Proteomic analysis of Chinese hamster ovary cells.

To complement the recent genomic sequencing of Chinese hamster ovary (CHO) cells, proteomic analysis was performed on CHO cells including the cellular proteome, secretome, and glycoproteome using tandem mass spectrometry (MS/MS) of multiple fractions obtained from gel electrophoresis, multidimensional liquid chromatography, and solid phase extraction of glycopeptides (SPEG). From the 120 different mass spectrometry analyses generating 682,097 MS/MS spectra, 93,548 unique peptide sequences were identified with at most 0.02 false discovery rate (FDR). A total of 6164 grouped proteins were identified from both glycoproteome and proteome analysis, representing an 8-fold increase in the number of proteins currently identified in the CHO proteome. Furthermore, this is the first proteomic study done using the CHO genome exclusively, which provides for more accurate identification of proteins. From this analysis, the CHO codon frequency was determined and found to be distinct from humans, which will facilitate expression of human proteins in CHO cells. Analysis of the combined proteomic and mRNA data sets indicated the enrichment of a number of pathways including protein processing and apoptosis but depletion of proteins involved in steroid hormone and glycosphingolipid metabolism. Five-hundred four of the detected proteins included N-acetylation modifications, and 1292 different proteins were observed to be N-glycosylated. This first large-scale proteomic analysis will enhance the knowledge base about CHO capabilities for recombinant expression and provide information useful in cell engineering efforts aimed at modifying CHO cellular functions.

[1]  Mark Leonard,et al.  Sustained productivity in recombinant Chinese Hamster Ovary (CHO) cell lines: proteome analysis of the molecular basis for a process-related phenotype , 2011, BMC biotechnology.

[2]  Prateek Gupta,et al.  Genomics and proteomics in process development: opportunities and challenges. , 2007, Trends in biotechnology.

[3]  Ronald J. Moore,et al.  Reversed‐phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells , 2011, Proteomics.

[4]  Proteomic understanding of intracellular responses of recombinant Chinese hamster ovary cells cultivated in serum-free medium supplemented with hydrolysates , 2011, Applied Microbiology and Biotechnology.

[5]  G. Lee,et al.  Limitations to the comparative proteomic analysis of thrombopoietin producing Chinese hamster ovary cells treated with sodium butyrate. , 2008, Journal of biotechnology.

[6]  Mamoun Ahram,et al.  Identification of shed proteins from chinese hamster ovary cells: Application of statistical confidence using human and mouse protein databases , 2005, Proteomics.

[7]  Nikolaj Blom,et al.  BIOINFORMATICS APPLICATIONS NOTE Sequence analysis NetAcet: prediction of N-terminal acetylation sites , 2004 .

[8]  Kelvin H. Lee,et al.  The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line , 2011, Nature Biotechnology.

[9]  W. Scheper,et al.  Long-range RNA interaction of two sequence elements required for endonucleolytic cleavage of human insulin-like growth factor II mRNAs , 1995, Molecular and cellular biology.

[10]  Nichole L. King,et al.  Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry , 2004, Genome Biology.

[11]  S. Loening,et al.  Different mRNA and protein expression of matrix metalloproteinases 2 and 9 and tissue inhibitor of metalloproteinases 1 in benign and malignant prostate tissue. , 2002, European urology.

[12]  Åke Björck,et al.  Numerical methods for least square problems , 1996 .

[13]  Ryan Nolan,et al.  Dynamic model of CHO cell metabolism. , 2011, Metabolic engineering.

[14]  Jerry D. Holman,et al.  Identifying Proteomic LC‐MS/MS Data Sets with Bumbershoot and IDPicker , 2012, Current protocols in bioinformatics.

[15]  Yuan Tian,et al.  GlycoFly: a database of Drosophila N-linked glycoproteins identified using SPEG--MS techniques. , 2011, Journal of proteome research.

[16]  A. Agresti.  [A Survey of Exact Inference for Contingency Tables]: Rejoinder , 1992 .

[17]  Ruedi Aebersold,et al.  Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry , 2003, Nature Biotechnology.

[18]  D. Tabb,et al.  TagRecon: high-throughput mutation identification through sequence tagging. , 2010, Journal of proteome research.

[19]  M. Mann,et al.  Universal sample preparation method for proteome analysis , 2009, Nature Methods.

[20]  B. Beutler,et al.  Identification of a common nucleotide sequence in the 3'-untranslated region of mRNA molecules specifying inflammatory mediators. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[21]  A. Skoultchi,et al.  Coupling of replication type histone mRNA levels to DNA synthesis requires the stem-loop sequence at the 3' end of the mRNA. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[22]  M. Gerstein,et al.  Comparing protein abundance and mRNA expression levels on a genomic scale , 2003, Genome Biology.

[23]  James R. Knight,et al.  A Protein Interaction Map of Drosophila melanogaster , 2003, Science.

[24]  Patrick G. A. Pedrioli,et al.  A high-quality catalog of the Drosophila melanogaster proteome , 2007, Nature Biotechnology.

[25]  Akhilesh Pandey,et al.  Quantitative proteomics using stable isotope labeling with amino acids in cell culture , 2008, Nature Protocols.

[26]  M. Al‐Rubeai,et al.  Using cell engineering and omic tools for the improvement of cell culture processes , 2007, Cytotechnology.

[27]  D. Tabb,et al.  MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. , 2007, Journal of proteome research.

[28]  M. Washburn,et al.  Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors , 2006, Proceedings of the National Academy of Sciences.

[29]  M. Betenbaugh,et al.  A comparison of the properties of a Bcl-xL variant to the wild-type anti-apoptosis inhibitor in mammalian cell cultures. , 2003, Metabolic engineering.

[30]  M. Betenbaugh,et al.  Aven and Bcl‐xL enhance protection against apoptosis for mammalian cells exposed to various culture conditions , 2004, Biotechnology and bioengineering.

[31]  Kelvin H. Lee,et al.  Genomic sequencing and analysis of a Chinese hamster ovary cell line using Illumina sequencing technology , 2011, BMC Genomics.

[32]  S. Peltz,et al.  The poly(A)-poly(A)-binding protein complex is a major determinant of mRNA stability in vitro , 1989, Molecular and cellular biology.

[33]  L. Zhang,et al.  Development of a highly efficient 2-D system with a serially coupled long column and its application in identification of rat brain integral membrane proteins with ionic liquids-assisted solubilization and digestion. , 2011, Journal of proteome research.

[34]  Nandini A. Sahasrabuddhe,et al.  A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry. , 2011, Genome research.

[35]  J. Ross,et al.  mRNA stability in mammalian cells. , 1995, Microbiological reviews.

[36]  Yuan Tian,et al.  Solid-phase extraction of N-linked glycopeptides , 2007, Nature Protocols.

[37]  Yeon‐Gu Kim,et al.  Assessment of cell engineering strategies for improved therapeutic protein production in CHO cells , 2008, Biotechnology journal.

[38]  L. Hurst,et al.  Evidence for selection on synonymous mutations affecting stability of mRNA secondary structure in mammals , 2005, Genome Biology.

[39]  B. Ingalls,et al.  Proteomics analysis of chinese hamster ovary cells undergoing apoptosis during prolonged cultivation , 2011, Cytotechnology.

[40]  Yizhar Lavner,et al.  Codon bias as a factor in regulating expression via translation rate in the human genome. , 2005, Gene.

[41]  J. Plotkin,et al.  Synonymous but not the same: the causes and consequences of codon bias , 2011, Nature Reviews Genetics.

[42]  Mark Leonard,et al.  Microarray and proteomics expression profiling identifies several candidates, including the valosin‐containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines , 2010, Biotechnology and bioengineering.

[43]  Gary Walsh,et al.  Biopharmaceutical benchmarks 2010 , 2010, Nature Biotechnology.

[44]  A. Helwak,et al.  High Guanine and Cytosine Content Increases mRNA Levels in Mammalian Cells , 2006, PLoS biology.

[45]  J. Seilhamer,et al.  A comparison of selected mRNA and protein abundances in human liver , 1997, Electrophoresis.

[46]  Wei-Shou Hu,et al.  Genomic and proteomic exploration of CHO and hybridoma cells under sodium butyrate treatment , 2008, Biotechnology and bioengineering.

[47]  T. Ikemura.  Codon usage and tRNA content in unicellular and multicellular organisms. , 1985, Molecular biology and evolution.

[48]  Niki S. C. Wong,et al.  Engineering mammalian cells in bioprocessing – current achievements and future perspectives , 2010, Biotechnology and applied biochemistry.

[49]  R. Klausner,et al.  Iron regulation of transferrin receptor mRNA levels requires iron‐responsive elements and a rapid turnover determinant in the 3′ untranslated region of the mRNA. , 1989, The EMBO journal.

[50]  M. Betenbaugh,et al.  Comparison of Bcl-2 to a Bcl-2 deletion mutant for mammalian cells exposed to culture insults. , 2001, Biotechnology and bioengineering.

[51]  F. H. Schmidt,et al.  The role of mRNA and protein stability in gene expression , 1989, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[52]  W. Hancock,et al.  Proteomic profiling of a high-producing Chinese hamster ovary cell culture. , 2009, Analytical chemistry.

[53]  G. Lee,et al.  Protein reference mapping of dihydrofolate reductase‐deficient CHO DG44 cell lines using 2‐dimensional electrophoresis , 2010, Proteomics.

[54]  Yuan Tian,et al.  GlycoFish: a database of zebrafish N-linked glycoproteins identified using SPEG method coupled with LC/MS. , 2011, Analytical chemistry.

[55]  J. Elf,et al.  Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage , 2003, Science.

[56]  D. Carlini.  Context-dependent codon bias and messenger RNA longevity in the yeast transcriptome. , 2005, Molecular biology and evolution.

[57]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[58]  K. Kobayashi,et al.  Combined transcriptome and proteome analysis as a powerful approach to study genes under glucose repression in Bacillus subtilis. , 2001, Nucleic acids research.

[59]  K. Champion,et al.  Proteomic studies support the use of multi‐product immunoassays to monitor host cell protein impurities , 2006, Proteomics.