sTREM2 Differentially Affects Cytokine Expression in Myeloid-Derived Cell Models via MAPK-JNK Signaling Pathway.

TREM2 is a critical innate immune receptor primarily expressed on myeloid-derived cells, such as microglia and macrophages. Mutations in TREM2 are linked to several neurodegenerative diseases including Alzheimer's disease (AD). TREM2 can be cleaved from the cell membrane and released as soluble TREM2 (sTREM2). sTREM2 levels are shown to peak prior to AD, with its levels fluctuating throughout disease progression. However, the mechanism by which sTREM2 may affect innate immune responses is largely uncharacterized. In this study, we investigated whether sTREM2 can induce inflammatory response in myeloid-derived THP-1 monocytes and macrophages and characterized the signaling mechanisms involved. Our results show that sTREM2 was capable of stimulating the expression of several inflammatory cytokines in THP-1 cells throughout the time course of 2 h to 8 h but inducing anti-inflammatory cytokine expression at later time points. A TREM2 antibody was capable of inhibiting the expression of some cytokines induced by sTREM2 but enhancing others. The complex of sTREM2/TREM2 antibody was shown to enhance IL-1ß expression, which was partially blocked by an NLRP3 specific inhibitor, indicating that the complex activated the NRLP3 inflammasome pathway. sTREM2 was also shown to have differential effects on cytokine expression in M0, M1, and M2 macrophages differentiated from THP-1 cells. sTREM2 has a more stimulating effect on cytokine expression in M0 macrophages, less of an effect on M2 macrophages, and some inhibitory effects on cytokine expression in M1 macrophages at early time points. Analyses of several signaling pathways revealed that sTREM2-induced expression of cytokines occurs mainly through MAPK-JNK signaling. Our work reveals differential effects of sTREM2 on cytokine expression profiles of THP-1 cells and macrophages and demonstrates that the MAPK-JNK signaling pathway is mainly responsible for sTREM2-induced cytokine expression.

[Differentially expressed microRNAs in peripheral blood mononuclear cells of ankylosing spondylitis patients and its correlation with immune inflammatory response].

Objective To investigate the differentially expressed miRNAs in peripheral blood mononuclear cells (PBMCs) of ankylosing spondylitis (AS) patients, and explore its relevance with the immune inflammatory responses. Methods Fifteen AS patients (AS group) and fifteen healthy volunteers (control group) were recruited in this research. High-throughput RNA sequencing was used to screen miRNA expression in PBMCs. Real-time quantitative PCR was used to detect the six differentially expressed miRNAs. ELISA was applied to test the levels of proinflammatory cytokines, such as TNF-α, IL-1ß, IL-17, and IL-23. Finally, Spearman correlation analysis was conducted to study the correlations of differentially expressed miRNAs with disease activity indicators and immune inflammatory markers. Results Forty-four miRNAs were significantly differentially expressed in AS patients, manifested as 22 up-regulated and 22 down-regulated (fold change≥1). Among them, miR-1-3p and miR-133a-5p were up-regulated obviously, while miR-127-5p, miR-345-3p and miR-136-3p were down-regulated significantly. TNF-α, IL-1ß, IL-17 and IL-23 were significantly increased simultaneously in AS patients. Moreover, miR-1-3p was positively correlated with TNF-α, CRP and BASDAI score; miR-133a-5p was positively correlated with TNF-α; miR-127-5p was negatively correlated with ESR and VAS; miR-345-3p was negatively correlated with IL-17; miR-136-3p was negatively correlated with IL-17 and BASDAI score. Conclusion The miRNAs are abnormally expressed in PBMCs of AS patients, and the differentially expressed miRNAs are associated with disease activity indicators and immune inflammatory cytokines.

Blood-Brain Barrier (BBB)-Crossing Strategies for Improved Treatment of CNS Disorders.

Blood-brain barrier (BBB) is a special biological property of the brain neurovascular unit (including brain microvessels and capillaries), which facilitates the transport of nutrients into the central nervous system (CNS) and exchanges metabolites but restricts passage of blood-borne neurotoxic substances and drugs/xenobiotics into CNS. BBB plays a crucial role in maintaining the homeostasis and normal physiological functions of CNS but severely impedes the delivery of drugs and biotherapeutics into CNS for treatment of neurological disorders. A variety of technologies have been developed in the past decade for brain drug delivery. Most of these technologies are still in preclinical stage and some are undergoing clinical studies. Only a few have been approved by regulatory agencies for clinical applications. This chapter will overview the strategies and technologies/approaches for brain drug delivery and discuss some of the recent advances in the field.

Differential Expression of ABC Transporter Genes in Brain Vessels vs. Peripheral Tissues and Vessels from Human, Mouse and Rat.

BACKGROUND: ATP-binding cassette (ABC) transporters comprise a superfamily of genes encoding membrane proteins with nucleotide-binding domains (NBD). These transporters, including drug efflux across the blood-brain barrier (BBB), carry a variety of substrates through plasma membranes against substrate gradients, fueled by hydrolyzing ATP. The expression patterns/enrichment of ABC transporter genes in brain microvessels compared to peripheral vessels and tissues are largely uncharacterized. METHODS: In this study, the expression patterns of ABC transporter genes in brain microvessels, peripheral tissues (lung, liver and spleen) and lung vessels were investigated using RNA-seq and WesTM analyses in three species: human, mouse and rat. RESULTS: The study demonstrated that ABC drug efflux transporter genes (including ABCB1, ABCG2, ABCC4 and ABCC5) were highly expressed in isolated brain microvessels in all three species studied; the expression of ABCB1, ABCG2, ABCC1, ABCC4 and ABCC5 was generally higher in rodent brain microvessels compared to those of humans. In contrast, ABCC2 and ABCC3 expression was low in brain microvessels, but high in rodent liver and lung vessels. Overall, most ABC transporters (with the exception of drug efflux transporters) were enriched in peripheral tissues compared to brain microvessels in humans, while in rodent species, additional ABC transporters were found to be enriched in brain microvessels. CONCLUSIONS: This study furthers the understanding of species similarities and differences in the expression patterns of ABC transporter genes; this is important for translational studies in drug development. In particular, CNS drug delivery and toxicity may vary among species depending on their unique profiles of ABC transporter expression in brain microvessels and BBB.

Hierarchical One-Class Model With Subnetwork for Representation Learning and Outlier Detection.

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at https://github.com/W1AE/OCC.

Semisupervised Manifold Regularization via a Subnetwork-Based Representation Learning Model.

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature encoding, which is generally less effective in data representation and feature learning. To alleviate this deficiency, we introduce a new semisupervised multilayer subnet neural network called SS-MSNN. The key contributions of this article are as follows: 1) a novel MPI-based MR model using the subnetwork structure is introduced. The subnet model is utilized to enrich the latent space representations iteratively; 2) a one-step training process to learn the discriminative encoding is proposed. The proposed SS-MSNN learns parameters by directly optimizing the entire network, accepting input from one end, and producing output at the other end; and 3) a new semisupervised dataset called HFSWR-RDE is built for this research. Experimental results on multiple domains show that the SS-MSNN achieves promising performance over the other semisupervised learning algorithms, demonstrating fast inference speed and better generalization ability.

RAC1, a Potential Diagnostic and Prognostic Marker for Diffuse Large B Cell Lymphoma.

The gene changes for diagnosis and prognosis of diffuse large B cell lymphoma (DLBCL) still remain unclear. RAC1 was reported to be asso;ciated with the B cell receptor signal pathway, but its relations with DLBCL have not yet been systematically explored. In this study, we have conducted molecular, bioinformatics and clinical analyses by using publicly available data from The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test and logistic regression were performed to evaluate the association between RAC1 and clinical features in patients. Kaplan-Meier and Cox regression methods were used to examine the impacts of RAC1 expression level on overall survival, and a nomogram was performed to illustrate the correlation between RAC1 and the risk of DLBCL. Our results revealed that the expression level of RAC1 in DLBCL was higher than that in normal tissues or lymphadenitis. High-level expression of RAC1 was significantly associated with clinical stage, as well as being an independent factor affecting overall survival. RAC1 was negatively correlated with Bruton's tyrosine kinase (BTK). The association between RAC1 gene expression and the risk of DLBCL was presented in a nomogram. In conclusion, RAC1 expression patterns may be used to predict the development and prognosis of DLBCL.

Multimodal Moore-Penrose Inverse-Based Recomputation Framework for Big Data Analysis.

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://github.com/W1AE/Retraining.

Differentiation of Human Induced Pluripotent Stem Cells (hiPSC) into Endothelial-Type Cells and Establishment of an In Vitro Blood-Brain Barrier Model.

Development of central nervous system (CNS) therapeutics and their brain delivery is impeded by the presence of the blood-brain barrier (BBB). In vitro BBB models, in particular human in vitro BBB models, are critical tools for CNS drug research and development. However, the availability of primary human microvascular endothelial cells is very limited for in vitro modeling. Advances in human induced pluripotent stem cell (hiPSC) technologies provide reproducible human cell resources for scientific research, regenerative medicine, and in vitro modeling. In particular, the differentiation of hiPSC into brain endothelial cells provides scalable, renewable and unlimited cells for in vitro BBB modeling that enables rapid screening of CNS drugs in terms of their BBB permeability. The following protocols provide a general guideline for hiPSC culture, differentiation of hiPSC into endothelial cells (hiPSC-ECs), generation of rat primary astrocytes, and establishment of a two-chamber co-culture in vitro BBB model.

Meta-Analysis and Structural Dynamics of the Emergence of Genetic Variants of SARS-CoV-2.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late December 2019 in Wuhan, China, and is the causative agent for the worldwide COVID-19 pandemic. SARS-CoV-2 is a positive-sense single-stranded RNA virus belonging to the betacoronavirus genus. Due to the error-prone nature of the viral RNA-dependent polymerase complex, coronaviruses are known to acquire new mutations at each cycle of genome replication. This constitutes one of the main factors driving the evolution of its relatively large genome and the emergence of new genetic variants. In the past few months, the identification of new B.1.1.7 (United Kingdom), B.1.351 (South Africa), and P.1 (Brazil) variants of concern (VOC) has highlighted the importance of tracking the emergence of mutations in the SARS-CoV-2 genome that impact transmissibility, virulence, and immune and neutralizing antibody escape. Here we analyzed the appearance and prevalence trajectory over time of mutations that appeared in all SARS-CoV-2 genes from December 2019 to April 2021. The goal of the study was to identify which genetic modifications are the most frequent and study the dynamics of their propagation, their incorporation into the consensus sequence, and their impact on virus biology. We also analyzed the structural properties of the spike glycoprotein of the B.1.1.7, B.1.351, and P.1 variants for its binding to the host receptor ACE2. This study offers an integrative view of the emergence, disappearance, and consensus sequence integration of successful mutations that constitute new SARS-CoV-2 variants and their impact on neutralizing antibody therapeutics and vaccines.

Donor-Acceptor Type Covalent Organic Frameworks.

Intermolecular charge transfer (ICT) effect has been widely studied in both small molecules and linear polymers. Covalently-bonded donor-acceptor pairs with tunable bandgaps and photoelectric properties endow these materials with potential applications in optoelectronics, fluorescent bioimaging, and sensors, etc. However, owing to the lack of charge transfer pathway or effective separation of charge carriers, unfavorable charge recombination gives rise to inevitable energy loss. Covalent organic frameworks (COFs) can be mediated with various geometry- and property-tailored building blocks, where donor (D) and acceptor (A) segments are connected by covalent bonds and can be finely arranged to form highly ordered networks (namely D-A COFs). The unique structural features of D-A COFs render the formation of segregated D-A stacks, thus provides pathways and channels for effective charge carriers transport. This review highlights the significant progress on D-A COFs over the past decade with emphasis on design principles, growing structural diversities, and promising application potentials.

Semicovariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and Other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality.

Complex modeling has received significant attention in recent years and is increasingly used to explain statistical phenomena with increasing and decreasing fluctuations, such as the similarity or difference of spike protein charge patterns of coronaviruses. Different from the existing covariance or correlation coefficient methods in traditional integer dimension construction, this study proposes a simplified novel fractional dimension derivation with the exact Excel tool algorithm. It involves the fractional center moment extension to covariance, which results in a complex covariance coefficient that is better than the Pearson correlation coefficient, in the sense that the nonlinearity relationship can be further depicted. The spike protein sequences of coronaviruses were obtained from the GenBank and GISAID databases, including the coronaviruses from pangolin, bat, canine, swine (three variants), feline, tiger, SARS-CoV-1, MERS, and SARS-CoV-2 (including the strains from Wuhan, Beijing, New York, German, and the UK variant B.1.1.7) which were used as the representative examples in this study. By examining the values above and below the average/mean based on the positive and negative charge patterns of the amino acid residues of the spike proteins from coronaviruses, the proposed algorithm provides deep insights into the nonlinear evolving trends of spike proteins for understanding the viral evolution and identifying the protein characteristics associated with viral fatality. The calculation results demonstrate that the complex covariance coefficient analyzed by this algorithm is capable of distinguishing the subtle nonlinear differences in the spike protein charge patterns with reference to Wuhan strain SARS-CoV-2, which the Pearson correlation coefficient may overlook. Our analysis reveals the unique convergent (positive correlative) to divergent (negative correlative) domain center positions of each virus. The convergent or conserved region may be critical to the viral stability or viability; while the divergent region is highly variable between coronaviruses, suggesting high frequency of mutations in this region. The analyses show that the conserved center region of SARS-CoV-1 spike protein is located at amino acid residues 900, but shifted to the amino acid residues 700 in MERS spike protein, and then to amino acid residues 600 in SARS-COV-2 spike protein, indicating the evolution of the coronaviruses. Interestingly, the conserved center region of the spike protein in SARS-COV-2 variant B.1.1.7 shifted back to amino acid residues 700, suggesting this variant is more virulent than the original SARS-COV-2 strain. Another important characteristic our study reveals is that the distance between the divergent mean and the maximal divergent point in each of the viruses (MERS > SARS-CoV-1 > SARS-CoV-2) is proportional to viral fatality rate. This algorithm may help to understand and analyze the evolving trends and critical characteristics of SARS-COV-2 variants, other coronaviral proteins and viruses.

Strategies for delivering therapeutics across the blood-brain barrier.

Achieving sufficient delivery across the blood-brain barrier is a key challenge in the development of drugs to treat central nervous system (CNS) disorders. This is particularly the case for biopharmaceuticals such as monoclonal antibodies and enzyme replacement therapies, which are largely excluded from the brain following systemic administration. In recent years, increasing research efforts by pharmaceutical and biotechnology companies, academic institutions and public-private consortia have resulted in the evaluation of various technologies developed to deliver therapeutics to the CNS, some of which have entered clinical testing. Here we review recent developments and challenges related to selected blood-brain barrier-crossing strategies - with a focus on non-invasive approaches such as receptor-mediated transcytosis and the use of neurotropic viruses, nanoparticles and exosomes - and analyse their potential in the treatment of CNS disorders.

Multimodel Feature Reinforcement Framework Using Moore-Penrose Inverse for Big Data Analysis.

Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore-Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from  âˆ¼  1 K to  âˆ¼ 2 M show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods.

Differential expression of receptors mediating receptor-mediated transcytosis (RMT) in brain microvessels, brain parenchyma and peripheral tissues of the mouse and the human.

Receptor-mediated transcytosis (RMT) is a principal pathway for transport of macromolecules essential for brain function across the blood-brain barrier (BBB). Antibodies or peptide ligands which bind RMT receptors are often co-opted for brain delivery of biotherapeutics. Constitutively recycling transferrin receptor (TfR) is a prototype receptor utilized to shuttle therapeutic cargos across the BBB. Several other BBB-expressed receptors have been shown to mediate transcytosis of antibodies or protein ligands including insulin receptor (INSR) and insulin-like growth factor-1 receptor (IGF1R), lipid transporters LRP1, LDLR, LRP8 and TMEM30A, solute carrier family transporter SLC3A2/CD98hc and leptin receptor (LEPR). In this study, we analyzed expression patterns of genes encoding RMT receptors in isolated brain microvessels, brain parenchyma and peripheral organs of the mouse and the human using RNA-seq approach. IGF1R, INSR and LRP8 were highly enriched in mouse brain microvessels compared to peripheral tissues. In human brain microvessels only INSR was enriched compared to either the brain or the lung. The expression levels of SLC2A1, LRP1, IGF1R, LRP8 and TFRC were significantly higher in the mouse compared to human brain microvessels. The protein expression of these receptors analyzed by Western blot and immunofluorescent staining of the brain microvessels correlated with their transcript abundance. This study provides a molecular transcriptomics map of key RMT receptors in mouse and human brain microvessels and peripheral tissues, important to translational studies of biodistribution, efficacy and safety of antibodies developed against these receptors.

Author Correction: HIF1α inhibition facilitates Leflunomide-AHR-CRP signaling to attenuate bone erosion in CRP-aberrant rheumatoid arthritis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

"Efficacy and safety of Xinfeng capsule in the treatment of osteoarthritis: a multicenter, randomized, double-blinded, controlled trial".

OBJECTIVE: To evaluate the efficacy and safety of Xinfeng capsule (XFC) in patients with osteoarthritis (OA). METHODS: This was a multicenter, double-blinded, randomized, controlled, clinical trial. Patients with OA were assigned to the XFC group [treated with XFC and a glucosamine (GS) placebo, n = 129] or the GS group (treated with GS and an XFC placebo, n = 126). Both groups were treated for 4 weeks. The primary endpoint was the difference between the two groups in the Western Ontario and McMaster Universities OA (WOMAC) index total score at 4th week. The secondary endpoints were the visual analogue scale for pain, Lequesne index, function influence index rating, quality of life as assessed by the Short Form-36, erythrocyte sedimentation rate, and C-reactive protein concentration at baseline and at second week and 4th week. Bone mineral density were checked by X ray absorptiometry at baseline and 4th week. RESULTS: After 4 weeks of treatment, all patients in both groups showed similar significant improvements compared with baseline. There were no significant differences between groups regarding pain relief, bilateral femoral bone mineral density, and laboratory indices such as erythrocyte sedimentation rate and C-reactive protein concentration. Both groups had a significantly lower function influence index rating score and curative effect for each sign/symptom in week 4 than in week 0, and these changes did not significantly differ between groups. XFC was superior to GS in improving the WOMAC index total score, WOMAC scores for function and stiffness, integrated symptoms, physiological function, energy, emotional function, mental health, and health?changes. Fourteen adverse reactions were reported, and the incidence of adverse reactions did not significantly differ between groups. The most common adverse reactions were hepatic impairment, kidney functional damage, gastrectasia, and facial skin allergy. The types of adverse reactions did not differ between groups. CONCLUSION: XFC is effective and safe in the treatment of OA. XFC was superior to GS in improving the WOMAC index total score, WOMAC scores for pain, stiffness, and function, visual analogue scale for pain, Lequesne index, and Short Form-36 quality of life.

HIF1α inhibition facilitates Leflunomide-AHR-CRP signaling to attenuate bone erosion in CRP-aberrant rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory disorder characterized by progressive bone erosion. Leflunomide is originally developed to suppress inflammation via its metabolite A77 1726 to attenuate bone erosion. However, distinctive responsiveness to Leflunomide is observed among RA individuals. Here we show that Leflunomide exerts immunosuppression but limited efficacy in RA individuals distinguished by higher serum C-reactive protein (CRPHigher, CRPH), whereas the others with satisfactory responsiveness to Leflunomide show lower CRP (CRPLower, CRPL). CRP inhibition decreases bone erosion in arthritic rats. Besides the immunomodulation via A77 1726, Leflunomide itself induces AHR-ARNT interaction to inhibit hepatic CRP production and attenuate bone erosion in CRPL arthritic rats. Nevertheless, high CRP in CRPH rats upregulates HIF1α, which competes with AHR for ARNT association and interferes Leflunomide-AHR-CRP signaling. Hepatocyte-specific HIF1α deletion or a HIF1α inhibitor Acriflavine re-activates Leflunomide-AHR-CRP signaling to inhibit bone erosion. This study presents a precision medicine-based therapeutic strategy for RA.

A Novel Chinese Medicine, Xinfeng Capsule, Modulates Proinflammatory Cytokines via Regulating the Toll-Like Receptor 4 (TLR4)/Mitogen-Activated Protein Kinase (MAPK)/Nuclear Kappa B (NF-κB) Signaling Pathway in an Adjuvant Arthritis Rat Model.

BACKGROUND Rheumatoid arthritis (RA) is a chronic autoimmune disease targeting joints. This research aimed to explore the effects of Xinfeng capsules (XFC) on cardiac injury in adjuvant arthritis (AA) model rats and assessed the associated mechanism. MATERIAL AND METHODS An adjuvant arthritis (AA) rat model was established by intracutaneously injection with Freund's complete adjuvant (FCA). Model rats were divided into 4 groups: an AA model group, an astragalus polysaccharides (APS) group, a methotrexate (MTX) group, and an XFC and triptolide (TPT) group. Hematoxylin-eosin (HE) staining was used to observe histopathologic changes. TUNEL assay was utilized to evaluate the apoptosis of cardiomyocytes. ELISA was utilized to evaluate levels of tumor necrosis factor alpha (TNF-alpha), interleukin 17 (IL-17), and interleukin 6 (IL-6) in myocardial tissues. Quantitative RT-PCR (qRT-PCR) was used to detect microRNA-21 (miRNA21) levels. Mitogen-activated protein kinase (MAPK)/p38, Toll-like receptor 4 (TLR4), and nuclear kappa B (NF-kappaB)/p65 levels were evaluated using Western blot. RESULTS XFC significantly improved proinflammatory response compared to the AA model group (p<0.05). XFC treatment significantly decreased the number of cells staining TUNEL-positive compared with the model group (p<0.05). XFC treatment significantly reduced TNF-alpha, IL-17, and IL-6 levels in myocardial tissues compared to the model group (p<0.05). Levels of miRNA21 were significantly lower in the XFC group compared to the AA model group (p<0.05). TLR4, MAPK/p38, and NF-kappaB/p65 expression levels were significantly lower in the XFC group than in the model group (p<0.05). CONCLUSIONS Xinfeng capsule, a traditional Chinese medicine preparation, protects against cardiac injury in AA rats by modulating proinflammatory cytokines expression via the TLR4/MAPK/NF-kappaB signaling pathway.

Efficacy of leflunomide combined with ligustrazine in the treatment of rheumatoid arthritis: prediction with network pharmacology and validation in a clinical trial.

BACKGROUND: Leflunomide (LEF) is a first-line disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, there are still a few nonresponders. It is logical to suggest that employing combinations including LEF that produce synergistic effects in terms of pharmacological activity is a promising strategy to improve clinical outcomes. METHODS: We propose a novel approach for predicting LEF combinations through investigating the potential effects of drug targets on the disease signaling network. We first constructed an RA signaling network with disease-associated driver genes. Thousands of available FDA-approved and investigational compounds were then selected based on a drug-RA network, which was generated using an algorithm model named synergistic score that combines chemical structure, functional prediction and target pathway. We then validated our predicted combination in a prospective clinical trial. RESULTS: Ligustrazine (LIG), a key component of the Chinese herb Chuanxiong and an approved drug in China, ranked first according to synergistic score. In the clinical trial, after 48 weeks, the American College of Rheumatology (ACR) 20 response rate was significantly lower (P < 0.05) in the LEF group [58.8% (45.4%, 72.3%)] than in the LEF + LIG group [78.7% (68.5%, 89.0%)]. Consistently, the erosion score was lower in patients treated with LEF + LIG than in those treated with LEF (0.34 ± 0.20 vs 1.12 ± 0.30, P < 0.05). CONCLUSIONS: Our algorithm combines structure and target pathways into one model that predicted that the combination of LEF and LIG can reduce joint inflammation and attenuate bone erosion in RA patients. To our knowledge, this study is the first to apply this paradigm to evaluate drug combination hypotheses.