RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility

MOPSOGAT: Predicting CircRNA-Disease Associations via Improved Multi-objective Particle Swarm Optimization and Graph Attention Network

Recently increasing researches have discovered that circRNAs are remarkably reliable in organisms and play a crucial role as marker in many diseases. Although deep learning techniques has been universally applied to investigate the relationship of circRNA-disease, optimizing many parameters involved in these techniques for best performance has been a challenge. Therefore, we present, for the first time, a multi-objective particle swarm optimization algorithm to optimize the parameters in a graph attention network, ensuring that the model operates at peak efficiency. In addition, it also limits feature learning due to uneven distribution of different node types in heterogeneous graphs based on association relationships. We suggest a unique approach, MOPSOGAT, to overcome the aforementioned problems. MOPSOGAT is a method for predicting circRNA-disease associations utilizing the improved multi-objective particle swarm optimization (MOPSO) and the graph attention network. Initially, we obtain node sequences by utilizing multiple circRNA similarities and disease phenotypic similarities, and employing a heterogeneous graph with random walks incorporating jump and stay strategies. These sequences are then processed using word2vec to derive the neighbor vectors of the nodes, thus providing initial embeddings for circRNAs and diseases. Subsequently, in order to model convergence and diversity of the Pareto front solutions, an improved MOPSO algorithm is used to iteratively search for optimal solutions in the parameter space. After MOPSO optimization, parameters are fed into a graph attention network to further refine the model embedding. As a result, MOPSOGAT performs better than deep learning based methods, solely multi-objective optimization-based methods and machine learning-based ways. Moreover, the potential associations predicted by MOPSOGAT have been validated through case studies, further demonstrating the potential of MOPSOGAT in future biomedical research.

Landscape of lncRNAs expressed in Mexican patients with triple‑negative breast cancer

Long non‑coding RNAs (lncRNAs) are key regulators of gene expression, that can regulate a range of carcinogenic processes. Moreover, they exhibit stability in biological fluids, with some displaying tissue specificity. As their expression depends on specific conditions or is linked to the regulation of particular signaling pathways, lncRNAs are promising candidates for providing insights into the likely progression of the disease. This allows for the stratification of patients based on their risk of progression, making them potential prognostic biomarkers in various types of cancer. In addition, the tissue‑specific expression profile of lncRNAs renders them ideal candidates for detection, prognosis and monitoring of cancer progression. The present study aims to provide an overview of differentially expressed lncRNAs in Mexican patients with triple‑negative breast cancer (TNBC), a subtype of breast cancer. The aim was to identify potential prognostic biomarkers that can be applied to improve the clinical management of Mexican patients with TNBC. Human Transcriptome Array 2.0 microarrays were used to analyze the transcriptome of TNBC and luminal tumors, which are reported to have a good prognosis amongst aggressive tumor types. Subsequently, results from these microarrays were validated in a cohort from The Cancer Genome Atlas, an independent cohort of Mexican patients and in breast cancer cell lines (MCF7, ZR75, T47D, MDA‑MB‑231, MDA‑MB‑468 and BT20). A total of 746 differentially expressed transcripts were identified, including 102 lncRNAs in TNBC compared with luminal tumors. Among the lncRNAs with the most significant changes in expression levels, SOX9‑AS was highly expressed in TNBC, whereas the expression of Lnc‑peroxidasin‑3:1 (Lnc‑PXDN‑3:1), Lnc‑RNA Synapse Defective Rho GTPase Homolog (Lnc‑SYDE) and long intergenic non‑coding RNA (LINC)01087 were decreased. In addition, the low expression of lncRNA LINC01087, LINC02568, ACO22196, and lncRNA eosinophil granule ontogeny transcript (Lnc‑EGOT) was associated with poor overall survival (OS). Further analysis revealed that the high expression levels of Lnc‑PXDN‑3:1, Lnc RNA fibrous sheath interacting protein 1‑6:3 and (LINC)00182 were associated with reduced survival in patients with the luminal subtype of breast cancer. Similarly, low expression levels of lncRNAs such as GATA binding protein 3‑1 (Lnc‑GATA‑3‑1), LINC01087, and BX679671.1 in luminal subtypes of breast cancer, as well as LINC00504 and LncRNA rho guanine nucleotide exchange factor 38 intronic transcript 1 (Lnc‑ARHGEF38‑IT1) in basal subtypes have been linked to poorer survival. The interactions and functions of LINC01087 were then investigated, revealing the interaction of LINC01087 with RNAs and transcription factors, highlighting their potential involvement in the estrogen receptor pathway. The present study provided a detailed analysis of the expression of lncRNAs in TNBC, which highlights the role of lncRNAs as a biomarker in the survival outcomes of patients with breast cancer to improve the understanding of transcriptional regulation in TNBC.

Improving prediction of bacterial sRNA regulatory targets with expression data

Small regulatory RNAs (sRNAs) are widespread in bacteria. However, characterizing the targets of sRNA regulation in a way that scales with the increasing number of identified sRNAs has proven challenging. Computational methods offer one means for efficient characterization of sRNA targets, but the sensitivity and precision of such computational methods is limited. Here, we investigate whether publicly available expression data from RNA-seq experiments can improve the accuracy of computational prediction of sRNA regulatory targets. Using compendia of 2143 Escherichia coli RNA-seq samples and 177 Salmonella RNA-seq samples, we identify groups of co-expressed genes in each organism and incorporate this expression information into computational prediction of sRNA targets based on machine learning methods. We find that integrating expression information significantly improves the accuracy of computational results. Further, we observe that computational methods perform better when trained on smaller, higher quality sets of targets rather than on larger, noisier sets of targets identified by high-throughput methods.

Integrative analysis of cuproptosis-related lncRNAs for prognostic risk assessment and tumor immune microenvironment evaluation in laryngeal squamous cell carcinoma

Long non-coding RNAs (lncRNAs) play a pivotal role in tumor prognostic models. This study targets cuproptosis-related lncRNAs in laryngeal squamous cell carcinoma (LSCC) using RNA-seq data from The Cancer Genome Atlas (TCGA)-LSCC. Differentially expressed genes were identified, and Pearson correlation analysis pinpointed cuproptosis-related lncRNAs. Combining clinical data, a prognostic risk model was constructed using LASSO Cox regression and Cox proportional hazards analyses. Nine lncRNAs (LINCO1473, SNHG12, AC007938.3, AC040970.1, AC023669.1, AL158166.2, GIHCG, AC007240.3, and AC011370.1) were identified, with GIHCG showing significant correlation with LSCC prognosis. GIHCG's competing endogenous RNAs (ceRNA) and co-expression networks (CEN) were established, revealing sensitivity to drugs like BMS-509744, YM155, and KU-55933. Silencing of GIHCG inhibited migration, invasion, EMT, and other biological processes in LSCC cells, suggesting GIHCG as a potential therapeutic target. Moreover, METTL16-mediated m6A methylation regulates GIHCG expression. In conclusion, this study successfully established a prognostic model comprising nine cuproptosis-related lncRNAs, accurately predicting LSCC prognosis, and highlighted the crucial role of GIHCG as a novel nucleic acid biomarker in regulating LSCC progression.

Pseudogene Lamr1-ps1 Aggravates Early Spatial Learning Memory Deficits in Alzheimer's Disease Model Mice

Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, alterations in the expression of non-coding RNAs (ncRNAs) have emerged as pivotal events. In this study, we focused on the aberrant expression of ncRNAs and revealed that Lamr1-ps1, a pseudogene of the laminin receptor, significantly exacerbates early spatial learning and memory deficits in APP/PS1 mice. Through a combination of bioinformatics prediction and experimental validation, we identified the miR-29c/Bace1 pathway as a potential regulatory mechanism by which Lamr1-ps1 influences AD pathology. Importantly, augmenting the miR-29c-3p levels in mice ameliorated memory deficits, underscoring the therapeutic potential of targeting miR-29c-3p in early AD intervention. This study not only provides new insights into the role of pseudogenes in AD but also consolidates a foundational basis for considering miR-29c as a viable therapeutic target, offering a novel avenue for AD research and treatment strategies.

ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana

Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. The platform is freely accessible at https://www.nipgr.ac.in/ANNInter/.

The miR-6779/XIAP axis alleviates IL-1β-induced chondrocyte senescence and extracellular matrix loss in osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a long-term degenerative joint disease worsening over time. Aging and chondrocyte senescence contribute to OA progression. MicroRNAs have been confirmed to regulate different cellular processes. They contribute to OA pathology and may help to identify novel biomarkers and therapies for OA.

METHODS

This study used bioinformatics and experimental investigations to analyze and validate differentially expressed miRNAs in OA that might affect chondrocyte apoptosis and senescence.

RESULTS

miR-6779 was found to be significantly down-regulated in OA. Seventy-six of the predicted and miR-6779 targeted genes and the OA-associated disease genes overlapped, and these were enriched in cell proliferation, cell apoptosis, and cell cycle. miR-6779 overexpression remarkably attenuated IL-1β effects on chondrocytes by reducing MMP3 and MMP13 levels, promoting cell apoptosis, suppressing cell senescence, and increasing caspase-3, caspase-9 and reducing P16 and P21 levels. miR-6779 targeted inhibition of X-linked inhibitor of apoptosis protein (XIAP) expression. XIAP knockdown partially improved IL-1β-induced chondrocyte senescence and dysfunction. Lastly, when co-transfected with a miR-6779 agomir, the XIAP overexpression vector partially attenuated the effects of miR-6779 overexpression on chondrocytes; miR-6779 improved IL-1β-induced senescence and dysfunction in chondrocytes through targeting XIAP.

CONCLUSION

miR-6779 is down-regulated, and XIAP is up-regulated in OA cartilage and IL-1β-treated chondrocytes. miR-6779 inhibits XIAP expression, thereby promoting senescent chondrocyte cell apoptosis and reducing chondrocyte senescence and ECM loss through XIAP.

Gene and transcript expression patterns, coupled with isoform switching and long non-coding RNA dynamics in adipose tissue, underlie the longevity of Ames dwarf mice

Our study investigates gene expression in adipose tissue of Ames dwarf (df/df) mice, whose deficiency in growth hormone is linked to health and extended lifespan. Recognizing adipose tissue influence on metabolism, aging, and related diseases, we aim to understand its contribution to the health and longevity of df/df mice. We have identified gene and transcript expression patterns associated with critical biological functions, including metabolism, stress response, and resistance to cancer. Intriguingly, we identified genes that, despite maintaining unchanged expression levels, switch between different isoforms, impacting essential cellular functions such as tumor suppression, oncogenic activity, ATP transport, and lipid biosynthesis and storage. The isoform switching is associated with changes in protein domains, retention of introns, initiation of nonsense-mediated decay, and emergence of intrinsically disordered regions. Moreover, we detected various alternative splicing events that may drive these structural alterations. We also found changes in the expression of long non-coding RNAs (lncRNAs) that may be involved in the aging process and disease resistance by regulating crucial genes in survival and metabolism. Through weighted gene co-expression network analysis, we have linked four lncRNAs with 29 genes, which contribute to protein complexes such as the Mili-Tdrd1-Tdrd12 complex. Beyond safeguarding DNA integrity, this complex also has a wider impact on gene regulation, chromatin structure, and metabolic control. Our detailed investigation provides insight into the molecular foundations of the remarkable health and longevity of df/df mice, emphasizing the significance of adipose tissue in aging and identifying new avenues for health-promoting therapeutic strategies.

Bioinformatics analysis and experimental validation of C6orf120 as a potential prognostic marker and therapeutic target for liver hepatocellular carcinoma

The C6orf120 gene is a novel gene whose function has not been fully defined. Previous studies have associated it with various liver pathologies, but its specific role in hepatocellular carcinoma (LIHC) remains unclear. This study aimed to investigate the diagnostic and prognostic value of C6orf120 in LIHC, as well as its potential biological functions. In this preliminary research, we utilized data from various databases and bioinformatics tools, including TCGA, GEO, TIMER2, HPA, GEPIA, Linkeomics, Metascape, CIBERSORT, TargetScan, DIANA-microT, RNAinter, and ENCORI, to analyze the expression patterns and mechanisms of C6orf120 in LIHC. Our bioinformatics analysis revealed that C6orf120 is upregulated in LIHC and may serve as a diagnostic and prognostic biomarker. The aberrant expression of C6orf120 in LIHC was further supported by clinical samples and cell lines. In vitro experiments demonstrated that the knockdown of C6orf120 in HepG2 cells significantly reduced migration capacity without affecting proliferation. Additionally, the downregulation of C6orf120 in LIHC cells appeared to inhibit endothelial cell migration and angiogenesis, which are critical in tumorigenesis and development. In conclusion, our findings suggest that C6orf120 could serve as a novel diagnostic and prognostic biomarker for LIHC and is expected to be a prognostic marker and a potential therapeutic target in the clinical management of LIHC.

RNA binding proteins (RBPs) on genetic stability and diseases

RNA-binding proteins (RBPs) are integral components of cellular machinery, playing crucial roles in the regulation of gene expression and maintaining genetic stability. Their interactions with RNA molecules govern critical processes such as mRNA splicing, stability, localization, and translation, which are essential for proper cellular function. These proteins interact with RNA molecules and other proteins to form ribonucleoprotein complexes (RNPs), hence controlling the fate of target RNAs. The interaction occurs via RNA recognition motif, the zinc finger domain, the KH domain and the double stranded RNA binding motif (all known as RNA-binding domains (RBDs). These domains are found within the coding sequences (intron and exon domains), 5' untranslated regions (5'UTR) and 3' untranslated regions (3'UTR). Dysregulation of RBPs can lead to genomic instability, contributing to various pathologies, including cancer neurodegenerative diseases, and metabolic disorders. This study comprehensively explores the multifaceted roles of RBPs in genetic stability, highlighting their involvement in maintaining genomic integrity through modulation of RNA processing and their implications in cellular signalling pathways. Furthermore, it discusses how aberrant RBP function can precipitate genetic instability and disease progression, emphasizing the therapeutic potential of targeting RBPs in restoring cellular homeostasis. Through an analysis of current literature, this study aims to delineate the critical role of RBPs in ensuring genetic stability and their promise as targets for innovative therapeutic strategies.

The Relevance of the Accurate Annotation of Micro and Long Non-Coding RNA Interactions for the Development of Therapies

A large fraction of the human genome is transcribed in RNA molecules that do not encode for proteins but that do have a crucial role in regulating almost every level of gene expression and, thus, define the specific phenotype of each cell. These non-coding RNAs include well-characterized microRNAs and thousands of less-defined longer transcripts, named long non-coding RNAs. Both types markedly affect the onset and the progression of numerous pathologies, ranging from cancer to vascular and neuro-degenerative diseases. In recent years, a substantial effort has been made to design drugs targeting ncRNAs, and promising advancements have been produced from micro-RNA mimics and inhibitors. Each ncRNA controls several targets, and the overall effect of its inhibition or overexpression depends on the function of the set of genes it regulates. Therefore, in selecting the most appropriate target, and predicting the final outcome of ncRNA-based therapies, it is crucial to have and utilize detailed and accurate knowledge of their functional interactions. In this review, I recapitulate the principal resources which collect information on microRNA and lncRNA networks, focusing on the non-homogeneity of the data that result from disparate approaches. I highlight the role of RNA identifiers and interaction evidence standardization in helping the user to filter and integrate data derived from different databases in a reliable functional web of regulative relations.

RNA-protein interaction prediction using network-guided deep learning

Accurate computational determination of RNA-protein interactions remains challenging, particularly when encountering unknown RNAs and proteins. The limited number of RNAs and their flexibility constrained the effectiveness of the deep-learning models for RNA-protein interaction prediction. Here, we introduce ZHMolGraph, which integrates graph neural network and unsupervised large language models to predict RNA-protein interaction. We validate ZHMolGraph predictions on two benchmark datasets and outperform the current best methods. For the dataset of entirely unknown RNAs and proteins, ZHMolGraph shows an improvement in achieving high AUROC of 79.8% and AUPRC of 82.0%. This represents a substantial improvement of 7.1%-28.7% in AUROC and 4.6%-30.0% in AUPRC over other methods. We utilize ZHMolGraph to enhance the challenging SARS-CoV-2 RPI and unbound RNA-protein complex predictions. Such enhancements make ZHMolGraph a reliable option for genome-wide RNA-protein prediction. ZHMolGraph holds broad potential for modeling and designing RNA-protein complexes.

Characterization of lncRNA-Driven Networks in Portal Vein Tumor Thrombosis: Implications for Hepatocellular Carcinoma Progression

Background: Portal vein tumor thrombosis (PVTT) is a frequent and serious complication of advanced hepatocellular carcinoma (HCC) that often results in poor prognosis. Although PVTT holds significant clinical relevance, the molecular mechanisms driving its formation are not well understood. Long non-coding RNAs (lncRNAs) have emerged as potential contributors to PVTT progression, prompting this study to explore lncRNAs as potential biomarkers for PVTT. Methods: We analyzed publicly available datasets from the Gene Expression Omnibus to identify differentially expressed lncRNAs and mRNAs across three comparisons: normal vs. HCC, normal vs. PVTT, and HCC vs. PVTT. Transcriptional profiles were characterized, and proteins interacting with HCC- and PVTT-specific lncRNAs were screened using online databases, revealing that all interacting proteins were transcription factors (TFs). We constructed lncRNA-TF-target gene regulatory networks by intersecting TF target genes with differentially expressed genes (DEGs) from each comparison. Protein-protein interaction (PPI) network analysis was performed to identify key clusters and hub genes, with TFs such as AR and ESR1 being highlighted. Gene Ontology analyses were conducted to understand the biological functions of the regulatory networks. Results: The study identified distinct transcriptional profiles for normal, HCC, and PVTT samples. Key regulatory networks, involving lncRNAs, TFs, and target genes, were constructed, and significant hub genes, including AR and ESR1, were identified as potential therapeutic targets. PPI network analysis revealed important clusters associated with PVTT progression, while Gene Ontology analyses provided insights into relevant biological functions. Conclusions: This study presents a novel framework for understanding lncRNA-TF-mediated gene regulation in PVTT. It identifies potential therapeutic targets and prognostic biomarkers that could facilitate the development of targeted therapies for PVTT, offering new opportunities to improve clinical outcomes.

A Regulatory Circuits Analysis Tool, "miRCuit," Helps Reveal Breast Cancer Pathways: Toward Systems Medicine in Oncology

A systems medicine understanding of the regulatory molecular circuits that underpin breast cancer is essential for early cancer detection and precision/personalized medicine in clinical oncology. Transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) control gene expression and cell biology, and by extension, serve as pillars of the regulatory circuits that determine human health and disease. We report here the development of a regulatory circuit analysis program, miRCuit, constructing 10 different types of regulatory elements involving messenger RNA, miRNA, lncRNA, and TFs. Using the miRCuit, we analyzed expression profiling data from 179 invasive ductal breast carcinoma and 51 normal tissue samples from the Gene Expression Omnibus database. We identified eight circuit types along with two special types of circuits, one of which highlighted the significant roles of lncRNA CASC15, miR-130b-3p, and TF KLF5 in breast cancer development and progression. These findings advance our understanding of the regulatory molecules associated with breast cancer. Moreover, miRCuit offers a new avenue for users to construct circuits from regulatory molecules for potential applications to decipher disease pathogenesis.

Unveiling the functions of five recently characterized lncRNAs in cancer progression

Numerous studies over the past few decades have shown that RNAs are multifaceted, multifunctional regulators of most cellular processes, contrary to the initial belief that they only act as mediators for translating DNA into proteins. LncRNAs, which refer to transcripts longer than 200nt and lack the ability to code for proteins, have recently been identified as central regulators of a variety of biochemical and cellular processes, particularly cancer. When they are abnormally expressed, they are closely associated with tumor occurrence, metastasis, and tumor staging. Therefore, through searches on Google Scholar, PubMed, and CNKI, we identified five five recently characterized lncRNAs-Lnc-SLC2A12-10:1, LncRNA BCRT1, lncRNA IGFBP4-1, LncRNA PCNAP1, and LncRNA CDC6-that have been linked to the promotion of cancer cell proliferation, invasion, and metastasis. Consequently, this review encapsulates the existing research and molecular underpinnings of these five newly identified lncRNAs across various types of cancer. It suggests that these novel lncRNAs hold potential as independent biomarkers for clinical diagnosis and prognosis, as well as candidates for therapeutic intervention. In parallel, we discuss the challenges inherent in the research on these five newly discovered lncRNAs and look forward to the avenues for future exploration in this field.

Extraction of coronary thrombus-derived exosomes from patients with acute myocardial infarction and its effect on the function of adventitial cells

BACKGROUND

Type I acute myocardial infarction (T1MI) has a very high morbidity and mortality rate. The role of thrombus-derived exosomes (TEs) in T1MI is unclear.

METHODS

The objective of this study was to identify the optimal thrombolytic drug and concentration for extracting TEs. To this end, a series of time and concentration combinations were tested. Subsequently, the effect of TEs on thrombus-adjacent cells was investigated. Finally, we conducted lncRNA microarray analysis on the extracted TEs (GSE213115).

RESULTS

TEs has been demonstrated to promote necrosis, autophagy, and ferroptosis of human cardiomyocytes, while inhibiting the proliferation and migration of human umbilical vein endothelial cells (HUVECs). Furthermore, TEs can stimulate the proliferation and migration of smooth muscle cells, and induce a transformation from a contractile to a secretory phenotype. Bioinformatics analysis revealed that five lncRNAs, AC068418.2, AC010186.3, AL031430.1, AC121333.1, and AL136526.1, exhibited significant differential expression in TE and regulated cell autophagy and ferroptosis by directly binding to TP53, TP63, and RELA, respectively.

CONCLUSIONS

We demonstrate that TEs as a potential target and research direction for the treatment of heart failure after T1MI. TEs may regulate ferroptosis and autophagy in thrombus-adjacent cells through the enrichment of certain lncRNAs.

Citrullinated IGF2BP1 promotes rheumatoid synovial aggression via increasing the mRNA stability of SEMA3D

Protein citrullination modification plays a pivotal role in the pathogenesis of rheumatoid arthritis (RA), and anti-citrullinated protein antibodies (ACPAs) are extensively employed for clinical diagnosis of RA. However, there remains limited understanding regarding specific citrullinated proteins and their implications in the progression of RA. In this study, we screen and verify insulin-like growth factor-2 mRNA binding protein 1 (IGF2BP1) as a novel citrullinated protein with significantly elevated citrullinated level in RA. Autoantibodies against citrullinated IGF2BP1 are further detected in serum and synovial fluid samples from RA patients, which are positively correlated with erythrocyte sedimentation rate (ESR) and disease activity score 28 (DAS28). Transcriptomic sequencing and functional verification show that citrullination at the R167 site of IGF2BP1 promotes the proliferation, migration, and invasion of RA fibroblast-like synoviocytes (RA-FLSs) by improving the mRNA stability of Semaphorin 3D (SEMA3D). Experiments in collagen-induced arthritis (CIA) mice, the classical animal model of RA, show that IGF2BP1 R176K point mutation (Igf2bp1R167K/R167K) mice exert reduced inflammatory response, clinical scores, and joint destruction. At a molecular level, citrullination of IGF2BP1 promotes the stability of SEMA3D mRNA by promoting the interaction between IGF2BP1 and its cofactor ELAV-like protein 1 (ELAVL1), thereby promoting the invasiveness of RA-FLSs. In this study, a new citrullinated protein of IGF2BP1 is discovered, and the molecular mechanism of its citrullinated modification promoting the progression of RA disease is elucidated, which provides theoretical basis for the diagnosis and treatment of RA.

MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks

BACKGROUND

MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.

RESULTS

In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations. Firstly, MDFGNN-SMMA extracted features of Atom Pairs fingerprints and Molecular ACCess System fingerprints to derive fusion feature vectors for small molecules (SMs). The K-mer features were employed to generate the initial feature vectors for miRNAs. Secondly, cosine similarity measures were computed to construct the adjacency matrices for SMs and miRNAs, respectively. Thirdly, these feature vectors and adjacency matrices were input into a model comprising GAT and GraphSAGE, which were utilized to generate the final feature vectors for SMs and miRNAs. Finally, the averaged final feature vectors were utilized as input for a multilayer perceptron to predict the associations between SMs and miRNAs.

CONCLUSIONS

The performance of MDFGNN-SMMA was assessed using 10-fold cross-validation, demonstrating superior compared to the four state-of-the-art models in terms of both AUC and AUPR. Moreover, the experimental results of an independent test set confirmed the model's generalization capability. Additionally, the efficacy of MDFGNN-SMMA was substantiated through three case studies. The findings indicated that among the top 50 predicted miRNAs associated with Cisplatin, 5-Fluorouracil, and Doxorubicin, 42, 36, and 36 miRNAs, respectively, were corroborated by existing literature and the RNAInter database.

Integrated Analysis of microRNA Targets Reveals New Insights into Transcriptional-Post-Transcriptional Regulatory Cross-Talk

It is becoming increasingly clear that microRNAs are key players in gene regulatory networks, modulating gene expression at post-transcriptional level. Their involvement in almost all cellular processes predicts their role in diseases, and several microRNA-based therapeutics are currently undergoing clinical testing. Despite their undeniable relevance and the substantial body of literature demonstrating their role in cancer and other pathologies, the identification of functional interactions is still challenging. To address this issue, several resources have been developed to collect information from the literature, according to different criteria and reliability scores. In the present study, we have constructed a network of verified microRNA-mRNA interactions by integrating strong-evidence couples from different resources. Our analysis of the resulting network reveals that only one-fifth of the human genes exhibits experimental validated regulation by microRNAs. A very small subset of them is controlled by more than 20 microRNAs, and these hubs are highly enriched of pivotal transcription factors and regulatory proteins, strongly suggesting a complex interplay and a combinatorial effect between transcriptional and post-transcriptional gene control. Data analysis also reveals that several microRNAs control multiple targets involved in the same pathway or biological process, likely contributing to the coordinated control of the protein levels.

RNALocate v3.0: Advancing the Repository of RNA Subcellular Localization with Dynamic Analysis and Prediction

Subcellular localization of RNA is a crucial mechanism for regulating diverse biological processes within cells. Dynamic RNA subcellular localizations are essential for maintaining cellular homeostasis; however, their distribution and changes during development and differentiation remain largely unexplored. To elucidate the dynamic patterns of RNA distribution within cells, we have upgraded RNALocate to version 3.0, a repository for RNA-subcellular localization (http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/). RNALocate v3.0 incorporates and analyzes RNA subcellular localization sequencing data from over 850 samples, with a specific focus on the dynamic changes in subcellular localizations under various conditions. The species coverage has also been expanded to encompass mammals, non-mammals, plants and microbes. Additionally, we provide an integrated prediction algorithm for the subcellular localization of seven RNA types across eleven subcellular compartments, utilizing convolutional neural networks (CNNs) and transformer models. Overall, RNALocate v3.0 contains a total of 1 844 013 RNA-localization entries covering 26 RNA types, 242 species and 177 subcellular localizations. It serves as a comprehensive and readily accessible data resource for RNA-subcellular localization, facilitating the elucidation of cellular function and disease pathogenesis.

SubCELL: the landscape of subcellular compartment-specific molecular interactions

The subcellular compartment-specific molecular interactions (SCSIs) are the building blocks for most molecular functions, biological processes and disease pathogeneses. Extensive experiments have therefore been conducted to accumulate the valuable information of SCSIs, but none of the available databases has been constructed to describe those data. In this study, a novel knowledge base SubCELL is thus introduced to depict the landscape of SCSIs among DNAs/RNAs/proteins. This database is UNIQUE in (a) providing, for the first time, the experimentally-identified SCSIs, (b) systematically illustrating a large number of SCSIs inferred based on well-established method and (c) collecting experimentally-determined subcellular locations for the DNAs/RNAs/proteins of diverse species. Given the essential physiological/pathological role of SCSIs, the SubCELL is highly expected to have great implications for modern molecular biological study, which can be freely accessed with no login requirement at: https://idrblab.org/subcell/.

RPS 2.0: an updated database of RNAs involved in liquid-liquid phase separation

Liquid-liquid phase separation (LLPS) is a crucial process for the formation of biomolecular condensates such as coacervate droplets, P-bodies and stress granules, which play critical roles in many physiological and pathological processes. Increasing studies have shown that not only proteins but also RNAs play a critical role in LLPS. To host LLPS-associated RNAs, we previously developed a database named 'RPS' in 2021. In this study, we present an updated version RPS 2.0 (https://rps.renlab.cn/) to incorporate the newly generated data and to host new LLPS-associated RNAs driven by post-transcriptional regulatory mechanisms. Currently, RPS 2.0 hosts 171 301 entries of LLPS-associated RNAs in 24 different biomolecular condensates with four evidence types, including 'Reviewed', 'High-throughput (LLPS enrichment)', 'High-throughput (LLPS perturbation)' and 'Predicted', and five event types, including 'Expression', 'APA', 'AS', 'A-to-I' and 'Modification'. Additionally, extensive annotations of LLPS-associated RNAs are provided in RPS 2.0, including RNA sequence and structure features, RNA-protein/RNA-RNA interactions, RNA modifications, as well as diseases related annotations. We expect that RPS 2.0 will further promote research of LLPS-associated RNAs and deepen our understanding of the biological functions and regulatory mechanisms of LLPS.

Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis

While ultrasonography effectively diagnoses Hashimoto's thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into disease mechanisms. This study aimed to identify HT-associated RNA signatures and investigate their potential for enhanced molecular characterization. Samples comprising 31 HT patients and 30 healthy controls underwent RNA sequencing of peripheral blood. Differential expression analysis identified transcriptomic features, which were integrated using multi-omics factor analysis. Pathway enrichment, co-expression, and regulatory network analyses were performed. A novel machine-learning model was developed for HT molecular characterization using stacking techniques. HT patients exhibited increased thyroid volume, elevated tissue hardness, and higher antibody levels despite being in the early subclinical stage. Analysis identified 79 HT-associated transcriptomic features (3 mRNA, 6 miRNA, 64 lncRNA, 6 circRNA). Co-expression (77 nodes, 266 edges) and regulatory (18 nodes, 45 edges) networks revealed significant hub genes and modules associated with HT. Enrichment analysis highlighted dysregulation in immune system, cell adhesion and migration, and RNA/protein regulation pathways. The novel stacking-model achieved 95% accuracy and 97% AUC for HT molecular characterization. This study demonstrates the value of transcriptome analysis in uncovering HT-associated signatures, providing insights into molecular changes and potentially guiding future research on disease mechanisms and therapeutic strategies.

Review of the Different Outcomes Produced by Genetic Knock Out of the Long Non-coding microRNA-host-gene MIR22HG versus Pharmacologic Antagonism of its Intragenic microRNA product miR-22-3p

BACKGROUND

Publications reveal different outcomes achieved by genetically knocking out a long non-coding microRNA-host-gene (lncMIRHG) versus the administration of pharmacologic antagomirs specifically targeting the guide strand of such intragenic microRNA. This suggests that lncMIRHGs may perform diverse functions unrelated to their role as intragenic miRNA precursors.

OBJECTIVE

This review synthesizes in silico, in vitro, and in vivo findings from our lab and others to compare the effects of knocking out the long non-coding RNA MIR22HG, which hosts miR- 22, versus administering pharmacological antagomirs targeting miR-22-3p.

METHODS

In silico analyses at the gene, pathway, and network levels reveal both distinct and overlapping targets of hsa-miR-22-3p and its host gene, MIR22HG. While pharmacological antagomirs targeting miR-22-3p consistently improve various metabolic parameters in cell culture and animal models across multiple studies, genetic knockout of MIR22HG yields inconsistent results among different research groups.

RESULTS

Additionally, MIR22HG functions as a circulating endogenous RNA (ceRNA) or "sponge" that simultaneously modulates multiple miRNA-mRNA interactions by competing for binding to several miRNAs.

CONCLUSIONS

From a therapeutic viewpoint, genetic inactivation of a lncMIRHG and pharmacologic antagonism of the guide strand of its related intragenic miRNA produce different results. This should be expected as lncMIRHGs play dual roles, both as lncRNA and as a source for primary miRNA transcripts.

Computational Prediction of Gene Regulation by lncRNAs

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200 nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells

Improving the selectivity and effectiveness of drugs represents a crucial issue for future therapeutic developments in immuno-oncology. Traditional bulk transcriptomics faces limitations in this context for the early phase of target discovery as resulting gene expression levels represent the average measure from multiple cell populations. Alternatively, single cell RNA sequencing can dive into unique cell populations transcriptome, facilitating the identification of specific targets. Here, we generated Tumor-Infiltrating regulatory T cells (TI-Tregs) and exhausted T cells (Tex) gene signatures from a single cell RNA-seq pan-cancer T cell atlas. To overcome noise and sparsity inherent to single cell transcriptomics, we then propagated the gene signatures by diffusion in a protein-protein interaction network using the Patrimony high-throughput computing platform. This methodology enabled the refining of signatures by rescoring genes based on their biological connectivity and shed light not only on processes characteristics of TI-Treg and Tex development and functions but also on their immunometabolic specificities. The combined use of single cell transcriptomics and network propagation may thus represent an innovative and effective methodology for the characterization of cell populations of interest and eventually the development of new therapeutic strategies in immuno-oncology.

Profiling of snoRNAs in Exosomes Secreted from Cells Infected with Influenza A Virus

Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from exosomes of influenza (IAV)-infected and non-infected MDCK cells using high-throughput sequencing. The analysis revealed 133 significantly differentially regulated snoRNAs (131 upregulated and 2 downregulated), including 93 SNORD, 38 SNORA, and 2 SCARNA. The most upregulated was SNORD58 (log2FoldChange = 9.61), while the only downregulated snoRNAs were SNORD3 (log2FC = -2.98) and SNORA74 (log2FC = -2.67). Several snoRNAs previously described as involved in viral infections were upregulated, including SNORD27, SNORD28, SNORD29, SNORD58, and SNORD44. In total, 533 interactors of dysregulated snoRNAs were identified using the RNAinter database with an assigned confidence score ≥ 0.25. The main groups of predicted interactors were transcription factors (TFs, 169 interactors) and RNA-binding proteins (RBPs, 130 interactors). Among the most important were pioneer TFs such as POU5F1, SOX2, CEBPB, and MYC, while in the RBP category, notable interactors included Polr2a, TNRC6A, IGF2BP3, and FMRP. Our results suggest that snoRNAs are involved in pro-viral activity, although follow-up studies including experimental validation would be beneficial.

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Furthermore, these studies link localization to RNA isoform expression, especially 3' Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enables the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single-cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3' UTR processing and detected examples of significant correlations between 3' UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the mouse brain highly correlated with regulated 3' UTR processing that includes the use of unannotated, but highly conserved, 3' ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3' UTRs for functional prediction and study. In these studies of data from mice, SPRAWL predicts that 3' UTR regulation of subcellular localization may be more pervasive than currently known.

Localization is the key to action: regulatory peculiarities of lncRNAs

To understand the transcriptomic profile of an individual cell in a multicellular organism, we must comprehend its surrounding environment and the cellular space where distinct molecular stimuli responses are located. Contradicting the initial perception that RNAs were nonfunctional and that only a few could act in chromatin remodeling, over the last few decades, research has revealed that they are multifaceted, versatile regulators of most cellular processes. Among the various RNAs, long non-coding RNAs (LncRNAs) regulate multiple biological processes and can even impact cell fate. In this sense, the subcellular localization of lncRNAs is the primary determinant of their functions. It affects their behavior by limiting their potential molecular partner and which process it can affect. The fine-tuned activity of lncRNAs is also tissue-specific and modulated by their cis and trans regulation. Hence, the spatial context of lncRNAs is crucial for understanding the regulatory networks by which they influence and are influenced. Therefore, predicting a lncRNA's correct location is not just a technical challenge but a critical step in understanding the biological meaning of its activity. Hence, examining these peculiarities is crucial to researching and discussing lncRNAs. In this review, we debate the spatial regulation of lncRNAs and their tissue-specific roles and regulatory mechanisms. We also briefly highlight how bioinformatic tools can aid research in the area.

Exploring an novel diagnostic gene of trastuzumab-induced cardiotoxicity based on bioinformatics and machine learning

Trastuzumab (Tra)-induced cardiotoxicity (TIC) is a serious side effect of cancer chemotherapy, which can seriously harm the health of cancer patients. However, there is currently a lack of effective and reliable biomarkers for the early diagnosis of TIC in clinical practice. Therefore, we screened the TIC candidate diagnostic gene solute carrier family 6 member 6 (SLC6A6) by combining multi-machine learning algorithm based on bioinformatics. In addition, cross-validation showed that SLC6A6 had a consistent expression trend in multi-data-sets. To further explore the diagnostic capability of SLC6A6 in TIC, we constructed a nomogram diagnostic model based on SLC6A6 expression level, and receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis proved that SLC6A6 had good diagnostic capability. In order to further verify the TIC expression of SLC6A6 in the real world, we have constructed cell and animal models. Animal experiments showed that left ventricular ejection fraction (LVEF) was significantly decreased (from 65.01 ± 3.30% and 351.32 ± 3.51%, p < 0.0001) after Tra injection, and severe cardiac function was impaired. Similarly, RT-QPCR demonstrated that SLC6A6 was significantly downregulated in Tra-treated cardiomyocytes in vitro and in vivo. Our study suggests that the differential expression of SLC6A6 in vitro and in vivo models is associated with TIC, which may be a candidate diagnostic gene for the early occurrence and development of TIC and a potential therapeutic target.

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

The Oncogenic Role of VWA8-AS1, a Long Non-Coding RNA, in Epstein-Barr Virus-Associated Oral Squamous Cell Carcinoma: An Integrative Transcriptome and Functional Analysis

Dysregulated long non-coding RNA (lncRNA) expression is linked to various cancers and may be influenced by oncogenic Epstein-Barr virus (EBV) infection, a known and detectable risk factor in oral squamous cell carcinoma (OSCC) patients. However, research on the oncogenic role of EBV-induced lncRNAs in OSCC is limited. To identify lncRNA-associated EBV infection and OSCC carcinogenesis, the differential expression of RNA-seq datasets from paired normal adjacent and OSCC tissues, and microarray data from EBV-negative and EBV-positive SCC25 cells, were identified and selected, respectively, for interaction, functional analysis, and CCK-8 cell proliferation, wound healing, and invasion Transwell assays. In OSCC tissues, 6731 differentially expressed lncRNAs were identified when compared to normal tissues from RNA-seq datasets, with 295 linked to EBV-induced OSCC carcinogenesis from microarray datasets. The EBV-induced lncRNA VWA8-AS1 showed significant upregulation in EBV-positive SCC25 cells and EBV-infected adjacent and OSCC tissue samples. VWA8-AS1 potentially promotes OSCC via the lncRNA-miRNA-mRNA axis or direct protein interactions, affecting various cellular processes. Studies in OSCC cell lines revealed that elevated VWA8-AS1 levels enhanced cell migration and invasion. This study demonstrates VWA8-AS1's contribution to tumor progression and possible interactions with its targets in OSCC, offering insights for future research on functional mechanisms and therapeutic targets in EBV-associated OSCC.

Emerging roles of long non-coding RNA FOXP4-AS1 in human cancers: From molecular biology to clinical application

Forkhead box P4 antisense RNA 1 (FOXP4-AS1) is a long non-coding RNA (lncRNA) situated on the human chromosome 6p21.1 locus. Previous research has demonstrated that FOXP4-AS1 is dysregulated in various cancers and exhibits a dual purpose as a tumor suppressor or oncogene in specific types of cancer. The levels of FOXP4-AS1 are significantly correlated with clinical features of cancer as well as prognosis. Additionally, FOXP4-AS1 is stimulated by transcription factors ATF3, YY1, PAX5, and SP4. The molecular mechanisms of FOXP4-AS1 in cancer are quite complex. It competitively sponges multiple miRNAs, bidirectionally regulates the levels of host gene FOXP4, activates the PI3K/AKT, Wnt/β-catenin, and ERK/MAPK signaling pathways, and recruits chromatin-modifying enzymes or interacts with other proteins to regulate malignant phenotypes of tumors, including proliferation, invasion, epithelial-mesenchymal transition (EMT), and angiogenesis. In this review, we provide an overview of the latest developments in FOXP4-AS1 oncology research, outlines its molecular regulatory networks in cancer, and discusses its prospective relevance as a cancer therapeutic target as well as a biomarker for prognosis and diagnosis.

Integration of genomic and transcriptomic layers in RNA-Seq data leads to protein interaction modules with improved Alzheimer's disease associations

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it is currently untreatable. RNA sequencing (RNA-Seq) is commonly used in the literature to identify AD-associated molecular mechanisms by analysing changes in gene expression. RNA-Seq data can also be used to detect genomic variants, enabling the identification of the genes with a higher load of deleterious variants in patients compared with controls. Here, we analysed AD RNA-Seq datasets to obtain differentially expressed genes and genes with a higher load of pathogenic variants in AD, and we combined them in a single list. We mapped these genes on a human protein-protein interaction network to discover subnetworks perturbed by AD. Our results show that utilizing gene pathogenicity information from RNA-Seq data positively contributes to the disclosure of AD-related mechanisms. Moreover, dividing the discovered subnetworks into highly connected modules reveals a clearer picture of altered molecular pathways that, otherwise, would not be captured. Repeating the whole pipeline with human metabolic network genes led to results confirming the positive contribution of gene pathogenicity information and enabled a more detailed identification of altered metabolic pathways in AD.

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network

Investigating the associations between circRNA and diseases is vital for comprehending the underlying mechanisms of diseases and formulating effective therapies. Computational prediction methods often rely solely on known circRNA-disease data, indirectly incorporating other biomolecules' effects by computing circRNA and disease similarities based on these molecules. However, this approach is limited, as other biomolecules also play significant roles in circRNA-disease interactions. To address this, we construct a comprehensive heterogeneous network incorporating data on human circRNAs, diseases, and other biomolecule interactions to develop a novel computational model, circ2DGNN, which is built upon a heterogeneous graph neural network. circ2DGNN directly takes heterogeneous networks as inputs and obtains the embedded representation of each node for downstream link prediction through graph representation learning. circ2DGNN employs a Transformer-like architecture, which can compute heterogeneous attention score for each edge, and perform message propagation and aggregation, using a residual connection to enhance the representation vector. It uniquely applies the same parameter matrix only to identical meta-relationships, reflecting diverse parameter spaces for different relationship types. After fine-tuning hyperparameters via five-fold cross-validation, evaluation conducted on a test dataset shows circ2DGNN outperforms existing state-of-the-art(SOTA) methods.

sChemNET: a deep learning framework for predicting small molecules targeting microRNA function

MicroRNAs (miRNAs) have been implicated in human disorders, from cancers to infectious diseases. Targeting miRNAs or their target genes with small molecules offers opportunities to modulate dysregulated cellular processes linked to diseases. Yet, predicting small molecules associated with miRNAs remains challenging due to the small size of small molecule-miRNA datasets. Herein, we develop a generalized deep learning framework, sChemNET, for predicting small molecules affecting miRNA bioactivity based on chemical structure and sequence information. sChemNET overcomes the limitation of sparse chemical information by an objective function that allows the neural network to learn chemical space from a large body of chemical structures yet unknown to affect miRNAs. We experimentally validated small molecules predicted to act on miR-451 or its targets and tested their role in erythrocyte maturation during zebrafish embryogenesis. We also tested small molecules targeting the miR-181 network and other miRNAs using in-vitro and in-vivo experiments. We demonstrate that our machine-learning framework can predict bioactive small molecules targeting miRNAs or their targets in humans and other mammalian organisms.

MiR-125b-5p alleviates pulmonary fibrosis by inhibiting TGFβ1-mediated epithelial-mesenchymal transition via targeting BAK1

This study explores the role and potential mechanisms of microRNA-125b-5p (miR-125b-5p) in pulmonary fibrosis (PF). PF is a typical outcome of many chronic lung diseases, with poor prognosis and the lack of appropriate medical treatment because PF's molecular mechanisms remain poorly understood. In this study, using in vitro and in vivo analyses, we find that miR-125b-5p is likely a potent regulator of lung fibrosis. The findings reveal that, on the one hand, miR-125b-5p not only specifically decreases in the epithelial-mesenchymal transition (EMT) of lung epithelial cells, but also shows a downregulation trend in the lung tissues of mice with PF. On the other hand, overexpression of miR-125b-5p on the cellular and animal levels downregulates EMT and fibrotic phenotypes, respectively. To clarify the molecular mechanism of the "therapeutic" effect of miR-125b-5p, we use the target prediction tool combined with a dual luciferase assay and complete a rescue experiment by constructing the overexpression vector of the target gene Bcl-2 homologous antagonist/ killer (BAK1), thus confirming that miR-125b-5p can effectively inhibit EMT and fibrosis process by targeting BAK1 gene. MiR-125b-5p inhibits the EMT in lung epithelial cells by negatively regulating BAK1, while overexpression of miR-125b-5p can alleviate lung fibrosis. The findings suggest that MiR-125b-5p/BAK1 can serve as a potential treatment target for PF.

Identification of molecular targets of Hypericum perforatum in blood for major depressive disorder: a machine-learning pharmacological study

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericum perforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD.

METHODS

We performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD.

RESULTS

Differential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking.

CONCLUSIONS

The active constituents of Hypericum perforatum can act on MDD and key targets and pathways of this action were identified.

Identification of key ubiquitination-related genes in gestational diabetes mellitus: A bioinformatics-driven study

Background and Aims

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that occurs during pregnancy. This study aimed to identify key ubiquitination-related genes associated with GDM pathogenesis.

Methods

Microarray data from GSE154377 was analyzed to identify differentially expressed genes (DEGs) in GDM vs normal pregnancy samples. Weighted gene co-expression network analysis was performed on ubiquitination-related genes. Functional enrichment, protein-protein interaction network, and TF-mRNA-miRNA interaction network analyses were conducted on differentially expressed ubiquitination-related genes (DE-URGs).

Results

We identified 2337 DEGs and 65 DE-URGs in GDM. Functional enrichment analysis of the 65 DE-URGs revealed involvement in protein ubiquitination and ubiquitin-dependent catabolic processes. Protein-protein interaction network analysis identified 8 hub genes, including MAP1LC3C, USP26, USP6, UBE2U, USP2, USP43, UCHL1, and USP44. ROC curve analysis showed these hub genes have high diagnostic accuracy for GDM (AUC > 0.6). The TF-mRNA-miRNA interaction network suggested USP2 and UCHL1 may be key ubiquitination genes in GDM.

Conclusion

In conclusion, this study contributes to our understanding of the molecular landscape of GDM by uncovering key ubiquitination-related genes. These findings may serve as a foundation for further investigations, offering potential biomarkers and therapeutic targets for clinical applications in GDM management.

The regulatory landscape of interacting RNA and protein pools in cellular homeostasis and cancer

Biological systems encompass intricate networks governed by RNA-protein interactions that play pivotal roles in cellular functions. RNA and proteins constituting 1.1% and 18% of the mammalian cell weight, respectively, orchestrate vital processes from genome organization to translation. To date, disentangling the functional fraction of the human genome has presented a major challenge, particularly for noncoding regions, yet recent discoveries have started to unveil a host of regulatory functions for noncoding RNAs (ncRNAs). While ncRNAs exist at different sizes, structures, degrees of evolutionary conservation and abundances within the cell, they partake in diverse roles either alone or in combination. However, certain ncRNA subtypes, including those that have been described or remain to be discovered, are poorly characterized given their heterogeneous nature. RNA activity is in most cases coordinated through interactions with RNA-binding proteins (RBPs). Extensive efforts are being made to accurately reconstruct RNA-RBP regulatory networks, which have provided unprecedented insight into cellular physiology and human disease. In this review, we provide a comprehensive view of RNAs and RBPs, focusing on how their interactions generate functional signals in living cells, particularly in the context of post-transcriptional regulatory processes and cancer.

Genome-Wide Mapping of RNA-Protein Associations via Sequencing

RNA-protein interactions are crucial for regulating gene expression and cellular functions, with their dysregulation potentially impacting disease progression. Systematically mapping these interactions is resource-intensive due to the vast number of potential RNA and protein interactions. Here, we introduce PRIM-seq (Protein-RNA Interaction Mapping by sequencing), a method for the concurrent de novo identification of RNA-binding proteins (RBPs) and the elucidation of their associated RNAs. PRIM-seq works by converting each RNA-protein pair into a unique chimeric DNA sequence, which is then decoded through DNA sequencing. Applied to two human cell types, PRIM-seq generated a comprehensive human RNA-protein association network (HuRPA), consisting of more than 350,000 RNA-proteins pairs involving approximately 7,000 RNAs and 11,000 proteins. The data revealed an enrichment of previously reported RBPs and RNA-protein interactions within HuRPA. We also identified LINC00339 as a protein-associating non-coding RNA and PHGDH as an RNA-associating protein. Notably, PHGDH interacts with BECN1 and ATF4 mRNAs, suppressing their protein expression and consequently inhibiting autophagy, apoptosis, and neurite outgrowth while promoting cell proliferation. PRIM-seq offers a powerful tool for discovering RBPs and RNA-protein associations, contributing to more comprehensive functional genome annotations.

Maternal immune activation upregulates the AU020206-IRFs-STAT1 axis in modulating cytokine production in the brain

Maternal immune activation (MIA) is reported to increase the risk of psychiatric disorders in the offspring. However, the underlying mechanism remains unclear. Methods: We constructed a MIA mouse model by intraperitoneal injection of LPS into pregnant mice and evaluated the behaviors and gene expression profiles in the brains of the female and male offspring, respectively. Results: We found that the MIA female offspring exhibited increased anxiety and a large number of differentially expressed genes (DEGs) in the brain, which were enriched with candidate gene sets of psychiatric disorders and immune functions. In contrast, the MIA male offspring exhibited no significant abnormal behaviors and only a small number of DEGs that were not enriched with disease genes and immune functions. Therefore, we further pursued the downstream study on the molecular mechanism underlying the increased anxiety in the female offspring. We identified the lncRNA AU020206-IRFs-STAT1-cytokine axis by integrating lncRNA-protein interaction data and TF-promoter interaction data, and verified the axis in vitro and in vivo. Conclusion: This study illustrates that MIA upregulates the AU020206-IRFs-STAT1 axis in controlling the brain immunity linked to abnormal behaviors, providing a basis for understanding the role of MIA in psychiatric disorders.

PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models, which can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

Deep learning of multimodal networks with topological regularization for drug repositioning

MOTIVATION

Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies.

RESULTS

STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git .

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.

Methylome analysis of endothelial cells suggests new insights on sporadic brain arteriovenous malformation

Arteriovenous malformation of the brain (bAVM) is a vascular phenotype related to brain defective angiogenesis. Involved vessels show impaired expression of vascular differentiation markers resulting in the arteriolar to venule direct shunt. In order to clarify aberrant gene expression occurring in bAVM, here we describe results obtained by methylome analysis performed on endothelial cells (ECs) isolated from bAVM specimens, compared to human cerebral microvascular ECs. Results were validated by quantitative methylation-specific PCR and quantitative realtime-PCR. Differential methylation events occur in genes already linked to bAVM onset, as RBPJ and KRAS. However, among differentially methylated genes, we identified EPHB1 and several other loci involved in EC adhesion as well as in EC/vascular smooth muscle cell (VSMC) crosstalk, suggesting that only endothelial dysfunction might not be sufficient to trigger the bAVM phenotype. Moreover, aberrant methylation pattern was reported for many lncRNA genes targeting transcription factors expressed during neurovascular development. Among these, the YBX1 that was recently shown to target the arteridin coding gene. Finally, in addition to the conventional CpG methylation, we further considered the role of impaired CHG methylation, mainly occurring in brain at embryo stage. We showed as differentially CHG methylated genes are clustered in pathways related to EC homeostasis, as well as to VSMC-EC crosstalk, suggesting as impairment of this interaction plays a prominent role in loss of vascular differentiation, in bAVM phenotype.

Role of mitochondrial lncRNA GAS5 in the pathogenesis of Multiple Sclerosis: interfering with the release of miR-651-5p-enriched exosomes from microglia cells.. [DOI: 10.21203/rs.3.rs-4673502/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Abstract

Multiple Sclerosis (MS) arises from immune system dysfunction and damage to the myelin sheath within the CNS. At various stages of MS, analyzing blood samples has the potential to help differentiate between individuals with MS and those without, detect the early onset of the disease, or distinguish between different types of MS. Long non-coding RNA (lncRNA) growth arrest-specific 5 (GAS5) serves a pivotal role in governing cell growth and arrest, as well as modulating the immune system by acting as the glucocorticoid receptor. This research aims to explore GAS5 expression in peripheral blood mononuclear cells (PBMCs) of Relapsing-Remitting MS (RRMS) patients and evaluate its targeted miRNAs in exosomes. Our findings revealed an elevated expression level of GAS5 in RRMS patients in contrast to control groups (P-value = 0.0121), and GAS5 demonstrated diagnostic potential for RRMS, with an AUC of 0.6498. The in-silico analysis revealed that hsa-miR-651-5p emerged as a central component in the regulatory network of GAS5, with its target genes primarily implicated in transcription and apoptosis regulation. Additionally, RUNX1, YY1, GSK3B, FMR1, and KLF2 were identified as entities linked to GAS5. In this regard, our findings indicate a significant association between redox imbalance and the dysregulation of GAS5 and miR-651-5p expression in the HMC3 cell line. Given the increased expression of miR-651-5p in exosomes under stress, the transport of miR-651-5p into serum exosomes may be varied and related to GAS5 expression in PBMCs of MS subtypes. In conclusion, GAS5 can serve as a mitochondrial marker for RRMS, and redox imbalance appears to influence its regulation, highlighting its role in the cellular stress response. Future research is suggested to focus on elucidating the molecular mechanisms underlying GAS5/miR-651-5p interaction to better understand this process.

circADAMTS6 via stabilizing CAMK2A is involved in smoking-induced emphysema through driving M2 macrophage polarization

Cigarette smoke (CS), an indoor environmental pollutant, is a prominent risk factor for emphysema, which is a pathological feature of chronic obstructive pulmonary disease (COPD). Emerging function of circRNAs in immune responses and disease progression shed new light to explore the pathogenesis of emphysema. In this research, we demonstrated, by single-cell RNA sequencing (scRNAseq), that the ratio of M2 macrophages were increased in lung tissues of humans and mice with smoking-related emphysema. Further, our data showed that circADAMTS6 was associated with cigarette smoke extract (CSE)-induced M2 macrophage polarization. Mechanistically, in macrophages, circADAMTS6 stabilized CAMK2A mRNA via forming a circADAMTS6/IGF2BP2/CAMK2A RNA-protein ternary complex to activate CREB, which drives M2 macrophage polarization and leads to emphysema. In addition, in macrophages of mouse lung tissues, downregulation of circADAMTS6 reversed M2 macrophage polarization, the proteinase/anti-proteinase imbalance, and the elastin degradation, which protecting against CS-induced emphysema. Moreover, for macrophages and in a model with co-cultured lung organoids, the target of circADAMTS6 restored the growth of lung organoids compared to CSE-treated macrophages. Our results also demonstrated that, for smokers and COPD smokers, elevation of circADAMTS6 negatively correlated with lung function. Overall, this study reveals a novel mechanism for circADAMTS6-driven M2 macrophage polarization in smoking-related emphysema and postulates that circADAMTS6 could serve as a diagnostic and therapeutic marker for smoking-related emphysema.

CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral-host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition-transition-distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein-human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage.

Epigenetic mechanism of RBM15 in affecting cisplatin resistance in laryngeal carcinoma cells by regulating ferroptosis

Laryngeal carcinoma (LC) is a common cancer of the respiratory tract. This study aims to investigate the role of RNA-binding motif protein 15 (RBM15) in the cisplatin (DDP) resistance of LC cells. LC-DDP-resistant cells were constructed. RBM15, lysine-specific demethylase 5B (KDM5B), lncRNA Fer-1 like family member 4 (FER1L4), lncRNA KCNQ1 overlapping transcript 1 (KCNQ1OT1), glutathione peroxidase 4 (GPX4), and Acyl-CoA synthetase long-chain family (ACSL4) was examined. Cell viability, IC50, and proliferation were assessed after RBM15 downregulation. The enrichment of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) and N6-methyladenosine (m6A) on KDM5B was analyzed. KDM5B mRNA stability was measured after actinomycin D treatment. A tumor xenograft assay was conducted to verify the role of RBM15 in LC. Results showed that RBM15 was upregulated in LC and its knockdown decreased IC50, cell viability, proliferation, glutathione, and upregulated iron ion content, ROS, malondialdehyde, ACSL4, and ferroptosis. Mechanistically, RBM15 improved KDM5B stability in an IGF2BP3-dependent manner, resulting in FER1L4 downregulation and GPX4 upregulation. KDM5B increased KCNQ1OT1 and inhibited ACSL4. KDM5B/KCNQ1OT1 overexpression or FER1L4 knockdown promoted DDP resistance in LC by inhibiting ferroptosis. In conclusion, RBM15 promoted KDM5B expression, and KDM5B upregulation inhibited ferroptosis and promoted DDP resistance in LC by downregulating FER1L4 and upregulating GPX4, as well as by upregulating KCNQ1OT1 and inhibiting ACSL4. Silencing RBM15 inhibited tumor growth in vivo.