Deep learning based DNA:RNA triplex forming potential prediction

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

RNA-DNA triplexes: molecular mechanisms and functional relevance

Interactions of RNA with DNA are principles of gene expression control that have recently gained considerable attention. Among RNA-DNA interactions are R-loops and RNA-DNA hybrid G-quadruplexes, as well as RNA-DNA triplexes. It is proposed that RNA-DNA triplexes guide RNA-associated regulatory proteins to specific genomic locations, influencing transcription and epigenetic decision making. Although triplex formation initially was considered solely an in vitro event, recent progress in computational, biochemical, and biophysical methods support in vivo functionality with relevance for gene expression control. Here, we review the central methodology and biology of triplexes, outline paradigms required for triplex function, and provide examples of physiologically important triplex-forming long non-coding RNAs.

The biological role of lncRNAs in the acute lymphocytic leukemia: An updated review

The cause of leukemia, a common malignancy of the hematological system, is unknown. The structure of long non-coding RNAs (lncRNAs) is similar to mRNA but no ability to encode proteins. Numerous malignancies, including different forms of leukemia, are linked to Lnc-RNAs. It is verified that the carcinogenesis and growth of a variety of human malignancies are significantly influenced by aberrant lncRNA expression. The body of evidence linking various types of lncRNAs to the etiology of leukemia has dramatically increased during the past ten years. Some lncRNAs are therefore anticipated to function as novel therapeutic targets, diagnostic biomarkers, and clinical outcome predictions. Additionally, these lncRNAs may provide new therapeutic options and insight into the pathophysiology of diseases, particularly leukemia. Thus, this review outlines the present comprehension of leukemia-associated lncRNAs.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

Analyzes In Silico Indicate the lncRNAs MIR31HG and LINC00939 as Possible Epigenetic Inhibitors of the Osteogenic Differentiation in PDLCs

Chromatin conformation, DNA methylation pattern, transcriptional profile, and non-coding RNAs (ncRNAs) interactions constitute an epigenetic pattern that influences the cellular phenotypic commitment and impacts the clinical outcomes in regenerative therapies. Here, we investigated the epigenetic landscape of the SP7 transcriptor factor (SP7) and Distal-Less Homeobox 4 (DLX4) osteoblastic transcription factors (TFs), in human periodontal ligament mesenchymal cells (PDLCs) with low (l-PDLCs) and high (h-PDLCs) osteogenic potential. Chromatin accessibility (ATAC-seq), genome DNA methylation (Methylome), and RNA sequencing (RNA-seq) assays were performed in l- and h-PDLCs, cultured at 10 days in non-induced (DMEM) and osteogenic (OM) medium in vitro. Data were processed in HOMER, Genome Studio, and edgeR programs, and metadata was analyzed by online bioinformatics tools and in R and Python environments. ATAC-seq analyses showed the TFs genomic regions are more accessible in l-PDLCs than in h-PDLCs. In Methylome analyses, the TFs presented similar average methylation intensities (AMIs), without differently methylated probes (DMPs) between l- and h-PDLCs; in addition, there were no differences in the expression profiles of TFs signaling pathways. Interestingly, we identified the long non-coding RNAs (lncRNAs), MIR31HG and LINC00939, as upregulated in l-PDLCs, in both DMEM and OM. In the following analysis, the web-based prediction tool LncRRIsearch predicted RNA:RNA base-pairing interactions between SP7, DLX4, MIR31HG, and LINC00939 transcripts. The machine learning program TriplexFPP predicted DNA:RNA triplex-forming potential for the SP7 DNA site and for one of the LINC00939 transcripts (ENST00000502479). PCR data confirmed the upregulation of MIR31HG and LINC00939 transcripts in l-PDLCs (× h-PDLCs) in both DMEM and OM (p < 0.05); conversely, SP7 and DLX4 were downregulated, confirming those results observed in the RNA-Seq analysis. Together, these results indicate the lncRNAs MIR31HG and LINC00939 as possible epigenetic inhibitors of the osteogenic differentiation in PDLCs by (post)transcriptional and translational repression of the SP7 and DLX4 TFs.

3plex enables deep computational investigation of triplex forming lncRNAs

Long non-coding RNAs (lncRNAs) regulate gene expression through different molecular mechanisms, including DNA binding via the formation of RNA:DNA:DNA triple helices (TPXs). Despite the increasing amount of experimental evidence, TPXs investigation remains challenging. Here we present 3plex, a software able to predict TPX interactions in silico. Given an RNA sequence and a set of DNA sequences, 3plex integrates 1) Hoogsteen pairing rules that describe the biochemical interactions between RNA and DNA nucleotides, 2) RNA secondary structure prediction and 3) determination of the TPX thermal stability derived from a collection of TPX experimental evidences. We systematically collected and uniformly re-analysed published experimental lncRNA binding sites on human and mouse genomes. We used these data to evaluate 3plex performance and showed that its specific features allow a reliable identification of TPX interactions. We compared 3plex with the other available software and obtained comparable or even better accuracy at a fraction of the computation time. Interestingly, by inspecting collected data with 3plex we found that TPXs tend to be shorter and more degenerated than previously expected and that the majority of analysed lncRNAs can directly bind to the genome by TPX formation. Those results suggest that an important fraction of lncRNAs can exert its biological function through this mechanism. The software is available at https://github.com/molinerisLab/3plex.

Approaches for Modes of Action Study of Long Non-Coding RNAs: From Single Verification to Genome-Wide Determination

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides (nt) that are not translated into known functional proteins. This broad definition covers a large collection of transcripts with diverse genomic origins, biogenesis, and modes of action. Thus, it is very important to choose appropriate research methodologies when investigating lncRNAs with biological significance. Multiple reviews to date have summarized the mechanisms of lncRNA biogenesis, their localization, their functions in gene regulation at multiple levels, and also their potential applications. However, little has been reviewed on the leading strategies for lncRNA research. Here, we generalize a basic and systemic mind map for lncRNA research and discuss the mechanisms and the application scenarios of ‘up-to-date’ techniques as applied to molecular function studies of lncRNAs. Taking advantage of documented lncRNA research paradigms as examples, we aim to provide an overview of the developing techniques for elucidating lncRNA interactions with genomic DNA, proteins, and other RNAs. In the end, we propose the future direction and potential technological challenges of lncRNA studies, focusing on techniques and applications.

Recent Development in Biomedical Applications of Oligonucleotides with Triplex-Forming Ability

A DNA structure, known as triple-stranded DNA, is made up of three oligonucleotide chains that wind around one another to form a triple helix (TFO). Hoogsteen base pairing describes how triple-stranded DNA may be built at certain conditions by the attachment of the third strand to an RNA, PNA, or DNA, which might all be employed as oligonucleotide chains. In each of these situations, the oligonucleotides can be employed as an anchor, in conjunction with a specific bioactive chemical, or as a messenger that enables switching between transcription and replication through the triplex-forming zone. These data are also considered since various illnesses have been linked to the expansion of triplex-prone sequences. In light of metabolic acidosis and associated symptoms, some consideration is given to the impact of several low-molecular-weight compounds, including pH on triplex production in vivo. The review is focused on the development of biomedical oligonucleotides with triplexes.

Computational Methods to Study DNA:DNA:RNA Triplex Formation by lncRNAs

Long non-coding RNAs (lncRNAs) impact cell function via numerous mechanisms. In the nucleus, interactions between lncRNAs and DNA and the consequent formation of non-canonical nucleic acid structures seems to be particularly relevant. Along with interactions between single-stranded RNA (ssRNA) and single-stranded DNA (ssDNA), such as R-loops, ssRNA can also interact with double-stranded DNA (dsDNA) to form DNA:DNA:RNA triplexes. A major challenge in the study of DNA:DNA:RNA triplexes is the identification of the precise RNA component interacting with specific regions of the dsDNA. As this is a crucial step towards understanding lncRNA function, there exist several computational methods designed to predict these sequences. This review summarises the recent progress in the prediction of triplex formation and highlights important DNA:DNA:RNA triplexes. In particular, different prediction tools (Triplexator, LongTarget, TRIPLEXES, Triplex Domain Finder, TriplexFFP, TriplexAligner and Fasim-LongTarget) will be discussed and their use exemplified by selected lncRNAs, whose DNA:DNA:RNA triplex forming potential was validated experimentally. Collectively, these tools revealed that DNA:DNA:RNA triplexes are likely to be numerous and make important contributions to gene expression regulation.

Identification of Long Noncoding RNAs That Exert Transcriptional Regulation by Forming RNA-DNA Triplexes in Prostate Cancer

Long noncoding RNAs (lncRNAs) are involved in transcriptional regulation, and their deregulation is associated with the development of various human cancers, including prostate cancer (PCa). However, their underlying mechanisms remain unclear. In this study, lncRNAs that interact with DNA and regulate mRNA transcription in PCa were screened and identified to promote PCa development. First, 4195 protein-coding genes (PCGs, mRNAs) were obtained from the The Cancer Genome Atlas (TCGA) database, in which 1148 lncRNAs were differentially expressed in PCa. Then, 44,270 pairs of co-expression relationships were calculated between 612 lncRNAs and 2742 mRNAs, of which 42,596 (96%) were positively correlated. Among the 612 lncRNAs, 392 had the potential to interact with the promoter region to form DNA:DNA:RNA triplexes, from which lncRNA AD000684.2(AC002128.1) was selected for further validation. AC002128.1 was highly expressed in PCa. Furthermore, AD000684.2 positively regulated the expression of the correlated genes. In addition, AD000684.2 formed RNA-DNA triplexes with the promoter region of the regulated genes. Functional assays also demonstrated that lncRNA AD000684.2 promotes cell proliferation and motility, as well as inhibits apoptosis, in PCa cell lines. The results suggest that AD000684.2 could positively regulate the transcription of target genes via triplex structures and serve as a candidate prognostic biomarker and target for new therapies in human PCa.

Potential Involvement of LncRNAs in Cardiometabolic Diseases

Characterized by cardiovascular disease and diabetes, cardiometabolic diseases are a major cause of mortality around the world. As such, there is an urgent need to understand the pathogenesis of cardiometabolic diseases. Increasing evidence suggests that most of the mammalian genome are transcribed as RNA, but only a few percent of them encode for proteins. All of the RNAs that do not encode for proteins are collectively called non-protein-coding RNAs (ncRNAs). Among these ncRNAs, long ncRNAs (lncRNAs) are considered as missing keys to understand the pathogeneses of various diseases, including cardiometabolic diseases. Given the increased interest in lncRNAs, in this study, we will summarize the latest trend in the lncRNA research from the perspective of cardiometabolism and disease by focusing on the major risk factors of cardiometabolic diseases: obesity, cholesterol, diabetes, and hypertension. Because genetic inheritance is unavoidable in cardiometabolic diseases, we paid special attention to the genetic factors of lncRNAs that may influence cardiometabolic diseases.

A universal model of RNA.DNA:DNA triplex formation accurately predicts genome-wide RNA-DNA interactions

RNA.DNA:DNA triple helix (triplex) formation is a form of RNA-DNA interaction which regulates gene expression but is difficult to study experimentally in vivo. This makes accurate computational prediction of such interactions highly important in the field of RNA research. Current predictive methods use canonical Hoogsteen base pairing rules, which whilst biophysically valid, may not reflect the plastic nature of cell biology. Here, we present the first optimization approach to learn a probabilistic model describing RNA-DNA interactions directly from motifs derived from triplex sequencing data. We find that there are several stable interaction codes, including Hoogsteen base pairing and novel RNA-DNA base pairings, which agree with in vitro measurements. We implemented these findings in TriplexAligner, a program that uses the determined interaction codes to predict triplex binding. TriplexAligner predicts RNA-DNA interactions identified in all-to-all sequencing data more accurately than all previously published tools in human and mouse and also predicts previously studied triplex interactions with known regulatory functions. We further validated a novel triplex interaction using biophysical experiments. Our work is an important step towards better understanding of triplex formation and allows genome-wide analyses of RNA-DNA interactions.

LncRNA LYPLAL1-AS1 rejuvenates human adipose-derived mesenchymal stem cell senescence via transcriptional MIRLET7B inactivation

Background

Mesenchymal stem cell (MSC) senescence is a phenotype of aging. Long noncoding RNAs (lncRNAs) are emerging as potential key regulators of senescence. However, the role of lncRNAs in MSC senescence remains largely unknown.

Results

We performed transcriptome analysis in senescent human adipose-derived MSCs (hADSCs) and identified that the lncRNA LYPLAL1 antisense RNA1 (LYPLAL1-AS1) was significantly downregulated in senescent hADSCs. LYPLAL1-AS1 expression in peripheral blood was lower in middle-aged healthy donors than in young adult donors, and correlated negatively with age. Knockdown of LYPLAL1-AS1 accelerated hADSC senescence, while LYPLAL1-AS1 overexpression attenuated it. Chromatin isolation by RNA purification (ChIRP) sequencing indicated that LYPLAL1-AS1 bound to the MIRLET7B promoter region and suppressed its transcription activity, as demonstrated by dual-luciferase assay. miR-let-7b, the transcript of MIRLET7B, was upregulated during hADSC senescence and was regulated by LYPLAL1-AS1. Furthermore, miR-let-7b mimics promoted hADSC senescence, while the inhibitors repressed it. Finally, LYPLAL1-AS1 overexpression reversed miR-let-7b-induced hADSC senescence.

Conclusions

Our data demonstrate that LYPLAL1-AS1 rejuvenates hADSCs through the transcriptional inhibition of MIRLET7B. Our work provides new insights into the mechanism of MSC senescence and indicates lncRNA LYPLAL1-AS1 and miR-let-7b as potential therapeutic targets in aging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00782-x.

Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: racial, ethnic, and geographical considerations

INTRODUCTION

Advances in high-throughput sequencing have greatly advanced our understanding of long non-coding RNAs (lncRNAs) in a relatively short period of time. This has expanded our knowledge of cancer, particularly how lncRNAs drive many important cancer phenotypes via their regulation of gene expression.

AREAS COVERED

Men of African descent are disproportionately affected by PC in terms of incidence, morbidity, and mortality. LncRNAs could serve as biomarkers to differentiate low-risk from high-risk diseases. Additionally, they may represent therapeutic targets for advanced and castrate-resistant cancer. We review current research surrounding lncRNAs and their association with PC. We discuss how lncRNAs can provide new insights and diagnostic biomarkers for African American men. Finally, we review advances in computational approaches that predict the regulatory effects of lncRNAs in cancer.

EXPERT OPINION

PC diagnostic biomarkers that offer high specificity and sensitivity are urgently needed. PC specific lncRNAs are compelling as diagnostic biomarkers owing to their high tissue and tumor specificity and presence in bodily fluids. Recent studies indicate that PCA3 clinical utility might be restricted to men of European descent. Further work is required to develop lncRNA biomarkers tailored for men of African descent.

Modelling the bioinformatics tertiary analysis research process

Background

With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate.

Methods

To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities.

Results

The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools.

Conclusions

To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04310-5.

RNA:DNA triple helices: from peculiar structures to pervasive chromatin regulators

The genomes of complex eukaryotes largely contain non-protein-coding DNA, which is pervasively transcribed into a plethora of non-coding RNAs (ncRNAs). The functional importance of many of these ncRNAs has been investigated in the last two decades, revealing their crucial and multifaceted roles in chromatin regulation. A common mode of action of ncRNAs is the recruitment of chromatin modifiers to specific regions in the genome. Whereas many ncRNA-protein interactions have been characterised in detail, binding of ncRNAs to their DNA target sites is much less understood. Recently developed RNA-centric methods have mapped the genome-wide distribution of ncRNAs, however, how ncRNAs achieve locus-specificity remains mainly unresolved. In terms of direct RNA-DNA interactions, two kinds of triple-stranded structures can be formed: R-loops consisting of an RNA:DNA hybrid and a looped out DNA strand, and RNA:DNA triple helices (triplexes), in which the RNA binds to the major groove of the DNA double helix by sequence-specific Hoogsteen base pairing. In this essay, we will review the current knowledge about RNA:DNA triplexes, summarising triplex formation rules, detection methods, and ncRNAs reported to engage in triplexes. While the functional characterisation of RNA:DNA triplexes is still anecdotal, recent advances in high-throughput and computational analyses indicate their widespread distribution in the genome. Thus, we are witnessing a paradigm shift in the appreciation of RNA:DNA triplexes, away from exotic structures towards a prominent mode of ncRNA-chromatin interactions.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.