Reversible Mn2+/Mn4+ double redox in lithium-excess cathode materials.

There is an urgent need for low-cost, resource-friendly, high-energy-density cathode materials for lithium-ion batteries to satisfy the rapidly increasing need for electrical energy storage. To replace the nickel and cobalt, which are limited resources and are associated with safety problems, in current lithium-ion batteries, high-capacity cathodes based on manganese would be particularly desirable owing to the low cost and high abundance of the metal, and the intrinsic stability of the Mn4+ oxidation state. Here we present a strategy of combining high-valent cations and the partial substitution of fluorine for oxygen in a disordered-rocksalt structure to incorporate the reversible Mn2+/Mn4+ double redox couple into lithium-excess cathode materials. The lithium-rich cathodes thus produced have high capacity and energy density. The use of the Mn2+/Mn4+ redox reduces oxygen redox activity, thereby stabilizing the materials, and opens up new opportunities for the design of high-performance manganese-rich cathodes for advanced lithium-ion batteries.

BERN2: an advanced neural biomedical named entity recognition and normalization tool.

In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction. AVAILABILITY AND IMPLEMENTATION: Web service of BERN2 is publicly available at http://bern2.korea.ac.kr. We also provide local installation of BERN2 at https://github.com/dmis-lab/BERN2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method.

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values).

MOTIVATION: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. RESULTS: Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Potent inhibition of human tyrosinase inhibitor by verproside from the whole plant of Pseudolysimachion rotundum var. subintegrum.

Affinity-based ultrafiltration-mass spectrometry coupled with ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry was utilised for the structural identification of direct tyrosinase ligands from a crude Pseudolysimachion rotundum var. subintegrum extract. False positives were recognised by introducing time-dependent inhibition in the control for comparison. The P. rotundum extract contained nine main metabolites in the UPLC-QTOF-MS chromatogram. However, four metabolites were reduced after incubation with tyrosinase, indicating that these metabolites were bound to tyrosinase. The IC50 values of verproside (1) were 31.2 µM and 197.3 µM for mTyr and hTyr, respectively. Verproside showed 5.6-fold higher efficacy than that of its positive control (kojic acid in hTyr). The most potent tyrosinase inhibitor, verproside, features a 3,4-dihydroxybenzoic acid moiety on the iridoid glycoside and inhibits tyrosinase in a time-dependent and competitive manner. Among these three compounds, verproside is bound to the active site pocket with a docking energy of -6.9 kcal/mol and four hydrogen bonding interactions with HIS61 and HIS85.

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.

Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.

Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein-ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination.

Verproside, the Most Active Ingredient in YPL-001 Isolated from Pseudolysimachion rotundum var. subintegrum, Decreases Inflammatory Response by Inhibiting PKCδ Activation in Human Lung Epithelial Cells.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease which causes breathing problems. YPL-001, consisting of six iridoids, has potent inhibitory efficacy against COPD. Although YPL-001 has completed clinical trial phase 2a as a natural drug for COPD treatment, the most effective iridoid in YPL-001 and its mechanism for reducing airway inflammation remain unclear. To find an iridoid most effectively reducing airway inflammation, we examined the inhibitory effects of the six iridoids in YPL-001 on TNF or PMA-stimulated inflammation (IL-6, IL-8, or MUC5AC) in NCI-H292 cells. Here, we show that verproside among the six iridoids most strongly suppresses inflammation. Both TNF/NF-κB-induced MUC5AC expression and PMA/PKCδ/EGR-1-induced IL-6/-8 expression are successfully reduced by verproside. Verproside also shows anti-inflammatory effects on a broad range of airway stimulants in NCI-H292 cells. The inhibitory effect of verproside on the phosphorylation of PKC enzymes is specific to PKCδ. Finally, in vivo assay using the COPD-mouse model shows that verproside effectively reduces lung inflammation by suppressing PKCδ activation and mucus overproduction. Altogether, we propose YPL-001 and verproside as candidate drugs for treating inflammatory lung diseases that act by inhibiting PKCδ activation and its downstream pathways.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

MOTIVATION: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. RESULTS: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. AVAILABILITY AND IMPLEMENTATION: We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

3,4-dihydroxytoluene, a metabolite of rutin, suppresses the progression of nonalcoholic fatty liver disease in mice by inhibiting p300 histone acetyltransferase activity.

3,3',4',5,7-Pentahydroxyflavone-3-rhamnoglucoside (rutin) is a flavonoid with a wide range of pharmacological activities. Dietary rutin is hardly absorbed because the microflora in the large intestine metabolize rutin into a variety of compounds including quercetin and phenol derivatives such as 3,4-dihydroxyphenolacetic acid (DHPAA), 3,4-dihydroxytoluene (DHT), 3,4-hydroxyphenylacetic acid (HPAA) and homovanillic acid (HVA). We examined the potential of rutin and its metabolites as novel histone acetyltransferase (HAT) inhibitors. DHPAA, HPAA and DHT at the concentration of 25 µM significantly inhibited in vitro HAT activity with DHT having the strongest inhibitory activity. Furthermore, DHT was shown to be a highly efficient inhibitor of p300 HAT activity, which corresponded with its high degree of inhibition on intracellular lipid accumulation in HepG2 cells. Docking simulation revealed that DHT was bound to the p300 catalytic pocket, bromodomain. Drug affinity responsive target stability (DARTS) analysis further supported the possibility of direct binding between DHT and p300. In HepG2 cells, DHT concentration-dependently abrogated p300-histone binding and induced hypoacetylation of histone subunits H3K9, H3K36, H4K8 and H4K16, eventually leading to the downregulation of lipogenesis-related genes and attenuating lipid accumulation. In ob/ob mice, administration of DHT (10, 20 mg/kg, iv, every other day for 6 weeks) dose-dependently improved the NAFLD pathogenic features including body weight, liver mass, fat mass, lipid accumulation in the liver, and biochemical blood parameters, accompanied by the decreased mRNA expression of lipogenic genes in the liver. Our results demonstrate that DHT, a novel p300 histone acetyltransferase inhibitor, may be a potential preventive or therapeutic agent for NAFLD.

ReSimNet: drug response similarity prediction using Siamese neural networks.

MOTIVATION: Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. RESULTS: We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. AVAILABILITY AND IMPLEMENTATION: The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rational design of small molecule RHOA inhibitors for gastric cancer.

Previously, we identified Ras homologous A (RHOA) as a major signaling hub in gastric cancer (GC), the third most common cause of cancer death in the world, prompting us to rationally design an efficacious inhibitor of this oncogenic GTPase. Here, based on that previous work, we extend those computational analyses to further pharmacologically optimize anti-RHOA hydrazide derivatives for greater anti-GC potency. Two of these, JK-136 and JK-139, potently inhibited cell viability and migration/invasion of GC cell lines, and mouse xenografts, diversely expressing RHOA. Moreover, JK-136's binding affinity for RHOA was >140-fold greater than Rhosin, a nonclinical RHOA inhibitor. Network analysis of JK-136/-139 vs. Rhosin treatments indicated downregulation of the sphingosine-1-phosphate, as an emerging cancer metabolic pathway in cell migration and motility. We assert that identifying and targeting oncogenic signaling hubs, such as RHOA, represents an emerging strategy for the design, characterization, and translation of new antineoplastics, against gastric and other cancers.

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition.

BACKGROUND: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. RESULTS: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. CONCLUSIONS: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.

Differential effects, on oncogenic pathway signalling, by derivatives of the HNF4 α inhibitor BI6015.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease with few "targeted" therapeutic options. Previously, we demonstrated involvement of the transcription factor HNF4α in human GC tumours, and the developmental signal mediator, WNT5A, as a prognostic GC biomarker. One previously developed HNF4α antagonist, BI6015, while not advancing beyond preclinical stages, remains useful for studying GC. METHODS: Here, we characterised the antineoplastic signalling activity of derivatives of BI6015, including transfer of the nitro group from the para position, relative to a methyl group on its benzene ring, to the ortho- and meta positions. We assessed binding efficacy, through surface plasmon resonance and docking studies, while biologic activity was assessed by antimitogenic efficacy against a panel of GC cell lines, and dysregulated transcriptomes, followed by pathway and subpathway analysis. RESULTS: The para derivative of BI6105 was found substantially more growth inhibitory, and effective, in downregulating numerous oncogenic signal pathways, including the embryonic cascade WNT. The ortho and meta derivatives, however, failed to downregulate WNT or other embryonic signalling pathways, unable to suppress GC growth. CONCLUSION: Straightforward strategies, employing bioinformatics analyses, to facilitate the effective design and development of "druggable" transcription factor inhibitors, are useful for targeting specific oncogenic signalling pathways, in GC and other cancers.

Dual BACE1 and Cholinesterase Inhibitory Effects of Phlorotannins from Ecklonia cava-An In Vitro and in Silico Study.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a multifactorial nature. ß-Secretase (BACE1) and acetylcholinesterase (AChE), which are required for the production of neurotoxic ß-amyloid (Aß) and the promotion of Aß fibril formation, respectively, are considered as prime therapeutic targets for AD. In our efforts towards the development of potent multi-target, directed agents for AD treatment, major phlorotannins such as eckol, dieckol, and 8,8'-bieckol from Ecklonia cava (E. cava) were evaluated. Based on the in vitro study, all tested compounds showed potent inhibitory effects on BACE1 and AChE. In particular, 8,8'-bieckol demonstrated the best inhibitory effect against BACE1 and AChE, with IC50 values of 1.62 ± 0.14 and 4.59 ± 0.32 µM, respectively. Overall, kinetic studies demonstrated that all the tested compounds acted as dual BACE1 and AChE inhibitors in a non-competitive or competitive fashion, respectively. In silico docking analysis exhibited that the lowest binding energies of all compounds were negative, and specifically different residues of each target enzyme interacted with hydroxyl groups of phlorotannins. The present study suggested that major phlorotannins derived from E. cava possess significant potential as drug candidates for therapeutic agents against AD.

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.

Mutations in DDX58, which encodes RIG-I, cause atypical Singleton-Merten syndrome.

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.Glu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373Ala) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies.

Identification of a rare homozygous c.790C>T variation in the TFB2M gene in Korean patients with autism spectrum disorder.

Mitochondrial dysfunction and subsequent enhanced oxidative stress is implicated in the pathogenesis of autism spectrum disorder (ASD). Mitochondrial transcription factor B2 (TFB2M) is an essential protein in mitochondrial gene expression. No reports have described TFB2M mutations and variations involved in any human diseases. We identified a rare homozygous c.790C>T (His264Tyr) variation in TFB2M gene in two Korean siblings with ASD by whole-exome sequencing. The roles of the TFB2M variation in the pathogenesis of ASD were investigated. Patient fibroblasts revealed increased transcription of mitochondrial genes and mitochondrial function in terms of ATP, membrane potential, oxygen consumption, and reactive oxygen species (ROS). Overexpression of the TFB2M variant in primary-cultured fibroblasts demonstrated significantly increased transcription of mitochondrial genes and mitochondrial function compared with overexpression of wild-type TFB2M. Molecular dynamics simulation of the TFB2M variant protein suggested an increase in the rigidity of the hinge region, which may cause alterations in loading and/or unloading of TFB2M on target DNA. Our results suggest that augmentation of mitochondrial gene expression and subsequent enhancement of mitochondrial function may be associated with the pathogenesis of ASD in Korean patients.

Fisetin inhibits TNF-α/NF-κB-induced IL-8 expression by targeting PKCδ in human airway epithelial cells.

Fisetin (3,7,3',4'-tetrahydroxyflavone), a natural flavonoid, is a therapeutic agent for respiratory inflammatory diseases such as chronic obstructive pulmonary disease (COPD). However, detailed molecular mechanisms regarding the target protein of fisetin remain unknown. Fisetin significantly reduces tumour necrosis factor alpha (TNF-α)-induced interleukin (IL)-8 levels by inhibiting both nuclear factor kappa B (NF-κB) transcriptional activity and the phosphorylation of its upstream effectors. We show that fisetin prevents interactions between protein kinase C (PKC)δ and TNF receptor-associated factor 2 (TRAF2), thereby inhibiting the inhibitor of kappa B kinase (IKK)/NF-κB downstream signalling cascade. Furthermore, we found that fisetin directly binds to PKCδ in vitro. Our findings provide evidence that fisetin inhibits the TNF-α-activated IKK/NF-κB cascade by targeting PKCδ, thereby mediating inflammatory diseases such as COPD. These data suggest that fisetin is a good therapeutic drug for the treatment of inflammatory lung diseases, such as COPD, by inhibiting the TNF-α/NF-κB signalling pathway.