YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

Quantification of Heavy Metals in Mining Affected Soil and Their Bioaccumulation in Native Plant Species

Several anthropogenic and natural sources are considered as the primary sources of toxic metals in the environment. The current study investigates the level of heavy metals contamination in the flora associated with serpentine soil along the Mafic and Ultramafic rocks northern-Pakistan. Soil and wild native plant species were collected from chromites mining affected areas and analyzed for heavy metals (Cr, Ni, Fe, Mn, Co, Cu and Zn) using atomic absorption spectrometer (AAS-PEA-700). The heavy metal concentrations were significantly (p < 0.01) higher in mine affected soil as compared to reference soil, however Cr and Ni exceeded maximum allowable limit (250 and 60 mg kg^?1, respectively) set by SEPA for soil. Inter-metal correlations between soil, roots and shoots showed that the sources of contamination of heavy metals were mainly associated with chromites mining. All the plant species accumulated significantly higher concentrations of heavy metals as compared to reference plant. The open dumping of mine wastes can create serious problems (food crops and drinking water contamination with heavy metals) for local community of the study area. The native wild plant species (Nepeta cataria, Impatiens bicolor royle, Tegetis minuta) growing on mining affected sites may be used for soil reclamation contaminated with heavy metals.     

Assessment of Heavy Metal Pollution in Water and Sediments from the Ghalechay River,Baychebagh Copper Mine Area,Iran

The concentrations of selected heavy metals in sediments and waters in Baychebagh copper mine were determined using ICP-OES. Except for Co, the average concentrations of Cd, Cu, Pb, and Zn in sediments from the Ghalechay River in the district exceed the world-average shale and continental upper crust value. Enrichment factors for Pb, Cu, and Cd were significantly enriched in sediments, indicating environmental contamination. Geoaccumulation index calculated for different sampling stations indicates that the sediments are unpolluted with respect to Co and Zn while unpolluted to moderately polluted with Cu and highly polluted with Pb and Cd. The Sediment Quality Guidelines (SQGs) suggest that Cd and Pb may pose the highest risk for the environment. Sequential extraction analyses of sediments revealed that Cu, Co, Pb, and Zn bound to extractable, carbonate, reducible and oxidizable fractions are lower than residual fraction. About 10% of the total Pb was associated with the exchangeable fraction, indicating remobilization, while Cd (89%), Pb (73%) Co (58%), Cu (76%), and Zn (68%) closely associated with the residual and oxidizable fractions, resulting in their environmental immobility. The residual forms are not expected to be released under normal conditions in the river and could be considered an inert phase.     

Integration,visualization and analysis of human interactome

Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of ‘omics’ fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.     

New surface contacts formed upon reductive lysine methylation: Improving the probability of protein crystallization

Surface lysine methylation (SLM) is a technique for improving the rate of success of protein crystallization by chemically methylating lysine residues. The exact mechanism by which SLM enhances crystallization is still not clear. To study these mechanisms, and to analyze the conditions where SLM will provide the optimal benefits for rescuing failed crystallization experiments, we compared 40 protein structures containing N,N-dimethyl-lysine (dmLys) to a nonredundant set of 18,972 nonmethylated structures from the PDB. By measuring the relative frequency of intermolecular contacts (where contacts are defined as interactions between the residues in proximity with a distance of 3.5 Å or less) of basic residues in the methylated versus nonmethylated sets, dmLys-Glu contacts are seen more frequently than Lys-Glu contacts. Based on observation of the 10 proteins with both native and methylated structures, we propose that the increased rate of contact for dmLys-Glu is due to both a slight increase in the number of amine-carboxyl H-bonds and to the formation of methyl C–H···O interactions. By comparing the relative contact frequencies of dmLys with other residues, the mechanism by which methylation of lysines improves the formation of crystal contacts appears to be similar to that of Lys to Arg mutation. Moreover, analysis of methylated structures with the surface entropy reduction (SER) prediction server suggests that in many cases SLM of predicted SER sites may contribute to improved crystallization. Thus, tools that analyze protein sequences and mark residues for SER mutation may identify proteins with good candidate sites for SLM.     

tsORFdb: Theoretical Small Open Reading Frames (ORFs) database and massProphet: Peptide Mass Fingerprinting (PMF) tool for unknown small functional ORFs

Peptide mass fingerprinting (PMF) has become one of the most widely used methods for rapid identification of proteins in proteomics research. Many peaks, however, remain unassigned after PMF analysis, partly because of post-translational modification and the limited scope of protein sequences. Almost all PMF tools employ only known or predicted protein sequences and do not include open reading frames (ORFs) in the genome, which eliminates the chance of finding novel functional peptides. Unlike most tools that search protein sequences from known coding sequences, the tool we developed uses a database for theoretical small ORFs (tsORFs) and a PMF application using a tsORFs database (tsORFdb). The tsORFdb is a database for ORFeome that encompasses all potential tsORFs derived from whole genome sequences as well as the predicted ones. The massProphet system tries to extend the search scope to include the ORFeome using the tsORFdb. The tsORFdb and massProphet should be useful for proteomics research to give information about unknown small ORFs as well as predicted and registered proteins.     

陡坡无土排岩场植被生态修复技术

针对无土陡坡排岩场特殊的立地条件,以城市污泥、粉煤灰等固体废弃物配制的人工土壤进行基质改良的基础上,以坑植式种植刺槐(Robina pseudoacacia)及棉槐(Amor-pha fruticosa),同时在种植坑中撒播牧草以培肥土壤,促进乔木生长固坡。结果表明,在人工土壤Ⅱ(粉煤灰∶城市污泥=1∶1)上种植的刺槐有较好的成活率(70%)和生长势,说明该修复技术在实现以废治废,变废为宝的同时,既解决了矿山废弃地修复中珍贵的土源问题,又改善了矿区生态环境。     

Using reaction mechanism to measure enzyme similarity

The concept of reaction similarity has been well studied in terms of the overall transformation associated with a reaction, but not in terms of mechanism. We present the first method to give a quantitative measure of the similarity of reactions based upon their explicit mechanisms. Two approaches are presented to measure the similarity between individual steps of mechanisms: a fingerprint-based approach that incorporates relevant information on each mechanistic step; and an approach based only on bond formation, cleavage and changes in order. The overall similarity for two reaction mechanisms is then calculated using the Needleman-Wunsch alignment algorithm. An analysis of MACiE, a database of enzyme mechanisms, using our measure of similarity identifies some examples of convergent evolution of chemical mechanisms. In many cases, mechanism similarity is not reflected by similarity according to the EC system of enzyme classification. In particular, little mechanistic information is conveyed by the class level of the EC system.     

Data-Mining Discovery of Pattern and Process in Ecological Systems

ABSTRACT Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data-mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data-mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.     

Identification of 10 882 porcine microsatellite sequences and virtual mapping of 4528 of these sequences

A total of 10 882 porcine microsatellite repeats were identified in genomic shotgun sequences from the Sino-Danish Pig Genome Sequencing Consortium (http://www.piggenome.dk). Of these, 4528 microsatellites were placed on a pig-human comparative map by blast analysis of porcine sequences against the human genome (blast cut-off threshold =1 x 10(-5)). All microsatellite sequences placed on the comparative map are accessible at http://www.animalgenome.org/QTLdb/pig.html. These sequences increase the number of identified microsatellites in the porcine genome by several orders of magnitude. They are a new resource of microsatellite sequences for generating markers to be used in linkage studies and in fine mapping and positional cloning of quantitative trait loci.