A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering
Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-11-01
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| Series: | Current Research in Biotechnology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590262820300010 |
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| author | Chenguang Liang Austin W.T. Chiang Anders H. Hansen Johnny Arnsdorf Sanne Schoffelen James T. Sorrentino Benjamin P. Kellman Bokan Bao Bjørn G. Voldborg Nathan E. Lewis |
| author_facet | Chenguang Liang Austin W.T. Chiang Anders H. Hansen Johnny Arnsdorf Sanne Schoffelen James T. Sorrentino Benjamin P. Kellman Bokan Bao Bjørn G. Voldborg Nathan E. Lewis |
| author_sort | Chenguang Liang |
| collection | DOAJ |
| description | Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modeling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation. |
| first_indexed | 2024-12-19T13:31:11Z |
| format | Article |
| id | doaj.art-b79c6ab39ef84984b515ef1fae17638c |
| institution | Directory Open Access Journal |
| issn | 2590-2628 |
| language | English |
| last_indexed | 2024-12-19T13:31:11Z |
| publishDate | 2020-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Biotechnology |
| spelling | doaj.art-b79c6ab39ef84984b515ef1fae17638c2022-12-21T20:19:21ZengElsevierCurrent Research in Biotechnology2590-26282020-11-0122236A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineeringChenguang Liang0Austin W.T. Chiang1Anders H. Hansen2Johnny Arnsdorf3Sanne Schoffelen4James T. Sorrentino5Benjamin P. Kellman6Bokan Bao7Bjørn G. Voldborg8Nathan E. Lewis9Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USADepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USAThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, DenmarkThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, DenmarkThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, DenmarkDepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USADepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USADepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USAThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, DenmarkDepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USA; Corresponding author at: 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA.Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modeling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.http://www.sciencedirect.com/science/article/pii/S2590262820300010Glycosylation modelGlycomicsSystems glycobiologyGlycoengineeringIsozyme specificityGlycosyltransferase interactions |
| spellingShingle | Chenguang Liang Austin W.T. Chiang Anders H. Hansen Johnny Arnsdorf Sanne Schoffelen James T. Sorrentino Benjamin P. Kellman Bokan Bao Bjørn G. Voldborg Nathan E. Lewis A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering Current Research in Biotechnology Glycosylation model Glycomics Systems glycobiology Glycoengineering Isozyme specificity Glycosyltransferase interactions |
| title | A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| title_full | A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| title_fullStr | A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| title_full_unstemmed | A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| title_short | A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| title_sort | markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering |
| topic | Glycosylation model Glycomics Systems glycobiology Glycoengineering Isozyme specificity Glycosyltransferase interactions |
| url | http://www.sciencedirect.com/science/article/pii/S2590262820300010 |
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