TarPmiR: a new approach for microRNA target site prediction

Advanced computational predictive models of miRNA-mRNA interaction efficiency

The modeling of miRNA-mRNA interactions holds significant implications for synthetic biology and human health. However, this research area presents specific challenges due to the multifaceted nature of mRNA downregulation by miRNAs, influenced by numerous factors including competition or synergism among miRNAs and mRNAs. In this study, we present an improved computational model for predicting miRNA-mRNA interactions, addressing aspects not previously modeled. Firstly, we integrated a novel set of features that significantly enhanced the predictor's performance. Secondly, we demonstrated the cell-specific nature of certain aspects of miRNA-mRNA interactions, highlighting the importance of designing models tailored to specific cell types for improved accuracy. Moreover, we introduce a miRNA binding site interaction model (miBSIM) that, for the first time, accounts for both the distribution of miRNA binding sites along the mRNA and their respective strengths in regulating mRNA stability. Our analysis suggests that distant miRNA sites often compete with each other, revealing the intricate interplay of binding site interactions. Overall, our new predictive model shows a significant improvement of up to 6.43% over previous models in the field. The code of our model is available at https://www.cs.tau.ac.il/~tamirtul/miBSIM.

Notch signaling regulates mineralization via microRNA modulation in dental pulp stem cells

OBJECTIVES

This study investigated the miRNA expression profile in Notch-activated human dental stem pulp stem cells (DPSCs) and validated the functions of miRNAs in modulating the odonto/osteogenic properties of DPSCs.

METHODS

DPSCs were treated with indirect immobilized Jagged1. The miRNA expression profile was examined using NanoString analysis. Bioinformatic analysis was performed, and miRNA expression was validated. Odonto/osteogenic differentiation was examined using alkaline phosphatase staining, Alizarin Red S staining, as well as odonto/osteogenic-related gene and protein expression.

RESULTS

Fourteen miRNAs were differentially expressed in Jagged1-treated DPSCs. Pathway analysis revealed that altered miRNAs were associated with TGF-β, Hippo, ErbB signalling pathways, FoxO and Ras signalling. Target prediction analysis demonstrated that 7604 genes were predicted to be targets for these altered miRNAs. Enrichment analysis revealed relationships to various DNA bindings. Among differentially expressed miRNA, miR-296-3p and miR-450b-5p were upregulated under Jagged1-treated conditions. Overexpression of miR-296-3p and miR-450b-5p enhanced mineralization and upregulation of odonto/osteogenic-related genes, whereas inhibition of these miRNAs revealed opposing results. The miR-296-3p and miR-450b-5p inhibitors attenuated the effects of Jagged1-induced mineralization in DPSCs.

CONCLUSIONS

Jagged-1 promotes mineralization in DPSCs that are partially regulated by miRNA. The novel understanding of these miRNAs could lead to innovative controlled mechanisms that can be applied to modulate biology-targeted dental materials.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Differential expression of ion channel coding genes in the endometrium of women experiencing recurrent implantation failures

Our study probed the differences in ion channel gene expression in the endometrium of women with Recurrent Implantation Failure (RIF) compared to fertile women. We analyzed the relative expression of genes coding for T-type Ca2+, ENaC, CFTR, and KCNQ1 channels in endometrial samples from 20 RIF-affected and 10 control women, aged 22-35, via microarray analysis and quantitative real-time PCR. Additionally, we examined DNA methylation in the regulatory region of KCNQ1 using ChIP real-time PCR. The bioinformatics component of our research included Gene Ontology analysis, protein-protein interaction networks, and signaling pathway mapping to identify key biological processes and pathways implicated in RIF. This led to the discovery of significant alterations in the expression of ion channel genes in RIF women's endometrium, most notably an overexpression of CFTR and reduced expression of SCNN1A, SCNN1B, SCNN1G, CACNA1H, and KCNQ1. A higher DNA methylation level of KCNQ1's regulatory region was also observed in RIF patients. Gene-set enrichment analysis highlighted a significant presence of genes involved with ion transport and membrane potential regulation, particularly in sodium and calcium channel complexes, which are vital for cation movement across cell membranes. Genes were also enriched in broader ion channel and transmembrane transporter complexes, underscoring their potential extensive role in cellular ion homeostasis and signaling. These findings suggest a potential involvement of ion channels in the pathology of implantation failure, offering new insights into the mechanisms behind RIF and possible therapeutic targets.

miRNA-protein-metabolite interaction network reveals the regulatory network and players of pregnancy regulation in dairy cows

Pregnancy is a complex process involving complex molecular interaction networks, such as between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite interactions. Advances in technology have led to the identification of many pregnancy-associated microRNA (miRNA), protein, and metabolite fingerprints in dairy cows. An array of miRNA, protein, and metabolite fingerprints produced during the early pregnancy of dairy cows were described. We have found the in silico interaction networks between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite. We have manually constructed miRNA-protein-metabolite interaction networks such as bta-miR-423-3p-IGFBP2-PGF2α interactomes. This interactome is obtained by manually combining the interaction network formed between bta-miR-423-3p-IGFBP2 and the interaction network between IGFBP2-PGF2α with IGFBP2 as a common interactor with bta-miR-423-3p and PGF2α with the provided sources of evidence. The interaction between bta-miR-423-3p and IGFBP2 has many sources of evidence including a high miRanda score of 169, minimum free energy (MFE) score of -25.14, binding probability (p) of 1, and energy of -25.5. The interaction between IGFBP2 and PGF2α occurs at high confidence scores (≥0.7 or 70%). Interestingly, PGF2α is also found to interact with different metabolites, such as PGF2α-PGD2, PGF2α-thromboxane B2, PGF2α-PGE2, and PGF2α-6-keto-PGF1α at high confidence scores (≥0.7 or 70%). Furthermore, the interactions between C3-PGE2, C3-PGD2, PGE2-PGD2, PGD2-thromboxane B2, PGE2-thromboxane B2, 6-keto-PGF1α-thromboxane B2, and PGE2-6-keto-PGF1α were also obtained at high confidence scores (≥0.7 or 70%). Therefore, we propose that miRNA-protein-metabolite interactomes involving miRNA, protein, and metabolite fingerprints of early pregnancy of dairy cows such as bta-miR-423-3p, IGFBP2, PGF2α, PGD2, C3, PGE2, 6-keto-PGF1 alpha, and thromboxane B2 may form the key regulatory networks and players of pregnancy regulation in dairy cows. This is the first study involving miRNA-protein-metabolite interactomes obtained in the early pregnancy stage of dairy cows.

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNA-target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field.

Piezo1 - Serine/threonine-protein phosphatase 2A - Cofilin1 biochemical mechanotransduction axis controls F-actin dynamics and cell migration

This study sheds light on a ground-breaking biochemical mechanotransduction pathway and reveals how Piezo1 channels orchestrate cell migration. We observed an increased cell migration rate in HEK293T (HEK) cells treated with Yoda1, a Piezo1 agonist, or in HEK cells overexpressing Piezo1 (HEK + P). Conversely, a significant reduction in cell motility was observed in HEK cells treated with GsMTx4 (a channel inhibitor) or upon silencing Piezo1 (HEK-P). Our findings establish a direct correlation between alterations in cell motility, Piezo1 expression, abnormal F-actin microfilament dynamics, and the regulation of Cofilin1, a protein involved in severing F-actin microfilaments. Here, the conversion of inactive pCofilin1 to active Cofilin1, mediated by the serine/threonine-protein phosphatase 2A catalytic subunit C (PP2AC), resulted in increased severing of F-actin microfilaments and enhanced cell migration in HEK + P cells compared to HEK controls. However, this effect was negligible in HEK-P and HEK cells transfected with hsa-miR-133b, which post-transcriptionally inhibited PP2AC mRNA expression. In summary, our study suggests that Piezo1 regulates cell migration through a biochemical mechanotransduction pathway involving PP2AC-mediated Cofilin1 dephosphorylation, leading to changes in F-actin microfilament dynamics.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning

MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA-based drug development. Datasets of high-throughput direct bona fide miRNA-target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.

Recommendations for detection, validation, and evaluation of RNA editing events in cardiovascular and neurological/neurodegenerative diseases

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.

LncRNA LUESCC promotes esophageal squamous cell carcinoma by targeting the miR-6785-5p/NRSN2 axis

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent gastrointestinal malignancies with high mortality worldwide. Emerging evidence indicates that long noncoding RNAs (lncRNAs) are involved in human cancers, including ESCC. However, the detailed mechanisms of lncRNAs in the regulation of ESCC progression remain incompletely understood. LUESCC was upregulated in ESCC tissues compared with adjacent normal tissues, which was associated with gender, deep invasion, lymph node metastasis, and poor prognosis of ESCC patients. LUESCC was mainly localized in the cytoplasm of ESCC cells. Knockdown of LUESCC inhibited cell proliferation, colony formation, migration, and invasion in vitro and suppressed tumor growth in vivo. Mechanistic investigation indicated that LUESCC functions as a ceRNA by sponging miR-6785-5p to enhance NRSN2 expression, which is critical for the malignant behaviors of ESCC. Furthermore, ASO targeting LUESCC substantially suppressed ESCC both in vitro and in vivo. Collectively, these data demonstrate that LUESCC may exerts its oncogenic role by sponging miR-6785-5p to promote NRSN2 expression in ESCC, providing a potential diagnostic marker and therapeutic target for ESCC patients.

CircHIPK3 regulates fatty acid metabolism through miR-637/FASN axis to promote esophageal squamous cell carcinoma

The oncogenic role of circRNA in cancers including esophageal cancer (EC) has been well studied. However, whether and how circRNAs are involved in cancer cell metabolic processes remains largely unknown. Here, we reported that circRNA, circHIPK3, is highly expressed in ESCC cell lines and tissues. Knockdown of circHIPK3 significantly restrained cell proliferation, colony formation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, circHIPK3 was found to act as a ceRNA by sponging miR-637 to regulate FASN expression and fatty acid metabolism in ESCC cells. Anti-sense oligonucleotide (ASO) targeting circHIPK3 substantially inhibited ESCC both in vitro and in vivo. Therefore, these results uncover a modulatory axis constituting of circHIPK3/miR-637/FASN may be a potential biomarker and therapeutic target for ESCC in the clinic.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

Species-specific microRNA discovery and target prediction in the soybean cyst nematode

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).

microRNAs slow translating ribosomes to prevent protein misfolding in eukaryotes

Slower translation rates reduce protein misfolding. Such reductions in speed can be mediated by the presence of non-optimal codons, which allow time for proper folding to occur. Although this phenomenon is conserved from bacteria to humans, it is not known whether there are additional eukaryote-specific mechanisms which act in the same way. MicroRNAs (miRNAs), not present in prokaryotes, target both coding sequences (CDS) and 3' untranslated regions (UTR). Given their low suppressive efficiency, it has been unclear why miRNAs are equally likely to bind to a CDS. Here, we show that miRNAs transiently stall translating ribosomes, preventing protein misfolding with little negative effect on protein abundance. We first analyzed ribosome profiles and miRNA binding sites to examine whether miRNAs stall ribosomes. Furthermore, either global or specific miRNA deficiency accelerated ribosomes and induced aggregation of a misfolding-prone polypeptide reporter. These defects were rescued by slowing ribosomes using non-cleaving shRNAs as miRNA mimics. We finally show that proinsulin misfolding, associated with type II diabetes, was resolved by non-cleaving shRNAs. Our findings provide a eukaryote-specific mechanism of co-translational protein folding and a previously unknown mechanism of action to target protein misfolding diseases.

Estrogen-Induced LncRNA, LINC02568, Promotes Estrogen Receptor-Positive Breast Cancer Development and Drug Resistance Through Both In Trans and In Cis Mechanisms

Endocrine therapy is the frontline treatment for estrogen receptor (ER) positive breast cancer patients. However, the primary and acquired resistance to endocrine therapy drugs remain as a major challenge in the clinic. Here, this work identifies an estrogen-induced lncRNA, LINC02568, which is highly expressed in ER-positive breast cancer and functional important in cell growth in vitro and tumorigenesis in vivo as well as endocrine therapy drug resistance. Mechanically, this work demonstrates that LINC02568 regulates estrogen/ERα-induced gene transcriptional activation in trans by stabilizing ESR1 mRNA through sponging miR-1233-5p in the cytoplasm. Meanwhile, LINC02568 contributes to tumor-specific pH homeostasis by regulating carbonic anhydrase CA12 in cis in the nucleus. The dual functions of LINC02568 together contribute to breast cancer cell growth and tumorigenesis as well as endocrine therapy drug resistance. Antisense oligonucleotides (ASO) targeting LINC02568 significantly inhibits ER-positive breast cancer cell growth in vitro and tumorigenesis in vivo. Furthermore, combination treatment with ASO targeting LINC02568 and endocrine therapy drugs or CA12 inhibitor U-104 exhibits synergistic effects on tumor growth. Taken together, the findings reveal the dual mechanisms of LINC02568 in regulating ERα signaling and pH homeostasis in ER-positive breast cancer, and indicated that targeting LINC02568 might represent a potential therapeutic avenue in the clinic.

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging

Motivation

Circular RNAs (circRNAs) are long noncoding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs.

Results

We developed circRNA-sponging, a nextflow pipeline that (i) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (ii) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (iii) performs differential expression analysis, (iv) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (v) predicts miRNA binding sites on circRNAs, (vi) systematically investigates potential circRNA-miRNA sponging events, (vii) creates a network of competing endogenous RNAs and (viii) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues, where we identified two distinct types of circRNAs characterized by a specific ratio of the number of the binding site to the length of the transcript. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research.

Availability and implementation

https://github.com/biomedbigdata/circRNA-sponging.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging

MOTIVATION

Circular RNAs (circRNAs) are long non-coding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs.

RESULTS

We developed circRNA-sponging, a nextflow pipeline that (1) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (2) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (3) performs differential expression analysis, (4) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (5) predicts miRNA binding sites on circRNAs, (6) systematically investigates potential circRNA-miRNA sponging events, (7) creates a network of competing endogenous RNAs, and (8) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues, where we identified two distinct types of circRNAs characterized by a specific ratio of the number of the binding site to the length of the transcript. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research.

AVAILABILITY

https://github.com/biomedbigdata/circRNA-sponging Contact: markus.daniel.hoffmann@tum.de; markus.list@tum.de Supplementary Material: Supplementary data are available at Bioinformatic Advances online.

CircPVT1 promotes ER-positive breast tumorigenesis and drug resistance by targeting ESR1 and MAVS

The molecular mechanisms underlying estrogen receptor (ER)-positive breast carcinogenesis and endocrine therapy resistance remain incompletely understood. Here, we report that circPVT1, a circular RNA generated from the lncRNA PVT1, is highly expressed in ERα-positive breast cancer cell lines and tumor samples and is functionally important in promoting ERα-positive breast tumorigenesis and endocrine therapy resistance. CircPVT1 acts as a competing endogenous RNA (ceRNA) to sponge miR-181a-2-3p, promoting the expression of ESR1 and downstream ERα-target genes and breast cancer cell growth. Furthermore, circPVT1 directly interacts with MAVS protein to disrupt the RIGI-MAVS complex formation, inhibiting type I interferon (IFN) signaling pathway and anti-tumor immunity. Anti-sense oligonucleotide (ASO)-targeting circPVT1 inhibits ERα-positive breast cancer cell and tumor growth, re-sensitizing tamoxifen-resistant ERα-positive breast cancer cells to tamoxifen treatment. Taken together, our data demonstrated that circPVT1 can work through both ceRNA and protein scaffolding mechanisms to promote cancer. Thus, circPVT1 may serve as a diagnostic biomarker and therapeutic target for ERα-positive breast cancer in the clinic.

Identification and characterization of miRNAs in spleens of sheep subjected to repetitive vaccination

Accumulative evidence has shown that short non-coding RNAs such as miRNAs can regulate the innate and adaptive immune responses. Aluminium hydroxide is a commonly used adjuvant in human and veterinary vaccines. Despite its extended use, its mechanism of action is not fully understood and very few in vivo studies have been done to enhance understanding at the molecular level. In this work, we took advantage of a previous long-term experiment in which lambs were exposed to three different treatments by parallel subcutaneous inoculations with aluminium-containing commercial vaccines, an equivalent dose of aluminium or mock injections. Spleen samples were used for miRNA-seq. A total of 46 and 16 miRNAs were found differentially expressed when animals inoculated with commercial vaccines or the adjuvant alone were compared with control animals, respectively. Some miRNAs previously related to macrophage polarization were found dysregulated exclusively by the commercial vaccine treatment but not in the aluminium inoculated animals. The dysregulated miRNAs in vaccine group let-7b-5p, miR-29a-3p, miR-27a and miR-101-3p are candidates for further research, since they may play key roles in the immune response induced by aluminium adjuvants added to vaccines. Finally, protein-protein interaction network analysis points towards leucocyte transendothelial migration as a specific mechanism in animals receiving adjuvant only.

Systemic circulating microRNA landscape in Lynch syndrome

Circulating microRNAs (c-miRs) are small noncoding RNA molecules that migrate throughout the body and regulate gene expression. Global c-miR expression patterns (c-miRnomes) change with sporadic carcinogenesis and have predictive potential in early detection of cancers. However, there are no studies that have assessed whether c-miRnomes display similar potential in carriers of inherited pathogenic mismatch-repair gene variants (path_MMR), known as Lynch syndrome (LS), who are predisposed to highly increased cancer risk. Using high-throughput sequencing and bioinformatic approaches, we conducted an exploratory analysis to characterize systemic c-miRnomes of path_MMR carriers, sporadic rectal cancer patients and non-LS controls. We showed for the first time that cancer-free path_MMR carriers have a systemic c-miRnome of 40 differentially expressed c-miRs that can distinguish them from non-LS controls. The systemic c-miRnome of cancer-free path_MMR carriers also resembles the systemic c-miRnomes of cancer patients with or without path_MMR. Our pathway analysis linked the found differentially expressed c-miRs to carcinogenesis. A total of 508 putative target genes were identified for 32 out of 40 differentially expressed c-miRs, and 238 of them were enriched in cancer-related pathways. The most enriched c-miR-target genes include well-known oncogenes and tumor suppressor genes such as BCL2, AKT3, PIK3CA, KRAS, NRAS, CDKN1A and PIK3R1. Taken together, our findings suggest that LS and sporadic carcinogenesis share common biological pathways and alterations in these pathways can produce a c-miR signature which can track potential oncogenic stress in cancer-free path_MMR carriers. Therefore, c-miRs hold potential in monitoring the LS risk stratification patterns during clinical surveillance or cancer management.

FUS Alters circRNA Metabolism in Human Motor Neurons Carrying the ALS-Linked P525L Mutation

Deregulation of RNA metabolism has emerged as one of the key events leading to the degeneration of motor neurons (MNs) in Amyotrophic Lateral Sclerosis (ALS) disease. Indeed, mutations on RNA-binding proteins (RBPs) or on proteins involved in aspects of RNA metabolism account for the majority of familiar forms of ALS. In particular, the impact of the ALS-linked mutations of the RBP FUS on many aspects of RNA-related processes has been vastly investigated. FUS plays a pivotal role in splicing regulation and its mutations severely alter the exon composition of transcripts coding for proteins involved in neurogenesis, axon guidance, and synaptic activity. In this study, by using in vitro-derived human MNs, we investigate the effect of the P525L FUS mutation on non-canonical splicing events that leads to the formation of circular RNAs (circRNAs). We observed altered levels of circRNAs in FUS^P525L MNs and a preferential binding of the mutant protein to introns flanking downregulated circRNAs and containing inverted Alu repeats. For a subset of circRNAs, FUS^P525L also impacts their nuclear/cytoplasmic partitioning, confirming its involvement in different processes of RNA metabolism. Finally, we assess the potential of cytoplasmic circRNAs to act as miRNA sponges, with possible implications in ALS pathogenesis.

Translational control in cortical development

Differentiation of specific neuronal types in the nervous system is worked out through a complex series of gene regulation events. Within the mammalian neocortex, the appropriate expression of key transcription factors allocates neurons to different cortical layers according to an inside-out model and endows them with specific properties. Precise timing is required to ensure the proper sequential appearance of key transcription factors that dictate the identity of neurons within the different cortical layers. Recent evidence suggests that aspects of this time-controlled regulation of gene products rely on post-transcriptional control, and point at micro-RNAs (miRs) and RNA-binding proteins as important players in cortical development. Being able to simultaneously target many different mRNAs, these players may be involved in controlling the global expression of gene products in progenitors and post-mitotic cells, in a gene expression framework where parallel to transcriptional gene regulation, a further level of control is provided to refine and coordinate the appearance of the final protein products. miRs and RNA-binding proteins (RBPs), by delaying protein appearance, may play heterochronic effects that have recently been shown to be relevant for the full differentiation of cortical neurons and for their projection abilities. Such heterochronies may be the base for evolutionary novelties that have enriched the spectrum of cortical cell types within the mammalian clade.

P-TarPmiR accurately predicts plant-specific miRNA targets

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

MicroRNA Profiling Using a PCR-Based Method

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in the post-transcriptional regulation of specific mRNA targets, thus possibly controlling many biological processes. The miRNA profiling analysis can contribute to understanding several signaling pathways, as biomarkers for molecular diagnostic, as well as potential to be used as therapeutic targets. The miRNAs expression can be analyzed by quantitative reverse transcription PCR (RT-qPCR), microarrays, and RNA sequencing. The RT-qPCR method is sensitive and specific and has a lower cost when compared to other techniques as microarrays and RNA sequencing. Therefore, the protocol presented in this chapter describes step by step all the details to perform miRNA analysis using primer-based RT-qPCR.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Circular RNA profile in Graves' disease and potential function of hsa_circ_0090364

Background

Graves' disease is a common autoimmune disease. Cytokines and their signalling pathways play a major part in the pathogenesis of Graves' disease; however, the underlying mechanism needs to be clarified.

Aims

The aim of this study was to explore whether circular RNAs participate in the immunological pathology of Graves' disease via cytokine-related signalling pathways.

Methods

Bioinformatics analysis was performed to identify differentially expressed circular RNAs and their targets and associated pathways. A total of three patients with Graves' disease and three sex- and age-matched healthy controls were enrolled for validation with microarray analysis and real-time quantitative PCR (qPCR). An additional 24 patients with Graves' disease and 24 gender- and age-matched controls were included for validation by real-time fluorescent qPCR. Flow cytometry and CCK8 assays were used to detect the apoptotic and proliferative levels of Jurkat cells (T lymphocytes) with the silenced expression of circRNA. ELISA was performed to detect the growth and apoptosis-related proteins. The competition mechanism of endogenous RNA was explored by real-time fluorescence qPCR.

Results

A total of 366 significantly differentially expressed circular RNAs were identified in the Graves' disease group compared to healthy controls. The level of hsa_circ_0090364 was elevated in Graves' disease patients and positively correlated with thyroid-stimulating hormone receptor antibodies. Further analyses suggested that hsa_circ_0090364 may regulate the JAK-STAT pathway via the hsa-miR-378a-3p/IL-6ST/IL21R axis to promote cell growth.

Conclusions

These results provide novel clues into the pathophysiological mechanisms of Graves' disease and potential targets for drug treatment.

The Network of miRNA-mRNA Interactions in Circulating T Cells of Patients Following Major Trauma - A Pilot Study

Purpose

Following major trauma, genes involved in adaptive immunity are downregulated, which accompanies the upregulation of genes involved in systemic inflammatory responses. This study investigated microRNA (miRNA)-mRNA interactome dysregulation in circulating T cells of patients with major trauma.

Patients and Methods

This study included adult trauma patients who had an injury severity score ≥16 and required ventilator support for more than 48 h in the intensive care unit. Next-generation sequencing was used to profile the miRNAs and mRNAs expressed in CD3+ T cells isolated from patient blood samples collected during the injury and recovery stages.

Results

In the 26 studied patients, 9 miRNAs (hsa-miR-16-2-3p, hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-192-5p, hsa-miR-197-3p, hsa-miR-23a-3p, hsa-miR-26b-5p, hsa-miR-223-3p, and hsa-miR-485-5p) were significantly upregulated, while 58 mRNAs were significantly downregulated in T cells following major trauma. A network consisting of 8 miRNAs and 22 mRNAs interactions was revealed by miRWalk, with three miRNAs (hsa-miR-185-5p, hsa-miR-197-3p, and hsa-miR-485-5p) acting as hub genes that regulate the network. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that “chemokine signaling pathway” was the predominant pathway.

Conclusion

The study revealed a miRNA-mRNA interactome consisting of 8 miRNAs and 22 mRNAs that are predominantly involved in chemokine signaling in circulating T cells of patients following major trauma.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.

Identification of Key Endometrial MicroRNAs and Their Target Genes Associated With Pathogenesis of Recurrent Implantation Failure by Integrated Bioinformatics Analysis

Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation.

Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA–target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA–target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR).

Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA–target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR (p < 0.05).

Conclusion: We constructed miRNA–target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.

Identification and characterization of virus-encoded circular RNAs in host cells

Emerging evidence has identified viral circular RNAs (circRNAs) in human cells infected by viruses, interfering with the immune system and inducing diseases including human cancer. However, the biogenesis and regulatory mechanisms of virus-encoded circRNAs in host cells remain unknown. In this study, we used the circRNA detection tool CIRI2 to systematically determine the virus-encoded circRNAs in virus-infected cancer cell lines and cancer patients, by analysing RNA-Seq datasets derived from RNase R-treated samples. Based on the thousands of viral circRNAs we identified, the biological characteristics and potential roles of viral circRNAs in regulating host cell function were determined. In addition, we developed a Viral-circRNA Database (http://www.hywanglab.cn/vcRNAdb/), which is open to all users to search, browse and download information on circRNAs encoded by viruses upon infection.

tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction

tRNA fragments (tRFs) are small RNAs comparable to the size and function of miRNAs. tRFs are generally Dicer independent, are found associated with Ago, and can repress expression of genes post-transcriptionally. Given that this expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, Gene Ontology analysis suggests that among the tRFs conserved between mice and humans, the predicted targets are enriched significantly in neuronal function, and we show this specifically for tRF-3009a. These improvements to tRF target prediction further our understanding of tRF function broadly across species and provide avenues for testing novel roles for tRFs in biology. We have created a publicly available website for the targets of tRFs predicted by tRForest.

Role of miRNAs in Human T Cell Leukemia Virus Type 1 Induced T Cell Leukemia: A Literature Review and Bioinformatics Approach

Human T cell leukemia virus type 1 (HTLV-1) was identified as the first pathogenic human retrovirus and is estimated to infect 5 to 10 million individuals worldwide. Unlike other retroviruses, there is no effective therapy to prevent the onset of the most alarming diseases caused by HTLV-1, and the more severe cases manifest as the malignant phenotype of adult T cell leukemia (ATL). MicroRNA (miRNA) dysfunction is a common feature of leukemogenesis, and it is no different in ATL cases. Therefore, we sought to analyze studies that reported deregulated miRNA expression in HTLV-1 infected cells and patients' samples to understand how this deregulation could induce malignancy. Through in silico analysis, we identified 12 miRNAs that stood out in the prediction of targets, and we performed functional annotation of the genes linked to these 12 miRNAs that appeared to have a major biological interaction. A total of 90 genes were enriched in 14 KEGG pathways with significant values, including TP53, WNT, MAPK, TGF-β, and Ras signaling pathways. These miRNAs and gene interactions are discussed in further detail for elucidation of how they may act as probable drivers for ATL onset, and while our data provide solid starting points for comprehension of miRNAs' roles in HTLV-1 infection, continuous effort in oncologic research is still needed to improve our understanding of HTLV-1 induced leukemia.

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks

Background

Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software.

Results

Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time.

Conclusions

SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04695-x.

Time-Restricted Eating Regimen Differentially Affects Circulatory miRNA Expression in Older Overweight Adults

Time-restricted eating (TRE), a popular form of intermittent fasting, has been demonstrated to provide multiple health benefits, including an extension of healthy lifespan in preclinical models. While the specific mechanisms remain elusive, emerging research indicates that one plausible mechanism through which TRE may confer health benefits is by influencing the expression of the epigenetic modulator circulatory miRNAs, which serve as intercellular communicators and are dysregulated in metabolic disorders, such as obesity. Therefore, the goal of this pilot study is to examine the effects of a 4-week TRE regimen on global circulatory miRNA from older (≥65 years) overweight participants. Pre- and post-TRE regimen serum samples from nine individuals who participated in the Time to Eat clinical trial (NCT03590847) and had a significant weight loss (2.6 kg, p < 0.01) were analyzed. The expressions of 2083 human miRNAs were quantified using HTG molecular whole transcriptome miRNA assay. In silico analyses were performed to determine the target genes and biological pathways associated with differentially expressed miRNAs to predict the metabolic effects of the TRE regimen. Fourteen miRNAs were differentially expressed pre- and post-TRE regimen. Specifically, downregulated miRNA targets suggested increased expression of transcripts, including PTEN, TSC1, and ULK1, and were related to cell growth and survival. Furthermore, the targets of downregulated miRNAs were associated with Ras signaling (cell growth and proliferation), mTOR signaling (cell growth and protein synthesis), insulin signaling (glucose uptake), and autophagy (cellular homeostasis and survival). In conclusion, the TRE regimen downregulated miRNA, which, in turn, could inhibit the pathways of cell growth and activate the pathways of cell survival and might promote healthy aging. Future mechanistic studies are required to understand the functional role of the miRNAs reported in this study.

Elevated Expression of miR-200c/141 in MDA-MB-231 Cells Suppresses MXRA8 Levels and Impairs Breast Cancer Growth and Metastasis In Vivo

Breast cancer cells with mesenchymal characteristics, particularly the claudin-low subtype, express extremely low levels of miR-200s. Therefore, this study examined the functional impact of restoring miR-200 expression in a human claudin-low breast cancer cell line MDA-MB-231. MDA-MB-231 cells were stably transfected with a control vector (MDA-231EV) or the miR-200c/141 cluster (MDA-231c141). Injection of MDA-231c141 cells into the 4th mammary gland of NCG mice produced tumors that developed significantly slower than tumors produced by MDA-231EV cells. Spontaneous metastasis to the lungs was also significantly reduced in MDA-231c141 cells compared to MDA-231EV cells. RNA sequencing of MDA-231EV and MDA-231c141 tumors identified genes including MXRA8 as being downregulated in the MDA-231c141 tumors. MXRA8 was further investigated as elevated levels of MXRA8 were associated with reduced distant metastasis free survival in breast cancer patients. Quantitative RT-PCR and Western blotting confirmed that MXRA8 expression was significantly higher in mammary tumors induced by MDA-231EV cells compared to those induced by MDA-231c141 cells. In addition, MXRA8 protein was present at high levels in metastatic tumor cells found in the lungs. This is the first study to implicate MXRA8 in human breast cancer, and our data suggests that miR-200s inhibit growth and metastasis of claudin-low mammary tumor cells in vivo through downregulating MXRA8 expression.

Nuclear microRNAs release paused Pol II via the DDX21-CDK9 complex

RNA activation (RNAa) is an uncharacterized mechanism of transcriptional activation mediated by small RNAs, such as microRNAs (miRNAs). A critical issue in RNAa research is that it is difficult to distinguish between changes in gene expression caused indirectly by post-transcriptional regulation and direct induction of gene expression by RNAa. Therefore, in this study, we seek to identify a key factor involved in RNAa, using the induction of ZMYND10 by miR-34a as a system to evaluate RNAa. We identify the positive transcription elongation factors CDK9 and DDX21, which form a complex with nuclear AGO and TNRC6A, as important transcriptional activators of RNAa. In addition, we find that inhibition of DDX21 suppresses RNAa by miR-34a and other miRNAs without inhibiting post-transcriptional regulation. Our findings reveal a strong connection between RNAa and release of paused Pol II, facilitating RNAa research by making it possible to separately analyze post-transcriptional regulation and RNAa.

Long non-coding RNA LINC00680 functions as a ceRNA to promote esophageal squamous cell carcinoma progression through the miR-423-5p/PAK6 axis

Background

Esophageal squamous cell carcinoma (ESCC) is a common invasive malignancy worldwide with poor clinical outcomes. Increasing amount of long non-coding RNAs (lncRNAs) have been reported to be involved in cancer development. However, lncRNAs that are functional in ESCC and the underlying molecular mechanisms remain largely unknown.

Methods

Transcriptomic analysis was performed to identify dysregulated lncRNAs in ESCC tissue samples. The high expression of LINC00680 in ESCC was validated by RT-qPCR, and the oncogenic functions of LINC00680 was investigated by cell proliferation, colony formation, migration and invasion assays in ESCC cells in vitro and xenografts derived from ESCC cells in mice. RNA-seq, competitive endogenous RNA (ceRNA) network analysis, and luciferase reporter assays were carried out to identify LINC00680 target genes and the microRNAs (miRNAs) bound to LINC00680. Antisense oligonucleotides (ASOs) were used for in vivo treatment.

Results

Transcriptome profiling revealed that a large number of lncRNAs was dysregulated in ESCC tissues. Notably, LINC00680 was highly expressed, and upregulation of LINC00680 was associated with large tumor size, advanced tumor stage, and poor prognosis. Functionally, knockdown of LINC00680 restrained ESCC cell proliferation, colony formation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, LINC00680 was found to act as a ceRNA by sponging miR-423-5p to regulate PAK6 (p21-activated kinase 6) expression in ESCC cells. The cell viability and motility inhibition induced by LINC00680 knockdown was significantly reversed upon PAK6 restoration and miR-423-5p inhibition. Furthermore, ASO targeting LINC00680 substantially suppressed ESCC both in vitro and in vivo.

Conclusions

An oncogenic lncRNA, LINC00680, was identified in ESCC, which functions as a ceRNA by sponging miR-423-5p to promote PAK6 expression and ESCC. LINC00680/miR-423-5p/PAK6 axis may serve as promising diagnostic and prognostic biomarkers and therapeutic targets for ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01539-3.

Competing endogenous RNA network mediated by circ_3205 in SARS-CoV-2 infected cells

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new member of the Betacoronaviridae family, responsible for the recent pandemic outbreak of COVID-19. To start exploring the molecular events that follow host cell infection, we queried VirusCircBase and identified a circular RNA (circRNA) predicted to be synthesized by SARS-CoV-2, circ_3205, which we used to probe: (i) a training cohort comprised of two pools of cells from three nasopharyngeal swabs of SARS-CoV-2 infected (positive) or uninfected (negative, UCs) individuals; (ii) a validation cohort made up of 12 positive and 3 negative samples. The expression of circRNAs, miRNAs and miRNA targets was assayed through real-time PCR. CircRNA-miRNA interactions were predicted by TarpMiR, Analysis of Common Targets for circular RNAs (ACT), and STarMir tools. Enrichment of the biological processes and the list of predicted miRNA targets were retrieved from DIANA miRPath v3.0. Our results showed that the predicted SARS-CoV-2 circ_3205 was expressed only in positive samples and its amount positively correlated with that of SARS-CoV-2 Spike (S) mRNA and the viral load (r values = 0.80952 and 0.84867, Spearman's correlation test, respectively). Human (hsa) miR-298 was predicted to interact with circ_3205 by all three predictive tools. KCNMB4 and PRKCE were predicted as hsa-miR-298 targets. Interestingly, the function of both is correlated with blood coagulation and immune response. KCNMB4 and PRKCE mRNAs were upregulated in positive samples as compared to UCs (6 and 8.1-fold, p values = 0.049 and 0.02, Student's t test, respectively) and their expression positively correlated with that of circ_3205 (r values = 0.6 and 0.25, Spearman's correlation test, respectively). We propose that our results convincingly suggest that circ_3205 is a circRNA synthesized by SARS-CoV-2 upon host cell infection and that it may behave as a competitive endogenous RNA (ceRNA), sponging hsa-miR-298 and contributing to the upregulation of KCNMB4 and PRKCE mRNAs.

microRNA Profile Associated with Positive Lymph Node Metastasis in Early-Stage Cervical Cancer

Lymph node metastasis (LNM) is an important prognostic factor in cervical cancer (CC). In early stages, the risk of LNM is approximately 3.7 to 21.7%, and the 5-year overall survival decreases from 80% to 53% when metastatic disease is identified in the lymph nodes. Few reports have analyzed the relationship between miRNA expression and the presence of LNM. The aim of this study was to identify a subset of miRNAs related to LNM in early-stage CC patients. Formalin-fixed paraffin-embedded tissue blocks were collected from patients with early-stage CC treated by radical hysterectomy with lymphadenectomy. We analyzed samples from two groups of patients—one group with LNM and the other without LNM. Global miRNA expression was identified by microarray analysis, and cluster analysis was used to determine a subset of miRNAs associated with LNM. Microarray expression profiling identified a subset of 36 differentially expressed miRNAs in the two groups (fold change (FC) ≥ 1.5 and p < 0.01). We validated the expression of seven miRNAs; miR-487b, miR-29b-2-5p, and miR-195 were underexpressed, and miR-92b-5p, miR-483-5p, miR-4534, and miR-548ac were overexpressed according to the microarray experiments. This signature exhibited prognostic value for identifying early-stage CC patients with LNM. These findings may help detect LNM that cannot be observed in imaging studies.

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.

Transgenic overexpression of the miR-200b/200a/429 cluster inhibits mammary tumor initiation

The miR-200 family consists of five members expressed as two clusters: miR-200c/141 cluster and miR-200b/200a/429 cluster. In the mammary gland, miR-200s maintain epithelial identity by decreasing the expression of mesenchymal markers leading to high expression of epithelial markers. While the loss of miR-200s is associated with breast cancer growth and metastasis the impact of miR-200 expression on mammary tumor initiation has not been investigated. Using mammary specific expression of the miR-200b/200a/429 cluster in transgenic mice, we found that elevated expression miR-200s could almost completely prevent mammary tumor development. Only 1 of 16 MTB-IGFIRba429 transgenic mice (expressing both the IGF-IR and miR-200b/200a/429 transgenes) developed a mammary tumor while 100% of MTB-IGFIR transgenic mice (expressing only the IGF-IR transgene) developed mammary tumors. RNA sequencing, qRT-PCR, and immunohistochemistry of mammary tissue from 55-day old mice found Spp1, Saa1, and Saa2 to be elevated in mammary tumors and inhibited by miR-200b/200a/429 overexpression. This study suggests that miR-200s could be used as a preventative strategy to protect women from developing breast cancer. One concern with this approach is the potential negative impact miR-200 overexpression may have on mammary function. However, transgenic overexpression of miR-200s, on their own, did not significantly impact mammary ductal development indicating the miR-200 overexpression should not significantly impact mammary function. Thus, this study provides the initial foundation for using miR-200s for breast cancer prevention and additional studies should be performed to identify strategies for increasing mammary miR-200 expression and determine whether miR-200s can prevent mammary tumor initiation by other genetic alterations.

Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network

Predicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framework based on heterogeneous network representation, MDCNN (Metapath-Based Deep Convolutional Neural Network), to predict the associations between miRNAs and target genes. MDCNN samples the paths between the node pairs in the form of meta-path based on the heterogeneous information network (HIN) about miRNAs and target genes. Then the node feature and the path feature which is learned by the Deep Convolutional Neural Network (DCNN) are spliced together as the representation of the miRNA-target gene, to predict the miRNA-target gene interactions. The experiment results indicate that the performance of MDCNN outperforms other methods in multiple validation metrics by fivefold cross validation. We set an ablation study to identify the necessity of miRNA similarity and target gene similarity for improving the prediction ability of MDCNN. The case studies on hsa-miR-26b-5p and CDKN1A further demonstrates that MDCNN can successfully predict potential miRNA-target gene interactions.

LRPPRC regulates redox homeostasis via the circANKHD1/FOXM1 axis to enhance bladder urothelial carcinoma tumorigenesis

Reactive oxygen species (ROS) which are continuously generated mainly by mitochondria, have been proved to play an important role in the stress signaling of cancer cells. Moreover, pentatricopeptide repeat (PPR) proteins have been suggested to take part in mitochondrial metabolism. However, the mechanisms integrating the actions of these distinct networks in urothelial carcinoma of the bladder (UCB) pathogenesis are elusive. In this study, we found that leucine rich pentatricopeptide repeat containing (LRPPRC) was frequently upregulated in UCB and that it was an independent prognostic factor in UCB. We further revealed that LRPPRC promoted UCB tumorigenesis by regulating the intracellular ROS homeostasis. Mechanistically, LRPPRC modulates ROS balance and protects UCB cells from oxidative stress via mt-mRNA metabolism and the circANKHD1/FOXM1 axis. In addition, the SRA stem-loop interacting RNA binding protein (SLIRP) directly interacted with LRPPRC to protect it from ubiquitination and proteasomal degradation. Notably, we showed that LRPPRC modulated the tumorigenesis of UCB cells in a circANKHD1-FOXM1-dependent manner. In conclusion, LRPPRC exerts critical roles in regulating UCB redox homeostasis and tumorigenesis, and is a prognostic factor for UCB; suggesting that LRPPRC may serve as an exploitable therapeutic target in UCB.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.