CD28-signaling can be partially compensated in CD28-knockout mice but is essential for virus elimination in a murine model of multiple sclerosis.

The intracerebral infection of mice with Theiler's murine encephalomyelitis virus (TMEV) represents a well-established animal model for multiple sclerosis (MS). Because CD28 is the main co-stimulatory molecule for the activation of T cells, we wanted to investigate its impact on the course of the virus infection as well as on a potential development of autoimmunity as seen in susceptible mouse strains for TMEV. In the present study, 5 weeks old mice on a C57BL/6 background with conventional or tamoxifen-induced, conditional CD28-knockout were infected intracerebrally with TMEV-BeAn. In the acute phase at 14 days post TMEV-infection (dpi), both CD28-knockout strains showed virus spread within the central nervous system (CNS) as an uncommon finding in C57BL/6 mice, accompanied by histopathological changes such as reduced microglial activation. In addition, the conditional, tamoxifen-induced CD28-knockout was associated with acute clinical deterioration and weight loss, which limited the observation period for this mouse strain to 14 dpi. In the chronic phase (42 and 147 dpi) of TMEV-infection, surprisingly only 33% of conventional CD28-knockout mice showed chronic TMEV-infection with loss of motor function concomitant with increased spinal cord inflammation, characterized by T- and B cell infiltration, microglial activation and astrogliosis at 33-42 dpi. Therefore, the clinical outcome largely depends on the time point of the CD28-knockout during development of the immune system. Whereas a fatal clinical outcome can already be observed in the early phase during TMEV-infection for conditional, tamoxifen-induced CD28-knockout mice, only one third of conventional CD28-knockout mice develop clinical symptoms later, accompanied by ongoing inflammation and an inability to clear the virus. However, the development of autoimmunity could not be observed in this C57BL/6 TMEV model irrespective of the time point of CD28 deletion.

Parthenolide Suppresses T Helper 17 and Alleviates Experimental Autoimmune Encephalomyelitis.

T helper (Th) cells play crucial roles in inflammation and adaptive immune system. Importantly, Th17 cells, a major pathogenic Th cell subset, are involved in the pathogenesis of multiple sclerosis (MS) and its classical animal modal experimental autoimmune encephalomyelitis (EAE). Previous studies have shown that parthenolide (PTL), a sesquiterpene lactone, possesses potent anti-cancer and anti-inflammatory activities. However, the immunosuppressive effect of PTL on the pathogenic Th17 cell and MS is unclear. In this study, we showed that PTL treatment could alleviate clinical symptoms by inhibiting inflammatory cell infiltration, reducing inflammation and demyelination of CNS. In addition, the mRNA expression of cytokines and inflammatory factors in CD4+ T cells, especially Th1 and Th17 cells, reduced in both CNS and peripheral immune tissue of EAE mice. Furthermore, PTL could inhibit the reactivation of MOG-specific T cells and the differentiation of naïve CD4+ T cells into Th17 cells in vitro. We also found that PTL inhibited nuclear factor kappa B (NF-κB) signaling and retinoid-related orphan receptor-γt (RORγt) in mouse Th17 cell and human Jurkat cell line. Taken together, our data demonstrated a critical immune-suppressive effect of PTL on autoimmune inflammation through regulating Th17 cells and the NF-κB/RORγt pathway.

Analysis of brain functional connectivity network in MS patients constructed by modular structure of sparse weights from cognitive task-related fMRI.

Cognitive dysfunction in multiple sclerosis (MS) seems to be the result of neural disconnections, leading to a wide range of brain functional network alterations. It is assumed that the analysis of the topological structure of brain connectivity network can be used to assess cognitive impairments in MS disease. We aimed to identify these brain connectivity pattern alterations and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain networks construction for better exhibition of changes, inducing the improved reflection of functional organization structure should be precisely considered. In this paper, we strove to introduce a framework for modeling the functional connectivity network by considering the two most important intrinsic sparse and modular structures of brain. For the proposed approach, we first derived group-wise sparse representation via learning a common over-complete dictionary matrix from the aggregated cognitive task-based functional magnetic resonance imaging (fMRI) data of all subjects of the two groups to be able to investigate between-group differences. We then applied the modularity concept on achieved sparse coefficients to compute the connectivity strength between the two brain regions. We examined the changes in network topological properties between relapsing-remitting MS (RRMS) and matched HC groups by considering the pairwise connections of regions of the resulted weighted networks and extracting graph-based measures. We found that the informative brain regions were related to their important connectivity weights, which could distinguish MS patients from the healthy controls. The experimental findings also proved the discrimination ability of the modularity measure among all the global features. In addition, we identified such local feature subsets as eigenvector centrality, eccentricity, node strength, and within-module degree, which significantly differed between the two groups. Moreover, these nodal graph measures have been served as the detectors of brain regions, affected by different cognitive deficits. In general, our findings illustrated that integration of sparse representation, modular structure, and pairwise connectivity strength in combination with the graph properties could help us with the early diagnosis of cognitive alterations in the case of MS.

A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors.

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer's disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells.