scholarly journals Therapies Targeted at Non-Coding RNAs in Prevention and Limitation of Myocardial Infarction and Subsequent Cardiac Remodeling—Current Experience and Perspectives

2021 ◽  
Vol 22 (11) ◽  
pp. 5718
Author(s):  
Michal Kowara ◽  
Sonia Borodzicz-Jazdzyk ◽  
Karolina Rybak ◽  
Maciej Kubik ◽  
Agnieszka Cudnoch-Jedrzejewska
Keyword(s):  
Myocardial Infarction ◽  
Atherosclerotic Plaque ◽  
Cardiac Remodeling ◽  
Therapeutic Option ◽  
Circular Rna ◽  
Plaque Progression ◽  
Cardiac Damage ◽  
Non Coding Rna ◽  
Causes Of Mortality ◽  
Long Non Coding Rna

Myocardial infarction is one of the major causes of mortality worldwide and is a main cause of heart failure. This disease appears as a final point of atherosclerotic plaque progression, destabilization, and rupture. As a consequence of cardiomyocytes death during the infarction, the heart undergoes unfavorable cardiac remodeling, which results in its failure. Therefore, therapies aimed to limit the processes of atherosclerotic plaque progression, cardiac damage during the infarction, and subsequent remodeling are urgently warranted. A hopeful therapeutic option for the future medicine is targeting and regulating non-coding RNA (ncRNA), like microRNA, circular RNA (circRNA), or long non-coding RNA (lncRNA). In this review, the approaches targeted at ncRNAs participating in the aforementioned pathophysiological processes involved in myocardial infarction and their outcomes in preclinical studies have been concisely presented.

scholarly journals CIRCULAR RNA – miRNA MEDIATED INTERACTION IN MYOCARDIAL INFARCTION

2021 ◽  
Vol 27 (2) ◽  
pp. 3793-3798
Author(s):  
Yordanka Doneva ◽  
◽  
Veselin Valkov ◽  
Yavor Kashlov ◽  
Galya Mihaylova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Circular Rna ◽  
Biological Functions ◽  
Non Coding Rna ◽  
Circular Structure ◽  
Wide Range ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Circular RNA (circRNAs) belong to the long non-coding RNA family, but unlike the linear RNA in circular RNA, the 3’ and 5’ end in the RNA molecule are joined together, forming their circular structure. Until recently, circRNAs have been believed to be a side product of splicing, but now it is known that they have a wide range of biological functions, from regulators of gene expression to regulators of other non-coding RNAs - microRNAs (miRNAs). CircRNAs have the potential of being therapeutic targets and biomarkers for diseases. There are little data and only several investigations about this type of RNAs in myocardial infarction in humans. This review summarizes the role of some new circRNA – miRNA interactions in the development of Myocardial Infarction.

scholarly journals The Role of non-coding RNA in Cardiac Repair and Regeneration after Myocardial Infarction

2021 ◽  
Vol 04 (06) ◽  
pp. 01-13
Author(s):  
Yongjun Li
Keyword(s):  
Myocardial Infarction ◽  
Life Quality ◽  
Circular Rna ◽  
Myocardial Repair ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Wide Range ◽  
High Incidence ◽  
Long Non Coding Rna

Myocardial infarction (MI), one of the cardiovascular diseases (CVDs) with high incidence and mortality rate, seriously endangers human health. The poor ways of fully repairing and regenerating the infarcted myocardium may have an impact on people's life quality, therefore scientists have devoted continuously to exploring the way of myocardial repair after MI so as to strive for a better prognosis of these patients. In recent years, non-coding RNAs (ncRNAs) have been identified and become one of the exciting fields of research in the development of CVDs. In a wide range of areas, more and more research has found that ncRNAs play important roles in myocardial repair. This review mainly introduces some strategies for myocardial repair and the role or mechanism of microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA) and circRNA/lncRNA-miRNA-mRNA regulatory axis in the repair of myocardial tissue after MI, in order to build a better understanding and find new therapeutic targets for MI.

scholarly journals Aging-regulated anti-apoptotic long non-coding RNA Sarrah augments recovery from acute myocardial infarction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
D. Julia Trembinski ◽  
Diewertje I. Bink ◽  
Kosta Theodorou ◽  
Janina Sommer ◽  
Ariane Fischer ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Non Coding Rna ◽  
Long Non Coding Rna

scholarly journals Long non-coding RNA MALAT1 promotes cardiac remodeling in hypertensive rats by inhibiting the transcription of MyoD

Aging ◽  
2019 ◽  
Vol 11 (20) ◽  
pp. 8792-8809
Author(s):  
Dan Li ◽  
Chunling Zhang ◽  
Jian Li ◽  
Jinna Che ◽  
Xuecheng Yang ◽  
...  
Keyword(s):  
Cardiac Remodeling ◽  
Hypertensive Rats ◽  
Non Coding Rna ◽  
Long Non Coding Rna

scholarly journals Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

2021 ◽  
Author(s):  
Yunhe Liu ◽  
Qiqing Fu ◽  
Xueqing Peng ◽  
Chaoyu Zhu ◽  
Gang Liu ◽  
...  
Keyword(s):  
Network Architecture ◽  
Enrichment Analysis ◽  
Circular Rna ◽  
Biological Processes ◽  
Non Coding Rna ◽  
Strong Representation ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Sparse Features ◽  
Long Non Coding Rna

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture, which can be fed with raw sequence, to learn the sparse features in sequences and accomplish the identification task for circRNAs. The model outperformed previously reported models. Following the effectiveness validation of the attention score by the handwritten digit dataset, the key sequence loci underlying circRNAs recognition were obtained based on the corresponding attention score. Moreover, the motif enrichment analysis of the extracted key sequences identified some of the key motifs for circRNA formation. In conclusion, we designed a deep learning network architecture suitable for gene sequence learning with sparse features and implemented to the circRNA identification, and the network has a strong representation capability with its indication of some key loci.

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