Brewster-Angle Variable Polarization Spectroscopy of Colloidal Au-Nanospheres and -Nanorods at the Silicon Surface

Colloidal Au-nanospheres and -nanorods were chemically synthesized from chloroauric acid containing solutions and adsorbed to hydrogen-terminated p-Si(111) surfaces. A comparative analysis of the respective plasmon resonance modes, in solution and at the silicon surface, was carried out by in situ optical transmission and surface reflection techniques. In solution, the absorbance was determined by VIS transmission spectroscopy and compared to Mie theory as well as finite difference time domain calculations. At the p-Si(111) surface, the reflectance was analyzed for the first time by Brewster-angle variable polarization spectroscopy (BA-VPS) and discussed in terms of anisotropic uniaxial thin-film properties. With BA-VPS, the strengths of parallel and orthogonal electric field components of the incident wave can be varied relative to the surface plane. Consequently, opposite changes of transverse and longitudinal resonance strengths are detected upon gradually incremented light polarization angles. Model considerations confirm that spherical and elongated particles can thereby be distinguished. The influence of particle–surface interaction and the dielectric environment is finally discussed

Modeling Genome-Wide Dynamic Regulatory Network in Mouse Lungs with Influenza Infection Using High-Dimensional Ordinary Differential Equations

<div><p>The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.</p></div

The inward and outward regulations in the module-based regulatory network.

<p>The negative sign indicates a negative coefficient in the linear ODE model; otherwise the coefficient is positive. The underlined modules are hub modules with the most outward regulations.</p

The road map of the proposed pipeline for reconstructing genome-wide dynamic GRNs.

<p>The road map of the proposed pipeline for reconstructing genome-wide dynamic GRNs.</p

Overview of the temporal variations of DE genes.

<p>(a) Correlation matrix between every pair of time points indicates three major transcriptional phases. (b) Estimates for the first two eigenfunctions (first-black solid, second-red dashed) for the DE genes.</p

Temporal expression profiles of DE genes.

<p>(a) The DE genes are clustered into 20 modules (M1–M20) and the number of genes in each module is displayed in the parentheses. Shown are standardized gene expressions normalized to day 0. (b) The smoothed mean expression curve obtained from (6) (red solid) for each module overlaid with the refined estimate from the linear ODE model (blue dashed).</p

Intra-module regulatory relationships for four modules M1 (a), M4 (b), M6 (c) and M7 (d).

<p>TFs are shown in aquamarine and target genes are shown in green. Isolated TFs and genes are not shown.</p

ATP Hydrolyzing Salivary Enzymes of Caterpillars Suppress Plant Defenses

<div><p>The oral secretions of herbivores are important recognition cues that can be used by plants to mediate induced defenses. In this study, a degradation of adenosine-5′-triphosphate (ATP) in tomato leaves was detected after treatment with <em>Helicoverpa zea</em> saliva. Correspondingly, a high level of ATPase activity in saliva was detected and three ATP hydrolyzing enzymes: apyrase, ATP synthase and ATPase 13A1 were identified in salivary glands. To determine the functions of these proteins in mediating defenses, they were cloned from <em>H. zea</em> and expressed in <em>Escherichia coli</em>. By applying the purified expressed apyrase, ATP synthase or ATPase 13A1 to wounded tomato leaves, it was determined that these ATP hydrolyzing enzymes suppressed the defensive genes regulated by the jasmonic acid and ethylene pathways in tomato plant. Suppression of glandular trichome production was also observed after treatment. Blood-feeding arthropods employ 5′-nucleotidase family of apyrases to circumvent host responses and the <em>H. zea</em> apyrase, is also a member of this family. The comparatively high degree of sequence similarity of the <em>H. zea</em> salivary apyrase with mosquito apyrases suggests a broader evolutionary role for salivary apyrases than previously envisioned.</p> </div

Evolution of the miR-290–295/miR-371–373 Cluster Family Seed Repertoire

<div><p>Expression of the mouse miR-290–295 cluster and its miR-371–373 homolog in human is restricted to early embryos, primordial germ cells, the germ line stem cell compartment of the adult testis and to stem cell lines derived from the early embryonic lineages. Sequencing data suggest considerable seed diversification between the seven homologous pre-miRNAs of miR-290–295 but it is not clear if all of the implied miR-290–295 seeds are also conserved in the human miR-371–373 cluster, which consists of only three homologous pre-miRNAs. By employing miRNA target reporters we show that most, if not all, seeds in miR-290–295 are represented in miR-371–373. In the mouse, pre-miR-290, pre-miR-292 and pre-miR-293 express subsets of the miRNA isoforms processed from the single human pre-miR-371. Comparison of the possible miR-290–295/miR-371–373 seed repertoires in placental mammals suggests a model for the evolution of this miRNA cluster family, which would be otherwise difficult to deduce based solely on pre-miRNA sequence comparisons. The conservation of co-expressed seeds that is characteristic of miR-290–295/miR-371–373 should be taken into account in models of the corresponding miRNA-target interaction networks.</p></div

Short RNA 5′-end distributions in pre-miR-290–295 and pre-miR-371–373 sequencing data.

<p>The frequencies of observed 5′-ends of RNA reads in various short RNA sequencing datasets are plotted as a function of pre-miRNA sequence position. pre-miRNA sequence co-ordinates are given with respect to the 5p0 and 3p0 positions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone-0108519-g001" target="_blank">Figure 1A</a> and the sum of 5′-end frequencies is normalized to 1 for each individual pre-miRNA. For pre-miR-290-295 the top panels show total ES cell RNA and HEK-293 ectopic overexpression RNA sequencing data (Total1-3, Ectopic) and the bottom panels show HITS-CLIP data. Dataset Total1 is the total RNA dataset from ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519-Leung1" target="_blank">[27]</a>, Total 2 and Total 3 are respectively the J1 and Dcr^+/+ total RNA datasets from ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519-Calabrese1" target="_blank">[16]</a> and Total 4 is from ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519-Chiang1" target="_blank">[6]</a>. CLIP1-3 correspond to datasets WT1A, WT1B and WT2 from ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519-Leung1" target="_blank">[27]</a>. The panels corresponding to pre-miR-371-373 show total RNA sequencing data from undifferentiated human ES cells (Undifferentiated) and human ES cells that have been differentiated into embryoid bodies (Differentiated) according to ref <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519-Morin1" target="_blank">[12]</a>. The data for pre-miR-291b, which yields very few reads in all datasets and is, thus, noisy is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108519#pone.0108519.s001" target="_blank">Figure S1</a>.</p