Lewis N.E.,CHOmics Inc. |
Liu X.,BGI Shenzhen |
Liu X.,Intrexon Corporation |
Li Y.,BGI Shenzhen |
And 28 more authors.
Nature Biotechnology | Year: 2013
Chinese hamster ovary (CHO) cells, first isolated in 1957, are the preferred production host for many therapeutic proteins. Although genetic heterogeneity among CHO cell lines has been well documented, a systematic, nucleotide-resolution characterization of their genotypic differences has been stymied by the lack of a unifying genomic resource for CHO cells. Here we report a 2.4-Gb draft genome sequence of a female Chinese hamster, Cricetulus griseus, harboring 24,044 genes. We also resequenced and analyzed the genomes of six CHO cell lines from the CHO-K1, DG44 and CHO-S lineages. This analysis identified hamster genes missing in different CHO cell lines, and detected ≥3.7 million single-nucleotide polymorphisms (SNPs), 551,240 indels and 7,063 copy number variations. Many mutations are located in genes with functions relevant to bioprocessing, such as apoptosis. The details of this genetic diversity highlight the value of the hamster genome as the reference upon which CHO cells can be studied and engineered for protein production. © 2013 Nature America, Inc. All rights reserved.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2014
DESCRIPTION (provided by applicant): Red blood cells (RBC) stored in approved additive solutions undergo a set of metabolic and physicochemical changes referred to as 'storage lesions' reducing the efficacy and safety of older transfused RBC units. Thoughthe consequences of the storage lesion are slowly becoming well documented, a major reason for delayed progress in developing new technologies for quality and safety of RBC transfusion is the lack of global understanding of metabolic decline during storage. There has been interest to utilize high-throughput metabolite profiling for global understanding of RBC metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic networks, and data-driven kinetic models to fully interpret and analyze RBC metabolite-profiles in a complete network context. The program will utilize time- course global, quant