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Flavonoids and liver protection

Flavonoids and liver protection

Wu KC, Proyection Y-L, Kuo Y-H, Flavonoids and liver protection S-S, Flavonoids and liver protection Lived, Chang Y-S. Anf Ameliorate High-Fat-Diet Feeding Plus Flavonoida Ethanol Binge-Induced Steatohepatitis through Inhibiting Inflammatory Caspasedependent Pyroptosis. Pradere, J. Our data Performance nutrition for seniors Flavonoids and liver protection the levels of Notch1 expression in fibrotic livers were marked increase when compared with those in the normal control livers; however, quercetin-treated fibrotic mice decreased the levels of Notch1 gene and protein expression when compared with vehicle-treated fibrotic animals Figures 8B,C. We constructed and visualized a compound-target network between the selected flavonoids and the target protein using assembled experimentally validated and predicted CTIs Figure 3C. Mazidi, M. Using the predicted target information, it was possible to measure the network proximity between all flavonoid targets and NAFLD-associated proteins.

Flavonoids and liver protection -

Nowadays, hepatic fibrosis is viewed as a dynamic process characterized by the massive excess deposition of extracellular matrix ECM in the liver Friedman, ; Tsuchida and Friedman, It has been generally accepted that resident hepatic stellate cells HSCs , which become activated and transdifferentiate into myofibroblast-like cells in response to chronic liver injury, are the major source of ECM during the process of liver fibrogenesis Pellicoro et al.

It has become evident that HSCs activation results from the inflammatory activity of liver immune cells, predominantly macrophages Pellicoro et al.

Of note, hepatic macrophages can directly mediate the behavior of HSCs and other myofibroblasts by producing a range of cytokines, chemokines, and other soluble mediates Pellicoro et al. Additionally, activated myofibroblasts can amplify inflammatory responses by inducing the infiltration of macrophages and further secreting cytokines Duffield et al.

Given the critical regulatory role of macrophages in HSCs activation and liver fibrosis, we believe that it provides therapeutic targets promising application in the future. The prevailing concept indicates that hepatic macrophages can arise either from proliferating resident macrophages, or from circulating bone marrow BM -derived monocytes, which are recruited to the injured liver Duffield et al.

Macrophages are highly plastic cells that can be altered depending on the tissue microenvironment Tacke and Zimmermann, ; Wynn and Vannella, Its polarization statue to M1 or M2 is often used to characterize macrophages; in which M1 macrophages exhibit an inflammatory phenotype while M2 macrophages are alternatively activated, including an anti-inflammatory phenotype Beljaars et al.

Moreover, increasing evidence suggests that M1 macrophages activation plays a critical role in liver inflammation and fibrosis Beljaars et al. Additionally, inflammatory cytokines, including transforming growth factor-β1 TGF-β1 , tumor necrosis factor TNF -α, interleukin IL -1β, and IL-6, released from these cells trigger local inflammatory responses and perpetuate inflammation as well as HSCs activation Sica et al.

By contrast, emerging evidence suggested that alternative M2 macrophages attenuated hepatic steatosis and inflammation, and have a pivotal role in the resolution of fibrosis Beljaars et al. Furthermore, macrophage polarization is regulated by several key molecular mechanisms, including epigenetic regulators, transcription factors, posttranscriptional regulators, and some signaling pathways Sica and Mantovani, ; Sica et al.

Moreover, it has been reported that the M1 macrophage phenotype was controlled by several molecular signaling or transcription factors, including Notch1 signaling, transducer and activator of transcription 1 STAT1 , and interferon-regulatory factor IRF 5 Lawrence and Natoli, ; Sica et al.

Quercetin is known to possess various biological and pharmacological activities including antioxidant, antiviral, anti-inflammatory, anti-proliferative, and antifibrotic effects Marcolin et al. Indeed, the beneficial effects of quercetin on liver injury and fibrosis have been confirmed by several animal models Hernandez-Ortega et al.

FIGURE 1. Quercetin inhibited liver inflammation and fibrosis in CCl 4 -treated mice. C Sirius red staining of liver sections original magnification: × E Hepatic fibrotic area based on Sirius red staining.

However, the precise mechanisms of quercetin on liver fibrosis are incompletely understood. Thus, further studies are needed to define the mechanisms underlying anti-inflammatory and antifibrotic activity of quercetin that hold promise for translation into human treatments.

Notably, it has been reported that quercetin attenuated inflammation in human and mouse macrophages in vitro upon injury Overman et al. In the light of these findings, we thus hypothesized that the antifibrotic effects of quercetin should be involved in regulating activation and polarization of hepatic macrophages.

Carbon tetrachloride CCl 4 , quercetin, dimethyl sulfoxide DMSO , olive oil, 1,4-diazabicyclo[2. Louis, MO, United States.

For in vivo and in vitro experiments, quercetin was diluted immediately in DMSO solution before administration. Mice had unrestricted access to food and water.

Fibrosis in mice was injected intraperitoneally i. biweekly for 8 weeks with 0. Fifty-five mice were randomly divided into four groups as described previously [20]. After 8 weeks of treatment with CCl 4 , mice were sacrificed with pentobarbital, mouse livers were removed to examine for fibrosis.

The dose of quercetin for this experiment was based on the previous studies in mice Hernandez-Ortega et al. All animal experiments were performed according to institutional guidelines and regulations and approved by the Animal Care Committee of Fudan University Shanghai, China. Raw Louis, MO, United States and cultured in undifferentiated Raw macrophages conditioned medium.

Briefly, Raw All incubations were performed in cells under the three or four passages Li et al. In experiments assessing the effects of quercetin on macrophages activation and polarization macrophages, Raw For selective experiments, cells were co-cultured with quercetin 50 μM ; and parallel cultures were treated with an equivalent volume of DMSO 0.

Quercetin concentration 50 μM for macrophage treatment was used in our cell experiments based on previous in vitro bioactivity work Kobuchi et al. After 24 h of co-culture at 37°C, cells were then washed and harvested by centrifugation for immunofluorescence analysis, RNA harvesting, and protein isolation Li et al.

All measurements were performed in triplicate using different batches of wells. Staining and quantitative RT-PCR analysis were performed on three independent experiments.

The absorbance at nm was measured with Flexstation 3 Multimode Microplate Reader Molecular Device. Experiments were conducted in triplicate independently, and data are presented as means ± SD.

Liver fibrosis was assessed by measurement of the Sirius-red positive area, which was measured in six low power × fields per slide using ImageJ 1.

The assessment of the preceding scores was uniformly performed under × magnification in 10 fields per sample. For immunohistochemical analysis, sections of formalin-fixed, paraffin-embedded liver tissue were cut 4 μm, dewaxed, hydrated, and subjected to heat-induced antigen retrieval according to standard protocols as previously reported Li et al.

The intensity of collagen III and IV immunostaining in tissue sections was quantified using five representative sections of each slide and determined for five animals in each group, and the area of staining was analyzed as a percentage of the total area.

Desmin-positive area was quantified in five random non-overlapping × fields and determined for six animals in each group. The immunostaining signaling was quantified at a fixed threshold using free software NIH ImageJ 1.

Details on the immunofluorescence methodology can be found in our previous reports Li et al. Briefly, freshly dissected liver tissues were OCT-embedded and the sections 10 μm in thickness were cut with a cryotome Cryostat Leica, , Germany.

Alexa Fluor Donkey anti-mouse and Alexa Fluor Donkey anti-rabbit secondary antibodies Yeasen Biotechnology, Shanghai, China were incubated at in PBS for 1 h at RT. Finally, the stained tissues were analyzed by fluorescence microscopy BX51, Olympus, Japan.

Sections were washed twice with PBS and incubated with fluorescein-labeled secondary antibody at a dilution of for 1 h at RT in the dark. Slides were mounted in mounting media with DAPI for 40 min at RT. After washing twice with PBS, the slides were covered with DABCO and images were captured by fluorescence microscopy IX51, Olympus, Japan.

Frozen liver tissue was homogenized in radio immunoprecipitation assay buffer RIPA buffer by adding protease inhibitor Cocktail Roche and phosphatase inhibitors Cocktail Sigma, St.

Louis, MO, United States , and then centrifuged at 10, × g at 4°C for 20 min Li et al. Protein extraction from Raw Protein concentration was quantified with the Bicinchoninic Acid Protein Colorimetric Assay kits BMI, Shanghai, China with BSA as the standard.

Equal amounts of proteins were separated by electrophoresis on 7. The membranes were washed with TBST and then incubated with goat anti-rabbit, anti-mouse, or anti-rat secondary antibodies for 2 h at RT.

GAPDH or β-actin dilution was used as internal control, respectively. After washing off the unbound antibody with TBST, the expression of the antibody-linked protein was determined by an ECL TM Western Blotting Detection Reagents Amersham Pharmacia Biotech Inc.

The intensity of the western blot bands was performed using NIH ImageJ software. Expression levels were evaluated by quantification of the relative density of each band normalized to that of the corresponding GAPDH or β-actin band density Li et al.

RNA was reverse-transcribed with random hexamers and avian myeloblastosis virus reverse transcriptase using a commercial kit Perfect Real Time, SYBR ® PrimeScriP TM TaKaRa, Japan.

Quantitative RT-PCR was performed for assessment of mRNA expression on the ABI Prism Sequence Detection system Applied Biosystems, Tokyo, Japan as previously reported Li et al. Sequences of primers for target genes were purchased from Sangon Biotech Co.

Shanghai, China and listed in Table 1. The reactions were run in triplicates using SYBR green gene expression assays. The relative change was normalized to endogenous GAPDH mRNA using the formula 2 -ΔΔC t Li et al.

All data are presented as the mean ± SD. Statistical analysis was performed with GraphPad Prism 7. In all comparisons, a P -value less than 0. Remarkably, histological examination revealed that repeated administration of CCl 4 induced the formation of necrotic areas and inflammation in the liver, with obvious alteration of the sinusoidal and lobular architecture of the liver Figure 1B.

Control oil-injected mice treated with quercetin did not show any liver injury and inflammation; similar to oil-treated animal administrated with vehicle Figure 1B. Consistent with these results, the necroinflammatory injury score was lower in fibrotic mice treated with quercetin than that in fibrotic mice treated with DMSO 2.

Fibrillar collagen deposition in livers could reflect the severity of fibrosis, which was assessed by examining Sirius red-stained liver sections. Our results revealed that mice-repeated injections of CCl 4 for 8 weeks induced obviously ECM proteins accumulation, with the formation of bridging fibrosis Figure 1C.

While there are only thin layers of collagen surrounded the portal tracts and central veins in the liver from normal control animals. However, fibrotic mice given oral quercetin treatment displayed thinner septa and more preserved hepatic parenchyma than fibrotic animals given vehicle DMSO treatment Figure 1C.

Furthermore, collagen deposition in the liver of CCl 4 -treated mice was confirmed by computerized image analysis of the fibrotic area, whereas fibrotic mice treated with quercetin markedly attenuated the progression of CCl 4 -induced fibrosis when compared with vehicle-treated fibrotic mice 4.

Similarly, we observed that the mean fibrosis score was significantly lower in fibrotic mice also given quercetin treatment than that in fibrotic mice given vehicle treatment 2. Additionally, immunohistochemical evaluation revealed that the deposition of intrahepatic collagen III and IV was increased in fibrotic mice induced by CCl 4 for 8 weeks, whereas co-treatment with quercetin attenuated these collagen accumulations in the liver when compared with DMSO-treated control Figure 2A.

These results were further confirmed by quantification of collagen III or collagen IV immunopositive areas; indicating that fibrotic mice treated with quercetin significantly reduced the deposition of collagen in the liver when compared with vehicle-treated control animal Figure 2B.

Moreover, we also assessed the expression levels of the markers of profibrogenic genes, such as Col3α1 , Col4α1 , connective tissue growth factor Ctgf , and tissue inhibitor of metalloproteinase-1 Timp We found that the levels of those profibrogenic genes were observably enhanced in CCl 4 -induced mice when compared with oil-treated normal control; however, quercetin treatment obviously inhibited the profibrogenic effects of CCl 4 injection and decreased the abundance of these genes expression as compared to vehicle-treated animals Figure 2C.

FIGURE 2. Quercetin inhibited liver fibrotic markers expression in CCl 4 -induced mouse fibrotic liver model.

A Representative microscopy images of Collagen III and Collagen IV immunohistochemistry in the liver original magnification, × B Quantitative analysis of Collagen III- and Collagen IV-positive area by ImageJ software NIH.

Taken together, these results indicated that quercetin strikingly attenuated liver inflammation and fibrogenesis in CCl 4 -induced liver fibrosis mouse model.

In order to investigate whether quercetin affects the activation of HSCs in the liver, we examined the expression of HSC-specific marker with immunohistochemical IHC staining.

In our previous study, we demonstrated that quercetin inhibited α-SMA expression at gene and protein level both in vivo and in vitro Li et al.

To provide additional support evidence, we here examined other HSCs activated markers such as desmin and vimentin Bansal et al. Indeed, as revealed by immunostaining, there were markedly strong desmin signals in the fibrotic septa in the CCl 4 -induced livers, while only faint staining for desmin in livers from normal mice; however, there was relatively weak intensity of desmin staining in livers from fibrotic mice receiving quercetin treatment when compared with those fibrotic mice receiving DMSO treatment Figure 3A.

Furthermore, computer-assisted semi-quantitative analysis showed that the number of desmin-positive cells was markedly decreased in livers from quercetin-treated fibrotic mice than those from vehicle-treated control mice 3.

These results were also confirmed by western blot analysis and quantitative RT-PCR experiments, indicating that there was lower expression in the levels of desmin gene and protein after chronic CCl 4 mice receiving quercetin compared with those mice receiving vehicle Figures 3C,D.

In addition, there was a corresponding reduction in mRNA expression levels of vimentin Figure 3D. FIGURE 3. Quercetin inhibited hepatic stellate cells HSCs activation in CCl 4 -treated mice. A Representative microscopy images of desmin staining magnification: × in the liver.

B Quantification of desmin-positive area by ImageJ software NIH. Results were normalized relative to GAPDH expression and expressed as mean ± SD fold change over normal control mice. Collectively, these findings indicated that quercetin treatment efficiently reduced HSC-derived myofibroblasts activation in mice induced by CCl 4.

Remarkably, these positive macrophages were predominantly observed in the scars of fibrotic livers. However, the number of macrophages infiltration in livers was markedly reduced in fibrotic mice receiving quercetin treatment when compared with those mice given DMSO treatment Figures 4A,B.

Taken together, these findings suggested that quercetin treatment significantly reduced massive hepatic macrophage recruitment to the injured liver. FIGURE 4. Quercetin inhibited massive macrophage recruitment into the fibrotic livers of CCl 4 -induced mice. Please note that these are observational findings based on a sample size of nearly 18, adults from the United States.

Take a look at my flavonoid-finder below, while keeping in mind even more recent research suggests that the anthocyanin and isoflavone groups may be the most beneficial flavonoids for liver health.

I can help, reach out today to learn more about my new fatty liver nutrition coaching program. Eat More Flavonoids To Reduce Fatty Liver Risk Posted on May 3, December 11, by Andy the RD. PREVIOUS POST. Liver cirrhosis. Article Google Scholar. Arzumanyan A, Reis HM, Feitelson MA.

Pathogenic mechanisms in HBV- and HCV-associated hepatocellular carcinoma. Nat Rev Cancer. Article CAS Google Scholar. Moradpour D, Blum HE. Pathogenesis of hepatocellular carcinoma.

Eur J Gastroenterol Hepatol. Shokoohinia Y, Rashidi M, Hosseinzadeh L, Jelodarian Z. QuercetinO-beta-D-glucopyranoside, a dietary flavonoid, protects PC12 cells from H 2 O 2 -induced cytotoxicity through inhibition of reactive oxygen species. Food Chem.

Sonoki H, Sato T, Endo S, Matsunaga T, Yamaguchi M, Yamazaki Y, Sugatani J, Ikari A. Quercetin decreases Claudin-2 expression mediated by up-regulation of microRNA miR in lung adenocarcinoma A cells.

Pinelo M, Manzocco L, Nunez MJ, Nicoli MC. Interaction among phenols in food fortification: negative synergism on antioxidant capacity.

J Agric Food Chem. Hatahet T, Morille M, Shamseddin A, Aubert-Pouessel A, Devoisselle JM, Begu S. Dermal quercetin lipid nanocapsules: influence of the formulation on antioxidant activity and cellular protection against hydrogen peroxide.

Int J Pharm. Kemelo MK, Pierzynova A, Kutinova Canova N, Kucera T, Farghali H. The involvement of sirtuin 1 and heme oxygenase 1 in the hepatoprotective effects of quercetin against carbon tetrachloride-induced sub-chronic liver toxicity in rats.

Chem Biol Interact. Ghosh A, Mandal AK, Sarkar S, Das N. Hepatoprotective and neuroprotective activity of liposomal quercetin in combating chronic arsenic induced oxidative damage in liver and brain of rats.

Drug Deliv. Ji LL, Sheng YC, Zheng ZY, Shi L, Wang ZT. The involvement of pKeap1-Nrf2 antioxidative signaling pathway and JNK in the protection of natural flavonoid quercetin against hepatotoxicity. Free Radic Biol Med. Afifi NA, Ibrahim MA, Galal MK. Hepatoprotective influence of quercetin and ellagic acid on thioacetamide-induced hepatotoxicity in rats.

Can J Physiol Pharmacol. Zhuo H, Zheng B, Liu J, Huang Y, Wang H, Zheng D, Mao N, Meng J, Zhou S, Zhong L, Zhao Y. Efficient targeted tumor imaging and secreted endostatin gene delivery by anti-CD immunoliposomes. J Exp Clin Cancer Res. Wang G, Wang JJ, Yang GY, Du SM, Zeng N, Li DS, Li RM, Chen JY, Feng JB, Yuan SH, Ye F.

Effects of quercetin nanoliposomes on C6 glioma cells through induction of type III programmed cell death. Int J Nanomedicine. Mukhopadhyay P, Maity S, Mandal S, Chakraborti AS, Prajapati AK, Kundu PP.

Preparation, characterization and in vivo evaluation of pH sensitive, safe quercetin-succinylated chitosan-alginate core-shell-corona nanoparticle for diabetes treatment. Carbohydr Polym. Zhang YD, Wang JW, Liu XY, Zhao ZY, Zhang LH, Long B.

Study on distribution of liposome nanoparticles loaded quercetin in rats. Chin Med Eng. CAS Google Scholar. Zhang HY, Han DW, Zhao ZF, Liu MS, Wu YJ, Chen XM, Ji C. Multiple pathogenic factor-induced complications of cirrhosis in rats: a new model of hepatopulmonary syndrome with intestinal endotoxemia.

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Combined activities of JNK1 and JNK2 in hepatocytes protect against toxic liver injury. Wang L, Zhang W, Ge CH, Yin RH, Xiao Y, Zhan YQ, Yu M, Li CY, Ge ZQ, Yang XM.

Wang X, Fang C, Tian S, Zhu X, Yang L, Huang Z, Li H, Dusp14 protects against hepatic ischemia-reperfusion injury via Tak1 suppression. J Hepatol. Nakao T, Ono Y, Dai H, Nakano R, Perez-Gutierrez A, Camirand G, Huang H, Geller DA, Thomson AW.

Qiao H, Zhou Y, Qin X, Cheng J, He Y, Jiang Y. NADPH oxidase signaling pathway mediates Mesenchymal stem cell-induced inhibition of hepatic stellate cell activation.

Stem Cells Int. Jia FF, Tan ZR, McLeod HL, Chen Y, Ou-Yang DS, Zhou HH. Effects of quercetin on pharmacokinetics of cefprozil in Chinese-Han male volunteers. Tzankova V, Aluani D, Kondeva-Burdina M, Yordanov Y, Odzhakov F, Apostolov A, Yoncheva K. Biomed Pharmacother. Gupta V, Sharma R, Bansal P, Kaur G.

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Li X, Jin Q, Yao Q, Xu B, Li L, Zhang S, Tu C. The flavonoid Quercetin ameliorates liver inflammation and fibrosis by regulating hepatic macrophages activation and polarization in mice.

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Pharmacol Rep. Download references. The design of the study, collection, analysis, and interpretation of data was supported by the grant Hunan Provincial Natural Science Foundation of China grant no.

You can also search for this author in PubMed Google Scholar. XL and YZ performed animals experiments, data acquisition, the decision to publish and manuscript preparation. LL revised the manuscript and provided valuable advices.

YP and YH contributed to the image modification. PY helped finish some experimental works.

Nonalcoholic fatty liver disease NAFLD is the prottection common protectlon of Flavonoids and liver protection liver disease and lacks guaranteed pharmacological therapeutic options. Flavonoid-target Hunger control and cravings information was obtained from combining experimentally validated data Flavlnoids Flavonoids and liver protection obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD.

Flavonoids and liver protection -

Whether you are looking at mental health, longevity, insulin resistance, inflammation or otherwise — flavonoids keep popping up. Fortunately, a paper out of The Journal Of Nutritional Biochemistry , had some answers for me.

Please note that these are observational findings based on a sample size of nearly 18, adults from the United States. Take a look at my flavonoid-finder below, while keeping in mind even more recent research suggests that the anthocyanin and isoflavone groups may be the most beneficial flavonoids for liver health.

I can help, reach out today to learn more about my new fatty liver nutrition coaching program. Eat More Flavonoids To Reduce Fatty Liver Risk Posted on May 3, December 11, by Andy the RD. PREVIOUS POST. NEXT POST. Mitra et al. Recently, NAFLD has become one of the most important medical issues worldwide; however, there has been no effective therapeutic approach.

Thus, the development of anti-NAFLD effect-guaranteed therapeutics is urgently required. Natural products, such as flavonoids, have frequently been investigated in NAFLD models and have shown beneficial effects in clinical and preclinical studies. A multiethnic clinical study found that the intake of flavonoids reduces the risk of NAFLD and assists in normalizing NAFLD status by attenuating the fatty liver index, serum aspartate aminotransferase, and alanine aminotransferase Mazidi et al.

In addition, narrative reviews suggested that the pharmacological properties of flavonoids against NAFLD are primarily exerted by acting on multiple targets involved in oxidative stress, inflammation, and lipid metabolism Jadeja and Devkar, ; Qiu et al.

Despite their therapeutic potential, the majority of flavonoids have not been identified for their NAFLD treatment potential due to the excessive cost and labor required to perform biochemical analysis. Therefore, there is still a pressing need for an alternative strategy to predict potential flavonoids with in-depth mechanisms for prevention and treatment of NAFLD.

A network medicine framework, based on the molecular interactions of comprehensive subcellular networks, has emerged as a promising platform for identifying rational drug target and novel indication Guney et al.

The key finding of the framework is that the closer the targets of a compound are to disease proteins on a human protein-protein interaction PPI network, the more likely that the compound will affect the disease phenotype. A recent study also revealed that the framework can discover the therapeutic effects of polyphenols, suggesting the possibility of discovering potential natural products for NAFLD treatment do Valle et al.

However, a sufficient amount of compound-target interaction CTI information is an essential prerequisite for exploring the therapeutic potential of natural products using the developed framework.

Unfortunately, the CTIs of natural products are currently largely unknown, making it difficult to fully apply network medicine frameworks to natural products.

One way to complement the limited coverage of the framework is to leverage machine-learning prediction methods. Using abundant chemical and biological data, machine-learning techniques have been successfully applied in various applications and archives, including CTI prediction Luo et al.

In particular, Lee et al. recently developed a state-of-the-art algorithm, AI-DTI, that predicts activatory and inhibitory targets based on mol2vec and genetically perturbed transcriptome Lee et al.

The model outperformed a previous model that predicted activatory and inhibitory targets, supporting the accuracy and reliability of the model.

One of the key features of this model is that only the 2D structure of a small molecule is required for CTI prediction, highlighting the practical applicability of CTI prediction to natural products. Here, we aimed to investigate potential flavonoids that exert beneficial effects on NAFLD by combining an AI-DTI model and the network medicine framework Figure 1.

Focusing on flavonoids, which have been widely studied for NAFLD, allowed us to comprehensively evaluate the reliability of the prediction results and lead to identifying more promising candidates.

We evaluated whether proximity measurements could rediscover known beneficial effects and their potential mechanisms in order to test the validity of the predicted results.

Finally, the potential of the candidate flavonoids was evaluated using a free fatty acid FFA -induced HepG2 and AML12 cell models. Altogether, we believe that this study systematically revealed flavonoids that can be used against NAFLD, along with testable molecular mechanistic hypotheses.

FIGURE 1. Integrated workflow for investigating candidate flavonoids and their potential mechanisms for NAFLD. A Data collection. Human PPI network, compound-target network of flavonoids, NAFLD-associated proteins were collected from various datasets and databases.

B Prioritizing flavonoids based on network proximity. Average closest distance d c and its relative distance Z d c were calculated to screen potential flavonoids for NAFLD treatment under the human PPI network. C Experimental validation.

The preventive effects of candidate flavonoids against NAFLD were evaluated using FFA-induced in vitro model, molecular docking, and network analysis. Flavonoids were retrieved from the Phenol-Explorer database version 3.

For the analysis, we only considered flavonoids that 1 could be mapped to PubChem IDs, and 2 where 2D structures in SMILES format were available. A quantitative estimate of drug-likeness QED Bickerton et al. Following this, 59 flavonoids were considered candidates with favorable pharmacokinetic properties that could be used in subsequent analyses.

QED was calculated using the RDkit module in Python 3. The potential target profiles of flavonoids were predicted by AI-DTI, a practically useful algorithm developed for predicting activatory and inhibitory targets of compounds Lee et al. AI-DTI consists of two models that predict activatory or inhibitory CTIs.

When an input query drug-target pair is received, AI-DTI transforms it into activatory and inhibitory DTI feature vectors, and then infers activatory and inhibitory interaction using each prediction model.

The input features for the model are constructed by concatenating the vectors of compounds and targets derived from the mol2vec method Jaeger et al. For activatory CTIs, the feature vector was represented as a concatenated form of the compound vector calculated by mol2vec and the representative vectors of activatory targets.

For inhibitory CTIs, the feature vector was constructed as a concatenated form of the compound vector calculated by mol2vec and representative vectors of inhibitory targets. Each model is trained to discriminate between known and unknown CTIs based on a dataset consisting of the constructed input features and their labels.

The trained model predicts the likelihood score that the compound would activate or inhibit the protein using the input vector of the compound and the target of interest. In this study, we used an AI-DTI model trained on the extended dataset with an optimized cascaded deep forest model trained on the extended dataset that can predict a wider target with best performance.

Network pharmacological analysis was conducted by constructing a compound-target network for flavonoids and analyzing the constructed network. A compound—target network is a bipartite network in which nodes are defined as compounds and targets, and the edges between compounds and targets are defined as CTIs.

The compound-target network was constructed and visualized using Cytoscape version 3. org , v. PANTHER is widely used as a comprehensive resource for gene function classification and genome-wide data analysis. NAFLD-associated proteins were obtained from the Comparative Toxicogenomics Database CTD and the literature.

CTD is a publicly available database that aims to advance the understanding of the effects of environmental exposure on human health, providing manually curated information, such as disease-gene associations Davis et al.

We manually added 14 additional NAFLD-associated proteins from the literatures Millar et al. et al. To identify biological functions at the process level, proteins were grouped as follows: lipid metabolism lipid metabolic process, GO: , inflammation regulation of inflammatory response, GO: , oxidative stress response to oxidative stress, GO: , and others.

In this study, 85 proteins related to various biological processes were identified as NAFLD-related proteins Supplementary Table S1. The human PPI network is a set of PPIs that occur in human cells. The PPI network used in this study was obtained from the data built by do Valle et al.

Briefly, they assembled the human interactome from 16 databases containing six different types of PPIs: 1 binary PPIs tested by high-throughput yeast two-hybrid experiments Rolland et al.

The genes were mapped to their Entrez IDs based on the National Center for Biotechnology Information NCBI database and their official gene symbols. The constructed network included , PPIs, connecting 17, unique proteins. The proximity of NAFLD-associated proteins and flavonoids was assessed using a distance metric proposed by Guney et al.

First, the average closest distance d c S , T between NAFLD-associated proteins and flavonoid targets is defined as follows:. S denotes a set of NAFLD-associated proteins, T denotes the set of flavonoid targets, and d s , t denotes the shortest path length between nodes s and t in the network.

A relative distance metric Z d c was then calculated by comparing the d c S , T to a reference distribution describing random expectations. The reference distribution is constructed by iteratively calculating the expected distances between two randomly selected groups of proteins matching the size and degrees of NAFLD—associated proteins and flavonoid targets in the network.

The relative distance Z d c is defined as follows:. μ c S , T denotes the mean and σ c S , T denotes standard deviation of the reference distribution, respectively. The closest and relative distances were calculated in python 3. The molecular docking method was used to study the binding affinities and conformations of glycitin and its predicted targets.

The web server CB-Dock was used to perform molecular docking simulations Liu et al. org Burley et al. These files were uploaded and submitted to the CB-Dock server. The result table lists the vina scores, cavity sizes, docking centers, and sizes of the predicted cavities. Once a ligand in the table is selected, the structure in the interactive 3D graphics is visualized.

Ligplot software was used for 2D visualization of the interactions between proteins and a ligand. HepG2 cell line Korean Cell Line Bank, Seoul, Republic of Korea and RAW AML12 cell line ATCC, VA. Cells were treated with 2, 20, or μM of glycitin, choerospondin, glycitein, and daidzin MedChemExpress, NJ, United States for 24 h.

Absorbance was measured using a UV spectrophotometer at nm Molecular Devices, CA, United States. Lipid accumulation was detected using optical microscopy.

Absorbance was measured using a UV spectrophotometer at nm Molecular Devices. Under the same cell culture conditions as the Oil Red O staining assay, intracellular TG levels were measured using an enzymatic detection kit Asan Pharmaceuticals, Seoul, Republic of Korea.

Total protein concentration was measured using a bicinchoninic acid protein assay kit BCA1 and B, Sigma-Aldrich, MO, United States. The absorbance was measured using a UV spectrophotometer at nm Molecular Devices.

Briefly, cells were incubated with 10 μM DCFH-DA for 30 min at 37°C in the dark. Intracellular ROS production was measured using an Axiophot microscope Carl Zeiss, Jena, Germany. After incubation for 15 min at 37°C, absorbance was measured using a UV spectrophotometer at nm Molecular Devices.

Under the same cell culture conditions as the NO detection assay, the proinflammatory cytokine TNF-α level of supernatants was measured using a commercially available enzyme immunoassay EIA kit for TNF-α BD Biosciences, San jose, CA, United States. Under the same cell culture conditions as the Oil Red O staining assay, total mRNA was extracted using QIAzol reagent Qiagen, CA, United States.

After synthesis of cDNA using a High-Capacity cDNA Reverse Transcription Kit Ambion, Austin, TX, United States , real-time PCR was performed using SYBR Green PCR Master Mix Applied Biosystems; Foster City, CA, United States.

PCR amplification was performed using a Rotor-Gene Q Qiagen, Hilden, Germany with standard protocol. The quantitative cycle threshold value of each gene was normalized with that of GAPDH. Information of the primer sequences is summarized in Supplementary Table S2. Statistical analyses were performed using Python version 3.

For the two-sample test, Shapiro—Wilk test was used to assess whether the data were normally distributed. When the normality was rejected, the Mann-Whitney U test was applied. For the multiple comparison test, Shapiro—Wilk test was used to assess whether the data were normally distributed.

We initially identified flavonoids from Phenol-Explorer. This threshold represents the average QED value of FDA-approved drugs, and compounds with QED values above the threshold are considered to have favorable pharmacokinetic properties. As a result, 59 flavonoids were selected and included in our study Figure 2A and Supplementary Table S3.

To describe their chemical diversity, we visualized the subclass distribution of flavonoids. The results showed that the selected flavonoids were distributed across nine subclasses.

Among the subclasses, flavones, flavanones, and flavonols were the top three subclasses with 20, 11, and 10 compounds, respectively Figure 2B.

FIGURE 2. Selection process for flavonoids evaluated in this study and their chemical distribution. A The flowchart of selecting the flavonoids B Distribution of flavonoids across its subclasses.

We first retrieved validated CTIs between 27 flavonoids and protein targets from the DrugBank version 5. Therefore, we utilized our recently developed algorithm, AI-DTI, to predict activatory and inhibitory targets of selected flavonoids.

We constructed an input vector for all predictable flavonoid and protein target pairs, and then predicted the likelihood score of the CTI using a pretrained model. As a result, we additionally secured activatory CTIs and 1, inhibitory CTIs between 59 selected flavonoids and 73 protein targets Figure 3A and Supplementary Table S4.

FIGURE 3. A compound-target network for selected flavonoids and its property. DrugBank and TTD contain experimentally validated DTIs, and AI-DTI is employed to predict the activity-inhibitory target of flavonoids.

B Distribution of the number of flavonoid targets. C Compound-target network for flavonoids. Circles and diamonds denote protein targets and compounds, respectively. We tested the reliability of the predicted results by comparing whether the overlapped number of CTIs between the experimentally validated results and predicted results was higher than the values in the null distribution.

The values of the null distribution were obtained by randomly selecting the potential combinations of flavonoids and predictable targets in AI-DTI and then repeatedly calculating the number of overlapped CTIs between validated results and selected results.

We found that AI-DTI successfully recovered 11 validated CTIs that did not appear in the training dataset. We constructed and visualized a compound-target network between the selected flavonoids and the target protein using assembled experimentally validated and predicted CTIs Figure 3C.

We revealed that the average number of targets for flavonoids was These results show that AI-DTI can provide accurate and sufficient CTI information for subsequent analyses.

Next, we attempted to identify candidate flavonoids that exert beneficial effects on NAFLD by employing a network medicine framework. We calculated the network proximity between flavonoid targets and NAFLD-associated 85 proteins using the closest measure, d c and Z d c , representing the average shortest path length and its relative distance between each flavonoid target and the nearest disease protein, respectively Figure 1B , see Materials and Methods for details.

The measured proximity and direct interactions between the flavonoids and NAFLD-associated proteins are summarized in Supplementary Table S5. For example, daidzein and daidzin, an isoflavone phytoestrogen found in soy, and its metabolites are produced by human intestinal microflora.

An in vivo study found anti-steatotic effects of daidzein through direct regulation of hepatic de novo lipogenesis and insulin signaling, and the indirect control of adiposity and adipocytokines by altering adipocyte metabolism.

We extended our search range to assess whether the top-predicted flavonoids and their metabolic byproducts have reported beneficial effects on NAFLD. Our results also revealed that the majority of flavonoids proximal to NAFLD-associated proteins have therapeutic effects on NAFLD, indicating that proximity score successfully rediscovered the known therapeutic effect of flavonoids on NAFLD Table 1.

TABLE 1. Top network-predicted candidate flavonoids for NAFLD with available literature-derived evidence. To test the mechanistic interpretability of the framework, we evaluated whether the mechanisms of flavonoids could be explained at the network level.

We considered three flavonoids, dihydroquercetin, nobliletin, and butein, which are highly ranked in proximity measure, and have evidence reported for its native molecule itself. We visualized networks focusing on selected flavonoids and their protein targets and biomarkers whose expression was measured in previous studies Figure 4.

We then explored whether the association between the target of flavonoids and the measured biomarker could explain the results of previous reports. FIGURE 4. A compound-protein network between selected flavonoids and NAFLD-associated proteins. A network including the interaction between flavonoid targets and NAFLD-associated proteins was constructed to elucidate the mechanisms of dihydroquercetin, butein, and nobiletin.

Black and red arrows indicate interactions consistent or inconsistent with the inferred mechanism of flavonoids against NAFLD, respectively. Dihydroquercetin, also called taxifolin, was reported to ameliorate high-fat diet feeding plus acute ethanol-binding induced steatohepatitis by upregulating PPARγ levels and suppressing the expression of interleukin IL -1β and caspase-1 Zhan et al.

Our results showed that dihydroquercetin activates PPARG, which supports the notion that upregulated PPARγ expression can be caused by the direct effect of dihydroquercetin.

In addition, we infer that the inhibitory effects of dihydroquercetin on caspase-1 and IL-1β can be derived from the inhibitory effects of dihydroquercetin on the androgen acceptor. This hypothesis is supported by a previous study showing that the androgen receptor is a promising regulator of caspase-1 activity, which is responsible for the subsequent activation of pro-inflammatory cytokines, including IL-1β Duez and Pourcet, Alternatively, butein exerts its antiproliferative and proapoptotic effects on NAFLD by suppressing STAT3 and JNK signaling Moon et al.

The constructed network showed that STAT3 and MAPK8 JNK1 interacted closely with the butein target EGFR. Moreover, considering that EGFR is an upstream regulator of STAT3 and MPAK8 JNK1 , we can infer the potential mechanisms by which butane affects STAT3 and MAPK by regulating EGFR.

Taken together, we found that molecular interactions between the flavonoid target and the measured biomarker provide potential network-level mechanisms for certain flavonoids. In contrast, we found that associations between certain flavonoid targets and NAFLD-associated proteins does not aid in the interpretation of mechanisms.

For example, previous studies have reported that dihydroquercetin inhibits the expression of IL-1B, which can lead to a hypothesis that its effect is exerted by the inhibitory effect of dihydroquercetin on interacting PTPN1, which interact with IL-1β.

However, a previous report revealed that PTPN1 inhibition rather further increases the effectiveness of inflammatory cytokines, including IL-1β Chen et al.

Furthermore, we could not identify any direct neighbors between the target of noviletin and previously measured biomarkers. These results indicate that the molecular interactions between flavonoid targets and measured biomarkers should be meticulously interpreted, considering the disease and model-specific contexts.

Based on the results from the proximity distances, we further evaluated whether unknown flavonoids, whose targets are proximal to NAFLD-associated proteins, could exhibit beneficial effects on NAFLD. We considered the following four flavonoids: glycitin and choerospondin, which are unreported and commercially available flavonoids that are proximal to NAFLD-associated proteins, and glycitein metabolites of glycitin , and daidzin the most proximal flavonoids with reported evidence.

To ensure appropriate dose of four flavonoids 0— μM , the cell viability was evaluated using a cell counting kit-8 CCK8 assays. Therefore, further investigations for evaluating anti-NAFLD activity were performed using a single dose 20 μM of four flavonoids.

To evaluate the beneficial effects of flavonoids against NAFLD, we adopted an FFA-induced hepatic steatosis cell model, which is commonly used to generate a cellular model of NAFLD Müller and Sturla, For our experimental purpose, instead of palmitic acid that induces lipotoxicity, the FFA mixture ratio of 2: 1, oleic acid: palmitic acid was used.

The FFAs predominantly induced NAFLD-like in vitro conditions in HepG2 cells, as evidenced by increases in Oil Red O histological observations approximately 3-folds and TG contents approximately 3. In addition, these anti-NAFLD properties were re-validated in a normal murine hepatocyte AML12 cells Figures 5A,B.

Taken together, these results indicate that the proximity scores are reliable traits for predicting novel candidates for preventing NAFLD progression.

FIGURE 5. Effects of four isoflavones against NAFLD conditions in both HepG2 and AML12 cells. A Hepatic lipid accumulations and ROS productions were determined using Oil Red O staining and DCFH-DA assay, respectively. B Intracellular lipid accumulations in HepG2 and AML12 cells and C TG contents in HepG2 cells were quantified.

We further investigated the potential mechanisms focusing on glycitin, and all the identified targets were predicted using AI-DTI. Reliability of predicted interactions was firstly evaluated by analyzing molecular docking potentials.

Briefly, the structure of glycitin was uploaded to CB-dock Liu et al. For each process, blind docking was carried out to detect suitable binding sites for glycitin and calculate the vina score, which is an estimate of the logarithm of the free binding energy. A cross-validation study was further performed using another docking web server, COACH-D Wu Q.

We visualized the molecular interaction between glycitin and PDE5A, which had the lowest vina score. Our results show that glycitin exhibits a strong binding affinity to the predicted target, which supports the reliability of the predicted results.

FIGURE 6. Molecular docking validation and potential mechanism of glycitin. A Molecular docking results between glycitin and its predicted target and its representative example. B Discovered target-NAFLD associated protein network for glycitin. A diamond denotes a flavonoid and circles denote protein targets.

The border and color of the circle denote the predicted target or related process-level function, respectively. We then conducted an overrepresentation test and network analysis to identify the potential mechanisms of glycitin at the level of biological processes and molecular interactions.

This association indicates that the mechanisms of glycitin involve lipid metabolism and inflammation, which are key regulators of the pathogenesis and progression of NAFLD Buzzetti et al. We also visualized the molecular interactions between the target of glycitin and NAFLD-associated protein.

We found that glycitin targets interact with NAFLD-associated proteins associated with various functions, including oxidative stress Figure 6B.

The close interaction between them indicates that the antioxidant capacity of glycitin may be exerted by regulating the function of these proteins via the cellular network.

These results indicate that the anti-NAFLD effect of glycitin may be exerted by regulating the functions of various proteins related to metabolism, oxidative stress, and inflammation at the biological process and network level. For the experimental verification of above predicted molecular interactions, we explored the mRNA gene expression in each of lipid metabolism, oxidative stress and inflammation using quantitative PCR method.

FIGURE 7. Experimental investigating potential mechanism of glycitin. A Intracellular ROS production was determined using DCFH-DA fluorescence assay, and B it was quantified in HepG2 cells. C The mRNA expressions of lipid metabolism- and antioxidant-related genes were measured using quantitative real-time PCR method in HepG2 cells.

According to AI-DTI predictions, we additionally investigated the anti-inflammatory properties of flavonoids using LPS-induced inflammatory macrophage model. In RAW In this study, we discovered candidate flavonoids that exert beneficial effects on NAFLD using a comprehensive strategy that combines AI-DTI and network medicine framework.

The AI-DTI model provided activatory and inhibitory CTIs for all included flavonoids, and the hypergeometric test supported the reliability and accuracy of the CTI prediction results. The measured proximity successfully recovered known flavonoids that exhibited therapeutic effects on NAFLD, along with potential mechanisms.

Then, we performed in vitro experiments using HepG2 and AML12 cells of the NAFLD model, which were triggered by FFA. The results showed that glycitin significantly attenuated lipid accumulation in hepatocytes cells, conferring its therapeutic potential against NAFLD.

The potential mechanisms of glycitin were proposed and supported by molecular docking, overrepresentation analysis, and network analysis. We believe that our study can contribute significantly to the discovery of novel candidates and clinical implications for NAFLD.

To the best of our knowledge, this study is the first to systematically identify candidates for NAFLD using network proximity measure. Network-based prediction prioritizes candidate drugs under the assumption that the emergence of disease is related to the breakdown of a coordinated function of a distinct group.

This hypothesis is consistent with the pathogenesis of NAFLD, which involves multifactorial pathogenic properties. Our findings also suggest that network-based approach could be a promising strategy for discovering candidate drugs for NAFLD.

Editor-in-Chief: Francis J. Castellino Dean Emeritus, College of Science Kleiderer-Pezold Professor of Protevtion Flavonoids and liver protection, W. Proteciton Center Glucagon hormone pathway Transgene Research Flavonoids and liver protection Hall, University of Notre Dame Notre Dame, IN USA. ISSN Print : ISSN Online : DOI: Background: The liver is one of the crucial organs in humans and is responsible for the regulation of diverse processes, including metabolism, secretion, and detoxification.


The BEST Foods to Clean Out Your Liver

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