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PXD035745-1

PXD035745 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleProteomic Analysis Based on TMT Regarding the Therapeutic Action of Rhizoma Drynariae on Rats in an Osteoporosis Model
DescriptionBackground: Primary osteoporosis has increasingly become one of the risk factors affecting human health, and the clinical effect and action mechanism of traditional Chinese medicine in the treatment of primary osteoporosis have been widely studied. Previous studies have confirmed that in traditional Chinese medicine , Drynaria rhizome has a role in improving bone density. In this study, TMT-based proteomic analysis was conducted to derive potential targets for Drynaria rhizome treatment in postmenopausal osteoporosis. Methods: The model group (OVX) and experimental group (OVXDF) for menopausal osteoporosis were established using the universally acknowledged ovariectomy method, and the latter group received intragastric administration of 8.1 g /kg-1 of Drynaria rhizome for 12 weeks. After 12 weeks, femurs of rats selected for this study were examined with a bone mineral density (BMD) test, Micro-CT, ELISABiochemical testing, hematoxylin and eosin (HE) staining, and immunohistochemistry. A certain portion of the bone tissue was studied with a TMT-based proteomic analysis and functional and pathway enrichment analysis. Finally, key target genes were selected for Western blotting for validation. Results: The comparison of the OVXDF and OVX groups indicated that Drynaria rhizome could improve bone density. In the TMT-based proteomic analysis, the comparison of these two groups revealed a total of 126 differentially expressed proteins (DEPs), of which 62 were upregulated and 64 were downregulated. Further, by comparing the differential genes between the OVXDF and OVX groups and between the OVX and SHAM groups, we concluded that the 27 differential genes were significantly changed in the rats selected for the osteoporosis model after Drynaria rhizome intragastric administration. The gene ontology (GO) enrichment analysis of DEPs showed that molecular function was mainly involved in biological processes, such as glucose metabolism, carbohydrate metabolism, immune responses, and aging. A Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEPs revealed that multiple differential genes were enriched in the estrogen and peroxisome proliferator-activated receptor (PPAR) signaling pathways. Relationships with nitrogen metabolism, glycerophospholipid metabolism, secretion systems, and tumor diseases were also observed. Western blotting was consistent with the analysis. Conclusions: We used TMT-based proteomics to analyze the positive effects of TCM Drynaria rhizome, which can regulate related proteins through the unique roles of multiple mechanisms, targets, and pathways. This treatment approach can regulate oxidative stress, improve lipid metabolism, reduce the inflammatory response mechanism, and improve bone density. These benefits highlight the unique advantages of TCM in the treatment of primary osteoporosis.
HostingRepositoryPRIDE
AnnounceDate2024-10-17
AnnouncementXMLSubmission_2024-10-17_02:39:05.082.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterSU HUI
SpeciesList scientific name: Rattus norvegicus (Rat); NCBI TaxID: 10116;
ModificationListNo PTMs are included in the dataset
InstrumentLTQ
Dataset History
RevisionDatetimeStatusChangeLog Entry
02022-08-02 10:21:56ID requested
12024-10-17 02:39:05announced
Publication List
Su H, Yan B, Wang R, Li Z, Xu Z, Xue H, Tan G, on Rats in an Osteoporosis Model. Comb Chem High Throughput Screen, 27(15):2223-2238(2024) [pubmed]
10.2174/0113862073261905231110061401;
Keyword List
submitter keyword: Osteoporosis, TMT ., Rats,Rhizoma Drynariae, Ovariectomized
Contact List
SU HUI
contact affiliationShandong University of Traditional Chinese Medicine
contact emailsuhui1334676310@163.com
lab head
SU HUI
contact affiliationShandong University of Traditional Chinese Medicine
contact emailsuhui1334676310@163.com
dataset submitter
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