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The effect of using an inappropriate protein database for proteomic data analysis.

ABSTRACT: A recent study by Bromenshenk et al., published in PLoS One (2010), used proteomic analysis to identify peptides purportedly of Iridovirus and Nosema origin; however the validity of this finding is controversial. We show here through re-analysis of a subset of this data that many of the spectra identified by Bromenshenk et al. as deriving from Iridovirus and Nosema proteins are actually products from Apis mellifera honey bee proteins. We find no reliable evidence that proteins from Iridovirus and Nosema are present in the samples that were re-analyzed. This article is also intended as a learning exercise for illustrating some of the potential pitfalls of analysis of mass spectrometry proteomic data and to encourage authors to observe MS/MS data reporting guidelines that would facilitate recognition of analysis problems during the review process.

SUBMITTER: Knudsen GM

PROVIDER: S-EPMC3114852 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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The effect of using an inappropriate protein database for proteomic data analysis.

Knudsen Giselle M GM Chalkley Robert J RJ

PloS one 20110614 6

A recent study by Bromenshenk et al., published in PLoS One (2010), used proteomic analysis to identify peptides purportedly of Iridovirus and Nosema origin; however the validity of this finding is controversial. We show here through re-analysis of a subset of this data that many of the spectra identified by Bromenshenk et al. as deriving from Iridovirus and Nosema proteins are actually products from Apis mellifera honey bee proteins. We find no reliable evidence that proteins from Iridovirus an ...[more]

PMID: 21695130

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Project description:BackgroundRecombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database.ResultsThe NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and beta-conglycinin chains.The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified.ConclusionsNanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.

Project description:Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

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The effect of using an inappropriate protein database for proteomic data analysis.

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The effect of using an inappropriate protein database for proteomic data analysis.

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