Periodic Reporting for period 2 - AgingTimer (Systems biology of the individual stochastic timer of aging)
Période du rapport: 2021-08-01 au 2023-01-31
Aging is the biggest risk factor for frailty and death. However, we lack basic understanding of a fundamental question: Why do genetically identical organisms raised in the same conditions get sick and die at different times? If we understood the stochastic timer that drives aging in each individual, we could devise ways to turn back the timer and treat age-related diseases, extending the healthy lifespan. This requires addressing both molecular and social factors that vary between individuals, such as socioeconomic status in humans and social ranking in mice, which impact every aspect of aging. This project aims to identify the stochastic timer of aging and develop methods to read the timer and turn it back. We use mice as a tractable organism relevant to human aging, and combine three disciplines: 1) systems biology to mathematically define the stochastic timer of aging and the basic concepts needed to understand its production, removal and noise processes; 2) neurobiology of behavioral individuality; and 3) biology of cellular senescence, which studies the most promising candidate for the timer: senescent cells that accumulate with age, causing chronic inflammation and whose removal delays age-related decline. This study will provide basic understanding of the timer of aging and provide ways to read the timer. Moreover, we will offer new ways to set back the timer in order to address age-related diseases and functional decline.
The AgingTimer project has adapted to COVID-19 by developing experimental and computational infrastructure that will enable us to meet our goals. First, we overcame the major challenge of quantitating senescent cells in vivo, our prime candidate for the aging timer. To do so, we achieved a breakthrough by developing a FACS-based method to identify senescent cells in tissues using an effective antibody against p16. This method allows using cell-type specific markers to identify and quantify senescent cells in tissues, and allows sorting out specific senescent cells from tissues for their molecular analysis. This method will allow us to achieve the goals of the proposal in a more efficient manner as it allows quantification and molecular characterization of senescent cells from different tissues at any time point of life in wild type mice. Our initial experiments using this system have already identified a novel type of senescent cells. Second, we established a new model organism to study aging, starved E. coli cells. These cells share the mortality law that mammals show, called the Gompertz law, but have lifespans of days and hence offer powerful experimental benefits to ask about universals of aging across kingdoms.
Thirdly, we achieved a proof-of concept for measuring the effect of senescent cells on social behavior. To do so, we demonstrated that continuous video monitoring can detect behavioral effects in mice that were manipulated to have excess senescent cells. We were also able to measure the clearance rate of senescent cells in socially isolated versus non-isolated mice. These studies begin to bridges the gap between molecular studies of aging and studies of social behavior. To tie these themes together, we developed mathematical models that connect senescent cell abundance to disease incidence. To do so, we combined out dynamical equations for senescent cell dynamics with newly available large-scale medical datasets from Israel with incidence of hundreds of age-related diseases in 50 million life-years of data. These models provide a potential link between the mouse-centered AgingTimer project and future applications to human health.
Thirdly, we achieved a proof-of concept for measuring the effect of senescent cells on social behavior. To do so, we demonstrated that continuous video monitoring can detect behavioral effects in mice that were manipulated to have excess senescent cells. We were also able to measure the clearance rate of senescent cells in socially isolated versus non-isolated mice. These studies begin to bridges the gap between molecular studies of aging and studies of social behavior. To tie these themes together, we developed mathematical models that connect senescent cell abundance to disease incidence. To do so, we combined out dynamical equations for senescent cell dynamics with newly available large-scale medical datasets from Israel with incidence of hundreds of age-related diseases in 50 million life-years of data. These models provide a potential link between the mouse-centered AgingTimer project and future applications to human health.
Progress beyond the state of the art
Our research already achieved several goals of our proposal. One of the main goals was to develop new methods in order to manipulate senescent cells as a timer of aging. Indeed, using new state of the art approaches we discovered a new method to remove senescent cells. This method is based on the most novel scientific technological developments and goes beyond the knowledge that was available before we started this project. We have already successfully used this method to eliminate senescent cells of a specific type that we identified using novel single-cell approach we established.
We also developed a new methodology to increase the number of senescent cells in a single organ to study the impact of such intervention on the aging timer on the level of the organism. This approach goes beyond our initial thoughts and models for the first time a situation when injury or disease damages a specific organ in our body and enables us to evaluate the effect of this on the aging timer.
From the system biology perspective of our project we developed the first mathematical model that connects human disease incidence from medical records to senescent cell dynamics. In an innovative manner this model connects increase of specific disease incidence with an increase in the number of senescent cells with age.
Expected results until the end of the project
Notwithstanding the restrictions imposed by COVID-19 pandemic we have already got significant amount of results which address aims of our proposal.
Expected results till the end of the project are:
1. Characterizing behavioral changes during aging and finding a link between social isolation, social ranking and accumulation of senescent cells.
2. Identifying non-invasive markers of aging that is coordinated with amount of senescent cells during aging. This will be performed based on the single cell approaches we already developed.
3. Development of a full stochastic model of SnCs that can explain the Gompertz law of mortality and the exponentially increasing incidence of age-related disease with age, as a function of social behavior and sex. This model will allow to establish the risks of death and disease as a function of total and organ-specific senescent cells
4. Determine which behavioral, physiological and histological aspects of aging are reversible and which are not. We will use novel methods we have already developed for elimination of a specific population of senescent cells achieve these results.
Our research already achieved several goals of our proposal. One of the main goals was to develop new methods in order to manipulate senescent cells as a timer of aging. Indeed, using new state of the art approaches we discovered a new method to remove senescent cells. This method is based on the most novel scientific technological developments and goes beyond the knowledge that was available before we started this project. We have already successfully used this method to eliminate senescent cells of a specific type that we identified using novel single-cell approach we established.
We also developed a new methodology to increase the number of senescent cells in a single organ to study the impact of such intervention on the aging timer on the level of the organism. This approach goes beyond our initial thoughts and models for the first time a situation when injury or disease damages a specific organ in our body and enables us to evaluate the effect of this on the aging timer.
From the system biology perspective of our project we developed the first mathematical model that connects human disease incidence from medical records to senescent cell dynamics. In an innovative manner this model connects increase of specific disease incidence with an increase in the number of senescent cells with age.
Expected results until the end of the project
Notwithstanding the restrictions imposed by COVID-19 pandemic we have already got significant amount of results which address aims of our proposal.
Expected results till the end of the project are:
1. Characterizing behavioral changes during aging and finding a link between social isolation, social ranking and accumulation of senescent cells.
2. Identifying non-invasive markers of aging that is coordinated with amount of senescent cells during aging. This will be performed based on the single cell approaches we already developed.
3. Development of a full stochastic model of SnCs that can explain the Gompertz law of mortality and the exponentially increasing incidence of age-related disease with age, as a function of social behavior and sex. This model will allow to establish the risks of death and disease as a function of total and organ-specific senescent cells
4. Determine which behavioral, physiological and histological aspects of aging are reversible and which are not. We will use novel methods we have already developed for elimination of a specific population of senescent cells achieve these results.