Mechanistic dissection of eukaryotic protein biogenesis and degradation pathways

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Principal Investigator: Vladimir  Denic
Organization: HARVARD UNIVERSITY
Fiscal Year: 2024
Award: $575,852
Funding agency: National Institute of General Medical Sciences

Abstract
My lab currently has two main areas of interest: 1) a new chaperone system (eFOLD) that enables biogenesis
of eukaryotic translation elongation factor 1 alpha (eEF1A); 2) endoplasmic reticulum (ER) and peroxisome
degradation by selective autophagy. Errors in eEF1A biogenesis result in rapid degradation by the
ubiquitin-proteasome system (UPS) thus making protein degradation a natural link between the two areas.
Both eFOLD and selective autophagy are controlled by distinct stress responses (e.g. heat shock vs. amino
acid starvation) but jointly serve as effectors of protein homeostasis (proteostasis). Both areas raise similar
questions regarding substrate selectivity: How does a specific eFOLD chaperone co-translationally recognize
an aggregation-prone region of eEF1A nascent chains? How is terminally misfolded eEF1A recognized for
degradation by the UPS? What signals on damaged or unwanted organelles are detected by specific
autophagy receptors to orchestrate encapsulation of organelle targets into autophagosomes? To answer
these questions, we are dissecting biogenesis and degradation mechanisms that select substrates of grossly
different sizes, respond to distinct physiological cues, and have widely different temporal dynamics. Using
yeast and human cell culture in parallel, we are exploring conserved aspects of eFOLD and selective
autophagy mechanisms shared by each species, as well as species-specific adaptations. Broadly speaking,
our projects spawn from identification of missing factors by genetic screening or biochemical purification but
all seek a deep mechanistic understanding of mutant phenotypes through biochemical reconstitution with
purified components and protein structure-function analysis. Along this path, we iteratively test our
hypotheses by genomics, quantitative cell microscopy, and theoretical modeling approaches.

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