Immunology of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) leading to demyelination, axonal damage, and progressive neurologic disability. The development of MS is influenced by environmental factors, particularly the Epstein-Barr virus (EBV), and genetic factors, which include specific HLA types, particularly DRB1*1501-DQA1*0102-DQB1*0602, and a predisposition to autoimmunity in general. MS patients have increased circulating T-cell and antibody reactivity to myelin proteins and gangliosides. It is proposed that the role of EBV is to infect autoreactive B cells that then seed the CNS and promote the survival of autoreactive T cells there. It is also proposed that the clinical attacks of relapsing-remitting MS are orchestrated by myelin-reactive T cells entering the white matter of the CNS from the blood, and that the progressive disability in primary and secondary progressive MS is caused by the action of autoantibodies produced in the CNS by ­meningeal lymphoid follicles with germinal centers

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

BACKGROUND: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. METHODS: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. RESULTS: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). CONCLUSIONS: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

KIR+CD8+ T cells suppress pathogenic T cells and are active in autoimmune diseases and COVID-19

Ly49+CD8+ T cells are a subset of CD8+ T cells that show immunoregulatory activity in mice. Li et al. report the existence of a similar CD8+ T cell subset in humans that expresses killer cell immunoglobulin-like receptors (KIRs), a functional parallel of the mouse Ly49 family (see the Perspective by Levescot and Bensussan). These cells, which can suppress self-reactive CD4+ T cells, were more abundant in patients with autoimmune conditions such as celiac disease, multiple sclerosis, and lupus, as well as in patients infected with influenza virus or severe acute respiratory syndrome coronavirus 2. When mice selectively deficient in Ly49+CD8+ T cells were infected with viruses, they showed normal antiviral immune responses but eventually developed symptoms of autoimmune disease. KIR+CD8+ T cells may therefore be an important therapeutic target for the control of autoimmune diseases such as “long COVID” that emerge after viral infections. —ST

Biomarkers of Multiple Sclerosis

The search for an ideal multiple sclerosis biomarker with good diagnostic value, prognostic reference and an impact on clinical outcome has yet to be realized and is still ongoing. The aim of this review is to establish an overview of the frequent biomarkers for multiple sclerosis that exist to date. The review summarizes the results obtained from electronic databases, as well as thorough manual searches. In this review the sources and methods of biomarkers extraction are described; in addition to the description of each biomarker, determination of the prognostic, diagnostic, disease monitoring and treatment response values besides clinical impact they might possess. We divided the biomarkers into three categories according to the achievement method: laboratory markers, genetic-immunogenetic markers and imaging markers. We have found two biomarkers at the time being considered the gold standard for MS diagnostics. Unfortunately, there does not exist a single solitary marker being able to present reliable diagnostic value, prognostic value, high sensitivity and specificity as well as clinical impact. We need more studies to find the best biomarker for MS.publishersversionPeer reviewe

A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis.

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available

A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available

Transcriptional expression patterns triggered by chemically distinct neuroprotective molecules

Glutamate mediated excitotoxicity has been purported to underlie many neurodegenerative disorders. A subtype of glutamate receptors, namely N-methyl-D-aspartate (NMDA) receptors, has been recognized as potential targets for neuroprotection. To increase our understanding of the mechanisms that underlie this neuroprotection, we employed a mouse model of glutamate receptor induced excitotoxic injury. Primary cortical neurons derived from postnatal day-0 CD-1 mice were cultured in the presence or absence of neuroprotective molecules and exposed to NMDA. Following a recovery period, whole genome expression was measured by microarray analysis. We used a combination of database and text mining, as well as systems modeling to identify signatures within the differentially expressed genes. While molecules differed in their mechanisms of action, we found significant overlap in the expression of a core group of genes and pathways. Many of these molecules have clear links to neuronal protection and survival, including ion channels, transporters, as well as signaling pathways including the mitogen-activated protein kinase (MAPK), the Toll-like receptor (TLR), and the hypoxic inducible factor (HIF). Within the TLR pathway, we also discovered a significant enrichment of interferon regulatory factor 7 (IRF7) regulated genes. Knockdown of Irf7 by RNA interference resulted in reduced survival following NMDA treatment. Given the prominent role that IRF7 plays in the transduction of type-I interferons (IFN), we also tested whether type-I IFNs alone functioned as neuroprotective agents and found that type-I IFNs were sufficient to promote neuronal survival. Our data suggest that the TLR/IRF7/IFN axis plays a significant role in recovery from glutamate induced excitotoxicity