Systems biology of the individual stochastic timer of aging

Informations projet

AgingTimer

N° de convention de subvention: 856487

DOI

10.3030/856487

Date de signature de la CE 16 Janvier 2020

Date de début 1 Février 2020

Date de fin 31 Juillet 2027

Financé au titre de

• EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 8 687 500,00

Contribution de l’UE

€ 8 687 500,00

Coordonné par

WEIZMANN INSTITUTE OF SCIENCE
Israel

Periodic Reporting for period 4 - AgingTimer (Systems biology of the individual stochastic timer of aging)

Période du rapport: 2024-08-01 au 2026-01-31

Résumé du contexte et des objectifs généraux du projet

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.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

The AgingTimer project has developed experimental and computational infrastructure that will enable us to meet our goals - and be the first major study that combines theoretical modeling, cell aging biology and social factors in aging. First, we made major strides in the 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 and two other markers. This method allows using cell-type specific markers to identify and quantify senescent cells in tissues. Our initial experiments using this system have already identified two major therapeutic strategies- using immune checkpoint inhibitors and ribosome biogenesis inhibitors to combat senescent cells. Second, we established a new model organism to study aging, starved E. coli cells. We uncovered a striking universality in the way that damage stochastically accumulates that spans from bacteria to mammals, allowing us accurate modeling of the timer of aging.
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, and to understand the role of sex in social hierarchy formation. These studies begin to bridge 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 our 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 AgingTimer project and future applications to human health.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

Progress beyond the state of the art
Our research already made significant progress on the 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 new methods 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 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
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.