Lifelong behavioral screen reveals an architecture of vertebrate aging.
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| Title: | Lifelong behavioral screen reveals an architecture of vertebrate aging. |
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| Authors: | Bedbrook, Claire N. (AUTHOR), Nath, Ravi D. (AUTHOR), Zhang, Libby (AUTHOR), Linderman, Scott W. (AUTHOR), Brunet, Anne (AUTHOR), Deisseroth, Karl (AUTHOR) |
| Source: | Science. 3/12/2026, Vol. 391 Issue 6790, p1-19. 19p. |
| Subjects: | Animal behavior, Aging, Life expectancy, Transcriptomes, Life history theory, Animal models for aging, Machine learning |
| Abstract: | Mapping behavior of individual vertebrate animals across lifespan could provide an unprecedented view into the lifelong process of aging. We created a platform for high-resolution continuous behavioral tracking of the African killifish across natural lifespan from adolescence to death. We found that animals follow distinct individual aging trajectories. The behaviors of long-lived animals differed markedly from those of short-lived animals, even relatively early in life, and were linked to organ-specific transcriptomic shifts. Machine-learning models accurately inferred age and even forecasted an individual's future lifespan, given only behavior at a young age. Finally, we found that animals progressed through adulthood in a sequence of stable and stereotyped behavioral stages with abrupt transitions, revealing precise structure for an architecture of aging. Editor's summary: Continuous recording of a vertebrate's adult life from adolescence until death would provide a complete view into the behavioral architecture of aging. However, the long time scale and complexity of vertebrate aging have so far precluded such observations. Bedbrook et al. leveraged major advances in machine learning and computer vision to follow nearly every moment of the adult life of a naturally short-lived vertebrate, the African killifish. The authors found that individual animals followed distinct aging trajectories defined by abrupt transitions in behavior. These results might lead to better understanding of the aging process in other vertebrates, including humans. —Mattia Maroso INTRODUCTION: With increased age comes a substantially increased risk of devastating illnesses, including cancers, cardiovascular diseases, and dementias. As elderly populations grow, so does the urgency to better understand the lifelong process of aging. We hypothesized that fundamental insights into the dynamic process of aging could come from continuously observing individuals across life until aging-related death. However, given the long timescale of vertebrate aging, continuous observation across an individual vertebrate animal's life has not been practical. RATIONALE: We studied the African turquoise killifish, a vertebrate model for aging with a naturally compressed lifespan (median 4 to 8 months), to continuously record behavior from puberty to death and explore the architecture of adult lifespan progression and aging. Behavior is a rich readout of animal state that integrates diverse features of multiorgan system physiology, including the core nervous system functions of sensation, cognition, and action. We designed an unbiased screen to systematically explore how behavioral patterns change with age and to determine whether behavior could predict future aging differences and even remaining lifespan. This unbiased approach also allowed us to explore the impact of interventions on behavior and to test for the presence of behavioral stages that define progression through adult life. RESULTS: We built a system to continuously screen behavior across the entire adult lifespan of individual animals, which enabled the initial investigation of vertebrate multidimensional behavioral dynamics spanning timescales from single video frames (tens of milliseconds) to whole lifespans (~250 days from puberty to death) in a systematic and quantitative manner. With this view into the process of aging, we discovered that behavioral trajectories of short-lived animals are distinct from those of long-lived animals. Based on these behavioral trajectories, we separated chronologically age-matched animals predicted to be long- versus short-lived and performed multiorgan transcriptomic profiling. Animals destined for a long lifespan exhibited transcriptomic changes in ribosomal and metabolic pathways but not in other key aging-related pathways such as inflammation. Through our machine-learning models, we found that the behavior of an individual at a relatively young age sufficed to predict future short or long lifespan, and key predictive behaviors appeared to be conserved across the animal kingdom. The noninvasive behavioral screen also enabled the exploration of how a human-relevant longevity intervention (dietary restriction) influences the process of aging. Finally, our continuous tracking of the aging process from adolescence to death revealed the surprising observation that animals exhibit notable transitions between stereotyped behavioral stages at distinct ages. These data suggest a model for the architecture of adult life progression, in which the process of aging encompasses the succession of discrete life stages, rather than a gradual continuous decline. CONCLUSION: We designed and built a platform to continuously track naturalistic behavior across lifespan from adolescence to death, allowing for a high-resolution unbiased screen of the process of aging in vertebrates. We found that animal behavior can be a highly informative noninvasive readout of the process of aging and that vertebrate animals progress through adulthood in an orderly sequence of stable and stereotyped behavioral stages. The lifespan architecture described here advances basic understanding of biological aging and may also enable targeted mechanistic and therapeutic discovery work that is relevant to human aging and age-related disease. Lifelong behavioral screen.: To investigate the lifelong progression of aging, we continuously tracked behavior from adolescence to death in the naturally short-lived African turquoise killifish. Naturalistic behavioral readout, which is noninvasive, revealed individual animal aging trajectories across life. Behavior not only can be used to infer age but also can forecast future lifespan. The continuous nature of behavioral tracking suggests an architecture of aging whereby individuals progress through adulthood in an orderly sequence of stable and stereotyped behavioral stages. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Mapping behavior of individual vertebrate animals across lifespan could provide an unprecedented view into the lifelong process of aging. We created a platform for high-resolution continuous behavioral tracking of the African killifish across natural lifespan from adolescence to death. We found that animals follow distinct individual aging trajectories. The behaviors of long-lived animals differed markedly from those of short-lived animals, even relatively early in life, and were linked to organ-specific transcriptomic shifts. Machine-learning models accurately inferred age and even forecasted an individual's future lifespan, given only behavior at a young age. Finally, we found that animals progressed through adulthood in a sequence of stable and stereotyped behavioral stages with abrupt transitions, revealing precise structure for an architecture of aging. Editor's summary: Continuous recording of a vertebrate's adult life from adolescence until death would provide a complete view into the behavioral architecture of aging. However, the long time scale and complexity of vertebrate aging have so far precluded such observations. Bedbrook et al. leveraged major advances in machine learning and computer vision to follow nearly every moment of the adult life of a naturally short-lived vertebrate, the African killifish. The authors found that individual animals followed distinct aging trajectories defined by abrupt transitions in behavior. These results might lead to better understanding of the aging process in other vertebrates, including humans. —Mattia Maroso INTRODUCTION: With increased age comes a substantially increased risk of devastating illnesses, including cancers, cardiovascular diseases, and dementias. As elderly populations grow, so does the urgency to better understand the lifelong process of aging. We hypothesized that fundamental insights into the dynamic process of aging could come from continuously observing individuals across life until aging-related death. However, given the long timescale of vertebrate aging, continuous observation across an individual vertebrate animal's life has not been practical. RATIONALE: We studied the African turquoise killifish, a vertebrate model for aging with a naturally compressed lifespan (median 4 to 8 months), to continuously record behavior from puberty to death and explore the architecture of adult lifespan progression and aging. Behavior is a rich readout of animal state that integrates diverse features of multiorgan system physiology, including the core nervous system functions of sensation, cognition, and action. We designed an unbiased screen to systematically explore how behavioral patterns change with age and to determine whether behavior could predict future aging differences and even remaining lifespan. This unbiased approach also allowed us to explore the impact of interventions on behavior and to test for the presence of behavioral stages that define progression through adult life. RESULTS: We built a system to continuously screen behavior across the entire adult lifespan of individual animals, which enabled the initial investigation of vertebrate multidimensional behavioral dynamics spanning timescales from single video frames (tens of milliseconds) to whole lifespans (~250 days from puberty to death) in a systematic and quantitative manner. With this view into the process of aging, we discovered that behavioral trajectories of short-lived animals are distinct from those of long-lived animals. Based on these behavioral trajectories, we separated chronologically age-matched animals predicted to be long- versus short-lived and performed multiorgan transcriptomic profiling. Animals destined for a long lifespan exhibited transcriptomic changes in ribosomal and metabolic pathways but not in other key aging-related pathways such as inflammation. Through our machine-learning models, we found that the behavior of an individual at a relatively young age sufficed to predict future short or long lifespan, and key predictive behaviors appeared to be conserved across the animal kingdom. The noninvasive behavioral screen also enabled the exploration of how a human-relevant longevity intervention (dietary restriction) influences the process of aging. Finally, our continuous tracking of the aging process from adolescence to death revealed the surprising observation that animals exhibit notable transitions between stereotyped behavioral stages at distinct ages. These data suggest a model for the architecture of adult life progression, in which the process of aging encompasses the succession of discrete life stages, rather than a gradual continuous decline. CONCLUSION: We designed and built a platform to continuously track naturalistic behavior across lifespan from adolescence to death, allowing for a high-resolution unbiased screen of the process of aging in vertebrates. We found that animal behavior can be a highly informative noninvasive readout of the process of aging and that vertebrate animals progress through adulthood in an orderly sequence of stable and stereotyped behavioral stages. The lifespan architecture described here advances basic understanding of biological aging and may also enable targeted mechanistic and therapeutic discovery work that is relevant to human aging and age-related disease. Lifelong behavioral screen.: To investigate the lifelong progression of aging, we continuously tracked behavior from adolescence to death in the naturally short-lived African turquoise killifish. Naturalistic behavioral readout, which is noninvasive, revealed individual animal aging trajectories across life. Behavior not only can be used to infer age but also can forecast future lifespan. The continuous nature of behavioral tracking suggests an architecture of aging whereby individuals progress through adulthood in an orderly sequence of stable and stereotyped behavioral stages. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00368075 |
| DOI: | 10.1126/science.aea9795 |
