Integration of the transcriptome and glycome for identification of glycan cell signatures.

Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model tha...

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Autores principales:	Sandra V Bennun, Kevin J Yarema, Michael J Betenbaugh, Frederick J Krambeck
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/cddeeac423a2421e825885b2eba762b7
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spelling	oai:doaj.org-article:cddeeac423a2421e825885b2eba762b72021-11-18T05:52:33ZIntegration of the transcriptome and glycome for identification of glycan cell signatures.1553-734X1553-735810.1371/journal.pcbi.1002813https://doaj.org/article/cddeeac423a2421e825885b2eba762b72013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23326219/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Le(y) epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.Sandra V BennunKevin J YaremaMichael J BetenbaughFrederick J KrambeckPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 1, p e1002813 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sandra V Bennun Kevin J Yarema Michael J Betenbaugh Frederick J Krambeck Integration of the transcriptome and glycome for identification of glycan cell signatures.
description	Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Le(y) epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.
format	article
author	Sandra V Bennun Kevin J Yarema Michael J Betenbaugh Frederick J Krambeck
author_facet	Sandra V Bennun Kevin J Yarema Michael J Betenbaugh Frederick J Krambeck
author_sort	Sandra V Bennun
title	Integration of the transcriptome and glycome for identification of glycan cell signatures.
title_short	Integration of the transcriptome and glycome for identification of glycan cell signatures.
title_full	Integration of the transcriptome and glycome for identification of glycan cell signatures.
title_fullStr	Integration of the transcriptome and glycome for identification of glycan cell signatures.
title_full_unstemmed	Integration of the transcriptome and glycome for identification of glycan cell signatures.
title_sort	integration of the transcriptome and glycome for identification of glycan cell signatures.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/cddeeac423a2421e825885b2eba762b7
work_keys_str_mv	AT sandravbennun integrationofthetranscriptomeandglycomeforidentificationofglycancellsignatures AT kevinjyarema integrationofthetranscriptomeandglycomeforidentificationofglycancellsignatures AT michaeljbetenbaugh integrationofthetranscriptomeandglycomeforidentificationofglycancellsignatures AT frederickjkrambeck integrationofthetranscriptomeandglycomeforidentificationofglycancellsignatures
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Integration of the transcriptome and glycome for identification of glycan cell signatures.

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