Next Article in Journal
Elucidating the Epigenetic and Protein Interaction Landscapes in Amyotrophic Lateral Sclerosis: An Integrated Bioinformatics Analysis
Previous Article in Journal
The Cognitive Reserve May Influence Fatigue after Rehabilitation in Progressive Multiple Sclerosis: A Secondary Analysis of the RAGTIME Trial
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Multiple Sclerosis: Immune Cells, Histopathology, and Therapeutics

1
Vascular Immunology Unit, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2050, Australia
2
Liver Injury & Cancer Program, Centenary Institute, Sydney 2050, Australia
3
Human Immunology Laboratory, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2050, Australia
*
Authors to whom correspondence should be addressed.
Sclerosis 2024, 2(3), 117-139; https://doi.org/10.3390/sclerosis2030009
Submission received: 29 April 2024 / Revised: 31 May 2024 / Accepted: 23 June 2024 / Published: 27 June 2024

Abstract

:
Multiple sclerosis (MS) is an inflammatory demyelinating disease affecting the central nervous system (CNS). In MS, oligodendrocytes and myelin that surround axons to facilitate transmission of neuronal signals are destroyed by adaptive and innate immune cells, resulting in the formation of demyelinating plaques. For many years, research into MS pathophysiology has identified immune cell populations in lesions such as T cells, B cells, and myeloid and innate lymphoid cells. In this review, we discuss the involvement of these immune cells in MS pathophysiology and demonstrate how findings from histopathology studies and single-cell analyses in animal and human models have identified which immune cell subsets contribute to disease. This knowledge has facilitated the introduction of numerous immune-targeted therapeutics towards CD20, CD52, interferon-beta, sphingosine-1-phosphate receptor, Bruton’s tyrosine kinase, and many more. These treatments have shown effective reduction in new lesion formation and management of symptoms in MS patients. Furthermore, as MS is a chronic disease, these therapeutics slow disease progression, reduce cognitive disabilities, and prevent relapses. Further research is required to develop a cure for MS with limited side effects. The ongoing research that utilises innovative methods to identify and assess MS pathophysiology could transform the treatment landscape for patients in the future.

1. Introduction

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) affecting approximately 2.8 million people worldwide [1]. MS is the most significant cause of neurological disability in young adults aged between 20 and 40 years, detrimentally altering their most productive years in life [2]. In MS, myelin and myelin-producing cells, the oligodendrocytes, are destroyed by the body’s immune system, preventing neurons from transmitting information in the CNS [3]. As a result, patients experience cognitive deficits, fatigue, muscle weakness, sensory disturbances, impaired coordination, and permanent neurological disability, which significantly reduce quality of life [4]. This inflammatory demyelinating disease occurs when the blood–brain barrier (BBB) is compromised, enabling immune cells to bypass and infiltrate the CNS [5]. The BBB is a highly regulated interface that separates circulating blood from the CNS; however, in MS, this interface is disrupted by pro-inflammatory cytokines and chemokines [6]. Expression of adhesion markers on the endothelium and increased permeability of the BBB permit transmigration of activated leukocytes, T cells, B cells, and monocytes/macrophages into the perivascular space. These immune cells generate cytotoxic factors and react with autoantigens and microglia to perpetuate inflammation and damage myelin and axons [2,5]. Moreover, recent therapeutic advances in MS have targeted these immune cell populations, with strategies aimed at modulating their activation, trafficking, and effector functions. Extensive research into MS pathogenesis has introduced many targets for treatment of MS. In this review, we discuss these novel targets and the medications currently used by MS patients. Furthermore, the valuable contribution of methods such as histopathology and single-cell analysis in human and animal models of MS is discussed, as well as how they contribute to our knowledge of MS pathogenesis.

1.1. Aetiology

Because MS involves a complex and multifaceted inflammatory response, the precise aetiology remains elusive. However, extensive research now shows genetic predisposition, environmental triggers, and intricate immunological dysregulation as pivotal factors influencing the onset and progression of MS [7,8]. Genetic factors influence an individual’s susceptibility to MS, with over 233 gene variants identified [9]. Findings from family and epidemiological studies identified specific human leukocyte antigen (HLA) alleles on chromosome 6 linked to the risk of developing MS [10]. Among the HLA class alleles, DRB1*15:01 [11], *03:01 [12], and *13:03 [13] exhibit a robust association with MS diagnosis [14]. Several non-HLA genes associated with immune system function and myelin maintenance have also been identified. For example, the interleukin-2 receptor alpha (IL2RA) gene, which is involved in T-cell regulation, has a single-nucleotide polymorphism (SNP) that impacts CD4 T-cell differentiation and T regulatory cell (Treg) suppressive function [15]. Similarly, gene variants for immune cell development such as interleukin-7 receptor [16,17], tumour necrosis factor receptor superfamily member 1A, and interferon regulatory factor 8 [18] have been linked with high risk of MS. Indeed, genetic predispositions also influence an individual’s response to medications. For example, interferon-beta-treated MS patients with the IFNAR1 allele respond more positively to treatment [19]. Interestingly, MS patients with the CCR5 deletion allele presented beneficial interferon-beta treatment efficacy, whereas interferon-beta non-responders expressed the CCR5 gene [20]. Similarly, a significant proportion of relapsing–remitting MS patients responding to glatiramer acetate displayed the presence of the HLADRB1 1501 allele [21]. Furthermore, a study identified a positive association between the anti-VLA-4 drug natalizumab with GSTP1 and NQ01 wild-type genotypes where treatment efficacy was increased [22]. More work is required to investigate the allelic combinations in response to anti-CD20 and anti-CD52 treatment. These studies confirm pharmacogenetic associations of therapeutics in MS patients, highlighting the importance of tailoring treatments for individuals to ensure beneficial outcomes.
Environmental factors also play a significant role in MS and can synergise with genetic predisposition to influence disease susceptibility. Vitamin D deficiency as a result of reduced sun exposure, inadequate diet, or mutations in the vitamin D receptor (VDR) gene has been linked to an increased risk of developing MS [23,24,25]. Vitamin D supplementation has reduced the risk of developing MS by 40% [23,24]. Cigarette smoking is another environmental factor, increasing the risk of MS 1.5 times compared to non-smokers [26,27]. Epigenetic mechanisms also play a role in MS pathogenesis. Specifically, DNA methylation patterns, histone modification, and miRNA profiles have been identified in MS patient plasma and B and T cells, and these are distinctly different to healthy individuals [28,29]. These epigenetic changes can be a potential diagnostic tool since markers were detected in patient blood and cerebrospinal fluid [30]. In addition, viral infections have also been implicated as potential triggers for MS development, and this has been observed with Epstein–Barr virus (EBV). In a longitudinal study monitoring adults recruited in the US military over 20 years, the risk of MS after EBV infection increased 32-fold [31]. Indeed, these findings support more recent studies showing EBV-specific T cells in MS patient peripheral blood and post-mortem brain tissue [32,33,34]. Further investigations have suggested the possibility of EBV triggering CNS autoimmunity through EBV nuclear antigen 1 and glial cell adhesion molecule, which can lead to MS [35]. Further investigations are required to interpret this link between EBV and MS, which could promote novel treatment options and strategies for prevention. There are numerous other pathogens that have been potentially linked to MS aetiology, including viruses, notably Semliki Forest virus, Japanese macaque encephalomyelitis rhadinovirus, Coxsackie viruses, herpes viruses (particularly HHV6), human endogenous retroviruses, cytomegalovirus (CMV), as well as measles, rubella, and varicella zoster viruses, and even SARS-CoV2, but also Helicobacter pylori, Chlamydia pneumoniae, or Borrelia burgdorferi [36,37,38,39].

1.2. Clinical Course of MS

Often, MS progression adheres to distinct patterns with initial relapses and remissions giving way to gradual accumulation of disability in later phases. Patients with MS are classified into one of four categories according to the 2017 revised McDonald criteria [40]: (1) clinically isolated syndrome (CIS) describes the first episode of neurological symptoms that lasts at least 24 h, often considered an initial manifestation of MS [41,42]; (2) relapsing–remitting MS (RRMS) is characterised by intermittent episodes of worsening neurologic function followed by periods of partial or complete recovery [43]; (3) secondary progressive MS (SPMS) prominently involves axonal loss and neurodegeneration, resulting in gradual worsening of the disease without distinct relapses and remissions [44,45]; (4) primary progressive MS (PPMS) is characterised by a continuous deterioration in neurological function, resulting in severe disability over time [46,47]. RRMS is the most prevalent form of MS, comprising 85% of total MS patients, while 10–15% of patients are classified under PPMS [46,48]. Approximately 90% of untreated individuals with RRMS will transition to SPMS, where the disease worsens more steadily [49,50].

2. Immune Cells Involved in MS Pathology

MS is characterised by inflammatory lesions and demyelinating plaques in the CNS composed of immune cells (Figure 1). These immune cells transmigrate through the BBB at post-capillary venules into the perivascular space [51] and accumulate around blood vessels to form perivascular cuffs. Perivascular cuffs observed in acute phases of inflammation differ from chronic lesions, which are characterised by B-cell aggregations known as follicles [52]. It is known that adaptive immune cells (T and B cells) are a key contributor to MS, but more studies have emphasised a pivotal role for innate lymphoid and myeloid cells. Discussed below are the immune cells that promote neuroinflammation in MS.

2.1. Adaptive Immune Cells: T and B Cells

Autoreactive T cells, particularly CD4+ T cells, play a pivotal role in initiating and perpetuating the autoimmune response against the CNS. They recognise myelin antigens, such as myelin basic protein (MBP), proteolipid protein (PLP), and myelin oligodendrocyte glycoprotein (MOG), which are essential components of the myelin sheath [53]. Recognition of these self-antigens activates the autoreactive T cells, resulting in proliferation and differentiation into effector T cells [54]. Primary responses to these auto-antigens likely occurs outside the CNS [55]. The activated T cells transmigrate through the BBB via upregulation of adhesion molecules on endothelial cells such as vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) and are further influenced by chemokines [56,57]. Once in the CNS, autoreactive T cells release pro-inflammatory cytokines, such as interferon-γ (IFN-γ) and tumour necrosis factor (TNF), triggering an inflammatory cascade [58]. This inflammatory milieu further contributes to the disruption of the BBB, allowing infiltration of other immune cells into the CNS [59]. CD8+ T cells can also detect CNS antigens, resulting in their activation and release of pro-inflammatory cytokines, which exacerbates the immune response [60] and destroys oligodendrocytes and axons through the release of perforin and granzyme [61]. The persistent inflammation and demyelination creates a self-sustaining loop that perpetuates the chronic nature of MS. Additionally, Tregs play a vital role in modulating the immune response and maintaining peripheral tolerance [54]. However, in MS, Tregs become dysfunctional and contribute to the breakdown of immune tolerance by releasing greater amounts of IFN-γ and have reduced suppressive function [62], allowing for the sustained attack on myelin [63].
B cells play multifaceted roles, by functioning as antigen-presenting cells (APCs) and producing antibodies against myelin components [64], such as MBP and MOG. These autoantibodies contribute to the formation of immune complexes that activate complement cascades and induce inflammation, resulting in demyelination [64,65]. The presence of these autoantibodies in the CNS is indicative of B-cell dysregulation. Additionally, B cells interact with T cells and facilitate the activation of autoreactive T cells specific to myelin antigens [66]. This process perpetuates the immune response and sustains chronic inflammation in the CNS, contributing to the progression of MS. Moreover, regulatory B cells (Bregs) can modulate the immune response, but in MS, Bregs are dysregulated, which compromises their ability to restrain pro-inflammatory responses, exacerbating the autoimmune attack on myelin [62,67]. The balance between pro-inflammatory and regulatory B-cell functions is crucial for maintaining immune homeostasis, and thus dysregulation contributes to the chronic inflammation observed in MS [62,68,69].
The interplay between T cells and B cells is central to the pathogenesis and progression of MS. T cells recognise myelin antigens and secrete pro-inflammatory cytokines to activate B cells and monocytes [70]. B cells, in turn, amplify the autoimmune response by antigen presentation to T cells via co-stimulatory molecules CD80 and CD86, resulting in T-cell activation and proliferation [71]. Furthermore, B cells induce pro-inflammatory T-cell function, notably via CD40–CD40L(CD154) interaction [72]. In addition, B cells release cytokines that promote inter-cell signalling; for example, IL-6 production by B cells induces Th17 differentiation [73]. B cells also influence monocyte activity by regulating TNF production in pro-inflammatory monocytes [71]; how this contributes to MS pathogenesis requires further investigation. Collectively, these immune cells interact with each other to promote and exacerbate CNS inflammation in MS.

2.2. Myeloid Cells: Monocytes, Macrophages, and Dendritic Cells

In MS, monocytes contribute to the phagocytosis of myelin debris, leading to the presentation of myelin antigens to T cells and the amplification of the adaptive immune response. Within the monocyte population, there are three main subsets: classical (CD14++CD16), intermediate (CD14++CD16+), and non-classical (CD14+CD16++), which exhibit diverse functions [74]. Classical monocytes can infiltrate the CNS and contribute to the clearance of myelin debris. However, they can also exacerbate inflammation by releasing pro-inflammatory cytokines and promoting the activation of other immune cells [75]. Intermediate monocytes have been implicated in the pathogenesis of MS through their ability to produce inflammatory cytokines and chemokines. These cells exhibit an enhanced capacity for antigen presentation, contributing to the activation of autoreactive T cells and perpetuating the inflammatory cascade in MS lesions [76]. Non-classical monocytes are involved in immune surveillance and patrolling the vasculature [77]. In MS, they have been associated with the resolution of inflammation through anti-inflammatory cytokine production and phagocytosis of apoptotic cells [78]. However, dysregulation of non-classical monocytes leads to an impaired resolution of inflammation.
Upon entering the CNS, classical monocytes undergo maturation into macrophages, adopting a pro-inflammatory phenotype [79]. Activated macrophages release cytokines, such as TNF and IL-1, which exacerbate the inflammatory milieu and contribute to the demyelination observed in MS lesions [80]. The dysregulation of the balance between M1 and M2 macrophages is a key factor in the pathogenesis of MS. An excess of M1 polarisation contributes to the initiation of inflammation and demyelination. As the disease progresses, there is a shift from M1 to M2 polarisation to resolve the inflammation and promote tissue repair [81]. However, this response is often insufficient, leading to chronic inflammation and neurodegeneration. M1 macrophages, often referred to as pro-inflammatory or classically activated macrophages, are implicated in the initiation and propagation of the inflammatory cascade in MS. These macrophages release pro-inflammatory cytokines such as IL-1β, TNF, and IL-6 and recruit other immune cells, such as T cells, that further amplify the inflammatory response [82]. Additionally, M1 macrophages contribute to the oxidative stress and tissue damage by producing reactive oxygen species (ROS) and nitric oxide, exacerbating the neuroinflammatory environment in MS lesions. Conversely, M2 macrophages, known as anti-inflammatory macrophages, are associated with tissue repair and resolution of inflammation. In MS, M2 macrophages promote tissue regeneration and clearance of myelin debris [83]. They also secrete anti-inflammatory cytokines, such as IL- 10 and TGF-β, to dampen the inflammatory response and support tissue repair. Additionally, M2 macrophages release factors to stimulate oligodendrocyte precursor cell differentiation and myelin formation [84].
Perivascular macrophages (PVMs) are located around blood vessels in the perivascular space and have been implicated in various CNS pathologies (Figure 2). PVMs function as APCs by extending their cellular processes across the endothelium into the vessel lumen to present antigens to T lymphocytes to encourage CD8 T-cell transmigration into the CNS [85,86,87]. Brain PVMs present antigens derived from MBP to induce an autoimmune response that damages myelin [88]. In MS, PVMs accumulate around blood vessels in the brain and have shown to associate with disease progression [89].
Dendritic cells (DCs) are involved in the initiation and modulation of the autoimmune response against CNS antigens. DCs and follicular DCs (FDCs) within the peripheral lymphoid organs and at the CNS in the perivascular space function as APCs to present myelin-derived antigens to activate autoreactive T cells [90]. Activated T cells migrate into the CNS, perpetuate inflammation, and contribute to the demyelination observed in MS lesions [91]. Furthermore, DCs are implicated in the maintenance of immune tolerance, and alterations in DC function can lead to the presentation of self-antigens in an immunogenic manner [92], exacerbating autoimmune responses [93]. DCs also contribute to the modulation of other immune cells, such as B cells and Tregs, influencing the overall immune balance in MS [92]. Follicular DCs expressing CXCL13 act as APCs to B cells [94]. Additionally, DCs participate in the formation of organised lymphoid aggregates known as tertiary lymphoid structures. These structures can form within the CNS, further highlighting the role of DCs in sustaining local inflammation [95].

2.3. Microglia

Microglia, the resident macrophages of the CNS, play a pivotal role in the initiation and propagation of neuroinflammation in MS through a myriad of interactions with other immune cells, neurons, and glial cells [96]. Microglia act as key regulators of the immune response in the CNS; however, in MS, these cells become activated in response to the presence of myelin debris and inflammatory signals [97]. Activated microglia release pro-inflammatory cytokines, such as TNF and IL-1β, and contribute to inflammation and recruitment of peripheral immune cells into the CNS, resulting in demyelination [98]. Additionally, microglia play a crucial role in phagocytosing myelin debris and apoptotic cells [99]. However, the process is dysregulated in MS, leading to the degradation of healthy myelin and axons. This dysregulated phagocytosis further exacerbates the neuroinflammatory response. Moreover, chronic microglial activation leads to the release of ROS and nitrogen species, creating an oxidative stress environment that contributes to neurodegeneration [100,101]. Continuous activation of microglia in the absence of effective resolution mechanisms further contributes to the disease process.

2.4. Innate Lymphoid and Natural Killer Cells

Innate lymphoid cells (ILCs) represent a subset of lymphocytes that lack antigen-specific receptors but play crucial roles in the innate immune response. While their involvement in MS is not well understood, evidence suggests that ILCs contribute to the initiation and progression of the disease [102]. There are five different ILC subsets: group 1 (ILC1), group 2 (ILC2), group 3 (ILC3), lymphoid tissue inducers, and cytotoxic natural killer (NK) cells. ILCs are non-cytotoxic tissue resident cells that resemble CD4 T helper (Th) cells in function by producing cytokines in response to different stimuli [103]. For example, ILC1 (which is similar to Th1 cells) protects against intracellular bacteria, parasites, viruses, and tumour cells, while ILC2 (similar to Th2 cells) defends against extracellular parasites, asthma, and allergens, and ILC3 (similar to Th17 cells) combats bacteria and fungi and is involved in chronic inflammation [104]. In MS pathogenesis, ILC1 and ILC3 subsets promote inflammation within the CNS [104]. These cells produce pro-inflammatory cytokines such as IFN-γ and IL-17, which can exacerbate the inflammatory response and contribute to tissue damage [105]. ILCs may also modulate the activity of other immune cells, such as T cells and microglia [106]. Interactions between ILCs and resident immune cells can further amplify the inflammatory cascade, creating a microenvironment conducive to autoimmune responses [107]. In fact, excessive pro-inflammatory cytokine production from ILCs has been suggested to influence blood–brain barrier function, further facilitating immune cell migration and neuroinflammation in the CNS [108].
NK cells are cytotoxic cells that play a crucial role in immune surveillance by detecting and eliminating infected or transformed cells without prior sensitisation [109,110]. In MS, studies have reported differences in the numbers and functions of NK cells in the peripheral blood and CNS, suggesting dysregulation of NK-cell activity [111,112,113]. NK cells can modulate the adaptive immune response by influencing the activation and differentiation of T cells that are involved in the development of MS lesions [112,114]. Dysfunctional NK cells create an imbalance between regulatory and effector T-cell populations, leading to an exaggerated autoimmune response against myelin components [115,116,117]. NK cells can contribute to the breakdown of the BBB through the release of cathepsin D, cytokines, and chemokines, which can enhance the migration of immune cells into the CNS [118]. This process further exacerbates inflammation and formation of demyelinating lesions [119]. In addition, NK cells are also involved in the clearance of stressed or damaged cells, including oligodendrocytes in the CNS [120]. Dysregulated NK-cell activity may contribute to the elimination of oligodendrocytes, thereby exacerbating demyelination and axonal damage [121].

3. MS Pathogenesis with Histopathology and Single-Cell Analysis

Understanding how immune cells contribute to neuroinflammation in MS is critical for developing new treatments. Research is ongoing to characterise the pathogenesis of MS, and here, we discuss the significant discoveries identified by histopathology and single-cell analysis.

3.1. Histopathology

Histopathology involves the study of diseased tissues, and this can be performed in vivo using magnetic resonance imaging (MRI) or by preparation of tissue ex vivo for immunohistochemistry. MRI is a key tool used clinically to visualise active MS lesions and measure atrophy in patients that allows monitoring of disease progression [122]. It is sensitive at detecting white and grey matter T1 and T2 weighted ratio to determine lesion size and volume; however, the level of neurodegeneration at different phases of MS is difficult to determine [123]. Greater insight into the extent of neuronal damage can be determined with magnetic resonance spectroscopy and optical coherence tomography [124,125]. Indeed, combining techniques provides greater pathological details such as PET and MRI imaging, which differentiates MS subtypes by representing greater homogeneous and rim lesions in patients with PPMS and SPMS compared to RRMS [126]. Furthermore, a combinatorial assessment of MRI with single-nucleus RNA sequencing identified the interplay of different immune cells in the inflammatory edge of demyelinated white-matter lesions; however, this only assessed mRNA transcripts [127]. While in vivo imaging is a practical diagnostic modality, knowledge about the immune components that form the lesion is still lacking.
Histology has been the most valuable and well-established technique to identify demyelinated focal plaques and can additionally determine localisation of immune cell sub-populations in lesions. Acute active plaques observed in RRMS and CIS are identified by hypercellular regions entirely filled with macrophages, myelin debris, lymphocytes, and microglia [128]. Chronic active lesions in SPMS and PPMS are advanced and distinctively demarcated with macrophages, myelin-filled macrophages, microglia, and lymphocytes, while inactive chronic MS lesions are identified by a demyelinated, hypocellular core, surrounded by activated microglia and macrophages at the lesion rim [129]. Immunohistochemical staining of active demyelinating MS lesions shows that these microglia and astrocyte-like cells express CD86 and PD-L1, respectively, which were absent in normal white matter from healthy donors [130]. These active demyelinating MS lesions also present expression of M1 markers such as CD40, CD32, CD64, and CD86 in activated macrophages and microglia [131]. Further, macrophages containing myelin express M2 markers, mannose receptor, and CD163, and they are localised to the perivascular space in active lesions. PVMs expressing CD163 and MHC class II molecule HLA-DR are found localised around blood vessels in demyelinating lesions and are associated with myelin degradation [132]. Further analysis of these PVMs in acute active lesions showed double positive staining for MBP and CD163, indicating that PVMs can ingest MBP and act as APCs to promote immune attack on myelin [88].
MS patients with acute lesions also display perivascular cuffs made of T and B cells [7,133]. CD3+, CD4+, and CD8+ T cells populate white-matter MS lesions, and these CD8+ T cells have a tissue resident effector memory cell phenotype and express CD69, CD44, CXCR6, GPR56, granzyme, and CD103 [34,130,134,135]. Furthermore, perivascular T-cell cuffs express large populations of CD3+ and CD103+ tissue resident memory T cells, which are co-localised with CD20+ B cells and HLA- and CD163-expressing macrophages, indicating the presence of antigen presentation and reactivation of T cells in lesions [134]. Interestingly, lymphocytes stained with the proliferation marker PCNA revealed greater T-cell proliferation in lesions of RRMS patients compared to PPMS, whereas B-cell proliferation was observed in some patients [135]. B cells are commonly localised to the perivascular space, with very few found in the centre of plaques, and indeed, B-cell infiltration in MS patients is uniquely higher compared to other inflammatory diseases except autoimmune human encephalitis [135]. CD20+ B cells, localised to perivascular regions, were identified in PPMS and SPMS patients and associated with mitochondrial damage, an indication for severe pathology [136]. Furthermore, CD20+ B cells can also accumulate in meningeal lymphoid-like structures and are associated with cortical grey matter in SPMS patients [94]. These ectopic B-cell follicles can be extensive in SPMS and also contain a population of CD35+ and CXCL13+ stromal cells and FDC, suggesting their involvement in promoting B-cell recruitment and activation since B cells were positive for Ki67 [137]. PPMS patients can present with diffuse follicles that contain significant B- and T-cell infiltration but do not have FDC, positive staining for Ki67, or follicle-like organisation as observed in SPMS patients; however, both clinical forms are associated with severe CNS damage [138,139]. The study of MS pathology using histology is a valuable source for providing information about the location of immune cells involved in MS neurodegeneration and disease severity; however, this requires post-mortem tissue. Human tissues remain a valuable source for studying MS pathology; however, animal models of MS have been pivotal in unravelling the complex pathophysiology of the disease. Experimental autoimmune encephalomyelitis (EAE) remains the most used animal model, induced by immunisation with myelin-derived antigens or peptides to induce autoimmune activation that causes axonal damage and mimics MS pathology [140]. These models have been vital in understanding the role of various immune cells, cytokines, and inflammatory pathways in disease development and progression.

3.2. Single-Cell Analysis

Single-cell analysis of each immune cell involved in MS pathogenesis can be investigated with single-cell RNA sequencing (scRNA-seq), flow cytometry, and mass cytometry. RNA sequencing is a genomic assessment of the expression of RNA molecules, and in scRNA-seq, each individual cell is profiled. Transcriptomics analysis of cells in RRMS and CIS patient cerebrospinal fluid (CSF) identified an increased population of myeloid DCs, monocytes, B cells, CD4+ T cells, Tregs, and helper T cells compared to healthy patients [141]. Furthermore, scRNA-seq of isolated peripheral blood mononuclear cells (PBMCs) from MS patients demonstrated increased inflammatory pathways activated in RRMS, while SPMS patients showed reduced activity for neuronal repair [142]. Whether these RNA readouts reflect the protein level is unclear, but combining scRNA-seq with immunohistochemistry could confirm this. For example, scRNA-seq analysis of normal-appearing white matter (NAWM) brain tissue samples from PPMS patients identified an enrichment in expression of inflammatory and cellular stress genes in brain macrophages [143]. Further immunohistochemistry found these to be PVMs localised near blood vessels and parenchyma with positive expression of FCGR2B and HLA-DR, markers associated with inflammation and antigen presentation. Transcriptomic studies provide massive datasets about single-cell activity, biological processes, and pathways altered with disease, which can be complex to interpret and require expertise in bioinformatics.
Flow cytometry uses fluorophore conjugated antibodies and lasers to rapidly analyse single cells in solution, generating a scattered and fluorescent light signal that provides biological and physical properties of cells. Because lymphocyte subpopulations can be characterised by a variety of molecules expressed on the cell surface, antibodies are utilised to distinguish sub-populations of lymphocytes that are otherwise morphologically similar. This has been demonstrated where flow cytometry analysis of isolated PBMCs showed increased CD14+HLA-DR- monocytic myeloid-derived suppressor cells, CD14+CD16+ inflammatory monocytes, and CD4+T-helper in RRMS patients compared to healthy controls [144]. Furthermore, targeted flow cytometry presented a loss in circulating CD8+CD161high T cells in PPMS, defining a shift in expression of markers with disease progression [145]. More recent studies use flow cytometry to assess extracellular vesicles (EVs) in MS patients, an emerging area that suggests EVs as a biomarker for disease [146]. Interestingly, we combined flow cytometry with trans-endothelial migration (TEM) to identify immune cell migration across a BBB. In this in vitro BBB model, fresh PBMCs from patients were assessed on their migratory ability across an inflamed endothelial monolayer and examined for their phenotypic expression of immune cell markers using flow cytometry [56]. This study showed that leukocytes treated with the sphingosine-1-phosphate modulator drug, fingolimod, had markedly reduced migration. Specifically, these cells were CD3+ T cells, effector memory CD4+ T cells, naïve CD19+ B cells, and NK cells. In another study investigating the effect of cladribine treatment on patient PBMCs, cell migration of intermediate monocytes and both CD4+ effector memory and CD8+ central memory T-cell migration were reduced [147,148]. These methods provide critical knowledge about the immune cell subtypes that are involved in MS pathogenesis.
Mass cytometry enables high-dimensional assessment of complex phenotypes by replacing fluorochromes with heavy metal isotopes conjugated to antibodies [149]. Samples may be analysed in solution, or tissue sections may be prepared for analysis by imaging mass cytometry, both of which provide valuable information [150]. By using heavy metals, mass cytometry eliminates the issue of spectral overlap observed in flow cytometry. Compared to scRNA-seq, cytometry allows the assessment of post-translational modification of proteins including protein phosphorylation. Mass cytometry has provided new insights in the field of MS. For example, CyTOF differentiated myeloid cell populations in active lesions and NAWM between PPMS and non-MS donors, by measuring 74 proteins, which indicated an enrichment of phagocytic and activated microglia in active PMS lesions [151]. Similarly, our study designed a panel of B-cell markers to assess differences between CIS vs. MS and active vs. inactive MS, whereby nine IgG3+ B-cell subsets were identified. We further observed significant changes in memory B-cell subsets between CIS and MS patients that corresponded with serum IgG3 levels [152]. Another study demonstrated an increased abundance of both a T-bet-expressing B-cell subset and a CD206+ classical monocyte subset in early MS [153]. Compared to immunohistochemistry, imaging mass cytometry provided better discrimination of the location of immune cell subsets in patient tissues [154]. Single-cell analysis has the potential to provide a comprehensive analysis of cell phenotypes, functional states, and cell–cell interactions in relation to lesion morphometry and demyelinating activity. These techniques have the potential to lead to new discoveries, improved diagnosis, and novel treatments for MS patients.

4. Disease-Modifying Therapies (DMTs) to Treat MS

Significant findings from histopathology in animal MS models and human samples and single-cell analysis have identified immune cell subsets involved in MS pathogenesis. As a result, this has contributed to the development of many targets for MS treatments, including CD20, CD52, α4β1-integrin, sphingosine 1-phosphate receptor, interferon-beta, and Bruton’s tyrosine kinase (Figure 3). These targets have generated DMTs that improved MS prognosis by modulating the immune system to suppress inflammation, inhibit migration of lymphocytes across the BBB, and decrease the formation of new lesions [155]. DMTs alleviate symptoms, slow disease progression, and reduce disabilities and relapses while improving overall quality of life, thereby ameliorating the impact of MS [156]. Most DMTs are effective at treating RRMS but have been far less effective in influencing the course of SPMS and PPMS [157,158]. It is also important to note that the impact of DMTs for newly diagnosed RRMS patients is more effective when introduced at early stages of the disease [156,159]. There are many Food and Drug Administration (FDA)-approved DMTs commercially available to MS patients, with several others being assessed in clinical studies as discussed below. Research is still ongoing to develop new DMTs that treat MS patients with limited side effects.

4.1. Immunomodulators

Immunomodulatory therapies aim to stimulate or suppress the immune system to help induce an effective response to disease. The first FDA-approved DMT for MS was interferon-beta (IFN-β) therapy in 1993 [160]. IFN-β is a signalling protein of the immune system, though its mechanism of action is not completely understood. Studies suggest IFN-β is involved in antiviral, anti-proliferative, and immunomodulatory properties [161]. Recombinant IFN-β 1-a and 1-b are FDA-approved and used to treat MS patients. Clinical trials demonstrated significant reductions in disease severity supported by reduced lesions in MRI scans [160,162,163,164,165]. However, side effects were frequently observed where patients develop flu-like symptoms and reactions to intramuscular injections. Another immunomodulatory drug is glatiramer acetate (GA), which is a random polymer of the four amino acids found in MBP that suppresses T-cell activity and promotes APCs to switch to an anti-inflammatory phenotype [166]. GA was approved in 1996 as a daily subcutaneous injection with no severe side effects, with the exception of transient post-injection reactions [167]. GA treatment vs. placebo showed reduced relapse rates in RRMS patients over 2 years, with improved disability outcomes [168]. Dimethyl fumarate (DF) is an oral immunomodulator involved in nuclear factor erythroid 2-related factor 2 activation, which promotes an anti-inflammatory and anti-oxidative effect on immune cells [169]. In a phase 3 clinical trial, relapse rates reduced by 44% for MS patients treated with DF compared to placebo over 2 years [170]. However, some patients experienced flushing and gastrointestinal side effects.

4.2. Leukocyte Depletion and Cytolysis

Many of the DMTs used to treat patients display potent effects on leukocyte numbers and activity in MS. For example, ofatumumab and ocrelizumab are human anti-CD20 monoclonal antibody therapies that induce cell cytotoxicity in CD20+ cells [171,172,173]. Patients treated with ofatumumab showed depleted B-cell numbers, reduced lesion size, and improvements in symptoms with limited infusion-related reactions [174,175,176,177,178,179]. Because low doses of ofatumumab are self-administered monthly, this treatment has become convenient, cost-effective, and burden-free [180]. Alemtuzumab is another human monoclonal antibody that binds to CD52+ cells to induce complement-dependent cytolysis [181,182]. MS patients treated with alemtuzumab had reduced relapses and disability compared to IFN-β treatment [183]. After a year of treatment, MRI scans showed reduced disease activity [184]. Our lab studied the effect of alemtuzumab on circulating immune cells isolated from RRMS patients before and after treatment and assessed immune markers by mass cytometry. We identified that alemtuzumab treatment restored expression of B-cell linker protein, CD40, and CD210 on IgA+ and IgG1+ B cells that were altered in RRMS patients [185]. In a prospective study, circulating ILC1 levels increased with alemtuzumab treatment with no changes in NK levels except during relapse [186]. These findings suggest alemtuzumab promotes a more tolerant immune phenotype in MS. We also found that alemtuzumab decreased CD4 effector memory T cells and CD8 central memory T-cell migration in our TEM study [187]. As an immunosuppressive drug, side effects experienced by patients treated with alemtuzumab include autoimmune hyperthyroidism and immune thrombocytopenic purpura [188,189,190].
Small-molecule drugs are also available as oral medications such as cladribine and teriflunomide. Cladribine is a synthetic adenosine deaminase taken up by cells and phosphorylated to its active metabolite cladribine triphosphate to inhibit DNA synthesis and repair, resulting in consecutive cell apoptosis [191,192]. Clinical trials showed a reduction in relapse rates and disability progression in cladribine-treated MS patients compared to placebo [193,194,195]. Remarkably, lesion numbers were reduced as early as six months after starting cladribine treatment in RRMS patients [196]. In a TEM assay, we showed that cladribine reduced the migratory ability of CD4 effector memory T cells, CD8 central memory T cells, and intermediate monocytes during MS [147,148]. Furthermore, mass cytometry found cladribine-treated RRMS patients had a reduction in all ILC subsets except for ILC2 [197]. These findings suggest cladribine tablets are semi-selective at immune cell depletion, resulting in the emergence of a new, expectedly more tolerant, immune system [198]. Teriflunomide is an active metabolite derived from leflunomide that selectively inhibits dihydroorotate dehydrogenase, a mitochondrial enzyme involved in lymphocyte production [199]. Clinical trials studying the effect of teriflunomide treatment reported reduced relapse rates and disability outcomes for MS patients [200]. Side effects were observed with teriflunomide such as alopecia, nausea, and diarrhoea.

4.3. Leukocyte Transmigration Inhibitors

Leukocyte TEM across the BBB is promoted by the expression of adhesion molecules such as VCAM-1, ICAM-1, E-, and P-selectin on endothelial cells [201]. A selective adhesion molecule inhibitor such as natalizumab blocks the ability of leukocytes to attach to the endothelium. Natalizumab is a humanised IgG4 monoclonal antibody that binds to α4β1-integrin or VLA-4 on leukocytes to prevent interaction with VCAM-1 on endothelial cells [202,203]. Clinical trials showed effective results in 90% of RRMS patients with reduced relapses, lesions size, and disability outcomes [204,205]. Compared to fingolimod, natalizumab-treated patients have reduced rates of treatment discontinuation and display no sign of relapse after two years [206]. However, a 10-year follow-up study showed 52% of patients discontinued natalizumab treatment, with 27% displayed worsening disabilities and 1 in 1000 treated patients presenting with progressive multifocal leukoencephalopathy, a rare immunosuppressive disease [207,208]. These observations suggest that its long-term effects may not be apparent.
Another inhibitor of leukocyte migration is the sphingosine-1-phosphate (S1P) receptor modulator, which includes fingolimod, ozanimod, siponimod, and ponesimod [167]. Fingolimod is a small-molecule oral medication that binds to S1P receptors on immune cells causing internalisation and degradation of the receptor, thus preventing lymphocyte egress from lymphoid tissue and into the CNS [209]. Clinical trials comparing fingolimod to placebo treatment in MS patients showed reduced lesions on MRI scans, and these effects were still observed in the long term with low disability progression, improved quality of life, and no side effects [210,211,212]. Indeed, our studies demonstrated the immunomodulatory effect of fingolimod on T-cell, B-cell, and NK-cell migration across a BBB model. Specifically, CD4 effector memory T-cell migration was reduced following fingolimod treatment [56]. Clinical trials reported patients experiencing side effects with fingolimod treatment such as fingolimod-associated macular oedema and cardiac complications [211,213]. Hence, the more recently FDA-approved second-generation S1P receptor modulators, ozanimod, siponimod, and ponesimod, have better specificity and activity for S1P receptors to reduce side effects and improve clinical benefits [214].
Additional studies have been conducted demonstrating potential targets for regulating leukocyte transmigration in MS. For example, activated leukocyte cell adhesion molecule (ALCAM-1) is involved in B-cell migration across the CNS, whereby ALCAM+ B cells have been identified in peripheral blood and MS brain lesions [215]. Further, blocking ALCAM in a mouse model of EAE reduced B-cell transmigration and disease progression. Osteopontin (OPN) is another potential target, with recent studies detecting elevated levels of serum anti-OPN autoantibodies in MS patients [216]. These novel targets represent a promising treatment option for inhibiting leukocyte transmigration in MS.

4.4. Bruton’s Tyrosine Kinase (BTK) Inhibitors

BTK inhibitors are an emerging field for MS treatment. BTK is an intracellular signalling protein that belongs to the tyrosine kinase family and is downstream of the B-cell receptor (BCR) [217]. Signalling pathways modulated by BTK activity include nuclear factor of activated T cells (NFAT), nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), and extracellular signal-regulated kinase (ERK). These signalling pathways promote B-cell development, maturation, activation, proliferation, survival, and differentiation into memory B cells and antigen-presenting plasma cells [218]. BTK is also expressed by other immune cells such as T cells for activation and proliferation [219,220], microglia for neuroinflammation, and macrophages to promote phagocytosis and cytokine production [221]. Increased BTK activity contributes to CNS inflammation in MS [222]. RRMS and SPMS patients displayed higher levels of phosphorylated BTK protein expression in blood-derived class-switched memory B cells compared to CIS and healthy controls [223]. Histopathological studies assessing MS brain tissue have also presented increased expression of BTK protein in acute and chronic active lesions [224]. Furthermore, these active lesions showed positive BTK staining in B cells, microglia, and macrophages while in chronic active lesions, only the rim showed positive BTK staining in microglia. These findings demonstrate the importance of BTK activity in MS pathogenesis and its potential to be a promising therapeutic target.
BTK inhibitors modulate immune cell function by reducing inflammation and neurodegeneration, and include ibrutinib, evobrutinib, orelabrutinib, tolebrutinib, remibrutinib, and fenebrutinb [225,226]. All are currently being evaluated in clinical trials except for ibrutinib, which is FDA-approved [227]. Each BTK inhibitor differs in its selectivity, binding ability, and strength of inhibition; for example, higher concentrations of evobrutinib are required to induce the same effects as other inhibitors [228]. Phase 2 clinical trials assessing evobrutinib demonstrated reduced lesion size in one year and number of lesions over two years [229,230]. Likewise, tolebruitinib treatment demonstrated a reduced number of new lesions in RRMS patient over 12 weeks in a phase 2b study [231]. Remarkably, these studies observed limited side effects to treatments. Furthermore, these BTK inhibitors are small molecules that can pass the BBB to target activated immune cells within the CNS and promote effective treatment [232].

5. Conclusions

MS is a chronic disease that initially presents as relapses and then progressively leads to severe disability if not managed and treated early. MS pathogenesis is complex and influenced by various immune cells. Research is ongoing to determine the cause and define the pathology that affects many young individuals. Our understanding of immune cell subsets involved in MS pathogenesis has expanded with the contribution of histopathology and single-cell analysis. Using histopathology and immuno-staining, the localisation of CD3+ and CD103+ T cells and CD20+ B cells in perivascular cuffs has been established, and the contribution of stromal cells and FDC to ectopic B-cell follicles has been specified. Furthermore, single-cell analysis by techniques such as flow cytometry, scRNA-seq, and mass cytometry developed the ability to identify immune cell phenotypes in MS patient tissue, blood, and CSF samples. These studies have led to the introduction of targeted therapeutics to CD52, CD20, S1P1, and BTK for the treatment of MS. With ongoing research and novel treatments being tested in clinical trials and investigations to further understand MS pathogenesis, more effective therapeutics to treat MS are a promising goal for the near future.

Author Contributions

Conceptualisation, writing, and visualisation—M.S.P., L.Y.L., F.M.-W., S.H. and G.E.G.; review and editing—M.S.P., L.Y.L., F.M.-W., E.J.J., M.P., S.H. and G.E.G.; supervision—G.E.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Walton, C.; King, R.; Rechtman, L.; Kaye, W.; Leray, E.; Marrie, R.A.; Robertson, N.; La Rocca, N.; Uitdehaag, B.; van der Mei, I.; et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult. Scler. 2020, 26, 1816–1821. [Google Scholar] [CrossRef] [PubMed]
  2. Hemmer, B.; Kerschensteiner, M.; Korn, T. Role of the innate and adaptive immune responses in the course of multiple sclerosis. Lancet Neurol. 2015, 14, 406–419. [Google Scholar] [CrossRef]
  3. Compston, A.; Coles, A. Multiple sclerosis. Lancet 2008, 372, 1502–1517. [Google Scholar] [CrossRef] [PubMed]
  4. Ghasemi, N.; Razavi, S.; Nikzad, E. Multiple Sclerosis: Pathogenesis, Symptoms, Diagnoses and Cell-Based Therapy. Cell J. 2017, 19, 1–10. [Google Scholar] [CrossRef] [PubMed]
  5. Ortiz, G.G.; Pacheco-Moises, F.P.; Macias-Islas, M.A.; Flores-Alvarado, L.J.; Mireles-Ramirez, M.A.; Gonzalez-Renovato, E.D.; Hernandez-Navarro, V.E.; Sanchez-Lopez, A.L.; Alatorre-Jimenez, M.A. Role of the blood-brain barrier in multiple sclerosis. Arch. Med. Res. 2014, 45, 687–697. [Google Scholar] [CrossRef] [PubMed]
  6. Holman, D.W.; Klein, R.S.; Ransohoff, R.M. The blood-brain barrier, chemokines and multiple sclerosis. Biochim. Biophys. Acta 2011, 1812, 220–230. [Google Scholar] [CrossRef]
  7. Dendrou, C.A.; Fugger, L.; Friese, M.A. Immunopathology of multiple sclerosis. Nat. Rev. Immunol. 2015, 15, 545–558. [Google Scholar] [CrossRef] [PubMed]
  8. Bernard, C.C.; Kerlero de Rosbo, N. Multiple sclerosis: An autoimmune disease of multifactorial etiology. Curr. Opin. Immunol. 1992, 4, 760–765. [Google Scholar] [CrossRef] [PubMed]
  9. International Multiple Sclerosis Genetics, C. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 2019, 365, eaav7188. [Google Scholar] [CrossRef]
  10. Lin, X.; Deng, F.Y.; Lu, X.; Lei, S.F. Susceptibility Genes for Multiple Sclerosis Identified in a Gene-Based Genome-Wide Association Study. J. Clin. Neurol. 2015, 11, 311–318. [Google Scholar] [CrossRef]
  11. Mosca, L.; Mantero, V.; Penco, S.; La Mantia, L.; De Benedetti, S.; Marazzi, M.R.; Spreafico, C.; Erminio, C.; Grassi, L.; Lando, G.; et al. HLA-DRB1*15 association with multiple sclerosis is confirmed in a multigenerational Italian family. Funct. Neurol. 2017, 32, 83–88. [Google Scholar] [CrossRef] [PubMed]
  12. Marrosu, M.G.; Murru, M.R.; Costa, G.; Cucca, F.; Sotgiu, S.; Rosati, G.; Muntoni, F. Multiple sclerosis in Sardinia is associated and in linkage disequilibrium with HLA-DR3 and -DR4 alleles. Am. J. Hum. Genet. 1997, 61, 454–457. [Google Scholar] [CrossRef]
  13. Kwon, O.J.; Karni, A.; Israel, S.; Brautbar, C.; Amar, A.; Meiner, Z.; Abramsky, O.; Karussis, D. HLA class II susceptibility to multiple sclerosis among Ashkenazi and non-Ashkenazi Jews. Arch. Neurol. 1999, 56, 555–560. [Google Scholar] [CrossRef] [PubMed]
  14. Hollenbach, J.A.; Oksenberg, J.R. The immunogenetics of multiple sclerosis: A comprehensive review. J. Autoimmun. 2015, 64, 13–25. [Google Scholar] [CrossRef] [PubMed]
  15. Buhelt, S.; Sondergaard, H.B.; Oturai, A.; Ullum, H.; von Essen, M.R.; Sellebjerg, F. Relationship between Multiple Sclerosis-Associated IL2RA Risk Allele Variants and Circulating T Cell Phenotypes in Healthy Genotype-Selected Controls. Cells 2019, 8, 634. [Google Scholar] [CrossRef] [PubMed]
  16. Liu, H.; Huang, J.; Dou, M.; Liu, Y.; Xiao, B.; Liu, X.; Huang, Z. Variants in the IL7RA gene confer susceptibility to multiple sclerosis in Caucasians: Evidence based on 9734 cases and 10,436 controls. Sci. Rep. 2017, 7, 1207. [Google Scholar] [CrossRef] [PubMed]
  17. Galarza-Munoz, G.; Briggs, F.B.S.; Evsyukova, I.; Schott-Lerner, G.; Kennedy, E.M.; Nyanhete, T.; Wang, L.; Bergamaschi, L.; Widen, S.G.; Tomaras, G.D.; et al. Human Epistatic Interaction Controls IL7R Splicing and Increases Multiple Sclerosis Risk. Cell 2017, 169, 72–84.e13. [Google Scholar] [CrossRef] [PubMed]
  18. De Jager, P.L.; Jia, X.; Wang, J.; de Bakker, P.I.; Ottoboni, L.; Aggarwal, N.T.; Piccio, L.; Raychaudhuri, S.; Tran, D.; Aubin, C.; et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat. Genet. 2009, 41, 776–782. [Google Scholar] [CrossRef]
  19. Kulakova, O.G.; Tsareva, E.Y.; Boyko, A.N.; Shchur, S.G.; Gusev, E.I.; Lvovs, D.; Favorov, A.V.; Vandenbroeck, K.; Favorova, O.O. Allelic combinations of immune-response genes as possible composite markers of IFN-beta efficacy in multiple sclerosis patients. Pharmacogenomics 2012, 13, 1689–1700. [Google Scholar] [CrossRef]
  20. Kulakova, O.G.; Tsareva, E.Y.; Lvovs, D.; Favorov, A.V.; Boyko, A.N.; Favorova, O.O. Comparative pharmacogenetics of multiple sclerosis: IFN-beta versus glatiramer acetate. Pharmacogenomics 2014, 15, 679–685. [Google Scholar] [CrossRef]
  21. Fusco, C.; Andreone, V.; Coppola, G.; Luongo, V.; Guerini, F.; Pace, E.; Florio, C.; Pirozzi, G.; Lanzillo, R.; Ferrante, P.; et al. HLA-DRB1*1501 and response to copolymer-1 therapy in relapsing-remitting multiple sclerosis. Neurology 2001, 57, 1976–1979. [Google Scholar] [CrossRef] [PubMed]
  22. Alexoudi, A.; Zachaki, S.; Stavropoulou, C.; Gavrili, S.; Spiliopoulou, C.; Papadodima, S.; Karageorgiou, C.E.; Sambani, C. Possible Implication of GSTP1 and NQO1 Polymorphisms on Natalizumab Response in Multiple Sclerosis. Ann. Clin. Lab. Sci. 2016, 46, 586–591. [Google Scholar] [PubMed]
  23. Kampman, M.T.; Steffensen, L.H. The role of vitamin D in multiple sclerosis. J. Photochem. Photobiol. B 2010, 101, 137–141. [Google Scholar] [CrossRef] [PubMed]
  24. Sintzel, M.B.; Rametta, M.; Reder, A.T. Vitamin D and Multiple Sclerosis: A Comprehensive Review. Neurol. Ther. 2018, 7, 59–85. [Google Scholar] [CrossRef] [PubMed]
  25. Cancela Diez, B.; Perez-Ramirez, C.; Maldonado-Montoro, M.D.M.; Carrasco-Campos, M.I.; Sanchez Martin, A.; Pineda Lancheros, L.E.; Martinez-Martinez, F.; Calleja-Hernandez, M.A.; Ramirez-Tortosa, M.C.; Jimenez-Morales, A. Association between polymorphisms in the vitamin D receptor and susceptibility to multiple sclerosis. Pharmacogenet. Genom. 2021, 31, 40–47. [Google Scholar] [CrossRef]
  26. Wingerchuk, D.M. Smoking: Effects on multiple sclerosis susceptibility and disease progression. Ther. Adv. Neurol. Disord. 2012, 5, 13–22. [Google Scholar] [CrossRef] [PubMed]
  27. Manouchehrinia, A.; Huang, J.; Hillert, J.; Alfredsson, L.; Olsson, T.; Kockum, I.; Constantinescu, C.S. Smoking Attributable Risk in Multiple Sclerosis. Front. Immunol. 2022, 13, 840158. [Google Scholar] [CrossRef]
  28. Lindberg, R.L.; Hoffmann, F.; Mehling, M.; Kuhle, J.; Kappos, L. Altered expression of miR-17-5p in CD4+ lymphocytes of relapsing-remitting multiple sclerosis patients. Eur. J. Immunol. 2010, 40, 888–898. [Google Scholar] [CrossRef] [PubMed]
  29. Liggett, T.; Melnikov, A.; Tilwalli, S.; Yi, Q.; Chen, H.; Replogle, C.; Feng, X.; Reder, A.; Stefoski, D.; Balabanov, R.; et al. Methylation patterns of cell-free plasma DNA in relapsing-remitting multiple sclerosis. J. Neurol. Sci. 2010, 290, 16–21. [Google Scholar] [CrossRef]
  30. Haghikia, A.; Haghikia, A.; Hellwig, K.; Baraniskin, A.; Holzmann, A.; Decard, B.F.; Thum, T.; Gold, R. Regulated microRNAs in the CSF of patients with multiple sclerosis: A case-control study. Neurology 2012, 79, 2166–2170. [Google Scholar] [CrossRef]
  31. Bjornevik, K.; Cortese, M.; Healy, B.C.; Kuhle, J.; Mina, M.J.; Leng, Y.; Elledge, S.J.; Niebuhr, D.W.; Scher, A.I.; Munger, K.L.; et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Science 2022, 375, 296–301. [Google Scholar] [CrossRef]
  32. Schneider-Hohendorf, T.; Gerdes, L.A.; Pignolet, B.; Gittelman, R.; Ostkamp, P.; Rubelt, F.; Raposo, C.; Tackenberg, B.; Riepenhausen, M.; Janoschka, C.; et al. Broader Epstein-Barr virus-specific T cell receptor repertoire in patients with multiple sclerosis. J. Exp. Med. 2022, 219, e20220650. [Google Scholar] [CrossRef] [PubMed]
  33. Serafini, B.; Rosicarelli, B.; Veroni, C.; Mazzola, G.A.; Aloisi, F. Epstein-Barr Virus-Specific CD8 T Cells Selectively Infiltrate the Brain in Multiple Sclerosis and Interact Locally with Virus-Infected Cells: Clue for a Virus-Driven Immunopathological Mechanism. J. Virol. 2019, 93, e00980-19. [Google Scholar] [CrossRef]
  34. van Nierop, G.P.; van Luijn, M.M.; Michels, S.S.; Melief, M.J.; Janssen, M.; Langerak, A.W.; Ouwendijk, W.J.D.; Hintzen, R.Q.; Verjans, G. Phenotypic and functional characterization of T cells in white matter lesions of multiple sclerosis patients. Acta Neuropathol. 2017, 134, 383–401. [Google Scholar] [CrossRef]
  35. Lanz, T.V.; Brewer, R.C.; Ho, P.P.; Moon, J.S.; Jude, K.M.; Fernandez, D.; Fernandes, R.A.; Gomez, A.M.; Nadj, G.S.; Bartley, C.M.; et al. Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature 2022, 603, 321–327. [Google Scholar] [CrossRef] [PubMed]
  36. Marrodan, M.; Alessandro, L.; Farez, M.F.; Correale, J. The role of infections in multiple sclerosis. Mult. Scler. 2019, 25, 891–901. [Google Scholar] [CrossRef] [PubMed]
  37. Donati, D. Viral infections and multiple sclerosis. Drug Discov. Today Dis. Models 2020, 32, 27–33. [Google Scholar] [CrossRef] [PubMed]
  38. Sedighi, B.; Haghdoost, A.; Jangipour Afshar, P.; Abna, Z.; Bahmani, S.; Jafari, S. Multiple sclerosis and COVID-19: A retrospective study in Iran. PLoS ONE 2023, 18, e0283538. [Google Scholar] [CrossRef] [PubMed]
  39. Landry, R.L.; Embers, M.E. The Probable Infectious Origin of Multiple Sclerosis. NeuroSci 2023, 4, 211–234. [Google Scholar] [CrossRef]
  40. Thompson, A.J.; Baranzini, S.E.; Geurts, J.; Hemmer, B.; Ciccarelli, O. Multiple sclerosis. Lancet 2018, 391, 1622–1636. [Google Scholar] [CrossRef]
  41. Miller, D.H.; Chard, D.T.; Ciccarelli, O. Clinically isolated syndromes. Lancet Neurol. 2012, 11, 157–169. [Google Scholar] [CrossRef] [PubMed]
  42. Miller, D.; Barkhof, F.; Montalban, X.; Thompson, A.; Filippi, M. Clinically isolated syndromes suggestive of multiple sclerosis, part I: Natural history, pathogenesis, diagnosis, and prognosis. Lancet Neurol. 2005, 4, 281–288. [Google Scholar] [CrossRef] [PubMed]
  43. Steinman, L. Immunology of relapse and remission in multiple sclerosis. Annu. Rev. Immunol. 2014, 32, 257–281. [Google Scholar] [CrossRef] [PubMed]
  44. Klineova, S.; Lublin, F.D. Clinical Course of Multiple Sclerosis. Cold Spring Harb. Perspect. Med. 2018, 8, a028928. [Google Scholar] [CrossRef] [PubMed]
  45. Kutzelnigg, A.; Lucchinetti, C.F.; Stadelmann, C.; Bruck, W.; Rauschka, H.; Bergmann, M.; Schmidbauer, M.; Parisi, J.E.; Lassmann, H. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 2005, 128, 2705–2712. [Google Scholar] [CrossRef] [PubMed]
  46. Fitzner, D.; Simons, M. Chronic progressive multiple sclerosis—Pathogenesis of neurodegeneration and therapeutic strategies. Curr. Neuropharmacol. 2010, 8, 305–315. [Google Scholar] [CrossRef] [PubMed]
  47. Bramow, S.; Frischer, J.M.; Lassmann, H.; Koch-Henriksen, N.; Lucchinetti, C.F.; Sorensen, P.S.; Laursen, H. Demyelination versus remyelination in progressive multiple sclerosis. Brain 2010, 133, 2983–2998. [Google Scholar] [CrossRef]
  48. McKay, K.A.; Kwan, V.; Duggan, T.; Tremlett, H. Risk factors associated with the onset of relapsing-remitting and primary progressive multiple sclerosis: A systematic review. Biomed. Res. Int. 2015, 2015, 817238. [Google Scholar] [CrossRef] [PubMed]
  49. Miller, D.H.; Leary, S.M. Primary-progressive multiple sclerosis. Lancet Neurol. 2007, 6, 903–912. [Google Scholar] [CrossRef]
  50. Trojano, M.; Paolicelli, D.; Bellacosa, A.; Cataldo, S. The transition from relapsing-remitting MS to irreversible disability: Clinical evaluation. Neurol. Sci. 2003, 24 (Suppl. 5), S268–S270. [Google Scholar] [CrossRef]
  51. Greiner, T.; Kipp, M. What Guides Peripheral Immune Cells into the Central Nervous System? Cells 2021, 10, 2041. [Google Scholar] [CrossRef] [PubMed]
  52. Hawke, S.; Stevenson, P.G.; Freeman, S.; Bangham, C.R. Long-term persistence of activated cytotoxic T lymphocytes after viral infection of the central nervous system. J. Exp. Med. 1998, 187, 1575–1582. [Google Scholar] [CrossRef] [PubMed]
  53. Greer, J.M. Autoimmune T-cell reactivity to myelin proteolipids and glycolipids in multiple sclerosis. Mult. Scler. Int. 2013, 2013, 151427. [Google Scholar] [CrossRef] [PubMed]
  54. Goverman, J. Autoimmune T cell responses in the central nervous system. Nat. Rev. Immunol. 2009, 9, 393–407. [Google Scholar] [CrossRef] [PubMed]
  55. Stevenson, P.G.; Austyn, J.M.; Hawke, S. Uncoupling of virus-induced inflammation and anti-viral immunity in the brain parenchyma. J. Gen. Virol. 2002, 83, 1735–1743. [Google Scholar] [CrossRef] [PubMed]
  56. Hawke, S.; Zinger, A.; Juillard, P.G.; Holdaway, K.; Byrne, S.N.; Grau, G.E. Selective modulation of trans-endothelial migration of lymphocyte subsets in multiple sclerosis patients under fingolimod treatment. J. Neuroimmunol. 2020, 349, 577392. [Google Scholar] [CrossRef] [PubMed]
  57. Eva, L.; Ples, H.; Covache-Busuioc, R.A.; Glavan, L.A.; Bratu, B.G.; Bordeianu, A.; Dumitrascu, D.I.; Corlatescu, A.D.; Ciurea, A.V. A Comprehensive Review on Neuroimmunology: Insights from Multiple Sclerosis to Future Therapeutic Developments. Biomedicines 2023, 11, 2489. [Google Scholar] [CrossRef] [PubMed]
  58. Kaskow, B.J.; Baecher-Allan, C. Effector T Cells in Multiple Sclerosis. Cold Spring Harb. Perspect. Med. 2018, 8, a029025. [Google Scholar] [CrossRef] [PubMed]
  59. Yarlagadda, A.; Alfson, E.; Clayton, A.H. The blood brain barrier and the role of cytokines in neuropsychiatry. Psychiatry 2009, 6, 18–22. [Google Scholar]
  60. Wagner, C.A.; Roque, P.J.; Mileur, T.R.; Liggitt, D.; Goverman, J.M. Myelin-specific CD8+ T cells exacerbate brain inflammation in CNS autoimmunity. J. Clin. Investig. 2020, 130, 203–213. [Google Scholar] [CrossRef]
  61. Neumann, H.; Medana, I.M.; Bauer, J.; Lassmann, H. Cytotoxic T lymphocytes in autoimmune and degenerative CNS diseases. Trends Neurosci. 2002, 25, 313–319. [Google Scholar] [CrossRef] [PubMed]
  62. Vasileiadis, G.K.; Dardiotis, E.; Mavropoulos, A.; Tsouris, Z.; Tsimourtou, V.; Bogdanos, D.P.; Sakkas, L.I.; Hadjigeorgiou, G.M. Regulatory B and T lymphocytes in multiple sclerosis: Friends or foes? Autoimmun. Highlights 2018, 9, 9. [Google Scholar] [CrossRef] [PubMed]
  63. Viglietta, V.; Baecher-Allan, C.; Weiner, H.L.; Hafler, D.A. Loss of functional suppression by CD4+CD25+ regulatory T cells in patients with multiple sclerosis. J. Exp. Med. 2004, 199, 971–979. [Google Scholar] [CrossRef] [PubMed]
  64. McLaughlin, K.A.; Wucherpfennig, K.W. B cells and autoantibodies in the pathogenesis of multiple sclerosis and related inflammatory demyelinating diseases. Adv. Immunol. 2008, 98, 121–149. [Google Scholar] [CrossRef]
  65. Lubetzki, C.; Stankoff, B. Demyelination in multiple sclerosis. Handb. Clin. Neurol. 2014, 122, 89–99. [Google Scholar] [CrossRef] [PubMed]
  66. Molnarfi, N.; Schulze-Topphoff, U.; Weber, M.S.; Patarroyo, J.C.; Prod’homme, T.; Varrin-Doyer, M.; Shetty, A.; Linington, C.; Slavin, A.J.; Hidalgo, J.; et al. MHC class II-dependent B cell APC function is required for induction of CNS autoimmunity independent of myelin-specific antibodies. J. Exp. Med. 2013, 210, 2921–2937. [Google Scholar] [CrossRef]
  67. Matsushita, T.; Yanaba, K.; Bouaziz, J.D.; Fujimoto, M.; Tedder, T.F. Regulatory B cells inhibit EAE initiation in mice while other B cells promote disease progression. J. Clin. Investig. 2008, 118, 3420–3430. [Google Scholar] [CrossRef] [PubMed]
  68. Knippenberg, S.; Peelen, E.; Smolders, J.; Thewissen, M.; Menheere, P.; Cohen Tervaert, J.W.; Hupperts, R.; Damoiseaux, J. Reduction in IL-10 producing B cells (Breg) in multiple sclerosis is accompanied by a reduced naive/memory Breg ratio during a relapse but not in remission. J. Neuroimmunol. 2011, 239, 80–86. [Google Scholar] [CrossRef]
  69. Staun-Ram, E.; Miller, A. Effector and regulatory B cells in Multiple Sclerosis. Clin. Immunol. 2017, 184, 11–25. [Google Scholar] [CrossRef]
  70. Sun, L.; Su, Y.; Jiao, A.; Wang, X.; Zhang, B. T cells in health and disease. Signal Transduct. Target. Ther. 2023, 8, 235. [Google Scholar] [CrossRef]
  71. Gharibi, T.; Babaloo, Z.; Hosseini, A.; Marofi, F.; Ebrahimi-Kalan, A.; Jahandideh, S.; Baradaran, B. The role of B cells in the immunopathogenesis of multiple sclerosis. Immunology 2020, 160, 325–335. [Google Scholar] [CrossRef] [PubMed]
  72. Elgueta, R.; Benson, M.J.; de Vries, V.C.; Wasiuk, A.; Guo, Y.; Noelle, R.J. Molecular mechanism and function of CD40/CD40L engagement in the immune system. Immunol. Rev. 2009, 229, 152–172. [Google Scholar] [CrossRef] [PubMed]
  73. Korn, T.; Mitsdoerffer, M.; Croxford, A.L.; Awasthi, A.; Dardalhon, V.A.; Galileos, G.; Vollmar, P.; Stritesky, G.L.; Kaplan, M.H.; Waisman, A.; et al. IL-6 controls Th17 immunity in vivo by inhibiting the conversion of conventional T cells into Foxp3+ regulatory T cells. Proc. Natl. Acad. Sci. USA 2008, 105, 18460–18465. [Google Scholar] [CrossRef] [PubMed]
  74. Ziegler-Heitbrock, L.; Ancuta, P.; Crowe, S.; Dalod, M.; Grau, V.; Hart, D.N.; Leenen, P.J.; Liu, Y.J.; MacPherson, G.; Randolph, G.J.; et al. Nomenclature of monocytes and dendritic cells in blood. Blood 2010, 116, e74–e80. [Google Scholar] [CrossRef] [PubMed]
  75. Sampath, P.; Moideen, K.; Ranganathan, U.D.; Bethunaickan, R. Monocyte Subsets: Phenotypes and Function in Tuberculosis Infection. Front. Immunol. 2018, 9, 1726. [Google Scholar] [CrossRef]
  76. Ziegler-Heitbrock, L. Blood Monocytes and Their Subsets: Established Features and Open Questions. Front. Immunol. 2015, 6, 423. [Google Scholar] [CrossRef] [PubMed]
  77. Wong, K.L.; Tai, J.J.; Wong, W.C.; Han, H.; Sem, X.; Yeap, W.H.; Kourilsky, P.; Wong, S.C. Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets. Blood 2011, 118, e16–e31. [Google Scholar] [CrossRef] [PubMed]
  78. Kapellos, T.S.; Bonaguro, L.; Gemund, I.; Reusch, N.; Saglam, A.; Hinkley, E.R.; Schultze, J.L. Human Monocyte Subsets and Phenotypes in Major Chronic Inflammatory Diseases. Front. Immunol. 2019, 10, 2035. [Google Scholar] [CrossRef] [PubMed]
  79. Jakubzick, C.V.; Randolph, G.J.; Henson, P.M. Monocyte differentiation and antigen-presenting functions. Nat. Rev. Immunol. 2017, 17, 349–362. [Google Scholar] [CrossRef]
  80. Sica, A.; Mantovani, A. Macrophage plasticity and polarization: In vivo veritas. J. Clin. Investig. 2012, 122, 787–795. [Google Scholar] [CrossRef]
  81. Radandish, M.; Khalilian, P.; Esmaeil, N. The Role of Distinct Subsets of Macrophages in the Pathogenesis of MS and the Impact of Different Therapeutic Agents on These Populations. Front. Immunol. 2021, 12, 667705. [Google Scholar] [CrossRef]
  82. Murray, P.J.; Wynn, T.A. Protective and pathogenic functions of macrophage subsets. Nat. Rev. Immunol. 2011, 11, 723–737. [Google Scholar] [CrossRef]
  83. Gundra, U.M.; Girgis, N.M.; Ruckerl, D.; Jenkins, S.; Ward, L.N.; Kurtz, Z.D.; Wiens, K.E.; Tang, M.S.; Basu-Roy, U.; Mansukhani, A.; et al. Alternatively activated macrophages derived from monocytes and tissue macrophages are phenotypically and functionally distinct. Blood 2014, 123, e110–e122. [Google Scholar] [CrossRef] [PubMed]
  84. Miron, V.E. Microglia-driven regulation of oligodendrocyte lineage cells, myelination, and remyelination. J. Leukoc. Biol. 2017, 101, 1103–1108. [Google Scholar] [CrossRef] [PubMed]
  85. Goddery, E.N.; Fain, C.E.; Lipovsky, C.G.; Ayasoufi, K.; Yokanovich, L.T.; Malo, C.S.; Khadka, R.H.; Tritz, Z.P.; Jin, F.; Hansen, M.J.; et al. Microglia and Perivascular Macrophages Act as Antigen Presenting Cells to Promote CD8 T Cell Infiltration of the Brain. Front. Immunol. 2021, 12, 726421. [Google Scholar] [CrossRef] [PubMed]
  86. Barkauskas, D.S.; Evans, T.A.; Myers, J.; Petrosiute, A.; Silver, J.; Huang, A.Y. Extravascular CX3CR1+ cells extend intravascular dendritic processes into intact central nervous system vessel lumen. Microsc. Microanal. 2013, 19, 778–790. [Google Scholar] [CrossRef]
  87. Junt, T.; Moseman, E.A.; Iannacone, M.; Massberg, S.; Lang, P.A.; Boes, M.; Fink, K.; Henrickson, S.E.; Shayakhmetov, D.M.; Di Paolo, N.C.; et al. Subcapsular sinus macrophages in lymph nodes clear lymph-borne viruses and present them to antiviral B cells. Nature 2007, 450, 110–114. [Google Scholar] [CrossRef]
  88. Zhang, Z.; Zhang, Z.Y.; Schittenhelm, J.; Wu, Y.; Meyermann, R.; Schluesener, H.J. Parenchymal accumulation of CD163+ macrophages/microglia in multiple sclerosis brains. J. Neuroimmunol. 2011, 237, 73–79. [Google Scholar] [CrossRef]
  89. Polfliet, M.M.; van de Veerdonk, F.; Dopp, E.A.; van Kesteren-Hendrikx, E.M.; van Rooijen, N.; Dijkstra, C.D.; van den Berg, T.K. The role of perivascular and meningeal macrophages in experimental allergic encephalomyelitis. J. Neuroimmunol. 2002, 122, 1–8. [Google Scholar] [CrossRef]
  90. Chastain, E.M.; Duncan, D.S.; Rodgers, J.M.; Miller, S.D. The role of antigen presenting cells in multiple sclerosis. Biochim. Biophys. Acta 2011, 1812, 265–274. [Google Scholar] [CrossRef]
  91. Fletcher, J.M.; Lalor, S.J.; Sweeney, C.M.; Tubridy, N.; Mills, K.H. T cells in multiple sclerosis and experimental autoimmune encephalomyelitis. Clin. Exp. Immunol. 2010, 162, 1–11. [Google Scholar] [CrossRef] [PubMed]
  92. Nuyts, A.H.; Lee, W.P.; Bashir-Dar, R.; Berneman, Z.N.; Cools, N. Dendritic cells in multiple sclerosis: Key players in the immunopathogenesis, key players for new cellular immunotherapies? Mult. Scler. 2013, 19, 995–1002. [Google Scholar] [CrossRef] [PubMed]
  93. Hopp, A.K.; Rupp, A.; Lukacs-Kornek, V. Self-antigen presentation by dendritic cells in autoimmunity. Front. Immunol. 2014, 5, 55. [Google Scholar] [CrossRef] [PubMed]
  94. Serafini, B.; Rosicarelli, B.; Magliozzi, R.; Stigliano, E.; Aloisi, F. Detection of ectopic B-cell follicles with germinal centers in the meninges of patients with secondary progressive multiple sclerosis. Brain Pathol. 2004, 14, 164–174. [Google Scholar] [CrossRef] [PubMed]
  95. Pikor, N.B.; Prat, A.; Bar-Or, A.; Gommerman, J.L. Meningeal Tertiary Lymphoid Tissues and Multiple Sclerosis: A Gathering Place for Diverse Types of Immune Cells during CNS Autoimmunity. Front. Immunol. 2015, 6, 657. [Google Scholar] [CrossRef] [PubMed]
  96. Ponomarev, E.D.; Shriver, L.P.; Maresz, K.; Dittel, B.N. Microglial cell activation and proliferation precedes the onset of CNS autoimmunity. J. Neurosci. Res. 2005, 81, 374–389. [Google Scholar] [CrossRef]
  97. Luo, C.; Jian, C.; Liao, Y.; Huang, Q.; Wu, Y.; Liu, X.; Zou, D.; Wu, Y. The role of microglia in multiple sclerosis. Neuropsychiatr. Dis. Treat. 2017, 13, 1661–1667. [Google Scholar] [CrossRef] [PubMed]
  98. Benveniste, E.N. Role of macrophages/microglia in multiple sclerosis and experimental allergic encephalomyelitis. J. Mol. Med. 1997, 75, 165–173. [Google Scholar] [CrossRef] [PubMed]
  99. Neumann, H.; Kotter, M.R.; Franklin, R.J. Debris clearance by microglia: An essential link between degeneration and regeneration. Brain 2009, 132, 288–295. [Google Scholar] [CrossRef]
  100. Smith, K.J.; Kapoor, R.; Felts, P.A. Demyelination: The role of reactive oxygen and nitrogen species. Brain Pathol. 1999, 9, 69–92. [Google Scholar] [CrossRef]
  101. Zeis, T.; Probst, A.; Steck, A.J.; Stadelmann, C.; Bruck, W.; Schaeren-Wiemers, N. Molecular changes in white matter adjacent to an active demyelinating lesion in early multiple sclerosis. Brain Pathol. 2009, 19, 459–466. [Google Scholar] [CrossRef] [PubMed]
  102. Romero-Suarez, S.; Del Rio Serrato, A.; Bueno, R.J.; Brunotte-Strecker, D.; Stehle, C.; Figueiredo, C.A.; Hertwig, L.; Dunay, I.R.; Romagnani, C.; Infante-Duarte, C. The Central Nervous System Contains ILC1s That Differ From NK Cells in the Response to Inflammation. Front. Immunol. 2019, 10, 2337. [Google Scholar] [CrossRef] [PubMed]
  103. Vivier, E.; Artis, D.; Colonna, M.; Diefenbach, A.; Di Santo, J.P.; Eberl, G.; Koyasu, S.; Locksley, R.M.; McKenzie, A.N.J.; Mebius, R.E.; et al. Innate Lymphoid Cells: 10 Years On. Cell 2018, 174, 1054–1066. [Google Scholar] [CrossRef] [PubMed]
  104. Artis, D.; Spits, H. The biology of innate lymphoid cells. Nature 2015, 517, 293–301. [Google Scholar] [CrossRef]
  105. Krabbendam, L.; Bernink, J.H.; Spits, H. Innate lymphoid cells: From helper to killer. Curr. Opin. Immunol. 2021, 68, 28–33. [Google Scholar] [CrossRef] [PubMed]
  106. Grigg, J.B.; Shanmugavadivu, A.; Regen, T.; Parkhurst, C.N.; Ahmed, A.; Joseph, A.M.; Mazzucco, M.; Gronke, K.; Diefenbach, A.; Eberl, G.; et al. Antigen-presenting innate lymphoid cells orchestrate neuroinflammation. Nature 2021, 600, 707–712. [Google Scholar] [CrossRef]
  107. Sadeghi Hassanabadi, N.; Broux, B.; Marinovic, S.; Gotthardt, D. Innate Lymphoid Cells—Neglected Players in Multiple Sclerosis. Front. Immunol. 2022, 13, 909275. [Google Scholar] [CrossRef]
  108. Si, Y.; Zhang, Y.; Zuloaga, K.; Yang, Q. The role of innate lymphocytes in regulating brain and cognitive function. Neurobiol. Dis. 2023, 179, 106061. [Google Scholar] [CrossRef]
  109. Gandhi, R.; Laroni, A.; Weiner, H.L. Role of the innate immune system in the pathogenesis of multiple sclerosis. J. Neuroimmunol. 2010, 221, 7–14. [Google Scholar] [CrossRef]
  110. Strowig, T.; Brilot, F.; Munz, C. Noncytotoxic functions of NK cells: Direct pathogen restriction and assistance to adaptive immunity. J. Immunol. 2008, 180, 7785–7791. [Google Scholar] [CrossRef]
  111. De Jager, P.L.; Rossin, E.; Pyne, S.; Tamayo, P.; Ottoboni, L.; Viglietta, V.; Weiner, M.; Soler, D.; Izmailova, E.; Faron-Yowe, L.; et al. Cytometric profiling in multiple sclerosis uncovers patient population structure and a reduction of CD8low cells. Brain 2008, 131, 1701–1711. [Google Scholar] [CrossRef] [PubMed]
  112. Gross, C.C.; Schulte-Mecklenbeck, A.; Runzi, A.; Kuhlmann, T.; Posevitz-Fejfar, A.; Schwab, N.; Schneider-Hohendorf, T.; Herich, S.; Held, K.; Konjevic, M.; et al. Impaired NK-mediated regulation of T-cell activity in multiple sclerosis is reconstituted by IL-2 receptor modulation. Proc. Natl. Acad. Sci. USA 2016, 113, E2973–E2982. [Google Scholar] [CrossRef] [PubMed]
  113. Vranes, Z.; Poljakovic, Z.; Marusic, M. Natural killer cell number and activity in multiple sclerosis. J. Neurol. Sci. 1989, 94, 115–123. [Google Scholar] [CrossRef] [PubMed]
  114. Lu, L.; Ikizawa, K.; Hu, D.; Werneck, M.B.; Wucherpfennig, K.W.; Cantor, H. Regulation of activated CD4+ T cells by NK cells via the Qa-1-NKG2A inhibitory pathway. Immunity 2007, 26, 593–604. [Google Scholar] [CrossRef] [PubMed]
  115. Jiang, W.; Chai, N.R.; Maric, D.; Bielekova, B. Unexpected role for granzyme K in CD56bright NK cell-mediated immunoregulation of multiple sclerosis. J. Immunol. 2011, 187, 781–790. [Google Scholar] [CrossRef] [PubMed]
  116. Gross, C.C.; Schulte-Mecklenbeck, A.; Wiendl, H.; Marcenaro, E.; Kerlero de Rosbo, N.; Uccelli, A.; Laroni, A. Regulatory Functions of Natural Killer Cells in Multiple Sclerosis. Front. Immunol. 2016, 7, 606. [Google Scholar] [CrossRef] [PubMed]
  117. Hoglund, R.A.; Maghazachi, A.A. Multiple sclerosis and the role of immune cells. World J. Exp. Med. 2014, 4, 27–37. [Google Scholar] [CrossRef] [PubMed]
  118. Yu, D.; Cai, W.; Chen, X.; Lu, D.; Hu, M.; Lu, T.; Qin, B.; Wu, A.; Ruan, H.; Lu, Y.; et al. Natural Killer Cells Disrupt Nerve Fibers by Granzyme H in Atheriosclerotic Cerebral Small Vessel Disease. J. Gerontol. A Biol. Sci. Med. Sci. 2023, 78, 414–423. [Google Scholar] [CrossRef] [PubMed]
  119. Belien, J.; Goris, A.; Matthys, P. Natural Killer Cells in Multiple Sclerosis: Entering the Stage. Front. Immunol. 2022, 13, 869447. [Google Scholar] [CrossRef]
  120. Darlington, P.J.; Podjaski, C.; Horn, K.E.; Costantino, S.; Blain, M.; Saikali, P.; Chen, Z.; Baker, K.A.; Newcombe, J.; Freedman, M.; et al. Innate immune-mediated neuronal injury consequent to loss of astrocytes. J. Neuropathol. Exp. Neurol. 2008, 67, 590–599. [Google Scholar] [CrossRef]
  121. Chanvillard, C.; Jacolik, R.F.; Infante-Duarte, C.; Nayak, R.C. The role of natural killer cells in multiple sclerosis and their therapeutic implications. Front. Immunol. 2013, 4, 63. [Google Scholar] [CrossRef] [PubMed]
  122. Filippi, M.; Rocca, M.A.; De Stefano, N.; Enzinger, C.; Fisher, E.; Horsfield, M.A.; Inglese, M.; Pelletier, D.; Comi, G. Magnetic resonance techniques in multiple sclerosis: The present and the future. Arch. Neurol. 2011, 68, 1514–1520. [Google Scholar] [CrossRef] [PubMed]
  123. Margoni, M.; Pagani, E.; Meani, A.; Storelli, L.; Mesaros, S.; Drulovic, J.; Barkhof, F.; Vrenken, H.; Strijbis, E.; Gallo, A.; et al. Exploring in vivo multiple sclerosis brain microstructural damage through T1w/T2w ratio: A multicentre study. J. Neurol. Neurosurg. Psychiatry 2022, 93, 741–752. [Google Scholar] [CrossRef] [PubMed]
  124. Ek, S.Z.; CakiroGlu, M.; Oz, C.; AralaSmak, A.; Karadel, I.H.; Ozcan, M.E. Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: A magnetic resonance spectroscopy study based on machine learning. Arq. Neuropsiquiatr. 2020, 78, 789–796. [Google Scholar] [CrossRef]
  125. Eklund, A.; Huang-Link, Y.; Kovacsovics, B.; Dahle, C.; Vrethem, M.; Lind, J. OCT and VEP correlate to disability in secondary progressive multiple sclerosis. Mult. Scler. Relat. Disord. 2022, 68, 104255. [Google Scholar] [CrossRef] [PubMed]
  126. Hamzaoui, M.; Garcia, J.; Boffa, G.; Lazzarotto, A.; Absinta, M.; Ricigliano, V.A.G.; Soulier, T.; Tonietto, M.; Gervais, P.; Bissery, A.; et al. Positron Emission Tomography with [(18) F]-DPA-714 Unveils a Smoldering Component in Most Multiple Sclerosis Lesions which Drives Disease Progression. Ann. Neurol. 2023, 94, 366–383. [Google Scholar] [CrossRef] [PubMed]
  127. Absinta, M.; Maric, D.; Gharagozloo, M.; Garton, T.; Smith, M.D.; Jin, J.; Fitzgerald, K.C.; Song, A.; Liu, P.; Lin, J.P.; et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature 2021, 597, 709–714. [Google Scholar] [CrossRef] [PubMed]
  128. Popescu, B.F.; Pirko, I.; Lucchinetti, C.F. Pathology of multiple sclerosis: Where do we stand? Continuum 2013, 19, 901–921. [Google Scholar] [CrossRef] [PubMed]
  129. Konjevic Sabolek, M.; Held, K.; Beltran, E.; Niedl, A.G.; Meinl, E.; Hohlfeld, R.; Lassmann, H.; Dornmair, K. Communication of CD8(+) T cells with mononuclear phagocytes in multiple sclerosis. Ann. Clin. Transl. Neurol. 2019, 6, 1151–1164. [Google Scholar] [CrossRef]
  130. Smolders, J.; Heutinck, K.M.; Fransen, N.L.; Remmerswaal, E.B.M.; Hombrink, P.; Ten Berge, I.J.M.; van Lier, R.A.W.; Huitinga, I.; Hamann, J. Tissue-resident memory T cells populate the human brain. Nat. Commun. 2018, 9, 4593. [Google Scholar] [CrossRef]
  131. Vogel, D.Y.; Vereyken, E.J.; Glim, J.E.; Heijnen, P.D.; Moeton, M.; van der Valk, P.; Amor, S.; Teunissen, C.E.; van Horssen, J.; Dijkstra, C.D. Macrophages in inflammatory multiple sclerosis lesions have an intermediate activation status. J. Neuroinflamm. 2013, 10, 35. [Google Scholar] [CrossRef] [PubMed]
  132. Kim, W.K.; Alvarez, X.; Fisher, J.; Bronfin, B.; Westmoreland, S.; McLaurin, J.; Williams, K. CD163 identifies perivascular macrophages in normal and viral encephalitic brains and potential precursors to perivascular macrophages in blood. Am. J. Pathol. 2006, 168, 822–834. [Google Scholar] [CrossRef] [PubMed]
  133. Lassmann, H. Multiple Sclerosis Pathology. Cold Spring Harb. Perspect. Med. 2018, 8, a028936. [Google Scholar] [CrossRef] [PubMed]
  134. Fransen, N.L.; Hsiao, C.C.; van der Poel, M.; Engelenburg, H.J.; Verdaasdonk, K.; Vincenten, M.C.J.; Remmerswaal, E.B.M.; Kuhlmann, T.; Mason, M.R.J.; Hamann, J.; et al. Tissue-resident memory T cells invade the brain parenchyma in multiple sclerosis white matter lesions. Brain 2020, 143, 1714–1730. [Google Scholar] [CrossRef] [PubMed]
  135. Machado-Santos, J.; Saji, E.; Troscher, A.R.; Paunovic, M.; Liblau, R.; Gabriely, G.; Bien, C.G.; Bauer, J.; Lassmann, H. The compartmentalized inflammatory response in the multiple sclerosis brain is composed of tissue-resident CD8+ T lymphocytes and B cells. Brain 2018, 141, 2066–2082. [Google Scholar] [CrossRef] [PubMed]
  136. Moccia, M.; Haider, L.; Eshaghi, A.; van de Pavert, S.H.P.; Brescia Morra, V.; Patel, A.; Wheeler-Kingshott, C.A.M.; Barkhof, F.; Ciccarelli, O. B Cells in the CNS at Postmortem Are Associated with Worse Outcome and Cell Types in Multiple Sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 2022, 9, e1108. [Google Scholar] [CrossRef] [PubMed]
  137. Magliozzi, R.; Howell, O.; Vora, A.; Serafini, B.; Nicholas, R.; Puopolo, M.; Reynolds, R.; Aloisi, F. Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology. Brain 2007, 130, 1089–1104. [Google Scholar] [CrossRef]
  138. Choi, S.R.; Howell, O.W.; Carassiti, D.; Magliozzi, R.; Gveric, D.; Muraro, P.A.; Nicholas, R.; Roncaroli, F.; Reynolds, R. Meningeal inflammation plays a role in the pathology of primary progressive multiple sclerosis. Brain 2012, 135, 2925–2937. [Google Scholar] [CrossRef]
  139. Silva, B.A.; Miglietta, E.; Ferrari, C.C. Insights into the role of B cells in the cortical pathology of Multiple sclerosis: Evidence from animal models and patients. Mult. Scler. Relat. Disord. 2021, 50, 102845. [Google Scholar] [CrossRef]
  140. Robinson, A.P.; Harp, C.T.; Noronha, A.; Miller, S.D. The experimental autoimmune encephalomyelitis (EAE) model of MS: Utility for understanding disease pathophysiology and treatment. Handb. Clin. Neurol. 2014, 122, 173–189. [Google Scholar] [CrossRef]
  141. Schafflick, D.; Xu, C.A.; Hartlehnert, M.; Cole, M.; Schulte-Mecklenbeck, A.; Lautwein, T.; Wolbert, J.; Heming, M.; Meuth, S.G.; Kuhlmann, T.; et al. Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis. Nat. Commun. 2020, 11, 247. [Google Scholar] [CrossRef] [PubMed]
  142. Nali, L.H.; Olival, G.S.; Sousa, F.T.G.; de Oliveira, A.C.S.; Montenegro, H.; da Silva, I.T.; Dias-Neto, E.; Naya, H.; Spangenberg, L.; Penalva-de-Oliveira, A.C.; et al. Whole transcriptome analysis of multiple Sclerosis patients reveals active inflammatory profile in relapsing patients and downregulation of neurological repair pathways in secondary progressive cases. Mult. Scler. Relat. Disord. 2020, 44, 102243. [Google Scholar] [CrossRef] [PubMed]
  143. Miedema, A.; Gerrits, E.; Brouwer, N.; Jiang, Q.; Kracht, L.; Meijer, M.; Nutma, E.; Peferoen-Baert, R.; Pijnacker, A.T.E.; Wesseling, E.M.; et al. Brain macrophages acquire distinct transcriptomes in multiple sclerosis lesions and normal appearing white matter. Acta Neuropathol. Commun. 2022, 10, 8. [Google Scholar] [CrossRef] [PubMed]
  144. D’Amico, E.; Zanghi, A.; Parrinello, N.L.; Romano, A.; Palumbo, G.A.; Chisari, C.G.; Toscano, S.; Raimondo, F.D.; Zappia, M.; Patti, F. Immunological Subsets Characterization in Newly Diagnosed Relapsing-Remitting Multiple Sclerosis. Front. Immunol. 2022, 13, 819136. [Google Scholar] [CrossRef] [PubMed]
  145. Acquaviva, M.; Bassani, C.; Sarno, N.; Dalla Costa, G.; Romeo, M.; Sangalli, F.; Colombo, B.; Moiola, L.; Martinelli, V.; Comi, G.; et al. Loss of Circulating CD8+ CD161(high) T Cells in Primary Progressive Multiple Sclerosis. Front. Immunol. 2019, 10, 1922. [Google Scholar] [CrossRef] [PubMed]
  146. Blandford, S.N.; Fudge, N.J.; Corkum, C.P.; Moore, C.S. Analysis of Plasma Using Flow Cytometry Reveals Increased Immune Cell-Derived Extracellular Vesicles in Untreated Relapsing-Remitting Multiple Sclerosis. Front. Immunol. 2022, 13, 803921. [Google Scholar] [CrossRef]
  147. Ford, R.K.; Juillard, P.; Hawke, S.; Grau, G.E.; Marsh-Wakefield, F. Cladribine Reduces Trans-Endothelial Migration of Memory T Cells across an In Vitro Blood-Brain Barrier. J. Clin. Med. 2022, 11, 6006. [Google Scholar] [CrossRef] [PubMed]
  148. Lin, L.Y.; Juillard, P.; Hawke, S.; Marsh-Wakefield, F.; Grau, G.E. Oral Cladribine Impairs Intermediate, but Not Conventional, Monocyte Transmigration in Multiple Sclerosis Patients across a Model Blood-Brain Barrier. Int. J. Mol. Sci. 2023, 24, 6487. [Google Scholar] [CrossRef]
  149. Marsh-Wakefield, F.M.; Mitchell, A.J.; Norton, S.E.; Ashhurst, T.M.; Leman, J.K.; Roberts, J.M.; Harte, J.E.; McGuire, H.M.; Kemp, R.A. Making the most of high-dimensional cytometry data. Immunol. Cell Biol. 2021, 99, 680–696. [Google Scholar] [CrossRef]
  150. Fernandez-Zapata, C.; Leman, J.K.H.; Priller, J.; Bottcher, C. The use and limitations of single-cell mass cytometry for studying human microglia function. Brain Pathol. 2020, 30, 1178–1191. [Google Scholar] [CrossRef]
  151. Bottcher, C.; van der Poel, M.; Fernandez-Zapata, C.; Schlickeiser, S.; Leman, J.K.H.; Hsiao, C.C.; Mizee, M.R.; Adelia; Vincenten, M.C.J.; Kunkel, D.; et al. Single-cell mass cytometry reveals complex myeloid cell composition in active lesions of progressive multiple sclerosis. Acta Neuropathol. Commun. 2020, 8, 136. [Google Scholar] [CrossRef] [PubMed]
  152. Marsh-Wakefield, F.; Ashhurst, T.; Trend, S.; McGuire, H.M.; Juillard, P.; Zinger, A.; Jones, A.P.; Kermode, A.G.; Hawke, S.; Grau, G.E.; et al. IgG(3) (+) B cells are associated with the development of multiple sclerosis. Clin. Transl. Immunol. 2020, 9, e01133. [Google Scholar] [CrossRef] [PubMed]
  153. Couloume, L.; Ferrant, J.; Le Gallou, S.; Mandon, M.; Jean, R.; Bescher, N.; Zephir, H.; Edan, G.; Thouvenot, E.; Ruet, A.; et al. Mass Cytometry Identifies Expansion of T-bet(+) B Cells and CD206(+) Monocytes in Early Multiple Sclerosis. Front. Immunol. 2021, 12, 653577. [Google Scholar] [CrossRef] [PubMed]
  154. Ramaglia, V.; Sheikh-Mohamed, S.; Legg, K.; Park, C.; Rojas, O.L.; Zandee, S.; Fu, F.; Ornatsky, O.; Swanson, E.C.; Pitt, D.; et al. Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry. Elife 2019, 8, e48051. [Google Scholar] [CrossRef] [PubMed]
  155. Bierhansl, L.; Hartung, H.P.; Aktas, O.; Ruck, T.; Roden, M.; Meuth, S.G. Thinking outside the box: Non-canonical targets in multiple sclerosis. Nat. Rev. Drug Discov. 2022, 21, 578–600. [Google Scholar] [CrossRef] [PubMed]
  156. Filippi, M.; Amato, M.P.; Centonze, D.; Gallo, P.; Gasperini, C.; Inglese, M.; Patti, F.; Pozzilli, C.; Preziosa, P.; Trojano, M. Early use of high-efficacy disease—Modifying therapies makes the difference in people with multiple sclerosis: An expert opinion. J. Neurol. 2022, 269, 5382–5394. [Google Scholar] [CrossRef] [PubMed]
  157. Wingerchuk, D.M.; Carter, J.L. Multiple sclerosis: Current and emerging disease-modifying therapies and treatment strategies. Mayo Clin. Proc. 2014, 89, 225–240. [Google Scholar] [CrossRef]
  158. Bossart, J.; Kamm, C.P.; Kaufmann, M.; Stanikic, M.; Puhan, M.A.; Kesselring, J.; Zecca, C.; Gobbi, C.; Rapold, I.; Kurmann, R.; et al. Real-world disease-modifying therapy usage in persons with relapsing-remitting multiple sclerosis: Cross-sectional data from the Swiss Multiple Sclerosis Registry. Mult. Scler. Relat. Disord. 2022, 60, 103706. [Google Scholar] [CrossRef]
  159. Biotti, D.; Ciron, J. First-line therapy in relapsing remitting multiple sclerosis. Rev. Neurol. 2018, 174, 419–428. [Google Scholar] [CrossRef]
  160. Paty, D.W.; Li, D.K. Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial. UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group. Neurology 1993, 43, 662–667. [Google Scholar] [CrossRef]
  161. Jakimovski, D.; Kolb, C.; Ramanathan, M.; Zivadinov, R.; Weinstock-Guttman, B. Interferon beta for Multiple Sclerosis. Cold Spring Harb. Perspect. Med. 2018, 8, a032003. [Google Scholar] [CrossRef] [PubMed]
  162. Jacobs, L.D.; Beck, R.W.; Simon, J.H.; Kinkel, R.P.; Brownscheidle, C.M.; Murray, T.J.; Simonian, N.A.; Slasor, P.J.; Sandrock, A.W. Intramuscular interferon beta-1a therapy initiated during a first demyelinating event in multiple sclerosis. CHAMPS Study Group. N. Engl. J. Med. 2000, 343, 898–904. [Google Scholar] [CrossRef] [PubMed]
  163. Kappos, L.; Polman, C.H.; Freedman, M.S.; Edan, G.; Hartung, H.P.; Miller, D.H.; Montalban, X.; Barkhof, F.; Bauer, L.; Jakobs, P.; et al. Treatment with interferon beta-1b delays conversion to clinically definite and McDonald MS in patients with clinically isolated syndromes. Neurology 2006, 67, 1242–1249. [Google Scholar] [CrossRef] [PubMed]
  164. Comi, G.; Filippi, M.; Barkhof, F.; Durelli, L.; Edan, G.; Fernandez, O.; Hartung, H.; Seeldrayers, P.; Sorensen, P.S.; Rovaris, M.; et al. Effect of early interferon treatment on conversion to definite multiple sclerosis: A randomised study. Lancet 2001, 357, 1576–1582. [Google Scholar] [CrossRef] [PubMed]
  165. PRISMS (Prevention of Relapses and Disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis) Study Group. Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. Lancet 1998, 352, 1498–1504. [Google Scholar] [CrossRef]
  166. Prod’homme, T.; Zamvil, S.S. The Evolving Mechanisms of Action of Glatiramer Acetate. Cold Spring Harb. Perspect. Med. 2019, 9, a029249. [Google Scholar] [CrossRef] [PubMed]
  167. Edinger, A.; Habibi, M. The evolution of multiple sclerosis disease-modifying therapies: An update for pharmacists. Am. J. Health Syst. Pharm. 2024, 81, 37–55. [Google Scholar] [CrossRef] [PubMed]
  168. Johnson, K.P.; Brooks, B.R.; Cohen, J.A.; Ford, C.C.; Goldstein, J.; Lisak, R.P.; Myers, L.W.; Panitch, H.S.; Rose, J.W.; Schiffer, R.B. Copolymer 1 reduces relapse rate and improves disability in relapsing-remitting multiple sclerosis: Results of a phase III multicenter, double-blind placebo-controlled trial. The Copolymer 1 Multiple Sclerosis Study Group. Neurology 1995, 45, 1268–1276. [Google Scholar] [CrossRef]
  169. Yadav, S.K.; Soin, D.; Ito, K.; Dhib-Jalbut, S. Insight into the mechanism of action of dimethyl fumarate in multiple sclerosis. J. Mol. Med. 2019, 97, 463–472. [Google Scholar] [CrossRef]
  170. Fox, R.J.; Miller, D.H.; Phillips, J.T.; Hutchinson, M.; Havrdova, E.; Kita, M.; Yang, M.; Raghupathi, K.; Novas, M.; Sweetser, M.T.; et al. Placebo-controlled phase 3 study of oral BG-12 or glatiramer in multiple sclerosis. N. Engl. J. Med. 2012, 367, 1087–1097. [Google Scholar] [CrossRef]
  171. Kosmas, C.; Stamatopoulos, K.; Stavroyianni, N.; Tsavaris, N.; Papadaki, T. Anti-CD20-based therapy of B cell lymphoma: State of the art. Leukemia 2002, 16, 2004–2015. [Google Scholar] [CrossRef] [PubMed]
  172. Cragg, M.S.; Morgan, S.M.; Chan, H.T.; Morgan, B.P.; Filatov, A.V.; Johnson, P.W.; French, R.R.; Glennie, M.J. Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts. Blood 2003, 101, 1045–1052. [Google Scholar] [CrossRef] [PubMed]
  173. Mulero, P.; Midaglia, L.; Montalban, X. Ocrelizumab: A new milestone in multiple sclerosis therapy. Ther. Adv. Neurol. Disord. 2018, 11, 1756286418773025. [Google Scholar] [CrossRef] [PubMed]
  174. Sorensen, P.S.; Lisby, S.; Grove, R.; Derosier, F.; Shackelford, S.; Havrdova, E.; Drulovic, J.; Filippi, M. Safety and efficacy of ofatumumab in relapsing-remitting multiple sclerosis: A phase 2 study. Neurology 2014, 82, 573–581. [Google Scholar] [CrossRef] [PubMed]
  175. Bar-Or, A.; O’Brien, S.M.; Sweeney, M.L.; Fox, E.J.; Cohen, J.A. Clinical Perspectives on the Molecular and Pharmacological Attributes of Anti-CD20 Therapies for Multiple Sclerosis. CNS Drugs 2021, 35, 985–997. [Google Scholar] [CrossRef] [PubMed]
  176. Hauser, S.L.; Bar-Or, A.; Cohen, J.A.; Comi, G.; Correale, J.; Coyle, P.K.; Cross, A.H.; de Seze, J.; Leppert, D.; Montalban, X.; et al. Ofatumumab versus Teriflunomide in Multiple Sclerosis. N. Engl. J. Med. 2020, 383, 546–557. [Google Scholar] [CrossRef] [PubMed]
  177. Bar-Or, A.; Grove, R.A.; Austin, D.J.; Tolson, J.M.; VanMeter, S.A.; Lewis, E.W.; Derosier, F.J.; Lopez, M.C.; Kavanagh, S.T.; Miller, A.E.; et al. Subcutaneous ofatumumab in patients with relapsing-remitting multiple sclerosis: The MIRROR study. Neurology 2018, 90, e1805–e1814. [Google Scholar] [CrossRef] [PubMed]
  178. Yu, H.; Graham, G.; David, O.J.; Kahn, J.M.; Savelieva, M.; Pigeolet, E.; Das Gupta, A.; Pingili, R.; Willi, R.; Ramanathan, K.; et al. Population Pharmacokinetic-B Cell Modeling for Ofatumumab in Patients with Relapsing Multiple Sclerosis. CNS Drugs 2022, 36, 283–300. [Google Scholar] [CrossRef] [PubMed]
  179. Hauser, S.L.; Cross, A.H.; Winthrop, K.; Wiendl, H.; Nicholas, J.; Meuth, S.G.; Giacomini, P.S.; Sacca, F.; Mancione, L.; Zielman, R.; et al. Safety experience with continued exposure to ofatumumab in patients with relapsing forms of multiple sclerosis for up to 3.5 years. Mult. Scler. 2022, 28, 1576–1590. [Google Scholar] [CrossRef]
  180. Hauser, S.L.; Kappos, L.; Bar-Or, A.; Wiendl, H.; Paling, D.; Williams, M.; Gold, R.; Chan, A.; Milo, R.; Das Gupta, A.; et al. The Development of Ofatumumab, a Fully Human Anti-CD20 Monoclonal Antibody for Practical Use in Relapsing Multiple Sclerosis Treatment. Neurol. Ther. 2023, 12, 1491–1515. [Google Scholar] [CrossRef]
  181. Rao, S.P.; Sancho, J.; Campos-Rivera, J.; Boutin, P.M.; Severy, P.B.; Weeden, T.; Shankara, S.; Roberts, B.L.; Kaplan, J.M. Human peripheral blood mononuclear cells exhibit heterogeneous CD52 expression levels and show differential sensitivity to alemtuzumab mediated cytolysis. PLoS ONE 2012, 7, e39416. [Google Scholar] [CrossRef] [PubMed]
  182. Hu, Y.; Turner, M.J.; Shields, J.; Gale, M.S.; Hutto, E.; Roberts, B.L.; Siders, W.M.; Kaplan, J.M. Investigation of the mechanism of action of alemtuzumab in a human CD52 transgenic mouse model. Immunology 2009, 128, 260–270. [Google Scholar] [CrossRef] [PubMed]
  183. Coles, A.J.; Fox, E.; Vladic, A.; Gazda, S.K.; Brinar, V.; Selmaj, K.W.; Bass, A.D.; Wynn, D.R.; Margolin, D.H.; Lake, S.L.; et al. Alemtuzumab versus interferon beta-1a in early relapsing-remitting multiple sclerosis: Post-hoc and subset analyses of clinical efficacy outcomes. Lancet Neurol. 2011, 10, 338–348. [Google Scholar] [CrossRef] [PubMed]
  184. Steingo, B.; Al Malik, Y.; Bass, A.D.; Berkovich, R.; Carraro, M.; Fernandez, O.; Ionete, C.; Massacesi, L.; Meuth, S.G.; Mitsikostas, D.D.; et al. Long-term efficacy and safety of alemtuzumab in patients with RRMS: 12-year follow-up of CAMMS223. J. Neurol. 2020, 267, 3343–3353. [Google Scholar] [CrossRef] [PubMed]
  185. Marsh-Wakefield, F.; Juillard, P.; Ashhurst, T.M.; Juillard, A.; Shinko, D.; Putri, G.H.; Read, M.N.; McGuire, H.M.; Byrne, S.N.; Hawke, S.; et al. Peripheral B-cell dysregulation is associated with relapse after long-term quiescence in patients with multiple sclerosis. Immunol. Cell Biol. 2022, 100, 453–467. [Google Scholar] [CrossRef] [PubMed]
  186. Aglas-Leitner, F.; Juillard, P.; Juillard, A.; Byrne, S.N.; Hawke, S.; Grau, G.E.; Marsh-Wakefield, F. Circulating CCR6(+)ILC proportions are lower in multiple sclerosis patients. Clin. Transl. Immunol. 2022, 11, e1426. [Google Scholar] [CrossRef] [PubMed]
  187. Nguyen, K.; Juillard, P.; Hawke, S.; Grau, G.E.; Marsh-Wakefield, F. Trans-Endothelial Migration of Memory T Cells Is Impaired in Alemtuzumab-Treated Multiple Sclerosis Patients. J. Clin. Med. 2022, 11, 6266. [Google Scholar] [CrossRef] [PubMed]
  188. Coles, A.J.; Wing, M.; Smith, S.; Coraddu, F.; Greer, S.; Taylor, C.; Weetman, A.; Hale, G.; Chatterjee, V.K.; Waldmann, H.; et al. Pulsed monoclonal antibody treatment and autoimmune thyroid disease in multiple sclerosis. Lancet 1999, 354, 1691–1695. [Google Scholar] [CrossRef] [PubMed]
  189. Cohen, J.A.; Coles, A.J.; Arnold, D.L.; Confavreux, C.; Fox, E.J.; Hartung, H.P.; Havrdova, E.; Selmaj, K.W.; Weiner, H.L.; Fisher, E.; et al. Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: A randomised controlled phase 3 trial. Lancet 2012, 380, 1819–1828. [Google Scholar] [CrossRef]
  190. Coles, A.J.; Twyman, C.L.; Arnold, D.L.; Cohen, J.A.; Confavreux, C.; Fox, E.J.; Hartung, H.P.; Havrdova, E.; Selmaj, K.W.; Weiner, H.L.; et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: A randomised controlled phase 3 trial. Lancet 2012, 380, 1829–1839. [Google Scholar] [CrossRef]
  191. Meuth, S.G.; Ruck, T.; Aktas, O.; Hartung, H.P. [Cladribine tablets: Oral immunotherapy of relapsing-remitting multiple sclerosis with short yearly treatment periods]. Nervenarzt 2018, 89, 895–907. [Google Scholar] [CrossRef] [PubMed]
  192. Gregson, A.; Thompson, K.; Tsirka, S.E.; Selwood, D.L. Emerging small-molecule treatments for multiple sclerosis: Focus on B cells. F1000Res 2019, 8, F1000 Faculty Rev-245. [Google Scholar] [CrossRef] [PubMed]
  193. Giovannoni, G.; Comi, G.; Cook, S.; Rammohan, K.; Rieckmann, P.; Soelberg Sorensen, P.; Vermersch, P.; Chang, P.; Hamlett, A.; Musch, B.; et al. A placebo-controlled trial of oral cladribine for relapsing multiple sclerosis. N. Engl. J. Med. 2010, 362, 416–426. [Google Scholar] [CrossRef] [PubMed]
  194. Giovannoni, G.; Soelberg Sorensen, P.; Cook, S.; Rammohan, K.; Rieckmann, P.; Comi, G.; Dangond, F.; Adeniji, A.K.; Vermersch, P. Safety and efficacy of cladribine tablets in patients with relapsing-remitting multiple sclerosis: Results from the randomized extension trial of the CLARITY study. Mult. Scler. 2018, 24, 1594–1604. [Google Scholar] [CrossRef] [PubMed]
  195. Sorensen, P.S.; Pontieri, L.; Joensen, H.; Heick, A.; Rasmussen, P.V.; Schafer, J.; Ratzer, R.; Pihl, C.E.; Sellebjerg, F.; Magyari, M. Real-world experience of cladribine treatment in relapsing-remitting multiple sclerosis: A Danish nationwide study. Mult. Scler. Relat. Disord. 2023, 70, 104491. [Google Scholar] [CrossRef] [PubMed]
  196. de Stefano, N.; Barkhof, F.; Montalban, X.; Achiron, A.; Derfuss, T.; Chan, A.; Hodgkinson, S.; Prat, A.; Leocani, L.; Schmierer, K.; et al. Early Reduction of MRI Activity during 6 Months of Treatment with Cladribine Tablets for Highly Active Relapsing Multiple Sclerosis: MAGNIFY-MS. Neurol. Neuroimmunol. Neuroinflamm. 2022, 9, e1187. [Google Scholar] [CrossRef] [PubMed]
  197. Aglas-Leitner, F.T.; Juillard, P.; Juillard, A.; Byrne, S.N.; Hawke, S.; Grau, G.E.; Marsh-Wakefield, F. Mass cytometry reveals cladribine-induced resets among innate lymphoid cells in multiple sclerosis. Sci. Rep. 2022, 12, 20411. [Google Scholar] [CrossRef]
  198. Lunemann, J.D.; Ruck, T.; Muraro, P.A.; Bar-Or, A.; Wiendl, H. Immune reconstitution therapies: Concepts for durable remission in multiple sclerosis. Nat. Rev. Neurol. 2020, 16, 56–62. [Google Scholar] [CrossRef]
  199. Vermersch, P.; Oh, J.; Cascione, M.; Oreja-Guevara, C.; Gobbi, C.; Travis, L.H.; Myhr, K.M.; Coyle, P.K. Teriflunomide vs injectable disease modifying therapies for relapsing forms of MS. Mult. Scler. Relat. Disord. 2020, 43, 102158. [Google Scholar] [CrossRef]
  200. O’Connor, P.; Wolinsky, J.S.; Confavreux, C.; Comi, G.; Kappos, L.; Olsson, T.P.; Benzerdjeb, H.; Truffinet, P.; Wang, L.; Miller, A.; et al. Randomized trial of oral teriflunomide for relapsing multiple sclerosis. N. Engl. J. Med. 2011, 365, 1293–1303. [Google Scholar] [CrossRef]
  201. Milosevic, N.; Rutter, M.; David, A. Endothelial Cell Adhesion Molecules- (un)Attainable Targets for Nanomedicines. Front. Med. Technol. 2022, 4, 846065. [Google Scholar] [CrossRef] [PubMed]
  202. Zohren, F.; Toutzaris, D.; Klarner, V.; Hartung, H.P.; Kieseier, B.; Haas, R. The monoclonal anti-VLA-4 antibody natalizumab mobilizes CD34+ hematopoietic progenitor cells in humans. Blood 2008, 111, 3893–3895. [Google Scholar] [CrossRef] [PubMed]
  203. Hutchinson, M. Natalizumab: A new treatment for relapsing remitting multiple sclerosis. Ther. Clin. Risk Manag. 2007, 3, 259–268. [Google Scholar] [CrossRef] [PubMed]
  204. Polman, C.H.; O’Connor, P.W.; Havrdova, E.; Hutchinson, M.; Kappos, L.; Miller, D.H.; Phillips, J.T.; Lublin, F.D.; Giovannoni, G.; Wajgt, A.; et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N. Engl. J. Med. 2006, 354, 899–910. [Google Scholar] [CrossRef] [PubMed]
  205. Nakamura, K.; Sun, Z.; Hara-Cleaver, C.; Bodhinathan, K.; Avila, R.L. Natalizumab reduces loss of gray matter and thalamic volume in patients with relapsing-remitting multiple sclerosis: A post hoc analysis from the randomized, placebo-controlled AFFIRM trial. Mult. Scler. 2024, 30, 687–695. [Google Scholar] [CrossRef] [PubMed]
  206. Boziki, M.; Bakirtzis, C.; Giantzi, V.; Sintila, S.A.; Kallivoulos, S.; Afrantou, T.; Nikolaidis, I.; Ioannidis, P.; Karapanayiotides, T.; Koutroulou, I.; et al. Long-Term Efficacy Outcomes of Natalizumab vs. Fingolimod in Patients With Highly Active Relapsing-Remitting Multiple Sclerosis: Real-World Data From a Multiple Sclerosis Reference Center. Front. Neurol. 2021, 12, 699844. [Google Scholar] [CrossRef] [PubMed]
  207. Butzkueven, H.; Kappos, L.; Wiendl, H.; Trojano, M.; Spelman, T.; Chang, I.; Kasliwal, R.; Jaitly, S.; Campbell, N.; Ho, P.R.; et al. Long-term safety and effectiveness of natalizumab treatment in clinical practice: 10 years of real-world data from the Tysabri Observational Program (TOP). J. Neurol. Neurosurg. Psychiatry 2020, 91, 660–668. [Google Scholar] [CrossRef] [PubMed]
  208. Berger, J.R.; Koralnik, I.J. Progressive multifocal leukoencephalopathy and natalizumab--unforeseen consequences. N. Engl. J. Med. 2005, 353, 414–416. [Google Scholar] [CrossRef]
  209. Pham, T.H.; Okada, T.; Matloubian, M.; Lo, C.G.; Cyster, J.G. S1P1 receptor signaling overrides retention mediated by G alpha i-coupled receptors to promote T cell egress. Immunity 2008, 28, 122–133. [Google Scholar] [CrossRef]
  210. Uzunkopru, C.; Beckmann, Y.; Ture, S. Long-Term Effectiveness of Fingolimod for Multiple Sclerosis in a Real-World Clinical Setting. Eur. Neurol. 2021, 84, 200–205. [Google Scholar] [CrossRef]
  211. Kappos, L.; Radue, E.W.; O’Connor, P.; Polman, C.; Hohlfeld, R.; Calabresi, P.; Selmaj, K.; Agoropoulou, C.; Leyk, M.; Zhang-Auberson, L.; et al. A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N. Engl. J. Med. 2010, 362, 387–401. [Google Scholar] [CrossRef] [PubMed]
  212. Calabresi, P.A.; Radue, E.W.; Goodin, D.; Jeffery, D.; Rammohan, K.W.; Reder, A.T.; Vollmer, T.; Agius, M.A.; Kappos, L.; Stites, T.; et al. Safety and efficacy of fingolimod in patients with relapsing-remitting multiple sclerosis (FREEDOMS II): A double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Neurol. 2014, 13, 545–556. [Google Scholar] [CrossRef] [PubMed]
  213. Cohen, J.A.; Barkhof, F.; Comi, G.; Hartung, H.P.; Khatri, B.O.; Montalban, X.; Pelletier, J.; Capra, R.; Gallo, P.; Izquierdo, G.; et al. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N. Engl. J. Med. 2010, 362, 402–415. [Google Scholar] [CrossRef] [PubMed]
  214. Chun, J.; Giovannoni, G.; Hunter, S.F. Sphingosine 1-phosphate Receptor Modulator Therapy for Multiple Sclerosis: Differential Downstream Receptor Signalling and Clinical Profile Effects. Drugs 2021, 81, 207–231. [Google Scholar] [CrossRef] [PubMed]
  215. Michel, L.; Grasmuck, C.; Charabati, M.; Lecuyer, M.A.; Zandee, S.; Dhaeze, T.; Alvarez, J.I.; Li, R.; Larouche, S.; Bourbonniere, L.; et al. Activated leukocyte cell adhesion molecule regulates B lymphocyte migration across central nervous system barriers. Sci. Transl. Med. 2019, 11, eaaw0475. [Google Scholar] [CrossRef] [PubMed]
  216. Clemente, N.; Comi, C.; Raineri, D.; Cappellano, G.; Vecchio, D.; Orilieri, E.; Gigliotti, C.L.; Boggio, E.; Dianzani, C.; Sorosina, M.; et al. Role of Anti-Osteopontin Antibodies in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis. Front. Immunol. 2017, 8, 321. [Google Scholar] [CrossRef] [PubMed]
  217. Garg, N.; Padron, E.J.; Rammohan, K.W.; Goodman, C.F. Bruton’s Tyrosine Kinase Inhibitors: The Next Frontier of B-Cell-Targeted Therapies for Cancer, Autoimmune Disorders, and Multiple Sclerosis. J. Clin. Med. 2022, 11, 6139. [Google Scholar] [CrossRef]
  218. Kramer, J.; Bar-Or, A.; Turner, T.J.; Wiendl, H. Bruton tyrosine kinase inhibitors for multiple sclerosis. Nat. Rev. Neurol. 2023, 19, 289–304. [Google Scholar] [CrossRef] [PubMed]
  219. Li, R.; Tang, H.; Burns, J.C.; Hopkins, B.T.; Le Coz, C.; Zhang, B.; de Barcelos, I.P.; Romberg, N.; Goldstein, A.C.; Banwell, B.L.; et al. BTK inhibition limits B-cell-T-cell interaction through modulation of B-cell metabolism: Implications for multiple sclerosis therapy. Acta Neuropathol. 2022, 143, 505–521. [Google Scholar] [CrossRef]
  220. Xia, S.; Liu, X.; Cao, X.; Xu, S. T-cell expression of Bruton’s tyrosine kinase promotes autoreactive T-cell activation and exacerbates aplastic anemia. Cell Mol. Immunol. 2020, 17, 1042–1052. [Google Scholar] [CrossRef]
  221. Touil, H.; Li, R.; Zuroff, L.; Moore, C.S.; Healy, L.; Cignarella, F.; Piccio, L.; Ludwin, S.; Prat, A.; Gommerman, J.; et al. Cross-talk between B cells, microglia and macrophages, and implications to central nervous system compartmentalized inflammation and progressive multiple sclerosis. EBioMedicine 2023, 96, 104789. [Google Scholar] [CrossRef] [PubMed]
  222. Neys, S.F.H.; Hendriks, R.W.; Corneth, O.B.J. Targeting Bruton’s Tyrosine Kinase in Inflammatory and Autoimmune Pathologies. Front. Cell Dev. Biol. 2021, 9, 668131. [Google Scholar] [CrossRef] [PubMed]
  223. Rijvers, L.; van Langelaar, J.; Bogers, L.; Melief, M.J.; Koetzier, S.C.; Blok, K.M.; Wierenga-Wolf, A.F.; de Vries, H.E.; Rip, J.; Corneth, O.B.; et al. Human T-bet+ B cell development is associated with BTK activity and suppressed by evobrutinib. JCI Insight 2022, 7, e160909. [Google Scholar] [CrossRef] [PubMed]
  224. Elkjaer, M.L.; Waede, M.R.; Kingo, C.; Damsbo, K.; Illes, Z. Expression of Bruton s tyrosine kinase in different type of brain lesions of multiple sclerosis patients and during experimental demyelination. Front. Immunol. 2023, 14, 1264128. [Google Scholar] [CrossRef] [PubMed]
  225. Zain, R.; Vihinen, M. Structure-Function Relationships of Covalent and Non-Covalent BTK Inhibitors. Front. Immunol. 2021, 12, 694853. [Google Scholar] [CrossRef] [PubMed]
  226. Amin, M.; Hersh, C.M. Updates and advances in multiple sclerosis neurotherapeutics. Neurodegener. Dis. Manag. 2023, 13, 47–70. [Google Scholar] [CrossRef] [PubMed]
  227. Liu, J.; Chen, C.; Wang, D.; Zhang, J.; Zhang, T. Emerging small-molecule inhibitors of the Bruton’s tyrosine kinase (BTK): Current development. Eur. J. Med. Chem. 2021, 217, 113329. [Google Scholar] [CrossRef] [PubMed]
  228. Angst, D.; Gessier, F.; Janser, P.; Vulpetti, A.; Walchli, R.; Beerli, C.; Littlewood-Evans, A.; Dawson, J.; Nuesslein-Hildesheim, B.; Wieczorek, G.; et al. Discovery of LOU064 (Remibrutinib), a Potent and Highly Selective Covalent Inhibitor of Bruton’s Tyrosine Kinase. J. Med. Chem. 2020, 63, 5102–5118. [Google Scholar] [CrossRef] [PubMed]
  229. Montalban, X.; Arnold, D.L.; Weber, M.S.; Staikov, I.; Piasecka-Stryczynska, K.; Willmer, J.; Martin, E.C.; Dangond, F.; Syed, S.; Wolinsky, J.S.; et al. Placebo-Controlled Trial of an Oral BTK Inhibitor in Multiple Sclerosis. N. Engl. J. Med. 2019, 380, 2406–2417. [Google Scholar] [CrossRef]
  230. Arnold, D.L.; Elliott, C.; Martin, E.C.; Hyvert, Y.; Tomic, D.; Montalban, X. Effect of Evobrutinib on Slowly Expanding Lesion Volume in Relapsing Multiple Sclerosis: A Post Hoc Analysis of a Phase 2 Trial. Neurology 2024, 102, e208058. [Google Scholar] [CrossRef]
  231. Reich, D.S.; Arnold, D.L.; Vermersch, P.; Bar-Or, A.; Fox, R.J.; Matta, A.; Turner, T.; Wallstrom, E.; Zhang, X.; Mares, M.; et al. Safety and efficacy of tolebrutinib, an oral brain-penetrant BTK inhibitor, in relapsing multiple sclerosis: A phase 2b, randomised, double-blind, placebo-controlled trial. Lancet Neurol. 2021, 20, 729–738. [Google Scholar] [CrossRef] [PubMed]
  232. Bross, M.; Hackett, M.; Bernitsas, E. Approved and Emerging Disease Modifying Therapies on Neurodegeneration in Multiple Sclerosis. Int. J. Mol. Sci. 2020, 21, 4312. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Transmigration of immune cells across the blood–brain barrier in multiple sclerosis. The blood vessel wall is lined with endothelial cells and supported by pericytes and astrocytes to form the blood–brain barrier. In multiple sclerosis, the blood–brain barrier is compromised and permits the migration of monocytes, T cells, and B cells across the wall into the central nervous system. These immune cells form perivascular cuffs and B-cell follicles in the brain observed in acute and chronic multiple sclerosis, respectively. Abbreviations: EC: endothelial cell; P: pericyte; A: astrocyte; Mo: monocyte; FDC: follicular dendritic cell.
Figure 1. Transmigration of immune cells across the blood–brain barrier in multiple sclerosis. The blood vessel wall is lined with endothelial cells and supported by pericytes and astrocytes to form the blood–brain barrier. In multiple sclerosis, the blood–brain barrier is compromised and permits the migration of monocytes, T cells, and B cells across the wall into the central nervous system. These immune cells form perivascular cuffs and B-cell follicles in the brain observed in acute and chronic multiple sclerosis, respectively. Abbreviations: EC: endothelial cell; P: pericyte; A: astrocyte; Mo: monocyte; FDC: follicular dendritic cell.
Sclerosis 02 00009 g001
Figure 2. The involvement of perivascular macrophages and follicular dendritic cells in multiple sclerosis. Perivascular macrophages reside within the perivascular space around blood vessels, acting as antigen-presenting cells. These cells can extend their processes through the endothelium to interact with T cells in the blood vessel lumen and promote transmigration. Follicular dendritic cells also present similar functions by acting as antigen-presenting cells to leukocytes in the brain. These immune cells in the brain promote autoimmune responses, leading to axonal damage. Abbreviations: EC: endothelial cell; P: pericyte; A: astrocyte; PVM: perivascular macrophage; Mo: monocyte; FDC: follicular dendritic cell; M: microglia; VCAM-1: vascular cell adhesion molecule-1; MHC: major histocompatibility complex; TREM-2: triggering receptor expressed on myeloid cells 2.
Figure 2. The involvement of perivascular macrophages and follicular dendritic cells in multiple sclerosis. Perivascular macrophages reside within the perivascular space around blood vessels, acting as antigen-presenting cells. These cells can extend their processes through the endothelium to interact with T cells in the blood vessel lumen and promote transmigration. Follicular dendritic cells also present similar functions by acting as antigen-presenting cells to leukocytes in the brain. These immune cells in the brain promote autoimmune responses, leading to axonal damage. Abbreviations: EC: endothelial cell; P: pericyte; A: astrocyte; PVM: perivascular macrophage; Mo: monocyte; FDC: follicular dendritic cell; M: microglia; VCAM-1: vascular cell adhesion molecule-1; MHC: major histocompatibility complex; TREM-2: triggering receptor expressed on myeloid cells 2.
Sclerosis 02 00009 g002
Figure 3. Targets for disease-modifying therapies in multiple sclerosis. Immunomodulatory therapies like interferon-β, glatiramer acetate, and dimethyl fumarate target immune cells to reduce inflammatory responses in disease. Leukocyte depletion and cytolysis are induced by anti-CD20 therapies such as ofatumumab and ocrelizumab, anti-CD52 by alemtuzumab, and small molecule drugs such as cladribine and teriflunomide. Leukocyte transmigration into the CNS is inhibited by the anti-VLA-4 antibody natalizumab, and cell emigration from lymph nodes is prevented by sphingosine-1-phosphate receptor modulator fingolimod. An emerging target for multiple sclerosis treatment is Bruton’s tyrosine kinase inhibitors, which include ibrutinib, evobrutinib, tolebrutinib, and remibrutinib. These BTK inhibitors block BTK activity in leukocytes and microglia, preventing inflammation. Additional markers such as ALCAM-1 and osteopontin are possible targets for treatment. Abbreviations: EC: endothelial cell; P: pericyte; S1PR: sphingosine-1-phosphate receptor; VCAM-1: vascular cell adhesion molecule-1; BTK: Bruton’s tyrosine kinase; OPN: osteopontin.
Figure 3. Targets for disease-modifying therapies in multiple sclerosis. Immunomodulatory therapies like interferon-β, glatiramer acetate, and dimethyl fumarate target immune cells to reduce inflammatory responses in disease. Leukocyte depletion and cytolysis are induced by anti-CD20 therapies such as ofatumumab and ocrelizumab, anti-CD52 by alemtuzumab, and small molecule drugs such as cladribine and teriflunomide. Leukocyte transmigration into the CNS is inhibited by the anti-VLA-4 antibody natalizumab, and cell emigration from lymph nodes is prevented by sphingosine-1-phosphate receptor modulator fingolimod. An emerging target for multiple sclerosis treatment is Bruton’s tyrosine kinase inhibitors, which include ibrutinib, evobrutinib, tolebrutinib, and remibrutinib. These BTK inhibitors block BTK activity in leukocytes and microglia, preventing inflammation. Additional markers such as ALCAM-1 and osteopontin are possible targets for treatment. Abbreviations: EC: endothelial cell; P: pericyte; S1PR: sphingosine-1-phosphate receptor; VCAM-1: vascular cell adhesion molecule-1; BTK: Bruton’s tyrosine kinase; OPN: osteopontin.
Sclerosis 02 00009 g003
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Patil, M.S.; Lin, L.Y.; Marsh-Wakefield, F.; James, E.J.; Palendira, M.; Hawke, S.; Grau, G.E. Multiple Sclerosis: Immune Cells, Histopathology, and Therapeutics. Sclerosis 2024, 2, 117-139. https://doi.org/10.3390/sclerosis2030009

AMA Style

Patil MS, Lin LY, Marsh-Wakefield F, James EJ, Palendira M, Hawke S, Grau GE. Multiple Sclerosis: Immune Cells, Histopathology, and Therapeutics. Sclerosis. 2024; 2(3):117-139. https://doi.org/10.3390/sclerosis2030009

Chicago/Turabian Style

Patil, Manisha S., Linda Y. Lin, Felix Marsh-Wakefield, Elizaveta J. James, Mainthan Palendira, Simon Hawke, and Georges E. Grau. 2024. "Multiple Sclerosis: Immune Cells, Histopathology, and Therapeutics" Sclerosis 2, no. 3: 117-139. https://doi.org/10.3390/sclerosis2030009

APA Style

Patil, M. S., Lin, L. Y., Marsh-Wakefield, F., James, E. J., Palendira, M., Hawke, S., & Grau, G. E. (2024). Multiple Sclerosis: Immune Cells, Histopathology, and Therapeutics. Sclerosis, 2(3), 117-139. https://doi.org/10.3390/sclerosis2030009

Article Metrics

Back to TopTop