Shu: visualization of high-dimensional biological pathways.

SUMMARY: Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. In addition, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines. AVAILABILITY AND IMPLEMENTATION: Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web application is deployed at https://biosustain.github.io/shu.

Comparative constraint-based modelling of fruit development across species highlights nitrogen metabolism in the growth-defence trade-off.

Although primary metabolism is well conserved across species, it is useful to explore the specificity of its network to assess the extent to which some pathways may contribute to particular outcomes. Constraint-based metabolic modelling is an established framework for predicting metabolic fluxes and phenotypes and helps to explore how the plant metabolic network delivers specific outcomes from temporal series. After describing the main physiological traits during fruit development, we confirmed the correlations between fruit relative growth rate (RGR), protein content and time to maturity. Then a constraint-based method is applied to a panel of eight fruit species with a knowledge-based metabolic model of heterotrophic cells describing a generic metabolic network of primary metabolism. The metabolic fluxes are estimated by constraining the model using a large set of metabolites and compounds quantified throughout fruit development. Multivariate analyses showed a clear common pattern of flux distribution during fruit development with differences between fast- and slow-growing fruits. Only the latter fruits mobilise the tricarboxylic acid cycle in addition to glycolysis, leading to a higher rate of respiration. More surprisingly, to balance nitrogen, the model suggests, on the one hand, nitrogen uptake by nitrate reductase to support a high RGR at early stages of cucumber and, on the other hand, the accumulation of alkaloids during ripening of pepper and eggplant. Finally, building virtual fruits by combining 12 biomass compounds shows that the growth-defence trade-off is supported mainly by cell wall synthesis for fast-growing fruits and by total polyphenols accumulation for slow-growing fruits.

Omics data for sampling thermodynamically feasible kinetic models.

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum.

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

multiTFA: a Python package for multi-variate thermodynamics-based flux analysis.

MOTIVATION: We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. RESULTS: We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. AVAILABILITY AND IMPLEMENTATION: Our framework along with documentation is available on https://github.com/biosustain/multitfa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Awakening sleeping beauty: production of propionic acid in Escherichia coli through the sbm operon requires the activity of a methylmalonyl-CoA epimerase.

BACKGROUND: Propionic acid is used primarily as a food preservative with smaller applications as a chemical building block for the production of many products including fabrics, cosmetics, drugs, and plastics. Biological production using propionibacteria would be competitive against chemical production through hydrocarboxylation of ethylene if native producers could be engineered to reach near-theoretical yield and good productivity. Unfortunately, engineering propionibacteria has proven very challenging. It has been suggested that activation of the sleeping beauty operon in Escherichia coli is sufficient to achieve propionic acid production. Optimising E. coli production should be much easier than engineering propionibacteria if tolerance issues can be addressed. RESULTS: Propionic acid is produced in E. coli via the sleeping beauty mutase operon under anaerobic conditions in rich medium via amino acid degradation. We observed that the sbm operon enhances amino acids degradation to propionic acid and allows E. coli to degrade isoleucine. However, we show here that the operon lacks an epimerase reaction that enables propionic acid production in minimal medium containing glucose as the sole carbon source. Production from glucose can be restored by engineering the system with a methylmalonyl-CoA epimerase from Propionibacterium acidipropionici (0.23 ± 0.02 mM). 1-Propanol production was also detected from the promiscuous activity of the native alcohol dehydrogenase (AdhE). We also show that aerobic conditions are favourable for propionic acid production. Finally, we increase titre 65 times using a combination of promoter engineering and process optimisation. CONCLUSIONS: The native sbm operon encodes an incomplete pathway. Production of propionic acid from glucose as sole carbon source is possible when the pathway is complemented with a methylmalonyl-CoA epimerase. Although propionic acid via the restored succinate dissimilation pathway is considered a fermentative process, the engineered pathway was shown to be functional under anaerobic and aerobic conditions.

Genetic and biochemical characterization of genes involved in hyaluronic acid synthesis in Streptococcus zooepidemicus.

The biosynthetic pathway for hyaluronic acid (HA) has been proposed; however, a thorough genetic and functional analysis is required to further elucidate the roles of genes involved in HA production. Previously, we developed a markerless gene-deletion system for Streptococcus zooepidemicus and confirmed that hasA is essential for HA synthesis. Here, we constructed a comprehensive set of deletion mutants and investigated the roles of ten additional predicted genes in the HA synthetic pathway. Phenotypic assays revealed that all ten genes play a role in cell growth and/or HA synthesis. As expected, the deletion of hasA or hasB abolished HA production with little effect on growth, while the deletion of genes that are also required for peptidoglycan biosynthesis (hasE, glmM, and glmS) significantly reduced cell growth and HA production. Either of the glmU homologues (hasD and gcaD) was sufficient for optimal growth and the mucoid phenotype, while no double mutant could be isolated. Of the two UDP-glucose pyrophosphorylase (UGPase) paralogues, the operon-encoded hasC1 was responsible for 65 % of the activity, while hasC2 was responsible for the remaining 35 %. The deletion of hasC1 had no effect on cell growth and caused only a moderate decrease in the UDP-glucose level and HA production. The deletion of both hasC1 and hasC2 resulted in a severe growth defect and negligible UDP-glucose accumulation, HA production, and pyrophosphorylase activity. Of the two phosphoglucomutase paralogues, pgm1 and pgm2, the former is responsible for around 10 % of activity, while the latter is responsible for 90 %. The deletion of pgm1 showed no apparent effect on HA synthesis and growth, while the deletion of pgm2 resulted in the abolishment of HA synthesis and a significantly slower growth. These results should guide the metabolic engineering of S. zooepidemicus to improve HA productivity and quality.

Control of chitin and N-acetylglucosamine utilization in Saccharopolyspora erythraea.

Chitin degradation and subsequent N-acetylglucosamine (GlcNAc) catabolism is thought to be a common trait of a large majority of actinomycetes. Utilization of aminosugars had been poorly investigated outside the model strain Streptomyces coelicolor A3(2), and we examined here the genetic setting of the erythromycin producer Saccharopolyspora erythraea for GlcNAc and chitin utilization, as well as the transcriptional control thereof. Sacch. erythraea efficiently utilize GlcNAc most likely via the phosphotransferase system (PTS(GlcNAc)); however, this strain is not able to grow when chitin or N,N'-diacetylchitobiose [(GlcNAc)2] is the sole nutrient source, despite a predicted extensive chitinolytic system (chi genes). The inability of Sacch. erythraea to utilize chitin and (GlcNAc)2 is probably because of the loss of genes encoding the DasABC transporter for (GlcNAc)2 import, and genes for intracellular degradation of (GlcNAc)2 by ß-N-acetylglucosaminidases. Transcription analyses revealed that in Sacch. erythraea all putative chi and GlcNAc utilization genes are repressed by DasR, whereas in Strep. coelicolor DasR displayed either activating or repressing functions whether it targets genes involved in the polymer degradation or genes for GlcNAc dimer and monomer utilization, respectively. A transcriptomic analysis further showed that GlcNAc not only activates the transcription of GlcNAc catabolism genes but also activates chi gene expression, as opposed to the previously reported GlcNAc-mediated catabolite repression in Strep. coelicolor. Finally, synteny exploration revealed an identical genetic background for chitin utilization in other rare actinomycetes, which suggests that screening procedures that used only the chitin-based protocol for selective isolation of antibiotic-producing actinomycetes could have missed the isolation of many industrially promising strains.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Adaptive laboratory evolution of Clostridium autoethanogenum to metabolize CO2 and H2 enhances growth rates in chemostat and unravels proteome and metabolome alterations.

Gas fermentation of CO2 and H2 is an attractive means to sustainably produce fuels and chemicals. Clostridium autoethanogenum is a model organism for industrial CO to ethanol and presents an opportunity for CO2-to-ethanol processes. As we have previously characterized its CO2/H2 chemostat growth, here we use adaptive laboratory evolution (ALE) with the aim of improving growth with CO2/H2. Seven ALE lineages were generated, all with improved specific growth rates. ALE conducted in the presence of 2% CO along with CO2/H2 generated Evolved lineage D, which showed the highest ethanol titres amongst all the ALE lineages during the fermentation of CO2/H2. Chemostat comparison against the parental strain shows no change in acetate or ethanol production, while Evolved D could achieve a higher maximum dilution rate. Multi-omics analyses at steady state revealed that Evolved D has widespread proteome and intracellular metabolome changes. However, the uptake and production rates and titres remain unaltered until investigating their maximum dilution rate. Yet, we provide numerous insights into CO2/H2 metabolism via these multi-omics data and link these results to mutations, suggesting novel targets for metabolic engineering in this bacterium.

Synergistic investigation of natural and synthetic C1-trophic microorganisms to foster a circular carbon economy.

A true circular carbon economy must upgrade waste greenhouse gases. C1-based biomanufacturing is an attractive solution, in which one carbon (C1) molecules (e.g. CO2, formate, methanol, etc.) are converted by microbial cell factories into value-added goods (i.e. food, feed, and chemicals). To render C1-based biomanufacturing cost-competitive, we must adapt microbial metabolism to perform chemical conversions at high rates and yields. To this end, the biotechnology community has undertaken two (seemingly opposing) paths: optimizing natural C1-trophic microorganisms versus engineering synthetic C1-assimilation de novo in model microorganisms. Here, we pose how these approaches can instead create synergies for strengthening the competitiveness of C1-based biomanufacturing as a whole.

Protocol for absolute quantification of proteins in Gram-negative bacteria based on QconCAT-based labeled peptides.

Mass-spectrometry-based absolute protein quantification uses labeled quantification concatamer (QconCAT) as internal standards (ISs). To calculate the amount of protein(s), the ion intensity ratio between the analyte and its cognate IS is compared in each biological sample. The present protocol describes a systematic workflow to design, produce, and purify QconCATs and to quantify soluble proteins in Pseudomonas putida KT2440. Our methodology enables the quantification of detectable peptide and serves as a versatile platform to produce ISs for different biological systems.

An automated workflow for multi-omics screening of microbial model organisms.

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

RNA-seq Sample Preparation Kits Strongly Affect Transcriptome Profiles of a Gas-Fermenting Bacterium.

Transcriptome analysis via RNA sequencing (RNA-seq) has become a standard technique employed across various biological fields of study. The rapid adoption of the RNA-seq approach has been mediated, in part, by the development of different commercial RNA-seq library preparation kits compatible with standard next-generation sequencing (NGS) platforms. Generally, the essential steps of library preparation, such as rRNA depletion and first-strand cDNA synthesis, are tailored to a specific group of organisms (e.g., eukaryotes versus prokaryotes) or genomic GC content. Therefore, the selection of appropriate commercial products is of crucial importance to capture the transcriptome of interest as closely to the native state as possible without introduction of technical bias. However, researchers rarely have the resources and time to test various commercial RNA-seq kits for their samples. This work reports a side-by-side comparison of RNA-seq data from Clostridium autoethanogenum obtained using three commercial rRNA removal and strand-specific library construction products of NuGEN Technologies, Qiagen, and Zymo Research and assesses their performance relative to published data. While all three vendors advertise their products as suitable for prokaryotes, we found significant differences in their performance regarding rRNA removal, strand specificity, and most importantly, transcript abundance distribution profiles. Notably, RNA-seq data obtained with Qiagen products were most similar to published data and delivered the best results in terms of library strandedness and transcript abundance distribution range. Our results highlight the importance of finding appropriate organism-specific workflows and library preparation products for RNA-seq studies. IMPORTANCE RNA-seq is a powerful technique for transcriptome profiling while involving elaborate sample processing before library sequencing. We show that RNA-seq library preparation kits can strongly affect the outcome of an RNA-seq experiment. Although library preparation benefits from the availability of various commercial kits, choosing appropriate products for the specific samples can be challenging for new users or for users working with unconventional organisms. Evaluating the performance of different commercial products requires significant financial and time investments infeasible for most researchers. Therefore, users are often guided in their choice of kits by published data involving similar input samples. We conclude that important consideration should be given to selecting sample processing workflows for any given organism.

Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum.

Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H2, or CO+CO2+H2). We detect the prioritization of proteome allocation for C1 fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H2 metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations. IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.

C4GEM, a genome-scale metabolic model to study C4 plant metabolism.

Leaves of C(4) grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C(3) plants, where photosynthetic CO(2) fixation proceeds in the mesophyll (M), the fixation process in C(4) plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C(4) genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C(4) photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C(4) subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C(4) model have been associated with well-annotated C(4) species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C(4) photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C(4) photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C(4) subtypes. Effects of CO(2) leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS cells during photosynthesis and can be used to explore C(4) plant metabolism.

AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis.

Genome-scale metabolic network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale metabolic network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed network was transformed into an in silico metabolic flux model of plant metabolism and validated through the simulation of plant metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.

Hyaluronan molecular weight is controlled by UDP-N-acetylglucosamine concentration in Streptococcus zooepidemicus.

The molecular weight of hyaluronan is important for its rheological and biological function. The molecular mechanisms underlying chain termination and hence molecular weight control remain poorly understood, not only for hyaluronan synthases but also for other beta-polysaccharide synthases, e.g. cellulose, chitin, and 1,3-betaglucan synthases. In this work, we manipulated metabolite concentrations in the hyaluronan pathway by overexpressing the five genes of the hyaluronan synthesis operon in Streptococcus equi subsp. zooepidemicus. Overexpression of genes involved in UDP-glucuronic acid biosynthesis decreased molecular weight, whereas overexpression of genes involved in UDP-N-acetylglucosamine biosynthesis increased molecular weight. The highest molecular mass observed was at 3.4 +/- 0.1 MDa twice that observed in the wild-type strain, 1.8 +/- 0.1 MDa. The data indicate that (a) high molecular weight is achieved when an appropriate balance of UDP-N-acetylglucosamine and UDP-glucuronic acid is achieved, (b) UDP-N-acetylglucosamine exerts the dominant effect on molecular weight, and (c) the wild-type strain has suboptimal levels of UDP-N-acetylglucosamine. Consistent herewith molecular weight correlated strongly (rho = 0.84, p = 3 x 10(-5)) with the concentration of UDP-N-acetylglucosamine. Data presented in this paper represent the first model for hyaluronan molecular weight control based on the concentration of activated sugar precursors. These results can be used to engineer strains producing high molecular weight hyaluronan and may provide insight into similar polymerization mechanisms in other polysaccharides.

Multicopy Targeted Integration for Accelerated Development of High-Producing Chinese Hamster Ovary Cells.

The ever-growing biopharmaceutical industry relies on the production of recombinant therapeutic proteins in Chinese hamster ovary (CHO) cells. The traditional timelines of CHO cell line development can be significantly shortened by the use of targeted gene integration (TI). However, broad use of TI has been limited due to the low specific productivity (qP) of TI-generated clones. Here, we show a 10-fold increase in the qP of therapeutic glycoproteins in CHO cells through the development and optimization of a multicopy TI method. We used a recombinase-mediated cassette exchange (RMCE) platform to investigate the effect of gene copy number, 5' and 3' gene regulatory elements, and landing pad features on qP. We evaluated the limitations of multicopy expression from a single genomic site as well as multiple genomic sites and found that a transcriptional bottleneck can appear with an increase in gene dosage. We created a dual-RMCE system for simultaneous multicopy TI in two genomic sites and generated isogenic high-producing clones with qP of 12-14 pg/cell/day and product titer close to 1 g/L in fed-batch. Our study provides an extensive characterization of the multicopy TI method and elucidates the relationship between gene copy number and protein expression in mammalian cells. Moreover, it demonstrates that TI-generated CHO cells are capable of producing therapeutic proteins at levels that can support their industrial manufacture.