Identification Of Long Non-coding RNAs Research Articles

In silico genome wide identification of long non-coding RNAs differentially expressed during Candida auris host pathogenesis.

Candida auris is an invasive fungal pathogen of high concern due to acquired drug tolerance against antifungals used in clinics. The prolonged persistence on biotic and abiotic surfaces can result in onset of hospital outbreaks causing serious health threat. An in depth understanding of pathology of C. auris is highly desirable for development of efficient therapeutics. Non-coding RNAs play crucial role in fungal pathology. However, the information about ncRNAs is scanty to be utilized. Herein our aim is to identify long noncoding RNAs with potent role in pathobiology of C. auris. Thereby, we analyzed the transcriptomics data of C. auris infection in blood for identification of potential lncRNAs with regulatory role in determining invasion, survival or drug tolerance under infection conditions. Interestingly, we found 275 lncRNAs, out of which 253 matched with lncRNAs reported in Candidamine, corroborating for our accurate data analysis pipeline. Nevertheless, we obtained 23 novel lncRNAs not reported earlier. Three lncRNAs were found to be under expressed throughout the course of infection, in the transcriptomics data. 16 of potent lncRNAs were found to be coexpressed with coding genes, emphasizing for their functional role. Noteworthy, these ncRNAs are expressed from intergenic regions of the genes associated with transporters, metabolism, cell wall biogenesis. This study recommends for possible association between lncRNA expression and C. auris pathogenesis.

Identification of long non-coding RNAs in response to microsporidia infection in Silkworm, Bombyx mori.

Microsporidia Nosema bombycis (Nb) is a cellular parasite responsible for pébrine disease in silkworms, significantly impacting the sericulture industry. Long non-coding RNAs (lncRNAs), which are RNA fragments longer than 200 nucleotides, are pivotal in a range of cellular and physiological functions. However, the potential role of silkworm lncRNAs in response to Nb infection remains unknown. This study conducted transcriptome sequencing on both larvae and Nb-infected midguts of silkworms, identifying 1,440 lncRNAs across all examined midgut samples. Within the Nb-infected group, 42 differentially expressed lncRNAs (DElncRNAs) and 305 differentially expressed mRNAs (DEmRNAs) were detected. Functional annotation and pathway analysis showed that these DEmRNAs are mostly involved in metabolism, apoptosis, autophagy, and other key pathways. The co-expression network of DEmRNAs and DElncRNAs illustrates that 1 gene could be regulated by multiple lncRNAs and 1 lncRNA may target multiple genes, indicating that the regulation of lncRNA is intricate and networked. In addition, the DElncRNA-miRNA-mRNA network showed that some DElncRNAs may be involved in the immune response and metabolism through miRNA. Notably, the study observed an increase in lncRNA MSTRG857.1 following Nb infection, which may promote Nb proliferation. These findings offer insights into the complex interplay between insects and microsporidia.

Systematic Identification of Long Noncoding RNAs during Three Key Organogenesis Stages in Zebrafish.

Thousands of lncRNAs have been found in zebrafish embryogenesis and adult tissues, but their identification and organogenesis-related functions have not yet been elucidated. In this study, high-throughput sequencing was performed at three different organogenesis stages of zebrafish embryos that are important for zebrafish muscle development. The three stages were 10 hpf (hours post fertilization) (T1), 24 hpf (T2), and 36 hpf (T3). LncRNA gas5, associated with muscle development, was screened out as the next research target by high-throughput sequencing and qPCR validation. The spatiotemporal expression of lncRNA gas5 in zebrafish embryonic muscle development was studied through qPCR and in situ hybridization, and functional analysis was conducted using CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/Cas9, CRISPR/Cas9). The results were as follows: (1) A total of 1486 differentially expressed lncRNAs were identified between T2 and T1, among which 843 lncRNAs were upregulated and 643 were downregulated. The comparison with T3 and T2 resulted in 844 differentially expressed lncRNAs, among which 482 lncRNAs were upregulated and 362 lncRNAs were downregulated. A total of 2137 differentially expressed lncRNAs were found between T3 and T1, among which 1148 lncRNAs were upregulated and 989 lncRNAs were downregulated, including lncRNA gas5, which was selected as the target gene. (2) The results of spatiotemporal expression analysis showed that lncRNA gas5 was expressed in almost all detected embryos of different developmental stages (0, 2, 6, 10, 16, 24, 36, 48, 72, 96 hpf) and detected tissues of adult zebrafish. (3) After lncRNA gas5 knockout using CRISPR/Cas9 technology, the expression levels of detected genes related to muscle development and adjacent to lncRNA gas5 were more highly affected in the knockout group compared with the control group, suggesting that lncRNA gas5 may play a role in embryonic muscle development in zebrafish. (4) The results of the expression of the skeletal myogenesis marker myod showed that the expression of myod in myotomes was abnormal, suggesting that skeletal myogenesis was affected after lncRNA gas5 knockout. The results of this study provide an experimental basis for further studies on the role of lncRNA gas5 in the embryonic skeletal muscle development of zebrafish.

LncRNA-miRNA-mRNA network in kidney transcriptome of Labeo rohita under hypersaline environment

The present study describes the kidney transcriptome of Labeo rohita, a freshwater fish, exposed to gradually increased salinity concentrations (2, 4, 6 and 8ppt). A total of 10.25 Gbps data was generated, and a suite of bioinformatics tools, including FEELnc, CPC2 and BLASTn were employed for identification of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs). Our analysis revealed a total of 170, 118, 99, and 269 differentially expressed lncRNA and 120, 118, 99, and 124 differentially expressed miRNAs in 2, 4, 6 and 8 ppt treatment groups respectively. Two competing endogenous RNA (ceRNA) networks were constructed i.e. A* ceRNA network with up-regulated lncRNAs and mRNAs, down-regulated miRNAs; and B* ceRNA network vice versa. 2ppt group had 131 and 83 lncRNA-miRNA-mRNA pairs in A* and B* networks, respectively. 4ppt group featured 163 pairs in A* network and 191 in B* network, while the 6ppt had 103 and 105 pairs. 8ppt group included 192 and 174 pairs. These networks illuminate the intricate RNA interactions in freshwater fish to varying salinity conditions.

Functional identification of long non-coding RNAs induced by PM2.5 in microglia through microarray analysis

As a dominating air pollutant, atmospheric fine particulate matter within 2.5 μm in diameter (PM2.5) has attracted increasing attention from the researchers all over the world, which will lead to various adverse effects on the central nervous system (CNS), yet the potential mechanism is unclear. In this study, the microglia (BV2 cell line) were exposed to different concentrations of PM2.5 (5, 10 and 20 μg/cm2) for 24 h. It was found that PM2.5 could result in adverse effects on microglia such as decreased cell viability, structural damage and even cell death. And it was reported that long non-coding RNAs (lncRNAs) could participate in multitudinous neurological diseases. Therefore, the microarray analysis was conducted in order to disclose the underlying neurotoxicity mechanism of PM2.5 by ascertaining the differentially expressed lncRNAs (DElncRNAs). The consequences indicated that the DElncRNAs were enriched in various biological pathways, including ferroptosis, IL-17 signaling pathway and NOD-like receptor signaling pathway. Moreover, the cis- and trans-regulated mRNAs by DElncRNAs as well as the corresponding transcriptional factors (TFs) were observed, such as CEBPA, MYC, MEIS1 and KLF4. In summary, our study supplies some candidate libraries and potential preventive target against PM2.5-induced toxicity through targeting lncRNAs. Furthermore, the post-transcriptional regulation will contribute to the future research on PM2.5-induced neurotoxicity.

Identification of long noncoding RNAs of silkworm at the early stage of Bombyx mori bidensovirus infection.

Bombyx mori bidensovirus (BmBDV) is one of the most important pathogens of silkworm. It mainly infects midgut cells of silkworm and causes losses to the sericulture industry. Long noncoding RNAs (lncRNAs) have been reported to play an important role in the regulation of antiviral immune response in silkworm. To explore whether lncRNAs are involved in BmBDV infection and immune response of silkworm, we performed a comparative transcriptome analysis to identify the lncRNAs and mRNAs between the BmBDV infected and noninfected silkworm larvae at the early stage. A total of 16,069 genes and 974 candidate lncRNAs were identified, among which 142 messenger RNA (mRNAs) and four lncRNAs were differentially expressed (DE). Target gene prediction revealed that 142 DEmRNAs were coexpressed with four DElncRNAs, suggesting that the expression of mRNA is mainly affected through trans-regulation activities. A regulatory network of DElncRNAs and DEmRNAs was constructed, showing that many genes targeted by different DElncRNAs are involved in metabolism and immunity, which implies that these genes and lncRNAs play an important role in the replication of BmBDV. Our results will help us to improve our understanding of lncRNA-mediated regulatory roles in BmBDV infection, providing a new perspective for further exploring the interaction between host and BmBDV.

Genome-wide identification of long non-coding RNAs and their potential functions in radish response to salt stress.

Long non-coding RNAs (lncRNAs) are increasingly recognized as cis- and trans-acting regulators of protein-coding genes in plants, particularly in response to abiotic stressors. Among these stressors, high soil salinity poses a significant challenge to crop productivity. Radish (Raphanus sativus L.) is a prominent root vegetable crop that exhibits moderate susceptibility to salt stress, particularly during the seedling stage. Nevertheless, the precise regulatory mechanisms through which lncRNAs contribute to salt response in radish remain largely unexplored. In this study, we performed genome-wide identification of lncRNAs using strand-specific RNA sequencing on radish fleshy root samples subjected to varying time points of salinity treatment. A total of 7,709 novel lncRNAs were identified, with 363 of them displaying significant differential expression in response to salt application. Furthermore, through target gene prediction, 5,006 cis- and 5,983 trans-target genes were obtained for the differentially expressed lncRNAs. The predicted target genes of these salt-responsive lncRNAs exhibited strong associations with various plant defense mechanisms, including signal perception and transduction, transcription regulation, ion homeostasis, osmoregulation, reactive oxygen species scavenging, photosynthesis, phytohormone regulation, and kinase activity. Notably, this study represents the first comprehensive genome-wide analysis of salt-responsive lncRNAs in radish, to the best of our knowledge. These findings provide a basis for future functional analysis of lncRNAs implicated in the defense response of radish against high salinity, which will aid in further understanding the regulatory mechanisms underlying radish response to salt stress.

Computational identification of long non-coding RNAs associated with graphene therapy in glioblastoma multiforme.

Glioblastoma multiforme represents the most prevalent primary malignant brain tumour, while long non-coding RNA assumes a pivotal role in the pathogenesis and progression of glioblastoma multiforme. Nonetheless, the successful delivery of long non-coding RNA-based therapeutics to the tumour site has encountered significant obstacles attributable to inadequate biocompatibility and inefficient drug delivery systems. In this context, the use of a biofunctional surface modification of graphene oxide has emerged as a promising strategy to surmount these challenges. By changing the surface of graphene oxide, enhanced biocompatibility can be achieved, facilitating efficient transport of long non-coding RNA-based therapeutics specifically to the tumour site. This innovative approach presents the opportunity to exploit the therapeutic potential inherent in long non-coding RNA biology for treating glioblastoma multiforme patients. This study aimed to extract relevant genes from The Cancer Genome Atlas database and associate them with long non-coding RNAs to identify graphene therapy-related long non-coding RNA. We conducted a series of analyses to achieve this goal, including univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression. The resulting graphene therapy-related long non-coding RNAs were utilized to develop a risk score model. Subsequently, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses on the identified graphene therapy-related long non-coding RNAs. Additionally, we employed the risk model to construct the tumour microenvironment model and analyse drug sensitivity. To validate our findings, we referenced the IMvigor210 immunotherapy model. Finally, we investigated differences in the tumour stemness index. Through our investigation, we identified four promising graphene therapy-related long non-coding RNAs (AC011405.1, HOXC13-AS, LINC01127 and LINC01574) that could be utilized for treating glioblastoma multiforme patients. Furthermore, we identified 16 compounds that could be utilized in graphene therapy. Our study offers novel insights into the treatment of glioblastoma multiforme, and the identified graphene therapy-related long non-coding RNAs and compounds hold promise for further research in this field. Furthermore, additional biological experiments will be essential to validate the clinical significance of our model. These experiments can help confirm the potential therapeutic value and efficacy of the identified graphene therapy-related long non-coding RNAs and compounds in treating glioblastoma multiforme.

IncRna: The R Package for Optimizing lncRNA Identification Processes.

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.

Involvement Value of FLT-3, c-Myc, STAT3, p27, and HOTAIR Gene Expression in Acute Myeloid Leukemia Patients: A Molecular Perspective to a Novel Leukemogenesis Mechanism.

Background: The identification of long non-coding RNAs (lncRNAs) in the pathogenesis of acute myeloid leukemia (AML) has marked a new era in the molecular understating of the disease. This study investigated the correlation between the changes in the expression of lncRNAs, including HOTAIR, PVT-1, and CRNDE, and the alteration in the expression profile of FLT-3, c-Myc, STAT3, STAT5, and p27 in AML patients. Materials and Methods: Blood samples were collected from forty-one newly diagnosed AML patients and ten healthy individuals to evaluate the expression levels of the study genes using qRT-PCR analysis. The probable correlation between the gene expressions was determined using Pearson's correlation test. Results: The results showed that while there was a significant elevation in the expression of FLT3, c-Myc, STAT3, and HOTAIR, p27 expression remarkably diminished in AML patients compared to the control group. Also, a correlation was found between the expression of FLT-3 and p27 and the expression of HOTAIR and STAT3. It was assumed that FLT-3 had a role in increasing the proliferative and survival capacity of AML cells, at least partly, through c-Myc-mediated suppression of p27. Moreover, lncRNA HOTAIR showed to be involved in leukemia proliferation assumably by enhancing the expression of STAT3. Conclusion: Overall, the results of gene profile analysis suggested that studying the expression of HOTAIR, FLT-3, c-Myc, STAT3, and p27 could be helpful to AML patients, and each of these genes could be a valuable target for pharmaceutic intervention.

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

Identification of long noncoding RNAs with aberrant expression in prostate cancer metastases.

Prostate cancer (PCa) is the second-most common cause of male cancer-related death in western industrialized countries, and the emergence of metastases is a key challenge in the treatment of PCa. Accumulating studies have shown that long noncoding RNAs (lncRNAs) play an important role in the regulation of diverse cellular and molecular processes during the development and progression of cancer. Here, we utilized a unique cohort of castration-resistant prostate cancer metastases (mCRPC) and corresponding localized tumors and RNA sequencing (RNA-seq). First, we showed that patient-to-patient variability accounted for most of the variance in lncRNA expression between the samples, suggesting that genomic alterations in the samples are the main drivers of lncRNA expression in PCa metastasis. Subsequently, we identified 27 lncRNAs with differential expression (DE-lncRNAs) between metastases and corresponding primary tumors, suggesting that they are mCRPC-specific lncRNAs. Analyses of potential regulation by transcription factors (TFs) revealed that approximately half of the DE-lncRNAs have at least one binding site for the androgen receptor in their regulatory regions. In addition, TF enrichment analysis revealed the enrichment of binding sites for PCa-associated TFs, such as FOXA1 and HOXB13, in the regulatory regions of the DE-lncRNAs. In a cohort of prostatectomy-treated prostate tumors, four of the DE-lncRNAs showed association with progression-free time and two of them (lnc-SCFD2-2 and lnc-R3HCC1L-8) were independent prognostic markers. Our study highlights several mCRPC-specific lncRNAs that might be important in the progression of the disease to the metastatic stage and may also serve as potential biomarkers for aggressive PCa.

Identification of long non-coding RNA in formaldehyde-induced cardiac dysplasia in rats.

Formaldehyde exposure during pregnancy can cause fetal congenital heart disease (CHD). However, the regulatory mechanism remains unclear. Studies on the biology of long non-coding RNAs (lncRNAs) show that lncRNAs can influence cardiac development and disease. However, expression patterns and regulatory mechanisms of action of lncRNAs in formaldehyde-induced CHD remain unclear. We used high-throughput sequencing strategies as a means of identifying lncRNA expression profiles in heart tissues of normal and formaldehyde-exposed newborn rats. Overall, 763 differentially expressed lncRNAs were identified, including 325 and 438 that were respectively up-regulated and down-regulated. GO and KEGG analyses indicated that the Ras and hedgehog signaling pathways may be important regulatory pathways in CHD caused by exposure to formaldehyde. A lncRNA-miRNA-mRNA co-expression network was constructed and a key miRNA, rno-miR-665, was identified. Furthermore, qRT-PCR analysis verified that the novel lncRNAs: MSTRG.27313.2, MSTRG.30629.2, MSTRG.36520.33, MSTRG.91234.1, and MSTRG.91233.9, were upregulated in the formaldehyde-exposed group. These differentially expressed lncRNAs identified during formaldehyde-induced CHD in newborn rats help explain CHD pathogenesis and provide an effective reference for diagnosing and treating CHD.

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model.

Long non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively. An online web server is provided to the community for easy use and efficient identification of lncRNA, available at http://39.106.16.168/lncDLSM.

Identification of long noncoding RNAs involved in plumule-vernalization of Chinese cabbage.

Vernalization is a phenomenon in which plants must undergo a period of continuous low temperatures to change from the vegetative growth stage to the reproductive growth stage. Chinese cabbage is a heading vegetable, and flowering time is an essential developmental trait. Premature vernalization leads to premature bolting, which causes a loss of product value and yield. While research into vernalization has provided a wealth of information, a complete understanding of the molecular mechanism for controlling vernalization requirements has not yet been elucidated. In this study, using high-throughput RNA sequencing, we analyzed the plumule-vernalization response of mRNA and long noncoding RNA in the bolting-resistant Chinese cabbage double haploid (DH) line 'Ju Hongxin' (JHX). A total of 3382 lncRNAs were identified, of which 1553 differentially expressed (DE) lncRNAs were characterized as plumule-vernalization responses. The ceRNA network revealed that 280 ceRNA pairs participated in the plumule-vernalization reaction of Chinese cabbage. Through identifying DE lncRNAs in Chinese cabbage and analyzing anti-, cis-, and trans-functional analysis, some candidate lncRNAs related to vernalization promoting flowering of Chinese cabbage and their regulated mRNA genes were found. Moreover, the expression of several critical lncRNAs and their targets was verified using qRT-PCR. Furthermore, we identified the candidate plumule-vernalization-related long noncoding RNAs that regulate BrFLCs in Chinese cabbage, which was interesting and different from previous studies and was a new discovery. Our findings expand the knowledge of lncRNAs in the vernalization of Chinese cabbage, and the identified lncRNAs provide rich resources for future comparative and functional studies.

Identification of long non-coding RNA and circular RNA associated networks in cellular stress responses.

Mammalian cells employ various adaptive responses to cope with multiple stresses to maintain homeostasis. Functional roles of non-coding RNAs (ncRNAs) in response to cellular stresses have been proposed, and systematical investigations about the crosstalk among distinct types of RNAs are required. Here, we challenged HeLa cells with thapsigargin (TG) and glucose deprivation (GD) treatments to induce endoplasmic reticulum (ER) and metabolic stresses, respectively. Ribosomal RNA (rRNA)-depleted RNA sequencing (RNA-seq) was then performed. Characterization of the RNA-seq data revealed a series of differentially expressed long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) with parallel changes responsive to both stimuli. We further constructed the lncRNA/circRNA-mRNA co-expressing network, competing endogenous RNA (ceRNA) network in the lncRNA/circRNA-miRNA-mRNA axis, and lncRNA/circRNA-RNA binding protein (RBP) interactome map. These networks indicated the potential cis and/or trans regulatory roles of lncRNAs and circRNAs. Moreover, Gene Ontology analysis demonstrated that these identified ncRNAs were associated with several essential biological processes known to be related to cellular stress responses. In conclusion, we systematically established functional regulatory networks of lncRNA/circRNA-mRNA, lncRNA/circRNA-miRNA-mRNA and lncRNA/circRNA-RBP to perceive the potential interactions and biological processes during cellular stresses. These results provided insights in ncRNA regulatory networks of stress responses and the basis for further identification of pivotal factors involved in cellular stress responses.

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.

Systematic Identification of Long Non-Coding RNAs under Allelopathic Interference of Para-Hydroxybenzoic Acid in S. lycopersicum

The importance of long noncoding RNAs (lncRNAs) in plant development has been established, but a systematic analysis of the lncRNAs expressed during plant allelopathy has not been carried out. We performed RNA-seq experiments on S. lycopersicum subjected to different levels of para-hydroxybenzoic acid (PHBA) stress during plant allelopathy and identified 61,729 putative lncRNAs. Of these, 7765 lncRNAs cis-regulated 5314 protein-coding genes (PGs). Among these genes, 1116 lncRNAs and 2239 PGs were involved in a complex web of transcriptome regulation, and we divided these genes into 12 modules. Within these modules, 458 lncRNAs and 975 target genes were found to be highly correlated. Additionally, 989 lncRNAs trans-regulated 1765 PGs, and we classified them into 11 modules, within which 335 lncRNAs were highly correlated with their 633 corresponding target genes. Only 98 lncRNAs in S. lycopersicum had homologs in the lncRNA database of Arabidopsis thaliana, all of which were affected by the PHBA treatments. MiRNAs that interacted with both mRNAs and lncRNAs were selected on the basis of weighted correlation network analysis (WGCNA) results to make lncRNA-miRNA-mRNA triplets. Our study presents a systematic identification of lncRNAs involved in plant allelopathy in S. lycopersicum and provides research references for future studies.

Identification of long non-coding RNA using single nucleotide epimutation analysis: a novel gene discovery approach

BackgroundLong non-coding RNAs (lncRNAs) are involved in a variety of mechanisms related to tumorigenesis by functioning as oncogenes or tumor-suppressors or even harboring oncogenic and tumor-suppressing effects; representing a new class of cancer biomarkers and therapeutic targets. It is predicted that more than 35,000 ncRNA especially lncRNA are positioned at the intergenic regions of the human genome. Emerging research indicates that one of the key pathways controlling lncRNA expression and tissue specificity is epigenetic regulation.MethodsIn the current article, a novel approach for lncRNA discovery based on the intergenic position of most lncRNAs and a single CpG site methylation level representing epigenetic characteristics has been suggested.ResultsUsing this method, a novel antisense lncRNA named LINC02892 presenting three transcripts without the capacity of coding a protein was found exhibiting nuclear, cytoplasmic, and exosome distributions.ConclusionThe current discovery strategy could be applied to identify novel non-coding RNAs influenced by methylation aberrations.

Identification of long non-coding RNAs involved in floral scent of Rosa hybrida.

Long non-coding RNAs (lncRNAs) were found to play important roles in transcriptional, post-transcriptional, and epigenetic gene regulation in various biological processes. However, lncRNAs and their regulatory roles remain poorly studied in horticultural plants. Rose is economically important not only for their wide use as garden and cut flowers but also as important sources of natural fragrance for perfume and cosmetics industry, but presently little was known about the regulatory mechanism of the floral scent production. In this paper, a RNA-Seq analysis with strand-specific libraries, was performed to rose flowers in different flowering stages. The scented variety ‘Tianmidemeng’ (Rosa hybrida) was used as plant material. A total of 13,957 lncRNAs were identified by mining the RNA-Seq data, including 10,887 annotated lncRNAs and 3070 novel lncRNAs. Among them, 10,075 lncRNAs were predicted to possess a total of 29,622 target genes, including 54 synthase genes and 24 transcription factors related to floral scent synthesis. 425 lncRNAs were differentially expressed during the flowering process, among which 19 were differentially expressed among all the three flowering stages. Using weighted correlation network analysis (WGCNA), we correlate the differentially-expressed lncRNAs to synthesis of individual floral scent compounds. Furthermore, regulatory function of one of candidate lncRNAs for floral scent synthesis was verified using VIGS method in the rose. In this study, we were able to show that lncRNAs may play important roles in floral scent production in the rose. This study also improves our understanding of how plants regulate their secondary metabolism by lncRNAs.