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24 pages, 9555 KiB  
Article
A Novel Prognostic Signature of Mitophagy-Related E3 Ubiquitin Ligases in Breast Cancer
by Kangjing Bian, Chihyu Yang, Feng Zhang and Lei Huang
Int. J. Mol. Sci. 2025, 26(4), 1551; https://doi.org/10.3390/ijms26041551 - 12 Feb 2025
Viewed by 440
Abstract
Mitophagy plays a critical role in maintaining mitochondrial quality and cellular homeostasis. But the specific contribution of mitophagy-related E3 ubiquitin ligases to prognoses remains largely unexplored. In this study, we identified a novel mitophagy-related E3 ubiquitin ligase prognostic signature using least absolute shrinkage [...] Read more.
Mitophagy plays a critical role in maintaining mitochondrial quality and cellular homeostasis. But the specific contribution of mitophagy-related E3 ubiquitin ligases to prognoses remains largely unexplored. In this study, we identified a novel mitophagy-related E3 ubiquitin ligase prognostic signature using least absolute shrinkage and selector operator (LASSO) and multivariate Cox regression analyses in breast cancer. Based on median risk scores, patients were divided into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore the biological differences between the two groups. Immune infiltration, drug sensitivity, and mitochondrial-related phenotypes were also analyzed to evaluate the clinical implications of the model. A four-gene signature (ARIH1, SIAH2, UBR5, and WWP2) was identified, and Kaplan–Meier analysis demonstrated that the high-risk group had significantly worse overall survival (OS). The high-risk patients exhibited disrupted mitochondrial metabolism and immune dysregulation with upregulated immune checkpoint molecules. Additionally, the high-risk group exhibited higher sensitivity to several drugs targeting the Akt/PI3K/mTORC1 signaling axis. Accompanying mitochondrial metabolic dysregulation, mtDNA stress was elevated, contributing to activation of the senescence-associated secretory phenotype (SASP) in the high-risk group. In conclusion, the identified signature provides a robust tool for risk stratification and offers insights into the interplay between mitophagy, immune modulation, and therapeutic responses for breast cancer. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Cancer Invasion and Metastasis)
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Figure 1

Figure 1
<p>Workflow diagram, showing the flowchart graph of this research.</p>
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<p>LASSO regression and clinical analyses of prognostic model in breast cancer. (<b>A</b>,<b>B</b>) Results of LASSO regression analysis. (<b>C</b>) Kaplan–Meier survival curves of 4 mitophagy-related E3 ubiquitin ligases (ARIH1, SIAH2, UBR5, and WWP2) in BRCA. (<b>D</b>) Multivariate Cox regression analysis of the four selected E3 ubiquitin ligases (ARIH1, SIAH2, UBR5, and WWP2). HRs with 95% CI are displayed. The square represents the HR, and the dashed line indicates 95% CI.</p>
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<p>Validation of the ASUW prognostic model in TCGA-BRCA and GSE25066 databases. (<b>A</b>,<b>B</b>) Kaplan–Meier survival curves for high-risk and low-risk groups in the TCGA-BRCA cohort and GSE25066 cohort, based on the ASUW risk regression model. (<b>C</b>,<b>D</b>) The distribution of the risk scores, with scatter plots showing whether the samples were alive and heatmaps for the four E3 ubiquitin ligases in the TCGA-BRCA cohort and GSE25066 cohort. Top: Risk scores for each patient, classified as high-risk (red) and low-risk (blue) groups. Middle: Survival time and status (alive or dead). Bottom: Heatmaps of expression levels of the four E3 ubiquitin ligases in two groups. (<b>E</b>) ROC curve analysis for 5-, 10-, and 15-year survival predictions in the TCGA-BRCA cohort. (<b>F</b>) ROC curve analysis for 1-, 3-, and 5-year survival predictions in the GSE25066 cohort.</p>
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<p>Enrichment analyses of DEGs of two groups. (<b>A</b>) GO enrichment analysis of DEGs between the high- and low-risk groups based on the ASUW model. The size of the dots indicates the ratio of genes enriched, while the colors represent the adjusted <span class="html-italic">p</span> value. (<b>B</b>) GSEA of “Hallmark gene sets” between high-risk and low-risk groups. Significant pathways were categorized as activated (left panel) or suppressed (right panel). The size of the dots represents the ratio of enriched genes, while the color indicates the adjusted <span class="html-italic">p</span> value. (<b>C</b>) KEGG enrichment analysis of DEGs. A chord diagram demonstrates the associations between the 5 most enriched KEGG pathways and the corresponding genes. The colors of the bands correspond to log (foldchange (FC)) values, showing upregulated (red) and downregulated (blue) genes in the high-risk group. (<b>D</b>) GSEA of “Reactome gene sets” between high-risk and low-risk groups. Significant pathways were categorized as activated (left panel) or suppressed (right panel). The size of the dots represents the ratio of enriched genes, while the color indicates the adjusted <span class="html-italic">p</span> value.</p>
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<p>Immune-related analysis between two groups. (<b>A</b>) Total ESTIMATE score, indicating tumor purity and immune-stromal component differences (<span class="html-italic">p</span> = 0.00016). (<b>B</b>) Immune score, indicating immune infiltration differences (<span class="html-italic">p</span> = 0.0013). (<b>C</b>) Stromal score, indicating stromal content differences (<span class="html-italic">p</span> = 0.0011). (<b>D</b>) Tumor purity score, indicating tumor purity differences (<span class="html-italic">p</span> = 0.00016). (<b>E</b>) Comparison of immune checkpoint-related gene expression between the two risk groups. (<b>F</b>) Proportions of immune cell types with significant differences between high- and low-risk groups. Statistical significance is indicated as not significant (ns). * <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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<p>Predicted drug sensitivity differences between two groups. The boxplots depict the predicted drug sensitivity (IC50) of 12 drugs between the high-risk (red) and low-risk (blue) groups as predicted by oncoPredict analysis. Drugs exhibiting statistically significant differences in sensitivity (<span class="html-italic">p</span> &lt; 0.05) are shown. The high-risk group displayed higher sensitivity to drugs including MK-2206, pictilisib, rapamycin, sorafenib, GSK1904529A, uprosertib, LGK974, elephantin, AZD5363, ipatasertib, and AT13148. Conversely, the low-risk group demonstrated greater sensitivity to BI-2536. Statistical significance is indicated as * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Comparative expression analysis of mtDNA stress and SASP- and VDIM-related genes between two groups. (<b>A</b>) Differential expression levels of mtDNA stress-related genes between high- and low-risk groups. (<b>B</b>) Differential expression levels of SASP-related genes. (<b>C</b>) Differential expression levels of critical proteins involved in the VDIM process. Statistical significance is indicated as not significant (ns). * <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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21 pages, 1861 KiB  
Article
Rapid Detection of PML::RARA Fusions in Acute Promyelocytic Leukemia: CRISPR/Cas9 Nanopore Sequencing with Adaptive Sampling
by William Middlezong, Victoria Stinnett, Michael Phan, Brian Phan, Laura Morsberger, Melanie Klausner, Jen Ghabrial, Natalie DeMetrick, Jing Zhu, Trisha James, Aparna Pallavajjala, Christopher D. Gocke, Maria R. Baer and Ying S. Zou
Biomolecules 2024, 14(12), 1595; https://doi.org/10.3390/biom14121595 - 13 Dec 2024
Viewed by 1160
Abstract
Acute promyelocytic leukemia (APL) accounts for approximately 10–15% of newly diagnosed acute myeloid leukemia cases and presents with coagulopathy and bleeding. Prompt diagnosis and treatment are required to minimize early mortality in APL as initiation of all-trans retinoic acid therapy rapidly reverses coagulopathy. [...] Read more.
Acute promyelocytic leukemia (APL) accounts for approximately 10–15% of newly diagnosed acute myeloid leukemia cases and presents with coagulopathy and bleeding. Prompt diagnosis and treatment are required to minimize early mortality in APL as initiation of all-trans retinoic acid therapy rapidly reverses coagulopathy. The PML::RARA fusion is a hallmark of APL and its rapid identification is essential for rapid initiation of specific treatment to prevent early deaths from coagulopathy and bleeding and optimize patient outcomes. Given limitations and long turnaround time of current gene fusion diagnostic strategies, we have developed a novel amplification-free nanopore sequencing-based approach with low cost, easy setup, and fast turnaround time. We termed the approach CRISPR/Cas9-enriched nanopore sequencing with adaptive sampling (CENAS). Using CENAS, we successfully sequenced breakpoints of typical and atypical PML::RARA fusions in APL patients. Compared with the standard-of-care genetic diagnostic tests, CENAS achieved good concordance in detecting PML::RARA fusions in this study. CENAS allowed for the identification of sequence information of fusion breakpoints involved in typical and atypical PML::RARA fusions and identified additional genes (ANKFN1 and JOSD1) and genomic regions (13q14.13) involving the atypical fusions. To the best of our knowledge, involvements of the ANKFN1 gene, the JOSD1 gene, and the 13q14.13 genomic region flanking with the SIAH3 and ZC3H13 genes have not been reported in the atypical PML::RARA fusions. CENAS has great potential to develop as a point-of-care test enabling immediate, low-cost bedside diagnosis of APL patients with a PML::RARA fusion. Given the early death rate in APL patients still reaches 15%, and ~10% of APL patients are resistant to initial therapy or prone to relapse, further sequencing studies of typical and atypical PML::RARA fusion might shed light on the pathophysiology of the disease and its responsiveness to treatment. Understanding the involvement of additional genes and positional effects related to the PML and RARA genes could shed light on their role in APL and may aid in the development of novel targeted therapies. Full article
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Graphical abstract

Graphical abstract
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<p>The CRISPR/Cas9-enriched nanopore sequencing with the adaptive sampling (CENAS) approach to reveal <span class="html-italic">PML::RARA</span> fusions in APL patients. The entire procedure includes DNA extraction from blood or marrow, CRISPR/Cas9-guided enrichment of targeted <span class="html-italic">PML</span> and <span class="html-italic">RARA</span> genomic regions, nanopore sequencing with adaptive sampling, and sequencing data analysis to reveal sequences involving <span class="html-italic">PML::RARA</span> fusions. hr: hours; min: minutes.</p>
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<p>Atypical <span class="html-italic">PML::RARA</span> fusions in case #13. (<b>a</b>) Partial karyogram shows a t(15;22;17) three-way translocation (white arrows for abnormal derivative chromosomes). Below: Interphase FISH shows an atypical FISH signal pattern (2R2G1F). The red arrows point to a <span class="html-italic">PML::RARA</span> fusion. (<b>b</b>) CENAS shows sequence reads of a <span class="html-italic">PML::RARA</span> fusion involving the <span class="html-italic">PML</span> gene on chromosome 15q and the <span class="html-italic">RARA</span> gene on 17q (red arrows), and a <span class="html-italic">JOSD1::RARA</span> fusion involving the <span class="html-italic">RARA</span> gene on 17q and the <span class="html-italic">JOSD1</span> gene on chromosome 22 (green arrows). Sequences were aligned to human genome builder GRCh37/hg19. (<b>c</b>) Diagram of the t(15;22;17) three-way translocation and atypical <span class="html-italic">PML::RARA</span> fusion. Black arrows point to derivative chromosomes involved in this translocation.</p>
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<p>Cryptic <span class="html-italic">PML::RARA</span> fusion in case #12. (<b>a</b>) Partial karyogram shows a t(13;15) reciprocal translocation (white arrows for abnormal derivative chromosomes) with two normal chromosomes 17. Interphase FISH shows an atypical FISH signal pattern (1R2G1F). The red arrows point to a <span class="html-italic">PML::RARA</span> fusion. Metaphase FISH shows an insertional <span class="html-italic">PML::RARA</span> fusion into the derivative chromosome 15 formed by t(13;15) translocation, ish der(15)t(13;15)(q14;q24) ins(15;17)(q24;q21q21) (<span class="html-italic">PML</span> +, <span class="html-italic">RARA</span> +). (<b>b</b>) CENAS shows sequence reads of a complex atypical <span class="html-italic">PML::RARA</span> fusion involving chromosomes 13, 15, and 17. Red arrows point to a <span class="html-italic">PML::RARA</span> fusion/t(15;17) translocation, green arrows point to a fusion of the <span class="html-italic">PML</span> gene and 13q14.13 (chr13:46,515,010), and black arrows point to fusions of the <span class="html-italic">RARA</span> gene and 13q14.13 (chr13:46,515,891). These data support the presence of complex fusions and rearrangements. Sequences were aligned to human genome builder GRCh37/hg19.</p>
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<p>Atypical <span class="html-italic">PML::RARA</span> fusion in case #11. (<b>a</b>) Interphase FISH shows an atypical FISH signal pattern (1R2G1F). The red arrows point to a <span class="html-italic">PML::RARA</span> fusion. (<b>b</b>) CENAS reveals an atypical <span class="html-italic">PML::RARA</span> fusion (a likely insertional fusion). Red arrows point to a <span class="html-italic">PML::RARA</span> fusion, green arrows point to the <span class="html-italic">PML</span> gene fused to the <span class="html-italic">ANKFN1</span> gene on 17q22, and black arrows point to the <span class="html-italic">RARA</span> gene fused to the <span class="html-italic">ANKFN1</span> gene on 17q22. These data suggest the presence of a likely insertional <span class="html-italic">PML::RARA</span> fusion into a derivative chromosome 17. Sequences were aligned to human genome builder GRCh37/hg19.</p>
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<p>Multiple <span class="html-italic">PML::RARA</span> fusions in case #14. (<b>a</b>) Partial karyogram shows der(15), −16, +17, ider(17)(q10)t(15;17)x2, and +22 (white arrows for abnormalities). The red box shows isoderivative (ider) chromosome 17q involving t(15;17) translocations on both 17q arms. Interphase FISH reveals multiple fusions. (<b>b</b>) CENAS displays a higher number of sequencing fusion reads (indicated by red arrows) compared to non-fusion reads, due to the presence of multiple <span class="html-italic">RARA::PML</span> fusions in this case. Sequences were aligned to human genome builder GRCh37/hg19.</p>
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23 pages, 9129 KiB  
Article
Virtual Screening, Molecular Dynamics, and Mechanism Study of Homeodomain-Interacting Protein Kinase 2 Inhibitor in Renal Fibroblasts
by Xinlan Hu, Yan Wu, Hanyi Ouyang, Jiayan Wu, Mengmeng Yao, Zhuo Chen and Qianbin Li
Pharmaceuticals 2024, 17(11), 1420; https://doi.org/10.3390/ph17111420 - 23 Oct 2024
Viewed by 1186
Abstract
Background/Objectives: Homeodomain-interacting protein kinase 2 (HIPK2) is critically involved in the progression of renal fibrosis. This study aims to identify and characterize a novel HIPK2 inhibitor, CHR-6494, and investigate its therapeutic potential. Methods: Using structure-based virtual screening and molecular dynamics simulations, we identified [...] Read more.
Background/Objectives: Homeodomain-interacting protein kinase 2 (HIPK2) is critically involved in the progression of renal fibrosis. This study aims to identify and characterize a novel HIPK2 inhibitor, CHR-6494, and investigate its therapeutic potential. Methods: Using structure-based virtual screening and molecular dynamics simulations, we identified CHR-6494 as a potent HIPK2 inhibitor with an IC50 of 0.97 μM. The effects of CHR-6494 on the phosphorylation of p53 in Normal Rattus norvegicus kidney cells (NRK-49F) induced by transforming growth factor-β (TGF-β) were assessed, along with its impact on TGF-β signaling and downstream profibrotic markers. Results: CHR-6494 significantly reduces p53 phosphorylation induced by TGF-β and enhances the interaction between HIPK2 and seven in absentia 2 (SIAH2), facilitating HIPK2 degradation via proteasomal pathways. Both CHR-6494 and Abemaciclib inhibit NRK-49F cell proliferation and migration induced by TGF-β, suppressing TGF-β/Smad3 signaling and decreasing profibrotic markers such as Fibronectin I (FN-I) Collagen I and α-smooth muscle actin (α-SMA). Additionally, these compounds inhibit nuclear factor kappa-B (NF-κB) signaling and reduce inflammatory cytokine expression. Conclusions: The study highlights the dual functionality of HIPK2 kinase inhibitors like CHR-6494 and Abemaciclib as promising therapeutic candidates for renal fibrosis and inflammation. The findings provide new insights into HIPK2 inhibition mechanisms and suggest pathways for the design of novel HIPK2 inhibitors in the future. Full article
(This article belongs to the Special Issue Small-Molecule Inhibitors for Novel Therapeutics)
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Graphical abstract

Graphical abstract
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<p>Representative structures of HIPK2 inhibitors.</p>
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<p>Screening out four compounds in virtual screening. (<b>A</b>) Virtual screening flowchart. MDS stands for Molecular Dynamics Simulation. Yellow indicates molecular and grey indicates protein residues. (<b>B</b>) After MOE docking screening, 12 compounds were found. Grey indicates protein residues and green indicates 12 compounds. (<b>C</b>) The scores for these 12 molecules after docking with Gnina are shown. Blue indicates docking at the ligand binding site while red indicates docking on the entire protein.</p>
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<p>Schematic diagram of molecular docking of the four compounds obtained through virtual screening. (<b>A</b>) The structural formulas of the four compounds. (<b>B</b>–<b>E</b>) represent the docking results for each molecule. Yellow molecules represent the results obtained through MOE docking, green molecules represent docking at the binding site using Gnina, and purple molecules represent docking on the entire protein using Gnina.</p>
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<p>Interaction diagrams derived from 100 ns of MD simulation trajectories, depicting plots of HIPK2 with four compounds. (<b>A</b>) The plot of RMSD values over 100 ns for the five complexes. (<b>B</b>) The plot of RMSF values over 100 ns for the five complexes. (<b>C</b>) The plot of Rg values over 100 ns for the five complexes. (<b>D</b>) The plot of hydrogen bond numbers over 100 ns for the five complexes.</p>
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<p>Molecular dynamics simulations, utilizing free energy landscape plots, elucidated the binding modes of HIPK2 with four compounds. The left graphs of (<b>A</b>–<b>E</b>) ((<b>A</b>) MU135-HIPK2, (<b>B</b>) T2476-HIPK2, (<b>C</b>) T16550-HIPK2, (<b>D</b>) T15617-HIPK2, (<b>E</b>) T9521-HIPK2) respectively display the free energy landscape plots, with RMSD on the horizontal axis and Rg on the vertical axis. The blue regions represent areas of lower energy, indicating relative stability of the protein complexes. The right graphs of (<b>A</b>–<b>E</b>) illustrate conformations of the protein–ligand complexes extracted from the lowest energy points on the free energy landscape plots.</p>
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<p>Interaction diagrams derived from 200 ns MD simulation trajectories, depicting plots of HIPK2 with Abemaciclib and CHR-6494. The blue regions represent Abemaciclib–HIPK2, while the red regions represent CHR-6494-HIPK2. (<b>A</b>) The plot of RMSD values over 200 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2. (<b>B</b>) The plot of RMSF values over 200 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2. (<b>C</b>) The plot of RMSF values over 200 ns for Abemaciclib and CHR-6494. (<b>D</b>) The plot of hydrogen bond numbers over 200 ns for Abemaciclib and CHR-6494 bound to HIPK2. (<b>E</b>) The plot of binding energy from 120 to 140 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2, calculated by MM-PBSA. (<b>F</b>) The plot of ΔE<sub>MM</sub> (the total potential energy of the system) from 120 to 140 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2. (<b>G</b>) The plot of ΔE<sub>polar</sub> (polar interaction) from 120 to 140 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2. (<b>H</b>) The plot of ΔE<sub>nonpolar</sub> (nonpolar interaction) values from 120 to 140 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2. (<b>I</b>) The plot of interaction energy from 120 to 140 ns for Abemaciclib–HIPK2 and CHR-6494-HIPK2.</p>
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<p>CHR-6494 and Abemaciclib suppress the proliferation and migration of TGF-β-induced NRK-49F cells. (<b>A</b>) Representative images of the colony formation assay. (<b>B</b>) Representative images of the cell scratch assay. (<b>C</b>) Quantitative data analysis of colony numbers for Abemaciclib and CHR-6494 in NRK-49F cells induced by 10 ng/mL of TGF-β. (<b>D</b>) Quantitative data analysis of cell migration distance for Abemaciclib and CHR-6494 in NRK-49F cells induced by 10 ng/mL of TGF-β. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group.</p>
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<p>CHR-6494 inhibits multiple profibrotic signaling pathways in NRK-49F cells treated with TGF-β. (<b>A</b>) The role of HIPK2 in modulating signaling pathways and associated regulatory factors was investigated. (<b>B</b>) The expression levels of Fn-I, Collagen I, p-p53 (Ser46), p-Smad 3, and α-SMA proteins were measured by Western blot analysis. (<b>C</b>–<b>G</b>) Quantification of the ratios of Fn-I, Collagen I, p-p53 (Ser 46), p-smad 3, and α-SMA normalized to β-actin. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 versus the Control group, **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group.</p>
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<p>CHR-6494 and Abemaciclib mitigate NF-κB activation in HK-2 cells treated with 10 ng/mL TNF-α for 24 h in vitro. (<b>A</b>) The expression levels of p-p65, p65, and IL-6 proteins were measured by Western blot analysis. (<b>B</b>) Quantification of the ratios of p-p65, p65, and IL-6 normalized to β-actin. (<b>C</b>) Quantification of the ratios of p-p65 normalized to p65. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 versus the Control group, **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, versus the Control + TGF-β group.</p>
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<p>CHR-6494 mitigates the heightened expression of HIPK2 in NRK-49F cells induced by 10 ng/mL of TGF-β for 24 h in vivo. (<b>A</b>) The expression levels of HIPK2 proteins were measured by Western blot analysis. (<b>B</b>) Quantification of the ratios of HIPK2 normalized to β-actin. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, # <span class="html-italic">p</span> &lt; 0.05 versus the Control group, **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group. (<b>C</b>) The mRNA levels of HIPK2 in the NRK-49F cells were determined by real-time polymerase chain reaction and presented as fold induction over control. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, # <span class="html-italic">p</span> &lt; 0.05 versus the Control group, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group. (<b>D</b>) The expression levels of HIPK2 proteins were measured by Western blot analysis under the condition of MG132 treatment. (<b>E</b>) Quantification of the ratios of HIPK2 normalized to β-actin was performed with MG132 treatment. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, **** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group. (<b>F</b>) Co-IP results indicate that CHR-6494 promotes ubiquitination of HIPK2 in NRK-49F cells. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3.</p>
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<p>CHR-6494 and Abemaciclib enhance TGF-β-induced apoptosis in NRK-49F cells treated with 10 ng/mL of TGF-β for 24 h in vivo. (<b>A</b>,<b>C</b>) Scattergram of Abemaciclib and CHR-6494 on the apoptosis and (<b>B</b>,<b>D</b>) quantitative data analysis of apoptotic NRK-49F. “0/+,4/+,8/+,12/+,18/+” means NRK-49F cells after TGF-β stimulation, treated with different concentrations of Abemaciclib or CHR-6494. (<b>E</b>) The expression levels of caspase 3 and cleaved caspase 3 proteins were measured by Western blot analysis. (<b>F</b>) Quantification of the ratios of cleaved caspase 3 normalized to caspase 3. Data are presented as mean ± SEM, <span class="html-italic">n</span> = 3; “ns” stands for no significant difference, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus the Control + TGF-β group.</p>
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17 pages, 4440 KiB  
Article
Reactive Oxygen Species Induction by Hepatitis B Virus: Implications for Viral Replication in p53-Positive Human Hepatoma Cells
by Yuna Jeong, Jiwoo Han and Kyung Lib Jang
Int. J. Mol. Sci. 2024, 25(12), 6606; https://doi.org/10.3390/ijms25126606 - 15 Jun 2024
Cited by 3 | Viewed by 1229
Abstract
Hepatitis B virus (HBV) infects approximately 300 million people worldwide, causing chronic infections. The HBV X protein (HBx) is crucial for viral replication and induces reactive oxygen species (ROS), leading to cellular damage. This study explores the relationship between HBx-induced ROS, p53 activation, [...] Read more.
Hepatitis B virus (HBV) infects approximately 300 million people worldwide, causing chronic infections. The HBV X protein (HBx) is crucial for viral replication and induces reactive oxygen species (ROS), leading to cellular damage. This study explores the relationship between HBx-induced ROS, p53 activation, and HBV replication. Using HepG2 and Hep3B cell lines that express the HBV receptor NTCP, we compared ROS generation and HBV replication relative to p53 status. Results indicated that HBV infection significantly increased ROS levels in p53-positive HepG2-NTCP cells compared to p53-deficient Hep3B-NTCP cells. Knockdown of p53 reduced ROS levels and enhanced HBV replication in HepG2-NTCP cells, whereas p53 overexpression increased ROS and inhibited HBV replication in Hep3B-NTCP cells. The ROS scavenger N-acetyl-L-cysteine (NAC) reversed these effects. The study also found that ROS-induced degradation of the HBx is mediated by the E3 ligase Siah-1, which is activated by p53. Mutations in p53 or inhibition of its transcriptional activity prevented ROS-mediated HBx degradation and HBV inhibition. These findings reveal a p53-dependent negative feedback loop where HBx-induced ROS increases p53 levels, leading to Siah-1-mediated HBx degradation and HBV replication inhibition. This study offers insights into the molecular mechanisms of HBV replication and identifies potential therapeutic targets involving ROS and p53 pathways. Full article
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Figure 1
<p>Exploring the relationship between ROS induction and HBV replication in p53-positive human hepatoma cells: (<b>a</b>) HepG2-NTCP and Hep3B-NTCP cells were infected with HBV at 10 or 50 MOI per cell for 24 h in DMEM with 2% DMSO and 4% PEG 8000. The cells were washed twice with DMEM without serum and incubated for an additional three days in DMEM with 3% FBS, 2% DMSO, and 4% PEG 8000. Cell lysates were analyzed by western blotting to measure the levels of indicated proteins. (<b>b</b>) Levels of HBeAg derived from the cells prepared in (<b>a</b>) were detected by ELISA. Results are presented as mean ± standard deviation from five independent experiments (<span class="html-italic">n</span> = 5). (<b>c</b>) Levels of extracellular HBV DNA from (<b>a</b>) were determined using both conventional PCR and quantitative real-time PCR (qPCR) (<span class="html-italic">n</span> = 4). (<b>d</b>) Intracellular ROS levels were assessed with chloromethyl dichlorodihydrofluorescein diacetate (CM-H2DCFDA; Invitrogen), a widely used H<sub>2</sub>O<sub>2</sub>-specific probe, in intact cells [<a href="#B30-ijms-25-06606" class="html-bibr">30</a>]. The conversion of CM-H2DCFDA to the green fluorescent product, DCF, was measured using a microplate reader with excitation and emission wavelengths of 485 nm and 535 nm, respectively (<span class="html-italic">n</span> = 4).</p>
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<p>HBV activates p53 to amplify ROS during infection in human hepatoma cells. (<b>a</b>) HepG2-NTCP and Hep3B-NTCP cells were transfected with p53 shRNA and p53 expression plasmid for 24 h. Cells were then infected with HBV at 50 MOI for an additional 3 days. Intracellular ROS levels were analyzed as in <a href="#ijms-25-06606-f001" class="html-fig">Figure 1</a>d (<span class="html-italic">n</span> = 4). (<b>b</b>) The levels of the specified proteins were measured by western blotting. Each protein band’s normalized intensity was calculated relative to the housekeeping protein γ-tubulin to measure protein expression levels across samples. (<b>c</b>) Extracellular HBV DNA levels were quantified using qPCR (<span class="html-italic">n</span> = 4).</p>
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<p>HBV induces ROS generation to inhibit virus replication via a negative feedback loop. (<b>a</b>,<b>d</b>) HepG2-NTCP and Hep3B-NTCP cells were infected with HBV for 48 h. Cells were then treated with NAC at the specified concentrations for 24 h before harvesting. ROS levels in cells were measured using CM-H2DCFDA (<span class="html-italic">n</span> = 5). (<b>b</b>,<b>e</b>) Levels of p53, HBx, HBsAg, and γ-tubulin in cell lysates were analyzed by western blotting. (<b>c</b>,<b>f</b>) Extracellular HBV DNA levels were quantified using conventional PCR and qPCR (<span class="html-italic">n</span> = 3).</p>
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<p>The transcriptional activity of p53 is crucial for the ROS-induced suppression of HBV replication. (<b>a</b>–<b>c</b>) Hep3B-NTCP cells were transfected with an expression plasmid encoding wild type (WT) p53, p53 R175H, and p53 R248Q for 24 h and then infected with HBV 50 MOI for an additional 3 days. (<b>d</b>–<b>f</b>) HepG2-NTCP cells and Hep3B-NTCP cells were infected with HBV, followed by treatment with PFT-α at the indicated concentrations. For lanes 7 and 10, Hep3B-NTCP cells were transfected with indicated plasmid before infection. (<b>a</b>,<b>d</b>) Levels of the intracellular proteins were determined by western blotting. (<b>b</b>,<b>e</b>) Levels of extracellular HBeAg were determined by ELISA (<span class="html-italic">n</span> = 3). (<b>c</b>,<b>f</b>) Levels of ROS were determined after treatment with CM-H2DCFDA (<span class="html-italic">n</span> = 3). (<b>a</b>,<b>d</b>) Each protein band’s normalized intensity was calculated relative to the housekeeping protein γ-tubulin to measure protein expression levels across samples.</p>
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<p>ROS reduces HBx levels to suppress HBV replication in human hepatoma cells. (<b>a</b>) HepG2-NTCP cells were infected with either WT HBV or HBx-null HBV for 3 days. If required, cells were treated with NAC for 24 h before harvesting. For lanes 3 and 5 in (<b>d</b>–<b>f</b>), pCMV-HA-HBx was transfected 24 h before HBV infection. (<b>a</b>,<b>d</b>) The levels of the indicated proteins were measured by western blotting. (<b>b</b>,<b>e</b>) Extracellular HBV DNA levels were quantified using conventional PCR and qPCR (<span class="html-italic">n</span> = 3). (<b>c</b>,<b>f</b>) ROS levels were measured after treatment with CM-H2DCFDA (<span class="html-italic">n</span> = 3). (<b>a</b>,<b>d</b>) The normalized intensity of each protein band was calculated relative to the housekeeping protein γ-tubulin to compare protein expression levels across samples.</p>
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<p>ROS drives Siah-1-mediated proteasomal degradation of HBx in a p53-reliant manner. HepG2 and Hep3B cells were transfected with an <span class="html-italic">HBx</span> expression plasmid for 48 h in the presence or absence of NAC. (<b>a</b>,<b>c</b>) ROS levels were determined (<span class="html-italic">n</span> = 4). (<b>b</b>,<b>d</b>) Levels of the indicated proteins were determined by western blotting. (<b>e</b>) Cells were treated with 50 μg/mL of cycloheximide (CHX) for the indicated time to inhibit further protein synthesis before harvesting, followed by western blotting. Each band was quantified using ImageJ image-analysis software (Version 2.1.0, NIH) to determine the half-life (t<sub>1/2</sub>) of HBx protein (<b>f</b>) HA-Ub expression plasmid was added in the transfection cocktails. Ubiquitin-conjugated HBx products were immunoprecipitated with anti-HA antibodies and pulled down with magnetic beads. Western blotting was performed to measure levels of HBx, p53, Siah-1, and Ub-complexed HBx. The input lanes show the levels of the proteins. (<b>g</b>) Cells were mock-treated or treated with 10 μM MG132 for 4 h before protein sampling. (<b>b</b>,<b>d</b>,<b>e</b>,<b>f</b>) Each protein band’s normalized intensity was calculated relative to the housekeeping protein γ-tubulin to measure protein expression levels across samples.</p>
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19 pages, 5086 KiB  
Article
Hydrogen Peroxide Inhibits Hepatitis B Virus Replication by Downregulating HBx Levels via Siah-1-Mediated Proteasomal Degradation in Human Hepatoma Cells
by Hyunyoung Yoon, Hye-Kyoung Lee and Kyung Lib Jang
Int. J. Mol. Sci. 2023, 24(17), 13354; https://doi.org/10.3390/ijms241713354 - 28 Aug 2023
Cited by 3 | Viewed by 1929
Abstract
The hepatitis B virus (HBV) is constantly exposed to significant oxidative stress characterized by elevated levels of reactive oxygen species (ROS), such as H2O2, during infection in hepatocytes of patients. In this study, we demonstrated that H2O [...] Read more.
The hepatitis B virus (HBV) is constantly exposed to significant oxidative stress characterized by elevated levels of reactive oxygen species (ROS), such as H2O2, during infection in hepatocytes of patients. In this study, we demonstrated that H2O2 inhibits HBV replication in a p53-dependent fashion in human hepatoma cell lines expressing sodium taurocholate cotransporting polypeptide. Interestingly, H2O2 failed to inhibit the replication of an HBV X protein (HBx)-null HBV mutant, but this defect was successfully complemented by ectopic expression of HBx. Additionally, H2O2 upregulated p53 levels, leading to increased expression of seven in absentia homolog 1 (Siah-1) levels. Siah-1, an E3 ligase, induced the ubiquitination-dependent proteasomal degradation of HBx. The inhibitory effect of H2O2 was nearly abolished not only by treatment with a representative antioxidant, N-acetyl-L-cysteine but also by knockdown of either p53 or Siah-1 using specific short hairpin RNA, confirming the role of p53 and Siah-1 in the inhibition of HBV replication by H2O2. The present study provides insights into the mechanism that regulates HBV replication under conditions of oxidative stress in patients. Full article
(This article belongs to the Special Issue Molecular Pathogenesis and Therapeutics in Viral Hepatitis)
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Figure 1
<p>H<sub>2</sub>O<sub>2</sub> inhibits HBV replication in a p53-dependent manner. HepG2-NTCP and Hep3B-NTCP cells were infected with HBV at the indicated multiplicity of infection (MOI) per cell for 24 h in DMEM containing 2% DMSO and 4% PEG 8000, washed twice with serum-free DMEM, and then incubated for an additional three days in DMEM containing 3% FBS, 2% DMSO, and 4% PEG 8000. Cells were either mock-treated or treated with the indicated concentrations of H<sub>2</sub>O<sub>2</sub> for 24 h before harvesting. Cells were transfected with either an empty vector or p53 expression plasmid for 24 h prior to infection for (<b>i</b>–<b>l</b>). (<b>a</b>,<b>e</b>,<b>i</b>) Cell lysates were subjected to Western blotting to determine levels of p53, Siah-1, HBx, HBsAg, and γ-tubulin. The protein bands were quantified using Image J image analysis software version 1.8.0 (NIH) to show the level of HBx relative to the loading control (γ-tubulin). The SDS-PAGE gels stained with Coomassie brilliant blue were provided to show that H<sub>2</sub>O<sub>2</sub> treatment did not non-specifically affect protein expression patterns under our experimental conditions. (<b>b</b>,<b>f</b>,<b>j</b>) The levels of HBV particles released from the cells prepared in (<b>a</b>,<b>e</b>,<b>i</b>) were measured by both conventional PCR and quantitative real-time PCR (qPCR). Results are shown as mean ± standard deviation obtained from four independent experiments (<span class="html-italic">n</span> = 4). (<b>c</b>,<b>g</b>,<b>k</b>) Levels of intracellular reactive oxygen species (ROS) were determined by using a fluorescent dye, chloromethyl dichlorodihydrofluorescein diacetate (CM-H<sub>2</sub>DCFDA), as described before [<a href="#B43-ijms-24-13354" class="html-bibr">43</a>]. (<b>d</b>,<b>h</b>,<b>l</b>) Cell viability was measured by MTT assays (<span class="html-italic">n</span> = 4).</p>
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<p>N-Acetylcysteine as an antioxidant stimulates HBV replication. HepG2-NTCP cells and Hep3B cells expressing p53 were infected with HBV, as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>, for four days in the presence of the indicated concentration of NAC. (<b>a</b>,<b>d</b>) Intracellular ROS levels were determined as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>c (<span class="html-italic">n</span> = 3). (<b>b</b>,<b>e</b>) Levels of the indicated proteins were determined by western blotting. (<b>c</b>,<b>f</b>) Levels of extracellular HBV DNA were determined by both conventional PCR and qPCR (<span class="html-italic">n</span> = 3).</p>
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<p>H<sub>2</sub>O<sub>2</sub> inhibits HBV replication by lowering HBx levels. HepG2 cells were transfected with a 1.2-mer WT HBV replicon (1.2-mer WT) or its HBx-null counterpart (1.2-mer HBx-null), along with or without an HBx expression plasmid for 24 h, and then treated with the indicated concentrations of H<sub>2</sub>O<sub>2</sub> for an additional 24 h. (<b>a</b>,<b>d</b>) Cells were subjected to ROS detection assay as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>c. (<b>b</b>,<b>e</b>) Levels of the indicated proteins were determined via Western blotting. Levels of HBx and HBsAg were quantified as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>a. (<b>c</b>,<b>f</b>) Levels of extracellular HBV DNA were determined by both conventional PCR and qPCR (<span class="html-italic">n</span> = 3).</p>
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<p>H<sub>2</sub>O<sub>2</sub> downregulates HBx levels by elevating p53 levels in human hepatoma cells. (<b>a</b>) Flow cytometric histograms of HBV-infected HepG2-NTCP and Hep3B-NTCP cells. Cells were infected with HBV in the presence and absence of H<sub>2</sub>O<sub>2</sub> as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a> and then analyzed by flow cytometry to determine the proportion of cells expressing p53 and HBx. (<b>b</b>–<b>f</b>) HepG2 and Hep3B cells were transiently transfected with the indicated amounts of HBx expression plasmid along with scrambled (SC) shRNA, p53 shRNA, or p53 expression plasmid for 24 h and treated with H<sub>2</sub>O<sub>2</sub> at the indicated concentration for an additional 24 h, followed by Western blotting. Levels of HBx were quantified as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>a.</p>
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<p>H<sub>2</sub>O<sub>2</sub> lowers HBx levels by inducing Siah-1-mediated ubiquitination and proteasomal degradation in a p53-dependent fashion. (<b>a</b>,<b>b</b>) HepG2 and Hep3B cells were transfected with the indicated amounts of HBx expression plasmid along with Siah-1, SC shRNA, Siah-1 shRNA, or p53 expression plasmid for 24 h and treated with H<sub>2</sub>O<sub>2</sub> at the indicated concentration for an additional 24 h, followed by Western blotting. (<b>c</b>) HepG2 and Hep3B cells prepared as in (<b>a</b>) were treated with 50 μM cycloheximide (CHX) for the indicated period before harvesting, followed by Western blotting. The levels of HBx and γ-tubulin were quantified, as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>a, to determine the half-life (t<sub>1/2</sub>) of HBx. (<b>d</b>) HepG2 and Hep3B cells were transfected with the indicated plasmids for 24 h and treated with H<sub>2</sub>O<sub>2</sub> for an additional 24 h as in (<b>a</b>). The HA-Ub expression plasmid was included in the transfection mixtures. Total HBx protein in cell lysates was immunoprecipitated with an anti-HBx antibody and subjected to Western blotting using anti-Siah-1, anti-HBx, and anti-HA antibodies to detect Siah-1, HBx, and HA-Ub-complexed HBx, respectively. The input shows the levels of the indicated proteins in the cell lysates. (<b>e</b>) HepG2 cells prepared as above were treated with 10 μM MG132 for 4 h before harvesting, followed by Western blotting.</p>
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<p>H<sub>2</sub>O<sub>2</sub> inhibits HBV replication by inducing Siah-1-mediated proteasomal degradation of HBx in a p53-dependent fashion. (<b>a</b>,<b>b</b>) HepG2 and Hep3B cells were transiently transfected with the indicated amounts of HBx expression plasmid and pHBV-luc, which contains the HBV core promoter/enhancer, for 24 h and treated with H<sub>2</sub>O<sub>2</sub> for an additional 24 h, followed by luciferase assay. The values show the relative luciferase activity compared to the basal level of the control (<span class="html-italic">n</span> = 4). (<b>c</b>,<b>d</b>) HepG2-NTCP and Hep3B-NTCP cells were transfected with SC shRNA and Siah-1 shRNA plasmids for 24 h and then either mock-infected or infected with HBV for an additional 4 days. Cells were treated with H<sub>2</sub>O<sub>2</sub> at the indicated concentration for 24 h before harvesting, followed by Western blotting. Levels of HBx were quantified as described in <a href="#ijms-24-13354-f001" class="html-fig">Figure 1</a>a. (<b>e</b>,<b>f</b>) The levels of HBV particles released from the cells prepared in (<b>c</b>,<b>d</b>) were determined by qPCR (<span class="html-italic">n</span> = 4).</p>
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18 pages, 5467 KiB  
Article
All-trans Retinoic Acid Inhibits Hepatitis B Virus Replication by Downregulating HBx Levels via Siah-1-Mediated Proteasomal Degradation
by Jiwoo Han and Kyung Lib Jang
Viruses 2023, 15(7), 1456; https://doi.org/10.3390/v15071456 - 27 Jun 2023
Cited by 4 | Viewed by 1673
Abstract
All-trans retinoic acid (ATRA), the most biologically active metabolite of vitamin A, is known to abolish the potential of HBx to downregulate the levels of p14, p16, and p21 and to stimulate cell growth during hepatitis B virus (HBV) infection, contributing to [...] Read more.
All-trans retinoic acid (ATRA), the most biologically active metabolite of vitamin A, is known to abolish the potential of HBx to downregulate the levels of p14, p16, and p21 and to stimulate cell growth during hepatitis B virus (HBV) infection, contributing to its chemopreventive and therapeutic effects against HBV-associated hepatocellular carcinoma. Here, we demonstrated that ATRA antagonizes HBx to inhibit HBV replication. For this effect, ATRA individually or in combination with HBx upregulated p53 levels, resulting in upregulation of seven in absentia homolog 1 (Siah-1) levels. Siah-1, an E3 ligase, induces ubiquitination and proteasomal degradation of HBx in the presence of ATRA. The ability of ATRA to induce Siah-1-mediated HBx degradation and the subsequent inhibition of HBV replication was proven in an in vitro HBV replication model. The effects of ATRA became invalid when either p53 or Siah-1 was knocked down by a specific shRNA, providing direct evidence for the role of p53 and Siah-1 in the negative regulation of HBV replication by ATRA. Full article
(This article belongs to the Special Issue Ubiquitin and Ubiquitin-Like Pathways in Viral Infection 2023)
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Figure 1
<p>ATRA inhibits HBV replication in a p53-dependent manner. HepG2-NTCP and Hep3B-NTCP cells were infected with HBV at the indicated MOI for 24 h, washed twice with serum-free DMEM, and then incubated for an additional 3 days in DMEM containing 3% FBS, 4% PEG 8000, and 2% DMSO. Cells were either mock-treated or treated with the indicated concentrations of ATRA for 24 h before harvesting. (<b>A</b>,<b>B</b>) Cell lysates were subjected to western blotting to measure levels of p53, Siah-1, HBx, HBcAg, HBsAg, and γ-tubulin. (<b>C</b>,<b>D</b>) The levels of HBV particles released from the cells prepared in (<b>A</b>,<b>B</b>) were determined using conventional PCR and quantitative real-time PCR (qPCR). Results are shown as means ± standard deviation (SD) obtained from four independent experiments (<span class="html-italic">n</span> = 4). (<b>E</b>,<b>F</b>) Cell viability was measured using the MTT assay (<span class="html-italic">n</span> = 9).</p>
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<p>ATRA activates p53 to inhibit HBV replication in human hepatoma cells. (<b>A</b>) HepG2-NTCP cells were transiently transfected with the indicated amounts of p53, SC shRNA, and p53 shRNA plasmids for 24 h and then infected with HBV for 4 days as described in <a href="#viruses-15-01456-f001" class="html-fig">Figure 1</a>, followed by western blotting analysis. For lanes 5 and 6, cells were treated with ATRA for 24 h before harvesting. (<b>B</b>) Levels of HBV particles in cell supernatants prepared in (<b>A</b>) were determined using qPCR (<span class="html-italic">n</span> = 3). (<b>C</b>) Hep3B-NTCP cells were transiently transfected with p53 expression plasmid for 24 h and then infected with HBV for 4 days as in <a href="#viruses-15-01456-f001" class="html-fig">Figure 1</a>, followed by western blotting analysis. For lanes 5 to 7, cells were treated with ATRA for 24 h before harvesting. (<b>D</b>) Levels of HBV particles in cell supernatants prepared in (<b>C</b>) were determined using qPCR (<span class="html-italic">n</span> = 3). (<b>E</b>) HepG2-NTCP cells were transfected with p53 expression plasmid for 24 h and infected with HBV for an additional 4 days in the presence or absence of pifithrin-alpha (PFT-α), followed by western blotting analysis. For lanes 3 and 6, cells were treated with ATRA for 24 h before harvesting. (<b>F</b>) Levels of HBV particles in culture supernatants prepared in (<b>E</b>) were measured using qPCR (<span class="html-italic">n</span> = 4).</p>
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<p>ATRA downregulates HBx levels to inhibit HBV replication in human hepatoma cells. HepG2-NTCP and Hep3B-NTCP cells were transiently transfected with a 1.2-mer WT HBV replicon (1.2-mer WT) or its HBx-null counterpart (1.2-mer HBx-null) with or without an HBx expression plasmid for 24 h and then treated with the indicated concentrations of ATRA for an additional 24 h. (<b>A</b>,<b>C</b>,<b>E</b>) Levels of the indicated proteins were measured by western blotting analysis. (<b>B</b>,<b>D</b>,<b>F</b>) Levels of extracellular HBV DNA were determined using qPCR (<span class="html-italic">n</span> = 3).</p>
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<p>ATRA activates p53 to downregulate HBx levels in human hepatoma cells. (<b>A</b>,<b>B</b>,<b>E</b>–<b>H</b>) HepG2 and Hep3B cells were transiently transfected with HBx expression plasmid along with SC shRNA, p53 shRNA, or p53 expression plasmid for 24 h and either mock-treated or treated with ATRA for an additional 24 h, followed by western blotting analysis. For (<b>G</b>,<b>H</b>), cells were incubated in the presence or absence of PFT-α. (<b>C</b>,<b>D</b>) HepG2 and Hep3B cells were transfected with HBx expression plasmid and pHBV-luc, which contains the HBV core promoter [<a href="#B28-viruses-15-01456" class="html-bibr">28</a>], for 24 h and then treated with the indicated amounts of ATRA for an additional 24 h, followed by the luciferase assay. Values indicate relative luciferase activity compared to the basal levels of the control (<span class="html-italic">n</span> = 3).</p>
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<p>ATRA activates Siah-1 expression via activation of p53 to downregulate HBx levels. (<b>A</b>,<b>B</b>) HepG2 cells were transiently transfected with HBx expression plasmid along with Siah-1, SC shRNA, or Siah-1 shRNA for 24 h and treated with ATRA for an additional 24 h, followed by western blotting analysis. (<b>C</b>,<b>D</b>) Hep3B cells were transiently transfected with HBx expression plasmid along with Siah-1, SC shRNA, or Siah-1 shRNA for 24 h and treated with ATRA for an additional 24 h, followed by western blotting analysis.</p>
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<p>ATRA downregulates HBx levels via Siah-1-mediated ubiquitination and proteasomal degradation in a p53-dependent manner. (<b>A</b>) HepG2 cells transiently transfected with HBx for 48 h were treated with 50 μM cycloheximide (CHX) for the indicated time before harvesting to block further protein synthesis, followed by western blotting analysis. Levels of HBx and γ-tubulin were quantified using ImageJ 1.53k image analysis software (NIH) to reveal the levels of HBx relative to the loading control (γ-tubulin), which were used to determine HBx t<sub>1/2</sub>. (<b>B</b>) HepG2 cells were transiently transfected with either empty vector or HBx expression plasmid along with the indicated plasmids for 24 h and treated with ATRA for an additional 24 h. Total HBx protein in cell lysates was immunoprecipitated with an anti-HBx antibody and subjected to western blotting using anti-HBx, anti-Siah-1, and anti-HA antibodies to detect HBx, Siah-1, and HA-Ub-complexed HBx, respectively. The input indicated the levels of the indicated proteins in the cell lysates. (<b>C</b>) HepG2 cells transiently transfected with HBx expression plasmid and then treated with ATRA as above were treated with MG132 for 4 h before harvesting, followed by western blotting analysis.</p>
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<p>ATRA downregulates HBx levels via Siah-1-mediated proteasomal degradation to inhibit HBV replication in a p53-dependent manner. HepG2-NTCP and Hep3B-NTCP cells were transfected with the indicated amounts of SC shRNA and Siah-1 shRNA plasmids for 24 h and then either mock-infected or infected with HBV for 4 days. Cells were treated with ATRA at the indicated concentration for 24 h before harvesting. For (<b>A</b>), pHA-Ub was included in the transfection mixtures. (<b>A</b>) Total HBx protein was immunoprecipitated with an anti-HBx antibody as in <a href="#viruses-15-01456-f006" class="html-fig">Figure 6</a>B and subjected to western blotting using anti-HBx, anti-Siah-1, and anti-HA antibodies to detect HBx, Siah-1, and HA-Ub-complexed HBx, respectively. The input indicated the levels of the indicated proteins in the cell lysates. (<b>B</b>) Levels of the indicated proteins were measured using western blotting analysis. (<b>C</b>,<b>D</b>) Levels of HBV particles in the culture supernatants were measured using qPCR (<span class="html-italic">n</span> = 4).</p>
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12 pages, 3591 KiB  
Article
Structural Basis of the Interaction between Human Axin2 and SIAH1 in the Wnt/β-Catenin Signaling Pathway
by Lianqi Chen, Yan-Ping Liu, Li-Fei Tian, Mingzhou Li, Shuyu Yang, Song Wang, Wenqing Xu and Xiao-Xue Yan
Biomolecules 2023, 13(4), 647; https://doi.org/10.3390/biom13040647 - 4 Apr 2023
Cited by 3 | Viewed by 3100
Abstract
The scaffolding protein Axin is an important regulator of the Wnt signaling pathway, and its dysfunction is closely related to carcinogenesis. Axin could affect the assembly and dissociation of the β-catenin destruction complex. It can be regulated by phosphorylation, poly-ADP-ribosylation, and ubiquitination. The [...] Read more.
The scaffolding protein Axin is an important regulator of the Wnt signaling pathway, and its dysfunction is closely related to carcinogenesis. Axin could affect the assembly and dissociation of the β-catenin destruction complex. It can be regulated by phosphorylation, poly-ADP-ribosylation, and ubiquitination. The E3 ubiquitin ligase SIAH1 participates in the Wnt pathway by targeting various components for degradation. SIAH1 is also implicated in the regulation of Axin2 degradation, but the specific mechanism remains unclear. Here, we verified that the Axin2-GSK3 binding domain (GBD) was sufficient for SIAH1 binding by the GST pull-down assay. Our crystal structure of the Axin2/SIAH1 complex at 2.53 Å resolution reveals that one Axin2 molecule binds to one SIAH1 molecule via its GBD. These interactions critically depend on a highly conserved peptide 361EMTPVEPA368 within the Axin2-GBD, which forms a loop and binds to a deep groove formed by β1, β2, and β3 of SIAH1 by the N-terminal hydrophilic amino acids Arg361 and Thr363 and the C-terminal VxP motif. The novel binding mode indicates a promising drug-binding site for regulating Wnt/β-catenin signaling. Full article
(This article belongs to the Section Biomacromolecules: Proteins)
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Figure 1
<p>Biochemical characterization of the Axin2/SIAH1 interaction. (<b>A</b>) Schematic diagram of the human Axin2 and SIAH1 conserved domains. (<b>B</b>) Sequence alignment of Axin2-GBD indicated it is conserved in vertebrate evolution, in which the highly conserved peptide, including the VxP motif, are shown in red. Abbreviations: Hs, <span class="html-italic">Homo sapiens</span>; Bt, <span class="html-italic">Bos taurus</span>; Mm, <span class="html-italic">Mus musculus</span>; La, <span class="html-italic">Loxodonta africanar</span>; Dr, <span class="html-italic">Danio rerio</span>; Ac, <span class="html-italic">Anolis carolinensis</span>. (<b>C</b>) The protein disorder tendency prediction of the full length human Axin2, using the GeneSilico MetaDisorder server (<a href="http://iimcb.genesilico.pl/metadisorder/" target="_blank">http://iimcb.genesilico.pl/metadisorder/</a> accessed on 21 March 2023). The X-axis represents residues 1–801 of the full-length human Axin2 protein, whereas the Y-axis is the disorder tendency score for each residue in the context of the Axin2 sequence. The regions with the disorder tendency higher than 0.5 have a high tendency to be structurally disordered. Two different prediction methods, IPDA and PRDOS, are shown in blue and orange, respectively. (<b>D</b>) GST pull-down assays of the Axin2-GBD/SIAH1-SBD interaction by using the SDS-PAGE and Western blot.</p>
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<p>Binding affinities of Axin-GDB (<b>left</b>) or Axin-GDBΔ (<b>right</b>) with SIAH1-SBD, as measured by BLI assays.</p>
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<p>Overall structure of the Axin2/SIAH1 complex. Cartoon representation of the Axin2/SIAH1 complex with secondary structural elements labeled (α: α-helix; β: β-strand); β-strand is represented in cyan, α-helix is represented in yellow; Axin2-GBD are shown in purple.</p>
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<p>Axin2 peptide<sub>361–368</sub> binds to the groove formed by β1, β2, and β3 of SIAH1. (<b>A</b>) The 2Fo-Fc electron density map (blue 1.2σ) of Axin2<sub>361–368</sub>. SIAH1 is represented in cyan, Axin2 is represented in purple. (<b>B</b>) Axin2<sub>361–368</sub> fits into the deep groove of SIAH1 as shown on the electrostatic surface.</p>
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<p>Wall-eyed view showing the critical residues in the Axin2/SIAH1 interface. SIAH1 is shown in cyan, Axin2 in purple, and the interacting amino acid residues are connected by black dotted lines representing the distance.</p>
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<p>Binding affinities of Axin2 mutants with wt Axin2<sub>356−368</sub> or wt SIAH1−SBD with SIAH1−SBD mutants or Axin2<sub>356−368</sub> mutants measured by ITC200 assays. The K<span class="html-italic"><sub>D</sub></span> value of wt Axin2<sub>356−368</sub>/wt SIAH1−SBD is 8.77 μM. ND: no detectable binding.</p>
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<p>Structural comparisons of Axin2/SIAH1 and Axin1/SIAH. (<b>A</b>) Schematic diagram of the human Axin1 and Axin2 conserved domains. (<b>B</b>) Sequence alignment of the conserved VxP motifs in Axin2 and Axin1, where the highly conserved amino acids around the VxP motif and the VxP motif are shown with asterisks. Abbreviations: Hs, <span class="html-italic">Homo sapiens</span>; Mm, <span class="html-italic">Mus musculus</span>; Dr, <span class="html-italic">Danio rerio</span>. (<b>C</b>) Residues involved in interface interactions between the Axin1 or Axin2 and SIAH1. Axin2/SIAH1 is represented in purple and cyan, Axin1/SIAH1 is represented in yellow and gray.</p>
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14 pages, 7902 KiB  
Article
Genome-Wide Association Analysis of Fruit Shape-Related Traits in Areca catechu
by Hao Ding, Guangzhen Zhou, Long Zhao, Xinyu Li, Yicheng Wang, Chengcai Xia, Zhiqiang Xia and Yinglang Wan
Int. J. Mol. Sci. 2023, 24(5), 4686; https://doi.org/10.3390/ijms24054686 - 28 Feb 2023
Cited by 4 | Viewed by 2681
Abstract
The areca palm (Areca catechu L.) is one of the most economically important palm trees in tropical areas. To inform areca breeding programs, it is critical to characterize the genetic bases of the mechanisms that regulate areca fruit shape and to identify [...] Read more.
The areca palm (Areca catechu L.) is one of the most economically important palm trees in tropical areas. To inform areca breeding programs, it is critical to characterize the genetic bases of the mechanisms that regulate areca fruit shape and to identify candidate genes related to fruit-shape traits. However, few previous studies have mined candidate genes associated with areca fruit shape. Here, the fruits produced by 137 areca germplasms were divided into three categories (spherical, oval, and columnar) based on the fruit shape index. A total of 45,094 high-quality single-nucleotide polymorphisms (SNPs) were identified across the 137 areca cultivars. Phylogenetic analysis clustered the areca cultivars into four subgroups. A genome-wide association study that used a mixed linear model identified the 200 loci that were the most significantly associated with fruit-shape traits in the germplasms. In addition, 86 candidate genes associated with areca fruit-shape traits were further mined. Among the proteins encoded by these candidate genes were UDP-glucosyltransferase 85A2, the ABA-responsive element binding factor GBF4, E3 ubiquitin-protein ligase SIAH1, and LRR receptor-like serine/threonine-protein kinase ERECTA. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed that the gene that encoded UDP-glycosyltransferase, UGT85A2, was significantly upregulated in columnar fruits as compared to spherical and oval fruits. The identification of molecular markers that are closely related to fruit-shape traits not only provides genetic data for areca breeding, but it also provides new insights into the shape formation mechanisms of drupes. Full article
(This article belongs to the Collection Feature Papers in Molecular Plant Sciences)
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<p>Distribution of variation detection types in areca catechu genome. SNP: single nucleotide polymorphism; InDel: insertion and deletion. The outermost circle shows the 16 chromosomes of <span class="html-italic">A</span>. <span class="html-italic">catechu</span>; purple dots in the middle circle show the distribution of SNPs across the genome; blue dots in the innermost circle show the distribution of InDels across the genome.</p>
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<p>SNP location and functional annotation. (<b>A</b>) The distribution of SNP variation sites in different locations. (<b>B</b>) The relative abundance of types of SNP mutations (missense, nonsense, and silent).</p>
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<p>Genetic relationship analysis. Neighbor-joining phylogeny constructed based on 137 areca germplasms. Roman numbers in the outermost circle indicate subgroups. Branch color corresponds to fruit shape, with red (I), blue (II), light blue (III) and green (IV) corresponding to areca germplasms with oval, spherical, mixed shape and columnar fruits, respectively.</p>
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<p>Genome-wide association analysis of the fruit shapes of areca germplasms. Manhattan plot shows the results of the whole-genome association analysis of fruit shape.</p>
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<p>Functional annotation of candidate genes. (<b>A</b>) Gene Ontology (GO) annotation of the candidate genes. (<b>B</b>) Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation of the candidate genes.</p>
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<p>Tissue expression patterns of candidate genes annotated with areca fruit-shape traits. P1, P2, and P3: areca nut pericarp after 3, 6, and 9 months of fertilization, respectively; E1, E2, and E3: areca nut endosperm after 3, 6, and 9 months of fertilization, respectively; FF: female flower; MF: male flower; UR: underground root; AR: aerial root; L: leaf; LV: leaf vein.</p>
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<p>Verification of candidate genes. (<b>A</b>) Chemical structures of compounds and biosynthetic pathways related to UGT85A2. (<b>B</b>) The complete predicted structure of AcUGT85A2 (<b>upper left</b>), the functional domain of UDP-glycosyltransferase (<b>upper right</b>), the complete predicted structure of AtUGT85A2 (<b>lower left</b>), and the functional domain of UDP-glycosyltransferase (<b>lower right</b>). Differences in the amino acid sequences of the conserved functional domains of AcUGT85A2 and AtUGT85A2; fully conserved residues are shaded in gray. (<b>C</b>) Experimental verification of <span class="html-italic">AcUGT85A2</span> expression levels using qRT-PCR. Statistical significance was assessed by an unpaired two-tailed Student’s t-test Symbols for statistical significance levels: *: significant differences (<span class="html-italic">p</span> &lt; 0.05), ns: no significant differences, n = 3.</p>
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<p>Expression patterns of genes involved in the cytokinin (CK) synthesis pathway. (<b>A</b>) The CK synthesis pathway. (<b>B</b>) Experimental verification of the expression patterns of genes associated with the CK synthesis pathway using real-time fluorescent quantitative polymerase chain reaction (qRT-PCR). Statistical significance was assessed by an unpaired two-tailed Student’s t-test Symbols for statistical significance levels: *: significant differences (<span class="html-italic">p</span> &lt; 0.05), ***: extremely significant differences (<span class="html-italic">p</span> &lt; 0.001), ns: no significant differences, n = 3.</p>
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18 pages, 3682 KiB  
Article
Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq
by Jieyun Xu, Shijie Qin, Yunmeng Yi, Hanyu Gao, Xiaoqi Liu, Fei Ma and Miao Guan
Int. J. Mol. Sci. 2022, 23(17), 9936; https://doi.org/10.3390/ijms23179936 - 1 Sep 2022
Cited by 12 | Viewed by 6620
Abstract
Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in [...] Read more.
Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients. Full article
(This article belongs to the Section Molecular Oncology)
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Graphical abstract

Graphical abstract
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<p>Workflow of delving into the heterogeneity of different breast cancer (BC) subtypes and constructing subtype specific prognostic models. In objective 1, a comparative analysis of three BC subtypes was performed based on the heterogeneity of cancer cells in gene expression, transcriptional regulatory networks and cellular communication. In objective 2, survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models using cancer cell specific upregulated genes. The accuracy of the prognostic models was then validated using external validation cohorts.</p>
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<p>Identification of tumor cells from epithelia. (<b>A</b>) Uniform manifold approximation and projection (UMAP) cluster plot of different BC cell types. (<b>B</b>) Expression levels of cellular markers corresponding to different BC cell types. (<b>C</b>) Unsupervised clustering of inferred large-scale copy number variations (CNVs) to identify cancer cells from epithelia cells. Epithelial cells and reference cells (B cells, T cells and endothelial cells) are in the y-axis and chromosomal regions in the x-axis. (<b>D</b>) Violin plot showing the differences of CNVs scores among 9 clusters. (<b>E</b>) UMAP cluster plot showing the distribution of normal epithelial cells and cancer cells. (<b>F</b>) The proportion of cancer cells and normal epithelial cells of the three subtypes of BC.</p>
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<p>Functions and gene regulatory networks of subtype-specific upregulated genes. (<b>A</b>) Intersection of differentially upregulated genes in cancer cells compared with normal epithelial cells (<b>left</b>) and differentially upregulated compared with other subtypes (<b>right</b>). (<b>B</b>) GO biological process function enrichment of specific upregulation genes in cancer cells of three BC subtypes. (<b>C</b>) Dotplot showing TFs specifically enriched in cancer cells of different BC subtypes. (<b>D</b>–<b>F</b>) Key TFs and their highly upregulated target genes in (<b>D</b>) luminal BC, (<b>E</b>) TNBC and (<b>F</b>) HER2+ BC tumor cells.</p>
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<p>Cell communication of cancer cells leads to BC inflammatory microenvironment. (<b>A</b>) Overall intercellular communication profiles of the three BC subtypes. (<b>B</b>) Intercellular communication frequency maps of cancer cells of three BC subtypes. (<b>C</b>) Receptor–ligand pairs for communication between cancer cells and other cells of the three BC subtypes. (<b>D</b>) GO biological process enrichment analysis of receptor–ligand genes for communication between BC cancer cells and other cells. (<b>E</b>) Scores of cellular inflammatory responses in the three BC subtypes.</p>
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<p>Construction and evaluation of the prognostic risk model for luminal BC. (<b>A</b>) Expression levels of prognostic factors between normal and cancer cells. (<b>B</b>) Expression levels of prognostic factors in cancer cells of luminal BC and other subtypes. (<b>C</b>) HR and <span class="html-italic">p</span> values of prognostic factors by univariate Cox regression. (<b>D</b>) Risk curves and scatter plots of sample survival probability for each sample reordered by risk score, heatmap of expression of prognostic factors in low-risk and high-risk groups. (<b>E</b>) Differences in overall survival between high-risk and low-risk groups in the training cohort of luminal BC. (<b>F</b>) Receiver operating characteristic curve (ROC) analysis of the sensitivity and specificity of the prognostic model in the training cohort of luminal BC. (<b>G</b>) Differences in overall survival between high-risk and low-risk groups in the external validation cohort of luminal BC. (<b>H</b>) ROC analysis of the sensitivity and specificity of the prognostic model in the external validation cohort of luminal BC.</p>
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<p>Construction and evaluation of the prognostic risk model for HER2+ BC. (<b>A</b>) Expression levels of prognostic factors between normal and cancer cells. (<b>B</b>) Expression levels of prognostic factors in cancer cells of HER2+ BC and other subtypes. (<b>C</b>) HR and <span class="html-italic">p</span> values of prognostic factors by univariate Cox regression. (<b>D</b>) Risk curves and scatter plots of sample survival probability for each sample reordered by risk score, heatmap of expression of prognostic factors in low-risk and high-risk groups. (<b>E</b>) Differences in overall survival between high-risk and low-risk groups in the training cohort of HER2+ BC. (<b>F</b>) Receiver operating characteristic curve (ROC) analysis of the sensitivity and specificity of the prognostic model in the training cohort of HER2+ BC. (<b>G</b>) Differences in overall survival between high-risk and low-risk groups in the external validation cohort of HER2+ BC. (<b>H</b>) ROC analysis of the sensitivity and specificity of the prognostic model in the external validation cohort of HER2+ BC.</p>
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<p>Construction and evaluation of the prognostic risk model for TNBC. (<b>A</b>) Expression levels of prognostic factors between normal and cancer cells. (<b>B</b>) Expression levels of prognostic factors in cancer cells of TNBC and other subtypes. (<b>C</b>) HR and <span class="html-italic">p</span> values of prognostic factors by univariate Cox regression. (<b>D</b>) Risk curves and scatter plots of sample survival probability for each sample reordered by risk score, heatmap of expression of prognostic factors in low-risk and high-risk groups. (<b>E</b>) Differences in overall survival between high-risk and low-risk groups in the training cohort of TNBC. (<b>F</b>) Receiver operating characteristic curve (ROC) analysis of the sensitivity and specificity of the prognostic model in the training cohort of TNBC. (<b>G</b>) Differences in overall survival between high-risk and low-risk groups in the external validation cohort of TNBC. (<b>H</b>) ROC analysis of the sensitivity and specificity of the prognostic model in the external validation cohort of TNBC.</p>
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44 pages, 1418 KiB  
Review
The Role of NEDD4 E3 Ubiquitin–Protein Ligases in Parkinson’s Disease
by James A. Conway, Grant Kinsman and Edgar R. Kramer
Genes 2022, 13(3), 513; https://doi.org/10.3390/genes13030513 - 14 Mar 2022
Cited by 21 | Viewed by 6622
Abstract
Parkinson’s disease (PD) is a debilitating neurodegenerative disease that causes a great clinical burden. However, its exact molecular pathologies are not fully understood. Whilst there are a number of avenues for research into slowing, halting, or reversing PD, one central idea is to [...] Read more.
Parkinson’s disease (PD) is a debilitating neurodegenerative disease that causes a great clinical burden. However, its exact molecular pathologies are not fully understood. Whilst there are a number of avenues for research into slowing, halting, or reversing PD, one central idea is to enhance the clearance of the proposed aetiological protein, oligomeric α-synuclein. Oligomeric α-synuclein is the main constituent protein in Lewy bodies and neurites and is considered neurotoxic. Multiple E3 ubiquitin-protein ligases, including the NEDD4 (neural precursor cell expressed developmentally downregulated protein 4) family, parkin, SIAH (mammalian homologues of Drosophila seven in absentia), CHIP (carboxy-terminus of Hsc70 interacting protein), and SCFFXBL5 SCF ubiquitin ligase assembled by the S-phase kinase-associated protein (SKP1), cullin-1 (Cul1), a zinc-binding RING finger protein, and the F-box domain/Leucine-rich repeat protein 5-containing protein FBXL5), have been shown to be able to ubiquitinate α-synuclein, influencing its subsequent degradation via the proteasome or lysosome. Here, we explore the link between NEDD4 ligases and PD, which is not only via α-synuclein but further strengthened by several additional substrates and interaction partners. Some members of the NEDD4 family of ligases are thought to crosstalk even with PD-related genes and proteins found to be mutated in familial forms of PD. Mutations in NEDD4 family genes have not been observed in PD patients, most likely because of their essential survival function during development. Following further in vivo studies, it has been thought that NEDD4 ligases may be viable therapeutic targets in PD. NEDD4 family members could clear toxic proteins, enhancing cell survival and slowing disease progression, or might diminish beneficial proteins, reducing cell survival and accelerating disease progression. Here, we review studies to date on the expression and function of NEDD4 ubiquitin ligases in the brain and their possible impact on PD pathology. Full article
(This article belongs to the Special Issue Preclinical and Clinical Genetics in Parkinson’s Disease)
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<p>NEDD4-mediated ubiquitination of protein substrates. Attachment of ubiquitin (Ub) to proteins is usually catalysed by an enzymatic cascade of a ubiquitin-activating enzyme E1, a ubiquitin-binding/conjugating enzyme E2, and a ubiquitin–protein ligase enzyme E3 that catalyses the transfer of the C-terminal carboxyl group of ubiquitin to the lysine (K) ε-amino group of the specific substrate. The process of ubiquitination can occur on transmembrane proteins (e.g., RET, ion channels) and on intracellular proteins (e.g., α-synuclein). The fate of the protein is dependent upon the number of ubiquitin moieties attached to each other on a substrate and which amino acid in ubiquitin the chain is extended: one of the seven lysines (K6, K11, K27, K29, K33, K48, K63) or, through its N-terminal, methionine (M1). Monoubiquitination and multimonoubiquitination of a transmembrane protein generally result in its transport, internalisation, and recycling. Linear and branched polyubiquitination with K48-linked chains results in proteasomal degradation of the substrate, and that with K63 extension regulates protein–protein interactions, protein activity, DNA repair, autophagy, immunity, inflammation, and protein trafficking to the lysosome [<a href="#B9-genes-13-00513" class="html-bibr">9</a>]. The primary role(s) of each of the eight distinct polyubiquitin chains formed at one of the seven lysine residues or the primary methionine are indicated (orange box) [<a href="#B4-genes-13-00513" class="html-bibr">4</a>,<a href="#B5-genes-13-00513" class="html-bibr">5</a>,<a href="#B8-genes-13-00513" class="html-bibr">8</a>]. ER = endoplasmic reticulum; ERAD = Endoplasmic-reticulum-associated protein degradation; TCR = T-cell receptor; TLR2/4 = Toll-like receptor 2 and 4.</p>
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<p>Schematic structural representation of NEDD4-1 and NEDD4-2 proteins in humans, mice, and fruit flies. The NEDD4 family of ligases is defined by its modular structure, a lipid-binding/Ca<sup>2+</sup> (C2) domain at the N-terminus, a number of WW domains in the middle section, and a HECT ubiquitin ligase at the C-terminus, the latter of which is required for its E3 ubiquitin ligase function. NEDD4’s WW domains can interact with PY (proline-tyrosine) motifs to recruit them for ubiquitination. This includes NEDD4’s own PY motifs located on the C terminus. Alternative splicing in mice has led some NEDD4-2 variants to lack a C2 domain, although in neurons, NEDD4-2 predominantly contains a C2 domain. WW domains regulate substrate recruitment for ubiquitination and may be expanded in higher-order organisms [<a href="#B111-genes-13-00513" class="html-bibr">111</a>,<a href="#B215-genes-13-00513" class="html-bibr">215</a>]. Common NEDD4 phosphorylation sites are indicated in red.</p>
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14 pages, 3311 KiB  
Article
Ochratoxin A Induces Steatosis via PPARγ-CD36 Axis
by Qian-Wen Zheng, Xu-Fen Ding, Hui-Jun Cao, Qian-Zhi Ni, Bing Zhu, Ning Ma, Feng-Kun Zhang, Yi-Kang Wang, Sheng Xu, Tian-Wei Chen, Ji Xia, Xiao-Song Qiu, Dian-Zhen Yu, Dong Xie and Jing-Jing Li
Toxins 2021, 13(11), 802; https://doi.org/10.3390/toxins13110802 - 13 Nov 2021
Cited by 19 | Viewed by 3732
Abstract
Ochratoxin A(OTA) is considered to be one of the most important contaminants of food and feed worldwide. The liver is one of key target organs for OTA to exert its toxic effects. Due to current lifestyle and diet, nonalcoholic fatty liver disease (NAFLD) [...] Read more.
Ochratoxin A(OTA) is considered to be one of the most important contaminants of food and feed worldwide. The liver is one of key target organs for OTA to exert its toxic effects. Due to current lifestyle and diet, nonalcoholic fatty liver disease (NAFLD) has been the most common liver disease. To examine the potential effect of OTA on hepatic lipid metabolism and NAFLD, C57BL/6 male mice received 1 mg/kg OTA by gavage daily. Compared with controls, OTA increased lipid deposition and TG accumulation in mouse livers. In vitro OTA treatment also promoted lipid droplets accumulation in primary hepatocytes and HepG2 cells. Mechanistically, OTA prevented PPARγ degradation by reducing the interaction between PPARγ and its E3 ligase SIAH2, which led to activation of PPARγ signaling pathway. Furthermore, downregulation or inhibition of CD36, a known of PPARγ, alleviated OTA-induced lipid droplets deposition and TG accumulation. Therefore, OTA induces hepatic steatosis via PPARγ-CD36 axis, suggesting that OTA has an impact on liver lipid metabolism and may contribute to the development of metabolic diseases. Full article
(This article belongs to the Special Issue Mycotoxins Study: Identification and Control)
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Figure 1
<p>Effects of OTA on lipid accumulation in HepG2 cells. HepG2 cells are treated with OTA at the concentrations of 5, 10 and 15 μM for 24 h, then intracellular lipid droplets are labeled with BODIPY (<b>a</b>) and TG contents are determined (<b>c</b>). HepG2 cells are treated with OTA at concentrations of 10 μM for 6, 12, 24 and 48 h, then intracellular lipid droplets are labeled with BODIPY (<b>b</b>) and TG contents are determined (<b>d</b>). Data shown as the mean ± S.E.M. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">n</span> = 6 biological replicates.</p>
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<p>Effects of OTA on lipid accumulation in primary hepatocytes. (<b>a</b>) Primary hepatocytes are treated with OTA at the concentrations of 5, 10 and 15 μM for 24 h, then intracellular lipid droplets are labeled with BODIPY (<b>a</b>) and TG contents are determined (<b>c</b>). Primary hepatocytes are treated with OTA at concentrations of 10 μM for 6, 12, 24 and 48 h, then intracellular lipid droplets are labeled with BODIPY (<b>b</b>) and TG contents are determined (<b>d</b>). Data shown as the mean ± S.E.M. * <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 6 biological replicates.</p>
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<p>OTA induces simple steatosis in vivo. Mice are treated with OTA or NaHCO3 for 12 weeks (n = 12 in each group). Effects of OTA on mouse: body weight (<b>a</b>), liver weight (<b>b</b>), representative images of livers (<b>c</b>), H&amp;E and Oil Red O staining of liver sections of control and OTA-treated mice (<b>d</b>), liver TG contents and serum TG levels (<b>e</b>), serum ALT levels and AST levels (<b>f</b>). Data shown as the mean ± S.E.M. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">ns</span> means no significant difference.</p>
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<p>Reprogramming of hepatic gene expression after OTA treatment. (<b>a</b>) Heatmap for clustering analysis of genes in the livers of control and OTA-treated mice. (<b>b</b>) KEGG analysis showing top 15 enriched pathways in OTA-treated livers with <span class="html-italic">p</span> &lt; 0.05. (<b>c</b>) Results of GSEA showed PPAR signaling pathway were differentially enriched upon OTA treatment. (<b>d</b>) Heatmaps of hepatic RNA-seq raw gene counts for PPAR signaling pathway. (<b>e</b>) Examination of top upregulated genes associated with lipid metabolism in livers of control and OTA-treated mice (<span class="html-italic">n</span> = 12 in each group) by qPCR. (<b>f</b>) Examination of top upregulated genes associated with lipid metabolism in HepG2 cells by qPCR (<span class="html-italic">n</span> = 6 biological replicates). Data shown as the mean ± S.E.M. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>OTA induces hepatic steatosis through upregulation and activation of PPARγ. (<b>a</b>) Western blot analysis of PPARγ expression in livers of control and OTA-treated mice. (<b>b</b>) Immunohistochemical staining of PPARγ in liver tissues of control and OTA-treated mice. (<b>c</b>) PPARγ protein expression in control and OTA-treated HepG2 cells. (<b>d</b>) Western blot analysis showing effect of OTA on PPARγ expression in cytosolic and nuclear fractions of HepG2 cells. (<b>e</b>) Expression and localization of PPARγ under indicated treatment is examined by immunofluorescence. Nuclei were stained with DAPI, and (<b>f</b>) intracellular lipid droplets are labeled with BODIPY.</p>
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<p>OTA regulates PPARγ by post-translational modification. (<b>a</b>) PPARγ mRNA expression in livers of control and OTA-treated mice (<span class="html-italic">n</span> = 12 in each group). (<b>b</b>) Influence of OTA on the ubiquitination of PPARγ is examined in HEK293T and HepG2 cells. (<b>c</b>) PPARγ expression after OTA and cycloheximide (CHX) treatment. (<b>d</b>) Western blot analysis of SIAH2 expression in livers of control and OTA-treated mice. (<b>e</b>) Endogenous interaction between PPARγ and SIAH2 is examined by immunoprecipitation with SIAH2 antibody in control and OTA-treated HepG2 cells. (<b>f</b>) HepG2 cells are treated with OTA at the concentrations of 5, 10 and 15 μM for 24 h, and endogenous interaction between PPARγ and SIAH2 is examined by immunoprecipitation with SIAH2 antibody. Data shown as the mean ± S.E.M. <span class="html-italic">ns</span> means no significant difference.</p>
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<p>OTA-induced hepatic steatosis is CD36-dependent. (<b>a</b>) Western blot analysis of CD36 expression in HepG2 cells under indicated treatment. (<b>b</b>) Western blot analysis of CD36 expression in livers of control and OTA-treated mice. (<b>c</b>) Immunohistochemical staining of CD36 in liver tissues of control and OTA-treated mice. (<b>d</b>) Knockdown efficiency of CD36 in HepG2 cells. (<b>e</b>) BODIPY staining of lipid droplets in control and CD36-knockdown HepG2 cells treated with DMSO and OTA. (<b>f</b>) Left, TG contents in control and CD36-knockdown HepG2 cells treated with DMSO and OTA. Right, TG contents in HepG2 cells under indicated treatment (<span class="html-italic">n</span> = 6 biological replicates). Data shown as the mean ± S.E.M. * <span class="html-italic">p</span> &lt;0.05 vs. shControl DMSO treatment; and # <span class="html-italic">p</span> &lt; 0.05 vs. shControl OTA treatment.</p>
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13 pages, 1420 KiB  
Review
Modification of Glyceraldehyde-3-Phosphate Dehydrogenase with Nitric Oxide: Role in Signal Transduction and Development of Apoptosis
by Vladimir I. Muronetz, Maria V. Medvedeva, Irina A. Sevostyanova and Elena V. Schmalhausen
Biomolecules 2021, 11(11), 1656; https://doi.org/10.3390/biom11111656 - 8 Nov 2021
Cited by 14 | Viewed by 3049
Abstract
This review focuses on the consequences of GAPDH S-nitrosylation at the catalytic cysteine residue. The widespread hypothesis according to which S-nitrosylation causes a change in GAPDH structure and its subsequent binding to the Siah1 protein is considered in detail. It is [...] Read more.
This review focuses on the consequences of GAPDH S-nitrosylation at the catalytic cysteine residue. The widespread hypothesis according to which S-nitrosylation causes a change in GAPDH structure and its subsequent binding to the Siah1 protein is considered in detail. It is assumed that the GAPDH complex with Siah1 is transported to the nucleus by carrier proteins, interacts with nuclear proteins, and induces apoptosis. However, there are several conflicting and unproven elements in this hypothesis. In particular, there is no direct confirmation of the interaction between the tetrameric GAPDH and Siah1 caused by S-nitrosylation of GAPDH. The question remains as to whether the translocation of GAPDH into the nucleus is caused by S-nitrosylation or by some other modification of the catalytic cysteine residue. The hypothesis of the induction of apoptosis by oxidation of GAPDH is considered. This oxidation leads to a release of the coenzyme NAD+ from the active center of GAPDH, followed by the dissociation of the tetramer into subunits, which move to the nucleus due to passive transport and induce apoptosis. In conclusion, the main tasks are summarized, the solutions to which will make it possible to more definitively establish the role of nitric oxide in the induction of apoptosis. Full article
(This article belongs to the Special Issue Supramolecular Protein Structures)
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<p>A scheme of apoptosis induced by various modifications of GAPDH. Modifications of the catalytic cysteine in GAPDH decrease the affinity of NAD<sup>+</sup> for the protein, which results in a release of NAD<sup>+</sup> from the active center, destabilization of the tetramer, and its dissociation into subunits. The subunits penetrate to the nucleus via passive transport. In the nucleus, unfolding of the GAPDH subunits leads to the exposure of the nuclear export signal (NES) and the subsequent transport of the unfolded GAPDH subunits to the cytoplasm, where they form aggregates (according to [<a href="#B34-biomolecules-11-01656" class="html-bibr">34</a>,<a href="#B35-biomolecules-11-01656" class="html-bibr">35</a>,<a href="#B37-biomolecules-11-01656" class="html-bibr">37</a>,<a href="#B39-biomolecules-11-01656" class="html-bibr">39</a>]).</p>
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<p>Scheme of NO-induced apoptosis mediated by GAPDH proposed by A. Sawa [<a href="#B49-biomolecules-11-01656" class="html-bibr">49</a>], with the depiction of the tetrameric GAPDH structure. <span class="html-italic">S</span>-nitrosylation of GAPDH promotes its binding with Siah1. Siah1, which possesses a nuclear localization signal (NLS), translocates GAPDH to the nucleus. In the nucleus, GAPDH interacts with nuclear proteins, leading to apoptosis.</p>
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<p>Relationships between different modifications of GAPDH. One of four identical subunits is shown. C<sub>152</sub> is the catalytic cysteine residue; C<sub>156</sub> is the cysteine residue that is not involved in catalysis.</p>
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31 pages, 6685 KiB  
Article
A Critical Success Factor Framework for Implementing Sustainable Innovative and Affordable Housing: A Systematic Review and Bibliometric Analysis
by Alireza Moghayedi, Bankole Awuzie, Temitope Omotayo, Karen Le Jeune, Mark Massyn, Christiana Okobi Ekpo, Manfred Braune and Paimaan Byron
Buildings 2021, 11(8), 317; https://doi.org/10.3390/buildings11080317 - 23 Jul 2021
Cited by 59 | Viewed by 15409
Abstract
The actualization of affordable housing remains a challenge. This challenge is exacerbated by the increasing societal demand for the incorporation of sustainability principles into such housing types to improve levels of occupant health and well-being whilst avouching the desired levels of affordability. Innovative [...] Read more.
The actualization of affordable housing remains a challenge. This challenge is exacerbated by the increasing societal demand for the incorporation of sustainability principles into such housing types to improve levels of occupant health and well-being whilst avouching the desired levels of affordability. Innovative technologies and practices have been described as beneficial to the effectuation of sustainable affordable housing. However, knowledge concerning the deployment of innovative technologies and practices in sustainable affordable housing (sustainable, innovative, affordable housing—SIAH) delivery remains nascent. Consequently, there is a lack of a common ontology among stakeholders concerning how to realize SIAH. This study aims to contribute toward the development of this body of knowledge through the establishment of the critical success factors (CSFs) for effective SIAH implementation. To achieve this objective, a systematic review and bibliometric analysis focusing on a juxtaposition of sustainable, innovative and affordable housing concepts was carried out based on the relevant literature. This led to the identification and clustering of CSFs for these housing concepts at individual levels and as a collective (SIAH). The findings of the study consisted of the establishment of four distinct yet interrelated facets through which SIAH can be achieved holistically, namely, housing design, house element, housing production method and housing technology. A total of 127 CSFs were found to be aligned to these facets, subsequently clustered, and conclusively used for the development of a SIAH CSF framework. The most frequently occurring CSFs with predominant interconnections were the utilization of energy-efficient systems/fittings, tenure security, a comfortable and healthy indoor environment, affordable housing price in relation to income and using water-efficient systems/fittings CSFs, and establishing the emergent SIAH CSF framework. The framework in this study is useful in the documentation of SIAH features for construction projects and further studies into SIAH CSFs. Full article
(This article belongs to the Collection Sustainable Buildings in the Built Environment)
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<p>Research protocol for the study.</p>
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<p>Temporal distribution and trend of SIAH CSFs.</p>
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<p>Publication territory in SIAH CSFs.</p>
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<p>Clustered SIAH CSFs network.</p>
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<p>Design CSFs network.</p>
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<p>House element CSFs network.</p>
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<p>Method CSFs network.</p>
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<p>Technology CSFs network.</p>
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<p>SIAH CSFs framework.</p>
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18 pages, 8077 KiB  
Article
Melatonin Treatment Improves Renal Fibrosis via miR-4516/SIAH3/PINK1 Axis
by Yeo Min Yoon, Gyeongyun Go, Sungtae Yoon, Ji Ho Lim, Gaeun Lee, Jun Hee Lee and Sang Hun Lee
Cells 2021, 10(7), 1682; https://doi.org/10.3390/cells10071682 - 3 Jul 2021
Cited by 23 | Viewed by 3945
Abstract
Dysregulation in mitophagy, in addition to contributing to imbalance in the mitochondrial dynamic, has been implicated in the development of renal fibrosis and progression of chronic kidney disease (CKD). However, the current understanding of the precise mechanisms behind the pathogenic loss of mitophagy [...] Read more.
Dysregulation in mitophagy, in addition to contributing to imbalance in the mitochondrial dynamic, has been implicated in the development of renal fibrosis and progression of chronic kidney disease (CKD). However, the current understanding of the precise mechanisms behind the pathogenic loss of mitophagy remains unclear for developing cures for CKD. We found that miR-4516 is downregulated and its target SIAH3, an E3 ubiquitin protein ligase that reduces PINK1 accumulation to damaged mitochondria, is upregulated in the renal cortex of CKD mice. Here, we demonstrated that melatonin injection induces miR-4516 expression and suppresses SIAH3, and promotes PINK1/Parkin-mediated mitophagy. Furthermore, we demonstrated that melatonin injection attenuates the pathological features of CKD by improving mitochondrial homeostasis. Our data supports that mitochondrial autophagy regulation by activating miR-4516/SIAH3/PINK1 mitophagy signaling axis can be a viable new strategy for treating CKD. Full article
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<p>The miR-4516/SIAH3 pathway is involved in dysregulated mitophagy in fibrotic kidney of CKD mice. (<b>A</b>) H&amp;E staining of kidney cortex of CKD mice with healthy control (scale bar = 100 μm). (<b>B</b>) Representative TEM images of mitochondria in renal cortex of healthy and CKD mice (scale bar = 1 μm). (<b>C</b>,<b>D</b>) Quantification of mitochondrial size and number of abnormal mitochondria in renal cortex of healthy and CKD mice (<span class="html-italic">n</span> = 3). (<b>E</b>) Expression of p-DRP1 (S637), DRP1, MFN1, and OPA1 in renal cortex of CKD mice with healthy control. (<b>F</b>) Immunofluorescence data for lysosomal marker LAMP-1 (green) and mitochondrial marker COX4 (red) in CKD renal cortex region with healthy control (scale bar = 20 μm). (<b>G</b>) Expression of P62 and LC3B in renal cortex of CKD mice with healthy control. Protein expression level were quantified by densitometry and normalized to α-tubulin levels (<span class="html-italic">n</span> = 3). (<b>H</b>) Level of miR-4516 in renal cortex of CKD mouse as measured by qRT-PCR. (<b>I</b>) Expression of PINK1 in renal cortex of CKD mice. (<b>J</b>) Potential target genes of miR-4516 related to E3 ubiquitin protein ligase as reported by three different microRNA databases. SIAH3 was suggested by all three databases as indicated at the intersection of the Venn diagram. (<b>K</b>) Representative IHC images for SIAH3 in renal cortex of CKD mice with healthy control. Protein expression level was quantified by densitometry and normalized to α-tubulin levels (<span class="html-italic">n</span> = 3). The values represent mean ± SEM, * <span class="html-italic">p</span>&lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared with renal cortex of healthy mice. Statistical significance was assessed with unpaired Student’s <span class="html-italic">t</span>-test. The α-tubulin was used as Western blot loading control for whole tissue lysates.</p>
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<p>Melatonin-induced miR-4516 promotes PINK1/Parkin-mediated mitophagy by suppressing SIAH3. (<b>A</b>) Expression of SIAH3 and PINK1 in melatonin-treated TH1 cells (1 μM for 24 h) or pretreated with the luzindole (1 μM for 48 h) under <span class="html-italic">p</span>-Cresol exposure (0.5 mM for 72 h) (<span class="html-italic">n</span> = 3). Protein expression level was quantified by densitometry and normalized to β-actin or VDAC1 levels (<span class="html-italic">n</span> = 3). (<b>B</b>) Expression of SIAH3 and PINK1 in melatonin-treated TH1 cells (1 μM for 24 h) or pretreated with the miR-4516 inhibitor (50 nM for 48 h) under <span class="html-italic">p</span>-Cresol exposure (0.5 mM for 72 h) (<span class="html-italic">n</span> = 3). Protein expression level was quantified by densitometry and normalized to β-actin or VDAC1 levels (<span class="html-italic">n</span> = 3). (<b>C</b>) After immunoprecipitation against PINK1, the precipitates were analyzed by immunoblotting with SIAH3 antibody. Proteins signals were quantified by densitometry and were normalized to PINK1 levels (<span class="html-italic">n</span> = 3). (<b>D</b>) PINK1 immunoprecipitates were analyzed by immunoblotting with the ubiquitin antibody. (<b>E</b>) LC3B-II/LC3B-I ratio and P62 expression level was measured with immunoblotting. Protein expression was quantified by densitometry and normalized to VDAC1 levels (<span class="html-italic">n</span> = 3). (<b>F</b>) The percentage of autophagy positive cells in melatonin-treated TH1 cells (1 μM for 24 h) under <span class="html-italic">p</span>-Cresol exposure (0.5 mM for 72 h). TH1 cells were pretreated with the miR-4516 inhibitor (50 nM for 48 h) before melatonin treatment (<span class="html-italic">n</span> = 3). All reported values represent mean ± SEM, * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span> &lt; 0.01 versus control; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus <span class="html-italic">p</span>-Cresol exposure; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 <sup><span>$</span><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.01 versus melatonin-treated cells under <span class="html-italic">p</span>-Cresol exposure. The β-actin or VDAC1 was used as Western blot loading control for whole cell lysates or mitochondrial fraction respectively.</p>
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<p>Melatonin-induced miR-4516 rescues abnormal mitochondrial functions. (<b>A</b>) Representative TEM images for TH1 cells either treated with <span class="html-italic">p</span>-Cresol alone, melatonin under <span class="html-italic">p</span>-Cresol exposure, or miR-4516 inhibitor (50 nM for 48 h) before melatonin treatment, compared with TH1 control (scare bar = 1 μm). (<b>B</b>,<b>C</b>) Measurement of mitochondrial area and number of abnormal mitochondria in each experimental group (<span class="html-italic">n</span> = 3). (<b>D</b>) The effects of melatonin on p-DRP1, DRP1, MFN1, and OPA1 were reversed with miR-4516 inhibitor. Protein expression level was detected using western blot, quantified by densitometry, and normalized to DRP1 or VDAC1 levels (<span class="html-italic">n</span> = 3) respectively. (<b>E</b>,<b>F</b>) Measurement of TMRE (<b>E</b>) and MitoSOX (<b>F</b>) positive cells for each group (<span class="html-italic">n</span> = 3). The values represent mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus control; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus <span class="html-italic">p</span>-Cresol exposure; <sup><span>$</span><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.01 versus melatonin-treated cells in <span class="html-italic">p</span>-Cresol exposure. The β-actin or VDAC1 was used as Western blot loading control for whole cell lysates or mitochondrial fraction, respectively.</p>
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<p>Melatonin-induced miR-4516 improves mitochondrial dynamics and enhances PINK1/Parkin-mediated mitophagy in the CKD mouse model. (<b>A</b>) Representative TEM images of mitochondria in renal cortex of CKD mice either treated with melatonin (0.2 mg/kg), or both melatonin and miR-4516 inhibitor (300 nM). Each group received two intraperitoneal injections per week (every 3–4 days)—a total of 4 injections for 2 weeks. All comparisons were made against healthy kidney control (scare bar = 1 μm) (<b>B</b>,<b>C</b>) Measurement of mitochondrial area and number of abnormal mitochondria in renal cortex of each groups (<span class="html-italic">n</span> = 3). (<b>D</b>) The expression of p-DRP1, DRP1, MFN1, and OPA1 in renal cortex of each group. Protein expression level were quantified by densitometry and normalized to DRP1 or α-tubulin levels (<span class="html-italic">n</span> = 3). (<b>E</b>) Immunofluorescence staining for LAMP-1 (green) and COX4 (red) in renal cortex of each group. Scare bar = 20 μm. (<b>F</b>) Expression of LC3B-II/LC3B-I ratio and P62 in renal cortex of each groups (<span class="html-italic">n</span> = 3). The values represent mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus healthy kidney cortex; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus PBS; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05, <sup><span>$</span><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.01 versus melatonin. The α-tubulin was used as Western blot loading control for whole tissue lysates.</p>
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<p>Melatonin injection ameliorates fibrotic CKD and improves kidney function in a CKD mouse model via miR-4516/SIAH3/PINK1 pathway. (<b>A</b>) miR-4516 expression level in renal cortex of healthy kidney control and CKD mice either treated with melatonin, or both melatonin and miR-4516 inhibitor, measured by qRT-PCR (<span class="html-italic">n</span> = 3). (<b>B</b>) Expression of SIAH3 and PINK1 in renal cortex of each groups. Protein expression level was quantified by densitometry and normalized to α-tubulin levels (<span class="html-italic">n</span> = 3). (<b>C</b>) IHC staining for SIAH3 in renal cortex of each mouse group. (<b>D</b>) H&amp;E staining of renal cortex of each mouse group. (<b>E</b>,<b>F</b>) Measurement of blood urea nitrogen (<b>E</b>) and creatinine (<b>F</b>) level in serum of each mouse group (<span class="html-italic">n</span> = 5). The values represent mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus healthy kidney control; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus PBS; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05, <sup><span>$</span><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.01 versus melatonin. The α-tubulin was used as Western blot loading control for whole tissue lysates.</p>
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<p>The schematic representation of miR-4516/SIAH3/PINK1 mitophagy pathway in CKD. Schematic representation of the proposed mechanisms by which melatonin-induced miR-4516 inhibits the progression of renal cortical fibrosis via PINK1/Parkin-mediated mitophagy signaling axis. Melatonin-induced miR-4516 suppresses the expression of SIAH3, which is overexpressed in CKD renal cortex, promotes PINK1/Parkin-mediated mitophagy, which reduces dysfunctional mitochondria, and improves kidney function.</p>
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12 pages, 2577 KiB  
Article
Effects of Shade Net Colors on Mineral Elements and Postharvest Shelf Life and Quality of Fresh Fig (Ficus carica L.) under Rain-Fed Condition
by Ali Jokar, Hamid Zare, Abdolrasool Zakerin and Abdolhossein Aboutalebi Jahromi
Horticulturae 2021, 7(5), 93; https://doi.org/10.3390/horticulturae7050093 - 1 May 2021
Cited by 11 | Viewed by 3277
Abstract
Photoselective netting is well known for filtering the intercepted solar radiation, thus affecting light quality. While its effects on leaf mineral elements have been well investigated, how color netting affects fruit mineral nutrients remains elusive. This study was conducted to evaluate the effects [...] Read more.
Photoselective netting is well known for filtering the intercepted solar radiation, thus affecting light quality. While its effects on leaf mineral elements have been well investigated, how color netting affects fruit mineral nutrients remains elusive. This study was conducted to evaluate the effects of shade provided by blue and yellow nets on mineral nutrients of fig trees under rain-fed conditions. The experiment was arranged as a split-plot treatment in a randomized complete block design with three replications. Cultivars “Sabz” and “Siah” were covered with color nets or left uncovered (as the control group). The highest nitrogen content (8710 ppm) was recorded for cultivar “Sabz” covered with blue net. Color nets enhanced calcium concentration in cultivar “Siah”. Covering fig trees with yellow net increased magnesium content in cultivar “Siah” and phosphorus content in cultivar “Sabz”. Our observation showed the significant positive effect of photo selective nets on postharvest quality, by decreasing fig fruit weight loss and extending shelf life of fruits. In general, color nets as a new agro-technological approach can maintain fruit nutrition under rain-fed conditions and increase postharvest shelf life and quality of fresh fig. Full article
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<p>Loading plot for factor analysis.</p>
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