Model Organisms: The holy grail of longevity research
Aging is a major risk factor for numerous chronic diseases, such as dementia, metabolic syndromes, and cancers, (Hou et al., 2019; Kennedy et al., 2014; Niccoli and Partridge, 2012), and age-related declines in health are poised to become significant economic and clinical challenges (European Commission, 2014). As a consequence, researchers, governments and drug companies have been trying to identify how aging is influenced by lifestyle choices and by biological, environmental and socio-economic factors (Crane et al., 2022). A key challenge is to develop innovative approaches that can help us to better understand the biology of aging and to accurately quantify age-dependent changes in physiology and cognition. The latter is necessary to evaluate the costs and benefits of potential interventions. Now, in eLife, Anne Brunnet (Stanford University) and colleagues – including Andrew McKay, Emma Costa and Jingxun Chen as joint first authors – report a fresh dimension to this quest (McKay et al., 2022).
Finding appropriate animal models is crucial in biomedicine, but it is rare for a single species (such as mice) to have all the characteristics required and also capture all the aspects of a target species (such as humans). The observation that “all models are wrong, but some are useful” captures this concept succinctly (even if it was first made about statistical models, not animal models; Box, 1976). However, it is possible to overcome this limitation by having a diverse pool of animal models that can help uncover fundamentally conserved phenomena and fuel innovative thinking (Mathuru et al., 2020). This is particularly relevant for aging studies, where longevity can be affected by species-specific adaptations and dramatically divergent evolutionary trajectories.
McKay et al. showcase new technologies and resources for the African turquoise killifish, Nothobranchius furzeri (Figure 1A). Compared to other vertebrate model organisms, killifish have an extremely short life cycle, during which they go through all the stages of life – from a larva to a senile adult – within a few weeks (Harel et al., 2015; Reichard and Polačik, 2019; Terzibasi Tozzini and Cellerino, 2020; Valenzano et al., 2015).
For the experiments, adult fish were housed in individual transparent tanks. McKay et al. designed an automated feeding system, which has several advantages over the conventional, manual handling systems: it is less invasive; it is more precise and flexible; and it can be deployed across a large number of individual tanks. It is also designed to be open-source, easily transferable, and built from only 25 widely available components.
In a proof-of-concept study, McKay et al. used their design to compare the impact of a favorable diet and a restricted diet on aging. Their results suggest that males (but not females) raised on a restricted diet live longer, and that this change is accompanied by changes in the transcriptional profiles of liver cells (Figure 1B). Sex-specific effects of diet have also been seen in mammals, which suggest that this phenomenon may be widespread among vertebrates. The sex-specific gene expression and their potential connection to lifespan differences raises many interesting questions for future research.
A critical factor in longevity studies is to study the impact of interventions on cognition. Towards this goal, McKay et al. included a red LED light in the design of their automated feeding system: this light switched on a few seconds before the fish were fed, so they can learn to associate the red light with food delivery. After a few repetitions, if the fish learned this association, they would react to the red light switching on as if they expected to be fed. This assay allowed McKay et al. to test the learning abilities and memory retention of the killifish in their home tanks, and how these were affected by age. Even though the design of the associative learning paradigm was simple, McKay et al. were able to demonstrate that killifish rapidly learned such associations in five to eight repetitions.
Overall, the study of McKay et al. opens up a number of exciting possibilities for future studies using killifish. For instance, experiments could focus on investigating how exactly dietary restriction, drug interventions, and sex-specific effects in gene expression intersect with cognitive fitness. The automated feeder should also be useful for studies looking at the effect of specific diet schedules, nutrients and circadian rhythms on longevity. Finally, as new technologies for killifish mature further (Platzer and Englert, 2016), comparative multi-species studies with other species – notably medaka and zebrafish – will become more realistic and offer the promise of even deeper insights into the biology of aging in vertebrates.
References
-
Science and statisticsJournal of the American Statistical Association 71:791–799.https://doi.org/10.1080/01621459.1976.10480949
-
Ageing as a risk factor for neurodegenerative diseaseNature Reviews. Neurology 15:565–581.https://doi.org/10.1038/s41582-019-0244-7
-
Why behavioral neuroscience still needs diversity?: A curious case of a persistent needNeuroscience and Biobehavioral Reviews 116:130–141.https://doi.org/10.1016/j.neubiorev.2020.06.021
-
Ageing as a risk factor for diseaseCurrent Biology 22:R741–R752.https://doi.org/10.1016/j.cub.2012.07.024
-
Nothobranchius furzeri: a model for aging research and moreTrends in Genetics 32:543–552.https://doi.org/10.1016/j.tig.2016.06.006
Article and author information
Author details
Publication history
- Version of Record published: December 23, 2022 (version 1)
Copyright
© 2022, Mathuru
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,409
- views
-
- 89
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
-
- Genetics and Genomics
- Neuroscience
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer’s disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.