Dysregulation of long non-coding RNAs and their mechanisms in Huntington's disease

Brain lncRNA-mRNA co-expression regulatory networks and alcohol use disorder

Prolonged alcohol consumption can disturb the expression of both coding and noncoding genes in the brain. These dysregulated genes may co-express in modules and interact within networks, consequently influencing the susceptibility to developing alcohol use disorder (AUD). In the present study, we performed an RNA-seq analysis of the expression of both long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in 192 postmortem tissue samples collected from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control subjects of European ancestry. Applying the limma-voom method, we detected a total of 57 lncRNAs and 51 mRNAs exhibiting significant differential expression (Padj < 0.05 and fold-change ≥2) across at least one of the eight brain regions investigated. Machine learning analysis further confirmed the potential of these top genes in predicting AUD. Through Weighted Gene Co-expression Network Analysis (WGCNA), we identified distinct lncRNA-mRNA co-expression modules associated with AUD in each of the eight brain regions. Additionally, lncRNA-mRNA co-expression networks were constructed for each brain region using Cytoscape to reveal gene regulatory interactions implicated in AUD. Hub genes within these networks were found to be enriched in several key KEGG pathways, including Axon Guidance, MAPK Signaling, p53 Signaling, Adherens Junction, and Neurodegeneration. Our results underscore the significance of networks involving AUD-associated lncRNAs and mRNAs in modulating neuroplasticity in response to alcohol exposure. Further elucidating these molecular mechanisms holds promise for the development of targeted therapeutic interventions for AUD.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

Long non-coding RNAs in schizophrenia: Genetic variations, treatment markers and potential targeted signaling pathways

Schizophrenia (SZ), a complex and debilitating spectrum of psychiatric disorders, is now mainly attributed to multifactorial etiology that includes genetic and environmental factors. Long non-coding RNAs (lncRNAs) are gaining popularity as a way to better understand the comprehensive mechanisms beneath the clinical manifestation of SZ. Only in recent years has it been elucidated that mammalian genomes encode thousands of lncRNAs. Strikingly, roughly 30-40% of these lncRNAs are extensively expressed in different regions across the brain, which may be closely associated with SZ. The therapeutic and adverse effects of atypical antipsychotic drugs (AAPDs) are partially reflected by their role in the regulation of lncRNAs. This begs the question directly, do any lncRNAs exist as biomarkers for AAPDs treatment? Furthermore, we comprehend a range of mechanistic investigations that have revealed the regulatory roles for lncRNAs both involved in the brain and the periphery of SZ. More crucially, we also combine insights from a variety of signaling pathways to argue that lncRNAs probably play critical roles in SZ via their interactive downstream factors. This review provides a thorough understanding regarding dysregulation of lncRNAs, corresponding genetic alternations, as well as their potential regulatory roles in the pathology of SZ, which might help reveal useful therapeutic targets in SZ.

Effects of Maternal High-Fructose Diet on Long Non-Coding RNAs and Anxiety-like Behaviors in Offspring

Increased fructose intake is an international issue. A maternal high-fructose diet during gestation and lactation could affect nervous system development in offspring. Long non-coding RNA (lncRNA) plays an important role in brain biology. However, the mechanism whereby maternal high-fructose diets influence offspring brain development by affecting lncRNAs is still unclear. Here, we administered 13% and 40% fructose water to establish a maternal high-fructose diet model during gestation and lactation. To determine lncRNAs and their target genes, full-length RNA sequencing was performed using the Oxford Nanopore Technologies platform, and 882 lncRNAs were identified. Moreover, the 13% fructose group and the 40% fructose group had differentially expressed lncRNA genes compared with the control group. Enrichment analyses and co-expression analyses were performed to investigate the changes in biological function. Furthermore, enrichment analyses, behavioral science experiments, and molecular biology experiments all indicated that the fructose group offspring showed anxiety-like behaviors. In summary, this study provides insight into the molecular mechanisms underlying maternal high-fructose diet-induced lncRNA expression and co-expression of lncRNA and mRNA.

mRNA and lncRNA co-expression network in mice of acute intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a severe subtype of stroke lacking effective pharmacological targets. Long noncoding RNA (lncRNA) has been confirmed to participate in the pathophysiological progress of various neurological disorders. However, how lncRNA affects ICH outcomes in the acute phase is not completely clear. In this study, we aimed to reveal the relationship of lncRNA-miRNA-mRNA following ICH.

Method

We conducted the autologous blood injection ICH model and extracted total RNAs on day 7. Microarray scanning was used to obtain mRNA and lncRNA profiles, which were validated by RT-qPCR. GO/KEGG analysis of differentially expressed mRNAs was performed using the Metascape platform. We calculated the Pearson correlation coefficients (PCCs) of lncRNA-mRNA for co-expression network construction. A competitive endogenous (Ce-RNA) network was established based on DIANALncBase and miRDB database. Finally, the Ce-RNA network was visualized and analyzed by Cytoscape.

Results

In total, 570 differentially expressed mRNAs and 313 differentially expressed lncRNAs were identified (FC ≥ 2 and value of p <0.05). The function of differentially expressed mRNAs was mainly enriched in immune response, inflammation, apoptosis, ferroptosis, and other typical pathways. The lncRNA-mRNA co-expression network contained 57 nodes (21 lncRNAs and 36 mRNAs) and 38 lncRNA-mRNA pairs. The ce-RNA network was generated with 303 nodes (29 lncRNAs, 163 mRNAs, and 111 miRNAs) and 906 edges. Three hub clusters were selected to indicate the most significant lncRNA-miRNA-mRNA interactions.

Conclusion

Our study suggests that the top differentially expressed RNA molecules may be the biomarker of acute ICH. Furthermore, the hub lncRNA-mRNA pairs and lncRNA-miRNA-mRNA correlations may provide new clues for ICH treatment.

The emerging role of long non-coding RNAs, microRNAs, and an accelerated epigenetic age in Huntington’s disease

Huntington’s disease (HD) is a dominantly inherited neurodegenerative disease with variable clinical manifestations. Recent studies highlighted the contribution of epigenetic alterations to HD progress and onset. The potential crosstalk between different epigenetic layers and players such as aberrant expression of non-coding RNAs and methylation alterations has been found to affect the pathogenesis of HD or mediate the effects of trinucleotide expansion in its pathophysiology. Also, microRNAs have been assessed for their roles in the modulation of HD manifestations, among them are miR-124, miR-128a, hsa-miR-323b-3p, miR-432, miR-146a, miR-19a, miR-27a, miR-101, miR-9*, miR-22, miR-132, and miR-214. Moreover, long non-coding RNAs such as DNM3OS, NEAT1, Meg3, and Abhd11os are suggested to be involved in the pathogenesis of HD. An accelerated DNA methylation age is another epigenetic signature reported recently for HD. The current literature search collected recent findings of dysregulation of miRNAs or lncRNAs as well as methylation changes and epigenetic age in HD.

Long Intergenic Noncoding RNAs Affect Biological Pathways Underlying Autoimmune and Neurodegenerative Disorders

Long intergenic noncoding RNAs (lincRNAs) are a class of independently transcribed molecules longer than 200 nucleotides that do not overlap known protein-coding genes. LincRNAs have diverse roles in gene expression and participate in a spectrum of biological processes. Dysregulation of lincRNA expression can abrogate cellular homeostasis, cell differentiation, and development and can also deregulate the immune and nervous systems. A growing body of literature indicates their important and multifaceted roles in the pathogenesis of several different diseases. Furthermore, certain lincRNAs can be considered potential therapeutic targets and valuable diagnostic or prognostic biomarkers capable of predicting the onset of a disease, its degree of activity, or the progression phase. In this review, we discuss possible mechanisms and molecular functions of lincRNAs in the pathogenesis of selected autoimmune and neurodegenerative disorders: multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, Sjögren's syndrome, Huntington's disease, Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis. This summary can provide new ideas for future research, diagnosis, and treatment of these highly prevalent and devastating diseases.

Regulation of Oxidative Stress by Long Non-coding RNAs in Central Nervous System Disorders

Central nervous system (CNS) disorders, such as ischemic stroke, Alzheimer’s disease, Parkinson’s disease, spinal cord injury, glioma, and epilepsy, involve oxidative stress and neuronal apoptosis, often leading to long-term disability or death. Emerging studies suggest that oxidative stress may induce epigenetic modifications that contribute to CNS disorders. Non-coding RNAs are epigenetic regulators involved in CNS disorders and have attracted extensive attention. Long non-coding RNAs (lncRNAs) are non-coding RNAs more than 200 nucleotides long and have no protein-coding function. However, these molecules exert regulatory functions at the transcriptional, post-transcriptional, and epigenetic levels. However, the major role of lncRNAs in the pathophysiology of CNS disorders, especially related to oxidative stress, remains unclear. Here, we review the molecular functions of lncRNAs in oxidative stress and highlight lncRNAs that exert positive or negative roles in oxidation/antioxidant systems. This review provides novel insights into the therapeutic potential of lncRNAs that mediate oxidative stress in CNS disorders.