Dataset Information

Metaprotein expression modeling for label-free quantitative proteomics.

ABSTRACT:

Background

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.

Results

We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.

Conclusions

Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

SUBMITTER: Lucas JE

PROVIDER: S-EPMC3436780 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Publications

Metaprotein expression modeling for label-free quantitative proteomics.

Lucas Joseph E JE Thompson J Will JW Dubois Laura G LG McCarthy Jeanette J Tillmann Hans H Thompson Alexander A Shire Norah N Hendrickson Ron R Dieguez Francisco F Goldman Phyllis P Schwarz Kathleen K Patel Keyur K McHutchison John J Moseley M Arthur MA

BMC bioinformatics 20120504

<h4>Background</h4>Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between exp ...[more]

PMID: 22559859

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Project description:To better understand light regulation of C(4) plant maize development, we investigated dynamic proteomic differences between green seedlings (control), etiolated seedlings, and etiolated seedlings illuminated for 6 or 12 h using a label-free quantitative proteomics approach based on nanoscale ultraperformance liquid chromatography-ESI-MS(E). Among more than 400 proteins identified, 73 were significantly altered during etiolated maize seedling greening. Of these 73 proteins, 25 were identified as membrane proteins that seldom had been identified with two-dimensional electrophoresis methods, indicating the power of our label-free method for membrane protein identification; 31 were related to light reactions of chlorophyll biosynthesis, photosynthesis, and photosynthetic carbon assimilation. The expression of photosystem II subunits was highly sensitive to light; most of them were not identified in etiolated maize seedlings but drastically increased upon light exposure, indicating that the complex process of biogenesis of the photosynthetic apparatus correlates with the transition from a dark-grown to a light-grown morphology. However, transcriptional analysis indicated that most transcripts encoding these proteins were not regulated by light. In contrast, the levels of mRNAs and proteins for enzymes involved in carbon assimilation were tightly regulated by light. Additionally phosphoenolpyruvate carboxykinase, the key enzyme of the phosphoenolpyruvate carboxykinase C(4) pathway, was more tightly regulated by light than the key enzymes of the NADP-malic enzyme C(4) pathway. Furthermore phosphoenolpyruvate carboxylase 1C, which was originally reported to be specifically expressed in roots, was also identified in this study; expression of this enzyme was more sensitive to light than its isoforms. Taken together, these results represent a comprehensive dynamic protein profile and light-regulated network of C(4) plants for etiolated seedling greening and provide a basis for further study of the mechanism of gene function and regulation in light-induced development of C(4) plants.

Dataset Information

Metaprotein expression modeling for label-free quantitative proteomics.

Background

Results

Conclusions

Publications

Metaprotein expression modeling for label-free quantitative proteomics.

Similar Datasets

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