Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry

Unique and conserved MicroRNAs in wheat chromosome 5D revealed by next-generation sequencing

MicroRNAs are a class of short, non-coding, single-stranded RNAs that act as post-transcriptional regulators in gene expression. miRNA analysis of Triticum aestivum chromosome 5D was performed on 454 GS FLX Titanium sequences of flow sorted chromosome 5D with a total of 3,208,630 good quality reads representing 1.34x and 1.61x coverage of the short (5DS) and long (5DL) arms of the chromosome respectively. In silico and structural analyses revealed a total of 55 miRNAs; 48 and 42 miRNAs were found to be present on 5DL and 5DS respectively, of which 35 were common to both chromosome arms, while 13 miRNAs were specific to 5DL and 7 miRNAs were specific to 5DS. In total, 14 of the predicted miRNAs were identified in wheat for the first time. Representation (the copy number of each miRNA) was also found to be higher in 5DL (1,949) compared to 5DS (1,191). Targets were predicted for each miRNA, while expression analysis gave evidence of expression for 6 out of 55 miRNAs. Occurrences of the same miRNAs were also found in Brachypodium distachyon and Oryza sativa genome sequences to identify syntenic miRNA coding sequences. Based on this analysis, two other miRNAs: miR1133 and miR167 were detected in B. distachyon syntenic region of wheat 5DS. Five of the predicted miRNA coding regions (miR6220, miR5070, miR169, miR5085, miR2118) were experimentally verified to be located to the 5D chromosome and three of them : miR2118, miR169 and miR5085, were shown to be 5D specific. Furthermore miR2118 was shown to be expressed in Chinese Spring adult leaves. miRNA genes identified in this study will expand our understanding of gene regulation in bread wheat

Oroxylin A promotes PTEN-mediated negative regulation of MDM2 transcription via SIRT3-mediated deacetylation to stabilize p53 and inhibit glycolysis in wt-p53 cancer cells

Introduction p53 plays important roles in regulating the metabolic reprogramming of cancer, such as aerobic glycolysis. Oroxylin A is a natural active flavonoid with strong anticancer effects both in vitro and in vivo. Methods wt-p53 (MCF-7 and HCT116 cells) cancer cells and p53-null H1299 cancer cells were used. The glucose uptake and lactate production were analyzed using Lactic Acid production Detection kit and the Amplex Red Glucose Assay Kit. Then, the protein levels and RNA levels of p53, mouse double minute 2 (MDM2), and p53-targeted glycolytic enzymes were quantified using Western blotting and quantitative polymerase chain reaction (PCR), respectively. Immunoprecipitation were performed to assess the binding between p53, MDM2, and sirtuin-3 (SIRT3), and the deacetylation of phosphatase and tensin homolog (PTEN). Reporter assays were performed to assess the transcriptional activity of PTEN. In vivo, effects of oroxylin A was investigated in nude mice xenograft tumor-inoculated MCF-7 or HCT116 cells. Results Here, we analyzed the underlying mechanisms that oroxylin A regulated p53 level and glycolytic metabolism in wt-p53 cancer cells, and found that oroxylin A inhibited glycolysis through upregulating p53 level. Oroxylin A did not directly affect the transcription of wt-p53, but suppressed the MDM2-mediated degradation of p53 via downregulating MDM2 transcription in wt-p53 cancer cells. In further studies, we found that oroxylin A induced a reduction in MDM2 transcription by promoting the lipid phosphatase activity of phosphatase and tensin homolog, which was upregulated via sirtuin3-mediated deacetylation. In vivo, oroxylin A inhibited the tumor growth of nude mice-inoculated MCF-7 or HCT116 cells. The expression of MDM2 protein in tumor tissue was downregulated by oroxylin A as well. Conclusions These results provide a p53-independent mechanism of MDM2 transcription and reveal the potential of oroxylin A on glycolytic regulation in both wt-p53 and mut-p53 cancer cells. The studies have important implications for the investigation on anticancer effects of oroxylin A, and provide the academic basis for the clinical trial of oroxylin A in cancer patients

Self-Assembled Polymeric Micellar Nanoparticles as Nanocarriers for Poorly Soluble Anticancer Drug Ethaselen

A series of monomethoxy poly(ethylene glycol)-poly(lactide) (mPEG-PLA) diblock copolymers were synthesized, and mPEG-PLA micelle was fabricated and used as a nanocarrier for solubilization and delivery of a promising anticancer drug ethaselen. Ethaselen was efficiently encapsulated into the micelles by the dialysis method, and the solubility of ethaselen in water was remarkably increased up to 82 μg/mL before freeze-drying. The mean diameter of ethaselen-loaded micelles ranged from 51 to 98 nm with a narrow size distribution and depended on the length of PLA block. In vitro hemolysis study indicated that mPEG-PLA copolymers and ethaselen-loaded polymeric micelles had no hemolytic effect on the erythrocyte. The enhanced antitumor efficacy and reduced toxic effect of ethaselen-loaded polymeric micelle when compared with ethaselen-HP-β-CD inclusion were observed at the same dose in H22human liver cancer cell bearing mouse models. These suggested that mPEG-PLA polymeric micelle nanoparticles had great potential as nanocarriers for effective solubilization of poorly soluble ethaselen and further reducing side effects and toxicities of the drug

Predicting RNA-Protein Interactions Using Only Sequence Information

<p>Abstract</p> <p>Background</p> <p>RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.</p> <p>Results</p> <p>We propose <b><it>RPISeq</it></b>, a family of classifiers for predicting <b><it>R</it></b>NA-<b><it>p</it></b>rotein <b><it>i</it></b>nteractions using only <b><it>seq</it></b>uence information. Given the sequences of an RNA and a protein as input, <it>RPIseq </it>predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of <it>RPISeq </it>are presented: <it>RPISeq-SVM</it>, which uses a Support Vector Machine (SVM) classifier and <it>RPISeq-RF</it>, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), <it>RPISeq </it>achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of <it>RPISeq </it>was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, <it>RPISeq </it>classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from <it>E. coli, S. cerevisiae, D. melanogaster, M. musculus</it>, and <it>H. sapiens</it>.</p> <p>Conclusions</p> <p>Our experiments with <it>RPISeq </it>demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. <it>RPISeq </it>offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. <it>RPISeq </it>is freely available as a web-based server at <url>http://pridb.gdcb.iastate.edu/RPISeq/.</url></p

Phosphorescent Energy Downshifting for Diminishing Surface Recombination in Silicon Nanowire Solar Cells

Molecularly engineered Ir(III) complexes can transfer energy from short-wavelength photons (lambda &lt; 450 nm) to photons of longer wavelength (lambda &gt; 500 nm), which can enhance the otherwise low internal quantum efficiency (IQE) of crystalline Si (c-Si) nanowire solar cells (NWSCs) in the shortwavelength region. Herein, we demonstrate a phosphorescent energy downshifting system using Ir(III) complexes at short wavelengths (300-450 nm) to diminish the severe surface recombination that occurs in c-Si NWSCs. The developed Ir(III) complexes can be considered promising energy converters because they exhibit superior intrinsic properties such as a high quantum yield, a large Stokes shift, a long exciton diffusion length in crystalline film, and a reproducible synthetic procedure. Using the developed 1011) complexes, highly crystalline energy downshifting layers were fabricated by ultrasonic spray deposition to enhance the photoluminescence efficiency by increasing the radiative decay. With the optimized energy downshifting layer, our 1cm(2) c-Si NWSCs with Ir(III) complexes exhibited a higher IQE value for short-wavelength light (300-450 nm) compared with that of bare Si NWSCs without Ir(III) complexes, resulting in a notable increase in the short-circuit current density (from 34.4 mA.cm(-2) to 36.5 mA.cm(-2) )

Integrative Genomic Data Mining for Discovery of Potential Blood-Borne Biomarkers for Early Diagnosis of Cancer

Background: With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types. Methodology/Principal Findings: In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function. Conclusion/Significance: The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile