Proteomic applications of automated GPCR classification

The G-protein coupled receptor (GPCR) superfamily fulfils various metabolic functions and interacts with a diverse range of ligands. There is a lack of sequence similarity between the six classes that comprise the GPCR superfamily. Moreover, most novel GPCRs found have low sequence similarity to other family members which makes it difficult to infer properties from related receptors. Many different approaches have been taken towards developing efficient and accurate methods for GPCR classification, ranging from motif-based systems to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of their amino acid sequences. This review describes the inherent difficulties in developing a GPCR classification algorithm and includes techniques previously employed in this area.     

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.     

Microbial consortia in mesocosm bioremediation trial using oil sorbents, slow-release fertilizer and bioaugmentation

An experimental prototype oil boom including oil sorbents, slow-release fertilizers and biomass of the marine oil-degrading bacterium, Alcanivorax borkumensis , was applied for sorption and degradation of heavy fuel oil in a 500-L mesocosm experiment. Fingerprinting of DNA and small subunit rRNA samples for microbial activity conducted to study the changes in microbial communities of both the water body and on the oil sorbent surface showed the prevalence of A. borkumensis on the surface of the oil sorbent. Growth of this obligate oil-degrading bacterium on immobilized oil coincided with a 30-fold increase in total respiration. A number of DNA and RNA signatures of aromatic hydrocarbon-degrading bacteria were detected both in samples of water body and on oil sorbent. Ultimately, the heavy fuel oil in this mesocosm study was effectively removed from the water body. This is the first study to successfully investigate the fate of oil-degrading microbial consortia in an experimental prototype for a bioremediation strategy in offshore, coastal or ship-bound oil spill mitigation using a combination of mechanical and biotechnological techniques.     

Mixed aerobic and anaerobic microbial communities in benzene-contaminated groundwater

Aims:  To investigate the factors affecting benzene biodegradation and microbial community composition in a contaminated aquifer.
Methods and Results:  We identified the microbial community in groundwater samples from a benzene-contaminated aquifer situated below a petrochemical plant. Eleven out of twelve groundwater samples with in situ dissolved oxygen concentrations between 0 and 2·57 mg l⁻¹ showed benzene degradation in aerobic microcosm experiments, whereas no degradation in anaerobic microcosms was observed. The lack of aerobic degradation in the remaining microcosm could be attributed to a pH of 12·1. Three groundwaters, examined by 16S rRNA gene clone libraries, with low in situ oxygen concentrations and high benzene levels, each had a different dominant aerobic (or denitrifying) population, either Pseudomonas , Polaromonas or Acidovorax species. These groundwaters also had syntrophic organisms, and aceticlastic methanogens were detected in two samples. The alkaline groundwater was dominated by organisms closely related to Hydrogenophaga .
Conclusions:  Results show that pH 12·1 is inimical to benzene biodegradation, and that oxygen concentrations below 0·03 mg l⁻¹ can support aerobic benzene-degrading communities.
Significance and Impact of the Study:  These findings will help to guide the treatment of contaminated groundwaters, and raise questions about the extent to which aerobes and anaerobes may interact to effect benzene degradation.     

The 'pH optimum anomaly' of intracellular enzymes of Ferroplasma acidiphilum

A wide range of microorganisms, the so-called acidophiles, inhabit acidic environments and grow optimally at pH values between 0 and 3. The intracellular pH of these organisms is, however, close to neutrality or slightly acidic. It is to be expected that enzymatic activities dedicated to extracellular functions would be adapted to the prevailing low pH of the environment (0-3), whereas intracellular enzymes would be optimally active at the near-neutral pH of the cytoplasm (4.6-7.0). The genes of several intracellular or cell-bound enzymes, a carboxylesterase and three alpha-glucosidases, from Ferroplasma acidiphilum, a cell wall-lacking acidophilic archaeon with a growth optimum at pH 1.7, were cloned and expressed in Escherichia coli, and their products purified and characterized. The Ferroplasmaalpha-glucosidases exhibited no sequence similarity to known glycosyl hydrolases. All enzymes functioned and were stable in vitro in the pH range 1.7-4.0, and had pH optima much lower than the mean intracellular pH of 5.6. This 'pH optimum anomaly' suggests the existence of yet-undetected cellular compartmentalization providing cytoplasmic pH patchiness and low pH environments for the enzymes we have analysed.     

Modification of coronary artery disease progression by cholesterol-lowering therapy: the angiographic studies

Large randomized placebo-controlled trials have demonstrated that cholesterol lowering with statin therapy reduces the incidence of adverse cardiac events. Smaller angiographic studies have shown that coronary artery disease progression can be slowed and, in some cases, reversed by cholesterol-lowering interventions. These anatomical changes, however, are small and occur too slowly to account for the early clinical benefit. Current evidence suggests that plaque stabilization is the most important mechanism, by which cholesterol-lowering therapy reduces both the incidence of adverse cardiac events and coronary artery disease progression.     

Compatible solutes protect against chaotrope (ethanol)-induced, nonosmotic water stress

Water stress is one of the major stresses experienced by cellular systems and can take a number of distinct forms. In response to turgor-related osmotic stress, cells produce compatible solutes that are macromolecule protectants and also counteract the outflow of water from stressed cells. In this report we show that the germination of conidia of Aspergillus nidulans, a sensitive indicator of water stress, in the presence of ethanol is correlated with the intracellular concentration of the compatible solutes glycerol and erythritol, which protect against both osmotic and nonturgor forms of water stress.