A systematic evaluation of bioinformatics tools for identification of long noncoding RNAs

Update on functional analysis of long non-coding RNAs in common crops

With the rapid advances in next-generation sequencing technology, numerous non-protein-coding transcripts have been identified, including long noncoding RNAs (lncRNAs), which are functional RNAs comprising more than 200 nucleotides. Although lncRNA-mediated regulatory processes have been extensively investigated in animals, there has been considerably less research on plant lncRNAs. Nevertheless, multiple studies on major crops showed lncRNAs are involved in crucial processes, including growth and development, reproduction, and stress responses. This review summarizes the progress in the research on lncRNA roles in several major crops, presents key strategies for exploring lncRNAs in crops, and discusses current challenges and future prospects. The insights provided in this review will enhance our comprehension of lncRNA functions in crops, with potential implications for improving crop genetics and breeding.

Navigating the landscape of epitranscriptomics and host immunity

RNA modifications, also termed epitranscriptomic marks, encompass chemical alterations to individual nucleotides, including processes such as methylation and editing. These marks contribute to a wide range of biological processes, many of which are related to host immune system defense. The functions of immune-related RNA modifications can be categorized into three main groups: regulation of immunogenic RNAs, control of genes involved in innate immune response, and facilitation of adaptive immunity. Here, we provide an overview of recent research findings that elucidate the contributions of RNA modifications to each of these processes. We also discuss relevant methods for genome-wide identification of RNA modifications and their immunogenic substrates. Finally, we highlight recent advances in cancer immunotherapies that aim to reduce cancer cell viability by targeting the enzymes responsible for RNA modifications. Our presentation of these dynamic research avenues sets the stage for future investigations in this field.

Long non-coding RNAs mediate fish gene expression in response to ocean acidification

The majority of the transcribed genome does not have coding potential but these non-coding transcripts play crucial roles in transcriptional and post-transcriptional regulation of protein-coding genes. Regulation of gene expression is important in shaping an organism's response to environmental changes, ultimately impacting their survival and persistence as population or species face global change. However, the roles of long non-coding RNAs (lncRNAs), when confronted with environmental changes, remain largely unclear. To explore the potential role of lncRNAs in fish exposed to ocean acidification (OA), we analyzed publicly available brain RNA-seq data from a coral reef fish Acanthochromis polyacanthus. We annotated the lncRNAs in its genome and examined the expression changes of intergenic lncRNAs (lincRNAs) between A. polyacanthus samples from a natural CO2 seep and a nearby control site. We identified 4728 lncRNAs, including 3272 lincRNAs in this species. Remarkably, 93.03% of these lincRNAs were species-specific. Among the 125 highly expressed lincRNAs and 403 differentially expressed lincRNAs in response to elevated CO2, we observed that lincRNAs were either neighboring or potentially trans-regulating differentially expressed coding genes associated with pH regulation, neural signal transduction, and ion transport, which are known to be important in the response to OA in fish. In summary, lncRNAs may facilitate fish acclimation and mediate the responses of fish to OA by modulating the expression of crucial coding genes, which offers insight into the regulatory mechanisms underlying fish responses to environmental changes.

The VAX2-LINC01189-hnRNPF signaling axis regulates cell invasion and migration in gastric cancer

Transcription factors (TFs) and long noncoding RNAs (lncRNAs) contribute to gastric cancer (GC). However, the roles of TFs and lncRNAs in the invasion and metastasis of GC remain largely unknown. Here, we observed that the transcription factor VAX2 is significantly upregulated in GC cells and tissues and acts as an oncogene. Moreover, high VAX2 expression is associated with the advancement of tumors in GC. In terms of functionality, the enforced expression of VAX2 promotes the proliferation and metastasis of GC cells. Mechanistically, VAX2 specifically interacts with the LINC01189 promoter and represses LINC01189 transcription. Furthermore, LINC01189 exhibits significant downregulation in GC and functions as a suppressor gene. Functionally, it inhibits migratory and invasive abilities in GC cells. In the context of GC metastasis, VAX2 plays a role in modulating it by trans-repressing the expression of LINC01189. Additionally, LINC01189 binds to hnRNPF to enhance hnRNPF degradation through ubiquitination. The cooperation between LINC01189 and hnRNPF regulates GC cell invasion and migration. In addition, both VAX2 and hnRNPF are highly expressed, while LINC01189 is expressed in at low levels in GC tissues compared to normal gastric tissues. Our study suggests that VAX2 expression facilitates, while LINC01189 expression suppresses, metastasis and that the VAX2-LINC01189-hnRNPF axis plays a contributory role in GC development.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Analysis of lncRNAs in Lupinus mutabilis (Tarwi) and Their Potential Role in Drought Response

Lupinus mutabilis is a legume with high agronomic potential and available transcriptomic data for which lncRNAs have not been studied. Therefore, our objective was to identify, characterize, and validate the drought-responsive lncRNAs in L. mutabilis. To achieve this, we used a multilevel approach based on lncRNA prediction, annotation, subcellular location, thermodynamic characterization, structural conservation, and validation. Thus, 590 lncRNAs were identified by at least two algorithms of lncRNA identification. Annotation with the PLncDB database showed 571 lncRNAs unique to tarwi and 19 lncRNAs with homology in 28 botanical families including Solanaceae (19), Fabaceae (17), Brassicaceae (17), Rutaceae (17), Rosaceae (16), and Malvaceae (16), among others. In total, 12 lncRNAs had homology in more than 40 species. A total of 67% of lncRNAs were located in the cytoplasm and 33% in exosomes. Thermodynamic characterization of S03 showed a stable secondary structure with -105.67 kcal/mol. This structure included three regions, with a multibranch loop containing a hairpin with a SECIS-like element. Evaluation of the structural conservation by CROSSalign revealed partial similarities between L. mutabilis (S03) and S. lycopersicum (Solyc04r022210.1). RT-PCR validation demonstrated that S03 was upregulated in a drought-tolerant accession of L. mutabilis. Finally, these results highlighted the importance of lncRNAs in tarwi improvement under drought conditions.

Long Non-Coding RNAs of Plants in Response to Abiotic Stresses and Their Regulating Roles in Promoting Environmental Adaption

Abiotic stresses triggered by climate change and human activity cause substantial agricultural and environmental problems which hamper plant growth. Plants have evolved sophisticated mechanisms in response to abiotic stresses, such as stress perception, epigenetic modification, and regulation of transcription and translation. Over the past decade, a large body of literature has revealed the various regulatory roles of long non-coding RNAs (lncRNAs) in the plant response to abiotic stresses and their irreplaceable functions in environmental adaptation. LncRNAs are recognized as a class of ncRNAs that are longer than 200 nucleotides, influencing a variety of biological processes. In this review, we mainly focused on the recent progress of plant lncRNAs, outlining their features, evolution, and functions of plant lncRNAs in response to drought, low or high temperature, salt, and heavy metal stress. The approaches to characterize the function of lncRNAs and the mechanisms of how they regulate plant responses to abiotic stresses were further reviewed. Moreover, we discuss the accumulating discoveries regarding the biological functions of lncRNAs on plant stress memory as well. The present review provides updated information and directions for us to characterize the potential functions of lncRNAs in abiotic stresses in the future.

Transcriptomes of Zebrafish in Early Stages of Multiple Viral Invasions Reveal the Role of Sterols in Innate Immune Switch-On

Although it is widely accepted that in the early stages of virus infection, fish pattern recognition receptors are the first to identify viruses and initiate innate immune responses, this process has never been thoroughly investigated. In this study, we infected larval zebrafish with four different viruses and analyzed whole-fish expression profiles from five groups of fish, including controls, at 10 h after infection. At this early stage of virus infection, 60.28% of the differentially expressed genes displayed the same expression pattern across all viruses, with the majority of immune-related genes downregulated and genes associated with protein synthesis and sterol synthesis upregulated. Furthermore, these protein synthesis- and sterol synthesis-related genes were strongly positively correlated in the expression pattern of the rare key upregulated immune genes, IRF3 and IRF7, which were not positively correlated with any known pattern recognition receptor gene. We hypothesize that viral infection triggered a large amount of protein synthesis that stressed the endoplasmic reticulum and the organism responded to this stress by suppressing the body's immune system while also mediating an increase in steroids. The increase in sterols then participates the activation of IRF3 and IRF7 and triggers the fish's innate immunological response to the virus infection.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Profiling the Spatial Expression Pattern and ceRNA Network of lncRNA, miRNA, and mRNA Associated with the Development of Intermuscular Bones in Zebrafish

Intermuscular bones (IBs) are small spicule-like bones in the muscular septum of fish, which affect their edible and economic value. The molecular mechanism of IB development is still uncertain. Numerous studies have shown that the ceRNA network, which is composed of mRNA, lncRNA, and miRNA, plays an important regulatory role in bone development. In this study, we compared the mRNA, lncRNA, and miRNA expression profiles in different IB development segments of zebrafish. The development of IBs includes two main processes, which are formation and growth. A series of genes implicated in the formation and growth of IBs were identified through gene differential expression analysis and expression pattern analysis. Functional enrichment analysis showed that the functions of genes implicated in the regulation of the formation and growth of IBs were quite different. Ribosome and oxidative phosphorylation signaling pathways were significantly enriched during the formation of IBs, suggesting that many proteins are required to form IBs. Several pathways known to be associated with bone development have been shown to play an important role in the growth of IBs, including calcium, ECM-receptor interaction, Wnt, TGF-β, and hedgehog signaling pathways. According to the targeting relationship and expression correlation of mRNA, lncRNA, and miRNA, the ceRNA networks associated with the growth of IBs were constructed, which comprised 33 mRNAs, 9 lncRNAs, and 7 miRNAs. This study provides new insight into the molecular mechanism of the development of IBs.

A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs

Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives and negatives in datasets and the lower performance of end-to-end deep learning models. We also derived a new dataset, RNAChallenge, from the benchmark considering hard instances that may include potential false alarms. The best and least well performing models under- and overfit the dataset, respectively, thereby serving a dual purpose. For computational approaches, it will be valuable to develop accurate and unbiased models. The identification of false alarms will be of interest for genome annotators, and experimental study of hard RNAs will help to untangle the complexity of the RNA world.

Dynamic Transcriptional Landscape of Grass Carp (Ctenopharyngodon idella) Reveals Key Transcriptional Features Involved in Fish Development

A high-quality baseline transcriptome is a valuable resource for developmental research as well as a useful reference for other studies. We gathered 41 samples representing 11 tissues/organs from 22 important developmental time points within 197 days of fertilization of grass carp eggs in order to systematically examine the role of lncRNAs and alternative splicing in fish development. We created a high-quality grass carp baseline transcriptome with a completeness of up to 93.98 percent by combining strand-specific RNA sequencing and single-molecule real-time RNA sequencing technologies, and we obtained temporal expression profiles of 33,055 genes and 77,582 transcripts during development and tissue differentiation. A family of short interspersed elements was preferentially expressed at the early stage of zygotic activation in grass carp, and its possible regulatory components were discovered through analysis. Additionally, after thoroughly analyzing alternative splicing events, we discovered that retained intron (RI) alternative splicing events change significantly in both zygotic activation and tissue differentiation. During zygotic activation, we also revealed the precise regulatory characteristics of the underlying functional RI events.

p53-Induced LINC00893 Regulates RBFOX2 Stability to Suppress Gastric Cancer Progression

Long noncoding RNAs (lncRNAs) have been reported to regulate diverse tumorigenic processes. However, little is known about long intergenic non-protein coding RNA 00893 (LINC00893) and its role in gastric cancer (GC). Herein we investigated its biological functions and molecular mechanism in GC. LINC00893 was decreased in GC tissues but significantly elevated in AGS cells after treatment with Nutlin-3. In GC patients, it was found that low expression of LINC00893 was correlated with tumor growth, metastasis and poor survival. Functionally, overexpression of LINC00893 suppressed the proliferation, migration and invasion of GC cells. Mechanistically, LINC00893 regulated the expression of epithelial-mesenchymal transition (EMT)-related proteins by binding to RNA binding fox-1 homolog 2 (RBFOX2) and promoting its ubiquitin-mediated degradation, thus suppressing the EMT and related functions of GC. In addition, the transcription factor p53 can regulate the expression of LINC00893 in an indirect way. Taken together, these results suggested that LINC00893 regulated by p53 repressed GC proliferation, migration and invasion by functioning as a binding site for RBFOX2 to regulate its stability and the expression of EMT-related proteins. LINC00893 acts as a tumor-inhibiting lncRNA that is induced by p53 in GC and regulates EMT by binding to RBFOX2, thus providing a novel experimental basis for the clinical treatment of GC.

Comprehensive analysis of hub mRNA, lncRNA and miRNA, and associated ceRNA networks implicated in grass carp (Ctenopharyngodon idella) growth traits

Grass carp (Ctenopharyngodon idella) is the most productive freshwater aquaculture fish in worldwide. However, the molecular mechanism of its growth traits has not been fully elucidated. Whole transcriptome analysis of the brain and hepatopancreas of 29 six-month-old grass carp with different growth rates was performed. Weighted gene co-expression network analysis (WGCNA) was used to construct a weighted gene co-expression network of mRNA, miRNA and lncRNA separately. A total of 35 hub mRNAs, 47 hub lncRNAs and 33 hub miRNAs were identified from the brain, 37 hub mRNAs, 110 hub lncRNAs and 36 hub miRNAs were identified from the hepatopancreas. The ceRNA networks in the brain and hepatopancreas were involved in brain development and nutrient metabolism, respectively. Overall, this is the first investigation of the growth-related transcriptomic characteristics in the brain and hepatopancreas of grass carp, thus will help us to further explore the molecular mechanism of grass carp growth rate.

A systematic evaluation of the computational tools for lncRNA identification

The computational identification of long non-coding RNAs (lncRNAs) is important to study lncRNAs and their functions. Despite the existence of many computation tools for lncRNA identification, to our knowledge, there is no systematic evaluation of these tools on common datasets and no consensus regarding their performance and the importance of the features used. To fill this gap, in this study, we assessed the performance of 17 tools on several common datasets. We also investigated the importance of the features used by the tools. We found that the deep learning-based tools have the best performance in terms of identifying lncRNAs, and the peptide features do not contribute much to the tool accuracy. Moreover, when the transcripts in a cell type were considered, the performance of all tools significantly dropped, and the deep learning-based tools were no longer as good as other tools. Our study will serve as an excellent starting point for selecting tools and features for lncRNA identification.