Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow

Harnessing metabolic plasticity in CHO cells for enhanced perfusion cultivation

Chinese Hamster Ovary (CHO) cells have rapidly become a cornerstone in biopharmaceutical production. Recently, a reinvigoration of perfusion culture mode in CHO cell cultivation has been observed. However, most cell lines currently in use have been engineered and adapted for fed-batch culture methods, and may not perform optimally under perfusion conditions. To improve the cell's resilience and viability during perfusion culture, we cultured a triple knockout CHO cell line, deficient in three apoptosis related genes BAX, BAK, and BOK in a perfusion system. After 20 days of culture, the cells exhibited a halt in cell proliferation. Interestingly, following this phase of growth arrest, the cells entered a second growth phase. During this phase, the cell numbers nearly doubled, but cell specific productivity decreased. We performed a proteomics investigation, elucidating a distinct correlation between growth arrest and cell cycle arrest and showing an upregulation of the central carbon metabolism and oxidative phosphorylation. The upregulation was partially reverted during the second growth phase, likely caused by intragenerational adaptations to stresses encountered. A phase-dependent response to oxidative stress was noted, indicating glutathione has only a secondary role during cell cycle arrest. Our data provides evidence of metabolic regulation under high cell density culturing conditions and demonstrates that cell growth arrest can be overcome. The acquired insights have the potential to not only enhance our understanding of cellular metabolism but also contribute to the development of superior cell lines for perfusion cultivation.

Amino acid degradation pathway inhibitory by-products trigger apoptosis in CHO cells

Chinese hamster ovary (CHO) cells are widely used to produce complex biopharmaceuticals. Improving their productivity is necessary to fulfill the growing demand for such products. One way to enhance productivity is by cultivating cells at high densities, but inhibitory by-products, such as metabolite derivatives from amino acid degradation, can hinder achieving high cell densities. This research examines the impact of these inhibitory by-products on high-density cultures. We cultured X1 and X2 CHO cell lines in a small-scale semi-perfusion system and introduced a mix of inhibitory by-products on day 10. The X1 and X2 cell lines were chosen for their varied responses to the by-products; X2 was susceptible, while X1 survived. Proteomics revealed that the X2 cell line presented changes in the proteins linked to apoptosis regulation, cell building block synthesis, cell growth, DNA repair, and energy metabolism. We later used the AB cell line, an apoptosis-resistant cell line, to validate the results. AB behaved similar to X1 under stress. We confirmed the activation of apoptosis in X2 using a caspase assay. This research provides insights into the mechanisms of cell death triggered by inhibitory by-products and can guide the optimization of CHO cell culture for biopharmaceutical manufacturing.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications

Much of the biopharmaceutical industry's success over the past 30 years has relied on products derived from Chinese Hamster Ovary (CHO) cell lines. During this time, improvements in mammalian cell cultures have come from cell line development and process optimization suited for large-scale fed-batch processes. Originally developed for high cell densities and sensitive products, perfusion processes have a long history. Driven by high volumetric titers and a small footprint, perfusion-based bioprocess research has regained an interest from academia and industry. The recent pandemic has further highlighted the need for such intensified biomanufacturing options. In this review, we outline the technical history of research in this field as it applies to biologics production in CHO cells. We demonstrate a number of emerging trends in the literature and corroborate these with underlying drivers in the commercial space. From these trends, we speculate that the future of perfusion bioprocesses is bright and that the fields of media optimization, continuous processing, and cell line engineering hold the greatest potential. Aligning in its continuous setup with the demands for Industry 4.0, perfusion biomanufacturing is likely to be a hot topic in the years to come.

A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells

Chinese hamster ovary (CHO) cells are widely used for producing biopharmaceuticals, and engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the published Chinese hamster genome sequence were computationally predicted and are frequently inaccurate. Here, we use nascent transcription start site sequencing methods to revise TSS annotations for 15 308 Chinese hamster genes and 3034 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. We further capture tens of thousands of putative transcribed enhancer regions with this method. Our revised TSSs improves upon the RefSeq annotation by revealing core sequence features of gene regulation such as the TATA box and the Initiator and, as exemplified by targeting the glycosyltransferase gene Mgat3, facilitate activating silent genes by CRISPRa. Together, we envision our revised annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

Integrative overview of antibodies against SARS-CoV-2 and their possible applications in COVID-19 prophylaxis and treatment

SARS-CoV-2 is a novel β-coronavirus that caused the COVID-19 pandemic disease, which spread rapidly, infecting more than 134 million people, and killing almost 2.9 million thus far. Based on the urgent need for therapeutic and prophylactic strategies, the identification and characterization of antibodies has been accelerated, since they have been fundamental in treating other viral diseases. Here, we summarized in an integrative manner the present understanding of the immune response and physiopathology caused by SARS-CoV-2, including the activation of the humoral immune response in SARS-CoV-2 infection and therefore, the synthesis of antibodies. Furthermore, we also discussed about the antibodies that can be generated in COVID-19 convalescent sera and their associated clinical studies, including a detailed characterization of a variety of human antibodies and identification of antibodies from other sources, which have powerful neutralizing capacities. Accordingly, the development of effective treatments to mitigate COVID-19 is expected. Finally, we reviewed the challenges faced in producing potential therapeutic antibodies and nanobodies by cell factories at an industrial level while ensuring their quality, efficacy, and safety.

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).

A Chemometric Tool to Monitor and Predict Cell Viability in Filamentous Fungi Bioprocesses Using UV Chromatogram Fingerprints

Monitoring process variables in bioprocesses with complex expression systems, such as filamentous fungi, requires a vast number of offline methods or sophisticated inline sensors. In this respect, cell viability is a crucial process variable determining the overall process performance. Thus, fast and precise tools for identification of key process deviations or transitions are needed. However, such reliable monitoring tools are still scarce to date or require sophisticated equipment. In this study, we used the commonly available size exclusion chromatography (SEC) HPLC technique to capture impurity release information in Penicillium chrysogenum bioprocesses. We exploited the impurity release information contained in UV chromatograms as fingerprints for development of principal component analysis (PCA) models to descriptively analyze the process trends. Prediction models using well established approaches, such as partial least squares (PLS), orthogonal PLS (OPLS) and principal component regression (PCR), were made to predict the viability with model accuracies of 90% or higher. Furthermore, we demonstrated the platform applicability of our method by monitoring viability in a Trichoderma reesei process for cellulase production. We are convinced that this method will not only facilitate monitoring viability of complex bioprocesses but could also be used for enhanced process control with hybrid models in the future.

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

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.

Accelerating bioprocess development by analysis of all available data: A USP case study

Bioprocess development generates extensive datasets from different unit operations and sources (e.g. time series, quality measurements). The development of such processes can be accelerated by evaluating all data generated during the experimental design. This can only be achieved by having a clearly defined data logging and analysis strategy. The latter is described in this manuscript. It consists in a combination of a feature based approach along with principal component analysis and partial least square regression. Application of this combined strategy is illustrated by applying it in an upstream processing (USP) case study. Data from the development and optimization of an animal component free USP of Sabin inactivated poliovirus vaccine (sIPV) was evaluated. During process development, 26 bioreactor runs at scales ranging from 2.3 to 16 L were performed. Several operational parameters were varied, and data was routinely analyzed following a design of experiments (DoE) methodology. With the strategy described here, it became possible to scrutinize all data from the 26 runs in a single data study. This included the DoE response parameters, all data generated by the bioreactor control systems, all offline data, and its derived calculations. This resulted in a more detailed, reliable and exact view on the most important parameters affecting bioreactor performance. In this case study, the strategy was applied for the analysis of previously produced data. Further development will use this data analysis methodology for continuous enhancing and accelerating process development, intensified DoE and integrated process modelling.

Proteogenomic Annotation of Chinese Hamsters Reveals Extensive Novel Translation Events and Endogenous Retroviral Elements

A high-quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrect predictions of translated sequence, inaccurate 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 3529 new proteins compared to the hamster RefSeq protein annotation and 2256 novel translational events (e.g., alternative splices, mutations, and 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 119 type-C retroviruses, thus enabling future efforts to eliminate retroviruses to reduce the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate resource for CHO cell line engineering, by facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling

Temperature shift (TS) to a hypothermic condition has been widely used during protein production processes that use Chinese hamster ovary (CHO) cells. The effect of temperature on cell growth, metabolites, protein titer and quality depends on cell line, product, and other bioreactor conditions. Due to the large numbers of experiments, which typically last 2-3 weeks each, limited systematic TS studies have been reported with multiple shift temperatures and steps at different times. Here, we systematically studied the effect of temperature on cell culture performance for the production of two monoclonal antibodies by industrial GS and DG44 CHO cell lines. Three 2-8 day short-duration methods were developed and validated for researching the effect of many different temperatures on CHO cell culture and quality attributes. We found that minor temperature differences (1-1.5 °C) affected cell culture performance. The kinetic parameters extracted from the short duration data were subsequently used to compute and predict cell culture performance in extended duration of 10-14 days with multiple TS conditions for both CHO cell lines. These short-duration culture methods with kinetic modeling tools may be used for effective TS optimization to achieve the best profiles for cell growth, metabolites, titer and quality attributes. Although only three short-duration methods were developed with two CHO cell lines, similar short-duration methods with kinetic modeling may be applied for different hosts, including both microbial and other mammalian cells.

Comparison of data science workflows for root cause analysis of bioprocesses

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.

The emerging role of systems biology for engineering protein production in CHO cells

To meet the ever-growing demand for effective, safe, and affordable protein therapeutics, decades of intense efforts have aimed to maximize the quantity and quality of recombinant proteins produced in CHO cells. Bioprocessing innovations and cell engineering efforts have improved product titer; however, uncharacterized cellular processes and gene regulatory mechanisms still hinder cell growth, specific productivity, and protein quality. Herein, we summarize recent advances in systems biology and data-driven approaches aiming to unravel how molecular pathways, cellular processes, and extrinsic factors (e.g. media supplementation) influence recombinant protein production. In particular, as the available omics data for CHO cells continue to grow, predictive models and screens will be increasingly used to unravel the biological drivers of protein production, which can be used with emerging genome editing technologies to rationally engineer cells to further control the quantity, quality and affordability of many biologic drugs.

CHO-Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.

A chimeric HS4 insulator-scaffold attachment region enhances transgene expression in transfected Chinese hamster ovary cells

Chinese hamster ovary (CHO) cells are one of the most commonly used expression systems for the production of recombinant proteins but low levels of transgene expression and transgene silencing are frequently encountered. Epigenetic regulatory elements such as the chicken β-globin locus control region hypersensitive site 4 (HS4) and scaffold/matrix attachment regions (S/MARs) have positive effects on transgene expression. In this study, a chimeric HS4-SAR was cloned upstream or downstream of an enhanced green fluorescent protein (eGFP) expression cassette in a eukaryotic vector, and the resulting vectors were transfected into CHO cells. eGFP was detected by flow cytometry. Real-time quantitative PCR (qPCR) was used to determine copy numbers of the stably transfected cells. And fluorescence in situ hybridization (FISH) was used to detect the status of vector in the host cell chromosome. The results showed that HS4-SAR positioned downstream of the expression cassette could enhance eGFP expression by 4.83-fold compared with the control vector. There may not be a relationship between transgene copy number and gene expression level. HS4-SAR did not appear to alter the integration of the transgene into the host cell chromosome or its position in the chromosome. We found a synthetic chimeric HS4-SAR positively increased transgene expression in CHO cells.

Human protein secretory pathway genes are expressed in a tissue-specific pattern to match processing demands of the secretome

Protein secretory pathway in eukaryal cells is responsible for delivering functional secretory proteins. The dysfunction of this pathway causes a range of important human diseases from congenital disorders to cancer. Despite the piled-up knowledge on the molecular biology and biochemistry level, the tissue-specific expression of the secretory pathway genes has not been analyzed on the transcriptome level. Based on the recent RNA-sequencing studies, the largest fraction of tissue-specific transcriptome encodes for the secretome (secretory proteins). Here, the question arises that if the expression levels of the secretory pathway genes have a tissue-specific tuning. In this study, we tackled this question by performing a meta-analysis of the recently published transcriptome data on human tissues. As a result, we detected 68 as called “extreme genes” which show an unusual expression pattern in specific gene families of the secretory pathway. We also inspected the potential functional link between detected extreme genes and the corresponding tissues enriched secretome. As a result, the detected extreme genes showed correlation with the enrichment of the nature and number of specific post-translational modifications in each tissue’s secretome. Our findings conciliate both the housekeeping and tissue-specific nature of the protein secretory pathway, which we attribute to a fine-tuned regulation of defined gene families to support the diversity of secreted proteins and their modifications.

The secretory pathway, an ubiquitous cellular machinery in human cells, is here shown to have tissue-specific characteristics. A research team led by Prof. Jens Nielsen at the Chalmers University of Technology performed a meta-analysis of the gene expressions of the secretory pathway’ component. They detected that even though most of these components are expressed in all tissues, there exist distinct components with fine-tuned expression. They further evaluated the functional link between the detected tuning and the processes that are demanded to make a set of tissue-specific proteins such as endocrine system hormones or their receptors. These findings open up a new avenue to understand the function of the secretion pathway in human tissues with possible applications for improving production of pharmaceutical proteins and getting more insight into the mechanisms underlying diseases such as diabetes that are connected with the endocrine system.

The enhancement of antibody concentration and achievement of high cell density CHO cell cultivation by adding nucleoside

Recently, with the dramatic increase in demand for therapeutic antibodies, Chinese hamster ovary (CHO) cell culture systems have made significant progress in recombinant antibody production. Over the past two decades, recombinant antibody productivity has been improved by more than 100-fold. Medium optimization has been identified as an important key approach for increasing product concentrations. In this study, we evaluated the effects of deoxyuridine addition to fed-batch cultures of antibody-expressing CHO cell lines. Furthermore, we investigated the effects of combined addition of deoxyuridine, thymidine, and deoxycytidine. Our results suggest that addition of these pyrimidine nucleosides can increase CHO cell growth, with no significant change in the specific production rate. As a result of the increased cell growth, the antibody concentration was elevated and we were able to achieve more than 9 g/L during 16 days of culture. Similar effects of nucleoside addition were observed in fed-batch cultures of a Fab fragment-expressing CHO cell line, and the final Fab fragment concentration was more than 4 g/L. This nucleoside addition strategy could be a powerful platform for efficient antibody production.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

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.