Dataset Information

Proteome allocation constraints determine cellular growth rates and demand fitness trade-offs

ABSTRACT: Protein synthesis is costly and the proteome size is constrained. Using a genome-scale computational model of proteome allocation together with absolute proteomics data sets from many growth environments, we determine how these fundamental limitations constrain growth and fitness in Escherichia coli. First, we show that the observed variation in growth rates across environments is largely determined by the expression of protein not utilized for growth in a given environment. We then elucidate the overall transcriptional regulatory logic that underlies the expression of unused protein. We systematically classify the unused proteome into segments devoted to environmental readiness and stress resistance functions. While expression of these proteome segments incurs a fitness cost of decreased growth in a fixed environment, they provide fitness benefits in a changing environment. Thus, the systems biology of the prokaryotic proteome can be quantitatively understood based on resource allocation to growth, environmental readiness, and stress resistance functions.

ORGANISM(S): Escherichia coli str. K-12 substr. MG1655

PROVIDER: GSE72020 | GEO | 2018/08/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increases the predictability of adaptive evolution. mRNA from each evolved clone or from the ancestral strain growing in the specificied nitrogen-limited condition was co-hybridized with mRNA from the ancestral strain grown in ammonium limited media

Project description:One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increases the predictability of adaptive evolution. DNA from each evolved clone or population is hybridized vs DNA from the ancestral strain

Dataset Information

Proteome allocation constraints determine cellular growth rates and demand fitness trade-offs

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets