Comparative genomic analysis of two-component regulatory proteins in Pseudomonas syringae

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas syringae </it>is a widespread bacterial plant pathogen, and strains of <it>P. syringae </it>may be assigned to different pathovars based on host specificity among different plant species. The genomes of <it>P. syringae </it>pv. <it>syringae </it>(<it>Psy</it>) B728a, pv. <it>tomato </it>(<it>Pto</it>) DC3000 and pv. <it>phaseolicola </it>(<it>Pph</it>) 1448A have been recently sequenced providing a major resource for comparative genomic analysis. A mechanism commonly found in bacteria for signal transduction is the two-component system (TCS), which typically consists of a sensor histidine kinase (HK) and a response regulator (RR). <it>P. syringae </it>requires a complex array of TCS proteins to cope with diverse plant hosts, host responses, and environmental conditions.</p> <p>Results</p> <p>Based on the genomic data, pattern searches with Hidden Markov Model (HMM) profiles have been used to identify putative HKs and RRs. The genomes of <it>Psy </it>B728a, <it>Pto </it>DC3000 and <it>Pph </it>1448A were found to contain a large number of genes encoding TCS proteins, and a core of complete TCS proteins were shared between these genomes: 30 putative TCS clusters, 11 orphan HKs, 33 orphan RRs, and 16 hybrid HKs. A close analysis of the distribution of genes encoding TCS proteins revealed important differences in TCS proteins among the three <it>P. syringae </it>pathovars.</p> <p>Conclusion</p> <p>In this article we present a thorough analysis of the identification and distribution of TCS proteins among the sequenced genomes of <it>P. syringae</it>. We have identified differences in TCS proteins among the three <it>P. syringae </it>pathovars that may contribute to their diverse host ranges and association with plant hosts. The identification and analysis of the repertoire of TCS proteins in the genomes of <it>P. syringae </it>pathovars constitute a basis for future functional genomic studies of the signal transduction pathways in this important bacterial phytopathogen.</p

Effect of Air Injection on Nucleation Rates: An Approach from Induction Time Statistics

From disruption of the supersaturated solution to improved mass transfer in the crystallizing suspension, the introduction of a moving gas phase in a crystallizer could lead to improved rates of nucleation and crystal growth. In this work, saturated air has been injected to batch crystallizers to study the effects on formation of the first crystal and subsequent turbidity buildup. To account for the typically large sample-to-sample variation, nucleation rates were evaluated for a large number of replicates using probability distributions of induction times. The slope and the intercept of the distributions were studied independently, allowing the simultaneous determination of the mean induction time and a certain detection delay related to the rate of crystal growth after formation of the first nucleus. When saturated air was injected in aqueous glycine solutions, the average detection delay was reduced from 69 to 13 min, and the mean induction time decreased from 128 to 36 min. The effect on aqueous solutions of l-arginine was less apparent, with a detection delay reduction from 15 to 3 min, and no significant changes on the rate of primary nucleation. These results demonstrate the potential of this technique for reduction in nucleation induction time and improved mass deposition rates in crystallization operations

Osmosis in a minimal model system

Osmosis plays a central role in the function of living and soft matter systems. While the thermodynamics of osmosis is well understood, the underlying microscopic dynamical mechanisms remain the subject of discussion. Unraveling these mechanisms is a crucial prerequisite for eventually understanding osmosis in non-equilibrium systems. Here, we investigate the microscopic basis of osmosis, in a system at equilibrium, using molecular dynamics simulations of a minimal model in which repulsive solute and solvent particles differ only in their interactions with an external potential. For this system, we can derive a simple virial-like relation for the osmotic pressure. Our simulations support an intuitive picture in which the solvent concentration gradient, at osmotic equilibrium, arises from the balance between an outward force, caused by the increased total density in the solution, and an inward diffusive flux caused by the decreased solvent density in the solution. While more complex effects may occur in other osmotic systems, they are not required for a description of the basic physics of osmosis in this minimal model.Comment: 10 pages, 8 figure