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Improving the chemotherapy resistance of temozolomide (TMZ) is of great significance in the treatment of glioblastoma multiforme (GBM). Long non‐coding RNA just proximal to the X‐inactive specific transcript (JPX) has been proven to be involved in cancer progression. However, the intrinsic significance and molecular mechanism by which JPX orchestrates GBM progression and TMZ chemotherapy resistance remain poorly understood. Here, JPX was found to be significantly elevated in GBM tissues and cell lines, and patients with high expressions of JPX showed significantly worse prognoses. Functional experiments revealed its carcinogenic roles in GBM cell proliferation, TMZ chemoresistance, anti‐apoptosis, DNA damage repair, and aerobic glycolysis. Mechanistically, JPX formed a complex with phosphoinositide dependent kinase‐1 (PDK1) messenger RNA (mRNA) and promoted its stability and expression. Furthermore, an RNA immunoprecipitation (RIP) experiment showed that JPX interacted with N6‐methyladenosine (m6A) demethylase FTO alpha‐ketoglutarate dependent dioxygenase (FTO) and enhanced FTO‐mediated PDK1 mRNA demethylation. JPX exerted its GBM‐promotion effects through the FTO/PDK1 axis. Taken together, these findings reveal the key role of JPX in promoting GBM aerobic glycolysis and TMZ chemoresistance in an m6A‐dependent manner. Thus, it comprises a promising novel therapeutic target for GBM chemotherapy. Our findings uncover a key role of just proximal to the X‐inactive specific transcript in promoting glioblastoma multiforme (GBM) aerobic glycolysis and temozolomide chemoresistance in a N6‐methyladenosine (m6A)‐dependent manner, providing a promising novel therapeutic target for GBM chemotherapy.

Alzheimer's disease (AD) is a common neurodegenerative disorder affecting older adults, characterized by progressive cognitive decline and pathological features such as amyloid plaque deposition, neuronal loss, and synaptic reduction. RNA N6-methyladenosine (m6A) methylation is prevalent in the brain and is intricately linked to synaptic plasticity, learning, and memory in AD. However, the precise mechanisms underlying these associations remain elusive.BackgroundAlzheimer's disease (AD) is a common neurodegenerative disorder affecting older adults, characterized by progressive cognitive decline and pathological features such as amyloid plaque deposition, neuronal loss, and synaptic reduction. RNA N6-methyladenosine (m6A) methylation is prevalent in the brain and is intricately linked to synaptic plasticity, learning, and memory in AD. However, the precise mechanisms underlying these associations remain elusive.This study employed the overexpression of methyltransferase-like protein 16 (METTL16), or overexpression of methionine adenosyltransferase 2A (MAT2A), or a combination of METTL16 overexpression with MAT2A knockdown to explore the influence of METTL16 on the regulation of MAT2A in cognitive function, hippocampal synaptic plasticity, and amyloid-beta (Aβ1-42) metabolism in 5 × FAD mice.MethodsThis study employed the overexpression of methyltransferase-like protein 16 (METTL16), or overexpression of methionine adenosyltransferase 2A (MAT2A), or a combination of METTL16 overexpression with MAT2A knockdown to explore the influence of METTL16 on the regulation of MAT2A in cognitive function, hippocampal synaptic plasticity, and amyloid-beta (Aβ1-42) metabolism in 5 × FAD mice.Our findings indicated a reduction in m6A methylation levels and the expression of METTL16 and MAT2A in the hippocampus of 5 × FAD mice. Overexpression of METTL16 led to an increase in overall m6A methylation levels, furthermore, overexpression of either METTL16 or MAT2A enhanced learning and memory in 5 × FAD mice, elevated the expression levels of postsynaptic density 95 (PSD95) and synaptophysin (Syp), increased dendritic spine density, and decreased the accumulation of Aβ1-42 in the hippocampus. In the hippocampus of 5 × FAD mice, METTL16 was found to upregulate both the protein and mRNA levels of MAT2A, as well as enhance MAT2A mRNA m6A methylation levels. Concurrent, overexpression of METTL16 and knockdown of MAT2A in the hippocampus resulted in impaired learning and memory in 5 × FAD mice, alongside a reduction in synaptic protein expression and dendritic spine density, and an increase in Aβ1-42 accumulation.ResultsOur findings indicated a reduction in m6A methylation levels and the expression of METTL16 and MAT2A in the hippocampus of 5 × FAD mice. Overexpression of METTL16 led to an increase in overall m6A methylation levels, furthermore, overexpression of either METTL16 or MAT2A enhanced learning and memory in 5 × FAD mice, elevated the expression levels of postsynaptic density 95 (PSD95) and synaptophysin (Syp), increased dendritic spine density, and decreased the accumulation of Aβ1-42 in the hippocampus. In the hippocampus of 5 × FAD mice, METTL16 was found to upregulate both the protein and mRNA levels of MAT2A, as well as enhance MAT2A mRNA m6A methylation levels. Concurrent, overexpression of METTL16 and knockdown of MAT2A in the hippocampus resulted in impaired learning and memory in 5 × FAD mice, alongside a reduction in synaptic protein expression and dendritic spine density, and an increase in Aβ1-42 accumulation.The present study demonstrated that METTL16 enhances learning and memory in 5 × FAD mice by regulating MAT2A mRNA m6A methylation, which leads to increased expression levels of PSD95 and Syp, greater dendritic spine density, and reduced Aβ1-42 accumulation in the hippocampus. These findings reveal a novel approach for investigating the pathophysiological role of METTL16 in AD and offer new insights for developing of potential therapeutic targets for AD.ConclusionThe present study demonstrated that METTL16 enhances learning and memory in 5 × FAD mice by regulating MAT2A mRNA m6A methylation, which leads to increased expression levels of PSD95 and Syp, greater dendritic spine density, and reduced Aβ1-42 accumulation in the hippocampus. These findings reveal a novel approach for investigating the pathophysiological role of METTL16 in AD and offer new insights for developing of potential therapeutic targets for AD.

Background N6-methyladenosine (m6A) methylation modification is involved in the regulation of various biological processes, including inflammation, antitumor, and antiviral immunity. However, the role of m6A modification in the pathogenesis of autoimmune diseases has been rarely reported. Methods Based on a description of m6A modification and the corresponding research methods, this review systematically summarizes current insights into the mechanism of m6A methylation modification in autoimmune diseases, especially its contribution to rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Results By regulating different biological processes, m6A methylation is involved in the pathogenesis of autoimmune diseases and provides a promising biomarker for the diagnosis and treatment of such diseases. Notably, m6A methylation modification is involved in regulating a variety of immune cells and mitochondrial energy metabolism. In addition, m6A methylation modification plays a role in the pathological processes of RA, and m6A methylation-related genes can be used as potential targets in RA therapy. Conclusions M6A methylation modification plays an important role in autoimmune pathological processes such as RA and SLE and represents a promising new target for clinical diagnosis and treatment, providing new ideas for the treatment of autoimmune diseases by targeting m6A modification-related pathways.

Gastric cancer (GC) is the fifth most common cancer and the third deadliest cancer in the world, and the occurrence and development of GC are influenced by epigenetics. Methyltransferase-like 3 (METTL3) is a prominent RNA n6-adenosine methyltransferase (m6A) that plays an important role in tumor growth by controlling the work of RNA. This study aimed to reveal the biological function and molecular mechanism of METTL3 in GC. The expression level of METTL3 in GC tissues and cells was detected by qPCR, Western blot and immunohistochemistry, and the expression level and prognosis of METTL3 were predicted in public databases. CCK-8, colony formation, transwell and wound healing assays were used to study the effect of METTL3 on GC cell proliferation and migration. In addition, the enrichment effect of METTL3 on DEK mRNA was detected by the RIP experiment, the m6A modification effect of METTL3 on DEK was verified by the MeRIP experiment and the mRNA half-life of DEK when METTL3 was overexpressed was detected. The dot blot assay detects m6A modification at the mRNA level. The effect of METTL3 on cell migration ability in vivo was examined by tail vein injection of luciferase-labeled cells. The experimental results showed that METTL3 was highly expressed in GC tissues and cells, and the high expression of METTL3 was associated with a poor prognosis. In addition, the m6A modification level of mRNA was higher in GC tissues and GC cell lines. Overexpression of METTL3 in MGC80-3 cells and AGS promoted cell proliferation and migration, while the knockdown of METTL3 inhibited cell proliferation and migration. The results of in vitro rescue experiments showed that the knockdown of DEK reversed the promoting effects of METTL3 on cell proliferation and migration. In vivo experiments showed that the knockdown of DEK reversed the increase in lung metastases caused by the overexpression of METTL3 in mice. Mechanistically, the results of the RIP experiment showed that METTL3 could enrich DEK mRNA, and the results of the MePIP and RNA half-life experiments indicated that METTL3 binds to the 3’UTR of DEK, participates in the m6A modification of DEK and promotes the stability of DEK mRNA. Ultimately, we concluded that METTL3 promotes GC cell proliferation and migration by stabilizing DEK mRNA expression. Therefore, METTL3 is a potential biomarker for GC prognosis and a therapeutic target.

Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

Spinal cord injury (SCI) is a serious central nervous system disease with no effective treatment strategy presently due to its complex pathogenic mechanism. N6-methyladenosine (m6A) methylation modification plays an important role in diverse physiological and pathological processes. However, our understanding of the potential mechanisms of messenger RNA (mRNA) and long non-coding RNAs (lncRNA) m6A methylation in SCI is currently limited. Here, comprehensive m6A profiles and gene expression patterns of mRNAs and lncRNAs in spinal cord tissues after SCI were identified using microarray analysis of immunoprecipitated methylated RNAs. A total of 3745 mRNAs (2343 hypermethylated and 1402 hypomethylated) and 738 lncRNAs (488 hypermethylated and 250 hypomethylated) were differentially methylated with m6A modifications in the SCI and sham rats. Functional analysis revealed that differentially m6A-modified mRNAs were mainly involved in immune inflammatory response, nervous system development, and focal adhesion pathway. In contrast, differentially m6A-modified lncRNAs were mainly related to antigen processing and presentation, the apoptotic process, and the mitogen-activated protein kinases (MAPKs) signaling pathway. In addition, combined analysis of m6A methylation and RNA expression results revealed that 1636 hypermethylated mRNAs and 262 hypermethylated lncRNAs were up-regulated, and 1571 hypomethylated mRNAs and 204 lncRNAs were down-regulated. Furthermore, we validated the altered levels of m6A methylation and RNA expression of five mRNAs (CD68, Gpnmb, Lilrb4, Lamp5, and Snap25) and five lncRNAs (XR_360518, uc.393 + , NR_131064, uc.280 − , and XR_597251) using MeRIP-qPCR and qRT-PCR. This study expands our understanding of the molecular mechanisms underlying m6A modification in SCI and provides novel insights to promote functional recovery after SCI.

Prostate-related diseases, including prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa), represent significant threats to the health of the aging male population worldwide. Despite their prevalence, the pathogenesis of prostate-related diseases has not been elucidated. Recent studies have shown that N 6 -methyladenosine (m 6 A) modification is widely involved in the progression of prostate-related diseases. In this review, we summarized recent advances in understanding the core m 6 A regulatory machinery comprising writers such as the methyltransferase-like 3 (METTL3)-METTL14 complex, erasers including fat mass and obesity-associated protein (FTO) and AlkB homolog 5 (ALKBH5), and readers, including the YTH domain-containing family proteins (YTHDFs), YTHDC proteins, insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs), and heterogeneous nuclear ribonucleoproteins (HNRNPs). Specifically, we elucidated how dysregulation of these components drives disease progression via alterations in cellular proliferation, differentiation, inflammatory responses, and stem cell dynamics. Notably, m 6 A modifications help shape the immunosuppressive landscape in PCa by modulating immune checkpoint expression, cytokine networks, and immune cell infiltration, thereby critically influencing therapeutic responses to immunotherapy. Furthermore, this review highlights the emerging diagnostic potential and therapeutic viability of m 6 A-targeted strategies, offering valuable insights for future clinical translation in prostate-related diseases.

Background As a common and abundant RNA methylation modification, N6-methyladenosine (m 6 A) is widely spread in various species' transcriptomes, and it is closely related to the occurrence and development of various life processes and diseases. Thus, accurate identification of m 6 A methylation sites has become a hot topic. Most biological methods rely on high-throughput sequencing technology, which places great demands on the sequencing library preparation and data analysis. Thus, various machine learning methods have been proposed to extract various types of features based on sequences, then occupied conventional classifiers, such as SVM, RF, etc., for m 6 A methylation site identification. However, the identification performance relies heavily on the extracted features, which still need to be improved. Results This paper mainly studies feature extraction and classification of m 6 A methylation sites in a natural language processing way, which manages to organically integrate the feature extraction and classification simultaneously, with consideration of upstream and downstream information of m 6 A sites. One-hot, RNA word embedding, and Word2vec are adopted to depict sites from the perspectives of the base as well as its upstream and downstream sequence. The BiLSTM model, a well-known sequence model, was then constructed to discriminate the sequences with potential m 6 A sites. Since the above-mentioned three feature extraction methods focus on different perspectives of m 6 A sites, an ensemble deep learning predictor (EDLm 6 APred) was finally constructed for m 6 A site prediction. Experimental results on human and mouse data sets show that EDLm 6 APred outperforms the other single ones, indicating that base, upstream, and downstream information are all essential for m 6 A site detection. Compared with the existing m 6 A methylation site prediction models without genomic features, EDLm 6 APred obtains 86.6% of the area under receiver operating curve on the human data sets, indicating the effectiveness of sequential modeling on RNA. To maximize user convenience, a webserver was developed as an implementation of EDLm 6 APred and made publicly available at www.xjtlu.edu.cn/biologicalsciences/EDLm6APred . Conclusions Our proposed EDLm 6 APred method is a reliable predictor for m 6 A methylation sites.

Aging is a risk factor for atrial fibrillation (AF). In 19‐month‐old mice, increases in AF inducibility are associated with enhanced protein levels of fat mass and obesity‐associated protein (Fto), and reduced N6‐methyladenosine (m6A) modification in atrial tissue. Whether Fto‐regulated m6A demethylation is involved in aging‐induced AF remains unclear. AF inducibility and electrophysiology were performed through programmed stimulation and optical mapping. The intensities of slow delayed rectifier potassium currents (IKs) were measured by patch‐clamp. m6A‐sequencing revealed that Kcne1 mRNA was m6A‐demethylated in aging mouse atria. Kcne1 knockdown in 2‐month‐old mice increased AF inducibility. Aging mice with cardiomyocyte‐specific Fto knockout had increased Kcne1 mRNA and protein levels, with reduced susceptibility to AF. Additionally, overexpression of wild‐type Fto, rather than a catalytically inactive mutant in 2‐month‐old mice, reduced Kcne1 protein levels, leading to enhanced IKs current and AF inducibility. Furthermore, the negative relationship between FTO and KCNE1 was confirmed in left atrial appendage samples from AF patients. In iPSC‐derived atrial cardiomyocytes, FTO‐mediated KCNE1 demethylation repressed KCNE1 pre‐mRNA splicing, mRNA nuclear export, and translational efficacy. Collectively, aging‐induced elevation of Fto represses m6A methylation of Kcne1, which in turn leads to reductions in Kcne1 mRNA and protein levels in atrial cardiomyocytes, thereby increasing AF inducibility. Aging‐induced accumulation of PGE2 in cardiomyocytes blocks the degradation of Fto. Elevation of Fto in the atria represses m6A methylation of Kcnel which, in turn, leads to the reduction of Kcnel mRNA and protein. Reduced Kcnel enhances IKs currents and thereby shortens APD in atrial cardiomyocytes and increases AF inducibility.

Both RNA N6-methyladenosine (m 6 A) modification of SARS-CoV-2 and immune characteristics of the human body have been reported to play an important role in COVID-19, but how the m 6 A methylation modification of leukocytes responds to the virus infection remains unknown. Based on the RNA-seq of 126 samples from the GEO database, we disclosed that there is a remarkably higher m 6 A modification level of blood leukocytes in patients with COVID-19 compared to patients without COVID-19, and this difference was related to CD4 + T cells. Two clusters were identified by unsupervised clustering, m 6 A cluster A characterized by T cell activation had a higher prognosis than m 6 A cluster B. Elevated metabolism level, blockage of the immune checkpoint, and lower level of m6A score were observed in m 6 A cluster B. A protective model was constructed based on nine selected genes and it exhibited an excellent predictive value in COVID-19. Further analysis revealed that the protective score was positively correlated to HFD45 and ventilator-free days, while negatively correlated to SOFA score, APACHE-II score, and crp. Our works systematically depicted a complicated correlation between m 6 A methylation modification and host lymphocytes in patients infected with SARS-CoV-2 and provided a well-performing model to predict the patients’ outcomes.