Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis
Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compi...
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BMC
2025-01-01
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| Series: | Particle and Fibre Toxicology |
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| Online Access: | https://doi.org/10.1186/s12989-024-00616-3 |
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| author | Jiayu Cao Yuhui Yang Xi Liu Yang Huang Qianqian Xie Aliaksei Kadushkin Mikhail Nedelko Di Wu Noel J. Aquilina Xuehua Li Xiaoming Cai Ruibin Li |
| author_facet | Jiayu Cao Yuhui Yang Xi Liu Yang Huang Qianqian Xie Aliaksei Kadushkin Mikhail Nedelko Di Wu Noel J. Aquilina Xuehua Li Xiaoming Cai Ruibin Li |
| author_sort | Jiayu Cao |
| collection | DOAJ |
| description | Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis. |
| format | Article |
| id | doaj-art-00ec5156ea334799927891f2cac0e3aa |
| institution | Kabale University |
| issn | 1743-8977 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Particle and Fibre Toxicology |
| spelling | doaj-art-00ec5156ea334799927891f2cac0e3aa2025-01-19T12:04:52ZengBMCParticle and Fibre Toxicology1743-89772025-01-0122111810.1186/s12989-024-00616-3Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosisJiayu Cao0Yuhui Yang1Xi Liu2Yang Huang3Qianqian Xie4Aliaksei Kadushkin5Mikhail Nedelko6Di Wu7Noel J. Aquilina8Xuehua Li9Xiaoming Cai10Ruibin Li11School of Public Health, Suzhou Medical School, Soochow UniversitySchool of Public Health, Suzhou Medical School, Soochow UniversityState Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Suzhou Medical School, Soochow UniversityKey Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of TechnologyState Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Suzhou Medical School, Soochow UniversityDepartment of Biological Chemistry, Belarusian State Medical UniversityB.I. Stepanov Institute of Physics of National Academy of Sciences of BelarusState Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Suzhou Medical School, Soochow UniversityDepartment of Chemistry, University of MaltaKey Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of TechnologySchool of Public Health, Suzhou Medical School, Soochow UniversityState Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Suzhou Medical School, Soochow UniversityAbstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.https://doi.org/10.1186/s12989-024-00616-3lung fibrosispredictive toxicologynanosafetynanotoxicitybiotransformation |
| spellingShingle | Jiayu Cao Yuhui Yang Xi Liu Yang Huang Qianqian Xie Aliaksei Kadushkin Mikhail Nedelko Di Wu Noel J. Aquilina Xuehua Li Xiaoming Cai Ruibin Li Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis Particle and Fibre Toxicology lung fibrosis predictive toxicology nanosafety nanotoxicity biotransformation |
| title | Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis |
| title_full | Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis |
| title_fullStr | Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis |
| title_full_unstemmed | Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis |
| title_short | Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis |
| title_sort | deciphering key nano bio interface descriptors to predict nanoparticle induced lung fibrosis |
| topic | lung fibrosis predictive toxicology nanosafety nanotoxicity biotransformation |
| url | https://doi.org/10.1186/s12989-024-00616-3 |
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