CLEAN WATER: water detoxification using innovative photocatalysts

Environmental pollution abatement and especially the growing demand for clean water pose one of the most severe challenges worldwide. Besides the scarcity of water resources, the presence of hazardous chemicals with serious adverse health effects, even at extremely low concentrations, impose serious considerations for the quality of drinking water. The rapid evolution of nanoscale science and technology has dramatically expanded the materials’ application potential towards radically new or multifunctional properties rendering nanotechnology an indispensable component in shaping modern environmental science. The nanoscale-perspective maintaining the integrity of the environment is currently the stimulus for the development of innovative and cost-effective functional materials and sustainable processes for water treatment and purification. The CLEAN WATER (EU FP7 collaborative project) aims at the development of an innovative and efficient water detoxification technology exploiting solar energy and nano-engineered titania photocatalysts in combination with nanofiltration membranes. In this approach, nanostructured titania with high UV–visible response will be synthesized and stabilized on nanotubular membranes of controlled pore size and retention efficiency as well as on carbon nanotubes exploiting their high surface area to achieve photocatalytically active nanocomposite membranes. Comparative evaluation of the UV–visible and solar light efficiency of the modified titania photocatalysts for water detoxification will be intensively investigated on various target pollutants ranging from classical water contaminants such us phenols, pesticides and azo-dyes to the extremely hazardous cyanobacterial toxins and emerging endocrine disrupting compounds in order to evaluate/optimize the materials performance and validate their competence on water treatment. Particular efforts will be devoted to the analysis and quantification of degradation products as well as their toxicity. All these will be the crucial components for the fabrication of innovative continuous flow photocatalytic-disinfection-membrane reactors for the implementation of sustainable and cost effective water treatment technologies based on nano-engineered materials.     

Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

Background

Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal.

Principal Findings

To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results.

Conclusions

Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).     

Rheb: Enrichment beyond the brain

Comment on: Goorden SM, et al. Mol Cell Biol 2011; 31:1672-8.     

PAGE: Parametric Analysis of Gene Set Enrichment

Background  

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.     

PHYSEAN: PHYsical SEquence ANalysis for the identification of protein domains on the basis of physical and chemical properties of amino acids

MOTIVATION: PHYSEAN predicts protein classes with highly variable sequences on the basis of their physical, chemical and biological characteristics such as diverse hydrophobicity, structural propensity and steric properties. These characteristics, calculated from multiple positions in a sequence, may be conserved even between sequences that fail to produce alignments at any acceptable level of statistical significance. PHYSEAN complements methods that require sequence alignments (BLAST, FASTA, dynamic programming) by adding less residue- and position-specific physicochemical information on the protein or the domain. RESULTS: We predict proteins or their domains like signal peptides using physical, chemical, geometric, and biological properties of the 20 amino acids. This comprehensive set of properties may cover the diagnostic functional and structural aspects of a domain or a protein class. We automatically select and weight a subset of properties so as to discriminate between, e.g., signal peptides and amino-termini of cytosolic proteins with the lowest number of incorrect predictions. This optimal selection of properties and their weights significantly decreases the number of incorrect predictions as compared to any single property or any combination of unweighted properties. Weights have been optimized by high-performance linear programming models that systematically find the optimal solution from among an astronomic number of property/weight combinations. PHYSEAN's performance is demonstrated by highly accurate predictions of signal peptides (the vehicles for protein transport across membranes) and their cleavage sites. The results indicate reliable predictions are possible even in the lack of sequence conservation using an automated physical and chemical analysis of proteins.     

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

A basic necessity for researchers studying adaptive immunity with in vivo experimental models is an ability to identify T cells based on their T cell antigen receptor (TCR) specificity. Many indirect methods are available in which a bulk population of T cells is stimulated in vitro with a specific antigen and epitope-specific T cells are identified through the measurement of a functional response such as proliferation, cytokine production, or expression of activation markers¹. However, these methods only identify epitope-specific T cells exhibiting one of many possible functions, and they are not sensitive enough to detect epitope-specific T cells at naive precursor frequencies. A popular alternative is the TCR transgenic adoptive transfer model, in which monoclonal T cells from a TCR transgenic mouse are seeded into histocompatible hosts to create a large precursor population of epitope-specific T cells that can be easily tracked with the use of a congenic marker antibody^2,3. While powerful, this method suffers from experimental artifacts associated with the unphysiological frequency of T cells with specificity for a single epitope^4,5. Moreover, this system cannot be used to investigate the functional heterogeneity of epitope-specific T cell clones within a polyclonal population.The ideal way to study adaptive immunity should involve the direct detection of epitope-specific T cells from the endogenous T cell repertoire using a method that distinguishes TCR specificity solely by its binding to cognate peptide:MHC (pMHC) complexes. The use of pMHC tetramers and flow cytometry accomplishes this⁶, but is limited to the detection of high frequency populations of epitope-specific T cells only found following antigen-induced clonal expansion. In this protocol, we describe a method that coordinates the use of pMHC tetramers and magnetic cell enrichment technology to enable detection of extremely low frequency epitope-specific T cells from mouse lymphoid tissues^3,7. With this technique, one can comprehensively track entire epitope-specific populations of endogenous T cells in mice at all stages of the immune response.     

Enrichment and ecosystem stability: effect of toxic food

Enrichment in resource availability theoretically destabilizes predator-prey dynamics (the paradox of enrichment). However, a minor change in the resource stoichiometry may make a prey toxic for the predator, and the presence of toxic prey affects the dynamics significantly. Here, theoretically we explore how, at increased carrying capacity, a toxic prey affects the oscillation or destabilization of predator-prey dynamics, and how its presence influences the growth of the predator as well as that of a palatable prey. Mathematical analysis determines the bounds on the food toxicity that allow the coexistence of a predator along with a palatable and a toxic prey. The overall results demonstrate that toxic food counteracts oscillation (destabilization) arising from enrichment of resource availability. Moreover, our results show that, at increased resource availability, toxic food that acts as a source of extra mortality may increase the abundance of the predator as well as that of the palatable prey.     

Enrichment of bitumen-degrading microorganisms

Enrichment cultures in a percolation apparatus with two different soil types supplemented with bitumen, were used to follow growth on this complex substrate. Microscopic techniques allowed visualisation of bacteria on solid surfaces of bitumen and soil particles; quantification was only possible in the percolation fluid. In the latter, changes in pH and amount of organic material provided evidence for metabolic activities and degradation of the substrate bitumen.     

Highly Efficient Phosphopeptide Enrichment by Calcium Phosphate Precipitation Combined with Subsequent IMAC Enrichment

A new method for enrichment of phosphopeptides in complex mixtures derived by proteolytic digestion of biological samples has been developed. The method is based on calcium phosphate precipitation of the phosphopeptides prior to further enrichment with established affinity enrichment methods. Calcium phosphate precipitation combined with phosphopeptide enrichment using Fe(III) IMAC provided highly selective enrichment of phosphopeptides. Application of the method to a complex peptide sample derived from rice embryo resulted in more than 90% phosphopeptides in the enriched sample as determined by mass spectrometry. Introduction of a two-step IMAC enrichment procedure after calcium phosphate precipitation resulted in observation of an increased number of phosphopeptides.     

GSEA-P: a desktop application for Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) is a computational method that assesses whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. We report the availability of a new version of the Java based software (GSEA-P 2.0) that represents a major improvement on the previous release through the addition of a leading edge analysis component, seamless integration with the Molecular Signature Database (MSigDB) and an embedded browser that allows users to search for gene sets and map them to a variety of microarray platform formats. This functionality makes it possible for users to directly import gene sets from MSigDB for analysis with GSEA. We have also improved the visualizations in GSEA-P 2.0 and added links to a new form of concise gene set annotations called Gene Set Cards. These additions, as well as other improvements suggested by over 3500 users who have downloaded the software over the past year have been incorporated into this new release of the GSEA-P Java desktop program. AVAILABILITY: GSEA-P 2.0 is freely available for academic and commercial users and can be downloaded from http://www.broad.mit.edu/GSEA