QB3 Postdoc Seminar

Seminar | March 15 | 4:30-5:30 p.m. | 177 Stanley Hall

 QB3 - California Institute for Quantitative Biosciences

Emily Blythe , Martin Lab
Development of an in vitro substrate to characterize p97 unfoldase activity.

Valosin-containing protein (VCP/p97) is an essential AAA ATPase involved in many cellular pathways, such as ER-associated degradation, Golgi reassembly after mitosis, and aggregate clearance. In these pathways, p97 separates ubiquitylated proteins from complexes or membranes and, in many cases, targets them to the proteasome for degradation. The nature of p97 substrates – unstable, heterogeneous, and difficult to purify from their cellular contexts – had long prevented the detailed biochemical study of p97’s purported basic function: protein unfolding. To address this barrier, we developed a model p97 substrate and demonstrated explicitly for the first time that p97, in complex with its adaptors Npl4-Ufd1, unfolds proteins. Using this system, we then explored the effects of mutations in p97 that cause Multisystem Proteinopathy, a neurodegenerative disease. We found that these mutants have a moderate acceleration of unfoldase activity and a large increase in Npl4-Ufd1 affinity, but further work is needed to examine the ramifications of this gain-of-function in the cell.

Benjamin Smarr | Kriegsfeld lab
Timing systems in biology enable variance reduction and prediction of future states.

Mammals show many of the biological rhythms of all life on earth - ultradian metabolic cycles across hours, circadian daily rhythms, and seasonal changes. In addition, they have ovulatory rhythms, and complex inter-relations between the cyclic output of different organs and tissue systems, as in heart rate, temperature, and digestion. Using a range of numerical approaches to parsing information from continuous, within-individual, physiological timeseries data, it is possible to reduce the variability of measurements by contextualizing them in cyclic phase. Additionally, when structured in time, these data become signals amenable to signal processing approaches, which can be used to enhance detection and prediction of specific changes, as in disease emergence and environmental disruption. Additionally, these analyses allow more nuanced assessment of diversity, capturing structures underneath variables like age, sex, and inter-individual difference.