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mEnrich-seq: methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome

Nature Methods volume 21, pages 236–246 (2024)Cite this article

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Abstract

Metagenomics has enabled the comprehensive study of microbiomes. However, many applications would benefit from a method that sequences specific bacterial taxa of interest, but not most background taxa. We developed mEnrich-seq (in which ‘m’ stands for methylation and seq for sequencing) for enriching taxa of interest from metagenomic DNA before sequencing. The core idea is to exploit the self versus nonself differentiation by natural bacterial DNA methylation and rationally choose methylation-sensitive restriction enzymes, individually or in combination, to deplete host and background taxa while enriching targeted taxa. This idea is integrated with library preparation procedures and applied in several applications to enrich (up to 117-fold) pathogenic or beneficial bacteria from human urine and fecal samples, including species that are hard to culture or of low abundance. We assessed 4,601 bacterial strains with mapped methylomes so far and showed broad applicability of mEnrich-seq. mEnrich-seq provides microbiome researchers with a versatile and cost-effective approach for selective sequencing of diverse taxa of interest.

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Fig. 1: mEnrich-seq workflow and its application.
Fig. 2: Validation of mEnrich-seq.
Fig. 3: Enrichment of A. muciniphila from fecal samples by mEnrich-seq.
Fig. 4: Enrichment sequencing of bacterial taxa based on de novo discovered methylation motifs.
Fig. 5: Assessing the broad applicability of mEnrich-seq.

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Data availability

All sequencing data generated for this study have been uploaded to the Sequence Read Archive under the BioProject PRJNA896537. Source data are provided with this paper.

Code availability

Primary sequence data analysis was done using open-source software tools as described in the Methods. Custom scripts used to analyze motif frequency are available in Zenodo at https://doi.org/10.5281/zenodo.8330164 (ref. 71).

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Acknowledgements

We thank the Icahn School of Medicine at Mount Sinai colleagues J. Faith for helping with the characterization of the adult fecal sample and F. Samaroo for helping with urine sample processing; L. Davey and R. Valdivia at Duke University School of Medicine for help with isolation of A. muciniphila; and the M. Blaser laboratory at the Rutgers University and other research teams that collected the infant fecal samples for The Early Childhood Antibiotics and the Microbiome study. This work was supported by grant no. R35 GM139655 (G.F.) from the National Institutes of Health. G.F. is a Hirschl Research Scholar by Irma T. Hirschl/Monique Weill-Caulier Trust and 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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Authors and Affiliations

  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lei Cao, Yimeng Kong, Yu Fan, Mi Ni, Alan Tourancheau, Magdalena Ksiezarek, Edward A. Mead & Gang Fang

  2. Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tonny Koo & Melissa Gitman

  3. Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, USA

    Xue-Song Zhang

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Contributions

G.F. conceived and supervised the project. L.C. and G.F. designed the methods. L.C. performed all mEnrich-seq experiments and sequencing. Y.K., Y.F., M.N. and A.T. performed the evaluation of mEnrich-seq and analyzed mEnrich-seq data across the applications. M.K. helped with the data analysis of mEnrich-seq of the urine samples. T.K. and M.G. helped with the characterization of urine samples and E. coli strains. X.-S.Z. helped with the characterization of the infant fecal samples. L.C., M.N. and E.A.M. performed additional sequencing. L.C., Y.F., M.N., X.-S.Z. and G.F. performed additional data analyses. L.C. and G.F. wrote the manuscript with inputs and comments from all coauthors.

Corresponding author

Correspondence to Gang Fang.

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Competing interests

L.C. and G.F. are the coinventors of a US provisional patent application (application no.: 63/382,616) based on the method described in this work. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 mEnrich-seq of urine samples using Nanopore Flongle flow cells.

a, Three urine samples were sequenced as the input: UTI-1 contained mostly bacterial DNA, while UTI-2 and UTI-3 were dominated by human DNA. b, Scatter plots for six urine samples (UTI-1 to -6) showing the percentage of reads mapped to E. coli, human and other taxa from the input (x-axis) vs. mEnrich-seq (y-axis). The UTI-1 to -3 samples demonstrate consistent patterns as shown in Fig. 2 in the main text (MinION flow cell); the additional three UTI urine samples (UTI -4 to -6) are included to illustrate the consistent enrichment of E. coli by mEnrich-seq. Across all six samples, E. coli reads are significantly enriched in mEnrich-seq. In UTI-2, UTI-3, UTI-4, and UTI-6, the proportion of reads corresponding to the human genome are significantly lower due to cleavage by DpnII in mEnrich-seq. c, Fold enrichment of E. coli reads from mEnrich-seq compared to the input for the six urine samples.

Source data

Extended Data Fig. 2 mEnrich-seq data of UTI-1 include reads that have high-confidence mapping to E. coli yet carry additional genes beyond the isolated strain from standard urine culture.

a, Top: 10 reads with highly confident mapping to E. coli from mEnrich-seq of UTI-1 urine sample, consistently supporting a tet(A) resistant gene (from AF534183, blue boxes). For each read, lengths of the sequences flanking the resistance gene were shown on both sides. The identities between the reads and tet(A) were shown inside the blue boxes. Bottom, reads alignment to tet(A) was performed with Clustalw2 and colored with MView. b, Validation of tet(A) in UTI-1 sample. Agarose gel electrophoresis of the tet(A) regions amplified once by two sets of primers (of lengths 1150 bp and 900 bp, respectively). The bands (in red square) were further purified for gene sequence confirmation by Sanger sequencing (Supplementary Information).

Source data

Extended Data Fig. 3 Confirmation of DNA 6 mA methylation at RA6mATTY motif sites using PacBio sequencing of two A. muciniphila strains.

a, RA6mATTY (mediated by MTase AmuORF1905P) is one of the five methylated motifs detected in A. muciniphila (ATCC BAA-835) by PacBio sequencing as summarized in REBASE. b, DNA 6 mA methylation at RAATTY motif sites (light blue) is confirmed based on the high IPD ratios observed in PacBio sequencing data of the A. muciniphila strain isolated from the infant fecal sample GUT-3. c, DNA 6mA methylation at RAATTY motif sites (purple) is confirmed based on the high IPD ratio observed in PacBio SMRT sequencing data of another A. muciniphila strain.

Extended Data Fig. 4 De novo methylation motif discovery from an initial shallow sequencing run (400k PacBio Sequel II CCS reads).

Many methylation motifs were discovered from SMRT sequencing data, even those from bacterial genomes with ~10% completeness. The relative abundance of each bacterial genome is included in the x-axis label in red color. 4-mer motifs (those with four non-degenerate bases) and similarly 5-mer and 6-mer motifs are color coded.

Source data

Extended Data Fig. 5 Direct comparison shows that the mEnrich-seq design has better fold enrichment than REMoDE.

a, Agarose gel electrophoresis of Mock-3 (a mock with five species, see Supplementary Information) after treatment by RE and T5 exonuclease for once. Left, Agarose gel electrophoresis of the mock sample digested by RE, XapI. Due to the sparse RE recognition sites in the background genomes in the mock microbiome sample, relatively long DNA fragments from the background genomes remain even after RE digestion; Right, The gel image of the mock sample digested by RE and T5. b, A direct comparison between mEnrich-seq and REMoDE was performed using the same mock microbiome sample (Mock-3), and the same sequencing platform (Illumina). To be compatible with the short-read sequencing platform, the library of mEnrich-seq was prepared according to the ‘Compatibility between mEnrich-seq and other sequencing platforms’ section in the Supplementary Information.

Source data

Extended Data Fig. 6 mEnrich-seq design has better fold enrichment than REMoDE when they are compared with the same, real microbiome samples with DNA fragmentation and damages.

a, Agarose gel electrophoresis tested once on genomic DNA isolated from Mock-3 (illustrating the ideal high molecular weight gDNA from cell culture) and two infant fecal samples (representing DNA quality in real applications). b, Comparing the fold enrichment of A. muciniphila from two infant fecal samples using mEnrich-seq, REMoDE, or REMoDE with DNA repair beforehand (see Supplementary Information). As DNA repair is part of mEnrich-seq by default, this step is also applied to REMoDE for a fair comparison.

Source data

Extended Data Fig. 7 Evaluation of the benefit of adapter ligation before vs. after RE-digestion using the Mock-3.

The bar graph demonstrates that adapter ligation before RE-digestion indeed has better fold enrichment (see Methods in Supplementary Information).

Source data

Extended Data Fig. 8 An alternative adapter has comparable enrichment efficiency as the original adapter.

The fold enrichment of A. muciniphila (using XapI as RE) was tested on Mock-3 with either the original adapter or the alternative adapter (sequences in Supplementary Table 9).

Source data

Extended Data Fig. 9 GC content does not introduce systematic bias on read coverage across the E. coli genome analyzed by mEnrich-seq.

Analysis was performed on Mock-1 and Mock-2 (E. coli relative abundance as 0.91% and 1.20%, see Supplementary Information). For a fair comparison, the same yield of data from both input and mEnrich-seq were used in data analysis and figures. Each dot represents a 5 kb genomic region, colored by GC content. X-axis: number of input reads mapped to each 5 kb genomic region; Y-axis: number of mEnrich-seq reads mapped to each 5 kb genomic region. To ease visualization, a random value was added on x-values to avoid overlapping dots.

Source data

Extended Data Fig. 10 GC content does not introduce systematic bias on read coverage across the A. muciniphila genome by mEnrich-seq.

The same type of scatter plots (as the ones above for E. coli) made with sequencing data from three human gut samples (A. muciniphila relative abundance: 0.48%, 0.52% and 1.52%, respectively) to characterize the enrichment of A. muciniphila. Consistent with the Circos plot for the same samples, read depths were capped at the 95% quantile to ease visualization.

Source data

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–4, protocols and unprocessed gels for Figs. 3 and 4.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–13.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Figs. 1 and 4–10

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed gel.

Source Data Extended Data Fig. 5

Unprocessed gel.

Source Data Extended Data Fig. 6

Unprocessed gel.

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Cao, L., Kong, Y., Fan, Y. et al. mEnrich-seq: methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome. Nat Methods 21, 236–246 (2024). https://doi.org/10.1038/s41592-023-02125-1

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