A Two-Hit Trigger for siRNA Biogenesis in Plants

SummaryIn Arabidopsis, microRNA-directed cleavage can define one end of RNAs that then generate phased siRNAs. However, most miRNA-targeted RNAs do not spawn siRNAs, suggesting the existence of additional determinants within those that do. We find that in moss, phased siRNAs arise from regions flanked by dual miR390 cleavage sites. AtTAS3, an siRNA locus important for development and conserved among higher plants, also has dual miR390 complementary sites. Both sites bind miR390 in vitro and are functionally required in Arabidopsis, but cleavage is undetectable at the 5′ site—demonstrating that noncleavable sites can be functional in plants. Phased siRNAs also emanate from the bounded regions of every Arabidopsis gene with two known microRNA/siRNA complementary sites, but only rarely from genes with single sites. Therefore, two “hits,”—often, but not always, two cleavage events—constitute a conserved trigger for siRNA biogenesis, a finding with implications for recognition and silencing of aberrant RNA

Strong Coupling Constant from the Photon Structure Function

We extract the value of the strong coupling constant alpha_s from a single-parameter pointlike fit to the photon structure function F_2^gamma at large x and Q^2 and from a first five-parameter full (pointlike and hadronic) fit to the complete F_2^gamma data set taken at PETRA, TRISTAN, and LEP. In next-to-leading order and the MSbar renormalization and factorization schemes, we obtain alpha_s(m_Z)=0.1183 +/- 0.0050(exp.)^+0.0029_-0.0028(theor.) [pointlike] and alpha_s(m_Z)=0.1198 +/- 0.0028(exp.)^+0.0034_-0.0046(theor.) [pointlike and hadronic]. We demonstrate that the data taken at LEP have reduced the experimental error by about a factor of two, so that a competitive determination of alpha_s from F_2^gamma is now possible.Comment: 11 pages, 2 tables, 2 figures. Version accepted for publication by Phys. Rev. Let

Nuclear Inelastic X-Ray Scattering of FeO to 48 GPa

The partial density of vibrational states has been measured for Fe in compressed FeO (w\"ustite) using nuclear resonant inelastic x-ray scattering. Substantial changes have been observed in the overall shape of the density of states close to the magnetic transiton around 20 GPa from the paramagnetic (low pressure) to the antiferromagnetic (high pressure) state. Our data indicate a substantial softening of the aggregate sound velocities far below the transition, starting between 5 and 10 GPa. This is consistent with recent radial x-ray diffraction measurements of the elastic constants in FeO. The results indicate that strong magnetoelastic coupling in FeO is the driving force behind the changes in the phonon spectrum of FeO.Comment: 4 pages, 4 figure

A database of microRNA expression patterns in Xenopus laevis

MicroRNAs (miRNAs) are short, non-coding RNAs around 22 nucleotides long. They inhibit gene expression either by translational repression or by causing the degradation of the mRNAs they bind to. Many are highly conserved amongst diverse organisms and have restricted spatio-temporal expression patterns during embryonic development where they are thought to be involved in generating accuracy of developmental timing and in supporting cell fate decisions and tissue identity. We determined the expression patterns of 180 miRNAs in Xenopus laevis embryos using LNA oligonucleotides. In addition we carried out small RNA-seq on different stages of early Xenopus development, identified 44 miRNAs belonging to 29 new families and characterized the expression of 5 of these. Our analyses identified miRNA expression in many organs of the developing embryo. In particular a large number were expressed in neural tissue and in the somites. Surprisingly none of the miRNAs we have looked at show expression in the heart. Our results have been made freely available as a resource in both XenMARK and Xenbase

MicroRNAs in cardiac arrhythmia: DNA sequence variation of MiR-1 and MiR-133A in long QT syndrome.

Long QT syndrome (LQTS) is a genetic cardiac condition associated with prolonged ventricular repolarization, primarily a result of perturbations in cardiac ion channels, which predisposes individuals to life-threatening arrhythmias. Using DNA screening and sequencing methods, over 700 different LQTS-causing mutations have been identified in 13 genes worldwide. Despite this, the genetic cause of 30-50% of LQTS is presently unknown. MicroRNAs (miRNAs) are small (∼ 22 nucleotides) noncoding RNAs which post-transcriptionally regulate gene expression by binding complementary sequences within messenger RNAs (mRNAs). The human genome encodes over 1800 miRNAs, which target about 60% of human genes. Consequently, miRNAs are likely to regulate many complex processes in the body, indeed aberrant expression of various miRNA species has been implicated in numerous disease states, including cardiovascular diseases. MiR-1 and MiR-133A are the most abundant miRNAs in the heart and have both been reported to regulate cardiac ion channels. We hypothesized that, as a consequence of their role in regulating cardiac ion channels, genetic variation in the genes which encode MiR-1 and MiR-133A might explain some cases of LQTS. Four miRNA genes (miR-1-1, miR-1-2, miR-133a-1 and miR-133a-2), which encode MiR-1 and MiR-133A, were sequenced in 125 LQTS probands. No genetic variants were identified in miR-1-1 or miR-133a-1; but in miR-1-2 we identified a single substitution (n.100A> G) and in miR-133a-2 we identified two substitutions (n.-19G> A and n.98C> T). None of the variants affect the mature miRNA products. Our findings indicate that sequence variants of miR-1-1, miR-1-2, miR-133a-1 and miR-133a-2 are not a cause of LQTS in this cohort

Physics Opportunities of e+e- Linear Colliders

We describe the anticipated experimental program of an e+e- linear collider in the energy range 500 GeV -- 1.5 TeV. We begin with a description of current collider designs and the expected experimental environment. We then discuss precision studies of the W boson and top quark. Finally, we review the range of models proposed to explain the physics of electroweak symmetry breaking and show, for each case, the central role that the linear collider experiments will play in elucidating this physics. (to appear in Annual Reviews of Nuclear and Particle Science)Comment: 93 pages, latex + 23 figures; typos corrections + 1 reference adde

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Performance evaluation of commercial miRNA expression array platforms

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNA) are short, endogenous transcripts that negatively regulate the expression of specific mRNA targets. The relative abundance of miRNAs is linked to function <it>in vivo </it>and miRNA expression patterns are potentially useful signatures for the development of diagnostic, prognostic and therapeutic biomarkers.</p> <p>Finding</p> <p>We compared the performance characteristics of four commercial miRNA array technologies and found that all platforms performed well in separate measures of performance.</p> <p>Conclusions</p> <p>The Ambion and Agilent platforms were more accurate, whereas the Illumina and Exiqon platforms were more specific. Furthermore, the data analysis approach had a large impact on the performance, predominantly by improving precision.</p

SFSSClass: an integrated approach for miRNA based tumor classification

Background: MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. Results: In this article, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is similar to 76.5%, the accuracy of SFSSClass is found to be similar to 82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass (integration of SAMBA, cancer-miRNA network and USC) over USC (eg., similar to 70.5% for SFSSClass versus similar to 58.8% in classifying a set of 17 MDT samples from 9 tumor types, similar to 91.7% for SFSSClass versus similar to 75% in classifying 12 cell lines from 6 tumor types and similar to 382.3% for SFSSClass versus similar to 41.2% in classifying 17 PDT samples from 11 tumor types). Conclusion: In this article, we develop the SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low interclass similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches