Biological, antigenic and phylogenetic characterization of the flavivirus Alfuy

Alfuy virus (ALFV) is classified as a subtype of the flavivirus Murray Valley encephalitis virus (MVEV); however, despite preliminary reports of antigenic and ecological similarities with MVEV, ALFV has not been associated with human disease. Here, it was shown that ALFV is at least 10(4)-fold less neuroinvasive than MVEV after peripheral inoculation of 3-week-old Swiss outbred mice, but ALFV demonstrates similar neurovirulence. In addition, it was shown that ALFV is partially attenuated in mice that are deficient in alpha/beta interferon responses, in contrast to MVEV which is uniformly lethal in these mice. To assess the antigenic relationship between these viruses, a panel of monoclonal antibodies was tested for the ability to bind to ALFV and MVEV in ELISA. Although the majority of monoclonal antibodies recognized both viruses, confirming their antigenic similarity, several discriminating antibodies were identified. Finally, the entire genome of the prototype strain of ALFV (MRM3929) was sequenced and phylogenetically analysed. Nucleotide (73%) and amino acid sequence (83 %) identity between ALFV and IMVEV confirmed previous reports of their close relationship. Several nucleotide and amino acid deletions and/or substitutions with putative functional significance were identified in ALFV, including the abolition of a conserved glycosylation site in the envelope protein and the deletion of the terminal dinucleotide 5'-CUOH-3' found in all other members of the genus. These findings confirm previous reports that ALFV is closely related to IMVEV, but also highlights significant antigenic, genetic and phenotypic divergence from MVEV. Accordingly, the data suggest that ALFV is a distinct species within the serogroup Japanese encephalitis virus.

Construction and analysis of a metagenomic library from an enhanced biological phosphorus removal biomass

The enhanced biological phosphorus removal (EBPR) process is regularly used for the treatment of wastewater, but suffers from erratic performance. Successful EBPR relies on the growth of bacteria called polyphosphate-accumulating organisms (PAOs), which store phosphorus intracellularly as polyphosphate, thus removing it from wastewater. Metabolic models have been proposed which describe the measured chemical transformations, however genetic evidence is lacking to confirm these hypotheses. The aim of this research was to generate a metagenomic library from biomass enriched in PAOs as determined by phenotypic data and fluorescence in situ hybridisation (FISH) using probes specific for the only described PAO to date, Candidatus Accumulibacter phosphatis. DNA extraction methods were optimised and two fosmid libraries were constructed which contained 93 million base pairs of metagenomic data. Initial screening of the library for 16S rRNA genes revealed fosmids originating from a range of non-pure-cultured wastewater bacteria. The metagenomic libraries constructed will provide the ability to link phylogenetic and metabolic data for bacteria involved in nutrient removal from wastewater. Keywords DNA extraction; EBPR; metagenomic library; 16S rRNA gene.

Modelling biological processes under anaerobic conditions through integrating titrimetric and off-gas measurements - applied to EBPR systems

An innovative method for modelling biological processes under anaerobic conditions is presented and discussed. The method is based on titrimetric and off-gas measurements. Titrimetric data is recorded as the addition rate of hydroxyl ions or protons that is required to maintain pH in a bioreactor at a constant level. An off-gas analysis arrangement measures, among other things, the transfer rate of carbon dioxide. The integration of these signals results in a continuous signal which is solely related to the biological reactions. When coupled with a mathematical model of the biological reactions, the signal allows a detailed characterisation of these reactions, which would otherwise be difficult to achieve. Two applications of the method to the enhanced biological phosphorus removal processes are presented and discussed to demonstrate the principle and effectiveness of the method.

Predicting population dynamics of weed biological control agents: science or gazing into crystal balls?

Various factors can influence the population dynamics of phytophages post introduction, of which climate is fundamental. Here we present an approach, using a mechanistic modelling package (CLIMEX), that at least enables one to make predictions of likely dynamics based on climate alone. As biological control programs will have minimal funding for basic work (particularly on population dynamics), we show how predictions can be made using a species geographical distribution, relative abundance across its range, seasonal phenology and laboratory rearing data. Many of these data sets are more likely to be available than long-term population data, and some can be incorporated into the exploratory phase of a biocontrol program. Although models are likely to be more robust the more information is available, useful models can be developed using information on species distribution alone. The fitted model estimates a species average response to climate, and can be used to predict likely geographical distribution if introduced, where the agent is likely to be more abundant (i.e. good locations) and more importantly for interpretation of release success, the likely variation in abundance over time due to intra- and inter-year climate variability. The latter will be useful in predicting both the seasonal and long-term impacts of the potential biocontrol agent on the target weed. We believe this tool may not only aid in the agent selection process, but also in the design of release strategies, and for interpretation of post-introduction dynamics and impacts. More importantly we are making testable predictions. If biological control is to become more of a science making and testing such hypothesis will be a key component.

Sensitivity of marine-reserve design to the spatial resolution of socioeconomic data

Socioeconomic considerations should have an important place in reserve design, Systematic reserve-selection tools allow simultaneous optimization for ecological objectives while minimizing costs but are seldom used to incorporate socioeconomic costs in the reserve-design process. The sensitivity of this process to biodiversity data resolution has been studied widely but the issue of socioeconomic data resolution has not previously been considered. We therefore designed marine reserves for biodiversity conservation with the constraint of minimizing commercial fishing revenue losses and investigated how economic data resolution affected the results. Incorporating coarse-resolution economic data from official statistics generated reserves that were only marginally less costly to the fishery than those designed with no attempt to minimize economic impacts. An intensive survey yielded fine-resolution data that, when incorporated in the design process, substantially reduced predicted fishery losses. Such an approach could help minimize fisher displacement because the least profitable grounds are selected for the reserve. Other work has shown that low-resolution biodiversity data can lead to underestimation of the conservation value of some sites, and a risk of overlooking the most valuable areas, and we have similarly shown that low-resolution economic data can cause underestimation of the profitability of some sites and a risk of inadvertently including these in the reserve. Detailed socioeconomic data are therefore an essential input for the design of cost-effective reserve networks.

The discriminative bilateral filter: An enhanced denoising filter for electron microscopy data

Advances in three-dimensional (313) electron microscopy (EM) and image processing are providing considerable improvements in the resolution of subcellular volumes, macromolecular assemblies and individual proteins. However, the recovery of high-frequency information from biological samples is hindered by specimen sensitivity to beam damage. Low dose electron cryo-microscopy conditions afford reduced beam damage but typically yield images with reduced contrast and low signal-to-noise ratios (SNRs). Here, we describe the properties of a new discriminative bilateral (DBL) filter that is based upon the bilateral filter implementation of Jiang et al. (Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chin, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struc. Biol. 128, 82-97.). In contrast to the latter, the DBL filter can distinguish between object edges and high-frequency noise pixels through the use of an additional photometric exclusion function. As a result, high frequency noise pixels are smoothed, yet object edge detail is preserved. In the present study, we show that the DBL filter effectively reduces noise in low SNR single particle data as well as cellular tomograms of stained plastic sections. The properties of the DBL filter are discussed in terms of its usefulness for single particle analysis and for pre-processing cellular tomograms ahead of image segmentation. (c) 2006 Elsevier Inc. All rights reserved.

Using continuous plankton recorder data

The continuous plankton recorder (CPR) survey is the largest multi-decadal plankton monitoring programme in the world. It was initiated in 1931 and by the end of 2004 had counted 207,619 samples and identified 437 phyto- and zoo-plankton taxa throughout the North Atlantic. CPR data are used extensively by the research community and in recent years have been used increasingly to underpin marine management. Here, we take a critical look at how best to use CPR data. We first describe the CPR itself, CPR sampling, and plankton counting procedures. We discuss the spatial and temporal biases in the Survey, summarise environmental data that have not previously been available, and describe the new data access policy. We supply information essential to using CPR data, including descriptions of each CPR taxonomic entity., the idiosyncrasies associated with counting many of the taxa, the logic behind taxonomic changes in the Survey, the semi-quantitative nature of CPR sampling, and recommendations on choosing the spatial and temporal scale of study. This forms the basis for a broader discussion on how to use CPR data for deriving ecologically meaningful indices based on size, functional groups and biomass that can be used to support research and management. This contribution should be useful for plankton ecologists, modellers and policy makers that actively use CPR data. (c) 2005 Elsevier Ltd. All rights reserved.

Determinants of biological activity of nanomaterials in innate immunity cells: the role of aspect ratio, environmental contaminants and protein corona

As defined by the European Union, “ ’Nanomaterial’ (NM) means a natural, incidental or manufactured material containing particles, in an unbound state or as an aggregate or agglomerate, where, for 50 % or more of the particles in the number size distribution, one or more external dimensions is in the size range 1 nm-100 nm ” (2011/696/UE). Given their peculiar physico-chemical features, nanostructured materials are largely used in many industrial fields (e.g. cosmetics, electronics, agriculture, biomedical) and their applications have astonishingly increased in the last fifteen years. Nanostructured materials are endowed with very large specific surface area that, besides making them very useful in many industrial processes, renders them very reactive towards the biological systems and, hence, potentially endowed with significant hazard for human health. For these reasons, in recent years, many studies have been focused on the identification of toxic properties of nanostructured materials, investigating, in particular, the mechanisms behind their toxic effects as well as their determinants of toxicity. This thesis investigates two types of nanostructured TiO2 materials, TiO2 nanoparticles (NP), which are yearly produced in tonnage quantities, and TiO2 nanofibres (NF), a relatively novel nanomaterial. Moreover, several preparations of MultiWalled Carbon Nanotubes (MWCNT), another nanomaterial widely present in many products, are also investigated.- Although many in vitro and in vivo studies have characterized the toxic properties of these materials, the identification of their determinants of toxicity is still incomplete. The aim of this thesis is to identify the structural determinants of toxicity, using several in vitro models. Specific fields of investigation have been a) the role of shape and the aspect ratio in the determination of biological effects of TiO2 nanofibres of different length; b) the synergistic effect of LPS and TiO2 NP on the expression of inflammatory markers and the role played therein by TLR-4; c) the role of functionalization and agglomeration in the biological effects of MWCNT. As far as biological effects elicited by TiO2 NF are concerned, the first part of the thesis demonstrates that long TiO2 nanofibres caused frustrated phagocytosis, cytotoxicity, hemolysis, oxidative stress and epithelial barrier perturbation. All these effects were mitigated by fibre shortening through ball-milling. However, short TiO2 NF exhibited enhanced ability to activate acute pro-inflammatory effects in macrophages, an effect dependent on phagocytosis. Therefore, aspect ratio reduction mitigated toxic effects, while enhanced macrophage activation, likely rendering the NF more prone to phagocytosis. These results suggest that, under in vivo conditions, short NF will be associated with acute inflammatory reaction, but will undergo a relatively rapid clearance, while long NF, although associated with a relatively smaller acute activation of innate immunity cells, are not expected to be removed efficiently and, therefore, may be associated to chronic inflammatory responses. As far as the relationship between the effects of TiO2 NP and LPS, investigated in the second part of the thesis, are concerned, TiO2 NP markedly enhanced macrophage activation by LPS through a TLR-4-dependent intracellular pathway. The adsorption of LPS onto the surface of TiO2 NP led to the formation of a specific bio-corona, suggesting that, when bound to TiO2 NP, LPS exerts a much more powerful pro-inflammatory effect. These data suggest that the inflammatory changes observed upon exposure to TiO2 NP may be due, at least in part, to their capability to bind LPS and, possibly, other TLR agonists, thus enhancing their biological activities. Finally, the last part of the thesis demonstrates that surface functionalization of MWCNT with amino or carboxylic groups mitigates the toxic effects of MWCNT in terms of macrophage activation and capability to perturb epithelial barriers. Interestingly, surface chemistry (in particular surface charge) influenced the protein adsorption onto the MWCNT surface, allowing to the formation of different protein coronae and the tendency to form agglomerates of different size. In particular functionalization a) changed the amount and the type of proteins adsorbed to MWCNT and b) enhanced the tendency of MWCNT to form large agglomerates. These data suggest that the different biological behavior of functionalized and pristine MWCNT may be due, at least in part, to the different tendency to form large agglomerates, which is significantly influenced by their different capability to interact with proteins contained in biological fluids. All together, these data demonstrate that the interaction between physico-chemical properties of nanostructured materials and the environment (cells + biological fluids) in which these materials are present is of pivotal importance for the understanding of the biological effects of NM. In particular, bio-persistence and the capability to elicit an effective inflammatory response are attributable to the interaction between NM and macrophages. However, the interaction NM-cells is heavily influenced by the formation at the nano-bio interface of specific bio-coronae that confer a novel biological identity to the nanostructured materials, setting the basis for their specific biological activities.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Synthesis and biological properties of 2-phenylbenzothiazole derivatives

2-Phenylbenzothiazoles have structural similarities to the antioestrogenic 2-phenylindole, zindoxifene and to the oestrogenic isoflavone, genistein which also inhibits tyrosine kinases. Hydroxylated 2-phenylbenzothiazole derivatives were therefore produced and tested for oestrogenic and tyrosine kinase inhibitory activity. Synthesis of methoxy substituted 2-phenylbenzothiazoles was via the Jacobson method, demethylation being effected by boron tribromide at -70oC. Three amino substituted 2-phenylbenzothiazoles were also synthesised and tested for activity. Data is presented for oestrogen receptor binding activity, aromatase inhibitory activity, epidermal growth factor receptor tyrosine kinase (EGFRTK) inhibitory activity and cytotoxicity to ANN-1, 3T3, MCF-7 and WIDR cells. Oestrogen receptor binding affinity (RBA) was shown by five of the nine compounds tested. 2-(4-hydroxy)-6-hydroxybenzo-thiazole was the most active of the benzothiazoles tested (RBA 0.7). This is low but comparable to that of genistein. EGFRTK inhibitory activity was shown by four of the six benzothiazole derivatives tested; activity was comparable to that of genistein. Cytotoxicity assays have shown no selective toxicity of 2-phenylbenzothiazoles to any of the cell lines tested. Toxicity to MCF-7 cells was similar to that for other cell lines despite some compounds showing oestrogen receptor binding capacity. Amino-substituted 2-phenylbenzothiazoles showed selective toxicity towards transformed ANN-1 cells compared to normal 3T3 cells but the mechanism of this selectivity has not been established. Molecular modelling techniques, including CHEM-X, QUANTA and MOPAC were used to compare known ATP-competitive tyrosine kinase inhibitors with a model of ATP built from the crystal structure of the ATP-phosphoglycerate kinase complex. Structural features thought to be important to kinase inhibition were found and used to suggest further 2-phenylbenzothiazole analogues which may have improved activity.