Toll-like receptors 2 and 4 expression on peripheral blood lymphocytes and neutrophils of Egyptian multiple sclerosis patients

Therapeutic effects and mechanisms of fecal microbiota transplantation on EAE partly through HPA axis-mediated neuroendocrine regulation

Background

The pathogenesis of multiple sclerosis (MS) may be closely related to immune regulation and inflammatory cytokines induced by specific flora. Repairing the intestinal flora may alter the immune response in MS patients, thus opening up novel approaches for the treatment of MS.

Objective

We aimed to test the therapeutic effect of fecal microbiota transplantation (FMT) on experimental autoimmune encephalomyelitis (EAE) and the characteristics of intestinal microbiota composition changes, explore the potential mechanisms of FMT treatment.

Methods

EAE animals were treated with FMT, with the therapeutic effects were evaluated by observing neurological scores and measuring serum levels of cortisol, IL-17, and TLR-2. Fecal microbiome 16S rRNA sequencing was used to profile changes in microbiota composition, and adrenalectomy pretreatment was used to test whether FMT effects were dependent on HPA axis function.

Results

FMT improved neurological function and reduced serum IL-17 to levels that were close to the control group. FMT reestablished intestinal homeostasis by altering the structure of the intestinal flora, increasing the abundance of beneficial flora, and regulating intestinal metabolites. We found that the therapeutic effects of FMT depended partly on the efferent function of the HPA axis; surgical disruption of the HPA axis altered the abundance and diversity of the intestinal flora.

Conclusion

FMT showed a neuroprotective effect on EAE by increasing the abundance of the beneficial flora, rebuilding intestinal homeostasis, reducing IL-17 and cortisol serum levels, and promoting serum TLR-2; the therapeutic effect of FMT on EAE is partly dependent on the HPA axis.

Diagnostic efficacy of long non-coding RNAs in multiple sclerosis: a systematic review and meta-analysis

Background

Currently, an increasing body of research suggests that blood-based long non-coding RNAs (lncRNAs) could serve as biomarkers for diagnosing multiple sclerosis (MS). This meta-analysis evaluates the diagnostic capabilities of selected lncRNAs in distinguishing individuals with MS from healthy controls and in differentiating between the relapsing and remitting phases of the disease.

Methods

We conducted comprehensive searches across seven databases in both Chinese and English to identify relevant studies, applying stringent inclusion and exclusion criteria. The quality of the selected references was rigorously assessed using the QUADAS-2 tool. The analysis involved calculating summarized sensitivity (SSEN), specificity (SSPE), positive likelihood ratio (SPLR), negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR) with 95% confidence intervals (CIs). Accuracy was assessed using summary receiver operating characteristic (SROC) curves.

Results

Thirteen high-quality studies were selected for inclusion in the meta-analysis. Our meta-analysis assessed the combined diagnostic performance of lncRNAs in distinguishing MS patients from healthy controls. We found a SSEN of 0.81 (95% CI: 0.74-0.87), SSPE of 0.84 (95% CI: 0.78-0.89), SPLR of 5.14 (95% CI: 3.63-7.28), SNLR of 0.22 (95% CI: 0.16-0.31), and DOR of 23.17 (95% CI: 14.07-38.17), with an AUC of 0.90 (95% CI: 0.87-0.92). For differentiating between relapsing and remitting MS, the results showed a SSEN of 0.79 (95% CI: 0.71-0.85), SSPE of 0.76 (95% CI: 0.64-0.85), SPLR of 3.34 (95% CI: 2.09-5.33), SNLR of 0.28 (95% CI: 0.19-0.40), and DOR of 12.09 (95% CI: 5.70-25.68), with an AUC of 0.84 (95% CI: 0.81-0.87).

Conclusion

This analysis underscores the significant role of lncRNAs as biomarkers in MS diagnosis and differentiation between its relapsing and remitting forms.

Constructing a Multiple Sclerosis Diagnosis Model Based on Microarray

Introduction

Multiple sclerosis is an immune-mediated demyelinating disorder of the central nervous system. Because of the complexity of etiology, pathology, clinical manifestations, and the diversity of classification, the diagnosis of MS is very difficult. We found that McDonald Criteria is very strict and relies heavily on the evidence for DIS and DIT. Therefore, we hope to find a new method to supplement the evidence and improve the accuracy of MS diagnosis.

Results

We finally selected GSE61240, GSE18781, and GSE185047 based on the GPL570 platform to build a diagnosis model. We initially selected 54 MS susceptibility locus genes identified by IMSGC and WTCCC2 as predictors for the model. After Random Forests and other series of screening, the logistic regression model was established with 4 genes as the final predictors. In external validation, the model showed high accuracy with an AUC of 0.96 and an accuracy of 86.30%. Finally, we established a nomogram and an online prediction tool to better display the diagnosis model.

Conclusion

The diagnosis model based on microarray data in this study has a high degree of discrimination and calibration in the validation set, which is helpful for diagnosis in the absence of evidence for DIS and DIT. Only one SLE case was misdiagnosed as MS, indicating that the model has a high specificity (93.93%), which is useful for differential diagnosis. The significance of the study lies in proving that it is feasible to identify MS by peripheral blood RNA, and the further application of the model and be used as a supplement to McDonald Criteria still need to be trained with larger sample size.

Toll-like Receptor 2 in Autoimmune Inflammation

TLR signaling is critical for broad scale immune recognition of pathogens and/or danger molecules. TLRs are particularly important for the activation and the maturation of cells comprising the innate immune response. In recent years it has become apparent that several different TLRs regulate the function of lymphocytes as well, albeit to a lesser degree compared to innate immunity. TLR2 heterodimerizes with either TLR1 or TLR6 to broadly recognize bacterial lipopeptides as well as several danger-associated molecular patterns. In general, TLR2 signaling promotes immune cell activation leading to tissue inflammation, which is advantageous for combating an infection. Conversely, inappropriate or dysfunctional TLR2 signaling leading to an overactive inflammatory response could be detrimental during sterile inflammation and autoimmune disease. This review will highlight and discuss recent research advances linking TLR2 engagement to autoimmune inflammation.

Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis

The purpose of this study was to identify biomarkers and construct a diagnostic prediction model for multiple sclerosis (MS). Microarray datasets in the Gene Expression Omnibus (GEO) were downloaded. Weighted gene coexpression analysis (WGCNA) was used to search for hub modules and biomarkers related to MS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to roughly define their biological functions and pathways. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to identify the diagnostic biomarkers and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were used to judge the diagnostic predictive ability. In addition, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to calculate the proportion of 22 kinds of immune cells. GSE41850 was used as the training set, and GSE17048 was used as the test set. WGCNA revealed one hub module containing 165 hub genes. Most of their biological functions and pathways are related to cell metabolism and immune cell activation. The diagnostic nomogram contained ARPC5, ROD1, UBQLN2, ZNF281, ABCA1 and FAS. The ROC curve and the calibration curve of the training set and test set confirmed that the nomogram had great prediction ability. In addition, monocytes and M0 macrophages were significantly different between MS patients and healthy people. The expression of ARPC5, ZNF281 and ABCA1 is correlated with M0 macrophages. The nomogram provides new insights and contributes to the accurate diagnosis of MS.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02675-1.