The LncRNA Connectivity Map: Using LncRNA Signatures to Connect Small Molecules, LncRNAs, and Diseases

HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways

Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery. While existing drug-target databases have contributed valuable resources, they exhibit structural and functional fragmentation due to heterogeneous data sources and annotation standards. Building upon the high-confidence drug-gene interactions curated in HCDT 1.0, we present HCDT 2.0, a comprehensive and standardized resource that expands the scope through multiomics data integration. This update incorporates three-dimensional interactions including drug-gene, drug-RNA and drug-pathway interactions. The current version contains 1,284,353 curated interactions: 1,224,774 drug-gene pairs (678,564 drugs × 5,692 genes), 11,770 drug-RNA mappings (316 drugs × 6,430 RNAs), and 47,809 drug-pathway links (6,290 drugs × 3,143 pathways), alongside 16,317 drug-disease associations. To enhance biological interpretability, we further integrated pathway-gene and RNA-gene regulatory relationships. In addition, we integrated 38,653 negative DTIs covering 26,989 drugs and 1,575 genes. This integrative framework not only addresses critical gaps in cross-scale data representation but also establishes a robust foundation for systems pharmacology applications, including drug repurposing, adverse event prediction, and precision oncology strategies.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

Non-coding RNAs and their bioengineering applications for neurological diseases

Engineering of cellular biomolecules is an emerging landscape presenting creative therapeutic opportunities. Recently, several strategies such as biomimetic materials, drug-releasing scaffolds, stem cells, and dynamic culture systems have been developed to improve specific biological functions, however, have been confounded with fundamental and technical roadblocks. Rapidly emerging investigations on the bioengineering prospects of mammalian ribonucleic acid (RNA) is expected to result in significant biomedical advances. More specifically, the current trend focuses on devising non-coding (nc) RNAs as therapeutic candidates for complex neurological diseases. Given the pleiotropic and regulatory role, ncRNAs such as microRNAs and long non-coding RNAs are deemed as attractive therapeutic candidates. Currently, the list of non-coding RNAs in mammals is evolving, which presents the plethora of hidden possibilities including their scope in biomedicine. Herein, we critically review on the emerging repertoire of ncRNAs in neurological diseases such as Alzheimer’s disease, Parkinson’s disease, neuroinflammation and drug abuse disorders. Importantly, we present the advances in engineering of ncRNAs to improve their biocompatibility and therapeutic feasibility as well as provide key insights into the applications of bioengineered non-coding RNAs that are investigated for neurological diseases.

Long Noncoding RNAs in Neurodegenerative Diseases: Pathogenesis and Potential Implications as Clinical Biomarkers

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), are progressive and ultimately fatal. NDD onset is influenced by several factors including heredity and environmental cues. Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules with: (i) lengths greater than 200 nucleotides, (ii) diverse biological functions, and (iii) highly conserved structures. They directly interact with molecules such as proteins and microRNAs and subsequently regulate the expression of their targets at the genetic, transcriptional, and post-transcriptional levels. Emerging studies indicate the important roles of lncRNAs in the progression of neurological diseases including NDDs. Additionally, improvements in detection technologies have enabled quantitative lncRNA detection and application to circulating fluids in clinical settings. Here, we review current research on lncRNAs in animal models and patients with NDDs. We also discuss the potential applicability of circulating lncRNAs as biomarkers in NDD diagnostics and prognostics. In the future, a better understanding of the roles of lncRNAs in NDDs will be essential to exploit these new therapeutic targets and improve noninvasive diagnostic methods for diseases.

From Junk to Function: LncRNAs in CNS Health and Disease

Recent advances in RNA sequencing technologies helped to uncover the existence of tens of thousands of long non-coding RNAs (lncRNAs) that arise from the dark matter of the genome. These lncRNAs were originally thought to be transcriptional noise but an increasing number of studies demonstrate that these transcripts can modulate protein-coding gene expression by a wide variety of transcriptional and post-transcriptional mechanisms. The spatiotemporal regulation of lncRNA expression is particularly evident in the central nervous system, suggesting that they may directly contribute to specific brain processes, including neurogenesis and cellular homeostasis. Not surprisingly, lncRNAs are therefore gaining attention as putative novel therapeutic targets for disorders of the brain. In this review, we summarize the recent insights into the functions of lncRNAs in the brain, their role in neuronal maintenance, and their potential contribution to disease. We conclude this review by postulating how these RNA molecules can be targeted for the treatment of yet incurable neurological disorders.

LncRNA HCG11/miR-579-3p/MDM2 axis modulates malignant biological properties in pancreatic carcinoma via Notch/Hes1 signaling pathway

BACKGROUND

Increasing reports have revealed that dysregulated expression of long non-coding RNAs (lncRNAs) is involved in pancreatic carcinoma progression. This study intends to explore the function and molecular mechanism of lncRNA HLA complex group 11 (HCG11) in pancreatic carcinoma.

METHODS

The expression profiles of HCG11 in pancreatic carcinoma samples were detected by qPCR. Bioinformatics analysis was applied to detect the associations among HCG11/miR-579-3p/MDM2. The malignant properties of pancreatic carcinoma cells were measured by numerous biological assays. Xenograft model was exploited to detect the effect of HCG11 on tumor growth.

RESULTS

A significant increase of HCG11 was occurred in pancreatic carcinoma samples. Knockdown of HCG11 suppressed the progression of pancreatic carcinoma cells. Bioinformatics analysis revealed that HCG11 upregulated MDM2 expression by competitively targeting miR-579-3p. The rescue assays showed that miR-579-3p reversed cell behaviors caused by HCG11, and MDM2 reversed cell properties induced by miR-579-3p. The Notch1 intracellular domain (NICD) and Hes1 protein levels were increased by overexpression of HCG11/MDM2. The tumor growth was suppressed after depletion of HCG11, followed by suppressing Ki67, PCNA and Vimentin expression, increasing TUNEL-positive cells and E-cadherin expression.

CONCLUSIONS

Our observations highlighted that HCG11 contributed to the progression of pancreatic carcinoma by promoting growth and aggressiveness, and inhibiting apoptosis via miR-579-3p/MDM2/Notch/Hes1 axis.

Molecular landscape of long noncoding RNAs in brain disorders

According to current paradigms, various risk factors, such as genetic mutations, oxidative stress, neural network dysfunction, and abnormal protein degradation, contribute to the progression of brain disorders. Through the cooperation of gene transcripts in biological processes, the study of noncoding RNAs can lead to insights into the cause and treatment of brain disorders. Recently, long noncoding RNAs (lncRNAs) which are longer than 200 nucleotides in length have been suggested as key factors in various brain disorders. Accumulating evidence suggests the potential of lncRNAs as diagnostic or prognostic biomarkers and therapeutic targets. High-throughput screening-based sequencing has been instrumental in identification of lncRNAs that demand new approaches to understanding the progression of brain disorders. In this review, we discuss the recent progress in the study of lncRNAs, and addresses the pathogenesis of brain disorders that involve lncRNAs and describes the associations of lncRNAs with neurodegenerative disorders such as Alzheimer disease (AD), Parkinson disease (PD), and neurodevelopmental disorders. We also discuss potential targets of lncRNAs and their promise as novel therapeutics and biomarkers in brain disorders.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Down-Regulation of LINC00460 Represses Metastasis of Colorectal Cancer via WWC2

BACKGROUND

Colorectal cancer (CRC) is one of the most prevalent cancers and a common cause of cancer-related death. Long noncoding RNAs have been reported to play an essential role in the development of CRC.

AIMS

This study aimed to investigate the possible function of LINC00460 in CRC.

METHODS

Initially, microarray-based gene expression profiling of CRC was employed to identify differentially expressed genes. Next, the expression of LINC00460 was examined and the cell line presenting with the highest LINC00460 expression was selected for subsequent experimentation. Then, the interaction among LINC00460, ERG, and WWC2 was identified. The effect of LINC00460 on proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT)-related factors as well as tumorigenicity of transfected cells was examined with gain- and loss-of-function experiments.

RESULTS

LINC00460 was robustly induced while WWC2 was poorly expressed in CRC. In addition, LINC00460 could down-regulate WWC2 through interaction with ERG, which led to promoted invasion, migration, and EMT of CRC cells in addition to tumor growth in vivo. Besides, down-regulation of LINC00460 exerted inhibitory effect on these biological activities.

CONCLUSION

Taken together, the key findings of the current study provided evidence suggesting that silencing of LINC00460 could potentially suppress EMT of CRC cells by increasing WWC2 via ERG, and highlighting that knockdown of LINC00460 could serve as a therapeutic target for CRC treatment.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs

Long noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug’s gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at https://github.com/wangyc82/ALACD-v1.

LncRNAs are RNA transcripts that are longer than 200 bp and do not encode proteins. Recent experimental studies have indicated the crucial role of lncRNAs in cancer. We proposed a computational model, ALACD, to understand a lncRNA’s molecular mechanism by associating it with a drug through the drug’s target genes. ALACD reveals lncRNAs, the associated anti-cancer drug, and the induced gene signatures that are involved in the regulation of cancer. Furthermore, these cancer-related lncRNAs are differentially expressed in cancer patients and closely associated with patient survival.

System level characterization of small molecule drugs and their affected long noncoding RNAs

Long noncoding RNAs (lncRNAs) have multiple regulatory roles and are involved in many human diseases. A potential therapeutic strategy based on targeting lncRNAs was recently developed. To gain insight into the global relationship between small molecule drugs and their affected lncRNAs, we constructed a small molecule lncRNA network consisting of 1206 nodes (1033 drugs and 173 lncRNAs) and 4770 drug-lncRNA associations using LNCmap, which reannotated the microarray data from the Connectivity Map (CMap) database. Based on network biology, we found that the connected drug pairs tended to share the same targets, indications, and side effects. In addition, the connected drug pairs tended to have a similar structure. By inferring the functions of lncRNAs through their co-expressing mRNAs, we found that lncRNA functions related to the modular interface were associated with the mode of action or side effects of the corresponding connected drugs, suggesting that lncRNAs may directly/indirectly participate in specific biological processes after drug administration. Finally, we investigated the tissue-specificity of drug-affected lncRNAs and found that some kinds of drugs tended to have a broader influence (e.g. antineoplastic and immunomodulating drugs), whereas some tissue-specific lncRNAs (nervous system) tended to be affected by multiple types of drugs.

Long Noncoding RNAs in Acute Myeloid Leukemia: Functional Characterization and Clinical Relevance

Acute Myeloid Leukemia (AML) is the most common form of leukemia in adults with an incidence of 4.3 per 100,000 cases per year. Historically, the identification of genetic alterations in AML focused on protein-coding genes to provide biomarkers and to understand the molecular complexity of AML. Despite these findings and because of the heterogeneity of this disease, questions as to the molecular mechanisms underlying AML development and progression remained unsolved. Recently, transcriptome-wide profiling approaches have uncovered a large family of long noncoding RNAs (lncRNAs). Larger than 200 nucleotides and with no apparent protein coding potential, lncRNAs could unveil a new set of players in AML development. Originally considered as dark matter, lncRNAs have critical roles to play in the different steps of gene expression and thus affect cellular homeostasis including proliferation, survival, differentiation, migration or genomic stability. Consequently, lncRNAs are found to be differentially expressed in tumors, notably in AML, and linked to the transformation of healthy cells into leukemic cells. In this review, we aim to summarize the knowledge concerning lncRNAs functions and implications in AML, with a particular emphasis on their prognostic and therapeutic potential.

Long non-coding RNA MACC1-AS1 promoted pancreatic carcinoma progression through activation of PAX8/NOTCH1 signaling pathway

BACKGROUND

Accumulated evidences have demonstrated that long non-coding RNAs (lncRNAs) are dysregulated and correlate with the pathophysiological basis of malignant tumors. The objective of this research is to uncover the possible molecular mechanism of MACC1-AS1 regarding the regulation of pancreatic carcinoma (PC) metastasis.

METHODS

lncRNA microarray and qRT-PCR were applied to identify differentially expressed lncRNA profile in PC. The function and role of MACC1-AS1 in PC were assessed via in vitro as well as in vivo assays. Luciferase analyses, RNA immunoprecipitation, and RNA pull-down were performed to determined the underlying MACC1-AS1 mechanisms.

RESULTS

Numbers of differentially expressed lncRNAs in PC were identified via lncRNA microarrays, among which MACC1-AS1 was revealed as the most abundant lncRNA. The upregulation of MACC1-AS1 in PC was further confirmed in two expanded PC cohorts, which showed that MACC1-AS1 expression was upregulated in those PC patients with poor survival. Functionally, knockdown of MACC1-AS1 inhibited the proliferation as well as metastasis of PC cells. Meanwhile, MACC1-AS1 upregulated the expression of PAX8 protein, which promoted aerobic glycolysis and activated NOTCH1 signaling. Additionally, PAX8 was upregulated in PC tissues, which was correlated with the expression of MACC1-AS1 and the overall survival of PC patients.

CONCLUSIONS

Together, our findings indicate a critical role of MACC1-AS1/PAX8/NOTCH1 signaling, which may be an alternative treatment target in PC therapy.

RNA Biology Provides New Therapeutic Targets for Human Disease

RNA is the messenger molecule that conveys information from the genome and allows the production of biomolecules required for life in a responsive and regulated way. Most genes are able to produce multiple mRNA products in response to different internal or external environmental signals, in different tissues and organs, and at specific times in development or later life. This fine tuning of gene expression is dependent on the coordinated effects of a large and intricate set of regulatory machinery, which together orchestrate the genomic output at each locus and ensure that each gene is expressed at the right amount, at the right time and in the correct location. This complexity of control, and the requirement for both sequence elements and the entities that bind them, results in multiple points at which errors may occur. Errors of RNA biology are common and found in association with both rare, single gene disorders, but also more common, chronic diseases. Fortunately, complexity also brings opportunity. The existence of many regulatory steps also offers multiple levels of potential therapeutic intervention which can be exploited. In this review, I will outline the specific points at which coding RNAs may be regulated, indicate potential means of intervention at each stage, and outline with examples some of the progress that has been made in this area. Finally, I will outline some of the remaining challenges with the delivery of RNA-based therapeutics but indicate why there are reasons for optimism.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

MicroRNA-small molecule association identification: from experimental results to computational models

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.

In silico analysis of the potential mechanism of telocinobufagin on breast cancer MCF-7 cells

BACKGROUNDS AND AIMS

The extractives from a ChanSu, traditional Chinese medicine, have been discovered to possess anti-inflammatory and tumor-suppressing abilities. However, the molecular mechanism of telocinobufagin, a compound extracted from ChanSu, on breast cancer cells has not been clarified. The aim of this study is to investigate the underlying mechanism of telocinobufagin on breast cancer cells.

METHODS AND MATERIALS

The differentially expressed genes after telocinobufagin treatment on breast cancer cells were searched and downloaded from Gene Expression Omnibus (GEO), ArrayExpress and literatures. Bioinformatics tools were applied to further explore the potential mechanism of telocinobufagin in breast cancer using the Kyoto Encyclopedia of genes and genomes (KEGG) pathway, Gene ontology (GO) enrichment, panther, and protein-protein interaction analyses. To better comprehend the role of telocinobufagin in breast cancer, we also queried the Connectivity Map using the gene expression profiles of telocinobufagin treatment.

RESULTS

One GEO accession (GSE85871) provided 1251 differentially expressed genes after telocinobufagin treatment on MCF-7 cells. The pathway of neuroactive ligand-receptor interaction, cell adhesion molecules (CAMs), intestinal immune network for IgA production, hematopoietic cell lineage and calcium signaling pathway were the key pathways from KEGG analysis. IGF1 and KSR1, owning to higher protein levels in breast cancer tissues, IGF1 and KSR1 could be the hub genes related to telocinobufagin treatment. It was indicated that the molecular mechanism of telocinobufagin resembled that of fenspiride.

CONCLUSIONS

Telocinobufagin might regulate neuroactive ligand-receptor interaction pathway to exert its influences in breast cancer MCF-7 cells, and its molecular mechanism might share some similarities with fenspiride. This study only presented a comprehensive picture of the role of telocinobufagin in breast cancer MCF-7 cells using big data. However, more thorough and deeper researches are required to add to the validity of this study.

Drosophila as a Model to Gain Insight into the Role of lncRNAs in Neurological Disorders

It is now clear that the majority of transcription in humans results in the production of long non-protein-coding RNAs (lncRNAs) with a variable length spanning from 200 bp up to several kilobases. To date, we have a limited understanding of the lncRNA function, but a huge number of evidences have suggested that lncRNAs represent an outstanding asset for cells. In particular, temporal and spatial expression of lncRNAs appears to be important for proper neurological functioning. Stunningly, abnormal lncRNA function has been found as being critical for the onset of neurological disorders. This chapter focus on the lncRNAs with a role in diseases affecting the central nervous system with particular regard for the lncRNAs causing those neurodegenerative diseases that exhibit dementia and/or motor dysfunctions. A specific section will be dedicated to the human neuronal lncRNAs that have been modelled in Drosophila. Finally, even if only few examples have been reported so far, an overview of the Drosophila lncRNAs with neurological functions will be also included in this chapter.