| Title |
Identification of regulatory motifs in the CHO genome for stable monoclonal antibody production
|
|---|---|
| Published in |
Methods in Cell Science, August 2016
|
| DOI | 10.1007/s10616-016-0017-8 |
| Pubmed ID | |
| Authors |
Yasuhiro Takagi, Tomomi Yamazaki, Kenji Masuda, Shigeaki Nishii, Bunsei Kawakami, Takeshi Omasa |
| Abstract |
Chinese hamster ovary (CHO) cell lines are widely used for therapeutic protein production. When a transgene is integrated into the genome of a CHO cell, the expression level is highly dependent on the site of integration because of positional effects such as gene silencing. To overcome negative positional effects and establish stable CHO cell lines with high productivity, several regulatory DNA elements are used in vector construction. Previously, we established the CHO DR1000L-4N cell line, a stable and high copy number Dhfr gene-amplified cell line. It was hypothesized that the chromosomal location of the exogenous gene-amplified region in the CHO DR1000L-4N genome contains regulatory motifs for stable protein production. Therefore, we isolated DNA regulatory motifs from the CHO DR1000L-4N cell line and determined whether these motifs act as an insulator. Our results suggest that stable expression of a transgene can be promoted by the CHO genome sequence, and it would be a powerful tool for therapeutic protein manufacturing. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 2 | 4% |
| Unknown | 53 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 25% |
| Researcher | 12 | 22% |
| Other | 6 | 11% |
| Student > Doctoral Student | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 5 | 9% |
| Unknown | 11 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 38% |
| Agricultural and Biological Sciences | 10 | 18% |
| Engineering | 6 | 11% |
| Chemical Engineering | 4 | 7% |
| Unspecified | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 12 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2016.
All research outputs
#22,756,649
of 25,371,288 outputs
Outputs from Methods in Cell Science
#908
of 1,026 outputs
Outputs of similar age
#314,750
of 355,178 outputs
Outputs of similar age from Methods in Cell Science
#7
of 10 outputs
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