Long non-coding RNAs and their involvement in bipolar disorders

Study on exosomes for identifying bipolar disorder in early stage: A cross-sectional and validation study protocol

BACKGROUND

The difficulty is remained to accurately distinguish bipolar disorder (BD) from major depressive disorder (MDD) in early stage, with a delayed diagnosis for 5-10 years. BD patients are often treated with antidepressants systematically due to being diagnosed with MDD, affecting the disease course and clinical outcomes. The current study aims to explore the role of plasma exosomes as biomarker to distinguish BD from MDD in early stage.

METHODS

Two stages are included. The first stage is a cross-sectional study, comparing the concentrations of plasma exosome microRNA and related proteins among BD group, MDD group, and healthy controls (HC) group (n = 40 respectively), to identify target biomarkers preliminarily. The "Latent Class Analysis" and "Receiver Operating Characteristic" analysis will be performed to determine the optimal concentration range for each biomarker. The second stage is to validate target markers in subjects, coming from an ongoing study focusing on patients with a first depressive episode. All target biomarkers will be test in plasma samples reserved at the initial stage to detect whether the diagnosis indicated by biomarker level is consistent with the diagnosis by DSM-5. Furthermore, the correlation between specific biomarkers and the manic episode, suicidal ideation, and adverse reactions will also be observed.

DISCUSSION

Exosome-derived microRNA and related proteins have potential in serving as a good medium for exploring mental disorders because it can pass through the blood-brain barrier bidirectionally and convey a large amount of information stably. Improving the early diagnosis of BD would help implement appropriate intervention strategy as early as possible and significantly reduce the burden of disease.

The potential applications of artificially modified exosomes derived from mesenchymal stem cells in tumor therapy

Mesenchymal stem cells (MSCs) have tumor-homing ability and play critical roles in tumor treatment, but their dual influences on tumor progression limit their therapeutic applications. Exosomes derived from MSCs (MSC-exosomes) exhibit great potential in targeted tumor treatment due to their advantages of high stability, low immunogenicity, good biocompatibility, long circulation time and homing characteristics. Furthermore, the artificial modification of MSC-exosomes could amplify their advantages and their inhibitory effect on tumors and could overcome the limit of tumor-promoting effect. In this review, we summarize the latest therapeutic strategies involving artificially modified MSC-exosomes in tumor treatment, including employing these exosomes as nanomaterials to carry noncoding RNAs or their inhibitors and anticancer drugs, and genetic engineering modification of MSC-exosomes. We also discuss the feasibility of utilizing artificially modified MSC-exosomes as an emerging cell-free method for tumor treatment and related challenges.

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 noncoding RNA NONHSAG045500 regulates serotonin transporter to ameliorate depressive-like behavior via the cAMP-PKA-CREB signaling pathway in a model of perinatal depression

OBJECTIVE

Perinatal depression (PND) is the most common complication of childbirth and negatively affects the mother. Long noncoding RNA (lncRNA) NONHSAG045500 inhibits the expression of 5-hydroxytryptamine (5-HT) transporter (i.e. serotonin transporter [SERT]) and produces an antidepressant effect. This study aimed to identify a link between the lncRNA NONHSAG045500 and the pathogenesis of PND.

METHODS

Female C57BL/6 J mice were divided into normal control group (control group, n = 15), chronic unpredictable stress (CUS) model group (PND group, n = 15), lncRNA NONHSAG045500-overexpressed group (LNC group, sublingual intravenous injection of NONHSAG045500 overexpression cells for 7 days, n = 15), and escitalopram treatment group (i.e. the selective serotonin reuptake inhibitor [SSRI] group, with escitalopram administered from the 10th day after pregnancy to the 10th day after delivery, n = 15). Control group mice were conceived normally, whereas, in the other groups, a CUS model was established before mice were conceived. Depressive-like behaviour was assessed via sucrose preference, forced swimming, and open-field tests. The expression levels of 5-HT, SERT, and cAMP-PKA-CREB pathway-related proteins in the prefrontal cortex were detected on the 10th day after delivery.

RESULTS

Mice in the PND group exhibited significant depressive-like behaviours compared with those in the control group, indicating that the PND model was successfully established. The expression of lncRNA NONHSAG045500 was markedly decreased in the PND group compared with that in the control group. After treatment, both LNC and SSRI groups showed a significant improvement in depression-like behaviour, and the expression of 5-HT in the prefrontal cortex was increased in these groups compared with that in the PND group. In addition, the LNC group displayed lower expression of SERT and higher expression of cAMP, PKA, and CREB when in comparison to PND group.

CONCLUSION

NONHSAG045500 mediates the development of PND mainly by activating the cAMP-PKA-CREB pathway, increasing the level of 5-HT, and decreasing the expression of SERT.

Reduction of GAS5 and FOXD3-AS1 long non-coding RNAs in patients with bipolar disorder

Bipolar disorder (BD) patients suffer from severe disability and premature death because of failure in prognosis, diagnosis, and treatment. Although neural mechanisms of bipolar have not been fully discovered, studies have shown long noncoding RNAs (lncRNAs) can play an important role in signaling pathways such as PI3K/AKT pathway. There has been little study on deregulated lncRNAs and the lncRNAs' mode of action in the BD. Hence, we aimed to investigate the expression of PI3K/AKT pathway-related lncRNAs named TUG1, GAS5, and FOXD3-AS1 lncRNAs in the PMBC in 50 bipolar patients and 50 healthy controls. Our results showed that FOXD3-AS1 and GAS5 under-expressed significantly in bipolar patients compared to healthy controls (P = 0.0028 and P < 0.0001 respectively). Moreover, after adjustment, all P values remained significant (q value < 0.0001). According to the ROC curve, AUC (area under the curve), specificity, and sensitivity of these lncRNAs, GAS5 and FOXD3-AS1 might work as BD candidate diagnostic biomarkers. Taken together, the current results highlight that the dysregulation of FOXD3-AS1 and GAS5 may be associated with an increased risk of BD.

A gene expression analysis of long non-coding RNAs NKILA and PACER as well as their target genes, NF-κB and cox-2 in bipolar disorder patients

Bipolar disorder (BD) is a severe condition characterized by periods of mania and depression. Despite advances in the neurobiology of bipolar disorder, the exact etiology of the disease remains unclear. There is evidence that Inflammation is associated with bipolar disorder. COX-2 and NF-κB are two critical mediators in the inflammatory pathways. Long non-coding RNAs (lncRNAs) are a new class of non-coding RNAs that play a wide range of roles, especially in developing and maintaining normal brain functions. Two lncRNAs called PACER and NKILA control the expression of COX-2 and NF-κB genes, respectively. In this study, Expression levels of PACER and NKILA lncRNAs, as well as, COX-2 and NF-κB genes were measured in fifty patients with bipolar disorder and 50 healthy individuals by real-time PCR. Expression levels of NKILA and COX2 were considerably reduced in BD patients compared with healthy controls. Such significant downregulation in the expression of NKILA and PACER was only observed in male patients with BD compared with male healthy subjects. Also, according to the results of the ROC curve, the area under curve values for NKILA and COX2 were 0.68 and 0.52 respectively. Consequently, the NKILA gene could be considered a biomarker. By examining the degree of pairwise correlation between genes, all genes had a significant positive correlation with each other. Taken together, these results revealed a function for NKILA and PACER lncRNAs in the pathogenesis of BD.

Genomic regulatory sequences in the pathogenesis of bipolar disorder

The lifetime prevalence of bipolar disorder is estimated to be about 2%. Epigenetics defines regulatory mechanisms that determine relatively stable patterns of gene expression by controlling all key steps, from DNA to messenger RNA to protein. This Mini Review highlights recent discoveries of modified epigenetic control resulting from genetic variants associated with bipolar disorder in genome-wide association studies. The revealed epigenetic abnormalities implicate gene transcription and post-transcriptional regulation. In the light of these discoveries, the Mini Review focuses on the genes PACS1, MCHR1, DCLK3, HAPLN4, LMAN2L, TMEM258, GNL3, LRRC57, CACNA1C, CACNA1D, and NOVA2 and their potential biological role in the pathogenesis of bipolar disorder. Molecular mechanisms under control of these genes do not translate into a unified picture and substantially more research is needed to fill the gaps in knowledge and to solve current limitations in prognosis and treatment of bipolar disorder. In conclusion, the genetic and functional studies confirm the complex nature of bipolar disorder and indicate future research directions to explore possible targeted treatment options, eventually working toward a personalized approach.

Comprehensive Analysis of NPSR1-AS1 as a Novel Diagnostic and Prognostic Biomarker Involved in Immune Infiltrates in Lung Adenocarcinoma

The incidence of lung adenocarcinoma (LUAD), the most common subtype of lung cancer, continues to make lung cancer the largest cause of cancer-related deaths worldwide. Long noncoding RNAs (lncRNAs) have been shown to have a significant role in both the onset and progression of lung cancer. In this study, we aimed to investigate the clinical significance and underlying mechanism of lncRNA NPSR1-AS1 (NPSR1-AS1) in LUAD. First, we performed an analysis on TCGA and identified 229 differentially expressed lncRNAs (DELs) (including 216 upregulated lncRNAs and 13 downregulated lncRNAs). Then, we carried out a screening of the lncRNAs associated with survival, and a total of 382 survival-related lncRNAs were found. 15 survival-related DELs were identified. Among them, our attention focused on NPSR1-AS1. We found that the expression of NPSR1-AS1 was much higher in LUAD specimens compared to nontumor tissues. According to the results of the ROC assays, high NPSR1-AS1 expression had an AUC value of 0.904 for LUAD, with a 95% confidence interval ranging from 0.881 to 0.927. The expression of NPSR1-AS1 was shown to be significantly elevated in a wide variety of cancers, according to the findings of a pancancer investigation. Functional enrichment analysis confirmed that NPSR1-AS1 was involved in LUAD progression via regulating several tumor-related pathways. Patients with high levels of NPSR1-AS1 expression were shown to have a shorter disease-specific survival (DSS) or overall survival (OS) than those with low levels of NPSR1-AS1 expression, according to the findings of a clinical investigation. It was determined by multivariate analysis that NPSR1-AS1 expressions served as an independent prognostic factor for the overall survival of LUAD patients. The results of immune cell infiltration revealed that the expressions of NPSR1-AS1 were negatively associated with CD8 T cells, pDC, cytotoxic cells, mast cells, iDC, neutrophils, NK CD56dim cells, DC, Th17 cells, Tgd, and macrophages, while they were positively associated with NK CD56bright cells and B cells. Overall, our findings revealed that NPSR1-AS1 could serve as a potential biomarker to assess the clinical outcome and immune infiltration level in LUAD.

Long noncoding RNA HOTAIR polymorphisms and susceptibility to bipolar disorder: a preliminary case-control study

Recent studies have shown that long noncoding RNAs contribute to the pathogenesis of bipolar disorder (BD). In this study, we genotyped four HOX Transcript Antisense Intergenic RNA (HOTAIR) gene polymorphisms to investigate if these variations could affect the risk of BD and its clinical subtypes. A total of 357 subjects, comprised of 194 BD patients and 163 age-matched healthy controls, were enrolled. Genotyping was carried out using PCR-RFLP and ARMS-PCR methods. We detected significant associations between the HOTAIR gene rs1899663 G/T, rs12826786 C/T, rs4759314 A/G, and rs920778 C/T polymorphism and the risk of BD under allelic, recessive, dominant, and codominant contrasted genetic models. The CT genotype of rs920778 C/T, GT genotype of rs1899663 G/T, and CT genotype of rs12826786 C/T polymorphisms enhanced the risk of BD type II (BDII). In contrast, the GG genotype of rs4759314 A/G polymorphism significantly diminished BDII risk by 83%. A positive association was noticed between CTTA and CTCG haplotypes of rs920778/rs1899663/rs12826786/rs4759314 and BD risk. Our findings reveal an interactive effect of HOTAIR polymorphisms on the development of BD and its subtypes. Further functional studies are needed to elucidate the role of these variations on HOTAIR expression and epigenetic status.

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.