Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: A proteomic study of L-DOPA induced dyskinesia in an animal model of Parkinson's disease using DIGE
Identifieur interne : 000665 ( Ncbi/Curation ); précédent : 000664; suivant : 000666Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: A proteomic study of L-DOPA induced dyskinesia in an animal model of Parkinson's disease using DIGE
Auteurs : Kim Kultima [Suède] ; Birger Scholz [Suède] ; Henrik Alm [Suède] ; Karl Sköld [Suède] ; Marcus Svensson [Suède] ; Alan R. Crossman [Royaume-Uni] ; Erwan Bezard [France] ; Per E. Andrén [Suède] ; Ingrid Lönnstedt [Suède]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2006.
Abstract
Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized.
In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a
Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration.
There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.
Further, by using the proposed method, Differential Expression in Predefined Proteins
Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying
Url:
DOI: 10.1186/1471-2105-7-475
PubMed: 17067368
PubMed Central: 1635739
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PMC:1635739Le document en format XML
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<author><name sortKey="Skold, Karl" sort="Skold, Karl" uniqKey="Skold K" first="Karl" last="Sköld">Karl Sköld</name>
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<author><name sortKey="Svensson, Marcus" sort="Svensson, Marcus" uniqKey="Svensson M" first="Marcus" last="Svensson">Marcus Svensson</name>
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<author><name sortKey="Crossman, Alan R" sort="Crossman, Alan R" uniqKey="Crossman A" first="Alan R" last="Crossman">Alan R. Crossman</name>
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<author><name sortKey="Bezard, Erwan" sort="Bezard, Erwan" uniqKey="Bezard E" first="Erwan" last="Bezard">Erwan Bezard</name>
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<country xml:lang="fr">France</country>
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<region type="old region">Aquitaine</region>
<settlement type="city">Bordeaux</settlement>
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<author><name sortKey="Andren, Per E" sort="Andren, Per E" uniqKey="Andren P" first="Per E" last="Andrén">Per E. Andrén</name>
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<author><name sortKey="Lonnstedt, Ingrid" sort="Lonnstedt, Ingrid" uniqKey="Lonnstedt I" first="Ingrid" last="Lönnstedt">Ingrid Lönnstedt</name>
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<country xml:lang="fr">Suède</country>
<wicri:regionArea>Department of Pharmaceutical Biosciences, Division of Toxicology, Uppsala University, BMC, Box 594, SE-75124 Uppsala</wicri:regionArea>
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<region nuts="1">Svealand</region>
<region nuts="1">East Middle Sweden</region>
</placeName>
</affiliation>
<affiliation wicri:level="4"><nlm:aff id="I6">Department of Mathematics, Uppsala University, Box 480, SE-75106 Uppsala, Sweden</nlm:aff>
<country xml:lang="fr">Suède</country>
<wicri:regionArea>Department of Mathematics, Uppsala University, Box 480, SE-75106 Uppsala</wicri:regionArea>
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<series><title level="j">BMC Bioinformatics</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized.</p>
<p>In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a <italic>set </italic>
of proteins shows an association with the predefined phenotypes of interest. This method was applied to our data generated from a monkey model (<italic>Macaca fascicularis</italic>
) of Parkinson's disease.</p>
</sec>
<sec><title>Results</title>
<p>Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration.</p>
<p>There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.</p>
<p>Further, by using the proposed method, Differential Expression in Predefined Proteins <italic>Sets </italic>
(DEPPS), several <italic>sets </italic>
of proteins associated with the priming effects of L-DOPA in the striatum in parkinsonian animals were identified. Three of these <italic>sets </italic>
are proteins involved in energy metabolism and one <italic>set </italic>
involved proteins which are part of the microtubule cytoskeleton.</p>
</sec>
<sec><title>Conclusion</title>
<p>Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying <italic>sets </italic>
of proteins the interpretation of the results are probably more accurate and biologically informative.</p>
</sec>
</div>
</front>
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