Control of gene expression by dynamic metabolic oscillations

Summary
Emerging evidence shows that changes in cellular metabolism can induce broad shifts in gene
expression, but the mechanisms underlying this connection are not fully understood. Previous
work has not examined the impact of temporal dynamics on metabolism-induced gene
expression. Recent work in systems biology has shown that oscillations in upstream inputs can
be filtered by gene expression machinery to modulate gene expression, through a process
termed dynamic filtering. Additionally, we have recently shown that cellular metabolic status
fluctuates rapidly in response to various forms of metabolic stress. These cycles drive
asynchronous oscillating activity of transcription factors including FOXO3, a key regulator of
stress genes that plays a role in aging, and TFEB, a central regulator of lysosome and
autophagy genes. We therefore hypothesize that oscillations in metabolic state drive gene
expression programs that are distinct from those under static unstressed conditions. We
propose that dynamics-sensitive gene expression programs can influence cell fate decisions
such as differentiation, cell growth, senescence, inflammation, and drug sensitivity. In this
project, we will investigate how metabolic oscillations control the expression of TFEB and
FOXO3 target genes. We will use live-cell reporters, inducible expression constructs, and other
methods to measure key kinetic parameters in the transcription and translation of target genes.
To identify broader gene expression programs modulated by metabolic dynamics, we will use
mathematical modeling in combination with transcriptome-level profiling. Functional assays will
be used to test how dynamically sensitive gene expression programs alter cell fates. We expect
our study to establish an important unexplored mechanism that explains how short-term
regulation of cellular metabolic status influences chronic diseases including cancer, diabetes,
and aging. Our results will address the outstanding question of how pharmacological metabolic
inhibitors such as metformin provide benefits in cancer and aging. The models generated will
establish a new approach to evaluate candidate pharmacological compounds.

Grant Number: 5R35GM139621-05
NIH Institute/Center: NIH
Principal Investigator: John Albeck