Mark A. J. Roberts

ParA-like protein uses nonspecific chromosomal DNA binding to partition protein complexes

Recent data have shown that plasmid partitioning Par-like systems are used by some bacterial cells to control localization of protein complexes. Here we demonstrate that one of these homologs, PpfA, uses nonspecific chromosome binding to separate cytoplasmic clusters of chemotaxis proteins upon division. Using fluorescent microscopy and point mutations, we show dynamic chromosome binding and Walker-type ATPase activity are essential for cluster segregation. The N-terminal domain of a cytoplasmic chemoreceptor encoded next to ppfA is also required for segregation, probably functioning as a ParB analog to control PpfA ATPase activity. An orphan ParA involved in segregating protein clusters therefore uses a similar mechanism to plasmid-segregating ParA/B systems and requires a partner protein for function. Given the large number of genomes that encode orphan ParAs, this may be a common mechanism regulating segregation of proteins and protein complexes.

A bifunctional kinase-phosphatase in bacterial chemotaxis

Phosphorylation-based signaling pathways employ dephosphorylation mechanisms for signal termination. Histidine to aspartate phosphosignaling in the two-component system that controls bacterial chemotaxis has been studied extensively. Rhodobacter sphaeroides has a complex chemosensory pathway with multiple homologues of the Escherichia coli chemosensory proteins, although it lacks homologues of known signal-terminating CheY-P phosphatases, such as CheZ, CheC, FliY or CheX. Here, we demonstrate that an unusual CheA homologue, CheA3, is not only a phosphodonor for the principal CheY protein, CheY6, but is also is a specific phosphatase for CheY6-P. This phosphatase activity accelerates CheY6-P dephosphorylation to a rate that is comparable with the measured stimulus response time of approximately 1 s. CheA3 possesses only two of the five domains found in classical CheAs, the Hpt (P1) and regulatory (P5) domains, which are joined by a 794-amino acid sequence that is required for phosphatase activity. The P1 domain of CheA3 is phosphorylated by CheA4, and it subsequently acts as a phosphodonor for the response regulators. A CheA3 mutant protein without the 794-amino acid region lacked phosphatase activity, retained phosphotransfer function, but did not support chemotaxis, suggesting that the phosphatase activity may be required for chemotaxis. Using a nested deletion approach, we showed that a 200-amino acid segment of CheA3 is required for phosphatase activity. The phosphatase activity of previously identified nonhybrid histidine protein kinases depends on the dimerization and histidine phosphorylation (DHp) domains. However, CheA3 lacks a DHp domain, suggesting that its phosphatase mechanism is different from that of other histidine protein kinases.

A model invalidation-based approach for elucidating biological signalling pathways, applied to the chemotaxis pathway in R. sphaeroides.

BackgroundDeveloping methods for understanding the connectivity of signalling pathways is a major challenge in biological research. For this purpose, mathematical models are routinely developed based on experimental observations, which also allow the prediction of the system behaviour under different experimental conditions. Often, however, the same experimental data can be represented by several competing network models.ResultsIn this paper, we developed a novel mathematical model/experiment design cycle to help determine the probable network connectivity by iteratively invalidating models corresponding to competing signalling pathways. To do this, we systematically design experiments in silico that discriminate best between models of the competing signalling pathways. The method determines the inputs and parameter perturbations that will differentiate best between model outputs, corresponding to what can be measured/observed experimentally. We applied our method to the unknown connectivities in the chemotaxis pathway of the bacterium Rhodobacter sphaeroides. We first developed several models of R. sphaeroides chemotaxis corresponding to different signalling networks, all of which are biologically plausible. Parameters in these models were fitted so that they all represented wild type data equally well. The models were then compared to current mutant data and some were invalidated. To discriminate between the remaining models we used ideas from control systems theory to determine efficiently in silico an input profile that would result in the biggest difference in model outputs. However, when we applied this input to the models, we found it to be insufficient for discrimination in silico. Thus, to achieve better discrimination, we determined the best change in initial conditions (total protein concentrations) as well as the best change in the input profile. The designed experiments were then performed on live cells and the resulting data used to invalidate all but one of the remaining candidate models.ConclusionWe successfully applied our method to chemotaxis in R. sphaeroides and the results from the experiments designed using this methodology allowed us to invalidate all but one of the proposed network models. The methodology we present is general and can be applied to a range of other biological networks.

Adaptation and control circuits in bacterial chemotaxis

Bacteria are capable of sensing and responding to changes in their environment. One of the ways they do this is via chemotaxis, regulating swimming behaviour. The chemotaxis pathway senses chemoattractant gradients and uses a feedback loop to change the bacterial swimming pattern; this feedback loop differs in detail between species. In the present article, we summarize the current understanding of the regulatory mechanisms in three species and how these pathways can be viewed and analysed through the ideas of feedback control systems engineering.

Feedback control architecture and the bacterial chemotaxis network.

Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to ‘reset’ (adapt) the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a ‘cascade control’ feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance.

Positioning of chemosensory proteins and FtsZ through the Rhodobacter sphaeroides cell cycle

Bacterial chemotaxis depends on signalling through large protein complexes. Each cell must inherit a complex on division, suggesting some co‐ordination with cell division. In Escherichia coli the membrane‐spanning chemosensory complexes are polar and new static complexes form at pre‐cytokinetic sites, ensuring positioning at the new pole after division and suggesting a role for the bacterial cytoskeleton. Rhodobacter sphaeroides has both membrane‐associated and cytoplasmic, chromosome‐associated chemosensory complexes. We followed the relative positions of the two chemosensory complexes, FtsZ and MreB in aerobic and in photoheterotrophic R. sphaeroides cells using fluorescence microscopy. FtsZ forms polar spots after cytokinesis, which redistribute to the midcell forming nodes from which FtsZ extends circumferentially to form the Z‐ring. Membrane‐associated chemosensory proteins form a number of dynamic unit‐clusters with mature clusters containing about 1000 CheW3 proteins. Individual clusters diffuse randomly within the membrane, accumulating at new poles after division but not colocalizing with either FtsZ or MreB. The cytoplasmic complex colocalizes with FtsZ at midcells in new‐born cells. Before cytokinesis one complex moves to a daughter cell, followed by the second moving to the other cell. These data indicate that two homologous complexes use different mechanisms to ensure partitioning, and neither complex utilizes FtsZ or MreB for positioning.

Two Chemosensory Operons of Rhodobacter sphaeroides Are Regulated Independently by Sigma 28 and Sigma 54

Rhodobacter sphaeroides has a complex chemosensory system, with several loci encoding multiple homologues of the components required for chemosensing in Escherichia coli. The operons cheOp2 and cheOp3 each encode complete pathways, and both are essential for chemosensing. The components of cheOp2 are predominantly localized to the cell pole, whereas those encoded by cheOp3 are predominantly targeted to a discrete cluster in the cytoplasm. Here we show that the expression of the two pathways is regulated independently. Overlapping promoters recognized by sigma(28) and sigma(70) RNAP holoenzyme transcribe cheOp2, whereas cheOp3 is regulated by one of the four sigma(54) homologues, RpoN3. The different regulation of these operons may reflect the need for balancing responses to extra- and intracellular signals under different growth conditions.

Bacterial cell identification in differential interference contrast microscopy images

BackgroundMicroscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.ResultsWe have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.ConclusionsResults demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

Synthetic biology: biology by design

Synthetic biology can be defined as the design and construction of novel biologically based parts, devices and systems, as well as the redesign of existing natural biological systems, for useful purposes. It builds on genetic engineering, being design-driven genetic engineering encompassing engineering concepts of standardization and abstraction (Endy, 2005). One of the technical advances that has significantly increased the ability to undertake synthetic biology has been to artificially synthesize DNA, and thus create DNA parts. So far, the peak achievement has been the synthesis and assembly of a small bacterial genome which was transferred to a bacterial cell devoid of DNA to create a novel replicating micro-organism (Gibson et al., 2010). A great diversity of synthetic biology applications exists, many in the early research phase, which include using microbes as biofactories or as biological computers (Bonnet et al., 2012; Oldham et al., 2012). In this issue of Microbiology we have assembled a collection of papers to showcase the current state of synthetic biology research, and to convey the potential impact of synthetic biology on biological sciences. The synthetic biology field itself is diverse but can broadly be divided into two main themes: bottom-up approaches creating truly artificial life de novo, and top-down approaches to design systems based on known biology to perform a specific task. The latter can involve designing metabolic and signalling pathways inside cells to achieve a specific purpose. Within this top-down design, biological elements (promoters, gene products, etc.) can be thought of as parts being assembled into a system. The top-down approach has the advantage of using the host cell (termed the chassis) and being able to make use of the co-factors, metabolites, transcription pathways and other components that it already possesses, but does have the potential disadvantage of potential crosstalk between the endogenous systems present in the chassis and the introduced synthetic systems (Saito, 2010; Verhamme et al., 2002). The papers within this issue focus on the top-down approach. As the aim of synthetic biology is to design a system to achieve a required outcome, researchers rely heavily on in silico modelling and whole-system analysis (’omic analysis) to provide data about the effect of perturbations, allowing parts encoded in DNA to be characterized and optimized. The data provide the basis to bring component parts together in different combinations to produce predictable devices with the outcomes predicted through modelling. An example of developing these standard parts is given in the Bartosiak-Jentys et al. (2013) paper, which describes the creation of a modular system for the design of Geobacillus and defins the parts that are created. The authors also describe how this may be applied in the design of improved bioethanol-producing strains. The parts themselves can be specifically modified to alter the desired outcome. The review in this issue by Arpino et al. (2013) describes these design parameters and how they can be modified in both prokaryote and eukaryote microbial systems. For example, different ribosome-binding sites can alter protein copy number, resulting in different outcomes from the synthetic system. Once defined, there is the ability to add these parts together to produce a system with a predictable defined output. A nice example of how using a small range of defined parts can be used to generate complex outcomes is presented by Chang et al. (2013). Here the authors use a simple bacterial two-component system to produce a range of outputs through careful variation of a phosphatase. This paper also highlights the utility of model-based design. Such technology has huge potential applications in industry. Being able to synthesize products in a biologically controlled way opens up new methods of manufacture, as highlighted in the Donald et al. (2013) paper. In this work the authors describe how expression can be optimized using synthetic biological approaches to modify the chassis, in this case to produce a vaccine. Synthetic biology has been identified as a technology that has huge potential to transform the way we work, and this step change in our use of biology has been recognized not just in the scientific community but in the wider social sphere as well. As a result, a number of governments have been shaping policy and developing science funding to specifically support synthetic biology (Pei et al., 2012; Zhang, 2011). In particular, how does current international legislation apply to synthetic biology (Bubela et al., 2012)? In the UK, for example, a cross-government group (The Synthetic Biology Roadmap Coordination Group, 2012) developed the Synthetic Biology Roadmap to bring together all the different interested parties and communities and to identify what government support is needed to develop this science within the UK, both for pure understanding and to drive translational research. The Roadmap also considered approaches to the ethical and legal issues. These latter areas have been raised as a matter of concern in a number of countries, and highlighted recently in the US (Roehr, 2010), reflecting the ethical, safety and regulatory considerations that apply to any new technology but, given the potential for self-replication, have particular significance in synthetic biology. With the emphasis on making manipulation of biology easier, synthetic biology also raises significant implications of dual use of synthetic biology for nefarious purposes, which also need consideration in ethical, legal and regulatory contexts (Samuel et al., 2009). Such considerations have also been the basis of activity within national learned academies, culminating in the six academies symposia between the science and engineering academies of the UK, China and the US. These meetings resulted in opinion pieces regarding ways of progressing synthetic biology research for the benefit of humanity while avoiding the potential pitfalls (OECD & The Royal Society, 2011). These issues will become especially important if we consider the possible environmental release of biological devices. Developing methods to contain and control the biological devices that we produce is thus a significant area of research. A review article in this issue by Wright et al. (2013) looks at current research in this area, and how scientific solutions can give us control over the spread of the synthetic systems we design. As indicated above, synthetic biology is a priority area for funding in a number of countries. This is now evolving into an internationally structured area, with larger international research networks being established, such as the EraSynBio network between funding bodies in both Europe and the US. Just as the technology requires a multi-disciplinary effort, so the science requires an international approach and frameworks. If, for example, there are to be standardized biological parts, such as using the Biobricks standard (Canton et al., 2008), researchers will have to work together, and within their domestic regulations, to achieve that. The articles published in this issue highlight the promise and hurdles that synthetic biology must overcome to produce the future designer microbes that could transform our world. Quite what that future will be is left for the reader to imagine, but there can be no doubt synthetic biology will play an important role.

Feedback control architecture of the R. sphaeroides chemotaxis network

This paper investigates the chemotaxis behavior of the bacterium R. sphaeroides. We review the results of a recent study comparing different possible mathematical models of this bacteriums chemotaxis decision mechanisms. It was found that only one of the aforesaid models could explain the experimental chemotactic response data. From a control theoretic perspective, we show that, compared to the other models posed, this model exhibits better and more robust chemotactic performance. This decision mechanism parallels a feedback architecture that has been used extensively to improve performance in engineered systems. We suggest that this mechanism may play a role in maintaining the chemotactic performance of this and potentially other bacteria.