DOI: 10.5061/dryad.pnvx0k6v8
title: Microbiome diversity protects against pathogens by nutrient blocking

description: The human gut microbiome plays an important role in resisting colonisation
 of the host by pathogens, but we lack the ability to predict which
 communities will be protective. We studied how human gut bacteria
 influence colonisation of two major bacterial pathogens, both in
 vitro and in gnotobiotic mice. While single species alone had
 negligible effects, colonisation resistance greatly increased with
 community diversity. Moreover, this community-level resistance rested
 critically upon certain species being present. We explain these ecological
 patterns via the collective ability of resistant communities to consume
 nutrients that overlap with those used by the pathogen. Further, we apply
 our findings to successfully predict communities that resist a novel
 target strain. Our work provides a reason why microbiome diversity is
 beneficial and suggests a route for the rational design of
 pathogen-resistant communities.
description: Various methods were used to collect data provided in this
 dataset including plate-reader based measurments of optical density or
 luminescence, flow cytometry, colony counting, and metagenomic sequencing.
 Raw data is provided in this dataset and allows re-plotting of figures in
 the manuscript.
description: # Microbiome diversity protects against pathogens by nutrient blocking
 [https://doi.org/10.5061/dryad.pnvx0k6v8](https://doi.org/10.5061/dryad.pnvx0k6v8) Give a brief summary of dataset contents, contextualized in experimental procedures and results. ## Description of the data and file structure These files provide the necessary data to recreate the figures in the associated manuscript, and also provides additional raw data. **LEAD CONTACT**: Prof. Kevin R Foster. Departments of Biology and Biochemistry, University of Oxford, Oxford, United Kingdom, kevin.foster@biology.ox.ac.uk **DATE AND LOCATION OF DATA COLLECTION**: 01/2020 - 04/2023, University of Oxford, United Kingdom **BACKGROUND FOR DATA**: The human gut microbiome plays an important role in resisting colonization of the host by pathogens, but we lack the ability to predict which communities will be protective. We studied how human gut bacteria influence colonization of two major bacterial pathogens, both in vitro and in gnotobiotic mice. While single species alone had negligible effects, colonization resistance greatly increased with community diversity. Moreover, this community-level resistance rested critically upon certain species being present. We explain these ecological patterns via the collective ability of resistant communities to consume nutrients that overlap with those used by the pathogen. Further, we apply our findings to successfully predict communities that resist a novel target strain. Our work provides a reason why microbiome diversity is beneficial and suggests a route for the rational design of pathogen-resistant communities. **FILES ASSOCIATED WITH THIS DATASET**: SourceData\_SpraggeBakkerenetal\_Fig1.xlsx - Experimental outline of the initial screen (A) and phylogenetic tree depicting the relationship of the 100 screened symbiont strains to the pathogens and how each strain performed in the screen (B,C). Experimental outline of the in vitro extended competition assay (D) and pathogen abundance on day two of the extended competition assay when in competition against each of the ten best-ranked individual symbionts from the screen, as well as against the communities of the ten best-ranked and ten worst-ranked strains (E,F). SourceData\_SpraggeBakkerenetal\_Fig2.xlsx - Results of the in vitro extended competition assay (readout is pathogen abundance on day two of the assay) when randomly selected symbiont communities of differing diversity (ranging from 1 to 50 species) and composition were tested for their colonisation resistance ability against the pathogens. (A,B) show the initial communities tested and (B,C) show the same but with the additional E. coli-containing communities. (E,F) shows the results of the extended competition assay (pathogen abundance, day two) when E. coli IAI1 is swapped out of the ten best-ranked strains and replaced with other E. coli strains. SourceData\_SpraggeBakkerenetal\_Fig3.xlsx - (A) shows the experimental outline for the in vivo experiments using germ-free mice. (B,C) show the diversity of the gavaged symbiont inoculums and the faecal communities on the day of pathogen gavage (day 0 post infection), as measured by metagenomics. (D,E) show pathogen abundance in the mouse faeces when sampled 24 hours after pathogen gavage, for each of the different treatments. SourceData\_SpraggeBakkerenetal\_Fig4.xlsx - (A,B) show that pathogen abundance on day two of the in vitro extended competition assay decreases as the degree of protein family overlap of the symbiont community with the pathogen increases. (C,D) show that as carbon source overlap between the symbiont communities and the pathogen increases, pathogen abundance on day two of the extended competition assay decreases. (E,F) show that when the private nutrient galactitol is supplemented during the in vitro extended competition assay, it enables the pathogen to invade the community of the ten best-ranked symbionts. SourceData\_SpraggeBakkerenetal\_Fig5.xlsx - (A) shows that as the number of species increases in a symbiont community, carbon source overlap with the pathogenic E. coli strain increases. (B) shows the abundance of the pathogenic E. coli strain on day two of the extended competition assay when tested against the predicted worst and best symbiont communities at each diversity level (predictions based on carbon source overlap).(C) shows that as the number of symbiont species increases in a community, protein family overlap with the pathogenic E. coli increases. (D) shows a closer view of the E. coli-containing communities in (C). (E) shows the experimental validation of the predicted best and worst performing symbiont communities against the pathogenic E. coli (predictions based on the protein family overlap). (F) shows the results of the five predicted best and five predicted worst five-member symbiont communities against the pathogenic E. coli. SourceData\_SpraggeBakkerenetal\_FigS1.xlsx - Human gut symbiont strains vary in their ability to inhibit growth of K. pneumoniae and S. Typhimurium in the luminescence screen. (A,B) show waterfall plots of the ecological invasion assays, (C,D) show waterfall plots of the competition screen. (E,F) show correlations of the ecological invasion and competition assays. SourceData\_SpraggeBakkerenetal\_FigS2.xlsx - Community diversity negatively correlates with pathogen abundance for E. coli-containing communities in the in vitro extended competition assay. SourceData\_SpraggeBakkerenetal\_FigS3.xlsx - Comparison to a null model where the effect of each species proportionally restricts pathogen growth in an additive manner. (A,B) show the null model for the in vitro data and (C,D) show the null model for the in vivo data. SourceData\_SpraggeBakkerenetal\_FigS4.xlsx - Combinations of multiple species are important for colonization resistance to each pathogen. (A,B) show the same data as in Figure 2B,C but the communities containing the other key members are highlighted in different colours. (C,D) show drop-out experiments where the ten-species communities are constructed without the various key members. SourceData\_SpraggeBakkerenetal\_FigS5.xlsx - Metagenomic sequencing shows that germ-free mice ravaged with more diverse communities were colonized with a higher number of bacterial strains. (A,B) show the species richness of the gavaged symbiont inoculums and the faeces on the day of pathogen gavage. (C,D) show the results of the metagenomic sequencing and the relative abundances of the identified species. SourceData\_SpraggeBakkerenetal\_FigS6.xlsx - Pathogen abundance in mouse faeces at later timepoints, specifically at 24, 48, 72 and 96 hours post pathogen gavage. SourceData\_SpraggeBakkerenetal\_FigS7.xlsx - The number of protein families increase proportional to community diversity SourceData\_SpraggeBakkerenetal\_FigS8.xlsx - The randomly chosen communities used in the in vitro experiments overlaid on all possible combinations of the 10 best-ranked species, showing that the selection tested was representative. (A,B) show the protein family overlap of the communities with the pathogens, (C,D) show the carbon source use overlap of the symbiont communities with the pathogens. SourceData\_SpraggeBakkerenetal\_FigS9.xlsx - Protein family overlap between single strains or communities and the pathogen shows that both diversity and key members (E. coli) are important in explaining predicted colonization resistance. (A,B) shows that as the number of species in a symbiont community increases, so does protein family overlap with the pathogen. (C,D) shows the protein family overlap of the ten best-ranked individual symbiont strains with the pathogens. (E,F) show that as protein family overlap with the pathogen increases, pathogen abundance on day two of the extended competition decreases. SourceData\_SpraggeBakkerenetal\_FigS10.xlsx - Individual carbon source utilization profiles of the 10 best-ranked symbiont strains for K. pneumoniae and S. Typhimurium (16 symbiont strains total) in (A) and their overlap with the pathogens in (B,C). SourceData\_SpraggeBakkerenetal\_FigS11.xlsx - The protein family overlap and carbon source overlap prediction approaches are positively correlated SourceData\_SpraggeBakkerenetal\_FigS12.xlsx - Spent media experiment SourceData\_SpraggeBakkerenetal\_FigS13.xlsx - Carbon source utilization overlap of the individual 16 symbiont strains with AMR E. coli ## Sharing/Access information There is no restriction to sharing of this data. All data generated or analysed during this study are included in the manuscript and supporting files (https://doi.org/10.5061/dryad.pnvx0k6v8). All Illumina sequencing data are publicly available from the NCBI repository (NCBI accession number JAVXZX000000000, BioProject PRJNA1021490). ## Code/Software All code needed for analysis or to generate plots are available at https://github.com/MartinTJahn/Nutrient\_blocking