First Steps Towards an Understanding of a Mode ofCarcinogenic Action for Vanadium Pentoxide

Inhalation of vanadium pentoxide clearly increases the incidence of alveolar/bronchiolar neoplasms in male and female B6C3F1 mice at all concentrations tested (1, 2 or 4 mg/m3), whereas responses in F344/N rats was, at most, ambiguous. While vanadium pentoxide is mutagenic in vitro and possibly in vivo in mice, this does not explain the species or site specificity of the neoplastic response. A nose-only inhalation study was conducted in female B6C3F1 mice (0, 0.25, 1 and 4 mg/m3, 6 h/day for 16 days) to explore histopathological, biochemical (α-tocopherol, glutathione and F2-isoprostane) and genetic (comet assays and 9 specific DNA-oxo-adducts) changes in the lungs. No treatment related histopathology was observed at 0.25 mg/m3. At 1 and 4 mg/m3, exposure-dependent increases were observed in lung weight, alveolar histiocytosis, sub-acute alveolitis and/or granulocytic infiltration and a generally time-dependent increased cell proliferation rate of histiocytes. Glutathione was slightly increased, whereas there were no consistent changes in α-tocopherol or 8-isoprostane F2α. There was no evidence for DNA strand breakage in lung or BAL cells, but there was an increase in 8-oxodGuo DNA lesions that could have been due to vanadium pentoxide induction of the lesions or inhibition of repair of spontaneous lesions. Thus, earlier reports of histopathological changes in the lungs after inhalation of vanadium pentoxide were confirmed, but no evidence has yet emerged for a genotoxic mode of action. Evidence is weak for oxidative stress playing any role in lung carcinogenesis at the lowest effective concentrations of vanadium pentoxide

Consistency analysis of metabolic correlation networks

<p>Abstract</p> <p>Background</p> <p>Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network).</p> <p>Results</p> <p>Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network.</p> <p>Conclusion</p> <p>The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles.</p

Bridge deck flutter derivatives: efficient numerical evaluation exploiting their interdependence

Increasing the efficiency in the process to numerically compute the flutter derivatives of bridge deck sections is desirable to advance the application of CFD based aerodynamic design in industrial projects. In this article, a 2D unsteady Reynolds-averaged Navier-Stokes (URANS) approach adopting Menter׳s SST k-ω turbulence model is employed for computing the flutter derivatives and the static aerodynamic characteristics of two well known examples: a rectangular cylinder showing a completely reattached flow and the generic G1 section representative of streamlined deck sections. The analytical relationships between flutter derivatives reported in the literature are applied with the purpose of halving the number of required numerical simulations for computing the flutter derivatives. The solver of choice has been the open source code OpenFOAM. It has been found that the proposed methodology offers results which agree well with the experimental data and the accuracy of the estimated flutter derivatives is similar to the results reported in the literature where the complete set of numerical simulations has been performed for both heave and pitch degrees of freedom

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes

A Non-Targeted Approach Unravels the Volatile Network in Peach Fruit

Volatile compounds represent an important part of the plant metabolome and are of particular agronomic and biological interest due to their contribution to fruit aroma and flavor and therefore to fruit quality. By using a non-targeted approach based on HS-SPME-GC-MS, the volatile-compound complement of peach fruit was described. A total of 110 volatile compounds (including alcohols, ketones, aldehydes, esters, lactones, carboxylic acids, phenolics and terpenoids) were identified and quantified in peach fruit samples from different genetic backgrounds, locations, maturity stages and physiological responses. By using a combination of hierarchical cluster analysis and metabolomic correlation network analysis we found that previously known peach fruit volatiles are clustered according to their chemical nature or known biosynthetic pathways. Moreover, novel volatiles that had not yet been described in peach were identified and assigned to co-regulated groups. In addition, our analyses showed that most of the co-regulated groups showed good intergroup correlations that are therefore consistent with the existence of a higher level of regulation orchestrating volatile production under different conditions and/or developmental stages. In addition, this volatile network of interactions provides the ground information for future biochemical studies as well as a useful route map for breeding or biotechnological purposes

\u3ci\u3eSenecio Conrathii\u3c/i\u3e N.E.Br. (Asteraceae), a New Hyperaccumulator of Nickel from Serpentinite Outcrops of the Barberton Greenstone Belt, South Africa

Five nickel hyperaccumulators belonging to the Asteraceae are known from ultramafic outcrops in South Africa. Phytoremediation applications of the known hyperaccumulators in the Asteraceae, such as the indigenous Berkheya coddii Roessler, are well reported and necessitate further exploration to find additional species with such traits. This study targeted the most frequently occurring species of the Asteraceae on eight randomly selected serpentinite outcrops of the Barberton Greenstone Belt. Twenty species were sampled, including 12 that were tested for nickel accumulation for the first time. Although the majority of the species were excluders, the known hyperaccumulators Berkheya nivea N.E.Br. and B. zeyheri (Sond. & Harv.) Oliv. & Hiern subsp. rehmannii (Thell.) Roessler var. rogersiana (Thell.) Roessler hyperaccumulated nickel in the leaves at expected levels. A new hyperaccumulator of nickel was discovered, Senecio conrathii N.E.Br., which accumulated the element in its leaves at 1695 ± 637 µg g−1 on soil with a total and exchangeable nickel content of 503 mg kg−1 and 0.095 µg g−1, respectively. This makes it the third known species in the Senecioneae of South Africa to hyperaccumulate nickel after Senecio anomalochrous Hilliard and Senecio coronatus (Thunb.) Harv., albeit it being a weak accumulator compared with the latter. Seven tribes in the Asteraceae have now been screened for hyperaccumulation in South Africa, with hyperaccumulators only recorded for the Arctoteae and Senecioneae. This suggests that further exploration for hyperaccumulators should focus on these tribes as they comprise all six species (of 68 Asteraceae taxa screened thus far) to hyperaccumulate nickel

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship