Universal architecture of bacterial chemoreceptor arrays

Chemoreceptors are key components of the high-performance signal transduction system that controls bacterial chemotaxis. Chemoreceptors are typically localized in a cluster at the cell pole, where interactions among the receptors in the cluster are thought to contribute to the high sensitivity, wide dynamic range, and precise adaptation of the signaling system. Previous structural and genomic studies have produced conflicting models, however, for the arrangement of the chemoreceptors in the clusters. Using whole-cell electron cryo-tomography, here we show that chemoreceptors of different classes and in many different species representing several major bacterial phyla are all arranged into a highly conserved, 12-nm hexagonal array consistent with the proposed “trimer of dimers” organization. The various observed lengths of the receptors confirm current models for the methylation, flexible bundle, signaling, and linker sub-domains in vivo. Our results suggest that the basic mechanism and function of receptor clustering is universal among bacterial species and was thus conserved during evolution

Evolution of phototaxis

Phototaxis in the broadest sense means positive or negative displacement along a light gradient or vector. Prokaryotes most often use a biased random walk strategy, employing type I sensory rhodopsin photoreceptors and two-component signalling to regulate flagellar reversal. This strategy only allows phototaxis along steep light gradients, as found in microbial mats or sediments. Some filamentous cyanobacteria evolved the ability to steer towards a light vector. Even these cyanobacteria, however, can only navigate in two dimensions, gliding on a surface. In contrast, eukaryotes evolved the capacity to follow a light vector in three dimensions in open water. This strategy requires a polarized organism with a stable form, helical swimming with cilia and a shading or focusing body adjacent to a light sensor to allow for discrimination of light direction. Such arrangement and the ability of three-dimensional phototactic navigation evolved at least eight times independently in eukaryotes. The origin of three-dimensional phototaxis often followed a transition from a benthic to a pelagic lifestyle and the acquisition of chloroplasts either via primary or secondary endosymbiosis. Based on our understanding of the mechanism of phototaxis in single-celled eukaryotes and animal larvae, it is possible to define a series of elementary evolutionary steps, each of potential selective advantage, which can lead to pelagic phototactic navigation. We can conclude that it is relatively easy to evolve phototaxis once cell polarity, ciliary swimming and a stable cell shape are present

Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations

We review the application of mathematical modeling to understanding the behavior of populations of chemotactic bacteria. The application of continuum mathematical models, in particular generalized Keller–Segel models, is discussed along with attempts to incorporate the microscale (individual) behavior on the macroscale, modeling the interaction between different species of bacteria, the interaction of bacteria with their environment, and methods used to obtain experimentally verified parameter values. We allude briefly to the role of modeling pattern formation in understanding collective behavior within bacterial populations. Various aspects of each model are discussed and areas for possible future research are postulated

Logarithmic sensing in Bacillus subtilis aerotaxis

Aerotaxis, the directed migration along oxygen gradients, allows many microorganisms to locate favorable oxygen concentrations. Despite oxygen’s fundamental role for life, even key aspects of aerotaxis remain poorly understood. In Bacillus subtilis, for example, there is conflicting evidence of whether migration occurs to the maximal oxygen concentration available or to an optimal intermediate one, and how aerotaxis can be maintained over a broad range of conditions. Using precisely controlled oxygen gradients in a microfluidic device, spanning the full spectrum of conditions from quasi-anoxic to oxic (60 n mol/l–1 m mol/l), we resolved B. subtilis’ ‘oxygen preference conundrum’ by demonstrating consistent migration towards maximum oxygen concentrations (‘monotonic aerotaxis’). Surprisingly, the strength of aerotaxis was largely unchanged over three decades in oxygen concentration (131 n mol/l–196 μ mol/l). We discovered that in this range B. subtilis responds to the logarithm of the oxygen concentration gradient, a rescaling strategy called ‘log-sensing’ that affords organisms high sensitivity over a wide range of conditions. In these experiments, high-throughput single-cell imaging yielded the best signal-to-noise ratio of any microbial taxis study to date, enabling the robust identification of the first mathematical model for aerotaxis among a broad class of alternative models. The model passed the stringent test of predicting the transient aerotactic response despite being developed on steadystate data, and quantitatively captures both monotonic aerotaxis and log-sensing. Taken together, these results shed new light on the oxygen-seeking capabilities of B. subtilis and provide a blueprint for the quantitative investigation of the many other forms of microbial taxis

Genome Sequence of a Mesophilic Hydrogenotrophic Methanogen Methanocella paludicola, the First Cultivated Representative of the Order Methanocellales

We report complete genome sequence of a mesophilic hydrogenotrophic methanogen Methanocella paludicola, the first cultured representative of the order Methanocellales once recognized as an uncultured key archaeal group for methane emission in rice fields. The genome sequence of M. paludicola consists of a single circular chromosome of 2,957,635 bp containing 3004 protein-coding sequences (CDS). Genes for most of the functions known in the methanogenic archaea were identified, e.g. a full complement of hydrogenases and methanogenesis enzymes. The mixotrophic growth of M. paludicola was clarified by the genomic characterization and re-examined by the subsequent growth experiments. Comparative genome analysis with the previously reported genome sequence of RC-IMRE50, which was metagenomically reconstructed, demonstrated that about 70% of M. paludicola CDSs were genetically related with RC-IMRE50 CDSs. These CDSs included the genes involved in hydrogenotrophic methane production, incomplete TCA cycle, assimilatory sulfate reduction and so on. However, the genetic components for the carbon and nitrogen fixation and antioxidant system were different between the two Methanocellales genomes. The difference is likely associated with the physiological variability between M. paludicola and RC-IMRE50, further suggesting the genomic and physiological diversity of the Methanocellales methanogens. Comparative genome analysis among the previously determined methanogen genomes points to the genome-wide relatedness of the Methanocellales methanogens to the orders Methanosarcinales and Methanomicrobiales methanogens in terms of the genetic repertoire. Meanwhile, the unique evolutionary history of the Methanocellales methanogens is also traced in an aspect by the comparative genome analysis among the methanogens

Motility, chemokinesis, and methylation-independent chemotaxis in Azospirillum brasilense.

Observations of free-swimming and antibody-tethered Azospirillum brasilense cells showed that their polar flagella could rotate in both clockwise and counterclockwise directions. Rotation in a counterclockwise direction caused forward movement of free-swimming cells, whereas the occasional change in the direction of rotation to clockwise caused a brief reversal in swimming direction. The addition of a metabolizable chemoattractant, e.g., malate or proline, had two distinct effects on the swimming behavior of the bacteria: (i) a short-term decrease in reversal frequency from 0.33 to 0.17 s-1 and (ii) a long-term increase in the mean population swimming speed from 13 to 23 microns s-1. A. brasilense therefore shows both chemotaxis and chemokinesis in response to temporal gradients of some chemoeffectors. Chemokinesis was dependent on the growth state of the cells and may depend on an increase in the electrochemical proton gradient above a saturation threshold. Analysis of behavior of a methionine auxotroph, assays of in vivo methylation, and the use of specific antibodies raised against the sensory transducer protein Tar of Escherichia coli all failed to demonstrate the methylation-dependent pathway for chemotaxis in A. brasilense. The range of chemicals to which A. brasilense shows chemotaxis and the lack of true repellents indicate an alternative chemosensory pathway probably based on metabolism of chemoeffectors

Model of Bacterial Band Formation in Aerotaxis

Aerotaxis is a particular form of “energy taxis”. It is based on a largely elusive signal transduction machinery. In aerotaxis, oxygen dissolved in water plays the role of both attractant (at moderate concentrations) and repellent (at high and low concentrations). Cells swimming from favorable oxygen concentrations into regions with unfavorable concentrations increase the frequency of reversals, turn back into the favorable domain, and become effectively trapped there. At the same time, bacteria consume oxygen, creating an oxygen gradient. This behavior leads to a pattern formation phenomenon: bacteria self-organize into a dense band at a certain distance from the air-water interface. We incorporate experimental observations of the aerotactic bacterium, Azospirillum brasilense, into a mathematical model. The model consists of a system of differential equations describing swimming bacterial cells and diffusing oxygen. The cells' frequency of reversals depends on the concentration of oxygen and its time derivative while oxygen is depleted by the bacteria. We suggest a hypothetical model of energy sensing mediated by aerotactic receptors Aer and Tsr. Computer simulations and analysis of the model equations allow comparisons of theoretical and experimental results and provide insight into the mechanisms of bacterial pattern formation and underlying signal transduction machinery. We make testable predictions about position and density of the bacterial band