Characterization of the molecular mechanisms of chronic ethanol exposure: A proteomics based approach
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Alcoholism is one of the major health problems world over. Chronic ethanol exposure is characterized by a number of neuro-adaptive as well as neurotoxic changes in the central nervous system. However, the molecular mechanisms responsible for these changes are unclear. A proteomic approach utilizing two-dimensional electrophoresis (2-DE) and matrix assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry was undertaken to identify brain proteins whose abundance were altered by chronic ethanol exposure. As previous studies have shown zebrafish are sensitive to pharmacological concentrations of ethanol and also exhibit tolerance to ethanol, these animals were used as a model system. In these studies, some novel protein targets have been identified as well as some putative previous targets of chronic ethanol exposure substantiated. Eight protein spots, VDAC-1 (voltage dependent anion channel 1), VDAC-2, apolipoprotein A1, Hsp70, Goa, aspartate aminotransferase, vacuolar ATPase, and lysosomal ATPase were unambiguously identified as differentially expressed with chronic ethanol exposure. Further examination into these proteins has the potential to unlock new pathways for understanding the mechanisms of ethanol toxicity. During the course of our studies, certain post-experimental factors that limited the analysis of 2-DE were identified. These areas of concerns were addressed in an attempt to minimize the variability and facilitate reliable protein identification using 2-DE. Though often ignored, software analyses of 2-DE gel images present a considerable source of variability in the analysis of proteins. In particular, cropping of gel images prior to quantitative 2-DE analysis has been shown to contribute a significant amount of variability to image analysis. To address this problem, a simple, reliable and objective method of cropping 2-DE gel images was devised that consequently minimized the variability in 2-DE image analysis. Identification of proteins by mass spectrometry (MS) is an essential step in proteomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. To this end, a value-based scoring system was developed that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis. Missing spot values pose a considerable problem in 2-DE analysis. This problem was addressed by "imputation"; that is, the missing spot values are replaced with values that use information from the protein spots that are present. Different methods of imputation were evaluated, and their impact on univariant statistical analysis was determined. To account for the large number of statistical comparisons being made in 2-DE analysis, multiple testing adjustments were proposed in an attempt to decrease the incidence of false positives. Though useful to control false positives, this approach comes at an expense of an increase in false negatives. Accordingly, a zonal approach was developed that allows a "triage" of protein analysis based on the objective of the study.