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A high quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely onde novogene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrectly predictions of translated sequence, incorrect splice isoforms and missing genes. Here we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 4,333 new proteins compared to the hamster RefSeq protein annotation and 2,503 novel translational events (e.g., alternative splices, mutations, novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 131 type-C retroviruses, thus enabling future efforts to eliminate retroviruses by reducing the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate platform for guiding CHO cell line engineering, including facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

Background Meningiomas are heterogeneous, with differences in anatomical, histopathological, and clinical characteristics. Such spatial variability in meningioma biology is thought to result from differences in the expression of critical developmental regulators. We hypothesized that the variability in meningioma biology would follow gradients such as in embryology and tested a cohort of 366 meningiomas for histopathological and immunohistochemical gradients. Methods The medical records from 366 patients treated for meningiomas from 2003 to 2016 were retrospectively analyzed for age, gender, anatomical localization, recurrence-free survival, overall survival, histopathological diagnosis, and immunohistochemistry findings for 6 markers: epithelial membrane antigen (EMA), progesterone receptor (PR), CD34, S100, p53, and Ki-67 labeling index. Results EMA, PR, S100, p53, and CD34 were expressed in 94%, 73%, 49%, 26%, and 23% of the tumors, respectively. p53 expression correlated positively with Ki-67 and World Health Organization (WHO) grade (rτ = 0.31 and rτ = 0.4, respectively). PR positivity correlated inversely with S100, p53, Ki-67, and WHO grade (rτ = −0.19, rτ = −0.14, rτ = −0.15, and rτ = −0.16, respectively). All secretory meningiomas were positive for EMA and PR and negative for S100, and this pattern exhibited a rostrocaudal gradient. The overall proportion of EMA+PR+S100− cases was significantly lower in the cranial vault (30.3%) than in the skull base (45.89%; P = 0.021). The proportion of WHO grade II-III tumors was greater in cranial vault than in skull base meningiomas. Conclusions Unsupervised methods detected an association between the anatomical location and tumor biology in meningiomas. Unlike the categorical associations that former studies had indicated, the present study revealed a rostrocaudal gradient in both the cranial vault and the skull base, correlating with human developmental biology.

A combined lipidomics and transcriptomics analysis was performed on mouse myeloma SP2/0, Chinese hamster ovary (CHO), and human embryonic kidney (HEK) cells in order to compare widely used mammalian expression systems. Initial thin layer chromatography (TLC) analysis indicated that phosphatidylethanolamine (PE) and phosphatidylcholine (PC) were the major lipid components in all cell lines with lower amounts of sphingomyelin (SM) in SP2/0 compared to CHO and HEK, which was subsequently confirmed and expanded upon following mass spectrometry (MS) analysis. HEK contained 4-10-fold higher amounts of lyso phosphatidylethanolamine (LPE) and 2-4-fold higher amounts of lyso phosphatidylcholine (LPC) compared to SP2/0 and CHO cell lines. C18:1 followed by C16:1 were the main contributors to the difference in both LPE and LPC levels. Alternatively, the SP2/0 cell line exhibited 30-65-fold lower amounts of SM principally in the amount of 16:0. By mapping the transcriptomics data to KEGG pathways, we found expression levels of secretory phospholipase A2 (sPLA2), lysophospholipid acyltransferase (LPEAT), lysophosphatidylcholine acyltransferase (LPCAT), and lysophospholipase (LYPLA) can contribute to the differences in LPE and LPC. Sphingomyelin synthases (SMS) and sphingomyelin phosphodiesterase (SMase) enzymes may play roles in SM differences across the three cell lines. The results of this study provide insights that will aid the understanding of the physiological and secretory differences across recombinant protein production systems.

Chinese hamster ovary (CHO) cells are the predominant production vehicle for biotherapeutics. Quantitative proteomics data were obtained from two CHO cell lines (CHO-S and CHO DG44) and compared with seven Chinese hamster (Cricetulus griseus) tissues (brain, heart, kidney, liver, lung, ovary and spleen) by tandem mass tag (TMT) labeling followed by mass spectrometry, providing a comprehensive hamster tissue and cell line proteomics atlas. Of the 8470 unique proteins identified, high similarity was observed between CHO-S and CHO DG44 and included increases in proteins involved in DNA replication, cell cycle, RNA processing, and chromosome processing. Alternatively, gene ontology and pathway analysis in tissues indicated increased protein intensities related to important tissue functionalities. Proteins enriched in the brain included those involved in acidic amino acid metabolism, Golgi apparatus, and ion and phospholipid transport. The lung showed enrichment in proteins involved in BCAA catabolism, ROS metabolism, vesicle trafficking, and lipid synthesis while the ovary exhibited enrichments in extracellular matrix and adhesion proteins. The heart proteome included vasoconstriction, complement activation, and lipoprotein metabolism enrichments. These detailed comparisons of CHO cell lines and hamster tissues will enhance understanding of the relationship between proteins and tissue function and pinpoint potential pathways of biotechnological relevance for future cell engineering.

Derivitization of peptides with isobaric tags such as iTRAQ and TMT is widely employed in proteomics due to their compatibility with multiplex quantitative measurements. We recently made publicly available a large peptide library derived from iTRAQ 4-plex labeled spectra. This resource has not been used for identifying peptides labeled with related tags with different masses, because values for virtually all masses of precursor and most product ions would differ for ions containing the different tags as well as containing different tag-specific peaks. We describe a method for interconverting spectra from iTRAQ 4-plex to TMT (6- and 10-plex) and to iTRAQ 8-plex. We interconvert spectra by appropriately mass shifting sequence ions and discarding derivative-specific peaks. After this "cleaning" of search spectra, we demonstrate that the converted libraries perform well in terms of peptide spectral matches. This is demonstrated by comparing results using sequence database searches as well as by comparing search effectiveness using original and converted libraries. At 1% FDR TMT labeled query spectra match 97% as many spectra against a converted iTRAQ library as compared to an original TMT library. Overall this interconversion strategy provides a practical way to extend results from one derivatization method to others that share related chemistry and do not significantly alter fragmentation profiles.

Mammalian expression systems such as Chinese hamster ovary (CHO), mouse myeloma (NS0), and human embryonic kidney (HEK) cells serve a critical role in the biotechnology industry as the production host of choice for recombinant protein therapeutics. Most of the recombinant biologics are glycoproteins that contain complex oligosaccharide or glycan attachments representing a principal component of product quality. Both N-glycans and O-glycans are present in these mammalian cells, but the engineering of N-linked glycosylation is of critical interest in industry and many efforts have been directed to improve this pathway. This is because altering the N-glycan composition can change the product quality of recombinant biotherapeutics in mammalian hosts. In addition, sialylation and fucosylation represent components of the glycosylation pathway that affect circulatory half-life and antibody-dependent cellular cytotoxicity, respectively. In this chapter, we first offer an overview of the glycosylation, sialylation, and fucosylation networks in mammalian cells, specifically CHO cells, which are extensively used in antibody production. Next, genetic engineering technologies used in CHO cells to modulate glycosylation pathways are described. We provide examples of their use in CHO cell engineering approaches to highlight these technologies further. Specifically, we describe efforts to overexpress glycosyltransferases and sialyltransfereases, and efforts to decrease sialidase cleavage and fucosylation. Finally, this chapter covers new strategies and future directions of CHO cell glycoengineering, such as the application of glycoproteomics, glycomics, and the integration of 'omics' approaches to identify, quantify, and characterize the glycosylated proteins in CHO cells. Graphical Abstract.

Mammalian expression systems such as Chinese hamster ovary (CHO), mouse myeloma (NS0), and human embryonic kidney (HEK) cells serve a critical role in the biotechnology industry as the production host of choice for recombinant protein therapeutics. Most of the recombinant biologics are glycoproteins that contain complex oligosaccharide or glycan attachments representing a principal component of product quality. Both N-glycans and O-glycans are present in these mammalian cells, but the engineering of N-linked glycosylation is of critical interest in industry and many efforts have been directed to improve this pathway. This is because altering the N-glycan composition can change the product quality of recombinant biotherapeutics in mammalian hosts. In addition, sialylation and fucosylation represent components of the glycosylation pathway that affect circulatory half-life and antibody-dependent cellular cytotoxicity, respectively. In this chapter, we first offer an overview of the glycosylation, sialylation, and fucosylation networks in mammalian cells, specifically CHO cells, which are extensively used in antibody production. Next, genetic engineering technologies used in CHO cells to modulate glycosylation pathways are described. We provide examples of their use in CHO cell engineering approaches to highlight these technologies further. Specifically, we describe efforts to overexpress glycosyltransferases and sialyltransfereases, and efforts to decrease sialidase cleavage and fucosylation. Finally, this chapter covers new strategies and future directions of CHO cell glycoengineering, such as the application of glycoproteomics, glycomics, and the integration of ‘omics’ approaches to identify, quantify, and characterize the glycosylated proteins in CHO cells.

Chinese hamster ovary (CHO) cells are the preferred host cell line for manufacturing a variety of complex biotherapeutic drugs including monoclonal antibodies. We performed a proteomics and bioinformatics analysis on the spent medium from adherent CHO cells. Supernatant from CHO-K1 culture was collected and subjected to in-solution digestion followed by LC/LC-MS/MS analysis, which allowed the identification of 3281 different host cell proteins (HCPs). To functionally categorize them, we applied multiple bioinformatics tools to the proteins identified in our study including SignalP, TargetP, SecretomeP, TMHMM, WoLF PSORT, and Phobius. This analysis provided information on the presence of signal peptides, transmembrane domains, and cellular localization and showed that both secreted and intracellular proteins were constituents of the supernatant. Identified proteins were shown to be localized to the secretory pathway including ones playing roles in cell growth, proliferation, and folding as well as those involved in protein degradation and removal. After combining proteins predicted to be secreted or having a signal peptide, we identified 1015 proteins, which we termed as CHO supernatant-ome (CHO-SO), or superome. As a part of this effort, we created a publically accessible web-based tool called GO-CHO to functionally categorize proteins found in CHO-SO and identify enriched molecular functions, biological processes, and cellular components. We also used a tool to evaluate the immunogenicity potential of high-abundance HCPs. Among enriched functions were catalytic activity and structural constituents of the cytoskeleton. Various transport related biological processes, such as vesicle mediated transport, were found to be highly enriched. Extracellular space and vesicular exosome associated proteins were found to be the most enriched cellular components. The superome also contained proteins secreted from both classical and nonclassical secretory pathways. The work and database described in our study will enable the CHO community to rapidly identify high-abundance HCPs in their cultures and therefore help assess process and purification methods used in the production of biologic drugs.

In this study, omics-based analysis tools were used to explore the effect of glucose starvation and culture duration on monoclonal antibody (mAb) production in fed-batch CHO cell culture to gain better insight into how these parameters can be controlled to ensure optimal mAb productivity and quality. Titer and N-glycosylation of mAbs, as well as proteomic signature and metabolic status of the production cells in the culture were assessed. We found that the impact of glucose starvation on the titer and N-glycosylation of mAbs was dependent on the degree of starvation during early stationary phase of the fed-batch culture. Higher degree of glucose starvation reduced intracellular concentrations of UDP-GlcNAc and UDP-GalNAc, but increased the levels of UDP-Glc and UDP-Gal. Increased GlcNAc and Gal occupancy correlated well with increased degree of glucose starvation, which can be attributed to the interplay between the dilution effect associated with change in specific productivity of mAbs and the changed nucleotide sugar metabolism. Herein, we also show and discuss that increased cell culture duration negatively affect the maturation of glycans. In addition, comparative proteomics analysis of cells was conducted to observe differences in protein abundance between early growth and early stationary phases. Generally higher expression of proteins involved in regulating cellular metabolism, extracellular matrix, apoptosis, protein secretion and glycosylation was found in early stationary phase. These analyses offered a systematic view of the intrinsic properties of these cells and allowed us to explore the root causes correlating culture duration with variations in the productivity and glycosylation quality of monoclonal antibodies produced with CHO cells.

Recent advancements in proteomics have enabled the generation of high-quality data sets useful for applications ranging from target and monoclonal antibody (mAB) discovery to bioprocess optimization. Comparative proteomics approaches have recently been used to identify novel disease targets in oncology and other disease conditions. Proteomics has also been applied as a new avenue for mAb discovery. Finally, CHO and Escherichia coli cells represent the dominant production hosts for biopharmaceutical development, yet the physiology of these cells types has yet to be fully established. Proteomics approaches can provide new insights into these cell types, aiding in recombinant protein production, cell growth regulation, and medium formulation. Optimization of sample preparations and protein database developments are enhancing the quantity and accuracy of proteomic results. In these ways, innovations in proteomics are enriching biotechnology and bioprocessing research across a wide spectrum of applications.

Chinese hamster ovary cells represent the dominant host for therapeutic recombinant protein production. However, few large-scale data sets have been generated to characterize this host organism and derived CHO cell lines at the proteomics level. Consequently, an extensive label-free quantitative proteomics analysis of two cell lines (CHO-S and CHO DG44) and two Chinese hamster tissues (liver and ovary) was used to identify a total of 11 801 unique proteins containing at least two unique peptides. 9359 unique proteins were identified specifically in the cell lines, representing a 56% increase over previous work. Additionally, 6663 unique proteins were identified across liver and ovary tissues, providing the first Chinese hamster tissue proteome. Protein expression was more conserved within cell lines during both growth phases than across cell lines, suggesting large genetic differences across cell lines. Overall, both gene ontology and KEGG pathway analysis revealed enrichment of cell-cycle activity in cells. In contrast, upregulated molecular functions in tissue include glycosylation and lipid transporter activity. Furthermore, cellular components including Golgi apparatus are upregulated in both tissues. In conclusion, this large-scale proteomics analysis enables us to delineate specific changes between tissues and cells derived from these tissues, which can help explain specific tissue function and the adaptations cells incur for applications in biopharmaceutical productions.

Mass spectrometry is being used to identify protein biomarkers that can facilitate development of drug treatment. Mass spectrometry-based labeling proteomic experiments result in complex proteomic data that is hierarchical in nature often with small sample size studies. The generalized linear model (GLM) is the most popular approach in proteomics to compare protein abundances between groups. However, GLM does not address all the complexities of proteomics data such as repeated measures and variance heterogeneity. Linear models for microarray data (LIMMA) and mixed models are two approaches that can address some of these data complexities to provide better statistical estimates. We compared these three statistical models (GLM, LIMMA, and mixed models) under two different normalization approaches (quantile normalization and median sweeping) to demonstrate when each approach is the best for tagged proteins. We evaluated these methods using a spiked-in data set of known protein abundances, a systemic lupus erythematosus (SLE) data set, and simulated data from multiplexed labeling experiments that use tandem mass tags (TMT). Data are available via ProteomeXchange with identifier PXD005486. We found median sweeping to be a preferred approach of data normalization, and with this normalization approach there was overlap with findings across all methods with GLM being a subset of mixed models. The conclusion is that the mixed model had the best type I error with median sweeping, whereas LIMMA had the better overall statistical properties regardless of normalization approaches.

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology., The secretory pathway is used in the production of most biopharmaceuticals, but the associated biosynthetic costs are little understood. Here, the authors integrate the core secretory pathway into genome-scale metabolic models of human, mouse, and CHO cells, enabling in silico analysis.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

The number of proteins encoded in the human genome has been estimated at between 20,000 and 25,000, despite estimates that the entire proteome contains more than a million proteins. One reason for this difference is due to many post-translational modifications of protein that contribute to proteome complexity. Among these, glycosylation is of particular relevance because it serves to modify a large number of cellular proteins. Glycogenomics, glycoproteomics, glycomics, and glycoinformatics are helping to accelerate our understanding of the cellular events involved in generating the glycoproteome, the variety of glycan structures possible, and the importance of roles that glycans play in therapeutics and disease. Indeed, interest in glycosylation has expanded rapidly over the past decade, as large amounts of experimental 'omics data relevant to glycosylation processing have accumulated. Furthermore, new and more sophisticated glycoinformatics tools and databases are now available for glycan and glycosylation pathway analysis. Here, we summarize some of the recent advances in both experimental profiling and analytical methods involving N- and O-linked glycosylation processing for biotechnological and medically relevant cells together with the unique opportunities and challenges associated with interrogating and assimilating multiple, disparate high-throughput glycosylation data sets. This emerging era of advanced glycomics will lead to the discovery of key glycan biomarkers linked to diseases and help establish a better understanding of physiology and improved control of glycosylation processing in diverse cells and tissues important to disease and production of recombinant therapeutics. Furthermore, methodologies that facilitate the integration of glycomics measurements together with other 'omics data sets will lead to a deeper understanding and greater insights into the nature of glycosylation as a complex cellular process.

Protein secretion and vesicular trafficking in mammalian cells rely on several key lipids including sphingolipids, phospholipids, and neutral lipids crucial to protein processing and other intracellular events. Proteins interact with these lipids to alter the shape of lipid bilayer, thereby playing a pivotal role in cellular sorting. Although some efforts have elucidated the role of these components, extensive studies are needed to further decipher the protein-lipid interactions along with the effect of membrane curvature and rafts in sorting of proteins. The regulatory role of proteins in subcellular localization and metabolism of lipids also needs to be described. Recent studies on the role of lipid-protein interactions in modulating membrane shape, signal transduction, and vesicular trafficking are presented in this review.

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.

In this study, omics-based analysis tools were used to explore the effect of glucose starvation and culture duration on monoclonal antibody (mAb) production in fed-batch CHO cell culture to gain better insight into how these parameters can be controlled to ensure optimal mAb productivity and quality. Titer and N-glycosylation of mAbs, as well as proteomic signature and metabolic status of the production cells in the culture were assessed. We found that the impact of glucose starvation on the titer and N-glycosylation of mAbs was dependent on the degree of starvation during early stationary phase of the fed-batch culture. Higher degree of glucose starvation reduced intracellular concentrations of UDP-GlcNAc and UDP-GalNAc, but increased the levels of UDP-Glc and UDP-Gal. Increased GlcNAc and Gal occupancy correlated well with increased degree of glucose starvation, which can be attributed to the interplay between the dilution effect associated with change in specific productivity of mAbs and the changed nucleotide sugar metabolism. Herein, we also show and discuss that increased cell culture duration negatively affect the maturation of glycans. In addition, comparative proteomics analysis of cells was conducted to observe differences in protein abundance between early growth and early stationary phases. Generally higher expression of proteins involved in regulating cellular metabolism, extracellular matrix, apoptosis, protein secretion and glycosylation was found in early stationary phase. These analyses offered a systematic view of the intrinsic properties of these cells and allowed us to explore the root causes correlating culture duration with variations in the productivity and glycosylation quality of monoclonal antibodies produced with CHO cells.

The genetic sequencing of Chinese hamster ovary cells has initiated a systems biology era for biotechnology applications. In addition to genomics, critical ‘omics data sets also include proteomics, transcriptomics and metabolomics. Recently, the use of proteomics in cell lines for recombinant protein production has increased significantly because proteomics can track changes in protein levels for different cell lines over time, which can be advantageous for bioprocess development and optimization. Specifically, the identification of proteins that affect cell culture processes can aid efforts in media development and cell line engineering to improve growth or productivity, delay the onset of apoptosis, or utilize nutrients efficiently. Mass-spectrometry based and other proteomics methods can provide for the detection of thousands of proteins from cell culture and bioinformatics analysis serves to identify and quantify protein levels. Optimizations of sample preparations and database development, including a detailed CHO proteome now available, have improved the quantity and accuracy of identified proteins. The applications are widespread and expanding, thus suggesting numerous applications of proteomics and combined ‘omics experiments in coming years.