Evidence map›Paper›PMID 33870466›Full record

ArticleClinical rheumatology2021

Comparative transcriptomics and network pharmacology analysis to identify the potential mechanism of celastrol against osteoarthritis.

Siming Dai, Hui Wang, Meng Wang, Yue Zhang, Zhiyi Zhang, Zhiguo Lin

Abstract read
PubMed Publisher
In one paragraph

Article in Clinical rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.8field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. The Liver-Protective Effects of the Essential Oil fromAntioxidants (Basel, Switzerland) · 2024
    Article
  5. Review
  6. Autophagy in the pharmacological activities of celastrol (Review).Experimental and therapeutic medicine · 2023
    Review
  7. Review
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Siming DaiDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang, China.ORCID https://orcid.org/0000-0002-7082-1432
Hui WangDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang, China.
Meng WangKey Laboratory of Basic and Applied Research in North Medicine, Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China.
Yue ZhangDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang, China.
Zhiyi ZhangDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang, China. zhangzhiyi2014@163.com.ORCID http://orcid.org/0000-0003-3951-3834
Zhiguo LinDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang, China. 1977linzhiguo@163.com.
Harbin Medical University · CNHeilongjiang University of Chinese Medicine · CN

Funding

Harbin Science and Technology Bureau 2017RAQXJ196National Natural Science Foundation of China 81771748
6 · The paper itself

Abstract

introductionCelastrol is a promising therapeutic agent for the treatment of osteoarthritis (OA). However, the mechanism of action of celastrol is unclear. This study was aiming to identify the potential function of celastrol on OA and determine its underlying mechanism.

methodCelastrol targets were collected from web database searches and literature review, while pathogenic OA targets were obtained from Online Mendelian Inheritance in Man (OMIM) and GeneCards databases. Transcriptomics data was sequenced using an Illumina HiSeq 4000 platform. Celastrol-OA overlapping genes were then identified followed by prediction of the potential function and signaling pathways associated with celastrol using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A celastrol-target network was constructed to identify the candidate core targets of celastrol. The predictions were then validated by performing molecular docking and molecular dynamics simulation studies.

resultsIn total, 96 genes were identified as the putative celastrol targets for treatment of OA. These genes were possibly involved in cell phenotype changes including response to lipopolysaccharide and oxidative stress as well as in cell apoptosis and aging. The genes also induced the mTOR pathway and AGE-RAGE signaling pathway at the intracellular level. Additionally, results indicated that 13 core targets including mTOR, TP53, MMP9, EGFR, CCND1, MAPK1, STAT3, VEGFA, CASP3, TNF, MYC, ESR1, and PTEN were likely direct targets of celastrol in OA. Finally, mTOR was determined as the most likely therapeutic target of celastrol in OA.

conclusionThis study provides a basic understanding and novel insight into the potential mechanism of celastrol against OA. Key Points • Our study provides a strong indication that further study of celastrol therapy in OA is required. • mTOR is the most likely therapeutic target of celastrol in OA.

Indexed as

Drugs, Chinese HerbalOsteoarthritisHumansMolecular Docking SimulationPentacyclic TriterpenesTranscriptomecelastrolDrugs, Chinese HerbalPentacyclic TriterpenesCelastrolComputer-aided drug designMechanismNetwork pharmacologyOsteoarthritisTranscriptomics

Identifiers

PMID33870466
OpenAlexW3155935788

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.