2016.08.22. Cross-species interactome mapping reveals network evolution principles from yeasts to human

2019-07-07 00:34:38 0

 北京大学定量生物学中心

学术报告

报告人: Haiyuan Yu, Associate Professor

Department of Biological Statistics and Computational Biology

Weill Institute for Cell and Molecular Biology

Cornell University

题目: Cross-species interactome mapping reveals network evolution principles from yeasts to human

时 间:2016-8-22(周一),13:00-14:00

地 点:北京大学老化学楼东配楼一层101会议室

主持人:邓明华教授 李方廷副教授

ABSTRACT

The Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae with similarly facile genetics. Yet, it is significantly under-studied with little functional genomic information available. Here, we screened the whole fission yeast proteome three times (>75 million protein pairs) to generate the first high-coverage high-quality binary interactome network for S. pombe, FissionNet, comprising ~2300 interactions among ~1300 proteins. ~50% of these interactions were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

报告人简介:

Haiyuan Yu is the associate professor at Conell University. His lab is affiliated with the Department of Biological Statistics and Computational Biology, the Department of Computer Science, the Weill Institute for Cell and Molecular Biology, the Tri-Institutional Training Program in Computational Biology and Medicine, and the Center for Vertebrate Genomics at Cornell University.

They perform research in the broad area of Network Systems Biology with both high-throughput experimental (see Vo et al., Cell 2016) and integrative computational (see Wang et al., Nature Biotechnology 2012) methodologies, aiming to understand gene functions and their relationships within complex molecular networks and how perturbations to such systems may lead to various human diseases. The complexity of biological systems calls for building experimentally-verified computational models based on high-quality large-scale datasets, which is truly the future of biomedical research and the main theme of the lab. Their research is focused in five main areas (http://www.yulab.org/) :

1)    Functional and Comparative Genomics

2)    Molecular and Dynamic Proteomics

3)    Structural Genomics and Simulations

4)    Algorithms and Tools

5)    Technology Development