Differential expression of m5C RNA methyltransferase genes NSUN6 and NSUN7 in Alzheimer's disease and traumatic brain injury

m5C RNA methylation: a potential mechanism for infectious Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder caused by a variety of factors, including age, genetic susceptibility, cardiovascular disease, traumatic brain injury, and environmental factors. The pathogenesis of AD is largely associated with the overproduction and accumulation of amyloid-β peptides and the hyperphosphorylation of tau protein in the brain. Recent studies have identified the presence of diverse pathogens, including viruses, bacteria, and parasites, in the tissues of AD patients, underscoring the critical role of central nervous system infections in inducing pathological changes associated with AD. Nevertheless, it remains unestablished about the specific mechanism by which infections lead to the occurrence of AD. As an important post-transcriptional RNA modification, RNA 5-methylcytosine (m5C) methylation regulates a wide range of biological processes, including RNA splicing, nuclear export, stability, and translation, therefore affecting cellular function. Moreover, it has been recently demonstrated that multiple pathogenic microbial infections are associated with the m5C methylation of the host. However, the role of m5C methylation in infectious AD is still uncertain. Therefore, this review discusses the mechanisms of pathogen-induced AD and summarizes research on the molecular mechanisms of m5C methylation in infectious AD, thereby providing new insight into exploring the mechanism underlying infectious AD.

Alterations of mRNAs and Non-coding RNAs Associated with Neuroinflammation in Alzheimer's Disease

Alzheimer's disease is a neurodegenerative pathology whose pathognomonic hallmarks are increased generation of β-amyloid (Aβ) peptide, production of hyperphosphorylated (pTau), and neuroinflammation. The last is an alteration closely related to the progression of AD and although it is present in multiple neurodegenerative diseases, the pathophysiological events that characterize neuroinflammatory processes vary depending on the disease. In this article, we focus on mRNA and non-coding RNA alterations as part of the pathophysiological events characteristic of neuroinflammation in AD and the influence of these alterations on the course of the disease through interaction with multiple RNAs related to the generation of Aβ, pTau, and neuroinflammation itself.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Dysregulation of RNA modification systems in clinical populations with neurocognitive disorders

ABSTRACT

The study of modified RNA known as epitranscriptomics has become increasingly relevant in our understanding of disease-modifying mechanisms. Methylation of N6 adenosine (m6A) and C5 cytosine (m5C) bases occur on mRNAs, tRNA, mt-tRNA, and rRNA species as well as non-coding RNAs. With emerging knowledge of RNA binding proteins that act as writer, reader, and eraser effector proteins, comes a new understanding of physiological processes controlled by these systems. Such processes when spatiotemporally disrupted within cellular nanodomains in highly specialized tissues such as the brain, give rise to different forms of disease. In this review, we discuss accumulating evidence that changes in the m6A and m5C methylation systems contribute to neurocognitive disorders. Early studies first identified mutations within FMR1 to cause intellectual disability Fragile X syndromes several years before FMR1 was identified as an m6A RNA reader protein. Subsequently, familial mutations within the m6A writer gene METTL5, m5C writer genes NSUN2, NSUN3, NSUN5, and NSUN6, as well as THOC2 and THOC6 that form a protein complex with the m5C reader protein ALYREF, were recognized to cause intellectual development disorders. Similarly, differences in expression of the m5C writer and reader effector proteins, NSUN6, NSUN7, and ALYREF in brain tissue are indicated in individuals with Alzheimer's disease, individuals with a high neuropathological load or have suffered traumatic brain injury. Likewise, an abundance of m6A reader and anti-reader proteins are reported to change across brain regions in Lewy bodies diseases, Alzheimer's disease, and individuals with high cognitive reserve. m6A-modified RNAs are also reported significantly more abundant in dementia with Lewy bodies brain tissue but significantly reduced in Parkinson's disease tissue, whilst modified RNAs are misplaced within diseased cells, particularly where synapses are located. In parahippocampal brain tissue, m6A modification is enriched in transcripts associated with psychiatric disorders including conditions with clear cognitive deficits. These findings indicate a diverse set of molecular mechanisms are influenced by RNA methylation systems that can cause neuronal and synaptic dysfunction underlying neurocognitive disorders. Targeting these RNA modification systems brings new prospects for neural regenerative therapies.

Comprehensive review on lipid metabolism and RNA methylation: Biological mechanisms, perspectives and challenges

Adipose tissue plays a crucial role in maintaining energy balance, regulating hormones, and promoting metabolic health. To address disorders related to obesity and develop effective therapies, it is essential to have a deep understanding of adipose tissue biology. In recent years, RNA methylation has emerged as a significant epigenetic modification involved in various cellular functions and metabolic pathways. Particularly in the realm of adipogenesis and lipid metabolism, extensive research is ongoing to uncover the mechanisms and functional importance of RNA methylation. Increasing evidence suggests that RNA methylation plays a regulatory role in adipocyte development, metabolism, and lipid utilization across different organs. This comprehensive review aims to provide an overview of common RNA methylation modifications, their occurrences, and regulatory mechanisms, focusing specifically on their intricate connections to fat metabolism. Additionally, we discuss the research methodologies used in studying RNA methylation and highlight relevant databases that can aid researchers in this rapidly advancing field.

Understanding the (epi)genetic dysregulation in Parkinson's disease through an integrative brain competitive endogenous RNA network

Parkinson's disease (PD) is a rapidly growing neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra pars compacta (SN) and aggregation of α-synuclein. Its aetiology involves a multifaceted interplay among genetic, environmental, and epigenetic factors. We integrated brain gene expression data from PD patients to construct a comprehensive regulatory network encompassing messenger RNAs (mRNAs), microRNAs (miRNAs), circular RNAs (circRNAs) and, for the first time, RNA binding proteins (RBPs). Expression data from the SN of PD patients and controls were systematically selected from public databases to identify combined differentially expressed genes (DEGs). Brain co-expression analysis revealed modules comprising significant DEGs that function cooperatively. The relationships among co-expressed DEGs, miRNAs, circRNAs, and RBPs revealed an intricate competitive endogenous RNA (ceRNA) network responsible for post-transcriptional dysregulation in PD. Many genes in the ceRNA network, including the TOMM20 and HMGCR genes, overlap with the most relevant genes in our previous Alzheimer's disease-associated ceRNA network, suggesting common underlying mechanisms between both conditions. Moreover, in the ceRNA subnetwork, the RBP Aly/REF export factor (ALYREF), which acts as an RNA 5-methylcytosine(m5C)-binding protein, stood out. Our data sheds new light on the potential role of brain ceRNA networks in PD pathogenesis.

Exploring the importance of m5c in the diagnosis and subtype classification of COPD using the GEO database

BACKGROUND

5-Methylcytosine (m5C) is an mRNA modifier that is associated with the occurrence and development of viral infection, pulmonary fibrosis, lung cancer, and other diseases. However, the role of m5C regulators in chronic obstructive pulmonary disease (COPD) remains unknown.

METHODS

In this study, by analysing the GSE42057 dataset, the differential expression of m5c regulators in the COPD group and control group was obtained, and a correlation analysis was conducted. The random forest model and support vector machine model were used to predict the occurrence of COPD. A nomogram model was also constructed to predict the prevalence of COPD. The COPD patients were divided into subtypes by consistent cluster analysis based on m5c methylation regulators. Immune cell infiltration was performed on the m5c methylation subtypes. Differentially expressed genes (DEGs) between m5c methylation subtypes were screened, and the DEGs were analysed by Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we verified the expression of several m5C regulators and related pathways using a COPD cell model.

RESULTS

Seven m5c methylation regulators were differentially expressed. The random forest model based on the above genes was the most accurate for predicting the occurrence of COPD. A nomogram model based on the above genes could also accurately predict the prevalence of COPD, and the implementation of these models could benefit COPD patients. The consistent cluster analysis divided the COPD patients into two subtypes (Cluster A and Cluster B). The main component analysis algorithm determined the m5c methylation subtypes and found that patients in Cluster A had a higher m5c score than those in Cluster B. GO analysis of the DEGs between the m5c methylation COPD patient subtypes revealed that DEGS were mainly enriched in leukocyte-mediated immunity and regulation of T-cell activation. KEGG analysis revealed that DEGS were mainly enriched in Th1 and Th2 cell differentiation, neutrophil extracellular trap formation, and the NF-κB signalling pathway. Immunocyte correlation analysis revealed that Cluster B was associated with neutrophil- and macrophage-mediated immunity, while Cluster A was associated with CD4 + T-cell- and CD8 + T-cell-mediated immunity. Cell experiments have also verified some of the above research results.

CONCLUSION

The diagnosis and subtype classification of COPD patients based on m5c regulators may provide a new strategy for the diagnosis and treatment of COPD.