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Microbial-enrichment method enables high-throughput metagenomic characterization from host-rich samples

Nature Methods volume 20, pages 1672–1682 (2023)Cite this article

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Abstract

Host–microbe interactions have been linked to health and disease states through the use of microbial taxonomic profiling, mostly via 16S ribosomal RNA gene sequencing. However, many mechanistic insights remain elusive, in part because studying the genomes of microbes associated with mammalian tissue is difficult due to the high ratio of host to microbial DNA in such samples. Here we describe a microbial-enrichment method (MEM), which we demonstrate on a wide range of sample types, including saliva, stool, intestinal scrapings, and intestinal mucosal biopsies. MEM enabled high-throughput characterization of microbial metagenomes from human intestinal biopsies by reducing host DNA more than 1,000-fold with minimal microbial community changes (roughly 90% of taxa had no significant differences between MEM-treated and untreated control groups). Shotgun sequencing of MEM-treated human intestinal biopsies enabled characterization of both high- and low-abundance microbial taxa, pathways and genes longitudinally along the gastrointestinal tract. We report the construction of metagenome-assembled genomes directly from human intestinal biopsies for bacteria and archaea at relative abundances as low as 1%. Analysis of metagenome-assembled genomes reveals distinct subpopulation structures between the small and large intestine for some taxa. MEM opens a path for the microbiome field to acquire deeper insights into host–microbe interactions by enabling in-depth characterization of host-tissue-associated microbial communities.

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Fig. 1: Comparison of the performance of the MEM with published host-depletion methods.
Fig. 2: Microbial enrichment of stool and saliva after host depletion by MEM as confirmed by shotgun sequencing.
Fig. 3: Analysis of microbial enrichment in paired human intestinal biopsies processed with and without MEM.
Fig. 4: Shotgun sequencing of MEM-treated human intestinal biopsies.
Fig. 5: MAG construction with MEM-treated human intestinal biopsies performed from shotgun metagenomic sequencing.
Fig. 6: Interindividual and intraindividual bacterial biodiversity present along GI tract.

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Data availability

The datasets generated and analyzed during the current study are available at CaltechDATA, https://doi.org/10.22002/gx69z-wec80. Microbial sequencing data are available at NCBI Accession no. PRJNA991155. Sequencing data from human samples have been host scrubbed using STAT78 sra-human-scrubber (https://github.com/ncbi/sra-human-scrubber) followed by alignment to CHM13 (ref. 79). Source data are provided with this paper.

Code availability

The code used in data processing and analysis is available at CaltechDATA, https://doi.org/10.22002/gx69z-wec80.

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Acknowledgements

We acknowledge assistance with animal experiments from Caltech Office of Laboratory Animal Research. We thank M. Ratanapanichkich (California Institute of Technology) for assistance on manual refinement of metagenomic bins and feedback on figure design. We thank A. Carter (California Institute of Technology) for assistance with Quant-seq library preparation, ddPCR measurements and feedback during manuscript preparation. We thank M. Cooper (California Institute of Technology) for identifying appropriate statistical tests, guidance during Quant-seq analysis and feedback on figure design. We thank S. R. Bogatyrev for preliminary investigations, discussions and advice. We thank O. Pradhan (California Institute of Technology) and R. Akana (California Institute of Technology) for advice and feedback during manuscript preparation. We thank B. McDonald (University of Chicago) for providing his expertise and advice on clinical sample collection and processing. We thank A. Wang (University of Chicago) for her assistance in the processing of the human tissue for Figs. 3–6. We thank N. Shelby (California Institute of Technology) for contributions to writing and editing this manuscript. This work was funded in part by a grant from the Kenneth Rainin Foundation (grant no. 2018-1207 to R.F.I.), the Army Research Office Multidisciplinary University Research Initiative (grant no. W911NF-17-1-0402 to R.F.I.), the Jacobs Institute for Molecular Engineering for Medicine, a NIH NIDDK grant (no. RC2 DK133947 to R.F.I. and B.J.), a National Science Foundation Graduate Research Fellowship (grant no. DGE‐1745301 to N.J.W.-W.), and a National Institutes of Health Biotechnology Leadership Pre-doctoral Training Program fellowship from Caltech’s Donna and Benjamin M. Rosen Bioengineering Center (grant no. T32GM112592, to J.T.B.), a Helmsley Foundation grant (to F.T.), a NIH NIDDK grant (no. RC2 DK122394, to F.T.), a F30 (grant no. 5F30DK121470, to D.G.S.), a R01 (grant no. DK067180, to B.J.) and the Digestive Diseases Research Core Center grant no. P30 DK42086 at the University of Chicago (to B.J.). The funders had no role in the design of the study, the collection, analysis and interpretation of data, nor in writing the manuscript.

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Author notes

  1. These authors contributed equally: Natalie J. Wu-Woods, Jacob T. Barlow.

Authors and Affiliations

  1. Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA

    Natalie J. Wu-Woods, Jacob T. Barlow & Rustem F. Ismagilov

  2. Department of Medicine, The University of Chicago, Chicago, IL, USA

    Florian Trigodet, Dustin G. Shaw & Bana Jabri

  3. Committee on Immunology, The University of Chicago, Chicago, IL, USA

    Dustin G. Shaw & Bana Jabri

  4. Department of Pathology, The University of Chicago, Chicago, IL, USA

    Dustin G. Shaw & Bana Jabri

  5. Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, USA

    Anna E. Romano & Rustem F. Ismagilov

  6. Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, USA

    A. Murat Eren

  7. Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany

    A. Murat Eren

  8. Alfred-Wegener-Institute for Marine and Polar Research, Bremerhaven, Germany

    A. Murat Eren

  9. Helmholtz Institute for Functional Marine Biodiversity, Oldenburg, Germany

    A. Murat Eren

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Contributions

N.J.W.-W. and J.T.B. conceived and optimized MEM. J.T.B. designed sample collection and analyzed 16S sequencing. D.G.S. codesigned and performed human biopsy collection. N.J.W.-W. and F.T. analyzed shotgun sequencing. A.E.R. performed library preparation. R.F.I. contributed to the design and implementation of the study and to obtaining funding. A.M.E. oversaw the bioinformatic analysis, contributed to the design and implementation of the study and to obtaining funding. B.J. supervised the clinical work, contributed to the design and implementation of the study and to obtaining funding. All authors edited the manuscript. A detailed author contribution statement is available in the Supplementary Information.

Corresponding author

Correspondence to Rustem F. Ismagilov.

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Competing interests

The work in this paper is the subject of a patent application filed by Caltech (R.F.I., N.J.W.-W., J.T.B. and A.E.R.). The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Correlation between bacterial load and non-host reads.

Shotgun sequencing was performed on longitudinally sampled intestinal biopsies after processing with host depletion (N = 60 biological replicates). Roughly 25 million reads on average were obtained for each biopsy and all samples fit on a single NovaSeq S1 flowcell. After host-filtering an average of 2 million reads were remaining with a range from 2E4 reads to 2E7 reads. For each box, the middle horizontal line denotes mean values, boxes extend to the 25th and 75th percentile, and whiskers extend to the 1.5 interquartile range. The variability in non-host reads remaining had a strong correlation (Spearman, r = 0.79) with the total microbial load as measured by digital PCR. This strong correlation indicated that our process was achieving a relatively uniform depletion across all samples. Additionally, the strong correlation indicates that the majority of non-human reads in our samples come from bacteria picked up by the 16S primers used for total microbial load quantification.

Source data

Extended Data Fig. 2 Bacterial loads of longitudinal biopsies.

16S rRNA gene copies were quantified as a proxy for bacterial load for all biopsies. Samples were plotted by participant and then by location. (N = 3 biological replicates for each location for each participant, LOB refers to limit of blank defined as LoB = meanblank + 1.645[SDblank] based on three processing blanks).

Source data

Extended Data Fig. 3 Longitudinal variation at the pathways and gene-level.

PCA analysis was performed on all 60 longitudinal samples grouped by participant (CT7, CT8, CT12, CT13, and CT14). Shotgun-sequencing data was annotated for pathways and genes through HUMAnN 3 without the taxonomic-profile flag. A) PCA on relative abundance of all pathways. B) PCA on relative abundance of completed pathways (defined as above 90% of modules being present). C) PCA on relative abundance of all genes. D) PCA on relative abundance of the top 5,000 most abundant genes in each participant.

Source data

Extended Data Fig. 4 Archaeon Methanobrevibacter smithii found along the lower GI tract.

From shotgun sequencing, we detected participant CT12 had low levels of Methanobrevibacter smithii present in the terminal ileum, descending colon, and rectal biopsies (N = 3 biological replicates; error bars are 95% CI centered on the mean). MAG construction was performed on co-assembly of all biopsies taken from the terminal ileum and descending colon to reconstruct a full Methanobrevibacter smithii genome (completeness: 100%, redundancy: 0%).

Source data

Extended Data Fig. 5 Ruminococcus bromii strain variants at the nucleotide (SNV), codon (SCV), and amino acid (AA) level.

SNVs present in R. bromii above the threshold of 21% deviation from reference were analyzed at the codon and translated-level to determine if SNVs may indicate a functional change. The fixation index for each level of analysis were plotted.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Author Contact Information/ORCID.

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Supplementary Tables 1–11

A description for each table can be found as a note in the first cell of each sheet.

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Wu-Woods, N.J., Barlow, J.T., Trigodet, F. et al. Microbial-enrichment method enables high-throughput metagenomic characterization from host-rich samples. Nat Methods 20, 1672–1682 (2023). https://doi.org/10.1038/s41592-023-02025-4

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