Camila A. Orellana

Recon 2.2: from reconstruction to model of human metabolism

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

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.

High-Antibody-Producing Chinese Hamster Ovary Cells Up-Regulate Intracellular Protein Transport and Glutathione Synthesis

Chinese hamster ovary (CHO) cells are the preferred production host for therapeutic monoclonal antibodies (mAb) due to their ability to perform post-translational modifications and their successful approval history. The completion of the genome sequence for CHO cells has reignited interest in using quantitative proteomics to identify markers of good production lines. Here we applied two different proteomic techniques, iTRAQ and SWATH, for the identification of expression differences between a high- and low-antibody-producing CHO cell lines derived from the same transfection. More than 2000 proteins were quantified with 70 of them classified as differentially expressed in both techniques. Two biological processes were identified as differentially regulated by both methods: up-regulation of glutathione biosynthesis and down-regulation of DNA replication. Metabolomic analysis confirmed that the high producing cell line displayed higher intracellular levels of glutathione. SWATH further identified up-regulation of actin filament processes and intracellular transport and down regulation of several growth-related processes. These processes may be important for conferring high mAb production and as such are promising candidates for targeted engineering of high-expression cell lines.

Mathematical Modeling of Elution Curves for a Protein Mixture in Ion Exchange Chromatography Applied to High Protein Concentration

Protein elution curves in ion exchange chromatography (IEC) were simulated with a rate model. Three pure proteins and their mixture were used (α‐lactalbumin, BSA, and conalbumin) under different operational conditions. The anionic matrix Q‐Sepharose FF was used packed in a 1 mL column. A high protein concentration (37.5 mg/mL of total protein injected into the column) was used in order to extend the utility of the model. Mass transfer parameters were calculated using empiric correlations, where the axial dispersion was negligible (Pe > 300) and the mass transfer was controlled by the intraparticle diffusion (Bi > 10). The model assumes a modulator–eluite relationship were the equilibrium constant of the Langmuir isotherm was a function of salt concentration. Adsorption kinetic parameters were estimated from experimental data. The parameters for pure proteins were determined, and elution curves for changes in flow rate, ionic strength gradient, concentration, and sample size were predicted by the model. Then the kinetic parameters of the mixture were determined under the same operational conditions and some of the parameters had to be modified to take into account effects such as protein–protein interactions, competition, and displacement. Experimental elution curves obtained for changes in operational conditions such as flow rate and ionic strength gradient were simulated by the rate model for the protein mixture with a relative error in retention time of visible peaks <5%. IEC operational conditions and the peak fraction collection can be selected using a cost function of the production process which considers yield, purity, concentration, and process time that are obtained from simulations. Operational conditions that gave the minimum cost were selected. Simulations allows to diminish experimental time and cost. Biotechnol. Bioeng. 2009; 104: 572–581

RNA-seq highlights high clonal variation in monoclonal antibody producing CHO cells

The development of next-generation sequencing technologies has opened new opportunities to better characterize complex eukaryotic cells. Chinese hamster ovary (CHO) cells play a primary role in therapeutic protein production, with currently five of the top ten blockbuster drugs produced in CHO. However, engineering superior CHO cells with improved production features has had limited success to date and cell lines are still developed through the generation and screening of large strain pools. Here, we applied RNA sequencing to contrast a high and a low monoclonal antibody producing cell line. Rigorous experimental design achieved high reproducibility between biological replicates, remarkably reducing variation to less than 10%. More than 14 000 gene-transcripts are identified and surprisingly 58% are classified as differentially expressed, including 2900 with a fold change higher than 1.5. The largest differences are found for gene-transcripts belonging to regulation of apoptosis, cell death, and protein intracellular transport GO term classifications, which are found to be significantly enriched in the high producing cell line. RNA sequencing is also performed on subclones, where down-regulation of genes encoding secreted glycoproteins is found to be the most significant change. The large number of significant differences even between subclones challenges the notion of identifying and manipulating a few key genes to generate high production CHO cell lines.

Overexpression of the regulatory subunit of glutamate-cysteine ligase enhances monoclonal antibody production in CHO cells

For decades, Chinese hamster ovary (CHO) cells have been the preferred host for therapeutic monoclonal antibody (mAb) production; however, increasing mAb titer by rational engineering remains a challenge. Our previous proteomic analysis in CHO cells suggested that a higher content of glutathione (GSH) might be related to higher productivity. GSH is an important antioxidant, cell detoxifier, and is required to ensure the formation of native disulfide bonds in proteins. To investigate the involvement of GSH in mAb production, we generated stable CHO cell lines overexpressing genes involved in the first step of GSH synthesis; namely the glutamate‐cysteine ligase catalytic subunit (Gclc) and the glutamate‐cysteine ligase modifier subunit (Gclm). The two genes were reconstructed from our RNA‐Seq de novo assembly and then were functionally annotated. Once the sequences of the genes were confirmed using proteogenomics, a transiently expressed mAb was introduced into cell lines overexpressing either Gclc or Gclm. The new cell lines were compared for mAb production to the parental cell line and changes at the proteome level were measured using SWATH. As per our previous proteomics observations, overexpressing Gclm improved productivity, titer, and the frequency of high producer clones by 70%. In contrast, overexpressing Gclc, which produced a higher amount of GSH, did not increase mAb production. We show that GSH cannot be linked to higher productivity and that Gclm may be controlling other cellular processes involved in mAb production yet to be elucidated. Biotechnol. Bioeng. 2017;114: 1825–1836.

Multi-omics approach for comparative studies of monoclonal antibody producing CHO cells

Background Monoclonal antibody (mAb) therapy has revolutionized the treatment of a vast range of diseases, mostly in the areas of oncology and autoimmune/inflammatory disorders [1]. With a world market exceeding 60 billion USD per year and six mAb related products in the top 10 selling drugs, the industry continues to grow at a fast rate [2]. Chinese hamster ovary (CHO) cells are the preferred production host for therapeutic production of mAbs due to their efficiency in performing post-translational modifications and their ability to produce proteins with similar properties to native human proteins [3]. Surprisingly, despite products varying only by a few amino acids in the variable region of a MAb, each production cell line is still developed by generating and screening a large strain pool, and generally the production process has to be reoptimised. Systems biology can be used as a powerful tool for the identification of key markers of good production lines, with the aim of engineering superior host lines that more reliably produce good production clones. To date systems biology efforts have been hampered by the need to use the mouse, rat and/or human genome as a reference and has suffered from the inherent limitation in coverage of 2-dimensional gel electrophoresis or mouse or CHO cDNA microarrays. The development of new techniques such as RNA sequencing for transcriptome analysis and LC-MS/MS for proteome analysis combined with the recent release of the CHO genome has reignited interest in using quantitative proteomics and transcriptomics to study high productivity cell lines. Materials and methods Here we applied the latest generation of tools to two CHO cell lines that produce different levels of mAb, as described in Orellana et al [4]. The two cell lines were derived from one transfection pool using the same plasmid carrying genes for a monoclonal antibody. For each cell line, three independent vials were thawed and passaged for two weeks prior to bioreactor inoculation. Cells were cultivated in 700 ml EX-CELL CD CHO Fusion Medium (Sigma Aldrich) containing 25 μM L-Methionine sulfoximine as selection, in a 1L Mini-Bioreactor (Applikon Biotechnologies) operated at 125 rpm stirring speed, 37°C, pH 6.9 and dissolved oxygen at 50% air saturation. RNA and protein were extracted from cells harvested in mid exponential phase. RNA samples were analysed with RNA sequencing (RNA-Seq) using the Illumina Hiseq2000 platform and 100 bp paired-end reads. TopHat and Cufflinks open-source software [5] were used with default settings for gene expression analysis, using the CHO genome as reference. Protein samples were analysed using SWATH [6]. The Paragon Algorithm from ProteinPilot v4.5 (ABSciex, Forster City CA) [7], PeakView v.1.2 software (ABSciex, Forster City CA) and the R package Limma [8] were used for data analysis. Transcripts and proteins were classified as differentially expressed if the adjusted p-value (Benjamini-Hochberg) was lower than 0.05.Gene set enrichment analysis was performed using DAVID Bioinformatics functional annotation tool [9].