Identification Of Non-coding RNAs Research Articles

Competing Endogenous RNAs (ceRNAs) and Application of Their Regulatory Networks in Complex Traits and Diseases of Ruminants

This manuscript summarizes information on the diverse range of RNA molecules and their role as competing endogenous RNAs (ceRNAs). Moreover, it provides an overview of ceRNA regulatory networks and their applications in ruminant biology. Knowledge of co-expression networks has increased with microarrays, RNA-seq, and scRNA-seq characterizing molecular mediators across various biological scales, using sequences from numerous blood and tissue samples. By synthesizing existing knowledge, this study summarizes interactions between coding and non-coding RNAs through microRNA response elements (MREs), elucidating large-scale regulatory networks throughout the transcriptome that influence the expression and activities of various ceRNAs. Identification of non-coding RNAs with important regulatory functions will revolutionize understanding of RNA biology, shifting from an mRNA-centric model to a complex network of RNA crosstalk. The ceRNA networks offer a more comprehensive and arguably more realistic perspective compared to protein–protein interaction (PPI) networks and weighted gene co-expression networks (WGCN). These ceRNA regulatory networks can describe potential molecular regulatory mechanisms related to functional and economically important traits in ruminants, plus contribute to disease and pathology research, by elucidating pathogenesis and potential drug effects in disease and cancer models. Furthermore, they can provide insights into farm animal biology, e.g., reproductive traits in goats and sheep, regulation of fat metabolism in beef cattle, heat stress responses, and lactation regulation in dairy cattle, fertility and muscle characteristics in buffalo, and resistance to high-salt and water-deprivation conditions in camels. In conclusion, ceRNA and associated regulatory networks should promote a new understanding of molecular mechanisms and identify candidate genes and metabolic-signaling pathways in ruminants.

Non-coding RNA identification with pseudo RNA sequences and feature representation learning

Distinguishing non-coding RNAs (ncRNAs) from coding RNAs is very important in bioinformatics. Although many methods have been proposed for solving this task, it remains highly challenging to further improve the accuracy of ncRNA identification. In this paper, we propose a coding potential predictor using feature representation learning based on pseudo RNA sequences named CPPFLPS. In this method, we use the pseudo RNA sequences generated by simulating RNA sequence mutations as new samples for data augmentation, and six string operations simulating RNA sequence mutations are considered: base replacement, base insertion, base deletion, subsequence reversion, subsequence repetition and subsequence deletion. In the feature representation learning framework, different types of pseudo RNA sequences are added to the training set to form new training sets that can be used to train baseline classifiers, thus obtaining baseline models. The resulting labels of these baseline models are used as feature vectors to represent RNA sequences, and the resulting feature vectors acquired after feature selection are used to train a predictive model for distinguishing ncRNAs from coding RNAs. Our method achieves better performance compared with that of existing state-of-the-art methods. The implementation of the proposed method is available at https://github.com/chenxgscuec/CPPFLPS.

Comprehensive RNA-Seq Analysis Pipeline for Non-Model Organisms and Its Application in Schmidtea mediterranea.

RNA sequencing (RNA-seq) is a high-throughput technology that provides in-depth information on transcriptome. The advancement and dropping costs of RNA sequencing, accompanied by more available reference genomes for different species, make transcriptome analysis in non-model organisms possible. Current obstacles in analyzing RNA-seq data include a lack of functional annotation, which may complicate the process of linking genes to corresponding functions. Here, we provide a one-stop RNA-seq analysis pipeline, PipeOne-NM, for transcriptome functional annotation, non-coding RNA identification, and transcripts alternative splicing analysis of non-model organisms, intended for use with Illumina platform-based RNA-seq data. We performed PipeOne-NM on 237 Schmidtea mediterranea RNA-seq runs and assembled a transcriptome with 84,827 sequences from 49,320 genes, identifying 64,582 mRNA from 35,485 genes, 20,217 lncRNA from 17,084 genes, and 3481 circRNAs from 1103 genes. In addition, we performed a co-expression analysis of lncRNA and mRNA and identified that 1319 lncRNA co-express with at least one mRNA. Further analysis of samples from S. mediterranea sexual and asexual strains revealed the role of sexual reproduction in gene expression profiles. Samples from different parts of asexual S. mediterranea revealed that differential expression profiles of different body parts correlated with the function of conduction of nerve impulses. In conclusion, PipeOne-NM has the potential to provide comprehensive transcriptome information for non-model organisms on a single platform.

Identification of non-coding RNAs and their functional network associated with optic nerve invasion in retinoblastoma

Optic nerve invasion (ONI) is an important high-risk feature and prognostic indicator of retinoblastoma (RB). Emerging evidence has revealed that non-coding RNAs (ncRNAs) play important roles in tumor perineural invasion (PNI). Nevertheless, the regulatory role of ncRNAs in the ONI of RB is poorly understood. In the current study, whole-transcriptome sequencing was performed to assess the expression profiles of ncRNAs and mRNAs in RB tissues, with or without ONI. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we predicted the biological functions of differentially expressed (DE) mRNAs. We then constructed competing endogenous RNA (ceRNA) regulatory networks based on bioinformatics analysis. The hsa_circ_0015965/lncRNA MEG3-hsa-miR-378a-5p-NOTCH1 pathway was selected and validated by real-time qPCR, western blotting, and dual luciferase reporter assays. Moreover, we demonstrated that NOTCH1 promotes the malignant progression of RB. Taken together, our results provide novel insights into the mechanism underlying optic nerve invasion in RB.

Identification of Non-Coding RNAs Based on Alignment-Free Features in Crassostrea gigas (Pacific Oyster) Transcriptome

Identification of Non-Coding RNAs Based on Alignment-Free Features in Crassostrea gigas (Pacific Oyster) Transcriptome

PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.

There is evidence that non-coding RNAs play significant roles in the regulation of nutrient homeostasis, development, and stress responses in plants. Accurate identification of ncRNAs is the first step in determining their function. While a number of machine learning tools have been developed for ncRNA identification, no dedicated tool has been developed for ncRNA identification in plants. Here, an automated machine learning tool, PINC is presented to identify ncRNAs in plants using RNA sequences. First, we extracted 91 features from the sequence. Second, we combined the F-test and variance threshold for feature selection to find 10 features. The AutoGluon framework was used to train models for robust identification of non-coding RNAs from datasets constructed for four plant species. Last, these processes were combined into a tool, called PINC, for the identification of plant ncRNAs, which was validated on nine independent test sets, and the accuracy of PINC ranged from 92.74% to 96.42%. As compared with CPC2, CPAT, CPPred, and CNIT, PINC outperformed the other tools in at least five of the eight evaluation indicators. PINC is expected to contribute to identifying and annotating novel ncRNAs in plants.

Disease-Associated Regulation of Non-Coding RNAs by Resveratrol: Molecular Insights and Therapeutic Applications.

There have been significant advances, particularly over the last 20 years, in the identification of non-coding RNAs (ncRNAs) and their pathophysiological role in a wide range of disease states, particularly cancer and other chronic conditions characterized by excess inflammation and oxidative stress such as atherosclerosis, diabetes, obesity, multiple sclerosis, osteoporosis, liver and lung fibrosis. Such discoveries have potential therapeutic implications as a better understanding of the molecular mechanisms underpinning the effects of ncRNAs on critical homeostatic control mechanisms and biochemical pathways might lead to the identification of novel druggable targets. In this context, increasing evidence suggests that several natural compounds can target ncRNAs at different levels and, consequently, influence processes involved in the onset and progression of disease states. The natural phenol resveratrol has been extensively studied for therapeutic purposes in view of its established anti-inflammatory and antioxidant effects, particularly in disease states such as cancer and cardiovascular disease that are associated with human aging. However, increasing in vitro and in vivo evidence also suggests that resveratrol can directly target various ncRNAs and that this mediates, at least in part, its potential therapeutic effects. This review critically appraises the available evidence regarding the resveratrol-mediated modulation of different ncRNAs in a wide range of disease states characterized by a pro-inflammatory state and oxidative stress, the potential therapeutic applications, and future research directions.

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification.

Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are more time-consuming and expensive than computational approaches. Several tools are available for identifying long non-coding RNAs. These tools, however, did not explain the features in their tools that contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that Xlnc1DCNN outperformed other state-of-the-art tools in terms of accuracy and F1-score. The explanation results revealed that lncRNA transcripts were mainly identified as sequences with no conserved regions, short patterns with unknown functions, or only regions of transmembrane helices while protein-coding transcripts were mostly classified by conserved protein domains or families. The explanation results also conveyed the probably inconsistent annotations among the public databases, lncRNA transcripts which contain protein domains, protein families, or intrinsically disordered regions (IDRs). Xlnc1DCNN is freely available at https://github.com/cucpbioinfo/Xlnc1DCNN.

Identification of non-coding RNAs in the hyper thermophilic bacterium Thermotoga maritima MSB8 through comparative genomics and in-silico analyses

Identification of non-coding RNAs in the hyper thermophilic bacterium Thermotoga maritima MSB8 through comparative genomics and in-silico analyses

IPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM.

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

Prediction of microRNAs in Pseudomonas syringae pv. tomato DC3000 and their potential target prediction in Solanum lycopersicum

MicroRNAs (miRNAs) regulate gene expression at the post-transcription level. It is a well explored field of non-coding RNAs, except in bacterial genomes. Here we investigated the transcriptome of Pseudomonas syringae pv. tomato DC3000 (PsPto) for the identification of non-coding RNAs. A total of 52 putative mature miRNAs were identified from the genome of PsPto using miRDeep* and CID-miRNA methods. One PsPto miRNA shared a common sequence with the reported miRNAs of E. coli DH10B, and two PsPto miRNAs shared a common sequence with the reported miRNAs of S. mutans ATCC 25175. Seven putative sequence homology miRNAs were found in different Thalassospira species. The present study provides the inclusive list of computationally identified miRNAs and filled up the existing knowledge gap on the presence of miRNAs in the PsPto genome. Moreover, a total of 93 target genes in tomato (Solanum lycopersicum) were identified for 46 PsPto miRNAs by using psRNATarget. The majority of the targets are transcription factors that play important roles in tomato development, including stress response, structural component, development, and metabolism. The results of this study will help to explain the function of miRNAs in S. lycopersicum and are intended to set the basis for further studies into PsPto miRNAs. Overall, this study can initiate further study on miRNA in regulating the growth and development of the important food tomato.

The role of non-coding RNAs in drug resistance of oral squamous cell carcinoma and therapeutic potential.

Oral squamous cell carcinoma (OSCC), the eighth most prevalent cancer in the world, arises from the interaction of multiple factors including tobacco, alcohol consumption, and betel quid. Chemotherapeutic agents such as cisplatin, 5‐fluorouracil, and paclitaxel have now become the first‐line options for OSCC patients. Nevertheless, most OSCC patients eventually acquire drug resistance, leading to poor prognosis. With the discovery and identification of non‐coding RNAs (ncRNAs), the functions of dysregulated ncRNAs in OSCC development and drug resistance are gradually being widely recognized. The mechanisms of drug resistance of OSCC are intricate and involve drug efflux, epithelial‐mesenchymal transition, DNA damage repair, and autophagy. At present, strategies to explore the reversal of drug resistance of OSCC need to be urgently developed. Nano‐delivery and self‐cellular drug delivery platforms are considered as effective strategies to overcome drug resistance due to their tumor targeting, controlled release, and consistent pharmacokinetic profiles. In particular, the combined application of new technologies (including CRISPR systems) opened up new horizons for the treatment of drug resistance of OSCC. Hence, this review explored emerging regulatory functions of ncRNAs in drug resistance of OSCC, elucidated multiple ncRNA‐meditated mechanisms of drug resistance of OSCC, and discussed the potential value of drug delivery platforms using nanoparticles and self‐cells as carriers in drug resistance of OSCC.

Differential Spatio-Temporal Regulation of T-Box Gene Expression by microRNAs during Cardiac Development.

Cardiovascular development is a complex process that starts with the formation of symmetrically located precardiac mesodermal precursors soon after gastrulation and is completed with the formation of a four-chambered heart with distinct inlet and outlet connections. Multiple transcriptional inputs are required to provide adequate regional identity to the forming atrial and ventricular chambers as well as their flanking regions; i.e., inflow tract, atrioventricular canal, and outflow tract. In this context, regional chamber identity is widely governed by regional activation of distinct T-box family members. Over the last decade, novel layers of gene regulatory mechanisms have been discovered with the identification of non-coding RNAs. microRNAs represent the most well-studied subcategory among short non-coding RNAs. In this study, we sought to investigate the functional role of distinct microRNAs that are predicted to target T-box family members. Our data demonstrated a highly dynamic expression of distinct microRNAs and T-box family members during cardiogenesis, revealing a relatively large subset of complementary and similar microRNA–mRNA expression profiles. Over-expression analyses demonstrated that a given microRNA can distinctly regulate the same T-box family member in distinct cardiac regions and within distinct temporal frameworks, supporting the notion of indirect regulatory mechanisms, and dual luciferase assays on Tbx2, Tbx3 and Tbx5 3′ UTR further supported this notion. Overall, our data demonstrated a highly dynamic microRNA and T-box family members expression during cardiogenesis and supported the notion that such microRNAs indirectly regulate the T-box family members in a tissue- and time-dependent manner.

Non-coding RNA Identification in Osteonecrosis of the Femoral Head Using Competitive Endogenous RNA Network Analysis.

ObjectiveTo investigate the regulatory network of long non‐coding RNA (lncRNA) as competing endogenous RNAs (ceRNAs) in osteonecrosis of the femoral head (ONFH).MethodsThe gene expression profile GSE74089 of ONFH and microRNA (miRNA) expression profile of GSE89587 were obtained from the Gene Expression Omnibus (GEO) database. The GSE74089 contained four ONFH samples and four controls. The GSE89587 included 10 ONFH samples and 10 control samples. The differentially expressed lncRNAs (DE‐lncRNAs) and DE‐mRNAs between ONFH group and control group were identified from GSE74089 using the limma package based on criteria of adjusted P value <0.05 and |log fold change (FC)| ≥2. The DEmiRNAs between ONFH group and control group were screened from GSE89587 on the basis of adjusted P value <0.05. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for DE‐mRNAs were analyzed using DAVID 6.7 and GSEA 3.0, respectively. Coexpressed lncRNA‐mRNA pairs were identified by corr.test method in R based on the criteria of adjusted P value <0.01 and |r| ≥ 0.9. A ceRNA network was constructed and visualized using cytoscape 3.7.0 by integrating the DE‐lncRNA, DE‐miRNA, and DEmRNA data. The key mRNAs and lncRNAs in the ceRNA network were further validated in an independent dataset of GSE123568.ResultsBased on our analysis, a total of 28 DE‐lncRNAs, 1403 DE‐mRNAs, and 134 DE‐miRNAs were identified, respectively. The DE‐mRNAs were significantly enriched in the function of “skeletal system development,” “collagen fibril organization,” “blood vessel development,” and “regulation of nervous system development.” Besides, 72 KEGG pathways, including eight active pathways and 64 suppressed pathways were identified, including which immune pathway was the most significantly activated one and which ribosome‐related function was the most suppressed. A co‐expression network including 161 DE‐mRNAs and 16 DE‐lncRNAs was built. Highly connected nodes were identified among lncRNAs such as H19, C20orf203, LINC00355, SFTA3, CRNDE, CASC2, LINC00494, C9orf163, C10orf91, and LINC00301. The ceRNA network indicated that lncRNA H19 functioned as a ceRNA of hsa‐miR‐519b‐3p and hsa‐miR‐296‐5p in ANKH and ECHDC1 regulation; lncRNA C9orf163 functioned as a ceRNA of hsa‐miR‐424‐5p in CCNT1 regulation. The expression trends of ANKH, CCNT1, and C9orf163 were successfully validated in independent dataset of GSE123568.ConclusionThe ceRNAs of lncRNA H19‐ hsa‐miR‐519b‐3p/hsa‐miR‐296‐5p‐ANKH and lncRNA c9orf163‐ hsa‐miR‐424‐5p‐CCNT1 might play important roles in ONFH development. Our research provided an understanding of the important role of lncRNA–related ceRNAs in ONFH.

LncRNADetector: a bioinformatics pipeline for long non-coding RNA identification and MAPslnc: a repository of medicinal and aromatic plant lncRNAs

ABSTRACT Long non-coding RNAs (lncRNAs) are an emerging class of non-coding RNAs and potent regulatory elements in the living cells. High throughput RNA sequencing analyses have generated a tremendous amount of transcript sequence data. A large proportion of these transcript sequences does not code for proteins and are known as non-coding RNAs. Among them, lncRNAs are a unique class of transcripts longer than 200 nucleotides with diverse biological functions and regulatory mechanisms. Recent emerging studies and next-generation sequencing technologies show a substantial amount of lncRNAs within the plant genome, which are yet to be identified. The computational identification of lncRNAs from these transcripts is a challenging task due to the involvement of a series of filtering steps. We have developed lncRNADetector, a bioinformatics pipeline for the identification of novel lncRNAs, especially from medicinal and aromatic plant (MAP) species. The lncRNADetector has been utilized to analyse and identify more than 88,459 lncRNAs from 21 species of MAPs. To provide a knowledge resource for the plant research community towards elucidating the diversity of biological roles of lncRNAs, the information generated about MAP lncRNAs (post-filtering steps) through lncRNADetector has been stored and organized in MAPslnc database (MAPslnc, https://lncrnapipe.cimap.res.in). The lncRNADetector web server and MAPslnc database have been developed in order to facilitate researchers for accurate identification of lncRNAs from the next-generation sequencing data of different organisms for downstream studies. To the best of our knowledge no such MAPslnc database is available till date.

Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells

BackgroundRetinol (RO) and its active metabolite retinoic acid (RA) are major regulators of gene expression in vertebrates and influence various processes like organ development, cell differentiation, and immune response. To characterize a general transcriptomic response to RA-exposure in vertebrates, independent of species- and tissue-specific effects, four publicly available RNA-Seq datasets from Homo sapiens, Mus musculus, and Xenopus laevis were analyzed. To increase species and cell-type diversity we generated RNA-seq data with chicken hepatocellular carcinoma (LMH) cells. Additionally, we compared the response of LMH cells to RA and RO at different time points.ResultsBy conducting a transcriptome meta-analysis, we identified three retinoic acid response core clusters (RARCCs) consisting of 27 interacting proteins, seven of which have not been associated with retinoids yet. Comparison of the transcriptional response of LMH cells to RO and RA exposure at different time points led to the identification of non-coding RNAs (ncRNAs) that are only differentially expressed (DE) during the early response.ConclusionsWe propose that these RARCCs stand on top of a common regulatory RA hierarchy among vertebrates. Based on the protein sets included in these clusters we were able to identify an RA-response cluster, a control center type cluster, and a cluster that directs cell proliferation. Concerning the comparison of the cellular response to RA and RO we conclude that ncRNAs play an underestimated role in retinoid-mediated gene regulation.

Using the Chou’s Pseudo Component to Predict the ncRNA Locations Based on the Improved K-Nearest Neighbor (iKNN) Classifier

Background: The non-coding RNA identification at the organelle genome level is a challenging task. In our previous work, an ncRNA dataset with less than 80% sequence identity was built, and a method incorporating an increment of diversity combining with support vector machine method was proposed. Objective: Based on the ncRNA_361 dataset, a novel decision-making method-an improved KNN (iKNN) classifier was proposed. Methods: In this paper, based on the iKNN algorithm, the physicochemical features of nucleotides, the degeneracy of genetic codons, and topological secondary structure were selected to represent the effective ncRNA characters. Then, the incremental feature selection method was utilized to optimize the feature set. Results: The results of iKNN indicated that the decision-making method of mean value is distinctly superior to the traditional decision-making method of majority vote the Increment of Diversity Combining Support Vector Machine (ID-SVM). The iKNN algorithm achieved an overall accuracy of 97.368% in the jackknife test, when k=3. Conclusion: It should be noted that the triplets of the structure-sequence mode under reading frames not only contains the entire sequence information but also reflects whether the base was paired or not, and the secondary structural topological parameters further describe the ncRNA secondary structure on the spatial level. The ncRNA dataset and the iKNN classifier are freely available at http://202.207.14.87:8032/fuwu/iKNN/index.asp.

Identification of long non-coding RNA and circular RNA in mice after intra-tracheal instillation with fine particulate matter

BackgroundFine particulate matter (PM2.5) exposure has been proved to be associated with respiratory diseases in epidemiological studies, but the underlying mechanisms are not clear. One of the most important mechanisms involved is inflammation. Non-coding RNAs are proposed to play crucial roles in epigenetic modulation and post-transcriptional regulation. Identification of non-coding RNAs can show us the new insight into the molecular toxicity of PM2.5. Materials and methodsIntra-tracheal instillation of saline or PM2.5 was performed in BALB/c Mice once a week for consecutive eight weeks. Genomewide transcriptome profiling of coding genes, long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in mice lung were done by ribosomal RNA-depleted RNA sequencing. Lung histological alternations were observed in haematoxylin and eosin (HE) staining sections. The expressions of pro-inflammatory cytokines and Nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome were quantified by qRT-PCR、ELISA and Western blot. Results1873 coding genes, 885 lncRNAs and 142 circRNAs were differentially expressed in lung tissues of the saline and PM2.5 exposed mice. The upregulated expressions of lncRNA NONMMUT065867, lncRNA NONMMUT064312, lncRNA NONMMUT018123 and the downregulated expressions of circRNA CBT15_circR_1011, circRNA mm9_circ_005915 were identified by qRT-PCR in PM2.5 group. The pulmonary inflammation score was higher in PM2.5 group. What’s more, the expressions of pro-inflammatory cytokines and NLRP3 inflammasome were upregulated in PM2.5 exposed mice. ConclusionPM2.5 causes lung inflammation and increases the expression of NLRP3 inflammasome. The identified novel lncRNAs and circRNAs may paly important role in the development of lung inflammation caused by PM2.5.

ALDH1A3-regulated long non-coding RNA NRAD1 is a potential novel target for triple-negative breast tumors and cancer stem cells

To discover novel therapeutic targets for triple-negative breast cancer (TNBC) and cancer stem cells (CSCs), we screened long non-coding RNAs (lncRNAs) most enriched in TNBCs for high expression in CSCs defined by high Aldefluor activity and associated with worse patient outcomes. This led to the identification of non-coding RNA in the aldehyde dehydrogenase 1 A pathway (NRAD1), also known as LINC00284. Targeting NRAD1 in TNBC tumors using antisense oligonucleotides reduced cell survival, tumor growth, and the number of cells with CSC characteristics. Expression of NRAD1 is regulated by an enzyme that causes Aldefluor activity in CSCs, aldehyde dehydrogenase 1A3 (ALDH1A3) and its product retinoic acid. Cellular fractionation revealed that NRAD1 is primarily nuclear localized, which suggested a potential function in gene regulation. This was confirmed by transcriptome profiling and chromatin isolation by RNA purification, followed by sequencing (ChIRP-seq), which demonstrated that NRAD1 has enriched chromatin interactions among the genes it regulates. Gene Ontology enrichment analysis revealed that NRAD1 regulates expression of genes involved in differentiation and catabolic processes. NRAD1 also contributes to gene expression changes induced by ALDH1A3; thereby, the induction of NRAD1 is a novel mechanism through which ALDH1A3 regulates gene expression. Together, these data identify lncRNA NRAD1 as a downstream effector of ALDH1A3, and a target for TNBCs and CSCs, with functions in cell survival and regulation of gene expression.

The Role of MicroRNAs in the Osseointegration Process

Regulation of cellular function is key to bone formation at endosseous implant surfaces. Osseointegration was "discovered" prior to the discovery of genetic regulation of osteoinduction or characterization of mesenchymal stem cells. Understanding osseointegration in cellular and molecular terms has benefited from genome-wide characterization of this healing process at endosseous implants in vivo. These in vivo studies also demonstrate a role for osteoprogenitor cells and cells involved in immune regulation and osteoclastogenesis. The identification of noncoding RNAs, including microRNAs, as key factors controlling cell function has highlighted the role of microRNAs in cell differentiation control. This review summarizes emerging in vitro and in vivo investigations emphasizing the role of microRNAs in the osseointegration process. Many microRNAs influence key osteoinductive pathways controlling Osterix, runt-related transcription factor 2 (RUNX2), and bone morphogenetic protein (BMP)/SMAD function. Others influence the monocyte/macrophage lineage. While significant progress has been made in elucidating the mechanisms associated with the regulation of surface modulation of osteoblast differentiation by microRNAs, knowledge gaps are evident in the identification and characterization of microRNAs linked to osseointegration. Given existing knowledge regarding the varied expression of microRNAs and their role in inflammation, it is important to understand how microRNA expression may influence the process of bone accrual at implant surfaces during osseointegration.