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Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation

Nature Ecology & Evolution volume 4, pages 1385–1394 (2020)Cite this article

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Abstract

In an era of pervasive anthropogenic ecological disturbances, there is a pressing need to understand the factors that constitute community response and resilience. A detailed understanding of disturbance response needs to go beyond associations and incorporate features of disturbances, species traits, rapid evolution and dispersal. Multispecies microbial communities that experience antibiotic perturbation represent a key system with important medical dimensions. However, previous microbiome studies on this theme have relied on high-throughput sequencing data from uncultured species without the ability to explicitly account for the role of species traits and immigration. Here, we serially passage a 34-species defined bacterial community through different levels of pulse antibiotic disturbance, manipulating the presence or absence of species immigration. To understand the ecological community response measured using amplicon sequencing, we combine initial trait data measured for each species separately and metagenome sequencing data revealing adaptive mutations during the experiment. We found that the ecological community response was highly repeatable within the experimental treatments, which could be attributed in part to key species traits (antibiotic susceptibility and growth rate). Increasing antibiotic levels were also coupled with an increasing probability of species extinction, making species immigration critical for community resilience. Moreover, we detected signals of antibiotic-resistance evolution occurring within species at the same time scale, leaving evolutionary changes in communities despite recovery at the species compositional level. Together, these observations reveal a disturbance response that presents as classic species sorting, but is nevertheless accompanied by rapid within-species evolution.

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Fig. 1: Experimental design.
Fig. 2: Community dynamics during the antibiotic pulse experiment.
Fig. 3: Community response to antibiotic perturbation.
Fig. 4: Competitive fitness of species in replicate communities during and after recovery from intermediate-level antibiotic pulses in the absence of immigration.
Fig. 5: Repeatability of community trajectories, assessed using the diversity dissimilarity index.
Fig. 6: Targets of adaptive mutations reaching high frequencies (>0.3 to fixation) in high-abundance species during or after recovery from antibiotic pulses.

Data availability

Raw sequencing data (FASTQ files) have been deposited at the NCBI Sequence Read Archive (SRA) under the accession number PRJNA632457. All preprocessed data needed to reproduce the downstream analyses and figures are available at GitHub (https://github.com/johannescairns/repeatable_dynamics; permanent URL, https://doi.org/10.5281/zenodo.3908935).

Code availability

All code needed to reproduce the downstream analyses and figures are available at GitHub (https://github.com/johannescairns/repeatable_dynamics; permanent URL, https://doi.org/10.5281/zenodo.3908935).

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Acknowledgements

We thank A. Ronkainen and P. Typpö for technical assistance. This research was funded by the Academy of Finland (grant nos. 106993, to T.H., and 313270, to V.M.), Jenny and Antti Wihuri Foundation (grant no. 190040 to J.C.) and the Heisenberg Stipend from the German Research Foundation (DFG; grant no. 4135/9, to L.B.).

Author information

Author notes

  1. These authors contributed equally: Johannes Cairns, Roosa Jokela.

Authors and Affiliations

  1. Wellcome Sanger Institute, Cambridge, UK

    Johannes Cairns

  2. Organismal and Evolutionary Biology Research Programme (OEB), Department of Computer Science, University of Helsinki, Helsinki, Finland

    Johannes Cairns & Ville Mustonen

  3. Department of Microbiology, University of Helsinki, Helsinki, Finland

    Johannes Cairns, Roosa Jokela & Teppo Hiltunen

  4. Human Microbiome Research Program (HUMI), Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Roosa Jokela

  5. Community Dynamics Group, Department of Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, Plön, Germany

    Lutz Becks

  6. Aquatic Ecology and Evolution, Limnological Institute University Konstanz, Konstanz, Germany

    Lutz Becks

  7. Helsinki Institute for Information Technology, Institute of Biotechnology, University of Helsinki, Helsinki, Finland

    Ville Mustonen

  8. Department of Biology, University of Turku, Turku, Finland

    Teppo Hiltunen

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Contributions

Design of serial passage experiment: T.H., J.C. and L.B. Performing serial passage experiment: R.J. Design of data analysis: J.C., V.M. and L.B. Performing data analysis: J.C. Interpreting results: J.C., T.H., L.B. and V.M. J.C. wrote the first manuscript draft, with contributions from all of the authors. All of the authors approved the final version of the manuscript.

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Correspondence to Johannes Cairns or Teppo Hiltunen.

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Extended data

Extended Data Fig. 1

A t-SNE map showing de novo community clustering before, during and after recovery from antibiotic pulse at different antibiotic levels with or without immigration (N = 190). The different antibiotic levels are indicated by color coding, and the time points relative to the antibiotic pulse are indicated by different shapes. Low, intermediate and high antibiotic levels correspond to 4, 16 and 128 μg ml–1 streptomycin, respectively. All data points originate from the same t-SNE analysis and have been separated into two panels (with same arbitrary axis units) only for the sake of visual clarity of immigration effect (at high antibiotic level, post-recovery communities indicated by diamonds more often resume pre-disturbance composition in upper left-hand region).

Extended Data Fig. 2

The extinction probability of species as a function of antibiotic level and the presence/absence of immigration (binomial glm estimate ± 95 % confidence intervals). Extinction is defined as the absence of a species after the antibiotic pulse (day 32 onwards) that was present prior to the pulse (day 16), and has been computed only for the species fulfilling these criteria in at least one experimental community (in total, 146 cases of extinction were observed). Low, intermediate and high antibiotic levels correspond to 4, 16 and 128 μg ml–1 streptomycin, respectively.

Extended Data Fig. 3

Bacterial biomass estimated by optical density (OD) at 600 nm at different levels of antibiotic pulse (expressed in μg ml–1) in the pre-disturbance (day 16), post-disturbance (day 32) and post-recovery (day 48) phases (mean ± standard deviation).

Extended Data Fig. 4

Global comparative view of community composition as shown by Kullback-Leibler (KL) divergence across all samples (N = 190). The color scale from blue to red indicates the degree to which community composition differs between two communities. KL divergence has been computed from species compositional data. The heat map has been color-annotated for the different immigration and antibiotic treatments and experimental phases. Low, intermediate and high antibiotic levels correspond to 4, 16 and 128 μg ml–1 streptomycin, respectively.

Extended Data Fig. 5

Competitive fitness landscapes across all samples during the antibiotic pulse and recovery phases (N = 126). The color scale from blue to red indicates the degree to which the competitive fitness landscapes are correlated between two communities. Correlations have been computed from competitive fitness data for each species in the communities. The heat map has been color-annotated for the different immigration and antibiotic treatments and experimental phases. Low, intermediate and high antibiotic levels correspond to 4, 16 and 128 μg ml–1 streptomycin, respectively. The black boxes show the data presented at species-level detail in Fig. 4 in the main text.

Extended Data Fig. 6

Correlation of competitive fitness landscapes within replicates in each experimental treatment (mean + 95 % confidence interval). Correlations have been computed from competitive fitness data for each species in the communities. The figure aggregates the pairwise correlation values shown for each replicate pair within treatments in Figure S4. Low, intermediate and high antibiotic levels correspond to 4, 16 and 128 μg ml–1 streptomycin, respectively.

Extended Data Fig. 7

Percentage of variance in the competitive fitness of species explained by the experimental treatments (antibiotic level and presence / absence of species immigration) and species traits (antibiotic MIC and intrinsic growth rate). The variance partitioning is based on ANOVA on competitive fitness performed separately for the antibiotic pulse and recovery phases (detailed results are presented in Supplementary Tables 1 and 2).

Extended Data Fig. 8

Illumina read recruitment (median per species) in whole genome alignments for deep sequencing data (two upper panels) or raw 16S rRNA amplicon data (bottom panel). The HAMBI codes of the species are indicated in the horizontal axis, with two exceptions: K12 and RP4 denote the chromosome and plasmid sequence, respectively, from E. coli JE2571. Read recruitment in whole genome alignments is indicated as number of reads (100 bp) in 1,000 bp blocks, and needs to be divided by 10 ((100 bp × read count)/(1,000 bp block)) to obtain an estimate of genome coverage. For instance, a median read count of 1,000 corresponds to roughly 100× genome coverage. In the uppermost panel, deep sequencing data was mapped separately to the genome of each individual species, and in the middle panel, the data was mapped to a multi-FASTA file containing all the genomes, producing comparable results. 16S rRNA amplicon read counts have been normalized to 15,000 reads per sample.

Extended Data Fig. 9

Deep sequencing read recruitment across the genomes of abundant species. Genomic position is indicated as relative position (0–1) across the whole chromosome for closed genomes or largest contig for draft genomes. Read recruitment in whole genome alignments is indicated as number of reads (100 bp) in 1,000 bp blocks, and needs to be divided by 10 ((100 bp × read count)/(1,000 bp block)) to obtain an estimate of genome coverage.

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Cairns, J., Jokela, R., Becks, L. et al. Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation. Nat Ecol Evol 4, 1385–1394 (2020). https://doi.org/10.1038/s41559-020-1272-9

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