Proteomic analysis of the bacterial cell cycle

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Timedependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.Keywords: evolutionary optimization; mathematical modelling; metabolic regulation; gene expression.Microarray technologies provide the means to measure simultaneously the expression patterns of thousands of genes [1,2]. These expression data and the availability of more than 80 fully sequenced genomes represent an enormous quantity of experimental data. The conversion of this genomic information into knowledge on phenotype characteristics such as metabolic pathways or signal transduction networks is a challenging task that cannot be effectively tackled without broad application of theoretical and computational methods.Time resolved tracing of expression levels for large sets of genes has provided evidence that mRNA levels of metabolic enzymes often change within the same time scale as variations of external conditions [1][2][3][4]. Quantitative simulation of these time dependent gene expression patterns meets with difficulties due to incomplete knowledge of the underlying regulatory mechanisms. However, statistical methods have been successfully applied, such as cluster analysis of time-dependent gene expression patterns for identifying functionally related proteins [5][6][7][8][9][10].It has been stressed that even without detailed knowledge of gene regulatory mechanisms phenotype properties can be rationalized by evolutionary optimization principles [11]. The basis of this approach is the hypothesis that a permanent change of phenotype properties due to mutation and selection leads to an optimal adaptation of an organism to given environmental conditions. Most optimization studies in the field of metabolic regulation are aimed at prediction of time independent characteristics of enzymes ensuring optimal performance of metabolic pathways [11][12][13][14][15]. The microarray data suggests applying optimization concepts to also explain time courses of enzyme concentrations.The basic idea of our paper is that time dependent gene expression enables cells to adapt their metabolic capabilities in an optimal way to varying external conditions. Our approach consists in (a) establishing a mathematical model of the metabolic pathways under consideration, (b) defining a performance function to ev...

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“…Clustered expression profiles show wave-like temporal changes of mRNA levels [25]. Their findings are supported by proteomic analyses [26].…”

Section: Discussionsupporting

confidence: 61%

Prediction of temporal gene expression

Klipp

Heinrich

Holzhütter

2002

European Journal of Biochemistry

103

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“…A previous study of selected yeast proteins (including several analyzed here) implied that they were not degraded in exponential growth (21), but the labeling/chase time periods were short, which serves to emphasize the importance of the extended labeling/chase protocol in accessing lower turnover rates. Moreover, the radiolabeling approach used previously (21,22) cannot readily be combined with the identification step, and autoradiography of the entire protein spot means that the enhanced statistical certainty deriving from multiple peptides is inaccessible.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of Protein Turnover, a Missing Dimension in Proteomics

Pratt

Petty

Riba-Garcia

et al. 2002

Molecular & Cellular Proteomics

375

391

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Functional genomic experiments frequently involve a comparison of the levels of gene expression between two or more genetic, developmental, or physiological states. Such comparisons can be carried out at either the RNA (transcriptome) or protein (proteome) level, but there is often a lack of congruence between parallel analyses using these two approaches. To fully interpret protein abundance data from proteomic experiments, it is necessary to understand the contributions made by the opposing processes of synthesis and degradation to the transition between the states compared. Thus, there is a need for reliable methods to determine the rates of turnover of individual proteins at amounts comparable to those obtained in proteomic experiments. Here, we show that stable isotope-labeled amino acids can be used to define the rate of breakdown of individual proteins by inspection of mass shifts in tryptic fragments. The approach has been applied to an analysis of abundant proteins in glucoselimited yeast cells grown in aerobic chemostat culture at steady state. The average rate of degradation of 50 proteins was 2.2%/h, although some proteins were turned over at imperceptible rates, and others had degradation rates of almost 10%/h. This range of values suggests that protein turnover is a significant missing dimension in proteomic experiments and needs to be considered when assessing protein abundance data and comparing it to the relative abundance of cognate mRNA species.

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“…In C. crescentus, protein degradation plays an important role in controlling cell cycle progression (Domian et al 1997;Jenal and Fuchs 1998;Grü nenfelder et al 2001). Caulobacter cells divide asymmetrically to produce two distinct daughter cells, a smaller motile swarmer cell and a larger surface-adherent stalked cell.…”

mentioning

confidence: 99%

Second messenger-mediated spatiotemporal control of protein degradation regulates bacterial cell cycle progression

Duerig¹,

Abel²,

Folcher³

et al. 2009

Genes Dev.

Self Cite

286

367

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Second messengers control a wide range of important cellular functions in eukaryotes and prokaryotes. Here we show that cyclic di-GMP, a global bacterial second messenger, promotes cell cycle progression in Caulobacter crescentus by mediating the specific degradation of the replication initiation inhibitor CtrA. During the G1-to-Sphase transition, both CtrA and its cognate protease ClpXP dynamically localize to the old cell pole, where CtrA is rapidly degraded. Sequestration of CtrA to the cell pole depends on PopA, a newly identified cyclic di-GMP effector protein. PopA itself localizes to the cell pole and directs CtrA to this subcellular site via the direct interaction with a mediator protein, RcdA. We present evidence that c-di-GMP regulates CtrA degradation during the cell cycle by controlling the dynamic sequestration of the PopA recruitment factor to the cell pole. Furthermore, we show that cell cycle timing of CtrA degradation relies on converging pathways responsible for substrate and protease localization to the old cell pole. This is the first report that links cyclic di-GMP to protein dynamics and cell cycle control in bacteria.[Keywords: PopA; cyclic di-GMP; protein degradation; Caulobacter crescentus; cell cycle; second messenger] Supplemental material is available at http://www.genesdev.org.

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Proteomic analysis of the bacterial cell cycle

Cited by 122 publications

References 51 publications

Prediction of temporal gene expression

Prediction of temporal gene expression

Dynamics of Protein Turnover, a Missing Dimension in Proteomics

Second messenger-mediated spatiotemporal control of protein degradation regulates bacterial cell cycle progression

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