Unknown

Dataset Information

0

Effects of artemisinin on ventricular arrhythmias in response to left ventricular afterload increase and microRNA expression profiles in Wistar rats.


ABSTRACT:

Background

Patients with dilated cardiomyopathy, increased ventricular volume, pressure overload or dysynergistic ventricular contraction and relaxation are susceptible to develop serious ventricular arrhythmias (VA). These phenomena are primarily based on a theory of mechanoelectric feedback, which reflects mechanical changes that produce alterations in electrical activity. However, very few systematic studies have provided evidence of the preventive effects of artemisinin (ART) on VA in response to left ventricle (LV) afterload increases. MicroRNAs (miRNAs) are endogenous small non-coding RNAs that regulate expression of multiple genes by suppressing mRNAs post-transcriptionally.

Aims

The aims of this study were to investigate preventive effects of ART on mechanical VA and the underling molecular mechanisms of differentially expressed miRNAs (DEMs).

Methods

For the study, 70 male Wistar rats were randomly divided into seven groups: group 1 was a control group (sham surgery); group 2 was a model group that underwent transverse aortic constriction (TAC) surgery; groups 3, 4, 5 and 6 were administered ART 75, 150, 300 and 600 mg/kg before TAC surgery, respectively; and group 7 was administered verapamil (VER) 1 mg/kg before TAC surgery. A ventricular arrhythmia score (VAS) was calculated to evaluate preventive effects of ART and VER on mechanical VA. The high throughput sequencing-based approach provided DEMs that were altered by ART pretreatment between group 2 and group 4. All predicted mRNAs of DEMs were enriched by gene ontology (GO) and Kyoto Encyclopedia annotation of Genes and Genomes (KEGG) databases. These DEMs were validated by a real time quantitative polymerase chain reaction (RT-qPCR).

Results

The average VASs of groups 3, 4, 5, 6 and 7 were significantly reduced compared with those of group 2 (2.70 ± 0.48, 1.70 ± 0.95, 2.80 ± 0.79, 2.60 ± 0.97, 1.40 ± 0.52, vs 3.70 ± 0.67, p < 0.01, respectively). The three top GO terms were neuron projection, organ morphogenesis and protein domain specific binding. KEGG enrichment of the 16 DEMs revealed that MAPK, Wnt and Hippo signaling pathways were likely to play a substantial role in the preventive effects of ART on mechanical VA in response to LV afterload increases. All candidate DEMs with the exception of rno-miR-370-3p, rno-miR-6319, rno-miR-21-3p and rno-miR-204-5p showed high expression levels validated by RT-qPCR.

Conclusions

Artemisinin could prevent mechanical VA in response to LV afterload increases. Validated DEMs could be biomarkers and therapeutic targets of ART regarding its prevention of VA induced by pressure overload. The KEGG pathway and GO annotation analyses of the target mRNAs could indicate the potential functions of candidate DEMs. These results will help to elucidate the functional and regulatory roles of candidate DEMs associated with antiarrhythmic effects of ART.

SUBMITTER: Xu X 

PROVIDER: S-EPMC6304267 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

altmetric image

Publications

Effects of artemisinin on ventricular arrhythmias in response to left ventricular afterload increase and microRNA expression profiles in Wistar rats.

Xu Xue X   Zhang Qiang Q   Song Huanqiu H   Ao Zhuo Z   Li Xiang X   Cheng Cheng C   Shi Maojing M   Fu Fengying F   Sun Chengtao C   Liu Yuansheng Y   Han Dong D  

PeerJ 20181220


<h4>Background</h4>Patients with dilated cardiomyopathy, increased ventricular volume, pressure overload or dysynergistic ventricular contraction and relaxation are susceptible to develop serious ventricular arrhythmias (VA). These phenomena are primarily based on a theory of mechanoelectric feedback, which reflects mechanical changes that produce alterations in electrical activity. However, very few systematic studies have provided evidence of the preventive effects of artemisinin (ART) on VA i  ...[more]

Similar Datasets

| S-EPMC4265552 | biostudies-literature
| S-EPMC2013974 | biostudies-other
| S-EPMC5844246 | biostudies-literature
| S-EPMC6544215 | biostudies-literature
| S-EPMC7573374 | biostudies-literature
| S-EPMC4956565 | biostudies-literature
| S-EPMC5073997 | biostudies-literature
| S-EPMC1572753 | biostudies-other
| S-EPMC11286422 | biostudies-literature
| S-EPMC6160394 | biostudies-literature