Genome-wide identification of clusters of predicted microRNA binding sites as microRNA sponge candidates

The transcriptomic landscape of neurons carrying PSEN1 mutations reveals changes in extracellular matrix components and non-coding gene expression

Alzheimer's disease (AD) is a progressive and irreversible brain disorder, which can occur either sporadically, due to a complex combination of environmental, genetic, and epigenetic factors, or because of rare genetic variants in specific genes (familial AD, or fAD). A key hallmark of AD is the accumulation of amyloid beta (Aβ) and Tau hyperphosphorylated tangles in the brain, but the underlying pathomechanisms and interdependencies remain poorly understood. Here, we identify and characterise gene expression changes related to two fAD mutations (A79V and L150P) in the Presenilin-1 (PSEN1) gene. We do this by comparing the transcriptomes of glutamatergic forebrain neurons derived from fAD-mutant human induced pluripotent stem cells (hiPSCs) and their individual isogenic controls generated via precision CRISPR/Cas9 genome editing. Our analysis of Poly(A) RNA-seq data detects 1111 differentially expressed coding and non-coding genes significantly altered in fAD. Functional characterisation and pathway analysis of these genes reveal profound expression changes in constituents of the extracellular matrix, important to maintain the morphology, structural integrity, and plasticity of neurons, and in genes involved in calcium homeostasis and mitochondrial oxidative stress. Furthermore, by analysing total RNA-seq data we reveal that 30 out of 31 differentially expressed circular RNA genes are significantly upregulated in the fAD lines, and that these may contribute to the observed protein-coding gene expression changes. The results presented in this study contribute to a better understanding of the cellular mechanisms impacted in AD neurons, ultimately leading to neuronal damage and death.

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

The upregulation of CLGN in hepatocellular carcinoma is potentially regulated by hsa-miR-194-3p and associated with patient progression

Background

Patients with hepatocellular carcinoma (HCC) have poor prognosis, especially in advanced stages. Targeted therapy is the main treatment for advanced HCC patients, but the optimal targets for HCC remain poorly understood. The main purpose of this study was to identify potential novel prognostic markers and therapeutic targets.

Methods

Firstly, differentially expressed genes (DEGs) in HCC were identified from the Gene Expression Omnibus (GEO) database. The expression, significance in prognosis, and potential mechanisms of DEGs were analyzed using GEPIA, TIMER, HPA, Kaplan Meier Plotter, CBioPortal, miRWalk, TargetScan, and ENCORI databases. Immunohistochemical staining was used to determine the protein expression levels of potential candidate genes.

Results

The mRNA levels of MND1, STXBP6, and CLGN were significantly increased in HCC (p< 0.01). HCC patients with elevated CLGN mRNA levels had poorer overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and disease-specific survival (DSS) (p < 0.05). Higher MND1 mRNA levels significantly correlated with poorer DFS in HCC patients (p< 0.05). However, there was no significant correlation between STXBP6 expression and prognosis of HCC (p> 0.05). Further analysis revealed that patients with elevated CLGN mRNA expression in advanced pathology stages had poorer prognosis (p< 0.01). In addition, CLGN protein levels were elevated in HCC compared to their levels in normal tissues. The mRNA levels of CLGN had no significant correlation with the abundance of six common tumor infiltrating lymphocytes in HCC (COR < 0.5). Moreover, the mutation rate of CLGN was less than 1% in HCC patients (10/1089). Finally, the expression level of hsa-miR-194-3p in HCC was significantly lower than that in normal tissues (p < 0.05), and prognosis of HCC with low expression of hsa-miR-194 was poor (p < 0.05).

Conclusion

The upregulation of CLGN in HCC is significantly associated with poor patient prognosis, especially in the advanced stages, and may be regulated by hsa-miR-194-3p. These findings suggest that CLGN may be closely related to the progression of HCC, and is a potential therapeutic target and prognostic indicator for patients with advanced HCC.

MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction

Circular RNA (circRNA) is closely involved in physiological and pathological processes of many diseases. Discovering the associations between circRNAs and diseases is of great significance. Due to the high-cost to verify the circRNA-disease associations by wet-lab experiments, computational approaches for predicting the associations become a promising research direction. In this paper, we propose a method, MDGF-MCEC, based on multi-view dual attention graph convolution network (GCN) with cooperative ensemble learning to predict circRNA-disease associations. First, MDGF-MCEC constructs two disease relation graphs and two circRNA relation graphs based on different similarities. Then, the relation graphs are fed into a multi-view GCN for representation learning. In order to learn high discriminative features, a dual-attention mechanism is introduced to adjust the contribution weights, at both channel level and spatial level, of different features. Based on the learned embedding features of diseases and circRNAs, nine different feature combinations between diseases and circRNAs are treated as new multi-view data. Finally, we construct a multi-view cooperative ensemble classifier to predict the associations between circRNAs and diseases. Experiments conducted on the CircR2Disease database demonstrate that the proposed MDGF-MCEC model achieves a high area under curve of 0.9744 and outperforms the state-of-the-art methods. Promising results are also obtained from experiments on the circ2Disease and circRNADisease databases. Furthermore, the predicted associated circRNAs for hepatocellular carcinoma and gastric cancer are supported by the literature. The code and dataset of this study are available at https://github.com/ABard0/MDGF-MCEC.

Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction

Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.

A regulatory miRNA-mRNA network is associated with transplantation response in acute kidney injury

Background

Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA–mRNA network.

Results

Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA–mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA–mRNA network by Cytoscape. The miRNA–mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K–Akt/HIF-1/Ras/MAPK signaling pathways.

Conclusion

We constructed a regulatory miRNA–mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00363-y.

A holistic miRNA-mRNA module discovery

The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. In this paper we will focus on miRNA-mRNA modules, where regulation jointly occurs in miRNA-mRNA pairs. Namely, we propose a holistic procedure to identify miRNA-mRNA modules within a population of candidate pairs. Since current methods still leave open issues, we adopt the strategy of postponing any decision on the value of the module ingredients exactly at the end, i.e. at the moment of biologically exploiting the results. This diverts chains of statistical tests into sequences of specially-devised-evolving metrics on the possible solutions. This strategy is rather expensive under a computational perspective, so needing implementations on HPC. The reward stands in the discovery of new modules, possibly hosting non differentially expressed miRNAs and mRNAs and pairs containing genes that currently are considered not targeted. In the paper we implement the procedure on a Multiple Myeloma dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues. These results, jointly with the normal manageability of the computations, suggest that the discovery procedure may be profitably extended to a wide spectrum of diseases where miRNA-mRNA interactions play a relevant role.

The bioinformatics toolbox for circRNA discovery and analysis

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

Novel circRNA discovery in sheep shows evidence of high backsplice junction conservation

Circular RNAs (circRNAs) are covalently closed circular non-coding RNAs. Due to their structure, circRNAs are more stable and have longer half-lives than linear RNAs making them good candidates for disease biomarkers. Despite the scientific relevance of these molecules, the study of circRNAs in non-model organisms is still in its infancy. Here, we analyse total RNA-seq data to identify circRNAs in sheep from peripheral blood mononuclear cells (PBMCs) and parietal lobe cortex. Out of 2510 and 3403 circRNAs detected in parietal lobe cortex and in PBMCs, a total of 1379 novel circRNAs were discovered. Remarkably, around 63% of all detected circRNAs were found to be completely homologous to a circRNA annotated in human. Functional enrichment analysis was conducted for both tissues based on GO terms and KEGG pathways. The enriched terms suggest an important role of circRNAs from encephalon in synaptic functions and the involvement of circRNAs from PBMCs in basic immune system functions. In addition to this, we investigated the role of circRNAs in repetitive vaccination experiments via differential expression analysis and did not detect any significant relationship. At last, our results support both the miRNA sponge and the miRNA shuttle functions of CDR1-AS in sheep brain. To our knowledge, this is the first study on circRNA annotation in sheep PBMCs or parietal lobe cortex samples.

Investigation of miRNA and mRNA Co-expression Network in Ependymoma

Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.

Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skeletal muscle to nutrient supply

BACKGROUND

The role of non-coding RNAs in the porcine muscle metabolism is poorly understood, with few studies investigating their expression patterns in response to nutrient supply. Therefore, we aimed to investigate the changes in microRNAs (miRNAs), long intergenic non-coding RNAs (lincRNAs) and mRNAs muscle expression before and after food intake.

RESULTS

We measured the miRNA, lincRNA and mRNA expression levels in the gluteus medius muscle of 12 gilts in a fasting condition (AL-T0) and 24 gilts fed ad libitum during either 5 h. (AL-T1, N = 12) or 7 h. (AL-T2, N = 12) prior to slaughter. The small RNA fraction was extracted from muscle samples retrieved from the 36 gilts and sequenced, whereas lincRNA and mRNA expression data were already available. In terms of mean and variance, the expression profiles of miRNAs and lincRNAs in the porcine muscle were quite different than those of mRNAs. Food intake induced the differential expression of 149 (AL-T0/AL-T1) and 435 (AL-T0/AL-T2) mRNAs, 6 (AL-T0/AL-T1) and 28 (AL-T0/AL-T2) miRNAs and none lincRNAs, while the number of differentially dispersed genes was much lower. Among the set of differentially expressed miRNAs, we identified ssc-miR-148a-3p, ssc-miR-22-3p and ssc-miR-1, which play key roles in the regulation of glucose and lipid metabolism. Besides, co-expression network analyses revealed several miRNAs that putatively interact with mRNAs playing key metabolic roles and that also showed differential expression before and after feeding. One case example was represented by seven miRNAs (ssc-miR-148a-3p, ssc-miR-151-3p, ssc-miR-30a-3p, ssc-miR-30e-3p, ssc-miR-421-5p, ssc-miR-493-5p and ssc-miR-503) which putatively interact with the PDK4 mRNA, one of the master regulators of glucose utilization and fatty acid oxidation.

CONCLUSIONS

As a whole, our results evidence that microRNAs are likely to play an important role in the porcine skeletal muscle metabolic adaptation to nutrient availability.

Role of miRNA sponges in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. HCC patients are commonly diagnosed at an advanced stage, for which highly effective therapies are limited. Hence, there is a growing need to discover promising biomarkers for HCC diagnosis, and in this context, microRNAs (miRNAs) hold great promise. MiRNAs function as gene expression regulators by directly binding messenger RNAs (mRNAs) and subsequently causing suppression of mRNA translation or degradation of target mRNAs. Two major types of noncoding RNAs act as competing endogenous sponges: circular RNAs and long non-coding RNAs.They can competitively bind to miRNA through miRNA response elements (MREs), thereby reducing the number of miRNAs binding mRNAs and regulating the expression of downstream target genes of miRNAs at the posttranscriptional level. The relationship between single miRNA sponge and HCC has been explored. However, comprehensive reviews on the sponge's function in HCC are lacking. In this review, we describe the methods to find endogenous sponges and construct exogenous sponges, and briefly compare endogenous and exogenous sponges. We also summarize the current progress on the functional role of miRNA sponges in HCC pathogenesis and present their potential value as diagnostic biomarkers and therapeutic targets. In-depth investigations on the function and mechanism of miRNA sponges in HCC will enrich our knowledge of HCC pathogenesis and contribute to the development of effective diagnostic biomarkers and therapeutic targets for HCC.

The rs17084733 variant in the KIT 3' UTR disrupts a miR-221/222 binding site in gastrointestinal stromal tumour: a sponge-like mechanism conferring disease susceptibility

Several miRNAs are dysregulated in gastrointestinal stromal tumours (GIST), and miR-221/222 appear to have a prominent role in GIST biology. Therefore, we investigated the role of DNA variants located in miR-221/222 precursor sequences and their target KIT 3'UTR. Ninety-five polymorphisms were analysed in 115 GIST cases and 88 healthy controls. KIT 3'UTR rs17084733 and pri-miR-222 rs75246947 were found significantly associated with GIST susceptibility. Specifically, KIT rs17084733 A allele was more common in GIST, particularly in KIT wild-type (WT) patients (Padj = 0.017). rs17084733 variant is located within one of the three miR-221/222 binding sites in the KIT 3'UTR, resulting in a mismatch in this seed region. Conversely, KIT mRNA levels were lower in patients carrying the variant allele, except for KIT mutant GIST. Luciferase assay data in GIST cells, generated using a construct containing all the three miR-221/222 binding sites, are consistent with KIT mRNA levels in GIST patients. Reporter assay data, generated using a construct containing only the site encompassing rs17084733, confirmed that this is a functional variant disrupting the miR-221/222 binding site. In conclusion, this is the first study investigating the role of SNPs on miR-221/222 precursor sequences and their binding region on KIT 3'UTR in GIST. We identified the KIT variant rs17084733 as a possible novel genetic biomarker for risk of developing KIT-WT GIST. Moreover, our findings suggest the role of one of the three miR-221/222 binding sites on KIT 3'UTR as endogenous sponge, soaking up and subtracting miR-221/222 to the other two sites characterized by a higher affinity.