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Background Brain innate immunity is vital for maintaining normal brain functions. Immune homeostatic imbalances play pivotal roles in the pathogenesis of neurological diseases including Parkinson’s disease (PD). However, the molecular and cellular mechanisms underlying the regulation of brain innate immunity and their significance in PD pathogenesis are still largely unknown. Methods Cre-inducible diphtheria toxin receptor (iDTR) and diphtheria toxin-mediated cell ablation was performed to investigate the impact of neuron-glial antigen 2 (NG2) glia on the brain innate immunity. RNA sequencing analysis was carried out to identify differentially expressed genes in mouse brain with ablated NG2 glia and lipopolysaccharide (LPS) challenge. Neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice were used to evaluate neuroinflammatory response in the presence or absence of NG2 glia. The survival of dopaminergic neurons or glial cell activation was evaluated by immunohistochemistry. Co-cultures of NG2 glia and microglia were used to examine the influence of NG2 glia to microglial activation. Results We show that NG2 glia are required for the maintenance of immune homeostasis in the brain via transforming growth factor-β2 (TGF-β2)-TGF-β type II receptor (TGFBR2)-CX3C chemokine receptor 1 (CX3CR1) signaling, which suppresses the activation of microglia. We demonstrate that mice with ablated NG2 glia display a profound downregulation of the expression of microglia-specific signature genes and remarkable inflammatory response in the brain following exposure to endotoxin lipopolysaccharides. Gain- or loss-of-function studies show that NG2 glia-derived TGF-β2 and its receptor TGFBR2 in microglia are key regulators of the CX3CR1-modulated immune response. Furthermore, deficiency of NG2 glia contributes to neuroinflammation and nigral dopaminergic neuron loss in MPTP-induced mouse PD model. Conclusions These findings suggest that NG2 glia play a critical role in modulation of neuroinflammation and provide a compelling rationale for the development of new therapeutics for neurological disorders.

Astrocytes contribute to chronic neuroinflammation in a variety of neurodegenerative diseases, including Parkinson's disease (PD), the most common movement disorder. However, the precise role of astrocytes in neuroinflammation remains incompletely understood. Herein, we show that regulator of G-protein signaling 5 (RGS5) promotes neurodegenerative process through augmenting astrocytic tumor necrosis factor receptor (TNFR) signaling. We found that selective ablation of Rgs5 in astrocytes caused an inhibition in the production of cytokines resulting in mitigated neuroinflammatory response and neuronal survival in animal models of PD, whereas overexpression of Rgs5 had the opposite effects. Mechanistically, RGS5 switched astrocytes from neuroprotective to pro-inflammatory property via binding to the receptor TNFR2. RGS5 also augmented TNFR signaling-mediated pro-inflammatory response by interacting with the receptor TNFR1. Moreover, interrupting RGS5/TNFR interaction by either RGS5 aa 1–108 or small molecular compounds feshurin and butein, suppressed astrocytic cytokine production. We showed that the transcription of astrocytic RGS5 was controlled by transcription factor early B cell factor 1 whose expression was reciprocally influenced by RGS5-modulated TNF signaling. Thus, our study indicates that beyond its traditional role in G-protein coupled receptor signaling, astrocytic RGS5 is a key modulator of TNF signaling circuit with resultant activation of astrocytes thereby contributing to chronic neuroinflammation. Blockade of the astrocytic RGS5/TNFR interaction is a potential therapeutic strategy for neuroinflammation-associated neurodegenerative diseases.

Background Multiple sclerosis (MS) is a CNS autoimmune disease that is characterized by demyelination, neuroinflammation, and neuronal loss. Although current chemotherapies and immunotherapies for MS efficiently mitigate disease activity and relief clinical symptoms, neuroprotection targeting axon/neurons remains therapeutically challenging. Accumulating evidence has suggested that poor oligodendrocyte precursor cell (OPC) proliferation and subsequent migration/differentiation significantly impedes remyelination during MS. However, the molecular mechanisms underlying OPC dysfunction during MS remain elusive. Methods Re-analysis of single-cell sequencing data from human and mouse oligodendrocyte lineage cells (OLCs), along with staining results from brain sections of MS patients, was conducted to investigate the role of 6-pyruvoyl-tetrahydropterin synthase (PTS) in determining oligodendrocyte fate. To assess PTS function in MS, conditional knockout mice targeting OLCs were employed. Transcriptomic analyses further illuminated the molecular mechanisms through which PTS influences the disease process. Results PTS in OPC is a suppressor of OPC proliferation that contributes to demyelination in MS. We found that PTS expression in OPC was aberrantly increased in patients with MS. Mice lacking Pts specifically in OPC displayed ameliorated experimental autoimmune encephalitis (EAE) severity, a mouse model of MS, which were accompanied by attenuated demyelination. Moreover, selective suppression of Pts in OPC facilitated OPC proliferation and remyelination during EAE. Mechanistically, the knockdown of Pts activated the cholesterol biosynthesis pathway leading to enhanced OPC proliferation. Conclusions Our findings suggest that PTS is a negative regulator of OPC proliferation, and its disinhibition could offer a potential therapeutic target for MS. Graphical Abstract

Background Parkinson’s disease (PD) is characterized by a chronic loss of dopaminergic neurons and the presence of proteinaceous inclusions (Lewy bodies) within some remaining neurons in the substantia nigra. Recently, astroglial inclusion body has also been found in some neurodegenerative diseases including PD. However, the underlying molecular mechanisms of how astroglial protein aggregation forms remain largely unknown. Here, we investigated the contribution of αB-crystallin (CRYAB), a small heat shock protein, in α-synuclein inclusion formation in astrocytes. Methods Small interfering RNA (siRNA)-mediated CRYAB (siCRYAB) knockdown or CRYAB overexpression was performed to investigate the impact of CRYAB on the autophagy in human glioblastoma cell line U251 cells. Co-immunoprecipitation (co-IP) and immunoblotting were used to dissect the interaction among multiple proteins. The clearance of α-synuclein in vitro was evaluated by immunocytochemistry. CRYAB transgenic mice and transgenic mice overexpressing A30P mutant form of human α-synuclein were used to examine the influence of CRYAB to α-synuclein accumulation in vivo. Results We found that knockdown of CRYAB in U251 cells or primary cultured astrocytes resulted in a marked augmentation of autophagy activity. In contrast, exogenous CRYAB disrupted the assembly of the BAG3-HSPB8-HSC70 complex via binding with BAG3, thereby suppressing the autophagy activity. Furthermore, CRYAB-regulated autophagy has relevance to PD pathogenesis. Knockdown of CRYAB remarkably promoted cytoplasmic clearance of α-synuclein preformed fibrils (PFFs). Conversely, selective overexpression of CRYAB in astrocytes markedly suppressed autophagy leading to the accumulation of α-synuclein aggregates in the brain of transgenic mice expressing human α-synuclein A30P mutant. Conclusions This study reveals a novel function for CRYAB as a natural inhibitor of astrocytic autophagy and shows that knockdown of CYRAB may provide a therapeutic target against proteinopathies such as synucleinopathies.

Chronic inflammation is a feature of the ageing brain and some neurodegenerative diseases; the authors show that astrocytes normally suppress neuroinflammation through activation of their DRD2 receptor by CRYAB, potentially opening new avenues for treatments. Role of dopamine D2 receptor in innate immunity This study identifies the dopamine D2 receptor (DRD2) in astrocytes as an important component in the control of innate immunity in the central nervous system (CNS). The small heat-shock protein αB-crystallin, which has anti-inflammatory and neuroprotective activities, is shown to be critical for the effect. In mice lacking the Drd2 gene, several areas of the CNS show signs of inflammation, and increased vulnerability to neurotoxins. Chronic inflammation is a feature of the ageing brain and some neurodegenerative diseases, and this work suggests the CNS astrocyte-mediated innate immune response as a possible drug target in ageing and disease. Chronic neuroinflammation is a common feature of the ageing brain and some neurodegenerative disorders. However, the molecular and cellular mechanisms underlying the regulation of innate immunity in the central nervous system remain elusive. Here we show that the astrocytic dopamine D2 receptor (DRD2) modulates innate immunity through αB-crystallin (CRYAB), which is known to suppress neuroinflammation 1 , 2 . We demonstrate that knockout mice lacking Drd2 showed remarkable inflammatory response in multiple central nervous system regions and increased the vulnerability of nigral dopaminergic neurons to neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced neurotoxicity 3 . Astrocytes null for Drd2 became hyper-responsive to immune stimuli with a marked reduction in the level of CRYAB. Preferential ablation of Drd2 in astrocytes robustly activated astrocytes in the substantia nigra. Gain- or loss-of-function studies showed that CRYAB is critical for DRD2-mediated modulation of innate immune response in astrocytes. Furthermore, treatment of wild-type mice with the selective DRD2 agonist quinpirole increased resistance of the nigral dopaminergic neurons to MPTP through partial suppression of inflammation. Our study indicates that astrocytic DRD2 activation normally suppresses neuroinflammation in the central nervous system through a CRYAB-dependent mechanism, and provides a new strategy for targeting the astrocyte-mediated innate immune response in the central nervous system during ageing and disease.

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling, RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show that RotEasy possesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.

The three-dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of direct factor Xa (FXa) inhibitors using AutoGPA-based modeling method in this paper. A training set of 38 molecules and a test set containing 10 molecules were used to build the 3D-QSAR model and validate the derived model, respectively. The developed model with correlation coefficients (r2) of 0.8564 and cross-validated correlation coefficients (q2) of 0.6721 were validated by an external test set of 10 molecules with predicted correlation coefficient (rpred2) of 0.6077. Docking study of FXa inhibitors and FXa active site was performed to check the induced pharmacophore query and comparative molecular field analysis (CoMFA) contour maps using MOE2012.10. It was proved to be coincidence with the interaction information between ligand and FXa active site and was rendered to provide a useful tool to improve FXa inhibitors.

Cost-sensitive boosting algorithms have proven successful for solving the difficult class imbalance problems. However, the influence of misclassification costs and imbalance level on the algorithm performance is still not clear. The present paper aims to conduct an empirical comparison of six representative cost-sensitive boosting algorithms, including AdaCost, CSB1, CSB2, AdaC1, AdaC2, and AdaC3. These algorithms are thoroughly evaluated by a comprehensive suite of experiments, in which nearly fifty thousands classification models are trained on 17 real-world imbalanced data sets. Experimental results show that AdaC serial algorithms generally outperform AdaCost and CSB when dealing with different imbalance level data sets. Furthermore, the optimality of AdaC2 algorithm stands out around the misclassification costs setting: CN=0.7, CP=1, especially for dealing with strongly imbalanced data sets. In the case of data sets with a low-level imbalance, there is no significant difference between the AdaC serial algorithms. In addition, the results indicate that AdaC1 is comparatively insensitive to the misclassification costs, which is consistent with the finding of the preceding research work.

Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling, RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show that RotEasy possesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.