Roles of the spiA gene from Salmonella enteritidis in biofilm formation and virulence

Salmonella enteritidis has emerged as one of the most important food-borne pathogens for humans, and the formation of biofilms by this species may improve its resistance to disadvantageous conditions. The spiA gene of Salmonella typhimurium is essential for its virulence in host cells. However, the roles of the spiA gene in biofilm formation and virulence of S. enteritidis remain unclear. In this study we constructed a spiA gene mutant with a suicide plasmid. Phenotypic and biological analysis revealed that the mutant was similar to the wild-type strain in growth rate, morphology, and adherence to and invasion of epithelial cells. However, the mutant showed reduced biofilm formation in a quantitative microtitre assay and by scanning electron microscopy, and significantly decreased curli production and intracellular proliferation of macrophages during the biofilm phase. In addition, the spiA mutant was attenuated in a mouse model in both the exponential growth and biofilm phases. These data indicate that the spiA gene is involved in both biofilm formation and virulence of S. enteritidis

22.運動負荷時の血小板機能とヘモレオロジー(第636回千葉医学会例会・第3回千葉大学第三内科懇話会)

Additional file 2. Changes of residual nitrate concentration during the experiments

Environmental flow mechanism and management for river–lake-marsh systems

Sustaining environmental flows (e-flows) is a basic requirement for river, lake and marsh management. Rivers, lakes and marshes have close hydrological and ecological connections in a river basin, and form river–lake-marsh systems. Any measure that is designed to address problems related to only one of these water bodies in isolation of the others may lead to unexpected, undesired and sub-optimal consequences for one or all of the water bodies. Papers were selected for this special issue ‘Environmental Flow Mechanism and Management for River-Lake-Marsh Systems’ to illustrate recent advances in revealing new mechanisms connecting hydrological and environmental/ecological processes, developing new methods for e-flow calculation, and establishing new measures for e-flow management

Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data

The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells

Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data

The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells

Phase Synchronization and Dynamic Behavior of a Novel Small Heterogeneous Coupled Network

Studying the firing dynamics and phase synchronization behavior of heterogeneous coupled networks helps us understand the mechanism of human brain activity. In this study, we propose a novel small heterogeneous coupled network in which the 2D Hopfield neural network (HNN) and the 2D Hindmarsh–Rose (HR) neuron are coupled through a locally active memristor. The simulation results show that the network exhibits complex dynamic behavior and is different from the usual phase synchronization. More specifically, the membrane potential of the 2D HR neuron exhibits five stable firing modes as the coupling parameter k1 changes. In addition, it is found that in the local region of k1, the number of spikes in bursting firing increases with the increase in k1. More interestingly, the network gradually changes from synchronous to asynchronous during the increase in the coupling parameter k1 but suddenly becomes synchronous around the coupling parameter k1 = 1.96. As far as we know, this abnormal synchronization behavior is different from the existing findings. This research is inspired by the fact that the episodic synchronous abnormal firing of excitatory neurons in the hippocampus of the brain can lead to diseases such as epilepsy. This helps us further understand the mechanism of brain activity and build bionic systems. Finally, we design the simulation circuit of the network and implement it on an STM32 microcontroller

Transcriptome Analysis Reveals the Effect of Low NaCl Concentration on Osmotic Stress and Type III Secretion System in <i>Vibrio parahaemolyticus</i>

Vibrio parahaemolyticus is a moderately halophilic foodborne pathogen that is mainly distributed in marine and freshwater environments. The transition of V. parahaemolyticus between aquatic ecosystems and hosts is essential for infection. Both freshwater and host environments have low salinity. In this study, we sought to further investigate the effects of low salinity (0.5% NaCl) on the fitness and virulence of V. parahaemolyticus. We found that V. parahaemolyticus could survive in Luria–Bertani (LB) and M9 mediums with different NaCl concentrations, except for the M9 medium containing 9% NaCl. Our results further showed that V. parahaemolyticus cultured in M9 medium with 0.5% NaCl had a higher cell density than that cultured at other NaCl concentrations when it entered the stationary phase. Therefore, we compared the transcriptomes of V. parahaemolyticus wild type (WT) cultured in an M9 medium with 0.5% and 3% NaCl at the stationary phase using RNA-seq. A total of 658 genes were significantly differentially expressed in the M9 medium with 0.5% NaCl, including regulators, osmotic adaptive responses (compatible solute synthesis systems, transporters, and outer membrane proteins), and virulence factors (T3SS1 and T6SS1). Furthermore, a low salinity concentration in the M9 medium induced the expression of T3SS1 to mediate the cytotoxicity of V. parahaemolyticus to HeLa cells. Similarly, low salinity could also induce the secretion of the T3SS2 translocon protein VPA1361. These factors may result in the high pathogenicity of V. parahaemolyticus in low-salinity environments. Taken together, these results suggest that low salinity (0.5% NaCl) could affect gene expression to mediate fitness and virulence, which may contribute to the transition of V. parahaemolyticus between aquatic ecosystems and the host

Research and Application Progress of the Active Metabolites of Probiotics

Probiotics is an intriguing research focus due to its multiple beneficial effects on host, which is closely associated with its bioactive metabolites, including extracellular polysaccharides, bacteriocins, vitamins, organic acids and short-chain fatty acids. They are widely used in medical treatment, food antiseptic, animal husbandry and other industrial areas for their broad beneficial effects, such as anti-inflammation, anti-oxidation, immune regulation and preventive and therapeutic properties against metabolic diseases. Even though the functionalities and application potential of probiotics are extensively studied, further efforts are needed to focus on the increase of their productivity, reducing the cost of production and purification and exploring the cellular and molecular mechanisms. Here we review the category, functions and applications of probiotic metabolites, and would provide more insights for their utilizations in food, medicine, health care and animal husbandry