Rapid responses of ribosomal RNA synthesis to nutrient shifts

Effects of amino acid starvation on RelA diffusive behavior in live Escherichia coli

During amino acid starvation, bacterial cells rapidly synthesize the nucleotides (p)ppGpp, causing a massive re-programming of the transcriptional profile known as the stringent response. The (p)ppGpp synthase RelA is activated by ribosomes harboring an uncharged tRNA at the A site. It is unclear whether synthesis occurs while RelA is bound to the ribosome or free in the cytoplasm. We present a study of three Escherichia coli strains, each expressing a different RelA-fluorescent protein (RelA-FP) construct: RelA-YFP, RelA-mEos2 and RelA-Dendra2. Single-molecule localization and tracking studies were carried out under normal growth conditions and during amino acid starvation. Study of three labeling schemes enabled us to assess potential problems with FP labeling of RelA. The diffusive trajectories and axial spatial distributions indicate that amino acid starvation induces net binding of all three RelA-FP constructs to 70S ribosomes. The data are most consistent with a model in which RelA synthesizes (p)ppGpp while bound to the 70S ribosome. We suggest a 'short hopping time' model of RelA activity during starvation. Our results contradict an earlier study of RelA-Dendra2 diffusion that inferred off-ribosome synthesis of (p)ppGpp. The reasons for the discrepancy remain unclear.

FtsH-mediated coordination of lipopolysaccharide biosynthesis in Escherichia coli correlates with the growth rate and the alarmone (p)ppGpp

The outer membrane is the first line of defense for Gram-negative bacteria and serves as a major barrier for antibiotics and other harmful substances. The biosynthesis of lipopolysaccharides (LPS), the essential component of the outer membrane, must be tightly controlled as both too much and too little LPS are toxic. In Escherichia coli, the cellular level of the key enzyme LpxC, which catalyzes the first committed step in LPS biosynthesis, is adjusted by proteolysis carried out by the essential and membrane-bound protease FtsH. Here, we demonstrate that LpxC is degraded in a growth rate-dependent manner with half-lives between 4 min and >2 h. According to the cellular demand for LPS biosynthesis, LpxC is degraded during slow growth but stabilized when cells grow rapidly. Disturbing the balance between LPS and phospholipid biosynthesis in favor of phospholipid production in an E. coli strain encoding a hyperactive FabZ protein abolishes growth rate dependency of LpxC proteolysis. Lack of the alternative sigma factor RpoS or inorganic polyphosphates, which are known to mediate growth rate-dependent gene regulation in E. coli, did not affect proteolysis of LpxC. In contrast, absence of RelA and SpoT, which synthesize the alarmone (p)ppGpp, deregulated LpxC degradation resulting in rapid proteolysis in fast-growing cells and stabilization during slow growth. Our data provide new insights into the essential control of LPS biosynthesis in E. coli.

Inhibition of bacterial ribosome assembly: a suitable drug target?

The assembly of bacterial ribosomes is viewed with increasing interest as a potential target for new antibiotics. The in vivo synthesis and assembly of ribosomes are briefly reviewed here, highlighting the many ways in which assembly can be perturbed. The process is compared with the model in vitro process from which much of our knowledge is derived. The coordinate synthesis of the ribosomal components is essential for their ordered and efficient assembly; antibiotics interfere with this coordination and therefore affect assembly. It has also been claimed that the binding of antibiotics to nascent ribosomes prevents their assembly. These two contrasting models of antibiotic action are compared and evaluated. Finally, the suitability and tractability of assembly as a drug target are assessed.

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship.

Systems biology aims to understand the interactions of cellular components in a systemic manner. Mathematical modeling is critical to the integration and analysis of these components on a conceptual as well as mechanistic level. To date, detailed genome-scale reconstructions of metabolism have become available for a growing number of organisms. Although metabolism has an important role in cells, other cellular functions need to be considered as well, such as signaling, regulation, and macromolecular synthesis. For instance, the cellular machinery required for RNA and protein synthesis consists of a complex set of proteins. Here, we show that one can collect all of the necessary information for a prokaryotic organism to create a gene-specific, fine-grained representation of the macromolecular synthesis machinery. E. coli was chosen as a model organism because of the wealth of available information. The explicit representation of transcription and translation in terms of a mass-balanced network enables a detailed, quantitative accounting of the protein synthesis capabilities of E. coli in silico. Hence, this study demonstrates the feasibility of constructing very large networks and also represents a critical step toward building cellular models of growth that can account for gene-specific protein production in a stoichiometric fashion on the genome scale.