Bagic, M. | Search bioRxiv

bioRxiv2024

AEGIS: individual-based modeling of life history evolution

Nature presents a staggering diversity of life history strategies, ranging from rapid to slow onset of sexual maturity, short or long life, low or high number of offspring, and much more. Each species-specific life history trait reflects on the one hand specific adaptations to unique environments, e.g., nutrient availability, predation, parasite load, seasonality; and on the other hand, depends on past demographic constraints, such as population bottlenecks, migrations, etc. Studying life history diversity in nature and in the laboratory ultimately aims to identify the ecological, demographic, and intrinsic causes contributing to species-specific growth rate distributions, lifetime reproductive outcomes, as well as lifespans. However, for most species, we cannot rewind the evolutionary and demographic past to identify the causal chain of events leading to the present life history traits. We can infer past events only by sampling extant populations. In silico evolution has the advantage of providing complete time resolution for the events driving life history evolution and enables to directly test the impact of ecological and demographic variables on the evolution of life history traits. We developed AEGIS (Aging of Evolving Genomes In Silico), a software for individual-based modeling of life history trait evolution at the genotype and phenotype level. AEGIS models life history traits evolution in response to a set of factors, including resource availability, extrinsic mortality induced by predators or parasites, different levels of germline mutation rates, population size, sexual vs. asexual reproduction, and more. AEGIS serves as a powerful tool to model life history evolution and allows for parameter inference against ground truths. AEGIS can help generate estimates for the evolution of different life history traits, such as age-dependent mortality and reproduction, in response to different selective pressures and intrinsic genetic constraints.

evolutionary biology↗

bioRxiv2022

Population size shapes the evolution of lifespan

Biological aging results from the age-dependent change in the force of natural selection, which increases the probability of germline variants that limit survival to accumulate in genes acting predominantly in late life1. The evolutionary mechanisms underlying the accumulation of neutral mutations and antagonistically pleiotropic gene variants that cause biological aging have been analyzed to date under the assumption of infinitely large population size. However, even though population size importantly shapes genetic and phenotype variation via drift and selection2,3, we still have a limited understanding of how finite population size impacts the evolution of mortality at the population level. Here, we study the impact of population size on lifespan evolution under mutation accumulation and antagonistic pleiotropy. We found that larger population size leads to lower age-independent, as well as age-dependent mortality under mutation accumulation, due to more effective purifying selection against deleterious germline variants. Strikingly, large population size can lead to extended lifespan under antagonistic pleiotropy, due to more effective positive selection on gene variants increasing survival in early-life, while leading to increased post-maturation mortality. Our findings provide a comprehensive numerical framework for the two major evolutionary genetic theories of aging and reveal a fundamental and yet non-appreciated role for population size in the evolution of mortality trajectories.

evolutionary biology↗