October 5 2016

12:00 LSB 2320

Sagi Snir
University of Haifa
Department of Evolutionary and Environmental Biology

A Universal PaceMaker as a Better Explanation of Evolution and Aging A Universal PaceMaker as a Better Explanation of Evolution and Aging


One of the central and most fundamental, yet debated, concepts of evolution is the Molecular Clock (MC) hypothesis, according to which each gene evolves at a characteristic, near constant rate. Numerous studies support the Molecular Clock hypothesis in principle but also show that the clock is indeed very dispersed. Independently, along with observed violations of the MC model, several comparative genomic studies pointed at gene rate correlations during various periods in life history. In particular, analysis of thousands of genes across long evolutionary spans, reveal surprising and unexpected correlation of evolutionary rates of the different genes within each evolving genome. This correlation could be explained by a new rigorous model that we devised, denoted Universal PaceMaker (UPM) of genome evolution. The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes, but independently across all evolving lineages. Alternatively, however, this correlation between the evolutionary rates of genes could be a simple consequence of MC. By fitting thousands of genes' histories from data sets spanning almost all life history, we were able to show that the goodness of fit for the UPM model was better than the fit for the MC model, with overwhelming statistical significance. These results reveal a universal, genome-wide pacemaker of evolution that could have been in operation throughout the history of life. The generality of UPM can be adapted to other processes and settings. In a recent collaboration at UCLA, we formulated aging as a process adhering to the UPM model. Under this formulation we can trace both individual (biological) aging as well as trends of aging in a population. In the talk, I will discuss these and further efforts including our recent results basedon methylation data.

Based on joint works with Eugene Koonin and Yuri Wolf from the NIH and Matteo Pellegrini from UCLA.











































































































































































































































































































































































































































































































































































































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