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.

Pseudomonas putida S12 could adapt to grow on styrene in a two-phase styrene-water system. Acetate was toxic for P. putida S12, but cells were similarly able to adapt to higher acetate concentrations. Only by using these acetate-adapted cells was growth observed in the presence of supersaturating concentrations of toxic nonmetabolizable solvents such as toluene.

Adaptation of Pseudomonas putida S12 to high concentrations of styrene and other organic solvents.

A gram-negative, peritrichously flagellated rod, tentatively identified as an Alcaligenes sp., was isolated from a mixture of soil and water samples by using 1,3-dichlorobenzene as the sole carbon and energy source. During growth on 1,3-dichlorobenzene, almost stoichiometric amounts of chloride were released. Simultaneous adaptation studies, as well as enzyme studies, indicated that 1,3-dichlorobenzene was metabolized via 3,5-dichloro-cis-1,2-dihydroxycyclohexa-3,5-diene to 3,5-dichlorocatechol. Subsequently, the latter product was cleaved, yielding 2,4-dichloromuconate. No initial hydrolytic step yielding 3-chlorophenol was detected in this species.

Microbial degradation of 1,3-dichlorobenzene.

Removal of organic compounds like toluene from waste gases with a trickle-bed reactor can result in clogging of the reactor due to the formation of an excessive amount of biomass. We therefore limited the amount of nutrients available for growth, to prevent clogging of the reactor. As a consequence of this nutrient limitation a lower removal rate was observed. However, when a fungal culture was used to inoculate the reactor, the toluene removal rate under nutrient limiting conditions was higher. Over a period of 375 days, an average removal rate of 27 g C/(m(3) h) was obtained with the reactor inoculated with the fungal culture. From the carbon balance over the reactor and the nitrogen availability it was concluded that, under these nutrient-limited conditions, large amounts of carbohydrates are probably formed. We also studied the application of a NaOH wash to remove excess biomass, as a method to prevent clogging. Under these conditions an average toluene removal rate of 35 g C/(m(3) h) was obtained. After about 50 days there was no net increase in the biomass content of the reactor. The amount of biomass which was formed in the reactor equaled the amount removed by the NaOH wash.

Prevention of clogging in a biological trickle-bed reactor removing toluene from contaminated air.

A 66 dm3 trickle-bed bioreactor was constructed to assess the possibilities of eliminating dichloromethane from industrial waste gases. The trickle-bed bioreactor was filled with a randomly-stacked polypropylene packing material over which a liquid phase was circulated. The pH of the circulating liquid was externally controlled at a value of 7 and the temperature was maintained at 25 °C. The packing material was very quickly covered by a dichloromethane-degrading biofilm which thrived on the dichloromethane supplied via the gas phase. The biological system was very stable and not sensitive to fluctuations in the dichloromethane supply. Removal of dichloromethane from synthetic waste gas was possible down to concentrations well below the maximal allowable concentration of 150mg/m3 required by West-German law for gaseous emissions. At higher dichloromethane concentrations specific dichloromethane degradation rates of 200 g h−1 m−3 were possible. At very low inlet concentrations, dichloromethane elimination was completely mass transfer limited.

Dichloromethane removal from waste gases with a trickle-bed bioreactor.

Mycobacterium L1 can grow on vinyl chloride as sole carbon and energy source. Application of this bacterium to remove vinyl chloride from waste gases is proposed. From air containing 1% vinyl chloride 93% of the vinyl chloride was removed by passing the air through a fermentor containing a growing population ofMycobacterium L1.

Sybe Hartmans

Papers

Adaptation of Pseudomonas putida S12 to high concentrations of styrene and other organic solvents.

Microbial degradation of 1,3-dichlorobenzene.

Prevention of clogging in a biological trickle-bed reactor removing toluene from contaminated air.

Dichloromethane removal from waste gases with a trickle-bed bioreactor.

Bacterial degradation of vinyl chloride