Serum molecular biomarkers in neuromyelitis optica and multiple sclerosis
Introduction
Neuromyelitis optica spectrum disorder (NMOSD) is a rare and severe inflammatory demyelinating disorder of the central nervous system (CNS), which mainly affects the optic nerves and spinal cord (Wingerchuk et al., 2007; Jarius et al., 2008). In 2006, a specific serum biomarker, an autoantibody against aquaporin-4 (AQP4-IgG), helped distinguish NMOSD from another disease that affects the CNS, multiple sclerosis (MS) (Jarius et al., 2007; Lennon et al., 2004; Wingerchuk et al., 2006). AQP4 is the most abundant water channel in the mammalian central nervous system (CNS). It is highly expressed in the membrane of astrocytic end feet at the interfaces between blood or cerebrospinal fluid (CSF) and brain parenchyma (Lennon et al., 2005). Binding of AQP4–IgG to astrocytic AQP4 initiates neuroinflammation (Takahashi et al., 2007). In addition to AQP4-IgG, growing evidence suggests that complements, cytokines, and chemokines also contribute to the complex pathogenesis of NMOSD (Lucchinetti et al., 2002; Uzawa et al., 2010, 2014). Several reports have found that certain cytokines (IL-6, IL-8, GCSF, IL-17) are elevated in the CSF of NMOSD patients (Uzawa et al., 2009). The anti-IL-6 receptor monoclonal antibody, tocilizumab, blocks IL-6 signaling and is highly effective for refractory patients with NMOSD (Araki et al., 2013).
Additional serological biomarkers for diagnosing NMOSD are desperately needed. First, not all NMOSD patients are AQP4-IgG seropositive (anti-AQP4-IgG(+)), and previous studies suggest that the etiology, epidemiology and clinical presentations are different for NMOSD patients who are anti-AQP4-IgG(+) and AQP4-IgG seronegative (anti-AQP4-IgG(-)) (Fujihara and Leite, 2013; Jarius et al., 2012a). Since 10–20% of NMOSD patients are anti-AQP4-IgG(-), false negative results may occur when AQP4-IgG tests are employed (Jarius et al., 2012b; Jarius and Wildemann, 2013). Moreover, most studies are based on data from anti-AQP4-IgG(+) NMOSD patients only. Second, NMOSD can often be misdiagnosed as MS, thus resulting in inappropriate or delayed treatment. For example, some MS therapies, such as interferon-b, natalizumab, or fingolimod, may be ineffective or harmful to NMOSD patients, supporting the idea that NMOSD is distinct from MS and dominated by humoral mechanisms (Trebst et al., 2014). Molecular profiling of NMOSD and MS may help guide patient care and novel treatment strategies.
In this study, the concentrations of 200 proteins in the serum of healthy controls (HCs), anti-AQP4-IgG(+) NMOSD patients, anti-AQP4-IgG(-) NMOSD patients, and MS patients were determined using high-density antibody arrays. Differential expression analyses were performed to compare the profiles between NMOSD and HCs, MS and HCs, NMOSD and MS, anti-AQP4-IgG(+) and anti-AQP4-IgG(-) NMOSD, and anti-AQP4-IgG(-) NMOSD and MS. Among the differentially-expressed proteins identified, we show that Epidermal Growth Factor (EGF) is upregulated in NMOSD patients, but not in MS patients, compared to HCs. Adding serum from NMOSD patients to immortalized hCMEC/D3 cells results in decreased expression of Claudin-5, a tight junction protein involved in maintaining the blood-brain barrier (BBB). However, the effect was not observed with serum pretreated with an antibody that neutralizes EGF activity, thus demonstrating that EGF directly regulates the levels of Claudin-5. Altogether, our data provide further insight into the pathogenesis of NMOSD and identifies potential biomarkers that may aid in the more accurate diagnosis of NMOSD patients.
Section snippets
Study participants
A total of 34 healthy controls (HCs), 41 NMOSD patients (32 anti-AQP4-IgG(+) patients, 9 anti-AQP4-IgG(-) patients), and 12 MS patients who fulfilled the diagnostic criteria (Wingerchuk et al., 2015; Thompson et al., 2018) were enrolled in the study. Serum AQP4 and MOG auto-antibodies were detected using cell-based assays as previously described (Long et al., 2017; Chen et al., 2018). A dilution of 1:10 was the cutoff for positive and negative cases. The AQP4-IgG titers are shown in Table 1.
Protein profiling using antibody arrays
Patient demographic and clinical characteristics are described in Tables 1 and 2. The quantitative levels of 200 serological proteins of 34 HCs, 41 NMOSD patients, and 12 MS patients were measured using antibody arrays. Distinct expression levels between the different groups were revealed. Thirty-nine (39), 12, and 18 cytokines were differentially expressed between HCs and NMOSD patients (Fig. 1), HCs and MS patients (Fig. 2), and NMOSD and MS (Fig. 3) patients, respectively. In addition, 36
Discussion
The interleukin (IL)-17 superfamily, a relatively new family of cytokines, consists of six ligands (IL-17A to IL-17F). IL-17A and IL-17F are mainly produced by T helper 17 (Th17) cells, which are the key cell effectors of autoimmune inflammatory demyelination diseases, including MS and NMOSD (Agasing et al., 2020). A previous study indicated that IL-17A augments reactive oxygen species production that leads to the breakdown of the BBB (Huppert et al., 2010). Unlike IL-17A and IL-17F, IL-17B is
Conclusion
Our study used high density antibody arrays to identify DEPs between HCs, anti-AQP4-IgG(+) NMOSD, anti-AQP4-IgG(-) NMOSD and MS patients. We show that serological IL-17B is significantly upregulated in both NMOSD and MS patients compared to HCs. Serological VEGF, MPIF-1 and NrCAM were positively associated with AQP4-IgG titer. We also demonstrated that EGF may be involved in the breakdown of the BBB by downregulating Claudin-5 in NMOSD patients.
CRediT authorship contribution statement
Cong-Cong Fu: Writing – original draft, Writing – review & editing, Visualization, Data curation. Cong Gao: Visualization. Hui-Hua Zhang: Data curation. Ying-Qing Mao: Data curation, Writing – original draft, Formal analysis. Jing-Qiao Lu: Data curation. Brianne Petritis: Writing – original draft, Writing – review & editing. Andy S. Huang: Data curation, Writing – original draft, Formal analysis. Xin-Guang Yang: Visualization. You-Ming Long: Funding acquisition, Visualization. Ruo-Pan Huang:
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