Design status of ASPIICS, an externally occulted coronagraph for PROBA-3

The "sonic region" of the Sun corona remains extremely difficult to observe with spatial resolution and sensitivity sufficient to understand the fine scale phenomena that govern the quiescent solar corona, as well as phenomena that lead to coronal mass ejections (CMEs), which influence space weather. Improvement on this front requires eclipse-like conditions over long observation times. The space-borne coronagraphs flown so far provided a continuous coverage of the external parts of the corona but their over-occulting system did not permit to analyse the part of the white-light corona where the main coronal mass is concentrated. The proposed PROBA-3 Coronagraph System, also known as ASPIICS (Association of Spacecraft for Polarimetric and Imaging Investigation of the Corona of the Sun), with its novel design, will be the first space coronagraph to cover the range of radial distances between ~1.08 and 3 solar radii where the magnetic field plays a crucial role in the coronal dynamics, thus providing continuous observational conditions very close to those during a total solar eclipse. PROBA-3 is first a mission devoted to the in-orbit demonstration of precise formation flying techniques and technologies for future European missions, which will fly ASPIICS as primary payload. The instrument is distributed over two satellites flying in formation (approx. 150m apart) to form a giant coronagraph capable of producing a nearly perfect eclipse allowing observing the sun corona closer to the rim than ever before. The coronagraph instrument is developed by a large European consortium including about 20 partners from 7 countries under the auspices of the European Space Agency. This paper is reviewing the recent improvements and design updates of the ASPIICS instrument as it is stepping into the detailed design phase

Microarray Analysis of Gene Expression during Bacteriophage T4 Infection

AbstractGenomic microarrays were used to examine the complex temporal program of gene expression exhibited by bacteriophage T4 during the course of development. The microarray data confirm the existence of distinct early, middle, and late transcriptional classes during the bacteriophage replicative cycle. This approach allows assignment of previously uncharacterized genes to specific temporal classes. The genomic expression data verify many promoter assignments and predict the existence of previously unidentified promoters

Living with Genome Instability: the Adaptation of Phytoplasmas to Diverse Environments of Their Insect and Plant Hosts

Phytoplasmas (“Candidatus Phytoplasma,” class Mollicutes) cause disease in hundreds of economically important plants and are obligately transmitted by sap-feeding insects of the order Hemiptera, mainly leafhoppers and psyllids. The 706,569-bp chromosome and four plasmids of aster yellows phytoplasma strain witches' broom (AY-WB) were sequenced and compared to the onion yellows phytoplasma strain M (OY-M) genome. The phytoplasmas have small repeat-rich genomes. This comparative analysis revealed that the repeated DNAs are organized into large clusters of potential mobile units (PMUs), which contain tra5 insertion sequences (ISs) and genes for specialized sigma factors and membrane proteins. So far, these PMUs appear to be unique to phytoplasmas. Compared to mycoplasmas, phytoplasmas lack several recombination and DNA modification functions, and therefore, phytoplasmas may use different mechanisms of recombination, likely involving PMUs, for the creation of variability, allowing phytoplasmas to adjust to the diverse environments of plants and insects. The irregular GC skews and the presence of ISs and large repeated sequences in the AY-WB and OY-M genomes are indicative of high genomic plasticity. Nevertheless, segments of ∼250 kb located between the lplA and glnQ genes are syntenic between the two phytoplasmas and contain the majority of the metabolic genes and no ISs. AY-WB appears to be further along in the reductive evolution process than OY-M. The AY-WB genome is ∼154 kb smaller than the OY-M genome, primarily as a result of fewer multicopy sequences, including PMUs. Furthermore, AY-WB lacks genes that are truncated and are part of incomplete pathways in OY-M

Genomic Analyses Reveal Broad Impact of miR-137 on Genes Associated with Malignant Transformation and Neuronal Differentiation in Glioblastoma Cells

<div><p>miR-137 plays critical roles in the nervous system and tumor development; an increase in its expression is required for neuronal differentiation while its reduction is implicated in gliomagenesis. To evaluate the potential of miR-137 in glioblastoma therapy, we conducted genome-wide target mapping in glioblastoma cells by measuring the level of association between PABP and mRNAs in cells transfected with miR-137 mimics vs. controls via RIPSeq. Impact on mRNA levels was also measured by RNASeq. By combining the results of both experimental approaches, 1468 genes were found to be negatively impacted by miR-137 – among them, 595 (40%) contain miR-137 predicted sites. The most relevant targets include oncogenic proteins and key players in neurogenesis like c-KIT, YBX1, AKT2, CDC42, CDK6 and TGFβ2. Interestingly, we observed that several identified miR-137 targets are also predicted to be regulated by miR-124, miR-128 and miR-7, which are equally implicated in neuronal differentiation and gliomagenesis. We suggest that the concomitant increase of these four miRNAs in neuronal stem cells or their repression in tumor cells could produce a robust regulatory effect with major consequences to neuronal differentiation and tumorigenesis.</p></div

miR-137 potentially shares targets with miRNAs implicated in neurogenesis and gliomagenesis.

<p><b>A</b>) Venn diagram displays the potential overlap between miR-137 identified targets and predicted miR-7, -124 and -128 targets obtained from TargetScan, miRanda, and PicTar. Table indicates that overlaps are significant according to Hypergeometric test. <b>B</b>) U251 glioblastoma cells were transfected with either miR-7, miR-124, and miR-128 (as listed) or control mimics. Western analyses show the impact on protein levels of a set of genes affected by miR-7, miR-124, or miR-128 transfection that were identified by the bioinformatics analyses. Data was analyzed with Student's <i>t</i>-test and is presented as the mean ± standard deviation. * indicates p≤0.05, ** indicates p≤0.01, and *** indicates p≤0.001. Neurofilament was not detected in U343 cells.</p

Analysis of miR-137 impact on glioblastoma expression by two genomic approaches.

<p><b>A</b>) Venn diagram shows the number of genes affected by miR-137 mimics transfection in U251 cells obtained by RNASeq and RIPSeq approaches. <b>B</b>) Venn diagram shows the number of genes identified by our approaches containing miR-137 predicted targets according to TargetScan, Miranda or Pictar. <b>C</b>) Hypergeometric test determined the significance of results of the two approaches used to identify miR-137 targets. <b>D</b>) Venn diagram shows the distribution of genes containing miR-137 predicated sites (from B) according to prediction tools. <b>E</b>) Venn diagram showing the number of genes identified by either the RIPSeq assay or the RNASeq assay in U251 cells, U343 cells, and both cell types. <b>F</b>) As in panel E, but considering only genes that were predicted as miR137 targets by Miranda, Pictar or TargetScan. <b>G</b>) Venn diagram showing the number of genes identified as miR137 targets in U343 cells by the RIPSeq assay, the RNASeq assay and both. <b>H</b>) As in panel G, but considering only genes that were predicted as miR137 targets by Miranda, Pictar or TargetScan. <b>I</b>) The average log10-fold-change in U251 cells (y-axis) and U343 cells (x-axis) is shown for all genes identified as miR137 targets by the RIPSeq assay in either U251 or U343 cells; genes (points) are colored by whether they were identified in only U251 cells, only U343 cells, or both. <b>J</b>) As in panel I, but for the RNASeq assay.</p

Most relevant oncogenes affected by miR-137 transfection with previously described function in gliomagenesis.

<p>Most relevant oncogenes affected by miR-137 transfection with previously described function in gliomagenesis.</p

Impact of miR-137 mimics transfection on cancer relevant processes.

<p><b>A</b>) miR-137 transfection into U251 glioblastoma cells inhibits cell proliferation (p<0.001, multiple comparison tests between groups). U251 cells were transfected with miR-137 or control miRNA and plated at a low density (500 cells per well). Cell proliferation was monitored using the Essen Bioscience IncuCyte automated microscope system and read out as percentage confluence. The experiment was monitored over a course of 6 days. The data was analyzed using analysis of variance, and the data is presented as the mean ± standard deviation. <b>B</b>) miR-137 transfection into U251 glioblastoma results in increased apoptosis, measured by caspase-3/-7 luminescent assay. Caspase-3/-7 activity, an indicator of apoptosis induction, increased after miR-137 transfection, as compared to the control miRNA transfection. Data was analyzed with Student's <i>t</i>-test and is presented as the mean ± standard deviation. *** indicates p≤0.001. <b>C</b>) miR-137 transfection into U251 glioblastoma cells results in increased apoptosis, measured by Western blot of poly(ADP) ribose polymerase (PARP) cleavage. PARP cleavage, an end event of apoptosis, increased after miR-137 transfection, as compared to the control miRNA transfection. Etoposide (25 µM) was used as an inducer of apoptosis. Data was analyzed with Student's <i>t</i>-test and is presented as the mean ± standard deviation. ** indicates p≤0.01. α-tubulin was included as an endogenous loading control. <b>D</b>) miR-137 transfection into glioblastoma cells results in decreased ability to migration (p<0.001, multiple comparison tests between groups). U251 glioblastoma cells were transfected with miR-137 or control miRNA. An <i>in vitro</i> scratch assay was used to measure the ability for cell migration. The assay was monitored using Essen Bioscience IncuCyte automated microscope system. The data was analyzed using analysis of variance, and the data is presented as the mean ± standard deviation.</p

Expression analysis of miR-137 and its targets in the TCGA dataset.

<p><b>A</b>) Shown is the total number and proportion of TCGA samples analyzed with up- or down-regulation of miR137 (see methods for details). Error-bars are 95% confidence interval, and *** indicates p≤0.001, binomial test (two tailed, null hypothesis is a true proportion of 0.5). <b>B</b>) The z-score of gene expression in 261 TCGA samples for the 595 miR137 target genes relative to normal tissue, stratified by GBM type as reported by Kim et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085591#pone.0085591-Kim1" target="_blank">[1]</a>. <b>C</b>) The number and proportion of up/down regulated genes amongst the 595 identified miR137 target genes in 261 TCGA GBM samples. Four different thresholds for significant change are shown (absolute value of z-score relative to normal tissue greater than 2, 3, 4 or 5). Samples are stratified by GBM type (x-axis), as defined in Kim et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085591#pone.0085591-Kim1" target="_blank">[1]</a>; if a gene is up or down regulated in multiple samples, it is counted once for each sample showing a significant change. <b>D</b>) Proportion of genes with positive or negative correlation with miR137 (x-axis) for increasing correlation coefficient threshold cut-offs (y-axis); higher correlations tend to be predominantly negative.</p