Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis

A machine learning approach to determine the risk factors for fall in multiple sclerosis

BACKGROUND

Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial. This study aims to investigate the contributing factors to falls in multiple sclerosis using a machine learning approach.

METHODS

This cross-sectional study was conducted with 253 PwMS admitted to the outpatient clinic of a university hospital between February and August 2023. A sociodemographic data collection form, Fall Efficacy Scale (FES-I), Berg Balance Scale (BBS), Fatigue Severity Scale (FSS), Expanded Disability Status Scale (EDSS), Multiple Sclerosis Impact Scale (MSIS-29), and Timed 25 Foot Walk Test (T25-FW) were used for data collection. Gradient-boosting algorithms were employed to predict the important variables for falls in PwMS. The XGBoost algorithm emerged as the best performed model in this study.

RESULTS

Most of the participants (70.0%) were female, with a mean age of 40.44 ± 10.88 years. Among the participants, 40.7% reported a fall history in the last year. The area under the curve value of the model was 0.713. Risk factors of falls in PwMS included MSIS-29 (0.424), EDSS (0.406), marital status (0.297), education level (0.240), disease duration (0.185), age (0.130), family type (0.119), smoking (0.031), income level (0.031), and regular exercise habit (0.026).

CONCLUSIONS

In this study, smoking and regular exercise were the modifiable factors contributing to falls in PwMS. We recommend that clinicians facilitate the modification of these factors in PwMS. Age and disease duration were non-modifiable factors. These should be considered as risk increasing factors and used to identify PwMS at risk. Interventions aimed at reducing MSIS-29 and EDSS scores will help to prevent falls in PwMS. Education of individuals to increase knowledge and awareness is recommended. Financial support policies for those with low income will help to reduce the risk of falls.

Modulating the RPS27A/PSMD12/NF-κB pathway to control immune response in mouse brain ischemia-reperfusion injury

BACKGROUND

Investigating immune cell infiltration in the brain post-ischemia-reperfusion (I/R) injury is crucial for understanding and managing the resultant inflammatory responses. This study aims to unravel the role of the RPS27A-mediated PSMD12/NF-κB axis in controlling immune cell infiltration in the context of cerebral I/R injury.

METHODS

To identify genes associated with cerebral I/R injury, high-throughput sequencing was employed. The potential downstream genes were further analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analyses. For experimental models, primary microglia and neurons were extracted from the cortical tissues of mouse brains. An in vitro cerebral I/R injury model was established in microglia using the oxygen-glucose deprivation/reoxygenation (OGD/R) technique. In vivo models involved inducing cerebral I/R injury in mice through the middle cerebral artery occlusion (MCAO) method. These models were used to assess neurological function, immune cell infiltration, and inflammatory factor release.

RESULTS

The study identified RPS27A as a key player in cerebral I/R injury, with PSMD12 likely acting as its downstream regulator. Silencing RPS27A in OGD/R-induced microglia decreased the release of inflammatory factors and reduced neuron apoptosis. Additionally, RPS27A silencing in cerebral cortex tissues mediated the PSMD12/NF-κB axis, resulting in decreased inflammatory factor release, reduced neutrophil infiltration, and improved cerebral injury outcomes in I/R-injured mice.

CONCLUSION

RPS27A regulates the expression of the PSMD12/NF-κB signaling axis, leading to the induction of inflammatory factors in microglial cells, promoting immune cell infiltration in brain tissue, and exacerbating brain damage in I/R mice. This study introduces novel insights and theoretical foundations for the treatment of nerve damage caused by I/R, suggesting that targeting the RPS27A and downstream PSMD12/NF-κB signaling axis for drug development could represent a new direction in I/R therapy.

B cell depletion attenuates CD27 signaling of T helper cells in multiple sclerosis

Multiple sclerosis is a chronic inflammatory disease of the central nervous system. Whereas T cells are likely the main drivers of disease development, the striking efficacy of B cell-depleting therapies (BCDTs) underscore B cells' involvement in disease progression. How B cells contribute to multiple sclerosis (MS) pathogenesis-and consequently the precise mechanism of action of BCDTs-remains elusive. Here, we analyze the impact of BCDTs on the immune landscape in patients with MS using high-dimensional single-cell immunophenotyping. Algorithm-guided analysis reveals a decrease in circulating T follicular helper-like (Tfh-like) cells alongside increases in CD27 expression in memory T helper cells and Tfh-like cells. Elevated CD27 indicates disrupted CD27/CD70 signaling, as sustained CD27 activation in T cells leads to its cleavage. Immunohistological analysis shows CD70-expressing B cells at MS lesion sites. These results suggest that the efficacy of BCDTs may partly hinge upon the disruption of Th cell and B cell interactions.

Application of OpenArray Technology to Assess Changes in the Expression of Functionally Significant Genes in the Substantia Nigra of Mice in a Model of Parkinson's Disease

Studying the molecular mechanisms of the pathogenesis of Parkinson's disease (PD) is critical to improve PD treatment. We used OpenArray technology to assess gene expression in the substantia nigra (SN) cells of mice in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD and in controls. Among the 11 housekeeping genes tested, Rps27a was taken as the reference gene due to its most stable expression in normal and experimental conditions. From 101 genes encoding functionally significant proteins of nigrostriatal dopaminergic neurons, 57 highly expressed genes were selected to assess their expressions in the PD model and in the controls. The expressions of Th, Ddc, Maoa, Comt, Slc6a3, Slc18a2, Drd2, and Nr4a2 decreased in the experiment compared to the control, indicating decreases in the synthesis, degradation, and transport of dopamine and the impaired autoregulation of dopaminergic neurons. The expressions of Tubb3, Map2, Syn1, Syt1, Rab7, Sod1, Cib1, Gpx1, Psmd4, Ubb, Usp47, and Ctsb genes were also decreased in the MPTP-treated mice, indicating impairments of axonal and vesicular transport and abnormal functioning of the antioxidant and ubiquitin-proteasome systems in the SN. The detected decreases in the expressions of Snca, Nsf, Dnm1l, and Keap1 may serve to reduce pathological protein aggregation, increase dopamine release in the striatum, prevent mitophagy, and restore the redox status of SN cells.

A 4D transcriptomic map for the evolution of multiple sclerosis-like lesions in the marmoset brain

Single-time-point histopathological studies on postmortem multiple sclerosis (MS) tissue fail to capture lesion evolution dynamics, posing challenges for therapy development targeting development and repair of focal inflammatory demyelination. To close this gap, we studied experimental autoimmune encephalitis (EAE) in the common marmoset, the most faithful animal model of these processes. Using MRI-informed RNA profiling, we analyzed ~600,000 single-nucleus and ~55,000 spatial transcriptomes, comparing them against EAE inoculation status, longitudinal radiological signals, and histopathological features. We categorized 5 groups of microenvironments pertinent to neural function, immune and glial responses, tissue destruction and repair, and regulatory network at brain borders. Exploring perilesional microenvironment diversity, we uncovered central roles of EAE-associated astrocytes, oligodendrocyte precursor cells, and ependyma in lesion formation and resolution. We pinpointed imaging and molecular features capturing the pathological trajectory of WM, offering potential for assessing treatment outcomes using marmoset as a platform.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Analysis of the potential relationship between COVID-19 and Behcet's disease using transcriptome data

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Shared Molecular Signatures Across Zika Virus Infection and Multiple Sclerosis Highlight AP-1 Transcription Factor as a Potential Player in Post-ZIKV MS-Like Phenotypes

Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis-like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual.

A Deep Learning Approach for Predicting Multiple Sclerosis

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

Multifaceted functions of RPS27a: An unconventional ribosomal protein

The ribosomal protein S27a (RPS27a) is cleaved from the fusion protein ubiquitin-RPS27a (Ub-RPS27a). Generally, Ub and RPS27a are coexpressed as a fusion protein but function independently after Ub is cleaved from RPS27a by a deubiquitinating enzyme. As an RP, RPS27a assembles into ribosomes, but it also functions independently of ribosomes. RPS27a is involved in the development and poor prognosis of various cancers, such as colorectal cancer, liver cancer, chronic myeloid leukemia, and renal carcinoma, and is associated with poor prognosis. Notably, the murine double minute 2/P53 axis is a major pathway through which RPS27a regulates cancer development. Moreover, RPS27a maintains sperm motility, regulates winged aphid indirect flight muscle degeneration, and facilitates plant growth. Additionally, RPS27a is a metalloprotein and mercury (Hg) biomarker. In the present review, we described the origin, structure, and biological functions of RPS27a.

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.

Identification of lncRNA and mRNA Expression Profile in Relapsed Graves' Disease

Background: Graves’ disease (GD) is a common autoimmune disease, and its pathogenesis is unclear. Studies have found that the occurrence of GD is related to the immune disorder caused by the interaction of genetic susceptibility and environmental factors. The CD4⁺ T cell subset is closely related to the immune disorder of GD. LncRNAs are RNA molecules with a length of more than 200 nt and are involved in a variety of autoimmune diseases. However, the roles of lncRNAs in recurrent GD are still elusive. The purpose of this study is to identify lncRNA and mRNA expression profile in relapsed Graves’ disease.

Method: CD4⁺ T cells from 12 recurrent GD and 8 healthy controls were collected for high-throughput sequencing. The gene-weighted co-expression network analysis (WGCNA) was used to construct the co-expression module relevant to recurrent GD, and the key genes in the module were verified by RT-PCR.

Results: There are 602 upregulated lncRNAs and 734 downregulated lncRNAs in CD4⁺ T cells in recurrent GD patients compared with the healthy controls. The module most relevant to GD recurrence was constructed using WGCNA, and the key genes in the module were verified by RT-PCR. We found that the expression of RPL8, OAS2, NFAT5, DROSHA, NONHSAT093153.2, NONHSAT118924.2, and NONHSAT209004.1 was significantly decreased in GD group (p < 0.001, p < 0.001, p < 0.01, p < 0.05, p < 0.001, p < 0.05, and p < 0.01, respectively).

Conclusion: LncRNAs are closely related to the recurrence of GD. For the first time, we constructed the expression profile of lncRNAs and mRNAs in CD4⁺ T cells in recurrent GD patients.

Attention to time-of-day variability improves the reproducibility of gene expression patterns in multiple sclerosis

Low reproducibility in gene expression profiles has been observed in transcriptome studies, and this often limits applying findings to clinical practice. Here, we show time-of-day effects on gene expression and analytical schemes to increase the reproducibility in expression patterns. We recruited patients with relapsing-remitting multiple sclerosis (RRMS) and healthy subjects and collected blood from individuals twice a day, day (2 pm) and night (9 pm). RNA sequencing analyses found that gene expression in RRMS in relapse (Relapse) is significantly changed at night compared with either Relapse at day or RRMS in remission (Remission). Gene set overrepresentation analysis demonstrated that gene sets significantly changed in Relapse at night are enriched to immune responses related to MS pathology. In those gene sets, 68 genes are significantly changed expression in Relapse at night compared with Relapse at day and Remission. This supports that times of sample collections should be standardized to obtain reproducible gene expression patterns.

•

Times of day affect gene expression patterns in patients with RRMS in relapse

•

Transcriptome profiles in Relapse are changed from day to night

•

In Relapse, immune response-related genes change the expression at night