Long non-coding RNAs in Huntington's disease neurodegeneration

Effects of MicroRNAs and Long Non-coding RNAs on Beneficial Action of Exercise on Cognition in Degenerative Diseases: A Review

Recent research has exposed a growing body of proof underscoring the importance of microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) in maintaining the physical composition of neurons and influencing cognitive functioning in both standard and atypical circumstances. Extensive research has been conducted on the possible application of miRNAs and lncRNAs as biomarkers for various diseases, with a particular focus on brain disorders, as they possess remarkable durability in cell-free surroundings and can endure repeated freezing and thawing processes. It is intriguing to note that miRNAs and lncRNAs have the ability to function through paracrine mechanisms, thereby playing a role in communication between different organs. Recent research has proposed that the improvement of cognitive abilities through physical exercise in mentally healthy individuals is a valuable method for uncovering potential connections between miRNAs, or microRNAs, and lncRNAs, and human cognitive function. The process of cross-correlating data from disease models and patients with existing data will be crucial in identifying essential miRNAs and lncRNAs, which can potentially act as biomarkers or drug targets in the treatment of cognitive disorders. By combining this method with additional research in animal models, we can determine the function of these molecules and their potential impact on therapy. This article discusses the latest research about the primary miRNAs, lncRNAs, and their exosomes that are affected by physical activity in terms of human cognitive function.

Long non-coding RNAs with essential roles in neurodegenerative disorders

ABSTRACT

Recently, with the advent of high-resolution and high-throughput sequencing technologies, an increasing number of long non-coding RNAs (lncRNAs) have been found to be involved in the regulation of neuronal function in the central nervous system with specific spatiotemporal patterns, across different neurodegenerative diseases. However, the underlying mechanisms of lncRNAs during neurodegeneration remain poorly understood. This review provides an overview of the current knowledge of the biology of lncRNAs and focuses on introducing the latest identified roles, regulatory mechanisms, and research status of lncRNAs in Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Finally, this review discusses the potential values of lncRNAs as diagnostic biomarkers and therapeutic targets for neurodegenerative diseases, hoping to provide broader implications for developing effective treatments.

Hidden regulators: the emerging roles of lncRNAs in brain development and disease

Long non-coding RNAs (lncRNAs) have emerged as critical players in brain development and disease. These non-coding transcripts, which once considered as "transcriptional junk," are now known for their regulatory roles in gene expression. In brain development, lncRNAs participate in many processes, including neurogenesis, neuronal differentiation, and synaptogenesis. They employ their effect through a wide variety of transcriptional and post-transcriptional regulatory mechanisms through interactions with chromatin modifiers, transcription factors, and other regulatory molecules. Dysregulation of lncRNAs has been associated with certain brain diseases, including Alzheimer's disease, Parkinson's disease, cancer, and neurodevelopmental disorders. Altered expression and function of specific lncRNAs have been implicated with disrupted neuronal connectivity, impaired synaptic plasticity, and aberrant gene expression pattern, highlighting the functional importance of this subclass of brain-enriched RNAs. Moreover, lncRNAs have been identified as potential biomarkers and therapeutic targets for neurological diseases. Here, we give a comprehensive review of the existing knowledge of lncRNAs. Our aim is to provide a better understanding of the diversity of lncRNA structure and functions in brain development and disease. This holds promise for unravelling the complexity of neurodevelopmental and neurodegenerative disorders, paving the way for the development of novel biomarkers and therapeutic targets for improved diagnosis and treatment.

lncRNA Biomarkers of Glioblastoma Multiforme

Long noncoding RNAs (lncRNAs) are RNA molecules of 200 nucleotides or more in length that are not translated into proteins. Their expression is tissue-specific, with the vast majority involved in the regulation of cellular processes and functions. Many human diseases, including cancer, have been shown to be associated with deregulated lncRNAs, rendering them potential therapeutic targets and biomarkers for differential diagnosis. The expression of lncRNAs in the nervous system varies in different cell types, implicated in mechanisms of neurons and glia, with effects on the development and functioning of the brain. Reports have also shown a link between changes in lncRNA molecules and the etiopathogenesis of brain neoplasia, including glioblastoma multiforme (GBM). GBM is an aggressive variant of brain cancer with an unfavourable prognosis and a median survival of 14-16 months. It is considered a brain-specific disease with the highly invasive malignant cells spreading throughout the neural tissue, impeding the complete resection, and leading to post-surgery recurrences, which are the prime cause of mortality. The early diagnosis of GBM could improve the treatment and extend survival, with the lncRNA profiling of biological fluids promising the detection of neoplastic changes at their initial stages and more effective therapeutic interventions. This review presents a systematic overview of GBM-associated deregulation of lncRNAs with a focus on lncRNA fingerprints in patients' blood.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

Aerobic exercise suppresses cognitive injury in patients with Alzheimer's disease by regulating long non-coding RNA TUG1

BACKGROUND

Alzheimer's disease (AD) is the primary reason for disability of the elderly. This article studied the diagnostic possibility of TUG1 and its potential mechanism in the regulation of aerobic exercise (AE) on AD.

METHODS

77 AD patients undertook a three-month-long cycling exercise, and 77 healthy controls were recruited. Polymerase Chain Reaction amplification was applied to assess the expression of TUG1 and miR-129-5p. The diagnostic possibility was manifested by the receiver operating characteristic (ROC) curve. Spearman correlation analyzed the interrelationships between TUG1 and AD. In vivo, the APP/PS1 double transgenic mouse models of AD were included for rescue experiments. Morris water maze (MWM) was performed to assess cognitive function of AD mice.

RESULTS

The content of TUG1 was ascended in AD patients and was diminished after AE. The increase of TUG1 indicated the high risk of the occurrence of AD. TUG1 was closely connected to the cognitive assessment tools of AD patients. The TUG1/ miR-129-5p axis was the regulator of the regulation of AE in AD mice.

CONCLUSION

TUG1 was involved in AD development and targeted miR-129-5p to participate in the regulation of AE.

Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice

A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.

Abnormal expression of long non-coding RNAs RMRP, CTC-487M23.5, and DGCR5 in the peripheral blood of patients with Bipolar disorder

Long non-coding RNAs (lncRNAs) have been recently considered as one of the regulatory mechanisms of the nervous system. Hence, lncRNAs may be considered diagnostic biomarkers for bipolar disorder (BD). We aimed to investigate the expression of RMRP, CTC-487M23.5, and DGCR5 lncRNAs in bipolar patients. The levels of these three lncRNAs were measured in peripheral blood mononuclear cells (PBMCs) of 50 BD patients and 50 healthy subjects by real-time PCR. Moreover, we performed a ROC curve analysis between the gene expression and some clinical features of BD patients. Significant upregulation of RMRP and CTC-487M23.5 and no significant change in levels of DGCR5 was observed in BD individuals compared with controls. Also, we found upregulation of RMRP and downregulation of CTC-487M23.5 and DGCR5 in females with BD. The areas under the ROC curve (AUC) for RMRP and CTC-487M23.5 lncRNAs were 0.80 and 0.61, respectively. There was no significant correlation between the expression of these three lncRNAs and clinical features in PBMCs of BD patients. These results suggest a role for RMRP and CTC-487M23.5 in the pathogenesis of bipolar disorder. Moreover, the peripheral expression of these two lncRNAs might be beneficial as potential biomarkers for BD.

Cognitive Dysfunction and Exercise: From Epigenetic to Genetic Molecular Mechanisms

Maintaining good health is crucial, and exercise plays a vital role in achieving this goal. It offers a range of positive benefits for cognitive function, regardless of age. However, as our population ages and life expectancy increases, cognitive impairment has become a prevalent issue, often coexisting with age-related neurodegenerative conditions. This can result in devastating consequences such as memory loss, difficulty speaking, and confusion, greatly hindering one's ability to lead an ordinary life. In addition, the decrease in mental capacity has a significant effect on an individual's physical and emotional well-being, greatly reducing their overall level of contentment and causing a significant financial burden for communities. While most current approaches aim to slow the decline of cognition, exercise offers a non-pharmacological, safe, and accessible solution. Its effects on cognition are intricate and involve changes in the brain's neural plasticity, mitochondrial stability, and energy metabolism. Moreover, exercise triggers the release of cytokines, playing a significant role in the body-brain connection and its impact on cognition. Additionally, exercise can influence gene expression through epigenetic mechanisms, leading to lasting improvements in brain function and behavior. Herein, we summarized various genetic and epigenetic mechanisms that can be modulated by exercise in cognitive dysfunction.

Long non-coding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity

The current outbreak of coronavirus disease 2019 (COVID-19) is a global emergency, as its rapid spread and high mortality rate, which poses a significant threat to public health. Innate immunity plays a crucial role in the primary defense against infections, and recent studies have highlighted the pivotal regulatory function of long non-coding RNAs (lncRNAs) in innate immune responses. This study aims to assess the circulating levels of lncRNAs namely ANRIL, THRIL, NEAT1, and MALAT1 in the blood of moderate and severe SARS-CoV-2 infected patients, in comparison to healthy individuals. Additionally, it aims to explore the potential of these lncRNAs as biomarkers for determining the severity of the disease. The blood samples were collected from a total of 38 moderate and 25 severe COVID-19 patients, along with 30 healthy controls. The total RNA was extracted and qPCR was performed to evaluate the blood levels of the lncRNAs. The results indicate significantly higher expression levels of lncRNAs ANRIL and THRIL in severe patients when compared to moderate patients (P value = 0.0307, P value = 0.0059, respectively). Moreover, the expression levels of lncRNAs ANRIL and THRIL were significantly up-regulated in both moderate and severe patients in comparison to the control group (P value < 0.001, P value < 0.001, P value = 0.001, P value < 0.001, respectively). The expression levels of lncRNA NEAT1 were found to be significantly higher in both moderate and severe COVID-19 patients compared to the healthy group (P value < 0.001, P value < 0.001, respectively), and there was no significant difference in the expression levels of NEAT1 between moderate and severe patients (P value = 0.6979). The expression levels of MALAT1 in moderate and severe patients did not exhibit a significant difference compared to the control group (P value = 0.677, P value = 0.764, respectively). Furthermore, the discriminative power of ANRIL and THRIL was significantly higher in the severe patient group than the moderate group (Area under curve (AUC) = 0.6879; P-value = 0.0122, AUC = 0.6947; P-value = 0.0093, respectively). In conclusion, the expression levels of the lncRNAs ANRIL and THRIL are correlated with the severity of COVID-19 and can be regarded as circulating biomarkers for disease progression.

Blood transcriptome sequencing identifies biomarkers able to track disease stages in spinocerebellar ataxia type 3

Transcriptional dysregulation has been described in spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD), an autosomal dominant ataxia caused by a polyglutamine expansion in the ataxin-3 protein. As ataxin-3 is ubiquitously expressed, transcriptional alterations in blood may reflect early changes that start before clinical onset and might serve as peripheral biomarkers in clinical and research settings. Our goal was to describe enriched pathways and report dysregulated genes, which can track disease onset, severity or progression in carriers of the ATXN3 mutation (pre-ataxic subjects and patients). Global dysregulation patterns were identified by RNA sequencing of blood samples from 40 carriers of ATXN3 mutation and 20 controls and further compared with transcriptomic data from post-mortem cerebellum samples of MJD patients and controls. Ten genes-ABCA1, CEP72, PTGDS, SAFB2, SFSWAP, CCDC88C, SH2B1, LTBP4, MEG3 and TSPOAP1-whose expression in blood was altered in the pre-ataxic stage and simultaneously, correlated with ataxia severity in the overt disease stage, were analysed by quantitative real-time PCR in blood samples from an independent set of 170 SCA3/MJD subjects and 57 controls. Pathway enrichment analysis indicated the Gαi signalling and the oestrogen receptor signalling to be similarly affected in blood and cerebellum. SAFB2, SFSWAP and LTBP4 were consistently dysregulated in pre-ataxic subjects compared to controls, displaying a combined discriminatory ability of 79%. In patients, ataxia severity was associated with higher levels of MEG3 and TSPOAP1. We propose expression levels of SAFB2, SFSWAP and LTBP4 as well as MEG3 and TSPOAP1 as stratification markers of SCA3/MJD progression, deserving further validation in longitudinal studies and in independent cohorts.

From Pathogenesis to Therapeutics: A Review of 150 Years of Huntington's Disease Research

Huntington's disease (HD) is a debilitating neurodegenerative genetic disorder caused by an expanded polyglutamine-coding (CAG) trinucleotide repeat in the huntingtin (HTT) gene. HD behaves as a highly penetrant dominant disorder likely acting through a toxic gain of function by the mutant huntingtin protein. Widespread cellular degeneration of the medium spiny neurons of the caudate nucleus and putamen are responsible for the onset of symptomology that encompasses motor, cognitive, and behavioural abnormalities. Over the past 150 years of HD research since George Huntington published his description, a plethora of pathogenic mechanisms have been proposed with key themes including excitotoxicity, dopaminergic imbalance, mitochondrial dysfunction, metabolic defects, disruption of proteostasis, transcriptional dysregulation, and neuroinflammation. Despite the identification and characterisation of the causative gene and mutation and significant advances in our understanding of the cellular pathology in recent years, a disease-modifying intervention has not yet been clinically approved. This review includes an overview of Huntington's disease, from its genetic aetiology to clinical presentation and its pathogenic manifestation. An updated view of molecular mechanisms and the latest therapeutic developments will also be discussed.

Characterization of SOD1-DT, a Divergent Long Non-Coding RNA in the Locus of the SOD1 Human Gene

Researchers studying Amyotrophic Lateral Sclerosis (ALS) have made significant efforts to find a unique mechanism to explain the etiopathology of the different forms of the disease. However, despite several mutations associated with ALS having been discovered in recent years, the link between the mutated genes and its onset has not yet been fully elucidated. Among the genes associated with ALS, superoxide dismutase 1 (SOD1) was the first to be identified, but its role in the etiopathogenesis of the disease is still unclear. In recent years, research has been focused on the non-coding part of the genome to fully understand the mechanisms underlying gene regulation. Non-coding RNAs are conserved molecules and are not usually translated in protein. A total of 98% of the human genome is composed of non-protein coding sequences with roles in the transcriptional and post-transcriptional regulation of gene expression. In this study, we characterized a divergent nuclear lncRNA (SOD1-DT) transcribed in the antisense direction from the 5' region of the SOD1 coding gene in both the SH-SY5Y cell line and fibroblasts derived from ALS patients. Interestingly, this lncRNA seems to regulate gene expression, since its inhibition leads to the upregulation of surrounding genes including SOD1. SOD1-DT represents a very complex molecule, displaying allelic and transcriptional variability concerning transposable elements (TEs) included in its sequence, widening the scenario of gene expression regulation in ALS disease.

The Emerging Role of Autophagy-Associated lncRNAs in the Pathogenesis of Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) have become a significant global public health problem and a major societal burden. The World Health Organization predicts that NDDs will overtake cancer as the second most common cause of human mortality within 20 years. Thus, it is urgently important to identify pathogenic and diagnostic molecular markers related to neurodegenerative processes. Autophagy is a powerful process for removing aggregate-prone proteins in neurons; defects in autophagy are often associated with the pathogenesis of NDDs. Long non-coding RNAs (lncRNAs) have been suggested as key regulators in neurodevelopment; aberrant regulation of lncRNAs contributes to neurological disorders. In this review, we summarize the recent progress in the study of lncRNAs and autophagy in the context of neurodegenerative disorders, especially Alzheimer's disease (AD) and Parkinson's disease (PD). The information presented here should provide guidance for future in-depth investigations of neurodegenerative processes and related diagnostic molecular markers and treatment targets.

lncRNA NEAT1: Key player in neurodegenerative diseases

Neurodegenerative diseases are the most common causes of disability worldwide. Given their high prevalence, devastating symptoms, and lack of definitive diagnostic tests, there is an urgent need to identify potential biomarkers and new therapeutic targets. Long non-coding RNAs (lncRNAs) have recently emerged as powerful regulatory molecules in neurodegenerative diseases. Among them, lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) has been reported to be upregulated in Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). However, whether this is part of a protective or harmful mechanism is still unclear. This review summarizes our current knowledge of the role of NEAT1 in neurodegenerative diseases and its association with the characteristic aggregation of misfolded proteins: amyloid-β and tau in AD, α-synuclein in PD, mutant huntingtin in HD, and TAR DNA-binding protein-43 fused in sarcoma/translocated in liposarcoma in ALS. The aim of this review is to stimulate further research on more precise and effective treatments for neurodegenerative diseases.

The Role of miR-128 in Neurodegenerative Diseases

Several neurodegenerative disorders are characterized by the accumulation of misfolded proteins and are collectively known as proteinopathies. Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD) represent some of the most common neurodegenerative disorders whose steady increase in prevalence is having a major socio-economic impact on our society. Multiple laboratories have reported hundreds of changes in gene expression in selective brain regions of AD, PD, and HD brains. While the mechanisms underlying these changes remain an active area of investigation, alterations in the expression of noncoding RNAs, which are common in AD, PD, and HD, may account for some of the changes in gene expression in proteinopathies. In this review, we discuss the role of miR-128, which is highly expressed in mammalian brains, in AD, PD, and HD. We highlight how alterations in miR-128 may account, at least in part, for the gene expression changes associated with proteinopathies. Indeed, miR-128 is involved, among other things, in the regulation of neuronal plasticity, cytoskeletal organization, and neuronal death, events linked to various proteinopathies. For example, reducing the expression of miR-128 in a mouse model of AD ameliorates cognitive deficits and reduces neuropathology. Overall, the data in the literature suggest that targeting miR-128 might be beneficial to mitigate the behavioral phenotype associated with these diseases.

Recent advances in predicting lncRNA-disease associations based on computational methods

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Research progress of lncRNA and miRNA in hepatic ischemia-reperfusion injury

BACKGROUND

Hepatic ischemia-reperfusion injury (HIRI) is a common complication of liver surgeries, such as hepatectomy and liver transplantation. In recent years, several non-coding RNAs (ncRNAs) including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been identified as factors involved in the pathological progression of HIRI. In this review, we summarized the latest research on lncRNAs, miRNAs and the lncRNA-miRNA regulatory networks in HIRI.

DATA SOURCES

The PubMed and Web of Science databases were searched for articles published up to December 2021 using the following keywords: "hepatic ischemia-reperfusion injury", "lncRNA", "long non-coding RNA", "miRNA" and "microRNA". The bibliography of the selected articles was manually screened to identify additional studies.

RESULTS

The mechanism of HIRI is complex, and involves multiple lncRNAs and miRNAs. The roles of lncRNAs such as AK139328, CCAT1, MALAT1, TUG1 and NEAT1 have been established in HIRI. In addition, numerous miRNAs are associated with apoptosis, autophagy, oxidative stress and cellular inflammation that accompany HIRI pathogenesis. Based on the literature, we conclude that four lncRNA-miRNA regulatory networks mediate the pathological progression of HIRI. Furthermore, the expression levels of some lncRNAs and miRNAs undergo significant changes during the progression of HIRI, and thus are potential prognostic markers and therapeutic targets.

CONCLUSIONS

Complex lncRNA-miRNA-mRNA networks regulate HIRI progression through mutual activation and antagonism. It is necessary to screen for more HIRI-associated lncRNAs and miRNAs in order to identify novel therapeutic targets.

GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning

In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

Expression of Long Noncoding RNAs in Fibroblasts from Mucopolysaccharidosis Patients

In this report, changes in the levels of various long non-coding RNAs (lncRNAs) were demonstrated for the first time in fibroblasts derived from patients suffering from 11 types/subtypes of mucopolysaccharidosis (MPS). Some kinds of lncRNA (SNHG5, LINC01705, LINC00856, CYTOR, MEG3, and GAS5) were present at especially elevated levels (an over six-fold change relative to the control cells) in several types of MPS. Some potential target genes for these lncRNAs were identified, and correlations between changed levels of specific lncRNAs and modulations in the abundance of mRNA transcripts of these genes (HNRNPC, FXR1, TP53, TARDBP, and MATR3) were found. Interestingly, the affected genes code for proteins involved in various regulatory processes, especially gene expression control through interactions with DNA or RNA regions. In conclusion, the results presented in this report suggest that changes in the levels of lncRNAs can considerably influence the pathomechanism of MPS through the dysregulation of the expression of certain genes, especially those involved in the control of the activities of other genes.

Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction

MOTIVATION

Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss.

RESULTS

This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Exosomal miR-29a Derived from Bone Marrow Mesenchymal Stem Cells Promotes Mouse Bone Development and Formation

Bone undergoes constant remodeling during development, and the maintenance of its function requires a dynamic balance between bone formation and resorption by osteoclasts. With unique bone resorption capabilities, as large multinucleated cells, osteocytes participate in bone remodeling and they are produced by the mononuclear/macrophage cells under activation of Wnt and Runx2. The mechanism underlying osteogenesis remains unclear. We investigated the impact of exosomal miR-29a derived from BMSCs on bone development and formation. In this study, BMSCs were transfected and then injected into mice followed by analysis of femur and skull development and regeneration by HE staining and CT scanning, and the expression of DKK1, Runx-2, and osteogenic biomarkers (Osterix, Satb2, ALP, and BSP) by western blot and RT-qPCR. Compared with mice in miR-29a inhibitor group, the femur and skull of mice in miRNA NC group were more complete. miR-29a derived from BMSCs induced a decrease of DKK1 expression and increase of the expression of β-catenin and osteogenic transcription factors. In conclusion, this study demonstrates that BMSC-derived exosomes miR-29a facilitates osteogenesis in mice through inhibition of DKK1 expression.

Regulation of LncRNAs and microRNAs in neuronal development and disease

Non-coding RNAs (ncRNAs) are RNAs that do not encode proteins but play important roles in regulating cellular processes. Multiple studies over the past decade have demonstrated the role of microRNAs (miRNAs) in cancer, in which some miRNAs can act as biomarkers or provide therapy target. Accumulating evidence also points to the importance of long non-coding RNAs (lncRNAs) in regulating miRNA-mRNA networks. An increasing number of ncRNAs have been shown to be involved in the regulation of cellular processes, and dysregulation of ncRNAs often heralds disease. As the population ages, the incidence of neurodegenerative diseases is increasing, placing enormous pressure on global health systems. Given the excellent performance of ncRNAs in early cancer screening and treatment, here we attempted to aggregate and analyze the regulatory functions of ncRNAs in neuronal development and disease. In this review, we summarize current knowledge on ncRNA taxonomy, biogenesis, and function, and discuss current research progress on ncRNAs in relation to neuronal development, differentiation, and neurodegenerative diseases.

The Mechanism Underlying the Regulation of Long Non-coding RNA MEG3 in Cerebral Ischemic Stroke

Cerebral ischemic stroke is one of the leading causes of morbidity and mortality worldwide, and rapidly increasing annually with no more effective therapeutic measures. Thus, the novel diagnostic and prognostic biomarkers are urgent to be identified for prevention and therapy of ischemic stroke. Recently, long noncoding RNAs (lncRNAs), a major family of noncoding RNAs with more than 200 nucleotides, have been considered as new targets for modulating pathological process of ischemic stroke. In this review, we summarized that the lncRNA-maternally expressed gene 3 (MEG3) played a critical role in promotion of neuronal cell death and inhibition of angiogenesis in response to hypoxia or ischemia condition, and further described the challenge of overcrossing blood-brain barrier (BBB) and determination of optimal carrier for delivering lncRNA' drugs into the specific brain regions. In brief, MEG3 will be a potential diagnostic biomarker and drug target in treatment and therapy of ischemic stroke in the future.

LncRNA Taurine Up-Regulated 1 plays a proapoptotic role by regulating nuclear-cytoplasmic shuttle of HuR under the condition of neuronal ischemia

The study aimed to identify TUG1 as an essential regulator of apoptosis in HT22 (mouse hippocampal neuronal cells) by direct interaction with the RNA-binding protein HuR. In order to study the role of TUG1 in the context of ischemia, we used mouse hippocampal neuronal cells treated with oxyglucose deprivation to establish an in-vitro ischemia model. A bioinformatic analysis and formaldehyde RNA immunoprecipitation (fRIP) were used to investigate the biological functions. A Western blot assay and reverse transcription polymerase chain reaction were used to explore the expression of the molecules involved. A cell proliferation and cytotoxicity assay was performed to detect neuronal apoptosis. TUG1 exhibits a localization-specific expression pattern in HT22 cells under OGD treatment. The bioinformatics analysis showed a strong correlation between the TUG1 and HuR as predicted, and this interaction was subsequently confirmed by fRIP-qPCR. We found that HuR was translocated from the nucleus to the cytoplasm after ischemia treatment and subsequently targeted and stabilized COX-2 mRNA, which led to elevated COX-2 mRNA levels and apoptosis of the HT22 cells. Furthermore, nuclear-specific disruption of TUG1 prevented the translocation of HuR to the cytoplasm and decreased COX-2 mRNA expression, resulting in increased cell viability and partially reversed apoptosis. In conclusion, it was demonstrated that TUG1 accelerates the process of apoptosis by promoting the transfer of HuR to the cytoplasm and stabilizing COX-2 mRNA. These results provide useful information concerning a therapeutic target for ischemic stroke.

The lncRNA NEAT1 Mediates Neuronal Cell Autophagy and Related Protein Expression After Cerebral Ischemia‒Reperfusion Injury

The present study focuses on the role of the long noncoding RNA (lncRNA) NEAT1 in regulating autophagy during the ischemia‒reperfusion (I/R) injury process and its possible regulatory mechanism based on the results of laboratory experiments. Neuro-2a (N2a) cells and BV-2 microglial cells were cultured separately, and oxygen-glucose deprivation/reoxygenation (OGD/R) was induced in vitro to mimic cerebral I/R injury. The expression of lncRNA NEAT1 was measured after reoxygenation for different durations, and the results showed that NEAT1 expression was significantly different after OGD/R for 12 h; thus, cell models of NEAT1 overexpression and knockdown were constructed. Knockdown of NEAT1 effectively relieved reperfusion injury. In an N2a and BV-2 cell coculture system, knockdown of NEAT1 reduced autophagic flow in neuronal cells after reperfusion. To clarify the mechanism of NEAT1 after neuronal I/R injury, label-free quantitative proteomics (LFQ) was used to identify the differentially expressed proteins (DEPs) in NEAT1 knockdown neurons after OGD/R for 12 h. Additionally, Gene Ontology (GO) enrichment, protein‒protein interaction (PPI) network and parallel-reaction monitoring (PRM) quantitative analyses were carried out; the results showed that the expression levels of the autophagy-related proteins Gaa, Glb1, Prkaa1, Kif23, Sec24a and Vps25 were significantly reduced and that these proteins interact. In summary, this study shows that NEAT1 can regulate the interactions between autophagy-related proteins after neuronal I/R injury, reducing the level of autophagy and relieving neuronal reperfusion injury.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters

Long non-coding RNA (lncRNA) plays important roles in a series of biological processes. The transcription of lncRNA is regulated by its promoter. Hence, accurate identification of lncRNA promoter will be helpful to understand its regulatory mechanisms. Since experimental techniques remain time consuming for gnome-wide promoter identification, developing computational tools to identify promoters are necessary. However, only few computational methods have been proposed for lncRNA promoter prediction and their performances still have room to be improved. In the present work, a convolutional neural network based model, called DeepLncPro, was proposed to identify lncRNA promoters in human and mouse. Comparative results demonstrated that DeepLncPro was superior to both state-of-the-art machine learning methods and existing models for identifying lncRNA promoters. Furthermore, DeepLncPro has the ability to extract and analyze transcription factor binding motifs from lncRNAs, which made it become an interpretable model. These results indicate that the DeepLncPro can server as a powerful tool for identifying lncRNA promoters. An open-source tool for DeepLncPro was provided at https://github.com/zhangtian-yang/DeepLncPro.

Current Diagnostic Methods and Non-Coding RNAs as Possible Biomarkers in Huntington’s Disease

Whether as a cause or a symptom, RNA transcription is recurrently altered in pathologic conditions. This is also true for non-coding RNAs, with regulatory functions in a variety of processes such as differentiation, cell identity and metabolism. In line with their increasingly recognized roles in cellular pathways, RNAs are also currently evaluated as possible disease biomarkers. They could be informative not only to follow disease progression and assess treatment efficacy in clinics, but also to aid in the development of new therapeutic approaches. This is especially important for neurological and genetic disorders, where the administration of appropriate treatment during the disease prodromal stage could significantly delay, if not halt, disease progression. In this review we focus on the current status of biomarkers in Huntington’s Disease (HD), a fatal hereditary and degenerative disease condition. First, we revise the sources and type of wet biomarkers currently in use. Then, we explore the feasibility of different RNA types (miRNA, ncRNA, circRNA) as possible biomarker candidates, discussing potential advantages, disadvantages, sources of origin and the ongoing investigations on this topic.

Heterogeneous graph neural network for lncRNA-disease association prediction

Identifying lncRNA-disease associations is conducive to the diagnosis, treatment and prevention of diseases. Due to the expensive and time-consuming methods verified by biological experiments, prediction methods based on computational models have gradually become an important means of lncRNA-disease associations discovery. However, existing methods still have challenges to make full use of network topology information to identify potential associations between lncRNA and disease in multi-source data. In this study, we propose a novel method called HGNNLDA for lncRNA-disease association prediction. First, HGNNLDA constructs a heterogeneous network composed of lncRNA similarity network, lncRNA-disease association network and lncRNA-miRNA association network; Then, on this heterogeneous network, various types of strong correlation neighbors with fixed size are sampled for each node by restart random walk; Next, the embedding information of lncRNA and disease in each lncRNA-disease association pair is obtained by the method of type-based neighbor aggregation and all types combination though heterogeneous graph neural network, in which attention mechanism is introduced considering that different types of neighbors will make different contributions to the prediction of lncRNA-disease association. As a result, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) under fivefold cross-validation (5FCV) are 0.9786 and 0.8891, respectively. Compared with five state-of-art prediction models, HGNNLDA has better prediction performance. In addition, in two types of case studies, it is further verified that our method can effectively predict the potential lncRNA-disease associations, and have ability to predict new diseases without any known lncRNAs.

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.

The potential role of serum expression profile of long non coding RNAs, Cox2 and HOTAIR as novel diagnostic biomarkers in systemic lupus erythematosus

Background

The role of the long non-coding RNAs (lncRNAs) in the pathogenesis of systemic lupus erythematosus (SLE) is mostly unknown, despite increasing evidence that lncRNAs extensively participate in physiological and pathological conditions.

Aim

To detect the level of lncRNA-Cox2, HOTAIR, IL-6, and MMP-9 in the serum of SLE patients and to correlate these levels with disease activity and patients’ clinical and laboratory data to evaluate the value of these biomarkers for SLE diagnosis and assessment of disease activity.

Methods

Blood samples from 58 SLE patients, and 60 healthy controls (HCs) were used for detection of lncRNAs-Cox2 and HOTAIR expression levels by real-time polymerase chain reaction. Both IL-6 and MMP-9 serum levels were assayed by enzyme-linked immunosorbent assay. Lupus activity was assessed with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI).

Results

The serum expression levels of lncRNA-Cox2 and HOTAIR were significantly up-regulated in SLE patients vs HCs (fold change [median (IQR) was 1.29(0.81–1.71, P<0.0001) and 2.68(0.95–3.67), P = 0.038) for lncRNA-Cox2 and HOTAIR, respectively. Serum levels of both IL-6 and MMP-9 were significantly high in SLE patients compared with HCs (P≤0.001 for each). The up-regulated lncRNA-Cox2 was positively associated with the presence of neurological manifestations in SLE patients (P = 0.007). Furthermore, HOTAIR expression level had significantly positive correlation with IL-6 (r = 0.578, P<0.0001), MMP-9 level (r = 0.762, P<0.0001), nephritis grades (r = 0.296, P = 0.024) and proteinuria (r = 0.287, P = 0.035). LncRNA-Cox2 showed sensitivity and specificity 72.4%, and 100.0% respectively. HOTAIR sensitivity was 60.3%, and specificity was 100.0%. By multiple logistic regression analysis, lncRNA-Cox2 and HOTAIR were found as SLE independent predictors.

Conclusion

LncRNA-COX2 and HOTAIR can be used as new non-invasive biomarkers for the diagnosis of SLE.

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.

Bone Marrow Mesenchymal Stem Cell-Exosomes (BMSC-ExO) Promote Osteogenic Differentiation In Vitro and Osteogenesis In Vivo by Regulating miR-318/Runt-Related Transcription Factor 2 (RUNX2)

Primary osteoporosis (PMOP) is characterized by bone mass reduction and bone microstructure destruction, increased bone fragility and prone to fracture, which is partially caused by ovarian dysfunction and decreased estrogen content. Bone marrow mesenchymal stem cell exosomes (BMSC-ExO) can improve PMOP. In this study, BMSC-EXO was used to study the role and function of miR-318 and Runx2 in PMOP. Human osteogenitor cells were isolated from PMOP patients with primary osteoporosis. After BMSC-exo treatment, miR-318 and Runx 2 level was tested by RT-qPCR and Western blot. In addition, mice in OVX group were treated with BMSC-ExO (bilateral ovaries were removed) to observe the effect of BMSC-ExO on bone tissue. Our results showed that BMSC-exo treatment significantly decreased miR-318 level, upregulated RUNX2 expression and increased ALP activity. In addition, BMSC-exo administration ameliorated the declined bone mass and bone formation in osteoporotic femurs in OVX mice. In conclusion, BMSC-Exo enhances Runx2 levels by down-regulation of miR-318, thereby promoting osteogenic differentiation of osteogenitor cells, providing new potential therapeutic targets for treating PMOP.

The Emerging Roles of Long Non-Coding RNAs in Intellectual Disability and Related Neurodevelopmental Disorders

In the human brain, long non-coding RNAs (lncRNAs) are widely expressed in an exquisitely temporally and spatially regulated manner, thus suggesting their contribution to normal brain development and their probable involvement in the molecular pathology of neurodevelopmental disorders (NDD). Bypassing the classic protein-centric conception of disease mechanisms, some studies have been conducted to identify and characterize the putative roles of non-coding sequences in the genetic pathogenesis and diagnosis of complex diseases. However, their involvement in NDD, and more specifically in intellectual disability (ID), is still poorly documented and only a few genomic alterations affecting the lncRNAs function and/or expression have been causally linked to the disease endophenotype. Considering that a significant fraction of patients still lacks a genetic or molecular explanation, we expect that a deeper investigation of the non-coding genome will unravel novel pathogenic mechanisms, opening new translational opportunities. Here, we present evidence of the possible involvement of many lncRNAs in the etiology of different forms of ID and NDD, grouping the candidate disease-genes in the most frequently affected cellular processes in which ID-risk genes were previously collected. We also illustrate new approaches for the identification and prioritization of NDD-risk lncRNAs, together with the current strategies to exploit them in diagnosis.

Downregulation of CRTC1 Is Involved in CUMS-Induced Depression-Like Behavior in the Hippocampus and Its RNA Sequencing Analysis

Chronic stress is an important risk factor for mood disorders including depression. The decreased level of CREB (cAMP-responsive element binding)-regulated transcription coactivator 1 (CRTC1) expression in hippocampus may be involved in depression-like behavior in some stress-induced depression models. But the mechanism of CRTC1 in mediating depression-like behavior remains unknown. In this study, chronic unpredictable mild stress (CUMS)-treated mice showed depression-like behavior accompanied by the downregulation of CRTC1 in the hippocampus. Adeno-associated virus (AAV)-CRTC1-mediated overexpression of CRTC1 in the hippocampus by stereotactic brain injection could significantly prevent depression-like behavior in CUMS-treated mice. The above data reveal that the downregulation of hippocampal CRTC1 expression participates in CUMS-induced depression-like behavior. In order to explore the key targets regulated by CRTC1, AAV-mediated CRTC1 short hairpin (shRNA) was constructed to achieve knockdown of CRTC1 in the hippocampus, and then the hippocampi were collected for RNA-sequencing (RNA-seq). The RNA-seq data show that upregulated genes were enriched in stress and immune system-associated GO terms and pathways such as response to stress and external stimulus and regulation of immune response and that downregulated genes were enriched in neural activity such as synaptic transmission and cognitive behavior. We further provided RT-qPCR data that the inflammation-related factors including Gpr84, Tlr2, Lyz2, and Icam1 were significantly upregulated in the hippocampus of both CUMS- and CRTC1 shRNA-induced models, some of them were also validated in protein levels by Western blotting. We propose a hypothesis that CUMS induces downregulation of CRTC1, which might lead to depression-like behavior via neuroinflammation pathway. This study provides new explanation for the inflammatory hypothesis of depression and some clues for exploring the molecular mechanism of CRTC1 regulation.

Significant reduction of long non-coding RNAs expression in bipolar disorder

Long non-coding RNAs (lncRNAs) have been recently emerged as critical modulators of oxidative stress pathway. Likewise, rising evidence currently highlights dysfunction of oxidative stress pathways in bipolar disorder (BD) patients.In the current study, we evaluated the expression levels of H19, SCAL1 (LUCAT1), RMST, MEG3 and MT1DP lncRNAs in the PBMC from 50 patients with BD and 50 control subjects (male/female ratio in each group: 70%/30%). Expression levels of SCAL1, RMST and MEG3 but not H19 and MT1DP were considerably decreased in BD patients compared with healthy individuals. Such significant decrease in the expression of MEG3, RMST and SCAL1 was only reported in male BD patients compared with male controls. Substantial pairwise correlations were observed between expression levels of these lncRNAs in BD subjects. The area under curve values for RMST, MEG3 and SCAL1 were 0.70, 0.63 and 0.61 respectively. On the basis of this finding, RMST had the best efficiency in the discrimination of disease status between BD patients and controls. Taken together, the current results suggest a role for MEG3, RMST and SCAL1 lncRNAs in the pathogenesis of BD. In addition, peripheral expression levels of these lncRNAs might serve as potential peripheral markers for BD.

Analysis of multi-omics data on the relationship between epigenetic changes and nervous system disorders caused by exposure to environmentally harmful substances

Environmentally hazardous substances and exposure to these can cause various diseases. Volatile organic compounds can easily evaporate into the atmosphere, thereby exerting toxic effects through either the skin or respiratory tract exposures. Toluene, a neurotoxin, has been widely used in various industries. However, it has a detrimental effect on the nervous system (such as hallucinations or memory impairment), while data on the mechanism underlaying its harmful effects remain limited. Therefore, this study investigates the effect of toluene on the nervous system via epigenetic and genetic changes of toluene-exposed individuals. We identified significant epigenetic changes and confirmed that the affected abnormally expressed genes negatively influenced the nervous system. In particular, we confirmed that the miR-15 family, upregulated by toluene, downregulated ABL2, which could affect the R as signaling pathway resulting in neuronal structural abnormalities. Our study suggests that miR-15a-5p, miR-15b-5p, miR-16-5p, miR-301a-3p, and lncRNA NEAT1 may represent effective epigenomic markers associated with neurodegenerative diseases caused by toluene.

A learning-based method to predict LncRNA-disease associations by combining CNN and ELM

Background

lncRNAs play a critical role in numerous biological processes and life activities, especially diseases. Considering that traditional wet experiments for identifying uncovered lncRNA-disease associations is limited in terms of time consumption and labor cost. It is imperative to construct reliable and efficient computational models as addition for practice. Deep learning technologies have been proved to make impressive contributions in many areas, but the feasibility of it in bioinformatics has not been adequately verified.

Results

In this paper, a machine learning-based model called LDACE was proposed to predict potential lncRNA-disease associations by combining Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN). Specifically, the representation vectors are constructed by integrating multiple types of biology information including functional similarity and semantic similarity. Then, CNN is applied to mine both local and global features. Finally, ELM is chosen to carry out the prediction task to detect the potential lncRNA-disease associations. The proposed method achieved remarkable Area Under Receiver Operating Characteristic Curve of 0.9086 in Leave-one-out cross-validation and 0.8994 in fivefold cross-validation, respectively. In addition, 2 kinds of case studies based on lung cancer and endometrial cancer indicate the robustness and efficiency of LDACE even in a real environment.

Conclusions

Substantial results demonstrated that the proposed model is expected to be an auxiliary tool to guide and assist biomedical research, and the close integration of deep learning and biology big data will provide life sciences with novel insights.

LncRNA PTCSC3 is upregulated in osteoporosis and negatively regulates osteoblast apoptosis

BACKGROUND

It is known that long non-coding RNA (lncRNA) PTCSC3 is involved in thyroid cancer and glioma, but its function in osteoporosis is unknown. The aim of our study was to investigate the role of lncRNA PTCSC3 in osteoporosis.

METHODS

A total of 80 patients with osteoporosis (4 clinical stages) and four corresponding groups of healthy controls were enrolled. Plasma PTCSC3 levels in the 80 osteoporosis patients and 80 healthy volunteers were measured using RT-qPCR. The diagnostic potential of plasma PTCSC3 for osteoporosis was evaluated by ROC curve analysis with healthy volunteers as the true negative cases and corresponding osteoporosis patients as the true positive cases.

RESULTS

PTCSC3 was upregulated in osteoporosis patients compared with healthy controls. PTCSC3 levels increased with osteoporosis stages increasing, but not with healthy controls aging. PTCSC3 overexpression separated each stage of osteoporosis from corresponding controls. PTCSC3 overexpression promoted while PTCSC3 silencing inhibited osteoblast apoptosis. However, PTCSC3 overexpression and silencing showed no significant effect on osteoclast apoptosis. LncRNA PTCSC3 was upregulated in osteoporosis and negatively regulated osteoblast apoptosis.

CONCLUSION

LncRNA PTCSC3 may serve as a potential therapeutic target for osteoporosis.

Roles of Long Non-coding RNAs in the Development of Aging-Related Neurodegenerative Diseases

Aging-related neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), are gradually becoming the primary burden of society and cause significant health-care concerns. Aging is a critical independent risk factor for neurodegenerative diseases. The pathological alterations of neurodegenerative diseases are tightly associated with mitochondrial dysfunction, inflammation, and oxidative stress, which in turn stimulates the further progression of neurodegenerative diseases. Given the potential research value, lncRNAs have attracted considerable attention. LncRNAs play complex and dynamic roles in multiple signal transduction axis of neurodegeneration. Emerging evidence indicates that lncRNAs exert crucial regulatory effects in the initiation and development of aging-related neurodegenerative diseases. This review compiles the underlying pathological mechanisms of aging and related neurodegenerative diseases. Besides, we discuss the roles of lncRNAs in aging. In addition, the crosstalk and network of lncRNAs in neurodegenerative diseases are also explored.

Non-Coding RNAs Regulate the Resistance to Anti-EGFR Therapy in Colorectal Cancer

Colorectal cancer (CRC) is the third prevalent cancer worldwide, the morbidity and mortality of which have been increasing in recent years. As molecular targeting agents, anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (McAbs) have significantly increased the progression-free survival (PFS) and overall survival (OS) of metastatic CRC (mCRC) patients. Nevertheless, most patients are eventually resistant to anti-EGFR McAbs. With the intensive study of the mechanism of anti-EGFR drug resistance, a variety of biomarkers and pathways have been found to participate in CRC resistance to anti-EGFR therapy. More and more studies have implicated non-coding RNAs (ncRNAs) primarily including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are widely involved in tumorigenesis and tumor progression. They function as essential regulators controlling the expression and function of oncogenes. Increasing data have shown ncRNAs affect the resistance of molecular targeted drugs in CRC including anti-EGFR McAbs. In this paper, we have reviewed the advance in mechanisms of ncRNAs in regulating anti-EGFR McAbs therapy resistance in CRC. It provides insight into exploring ncRNAs as new molecular targets and prognostic markers for CRC.

Genomic and functional conservation of lncRNAs: lessons from flies

Over the last decade, the increasing interest in long non-coding RNAs (lncRNAs) has led to the discovery of these transcripts in multiple organisms. LncRNAs tend to be specifically, and often lowly, expressed in certain tissues, cell types and biological contexts. Although lncRNAs participate in the regulation of a wide variety of biological processes, including development and disease, most of their functions and mechanisms of action remain unknown. Poor conservation of the DNA sequences encoding for these transcripts makes the identification of lncRNAs orthologues among different species very challenging, especially between evolutionarily distant species such as flies and humans or mice. However, the functions of lncRNAs are unexpectedly preserved among different species supporting the idea that conservation occurs beyond DNA sequences and reinforcing the potential of characterising lncRNAs in animal models. In this review, we describe the features and roles of lncRNAs in the fruit fly Drosophila melanogaster, focusing on genomic and functional comparisons with human and mouse lncRNAs. We also discuss the current state of advances and limitations in the study of lncRNA conservation and future perspectives.

Long non-coding RNA lncC11orf54-1 modulates neuroinflammatory responses by activating NF-κB signaling during meningitic Escherichia coli infection

Escherichia coli is the most common gram-negative pathogenic bacterium causing meningitis. It penetrates the blood–brain barrier (BBB) and activates nuclear factor kappa B (NF-κB) signaling, which are vital events leading to the development of meningitis. Long non-coding RNAs (lncRNAs) have been implicated in regulating neuroinflammatory signaling, and our previous study showed that E. coli can induce differential expression of lncRNAs, including lncC11orf54-1, in human brain microvascular endothelial cells (hBMECs). The hBMECs constitute the structural and functional basis for the BBB, however, it is unclear whether lncRNAs are involved in the regulation of inflammatory responses of hBMECs during meningitic E. coli infection. In this study, we characterized an abundantly expressed lncRNA, lncC11orf54-1, which was degraded by translocated coilin to produce mgU2-19 and mgU2-30 in hBMECs during E. coli infection. Functionally, lncC11orf54-1-originated non-coding RNA mgU2-30 interacted with interleukin-1 receptor-associated kinase 1 (IRAK1) to induce its oligomerization and autophosphorylation, thus promoting the activation of NF-κB signaling and facilitating the production of pro-inflammatory cytokines. In summary, our study uncovers the involvement of lncC11orf54-1 in IRAK1–NF-κB signaling, and it functions as a positive regulator of inflammatory responses in meningitic E. coli-induced neuroinflammation, which may be a valuable therapeutic and diagnostic target for bacterial meningitis.

LncRNAs as the Regulators of Brain Function and Therapeutic Targets for Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common type of dementia and a serious threat to the health and safety of the elderly population. It has become an emerging public health problem and a major economic and social burden. However, there is currently no effective treatment for AD. Although the mechanism of AD pathogenesis has been investigated substantially, the full range of molecular factors that contribute to its development remain largely unclear. In recent years, accumulating evidence has revealed that long non-coding RNAs (lncRNAs), a type of non-coding RNA longer than 200 nucleotides, play important roles in multiple biological processes involved in AD pathogenesis. With the further exploration of genomics, the role of lncRNA in the pathogenesis of AD has been phenotypically or mechanistically studied. Herein, we systematically review the current knowledge about lncRNAs implicated in AD and elaborate on their main regulatory pathways, which may contribute to the discovery of novel therapeutic targets and drugs for AD.

Long Non-Coding RNA Lacuna Regulates Neuronal Differentiation of Neural Stem Cells During Brain Development

Although long non-coding RNAs (lncRNAs) is one of the most abundant classes of RNAs encoded within the mammalian genome and are highly expressed in the adult brain, they remain poorly characterized and their roles in the brain development are not well understood. Here we identify the lncRNA Lacuna (also catalogued as NONMMUT071331.2 in NONCODE database) as a negative regulator of neuronal differentiation in the neural stem/progenitor cells (NSCs) during mouse brain development. In particular, we show that Lacuna is transcribed from a genomic locus near to the Tbr2/Eomes gene, a key player in the transition of intermediate progenitor cells towards the induction of neuronal differentiation. Lacuna RNA expression peaks at the developmental time window between E14.5 and E16.5, consistent with a role in neural differentiation. Overexpression experiments in ex vivo cultured NSCs from murine cortex suggest that Lacuna is sufficient to inhibit neuronal differentiation, induce the number of Nestin+ and Olig2+ cells, without affecting proliferation or apoptosis of NSCs. CRISPR/dCas9-KRAB mediated knockdown of Lacuna gene expression leads to the opposite phenotype by inducing neuronal differentiation and suppressing Nestin+ and Olig2+ cells, again without any effect on proliferation or apoptosis of NSCs. Interestingly, despite the negative action of Lacuna on neurogenesis, its knockdown inhibits Eomes transcription, implying a simultaneous, but opposite, role in facilitating the Eomes gene expression. Collectively, our observations indicate a critical function of Lacuna in the gene regulation networks that fine tune the neuronal differentiation in the mammalian NSCs.

LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification

BACKGROUND

Long noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experiments are costly and time-consuming. Most computational methods failed to observe the imbalanced characterize of lncRNA-protein interaction (LPI) data. More importantly, they were measured based on a unique dataset, which produced the prediction bias.

RESULTS

In this study, we develop an Ensemble framework (LPI-EnEDT) with Extra tree and Decision Tree classifiers to implement imbalanced LPI data classification. First, five LPI datasets are arranged. Second, lncRNAs and proteins are separately characterized based on Pyfeat and BioTriangle and concatenated as a vector to represent each lncRNA-protein pair. Finally, an ensemble framework with Extra tree and decision tree classifiers is developed to classify unlabeled lncRNA-protein pairs. The comparative experiments demonstrate that LPI-EnEDT outperforms four classical LPI prediction methods (LPI-BLS, LPI-CatBoost, LPI-SKF, and PLIPCOM) under cross validations on lncRNAs, proteins, and LPIs. The average AUC values on the five datasets are 0.8480, 0,7078, and 0.9066 under the three cross validations, respectively. The average AUPRs are 0.8175, 0.7265, and 0.8882, respectively. Case analyses suggest that there are underlying associations between HOTTIP and Q9Y6M1, NRON and Q15717.

CONCLUSIONS

Fusing diverse biological features of lncRNAs and proteins and exploiting an ensemble learning model with Extra tree and decision tree classifiers, this work focus on imbalanced LPI data classification as well as interaction information inference for a new lncRNA (or protein).