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The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification

Proteins are the building blocks of life. While proteins and their localization within cells and sub-cellular compartments are well defined, the proteins predicted to be secreted to form the extracellular matrix - or matrisome - remain elusive in the model organism C. elegans. Here, we used a bioinf...

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Autores principales: Teuscher, Alina C., Jongsma, Elisabeth, Davis, Martin N., Statzer, Cyril, Gebauer, Jan M., Naba, Alexandra, Ewald, Collin Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852208/
https://www.ncbi.nlm.nih.gov/pubmed/33543001
http://dx.doi.org/10.1016/j.mbplus.2018.11.001
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author Teuscher, Alina C.
Jongsma, Elisabeth
Davis, Martin N.
Statzer, Cyril
Gebauer, Jan M.
Naba, Alexandra
Ewald, Collin Y.
author_facet Teuscher, Alina C.
Jongsma, Elisabeth
Davis, Martin N.
Statzer, Cyril
Gebauer, Jan M.
Naba, Alexandra
Ewald, Collin Y.
author_sort Teuscher, Alina C.
collection PubMed
description Proteins are the building blocks of life. While proteins and their localization within cells and sub-cellular compartments are well defined, the proteins predicted to be secreted to form the extracellular matrix - or matrisome - remain elusive in the model organism C. elegans. Here, we used a bioinformatic approach combining gene orthology and protein structure analysis and an extensive curation of the literature to define the C. elegans matrisome. Similar to the human genome, we found that 719 out of ~20,000 genes (~4%) of the C. elegans genome encodes matrisome proteins, including 181 collagens, 35 glycoproteins, 10 proteoglycans, and 493 matrisome-associated proteins. We report that 173 out of the 181 collagen genes are unique to nematodes and are predicted to encode cuticular collagens, which we are proposing to group into five clusters. To facilitate the use of our lists and classification by the scientific community, we developed an automated annotation tool to identify ECM components in large datasets. We also established a novel database of all C. elegans collagens (CeColDB). Last, we provide examples of how the newly defined C. elegans matrisome can be used for annotations and gene ontology analyses of transcriptomic, proteomic, and RNAi screening data. Because C. elegans is a widely used model organism for high throughput genetic and drug screens, and to study biological and pathological processes, the conserved matrisome genes may aid in identifying potential drug targets. In addition, the nematode-specific matrisome may be exploited for targeting parasitic infection of man and crops.
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spelling pubmed-78522082021-02-03 The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification Teuscher, Alina C. Jongsma, Elisabeth Davis, Martin N. Statzer, Cyril Gebauer, Jan M. Naba, Alexandra Ewald, Collin Y. Matrix Biol Plus Article Proteins are the building blocks of life. While proteins and their localization within cells and sub-cellular compartments are well defined, the proteins predicted to be secreted to form the extracellular matrix - or matrisome - remain elusive in the model organism C. elegans. Here, we used a bioinformatic approach combining gene orthology and protein structure analysis and an extensive curation of the literature to define the C. elegans matrisome. Similar to the human genome, we found that 719 out of ~20,000 genes (~4%) of the C. elegans genome encodes matrisome proteins, including 181 collagens, 35 glycoproteins, 10 proteoglycans, and 493 matrisome-associated proteins. We report that 173 out of the 181 collagen genes are unique to nematodes and are predicted to encode cuticular collagens, which we are proposing to group into five clusters. To facilitate the use of our lists and classification by the scientific community, we developed an automated annotation tool to identify ECM components in large datasets. We also established a novel database of all C. elegans collagens (CeColDB). Last, we provide examples of how the newly defined C. elegans matrisome can be used for annotations and gene ontology analyses of transcriptomic, proteomic, and RNAi screening data. Because C. elegans is a widely used model organism for high throughput genetic and drug screens, and to study biological and pathological processes, the conserved matrisome genes may aid in identifying potential drug targets. In addition, the nematode-specific matrisome may be exploited for targeting parasitic infection of man and crops. Elsevier 2019-02-21 /pmc/articles/PMC7852208/ /pubmed/33543001 http://dx.doi.org/10.1016/j.mbplus.2018.11.001 Text en © 2018 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Teuscher, Alina C.
Jongsma, Elisabeth
Davis, Martin N.
Statzer, Cyril
Gebauer, Jan M.
Naba, Alexandra
Ewald, Collin Y.
The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title_full The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title_fullStr The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title_full_unstemmed The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title_short The in-silico characterization of the Caenorhabditis elegans matrisome and proposal of a novel collagen classification
title_sort in-silico characterization of the caenorhabditis elegans matrisome and proposal of a novel collagen classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852208/
https://www.ncbi.nlm.nih.gov/pubmed/33543001
http://dx.doi.org/10.1016/j.mbplus.2018.11.001
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