北京大学定量生物学中心

学术报告

题    目: Resource conservation manifests in the genetic code

报告人: Dr. David Zeevi

Center for Studies in Physics and Biology, the Rockefeller University

时    间: 10月19日（周一）9:00-10:00

地    点: Online (Zoom会议)

会议 ID：627 9242 4126

https://zoom.com.cn/j/62792424126

主持人: Lucas Carey

摘 要:

Nutrient limitation is a strong selective force, driving competition for resources. However, much is unknown about how selective pressures resulting from nutrient limitation shape microbial coding sequences. Here, we study this ‘resource-driven’ selection using metagenomic and single-cell data of marine microbes, alongside environmental measurements. We show that a significant portion of the selection exerted on microbes is explained by the environment and is strongly associated with nitrogen availability. We further demonstrate that this resource conservation optimization is encoded in the structure of the standard genetic code, providing robustness against mutations that increase carbon and nitrogen incorporation into protein sequences. Overall, we demonstrate that nutrient conservation exerts a significant selective pressure on coding sequences and may have even contributed to the evolution of the genetic code.

报告人简介：

 David Zeevi is an independent postdoctoral fellow at the Rockefeller University Center for Studies in Physics and Biology. His research focuses on developing computational methods for studying the human gut and marine microbiomes, and their contribution to human and environmental health. David applies these tools in clinical settings in order to understand the relationship between nutrition, health, and gut microbes in humans; and in environmental settings in order to find new microbial mechanisms for combating pollution. David has coauthored several publications in the human microbiome field, linking the microbiome to the effects of artificial sweeteners (Suez et al., Nature 2014) and host circadian rhythm (Thaiss et al., Cell 2015), inferring bacterial growth dynamics (Korem et al., Science 2015), predicting the glycemic responses of individuals to complex meals (Zeevi et al., Cell 2015; Korem et al., Cell Metab 2017), and characterizing microbial genomic variability across individuals (Zeevi et al., Nature 2019).