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Label-free quantitative proteomics and N-glycoproteomics analysis of KRAS-activated human bronchial epithelial cells.

ABSTRACT: Mutational activation of KRAS promotes various malignancies, including lung adenocarcinoma. Knowledge of the molecular targets mediating the downstream effects of activated KRAS is limited. Here, we provide the KRAS target proteins and N-glycoproteins using human bronchial epithelial cells with and without the expression of activated KRAS (KRAS(V12)). Using an OFFGEL peptide fractionation and hydrazide method combined with subsequent LTQ-Orbitrap analysis, we identified 5713 proteins and 608 N-glycosites on 317 proteins in human bronchial epithelial cells. Label-free quantitation of 3058 proteins (?2 peptides; coefficient of variation (CV) ? 20%) and 297 N-glycoproteins (CV ? 20%) revealed the differential regulation of 23 proteins and 14 N-glycoproteins caused by activated KRAS, including 84% novel ones. An informatics-assisted IPA-Biomarker® filter analysis prioritized some of the differentially regulated proteins (ALDH3A1, CA2, CTSD, DST, EPHA2, and VIM) and N-glycoproteins (ALCAM, ITGA3, and TIMP-1) as cancer biomarkers. Further, integrated in silico analysis of microarray repository data of lung adenocarcinoma clinical samples and cell lines containing KRAS mutations showed positive mRNA fold changes (p < 0.05) for 61% of the KRAS-regulated proteins, including biomarker proteins, CA2 and CTSD. The most significant discovery of the integrated validation is the down-regulation of FABP5 and PDCD4. A few validated proteins, including tumor suppressor PDCD4, were further confirmed as KRAS targets by shRNA-based knockdown experiments. Finally, the studies on KRAS-regulated N-glycoproteins revealed structural alterations in the core N-glycans of SEMA4B in KRAS-activated human bronchial epithelial cells and functional role of N-glycosylation of TIMP-1 in the regulation of lung adenocarcinoma A549 cell invasion. Together, our study represents the largest proteome and N-glycoproteome data sets for HBECs, which we used to identify several novel potential targets of activated KRAS that may provide insights into KRAS-induced adenocarcinoma and have implications for both lung cancer therapy and diagnosis.

SUBMITTER: Sudhir PR

PROVIDER: S-EPMC3494152 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Label-free quantitative proteomics and N-glycoproteomics analysis of KRAS-activated human bronchial epithelial cells.

Sudhir Putty-Reddy PR Chen Chein-Hung CH Pavana Kumari Madireddy M Wang Mei-Jung MJ Tsou Chih-Chiang CC Sung Ting-Yi TY Chen Jeou-Yuan JY Chen Chung-Hsuan CH

Molecular & cellular proteomics : MCP 20120703 10

Mutational activation of KRAS promotes various malignancies, including lung adenocarcinoma. Knowledge of the molecular targets mediating the downstream effects of activated KRAS is limited. Here, we provide the KRAS target proteins and N-glycoproteins using human bronchial epithelial cells with and without the expression of activated KRAS (KRAS(V12)). Using an OFFGEL peptide fractionation and hydrazide method combined with subsequent LTQ-Orbitrap analysis, we identified 5713 proteins and 608 N-g ...[more]

PMID: 22761399

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Project description:BackgroundLabel-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.ResultsWe 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.ConclusionsMass-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.

Dataset Information

Label-free quantitative proteomics and N-glycoproteomics analysis of KRAS-activated human bronchial epithelial cells.

Publications

Label-free quantitative proteomics and N-glycoproteomics analysis of KRAS-activated human bronchial epithelial cells.

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