exacerbation), during initial pulmonary exacerbation and post-exacerbation in those experiencing a second exacerbation in <12 weeks (red) and >12 weeks (blue). Box plots reflect median and IQRs with whiskers bounding non-outlier values. ns; not significant (Kruskal-wallis). d, Correlation analysis of microbial abundance and (e) microbial interactions associated with time to next exacerbation. Heatmaps illustrate significant (p < 0.05) associated correlations with colour indicating the strength of correlation (Spearman’s ρ). Vertical text font colouration indicates kingdom membership; blue = bacteria, green = fungi, red = virus. Psuedom. = Pseudomonas, Strep. = Streptococcus, Neiss. = Neisseria, Sacch. = Saccharomyces. A multivariate adaptive regression spline (MARS) was implemented with both microbes and interactions (strength of interaction; edge weight) as predictor variables for ‘time to next exacerbation’ defined as; <12 weeks or >12weeks. Feature importance plots based on generalized cross-validation scores (gcv) illustrating the most important (f) microbes and (g) microbial interactions predicting the time to next exacerbation across baseline, exacerbation and post-exacerbation timepoints. Computed R-squared (RSq) and Generalized R-squared metric (GRsq) are indicated in table (h) reflecting goodness of the fit for each model.

Extended Data Fig. 8 Analysis of lung function and breathlessness in functionally-defined patient clusters.

Box and whisker plots showing (a) lung function (as FEV1 % predicted, p = 3.5 × 10−2) and (b) breathlessness (symptom) score (as MMRC) in patients clustered according to functional analysis of sputum-derived metagenomes (FC1; n = 116, FC2; n = 50)). Box plots reflect median and IQRs with whiskers bounding non-outlier values. Significance levels for the observed between-group differences are indicated as follows: ns: non-significant; *p < 0.05 (Mann-Whitney U). FEV1% = forced expiratory volume in the 1st second (% predicted). MMRC = modified medical research council.

Extended Data Fig. 9 Metagenomic analysis of the virome in an independent cohort of n = 166 bronchiectasis patients recruited from Singapore, Malaysia, Scotland, and Italy.

To gain further insight into the respiratory virome we conducted an analysis of n = 166 metagenomes from sputum. a, A schematic overview of the analysis pipeline used to construct virome profiles. Virome profiles were derived by first generating a metagenomic assembly using all available metagenomic sequences trimmed and free of contaminating human DNA followed by detection of viral contigs using Virfinder with subsequent refinement using CONCOCT yielding 702 contigs present in at least 1% relative abundance in 5% of patients. Taxonomic assignment was performed using Demovir and BLAST. b, Relative abundance of viral families identified across samples from Singapore (SG) Kuala Lumpur (KL) Scotland (DD) and Italy (IT). c, Relative abundance of viral families identified in wSNF clusters SC1 and SC2. d, LEfSe discriminant taxa analysis comparing viral families observed in SC1 and SC2. e, BLAST analysis of identified viral contigs in SC1 and SC2. f, Analysis of antibiotic resistance gene family distribution in SC1 versus SC2 (RPKM - Reads Per Kilobase of transcript, per Million mapped reads).

Extended Data Fig. 10 Assessment of background DNA contamination in sequencing blank extraction controls.

Read counts of taxonomically assigned sequences for (a) 16 s rRNA, (b) fungal ITS and (c) WGS metagenomic classification. Open circles represent samples while red squares indicate blank DNA extraction controls. Pooled blanks (n = 4) were sequenced for 16 s rRNA and ITS sequencing runs while four (un-pooled) extraction blanks were sequenced separately for WGS metagenomic analysis (B1-B4). Observed profiles of (d) bacterial, (e) fungal and (f) metagenomically assigned taxa identified in sequencing reads derived from DNA extraction blanks compared to aggregated sample profiles are indicated.

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Mac Aogáin, M., Narayana, J.K., Tiew, P.Y. et al. Integrative microbiomics in bronchiectasis exacerbations. Nat Med 27, 688–699 (2021). https://doi.org/10.1038/s41591-021-01289-7

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