| Chapter title |
Glycoengineering of CHO Cells to Improve Product Quality
|
|---|---|
| Chapter number | 2 |
| Book title |
Heterologous Protein Production in CHO Cells
|
| Published in |
Methods in molecular biology, May 2017
|
| DOI | 10.1007/978-1-4939-6972-2_2 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6971-5, 978-1-4939-6972-2
|
| Authors |
Wang, Qiong, Yin, Bojiao, Chung, Cheng-Yu, Betenbaugh, Michael J., Qiong Wang, Bojiao Yin, Cheng-Yu Chung, Michael J. Betenbaugh |
| Editors |
Paula Meleady |
| Abstract |
Chinese hamster ovary (CHO) cells represent the predominant platform in biopharmaceutical industry for the production of recombinant biotherapeutic proteins, especially glycoproteins. These glycoproteins include oligosaccharide or glycan attachments that represent one of the principal components dictating product quality. Especially important are the N-glycan attachments present on many recombinant glycoproteins of commercial interest. Furthermore, altering the glycan composition can be used to modulate the production quality of a recombinant biotherapeutic from CHO and other mammalian hosts. This review first describes the glycosylation network in mammalian cells and compares the glycosylation patterns between CHO and human cells. Next genetic strategies used in CHO cells to modulate the sialylation patterns through overexpression of sialyltransfereases and other glycosyltransferases are summarized. In addition, other approaches to alter sialylation including manipulation of sialic acid biosynthetic pathways and inhibition of sialidases are described. Finally, this review also covers other strategies such as the glycosylation site insertion and manipulation of glycan heterogeneity to produce desired glycoforms for diverse biotechnology applications. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 16% |
| Researcher | 11 | 16% |
| Student > Master | 6 | 9% |
| Student > Bachelor | 5 | 7% |
| Other | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 24 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 30% |
| Chemical Engineering | 9 | 13% |
| Agricultural and Biological Sciences | 3 | 4% |
| Chemistry | 3 | 4% |
| Immunology and Microbiology | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 26 | 37% |