Screening and Identification of Yeasts Antagonistic to Pathogenic Fungi Show a Narrow Optimal pH Range for Antagonistic Activity

Microbes have evolved ways of interference competition to gain advantage over their ecological competitors. The use of secreted antagonistic compounds by yeast cells is one of the prominent examples. Although this killer behavior has been thoroughly studied in laboratory yeast strains, our knowledge of the antagonistic specificity of killer effects in nature remains limited. In this study, yeast strains were collected from various niches and screened for antagonistic activity against one toxin-sensitive strain of Saccharomyces cerevisiae and three pathogenic fungi. We demonstrate that some strains with antagonistic activity against these pathogenic fungi can be found in antagonist culture tests. These yeasts were identified as members of Trichosporon asahii, Candida stellimalicola, Wickerhamomyces anomalus, Ustilago esculenta, Aureobasidium pullulans, and Pichia kluyveri. The results indicated that the antagonistic activity of these killer yeasts has a narrow optimal pH range. Furthermore, we found that the antagonistic activity of some species is strain-dependent

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

Proteomic profiling reveals α1-antitrypsin, α1-microglobulin, and clusterin as preeclampsia-related serum proteins in pregnant women

AbstractObjectivePreeclampsia is a major cause of mortality in pregnant women but the underlying mechanism remains unclear to date. In this study, we attempted to identify candidate proteins that might be associated with preeclampsia in pregnant women by means of proteomics tools.Materials and methodsDifferentially expressed proteins in serum samples obtained from pregnant women with severe preeclampsia (n = 8) and control participants (n = 8) were identified using two-dimensional gel electrophoresis (2-DE) followed by peptide mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS). Additional serum samples from 50 normal and 41 pregnant women with severe preeclampsia were analyzed by immunoassay for validation.ResultsTen protein spots were found to be upregulated significantly in women with severe preeclampsia. These protein spots had the peptide mass fingerprints matched to α1-antitrypsin, α1-microglobulin, clusterin, and haptoglobin. Immunoassays in an independent series of serum samples showed that serum α1-antitrypsin, α1-microglobulin, and clusterin levels of severe preeclampsia patients (n = 41) were significantly higher than those in the normal participants (n = 50; α1-antitrypsin 295.95 ± 50.94 mg/dL vs. 259.31 ± 33.90 mg/dL, p = 0.02; α1-microglobulin 0.029 ± 0.004 mg/mL vs. 0.020 ± 0.004 mg/mL, p < 0.0001; clusterin 77.6 ± 16.15 μg/dL vs. 67.6 ± 15.87 μg/dL, p < 0.05).ConclusionIdentification of these proteins by proteomics analysis enables further understanding of the pathophysiology of preeclampsia. Further studies are warranted to investigate the role of these biomarkers in prediction of this disease

Brazilein from Caesalpinia sappan

Brazilein, a natural, biologically active compound from Caesalpinia sappan L., has been shown to exhibit anti-inflammatory and antioxidant properties and to inhibit the growth of several cancer cells. This study verifies the antioxidant and antitumor characteristics of brazilein in skin cancer cells and is the first time to elucidate the inhibition mechanism of adipocyte differentiation, cestocidal activities against Hymenolepis nana, and reduction of spontaneous movement in Anisakis simplex. Brazilein exhibits an antioxidant capacity as well as the ability to scavenge DPPH• and ABTS•+ free radicals and to inhibit lipid peroxidation. Brazilein inhibited intracellular lipid accumulation during adipocyte differentiation in 3T3-L1 cells and suppressed the induction of peroxisome proliferator-activated receptor γ (PPARγ), the master regulator of adipogenesis, suggesting that brazilein presents the antiobesity effects. The toxic effects of brazilein were evaluated in terms of cell viability, induction of apoptosis, and the activity of caspase-3 in BCC cells. The inhibition of the growth of skin cancer cells (A431, BCC, and SCC25) by brazilein is greater than that of human skin malignant melanoma (A375) cells, mouse leukemic monocyte macrophage (RAW 264.7 cells), and noncancerous cells (HaCaT and BNLCL2 cells). The anthelmintic activities of brazilein against Hymenolepis nana are better than those of Anisakis simplex

Oxidized-monolayer Tunneling Barrier for Strong Fermi-level Depinning in Layered InSe Transistors

In 2D-semiconductor-based field-effect transistors and optoelectronic devices, metal-semiconductor junctions are one of the crucial factors determining device performance. The Fermi-level (FL) pinning effect, which commonly caused by interfacial gap states, severely limits the tunability of junction characteristics, including barrier height and contact resistance. A tunneling contact scheme has been suggested to address the FL pinning issue in metal-2D-semiconductor junctions, whereas the experimental realization is still elusive. Here, we show that an oxidized-monolayer-enabled tunneling barrier can realize a pronounced FL depinning in indium selenide (InSe) transistors, exhibiting a large pinning factor of 0.5 and a highly modulated Schottky barrier height. The FL depinning can be attributed to the suppression of metal- and disorder-induced gap states as a result of the high-quality tunneling contacts. Structural characterizations indicate uniform and atomically thin surface oxidation layer inherent from nature of van der Waals materials and atomically sharp oxide-2D-semiconductor interfaces. Moreover, by effectively lowering the Schottky barrier height, we achieve an electron mobility of 2160 cm$^2$/Vs and a contact barrier of 65 meV in two-terminal InSe transistors. The realization of strong FL depinning in high-mobility InSe transistors with the oxidized monolayer presents a viable strategy to exploit layered semiconductors in contact engineering for advanced electronics and optoelectronics

Investigations of the effect of pore size of ceramic membranes on the pilot-scale removal of oil from oil-water emulsion

Oil-water emulsions are one of the most serious pollutants because of the large quantities produced by various industries, such as the petrochemical, oil and gas industries. One of the major methods to remove oil from wastewater is filtration using ceramic tubular microfiltration membranes. However, such membranes are vulnerable to fouling, which causes operational impairment. The aims of this work are to study the influence of membrane pore size on permeate flux and oil removal efficiency at different operating parameters and the reduction in fouling when used in combination with hybrid Coagulation/sand filter-MF pre-treatment process. The droplet size of the oil-water emulsion has an interaction with the pore size of the ceramic membrane. Therefore, each pore size may be optimal, depending upon the concentration of oil in the emulsion, and hence droplet size. Steady-state flux and oil removal efficiency were found to b highest for hybrid coagulation/sand filter –MF due to a reduction of membrane fouling by reducing the oil concentration in the inlet emulsion to the ceramic membrane