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Silica nanoparticle-induced cytotoxicity is predicted using penta-omics and alleviated by glutamate dehydrogenase activation in vitro and in vivo

  • 황지수 (아주대학교 일반대학원)

초록/요약

Nanoparticles (NPs) are extensively applied in nanomedicine due to the development of nanotechnology. Among the various NPs, silica nanoparticles (SiNPs) have obtained widespread use due to their favorable biocompatibility and adjustable surface modification. However, their large surface-to-volume and surface reactivity have also raised concerns about potential toxicity, leading to wide-ranging research in various organs. Nonetheless, research focused on the heart is relatively limited. In this study, the cardiotoxicity of 50 nm SiNPs was examined through both in vitro and in vivo biological analyses, incorporating integrated penta-omics. Exposure of cardiomyocytes to SiNPs elicited a series of biological adverse effects, such as excessive oxidative stress, a decline in cell viability, ATP level, and mitochondrial DNA content, as well as imbalanced calcium homeostasis, collectively indicating the occurrence of mitochondrial dysfunction. These effects were analyzed using penta- omic datasets by bioinformatic software and predicted as single and integrated networks. In addition, several machine learning techniques identified key molecules contributing to these impairments and increased risk of heart failure (HF). The alterations observed in mitochondrial metabolism offer a promising avenue for targeting glutamate dehydrogenase (GDH), potentially activating the mitochondrial function. Consequently, the administration of GDH activator mitigated these toxicities on cardiomyocytes, all attributed to the improved mitochondrial function. Moreover, cardiac damages including fibrosis, inflammation and stress signals by isoproterenol (ISO) and SiNPs in mice were alleviated by GDH activator. These findings highlight the comprehensive mechanism of cardiotoxicity by SiNPs and suggest that GDH activators as one of the therapeutic candidates. Keywords: Silica nanoparticles, Cardiotoxicity, Mitochondrial dysfunction, Glutamate dehydrogenase, Penta-omics

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목차

1. Introduction 1
2. Materials and Methods 4
2.1. Nanoparticles 4
2.2. Scanning electron microscopy (SEM) 4
2.3. Cell culture 4
2.4. Evaluation of cell viability 5
2.5. Evaluation of intracellular ATP level 5
2.6. Evaluation of intracellular ROS level 6
2.7. Evaluation of calcium level 6
2.8. Transmission electron microscopy (TEM) 6
2.9. Isolation of RNA and quantitative real-time PCR (qRT-PCR) 7
2.10. Total RNA isolation and cDNA library preparation for transcriptomic analysis 7
2.11. RNA-seq data analysis for transcriptomic profiling 8
2.12. cDNA library preparation and data processing for microRNA (miRNA) profiling 9
2.13. Isolation of miRNA and quantification by qRT-PCR 10
2.14. Preparation of sample for proteomic analysis 10
2.15. High pH reversed-phase liquid chromatography for peptide fractionation 11
2.16. Phosphopeptide enrichment 12
2.17 Liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis for proteomic and phosphoproteomic profiling 12
2.18. Identification and quantification of peptides for proteomic and phosphoproteomic analysis 13
2.19. Sample preparation for metabolomic profiling 14
2.20. Gas chromatographic analysis with MS/MS (GC-MS/MS) for metabolomic analysis 15
2.21. LC-MS/MS for metabolomic analysis 15
2.22. Functional network analysis of biological functions using Ingenuity Pathway Analysis (IPA) 16
2.23. Dimensionality reduction and clustering 16
2.24. Evaluation of glutamate dehydrogenase activity 17
2.25. Evaluation of GSH level 17
2.26. Immunoblotting 18
2.27. Animal studies 19
2.28. Hematoxylin and eosin staining 19
2.29. Sirius Red staining 20
2.30. Echocardiography 20
2.31. Statistical analysis 20
3. Results 21
3.1. Phenotypic characterization of SiNPs-treated HL-1 cells 21
3.2. Analysis and prediction of biological functional changes with transcriptomic and miRNAomic profiling in SiNPs-treated HL-1 cells 24
3.3. Analysis and prediction of biological functional changes with proteomic and phosphoproteomic profiling in SiNPs-treated HL-1 cells 28
3.4. Analysis and prediction of biological functional changes with metabolomic profiling and integration of penta-omic dataset 32
3.5. Identification of key molecules from penta-omic network using machine learning techniques 35
3.6. Decreased glutamate dehydrogenase activity in SiNPs-treated HL-1 cells 38
3.7. Mitigation of SiNPs-induced toxicity in HL-1 cells through BCH-mediated glutamate dehydrogenase activation in vitro 41
3.8. Attenuation of in vivo SiNPs-induced cardiac fibrosis and inflammation by BCH administration via inhibition of p65 and JNK signaling pathways 44
4. Discussion 48
CONCLUSION. 52
APPENDIX 1 : Supplementary Tables 53
Appendix 1.1. Sequences of primers for qRT-PCR for mitochondrial DNA copy number analysis, transcriptomic network-related genes, inflammatory and fibrotic markers 53
Appendix 1.2. Sequences of primers for qRT-PCR for miRNAomic network-related gene 55
Appendix 1.3. Top 20 toxicological functions annotation generated by IPA in the transcriptome of 0.5 μg/μL SiNPs-treated HL-1 cells 56
Appendix 1.4. Profiles of factors in the transcriptomic network of 0.5 μg/μL SiNPs-treated HL-1 cells 57
Appendix 1.5. Top 20 toxicological functions annotation generated by IPA in the miRNAome of 0.5 μg/μL SiNPs-treated HL-1 cells 63
Appendix 1.6. Profile of factor in the miRNAomic network of 0.5 μg/μL SiNPs-treated HL-1 cells 64
Appendix 1.7. Top 20 toxicological functions annotation generated by IPA in the proteome of 0.5 μg/μL SiNPs-treated HL-1 cells 65
Appendix 1.8. Profiles of factors in the proteomic network of 0.5 μg/μL SiNPs-treated HL-1 cells 66
Appendix 1.9. Top 20 toxicological functions annotation generated by IPA in the phosphoproteome of 0.5 μg/μL SiNPs-treated HL-1 cells 70
Appendix 1.10. Profiles of factors in the phosphoproteomic network of 0.5 μg/μL SiNPs-treated HL-1 cells 71
Appendix 1.11. Top 20 Toxicological functions annotation generated by IPA in the metabolome of 0.5 μg/μL SiNPs-treated HL-1 cells 74
Appendix 1.12. Profiles of factors in the metabolomic network of 0.5 μg/μL SiNPs-treated HL-1 cells 75
Appendix 1.13. Average silhouette scores in nine combinations of dimensionality reduction techniques and clustering algorithms 76
APPENDIX 2 : Supplementary Figures 77
Appendix 2.1. Morphology of SiNPs 77
Appendix 2.2. Cell viability of HL-1 cells treated with SiNPs for 12 h in a dose-dependent manner 78
Appendix 2.3. Transcriptomic network analysis of 0.5 μg/μL SiNPs-treated HL- 1 cells 79
Appendix 2.4. Predictive transcriptomic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 80
Appendix 2.5. Proteomic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 81
Appendix 2.6. Predictive proteomic analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 82
Appendix 2.7. Phosphoproteomic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 83
Appendix 2.8. Predictive phosphoproteomic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 84
Appendix 2.9. Identification of affected phosphorylation sites after SiNPs treatment for 12 h 85
Appendix 2.10. Metabolomic network analysis of 0.5 μg/μL SiNPs-treated HL- 1 cells 87
Appendix 2.11. Predictive metabolomic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 88
Appendix 2.12. Penta-omic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 89
Appendix 2.13. Predictive penta-omic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells 90
Appendix 2.14. Visualization of scatter plots following dimensionality reduction techniques 91
Appendix 2.15. Penta-omic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells following nine combinations of machine learning techniques 92
Appendix 2.16. Predictive penta-omic network analysis of 0.5 μg/μL SiNPs-treated HL-1 cells following nine combinations of machine learning techniques 93
Appendix 2.17. Glutamate dehydrogenase activity under various concentrations of BCH on HL-1 cells for 12 h 94
Appendix 2.18. Cell viability under various concentrations of BCH on HL-1 cells for 12 h 95
Appendix 2.19. Transcriptomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 96
Appendix 2.20. Prediction in transcriptomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 97
Appendix 2.21. Proteomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 98
Appendix 2.22. Prediction in proteomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 99
Appendix 2.23. Phosphoproteomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 100
Appendix 2.24. Prediction in phosphoproteomic network analysis related to DNA damage response of 0.5 μg/μL SiNPs-treated HL-1 cells 101
Appendix 2.25. Echocardiographic analysis in mice with combinatory treatment of isoproterenol, SiNPs, and BCH 102
Appendix 2.26. Alteration in free fatty acids of SiNPs-treated HL-1 cells 103
Appendix 2.27. Transcriptomic network analysis related to membrane integrity, contractility, and heart failure of 0.5 μg/μL SiNPs-treated HL-1 cells 104
Appendix 2.28. Prediction in transcriptomic network analysis related to membrane integrity, contractility, and heart failure of 0.5 μg/μL SiNPs-treated HL-1 cells 105
Appendix 2.29. Phosphoproteomic network related to membrane integrity, contractility, and heart failure of 0.5 μg/μL SiNPs-treated HL-1 cells 106
Appendix 2.30. Prediction in phosphoproteomic network related to membrane integrity, contractility, and heart failure of 0.5 μg/μL SiNPs-treated HL-1 cells 107
REFERENCES 108

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