Genome-Wide Analysis of the <i>Salmonella</i> Fis Regulon and Its Regulatory Mechanism on Pathogenicity Islands

<div><p>Fis, one of the most important nucleoid-associated proteins, functions as a global regulator of transcription in bacteria that has been comprehensively studied in <i>Escherichia coli</i> K12. Fis also influences the virulence of <i>Salmonella enterica</i> and pathogenic <i>E. coli</i> by regulating their virulence genes, however, the relevant mechanism is unclear. In this report, using combined RNA-seq and chromatin immunoprecipitation (ChIP)-seq technologies, we first identified 1646 Fis-regulated genes and 885 Fis-binding targets in the <i>S. enterica</i> serovar Typhimurium, and found a Fis regulon different from that in <i>E. coli</i>. Fis has been reported to contribute to the invasion ability of <i>S. enterica</i>. By using cell infection assays, we found it also enhances the intracellular replication ability of <i>S. enterica</i> within macrophage cell, which is of central importance for the pathogenesis of infections. <i>Salmonella</i> pathogenicity islands (SPI)-1 and SPI-2 are crucial for the invasion and survival of <i>S. enterica</i> in host cells. Using mutation and overexpression experiments, real-time PCR analysis, and electrophoretic mobility shift assays, we demonstrated that Fis regulates 63 of the 94 <i>Salmonella</i> pathogenicity island (SPI)-1 and SPI-2 genes, by three regulatory modes: i) binds to SPI regulators in the gene body or in upstream regions; ii) binds to SPI genes directly to mediate transcriptional activation of themselves and downstream genes; iii) binds to gene encoding OmpR which affects SPI gene expression by controlling SPI regulators SsrA and HilD. Our results provide new insights into the impact of Fis on SPI genes and the pathogenicity of <i>S. enterica.</i></p></div

Synthesis of Ultra-High-Molecular-Weight Polyethylene by Transition-Metal-Catalyzed Precipitation Polymerization

Ultra-high-molecular-weight polyethylene (UHMWPE) plays an important role in many important fields as engineering plastics. In this contribution, a precipitation polymerization strategy is developed by combination of highly active phosphino-phenolate nickel catalysts with polymer-insoluble solvent (heptane) to access UHMWPE (Mn up to 8.3 × 106 g mol–1) with good product morphology, free-flowing characteristics, and great mechanical properties. Compared with the academically commonly used aromatic solvent (toluene), the utilization of heptane offers simultaneous enhancement in important parameters including activity, polymer molecular weight, and catalyst thermal stability. This system can also generate polar functionalized UHMWPE with molecular weight of up to 1.6 × 106 g mol–1 in the copolymerization of ethylene with polar comonomers. More importantly, this precipitation polymerization strategy is generally applicable to several representative transition metal catalyst systems, leading to UHMWPE synthesis with good product morphology control

Mutual information distribution.

<p>A) Distribution of mutual information in SORN with plasticity in the first block of training. B) Distribution of mutual information in SORN with plasticity after training (last block), which becomes higher with training compared with the first block. C) Distribution of mutual information in SORN without STDP and IP plasticity.</p

Structure of Self-Organizing Recurrent Neural Network (SORN).

<p>Input units (cyan) directly receive external input in a non-overlapping way and connect to other excitatory reservoir units (blue). Excitatory reservoir units are also connected to inhibitory units, as well as the output units. The weights between the reservoir units and the output units are trained with supervised methods.</p

Analysis of changes in input weight vectors over training.

<p>(A) Euclidean distance of all 300 weight vectors from the initial weight across training in Experiment 1 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005632#pcbi.1005632.ref024" target="_blank">24</a>] projected into the space of the first three PCs of all weight vectors. The blue part of each curves corresponds to training on sequence S1 and the red part corresponds to training on sequence S2. (B) Change in angle of the weights versus the total distance of the weights to the initial weight across training in Experiment 1. The angle was computed through the dot product in the full 300 dimensional space of weights. The total distance of weights was computed as the Euclidean distance of the weight vectors to the initial weight vector . The blue data points correspond to the respective values after the first 1600 training steps and the red data points correspond to the weights after the last training step. The black data points correspond to the changes in angle versus distance in weights across training after switching from sequence S1 to sequence S2. Panels (C,D) as Panel (A, B) but for the data of Experiment 2 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005632#pcbi.1005632.ref025" target="_blank">25</a>].</p

Selectivity indices of neurons in SORN network.

<p>(A) Distribution of selectivity indices in SORN with plasticity in the first block of training. (B) Distribution of selectivity indices in SORN with plasticity after training (last block), which becomes higher with training compared with the first block. (C) Distribution of selectivity indices in SORN without STDP and IP plasticity. A value of zero indicates that the neuron has identical responses to all stimuli; a value of 1 indicates activation by one stimulus and silence to all other stimuli. In SORN, neurons are firing highly selectively, with the indices reaching values between 0.9 and 1.0.</p

Seperability of SORN with all three plasticity mechanisms turned on (solid) or with STDP and IP turned off (dotted).

<p>To be intuitively comparable to experimental data, the Y-axis is plotted upside-down.</p

Anterograde and retrograde facilitation and interference effects across task similiarities and training schedules.

<p>A) Network performance as a function of the number of training trials for interleaved training with low task similarity. B) Performance for blocked training with low task similarity. C) Performance for interleaved training with high task similarity. D) Performance for blocked training with high task similarity. E) Anterograde effects in learning as quantified by the difference in error rates between the first 10 training trials on task S2 and the first 10 training trials on task S1. F) Retrograde effects in learning as quantified by the difference in error rates between the end of training task S1 and testing on S1 after training on all tasks. Note that facilitation effects correspond to positive values while interference effects correspond to negative values in both E) and F).</p

The joint probability between inputs and neuron firing.

<p>The vertical axis corresponds to the network’s 300 model neurons, and the horizontal axis corresponds to the input sequence element of the total length 20. (A) Joint probability of inputs and neuron firing in SORN with plasticity in the first block of training. (B) Joint probability of inputs and neuron firing in SORN with plasticity in the last block of training. Compared with the left subplot, the joint probability becomes higher with training and the firing of neurons is sparse. (C) Joint probability of inputs and neuron firing in SORN without STDP and IP.</p

Ground-State Proton Transfer Kinetics in Green Fluorescent Protein

Proton transfer plays an important role in the optical properties of green fluorescent protein (GFP). While much is known about excited-state proton transfer reactions (ESPT) in GFP occurring on ultrafast time scales, comparatively little is understood about the factors governing the rates and pathways of ground-state proton transfer. We have utilized a specific isotopic labeling strategy in combination with one-dimensional ¹³C nuclear magnetic resonance (NMR) spectroscopy to install and monitor a ¹³C directly adjacent to the GFP chromophore ionization site. The chemical shift of this probe is highly sensitive to the protonation state of the chromophore, and the resulting spectra reflect the thermodynamics and kinetics of the proton transfer in the NMR line shapes. This information is complemented by time-resolved NMR, fluorescence correlation spectroscopy, and steady-state absorbance and fluorescence measurements to provide a picture of chromophore ionization reactions spanning a wide time domain. Our findings indicate that proton transfer in GFP is described well by a two-site model in which the chromophore is energetically coupled to a secondary site, likely the terminal proton acceptor of ESPT, Glu222. Additionally, experiments on a selection of GFP circular permutants suggest an important role played by the structural dynamics of the seventh β-strand in gating proton transfer from bulk solution to the buried chromophore