Abstract
The African turquoise killifish is an exciting new vertebrate model for aging studies. A significant challenge for any model organism is the control over its diet in space and time. To address this challenge, we created an automated and networked fish feeding system. Our automated feeder is designed to be open-source, easily transferable, and built from widely available components. Compared to manual feeding, our automated system is highly precise and flexible. As a proof-of-concept for the feeding flexibility of these automated feeders, we define a favorable regimen for growth and fertility for the African killifish and a dietary restriction regimen where both feeding time and quantity are reduced. We show that this dietary restriction regimen extends lifespan in males (but not in females) and impacts the transcriptomes of killifish livers in a sex-specific manner. Moreover, combining our automated feeding system with a video camera, we establish a quantitative associative learning assay to provide an integrative measure of cognitive performance for the killifish. The ability to precisely control food delivery in the killifish opens new areas to assess lifespan and cognitive behavior dynamics and to screen for dietary interventions and drugs in a scalable manner previously impossible with traditional vertebrate model organisms.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have performed new experiments and analyses to identify in an unbiased manner the transcriptomic changes associated with the sex differences in lifespan extension by dietary restriction. This transcriptomic analysis reveals sexual dimorphism in liver inflammation, metabolism, and stress responses in response to dietary restriction in the liver, some of which has also been observed in mammals. Our study uncovers genes that exhibit a striking sex specificity in gene expression, which could underlie the lifespan differences between sexes and extend to other species. These new data have been included in new Figure 5 and the associated figure supplements and the corresponding text. We have now bolstered our behavioral analysis by generating a new automated analysis pipeline and by benchmarking it on thorough manual analysis. Specifically, we have (1) generated a robust automated pipeline for unbiased quantification of learning sessions by measuring the fish's velocity in successive trials; (2) calculated different metrics of success for associative learning behavior; (3) better defined how the learning index is calculated; (4) plotted the learning index as a continuum of different ages and sexes, in addition to binning; (5) performed power analysis to determine the number of individuals required for well-powered behavior experiments. These new analyses have been included in new Figure 6 and the associated figure supplements and described in the corresponding text.
