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15 pages, 958 KiB  
Review
The Role of circHIPK3 in Tumorigenesis and Its Potential as a Biomarker in Lung Cancer
by Eryk Siedlecki, Piotr Remiszewski and Rafał Stec
Cells 2024, 13(17), 1483; https://doi.org/10.3390/cells13171483 - 4 Sep 2024
Viewed by 391
Abstract
Lung cancer treatment and detection can be improved by the identification of new biomarkers. Novel approaches in investigating circular RNAs (circRNAs) as biomarkers have yielded promising results. A circRNA molecule circHIPK3 was found to be widely expressed in non-small-cell lung cancer (NSCLC) cells, [...] Read more.
Lung cancer treatment and detection can be improved by the identification of new biomarkers. Novel approaches in investigating circular RNAs (circRNAs) as biomarkers have yielded promising results. A circRNA molecule circHIPK3 was found to be widely expressed in non-small-cell lung cancer (NSCLC) cells, where it plays a crucial role in lung cancer tumorigenesis. CircHIPK3 promotes lung cancer progression by sponging oncosuppressive miRNAs such as miR-124, miR-381-3p, miR-149, and miR-107, which results in increased cell proliferation, migration, and resistance to therapies. Inhibiting circHIPK3 has been demonstrated to suppress tumour growth and induce apoptosis, which suggests its potential use in the development of new lung cancer treatment strategies targeting circHIPK3-related pathways. As a biomarker, circHIPK3 shows promise for early detection and monitoring of lung cancer. CircHIPK3 increased expression levels in lung cancer cells, and its potential link to metastasis risk highlights its clinical relevance. Given the promising preliminary findings, more clinical trials are needed to validate circHIPK3 efficacy as a biomarker. Moreover, future research should determine if the mechanisms discovered in NSCLC apply to small cell lung cancer (SCLC) to investigate circHIPK3-targeted therapies for SCLC. Full article
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Figure 1
<p>Biogenesis of circHIPK3 involving intron-pairing-driven circularization model and subsequent sponging of miRNAs in the cytoplasm. CircHIPK3 functions as a miRNA sponge, influencing mRNA expression at the post-transcriptional level and thereby promoting or inhibiting progression in various cancers. This circRNA is formed in the nucleus and exported via the nuclear pore to the cytoplasm, where it binds with the miRNAs. The miRNAs are present in the cytoplasm and formed out of pre-miRNAs in the nucleus.</p>
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<p>Location on 11p13 chromosome and the formation of circHIPK3 out of exon 2 surrounded by Alu repeats. CircHIPK3 is formed primarily in an intron-pairing-driven circularization model, which is also known as the direct back-splicing mechanism. Reverse complementary sequences flanking introns facilitate the process of back-splicing. These flanking complementary sequences, particularly Alu elements, are essential for exon circularization. Perfectly matched complementary sequences enhance the expression of circRNAs.</p>
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19 pages, 855 KiB  
Review
HIPK2 in Colon Cancer: A Potential Biomarker for Tumor Progression and Response to Therapies
by Alessandra Verdina, Alessia Garufi, Valerio D’Orazi and Gabriella D’Orazi
Int. J. Mol. Sci. 2024, 25(14), 7678; https://doi.org/10.3390/ijms25147678 - 12 Jul 2024
Viewed by 823
Abstract
Colon cancer, one of the most common and fatal cancers worldwide, is characterized by stepwise accumulation of specific genetic alterations in tumor suppressor genes or oncogenes, leading to tumor growth and metastasis. HIPK2 (homeodomain-interacting protein kinase 2) is a serine/threonine protein kinase and [...] Read more.
Colon cancer, one of the most common and fatal cancers worldwide, is characterized by stepwise accumulation of specific genetic alterations in tumor suppressor genes or oncogenes, leading to tumor growth and metastasis. HIPK2 (homeodomain-interacting protein kinase 2) is a serine/threonine protein kinase and a “bona fide” oncosuppressor protein. Its activation inhibits tumor growth mainly by promoting apoptosis, while its inactivation increases tumorigenicity and resistance to therapies of many different cancer types, including colon cancer. HIPK2 interacts with many molecular pathways by means of its kinase activity or transcriptional co-repressor function modulating cell growth and apoptosis, invasion, angiogenesis, inflammation and hypoxia. HIPK2 has been shown to participate in several molecular pathways involved in colon cancer including p53, Wnt/β-catenin and the newly identified nuclear factor erythroid 2 (NF-E2) p45-related factor 2 (NRF2). HIPK2 also plays a role in tumor–host interaction in the tumor microenvironment (TME) by inducing angiogenesis and cancer-associated fibroblast (CAF) differentiation. The aim of this review is to assess the role of HIPK2 in colon cancer and the underlying molecular pathways for a better understanding of its involvement in colon cancer carcinogenesis and response to therapies, which will likely pave the way for novel colon cancer therapies. Full article
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<p>Schematic representation of the risk factors of colon cancer development.</p>
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<p>Schematic representation of the steps of colon cancer development. The passages from polyp to benign adenoma, malignant adenoma, carcinoma (represented by the pink/yellow pentagons), in the colon regions, and liver metastasis, include the reduction of oncosuppressor genes (blue triangle) and the activation of oncogenes with increased chromosomal instability (red triangle).</p>
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<p>Schematic representation of the HIPK2 role in the TME (tumor microenvironment). HIPK2 can be inhibited by exomiR-1299, HIF-1 and NRF2 (blocking black lines). HIF-1 and NRF2 can sustain their oncogenic ability (↑↓). HIPK2 inhibition leads to increased (red arrow) differentiation of CAF (cancer-associated fibroblasts); to induction of angiogenesis by targeting endothelial cells with secreted (red arrow) VEGF (vascular endothelial growth factor) by HIF-1, β-catenin or COX pathways; and to block (red arrow) dendritic cell (DC) maturation by means of PGE2 (prostaglandin E2) production.</p>
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26 pages, 2337 KiB  
Article
Transcriptome Profiling of Oncorhynchus mykiss Infected with Low or Highly Pathogenic Viral Hemorrhagic Septicemia Virus (VHSV)
by Lorena Biasini, Gianpiero Zamperin, Francesco Pascoli, Miriam Abbadi, Alessandra Buratin, Andrea Marsella, Valentina Panzarin and Anna Toffan
Microorganisms 2024, 12(1), 57; https://doi.org/10.3390/microorganisms12010057 - 28 Dec 2023
Cited by 1 | Viewed by 1099
Abstract
The rainbow trout (Oncorhynchus mykiss) is the most important produced species in freshwater within the European Union, usually reared in intensive farming systems. This species is highly susceptible to viral hemorrhagic septicemia (VHS), a severe systemic disease widespread globally throughout the [...] Read more.
The rainbow trout (Oncorhynchus mykiss) is the most important produced species in freshwater within the European Union, usually reared in intensive farming systems. This species is highly susceptible to viral hemorrhagic septicemia (VHS), a severe systemic disease widespread globally throughout the world. Viral hemorrhagic septicemia virus (VHSV) is the etiological agent and, recently, three classes of VHSV virulence (high, moderate, and low) have been proposed based on the mortality rates, which are strictly dependent on the viral strain. The molecular mechanisms that regulate VHSV virulence and the stimulated gene responses in the host during infection are not completely unveiled. While some preliminary transcriptomic studies have been reported in other fish species, to date there are no publications on rainbow trout. Herein, we report the first time-course RNA sequencing analysis on rainbow trout juveniles experimentally infected with high and low VHSV pathogenic Italian strains. Transcriptome analysis was performed on head kidney samples collected at different time points (1, 2, and 5 days post infection). A large set of notable genes were found to be differentially expressed (DEGs) in all the challenged groups (e.s. trim63a, acod1, cox-2, skia, hipk1, cx35.4, ins, mtnr1a, tlr3, tlr7, mda5, lgp2). Moreover, the number of DEGs progressively increased especially during time with a greater amount found in the group infected with the high VHSV virulent strain. The gene ontology (GO) enrichment analysis highlighted that functions related to inflammation were modulated in rainbow trout during the first days of VHSV infection, regardless of the pathogenicity of the strain. While some functions showed slight differences in enrichments between the two infected groups, others appeared more exclusively modulated in the group challenged with the highly pathogenic strain. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Kaplan–Meier survival curves and qRT-PCR results. (<b>a</b>) Mortality challenge: survival curves of VHSV-L e VHSV-H in rainbow trout. Trial carried out by bath immersion. The <span class="html-italic">y</span>-axis reports the survival rate; the <span class="html-italic">x</span>-axis reports the observation period expressed as days post infection (dpi). Step curves represent the survival rate of challenged fish in each experimental group. (<b>b</b>) Transcriptomic challenge: qRT-PCR results. In the <span class="html-italic">x</span>-axis the results are grouped according to the sampling day post-infection (dpi). The <span class="html-italic">y</span>-axis shows viral target gene copy numbers (CN) detected in SPL or HK and reported on logarithmic scale. Values are expressed as means of normalized CN in 1 ng of total RNA ± SEM (<span class="html-italic">n</span> = 5). * = <span class="html-italic">p</span>-value &lt; 0.001.</p>
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<p>Principal component analysis of the normalized gene counts of the host genes. The <span class="html-italic">x</span>- and <span class="html-italic">y</span>-axes show the two dimensions that explain the overall amount of variance related to gene expression levels. Each replicate is represented by a colored numerical code based on the challenge group and the time elapsed since infection.</p>
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<p>Differentially expressed gene numbers in head kidney of rainbow trout infected with VHSV High or Low virulent strains. At each time point, the total number of genes is shown in a stacked bar chart. Red and blue colors represent up- and down-regulated genes, respectively.</p>
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<p>Number of common and specific genes observed after infection. Venn diagrams show: (<b>a</b>) the common and specific differentially expressed genes (DEGs) between different time points of VHSV-L challenged group; (<b>b</b>) VHSV-H challenged group; (<b>c</b>) between the important time points of both challenged groups. Percentages of common and specific genes reported in (<b>a</b>,<b>b</b>) are computed relative to the total number of DEGs in each time point, while in (<b>c</b>) they refer to the total number of DEGs overall the two infections at both time points considered.</p>
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<p>GO enrichment for the L and H VHSV challenges. Dotplot representing the most specific enriched GO terms for all the analyzed time points in both infections. L1, L2, L5 refer to time points 1, 2, 5 dpi in the VHSV-L challenge; similarly, H1, H2, H5 refer to time points 1, 2, 5 dpi in the VHSV-H challenge. Statistically significant enrichments (FDR &lt; 0.05) are presented, and the -Log FDR is shown.</p>
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21 pages, 5938 KiB  
Article
On the Prevalence and Roles of Proteins Undergoing Liquid–Liquid Phase Separation in the Biogenesis of PML-Bodies
by Sergey A. Silonov, Yakov I. Mokin, Eugene M. Nedelyaev, Eugene Y. Smirnov, Irina M. Kuznetsova, Konstantin K. Turoverov, Vladimir N. Uversky and Alexander V. Fonin
Biomolecules 2023, 13(12), 1805; https://doi.org/10.3390/biom13121805 - 18 Dec 2023
Viewed by 1932
Abstract
The formation and function of membrane-less organelles (MLOs) is one of the main driving forces in the molecular life of the cell. These processes are based on the separation of biopolymers into phases regulated by multiple specific and nonspecific inter- and intramolecular interactions. [...] Read more.
The formation and function of membrane-less organelles (MLOs) is one of the main driving forces in the molecular life of the cell. These processes are based on the separation of biopolymers into phases regulated by multiple specific and nonspecific inter- and intramolecular interactions. Among the realm of MLOs, a special place is taken by the promyelocytic leukemia nuclear bodies (PML-NBs or PML bodies), which are the intranuclear compartments involved in the regulation of cellular metabolism, transcription, the maintenance of genome stability, responses to viral infection, apoptosis, and tumor suppression. According to the accepted models, specific interactions, such as SUMO/SIM, the formation of disulfide bonds, etc., play a decisive role in the biogenesis of PML bodies. In this work, a number of bioinformatics approaches were used to study proteins found in the proteome of PML bodies for their tendency for spontaneous liquid–liquid phase separation (LLPS), which is usually caused by weak nonspecific interactions. A total of 205 proteins found in PML bodies have been identified. It has been suggested that UBC9, P53, HIPK2, and SUMO1 can be considered as the scaffold proteins of PML bodies. It was shown that more than half of the proteins in the analyzed proteome are capable of spontaneous LLPS, with 85% of the analyzed proteins being intrinsically disordered proteins (IDPs) and the remaining 15% being proteins with intrinsically disordered protein regions (IDPRs). About 44% of all proteins analyzed in this study contain SUMO binding sites and can potentially be SUMOylated. These data suggest that weak nonspecific interactions play a significantly larger role in the formation and biogenesis of PML bodies than previously expected. Full article
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<p>PML protein domain organization scheme. All PML isoforms have the same N-terminal part that contains RING (R), B-Box1 (B1), B-Box2 (B2), and coiled-coil (CC) domains. The majority of PML isoforms contain a nuclear localization signal (NLS).</p>
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<p>Scheme illustrating the design of our study.</p>
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<p>Venn diagram of three sets of proteins potentially included in PML bodies. Blue circle represents the set based on the analysis of the PML interactome in the BIOGRID database. Green circle represents the set according to the analysis of the GO:0016605 and SL-0465 databases. Yellow circle represents the set collected during the analysis of literary sources.</p>
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<p>Analysis of the interactome of PML bodies’ scaffold proteins. (<b>A</b>) Venn diagram represents the intersection of the interactomes of four PML bodies’ scaffold proteins (PML, DAXX, SP100, and CREBB(CBP)). Data were obtained using BioGRID v4.4. (<b>B</b>) Interaction map for eight potential PML body envelope proteins based on the statistical representation in the BIOGRID (evidence score) database. Yellow shows results with weak statistics (data from one experiment), blue shows data with strong statistics (&gt;10 experiments). The heat map was constructed using CytoScape [<a href="#B221-biomolecules-13-01805" class="html-bibr">221</a>]. (<b>C</b>) Table of results for predicting the propensity of detected proteins to undergo spontaneous phase separation using FuzDrop and PSPrediction scores.</p>
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<p>Analysis of global protein disorder within the PML-body proteome. (<b>A</b>) The output of PONDR<sup>®</sup> VSL2, where the PONDR<sup>®</sup> VSL2 score denotes the mean disorder score (MDS) for a given protein. This score is derived by summing up the per-residue disorder scores and dividing this sum by the number of residues in a query sequence. The PONDR<sup>®</sup> VSL2 (%) corresponds to the percentage of predicted intrinsically disordered residues (PPIDR), indicating the proportion of residues with disorder scores above 0.5. Color-coded blocks depict distinct regions within the MDS-PPIDR phase space based on the degree of protein (dis)order: mostly disordered proteins are found in the red colored block, moderately disordered proteins are within the pink and light-pink blocks, and predominantly ordered proteins are positioned in the blue and light-blue blocks. Dark-colored background areas (blue, pink, and red) indicate concordance between the two parameters; i.e., MDS and PPIDR, while light-blue and light-pink colors signify areas where only one criterion aligns. The delineation of colored regions is contingent upon the arbitrary yet accepted cutoffs for PPDR (<span class="html-italic">x</span>-axis) and MDS (<span class="html-italic">y</span>-axis). (<b>B</b>) The cumulative distribution function and charge–hydropathy plot (ΔCDF-ΔCH) for the nuclear PML-body proteome comprising 205 proteins. The Y-coordinate is determined by the distance of each protein from the boundary in the CH plot, while the X-coordinate is calculated as the average distance of the protein’s CDF curve from the CDF boundary. The protein classification is based on the quadrant in which it is positioned. In Quadrant 1 (Q1), 70 proteins (34.1%) are projected to be ordered according to CDF and exhibit compact characteristics in the CH plot. In Quadrant 2 (Q2), 88 proteins (42.9%) are anticipated to be ordered/compact based on the CH plot but display disorder as per the CDF plot. Quadrant 3 (Q3) encompasses 44 proteins (21.5%) predicted to be disordered based on the CH plot and CDF. Quadrant 4 (Q4) includes 3 proteins (1.5%) anticipated to be disordered according to the CH plot but ordered according to the CDF analysis.</p>
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<p>Proteome analysis of PML bodies. (<b>A</b>) Correlation of the results of two predictors (FuzDrop and PSPredictor) and the results in the form of a pie diagram. (<b>B</b>) Proportion of proteins potentially capable (SUMO) and incapable (NoSUMO) of SUMOylation in the analyzed set of proteins. (<b>C</b>) Share of LLPS drivers/clients. (<b>D</b>) Proportion of disordered proteins (IDPs) and proteins with intrinsically disordered protein regions (IDPRs). (<b>E</b>) Proportion of proteins potentially carrying a negative charge, a weak positive charge, and a positive charge. Proportion of proteins potentially capable (SUMO) and incapable (NoSUMO) of SUMOylation for LLPS (<b>F</b>), IDP (<b>G</b>) and weakly positive (<b>H</b>) drivers.</p>
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<p>Result of GO analysis for proteins potentially prone to LLPS. Biological process and molecular functions are shown.</p>
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12 pages, 2465 KiB  
Article
New Copper-Based Metallodrugs with Anti-Invasive Capacity
by Alessia Garufi, Francesca Scarpelli, Loredana Ricciardi, Iolinda Aiello, Gabriella D’Orazi and Alessandra Crispini
Biomolecules 2023, 13(10), 1489; https://doi.org/10.3390/biom13101489 - 7 Oct 2023
Viewed by 1724
Abstract
While metal-based complexes are deeply investigated as anticancer chemotherapeutic drugs, fewer studies are devoted to their anti-invasive activity. Herein, two copper (Cu)(II) tropolone derivatives, [Cu(Trop)Cl] and [Cu(Trop)Sac], both containing the N,N-chelated 4,4′-bishydroxymethyl-2,2′-bipyridne ligand, were evaluated for their anticancer and anti-invasive properties. RKO (RKO-ctr) [...] Read more.
While metal-based complexes are deeply investigated as anticancer chemotherapeutic drugs, fewer studies are devoted to their anti-invasive activity. Herein, two copper (Cu)(II) tropolone derivatives, [Cu(Trop)Cl] and [Cu(Trop)Sac], both containing the N,N-chelated 4,4′-bishydroxymethyl-2,2′-bipyridne ligand, were evaluated for their anticancer and anti-invasive properties. RKO (RKO-ctr) colon cancer cells and their derivatives undergoing stable small interference (si) RNA for HIPK2 protein (RKO-siHIPK2) with acquisition of pro-invasive capacity were used. The results demonstrate that while [Cu(Trop)Sac] did not show cytotoxic activity, [Cu(Trop)Cl] induced cell death in both RKO-ctr and RKO-siHIPK2 cells, indicating that structural changes on substituting the coordinated chloride ligand with saccharine (Sac) could be a key factor in suppressing mechanisms of cellular death. On the other hand, both [Cu(Trop)Sac] and [Cu(Trop)Cl] complexes counteracted RKO-siHIPK2 cell migration in the wound healing assay. The synergic effect exerted by the concomitant presence of both tropolone and saccharin ligands in [Cu(Trop)Sac] was also supported by its significant inhibition of RKO-siHIPK2 cell migration compared to the free Sac ligand. These data suggest that the two Cu(II) tropolone derivatives are also interesting candidates to be further tested in in vivo models as an anti-invasive tumor strategy. Full article
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<p>Molecular structures of the (<b>a</b>) [(bpy-OH)Cu(Trop)(Cl)]H<sub>2</sub>O and [Cu(Trop)Sac] (<b>b</b>) complexes.</p>
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<p>Absorption spectra over time (<span class="html-italic">t</span> = 0, 3, 6, 24 and 48 h) of [Cu(Trop)Cl] (<b>a</b>) and [Cu(Trop)Sac] (<b>b</b>) complexes in DMSO/buffer solution (DMSO 0.5% <span class="html-italic">v</span>/<span class="html-italic">v</span>) at room temperature, 1 × 10<sup>−5</sup> M.</p>
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<p>Anticancer activity of the [Cu(Trop)Sac] complex. RKO-ctr and RKO-siHIPK2 colon cancer cells were left untreated or treated with the indicated amount of (<b>a</b>) [Cu(Trop)Sac] complex or (<b>b</b>) saccharine for 24 h before measuring cell survival by XTT assay. (<b>c</b>) Western blot analysis of the indicated proteins in RKO-ctr and RKO-siHIPK2 colon cancer cells left untreated or treated with the indicated amount of [Cu(Trop)Sac] complex for 24 h (original images can be found in <a href="#app1-biomolecules-13-01489" class="html-app">Figure S1</a>). The ratio of protein levels vs. βactin, used as a protein loading control, was measured by densitometric analysis and is reported. * <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Cell migration following [Cu(Trop)Sac] treatment. (<b>a</b>) Wound healing assay on RKO-ctr and RKO-siHIPK2 during time after scratch. Wound healing assay following [Cu(Trop)Sac] or saccharine (Sac) (5 µM) treatment on ((<b>b</b>), <b>left panel</b>) RKO-siHIPK2 and ((<b>b</b>), <b>right panel</b>) RKO-ctr cells. (<b>c</b>) Histograms showing the percentage of RKO-siHIPK2 cell migration treated as in (<b>b</b>). * <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Anticancer activity of the [Cu(Trop)Cl] precursor. RKO-ctr and RKO-siHIPK2 colon cancer cells were left untreated or treated with the indicated amount of (<b>a</b>) [Cu(Trop)Cl] precursor for 16 and 24 h before measuring cell viability by Trypan blue assay. (<b>b</b>) Wound healing assay on RKO-siHIPK2 cells mock-treated or treated with [Cu(Trop)Cl] (1 and 5 µM) for the indicated time. (<b>c</b>) Histograms showing the percentage of RKO-siHIPK2 cell migration treated as in (<b>b</b>). * <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Absorption spectra of DPPH (control, black line), [Cu(Trop)Cl] (red line), and [Cu(Trop)Sac] (blue line) ethanolic solution after incubation for 3 (<b>a</b>), 24 (<b>b</b>), 48 (<b>c</b>), and 72 (<b>d</b>) hours.</p>
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16 pages, 1235 KiB  
Review
Progress in circRNA-Targeted Therapy in Experimental Parkinson’s Disease
by Simoneide Souza Titze-de-Almeida and Ricardo Titze-de-Almeida
Pharmaceutics 2023, 15(8), 2035; https://doi.org/10.3390/pharmaceutics15082035 - 28 Jul 2023
Cited by 4 | Viewed by 1493
Abstract
Circular RNAs (circRNAs) are single-stranded RNA molecules often circularized by backsplicing. Growing evidence implicates circRNAs in the underlying mechanisms of various diseases, such as Alzheimer’s and Parkinson’s disease (PD)—the first and second most prevalent neurodegenerative disorders. In this sense, circSNCA, circHIPK2, circHIPK3, and [...] Read more.
Circular RNAs (circRNAs) are single-stranded RNA molecules often circularized by backsplicing. Growing evidence implicates circRNAs in the underlying mechanisms of various diseases, such as Alzheimer’s and Parkinson’s disease (PD)—the first and second most prevalent neurodegenerative disorders. In this sense, circSNCA, circHIPK2, circHIPK3, and circSLC8A1 are circRNAs that have been related to the neurodegenerative process of PD. Gain-of-function and loss-of-function studies on circRNAs have shed light on their roles in the pathobiology of various diseases. Gain-of-function approaches typically employ viral or non-viral vectors that hyperexpress RNA sequences capable of circularizing to form the specific circRNA under investigation. In contrast, loss-of-function studies utilize CRISPR/Cas systems, antisense oligonucleotides (ASOs), or RNAi techniques to knock down the target circRNA. The role of aberrantly expressed circRNAs in brain pathology has raised a critical question: could circRNAs serve as viable targets for neuroprotective treatments? Translating any oligonucleotide-based therapy, including those targeting circRNAs, involves developing adequate brain delivery systems, minimizing off-target effects, and addressing the high costs of treatment. Nonetheless, RNAi-based FDA-approved drugs have entered the market, and circRNAs have attracted significant attention and investment from major pharmaceutical companies. Spanning from bench to bedside, circRNAs present a vast opportunity in biotechnology for oligonucleotide-based therapies designed to slow or even halt the progression of neurodegenerative diseases. Full article
(This article belongs to the Special Issue Recent Trends in Oligonucleotide Based Therapies)
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<p>Schematic representation of circRNA biogenesis. Precursor mRNA (pre-mRNA) is the primary RNA transcript synthesized on a DNA template in the cell nucleus. Pre-mRNA can follow two distinct cellular pathways. In the upward direction, pre-mRNA gives rise to 5′-3′ mRNA sequences through canonical splicing (<b>a</b>), which removes introns and generates a molecule to be exported to the cytoplasm for guiding protein synthesis. In the alternate downward direction, pre-mRNA produces circRNAs through back-splicing reactions (<b>b</b>). Intronic circRNAs (ciRNA) (<b>c</b>) are formed by a lariat-driven cyclization process that exclusively involves introns. Specific sequence elements adjacent to an exon, rich in guanine (G) and uracil (U) (depicted in blue), bind to an 11-nucleotide cytosine (C)-rich sequence (shown in red) near another exon. This binding allows evasion of the debranching reaction and prevents degradation by exonucleases. Reverse complementary sequences and/or RNA-binding proteins (RBP) (<b>d</b>) act to bring the nucleotide sequences into close proximity. This process facilitates the back-splicing reaction, leading to the generation of exonic circRNA (EciRNA) (<b>e</b>) or exon-intron circRNA (EIciRNA) (<b>f</b>). Abbreviations: mRNA, messenger RNA; pre-mRNA, precursor mRNA; ciRNA, intronic circRNA; EciRNA, exonic circRNA; EIciRNA, exon-intron circRNA; E, exon; I, intron; RBP, RNA-binding proteins.</p>
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<p>Alpha-synuclein (SNCA), circSNCA, and the cellular stressors acting on the neurodegenerative process of PD. (<b>a</b>) The CDS region of SNCA mRNA originates the (<b>b</b>) normal folded SNCA protein, which, under (<b>c</b>) oxidative stress, causes the formation of (<b>d</b>) SNCA aggregates. These aggregates induce toxicity and further neurodegeneration. The 3′UTR region of SNCA mRNA originates (<b>e</b>) circSNCA, which acts as a sponge for miR-7 (<b>f</b>,<b>g</b>), leading to an increase in pro-apoptotic proteins and neurodegeneration. Abbreviations: 5′UTR, 5′untranslated region; 3′UTR, 3′untranslated region; CDS, coding sequence.</p>
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<p>Strategies for targeting circRNAs. The four most common methods to modify circRNA content include viral and non-viral vectors, AONs, and small-interfering RNAs (siRNAs). On the left, engineered viral vectors with specific cassettes enter the cell without carriers and overexpress a specific circRNA. The other strategies for targeting circRNAs require a nanoparticle to carry nucleic acids (siRNA, AON and plasmid) across the plasma membrane into the cytosol, which then form endocytic vesicles. Upon vesicle disruption, the plasmids or oligonucleotides are free to perform their functions. Plasmids are circular double-stranded DNA sequences engineered to overexpress single-stranded RNA that can circularize and form the intended circRNA, providing gain-of-function effects similar to viral vectors. In contrast, loss-of-function effects are achieved with AONs, single-stranded RNA sequences that bind through Watson–Crick base pairing to complementary nucleotides in circRNA, blocking protein interaction sites. Additionally, siRNAs are short double-stranded RNA molecules that trigger RNAi mechanisms involving the RISC complex, leading to circRNA cleavage. Abbreviations: circRNA, circular RNA; siRNA, small-interfering RNA; RISC, RNA-inducing silencing complex.</p>
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14 pages, 3059 KiB  
Article
Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations
by Ziqi Zhong, Ziyi Wang, Xinfeng Xie, Shuaishuai Tian, Feifan Wang, Qishan Wang, Shiheng Ni, Yuchun Pan and Qian Xiao
Animals 2023, 13(12), 2010; https://doi.org/10.3390/ani13122010 - 16 Jun 2023
Cited by 6 | Viewed by 1894
Abstract
Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, [...] Read more.
Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (FST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations. Full article
(This article belongs to the Collection Genetic Diversity in Livestock and Companion Animals)
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<p>SNP characteristics of indigenous pig population. (<b>A</b>) This figure shows the annotation of all variations within the three indigenous pig populations, with each variation area annotated as a percentage. (<b>B</b>) This figure displays the annotated variation information within the coding region of the three indigenous pig populations.</p>
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<p>Genetic diversity of indigenous pigs and commercial pigs. (<b>A</b>) Drawn violin plots for nucleotide diversity in each population. (<b>B</b>) Drawn LD decay plots for each population. This plot describes the change in the degree of linkage disequilibrium (LD) between two loci along the distance. (<b>C</b>) Heatmap of <span class="html-italic">F<sub>ST</sub></span> distance between populations.</p>
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<p>Analysis of genetic structure of the tested pigs. WZS, Wuzhishan pigs; TC, Tunchang pigs; DA, Dingan pigs; LR, Landrace pigs; LW, Large White pigs. (<b>A</b>) Neighbor-joining tree for all individuals. (<b>B</b>) Plot of the first and second principal components resulting from a principal component analysis of all pig populations. (<b>C</b>) The plot of population structure for all pig populations (K = 2–4). Different colors represent different clusters.</p>
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<p>Manhattan plot of selective signatures by <span class="html-italic">F<sub>ST</sub></span> in the Chinese indigenous pigs. The red dotted line means the threshold for classifying outliers in the heat group (top 1%). Different colors are used to distinguish the neighboring chromosomes. Several related candidate genes are also highlighted.</p>
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<p>GO terms and KEGG pathways were drawn for the candidate exonic genes that were screened based on <span class="html-italic">F<sub>ST</sub></span>. (<b>A</b>) Bubble chart illustrating Gene Ontology (GO) Biological Process enrichment analysis results. (<b>B</b>) Bubble chart demonstrating Gene Ontology (GO) Cellular Component enrichment analysis findings. (<b>C</b>) Bubble chart highlighting Gene Ontology (GO) Molecular Function enrichment analysis outcomes. (<b>D</b>) Bubble chart illustrating KEGG pathway analysis results of selected candidate genes.</p>
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16 pages, 4048 KiB  
Article
Inverse Impact of Cancer Drugs on Circular and Linear RNAs in Breast Cancer Cell Lines
by Anna Terrazzan, Francesca Crudele, Fabio Corrà, Pietro Ancona, Jeffrey Palatini, Nicoletta Bianchi and Stefano Volinia
Non-Coding RNA 2023, 9(3), 32; https://doi.org/10.3390/ncrna9030032 - 19 May 2023
Viewed by 2400
Abstract
Altered expression of circular RNAs (circRNAs) has previously been investigated in breast cancer. However, little is known about the effects of drugs on their regulation and relationship with the cognate linear transcript (linRNA). We analyzed the dysregulation of both 12 cancer-related circRNAs and [...] Read more.
Altered expression of circular RNAs (circRNAs) has previously been investigated in breast cancer. However, little is known about the effects of drugs on their regulation and relationship with the cognate linear transcript (linRNA). We analyzed the dysregulation of both 12 cancer-related circRNAs and their linRNAs in two breast cancer cell lines undergoing various treatments. We selected 14 well-known anticancer agents affecting different cellular pathways and examined their impact. Upon drug exposure circRNA/linRNA expression ratios increased, as a result of the downregulation of linRNA and upregulation of circRNA within the same gene. In this study, we highlighted the relevance of identifying the drug-regulated circ/linRNAs according to their oncogenic or anticancer role. Interestingly, VRK1 and MAN1A2 were increased by several drugs in both cell lines. However, they display opposite effects, circ/linVRK1 favors apoptosis whereas circ/linMAN1A2 stimulates cell migration, and only XL765 did not alter the ratio of other dangerous circ/linRNAs in MCF-7. In MDA-MB-231 cells, AMG511 and GSK1070916 decreased circGFRA1, as a good response to drugs. Furthermore, some circRNAs might be associated with specific mutated pathways, such as the PI3K/AKT in MCF-7 cells with circ/linHIPK3 correlating to cancer progression and drug-resistance, or NHEJ DNA repair pathway in TP-53 mutated MDA-MB-231 cells. Full article
(This article belongs to the Special Issue ncRNAs to Target Molecular Pathways)
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Graphical abstract

Graphical abstract
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<p>Drug-mediated dysregulation of the circRNA and linRNA pairs in MCF-7 cells. On the top, differential expression of circRNA (<b>black</b>) and linRNA (<b>grey</b>), as FC (2<sup>−ΔΔCT</sup>) of treated vs. untreated samples (Student’s <span class="html-italic">t</span>-test, two-tail paired). Boxplots were obtained using GraphPad Prism version 5.01. On the bottom, FC values and “FC circRNA/FC linRNA” ratio are reported for each pair. In bold, the values of the statistically significant FC circRNA and FC linRNA ± SD, as previously reported in <a href="#ncrna-09-00032-t004" class="html-table">Table 4</a>. In <b>red</b>, the pairs with a ratio &gt; 2.00.</p>
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<p>Drug-mediated dysregulation of the circRNA and linRNA pairs in MDA-MB-231 cells. On the top, differential expression of circRNA (<b>black</b>) and linRNA (<b>grey</b>), as FC (2<sup>−ΔΔCT</sup>) of treated vs. untreated samples (Student’s <span class="html-italic">t</span>-test, two-tail paired). Boxplots were obtained using GraphPad Prism version 5.01. On the bottom, FC values and “FC circRNA/FC linRNA” ratio are reported for each pair. In bold, the values of the statistically significant FC circRNA and FC linRNA ± SD, as previously reported in <a href="#ncrna-09-00032-t005" class="html-table">Table 5</a>. In <b>red</b>, the pairs with a ratio &gt; 2.00.</p>
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<p>Pathways affected by drugs deregulating circRNAs and linRNAs. Some icons of this figure were created on Biorender.com (accessed on 17 January 2023). The cartoon depicts the results of circRNA and linRNA analysis linked to the pathways regulated by the indicated drug (in <b>red</b>). Pointed arrows indicate activation, while flat-tipped arrows inhibition of targets. The circ/linRNAs of each cell line are represented in the marked white squares, in <b>blue</b> the ones detected in MCF-7 and in black those in MDA-MB-231 cells.</p>
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15 pages, 1049 KiB  
Review
The Sweet Side of HIPK2
by Alessia Garufi, Valerio D’Orazi, Giuseppa Pistritto, Mara Cirone and Gabriella D’Orazi
Cancers 2023, 15(10), 2678; https://doi.org/10.3390/cancers15102678 - 9 May 2023
Cited by 4 | Viewed by 1977
Abstract
HIPK2 is an evolutionary conserved protein kinase which modulates many molecular pathways involved in cellular functions such as apoptosis, DNA damage response, protein stability, and protein transcription. HIPK2 plays a key role in the cancer cell response to cytotoxic drugs as its deregulation [...] Read more.
HIPK2 is an evolutionary conserved protein kinase which modulates many molecular pathways involved in cellular functions such as apoptosis, DNA damage response, protein stability, and protein transcription. HIPK2 plays a key role in the cancer cell response to cytotoxic drugs as its deregulation impairs drug-induced cancer cell death. HIPK2 has also been involved in regulating fibrosis, angiogenesis, and neurological diseases. Recently, hyperglycemia was found to positively and/or negatively regulate HIPK2 activity, affecting not only cancer cell response to chemotherapy but also the progression of some diabetes complications. The present review will discuss how HIPK2 may be influenced by the high glucose (HG) metabolic condition and the consequences of such regulation in medical conditions. Full article
(This article belongs to the Special Issue Apoptosis in Cancer 2.0)
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<p>Schematic representation of HIPK2 apoptotic activity. Following activation by drugs and radiations (UV and IR) (red arrow), HIPK2 activates (↑) or inhibits (↓) molecules involved in apoptosis regulation, inducing apoptosis.</p>
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<p>Schematic representation of the regulatory balance between HIF-1 and HIPK2 (red symbols) (see text). (<b>A</b>) HIPK2 activation inhibits HIF-1α expression (↓) and, therefore, HIF-1 activity, impairing the HIF-1-induced angiogenesis, chemoresistance, and tumor invasion (dotted blue arrow); (<b>B</b>) Hypoxia induces HIF-1 transcription (↑) which, through its targets WSB1 and/or Siah-1/2, induces HIPK2 proteasomal degradation (blue dots), impairing the HIPK2-induced apoptosis (dotted blue arrow).</p>
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<p>Schematic representation of HIPK2/p53 apoptotic regulation by HG. <b>Left panel</b>: lethal damage, such as that triggered by drugs, activates HIPK2 to induce p53 phosphorylation in serine 46 (pSer46) that consequently induces apoptosis through Puma transcription. <b>Right panel</b>: high glucose induces HIPK2 degradation (green squares) through the PP2A/HIF-1/Siah-2 axis, impairing the apoptotic activity of p53 that is instead switched toward transcription of the autophagic gene DRAM.</p>
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<p>Schematical representation of the outcome of HIPK2-induced regulation of insulin promoter factor (IPF)-1/pancreatic duodenal homeobox (PDX)-1 transcription factor. HIPK2 phosphorylates and induces the transcriptional activity of IPF/PDX1 (blue arrow), which plays a crucial role in both pancreas development and maintenance of mature β-cell function for insulin production (blue arrow). High glucose (HG) inhibits PDX1 phosphorylation, likely through HG-induced HIPK2 inhibition (dot red arrow), which correlates with diabetes (blue arrow).</p>
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<p>Schematic representation of HIPK2 regulation in diabetes complications. (<b>A</b>) High glucose (HG) induces overexpression (blue ↑) of miR-423-5p that targets and downregulates (red ↓) HIPK2: the HIPK2 downregulation de-represses the HIF-1/VEGF axis which induces angiogenesis in hREC (human retinal endothelial cells) (red arrow), contributing to diabetic retinopathy (DR). (<b>B</b>) HG induces overexpression (blue ↑) of HIPK2 in HUVECs (human umbilical vascular endothelial cells) which downregulates the HIF-1/VEGF-pathway, impairing angiogenesis and wound healing (dotted arrow) and contributing to diabetic foot ulcer (DFU). Upregulation (blue arrow) of miR-221-3p block HIPK2 (red symbol) counteracting the inhibition of angiogenesis induced by HG. (<b>C</b>) In diabetic nephropathy (DN), the overexpression (blue ↑) of HIPK2, as a consequence of Siah-2 inhibition (red ↓) or ASH2L upregulation (blue ↑), contributes to kidney fibrosis by p53-induced apoptosis of RTECs (renal tubular epithelial cells) (red arrow) and upregulation of epithelial–mesenchymal transition (EMT) and fibrosis markers (red arrow). See text for details.</p>
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25 pages, 6674 KiB  
Article
Concurrent Activation of Both Survival-Promoting and Death-Inducing Signaling by Chloroquine in Glioblastoma Stem Cells: Implications for Potential Risks and Benefits of Using Chloroquine as Radiosensitizer
by Andreas Müller, Patrick Weyerhäuser, Nancy Berte, Fitriasari Jonin, Bogdan Lyubarskyy, Bettina Sprang, Sven Rainer Kantelhardt, Gabriela Salinas, Lennart Opitz, Walter Schulz-Schaeffer, Alf Giese and Ella L. Kim
Cells 2023, 12(9), 1290; https://doi.org/10.3390/cells12091290 - 30 Apr 2023
Cited by 3 | Viewed by 2040
Abstract
Lysosomotropic agent chloroquine was shown to sensitize non-stem glioblastoma cells to radiation in vitro with p53-dependent apoptosis implicated as one of the underlying mechanisms. The in vivo outcomes of chloroquine or its effects on glioblastoma stem cells have not been previously addressed. This [...] Read more.
Lysosomotropic agent chloroquine was shown to sensitize non-stem glioblastoma cells to radiation in vitro with p53-dependent apoptosis implicated as one of the underlying mechanisms. The in vivo outcomes of chloroquine or its effects on glioblastoma stem cells have not been previously addressed. This study undertakes a combinatorial approach encompassing in vitro, in vivo and in silico investigations to address the relationship between chloroquine-mediated radiosensitization and p53 status in glioblastoma stem cells. Our findings reveal that chloroquine elicits antagonistic impacts on signaling pathways involved in the regulation of cell fate via both transcription-dependent and transcription-independent mechanisms. Evidence is provided that transcriptional impacts of chloroquine are primarily determined by p53 with chloroquine-mediated activation of pro-survival mevalonate and p21-DREAM pathways being the dominant response in the background of wild type p53. Non-transcriptional effects of chloroquine are conserved and converge on key cell fate regulators ATM, HIPK2 and AKT in glioblastoma stem cells irrespective of their p53 status. Our findings indicate that pro-survival responses elicited by chloroquine predominate in the context of wild type p53 and are diminished in cells with transcriptionally impaired p53. We conclude that p53 is an important determinant of the balance between pro-survival and pro-death impacts of chloroquine and propose that p53 functional status should be taken into consideration when evaluating the efficacy of glioblastoma radiosensitization by chloroquine. Full article
(This article belongs to the Special Issue Cell Death Mechanisms and Therapeutic Opportunities in Glioblastoma)
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Figure 1
<p>Effects of ClQ on GSCs proliferation in vitro. GSCs were treated with ClQ (30 µM), irradiation (IR, 2.5 Gy) or combination of ClQ+IR for 72 h and analyzed by immunofluorescence staining for Ki-67. Summary of the data from three independent experiments. Statistical significance was determined using Student’s <span class="html-italic">t</span>-test. (*), <span class="html-italic">p</span> ≤ 0.05; (**); <span class="html-italic">p</span> ≤ 0.01; (***), <span class="html-italic">p</span> ≤ 0.001. “ns”, not significant.</p>
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<p>Effects of ClQ on GSCs viability in vitro. GSCs were treated with ClQ (30 µM), irradiation (IR, 2.5 Gy) or combination of ClQ+IR for 72 h and assessed for the sub-G1 content by flow cytometry. Summary of the data obtained from three independent experiments. Statistical significance was determined by an unpaired <span class="html-italic">t</span>-test with Welch’s correction. (*), <span class="html-italic">p</span> ≤ 0.05; (***), <span class="html-italic">p</span> ≤ 0.001; (****), <span class="html-italic">p</span> ≤ 0.0001, “ns”, not significant.</p>
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<p>Effects of ClQ in vivo. Survival analyses of GSC xenografted mice treated with ClQ (<b>a</b>), radiation (<b>b</b>) or combination of ClQ and IR (<b>c</b>). Solid lines correspond to sham-treated control groups. Kaplan–Meier curves of mice survival were determined using the log-rank test.</p>
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<p>Effects of ClQ in vivo. Survival analyses of GSC xenografted mice treated with ClQ (<b>a</b>), radiation (<b>b</b>) or combination of ClQ and IR (<b>c</b>). Solid lines correspond to sham-treated control groups. Kaplan–Meier curves of mice survival were determined using the log-rank test.</p>
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<p>Effects of ClQ on p53, p53-Ser46P and p21 proteins. Top, representative blots for wtp53 or R273H-p53 expressing GSCs treated with ClQ for 24 h and 48 h. Protein loading was ascertained by probing for the mitochondrial resident mtHSP70. Graph shows quantitative evaluations of p53 and p21 levels by densitometry, in untreated or ClQ-treated GSCs. For total protein normalization, mitochondrial HSP70 or b-actin were used as internal loading controls. Data from three independent experiments were analyzed for each line.</p>
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<p>Effects of ClQ on p53, p53-Ser46P and p21 proteins. Top, representative blots for wtp53 or R273H-p53 expressing GSCs treated with ClQ for 24 h and 48 h. Protein loading was ascertained by probing for the mitochondrial resident mtHSP70. Graph shows quantitative evaluations of p53 and p21 levels by densitometry, in untreated or ClQ-treated GSCs. For total protein normalization, mitochondrial HSP70 or b-actin were used as internal loading controls. Data from three independent experiments were analyzed for each line.</p>
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<p>Assessment of p53 and MDM2-Ser395P proteins in wtp53 expressing GSCs. Representative blot for wtp53 GSCs treated with ClQ for 24 h and 48 h. Experiments were performed at least three times.</p>
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<p>Dual effect of ClQ on ATM phosphorylation at Ser1981 and structural integrity of the ATM-Ser1981P protein. Top panel shows representative blots for ATM-Ser1981P in wtp53 (#993), R273H-p53 (G112) or p53-null GSCs after 72 h of treatment with ClQ. Graph shows the results of quantitative evaluations of the full-length and truncated ATM-Ser1891P levels by densitometry (n = 3 for each line). For total protein normalization, mitochondrial HSP70 or b-actin were used as internal loading controls.</p>
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<p>Assessments of HIPK2 proteins in GSCs differing for the p53 status. Western blot data for total and Tyr361P phosphorylated HIPK2 in GSCs expressing wtp53 (#993), R273H-p53 (G112) or p53-null GSCs after 72 h of treatment with ClQ. Top panel shows representative blots for total HIPK2 and HIPK2-Tyr361P isoform in wtp53 (#993), R273H-p53 (G112) or p53-null GSCs after 72 h of treatment with ClQ. Graph shows the results of quantitative evaluations by densitometry (n = 3 for each line). For total protein normalization, mitochondrial HSP70 or b-actin were used as internal loading controls.</p>
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<p>Schematic presentation of ClQ_DEGs identified in GSCs differing for the p53 status. “p53RGs”, p53-regulated genes. “up”, upregulated ClQ_DEGs. “down”, down-regulated ClQ_DEGs. Encircled numbers correspond to known p53RGs.</p>
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<p>Effects of ClQ on apoptosis signaling pathways. Readouts from the APOSIG arrays incubated with cell lysates of (<b>a</b>) wtp53 or (<b>b</b>) R273H GSCs either untreated or treated with ClQ for 72 h and graphical presentation of the quantified readouts.</p>
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<p>Effect of ClQ on the abundance of AKT kinase. Top panel shows representative blots for total HIPK2 and HIPK2-Tyr361P isoform in wtp53 (#993), R273H-p53 (G112) or p53-null GSCs after 72 h of treatment with ClQ. Graph shows the results of quantitative evaluations of datasets from independent experiments (n = 3 for each line) by densitometry. For total protein normalization, mitochondrial HSP70 or b-actin were used as internal loading controls.</p>
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<p>Effects of ClQ on the autophagic activity in GSCs differing for p53 status. Western blot assessments of late autophagy markers p63 and LC3B-II in untreated or ClQ-treated (72 h) GSCs. Protein loading was ascertained by probing for β-actin.</p>
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<p>Schematic summary of main results integrated into the known networks of survival or death pathways. Green lines indicate molecular impacts of ClQ identified in this study. Solid and dashed indicate, respectively, sustained or diminished signaling in the context of wtp53 or transcriptionally impaired p53.</p>
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31 pages, 27085 KiB  
Article
Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Kidney Cancers
by Ioanna Ioannou, Angeliki Chatziantoniou, Constantinos Drenios, Panayiota Christodoulou, Malamati Kourti and Apostolos Zaravinos
Int. J. Mol. Sci. 2023, 24(7), 6577; https://doi.org/10.3390/ijms24076577 - 31 Mar 2023
Cited by 2 | Viewed by 3017
Abstract
There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we [...] Read more.
There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we reanalyzed 10 Gene Expression Omnibus (GEO) studies to detect and annotate co-deregulated signatures across different subtypes of kidney cancer or in single-gene perturbation experiments in kidney cancer cells and/or tissue. Using a systems biology approach, we aimed to decipher the networks they form along with their upstream regulators. Differential expression and upstream regulators, including transcription factors [MYC proto-oncogene (MYC), CCAAT enhancer binding protein delta (CEBPD), RELA proto-oncogene, NF-kB subunit (RELA), zinc finger MIZ-type containing 1 (ZMIZ1), negative elongation factor complex member E (NELFE) and Kruppel-like factor 4 (KLF4)] and protein kinases [Casein kinase 2 alpha 1 (CSNK2A1), mitogen-activated protein kinases 1 (MAPK1) and 14 (MAPK14), Sirtuin 1 (SIRT1), Cyclin dependent kinases 1 (CDK1) and 4 (CDK4), Homeodomain interacting protein kinase 2 (HIPK2) and Extracellular signal-regulated kinases 1 and 2 (ERK1/2)], were computed using the Characteristic Direction, as well as GEO2Enrichr and X2K, respectively, and further subjected to GO and KEGG pathways enrichment analyses. Furthermore, using CMap, DrugMatrix and the LINCS L1000 chemical perturbation databases, we highlight putative repurposing drugs, including Etoposide, Haloperidol, BW-B70C, Triamterene, Chlorphenesin, BRD-K79459005 and β-Estradiol 3-benzoate, among others, that may reverse the expression of the identified co-DEGs in kidney cancers. Of these, the cytotoxic effects of Etoposide, Catecholamine, Cyclosporin A, BW-B70C and Lasalocid sodium were validated in vitro. Overall, we identified critical co-DEGs across different subtypes in kidney cancer, and our results provide an innovative framework for their potential use in the future. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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<p>The bar charts (left) depict the top 10 enriched Gene Ontology (GO) terms (<b>a</b>) and KEGG pathways (<b>b</b>) in which the top 250 co-upregulated genes in RCC participate, along with their corresponding <span class="html-italic">p</span>-values. Asterisks (*) indicate the terms with significant adjusted <span class="html-italic">p</span>-values (&lt;0.05). The scatterplots (right) were created using UMAP and depict clusters of similar gene sets. The significantly enriched terms of the associated gene sets are denoted.</p>
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<p>The bar charts (left) depict the top 10 enriched Gene Ontology (GO) terms (<b>a</b>) and KEGG pathways (<b>b</b>) in which the top 250 co-downregulated genes in RCC participate, along with their corresponding <span class="html-italic">p</span>-values. Asterisks (*) indicate the terms with significant adjusted <span class="html-italic">p</span>-values (&lt;0.05). The scatterplots (right) were created using UMAP and depict clusters of similar gene sets. The significantly enriched terms of the associated gene sets (also highlighted in blue color in the bar charts) are denoted. The GO and KEGG terms appearing in grey color in the bar charts, are non-significant.</p>
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<p>Upstream regulatory networks for co-upregulated (<b>a</b>) and co-downregulated (<b>b</b>) gene signatures in kidney cancers vs. the normal tissue. The networks depict transcription factors (TFs, red nodes), intermediate proteins (grey nodes) and kinases (blue nodes). Grey edges indicate PPI interactions and green edges depict kinase-driven phosphorylation events. Node size is relative to the levels of expression degree. Upstream regulatory networks were constructed using the eXpression2Kinases (X2K) algorithm.</p>
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<p>The bar charts (left) depict the top 10 enriched Gene Ontology (GO) terms (<b>a</b>) and the KEGG pathways (<b>b</b>) for the top 250 co-upregulated genes in kidney cancer cells with a single-gene perturbation, along with their corresponding <span class="html-italic">p</span>-values. Asterisks (*) indicate the terms with significant adjusted <span class="html-italic">p</span>-values (&lt;0.05). The scatterplots (right) were created using UMAP and depict clusters of similar gene sets. The significantly enriched terms of the associated gene sets are denoted.</p>
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<p>The bar charts (left) depict the top 10 enriched Gene Ontology (GO) terms (<b>a</b>) and the KEGG pathways (<b>b</b>) for the top 250 co-downregulated genes in RCC with a single-gene perturbation, along with their corresponding <span class="html-italic">p</span>-values. Asterisks (*) indicate the terms with significant adjusted <span class="html-italic">p</span>-values (&lt;0.05). The scatterplots (right) were created using UMAP and depict clusters of similar gene sets. The significantly enriched terms of the associated gene sets are denoted.</p>
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<p>Upstream regulatory networks for co-upregulated (<b>a</b>) and co-downregulated (<b>b</b>) gene signatures in single-gene perturbation experiments in kidney cancers vs. the normal tissue. The networks depict transcription factors (TFs, red nodes), intermediate proteins (grey nodes) and kinases (blue nodes). Grey edges indicate PPI interactions and green edges depict kinase-driven phosphorylation events. Node size is relative to expression. Upstream regulatory networks were constructed using the eXpression2Kinases (X2K) algorithm.</p>
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<p>Validation of the co-UP (<b>a</b>) and co-DOWN (<b>c</b>) gene signatures in the TCGA-KICH, TCGA-KIRC and TCGA-KIRP datasets, as well as in four mRNA clusters in KIRC (<b>b</b>,<b>d</b>). Results with a |log<sub>2</sub>FC ≥ 1| and <span class="html-italic">p</span>-value &lt; 0.01 were considered statistically significant (*). The expression of KSR1 and MPEG1, two genes within the co-UP signature, were validated across different immune (<b>e</b>) and molecular (<b>f</b>) subtypes in kidney cancer. The expression of SLC16A9 and CLDN2, two genes within the co-DOWN signature, were also validated across different immune (<b>g</b>) and molecular (<b>h</b>) subtypes in kidney cancer.</p>
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<p>(<b>a</b>) Analysis of the differential expression between different kidney tumors in the TCGA dataset (KIRP, KIRC and KICH) and their adjacent normal samples. The bubble colors from purple to red represent the fold change between kidney tumor and normal samples. The size of each dot correlates the significance of FDR. The dots were filtered by the fold change (FC &gt; 2) and statistical significance (FDR ≤ 0.05). Detailed expression of MYC, MAPK14 and CDK4 in KIRC, KICH and KIRP, respectively, are shown to the right. (<b>b</b>) Estimation of patient survival differences (OS, PFS, DSS and DFI) between high and low gene expression groups. The colors from blue to red represent the hazard ratio (HR) and size represents statistical significance in the bubble plot. The black outline border indicates Cox <span class="html-italic">p</span> ≤ 0.05. (<b>c</b>) Exemplary associations of high CDK4 and CDK1 with worse prognosis in KIRP are depicted in the Kaplan–Meier curves.</p>
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<p>The heatmap (<b>a</b>) and trend plot (<b>b</b>) present the gene mRNA expression profile from stage I to stage IV of kidney cancers (KICH, KIRC and KIRP) in the TCGA database. The trend line colors from blue to red represents the tendency from fall to rise. The <span class="html-italic">p</span>-values were calculated using the Mann–Kendall test for trend analysis. <span class="html-italic">p</span>-values &lt; 0.05 (*), &lt;0.01 (**), &lt;0.001 (***) or &lt;0.0001 (****) were considered statistically significant, else if <span class="html-italic">p</span>-values were &gt;0.05, were considered non-significant (NS). (<b>c</b>) Examples of the increased or decreased mRNA expression of various genes in pathologic and clinical stages of KICH, KIRC and KIRP. The Wilcoxon or ANOVA tests were used to assess statistical significance between 2 or &gt;2 stage groups, respectively. <span class="html-italic">p</span>-values &lt; 0.05 were considered statistically significant. NS, non-significant. (<b>d</b>) The percentage of cancers in which the mRNA expression of the genes of interest has a potential effect on pathway activity. Red color, activatory (A) effect; blue color, inhibitory (I) effect. The number in each cell indicates the percentage of cancer types in which each gene shows significant association with a specific pathway, among the three kidney tumor subtypes. (<b>e</b>) The box plots compare the GSVA scores between kidney cancer (KIRCH, KIRC and KIRP) and normal samples. GSVA scores represent the variation of gene set activity over a specific cancer sample population in an unsupervised manner, which was calculated through the GSVA R package. Briefly, the GSVA score represents the integrated level of the expression of the gene set, which is positively correlated with gene expression.</p>
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<p>Top 10 enriched compounds that can be used as repurposing drugs against the top 250 co-upregulated genes in RCC according to DrugMatrix (<b>a</b>) and cMap (<b>b</b>,<b>c</b>) databases. The repurposed drugs upregulating or downregulating the co-upregulated genes in RCC, according to Old cMAP analysis are depicted in (<b>b</b>,<b>c</b>), respectively. Asterisks (*) indicate the terms with significant adjusted <span class="html-italic">p</span>-values (&lt;0.05). The scatterplots (right) were created using UMAP and depict clusters of similar compounds. The significantly enriched terms of the associated gene sets are denoted.</p>
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<p>Top 10 enriched compounds that can be used as repurposing drugs against the top 250 co-upregulated (<b>a</b>–<b>c</b>) or down-regulated (<b>d</b>–<b>f</b>) genes in RCC according to DrugMatrix (<b>a</b>,<b>d</b>), cMap (<b>b</b>,<b>e</b>) and LINCS L1000 (<b>c</b>,<b>f</b>) databases. The significantly enriched terms of the associated compounds (<span class="html-italic">p</span> &lt; 0.001) are denoted with an asterisk (*).</p>
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<p>Curve fitting analysis to determine the effective inhibitory concentrations of BW-B70C, Lasalocid sodium, Ifosfamide, Catecholamine, Cyclosporin A and Etoposide on HEK-293 cells. The cytotoxicity effects of the drugs were measured using MTT assays (n = 3). To obtain IC50s of the drugs, an exponential two-phase decay model (marked as red line) was fitted to the dose-response curves (solid black lines) of single treatment of each drug on HEK-293 cells.</p>
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13 pages, 578 KiB  
Review
HIPK2 in Angiogenesis: A Promising Biomarker in Cancer Progression and in Angiogenic Diseases
by Alessia Garufi, Valerio D’Orazi, Giuseppa Pistritto, Mara Cirone and Gabriella D’Orazi
Cancers 2023, 15(5), 1566; https://doi.org/10.3390/cancers15051566 - 2 Mar 2023
Cited by 4 | Viewed by 1941
Abstract
Angiogenesis is the formation of new blood capillaries taking place from preexisting functional vessels, a process that allows cells to cope with shortage of nutrients and low oxygen availability. Angiogenesis may be activated in several pathological diseases, from tumor growth and metastases formation [...] Read more.
Angiogenesis is the formation of new blood capillaries taking place from preexisting functional vessels, a process that allows cells to cope with shortage of nutrients and low oxygen availability. Angiogenesis may be activated in several pathological diseases, from tumor growth and metastases formation to ischemic and inflammatory diseases. New insights into the mechanisms that regulate angiogenesis have been discovered in the last years, leading to the discovery of new therapeutic opportunities. However, in the case of cancer, their success may be limited by the occurrence of drug resistance, meaning that the road to optimize such treatments is still long. Homeodomain-interacting protein kinase 2 (HIPK2), a multifaceted protein that regulates different molecular pathways, is involved in the negative regulation of cancer growth, and may be considered a “bona fide” oncosuppressor molecule. In this review, we will discuss the emerging link between HIPK2 and angiogenesis and how the control of angiogenesis by HIPK2 impinges in the pathogenesis of several diseases, including cancer. Full article
(This article belongs to the Section Cancer Pathophysiology)
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<p>Schematic representation of the balance between hypoxia and HIPK2 in cancer. When hypoxia is activated ((<b>left</b>) panel) the hypoxia-inducible mechanisms inhibit HIPK2 and the effects of hypoxia (such as angiogenesis, chemoresistance, inhibition of apoptosis, and tumor growth) prevail. When HIPK2 is activated ((<b>right</b>) panel) the HIF-1-induced molecular mechanisms are inhibited and the antitumor effects (such as p53 activation, activation of apoptosis, and tumor regression) prevail.</p>
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17 pages, 2044 KiB  
Article
miRNA Expression Profiles of Mouse Round Spermatids in GRTH/DDX25-Mediated Spermiogenesis: mRNA–miRNA Network Analysis
by Rajakumar Anbazhagan, Raghuveer Kavarthapu, Ryan Dale, Kiersten Campbell, Fabio R. Faucz and Maria L. Dufau
Cells 2023, 12(5), 756; https://doi.org/10.3390/cells12050756 - 27 Feb 2023
Cited by 4 | Viewed by 1821
Abstract
GRTH/DDX25 is a testis-specific DEAD-box family of RNA helicase, which plays an essential role in spermatogenesis and male fertility. There are two forms of GRTH, a 56 kDa non-phosphorylated form and a 61 kDa phosphorylated form (pGRTH). GRTH-KO and GRTH Knock-In (KI) mice [...] Read more.
GRTH/DDX25 is a testis-specific DEAD-box family of RNA helicase, which plays an essential role in spermatogenesis and male fertility. There are two forms of GRTH, a 56 kDa non-phosphorylated form and a 61 kDa phosphorylated form (pGRTH). GRTH-KO and GRTH Knock-In (KI) mice with R242H mutation (lack pGRTH) are sterile with a spermatogenic arrest at step 8 of spermiogenesis due to failure of round spermatids (RS) to elongate. We performed mRNA-seq and miRNA-seq analysis on RS of WT, KI, and KO to identify crucial microRNAs (miRNAs) and mRNAs during RS development by establishing a miRNA–mRNA network. We identified increased levels of miRNAs such as miR146, miR122a, miR26a, miR27a, miR150, miR196a, and miR328 that are relevant to spermatogenesis. mRNA–miRNA target analysis on these DE-miRNAs and DE-mRNAs revealed miRNA target genes involved in ubiquitination process (Ube2k, Rnf138, Spata3), RS differentiation, and chromatin remodeling/compaction (Tnp1/2, Prm1/2/3, Tssk3/6), reversible protein phosphorylation (Pim1, Hipk1, Csnk1g2, Prkcq, Ppp2r5a), and acrosome stability (Pdzd8). Post-transcriptional and translational regulation of some of these germ-cell-specific mRNAs by miRNA-regulated translation arrest and/or decay may lead to spermatogenic arrest in KO and KI mice. Our studies demonstrate the importance of pGRTH in the chromatin compaction and remodeling process, which mediates the differentiation of RS into elongated spermatids through miRNA–mRNA interactions. Full article
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<p>Transcriptome analysis of RS from KO, KI, and WT mice. (<b>A</b>) MA plot (log2 fold change vs. average of counts) shows differentially expressed genes (DEGs) from KO and WT (<b>B</b>) MA plot (log2 fold change vs. average of counts) shows DEGs from KI and WT groups. The red dots indicate the significantly (<span class="html-italic">p</span> &lt; 0.1) upregulated or downregulated genes in each of RS of KO or KI compared to WT.</p>
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<p>GO functional enrichment analysis on DEGs from RS of KO and KI groups were grouped into different functional categories: biological process (BP), cellular component (CC), and molecular function (MF). (<b>A</b>) BP significantly enriched in GO analysis of DEGs (<b>B</b>) CC significantly altered in GO analysis of DEGs. The x-axis represents gene ratio = count/set size. The dot size represents the number of genes, and the color of each bar represents the padj-value.</p>
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<p>qRT-PCR analysis of selected DEGs of mRNA from RS of KO and KI groups. Bar graphs represent the fold change expression between (<b>A</b>) KO vs. WT or (<b>B</b>) KI vs. WT groups. Means ± SEM were determined from three independent qRT-PCR experiments with each sample run in triplicates. <span class="html-italic">p</span> values were calculated by a two-tailed Student t-test (asterisks indicate <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>miRNA differential expression in RS from KO, KI, and WT mice. (<b>A</b>) MA plot (log2 fold change vs average of counts) shows differentially enriched miRNAs from KO and WT group (<b>B</b>) MA plot (log2 fold change vs average of counts) shows DEGs from KI and WT group. The red dots indicate the significantly (<span class="html-italic">p</span> &lt; 0.1) upregulated or downregulated miRNAs in each of RS of KO or KI compared to WT.</p>
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<p>qRT-PCR analysis of selected miRNAs from RS of KO and KI groups. Bar graphs represent the fold change expression between (<b>A</b>) KO vs. WT or (<b>B</b>) KI vs. WT groups. Means ± SEM were determined from three independent qRT-PCR experiments with each sample run in triplicates using LNA probes. <span class="html-italic">p</span> values were calculated by two-tailed Student t-test (asterisks indicate <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation of mRNA–miRNA changes in the RS of KO, KI, and WT groups. mRNAs and miRNAs identified from the RS of (<b>A</b>) KO and WT group or (<b>B</b>) KI and WT group showing canonical correlation (Red, mRNA down and miRNA up or mRNA up and miRNA down) or non-canonical correlation (Black, mRNA down and miRNA down or mRNA up and miRNA up).</p>
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<p>Putative mRNA–miRNA target regulation (from miRNA and mRNA DEGs) in the RS of KO, KI, and WT groups. mRNA–miRNA pairs in (<b>A</b>) KO vs. WT or (<b>B</b>) KI vs. WT group showing interaction (network). mRNA–miRNA pairs (only miRNA and mRNA changed in RS) in the genotype of interest showing interaction (positive or negative) identified jointly across databases. Red circles = upregulated compared to WT. Blue circles = downregulated compared to WT. Darker color circles = higher magnitude change. Each line represents a connection of a miRNA to an mRNA if it was identified jointly across databases. Thicker line = canonical change “miRNA down and mRNA up or miRNA up and mRNA down”. Dotted line = non-canonical “miRNA down and mRNA down or miRNA up and mRNA down”. Bright red line (dotted or not) = KO and KI behave similarly. Black outline around a node (circle) = node was shared in both KO and KI DEGs. A white outline around a node means that the node was unique to that genotype.</p>
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<p><span class="html-italic">Tnp2</span> mRNA–miRNA122a interaction. Modulation of translation of <span class="html-italic">Tnp2</span> depends on the presence of <span class="html-italic">Tnp2</span> 3′ UTR region. (<b>A</b>) Schematic representation of the psiCheck2 reporter gene carrying the <span class="html-italic">Tnp2</span> coding sequence with or without 3′ UTR. (<b>B</b>) Relative luciferase activity in COS-1 cells co-transfected with <span class="html-italic">Tnp2</span> with 3′UTR reporter constructs (or <span class="html-italic">Tnp2</span> coding region without 3′UTR) and miR122a mimic and/or miR122a inhibitor or negative control. Asterisks (*) indicate statistically significant change between psi-<span class="html-italic">Tnp2</span> and psi-<span class="html-italic">Tnp2</span> + miR122a mimic group (Student’s t-test; <span class="html-italic">p</span> &lt; 0.05). All the data represent means ± SEM from three independent experiments, with each sample running in triplicates.</p>
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18 pages, 1359 KiB  
Review
HIPK2 as a Novel Regulator of Fibrosis
by Alessia Garufi, Giuseppa Pistritto and Gabriella D’Orazi
Cancers 2023, 15(4), 1059; https://doi.org/10.3390/cancers15041059 - 7 Feb 2023
Cited by 12 | Viewed by 2813
Abstract
Fibrosis is an unmet medical problem due to a lack of evident biomarkers to help develop efficient targeted therapies. Fibrosis can affect almost every organ and eventually induce organ failure. Homeodomain-interacting protein kinase 2 (HIPK2) is a protein kinase that controls several molecular [...] Read more.
Fibrosis is an unmet medical problem due to a lack of evident biomarkers to help develop efficient targeted therapies. Fibrosis can affect almost every organ and eventually induce organ failure. Homeodomain-interacting protein kinase 2 (HIPK2) is a protein kinase that controls several molecular pathways involved in cell death and development and it has been extensively studied, mainly in the cancer biology field. Recently, a role for HIPK2 has been highlighted in tissue fibrosis. Thus, HIPK2 regulates several pro-fibrotic pathways such as Wnt/β-catenin, TGF-β and Notch involved in renal, pulmonary, liver and cardiac fibrosis. These findings suggest a wider role for HIPK2 in tissue physiopathology and highlight HIPK2 as a promising target for therapeutic purposes in fibrosis. Here, we will summarize the recent studies showing the involvement of HIPK2 as a novel regulator of fibrosis. Full article
(This article belongs to the Special Issue Cancer Chemotherapy: Combination with Inhibitors)
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<p>Schematic representation showing the involvement of fibrosis in several diseases (as explained in the text). IPF: idiopathic pulmonary fibrosis. COPD: chronic obstructive pulmonary disease.</p>
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<p>Cellular mechanisms regulating fibrosis. Under pro-fibrotic stimuli, fibroblasts, among the several cell types, as explained in the text, differentiate into myofibroblasts that produce extracellular matrix (ECM) components leading to tissue fibrosis. Myofibroblasts cross-talk with macrophages and this interplay contributes to tissue fibrosis.</p>
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<p>The Wnt/β-catenin, TGF-β1/Smad3 and Notch pathways contribute to fibrogenesis by stimulating myofibroblast differentiation and proliferation in order to produce extracellular matrix components.</p>
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<p>(<b>a</b>) Schematic representation of the molecular pathways regulated by HIPK2 and the underlying outcome. (<b>b</b>) HIPK2 is activated by DNA damage and consequently activates the p53 apoptotic function. HIPK2 is inhibited by hypoxia and hyperglycemia and by the Siah-1/2, WSB1 and MDM2 ubiquitin ligases. Black arrows indicate activation.</p>
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<p>(<b>a</b>) HIPK2 role in kidney fibrosis. Upstream factors (HIV infection, oxidative stress-ROS) induce HIPK2 expression that consequently activates p53-dependent RTEC (renal tubular epithelial cells) apoptosis and the EMT (epithelial mesenchymal transition) markers by the pro-fibrotic pathways (Wnt/β-catenin, TGF-β and Notch), leading to kidney fibrosis. HIPK2 is overexpressed in kidney fibrosis, FSGS (focal segmental glomerulosclerosis), diabetic nephropathy, IgA nephropathy (IgAN) and unilateral urethral obstruction (UUO). NAC (N-acetylcysteine) inhibits ROS-induced HIPK2 upregulation. Black arrows indicate activation. (<b>b</b>) Schematic representation of HIPK2 inhibitors in renal fibrosis.</p>
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<p>(<b>a</b>) Role for HIPK2 in cardiac fibrosis in a transverse aortic constriction (TAC) cardiac fibrosis model. The molecular pathways activated by HIPK2 are shown and they induce both cardiomyocyte hypertrophy and cardiac fibroblast proliferation, inducing cardiac fibrosis. (<b>b</b>) Schematic representation showing how exercise reduces myocardial infarction (MI) by downregulating HIPK2 with a consequent reduction in p53-induced cardiomyocyte apoptosis. Black arrows indicate activation. Red arrows indicate reduction/inhibition.</p>
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<p>Summary of the HIPK2 expression in the several different fibrotic diseases discussed in the review. Black arrows indicate upregulation. Red arrows indicate downregulation. LOH = loss of heterozygosity.</p>
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18 pages, 4942 KiB  
Article
BMSC-Derived Exosomal CircHIPK3 Promotes Osteogenic Differentiation of MC3T3-E1 Cells via Mitophagy
by Shaoyang Ma, Sijia Li, Yuchen Zhang, Jiaming Nie, Jiao Cao, Ang Li, Ye Li and Dandan Pei
Int. J. Mol. Sci. 2023, 24(3), 2785; https://doi.org/10.3390/ijms24032785 - 1 Feb 2023
Cited by 8 | Viewed by 2673
Abstract
Exosome-based therapy is emerging as a promising strategy to promote bone regeneration due to exosomal bioactive cargos, among which circular RNA (circRNA) has recently been recognized as the key effector. The role of exosomal circRNA derived from bone marrow mesenchymal stem cells (BMSCs) [...] Read more.
Exosome-based therapy is emerging as a promising strategy to promote bone regeneration due to exosomal bioactive cargos, among which circular RNA (circRNA) has recently been recognized as the key effector. The role of exosomal circRNA derived from bone marrow mesenchymal stem cells (BMSCs) has not been well-defined. The present study aimed to clarify the regulatory function and molecular mechanism of BMSC-derived exosomal circRNA in osteogenesis. Exosomes derived from bone marrow mesenchymal stem cells (BMSC-Exos) were isolated and identified. BMSC-Exos’ pro-osteogenic effect on MC3T3-E1 cells was validated by alkaline phosphatase (ALP) activity and Alizarin Red staining. Through bioinformatic analysis and molecular experiments, circHIPK3 was selected and verified as the key circRNA of BMSC-Exos to promote osteoblast differentiation of MC3T3-E1 cells. Mechanistically, circHIPK3 acted as an miR-29a-5p sponge and functioned in mitophagy via targeting miR-29a-5p and PINK1. Additionally, we showed that the mitophagy level of MC3T3-E1 cells were mediated by BMSC-Exos, which promoted the osteogenic differentiation. Collectively, our results revealed an important role for BMSC-derived exosomal circHIPK3 in osteogenesis. These findings provide a potentially effective therapeutic strategy for bone regeneration. Full article
(This article belongs to the Special Issue Advances in circRNA Biology)
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<p>BMSC-Exos promoted osteogenesis. (<b>A</b>) Representative picture of BMSC-Exos collected by ultracentrifugation. (<b>B</b>) Size distribution profile of BMSC-Exos (<span class="html-italic">n</span> = 3). The dots represent the mean value, and the shading represents the SD. (<b>C</b>) Western blot analysis of exosome-specific markers (CD9, CD63, CD81 and TSG101) and negative marker (Calnexin). (<b>D</b>) Intercellular trafficking of BMSC-Exos. Cytomembranes were labeled by Dil (red); BMSC-Exos were labeled by DiO (Green); nuclei were labeled by DAPI (blue). (<b>E</b>) Schematic drawing of the Transwell protocol. (<b>F</b>) Cell proliferation assays. Each point represents the mean value from three independent samples. * <span class="html-italic">p &lt;</span> 0.05; ** <span class="html-italic">p &lt;</span> 0.01. (<b>G</b>) ALP staining (left) was examined and activity of ALP (right, <span class="html-italic">n</span> = 6) was analyzed by ALP activity assay kit. Bar 100 μm. (<b>H</b>) ARS staining was performed (left) and quantified using a microplate reader at 562 nm (right, <span class="html-italic">n</span> = 6). Bar: 100 μm. (<b>I</b>) Fold changes in the expression of RUNX2 and OCN in MC3T3-E1 cells relative to GAPDH (<span class="html-italic">n</span> = 6). (<b>J</b>) Western blot analysis (left) and quantification of Western blot results (right, <span class="html-italic">n</span> = 3). * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group; # <span class="html-italic">p &lt;</span> 0.05, ## <span class="html-italic">p &lt;</span> 0.01, ### <span class="html-italic">p &lt;</span> 0.001 vs. the BMSC group.</p>
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<p>CircHIPK3 was highly expressed in BMSC-Exos. (<b>A</b>) Heatmap of highly expressed circRNAs in BMSCs and BMSC-Exos. The color from blue (low) to red (high) represents expression levels of circRNAs. Each row in the map represents a sample and each column represents a gene. The red box represents circHIPK3 (circBase name: hsa_circ_0000284). (<b>B</b>) The relative levels of highly expressed circRNAs in BMSCs and BMSC-Exos. The relative expression in BMSCs (Y-axis), against those in BMSC-Exos (X-axis). (<b>C</b>) Schematic presentation of circHIPK3 location and the junction site sequence. (<b>D</b>) Agarose gel electrophoresis analysis of circHIPK3 PCR products of divergent primer and convergent primer. cDNA: complementary DNA, gDNA: genomic DNA. (<b>E</b>) The RNase R digestion resistance test confirmed the stability of circHIPK3 (<span class="html-italic">n</span> = 18). *** <span class="html-italic">p &lt;</span> 0.001.</p>
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<p>CircHIPK3 promoted osteogenic differentiation of MC3T3-E1 cells. (<b>A</b>) circHIPK3 levels and linear HIPK3 in BMSCs were measured by qRT-PCR after transfection with si-NC, si-circHIPK3-1 or si-circHIPK3-2. (<b>B</b>) circHIPK3 levels in exosomes derived from BMSCs treated with si-NC, si-circHIPK3-1 or si-circHIPK3-2 were measured by qRT-PCR. (<b>C</b>,<b>D</b>) circHIPK3 levels (<b>C</b>) and RUNX2 and OCN levels (<b>D</b>) in MC3T3-E1 cells treated with DMSO (control, <span class="html-italic">n</span> = 12) or exosomes derived from BMSCs treated DMSO (BMSC-Exos-control, <span class="html-italic">n</span> = 12), with si-NC (BMSC-Exos-si-NC, <span class="html-italic">n</span> = 12) or si-circHIPK3-1 (BMSC-Exos-si-circHIPK3, <span class="html-italic">n</span> = 12). *** <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>) ALP staining (left) was examined and activity of ALP (right, <span class="html-italic">n</span> = 6) was analyzed by ALP activity assay kit. Bar 100 μm. (<b>F</b>) ARS staining was performed (left) and quantified using a microplate reader at 562 nm (right, <span class="html-italic">n</span> = 6). Bar: 100 μm. *** <span class="html-italic">p &lt;</span> 0.001.</p>
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<p>CircHIPK3 served as a miRNA sponge of miR-29a-5p. (<b>A</b>) Three miRNAs (miR-29a-5p, miR-193b-3p and miR-558) were predicted to be candidate targets of circHIPK3. (<b>B</b>) Expressions of miR-29a-5p, miR-193b-3p and miR-558 in MC3T3-E1 cells treated with BMSC-Exos (<span class="html-italic">n</span> = 12). (<b>C</b>) Schematic representation of circHIPK3 with the predicted target site for miR-29a-5p. The mutant site of circHIPK3 is indicated (green). (<b>D</b>) Luciferase reporter analysis was performed to examine the binding ability between miR-29a-5p and circHIPK3. Reporter constructs containing either circHIPK3-WT or circHIPK3-Mut at the predicted miR-29a-5p target sequences were co-transfected into HEK293T cells, along with miR-29a-5p or miRNA NC mimics (<span class="html-italic">n</span> = 12). (<b>E</b>,<b>F</b>) RIP experiments were performed using the Ago2 antibody (E) and specific primers were used to detect the enrichment of circHIPK3 and miR-29a-5p in MC3T3-E1 ((<b>F</b>), <span class="html-italic">n</span> = 6). *** <span class="html-italic">p</span> &lt; 0.001. (<b>G</b>) Biotinylated-probe pull-down assay for circHIPK3 and miR-29a-5p by qRT-PCR in MC3T3-E1 cells (<span class="html-italic">n</span> = 6). The probe was designed according to the junction region of circHIPK3. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>CircHIPK3 promoted mitophagy of MC3T3-E1 cells through miR-29a-5p/PINK1 axis. (<b>A</b>) Schematic representation of PINK1 3′-UTR with the predicted target site for miR-29a-5p. The mutant site of PINK1 3′-UTR is indicated (green). Luciferase reporter analysis was performed to examine the binging ability between miR-29a-5p and PINK1 3′-UTR. Reporter constructs containing either PINK1 3′-UTR-wt or PINK1 3′-UTR-mut at the predicted miR-29a-5p target sequences were co-transfected into HEK293T cells, along with miR-29a-5p or miRNA NC mimics (<span class="html-italic">n</span> = 12). (<b>B</b>,<b>C</b>) Western blot assay and quantification of PINK1 in MC3T3-E1 cells transfected with miR-29a-5p or miRNA NC mimics (<span class="html-italic">n</span> = 3). *** <span class="html-italic">p</span> &lt; 0.001. (<b>D</b>,<b>E</b>) Western blot assay and quantification of PINK1 in MC3T3-E1 cells treated with PBS (control), BMSC-Exos (Exos), BMSC-Exos and miRNA NC (Exos + miRNA NC) or BMSC-Exos and miR-29a-5p mimic (Exos + miR-29a-5p) (<span class="html-italic">n</span> = 3). ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p &lt;</span> 0.001. (<b>F</b>,<b>G</b>) Representative LC3 immunofluorescence staining (LC3 stained with red and nuclei stained with blue) and quantitative results of LC3 intensity (<span class="html-italic">n</span> = 12). *** <span class="html-italic">p</span> &lt; 0.001. Bar: 20 μm.</p>
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<p>BMSC-Exos promoted osteogenic differentiation of MC3T3-E1 cell through mitophagy. (<b>A</b>,<b>B</b>) Western blot assay and quantification of osteogenesis-related genes (PINK1, Parkin and LC3-I/II) in MC3T3-E1 cells treated with PBS (Control), BMSC-Exos (Exos) or BMSC-Exos and CsA (Exos + CsA) (<span class="html-italic">n</span> = 3). ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>,<b>D</b>) Representative LC3 immunofluorescence staining (LC3 stained with red and nuclei stained with blue) and quantitative results of LC3 intensity (<span class="html-italic">n</span> = 12). *** <span class="html-italic">p</span> &lt; 0.001. Bar: 10 μm. (<b>E</b>,<b>F</b>) ALP staining was examined and activity of ALP was analyzed by ALP activity assay kit (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. Bar 100 μm. (<b>G</b>,<b>H</b>) ARS staining was performed and (H) quantified using a microplate reader at 562 nm (<span class="html-italic">n</span> = 6). *** <span class="html-italic">p</span> &lt; 0.001. Bar: 100 μm.</p>
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<p>CircHIPK3 is enriched in BMSC-Exos and promotes osteogenic differentiation of MC3T3-E1 cells by regulating mitophagy through miR-29a-5p/PINK1 axis.</p>
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