Abstract

The gut microbiome of animals, which serves important functions but can also contain potential pathogens, is to varying degrees under host genetic control. This can generate signals of phylosymbiosis, whereby gut microbiome composition matches host phylogenetic structure. However, the genetic mechanisms that generate phylosymbiosis and the scale at which they act remain unclear. Two non‐mutually exclusive hypotheses are that phylosymbiosis is driven by immunogenetic regions such as the major histocompatibility complex (MHC) controlling microbial composition, or by spatial structuring of neutral host genetic diversity via founder effects, genetic drift, or isolation by distance. Alternatively, associations between microbes and host phylogeny may be generated by their spatial autocorrelation across landscapes, rather than the direct effects of host genetics. In this study, we collected MHC, microsatellite, and gut microbiome data from separate individuals belonging to the Galápagos mockingbird species complex, which consists of four allopatrically distributed species. We applied multiple regression with distance matrices and Bayesian inference to test for correlations between average genetic and microbiome similarity across nine islands for which all three levels of data were available. Clustering of individuals by species was strongest when measured with microsatellite markers and weakest for gut microbiome distributions, with intermediate clustering of MHC allele frequencies. We found that while correlations between island‐averaged gut microbiome composition and both microsatellite and MHC dissimilarity existed across species, these relationships were greatly weakened when accounting for geographic distance. Overall, our study finds little support for large‐scale control of gut microbiome composition by neutral or adaptive genetic regions across closely related bird phylogenies, although this does not preclude the possibility that host genetics shapes gut microbiome at the individual level.

Highlights

  • The gut microbiome of animals is a source of both functionality and pathogens, and is to various degrees under host genetic control (Davenport, 2016; Kubinak et al, 2015; Kurilshikov et al, 2017)

  • Genetic distances based on both major histocompatibility complex (MHC) and microsatellite dissimilarity significantly correlated with gut microbiome dissimilarity in univariate multiple regressions on distance matrices (MRM) tests for both overall and island core microbiomes (Figure 3a,b,d,e; Table S2a,b, respectively)

  • This study aimed to quantify the effects of genetic dissimilarity based on host neutral and MHC allele frequencies on the gut microbiome amplicon sequence variants (ASVs) distributions of the Galápagos mockingbird species complex at the island population level

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Summary

| INTRODUCTION

The gut microbiome of animals is a source of both functionality and pathogens, and is to various degrees under host genetic control (Davenport, 2016; Kubinak et al, 2015; Kurilshikov et al, 2017). The accumulation of such neutral effects via founder effects, genetic drift, or isolation by distance, would together generate a distinct phylogenetic signal on the host-associated microbiome While both neutral and adaptive genetic regions have been implicated in being important for shaping gut microbial communities, the extent to which these mechanisms act together has not been explicitly tested. Community-wide genetic effects, whereby allele frequencies across islands drive differences in overall gut microbiome composition, are detectable within this framework We apply both MRM tests and BEDASSLE to test for the effects of neutral and MHC dissimilarity while controlling for geographic distance. Since phylogenetic signals in gut microbiomes have been shown to be weak yet detectable in avian clades (Kropáčková et al, 2017; Song et al, 2020; Trevelline et al, 2020), including Galápagos finches (Michel et al, 2018), we expect that adaptive immune genetic regions may have larger effects on microbiome composition than neutral markers in this study system

| MATERIAL AND METHODS
| DISCUSSION
Findings
| CONCLUSIONS
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