Academic journal article Alcohol Research

Proteomic Solutions for Analytical Challenges Associated with Alcohol Research

Academic journal article Alcohol Research

Proteomic Solutions for Analytical Challenges Associated with Alcohol Research

Article excerpt

KEY WORDS: Alcohol dependence; genetic theory of alcohol and other drug use; genetic trait; brain; animal models; proteins; protein analysis; proteomics; mass spectrometry; peptides.

Alcohol addiction is a complex disease with both hereditary and environmental influences. Because molecular determinants contributing to this phenotype are difficult to study in humans, numerous rodent models and conditioning paradigms1 have provided powerful tools to study the molecular complexities underlying these behavioral phenotypes (Crabbe 2002). In particular, specifically bred rodents (i.e., selected lines and inbred strains) that differ in voluntary alcohol drinking represent valuable tools to dissect the genetic components of alcoholism. However, because each model has distinct advantages, a combined comparison across datasets of different models for common changes in gene expression would provide more statistical power to detect reliable changes as opposed to the analysis of any one model. Indeed, meta-analyses of diverse gene expression datasets were recently performed to uncover genes related to the predisposition for a high alcohol intake. This large endeavor resulted in the identification of 3,800 unique genes that significantly and consistently changed between all included mouse lines and strains (Mulligan et al. 2006).

Similar experiments also are crucial at the protein level. However, these analyses are not yet possible. Proteins do not conform to any one uniform sample preparation method and/or biochemical analysis. They display a broad range of physical and chemical properties (e.g., molecular weight or hydrophobicity) and are expressed over a very large dynamic range (up to 8 orders of magnitude) (Anderson 2005; Ghaemmaghami et al. 2003). Further complicating global proteomic comparisons are the added considerations that proteins often undergo extensive covalent modifications and that protein functions often are regulated by complex protein-protein interactions and the specific location of the proteins in the cell (i.e., their subcellular localization) (Grant and Wu 2007). Furthermore, because the number of biological replicates involved in behavioral analyses typically is high, robust high-throughput proteomic platforms will be required to handle the multitude of protein samples that can potentially result from the various brain regions for the numerous animal models and paradigms. Finally, these effects often are monitored over time courses, again inflating the total number of samples that need to be analyzed and compared. This article summarizes some general strategies for large-scale, high-throughput protein analyses and describes two new proteomic strategies that appear promising for future studies in this field.


To overcome the complexities of proteins, proteomic methods routinely digest proteins into smaller pieces (i.e., peptides) prior to analysis. (Strategies using this approach are collectively termed shotgun proteomics.) The peptides then are separated by microcapillary chromatography and electrosprayed directly into a mass spectrometer placed at the outlet of the chromatography column for mass analysis and fragmentation of the peptides2 using a technique known as data-dependent acquisition. In data-dependent acquisition, the mass spectrometer software makes real-time decisions about how the mass spectrometer is scanned, depending on data acquired in prior scans. Thus, the mass spectrometer acquires a "survey" mass spectrum, followed by tandem mass spectra (MS/MS spectra) of precursor ions identified in the earlier survey mass spectrum. This data-dependent acquisition, although powerful, is normally focused on the most intense signals and will ultimately be limited by the complexity of the mixture and the scan speed of the mass spectrometer.

The mass spectrometer's ability to handle extremely complex mixtures of peptides can be enhanced either by biochemically fractionating the protein sample prior to digestion (thereby reducing the complexity of each resulting fractionated protein sample) or by adding an extra dimension of liquid chromatography (thereby increasing the separation on the peptide level) prior to mass spectrometry (Peng et al. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed


An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.