Population Diversity in Aging and Metabolic Stress: Using Systems Biology to Connect Molecular Networks and Phenotypic Outcomes: Dr. Evan Williams, Institute of Molecular Systems Biology, ETH Zurich
Seminar | April 1 | 4-5 p.m. | 114 Morgan Hall
Interactions between individuals' genetic backgrounds and their environments over a lifetime drive variation in the incidence and severity of metabolic disorders and age-related co-morbidities. We have followed a highly diverse set of 2210 mice belonging to 50 isogenic BXD strains on high and low fat diets across their lifespans. From this cohort, we built a biobank of 663 cases taken at 6, 12, 18, or 24 months-of-age in order to examine the extent to which metabolic networks respond to differences in age, diet, and genotype. Striking variations in phenotypic outcomes across the population emphasize the inadequacy of using population averages or single models. Thus, to understand the relationships between these gene-by-environment (GXE) effects, we have taken a multi-omics molecular approach to the liver. Implementing this approach first required us to improve the throughput and stability of SWATH proteomics, which is now suitable for the generation of large longitudinal datasets for systems genetics. We have used the resulting transcriptomic, proteomic, and metabolomic dataset systematically to identify genetic causes and regulatory networks driving phenotypic outcomes. In particular, genetic variants in key pathways of energy metabolismparticularly oxidative phosphorylation, cholesterol biosynthesis, and beta-oxidationall closely tie to genotype-specific responses to diet and age. Age-associated changes also prominently coincide with increasing variance in gene expression, indicating a possible degradation of regulatory homeostasis. Global shifts in energy metabolism are relatively concordant between transcriptomes and proteomes, but proteomic networks are statistically and functionally accentuated relative to mRNA networks, indicating tighter protein stoichiometry. This is particularly clear for large complexes such as the electron transport chain and the ribosomes, but is also evident for pathways of physically separate, but functionally related, proteins. Our work demonstrates ways in which proteomics and multi-omics, when linked with endogenous genetic variation, are permitting the identification of novel causal factors and networks that can lead to a more complete understanding of the consequences of variation in complex metabolic processes and the etiology of metabolic disorders. This analytical platform that we are building is an extensible and open experimental system to understand generally how GXE factors affect complex genome-to-phenome relations.
Evan Williams obtained his bachelor's degree in Bioengineering at Rice University in 2009 and his doctorate at the École Polytechnique Fédérale de Lausanne (EPFL) in 2015. At the EPFL, Evan worked in the laboratory of Johan Auwerx on understanding how varying genetic backgrounds and environments across mouse populations lead to divergent incidence and severity of metabolic diseases. These gene-by-environment interaction studies led to the identification of several novel gene functions, some with direct phenotypic impacts on complex metabolic diseases, and others on core cell processes such as the divergent formations of protein supercomplexes in the electron transport chain. Since finishing his PhD, Evan has worked in the group of Ruedi Aebersold at the Eidgenössische Technische Hochschule Zürich (ETHZ) on improving the throughput and reliability of proteomics for large and longitudinal studies, and on the merged application of multi-omics datasets for hypothesis discovery. Most recently, Evan has generated transcriptomic, proteomic, and metabolomic datasets to understanding how age plays an interacting role with genetics and environment to influence cellular metabolism and overall metabolic health. Evan's primary research interests are at the junction between systems genetics and molecular biology, particularly for the study of complex metabolic traits and pathways.