RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach

Regulatory role of lncH19 in RAC1 alternative splicing: implication for RAC1B expression in colorectal cancer

Aberrant alternative splicing events play a critical role in cancer biology, contributing to tumor invasion, metastasis, epithelial-mesenchymal transition, and drug resistance. Recent studies have shown that alternative splicing is a key feature for transcriptomic variations in colorectal cancer, which ranks third among malignant tumors worldwide in both incidence and mortality. Long non-coding RNAs can modulate this process by acting as trans-regulatory agents, recruiting splicing factors, or driving them to specific targeted genes. LncH19 is a lncRNA dis-regulated in several tumor types and, in colorectal cancer, it plays a critical role in tumor onset, progression, and metastasis. In this paper, we found, that in colorectal cancer cells, the long non-coding RNA H19 can bind immature RNAs and splicing factors as hnRNPM and RBFOX2. Through bioinformatic analysis, we identified 57 transcripts associated with lncH19 and containing binding sites for both splicing factors, hnRNPM, and RBFOX2. Among these transcripts, we identified the mRNA of the GTPase-RAC1, whose alternatively spliced isoform, RAC1B, has been ascribed several roles in the malignant transformation. We confirmed, in vitro, the binding of the splicing factors to both the transcripts RAC1 and lncH19. Loss and gain of expression experiments in two colorectal cancer cell lines (SW620 and HCT116) demonstrated that lncH19 is required for RAC1B expression and, through RAC1B, it induces c-Myc and Cyclin-D increase. In vivo, investigation from biopsies of colorectal cancer patients showed higher levels of all the explored genes (lncH19, RAC1B, c-Myc and Cyclin-D) concerning the healthy counterpart, thus supporting our in vitro model. In addition, we identified a positive correlation between lncH19 and RAC1B in colorectal cancer patients. Finally, we demonstrated that lncH19, as a shuttle, drives the splicing factors RBFOX2 and hnRNPM to RAC1 allowing exon retention and RAC1B expression. The data shown in this paper represent the first evidence of a new mechanism of action by which lncH19 carries out its functions as an oncogene by prompting colorectal cancer through the modulation of alternative splicing.

Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.

NKX2-1 gene is targeted by H19 lncRNA and is found to be overexpressed in benign nodular goiter tissues

OBJECTIVE

Nodular goiter may increase the risk of thyroid cancer, but the genetic factors contributing to nodular goiter are not well understood. There is an overexpression of H19 lncRNA in goiter tissue and its target remains unknown. In this study, we attempted to identify a new target for H19 in the context of goiter development.

METHODS

Using interaction energy calculations, the interaction between NKX2-1 mRNA and H19 lncRNA was examined. Putative microRNAs were found at the H19 lncRNA target site with the highest affinity for NKX2-1. RNAseq data was analyzed to determine the tissue specificity of gene expression. Samples were taken from 18 goiter and 18 normal tissues during thyroidectomy. The expression of NKX2-1 was determined by RT-qPCR using specific primers.

RESULTS

The interaction between NKX2-1 and H19 was characterized by six local base-pairing connections, with a maximum energy of -20.56 kcal/moL. Specifically, the sequence that displayed the highest affinity for binding with H19 overlapped with the binding site of has-miR-1827 to NKX2-1. It was found that NKX2-1 is exclusively co-expressed with H19 in normal thyroid tissue. As compared to adjacent normal tissues, nodular goiter tissues have a significant overexpression of NKX2-1 (relative expression = 1.195, p =  0.038).

CONCLUSION

NKX2-1 has been identified as the putative target of H19 lncRNA, which is overexpressed in nodular goiter tissues significantly.

LEVEL OF EVIDENCE: 4

lncRNA-mRNA Co-Expression and Regulation Analysis in Lung Fibroblasts from Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease marked by abnormal accumulation of extracellular matrix (ECM) due to dysregulated expression of various RNAs in pulmonary fibroblasts. This study utilized RNA-seq data meta-analysis to explore the regulatory network of hub long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in IPF fibroblasts. The meta-analysis unveiled 584 differentially expressed mRNAs (DEmRNA) and 75 differentially expressed lncRNAs (DElncRNA) in lung fibroblasts from IPF. Among these, BCL6, EFNB1, EPHB2, FOXO1, FOXO3, GNAI1, IRF4, PIK3R1, and RXRA were identified as hub mRNAs, while AC008708.1, AC091806.1, AL442071.1, FAM111A-DT, and LINC01989 were designated as hub lncRNAs. Functional characterization revealed involvement in TGF-β, PI3K, FOXO, and MAPK signaling pathways. Additionally, this study identified regulatory interactions between sequences of hub mRNAs and lncRNAs. In summary, the findings suggest that AC008708.1, AC091806.1, FAM111A-DT, LINC01989, and AL442071.1 lncRNAs can regulate BCL6, EFNB1, EPHB2, FOXO1, FOXO3, GNAI1, IRF4, PIK3R1, and RXRA mRNAs in fibroblasts bearing IPF and contribute to fibrosis by modulating crucial signaling pathways such as FoxO signaling, chemical carcinogenesis, longevity regulatory pathways, non-small cell lung cancer, and AMPK signaling pathways.

Monoallelically expressed noncoding RNAs form nucleolar territories on NOR-containing chromosomes and regulate rRNA expression

Out of the several hundred copies of rRNA genes arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, ~50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to the differential expression of rRNAs. However, the mechanism(s) controlling the dosage of active versus inactive rRNA genes within each NOR in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influence rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosomes. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs influencing rRNA expression by forming constrained nucleolar territories on individual NORs.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

Genome-wide analysis of long noncoding RNAs in response to salt stress in Nicotiana tabacum

BACKGROUND

Long noncoding RNAs (lncRNAs) have been shown to play important roles in the response of plants to various abiotic stresses, including drought, heat and salt stress. However, the identification and characterization of genome-wide salt-responsive lncRNAs in tobacco (Nicotiana tabacum L.) have been limited. Therefore, this study aimed to identify tobacco lncRNAs in roots and leaves in response to different durations of salt stress treatment.

RESULTS

A total of 5,831 lncRNAs were discovered, with 2,428 classified as differentially expressed lncRNAs (DElncRNAs) in response to salt stress. Among these, only 214 DElncRNAs were shared between the 2,147 DElncRNAs in roots and the 495 DElncRNAs in leaves. KEGG pathway enrichment analysis revealed that these DElncRNAs were primarily associated with pathways involved in starch and sucrose metabolism in roots and cysteine and methionine metabolism pathway in leaves. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 15 co-expression modules, with four modules strongly linked to salt stress across different treatment durations (MEsalmon, MElightgreen, MEgreenyellow and MEdarkred). Additionally, an lncRNA-miRNA-mRNA network was constructed, incorporating several known salt-associated miRNAs such as miR156, miR169 and miR396.

CONCLUSIONS

This study enhances our understanding of the role of lncRNAs in the response of tobacco to salt stress. It provides valuable information on co-expression networks of lncRNA and mRNAs, as well as networks of lncRNAs-miRNAs-mRNAs. These findings identify important candidate lncRNAs that warrant further investigation in the study of plant-environment interactions.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

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.

Ticarcillin degradation product thiophene acetic acid is a novel auxin analog that promotes organogenesis in tomato

Efficient regeneration of transgenic plants from explants after transformation is one of the crucial steps in developing genetically modified plants with desirable traits. Identification of novel plant growth regulators and developmental regulators will assist to enhance organogenesis in culture. In this study, we observed enhanced shoot regeneration from tomato cotyledon explants in culture media containing timentin, an antibiotic frequently used to prevent Agrobacterium overgrowth after transformation. Comparative transcriptome analysis of explants grown in the presence and absence of timentin revealed several genes previously reported to play important roles in plant growth and development, including Auxin Response Factors (ARFs), GRF Interacting Factors (GIFs), Flowering Locus T (SP5G), Small auxin up-regulated RNAs (SAUR) etc. Some of the differentially expressed genes were validated by quantitative real-time PCR. We showed that ticarcillin, the main component of timentin, degrades into thiophene acetic acid (TAA) over time. TAA was detected in plant tissue grown in media containing timentin. Our results showed that TAA is indeed a plant growth regulator that promotes root organogenesis from tomato cotyledons in a manner similar to the well-known auxins, indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA). In combination with the cytokinin 6-benzylaminopurine (BAP), TAA was shown to promote shoot organogenesis from tomato cotyledon in a concentration-dependent manner. To the best of our knowledge, the present study reports for the first time demonstrating the function of TAA as a growth regulator in a plant species. Our work will pave the way for future studies involving different combinations of TAA with other plant hormones which may play an important role in in vitro organogenesis of recalcitrant species. Moreover, the differentially expressed genes and long noncoding RNAs identified in our transcriptome studies may serve as contender genes for studying molecular mechanisms of shoot organogenesis.

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.

Neat1 lncRNA organizes the inflammatory gene expressions in the dorsal root ganglion in neuropathic pain caused by nerve injury

Primary sensory neurons regulate inflammatory processes in innervated regions through neuro-immune communication. However, how their immune-modulating functions are regulated in concert remains largely unknown. Here, we show that Neat1 long non-coding RNA (lncRNA) organizes the proinflammatory gene expressions in the dorsal root ganglion (DRG) in chronic intractable neuropathic pain in rats. Neat1 was abundantly expressed in the DRG and was upregulated after peripheral nerve injury. Neat1 overexpression in primary sensory neurons caused mechanical and thermal hypersensitivity, whereas its knockdown alleviated neuropathic pain. Bioinformatics analysis of comprehensive transcriptome changes indicated the inflammatory response was the most relevant function of genes upregulated through Neat1. Consistent with this, upregulation of proinflammatory genes in the DRG following nerve injury was suppressed by Neat1 knockdown. Expression changes of these proinflammatory genes were regulated through Neat1-mRNA interaction-dependent and -independent mechanisms. Notably, Neat1 increased proinflammatory genes by stabilizing its interacting mRNAs in neuropathic pain. Finally, Neat1 in primary sensory neurons contributed to spinal inflammatory processes that mediated peripheral neuropathic pain. These findings demonstrate that Neat1 lncRNA is a key regulator of neuro-immune communication in neuropathic pain.

lncRNA NORAD, soluble ICAM1 and their correlations may be related to the regulation of the tumor immune microenvironment in laryngeal squamous cell carcinoma (LSCC)

NORAD, non-coding RNA activated by DNA damage, is a Long non-coding RNA (lncRNA) transcript that modulates genome stability and has been reported to be dysregulated in different cancers. Although it has been reported to be upregulated in tumor cells mostly for solid organ cancers, it has also been reported to be downregulated in some cancers. Although the pathophysiological mechanism is not fully understood, a negative correlation between NORAD and intercellular cell adhesion molecule-1 (ICAM-1) has been shown in experimental models, but this situation has not been evaluated in terms of cancer. We aimed to evaluate the potential roles of these two biomarker candidates together and separately in the clinicopathological axis in Laryngeal squamous cell carcinoma (LSCC) in a case-control study setting. The interactions of NORAD and ICAM1 at the RNA level were evaluated interactively by the RIblast program. sICAM1 (soluble intercellular cell adhesion molecule-1) levels were determined by ELISA in one hundred and five individuals (forty-four LSCC, sixty-one control) and lncRNA NORAD expression in eighty-eight tissues (forty-four LSCC tumors, forty-four tumor-free surrounding tissues) was determined by Real-time PCR. While the energy treesholud was - 16 kcal/mol between NORAD and ICAM1, the total energy was 176.33 kcal/mol, and 9 base pair pairings from 4 critical points were detected. NORAD expression level was found to be higher in tumor surrounding tissue compared to tumor tissue, and sICAM1 was higher in the control group compared to LSCC (p = 0.004; p = 0.02). NORAD discreminte tumor surrounding tissue from tumor (AUC: 0.674; optimal sensitivity:87.50%; optimal specificity 54.55%; cut-off point as >1.58 fold change; P = 0.034). The sICAM1 level was found to be higher in the control (494,814 ± 93.64 ng/L) than LSCC (432.95 ± 93.64 ng/L) (p = 0.02). sICAM1 discreminte control group from LSCC (AUC: 0.624; optimal sensitivity 68,85%; optimal specificity 61,36%; cut-off point ≤115,0 ng/L; (p = 0.033). A very strong negative correlation was found between NORAD expression and patients' sICAM1 levels (r = -.967; n = 44; p = 0.033). sICAM1 levels were found to be 1.63 times higher in NORAD downregulated subjects compared to upregulated ones (p = 0.031). NORAD was 3.63 times higher in those with alcohol use, and sICAM 1 was 5.77 times higher in those without distant organ metastasis (p = 0.043; 0.004). The increased NORAD expression in the tumor microenvironment in LSCC, the activation of T cells via TCR signaling, and the decrease of sICAM in the control group in correlation with NORAD suggests that ICAM1 may be needed as a membrane protein in the tumor microenvironment. NORAD and ICAM1 may be functionally related to tumor microenvironment and immune control in LSCC.

Roles of lncRNAs in pancreatic ductal adenocarcinoma: Diagnosis, treatment, and the development of drug resistance

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, primarily due to its late diagnosis, high propensity to metastasis, and the development of resistance to chemo-/radiotherapy. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are intimately involved in the treatment resistance of pancreatic cancer cells via interacting with critical signaling pathways and may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC.

DATA SOURCES

We carried out a systematic review on lncRNAs-based research in the context of pancreatic cancer and presented an overview of the updated information regarding the molecular mechanisms underlying lncRNAs-modulated pancreatic cancer progression and drug resistance, together with their potential value in diagnosis, prognosis, and treatment of PDAC. Literature mining was performed in PubMed with the following keywords: long non-coding RNA, pancreatic ductal adenocarcinoma, pancreatic cancer up to January 2022. Publications relevant to the roles of lncRNAs in diagnosis, prognosis, drug resistance, and therapy of PDAC were collected and systematically reviewed.

RESULTS

LncRNAs, such as HOTAIR, HOTTIP, and PVT1, play essential roles in regulating pancreatic cancer cell proliferation, invasion, migration, and drug resistance, thus may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC. They participate in tumorigenesis mainly by targeting miRNAs, interacting with signaling molecules, and involving in the epithelial-mesenchymal transition process.

CONCLUSIONS

The functional lncRNAs play essential roles in pancreatic cancer cell proliferation, invasion, migration, and drug resistance and have potential values in diagnosis, prognostic prediction, and treatment of PDAC.

Construction of ceRNA Networks at Different Stages of Somatic Embryogenesis in Garlic

LncRNA (long non-coding RNA) and mRNA form a competitive endogenous RNA (ceRNA) network by competitively binding to common miRNAs. This network regulates various processes of plant growth and development at the post-transcriptional level. Somatic embryogenesis is an effective means of plant virus-free rapid propagation, germplasm conservation, and genetic improvement, which is also a typical process to study the ceRNA regulatory network during cell development. Garlic is a typical asexual reproductive vegetable. Somatic cell culture is an effective means of virus-free rapid propagation in garlic. However, the ceRNA regulatory network of somatic embryogenesis remains unclear in garlic. In order to clarify the regulatory role of the ceRNA network in garlic somatic embryogenesis, we constructed lncRNA and miRNA libraries of four important stages (explant stage: EX; callus stage: AC; embryogenic callus stage: EC; globular embryo stage: GE) in the somatic embryogenesis of garlic. It was found that 44 lncRNAs could be used as precursors of 34 miRNAs, 1511 lncRNAs were predicted to be potential targets of 144 miRNAs, and 45 lncRNAs could be used as eTMs of 29 miRNAs. By constructing a ceRNA network with miRNA as the core, 144 miRNAs may bind to 1511 lncRNAs and 12,208 mRNAs. In the DE lncRNA-DE miRNA-DE mRNA network of adjacent stages of somatic embryo development (EX-VS-CA, CA-VS-EC, EC-VS-GE), by KEGG enrichment of adjacent stage DE mRNA, plant hormone signal transduction, butyric acid metabolism, and C5-branched dibasic acid metabolism were significantly enriched during somatic embryogenesis. Since plant hormones play an important role in somatic embryogenesis, further analysis of plant hormone signal transduction pathways revealed that the auxin pathway-related ceRNA network (lncRNAs-miR393s-TIR) may play a role in the whole stage of somatic embryogenesis. Further verification by RT-qPCR revealed that the lncRNA125175-miR393h-TIR2 network plays a major role in the network and may affect the occurrence of somatic embryos by regulating the auxin signaling pathway and changing the sensitivity of cells to auxin. Our results lay the foundation for studying the role of the ceRNA network in the somatic embryogenesis of garlic.

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

Genome-wide analysis reveals the crucial role of lncRNAs in regulating the expression of genes controlling pollen development

The genomic location and stage-specific expression pattern of many long non-coding RNAs reveal their critical role in regulating protein-coding genes crucial in pollen developmental progression and male germ line specification. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 bp with no apparent protein-coding potential. Multiple investigations have revealed high expression of lncRNAs in plant reproductive organs in a cell and tissue-specific manner. However, their potential role as essential regulators of molecular processes involved in sexual reproduction remains largely unexplored. We have used developing field mustard (Brassica rapa) pollen as a model system for investigating the potential role of lncRNAs in reproductive development. Reference-based transcriptome assembly performed to update the existing genome annotation identified novel expressed protein-coding genes and long non-coding RNAs (lncRNAs), including 4347 long intergenic non-coding RNAs (lincRNAs, 1058 expressed) and 2,045 lncRNAs overlapping protein-coding genes on the opposite strand (lncNATs, 780 expressed). The analysis of expression profiles reveals that lncRNAs are significant and stage-specific contributors to the gene expression profile of developing pollen. Gene co-expression networks accompanied by genome location analysis identified 38 cis-acting lincRNA, 31 cis-acting lncNAT, 7 trans-acting lincRNA and 14 trans-acting lncNAT to be substantially co-expressed with target protein-coding genes involved in biological processes regulating pollen development and male lineage specification. These findings provide a foundation for future research aiming at developing strategies to employ lncRNAs as regulatory tools for gene expression control during reproductive development.

Web Services for RNA-RNA Interaction Prediction

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions

Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.

Dynamic landscape of long noncoding RNAs during leaf aging in Arabidopsis

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods

Motivation

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time.

Results

In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results.

Availability and implementation

The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Advances in endogenous RNA pull-down: A straightforward dextran sulfate-based method enhancing RNA recovery

Detecting RNA/RNA interactions in the context of a given cellular system is crucial to gain insights into the molecular mechanisms that stand beneath each specific RNA molecule. When it comes to non-protein coding RNA (ncRNAs), and especially to long noncoding RNAs (lncRNAs), the reliability of the RNA purification is dramatically dependent on their abundance. Exogenous methods, in which lncRNAs are in vitro transcribed and incubated with protein extracts or overexpressed by cell transfection, have been extensively used to overcome the problem of abundance. However, although useful to study the contribution of single RNA sub-modules to RNA/protein interactions, these exogenous practices might fail in revealing biologically meaningful contacts occurring in vivo and risk to generate non-physiological artifacts. Therefore, endogenous methods must be preferred, especially for the initial identification of partners specifically interacting with elected RNAs. Here, we apply an endogenous RNA pull-down to lncMN2-203, a neuron-specific lncRNA contributing to the robustness of motor neurons specification, through the interaction with miRNA-466i-5p. We show that both the yield of lncMN2-203 recovery and the specificity of its interaction with the miRNA dramatically increase in the presence of Dextran Sulfate Sodium (DSS) salt. This new set-up may represent a powerful means for improving the study of RNA-RNA interactions of biological significance, especially for those lncRNAs whose role as microRNA (miRNA) sponges or regulators of mRNA stability was demonstrated.

Genome-wide association study for systemic lupus erythematosus in an egyptian population

Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians-an admixed North African/Middle Eastern population-using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 109). We identified a novel genome-wide significant locus near IRS1/miR-5702 (Pcorrected = 1.98 × 10-8) and eight novel suggestive loci (Pcorrected < 1.0 × 10-5). We also replicated (Pperm < 0.01) 97 previously known loci with at least one associated nearby SNP, with ITGAM, DEF6-PPARD and IRF5 the top three replicated loci. SNPs correlated (r 2 > 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10-95 < p < 1.0 × 10-2) across diverse tissues. These loci are involved in cellular proliferation and invasion-pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis.

Predicting miRNA-lncRNA interactions on plant datasets based on bipartite network embedding method

The research of miRNA-lncRNA interactions (MLIs) has received great attention recently due to their vital roles in microbiology and profound significance in diseases. Currently, many related studies mainly focus on animals and the link prediction problem on plants is rarely discussed comprehensively. Motivated by this, we achieve link prediction task based on the concept of bipartite graph and verify encouraging performance of our conclusions by conducting experiments on plant datasets. In this work, we firstly extract attribute information and structure information as base features and further process these information for network embedding. Intra-partition and inter-partition proximity modelling are conducted to construct the loss function, which facilitates the training of parameters. Finally, the superiority of our presented approach is shown by carrying out experiments on four plant datasets, which reflects the significance of this work to the research of microbiology and disease.

Genome-wide analysis suggests the potential role of lncRNAs during seed development and seed size/weight determination in chickpea

The integrated transcriptome data analyses suggested the plausible roles of lncRNAs during seed development in chickpea. The candidate lncRNAs associated with QTLs and those involved in miRNA-mediated seed size/weight determination in chickpea have been identified. Long non-coding RNAs (lncRNAs) are important regulators of various biological processes. Here, we identified lncRNAs at seven successive stages of seed development in small-seeded and large-seeded chickpea cultivars. In total, 4751 lncRNAs implicated in diverse biological processes were identified. Most of lncRNAs were conserved between the two cultivars, whereas only a few of them were conserved in other plants, suggesting their species-specificity. A large number of lncRNAs differentially expressed between the two chickpea cultivars associated with seed development-related processes were identified. The lncRNAs acting as precursors of miRNAs and those mimicking target protein-coding genes of miRNAs involved in seed size/weight determination, including HAIKU1, BIG SEEDS1, and SHB1, were also revealed. Further, lncRNAs located within seed size/weight associated quantitative trait loci were also detected. Overall, we present a comprehensive resource and identified candidate lncRNAs that may play important roles during seed development and seed size/weight determination in chickpea.

Cpmer: A new conserved eEF1A2-binding partner that regulates Eomes translation and cardiomyocyte differentiation

Previous studies have shown that eukaryotic elongation factor 1A2 (eEF1A2) serves as an essential heart-specific translation elongation element and that its mutation or knockout delays heart development and causes congenital heart disease and death among species. However, the function and regulatory mechanisms of eEF1A2 in mammalian heart development remain largely unknown. Here we identified the long noncoding RNA (lncRNA) Cpmer (cytoplasmic mesoderm regulator), which interacted with eEF1A2 to co-regulate differentiation of mouse and human embryonic stem cell-derived cardiomyocytes. Mechanistically, Cpmer specifically recognized Eomes mRNA by RNA-RNA pairing and facilitated binding of eEF1A2 with Eomes mRNA, guaranteeing Eomes mRNA translation and cardiomyocyte differentiation. Our data reveal a novel functionally conserved lncRNA that can specifically regulate Eomes translation and cardiomyocyte differentiation, which broadens our understanding of the mechanism of lncRNA involvement in the subtle translational regulation of eEF1A2 during mammalian heart development.

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.

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.

Noncoding RNA crosstalk in brain health and diseases

The mammalian brain expresses several classes of noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs). These ncRNAs play vital roles in regulating cellular processes by RNA/protein scaffolding, sponging and epigenetic modifications during the pathophysiological conditions, thereby controlling transcription and translation. Some of these functions are the result of crosstalk between ncRNAs to form a competitive endogenous RNA network. These intricately organized networks comprise lncRNA/miRNA, circRNA/miRNA, or lncRNA/miRNA/circRNA, leading to crosstalk between coding and ncRNAs through miRNAs. The miRNA response elements predominantly mediate the ncRNA crosstalk to buffer the miRNAs and thereby fine-tune and counterbalance the genomic changes and regulate neuronal plasticity, synaptogenesis and neuronal differentiation. The perturbed levels and interactions of the ncRNAs could lead to pathologic events like apoptosis and inflammation. Although the regulatory landscape of the ncRNA crosstalk is still evolving, some well-known examples such as lncRNA Malat1 sponging miR-145, circRNA CDR1as sponging miR-7, and lncRNA Cyrano and the circRNA CDR1as regulating miR-7, has been shown to affect brain function. The ability to manipulate these networks is crucial in determining the functional outcome of central nervous system (CNS) pathologies. The focus of this review is to highlights the interactions and crosstalk of these networks in regulating pathophysiologic CNS function.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

Overexpression of DDIT4 and TPTEP1 are associated with metastasis and advanced stages in colorectal cancer patients: a study utilizing bioinformatics prediction and experimental validation

BACKGROUND

Various diagnostic and prognostic tools exist in colorectal cancer (CRC) due to multiple genetic and epigenetic alterations causing the disease. Today, the expression of RNAs is being used as prognostic markers for cancer.

METHODS

In the current study, various dysregulated RNAs in CRC were identified via bioinformatics prediction. Expression of several of these RNAs were measured by RT-qPCR in 48 tissues from CRC patients as well as in colorectal cancer stem cell-enriched spheroids derived from the HT-29 cell line. The relationships between the expression levels of these RNAs and clinicopathological features were analyzed.

RESULTS

Our bioinformatics analysis determined 11 key mRNAs, 9 hub miRNAs, and 18 lncRNAs which among them 2 coding RNA genes including DDIT4 and SULF1 as well as 3 non-coding RNA genes including TPTEP1, miR-181d-5p, and miR-148b-3p were selected for the further investigations. Expression of DDIT4, TPTEP1, and miR-181d-5p showed significantly increased levels while SULF1 and miR-148b-3p showed decreased levels in CRC tissues compared to the adjacent normal tissues. Positive relationships between DDIT4, SULF1, and TPTEP1 expression and metastasis and advanced stages of CRC were observed. Additionally, our results showed significant correlations between expression of TPTEP1 with DDIT4 and SULF1.

CONCLUSIONS

Our findings demonstrated increased expression levels of DDIT4 and TPTEP1 in CRC were associated with more aggressive tumor behavior and more advanced stages of the disease. The positive correlations between TPTEP1 as non-coding RNA and both DDIT4 and SULF1 suggest a regulatory effect of TPTEP1 on these genes.

Identification of 3'-UTR single nucleotide variants and prediction of select protein imbalance in mesial temporal lobe epilepsy patients

The genetic influence in epilepsy, characterized by unprovoked and recurrent seizures, is through variants in genes critical to brain development and function. We have carried out variant calling in Mesial Temporal Lobe Epilepsy (MTLE) patients by mapping the RNA-Seq data available at SRA, NCBI, USA onto human genome assembly hg-19. We have identified 1,75,641 SNVs in patient samples. These SNVs are distributed over 14700 genes of which 655 are already known to be associated with epilepsy. Large number of variants occur in the 3'-UTR, which is one of the regions involved in the regulation of protein translation through binding of miRNAs and RNA-binding proteins (RBP). We have focused on studying the structure-function relationship of the 3'-UTR SNVs that are common to at-least 10 of the 35 patient samples. For the first time we find SNVs exclusively in the 3'-UTR of FGF12, FAR1, NAPB, SLC1A3, SLC12A6, GRIN2A, CACNB4 and FBXO28 genes. Structural modelling reveals that the variant 3'-UTR segments possess altered secondary and tertiary structures which could affect mRNA stability and binding of RBPs to form proper ribonucleoprotein (RNP) complexes. Secondly, these SNVs have either created or destroyed miRNA-binding sites, and molecular modeling reveals that, where binding sites are created, the additional miRNAs bind strongly to 3'-UTR of only variant mRNAs. These two factors affect protein production thereby creating an imbalance in the amounts of select proteins in the cell. We suggest that in the absence of missense and nonsense variants, protein-activity imbalances associated with MTLE patients can be caused through 3'-UTR variants in relevant genes by the mechanisms mentioned above. 3'-UTR SNV has already been identified as causative variant in the neurological disorder, Tourette syndrome. Inhibition of these miRNA-mRNA bindings could be a novel way of treating drug-resistant MTLE patients. We also suggest that joint occurrence of these SNVs could serve as markers for MTLE. We find, in the present study, SNV-mediated destruction of miRNA binding site in the 3'-UTR of the gene encoding glutamate receptor subunit, and, interestingly, overexpression of one of this receptor subunit is also associated with Febrile Seizures.

Identification and characterization of long non-coding RNA and their response against citrus bark cracking viroid infection in Humulus lupulus

Long non-coding RNAs (lncRNAs) are a highly heterogeneous class of non-protein-encoding transcripts that play an essential regulatory role in diverse biological processes, including stress responses. The severe stunting disease caused by Citrus bark cracking viroid (CBCVd) poses a major threat to the production of Humulus lupulus (hop) plants. In this study, we systematically investigate the characteristics of the lncRNAs in hop and their role in CBCVd-infection using RNA-sequencing data. Following a stringent filtration criterion, a total of 3598 putative lncRNAs were identified with a high degree of certainty, of which 19% (684) of the lncRNAs were significantly differentially expressed (DE) in CBCVd-infected hop, which were predicted to be mainly involved in plant-pathogen interactions, kinase cascades, secondary metabolism and phytohormone signal transduction. Besides, several lncRNAs and CBCVd-responsive lncRNAs were identified as the precursor of microRNAs and predicted as endogenous target mimics (eTMs) for hop microRNAs involved in CBCVd-infection.

Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA-lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This paper proposes an ensemble deep learning model based on multi-level information enhancement and greedy fuzzy decision (PmliPEMG) for plant miRNA-lncRNA interaction prediction. The fusion complex features, multi-scale convolutional long short-term memory networks, and attention mechanism are adopted to enhance the sample information at the feature, scale, and model levels, respectively. An ensemble deep learning model is built based on a novel method (greedy fuzzy decision) which greatly improves the efficiency. The multi-level information enhancement and greedy fuzzy decision are verified to have the positive effects on prediction performance. PmliPEMG can be applied to the cross-species prediction. It shows better performance and stronger generalization ability than state-of-the-art predictors and may provide valuable references for related research.

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.

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

MOTIVATION

Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA.

RESULTS

In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA-gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90-0.98) and area under precision-recall curves (0.91-0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zty2009/LncRNA-target-gene.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

Identification of a three-long non-coding RNA signature for predicting survival of temozolomide-treated isocitrate dehydrogenase mutant low-grade gliomas

Temozolomide (TMZ) is the major chemotherapy agent in glioma, and isocitrate dehydrogenase (IDH) is a well-known prognostic marker in glioma. O6-methylguanine-DNA methyltransferase promoter methylation (MGMTmethyl) is a predictive biomarker in overall gliomas rather than in IDH mutant gliomas. To discover effective biomarkers that could predict TMZ efficacy in IDH mutant low-grade gliomas (LGGs), we retrieved data of IDH mutant LGGs from TMZ arm of the EORTC22033-26033 trial as the training-set (n = 83), analyzed correlations between long non-coding RNAs (lncRNAs) and progression-free survival (PFS) using Lasso-Cox regression, and created a risk score (RS) to stratify patients. We identified a three-lncRNA signature in TMZ-treated IDH mutant LGGs. All of the three lncRNAs, as well as the RS derived, were significantly correlated with PFS. Patients were classified into high-risk and low-risk groups according to RS. PFS of the high-risk group was significantly worse than that of the low-risk group (P < 0.001). AUCs of the three-, four-, and five-year survival probability predicted by RS were 0.73, 0.79, and 0.76, respectively. The predictive role of the three-lncRNA signature was further validated in an independent testing-set, the TCGA-LGGs, which resulted in a significantly worse PFS (P < 0.001) in the high-risk group. Three-, four-, and five-year survival probabilities predicted by RS were 0.65, 0.69, and 0.84, respectively. Functions of these three lncRNAs involve cell proliferation and differentiation, predicted by their targeting cancer genes. Conclusively, we created a scoring model based on the expression of three lncRNAs, which can effectively predict the survival of IDH mutant LGGs treated with TMZ.

Designing a general method for predicting the regulatory relationships between long noncoding RNAs and protein-coding genes based on multi-omics characteristics

MOTIVATION

Long noncoding RNA (lncRNA) has been verified to interact with other biomolecules especially protein-coding genes (PCGs), thus playing essential regulatory roles in life activities and disease development. However, the inner mechanisms of most lncRNA-PCG relationships are still unclear. Our study investigated the characteristics of true lncRNA-PCG relationships and constructed a novel predictor with machine learning algorithms.

RESULTS

We obtained the 307 true lncRNA-PCG pairs from database and found that there are significant differences in multiple characteristics between true and random lncRNA-PCG sets. Besides, 3-fold cross-validation and prediction results on independent test sets show the great AUC values of LR, SVM and RF, among which RF has the best performance with average AUC 0.818 for cross-validation, 0.823 and 0.853 for two independent test sets, respectively. In case study, some candidate lncRNA-PCG relationships in colorectal cancer were found and HOTAIR-COMP interaction was specially exemplified. The proportion of the reported pairs in the predicted positive results was significantly higher than that in negative results (P < 0.05).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10¹¹) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer

It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment

BACKGROUND

Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results.

RESULTS

Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations.

CONCLUSIONS

This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.

IntaRNAhelix-composing RNA–RNA interactions from stable inter-molecular helices boosts bacterial sRNA target prediction

Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures. While they typically predict RNA–RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens. One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA–RNA interactions. For example, often — even thermodynamically favorable — extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix. Another source is the consideration of instable and thus unlikely subinteractions that enable better scoring of longer interactions. In consequence, the efficient prediction methods that do not consider such effects show a high false positive rate.

To increase the prediction accuracy we devise IntaRNAhelix, a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitly model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool IntaRNA.

Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables more than 40-times faster computations. These results indicate — supporting our hypothesis — that stable helix composition increases the accuracy of interaction prediction models compared to the current state-of-the-art approach.

Targeting the TR4 nuclear receptor-mediated lncTASR/AXL signaling with tretinoin increases the sunitinib sensitivity to better suppress the RCC progression

Renal cell carcinoma (RCC) is one of the most lethal urological tumors. Using sunitinib to improve the survival has become the first-line therapy for metastatic RCC patients. However, the occurrence of sunitinib resistance in the clinical application has curtailed its efficacy. Here we found TR4 nuclear receptor might alter the sunitinib resistance to RCC via altering the TR4/lncTASR/AXL signaling. Mechanism dissection revealed that TR4 could modulate lncTASR (ENST00000600671.1) expression via transcriptional regulation, which might then increase AXL protein expression via enhancing the stability of AXL mRNA to increase the sunitinib resistance in RCC. Human clinical surveys also linked the expression of TR4, lncTASR, and AXL to the RCC survival, and results from multiple RCC cell lines revealed that targeting this newly identified TR4-mediated signaling with small molecules, including tretinoin, metformin, or TR4-shRNAs, all led to increase the sunitinib sensitivity to better suppress the RCC progression, and our preclinical study using the in vivo mouse model further proved tretinoin had a better synergistic effect to increase sunitinib sensitivity to suppress RCC progression. Future successful clinical trials may help in the development of a novel therapy to better suppress the RCC progression.

Genome-Wide Analysis of Long Intergenic Noncoding RNAs Responding to Low-Nutrient Conditions in Arabidopsis thaliana: Possible Involvement of Trans-Acting siRNA3 in Response to Low Nitrogen

Long intergenic noncoding RNAs (lincRNAs) play critical roles in transcriptional and post-transcriptional regulation of gene expression in a wide variety of organisms. Thousands of lincRNAs have been identified in plant genomes, although their functions remain mostly uncharacterized. Here, we report a genome-wide survey of lincRNAs involved in the response to low-nutrient conditions in Arabidopsis thaliana. We used RNA sequencing data derived from A. thaliana roots exposed to low levels of 12 different nutrients. Using bioinformatics approaches, 60 differentially expressed lincRNAs were identified that were significantly upregulated or downregulated under deficiency of at least one nutrient. To clarify their roles in nutrient response, correlations of expression patterns between lincRNAs and reference genes were examined across the 13 conditions (12 low-nutrient conditions and control). This analysis allowed us to identify lincRNA-RNA pairs with highly positive or negative correlations. In addition, calculating interaction energies of those pairs showed lincRNAs that may act as regulatory interactors; e.g. small interfering RNAs (siRNAs). Among them, trans-acting siRNA3 (TAS3), which is known to promote lateral root development by producing siRNA against Auxin response factor 2, 3, and 4, was revealed as a nitrogen (N)-responsive lincRNA. Furthermore, nitrate transporter 2 was identified as a potential target of TAS3-derived siRNA, suggesting that TAS3 participates in multiple pathways by regulating N transport and root development under low-N conditions. This study provides the first resource for candidate lincRNAs involved in multiple nutrient responses in plants.