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12 pages, 4007 KiB  
Article
Cardiomyocyte Regeneration in Human Myocarditis
by Andrea Frustaci, Eleonora Foglio, Federica Limana, Michele Magnocavallo, Emanuela Frustaci, Leonardo Lupacchini and Romina Verardo
Biomedicines 2024, 12(8), 1814; https://doi.org/10.3390/biomedicines12081814 - 9 Aug 2024
Viewed by 266
Abstract
Background: Newly generated cardiomyocytes (NGCs) concur with the recovery of human myocarditis occurring spontaneously in around 50% of cases. However, NGCs decline with age, and their modality of myocardial homing and integration are still unclear. Methods: We retrospectively assessed NGCs in 213 consecutive [...] Read more.
Background: Newly generated cardiomyocytes (NGCs) concur with the recovery of human myocarditis occurring spontaneously in around 50% of cases. However, NGCs decline with age, and their modality of myocardial homing and integration are still unclear. Methods: We retrospectively assessed NGCs in 213 consecutive patients with endomyocardial biopsy denoting acute myocarditis, with normal coronaries and valves. Tissue samples were processed for histology (H&E), immunohistochemistry for the evaluation of inflammatory infiltrates, immunostaining for alpha-sarcomeric-actin, junctional connexin-43, Ki-67, and phosphorylated STAT3 (p-STAT3), and Western blot (WB) for HMGB1. Frozen samples were analyzed using polymerase chain reaction (PCR) for cardiotropic viruses. Controls included 20 normal surgical biopsies. Results: NGCs were defined as small myocytes (diameter < 10 µm) with nuclear positivity to Ki-67 and p-STAT3 and positive immunostaining for cytoplasmic α-sarcomeric actin and connexin-43. Their number/mm2 in relation to age and pathway of integration was evaluated. NGCs crossed the membrane and grew integrated within the empty necrotic myocytes. NGC mean diameter was 6.6 ± 3.34 vs. 22.5 ± 3.11 µm adult cells; their number, in comparison to LVEF, was 86.3 ± 10.3/mm2 in patients between 18 and 40 years, 50.4 ± 13.8/mm2 in those between 41 and 60, and 15.1 ± 5.7/mm2 in those between 61 and 80. Control NGCs’ mean diameter was 0.2 ± 0.2 mm2. PCR was positive for viral genomes in 16% of cases; NGCs were not statistically different in viral and non-viral myocarditis. WB analysis revealed a higher expression of HMGB1 in myocarditis compared to myocardial controls. Conclusions: NGCs are constantly recognizable in acute human myocarditis. Their number declines with age. Their integration within necrotic myocytes allows for the preservation of the cardiac structure and function. Full article
(This article belongs to the Section Biomedical Engineering and Materials)
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Figure 1
<p>Homing and integration of NGCs into inflamed myocardium. (<b>A</b>) Several cells resembling small myocytes (arrows) approach an area of cardiomyocyte necrosis caused by lymphocytic myocarditis (H&amp;E 400×). (<b>B</b>) Immunohistochemistry for alpha-sarcomeric-actin (400×) shows the presence of contractile material in the small cells. (<b>C</b>) Multiple NGCs are lining up (arrows) to enter an empty cardiomyocyte, crossing its cell membrane (H&amp;E 400×). (<b>D</b>) NGCs (arrows) are growing inside of a cardiomyocyte that has undergone a myocytolytic process (H&amp;E, 400×).</p>
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<p>Myocarditis induces the formation of proliferating NGCs’ α-sarcomeric-expressing cells in the human left ventricle. Newly formed cells positive for α-sarcomeric actin (red fluorescence) expressed Ki-67 (green fluorescence), as indicated by the arrowheads. Red fluorescence indicates α-sarcomeric actin immunostaining; green fluorescence indicates Ki-67; blue fluorescence indicates DAPI staining of nuclei. Newly formed cells positive for α-sarcomeric actin (red fluorescence) expressed Ki-67 (green fluorescence), as indicated by the arrowheads. Bar = 5 µm (inserts: magnified version; bar = 2 µm or 1 µm).</p>
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<p>Newly generated cardiomyocytes in the human left ventricle were functionally competent. NGCs expressed connexin-43, detected as punctuate staining (green fluorescence) in the gap-junctional regions between cardiomyocytes in the left ventricular tissue. Newly formed α-sarcomeric-expressing cells expressed connexin-43. Red fluorescence indicates α-sarcomeric actin immunostaining; green fluorescence indicates connexin-43; blue fluorescence indicates DAPI staining of nuclei. Bar = 5 µm (inserts: magnified version; bar = 1 µm).</p>
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<p>STAT3 was activated in NGCs of patients diagnosed with myocarditis. NGCs expressed pSTAT3 in the nuclei (green fluorescence). Red fluorescence indicates α-sarcomeric actin immunostaining; green fluorescence indicates pSTAT3; blue fluorescence indicates DAPI staining of nuclei. Bar = 5 μm (inserts: magnified version; bar = 2 µm).</p>
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<p>HMGB1 expression is up-regulated in myocarditis. Western blot analysis showing the expression of HMGB1 in LV endomyocardial biopsies of myocarditis compared to controls (CTRL). The same filter was probed with anti-GAPDH pAb to show equal loading. Left panel: a representative Western blotting of three replicates is shown. Marker: lane 1 = CTRL 1, lane 2 = CTRL 2, lane 3 = pt 1, lane 4 = pt 2, lane 5 = pt 3, lane 6 = pt 4, and lane 7 = pt 5. Right panel: densitometric analysis of Western blot (mean values) from CTRL patients (n = 2) and patients with myocarditis (n = 5). Data are shown as means ± SEM. ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Distribution of newly generated cardiomyocytes in groups and controls and their correlation with age. (<b>A</b>) NGCs were statistically different in the three groups and between groups and controls (<span class="html-italic">p</span>-value between groups: 0.001). In the box plot are represented the median (line in the middle) and the middle “box” (1st–3rd quartile) of all values. The upper and lower whiskers represent scores outside of the middle 50%. (<b>B</b>) NGCs linearly decreased with age.</p>
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27 pages, 1374 KiB  
Review
Novel Anti-Cancer Stem Cell Compounds: A Comprehensive Review
by Shanchun Guo, Shilong Zheng, Mingli Liu and Guangdi Wang
Pharmaceutics 2024, 16(8), 1024; https://doi.org/10.3390/pharmaceutics16081024 - 1 Aug 2024
Viewed by 558
Abstract
Cancer stem cells (CSCs) possess a significant ability to renew themselves, which gives them a strong capacity to form tumors and expand to encompass additional body areas. In addition, they possess inherent resistance to chemotherapy and radiation therapies used to treat many forms [...] Read more.
Cancer stem cells (CSCs) possess a significant ability to renew themselves, which gives them a strong capacity to form tumors and expand to encompass additional body areas. In addition, they possess inherent resistance to chemotherapy and radiation therapies used to treat many forms of cancer. Scientists have focused on investigating the signaling pathways that are highly linked to the ability of CSCs to renew themselves and maintain their stem cell properties. The pathways encompassed are Notch, Wnt/β-catenin, hedgehog, STAT3, NF-κB, PI-3K/Akt/mTOR, sirtuin, ALDH, MDM2, and ROS. Recent studies indicate that directing efforts towards CSC cells is essential in eradicating the overall cancer cell population and reducing the likelihood of tumor metastasis. As our comprehension of the mechanisms that stimulate CSC activity, growth, and resistance to chemotherapy advances, the discovery of therapeutic drugs specifically targeting CSCs, such as small-molecule compounds, holds the potential to revolutionize cancer therapy. This review article examines and analyzes the novel anti-CSC compounds that have demonstrated effective and selective targeting of pathways associated with the renewal and stemness of CSCs. We also discussed their special drug metabolism and absorption mechanisms. CSCs have been the subject of much study in cancer biology. As a possible treatment for malignancies, small-molecule drugs that target CSCs are gaining more and more attention. This article provides a comprehensive review of the current state of key small-molecule compounds, summarizes their recent developments, and anticipates the future discovery of even more potent and targeted compounds, opening up new avenues for cancer treatment. Full article
(This article belongs to the Special Issue Novel Anti-cancer Compounds: Drug Metabolism and Absorption)
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<p>In the tumor CSC model, only CSCs have tumor-initiating capability.</p>
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<p>Chemical structures of targeted small-molecule compounds of cancer stem cells.</p>
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<p>Chemical structures of targeted small-molecule compounds of cancer stem cells.</p>
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<p>Chemical structures of targeted small-molecule compounds of cancer stem cells.</p>
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22 pages, 4352 KiB  
Article
Bufalin Suppresses Head and Neck Cancer Development by Modulating Immune Responses and Targeting the β-Catenin Signaling Pathway
by Nour Mhaidly, Noura Barake, Anne Trelcat, Fabrice Journe, Sven Saussez and Géraldine Descamps
Cancers 2024, 16(15), 2739; https://doi.org/10.3390/cancers16152739 - 1 Aug 2024
Viewed by 749
Abstract
Bufalin, a cardiotonic steroid derived from the Chinese toad (Bufo gargarizans), has demonstrated potent anticancer properties across various cancer types, positioning it as a promising therapeutic candidate. However, comprehensive mechanistic studies specific to head and neck cancers have been lacking. Our study aimed [...] Read more.
Bufalin, a cardiotonic steroid derived from the Chinese toad (Bufo gargarizans), has demonstrated potent anticancer properties across various cancer types, positioning it as a promising therapeutic candidate. However, comprehensive mechanistic studies specific to head and neck cancers have been lacking. Our study aimed to bridge this gap by investigating bufalin’s mechanisms of action in head and neck cancer cells. Using several methods, such as Western blotting, immunofluorescence, and flow cytometry, we observed bufalin’s dose-dependent reduction in cell viability, disruption of cell membrane integrity, and inhibition of colony formation in both HPV-positive and HPV-negative cell lines. Bufalin induces apoptosis through the modulation of apoptosis-related proteins, mitochondrial function, and reactive oxygen species production. It also arrests the cell cycle at the G2/M phase and attenuates cell migration while affecting epithelial–mesenchymal transition markers and targeting pivotal signaling pathways, including Wnt/β-catenin, EGFR, and NF-κB. Additionally, bufalin exerted immunomodulatory effects by polarizing macrophages toward the M1 phenotype, bolstering antitumor immune responses. These findings underscore bufalin’s potential as a multifaceted therapeutic agent against head and neck cancers, targeting essential pathways involved in proliferation, apoptosis, cell cycle regulation, metastasis, and immune modulation. Further research is warranted to validate these mechanisms and optimize bufalin’s clinical application. Full article
(This article belongs to the Special Issue Head and Neck Cancers—Novel Approaches and Future Outlook)
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Figure 1
<p>Bufalin’s effect on cancer cell proliferation. (<b>A</b>) Graphs showing cell viability percentages after treatment with varying concentrations of bufalin (0 to 80 nM). (<b>B</b>) Graphs illustrating the Cytotox green objective area (µm<sup>2</sup>) over time for three conditions: untreated; bufalin-treated at IC<sub>80</sub>; and cisplatin-treated positive control. Mean ± SD, Anova One Way, and Tukey’s post hoc test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001. (<b>C</b>) Clonogenic assays evaluating the effects of bufalin at 2, 3, 5, and 10 nM.</p>
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<p>Bufalin’s effects on cancer cell apoptosis. (<b>A</b>) Graphs illustrating the Annexin V green objective area (µm<sup>2</sup>) over time for three conditions: untreated, bufalin-treated at IC<sub>80</sub>, and cisplatin-treated positive control at a concentration of 30 µM. Mean + SD, Anova One Way, and Tukey’s post hoc test (*** <span class="html-italic">p</span> ≤ 0.001). (<b>B</b>) Western blot analysis of apoptosis markers following bufalin incubation for various times, ranging from 1 h to 48 h, in both cell lines. (<b>C</b>) Immunofluorescence images of TMRE representing the ΔΨm before and after 24 h of bufalin treatment at IC<sub>80</sub> (scale = 10 μm).</p>
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<p>Bufalin’s effects on oxidative stress. (<b>A</b>) Graphs illustrating the Mitosox Red objective area (µm<sup>2</sup>) over time for three conditions: untreated; bufalin-treated at IC<sub>80</sub>; and BSO-treated positive control. Mean + SD, Anova One Way, and Tukey’s post hoc (*** <span class="html-italic">p</span> ≤ 0.001). (<b>B</b>) Western blot analysis of NRF2 marker following 24 h of bufalin IC<sub>80</sub> incubation in both cell lines. (<b>C</b>) mRNA relative expression (2<sup>−ΔCt</sup>) of antioxidant enzymes before and after 24 h of bufalin treatment. Data are presented as Mean ± SD; <span class="html-italic">t</span>-test; * = <span class="html-italic">p</span> ≤ 0. 05; *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Bufalin effect on cell cycle regulation. (<b>A</b>) Western blot analysis of cell cycle-related markers following cell incubation with bufalin IC<sub>80</sub> for various times, ranging from 1 h to 48 h. (<b>B</b>) Immunofluorescence confirming increased expression of p21 after bufalin treatment in FaDu cells. (<b>C</b>) mRNA relative expression (2<sup>−ΔCt</sup>) showing upregulated p21 following bufalin treatment. (<b>D</b>) Flow cytometry data representing the distribution of cells in different cell cycle phases after 24 h of bufalin treatment in 93VU cells. Data are presented as Mean + SD; <span class="html-italic">t</span>-test; * = <span class="html-italic">p</span> ≤ 0. 05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Bufalin effect on migration and EMT process. (<b>A</b>) Representative photographs of cell migration assays using transwell chambers after 48 h of bufalin treatment at IC<sub>80</sub>. (<b>B</b>) Quantification of migrated cells by measuring relative absorbance at 570 nm. (<b>C</b>) RT-qPCR analysis of mRNA expression levels (2<sup>−ΔCt</sup>) for EMT markers (E-cadherin, vimentin, Twist1), normalized to 18S expression, mean ± SD; <span class="html-italic">t</span>-test; * = <span class="html-italic">p</span> ≤ 0.05; ** = <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Bufalin downstream via different pathways. (<b>A</b>) Western blot analysis showing the expression levels of p-β-catenin (Ser675), total β-catenin, EGFR, and p-STAT3 (Tyr605) bufalin treatment for various durations, ranging from 1 to 48 h. (<b>B</b>) Western blot analysis of the proto-oncogene c-Myc marker following 24 h of bufalin incubation in both cell lines. (<b>C</b>) Immunofluorescence confirming decreased expression of EGFR (scale = 10 μm), p-STAT3 (scale = 10 μm), and c-Myc (scale = 20 μm) in FaDu cells after 48 h of bufalin treatment.</p>
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<p>Bufalin effect (40 µM) on the reprogrammation of M2 toward M1 macrophages. (<b>A</b>) Evaluation of macrophage phenotypic markers CD86 and CD206 in M2-differentiated macrophages after 48 h of bufalin treatment by immunofluorescence (scale = 20 μm). (<b>B</b>) mRNA relative expression (RT-qPCR, 2<sup>−ΔCt</sup>) to characterize macrophage phenotype using M1 markers (CD86, IL-12, iNOS, NFκB) and M2 markers (CD206, CD163, IL10, and MIF). Mean ± SD, <span class="html-italic">t</span>-test (* = <span class="html-italic">p</span> ≤ 0.05; ** = <span class="html-italic">p</span> ≤ 0.01; *** = <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Proposal of the potential signaling pathways used by bufalin to induce apoptosis in HNC. Bufalin reduces β-catenin signaling activation by blocking p-β-catenin translocation to the nucleus and inhibiting the transcription of genes such as EGFR, Cyclin D1, and c-Myc. Inhibiting EGFR not only blocks the activation of the p-STAT3 pathway but also prevents the transcription of anti-apoptotic proteins like Bcl-xL and MCL-1. This inhibition shifts the balance toward pro-apoptotic factors, triggering apoptosis via the mitochondrial pathway. Consequently, cytochrome c and AIF are released into the cytosol, activating the caspase-3 cascade and initiating apoptosis. Additionally, the increase in reactive oxygen species (ROS) promotes apoptosis. Bufalin also blocks the cell cycle at the G2/M phase by inhibiting c-Myc. This action prevents the activation of cyclin D1 and promotes p21-mediated cell cycle arrest by blocking the formation of the CDK1/cyclin B complex. Finally, it prevents β-catenin nuclear translocation, preserving E-cadherin/β-catenin complex integrity and inhibiting epithelial–mesenchymal transition.</p>
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28 pages, 6100 KiB  
Review
Using microRNAs Networks to Understand Pancreatic Cancer—A Literature Review
by Oskar Przybyszewski, Michał Mik, Michał Nowicki, Michał Kusiński, Melania Mikołajczyk-Solińska and Agnieszka Śliwińska
Biomedicines 2024, 12(8), 1713; https://doi.org/10.3390/biomedicines12081713 - 1 Aug 2024
Viewed by 330
Abstract
Pancreatic cancer is a severe disease, challenging to diagnose and treat, and thereby characterized by a poor prognosis and a high mortality rate. Pancreatic ductal adenocarcinoma (PDAC) represents approximately 90% of pancreatic cancer cases, while other cases include neuroendocrine carcinoma. Despite the growing [...] Read more.
Pancreatic cancer is a severe disease, challenging to diagnose and treat, and thereby characterized by a poor prognosis and a high mortality rate. Pancreatic ductal adenocarcinoma (PDAC) represents approximately 90% of pancreatic cancer cases, while other cases include neuroendocrine carcinoma. Despite the growing knowledge of the pathophysiology of this cancer, the mortality rate caused by it has not been effectively reduced. Recently, microRNAs have aroused great interest among scientists and clinicians, as they are negative regulators of gene expression, which participate in many processes, including those related to the development of pancreatic cancer. The aim of this review is to show how microRNAs (miRNAs) affect key signaling pathways and related cellular processes in pancreatic cancer development, progression, diagnosis and treatment. We included the results of in vitro studies, animal model of pancreatic cancer and those performed on blood, saliva and tumor tissue isolated from patients suffering from PDAC. Our investigation identified numerous dysregulated miRNAs involved in KRAS, JAK/STAT, PI3/AKT, Wnt/β-catenin and TGF-β signaling pathways participating in cell cycle control, proliferation, differentiation, apoptosis and metastasis. Moreover, some miRNAs (miRNA-23a, miRNA-24, miRNA-29c, miRNA-216a) seem to be engaged in a crosstalk between signaling pathways. Evidence concerning the utility of microRNAs in the diagnosis and therapy of this cancer is poor. Therefore, despite growing knowledge of the involvement of miRNAs in several processes associated with pancreatic cancer, we are beginning to recognize and understand their role and usefulness in clinical practice. Full article
(This article belongs to the Special Issue MicroRNA and Its Role in Human Health)
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<p>The stages of PDAC development, including the progression of precursor lesions called pancreatic intraepithelial neoplasia (PanIN). The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>Synthesis of miRNA. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>PI3K/AKT signaling pathway and miRNAs in pancreatic cancer. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>KRAS signaling pathway and miRNAs in pancreatic cancer. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>JAK/STAT signaling pathway and miRNAs in pancreatic cancer. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>TGF-β signaling pathway and miRNAs in pancreatic cancer. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>Wnt/β-Catenin signaling pathway and miRNAs in pancreatic cancer. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>The scheme of cell cycle phases. Interphase: M/G1 checkpoint = initiation of the cell cycle; G1 phase = cell growth and protein synthesis; G0 phase = differentiation, rest from divisions; G1/S checkpoint = repair of errors in DNA; S phase = DNA replication; G2 phase = tubulin synthesis; G2/M checkpoint = repair of errors after replication. M phase (Mitosis): karyokinesis, cytokinesis, division of a cell into two cells. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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<p>The internal and external apoptosis pathways.</p>
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12 pages, 5557 KiB  
Article
The Mechanism of Action of the Active Ingredients of Coptidis rhizoma against Porcine Epidemic Diarrhea Was Investigated Using Network Pharmacology and Molecular Docking Technology
by Hong Zou, Zheng Niu, Zhangchen Tang, Peng Cheng, Yanling Yin, Gan Luo and Shilei Huang
Viruses 2024, 16(8), 1229; https://doi.org/10.3390/v16081229 - 31 Jul 2024
Viewed by 412
Abstract
The objective of this study was to elucidate the mechanism of action of the active components of Coptidis rhizoma against porcine epidemic diarrhea and to provide a theoretical foundation for further development of novel anti-PED therapeutic agents based on Coptidis rhizoma. The [...] Read more.
The objective of this study was to elucidate the mechanism of action of the active components of Coptidis rhizoma against porcine epidemic diarrhea and to provide a theoretical foundation for further development of novel anti-PED therapeutic agents based on Coptidis rhizoma. The potential targets of Coptidis rhizoma against PEDV were identified through a comprehensive literature review and analysis using the TCMSP pharmacological database, SwissDrugDesign database, GeneCards database, and UniProt database. Subsequently, the STRING database and Cytoscape 3.7.1 software were employed to construct a protein–protein interaction (PPI) network and screen key targets. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the identified targets. Molecular docking studies were performed using AutoDock 1.5.7 software to analyze the binding energy and modes of interaction between the active components of Coptidis rhizoma and the target proteins. The PyMOL 2.5.0a0 software was employed to visualize the docking results. Through comprehensive analysis, 74 specific targets of active components of Coptidis rhizoma against PEDV were identified. The core gene targets were screened, and an interaction network diagram was subsequently generated. Ultimately, 14 core targets were identified, with STAT3, ESR1, CASP3, and SRC exhibiting the most significant interactions. GO enrichment analysis revealed a total of 215 molecular items, including 48 biological function items, 139 biological process items, and 28 cellular component items. KEGG enrichment analysis identified 140 signaling pathways. Molecular docking analysis demonstrated that epiberberine and palmatine exhibited high binding affinity with STAT3 protein, worenine showed high binding affinity with ESR1 protein, obacunone exhibited high binding affinity with CASP3 protein, and epiberberine, obacunone, berberine, and berberruine exhibited high binding affinity with SRC protein. A network pharmacology and molecular docking technology approach was employed to screen six important active components of Coptidis rhizoma and four important potential targets against PEDV infection. The findings indicated that the active components of Coptidis rhizoma could serve as promising pharmaceutical agents for the prevention and control of PEDV, with significant potential for clinical application. Full article
(This article belongs to the Special Issue Porcine Enteric Viruses)
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Graphical abstract

Graphical abstract
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<p>Venn diagram of intersection gene target between active components of <span class="html-italic">Coptidis rhizoma</span> and PED.</p>
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<p>Network diagram of effective active components–intersection gene target of <span class="html-italic">Coptidis rhizoma</span>.</p>
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<p>Core gene targets and interaction network of effective active components of <span class="html-italic">Coptidis rhizoma</span> against PED.</p>
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<p>Bubble chart of active components in Coptidis Rhizoma drugs against PED. (<b>A</b>): Top 10 results of GO enrichment analysis of 215 biological functions, biological processes, and cellular components. (<b>B</b>): Top 10 results from KEGG enrichment analysis of 140 signaling pathways. Each bubble’s color represents the negative log10 value of the <span class="html-italic">p</span>-value, and the size of the bubble generally represents the number of genes involved in the pathway.</p>
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<p>Binding energies, binding sites, and modes of action of epiberberine, palmatine, worenine, obacunone, berberine, and berberruine and receptor proteins STAT3, ESR1, CASP3, and SRC. (<b>A</b>): Heatmap of binding energies between active drugs and STAT3, ESR1, CASP3, and SRC. (<b>B</b>): Schematic diagram of interaction patterns between epiberberine and palmatine with STAT3. (<b>C</b>): Schematic diagram of interaction patterns between worenine and ESR1. (<b>D</b>): Schematic diagram of interaction patterns between obacunone and CASP3. (<b>E</b>): Schematic diagram of interaction patterns between epiberberine, berberine, berberrubine, and obacunone with SRC.</p>
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<p>Binding energies, binding sites, and modes of action of epiberberine, palmatine, worenine, obacunone, berberine, and berberruine and receptor proteins STAT3, ESR1, CASP3, and SRC. (<b>A</b>): Heatmap of binding energies between active drugs and STAT3, ESR1, CASP3, and SRC. (<b>B</b>): Schematic diagram of interaction patterns between epiberberine and palmatine with STAT3. (<b>C</b>): Schematic diagram of interaction patterns between worenine and ESR1. (<b>D</b>): Schematic diagram of interaction patterns between obacunone and CASP3. (<b>E</b>): Schematic diagram of interaction patterns between epiberberine, berberine, berberrubine, and obacunone with SRC.</p>
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14 pages, 916 KiB  
Review
African Swine Fever Virus Immunosuppression and Virulence-Related Gene
by Tao Huang, Fangtao Li, Yingju Xia, Junjie Zhao, Yuanyuan Zhu, Yebing Liu, Yingjuan Qian and Xingqi Zou
Curr. Issues Mol. Biol. 2024, 46(8), 8268-8281; https://doi.org/10.3390/cimb46080488 - 31 Jul 2024
Viewed by 255
Abstract
African swine fever virus (ASFV), a highly contagious pathogen characterized by a complex structure and a variety of immunosuppression proteins, causes hemorrhagic, acute, and aggressive infectious disease that severely injures the pork products and industry. However, there is no effective vaccine or treatment. [...] Read more.
African swine fever virus (ASFV), a highly contagious pathogen characterized by a complex structure and a variety of immunosuppression proteins, causes hemorrhagic, acute, and aggressive infectious disease that severely injures the pork products and industry. However, there is no effective vaccine or treatment. The main reasons are not only the complex mechanisms that lead to immunosuppression but also the unknown functions of various proteins. This review summarizes the interaction between ASFV and the host immune system, along with the involvement of virulence-related genes and proteins, as well as the corresponding molecular mechanism of immunosuppression of ASFV, encompassing pathways such as cGAS-STING, nuclear factor kappa–light-chain-enhancer of activated B cells (NF-κB), Janus Kinase (JAK) and JAK Signal Transducers and Activators of Transcription (STAT), apoptosis, and other modulation. The aim is to summarize the dynamic process during ASFV infection and entry into the host cell, provide a rational insight into development of a vaccine, and provide a better clear knowledge of how ASFV impacts the host. Full article
(This article belongs to the Special Issue Innate Immunity Responds to Virus Infection)
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Figure 1
<p>The relations among cGAS-STING, NF-κB, and JAK/STAT signaling pathways. After invasion of ASFV into host cells, the dsDNA of ASFV binds to and activates cGAS, along with the cGAS-STING pathway, and TBKI is activated to phosphorylate IRF3 and initiate the NF-κB pathway, resulting in production of type I interferons. These type I interferons then activate and phosphorylate JAK1 and tyrosine kinase 2, subsequently phosphorylating STAT1 and STAT2. The phosphorylated STAT1 and STAT2 then bind to IRF9 to form the IFN-stimulated gene factor (ISGF) 3 complex; the ISGF3 complex can translocate into the nucleus and boost the activity of IFN-stimulated response element (ISRE), thereby increasing the expression of IFN-stimulated genes (ISGs), which play important roles in the cell’s response to viruses and pathogens. During the above processes, the ASFV is able to influence the normal function of various regulatory factors and promotes its pathogenicity.</p>
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<p>The molecular mechanisms of apoptosis, including intrinsic and extrinsic pathways. Even though these two pathways have distinct starting points, their processes interact with each other and ultimately converge to induce a common apoptotic response.</p>
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18 pages, 4874 KiB  
Article
A Novel Liquid Biopsy Method Based on Specific Combinations of Vesicular Markers Allows Us to Discriminate Prostate Cancer from Hyperplasia
by Emanuele Martorana, Gabriele Raciti, Raffaella Giuffrida, Elena Bruno, Vincenzo Ficarra, Giuseppe Mario Ludovico, Nazareno Roberto Suardi, Nunzio Iraci, Loredana Leggio, Benedetta Bussolati, Cristina Grange, Aurelio Lorico, Rosario Leonardi and Stefano Forte
Cells 2024, 13(15), 1286; https://doi.org/10.3390/cells13151286 - 31 Jul 2024
Viewed by 704
Abstract
Background: Prostate cancer is the second most common cancer in males worldwide, and its incidence is rising. Early detection is crucial for improving the outcomes, but the current screening methods have limitations. While prostate-specific antigen (PSA) testing is the most widely used screening [...] Read more.
Background: Prostate cancer is the second most common cancer in males worldwide, and its incidence is rising. Early detection is crucial for improving the outcomes, but the current screening methods have limitations. While prostate-specific antigen (PSA) testing is the most widely used screening tool, it has poor specificity, leading to a high rate of false positives and unnecessary biopsies. The existing biopsy techniques are invasive and are associated with complications. The liquid biopsy methods that analyze the biomarkers in blood or other bodily fluids offer a non-invasive and more accurate alternative for detecting and characterizing prostate tumors. Methods: Here, we present a novel liquid biopsy method for prostate cancer based on the identification of specific proteins in the extracellular vesicles isolated from the blood of patients with prostate cancer. Results: We observed that a specific combination of sEV proteins is a sensitive indicator of prostate cancer. Indeed, we found that the number of clusters expressed by specific combinations of either intra-vesicular (STAT3 and CyclinD1) or surface proteins (ERBB3, ALK, and CD81) allowed us to significantly discriminate the patients with prostate cancer from the individuals with hyperplasia. Conclusion: This new liquid biopsy method has the potential to improve prostate cancer screening by providing a non-invasive and more accurate diagnostic tool. Full article
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<p>EV characterization. (<b>A</b>) Nanotracking analysis of PCa and BPH samples showing EV size and concentration. (<b>B</b>) Representative images of sEV acquisitions by SEM at different magnifications. (<b>C</b>) Western Blot analysis on Jurkat (J) and vesicular lysates (1, 2, 3, and 4) to detect CD45, Alix, and β-actin. Abbreviations: PCa: prostate cancer; BPH: benign prostate hyperplasia; sEVs: small extracellular vesicles; SEM: scanning electron microscopy; J: Jurkat cell lysate; M: marker.</p>
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<p>Super-resolution microscopy-based EV characterization. Representative images of single sEVs expressing only one (single labelling) of the analyzed markers (<b>A</b>) and co-expressing two (<b>B</b>) or three (<b>C</b>) markers contemporarily (double and triple labelling, respectively). Super-resolution microscopy field of view of sEVs isolated from PCa sample with scale bar of 20 µm (<b>D</b>). Pie charts of BPH surface-only (<b>E</b>) and surface/intra-vesicular (<b>F</b>) markers distribution and of PCa surface-only (<b>G</b>) and surface/intra-vesicular (<b>H</b>) ones. Surface-only (<b>I</b>) and surface/intra-vesicular (<b>J</b>) cluster counts for both types of all analyzed samples. Abbreviations: sEVs: small extracellular vesicles; PCa: prostate cancer; BPH: benign prostate hyperplasia; AF: alexa fluor dye; CF: cyanine-based far red fluorescent dye.</p>
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<p>Super-resolution microscopy-based EV characterization. Representative images of single sEVs expressing only one (single labelling) of the analyzed markers (<b>A</b>) and co-expressing two (<b>B</b>) or three (<b>C</b>) markers contemporarily (double and triple labelling, respectively). Super-resolution microscopy field of view of sEVs isolated from PCa sample with scale bar of 20 µm (<b>D</b>). Pie charts of BPH surface-only (<b>E</b>) and surface/intra-vesicular (<b>F</b>) markers distribution and of PCa surface-only (<b>G</b>) and surface/intra-vesicular (<b>H</b>) ones. Surface-only (<b>I</b>) and surface/intra-vesicular (<b>J</b>) cluster counts for both types of all analyzed samples. Abbreviations: sEVs: small extracellular vesicles; PCa: prostate cancer; BPH: benign prostate hyperplasia; AF: alexa fluor dye; CF: cyanine-based far red fluorescent dye.</p>
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<p>The cluster counts in the tumor and hyperplasia samples for the surface and intra-vesicular targets. The subfigures represent clusters expressing ERBB3 and ALK (<b>A</b>); ERBB3, ALK, and CD81 (<b>B</b>); STAT3 (<b>C</b>); STAT3 and CD81 (<b>D</b>); STAT3 and CyclinD1 (<b>E</b>); and STAT3, CyclinD1, and CD81 (<b>F</b>). Abbreviations: PCa: prostate cancer; BPH: benign prostate hyperplasia. Symbols represent: “*” <span class="html-italic">p</span> &lt; 0.05; “ns” <span class="html-italic">p</span> ≥ 0.05; “•”refer to values out of ±1.5 * IQR.</p>
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<p>The receiver operating characteristic curves for the intra-vesicular and surface targets. ROC curves for intra-vesicular and surface targets with the most differentiated counts: ERBB3, ALK (<b>A</b>); ERBB3, ALK, and CD81 (<b>B</b>); STAT3 (<b>C</b>); STAT3 and CD81 (<b>D</b>); STAT3 and CyclinD1 (<b>E</b>); and STAT3, CyclinD1, and CD81 (<b>F</b>).</p>
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<p>K-means cluster analysis. The above graph was generated from the analysis of the four most characterizing targets: STAT3; STAT3 and CD81; STAT3 and CyclinD1; and STAT3, CyclinD1, and CD81.</p>
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<p>Cluster counts according to Gleason score for ERBB3 and CD81 markers. Boxplots show higher ERBB3 and CD81 levels in GS7(4 + 3) than those in GS7(3 + 4). These <span class="html-italic">p</span>-values were not adjusted due to small sample size. Symbol “•”represent values out of ±1.5 * IQR.</p>
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<p>Correlation plots of surface markers with PSA values. The presented plots highlight the correlation between the PSA values and the cluster counts for both the CD81, ALK surface protein (<b>A</b>) and CD81 alone (<b>B</b>), showing a weak-to-moderate but statistically significant correlation.</p>
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17 pages, 6956 KiB  
Article
Papain Suppresses Atopic Skin Inflammation through Anti-Inflammatory Activities Using In Vitro and In Vivo Models
by Hye-Min Kim, Yun-Mi Kang, Minho Lee and Hyo-Jin An
Antioxidants 2024, 13(8), 928; https://doi.org/10.3390/antiox13080928 - 30 Jul 2024
Viewed by 355
Abstract
Papain (PN) is a proteolytic enzyme derived from Carica Papaya L. While the pharmacological effects of PN have not been extensively studied compared to its enzymatic activity, PN also holds potential benefits beyond protein digestion. This study aimed to investigate the potential effects [...] Read more.
Papain (PN) is a proteolytic enzyme derived from Carica Papaya L. While the pharmacological effects of PN have not been extensively studied compared to its enzymatic activity, PN also holds potential benefits beyond protein digestion. This study aimed to investigate the potential effects of PN against skin inflammation in house dust mite Dermatophagoides farinae body (Dfb)-exposed NC/Nga atopic dermatitis (AD) mice and human HaCaT keratinocytes and their underlying mechanisms. The effects of PN on the skin were assessed via histological examination, measurements of transepidermal water loss (TEWL), quantitative reverse transcription-polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay. Our findings indicated that the oral intake of PN decreased the severity scores of lesions resembling AD, TEWL, and the levels of inflammatory cytokines and serum immunoglobulin E in Dfb-induced AD mice, along with a reduction in epidermal thickness and mast cell infiltration. Additionally, PN inhibited the activation of the mitogen-activated protein kinases (MAPKs) and the signal transducer and activator of transcription (STAT) pathways in Dfb-induced AD mice and HaCaT keratinocytes. Moreover, PN improved survival and reduced ROS production in H2O2-damaged HaCaT keratinocytes and enhanced the expression of antioxidant enzymes in Dfb-induced AD mice. Concludingly, the oral administration of PN suppressed inflammatory mediators and downregulated the MAPKs/STAT pathway, suggesting its potential role in AD pathogenesis. Full article
(This article belongs to the Section Health Outcomes of Antioxidants and Oxidative Stress)
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<p>Effects of papain (PN) on the clinical features of the skin of Dfb-induced atopic dermatitis (AD) mice. (<b>A</b>) Dermatitis scores were measured once a week for six weeks. The dermatitis score was defined as the sum of scores graded for each symptom. (<b>B</b>) Transepidermal water loss (TEWL) was measured by the end of six weeks. (<b>C</b>) Serum IgE level was measured using an ELISA kit. (<b>D</b>,<b>E</b>) Protein levels of TNF-α and IL-6 in dorsal skin tissue were determined using ELISA kits. (<b>F</b>,<b>G</b>) Total RNA was prepared from the dorsal skin tissue, and mRNA expression levels of TNF-α and IL-6 were determined via RT-qPCR. # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. Dfb-induced AD group.</p>
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<p>Effects of PN on histological alterations in the skin of Dfb-induced AD mice. (<b>A</b>) Hematoxylin and eosin (H&amp;E) staining of skin lesions in AD mice (scale bar = 200 μm). (<b>B</b>) Toluidine blue staining of skin lesions in AD mice (scale bar = 200 μm). (<b>C</b>) Determination of epidermal thickness. Through the H&amp;E-stained sections, epidermal thickness was measured under a microscope. (<b>D</b>) Through the toluidine blue-stained sections, mast cell infiltration was shown as the average count in five fields. This can be confirmed through the brown arrow. Data represent mean ± standard deviation (SD) from three independent experiments. ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; *** <span class="html-italic">p</span> &lt; 0.001 vs. Dfb-induced AD group.</p>
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<p>Effects of PN on AD-related cytokines and antioxidant makers in the skin of Dfb-induced AD mice. Total RNA was prepared from the dorsal skin tissue, and the mRNA expression levels of (<b>A</b>) IL-4, (<b>B</b>) IL-13, (<b>C</b>) TSLP, (<b>D</b>) IL-17A, (<b>E</b>) IL-17E, (<b>F</b>) IL-17F, (<b>G</b>) NRF-2, and (<b>H</b>) NQO1 were determined via RT-qPCR. Data are presented as the mean ± standard deviation from triplicate experiments. # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. Dfb-induced AD group.</p>
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<p>Effects of PN on the activation of MAPKs and STAT1 in the skin of Dfb-induced AD mice. Total proteins were prepared from the dorsal skin, and Western blotting was performed for the determination of (<b>A</b>) p-IκBα, p-STAT1, STAT1, (<b>B</b>) p-ERK, ERK, p-JNK, JNK, p-p38, and p38 using specific antibodies. β-actin was used as an internal control. Densitometric analysis was determined via Bio-Rad Quantity One<sup>®</sup> 5.x Software. Data are presented as the mean ± standard deviation from triplicate experiments. ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; *** <span class="html-italic">p</span> &lt; 0.001 vs. Dfb-induced AD group.</p>
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<p>Effects of PN on NF-κB activation in TNF-α/IFN-γ-stimulated HaCaT keratinocytes. (<b>A</b>) Viability of HaCaT keratinocytes was measured using MTT assay. *** <span class="html-italic">p</span> &lt; 0.001 vs. non-treated group. Total proteins were prepared, and Western blotting was performed for the determination of (<b>B</b>) HO-1, (<b>C</b>) p-IκBα, and NF-κB p65. β-actin, α-tubulin, and PARP were used as internal control. ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. TNF-α/IFN-γ-stimulated group.</p>
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<p>Effects of PN on the activation of MAPKs and STATs in TNF-α/IFN-γ-stimulated HaCaT keratinocytes. Total proteins were prepared, and Western blotting was performed for the determination of (<b>A</b>) p-STAT1, STAT1, p-STAT6, STAT6, (<b>B</b>) p-ERK, ERK, p-JNK, and JNK. β-actin was used as an internal control. Densitometric analysis was determined via Bio-Rad Quantity One<sup>®</sup> 5.x Software. Data are presented as the mean ± standard deviation from triplicate experiments. ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. TNF-α/IFN-γ-stimulated group.</p>
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<p>Effects of PN on oxidative damage in H<sub>2</sub>O<sub>2</sub>-induced HaCaT keratinocytes and Dfb-induced AD mice. (<b>A</b>) Viability of HaCaT keratinocytes after treatment with PN for 1 h and H<sub>2</sub>O<sub>2</sub> for 4 h, measured using Cell Counting Kit-8 assay. (<b>B</b>) HaCaT cells were labeled with a DCF-DA probe for fluorescent detection, and a representative ROS image was selected (10×). (<b>C</b>) Fluorescence microscopic image of ROS induced using H<sub>2</sub>O<sub>2</sub> after treatment with PN. Total proteins were prepared, and Western blotting was performed for the determination of (<b>D</b>) SOD1, SOD2, GPx-4, (<b>E</b>) NQO1, HO-1, and (<b>G</b>) Nrf2. β-actin was used as internal control. (<b>F</b>) Histological sections of dorsal skin tissue were immunohistochemically stained with HO-1 antibody, expressed as brown spots (Scale bar = 200 µm). Data are presented as the mean ± standard deviation from triplicate experiments. ### <span class="html-italic">p</span> &lt; 0.001 vs. control group; ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. TNF-α/IFN-γ-stimulated group or Dfb-induced AD group.</p>
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22 pages, 836 KiB  
Review
JAK Inhibitors in Rheumatoid Arthritis: Immunomodulatory Properties and Clinical Efficacy
by Kajetan Kiełbowski, Paulina Plewa, Aleksandra Wiktoria Bratborska, Estera Bakinowska and Andrzej Pawlik
Int. J. Mol. Sci. 2024, 25(15), 8327; https://doi.org/10.3390/ijms25158327 - 30 Jul 2024
Viewed by 507
Abstract
Rheumatoid arthritis (RA) is a highly prevalent autoimmune disorder. The pathogenesis of the disease is complex and involves various cellular populations, including fibroblast-like synoviocytes, macrophages, and T cells, among others. Identification of signalling pathways and molecules that actively contribute to the development of [...] Read more.
Rheumatoid arthritis (RA) is a highly prevalent autoimmune disorder. The pathogenesis of the disease is complex and involves various cellular populations, including fibroblast-like synoviocytes, macrophages, and T cells, among others. Identification of signalling pathways and molecules that actively contribute to the development of the disease is crucial to understanding the mechanisms involved in the chronic inflammatory environment present in affected joints. Recent studies have demonstrated that the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway regulates the behaviour of immune cells and contributes to the progression of RA. Several JAK inhibitors, such as tofacitinib, baricitinib, upadacitinib, and filgocitinib, have been developed, and their efficacy and safety in patients with RA have been comprehensively investigated in a number of clinical trials. Consequently, JAK inhibitors have been approved and registered as a treatment for patients with RA. In this review, we discuss the involvement of JAK/STAT signalling in the pathogenesis of RA and summarise the potential beneficial effects of JAK inhibitors in cells implicated in the pathogenesis of the disease. Moreover, we present the most important phase 3 clinical trials that evaluated the use of these agents in patients. Full article
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<p>The effects of tofacitinib, baricitinib, and peficitinib on various mechanisms involving fibroblast-like synoviocytes that take part in the pathogenesis of rheumatoid arthritis.</p>
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<p>An impact of JAK inhibitors on the behaviour of B cells.</p>
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20 pages, 8349 KiB  
Article
Single-Cell RNA-seq Analysis Reveals a Positive Correlation between Ferroptosis and Beta-Cell Dedifferentiation in Type 2 Diabetes
by Jiajing Ma, Xuhui Li, Xuesi Wan, Jinmei Deng, Yanglei Cheng, Boyuan Liu, Liehua Liu, Lijuan Xu, Haipeng Xiao and Yanbing Li
Biomedicines 2024, 12(8), 1687; https://doi.org/10.3390/biomedicines12081687 - 29 Jul 2024
Viewed by 404
Abstract
Insulin deficiency in patients with type 2 diabetes mellitus (T2D) is associated with beta-cell dysfunction, a condition increasingly recognized to involve processes such as dedifferentiation and apoptosis. Moreover, emerging research points to a potential role for ferroptosis in the pathogenesis of T2D. In [...] Read more.
Insulin deficiency in patients with type 2 diabetes mellitus (T2D) is associated with beta-cell dysfunction, a condition increasingly recognized to involve processes such as dedifferentiation and apoptosis. Moreover, emerging research points to a potential role for ferroptosis in the pathogenesis of T2D. In this study, we aimed to investigate the potential involvement of ferroptosis in the dedifferentiation of beta cells in T2D. We performed single-cell RNA sequencing analysis of six public datasets. Differential expression and gene set enrichment analyses were carried out to investigate the role of ferroptosis. Gene set variation and pseudo-time trajectory analyses were subsequently used to verify ferroptosis-related beta clusters. After cells were categorized according to their ferroptosis and dedifferentiation scores, we constructed transcriptional and competitive endogenous RNA networks, and validated the hub genes via machine learning and immunohistochemistry. We found that ferroptosis was enriched in T2D beta cells and that there was a positive correlation between ferroptosis and the process of dedifferentiation. Upon further analysis, we identified two beta clusters that presented pronounced features associated with ferroptosis and dedifferentiation. Several key transcription factors and 2 long noncoding RNAs (MALAT1 and MEG3) were identified. Finally, we confirmed that ferroptosis occurred in the pancreas of high-fat diet-fed mice and identified 4 proteins (NFE2L2, CHMP5, PTEN, and STAT3) that may participate in the effect of ferroptosis on dedifferentiation. This study helps to elucidate the interplay between ferroptosis and beta-cell health and opens new avenues for developing therapeutic strategies to treat diabetes. Full article
(This article belongs to the Section Endocrinology and Metabolism Research)
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<p>Integration, clustering, and cell proportion calculation of single-cell RNA sequencing (scRNA-seq) data. (<b>A</b>) Uniform Manifold Approximation and Projection (UMAP) plot showing elimination of batch effect. (<b>B</b>) Eighteen clusters visualized based on UMAP. (<b>C</b>) Cell populations identified by marker genes. (<b>D</b>) Comparison of cell proportions between type 2 diabetes mellitus (T2D)-affected islets and non-diabetic islets.</p>
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<p>The landscape of ferroptosis in T2D. (<b>A</b>) Volcano plot of differentially expressed genes (DEGs). (<b>B</b>) Venn diagram of DEGs and ferroptosis-related genes (FRGs) based on the FerrDb database. (<b>C</b>) Heatmap of the 24 ferroptosis-related DEGs. (<b>D</b>) Gene set enrichment analysis (GSEA) of the “WP_FERROPTOSIS” pathway. (<b>E</b>) Ferroptosis scores of T2D and non-diabetic samples.</p>
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<p>Dedifferentiation characteristics of beta cells. (<b>A</b>) Dot map of dedifferentiation-related genes. (<b>B</b>) Dedifferentiation scores of T2D and non-diabetic samples. (<b>C</b>) Dedifferentiation scores of “Ferro_high” and “Ferro_low” beta cells. (<b>D</b>) Proportion of 4 groups of beta cells between T2D and non-diabetic samples.</p>
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<p>Identification of ferroptosis-related clusters in beta cells. (<b>A</b>) UMAP plot showing 6 beta-cell clusters. (<b>B</b>) Comparison of beta-cell cluster proportions between T2D and non-diabetic tissues. (<b>C</b>) Expression of 5 markers of each cluster. (<b>D</b>) Ferroptosis scores of different beta-cell clusters.</p>
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<p>Gene set variation analysis (GSVA) of ferroptosis-related pathways in beta-cell clusters. (<b>A</b>) GSVA of oxidative-stress-related pathways. (<b>B</b>) GSVA of lipid-metabolism-related pathways. (<b>C</b>) GSVA of iron-related pathways.</p>
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<p>Cellular trajectory analysis in beta cells. (<b>A</b>) Pseudo-time trajectories of changes in FRGs. (<b>B</b>) Pseudo-time trajectories of changes in beta-identity-related genes.</p>
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<p>Construction of regulatory networks in beta cells. (<b>A</b>) DEGs of double-high cells compared with double-low cells. (<b>B</b>) Differentially activated transcription factors of double-high cells compared with double-low cells. (<b>C</b>) The transcriptional regulatory network (the orange circles represent TFs, and the green circles represent their target genes). (<b>D</b>) Differentially expressed long non-coding RNAs (DELs) of double-high cells compared with double-low cells. (<b>E</b>) The competitive endogenous RNA (ceRNA) network.</p>
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<p>Screening of key genes using machine learning methods. (<b>A</b>) Dispersion of Least Absolute Shrinkage and Selection Operator (LASSO) coefficients. Each curve represents the trajectory of each independent variable coefficient. (<b>B</b>) Tenfold cross-validation for LASSO model parameter selection tuning. (<b>C</b>) Random Forest (RF) algorithm plot illustrating the connection between error rate and tree count. The three lines in the figure represent, from bottom to top, the error rate of the first class, the overall error rate, and the error rate of the second class, respectively. (<b>D</b>) RF algorithm-based gene ranking according to relative importance. (<b>E</b>) Venn diagram demonstrating the key genes shared by the LASSO results, RF algorithm results, and DEGs of T2D. (<b>F</b>) Correlation analysis performed among key genes.</p>
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<p>Expression and receiver operator characteristic (ROC) curves of key genes in beta cells. (<b>A</b>) Box plots displaying the expression of 8 key genes in non-diabetic and T2D samples. (<b>B</b>) ROC curves of the 8 key genes in T2D. (**** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Ferroptosis markers in control and T2D mouse samples. (<b>A</b>) Body weight of control and high-fat diet(HFD) groups. (<b>B</b>) Fasting blood glucose was measured after a 6 h fast. (<b>C</b>) Glucose tolerance test (GTT) and AUC results of the control and HFD group (i.p. 2.0 g/kg glucose). (<b>D</b>) The serum iron level was measured using an iron assay kit. (<b>E</b>) The serum ferritin level was measured using a ferritin ELISA kit. (<b>F</b>) The iron level in pancreatic tissues was measured using an iron assay kit. (<b>G</b>) The malondialdehyde (MDA) level in pancreatic tissues was measured using an MDA assay kit. (<b>H</b>) The glutathione (GSH) level in pancreatic tissues was measured using a GSH assay kit. (n = 10 in each group; values are shown as means ± standard deviations. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Expression of hub genes in the islets of the control and HFD groups. (<b>A</b>) Immunohistochemical staining of the NFE2L2, CHMP5, PTEN, and STAT3 proteins in the islets of the control and HFD groups. (<b>B</b>) Quantitative analysis of immunohistochemical staining of the NFE2L2, CHMP5, PTEN, and STAT3 proteins. (n = 3 in each group; 4–6 islets per mouse were analyzed; values are shown as means ± standard deviations. *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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23 pages, 2156 KiB  
Review
IFNγ-Induced Bcl3, PD-L1 and IL-8 Signaling in Ovarian Cancer: Mechanisms and Clinical Significance
by Suprataptha U. Reddy, Fatema Zohra Sadia, Ales Vancura and Ivana Vancurova
Cancers 2024, 16(15), 2676; https://doi.org/10.3390/cancers16152676 - 27 Jul 2024
Viewed by 565
Abstract
IFNγ, a pleiotropic cytokine produced not only by activated lymphocytes but also in response to cancer immunotherapies, has both antitumor and tumor-promoting functions. In ovarian cancer (OC) cells, the tumor-promoting functions of IFNγ are mediated by IFNγ-induced expression of Bcl3, PD-L1 and IL-8/CXCL8, [...] Read more.
IFNγ, a pleiotropic cytokine produced not only by activated lymphocytes but also in response to cancer immunotherapies, has both antitumor and tumor-promoting functions. In ovarian cancer (OC) cells, the tumor-promoting functions of IFNγ are mediated by IFNγ-induced expression of Bcl3, PD-L1 and IL-8/CXCL8, which have long been known to have critical cellular functions as a proto-oncogene, an immune checkpoint ligand and a chemoattractant, respectively. However, overwhelming evidence has demonstrated that these three genes have tumor-promoting roles far beyond their originally identified functions. These tumor-promoting mechanisms include increased cancer cell proliferation, invasion, angiogenesis, metastasis, resistance to chemotherapy and immune escape. Recent studies have shown that IFNγ-induced Bcl3, PD-L1 and IL-8 expression is regulated by the same JAK1/STAT1 signaling pathway: IFNγ induces the expression of Bcl3, which then promotes the expression of PD-L1 and IL-8 in OC cells, resulting in their increased proliferation and migration. In this review, we summarize the recent findings on how IFNγ affects the tumor microenvironment and promotes tumor progression, with a special focus on ovarian cancer and on Bcl3, PD-L1 and IL-8/CXCL8 signaling. We also discuss promising novel combinatorial strategies in clinical trials targeting Bcl3, PD-L1 and IL-8 to increase the effectiveness of cancer immunotherapies. Full article
(This article belongs to the Special Issue IFN-Gamma Signaling in Cancer)
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<p>Schematic illustration of cellular sources and targets of IFNγ in OC TME. In addition to tumor-infiltrating lymphocytes (TILs), IFNγ can be produced by tumor-associated macrophages (TAMs) and monocytic cells (Monos) present in TME. The cellular targets of the produced IFNγ include OC cells and also TILs, TAMs, dendritic cells (DCs), myeloid-derived suppressor cells (MDSCs), as well as cancer-associated fibroblasts (CAFs). Created with BioRender.com with granted permission and license.</p>
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<p>Schematic illustration of the mechanisms regulating IFNγ-induced Bcl3, PD-L1 and IL-8 expression in OC cells. IFNγ binds to its receptors IFNGR1 and IFNGR2, resulting in the activation of JAK1 kinase, and increased expression of the transcription regulators IRF1, STAT1, p65 NFκB and Bcl3 in OC cells [<a href="#B47-cancers-16-02676" class="html-bibr">47</a>,<a href="#B49-cancers-16-02676" class="html-bibr">49</a>,<a href="#B50-cancers-16-02676" class="html-bibr">50</a>]. JAK1 phosphorylates STAT1 at Tyr-701, which is required for the nuclear translocation of STAT1. STAT1 is then phosphorylated at Ser-727, which promotes its recruitment to the Bcl3, PD-L1 and IL-8 promoters. In addition, IFNγ induces p300-mediated acetylation of p65 NFκB at K314/315, resulting in increased p65 transcriptional activity and recruitment to PD-L1 and IL-8 promoters [<a href="#B47-cancers-16-02676" class="html-bibr">47</a>,<a href="#B49-cancers-16-02676" class="html-bibr">49</a>]. IFNγ also induces acetylation of histones (Ac) at the Bcl3, PD-L1 and IL-8 promoters in OC cells, thus facilitating transcription factor recruitment and transcription [<a href="#B47-cancers-16-02676" class="html-bibr">47</a>,<a href="#B48-cancers-16-02676" class="html-bibr">48</a>,<a href="#B49-cancers-16-02676" class="html-bibr">49</a>,<a href="#B50-cancers-16-02676" class="html-bibr">50</a>,<a href="#B51-cancers-16-02676" class="html-bibr">51</a>]. Created with BioRender.com with granted permission and license.</p>
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<p>Bcl3 tumor-promoting functions in ovarian cancer. By inducing the expression of ceruloplasmin [<a href="#B96-cancers-16-02676" class="html-bibr">96</a>] and IL-8 [<a href="#B50-cancers-16-02676" class="html-bibr">50</a>,<a href="#B51-cancers-16-02676" class="html-bibr">51</a>], Bcl3 promotes proliferation, invasion and angiogenesis in OC cells. By inducing the expression of PD-L1 [<a href="#B47-cancers-16-02676" class="html-bibr">47</a>,<a href="#B49-cancers-16-02676" class="html-bibr">49</a>], Bcl3 also promotes immune escape in ovarian cancer. Created with BioRender.com.</p>
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<p>Mechanisms inducing IL-8 expression in ovarian cancer and the tumor promoting effects of IL-8 in OC cells.</p>
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<p>Bcl3, PD-L1 and IL-8 co-expression in ovarian cancer tissues. (<b>A</b>) Heatmap of Bcl3, PD-L1/CD274 and IL-8/CXCL8 mRNA co-expression in 379 OC samples in the GDC-TCGA database using the UCSC Xena platform. (<b>B</b>) Scatter plots showing associations between Bcl3 and PD-L1/CD274, between Bcl3 and IL-8/CXCL8 and between IL-8/CXCL8 and PD-L1/CD274 gene expression in GDC-TCGA ovarian cancer samples (<span class="html-italic">n</span> = 379) using the UCSC Xena browser.</p>
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<p>Model of how IFNγ induces Bcl3-dependent IL-8 and PD-L1 expression in OC cells. IFNγ induces first the expression of Bcl3 in OC cells, resulting in increased transcription of IL-8 and PD-L1. The increased expression of IL-8 and PD-L1 enhances OC cell proliferation, migration, invasion, epithelial-to-mesenchymal transition (EMT), metastasis, angiogenesis, cell stemness and immune escape.</p>
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37 pages, 1047 KiB  
Review
Balancing the Scales: The Dual Role of Interleukins in Bone Metastatic Microenvironments
by Ahmad Dawalibi, Amal Ahmed Alosaimi and Khalid S. Mohammad
Int. J. Mol. Sci. 2024, 25(15), 8163; https://doi.org/10.3390/ijms25158163 - 26 Jul 2024
Viewed by 449
Abstract
Bone metastases, a common and debilitating consequence of advanced cancers, involve a complex interplay between malignant cells and the bone microenvironment. Central to this interaction are interleukins (ILs), a group of cytokines with critical roles in immune modulation and inflammation. This review explores [...] Read more.
Bone metastases, a common and debilitating consequence of advanced cancers, involve a complex interplay between malignant cells and the bone microenvironment. Central to this interaction are interleukins (ILs), a group of cytokines with critical roles in immune modulation and inflammation. This review explores the dualistic nature of pro-inflammatory and anti-inflammatory interleukins in bone metastases, emphasizing their molecular mechanisms, pathological impacts, and therapeutic potential. Pro-inflammatory interleukins, such as IL-1, IL-6, and IL-8, have been identified as key drivers in promoting osteoclastogenesis, tumor proliferation, and angiogenesis. These cytokines create a favorable environment for cancer cell survival and bone degradation, contributing to the progression of metastatic lesions. Conversely, anti-inflammatory interleukins, including IL-4, IL-10, and IL-13, exhibit protective roles by modulating immune responses and inhibiting osteoclast activity. Understanding these opposing effects is crucial for developing targeted therapies aimed at disrupting the pathological processes in bone metastases. Key signaling pathways, including NF-κB, JAK/STAT, and MAPK, mediate the actions of these interleukins, influencing tumor cell survival, immune cell recruitment, and bone remodeling. Targeting these pathways presents promising therapeutic avenues. Current treatment strategies, such as the use of denosumab, tocilizumab, and emerging agents like bimekizumab and ANV419, highlight the potential of interleukin-targeted therapies in mitigating bone metastases. However, challenges such as therapeutic resistance, side effects, and long-term efficacy remain significant hurdles. This review also addresses the potential of interleukins as diagnostic and prognostic biomarkers, offering insights into patient stratification and personalized treatment approaches. Interleukins have multifaceted roles that depend on the context, including the environment, cell types, and cellular interactions. Despite substantial progress, gaps in research persist, particularly regarding the precise mechanisms by which interleukins influence the bone metastatic niche and their broader clinical implications. While not exhaustive, this overview underscores the critical roles of interleukins in bone metastases and highlights the need for continued research to fully elucidate their complex interactions and therapeutic potential. Addressing these gaps will be essential for advancing our understanding and treatment of bone metastases in cancer patients. Full article
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<p>Interleukin Regulation of Bone Remodeling. This figure illustrates the complex interactions between interleukins (ILs), immune cells, osteoblasts, and osteoclasts in the bone microenvironment, highlighting the role of these cytokines in the regulation of bone remodeling and formation. Osteoblasts, depicted in blue, produce RANKL (Receptor Activator of Nuclear factor Kappa-Β Ligand), shown as orange circles. RANKL binds to its receptor RANK, expressed on pre-osteoclasts (purple cells) and mature osteoclasts (multinucleated purple cells), promoting osteoclast differentiation and activation, leading to bone resorption. Immune Cells and interleukins: Immune cells (depicted in teal) secrete various interleukins that modulate osteoclastogenesis and osteoblastogenesis. Pro-osteoclastogenic Interleukins: IL-1, IL-6, and IL-17 (indicated by solid arrows) enhance the production of RANKL by osteoblasts and directly promote the differentiation of pre-osteoclasts into mature osteoclasts. Anti-osteoclastogenic Interleukins: IL-4, IL-10, and IL-35 (indicated by red inhibitory lines) inhibit osteoclast differentiation and activity by downregulating RANKL production or directly suppressing pre-osteoclast maturation. It can also simulate osteoblast proliferation.</p>
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<p>Interleukin Inhibitors and Denosumab in the Regulation of Osteoclastogenesis in Bone Metastases. This schematic illustrates the role of interleukins (IL) in the regulation of osteoclastogenesis and the therapeutic interventions targeting these pathways to manage bone metastases. Tumor cells in the bone microenvironment secrete IL-8 and IL-11, which stimulate pre-osteoclasts, promoting their differentiation into mature osteoclasts. IL-1 and IL-6 secreted by the tumor cells stimulate osteoblasts to secrete RANKL that bind to the RANK receptors on osteoclast to stimulate their activity. Osteoclasts contribute to bone resorption, which facilitates tumor growth and metastasis. The figure highlights the therapeutic targets and inhibitors involved in this process: canakinumab—An IL-1 inhibitor that blocks the signaling of IL-1, thereby reducing its stimulatory effect on osteoclastogenesis. Siltuximab—An IL-6 inhibitor that blocks IL-6, another cytokine involved in the promotion of osteoclast differentiation and activation. Both inhibitors work by preventing the activation of osteoclasts, thereby mitigating bone resorption and tumor progression. Additionally, the figure depicts the role of Denosumab, a monoclonal antibody that inhibits RANKL (Receptor Activator of Nuclear Factor Kappa-Β Ligand). RANKL is essential for the formation, function, and survival of osteoclasts. By binding to RANKL, Denosumab prevents it from interacting with its receptor RANK on pre-osteoclasts and osteoclasts, thereby inhibiting osteoclastogenesis and reducing bone resorption. This integrative approach of using interleukin inhibitors along with Denosumab offers a promising therapeutic strategy to manage bone metastases by targeting both the cytokine signaling pathways and the direct inhibition of osteoclast formation and activity.</p>
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23 pages, 3729 KiB  
Article
The Engineered Drug 3′UTRMYC1-18 Degrades the c-MYC-STAT5A/B-PD-L1 Complex In Vivo to Inhibit Metastatic Triple-Negative Breast Cancer
by Chidiebere U. Awah, Joo Sun Mun, Aloka Paragodaarachchi, Baris Boylu, Chika Ochu, Hiroshi Matsui and Olorunseun O. Ogunwobi
Cancers 2024, 16(15), 2663; https://doi.org/10.3390/cancers16152663 - 26 Jul 2024
Viewed by 487
Abstract
c-MYC is overexpressed in 70% of human cancers, including triple-negative breast cancer (TNBC), yet there is no clinically approved drug that directly targets it. Here, we engineered the mRNA-stabilizing poly U sequences within the 3′UTR of c-MYC to specifically destabilize and promote the [...] Read more.
c-MYC is overexpressed in 70% of human cancers, including triple-negative breast cancer (TNBC), yet there is no clinically approved drug that directly targets it. Here, we engineered the mRNA-stabilizing poly U sequences within the 3′UTR of c-MYC to specifically destabilize and promote the degradation of c-MYC transcripts. Interestingly, the engineered derivative outcompetes the endogenous overexpressed c-MYC mRNA, leading to reduced c-MYC mRNA and protein levels. The iron oxide nanocages (IO-nanocages) complexed with MYC-destabilizing constructs inhibited primary and metastatic tumors in mice bearing TNBC and significantly prolonged survival by degrading the c-MYC-STAT5A/B-PD-L1 complexes that drive c-MYC-positive TNBC. Taken together, we have described a novel therapy for c-MYC-driven TNBC and uncovered c-MYC-STAT5A/B-PD-L1 interaction as the target. Full article
(This article belongs to the Special Issue Triple Negative Breast Cancer Therapy Resistance and Metastasis)
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<p>In vivo, IO-nanocage-delivered destabilized 3′UTR of c-MYC degrades c-MYC and STAT5A/5B and shows significant survival outcome and the inhibition of primary and metastatic tumors in c-MYC-driven TNBC. (<b>A</b>) Bar chart shows the tumor volume measurement of animals bearing tumors: WT, treated with nanocage only, vector + IO-nanocage, 3′UTRMYC2-3 + IO-nanocage, 3′UTRMYC1-18 + IO-nanocage, and 3′UTRMYC1-14 + IO-nanocage, (N = 5). Paired two-tailed <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> = 0.0004, WT vs. 3′UTRMYC1-18 + IO, * <span class="html-italic">p</span> = 0.0021, 0.0015WT vs. 3′UTRMYC2-3 + IO, 3′UTRMYC1-14 + IO). (<b>B</b>) H&amp;E staining of primary tumor images from the different control groups and the treatment groups (N = 5). (<b>C</b>) H&amp;E staining of primary tumor images from the different control groups and the treatment groups (N = 5). (<b>D</b>) Bar chart of quantification of tumor lysis from the different control groups and treatment groups (** <span class="html-italic">p</span> = 0.0032, paired two-tailed <span class="html-italic">t</span>-test, WT vs. 3′UTRMYC1-18 + IO). (<b>E</b>) Immunofluorescence images of primary tumors from the controls and treatment groups stained with c-MYC (red), DAPI nuclei (blue) (N = 5). (<b>F</b>) Immunofluorescence images of primary tumors from the controls and the treatment groups stained with STAT5A/5B (red), DAPI nuclei (blue) (N = 5). (<b>G</b>) Kaplan–Meier survival plot of animal experiment. * <span class="html-italic">p</span> &lt; 0.0001 (WT, IO-nanocage only vs. 3′UTRMYC2-3, 1-18, and 1-14) (N = 5). (<b>H</b>) Quantification of STAT5A/5B in primary tumors from the controls and the treated groups. * <span class="html-italic">p</span> &lt; 0.03 (WT vs. IO-nanocage only, 3′UTRMYC2-3). * <span class="html-italic">p</span> &lt; 0.01 (WT vs. 3′UTRMYC1-18 and 1-14) (N = 5). (<b>I</b>) Quantification of c-MYC in primary tumors from the controls and the treated groups. * <span class="html-italic">p</span> &lt; 0.03 (WT vs. 3′UTRMYC2-3, 1-18, and 1-14) (N = 5).</p>
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<p>The engineered destabilized 3′UTR of c-MYC degrades MYC transcript and protein in TNBC. (<b>A</b>) Western blot shows c-MYC and GAPDH protein expression in MDA MB231 WT (wildtype or no intervention) cells, and cells treated with vector, IO-nanocage only, and 3′UTRMYC1-14, 3′UTRMYC1-18, and 3′UTRMYC2-3 via IO-nanocage delivery. N = 4. (<b>B</b>) Bar chart shows c-MYC mRNA expression normalized against GAPDH in MDA MB231 WT cells, and cells treated with vector, IO-nanocage only, and 3′UTRMYC1-14, 3′UTRMYC1-18, and 3′UTRMYC2-3 cells treated via IO-nanocage delivery. Two-tailed <span class="html-italic">t</span>-test, **** <span class="html-italic">p</span> &lt; 0.00001 (WT, vector, IO-nanocage versus 3′UTRMYC1-14, 1-18, and 2-3). N = 5. (<b>C</b>) Bar chart shows c-MYC mRNA expression normalized against GAPDH in MDA MB231 WT, vector, 3′UTRMYC2-3, 1-18, and 1-14, and BT474 cells expressing 3′UTRMYC1-14. Two-tailed <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.001 (WT vs. vector, 3′UTRMYC2-3 and1-14), **** <span class="html-italic">p</span> &lt; 0.00001 (WT vs. 3′UTRMYC1-18), *** <span class="html-italic">p</span> &lt; 0.0001 (WT vs. BT474 3′UTRMYC1-14). N = 5. (<b>D</b>) High-resolution transmission electron microscopy (HR-TEM) image displays IO-nanocages with consistent cuboidal structure and well-defined lattice structure with little to no aggregation. (<b>E</b>) Transmission electron microscopy (TEM) image of the loaded IO-nanocages (blue arrow) in displays of DNA plasmids wrapped around the surface of IO-nanocages (red arrow), 20× magnification. (<b>F</b>) Diagrammatic illustration of DNA constructs wrapped around the IO-nanocages (1:3). (<b>G</b>) Bar chart shows viability of cells treated with constructs. ** <span class="html-italic">p</span> &lt; 0.001 (WT vs. vector), *** <span class="html-italic">p</span> &lt; 0.0001 (WT vs. 3′UTRMYC2-3, 1-18, and 1-14). N = 6. (<b>H</b>) Heat map shows gene expression pattern changes of MYC interacting partners compared to controls and the 3′UTRMYC1-18-treated cells. (<b>I</b>) Western blot shows STAT5A/5B and GAPDH protein expression in MDA MB231 WT cells and cells treated with vector, nanocage only, 3′UTRMYC1-14, 3′UTRMYC1-18, and 3′UTRMYC2-3. N = 3. (<b>J</b>) Bar chart shows the quantification of STAT5A/5B protein expression normalized against GAPDH in the treated and control groups (*** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, two-tailed <span class="html-italic">t</span>-test). Original western blots are presented in <a href="#app1-cancers-16-02663" class="html-app">File S1</a>.</p>
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<p>3′UTRMYC1-18 degrades c-MYC in childhood cancers and prostate cancer and impaired their viability. (<b>A</b>) Image shows c-MYC expression in DAOY treated with the 3′UTRMYC1-18 and the controls. (<b>B</b>) Bar chart shows the quantification of c-MYC protein normalized against GAPDH in the treated and control cells (**** <span class="html-italic">p</span> &lt; 0.0001, two-tailed <span class="html-italic">t</span>-test). (<b>C</b>) Bar chart shows the quantification of the viability in the 3′UTRMYC1-18-treated cells and the control (*** <span class="html-italic">p</span> = 0.001, two-tailed <span class="html-italic">t</span>-test). (<b>D</b>) Heat map shows the expression of c-MYC and its interactome in the 3′UTRMYC1-18-treated cells and controls. (<b>E</b>–<b>G</b>) Images show c-MYC stain (red) and DAPI (blue) of the 3′UTRMYC1-18-treated cells and controls, quantified in (<b>H</b>). (<b>I</b>) Bar chart shows the quantification of the nuclear c-MYC stain in the 3′UTRMYC1-18-treated cells and controls (**** <span class="html-italic">p</span> &lt; 0.000121, two-tailed <span class="html-italic">t</span>-test). (<b>J</b>) Western blot image of the c-MYC and GAPDH protein expression in the C4-2B cells treated with the 3′UTRMYC1-18 and the controls. (<b>K</b>) Bar chart shows the quantification of c-MYC expression normalized against the GAPDH in the treated cells and controls (*** <span class="html-italic">p</span> &lt; 0.001, two-tailed <span class="html-italic">t</span>-test). (<b>L</b>) Bar chart shows the quantification of the viability of the 3′UTRMYC1-18-treated cells and the controls (**** <span class="html-italic">p</span> &lt; 0.0.0001, two-tailed <span class="html-italic">t</span>-test). Original western blots are presented in <a href="#app1-cancers-16-02663" class="html-app">File S1</a>.</p>
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<p>Loss of c-MYC leads to loss of STAT5A/5B and PD-L1 in vivo in responsive tumors. (<b>A</b>) Immunohistochemistry staining of PD-L1 in primary tumors from the control and treated groups; positive control is human tonsil; PD-L1 (brown); DAPI nuclei (blue) (N = 5). (<b>B</b>) Quantification of PD-L1 in primary tumors from the control and treated groups, *** <span class="html-italic">p</span> &lt; 0.0005 (WT, IO-nanocage + vector vs. 3′UTRMYC2-3, 1-18, and 1-14). (<b>C</b>) Head-to-head quantification of PD-L1 and c-MYC in the same primary tumors from the control and treated groups, *** <span class="html-italic">p</span> &lt; 0.005 (WT, IO-nanocage +vector vs. 3′UTRMYC2-3, 1-18, and 1-14). (<b>D</b>) Graph shows the correlation analysis between PD-L1 and c-MYC in the primary tumors, R<sup>2</sup> = 0.9356, <span class="html-italic">p</span> = 0.0071. (<b>E</b>) Head-to-head quantification of PD-L1 and STAT5A/5B in primary tumors from the control and treated groups, *** <span class="html-italic">p</span> &lt; 0.0005 (WT, nanocage +vector vs. 3′UTRMYC2-3, 1-18, and 1-14). (<b>F</b>) Graph shows the correlation analysis between PD-L1 and STAT5A/5B in the primary tumors, R<sup>2</sup> = 0.97, <span class="html-italic">p</span> = 0.0061. (<b>G</b>) Table shows the multiple correlation analysis between c-MYC, STAT5A/5B, and PD-L1 in the primary tumors from the control and treated groups.</p>
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12 pages, 1839 KiB  
Article
Dynamic Changes in Histone Modifications Are Associated with Differential Chromatin Interactions
by Yumin Nie and Mengjie Wang
Genes 2024, 15(8), 988; https://doi.org/10.3390/genes15080988 - 26 Jul 2024
Viewed by 400
Abstract
Eukaryotic genomes are organized into chromatin domains through long-range chromatin interactions which are mediated by the binding of architectural proteins, such as CTCF and cohesin, and histone modifications. Based on the published Hi-C and ChIP-seq datasets in human monocyte-derived macrophages, we identified 206 [...] Read more.
Eukaryotic genomes are organized into chromatin domains through long-range chromatin interactions which are mediated by the binding of architectural proteins, such as CTCF and cohesin, and histone modifications. Based on the published Hi-C and ChIP-seq datasets in human monocyte-derived macrophages, we identified 206 and 127 differential chromatin interactions (DCIs) that were not located within transcription readthrough regions in influenza A virus- and interferon β-treated cells, respectively, and found that the binding positions of CTCF and RAD21 within more than half of the DCI sites did not change. However, five histone modifications, H3K4me3, H3K27ac, H3K36me3, H3K9me3, and H3K27me3, showed significantly more dramatic changes than CTCF and RAD21 within the DCI sites. For H3K4me3, H3K27ac, H3K36me3, and H3K27me3, significantly more dramatic changes were observed outside than within the DCI sites. We further applied a motif scanning approach to discover proteins that might correlate with changes in histone modifications and chromatin interactions and found that PRDM9, ZNF384, and STAT2 frequently bound to DNA sequences corresponding to 1 kb genomic intervals with gains or losses of a histone modification within the DCI sites. This study explores the dynamic regulation of chromatin interactions and extends the current knowledge of the relationship between histone modifications and chromatin interactions. Full article
(This article belongs to the Section Epigenomics)
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<p>Hi-C contact maps of mock- and IAV-treated MDMs and two DCIs on chromosome 11. The blue and green square represented a strengthened and weakened DCI, respectively.</p>
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<p>Genomic distances between DCI sites and patterns of CTCF and RAD21 binding within DCI sites after IAV infection. (<b>A</b>) Proportions of DCI sites with different distances after IAV and IFN-β treatment of MDMs. (<b>B</b>) Gains and losses of CTCF and RAD21 binding within DCI sites after IAV infection. (<b>C</b>) Changes in the patterns of CTCF and RAD21 binding within DCI sites after IAV infection. For two genomic regions where a DCI occurred, 0, 1, and 2 represent binding peaks of CTCF or RAD21 overlapped with neither, either, and both of the genomic regions, respectively.</p>
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<p>Changes in the binding of CTCF and RAD21 and histone modifications within DCI sites after IAV infection. (<b>A</b>) Changes within all DCI sites. (<b>B</b>) Changes within weakened and strengthened loops. (<b>C</b>,<b>D</b>) Gains and losses of CTCF and RAD21 binding and histone modifications within weakened and strengthened loops. Here, 0 and 1 represent a transcription factor or histone modification being absent or present within an interval, respectively. *, **, and *** represent a <span class="html-italic">p</span>-value of less than 0.05, 0.005, and 0.001, respectively.</p>
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<p>Changes in the binding of CTCF and RAD21 and histone modifications around DCI sites after IAV infection (<b>A</b>) and IFN-β treatment (<b>B</b>). * <span class="html-italic">p</span>-value of less than 0.05 produced by a Wilcoxon rank-sum test.</p>
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<p>Transcription factors most frequently bound within four types of intervals within DCI sites after IAV infection. Nodes represent transcription factors and four types of intervals. For each type of interval, W and S represent weakened and strengthened loops, respectively, while 0→1 and 1→0 represent gain and loss of a histone modification, respectively. The width of the lines between transcription factors and types of intervals is proportional to the occurrence number of transcription factors within the intervals.</p>
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<p>Colocalization of CTCF, RAD21, and five histone modifications in mock-, IAV-, and IFN-β-treated MDMs. The lower and upper triangular heat maps show pairwise similarities in mock and IAV (or IFN-β) conditions, respectively.</p>
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26 pages, 9328 KiB  
Article
N-Myc and STAT Interactor is an Endometriosis Suppressor
by Yuri Park, Xiaoming Guan and Sang Jun Han
Int. J. Mol. Sci. 2024, 25(15), 8145; https://doi.org/10.3390/ijms25158145 - 26 Jul 2024
Viewed by 286
Abstract
In patients with endometriosis, refluxed endometrial fragments evade host immunosurveillance, developing into endometriotic lesions. However, the mechanisms underlying this evasion have not been fully elucidated. N-Myc and STAT Interactor (NMI) have been identified as key players in host immunosurveillance, including interferon (IFN)-induced cell [...] Read more.
In patients with endometriosis, refluxed endometrial fragments evade host immunosurveillance, developing into endometriotic lesions. However, the mechanisms underlying this evasion have not been fully elucidated. N-Myc and STAT Interactor (NMI) have been identified as key players in host immunosurveillance, including interferon (IFN)-induced cell death signaling pathways. NMI levels are markedly reduced in the stromal cells of human endometriotic lesions due to modulation by the Estrogen Receptor beta/Histone Deacetylase 8 axis. Knocking down NMI in immortalized human endometrial stromal cells (IHESCs) led to elevated RNA levels of genes involved in cell-to-cell adhesion and extracellular matrix signaling following IFNA treatment. Furthermore, NMI knockdown inhibited IFN-regulated canonical signaling pathways, such as apoptosis mediated by Interferon Stimulated Gene Factor 3 and necroptosis upon IFNA treatment. In contrast, NMI knockdown with IFNA treatment activated non-canonical IFN-regulated signaling pathways that promote proliferation, including β-Catenin and AKT signaling. Moreover, NMI knockdown in IHESCs stimulated ectopic lesions’ growth in mouse endometriosis models. Therefore, NMI is a novel endometriosis suppressor, enhancing apoptosis and inhibiting proliferation and cell adhesion of endometrial cells upon IFN exposure. Full article
(This article belongs to the Special Issue Endometriosis: From Molecular Basis to Therapy)
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<p>Reduced levels of NMI in endometriotic Lesions. (<b>A</b>) Protein levels of NMI, ERβ, and β-Actin in primary human endometrial stromal cells isolated from patients with endometrioma endometriosis (endometriosis) compared to those from normal endometrium (normal), as determined by Western blot analysis. (<b>B</b>) The protein ratio of NMI to β-Actin is shown in panel (<b>A</b>). (<b>C</b>) The protein ratio of ERβ to β-Actin is shown in panel (<b>A</b>). (<b>D</b>) Comparative analysis of NMI mRNA levels in human ectopic lesions versus normal endometrium, based on GSE25628 data. (<b>E</b>) Immunohistochemical analysis of NMI expression in normal human endometrium and human endometriotic lesions. Scale bar represents 100 µm. (<b>F</b>) Quantification of NMI protein levels in the epithelial compartment of both the endometrium and endometriotic lesions, as shown in panel (<b>E</b>). (<b>G</b>) Quantification of NMI protein levels in the stromal compartment of both the endometrium and endometriotic lesions, as illustrated in panel (<b>E</b>). NS: Non-Specific. Significance levels are indicated as follows: * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001, NS: Non-Specific determined by Student’s <span class="html-italic">t</span>-test.</p>
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<p>Modulation of NMI expression via the ERβ/HDAC8 axis in endometrial stromal cells. (<b>A</b>) Comparative analysis of NMI mRNA levels in ERβ-overexpressing eutopic endometrium and ectopic lesions versus control samples, illustrating the relative fold changes in NMI RNA. (<b>B</b>) Evaluation of NMI protein levels in ERβ-overexpressing immortalized human endometrial epithelial cells (IHEECs) and stromal cells (IHESCs) via Western blot. β-Tubulin served as a normalization control for NMI expression levels, while MYC-tagged ERβ levels were assessed using a MYC antibody. (<b>C</b>) ChIP-seq analysis in ERβ-overexpressing mouse ectopic lesions highlighted ERβ’s binding to the NMI locus promoter region as marked in the red dotted box, indicating direct regulatory actions. (<b>D</b>) HDAC8 protein expression in normal endometrium and ovarian endometriotic lesions was analyzed through immunohistochemistry using an HDAC8-specific antibody. Scale bar represents 100 µm. (<b>E</b>,<b>F</b>) Quantitative assessment of HDAC8 protein concentrations within the epithelial (<b>E</b>) and stromal (<b>F</b>) compartments of normal endometrium and ovarian endometriotic lesions, as shown in panel (<b>D</b>). (<b>G</b>) Quantitative RT-PCR was employed to measure HDAC8 mRNA levels in IHESCs treated with either control siRNA (siControl) or HDAC8-targeting siRNA (siHDAC8). (<b>H</b>) NMI mRNA levels in IHESCs subjected to siControl or siHDAC8 treatments were quantified using quantitative RT-PCR. (<b>I</b>) NCOR2 protein expression in normal endometrium and ovarian endometriotic lesions was determined via immunohistochemistry, utilizing an NCOR2 antibody. Scale bar represents 100µm. (<b>J</b>,<b>K</b>) Quantitative analysis of NCOR2 protein levels in the epithelial (<b>J</b>) and stromal (<b>K</b>) compartments of normal endometrium and ovarian endometriotic lesions, corresponding to panel (<b>I</b>). (<b>L</b>) Quantitative RT-PCR was used to ascertain NCOR2 mRNA levels in IHESCs treated with siControl or NCOR2-specific siRNA (siNCOR2). (<b>M</b>) Assessment of NMI mRNA levels in IHESCs following treatment with siControl or siNCOR2, determined by quantitative RT-PCR. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001, NS: Non-Specific determined by Student’s <span class="html-italic">t</span>-test.</p>
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<p>Suppression of IFNα-induced apoptosis in human endometrial stromal cells by NMI knockdown (KD). (<b>A</b>) Assessment of NMI protein levels in IHESCs treated with non-targeting shRNA (NT shRNA) and NMI-specific shRNA, analyzed via Western blot using an NMI antibody. β-Tubulin was employed as a normalization standard for NMI protein expression. (<b>B</b>) Evaluation of cell proliferation in control IHESCs and NMI KD-IHESCs following treatment with 0, 500, or 1000 units/mL of IFNA for 24 h, indicating the impact of NMI KD on cell growth under IFNA exposure. (<b>C</b>) Analysis of apoptosis signaling pathways in control KD (wild-type, WT) and NMI KD IHESCs post-treatment with 0, 500, or 1000 units/mL of IFNA for 24 h. Protein levels of caspase-3 (CASP3), cleaved CASP3, caspase-8 (CASP8), and cleaved CASP8 were quantified via Western blot. β-Tubulin levels served as a normalization control for the apoptosis-associated proteins. (<b>D</b>) Representative images of TUNEL assay in WT and NMI KD IHESCs post-treatment with 0 or 500 units/mL of IFNA for 24 h. TUNEL-positive cells and nuclei were stained in red and blue, respectively. The scale bar represents 100 µm. (<b>E</b>) Quantitative analysis of TUNEL positivity from (<b>D</b>). * <span class="html-italic">p &lt;</span> 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 determined by one-side ANOVA with a post-hoc Tukey test.</p>
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<p>Enhancement of extracellular matrix and cell adhesion signaling in human endometrial stromal cells by NMI KD. (<b>A</b>) Total read counts of each library. (<b>B</b>) Boxplot representing transformed expression levels. (<b>C</b>) Hierarchical clustering and (<b>D</b>) PCA analysis depicting substantial differences in thousands of genes induced by NMI knockdown in IHESCs upon IFNA treatment. (<b>E</b>) Relative fold change in NMI RNA levels in NMI KD IHESCs as compared to control KD IHESCs upon vehicle (NMI-KD_V) and IFNA (NMI-KD_INFA) treatment. (<b>F</b>) Volcano plot to define the differential gene expression profile between NMI KD and control KD (WT) IHESCs under vehicle treatment. Identification of up- or down-regulated genes with &gt;1 (−log10[FDR]) and &gt;2 (log2[Fold]) changes in NMI KD IHESCs compared to WT control IHESCs under vehicle treatment. (<b>G</b>) Gene ontology analysis heightened the cellular pathways linked to the up-and down-regulated genes in NMI KD IHESCs as compared with control KD IHESCs in Panel (<b>F</b>). The cellular pathways related to extracellular matrix and cell-to-cell adhesion are highlighted with red circles. (<b>H</b>) Volcano plot showing differential gene expression profile between NMI KD and control KD (WT) control IHESCs under IFNA treatment (500 units/mL) for 24 h. Identification of up- or down-regulated genes with &gt;1 (−log10[FDR]) and &gt;2 (log2[Fold]) changes in NMI KD IHESCs compared to WT control IHESCs under IFNA treatment. (<b>I</b>) Gene ontology analysis heightened the cellular pathways linked to the up-and down-regulated genes in NMI KD IHESCs as compared with control KD IHESCs upon IFNA treatment in Panel (<b>H</b>). The cellular pathways related to extracellular matrix and cell-to-cell adhesion are highlighted with red circles.</p>
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<p>Upregulation of genes involved in cell adhesion and extracellular matrix signaling in NMI KD IHESCs compared to control KD IHESCs. (<b>A</b>) mRNA fold changes in cell adhesion-related genes (MPZL2, ITGB3, JUP, and SORBS1) in control KD (WT) versus NMI KD IHESCs with vehicle treatment and (<b>B</b>) IFNA treatment at 500 units/mL for 24 h. (<b>C</b>) mRNA fold changes in genes associated with extracellular matrix signaling (MFAP5, ADAM19, VCAN, and HAPLN3) in WT versus NMI KD IHESCs with vehicle treatment and (<b>D</b>) IFNA treatment at 500 units/mL for 24 h. Significance levels are indicated as follows: * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001 determined by Student’s <span class="html-italic">t</span>-test.</p>
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<p>Suppression of ISGF3-mediated apoptosis and necroptosis in human endometrial stromal cells by NMI KD upon IFNA treatment. (<b>A</b>) Protein levels of ISGF3 complex components, IRF9, STAT1, p-STAT1(Tyr701), STAT2, and p-STAT2 (Tyr689) in control KD versus NMI KD IHESCs upon IFNA treatment determined by Western blotting. (<b>B</b>) RNA levels of IRF9, STAT1, and STAT3 normalized by 18sRNA in control KD versus NMI KD IHESCs upon IFNA treatment. (<b>C</b>) The ratio of p-STAT1/STAT1 and (<b>D</b>) the ratio of p-STAT2/STAT2 in control KD versus NMI KD IHESCs upon IFNA treatment determined by quantification of panel (<b>A</b>). (<b>E</b>) The relative fold change in RNA levels of genes involved in apoptosis in NMI KD IHESCs compared to control KD IHESCs upon IFNA treatment. (<b>F</b>) Protein levels of necroptosis components. RIP1, p-RIP1(Ser166), RIP3, p-RIP3(Ser227), MLKL, and p-MLKL(Ser358) in control KD (WT) versus NMI KD IHESCs following IFNA treatment. RNA levels of (<b>G</b>) RIP1, (<b>H</b>) IRIP3, and (<b>I</b>) MLKL normalized by 18sRNA in control KD versus NMI KD IHESCs upon IFNA treatment. The ratio of (<b>J</b>) p-RIP1/RIP1, (<b>K</b>) p-RIP2/RIP2, and (<b>L</b>) p-MLKL/MLKL in control KD versus NMI KD IHESCs upon IFNA treatment was determined by quantification of panel (<b>F</b>). Significance levels are indicated as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 determined by one-way AVONA with post-hoc Tukey test.</p>
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<p>Increasing IFNA non-canonical pathways in endometrial stromal cells by NMI KD. (<b>A</b>) Protein levels of β-Catenin, GSK3β, and p-GSK3β (Ser9) in control KD (WT) versus NMI KD IHESCs upon IFNA treatment. Protein levels of b-actin were determined to normalize the β-Catenin/GSK3β axis. RNA levels of (<b>B</b>) β-Catenin and (<b>C</b>) GSK3β normalized by 18sRNA were determined in control KD (WT) and NMI KD IHESCS upon IFNA treatment. Relative fold change in (<b>D</b>) the ratio of β-Catenin/b-Actin and (<b>E</b>) the ratio of p-GSK3β/GSK3β in control KD (WT) and NMI KD IHESCS upon IFNA treatment were calculated based on panel A. (<b>F</b>) The relative fold change in RNA levels of β-Catenin target genes in NMI KD IHESCs compared to control KD IHESCs. (<b>G</b>) Protein levels of PI3K, p-PI3K (Tyr 85/Tyr 55), AKT, and p-AKT (Ser473) in control KD (WT) and NMI KD IHESCs upon IFNA treatment. Protein levels of b-actin were determined to normalize the PI3K/p-PI3K and the AKT/p-AKT axis. (<b>H</b>) Relative fold change in the ratio of p-PI3K(Tyr85/55)/PI3K in control KD (WT) and NMI KD IHESCs upon IFNA treatment was quantified based on panel G. (<b>I</b>) Relative fold change in the ratio of p-AKT(Ser473)/AKT in control KD (WT) and NMI KD IHESCS upon IFNA treatment were calculated based on panel G. (<b>J</b>) RNA levels of PI3K normalized by 18sRNA were determined in control KD (WT) and NMI KD IHESCS upon IFNA treatment. (<b>K</b>) RNA levels of AKT normalized by 18sRNA were determined in control KD (WT) and NMI KD IHESCS upon IFNA treatment. RF: Relative fold change, NS: Non-Specific. Significance levels are indicated as follows: NS, Non-Specific determined by one-side ANOVA with a post-hoc Tukey test.</p>
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<p>Stimulation of growth of endometriotic lesions in mice by NMI KD endometrial stromal cells. (<b>A</b>) Bioluminescence images of endometriotic lesions generated by the mixture of luciferase-labeled immortalized human epithelial cells (LIHEEC) plus luciferase-labeled immortalized human stromal cells (LIHESC) named “Epi+Str” and a mixture of LIHEECS plus NMI KD LIHESCs named Epi+NMI KD-Str in SCID mice at 0 and 10 days post-endometriosis induction. (<b>B</b>) Quantification of bioluminescence signals depicted in panel (<b>A</b>). (<b>C</b>) Quantification of the number of ectopic lesions corresponding to the bioluminescence signals from panel A. Significance levels are indicated as follows: * <span class="html-italic">p</span> &lt; 0.05 determined by Students’ <span class="html-italic">t</span>-test.</p>
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<p>Proposed model of ERβ/HDAC8/NMI axis in endometriosis progression. ERβ/HDAC8 suppresses NMI expression in endometriosis, which subsequently modulates canonical and non-canonical pathways of IFNA.</p>
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