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Within the BEEP project (Biological Effects of Environmental Pollution in Marine Ecosystems) the Work Package 1 was addressed to the development of new and more sensitive biomarkers of exposure in several sentinel organisms. Within this framework, common mesocosm exposures of organic pollutants relevant for marine ecosystems were conducted in the facilities of Akvamiljø a/s (Stavanger, Norway). In the first experiment, Atlantic cod (Gadus morhua), turbot (Scophthalmus maximus) and shore crab (Carcinus maenas) were exposed to nonylphenol, North Sea crude oil and a combination of crude oil and alkylated phenols. Mussels (Mytilus edulis) were exposed to North Sea crude oil and a combination of crude oil, alkylated phenols and PAHs. In the second experiment, Atlantic cod, turbot, mussel and spider crab (Hyas araneus) were exposed to the plasticizers bisphenol A and diallyl phatalate and the brominated flame retardant BDE-47. The main purpose of the present study was to provide the 30 participating Institutes with samples which had been exposed to defined contaminant concentrations in a controlled laboratory exposure for 3 weeks. This paper describes the mesocosm experimental design, the transplantation and treatment of the organisms, and the contaminant exposures.

Compound-specific protein expression signatures (PESs) can be revealed by proteomic techniques. The SELDI-TOF MS approach is advantageous due to its simplicity and high-throughput capacity, however, there are concerns regarding the reproducibility of this method. The aim of this study was to define an estrogen-responsive PES in plasma of Atlantic cod (Gadus morhua) using the SELDI-TOF MS technique. Protein expression analysis of male cod exposed to 17β-estradiol (E2) showed that 27 plasma peaks were differentially expressed following exposure. The reproducibility of this result was evaluated by reanalyzing the samples six months later, and a significant change in expression was confirmed for 13 of the 27 peaks detected in the first analysis. The performance of the reproducible E2-responsive PES, constituting these 13 peaks, was then tested on samples from juvenile cod exposed to 4-nonylphenol, North Sea oil, or North Sea oil spiked with alkylphenols. Principal component analysis revealed that nonylphenol-exposed cod could be separated from unexposed cod based on the E2-responsive PES, indicating that the PES can be used to assess estrogenic exposure of both juvenile and adult specimens of cod. A targeted antibody-assisted SELDI-TOF MS approach was carried out in an attempt to identify the E2-responsive peaks. Results indicated that 2 peaks were fragments of the well-known biomarkers VTG and/or ZRP. In this study, the SELDI-TOF MS technology has shown its potential for defining compound-specific PESs in fish. Nevertheless, thorough validation of reproducibility, specificity and sensitivity of a PES is required before it can be applied in environmental monitoring. [Copyright &y& Elsevier]

Background: Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. The challenges associated with the analysis of large-scale multivariate proteomic datasets have been widely discussed in medical research and biomarker discovery. This concept has been introduced to ecotoxicology only recently, so data processing and classification analysis need to be refined before they can be readily applied in biomarker discovery and monitoring studies. Results: Data sets obtained from a case study of oil pollution in the Blue mussel were investigated for differential protein expression by retentate chromatography-mass spectrometry and decision tree classification. Different tissues and different settings were used to evaluate classifiers towards their discriminatory power. It was found that, due the intrinsic variability of the data sets, reliable classification of unknown samples could only be achieved on a broad statistical basis (n > 60) with the observed expression changes comprising high statistical significance and sufficient amplitude. The application of stringent criteria to guard against overfitting of the models eventually allowed satisfactory classification for only one of the investigated data sets and settings. Conclusion: Machine learning techniques provide a promising approach to process and extract informative expression signatures from high-dimensional mass-spectrometry data. Even though characterisation of the proteins forming the expression signatures would be ideal, knowledge of the specific proteins is not mandatory for effective class discrimination. This may constitute a new biomarker approach in ecotoxicology, where working with organisms, which do not have sequenced genomes render protein identification by database searching problematic. However, data processing has to be critically evaluated and statistical constraints have to be considered before supervised classification algorithms are employed. [ABSTRACT FROM AUTHOR]

Abstract: SELDI-TOF MS has been demonstrated as a powerful tool for biomarker discovery. However, a major disadvantage of SELDI-TOF MS is the lack of direct identification of the discriminatory peaks discovered. We describe a novel experimental identification strategy where peptides/proteins captured to a weak cation exchange ProteinArray surface (CM10) are eluted, and thereafter identified by utilizing a sensitive LC–MS/MS (i.e. LTQ Orbitrap). A mixture of four known proteins was used to test the novel experimental approach described, and all four proteins were successfully identified. Additionally, a biomarker candidate previously discovered in plasma of Atlantic cod (Gadus morhua) by SELDI-TOF MS was identified. Thus, this study indicated that a combination of on-chip elution and a highly sensitive LC–MS/MS system can be an alternative approach to identify biomarker candidates discovered by use of SELDI-TOF MS. [Copyright &y& Elsevier]