Roberto Tarantino , Halie Mei Jensen , Stephen D. Waldman
{"title":"软骨细胞代谢模型中的生物量积累:结合细胞外基质代理来预测组织工程结果","authors":"Roberto Tarantino , Halie Mei Jensen , Stephen D. Waldman","doi":"10.1016/j.ymben.2025.07.004","DOIUrl":null,"url":null,"abstract":"<div><div>Metabolic modeling in chondrocytes plays a pivotal role in advancing our understanding of cellular function. These techniques have been used to study degenerative joint diseases (e.g. osteoarthritis), mechanotransduction, and more recently to optimize strategies for cartilage tissue engineering. Incorporating tissue formation into metabolic flux analysis is inherently challenging due to the complexity of linking metabolic activity to extracellular matrix (ECM) accumulation. Many ECM macromolecules are synthesized using metabolites derived from central carbon metabolism, but direct modeling of their accumulation remains complex. This study establishes a novel methodology for incorporating ECM synthesis into metabolic flux analysis (MFA). By utilizing chondroitin sulfate and hydroxyproline as measurable metabolic proxies for proteoglycan and collagen production, we demonstrate a framework for linking metabolic inputs with tissue formation. Extracellular flux data for glucose, lactate, carbon dioxide, glutamine, and glutamate, along with mass isotopomer distributions, were sourced from previous studies involving three-dimensional high-density cultures of articular cartilage tissue constructs. Additionally, the conditioned culture media used in these studies was used to quantify the production rates of chondroitin sulfate and hydroxyproline. Using a modular network model, proteoglycan and collagen metabolism were assessed independently, and in combination, with sensitivity analyses on ECM retention assumptions. Predicted proteoglycan production aligned well with previously observed trends; however, predicted collagen production was less consistent. These findings offer a novel approach for linking metabolic inputs with ECM production, advancing our ability to predict tissue formation and address key challenges in cartilage tissue engineering.</div></div>","PeriodicalId":18483,"journal":{"name":"Metabolic engineering","volume":"92 ","pages":"Pages 1-12"},"PeriodicalIF":6.8000,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Biomass accumulation in chondrocyte metabolic modelling: Incorporating extracellular matrix proxies to predict tissue engineering outcomes\",\"authors\":\"Roberto Tarantino , Halie Mei Jensen , Stephen D. Waldman\",\"doi\":\"10.1016/j.ymben.2025.07.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Metabolic modeling in chondrocytes plays a pivotal role in advancing our understanding of cellular function. These techniques have been used to study degenerative joint diseases (e.g. osteoarthritis), mechanotransduction, and more recently to optimize strategies for cartilage tissue engineering. Incorporating tissue formation into metabolic flux analysis is inherently challenging due to the complexity of linking metabolic activity to extracellular matrix (ECM) accumulation. Many ECM macromolecules are synthesized using metabolites derived from central carbon metabolism, but direct modeling of their accumulation remains complex. This study establishes a novel methodology for incorporating ECM synthesis into metabolic flux analysis (MFA). By utilizing chondroitin sulfate and hydroxyproline as measurable metabolic proxies for proteoglycan and collagen production, we demonstrate a framework for linking metabolic inputs with tissue formation. Extracellular flux data for glucose, lactate, carbon dioxide, glutamine, and glutamate, along with mass isotopomer distributions, were sourced from previous studies involving three-dimensional high-density cultures of articular cartilage tissue constructs. Additionally, the conditioned culture media used in these studies was used to quantify the production rates of chondroitin sulfate and hydroxyproline. Using a modular network model, proteoglycan and collagen metabolism were assessed independently, and in combination, with sensitivity analyses on ECM retention assumptions. Predicted proteoglycan production aligned well with previously observed trends; however, predicted collagen production was less consistent. These findings offer a novel approach for linking metabolic inputs with ECM production, advancing our ability to predict tissue formation and address key challenges in cartilage tissue engineering.</div></div>\",\"PeriodicalId\":18483,\"journal\":{\"name\":\"Metabolic engineering\",\"volume\":\"92 \",\"pages\":\"Pages 1-12\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2025-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolic engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1096717625001089\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolic engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1096717625001089","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Biomass accumulation in chondrocyte metabolic modelling: Incorporating extracellular matrix proxies to predict tissue engineering outcomes
Metabolic modeling in chondrocytes plays a pivotal role in advancing our understanding of cellular function. These techniques have been used to study degenerative joint diseases (e.g. osteoarthritis), mechanotransduction, and more recently to optimize strategies for cartilage tissue engineering. Incorporating tissue formation into metabolic flux analysis is inherently challenging due to the complexity of linking metabolic activity to extracellular matrix (ECM) accumulation. Many ECM macromolecules are synthesized using metabolites derived from central carbon metabolism, but direct modeling of their accumulation remains complex. This study establishes a novel methodology for incorporating ECM synthesis into metabolic flux analysis (MFA). By utilizing chondroitin sulfate and hydroxyproline as measurable metabolic proxies for proteoglycan and collagen production, we demonstrate a framework for linking metabolic inputs with tissue formation. Extracellular flux data for glucose, lactate, carbon dioxide, glutamine, and glutamate, along with mass isotopomer distributions, were sourced from previous studies involving three-dimensional high-density cultures of articular cartilage tissue constructs. Additionally, the conditioned culture media used in these studies was used to quantify the production rates of chondroitin sulfate and hydroxyproline. Using a modular network model, proteoglycan and collagen metabolism were assessed independently, and in combination, with sensitivity analyses on ECM retention assumptions. Predicted proteoglycan production aligned well with previously observed trends; however, predicted collagen production was less consistent. These findings offer a novel approach for linking metabolic inputs with ECM production, advancing our ability to predict tissue formation and address key challenges in cartilage tissue engineering.
期刊介绍:
Metabolic Engineering (MBE) is a journal that focuses on publishing original research papers on the directed modulation of metabolic pathways for metabolite overproduction or the enhancement of cellular properties. It welcomes papers that describe the engineering of native pathways and the synthesis of heterologous pathways to convert microorganisms into microbial cell factories. The journal covers experimental, computational, and modeling approaches for understanding metabolic pathways and manipulating them through genetic, media, or environmental means. Effective exploration of metabolic pathways necessitates the use of molecular biology and biochemistry methods, as well as engineering techniques for modeling and data analysis. MBE serves as a platform for interdisciplinary research in fields such as biochemistry, molecular biology, applied microbiology, cellular physiology, cellular nutrition in health and disease, and biochemical engineering. The journal publishes various types of papers, including original research papers and review papers. It is indexed and abstracted in databases such as Scopus, Embase, EMBiology, Current Contents - Life Sciences and Clinical Medicine, Science Citation Index, PubMed/Medline, CAS and Biotechnology Citation Index.