DDX3X overexpression decreases dipeptide repeat proteins in a mouse model of C9ORF72-ALS/FTD.

Hexanucleotide repeat expansion in C9ORF72 (C9) is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). One of the proposed pathogenic mechanisms is the neurotoxicity arising from dipeptide repeat (DPR) proteins produced by repeat-associated non-AUG (RAN) translation. Therefore, reducing DPR levels emerges as a potential therapeutic strategy for C9ORF72-ALS/FTD. We previously identified an RNA helicase, DEAD-box helicase 3 X-linked (DDX3X), modulates RAN translation. DDX3X overexpression decreases poly-GP accumulation in C9ORF72-ALS/FTD patient-derived induced pluripotent stem cell (iPSC)-differentiated neurons (iPSNs) and reduces the glutamate-induced neurotoxicity. In this study, we examined the in vivo efficacy of DDX3X overexpression using a mouse model. We expressed exogenous DDX3X or GFP in the central nervous system (CNS) of the C9-500 ALS/FTD BAC transgenic or non-transgenic control mice using adeno-associated virus serotype 9 (AAV9). The DPR levels were significantly reduced in the brains of DDX3X-expressing C9-BAC mice compared to the GFP control even twelve months after virus delivery. Additionally, p62 aggregation was also decreased. No neuronal loss or neuroinflammatory response were detected in the DDX3X overexpressing C9-BAC mice. This work demonstrates that DDX3X overexpression effectively reduces DPR levels in vivo without provoking neuroinflammation or neurotoxicity, suggesting the potential of increasing DDX3X expression as a therapeutic strategy for C9ORF72-ALS/FTD.

Exploring the brain epitranscriptome: perspectives from the NSAS summit.

Increasing evidence reinforces the essential function of RNA modifications in development and diseases, especially in the nervous system. RNA modifications impact various processes in the brain, including neurodevelopment, neurogenesis, neuroplasticity, learning and memory, neural regeneration, neurodegeneration, and brain tumorigenesis, leading to the emergence of a new field termed neuroepitranscriptomics. Deficiency in machineries modulating RNA modifications has been implicated in a range of brain disorders from microcephaly, intellectual disability, seizures, and psychiatric disorders to brain cancers such as glioblastoma. The inaugural NSAS Challenge Workshop on Brain Epitranscriptomics hosted in Crans-Montana, Switzerland in 2023 assembled a group of experts from the field, to discuss the current state of the field and provide novel translational perspectives. A summary of the discussions at the workshop is presented here to simulate broader engagement from the general neuroscience field.

RBFOX2 recognizes N6-methyladenosine to suppress transcription and block myeloid leukaemia differentiation.

N6-methyladenosine (m6A) methylation can be deposited on chromatin-associated RNAs (caRNAs) by the RNA methyltransferase complex (MTC) to regulate chromatin state and transcription. However, the mechanism by which MTC is recruited to distinct genomic loci remains elusive. Here we identify RBFOX2, a well-studied RNA-binding protein, as a chromatin factor that preferentially recognizes m6A on caRNAs. RBFOX2 can recruit RBM15, an MTC component, to facilitate methylation of promoter-associated RNAs. RBM15 also physically interacts with YTHDC1 and recruits polycomb repressive complex 2 (PRC2) to the RBFOX2-bound loci for chromatin silencing and transcription suppression. Furthermore, we found that this RBFOX2/m6A/RBM15/YTHDC1/PRC2 axis plays a critical role in myeloid leukaemia. Downregulation of RBFOX2 notably inhibits survival/proliferation of acute myeloid leukaemia cells and promotes their myeloid differentiation. RBFOX2 is also required for self-renewal of leukaemia stem/initiation cells and acute myeloid leukaemia maintenance. Our study presents a pathway of m6A MTC recruitment and m6A deposition on caRNAs, resulting in locus-selective chromatin regulation, which has potential therapeutic implications in leukaemia.

Thorough statistical analyses of breast cancer co-methylation patterns.

BACKGROUND: Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). RESULTS: Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites' co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites' locations are significantly different from the genome-wide CG sites' locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. CONCLUSIONS: Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

Nuclear export and translation of circular repeat-containing intronic RNA in C9ORF72-ALS/FTD.

C9ORF72 hexanucleotide GGGGCC repeat expansion is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Repeat-containing RNA mediates toxicity through nuclear granules and dipeptide repeat (DPR) proteins produced by repeat-associated non-AUG translation. However, it remains unclear how the intron-localized repeats are exported and translated in the cytoplasm. We use single molecule imaging approach to examine the molecular identity and spatiotemporal dynamics of the repeat RNA. We demonstrate that the spliced intron with G-rich repeats is stabilized in a circular form due to defective lariat debranching. The spliced circular intron, instead of pre-mRNA, serves as the translation template. The NXF1-NXT1 pathway plays an important role in the nuclear export of the circular intron and modulates toxic DPR production. This study reveals an uncharacterized disease-causing RNA species mediated by repeat expansion and demonstrates the importance of RNA spatial localization to understand disease etiology.

Overexpression of long non-coding RNA H19 relieves hypoxia-induced injury by down-regulating microRNA-107 in neural stem cells.

BACKGROUND: Neonatal hypoxia-ischemia (HI) is one of the commonest conditions which seriously influences the development of infants' nervous system and causes series of neurological sequelaes. The aim of the present study was to analyze the potential regulatory mechanism of long non-coding (lnc) RNA H19 under hypoxia conditions. METHODS: Neural stem cells (NSCs) were incubated in hypoxic conditions for 8 h to induce hypoxia injury. qRT-PCR was performed to detect H19 or micro (miR)-107 expression. Cell Counting Kit-8 (CCK-8) assay and Annexin V-FITC/PI staining assay were employed to detect the effects of hypoxia on cell viability and apoptosis, respectively. Moreover, NSCs were transfected with H19 overexpressing plasmid or shRNA-H19 and then subjected to hypoxia treatment. The effects of H19/miR-107 on NSC cell biological behaviors were confirmed. Furthermore, the signaling pathways involved in HI were analyzed using western blot. RESULTS: Hypoxia treatment restrained cell viability and induced cell apoptosis in NSCs. Overexpression of lncRNA H19 attenuated hypoxia-induced NSCs injury, while knockdown of lncRNA H19 aggravated NSCs injury. Further experiments suggested that miR-107 up-regulation reversed the effects of lncRNA H19 overexpression on NSCs. Moreover, the activation of Wnt/ß-catenin and PI3K/AKT pathways triggered by H19 were reversed by miR-107 up-regulation in hypoxia-treated NSCs. CONCLUSION: LncRNA H19 overexpression attenuated hypoxia-induced NSCs injury and promoted activation of Wnt/ß-catenin and PI3K/AKT pathways through downregulating miR-107.

Statistical and bioinformatic analysis of hemimethylation patterns in non-small cell lung cancer.

BACKGROUND: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non-small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its significant impact. Therefore, it is important to identify cancer hemimethylation patterns. METHODS: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. Highly hemimethylated genes are then studied for their biological interactions using available bioinformatics tools. RESULTS: In this paper, we have conducted the first-ever investigation of hemimethylation in lung cancer. Our results show that hemimethylation does exist in lung cells either as singletons or clusters. Most clusters contain only two or three CpG sites. Polarity clusters are much shorter than regular clusters and appear less frequently. The majority of clusters found in tumor samples have no overlap with clusters found in normal samples, and vice versa. Several genes that are known to be associated with cancer are hemimethylated differently between the cancerous and normal samples. Furthermore, highly hemimethylated genes exhibit many different interactions with other genes that may be associated with cancer. Hemimethylation has diverse patterns and frequencies that are comparable between normal and tumorous cells. Therefore, hemimethylation may be related to both normal and tumor cell development. CONCLUSIONS: Our research has identified CpG clusters and genes that are hemimethylated in normal and lung tumor samples. Due to the potential impact of hemimethylation on gene expression and cell function, these clusters and genes may be important to advance our understanding of the development and progression of non-small cell lung cancer.

Preliminary Analysis of Within-Sample Co-methylation Patterns in Normal and Cancerous Breast Samples.

DNA methylation plays a significant role in regulating the expression of certain genes in both cancerous and normal breast tissues. It is therefore important to study within-sample co-methylation, ie, methylation patterns between consecutive sites in a chromosome. In this article, we develop 2 new methods to compare co-methylation patterns between normal and cancerous breast samples. In particular, we investigate the co-methylation patterns of 4 different methylation states/levels separately. Using these 2 methods, we focus on addressing the following questions: How often does 1 methylation state change to other methylation states and how is this change dependent on chromosome distance? What co-methylation patterns do normal and cancerous breast samples have? Do genomic sites with different methylation states/levels have different co-methylation patterns? Our results show that cancerous and normal co-methylation patterns are significantly different. We find that this difference exists even when the physical distance of 2 sites are less than 50 bases. Breast cancer cell lines tend to remain in the same methylation state more often than normal samples, especially for the no/low or high/full methylation states. We also find that the co-methylation region lengths for various methylation states (no/low, partial, and high/full methylation states) are very different. For example, the co-methylation region lengths for partial methylation regions are shorter than the unmethylated or fully methylated regions. Our research may provide a deep understanding of co-methylation patterns. These co-methylation patterns will aid in discovering and understanding new methylation events that may be related to novel biomarkers.

Hemimethylation Patterns in Breast Cancer Cell Lines.

DNA methylation is an epigenetic event that involves adding a methyl group to the cytosine (C) site, especially the one that pairs with a guanine (G) site (ie, CG or CpG site), in a human genome. This event plays an important role in both cancerous and normal cell development. Previous studies often assume symmetric methylation on both DNA strands. However, asymmetric methylation, or hemimethylation (methylation that occurs only on 1 DNA strand), does exist and has been reported in several studies. Due to the limitation of previous DNA methylation sequencing technologies, researchers could only study hemimethylation on specific genes, but the overall genomic hemimethylation landscape remains relatively unexplored. With the development of advanced next-generation sequencing techniques, it is now possible to measure methylation levels on both forward and reverse strands at all CpG sites in an entire genome. Analyzing hemimethylation patterns may potentially reveal regions related to undergoing tumor growth. For our research, we first identify hemimethylated CpG sites in breast cancer cell lines using Wilcoxon signed rank tests. We then identify hemimethylation patterns by grouping consecutive hemimethylated CpG sites based on their methylation states, methylation "M" or unmethylation "U." These patterns include regular (or consecutive) hemimethylation clusters (eg, "MMM" on one strand and "UUU" on another strand) and polarity (or reverse) clusters (eg, "MU" on one strand and "UM" on another strand). Our results reveal that most hemimethylation clusters are the polarity type, and hemimethylation does occur across the entire genome with notably higher numbers in the breast cancer cell lines. The lengths or sizes of most hemimethylation clusters are very short, often less than 50 base pairs. After mapping hemimethylation clusters and sites to corresponding genes, we study the functions of these genes and find that several of the highly hemimethylated genes may influence tumor growth or suppression. These genes may also indicate a progressing transition to a new tumor stage.

Overexpression of microRNA-423-3p indicates poor prognosis and promotes cell proliferation, migration, and invasion of lung cancer.

BACKGROUND: Lung cancer is one of the common malignant tumors worldwide with high incidence and mortality. MicroRNA-423-3p (miR-423-3p) acts as an oncogene in several types of cancers. The aim of this study is to reveal the clinical significance and biological function of miR-423-3p in lung cancer. METHODS: The expression of miR-423-3p was detected in lung cancer specimens by reverse transcription-quantitative polymerase chain reaction (qRT-PCR) assay. Kaplan-Meier survival and Cox regression analyses were used to investigate the prognostic significance of miR-423-3p in lung cancer. CCK-8 and Transwell assays were used to determine the functional role of miR-423-3p in lung cancer. RESULTS: We observed that miR-423-3p was significantly upregulated in lung cancer tissues and cell lines. Overexpression of miR-423-3p was significantly associated with lymph node metastasis, TNM stage, and poor prognosis. Multivariate Cox regression analysis results showed that miR-423-3p was an independent prognostic indicator for lung cancer patients. Results of functional analyses revealed that overexpression of miR-423-3p promoted cell proliferation, migration, and invasion in lung cancer cells. CONCLUSIONS: These results indicated that miR-423-3p acts as an oncogene and promotes cell proliferation migration, and invasion of lung cancer. And miR-423-3p may serve as a potential prognostic biomarker and therapeutic target for the treatment of lung cancer.

PAC, an evolutionarily conserved membrane protein, is a proton-activated chloride channel.

Severe local acidosis causes tissue damage and pain, and is one of the hallmarks of many diseases including ischemia, cancer, and inflammation. However, the molecular mechanisms of the cellular response to acid are not fully understood. We performed an unbiased RNA interference screen and identified PAC (TMEM206) as being essential for the widely observed proton-activated Cl- (PAC) currents (I Cl,H). Overexpression of human PAC in PAC knockout cells generated I Cl,H with the same characteristics as the endogenous ones. Zebrafish PAC encodes a PAC channel with distinct properties. Knockout of mouse Pac abolished I Cl,H in neurons and attenuated brain damage after ischemic stroke. The wide expression of PAC suggests a broad role for this conserved Cl- channel family in physiological and pathological processes associated with acidic pH.

Overriding FUS autoregulation in mice triggers gain-of-toxic dysfunctions in RNA metabolism and autophagy-lysosome axis.

Mutations in coding and non-coding regions of FUS cause amyotrophic lateral sclerosis (ALS). The latter mutations may exert toxicity by increasing FUS accumulation. We show here that broad expression within the nervous system of wild-type or either of two ALS-linked mutants of human FUS in mice produces progressive motor phenotypes accompanied by characteristic ALS-like pathology. FUS levels are autoregulated by a mechanism in which human FUS downregulates endogenous FUS at mRNA and protein levels. Increasing wild-type human FUS expression achieved by saturating this autoregulatory mechanism produces a rapidly progressive phenotype and dose-dependent lethality. Transcriptome analysis reveals mis-regulation of genes that are largely not observed upon FUS reduction. Likely mechanisms for FUS neurotoxicity include autophagy inhibition and defective RNA metabolism. Thus, our results reveal that overriding FUS autoregulation will trigger gain-of-function toxicity via altered autophagy-lysosome pathway and RNA metabolism function, highlighting a role for protein and RNA dyshomeostasis in FUS-mediated toxicity.

Integrative analysis of gene expression and methylation data for breast cancer cell lines.

BACKGROUND: The deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenetic event, plays an important role in cancer susceptibility. In this paper, we use DNA methylation and gene expression data integration and pathway analysis to further explore and understand the complex relationship between methylation and gene expression. RESULTS: Through linear modeling and analysis of variance, we obtain genes that show a significant correlation between methylation and gene expression. We then examine the functions and relationships of these genes using bioinformatic tools and databases. In particular, using ConsensusPathDB, we analyze the networks of statistically significant genes to identify hub genes, genes with a large number of links to other genes. We identify eight major hub genes, all in strong association with cancer susceptibility. Through further analysis of the function, gene expression level, and methylation level of these hub genes, we conclude that they are novel potential biomarkers for breast cancer. CONCLUSIONS: Our findings have various implications for cancer screening, early detection methods, and potential novel treatments for cancer. Researchers can also use our results to develop more effective methods for cancer study.

C9ORF72 GGGGCC repeat-associated non-AUG translation is upregulated by stress through eIF2α phosphorylation.

Hexanucleotide repeat expansion in C9ORF72 is the most frequent cause of both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Here we demonstrate that the repeat-associated non-AUG (RAN) translation of (GGGGCC) n -containing RNAs into poly-dipeptides can initiate in vivo without a 5'-cap. The primary RNA substrate for RAN translation of C9ORF72 sense repeats is shown to be the spliced first intron, following its excision from the initial pre-mRNA and transport to the cytoplasm. Cap-independent RAN translation is shown to be upregulated by various stress stimuli through phosphorylation of the α subunit of eukaryotic initiation factor-2 (eIF2α), the core event of an integrated stress response (ISR). Compounds inhibiting phospho-eIF2α-signaling pathways are shown to suppress RAN translation. Since the poly-dipeptides can themselves induce stress, these findings support a feedforward loop with initial repeat-mediated toxicity enhancing RAN translation and subsequent production of additional poly-dipeptides through ISR, thereby promoting progressive disease.

DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines.

Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I (2) statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating "hub genes" - heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a considerable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility.

Epigenetic Loss of MLH1 Expression in Normal Human Hematopoietic Stem Cell Clones is Defined by the Promoter CpG Methylation Pattern Observed by High-Throughput Methylation Specific Sequencing.

Normal human hematopoietic stem and progenitor cells (HPC) lose expression of MLH1, an important mismatch repair (MMR) pathway gene, with age. Loss of MMR leads to replication dependent mutational events and microsatellite instability observed in secondary acute myelogenous leukemia and other hematologic malignancies. Epigenetic CpG methylation upstream of the MLH1 promoter is a contributing factor to acquired loss of MLH1 expression in tumors of the epithelia and proximal mucosa. Using single molecule high-throughput bisulfite sequencing we have characterized the CpG methylation landscape from -938 to -337 bp upstream of the MLH1 transcriptional start site (position +0), from 30 hematopoietic colony forming cell clones (CFC) either expressing or not expressing MLH1. We identify a correlation between MLH1 promoter methylation and loss of MLH1 expression. Additionally, using the CpG site methylation frequencies obtained in this study we were able to generate a classification algorithm capable of sorting the expressing and non-expressing CFC. Thus, as has been previously described for many tumor cell types, we report for the first time a correlation between the loss of MLH1 expression and increased MLH1 promoter methylation in CFC derived from CD34+ selected hematopoietic stem and progenitor cells.

HMM-DM: identifying differentially methylated regions using a hidden Markov model.

DNA methylation is an epigenetic modification involved in organism development and cellular differentiation. Identifying differential methylations can help to study genomic regions associated with diseases. Differential methylation studies on single-CG resolution have become possible with the bisulfite sequencing (BS) technology. However, there is still a lack of efficient statistical methods for identifying differentially methylated (DM) regions in BS data. We have developed a new approach named HMM-DM to detect DM regions between two biological conditions using BS data. This new approach first uses a hidden Markov model (HMM) to identify DM CG sites accounting for spatial correlation across CG sites and variation across samples, and then summarizes identified sites into regions. We demonstrate through a simulation study that our approach has a superior performance compared to BSmooth. We also illustrate the application of HMM-DM using a real breast cancer dataset.

HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test.

DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher's exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher's exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

SRSF1-Regulated Alternative Splicing in Breast Cancer.

Splicing factor SRSF1 is upregulated in human breast tumors, and its overexpression promotes transformation of mammary cells. Using RNA-seq, we identified SRSF1-regulated alternative splicing (AS) targets in organotypic three-dimensional MCF-10A cell cultures that mimic a context relevant to breast cancer. We identified and validated hundreds of endogenous SRSF1-regulated AS events. De novo discovery of the SRSF1 binding motif reconciled discrepancies in previous motif analyses. Using a Bayesian model, we determined positional effects of SRSF1 binding on cassette exons: binding close to the 5' splice site generally promoted exon inclusion, whereas binding near the 3' splice site promoted either exon skipping or inclusion. Finally, we identified SRSF1-regulated AS events deregulated in human tumors; overexpressing one such isoform, exon-9-included CASC4, increased acinar size and proliferation, and decreased apoptosis, partially recapitulating SRSF1's oncogenic effects. Thus, we uncovered SRSF1 positive and negative regulatory mechanisms, and oncogenic AS events that represent potential targets for therapeutics development.

HMPL: A Pipeline for Identifying Hemimethylation Patterns by Comparing Two Samples.

DNA methylation (the addition of a methyl group to a cytosine) is an important epigenetic event in mammalian cells because it plays a key role in regulating gene expression. Most previous methylation studies assume that DNA methylation occurs on both positive and negative strands. However, a few studies have reported that in some genes, methylation occurs only on one strand (ie, hemimethylation) and has clustering patterns. These studies report that hemimethylation occurs on individual genes. It is unclear whether hemimethylation occurs genome-wide and whether there are hemimethylation differences between cancerous and noncancerous cells. To address these questions, we have developed the first-ever pipeline, named hemimethylation pipeline (HMPL), to identify hemimethylation patterns. Utilizing the available software and the newly developed Perl and R scripts, HMPL can identify hemimethylation patterns for a single sample and can also compare two different samples.