Artificial Selection and the Skin Microbiome Independently Predict Parasite Resistance

Abstract

Host responses to parasite infection involve several interacting systems. Host genetics determine much of the response, but it is increasingly clear that the host-associated microbiome also plays a role. Genetically determined systems and the microbiome can also interact; for example, the microbiome can modulate the immune response, and vice versa. However, it remains unclear how such interactions between the host immune system and the microbiome may influence the hosts overall response to parasites. To investigate how host genetics and the microbiome interact to shape responses to parasites, we imposed truncation selection on Trinidadian guppies (Poecilia reticulata) for low and high resistance to the specialist ectoparasite Gyrodactylus turnbulli. After 3-6 generations of breeding without parasites, we sampled the skin-associated microbiome and infected fish from each line. We applied Dirichlet Multinomial Modeling (DMM) machine-learning to identify bacterial community types across lines and evaluated how selection line and community type explained variations in infection severity. Our findings showed that among females, the resistant line had significantly lower infection severity, while the susceptible line had higher infection severity. Among males, only the susceptible line experienced higher infection severity compared to the other lines. Line did not explain skin microbial diversity, structure or composition. Our DMM analysis revealed three distinct bacterial community types, independent of artificial selection lines, which explained just as much variation in infection load as selection line. Overall, we found that the microbiome and host genetics independently predict infection severity, highlighting the microbiomes active role in host-parasite interactions.