HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.

Inflammatory cell death PANoptosis is induced by the anti-cancer curaxin CBL0137 via eliciting the assembly of ZBP1-associated PANoptosome.

OBJECTIVE: PANoptosis, a new form of regulated cell death, concomitantly manifests hallmarks for pyroptosis, apoptosis, and necroptosis. It has been usually observed in macrophages, a class of widely distributed innate immune cells in various tissues, upon pathogenic infections. The second-generation curaxin, CBL0137, can trigger necroptosis and apoptosis in cancer-associated fibroblasts. This study aimed to explore whether CBL0137 induces PANoptosis in macrophages in vitro and in mouse tissues in vivo. METHODS: Bone marrow-derived macrophages and J774A.1 cells were treated with CBL0137 or its combination with LPS for indicated time periods. Cell death was assayed by propidium iodide staining and immunoblotting. Immunofluorescence microscopy was used to detect cellular protein distribution. Mice were administered with CBL0137 plus LPS and their serum and tissues were collected for biochemical and histopathological analyses, respectively. RESULTS: The results showed that CBL0137 alone or in combination with LPS induced time- and dose-dependent cell death in macrophages, which was inhibited by a combination of multiple forms of cell death inhibitors but not each alone. This cell death was independent of NLRP3 expression. CBL0137 or CBL0137 + LPS-induced cell death was characterized by simultaneously increased hallmarks for pyroptosis, apoptosis and necroptosis, indicating that this is PANoptosis. Induction of PANoptosis was associated with Z-DNA formation in the nucleus and likely assembly of PANoptosome. ZBP1 was critical in mediating CBL0137 + LPS-induced cell death likely by sensing Z-DNA. Moreover, intraperitoneal administration of CBL0137 plus LPS induced systemic inflammatory responses and caused multi-organ (including the liver, kidney and lung) injury in mice due to induction of PANoptosis in these organs. CONCLUSIONS: CBL0137 alone or plus inflammatory stimulation induces PANoptosis both in vitro and in vivo, which is associated with systemic inflammatory responses in mice.

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network.

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.

Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.

Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion.

Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence drug efficacy. Therefore, research into the relationships between circRNAs and drugs can be of great significance in increasing the comprehension of circRNAs function, as well as contributing to the discovery of new drugs and the repurposing of existing drugs. However, it is time-consuming and costly to validate the function of circRNA with traditional medical research methods. Therefore, the development of efficient and accurate computational models that can assist in discovering the potential interactions between circRNAs and drugs is urgently needed. In this study, a novel method is proposed, called DHANMKF , that aims to predict potential circRNA-drug sensitivity interactions for further biomedical screening and validation. Firstly, multimodal networks were constructed by DHANMKF using multiple sources of information on circRNAs and drugs. Secondly, comprehensive intra-type and inter-type node representations were learned using bi-typed multi-relational heterogeneous graphs, which are attention-based encoders utilizing a hierarchical process. Thirdly, the multi-kernel fusion method was used to fuse intra-type embedding and inter-type embedding. Finally, the Dual Laplacian Regularized Least Squares method (DLapRLS) was used to predict the potential circRNA-drug sensitivity associations using the combined kernel in circRNA and drug spaces. Compared with the other methods, DHANMKF obtained the highest AUC value on two datasets. Code is available at https://github.com/cuntjx/DHANMKF .

Triptolide induces PANoptosis in macrophages and causes organ injury in mice.

Macrophages represent the first lines of innate defense against pathogenic infections and are poised to undergo multiple forms of regulated cell death (RCD) upon infections or toxic stimuli, leading to multiple organ injury. Triptolide, an active compound isolated from Tripterygium wilfordii Hook F., possesses various pharmacological activities including anti-tumor and anti-inflammatory effects, but its applications have been hampered by toxic adverse effects. It remains unknown whether and how triptolide induces different forms of RCD in macrophages. In this study, we showed that triptolide exhibited significant cytotoxicity on cultured macrophages in vitro, which was associated with multiple forms of lytic cell death that could not be fully suppressed by any one specific inhibitor for a single form of RCD. Consistently, triptolide induced the simultaneous activation of pyroptotic, apoptotic and necroptotic hallmarks, which was accompanied by the co-localization of ASC specks respectively with RIPK3 or caspase-8 as well as their interaction with each other, indicating the formation of PANoptosome and thus the induction of PANoptosis. Triptolide-induced PANoptosis was associated with mitochondrial dysfunction and ROS production. PANoptosis was also induced by triptolide in mouse peritoneal macrophages in vivo. Furthermore, triptolide caused kidney and liver injury, which was associated with systemic inflammatory responses and the activation of hallmarks for PANoptosis in vivo. Collectively, our data reveal that triptolide induces PANoptosis in macrophages in vitro and exhibits nephrotoxicity and hepatotoxicity associated with induction of PANoptosis in vivo, suggesting a new avenue to alleviate triptolide's toxicity by harnessing PANoptosis.

Dimethyl fumarate inhibits necroptosis and alleviates systemic inflammatory response syndrome by blocking the RIPK1-RIPK3-MLKL axis.

Necroptosis has been implicated in various inflammatory diseases including tumor-necrosis factor-α (TNF-α)-induced systemic inflammatory response syndrome (SIRS). Dimethyl fumarate (DMF), a first-line drug for treating relapsing-remitting multiple sclerosis (RRMS), has been shown to be effective against various inflammatory diseases. However, it is still unclear whether DMF can inhibit necroptosis and confer protection against SIRS. In this study, we found that DMF significantly inhibited necroptotic cell death in macrophages induced by different necroptotic stimulations. Both the autophosphorylation of receptor-interacting serine/threonine kinase 1 (RIPK1) and RIPK3 and the downstream phosphorylation and oligomerization of MLKL were robustly suppressed by DMF. Accompanying the suppression of necroptotic signaling, DMF blocked the mitochondrial reverse electron transport (RET) induced by necroptotic stimulation, which was associated with its electrophilic property. Several well-known anti-RET reagents also markedly inhibited the activation of the RIPK1-RIPK3-MLKL axis accompanied by decreased necrotic cell death, indicating a critical role of RET in necroptotic signaling. DMF and other anti-RET reagents suppressed the ubiquitination of RIPK1 and RIPK3, and they attenuated the formation of necrosome. Moreover, oral administration of DMF significantly alleviated the severity of TNF-α-induced SIRS in mice. Consistent with this, DMF mitigated TNF-α-induced cecal, uterine, and lung damage accompanied by diminished RIPK3-MLKL signaling. Collectively, DMF represents a new necroptosis inhibitor that suppresses the RIPK1-RIPK3-MLKL axis through blocking mitochondrial RET. Our study highlights DMF's potential therapeutic applications for treating SIRS-associated diseases.

["Component-target-efficacy" network analysis and experimental verification of Qingkailing Oral Preparation].

This study aims to explore the mechanism of Qingkailing(QKL) Oral Preparation's heat-clearing, detoxifying, mind-tranquilizing effects based on "component-target-efficacy" network. To be specific, the potential targets of the 23 major components in QKL Oral Preparation were predicted by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) and SwissTargetPrediction. The target genes were obtained based on UniProt. OmicsBean and STRING 10 were used for Gene Ontology(GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment of the targets. Cytoscape 3.8.2 was employed for visualization and construction of "component-target-pathway-pharmacological effect-efficacy" network, followed by molecular docking between the 23 main active components and 15 key targets. Finally, the lipopolysaccharide(LPS)-induced RAW264.7 cells were adopted to verify the anti-inflammatory effect of six monomer components in QKL Oral Preparation. It was found that the 23 compounds affected 33 key signaling pathways through 236 related targets, such as arachidonic acid metabolism, tumor necrosis factor α(TNF-α) signaling pathway, inflammatory mediator regulation of TRP channels, cAMP signaling pathway, cGMP-PKG signaling pathway, Th17 cell differentiation, interleukin-17(IL-17) signaling pathway, neuroactive ligand-receptor intera-ction, calcium signaling pathway, and GABAergic synapse. They were involved in the anti-inflammation, immune regulation, antipyretic effect, and anti-convulsion of the prescription. The "component-target-pathway-pharmacological effect-efficacy" network of QKL Oral Preparation was constructed. Molecular docking showed that the main active components had high binding affinity to the key targets. In vitro cell experiment indicated that the six components in the prescription(hyodeoxycholic acid, baicalin, chlorogenic acid, isochlorogenic acid C, epigoitrin, geniposide) can reduce the expression of nitric oxide(NO), TNF-α, and interleukin-6(IL-6) in cell supernatant(P<0.05). Thus, the above six components may be the key pharmacodynamic substances of QKL Oral Preparation. The major components in QKL Oral Prescription, including hyodeoxycholic acid, baicalin, chlorogenic acid, isochlorogenic acid C, epigoitrin, geniposide, cholic acid, isochlorogenic acid A, and γ-aminobutyric acid, may interfere with multiple biological processes related to inflammation, immune regulation, fever, and convulsion by acting on the key protein targets such as IL-6, TNF, prostaglandin-endoperoxide synthase 2(PTGS2), arachidonate 5-lipoxygenase(ALOX5), vascular cell adhesion molecule 1(VCAM1), nitric oxide synthase 2(NOS2), prostaglandin E2 receptor EP2 subtype(PTGER2), gamma-aminobutyric acid receptor subunit alpha(GABRA), gamma-aminobutyric acid type B receptor subunit 1(GABBR1), and 4-aminobutyrate aminotransferase(ABAT). This study reveals the effective components and mechanism of QKL Oral Prescription.

Erratum: Construction of a Competitive Endogenous RNA Network in Uterine Corpus Endometrial Carcinoma.

There was an error in Figure 4. The correct Figure 4 is provided below. The picture error would not affect the conclusion of the paper. Reference: Dong Ouyang, Ruyi Li, Yaxian Li, Xueqiong Zhu. Construction of a Competitive Endogenous RNA Network in Uterine Corpus Endometrial Carcinoma. Med Sci Monit 2019; 25:7998-8010. DOI: 10.12659/MSM.915798.

Comprehensive analysis of prognostic alternative splicing signature in cervical cancer.

BACKGROUND: Alternative splicing (AS) is a key factor in protein-coding gene diversity, and is associated with the development and progression of malignant tumours. However, the role of AS in cervical cancer is unclear. METHODS: The AS data for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq website. Few prognostic AS events were identified through univariate Cox analysis. We further identified the prognostic prediction models of the seven subtypes of AS events and assessed their predictive power. We constructed a clinical prediction model through global analysis of prognostic AS events and established a nomogram using the risk score calculated from the prognostic model and relevant clinical information. Unsupervised cluster analysis was used to explore the relationship between prognostic AS events in the model and clinical features. RESULTS: A total of 2860 prognostic AS events in cervical cancer were identified. The best predictive effect was shown by a single alternate acceptor subtype with an area under the curve of 0.96. Our clinical prognostic model included a nine-AS event signature, and the c-index of the predicted nomogram model was 0.764. SNRPA and CCDC12 were hub genes for prognosis-associated splicing factors. Unsupervised cluster analysis through the nine prognostic AS events revealed three clusters with different survival patterns. CONCLUSIONS: AS events affect the prognosis and biological progression of cervical cancer. The identified prognostic AS events and splicing regulatory networks can increase our understanding of the underlying mechanisms of cervical cancer, providing new therapeutic strategies.

A 7-lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma.

Current research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA-Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis-related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7-3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low-risk groups. The low-risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c-index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3-year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1-, 3-, and 5-year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA-messenger RNA (mRNA) of the 7-lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7-lncRNA signature were neuroactive ligand-receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7-lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow-up.

Chemotherapeutic paclitaxel and cisplatin differentially induce pyroptosis in A549 lung cancer cells via caspase-3/GSDME activation.

Gasdermin E (GSDME) has an important role in inducing secondary necrosis/pyroptosis. Upon apoptotic stimulation, it can be cleaved by activated caspase-3 to generate its N-terminal fragment (GSDME-NT), which executes pyroptosis by perforating the plasma membrane. GSDME is expressed in many human lung cancers including A549 cells. Paclitaxel and cisplatin are two representative chemotherapeutic agents for lung cancers, which induce apoptosis via different action mechanisms. However, it remains unclear whether they can induce GSDME-mediated secondary necrosis/pyroptosis in lung A549 cancer cells. Here we showed that both paclitaxel and cisplatin evidently induced apoptosis in A549 cells as revealed by the activation of multiple apoptotic markers. Notably, some of the dying cells displayed characteristic morphology of secondary necrosis/pyroptosis, by blowing large bubbles from the cellular membrane accompanied by caspase-3 activation and GSDME-NT generation. But the ability of cisplatin to induce this phenomenon was much stronger than that of paclitaxel. Consistent with this, cisplatin triggered much higher activation of caspase-3 and generation of GSDME-NT than paclitaxel, suggesting that the levels of secondary necrosis/pyroptosis correlated with the levels of active caspase-3 and GSDME-NT. Supporting this, caspase-3 specific inhibitor (Ac-DEVD-CHO) suppressed cisplatin-induced GSDME-NT generation and concurrently reduced the secondary necrosis/pyroptosis. Besides, GSDME knockdown significantly inhibited cisplatin- but not paclitaxel-induced secondary necrosis/pyroptosis. These results indicated that cisplatin induced higher levels of secondary necrosis/pyroptosis in A549 cells than paclitaxel, suggesting that cisplatin may provide additional advantages in the treatment of lung cancers with high levels of GSDME expression.

ATP induces caspase-3/gasdermin E-mediated pyroptosis in NLRP3 pathway-blocked murine macrophages.

ATP acts as a canonical activator to induce NLRP3 (NOD-like receptor family, pyrin domain containing 3) inflammasome activation in macrophages, leading to caspase-1/gasdermin D (GSDMD)-mediated pyroptosis. It remains unclear whether ATP can induce pyroptosis in macrophages when the NLRP3 pathway is blocked by pathogenic infection. In this study, we used cellular models to mimic such blockade of NLRP3 activation: bone marrow-derived macrophages (BMDMs) treated with NLRP3-specific inhibitor MCC950 and RAW264.7 cells deficient in ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain) expression. The results showed that ATP treatment induced lytic cell death morphologically resembling canonical pyroptosis in both MCC950-treated BMDMs and RAW264.7 cells, but did not cause the activation of caspase-1 (by detecting caspase-1p10 and mature interleukin-1ß) and cleavage of GSDMD. Instead, both apoptotic initiator (caspase-8 and -9) and executioner (caspase-3 and -7) caspases were evidently activated and gasdermin E (GSDME) was cleaved to generate its N-terminal fragment (GSDME-NT) which executes pyroptosis. The GSDME-NT production and lytic cell death induced by ATP were diminished by caspase-3 inhibitor. In BMDMs without MCC950 treatment, ATP induced the formation of ASC specks which were co-localized with caspase-8; with MCC950 treatment, however, ATP did not induced the formation of ASC specks. In RAW264.7 cells, knockdown of GSDME by small interfering RNA attenuated ATP-induced lytic cell death and HMGB1 release into culture supernatants. Collectively, our results indicate that ATP induces pyroptosis in macrophages through the caspase-3/GSDME axis when the canonical NLRP3 pathway is blocked, suggestive of an alternative mechanism for combating against pathogen evasion.

Relationship between polymorphisms of the lipid metabolism-related gene PLA2G16 and risk of colorectal cancer in the Chinese population.

This study aimed to investigate the relationship between polymorphisms in the lipid metabolism-related gene PLA2G16 encoding Group XVI phospholipase A2 and the risk of colorectal cancer (CRC) in the Chinese population. A total of 185 patients with CRC and 313 healthy controls were enrolled. Thirteen single nucleotide polymorphisms (SNPs) of PLA2G16 were genotyped with SNPscan™. Linkage disequilibrium and haplotypes were analysed using Haploview software. Multivariate logistic regression was used to determine the association between the various genotypes and CRC risk. We identified five PLA2G16 SNPs (rs11600655, rs3809072, rs3809073, rs640908 and rs66475048) that were associated with CRC risk after adjusting for age, sex and body mass index. Two haplotypes (CTC and GGA) of rs11600655, rs3809073 and rs3809072, were relevant to CRC risk. The rs11600655 polymorphism was also associated with lymph node metastasis and CRC staging, while rs3809073 and rs3809072 may affect transcriptional regulation of PLA2G16 by altering transcription factor binding. These findings suggest that PLA2G16 polymorphisms-especially CTC and GGA haplotypes-increase CRC susceptibility. Importantly, we showed that the rs11600655 CC, rs640908 CT and rs66475048 GA genotypes are independent risk factors for CRC in the Chinese population.

Aucubin Alleviates Seizures Activity in Li-Pilocarpine-Induced Epileptic Mice: Involvement of Inhibition of Neuroinflammation and Regulation of Neurotransmission.

Neuroinflammation and imbalance of neurotransmitters play pivotal roles in seizures and epileptogenesis. Aucubin (AU) is an iridoid glycoside derived from Eucommia ulmoides that possesses anti-inflammatory and neuroprotective effects. However, the anti-seizure effects of AU have not been reported so far. The present study was designed to investigate the effects of AU on pilocarpine (PILO) induced seizures and its role in the regulation of neuroinflammation and neurotransmission. We found that AU reduced seizure intensity and prolonged the latency of seizures. AU significantly attenuated the activation of astrocytes and microglia and reduced the levels of interleukine-1 beta (IL-1ß), high mobility group box 1 (HMGB1), tumor necrosis factor-α (TNF-α). Furthermore, the contents of γ-aminobutyric acid (GABA) were increased while the levels of glutamate were decreased in the hippocampus with AU treatment. The expression of γ-aminobutyric acid type A receptor subunit α1 (GABAARα1) and glutamate transporter-1 (GLT-1) protein were up-regulated in AU treatment group. However, AU had no significant effect on N-methyl-d-aspartate receptor subunit 2B (NR2B) expression in status epilepticus (SE). In conclusion, our findings provide the first evidence that AU can exert anti-seizure effects by attenuating gliosis and regulating neurotransmission. The results suggest that AU may be developed as a drug candidate for the treatment of epilepsy.

Construction of a Competitive Endogenous RNA Network in Uterine Corpus Endometrial Carcinoma.

BACKGROUND Long non-coding RNAs (lncRNAs) affect post-transcriptional regulation by interfering with microRNAs (miRNAs), and by acting as competitive endogenous RNAs (ceRNAs). The roles and mechanisms of lncRNAs as ceRNAs in the progression and prognosis of uterine corpus endometrial carcinoma are not well understood. MATERIAL AND METHODS We analyzed high-throughput transcriptome data downloaded from The Cancer Genome Atlas database for 548 patients with uterine corpus endometrial carcinoma, and the we constructed a ceRNA network. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses of differentially expressed messenger RNAs (DE-mRNAs) were performed using R software. Kaplan-Meier survival curves were generated for all RNAs in the ceRNA network. RESULTS We identified 2612 messenger RNAs (mRNAs), 1111 lncRNAs, and 187 miRNAs that were differentially expressed in uterine corpus endometrial carcinoma. We then identified mutual regulatory relationships between lncRNA-miRNA pairs and miRNA-mRNA pairs. A ceRNA regulatory network for uterine corpus endometrial carcinoma was successfully constructed, and consisted of 87 lncRNAs, 74 mRNAs, and 20 miRNAs. Nine lncRNAs, 3 miRNAs, and 22 mRNAs were associated with prognosis of uterine corpus endometrial carcinoma. We also analyzed the linear relationships between the expression of the 9 DE-lncRNAs and 22 DE-mRNAs with prognostic value. CONCLUSIONS Our study showed that the lncRNAs C2orf48 and LINC00261 might be key regulators of uterine corpus endometrial carcinoma and might serve as prognostic indicators. Our study contributes to the understanding of the molecular mechanisms of uterine corpus endometrial carcinoma, and it identifies lncRNAs that might serve as prognostic markers and therapeutic targets.

The Constipation-Relieving Property of d-Tagatose by Modulating the Composition of Gut Microbiota.

d-tagatose, a monosaccharide as well as a dietary supplement, has been reported as having a wide range of applicability in the food industry, however, the prebiotic activity, anticonstipation effects, and related mechanisms are still unclear. In this study, using the loperamide-induced constipation Kunming mice as the animal model, the effects of d-tagatose for the prevention of constipation were evaluated by gastrointestinal transit experiment and defecation experiment. Furthermore, the underlying mechanism was clarified by evaluating the change of the biochemical indicators and analyzing 16S rRNA amplicon of gut microbiota among the different mice groups. The results showed that the gastrointestinal transit rate, fecal number, and weight in six hours were significantly enhanced after the administration of d-tagatose. In addition, d-tagatose significantly increased the serum levels of acetylcholine (Ach) and substance P (SP), whereas the serum levels of nitric oxide (NO) were significantly decreased. Moreover, the 16S rRNA sequencing analysis revealed that the changes in the gut microbiota caused by constipation were restored by d-tagatose treatment. In conclusion, this study indicated that the administration of d-tagatose as a dietary supplement can effectively prevent and relieve constipation in Kunming mice, and it is a promising prebiotic candidate with constipation-relieving properties.

The potent mechanism-based inactivation of CYP2D6 and CYP3A4 with fusidic acid in in vivo bioaccumulation.

1. The accumulation of fusidic acid (FA) after multiple doses of FA has been reported on in previous studies but the related mechanisms have not been clarified fully. In the present study, we explain the mechanisms related to the mechanism-based inactivation of CYP2D6 and CYP3A4. 2. The irreversible inhibitory effects of FA on CYP2D6 and CYP3A4 were examined via a series of experiments, including: (a) time-, concentration- and NADPH-dependent inactivation, (b) substrate protection in enzyme inactivation and (c) partition ratio with recombinant human CYP enzymes. Metoprolol α-hydroxylation and midazolam 1'-hydroxylation were used as marker reactions for CYP2D6 and CYP3A4 activities, and HPLC-MS/MS measurement was also utilised. 3. FA caused to the time- and concentration-dependent inactivation of CYP2D6 and CYP3A4. About 55.8% of the activity of CYP2D6 and 75.8% of the activity of CYP3A4 were suppressed after incubation with 10 µM FA for 15 min. KI and kinact were found to be 2.87 µM and 0.033 min-1, respectively, for CYP2D6, while they were 1.95 µM and 0.029 min-1, respectively, for CYP3A4. Inhibition of CYP2D6 and CYP3A4 activity was found to require the presence of NADPH. Substrates of CYP2D6 and CYP3A4 showed that the enzymes were protected against the inactivation induced by FA. The estimated partition ratio for the inactivation was 7 for CYP2D6 and 12 for CYP3A4. 4. FA is a potent mechanism-based inhibitor of CYP2D6 and CYP3A4, which may explain the accumulation of FA in vivo.

The effect of SLCO1B1 polymorphism on the pharmacokinetics of atorvastatin and 2-hydroxyatorvastatin in healthy Chinese people.

The pharmacokinetics of statins show substantial inter-subject variability. Increasing systemic exposure of statins may lead to adverse drug reactions such as myopathy. The variation in statin pharmacokinetics is partly explained by genetic factors. OATP1B1, coded by SLCO1B1 transports a large number of therapeutic drugs, such as atorvastatin. Here we investigated the effect of SLCO1B1 polymorphism on the pharmacokinetics of atorvastatin and its metabolites. Two pharmacokinetic studies were conducted in Chinese Han volunteers and 132 volunteers were enrolled in our study as 72 in trial 1 and 60 in trial 2. A LC-MS/MS method was developed for the identification and quantification of atorvastatin acid and its metabolites. S LCO1B1 c.521T>C (rs4149056) was identified by the MALDI-TOF MS and Sequenom MassARRAY system. The distribution frequencies of SLCO1B1 c.521T>C were in agreement with Hardy-Weinberg equilibrium both in trial 1 and trial 2. In subjects with 521C allele the mean Cmax, AUC0-24h and AUC0-∞ of atorvastatin acid and 2-hydroxyatorvastatin acid were significantly higher than subjects with 521TT genotype, while the mean CL was lower. In conclusion, our results suggested that SLCO1B1 c.521T>C had an effect on the pharmacokinetics of atorvastatin and 2-hydroxyatorvastatin in Chinese Han population. Subjects with 521C allele have an increased risk of toxic effects caused by atorvastatin.