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Bacterial phylogeny structures soil resistomes across habitats


Ancient and diverse antibiotic resistance genes (ARGs) have previously been identified from soil1,2,3, including genes identical to those in human pathogens4. Despite the apparent overlap between soil and clinical resistomes4,5,6, factors influencing ARG composition in soil and their movement between genomes and habitats remain largely unknown3. General metagenome functions often correlate with the underlying structure of bacterial communities7,8,9,10,11,12. However, ARGs are proposed to be highly mobile4,5,13, prompting speculation that resistomes may not correlate with phylogenetic signatures or ecological divisions13,14. To investigate these relationships, we performed functional metagenomic selections for resistance to 18 antibiotics from 18 agricultural and grassland soils. The 2,895 ARGs we discovered were mostly new, and represent all major resistance mechanisms15. We demonstrate that distinct soil types harbour distinct resistomes, and that the addition of nitrogen fertilizer strongly influenced soil ARG content. Resistome composition also correlated with microbial phylogenetic and taxonomic structure, both across and within soil types. Consistent with this strong correlation, mobility elements (genes responsible for horizontal gene transfer between bacteria such as transposases and integrases) syntenic with ARGs were rare in soil by comparison with sequenced pathogens, suggesting that ARGs may not transfer between soil bacteria as readily as is observed between human pathogens. Together, our results indicate that bacterial community composition is the primary determinant of soil ARG content, challenging previous hypotheses that horizontal gene transfer effectively decouples resistomes from phylogeny13,14.

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Figure 1: Functional selections of 18 soil libraries yield diverse ARGs.
Figure 2: Resistance is encoded by diverse soil phyla.
Figure 3: Resistomes correlate with phylogeny across soil type and nitrogen amendment.
Figure 4: Pathogen ARGs show higher HGT potential than soil ARGs.

Accession codes

Primary accessions


Sequence Read Archive

Data deposits

All assembled sequences have been deposited to Genbank with accession numbers KJ691878KJ696532 and raw reads to SRA under the accession number SRP041174.


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We thank M. Pesesky for access to and assistance with the data set used to benchmark RAIphy’s performance, B. Wang for suggested improvements to Illumina library preparation, the Genome Technology Access Center at Washington University in St Louis for generating Illumina sequence data, M. Sherman for discussions on modelling pathogen HGT potential, and members of the Dantas laboratory for discussions on the results and analyses presented here. This work was supported by awards to G.D. through the Children’s Discovery Institute (MD-II-2011-117), the International Center for Advanced Renewable Energy and Sustainability at Washington University, the National Academies Keck Futures Initiatives (Synthetic Biology, SB2), and the NIH Director’s New Innovator Award (DP2-DK-098089). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. M.K.G. is supported by a Mr and Mrs Spencer T. Olin Fellowship for Women in Graduate Study at Washington University. K.J.F. received support from the NIGMS Cell and Molecular Biology Training Grant (GM 007067) and from the NHGRI Genome Analysis Training Program (T32 HG000045). K.J.F. and M.K.G. are NSF graduate research fellows (award number DGE-1143954).

Author information

Authors and Affiliations



N.F., C.L.L. and R.K. provided soils and 16S rRNA gene sequencing data; G.D. conceived the functional selections; S.P. created metagenomic libraries, performed functional selections, and prepared sequencing libraries; K.J.F. assembled sequence data from functional selections and annotated ARGs with assistance from M.K.G.; M.K.G. built the custom ARG profile HMM database; K.J.F. performed genomic and ecological analyses and wrote the manuscript with contributions from M.K.G., R.K., N.F. and G.D.

Corresponding author

Correspondence to Gautam Dantas.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Functional selections of 18 soil metagenomes for resistance against 18 antibiotics.

a, Phenotypic results of selections. A dark grey cell means that a resistance phenotype was observed whereas white cells indicate the absence of any drug-tolerant transformants. Grassland soils from CC are labelled in red and agricultural soils from KBS are labelled in blue. b, c, Alpha diversity representations. On the left is depicted the number of distinct ARG annotations observed as increasing numbers of ARGs are sampled from each soil. On the right, Shannon diversity scores (an ecological metric that quantifies within-sample diversity) are shown at each rarefaction step.

Extended Data Figure 2 Three prominent ARG classes are present in nearly all bacterial genomes and can provide antibiotic resistance when overexpressed.

a, Generalized as red circles are dihydrofolate reductases, D-alanine—D-alanine (D-ala D-ala) ligases, which are the molecular targets of the drugs trimethoprim (TR) and D-cycloserine (CY) respectively (black stars), and thymidylate synthases, which can provide trimethoprim resistance by circumventing the need for an active dihydrofolate reductase. When overexpressed in functional selections, these genes can provide antibiotic resistance. We found substantial diversity in these genes (average pairwise amino acid identity 39.3 ± 12.2%), suggesting that variants were captured from many bacterial lineages. b, Relative to other ARG mechanisms, large numbers of dihydrofolate reductases, thymidylate synthases, and D-ala D-ala ligases were found in all soils, with these ARGs representing 92.5% of resistance genes identified from selections containing trimethoprim or D-cycloserine antibiotics. Therefore, these selections encompass large genetic diversity, but constrained functional diversity, with a broad range of genes encoding limited functional traits. c, When considered in isolation, these functions were not different between the KBS and CC soils (P > 0.05, ANOSIM), indicating that trimethoprim and D-cycloserine resistance function is similarly distributed across the surveyed soil types.

Extended Data Figure 3 Total counts of β-lactamases recovered from antibiotic selections.

All soils (black), CC soils (red), and KBS soils (blue).

Extended Data Figure 4 Total counts of ARGs categorized by their predicted phylogenetic origin.

The number of ARGs is indicated on the y axis and the ARG types are colour-coded in the key.

Extended Data Figure 5 PCoA analysis plots of Bray–Curtis distances between soil resistomes.

The PCoA was calculated using all ORFs captured from functional selections without trimethoprim and D-cycloserine, and shows significant separation between CC (red) and KBS (blue) resistomes (P < 10−5, ANOSIM).

Extended Data Figure 6 PCoA across CC (red, grassland) and KBS (blue, agricultural) soils.

ac, PCoA generated from all 16S data available from ref. 8, using Bray–Curtis (a), weighted Unifrac (b) and unweighted Unifrac (c) dissimilarity metrics. Samples cluster by soil location and N level, as previously demonstrated. df, The same PCoA plots generated using only samples with sufficient 16S and resistome data (that is, those used in Procrustes and Mantel analyses). Excluding the two high-N KBS soils with insufficient resistome data eliminates the clustering pattern observed for KBS soils in ac. The asterisk denotes the high-N KBS soil common to both sets of analyses.

Extended Data Figure 7 Phylum level relative abundance of combined CC and KBS data sets for major soil bacteria.

a, 16s rRNA data are depicted in black. Phylogenetic inferences based on the sequence composition of the assembled, resistance-conferring DNA fragments are depicted in red. The relative abundances of Actinobacteria and Acidobacteria represent the largest discrepancies between data sets. b, Actinobacteria are most dramatically enriched in resistance-conferring DNA fragments, in accord with their role in producing antibiotics, but despite their high GC-content and predicted transcriptional incompatibilities with E. coli. Levels of Proteobacteria, the phylum to which E. coli belongs, are largely unchanged following functional selection, suggesting that any potential bias introduced to the selections by heterologous expression in E. coli is minimal compared to the effect of ARG-content of the source organisms.

Extended Data Figure 8 Procrustes analysis demonstrates that when soils cluster by bacterial composition, resistomes aggregate with phylogenetic groupings.

ac, Procrustes analysis of the ARG content (Bray–Curtis) of CC (red) and KBS (blue) soils compared to community composition calculated by Bray–Curtis (a), weighted Unifrac (b) and unweighted Unifrac (c) dissimilarity metrics. df, The same Procrustes transformations for CC soils only. For a given soil, black lines connect to functional resistome data while the green lines connect to points generated from 16S gene sequence data. The M2 fit reported is from a Procrustes transformation over the first two principal coordinates while the P-value is calculated from a distribution of empirically determined M2 values over 10,000 Monte Carlo label permutations. For M2/P values calculated using all principal coordinates, refer to Supplementary Table 8.

Extended Data Figure 9 Procrustes analysis demonstrates that when soils do not form distinct phylogenetic clusters, we are unable to detect significant correlation between ARG content and phylogenetic architecture.

See Extended Data Fig. 6 for the phylogenetic relationships between these soils. ac, Procrustes analysis of the ARG content (Bray–Curtis) of KBS (agricultural, blue) soils compared to 16S rRNA gene sequence using unweighted Unifrac (a), weighted Unifrac (b) and Bray–Curtis (c) similarity metrics. df, The same Procrustes transformations for the CC soils (grassland, red) without high-N amendment, showing that soil groupings must be distinguishable by bacterial composition to detect correlations with resistome content, regardless of soil type. For a given soil, black lines connect to functional resistome data while the green lines connect to points generated from 16S rRNA gene sequence data. The M2 fit reported is from a Procrustes transformation over the first two principal coordinates while the P value is calculated from a distribution of empirically determined M2 values over 10,000 Monte Carlo label permutations.

Extended Data Figure 10 Histogram of nucleotide per cent identity from pairwise alignments of all predicted mobility elements, suggesting that assembly does not inappropriately condense mobile DNA elements into too few sequences.

The blue trace depicts a normal distribution with the same mean and standard deviation empirically observed across all pairwise comparisons (n = 666).

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Supplementary information

Supplementary information

This file contains Supplementary Tables 1-9. (PDF 225 kb)

Supplementary Data 1

This file contains all recovered contigs, their ORFs, annotations, and information regarding the samples from which each contig was derived. (XLSX 364 kb)

Supplementary Data 2

This file outlines all 433 human pathogen genomes and 153 non-pathogenic soil genomes used in comparisons of resistance gene mobility in these different environments. (XLSX 33 kb)

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Forsberg, K., Patel, S., Gibson, M. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).

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