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Proteomic solutions for analytical challenges associated with alcohol research.

MacCoss MJ, Wu CC - Alcohol Res Health (2008)

Bottom Line: Proteins do not conform to any one uniform sample preparation method and/or biochemical analysis.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Genome Sciences at the University of Washington, Seattle, Washington.

ABSTRACT
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 paradigms have provided powerful tools to study the molecular complexities underlying these behavioral phenotypes. 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 . 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). 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)). 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.

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Related in: MedlinePlus

Finding differences between samples using differential mass spectrometry (dMS). Peptide maps are plotted as two-dimensional images following chromatogram alignment and intensity normalization. Statistical analysis software is used to find regions of mass-to-charge ratio (m/z) and retention time that differ in abundance between sample groups. Obvious visual differences (in the context of this figure) are illustrated in red.
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f9-arh-31-3-251: Finding differences between samples using differential mass spectrometry (dMS). Peptide maps are plotted as two-dimensional images following chromatogram alignment and intensity normalization. Statistical analysis software is used to find regions of mass-to-charge ratio (m/z) and retention time that differ in abundance between sample groups. Obvious visual differences (in the context of this figure) are illustrated in red.

Mentions: Because of this limitation, some groups have begun to focus their identification efforts only on peptides that differ in abundance between samples (Finney et al. 2008; Pasa-Tolic et al. 2002; Prakash et al. 2006; Wiener et al. 2004). This experimental workflow is similar to that used in a two-dimensional (2D) gel analysis,4 except that these analyses are performed on the peptide level using chromatographic separation and not on the protein level using gel electrophoresis. Specifically, in a 2D gel experiment, gels are run first to identify spots that differ between samples and then efforts are taken to determine the identities of the proteins in those spots. Similarly, in a “differential” mass spectrometry experiment, chromatographic separations are first carried out to identify peaks that differ in abundance between samples (see figure 9) and then efforts are taken to determine the identities of the peptides in those peaks. Peak abundances that change in a statistically meaningful way (see figure 9, red circles) then are analyzed again to assign peptide identities.


Proteomic solutions for analytical challenges associated with alcohol research.

MacCoss MJ, Wu CC - Alcohol Res Health (2008)

Finding differences between samples using differential mass spectrometry (dMS). Peptide maps are plotted as two-dimensional images following chromatogram alignment and intensity normalization. Statistical analysis software is used to find regions of mass-to-charge ratio (m/z) and retention time that differ in abundance between sample groups. Obvious visual differences (in the context of this figure) are illustrated in red.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860482&req=5

f9-arh-31-3-251: Finding differences between samples using differential mass spectrometry (dMS). Peptide maps are plotted as two-dimensional images following chromatogram alignment and intensity normalization. Statistical analysis software is used to find regions of mass-to-charge ratio (m/z) and retention time that differ in abundance between sample groups. Obvious visual differences (in the context of this figure) are illustrated in red.
Mentions: Because of this limitation, some groups have begun to focus their identification efforts only on peptides that differ in abundance between samples (Finney et al. 2008; Pasa-Tolic et al. 2002; Prakash et al. 2006; Wiener et al. 2004). This experimental workflow is similar to that used in a two-dimensional (2D) gel analysis,4 except that these analyses are performed on the peptide level using chromatographic separation and not on the protein level using gel electrophoresis. Specifically, in a 2D gel experiment, gels are run first to identify spots that differ between samples and then efforts are taken to determine the identities of the proteins in those spots. Similarly, in a “differential” mass spectrometry experiment, chromatographic separations are first carried out to identify peaks that differ in abundance between samples (see figure 9) and then efforts are taken to determine the identities of the peptides in those peaks. Peak abundances that change in a statistically meaningful way (see figure 9, red circles) then are analyzed again to assign peptide identities.

Bottom Line: Proteins do not conform to any one uniform sample preparation method and/or biochemical analysis.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Genome Sciences at the University of Washington, Seattle, Washington.

ABSTRACT
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 paradigms have provided powerful tools to study the molecular complexities underlying these behavioral phenotypes. 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 . 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). 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)). 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.

Show MeSH
Related in: MedlinePlus