Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

Transcriptional network analysis of peripheral blood leukocyte subsets in multiple sclerosis identifies a pathogenic role for a cytotoxicity-associated gene network in myeloid cells

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting predominantly adults. It is a complex disease associated with both environmental and genetic risk factors. Although over 230 risk single-nucleotide polymorphisms have been associated with MS, all are common human variants. The mechanisms by which they increase the risk of MS, however, remain elusive. We hypothesized that a complex genetic phenotype such as MS could be driven by coordinated expression of genes controlled by transcriptional regulatory networks. We, therefore, constructed a gene coexpression network from microarray expression analyses of five purified peripheral blood leukocyte subsets of 76 patients with relapsing remitting MS and 104 healthy controls. These analyses identified a major network (or module) of expressed genes associated with MS that play key roles in cell-mediated cytotoxicity which was downregulated in monocytes of patients with MS. Manipulation of the module gene expression was achieved in vitro through small interfering RNA gene knockdown of identified drivers. In a mouse model, network gene knockdown modulated the autoimmune inflammatory MS model disease-experimental autoimmune encephalomyelitis. This research implicates a cytotoxicity-associated gene network in myeloid cells in the pathogenesis of MS.

Identification of crucial inflammaging related risk factors in multiple sclerosis

Background

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.

Methods

First, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential "aging-inflammation-disease" triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.

Results

A series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.

Conclusion

In conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

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

Background

Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown.

Results

In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein–protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93).

Conclusion

This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

Extra-Virgin Olive Oil Modifies the Changes Induced in Non-Nervous Organs and Tissues by Experimental Autoimmune Encephalomyelitis Models

This study reveals the existence of oxidative stress (reactive oxygen species (ROS)) in non-nervous organs and tissues in multiple sclerosis (MS) by means of a model of experimental autoimmune encephalomyelitis (EAE) in rats. This model reproduces a similar situation to MS, as well as its relationship with intestinal microbiota starting from the changes in bacterial lipopolysaccharide levels (LPS) in the outer wall of the gram-negative bacteria. Finally, the administration of extra-virgin olive oil (EVOO), hydroxytirosol (HT), and oleic acid (OA) exert beneficial effects. Twenty-five Dark Agouti two-month-old male rats, weighing around 190 g, were distributed into the following groups: Control, EAE (experimental autoimmune encephalomyelitis group), EAE + EVOO, EAE + HT, and EAE + OA. The glutathione redox system with the EAE was measured in heart, kidney, liver, and small and large intestines. The LPS and the correlation with oxidative stress in the small and large intestines were also investigated. The results showed that (1) the oxidative damage in the EAE model affects non-nervous organs and tissues; (2) The LPS is related to inflammatory phenomena and oxidative stress in the intestinal tissue and in other organs; (3) The administration of EVOO, HT, and OA reduces the LPS levels at the same time as minimizing the oxidative damage; (4) EVOO, HT, and OA improve the disease's clinical score; and (5) on balance, EVOO offers a better neuroprotective effect.

Functional Implications of MicroRNAs in Crohn's Disease Revealed by Integrating MicroRNA and Messenger RNA Expression Profiling

Crohn's disease (CD) is a debilitating inflammatory bowel disease (IBD) that emerges due to the influence of genetic and environmental factors. microRNAs (miRNAs) have been identified in the tissue and sera of IBD patients and may play an important role in the induction of IBD. Our study aimed to identify differentially expressed miRNAs and miRNAs with the ability to alter transcriptome activity by comparing inflamed tissue samples with their non-inflamed counterparts. We studied changes in miRNA-mRNA interactions associated with CD by examining their differential co-expression relative to normal mucosa from the same patients. Correlation changes between the two conditions were incorporated into scores of predefined gene sets to identify biological processes with altered miRNA-mediated control. Our study identified 28 miRNAs differentially expressed (p-values < 0.01), of which 14 are up-regulated. Notably, our differential co-expression analysis highlights microRNAs (i.e., miR-4284, miR-3194 and miR-21) that have known functional interactions with key mechanisms implicated in IBD. Most of these miRNAs cannot be detected by differential expression analysis that do not take into account miRNA-mRNA interactions. The identification of differential miRNA-mRNA co-expression patterns will facilitate the investigation of the miRNA-mediated molecular mechanisms underlying CD pathogenesis and could suggest novel drug targets for validation.