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      MatrisomeDB: the ECM-protein knowledge database

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          Abstract

          The extracellular matrix (ECM) is a complex and dynamic meshwork of cross-linked proteins that supports cell polarization and functions and tissue organization and homeostasis. Over the past few decades, mass-spectrometry-based proteomics has emerged as the method of choice to characterize the composition of the ECM of normal and diseased tissues. Here, we present a new release of MatrisomeDB, a searchable collection of curated proteomic data from 17 studies on the ECM of 15 different normal tissue types, six cancer types (different grades of breast cancers, colorectal cancer, melanoma, and insulinoma) and other diseases including vascular defects and lung and liver fibroses. MatrisomeDB ( http://www.pepchem.org/matrisomedb) was built by retrieving raw mass spectrometry data files and reprocessing them using the same search parameters and criteria to allow for a more direct comparison between the different studies. The present release of MatrisomeDB includes 847 human and 791 mouse ECM proteoforms and over 350 000 human and 600 000 mouse ECM-derived peptide-to-spectrum matches. For each query, a hierarchically-clustered tissue distribution map, a peptide coverage map, and a list of post-translational modifications identified, are generated. MatrisomeDB is the most complete collection of ECM proteomic data to date and allows the building of a comprehensive ECM atlas.

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          Most cited references38

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          DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.

          The components of complex peptide mixtures can be separated by liquid chromatography, fragmented by tandem mass spectrometry, and identified by the SEQUEST algorithm. Inferring a mixture's source proteins requires that the identified peptides be reassociated. This process becomes more challenging as the number of peptides increases. DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches. The accompanying Contrast tool compares DTASelect results from multiple experiments. The two programs improve the speed and precision of proteomic data analysis.
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            Extracellular matrix-based materials for regenerative medicine

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              Extracellular matrix signatures of human mammary carcinoma identify novel metastasis promoters

              The extracellular matrix (ECM) is a major component of tumors and a significant contributor to cancer progression. In this study, we use proteomics to investigate the ECM of human mammary carcinoma xenografts and show that primary tumors of differing metastatic potential differ in ECM composition. Both tumor cells and stromal cells contribute to the tumor matrix and tumors of differing metastatic ability differ in both tumor- and stroma-derived ECM components. We define ECM signatures of poorly and highly metastatic mammary carcinomas and these signatures reveal up-regulation of signaling pathways including TGFβ and VEGF. We further demonstrate that several proteins characteristic of highly metastatic tumors (LTBP3, SNED1, EGLN1, and S100A2) play causal roles in metastasis, albeit at different steps. Finally we show that high expression of LTBP3 and SNED1 correlates with poor outcome for ER−/PR−breast cancer patients. This study thus identifies novel biomarkers that may serve as prognostic and diagnostic tools. DOI: http://dx.doi.org/10.7554/eLife.01308.001
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2020
                05 October 2019
                05 October 2019
                : 48
                : D1
                : D1136-D1144
                Affiliations
                [1 ] College of Pharmacy, University of Illinois at Chicago , Chicago, IL 60612, USA
                [2 ] Department of Physiology and Biophysics, University of Illinois at Chicago , Chicago, IL 60612, USA
                [3 ] Broad Institute , Cambridge, MA 02139, USA
                [4 ] University of Illinois at Chicago Cancer Center , Chicago, IL 60612, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 312 355 5417; Email: anaba@ 123456uic.edu
                Correspondence may also be addressed to Yu (Tom) Gao. Tel: +1 312 996 8087; Email: yugao@ 123456uic.edu
                Author information
                http://orcid.org/0000-0002-4796-5614
                Article
                gkz849
                10.1093/nar/gkz849
                6943062
                31586405
                1acc2e58-241f-4238-a61d-96ad77a35e26
                © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 30 September 2019
                : 13 September 2019
                : 19 August 2019
                Page count
                Pages: 9
                Funding
                Funded by: Department of Physiology and Biophysics at the University of Illinois at Chicago
                Funded by: College of Pharmacy 10.13039/100008901
                Funded by: University of Illinois at Chicago 10.13039/100008522
                Categories
                Database Issue

                Genetics
                Genetics

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