Back

Zebrafish (Danio rerio) behavioral phenotypes not underscored by different gut microbiota

Ayayee, P. A.; Wong, R. Y.

2024-05-31 animal behavior and cognition
10.1101/2024.05.29.596447 bioRxiv
Show abstract

Different animal behavioral phenotypes maintained and selectively bred over multiple generations may be underscored by dissimilar gut microbial community compositions or not have any significant dissimilarity in community composition. Operating within the microbiota-gut-brain axis framework, we anticipated differences in gut microbiome profiles between zebrafish (Danio rerio) selectively bred to display the bold and shy personality types. This would highlight gut microbe-mediated effects on host behavior. To this end, we amplified and sequenced a fragment of the 16S rRNA gene from the guts of bold and shy zebrafish individuals (n=10) via Miseq. We uncovered no significant difference in within-group microbial diversity nor between-group microbial community composition of the two behavioral phenotypes. Interestingly, though not statistically different, we determined that the gut microbial community of the bold phenotype was dominated by Burkholderiaceae, Micropepsaceae, and Propionibacteriaceae. In contrast, the shy phenotype was dominated by Beijerinckaceae, Pirelullacaeae, Rhizobiales_Incertis_Sedis, and Rubinishaeraceae. The absence of any significant difference in gut microbiota profiles between the two phenotypes would suggest that in this species, there might exist a stable "core" gut microbiome, regardless of behavioral phenotypes, and or possibly, a limited role for the gut microbiota in modulating this selected-for host behavior. This is the first study to characterize the gut microbial community of distinct innate behavioral phenotypes of the zebrafish (that are not considered dysbiotic states) and not rely on antibiotic or probiotic treatments to induce changes in behavior. Such studies are crucial to our understanding of the modulating impacts of the gut microbiome on normative animal behavior.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.