bioRxivEvolutionary BiologyPreprint
Convergent and divergent patterns of holobiont evolution in co-distributed chemosynthetic symbioses
Corinna BreusingiD, Caitlin Q. PlowmaniD, Shawn M. ArellanoiD, Andrew John DaviesiD, Brennan PhillipsiD, Craig M. YoungiD, Roxanne A. BeinartiD
Abstract
Intraspecific variation in microbial symbioses is shaped by a variety of ecological and evolutionary processes. In geographically separated populations, divergence in genomic traits is expected due to dispersal barriers and habitat differentiation, whereas sympatric populations might exhibit functional convergence to the characteristics of a shared environment. While these two processes have been well documented in many animal and plant species, they remain understudied in symbiotic systems. Here, we examined these processes in Atlantic cold seep mussel species that associate with environmentally acquired methane and/or sulfur-oxidizing bacteria. Using genomic methods, we reveal an eco-geographical barrier between the Western Atlantic and Gulf of Mexico that influences the distribution of symbiont species and the level of divergence between holobiont populations. While host populations were only slightly differentiated across this divide, symbiont populations were structured both between and within regions. The sulfur-oxidizing symbionts contained different regional types of dissimilatory nitrate reductase, possibly due to variation in oxygen availability. The methane-oxidizing symbionts showed evidence for convergent evolution, such as retention of a secondary, iron-dependent methane monooxygenase in Western Atlantic populations, driven by local copper limitation. These findings advance our understanding of divergent and convergent adaptive mechanisms underlying intraspecific host-symbiont variation in horizontally transmitted symbioses.
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: phylogenetics / evolution
- approach: statistical model
- Citations
- 0
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