Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation

Abstract

Shotgun metagenomics methods enable characterization of microbial communities in human microbiome and environmental samples. Assembly of metagenome sequences does not output whole genomes, so computational binning methods have been developed to cluster sequences into genome 'bins'. These methods exploit sequence composition, species abundance, or chromosome organization but cannot fully distinguish closely related species and strains. We present a binning method that incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing. Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins. We validate our method using synthetic and real microbiome sequences. In addition to genome binning, we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample. Incorporation of DNA methylation information into shotgun metagenomics analyses will complement existing methods to enable more accurate sequence binning.

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Figure 1: Overview of metagenomic binning using DNA methylation detected in SMRT long reads.
Figure 2: Metagenomic binning by methylation profiles.
Figure 3: Methylation profiles can link plasmids to the chromosomal DNA of their host species.
Figure 4: Binning SMRT reads using composition and DNA methylation profiles.

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NCBI Reference Sequence

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NCBI Reference Sequence

Sequence Read Archive

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Acknowledgements

We thank M. Lewis for her assistance in DNA extraction and A. Bashir for his guidance in computational matters. We also thank those who contributed to the generation of the publically available SMRT sequencing data for the 20-member Mock Community B. The work is funded by R01 GM114472 (G.F.) from the National Institutes of Health and Icahn Institute for Genomics and Multiscale Biology. G.F. is a Nash Family Research Scholar. This work was also supported in part through the computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai.

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Contributions

J.B. and G.F. designed the methods. J.B. developed the software package for all the proposed computational analyses. J.B., E.W.T., J.J.F. R.S., E.E.S. and G.F. contributed to experimental design. I.M., X.-S.Z., A.D.-R., R.C., E.W.T. and J.J.F. conducted the experiments. G.D. and R.S. designed and conducted sequencing. J.B., S.Z., E.W.T., J.J.F., R.S., E.E.S. and G.F. analyzed the data. J.B. and G.F. wrote the manuscript with inputs and comments from all co-authors. G.F. conceived and supervised the project.

Corresponding author

Correspondence to Gang Fang.

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Competing interests

E.E.S. is on the scientific advisory board of Pacific Biosciences. J.B. and G.F. are inventors of a US Provisional patent application (No. 62/525,908) that describes the method for methylation binning.

Integrated supplementary information

Supplementary Figure 1 Binning contigs from 8-species mock community.

(a) t-SNE scatter plot of 5-mer composition profiles for contigs and (b) scatter plot of contig GC-content vs. contig coverage.

Supplementary Figure 2 Shorter contigs contain fewer methylated motif sites.

After de novo assembly of reads from a mixture of eight bacterial species, the contigs belonging to C. bolteae were isolated. As the contig length decreases, it becomes less common for the contig to contain IPD values from the full diversity of motif sites that are methylated in C. bolteae, making it increasingly difficult to segregate smaller contigs based on contig methylation patterns alone.

Supplementary Figure 3 Composition and coverage-based binning methods applied to adult mouse gut microbiome assembly.

(a) Contig GC-content vs. coverage for adult mouse gut microbiome assembly, and (b) contig coverage plotted against the contig coverage using sequencing from a related sample.

Supplementary Figure 4 Infant gut microbiome contigs binned by sequence composition and methylation profiles.

(a) t-SNE map of 5-mer frequency features for contigs assembled from a mixture of two infant microbiome samples. Several clusters contain a mixture of species from the same genus. (b) t-SNE map of methylation features for the same contigs. (c) t-SNE map of the same contigs binned by both 5-mer frequency and methylation profiles (Online Methods), which resolve the contigs into mostly species-specific clusters. Kraken annotation relies on an existing reference database (Online Methods) and is therefore incomplete; contigs not generating a database hit are marked Unlabeled. Contigs <10kb are omitted.

Supplementary Figure 5 CONCOCT bins of the mouse gut microbiome.

Taxonomic composition of the 29 bins identified by CONCOCT in the mouse gut metagenomic assembly. Taxonomy is based on contig-level annotations by Kraken.

Supplementary Figure 6 Heatmaps of methylation profiles for K. pneumoniae.

(a) Hierarchical clustering of all known methylated motifs in REBASE for K. pneumoniae strain 234-12 and nine other species whose chromosomes have smaller sequence distance to the K. pneumoniae strain 234-12 plasmid (horizontal red bars) than its own host chromosome. (b) Hierarchical clustering of all motifs in REBASE for 25 strains of K. pneumoniae. The strains contain 17 unique methylation motifs, including CCAYNNNNNTCC that is observed solely in K. pneumoniae strain 234-12.

Supplementary Figure 7 Sequence composition t-SNE map of modified HMP mock community B.

5-mer frequency-based binning of assembled contigs and raw reads (length>15kb) from the log-abundance HMP mock community. Only the contigs are labeled (raw reads represented underneath contigs by density map) and the sum of assembled bases for each Kraken-annotated species is included in the legend.

Supplementary Figure 8 5-mer frequency-based binning of unaligned reads from the modified HMP mock community B.

(a) Read lengths between 5-10kb, and (b) read lengths between 10-15kb. The shorter read lengths result in more diffuse and overlapping clusters due to the increased variation in 5-mer frequency metrics on these shorter reads.

Supplementary Figure 9 t-SNE map of read-level methylation profiles for two H. pylori strains.

2D map of reads from each of the H. pylori strains, 26695 and J99, analyzed in the multi-strain synthetic mixture. 2D map generated using t-SNE, where the only features used in dimensionality reduction are methylation profiles of the reads.

Supplementary Figure 10 Comparison of abundance-matched SMRT vs. synthetic long read (SLR) sequencing coverage.

(a) Human Microbiome Project Mock Community B members in decreasing order of GC content in genome. The percentage of the reference positions covered by SLRs is consistently lower than the percentage covered by abundance-matched SMRT reads. (b) Coverage variation for alignments of abundance-matched SLR and SMRT reads. A significant number of bases in SLRs are aligned in the same regions, creating dramatic peaks in coverage. SMRT reads largely lack these peaks and have a more uniform coverage profile.

Supplementary Figure 11 Examples of uneven coverage in SLR.

Uneven coverage by synthetic long reads in a 40 kb region of the S. agalactiae genome (a), a 40 kb region of the S. aureus genome (b), and a 50 kb region of the P. aeruginosa genome (c).

Supplementary Figure 12 Genomewide coverage of SLR and SMRT reads for all genomes in HMP mock community B.

Genome-wide coverage of abundance-matched synthetic long reads (red lines) and SMRT reads (blue lines). Regions with zero coverage are highlighted for synthetic long reads (pink) and SMRT reads (light blue).

Supplementary Figure 13 Reference matches for bins identified from methylation profiles in mouse gut microbiome.

Dot plot visualizations created using mummerplot that show the top reference alignment for bins isolated from the mouse gut microbiome metagenomic assembly using only methylation profiles. See Supplementary Table 6 for details of these alignments and the matching reference sequences.

Supplementary Figure 14 Modified relative abundances in HMP mock community B.

Relative abundances of the 20-species in the Human Microbiome Project mock community B modified to follow a log-curve distribution.

Supplementary Figure 15 Sequence composition t-SNE map of unmodified HMP mock community B.

5-mer frequency-based binning of assembled contigs and raw reads (length>15kb) from the even-abundance HMP mock community B. Only the contigs are labeled (raw reads represented underneath contigs by density map) and the sum of assembled bases for each Kraken-annotated species is included in the legend.

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Supplementary Figures 1–15 Supplementary Methods (PDF 2223 kb)

Life Sciences Reporting Summary (PDF 176 kb)

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Supplementary tables 1–11 (ZIP 465 kb)

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Mbin Software package and relevant scripts (ZIP 43 kb)

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Beaulaurier, J., Zhu, S., Deikus, G. et al. Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation. Nat Biotechnol 36, 61–69 (2018). https://doi.org/10.1038/nbt.4037

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