Andreas Eriksson, Anne Richelle, Johan Trygg, Steffi Scholze, Shanti Pijeaud, Henrik Antti, Christoph Zehe, Izabella Surowiec, Pär Jonsson
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引用次数: 0
Abstract
Biopharmaceuticals are medical compounds derived from biological sources and are often manufactured by living cells, primarily Chinese hamster ovary (CHO) cells. CHO cells display variation among cell clones, leading to growth and productivity differences that influence the product's quantity and quality. The biological and environmental factors behind these differences are not fully understood. To identify metabolites with a consistent relationship to productivity or cell death over time, we analyzed the extracellular metabolome of 11 CHO clones with different growth and productivity characteristics over 14 days. However, in bioreactor processes, metabolic profiles and process variables are both strongly time-dependent, confounding the metabolite-process variable relationship. To address this, we customized an existing hierarchical approach for handling time dependency to highlight metabolites with a consistent correlation to a process variable over a selected timeframe. We benchmarked this new method against conventional orthogonal partial least squares (OPLS) models. Our hierarchical method highlighted several metabolites consistently related to productivity or cell death that the conventional method missed. These metabolites were biologically relevant; most were known already, but some that had not been reported in CHO literature before, such as 3-methoxytyrosine and succinyladenosine, had ties to cell death in studies with other cell types. The metabolites showed an inverse relationship with the response variables: those positively correlated with productivity were typically negatively correlated with the death rate, or vice versa. For both productivity and cell death, the citrate cycle and adjacent pathways (pyruvate, glyoxylate, pantothenate) were among the most important. In summary, we have proposed a new method to analyze time-dependent omics data in bioprocess production. This approach allowed us to identify metabolites tied to cell death and productivity that were not detected with traditional models.
Biotechnology JournalBiochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
8.90
自引率
2.10%
发文量
123
审稿时长
1.5 months
期刊介绍:
Biotechnology Journal (2019 Journal Citation Reports: 3.543) is fully comprehensive in its scope and publishes strictly peer-reviewed papers covering novel aspects and methods in all areas of biotechnology. Some issues are devoted to a special topic, providing the latest information on the most crucial areas of research and technological advances.
In addition to these special issues, the journal welcomes unsolicited submissions for primary research articles, such as Research Articles, Rapid Communications and Biotech Methods. BTJ also welcomes proposals of Review Articles - please send in a brief outline of the article and the senior author''s CV to the editorial office.
BTJ promotes a special emphasis on:
Systems Biotechnology
Synthetic Biology and Metabolic Engineering
Nanobiotechnology and Biomaterials
Tissue engineering, Regenerative Medicine and Stem cells
Gene Editing, Gene therapy and Immunotherapy
Omics technologies
Industrial Biotechnology, Biopharmaceuticals and Biocatalysis
Bioprocess engineering and Downstream processing
Plant Biotechnology
Biosafety, Biotech Ethics, Science Communication
Methods and Advances.