Genetic screening of the genes interacting with Drosophila FIG4 identified a novel link between CMT-causing gene and long noncoding RNAs

Muraoka, Yuuka; Nakamura, Aya; Tanaka, Ryo; Suda, Kojiro; Azuma, Yumiko; Kushimura, Yukie; Piccolo, Luca Lo; Yoshida, Hideki; Mizuta, Ikuko; Tokuda, Takahiko; Mizuno, Toshiki; Nakagawa, Masanori; Yamaguchi, Masamitsu

doi:10.1016/j.expneurol.2018.08.009

Cited by 31 publications

(29 citation statements)

References 46 publications

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“…Several studies using Drosophila as a model have uncovered relationships between lncRNAs and ALS. Knockdown of CR18854, an lncRNA associated with the RBP Staufen [71], rescues the climbing ability defects arising from dysregulated Cabeza (the orthologue of human FUS, hereafter referred to as dFUS) in Drosophila [99]. In contrast, knockdown of the lncRNA heat shock RNA ω (hsrω) in Drosophila MNs gives rise to severe motor deficiency by affecting presynaptic terminals.…”

Section: Other Lncrnas Implicated In Alsmentioning

confidence: 99%

Functional Roles of Long Non-coding RNAs in Motor Neuron Development and Disease

Chen

2020

J Biomed Sci

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Long non-coding RNAs (lncRNAs) have gained increasing attention as they exhibit highly tissue-and cell-type specific expression patterns. LncRNAs are highly expressed in the central nervous system and their roles in the brain have been studied intensively in recent years, but their roles in the spinal motor neurons (MNs) are largely unexplored. Spinal MN development is controlled by precise expression of a gene regulatory network mediated spatiotemporally by transcription factors, representing an elegant paradigm for deciphering the roles of lncRNAs during development. Moreover, many MN-related neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA), are associated with RNA metabolism, yet the link between MN-related diseases and lncRNAs remains obscure. In this review, we summarize lncRNAs known to be involved in MN development and disease, and discuss their potential future therapeutic applications.

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Section: Other Lncrnas Implicated In Alsmentioning

confidence: 99%

Functional Roles of Long Non-coding RNAs in Motor Neuron Development and Disease

Chen

2020

J Biomed Sci

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“…In the last decade, Drosophila lncRNA functions in vivo have been exposed constantly (Table 1). Drosophila lncRNAs act throughout diverse biological processes, including the development of embryos [12,13,67,68,69,70,71], bristles [72,73], gonadal cells [16,74], and neuromuscular junctions [15,75] (Figure 1). The mechanisms of these lncRNAs act in a similar way to those in other organisms, including the regulation of the activity of enzymes or the status of response elements, interaction with certain proteins, transcriptional interference by overlapping with target genes, and the production of miRNAs.…”

Section: Biological Functions Of Lncrnas In Drosophilamentioning

confidence: 99%

“…The mechanism of lncRNA-7SK in Drosophila , which is almost identical to that of its homolog in humans, may also result from the structural identity of the 7SK sequence in several regions between flies and humans [12]. LncRNAs in Drosophila have emerged as crucial regulators of organ development [13], neurological function [14,15], gonadal function [16], and stress response [17,18]. Here, we will briefly review the epigenetic, transcriptional, and post-transcriptional mechanisms of lncRNAs and then summarize their role in Drosophila with a particular emphasis on experimentally verified biological functions (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Insights into the Functions of LncRNAs in Drosophila

Tian

Yuán

et al. 2019

IJMS

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Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs longer than 200 nucleotides (nt). LncRNAs have high spatiotemporal specificity, and secondary structures have been preserved throughout evolution. They have been implicated in a range of biological processes and diseases and are emerging as key regulators of gene expression at the epigenetic, transcriptional, and post-transcriptional levels. Comparative analyses of lncRNA functions among multiple organisms have suggested that some of their mechanisms seem to be conserved. Transcriptome studies have found that some Drosophila lncRNAs have highly specific expression patterns in embryos, nerves, and gonads. In vivo studies of lncRNAs have revealed that dysregulated expression of lncRNAs in Drosophila may result in impaired embryo development, impaired neurological and gonadal functions, and poor stress resistance. In this review, we summarize the epigenetic, transcriptional, and post-transcriptional mechanisms of lncRNAs and mainly focus on recent insights into the transcriptome studies and biological functions of lncRNAs in Drosophila.

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“…Several neuromuscular diseases, e.g., Dystonia and Cardiomyopathy, and neurodegenerative disorders, e.g., Charcot-Marie-Tooth disease (CMTD), Parkinson s disease (PD), Frontotemporal dementia (FTD), Schizophrenia (SCZ) and Alzheimer's Disease (AD), were among the top 5 significant hits (Table 4). For some of these diseases, e.g., FTD, SCZ, PD, and CMTD, the genetic and phenotypic overlap with ALS has been shown previously [49][50][51][52][53][54][55]. Interestingly, type 2 diabetes was among the top 5 DisGeNet hits and has been proposed as an ALS risk factor and phenotypic modifier [56,57].…”

Section: Functional and Phenotypic Enrichment Analyses Of Predicted Amentioning

confidence: 95%

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis

Bean

Al‐Chalabi

Dobson

et al. 2020

Genes

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Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein–protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein–protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.

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Genetic screening of the genes interacting with Drosophila FIG4 identified a novel link between CMT-causing gene and long noncoding RNAs

Cited by 31 publications

References 46 publications

Functional Roles of Long Non-coding RNAs in Motor Neuron Development and Disease

Functional Roles of Long Non-coding RNAs in Motor Neuron Development and Disease

Insights into the Functions of LncRNAs in Drosophila

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis

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