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

Gutierrez, Jahir M.; Feizi, Amir; Li, Shangzhong; Kallehauge, Thomas Beuchert; Hefzi, Hooman; Grav, Lise Marie; Ley, Daniel; Hızal, Deniz Baycın; Betenbaugh, Michael J.; Voldborg, Bjørn Gunnar; Kildegaard, Helene Faustrup; Lee, Gyun Min; Palsson, Bernhard Ø.; Nielsen, Jens; Lewis, Nathan E.

doi:10.1038/s41467-019-13867-y

Cited by 94 publications

(59 citation statements)

References 66 publications

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“…Additionally, other features beyond metabolism, e.g., secretory pathway capacity, can influence cell growth and recombinant protein productivity. In mammalian cells, more than 25% of synthesized proteins (including enzymes and antibodies) are exported through the secretory pathway [47]. Therefore, a detailed understanding of the secretory pathway is valuable to be taken into account of protein productivity.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, a detailed understanding of the secretory pathway is valuable to be taken into account of protein productivity. Recently, Gutierrez et al (2020) integrated the core secretory pathways with the genome-scale metabolic model of CHO cells to capture the energetic and machinery demands of secreted proteins [47]. In the future, this new reconstructed model can be used to interrogate cellular activities more comprehensively and explore the critical features of productivity among different CHO cell lines.…”

Section: Discussionmentioning

confidence: 99%

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Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Huang

Yoon

2020

Processes

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Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Huang

Yoon

2020

Processes

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“…Consideration of secretion costs was implemented by joining a model of the CHO secretory pathway 68 onto our metabolic network model. The secretory pathway model included about 100 reactions for folding, transport, post-translational modification and related activities in the endoplasmic reticulum and Golgi body (Table S3).…”

Section: Methodsmentioning

confidence: 99%

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

Schinn

Morrison

Wei

et al. 2020

Preprint

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10Genome-scale metabolic models describe cellular metabolism with mechanistic detail. 11Given their high complexity, such models need to be parameterized correctly to yield 12 accurate predictions and avoid overfitting. Effective parameterization has been well-13 studied for microbial models, but it remains unclear for higher eukaryotes, including 14 mammalian cells. To address this, we enumerated model parameters that describe key 15 features of cultured mammalian cells -including cellular composition, bioprocess 16 performance metrics, mammalian-specific pathways, and biological assumptions behind 17 model formulation approaches. We tested these parameters by building thousands of 18 metabolic models and evaluating their ability to predict the growth rates of a panel of 19 phenotypically diverse Chinese Hamster Ovary cell clones. We found the following 20 considerations to be most critical for accurate parameterization: (1) cells limit metabolic 21 activity to maintain homeostasis, (2) cell morphology and viability change dynamically 22 during a growth curve, and (3) cellular biomass has a particular macromolecular 23 composition. Depending on parameterization, models predicted different metabolic 24 phenotypes, including contrasting mechanisms of nutrient utilization and energy 25 generation, leading to varying accuracies of growth rate predictions. Notably, accurate 26 parameter values broadly agreed with experimental measurements. These insights will 27 guide future investigations of mammalian metabolism. 28

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“…Although many components of the PSP that drive these important secretome changes have been studied individually, an investigation of how these constituents behave together as a system is lacking, particularly in the context of cancer. Recent efforts have begun to elucidate this system by exploring how PSP expression patterns compare to those of the secretome among different human tissues [ 8 ], and by developing genome-scale reconstructions of the PSP to mechanistically link these characteristics to the metabolic network [ 9 ]. We sought to further extend the investigation of the PSP through the application of machine learning (ML) approaches.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based investigation of the cancer protein secretory pathway

Saghaleyni

Muhammad

Bangalore³

et al. 2021

PLoS Comput Biol

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Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.

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

Cited by 94 publications

References 66 publications

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

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

Machine learning-based investigation of the cancer protein secretory pathway

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