Association of lncRNA with regulatory molecular factors in brain and their role in the pathophysiology of schizophrenia

Integrating mitoepigenetics into research in mood disorders: a state-of-the-art review

Mood disorders, including major depressive disorder and bipolar disorder, are highly prevalent and stand among the leading causes of disability. Despite the largely elusive nature of the molecular mechanisms underpinning these disorders, two pivotal contributors-mitochondrial dysfunctions and epigenetic alterations-have emerged as significant players in their pathogenesis. This state-of-the-art review aims to present existing data on epigenetic alterations in the mitochondrial genome in mood disorders, laying the groundwork for future research into their pathogenesis. Associations between abnormalities in mitochondrial function and mood disorders have been observed, with evidence pointing to notable changes in mitochondrial DNA (mtDNA). These changes encompass variations in copy number and oxidative damage. However, information on additional epigenetic alterations in the mitochondrial genome remains limited. Recent studies have delved into alterations in mtDNA and regulations in the mitochondrial genome, giving rise to the burgeoning field of mitochondrial epigenetics. Mitochondrial epigenetics encompasses three main categories of modifications: mtDNA methylation/hydroxymethylation, modifications of mitochondrial nucleoids, and mitochondrial RNA alterations. The epigenetic modulation of mitochondrial nucleoids, lacking histones, may impact mtDNA function. Additionally, mitochondrial RNAs, including non-coding RNAs, present a complex landscape influencing interactions between the mitochondria and the nucleus. The exploration of mitochondrial epigenetics offers valuable perspectives on how these alterations impact neurodegenerative diseases, presenting an intriguing avenue for research on mood disorders. Investigations into post-translational modifications and the role of mitochondrial non-coding RNAs hold promise to unravel the dynamics of mitoepigenetics in mood disorders, providing crucial insights for future therapeutic interventions.

Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond

BACKGROUND

Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age.

OBJECTIVE

The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations.

METHODS

We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology.

RESULTS

(1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear.

CONCLUSIONS

Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.

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.

Construction of a Diagnostic Model and a lncRNA-Associated ceRNA Network Based on Apoptosis-Related Genes for Schizophrenia

Methods

Gene expression profiles and apoptosis-related data were downloaded from the Gene Expression Omnibus and Molecular Signature databases, respectively. Apoptosis-related differentially expressed mRNAs (DEGs) and miRNAs (DEMs) from blood samples between the schizophrenia and healthy control individuals were screened. A diagnostic model was developed using the data from univariate and least absolute shrinkage and selection operator (LASSO) regression analyses, followed by validation using the GSE38485 dataset. Cases were divided into low-risk (LR) and high-risk (HR) groups based on the risk score of the model, and differences in immune gene sets and pathways between these two groups were compared. Finally, a ceRNA network was constructed by integrating long non-coding RNAs (lncRNAs), DEMs, and DEGs.

Results

A diagnostic model containing 15 apoptosis-related genes was developed and its diagnostic efficiency was found to be robust. The HR group was correlated with higher immune scores of chemokines, cytokines, and interleukins; it was also significantly involved in pathways such as pancreatic beta cells and early estrogen response. A ceRNA network composed of 2 lncRNAs, 14 miRNAs, and 5 mRNAs was established.

Conclusions

The established model is a potential tool to improve the diagnostic efficiency of patients with schizophrenia, and the nodes included in the ceRNA network might serve as biomarkers and therapeutic targets for schizophrenia.

Whole transcriptome analysis reveals dysregulation of molecular networks in schizophrenia

To characterize the regulatory relationships between different types of transcripts and the altered molecular networks in schizophrenia (SCZ), we performed a whole transcriptome study by quantifying mRNAs, long noncoding RNAs (lncRNAs), miRNAs, and circular RNAs (circRNAs) in the same individuals simultaneously. A total of 807 dysregulated genes showed differential expression in SCZ cases compared with controls. Network-based analysis revealed dysregulation of molecular networks in SCZ. Finally, integration of the transcriptome data with published data identified promising SCZ candidate genes. Our study reveals that dysregulated molecular networks and regulatory relationships between different types of transcript may have a role in SCZ.

Molecular hallmarks of long non-coding RNAs in aging and its significant effect on aging-associated diseases

Aging is linked to the deterioration of many physical and cognitive abilities and is the leading risk factor for Alzheimer's disease. The growing aging population is a significant healthcare problem globally that researchers must investigate to better understand the underlying aging processes. Advances in microarrays and sequencing techniques have resulted in deeper analyses of diverse essential genomes (e.g., mouse, human, and rat) and their corresponding cell types, their organ-specific transcriptomes, and the tissue involved in aging. Traditional gene controllers such as DNA- and RNA-binding proteins significantly influence such programs, causing the need to sort out long non-coding RNAs, a new class of powerful gene regulatory elements. However, their functional significance in the aging process and senescence has yet to be investigated and identified. Several recent researchers have associated the initiation and development of senescence and aging in mammals with several well-reported and novel long non-coding RNAs. In this review article, we identified and analyzed the evolving functions of long non-coding RNAs in cellular processes, including cellular senescence, aging, and age-related pathogenesis, which are the major hallmarks of long non-coding RNAs in aging.

LncRNAs in neuropsychiatric disorders and computational insights for their prediction

Long non-coding RNAs (lncRNAs) are 200 nucleotide extended transcripts that do not encode proteins or possess limited coding ability. LncRNAs epigenetically control several biological functions such as gene regulation, transcription, mRNA splicing, protein interaction, and genomic imprinting. Over the years, drastic progress in understanding the role of lncRNAs in diverse biological processes has been made. LncRNAs are reported to show tissue-specific expression patterns suggesting their potential as novel candidate biomarkers for diseases. Among all other non-coding RNAs, lncRNAs are highly expressed within the brain-enriched or brain-specific regions of the neural tissues. They are abundantly expressed in the neocortex and pre-mature frontal regions of the brain. LncRNAs are co-expressed with the protein-coding genes and have a significant role in the evolution of functions of the brain. Any deregulation in the lncRNAs contributes to disruptions in normal brain functions resulting in multiple neurological disorders. Neuropsychiatric disorders such as schizophrenia, bipolar disease, autism spectrum disorders, and anxiety are associated with the abnormal expression and regulation of lncRNAs. This review aims to highlight the understanding of lncRNAs concerning normal brain functions and their deregulation associated with neuropsychiatric disorders. We have also provided a survey on the available computational tools for the prediction of lncRNAs, their protein coding potentials, and sub-cellular locations, along with a section on existing online databases with known lncRNAs, and their interactions with other molecules.

Baihe Dihuang (Lilium Henryi Baker and Rehmannia Glutinosa) decoction attenuates somatostatin interneurons deficits in prefrontal cortex of depression via miRNA-144-3p mediated GABA synthesis and release

ETHNOPHARMACOLOGICAL RELEVANCE

Baihe Dihuang Decoction is a well-known traditional Chinese medicine prescription (Also known as Lilium Henryi Baker and Rehmannia Glutinosa Decoction, LBRD) composed of Lilium Henryi Baker bulb and raw juice from Rehmannia Glutinosa (Gaertn) DC with the curative efficacy of nourishing yin and clearing heat based on the Chinese herbal medicine theory. It has been used as routine medication in treating depression combined with conventional western medicine in China for years.

AIM OF THE STUDY

LBRD can attenuates GABAergic deficits in the medial prefrontal cortex (mPFC) of depression. This study aimed to investigate the mechanism of antidepressive properties of LBRD in the prefrontal GABAergic interneuron subtypes, including parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP)-positive neuron.

MATERIALS AND METHODS

In this project, chronic unpredicted mild stress paradigm was adopted to construct depression model. After treated with LBRD standard decoction and behaviors test, the level of GABA associated miRNA/mRNA and GABAergic subtype-specific markers were detected by qRT-PCR and Western blot. The lncRNAs/miRNAs/GABA regulatory axis was verified by luciferase reporter assay, RNA immunoprecipitation, RNA pull-down assay, and theses changes were measured in LBRD administration with the use of immunofluorescence staining and RNA-fluorescence in situ hybridization.

RESULTS

In the current study, we found that LBRD exhibited high efficacy based on the results of behavioral tests. Meanwhile, LBRD also improved the reduced GABA levels in depression by increasing the expression of lncRNA Neat1 and Malat1, as well as decreasing miRNA-144-3p and miRNA-15b-5p. Moreover, the level of Sst mRNA and protein that were harvested from the mPFC tissues of depression group was significantly lower than those in the control mice. While, these changes can be reverted by LBRD standard decoction administration. Whereas, neither chronic stress nor treatment can change the level of PV and VIP mRNAs and protein expression. In the SST-positive neuron of mPFC tissues, treatment with LBRD standard decoction resulted in the elevation of Gad-67, VGAT, GAT-3 and a reduction of miRNA-144-3p expression.

CONCLUSIONS

These findings suggested that LBRD antidepressant activities may be related to ameliorating the SST-positive neuron deficits via regulating the miRNA-144-3p mediated GABA synthesis and release.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

Meta-analysis of transcriptional regulatory networks for lipid metabolism in neural cells from schizophrenia patients based on an open-source intelligence approach

There have been a number of reports about the transcriptional regulatory networks in schizophrenia. However, most of these studies were based on a specific transcription factor or a single dataset, an approach that is inadequate to understand the diverse etiology and underlying common characteristics of schizophrenia. Here we reconstructed and compared the transcriptional regulatory network for lipid metabolism enzymes using 15 public transcriptome datasets of neural cells from schizophrenia patients. Since many of the well-known schizophrenia-related SNPs are in enhancers, we reconstructed a network including enhancer-dependent regulation and found that 53.3 % of the total number of edges (7,577 pairs) involved regulation via enhancers. By examining multiple datasets, we found common and unique transcriptional modes of regulation. Furthermore, enrichment analysis of SNPs that were connected with genes in the transcriptional regulatory networks by eQTL suggested an association with hematological cell counts and some other traits/diseases, whose relationship to schizophrenia was either not or insufficiently reported in previous studies. Based on these results, we suggest that in future studies on schizophrenia, information on genotype, comorbidities and hematological cell counts should be included, along with the transcriptome, for a more detailed genetic stratification and mechanistic exploration of schizophrenia.

A putative role for lncRNAs in epigenetic regulation of memory

The central dogma of molecular genetics is defined as encoded genetic information within DNA, transcribed into messenger RNA, which contain the instructions for protein synthesis, thus imparting cellular functionality and ultimately life. This molecular genetic theory has given birth to the field of neuroepigenetics, and it is now well established that epigenetic regulation of gene transcription is critical to the learning and memory process. In this review, we address a potential role for a relatively new player in the field of epigenetic crosstalk - long non-coding RNAs (lncRNAs). First, we briefly summarize epigenetic mechanisms in memory formation and examine what little is known about the emerging role of lncRNAs during this process. We then focus discussions on how lncRNAs interact with epigenetic mechanisms to control transcriptional programs under various conditions in the brain, and how this may be applied to regulation of gene expression necessary for memory formation. Next, we explore how epigenetic crosstalk in turn serves to regulate expression of various individual lncRNAs themselves. To highlight the importance of further exploring the role of lncRNA in epigenetic regulation of gene expression, we consider the significant relationship between lncRNA dysregulation and declining memory reserve with aging, Alzheimer's disease, and epilepsy, as well as the promise of novel therapeutic interventions. Finally, we conclude with a discussion of the critical questions that remain to be answered regarding a role for lncRNA in memory.