bioRxivGenomicsPreprint
Sexual reproduction impacts vertebrate organ aging
Jingxun CheniD, Aleksandra Tsenter, Emma K. CostaiD, Doudou Yu, Xiangping LiniD, Kenneth L. Chen, Veronica Augustina BotiD, Madeline J. HoushiD, Leo-Paul Heraud, S.V. Perry, Rishad C. Khondker, Michael P. SnyderiD, Ashley E. WebbiD, Tony Wyss‐CorayiD, Anne BrunetiD
Abstract
Sexual reproduction is a central component of life. Yet, an unanswered question remains: how does sexual reproduction shape physiology and health across an individual's lifetime? Unlike other external factors such as diet and exercise, the impact of sexual reproduction on organ health during aging is far less studied, particularly in vertebrates. Here, we develop the short-lived killifish as a vertebrate model to study how lifelong mating affects organ aging. We discover that mating regulates liver aging in a cell-type and sex-specific manner. In old female livers, mating dampened hepatocyte inflammation and reduced myeloid cell levels, and machine-learning models predicted that mating led to a 'younger' transcriptomic age in several liver cell types. Functionally, mating reduced hepatic accumulation of several lipid classes and was associated with enhanced female resilience against the liver toxin acetaminophen. More broadly, mating induced anti-inflammatory and metabolic rewiring transcriptomic signatures in the brain and gut, indicating that mating may affect vertebrate organ aging more globally. Using publicly available human datasets and plasma proteomics aging clocks, we observed an association between sexual experience and a 'younger' plasma proteomic age of several cell types, including hepatocytes. Our work provides evidence that lifelong mating reshapes the physiological context during aging in a vertebrate, highlighting an understudied connection between vertebrate sexual history, health, and aging.
- organism: zebrafish
- assay: bulk RNA-seq
- assay: proteomics
- research focus: differential expression
- research focus: cell type annotation
- research focus: benchmarking
- approach: machine learning (classical)
- Citations
- 0
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