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Peinado RDS, Martins LG, Pacca CC, Saivish MV, Borsatto KC, Nogueira ML, Tasic L, Arni RK, Eberle RJ, Coronado MA. HR-MAS NMR Metabolomics Profile of Vero Cells under the Influence of Virus Infection and nsP2 Inhibitor: A Chikungunya Case Study. Int J Mol Sci 2024; 25:1414. [PMID: 38338694 PMCID: PMC10855909 DOI: 10.3390/ijms25031414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The arbovirus Chikungunya (CHIKV) is transmitted by Aedes mosquitoes in urban environments, and in humans, it triggers debilitating symptoms involving long-term complications, including arthritis and Guillain-Barré syndrome. The development of antiviral therapies is relevant, as no efficacious vaccine or drug has yet been approved for clinical application. As a detailed map of molecules underlying the viral infection can be obtained from the metabolome, we validated the metabolic signatures of Vero E6 cells prior to infection (CC), following CHIKV infection (CV) and also upon the inclusion of the nsP2 protease inhibitor wedelolactone (CWV), a coumestan which inhibits viral replication processes. The metabolome groups evidenced significant changes in the levels of lactate, myo-inositol, phosphocholine, glucose, betaine and a few specific amino acids. This study forms a preliminary basis for identifying metabolites through HR-MAS NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Ressonance Spectroscopy) and proposing the affected metabolic pathways of cells following viral infection and upon incorporation of putative antiviral molecules.
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Affiliation(s)
- Rafaela dos S. Peinado
- Multiuser Center for Biomolecular Innovation, Department of Physics, Institute of Biosciences, Languages and Exact Sciences (Ibilce—UNESP), Sao Jose do Rio Preto, Sao Paulo 15054000, Brazil; (R.d.S.P.); (K.C.B.); (R.K.A.)
| | - Lucas G. Martins
- Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas 13083862, Brazil; (L.G.M.); (L.T.)
| | - Carolina C. Pacca
- Virology Research Laboratory, Medical School of Sao Jose do Rio Preto (FAMERP), Sao Paulo 15090000, Brazil; (C.C.P.); (M.V.S.); (M.L.N.)
| | - Marielena V. Saivish
- Virology Research Laboratory, Medical School of Sao Jose do Rio Preto (FAMERP), Sao Paulo 15090000, Brazil; (C.C.P.); (M.V.S.); (M.L.N.)
| | - Kelly C. Borsatto
- Multiuser Center for Biomolecular Innovation, Department of Physics, Institute of Biosciences, Languages and Exact Sciences (Ibilce—UNESP), Sao Jose do Rio Preto, Sao Paulo 15054000, Brazil; (R.d.S.P.); (K.C.B.); (R.K.A.)
| | - Maurício L. Nogueira
- Virology Research Laboratory, Medical School of Sao Jose do Rio Preto (FAMERP), Sao Paulo 15090000, Brazil; (C.C.P.); (M.V.S.); (M.L.N.)
| | - Ljubica Tasic
- Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas 13083862, Brazil; (L.G.M.); (L.T.)
| | - Raghuvir K. Arni
- Multiuser Center for Biomolecular Innovation, Department of Physics, Institute of Biosciences, Languages and Exact Sciences (Ibilce—UNESP), Sao Jose do Rio Preto, Sao Paulo 15054000, Brazil; (R.d.S.P.); (K.C.B.); (R.K.A.)
| | - Raphael J. Eberle
- Institute of Biological Information Processing IBI-7: Structural Biochemistry, Forschungszentrum Jülich, 52428 Jülich, Germany
- Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Mônika A. Coronado
- Institute of Biological Information Processing IBI-7: Structural Biochemistry, Forschungszentrum Jülich, 52428 Jülich, Germany
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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Special Issue: Mathematical Modeling and Control of Bioprocesses. Processes (Basel) 2022. [DOI: 10.3390/pr10071372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
This Special Issue (SI) of Processes on Mathematical Modeling and Control of Bioprocesses (MMCB) contains papers focusing, on the one hand, on mathematical modeling of biological processes at different scales ranging from microscopic to macroscopic levels and, on the other hand, on model-based estimation, optimization and control of these processes [...]
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