Abstract
Background: The relatively fast selection of symbiotic bacteria within hosts and the potential transmission of these bacteria across generations of hosts raise the question of whether interactions between host and bacteria support emergent adaptive capabilities beyond those of germ-free hosts. Results: To investigate possibilities for emergent adaptations that may distinguish composite host-microbiome systems from germ-free hosts, we introduce a population genetics model of a host-microbiome system with vertical transmission of bacteria. The host and its bacteria are jointly exposed to a toxic agent, creating a toxic stress that can be alleviated by selection of resistant individuals and by secretion of a detoxification agent ("detox"). We show that toxic exposure in one generation of hosts leads to selection of resistant bacteria, which in turn, increases the toxic tolerance of the host's offspring. Prolonged exposure to toxin over many host generations promotes anadditional form of emergent adaptation due to selection of hosts based on detox produced by their bacterial community as a whole (as opposed to properties of individual bacteria). Conclusions: These findings show that interactions between pure Darwinian selections of host and its bacteria can give rise to emergent adaptive capabilities, including Lamarckian-like adaptation of the host-microbiome system. Reviewers: This article was reviewed by Eugene Koonin, Yuri Wolf and Philippe Huneman.
Original language | English |
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Article number | 24 |
Number of pages | 13 |
Journal | Biology Direct |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - 26 Oct 2018 |
Keywords
- Darwinian selection
- Emergent adaptation
- Holobiont
- Host-microbiome interactions
- Lamarckian adaptation
- Population genetics
- Vertical and horizontal transmission
All Science Journal Classification (ASJC) codes
- Applied Mathematics
- General Biochemistry,Genetics and Molecular Biology
- Ecology, Evolution, Behavior and Systematics
- General Agricultural and Biological Sciences
- Immunology
- Modelling and Simulation