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15 pages, 2313 KiB  
Article
Effects of Pterostilbene on the Cell Division Cycle of a Neuroblastoma Cell Line
by Francesca Bruno, Flores Naselli, Desiree Brancato, Sara Volpes, Paola Sofia Cardinale, Salvatore Saccone, Concetta Federico and Fabio Caradonna
Nutrients 2024, 16(23), 4152; https://doi.org/10.3390/nu16234152 (registering DOI) - 29 Nov 2024
Viewed by 229
Abstract
Background. The “Cell Cycle Hypothesis” suggests that the abnormal re-entry of neurons into the cell division cycle leads to neurodegeneration, a mechanism supported by in vitro studies on neuronal-like cells treated with the hyperphosphorylating agent forskolin. Pterostilbene, a bioavailable compound found in foods [...] Read more.
Background. The “Cell Cycle Hypothesis” suggests that the abnormal re-entry of neurons into the cell division cycle leads to neurodegeneration, a mechanism supported by in vitro studies on neuronal-like cells treated with the hyperphosphorylating agent forskolin. Pterostilbene, a bioavailable compound found in foods such as blueberries and grapes, may exert neuroprotective effects and could serve as a potential adjunct therapy for neurodegenerative diseases. Methods. In this study, we investigated the effects of pterostilbene on neuronal-like cells derived from the human neuroblastoma SK-N-BE cell line, where cell cycle reactivation was induced by forskolin treatment. We analyzed molecular endpoints associated with differentiated versus replicative cell states, specifically the following: (a) the expression of cyclin CCND1, (b) the Ki67 cell proliferation marker, (c) the AT8 nuclear tau epitope, and (d) genome-wide DNA methylation changes. Results. Our findings indicate that pterostilbene exerts distinct effects on the cell division cycle depending on the cellular state, with neuroprotective benefits observed in differentiated neuronal-like cells, but not in cells undergoing induced division. Additionally, pterostilbene alters DNA methylation patterns. Conclusion. These results suggest that pterostilbene may offer neuroprotective advantages for differentiated neuronal-like cells. However, further studies are required to confirm these effects in vivo by examining specific biomarkers in human populations consuming pterostilbene-containing foods. Full article
11 pages, 1911 KiB  
Article
Hepatitis B Virus-Induced Resistance to Sorafenib and Lenvatinib in Hepatocellular Carcinoma Cells: Implications for Cell Viability and Signaling Pathways
by Narmen Esmael, Ido Lubin, Ran Tur-Kaspa and Romy Zemel
Cancers 2024, 16(22), 3763; https://doi.org/10.3390/cancers16223763 - 8 Nov 2024
Viewed by 460
Abstract
Background/Objectives: Sorafenib and lenvatinib are tyrosine kinase inhibitors used in hepatocellular carcinoma (HCC) treatment. This study investigates how hepatitis B virus (HBV) infection affects their efficacy in HepG2 hepatoma cells. Methods: HepG2 and HBV-infected HepG2/2215 cells were treated with varying concentrations [...] Read more.
Background/Objectives: Sorafenib and lenvatinib are tyrosine kinase inhibitors used in hepatocellular carcinoma (HCC) treatment. This study investigates how hepatitis B virus (HBV) infection affects their efficacy in HepG2 hepatoma cells. Methods: HepG2 and HBV-infected HepG2/2215 cells were treated with varying concentrations of both drugs. The cell viability, cell cycle gene expression, cycle progression, and phosphorylation levels of ERK and AKT were analyzed. Results: The HBV-infected cells showed significant alterations in their cell cycle gene expressions, with an 80-fold increase in CCND2 expression and a higher proportion of cells in the G2/M phase, indicating enhanced proliferation. While both drugs decreased HepG2 cell viability in a concentration-dependent manner, HBV infection conferred resistance, as evidenced by the increased viable cells in the HepG2/2215 cultures. Sorafenib and lenvatinib decreased key cyclin and cyclin-dependent kinase expressions in uninfected cells, with less effect on the HBV-infected cells. Both drugs lowered the pERK and pAKT levels in the HepG2 cells. In the HBV-infected cells, sorafenib reduced the pERK and pAKT levels to a lesser extent. However, treatment with lenvatinib elevated the levels of pERK and pAKT. Conclusions: In conclusion, HBV infection increases resistance to both sorafenib and lenvatinib in hepatoma cells by influencing the cell cycle regulatory genes and critical signaling pathways. However, the resistance mechanisms likely differ between the two medications. Full article
(This article belongs to the Special Issue Hepatitis Viruses and Cancer)
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Figure 1
<p>Sorafenib and lenvatinib treatment decreased the viability of HepG2 cells. HepG2 cells and HepG2/2215 HBV-infected cells (2215) were exposed to varying concentrations (0 to 14 µM) of (<b>A</b>) sorafenib or (<b>B</b>) lenvatinib for 24 h and analyzed for viability by AlamarBlue. A comparison of the effects of sorafenib and of lenvatinib in (<b>C</b>) HepG2 cells and in (<b>D</b>) HepG2/2215 cells is shown. The percentage of live cells was determined compared to the non-treated cells, set as 100%. The data are presented as the means ± standard deviations. <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, and *** <span class="html-italic">p</span> &lt; 0.0005.</p>
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<p>The effects of sorafenib and lenvatinib treatments on the expressions of cell cycle genes in the HepG2 and HepG2/2215 cell lines. QRT-PCR of cell cycle gene expression; <span class="html-italic">CDK1</span>, <span class="html-italic">CDK2</span>, <span class="html-italic">CDK3</span>, <span class="html-italic">CDK6</span>, <span class="html-italic">CCNB1</span>, <span class="html-italic">CCND1</span>, and <span class="html-italic">CCND2</span>. (<b>A</b>) HepG2 and HepG2/2215 cell lines without treatment; (<b>B</b>) HEPG2 cells treated with sorafenib (Sor) and (<b>C</b>) lenvatinib (Len); (<b>D</b>) HepG2/2215 cells treated with sorafenib and (<b>E</b>) lenvatinib. The results are shown as the relative quantity normalized to the RPLP11 mRNA values. The data are presented as the means ± standard deviations; <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.0001 and **** <span class="html-italic">p</span> &lt; 0.00005.</p>
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<p>The effect of sorafenib (Sor) and lenvatinib (Len) on cell cycle phases in HepG2/2215 cell line. Stacked bar graphs showing the percentage of cells in different phases of the cell cycle. Flow cytometry results shown distribution DNA content: G1 (black), S (dark gray), G2/M (light gray) phases in (<b>A</b>) HepG2 and HepG2/2215 cell line without chemotherapeutic treatments (<b>B</b>) HepG2 cells ± sorafenib and lenvatinib treatment (<b>C</b>) HepG2/2215 cells ± sorafenib and lenvatinib treatment. Data of three experiments are presented as mean ± standard deviation (numbers at the left side of the bars represent the mean of % cell number). <span class="html-italic">NS</span>—non significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>PERK and pAKT expression levels following treatment with sorafenib and lenvatinib. HepG2 and HepG2/2215 cells were treated with 10 µM sorafenib or 10 µM lenvatinib for 24 h, and the proteins were extracted and analyzed by Western blot for the protein levels of (<b>A</b>) pERK (<b>a</b>), ERK (<b>b</b>) and tubulin (as the internal control) (<b>c</b>) and for (<b>B</b>) pAKT (<b>a</b>) and tubulin (<b>b</b>). iBright analysis was used to quantify intensity, and the percentage of relative intensity, normalized to untreated cells, is displayed at the membrane’s bottom.</p>
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22 pages, 1438 KiB  
Article
Association of Genetic Variants at the CDKN1B and CCND2 Loci Encoding p27Kip1 and Cyclin D2 Cell Cycle Regulators with Susceptibility and Clinical Course of Chronic Lymphocytic Leukemia
by Lidia Ciszak, Agata Kosmaczewska, Edyta Pawlak, Irena Frydecka, Aleksandra Szteblich and Dariusz Wołowiec
Int. J. Mol. Sci. 2024, 25(21), 11705; https://doi.org/10.3390/ijms252111705 - 31 Oct 2024
Viewed by 557
Abstract
Beyond the essential role of p27Kip1 and cyclin D2 in cell cycle progression, they are also shown to confer an anti-apoptotic function in peripheral blood (PB) lymphocytes. Although the aberrant longevity and expression of p27Kip1 and cyclin D2 in leukemic cells [...] Read more.
Beyond the essential role of p27Kip1 and cyclin D2 in cell cycle progression, they are also shown to confer an anti-apoptotic function in peripheral blood (PB) lymphocytes. Although the aberrant longevity and expression of p27Kip1 and cyclin D2 in leukemic cells is well documented, the exact mechanisms responsible for this phenomenon have yet to be elucidated. This study was undertaken to determine the associations between polymorphisms in the CDKN1B and CCND2 genes (encoding p27Kip1 and cyclin D2, respectively) and susceptibility to chronic lymphocytic leukemia (CLL), as well as their influence on the expression of both cell cycle regulators in PB leukemic B cells and non-malignant T cells from untreated CLL patients divided according to the genetic determinants studied. Three CDKN1B single-nucleotide polymorphisms (SNPs), rs36228499, rs34330, and rs2066827, and three CCND2 SNPs, rs3217933, rs3217901, and rs3217810, were genotyped using a real-time PCR system. The expression of p27Kip1 and cyclin D2 proteins in both leukemic B cells and non-malignant T cells was determined using flow cytometry. We found that the rs36228499A and rs34330T alleles in CDKN1B and the rs3217810T allele in the CCND2 gene were more frequent in patients and were associated with increased CLL risk. Moreover, we observed that patients possessing the CCND2rs3217901G allele had lower susceptibility to CLL (most pronounced in the AG genotype). We also noticed that the presence of the CDKN1Brs36228499CC, CDKN1Brs34330CC, CDKN1Brs2066827TT, and CCND2rs3217901AG genotypes shortened the time to CLL progression. Statistically significant functional relationships were limited to T cells and assigned to CDKN1B polymorphic variants; carriers of the polymorphisms rs34330CC and rs36228499CC (determining the aggressive course of CLL) expressed a decrease in p27Kip1 and cyclin D2 levels, respectively. We indicate for the first time that genetic variants at the CDKN1B and CCND2 loci may be considered as a potentially low-penetrating risk factor for CLL and determining the clinical outcome. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1
<p>Cumulative probability of progression to a higher Rai stage, lymph node and organ progression, and treatment-free survival in CLL patients stratified according to the <span class="html-italic">CDKN1B</span> and <span class="html-italic">CCND2</span> gene polymorphisms. (<b>a</b>–<b>c</b>): Cumulative probability of progression to a higher Rai stage-free survival in CLL patients divided according to the genetic variants of the <span class="html-italic">CDKN1B</span>rs36228499 (<b>a</b>) and <span class="html-italic">CDKN1B</span>rs2066827 (<b>b</b>) polymorphic sites as well as the <span class="html-italic">CCND2</span>rs3217901 polymorphic locus (<b>c</b>). (<b>d</b>,<b>e</b>): Cumulative probability of lymph node-free survival in CLL patients stratified according to the genetic variants of the <span class="html-italic">CDKN1B</span>rs34330 (<b>d</b>) and <span class="html-italic">CCND2</span>rs3217810 (<b>e</b>) polymorphic sites. (<b>f</b>): Cumulative probability of treatment-free survival in CLL patients divided according to the genetic variants of the <span class="html-italic">CCND2</span>rs3217810 polymorphic locus. The <span class="html-italic">p</span>-value was obtained using the log rank test.</p>
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<p>Association between genetic variants of the <span class="html-italic">CDKN1B</span>rs36228499 polymorphic locus and cyclin D2 expression in CLL patients. (<b>a</b>,<b>d</b>) The graphs show the mean fluorescence intensity (MFI) of cyclin D2 protein in PB CD19+CD5+ (<b>a</b>) and CD3+ (<b>d</b>) cells in A− and A+ carriers of the <span class="html-italic">CDKN1B</span>rs36228499 polymorphic site. The horizontal lines represent the median values. Differences between studied groups were evaluated using the Mann–Whitney U test. (**) signifies a statistically significant difference <span class="html-italic">p</span> &lt; 0.01. (<b>b</b>,<b>c</b>,<b>e</b>,<b>f</b>): Cytometric analysis of cyclin D2 protein expression in CLL patients divided according to the genetic variants of the <span class="html-italic">CDKN1B</span>rs36228499 polymorphic locus. Histograms show cytometric analysis of cyclin D2 expression in PB CD19+CD5+ (<b>b</b>,<b>c</b>) and CD3+ (<b>e</b>,<b>f</b>) cells co-expressing cyclin D2 protein in A− and A+ carriers. PBMCs were gated using FSC/SSC profiles followed by gating on CD19+CD5+ (<b>b</b>,<b>c</b>) or CD3+ (<b>e</b>,<b>f</b>) to identify CD19+CD5+ and CD3+ cells for further analysis of cyclin D2 protein expression in PB CD19+CD5+ and CD3+ cells. Black line curves show cyclin D2-fluorescence of cells within PB CD19+CD5+ and CD3+ cells. Gray areas represent the isotype controls. The numbers located on the histograms represent the cyclin D2-dependent signal intensity (MFI).</p>
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14 pages, 13928 KiB  
Article
STAT3 Protein–Protein Interaction Analysis Finds P300 as a Regulator of STAT3 and Histone 3 Lysine 27 Acetylation in Pericytes
by Gautam Kundu, Maryam Ghasemi, Seungbin Yim, Ayanna Rohil, Cuiyan Xin, Leo Ren, Shraddha Srivastava, Akinwande Akinfolarin, Subodh Kumar, Gyan P. Srivastava, Venkata S. Sabbisetti, Gopal Murugaiyan and Amrendra K. Ajay
Biomedicines 2024, 12(9), 2102; https://doi.org/10.3390/biomedicines12092102 - 14 Sep 2024
Viewed by 1081
Abstract
Background: Signal transducer and activator of transcription 3 (STAT3) is a member of the cytoplasmic inducible transcription factors and plays an important role in mediating signals from cytokines, chemokines, and growth factors. We and others have found that STAT3 directly regulates pro-fibrotic signaling [...] Read more.
Background: Signal transducer and activator of transcription 3 (STAT3) is a member of the cytoplasmic inducible transcription factors and plays an important role in mediating signals from cytokines, chemokines, and growth factors. We and others have found that STAT3 directly regulates pro-fibrotic signaling in the kidney. The STAT3 protein–protein interaction plays an important role in activating its transcriptional activity. It is necessary to identify these interactions to investigate their function in kidney disease. Here, we investigated the protein–protein interaction among three species to find crucial interactions that can be targeted to alleviate kidney disease. Method: In this study, we examined common protein–protein interactions leading to the activation or downregulation of STAT3 among three different species: humans (Homo sapiens), mice (Mus musculus), and rabbits (Oryctolagus cuniculus). Further, we chose to investigate the P300 and STAT3 interaction and performed studies of the activation of STAT3 using IL-6 and inhibition of the P300 by its specific inhibitor A-485 in pericytes. Next, we performed immunoprecipitation to confirm whether A-485 inhibits the binding of P300 to STAT3. Results: Using the STRING application from ExPASy, we found that six proteins, including PIAS3, JAK1, JAK2, EGFR, SRC, and EP300, showed highly confident interactions with STAT3 in humans, mice, and rabbits. We also found that IL-6 treatment increased the acetylation of STAT3 and increased histone 3 lysine acetylation (H3K27ac). Furthermore, we found that the disruption of STAT3 and P300 interaction by the P300 inhibitor A-485 decreased STAT3 acetylation and H3K27ac. Finally, we confirmed that the P300 inhibitor A-485 inhibited the binding of STAT3 with P300, which inhibited its transcriptional activity by reducing the expression of Ccnd1 (Cyclin D1). Conclusions: Targeting the P300 protein interaction with STAT3 may alleviate STAT3-mediated fibrotic signaling in humans and other species. Full article
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
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Graphical abstract

Graphical abstract
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<p>The boxplot shows interaction score distribution (<span class="html-italic">X</span>-axis) for STAT3-interacting proteins based on sources of evidence (<span class="html-italic">Y</span>-axis). We found that the aggregated score (combined_score) is mainly driven by text mining, which is known to have noise and false positive results. In addition, there is almost no evidence for homology. This suggests that major sources of identifying STAT3-interacting proteins are experimentally determined or appear in similar pathways. Some of these proteins also show coexpression statistically derived from RNA-seq data.</p>
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<p>The heatmap shows the confidence score of every protein (shown as individual columns) estimated from each data source (shown as individual rows). The color represents the confidence score for each element in the metrics that ranges between [0, 1]. Clustering is performed on STAT3-associated proteins to group them based on scores from similar sources. Three groups of proteins are identified based on their association with STAT3 estimated from various sources. These sets of proteins are described in the heatmap.</p>
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<p>A protein–protein interaction network is created for STAT3-associated proteins from the STRING database. (<b>A</b>–<b>C</b>) Three different organisms (humans, mice, and rabbits) were selected to create the PPI network, where nodes are colored based on their cluster membership. Red represents the cluster with the largest number of proteins, green the cluster with a medium number, and blue the cluster with the smallest number of proteins. For each network, the annotation includes the total number of proteins interacting with STAT3, the total number of interactions amongst these proteins, and the total number of clusters identified. (<b>D</b>) The Venn diagram shows the overlap between these STAT3-interacting proteins in humans, mice, and rabbits. (<b>E</b>) The list of the top 25 proteins that are common in all three species is shown.</p>
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<p>The inhibition of P300 inhibits IL-6-induced STAT3 acetylation and its binding. Immunofluorescence staining shows that (<b>A</b>) IL-6 treatment increased the Histone 3 Lysine acetylation (H3K27ac), which was decreased by the P300 inhibitor (A-485). The right panels show the quantitation of image intensity as log corrected total cell fluorescence (CTCF). Data are represented as ±SEM. (<b>B</b>) IL-6 treatment increased the acetylation of STAT3 on the Lysine 685 residue, which was inhibited by A-485 in pericytes, 10T1/2. Scale bar = 10 μm. The right panels show the quantitation of image intensity as log CTCF. The data are represented as ±SEM. (<b>C</b>) RT-PCR to detect <span class="html-italic">Ccnd1</span> (<span class="html-italic">Cyclin D1</span>) gene expression using RT-PCR. Fold changes relative to Ctrl were plotted after normalizing them to <span class="html-italic">Gapdh</span>. Data are represented as ±SD. (<b>D</b>) The immunoprecipitation of STAT3 and immunoblotting for P300 showed the binding of STAT3 with P300 and a decrease in P300 binding following A-485 treatment in pericytes, 10T1/2. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001 and **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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19 pages, 8744 KiB  
Article
Cucurbitacin B Inhibits the Proliferation of WPMY-1 Cells and HPRF Cells via the p53/MDM2 Axis
by Yangtao Jin, Ping Zhou, Sisi Huang, Congcong Shao, Dongyan Huang, Xin Su, Rongfu Yang, Juan Jiang and Jianhui Wu
Int. J. Mol. Sci. 2024, 25(17), 9333; https://doi.org/10.3390/ijms25179333 - 28 Aug 2024
Cited by 1 | Viewed by 840
Abstract
Modern research has shown that Cucurbitacin B (Cu B) possesses various biological activities such as liver protection, anti-inflammatory, and anti-tumor effects. However, the majority of research has primarily concentrated on its hepatoprotective effects, with limited attention devoted to exploring its potential impact on [...] Read more.
Modern research has shown that Cucurbitacin B (Cu B) possesses various biological activities such as liver protection, anti-inflammatory, and anti-tumor effects. However, the majority of research has primarily concentrated on its hepatoprotective effects, with limited attention devoted to exploring its potential impact on the prostate. Our research indicates that Cu B effectively inhibits the proliferation of human prostate stromal cells (WPMY-1) and fibroblasts (HPRF), while triggering apoptosis in prostate cells. When treated with 100 nM Cu B, the apoptosis rates of WPMY-1 and HPRF cells reached 51.73 ± 5.38% and 26.83 ± 0.40%, respectively. In addition, the cell cycle assay showed that Cu B had a G2/M phase cycle arrest effect on WPMY-1 cells. Based on RNA-sequencing analysis, Cu B might inhibit prostate cell proliferation via the p53 signaling pathway. Subsequently, the related gene and protein expression levels were measured using quantitative real-time PCR (RT-qPCR), immunocytochemistry (ICC), and enzyme-linked immunosorbent assays (ELISA). Our results mirrored the regulation of tumor protein p53 (TP53), mouse double minute-2 (MDM2), cyclin D1 (CCND1), and thrombospondin 1 (THBS1) in Cu B-induced prostate cell apoptosis. Altogether, Cu B may inhibit prostate cell proliferation and correlate to the modulation of the p53/MDM2 signaling cascade. Full article
(This article belongs to the Special Issue Nutrients and Active Substances in Natural Products)
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Figure 1
<p>The chemical structure of cucurbitacin B.</p>
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<p>Effect of Cu B on the cell viability of WPMY-1 (<b>A</b>) and HPRF (<b>B</b>). Cu B and Doxa inhibited the proliferation of WPMY-1 and HPRF cells based on CCK-8 results. Prostate cells were treated with vehicle (0.1% DMSO), doxazosin (40 μM), or Cu B (12.5 nM, 25 nM, 50 nM, 100 nM, 200 nM) for 48 h and 72 h. The positive control drug doxazosin 40 μM significantly reduced the survival rate of both cells compared with the solvent control group. After 72 h of treatment with Cu B at medium to high concentrations (25 nM–200 nM), the survival rate of WPMY-1 cells was significantly reduced compared with the solvent control group. Only after 72 h of treatment with the highest concentration (200 nM) of Cu B was the survival rate of HPRF cells significantly reduced compared with the solvent control group. The data are presented as mean ± standard deviation (SD) of three independent experiments, analyzed using one-way analysis of variance (ANOVA), followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <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, compared with the control group. Cu B, cucurbitacin B; Doxa, doxazosin; CCK-8, counting kit-8; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts; DMSO, dimethyl sulfoxide.</p>
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<p>Effect of Cu B (50 nM–200 nM) and doxazosin (40 μM) on the cellular morphology of WPMY-1 and HPRF. Compared with the control group, Cu B treatment induced distinct morphological alterations, manifesting with cell shrinkage, rounding, and karyorrhexis. Cu B, cucurbitacin B; Doxa, doxazosin; CCK-8, counting kit-8; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts. Scale bar = 50 µM.</p>
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<p>Apoptosis of WPMY-1 and HPRF that were treated with Cu B and Doxa. Cu B and Doxa induced the apoptosis of prostate cells based on the results of flow cytometry analysis. In WPMY-1 cells, the apoptosis rate of the Doxa group and the middle-to-high concentration Cu B (50 nM–100 nM) group was significantly increased compared with the control group. In HPRF cells, only the apoptosis rate of the high-concentration Cu B (50 nM–100 nM) group was significantly increased compared with the control group. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <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, compared with the control group. Cu B, cucurbitacin B; Doxa, doxazosin; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts.</p>
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<p>Cell cycle results of WPMY-1 and HPRF cells treated with Cu B and Doxa. Flow cytometry analysis showed that Cu B could block the G2/M phase of WPMY-1 cells. In WPMY-1 cells, the proportion of G2/M phase cells in the high-concentration Cu B (100 nM) group was significantly increased compared with the control group, Doxa group, and Cu B (50 nM) group. No significant changes were observed in the proportion of cells in HPRF cells. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Cu B, cucurbitacin B; Doxa, doxazosin; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts.</p>
Full article ">Figure 5 Cont.
<p>Cell cycle results of WPMY-1 and HPRF cells treated with Cu B and Doxa. Flow cytometry analysis showed that Cu B could block the G2/M phase of WPMY-1 cells. In WPMY-1 cells, the proportion of G2/M phase cells in the high-concentration Cu B (100 nM) group was significantly increased compared with the control group, Doxa group, and Cu B (50 nM) group. No significant changes were observed in the proportion of cells in HPRF cells. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Cu B, cucurbitacin B; Doxa, doxazosin; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts.</p>
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<p>RNA-sequencing results of DEGs and KEGG pathway enrichment analysis in WPMY-1 cells after Cu B treatment for 48 h. Volcano chart (<b>A</b>–<b>C</b>) and column chart (<b>D</b>) showing the number of DEGs up- and down-regulated in the Cu B (25 nM) group versus the control, Cu B (50 nM) group versus the control, and Cu B (50 nM) group versus Cu B (25 nM) group. (<b>E</b>) KEGG enrichment analysis of DEGs between the Cu B (25 nM) group and the control group. (<b>F</b>) KEGG enrichment analysis of DEGs between the Cu B (50 nM) group and the control group (<span class="html-italic">p</span> = 0.037213701 for p53 signaling pathway). (<b>G</b>) KEGG enrichment analysis of DEGs between the Cu B (50 nM) group and the Cu B (25 nM) group. Cu B, cucurbitacin B; DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes. (<span class="html-italic">n</span> = 3).</p>
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<p>Cu B regulated the expression of selected genes in WPMY-1 and HPRF cells based on RT-qPCR results. (<b>A</b>) The gene expression level of <span class="html-italic">TP53</span> in Cu B-treated cells. (<b>B</b>) The gene expression level of <span class="html-italic">THBS1</span> in Cu B-treated cells. (<b>C</b>) The gene expression level of <span class="html-italic">MDM2</span> in Cu B-treated cells. (<b>D</b>) The gene expression level of <span class="html-italic">CCND1</span> in Cu B-treated cells. In WPMY-1 cells, compared with the control group, the expression of <span class="html-italic">MDM2</span> and <span class="html-italic">CCND1</span> genes was significantly down-regulated under the action of Cu B (50 nM) and Cu B (25 nM), respectively; the expression of <span class="html-italic">THBS1</span> gene was significantly up-regulated under the action of Cu B (50 nM). In HPRF cells, compared with the control group, the expression of <span class="html-italic">CCND1</span> gene was significantly up-regulated under the action of Cu B (50 nM); the expression of <span class="html-italic">THBS1</span> gene was significantly down-regulated under the action of Cu B (25 nM–50 nM). The expression of <span class="html-italic">TP53</span> gene was not affected by Cu B. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, compared with control. Cu B, cucurbitacin B; TP53, tumor protein p53; THBS1, thrombospondin 1; MDM2, mouse double minute-2; CCND1, cyclin D1; WPMY-1, human normal prostate stromal immortalized cell line; HPRF, human prostate fibroblasts; RT-qPCR, real-time quantitative polymerase chain reaction.</p>
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<p>Impact of Cu B on the modulation of TP53, THBS1, and MDM2 protein expressions in WPMY-1 cells. (<b>A</b>) Immunocytochemistry images of TP53, THBS1, and MDM2 in WPMY-1 cells (400×). (<b>B</b>) Effect of Cu B on TP53 expression. (<b>C</b>) Effect of Cu B on THBS1 expression. (<b>D</b>) Effect of Cu B on MDM2 expression. Scale bar = 50 μm. In WPMY-1 cells, compared with the control group, the high-dose Cu B (50 nM) group significantly up-regulated the expression of TP53 and THBS1, while the low-dose Cu B (12.5 nM) group significantly down-regulated the expression of MDM2. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <span class="html-italic">p</span> &lt; 0.05, compared with the control. Cu B, cucurbitacin B; TP53, tumor protein p53; THBS1, thrombospondin 1; MDM2, mouse double minute-2; AOD, average optical density.</p>
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<p>Impact of Cu B on the modulation of TP53 and MDM2 protein expressions in HPRF cells. (<b>A</b>) Immunocytochemistry images of TP53 and MDM2 in HPRF cells (400×). (<b>B</b>) Effect of Cu B on TP53 expression. (<b>C</b>) Effect of Cu B on MDM2 expression. Scale bar = 50 μm. Cu B had no significant effect on the expression of TP53 and MDM2. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. Cu B, cucurbitacin B; TP53, tumor protein p53; MDM2, mouse double minute-2; HPRF, human prostate fibroblasts; AOD, average optic density.</p>
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<p>ELISA analysis results of THBS1 in the prostate cell culture supernatant. Prostate cells were treated with vehicle (0.1% DMSO) and Cu B (12.5 nM, 25 nM, 50 nM) for 48 h. (<b>A</b>) THBS1 level in WPMY-1 cells. (<b>B</b>) THBS1 level in HPRF cells.; WPMY-1: human normal prostate stromal immortalized cell line; HPRF: human prostate fibroblasts. Cu B significantly increased the expression of THBS1 in two prostate cell types. The data are presented as mean ± SD of three independent experiments, analyzed using one-way ANOVA followed by least significant difference post-hoc test or Dunnett’s post-hoc test. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, compared with the control. ELISA, enzyme-linked immunosorbent assay; THBS1, thrombospondin 1; DMSO, dimethyl sulfoxide; Cu B, cucurbitacin B.</p>
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9 pages, 4225 KiB  
Communication
Doxycycline-Mediated Control of Cyclin D2 Overexpression in Human-Induced Pluripotent Stem Cells
by Aijun Qiao, Yuhua Wei, Yanwen Liu, Asher Kahn-Krell, Lei Ye, Thanh Nguyen and Jianyi Zhang
Int. J. Mol. Sci. 2024, 25(16), 8714; https://doi.org/10.3390/ijms25168714 - 9 Aug 2024
Viewed by 1045
Abstract
Previous studies have demonstrated that when the cyclin D2 (CCND2), a cell-cycle regulatory protein, is overexpressed in human-induced pluripotent stem cells (hiPSCs), cardiomyocytes (CMs) differentiated from these CCND2-overexpressing hiPSCs can proliferate after transplantation into infarcted hearts, which significantly improves the cells’ potency for [...] Read more.
Previous studies have demonstrated that when the cyclin D2 (CCND2), a cell-cycle regulatory protein, is overexpressed in human-induced pluripotent stem cells (hiPSCs), cardiomyocytes (CMs) differentiated from these CCND2-overexpressing hiPSCs can proliferate after transplantation into infarcted hearts, which significantly improves the cells’ potency for myocardial regeneration. However, persistent CM proliferation could lead to tumor growth or the development of arrhythmogenic complications; thus, the goal of the current study was to generate a line of hiPSCs in which CCND2 overexpression could be tightly controlled. First, we transfected hiPSCs with vectors coding for a doxycycline-inducible Tet-On transactivator and S. pyogenes dCas9 fused to the VPR activation domain; then, the same hiPSCs were engineered to express guide RNAs targeting the CCND2 promotor. Thus, treatment with doxycycline (dox) activated dCas9-VPR expression, and the guide RNAs directed dCas9-VPR to the CCND2 promoter, which activated CCND2 expression. Subsequent experiments confirmed that CCND2 expression was dox-dependent in this newly engineered line of hiPSCs (doxCCND2-hiPSCs): CCND2 protein was abundantly expressed after 48 h of treatment with dox and declined to near baseline level ~96 h after dox treatment was discontinued. Full article
(This article belongs to the Special Issue Molecular Biology of Stem Cells)
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Figure 1

Figure 1
<p>Strategy for generating <sup>dox</sup>dCas9-hiPSCs. (<b>A</b>) The all-in-one cassette used to generate <sup>dox</sup>dCas9-hiPSCs is displayed as a schematic. It includes a neomycin-resistance sequence (NEO<sup>R</sup>), an rtTA sequence (TetON) driven by the CAG promoter, and a sequence coding for dCas9-VPR linked to EGFP by a self-cleaving T2A peptide, which is driven by the TRE3G promoter. The sequence was inserted into the AAVS1 locus of hiPSCs via TALENS, and successfully transfected cells were collected via treatment with G418 (a neomycin analog) and flow-cytometry selection for EGFP fluorescence (SA: splice acceptor; NLS, 2XNLS, FLAG). (<b>B</b>) Genomic DNA primers were generated for two regions spanning the left AAVS1 and NEO<sup>R</sup> sequences and for three regions spanning the right AAVS1 and TRE3G sequences; then, (<b>C</b>) the successful insertion of the all-in-one cassette was confirmed via genomic DNA PCR.</p>
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<p>Dox treatment induced GFP expression and upregulated dCas9 and rtTA protein abundance in <sup>dox</sup>dCas9-hiPSCs. (<b>A</b>) GFP fluorescence was visualized in unpurified <sup>dox</sup>dCas9-hiPSCs (i.e., after G418 selection) that had been treated with or without 5 μg/mL dox (Dox+ or Dox-, respectively) for 48 h. (<b>B</b>) GFP fluorescence was visualized in purified <sup>dox</sup>dCas9-hiPSCs (i.e., after both G418 selection and flow-cytometry sorting for GFP fluorescence) that had been treated with or without dox for 48 h. (<b>C</b>) dCas9 and rtTA protein expression levels were evaluated by Western blot analysis in unpurified and purified <sup>dox</sup>dCas9-hiPSCs after treatment with or without dox for 48 h. Quantification of dCas9 (<b>D</b>) and rTTA (<b>E</b>) protein expressions after being normalized to β-tubulin protein. Statistical testing: Kruskal-Wallis test, sample size <span class="html-italic">n</span> = 3.</p>
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<p>Generation of <sup>dox</sup>CCND2-hiPSCs. (<b>A</b>) Five different CCND2 guide RNA sequences (gRNA-CCND2 numbers 1-5) were validated via PCR analysis with the U6 validation primer and the gRNA reverse primer. (<b>B</b>) GFP fluorescence was visualized in <sup>dox</sup>CCND2-hiPSCs that had been generated via transduction with lentiviruses coding for the CCND2 guide RNAs and in <sup>dox</sup>dCas9-hiPSCs that had been transduced with a control guide RNA after the cells were treated with dox for 48 h. (<b>C</b>) dCas9 and CCND2 protein expression levels were evaluated by Western blot analysis in <sup>dox</sup>dCas9-hiPSCs that had been transduced with a control guide RNA (gRNA-Control) or gRNA-CCND2 after treatment with (+) or without (-) dox for 48 h. Quantification of dCas9 (<b>D</b>) and CCND2 (<b>E</b>) protein expressions after being normalized to β-tubulin protein. Statistical testing: Kruskal-Wallis test, sample size <span class="html-italic">n</span> = 3.</p>
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<p>GFP and CCND2 expression in <sup>dox</sup>CCND2-hiPSCs can be turned on and off by dox treatment and withdrawal. <sup>dox</sup>CCND2-hiPSCs and <sup>dox</sup>dCas9-hiPSCs were treated with 5 μg/mL dox for 48 h; then, dox treatment was withdrawn, and the cells were maintained for an additional 96 h. (<b>A</b>) GFP fluorescence was visualized in <sup>dox</sup>CCND2-hiPSCs before dox treatment; after treatment with dox for 48 h (48 h Dox+); and then after the dox-treated cells had been maintained in the absence of dox for 24 (48 h+, 24 h-), 48 (48 h+, 48 h-), 72 (48 h+, 72 h-), and 96 h (48 h+, 96 h-). (<b>B</b>) dCas9 and CCND2 protein expression levels were evaluated by Western blot analysis in <sup>dox</sup>CCND2-hiPSCs in the absence of dox (-) and at the indicated time points after dox treatment and withdrawal. Quantification of dCas9 (<b>C</b>) and CCND2 (<b>D</b>) protein expressions after being normalized to β-tubulin protein. Statistical testing: Kruskal-Wallis test, sample size <span class="html-italic">n</span> = 4.</p>
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<p>Pluripotency of <sup>dox</sup>CCND2-hiPSCs. (<b>A</b>) <sup>dox</sup>CCND2-hiPSCs formed a teratoma containing mesodermal (cartilage), ectodermal (epidermis), and endodermal (glandular tissue) cells after implantation into an immunodeficient mouse. (<b>B</b>) A representative image (20× magnification) of CMs differentiated from <sup>dox</sup>CCND2-hiPSCs.</p>
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<p>Schematic summary of how dox treatment controls proliferation in CMs differentiated from <sup>dox</sup>CCND2-hiPSCs. A—Doxycycline induces a conformational change in rtTA, which binds the TREG3 promoter to activate dCas9-VPR expression. B—Guide RNAs (gRNA) direct dCas9-VPR to the CCND2 promoter, which activates CCND2 expression. C—CCND2 binds cyclin-dependent kinases 4/6 (CDK 4/6), which triggers signaling pathways that subsequently activate the G1-S phase transition of the cell cycle.</p>
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10 pages, 2382 KiB  
Article
LincRNA-EPS Promotes Proliferation of Aged Dermal Fibroblast by Inducing CCND1
by Liping Zhang, Iris C. Wang, Songmei Meng and Junwang Xu
Int. J. Mol. Sci. 2024, 25(14), 7677; https://doi.org/10.3390/ijms25147677 - 12 Jul 2024
Cited by 1 | Viewed by 983
Abstract
The aging process is linked to numerous cellular changes, among which are modifications in the functionality of dermal fibroblasts. These fibroblasts play a crucial role in sustaining the healing of skin wounds. Reduced cell proliferation is a hallmark feature of aged dermal fibroblasts. [...] Read more.
The aging process is linked to numerous cellular changes, among which are modifications in the functionality of dermal fibroblasts. These fibroblasts play a crucial role in sustaining the healing of skin wounds. Reduced cell proliferation is a hallmark feature of aged dermal fibroblasts. Long intergenic non-coding RNA (lincRNAs), such as LincRNA-EPS (Erythroid ProSurvival), has been implicated in various cellular processes. However, its role in aged dermal fibroblasts and its impact on the cell cycle and its regulator, Cyclin D1 (CCND1), remains unclear. Primary dermal fibroblasts were isolated from the skin of 17-week-old (young) and 88-week-old (aged) mice. Overexpression of LincRNA-EPS was achieved through plasmid transfection. Cell proliferation was detected using the MTT assay. Real-time PCR was used to quantify relative gene expressions. Our findings indicate a noteworthy decline in the expression of LincRNA-EPS in aged dermal fibroblasts, accompanied by reduced levels of CCND1 and diminished cell proliferation in these aging cells. Significantly, the overexpression of LincRNA-EPS in aged dermal fibroblasts resulted in an upregulation of CCND1 expression and a substantial increase in cell proliferation. Mechanistically, LincRNA-EPS induces CCND1 expression by sequestering miR-34a, which was dysregulated in aged dermal fibroblasts, and directly targeting CCND1. These outcomes underscore the crucial role of LincRNA-EPS in regulating CCND1 and promoting cell proliferation in aged dermal fibroblasts. Our study provides novel insights into the molecular mechanisms underlying age-related changes in dermal fibroblasts and their implications for skin wound healing. The significant reduction in LincRNA-EPS expression in aged dermal fibroblasts and its ability to induce CCND1 expression and enhance cell proliferation highlight its potential as a therapeutic target for addressing age-related skin wound healing. Full article
(This article belongs to the Special Issue Molecular and Cellular Perspectives on Wound Healing)
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Figure 1
<p>Reduced LincRNA-EPS and induced miR-34a expression in aged wounds. (<b>A</b>) Real-time qPCR analysis of LincRNA-EPS gene expression in young and aged mice wounds (mean ± SD, n = 3 per group). (<b>B</b>) Real-time qPCR analysis of miR-34a gene expression in young and aged mice wounds (mean ± SD, n = 3 per group). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Reduced LincRNA-EPS and induced miR-34a expression in aged dermal fibroblasts. (<b>A</b>) Real-time qPCR analysis of LincRNA-EPS gene expression in young and aged dermal fibroblast (mean ± SD, n = 3 per group). (<b>B</b>) Real-time qPCR analysis of miR-34a gene expression in young and aged dermal fibroblast (mean ± SD, n = 3 per group). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Reduced cell proliferation and CCND1 gene expression in aged dermal fibroblasts. (<b>A</b>) Young and aged dermal fibroblast cell proliferation analysis using MTT assay. (<b>B</b>) Real-time qPCR analysis of CCND1 gene expression in young and aged dermal fibroblast (mean ± SD, n = 3 per group). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>LincRNA-EPS overexpression induces CCND1 and cell proliferation. Dermal fibroblasts were transfected with pEPS (plasmid to overexpress LincRNA-EPS) or control plasmid (p3.1). (<b>A</b>) LincRNA-EPS overexpression via plasmid transfection confirmed by RT-qPCR. (<b>B</b>) The gene expression level of CCND1 was determined by RT-qPCR in EPS overexpression fibroblast or control transfected fibroblasts. (<b>C</b>) Cell proliferation between aged dermal fibroblast transfected with pEPS or control p3.1 using MTT assay. (<b>D</b>) Cell proliferation maker gene MKI67 was determined by RT-qPCR.</p>
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<p>MiR-34a overexpression reduces CCND1 and cell proliferation. (<b>A</b>) Bioinformatic analysis identified miR-34a binding site on CCND1 3′ UTR. (<b>B</b>) TR-qPCR analysis indicated that miR-34a was significantly induced in miR-34a mimic transfected cells. (<b>C</b>) Dermal fibroblasts were transfected with miR-34a mimic (miR-34a) or control mimic (Con). The gene expression level of CCND1 was determined by RT-qPCR in miR-34a overexpression fibroblast or control transfected fibroblasts. (<b>D</b>) Cell proliferation between dermal fibroblast transfected with miR-34a or control using MTT assay.</p>
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<p>miR-34a and LincRNA-EPS regulate each other. (<b>A</b>) miR-34a binding site on LincRNA-EPS by bioinformatics analysis. (<b>B</b>) Real-time qPCR analysis of miR-34a gene expression in young and aged dermal fibroblasts with EPS overexpression (mean ± SD, n = 3 per group). (<b>C</b>) Real-time qPCR analysis of LincRNA-EPS gene expression in young and aged dermal fibroblasts with miR-34a overexpression (mean ± SD, n = 3 per group). * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) Possible mechanisms of LinRNA-EPS/miR-34a signaling in aged dermal fibroblast.</p>
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14 pages, 1733 KiB  
Article
Post-GWAS Validation of Target Genes Associated with HbF and HbA2 Levels
by Cristian Antonio Caria, Valeria Faà, Susanna Porcu, Maria Franca Marongiu, Daniela Poddie, Lucia Perseu, Alessandra Meloni, Simona Vaccargiu and Maria Serafina Ristaldi
Cells 2024, 13(14), 1185; https://doi.org/10.3390/cells13141185 - 12 Jul 2024
Viewed by 1334
Abstract
Genome-Wide Association Studies (GWASs) have identified a huge number of variants associated with different traits. However, their validation through in vitro and in vivo studies often lags well behind their identification. For variants associated with traits or diseases of biomedical interest, this gap [...] Read more.
Genome-Wide Association Studies (GWASs) have identified a huge number of variants associated with different traits. However, their validation through in vitro and in vivo studies often lags well behind their identification. For variants associated with traits or diseases of biomedical interest, this gap delays the development of possible therapies. This issue also impacts beta-hemoglobinopathies, such as beta-thalassemia and sickle cell disease (SCD). The definitive cures for these diseases are currently bone marrow transplantation and gene therapy. However, limitations regarding their effective use restrict their worldwide application. Great efforts have been made to identify whether modulators of fetal hemoglobin (HbF) and, to a lesser extent, hemoglobin A2 (HbA2) are possible therapeutic targets. Herein, we performed the post-GWAS in vivo validation of two genes, cyclin D3 (CCND3) and nuclear factor I X (NFIX), previously associated with HbF and HbA2 levels. The absence of Ccnd3 expression in vivo significantly increased g (HbF) and d (HbA2) globin gene expression. Our data suggest that CCND3 is a possible therapeutic target in sickle cell disease. We also confirmed the association of Nfix with γ-globin gene expression and present data suggesting a possible role for Nfix in regulating Kruppel-like transcription factor 1 (Klf1), a master regulator of hemoglobin switching. This study contributes to filling the gap between GWAS variant identification and target validation for beta-hemoglobinopathies. Full article
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Graphical abstract

Graphical abstract
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<p>(<b>A</b>) Schematic representation of the human β-globin cluster transgene in ln72 mice, comprising four LCR DNAseI hypersensitivity sites and the entire β-globin gene cluster. (<b>B</b>) Breeding strategy adopted to obtain ln72 <span class="html-italic">Ccnd3</span>+/+, ln72 <span class="html-italic">Ccnd3</span>+/− and ln72 <span class="html-italic">Ccnd3</span>−/− mice. (<b>C</b>) γ-globin, (<b>D</b>) δ-globin, and (<b>E</b>) β-globin gene expression levels from embryos and adult mice obtained by RT-qPCR. Data are normalized to mouse <span class="html-italic">α-globin</span> gene expression and indicated as the mRNA fold change expression relative to the control values (ln72 <span class="html-italic">Ccnd3</span>+/+). Each datum is representative of five independent experiments (at least three mice in each experiment). The error bars represent the standard deviation from the mean. (<span class="html-italic">p</span>-value: * &lt; 0.05; ** &lt; 0.01; *** &lt; 0.001).</p>
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<p>(<b>A</b>) RBC, (<b>B</b>) MCV, (<b>C</b>) MCHC, (<b>D</b>) MCH, and (<b>E</b>) Hb parameters obtained from adult mice hematological analysis. Each datum is representative of five mice (<span class="html-italic">n</span> = 5). (<b>F</b>) Percentage of Ter119+ Cd71+ and Ter119+ Cd71−populations from flow cytometry analysis of adult mice of each genotype. Data is representative of five mice (<span class="html-italic">n</span> = 5). (<b>G</b>) Dot plot of representative erythropoiesis from mice of each genotype and relative percentages. (<span class="html-italic">p</span>-value: * &lt; 0.05; ** &lt; 0.01; *** &lt; 0.001).</p>
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<p>(<b>A</b>) Breeding strategy adopted to obtain ln72 <span class="html-italic">Nfix</span>+/+, ln72 <span class="html-italic">Nfix</span>+/− and ln72 <span class="html-italic">Nfix</span>−/− mice. (<b>B</b>) γ-globin and (<b>C</b>) β-globin genes levels from embryos and adult mice obtained by RT-qPCR. Data are normalized to mouse α-globin and indicated as fold change in mRNA expression relative to control values. Each datum is representative of five independent experiments (at least three mice in each experiment). The error bars represent one standard deviation from the mean. (<span class="html-italic">p</span>-value: * &lt; 0.05; ** &lt; 0.01).</p>
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<p>(<b>A</b>) Murine <span class="html-italic">Klf1</span> expression levels obtained by RT-qPCR of the three ln72 <span class="html-italic">Nfix</span> genotypes. (<b>B</b>) Percentage of murine Klf1 positive erythroblast cells from flow cytometry analysis of each group of adult mice. (<b>C</b>) Schematic representation of luciferase assay. (<b>D</b>) Luciferase assay results using the <span class="html-italic">Klf1</span> promoter in the absence (Kprom) or presence (Kprom + hNFIX) of the human NFIX protein in Hela cells. Results are normalized to luciferase activity for each sample and values are represented relative to the Kprom construct. (<b>E</b>) Schematic representation of transactivation assay. (<b>F</b>) Human <span class="html-italic">KLF1</span> gene expression level following transactivation with an expression vector (pEF5HA) containing human <span class="html-italic">NFIX</span> cDNA in HEL cells. RT-qPCR data are normalized to human α-globin gene expression and indicated as fold change in mRNA expression relative to control values. Each datum is representative of three independent triplicate experiments. The error bars represent one standard deviation from the mean. (<span class="html-italic">p</span>-value: * &lt; 0.05; ** &lt; 0.01; *** &lt; 0.001).</p>
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15 pages, 8402 KiB  
Article
Genetic Analysis of Egg Production Traits in Luhua Chickens: Insights from a Multi-Trait Animal Model and a Genome-Wide Association Study
by Qianwen Yang, Xubin Lu, Guohui Li, Huiyong Zhang, Chenghao Zhou, Jianmei Yin, Wei Han and Haiming Yang
Genes 2024, 15(6), 796; https://doi.org/10.3390/genes15060796 - 17 Jun 2024
Viewed by 1207
Abstract
Egg production plays a pivotal role in the economic viability of hens. To analyze the genetic rules of egg production, a total of 3151 Luhua chickens were selected, the egg production traits including egg weight at first laying (Start-EW), egg weight at 43 [...] Read more.
Egg production plays a pivotal role in the economic viability of hens. To analyze the genetic rules of egg production, a total of 3151 Luhua chickens were selected, the egg production traits including egg weight at first laying (Start-EW), egg weight at 43 weeks (EW-43), egg number at 43 weeks (EN-43), and total egg number (EN-All) were recorded. Then, the effects of related factors on egg production traits were explored, using a multi-trait animal model for genetic parameter estimation and a genome-wide association study (GWAS). The results showed that body weight at first egg (BWFE), body weight at 43 weeks (BW-43), age at first egg (AFE), and seasons had significant effects on the egg production traits. Start-EW and EW-43 had moderate heritability of 0.30 and 0.21, while EN-43 and EN-All had low heritability of 0.13 and 0.16, respectively. Start-EW exhibited a robust positive correlation with EW-43, while Start-EW was negatively correlated with EN-43 and EN-All. Furthermore, gene ontology (GO) results indicated that Annexin A2 (ANXA2) and Frizzled family receptor 7 (FZD7) related to EW-43, Cyclin D1 (CCND1) and A2B adenosine receptor (ADORA2B) related to EN-All, and have been found to be mainly involved in metabolism and growth processes, and deserve more attention and further study. This study contributes to accelerating genetic progress in improving low heritability egg production traits in layers, especially in Luhua chickens. Full article
(This article belongs to the Special Issue Poultry Breeding and Genetics)
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Figure 1
<p>Phenotypic traits correlation between egg production traits. Egg production traits include age at first egg (Start_Age), body weight at first egg (Body_Weight), egg weight at first laying (Start_EW), egg weight at 43 weeks (EW_43), body weight at 43 weeks (Body_Weight_43), egg number at 43 weeks (EN-43), and total egg number (EN-All). The ** and *** represent significant correlations at 0.05 and 0.01, respectively.</p>
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<p>Density distribution of Single Nucleotide Polymorphisms (SNPs) on chromosomes. After conducting quality control, a total of 60 Luhua chickens and 1,607,248 SNPs remained. The distribution of the filtered SNPs is displayed over the 39 chromosomes.</p>
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<p>Population structure demonstrated by principal component analysis. Principal component analysis (PCA) was conducted with the 1,607,248 SNPs for the 60 Luhua chickens. The population structure is demonstrated by the scatter plots.</p>
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<p>Manhattan plots of egg weight at first laying (<b>a</b>), egg weight at 43 weeks (<b>b</b>), egg number at 43 weeks (<b>c</b>) and total egg number (<b>d</b>). Manhattan plots established from the GWAS results of egg production traits in Luhua chickens. Manhattan plots display the negative logarithms of the observed <span class="html-italic">p</span> values for SNPs across 39 chormosomes.</p>
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<p>Manhattan plots of egg weight at first laying (<b>a</b>), egg weight at 43 weeks (<b>b</b>), egg number at 43 weeks (<b>c</b>) and total egg number (<b>d</b>). Manhattan plots established from the GWAS results of egg production traits in Luhua chickens. Manhattan plots display the negative logarithms of the observed <span class="html-italic">p</span> values for SNPs across 39 chormosomes.</p>
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<p>Gene ontology term results from egg weight at first laying traits (<b>a</b>), egg weight at 43 weeks traits (<b>b</b>), egg number at 43 weeks traits (<b>c</b>), and total egg number traits (<b>d</b>). GO analyses were conducted on the candidate genes with the smallest <span class="html-italic">p</span>-value by R package Cluster.</p>
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<p>Gene ontology term results from egg weight at first laying traits (<b>a</b>), egg weight at 43 weeks traits (<b>b</b>), egg number at 43 weeks traits (<b>c</b>), and total egg number traits (<b>d</b>). GO analyses were conducted on the candidate genes with the smallest <span class="html-italic">p</span>-value by R package Cluster.</p>
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14 pages, 2153 KiB  
Article
Host miR-146a-3p Facilitates Replication of Infectious Hematopoietic Necrosis Virus by Targeting WNT3a and CCND1
by Jingwen Huang, Shihao Zheng, Qiuji Li, Hongying Zhao, Xinyue Zhou, Yutong Yang, Wenlong Zhang and Yongsheng Cao
Vet. Sci. 2024, 11(5), 204; https://doi.org/10.3390/vetsci11050204 - 8 May 2024
Viewed by 1380
Abstract
Infectious hematopoietic necrosis virus (IHNV) is a serious pathogen that causes great economic loss to the salmon and trout industry. Previous studies showed that IHNV alters the expression patterns of splenic microRNAs (miRNAs) in rainbow trout. Among the differentially expressed miRNAs, miRNA146a-3p was [...] Read more.
Infectious hematopoietic necrosis virus (IHNV) is a serious pathogen that causes great economic loss to the salmon and trout industry. Previous studies showed that IHNV alters the expression patterns of splenic microRNAs (miRNAs) in rainbow trout. Among the differentially expressed miRNAs, miRNA146a-3p was upregulated by IHNV. However, it is unclear how IHNV utilizes miRNA146a-3p to escape the immune response or promote viral replication. The present study suggested that one multiplicity of infection (MOI) of IHNV induced the most significant miR-146a-3p expression at 1 day post infection (dpi). The upregulation of miR-146a-3p by IHNV was due to viral N, P, M, and G proteins and relied on the interferon (IFN) signaling pathway. Further investigation revealed that Wingless-type MMTV integration site family 3a (WNT3a) and G1/S-specific cyclin-D1-like (CCND1) are the target genes of miRNA-146a-3p. The regulation of IHNV infection by miRNA-146a-3p is dependent on WNT3a and CCND1. MiRNA-146a-3p was required for the downregulation of WNT3a and CCND1 by IHNV. Moreover, we also found that WNT3a and CCND1 are novel proteins that induce the type-I IFN response in RTG-2 cells, and both of them could inhibit the replication of IHNV. Therefore, IHNV-induced upregulation of miRNA-146a-3p promotes early viral replication by suppressing the type-I IFN response by targeting WNT3a and CCND1. This work not only reveals the molecular mechanism of miRNA-146a-3p during IHNV infection but also provides new antiviral targets for IHNV. Full article
(This article belongs to the Section Veterinary Microbiology, Parasitology and Immunology)
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Figure 1
<p>IHNV infection upregulated the expression of miRNA-146a-3p in RTG-2 cells. (<b>A</b>) RTG-2 cells were infected with IHNV at an MOI of 10, 1, and 0.1, and the level of miRNA-146a-3p at 1 day post infection (dpi) was measured by qRT-PCR. RTG-2 cells were infected with IHNV at an MOI of 1, and the levels of IHNV N gene (<b>B</b>) and miRNA-146a-3p (<b>C</b>) at 1, 2, and 3 dpi were measured by qRT-PCR. The ** indicates statistically significant differences (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>IHNV-induced miRNA-146a-3p expression was associated with IFN signaling. (<b>A</b>) RTG-2 cells were incubated with 10 μM of PDTC or CCCP for 24 h, followed by 1 MOI of IHNV infection. The level of the IFN gene was measured by qRT-PCR and compared with that of the IHNV-infected mock cells. (<b>B</b>) RTG-2 cells were transfected with 50 nM of STAT1-targeted siRNA, followed by incubation with recombinant iIFN1a protein at a concentration of 500 IU/mL. The levels of the STAT1 gene and Mx1 gene were measured by qRT-PCR and compared with that of the recombinant iIFN1a protein incubated mock cells. (<b>C</b>) RTG-2 cells were treated with PDTC, CCCP, IFN receptor-targeted siRNA, or STAT1-targeted siRNA, followed by 1 MOI of IHNV infection. The level of miRNA-146a-3p was measured by qRT-PCR and compared with that of the IHNV-infected mock cells. (<b>D</b>) RTG-2 cells were transfected with eight plasmids expressing each IHNV protein (among these, L protein was truncated to three segments), respectively. Twenty-four hours later, the expression of miRNA-146a-3p was measured by qRT-PCR. The ** indicates statistically significant differences (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Identification and characterization of the potential target genes of miRNA-146a-3p. (<b>A</b>) Validation of the potential target genes of miRNA-146a-3p by qRT-PCR. miRNA-146a-3p mimic or inhibitor was transfected into RTG-2. Twenty-four hours later, the levels of DAAM, SOS, CTBP, WNT3a, NOTCH, and CCND1 were measured by qRT-PCR. (<b>B</b>) Analysis of the potential target genes of miRNA-146a-3p in responding to IHNV infection. RTG-2 cells were infected with 1 MOI of IHNV or untreated, followed by the detection of genes using qRT-PCR. (<b>C</b>) Downregulations of the potential target genes by IHNV were dependent on miRNA-146a-3p. RTG-2 cells were transfected with miRNA-146a-3p inhibitor and NC inhibitor, followed by 1 MOI of IHNV infection. The * and ** indicate statistically significant differences (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>WNT3a and CCND1 were targeted by miRNA-146a-3p. (<b>A</b>) Western blotting analysis. RTG-2 cells were transfected with miRNA-146a-3p mimic or NC mimic and miRNA-146a-3p inhibitor or NC inhibitor for 24 h. Then, the cells were lysed and the target protein bands were detected using the corresponding antibody. (<b>B</b>) Alignment of miR-146a-3p and the putative target sequences in the 3′ UTR of WNT3a and CCND1. (<b>C</b>) HEK293T cells were transfected with pmirGLO-WNT3a, pmirGLO-WNT3a-MUT, pmirGLO-CCND1, or pmirGLO-CCND1-MUT, together with miR-146a-3p mimic or NC mimic, for 24 h as indicated. Luciferase activity was measured and normalized to Renilla luciferase activity. The * indicates statistically significant differences (* <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>miR-146a-3p regulation of IHNV infection was in the WNT3a and CCND1-dependent manner. RTG-2 cells were transfected with miR-146a-3p mimic or NC mimic, or cotransfected with miRNA-146a-3p mimic and pCCDN1/pWNT3a, or cotransfected with miRNA-146a-3p inhibitor and siCCDN1/siWNT3a. Twenty-four hours later, the expressions of the viral gene and IFN gene were detected by qRT-PCR. The amount of virus in the supernatant was measured using the TCID<sub>50</sub> assay. The * and ** indicate statistically significant differences (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>WNT3a and CCND1 inhibited IHNV replication. RTG-2 cells were transfected with pWNT3a, pCCND1, siWNT3a, and siCCND1, respectively. Twenty-four hours later, the cells were infected with IHNV. Another 24 h post infection, the expressions of viral gene and IFN-related genes were analyzed by qRT-PCR. The * and ** indicate statistically significant differences (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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15 pages, 4042 KiB  
Article
Targeting the Cell Cycle, RRM2 and NF-κB for the Treatment of Breast Cancers
by Nahid Sultana, Howard L. Elford and Jesika S. Faridi
Cancers 2024, 16(5), 975; https://doi.org/10.3390/cancers16050975 - 28 Feb 2024
Cited by 1 | Viewed by 1761
Abstract
A hallmark of cancer is the dysregulation of the cell cycle. The CDK4/6 inhibitor palbociclib is approved for treating advanced estrogen-receptor-positive breast cancer, but its success is limited by the development of acquired resistance owing to long-term therapy despite promising clinical outcomes. This [...] Read more.
A hallmark of cancer is the dysregulation of the cell cycle. The CDK4/6 inhibitor palbociclib is approved for treating advanced estrogen-receptor-positive breast cancer, but its success is limited by the development of acquired resistance owing to long-term therapy despite promising clinical outcomes. This situation necessitates the development of potential combination strategies. Here, we report that didox, an inhibitor of ribonucleotide reductase in combination with palbociclib, can overcome palbociclib resistance in ER-positive and ER-negative breast cancers. This study shows didox downregulates an element of the cell cycle checkpoint, cyclin D1, accompanied by a reduction in NF-κB activity in vitro and tumor growth inhibition of palbociclib-resistant ER positive breast cancer tumor growth in vivo. Furthermore, didox induces cell cycle arrest at G1 as well as reduces ROS generated by on-target effects of palbociclib on the cell cycle. Our current study also reports that the CCND1 and RRM2 upregulation associated with palbociclib-resistant breast cancers decreases upon ribonucleotide reductase inhibition. Our data present a novel and promising biomarker-driven combination therapeutic approach for the treatment of ER-positive and ER-negative breast cancers that involves the inhibition of the CDK4/6-cyclinD1/pRb cell cycle axis that merits further clinical investigation in human models. Full article
(This article belongs to the Special Issue Neoadjuvant Therapy of Breast Cancer)
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Figure 1
<p>Resistance to palbociclib alters expression of proteins involved in cell growth and cell cycle regulatory pathways. Western blot analysis of cellular and apoptotic proteins of MCF7 and MCF7 PR (<b>A</b>), Western blot analysis of MDA-MB-468 and MDA-MB-468 PR (<b>B</b>) cells treated with vehicle (NT), 600 µmol/L DDX, 1 µmol/L PLB, and combination of 600 µmol/L DDX + 1 µmol/L PLB. IC<sub>50</sub> values of drug palbociclib and didox in all cell lines (<b>C</b>). The uncropped blots are shown in <a href="#app1-cancers-16-00975" class="html-app">Supplementary Materials</a>.</p>
Full article ">Figure 1 Cont.
<p>Resistance to palbociclib alters expression of proteins involved in cell growth and cell cycle regulatory pathways. Western blot analysis of cellular and apoptotic proteins of MCF7 and MCF7 PR (<b>A</b>), Western blot analysis of MDA-MB-468 and MDA-MB-468 PR (<b>B</b>) cells treated with vehicle (NT), 600 µmol/L DDX, 1 µmol/L PLB, and combination of 600 µmol/L DDX + 1 µmol/L PLB. IC<sub>50</sub> values of drug palbociclib and didox in all cell lines (<b>C</b>). The uncropped blots are shown in <a href="#app1-cancers-16-00975" class="html-app">Supplementary Materials</a>.</p>
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<p>DDX inhibits cell cycle regulatory proteins and RRM2 in a dose-dependent manner in both parental and PLB-resistant ER+ and ER− breast cancer cell lines. Western blot analysis of cellular and apoptotic proteins in MCF7 (<b>A</b>) and MDA-MB-468 (<b>B</b>) cells treated with vehicle (NT), 30 µmol/L DDX, 100 µmol/L DDX, and 600 µmol/L DDX for 24 h. The uncropped blots are shown in <a href="#app1-cancers-16-00975" class="html-app">Supplementary Materials</a>.</p>
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<p>DDX alters the cell cycle and causes cell cycle arrest in MCF7 and MDA-MB-468 parental and palbociclib-resistant breast cancer cells at G1 and reduces ROS. Cell cycle analysis by flow cytometry of MCF7 and MCF7 PR (<b>A</b>), MDA-MB-468 and MDA-MB-468 PR (<b>B</b>) cells treated with NT, DDX 100 µmol/L, PLB 1 µmol/L and combination of DDX and PLB for 24 h. Difference between percent gated at G0/G1 in MCF7 and MCF7 PR (<b>C</b>), MDA-MB-468 and MDA-MB-468 PR (<b>D</b>) with same treatment conditions as before. DDX scavenges free radicals in MCF7 and MDA-MB-468 breast cancer cells and their palbociclib-resistant counterparts (<b>E</b>,<b>F</b>). Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3. ∗ <span class="html-italic">p</span> &lt; 0.009, ∗∗ <span class="html-italic">p</span> &lt; 0.01; significant difference between NT and DDX-, NT and D+P-treated breast cancer cells. % <span class="html-italic">p</span> &lt; 0.02, %% <span class="html-italic">p</span> &lt; 0.0002; significant difference between MCF7 and MCF7 PR, MDA-MB-468 and MDA-MB-468 PR breast cancer cells treated with PLB. &amp; <span class="html-italic">p</span> &lt; 0.0001; significant difference between NT and PLB. # <span class="html-italic">p</span> &lt; 0.001; significant difference between NT and DDX or D+P.</p>
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<p>Inhibition of ribonucleotide reductase reduces ER+ palbociclib-resistant tumor growth and decreases NF-κB activation, whereas palbociclib resistance increases CCND1 and RRM2 expression. Mice bearing MCF7 PR tumors displayed reduced tumor growth with D+P treatment when compared with PLB alone, DDX alone or NT (<b>A</b>). Promoter analysis of NF-κB in all breast cancer cells treated with DDX at a dose ranging from 30 µmol/L to 1 mmol/L for 6 h (<b>B</b>). RT-qPCR of CCND1 (<b>C</b>) and RRM2 (<b>D</b>) mRNA fold changes with didox 100 µmol/L for 6 h and 12 h compared to NT in ER+ and ER− breast cancer and their PLB-resistant counterparts. All experiments were carried out in triplicate. Comparisons between groups were made by two-sample <span class="html-italic">t</span> tests. ⍺ <span class="html-italic">p</span> &lt; 0.012, MCF7 PR PLB day 3 vs. MCF7 PR D+P day 3. ⍺⍺ <span class="html-italic">p</span> &lt; 0.0004, MCF7 PR PLB day 5 vs. MCF7 PR D+P day 5. ⍺⍺⍺ <span class="html-italic">p</span> &lt; 0.0043, MCF7 PR PLB day 7 vs. MCF7 PR D+P day 7. • <span class="html-italic">p</span> &lt; 0.05, MCF7 NT vs. DDX treatment 100 mmol/L and 600 mmol/L. ⎔ <span class="html-italic">p</span> &lt; 0.003; significant difference between MCF7 PR NT and DDX treatment 100 mmol/L and 600 mmol/L, ▼ <span class="html-italic">p</span> &lt; 0.003, MDA-MB-468 NT and DDX treatment 100 mmol/L and 600 mmol/L, ▽ <span class="html-italic">p</span> &lt; 0.0007 MDA-MB-468 PR NT and DDX treatment 600 mmol/L. ✭ <span class="html-italic">p</span> = 0.002; significant difference between MCF7 and MCF7 PR breast cancer cell lines. ✭✭ <span class="html-italic">p</span> = 0.0001; significant difference between MCF7 and MDA-MB-468 and MDA-MB-468 PR breast cancer cell lines. ✻ <span class="html-italic">p</span> &lt; 0.01 and ✻✻ <span class="html-italic">p</span> &lt; 0.0003; significant difference between MCF7 and MCF7 PR, MDA-MB-468, MDA-MB-468 PR breast cancer cells. ◇ <span class="html-italic">p</span> &lt; 0.0003 and ⦿ <span class="html-italic">p</span> &lt; 0.002; significant difference between MDA-MD-468 NT and DDX treatment 6 h and 12 h, MDA-MB-468 PR NT and DDX treatment 6 h and 12 h. ❖ <span class="html-italic">p</span> &lt; 0.03, MCF7 PR NT and DDX treatment 12 h. ▲ <span class="html-italic">p</span> &lt; 0.0003; significant difference between MDA-MD-468 NT and DDX treatment 6 h and 12 h, □ <span class="html-italic">p</span> &lt; 0.0002, MDA-MB-468 PR NT and DDX treatment 6 h and 12 h.</p>
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13 pages, 3536 KiB  
Article
Circular RNA circIGF2BP3 Promotes the Proliferation and Differentiation of Chicken Primary Myoblasts
by Xiaotong Wang, Junyuan Lin, Zhenhai Jiao, Li Zhang, Dongxue Guo, Lilong An, Tingting Xie and Shudai Lin
Int. J. Mol. Sci. 2023, 24(21), 15545; https://doi.org/10.3390/ijms242115545 - 24 Oct 2023
Cited by 4 | Viewed by 1633
Abstract
The quality and quantity of animal meat are closely related to the development of skeletal muscle, which, in turn, is determined by myogenic cells, including myoblasts and skeletal muscle satellite cells (SMSCs). Circular RNA, an endogenous RNA derivative formed through specific reverse splicing [...] Read more.
The quality and quantity of animal meat are closely related to the development of skeletal muscle, which, in turn, is determined by myogenic cells, including myoblasts and skeletal muscle satellite cells (SMSCs). Circular RNA, an endogenous RNA derivative formed through specific reverse splicing in mRNA precursors, has the potential to influence muscle development by binding to miRNAs or regulating gene expression involved in muscular growth at the transcriptional level. Previous high-throughput sequencing of circRNA in chicken liver tissue revealed a circular transcript, circIGF2BP3, derived from the gene encoding insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3). In this study, we confirmed the presence of the natural circular molecule of circIGF2BP3 through an RNase R enzyme tolerance assay. RT-qPCR results showed high circIGF2BP3 expression in the pectoral and thigh muscles of Yuexi frizzled feather chickens at embryonic ages 14 and 18, as well as at 7 weeks post-hatch. Notably, its expression increased during embryonic development, followed by a rapid decrease after birth. As well as using RT-qPCR, Edu, CCK-8, immunofluorescence, and Western blot techniques, we demonstrated that overexpressing circIGF2BP3 could promote the proliferation and differentiation of chicken primary myoblasts through upregulating genes such as proliferating cell nuclear antigen (PCNA), cyclin D1 (CCND1), cyclin E1 (CCNE1), cyclin dependent kinase 2 (CDK2), myosin heavy chain (MyHC), myoblast-determining 1 (MyoD1), myogenin (MyoG), and Myomaker. In conclusion, circIGF2BP3 promotes the proliferation and differentiation of myoblasts in chickens. This study establishes a foundation for further investigation into the biological functions and mechanisms of circIGF2BP3 in myoblasts proliferation and differentiation. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Circular features of the chicken circIGF2BP3. (<b>A</b>,<b>B</b>) Electrophoresis and sequencing results of the PCR product at the circIGF2BP3 junction site, respectively; (<b>C</b>,<b>D</b>) electrophoresis results and structural diagram of the full-length circIGF2BP3, respectively; (<b>E</b>) impact of RNase R treatment on circIGF2BP3 abundance and insulin-like growth factor 2 mRNA binding protein 3 (<span class="html-italic">IGF2BP3</span>) mRNA expression; (<b>F</b>) effect of different reverse transcription primers on the expression of circIGF2BP3 and <span class="html-italic">IGF2BP3</span> mRNA; (<b>G</b>) distribution of circIGF2BP3 and <span class="html-italic">IGF2BP3</span> mRNA in myoblasts. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 4). * <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.</p>
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<p>Tissue expression profiles of chicken circIGF2BP3 and <span class="html-italic">IGF2BP3</span> mRNA. (<b>A</b>,<b>C</b>,<b>E</b>) Expression patterns of circIGF2BP3 in <span class="html-italic">Yuexi frizzled feather chickens</span> at E14, E18, and 7W, respectively; (<b>B</b>,<b>D</b>,<b>F</b>) expression patterns of <span class="html-italic">IGF2BP3</span> mRNA in <span class="html-italic">Yuexi frizzled feather chickens</span> at E14, E18, and 7W, respectively. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 4). * <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.</p>
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<p>The temporal expression profile of circIGF2BP3 and <span class="html-italic">IGF2BP3</span> mRNA in chicken muscle tissue. (<b>A</b>,<b>C</b>) The expression pattern of circIGF2BP3 in the pectoral and thigh muscles of chickens at E10, E12, E14, E16, E18, and 1–8W, respectively; (<b>B</b>,<b>D</b>) the expression pattern of <span class="html-italic">IGF2BP3</span> mRNA in the pectoral and thigh muscles of chickens at E10, E12, E14, E16, E18, and 1-8W, respectively. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 4). * <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.</p>
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<p>The expression levels of circIGF2BP3, <span class="html-italic">IGF2BP3</span>, insulin-like growth factor 2 (<span class="html-italic">IGF2</span>), and myosin heavy chain (<span class="html-italic">MyHC</span>) mRNAs during the proliferation and differentiation of primary myoblasts in chickens. (<b>A</b>–<b>D</b>) The relative expression levels of circIGF2BP3, <span class="html-italic">IGF2BP3</span>, <span class="html-italic">IGF2</span>, and <span class="html-italic">MyHC</span> mRNAs were analyzed during the proliferation and differentiation of chicken primary myoblasts, respectively. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 4). * <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.</p>
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<p>Effect of circIGF2BP3 on the proliferation of primary myoblasts (Scale bar: 250 μm and 50 μm). (<b>A</b>) Results of the CCK-8 assay on chicken primary myoblasts after transfection with pCD2.1-circIGF2BP3 and pCD2.1-ciR (NC); (<b>B</b>) results of the EdU assay on primary myoblasts 48 h after transfection with pCD2.1-circIGF2BP3 and pCD2.1-ciR (NC) (40×, 200× magnification); (<b>C</b>) cell cycle analysis of primary myoblasts 48 h after circIGF2BP3 overexpression and transfection with pCD2.1-ciR; (<b>D</b>) relative expression levels of circIGF2BP3, <span class="html-italic">IGF2BP3</span>, <span class="html-italic">IGF2</span>, proliferating cell nuclear antigen (<span class="html-italic">PCNA</span>), cyclin D1 (<span class="html-italic">CCND1</span>), cyclin E1 (<span class="html-italic">CCNE1</span>), cyclin dependent kinase 2 (<span class="html-italic">CDK2</span>), and fibroblast growth factor 7 (<span class="html-italic">FGF7</span>) mRNAs in primary myoblasts 48 h post-circIGF2BP3 overexpression and transfection with pCD2.1-ciR; (<b>E</b>) protein levels of circIGF2BP3 and different proliferation marker genes after the overexpression of circIGF2BP3. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 3, 4, or 6). * <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.</p>
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<p>Effect of circIGF2BP3 on the differentiation of primary myoblasts (Scale bar: 50 μm). (<b>A</b>) MyHC immunofluorescent staining of primary myoblasts at 200× magnification. MyHC: indicated in red, serves as a molecular marker for myogenesis; DAPI staining in blue highlights cell nuclei; Merge illustrates the fusion of primary myoblasts into myotubes; the relative myotube area (%) after transfection with pCD2.1-circIGF2BP3 and pCD2.1-ciR (NC); (<b>B</b>) relative expression levels of circIGF2BP3, <span class="html-italic">IGF2BP3</span>, <span class="html-italic">IGF2</span>, <span class="html-italic">MyHC</span>, myoblast-determining 1 (<span class="html-italic">MyoD1</span>), myogenin (<span class="html-italic">MyoG</span>), and <span class="html-italic">Myomaker</span> mRNAs in primary myoblasts after 3 d of circIGF2BP3 overexpression and induced differentiation; (<b>C</b>) protein levels of myogenic marker genes after circIGF2BP3 overexpression and transfection with pCD2.1-ciR. Data is presented as mean ± SEM (<span class="html-italic">n</span> = 3 or 4). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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18 pages, 4439 KiB  
Article
Patulin Stimulates Progenitor Leydig Cell Proliferation but Delays Its Differentiation in Male Rats during Prepuberty
by Huitao Li, Ming Su, Hang Lin, Jingjing Li, Shaowei Wang, Lei Ye, Xingwang Li and Renshan Ge
Toxins 2023, 15(9), 581; https://doi.org/10.3390/toxins15090581 - 20 Sep 2023
Cited by 1 | Viewed by 1490
Abstract
Patulin is a mycotoxin with potential reproductive toxicity. We explored the impact of patulin on Leydig cell (LC) development in male rats. Male Sprague Dawley rats (21 days postpartum) were gavaged patulin at doses of 0.5, 1, and 2 mg/kg/day for 7 days. [...] Read more.
Patulin is a mycotoxin with potential reproductive toxicity. We explored the impact of patulin on Leydig cell (LC) development in male rats. Male Sprague Dawley rats (21 days postpartum) were gavaged patulin at doses of 0.5, 1, and 2 mg/kg/day for 7 days. Patulin markedly lowered serum testosterone at ≥0.5 mg/kg and progesterone at 1 and 2 mg/kg, while increasing LH levels at 2 mg/kg. Patulin increased the CYP11A1+ (cholesterol side-chain cleavage, a progenitor LC biomarker) cell number and their proliferation at 1 and 2 mg/kg. Additionally, patulin downregulated Lhcgr (luteinizing hormone receptor), Scarb1 (high-density lipoprotein receptor), and Cyp17a1 (17α-hydroxylase/17,20-lyase) at 1 and 2 mg/kg. It increased the activation of pAKT1 (protein kinase B), pERK1/2 (extracellular signal-related kinases 1 and 2), pCREB (cyclic AMP response binding protein), and CCND1 (cyclin D1), associated with cell cycle regulation, in vivo. Patulin increased EdU incorporation into R2C LC and stimulated cell cycle progression in vitro. Furthermore, patulin showed a direct inhibitory effect on 11β-HSD2 (11β-hydroxysteroid dehydrogenase 2) activity, which eliminates the adverse effects of glucocorticoids. This study provides insights into the potential mechanisms via which patulin affects progenitor LC development in young male rats. Full article
(This article belongs to the Section Mycotoxins)
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Graphical abstract

Graphical abstract
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<p>Structure of patulin, regimen, and hormonal levels. Chemical structure of patulin (<b>A</b>); regimen of patulin (<b>B</b>) (in vivo, including <a href="#toxins-15-00581-f001" class="html-fig">Figure 1</a>, <a href="#toxins-15-00581-f002" class="html-fig">Figure 2</a>, <a href="#toxins-15-00581-f003" class="html-fig">Figure 3</a>, <a href="#toxins-15-00581-f004" class="html-fig">Figure 4</a>, <a href="#toxins-15-00581-f005" class="html-fig">Figure 5</a> and <a href="#toxins-15-00581-f006" class="html-fig">Figure 6</a>); and serum testosterone (T) (<b>C</b>), progesterone (P4) (<b>D</b>), LH (<b>E</b>), T/LH ratio (<b>F</b>), P4/LH ratio (<b>G</b>), FSH (<b>H</b>), estradiol (E2) (<b>I</b>), and T/E2 ratio (<b>J</b>) levels. Means ± SEM; n = 6. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 show significant differences from the vehicle control.</p>
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<p>The effect of LC and Sertoli cell numbers after in vivo patulin exposure. (<b>A</b>–<b>D</b>): CYP11A1<sup>+</sup> LCs; (<b>E</b>–<b>H</b>): HSD11B1<sup>+</sup> LCs; (<b>I</b>–<b>L</b>): SOX9<sup>+</sup> Sertoli cells. (<b>A</b>,<b>E</b>,<b>I</b>): control; (<b>B</b>,<b>F</b>,<b>J</b>): 0.5 mg/kg/day patulin; (<b>C</b>,<b>G</b>,<b>K</b>): 1 mg/kg/day patulin; (<b>D</b>,<b>H</b>,<b>L</b>): 2 mg/kg/day patulin; (<b>M</b>,<b>N</b>,<b>O</b>): quantitative results. Means ± SEM; n = 6 (randomly selected). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 show significant differences from the vehicle control. Black arrows designate LCs. Green arrowheads designate Sertoli cells. Scale bar = 50 μm.</p>
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<p>Immunofluorescence staining of LC proliferation biomarker from rat testis after in vivo patulin exposure. (<b>A</b>–<b>D</b>): Immunofluorescence images of sections from rat testis after exposure to different doses of patulin (0, 0.5, 1, and 2 mg/kg/day). Each panel represents a different patulin concentration. (<b>E</b>–<b>H</b>): Enlarged images of specific regions within (<b>A</b>–<b>D</b>), indicated by yellow squares, highlighting the staining of PCNA (red—nucleus, proliferation biomarker) and CYP11A1 (green—cytoplasm, LC biomarker) and DAPI (blue—nucleus, counterstain). (<b>I</b>): Quantitative data for PCNA labeling (white arrow) of LCs. Means ± SEM; n = 6. * <span class="html-italic">p</span> &lt; 0.05 shows a significant difference from the vehicle control. Scale bar = 20 μm.</p>
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<p>Effects of patulin on mRNA expression in rat testis after in vivo patulin exposure. The testicular mRNA levels were measured using qPCR and adjusted to CYP11A1<sup>+</sup> LC number for LC genes. (<b>A</b>–<b>K</b>): LC genes (<span class="html-italic">Lhcgr</span>, <span class="html-italic">Scarb1</span>, <span class="html-italic">Star</span>, <span class="html-italic">Cyp11a1</span>, <span class="html-italic">Hsd3b1</span>, <span class="html-italic">Cyp17a1</span>, <span class="html-italic">Hsd17b3</span>, <span class="html-italic">Hsd11b1</span>, <span class="html-italic">Srd5a1</span>, <span class="html-italic">Insl3</span>, and <span class="html-italic">Akr1c14</span>); (<b>L</b>–<b>N</b>): Sertoli cell genes (<span class="html-italic">Fshr</span>, <span class="html-italic">Dhh</span>, and <span class="html-italic">Sox9</span>). Means ± SEM; n = 6. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 show significant differences from vehicle control.</p>
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<p>Levels of LC proteins in the testis after in vivo patulin exposure. (<b>A</b>): Western blot image; (<b>B</b>–<b>D</b>): Quantification of protein levels of LHCGR, SCARB1, CYP17A1 after normalized to ACTB (internal control). Mean ± SEM, n = 3–7 (randomly selected samples). * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, and *** <span class="html-italic">p &lt;</span> 0.001 show significant differences from vehicle control.</p>
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<p>Levels of signal proteins in the testis after in vivo patulin exposure. (<b>A</b>): Western blot image; (<b>B</b>–<b>G</b>): quantitative results. Protein levels were adjusted to ACTB (internal control). Means ± SEM; n = 4–9 (randomly selected testis samples). * <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 show significant differences from vehicle control.</p>
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<p>Patulin in vitro treatment regimen and cell viability, testosterone levels, and cell cycle analysis of R2C cells after patulin treatment for 24 h. (<b>A</b>): regimen; (<b>B</b>): cell viability (CCK8); (<b>C</b>): medium testosterone; (<b>D</b>): flow cytometry analysis of cell cycle; (<b>E</b>–<b>H</b>): quantification for G0/G1, S, G2/M, and S + G2/M phase populations, respectively. Means ± SEM; n = 3. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 show significant differences from the control.</p>
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<p>EdU incorporation into proliferative R2C cells after patulin in vitro treatment. (<b>A</b>): EdU (green nucleus), CYP11A1 (red cytoplasm), and DAPI (blue counterstain) in R2C cells after patulin treatment; (<b>B</b>): quantification of EdU labeling in R2C cells. Means ± SEM; n = 3. ** <span class="html-italic">p</span> &lt; 0.01 shows a significant difference from the control.</p>
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<p>Patulin’s inhibition, IC50, dithiothreitol (DTT) effect, and docking analysis of rat and human 11β-HSD2 enzymes. (<b>A</b>,<b>F</b>): Inhibition by patulin of rat and human 11β-HSD2, respectively; (<b>B</b>,<b>G</b>): IC<sub>50</sub> values for patulin for rat 11β-HSD2 and human 11β-HSD2, respectively; (<b>C</b>,<b>H</b>): effects of DTT on patulin-induced inhibition of rat and human 11β-HSD2, respectively. (<b>D</b>,<b>I</b>): Superimposed images of rat or human 11β-HSD2 with patulin and substrate cortisol, respectively. Red = patulin; blue = substrate cortisol. (<b>E</b>,<b>J</b>): Superimposed images of rat or human 11β-HSD2 with patulin and substrate cortisol, respectively. Purple = patulin, cyan = substrate cortisol, red circle = common contacting residues, and green dash line = hydrogen bonds. The Ki and ΔG values obtained through docking analysis are displayed beneath the panels of the figure, respectively, which contain the 3D/2D superimposed images. Means ± SEM; n = 4. * <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 show significant differences from the control.</p>
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<p>The mechanism of patulin action on main proteins in this study.</p>
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14 pages, 8739 KiB  
Article
Epigenetic Downregulation of Hsa-miR-193b-3p Increases Cyclin D1 Expression Level and Cell Proliferation in Human Meningiomas
by Paulina Kober, Beata Joanna Mossakowska, Natalia Rusetska, Szymon Baluszek, Emilia Grecka, Ryszard Konopiński, Ewa Matyja, Artur Oziębło, Tomasz Mandat and Mateusz Bujko
Int. J. Mol. Sci. 2023, 24(17), 13483; https://doi.org/10.3390/ijms241713483 - 30 Aug 2023
Cited by 1 | Viewed by 1306
Abstract
Meningiomas are common intracranial tumors in adults. Abnormal microRNA (miRNA) expression plays a role in their pathogenesis. Change in miRNA expression level can be caused by impaired epigenetic regulation of miRNA-encoding genes. We found the genomic region covering the MIR193B gene to be [...] Read more.
Meningiomas are common intracranial tumors in adults. Abnormal microRNA (miRNA) expression plays a role in their pathogenesis. Change in miRNA expression level can be caused by impaired epigenetic regulation of miRNA-encoding genes. We found the genomic region covering the MIR193B gene to be DNA hypermethylated in meningiomas based on analysis of genome-wide methylation (HumanMethylation450K Illumina arrays). Hypermethylation of MIR193B was also confirmed via bisulfite pyrosequencing. Both hsa-miR-193b-3p and hsa-miR-193b-5p are downregulated in meningiomas. Lower expression of hsa-miR-193b-3p and higher MIR193B methylation was observed in World Health Organization (WHO) grade (G) II/III tumors as compared to GI meningiomas. CCND1 mRNA was identified as a target of hsa-miR-193b-3p as further validated using luciferase reporter assay in IOMM-Lee meningioma cells. IOMM-Lee cells transfected with hsa-miR-193b-3p mimic showed a decreased cyclin D1 level and lower cell viability and proliferation, confirming the suppressive nature of this miRNA. Cyclin D1 protein expression (immunoreactivity) was higher in atypical than in benign meningiomas, accordingly to observations of lower hsa-miR-193b-3p levels in GII tumors. The commonly observed hypermethylation of MIR193B in meningiomas apparently contributes to the downregulation of hsa-miR-193b-3p. Since hsa-miR-193b-3p regulates proliferation of meningioma cells through negative regulation of cyclin D1 expression, it seems to be an important tumor suppressor in meningiomas. Full article
(This article belongs to the Section Molecular Neurobiology)
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Figure 1

Figure 1
<p>The results of comparison of DNA methylation profile in human meningiomas and normal meningeal samples. (<b>A</b>) The genomic map of differentially methylated regions (DMRs); (<b>B</b>) comparison of DNA methylation (%) at CpGs covered by HumanMethylation450 K probes located in <span class="html-italic">MIR193B</span> and <span class="html-italic">MIR365A</span> genes at chr16.p13.12; (<b>C</b>) Genomic map of chr16.p13.12 locus presenting the positions of <span class="html-italic">MIR193B</span> and <span class="html-italic">MIR365A</span> genes, the identified DMRs, and differentially methylated probes (DMPs) as well as DNA methylation level in meningiomas (pink line) and normal samples (blue line). CGI – CpG Island; FDR – false discovery rate.</p>
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<p>Relative expression level of hsa-miR-193b-3p, hsa-miR-193b-5p, hsa-miR-365a-3p, and hsa-miR-365a-5p in meningiomas (<span class="html-italic">n</span> = 58) and normal meninges (<span class="html-italic">n</span> = 4). For significant differences between normal meninges and meningiomas <span class="html-italic">p</span>-values are shown above the bars, ns—not significant.</p>
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<p>The comparison of benign meningiomas grade (G) I with atypical (GII) and anaplastic (GIII) meningiomas in terms of <span class="html-italic">MIR193B</span> DNA methylation and expression levels. (<b>A</b>) Comparison of the average methylation level at CpGs covered with bisulfite pyrosequencing assay. (<b>B</b>) Comparison of the expression levels of hsa-miR-193b-3p and hsa-miR-193b-5p. Each dot represents a particular tumor sample.</p>
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<p>Role of hsa-miR-193b-3p in the regulation of cyclin D1 expression in meningioma IOMM-Lee cell line. (<b>A</b>) Decrease of cyclin D1 expression in IOMM-Lee cells transfected with hsa-miR-193b-3p miRNA mimic (western blot membrane and densitometry result). (<b>B</b>) Location of the predicted hsa-miR-193b-3p target site in <span class="html-italic">CCND1</span> 3′ untranslated region (UTR). (<b>C</b>) The results of luciferase reporter assays verifying the interaction between the fragment of 3′UTR of <span class="html-italic">CCND1</span> (<span class="html-italic">CCND1</span> 3′UTR) and hsa-miR-193b-3p mimic (miR-193b). pmirGLO plasmid without any insert (empty vector) as well as pmirGLO plasmid with mutated sequence of 3′UTR of <span class="html-italic">CCND1</span> target site (<span class="html-italic">CCND1</span> m3′UTR) were used as controls. Asterisks indicate the statistical significance of differences (* <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).</p>
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<p>Suppressive role of hsa-miR-193b-3p in IOMM-Lee meningioma cells. (<b>A</b>) Reduction of viability in cells transfected with hsa-miR-193b-3p mimic (MTT assay). Two independent replicates of the experiment are presented. (<b>B</b>) Lowered proliferation of cells transfected with hsa-miR-193b-3p mimic (BrdU incorporation-based test). Two independent replicates of the experiment are presented. (<b>C</b>) The results of scratch assay show no difference in the migration of cells transfected with hsa-miR-193b-3p or negative control mimic. Images were taken with the use of Olympus CKX53 microscope with magnification × 40.</p>
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<p>Cyclin D1 protein expression in meningiomas. (<b>A</b>) Comparison of nuclear immunoreactivity against cyclin D1 in benign and atypical meningiomas, quantified with H-score. Each dot represents a particular tumor sample. (<b>B</b>) Representative examples of immunohistochemical staining.</p>
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15 pages, 2257 KiB  
Article
The Synergistic Antitumor Effect of Decitabine and Vorinostat Combination on HepG2 Human Hepatocellular Carcinoma Cell Line via Epigenetic Modulation of Autophagy–Apoptosis Molecular Crosstalk
by Basant M. Salama, Maged W. Helmy, Hosny Fouad, Marium M. Shamaa and Maha E. Houssen
Curr. Issues Mol. Biol. 2023, 45(7), 5935-5949; https://doi.org/10.3390/cimb45070375 - 16 Jul 2023
Cited by 4 | Viewed by 1895
Abstract
Hepatocellular carcinoma (HCC) is a worldwide health issue. Epigenetic alterations play a crucial role in HCC tumorigenesis. Using epigenetic modulators for HCC treatment confers a promising therapeutic effect. The aim of this study was to explore the effect of a decitabine (DAC) and [...] Read more.
Hepatocellular carcinoma (HCC) is a worldwide health issue. Epigenetic alterations play a crucial role in HCC tumorigenesis. Using epigenetic modulators for HCC treatment confers a promising therapeutic effect. The aim of this study was to explore the effect of a decitabine (DAC) and vorinostat (VOR) combination on the crosstalk between apoptosis and autophagy in the HCC HepG2 cell line at 24 h and 72 h. Median inhibitory concentrations (IC50s) of VOR and DAC were assessed in the HepG2 cell line. The activity of caspase-3 was evaluated colorimetrically, and Cyclin D1(CCND1), Bcl-2, ATG5, ATG7, and P62 levels were assessed using ELISA at different time intervals (24 h and 72 h), while LC3IIB and Beclin-1gene expression were measured by using qRT-PCR. The synergistic effect of VOR and DAC was confirmed due to the observed combination indices (CIs) and dose reduction indices (DRIs). The combined treatment with both drugs inhibited the proliferation marker (CCND1), and enhanced apoptosis compared with each drug alone at 24 h and 72 h (via active caspase-3 upregulation and Bcl-2 downregulation). Moreover, the combination induced autophagy as an early event via upregulation of Beclin-1, LC3IIB, ATG5, and ATG7 gene expression. The initial induction of autophagy started to decrease after 72 h due to Beclin-1 downregulation, and there was decreased expression of LC3IIB compared with the value at 24 h. Herein, epigenetic modulation via the VOR/DAC combination showed an antitumor effect through the coordination of an autophagy–apoptosis crosstalk and promotion of autophagy-induced apoptosis, which ultimately led to the cellular death of HCC cancer cells. Full article
(This article belongs to the Section Molecular Medicine)
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Figure 1
<p>Effects of VOR, DAC, and their combination on HepG2 cell viability after 24 h (<b>a</b>–<b>c</b>) and 72 h (<b>d</b>–<b>f</b>) treatment. The viability of HepG2 cells treated with vorinostat (VOR, 0.25–8 μM) (<b>a</b>,<b>d</b>), decitabine (DAC, 3.12–100 μM) (<b>b</b>,<b>e</b>), and combination of the two drugs (<b>c</b>,<b>f</b>). Data points indicate the mean ± SEM, each conducted in triplicate. *: A significant difference for VOR and/or DAC vs. the corresponding control group with <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>HepG2 cell line morphology after 24 h and 72 h treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR).</p>
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<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and their combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on proliferation and apoptosis markers in HepG2 cells. The levels of tumor markers of proliferation, (<b>a</b>) (Cyclin D1; CCND1), and apoptosis, (<b>b</b>) (active caspase-3), (<b>c</b>) (Bcl-2), were measured using ELISA or colorimetrically. Data represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. the DAC group, and Δ: <span class="html-italic">p</span> &lt; 0 05 vs. VOR group; these designations indicate statistically significant differences between groups at the same time interval, while significant differences between two time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and their combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on autophagy in HepG2 cells. qRT-PCR was used to determine the fold change (RQ) in <span class="html-italic">LC3II</span> (<b>a</b>) and <span class="html-italic">Beclin-1</span> (<b>b</b>) gene expression in each treatment group compared to the control group. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. the DAC group, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group. These designations indicate statistically significant differences between groups at the same time interval, while significant differences between two- time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on p62 expression in HepG2 cells measured using ELISA technique. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group.</p>
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<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on ATG5 (<b>a</b>) and ATG7 (<b>b</b>) expression in HepG2 cells measured using ELISA technique. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. DAC group, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group. These designations indicate statistically significant differences between groups at the same time interval, while significant differences between two time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
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