Prediction of glycan motifs using quantitative analysis of multi‐lectin binding: Motifs on MUC1 produced by cultured pancreatic cancer cells
Identifieur interne : 000939 ( Main/Exploration ); précédent : 000938; suivant : 000940Prediction of glycan motifs using quantitative analysis of multi‐lectin binding: Motifs on MUC1 produced by cultured pancreatic cancer cells
Auteurs : Calvin Mccarter ; Doron Kletter ; Huiyuan Tang ; Katie Partyka ; Yinjiao Ma [États-Unis] ; Sudhir Singh ; Jessica Yadav ; Marshall Bern ; Brian B. HaabSource :
- PROTEOMICS – Clinical Applications [ 1862-8346 ] ; 2013-10.
Abstract
Purpose: Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs. Experimental design: The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines. Results: The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates. Conclusions and clinical relevance: This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.
Url:
DOI: 10.1002/prca.201300069
Affiliations:
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Haab</name><affiliation></affiliation><affiliation></affiliation><affiliation><wicri:noCountry code="no comma">E-mail: brian.haab@vai.org</wicri:noCountry></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">PROTEOMICS – Clinical Applications</title><title level="j" type="sub">Glycan Biomarker Discovery</title><title level="j" type="alt">PROTEOMICS CLINICAL APPLICATIONS</title><idno type="ISSN">1862-8346</idno><idno type="eISSN">1862-8354</idno><imprint><biblScope unit="vol">7</biblScope><biblScope unit="issue">9-10</biblScope><biblScope unit="page" from="632">632</biblScope><biblScope unit="page" to="641">641</biblScope><biblScope unit="page-count">10</biblScope><date type="published" when="2013-10">2013-10</date></imprint><idno type="ISSN">1862-8346</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1862-8346</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">Purpose: Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs. Experimental design: The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines. Results: The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates. Conclusions and clinical relevance: This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Michigan</li></region></list><tree><noCountry><name sortKey="Bern, Marshall" sort="Bern, Marshall" uniqKey="Bern M" first="Marshall" last="Bern">Marshall Bern</name><name sortKey="Haab, Brian B" sort="Haab, Brian B" uniqKey="Haab B" first="Brian B." last="Haab">Brian B. Haab</name><name sortKey="Kletter, Doron" sort="Kletter, Doron" uniqKey="Kletter D" first="Doron" last="Kletter">Doron Kletter</name><name sortKey="Mccarter, Calvin" sort="Mccarter, Calvin" uniqKey="Mccarter C" first="Calvin" last="Mccarter">Calvin Mccarter</name><name sortKey="Partyka, Katie" sort="Partyka, Katie" uniqKey="Partyka K" first="Katie" last="Partyka">Katie Partyka</name><name sortKey="Singh, Sudhir" sort="Singh, Sudhir" uniqKey="Singh S" first="Sudhir" last="Singh">Sudhir Singh</name><name sortKey="Tang, Huiyuan" sort="Tang, Huiyuan" uniqKey="Tang H" first="Huiyuan" last="Tang">Huiyuan Tang</name><name sortKey="Yadav, Jessica" sort="Yadav, Jessica" uniqKey="Yadav J" first="Jessica" last="Yadav">Jessica Yadav</name></noCountry><country name="États-Unis"><region name="Michigan"><name sortKey="Ma, Yinjiao" sort="Ma, Yinjiao" uniqKey="Ma Y" first="Yinjiao" last="Ma">Yinjiao Ma</name></region><name sortKey="Ma, Yinjiao" sort="Ma, Yinjiao" uniqKey="Ma Y" first="Yinjiao" last="Ma">Yinjiao Ma</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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