| Autor: |
Yuqing Meng, Jiayun Chen, Yanqing Liu, Yongping Zhu, Yin-Kwan Wong, Haining Lyu, Qiaoli Shi, Fei Xia, Liwei Gu, Xinwei Zhang, Peng Gao, Huan Tang, Qiuyan Guo, Chong Qiu, Chengchao Xu, Xiao He, Junzhe Zhang, Jigang Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of Pharmaceutical Analysis, Vol 12, Iss 6, Pp 879-888 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2095-1779 |
| DOI: |
10.1016/j.jpha.2022.07.002 |
| Popis: |
The composition of serum is extremely complex, which complicates the discovery of new pharmacodynamic biomarkers via serum proteome for disease prediction and diagnosis. Recently, nanoparticles have been reported to efficiently reduce the proportion of high-abundance proteins and enrich low-abundance proteins in serum. Here, we synthesized a silica-coated iron oxide nanoparticle and developed a highly efficient and reproducible protein corona (PC)-based proteomic analysis strategy to improve the range of serum proteomic analysis. We identified 1,070 proteins with a median coefficient of variation of 12.56% using PC-based proteomic analysis, which was twice the number of proteins identified by direct digestion. There were also more biological processes enriched with these proteins. We applied this strategy to identify more pharmacodynamic biomarkers on collagen-induced arthritis (CIA) rat model treated with methotrexate (MTX). The bioinformatic results indicated that 485 differentially expressed proteins (DEPs) were found in CIA rats, of which 323 DEPs recovered to near normal levels after treatment with MTX. This strategy can not only help enhance our understanding of the mechanisms of disease and drug action through serum proteomics studies, but also provide more pharmacodynamic biomarkers for disease prediction, diagnosis, and treatment. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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