Evaluating oleaginous yeasts for enhanced microbial lipid production using sweetwater as a sustainable feedstock.

BACKGROUND: Yeasts exhibit promising potential for the microbial conversion of crude glycerol, owing to their versatility in delivering a wide range of value-added products, particularly lipids. Sweetwater, a methanol-free by-product of the fat splitting process, has emerged as a promising alternative feedstock for the microbial utilization of crude glycerol. To further optimize sweetwater utilization, we compared the growth and lipid production capabilities of 21 oleaginous yeast strains under different conditions with various glycerol concentrations, sweetwater types and pH. RESULTS: We found that nutrient limitation and the unique carbon composition of sweetwater boosted significant lipid accumulation in several strains, in particular Rhodosporidium toruloides NRRL Y-6987. Subsequently, to decipher the underlying mechanism, the transcriptomic changes of R. toruloides NRRL Y-6987 were further analyzed, indicating potential sugars and oligopeptides in sweetwater supporting growth and lipid accumulation as well as exogenous fatty acid uptake leading to the enhanced lipid accumulation. CONCLUSION: Our comparative study successfully demonstrated sweetwater as a cost-effective feedstock while identifying R. toluroides NRRL Y-6987 as a highly promising microbial oil producer. Furthermore, we also suggested potential sweetwater type and strain engineering targets that could potentially enhance microbial lipid production.

Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis.

There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins.

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.

Enhanced cell growth, production, and mAb quality produced in Chinese hamster ovary-K1 cells by supplementing polyamine in the media.

Polyamines such as putrescine (PUT), spermidine (SPD), and spermine (SPM) are amine group-containing biomolecules that regulate multiple intracellular functions such as proliferation, differentiation, and stress response in mammalian cells. Although these biomolecules can be generated intracellularly, lack of polyamine-synthesizing activity has occasionally been reported in a few mammalian cell lines such as Chinese hamster ovary (CHO)-K1; thus, polyamine supplementation in serum-free media is required to support cell growth and production. In the present study, the effects of biogenic polyamines PUT, SPD, and SPM in media on cell growth, production, metabolism, and antibody quality were explored in cultures of antibody-producing CHO-K1 cells. Polyamine withdrawal from media significantly suppressed cell growth and production. On the other hand, enhanced culture performance was achieved in polyamine-containing media conditions in a dose-dependent manner regardless of polyamine type. In addition, in polyamine-deprived medium, distinguishing metabolic features, such as enriched glycolysis and suppressed amino acid consumption, were observed and accompanied by higher heterogeneity of antibody quality compared with the optimal concentration of polyamines. Furthermore, an excessive concentration of polyamines negatively affected culture performance as well as antibody quality. Hence, the results suggest that polyamine-related metabolism needs to be further investigated and polyamines in cell growth media should be optimized as a controllable parameter in CHO cell culture bioprocessing. KEY POINTS: • Polyamine supplementation enhanced cell growth and production in a dose-dependent manner • Polyamine type and concentration in the media affected mAb quality • Optimizing polyamines in the media is suggested in CHO cell bioprocessing.

Data-driven and model-guided systematic framework for media development in CHO cell culture.

Proposed herein is a systematic media design framework that combines multivariate statistical approaches with in silico analysis of a genome-scale metabolic model of Chinese hamster ovary cell. The framework comprises sequential modules including cell culture and metabolite data collection, multivariate data analysis, in silico modeling and flux prediction, and knowledge-based identification of target media components. Two monoclonal antibody-producing cell lines under two different media conditions were used to demonstrate the applicability of the framework. First, the cell culture and metabolite profiles from all conditions were generated, and then statistically and mechanistically analyzed to explore combinatorial effects of cell line and media on intracellular metabolism. As a result, we found a metabolic bottleneck via a redox imbalance in the TCA cycle in the poorest growth condition, plausibly due to inefficient coenzyme q10-q10h2 recycling. Subsequent in silico simulation allowed us to suggest q10 supplementation to debottleneck the imbalance for the enhanced cellular energy state and TCA cycle activity. Finally, experimental validation was successfully conducted by adding q10 in the media, resulting in increased cell growth. Taken together, the proposed framework rationally identified target nutrients for cell line-specific media design and reformulation, which could greatly improve cell culture performance.

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures.

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS-R) with the flux balance analysis of a genome-scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha-1 antitrypsin producing CHO-DG44 cells grown in one commercial and another two in-house media under development. At the onset, PLS-R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found suboptimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modeling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells.

Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality.

A robust monoclonal antibody (mAb) bioprocess requires physiological parameters such as temperature, pH, or dissolved oxygen to be well-controlled as even small variations in them could potentially impact the final product quality. For instance, pH substantially affects N-glycosylation, protein aggregation, and charge variant profiles, as well as mAb productivity. However, relatively less is known about how pH jointly influences product quality and titer. In this study, we investigated the effect of pH on culture performance, product titer, and quality profiles by applying longitudinal multi-omics profiling, including transcriptomics, proteomics, metabolomics, and glycomics, at three different culture pH set points. The subsequent systematic analysis of multi-omics data showed that pH set points differentially regulated various intracellular pathways including intracellular vesicular trafficking, cell cycle, and apoptosis, thereby resulting in differences in specific productivity, product titer, and quality profiles. In addition, a time-dependent variation in mAb N-glycosylation profiles, independent of pH, was identified to be mainly due to the accumulation of mAb proteins in the endoplasmic reticulum disrupting cellular homeostasis over culture time. Overall, this multi-omics-based study provides an in-depth understanding of the intracellular processes in mAb-producing CHO cell line under varied pH conditions, and could serve as a baseline for enabling the quality optimization and control of mAb production.

Genome scale metabolic models and analysis for evaluating probiotic potentials.

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

Enzyme capacity-based genome scale modelling of CHO cells.

Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.

What CHO is made of: Variations in the biomass composition of Chinese hamster ovary cell lines.

BACKGROUND: Cell line-specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) cells. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions. RESULTS: We report total protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and detailed protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line- and condition-specific biomass functions for use in cell line-specific, genome-scale metabolic models of CHO. Finally, flux balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis. CONCLUSIONS: Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. However, the relative amino acid composition is independent of the cell line and condition and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and thus will have to be determined in each case. These cell line-specific variations in biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are rather dominated by inaccuracies in the exchange rate spectra. Cell-specific biomass variations only become important if the experimental errors in the exchange rate spectra drop below twenty percent.

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Multi-omics profiling of CHO parental hosts reveals cell line-specific variations in bioprocessing traits.

Chinese hamster ovary (CHO) cells are the most prevalent mammalian cell factories for producing recombinant therapeutic proteins due to their ability to synthesize human-like post-translational modifications and ease of maintenance in suspension cultures. Currently, a wide variety of CHO host cell lines has been developed; substantial differences exist in their phenotypes even when transfected with the same target vector. However, relatively less is known about the influence of their inherited genetic heterogeneity on phenotypic traits and production potential from the bioprocessing point of view. Herein, we present a global transcriptome and proteome profiling of three commonly used parental cell lines (CHO-K1, CHO-DXB11, and CHO-DG44) in suspension cultures and further report their growth-related characteristics, and N- and O-glycosylation patterns of host cell proteins (HCPs). The comparative multi-omics and subsequent genome-scale metabolic network model-based enrichment analyses indicated that some physiological variations of CHO cells grown in the same media are possibly originated from the genetic deficits, particularly in the cell-cycle progression. Moreover, the dihydrofolate reductase deficient DG44 and DXB11 possess relatively less active metabolism when compared to K1 cells. The protein processing abilities and the N- and O-glycosylation profiles also differ significantly across the host cell lines, suggesting the need to select host cells in a rational manner for the cell line development on the basis of recombinant protein being produced.

Ginseng Genome Database: an open-access platform for genomics of Panax ginseng.

BACKGROUND: The ginseng (Panax ginseng C.A. Meyer) is a perennial herbaceous plant that has been used in traditional oriental medicine for thousands of years. Ginsenosides, which have significant pharmacological effects on human health, are the foremost bioactive constituents in this plant. Having realized the importance of this plant to humans, an integrated omics resource becomes indispensable to facilitate genomic research, molecular breeding and pharmacological study of this herb. DESCRIPTION: The first draft genome sequences of P. ginseng cultivar "Chunpoong" were reported recently. Here, using the draft genome, transcriptome, and functional annotation datasets of P. ginseng, we have constructed the Ginseng Genome Database http://ginsengdb.snu.ac.kr /, the first open-access platform to provide comprehensive genomic resources of P. ginseng. The current version of this database provides the most up-to-date draft genome sequence (of approximately 3000 Mbp of scaffold sequences) along with the structural and functional annotations for 59,352 genes and digital expression of genes based on transcriptome data from different tissues, growth stages and treatments. In addition, tools for visualization and the genomic data from various analyses are provided. All data in the database were manually curated and integrated within a user-friendly query page. CONCLUSION: This database provides valuable resources for a range of research fields related to P. ginseng and other species belonging to the Apiales order as well as for plant research communities in general. Ginseng genome database can be accessed at http://ginsengdb.snu.ac.kr /.

Genome and evolution of the shade-requiring medicinal herb Panax ginseng.

Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications (WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane-type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome-scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics-assisted breeding or metabolic engineering.

BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals.

MOTIVATION: Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models. RESULTS: We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets. AVAILABILITY AND IMPLEMENTATION: MATLAB code is available at https://github.com/kms1041/BeReTa (github). CONTACT: byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

An LC-MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology.

INTRODUCTION: Given a raw LC-MS dataset, it is often required to rapidly generate initial hypotheses, in conjunction with other 'omics' datasets, without time-consuming lipid verifications. Furthermore, for meta-analysis of many datasets, it may be impractical to conduct exhaustive confirmatory analyses. In other cases, samples for validation may be difficult to obtain, replicate or maintain. Thus, it is critical that the computational identification of lipids is of appropriate accuracy, coverage, and unbiased by a researcher's experience and prior knowledge. OBJECTIVES: We aim to prescribe a systematic framework for lipid identifications, without usage of their characteristic retention-time by fully exploiting their underlying mass features. RESULTS: Initially, a hybrid technique, for deducing both common and distinctive daughter ions, is used to infer parent lipids from deconvoluted spectra. This is followed by parent confirmation using basic knowledge of their preferred product ions. Using the framework, we could achieve an accuracy of ~ 80% by correctly identified 101 species from 18 classes in Chinese hamster ovary (CHO) cells. The resulting inferences could explain the recombinant-producing capability of CHO-SH87 cells, compared to non-producing CHO-K1 cells. For comparison, a XCMS-based study of the same dataset, guided by a user's ad-hoc knowledge, identified less than 60 species of 12 classes from thousands of possibilities. CONCLUSION: We describe a systematic LC-MS-based framework that identifies lipids for rapid hypothesis generation.

In silico model-guided identification of transcriptional regulator targets for efficient strain design.

BACKGROUND: Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application. RESULTS: We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification. CONCLUSIONS: In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications.

In nature, microbes do not exist in isolation but co-exist in a variety of ecological and biological environments and on various host organisms. Due to their close proximity, these microbes interact among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrinsic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial communities. If detailed species-specific information is available, segregated models of individual organisms can be constructed and connected via metabolite exchanges; otherwise, the community may be represented as a lumped network of metabolic reactions. The constructed models can then be simulated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments. More importantly, such community models have been developed to study microbial interactions in various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights into the natural and synthetic microbial communities, and design interventions to achieve specific goals. Finally, potential directions for future development in metabolic modeling of microbial communities were also discussed.

Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures.

Chinese hamster ovary (CHO) cells have been widely used for producing many recombinant therapeutic proteins. Constraint-based modeling, such as flux balance analysis (FBA) and metabolic flux analysis (MFA), has been developing rapidly for the quantification of intracellular metabolic flux distribution at a systematic level. Such methods would produce detailed maps of flows through metabolic networks, which contribute significantly to better understanding of metabolism in cells. Although these approaches have been extensively established in microbial systems, their application to mammalian cells is sparse. This review brings together the recent development of constraint-based models and their applications in CHO cells. The further development of constraint-based modeling approaches driven by multi-omics datasets is discussed, and a framework of potential modeling application in cell culture engineering is proposed. Improved cell culture system understanding will enable robust developments in cell line and bioprocess engineering thus accelerating consistent process quality control in biopharmaceutical manufacturing.

Multi-omics data driven analysis establishes reference codon biases for synthetic gene design in microbial and mammalian cells.

In this study, we analyzed multi-omics data and subsets thereof to establish reference codon usage biases for codon optimization in synthetic gene design. Specifically, publicly available genomic, transcriptomic, proteomic and translatomic data for microbial and mammalian expression hosts, Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Chinese hamster ovary (CHO) cells, were compiled to derive their individual codon and codon pair frequencies. Then, host dependent and -omics specific codon biases were generated and compared by principal component analysis and hierarchical clustering. Interestingly, our results indicated the similar codon bias patterns of the highly expressed transcripts, highly abundant proteins, and efficiently translated mRNA in microbial cells, despite the general lack of correlation between mRNA and protein expression levels. However, for CHO cells, the codon bias patterns among various -omics subsets are not distinguishable, forming one cluster. Thus, we further investigated the effect of different input codon biases on codon optimized sequences using the codon context (CC) and individual codon usage (ICU) design parameters, via in silico case study on the expression of human IFNγ sequence in CHO cells. The results supported that CC is more robust design parameter than ICU for improved heterologous gene design.