MicroRNA target prediction: theory and practice

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Integrated miRNA/mRNA Counter-Expression Analysis Highlights Oxidative Stress-Related Genes CCR7 and FOXO1 as Blood Markers of Coronary Arterial Disease

Our interest in the mechanisms of atherosclerosis progression (ATHp) has led to the recent identification of 13 miRNAs and 1285 mRNAs whose expression was altered during ATHp. Here, we deepen the functional relationship among these 13 miRNAs and genes associated to oxidative stress, a crucial step in the onset and progression of vascular disease. We first compiled a list of genes associated to the response to oxidative stress (Oxstress genes) by performing a reverse Gene Ontology analysis (rGO, from the GO terms to the genes) with the GO terms GO0006979, GO1902882, GO1902883 and GO1902884, which included a total of 417 unique Oxstress genes. Next, we identified 108 putative targets of the 13 miRNAs among these unique Oxstress genes, which were validated by an integrated miRNA/mRNA counter-expression analysis with the 1285 mRNAs that yielded 14 genes, Map2k1, Mapk1, Mapk9, Dapk1, Atp2a2, Gata4, Fos, Egfr, Foxo1, Ccr7, Vkorc1l1, Rnf7, Kcnh3, and Mgat3. GO enrichment analysis and a protein–protein-interaction network analysis (PPI) identified most of the validated Oxstress transcripts as components of signaling pathways, highlighting a role for MAP signaling in ATHp. Lastly, expression of these Oxstress transcripts was measured in PBMCs from patients suffering severe coronary artery disease, a serious consequence of ATHp. This allowed the identification of FOXO1 and CCR7 as blood markers downregulated in CAD. These results are discussed in the context of the interaction of the Oxstress transcripts with the ATHp-associated miRNAs.

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks.

DeepMirTar: a deep-learning approach for predicting human miRNA targets

Motivation

MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates.

Results

In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance.

Availability and implementation

DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA.

Supplementary information

Supplementary data are available at Bioinformatics online.

miR-206 inhibits FN1 expression and proliferation and promotes apoptosis of rat type II alveolar epithelial cells

Bronchopulmonary dysplasia (BPD) is a syndrome of respiratory distress caused by chronic lung injury, primarily in preterm infants. miR-206 and fibronectin 1 (FN1) are associated with the development of BPD. The present study used rat type II alveolar epithelial cells (AECII) to investigate the underlying mechanisms of BPD. AECII were isolated using a primary cell culture prior to alkaline phosphatase staining and immunofluorescence of surfactant protein C (SP-C). These were used to verify the presence of AECII. AECII were then divided into four groups, which were transfected with four different plasmids. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to determine the relative expression of miR-206 in the each group. The gene and protein expression level of FN1 was detected by RT-qPCR and immunofluorescence. The proliferation of AECII in each of the four groups was evaluated using an MTT assay 48 h following transfection. The percentage of apoptotic cells was determined by flow cytometric analysis. The present study demonstrated that upregulation of miR-206 decreased the expression of FN1 (P<0.05) and low levels of miR-206 led to increased expression of FN1 (P<0.05) in AECII. Furthermore, the forced expression of miR-206 suppressed proliferation and promoted apoptosis of AECII while downregulation of miR-206 had the opposite effect (P<0.05). The results of the current study provide valuable insights into the prevention of BPD and suggest that miR-206 may be used as a potential molecular target for BPD therapy in the future.