Versatile interactions and bioinformatics analysis of noncoding RNAs

piRNAs as emerging biomarkers and physiological regulatory molecules in cardiovascular disease

Cardiovascular diseases (CVD) represent one of the most considerable global health threats, owing to their high incidence and mortality rates. Despite the ongoing advancements in detection, prevention, treatment, and prognosis of CVD, which have resulted in a decline in both incidence and mortality rates, CVD remains a major public health concern. Therefore, novel diagnostic biomarkers and therapeutic interventions are imperative to minimise the risk of CVD. Non-coding RNAs (ncRNAs) have recently gained increasing attention, with PIWI-interacting RNAs (piRNAs) emerging as a class of small ncRNAs traditionally recognised for their role in silencing transposons within cells. Although the functional roles of PIWI proteins and piRNAs in human cells remain unclear, growing evidence suggests that these molecules are gradually becoming valuable biomarkers for the diagnosis and treatment of CVD. This review provides a comprehensive summary of the latest studies on piRNAs in CVD. This review discusses the roles of piRNAs in various cardiovascular subtypes, including myocardial hypertrophy, heart failure, myocardial infarction, and cardiac regeneration. The perceived insights may contribute novel perspectives for the diagnosis and treatment of CVD.

The role of long non-coding RNAs in the development of diabetic kidney disease and the involved clinical application

Diabetic kidney disease (DKD), one of the common microvascular complications of diabetes, is increasing in prevalence worldwide and can lead to End-stage renal disease. However, there are still gaps in our understanding of the pathophysiology of DKD, and both current clinical diagnostic methods and treatment strategies have drawbacks. According to recent research, long non-coding RNAs (lncRNAs) are intimately linked to the developmental process of DKD and could be viable targets for clinical diagnostic decisions and therapeutic interventions. Here, we review recent insights gained into lncRNAs in pathological changes of DKD such as mesangial expansion, podocyte injury, renal tubular injury, and interstitial fibrosis. We also discuss the clinical applications of DKD-associated lncRNAs as diagnostic biomarkers and therapeutic targets, as well as their limitations and challenges, to provide new methods for the prevention, diagnosis, and treatment of DKD.

Non-coding RNAs and exosomal non-coding RNAs in diabetic retinopathy: A narrative review

Diabetic retinopathy (DR) is a devastating complication of diabetes, having extensive and resilient effects on those who suffer from it. As yet, the underlying cell mechanisms of this microvascular disorder are largely unclear. Recently, growing evidence suggests that epigenetic mechanisms can be responsible for gene deregulation leading to the alteration of key processes in the development and progression of DR, in addition to the widely recognized pathological mechanisms. It is noteworthy that seemingly unending epigenetic modifications, caused by a prolonged period of hyperglycemia, may be a prominent factor that leads to metabolic memory, and brings epigenetic entities such as non-coding RNA into the equation. Consequently, further investigation is necessary to truly understand this mechanism. Exosomes are responsible for carrying signals from cells close to the vasculature that are participating in abnormal signal transduction to faraway organs and cells by sailing through the bloodstream. These signs indicate metabolic disorders. With the aid of their encased structure, they can store diverse signaling molecules, which then can be dispersed into the blood, urine, and tears. Herein, we summarized various non-coding RNAs (ncRNAs) that are related to DR pathogenesis. Moreover, we highlighted the role of exosomal ncRNAs in this disease.

Human mtDNA-Encoded Long ncRNAs: Knotty Molecules and Complex Functions

Until a few decades ago, most of our knowledge of RNA transcription products was focused on protein-coding sequences, which were later determined to make up the smallest portion of the mammalian genome. Since 2002, we have learnt a great deal about the intriguing world of non-coding RNAs (ncRNAs), mainly due to the rapid development of bioinformatic tools and next-generation sequencing (NGS) platforms. Moreover, interest in non-human ncRNAs and their functions has increased as a result of these technologies and the accessibility of complete genome sequences of species ranging from Archaea to primates. Despite not producing proteins, ncRNAs constitute a vast family of RNA molecules that serve a number of regulatory roles and are essential for cellular physiology and pathology. This review focuses on a subgroup of human ncRNAs, namely mtDNA-encoded long non-coding RNAs (mt-lncRNAs), which are transcribed from the mitochondrial genome and whose disparate localisations and functions are linked as much to mitochondrial metabolism as to cellular physiology and pathology.

Circular RNAs: A New Approach to Multiple Sclerosis

Multiple sclerosis, a condition characterised by demyelination and axonal damage in the central nervous system, is due to autoreactive immune cells that recognise myelin antigens. Alteration of the immune balance can promote the onset of immune deficiencies, loss of immunosurveillance, and/or development of autoimmune disorders such as MS. Numerous enzymes, transcription factors, signal transducers, and membrane proteins contribute to the control of immune system activity. The "transcriptional machine" of eukaryotic cells is a complex system composed not only of mRNA but also of non-coding elements grouped together in the set of non-coding RNAs. Recent studies demonstrate that ncRNAs play a crucial role in numerous cellular functions, gene expression, and the pathogenesis of many immune disorders. The main purpose of this review is to investigate the role of circular RNAs, a previously unknown class of non-coding RNAs, in MS's pathogenesis. CircRNAs influence post-transcriptional control, expression, and functionality of a microRNA and epigenetic factors, promoting the development of typical MS abnormalities such as neuroinflammation, damage to neuronal cells, and microglial dysfunction. The increase in our knowledge of the role of circRNAs in multiple sclerosis could, in the future, modify the common diagnostic-therapeutic criteria, paving the way to a new vision of this neuroimmune pathology.

Regulatory Roles of Non-Coding RNAs in Cancer

For several decades, scientific research in cancer biology has focused mainly on the involvement of protein-coding genes [...].

Differentiation of Human Wharton Jelly Mesenchymal Stem Cells into Germ-Like Cells; emphasis on evaluation of Germ-long non-coding RNAs

BACKGROUND

The proliferation and differentiation of stem cells into Germ-Like Cells (GLCs) is mediated by several growth factors and specific genes, of which some are related to long non-coding RNAs (lncRNAs). We have developed a modified differentiation process and identified a panel of GermlncRNAs related to GLCs.

METHODS

Human Wharton Jelly Mesenchymal Stem Cells were treated with 25 ng/ml Bone Morphogenetic Protein (BMP)-4 and 10^- 5 M all-trans retinoic acid to differentiate them into germ-like cells. To confirm the differentiation, changes in the expression of Oct-4, C-kit, Stella, and Vasa genes were assessed using quantitative Real-Time PCR (qPCR) and immunocytochemistry. QPCR was also used before and after differentiation to evaluate the changes in a lncRNA panel, using a 96-well array. Statistical analysis of the data was performed by SPSS 21.

RESULTS

After 21 days of induction, the HWJ-MSCs derived germ-like cells were formed. Also, qPCR and immunocytochemistry showed that the pluripotent Oct4 marker was expressed in the undifferentiated HWJ-MSCs, but its expression gradually decreased in the differentiated cells. C-kit was expressed on days 7, 14, and 21 of differentiation. Both GLC markers of Stella and Vasa genes/proteins were present only in differentiated cells. Of the 44 lncRNA genes array, 36 of them showed an increase and eight genes showed a decrease.

CONCLUSION

Our study showed that BMP4 and RA are effective in inducing HWJ-MSCs differentiation into GLCs. In addition, our study for the first time showed changes in the lncRNAs expression during the differentiation of HWJ-MSCs into GLCs by using BMP4 and RA.

Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.

Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose

To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments. Atom-atom electrostatic interaction energies are subsequently computed using the predicted atomic multipole moments in the pilot system pentose of RNA. Here, the performance of the five methods is compared in terms of both the multipole moment prediction errors and the electrostatic energy prediction errors. For the predicted high-rank multipole moments of the four elements (O, C, N, and H) in capped pentose, ARDGPR and Kriging consistently outperform the other three methods. Therefore, the multipole moments predicted by the two best methods of ARDGPR and Kriging are then used to predict electrostatic interaction energy of each pentose. Finally, the absolute average energy errors of ARDGPR and Kriging are 1.83 and 4.33 kJ mol^-1, respectively. Compared to Kriging, the ARDGPR method achieves a 58% decrease in the absolute average energy error. These satisfactory results demonstrated that the ARDGPR method with the strong feature extraction ability can predict the electrostatic interaction energy of pentose in RNA correctly and reliably.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer

Background

The changes of lipid metabolism have been implicated in the development of many tumors, but its role in breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA.

Methods

We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set. Ingenuity Pathway Analysis (IPA) was applied to identify the potential pathways and functions of Differentially Expressed Genes (DEGs) related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature. Functional enrichment analysis of prognostic genes was achieved by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, clinical follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were screened by Connectivity Map (CMap) database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Furthermore, we separately validated the selected marker genes in BRCA samples and human breast cancer cell lines (MCF-7, MDA-MB-231).

Results

IPA and functional enrichment analysis demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in many lipid metabolism and BRCA pathological signatures. The prognostic classifier we constructed comprising SDC1 and SORBS1 can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes, of which 16 compounds could target both two prognostic genes were identified by CTD. The functions of the two prognostic genes in breast cancer cells were verified by cell function experiments.

Conclusion

Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This result highlighted a new perspective on the metabolic exploration of BRCA.

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.

MicroRNA Databases and Tools

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

Advances in the identification of long non-coding RNA binding proteins

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.

Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method

The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.

Integrative Analysis of the Roles of lncRNAs and mRNAs in Itaconate-Mediated Protection Against Liver Ischemia-Reperfusion Injury in Mice

Purpose

Itaconate is well known for its strong anti-inflammatory and antioxidant effect, but little is known about the potential role of long non-coding RNAs (lncRNAs) in the underlying mechanisms of hepatic ischemia-reperfusion (IR) injury. The aim of our study is to identify lncRNAs related to IR injury and itaconate-mediated protection and to demonstrate the mechanism by which itaconate acts in liver IR injury from the new perspective of lncRNAs.

Methods

4-Octyl itaconate (OI), a membrane-permeable derivative of itaconate, was used as a substitute for itaconate in our study. By using a mouse model of hepatic IR injury, serum and liver samples were collected to measure indexes of liver injury. Then, the liver samples of the mice were subjected to RNA sequencing (RNA-seq) and subsequent bioinformatics analysis.

Results

Itaconate attenuated liver IR injury. A total of 138 lncRNAs and 156 messenger RNAs (mRNAs) were markedly differentially expressed in the IR-damaged liver tissues pretreated with OI compared with the matched liver tissues treated with vehicle. Functional analysis indicated that lncRNAs may indirectly participate in the effects of itaconate. Furthermore, 41 mRNAs were examined for the protein-protein interaction (PPI) network analysis, and a key gene cluster was defined. Then, combined the coexpression analysis and the cis and trans regulatory function prediction of lncRNAs, some "candidate" lncRNA-mRNA pairs which might relate to itaconate-mediated liver protection were identified, while the relationship requires future validation.

Conclusion

Our study revealed that itaconate could protect the liver against IR injury and that lncRNAs might play a role in this process. Our study provides a novel way to investigate the mechanism by which itaconate affects hepatic IR injury and exerts its anti-inflammatory and antioxidative stress effects.

The Non-Coding RNA Landscape in IgA Nephropathy-Where Are We in 2021?

IgA nephropathy (IgAN) is the most commonly diagnosed primary glomerulonephritis worldwide. It is a slow progressing disease with approximately 30% of cases reaching end-stage kidney disease within 20 years of diagnosis. It is currently only diagnosed by an invasive biopsy and treatment options are limited. However, the current surge in interest in RNA interference is opening up new horizons for the use of this new technology in the field of IgAN management. A greater understanding of the fundamentals of RNA interference offers exciting possibilities both for biomarker discovery and, more importantly, for novel therapeutic approaches to target key pathogenic pathways in IgAN. This review aims to summarise the RNA interference literature in the context of microRNAs and their association with the multifaceted aspects of IgA nephropathy.

The Mechanistic Roles of ncRNAs in Promoting and Supporting Chemoresistance of Colorectal Cancer

Colorectal Cancer (CRC) is one of the most common gastrointestinal malignancies which has quite a high mortality rate. Despite the advances made in CRC treatment, effective therapy is still quite challenging, particularly due to resistance arising throughout the treatment regimen. Several studies have been carried out to identify CRC chemoresistance mechanisms, with research showing different signalling pathways, certain ATP binding cassette (ABC) transporters and epithelial mesenchymal transition (EMT), among others to be responsible for the failure of CRC chemotherapies. In the last decade, it has become increasingly evident that certain non-coding RNA (ncRNA) families are involved in chemoresistance. Research investigations have demonstrated that dysregulation of microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) contribute towards promoting resistance in CRC via different mechanisms. Considering the currently available data on this phenomenon, a better understanding of how these ncRNAs participate in chemoresistance can lead to suitable solutions to overcome this problem in CRC. This review will first focus on discussing the different mechanisms of CRC resistance identified so far. The focus will then shift onto the roles of miRNAs, lncRNAs and circRNAs in promoting 5-fluorouracil (5-FU), oxaliplatin (OXA), cisplatin and doxorubicin (DOX) resistance in CRC, specifically using ncRNAs which have been recently identified and validated under in vivo or in vitro conditions.

A novel riboswitch classification based on imbalanced sequences achieved by machine learning

Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches. The Python source code is available at https://github.com/Seasonsling/riboswitch.

Non-Coding RNAs and their Integrated Networks

Eukaryotic genomes are pervasively transcribed. Besides protein-coding RNAs, there are different types of non-coding RNAs that modulate complex molecular and cellular processes. RNA sequencing technologies and bioinformatics methods greatly promoted the study of ncRNAs, which revealed ncRNAs' essential roles in diverse aspects of biological functions. As important key players in gene regulatory networks, ncRNAs work with other biomolecules, including coding and non-coding RNAs, DNAs and proteins. In this review, we discuss the distinct types of ncRNAs, including housekeeping ncRNAs and regulatory ncRNAs, their versatile functions and interactions, transcription, translation, and modification. Moreover, we summarize the integrated networks of ncRNA interactions, providing a comprehensive landscape of ncRNAs regulatory roles.