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21 pages, 1486 KiB  
Systematic Review
Comparative Efficacy and Safety of Cardio-Renoprotective Pharmacological Interventions in Chronic Kidney Disease: An Umbrella Review of Network Meta-Analyses and a Multicriteria Decision Analysis
by Ioannis Bellos, Smaragdi Marinaki, Pagona Lagiou and Vassiliki Benetou
Biomolecules 2025, 15(1), 39; https://doi.org/10.3390/biom15010039 - 31 Dec 2024
Viewed by 303
Abstract
Sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1a), and non-steroidal mineralocorticoid receptor antagonists (ns-MRA) are promising treatments for chronic kidney disease. This umbrella review of network meta-analyses evaluated their effects on cardiovascular outcomes, kidney disease progression, and adverse events, using the [...] Read more.
Sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1a), and non-steroidal mineralocorticoid receptor antagonists (ns-MRA) are promising treatments for chronic kidney disease. This umbrella review of network meta-analyses evaluated their effects on cardiovascular outcomes, kidney disease progression, and adverse events, using the TOPSIS method to identify the optimal intervention based on P-scores. A total of 19 network meta-analyses and 44 randomized controlled trials involving 86,150 chronic kidney disease patients were included. Compared to placebo, SGLT2i were associated with reduced risks of cardiovascular events [Hazard ratio (HR): 0.776, 95% confidence intervals (CI): 0.727–0.998], kidney disease progression (HR: 0.679, 95% CI: 0.629–0.733), acute kidney injury (HR: 0.873, 95% CI: 0.773–0.907), and serious adverse events (HR: 0.881, 95% CI: 0.847–0.916). GLP1a and ns-MRA were also associated with significant reductions in cardiovascular and kidney-specific composite outcomes. Indirect evidence showed that SGLT2i demonstrated a lower risk of kidney disease progression compared to GLP1a (HR: 0.826, 95% CI: 0.716–0.952) and ns-MRA (HR: 0.818, 95% CI: 0.673–0.995), representing the best intervention across all endpoints. In conclusion, while SGLT2i, GLP1a, and ns-MRA all reduce cardiovascular and kidney disease risks in chronic kidney disease, SGLT2i appears to provide the most favorable balance of efficacy and safety. Full article
(This article belongs to the Special Issue New Insights into Cardiometabolic Diseases)
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Figure 1
<p>League table comparing interventions for the risk of the cardiovascular composite (lower half) and kidney-specific composite (upper half) outcome. Each cell presents the relative effects of interventions as hazard ratios with 95% confidence intervals. Statistically significant differences are indicated by asterisks.</p>
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<p>League tables comparing interventions for the risk of serious adverse events, adverse events leading to drug discontinuation, and acute kidney injury. Each cell presents the relative effects of interventions as hazard ratios with 95% confidence intervals. Statistically significant differences are indicated by asterisks.</p>
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17 pages, 9791 KiB  
Article
The Potential Mechanism of Alpiniae oxyphyllae Fructus Against Hyperuricemia: An Integration of Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Experiments
by Shuanggou Zhang, Yuanfei Yang, Ruohan Zhang, Jian Gao, Mengyun Wu, Jing Wang, Jun Sheng and Peiyuan Sun
Nutrients 2025, 17(1), 71; https://doi.org/10.3390/nu17010071 (registering DOI) - 28 Dec 2024
Viewed by 357
Abstract
Background: Alpiniae oxyphyllae Fructus (AOF) is a medicinal and edible resource that holds potential to ameliorate hyperuricemia (HUA), yet its mechanism of action warrants further investigation. Methods: We performed network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiments to [...] Read more.
Background: Alpiniae oxyphyllae Fructus (AOF) is a medicinal and edible resource that holds potential to ameliorate hyperuricemia (HUA), yet its mechanism of action warrants further investigation. Methods: We performed network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiments to investigate the potential action and mechanism of AOF against HUA. Results: The results indicate that 48 potential anti-HUA targets for 4 components derived from AOF were excavated and predicted through public databases. Gene Ontology (GO) enrichment analysis indicated that there are 190 entries related to biological process, 24 entries related to cellular component, 42 entries related to molecular function, and 44 entries related to Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. The results of molecular docking showed that the main active ingredients of AOF may have potential therapeutic effects on immune system disorders and inflammation caused by HUA by binding to targets including peroxisome-proliferator-activated receptor gamma (PPARG), estrogen receptor 1 (ESR1), prostaglandin G/H synthase 2 (PTGS2), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR). Subsequently, we further determined the stability of the complex between the core active ingredient and the core target proteins by molecular dynamics simulation. The results of cell experiments demonstrated that stigmasterol as the core active ingredient derived from AOF significantly upregulated the expression levels of ESR1 and PPARG (p < 0.001) to exert an anti-HUA effect. Conclusions: In summary, we have systematically elucidated that the mechanism of main active ingredients derived from AOF mainly exert their pharmacological effects by acting on multiple targets in this study. Our studies will provide a scientific basis for the precise development and utilization of AOF. Full article
(This article belongs to the Section Phytochemicals and Human Health)
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<p>Network pharmacology flowchart of AOF for improving HUA. ### <span class="html-italic">p</span> &lt; 0.001 vs. the control; *** <span class="html-italic">p</span> &lt; 0.001 vs. the UA.</p>
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<p>Target network of the active ingredients derived from AOF.</p>
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<p>The overlap between the targets associated with active components of AOF and the targets associated with HUA depicted by a Venn diagram.</p>
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<p>Network diagram of target-active ingredient disease for the anti-HUA efficacy of AOF.</p>
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<p>The PPI network of targets between HUA and AOF was constructed. (<b>A</b>) The PPI network diagram was derived from the STRING database. (<b>B</b>) Cytoscape software was utilized to identify the core targets within the protein interaction network. Nodes represent proteins, and edges indicate the connections between proteins. The size and color of the nodes correspond to the significance of each protein within the overall network.</p>
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<p>GO enrichment analysis (<b>A</b>,<b>C</b>,<b>E</b>) and the core targets against HUA (<b>B</b>,<b>D</b>,<b>F</b>).</p>
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<p>KEGG pathway enrichment analysis of potential targets for AOF intervening with HUA.</p>
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<p>The docking conformation between the main active ingredients of AOF and some key targets of HUA. Figures (<b>A</b>–<b>D</b>) display the molecular docking outcomes of stigmasterol with the following proteins: ESR1 (<b>A</b>), PPARG (<b>B</b>), HMGCR (<b>C</b>), and PTGS2 (<b>D</b>).</p>
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<p>Molecular dynamics simulation for stigmasterol bound to ESR1 or PPARG by using Gromacs 2020.6. (<b>A</b>) The RMSD plots for free ESR1 and the complex of stigmasterol–ESR1. (<b>B</b>) The RMSD plots for free PPARG and the complex of stigmasterol–PPARG. (<b>C</b>) The Rg plots for free ESR1 and the complex of stigmasterol–ESR1. (<b>D</b>) The Rg plots for free PPARG and the complex of stigmasterol–PPARG.</p>
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<p>The cytotoxicity and anti-HUA effect of stigmasterol on HK-2 cells. (<b>A</b>) The structural formula of stigmasterol. (<b>B</b>) Effects of different concentrations of stigmasterol on the cell viability of HK-2 cells. (<b>C</b>) Different concentrations of stigmasterol attenuated the inhibitory effect induced by UA on HK-2 cells. ### <span class="html-italic">p</span> &lt; 0.001 vs. the control; *** <span class="html-italic">p</span> &lt; 0.001 vs. the UA.</p>
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<p>Stigmasterol exhibited anti-HUA effects by promoting UA excretion via regulating ESR1 and PPARG. (<b>A</b>) Stigmasterol increased UA excretion in HK-2 cells. (<b>B</b>) Effects of different concentrations of stigmasterol on the expression of key target proteins were detected by Western blotting. (<b>C</b>) The protein expression levels of ESR1 were quantified. (<b>D</b>) The protein expression levels of PPARG were quantified. All values are expressed as the average of three independent experiments (mean ± SEM). ### <span class="html-italic">p</span> &lt; 0.001 vs. the control; *** <span class="html-italic">p</span> &lt; 0.001 vs. the UA.</p>
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26 pages, 18252 KiB  
Article
Amelioration of Inflammation in Rats with Experimentally Induced Asthma by Spenceria ramalana Trimen Polyphenols via the PI3K/Akt Signaling Pathway
by Zhaobin Xia, Xing Zhao, Lu Wang, Lin Huang, Yanwen Yang, Xiangyu Yin, Luyu He, Yuebumo Aga, Ankaer Kahaer, Shiyu Yang, Lili Hao and Chaoxi Chen
Int. J. Mol. Sci. 2025, 26(1), 165; https://doi.org/10.3390/ijms26010165 - 28 Dec 2024
Viewed by 275
Abstract
Asthma is a chronic inflammatory respiratory disease that affects millions globally and poses a serious public health challenge. Current therapeutic strategies, including corticosteroids, are constrained by variable patient responses and adverse effects. In this study, a polyphenolic extract derived from the Tibetan medicinal [...] Read more.
Asthma is a chronic inflammatory respiratory disease that affects millions globally and poses a serious public health challenge. Current therapeutic strategies, including corticosteroids, are constrained by variable patient responses and adverse effects. In this study, a polyphenolic extract derived from the Tibetan medicinal plant Spenceria ramalana Trimen (SRT) was employed and shown to improve experimentally (ovalbumin + cigarette smoke, OVA + CS) induced asthma in rats. Initially, the potential therapeutic mechanism of the polyphenolic components in SRT on OVA + CS-induced asthma was predicated by network pharmacology analysis. Subsequently, in vivo experiments identified that SRT polyphenols exhibit significant anti-asthmatic activities, primarily mediated by lowering inflammatory cell counts such as the WBC (white blood cell), eosinophils, and neutrophils, decreasing the expression of inflammatory cytokines (IL-4, IL-5, IL-13, and TNF-α), alleviating lung histological damage (reduced inflammation, collagen deposition, and mucus secretion), and enhancing the epithelial barrier integrity (upregulation of ZO-1, occludin, and claudin-1). Additionally, SRT polyphenols downregulated the PI3K/Akt (Phosphoinositide 3-kinase/protein kinase B) signaling pathway, improved gut microbiota disruption, and regulated fecal metabolites (glucose-6-glutamate, PS (16:0/0:0), 8-aminocaprylic acid, galactonic acid, Ascr#10, 2,3,4,5,6,7-hexahydroxyheptanoic acid, phosphodimethylethanolamine, muramic acid, 9-oxohexadeca-10e-enoic acid, and sedoheptulose) in asthmatic rats. In conclusion, SRT polyphenols exerted multifaceted protective effects against OVA + CS-induced asthma in rats, highlighting their potential value in preventing asthma via the PI3K/Akt signaling pathway. Full article
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<p>Network pharmacology analysis. (<b>A</b>) Flow chart for screening of potential active components. (<b>B</b>) Asthma-related targets from OMIM, Drugbank, TTD, DisGeNET, and GeneCards. (<b>C</b>) Venn diagram of 143 overlapping targets between SRT polyphenols and asthma. (<b>D</b>) PPI network of 143 overlapping targets. (<b>E</b>) GO function enrichment analysis. (<b>F</b>) KEGG enrichment analysis. (<b>G</b>) Component–target–pathway network (green nodes represent pathways, red nodes represent SRT polyphenols, and blue nodes represent targets). (<b>H</b>) Key components predicted for treatment of asthma.</p>
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<p>The establishment of an OVA + CS-induced asthma model. (<b>A</b>) Experimental design and dosing regimen. (<b>B</b>) Gross lesions in the lungs of asthmatic rats (white areas represent lesions and histological edema). (<b>C</b>) The W/D ratio of the lung. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols attenuated inflammatory responses in OVA + CS-induced asthmatic rats. (<b>A</b>–<b>C</b>) WBC count, percentages of eosinophils, and neutrophils in BALF. (<b>D</b>–<b>G</b>) ELISA-measured cytokine concentrations of IL-4, IL-5, IL-13, and TNF-α in serum. (<b>H</b>–<b>K</b>) Relative mRNA expression of <span class="html-italic">IL-4</span>, <span class="html-italic">IL-5</span>, <span class="html-italic">IL-13</span>, and <span class="html-italic">TNF-α</span> in lung tissues. Data are presented as mean ± SD. (n = 5–6). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols promoted lung epithelial barrier repair. (<b>A</b>) EB staining assay. (<b>B</b>) Quantification of dye in lung tissues. (<b>C</b>–<b>F</b>) Representative Western blot images and bar graphs showing relative expressions of ZO-1, occludin, and claudin-1. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of SRT polyphenols on airway remodeling. (<b>A</b>–<b>D</b>) H&amp;E, Masson, and PAS staining of lung tissues with quantitative evaluation of inflammatory response, collagen deposition, and mucus secretion (black arrows in H&amp;E staining indicate inflammatory cell infiltration; blue areas around airways in Masson staining represent collagen deposition; red arrows in PAS staining indicate mucus). (<b>E</b>–<b>H</b>) Immunohistochemical analysis for MMP9 and <span class="html-italic">α</span>-SMA with quantitative analysis. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols modulated the PI3K/Akt signaling pathways. (<b>A</b>–<b>E</b>) Representative Western blot images and bar graphs showing the relative expression levels of PIK3CA, Akt, and p-Akt. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>SRT polyphenols regulated the overall structure of the gut microbiota. (<b>A</b>) Petal plot of the ASV distribution. (<b>B</b>) Phylogenetic tree of the top 50 species. (<b>C</b>) Principal coordinates analysis (PCoA). (<b>D</b>) Hierarchical cluster analysis. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>SRT polyphenols altered the relative abundance of the gut microbiota. Microbial community structure at the (<b>A</b>) phylum and <b>(B</b>) genus levels. (<b>C</b>–<b>E</b>) Representative differentially enriched species at the genus level: <span class="html-italic">Prevotella</span>, <span class="html-italic">Romboutsia,</span> and <span class="html-italic">Parabacteroides</span>. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>LEfSe analysis. (<b>A</b>) Differential species score chart and (<b>B</b>) differential species annotation branching diagram (n = 5).</p>
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<p>Fecal metabolome analysis. (<b>A</b>–<b>C</b>) PCA analysis, volcano plots of differentially expressed metabolites, and KEGG enrichment analysis for NC vs. MO. (<b>D</b>–<b>F</b>) PCA analysis, volcano plots of differentially expressed metabolites, and KEGG enrichment analysis for MO vs. SRTH. (<b>G</b>,<b>H</b>) Overlapping differentially expressed metabolites and expression levels between NC vs. MO and MO vs. SRTH. (<b>I</b>) Spearman’s correlation analysis of key differentially expressed metabolites with asthma indicators and gut microbiota. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The underlying mechanism of the protective effect of SRT polyphenols against OVA + CS-induced asthma.</p>
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24 pages, 9504 KiB  
Article
Gegen Qinlian Decoction Attenuates Colitis-Associated Colorectal Cancer via Suppressing TLR4 Signaling Pathway Based on Network Pharmacology and In Vivo/In Vitro Experimental Validation
by Yaoyao Xu, Qiaoyan Cai, Chunyu Zhao, Weixiang Zhang, Xinting Xu, Haowei Lin, Yuxing Lin, Daxin Chen, Shan Lin, Peizhi Jia, Meiling Wang, Ling Zhang and Wei Lin
Pharmaceuticals 2025, 18(1), 12; https://doi.org/10.3390/ph18010012 - 25 Dec 2024
Viewed by 429
Abstract
Background: Gegen Qinlian Decoction (GQD), is used for intestinal disorders like ulcerative colitis, irritable bowel syndrome, and colorectal cancer. But the precise mechanisms underlying its anti-inflammatory and anti-tumor effects are not fully elucidated. Methods: Use network pharmacology to identify targets and [...] Read more.
Background: Gegen Qinlian Decoction (GQD), is used for intestinal disorders like ulcerative colitis, irritable bowel syndrome, and colorectal cancer. But the precise mechanisms underlying its anti-inflammatory and anti-tumor effects are not fully elucidated. Methods: Use network pharmacology to identify targets and pathways of GQD. In vivo (azoxymethane/dextran sodium sulfate (AOM/DSS)-induced colitis-associated colorectal cancer (CAC) mouse model) and in vitro (lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages) experiments were conducted to explore GQD’s anti-inflammatory and anti-tumor effects. We monitored mouse body weight and disease activity index (DAI), and evaluated colon cancer tissues using hematoxylin and eosin staining. Expression of Ki67 and F4/80 was determined by immunohistochemistry analysis. The protein levels of TLR4 signaling pathway were assessed by western blotting analysis. Enzyme-linked immunosorbent assay measured IL-1β, IL-6, and TNF-α levels. Immunofluorescence (IF) staining visualized NF-κB and IRF3 translocation. Results: There were 18, 9, 24 and 77 active ingredients in the four herbs of GQD, respectively, targeting 435, 156, 485 and 691 genes. Through data platform analysis, it was concluded that there were 1104 target genes of GQD and 2022 target genes of CAC. Moreover, there were 99 intersecting genes between GQD and CAC. The core targets of GQD contained NFKB1, IL1B, IL6, TLR4, and TNF, and GQD reduced inflammation by inhibiting the TLR4 signaling pathway. In vivo experiment, GQD increased mouse body weight, lowered DAI scores, while also alleviating histopathological changes in the colon and decreasing the expressions of Ki67 and F4/80 in the AOM/DSS-induced mice. GQD reduced IL-1β, IL-6, and TNF-α levels in the serum and downregulated TLR4, MyD88, and phosphorylation of IκBα, P65, and IRF3 in the colon tissue from AOM/DSS-induced mice. In vitro, GQD suppressed pro-inflammatory cytokines and TLR4 signaling pathway in the LPS-induced RAW264.7 cells, and combined with TAK242, it further reduced the phosphorylation of IκBα, P65. Conclusions: GQD mitigated CAC by inhibiting the TLR4 signaling pathway, offering a potential therapeutic approach for CAC management. Full article
(This article belongs to the Section Pharmacology)
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<p>Quality control of GQD using UPLC-MS. (<b>A</b>) The positive and negative ion chromatogram of GQD. (<b>B</b>) The peaks of reference substances. 1. Puerarin; 2. Liquiritin; 3. Puerarin 6″-O-xyloside; 4. Berberine; 5. Baicalin; 6. Glycyrrhizic acid. GQD: Gegen Qinlian Decoction.</p>
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<p>Active ingredients and target genes of GQD. Searching in the Batman-TCM database revealed compounds and target gene information for <span class="html-italic">Pueraria lobata</span> (Willd.) Ohwi, <span class="html-italic">Scutellaria baicalensis</span> Georgi, <span class="html-italic">Coptis chinensis</span> Franch, and <span class="html-italic">Glycyrrhiza uralensis</span> Fisch, with specific numbers listed. GQD: Gegen Qinlian Decoction.</p>
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<p>Identifying the intersecting genes between GQD and CAC as well as creating the compound-intersecting genes-disease association map. (<b>A</b>) Venny diagram of intersecting genes of GQD and CAC; Analysis of intersecting genes via protein-protein interaction network. (<b>B</b>) Development of the compound-targets network and compound-targets-pathway network from the 4 herbs of GQD. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer.</p>
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<p>KEGG and GO pathway enrichment analyses. (<b>A</b>) Analyzing the pathway enrichment of the intersecting genes between GQD and CAC. (<b>B</b>) GO analysis of biological processes, cellular components, and molecular functions associated with the therapeutic effects of GQD for CAC. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology.</p>
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<p>GQD attenuated the symptoms in AOM/DSS-induced CAC mice. (<b>A</b>) Sketch of the animal experimental design. (<b>B</b>) Body weight of three groups. n = 6. (<b>C</b>) The DAI score of three groups. n = 6. (<b>D</b>,<b>E</b>) The macroscopic pathology, as well as the length and weight of the mouse colon in three groups. The red arrow indicated the location of the tumor. n = 3. (<b>F</b>) The pathological morphology of the colon with different multiples via HE staining in three groups. n = 4. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; DAI: Disease Activity Index; HE: Hematoxylin and Eosin.</p>
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<p>GQD reduced the tumor incidence and improved the survival rate in AOM/DSS-induced CAC mice. (<b>A</b>) Comparison of colonic macroscopic morphology and number of tumors in three groups. The red arrow indicated the location of the tumor. n = 3. (<b>B</b>) The IHC staining results of Ki67 in three groups. The red arrow represented the characteristics of Ki67 positive expression. n = 4. (<b>C</b>) The survival rate of the three groups. n = 6. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; IHC: Immunohistochemistry.</p>
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<p>GQD inhibited inflammation by downregulating the TLR4-related signaling pathways in AOM/DSS-induced CAC mice. (<b>A</b>) Serum inflammatory factors in three groups. n = 4. (<b>B</b>) The protein expression of TLR4, Myd88, p-IκBα, IκBα, p-P65, P65, p-IRF3, and IRF3 in three groups. n = 3. (<b>C</b>) The IHC staining results of F4/80 in three groups. The red arrow represented the characteristics of F4/80 positive expression. n = 4. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; IHC: Immunohistochemistry.</p>
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<p>GQD inhibited the inflammatory cytokine secretion in LPS-induced RAW264.7 cells. (<b>A</b>) The impact of GQD on the viability of RAW264.7 cells via the CCK8 assay. n = 6. (<b>B</b>,<b>C</b>) The levels of IL-1β, IL-6, and TNF-α in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group; <sup>&amp;</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + 25 μg/mL GQD group; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + 50 μg/mL GQD group. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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<p>GQD inhibited TLR4-ralated signaling pathway in LPS-induced RAW264.7 cells. The protein expression of TLR4, Myd88, TRIF, p-IκBα, IκBα, p-P65, P65, p-IRF3, IFR3 in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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<p>GQD inhibited nuclear translocation of NF-κB, IRF3 in LPS-induced RAW264.7 cells. (<b>A</b>) The expression and nuclear translocation of NF-κB protein in each group by IF staining. n = 4. (<b>B</b>) The expression and nuclear translocation of IRF3 protein in each group by IF staining. n = 4. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide; IF: Immunofluorescence.</p>
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<p>Synergistic effect of GQD and TAK242 on the inhibiting TLR4-related signaling pathways. The protein expression of p-IκBα, IκBα, p-P65, P65, p-IRF3, IFR3 in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group; <sup>&amp;</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + GQD. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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23 pages, 10531 KiB  
Article
Investigation into the Potential Mechanism of Radix Paeoniae Rubra Against Ischemic Stroke Based on Network Pharmacology
by Tingyu Wen, Guang Xin, Qilong Zhou, Tao Wang, Xiuxian Yu, Yanceng Li, Shiyi Li, Ying Zhang, Kun Zhang, Ting Liu, Beiwei Zhu and Wen Huang
Nutrients 2024, 16(24), 4409; https://doi.org/10.3390/nu16244409 - 23 Dec 2024
Viewed by 379
Abstract
Background: Radix Paeoniae Rubra (RPR), an edible and medicinal Traditional Chinese Medicine (TCM), is extensively employed in therapeutic interventions of cardiovascular and cerebrovascular diseases. However, the curative effect of RPR on ischemic stroke remains ambiguous. This work integrated network pharmacology, molecular docking, and [...] Read more.
Background: Radix Paeoniae Rubra (RPR), an edible and medicinal Traditional Chinese Medicine (TCM), is extensively employed in therapeutic interventions of cardiovascular and cerebrovascular diseases. However, the curative effect of RPR on ischemic stroke remains ambiguous. This work integrated network pharmacology, molecular docking, and experimental validation to explore the mechanisms of RPR in treating ischemic stroke. Methods: In this study, we preliminarily elucidated the therapeutic effect and mechanism of RPR on ischemic stroke through network pharmacology, molecular docking analysis, and experimental verification. Results: The results indicated that RPR improved the neurological deficit scores, decreased the size of infarcts, and reduced brain edema symptoms in the tMCAO mice model. Furthermore, through network pharmacology and molecular docking, four core targets (MAPK3, TNF-α, MAPK14, and JNK) closely related to RPR’s treatment of ischemic stroke were identified, exhibiting strong affinity with two key active components of RPR: albiflorin (AF) and β-sitosterol (BSS). The Western blot showed the potential mechanism of RPR treatment for ischemic stroke by regulating the MAPK signaling pathway. Moreover, RPR and its main active ingredients exhibited a significant inhibitory effect on platelets. Conclusion: In conclusion, this study revealed that RPR alleviates ischemic injury by activating the MAPK signaling pathway, and its protective effect may partly stem from inhibiting platelet activation. This work may provide a scientific basis for the development and utilization of RPR as a natural edible material to prevent ischemic stroke and anti-platelet therapy. Full article
(This article belongs to the Special Issue Medicinal Plants and Natural Products for Human Health)
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Graphical abstract

Graphical abstract
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<p>The network pharmacology analysis of RPR against stroke: (<b>A</b>) “RPR-Component-Target” network. The pink nodes represent the ingredients of RPR, and the blue nodes represent the targets, (<b>B</b>) The Venn diagram of 128 targets intersected by RPR and stroke. (<b>C</b>) The PPI network of the 128 common targets. (<b>D</b>) The core targets of the 128 common targets ranked by degree value. The node size and degree value are positively correlated. (<b>E</b>–<b>G</b>) A cluster analysis identifies the top 3 core seed nodes of the core intersection targets.</p>
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<p>“Disease-Pathway-Target-Component-Drug” network. The orange nodes represent signaling pathway involved in stroke, the green nodes represent ingredients of RPR, and the blue nodes represent the common targets between stroke and RPR.</p>
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<p>The enrichment analysis of 128 intersected targets: (<b>A</b>) the top 20 GO enrichment terms (BP: biological process, CC: cellular component, and MF: molecular function); (<b>B</b>) Top 20 KEGG enrichment analysis item.</p>
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<p>The protective effect of RPR on tMCAO mice: (<b>A</b>) Representative images of TTC staining of mice brain tissue with different dosages of RPR for 3 d of preventive administration, cerebral infarction area circled by yellow dashed, <span class="html-italic">n</span> = 5; (<b>B</b>) The quantification results of brain infarction volume; (<b>C</b>) H&amp;E staining and Nissl staining images of brain tissues, <span class="html-italic">n</span> = 5. Scale bar = 50 μm. (<b>D</b>) Neurological score treated with different dosages of RPR, <span class="html-italic">n</span> = 5; (<b>E</b>,<b>F</b>) Representative Western blots showing ZO-1, Occludin, and Claudin- 5 levels, <span class="html-italic">n</span> = 5; (<b>G</b>,<b>H</b>) IL-6 level and IL-1β level in brain tissues (<span class="html-italic">n</span> = 5). The data are presented as mean ± SD. <span class="html-italic"># p</span> &lt; 0.05, <span class="html-italic">## p</span> &lt; 0.01, <span class="html-italic">### p</span> &lt; 0.001 vs. Sham group, <span class="html-italic">* p</span> &lt; 0.05, <span class="html-italic">** p</span> &lt; 0.01, <span class="html-italic">*** p</span> &lt; 0.001 vs. Model group, ns, no significance.</p>
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<p>RPR inhibited stroke via the MAPK signaling pathway. Western blotting and quantification analysis of p-p38, p38 (<b>A</b>), p-ERK, ERK (<b>B</b>), p-JNK, JNK (<b>C</b>), and TNF-α (<b>D</b>) in the brain tissue of tMCAO mice. <span class="html-italic">n</span> = 5. The data are presented as mean ± SD. <span class="html-italic">## p</span> &lt; 0.01 vs. Sham group, <span class="html-italic">* p</span> &lt; 0.05, <span class="html-italic">** p</span> &lt; 0.01, <span class="html-italic">*** p</span> &lt; 0.001 vs. Model group.</p>
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<p>Molecular docking: (<b>A</b>–<b>D</b>) The results of molecular docking of MAPK3 (<b>A</b>), TNF (<b>B</b>), MAPK14 (<b>C</b>), and JNK (<b>D</b>) with AF. (<b>E</b>–<b>H</b>) The results of molecular docking of MAPK3 (<b>E</b>), TNF (<b>F</b>), MAPK14 (<b>G</b>), and JNK (<b>H</b>) with BSS. (<b>I</b>) Heat maps of the docking binding energy of the top 6 core targets with the top 4 active compounds in RPR.</p>
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<p>Active compounds of RPR inhibit agonist-induced platelet aggregation and granules release: (<b>A</b>) The effects of RPR and the two active compounds, including AF and BSS on cell viability of platelets. (<b>B</b>–<b>E</b>) The effects of RPR and the two active compounds, including AF and BSS on aggregation induced by thrombin (<b>B1</b>,<b>B2</b>,<b>C</b>) and ADP (<b>D1</b>,<b>D2</b>,<b>E</b>). (<b>F</b>) The effects of RPR and the two active compounds, including AF and BSS, on the release of ATP secretion induced by thrombin (<b>F1</b>) and ADP (<b>F2</b>). (<b>G</b>) The effects of RPR and the two active compounds, including AF and BSS on the release of PF4 induced by thrombin (<b>G1</b>) and ADP (<b>G2</b>); <span class="html-italic">n</span> = 5. The data are presented as mean ± SD. <span class="html-italic">* p</span> &lt; 0.05, <span class="html-italic">** p</span> &lt; 0.01, <span class="html-italic">*** p</span>&lt; 0.001, <span class="html-italic"># p</span> &lt; 0.05, <span class="html-italic">## p</span> &lt; 0.01, ns, no significance.</p>
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<p>Active compounds of RPR inhibit platelet clot retraction and adhesion: (<b>A</b>) The effects of RPR and the two active compounds, including AF and BSS on platelet clot retraction within 40 min. (<b>B</b>) Statistics of the volume of clot retraction. (<b>C</b>) The effects of RPR and the two active compounds, including AF and BSS, on platelet adhesion within 45 min. (<b>D</b>) Statistics of the area of platelet adhesion, <span class="html-italic">n</span> = 5. Results were quantified and presented as mean ± SD, <span class="html-italic">* p</span> &lt; 0.05, <span class="html-italic">** p</span> &lt; 0.01, <span class="html-italic">*** p</span>&lt; 0.001 vs. PBS group, ns, no significance.</p>
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<p>The proposed mechanism of RPR therapeutic effects on ischemic stroke. RPR alleviates ischemic stroke by down-regulating MAPK pathways; furthermore, the protective effect may be partly due to the anti-platelet function of its active compounds.</p>
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21 pages, 5421 KiB  
Article
Modulation of TNFR 1-Triggered Inflammation and Apoptosis Signals by Jacaranone in Cancer Cells
by Jie Liu, Yang Xu, Guobin Xie, Bingjie Geng, Renjing Yang, Wenjing Tian, Haifeng Chen and Guanghui Wang
Int. J. Mol. Sci. 2024, 25(24), 13670; https://doi.org/10.3390/ijms252413670 - 20 Dec 2024
Viewed by 507
Abstract
Jacaranone derived from Senecio scandens, a traditional Chinese medicine used for centuries, has been documented to exhibit anti-inflammatory and antiproliferative properties in various tumor cell lines. However, the mechanism of action and relationship between inflammation and apoptosis induced by jacaranone remain inadequately [...] Read more.
Jacaranone derived from Senecio scandens, a traditional Chinese medicine used for centuries, has been documented to exhibit anti-inflammatory and antiproliferative properties in various tumor cell lines. However, the mechanism of action and relationship between inflammation and apoptosis induced by jacaranone remain inadequately elucidated. In this study, the targets of jacaranone and cancer were identified from various databases, while potential targets and pathways were predicted through the analysis of the protein–protein interactions (PPI) network and pathway enrichment. Through a comprehensive network pharmacology analysis and corroborating experimental findings, we revealed that jacaranone induces tumor cell death by fine-tuning the tumor necrosis factor receptor 1 (TNFR1) downstream signaling pathway. TNFR1 serves as a key node that assembles into complexes I and II, regulating pathways including the nuclear factor (NF)-κB signaling pathway and the cell apoptosis pathway, which play crucial roles in cellular life activities. Jacaranone successfully guides survival signaling pathways to apoptotic mechanisms by inhibiting the assembly of complex I and promoting the formation of complex II. In particular, the main action mechanism of jacaranone lies in inducing the degradation of the inhibitor of apoptosis protein (cIAP)-2. cIAP-2 serves as an E3 ubiquitin ligase that ubiquitinates receptor-interacting serine/threonine-protein kinase 1 (RIPK1), thereby hindering the formation of complex I and effectively reducing the phosphorylation of Inhibitor of κB kinase (IKK) β. When the deubiquitylation process of RIPK1 is triggered, it may promote the formation of complex II, which ultimately leads to cell apoptosis. This fully demonstrates the key role of jacaranone in regulating TNFR1 complexes, especially through the degradation of cIAP-2. Taken together, jacaranone hinders the assembly of TNFR1 complex I and promotes the formation of complex II to induce apoptosis of cancer cells. Our findings unveil a novel mechanism underlying jacaranone, while also presenting a fresh approach for the development of new pharmaceuticals. Full article
(This article belongs to the Special Issue Apoptosis and Cell Signaling in Disease)
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Figure 1
<p>(<b>A</b>) Structure of jacaranone and the flowchart of this study is based on the methods of network pharmacology. (<b>B</b>) PPI network of common targets between jacaranone and cancer. (<b>C</b>) PPI network of core targets between jacaranone and cancer. The intensity of the dots reflects the degree of protein enrichment in C7-regulated pathways or functions. (<b>D</b>) Go function enrichment diagram of the common targets of jacaranone and cancer. (BP, Biological Process; CC, Cell Component; MF, Molecular Function) (<b>E</b>) KEGG pathway diagram of the common targets of jacaranone and cancer.</p>
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<p>Jacaranone inhibits TNFα-induced activation of NF-κB. (<b>A</b>) The transcriptional activity of NF-κB induced by TNFα was inhibited by jacaranone. Plasmid pGL6-NF-κB and pRL-renilla were transfected into MCF7 cells, which were then treated with 20 ng/mL of TNFα along with varying concentrations (0, 1, 10 and 20 μM) of jacaranone for a duration of 24 h. Following transfection, luciferase activity was measured using a dual luciferin reporter gene kit, (n = 3). (<b>B</b>) Time-dependent inhibits the TNF-induced degradation of the IκBα effect of jacaranone. MCF7 cells treated with a vehicle or indicated concentration of jacaranone for 1 and 3 h. Subsequently, the cells were treated with TNFα at a concentration of 20 ng/mL for a period of 0.5 h. Western blotting techniques were used to detect the expression level of IκBα. In various experimental groups, Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) and β-tubulin were utilized as internal controls. (<b>C</b>) Dose-dependent inhibits the TNF-induced degradation of the IκBα effect of jacaranone. MCF7 cells were pre-treated with various concentrations (0, 1, 2.5, 5 and 10 μM) of jacaranone for a duration of 3 h. Subsequently, the cells were treated with TNFα at a concentration of 20 ng/mL for a period of 0.5 h. The expression levels of IκBα were assessed using western blotting techniques. In various experimental groups, GAPDH and β-tubulin were utilized as internal controls. (<b>D</b>) Dose size inhibits the TNF-induced degradation of the IκBα effect of jacaranone in various cancer cells. MDA-MB-231, HepG2, HeLan and PC3 cells were pre-treated with various concentrations (0, 1, 2.5, 5 and 10 μM) of jacaranone for a duration of 3 h. Subsequently, the cells were treated with TNFα at a concentration of 20 ng/mL for a period of 0.5 h. The expression levels of IκBα were assessed using western blotting techniques. In various experimental groups, GAPDH and β-tubulin were utilized as internal controls. (<b>E</b>) The nuclear transport of p65 induced by TNFα is inhibited by jacaranone. MCF7 cells were pre-treated with 10 μM jacaranone for 3 h and then exposed to 20 ng/mL TNFα for 0.5 h before sample collection. Control and treated cells were subjected to staining with a p65 antibody, while the nucleus was stained with 4′,6-diamidino-2-phenylindole (DAPI), followed by observation under a confocal laser microscope, Scale bar, 10 μm. ns: no significance, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0. 001, **** <span class="html-italic">p</span> &lt; 0.0001. All blots above are representative of one of three experiments.</p>
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<p>Jacaranone inhibits LPS/D-GalN induced inflammation in vivo. Mice were orally administered jacaranone at doses of 20 mg/kg or 50 mg/kg body weight, followed by an intraperitoneal injection of PBS or LPS/D-Gal (n = 4). (<b>A</b>,<b>B</b>) The liver sections were subjected to hematoxylin-eosin staining for the detection of necrosis and inflammatory cell infiltration (<b>A</b>), or CD68 staining for the identification of CD68 positive cells (<b>B</b>). Scale bar, 20 μm. (<b>C</b>) Western blot analysis was performed to assess the levels of IκBα and phosphorylated IKKα/β in control and treated tissues, with GAPDH and β-tubulin were served as a loading control for normalization purposes. ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001. Blots above are representative of one of three experiments.</p>
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<p>The degradation of cIAP2 induced by jacaranone played a crucial role in modulating inflammation and apoptosis. (<b>A</b>) The expression of cIAP2 protein was reduced by jacaranone treatment. MCF7 cells were treated with jacaranone (10 μM) for 3 h and 6 h, followed by TNFα (20 ng/mL) treatment for 0.5 h prior to sample collection. Western blot analysis was performed to assess the levels of cIAP1, cIAP2, XIAP, RIPK1, TRAF2, and IκBα. GAPDH was used as a loading control. (<b>B</b>,<b>C</b>) The expression of cIAP2 protein was reduced by jacaranone treatment. MDA-MB-231 (<b>B</b>) and HeLa (<b>C</b>) cells were treated with jacaranone (10 μM) for 3 h and 6 h, followed by TNF-α (20 ng/mL) treatment for 0.5 h prior to sample collection. Western blot analysis was performed to assess the levels of cIAP1 and cIAP2. GAPDH was used as a loading control. (<b>D</b>) The impact of jacaranone on cIAP2 mRNA levels was investigated in MCF7 cells. Cells were treated with jacaranone (10 μM) for 3 h, followed by TNFα (20 ng/mL) treatment for 0.5 h. RT-PCR was employed to assess the cellular mRNA levels. (<b>E</b>) The downregulation of cIAP2 expression induced by jacaranone can be effectively inhibited by MG132 treatment. MCF7 cells were pre-treated with 10 μM MG132 for 1 h prior to the administration of 10 μM jacaranone for 6 h. TNFα (20 ng/mL) was added 0.5 h before sample collection, and cIAP2 expression levels were assessed using immunoblotting techniques, with GAPDH and β-tubulin used as a reference for sample normalization. (<b>F</b>) The degradation of IκBα induced by TNFα is inhibited by jacaranone in a cIAP2-dependent manner. MCF7 cells were transfected with si-cIAP2. After 48 h of transfection, the cells were treated with jacaranone (10 μM) for 3 h and then stimulated with TNFα (20 ng/mL) for 0.5 h before sample collection. Western blotting was performed to analyze the levels of cIAP2 and IκBα, and GAPDH was used as a loading control. ns: no significance, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. All blots above are representative of one of three experiments.</p>
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<p>Jacaranone inhibits the formation of complex I and promotes the assembly of complex II. (<b>A</b>) The interaction among TNFR1 and RIPK1, TRAF2, cIAP1/2, and TRADD is inhibited by jacaranone. MCF7 cells were treated with 10 μM jacaranone for 3 h, followed by treatment with TNFα (20 ng/mL) for 0.5 h prior to sample collection. Immunoprecipitation was performed using a TNFR1 antibody, and the presence of RIPK1, TRAF2, cIAP1/2, TRADD, and TNFR1 proteins was detected through western blotting. (<b>B</b>) The ubiquitination of RIPK1 promoted by cIAP2 is inhibited by jacaranone. HEK293T cells were co-transfected with Myc-RIPK1, HA-Ub, and 3flag-cIAP2 plasmids, followed by treatment with jacaranone (10 μM) for 3 h after 24 h. (<b>C</b>) The phosphorylation of IKKβ is inhibited by jacaranone. MCF7 cells were pre-treated with 10 μM and 20 μM jacaranone for a duration of 3 h. Subsequently, the cells were treated with TNFα at a concentration of 20 ng/mL for a period of 0.5 h. The phosphorylation level of IKKβ were assessed using western blotting. Total IKKβ was used as an internal control. (<b>D</b>) 293T cells were transfected with Myc-FADD, treated with 10 μM jacaranone for 6 h, followed by treatment with TNFα (20 ng/mL) for 0.5 h prior to sample collection. Immunoprecipitation was performed using a Myc antibody, and the presence of RIPK1 and Myc were detected through western blotting. (<b>E</b>) MCF7 cells, treated with 10 μM jacaranone for 3, 6, 12, and 24 h, followed by treatment with TNFα (20 ng/mL) for 0.5 h. Immunoprecipitation was performed using a FADD antibody, and the presence of RIPK1 and FADD were detected through western blotting. (<b>F</b>) MCF7 cells, treated with 10 μM jacaranone for 6 h, followed by treatment with TNFα (20 ng/mL) for 0.5 h. Immunoprecipitation was performed using the caspase-8 antibody, and the presence of RIPK1, FADD, TRAF2, TRADD, and caspase-8 were detected through western blotting. ns: no significance, **** <span class="html-italic">p</span> &lt; 0.0001. All blots above are representative of one of three experiments.</p>
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<p>Jacaranone induces cancer apoptosis. (<b>A</b>) The apoptosis of jacaranone was evaluated using flow cytometry. MCF7 cells were exposed to a negative control, 20 ng/mL TNFα, 10 μM jacaranone, and a combination of both in a serum-free medium for a duration of 24 h. Subsequently, the cells were harvested and stained with propidium iodide and annexin V-FITC to detect apoptosis via flow cytometry, (n = 3). (<b>B</b>) The cleavage of caspase-8 induced by jacaranone was examined in MCF7 cells, both in the presence and absence of TNFα (20 ng/mL). Cells were treated with jacaranone (10 μM) for different time points: 6, 12, and 24 h for MCF7 cells. Western blot analysis was performed using equal amounts of total cell lysate to detect the expression levels of cleaved caspase-8 and PARP. GAPDH was used as a loading control. (<b>C</b>) The apoptosis of jacaranone can be suppressed by caspase inhibitors. HeLa cells were treated with TNFα (20 ng/mL), jacaranone (10 μM), and Z-VAD-FMK (10 μM) for 12 h, as depicted in the figure. Western blot analysis was conducted to assess the expression of cleaved caspase-8, while GAPDH was employed to determine sample size. (<b>D</b>) The cleavage of caspase-8 induced by jacaranone was examined in MDA-MB-231 and MCF-10A cells. Cells were treated with jacaranone (10 μM) for 24 h. Western blot analysis was performed using equal amounts of total cell lysate to detect the expression levels of cleaved caspase-8. GAPDH was used as a loading control. ns: no significance, **** <span class="html-italic">p</span> &lt; 0.0001. All blots above are representative of one of three experiments.</p>
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<p>Jacaranone inhibits tumor growth in spontaneous breast cancer. The effect of jacaranone (8 mg/kg and 20 mg/kg by intraperitoneal injection) on MMTV-PyMT mice were evaluated, (n = 6). (<b>A</b>) The mammary tumor weight was assessed. Jacaranone-treated MMTV-PyMT mice developed smaller and lighter tumors than DMSO-treated mice. (<b>B</b>) Representative immune histological images from MMTV-PyMT mice treated with jacaranone or DMSO. Scale bar, 20 μm. (<b>C</b>) cIAP1/2, RIPK1, and TRAF2 expression were detected through western blotting in control and jacaranone-treated tumors tissues, while β-actin was used as a loading control. ns: no significance, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. All blots above are representative of one of three experiments.</p>
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<p>Jacaranone suppresses MCF-7 xenograft tumor growth in nude mice. MCF7 xenografted mice were intragastric administered with DMSO (1% in volume) or jacaranone (100 mg/kg) once every 2 days for a duration of 12 days, (n = 3). (<b>A</b>) Tumor volume was evaluated every 3 days, revealing that jacaranone-treated MCF7 xenografted mice exhibited smaller tumors compared to the DMSO-treated group. (<b>B</b>) Body weight was measured every 3 days, indicating no significant difference between jacaranone-treated and DMSO-treated MCF7 xenografted mice. (<b>C</b>) Following sacrifice, tumors were isolated and weighed, demonstrating that jacaranone reduced tumor weight significantly (* <span class="html-italic">p</span> &lt; 0.05).</p>
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22 pages, 8084 KiB  
Article
Optimization of Extraction of Luteolin from Schisandra chinensis by Ionic Liquid–Enzyme Complex System and Antioxidant Study Analysis
by Jingwei Hao, Nan Dong, Yifan Sun, Xiaoxia Lu, Yingying Pei, Yi Zhou, Xiangkun Zhou and Heming Liu
Separations 2024, 11(12), 354; https://doi.org/10.3390/separations11120354 - 19 Dec 2024
Viewed by 439
Abstract
The luteolin in Schisandra chinensis [Schisandraceae Schisandra (Turcz.) Baill.] were extracted by ultrasonic extraction assisted by an ionic liquid–enzyme composite system, and the content of luteolins was determined using high-performance liquid chromatography (HPLC). This process was initially conducted through a one-factor experiment and [...] Read more.
The luteolin in Schisandra chinensis [Schisandraceae Schisandra (Turcz.) Baill.] were extracted by ultrasonic extraction assisted by an ionic liquid–enzyme composite system, and the content of luteolins was determined using high-performance liquid chromatography (HPLC). This process was initially conducted through a one-factor experiment and a Box–Behnken combinatorial design of response surface method. The extraction process was optimized, and the results demonstrated that the optimal extraction conditions were 13.31% enzyme addition, 0.53 mol/L ionic liquid concentration, 173.47 min ultrasonic shaking, and 0.2266 mg/g, which was 4.88 times higher than that of the traditional reflux extraction. Secondly, the antioxidant function of luteolins was studied based on network pharmacology. For the study of the antioxidant mechanism of luteolin, the herb group identification database, SwissTargetPrediction on luteolins target prediction, and GeneCards database to achieve the antioxidant target were used. For the analysis of the intersection of the target protein interactions, GO bioanalysis and KEGG signaling pathway enrichment analysis were used. There were 57 overlapping targets of luteolin and antioxidants, including AKT1, MMP9, ESR1, EGFR, and SRC. GO function and KEGG pathway enrichment analysis showed that luteolin antioxidants were related to zoerythromycin metabolic process, adriamycin metabolic process, negative regulation of apoptotic process, endocrine resistance and oxidoreductase. The key targets in the pathways, such as luteolin AKT1 and MMP9, exert antioxidant effects. The antioxidant activity of luteolins was investigated by determining the scavenging ability of luteolins against two types of free radicals: 2,2-bipyridine-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt (ABTS+) free radicals and 1,1-diphenyl-2-trinitrophenylhydrazine free radicals (DPPH-). The results of the antioxidant test demonstrated that the ABTS radical scavenging rate was 87.26%, and the DPPH radical scavenging rate was 93.85% when the quality concentration of Schisandra luteolins was 0.1 mg/g, indicating the potential of this natural antioxidant. This method of extracting Schisandra chinensis luteolins is highly productive, environmentally friendly, and practical, and it facilitates the development and utilization of industrial Schisandra chinensis. Full article
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<p>Standard curve of luteolin.</p>
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<p>The effect of ionic liquid type on luteolin yield (<b>a</b>); the effect of ionic liquid concentration on luteolin yield (<b>b</b>); the effect of enzyme addition amount on luteolin yield (<b>c</b>); the effect of solid–liquid ratio on luteolin yield (<b>d</b>), the effect of ultrasonic time on luteolin yield (<b>e</b>); and the effect of enzyme addition amount on luteolin yield. Effect of ultrasonic extraction temperature on luteolin yield (<b>f</b>); effect of enzymolysis time on luteolin yield (<b>g</b>); effect of enzymolysis temperature on luteolin yield (<b>h</b>). Different letters labeled in the bar graph indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) and the same letter indicates a non-significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>The effect of ionic liquid type on luteolin yield (<b>a</b>); the effect of ionic liquid concentration on luteolin yield (<b>b</b>); the effect of enzyme addition amount on luteolin yield (<b>c</b>); the effect of solid–liquid ratio on luteolin yield (<b>d</b>), the effect of ultrasonic time on luteolin yield (<b>e</b>); and the effect of enzyme addition amount on luteolin yield. Effect of ultrasonic extraction temperature on luteolin yield (<b>f</b>); effect of enzymolysis time on luteolin yield (<b>g</b>); effect of enzymolysis temperature on luteolin yield (<b>h</b>). Different letters labeled in the bar graph indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) and the same letter indicates a non-significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Response surface contours and curves between each factor and yield.</p>
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<p>Response surface contours and curves between each factor and yield.</p>
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<p>Interactive target information of luteolin and antioxidant.</p>
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<p>Drug—component—gene target network of luteolin antioxidant. The red triangle represents schisandra, the yellow triangle represents luteolin, and the blue rectangle represents the intersection target.</p>
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<p>PPI network of luteolin antioxidant function.</p>
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<p>Top 10 core targets in the PPI network of luteolin antioxidant function. The ranking of core targets is inversely proportional to the darkness of the color.</p>
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<p>Bubble diagram of GO function and KEGG path enrichment analysis. (<b>A</b>): BP; (<b>B</b>): MF; (<b>C</b>): CC; (<b>D</b>): KEGG.</p>
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<p>Bubble diagram of GO function and KEGG path enrichment analysis. (<b>A</b>): BP; (<b>B</b>): MF; (<b>C</b>): CC; (<b>D</b>): KEGG.</p>
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<p>Scavenging ability of luteolin on free radicals. Different letters labeled in the bar graph indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) and the same letter indicates a non-significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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15 pages, 15559 KiB  
Article
The Role of Flavonoids from Aurantii Fructus Immaturus in the Alleviation of Allergic Asthma: Theoretical and Practical Insights
by Jingwen Xue, Yuntong Liu, Qiushi Chen, Huimin Liu, Huijing Zhang, Bo Wang, Yongri Jin, Xuwen Li and Xiaolei Shi
Int. J. Mol. Sci. 2024, 25(24), 13587; https://doi.org/10.3390/ijms252413587 - 19 Dec 2024
Viewed by 335
Abstract
Flavonoids derived from plants in the citrus family can have an alleviating effect on allergic asthma. The aim of this study was to provide insights into the mechanisms by which these compounds exert their effects on allergic asthma by combining theoretical and practical [...] Read more.
Flavonoids derived from plants in the citrus family can have an alleviating effect on allergic asthma. The aim of this study was to provide insights into the mechanisms by which these compounds exert their effects on allergic asthma by combining theoretical and practical approaches. Aurantii Fructus Immaturus flavonoids (AFIFs) were obtained by solvent extraction and were determined by high performance liquid chromatography (HPLC). In vivo and in vitro experiments combined with network pharmacology, Mendelian randomization (MR) analysis and the AutoDock method were applied to study the mechanism of their effects. The main AFIFs were found to be hesperidin (13.21 mg/g), neohesperidin (287.26 mg/g), naringin (322.56 mg/g), and narirutin (19.35 mg/g). Based on the network pharmacology and MR analysis results, five targets Caspase 3 (CASP3), CyclinD1 (CCND1), Intercellular adhesion molecule (ICAM), erb-b2 receptor tyrosine kinase 2 (ERBB2), and rubisco accumulation factor 1 (RAF1) were selected, and the interactions between the AFIFs and the targets were studied using AutoDock Vina. The results indicated that glycosidic bonds play an important role in the interactions between AFIFs and both ERBB2 and RAF1. Full article
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<p>HPLC analysis of AFIFs and effects of AFIFs on body weights, rectal temperature, spleen, thymus index and inflammatory response in vivo. (<b>A</b>) HPLC of AFIFs; (<b>B</b>) Structures of hesperidin, neohesperidin, naringin, and narirutin. Red circle presents the different connection of glycosidic bonds; Blue circle presents the different connection of -OH and -OCH<sub>3</sub>; (<b>C</b>) Effects of AFIFs on body weight changes; (<b>D</b>) Effects of AFIFs on rectal temperature change; (<b>E</b>) Effects of AFIFs on thymus index; (<b>F</b>) Effects of AFIFs on spleen index; (<b>G</b>) Effects of AFIFs on total IgE; (<b>H</b>) Effects of AFIFs on IL-17A in BALF; (<b>I</b>) Effects of AFIFs on IL-13 in BALF. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, compared with the Control group; # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, compared with the Model group.</p>
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<p>(<b>A</b>) Histomorphological changes in the lungs of mice (Control); (<b>B</b>) Histomorphological changes in the lungs of mice (Model); (<b>C</b>) Histomorphological changes in the lungs of mice (AFIFs dose 10 mg/kg); (<b>D</b>) Histomorphological changes in the lungs of mice (AFIFs dose 50 mg/kg); (<b>E</b>) Histomorphological changes in the lungs of mice (AFIFs dose 100 mg/kg).</p>
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<p>Effects of AFIFs on mast cell degranulation. (<b>A</b>) Effects of AFIFs on cell viability; (<b>B</b>) Effects of AFIFs on β-HEX; (<b>C</b>) Effects of AFIFs on Ca<sup>2+</sup> influx; (<b>D</b>) Effects of AFIFs on IL-4 release; (<b>E</b>) Effects of AFIFs on histamine release. * <span class="html-italic">p</span> &lt; 0.05,** <span class="html-italic">p</span> &lt; 0.01, compared with the Control group; # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, compared with the Model group (0 μg/mL).</p>
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<p>Network pharmacology of AFIFs and allergic asthma. (<b>A</b>) The number of allergic asthma-related targets and AFIFs targets showed by Venn; (<b>B</b>) Network of 22 targets; (<b>C</b>) KEGG analysis; (<b>D</b>) GO-CC analysis; (<b>E</b>) GO-MF analysis; (<b>F</b>) GO-BP analysis.</p>
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<p>MR association between expression of gene <span class="html-italic">BAX</span>, <span class="html-italic">CASP3</span>, <span class="html-italic">CCND1</span>, <span class="html-italic">ERBB2</span>, <span class="html-italic">ICAM1</span>, <span class="html-italic">PEBP1</span>, <span class="html-italic">RAF1</span>, and allergic asthma outcomes. Red pots indicated Odds ratio &gt; 1, Green pots indicated Odds ratio &lt; 1.</p>
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<p>Molecular docking between key protein and AFIFs; color balls indicated amino acid residues of different targets. (<b>I</b>) interaction between hesperidin and key protein; (<b>II</b>) interaction between neohesperidin and key protein; (<b>III</b>) interaction between naringin and key protein; (<b>IV</b>) interaction between narirutin and key protein.</p>
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19 pages, 7471 KiB  
Article
Single-Cell RNA Sequencing, Cell Communication, and Network Pharmacology Reveal the Potential Mechanism of Senecio scandens Buch.-Ham in Hepatocellular Carcinoma Inhibition
by Jiayi Jiang, Haitao Wu, Xikun Jiang, Qing Ou, Zhanpeng Gan, Fangfang Han and Yongming Cai
Pharmaceuticals 2024, 17(12), 1707; https://doi.org/10.3390/ph17121707 - 18 Dec 2024
Viewed by 324
Abstract
Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing [...] Read more.
Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC. Methods: Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The “Climbing senecio-Hepatocellular Carcinoma Targets” network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio’s bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio’s primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization. Results: Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC. Conclusions: This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation. Full article
(This article belongs to the Section Pharmacology)
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<p>Identification and analysis of targets for <span class="html-italic">Senecio scandens</span> Buch.-Ham (Climbing senecio). (<b>a</b>) Venn diagram of Climbing senecio targets across TCMSP, TargetNet, Binding DB, and SwissTargetPrediction. (<b>b</b>) Enrichment analysis of Climbing senecio targets using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. (<b>c</b>) Comprehensive Gene Ontology (GO) enrichment analysis for Climbing senecio, including categories of biological processes (BP), cellular components (CC), and molecular functions (MF).</p>
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<p>Differentially expressed genes (DEGs) in the GSE54238 dataset. (<b>a</b>) The heatmap displaying the expression profiles of DEGs. (<b>b</b>) Volcano plot illustrating the distribution of DEGs. (<b>c</b>,<b>d</b>) Gene set enrichment analysis (GSEA) based on KEGG pathways.</p>
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<p>Weighted gene co-expression network analysis (WGCNA) of enrichment values. (<b>a</b>) Soft threshold selection. (<b>b</b>) WGCNA cluster dendrogram. (<b>c</b>) Gene module separation and cluster dendrogram in WGCNA, with different colors representing different modules. (<b>d</b>) Inter-module correlation. (<b>e</b>) Module-trait relationship analysis diagram for 17 modules. (<b>f</b>) Relationship between gene significance and brown module memberships.</p>
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<p>Key target identification and functional analysis. (<b>a</b>) The intersection of Climbing senecio-related genes with DEGs and WGCNA brown module genes. (<b>b</b>) Intersection of drug targets with Climbing senecio-related genes. (<b>c</b>) The Climbing senecio–HCC protein interaction network generated in Cytoscape3.10.1 showing Climbing senecio-related genes and drug targets. Green and light pink indicate both Climbing senecio-related genes and drug targets; light green indicates drug targets; light pink indicates Climbing senecio-related genes. (<b>d</b>) KEGG pathway analysis of key genes. (<b>e</b>) GO analysis of the primary cluster.</p>
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<p>Single-cell overview in HCC. (<b>a</b>) Unified clustering into 17 clusters. (<b>b</b>) Bubble charts at each gene table level. (<b>c</b>) Identification of nine clusters. (<b>d</b>) Proposed pathway of Climbing senecio’s action on HCC.</p>
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<p>Expression and distribution of the key cluster with receiver operating characteristic (ROC) curve analysis. (<b>a</b>) A boxplot depicting the differential expression of pivotal genes between normal and control tissues within the GSE54238. (<b>b</b>) The crucial targets are determined by the overlap of key clusters with genes associated with Climbing senecio and targets linked to HCC. (<b>c</b>) ROC curve analysis of three crucial targets.</p>
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<p>Key pathways in intercellular communication analysis. (<b>a</b>) Cellular interaction network. (<b>b</b>) Interaction between cell types. (<b>c</b>) Network of TGF-β, IL-1, CXCL, and VEGF signaling pathways.</p>
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<p>Immune filtration analysis of <span class="html-italic">AKR1B1</span>, <span class="html-italic">CA2</span>, <span class="html-italic">FOS</span>, <span class="html-italic">CXCL2</span>, <span class="html-italic">SRC</span>, <span class="html-italic">ABCC1</span>, and <span class="html-italic">PLIN1</span>. (<b>a</b>) Stacked column diagram of 20 types of immune cell infiltration in the GSE54238 dataset. (<b>b</b>) A box diagram illustrating the variation in infiltration levels of different immune cell types between diseased and normal samples. The “ns” (not significant) means there is no statistically significant difference. (<b>c</b>) A heatmap depicting the correlations between immune cell infiltration and the expression levels of <span class="html-italic">AKR1B1</span>, <span class="html-italic">CA2</span>, <span class="html-italic">FOS</span>, <span class="html-italic">CXCl2</span>, <span class="html-italic">SRC</span>, <span class="html-italic">ABCC1</span>, and <span class="html-italic">PLIN1</span>.</p>
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<p>Molecular docking analysis of Climbing senecio’s active ingredients with target proteins. (<b>a</b>) Visual docking diagram of Climbing senecio–<span class="html-italic">SRC</span> interaction. (<b>b</b>) Visual docking diagram of Climbing senecio–<span class="html-italic">FOS</span>.</p>
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21 pages, 11996 KiB  
Article
Molecular and Metabolic Regulation of Flavonoid Biosynthesis in Two Varieties of Dendrobium devonianum
by Ran Pu, Yawen Wu, Tian Bai, Yue Li, Xuejiao Li, Nengbo Li, Ying Zhou and Jingli Zhang
Curr. Issues Mol. Biol. 2024, 46(12), 14270-14290; https://doi.org/10.3390/cimb46120855 - 18 Dec 2024
Viewed by 499
Abstract
Dendrobium devonianum is an important medicinal plant, rich in flavonoid, with various pharmacological activities such as stomachic and antioxidant properties. In this study, we integrated metabolome and transcriptome analyses to reveal metabolite and gene expression profiles of D. devonianum, both green (GDd) and [...] Read more.
Dendrobium devonianum is an important medicinal plant, rich in flavonoid, with various pharmacological activities such as stomachic and antioxidant properties. In this study, we integrated metabolome and transcriptome analyses to reveal metabolite and gene expression profiles of D. devonianum, both green (GDd) and purple-red (RDd) of semi-annual and annual stems. A total of 244 flavonoid metabolites, mainly flavones and flavonols, were identified and annotated. Cyanidin and delphinidin were the major anthocyanidins, with cyanidin-3-O-(6″-O-p-Coumaroyl) glucoside and delphinidin-3-O-(6″-O-p-coumaroyl) glucoside being the highest relative content in the RDd. Differential metabolites were significantly enriched, mainly in flavonoid biosynthesis, anthocyanin biosynthesis, and flavone and flavonol biosynthesis pathways. Transcriptomic analysis revealed that high expression levels of structural genes for flavonoid and anthocyanin biosynthesis were the main reasons for color changes in D. devonianum stems. Based on correlation analysis and weighted gene co-expression network analysis (WGCNA) analysis, CHS2 (chalcone synthase) and UGT77B2 (anthocyanidin 3-O-glucosyltransferase) were identified as important candidate genes involved in stem pigmentation. In addition, key transcription factors (TFs), including three bHLH (bHLH3, bHLH4, bHLH5) and two MYB (MYB1, MYB2), which may be involved in the regulation of flavonoid biosynthesis, were identified. This study provides new perspectives on D. devonianum efficacy components and the Dendrobium flavonoids and stem color regulation. Full article
(This article belongs to the Special Issue Advanced Research in Plant Metabolomics, 2nd Edition)
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<p>Plant material and identification of flavonoid metabolites. (<b>A</b>) Experimental material: <span class="html-italic">D. devonianum</span> with green stems (GDd) and purple-red stems (RDd). (G1 and R1 are half-yearly growth period, G2 and R2 are one-yearly growth period). (<b>B</b>) Classification statistics of identified flavonoid metabolites. (<b>C</b>) Relative content of flavonoid metabolites in 4 samples. (<b>D</b>) Clustering heat map of 244 flavonoid metabolites. (<b>E</b>) Plot of OPLS-DA scores for each comparison group and point plots for validation of OPLS-DA using the 500 response method.</p>
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<p>Identification of differential metabolites: (<b>A</b>) Identification of the number of up-regulated (UP) and down-regulated (DOWN) differential metabolites (DAFs) among the four comparative groups. (<b>B</b>) Venn diagram of differential metabolites identified in comparison groups (G1 vs. R1, G2 vs. R2, G1 vs. G2 and R1 vs. R2). (<b>C</b>) Analysis of flavonoid metabolite expression module.</p>
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<p>Analysis of DEGs: (<b>A</b>) Statistics of the number of up-regulated (UP) and down-regulated (DOWN) DEGs among the four comparative groups. (<b>B</b>) Venn diagrams of DEGs in four comparison groups (G1 vs. R1, G2 vs. R2, G1 vs. G2 and R1 vs. R2). (<b>C</b>) KEGG enrichment analyses of DEGs in the G1 vs. R1. (<b>D</b>) KEGG enrichment analyses of DEGs in the G2 vs. R2. (<b>E</b>) KEGG enrichment analyses of DEGs in the G1 vs. G2. (<b>F</b>) KEGG enrichment analyses of DEGs in the R1 vs. R2.</p>
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<p>Gene trend expression analysis: (<b>A</b>) Analysis of genes clustering trend. (<b>B</b>) G_Cluster 2 KEGG enrichment analysis. (<b>C</b>) G_Cluster 3 KEGG enrichment analysis.</p>
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<p>(<b>A</b>) Correlation analysis of flavonoid metabolites with flavonoid biosynthesis genes (red color indicates high correlation, blue color indicates low correlation). (<b>B</b>) Flavonoid structural gene and metabolites network interactions graph (blue boxes indicate metabolites, orange circles indicate gene). (<b>C</b>) Network diagram of the correlation between key structural gene and TFs (pink diamonds indicate DEGs, blue circles indicate TFs).</p>
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<p>WGCNA results of <span class="html-italic">D. devonianum</span> stem genes and accumulation of flavonoid metabolites: (<b>A</b>) WGCNA co-expression hierarchical clustering tree (19 gene expression modules, different colors represent different gene modules). (<b>B</b>) Correlation analysis of the 19 gene modules with 16 flavonoid metabolites (the value in each box indicates the Pearson’s correlation coefficient between the modules and metabolites, and each bracketed value indicates the <span class="html-italic">p</span>-value; the color scale on the right side indicates the degree of correlation between the module and the metabolite, red color indicates high correlation). (<b>C</b>) Correlation network diagram of DEGs with flavonoid metabolites (orange boxes indicate metabolites, blue circles indicate DEGs).</p>
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<p>Dynamic expression analysis of genes in the flavonoid biosynthesis pathway of <span class="html-italic">D. devonianum</span>. (<span class="html-italic">PAL</span>: Phenylalanine ammonia-lyase; <span class="html-italic">4CL</span>: 4-coumarate--CoA ligase; <span class="html-italic">CYP73A</span>: Trans-cinnamate 4-monooxygenase; <span class="html-italic">CYP98A</span>: 5-O-(4-coumaroyl)-D-quinate 3′-monooxygenase; <span class="html-italic">CHS</span>: Chalcone synthase; <span class="html-italic">CHI</span>: Chalcone isomerase; <span class="html-italic">DFR</span>: Flavanone 4-reductase; <span class="html-italic">ANS</span>: Anthocyanidin synthase; <span class="html-italic">ANR</span>: Anthocyanidin reductase; <span class="html-italic">FG2</span>: Flavonol-3-O-glucoside L-rhamnosyltransferase; <span class="html-italic">BZ1/UGT77B2</span>: Anthocyanidin 3-O-glucosyltransferase; <span class="html-italic">5GT</span>: Anthocyanidin 3-O-glucoside 5-O-glucosyltransferase; <span class="html-italic">3GT</span>: Anthocyanin 3′-O-beta-glucosyltransferase; <span class="html-italic">GT1</span>: Anthocyanidin 5,3-O-glucosyltransferase; <span class="html-italic">3AT</span>: Anthocyanidin 3-O-glucoside 6″-O-acyltransferase).</p>
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<p>Expression analysis of key genes of flavonoid biosynthesis pathway. Values are mean ± SD (<span class="html-italic">n</span> = 3 independent measurements); different lowercase letters (a, b, c, d) indicate significant differences, <span class="html-italic">p</span> &lt; 0.05.</p>
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26 pages, 6218 KiB  
Article
Revealing the Mechanism of Hemerocallis citrina Baroni in Depression Treatment Through Integrated Network Pharmacology and Transcriptomic Analysis
by Shan Gao, Jihui Lu, Yixiao Gu, Yaozhi Zhang, Cheng Wang, Feng Gao, Ziqi Dai, Shujing Xu, Jindong Zhang, Yuqin Yang and Haimin Lei
Pharmaceuticals 2024, 17(12), 1704; https://doi.org/10.3390/ph17121704 - 17 Dec 2024
Viewed by 368
Abstract
Background/Objectives: Hemerocallis citrina Baroni (HCB) is a traditional herb for the treatment of depression in China. However, the active constituents and the underlying mechanisms of its antidepressant effects remain unclear. The aim of this study was to identify the bioactive constituents of [...] Read more.
Background/Objectives: Hemerocallis citrina Baroni (HCB) is a traditional herb for the treatment of depression in China. However, the active constituents and the underlying mechanisms of its antidepressant effects remain unclear. The aim of this study was to identify the bioactive constituents of HCB and elucidate its underlying mechanism for the treatment of depression. Methods: The constituents of HCB were systematically analyzed using UHPLC-Q-Orbitrap HRMS. Its antidepressant effect was evaluated by chronic unpredictable mild stress (CUMS)-induced depression. The mechanism of HCB in treating depression was investigated through network pharmacology and molecular docking. Subsequently, its potential mechanism for the treatment of depression was carried out by RNA sequencing. Finally, the mechanism was further verified by Western blot. Results: A total of 62 chemical constituents were identified from HCB using UHPLC-Q-Orbitrap HRMS, including 17 flavonoids, 11 anthraquinones, 11 alkaloids, 10 caffeoylquinic acid derivatives, five phenolic acids, five triterpenoids, and three phenylethanosides, 13 of which were identified as potential active constituents targeting 49 depression-associated proteins. Furthermore, HCB was found to significantly reduce cognitive impairment, anxiety-like behavior, and anhedonia-like behavior. The expression levels of 5-hydroxytryptamine (5-HT), dopamine (DA), and brain-derived neurotrophic factor (BDNF) were elevated in the hippocampal CA3 region. Results from network pharmacology and transcriptomics indicated that the PI3K/Akt/CREB signaling pathway is essential for the therapeutic effects of HCB on depression. Research in the field of molecular biology has conclusively demonstrated that HCB is associated with an increase in the expression levels of several important proteins. Specifically, there was a notable upregulation of phosphorylated PI3K (p-PI3K) relative to its unphosphorylated form PI3K, as well as an elevation in the ratio of phosphorylated Akt (p-Akt) to total Akt. Additionally, the study observed increased levels of phosphorylated CREB (p-CREB) compared to its unphosphorylated CREB. Conclusions: This study provides compelling evidence that HCB possesses the ability to mitigate the symptoms of depression through its influence on the PI3K/Akt/CREB signaling pathway. HCB could be developed as a promising therapeutic intervention for individuals struggling with depression, offering new avenues for treatment strategies that target this particular signaling mechanism. Full article
(This article belongs to the Special Issue Discovery of Novel Antidepressants and Anxiolytics)
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<p>Mass spectrogram of HCB. (<b>A</b>) TICC of HCB obtained in ESI+ mode. Product ion spectra of (<b>B</b>) kaempferol-3-rutinoside. (<b>C</b>) quercetin. (<b>D</b>) rutin. (<b>E</b>) kwanzoquinone G. (<b>F</b>) rhein. (<b>G</b>) gallic acid. (<b>H</b>) clionasterol. (<b>I</b>) adenosine.</p>
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<p>HCB improved CUMS mice depression-like behaviors. (<b>A</b>) Schematic diagram of experimental design. (<b>B</b>) Representative images of movement trajectory. (<b>C</b>) Total distance within 5 min in the OFT (<span class="html-italic">n</span> = 8). (<b>D</b>) Time spent in the central area in the OFT (<span class="html-italic">n</span> = 8). (<b>E</b>) Not moving time in the OFT within 5 min (<span class="html-italic">n</span> = 8). (<b>F</b>) Immobility time in the FST within 4 min (<span class="html-italic">n</span> = 8). (<b>G</b>) Changes in precent of sucrose preference in the SPT (<span class="html-italic">n</span> = 8). (<b>H</b>) The secretion levels of 5-hydroxytryptamine (<span class="html-italic">n</span> = 3). (<b>I</b>) The secretion levels of dopamine (<span class="html-italic">n</span> = 3). (<b>J</b>) The secretion levels of BDNF (<span class="html-italic">n</span> = 3). (<b>K</b>) The number of Nissl bodies in the hippocampal CA3 regions (<span class="html-italic">n</span> = 3). (<b>L</b>) Representative pictures of Nissl staining in the hippocampi. Data are presented as mean ± SEM, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. control group (C-group); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. model group (M-group).</p>
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<p>Analysis results of network pharmacology and molecular docking. (<b>A</b>) Venn mapping of HCB on depression. (<b>B</b>) PPI networks of candidate targets. (<b>C</b>) The network construction of compounds–targets–diseases. (<b>D</b>) GO enrichment analysis. (<b>E</b>) KEGG pathway analysis. (<b>F</b>) Molecular docking diagram of active constitutes and potential targets.</p>
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<p>RNA sequencing analysis of hippocampus. (<b>A</b>) Volcano map of DEGs. (<b>B</b>) Hierarchical clustering analysis of DEGs. (<b>C</b>) Functional annotation analysis of GO using DEGs. (<b>D</b>) Functional enrichment analysis of KEGG using DEGs.</p>
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<p>HCB regulated PI3K/Akt/CREB signaling pathway (<b>A</b>) Representative protein bands of PI3K, p-PI3K, Akt, p-Akt, CREB, and p-CREB in hippocampal. Statistical graphs of relative protein expression of ratio of p-PI3K/PI3K (<b>B</b>), PI3K/GAPDH (<b>C</b>), p-Akt/Akt (<b>D</b>), Akt/GAPDH (<b>E</b>), p-CREB/CREB (<b>F</b>), and CREB/GAPDH (<b>G</b>). Data are presented as mean ± SEM, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. control group (C-group); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. model group (M-group).</p>
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<p>Identification of antidepressant constitutes in HCB and its underlying mechanism on the treatment of depression.</p>
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18 pages, 13037 KiB  
Article
Bufadienolides from Chansu Injection Synergistically Enhances the Antitumor Effect of Erlotinib by Inhibiting the KRAS Pathway in Pancreatic Cancer
by Yanli Guo, Yu Jin, Jie Gao, Ding Wang, Yanming Wang, Liya Shan, Mengyu Yang, Xinzhi Li and Ketao Ma
Pharmaceuticals 2024, 17(12), 1696; https://doi.org/10.3390/ph17121696 - 16 Dec 2024
Viewed by 466
Abstract
Background and Objectives: The Chansu injection (CSI), a sterile aqueous solution derived from Chansu, is applied in clinical settings to support antitumor and anti-radiation treatments. CSI’s principal active components, bufadienolides (≥90%), demonstrate potential effects on pancreatic cancer (PDAC), but their underlying mechanisms remain [...] Read more.
Background and Objectives: The Chansu injection (CSI), a sterile aqueous solution derived from Chansu, is applied in clinical settings to support antitumor and anti-radiation treatments. CSI’s principal active components, bufadienolides (≥90%), demonstrate potential effects on pancreatic cancer (PDAC), but their underlying mechanisms remain unclear. This study aimed to elucidate the antitumor effects and pathways associated with CSI in PDAC. Methods: Network pharmacology and bioinformatics analyses explored CSI’s mechanisms against PDAC. MTT, colony-formation, and migration assays evaluated CSI’s impact on proliferation and migration in PANC-1 and MIA PACA-2 cells, both as a single agent and in combination with erlotinib (EGFR inhibitor). Cell cycle analysis employed flow cytometry. Animal experiments were performed on tumor-bearing mice, with targets and pathways assessed via molecular docking and western blotting. Results: CSI treatment suppressed PDAC cell proliferation and migration by inducing G2/M phase arrest. Network pharmacology, bioinformatics, and molecular docking indicated that CSI’s anti-PDAC effects may involve EGFR pathway modulation, with CSI lowering p-EGFR/KRAS/p-ERK1/2 pathway expressions in PDAC cells. Additionally, sustained KRAS activation in mediating erlotinib resistance in PDAC and CSI potentiated erlotinib’s antitumor effects through enhanced KRAS and p-ERK1/2 inhibition. CSI also enhanced erlotinib’s efficacy in tumor-bearing mice without causing detectable toxicity in renal, cardiac, or hepatic tissues at therapeutic doses. Conclusions: CSI as an adjuvant used in antitumor and anti-radiation therapies enhanced erlotinib’s antitumor effects through modulation of the KRAS pathway. CSI and erlotinib’s synergistic interaction represents a promising approach for addressing erlotinib resistance in PDAC treatment. Full article
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<p>CSI inhibited the proliferation and migration of PDAC cells. (<b>A</b>) PANC-1 and MIA PACA-2 cells were treated with 0.1325–80 μg/mL of CSI for 24 h or 48 h, and cell viability was assessed using the MTT assay (n = 3). (<b>B</b>,<b>C</b>) Colony-formation assays were performed on PANC-1 and MIA PACA-2 cells treated with 2.5, 5 or 10 μg/mL CSI for 48 h (n = 3). (<b>D</b>,<b>E</b>) Migrating assays were conducted with PANC-1 and MIA PACA-2 cells exposed to 2.5, 5, or 10 μg/mL CSI for 48 h (n = 5). The same volume of sterile water or DMSO was added to the control group. Data were expressed as mean ± standard error (SEM). Statistical significance was defined as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 versus the control group.</p>
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<p>CSI inhibited the growth of PDAC cells by inducing G2/M phase arrest. (<b>A</b>) PANC-1 and MIA PACA-2 cells were treated with 2.5, 5, or 10 μg/mL CSI for 24 h, and cell cycle distribution was analyzed using flow cytometry (n = 5). (<b>B</b>) The expression of cyclin B1 and CDK1 were determined using Western blot (n = 5). Data were expressed as mean ± standard error (SEM). Statistical significance was defined as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 versus the control group.</p>
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<p>Network pharmacological and bioinformatics analysis of CSI against PDAC. (<b>A</b>) Overview of the network pharmacological and bioinformatics analysis for CSI against PDAC. (<b>B</b>) Volcano map and Venn diagram depicting differentially expressed genes from GSE62165, GSE91035, GSE15471, and GSE16515. (<b>C</b>) PPI network of CSI active compounds and anti-pancreatic cancer targets. (<b>D</b>,<b>E</b>) GO and KEGG enrichment analyses of 155 common targets for CSI in pancreatic cancer.</p>
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<p>Network pharmacological and bioinformatics analysis of CSI against PDAC. (<b>A</b>) Top 10 hub targets of CSI in pancreatic cancer. (<b>B</b>) Survival curves of patients with PDAC based on EGFR expression. (<b>C</b>) Targets of CSI interactions within the EGFR inhibitor resistance pathway. (<b>D</b>) Molecular docking analysis of CSI active compounds with EGFR, with erlotinib used as a positive control.</p>
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<p>CSI treatment reduced the expression of p-EGFR, KRAS, and p-ERK1/2. (<b>A</b>) PANC-1 and MIA PACA-2 cells were treated with 2.5, 5, or 10 μg/mL CSI for 24 h, and the protein expression levels of p-EGFR, EGFR, KRAS, p-ERK1/2, and ERK1/2 were analyzed using Western blot (n = 5). (<b>B</b>) Quantification of Western blot results for p-EGFR, EGFR, KRAS, p-ERK1/2, and ERK1/2 (n = 5). Data were expressed as mean ± standard error (SEM). Statistical significance was defined as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 versus the control group.</p>
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<p>CSI synergistically enhanced the antitumor effect of erlotinib in vitro. Er (erlotinib), CSI (Chansu injection), and Er+CSI (erlotinib+Chansu injection). (<b>A</b>) The inhibitory effects of various concentrations of CSI (2.5, 5, 10 μg/mL) in combination with erlotinib (2, 4, 8 μM) on PANC-1 and MIA PACA-2 cells for 48 h were assessed (n = 5). (<b>B</b>) Colony-formation assays were conducted on PANC-1 cells and MIA PACA-2 treated with 10 μg/mL CSI, 2 μM erlotinib, or their combination for 48 h (n = 3). Data were expressed as mean ± standard error (SEM). Statistical significance was defined as ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, and as ## <span class="html-italic">p</span> &lt; 0.01 relative to the erlotinib (Er) group, <span class="html-italic">ns</span> represented no significant difference.</p>
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<p>Cotreatment of CSI and erlotinib inhibited KRAS and p-ERK1/2 in PDAC cells. Er (erlotinib), CSI (Chansu injection), and Er+CSI (erlotinib+Chansu injection). (<b>A</b>,<b>B</b>) PANC-1 and MIA PACA-2 cells were treated with 10 μg/mL CSI, 2 μM erlotinib or their combination for 48 h, and cell cycle distribution was analyzed using flow cytometry assays (n = 5). (<b>C</b>) Western blot analysis of p-EGFR, EGFR, KRAS, p-ERK1/2, ERK1/2, and CDK1 (n = 5). Data were expressed as mean ± standard error (SEM). Statistical significance was defined as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, as # <span class="html-italic">p</span> &lt; 0.05 and ## <span class="html-italic">p</span> &lt; 0.01 relative to the erlotinib (Er) group, and <span class="html-italic">ns</span> represented no significant difference.</p>
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<p>CSI enhanced the antitumor effect of Er (erlotinib) in tumor-bearing mice. (<b>A</b>) Images of tumor samples from PDAC-bearing mice (n = 6). (<b>B</b>) Effects of CSI and erlotinib combination on tumor weight in tumor-bearing mice (n = 6). (<b>C</b>) Effects of CSI and erlotinib combination on tumor volume (n = 6). (<b>D</b>) Representative H&amp;E staining images of tumor, heart, liver, and kidney tissues. (<b>E</b>) Effect of CSI and erlotinib combination on body weight (n = 6). (<b>F</b>) Effect of CSI and erlotinib combination on the weight of the heart, liver, and kidney in mice (n = 6). (<b>G</b>) Representative IHC staining images of p-EGFR, KRAS, and p-ERK of tumor. Data were expressed as mean ± standard error (SEM). Statistical significance was defined as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, and as ## <span class="html-italic">p</span> &lt; 0.01 relative to the erlotinib (Er) group.</p>
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20 pages, 5995 KiB  
Article
Pasteurization and the Potential Anti-Obesity Function of Fermented Beverages: A Significant Increase in Nitrogen-Containing Aromatic Heterocyclic Compound Content
by Xiurong Wu, Ting Wang, Xiangzhen Nie, Yanglin Wu, Jinghan Wang, Haoming Wang, Rui Dai, Ronghan Liu, Yingying Cui, Miaoting Su, Yang Qiu and Xiantao Yan
Fermentation 2024, 10(12), 646; https://doi.org/10.3390/fermentation10120646 - 16 Dec 2024
Viewed by 668
Abstract
Obesity is a chronic disease that profoundly impacts human health, and the role of plant-based formulas (PBFs) in combating obesity has garnered significant interest. Studies have revealed that fermentation significantly enhances the taste, aroma, quality, and health benefits of PBF water extract, with [...] Read more.
Obesity is a chronic disease that profoundly impacts human health, and the role of plant-based formulas (PBFs) in combating obesity has garnered significant interest. Studies have revealed that fermentation significantly enhances the taste, aroma, quality, and health benefits of PBF water extract, with pasteurization being the preferred sterilization technology. However, few studies have investigated the effects of pasteurization on the active components and potential functions of PBF water extract fermentation broth. To examine the impact of pasteurization on fermented water extract of Millettia speciosa Champ (FH08F) and its potential anti-obesity properties, the components of FH08F and thermal-pasteurized FH08F (FH08FS) were analyzed in this study. The analysis revealed a substantial rise in ester content following sterilization. This can be attributed to the acidic environment that promotes the esterification reaction during the heating phase. Network pharmacology was employed to thoroughly examine seven active components of upregulated compounds (URCs) with potential obesity targets, which constituted 92.97% of the total URC content, and four of them were nitrogen-containing aromatic heterocyclic compounds (NAHCs), which accounted for 90.33% of the total URC content. Upregulated NAHCs appear to actively contribute to efficacy against obesity. Molecular docking analyses have shown that theophylline, an NAHC, has the strongest binding affinity with the obesity-related target PTGS2 (Prostaglandin G/H synthase 2, 5FLG). These results imply that theophylline may directly activate PKA/PKG-mediated phosphorylated hormone-sensitive lipase (p-HSL), thereby promoting lipolysis through the cAMP signaling pathway and stimulating the catabolism of triglycerides (TGs) to combat obesity. In conclusion, pasteurization substantially alters the composition of FH08F, and NAHCs are likely to play a significant role in its potential anti-obesity function. These findings provide a scientific foundation for the potential therapeutic effect of FH08FS on obesity and associated metabolic diseases. Full article
(This article belongs to the Section Fermentation for Food and Beverages)
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Figure 1

Figure 1
<p>Analysis of compounds before and after pasteurization of FH08F: (<b>a</b>) Partial least squares discriminant analysis (PLS-DA) score plot of the FH08FS group versus the FH08F group. Component 1 (X-axis) represents the predicted score of the first principal component, showing the difference between the sample groups. Component 2 (Y-axis) represents the orthogonal principal component score, displaying the differences within the sample groups. Each scatter point represents a sample, and the green scatter points and red scatter points represent the FH08F and FH08FS groups, respectively. The region marked by the ellipse is the 95% confidence interval of the sample point. (<b>b</b>) Volcano plot. Red dots, blue dots, and gray dots represent significantly upregulated, significantly downregulated, and nonsignificantly different compounds, respectively. The dot size indicates the variable importance in projection (VIP) of each compound. (<b>c</b>) Results of differential compound category analysis of the FH08FS group vs. the FH08F group. The numbers on the bar chart represent the number of changed compound types. Red indicates significant upregulation, and blue indicates significant downregulation. (<b>d</b>) Terpenoids downregulated after pasteurization. (<b>e</b>) Proportion of each compound among the upregulated compounds (URCs). (<b>f</b>) The structure of nitrogen-containing aromatic heterocyclic compounds (NAHCs).</p>
Full article ">Figure 1 Cont.
<p>Analysis of compounds before and after pasteurization of FH08F: (<b>a</b>) Partial least squares discriminant analysis (PLS-DA) score plot of the FH08FS group versus the FH08F group. Component 1 (X-axis) represents the predicted score of the first principal component, showing the difference between the sample groups. Component 2 (Y-axis) represents the orthogonal principal component score, displaying the differences within the sample groups. Each scatter point represents a sample, and the green scatter points and red scatter points represent the FH08F and FH08FS groups, respectively. The region marked by the ellipse is the 95% confidence interval of the sample point. (<b>b</b>) Volcano plot. Red dots, blue dots, and gray dots represent significantly upregulated, significantly downregulated, and nonsignificantly different compounds, respectively. The dot size indicates the variable importance in projection (VIP) of each compound. (<b>c</b>) Results of differential compound category analysis of the FH08FS group vs. the FH08F group. The numbers on the bar chart represent the number of changed compound types. Red indicates significant upregulation, and blue indicates significant downregulation. (<b>d</b>) Terpenoids downregulated after pasteurization. (<b>e</b>) Proportion of each compound among the upregulated compounds (URCs). (<b>f</b>) The structure of nitrogen-containing aromatic heterocyclic compounds (NAHCs).</p>
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<p>Analysis of the targets associated with URCs and obesity: (<b>a</b>) Venn diagram of URCs and obesity-associated targets. This dataset included 193 URC-related targets (left), 1793 obesity-related targets (right), and 69 URCs and obesity-related targets. (<b>b</b>) URC anti-obesity PPI network. A larger area indicates larger nodes, a purple color indicates a stronger association and a lighter color indicates a weaker association. (<b>c</b>) The active ingredients of the URC–target–obesity interaction network. The active ingredients are sedanolide (Mol 3), ethyl-4-amino-2-(methylsulfanyl)-1,3-thiazole-5-carboxylate (Mol 10), nicotinamide (Mol 11), ethyl 2-{2-[(phenylsulfonyl)amino]-1,3-thiazol-4-yl}acetate (Mol 12), fumaric acid (Mol 14), theophylline (Mol 15), and 2-aminoethylphosphonate (Mol 17). (<b>d</b>) Degree values of the 7 active ingredients from <a href="#fermentation-10-00646-f002" class="html-fig">Figure 2</a>c. (<b>e</b>) Nineteen genes screened from the PPI network with BC, CC, DC, EC, LAC, and NC scores above the median value of 66 genes. (<b>f</b>) Seven core targets screened from the 19 targets with BC, CC, DC, EC, LAC, and NC scores above the median value.</p>
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<p>Analysis of the targets associated with URCs and obesity: (<b>a</b>) Venn diagram of URCs and obesity-associated targets. This dataset included 193 URC-related targets (left), 1793 obesity-related targets (right), and 69 URCs and obesity-related targets. (<b>b</b>) URC anti-obesity PPI network. A larger area indicates larger nodes, a purple color indicates a stronger association and a lighter color indicates a weaker association. (<b>c</b>) The active ingredients of the URC–target–obesity interaction network. The active ingredients are sedanolide (Mol 3), ethyl-4-amino-2-(methylsulfanyl)-1,3-thiazole-5-carboxylate (Mol 10), nicotinamide (Mol 11), ethyl 2-{2-[(phenylsulfonyl)amino]-1,3-thiazol-4-yl}acetate (Mol 12), fumaric acid (Mol 14), theophylline (Mol 15), and 2-aminoethylphosphonate (Mol 17). (<b>d</b>) Degree values of the 7 active ingredients from <a href="#fermentation-10-00646-f002" class="html-fig">Figure 2</a>c. (<b>e</b>) Nineteen genes screened from the PPI network with BC, CC, DC, EC, LAC, and NC scores above the median value of 66 genes. (<b>f</b>) Seven core targets screened from the 19 targets with BC, CC, DC, EC, LAC, and NC scores above the median value.</p>
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<p>GO and KEGG pathway enrichment analysis of the targets associated with URCs against obesity: (<b>a</b>) GO enrichment analysis; (<b>b</b>) KEGG pathway enrichment analysis.</p>
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<p>Heatmap of the molecular docking binding energy among 3 core active ingredients of URCs and 7 core targets.</p>
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<p>Molecular docking diagram of theophylline and 7 core targets, the orange represents the theophylline molecule: (<b>a</b>) Theophylline-IL6; (<b>b</b>) theophylline-AKT1; (<b>c</b>) theophylline-PPARG; (<b>d</b>) theophylline-PTGS2; (<b>e</b>) theophylline-ESR1; (<b>f</b>) theophylline-PPARA; (<b>g</b>) theophylline-REN.</p>
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25 pages, 31841 KiB  
Article
From Tea to Functional Foods: Exploring Caryopteris mongolica Bunge for Anti-Rheumatoid Arthritis and Unraveling Its Potential Mechanisms
by Xin Dong, Zhi Wang, Yao Fu, Yuxin Tian, Peifeng Xue, Yuewu Wang, Feiyun Yang, Guojing Li and Ruigang Wang
Nutrients 2024, 16(24), 4311; https://doi.org/10.3390/nu16244311 - 13 Dec 2024
Viewed by 464
Abstract
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and [...] Read more.
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and joint-protective effects remain unclear, limiting its development as a functional food. This study investigated the effects of aqueous CM extract on RA in collagen-induced arthritis (CIA) rats and explored the underlying mechanisms. Methods: Forty-eight female Sprague-Dawley rats were randomly assigned to six groups (n = 8): normal control, CIA model, methotrexate (MTX), and CM high-, middle-, and low-dose groups. Anti-inflammatory and joint-protective effects were evaluated using biochemical and histological analyses. To elucidate the mechanisms, we applied metabolomics, network pharmacology, and transcriptomics approaches. Results: The results demonstrated that CM extract effectively suppressed synovial inflammation in CIA rats, reducing joint degradation. CM’s anti-inflammatory effects were mediated through the TNF signaling pathway, modulating glycerophospholipid and amino acid metabolism, including reduced levels of tryptophan, LysoPC, and asparagine. Molecular docking identified scutellarin and apigenin as key bioactive compounds. Additionally, immunofluorescence analysis revealed CM’s therapeutic effects via TNF signaling inhibition and suppression of M1 macrophage polarization. Conclusions: These findings highlight the therapeutic potential of CM for RA and support its development as a functional food or pharmaceutical product. Full article
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Graphical abstract

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<p>Induction protocol for collagen-induced arthritis (CIA) in SD rats and the drug treatment schedule employed during the experiments.</p>
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<p>Network analysis of CM in treating rheumatoid arthritis. (<b>a</b>) Venn diagram showing 244 common targets between CM and rheumatoid arthritis. (<b>b</b>) STRING network visualization of the 244 common targets with topological analysis. (<b>c</b>) The top 10 significantly enriched terms in KEGG pathways. (<b>d</b>) Top 15 significantly enriched terms in biological processes. (<b>e</b>) Top 15 significantly enriched terms in cellular components (<b>e</b>). (<b>f</b>) Top 15 significantly enriched terms in molecular functions.</p>
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<p>Network analysis of CM in treating rheumatoid arthritis. (<b>a</b>) Venn diagram showing 244 common targets between CM and rheumatoid arthritis. (<b>b</b>) STRING network visualization of the 244 common targets with topological analysis. (<b>c</b>) The top 10 significantly enriched terms in KEGG pathways. (<b>d</b>) Top 15 significantly enriched terms in biological processes. (<b>e</b>) Top 15 significantly enriched terms in cellular components (<b>e</b>). (<b>f</b>) Top 15 significantly enriched terms in molecular functions.</p>
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<p>(<b>a</b>) Derived different MCODE clusters: Light yellow nodes were kernel genes in MCODE cluster1; Light orange nodes were kernel genes in MCODE cluster2; Pink nodes were kernel genes in MCODE cluster3; Light purple nodes were kernel genes in MCODE cluster4; Light blue nodes were kernel genes in MCODE cluster5. (<b>b</b>) The top 15 significantly enriched terms of MCODE cluster5 in KEGG pathways. (<b>c</b>) The top 5 enriched terms of MCODE cluster5 in biological processes (BP parts), cellular components (CC parts), and molecular functions (MF parts). (<b>d</b>) Illustration of the relevance among components of CM, the key targets, and diseases. Yellow nodes refer to RA; Pink nodes refer to the components contained in CM; Blue nodes refer to the key targets.</p>
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<p>(<b>a</b>) Derived different MCODE clusters: Light yellow nodes were kernel genes in MCODE cluster1; Light orange nodes were kernel genes in MCODE cluster2; Pink nodes were kernel genes in MCODE cluster3; Light purple nodes were kernel genes in MCODE cluster4; Light blue nodes were kernel genes in MCODE cluster5. (<b>b</b>) The top 15 significantly enriched terms of MCODE cluster5 in KEGG pathways. (<b>c</b>) The top 5 enriched terms of MCODE cluster5 in biological processes (BP parts), cellular components (CC parts), and molecular functions (MF parts). (<b>d</b>) Illustration of the relevance among components of CM, the key targets, and diseases. Yellow nodes refer to RA; Pink nodes refer to the components contained in CM; Blue nodes refer to the key targets.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Transcriptomic landscape of CM treating rheumatoid arthritis: (<b>a</b>) DEGs volcano plot; (<b>b</b>) DEGs heatmap. (<b>c</b>) The top 15 significantly enriched terms in KEGG pathways. (<b>d</b>) The top 5 enriched terms in biological processes (blue parts), cellular components (yellow parts), and molecular functions (pink parts).</p>
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<p>Transcriptomic landscape of CM treating rheumatoid arthritis: (<b>a</b>) DEGs volcano plot; (<b>b</b>) DEGs heatmap. (<b>c</b>) The top 15 significantly enriched terms in KEGG pathways. (<b>d</b>) The top 5 enriched terms in biological processes (blue parts), cellular components (yellow parts), and molecular functions (pink parts).</p>
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<p>(<b>a</b>) Venn diagram of common targets between CM, rheumatoid arthritis and DEGs. (<b>b</b>) 13 common targets via STRING network topological analysis. (<b>c</b>) Fluorescence quantitative PCR verification of core differential expressed gene. Molecular docking results, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. CIA group: (<b>d</b>) The heatmap of docking scores of key targets combining to 5 active compounds in CM. (<b>e</b>–<b>j</b>) The representative docking complex of key targets and compounds.</p>
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<p>(<b>a</b>) Venn diagram of common targets between CM, rheumatoid arthritis and DEGs. (<b>b</b>) 13 common targets via STRING network topological analysis. (<b>c</b>) Fluorescence quantitative PCR verification of core differential expressed gene. Molecular docking results, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. CIA group: (<b>d</b>) The heatmap of docking scores of key targets combining to 5 active compounds in CM. (<b>e</b>–<b>j</b>) The representative docking complex of key targets and compounds.</p>
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<p>Immunofluorescence double staining of M1 and M2 macrophages in rats’ joint tissues of each group (iNOS as the positive marker for M1 macrophages with green color, CD206 as the positive marker for M2 macrophages with red color, DAPI as the positive marker for cell nuclei with blue color, and Merge as the combined marker of all three).</p>
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20 pages, 5085 KiB  
Article
Antioxidant Effects and Potential Mechanisms of Citrus reticulata ‘Chachi’ Components: An Integrated Approach of Network Pharmacology and Metabolomics
by Jiahao Xiao, Tian Sun, Shengyu Jiang, Zhiqiang Xiao, Yang Shan, Tao Li, Zhaoping Pan, Qili Li and Fuhua Fu
Foods 2024, 13(24), 4018; https://doi.org/10.3390/foods13244018 - 12 Dec 2024
Viewed by 698
Abstract
Citrus reticulata ‘Chachi’ (CRC), recognized for its considerable edible and medicinal significance, is a valuable source of metabolites beneficial to human health. This research investigates the metabolic distinctions and antioxidant properties across four different parts of CRC, using multivariate statistical analysis to interpret [...] Read more.
Citrus reticulata ‘Chachi’ (CRC), recognized for its considerable edible and medicinal significance, is a valuable source of metabolites beneficial to human health. This research investigates the metabolic distinctions and antioxidant properties across four different parts of CRC, using multivariate statistical analysis to interpret metabolomic data and network pharmacology to identify potential antioxidant targets and relevant signaling pathways. The results indicate considerable metabolic differences in different parts of the sample, with 1622 metabolites showing differential expression, including 816 secondary metabolites, primarily consisting of terpenoids (31.02%) and flavonoids (25.22%). The dried mature citrus peel (CP) section demonstrates the highest level of total phenolics (6.8 mg/g), followed by the pulp without seed (PU) (4.52 mg/g), pulp with seed (PWS) (4.26 mg/g), and the seed (SE) (2.16 mg/g). Interestingly, targeted high-performance liquid chromatography of flavonoids reveals the highest level of nobiletin and tangeretin in CP, whereas PU has the highest level of hesperidin, narirutin, and didymin. Furthermore, all four sections of CRC exhibit robust antioxidant properties in in vitro assessments (CP > PU > PWS > SE). Lastly, the network pharmacology uncovered potential antioxidant mechanisms in CRC. This research offers deeper insights into the development and utilization of byproducts in the CRC processing industry. Full article
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Figure 1

Figure 1
<p>(<b>A</b>) Classification of primary metabolites. (<b>B</b>) Classification of secondary metabolites. (<b>C</b>) Three-dimensional PCA score plot. (<b>D</b>) PLS-DA score plot. (<b>E</b>) Permutation test plot with 200 permutations. (<b>F</b>) Sample correlation heat map.</p>
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<p>(<b>A</b>–<b>C</b>) OPLS-DA score plots. (<b>A</b>) CP and PU; (<b>B</b>) PWS and PU; (<b>C</b>) SE and PU. (<b>D</b>–<b>F</b>) Volcano plots of differential metabolite expression levels. (<b>D</b>) CP and PU; (<b>E</b>) PWS and PU; (<b>F</b>) SE and PU.</p>
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<p>(<b>A</b>) Venn diagram of differential metabolites; (<b>B</b>) Classification of 816 secondary differential metabolites.</p>
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<p>Heat maps display the levels of secondary differentially expressed metabolites in four parts of CRC. (<b>A</b>) Flavonoids; (<b>B</b>) Terpenoids; (<b>C</b>) Phenolic acids and derivatives; (<b>D</b>) Steroids and steroid derivatives.</p>
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<p>Heat maps display the levels of secondary differentially expressed metabolites in four parts of CRC. (<b>A</b>) Coumarins and derivatives; (<b>B</b>) Organic acids and derivatives; (<b>C</b>) Alkaloids and derivatives; (<b>D</b>) Others.</p>
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<p>Bubble chart of KEGG enrichment pathways for secondary differential metabolites.</p>
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<p>HPLC chromatograms of 16 flavonoid standards (1, Verbascoside; 2, Taxifolin; 3, Narirutin; 4, Naringin; 5, Hesperidin; 6, Neohesperidin; 7, Rutin; 8, Rhoifolin; 9, Diosmin; 10, Didymin; 11, Hesperetin; 12, Luteolin; 13, Diosmetin; 14, Sinensetin; 15, Nobiletin; 16, Tangeretin) and samples. (<b>A</b>) Flavonoid standard mixture, 283 nm. (<b>B</b>) Flavonoid standard mixture, 330 nm. (<b>C</b>) PU, 283 nm. (<b>D</b>) CP, 330 nm. (<b>E</b>) PWS, 283 nm. (<b>F</b>) SE, 283 nm.</p>
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<p>(<b>A</b>) Venn diagram of overlapping targets between flavonoid active components and oxidative damage. (<b>B</b>,<b>C</b>) Protein–protein interaction (PPI) analysis network diagram. (<b>C</b>) Top 10 GO enrichment analysis bar chart. (<b>D</b>) Top 20 KEGG enrichment analysis bubble chart of signaling pathways.</p>
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