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Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Nature Ecology & Evolution volume 4, pages 437–452 (2020)Cite this article

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Abstract

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.

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Fig. 1: Barcoding E. coli cells with Tn7 transposon machinery.
Fig. 2: Dynamics of barcoded lineage frequencies over evolution experiment.
Fig. 3: Dynamics of lineage diversity over time.
Fig. 4: Dynamics of lineage dissimilarity among populations over time.
Fig. 5: Reproducibility of individual lineage dynamics.
Fig. 6: Distinct patterns of trajectories from pre-existing and de novo beneficial mutations.

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Data availability

All raw barcode sequencing data used in this study is deposited in the National Center for Biotechnology Information Sequence Read Archive under BioProject accession numbers PRJNA592527 (initial barcode libraries), PRJNA592371 (time points from evolution experiment under increasing drug concentrations), and PRJNA592529 (time points from evolution experiment under constant drug concentrations). All other raw data is included in Supplementary Tables 1–4.

Code availability

All custom scripts used to analyse the data are available on request.

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Acknowledgements

We thank L. Zhao, J. Rojas Echenique, S. Levy and A. Pascual Garcia for advice on analysing the data, and D. Tawfik and A. Aharoni for comments and help with preparation of the manuscript. This work was supported by an F32 fellowship from the US National Institutes of Health (GM116217) and an Ambizione grant from the Swiss National Science Foundation (PZ00P3_180147) to M.M.; a grant from the Canadian Natural Sciences and Engineering Research Council (NSERC RN000524) to A.W.R.S.; and a personal Israel Science Foundation grant 1630/15 to S.B. We also acknowledge support from the FAS Division of Science, Research Computing Group at Harvard University for the computations performed on the Odyssey cluster.

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Author notes

  1. These authors contributed equally: Weronika Jasinska, Michael Manhart.

Authors and Affiliations

  1. Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Weronika Jasinska, Jesse Lerner & Shimon Bershtein

  2. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Michael Manhart

  3. Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland

    Michael Manhart

  4. Department of Biochemistry, University of Montreal, Montreal, Quebec, Canada

    Louis Gauthier & Adrian W. R. Serohijos

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Contributions

S.B. conceived the study. W.J. and J.L. performed the laboratory experiments. M.M. analysed sequencing data and developed ecological approach. L.G. and A.W.R.S. performed simulations. M.M., L.G., A.W.R.S. and S.B. interpreted the data. M.M. and S.B. wrote the manuscript. All authors approved of the final manuscript.

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Correspondence to Shimon Bershtein.

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Extended data

Extended Data Fig. 1 Initial barcode libraries.

(A) Distributions of barcode frequencies at different stages of library preparation. For a given frequency on the horizontal axis, the vertical axis shows the number of unique detected barcodes with that frequency. “Raw library” (blue): NextSeq Illumina sequencing of the barcode library as synthesized by IDT (prior to plasmid library creation). “Plasmid library” (red): MiSeq Illumina sequencing of barcodes incorporated into the Tn7 integration plasmid library. “Chromosomal library” (orange): NextSeq Illumina sequencing of the barcode library integrated into E. coli chromosomes and generated by PCR performed on chromosomal DNA pooled from four independent extractions. (B) Shannon entropy of nucleotides at each position in the 15 nt barcode for the same libraries in panel (A). The horizontal black line marks the entropy (ln 4≈1.386) of a maximally-random library where all nucleotides are equally abundant at each position. (C) Comparison of individual barcode frequencies in the raw library and in the plasmid library. Points are partially transparent to show their density; the dashed black line marks the line of identity. We also show the Pearson correlation coefficient R and estimated p-value for the frequencies (p-values are numerically indistinguishable from zero). (D) Same as (C) but comparing the plasmid library and the chromosomal library.

Extended Data Fig. 2 Drug concentrations and population growth over evolution experiment.

(A) Trajectories of low and ultra-low chloramphenicol (CMP) concentrations over time of the evolution experiment. (B) Same as (A) but for trimethoprim (TMP) conditions. (C) Approximate number of cells at the end of each passage for low and ultra-low CMP conditions, along with the populations evolved without drug. Lines are averages over all 14 replicate populations for each condition. (D) Same as (C) but for TMP conditions. (E) Same as (C) but showing the fold-change of population size during each passage on the vertical axis. (F) Same as (E) but for TMP conditions. Periodic oscillations in cell numbers and yields result from the fact that cultures were propagated in two intermittent growth regimes: 9 hours during the day, followed by 12 hours during the night (see Methods). Supplementary Table 2 contains raw OD data for each population at the end of each passage.

Extended Data Fig. 3 Evolution of resistance and growth traits.

(A) At several time points during the evolution experiment, we measured the chloramphenicol (CMP) IC50 of the barcoded populations evolved in low and ultra-low CMP as well as those evolving without drug. (B) Same as (A) but for trimethoprim (TMP). (C) Growth rate, measured in the absence of drug, of barcoded populations evolved in low and ultra-low CMP as well as without drug. Points represent the mean and error bars represent standard deviation over replicate measurements. (D) Same as (C) but for populations evolved in low and ultra-low TMP. Supplementary Table 3 contains raw growth curve data for the evolved populations at these different drug concentrations.

Extended Data Fig. 4 Trajectories of barcoded lineage frequencies.

Each row corresponds to a different antibiotic regimen, while each column corresponds to a different replicate. Individual panels show the frequency trajectories for barcoded lineages in single populations over time of the experiment; we only show lineages with mean frequency over time greater than 10-4. For the top 20 lineages in each population (ranked by mean frequency over time), we assign a unique color to each lineage that is consistent across panels, i.e., the same color represents the same barcode across panels (Supplementary Table 4). All lower-frequency lineages are gray and partially transparent to show their density. The dashed black line represents the frequency of reads without identified barcodes. Dots above each plot mark times at which the drug concentration for that population changed (Extended Data Fig. 2A,B).

Extended Data Fig. 5 Trajectories of barcoded lineage frequencies under constant conditions.

Same as Extended Data 4 but for evolution experiments with constant drug concentrations (see Methods).

Extended Data Fig. 6 Dynamics of lineage diversity over time under constant conditions.

Same as Fig. 3 but for evolution experiments with constant drug concentrations (see Methods).

Extended Data Fig. 7 Dynamics of lineage dissimilarity among populations over time under constant conditions.

Same as Fig. 4 but for evolution experiments with constant drug concentrations (see Methods).

Extended Data Fig. 8 Trajectories with pre-existing and de novo beneficial mutations under constant conditions.

Same as Fig. 6B,C but for evolution experiments with constant drug concentrations (see Methods).

Extended Data Fig. 9 Comparison of simulations with different sources of variation.

We simulated evolutionary dynamics of a barcoded population (see Methods) with different supplies of mutations. We plot trajectories of barcoded lineage frequencies over time for simulated populations with (A) neither pre-existing nor de novo mutations (neutral dynamics), (B) pre-existing mutations only (mean s=0.1), (C) de novo mutations only (mean s=0.05), and (D) both pre-existing mutations (mean s=0.01) and de novo mutations (mixed exponential distribution with 90% deleterious and 10% beneficial). In the top row, each color indicates the relative frequency of a particular lineage at every time point. For simplicity, we show only lineages with a minimum frequency of 10-4 (the gray area covers the frequency of all remaining lineages). In the bottom row, we show the frequency trajectories as lines on a log scale; lineages with a minimum frequency of 5×10-4 are colored while all other lineages above 10-4 are gray. Lineage frequencies represent the estimated frequency from subsampling 10% of the full simulated population.

Extended Data Fig. 10 Simulated dynamics of lineage diversity.

We simulated evolutionary dynamics of the barcoded population for two replicates of each condition: “neutral” (no mutations), “pre-existing dominated,” and “de novo dominated” (see Methods). (A) For each population, we calculated the effective number of barcoded lineages using the diversity index qD (Eq. 1; see also Methods) for three different values of q, which controls the weight of low- versus high-frequency lineages: 0D (number of unique barcodes, left), 1D (Shannon diversity, center), and ∞D (reciprocal of the maximum lineage frequency, right). (B) We also calculated the dissimilarity of lineages (Eq. 2; see also Methods) among all replicate populations in each condition, using q=0 (left), q=1 (center), and q=∞ (right). All diversity and dissimilarity values are based on perfect sampling of the population.

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Jasinska, W., Manhart, M., Lerner, J. et al. Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution. Nat Ecol Evol 4, 437–452 (2020). https://doi.org/10.1038/s41559-020-1103-z

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