Mechanisms of membrane toxicity of hydrocarbons.

Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

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Centering, scaling, and transformations: improving the biological information content of metabolomics data

Recent advances in molecular biology have extended our understanding of the metabolic processes related to microbial transformation of petroleum hydrocarbons. The physiological responses of microorganisms to the presence of hydrocarbons, including cell surface alterations and adaptive mechanisms for uptake and efflux of these substrates, have been characterized. New molecular techniques have enhanced our ability to investigate the dynamics of microbial communities in petroleum-impacted ecosystems. By establishing conditions which maximize rates and extents of microbial growth, hydrocarbon access, and transformation, highly accelerated and bioreactor-based petroleum waste degradation processes have been implemented. Biofilters capable of removing and biodegrading volatile petroleum contaminants in air streams with short substrate-microbe contact times ( 2 S and sulfoxides from petrochemical waste streams. Microbes also have potential for use in removal of nitrogen from crude oil leading to reduced nitric oxide emissions provided that technical problems similar to those experienced in biodesulfurization can be solved. Enzymes are being exploited to produce added-value products from petroleum substrates, and bacterial biosensors are being used to analyze petroleum-contaminated environments.

Recent Advances in Petroleum Microbiology

An overview of hydrogen production from biomass

Epidemiology of infection by nontuberculous mycobacteria.

Summary: A NADH- or NADPH-dependent alkene monooxygenase (AMO) activity has been detected in cell-free extracts of the ethene-utilizing Mycobacterium E3 and Mycobacterium aurum L1. The activity was not linear with protein concentration in the assay suggesting AMO is a multicomponent enzyme. The inhibition pattern of AMO activity was very similar to the inhibition patterns published for the three-component soluble methane monooxygenases. Fractionation of crude extracts revealed that combination of two fractions was required to restore alkene monooxygenase activity. The first fraction was inhibited by acetylene, indicating it contained an oxygenase component. The second fraction contained reductase activity which was absent from non-induced cells. This reductase activity is probably the NADH-acceptor reductase of AMO.

Alkene monooxygenase from Mycobacterium: a multicomponent enzyme.

The acceptor specificity of Streptococcus mutans GS-5 glucosyltransferase-D (GTF-D) was studied, particular the specificity toward non-saccharide compounds. Dihydroxy aromatic compounds like catechol, 4-methylcatechol, and 3-methoxycatechol were glycosylated by GTF-D with a high efficiency. Transglycosylation yields were 65°50°and 75°respectively, using 40 mM acceptor and 200 mM sucrose as glucosyl donor. 3-Methoxylcatchol was also glycosylated, though at a significantly lower rate. A number of other aromatic compounds such as phenol, 2-hydroxybenzaldehyde, 1,3-dihydroxybenzene, and 1,2-phenylethanediol were not glycosylated by GTF-D. Consequently GTF-D aromatic acceptors appear to require two adjacent aromatic hydroxyl groups. In order to facilitate the transglycosylation of less water-soluble acceptors the use of various water miscible organic solvents (cosolvents) was studied. The flavonoid catechin was used as a model acceptor. Bis-2-methoxyethyl ether (MEE) was selected as a useful cosolvent. In the presence of 15ov/v) MEE the specific catechin transglucosylation activity was increased 4-fold due to a 12-fold increase in catechin solubility. MEE (10-30␟/v) could also be used to allow the transglycosylation of catechol, 4-methylcatechol, and 3-methoxycatechol at concentrations (200 mM) otherwise inhibiting GTF-D transglycosylation activity.

Transglycosylation by Streptococcus mutans GS-5 glucosyltransferase-D: acceptor specificity and engineering of reaction conditions.

The potential of lyases for the industrial production of optically active compounds

Pseudomonas pseudoalcaligenes can only form d-malate from maleate after incubation of the cells with a solvent or a detergent. The effect of the detergent Triton X-100 on d-malate production was studied in more detail. The longer the cells were incubated with Triton X-100, the higher was the d-malate production activity, until the maximal malease activity was reached. Incubation of P. pseudoalcaligenes cells with Triton X-100 also resulted in an increase in the protein concentration of the supernatant, indicating that cell lysis had occurred. The rate at which the d-malate production activity increased was dependent on the Triton X-100 concentration and on the cell density. Also the rate at which lysis occurred depended on the Triton X-100 concentration.

Permeabilization and lysis of Pseudomonas pseudoalcaligenes cells by Triton X-100 for efficient production of D-malate.

Propene-grown Xanthobacter sp. strain Py2 cells can degrade trichloroethylene (TCE), but the transformation capacity of such cells was limited and depended on both the TCE concentration and the biomass concentration. Toxic metabolites presumably accumulated extracellularly, because the fermentation of glucose by yeast cells was inhibited by TCE degradation products formed by strain Py2. The affinity of the propene monooxygenase for TCE was low, and this allowed strain Py2 to grow on propene in the presence of TCE. During batch growth with propene and TCE, the TCE was not degraded before most of the propene had been consumed. Continuous degradation of TCE in a chemostat culture of strain Py2 growing with propene was observed with TCE concentrations up to 206 microns in the growth medium without washout of the fermentor occurring. At this TCE concentration the specific degradation rate was 1.5 nmol/min/mg of biomass. The total amount of TCE that could be degraded during simultaneous growth on propene depended on the TCE concentration and ranged from 0.03 to 0.34g of TCE per g of biomass. The biomass yield on propene was not affected by the cometabolic degradation of TCE.

Sybe Hartmans

Papers

Alkene monooxygenase from Mycobacterium: a multicomponent enzyme.

Transglycosylation by Streptococcus mutans GS-5 glucosyltransferase-D: acceptor specificity and engineering of reaction conditions.

The potential of lyases for the industrial production of optically active compounds

Permeabilization and lysis of Pseudomonas pseudoalcaligenes cells by Triton X-100 for efficient production of D-malate.

Continuous degradation of trichloroethylene by Xanthobacter sp. strain Py2 during growth on propene