Background and ObjectivesTo analyze (1) the effect of immunoglobulin G (IgG) from patients with anti–myelin oligodendrocyte glycoprotein antibody (MOG-Ab)–associated disorder on the blood-brain barrier (BBB) endothelial cells and (2) the positivity of glucose-regulated protein 78 (GRP78) antibodies in MOG-Ab–associated disorders.MethodsIgG was purified from sera with patients with MOG-Ab–associated disorder in the acute phase (acute MOG, n = 15), in the stable stage (stable MOG, n = 14), healthy controls (HCs, n = 9), and disease controls (DCs, n = 27). Human brain microvascular endothelial cells (BMECs) were incubated with IgG, and the number of nuclear NF-κB p65-positive cells in BMECs using high-content imaging system and the quantitative messenger RNA change in gene expression over the whole transcriptome using RNA-seq were analyzed. GRP78 antibodies from patient IgGs were detected by Western blotting.ResultsIgG in the acute MOG group significantly induced the nuclear translocation of NF-κB and increased the vascular cell adhesion molecule 1/intercellular adhesion molecule 1 expression/permeability of 10-kDa dextran compared with that from the stable MOG and HC/DC groups. RNA-seq and pathway analysis revealed that NF-κB signaling and oxidative stress (NQO1) play key roles. The NQO1 and Nrf2 protein amounts were significantly decreased after exposure to IgG in the acute MOG group. The rate of GRP78 antibody positivity in the acute MOG group (10/15, 67% [95% confidence interval, 38%–88%]) was significantly higher than that in the stable MOG group (5/14, 36% [13%–65%]), multiple sclerosis group (4/29, 14% [4%–32%]), the DCs (3/27, 11% [2%–29%]), or HCs (0/9, 0%). Removal of GRP78 antibodies from MOG-IgG reduced the effect on NF-κB nuclear translocation and increased permeability.DiscussionGRP78 antibodies may be associated with BBB dysfunction in MOG-Ab–associated disorder.

We study a model of an idiotypic immune network which was introduced by N. K. Jerne. It is known that in immune systems there generally exist several kinds of immune cells which can recognize any particular antigen. Taking this fact into account and assuming that each cell interacts with only a finite number of other cells, we analyze a large scale immune network via both numerical simulations and statistical mechanical methods, and show that the distribution of the concentrations of antibodies becomes non-trivial for a range of values of the strength of the interaction and the connectivity. §1. IntroductionThere are many numerical and theoretical studies of biological networks in which there is a global coupling between the constituent elements, e.g. Hopfield-type neural networks and networks of Kuramoto-type phase oscillators. 1), 2) In contrast, relatively few studies have been carried out of networks where each component interacts with only a small number of randomly selected other components. One such system is the immune network, introduced by Jerne 3) to explain the activation of immune cells in the absence of external stimulation.Let us briefly summarize the main mechanism of cell-interaction in the immune system. Its main constituents are B-lymphocytes (B-cells), T-lymphocytes (T-Cells) and antibodies produced by B-cells. B-cells and T-cells have receptors on their surfaces. The receptors of B-cells are antibodies, which recognize and connect to antigens in order to neutralize them; they have specific 3-dimensional structures which are called 'idiotypes'. A family of B-cells which are generated from a given B-cell is called a 'clone'. Hence, all members of a clone as well as the antibodies produced by this clone have the same idiotype. In general, each antibody could present several 3-dimensional structures which can be recognized by other B-cells. This then generates, indirectly, an effective interaction between antibodies. This paper is organized as follows. In §2 we formulate the model, followed by a summary of previous studies in §3. In §4, we present the results of our present study. Section 5 is devoted to a summary and a discussion.