A new HPLC-DAD-MS/MS method for the simultaneous determination of major compounds in the crude extract of Lychnophora salicifolia Mart. (Brazilian arnicao) leaves: Application to chemical variability evaluation

Lychnophora salicifolia Mart., which occurs in the Brazilian Cerrado in the states of Bahia and Minas Gerais as well as in the southeast of the state of Goias, is the most widely distributed and also the most polymorphic species of the genus. This plant is popularly known to have anti-inflammatory and analgesic activities. In this work, we have studied the variation in terms of polar metabolites of ninety-three Lychnophora salicifolia Mart, specimens collected from different regions of the Brazilian Cerrado. Identification of the constituents of this mixture was carried out by analysis of the UV spectra and MS data after chromatographic separation. Twenty substances were identified, including chlorogenic acid derivatives, a flavonoid C-glucoside, and other sesquiterpenes. The analytical method was validated, and the reliability and credibility of the results was ensured for the purposes of this study. The concentration range required for analysis of content variability within the analyzed group of specimens was covered with appropriate values of limits of detection and quantitation, as well as satisfactory precision and recovery. A quantitative variability was observed among specimens collected from the same location, but on average they were similar from a chemical viewpoint. In relation to the study involving specimens from different locations, there were both qualitative and quantitative differences among plants collected from different regions of Brazil. Statistical analysis revealed that there is a correlation between geographical localization and polar metabolites profile for specimens collected from different locations. This is evidence that the pattern of metabolites concentration depends on the geographical distribution of the specimens. (C) 2012 Elsevier B.V. All rights reserved.

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Identification of differentially expressed genes from Trichoderma harzianum during growth on cell wall of Fusarium solani as a tool for biotechnological application

Background: The species of T. harzianum are well known for their biocontrol activity against many plant pathogens. However, there is a lack of studies concerning its use as a biological control agent against F. solani, a pathogen involved in several crop diseases. In this study, we have used subtractive library hybridization (SSH) and quantitative real-time PCR (RT-qPCR) techniques in order to explore changes in T. harzianum genes expression during growth on cell wall of F. solani (FSCW) or glucose. RT-qPCR was also used to examine the regulation of 18 genes, potentially involved in biocontrol, during confrontation between T. harzianum and F. solani. Results: Data obtained from two subtractive libraries were compared after annotation using the Blast2GO suite. A total of 417 and 78 readable EST sequence were annotated in the FSCW and glucose libraries, respectively. Functional annotation of these genes identified diverse biological processes and molecular functions required during T. harzianum growth on FSCW or glucose. We identified various genes of biotechnological value encoding to proteins which function such as transporters, hydrolytic activity, adherence, appressorium development and pathogenesis. Fifteen genes were up-regulated and sixteen were down-regulated at least at one-time point during growth of T. harzianum in FSCW. During the confrontation assay most of the genes were up-regulated, mainly after contact, when the interaction has been established. Conclusions: This study demonstrates that T. harzianum expressed different genes when grown on FSCW compared to glucose. It provides insights into the mechanisms of gene expression involved in mycoparasitism of T. harzianum against F. solani. The identification and evaluation of these genes may contribute to the development of an efficient biological control agent.

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.

Expression of human protein S100A7 (psoriasin), preparation of antibody and application to human larynx squamous cell carcinoma

Background Up-regulation of S100A7 (Psoriasin), a small calcium-binding protein, is associated with the development of several types of carcinomas, but its function and possibility to serve as a diagnostic or prognostic marker have not been fully defined. In order to prepare antibodies to the protein for immunohistochemical studies we produced the recombinant S100A7 protein in E. coli. mRNA extracted from human tracheal tumor tissue which was amplified by RT-PCR to provide the region coding for the S100A7 gene. The amplified fragment was cloned in the vector pCR2.1-TOPO and sub-cloned in the expression vector pAE. The protein rS100A7 (His-tag) was expressed in E. coli BL21::DE3, purified by affinity chromatography on an Ni-NTA column, recovered in the 2.0 to 3.5 mg/mL range in culture medium, and used to produce a rabbit polyclonal antibody anti-rS100A7 protein. The profile of this polyclonal antibody was evaluated in a tissue microarray. Results The rS100A7 (His-tag) protein was homogeneous by SDS-PAGE and mass spectrometry and was used to produce an anti-recombinant S100A7 (His-tag) rabbit serum (polyclonal antibody anti-rS100A7). The molecular weight of rS100A7 (His-tag) protein determined by linear MALDI-TOF-MS was 12,655.91 Da. The theoretical mass calculated for the nonapeptide attached to the amino terminus is 12,653.26 Da (delta 2.65 Da). Immunostaining with the polyclonal anti-rS100A7 protein generated showed reactivity with little or no background staining in head and neck squamous cell carcinoma cells, detecting S100A7 both in nucleus and cytoplasm. Lower levels of S100A7 were detected in non-neoplastic tissue. Conclusions The polyclonal anti-rS100A7 antibody generated here yielded a good signal-to-noise contrast and should be useful for immunohistochemical detection of S100A7 protein. Its potential use for other epithelial lesions besides human larynx squamous cell carcinoma and non-neoplastic larynx should be explored in future.