Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota

Abstract

Amplicon sequencing (for example, of the 16S rRNA gene) identifies the presence and relative abundance of microbial community members. However, metagenomic sequencing is needed to identify the genetic content and functional potential of a community. Metagenomics is challenging in samples dominated by host DNA, such as those from the skin, tissue and respiratory tract. Here, we combine advances in amplicon and metagenomic sequencing with culture-enriched molecular profiling to study the human microbiota. Using the cystic fibrosis lung as an example, we cultured an average of 82.13% of the operational taxonomic units representing 99.3% of the relative abundance identified in direct sequencing of sputum samples; importantly, culture enrichment identified 63.3% more operational taxonomic units than direct sequencing. We developed the PLate Coverage Algorithm (PLCA) to determine a representative subset of culture plates on which to conduct culture-enriched metagenomics, resulting in the recovery of greater taxonomic diversity—including of low-abundance taxa—with better metagenome-assembled genomes, longer contigs and better functional annotations when compared to culture-independent methods. The PLCA is also applied as a proof of principle to a previously published gut microbiota dataset. Culture-enriched molecular profiling can be used to better understand the role of the human microbiota in health and disease.

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Fig. 1: The culture-enriched metagenomic sequencing workflow.
Fig. 2: The majority of the cystic fibrosis lung microbiota is culturable.
Fig. 3: Taxonomic diversity captured across culture enrichment conditions.
Fig. 4: The PLCA determines an optimal plate set for culture-enriched metagenomics.
Fig. 5: MAG and non-MAG bins resulting from culture-enriched and direct metagenomic sequencing of the first sample in the dataset.
Fig. 6: The taxonomic and functional diversity of direct and culture-enriched metagenomic sequencing.

Data availability

All sequencing results are publicly available (BioProject ID PRJNA503799). The PLCA algorithm is available from https://github.com/fwhelan/PLCA.

Code availability

All code developed by the authors is available under a GNU licence at http://github.com/fwhelan/PLCA and https://github.com/shekas3/BinTaxaAssigner.

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Acknowledgements

This research was funded in part by a Canadian Institutes of Health Research (CIHR) Doctoral Scholarship, a Cystic Fibrosis Canada (CFC) Studentship and a Marie Skłodowska-Curie Individual Fellowship (GA no. 793818) awarded to F.J.W., and grants from CIHR, CFC and a Tier 1 Canada Research Chair to M.G.S. We thank the patients and healthcare professionals at the Calgary Adult Cystic Fibrosis Clinic for their participation and assistance with this study. We acknowledge critical intellectual conversations with J.T. Lau and J.C. Szamosi. We wish to acknowledge that this research was conducted on traditional territory shared by the Haudenosaunee confederacy and the Anishinaabe nations as well as the peoples of the Treaty 7 region in Southern Alberta.

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Contributions

F.J.W. is the primary author of this prepared manuscript. H.R.R. and M.D.P. collected patient information and enrolled willing participants for this study. B.W. collected, processed and cultured all sputum samples in addition to all biological and technical controls. F.J.W. and S.A.S. isolated DNA from culture/sputum material, and ran PCR reactions to amplify the 16S rRNA gene variable 3 region. S.A.S. performed the enrichment of Stenotrophomonas. F.J.W. prepared DNA for metagenomic sequencing. F.J.W. processed and analysed all 16S rRNA gene and metagenomic sequencing results. S.S. provided code for the taxonomic assignment of metagenomic bins. F.J.W., M.D.P. and M.G.S. conceptualized the experimental outline. F.J.W. conducted all data analyses and wrote the manuscript. All authors edited and approved the manuscript.

Corresponding author

Correspondence to Michael G. Surette.

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Extended data

Extended Data Fig. 1

Media used for culture enrichment. Source data

Extended Data Fig. 2 Using more stringent abundance thresholds, the vast majority of the cystic fibrosis lung microbiota is still captured by culture-enriched methods. Culture-enriched 16S rRNA gene sequencing increases OTU recovery.

Using a cutoff of >= 0.1% relative abundance, 65.6% of the lung microbiota was identified only by culture across samples (a), with only 4.3% of OTUs not being cultured (b). c. In each sample, more OTUs were recovered via culture-enriched sequencing than by direct sequencing. d. Overall, an average of 65.5% of all OTUs were identified only via culture. e. The α-diversity of the original samples subject to culture enrichment. Source data

Extended Data Fig. 3 Culture enriches for low abundance taxa.

The first sample in our dataset was re-sequenced to a depth of 972,834 reads. A rank order curve, and associated quantification of the recovered OTUs (inset), illustrates the number of OTUs present in the original (41,199) and re-sequenced sample as well as at rarefied depths, indicating the increased recovery of cultured OTUs by direct sequencing as sequencing depth increases. The inset shows, at two different read cutoffs, that with increasing sequencing depth, the number of OTUs seen only in culture decreases.

Extended Data Fig. 4 No specific culture condition consistently recapitulates the originating sputum sample.

Principle coordinate analyses based on the Bray Curtis β-diversity metric indicates that, across all 20 samples, no culture condition consistently recapitulates the microbial community of the originating sputum sample. For a list of media abbreviations see Extended Data Table 1; Aer = aerobic, Ana = anaerobic.

Extended Data Fig. 5 The importance of selective and non-selective culture conditions in capturing the genus-level diversity of the cystic fibrosis microbiome.

This Figure is a labelled version of Fig. 3c.

Extended Data Fig. 6 The importance of selective and non-selective culture conditions in capturing the OTU-level diversity of the cystic fibrosis microbiome.

A heatmap indicates the breadth of culture conditions necessary to culture this community at the OTU level. Source data

Extended Data Fig. 7

The optimal plate sets needed to sequence all samples (S) in this dataset, and the number of OTUs which would be obtained. Source data

Extended Data Fig. 8 Culture-enriched metagenomic sequencing finds similar communities to 16S rRNA gene sequencing.

Comparisons of the bacterial composition of 16S rRNA gene sequencing to the 16S rRNA gene sequences obtained via whole-genome metagenomics reveal similar communities in the culture conditions amplified as part of the de novo PLCA (a-b) and adjusted PLCA plate sets. (c-d), Communities are compared visually using taxonomic summaries (a,c) and quantitatively using PCoA of the Bray Curtis β-diversity metric (b,d). 16S=16S rRNA gene sequencing; MG=shotgun metagenomic sequencing. Source data

Extended Data Fig. 9 The PLCA consistently recovers targeted OTUs and is not specific to the cystic fibrosis lung microbiota.

a, When the adjusted PLCA was applied to the 20 samples in this cystic fibrosis dataset, it consistently recovered the targeted OTUs (orange), though some (gray) were not recovered as metagenomic bins due to inadequate sequencing depth, or the inability to separate species into separate bins. The overlaying numbers represent the number of OTUs in each category. Additional species obtained as a consequence of being present on a plate with a targeted OTU are shown as yellow dots. b, The PLCA is not specific to the cystic fibrosis lung microbiota or to a particular set of culture conditions; here, we apply the PLCA to previously published culture-enriched gut microbiota data (reference 12). Even through the culture conditions used by Lau et al. differ from those used in this study, the PLCA still predicts successful recovery of almost all species at abundances above the PLCA thresholds (dotted line). Source data

Extended Data Fig. 10 Biological (a-b) and technical (c-d) replicates of sputum and culture-enriched 16S rRNA gene sequencing.

PCoA plots of the Bray Curtis distance between sputum profiles show close clustering of 3 sputum biological replicates prior to plating, n=6 (a), and cultured replicates (technical replicates after plating of 3 sputum samples x 2 culture replicates of 6 representative media types, n=108, c). Polygons/lines connect the replicates in each plot; specifically between duplicate sputum sampling of 3 individuals (labelled A-C) in a and between triplicate platings of sputums in c. In c, the legend labels refer to the media type (for example BHI), environmental condition (Aer, aerobic or Ana, anaerobic), and replicate number (1–6). Taxonomic summaries of biological samples prior to plating (b) and 3 technical replicates each of 2 sputum samples from 3 patients (d) show the consistency of these techniques. Labelling of biological (A-C) and technical (1–6) replicates is consistent with parts a and c. In cases of no visible growth (that is MAC_Aer_3 and MAC_Aer_4) no samples were collected. In some cases, there was some difference in abundance of organisms between replicates (for example KVLB 5,6 and McKay 5,6) on which there were low bacterial counts and only at the lowest dilution plated (the cultured organisms representing ~104 CFU/ml and less than 1% in the original sputum sample). These will be subject to more variability in plating.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Supplementary Tables 1–7.

Reporting Summary

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Whelan, F.J., Waddell, B., Syed, S.A. et al. Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota. Nat Microbiol 5, 379–390 (2020). https://doi.org/10.1038/s41564-019-0643-y

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