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Profiling lateral gene transfer events in the human microbiome using WAAFLE

Nature Microbiology volume 10, pages 94–111 (2025)Cite this article

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Abstract

Lateral gene transfer (LGT), also known as horizontal gene transfer, facilitates genomic diversification in microbial populations. While previous work has surveyed LGT in human-associated microbial isolate genomes, the landscape of LGT arising in personal microbiomes is not well understood, as there are no widely adopted methods to characterize LGT from complex communities. Here we developed, benchmarked and validated a computational algorithm (WAAFLE or Workflow to Annotate Assemblies and Find LGT Events) to profile LGT from assembled metagenomes. WAAFLE prioritizes specificity while maintaining high sensitivity for intergenus LGT. Applying WAAFLE to >2,000 human metagenomes from diverse body sites, we identified >100,000 high-confidence previously uncharacterized LGT (~2 per microbial genome-equivalent). These were enriched for mobile elements, as well as restriction–modification functions associated with the destruction of foreign DNA. LGT frequency was influenced by biogeography, phylogenetic similarity of involved pairs (for example, Fusobacterium periodonticum and F. nucleatum) and donor abundance. These forces manifest as networks in which hub taxa donate unequally with phylogenetic neighbours. Our findings suggest that human microbiome LGT may be more ubiquitous than previously described.

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Fig. 1: The WAAFLE algorithm is accurate for novel lateral transfer discovery in metagenomes.
Fig. 2: Rates of undirected intergenus LGT for HMP1-II metagenomes profiled by WAAFLE.
Fig. 3: Phylogenetic distance and donor abundance as determinants of LGT rate.
Fig. 4: The network of LGT events in the human microbiome.
Fig. 5: Molecular functions associated with predicted LGT events.
Fig. 6: Experimental support for novel predicted LGT events in human stool.

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Data availability

HMP1-II metagenomes are available from the HMP DACC (http://hmpdacc.org) and from SRA BioProjects PRJNA48479 and PRJNA275349. Metadata and precomputed taxonomic profiles for HMP1-II samples are also available from the HMP DACC. The HMP2 IBDMDB metagenomes used in this work are available from SRA BioProject PRJNA398089. Metadata for HMP2 samples are available from the IBDMDB website (https://ibdmdb.org). WAAFLE’s databases (as used in this work), synthetic evaluation contigs, HMP1-II and HMP2 assemblies, and LGT calls are available from the WAAFLE website (https://github.com/biobakery/waafle). Source data are provided with this paper.

Code availability

WAAFLE is a free and open-source Python 3 software package available from GitHub (https://github.com/biobakery/waafle) and PyPI (https://pypi.org/project/waafle/). Installation and usage details, including links to download the databases and analysis products used in this work, are expanded on the WAAFLE website (https://github.com/biobakery/waafle). Additional Python and R code used in the statistical analyses and visualizations from this work are available from the authors upon request.

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Acknowledgements

This work was supported by National Institutes of Health grants T32CA009001 (E.N.), U54DE023798 (C.H.), R24DK110499 (C.H.) and K23DK125838 (L.H.N.), the American Gastroenterological Association Research Foundation’s Research Scholars Award (L.H.N.), the Crohn’s and Colitis Foundation Career Development Award (L.H.N.), and the MGH/Chen Institute Transformative Scholars Award in Medicine (L.H.N.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We thank A. Pawluk, K. N. Thompson, K. Curry and T. Treangen for comments on the manuscript and helpful discussions; M. Michaud, C. Dulong and Y. Yan for help with validation experiments. Computational work was conducted on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University.

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Author notes

  1. These authors contributed equally: Tiffany Y. Hsu, Etienne Nzabarushimana.

  2. These authors jointly supervised this work: Long H. Nguyen, Eric A. Franzosa.

Authors and Affiliations

  1. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Tiffany Y. Hsu, Etienne Nzabarushimana, Curtis Huttenhower, Long H. Nguyen & Eric A. Franzosa

  2. Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Etienne Nzabarushimana & Long H. Nguyen

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Dennis Wong & Robert G. Beiko

  4. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Chengwei Luo, Curtis Huttenhower & Eric A. Franzosa

  5. Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada

    Morgan Langille

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Contributions

T.Y.H., E.N., L.H.N. and E.A.F. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. T.Y.H., E.N., C.H., L.H.N. and E.A.F. conceptualized and designed the project. All authors acquired, analysed and interpreted data, and critically reviewed the paper for important intellectual content. T.Y.H. and E.N. wrote the paper draft. T.Y.H. and E.N. performed statistical analysis. C.H. and L.H.N. obtained funding. C.H., L.H.N. and E.A.F. provided administrative, technical or material support. L.H.N. and E.A.F. supervised the project.

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Correspondence to Long H. Nguyen or Eric A. Franzosa.

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Extended data

Extended Data Fig. 1 Relative accuracy of WAAFLE, DarkHorse, and MetaCHIP on synthetic LGT and control contigs.

WAAFLE was penalized with a 20% holdout of its search database, while DarkHorse was evaluated using a translated version of the complete database, and MetaCHIP was evaluated without further constraints on its respective input. (a) DarkHorse only achieved non-negligible sensitivity (TPR) for the longest contigs (rightmost column) containing the most “extreme” LGT events (that is between pairs of species with the kingdom- or phylum-level LCAs). WAAFLE’s specificity (FPR) is stratified according to the taxonomic level of the LGT LCA as in Fig. 1 from the main text (for example an intragenus false positive is counted as a true negative at the family level; x-axis). This level of stratification was not possible for DarkHorse, and so a single FPR value is plotted at “genus” resolution for comparison. DarkHorse offered better specificity than WAAFLE on shorter contigs (where it made relatively few LGT calls) but not on the longest contigs. (b) Here, an additional comparison was performed between WAAFLE and MetaCHIP using a separate synthetic dataset designed for MetaCHIP compatibility. TPR and FPR were computed and plotted as in ‘(A)’ with TPR calculations restricted to taxonomic ranks assigned to at least 100 LGT LCAs (that is kingdom, order, and genus). Results are stratified according to the completeness of the metagenomic bins into which LGT and control contigs were grouped. WAAFLE’s sensitivity here was similar to that observed in the preceding evaluations and consistently higher than MetaCHIP. While MetaCHIP’s specificity was correspondingly very high, WAAFLE again exhibited a peak FPR of only ~0.5% at the intragenus level, improved at higher ranks. Notably, WAAFLE’s performance was not dependent on bin completeness, while MetaCHIP proved less sensitive to LGT events in less-complete bins.

Source data

Extended Data Fig. 2 Relationship between phylogenetic relatedness and WAAFLE performance.

(a) Distribution of phylogenetic distance (PD) by taxonomic rank. Genomes from the WAAFLE synthetic benchmarking dataset were compared with each dot representing the PD value of a pair of genomes which were then binned based on the level of their taxonomic LCA. The boxplot limits display the first and third quartiles, with a horizontal line indicating the median, the whiskers extend to 1.5x the interquartile range below Q1 and above Q3 (the inner fence) outside of which outliers are plotted as individual data points. Phylogenetic relatedness generally corresponds to the hierarchical rank of taxonomy. Nevertheless, taxa distantly related by taxonomy may have similar phylogenetic distances and vice versa. Taxonomic rank, s: species (n = 237), g: genus (n = 238), f: family (n = 233), o: order (n = 227), c: class (n = 216), p: phylum (n = 237), k: kingdom (n = 227). (b) The true positive rate (TPR) for WAAFLE calling an LGT based on PD. The relationship between phylogenetic relatedness and WAAFLE sensitivity follows an exponential fit of y = 0.779*(1-exp(−2.012*x)). The vertical dotted lines indicate the 90th percentile of intraspecies and intragenus PD values corresponding to panel A. (c) WAAFLE controlled the false positive rate for closely related genomes, similar to what was observed for genomes within the same genus. (d) Comparison of WAAFLE and MetaCHIP sensitivity using a MetaCHIP-compatible synthetic dataset. MetaCHIP had consistently lower sensitivity for detecting LGT among genomes whose PD is greater or equal to the 90th percentile of intragenus PD values. (e) MetaCHIP and WAAFLE each adequately controlled the false positive rate, which improved with greater PD.

Source data

Extended Data Fig. 3 Rates of undirected and directed inter-genus LGT for HMP1-II metagenomes profiled by WAAFLE.

LGT rates normalized to total assembly size in 1000 s of genes, stratified by body site and correspondingly colored by body area. (a) All inter-genus LGT events were considered, regardless of whether the donor and recipient clades were known. (b) Only directed inter-genus LGT events were considered (that is, cases where the donor and recipient clade were clear from gene adjacency). Major body sites are labeled in bold type. (c) Total assembly sizes for the same set of samples; only genes resolved to at least the genus level were counted. Only the first sequenced visit from each unique HMP1-II participant is plotted. Each box displays the first and third quartiles, with a horizontal line indicating the median, while the whiskers extend to 1.5x the interquartile range below Q1 and above Q3 (the inner fence) outside of which outliers are plotted as individual data points.

Source data

Extended Data Fig. 4 Differential abundance of putative antimicrobial resistance genes in LGT vs. non-LGT contigs.

(a) ARG enrichment patterns in HMP1-II metagenomes are similar across body areas and higher in LGT contigs. (b) Cross-tabulation of ARG abundance by resistance mechanism demonstrates that genes related to antibiotic inactivation are more abundant in LGT contigs. (c) Cross-tabulation of ARG genes by drug class demonstrates an enrichment of genes conferring resistance to ribosomal-targeting antimicrobial drugs in LGT. (d) Differential abundance of ARG families in LGT vs. non-LGT contigs. Specifically, those conferring resistance to antimicrobials acting on protein synthesis pathways (for example, ribosomal proteins like aminoglycosides) and folate synthesis (for example, sul genes) are enriched in LGT, while ARGs conferring resistance to drugs disrupting cell wall biosynthesis (for example, glycopeptides) are depleted. Heatmaps indicate the log-scaled fold enrichment of ARGs in LGT vs. non-LGT contigs.

Source data

Supplementary information

Supplementary Information

Supplementary Appendix.

Reporting Summary

Supplementary Tables 1–8

Supplementary Table 1 Assembly statistics of the HMP1-II metagenomes. Tables 2–5 Counts and rates of detected LGT. Tables 6 and 7 Molecular function data. Table 8 Experimental validation PCR data and results. All contigs and LGT profiles are provided on the WAAFLE website (https://github.com/biobakery/waafle).

Source data

Source Data Unprocessed Gels

Unprocessed gel images for both Fig. 6 and Supplementary Fig. 15.

Source Data Figs. 1–6 and Extended Data Figs. 1–4

Source data provided in separate, labelled sheets.

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Hsu, T.Y., Nzabarushimana, E., Wong, D. et al. Profiling lateral gene transfer events in the human microbiome using WAAFLE. Nat Microbiol 10, 94–111 (2025). https://doi.org/10.1038/s41564-024-01881-w

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