Phylogeny of Prokaryotes and Chloroplasts Revealed by a Simple Composition Approach on All Protein Sequences from Complete Genomes Without Sequence Alignment

The complete genomes of living organisms have provided much information on their phylogenetic relationships. Similarly, the complete genomes of chloroplasts have helped to resolve the evolution of this organelle in photosynthetic eukaryotes. In this paper we propose an alternative method of phylogenetic analysis using compositional statistics for all protein sequences from complete genomes. This new method is conceptually simpler than and computationally as fast as the one proposed by Qi et al. (2004b) and Chu et al. (2004). The same data sets used in Qi et al. (2004b) and Chu et al. (2004) are analyzed using the new method. Our distance-based phylogenic tree of the 109 prokaryotes and eukaryotes agrees with the biologists tree of life based on 16S rRNA comparison in a predominant majority of basic branching and most lower taxa. Our phylogenetic analysis also shows that the chloroplast genomes are separated to two major clades corresponding to chlorophytes s.l. and rhodophytes s.l. The interrelationships among the chloroplasts are largely in agreement with the current understanding on chloroplast evolution

Phase Separation and Magnetic Order in K-doped Iron Selenide Superconductor

Alkali-doped iron selenide is the latest member of high Tc superconductor family, and its peculiar characters have immediately attracted extensive attention. We prepared high-quality potassium-doped iron selenide (KxFe2-ySe2) thin films by molecular beam epitaxy and unambiguously demonstrated the existence of phase separation, which is currently under debate, in this material using scanning tunneling microscopy and spectroscopy. The stoichiometric superconducting phase KFe2Se2 contains no iron vacancies, while the insulating phase has a \surd5\times\surd5 vacancy order. The iron vacancies are shown always destructive to superconductivity in KFe2Se2. Our study on the subgap bound states induced by the iron vacancies further reveals a magnetically-related bipartite order in the superconducting phase. These findings not only solve the existing controversies in the atomic and electronic structures in KxFe2-ySe2, but also provide valuable information on understanding the superconductivity and its interplay with magnetism in iron-based superconductors

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

<p>Abstract</p> <p>Background</p> <p>Prediction of protein structural classes (<it>α</it>, <it>β</it>, <it>α </it>+ <it>β </it>and <it>α</it>/<it>β</it>) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.</p> <p>Results</p> <p>We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the <it>chaos game representation </it>is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using <it>recurrence quantification analysis</it>, <it>K-string based information entropy </it>and <it>segment-based analysis</it>. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at <url>http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/</url>.</p> <p>Conclusion</p> <p>The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of <it>α </it>helices and <it>β </it>strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.</p

Knockdown of CypA inhibits interleukin-8 (IL-8) and IL-8-mediated proliferation and tumor growth of glioblastoma cells through down-regulated NF-κB

Although cyclophilin A (CypA) has been reported to be over-expressed in cancer cells and solid tumors, its expression and role in glioblastomas have not been studied. Herein, we show that expression of CypA in human glioblastoma cell lines and tissues is significantly higher than in normal human astrocytes and normal counterparts of brain tissue. To determine the role of over-expressed CypA in glioblastoma, stable RNA interference (RNAi)-mediated knockdown of CypA (CypA KD) was performed in gliobastoma cell line U87vIII (U87MG · ΔEGFR). CypA KD stable single clones decrease proliferation, infiltration, migration, and anchorage-independent growth in vitro and with slower growth in vivo as xenografts in immunodeficient nude mice. We have also observed that knockdown of CypA inhibits expression of interleukin-8 (IL-8), a tumorigenic and proangiogenic cytokine. Conversely, enforced expression of CypA in the CypA KD cell line, Ud-12, markedly enhanced IL-8 transcripts and restored Ud-12 proliferation, suggesting that CypA-mediated IL-8 production provides a growth advantage to glioblastoma cells. CypA knockdown-mediated inhibition of IL-8 is due to reduced activity of NF-κB, which is one of the major transcription factors regulating IL-8 expression. These results not only establish the relevance of CypA to glioblastoma growth in vitro and in vivo, but also suggest that small interfering RNA-based CypA knockdown could be an effective therapeutic approach against glioblastomas