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Environmental selection and epistasis in an empirical phenotype–environment–fitness landscape

Nature Ecology & Evolution volume 6, pages 427–438 (2022)Cite this article

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Abstract

Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype–fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype–fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.

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Fig. 1: Workflow for generating fitness scores and EC50 values using DMS.
Fig. 2: Distributions of fitness and EC50 effects.
Fig. 3: Phenotype–fitness landscapes across different AMP concentrations.
Fig. 4: The VIM-2 phenotype–fitness landscape as a function of EC50 and AMP concentration.
Fig. 5: Sub-MIC selection behaviour of VIM-2 variants.
Fig. 6: Non-specific epistasis varies across selection conditions.

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Data availability

Sequencing data are available through the NCBI SRA, under BioProject accession PRJNA634597. All data generated or analysed during this study are included in this published article and its Supplementary Information.

Code availability

All code for analysis are publicly available. ‘DMS-FastQ-processing’ (v.1) can be found at https://github.com/johnchen93/DMS-FastQ-processing. ‘DMS-EC50’ (v.1) can be found at https://github.com/johnchen93/DMS-EC50.

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Acknowledgements

We thank A. Serohijos, P. Dasmeh and members of the Tokuriki laboratory for insightful comments on the manuscript. We thank the Canadian Institute of Health Research Foundation Grant (FDN-148437, N.T.) for the financial support. N.T. is a Michael Smith Foundation of Health Research career investigator.

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Authors and Affiliations

  1. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    J. Z. Chen & N. Tokuriki

  2. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    D. M. Fowler

  3. Department of Bioengineering, University of Washington, Seattle, WA, USA

    D. M. Fowler

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Contributions

J.Z.C. was responsible for conceptualization, software, formal analysis, investigation, methodology, writing the original draft and reviewing and editing it. D.M.F. contributed to software, validation, methodology and reviewed and edited the manuscript. N.T. was involved in conceptualization, supervision, funding acquisition, project administration and reviewing and editing of the manuscript.

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Correspondence to N. Tokuriki.

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Extended data

Extended Data Fig. 1 Example dose–response curves for EC50 calculation based on deep sequencing data.

Each pair of plots represent the two replicate dose–response curves of a single variant in the library. The solid curve indicates the results of fitting to the sigmoidal equation (2). The horizontal dashed line marks the average population size of the variant in the non-selected conditions. Dose–response curves shown here were randomly sampled from the entire pool of all variants with a successful fit.

Extended Data Fig. 2 Heat map of EC50 values for all variants.

Each cell in the heat map represents the EC50 value of a single amino acid variant. Synonymous variants are indicated by dark grey circles and variants that are not present in the library are in grey. The x-axis under the heat map indicates the wild-type residue and position, while the y-axis indicates the variant residue at that position. The colouring is scaled so that the centre of the colour range (white) is at the EC50-wt of 84 µg/mL.

Extended Data Fig. 3 Replicate correlation of DMS fitness scores.

Each scatterplot shows the replicate fitness scores of all variants under selection in (a) 4.0, (b) 8.0, (c) 16, (d) 32, (e) 64, (f) 128 and (g) 256 µg/mL AMP. In each panel, variants are coloured according to mutation type with the legend in the lower right. The solid black line indicate the line of best fit for a linear regression, with the R2 and P value of the regression above each plot.

Extended Data Fig. 4 Fit parameters for the phenotype–fitness relationship across AMP concentrations.

Each plot shows one of the final fitted value of a parameter in the four-parameter sigmoidal curve in equation (3) when fitted to the fitness scores and EC50 values from the AMP concentration indicated in the x-axis. (a) Plot for maximum fitness (fmax) (b) Plot for minimum fitness (fmin), (c) Plot for Hill coefficient (n) (d) Plot for the inflection point (X50). For all plots, solid points represent AMP concentrations where the linear portion of the fit were not limited by the AMP concentration range, while hollow points indicate the opposite. For d), a linear regression was conducted with the solid data points. The black line indicates the line of best fit for a linear regression, and the R2, P value and the equation of the fit shown at the top.

Extended Data Fig. 5 Fitted parameters of sigmoidal phenotype–fitness relationships.

a All parameters shown are calculated from a fit to equation (3). b Values are displayed as optimal fit ± S.D. The S.D.s are calculated as the square root of the variance for the parameter, estimated during fitting by the ‘SciPy.optimize.cuve_fit’ function.

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Supplementary Data

Supplementary Data 1, Fitness scores of VIM-2 variants across AMP concentrations; Data 2, EC50 of all VIM-2 variants; Data 3, EC50 of VIM-2 variants measured individually; Data 4, OD600 of VIM-2 libraries after 6 h of selection at 37 °C.

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Chen, J.Z., Fowler, D.M. & Tokuriki, N. Environmental selection and epistasis in an empirical phenotype–environment–fitness landscape. Nat Ecol Evol 6, 427–438 (2022). https://doi.org/10.1038/s41559-022-01675-5

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