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Keywords = EGFR and MAPK signaling

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9 pages, 1294 KiB  
Article
ARAF Amplification in Small-Cell Lung Cancer-Transformed Tumors Following Resistance to Epidermal Growth Factor Receptor–Tyrosine Kinase Inhibitors
by Ryo Kimura, Yuta Adachi, Kentaro Hirade, Satoru Kisoda, Shogo Yanase, Noriko Shibata, Makoto Ishii, Yutaka Fujiwara, Rui Yamaguchi, Yasuko Fujita, Waki Hosoda and Hiromichi Ebi
Cancers 2024, 16(20), 3501; https://doi.org/10.3390/cancers16203501 - 16 Oct 2024
Viewed by 363
Abstract
Background/Objectives: Although tyrosine kinase inhibitors (TKIs) targeting EGFR-activating mutations significantly improved the outcome of EGFR-mutant NSCLC, resistance inevitably emerges. Despite the heterogeneity of these resistance mechanisms, many induce activation of MAPK signaling in the presence of EGFR-TKIs. While ARAF gene amplification is identified [...] Read more.
Background/Objectives: Although tyrosine kinase inhibitors (TKIs) targeting EGFR-activating mutations significantly improved the outcome of EGFR-mutant NSCLC, resistance inevitably emerges. Despite the heterogeneity of these resistance mechanisms, many induce activation of MAPK signaling in the presence of EGFR-TKIs. While ARAF gene amplification is identified as a resistance mechanism that activates MAPK signaling by directly interacting with RAS, little is known about its clinicopathologic characteristics. Methods: We conducted a single-center retrospective analysis of the presence of ARAF amplification in re-biopsied samples in patients with EGFR-mutant NSCLC resistant to EGFR-TKIs. Demographic data, treatment course, and clinical molecular testing reports were extracted from electronic medical records. ARAF amplification was determined using a gene copy number assay. RNA sequence analysis was performed in patients with ARAF amplification as well as presenting histologic transformations to small-cell lung carcinoma (SCLC). Results: ARAF amplification was identified in five of ninety-seven patients resistant to erlotinib or gefitinib, and four of forty-eight patients resistant to Osimertinib. ARAF amplification was dominantly observed in female patients with EGFR exon 19 deletion. All ARAF-amplified tumors retained their founder EGFR mutation and were absent of secondary mutations. Two cases were found where ARAF amplification correlated with a histological transformation to SCLC. Conclusions: ARAF amplification was identified in 5–8% of EGFR-TKI-resistant tumors. The possible roles of ARAF in SCLC transformation warrant further investigation. Full article
(This article belongs to the Special Issue Advances in Molecular Oncology and Therapeutics)
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Figure 1
<p>The distribution of <span class="html-italic">ARAF</span> copy numbers and clinicopathologic characteristics is shown for patients resistant to first– and second–generation EGFR-TKIs (<b>A</b>,<b>B</b>) and Osimertinib (<b>C</b>,<b>D</b>). <a href="#cancers-16-03501-f001" class="html-fig">Figure 1</a>B and <a href="#cancers-16-03501-f001" class="html-fig">Figure 1</a>D provide the percentages of each characteristic within their respective categories. Blue represents <span class="html-italic">ARAF</span> non-amplified cases, while orange indicates <span class="html-italic">ARAF</span> amplified cases.</p>
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<p>Cases with <span class="html-italic">ARAF</span> amplification in SCLC-transformed tumors following resistance to EGFR-TKI. (<b>A</b>) Case with SCLC transformation after resistance to erlotinib treatment. A pre-treatment sample was taken at diagnosis using transbronchial lung biopsy (TBLB), and re-biopsied samples were taken at resistance to erlotinib treatment using CT-Guided needle biopsy (CTNB). Scale bar, 100 µm for low-power field and 50 µm for high-power field in CD56. IRI, irinotecan; AMR, amrubicin; DTX, docetaxel; PEM, pemetrexed. (<b>B</b>) Case with SCLC transformation after resistance to Osimertinib treatment. A pre-treatment sample was taken after resistance to gefitinib using CTNB, and re-biopsied samples were taken at resistance to Osimertinib using ultrasound-guided needle biopsy (USNB). Scale bar, 100 µm. ETP, etoposide. (<b>C</b>) Log 2-fold change in expression of the indicated genes at the time of resistance compared to the pre-treatment biopsy samples shown in (<b>A</b>,<b>B</b>). (<b>D</b>) GSEA data for the indicated signatures. (<b>E</b>) Immunohistochemistry staining for indicated antibodies. Scale bar, 100 µm for low-power field and 50 µm for high-power field in YAP.</p>
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17 pages, 2866 KiB  
Article
Metabolomic Profiling and Network Toxicology: Mechanistic Insights into Effect of Gossypol Acetate Isomers in Uterine Fibroids and Liver Injury
by Zishuo Liu, Hui Zhang and Jun Yao
Pharmaceuticals 2024, 17(10), 1363; https://doi.org/10.3390/ph17101363 - 12 Oct 2024
Viewed by 480
Abstract
Objective: Gossypol is a natural polyphenolic dialdehyde product that is primarily isolated from cottonseed. It is a racemized mixture of (−)-gossypol and (+)-gossypol that has anti-infection, antimalarial, antiviral, antifertility, antitumor and antioxidant activities, among others. Gossypol optical isomers have been reported to differ [...] Read more.
Objective: Gossypol is a natural polyphenolic dialdehyde product that is primarily isolated from cottonseed. It is a racemized mixture of (−)-gossypol and (+)-gossypol that has anti-infection, antimalarial, antiviral, antifertility, antitumor and antioxidant activities, among others. Gossypol optical isomers have been reported to differ in their biological activities and toxic effects. Method: In this study, we performed a metabolomics analysis of rat serum using 1H-NMR technology to investigate gossypol optical isomers’ mechanism of action on uterine fibroids. Network toxicology was used to explore the mechanism of the liver injury caused by gossypol optical isomers. SD rats were randomly divided into a normal control group; model control group; a drug-positive group (compound gossypol acetate tablets); high-, medium- and low-dose (−)-gossypol acetate groups; and high-, medium- and low-dose (+)-gossypol acetate groups. Result: Serum metabolomics showed that gossypol optical isomers’ pharmacodynamic effect on rats’ uterine fibroids affected their lactic acid, cholesterol, leucine, alanine, glutamate, glutamine, arginine, proline, glucose, etc. According to network toxicology, the targets of the liver injury caused by gossypol optical isomers included HSP90AA1, SRC, MAPK1, AKT1, EGFR, BCL2, CASP3, etc. KEGG enrichment showed that the toxicity mechanism may be related to pathways active in cancer, such as the PPAR signaling pathway, glycolysis/glycolysis gluconeogenesis, Th17 cell differentiation, and 91 other closely related signaling pathways. Conclusions: (−)-gossypol acetate and (+)-gossypol acetate play positive roles in the treatment and prevention of uterine fibroids. Gossypol optical isomers cause liver damage through multiple targets and pathways. Full article
(This article belongs to the Section Pharmacology)
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Graphical abstract

Graphical abstract
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<p>(<b>A</b>) 1H-NMR differential metabolite spectra of the serum from rats in each group. (1) Blank group; (2) uterine fibroid model group; (3) high-dose (+)-gossypol acetate group; (4) high-dose (−)-gossypol acetate group; and (5) positive control group. (<b>B</b>) Three-dimensional spatial distribution map of PLS-DA analysis. (1: positive control group; 2: high-dose (+)-gossypol acetate group; 3: high-dose (−)-gossypol acetate group; 4: model control group 5: normal control group). The corresponding metabolites 1–27 are listed in detail in <a href="#pharmaceuticals-17-01363-t001" class="html-table">Table 1</a>.</p>
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<p>Serum OPLS-DA scores of the rats in each group (<b>left</b>) with model verification (<b>right</b>). (<b>A</b>) Positive control group vs. model group; (<b>B</b>) high-dose (+)-gossypol acetate group vs. model group; (<b>C</b>) high-dose (−)-gossypol acetate group vs. model group; and (<b>D</b>) normal control group vs. model group.</p>
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<p>(<b>A</b>) Common targets of gossypol isomer-induced hepatotoxicity. (<b>B</b>) “component-target interaction” PPI network diagram (<b>C</b>) GO enrichment analysis of targets related to gossypol isomers. (<b>D</b>) KEGG enrichment analysis of targets related to gossypol isomers.</p>
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<p>(<b>A</b>) Common targets of gossypol isomer-induced hepatotoxicity. (<b>B</b>) “component-target interaction” PPI network diagram (<b>C</b>) GO enrichment analysis of targets related to gossypol isomers. (<b>D</b>) KEGG enrichment analysis of targets related to gossypol isomers.</p>
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17 pages, 3194 KiB  
Article
Lumican/Lumikine Promotes Healing of Corneal Epithelium Debridement by Upregulation of EGFR Ligand Expression via Noncanonical Smad-Independent TGFβ/TBRs Signaling
by Winston W. Y. Kao, Jianhua Zhang, Jhuwala Venkatakrishnan, Shao-Hsuan Chang, Yong Yuan, Osamu Yamanaka, Ying Xia, Tarsis F. Gesteira, Sudhir Verma, Vivien J. Coulson-Thomas and Chia-Yang Liu
Cells 2024, 13(19), 1599; https://doi.org/10.3390/cells13191599 - 24 Sep 2024
Viewed by 520
Abstract
The synthetic peptide of lumican C-terminal 13 amino acids with the cysteine replaced by an alanine, hereafter referred to as lumikine (LumC13C-A: YEALRVANEVTLN), binds to TGFβ type I receptor/activin-like kinase5 (TBR1/ALK5) in the activated TGFβ receptor complex to promote corneal epithelial [...] Read more.
The synthetic peptide of lumican C-terminal 13 amino acids with the cysteine replaced by an alanine, hereafter referred to as lumikine (LumC13C-A: YEALRVANEVTLN), binds to TGFβ type I receptor/activin-like kinase5 (TBR1/ALK5) in the activated TGFβ receptor complex to promote corneal epithelial wound healing. The present study aimed to identify the minimum essential amino acid epitope necessary to exert the effects of lumikine via ALK5 and to determine the role of the Y (tyrosine) residue for promoting corneal epithelium wound healing. This study also aimed to determine the signaling pathway(s) triggered by lumican–ALK5 binding. For such, adult Lum knockout (Lum−/−) mice (~8–12 weeks old) were subjected to corneal epithelium debridement using an Agerbrush®. The injured eyes were treated with 10 µL eye drops containing 0.3 µM synthetic peptides designed based on the C-terminal region of lumican for 5–6 h. To unveil the downstream signaling pathways involved, inhibitors of the Alk5 and EGFR signaling pathways were co-administered or not. Corneas isolated from the experimental mice were subjected to whole-mount staining and imaged under a ZEISS Observer to determine the distance of epithelium migration. The expression of EGFR ligands was determined following a scratch assay with HTCE (human telomerase-immortalized cornea epithelial cells) in the presence or not of lumikine. Results indicated that shorter LumC-terminal peptides containing EVTLN and substitution of Y with F in lumikine abolishes its capability to promote epithelium migration indicating that Y and EVTLN are essential but insufficient for Lum activity. Lumikine activity is blocked by inhibitors of Alk5, EGFR, and MAPK signaling pathways, while EGF activity is only suppressed by EGFR and MAPK inhibitors. qRT-PCR of scratched HTCE cells cultures treated with lumikine showed upregulated expression of several EGFR ligands including epiregulin (EREG). Treatment with anti-EREG antibodies abolished the effects of lumikine in corneal epithelium debridement healing. The observations suggest that Lum/lumikine binds Alk5 and promotes the noncanonical Smad-independent TGFβ/TBRs signaling pathways during the healing of corneal epithelium debridement. Full article
(This article belongs to the Section Cell Signaling)
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Figure 1
<p>Effects of anti-TGFβ antibodies on lumikine and EGF promoted the healing of cornea epithelium debridement. Adult 8–12-week-old <span class="html-italic">Lum<sup>−/−</sup></span> KO mice were subjected to epithelium debridement with Algerbrush<sup>®</sup>. The injured corneas were treated with 10 µL PBS eyedrops containing 0.3 µM lumikine (<b>A</b>), lumikine + Anti-TGFβ antibodies (10 µg/mL) (<b>B</b>), EGF (10 ng/mL) (<b>C</b>), and EGF + anti-TGFβ (10 µg/mL) (<b>D</b>) every 10 min for 3 h. Experimental mice were sacrificed and excised eyes were fixed in 4% PFA/PBS at room temperature for 1 h and then quenched with 0.1% NaBH<sub>4</sub>. Dissected corneas were stained with DAPI and Phalloidin red overnight. The migration of epithelium was measured by scanning the corneas with ZEISS Axio-observerZ inverted microscope as described in as described in Materials and Methods.</p>
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<p>Cumulative data of the effects of anti-TGFβ antibodies on cornea epithelial migration in lumikine- and EGF-treated corneas. Cornea epithelium debridement and cornea epithelial migration of injured corneas were determined as described in <a href="#cells-13-01599-f001" class="html-fig">Figure 1</a>. Each cornea was scanned at four different quarters of the specimens and the mean epithelial migration of each individual cornea was calculated. Effects of EGF were not affected by anti-TGFβ antibodies. PBS alone has little effect on epithelium migration at 3 h. Each dot represents the mean of epithelium migration in each of individual corneas. Probability <span class="html-italic">p</span> value *** &lt;0.001; ns, no significant difference.</p>
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<p>Identification of minimum and essential LumC peptides promoting healing of epithelial debridement. Adult 8–12-week-old <span class="html-italic">Lum<sup>−/−</sup></span> KO mice were subjected to epithelium debridement with Algerbrush<sup>®</sup>. Ten microliters of eye drops containing various Lum C-terminal peptides (0.3 µM in PBS) were administered to the epithelium-debrided corneas every 10 min for 4 h, processed for whole mount and then subjected to scanning for corneal epithelium migration with ZEISS Axio-observerZ as described in <a href="#cells-13-01599-f001" class="html-fig">Figure 1</a>. The data show the effects of different LumC terminal peptides on epithelial migration, i.e., lumikine (LumC13<sub>C-A</sub>), LumC18<sub>∆C5</sub> peptide missing the last five amino acids of C-terminal EVTLN, and shorter peptides missing the N-terminal amino acids, i.e., LumC10, LumC7, LumC5, and LumC4, failed to promote cornea epithelium migration. LumC5 has slightly higher capability in promoting epithelium than the rest of peptides missing the N-terminal amino acids except LumC7. Probability value: * &lt;0.05, **** &lt;0.0001; ns, no significant difference.</p>
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<p>Role of N-terminal Y residue in lumikine activity in promoting corneal epithelium migration. Lum<sup>−/−</sup> mice were used to determine the role of Y residue in lumikine function as described in <a href="#cells-13-01599-f001" class="html-fig">Figure 1</a>. The epithelium migration was evaluated in injured corneas treated with Hybrid1/3 peptide consisting of EVTLN but missing Y residue, and peptides in which Y is substituted by F, i.e., LumC13<sub>C-A/Y-F</sub> and Hybrid2/3<sub>Y-F</sub>. Both missing Y and substitution of F greatly reduce the peptides’ capacity in promoting epithelium migration. Probability value: *** &lt;0.001, **** &lt;0.0001.</p>
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<p>Effect of inhibitors of TGFβ receptor, EGF receptor, and MAPK signaling cascades on the epithelium migration promoted by lumikine. Experimental <span class="html-italic">Lum<sup>−/−</sup></span> mice were subjected to epithelium debridement (2 mm in diameter) and allowed to heal for 5 h. The injured corneas were treated every 10 min with 10 µL eye drops containing LumC13<sub>C-A</sub> (0.3 µM) and EGF (10 ng/mL) with inhibitors ALK5 inhibitor SB431542 (10 µM), EGFR inhibitor AG1478 (10 nM), pERK1/2 inhibitor (PD98059, 5 µM), PI3K inhibitor (wortmannin, 1 µM), and Src inhibitor (SrcI-1, 2 µM), respectively, as described in <a href="#cells-13-01599-f001" class="html-fig">Figure 1</a> and <a href="#cells-13-01599-f002" class="html-fig">Figure 2</a>. The experimental mice were euthanized and the epithelium migration was determined by whole count scanning of excised corneas stained with phalloidin and DAPI. Images were taken with a Zeiss Apotome microscope (Observer Z1). (<b>A</b>): both LumC13<sub>C-A</sub> and EGF promoted epithelial migration; (<b>B</b>): all inhibitors inhibited epithelium migration in PBS, except Alk5 inhibitor; (<b>C</b>): increased epithelium migration by LumC13<sub>C-A</sub> decreased in the presence of all inhibitors; (<b>D</b>): increased epithelium migration by EGF was inhibited by Ag1478 (ERK inhibitor), AG (EGFR inhibitors) and Wortmann (PI3K inhibitor), but not SB431542 (Alk5 inhibitor) and SrcI-1 (Src inhibitor). Probability value: * &lt;0.05, ** &lt;0.01, *** &lt;0.001, **** &lt;0.0001; ns, no significant difference.</p>
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<p>Administration of lumikine leads to EGFR activation. Epithelium-debrided corneas of <span class="html-italic">Lum<sup>−/−</sup></span> mice were treated with lumikine and PBS eyedrops for 6 h. Cryosections of experimental corneas were then subjected to immunofluorescence staining with antibodies against EGFR, phospho-Y1068 and -Y1069 of EGFR as described in Materials and Methods.</p>
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<p>Expression of EGFR ligands by HTCE cells treated with lumikine. Cultures of human telomerase-immortalized corneal epithelial (HTCE) cells at late-log phase were scratched and incubated basic medium containing 0.3 µM lumikine for 30 min and 2 h, cells harvested were subjected to qRT-PCR for expression of various EGFR ligands, i.e., HBEGFE (heparin-binding EGF), TGFα, EREG (epiregulin), BTC (betacellulin), EGF, and AREG (amphiregulin) as described in Materials and Methods. In 30 min, none of the ligands’ expressions were upregulated. At 2 h expression of all ligands were upregulated. However, both BTS and EGF have low expression levels, whereas HBEGF, TGFα, EREG, and AREG expression was upregulated. Probability values: * &lt;0.05, ** &lt;0.01, *** &lt;0.001, **** &lt;0.0001; ns, no significant difference.</p>
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<p>Effects of anti-EREG antibodies on migration of injured cornea epithelium treated with lumikine. Adult <span class="html-italic">Lumi<sup>−/−</sup></span> (8–12 weeks old) mice were subjected to corneal epithelium debridement. The injured corneas were treated with eye drops containing lumikine (0.3 µM), lumikine (0.3 µM) + goat anti-mouse EREG antibodies (20 µg/mL), (R &amp; D, CAT# AF10680). Probability values: **** &lt;0.0001; ns, no significant difference.</p>
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<p>Roles of Lum/lumikine in TGFβ/TBR (TGFβ receptor) signaling in the healing of corneal epithelium debridement. There are two TGFβ/TBRs signaling pathways: (1) Canonical Smad-dependent pathway binding of TGFβ to TBR2 (type 2 receptor) initiates autophosphorylation of TBR2 and forms tetrameric TBRs (TBR2<sub>2</sub>/ALK5<sub>2</sub>) in which several serine and threonine residues in ALK5 (TBR1) are phosphorylated by p-TBR2. The activated tetrameric TBRs subsequently phosphorylate Smad 2 and 3 that bind Smad 4 to form a Smad 2/3/4 complex and translocate to nuclei, where it binds other transcription factors and drives expression of TGFβ target genes, e.g., components of extracellular matrix. (2) The noncanonical Smad-independent pathway is characterized by the binding of SRC to free TBR2; the subsequent TGFβ binding to TBR2 triggers SRC phosphorylates 284Y of TBR2 and form a tetrameric TBR<sub>2</sub>/ALK5<sub>2</sub> complex that phosphorylates Alk5 Y residues and transduces the MAPK signaling cascades. The binding of free lumican (secreted by epithelial cells and/or keratocytes) and lumikine (LumC13<sub>C-A</sub>) to Alk5 lead to upregulated expression of EGFR ligands that switch on and feed forward the signaling cascades to p38MAPK/JNK and/or ERK pathways.</p>
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19 pages, 4526 KiB  
Article
Discovery of Coumarins from Zanthoxylum dimorphophyllum var. spinifoliumas and Their Potential against Rheumatoid Arthritis
by Caixia Du, Xingyu Li, Junlei Chen, Lili Luo, Chunmao Yuan, Jue Yang, Xiaojiang Hao and Wei Gu
Molecules 2024, 29(18), 4395; https://doi.org/10.3390/molecules29184395 - 16 Sep 2024
Viewed by 575
Abstract
In the present study, a series of coumarins, including eight undescribed bis-isoprenylated ones Spinifoliumin A-H, were isolated and identified from the aerial parts of Zanthoxylum dimorphophyllum var. spinifolium (ZDS), a plant revered in traditional Chinese medicine, particularly for treating rheumatoid arthritis (RA). The structures [...] Read more.
In the present study, a series of coumarins, including eight undescribed bis-isoprenylated ones Spinifoliumin A-H, were isolated and identified from the aerial parts of Zanthoxylum dimorphophyllum var. spinifolium (ZDS), a plant revered in traditional Chinese medicine, particularly for treating rheumatoid arthritis (RA). The structures of the compounds were elucidated using 1D and 2D NMR spectroscopy, complemented by ECD, [Rh2(OCOCF3)4]-induced ECD, Mo2(OAc)4 induced ECD, IR, and HR-ESI-MS mass spectrometry. A network pharmacology approach allowed for predicting their anti-RA mechanisms and identifying the MAPK and PI3K-Akt signaling pathways, with EGFR as a critical gene target. A CCK-8 method was used to evaluate the inhibition activities on HFLS-RA cells of these compounds. The results demonstrated that Spinifoliumin A, B, and D-H are effective at preventing the abnormal proliferation of LPS-induced HFLS-RA cells. The results showed that compounds Spinifoliumin A, D, and G can significantly suppress the levels of IL-1β, IL-6, and TNF-α. Moreover, molecular docking methods were utilized to confirm the high affinity between Spinifoliumin A, D, and G and EGFR, SRC, and JUN, which were consistent with the results of network pharmacology. This study provides basic scientific evidence to support ZDS’s traditional use and potential clinical application. Full article
(This article belongs to the Section Natural Products Chemistry)
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Figure 1
<p>Chemical structures of compounds <b>1</b>–<b>30</b>.</p>
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<p>The key HMBC and <sup>1</sup>H-<sup>1</sup>H COSY correlations of compounds <b>1</b>–<b>8</b>.</p>
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<p>ECD curve of the Rh<sub>2</sub>(OCOCF<sub>3</sub>)<sub>4</sub> complex of compound <b>1</b>.</p>
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<p>NOESY correlations of compound <b>7</b>.</p>
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<p>CD spectrum of <b>7</b> in DMSO containing Mo<sub>2</sub>(OAc)<sub>4</sub> with the inherent CDs subtracted.</p>
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<p>Experimental and calculated ECD spectra of compounds <b>7</b> and <b>8</b>.</p>
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<p>(<b>A</b>) Venn diagram showing the common target genes between ZDS and RA. (<b>B</b>,<b>C</b>) Overall PPI network and (<b>D</b>) the top 10 targets in order of degree value. (<b>E</b>) The top 30 KEGG pathways of hub genes and (<b>F</b>) the compound target pathway network.</p>
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<p>Cell viability of compounds <b>1</b>, <b>2</b>, and <b>4</b>–<b>8</b> of HFLS-RA. Data are expressed as mean ± SD (n = 3), vs. the control group, ns means non-significant and *** means <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effect of compounds <b>1</b>, <b>2</b>, and <b>4</b>–<b>8</b> on the proliferation viability of LPS (1 μg/mL)-induced HFLS-RA cells. Data are expressed as mean ± SD (n = 3), vs. the untreated group (Normol), ###, <span class="html-italic">p</span> &lt; 0.001 vs. LPS-induced group, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of compounds <b>1</b>, <b>5</b>, and <b>7</b> on the levels of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α in the LPS (1 μg/mL)-induced HFLS-RA cells. Data are expressed as mean ± SD (n = 3), vs. untreated group, ###, <span class="html-italic">p</span> &lt; 0.001, vs. LPS-induced group, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Docking results of the three active compounds of ZDS and the key RA-associated targets. (<b>A</b>) compound <b>1</b> and EGFR; (<b>B</b>) compound <b>5</b> and EGFR; (<b>C</b>) compound <b>5</b> and SRC; (<b>D</b>) compound <b>7</b> and EGFR; (<b>E</b>) compound <b>7</b> and SRC; (<b>F</b>) compound <b>7</b> and JUN.</p>
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20 pages, 9201 KiB  
Article
Epidermal Growth Factor Receptor Emerges as a Viable Target for Reducing Tumorigenicity of MDCK Cells
by Di Yang, Yuejiao Liao, Lingwei Huang, Jiachen Shi, Jiamin Wang, Zilin Qiao, Zhongren Ma and Sijiu Yu
Genes 2024, 15(9), 1208; https://doi.org/10.3390/genes15091208 - 14 Sep 2024
Viewed by 600
Abstract
The MDCK cell line is perceived as better than the embryos of hen eggs for the production of influenza vaccines, but the tumorigenicity of these cells is concerning. Epidermal growth factor receptor (EGFR) is likely to be a crucial target that contributes to [...] Read more.
The MDCK cell line is perceived as better than the embryos of hen eggs for the production of influenza vaccines, but the tumorigenicity of these cells is concerning. Epidermal growth factor receptor (EGFR) is likely to be a crucial target that contributes to the tumorigenicity of MDCK cells. In this study, EGFR-knockdown and EGFR-overexpression cell lines were established. EGFR’s influence on cell growth, migration, clonogenic ability, and flu virus susceptibility was evaluated in vitro, and its role in cell tumorigenicity was examined in nude mice. GST pull-down coupled with mass spectrometry (MS) and bioinformatics analysis identified EGFR-interacting proteins. The expression levels of these proteins, as well as those of PI3K–AKT- and MAPK–ERK-signaling-pathway-related molecules, were confirmed at both gene and protein levels. The result indicates that EGFR overexpression can enhance cell proliferation, migration, and clonal formation; EGFR knockdown could effectively curtail tumorigenesis and amplify the titers of influenza viruses in MDCK cells. An analysis of the underlying mechanism identified a total of 21 interacting proteins implicated in tumor formation, and among these, AKT1, CDK4, GNB2, and MAPK8 were confirmed at both gene and protein levels. EGFR can activate key factors of the PI3K–AKT signaling pathway, AKT and PI3K, and promote their phosphorylation levels. Consequently, we concluded that EGFR interacts with GNB2, facilitating transmembrane signal transduction, activating the PI3K–AKT signaling cascade, controlling cell cycle alterations, stimulating cell proliferation, and promoting tumorigenesis. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Tumorigenic gene expression in MDCK cell lines from various origins. **, ***, and **** were considered to indicate a highly significant difference (<span class="html-italic">p</span> &lt; 0.01); and ns was considered to indicate no significant difference (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p>(<b>A</b>) Clusters of monoclonal cells at day 1, 4, and 8 after transfection with lv-EGFR lentivirus. (<b>B</b>) Clusters of monoclonal cells at day 1, 4, and 8 after transfection with sh-EGFR lentivirus. (<b>C</b>) Target gene expression in clone cells with EGFR overexpression. (<b>D</b>) Target gene expression in clone cells with EGFR knockdown. (<b>E</b>) Western blotting was used to detect the expression bands and relative expression levels of EGFR protein in each lv-clone cell. (<b>F</b>) Western blotting was used to detect the expression bands and relative expression levels of EGFR protein in each sh-clone cell. **** was considered to indicate a highly significant difference (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>EGFR can stimulate the proliferation, migration, and clonal establishment of MDCK cells. (<b>A</b>) The effect of EGFR on cell proliferation was detected by CCK8 assay. (<b>B</b>) The morphology of lv-EGFR cells changed after 10 generations. (<b>C</b>) Results from the migration assay of each cell group at 0 and 12 h. (<b>D</b>) The RMR(%) of each cell at 12 h. (<b>E</b>) Graph of the dishes stained for colony formation of cells cultured for 6 days. (<b>F</b>) Number of clones formed from each cell group after 6 days of culture. * was considered to indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05); **, ***, and **** were considered to indicate a highly significant difference (<span class="html-italic">p</span> &lt; 0.01); and ns was considered to indicate no significant difference (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p>EGFR can enhance the tumorigenicity potential of MDCK cells in vivo. (<b>A</b>) Examination of the influence of EGFR on the tumorigenicity of MDCK cells based on xenografts in nude mice. (<b>B</b>) Correlation analysis of the mean tumor volumes among experimental groups in concurrent murine tumorigenesis trials. (<b>C</b>) Analysis of the trends in the changes in the mean mouse body weight across experimental groups within these mouse trials.</p>
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<p>Observations from the GST pull-down assay and subsequent mass spectrometric data evaluation. (<b>A</b>) The concentration of GST antibodies in the protein sample was determined by Western blotting prior to conducting the GST pull-down test. M: marker; EGFR–GST: EGFR–GST fusion protein synthesized via an inclusion body, a protein band was detected at approximately 66 kDa; GST: GST protein, a protein band was detected at ~25 kDa. (<b>B</b>) The expression of GST antibody in protein was detected by Western blotting after the pull-down test. M: marker; pull-down-CG: protein bands evident within the control group’s eluate, which displayed the previously identified ~25-kDa band; pull-down-EG: specific protein bands observed in the test group’s eluate, which corresponded to the preestablished ~66-kDa EGFR–GST protein; input: total protein content of the MDCK cell line employed in the study. (<b>C</b>) The protein expression in each group was detected by silver staining after the pull-down test. As noted earlier, this group mirrors those depicted in panel B. (<b>D</b>) Number of peptides of different lengths detected by MS. The horizontal coordinate is the number of amino acids that compose the peptide segment, namely, the length of the peptide segment, and the vertical coordinate is the number of peptide segments. (<b>E</b>) Number of proteins with different peptide numbers detected by MS. The horizontal coordinate is the number of peptides that make up the protein, and the vertical coordinate is the number of proteins. (<b>F</b>) Venn diagram visualizing the interacting protein network derived by MS analysis of the test versus control groups.</p>
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<p>Comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of interacting proteins. (<b>A</b>) The details related to the top five terms in the ‘Biological Process’, ‘Cellular Component’, and ‘Molecular Function’ categories found for the interacting proteins in the GO analysis are presented. (<b>B</b>) The biological pathways in which the interacting genes (proteins) are mapped onto the top 15 positions within a specifically defined pathway.</p>
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<p>Validation of genes and proteins interacting with EGFR. (<b>A</b>) The relative gene expression levels of 21 interacting proteins in EGFR knockdown cells and its control cells. (<b>B</b>) The relative gene expression levels of 21 interacting proteins in EGFR overexpression cells and its control cells. (<b>C</b>) Protein levels were detected by screening interacting proteins in different cell groups. (<b>D</b>) Histogram of quantized analysis of interacting proteins in different cell groups. * was considered to indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05); **, ***, and **** were considered to indicate a highly significant difference (<span class="html-italic">p</span> &lt; 0.01); and ns was considered to indicate no significant difference (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p>Protein expression and phosphorylation degree of key factors in MAPK–ERK and PI3K–AKT signaling pathway. (<b>A</b>) Western blot analysis of JUK1/2/3, ERK1/2, and their phosphorylation levels in different cell groups. (<b>B</b>) Histogram of quantized analysis of each strip in the MAPK–ERK signal path. (<b>C</b>) Western blot analysis of AKT, PI3K, and their phosphorylation levels in different cell groups. (<b>D</b>) Histogram of quantized analysis of each strip in the PI3K–AKT signal path. * was considered to indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05); **** was considered to indicate a highly significant difference (<span class="html-italic">p</span> &lt; 0.01); and ns was considered to indicate no significant difference (<span class="html-italic">p</span> ≥ 0.05).</p>
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11 pages, 1393 KiB  
Article
A Bioinformatic Analysis Predicts That Cannabidiol Could Function as a Potential Inhibitor of the MAPK Pathway in Colorectal Cancer
by Julianne du Plessis, Aurelie Deroubaix, Aadilah Omar and Clement Penny
Curr. Issues Mol. Biol. 2024, 46(8), 8600-8610; https://doi.org/10.3390/cimb46080506 - 5 Aug 2024
Viewed by 724
Abstract
Colorectal cancer (CRC), found in the intestinal tract, is initiated and progresses through various mechanisms, including the dysregulation of signaling pathways. Several signaling pathways, such as EGFR and MAPK, involved in cell proliferation, migration, and apoptosis, are often dysregulated in CRC. Although cannabidiol [...] Read more.
Colorectal cancer (CRC), found in the intestinal tract, is initiated and progresses through various mechanisms, including the dysregulation of signaling pathways. Several signaling pathways, such as EGFR and MAPK, involved in cell proliferation, migration, and apoptosis, are often dysregulated in CRC. Although cannabidiol (CBD) has previously induced apoptosis and cell cycle arrest in vitro in CRC cell lines, its effects on signaling pathways have not yet been determined. An in silico analysis was used here to assess partner proteins that can bind to CBD, and docking simulations were used to predict precisely where CBD would bind to these selected proteins. A survey of the current literature was used to hypothesize the effect of CBD binding on such proteins. The results predict that CBD could interact with EGFR, RAS/RAF isoforms, MEK1/2, and ERK1/2. The predicted CBD-induced inhibition might be due to CBD binding to the ATP binding site of the target proteins. This prevents the required phosphoryl transfer to activate substrate proteins and/or CBD binding to the DFG motif from taking place, thus reducing catalytic activity. Full article
(This article belongs to the Collection Bioinformatics Approaches to Biomedicine)
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<p>In silico analysis protocol. (<b>1</b>) The 3D structure of CBD was obtained from ChEBI as a .sdf file and uploaded to HT-Docking for binding prediction. (<b>2</b>) Pathway identification via Reactome and a literature review were conducted to identify suitable proteins involved in CRC. (<b>3</b>) Protein crystallography structure, in complex with a ligand, was obtained from Protein Data Bank, and then the ligand was removed using CCDC GOLD software. This new protein structure and CBD structure were uploaded to the CB dock 2 website to perform docking simulations. (<b>4</b>) The docked file was downloaded and opened using LigPlot to identify the amino acid residues involved in CBD binding to the protein. A literature review was then performed to predict the effect/s that CBD binding to these specific amino acids and binding sites could have on the target proteins’ functionality.</p>
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<p>Binding site of CBD to various MAPK pathway proteins visualized with LigPlot. (<b>A</b>) BRAF, (<b>B</b>) EGFR, (<b>C</b>) KRAS, and (<b>D</b>) MEK1. Green—CBD; red—hydrogen bond; blue—hydrophobic interaction. The 3D conformation of CBD binding to various MAPK-pathway proteins visualized with PyMol. (<b>E</b>) BRAF, (<b>F</b>) EGFR, (<b>G</b>) KRAS, and (<b>H</b>) MEK1. Green—CBD; turquoise—helix; red—sheets.</p>
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<p>Proposed schematic representation of the MAPK pathway with and without CBD. (<b>A</b>) In the absence of CBD, the MAPK pathway remains hyperactivated due to the unregulated activation of EGFR and MAPK pathway proteins. (<b>B</b>) In the presence of CBD, the proteins in the EGFR/MAPK pathway are inhibited through the abovementioned mechanisms. This will lead to the cascade-like prevention of target protein activation, resulting in a decrease in the activation and expression of proteins associated with cell migration, survival, and proliferation, among others.</p>
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24 pages, 2272 KiB  
Review
Natural Alkaloids in Cancer Therapy: Berberine, Sanguinarine and Chelerythrine against Colorectal and Gastric Cancer
by Anna Duda-Madej, Szymon Viscardi, Wiktoria Szewczyk and Ewa Topola
Int. J. Mol. Sci. 2024, 25(15), 8375; https://doi.org/10.3390/ijms25158375 - 31 Jul 2024
Viewed by 1371
Abstract
The rising incidence of colorectal cancer (CRC) and gastric cancer (GC) worldwide, coupled with the limited effectiveness of current chemotherapeutic agents, has prioritized the search for new therapeutic options. Natural substances, which often exhibit cytostatic properties, hold significant promise in this area. This [...] Read more.
The rising incidence of colorectal cancer (CRC) and gastric cancer (GC) worldwide, coupled with the limited effectiveness of current chemotherapeutic agents, has prioritized the search for new therapeutic options. Natural substances, which often exhibit cytostatic properties, hold significant promise in this area. This review evaluates the anticancer properties of three natural alkaloids—berberine, sanguinarine, and chelerythrine—against CRC and GC. In vivo and in vitro studies have demonstrated that these substances can reduce tumor volume and inhibit the epithelial–mesenchymal transition (EMT) of tumors. At the molecular level, these alkaloids disrupt key signaling pathways in cancer cells, including mTOR, MAPK, EGFR, PI3K/AKT, and NF-κB. Additionally, they exhibit immunomodulatory effects, leading to the induction of programmed cell death through both apoptosis and autophagy. Notably, these substances have shown synergistic effects when combined with classical cytostatic agents such as cyclophosphamide, 5-fluorouracil, cetuximab, and erlotinib. Furthermore, berberine has demonstrated the ability to restore sensitivity in individuals originally resistant to cisplatin GC. Given these findings, natural compounds emerge as a promising option in the chemotherapy of malignant gastrointestinal tumors, particularly in cases with limited treatment options. However, more research is necessary to fully understand their therapeutic potential. Full article
(This article belongs to the Special Issue Bioactive Compounds in Cancers)
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<p>Chemical structures of berberine (<b>A</b>), chelerythrine (<b>B</b>), and sanguinarine (<b>C</b>).</p>
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<p>Berberine-dependent anti-invasive impact on colorectal cancer cells (simplified scheme).</p>
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<p>Berberine-dependent inhibition of cell cycle of colorectal cancer cells in phase G0/G1.</p>
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<p>Berberine-dependent colorectal cancer cell apoptosis (simplified scheme).</p>
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<p>Spectrum of molecular effects of berberine on gastric cancer cells.</p>
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24 pages, 13228 KiB  
Article
Investigating the Molecular Mechanisms of Resveratrol in Treating Cardiometabolic Multimorbidity: A Network Pharmacology and Bioinformatics Approach with Molecular Docking Validation
by Wei Gong, Peng Sun, Xiujing Li, Xi Wang, Xinyu Zhang, Huimin Cui and Jianjun Yang
Nutrients 2024, 16(15), 2488; https://doi.org/10.3390/nu16152488 - 31 Jul 2024
Viewed by 1190
Abstract
Background: Resveratrol is a potent phytochemical known for its potential in treating cardiometabolic multimorbidity. However, its underlying mechanisms remain unclear. Our study systematically investigates the effects of resveratrol on cardiometabolic multimorbidity and elucidates its mechanisms using network pharmacology and molecular docking techniques. Methods: [...] Read more.
Background: Resveratrol is a potent phytochemical known for its potential in treating cardiometabolic multimorbidity. However, its underlying mechanisms remain unclear. Our study systematically investigates the effects of resveratrol on cardiometabolic multimorbidity and elucidates its mechanisms using network pharmacology and molecular docking techniques. Methods: We screened cardiometabolic multimorbidity-related targets using the OMIM, GeneCards, and DisGeNET databases, and utilized the DSigDB drug characterization database to predict resveratrol’s effects on cardiometabolic multimorbidity. Target identification for resveratrol was conducted using the TCMSP, SymMap, DrugBank, Swiss Target Prediction, CTD, and UniProt databases. SwissADME and ADMETlab 2.0 simulations were used to predict drug similarity and toxicity profiles of resveratrol. Protein–protein interaction (PPI) networks were constructed using Cytoscape 3.9.1 software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed via the DAVID online platform, and target-pathway networks were established. Molecular docking validated interactions between core targets and resveratrol, followed by molecular dynamics simulations on the optimal core proteins identified through docking. Differential analysis using the GEO dataset validated resveratrol as a core target in cardiometabolic multimorbidity. Results: A total of 585 cardiometabolic multimorbidity target genes were identified, and the predicted results indicated that the phytochemical resveratrol could be a major therapeutic agent for cardiometabolic multimorbidity. SwissADME simulations showed that resveratrol has potential drug-like activity with minimal toxicity. Additionally, 6703 targets of resveratrol were screened. GO and KEGG analyses revealed that the main biological processes involved included positive regulation of cell proliferation, positive regulation of gene expression, and response to estradiol. Significant pathways related to MAPK and PI3K-Akt signaling pathways were also identified. Molecular docking and molecular dynamics simulations demonstrated strong interactions between resveratrol and core targets such as MAPK and EGFR. Conclusions: This study predicts potential targets and pathways of resveratrol in treating cardiometabolic multimorbidity, offering a new research direction for understanding its molecular mechanisms. Additionally, it establishes a theoretical foundation for the clinical application of resveratrol. Full article
(This article belongs to the Section Phytochemicals and Human Health)
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<p>A detailed workflow of the network pharmacological investigation strategy for resveratrol in the treatment of CMM.</p>
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<p>Screening of resveratrol in CMM. (<b>A</b>) Venn diagram of intersecting target genes of resveratrol and CMM. (<b>B</b>) Protein–protein interaction network. (<b>C</b>) The top 20 proteins in the PPI network in terms of degree values.</p>
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<p>GO and KEGG enrichment analysis. (<b>A</b>) Bar chart of GO enrichment analysis. (<b>B</b>) Bubble plot of KEGG enrichment. (<b>C</b>) Results of KEGG enrichment analysis (different colors indicate different systems of action, and pathways in each system are arranged in descending order of the number of enriched genes). (<b>D</b>) KEGG key pathway network. (<b>E</b>) MAPK signaling pathway.</p>
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<p>GO and KEGG enrichment analysis. (<b>A</b>) Bar chart of GO enrichment analysis. (<b>B</b>) Bubble plot of KEGG enrichment. (<b>C</b>) Results of KEGG enrichment analysis (different colors indicate different systems of action, and pathways in each system are arranged in descending order of the number of enriched genes). (<b>D</b>) KEGG key pathway network. (<b>E</b>) MAPK signaling pathway.</p>
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<p>Cluster module analysis diagram of related protein targets of resveratrol in CMM. (<b>A</b>) Highly enriched terms of resveratrol in CMM. (<b>B</b>) Sub network specific to the interaction. (<b>C</b>) Cluster analysis of resveratrol in CMM.</p>
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<p>Cluster module analysis diagram of related protein targets of resveratrol in CMM. (<b>A</b>) Highly enriched terms of resveratrol in CMM. (<b>B</b>) Sub network specific to the interaction. (<b>C</b>) Cluster analysis of resveratrol in CMM.</p>
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<p>Component–target pathway diagram of resveratrol in CMM.</p>
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<p>Identification of core targets from resveratrol–PPI and drug–target path analysis.</p>
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<p>Molecular docking results in each target with resveratrol. (<b>A</b>) MAKP3, (<b>B</b>) EGFR, (<b>C</b>) FGFR1, (<b>D</b>) FGF2, (<b>E</b>) STAT5, and (<b>F</b>) STAT3.</p>
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<p>Molecular docking results in each target with resveratrol. (<b>A</b>) MAKP3, (<b>B</b>) EGFR, (<b>C</b>) FGFR1, (<b>D</b>) FGF2, (<b>E</b>) STAT5, and (<b>F</b>) STAT3.</p>
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<p>Molecular docking results in each target with resveratrol. (<b>A</b>) MAKP3, (<b>B</b>) EGFR, (<b>C</b>) FGFR1, (<b>D</b>) FGF2, (<b>E</b>) STAT5, and (<b>F</b>) STAT3.</p>
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<p>The fluctuation plot of the target protein–ligand complexes’ RMSD values.</p>
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<p>The fluctuation plot of the target protein–ligand complexes’ RMSF values. (<b>A</b>) MAKP3, (<b>B</b>) EGFR, (<b>C</b>) FGFR1, (<b>D</b>) FGF2, (<b>E</b>) STAT5, and (<b>F</b>) STAT3. Residues in contact with the ligand are marked in green.</p>
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<p>Hydrogen bond number of complexes. (<b>A</b>) MAKP3–resveratrol, (<b>B</b>) EGFR–resveratrol, (<b>C</b>) FGFR1–resveratrol, (<b>D</b>) FGF2–resveratrol, (<b>E</b>) STAT5–resveratrol, and (<b>F</b>) STAT3–resveratrol.</p>
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<p>Compactness of the protein according to Rg.</p>
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<p>Validation of core resveratrol targets using GEO datasets. (<b>A</b>) Volcano plot of the IS dataset GSE140275. (<b>B</b>) Volcano plot of the CHD dataset GSE12288. (<b>C</b>) Volcano plot of the DM dataset GSE29221. (<b>D</b>) Heatmap of differential genes in the IS dataset GSE140275. (<b>E</b>) Heatmap of differential genes in the CHD dataset GSE12288. (<b>F</b>) Heatmap of differential genes in the DM dataset GSE29221. (<b>G</b>) Validation of resveratrol core target genes using the GEO dataset.</p>
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<p>Validation of core resveratrol targets using GEO datasets. (<b>A</b>) Volcano plot of the IS dataset GSE140275. (<b>B</b>) Volcano plot of the CHD dataset GSE12288. (<b>C</b>) Volcano plot of the DM dataset GSE29221. (<b>D</b>) Heatmap of differential genes in the IS dataset GSE140275. (<b>E</b>) Heatmap of differential genes in the CHD dataset GSE12288. (<b>F</b>) Heatmap of differential genes in the DM dataset GSE29221. (<b>G</b>) Validation of resveratrol core target genes using the GEO dataset.</p>
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35 pages, 11739 KiB  
Article
Combining In Vitro, In Vivo, and Network Pharmacology Assays to Identify Targets and Molecular Mechanisms of Spirulina-Derived Biomolecules against Breast Cancer
by Soha Osama Hassanin, Amany Mohammed Mohmmed Hegab, Reham Hassan Mekky, Mohamed Adel Said, Mona G. Khalil, Alaaeldin Ahmed Hamza and Amr Amin
Mar. Drugs 2024, 22(7), 328; https://doi.org/10.3390/md22070328 - 22 Jul 2024
Cited by 1 | Viewed by 1293
Abstract
The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina [...] Read more.
The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina (SP) extract and understand its molecular mechanisms. The results showed that the administration of 1 g/kg of SP increased the antioxidant activity by raising levels of catalase (CAT) and superoxide dismutase (SOD), while decreasing the levels of malonaldehyde (MDA) and protein carbonyl. A histological examination revealed reduced tumor occurrence, decreased estrogen receptor expression, suppressed cell proliferation, and promoted apoptosis in SP protected animals. In addition, SP disrupted the G2/M phase of the MCF-7 cell cycle, inducing apoptosis and reactive oxygen species (ROS) accumulation. It also enhanced intrinsic apoptosis in MCF-7 cells by upregulating cytochrome c, Bax, caspase-8, caspase-9, and caspase-7 proteins, while downregulating Bcl-2 production. The main compounds identified in the LC-MS/MS study of SP were 7-hydroxycoumarin derivatives of cinnamic acid, hinokinin, valeric acid, and α-linolenic acid. These substances specifically targeted three important proteins: ERK1/2 MAPK, PI3K-protein kinase B (AKT), and the epidermal growth factor receptor (EGFR). Network analysis and molecular docking indicated a significant binding affinity between SP and these proteins. This was verified by Western blot analysis that revealed decreased protein levels of p-EGFR, p-ERK1/2, and p-AKT following SP administration. SP was finally reported to suppress MCF-7 cell growth and induce apoptosis by modulating the PI3K/AKT/EGFR and MAPK signaling pathways suggesting EGFR as a potential target of SP in breast cancer (BC) treatment. Full article
(This article belongs to the Special Issue Discovery of Marine-Derived Anticancer Agents)
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Graphical abstract
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<p>Standard mammary gland structure in histological sections (×200) of rat mammary tissues from the nontreated control (<b>a</b>) and treated SP (<b>b</b>) groups. Small mammary ducts are in the glands, partly surrounded by fibrous connective tissue (CT) and adipose tissues (ATs). Sections from the DMBA group (<b>c</b>–<b>f</b>) exhibit diverse histological alterations. (<b>c</b>) Dysplastic mammary gland (×200): the arrow indicates additional ducts and uneven cell division. Focal patches of dysplastic cells may be detected in these moderately dilated breast ductal tubules. (<b>d</b>) Fibroadenoma (×400): the arrow indicates a localized region of considerable ductal and epithelial hyperplasia, which inducts fibrosis (*). Invasive ductal carcinoma displays the proliferation of intraductal neoplastic epithelial cells with remarkable variations in cellular and nuclear sizes and shapes, which invaded the neighbor stroma, as seen in (<b>e</b>) (×400). The arrow represents a small lobular proliferation and localized epithelial hyperplasia with hyperchromatic enlarged nuclei. (<b>f</b>) (×400) The intraductal papillary carcinoma in situ: the arrow represents the micropapillae of neoplastic cells. (<b>g</b>,<b>h</b>) DMBA + SP group fibroadenoma (<b>g</b>) (×200) and stage of mammary gland cell death (<b>h</b>) (×400). (*) Fibrosis and CT connective tissues. H&amp;E staining was used on all sections.</p>
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<p>Figure displays immunohistochemical staining for PCNA and ER-α in breast sections, labeled as (<b>A</b>,<b>B</b>). Rats labeled as A and B were subjected to several treatments: (<b>a</b>) vehicle (as a control), SP (<b>b</b>), DMBA (<b>c</b>), and DMBA + SP (<b>d</b>) exhibiting PCNA- and ER-α-positive cell expressions in breast tissues. Photomicrographs and quantitative analysis (<b>C</b>,<b>D</b>) demonstrate the quantity of PCNA- and ER-α-positive cells The quantification of PCNA and ER-cells in each slice was conducted by enumerating the number of cells exhibiting brown staining positivity out of a total of 1000 cells seen at a magnification of 400×. Arrows indicate the presence of PCNA and ER-α-positive cells (H counter stained, 400×). Values expressed as mean ± SEM for six animals in each group. Significance was determined by one-way analysis of variance followed by a post hoc Dunnett’s test. * <span class="html-italic">p</span> &lt; 0.05 vs. control group; # <span class="html-italic">p</span> &lt; 0.05 vs. DMBA group.</p>
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<p>Figure displays the presence of TUNEL-positive cells in the mammary tissues of rats subjected to several treatments: (<b>a</b>) vehicle (as a control), SP (<b>b</b>), DMBA (<b>c</b>), and DMBA + SP (<b>d</b>). The semi-quantitative analysis (<b>e</b>) and the photomicrographs reveal the percentage of TUNEL-positive cells in various experimental groups. The percentage of TUNEL-positive cells in each slice was determined by quantifying the number of cells exhibiting brown staining using a 400× magnification, out of a total of 1000 cells. Arrows show TUNEL-positive cells (400×, H counterstained). Values expressed as mean ± SEM for six animals in each group. Significance was determined by one-way analysis of variance followed by a post hoc Dunnett’s test. * <span class="html-italic">p</span> &lt; 0.05 vs. control group; # <span class="html-italic">p</span> &lt; 0.05 vs. DMBA group.</p>
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<p>(<b>A</b>–<b>C</b>) Breast cancer cell growth decreased by SP. (<b>A</b>–<b>C</b>) illustrates how SP and DOX significantly reduce the viability of breast cancer cells. MCF-7 and MCF-7/ADR cells were treated with varying concentrations of SP and DOX, and 24 h later, their viability was evaluated. (<b>D</b>–<b>G</b>) demonstrate that SP produced cell cycle arrest using flow cytometry, a method by which the amount of cellular DNA was assessed following PI staining. (<b>H</b>–<b>J</b>) percentage of cells in the S, G1, G2, and M stages. In every instance, untreated cells in their growth media were used as controls. SP-treatment histograms for MCF-7 cells at zero (<b>D</b>), 10 µg/mL (<b>E</b>), and 25 µg/mL (<b>F</b>). (<b>G</b>) Three studies were used to calculate the average proportion of cells in each cell cycle phase. Cell apoptosis was observed using flow cytometry and an Annexin V/PI apoptosis detection kit (<b>H</b>–<b>J</b>). The dual parametric dot plots that incorporated PI fluorescence and Annexin V-FITC analysis reveal that early apoptotic cells are located in the bottom-right quadrant (Q4), late apoptotic cells in the top-right quadrant (Q2), and the viable cell population in the bottom-left quadrant (Q3). (<b>K</b>) demonstrates the percentage of cell necrosis and apoptosis. The reason for MCF-7 cell death is SP, which requires mitochondria. (<b>L</b>–<b>Q</b>) The expression of extrinsic and intrinsic apoptosis-related proteins (Bax, Bcl-2, cytochrome c, caspase 8, caspase 9, and caspase 7) was measured using microplate readers and ELISA kits following MCF-7 cells treated with suitable amounts of SP for 24 h. (<b>R</b>) The amount of ROS was measured using flow cytometry. The mean fluorescence density of the ROS level was calibrated using (<b>S</b>). All the data (n = 3) are displayed as mean ± SEM. One-way ANOVA was utilized first, and then Tukey’s post hoc analysis was carried out. <sup>a</sup> <span class="html-italic">p</span> &lt; 0.05 was obtained when compared to control cells.</p>
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<p>Base peak chromatograms (BPCs) of Spirulina extract in (<b>a</b>) the negative and (<b>b</b>) positive ionization modes; (<b>c</b>) bubble plot of the observed masses vs. the retention time in relation to metabolite classes; and (<b>d</b>) structures of the major compounds.</p>
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<p>Prediction of SP by network pharmacology for BC treatment. (<b>A</b>) Venn diagram of component target and disease target. (<b>B</b>) SP-ingredients target network: the green rectangles represent SP, the purple rectangles represent ingredients, the orange circles represent the top 10 targets correlated to BC by the PPI network, and the yellow circles represent the other targets. (<b>C</b>) GO enrichment analysis of results for BC treatment of SP. (<b>D</b>) KEGG pathway enrichment analysis of results for BC treatment of SP. (<b>E</b>) EGFR tyrosine kinase resistance pathway. (<b>F</b>) The component–target pathway network.</p>
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<p>Three-dimensional representation of the most potent compounds against target enzymes: (<b>A</b>) hinokinin, 442879/EGFR (Green), (<b>B</b>) hydroxylinoleic acid II, 5312775/EGFR (Green), (<b>C</b>) swainsonine, 51683/PI3K (Yellow), (<b>D</b>) p-dihydrocoumaric acid, 129846263/PI3K (Yellow), (<b>E</b>) hydroxylinoleic acid II, 5312775/MAPK(ERK) (Gray), and (<b>F</b>) peyssonoic acid B, 46178008/MAPK(ERK) (Gray).</p>
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<p>SP modulates MAPK and PI3K/Akt/EGFR signaling pathways in MCF-7 cells. In MCF-7 cells treated with low (10 μg/mL) and high (25 μg/mL) doses of SP for 24 h, the expression levels of p-EGFR, EGFR, p-AKT, AKT, p-ERK1/2, and ERK1/2 proteins are shown in the Western blot image in (<b>A</b>). Remarkably, the overall protein concentrations of EGFR, AKT, and ERK1/2 did not change with therapy. As a function of SP concentration, the relative protein expression levels of p-EGFR, EGFR, p-AKT, AKT, p-ERK1/2, and ERK1/2 are shown in (<b>B</b>–<b>D</b>). The control group consisted of untreated cells grown in their growth medium. The presentation of all data is as mean ± SEM (n = 3). After one-way ANOVA, Tukey’s post hoc analysis was carried out. <sup>a</sup> <span class="html-italic">p</span> &lt; 0.05 was obtained when compared to control cells.</p>
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22 pages, 7182 KiB  
Article
Bioactive Compounds in Citrus reticulata Peel Are Potential Candidates for Alleviating Physical Fatigue through a Triad Approach of Network Pharmacology, Molecular Docking, and Molecular Dynamics Modeling
by Amin Ullah, Qiuxi Sun, Jiangtao Li, Jinjie Li, Pipasha Khatun, Guangning Kou and Quanjun Lyu
Nutrients 2024, 16(12), 1934; https://doi.org/10.3390/nu16121934 - 18 Jun 2024
Cited by 1 | Viewed by 1058
Abstract
Physical fatigue (peripheral fatigue), which affects a considerable portion of the world population, is a decline in the ability of muscle fibers to contract effectively due to alterations in the regulatory processes of muscle action potentials. However, it lacks an efficacious therapeutic intervention. [...] Read more.
Physical fatigue (peripheral fatigue), which affects a considerable portion of the world population, is a decline in the ability of muscle fibers to contract effectively due to alterations in the regulatory processes of muscle action potentials. However, it lacks an efficacious therapeutic intervention. The present study explored bioactive compounds and the mechanism of action of Citrus reticulata peel (CR-P) in treating physical fatigue by utilizing network pharmacology (NP), molecular docking, and simulation-based molecular dynamics (MD). The bioactive ingredients of CR-P and prospective targets of CR-P and physical fatigue were obtained from various databases. A PPI network was generated by the STRING database, while the key overlapping targets were analyzed for enrichment by adopting KEGG and GO. The binding affinities of bioactive ingredients to the hub targets were determined by molecular docking. The results were further validated by MD simulation. Five bioactive compounds were screened, and 56 key overlapping targets were identified for CR-P and physical fatigue, whereas the hub targets with a greater degree in the PPI network were AKT1, TP53, STAT3, MTOR, KRAS, HRAS, JAK2, IL6, EGFR, and ESR1. The findings of the enrichment analysis indicated significant enrichment of the targets in three key signaling pathways, namely PI3K-AKT, MAPK, and JAK-STAT. The molecular docking and MD simulation results revealed that the bioactive compounds of CR-P exhibit a stronger affinity for interacting with the hub targets. The present work suggests that bioactive compounds of CR-P, specifically Hesperetin and Sitosterol, may ameliorate physical fatigue via the PI3K-AKT signaling pathway by targeting AKT1, KRAS, and MTOR proteins. Full article
(This article belongs to the Special Issue Natural Products and Human Health)
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<p>Targets collection from different databases. (<b>A</b>) Bioactive compounds related targets of CR-P; (<b>B</b>) physical fatigue-related targets.</p>
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<p>Venn diagram of CR-P and physical fatigue: the gold part represents CR-P with 619 potential targets, the green part represents physical fatigue with 228 potential targets, whereas the tan overlapping part represents 56 common targets between them.</p>
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<p>(<b>A</b>) PPI network of key targets of CR-P and physical fatigue by STRING database; (<b>B</b>) visualized PPI network by Cytoscape; (<b>C</b>) hub genes in the PPI network.</p>
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<p>Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. (<b>A</b>) Biological process; (<b>B</b>) cellular component; (<b>C</b>) molecular function; (<b>D</b>) GO bar chart; (<b>E</b>) KEGG enrichment.</p>
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<p>The compound–target–pathway network. The orange octagon in the middle represents CR-P, the green hexagons represent the bioactive compounds, the purple octagons represent the targets, whereas the blue ellipses represent the pathways in the network.</p>
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<p>Heatmap of binding affinity scores between bioactive compounds of CR-P and 10 hub targets (kcal/mol) (<span class="html-italic">n</span> = 50).</p>
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<p>Molecular docking of Hesperetin, Sitosterol, and Naringenin with AKT1, KRAS, and MTOR proteins. (<b>A</b>) AKT1–Hesperetin; (<b>B</b>) AKT1–Sitosterol, (<b>C</b>) KRAS–Hesperetin; (<b>D</b>) KRAS–Naringenin; (<b>E</b>) MTOR–Hesperetin; (<b>F</b>) MTOR–Sitosterol. The cyan color represents proteins, whereas the violet color represents the ligands.</p>
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<p>MD simulations of bioactive compounds and target proteins. (<b>A</b>) AKT1–Hesperetin/Sitosterol; (<b>B</b>) KRAS–Hesperetin/Naringenin; (<b>C</b>) MTOR–Hesperetin/Sitosterol.</p>
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19 pages, 4900 KiB  
Article
Unveiling the RKIP and EGFR Inverse Relationship in Solid Tumors: A Case Study in Cervical Cancer
by Diana Cardoso-Carneiro, Joana Pinheiro, Patrícia Fontão, Rosete Nogueira, Maria Gabriela-Freitas, Ana Raquel-Cunha, Adriana Mendes, Adhemar Longatto-Filho, Fábio Marques, Marise A. R. Moreira, Rui M. Reis and Olga Martinho
Cancers 2024, 16(12), 2182; https://doi.org/10.3390/cancers16122182 - 10 Jun 2024
Viewed by 1249
Abstract
Raf Kinase Inhibitor Protein (RKIP) is recognized as a bona fide tumor suppressor gene, and its diminished expression or loss is associated with the progression and poor prognosis of various solid tumors. It exerts multifaceted roles in carcinogenesis by modulating diverse intracellular signaling [...] Read more.
Raf Kinase Inhibitor Protein (RKIP) is recognized as a bona fide tumor suppressor gene, and its diminished expression or loss is associated with the progression and poor prognosis of various solid tumors. It exerts multifaceted roles in carcinogenesis by modulating diverse intracellular signaling pathways, including those governed by HER receptors such as MAPK. Given the significance of HER receptor overexpression in numerous tumor types, we investigated the potential oncogenic relationship between RKIP and HER receptors in solid tumors. Through a comprehensive in silico analysis of 30 TCGA PanCancer Atlas studies encompassing solid tumors (10,719 samples), we uncovered compelling evidence of an inverse correlation between RKIP and EGFR expression in solid tumors observed in 25 out of 30 studies. Conversely, a predominantly positive association was noted for the other HER receptors (ERBB2, ERBB3, and ERBB4). In particular, cervical cancer (CC) emerged as a tumor type exhibiting a robust inverse association between RKIP and EGFR expression, a finding that was further validated in a cohort of 202 patient samples. Subsequent in vitro experiments involving pharmacological and genetic modulation of EGFR and RKIP showed that RKIP depletion led to significant upregulation of EGFR mRNA levels and induction of EGFR phosphorylation. Conversely, EGFR overactivation decreased RKIP expression in CC cell lines. Additionally, we identified a common molecular signature among patients depicting low RKIP and high EGFR expression and demonstrated the prognostic value of this inverse correlation in CC patients. In conclusion, our findings reveal an inverse association between RKIP and EGFR expression across various solid tumors, shedding new light on the underlying molecular mechanisms contributing to the aggressive phenotype associated with RKIP and EGFR in cervical cancer. Full article
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<p>Correlation between RKIP and HER receptor expression in solid tumors—in silico analysis. Using TCGA data available at cBioPortal (<a href="http://www.cbioportal.org" target="_blank">www.cbioportal.org</a>, last accessed on 14 Apri 2024), we analyzed the 30 TCGA PanCancer Altlas studies concerning solid tumors, which comprise 10,719 samples, and co-expression plots were generated to determine the correlation level between RKIP (PEBP1) and HER receptor (EGFR, ERBB2, ERBB3, ERBB4) expression. HER and PEBP1 mRNA expression data (RNA Seq V2 RSEM) were used relative to diploid samples, and EGFR protein (Reverse-phase protein array-RPPA). The correlation levels were assessed using Spearman’s correlation coefficient (<span class="html-italic">p</span>). (<b>A</b>) Individual Spearman’s correlation coefficients (<a href="#app1-cancers-16-02182" class="html-app">Supplementary Materials Table S1</a>) regarding mRNA correlations are graphically represented in the heatmap. (<b>B</b>) Correlation levels between PEBP1 mRNA and EGFR protein, both total protein and the phosphorylated forms at tyrosine 1067 (PY1067) and tyrosine 1173 (PY1173). Spearman correlation coefficients are graphically represented as a heatmap for the 9 tumor types in each the inverse correlation with total protein was significant (<a href="#app1-cancers-16-02182" class="html-app">Supplementary Materials Table S2</a>). BRCA, STAD, LUSC, CESC, and BLCA are the 5 datasets in which the correlation was maintained at the phosphorylated level. PEBP1 expression was classified as positively (<span class="html-italic">p</span> &gt; 0; dark to green) or negatively (<span class="html-italic">p</span> &lt; 0; dark to red) correlated with HER expression.</p>
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<p>Correlation between RKIP and HER receptor expression in cervical cancer. (<b>A</b>) Immunohistochemistry analysis for RKIP in representative adenocarcinoma tissues of cervical cancer: (<b>a</b>) sample with low RKIP expression in the tumor and positive staining in the stroma; (<b>b</b>) sample showing positive staining in the stroma but with negative RKIP expression in the tumor; (<b>c</b>) sample with high nuclear expression of RKIP; (<b>d</b>) sample showing high cytoplasmatic RKIP expression. All pictures were taken at 200× magnification. (<b>B</b>) Violin plot showing the distribution of IHC scores for RKIP and HER receptors. For correlation analysis between RKIP and HER receptors, we considered the cases with IHC scores &gt; 4: 100/181 for RKIP, 7/176 for EGFR, 23/170 for HER2, 70/166 for HER3, 1/158 for HER4. (<b>C</b>) Kaplan–Meier survival analysis was conducted with positive (red) and negative (blue) RKIP protein expression and its association with overall survival in cervical adenocarcinoma (N = 90). (<b>D</b>) Kaplan–Meier survival analysis was performed at cBioPortal considering patient tumor samples with both RKIP-low and EGFR-high (PEBP1: EXP &lt; 0 and EGFR: EXP &gt; 0; N = 99) or both RKIP-high and EGFR-low (PEBP1: EXP &gt; 0 and EGFR: EXP &lt; 0; N = 107) mRNA expression and patients with only RKIP-low (PEBP1: EXP &lt; 0; N = 71) or RKIP-high (PEBP1: EXP &gt; 0; N = 26) expression. For survival analysis, the log-rank test (<span class="html-italic">p</span> &lt; 0.05) was considered statistically significant.</p>
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<p>Effect of EGFR on RKIP expression. (<b>A</b>) Analysis of p-EGFR (Tyr1068), EGFR, and RKIP protein expression levels was performed using Western blotting. (<b>B</b>) Analysis of EGFR and RKIP mRNA expression levels was performed by qPCR. The results were calibrated to β-actin, which was used as a reference gene (N = 3). (<b>C</b>) Cells were stimulated with 10 ng/mL EGF for 15 min. The assay, run, and the blot revelation were performed simultaneously with blot A. (<b>D</b>) Western blot quantification for RKIP relative expression. (<b>E</b>) SW756 cells were stimulated with 10 ng/mL EGF for 15, 20, 25, 30, and 35 min. The isolated protein was separated by Western blotting, which was quantified for RKIP and p-EGFR. (<b>F</b>) The cell lines were treated for 2 h with erlotinib (ER), at 2.5 µM, followed by 15 min of treatment with 10 ng/mL EGF. The blots were quantified below for RKIP and p-EGFR. The Western blots shown are representative of at least two independent assays and were quantified using band densitometry analysis with ImageJ software version 1.8. Relative protein expression for RKIP was calculated as the ratio with α-tubulin and for p-EGFR as a ratio with total EGFR. The results are shown as the mean values obtained after quantification. <span class="html-italic">p</span> &lt; 0.05 was considered statistically significant (*).</p>
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<p>Does RKIP modulate EGFR expression? (<b>A</b>) Western blot analysis of p-EGFR (Tyr1068), EGFR, and RKIP protein expression in the SiHa cell line genetically manipulated to obtain RKIP KO and overexpression (RKIP+) under EGF-stimulating conditions (10 ng/mL, 15 min). (<b>B</b>) The same western blot analysis as A, but for SW756 cells. On the right side of the blot, band quantification was performed by band densitometry analysis using ImageJ software version 1.8. (<b>C</b>) Analysis of EGFR and RKIP mRNA expression levels by qPCR under the same conditions as in A. The results were calibrated to β-actin, which was used as a reference gene (N = 3). (<b>D</b>) The same qPCR analysis as in C, except for the SW756 cell line. Western blots are representative of at least 2 independent assays. Relative protein expression for RKIP was calculated as the ratio with α-tubulin and for p-EGFR as a ratio with total EGFR. The results are shown as the mean value achieved after quantification. <span class="html-italic">p</span> &lt; 0.05 was considered statistically significant (*). CTR: CRISPR Control; KO: Knockout; Empty: pcDNA vector; RKIP+: pcDNA vector containing full RKIP cDNA.</p>
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<p>In silico analysis of protein expression signatures from cervical cancer patients with RKIP downregulation and EGFR upregulation. (<b>A</b>) Tumor samples from 48 patients with low RKIP mRNA expression (log RNA Seq V2 RSEM) and high EGFR protein expression (RPPA) were selected in cBioPortal using TCGA CSCC (Firehose legacy). Through enrichment analysis, 15 proteins were found to be highly expressed in this group of patients compared with the following groups of patients: RKIP-low mRNA expression and EGFR-high protein expression (PEBP1:EXP &lt; 0 and EGFR:PROT &gt; 0; N = 48); RKIP-high mRNA expression and EGFR-high protein expression (PEBP1:EXP &gt; 0 and EGFR:PROT &gt; 0; N = 30); RKIP-high mRNA expression and EGFR-low protein expression (PEBP1:EXP &gt; 0 and EGFR:PROT &lt; 0; N = 50); and RKIP-low mRNA and EGFR-low protein expression (PEBP1:EXP &lt; 0 and EGFR:PROT &lt; 0; N = 43). (<b>B</b>) Functional protein association network performed in STRING (<a href="https://string-db.org/" target="_blank">https://string-db.org/</a>, last accessed on 14 April 2024), showing a significant interaction within the network (PPI enrichment <span class="html-italic">p</span>-value = 2.22 × 10<sup>−16</sup>). (<b>C</b>) ShinyGo Lollipop plot results from the functional enrichment analysis showing the top 10 KEEG pathways enriched in the subset of enriched proteins. Enriched pathways were sorted considering the Fold Enrichment; the size of the circles is proportional to the Fold Enrichment, and the color of the bars corresponds to the <math display="inline"><semantics> <mrow> <mo>−</mo> </mrow> </semantics></math>log10(FDR) values.</p>
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26 pages, 1949 KiB  
Review
Small Molecule Therapeutics in the Pipeline Targeting for Triple-Negative Breast Cancer: Origin, Challenges, Opportunities, and Mechanisms of Action
by Nneoma James, Esther Owusu, Gildardo Rivera and Debasish Bandyopadhyay
Int. J. Mol. Sci. 2024, 25(11), 6285; https://doi.org/10.3390/ijms25116285 - 6 Jun 2024
Cited by 1 | Viewed by 1767
Abstract
Triple-negative breast cancer (TNBC) cells are devoid of estrogen receptors (ERs), progesterone receptor (PRs), and human epidermal growth factor receptor 2 (HER2), and it (TNBC) counts for about 10–15% of all breast cancers. TNBC is highly invasive, having a faster growth rate and [...] Read more.
Triple-negative breast cancer (TNBC) cells are devoid of estrogen receptors (ERs), progesterone receptor (PRs), and human epidermal growth factor receptor 2 (HER2), and it (TNBC) counts for about 10–15% of all breast cancers. TNBC is highly invasive, having a faster growth rate and a higher risk of metastasis and recurrence. Still, chemotherapy is one of the widely used options for treating TNBC. This study reviewed the histological and molecular characterization of TNBC subtypes, signaling pathways that are aberrantly expressed, and small molecules targeting these pathways, as either single agents or in combination with other therapeutic agents like chemotherapeutics, immunotherapeutics, and antibody–drug conjugates; their mechanisms of action, challenges, and future perspectives were also reviewed. A detailed analytical review was carried out using the literature collected from the SciFinder, PubMed, ScienceDirect, Google Scholar, ACS, Springer, and Wiley databases. Several small molecule inhibitors were found to be therapeutics for treating TNBC. The mechanism of action and the different signaling pathways through which the small molecules exert their effects were studied, including clinical trials, if reported. These small molecule inhibitors include buparlisib, everolimus, vandetanib, apatinib, olaparib, salidroside, etc. Some of the signaling pathways involved in TNBC, including the VEGF, PARP, STAT3, MAPK, EGFR, P13K, and SRC pathways, were discussed. Due to the absence of these biomarkers, drug development for treating TNBC is challenging, with chemotherapy being the main therapeutic agent. However, chemotherapy is associated with chemoresistance and a high toxicity to healthy cells as side effects. Hence, there is a continuous demand for small-molecule inhibitors that specifically target several signaling pathways that are abnormally expressed in TNBC. We attempted to include all the recent developments in this field. Any omission is truly unintentional. Full article
(This article belongs to the Section Biochemistry)
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<p>TNBC subtypes and their different gene expression patterns.</p>
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<p>Structures of buparlisib, ipatasertib, capivasertib and everolimus.</p>
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<p>Structures of vandetanib and apatinib.</p>
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<p>Structures of olaparib, veliparib, and talazoparib.</p>
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<p>Structures of ruxolitinib, LLL12B, flubendazole, and salinomycin.</p>
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<p>Structures of E6201, cobimetinib, nifetepimine, and BL-EI001.</p>
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<p>Structures of cannabidiol, varlitinib, salidroside, and vandetanib.</p>
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<p>Structure of dasatinib, BJ-2302, and 1j.</p>
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20 pages, 28743 KiB  
Article
Exploring the Underlying Mechanisms of Qingxing Granules Treating H1N1 Influenza Based on Network Pharmacology and Experimental Validation
by Hujun Du, Lianying Zhang, Haoxiang Sun, Shaoqin Zheng, Hongying Zhang, Shijia Yuan, Jiuyao Zhou, Zihao Fang, Jianping Song, Manxue Mei and Changsheng Deng
Pharmaceuticals 2024, 17(6), 731; https://doi.org/10.3390/ph17060731 - 5 Jun 2024
Viewed by 973
Abstract
Background: H1N1 is one of the major subtypes of influenza A virus (IAV) that causes seasonal influenza, posing a serious threat to human health. A traditional Chinese medicine combination called Qingxing granules (QX) is utilized clinically to treat epidemic influenza. However, its chemical [...] Read more.
Background: H1N1 is one of the major subtypes of influenza A virus (IAV) that causes seasonal influenza, posing a serious threat to human health. A traditional Chinese medicine combination called Qingxing granules (QX) is utilized clinically to treat epidemic influenza. However, its chemical components are complex, and the potential pharmacological mechanisms are still unknown. Methods: QX’s effective components were gathered from the TCMSP database based on two criteria: drug-likeness (DL ≥ 0.18) and oral bioavailability (OB ≥ 30%). SwissADME was used to predict potential targets of effective components, and Cytoscape was used to create a “Herb-Component-Target” network for QX. In addition, targets associated with H1N1 were gathered from the databases GeneCards, OMIM, and GEO. Targets associated with autophagy were retrieved from the KEGG, HAMdb, and HADb databases. Intersection targets for QX, H1N1 influenza, and autophagy were identified using Venn diagrams. Afterward, key targets were screened using Cytoscape’s protein–protein interaction networks built using the database STRING. Biological functions and signaling pathways of overlapping targets were observed through GO analysis and KEGG enrichment analysis. The main chemical components of QX were determined by high-performance liquid chromatography (HPLC), followed by molecular docking. Finally, the mechanism of QX in treating H1N1 was validated through animal experiments. Results: A total of 786 potential targets and 91 effective components of QX were identified. There were 5420 targets related to H1N1 and 821 autophagy-related targets. The intersection of all targets of QX, H1N1, and autophagy yielded 75 intersecting targets. Ultimately, 10 core targets were selected: BCL2, CASP3, NFKB1, MTOR, JUN, TNF, HSP90AA1, EGFR, HIF1A, and MAPK3. Identification of the main chemical components of QX by HPLC resulted in the separation of seven marker ingredients within 195 min, which are amygdalin, puerarin, baicalin, phillyrin, wogonoside, baicalein, and wogonin. Molecular docking results showed that BCL2, CASP3, NFKB1, and MTOR could bind well with the compounds. In animal studies, QX reduced the degenerative alterations in the lung tissue of H1N1-infected mice by upregulating the expression of p-mTOR/mTOR and p62 and downregulating the expression of LC3, which inhibited autophagy. Conclusions: According to this study’s network pharmacology analysis and experimental confirmation, QX may be able to treat H1N1 infection by regulating autophagy, lowering the expression of LC3, and increasing the expression of p62 and p-mTOR/mTOR. Full article
(This article belongs to the Section Pharmacology)
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<p>The technology roadmap of network pharmacology in this study.</p>
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<p>“Drug-Compound-Target” network of Qingxing granules.</p>
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<p>Core target screening of Qingxing granules. (<b>A</b>) Venn diagrams of QX “Component Target-Disease Target” and “Component Target-Disease Target-Autophagy Gene”; (<b>B</b>) PPI network of QX treatment for H1N1.</p>
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<p>GO enrichment analysis bubble chart.</p>
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<p>KEGG pathway enrichment analysis bubble chart.</p>
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<p>HPLC characteristic chromatogram of Qingxing granules.</p>
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<p>Molecular docking models of key compounds and core targets. (<b>a</b>) AmygdalinmTOR; (<b>b</b>) baicalein-mTOR; (<b>c</b>) baicalin-mTOR; (<b>d</b>) phillyrin-mTOR; (<b>e</b>) puerarin-mTOR; (<b>f</b>) wogonin-mTOR; (<b>g</b>) wogonoside-mTOR; (<b>h</b>) phillyrin-CASP3; (<b>i</b>) phillyrin-BCL2; (<b>j</b>) wogonoside-BCL2.</p>
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<p>Improvement effect of QX on mice infected with H1N1. (<b>a</b>) Changes in lung index of different groups of mice; (<b>b</b>) changes in H1N1 virus titers in lung tissues of mice from different groups; (<b>c</b>) variations in the overall white blood cell, lymphocyte, and monocyte characteristics in mouse blood; (<b>d</b>) the gross appearance of lung tissues from different groups of mice; (<b>e</b>) HE staining observations of mice in different groups. Scale bar = 100 µm; (<b>f</b>) The lung tissue was assessed using a histopathological grading scale, which ranged from 0 (indicating no alterations) to 3 (representing severe pathological conditions). Data are presented as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, compared with the virus group; <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, compared with the Control group.</p>
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<p>Qingxing granules affect mice infected with H1N1 through autophagy. (<b>a</b>) Observation of the number of autophagosomes and autolysosomes in the normal group, virus group, and medium-dose QX group mice under transmission electron microscopy. The red arrow points to the target as the autophagosome. The yellow arrow points to the autolysosome. Scale bar = 1 µm; (<b>b</b>) LC3, p62, and p-mTOR/mTOR expression measurements in mouse lung tissue employing Western blot research. Data are presented as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, compared with the virus group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, compared with the Control group.</p>
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25 pages, 2329 KiB  
Review
Molecular Mechanisms of N-Acetylcysteine in RSV Infections and Air Pollution-Induced Alterations: A Scoping Review
by August Wrotek, Artur Badyda and Teresa Jackowska
Int. J. Mol. Sci. 2024, 25(11), 6051; https://doi.org/10.3390/ijms25116051 - 31 May 2024
Viewed by 1287
Abstract
N-acetylcysteine (NAC) is a mucolytic agent with antioxidant and anti-inflammatory properties. The respiratory syncytial virus (RSV) is one of the most important etiological factors of lower respiratory tract infections, and exposure to air pollution appears to be additionally associated with higher RSV incidence [...] Read more.
N-acetylcysteine (NAC) is a mucolytic agent with antioxidant and anti-inflammatory properties. The respiratory syncytial virus (RSV) is one of the most important etiological factors of lower respiratory tract infections, and exposure to air pollution appears to be additionally associated with higher RSV incidence and disease severity. We aimed to systematically review the existing literature to determine which molecular mechanisms mediate the effects of NAC in an RSV infection and air pollution, and to identify the knowledge gaps in this field. A search for original studies was carried out in three databases and a calibrated extraction grid was used to extract data on the NAC treatment (dose, timing), the air pollutant type, and the most significant mechanisms. We identified only 28 studies conducted in human cellular models (n = 18), animal models (n = 7), and mixed models (n = 3). NAC treatment improves the barrier function of the epithelium damaged by RSV and air pollution, and reduces the epithelial permeability, protecting against viral entry. NAC may also block RSV-activated phosphorylation of the epidermal growth factor receptor (EGFR), which promotes endocytosis and facilitates cell entry. EGFR also enhances the release of a mucin gene, MUC5AC, which increases mucus viscosity and causes goblet cell metaplasia; the effects are abrogated by NAC. NAC blocks virus release from the infected cells, attenuates the cigarette smoke-induced shift from necrosis to apoptosis, and reverses the block in IFN-γ-induced antiviral gene expression caused by the inhibited Stat1 phosphorylation. Increased synthesis of pro-inflammatory cytokines and chemokines is induced by both RSV and air pollutants and is mediated by the nuclear factor kappa-B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways that are activated in response to oxidative stress. MCP-1 (monocyte chemoattractant protein-1) and RANTES (regulated upon activation, expressed and secreted by normal T cells) partially mediate airway hyperresponsiveness (AHR), and therapeutic (but not preventive) NAC administration reduces the inflammatory response and has been shown to reduce ozone-induced AHR. Oxidative stress-induced DNA damage and cellular senescence, observed during RSV infection and exposure to air pollution, can be partially reversed by NAC administration, while data on the emphysema formation are disputed. The review identified potential common molecular mechanisms of interest that are affected by NAC and may alleviate both the RSV infection and the effects of air pollution. Data are limited and gaps in knowledge include the optimal timing or dosage of NAC administration, therefore future studies should clarify these uncertainties and verify its practical use. Full article
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<p>PRISMA flow diagram.</p>
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<p>The number of studies in the review by year of publication.</p>
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<p>The models of the studies according to the specific variables analyzed in the review: blue—human cellular model, green—animal model, gray—mixed model. Abbreviations: RSV—respiratory syncytial virus, TiO<sub>2</sub>NP—titanium dioxide nanoparticles, PM—particulate matter, NO—nitric oxide.</p>
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<p>A diagram of the effects of NAC on the plausible mechanisms of RSV entry facilitated by air pollution and/or RSV itself. Solid dark blue arrows indicate the directions of the activity of RSV and/or air pollutants (the particular air pollutants are indicated in square brackets), with the directions of the effects marked by red or blue arrows (for increase and decrease, respectively); dashed pink lines show the effects of the activation of specific pathways; blind yellow arrows indicate the sites where NAC inhibits the mechanisms induced by RSV and/or air pollution; the solid red line represents the epithelium, and the dashed red line represents the endothelium. The pathways in which oxidative stress plays a significant role are marked with an asterisk (*). The figure is based on a literature search and is simplified for illustrative purposes only. Abbreviations: TiO<sub>2</sub>-NP—titanium dioxide nanoparticle, ICAM-1—Intercellular adhesion molecule-1, IFN-γ—interferon gamma, VCAM-1—vascular cell adhesion molecule 1, PECAM-1—platelet endothelial cell adhesion molecule-1, EGFR—epidermal growth factor receptor, sLex—E-selectin ligand, PSGL-1—P-selectin ligand, LFA-1—Lymphocyte function-associated antigen 1 (ICAM-1 ligand), VLA-4—very late antigen-4 (integrin α4β1, VCAM-1 ligand), αVβ3—PECAM-1 ligand.</p>
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<p>The effects of cigarette smoke on the RSV load, focusing on the mechanisms induced by cigarette smoke and inhibited by NAC. Solid dark blue arrows indicate the directions of the activity of human immune cells, RSV, and cigarette smoke, with the directions of the effects marked by red or blue arrows (for increase and decrease, respectively); dotted and dashed blue arrows indicate the directions of signal transduction and its effects, while blind yellow arrows indicate the inhibitory effect of NAC, which inhibits the mechanisms induced by cigarette smoke, and by cigarette smoke, which inhibits caspase activation; curved solid black line represents a cell. Abbreviations: IFN-γ—Interferon gamma; P—phosphoryl group; JAK—Janus kinase; STAT—signal transducer and activator of transcription; ROS—reactive oxygen species.</p>
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<p>The mechanisms of host response to an RSV infection exerted by the RSV and air pollutants. The exaggerated response includes an inflammatory response, airway hyperresponsiveness, emphysema formation, and mucus changes. Solid dark blue arrows indicate the directions of action of the RSV and/or air pollutants (the particular air pollutants are indicated in square brackets), with the directions of the effects indicated by red or blue arrows (for increase and decrease, respectively); in the case of proinflammatory chemokines and cytokines, the red arrows (only increase was seen) are not shown for better readability. The effects of the activation of the specific pathways are indicated by pink or green dashed lines (the latter are used to signal the results that are not directly derived from this scoping review); double-headed blind yellow arrows indicate the sites where NAC inhibits the mechanisms induced by RSV and/or air pollution. Abbreviations: IL-1 β, -6, -8, -13, and -18—interleukin-1 β, -6, -8, -13, and -18 (respectively), TNF-α—tumor necrosis factor alpha, MIP-2—macrophage inflammatory protein-2, MCP-1—monocyte chemoattractant protein-1, RANTES—regulated upon activation, normal T cell expressed and secreted, ASM—airway smooth muscle, VEGF—vascular endothelial growth factor, VEGFR—receptor for vascular endothelial growth factor, EGFR—epidermal growth factor receptor, MUC5AC—mucin-5AC, MMP-2, -9, and -12—matrix metalloproteinase 2, 9, and 12 (respectively), TiO<sub>2</sub>-NP—titanium dioxide nanoparticle, PM2.5—particulate matter 2.5, NO—nitric oxide, COX-2—cyclooxygenase 2. * NAC does not influence IL-8 levels, ** one of the studies showed only a trend towards reducing IL-8 levels by NAC, *** NAC reduces ASM mass, but not emphysema.</p>
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11 pages, 2251 KiB  
Article
The Invasion Factor ODZ1 Is Upregulated through an Epidermal Growth Factor Receptor-Induced Pathway in Primary Glioblastoma Cells
by Carlos Velasquez, Olga Gutierrez, Maria Carcelen and Jose L. Fernandez-Luna
Cells 2024, 13(9), 766; https://doi.org/10.3390/cells13090766 - 30 Apr 2024
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Abstract
We have previously shown that the transmembrane protein ODZ1 promotes cytoskeletal remodeling of glioblastoma (GBM) cells and invasion of the surrounding parenchyma through the activation of a RhoA–ROCK pathway. We also described that GBM cells can control the expression of ODZ1 through transcriptional [...] Read more.
We have previously shown that the transmembrane protein ODZ1 promotes cytoskeletal remodeling of glioblastoma (GBM) cells and invasion of the surrounding parenchyma through the activation of a RhoA–ROCK pathway. We also described that GBM cells can control the expression of ODZ1 through transcriptional mechanisms triggered by the binding of IL-6 to its receptor and a hypoxic environment. Epidermal growth factor (EGF) plays a key role in the invasive capacity of GBM. However, the molecular mechanisms that enable tumor cells to acquire the morphological changes to migrate out from the tumor core have not been fully characterized. Here, we show that EGF is able to induce the expression of ODZ1 in primary GBM cells. We analyzed the levels of the EGF receptor (EGFR) in 20 GBM primary cell lines and found expression in 19 of them by flow cytometry. We selected two cell lines that do or do not express the EGFR and found that EGFR-expressing cells responded to the EGF ligand by increasing ODZ1 at the mRNA and protein levels. Moreover, blockade of EGF-EGFR binding by Cetuximab, inhibition of the p38 MAPK pathway, or Additionally, the siRNA-mediated knockdown of MAPK11 (p38β MAPK) reduced the induction of ODZ1 in response to EGF. Overall, we show that EGF may activate an EGFR-mediated signaling pathway through p38β MAPK, to upregulate the invasion factor ODZ1, which may initiate morphological changes for tumor cells to invade the surrounding parenchyma. These data identify a new candidate of the EGF–EGFR pathway for novel therapeutic approaches. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Glioblastoma III)
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Figure 1

Figure 1
<p>The constitutively active mutant EGFRvIII correlates with higher levels of ODZ1. (<b>A</b>) ODZ1 expression was identified from RNA-seq data in TCGA datasets that provided information on a GBM cohort containing 214 patient samples carrying either the wild-type EGFR or the mutant variant EGFRvIII. (<b>B</b>) Schematic representation of the EGFR gene showing the PCR design to discriminate the EGFR from EGFRvIII. (<b>C</b>) PCR analysis of 20 primary GBM cell lines using the experimental design described in (<b>B</b>).</p>
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<p>Expression of the EGFR protein in primary GBM cells. (<b>A</b>) EGFR expression was determined by FACS analysis. Red line, anti-EGFR antibody. Black line, isotype-matched control antibody. (<b>B</b>) Mean fluorescence intensity of FACS results and the gene amplification status of the EGFR in all the cell lines. Cell lines are ordered in the same way as in <a href="#cells-13-00766-f001" class="html-fig">Figure 1</a>C. (<b>C</b>) Representative result of a MLPA assay showing amplification of the entire EGFR gene.</p>
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<p>EGF induces the expression of ODZ1. (<b>A</b>) FACS analysis of two GBM cell lines expressing, or not, EGFR (same as in <a href="#cells-13-00766-f002" class="html-fig">Figure 2</a>A). (<b>B</b>) GBM cells were incubated with EGF and the mRNA levels of ODZ1 were determined after 24 h by qPCR. Histograms represent the mean of three independent experiments ± S.D. Asterisks represent significant differences (** <span class="html-italic">p</span> &lt; 0.01). (<b>C</b>) Western Blot analysis confirmed that the exposure of GBM cells to EGF also promoted the expression of ODZ1 at the protein level. Additionally, αTubulin was used to ensure equal loading. Arrows indicate the native high-molecular-weight ODZ1 protein and its most frequent proteolytic fragment of 70 kDa. (<b>D</b>) Cells were incubated in the presence of EGF and migration was determined at different time points by using an in vitro wound healing assay.</p>
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<p>The blockade of the EGFR–p38 signaling downregulates the levels of ODZ1. (<b>A</b>) GBM cells were treated with EGF in the absence or in the presence of the EGFR-blocking antibody Cetuximab or the specific p38 MAPK inhibitor SB203580. Western Blot analysis confirmed that the blockade of EGFR/p38 inhibited the phosphorylation of MAPKAPK2 at Thr222, a target of p38, and reduced the expression of the ODZ1 protein. (<b>B</b>) Downregulation of ODZ1 following inhibition of EGFR/p38 was confirmed in two additional GBM cell lines. MAPKAPK2 and αTubulin were used to ensure equal loading.</p>
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<p>RNA interference by p38-specific siRNAs neutralizes the EGF-promoted upregulation of ODZ1. (<b>A</b>) GBM cells were transfected with two MAPK14-specific siRNAs or two control siRNAs. Following incubation with EGF, the protein levels of ODZ1 were determined by Western Blot analysis. (<b>B</b>) Same experimental design as in A but uses MAPK11-specific siRNAs. Additionally, αTubulin was used to ensure equal loading. (<b>C</b>) Quantification of ODZ1 protein levels of the experiment shown in B by using imageJ software (v1.53k). (<b>D</b>) Cells carrying siRNAs specific to MAPK11 were analyzed for the expression of either MAPK11 or MAPK14 mRNA by qPCR. Histograms represent the mean of three independent experiments ± S.D. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Schematic representation of the EGFR–p38–ODZ1 transcriptional pathway. EGF, present in the tumor microenvironment, binds to its receptor and triggers the activation of p38 MAPK, which induces a transcriptional mechanism to upregulate ODZ1, which is a known migration factor in GBM cells through the RhoA–ROCK pathway.</p>
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