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The microbiome extends host evolutionary potential

Abstract

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

Introduction

The “microbiome” has emerged as a key determinant of many aspects of organismal biology, capable of shaping developmental, physiological, and reproductive phenotypes1,2,3,4,5. Yet, the contribution of the microbiome to host adaptation remains an evolutionary puzzle6,7,8,9. Microbiomes are traditionally viewed as nongenetic, environmental factors that influence host phenotypes. However, unlike abiotic environmental conditions, the effects of microbial variation have a genetic basis and can evolve10, but are not always inherited in the same way as host genes6,7,9. While microbiomes have substantial phenotypic effects on their hosts, these effects strongly depend on the ecological context2,3,8. Despite their clear importance, the complex effects of microbial inheritance and genetics on host phenotypes remain underappreciated, limiting our ability to understand host-microbiome evolution.

In this perspective piece, we explore how the microbiome influences host phenotypic variation. If the microbiome expands the host genetic repertoire and influences trait heritability, then the microbiome may have substantial impacts on host phenotypic evolution. We propose a path forward by integrating the microbiome into quantitative genetic models. Models that account for patterns of microbial inheritance and phenotypic effects will allow researchers to make predictions into the microbial impact on host evolutionary trajectories. Using this perspective, we detail two common scenarios found in the literature. First, microbial variation may shift the mean phenotype of the population, as would be expected when hosts leverage the microbiome to become locally adapted. Second, microbial variation may change host phenotypic variance, which could reduce variance by buffering against host variation or increase variability within the population. These two patterns may occur together (e.g., shift in mean and decrease in variance) and will change how hosts explore their fitness landscape. We review approaches to measure host-microbiome evolution, addressing current theoretical and technical limitations. We conclude by suggesting key avenues of research powered by the integration of the microbiome into common evolutionary tools, like quantitative genetics and experimental evolution. Through this perspective piece, we highlight how considering shifts in phenotypic mean and variance will help elucidate how the microbiome influences host evolution.

Phenotypic effects: extending the host genetic repertoire

Dawkins’ “Extended Phenotype” recognized how organisms modify surrounding environments and ecological communities11,12. Through environmental modification, an organism’s phenotypic effects are extended beyond its own genome, suggesting evolution is influenced through interacting ecological communities. This theory, developed for free-living ecosystems, also applies to host-microbiome interactions12,13. The microbiome, with its consortium of genomes, extends the genetic repertoire of the host to form what some are now calling the “Extended Genotype” because the host integrates the extended effects of the microbiome into its phenotype3,14,15,16,17,18,19,20,21,22. This extended genetic repertoire may shape the distribution of host phenotypes within a population, and consequently, shape the evolutionary potential of the host.

To formalize these verbal arguments, there is a genuine need for quantitative genetic models that incorporate the contribution of the microbiome to host phenotype, and subsequently, response to selection (Box 1). Explicitly incorporating the genetic variance encoded by microbes (VG-MICRO) as one would for the other components of phenotypic variance (e.g., VG-HOST or VE) should be a useful starting point. Host phenotypic variance can then be decomposed as:

$${V}_{P}={V}_{{{{{{\rm{G-HOST}}}}}}}+{V}_{{{{{{\rm{G-MICRO}}}}}}}+{V}_{{{{{{\rm{E}}}}}}}$$
(1)

When VG-MICRO contributes to host phenotypic variance, the microbiome may shape the evolutionary potential for a population. For illustrative purposes, imagine a scenario where a microbe turns a host blue (i.e., VG-MICRO contributes substantially to VP), and this blue phenotype increases host fitness in a hypothetical environment. We would predict that these beneficial blue-inducing microbes will increase in frequency in the host population, shifting the mean host color. We consider this scenario as hosts leveraging locally adaptive microbes (Fig. 1). The evolutionary benefits in this scenario will depend on the match between phenotype and selective pressure. Second, microbial genetic variation may alter variance in host phenotypes within a population (Fig. 1). The microbiome may effectively act as a buffer against environmental perturbation, thus decreasing phenotypic variance. Under this scenario, modifying the degree of host phenotypic robustness may be advantageous for specialization, but may constrain future adaptation if the environment is not constant23,24. On the other hand, the microbiome may also increase host phenotypic variance. This may enable the simultaneous exploration of novel regions of fitness landscapes, potentially enhancing host evolutionary responses to rapidly changing environments25,26,27,28. We note that these scenarios are not mutually exclusive, and the microbiome may shape both mean and variance in a population. Finally, it is important to consider that not all host phenotypes will be influenced by the microbiome, and not all microbes will influence host phenotypes (e.g., VG-MICRO = 0). Naturally, many host-microbiome interactions are also pathogenic, but such relationships have been studied theoretically and empirically in disease ecology8,29 and could also be incorporated into our quantitative genetics framework. Here, however, we are primarily focusing on adaptive microbiomes.

Fig. 1: Microbial influence on host phenotypic variation.
figure1

The microbiome encodes many more genes than the host genome alone. Interactions with variation in the microbiome may alter the host genotype-phenotype map, shaping host phenotypic variance within populations. A First, some host phenotypes will not be affected by the microbiome (visualized in gray) and it is worth noting that not all microbes will influence host phenotypes. B The microbiome may shift the mean host phenotypes. The blue distribution is suggestive of when hosts leverage locally adaptive microbiomes to match local selective pressures. C Alternatively, the microbiome may also alter phenotypic variance (conceptualized in purple). D Finally, both the phenotypic mean and variance may be affected by the microbiome (in green). We note here that these scenarios are not mutually exclusive. Expanding host phenotypic variation through the microbiome may allow hosts to explore novel regions of fitness landscapes. These are conceptualized phenotypic distributions, and more experimental work is necessary to confirm how the microbiome affects host phenotypic distributions.

Using this simple quantitative genetics framework enables the partitioning of the genetic contribution of the microbiome to host phenotypic variance, and subsequently, host evolution. We explore how the two broad scenarios of the microbiome on host phenotypic effects described above (shifting mean phenotype and changing variance) can affect host evolutionary potential (Box 1). Overall, when the microbiome contributes to host phenotypes, the response to selection is amplified and shifts host evolutionary trajectory. However, the magnitude of microbial effects on host evolution will also strongly depend on the complexities of microbial inheritance.

A complex inheritance

Integrating microbial genetic variation into host evolutionary processes builds on ‘hologenome’ theory. The hologenome is defined as an evolutionary unit combining the eukaryotic host and all associated microbes15,30,31. Under this concept, evolution operates on this single unit because eukaryotic hosts are never isolated from microbes in the natural world15,22. Thus, the evolutionary fate of both hosts and microbes is intertwined. However, the intertwined fate of a single host-microbiome evolutionary unit is the focus of much criticism of the hologenome. One key criticism is that the host and microbiome rarely operate as one selective unit because transmission of the microbiome between host generations is rarely strictly vertical6,7,9. The microbiome can be transmitted via strict vertical transmission through embryos, but many microbes are also transmitted through quasi-vertical and environmental modes, like vaginal birth in mammals, regurgitation in birds, environmental inoculation in insects, or coprophagy in many taxa32,33. Environmental factors, like diet or climate, substantially influence the reservoir of possible microbial partners, leading to changes in the microbiome independent of any host evolution34,35,36. Host phenotypes also may be more strongly shaped by distinct microbial variation early in life, but because microbial dynamics operate at shorter timescales than host generations, those relevant microbes may not be faithfully transmitted across host generations8,37,38,39,40. Such environmental and demographic complexities muddle the inheritance of the microbiome, challenging our understanding of how and when the microbiome could influence host evolution6,7,8,9,31.

From a quantitative genetics perspective, heritability of the microbiome can be described as the proportion of microbial variation attributable to host genetic variance, just like any other complex trait41. Thus, heritable microbes are those where the relative abundance or community structure is associated with particular host genotypes41,42. Traditionally, only vertically transmitted microbes have been considered to be heritable, but the conflation of ‘microbial inheritance’ (i.e., transmission mode) and ‘microbial heritability’ (i.e., host genetic contribution to microbial variation) overlooks the many host systems that acquire their microbes through diverse transmission modes43. Indeed, in hosts dominated by environmentally acquired microbes, the contribution of host genetic variation to the relative abundance of particular microbes is as high as 42% in humans44,45, 39% in Drosophila46, and 25% in maize47. Interestingly, these and other studies show that not all components of the microbiome are heritable, with estimates ranging from 8 to b56% of microbes being transmitted faithfully45,47,48,49.

One might expect microbes with beneficial phenotypic effects to be more faithfully inherited. If a microbe contributes to a trait that promotes fitness in particular environments, then selection may reinforce faithful transmission to maintain the beneficial association, as observed in the many complex behaviors in host-microbe associations like aphid-Buchnera50, squid-Vibrio51, or beewolf-Streptomyces associations52. However, for many hosts, more complex communities are transmitted53,54,55. The drivers of this relationship between inheritance and phenotypic effects is not always clear. For example, in a UK twin study investigating the influence of host genetics on the microbiome, Methanobrevibacter species with the highest heritability had a strong association with low body-mass index44,45. However, another study in humans found low microbiome heritability, but microbial variation still explained 22-36% of metabolic traits35. Similarly, microbial variation explained 33% of weight gain in pigs48 and 13% of methane emissions in cows49 but also occurred largely independently of host genetic control of the microbiome. In other words, we still do not know whether the most heritable microbes explain the most significant variance in host traits.

Variation in transmission and the environment can impact the detection of heritable microbes. Controlling for transmission and environment are essential to identifying heritable microbes, and the disagreement between studies may result from differences in experimental design. The UK twin studies44,45 compared dizygotic and monozygotic twins to untangle maternal transmission and genetic contributions, while Rothschild et al.35 used large cohorts of unrelated individuals combined with metadata on diet and lifestyle to determine the environmental contribution. Animal and plant studies often use common garden experiments to expose different host genotypes to similar pools of available microbes46,47,48,49. The differences in these approaches may bias the ability to detect the contribution of heritable microbes to host trait variation, especially for hosts with largely environmentally acquired microbes. Despite these experimental limitations, these studies suggest that for a range of host traits, the microbiome contributes almost as much to phenotypic variance as do host genetics, even with the complicated inheritance of the microbiome.

Overall, these studies suggest that variation in how faithfully the microbiome is inherited may also influence host phenotypic variance. A quantitative genetics framework provides a path forward to partition the microbial contribution (VG-MICRO) to host phenotypes and allows us to further dissect the evolutionary consequences of this variation in transmission fidelity (Box 1). Through scaling the microbial effects by transmission fidelity, the microbiome could modulate the host response to selection. Microbes with phenotypic effects that are faithfully transmitted are likely to enhance the host phenotypic response to selection.

Our conceptualization suggests that the microbiome can modulate the host evolutionary potential, but there is currently limited empirical data to validate these conclusions. However, within the last 10 years, researchers have started to identify connections between the microbiome and host adaptation in diverse taxa and environments. We synthesized the literature and indeed found evidence suggestive of two common patterns in how the microbiome may shape host evolution connected to the processes suggested by our quantitative genetic framework: (1) hosts leverage locally adaptive microbes, shifting phenotypic means between host populations, and (2) the microbiome exposes novel host variation, changing host phenotypic variance within populations. These scenarios are not mutually exclusive, but as we explain below, may lead to different evolutionary trajectories for hosts.

Hosts leverage locally adaptive microbes

Hosts may leverage microbes to acquire new traits that are adaptive in the local environment. Locally adaptive microbes may facilitate the ability of hosts to explore the fitness landscape to match local environmental stressors by shifting host phenotypic means (Fig. 1). In particular, microbes with larger effective population sizes, rapid generation time, and pangenomes may evolve novel functions faster than their hosts56,57,58,59, and hosts may also acquire these adaptive microbes from the standing microbial variation in the local environment. If hosts can leverage locally adaptive microbes, then hosts can increase survivorship and/or reproduction to rapidly adapt to novel environments.

There are a number of remarkable studies that illustrate the substantial impact of locally adaptive microbes on host phenotypes and fitness. For example, bean bugs can gain pesticide resistance by acquiring a pesticide degrading bacterium, Burkholderia60,61,62 that is already present in the soil environment. Many other hosts utilize their microbiome to detoxify harmful chemicals. In habitats with toxic creosote plants, woodrats have a gut microbiome that can degrade phenolic toxins; exposure to creosote resin predictably structures this microbiome, enriching for Actinobacteria that degrade phenols63 and enabling the woodrat to occupy a specialized dietary niche63,64,65,66,67. Woodrat populations naïve to creosote have distinct microbiomes that do not degrade phenolic toxins even after creosote resin exposure68; however, transfer of the toxin-adapted microbiome to these woodrats increases woodrat survival on a creosote diet64. The microbiome can also facilitate survival in other kinds of stressful environments. Plants on geothermal soils are associated with the thermotolerant endophyte Curvularia69; thermotolerant Curvularia can increase survival up to 40 °C in non-adapted tomatoes, while Curvularia isolated from non-geothermal soils did not70. Additionally, salt tolerant fungal endophytes can confer salt tolerance to non-adapted plant populations70. Overall, these examples show that hosts can utilize specific microbes with large phenotypic effects to specialize and persist in novel environments, likely adapting more rapidly than the host genome alone could.

Microbial variation exposes novel host variation

During assembly of the microbiome, stochasticity and priority effects may create microbial variation between hosts within a population8,38,71. This change in microbial variation may increase phenotypic variability within populations (Fig. 1), enabling individuals to explore more phenotypic space26,28. Microbial variation would then enable the simultaneous exploration of different regions of fitness landscapes, changing the evolutionary trajectory of the host. We next review several examples that show how the microbiome increases phenotypic variance, but note that in natural populations, a combination of increases and decreases in phenotypic variance along with shifts in the mean likely occur.

Microbial variation increases host phenotypic variance in many organisms. For example, in Drosophila, different microbial communities increase the variance in larval development time, pupal weight, and adult weight compared between flies with different microbes and flies without microbes72,73. More so, different combinations of microbes shape life history traits in Drosophila nonlinearly, suggesting that microbial variation increases host phenotypic variance in complex ways74. Similarly, in Daphnia, different bacteria increase variance in body size and hatching success compared between sterile and among non-sterile treatments75. Microbiomes evolved for rapid flowering time in Arabidopsis increased variance in biomass compared to microbiomes evolved for slow flowering time76. In zebrafish, different bacteria species can either stimulate or suppress neutrophil responses, suggesting that variation in the microbiome is associated with varying immune responses to other bacteria77. In these cases, hosts are permissive to colonization by many microbes, and in turn, phenotypes respond to microbial variation. A major question for host-microbiome evolution is why some host phenotypes are responsive to diverse microbiomes while others maintain more specialized interactions?

The ability of the host to tune specific traits using diverse microbes may allow hosts to better match phenotypes to environmental stressors16,17, especially those stressors that may vary within populations. First, hosts that experience extreme variation in environment over their lifespan may need flexibility in their microbiome16. For example, coral microbiomes shift to protect against pathogens and extreme heat78,79,80. Bear microbiomes are enriched for microbes that harvest more energy during summer, most likely to ensure abundant fat accumulation before hibernation81. Second, the adaptive benefit of variable microbiomes may be especially important for organisms where high spatiotemporal variation in the environment occurs between parent and offspring. When offspring occupy different environments than parents, microbes may provide critical cues for local environments. For example, in Daphnia, microbes acquired from their local environment increased fecundity compared to those acquired via maternal transmission before diapause75. To best match phenotype to the local environment, hosts may frequently use microbial variation to alter developmental timing. Development may be especially responsive to microbial variation in many taxa, from insects to plants to crustaceans82. Developmental plasticity, through microbial variation, may expose novel phenotypic variation83, and this may in turn generate phenotypes that allow rapid adaptation to novel environments.

Particularly for organisms that live in fluctuating environments, the microbiome may enable a form of evolutionary bet-hedging. Bet-hedging occurs when phenotypic variation is maintained within a population to maximize long-term fitness of the lineage, despite shorter term fitness costs84. In the microbiome, bet-hedging could occur when hosts harbor associations with diverse microbes and responsive phenotypes. For some host systems, microbial variation may increase variability among individuals within a population, like when a diverse reservoir of microbes stochastically infects individuals, leading to increased inter-individual variation. Bet-hedging may also occur at the intra-individual scale, like when microbes have different phenotypic effects during different life stages. For example, studies have shown that Lactobacillus typically slows development in flies, suggesting different fitness costs in different environments72,73. However, on a nutrient poor diet, Lactobacillus increased in abundance and promoted larval growth85. These studies suggest the microbiome may enable a form of bet-hedging in Drosophila and potentially other hosts, but more work is necessary to explicitly test the role of the microbiome in bet-hedging86.

Long-term effects of the microbiome on host evolution

So far, we have focused on how variation in the microbiome shapes both the mean and variance of host phenotypic traits. When the phenotypic distribution changes, then the response to selection in the host also changes (Box 1). Over time, the adaptive potential of the microbiome will also leave signatures of selection in the host genome as well. The role of the microbiome in long-term host evolution is poorly understood, but by drawing on studies in evolutionary ecology and traditional symbioses, we can draw basic predictions for the long-term effects of microbiome-induced phenotypic variation on host evolution.

Relying on locally adaptive microbes may facilitate host trait evolution in the short term—but what are the long-term evolutionary consequences of host reliance on locally adaptive microbes? If beneficial, we may expect that the frequency of those microbes will increase within the host population, and in turn, this evolutionary benefit may allow hosts to rapidly cross the fitness landscape. Thus, hosts should evolve to maintain locally adaptive microbes and their phenotypic effects to either host traits or mitigation of the environmental stressors, much like genetic accommodation or niche construction83. Host taxa then specialize in these unique niches, as observed for taxa like bark beetles87, pine weevils88, and coffee berry borers89 that use their microbiomes to detoxify plant secondary compounds in specialized niches.

When host taxa experience similar ecological processes, the microbiome may converge to provide similar functions, like conifer pests that use microbes to degrade otherwise toxic terpenes87,88,90. In all of these cases, selection may favor specific functions to ensure consistent benefits to these specialized host–microbe associations. However, it is unclear whether selection operates first on the standing pool of microbes in the environment (that are then acquired by hosts), or if selection on the host exerts selective pressure on the microbiome, which may in turn evolve novel traits, benefiting the host. In some cases, like for bean bugs, pesticide application first increases pesticide resistant Burkholderia in the soil, and then bean bugs acquire the pesticide resistance through Burkholderia62.

It is worth considering that when microbial variation exposes novel host phenotypic variation, hosts must deal with inconsistent benefits of the microbiome in the short-term. The benefits and stability of a given host-microbiome association will ultimately be a function of the environmental context, with different expectations for hosts that inhabit stable versus more rapidly fluctuating environments (as discussed in Bruijning et al.86).

However, relying on environmentally acquired microbes increases the probability of random microbial acquisition, like pathogens and non-adaptive microbes. If specific microbes are consistently beneficial, hosts should evolve mechanisms to ensure faithful microbial transmission, similar to traditional symbioses91. The quantitative genetic framework introduced above is focused on a specific functional trait, but this framework may also be extended to selection on transmission mechanisms as well. For example, the bean bug acquires pesticide-resistant Burkholderia even at only 0.04% of the total soil microbiome62. With a unique physical sorting structure in the midgut that imposes competition between different potential microbes, the bean bug ensures the establishment of only beneficial Burkholderia species92,93. Hosts can evolve mechanisms to either screen potential symbionts, like in the bean bug-Burkholderia example, or other behaviors that promote the acquisition of beneficial microbes, like coprophagy32,33. While it is still contentious if most microbiomes behave like traditional symbioses, ultimately, strict vertical transmission and extreme reliance on specialized microbes is likely an evolutionary dead end, constraining hosts and microbes to the ecological conditions that generated the symbiosis94. Furthermore, when hosts leverage locally adaptive microbes, different populations will have different locally adaptive associations, which will likely lead to increased genetic differentiation between host populations. Overall, the challenge is to determine how microbial genetic variation is linked with and influences host genetic change during adaptation. Integrating the microbiome into quantitative genetics provides a framework to generate formal predictions. However, as discussed next, accurately quantifying microbial variation and its effects on host phenotypic variation by taking advantage of clever experimental design are necessary to test these hypotheses.

Experimental approaches to study the influence of the microbiome on host evolution

Above, we identified common scenarios of how the microbiome may influence host adaptation, with a focus on how host phenotypic distributions across individuals are shaped by microbial variation. With recent advances in sequencing technologies, microbial variation can be quantified at many different scales, from strains to whole community composition to complete ‘metagenomes’ (see Knight et al.95 for a recent review). A major challenge is to identify which scale of microbial variation (i.e., strain, community, metagenome) best measures microbial adaptation and influences host phenotypes8,95,96. Overcoming the technical hurdles in accurately quantifying microbial variation will be key to detecting how the microbiome influences the host evolutionary response.

The majority of host–microbiome interactions are characterized through “marker gene” studies (i.e., 16S rRNA for bacteria, ITS for fungi). Marker genes can classify microbial communities at broad taxonomic levels, are relatively inexpensive to sequence, and have established bioinformatic and analytical pipelines95. This approach demonstrates that microbial communities respond to many different kinds of selective pressures, like drought97,98, antibiotics99,100, different diets101, or warming environments102. Some studies show that microbial community diversity decreases in response to stressful environments98,99,100, while others show only shifts in community composition97,101,102. Marker gene studies are well-suited for taxonomically defining communities (though see103), but changes in microbial community composition do not always correlate to changes in host phenotype. For example, as measured by 16S rRNA marker genes, the macroalgae Ulva is colonized by different bacteria in different environments, but metagenomic data show the same core functional genes encoded by different bacterial taxa104. Similar patterns are observed in human microbiomes105 and the tree phyllosphere106,107. It remains unknown how taxonomic and functional identity provide different adaptive value for hosts. However, this conclusion may be strongly impacted by how microbial variation is characterized. Therefore, techniques beyond marker gene classification are needed to determine if and how microbial variation influences the host response to selection.

Strain-level analyses of microbial variation (i.e., polymorphisms and other genetic variants beyond marker genes) may provide more functional insight into the microbiome response to selection. For example, strain-level variation in honey bee microbes influences the response to pathogens or different diets for the hosts108. Strain-level analyses also provide insights into the stability of the microbiome across the lifetime of the host54,109,110 as well as transmission dynamics111,112. Metagenomes, which fully characterize the genes encoded by microbes, will provide crucial insights into functional variation of the microbiome95,108,113,114, like how bacterial metabolism varies across different niches within the same host115. This approach however remains costly, computationally intensive, and the results can be difficult to interpret. The emerging field of metatranscriptomics aims to study gene expression in the complex microbial communities95,116,117. Linking the metagenome to the metatranscriptome will be particularly insightful to understand how microbial variation influence host phenotypic variation95,118. For example, by comparing metagenomes to metatranscriptomes in the human gut, it was discovered that only a few specialized bacteria express unique biosynthesis pathways that may reflect the microbiome response to changing diets or other environmental perturbations in hosts118,119.

Experimental evolution can provide a powerful approach to investigate how microbial change interacts with host evolution13,120,121. In Drosophila, “Evolve and Resequence” (E&R) experiments, have uncovered many aspects of the genetic basis of adaptation to a diverse range of selective pressures122. E&R experiments use outbred populations, exert a selective pressure, and use sequencing to identify the genomic signatures of selection (Fig. 2). However, missing from this approach is whether and how the microbiome shapes the adaptive response of the host. As a first step in answering this question, the re-analysis of ten E&R experiments in D. melanogaster123 focusing on characterizing the microbial response to selection has been particularly insightful (Fig. 2). First, not all selective regimes had an effect on the microbiome. This observation is consistent with the idea that some phenotypes are more responsive to the influence of the microbiome than other phenotypes. Across the ten experiments, microbial diversity significantly decreased in four of the evolved populations (compared to the control populations), while it increased in one population. The decrease in diversity arises from a substantial increase in relative abundance for only a few bacteria, suggesting that flies are leveraging locally adaptive microbes. For example, for starvation resistance, Wolbachia increased in frequency following selection.

Fig. 2: Microbiome responds to host experimental evolution.
figure2

Experimental evolution is a powerful tool to understand the genetic basis of adaptation. A Hypothesized schematic of the microbial response during adaptation in flies. B Microbial diversity is frequently reduced in evolved populations during experimental evolution in flies. Data is reproduced from Henry & Ayroles123. Here, each point represents Shannon diversity of metagenomes from a pool of sequenced flies. Asterisks denote the experiments where microbial diversity is significantly different between control and evolved populations.

While these results are correlative, this E&R survey suggests that the microbiome also responded to various selective regimes, and that microbiomes evolved in tandem with their hosts. However, additional work is necessary to understand if and how microbial evolution in these experiments also influenced host evolution. Like many other studies, we can only observe the endpoint of the host-microbiome evolutionary trajectory. Ultimately, understanding how the microbiome alters the evolutionary trajectory in hosts is the critical missing link13,15,16,18,19,120,124,125,126. Monitoring the temporal dynamics as populations respond to selection should provide the missing link—specifically, does the microbiome adapt faster than the host? And how frequently does the host utilize this rapid microbial evolution? A recent study, where the temporal evolutionary dynamics were monitored, suggests that environmentally acquired Lactobacillus bacteria in Drosophila evolve rapidly to nutrient-poor diets, and Drosophila can leverage rapid microbial adaptation independent of their own evolution121. We propose that experimental evolution, combined with statistical tools used in quantitative genetics, will be an important tool to understand how the microbiome shapes host phenotypic distributions, patterns of inheritance, and how these together influence host evolutionary trajectories (Fig. 3).

Fig. 3: Partitioning microbial effects on host adaptation.
figure3

Experimental approaches developed for variance partitioning in quantitative genetics can be a powerful way to assess the influence of the microbiome on host evolution. A Experimental evolution will provide critical insights into how hosts and microbiomes respond to stressful environments. By including the microbiome (visualized as different colored circles) in experimental evolution, then genetic responses in both host and microbiome can be measured following selection. At the end of experimental evolution, we expect both host and microbiome to adapt - visualized as blue fly and blue/purple microbes. Microbial evolution may occur at the strain level (e.g., when beneficial mutations in particular genes drive adaptation to the novel selective pressure). Alternatively, selection may increase the frequency of a particular microbial taxon, leading to loss of microbial taxa in the adapted microbiome. To test how the microbiome interacts with the host genome to influence host phenotypes, one can perform fully factorial, reciprocal transplants between host, microbiome, and environment. B Key insights will be gained from examining the evolutionary trajectory of alleles that emerge or change in frequency during experimental evolution. C To test how microbial variation influences host phenotype, hosts can be inoculated with different levels of microbial variation. Removing the microbiome through antibiotics (or other manipulations) will show how hosts respond to perturbation to their microbiomes. D Finally, diallel crosses can be used to show how host and microbial genetic variation interact. Diallel crosses are performed by crossing all possible combinations between inbred lines to each other in a common environment (represented by fly colors). Rearing F1s in different microbial environments will enable partitioning of the additive and nonlinear, epistatic components between host and microbial genetic variation.

A practical outlook

Natural variation in the microbiome may also provide new tools to combat a range of challenges with practical applications. Indeed, there are several promising avenues of research leveraging natural microbial variation in fields ranging from conservation biology to agriculture. For example, variants of Pseudomonas bacteria isolated from caves help protect vulnerable bats from the devastating fungal infections of white-nose syndrome127,128. A similar strategy is also in development to protect amphibians from chytrid pathogens129. From a public health perspective, manipulating the microbiome in disease vectors may reduce vector competence130. The success of cancer management can be influenced by the presence of particular gut microbes in humans and finding novel probiotics to improve the efficacy of chemotherapy is under development131. Isolating host-associated microbiomes from organisms in extreme environments, like geothermal soils69,70, will serve as reservoir of adaptive microbes for agriculture in changing environments13,132. Natural variation in the microbiome represents a largely untapped resource with many novel solutions to address applied challenges.

Our quantitative genetics framework provides a useful starting point that can be further developed to leverage the microbiome in applied challenges. For conservation efforts, ensuring that microbial diversity is buffered from anthropogenic effects may help endangered organisms persist and restore both free-living and within-host ecosystems133,134,135. The effect may be similar to the microbiome increasing phenotypic variation to buffer fluctuating environments. Second, probiotics could be used to increase host resistance to specific challenges136, much like when the microbiome shifts host phenotypic distributions. The evolutionary persistence of microbiome engineering will depend on how selection operates on both host and microbiome13. For this to work, our framework suggests that maintaining transmission fidelity will enhance host response to selection (Box 1). Finally, many of the studies cited here utilize highly controlled environment to test the microbial contribution to host phenotypes. Identifying the processes that shape host-microbiome interactions in the wild is a major missing link135,137. Understanding the match between host phenotypic effects with the environment will be key to leveraging the microbiome for applied challenges.

Conclusions

In this perspective piece, we have identified scenarios of how the microbiome has the greatest potential to influence host evolution. Nevertheless, several key evolutionary questions remain to be addressed (see also Box 2): How does the microbiome change the host response to selection? What is the genetic basis of host responsiveness to microbial variation? Do the results of lab studies apply to microbiomes in the wild? Answering these questions will require a diversity of approaches using tools from quantitative genetics, community ecology, and genomics. Box 3

Several key analytical and theoretical gaps limit our current understanding of microbial influence on host evolution. More advanced bioinformatic tools are necessary to evaluate microbial diversity and its impact on host phenotypic diversity95. On the theoretical side, many aspects of the microbiome may poorly match the assumptions of theory developed for free-living organisms. Can we, for example, apply indirect selection12 or multilevel selection models (i.e., Price equation)96 to ask how independent or synergistic host and microbiome evolution are? One novel approach to detect the signatures of microbial variation shaping host evolution is ‘interspecies linkage disequilibrium’138, where selection may result in non-random allelic assortment of beneficial microbes and host genetic variation. The challenge is to identify how frequently variation in the microbiome increases host fitness, and whether this increases the probability of transmission across host generations.

Developing meaningful null models to describe host-microbiome evolution is an important and nontrivial challenge. A typical comparison would be between germ-free and conventionally reared hosts, where the null model would assume no difference. If hosts with a microbiome differed in their response to a selective pressure compared to the germ-free group, then the microbiome would be implicated in shaping host evolution. However, this may not be the most informative null, as how often have host organisms experienced a microbe-free world in their evolutionary history? Is the complete lack of a microbiome relevant to the evolutionary context in which organisms adapt to selective pressures? For some host taxa, recent work suggests that the modification and removal of the microbiome do not change host physiology139,140, and thus variation in the microbiome may not be evolutionary relevant for the host. First, we need more empirical data to calibrate the null model to understand how often the microbiome does not influence host phenotypes. Second, as outlined in Fig. 3, comparing evolved and ancestral microbiomes (and not only against germ-free conditions) across environments could greatly improve our understanding of host-microbiome evolution. Third, the null model should consider the balance in evolutionary interests between host and microbiome. We cannot assume that host-microbe associations evolve together to mutually benefit each partner6. Hosts and microbiomes will rarely have perfectly aligned evolutionary interests, suggesting a balance between cooperation and conflict6,8,37. Intergenomic conflict may maintain variation in microbial associations, but how often this influences host-microbiome evolution is not well understood141. Ultimately, the null hypothesis should drive appropriate experimental design to understand the balance between cooperation, conflict, and indifference between the host and microbiome during adaptation.

In conclusion, the contribution of the complex interplay between host and microbial genetic variation to host evolution is surprisingly understudied. Until recently, most quantitative and evolutionary genetic models addressing this topic have largely neglected the contribution of the microbiome to host phenotypic variance and evolution. Yet work from community ecology, quantitative genetics, and evolutionary biology suggest that the microbiome frequently shapes host phenotypic distributions across taxa and environments and may play a critical role in host evolution. Here, we have proposed a simple framework based on quantitative genetics that explicitly considers how genetic variation in the microbiome can extend the genetic repertoire of the host genome, influence host trait heritability, and subsequently impact host phenotypic evolution. Many challenges remain in this burgeoning field, but continuing advances in sequencing technology will facilitate the necessary characterization of host-microbiome evolutionary dynamics. Incorporating microbial variation into quantitative genetic models will provide fundamental novel insights into how selection operates across ecological and evolutionary scales.

References

  1. 1.

    Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota? Nat. Rev. Microbiol. 7, 887–894 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    Friesen, M. L. et al. Microbially mediated plant functional traits. Annu. Rev. Ecol. Evol. Syst. 42, 23–46 (2011).

    Article  Google Scholar 

  3. 3.

    McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Douglas, A. E. Symbiosis as a general principle in eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016113 (2014).

  5. 5.

    Stappenbeck, T. S. & Virgin, H. W. Accounting for reciprocal host-microbiome interactions in experimental science. Nature 534, 191–199 (2016).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Moran, N. A. & Sloan, D. B. The hologenome concept: helpful or hollow? PLoS Biol. 13, e1002311 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  7. 7.

    Douglas, A. E. & Werren, J. H. Holes in the hologenome: why host-microbe symbioses are not holobionts. MBio 7, e02099 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Morimoto, J. & Baltrus, D. A. The extended genotype: to what extent? A comment on Carthey et al. Trends Ecol. Evol. 34, 186–187 (2019).

  10. 10.

    Scheuring, I. & Yu, D. W. How to assemble a beneficial microbiome in three easy steps. Ecol. Lett. 15, 1300–1307 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Dawkins, R. The Extended Phenotype: The Long Reach of the Gene. (Oxford University Press, USA, 1982).

  12. 12.

    Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Mueller, U. G. & Sachs, J. L. Engineering Microbiomes to Improve Plant and Animal Health. Trends Microbiol 23, 606–617 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Bordenstein, S. R. & Theis, K. R. Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol. 13, e1002226 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  16. 16.

    Alberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. & Gilbert, M. T. P. Do vertebrate gut metagenomes confer rapid ecological adaptation? Trends Ecol. Evol. 31, 689–699 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Shapira, M. Gut Microbiotas and host evolution: scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Carrier, T. J. & Reitzel, A. M. The hologenome across environments and the implications of a host-associated microbial repertoire. Front. Microbiol. 8, 802 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Hurst, G. D. D. Extended genomes: symbiosis and evolution. Interface Focus 7, 20170001 (2017).

  20. 20.

    Sudakaran, S., Kost, C. & Kaltenpoth, M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol 25, 375–390 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Carthey, A. J. R., Gillings, M. R. & Blumstein, D. T. The extended genotype: microbially mediated olfactory communication. Trends Ecol. Evol. 33, 885–894 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Rosenberg, E. & Zilber-Rosenberg, I. The hologenome concept of evolution after 10 years. Microbiome 6, 78 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).

    Article  Google Scholar 

  24. 24.

    Piersma, T. & Drent, J. Phenotypic flexibility and the evolution of organismal design. Trends Ecol. Evol. 18, 228–233 (2003).

    Article  Google Scholar 

  25. 25.

    Lande, R. Natural selection and random genetic drift in phenotypic. Evolution. Evolution 30, 314–334 (1976).

    PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).

    Article  Google Scholar 

  27. 27.

    Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).

    Article  Google Scholar 

  28. 28.

    Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Metcalf, C. J. E. & Koskella, B. Protective microbiomes can limit the evolution of host pathogen defense. Evol. Lett. 3, 534–543 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Rosenberg, E., Koren, O., Reshef, L., Efrony, R. & Zilber-Rosenberg, I. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Theis, K. R. et al. Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. mSystems 1, e00028 (2016).

  32. 32.

    Funkhouser, L. J. & Bordenstein, S. R. Mom knows best: the universality of maternal microbial transmission. PLoS Biol. 11, e1001631 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Salem, H., Florez, L., Gerardo, N. & Kaltenpoth, M. An out-of-body experience: the extracellular dimension for the transmission of mutualistic bacteria in insects. Proc. R. Soc. Lond. B. 282, 20142957 (2015).

  34. 34.

    Vacher, C. et al. The phyllosphere: microbial jungle at the plant–climate interface. Annu. Rev. Ecol. Evol. Syst. 47, 1–24 (2016).

    Article  Google Scholar 

  35. 35.

    Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    ADS  CAS  PubMed  Article  Google Scholar 

  36. 36.

    Grieneisen, L. E. et al. Genes, geology and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc. R. Soc. B: Biol. Sci. 286, 20190431 (2019).

    Article  Google Scholar 

  37. 37.

    McKenney, E. A., Koelle, K., Dunn, R. R. & Yoder, A. D. The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Sprockett, D., Fukami, T. & Relman, D. A. Role of priority effects in the early-life assembly of the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 15, 197–205 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  39. 39.

    Uhr, G. T., Dohnalová, L. & Thaiss, C. A. The dimension of time in host-microbiome interactions. mSystems 4, e00216–18 (2019).

  40. 40.

    van Vliet, S. & Doebeli, M. The role of multilevel selection in host microbiome evolution. Proc. Natl Acad. Sci. U.S.A. 116, 20591–20597 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  41. 41.

    Benson, A. K. The gut microbiome—an emerging complex trait. Nat. Genet. 48, 1301 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    van Opstal, E. J. & Bordenstein, S. R. Rethinking heritability of the microbiome. Science 349, 1172–1173 (2015).

    ADS  PubMed  Article  Google Scholar 

  43. 43.

    Beilsmith, K. et al. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host–microbe interactions. Plant J. 97, 164–181 (2019).

  44. 44.

    Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Early, A. M., Shanmugarajah, N., Buchon, N. & Clark, A. G. Drosophila genotype influences commensal bacterial levels. PLoS ONE 12, e0170332 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  47. 47.

    Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl Acad. Sci. U. S. A. 115, 7368–7373 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Camarinha-Silva, A. et al. Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs. Genetics 206, 1637–1644 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Difford, G. F. et al. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genet 14, e1007580 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  50. 50.

    Koga, R., Meng, X.-Y., Tsuchida, T. & Fukatsu, T. Cellular mechanism for selective vertical transmission of an obligate insect symbiont at the bacteriocyte–embryo interface. Proc. Natl Acad. Sci. U.S.A. 109, E1230–E1237 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Kaltenpoth, M., Göttler, W., Herzner, G. & Strohm, E. Symbiotic bacteria protect wasp larvae from fungal infestation. Curr. Biol. 15, 475–479 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Clark, R. I. et al. Distinct shifts in microbiota composition during Drosophila aging impair intestinal function and drive mortality. Cell Rep. 12, 1656–1667 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. U.S.A. 112, E2930–E2938 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Lokmer, A. et al. Spatial and temporal dynamics of pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  56. 56.

    Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Lapierre, P. & Gogarten, J. P. Estimating the size of the bacterial pan-genome. Trends Genet 25, 107–110 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Koonin, E. V. & Wolf, Y. I. Evolution of microbes and viruses: a paradigm shift in evolutionary biology? Front. Cell. Infect. Microbiol. 2, 119 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  59. 59.

    Ferreiro, A., Crook, N., Gasparrini, A. J. & Dantas, G. Multiscale evolutionary dynamics of host-associated microbiomes. Cell 172, 1216–1227 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Kikuchi, Y., Hosokawa, T. & Fukatsu, T. Specific developmental window for establishment of an insect-microbe gut symbiosis. Appl. Environ. Microbiol. 77, 4075–4081 (2011).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Kikuchi, Y. et al. Symbiont-mediated insecticide resistance. Proc. Natl Acad. Sci. U.S.A. 109, 8618–8622 (2012).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Itoh, H. et al. Infection dynamics of insecticide-degrading symbionts from soil to insects in response to insecticide spraying. ISME J. 12, 909–920 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63.

    Kohl, K. D. & Dearing, M. D. The woodrat gut microbiota as an experimental system for understanding microbial metabolism of dietary toxins. Front. Microbiol. 7, 1165 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Dearing, M. D. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    Miller, A. W., Kohl, K. D. & Dearing, M. D. The gastrointestinal tract of the white-throated Woodrat (Neotoma albigula) harbors distinct consortia of oxalate-degrading bacteria. Appl. Environ. Microbiol. 80, 1595–1601 (2014).

    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

  66. 66.

    Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  68. 68.

    Kohl, K. D. & Dearing, M. D. Experience matters: prior exposure to plant toxins enhances diversity of gut microbes in herbivores. Ecol. Lett. 15, 1008–1015 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  69. 69.

    Redman, R. S., Sheehan, K. B., Stout, R. G., Rodriguez, R. J. & Henson, J. M. Thermotolerance generated by plant/fungal symbiosis. Science 298, 1581 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Rodriguez, R. J. et al. Stress tolerance in plants via habitat-adapted symbiosis. ISME J. 2, 404–416 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Miller, E. T., Svanbäck, R. & Bohannan, B. J. M. Microbiomes as Metacommunities: Understanding Host-Associated Microbes through Metacommunity Ecology. Trends Ecol. Evol. 33, 926–935 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  72. 72.

    Newell, P. D. & Douglas, A. E. Interspecies interactions determine the impact of the gut microbiota on nutrient allocation in Drosophila melanogaster. Appl. Environ. Microbiol. 80, 788–796 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  73. 73.

    Keebaugh, E. S., Yamada, R., Obadia, B., Ludington, W. B. & Ja, W. W. Microbial quantity impacts drosophila nutrition, development, and lifespan. Science 4, 247–259 (2018).

    CAS  Google Scholar 

  74. 74.

    Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. U.S.A. 115, E11951–E11960 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Mushegian, A. A., Walser, J.-C., Sullam, K. E. & Ebert, D. The microbiota of diapause: How host-microbe associations are formed after dormancy in an aquatic crustacean. J. Anim. Ecol. 87, 400–413 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  76. 76.

    Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  77. 77.

    Rolig, A. S., Parthasarathy, R., Burns, A. R., Bohannan, B. J. M. & Guillemin, K. Individual members of the microbiota disproportionately modulate host innate immune responses. Cell Host Microbe 18, 613–620 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Webster, N. S. & Reusch, T. B. H. Microbial contributions to the persistence of coral reefs. ISME J. 11, 2167–2174 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  79. 79.

    Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  80. 80.

    Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  81. 81.

    Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Metcalf, C. J. E., Henry, L. P., Rebolleda-Gómez, M. & Koskella, B. Why evolve reliance on the microbiome for timing of ontogeny? MBio 10, e01496-19 (2019).

  83. 83.

    Gilbert, S. F., Bosch, T. C. G. & Ledón-Rettig, C. Eco-Evo-Devo: developmental symbiosis and developmental plasticity as evolutionary agents. Nat. Rev. Genet. 16, 611–622 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  84. 84.

    Philippi, T. & Seger, J. Hedging one’s evolutionary bets, revisited. Trends Ecol. Evol. 4, 41–44 (1989).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  85. 85.

    Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 403–414 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  86. 86.

    Bruijning, M., Henry, L. P., Forsberg, S. K. G., Metcalf, C. J. E. & Ayroles, J. F. When the microbiome defines the host phenotype: selection on vertical transmission in varying environments. bioRxiv 2020.09.02.280040 (2020) https://doi.org/10.1101/2020.09.02.280040.

  87. 87.

    Boone, C. K. et al. Bacteria associated with a tree-killing insect reduce concentrations of plant defense compounds. J. Chem. Ecol. 39, 1003–1006 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  88. 88.

    Berasategui, A. et al. Gut microbiota of the pine weevil degrades conifer diterpenes and increases insect fitness. Mol. Ecol. 26, 4099–4110 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  89. 89.

    Ceja-Navarro, J. A. et al. Gut microbiota mediate caffeine detoxification in the primary insect pest of coffee. Nat. Commun. 6, 7618 (2015).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  90. 90.

    Berasategui, A. et al. The gut microbiota of the pine weevil is similar across Europe and resembles that of other conifer-feeding beetles. Mol. Ecol. 25, 4014–4031 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  91. 91.

    Sachs, J. L., Skophammer, R. G. & Regus, J. U. Evolutionary transitions in bacterial symbiosis. Proc. Natl Acad. Sci. U.S.A 108, 10800–10807 (2011).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  92. 92.

    Ohbayashi, T. et al. Insect’s intestinal organ for symbiont sorting. Proc. Natl Acad. Sci. U.S.A 112, E5179–E5188 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  93. 93.

    Itoh, H. et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc. Natl Acad. Sci. U.S.A. 116, 22673–22682 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  94. 94.

    Bennett, G. M. & Moran, N. A. Heritable symbiosis: the advantages and perils of an evolutionary rabbit hole. Proc. Natl Acad. Sci. 112, 10169–10176 (2015).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  95. 95.

    Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  96. 96.

    Klassen, J. L. Defining microbiome function. Nat. Microbiol 3, 864–869 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  97. 97.

    Lau, J. A. & Lennon, J. T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc. Natl Acad. Sci. U.S.A. 109, 14058–14062 (2012).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  98. 98.

    Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. U.S.A 115, E4284–E4293 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  99. 99.

    Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  100. 100.

    Raymann, K., Bobay, L.-M. & Moran, N. A. Antibiotics reduce genetic diversity of core species in the honeybee gut microbiome. Mol. Ecol. 27, 2057–2066 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  101. 101.

    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  102. 102.

    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  103. 103.

    Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of microbiome: attempting to find consensus ‘best practice’ for 16S microbiome studies. Appl. Environ. Microbiol. 84, e02627–17 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  104. 104.

    Roth-Schulze, A. J. et al. Functional biogeography and host specificity of bacterial communities associated with the Marine Green Alga Ulva spp. Mol. Ecol. 27, 1952–1965 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  105. 105.

    Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  106. 106.

    Meaden, S., Metcalf, C. J. E. & Koskella, B. The effects of host age and spatial location on bacterial community composition in the English Oak tree (Quercus robur). Environ. Microbiol. Rep. 8, 649–658 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  107. 107.

    Lambais, M. R., Barrera, S. E., Santos, E. C., Crowley, D. E. & Jumpponen, A. Phyllosphere metaproteomes of trees from the Brazilian atlantic forest show high levels of functional redundancy. Microb. Ecol. 73, 123–134 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  108. 108.

    Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  109. 109.

    Oh, J. et al. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  110. 110.

    Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  111. 111.

    Asnicar, F. et al. Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling. mSystems 2, e00164–16 (2017).

  112. 112.

    Yassour, M. et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe 24, 146–154.e4 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  113. 113.

    Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  114. 114.

    Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  115. 115.

    Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  116. 116.

    Jorth, P. et al. Metatranscriptomics of the human oral microbiome during health and disease. MBio 5, e01012–e01014 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  117. 117.

    Bashiardes, S., Zilberman-Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10, 19–25 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  118. 118.

    Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. U.S.A. 111, E2329–E2338 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  119. 119.

    Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  120. 120.

    Hoang, K. L., Morran, L. T. & Gerardo, N. M. Experimental evolution as an underutilized tool for studying beneficial animal–microbe interactions. Front. Microbiol. 7, 1444 (2016).

  121. 121.

    Martino, M. E. et al. Bacterial adaptation to the host’s diet is a key evolutionary force shaping drosophila-lactobacillus symbiosis. Cell Host Microbe 24, 109–119.e6 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  122. 122.

    Schlötterer, C., Kofler, R., Versace, E., Tobler, R. & Franssen, S. U. Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation. Heredity 114, 431–440 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  123. 123.

    Henry, L. P. & Ayroles, J. F. Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster. BMC Microbiol 21, 108 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  124. 124.

    Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  125. 125.

    Hendry, A. P. Eco-evolutionary Dynamics. (Princeton University Press, 2017).

  126. 126.

    Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  127. 127.

    Hoyt, J. R. et al. Bacteria isolated from bats inhibit the growth of Pseudogymnoascus destructans, the causative agent of white-nose syndrome. PLoS ONE 10, e0121329 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  128. 128.

    Cheng, T. L. et al. Efficacy of a probiotic bacterium to treat bats affected by the disease white-nose syndrome. J. Appl. Ecol. 54, 701–708 (2016).

    Article  Google Scholar 

  129. 129.

    Woodhams, D. C., Bletz, M., Kueneman, J. & McKenzie, V. Managing amphibian disease with skin microbiota. Trends Microbiol. 24, 161–164 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  130. 130.

    Weiss, B. & Aksoy, S. Microbiome influences on insect host vector competence. Trends Parasitol. 27, 514–522 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  131. 131.

    Zitvogel, L., Ma, Y., Raoult, D., Kroemer, G. & Gajewski, T. F. The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 359, 1366–1370 (2018).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  132. 132.

    Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  133. 133.

    Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  134. 134.

    Christian, N., Whitaker, B. K. & Clay, K. Microbiomes: unifying animal and plant systems through the lens of community ecology theory. Front. Microbiol. 6, 869 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  135. 135.

    Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc. Biol. Sci. 286, 20182448 (2019).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Mueller, E. A., Wisnoski, N. I., Peralta, A. L. & Lennon, J. T. Microbial rescue effects: how microbiomes can save hosts from extinction. Funct. Ecol. 34, 2055-2064 (2020).

  137. 137.

    Hird, S. M. Evolutionary biology needs wild microbiomes. Front. Microbiol. 8, 725 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  138. 138.

    Wade, M. J. The co-evolutionary genetics of ecological communities. Nat. Rev. Genet. 8, 185–195 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  139. 139.

    Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl Acad. Sci. 114, 9641–9646 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  140. 140.

    Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 (2019).

  141. 141.

    Heath, K. D. & Stinchcombe, J. R. Explaining mutualism variation: a new evolutionary paradox? Evolution 68, 309–317 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  142. 142.

    Sandoval-Motta, S., Aldana, M., Martínez-Romero, E. & Frank, A. The human microbiome and the missing heritability problem. Front. Genet. 8, 80 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  143. 143.

    Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  144. 144.

    Vollmar, S. et al. The gut microbial architecture of efficiency traits in the domestic poultry model species japanese quail (Coturnix japonica) assessed by mixed linear models. G3 10, 2553–2562 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  145. 145.

    Hoffmann, A. A., Sgrò, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32, 506–517 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  146. 146.

    Bruijning, M., Metcalf, C. J. E., Jongejans, E. & Ayroles, J. F. The evolution of variance control. Trends Ecol. Evol. 35, 22–33 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  147. 147.

    Douglas, G. M., Bielawski, J. P. & Langille, M. G. I. Re-evaluating the relationship between missing heritability and the microbiome. Microbiome 8, 87 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  148. 148.

    Johannes, F., Colot, V. & Jansen, R. C. Epigenome dynamics: a quantitative genetics perspective. Nat. Rev. Genet. 9, 883–890 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  149. 149.

    Slatkin, M. Epigenetic inheritance and the missing heritability problem. Genetics 182, 845–850 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  150. 150.

    Hernando-Herraez, I., Garcia-Perez, R., Sharp, A. J. & Marques-Bonet, T. DNA methylation: insights into human evolution. PLoS Genet 11, e1005661 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  151. 151.

    Pujol, B. et al. The missing response to selection in the wild. Trends Ecol. Evol. 33, 337–346 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  152. 152.

    Shaw, R. G. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193, 1–10 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  153. 153.

    Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol.17, e3000102 (2019).

  154. 154.

    Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  155. 155.

    Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).

    CAS  PubMed  Article  Google Scholar 

  156. 156.

    Guo, Y. et al. Networks underpinning symbiosis revealed through cross-species eQTL mapping. Genetics 206, 2175–2184 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  157. 157.

    Kohl, K. D. An introductory ‘how-to’ guide for incorporating microbiome research into integrative and comparative biology. Integr. Comp. Biol. 57, 674–681 (2017).

    PubMed  Article  Google Scholar 

  158. 158.

    Marchesi, J. R. & Ravel, J. The vocabulary of microbiome research: a proposal. Microbiome 3, 31 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank Sarah Kocher, Amanda Lea, Lindy McBride, Jess Metcalf, Simon Levin, and Luisa Pallares for helpful feedback. L.P.H. was supported by NSF-GRFP under grant DGE1656466, M.B. by NWO Rubicon (019.192EN.017), S.K.G.F. by Swedish Research Council (2017-06229) and EMBO (632-2017), and National Institutes of Health (NIH) grants GM124881 to J.F.A.

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L.P.H. and J.F.A. conceptualized the perspective. M.B. and S.K.G.F. developed the verbal quantitative genetic models. L.P.H. wrote the first draft, and all authors edited the text.

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Correspondence to Lucas P. Henry or Julien F. Ayroles.

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Henry, L.P., Bruijning, M., Forsberg, S.K.G. et al. The microbiome extends host evolutionary potential. Nat Commun 12, 5141 (2021). https://doi.org/10.1038/s41467-021-25315-x

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