Abstract
The myriad microorganisms that live in close association with humans have diverse effects on physiology, yet the molecular bases for these impacts remain mostly unknown1,2,3. Classical pathogens often invade host tissues and modulate immune responses through interactions with human extracellular and secreted proteins (the ‘exoproteome’). Commensal microorganisms may also facilitate niche colonization and shape host biology by engaging host exoproteins; however, direct exoproteome–microbiota interactions remain largely unexplored. Here we developed and validated a novel technology, BASEHIT, that enables proteome-scale assessment of human exoproteome–microbiome interactions. Using BASEHIT, we interrogated more than 1.7 million potential interactions between 519 human-associated bacterial strains from diverse phylogenies and tissues of origin and 3,324 human exoproteins. The resulting interactome revealed an extensive network of transkingdom connectivity consisting of thousands of previously undescribed host–microorganism interactions involving 383 strains and 651 host proteins. Specific binding patterns within this network implied underlying biological logic; for example, conspecific strains exhibited shared exoprotein-binding patterns, and individual tissue isolates uniquely bound tissue-specific exoproteins. Furthermore, we observed dozens of unique and often strain-specific interactions with potential roles in niche colonization, tissue remodelling and immunomodulation, and found that strains with differing host interaction profiles had divergent interactions with host cells in vitro and effects on the host immune system in vivo. Overall, these studies expose a previously unexplored landscape of molecular-level host–microbiota interactions that may underlie causal effects of indigenous microorganisms on human health and disease.
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Data availability
All data supporting this study are included in the paper and its associated supplementary tables or deposited in publicly available databases. Source Data is available for all figures (Figs. 1–5 and Extended Data Figs. 1–10). Raw BASEHIT sequence data were deposited and are available at the NCBI Sequence Read Archive with the BioProject identifier: PRJNA1039280. Mapped barcode data have been deposited and are available at Zenodo (https://doi.org/10.5281/zenodo.10606150)51. RNA sequencing data and whole-genome sequences for Staphylococcus strains were also deposited and can be found at PRJNA1039280. Public databases used: bioBakery 3 (https://github.com/biobakery), Species Genome Bin (http://segatalab.cibio.unitn.it/data/Pasolli_et_al.html), ProTraits (http://protraits.irb.hr/), UniProt (https://www.uniprot.org/), Gene Ontology (https://geneontology.org/), proteins physical properties55 and the Human Protein Atlas (https://www.proteinatlas.org). Source data are provided with this paper.
Code availability
The custom code for the analysis of BASEHIT data has been deposited and is available at Zenodo (https://doi.org/10.5281/zenodo.10606150)51.
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Acknowledgements
We thank all members of the Palm, Ring and Huttenhower laboratories for helpful advice and assistance. This work was supported by a grant from the Leona M. and Henry B. Helmsley Charitable Trust (3083 to N.W.P. and A.M.R.). N.W.P. is additionally supported by an NIH Director’s New Innovator Award (DP2DK125119), the NIA and NIGMS (R01AG068863 and RM1GM141649), a Pew Scholar Award, the Chan Zuckerberg Initiative, Aligning Science Across Parkinson’s, F. Hoffmann-La Roche Ltd, and gifts from the Mathers Family Foundation and Ludwig Family Foundation. A.M.R. is additionally supported by an NIH Director’s Early Independence Award (DP5OD023088), a Pew-Stewart Scholar award, and gifts from the Mathers Family Foundation, the Ludwig Family Foundation and the Robert T. McCluskey Foundation. C.E.R. and N.D.S. were supported by the National Science Foundation Graduate Research Fellowship Program. The computations in this paper were run in part on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University. Illustrations in Figs. 1a, 4a and 5a,c were generated with BioRender (https://biorender.com).
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C.E.R., N.D.S., N.W.P. and A.M.R. designed the study. C.E.R. and N.D.S. established the BASEHIT platform and performed BASEHIT screens. C.E.R., Y.D., S.F. and A.M.R. created the exoprotein yeast display library. A.R.G. developed the BASEHIT statistical model and performed associated analysis. E.A.F. performed the global network and phylogenetic analysis. N.D.S. and C.E.R. performed all other analyses. C.E.R., N.D.S., A.A.B. and Y.C. acquired and grew bacteria for BASEHIT screens. C.E.R., N.D.S. B.D.-L., J.A.G.-H., J.D.H. and T.A.R. contributed essential reagents for and performed orthogonal validations. N.D.S., B.D.-L. and J.A.G.-H. performed the in vitro functional experiments. N.D.S., Y.Y., M.T.N. and D.S. assessed potential phenotypes and performed the in vivo experiments. Y.Y. performed the whole-genome sequencing of Staphylococcus strains. C.G. and J.O. contributed Staphylococcus strains. A.L.M. assisted with the gnotobiotic mouse experiments. C.H., A.M.R. and N.W.P. supervised the study. C.E.R., N.D.S., A.R.G., E.A.F., C.H., A.M.R. and N.W.P. wrote the paper with input from all authors.
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C.E.R., N.W.P. and A.M.R. are inventors of patents related to the BASEHIT technology and specific host–microorganism interactions discovered through BASEHIT. N.W.P. is a co-founder of Artizan Biosciences and Design Pharmaceuticals. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Yeast exoproteome library composition and diversity and bacterial strain collection composition and diversity.
a, Extracellular protein sequences are curated and cloned into a standardized backbone featuring a C-terminal epitope tag. Proper display is confirmed via epitope tag staining, as well as binding by confirmation-specific antibodies or endogenous ligands for a subset of proteins. b, Schematic of expression construct used in the yeast display library. c, Proportion of the human exoproteome represented in the yeast display library. d, Each protein is represented by multiple barcodes, with a median of 20 barcodes per protein. Boxplot shows median, IQR, and whiskers extend to 1.5x IQR for n = 3,406 epitopes from 3,336 proteins in the library. e, Tissue expression (defined as Normalized Expression (NX) > 10 in the Human Protein Atlas) of proteins in the library, grouped by barrier, immune, and sterile tissues. f, Percentage of proteins in the library belonging to highly represented protein families. g, Number of strains from indicated genera, showing all genera with 9 or more strains. h, Number of strains from indicated species, showing all species with 5 or more strains. i, Number of strains from different body sites, showing all body sites with 5 or more strains.
Extended Data Fig. 2 BASEHIT optimization with AIEC identifies conditions that yield selectivity and specificity and are broadly specific across diverse known host-microbe interactions.
a, Enrichment of CD55 and CEACAM1 by AIEC using different bead:cell ratios. Enrichment is defined as the fold change in frequency of reads for the indicated protein in the post-selection library relative to the pre-selection library. Enrichment of both CD55 and CEACAM1 decreases with increasing cell:bead ratio. b, Enrichment of CD55 and CEACAM1 by AIEC labelled with variable concentrations of sulfo-NHS-biotin reagent. Increasing or decreasing concentrations of biotin decrease enrichment of CD55 and CEACAM1. c, Enrichment of CD55 and CEACAM1 by various E. coli strains with or without expression of Dr-family adhesins as indicated. CD55 and CEACAM1 are specifically enriched by the Dr-adhesin containing AIEC strain. d, Exoproteome-wide host exoprotein binding pattern of AIEC determined by BASEHIT. CD55 and CEACAM1 are enriched substantially more than any other protein. Data in a, b represent the mean ± s.d., from n = 3 independent samples. e, Diverse bacterial strains with previously described interactions with human exoproteins were screened by BASEHIT and assessed for enrichment. Interactions that were successfully detected by BASEHIT are shown as filled circles, while interactions that BASEHIT failed to detect are shown as empty circles. The overall rate of detection of previously reported interactions (54%) is shown in the pie chart on the right.
Extended Data Fig. 3 Impacts of biotinylation and bacterial cell density on the detection of interactions via BASEHIT.
a, Four bacterial strains with differing interaction profiles were grown and labeled with a titration of biotin ranging from 50 nM to 500 µM and then screened by BASEHIT. The enrichments of each protein hit are shown across all conditions, along with the enrichments of two predicted inert proteins — the coronaviral spike protein 229E-S1, and the arylsulfatase ARSA, which serve as internal negative controls. The biotin concentration used for labelling in our large-scale screen (5 µM) is highlighted in teal. Across all tested interactions, 5 µM biotin exhibited enrichments within two-fold of the “optimal” condition, and no appreciable enrichment of inert proteins was observed under any conditions. Data represent the mean ± s.d. from n = 3 independent experiments. b, Five strains were screened via BASEHIT at bacterial amounts ranging from 50 µL of 0.25 OD/mL to 10 OD/mL per well. The enrichments of hits identified in the BASEHIT screen, as well as the predicted inert proteins 229E-S1 and ARSA. The density used in our large-scale BASEHIT screen, 5 OD/mL, is highlighted in each graph. Across all tested interactions, an input of 50 µL of 5 OD/mL provided enrichment within two-fold of the “optimal” condition, and no appreciable enrichment of inert proteins was observed under any conditions. The density of bacterial particles was determined via volumetric counts for 97 strains used in our large-scale BASEHIT screen (all strains were at ~5 OD/mL). The five strains selected approximated the lower and upper bounds of particle density (~1 × 107 to ~3 × 108 particles/mL).
Extended Data Fig. 4 Modelling and scoring procedure metrics.
a, A histogram of the protein barcode representation in the input library. The wide spread on the log10 x-axis indicates a high degree of variability. The model accounts for this by using barcode input concentration as an offset term. Each tick mark across the x-axis below the histogram represents a protein. b, A Venn diagram showing interaction counts that pass each of the three hit-calling thresholds for the standard threshold set (95% interval excludes zero, estimated effect size > 0.5, and concordance score > 0.75). c, A plot of normalized counts demonstrating the utility of the concordance threshold. Both interactions shown have about the same interaction score (around 1.9) and similarly variable inputs in the Pre library (top panels), but the concordance between normalized output counts (bottom panels) in the TFF2:HM645 interaction is much higher than in SLC6A9:HM1171. Grey cells represent zero counts. d, A histogram of concordance scores for all interactions in the assay. Dashed vertical lines indicate the stringent and standard thresholds. e, Saturation curves from repeated rarefaction analysis. Given that both sets of thresholds have roughly plateaued, we can conclude that we have identified most of the interactions that are detectable under the experimental conditions. f, Comparison of the results of an initial run of the scoring method against five repeated runs where the standard deviation of the normal prior on interaction scores varied from 0.075 to 0.3. Each dot represents the score of a particular interaction. Only interactions that were a hit in at least one run are shown. The middle panel uses the same value as the initial run, showing the extent of Monte Carlo error. As expected, the rank and relative magnitude of scores are highly consistent between runs, while narrower priors lead to lower scores and fewer hits and wider priors lead to higher scores and more hits. The two distinct groups of interactions visible in the panels with wide priors represent subpopulations of interactions that are either more or less amenable to the zero-inflation component of the model.
Extended Data Fig. 5 Proteins from multiple tissues bind bacteria with a power-log distribution, and bacteria from different tissues or phyla show similar distributions of host protein binding.
a, Plot of number of bacterial strains bound (interaction called as a hit) for proteins expressed in multiple host tissues. Tissues expression is defined as Human Protein Atlas normalized expression NX > 10. b, Plot of number of proteins bound (interaction called as a hit) for all bacteria with hits as well as for all bacteria including non-binders. c, Same plot as b but depicting strains isolated from specific tissues. Maximum and mean reported for bacteria with one or more hits. d, Same plot as b but depicting strains from indicated phyla. Maximum and mean reported for bacteria with one or more hits.
Extended Data Fig. 6 Biophysical properties are significantly different between interacting and non-interacting proteins.
Proteins which bound at least one bacterial strain (“Targets”) are compared with “Non-targets” for various biophysical properties as indicated. FDR shown is for a two-tailed Wilcoxon Rank-Sum test. Box plots show median, IQR, and whiskers extending to 1.5x IQR, for n = 631 “Targets” and n = 2,705 “Non-targets”.
Extended Data Fig. 7 Relationships between similarity in strains’ interaction profiles and their phylogenetic distance.
a, We computed a phylogenetic tree over 108 genomes of tested strains based on ~ 400 broadly distributed protein families. We compared distances in this tree with similarity of strains’ interaction profiles using Spearman correlation (n = 5,565 strain pairs). Phylogenetic distance is expressed in units of amino acid substitutions per amino acid site. Interaction similarity was measured as the Jaccard overlap score between strains’ sets of human protein binding partners (ignoring strains with no binding partners). b, We separately considered the subset of n = 907 strain pairs with phylogenetic distance <0.02 substitutions per site, which was largely synonymous with a conspecific relationship in taxonomy. In both regimes, interaction similarity and phylogenetic distance were strongly and significantly negatively correlated. In both cases a two-tailed Mantel test with 104 permutations with FDR adjustments was performed.
Extended Data Fig. 8 Superbinder Staphylococcus show highly overlapping sub-networks.
a, Network of 7S. pasteuri and 8 other Staphylococcus superbinders, highlighted in green and orange respectively. The 5 proteins bound by the most strains are labeled. b, Overlap in interaction profiles across strains. Proteins are binned according to whether they are bound by more than half of the S. pasteuri strains (“Pasteuri core”), or by multiple or only one superbinder strains (“Multiple” and “Unique”, respectively). c, Top proteins bound by multiple superbinders. Overall interaction profiles of proteins bound by 7 or more superbinder strains are colored according to the strains they recognize, including all other Staphylococcus strains as well as non-Staphylococcus strains. d, Interactions for skin-expressed proteins CDSN, FAT2, and XG for all 519 bacterial strains organized by tissue of origin. Dashed red line at 0.5 represents hit threshold.
Extended Data Fig. 9 Phylogenetic specificity of interactions with tissue-specific proteins across all tested strains.
The interaction scores for all 519 tested strains are shown for the indicated proteins, which are highlighted in Fig. 4a. Strains are colored by phylum, and all scores above the hit threshold line at 0.5 are indicated and labeled with the genus of the strain. Parentheses indicate the frequency of hits within a genus.
Extended Data Fig. 10 Ruminococcus gnavus and Fusobacterium strains influence host cell binding and function.
a, Representative flow cytometry plots of CD7-binding and non-binding R. gnavus strains labelling mock, CD7-, and CD55-expressing EXPI293 cells as shown in Fig. 5b. b, Representative flow cytometry plots of THP-1 phagocytosis of CFSE-labelled Fusobacterium spp. and of fluorescein-labelled E. coli K12 BioParticles incubated with unlabelled Fusobacterium spp. from Fig. 5d,e.
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Sonnert, N.D., Rosen, C.E., Ghazi, A.R. et al. A host–microbiota interactome reveals extensive transkingdom connectivity. Nature 628, 171–179 (2024). https://doi.org/10.1038/s41586-024-07162-0
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DOI: https://doi.org/10.1038/s41586-024-07162-0
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