Role of Acetyl-Phosphate in Activation of the Rrp2-RpoN-RpoS Pathway in Borrelia burgdorferi

Borrelia burgdorferi, the Lyme disease spirochete, dramatically alters its transcriptome and proteome as it cycles between the arthropod vector and mammalian host. During this enzootic cycle, a novel regulatory network, the Rrp2-RpoN-RpoS pathway (also known as the σ54–σS sigma factor cascade), plays a central role in modulating the differential expression of more than 10% of all B. burgdorferi genes, including the major virulence genes ospA and ospC. However, the mechanism(s) by which the upstream activator and response regulator Rrp2 is activated remains unclear. Here, we show that none of the histidine kinases present in the B. burgdorferi genome are required for the activation of Rrp2. Instead, we present biochemical and genetic evidence that supports the hypothesis that activation of the Rrp2-RpoN-RpoS pathway occurs via the small, high-energy, phosphoryl-donor acetyl phosphate (acetyl∼P), the intermediate of the Ack-Pta (acetate kinase-phosphate acetyltransferase) pathway that converts acetate to acetyl-CoA. Supplementation of the growth medium with acetate induced activation of the Rrp2-RpoN-RpoS pathway in a dose-dependent manner. Conversely, the overexpression of Pta virtually abolished acetate-induced activation of this pathway, suggesting that acetate works through acetyl∼P. Overexpression of Pta also greatly inhibited temperature and cell density-induced activation of RpoS and OspC, suggesting that these environmental cues affect the Rrp2-RpoN-RpoS pathway by influencing acetyl∼P. Finally, overexpression of Pta partially reduced infectivity of B. burgdorferi in mice. Taken together, these findings suggest that acetyl∼P is one of the key activating molecule for the activation of the Rrp2-RpoN-RpoS pathway and support the emerging concept that acetyl∼P can serve as a global signal in bacterial pathogenesis

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

Mechanism of Disruption of the Amt-GlnK Complex by PII-Mediated Sensing of 2-Oxoglutarate

GlnK proteins regulate the active uptake of ammonium by Amt transport proteins by inserting their regulatory T-loops into the transport channels of the Amt trimer and physically blocking substrate passage. They sense the cellular nitrogen status through 2-oxoglutarate, and the energy level of the cell by binding both ATP and ADP with different affinities. The hyperthermophilic euryarchaeon Archaeoglobus fulgidus possesses three Amt proteins, each encoded in an operon with a GlnK ortholog. One of these proteins, GlnK2 was recently found to be incapable of binding 2-OG, and in order to understand the implications of this finding we conducted a detailed structural and functional analysis of a second GlnK protein from A. fulgidus, GlnK3. Contrary to Af-GlnK2 this protein was able to bind both ATP/2-OG and ADP to yield inactive and functional states, respectively. Due to the thermostable nature of the protein we could observe the exact positioning of the notoriously flexible T-loops and explain the binding behavior of GlnK proteins to their interaction partner, the Amt proteins. A thermodynamic analysis of these binding events using microcalorimetry evaluated by microstate modeling revealed significant differences in binding cooperativity compared to other characterized PII proteins, underlining the diversity and adaptability of this class of regulatory signaling proteins

Prediction of Cellular Burden with Host--Circuit Models

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modelling paradigm can facilitate fast and robust design cycles in synthetic biology

Achieving Optimal Growth through Product Feedback Inhibition in Metabolism

Recent evidence suggests that the metabolism of some organisms, such as Escherichia coli, is remarkably efficient, producing close to the maximum amount of biomass per unit of nutrient consumed. This observation raises the question of what regulatory mechanisms enable such efficiency. Here, we propose that simple product-feedback inhibition by itself is capable of leading to such optimality. We analyze several representative metabolic modules—starting from a linear pathway and advancing to a bidirectional pathway and metabolic cycle, and finally to integration of two different nutrient inputs. In each case, our mathematical analysis shows that product-feedback inhibition is not only homeostatic but also, with appropriate feedback connections, can minimize futile cycling and optimize fluxes. However, the effectiveness of simple product-feedback inhibition comes at the cost of high levels of some metabolite pools, potentially associated with toxicity and osmotic imbalance. These large metabolite pool sizes can be restricted if feedback inhibition is ultrasensitive. Indeed, the multi-layer regulation of metabolism by control of enzyme expression, enzyme covalent modification, and allostery is expected to result in such ultrasensitive feedbacks. To experimentally test whether the qualitative predictions from our analysis of feedback inhibition apply to metabolic modules beyond linear pathways, we examine the case of nitrogen assimilation in E. coli, which involves both nutrient integration and a metabolic cycle. We find that the feedback regulation scheme suggested by our mathematical analysis closely aligns with the actual regulation of the network and is sufficient to explain much of the dynamical behavior of relevant metabolite pool sizes in nutrient-switching experiments

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future

Synthetic biology approaches in drug discovery and pharmaceutical biotechnology

Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology