Modulation of nutrient precursors for controlling metabolic inhibitors by genome‐scale flux balance analysis
Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to i...
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Veröffentlicht in: | Biotechnology progress 2023-03, Vol.39 (2), p.e3313-n/a |
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description | Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by‐products. Generic CHO metabolic model was tailored and integrated with CHO fed‐batch metabolomic data to obtain a cell line‐ and process‐specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n‐acetylputrescine—all of which were previously reported to generate from endogenous cell metabolism—by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed‐batch feeding conditions one against another, from which the results were validated via a dose‐dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by‐products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors. |
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Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by‐products. Generic CHO metabolic model was tailored and integrated with CHO fed‐batch metabolomic data to obtain a cell line‐ and process‐specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n‐acetylputrescine—all of which were previously reported to generate from endogenous cell metabolism—by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed‐batch feeding conditions one against another, from which the results were validated via a dose‐dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by‐products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors.</description><identifier>ISSN: 8756-7938</identifier><identifier>EISSN: 1520-6033</identifier><identifier>DOI: 10.1002/btpr.3313</identifier><identifier>PMID: 36367527</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Accumulation ; Amino acids ; Amino Acids - metabolism ; Animals ; Batch Cell Culture Techniques - methods ; Cell culture ; Cell density ; CHO Cells ; Cricetinae ; Cricetulus ; Culture Media - chemistry ; culture medium development ; Design optimization ; Digital twins ; fed‐batch bioprocess ; flux modeling simulation ; Gene mapping ; Genomes ; Glycosylation ; Growth rate ; Inhibitors ; metabolic inhibitors ; metabolic shift ; Metabolism ; Metabolites ; Metabolomics ; Nutrient balance ; Nutrients ; Phenotypes ; Precursors ; therapeutic protein production</subject><ispartof>Biotechnology progress, 2023-03, Vol.39 (2), p.e3313-n/a</ispartof><rights>2022 American Institute of Chemical Engineers.</rights><rights>2023 American Institute of Chemical Engineers</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3883-e7a8028f72868d54f15b4b5419608cef66c58e6b51386cc0258bd34d87001acd3</citedby><cites>FETCH-LOGICAL-c3883-e7a8028f72868d54f15b4b5419608cef66c58e6b51386cc0258bd34d87001acd3</cites><orcidid>0000-0002-7496-5193 ; 0000-0002-5330-8784</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fbtpr.3313$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fbtpr.3313$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36367527$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hoang, Duc</creatorcontrib><creatorcontrib>Kuang, Bingyu</creatorcontrib><creatorcontrib>Liang, George</creatorcontrib><creatorcontrib>Wang, Zhao</creatorcontrib><creatorcontrib>Yoon, Seongkyu</creatorcontrib><title>Modulation of nutrient precursors for controlling metabolic inhibitors by genome‐scale flux balance analysis</title><title>Biotechnology progress</title><addtitle>Biotechnol Prog</addtitle><description>Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by‐products. Generic CHO metabolic model was tailored and integrated with CHO fed‐batch metabolomic data to obtain a cell line‐ and process‐specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n‐acetylputrescine—all of which were previously reported to generate from endogenous cell metabolism—by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed‐batch feeding conditions one against another, from which the results were validated via a dose‐dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by‐products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors.</description><subject>Accumulation</subject><subject>Amino acids</subject><subject>Amino Acids - metabolism</subject><subject>Animals</subject><subject>Batch Cell Culture Techniques - methods</subject><subject>Cell culture</subject><subject>Cell density</subject><subject>CHO Cells</subject><subject>Cricetinae</subject><subject>Cricetulus</subject><subject>Culture Media - chemistry</subject><subject>culture medium development</subject><subject>Design optimization</subject><subject>Digital twins</subject><subject>fed‐batch bioprocess</subject><subject>flux modeling simulation</subject><subject>Gene mapping</subject><subject>Genomes</subject><subject>Glycosylation</subject><subject>Growth rate</subject><subject>Inhibitors</subject><subject>metabolic inhibitors</subject><subject>metabolic shift</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Nutrient balance</subject><subject>Nutrients</subject><subject>Phenotypes</subject><subject>Precursors</subject><subject>therapeutic protein production</subject><issn>8756-7938</issn><issn>1520-6033</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kLtOwzAYRi0EoqUw8ALIEhNDii-x445QcZOKQKjMke04xZVrFzsRdOMReEaehJQWNqZ_Of_RpwPAMUZDjBA5V80yDinFdAf0MSMo44jSXdAXBeNZMaKiBw5SmiOEBOJkH_Qop7xgpOgDfx-q1snGBg9DDX3bRGt8A5fR6DamEBOsQ4Q6-CYG56yfwYVppArOamj9i1W2WUNqBWfGh4X5-vhMWjoDa9e-QyWd9NpA6aVbJZsOwV4tXTJH2zsAz9dX0_FtNnm4uRtfTDJNhaCZKaRARNQFEVxULK8xU7liOR5xJLSpOddMGK4YpoJrjQgTqqJ5JQqEsNQVHYDTjXcZw2trUlPOQxu7EakkAuF8RLrPjjrbUDqGlKKpy2W0CxlXJUblumy5Lluuy3bsydbYqoWp_sjflB1wvgHerDOr_03l5fTx6Uf5DS6jhbw</recordid><startdate>202303</startdate><enddate>202303</enddate><creator>Hoang, Duc</creator><creator>Kuang, Bingyu</creator><creator>Liang, George</creator><creator>Wang, Zhao</creator><creator>Yoon, Seongkyu</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-7496-5193</orcidid><orcidid>https://orcid.org/0000-0002-5330-8784</orcidid></search><sort><creationdate>202303</creationdate><title>Modulation of nutrient precursors for controlling metabolic inhibitors by genome‐scale flux balance analysis</title><author>Hoang, Duc ; Kuang, Bingyu ; Liang, George ; Wang, Zhao ; Yoon, Seongkyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3883-e7a8028f72868d54f15b4b5419608cef66c58e6b51386cc0258bd34d87001acd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accumulation</topic><topic>Amino acids</topic><topic>Amino Acids - metabolism</topic><topic>Animals</topic><topic>Batch Cell Culture Techniques - methods</topic><topic>Cell culture</topic><topic>Cell density</topic><topic>CHO Cells</topic><topic>Cricetinae</topic><topic>Cricetulus</topic><topic>Culture Media - chemistry</topic><topic>culture medium development</topic><topic>Design optimization</topic><topic>Digital twins</topic><topic>fed‐batch bioprocess</topic><topic>flux modeling simulation</topic><topic>Gene mapping</topic><topic>Genomes</topic><topic>Glycosylation</topic><topic>Growth rate</topic><topic>Inhibitors</topic><topic>metabolic inhibitors</topic><topic>metabolic shift</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolomics</topic><topic>Nutrient balance</topic><topic>Nutrients</topic><topic>Phenotypes</topic><topic>Precursors</topic><topic>therapeutic protein production</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hoang, Duc</creatorcontrib><creatorcontrib>Kuang, Bingyu</creatorcontrib><creatorcontrib>Liang, George</creatorcontrib><creatorcontrib>Wang, Zhao</creatorcontrib><creatorcontrib>Yoon, Seongkyu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biotechnology progress</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hoang, Duc</au><au>Kuang, Bingyu</au><au>Liang, George</au><au>Wang, Zhao</au><au>Yoon, Seongkyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modulation of nutrient precursors for controlling metabolic inhibitors by genome‐scale flux balance analysis</atitle><jtitle>Biotechnology progress</jtitle><addtitle>Biotechnol Prog</addtitle><date>2023-03</date><risdate>2023</risdate><volume>39</volume><issue>2</issue><spage>e3313</spage><epage>n/a</epage><pages>e3313-n/a</pages><issn>8756-7938</issn><eissn>1520-6033</eissn><abstract>Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by‐products. Generic CHO metabolic model was tailored and integrated with CHO fed‐batch metabolomic data to obtain a cell line‐ and process‐specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n‐acetylputrescine—all of which were previously reported to generate from endogenous cell metabolism—by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed‐batch feeding conditions one against another, from which the results were validated via a dose‐dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by‐products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36367527</pmid><doi>10.1002/btpr.3313</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-7496-5193</orcidid><orcidid>https://orcid.org/0000-0002-5330-8784</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accumulation Amino acids Amino Acids - metabolism Animals Batch Cell Culture Techniques - methods Cell culture Cell density CHO Cells Cricetinae Cricetulus Culture Media - chemistry culture medium development Design optimization Digital twins fed‐batch bioprocess flux modeling simulation Gene mapping Genomes Glycosylation Growth rate Inhibitors metabolic inhibitors metabolic shift Metabolism Metabolites Metabolomics Nutrient balance Nutrients Phenotypes Precursors therapeutic protein production |
title | Modulation of nutrient precursors for controlling metabolic inhibitors by genome‐scale flux balance analysis |
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