Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches
Background: Preeclampsia (PE) is the major cause of maternal and fetal morbidity and mortality, affecting 3–8% of all pregnancies around the globe. miRNAs are small, noncoding RNA molecules, which negatively regulate gene expression. Abnormally expressed miRNAs contribute to pregnancy complications...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-01-01
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| Series: | Hypertension in Pregnancy |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10641955.2016.1239736 |
| _version_ | 1797681586287673344 |
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| author | Orsolya Biró Bálint Nagy János Rigó |
| author_facet | Orsolya Biró Bálint Nagy János Rigó |
| author_sort | Orsolya Biró |
| collection | DOAJ |
| description | Background: Preeclampsia (PE) is the major cause of maternal and fetal morbidity and mortality, affecting 3–8% of all pregnancies around the globe. miRNAs are small, noncoding RNA molecules, which negatively regulate gene expression. Abnormally expressed miRNAs contribute to pregnancy complications such as PE. The aim of our study was to find possible regulatory mechanisms by system biology approaches, which are connected to the pathogenesis of PE. Methods: We integrated publicly available miRNA and gene expression profiles and created a network from the significant miRNA–mRNA pairs with the help of MAGIA and Cytoscape softwares. Two subnetworks were expanded by adding protein–protein interactions. Differentially expressed miRNAs were identified for the evaluation of their regulatory effect. We analyzed the miRNAs and their targets using different bioinformatics tools and through literature research. Results: Altogether, 52,603 miRNA–mRNA interactions were generated by the MAGIA web tool. The top 250 interactions were visualized and pairs with q < 0.0001 were analyzed, which included 85 nodes and 80 edges signalizing the connections between 52 regulated genes and 33 miRNAs. A total of 11 of the regulated genes are PE related and 9 of them were targeted by multiple miRNAs. A total of 8 miRNAs were associated with PE before, and 13 miRNAs regulated more than 1 mRNA. Hsa-mir-210 was the highest degree node in the network and its role in PE is well established. Conclusions: We identified several miRNA–mRNA regulatory mechanisms which may contribute to the pathogenesis of PE. Further investigations are needed to validate these miRNA–mRNA interactions and to enlighten the possibilities of developing potential therapeutic targets. |
| first_indexed | 2024-03-11T23:47:04Z |
| format | Article |
| id | doaj.art-6902ba7823294bdbacbb834616cc5212 |
| institution | Directory Open Access Journal |
| issn | 1064-1955 1525-6065 |
| language | English |
| last_indexed | 2024-03-11T23:47:04Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Hypertension in Pregnancy |
| spelling | doaj.art-6902ba7823294bdbacbb834616cc52122023-09-19T09:24:42ZengTaylor & Francis GroupHypertension in Pregnancy1064-19551525-60652017-01-01361909910.1080/10641955.2016.12397361239736Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approachesOrsolya Biró0Bálint Nagy1János Rigó2Semmelweis University, BudapestUniversity of DebrecenSemmelweis University, BudapestBackground: Preeclampsia (PE) is the major cause of maternal and fetal morbidity and mortality, affecting 3–8% of all pregnancies around the globe. miRNAs are small, noncoding RNA molecules, which negatively regulate gene expression. Abnormally expressed miRNAs contribute to pregnancy complications such as PE. The aim of our study was to find possible regulatory mechanisms by system biology approaches, which are connected to the pathogenesis of PE. Methods: We integrated publicly available miRNA and gene expression profiles and created a network from the significant miRNA–mRNA pairs with the help of MAGIA and Cytoscape softwares. Two subnetworks were expanded by adding protein–protein interactions. Differentially expressed miRNAs were identified for the evaluation of their regulatory effect. We analyzed the miRNAs and their targets using different bioinformatics tools and through literature research. Results: Altogether, 52,603 miRNA–mRNA interactions were generated by the MAGIA web tool. The top 250 interactions were visualized and pairs with q < 0.0001 were analyzed, which included 85 nodes and 80 edges signalizing the connections between 52 regulated genes and 33 miRNAs. A total of 11 of the regulated genes are PE related and 9 of them were targeted by multiple miRNAs. A total of 8 miRNAs were associated with PE before, and 13 miRNAs regulated more than 1 mRNA. Hsa-mir-210 was the highest degree node in the network and its role in PE is well established. Conclusions: We identified several miRNA–mRNA regulatory mechanisms which may contribute to the pathogenesis of PE. Further investigations are needed to validate these miRNA–mRNA interactions and to enlighten the possibilities of developing potential therapeutic targets.http://dx.doi.org/10.1080/10641955.2016.1239736preeclampsiaplacentamirnasystems biologynetwork analysis |
| spellingShingle | Orsolya Biró Bálint Nagy János Rigó Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches Hypertension in Pregnancy preeclampsia placenta mirna systems biology network analysis |
| title | Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches |
| title_full | Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches |
| title_fullStr | Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches |
| title_full_unstemmed | Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches |
| title_short | Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches |
| title_sort | identifying mirna regulatory mechanisms in preeclampsia by systems biology approaches |
| topic | preeclampsia placenta mirna systems biology network analysis |
| url | http://dx.doi.org/10.1080/10641955.2016.1239736 |
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