Competitive endogenous RNA network and pathway-based analysis of LncRNA single-nucleotide polymorphism in myasthenia gravis

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model

Background

Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The purpose of this study was to determine the value of Long chain non-coding RNA (LncRNA) RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 in the diagnosis of adult sepsis patients and to develop a Nomogram prediction model.

Methods

We screened adult sepsis microarray datasets GSE57065 and GSE95233 from the GEO database and performed differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning methods to find the genes by random forest (Random Forest), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), respectively, with GSE95233 as the training set and GSE57065 as the validation set. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), boxplot statistical analysis, and ROC analysis by Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine (SVM) machine learning methods were used to identify characteristic genes and build the Nomogram Prediction model.

Results

GSE95233 yielded a total of 1069 genes, 102 of which were sepsis-related and 22 of which were non-sepsis controls. GSE57065 yielded a total of 899 genes, with 467 up-regulated and 432 down-regulated, including 82 sepsis-related genes and 25 non-sepsis control genes. WGCNA analysis excluded outlier samples, leaving 2,029 genes for relationship analysis between sepsis- and non-sepsis patient-associated LncRNA network representation modules, as well as Wein plots of differential genes versus genes in key modules of weighted co-expression network analysis to analyze gene intersections. Machine Learning found the sepsis-related characteristic LncRNAs RP3-508I15.21, RP11-295G20.2, LDLRAD4-AS1, and CTD-2542L18.1. The datasets GSE95233 and GSE57065 were analyzed using Boxplot against the screened genes listed above, respectively. The p-value between the sepsis and non-sepsis groups was less than 0.05, indicating that anomalies were statistically significant. CTD-2542L18.1 in dataset GSE57065 had an AUC value of 0.638, which was less than 0.7 and did not indicate diagnostic significance, but RP3-508I15.21, RP11-295G20.2, and LDLRAD4-AS1 had AUC values more than 0.7 after ROC analysis. All four sepsis-associated LncRNA ROC analyses in dataset GSE95233 exhibited AUC values more than 0.7, indicating diagnostic significance.

Conclusion

LncRNAs RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 have some utility in the diagnosis and treatment of adult sepsis patients, as well as some reference importance in guiding the diagnosis and treatment of clinical sepsis.

Non-Coding RNAs in Myasthenia Gravis: From Immune Regulation to Personalized Medicine

Myasthenia gravis (MG) is an antibody-mediated autoimmune disorder characterized by altered neuromuscular transmission, which causes weakness and fatigability in the skeletal muscles. The etiology of MG is complex, being associated with multiple genetic and environmental factors. Over recent years, progress has been made in understanding the immunological alterations implicated in the disease, but the exact pathogenesis still needs to be elucidated. A pathogenic interplay between innate immunity and autoimmunity contributes to the intra-thymic MG development. Epigenetic changes are critically involved in both innate and adaptive immune response regulation. They can act as (i) pathological factors besides genetic predisposition and (ii) co-factors contributing to disease phenotypes or patient-specific disease course/outcomes. This article reviews the role of non-coding RNAs (ncRNAs) as epigenetic factors implicated in MG. Particular attention is dedicated to microRNAs (miRNAs), whose expression is altered in MG patients' thymuses and circulating blood. The long ncRNA (lncRNA) contribution to MG, although not fully characterized yet, is also discussed. By summarizing the most recent and fast-growing findings on ncRNAs in MG, we highlight the therapeutic potential of these molecules for achieving immune regulation and their value as biomarkers for the development of personalized medicine approaches to improve disease care.

Non-coding RNA and its network in the pathogenesis of Myasthenia Gravis

Myasthenia Gravis (MG) is a chronic autoimmune disease that primarily affects the neuromuscular junction, leading to muscle weakness in patients with this condition. Previous studies have identified several dysfunctions in thymus and peripheral blood mononuclear cells (PBMCs), such as the formation of ectopic germinal centers in the thymus and an imbalance of peripheral T helper cells and regulatory T cells, that contribute to the initiation and development of MG. Recent evidences suggest that noncoding RNA, including miRNA, lncRNA and circRNA may play a significant role in MG progression. Additionally, the network between these noncoding RNAs, such as the competing endogenous RNA regulatory network, has been found to be involved in MG progression. In this review, we summarized the roles of miRNA, lncRNA, and circRNA, highlighted their potential application as biomarkers in diagnosing MG, and discussed their potential regulatory networks in the abnormal thymus and PBMCs during MG development.

Small extracellular vesicle microRNAs in pediatric myasthenia gravis plasma and skeletal muscle

BACKGROUND

The diagnosis of myasthenia gravis (MG) in children remains difficult. Circulating small extracellular vesicle (sEV)-derived miRNAs (sEV-miRNAs) have been recognized as biomarkers of various diseases and can be excreted by different cell types. These biomarker candidates also play a vital role in autoimmune diseases via intercellular communication.

METHODS

In the present study, we used sEV isolation and purification methods to extract the plasma-derived sEV-miRNAs from children with MG and healthy controls. A small RNA sequencing analysis confirmed the miRNA expression features in plasma-derived sEVs from MG patients. The miRNA expression analysis in vitro was determined using microarray analysis. The enrichment and network analyses of altered sEV-miRNAs were performed using miRNA databases and Database for Annotation, Visualization, and Integrated Discovery website. Quantitative real-time polymerase chain reaction was performed for validation of sEV-miRNA. The diagnostic power of altered sEV-miRNAs was evaluated using receiver operating characteristic curve analyses.

RESULTS

Twenty-four sEV-miRNAs with altered expression level were identified between groups by DESeq2 method. The miRNAs were extracted from the sEVs, which were isolated from human primary skeletal muscle cell culture treated with mAb198. The target genes and enriched pathways of sEV-miRNAs partially overlapped between cell supernatant and plasma samples. The significantly downregulated miR-143-3p was validated in quantitative real-time polymerase chain reaction analysis.

CONCLUSIONS

For the first time, we report that plasma-derived sEV-miRNAs may act as novel circulating biomarkers and therapeutic targets in pediatric MG.

Study on the Potential Mechanism of Semen Strychni against Myasthenia Gravis Based on Network Pharmacology and Molecular Docking with Experimental Verification

Background

Semen Strychni (SS) is an effective Chinese medicine formula for treating myasthenia gravis (MG) in clinics. Nonetheless, its molecular mechanism is largely unknown.

Objective

Using network pharmacology, molecular docking, and experimental validation, we aim to identify the therapeutic effect of SS on MG and its underlying mechanism.

Methods

The main ingredients of SS and their targets and potential disease targets for MG were extracted from public databases. The protein-protein interaction (PPI) network was constructed using the STRING 11.0 database, and Cytoscape was used to identify the hub targets. In addition, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify molecular biological processes and signaling pathways. Then, AutoDock Via conducted molecular docking. The experimental autoimmune myasthenia gravis (EAMG) model in female Lewis rats, quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and enzyme-linked immunosorbent assay (ELISA) were performed to confirm the effect and mechanism of SS on MG.

Results

The following active compounds and hub targets were identified by screening and analyzing: isobrucine, vomicine, (S)-stylopine, strychnine, brucine-N-oxide, brucine and AKT1, MAPK1, MAPK14, CHRM1, ACHE, and CHRNA4. KEGG enrichment analyses indicated that the cholinergic synapse and neuroactive ligand-receptor interaction signaling pathway may be necessary. The results of molecular docking revealed that the main active ingredients bind well to the hub targets. In vivo experiments proved that SS could improve the weight loss and Lennon scores in the EAMG model. Experiments in molecular biology showed that SS could treat MG by affecting the cholinergic synapse through the respective antibody, receptor, and key enzymes in the cholinergic pathway.

Conclusion

This study provided a preliminary overview of the active constituents, primary targets, and potential pathways of SS against MG. SS ameliorated EAMG by regulating the cholinergic synaptic junction.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.