Abstract
Background: Cell growth underlies many key cellular and development processes,
yet a limited number of studies have been conducted on cell growth regulation.
Comprehensive studies at the transcriptional, proteomic and metabolic levels under
defined controlled conditions are presently lacking.
Results: Metabolic Control Analysis is being exploited in a Systems Biology
study of the eukaryotic cell. Using chemostat culture, we have measured the impact
of changes in flux (growth rate) on the transcriptome, proteome, endo- and exo-metabolomes
of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear
growth-rate-associated trends and discriminates between carbon-sufficient and
carbon-limited conditions. Genes consistently and significantly up-regulated with
increasing growth rate are frequently essential and encode evolutionarily conserved
proteins of known function that participate in many protein-protein interactions.
In contrast, more unknown, and fewer essential, genes are down-regulated with
increasing growth rate; their protein products rarely interact with one another.
A large proportion of yeast genes under positive growth-rate control share
orthologues with other eukaryotes, including humans. Significantly, transcription
of genes encoding components of the TOR complex (a major controller of eukaryotic
cell growth) is not subject to growth-rate regulation. Also, integrative studies
reveal the extent and importance of post-transcriptional control, patterns of
control of metabolic fluxes at the level of enzyme synthesis, and the relevance
of specific enzymatic reactions in the control of metabolic fluxes during cell growth.
Conclusions: This work constitutes a first comprehensive Systems Biology
study on growth-rate control in the eukaryotic cell. The results have direct
implications for advanced studies on cell growth, in vivo regulation of metabolic
fluxes for comprehensive metabolic engineering, and for the design of genome-scale
Systems Biology models of the eukaryotic cell.
Paper
Data
- Transcriptomics, proteomics and metabolomics data are available to download.
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