Evidence map›Paper›PMID 35287352›Full record

ArticlePeerJ2022

Analysis of the role and mechanism of EGCG in septic cardiomyopathy based on network pharmacology.

Ji Wu, Zhenhua Wang, Shanling Xu, Yang Fu, Yi Gao, Zuxiang Wu, Yun Yu, Yougen Yuan, Lin Zhou, Ping Li

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Ji WuDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Zhenhua WangDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Shanling XuDepartment of Cardiovascular, Medicine, Fuzhou First People's Hospital, Fu Zhou, China.
Yang FuDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Yi GaoDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Zuxiang WuDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Yun YuDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Yougen YuanDepartment of Cardiovascular, The Three Affiliated Hospital of Nanchang University, Nan Chang, China.
Lin ZhouDepartment of Cardiovascular, The Three Affiliated Hospital of Nanchang University, Nan Chang, China.
Ping LiDepartment of Cardiovascular, The Second Affiliated Hospital of Nanchang University, Nan Chang, China.
Second Affiliated Hospital of Nanchang University · CNNanchang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Septic cardiomyopathy (SC) is a common complication of sepsis that leads to an increase in mortality. The pathogenesis of septic cardiomyopathy is unclear, and there is currently no effective treatment. EGCG (epigallocatechin gallate) is a polyphenol that has anti-inflammatory, antiapoptotic, and antioxidative stress effects. However, the role of EGCG in septic cardiomyopathy is unknown. Methods: Network pharmacology was used to predict the potential targets and molecular mechanisms of EGCG in the treatment of septic cardiomyopathy, including the construction and analysis of protein-protein interaction (PPI) network, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and molecular docking. The mouse model of septic cardiomyopathy was established after intraperitoneal injection of LPS (lipopolysaccharide). The myocardial protective effect of EGCG on septic mice is observed by cardiac ultrasound and HE staining. RT-PCR is used to verify the expression level of the EGCG target in the septic cardiomyopathy mouse model. Results: A total of 128 anti-SC potential targets of EGCGareselected for analysis. The GO enrichment analysis and KEGG pathway analysis results indicated that the anti-SC targets of EGCG mainly participate in inflammatory and apoptosis processes. Molecular docking results suggest that EGCG has a high affinity for the crystal structure of six targets (IL-6 (interleukin-6), TNF (tumor necrosis factor), Caspase3, MAPK3 (Mitogen-activated protein kinase 3), AKT1, and VEGFA (vascular endothelial growth factor)), and the experimental verification result showed levated expression of these 6 hub targets in the LPS group, but there is an obvious decrease in expression in the LPS + EGCG group. The functional and morphological changes found by echocardiography and HE staining show that EGCG can effectively improve the cardiac function that is reduced by LPS. Conclusion: Our results reveal that EGCG may be a potentially effective drug to improve septic cardiomyopathy. The potential mechanism by which EGCG improves myocardial injury in septic cardiomyopathy is through anti-inflammatory and anti-apoptotic effects. The anti-inflammatory and anti-apoptotic effects of EGCG occur not only through direct binding to six target proteins (IL-6,TNF-α, Caspase3, MAPK3, AKT1, and VEGFA) but also by reducing their expression.

Indexed as

CardiomyopathiesNetwork PharmacologyAnimalsCatechinDisease Models, AnimalInterleukin-6LipopolysaccharidesMiceMolecular Docking SimulationTumor Necrosis Factor-alphaVascular Endothelial Growth Factor ACatechinepigallocatechin gallateInterleukin-6LipopolysaccharidesTumor Necrosis Factor-alphaVascular Endothelial Growth Factor AApoptosisEGCGInflammationNetwork pharmacologySeptic cardiomyopathy

Identifiers

PMID35287352
PMCPMC8917800
OpenAlexW4220822164

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Registered trials

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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 OpenQuestion graph.