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Development and application of a proteomic approach to the assessment of pollution in the marine environmentApraiz Larrucea, Itxaso January 2009 (has links)
Today, assessment of the health of coastal waters is recognized as being important for both the conservation of nature and well-being of humans. Anthropogenic pollution has been the focus of extensive research for some time and a variety of programs for the monitoring and assessment of environmental pollution have been developed. Determination of the levels of pollution in sensitive ‘sentinels’ such as mussels, allows monitoring of these levels in a given area over a prolonged period of time. Furthermore, the biological effects of pollution are reflected in a series of biomarkers, none of which provides a general picture of the sentinel’s state of health and all of which are individually specific for certain pollutants and influenced by both biotic and abiotic factors. In an attempt to improve biomonitoring of marine pollution, we have developed two proteomic approaches here. In the first portion of the thesis, a proteomic analysis was performed on peroxisomes isolated from mussels exposed either to one of three model anthropogenic pollutants, or two different types of crude oil, or from mussels exposed to the Prestige oil spill. Application of two-dimensional electrophoresis (2-DE) provided protein expression signatures (PES) for exposure to these different pollutants.Furthermore, several individual protein components of these PES could be putatively identified. In the second portion of this work, such analysis of subproteomes was developed further in order to improve the applicability of this approach to biomonitoring. A simple fractionation procedure in combination with liquid chromatography and 2-DE provided samples from mussels residing in different regions of a pollution gradient around the harbor of Gothenburg, as well as from mussels exposed to two types of fuel oil similar to that of the Prestige that were suitable for environmental proteomics. In addition, we constructed a model for this approach that can be cross-validated in the future and applied to assess sources of fuel oil pollution in connection with biomonitoring programs.
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