miRecords: an integrated resource for microRNA-target interactions

Expanding the immune-related targetome of miR-155-5p by integrating time-resolved RNA patterns into miRNA target prediction

The lack of a sufficient number of validated miRNA targets severely hampers the understanding of their biological function. Even for the well-studied miR-155-5p, there are only 239 experimentally validated targets out of 42,554 predicted targets. For a more complete assessment of the immune-related miR-155 targetome, we used an inverse correlation of time-resolved mRNA profiles and miR-155-5p expression of early CD4+ T cell activation to predict immune-related target genes. Using a high-throughput miRNA interaction reporter (HiTmIR) assay we examined 90 target genes and confirmed 80 genes as direct targets of miR-155-5p. Our study increases the current number of verified miR-155-5p targets approximately threefold and exemplifies a method for verifying miRNA targetomes as a prerequisite for the analysis of miRNA-regulated cellular networks.

Identification of puberty related miRNAs in the hypothalamus of female mice

BACKGROUND AND AIMS

Puberty is a crucial developmental stage marked by the transition from childhood to adulthood, organized by complex hormonal signaling within the neuroendocrine system. The hypothalamus, a central region in this system, regulates pubertal functions through the hypothalamic-pituitary-gonadal (HPG) axis. Gonadotropin-releasing hormone (GnRH) neurons, essential in puberty control, release GnRH in a pulsatile manner, initiating the production of sex hormones. Major influence in pubertal timing has been attributed to genetic predisposition, environmental factors, and nutritional status. MicroRNAs (miRNAs), small non-coding RNA molecules, have emerged as key regulators in various cellular processes by either repressing genes or activating them by inhibiting their repressors. The present study aims to investigate the involvement of miRNAs in the control of puberty.

METHODS

Small RNA sequencing was used to identify and compare the total population of miRNAs in the hypothalamus of female mice before, during and after puberty. Bioinformatic analysis was applied to analyse the expression profile of miRNAs with altered levels followed by pathway enrichment analysis.

RESULTS

Expression levels of several miRNAs were found up- or down-regulated from pre-pubertal to pubertal stage. Furthermore, monitoring the levels of these miRNAs at the post-pubertal stage revealed four expression patterns, in which pathway analysis displayed the associations of these miRNAs with developmental processes, cell cycle regulation, metabolic biosynthesis and epigenetic regulation.

CONCLUSION

The findings of the present study improve our understanding of the molecular pathways underlying puberty and stress the significance of miRNAs in fine-tuning gene expression within the hypothalamus during this critical developmental stage.

CircSPG21 ameliorates oxidative stress-induced senescence in nucleus pulposus-derived mesenchymal stem cells and mitigates intervertebral disc degeneration through the miR-217/SIRT1 axis and mitophagy

BACKGROUND

The microenvironment of intervertebral disc degeneration (IVDD) is characterized by oxidative stress, leading to the senescence of nucleus pulposus-derived mesenchymal stem cells (NPMSCs). The purpose of this study was to investigate the competitive endogenous RNA mechanism involved in the senescence of NPMSCs induced by tert-butyl hydroperoxide (TBHP).

METHODS

Bioinformatic analysis identified differentially expressed circRNAs. Interactions among circSPG21, miR-217, and the NAD-dependent protein deacetylase sirtuin-1 (SIRT1) were validated through dual-luciferase assays, RNA fluorescence in situ hybridization and RNA immune precipitation. β-Gal staining, EdU staining, Western blotting, JC-1 assays, cell cycle analysis, and quantitative reverse transcription PCR (RT‒qPCR) were used to examine the functions of these molecules in TBHP-induced senescent NPMSCs. The therapeutic effects of circSPG21 were evaluated in a rat IVDD model.

RESULTS

CircSPG21 expression was significantly decreased in both human and rat IVDD tissues, whereas miR-217 was upregulated and SIRT1 was downregulated. Overexpression of circSPG21 alleviated NPMSC senescence by reducing P21 and P53 levels and restoring mitophagy through Parkin. The protective effects of circSPG21 were mediated through the miR-217/SIRT1 axis, as SIRT1 knockdown attenuated these benefits. CircSPG21 also ameliorated disc degeneration in the IVDD rat model, highlighting its potential as a therapeutic target.

CONCLUSION

CircSPG21 reduces oxidative stress-induced NPMSC senescence through the miR-217/SIRT1 axis and mitophagy, providing new insights into IVDD and identifying circSPG21 as a potential therapeutic target for disc degeneration.

Predicting microRNA target genes using pan-cancer correlation patterns

The interaction relationship between miRNAs and genes is important as miRNAs play a crucial role in regulating gene expression. In the literature, several databases have been constructed to curate known miRNA target genes, which are valuable resources but likely only represent a small fraction of all miRNA-gene interactions. In this study, we constructed machine learning models to predict miRNA target genes that have not been previously reported. Using the miRNA and gene expression data from TCGA, we performed a correlation analysis between all miRNAs and all genes across multiple cancer types. The correlations served as features to describe each miRNA-gene pair. Using the existing databases of curated miRNA targets, we labeled the miRNA-gene pairs, and trained machine learning models to predict novel miRNA-gene interactions. For the miRNA-gene pairs that were consistently predicted across the models, we called them significant miRNA-gene pairs. Using held-out miRNA target databases and a literature survey, we validated 5.5% of the predicted significant miRNA-gene pairs. The remaining predicted miRNA-gene pairs could serve as hypotheses for experimental validation. Additionally, we explored several additional datasets that provided gene expression data before and after a specific miRNA perturbation and observed consistency between the correlation direction of predicted miRNA-gene pairs and their regulatory patterns. Together, this analysis revealed a novel framework for uncovering previously unidentified miRNA-gene relationships, enhancing the collective comprehension of miRNA-mediated gene regulation.

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning

Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions. The model allows for the interpretation of the respective importance of multi-omic and higher-order structural features, achieved state-of-the-art performance in the prediction of cancer genes across biological networks (including networks of interactions between miRNA and proteins, transcription factors and proteins, and transcription factors and miRNA) in pan-cancer and cancer-specific scenarios, and predicted 57 cancer-gene candidates (including three genes that had not been identified by other models) among 4,729 unlabelled genes across 8 pan-cancer datasets. The model's interpretability and generalization may facilitate the understanding of gene-related regulatory mechanisms and the discovery of new cancer genes.

Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs

MicroRNAs (miRNAs) are a class of small non-coding RNAs (sncRNAs) of length 21-25 nucleotides. These sncRNAs hybridize to repress their target genes and inhibit protein translation, thereby controlling regulatory functions in the cell. Integration of time-series matched small and RNA-seq data enables investigation of dynamic gene regulation through miRNAs during development or in response to a stimulus, such as stress. Here we summarize analysis strategies, such as probabilistic and regression-based models, that take advantage of the temporal dimension to investigate the complexity of miRNA regulation.

miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue

Acute kidney injury (AKI) is an important contributor to the development of chronic kidney disease (CKD). There is a need to understand molecular mediators that drive recovery and progression to CKD. In particular, the regulatory role of miRNAs in AKI is poorly understood. Herein, miRNA and mRNA sequencing were performed on biobanked human kidney tissues obtained during the routine care of subjects with a diagnosis of AKI, minimal change disease, or on nephrectomy tissue with no known kidney disease. mRNA analysis revealed that nephrectomy tissues exhibited an injury signature similar to that of AKI which was not identified in minimal change disease samples. The transcriptomic signature of human AKI was enriched in pathways involved in cell adhesion, epithelial-to-mesenchymal transition, and cell cycle arrest (eg, CDH6, ITGB6, CDKN1A). In AKI, up-regulation of miR-146a, miR-155, miR-142, and miR-122 was associated with pathways involved in immune cell recruitment, inflammation, and epithelial-to-mesenchymal transition. miR-122 and miR-146 were associated with down-regulation of DDR2 and IGFBP6, which are genes involved in the recovery and progression of kidney disease. These data provide integrated miRNA signatures that complement mRNA and other epigenetic data available in kidney atlases.

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.

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.

Molecular Profiling of A549 Cell-Derived Exosomes: Proteomic, miRNA, and Interactome Analysis for Identifying Potential Key Regulators in Lung Cancer

BACKGROUND/OBJECTIVES

Exosomes, nano-sized extracellular vesicles released by all cells, play a key role in intercellular communication and carry tumorigenic properties that impact surrounding or distant cells. The complexity of the exosomal molecular interactome and its effects on recipient cells still remain unclear. This study aims to decipher the molecular profile and interactome of lung adenocarcinoma A549 cell-derived exosomes using multi-omics and bioinformatics approaches.

METHODS

We performed comprehensive morphological and physicochemical characterization of exosomes isolated from cell culture supernatant of A549 cells in vitro, using DLS, cryo-TEM, Western blot, and flow cytometry. Proteomic and miRNA high-throughput profiling, coupled with bioinformatics network analysis, were applied to elucidate the exosome molecular cargo. A comparative miRNA analysis was also conducted with exosomes derived from normal lung fibroblast MRC-5 cells.

RESULTS

Exosomes exhibited an average size of ~40 nm and disk-shaped lipid bilayer structures, with tetraspanins CD9 and CD63 validated as exosomal markers. Proteomic analysis identified 68 proteins, primarily linked to the extracellular matrix organization and metabolic processes. miRNA sequencing revealed 72 miRNAs, notably hsa-miR-619-5p, hsa-miR-122-5p, hsa-miR-9901, hsa-miR-7704, and hsa-miR-151a-3p, which are involved in regulating metabolic processes, gene expression, and tumorigenic pathways. Th integration of proteomic and miRNA data through a proteogenomics approach identified dually affected genes including ERBB2, CD44, and APOE, impacted by both exosomal miRNA targeting and protein interactions through synergistic or antagonistic interactions. Differential analysis revealed a distinct miRNA profile in A549 exosomes, associated with cancer-related biological processes, compared to MRC-5 exosomes; notably, hsa-miR-619-5p emerged as a promising candidate for future clinical biomarker studies. The network analysis also revealed genes targeted by multiple upregulated tumor-associated miRNAs in potential exosome-recipient cells.

CONCLUSIONS

This integrative study provides insights into the molecular interactome of lung adenocarcinoma A549 cell-derived exosomes, providing a foundation for future research on exosomal cargo and its role in tumor cell communication, growth, and progression.

AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method

MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) networks to predict miRNA-gene associations. By enhancing feature extraction through autoencoders, AEmiGAP captures intricate, latent relationships between miRNAs and genes with unprecedented accuracy, outperforming all existing models in miRNA-gene association prediction. A thoroughly curated dataset of positive and negative miRNA-gene pairs was generated using distance-based filtering methods, significantly improving the model's AUC and overall predictive accuracy. Additionally, this study proposes two case studies to highlight AEmiGAP's application: first, a top 30 list of miRNA-gene pairs with the highest predicted association scores among previously unknown pairs, and second, a list of the top 10 miRNAs strongly associated with each of five key oncogenes. These findings establish AEmiGAP as a new benchmark in miRNA-gene association prediction, with considerable potential to advance both cancer research and precision medicine.

microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites

microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs. The multilayer sequence-based design enables the prediction of both host and virus-encoded miRNA interactions, providing for the first time up to 67% of direct genuine Epstein-Barr virus- and Kaposi's sarcoma-associated herpesvirus-derived miRNA-target pairs corresponding to one out of four binding events of virus-encoded miRNAs. microT-CNN fills the existing gap of the miRNA-target prediction by providing functional targets beyond the canonical sites, including 3' compensatory miRNA pairings, prompting 1.4-fold more validated miRNA binding events compared to other implementations and shedding light on previously unexplored facets of the miRNA interactome.

Circulating microRNAs associated with bronchodilator response in childhood asthma

BACKGROUND

Bronchodilator response (BDR) is a measure of improvement in airway smooth muscle tone, inhibition of liquid accumulation and mucus section into the lumen in response to short-acting beta-2 agonists that varies among asthmatic patients. MicroRNAs (miRNAs) are well-known post-translational regulators. Identifying miRNAs associated with BDR could lead to a better understanding of the underlying complex pathophysiology.

OBJECTIVE

The purpose of this study is to identify circulating miRNAs associated with bronchodilator response in asthma and decipher possible mechanism of bronchodilator response variation.

METHODS

We used available small RNA sequencing on blood serum from 1,134 asthmatic children aged 6 to 14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS). We filtered the participants into the highest and lowest bronchodilator response (BDR) quartiles and used DeSeq2 to identify miRNAs with differential expression (DE) in high (N = 277) vs. low (N = 278) BDR group. Replication was carried out in the Leukotriene modifier Or Corticosteroids or Corticosteroid-Salmeterol trial (LOCCS), an adult asthma cohort. The putative target genes of DE miRNAs were identified, and pathway enrichment analysis was performed.

RESULTS

We identified 10 down-regulated miRNAs having odds ratios (OR) between 0.37 and 0.76 for a doubling of miRNA counts and one up-regulated miRNA (OR = 2.26) between high and low BDR group. These were assessed for replication in the LOCCS cohort, where two miRNAs (miR-200b-3p and miR-1246) were associated. Further, functional annotation of 11 DE miRNAs were performed as well as of two replicated miRs. Target genes of these miRs were enriched in regulation of cholesterol biosynthesis by SREBPs, ESR-mediated signaling, G1/S transition, RHO GTPase cycle, and signaling by TGFB family pathways.

CONCLUSION

MiRNAs miR-1246 and miR-200b-3p are associated with both childhood and adult asthma BDR. Our findings add to the growing body of evidence that miRNAs play a significant role in the difference of asthma treatment response among patients as it points to genomic regulatory machinery underlying difference in bronchodilator response among patients.

TRIAL REGISTRATION

LOCCS cohort [ClinicalTrials.gov number NCT00156819, Registration date 20050912], GACRS cohort [ClinicalTrials.gov number NCT00021840].

Small Extracellular Vesicle-Associated MiRNAs in Polarized Retinal Pigmented Epithelium

Purpose

Oxidative stress in the retinal pigmented epithelium (RPE) has been implicated in age-related macular degeneration by impacting endocytic trafficking, including the formation, content, and secretion of extracellular vesicles (EVs). Using our model of polarized primary porcine RPE (pRPE) cells under chronic subtoxic oxidative stress, we tested the hypothesis that RPE miRNAs packaged into EVs are secreted in a polarized manner and contribute to maintaining RPE homeostasis.

Methods

Small EVs (sEVs) enriched for exosomes were isolated from apical and basal conditioned media from pRPE cells grown for up to four weeks with or without low concentrations of hydrogen peroxide using two sEV isolation methods, leading to eight experimental groups. The sEV miRNA expression was profiled using miRNA-Seq with Illumina MiSeq, followed by quality control and bioinformatics analysis for differential expression using the R computing environment. Expression of selected miRNAs were validated using qRT-PCR.

Results

We identified miRNA content differences carried by sEVs isolated using two ultracentrifugation-based methods. Regardless of the sEV isolation method, miR-182 and miR-183 were enriched in the cargo of apically secreted sEVs, and miR-122 in the cargo of basally secreted sEVs from RPE cells during normal homeostatic conditions. After oxidative stress, miR-183 levels were significantly decreased in the cargo of apically released sEVs from stressed RPE cells.

Conclusions

We curated RPE sEV miRNA datasets based on cell polarity and oxidative stress. Unbiased miRNA analysis identified differences based on polarity, stress, and sEV isolation methods. These findings suggest that miRNAs in sEVs may contribute to RPE homeostasis and function in a polarized manner.

Picalm, a novel regulator of GLUT4-trafficking in adipose tissue

OBJECTIVE

Picalm (phosphatidylinositol-binding clathrin assembly protein), a ubiquitously expressed clathrin-adapter protein, is a well-known susceptibility gene for Alzheimer's disease, but its role in white adipose tissue (WAT) function has not yet been studied. Transcriptome analysis revealed differential expression of Picalm in WAT of diabetes-prone and diabetes-resistant mice, hence we aimed to investigate the potential link between Picalm expression and glucose homeostasis, obesity-related metabolic phenotypes, and its specific role in insulin-regulated GLUT4 trafficking in adipocytes.

METHODS

Picalm expression and epigenetic regulation by microRNAs (miRNAs) and DNA methylation were analyzed in WAT of diabetes-resistant (DR) and diabetes-prone (DP) female New Zealand Obese (NZO) mice and in male NZO after time-restricted feeding (TRF) and alternate-day fasting (ADF). PICALM expression in human WAT was evaluated in a cross-sectional cohort and assessed before and after weight loss induced by bariatric surgery. siRNA-mediated knockdown of Picalm in 3T3-L1-cells was performed to elucidate functional outcomes on GLUT4-translocation as well as insulin signaling and adipogenesis.

RESULTS

Picalm expression in WAT was significantly lower in DR compared to DP female mice, as well as in insulin-sensitive vs. resistant NZO males, and was also reduced in NZO males following TRF and ADF. Four miRNAs (let-7c, miR-30c, miR-335, miR-344) were identified as potential mediators of diabetes susceptibility-related differences in Picalm expression, while 11 miRNAs (including miR-23a, miR-29b, and miR-101a) were implicated in TRF and ADF effects. Human PICALM expression in adipose tissue was lower in individuals without obesity vs. with obesity and associated with weight-loss outcomes post-bariatric surgery. siRNA-mediated knockdown of Picalm in mature 3T3-L1-adipocytes resulted in amplified insulin-stimulated translocation of the endogenous glucose transporter GLUT4 to the plasma membrane and increased phosphorylation of Akt and Tbc1d4. Moreover, depleting Picalm before and during 3T3-L1 differentiation significantly suppressed adipogenesis, suggesting that Picalm may have distinct roles in the biology of pre- and mature adipocytes.

CONCLUSIONS

Picalm is a novel regulator of GLUT4-translocation in WAT, with its expression modulated by both genetic predisposition to diabetes and dietary interventions. These findings suggest a potential role for Picalm in improving glucose homeostasis and highlight its relevance as a therapeutic target for metabolic disorders.

Beyond basic characterization and omics: Immunomodulatory roles of platelet-derived extracellular vesicles unveiled by functional testing

Renowned for their role in haemostasis and thrombosis, platelets are also increasingly recognized for their contribution in innate immunity, immunothrombosis and inflammatory diseases. Platelets express a wide range of receptors, which allows them to reach a variety of activation endpoints and grants them immunomodulatory functions. Activated platelets release extracellular vesicles (PEVs), whose formation and molecular cargo has been shown to depend on receptor-mediated activation and environmental cues. This study compared the immunomodulatory profiles of PEVs generated via activation of platelets by different receptors, glycoprotein VI, C-type lectin-like receptor 2 and combining all thrombin-collagen receptors. Functional assays in vivo in zebrafish and in vitro in human macrophages highlighted distinct homing and secretory responses triggered by the PEVs. In contrast, omics analyses of protein and miRNA cargo combined with physicochemical particle characterization found only subtle differences between the activated PEV types, which were insufficient to predict their different immunomodulatory functions. In contrast, constitutively released PEVs, formed in the absence of an exogenous activator, displayed a distinct immunomodulatory profile from the receptor-induced PEVs. Our findings underscore that PEVs are tunable through receptor-mediated activation. To truly comprehend their role(s) in mediating platelet functions among immune cells, conducting functional assays is imperative.

Deciphering regulatory patterns in a mouse model of hyperoxia-induced acute lung injury

Background

Oxygen therapy plays a pivotal role in treating critically ill patients in the intensive care unit (ICU). However, excessive oxygen concentrations can precipitate hyperoxia, leading to damage in multiple organs, with a notable effect on the lungs. Hyperoxia condition may lead to hyperoxia-induced acute lung injury (HALI), deemed as a milder form of acute respiratory distress syndrome (ARDS). Given its clinical importance and practical implications, there is a compelling need to investigate the underlying pathogenesis and comprehensively understand the regulatory mechanisms implicated in the development of HALI.

Results

In this study, we conducted a mouse model with HALI and performed regulatory mechanism analysis using RNA-seq on both HALI and control group. Comprehensive analysis revealed 727 genes of significant differential expression, including 248 long non-coding RNAs (lncRNAs). Also, alternative splicing events were identified from sequencing results. Notably, we observed up-regulation or abnormal alternative splicing of genes associated with immune response and ferroptosis under hyperoxia conditions. Utilizing weighted gene co-expression network analysis (WGCNA), we ascertained that genes involved in immune response formed a distinct cluster, showcasing an up-regulated pattern in hyperoxia, consistent with previous studies. Furthermore, a competing endogenous RNA (ceRNA) network was constructed, including 78 differentially expressed mRNAs and six differentially expressed lncRNAs, including H19. These findings uncover the intricate interplay of multiple transcriptional regulatory mechanisms specifically tailored to the pulmonary defense against HALI, substantiating the importance of these non-coding RNAs in this disease context.

Conclusions

Our results provide new insights into the potential mechanisms and underlying pathogenesis in the development of HALI at the post-transcriptional level. The findings of this study reveal potential regulatory interactions and biological roles of specific lncRNAs and genes, such as H19 and Sox9, encompassing driven gene expression patterns, alternative splicing events, and lncRNA-miRNA-mRNA ceRNA networks. These findings may pave the way for advancing therapeutic strategies and reducing the risk associated with oxygen treatment for patients.

Microparticle Mediated Delivery of Apelin Improves Heart Function in Post Myocardial Infarction Mice

BACKGROUND

Apelin is an endogenous prepropeptide that regulates cardiac homeostasis and various physiological processes. Intravenous injection has been shown to improve cardiac contractility in patients with heart failure. However, its short half-life prevents studying its impact on left ventricular remodeling in the long term. Here, we aim to study whether microparticle-mediated slow release of apelin improves heart function and left ventricular remodeling in mice with myocardial infarction (MI).

METHODS

A cardiac patch was fabricated by embedding apelin-containing microparticles in a fibrin gel scaffold. MI was induced via permanent ligation of the left anterior descending coronary artery in adult C57BL/6J mice followed by epicardial patch placement immediately after (acute MI) or 28 days (chronic MI) post-MI. Four groups were included in this study, namely sham, MI, MI plus empty microparticle-embedded patch treatment, and MI plus apelin-containing microparticle-embedded patch treatment. Cardiac function was assessed by transthoracic echocardiography. Cardiomyocyte morphology, apoptosis, and cardiac fibrosis were evaluated by histology. Cardioprotective pathways were determined by RNA sequencing, quantitative polymerase chain reaction, and Western blot.

RESULTS

The level of endogenous apelin was largely reduced in the first 7 days after MI induction and it was normalized by day 28. Apelin-13 encapsulated in poly(lactic-co-glycolic acid) microparticles displayed a sustained release pattern for up to 28 days. Treatment with apelin-containing microparticle-embedded patch inhibited cardiac hypertrophy and reduced scar size in both acute and chronic MI models, which is associated with improved cardiac function. Data from cellular and molecular analyses showed that apelin inhibits the activation and proliferation of cardiac fibroblasts by preventing transforming growth factor-β-mediated activation of Smad2/3 (supporessor of mothers against decapentaplegic 2/3) and downstream profibrotic gene expression.

CONCLUSIONS

Poly(lactic-co-glycolic acid) microparticles prolonged the apelin release time in the mouse hearts. Epicardial delivery of the apelin-containing microparticle-embedded patch protects mice from both acute and chronic MI-induced cardiac dysfunction, inhibits cardiac fibrosis, and improves left ventricular remodeling.

Guidelines for mitochondrial RNA analysis

Mitochondria are the energy-producing organelles of mammalian cells with critical involvement in metabolism and signaling. Studying their regulation in pathological conditions may lead to the discovery of novel drugs to treat, for instance, cardiovascular or neurological diseases, which affect high-energy-consuming cells such as cardiomyocytes, hepatocytes, or neurons. Mitochondria possess both protein-coding and noncoding RNAs, such as microRNAs, long noncoding RNAs, circular RNAs, and piwi-interacting RNAs, encoded by the mitochondria or the nuclear genome. Mitochondrial RNAs are involved in anterograde-retrograde communication between the nucleus and mitochondria and play an important role in physiological and pathological conditions. Despite accumulating evidence on the presence and biogenesis of mitochondrial RNAs, their study continues to pose significant challenges. Currently, there are no standardized protocols and guidelines to conduct deep functional characterization and expression profiling of mitochondrial RNAs. To overcome major obstacles in this emerging field, the EU-CardioRNA and AtheroNET COST Action networks summarize currently available techniques and emphasize critical points that may constitute sources of variability and explain discrepancies between published results. Standardized methods and adherence to guidelines to quantify and study mitochondrial RNAs in normal and disease states will improve research outputs, their reproducibility, and translation potential to clinical application.

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.

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Combined Analysis of Multi-Study miRNA and mRNA Expression Data Shows Overlap of Selected miRNAs Involved in West Nile Virus Infections

The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been analyzed by several studies under different conditions and with different outcomes. A systematic comparison is therefore necessary. Furthermore, additional information from mRNA target expression data has rarely been taken into account to understand miRNA expression profiles under WNV infections. We conducted a meta-analysis of publicly available miRNA expression data from multiple independent studies, and analyzed them in a harmonized way to increase comparability. In addition, we used gene-set tests on mRNA target expression data to further gain evidence about differentially expressed miRNAs. For this purpose, we also studied the use of target information from different databases. We detected a substantial number of miRNA that emerged as differentially expressed from several miRNA datasets, and from the mRNA target data analysis as well. When using mRNA target data, we found that the targetscan databases provided the most useful information. We demonstrated improved miRNA detection through research synthesis of multiple independent miRNA datasets coupled with mRNA target set testing, leading to the discovery of multiple miRNAs which should be taken into account for further research on the molecular mechanism of WNV infections.

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.

Exosomal Preconditioning of Human iPSC-Derived Cardiomyocytes Beneficially Alters Cardiac Electrophysiology and Micro RNA Expression

Experimental evidence, both in vitro and in vivo, has indicated cardioprotective effects of extracellular vesicles (EVs) derived from various cell types, including induced pluripotent stem cell-derived cardiomyocytes. The biological effects of EV secretion, particularly in the context of ischemia and cardiac electrophysiology, remain to be fully explored. Therefore, the goal of this study was to unveil the effects of exosome (EXO)-mediated cell-cell signaling during hypoxia by employing a simulated preconditioning approach on human-induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CMs). Electrophysiological activity of hIPSC-CMs was measured using a multielectrode array (MEA) system. A total of 16 h of hypoxic stress drastically increased the beat period. Moreover, hIPSC-CMs preconditioned with EXOs displayed significantly longer beat periods compared with non-treated cells after 16 h of hypoxia (+15.7%, p < 0.05). Furthermore, preconditioning with hypoxic EXOs resulted in faster excitation-contraction (EC) coupling compared with non-treated hIPSC-CMs after 16 h of hypoxia (-25.3%, p < 0.05). Additionally, microRNA (miR) sequencing and gene target prediction analysis of the non-treated and pre-conditioned hIPSC-CMs identified 10 differentially regulated miRs and 44 gene targets. These results shed light on the intricate involvement of miRs, emphasizing gene targets associated with cell survival, contraction, apoptosis, reactive oxygen species (ROS) regulation, and ion channel modulation. Overall, this study demonstrates that EXOs secreted by hIPSC-CM during hypoxia beneficially alter electrophysiological properties in recipient cells exposed to hypoxic stress, which could play a crucial role in the development of targeted interventions to improve outcomes in ischemic heart conditions.

Non-Coding RNAs Extended Omnigenic Module of Cancers

The emergence of cancers involves numerous coding and non-coding genes. Understanding the contribution of non-coding RNAs (ncRNAs) to the cancer neighborhood is crucial for interpreting the interaction between molecular markers of cancer. However, there is a lack of systematic studies on the involvement of ncRNAs in the cancer neighborhood. In this paper, we construct an interaction network which encompasses multiple genes. We focus on the fundamental topological indicator, namely connectivity, and evaluate its performance when applied to cancer-affected genes using statistical indices. Our findings reveal that ncRNAs significantly enhance the connectivity of affected genes and mediate the inclusion of more genes in the cancer module. To further explore the role of ncRNAs in the network, we propose a connectivity-based method which leverages the bridging function of ncRNAs across cancer-affected genes and reveals the non-coding RNAs extended omnigenic module (NeOModule). Topologically, this module promotes the formation of cancer patterns involving ncRNAs. Biologically, it is enriched with cancer pathways and treatment targets, providing valuable insights into disease relationships.

The microRNA expression in crypt-top and crypt-bottom colonic epithelial cell populations demonstrates cell-type specificity and correlates with endoscopic activity in ulcerative colitis

BACKGROUND AND AIMS

Colonic epithelial barrier dysfunction is one of the early events in ulcerative colitis (UC) and microRNAs (miRNAs) participate in its regulation. However, cell type-specific miRNome during UC is still unknown. Thus, we aimed to explore miRNA expression patterns in colon tissue and epithelial cells at active and quiescent UC.

METHODS

Small RNA-sequencing in colon tissue, crypt-bottom (CD44+), and crypt-top (CD66a+) colonic epithelial cells from two cohorts of UC patients (n=74) and healthy individuals (n=50) was performed. Data analysis encompassed differential expression, weighted gene co-expression network, correlation, gene-set enrichment analyses.

RESULTS

Differentially expressed colonic tissue miRNAs showed potential involvement in regulation of interleukin-4 and interleukin-13 signalling during UC. As this pathway plays role in intestinal barrier regulation, consecutive analysis of spatially distinct colonic epithelial cell populations was performed. Cell-type (crypt-top and crypt-bottom) specific miRNA expression patterns were identified in both active and quiescent UC. Target genes of differentially expressed epithelial miRNAs at different disease activity were overrepresented in epithelial cell migration and therefore intestinal barrier integrity regulation. The pro-inflammatory miRNA co-expression module M1 correlated with endoscopic disease activity and successfully distinguished active and quiescent UC not only in both epithelial cell populations, but also in the colon tissue. The anti-inflammatory module M2 was specific to crypt-bottom cells and significantly enriched in the quiescent UC patients.

CONCLUSIONS

miRNA expression was specific to colonic epithelial cell populations and UC state, reflecting endoscopic disease activity. Irrespective of the UC state, deregulated epithelial miRNAs were associated with regulation of intestinal barrier integrity.

A dataset of hidden small non-coding RNA in the testis of heat-stressed models revealed by Pandora-seq

Infertility, a worldwide reproductive health concern, impacts approximately one in five couples. Male infertility, stemming from spermatogenic dysfunction and reduced sperm quality, stands as a primary factor contributing to infertility. Given the global decrease in male fertility linked to environmental factors like the greenhouse effect, it is crucial to develop a comprehensive understanding of how increased temperatures impact both the quantity and quality of sperm. In this study, we utilized Pandora-seq technology to detect the small non-coding RNAs (sncRNAs) expression profile in the testicular tissue of heat-stressed mice. The investigation explores the dynamic shifts in sncRNAs within the mouse testis under heat stress, including miRNAs, tsRNAs, piRNAs, rsRNAs and other sncRNAs. Furthermore, we successfully identified differentially expressed sncRNAs in testicular tissues before and after heat stress. Subsequently, we conducted functional enrichment analysis on the potential predicted target genes of differentially expressed miRNAs and tsRNAs. These datasets will constitute a valuable foundational resource for further investigations into the decline in male reproductive capacity triggered by heat stress.

Unraveling the H19/GAS1 axis in recurrent implantation failure: A potential biomarker for diagnosis and insight into immune microenvironment alteration

Recurrent implantation failure (RIF) presents a significant clinical challenge due to the lack of established diagnostic and therapeutic guidelines. Emerging evidence underscores the crucial role of competitive endogenous RNA (ceRNA) regulatory networks in non-cancerous female reproductive disorders, yet the intricacies and operational characteristics of these networks in RIF are not fully understood. This study aims to demystify the ceRNA regulatory network and identify potential biomarkers for its diagnosis. We analyzed expression profiles of three RNA types (long noncoding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) sourced from the GEO database, leading to the identification of the H19-hsa-miR-301a-3p-GAS1 ceRNA network. This network demonstrates significant diagnostic relevance for RIF. Notably, the H19/GAS1 axis within this ceRNA network, identified through correlation analysis, emerged as a promising diagnostic marker, as evidenced by operating receiver operator characteristic (ROC) curve analysis. Further investigation into the binding potential of miR-301a-3p with H19 and GAS1 revealed a close association of these genes with endometrial disorders and embryo loss, as per the Comparative Toxicogenomics Database. Additionally, our immune infiltration analysis revealed a lower proportion of T cells gamma delta (γδ) in RIF, along with distinct differences in the expression of immune cell type-specific markers between fertile patients and those with RIF. We also observed a correlation between aberrant expression of H19/GAS1 and these immune markers, suggesting that the H19/GAS1 axis might play a role in modifying the immune microenvironment, contributing to the pathogenesis of RIF. In conclusion, the ceRNA-based H19/GAS1 axis holds promise as a novel diagnostic biomarker for RIF, potentially enhancing our understanding of its underlying mechanisms and improving the success rates of implantation.

The DNA-repair protein APE1 participates with hnRNPA2B1 to motif-enriched and prognostic miRNA secretion

The base excision repair (BER) Apurinic/apyrimidinic endonuclease 1 (APE1) enzyme is endowed with several non-repair activities including miRNAs processing. APE1 is overexpressed in many cancers but its causal role in the tumorigenic processes is largely unknown. We recently described that APE1 can be actively secreted by mammalian cells through exosomes. However, APE1 role in EVs or exosomes is still unknown, especially regarding a putative regulatory function on vesicular small non-coding RNAs. Through dedicated transcriptomic analysis on cellular and vesicular small RNAs of different APE1-depleted cancer cell lines, we found that miRNAs loading into EVs is a regulated process, dependent on APE1, distinctly conveying RNA subsets into vesicles. We identified APE1-dependent secreted miRNAs characterized by enriched sequence motifs and possible binding sites for APE1. In 33 out of 34 APE1-dependent-miRNA precursors, we surprisingly found EXO-motifs and proved that APE1 cooperates with hnRNPA2B1 for the EV-sorting of a subset of miRNAs, including miR-1246, through direct binding to GGAG stretches. Using TCGA-datasets, we showed that these miRNAs identify a signature with high prognostic significance in cancer. In summary, we provided evidence that the ubiquitous DNA-repair enzyme APE1 is part of the EV protein cargo with a novel post-transcriptional role for this ubiquitous DNA-repair enzyme that could explain its role in cancer progression. These findings could open new translational perspectives in cancer biology.

Deciphering the similarities and disparities of molecular mechanisms behind respiratory epithelium response to HCoV-229E and SARS-CoV-2 and drug repurposing, a systems biology approach

PURPOSE

Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications.

METHODS

We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed.

RESULTS

CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations.

CONCLUSION

We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.

Restricting Colorectal Cancer Cell Metabolism with Metformin: An Integrated Transcriptomics Study

BACKGROUND

Metformin is a first-line therapy for type 2 diabetes as it disrupts cellular metabolism. Despite the association between metformin and lower cancer incidence, the anti-tumour activity of the drug in colorectal cancer (CRC) is incompletely understood. This study identifies underlying molecular mechanisms by which metformin slows colorectal cancer cell proliferation by investigating metformin-associated microRNA (miRNA) and target gene pairs implicated in signalling pathways.

METHODS

The present study analysed changes in miRNAs and the coding transcriptome in CRC cells treated with a sublethal dose of metformin, followed by the contextual validation of potential miRNA-target gene pairs.

RESULTS

Analyses of small RNA and transcriptome sequencing data revealed 104 miRNAs and 1221 mRNAs to be differentially expressed in CRC cells treated with metformin for 72 h. Interaction networks between differentially expressed miRNAs and putative target mRNAs were identified. Differentially expressed genes were mainly implicated in metabolism and signalling processes, such as the PI3K-Akt and MAPK/ERK pathways. Further validation of potential miRNA-target mRNA pairs revealed that metformin induced miR-2110 and miR-132-3p to target PIK3R3 and, consequently, regulate CRC cell proliferation, cell cycle progression and the PI3K-Akt signalling pathway. Metformin also induced miR-222-3p and miR-589-3p, which directly target STMN1 to inhibit CRC cell proliferation and cell cycle progression.

CONCLUSIONS

This study identified novel changes in the coding transcriptome and small non-coding RNAs associated with metformin treatment of CRC cells. Integration of these datasets highlighted underlying mechanisms by which metformin impedes cell proliferation in CRC. Importantly, it identified the post-transcriptional regulation of specific genes that impact both metabolism and cell proliferation.

IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

Integrative experimental validation of concomitant miRNAs and transcription factors with differentially expressed genes in acute myocardial infarction

Identification of concomitant miRNAs and transcription factors (TFs) with differential expression (DEGs) in MI is crucial for understanding holistic gene regulation, identifying key regulators, and precision in biomarker and therapeutic target discovery. We performed a comprehensive analysis using Affymetrix microarray data, advanced bioinformatic tools, and experimental validation to explore potential biomarkers associated with human pathology. The search strategy includes the identification of the GSE83500 dataset, comprising gene expression profiles from aortic wall punch biopsies of MI and non-MI patients, which were used in the present study. The analysis identified nine distinct genes exhibiting DEGs within the realm of MI. miRNA-gene/TF and TF-gene/miRNA regulatory relations were mapped to retrieve interacting hub genes to acquire an MI miRNA-TF co-regulatory network. Furthermore, an animal model of I/R-induced MI confirmed the involved gene based on quantitative RT-PCR and Western blot analysis. The consequences of the bioinformatic tool substantiate the inference regarding the presence of three key hub genes (UBE2N, TMEM106B, and CXADR), a central miRNA (hsa-miR-124-3p), and sixteen TFs. Animal studies support the involvement of predicted genes in the I/R-induced myocardial infarction assessed by RT-PCR and Western blotting. Thus, the final consequences suggest the involvement of promising molecular pathways regulated by TF (p53/NF-κB1), miRNA (hsa-miR-124-3p), and hub gene (UBE2N), which may play a key role in the pathogenesis of MI.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

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.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

RBBP6 maintains glioblastoma stem cells through CPSF3-dependent alternative polyadenylation

Glioblastoma is one of the most lethal malignant cancers, displaying striking intratumor heterogeneity, with glioblastoma stem cells (GSCs) contributing to tumorigenesis and therapeutic resistance. Pharmacologic modulators of ubiquitin ligases and deubiquitinases are under development for cancer and other diseases. Here, we performed parallel in vitro and in vivo CRISPR/Cas9 knockout screens targeting human ubiquitin E3 ligases and deubiquitinases, revealing the E3 ligase RBBP6 as an essential factor for GSC maintenance. Targeting RBBP6 inhibited GSC proliferation and tumor initiation. Mechanistically, RBBP6 mediated K63-linked ubiquitination of Cleavage and Polyadenylation Specific Factor 3 (CPSF3), which stabilized CPSF3 to regulate alternative polyadenylation events. RBBP6 depletion induced shortening of the 3'UTRs of MYC competing-endogenous RNAs to release miR-590-3p from shortened UTRs, thereby decreasing MYC expression. Targeting CPSF3 with a small molecular inhibitor (JTE-607) reduces GSC viability and inhibits in vivo tumor growth. Collectively, RBBP6 maintains high MYC expression in GSCs through regulation of CPSF3-dependent alternative polyadenylation, providing a potential therapeutic paradigm for glioblastoma.

Identification and Validation of the Pyroptosis-Related Hub Gene Signature and the Associated Regulation Axis in Diabetic Keratopathy

Background

Diabetic keratopathy (DK) poses a significant challenge in diabetes mellitus, yet its molecular pathways and effective treatments remain elusive. The aim of our research was to explore the pyroptosis-related genes in the corneal epithelium of the streptozocin-induced diabetic rats.

Methods

After sixteen weeks of streptozocin intraperitoneal injection, corneal epithelium from three diabetic rats and three normal groups underwent whole-transcriptome sequencing. An integrated bioinformatics pipeline, including differentially expressed gene (DEG) identification, enrichment analysis, protein-protein interaction (PPI) network, coexpression, drug prediction, and immune deconvolution analyses, identified hub genes and key drivers in DK pathogenesis. These hub genes were subsequently validated in vivo through RT-qPCR.

Results

A total of 459 DEGs were screened out from the diabetic group and nondiabetic controls. Gene Set Enrichment Analysis highlighted significant enrichment of the NOD-like receptor, Toll-like receptor, and NF-kappa B signaling pathways. Intersection of DEGs and pyroptosis-related datasets showed 33 differentially expressed pyroptosis-related genes (DEPRGs) associated with pathways such as IL-17, NOD-like receptor, TNF, and Toll-like receptor signaling. A competing endogenous RNA network comprising 16 DEPRGs, 22 lncRNAs, 13 miRNAs, and 3 circRNAs was constructed. After PPI network, five hub genes (Nfkb1, Casp8, Traf6, Ptgs2, and Il18) were identified as upregulated in the diabetic group, and their expression was validated by RT-qPCR in streptozocin-induced rats. Immune infiltration characterization showed that diabetic corneas owned a higher proportion of resting mast cells, activated NK cells, and memory-resting CD4 T cells. Finally, several small compounds including all-trans-retinoic acid, Chaihu Shugan San, dexamethasone, and resveratrol were suggested as potential therapies targeting these hub genes for DK.

Conclusions

The identified and validated hub genes, Nfkb1, Casp8, Traf6, Ptgs2, and Il18, may play crucial roles in DK pathogenesis and serve as therapeutic targets.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Background

MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking.

Methods

Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules.

Results

Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA-mRNA-lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers.

Conclusion

ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Exploring miRNA-target gene pair detection in disease with coRmiT

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer

Combination therapy can greatly improve the efficacy of cancer treatment, so identifying the most effective drug combination and interaction can accelerate the development of combination therapy. Here we developed a computational network biological approach to identify the effective drug which inhibition risk pathway crosstalk of cancer, and then filtrated and optimized the drug combination for cancer treatment. We integrated high-throughput data concerning pan-cancer and drugs to construct miRNA-mediated crosstalk networks among cancer pathways and further construct networks for therapeutic drug. Screening by drug combination method, we obtained 687 optimized drug combinations of 83 first-line anticancer drugs in pan-cancer. Next, we analyzed drug combination mechanism, and confirmed that the targets of cancer-specific crosstalk network in drug combination were closely related to cancer prognosis by survival analysis. Finally, we save all the results to a webpage for query ( http://bio-bigdata.hrbmu.edu.cn/oDrugCP/ ). In conclusion, our study provided an effective method for screening precise drug combinations for various cancer treatments, which may have important scientific significance and clinical application value for tumor treatment.

TarBase-v9.0 extends experimentally supported miRNA-gene interactions to cell-types and virally encoded miRNAs

TarBase is a reference database dedicated to produce, curate and deliver high quality experimentally-supported microRNA (miRNA) targets on protein-coding transcripts. In its latest version (v9.0, https://dianalab.e-ce.uth.gr/tarbasev9), it pushes the envelope by introducing virally-encoded miRNAs, interactions leading to target-directed miRNA degradation (TDMD) events and the largest collection of miRNA-gene interactions to date in a plethora of experimental settings, tissues and cell-types. It catalogues ∼6 million entries, comprising ∼2 million unique miRNA-gene pairs, supported by 37 experimental (high- and low-yield) protocols in 172 tissues and cell-types. Interactions are annotated with rich metadata including information on genes/transcripts, miRNAs, samples, experimental contexts and publications, while millions of miRNA-binding locations are also provided at cell-type resolution. A completely re-designed interface with state-of-the-art web technologies, incorporates more features, and allows flexible and ingenious use. The new interface provides the capability to design sophisticated queries with numerous filtering criteria including cell lines, experimental conditions, cell types, experimental methods, species and/or tissues of interest. Additionally, a plethora of fine-tuning capacities have been integrated to the platform, offering the refinement of the returned interactions based on miRNA confidence and expression levels, while boundless local retrieval of the offered interactions and metadata is enabled.

Comparative Analysis of Published Database Predicting MicroRNA Binding in 3'UTR of mRNA in Diverse Species

BACKGROUND

Micro-RNAs are endogenous non-coding RNA moieties of 22-27 nucleotides that play a crucial role in the regulation of various biological processes and make them useful prognostic and diagnostic biomarkers. Discovery and experimental validation of miRNA is a laborious and time-consuming process. For early prediction, multiple bioinformatics databases are available for miRNA target prediction; however, their utility can confuse amateur researchers in selecting the most appropriate tools for their study.

OBJECTIVE

This descriptive review aimed to analyse the usability of the existing database based on the following criteria: accessibility, efficiency, interpretability, updatability, and flexibility for miRNA target prediction of 3'UTR of mRNA in diverse species so that the researchers can utilize the database most appropriate to their research.

METHODS

A systematic literature search was performed in PubMed, Google Scholar and Scopus databases up to November 2022. ≥10,000 articles found online, including ⁓130 miRNA tools, which contain various information on miRNA. Out of them, 31 databases that provide information on validated 3'UTR miRNAs target databases were included and analysed in this review.

RESULTS

These miRNA database tools are being used in varied areas of biological research to select the most suitable miRNA for their experimental validation. These databases, updated until the year 2021, consist of miRNA-related data from humans, animals, mice, plants, viruses etc. They contain 525-29806351 data entries, and information from most databases is freely available on the online platform.

CONCLUSION

Reviewed databases provide significant information, but not all information is accurate or up-to-date. Therefore, Diana-TarBase and miRWalk are the most comprehensive and up-to-date databases.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

Noncoding RNAs improve the predictive power of network medicine

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.