Ryan N Gutenkunst

Ryan N Gutenkunst

Associate Department Head, Molecular and Cellular Biology
Associate Professor, Applied BioSciences - GIDP
Associate Professor, Applied Mathematics - GIDP
Associate Professor, Cancer Biology -
Associate Professor, Ecology and Evolutionary Biology
Associate Professor, Genetics - GIDP
Associate Professor, Molecular and Cellular Biology
Associate Professor, Public Health
Associate Professor, Statistics-GIDP
Associate Professor, BIO5 Institute
Member of the Graduate Faculty
Director, Graduate Studies
Primary Department
Contact
(520) 626-0569

Work Summary

We learn history from the genomes of humans, tumors, and other species. Our studies reveal how evolution works at the molecular level, offering fundamental insight into how humans and pathogens adapt to challenges.

Research Interest

The Gutenkunst group studies the function and evolution of the complex molecular networks that comprise life. To do so, they integrate computational population genomics, bioinformatics, and molecular evolution. They focus on developing new computational methods to extract biological insight from genomic data and applying those methods to understand population history and natural selection.

Publications

Holmes, W. M., Mannakee, B. K., Gutenkunst, R. N., & Serio, T. R. (2014). Loss of amino-terminal acetylation suppresses a prion phenotype by modulating global protein folding. Nature communications, 5, 4383.

Amino-terminal acetylation is among the most ubiquitous of protein modifications in eukaryotes. Although loss of N-terminal acetylation is associated with many abnormalities, the molecular basis of these effects is known for only a few cases, where acetylation of single factors has been linked to binding avidity or metabolic stability. In contrast, the impact of N-terminal acetylation for the majority of the proteome, and its combinatorial contributions to phenotypes, are unknown. Here, by studying the yeast prion [PSI(+)], an amyloid of the Sup35 protein, we show that loss of N-terminal acetylation promotes general protein misfolding, a redeployment of chaperones to these substrates, and a corresponding stress response. These proteostasis changes, combined with the decreased stability of unacetylated Sup35 amyloid, reduce the size of prion aggregates and reverse their phenotypic consequences. Thus, loss of N-terminal acetylation, and its previously unanticipated role in protein biogenesis, globally resculpts the proteome to create a unique phenotype.

Mannakee, B. K., & Gutenkunst, R. N. (2016). Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution. PLoS genetics, 12(7), e1006132.

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

Casey, F. P., Baird, D., Feng, Q., Gutenkunst, R. N., Waterfall, J. J., Myers, C. R., Brown, K. S., Cerione, R. A., & Sethna, J. P. (2007). Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model. IET Systems Biology, 1(3), 190-202.

PMID: 17591178;Abstract:

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (β-Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system. © 2007 The Institution of Engineering and Technology.

Stuck, T. J., Mannakee, B. K., & Gutenkunst, R. N. (2017). The impact of genome-wide association studies on biomedical research is modest and declining. Human Molecular Genetics.
Smith, A. M., Adler, F. R., McAuley, J. L., Gutenkunst, R. N., Ribeiro, R. M., McCullers, J. A., & Perelson, A. S. (2011). Effect of 1918 PB1-F2 expression on influenza a virus infection kinetics. PLoS Computational Biology, 7(2).

PMID: 21379324;PMCID: PMC3040654;Abstract:

Relatively little is known about the viral factors contributing to the lethality of the 1918 pandemic, although its unparalleled virulence was likely due in part to the newly discovered PB1-F2 protein. This protein, while unnecessary for replication, increases apoptosis in monocytes, alters viral polymerase activity in vitro, enhances inflammation and increases secondary pneumonia in vivo. However, the effects the PB1-F2 protein have in vivo remain unclear. To address the mechanisms involved, we intranasally infected groups of mice with either influenza A virus PR8 or a genetically engineered virus that expresses the 1918 PB1-F2 protein on a PR8 background, PR8-PB1-F2(1918). Mice inoculated with PR8 had viral concentrations peaking at 72 hours, while those infected with PR8-PB1-F2(1918) reached peak concentrations earlier, 48 hours. Mice given PR8-PB1-F2(1918) also showed a faster decline in viral loads. We fit a mathematical model to these data to estimate parameter values. The model supports a higher viral production rate per cell and a higher infected cell death rate with the PR8-PB1-F2(1918) virus. We discuss the implications these mechanisms have during an infection with a virus expressing a virulent PB1-F2 on the possibility of a pandemic and on the importance of antiviral treatments.