Dataset Information

Novel MMP-9 substrates in cancer cells revealed by a label-free quantitative proteomics approach.

ABSTRACT: Matrix metalloproteinase-9 (MMP-9) is implicated in tumor metastasis as well as a variety of inflammatory and pathological processes. Although many substrates for MMP-9, including components of the extracellular matrix, soluble mediators such as chemokines, and cell surface molecules have been identified, we undertook a more comprehensive proteomics-based approach to identify new substrates to further understand how MMP-9 might contribute to tumor metastasis. Previous proteomics approaches to identify protease substrates have depended upon differential labeling of each sample. Instead we used a label-free quantitative proteomics approach based on ultraperformance LC-ESI-high/low collision energy MS. Conditioned medium from a human metastatic prostate cancer cell line, PC-3ML, in which MMP-9 had been down-regulated by RNA interference was compared with that from the parental cells. From more than 200 proteins identified, 69 showed significant alteration in levels after depletion of the protease (>+/-2-fold), suggesting that they might be candidate substrates. Levels of six of these (amyloid-beta precursor protein, collagen VI, leukemia inhibitory factor, neuropilin-1, prostate cancer cell-derived growth factor (PCDGF), and protease nexin-1 (PN-1)) were tested in the conditioned media by immunoblotting. There was a strong correlation between results by ultraperformance LC-ESI-high/low collision energy MS and by immunoblotting giving credence to the label-free approach. Further information about MMP-9 cleavage was obtained by comparison of the peptide coverage of collagen VI in the presence and absence of MMP-9 showing increased sensitivity of the C- and N-terminal globular regions over the helical regions. Susceptibility of PN-1 and leukemia inhibitory factor to MMP-9 degradation was confirmed by in vitro incubation of the recombinant proteins with recombinant MMP-9. The MMP-9 cleavage sites in PN-1 were sequenced. This study provides a new label-free method for degradomics cell-based screening leading to the identification of a series of proteins whose levels are affected by MMP-9, some of which are clearly direct substrates for MMP-9 and become candidates for involvement in metastasis.

SUBMITTER: Xu D

PROVIDER: S-EPMC2577209 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel MMP-9 substrates in cancer cells revealed by a label-free quantitative proteomics approach.

Xu Danmei D Suenaga Naoko N Edelmann Mariola J MJ Fridman Rafael R Muschel Ruth J RJ Kessler Benedikt M BM

Molecular & cellular proteomics : MCP 20080702 11

Matrix metalloproteinase-9 (MMP-9) is implicated in tumor metastasis as well as a variety of inflammatory and pathological processes. Although many substrates for MMP-9, including components of the extracellular matrix, soluble mediators such as chemokines, and cell surface molecules have been identified, we undertook a more comprehensive proteomics-based approach to identify new substrates to further understand how MMP-9 might contribute to tumor metastasis. Previous proteomics approaches to id ...[more]

PMID: 18596065

Similar Datasets

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

Novel MMP-9 substrates in cancer cells revealed by a label-free quantitative proteomics approach.

Publications

Novel MMP-9 substrates in cancer cells revealed by a label-free quantitative proteomics approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets