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Natural selection for imprecise vertical transmission in host–microbiota systems

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A Publisher Correction to this article was published on 11 March 2022

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Abstract

How and when the microbiome modulates host adaptation remains an evolutionary puzzle, despite evidence that the extended genetic repertoire of the microbiome can shape host phenotypes and fitness. One complicating factor is that the microbiome is often transmitted imperfectly across host generations, leading to questions about the degree to which the microbiome contributes to host adaptation. Here, using an evolutionary model, we demonstrate that decreasing vertical transmission fidelity can increase microbiome variation, and thus phenotypic variation, across hosts. When the most beneficial microbial genotypes change unpredictably from one generation to the next (for example, in variable environments), hosts can maximize fitness by increasing the microbiome variation among offspring, as this improves the chance of there being an offspring with the right microbial combination for the next generation. Imperfect vertical transmission can therefore be adaptive in varying environments. We characterize how selection on vertical transmission is shaped by environmental conditions, microbiome changes during host development and the contribution of other factors to trait variation. We illustrate how environmentally dependent microbial effects can favour intermediate transmission and set our results in the context of examples from natural systems. We also suggest research avenues to empirically test our predictions. Our model provides a basis to understand the evolutionary pathways that potentially led to the wide diversity of microbe transmission patterns found in nature.

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Fig. 1: Natural variation in microbiome transmission fidelity across the eukaryotic kingdom, resulting in variation in microbiome composition across hosts in a population.
Fig. 2: Interactions among transmission fidelity, host phenotypes and fitness, and the environment.
Fig. 3: Conceptual overview of how microbiome transmission could shape host phenotypic variance and when phenotypic variation among genetically similar individuals might be beneficial.
Fig. 4: Environmental predictability and variance together shape the optimal transmission fidelity.
Fig. 5: Including other sources of phenotypic variation among hosts changes selection on transmission fidelity.
Fig. 6: Environmental colonization and within-host proliferation alter host phenotypic variance.

Data availability

This study uses computer-generated datasets, which can be created using available R code.

Code availability

An interactive tool to run the model can be found at http://marjoleinbruijning.shinyapps.io/simulhostmicrobiome, and example R code is available on Github: http://github.com/marjoleinbruijning/microbiomeTransmission (https://doi.org/10.5281/zenodo.5534317)84.

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Acknowledgements

M.B. is supported by NWO Rubicon grant no. 019.192EN.017, C.J.E.M. by National Science Foundation grant no. 1753993 and J.F.A. by National Institutes of Health grant no. GM124881.

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M.B. and L.P.H. conceived the ideas. M.B. developed the modelling framework with strong input from L.P.H., S.K.G.F., C.J.E.M. and J.F.A. M.B. wrote the first draft, and all authors contributed substantially to revisions.

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Correspondence to Marjolein Bruijning.

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The authors declare no competing interests.

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Peer review information Nature Ecology and Evolution thanks Kevin Foster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Increased stochasticity in small host populations.

In Fig. 2, we show how the microbiome transmission fidelity shapes host phenotype distributions. In doing so, we simulated large populations, consisting of 500 individuals, in order to obtain robust results. This results in a limited role of stochasticity, explaining the relatively low variation across replicated simulations (see shaded regions in Fig. 2a,b). In smaller populations, however, populations are, unsurprisingly, more sensitive to stochastic processes. Here, we set transmission fidelity τ at 1, implying strict vertical transmission, and assessed the average deviation from P = 0 for varying population sizes. In small populations, there is an increase in the number of maladapted populations (that is a larger deviation from the optimal phenotype). Grey dots indicate individual simulations (30 per population size), red lines indicate median values for each population size. τ = 1; ω2 = 1; \(\sigma _\varphi ^2\)=2; Vα=0.01.

Extended Data Fig. 2 Results when the average phenotype is kept constant.

Plots show relationship between transmission fidelity and phenotypic variance (upper row), deviation from the long-term optimal mean (middle row) and long-term fitness (bottom row) when selection can shift the mean phenotype (left; corresponds to Fig. 2) or when keeping the mean phenotype fixed at 0 (right). This was done by mean-centering phenotypes in each time step, by subtracting each phenotype by the average time-specific phenotype. When we keep the mean host phenotype at 0, we can use Bull’s modeling framework30 to calculate long-term fitness (crosses in bottom right panel), based on the relation between transmission fidelity and phenotypic variance (Appendix S2).

Extended Data Fig. 3 The difference between heritability and inheritance.

Heritability (averaged across 5 replicated simulations) is a function of the transmission fidelity, colonization from the environment, and the number of microbial generations within a host generation. Heritability is measured as the slope of a regression between parent and offspring phenotypes upon the moment of reproduction, averaged across time steps. If there is only one microbial generation within a host generation and/or without colonization, the heritability equals the transmission fidelity. However, when one or both increase, heritability decreases, illustrating the difference between inheritance and heritability.

Extended Data Fig. 4 Microbial variation among hosts within a host generation.

Within one host generation, increased colonization from the microbial source pool decreases microbial variation among hosts, as predicted from metacommunity theory. Variation among hosts is calculated as the average microbial diversity within each host, divided by the total microbial diversity across all hosts.

Extended Data Fig. 5 Empirical approaches to testing how microbiome transmission can affect host fitness.

a) Soil transfers in plant microbiomes can be used to enforce strict environmental acquisition or vertical transmission. By either successively inoculating plant generations with their initial starting microbial community (upper row in panel A-i), or passaging the microbial community from the previous to the next generation (bottom row in panel A-i), transmission of microbes can be controlled. Each host generation, artificial selection can be used to select plants based on their phenotype (for example plant size, illustrated here), whereby selection regimes vary (imposing either constant or fluctuating selection). Based on our results, we expect that under constant selection, strict vertical transmission increases fitness compared to strict horizontal transmission, as it allows phenotype distributions to respond to selection. In contrast, under sufficiently large fluctuating selection, vertical transmission reduces phenotypic variation, decreasing long-term fitness. b) A single microbe with a clear effect on host performance can also be used to study selection on transmission fidelity. As discussed in the manuscript, aphid fitness effects of several vertically transmitted symbionts, as well as their environmental-dependence, are quite well understood45. This makes aphids arguably a suitable system to study selection on vertical transmission fidelity. To do so, one could vary the symbiont frequency in different aphid populations (panel B-i). Populations can be followed through time, while keeping symbiont frequencies constant. Based on our results, we expect that under constant selection, a symbiont frequency of 100% (or 0%) optimizes population growth (panel B-ii), which can be realized by perfect vertical transmission. Under fluctuating selection, some intermediate symbiont frequency might be favored (panel B-ii), which can be achieved by noisy vertical transmission.

Extended Data Fig. 6 Schematic overview of our model.

Upper panel shows the different steps of our simulations, and the parameters that we vary. Bottom panel shows the output that we obtain from each simulation.

Extended Data Fig. 7 The evolution of transmission fidelity in mixed populations, under different environmental conditions.

For the analyses presented in our main text, we assessed long-term fitness of each strategy (transmission fidelity) separately, and take the strategy with the highest long-term fitness (calculated as the geometric mean) for what would evolve in a mixed population. Here, we simulated dynamics of mixed populations (consisting of 5000 individuals), and show that this yield the same outcomes. We assigned to each host a random transmission fidelity at the start of a simulation run, and performed 5 replicated runs for each environmental condition. Three bottom plots show how the composition of transmission fidelities in a host population changes over time in specific simulation runs (letters A-C corresponding to scenarios depicted in upper graph).

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Bruijning, M., Henry, L.P., Forsberg, S.K.G. et al. Natural selection for imprecise vertical transmission in host–microbiota systems. Nat Ecol Evol 6, 77–87 (2022). https://doi.org/10.1038/s41559-021-01593-y

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