Physiological role of the GlnK signal transduction protein of Escherichia coli : survival of nitrogen starvation

Escherichia coli contains two PII-like signal trans-duction proteins, PII and GlnK, involved in nitrogen assimilation. We examined the roles of PII and GlnK in controlling expression of glnALG , glnK and nac during the transition from growth on ammonia to nitrogen starvation and vice versa. The PII protein exclusively controlled glnALG expression in cells adapted to growth on ammonia, but was unable to limit nac and glnK expression under conditions of nitrogen starvation. Conversely, GlnK was unable to limit glnALG expression in cells adapted to growth on ammonia, but was required to limit expression of the glnK and nac promoters during nitrogen starvation. In the absence of GlnK, very high expression of the glnK and nac promoters occurred in nitrogen-starved cells, and the cells did not reduce glnK and nac expression when given ammonia. Thus, one specific role of GlnK is to regulate the expression of Ntr genes during nitrogen starvation. GlnK also had a dramatic effect on the ability of cells to survive nitrogen starvation and resume rapid growth when fed ammonia. After being nitrogen starved for as little as 10 h, cells lacking GlnK were unable to resume rapid growth when given ammonia. In contrast, wild-type cells that were starved immediately resumed rapid growth when fed ammonia. Cells lacking GlnK also showed faster loss of viability during extended nitrogen starvation relative to wild-type cells. This complex phenotype resulted partly from the requirement for GlnK to regulate nac expression; deletion of nac restored wild-type growth rates after ammonia starvation and refeeding to cells lacking GlnK, but did not improve viability during nitrogen starvation. The specific roles of GlnK during nitrogen starvation were not the result of a distinct function of the protein, as expression of PII from the glnK promoter in cells lacking GlnK restored the wild-type phenotypes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72950/1/j.1365-2958.2002.03153.x.pd

Non-equilibrium phase transitions in biomolecular signal transduction

We study a mechanism for reliable switching in biomolecular signal-transduction cascades. Steady bistable states are created by system-size cooperative effects in populations of proteins, in spite of the fact that the phosphorylation-state transitions of any molecule, by means of which the switch is implemented, are highly stochastic. The emergence of switching is a nonequilibrium phase transition in an energetically driven, dissipative system described by a master equation. We use operator and functional integral methods from reaction-diffusion theory to solve for the phase structure, noise spectrum, and escape trajectories and first-passage times of a class of minimal models of switches, showing how all critical properties for switch behavior can be computed within a unified framework

Can MONDian vector theories explain the cosmic speed up ?

Generalized Einstein - Aether vector field models have been shown to provide, in the weak field regime, modifications to gravity which can be reconciled with the successfull MOND proposal. Very little is known, however, on the function F(K) defining the vector field Lagrangian so that an analysis of the viability of such theories at the cosmological scales has never been performed. As a first step along this route, we rely on the relation between F(K) and the MOND interpolating function $\mu(a/a_0)$ to assign the vector field Lagrangian thus obtaining what we refer to as "MONDian vector models". Since they are able by construction to recover the MOND successes on galaxy scales, we investigate whether they can also drive the observed accelerated expansion by fitting the models to the Type Ia Supernovae data. Should be this the case, we have a unified framework where both dark energy and dark matter can be seen as different manifestations of a single vector field. It turns out that both MONDian vector models are able to well fit the low redshift data on Type Ia Supernovae, while some tension could be present in the high z regime.Comment: 15 pages, 5 tables, 4 figures, accepted for publication on Physical Review

Nac-mediated repression of the serA promoter of Escherichia coli

Escherichia coli and related bacteria contain two paralogous PII-like proteins involved in nitrogen regulation, the glnB product, PII, and the glnK product, GlnK. Previous studies have shown that cells lacking both PII and GlnK have a severe growth defect on minimal media, resulting from elevated expression of the Ntr regulon. Here, we show that this growth defect is caused by activity of the nac product, Nac, a LysR-type transcription factor that is part of the Ntr regulon. Cells with elevated Ntr expression that also contain a null mutation in nac displayed growth rates on minimal medium similar to the wild type. When expressed from high-copy plasmids, Nac imparts a growth defect to wild-type cells in an expression level-dependent manner. Neither expression of Nac nor lack thereof significantly affected Ntr gene expression, suggesting that the activity of Nac at one or more promoters outside the Ntr regulon was responsible for its effects. The growth defect of cells lacking both PII and GlnK was also eliminated upon supplementation of minimal medium with serine or glycine for solid medium or with serine or glycine and glutamine for liquid medium. These observations suggest that high Nac expression results in a reduction in serine biosynthesis. β -Galactosidase activity expressed from a Mu d1 insertion in serA was reduced approximately 10-fold in cells with high Nac expression. We hypothesize that one role of Nac is to limit serine biosynthesis as part of a cellular mechanism to reduce metabolism in a co-ordinated manner when cells become starved for nitrogen.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72401/1/j.1365-2958.2002.02994.x.pd

Identification of direct residue contacts in protein-protein interaction by message passing

Understanding the molecular determinants of specificity in protein-protein interaction is an outstanding challenge of postgenome biology. The availability of large protein databases generated from sequences of hundreds of bacterial genomes enables various statistical approaches to this problem. In this context covariance-based methods have been used to identify correlation between amino acid positions in interacting proteins. However, these methods have an important shortcoming, in that they cannot distinguish between directly and indirectly correlated residues. We developed a method that combines covariance analysis with global inference analysis, adopted from use in statistical physics. Applied to a set of >2,500 representatives of the bacterial two-component signal transduction system, the combination of covariance with global inference successfully and robustly identified residue pairs that are proximal in space without resorting to ad hoc tuning parameters, both for heterointeractions between sensor kinase (SK) and response regulator (RR) proteins and for homointeractions between RR proteins. The spectacular success of this approach illustrates the effectiveness of the global inference approach in identifying direct interaction based on sequence information alone. We expect this method to be applicable soon to interaction surfaces between proteins present in only 1 copy per genome as the number of sequenced genomes continues to expand. Use of this method could significantly increase the potential targets for therapeutic intervention, shed light on the mechanism of protein-protein interaction, and establish the foundation for the accurate prediction of interacting protein partners.Comment: Supplementary information available on http://www.pnas.org/content/106/1/67.abstrac

Modular cell biology: retroactivity and insulation

Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic networks. Here, we study the effect of interconnections on the input–output dynamic characteristics of transcriptional components, focusing on a property, which we call ‘retroactivity', that plays a role analogous to non-zero output impedance in electrical systems. In transcriptional networks, retroactivity is large when the amount of transcription factor is comparable to, or smaller than, the amount of promoter-binding sites, or when the affinity of such binding sites is high. To attenuate the effect of retroactivity, we propose a feedback mechanism inspired by the design of amplifiers in electronics. We introduce, in particular, a mechanism based on a phosphorylation–dephosphorylation cycle. This mechanism enjoys a remarkable insulation property, due to the fast timescales of the phosphorylation and dephosphorylation reactions

Selective trapping of DNA using glass microcapillaries

We show experimentally that a cheap glass microcapillary can accumulate {\lambda}-phage DNA at its tip and deliver the DNA into the capillary using a combination of electro-osmotic flow, pressure-driven flow, and electrophoresis. We develop an efficient simulation model for this phenomenon based on the electrokinetic equations and the finite-element method. Using our model, we explore the large parameter space of the trapping mechanism by varying the salt concentration, the capillary surface charge, the applied voltage, the pressure difference, and the mobility of the analyte molecules. Our simulation results show that this system can be tuned to capture a wide range of analyte molecules, such as DNA or proteins, based on their electrophoretic mobility. Our method for separation and pre-concentration of analytes has implications for the development of low-cost lab-on-a-chip devices.Comment: 9 pages, 4 figure

Adaptable Functionality of Transcriptional Feedback in Bacterial Two-Component Systems

A widespread mechanism of bacterial signaling occurs through two-component systems, comprised of a sensor histidine kinase (SHK) and a transcriptional response regulator (RR). The SHK activates RR by phosphorylation. The most common two-component system structure involves expression from a single operon, the transcription of which is activated by its own phosphorylated RR. The role of this feedback is poorly understood, but it has been associated with an overshooting kinetic response and with fast recovery of previous interrupted signaling events in different systems. Mathematical models show that overshoot is only attainable with negative feedback that also improves response time. Our models also predict that fast recovery of previous interrupted signaling depends on high accumulation of SHK and RR, which is more likely in a positive feedback regime. We use Monte Carlo sampling of the parameter space to explore the range of attainable model behaviors. The model predicts that the effective feedback sign can change from negative to positive depending on the signal level. Variations in two-component system architectures and parameters may therefore have evolved to optimize responses in different bacterial lifestyles. We propose a conceptual model where low signal conditions result in a responsive system with effectively negative feedback while high signal conditions with positive feedback favor persistence of system output