Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences

BACKGROUND

MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models.

RESULTS

In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years.

CONCLUSIONS

We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.

A computational modeling of pri-miRNA expression

MicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies focus on mature miRNAs, which leaves many unknowns about primary miRNAs (pri-miRNAs). To fill the gap, we attempted to model the expression of pri-miRNAs in 1829 primary cell types, cell lines, and tissues in this study. We demonstrated that the expression of pri-miRNAs can be modeled well by the expression of specific sets of mRNAs, which we termed their associated mRNAs. These associated mRNAs differ from their corresponding target mRNAs and are enriched with specific functions. Most associated mRNAs of a miRNA are shared across conditions, while on average, about one-fifth of the associated mRNAs are condition-specific. Our study shed new light on understanding miRNA biogenesis and general gene transcriptional regulation.

Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response

Melanoma, the cancer of the melanocyte, is the deadliest form of skin cancer with an aggressive nature, propensity to metastasize and tendency to resist therapeutic intervention. Studies have identified that the re-emergence of developmental pathways in melanoma contributes to melanoma onset, plasticity, and therapeutic response. Notably, it is well known that noncoding RNAs play a critical role in the development and stress response of tissues. In this review, we focus on the noncoding RNAs, including microRNAs, long non-coding RNAs, circular RNAs, and other small RNAs, for their functions in developmental mechanisms and plasticity, which drive onset, progression, therapeutic response and resistance in melanoma. Going forward, elucidation of noncoding RNA-mediated mechanisms may provide insights that accelerate development of novel melanoma therapies.

Exosomal miR-18a-5p promotes EMT and metastasis of NPC cells via targeting BTG3 and activating the Wnt/β-catenin signaling pathway

This study investigated the underlying mechanism of miR-18a-5p regulating the proliferation, invasion, and metastasis of nasopharyngeal carcinoma (NPC) cells in vitro and in vivo to indicate the pathogenesis of NPC. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was utilized to determine miR-18a-5p expression level in NPC tissues and cell lines. Besides, 2,5-diphenyl-2 H-tetrazolium bromide (MTT) and colony formation assays were employed to detect the effect of miR-18a-5p expression level on NPC cell proliferation. Wound healing and Transwell assays were utilized to detect the effect of miR-18a-5p on NPC cell invasion and migration. The expression levels of epithelial-mesenchymal transition (EMT)-related proteins (Vimentin, N-cadherin, and E-cadherin) were identified by Western blot assay. After collecting exosomes from CNE-2 cells, it was found that exosomal miR-18a-5p secreted from NPC cells promoted NPC cell proliferation, migration, invasion, and EMT, whereas inhibition of miR-18a-5p expression level led to the opposite results. The dual-luciferase reporter assay showed that BTG anti-proliferation factor 3 (BTG3) was the target gene of miR-18a-5p, and BTG3 could overturn the effect of miR-18a-5p on NPC cells. Xenograft mouse model of NPC nude mice showed that miR-18a-5p promoted NPC growth and metastasis in vivo. This study revealed that exosomal miR-18a-5p derived from NPC cells promoted angiogenesis via targeting BTG3 and activating the Wnt/β-catenin signaling pathway.

Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome

Introduction: The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research. Methods: The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls. Results: The study identified 46 significantly differentially methylated (FDR p-value ≤ 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) ≥0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis. Conclusion: This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.

Identification of LncRNA-miRNA-mRNA ceRNA network in hypoxia-induced HTR-8/SVneo cells for preeclampsia

Preeclampsia (PE) is a common pregnancy complication, and placental hypoxia is one of its causes. We aimed to identify the transcriptional profile and construct a long non-coding RNAs (lncRNA)-centered competing endogenous RNAs (ceRNA) network in hypoxia-induced HTR8/SVneo cells. We used datasets from the GEO database to identify important pathways in PE. We performed microarray profiling and functional analysis to identify differentially expressed long non-coding RNAs (lncRNAs), differentially expressed profiles of microRNA (miRNAs), and differentially expressed profiles of messenger RNA (mRNAs) in hypoxia-induced HTR8/SVneo cells. The candidates were validated using quantitative reverse transcription polymerase chain reaction. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses were performed to understand the functional significance of differentially expressed genes. Finally, we constructed an lncRNA-centered ceRNA network. Several hub genes were validated both in placentas from PE and normal pregnancy, and in hypoxia-induced HTR8/SVneo cells. The hypoxic response pathway was involved in the pathophysiology of PE. Subsequently, we identified 536 differentially expressed profiles of lncRNAs (183 upregulated and 353 downregulated), 46 differentially expressed profiles of miRNAs (35 upregulated and 11 downregulated), and 2782 differentially expressed profiles of mRNAs (DEmRNAs) (1031 upregulated and 1751 downregulated) in hypoxia-induced HTR8/SVneo cells. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed potential pathways affected by these genes, such as angiogenesis, the HIF-1 signaling pathway, and the PI3K-Akt signaling pathway. The ceRNA network comprised 35 lncRNAs, 11 miRNAs, 27 mRNAs, and 2 hub lncRNAs, which might play a vital role in placental functions and PE. Our results revealed the transcriptome profile and constructed an lncRNA-centered ceRNA network in hypoxia-induced HTR8/SVneo cells, thereby providing potential therapeutic targets for PE.

lncRNA XIST/miR‑129‑2‑3p axis targets CCP110 to regulate the proliferation, invasion and migration of endometrial cancer cells

Centromere coiled-coil protein 110 (CCP110) plays a role in the development of several types of cancer; however, its regulatory mechanism and role in endometrial cancer is unclear. The present study revealed that CCP110 is regulated by a signaling pathway involving microRNA (miR/miRNA)-129-2-3p and the long non-coding RNA (lncRNA) X-inactive-specific transcript (XIST), and plays a role in controlling the proliferation, migration and invasion of endometrial cancer cells. CCP110 was upregulated in human endometrial cancer tissues, as revealed by immunohistochemistry, and high expression of the protein was related to reduced overall survival of the patients. Genetic knockdown of CCP110 by small interfering RNA promoted apoptosis and suppressed the proliferation, migration, invasion and colony formation of endometrial cancer cells significantly in the endometrial cancer Ishikawa and HEC-1B cell lines, as assessed by flow cytometry, and Cell Counting Kit-8, Transwell and colony formation assays. A bioinformatics analysis and luciferase reporter assay revealed that CCP110 is a target of miR-129-2-3p. Overexpression of miR-129-2-3p mimic fragments inhibited the proliferation, migration and invasion of endometrial cancer cells significantly, while co-overexpression of CCP110 counteracted these inhibitory effects. The expression level of the lncRNA XIST was upregulated significantly in endometrial cancer tissues, as assessed by reverse transcription-quantitative PCR assay, while that of miR-129-2-3p was downregulated significantly. A bioinformatics analysis and luciferase reporter assay showed that XIST could inhibit miR-129-2-3p via a miRNA sponge effect. Furthermore, co-overexpression of XIST antagonized the inhibitory effect of the miR-129-2-3p mimic on the luciferase reporter gene signal and protein expression of CCP110. Co-overexpression of XIST also abolished the inhibitory effect of the miR-129-2-3p mimic on the proliferation, migration and invasion of endometrial cancer cells. Overall, these data identified a novel regulatory mechanism of CCP110 involving XIST and miR-129-2-3p, which affected the development of endometrial carcinoma. CCP110, XIST and miR-129-2-3p could represent novel targets for the clinical treatment of endometrial cancer.

Construction of a potentially functional lncRNA-miRNA-mRNA network in sepsis by bioinformatics analysis

Objective: Sepsis is a common disease in internal medicine, with a high incidence and dangerous condition. Due to the limited understanding of its pathogenesis, the prognosis is poor. The goal of this project is to screen potential biomarkers for the diagnosis of sepsis and to identify competitive endogenous RNA (ceRNA) networks associated with sepsis.

Methods: The expression profiles of long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) were derived from the Gene Expression Omnibus (GEO) dataset. The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened by bioinformatics analysis. DEmRNAs were analyzed by protein-protein interaction (PPI) network analysis, transcription factor enrichment analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Set Enrichment Analysis (GSEA). After the prediction of the relevant database, the competitive ceRNA network is built in Cytoscape. The gene-drug interaction was predicted by DGIgb. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm five lncRNAs from the ceRNA network.

Results: Through Venn diagram analysis, we found that 57 DElncRNAs, 6 DEmiRNAs and 317 DEmRNAs expressed abnormally in patients with sepsis. GO analysis and KEGG pathway analysis showed that 789 GO terms and 36 KEGG pathways were enriched. Through intersection analysis and data mining, 5 key KEGG pathways and related core genes were revealed by GSEA. The PPI network consists of 247 nodes and 1,163 edges, and 50 hub genes are screened by the MCODE plug-in. In addition, there are 5 DElncRNAs, 6 DEmiRNAs and 28 DEmRNAs in the ceRNA network. Drug action analysis showed that 7 genes were predicted to be molecular targets of drugs. Five lncRNAs in ceRNA network are verified by qRT-PCR, and the results showed that the relative expression of five lncRNAs was significantly different between sepsis patients and healthy control subjects.

Conclusion: A sepsis-specific ceRNA network has been effectively created, which is helpful to understand the interaction between lncRNAs, miRNAs and mRNAs. We discovered prospective sepsis peripheral blood indicators and proposed potential treatment medicines, providing new insights into the progression and development of sepsis.

Characterization of Adrenal miRNA-Based Dysregulations in Cushing's Syndrome

MiRNAs are important epigenetic players with tissue- and disease-specific effects. In this study, our aim was to investigate the putative differential expression of miRNAs in adrenal tissues from different forms of Cushing’s syndrome (CS). For this, miRNA-based next-generation sequencing was performed in adrenal tissues taken from patients with ACTH-independent cortisol-producing adrenocortical adenomas (CPA), from patients with ACTH-dependent pituitary Cushing’s disease (CD) after bilateral adrenalectomy, and from control subjects. A confirmatory QPCR was also performed in adrenals from patients with other CS subtypes, such as primary bilateral macronodular hyperplasia and ectopic CS. Sequencing revealed significant differences in the miRNA profiles of CD and CPA. QPCR revealed the upregulated expression of miR-1247-5p in CPA and PBMAH (log2 fold change > 2.5, p < 0.05). MiR-379-5p was found to be upregulated in PBMAH and CD (log2 fold change > 1.8, p < 0.05). Analyses of miR-1247-5p and miR-379-5p expression in the adrenals of mice which had been exposed to short-term ACTH stimulation showed no influence on the adrenal miRNA expression profiles. For miRNA-specific target prediction, RNA-seq data from the adrenals of CPA, PBMAH, and control samples were analyzed with different bioinformatic platforms. The analyses revealed that both miR-1247-5p and miR-379-5p target specific genes in the WNT signaling pathway. In conclusion, this study identified distinct adrenal miRNAs as being associated with CS subtypes.

Upregulated lncARAT in Schwann cells promotes axonal regeneration by recruiting and activating proregenerative macrophages

Background

Axonal regeneration following peripheral nerve injury (PNI) depends on the complex interaction between Schwann cells (SCs) and macrophages, but the mechanisms underlying macrophage recruitment and activation in axonal regeneration remain unclear.

Methods

RNA sequencing (RNA-seq) was conducted to identify differentially expressed long noncoding RNAs (DElncRNAs) between crushed sciatic nerves and intact contralateral nerves. The putative role of lncRNAs in nerve regeneration was analyzed in vitro and in vivo.

Results

An lncRNA, called axon regeneration-associated transcript (lncARAT), was upregulated in SCs and SC-derived exosomes (SCs-Exo) after sciatic nerve injury. LncARAT contributed to axonal regeneration and improved motor function recovery. Mechanistically, lncARAT epigenetically activated C–C motif ligand 2 (CCL2) expression by recruiting KMT2A to CCL2 promoter, resulting in increased histone 3 lysine 4 trimethylation (H3K4me3) and CCL2 transcription in SCs. CCL2 facilitated the infiltration of macrophages into the injured nerves. Meanwhile, lncARAT-enriched exosomes were released from SCs and incorporated into macrophages. LncARAT functioned as an endogenous sponge to adsorb miRNA-329-5p in macrophages, resulting in increased suppressor of cytokine signaling (SOCS) 2 expression, which induced a proregenerative function of macrophages through a signal transducer and activator of transcription (STAT) 1/6-dependent pathway.

Conclusions

LncARAT may represent a promising therapeutic avenue for peripheral nerve repair.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00501-9.

A deep learning method for miRNA/isomiR target detection

Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.

Antagonistic regulatory effects of a single cis-acting expression quantitative trait locus between transcription and translation of the MRPL43 gene

Background

Heterogeneity of expression quantitative trait locus (eQTL) effects have been shown across gene expression processes. Knowledge on how to produce the heterogeneity is quite limited. This study aims to examine fluctuations in differential gene expression by alleles of sequence variants across expression processes.

Results

Genome-wide eQTL analyses with transcriptome-wide gene expression data revealed 20 cis-acting eQTLs associated simultaneously with mRNA expression, ribosome occupancy, and protein abundance. A 97 kb-long eQTL signal for mitochondrial ribosomal protein L43 (MRPL43) covered the gene, showing a heterogeneous effect size on gene products across expression stages. One allele of the eQTL was associated with increased mRNA expression and ribosome occupancy but decreased protein abundance. We examined the heterogeneity and found that the eQTL can be attributed to the independent functions of three nucleotide variants, with a strong linkage. NC_000010.11:g.100987606G > T, upstream of MRPL43, may regulate the binding affinity of transcription factors. NC_000010.11:g.100986746C > G, 3 bp from an MRPL43 splice donor site, may alter the splice site. NC_000010.11:g.100978794A > G, in the isoform with a long 3′-UTR, may strengthen the binding affinity of the microRNA. Individuals with the TGG haplotype at these three variants had higher levels of mRNA expression and ribosome occupancy than individuals with the GCA haplotype but lower protein levels, producing the flipped effect throughout the expression process.

Conclusions

These findings suggest that multiple functional variants in a linkage exert their regulatory functions at different points in the gene expression process, producing a complexity of single eQTLs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01057-7.

Dexmedetomidine Mitigates Microglial Activation Associated with Postoperative Cognitive Dysfunction by Modulating the MicroRNA-103a-3p/VAMP1 Axis

Surgery-induced microglial activation is critical in mediating postoperative cognitive dysfunction (POCD) in elderly patients, where the important protective effect of dexmedetomidine has been indicated. However, the mechanisms of action of dexmedetomidine during the neuroinflammatory response that underlies POCD remain largely unknown. We found that lipopolysaccharide (LPS) induced substantial inflammatory responses in primary and BV2 microglial cells. The screening of differentially expressed miRNAs revealed that miR-103a-3p was downregulated in these cell culture models. Overexpression of miR-103a-3p mimics and inhibitors suppressed and enhanced the release of inflammatory factors, respectively. VAMP1 expression was upregulated in LPS-treated primary and BV-2 microglial cells, and it was validated as a downstream target of miR-103-3p. VAMP1-knockdown significantly inhibited the LPS-induced inflammatory response. Dexmedetomidine treatment markedly inhibited LPS-induced inflammation and the expression of VAMP1, and miR-103a-3p expression reversed this inhibition. Moreover, dexmedetomidine mitigated microglial activation and the associated inflammatory response in a rat model of surgical trauma that mimicked POCD. In this model, dexmedetomidine reversed miR-103a-3p and VAMP1 expression; this effect was abolished by miR-103a-3p overexpression. Taken together, the data show that miR-103a-3p/VAMP1 is critical for surgery-induced microglial activation of POCD.

Elucidating miRNA Function in Cancer Biology via the Molecular Genetics’ Toolbox

Micro-RNA (miRNAs) are short non-coding RNAs of about 18–20 nucleotides in length and are implicated in many cellular processes including proliferation, development, differentiation, apoptosis and cell signaling. Furthermore, it is well known that miRNA expression is frequently dysregulated in many cancers. Therefore, this review will highlight the various mechanisms by which microRNAs are dysregulated in cancer. Further highlights include the abundance of molecular genetics tools that are currently available to study miRNA function as well as their advantages and disadvantages with a special focus on various CRISPR/Cas systems This review provides general workflows and some practical considerations when studying miRNA function thus enabling researchers to make informed decisions in regards to the appropriate molecular genetics tool to be utilized for their experiments.

<em>miR-19a</em> targeting <em>CLCA4</em> to regulate the proliferation, migration, and invasion of colorectal cancer cells

The role of miR-19a in colorectal cancer (CRC), a devastating disease with high mortality and morbidity, remains controversial. In the present study, we show that the level of miR-19a is significantly higher in clinical CRC tissue samples than in paracancerous tissue samples, and significantly higher in CRC cells lines HT29, SW480, and CaCO2 than in the normal human colon mucosal epithelial cell line NCM460. miR-19a mimics and inhibitors were synthesized and validated. Overexpression of miR-19a mimics significantly promoted, while miR-19a inhibitors inhibited, the proliferation, survival, migration, and invasion of SW480 and CaCO2 CRC cells. Furthermore, mRNA and protein levels of chloride channel accessory 4 (CLCA4) were lower in CRC cells and tissues. Bioinformatics and a luciferase reporter assay confirmed that CLCA4 was a miR-19a target. Further, miR-19a inhibition increased CLCA4 expression. The inhibitory effect of miR-19a on cell growth, survival, migration, and invasion was reversed by knockdown of CLCA4 expression. The data demonstrated that the miR-19a/CLCA4 axis modulates phospho-activation of the PI3K/AKT pathway in CRC cells. In conclusion, our results revealed that miR-19a overexpression decreases CLCA4 levels to promote CRC oncogenesis, suggesting that miR-19a inhibitors have potential applications for future therapeutic of CRC.

MicroRNAs miR-451a and Let-7i-5p Profiles in Circulating Exosomes Vary among Individuals with Different Sickle Hemoglobin Genotypes and Malaria

Sickle cell disease (SCD) occurs when two alleles of mutated hemoglobin (HbS or HbC) are inherited (HbSS and HbSC) rather than one (HbAS or HbAC), which indicates a person carries the sickle cell trait. The high prevalence of these two alleles in Africa have been associated with reduced malaria susceptibility. Recent in vitro research has been shown that microRNAs (miRNAs) miR-451a and let-7i-5p are differentially expressed in HbSS erythrocytes compared to healthy controls (HbAA) and are overexpressed in Plasmodium-infected malaria erythrocytes. However, these miRNAs have not been fully examined in the plasma of people with different sickle hemoglobin genotypes. Plasma circulating miRNAs are commonly encapsulated in extracellular vesicles, such as exosomes, and are thought to play a role in disease development. Circulating exosomal miR-451a and let-7i-5p were quantified from individuals with various hemoglobin genotypes (HbAA, HbAS, HbAC, HbSS, HbSC, and HbCC) with (+) and without (-) malaria. The results showed a higher level of exosomal let-7i-5p and miR-451a in HbSS-. Exosomal let-7i-5p and miR-451a levels were lower in HbSS+ compared to other genotypes. Based on the area under the curve (AUC) of the Receiver Operating Characteristics (ROCs), both exosomal miRNAs may be useful disease biomarkers for SCD with malaria. Finally, miR-451a and let-7i-5p modulate genes involved in inflammation, making them potential biomarkers of pathogenesis for both diseases.

Computational Detection of MicroRNA Targets

MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.

Linc-ROR facilitates progression and angiogenesis of hepatocellular carcinoma by modulating DEPDC1 expression

Linc-ROR have been well-demonstrated to play important roles in cancer progression and angiogenesis. However, the underlying oncogenic mechanism of Linc-ROR in hepatocellular carcinoma is poorly understood. In this study, we demonstrate that Linc-ROR plays an oncogenic role in part through its positive regulation of DEPDC1 expression. Mechanistically, Linc-ROR acts as competing endogenous RNA to stabilize DEPDC1 mRNA and regulates DEPDC1 mRNA stability by binding HNRNPK. Thus, these findings suggest that function of Linc-ROR-mediated DEPDC1 could predispose hepatocellular carcinoma patients to progression and angiogenesis, and may serve as a potential target for anticancer therapies.

MYCN in Neuroblastoma: "Old Wine into New Wineskins"

MYCN Proto-Oncogene, BHLH Transcription Factor (MYCN) has been one of the most studied genes in neuroblastoma. It is known for its oncogenetic mechanisms, as well as its role in the prognosis of the disease and it is considered one of the prominent targets for neuroblastoma therapy. In the present work, we attempted to review the literature, on the relation between MYCN and neuroblastoma from all possible mechanistic sites. We have searched the literature for the role of MYCN in neuroblastoma based on the following topics: the references of MYCN in the literature, the gene's anatomy, along with its transcripts, the protein's anatomy, the epigenetic mechanisms regulating MYCN expression and function, as well as MYCN amplification. MYCN plays a significant role in neuroblastoma biology. Its functions and properties range from the forming of G-quadraplexes, to the interaction with miRNAs, as well as the regulation of gene methylation and histone acetylation and deacetylation. Although MYCN is one of the most primary genes studied in neuroblastoma, there is still a lot to be learned. Our knowledge on the exact mechanisms of MYCN amplification, etiology and potential interventions is still limited. The knowledge on the molecular mechanisms of MYCN in neuroblastoma, could have potential prognostic and therapeutic advantages.

A two-microRNA signature predicts the progression of male thyroid cancer

In various cancers, microRNAs (miRNAs) are abnormally expressed, including thyroid cancer (TC). In recent years, the incidence of TC has increased annually around the world. Compared with female patients, male TC patients are more likely to have a postoperative recurrence and lymph node metastasis, and hence need second treatments. However, the molecular biological processes underlying this phenomenon are not understood. Therefore, we collected data on miRNA expression and clinical information of male TC patients from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs were identified between male TC tissues and matched normal tissues. The Kaplan–Meier method, univariate and multivariate Cox regressions, and receiver operating characteristic curve analyses were performed to assess the association between miRNAs and the disease-free survival of male TC patients. Gene Ontology (GO) and the Kyoto Encyclopaedia of Gene and Genome (KEGG) enrichment analyses were then used to explore the function of miRNA target genes. Furthermore, we evaluated the ability of the miRNA biomarker to predict survival in female TC patients. As a result, a total of 118 differentially expressed miRNAs were identified, including 25 upregulated and 93 downregulated miRNAs. Among them, miR-451a and miR-16-1-3p were confirmed to be independent prognostic factors for the disease-free survival rate. The target genes of miR-451a and miR-16-1-3p were identified, and functional analysis showed that these genes were enriched in 25 Go and KEGG accessions, including cell signal transduction, motor adhesion, phagocytosis, regulation of transcription, cell proliferation, angiogenesis, etc. Neither miR-451a and miR-16-1-3p, nor a prediction model based on both miRNAs effectively predicted survival in female TC patients. In conclusion, both miR-451a and miR-16-1-3p may play important roles in the processes of male TC. The two-miRNA signature involving miR-1258 and miR-193a may serve as a novel prognostic biomarker for male TC patients.

Suppression of autophagy promotes fibroblast activation in p53-deficient colorectal cancer cells

Deficiency of p53 in cancer cells activates the transformation of normal tissue fibroblasts into carcinoma-associated fibroblasts; this promotes tumor progression through a variety of mechanisms in the tumor microenvironment. The role of autophagy in carcinoma-associated fibroblasts in tumor progression has not been elucidated. We aimed to clarify the significance of autophagy in fibroblasts, focusing on the TP53 status in co-cultured human colorectal cancer cell lines (TP53-wild-type colon cancer, HCT116; TP53-mutant colon cancer, HT29; fibroblast, CCD-18Co) in vitro. Autophagy in fibroblasts was significantly suppressed in association with ACTA2, CXCL12, TGFβ1, VEGFA, FGF2, and PDGFRA mRNA levels, when co-cultured with p53-deficient HCT116^{sh p53} cells. Exosomes isolated from the culture media of HCT116^{sh p53} cells significantly suppressed autophagy in fibroblasts via inhibition of ATG2B. Exosomes derived from TP53-mutant HT29 cells also suppressed autophagy in fibroblasts. miR-4534, extracted from the exosomes of HCT116^{sh p53} cells, suppressed ATG2B in fibroblasts. In conclusion, a loss of p53 function in colon cancer cells promotes the activation of surrounding fibroblasts through the suppression of autophagy. Exosomal miRNAs derived from cancer cells may play a pivotal role in the suppression of autophagy.

MicroRNA-195 rescues ApoE4-induced cognitive deficits and lysosomal defects in Alzheimer's disease pathogenesis

Our recent findings link the apolipoprotein E4 (ApoE4)-specific changes in brain phosphoinositol biphosphate (PIP2) homeostasis to the susceptibility of developing Alzheimer's Disease (AD). In the present study, we have identified miR-195 as a top micro-RNA candidate involved in the ApoE/PIP2 pathway using miRNA profiles in human ROSMAP datasets and mouse microarray studies. Further validation studies have demonstrated that levels of miR-195 are significantly lower in human brain tissue of ApoE4+/- patients with clinical diagnosis of mild cognitive impairment (MCI) or early AD when compared to ApoE4-/- subjects. In addition, brain miR-195 levels are reduced along with disease progression from normal aging to early AD, and cerebrospinal fluid (CSF) miR-195 levels of MCI subjects are positively correlated with cognitive performances as measured by mini-mental status examination (MMSE) and negatively correlated with CSF tau levels, suggesting the involvement of miR-195 in early development of AD with a potential impact on cognition. Similar differences in miR-195 levels are seen in ApoE4+/+ mouse hippocampal brain tissue and cultured neurons when compared to ApoE3+/+ counterparts. Over-expressing miR-195 reduces expression levels of its top predicted target synaptojanin 1 (synj1), a brain PIP2-degrading enzyme. Furthermore, elevating miR-195 ameliorates cognitive deficits, amyloid plaque burden, and tau hyper-phosphorylation in ApoE4+/+ mice. In addition, elevating miR-195 rescues AD-related lysosomal defects in inducible pluripotent stem cells (iPSCs)-derived brain cells of ApoE4+/+ AD subjects while inhibiting miR-195 exacerbates these phenotypes. Together, our data uncover a novel regulatory mechanism of miR-195 targeted at ApoE4-associated brain PIP2 dyshomeostasis, cognitive deficits, and AD pathology.

Interleukin-1β enhances cell adhesion in human endothelial cells via microRNA-1914-5p suppression

Atherosclerosis is a chronic inflammatory disease and the underlying cause of most cardiovascular diseases. Interleukin (IL)-1β facilitates early atherogenic lesion formation by increasing monocyte adhesion to endothelial cells via upregulation of adhesion molecules, including intercellular adhesion molecule-1 (ICAM-1). MicroRNAs (miRNAs) have been shown to be associated with inflammatory conditions in the vascular system. The expression of circulating miR-1914-5p is reportedly downregulated in patients with cardiovascular diseases. However, the role of miR-1914-5p downregulation in IL-1β-induced endothelial cell dysfunction and the effect of miR-1914-5p on lesion formation remain unclear. Therefore, we investigated whether miR-1914-5p is associated with monocyte adhesion in human endothelial cells. IL-1β decreased miR-1914-5p expression in EA.hy926 cells. In addition, miR-1914-5p depletion enhanced ICAM-1 expression and monocyte adhesion in EA.hy926 cells. Moreover, miR-1914-5p mimic suppressed monocyte adhesion and ICAM-1 expression induced by IL-1β in endothelial cells. These results suggest that suppression of miR-1914-5p expression by IL-1β may be an important regulator in mediating monocyte adhesion in endothelial cells. Further investigation of miR-1914-5p may lead to the development of novel therapeutic strategies for atherosclerosis.

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.

Integrative Analysis Identified a 6-miRNA Prognostic Signature in Nasopharyngeal Carcinoma

Background

Nasopharyngeal carcinoma (NPC) is an Epstein–Barr virus-associated epithelial malignancy, which is rare in America but endemic in China. The current clinical gold TNM-based standard for prognosis may not be enough. Although some studies have reported that some miRNAs have a prognostic power in NPC, there is a scarcity of independent validation for these miRNAs.

Methods

In this work, we firstly conducted a literature review of all miRNA profiling datasets with survival information, then integrated miRNA expression data across different profiling platforms and built prognostic models using machine learning methods. The Kaplan–Meier method and log-rank tests were applied to estimate correlations of the miRNA signature with survival, and the area under the time-dependent ROC curve (AUC) and concordance index (C-index) were used to assess the predictive power of prognostic models. We also investigated the biological roles of the prognostic miRNAs through identifying their regulated genes and association with immune infiltration.

Results

We constructed a prognostic model based on 6-miRNA signature (ebv-miR-BART12, ebv-miR-BART15, miR-29c-3p, miR-30e-5p, hsa-miR-375-3p, has-miR-93-5p) using the elastic net penalized Cox regression model. The AUCs of our model predicting 1-, 3-, and 5-year overall survival rates were 0.90, 0.73, and 0.70 for the external validation dataset and showed better prognostic power than models using previously reported miRNA signatures. The 6-miRNA risk score was an independent prognostic predictor for overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS). It could stratify patients into low-risk and high-risk groups; patients in the low-risk group treated with concurrent chemotherapy had a better survival. On the basis that the 6-miRNA risk score improved the current clinical gold standard for prognosis, we built a nomogram integrating both clinical characterizations and the risk score to predict 3-, 5-, and 10-year overall survival. Functional analysis suggested that the six miRNAs mainly play roles in virus infection pathways and oncogenic signaling pathways and associated with B-cell expression.

Conclusion

We identified a 6-miRNA prognostic signature in nasopharyngeal carcinoma across miRNA profiling platforms and patient geographical difference, which showed good prediction capability in terms of OS, DFS, and MFS. The 6-miRNA risk score might be helpful for clinicians to make treatment strategies and predict patient outcomes.

Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods.

RESULTS

We examined the evolution of miRNA-target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA-target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species.

CONCLUSIONS

The transferability of miRNA-targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA-target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to "non-model" organisms for which minimal experimental data are available.

AVAILABILITY AND IMPLEMENTATION

The code is freely available at https://github.com/gbenor/TPVOD .

Differential microRNAs expression profiles in liver from three different lifestyle modification mice models

Background

MicroRNAs play an important role in many fundamental biological and pathological processes. Defining the microRNAs profile underlying the processes by beneficial and detrimental lifestyles, including caloric restriction (CR), exercise and high-fat diet (HF), is necessary for understanding both normal physiology and the pathogenesis of metabolic disease. We used the microarray to detect microRNAs expression in livers from CR, EX and HF mice models. After predicted potential target genes of differentially expressed microRNAs with four algorithms, we applied GO and KEGG to analyze the function of predicted microRNA targets.

Results

We describe the overall microRNAs expression pattern, and identified 84 differentially expressed microRNAs changed by one or two or even all the three lifestyle modifications. The common and different enriched categories of gene function and main biochemical and signal transduction pathways were presented.

Conclusions

We provided for the first time a comprehensive and thorough comparison of microRNAs expression profiles in liver among these lifestyle modifications. With this knowledge, our findings provide us with an overall vision of microRNAs in the molecular impact of lifestyle on health as well as useful clues for future and thorough research of the role of microRNAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07507-3.

Bioinformatics-Based Identification of HDAC Inhibitors as Potential Drugs to Target EGFR Wild-Type Non-Small-Cell Lung Cancer

Patients with EGFR-mutant non-small-cell lung cancer (NSCLC) greatly benefit from EGFR-tyrosine kinase inhibitors (EGFR-TKIs) while the prognosis of patients who lack EGFR-sensitive mutations (EGFR wild type, EGFR-WT) remains poor due to a lack of effective therapeutic strategies. There is an urgent need to explore the key genes that affect the prognosis and develop potentially effective drugs in EGFR-WT NSCLC patients. In this study, we clustered functional modules related to the survival traits of EGFR-WT patients using weighted gene co-expression network analysis (WGCNA). We used these data to establish a two-gene prognostic signature based on the expression of CYP11B1 and DNALI1 by combining the least absolute shrinkage and selection operator (LASSO) algorithms and Cox proportional hazards regression analysis. Following the calculation of risk score (RS) based on the two-gene signature, patients with high RSs showed a worse prognosis. We further explored targeted drugs that could be effective in patients with a high RS by the connectivity map (CMap). Surprisingly, multiple HDAC inhibitors (HDACis) such as trichostatin A (TSA) and vorinostat (SAHA) that may have efficacy were identified. Also, we proved that HDACis could inhibit the proliferation and metastasis of NSCLC cells in vitro. Taken together, our study identified prognostic biomarkers for patients with EGFR-WT NSCLC and confirmed a novel potential role for HDACis in the clinical management of EGFR-WT patients.

Validation of human microRNA target pathways enables evaluation of target prediction tools

MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome and that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool miRTaH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFB-signaling, and Parkinson's Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting.

Overexpression of Gjb4 impairs cell proliferation and insulin secretion in primary islet cells

OBJECTIVE

Altered gene expression contributes to the development of type 2 diabetes (T2D); thus, the analysis of differentially expressed genes between diabetes-susceptible and diabetes-resistant mouse models is an important tool for the determination of candidate genes that participate in the pathology. Based on RNA-seq and array data comparing pancreatic gene expression of diabetes-prone New Zealand Obese (NZO) mice and diabetes-resistant B6.V-ob/ob (B6-ob/ob) mice, the gap junction protein beta 4 (Gjb4) was identified as a putative novel T2D candidate gene.

METHODS

Gjb4 was overexpressed in primary islet cells derived from C57BL/6 (B6) mice and INS-1 cells via adenoviral-mediated infection. The proliferation rate of cells was assessed by BrdU incorporation, and insulin secretion was measured under low (2.8 mM) and high (20 mM) glucose concentration. INS-1 cell apoptosis rate was determined by Western blotting assessing cleaved caspase 3 levels.

RESULTS

Overexpression of Gjb4 in primary islet cells significantly inhibited the proliferation by 47%, reduced insulin secretion of primary islets (46%) and INS-1 cells (51%), and enhanced the rate of apoptosis by 63% in INS-1 cells. Moreover, an altered expression of the miR-341-3p contributes to the Gjb4 expression difference between diabetes-prone and diabetes-resistant mice.

CONCLUSIONS

The gap junction protein Gjb4 is highly expressed in islets of diabetes-prone NZO mice and may play a role in the development of T2D by altering islet cell function, inducing apoptosis and inhibiting proliferation.

The lncRNA MALAT1/miR-30/Spastin Axis Regulates Hippocampal Neurite Outgrowth

Spastin, a microtubule-severing enzyme, is important for neurite outgrowth. However, the mechanisms underlying the post-transcriptional regulation of spastin during microtubule-related processes are largely unknown. We demonstrated that the spastin expression level is controlled by a long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1)/microRNA-30 (miR-30) axis during neurite outgrowth. The miR-30 expression level decreased in hippocampal neurons with increasing days in culture, and miR-30 overexpression suppressed while miR-30 inhibition promoted neurite outgrowth in hippocampal neurons. Spastin was validated as a target gene of miR-30 using the luciferase reporter assay. The protein expression, microtubule severing activity, and neurite promoting effect of spastin were suppressed by the overexpression of miR-30 mimics and increased by miR-30 inhibitors. MALAT1 expression increased during neurite outgrowth and MALAT1 silencing impaired neurite outgrowth. miR-30 was a sponge target of MALAT1 and MALAT1/miR-30 altered neurite outgrowth in hippocampal neurons. MALAT1 overexpression reversed the inhibitory effect of miR-30 on the activity of a luciferase reporter construct containing spastin, as well as spastin mRNA and protein expression, indicating that spastin was a downstream effector of MALAT1/miR-30. The MALAT1/miR-30 cascade also modulated spastin-induced microtubule severing, and the MALAT1/miR-30/spastin axis regulated neurite outgrowth in hippocampal neurons. This study suggests a new mechanism governing neurite outgrowth in hippocampal neurons involving MALAT1/miR-30-regulated spastin expression.

Co‑expression network analysis identified specific miRNAs and genes in association with slow‑transit constipation

The pathogenesis of slow-transit constipation (STC) remains largely unclear, with the roles of microRNAs (miRs/miRNAs) yet to be determined. Co-expression network analysis of miRNAs in STC is crucial to elucidating potential underlying mechanisms. Weighted gene correlation network analysis was performed in the miRNA expression profile of STC (GSE57969). The key miRNA target genes were further functionally enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). A Protein-Protein Interaction (PPI) network was constructed, with a total of 12 color-clustered modules determined. Seven key miRNAs were established, including five miRNAs from the turquoise module (hsa-miR-20b, hsa-miR-128, hsa-miR-129-3p, hsa-miR-30b and hsa-miR-340), one miRNA from the blue module (hsa-miR-619) and one from the black module (hsa-miR-486-3p). A total of 2,077 key miRNA target genes were predicted. GO analysis revealed that the ‘protein modification process’ and ‘cellular protein modification process’ were the most significantly enriched processes in the ‘Biological Processes’ category, whereas the ‘nucleoplasm’ in ‘Cellular Components’ and ‘enzyme binding’ in ‘Molecular Functions’ were the most significantly enriched processes. The ‘cAMP signalling pathway’ was the top KEGG pathway. The hub genes identified from the PPI network included calmodulin (CALM)2, CALM1, histone deacetylase (HDAC)3, glycogen synthase kinase 3 β, HDAC9, heat-shock protein family A member 8, G-protein subunit γ (GNG)13, HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1, GNG10 and GNG7. This bioinformatics analysis demonstrated co-expressed miRNA networks with insightful genes and pathways associated with STC.

miR-19a/b and miR-20a Promote Wound Healing by Regulating the Inflammatory Response of Keratinocytes

Persistent and impaired inflammation impedes tissue healing and is a characteristic of chronic wounds. A better understanding of the mechanisms controlling wound inflammation is needed. In this study, we show that in human wound-edge keratinocytes, the expressions of microRNA (miR)-17, miR-18a, miR-19a, miR-19b, and miR-20a, which all belong to the miR-17∼92 cluster, are upregulated during wound repair. However, their levels are lower in chronic ulcers than in acute wounds at the proliferative phase. Conditional knockout of miR-17∼92 in keratinocytes as well as injection of miR-19a/b and miR-20a antisense inhibitors into wound edges enhanced inflammation and delayed wound closure in mice. In contrast, conditional overexpression of the miR-17∼92 cluster or miR-19b alone in mice keratinocytes accelerated wound closure in vivo. Mechanistically, miR-19a/b and miR-20a decreased TLR3-mediated NF-κB activation by targeting SHCBP1 and SEMA7A, respectively, reducing the production of inflammatory chemokines and cytokines by keratinocytes. Thus, miR-19a/b and miR-20a being crucial regulators of wound inflammation, the lack thereof may contribute to sustained inflammation and impaired healing in chronic wounds. In line with this, we show that a combinatory treatment with miR-19b and miR-20a improved wound healing in a mouse model of type 2 diabetes.

Bioinformatics analysis of microRNA linked to ubiquitin proteasome system in traumatic osteonecrosis of the femoral head

MicroRNAs (miRNAs) have been suggested to act critical roles in the pathophysiology of traumatic osteonecrosis of the femoral head (TONFH). Unfortunately, their roles in the development of TONFH are still ambiguous. The purpose of this study is to identify promising miRNA biomarkers in traumatic osteonecrosis development.We conducted a comprehensive bioinformatics analysis using microarray datasets downloaded from the Gene Expression Omnibus database, and compared the expression of miRNAs in the serum of TONFH patients with controls. Next, we performed target prediction, function enrichment analysis, and protein-protein interaction network analysis based on differentially expressed (DE) miRNAs.We identified 26 DE miRNAs that may contribute to the pathophysiology of TONFH. The miRNAs were linked to ubiquitin proteasome system including conjugating protein ligase activity, ubiquitin-protein ligase activity and ubiquitin mediated proteolysis 5 pathway, and we exposed miR-181a-5p and miR-140-5p as promising biomarkers in TONFH.A predicting model consisting of 5 miRNAs may help discriminating high-risk patients who might develop TONFH after femur neck fracture. Among DE miRNAs, MiR-181a-5p and miR-140-5p may contribute to the development femoral head osteonecrosis after femur neck fracture via ubiquitin proteasome system.

Integrated analysis of circRNA-miRNA-mRNA regulatory network identifies potential diagnostic biomarkers in diabetic foot ulcer

Diabetic foot ulcer (DFU) is a common and serious complication of diabetes mellitus, which influences patients’ quality of life. Recently, circRNA regulated the mRNA levels by functioning as miRNA sponge in various disease, including diabetes mellitus. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved in DFU remains obscure. The aim of this study is to construct a competing endogenous RNA (ceRNA) network and screen biological indicators as diagnostic factors in DFU.

All the differentially expressed circRNAs, miRNAs and mRNAs were derived from Gene Expression Omnibus database. Furthermore, circRNAs identified by cytoHubba analysis and miRNAs obtained by human miRNA-disease database were used to construct DFU-specific ceRNA network with intersection of mRNAs. Functional enrichment analysis displayed the function and pathway of dysregulated mRNAs. Hub genes with high diagnostic value were screened by ClusterONE, GO semantic similarity and receiver operating characteristic (ROC) curve.

Here, the ceRNA network consisted of 8 circRNAs, 11 miRNAs and 91 mRNAs. Functional enrichment analysis demonstrated diabetic complications-related pathway including TGF-beta, FoxO and Wnt signaling pathway. GO semantic similarity and ROC curve analysis showed 6 hub genes with high diagnostic value (the area under the ROC curve ≥ 0.8) in patients with DFU, including BCL2, CCND1, IRAK4, SMAD4, SP1 and SUFU, which were identified as potential target genes for DFU diagnosis. In conclusion, the present study looked at a circRNA-miRNA-mRNA regulatory network with DFU and screened the potential function of mRNA, then identified novel diagnostic biomarkers and therapeutic targets for patients with DFU.

MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network

Analysis of miRNA-target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high-priced, time-consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA-target mRNA relationships. In this study, a novel method called MIPDH is developed for miRNA-mRNA interaction prediction by using DeepWalk on a heterogeneous network. More specifically, MIPDH extracts two kinds of features, in which a biological behavior feature is learned using a network embedding algorithm on a constructed heterogeneous network derived from 17 kinds of associations among drug, disease, and 6 kinds of biomolecules, and the attribute feature is learned using the k-mer method on sequences of miRNAs and target mRNAs. Then, a random forest classifier is trained on the features combined with the biological behavior feature and attribute feature. When implementing a 5-fold cross-validation experiment, MIPDH achieved an average accuracy, sensitivity, specificity and AUC of 75.85, 74.37, 77.33%, and 0.8044, respectively. To further evaluate the performance of MIPDH, other classifiers and feature descriptors are conducted for comparisons. MIPDH can achieve a better performance. Additionally, case studies on hsa-miR-106b-5p, hsa-let-7d-5p, and hsa-let-7e-5p are also implemented. As a result, 14, 9, and 9 out of the top 15 targets that interacted with these miRNAs were verified using the experimental literature or other databases. All these prediction results indicate that MIPDH is an effective method for predicting miRNA-target mRNA interactions.

miRNA target identification and prediction as a function of time in gene expression data

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

CPPED1-targeting microRNA-371a-5p expression in human placenta associates with spontaneous delivery

MicroRNAs (miRNAs) are important regulators of gene expression, and their expression is associated with many physiological conditions. Here, we investigated potential associations between expression levels of miRNAs in human placenta and the onset of spontaneous term birth. Using RNA sequencing, we identified 54 miRNAs differentially expressed during spontaneous term labor compared to elective term births. Expression levels of 23 miRNAs were upregulated, whereas 31 were downregulated at least 1.5-fold. The upregulated miRNA miR-371a-5p putatively targets CPPED1, expression of which decreases during spontaneous birth. We used a luciferase reporter–based assay to test whether a miR-371a-5p mimic affected translation when it bound to the 3′ untranslated region of CPPED1. In this setting, the miR-371a-5p mimic resulted in lower luciferase activity, which suggests that miR-371a-5p regulates levels of CPPED1. In conclusion, inversely correlated levels of miR-371a-5p and CPPED1 suggest a role for both in spontaneous delivery.

Position-wise binding preference is important for miRNA target site prediction

MOTIVATION

It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms.

RESULTS

We developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools.

AVAILABILITY AND IMPLEMENTATION

The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of exosomal microRNA-125b-5p in conferring the metastatic phenotype among pancreatic cancer cells with different potential of metastasis

AIMS

To explore the pro-metastatic role of exosomes derived from highly invasive pancreatic cancer cell and the associated aberrant expression of exosomal microRNAs (miRNAs).

MAIN METHODS

Weakly invasive PC-1 cells were treated with exosomes of highly invasive PC-1.0 cells to determine the pro-metastatic effect of PC-1.0 derived exosomes. The exosomal miRNA profile was further investigated using high-throughput sequencing. The level of miR-125b-5p in highly and weakly invasive pancreatic cancer cells was further determined. Pancreatic cancer cells transfected with miR-125b-5p mimic and inhibitor were used to explore the effect of miR-125b-5p on migration, invasion and epithelial-to-mesenchymal transition (EMT). Treatment with PC-1.0 derived exosome and Western blot assay were performed to validate STARD13 as a target of exosomal miR-125b-5p in pancreatic cancer.

KEY FINDINGS

PC-1.0 derived exosomes promoted the migration and invasion of weakly invasive PC-1 cells. miRNA sequencing revealed 62 miRNAs upregulated in PC-1.0 derived exosomes. miR-125b-5p most significantly promoted migration and invasion and was associated with metastasis in pancreatic cancer. Further, miR-125b-5p was upregulated in highly invasive pancreatic cancer cells and increased migration, invasion, and EMT. Moreover, its upregulation was associated with activation of MEK2/ERK2 signaling. The tumor suppressor STARD13 was directly targeted by miR-125b-5p in pancreatic cancer, which was associated with good prognosis and was suppressed by exosomes derived from highly invasive cancer cells.

SIGNIFICANCE

This study explored the pro-metastatic role of exosomes derived from highly invasive pancreatic cancer cells and the associated aberrant expression of exosomal miRNAs, which may help to elucidate the metastatic mechanism of pancreatic cancer.

MicroRNA expression profiling in PBMCs of Indian water Buffalo (Bubalus bubalis) infected with Brucella and Johne's disease

Background

MicroRNAs play key roles in host-pathogen-interactions and disease pathogenesis. Our aim was to characterize the differentially expressed miRNAs in the blood cells of diseased (Brucellosis-positive, Johne’s disease-positive) and healthy- water buffaloes. The pooled small-RNA samples of each group were sequenced on Ion Torrent Personal Genome Machine (PGM) sequencer and the data were analyzed for differential expression.

Results

Here we identified 274 known miRNAs with bovine homologs and 36 novel mature-star miRNAs from the sequnces of small RNA libraries. Overall 195 miRNAs were common to all the three groups. Certain miRNAs such as bta-miR-21-5p, −26a, −29a/b, −30d − 103, − 140, − 150, − 191, − 374, − 1434-5p,-1260b, − 2484 and let-7 members were abundantly expressed in diseased groups. Bta-miR-1434-5p, − 188, −200c were up-regulated (> 1.5 folds) while bta-miR-27a-5p, −34b and -2285x were down-regulated (> 100 folds) in Brucellosis group. In Johne’s Disease group, only 3 miRNAs (bta-miR-1434-5p, − 2340 and − 2484) were up-regulated (> 1.5 folds). The functional classification of miRNA target genes into gene ontology (GO) terms indicated their involvement in innate immunity and cellular process of disease pathogenesis. Expression profile of four differentially expressed miRNAs (bta-miR-9-5p, − 677, − 331-3p and − 2440) and eight predicted target-genes were validated through reverse transcriptase qPCR.

Conclusion

This study provides a valuable frame of reference for elucidation of regulatory roles of miRNAs associated with disease pathogenesis in water buffaloes as well as identification of miRNA biomarkers for disease diagnosis and treatment.

Prediction of miRNA targets by learning from interaction sequences

MicroRNAs (miRNAs) are involved in a diverse variety of biological processes through regulating the expression of target genes in the post-transcriptional level. So, it is of great importance to discover the targets of miRNAs in biological research. But, due to the short length of miRNAs and limited sequence complementarity to their gene targets in animals, it is challenging to develop algorithms to predict the targets of miRNA accurately. Here we developed a new miRNA target prediction algorithm using a multilayer convolutional neural network. Our model learned automatically the interaction patterns of the experiment-validated miRNA:target-site chimeras from the raw sequence, avoiding hand-craft selection of features by domain experts. The performance on test dataset is inspiring, indicating great generalization ability of our model. Moreover, considering the stability of miRNA:target-site duplexes, our method also showed good performance to predict the target transcripts of miRNAs.

Age-Related Changes in miRNA Expression Influence GSTZ1 and Other Drug Metabolizing Enzymes

Previous work has shown that hepatic levels of human glutathione transferase zeta 1 (GSTZ1) protein, involved in tyrosine catabolism and responsible for metabolism of the investigational drug dichloroacetate, increase in cytosol after birth before reaching a plateau around age 7. However, the mechanism regulating this change of expression is still unknown, and previous studies showed that GSTZ1 mRNA levels did not correlate with GSTZ1 protein expression. In this study, we addressed the hypothesis that microRNAs (miRNAs) could regulate expression of GSTZ1. We obtained liver samples from donors aged less than 1 year or older than 13 years and isolated total RNA for use in a microarray to identify miRNAs that were downregulated in the livers of adults compared with children. From a total of 2578 human miRNAs tested, 63 miRNAs were more than 2-fold down-regulated in adults, of which miR-376c-3p was predicted to bind to the 3' untranslated region of GSTZ1 mRNA. There was an inverse correlation of miR-376c-3p and GSTZ1 protein expression in the liver samples. Using cell culture, we confirmed that miR-376c-3p could downregulate GSTZ1 protein expression. Our findings suggest that miR-376c-3p prevents production of GSTZ1 through inhibition of translation. These experiments further our understanding of GSTZ1 regulation. Furthermore, our array results provide a database resource for future studies on mechanisms regulating human hepatic developmental expression. SIGNIFICANCE STATEMENT: Hepatic glutathione transferase zeta 1 (GSTZ1) is responsible for metabolism of the tyrosine catabolite maleylacetoacetate as well as the investigational drug dichloroacetate. Through examination of microRNA (miRNA) expression in liver from infants and adults and studies in cells, we showed that expression of GSTZ1 is controlled by miRNA. This finding has application to the dosing regimen of the drug dichloroacetate. The miRNA expression profiles are provided and will prove useful for future studies of drug-metabolizing enzymes in infants and adults.

miRTRS: A Recommendation Algorithm for Predicting miRNA Targets

microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.

miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm

microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i.e., the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://github.com/hjiangcsu/miRTMC.

FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

MOTIVATION

MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest.

RESULTS

We demonstrate that FilTar can be used to: (i) provide sample specific 3'-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods.

AVAILABILITY AND IMPLEMENTATION

FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Increased estrogen levels altered microRNA expression in prostate and plasma of rats dosed with sex hormones

BACKGROUND

Elevated estrogen (E) levels caused by aging or exposure to endocrine disrupting chemicals are related to prostate disease development. Sixty to seventy percent of prostate cancer or benign prostatic hyperplasia patients are over the age of 65, while prostatitis is likely to occur in men under 45 years. MicroRNAs currently represent a class of distinctive biological indicators to be used for clinical disease diagnosis and treatment monitoring. This study aims to identify microRNAs that could serve as potential biomarkers for prostate disorders induced by elevated E levels according to their altered expression in prostate or plasma.

MATERIALS AND METHODS

Groups of Sprague Dawley rats (offspring) were dosed with estradiol benzoate (EB) on postnatal days 1, 3, and 5, and subcutaneously implanted with tubes containing testosterone (T)/E on postnatal day 90. Expression levels of prostate and plasma microRNAs were evaluated using microRNA microarray and validated via qRT-PCR. The expression levels of the potential targeted genes of a set of identified microRNAs were also examined by qRT-PCR.

RESULTS

Postnatal administration of EB, T, and E elevated serum E levels with decreased serum T levels in rats. Chronic inflammation was observed in the dorsolateral prostate. Significant changes in expression levels of several microRNAs (rno-miR-146-5p, rno-miR-329-3p, and rno-miR-126a-3p) in the dorsolateral prostate and of a microRNA (rno-miR-329-3p) in the plasma were found in the dosed rats. The target gene expression levels of the altered microRNAs also changed accordingly.

CONCLUSION

Chronic inflammation in the dorsolateral prostate of rats dosed with EB, T, and E resulted in deregulated expression in a set of microRNAs whose target genes were related to tumor growth or abnormal proliferation. Our findings suggest the identified microRNAs and their target genes the potential use as biomarkers to predict prostate cancer development. Validation using human samples is warranted.

Integrated analysis of a ceRNA network reveals potential prognostic lncRNAs in gastric cancer

Long noncoding RNAs (lncRNAs) have important biological functions as competing endogenous RNAs (ceRNAs) in tumors, yet the functions and regulatory mechanisms of lncRNA-related ceRNAs in gastric cancer have not been fully elucidated. In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network and identified potential lncRNA biomarkers in gastric cancer. Basing on the RNA profiles downloaded from The Cancer Genome Atlas (TCGA) platform, the gastric cancer-specific differentially expressed lncRNAs, miRNAs, and mRNAs were screened for constructing a ceRNA network using bioinformatic tools. The enrichment analysis of the biological processes in Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathways was performed on the ceRNA-related DEmRNAs. According to the modularization of protein-protein interaction (PPI) network, we extracted a ceRNA subnetwork and analyzed the correlation between the expression of the lncRNAs involved and specific clinical features of patients. Next, the expression of highly up-regulated in liver cancer (HULC) and RP11-314B1.2 showed significant changes in several pathological processes involved in gastric cancer, and nine lncRNAs were found to be correlated with the overall survival of patients with gastric cancer. Through the univariate and multivariate Cox regression analyses, two lncRNAs (LINC00106 and RP11-999E24.3) were identified and utilized to establish a risk score model for assessing the prognosis of patients. The analysis results were also partially verified using quantitative real-time PCR. The findings from this study indicate that HULC, RP11-314B1.2, LINC00106, and RP11-999E24.3 could be considered as potential therapeutic targets or prognostic biomarkers in gastric cancer, and provide a new perspective for cancer pathogenesis research.