Abstracts (first author)


Life-history constraints are exposed when genetically-modified mice compete with con-specifics

Author(s): Garratt M, Pichaud N, Brooks RC


An organism’s investment in life history traits such as growth, reproduction and lifespan will be constrained by a variety of physiological factors. With regards to constraints on lifespan, genetic modification of gene expression has been successfully used to uncover some of the molecular pathways that influence how long an organism lives. These techniques have the potential to reveal constraints on other life history traits as I will demonstrate in this talk. We used a genetically-modified strain of mouse to test how various components of life history are constrained by oxidative stress, a physiological condition also implicated in ageing. Mice that did not express a key antioxidant enzyme used to protect against oxidative stress, copper-zinc superoxide dismutase (Sod1), showed altered investment in behavioural, morphological and molecular aspects of reproduction and sexual signalling. These effects were also found to be more prominent when animals were maintained in a competitive environment. We then used phenotypic manipulations to increase investment in various reproductive traits in both genetically-modified and wild-type animals. This allowed us to determine how oxidative stress, the outcome of this gene knockout, influences an organism’s ability to increase metabolic rate, adjust mitochondrial function and limit further oxidative damage when investing in demanding periods of reproductive effort. Our results reveal that oxidative stress is one aspect of physiology that can directly reduce investment in reproduction. The examination of genetically modified animals in more ecologically-relevant conditions offers exciting opportunities to uncover the mechanistic constraints on life history evolution.



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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group