Recent development of bioinformatics tools for microRNA target prediction

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

microRNA858 represses the transcription factor gene SbMYB47 and regulates flavonoid biosynthesis in Scutellaria baicalensis

MicroRNAs (miRNAs) are noncoding endogenous single-stranded RNAs that regulate target gene expression by reducing their transcription and translation. Several miRNAs in plants function in secondary metabolism. The dried root of Scutellaria baicalensis Georgi is a traditional Chinese medicine that contains flavonoids (baicalin, wogonoside, and baicalein) as its main active ingredients. Although the S. baicalensis genome sequence has been published, information regarding its miRNAs is lacking. In this study, 12 small RNA libraries of different S. baicalensis tissues were compiled, including roots, stems, leaves, and flowers. A total of 129 miRNAs were identified, including 99 miRNAs from 27 miRNA families and 30 predicted miRNAs. Furthermore, 46 reliable target genes of 15 miRNA families were revealed using psRNATarget and confirmed by degradome sequencing. It was speculated that the microRNA858 (miR858)-SbMYB47 module might be involved in flavonoid biosynthesis. Transient assays in Nicotiana benthamiana leaves indicated that miR858 targets SbMYB47 and suppresses its expression. Artificial miRNA-mediated knockdown of miR858 and overexpression of SbMYB47 significantly increased the flavonoid content in S. baicalensis hairy roots, while SbMYB47 knockdown inhibited flavonoid accumulation. Yeast one-hybrid and dual-luciferase assays indicated that SbMYB47 directly binds to and activates the S. baicalensis phenylalanine ammonia-lyase 3 (SbPAL-3) and flavone synthase II (SbFNSⅡ-2) promoters. Our findings reveal the link between the miR858-SbMYB47 module and flavonoid biosynthesis, providing a potential strategy for the production of flavonoids with important pharmacological activities through metabolic engineering.

Pivotal Role of miRNA-lncRNA Interactions in Human Diseases

New technologies have shown that most of the genome comprises transcripts that cannot code for proteins and are referred to as non-coding RNAs (ncRNAs). Some ncRNAs, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are of substantial interest because of their critical function in controlling genes and numerous biological activities. The expression levels and function of miRNAs and lncRNAs are rigorously monitored throughout developmental processes and the maintenance of physiological homeostasis. Due to their critical roles, any dysregulation or changes in their expression can significantly influence the pathogenesis of various human diseases. The interactions between miRNAs and lncRNAs have been found to influence gene expression in various ways. These interactions significantly influence the understanding of disease etiology, cellular processes, and potential therapeutic targets. Different experimental and in silico methods can be used to investigate miRNA-lncRNA interactions. By aiding the elucidation of miRNA-lncRNA interactions and deepening the understanding of post-transcriptional gene regulation, researchers can open a new window for designing hypotheses, conducting experiments, and discovering methods for diagnosing and treating complex human diseases. This review briefly summarizes miRNA and lncRNA functions, discusses their interaction mechanisms, and examines the experimental and computational methods used to study these interactions. Additionally, we highlight significant studies on lncRNA and miRNA interactions in various diseases from 2000 to 2024, using the academic research databases such as PubMed, Google Scholar, ScienceDirect, and Scopus.

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

One-step reverse transcriptase-free miRNA detection system and its application for detection of gastrointestinal cancers

MicroRNAs (miRNAs) play pivotal roles in gene regulation and their dysregulation is implicated in various diseases, including cancer. Current methods for miRNA analysis often involve complex procedures and high costs, limiting their clinical utility. Therefore, there is a critical need for the development of simpler and more cost-effective miRNA detection techniques to enable early disease diagnosis. In this study, we introduce a novel one-enzyme for miRNA one-step detection method using Taq DNA polymerase, termed OSMOS-qPCR. We optimized the PCR buffer, PCR program, Taq DNA Polymerase concentrations and reverse PCR primer concentrations, resulted in a wide linear range from 100 fM to 0.001 fM (R2 > 0.98 for each miRNA), the detection limit for OSMOS-qPCR was 0.0025 fM. Furthermore, OSMOS-qPCR demonstrates excellent specificity to differentiation of less than 0.1 % nonspecific signal. Finally, we demonstrated the robust amplification efficiency, enabling the detection of trace amounts of cell-free miRNA in serum samples, and the excellent discrimination ability between gastrointestinal cancers and control subjects (AUC value = 1.0) if combined two miRNAs. The development of OSMOS-qPCR offering a simpler, cost-effective, and efficient detection method, has the potential to be non-invasive strategy for early detection of gastrointestinal cancers.

Plant-Derived Vesicle-like Nanoparticles: The Next-Generation Drug Delivery Nanoplatforms

A wide variety of natural bioactive compounds derived from plants have demonstrated significant clinical relevance in the treatment of various diseases such as cancer, chronic disease, and inflammation. An increasing number of studies have surfaced that give credence to the potential of plant-derived vesicle-like nanoparticles (PDVLNs) as compelling candidates for a drug delivery system (DDS). PDVLNs are cost-effective production, non-toxicity and non-immunogenicity and fascinating bi-ocompatibility. In this review, we attempt to comprehensively review and consolidate the position of PDVLNs as next-generation drug delivery nanoplatforms. We aim to give a quick glance to readers of the current developments of PDVLNs, including their biogenesis, characteristic features, composition, administration routes, advantages, and application. Further, we discuss the advantages and limitations of PDVLNs. We expect that the role of PDVLNs in drug delivery will be significantly enhanced, thus positioning them as the next generation of therapeutic modalities in the foreseeable future.

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.

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

Meta-analysis of miR-34 target mRNAs using an integrative online application

Members of the microRNA-34/miR-34 family are induced by the p53 tumor suppressor and themselves possess tumor suppressive properties, as they inhibit the translation of mRNAs that encode proteins involved in processes, such as proliferation, migration, invasion, and metastasis. Here we performed a comprehensive integrative meta-analysis of multiple computational and experimental miR-34 related datasets and developed tools to identify and characterize novel miR-34 targets. A miR-34 target probability score was generated for every mRNA to estimate the likelihood of representing a miR-34 target. Experimentally validated miR-34 targets were strongly enriched among mRNAs with the highest scores providing a proof of principle for our analysis. We integrated the results from the meta-analysis in a user-friendly METAmiR34TARGET website (www.metamir34target.com/) that allows to graphically represent the meta-analysis results for every mRNA. Moreover, the website harbors a screen function, which allows to select multiple miR-34-related criteria/analyses and cut-off values to facilitate the stringent and comprehensive prediction of relevant miR-34 targets in expression data obtained from cell lines and tumors/tissues. Furthermore, information on more than 200 miR-34 target mRNAs, that have been experimentally validated so far, has been integrated in the web-tool. The website and datasets provided here should facilitate further investigation into the mechanisms of tumor suppression by the p53/miR-34 connection and identification of potential cancer drug targets.

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Decoding the protein-ligand interactions using parallel graph neural networks

Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.

Hyperoxia Induced Bronchopulmonary Dysplasia-Like Inflammation via miR34a-TNIP2-IL-1β Pathway

Lung injury induced by oxygen is a key contributor to the pathogenesis of preterm infant bronchopulmonary dysplasia (BPD). To date, there are comprehensive therapeutic strategy for this disease, but the underlying mechanism is still in progress. By using lentivirus, we constructed microRNA34a (miR34a)-overexpressing or knockdown A549 cell lines, and exposure to hyperoxia to mimic oxygen induce lung injury. In this study, we investigated 4 proinflammatory cytokines, interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), angiopoietin-1 (Ang-1), and Cyclooxygenase-2 (COX-2) in the secreted sputum of infants who received mechanical ventilation, and found that IL-1β was substantially elevated in the first week after oxygen therapy and with no significant decrease until the fourth week, while TNF-α, Ang-1, and COX-2 were increased in the first week but decreased quickly in the following weeks. In addition, in vitro assay revealed that hyperoxia significantly increased the expression of miR-34a, which positively regulated the proinflammatory cytokine IL-1β in a time- and concentration-dependent manner in A549 cells. Overexpressing or knockdown miR34 would exacerbate or inhibit production of IL-1β and its upstream NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome signaling pathway. Mechanically, it's found that TNFAIP3 interacting protein 2 (TNIP2), an inhibitor of nuclear factor κB (NF-κB), is a direct target of miR34a, negatively regulated activation of NLRP3 inflammasome and the production of IL-1β. Overexpressing TNIP2 ameliorated hyperoxia-induced production of IL-1β and cell apoptosis. Our findings suggest that TNIP2 may be a potential clinical marker in the diagnosis of BPD.