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Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community

Nature Ecology & Evolution volume 7, pages 143–154 (2023)Cite this article

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Abstract

Species interactions drive evolution while evolution shapes these interactions. The resulting eco-evolutionary dynamics and their repeatability depend on how adaptive mutations available to community members affect fitness and ecologically relevant traits. However, the diversity of adaptive mutations is not well characterized, and we do not know how this diversity is affected by the ecological milieu. Here we use barcode lineage tracking to address this question in a community of yeast Saccharomyces cerevisiae and alga Chlamydomonas reinhardtii that have a net commensal relationship that results from a balance between competitive and mutualistic interactions. We find that yeast has access to many adaptive mutations with diverse ecological consequences, in particular those that increase and reduce the yields of both species. The presence of the alga does not change which mutations are adaptive in yeast (that is, there is no fitness trade-off for yeast between growing alone or with alga), but rather shifts selection to favour yeast mutants that increase the yields of both species and make the mutualism stronger. Thus, in the presence of the alga, adaptative mutations contending for fixation in yeast are more likely to enhance the mutualism, even though cooperativity is not directly favoured by natural selection in our system. Our results demonstrate that ecological interactions not only alter the trajectory of evolution but also dictate its repeatability; in particular, weak mutualisms can repeatably evolve to become stronger.

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Fig. 1: Growth of ancestral yeast and alga.
Fig. 2: The presence of the alga affects adaptation in yeast at the fitness and genetic levels.
Fig. 3: Adaptive mutations in yeast have diverse ecological consequences.
Fig. 4: The effects of adaptive mutations on life-history traits, fitness and yield.

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Data availability

All raw sequencing data are available on the US National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under BioProject PRJNA735257. Other input data can be found on Dryad82. Strains and other biological materials are available by request to S.K. Source data are provided with this paper.

Code availability

The latest version of the barcode counting software BarcodeCounter2 is available at https://github.com/sandeepvenkataram/BarcodeCounter2.git. Analysis scripts, including the version of BarcodeCounter2 used in this study, can be found on Dryad82.

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Acknowledgements

We thank G. Sherlock and K. Schwartz for providing the barcoded yeast library, S. Mayfield and F. Fields for laboratory equipment and help with algal husbandry, R. Dutton and M. Morin for help with sequencing, S. Rifkin and J. Bloom for help with microscopy, STARS students J. Yu and S. Rosemann for help with experiments, J. Meyer, A. Martsul and S. Sikaroodi for technical assistance, the Kryazhimskiy, Meyer and Hwa labs and D. Barrett, J. Borin, S. de Silva, S. Dunker, N. Garud, S. Harpole, C. Karakoç, H. Moeller, D. Petrov and P. Zee for feedback on the manuscript. Sequencing was done in part at the UCSD IGM Center (University of California, San Diego, La Jolla, CA). We acknowledge the San Diego Supercomputing Center for the use of the TSCC cluster for computing services. This project has been supported by the National Science Foundation CAREER grant 1846376 (E.F.Y.H.), Deutsches Zentrum für Integrative Biodiversitätsforschung (iDiv) grant DFG–FZT 118, 202548816 (E.F.Y.H.), BWF Career Award at the Scientific Interface grant 1010719.01 (S.K.), Alfred P. Sloan Foundation grant FG-2017-9227 (S.K.) and the Hellman Foundation (S.K.).

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Authors and Affiliations

  1. Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, USA

    Sandeep Venkataram, Huan-Yu Kuo & Sergey Kryazhimskiy

  2. Department of Physics, University of California San Diego, La Jolla, CA, USA

    Huan-Yu Kuo

  3. Department of Biology and Center for Biodiversity and Conservation Research, University of Mississippi, University, MS, USA

    Erik F. Y. Hom

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Contributions

Conceptualization (S.V., E.F.Y.H. and S.K.), methodology (S.V., H.-Y.K. and S.K.), data acquisition (S.V.), analysis (S.V., H.-Y.K. and S.K.), initial manuscript (S.V. and S.K.), editing (S.V., H.-Y.K., E.F.Y.H. and S.K.), supervision (E.F.Y.H. and S.K.) and funding (S.K. and E.F.Y.H.).

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Correspondence to Sergey Kryazhimskiy.

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Extended data

Extended Data Fig. 1 Per capita net population change for the ancestral yeast and alga.

Same data as in Fig. 1 (n = 6 except yeast alone where n = 4). Data points depict mean values ±1 SEM.

Source data

Extended Data Fig. 2 Frequency trajectories of barcoded lineages in yeast in the A-condition.

Each panel shows a BLT replicate population in the A-condition, as indicated. Lineage frequencies were measured at every odd cycle. Twenty random adapted lineages are shown in red, and twenty random neutral lineages are shown in blue.

Source data

Extended Data Fig. 3 Frequency trajectories of barcoded lineages in yeast in the C-condition.

Each panel shows a BLT replicate population in the C-condition, as indicated. Lineage frequencies were measured at every odd cycle. Twenty random adapted lineages are shown in red, and twenty random neutral lineages are shown in blue.

Source data

Extended Data Fig. 4 Distribution of adaptive mutations across the most common driver loci.

Driver loci with 5 or more mutations are shown. See Data S3 for the full distribution. Colors are the same as in Fig. 2.

Source data

Extended Data Fig. 5 Probabilities of observing adaptive mutations at the most common driver loci in the whole-genome sequencing data.

A. Shades of gray represent the probability of sampling at least one clone with a beneficial mutation that arises at a certain rate (y-axis) and provides a certain fitness benefit in the A-condition (x-axis). The most common driver loci are shown by points (colors are the same as in Fig. 2). The estimated beneficial mutation rate and the selection coefficient for each mutation class are given in Table S3. B. Same as A but for the C-condition. The mutation rate for each locus is assumed to be the same in both conditions, but the selection coefficients vary. C. Black points show the number of sequenced clones with a given driver mutation found per replicate population in either A- (left) or C-condition (right; n = 5 replicate populations per condition). Box and whiskers show the distributions of these numbers found in our simulations (mid-line shows the median, boxes show the 25th and 75th quantiles, whiskers show the 5th and 95th quantiles).

Source data

Extended Data Fig. 6 Distribution of yields in mutant communities weighted by frequencies of adaptive mutations.

The heatmap shows the ratio of estimated probability densities DC and DA of observing a given pair of yeast and alga yields in hypothetical communities formed by the alga ancestor and yeast mutants contending for fixation in the C- and A-conditions. Data points are identical to Fig. 3B in the main text. DA and DC are estimated by weighing each data point by the frequency of occurrence of the corresponding mutation among the sequenced A- and C-mutants, respectively (see Methods for details). Regions where either DA or DC falls below 0.03 are colored gray. YYC and YYA are normalized by the respective ancestral values.

Source data

Extended Data Fig. 7 Correlations between competitive fitness and yields.

Normalization is relative to the ancestor. Error bars represent ±1 SEM. For all panels, n = 31 for A-mutants and n = 28 for C-mutants with three replicate fitness measurements and two replicate YYC, AYC and YYA measurements. Pearson correlation coefficients (R) are reported for each panel, as are P-values calculated by a two-tailed permutation test.

Source data

Extended Data Fig. 8 Representative microscopy image showing lack of physical associations between yeast and algae cells.

Mutant culture formed by the C-mutant C2 (barcode ID 109098) is shown. Yeast and alga cells are indicated with arrows. Similar observations were made for 17 other mutants (available on Dryad82).

Extended Data Fig. 9 Relationship between growth rate, yields and fitness.

In all panels, normalization is relative to the ancestor. Error bars represent ±1 standard error of the mean. For all panels, n = 31 for A-mutants and n = 28 for C-mutants with three replicate fitness measurements and two replicate YYC, AYC and Δr measurements. Pearson correlation coefficients (R) are reported for each panel, as are P-values calculated by a two-tailed permutation test.

Source data

Extended Data Fig. 10 Relationship between carrying capacity, yields and fitness.

In all panels, normalization is relative to the ancestor. Error bars represent ±1 standard error of the mean. For all panels, n = 31 for A-mutants and n = 28 for C-mutants with three replicate fitness measurements and two replicate AYC, YYA and ΔK measurements. Pearson correlation coefficients (R) are reported for each panel, as are P-values calculated by a two-tailed permutation test.

Source data

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Venkataram, S., Kuo, HY., Hom, E.F.Y. et al. Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community. Nat Ecol Evol 7, 143–154 (2023). https://doi.org/10.1038/s41559-022-01923-8

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