An automated feeding system for the African killifish reveals the impact of diet on lifespan and allows scalable assessment of associative learning

  1. Andrew McKay
  2. Emma K Costa
  3. Jingxun Chen
  4. Chi-Kuo Hu
  5. Xiaoshan Chen
  6. Claire N Bedbrook
  7. Rishad C Khondker
  8. Mike Thielvoldt
  9. Param Priya Singh
  10. Tony Wyss-Coray
  11. Anne Brunet

  • Curated by eLife

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    McKay, et al. describe development of a new wireless, network-enabled automated feeder system with which diet amount and schedule can be controlled across individually housed killifish. The system is constructed using open-source components and software and is amenable to manufacture by individual research groups and is highly scalable. The authors then use this system to explore dietary restriction effects on killifish lifespan and to develop an associative learning assay, two important goals in the KF /longevity field. The authors demonstrate that precise control of food allows automated investigation of lifespan extension under calorie restriction conditions. Secondly, they show an exciting modification of the system that involves only addition of a simple LED light. This modification allows use of the system in an associative learning / conditioning paradigm. Finally, using this paradigm, they demonstrate an age-dependent decline in learning.

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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.

Article activity feed

  1. eLife

    eLife assessment

    McKay, et al. describe development of a new wireless, network-enabled automated feeder system with which diet amount and schedule can be controlled across individually housed killifish. The system is constructed using open-source components and software and is amenable to manufacture by individual research groups and is highly scalable. The authors then use this system to explore dietary restriction effects on killifish lifespan and to develop an associative learning assay, two important goals in the KF /longevity field. The authors demonstrate that precise control of food allows automated investigation of lifespan extension under calorie restriction conditions. Secondly, they show an exciting modification of the system that involves only addition of a simple LED light. This modification allows use of the system in an associative learning / conditioning paradigm. Finally, using this paradigm, they demonstrate an age-dependent decline in learning.

  2. eLife

    Reviewer #1 (Public Review):

    The authors describe a feeding system for killifish that allows high precision control of feeding amount and schedule on a per-tank basis. The system permits automation of this task using open-source and affordable components and software. Due to this emphasis, the system appears amenable to manufacture by individual research groups and the approach appears very scalable (although more detailed build, programming and assembly instructions and videos might be useful for groups with little experience with microcontrollers and manufacturing). An exciting aspect of the system is the possibility to modify the system for different purposes. For example, it might be possible to reduce the minimum feeding amount, thereby allowing more fine grained exploration of effects related to feeding shedule. I am very enthusiastic about the open-source "maker" aspects of this work.

    The authors next explore two interesting applications of the system. First, they show that precise control of food allows automated investigation of lifespan extension under calorie restriction (CR) conditions. This is an important use case for a system of this type and showing that it is fit for this application is important.

    Secondly, the authors show an exciting modification of the system that involves only addition of a simple red light LED. This modification allows use of the system in a associative learning / conditioning paradigm.

    Finally, they show that there is an age-dependent decline in learning as evaluated by this conditioning paradigm. I am very enthusiastic about this additional function and, again, this example demonstrates the flexibly and open nature of the technology, suggesting that others can likely modify and expand the system to suite their own questions and applications. In summary, I am enthusiastic about the technology described and about the approach by which the system was developed.

    However, at the current stage, the biological applications are essentially validation experiments - e.g. showing that CR can be implemented and that the system can be used for learning and memory experiments. Neither of these aspects is pursued beyond the basic validation experiments (showing that lifespan extension can be achieved and that there is age-dependent decline in associative learning).

  3. eLife

    Reviewer #2 (Public Review):

    Here, McKay, et al. describe a new automated system to feed killifish, and use it to explore dietary restriction effects on killifish lifespan and to develop an associative learning assay, two important goals in the KF/longevity field.

    Fig. 1-2- The first figures focus on the design and evaluation of the feeding system. It appears that the feeding system works well and achieves what the authors set out to do.
    Fig. 3 explains the DR and overfeeding setup, and effects on growth and reproduction; demonstrates that the automated feeding system does achieve DR.
    Fig. 4 explains the DR setup and results on male and female KF, highlighting the fact that DR only extends the lifespan of males. This sex-specific effect seems somewhat surprising, and warrants further follow-up studies.
    Fig. 5 describes the associative learning assay, which is based on the ability of the fish to sense a red light and learn that it is associated with feeding. It is great that the authors have been able to develop a learning assay, which will no doubt become an important tool in the killifish researcher's arsenal, but additional experiments are necessary to increase the general impact of the work.

    Overall, while the results seem sound, the current version of the manuscript may be pitched to a small audience (killifish researchers) who will benefit from the development of this methodology. Perhaps the paper could be re-structured to focus less on the methodology and more on the results, fleshing out the associative learning results even more (are there mutants that extend the length of associative learning? Does it require conserved genes? etc). Further exploration of the sex-specific effects of DR on lifespan (why does this only affect males) would also raise the general interest of the work, but both the DR and associative learning aspects of the paper would need to be studied quite a bit more to move this beyond a methods paper.

  4. eLife

    Reviewer #3 (Public Review):

    The work presented by McKay et al. details the development of a new wireless network-enabled automated feeder system with which diet amount and schedule can be controlled across individually housed killifish. The manuscript describes the characterization of the system and demonstrates the robustness, precision, and high fidelity in feeding control achieved due to modular design.

    The technique in principle can be applied to hundreds of tanks and to other species that are reared in similar tank system racks.

    Strengths:

    - The authors provide a convincing account of the use of automated feeder systems for implementing experiments where diet is controlled precisely. The experimental design allows the authors to clearly demonstrate feeding schedules optimal for killifish growth, reproduction, and longevity. Their characterization and results will be highly valuable for a growing community of researchers who are beginning to use killifish in laboratory settings and can choose the regimen most suited to their research goals. The system presented in this study may also allow for better husbandry practices with the potential to mimic the ephemeral natural habitats of this species more closely in the laboratory.
    - The authors also conducted additional experiments comparing restricted food delivery schedules. The conclusion they reach that a time and quantity restricted feeding regimen increases the lifespan of males based on this experiment is well justified from the data presented. The differences between the sexes are interesting to note as the authors observed similar results with two different cohorts, though cohorts can differ in the median and maximum lifespan.

    Weaknesses:

    - The authors imply the value of automated feeders is in scaling to hundreds of individual animals/tanks. I agree with the author's assessment of this need in research labs, however, it is not easy to infer exactly how many automated feeders were operating simultaneously in this study. Estimates of the costs of building, and operating (maintenance, server use, and cloud computing costs) for conducting 1 experiment (2 conditions, 24 animals per condition) running over 100 days will be valuable for other researchers interested in adapting this resource. A clearer supplementary video 1 that demonstrates the entire feeder properly, in the home tank will also be valuable for the researchers interested in adapting the system.
    - The proof of concept experiment showing associative learning is extremely interesting but is quite difficult to assess, based on the detail provided in the results and the method. The rationale behind key considerations for behavioral measures, whether based on previous studies or, due to technical constraints are difficult to judge. This needs a better description. In particular, results mention a "pipeline", but this is obscure, in the methods section. Clearer definitions would also be needed to evaluate if an objective scoring system for was used in measures such as the"startle" response. In principle, as all trajectories are recorded, it should be possible to describe a range of acceleration/velocity changes that quantify most parameters such as startle, unless it was manually scored. As this will be a first, clarity on how "early" and "late" sessions were categorized; exact experimental design on the number of trails that made up a session; whether all animals went through same number of trials in Figure 5, etc. will improve the description and future adaptations of the experimental design.
    - One more cautionary note is in the interpretation that young individuals had significantly higher learning index scores than old individuals, as the size of the effects can't be estimated from the type of data provided and the analysis used. Given the fairly small sample size for animals used in learning index calculations (< 15), and as the authors demonstrate in diet restriction experiments there can be cohort-dependent differences as well, I would caution against such an interpretation. The p values reported in Suppl. Figure 4E especially brings home the need to move away from dichotomous thinking of yes/no based on a threshold, without taking into account effect sizes. Please refer to this recent post in eNeuro on the inherent issues with such interpretations, and methods to overcome them (https://doi.org/10.1523/ENEURO.0091-21.2021). The deficiency in "old" may not be as large, and it would be important to interpret this appropriately. Other normalization issues, rather than learning could account for small differences between the young and the old. For instance, a small latency in the average velocity and/or other locomotion kinematics differences between fish categorized as old vs. young could result in the criterion of "3 seconds before the food drops" to meet the "threshold of learning" being unmet. The data available in the paper at present can't be used to evaluate such a point.

  5. Version published to 10.7554/elife.69008 on eLife
  6. Version published to 10.1101/2021.03.30.437790v2 on bioRxiv
  7. Version published to 10.1101/2021.03.30.437790v1 on bioRxiv