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24 pages, 2346 KiB  
Article
Multi-Omics Profiles of Small Intestine Organoids in Reaction to Breast Milk and Different Infant Formula Preparations
by Xianli Wang, Shangzhi Yang, Chengdong Zheng, Chenxuan Huang, Haiyang Yao, Zimo Guo, Yilun Wu, Zening Wang, Zhenyang Wu, Ruihong Ge, Wei Cheng, Yuanyuan Yan, Shilong Jiang, Jianguo Sun, Xiaoguang Li, Qinggang Xie and Hui Wang
Nutrients 2024, 16(17), 2951; https://doi.org/10.3390/nu16172951 - 2 Sep 2024
Viewed by 2330
Abstract
Ensuring optimal infant nutrition is crucial for the health and development of children. Many infants aged 0–6 months are fed with infant formula rather than breast milk. Research on cancer cell lines and animal models is limited to examining the nutrition effects of [...] Read more.
Ensuring optimal infant nutrition is crucial for the health and development of children. Many infants aged 0–6 months are fed with infant formula rather than breast milk. Research on cancer cell lines and animal models is limited to examining the nutrition effects of formula and breast milk, as it does not comprehensively consider absorption, metabolism, and the health and social determinants of the infant and its physiology. Our study utilized small intestine organoids induced from human embryo stem cell (ESC) to compare the nutritional effects of breast milk from five donors during their postpartum lactation period of 1–6 months and three types of Stage 1 infant formulae from regular retail stores. Using transcriptomics and untargeted metabolomics approaches, we focused on the differences such as cell growth and development, cell junctions, and extracellular matrix. We also analyzed the roles of pathways including AMPK, Hippo, and Wnt, and identified key genes such as ALPI, SMAD3, TJP1, and WWTR1 for small intestine development. Through observational and in-vitro analysis, our study demonstrates ESC-derived organoids might be a promising model for exploring nutritional effects and underlying mechanisms. Full article
(This article belongs to the Topic Advances in Animal-Derived Non-Cow Milk and Milk Products)
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Figure 1

Figure 1
<p>Transcriptome profiles of intestine organoids feeding by different infant formulae and breast milk. (<b>a</b>) PCA of samples in groups BM, PMF1, PMF2, PMF3, and control. PC1 and PC2 Scores of different samples are visualized, and the variance contributed by its corresponding component is presented. (<b>b</b>) GSVA analysis of each sample for GO terms associated with nutrition absorption in small intestine. (<b>c</b>) Venn graph of different groups’ DEG. (<b>d</b>,<b>e</b>) Gene over-representation analysis of GO of (<b>d</b>) unique DEG of infant formulae group and breast milk group presented in functionally grouped network with terms as nodes linked based on their kappa score level (≥0.3) using a Cytoscape plug-in clueGO, and (<b>e</b>) shared DEG of all infant formulae and breast milk presented in dendrogram using methods adopted by GeneTonic. (<b>f</b>) Pathway enrichment analysis of KEGG for shared DEG of all infant formulae and breastmilk. Top 12 enriched significant pathways (<span class="html-italic">p</span> value &lt; 0.05) ordered by count were presented.</p>
Full article ">Figure 2
<p>Metabolite profiles of breast milk and different infant formulae. (<b>a</b>) Inter-group PLSDA (Partial Least Square Discriminant Analysis). Variation contribution of each component was presented. (<b>b</b>) A hierarchical clustered heatmap of different metabolites identified by ANOVA analysis. (<b>c</b>) Venn plot of enriched pathways of differential metabolites of BM, PMF1, PMF2, and PMF3. (<b>d</b>) KEGG pathways enriched from differential metabolites of BM, PMF1, PMF2, and PMF3. Pathways satisfying <span class="html-italic">p</span> value &lt; 0.1 were presented. (<b>e</b>) KEGG pathways enriched from differential metabolites of different breast milk groups. Pathways satisfying <span class="html-italic">p</span> value &lt; 0.1 were presented.</p>
Full article ">Figure 3
<p>Pro-development effects of breast milk and different infant formulae on intestine organoids. (<b>a</b>) Radar plot of GO-enriched pathways’ z-score of breast milk group and PMF group. Methods are from GeneTonic, an R package for RNA-seq data. Z-score implies the intensity and direction of pathway enrichment. PMF means powder milk (infant formulae), gathering PMF1, PMF2, and PMF3 as one group. (<b>b</b>) GSEA results of GO: Canonical Wnt signaling pathway from shared DEG of BM, PMF1, PMF2, and PMF3. NES means normalized enrichment score. (<b>c</b>) Heatmap of core enrichment genes of GSEA: canonical Wnt signaling pathway. (<b>d</b>) Heatmap of GSVA score of selected pathways associated with growth and development of intestine for each sample. (<b>e</b>) Cilium assembly and epithelial tube formation’ highly correlated genes (GSVA score-gene, spearman correlation &gt; 0.8) and the metabolites highly correlated to them (gene-metabolite, spearman correlation &gt; 0.8). (<b>f</b>) mRNA expression level of ‘key genes’ measured by RT-qPCR technique (2<sup>−ΔΔCt</sup> method). All data are presented as mean ± SEM. * <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; ns, no significance, <span class="html-italic">p</span> ≥ 0.05.</p>
Full article ">Figure 4
<p>Breast milk and different infant formulae effects on cell junction assembly and regulation. (<b>a</b>) NES value of GSEA enrichment for breast milk and infant formulae. Here, treat infant formulae as one group: PMF. (<b>b</b>) Inter-PMF comparison of cell junction-related GO biological processes enriched by GSEA. NES of each GO term for each infant formula group was visualized. (<b>c</b>) mRNA level of claudin-1 (CLDN1) and ZO-1 (TJP1) quantified by RT-qPCR (2<sup>−ΔΔCt</sup> method). (<b>d</b>) Hierarchical clustered heatmap of expression of core enrichment genes in tight junction pathway. Genes were clustered in three modules, representing certain infant formula groups’ relatively higher expressed genes. (<b>e</b>) Protein–protein interactions, respectively, from each clustered module’s genes. Left-Up: from module representing PMF1. Right-Up: from module representing PMF3. Middle-Down: from module representing PMF2. All data are presented as mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01; ns, no significance, <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>Profiles of extracellular events of breast milk and infant formulae. (<b>a</b>) GSEA result for GO (CC, cell compartment): Extracellular matrix obtained from identified shared DEG of breast milk and different infant formulae. (<b>b</b>) Heatmap of expression of core enrichment genes in Extracellular Matrix (ECM) generated from GSEA. (<b>c</b>) “Hub genes” and their highly correlated metabolites identified from network of ECM-related DEGs of BM. (<b>d</b>) “Hub genes” and their highly correlated metabolites identified from network of ECM-related DEGs of PMF. (<b>e</b>) Major GO terms enriched from ECM-related DEG of BM in the form of functionally grouped networks. (<b>f</b>) Major GO terms enriched from ECM-related DEG of PMF in the form of functionally grouped networks, similar terms were fused by clueGO.</p>
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18 pages, 4067 KiB  
Article
CRKL Enhances YAP Signaling through Binding and JNK/JUN Pathway Activation in Liver Cancer
by Marie C. Wesener, Sofia M. E. Weiler, Michaela Bissinger, Tobias F. Klessinger, Fabian Rose, Sabine Merker, Marcin Luzarowski, Thomas Ruppert, Barbara Helm, Ursula Klingmüller, Peter Schirmacher and Kai Breuhahn
Int. J. Mol. Sci. 2024, 25(15), 8549; https://doi.org/10.3390/ijms25158549 - 5 Aug 2024
Cited by 1 | Viewed by 1564
Abstract
The Hippo pathway transducers yes-associated protein (YAP) and WW-domain containing transcription regulator 1 (WWTR1/TAZ) are key regulators of liver tumorigenesis, promoting tumor formation and progression. Although the first inhibitors are in clinical trials, targeting the relevant upstream regulators of YAP/TAZ activity could prove [...] Read more.
The Hippo pathway transducers yes-associated protein (YAP) and WW-domain containing transcription regulator 1 (WWTR1/TAZ) are key regulators of liver tumorigenesis, promoting tumor formation and progression. Although the first inhibitors are in clinical trials, targeting the relevant upstream regulators of YAP/TAZ activity could prove equally beneficial. To identify regulators of YAP/TAZ activity in hepatocarcinoma (HCC) cells, we carried out a proximity labelling approach (BioID) coupled with mass spectrometry. We verified CRK-like proto-oncogene adaptor protein (CRKL) as a new YAP-exclusive interaction partner. CRKL is highly expressed in HCC patients, and its expression is associated with YAP activity as well as poor survival prognosis. In vitro experiments demonstrated CRKL-dependent cell survival and the loss of YAP binding induced through actin disruption. Moreover, we delineated the activation of the JNK/JUN pathway by CRKL, which promoted YAP transcription. Our data illustrate that CRKL not only promoted YAP activity through its binding but also through the induction of YAP transcription by JNK/JUN activation. This emphasizes the potential use of targeting the JNK/JUN pathway to suppress YAP expression in HCC patients. Full article
(This article belongs to the Special Issue Liver Cancer 3.0)
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Figure 1
<p>BioID identifies novel YAP/TAZ interaction partners. (<b>A</b>,<b>B</b>) Volcano plots show significant (in red) interaction partners of YAP/TAZ, as identified by the BioID pulldown. BirA-only expression was used as negative control. Well-established YAP/TAZ binding partners are labelled. (<b>C</b>) Overlap analysis of the YAP and TAZ interactome revealed high redundancy between YAP and TAZ, but also showed a vast number of exclusive YAP candidates. (<b>D</b>) Pathway enrichment analysis using the STRING database showed the involvement of YAP/TAZ interaction partners in different cancer-relevant processes.</p>
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<p>CRK and CRKL are new YAP interaction partners. (<b>A</b>) Precipitation of either endogenous YAP (upper panel) or CRKL (lower panel) and co-precipitated CRKL or YAP in HLF cells, respectively, confirming the interaction between both proteins. IgG served as negative control. (<b>B</b>) In situ PLA confirmed the predicted interactions between YAP and CRK/CRKL and TAZ and CRK in HLF cells. For TAZ and CRKL, no clear interaction was detected. Negative controls used only one primary antibody for PLA. Scale bar: 20 µm. (<b>C</b>) The transcriptome data of HCC tissue and the corresponding surrounding liver tissue (N = 242) [<a href="#B29-ijms-25-08549" class="html-bibr">29</a>] were analyzed for the mRNA expression of CRK and CRKL. While the CRK expression was decreased, CRKL expression was increased in HCC tissue. Statistical test: Mann–Whitney-U. (<b>D</b>) Patients were stratified into high- and low-expression groups using the median as a cutoff. Kaplan–Meier plots show an increased survival probability for patients with high CRK expression and a significantly decreased probability for patients with high CRKL expression. Statistical test: log-rank test. <span class="html-italic">p</span> ≤ 0.01 **, <span class="html-italic">p</span> ≤ 0.001 ***.</p>
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<p>CRKL inhibition reduces cell viability. (<b>A</b>,<b>B</b>) The measurement of cell viability after different time points of CRKL and CRK inhibition in SNU182 and Hep3B cells showed reduced cell viability for CRKL inhibition. In contrast, CRK inhibition did not consistently affect cell viability. (<b>C</b>,<b>D</b>) CRKL but not CRK knockdown decreased cell survival in a colony formation assay. Exemplary images are shown. (<b>A</b>–<b>D</b>) Nonsense siRNA (nons.) transfected cells served as negative controls. (<b>A</b>–<b>D</b>) Statistical tests: Dunnett’s multiple comparison with nons. as reference. ns = not significant. <span class="html-italic">p</span> ≤ 0.05 *, <span class="html-italic">p</span> ≤ 0.01 **, <span class="html-italic">p</span> ≤ 0.001 ***.</p>
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<p>CRKL activates YAP signaling. (<b>A</b>) SiRNA-mediated inhibition of CRKL (48 h) reduced YAP target gene expression, as measured by semiquantitative real-time PCR. Gene expression was normalized to nonsense siRNA (nons.) control. (<b>B</b>) SiRNA knockdown of CRKL (48 h) reduced the expression of YAP targets AXL and CYR61, as shown by Western Immunoblot. Nonsense siRNA served as control. (<b>C</b>) Treatment with the actin inhibitors Blebbistatin (1 h) and Cytochalasin D (10 min) reduced YAP/CRKL interaction in SNU449 cells, as quantified by PLA. Phalloidin staining shows inhibition of actin cytoskeleton upon inhibitor treatments. DMSO treatment was used as control. YAP and CRKL antibody-only PLAs served as negative control for PLA. Scale bar: 20 µm. Statistical test: Dunnett’s multiple comparison with DMSO as reference. <span class="html-italic">p</span> ≤ 0.001 ***.</p>
Full article ">Figure 5
<p>CRKL activates JNK/JUN to induce YAP transcription. (<b>A</b>) The inhibition of CRKL reduced the YAP protein level, as shown by Western Immunoblot. Actin served as the loading control. (<b>B</b>) The YAP mRNA level was decreased after the knockdown of CRKL, as measured by real-time PCR. (<b>C</b>) CRKL inhibition reduced the phosphorylated JNK (pJNK) and JUN (pJUN) levels, as shown by Western Immunoblot. GAPDH served as the loading control. (<b>D</b>) SiRNA-mediated knockdown of JUN led to a reduction in YAP mRNA, as well as a reduction in the target gene expression, as shown by real-time PCR. (<b>E</b>) Treatment with the JNK inhibitor SP600125 for 24 h reduced YAP expression, as illustrated by real-time PCR. DMSO served as control. (<b>F</b>) The scheme shows JUN ChIPseq peaks at the genomic YAP locus for three different cell lines from the ENCODE project. A predicted JUN binding site is highlighted. The bar graph shows the results from JUN ChIP with the amplification of the predicted binding site compared to IgG precipitation and a negative downstream control region. For siRNA transfection, nonsense siRNAs (nons.) served as controls.</p>
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<p>CRKL expression correlates with YAP activation in HCC patients. (<b>A</b>) The immunohistochemical staining of a tissue-microarray against CRKL, YAP and KI67 illustrated higher CRKL and YAP protein expression in HCC tissues compared to non-tumor liver tissue. The staining intensity increased with tumor grading and KI67 positivity. Three HCC samples with an increasing tumor grade, as well as a non-tumor liver sample, are shown as examples. Spearman’s rank correlation revealed significant correlations between CRKL protein expression, YAP abundance, KI67 staining and tumor grading. (<b>B</b>) Spearman’s rank correlation showed a significant correlation between CRKL mRNA expression and the YAP mRNA levels in HCC patients (N = 242) [<a href="#B29-ijms-25-08549" class="html-bibr">29</a>]. (<b>C</b>) CRKL mRNA expression correlated with a YAP target gene signature (CIN25 [<a href="#B4-ijms-25-08549" class="html-bibr">4</a>]) in HCC patients (N = 242). For Spearman’s rank correlation, a score of the target signature was calculated by summing up the normalized expression values of the 25 CIN genes. <span class="html-italic">p</span> ≤ 0.001 ***.</p>
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15 pages, 2264 KiB  
Review
Hippo Signaling at the Hallmarks of Cancer and Drug Resistance
by Ramesh Kumar and Wanjin Hong
Cells 2024, 13(7), 564; https://doi.org/10.3390/cells13070564 - 22 Mar 2024
Cited by 6 | Viewed by 3257
Abstract
Originally identified in Drosophila melanogaster in 1995, the Hippo signaling pathway plays a pivotal role in organ size control and tumor suppression by inhibiting proliferation and promoting apoptosis. Large tumor suppressors 1 and 2 (LATS1/2) directly phosphorylate the Yki orthologs YAP (yes-associated protein) [...] Read more.
Originally identified in Drosophila melanogaster in 1995, the Hippo signaling pathway plays a pivotal role in organ size control and tumor suppression by inhibiting proliferation and promoting apoptosis. Large tumor suppressors 1 and 2 (LATS1/2) directly phosphorylate the Yki orthologs YAP (yes-associated protein) and its paralog TAZ (also known as WW domain-containing transcription regulator 1 [WWTR1]), thereby inhibiting their nuclear localization and pairing with transcriptional coactivators TEAD1-4. Earnest efforts from many research laboratories have established the role of mis-regulated Hippo signaling in tumorigenesis, epithelial mesenchymal transition (EMT), oncogenic stemness, and, more recently, development of drug resistances. Hippo signaling components at the heart of oncogenic adaptations fuel the development of drug resistance in many cancers for targeted therapies including KRAS and EGFR mutants. The first U.S. food and drug administration (US FDA) approval of the imatinib tyrosine kinase inhibitor in 2001 paved the way for nearly 100 small-molecule anti-cancer drugs approved by the US FDA and the national medical products administration (NMPA). However, the low response rate and development of drug resistance have posed a major hurdle to improving the progression-free survival (PFS) and overall survival (OS) of cancer patients. Accumulating evidence has enabled scientists and clinicians to strategize the therapeutic approaches of targeting cancer cells and to navigate the development of drug resistance through the continuous monitoring of tumor evolution and oncogenic adaptations. In this review, we highlight the emerging aspects of Hippo signaling in cross-talk with other oncogenic drivers and how this information can be translated into combination therapy to target a broad range of aggressive tumors and the development of drug resistance. Full article
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<p>Chemical structure of key TEAD inhibitors: IAG933 and the inhibitor 6 bind to the surface of TEAD at interface 3. Majority of inhibitors are designed against the central hydrophobic pocket. Lead molecules IK-930, VT107, GNE-7883, SWTX-143, Flufenamic acid (FA), and MGH-CP1 are non-covalent binders. K-975, mCMY020, MYF-03-176, and MYF-03-69 are covalent inhibitors targeting conserved cysteine residues in the pocket.</p>
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<p>Targeting adaptive resistance of KRAS G12C mutation: in mutant KRAS cancer cells, KRAS inhibitors trigger mis-localization of Scrib and nuclear localization of YAP. YAP/TAZ-TEAD signaling and MAPK reactivation fuel the process of adaptive resistance in the presence of KRAS G12Ci (left panel). The combination of small-molecule Pan-TEAD inhibitor impairs the YAP/TAZ-TEAD nuclear binding and expression of transcriptional target genes, limiting adaptive resistance to KRAS inhibitors. Created in BioRender.com (accessed on 27 February 2024).</p>
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<p>Strategy to target tumors with mutant EGFR and the development of drug resistance: in NSCLC, various forms of EGFR mutations led to intrinsic and acquired resistance in response to a tyrosine kinase inhibitor treatment. YAP/TAZ-TEAD engages the EMT transcription factor SLUG to directly repress pro-apoptotic BMF, limiting drug-induced apoptosis (<b>A</b>). The combination therapy of MEK/TEAD inhibitors disrupts the process of tumor evolution and development of drug resistance (<b>B</b>). Note: this model also covers a broad range of other EGFR mutations including EGFR exon 20 insertions and exon 21 mutations. Created in BioRender.com (accessed on 27 February 2022).</p>
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24 pages, 11226 KiB  
Article
Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD
by Ryan Kanai, Emily Norton, Patrick Stern, Richard O. Hynes and John M. Lamar
Cancers 2024, 16(5), 852; https://doi.org/10.3390/cancers16050852 - 20 Feb 2024
Cited by 1 | Viewed by 2463
Abstract
Targeted therapies are effective cancer treatments when accompanied by accurate diagnostic tests that can help identify patients that will respond to those therapies. The YAP/TAZ-TEAD axis is activated and plays a causal role in several cancer types, and TEAD inhibitors are currently in [...] Read more.
Targeted therapies are effective cancer treatments when accompanied by accurate diagnostic tests that can help identify patients that will respond to those therapies. The YAP/TAZ-TEAD axis is activated and plays a causal role in several cancer types, and TEAD inhibitors are currently in early-phase clinical trials in cancer patients. However, a lack of a reliable way to identify tumors with YAP/TAZ-TEAD activation for most cancer types makes it difficult to determine which tumors will be susceptible to TEAD inhibitors. Here, we used a combination of RNA-seq and bioinformatic analysis of metastatic melanoma cells to develop a YAP/TAZ gene signature. We found that the genes in this signature are TEAD-dependent in several melanoma cell lines, and that their expression strongly correlates with YAP/TAZ activation in human melanomas. Using DepMap dependency data, we found that this YAP/TAZ signature was predictive of melanoma cell dependence upon YAP/TAZ or TEADs. Importantly, this was not limited to melanoma because this signature was also predictive when tested on a panel of over 1000 cancer cell lines representing numerous distinct cancer types. Our results suggest that YAP/TAZ gene signatures like ours may be effective tools to predict tumor cell dependence upon YAP/TAZ-TEAD, and thus potentially provide a means to identify patients likely to benefit from TEAD inhibitors. Full article
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Figure 1
<p>YAP promotes melanoma metastasis in a TEAD- and transactivation domain-dependent manner. GFP-expressing A375 cells were stably transduced with a control empty vector (MSCV-IRES-Hygro) or the indicated YAP constructs and then assayed by (<b>A</b>) Western blot or for (<b>B</b>) TEAD transcriptional activity using a dual-luciferase reporter assay. (<b>C</b>) Cells from (<b>A</b>) were injected into NOD/SCID mice via the lateral tail vein and after 19 days, the numbers of GFP-positive metastases were counted in the lungs. (<b>D</b>) Fluorescent images of all 5 lung lobes from 1 representative animal of each group in panel (<b>C</b>). Lung lobes are outlined in each image. (<b>F</b>) RNA-seq was performed on 3 independent RNA samples from A375 cells expressing an empty vector control (MSCV-IRES-Hygro) or the indicated YAP constructs, and the number of genes significantly up- or downregulated (fold change &gt; 2, adjusted <span class="html-italic">p</span> Value &lt; 0.05) by each YAP construct relative to the control is shown. (<b>E</b>) Geneset enrichment analysis (GSEA) was run on the RNA-seq data from (<b>D</b>) to test for the enrichment of the indicated Hippo Pathway genesets in the A375-YAP<sup>2SA</sup> vs. A375-Control cells. The normalized enrichment score (NES) and false discovery rate (FDR) q Value are listed for each geneset. (<b>G</b>) The heatmap shows log2 fold change (log2FC) for each indicated comparison for all 1696 genes that were differentially expressed in the A375-YAP<sup>2SA</sup> vs. Control cells. The genes are arranged from most upregulated (green) to most downregulated (red) in the A375-YAP<sup>2SA</sup> cells. Data used to generate this heatmap can be found in <a href="#app1-cancers-16-00852" class="html-app">Table S2</a>. The plots in (<b>B</b>,<b>C</b>) show mean ± SD with each dot representing an <span class="html-italic">n</span>. <span class="html-italic">n</span> = 3 independent experiments in (<b>B</b>) and <span class="html-italic">n</span> = individual mice in (<b>C</b>). Statistical significance was determined using one-way ANOVA with Dunnett’s multiple comparisons test (<b>B</b>, <b>left</b> and <b>C</b>) or unpaired, two-tail <span class="html-italic">t</span>-test (<b>B</b>, <b>right</b>); ** <span class="html-italic">p</span> ≤ 0.01, **** <span class="html-italic">p</span> ≤ 0.0001, n.s. <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Identification of a YAP/TAZ gene signature in metastatic human melanoma cells. (<b>A</b>) Venn diagrams show overlap between genes upregulated (<b>left</b>) or downregulated (<b>right</b>) (≥2 fold, <span class="html-italic">p</span> Value &lt; 0.05) in the following: A375-YAP<sup>2SA</sup> vs. Control cells (our data); MeWo YAP<sup>5SA</sup> vs. Control cells ([<a href="#B6-cancers-16-00852" class="html-bibr">6</a>]); Control siRNA vs. YAP/TAZ siRNA transfected SK-MEL-28 or WM3248 cells (GSE68599). (<b>B</b>) The table shows fold change for established YAP/TAZ target genes. All changes are statistically significant (adjusted <span class="html-italic">p</span> Value <span class="html-italic">p</span> ≤ 0.001). (<b>C</b>) The heatmap shows fold change for the 132 YAP/TAZ dependent genes in our YAP/TAZ signature. White indicates the gene was not detected in A375, WM3248, and SK-MEL-28, or not differentially expressed in MeWo. The data used to generate this heatmap can be found in <a href="#app1-cancers-16-00852" class="html-app">Table S3</a>, Tab 6. (<b>D</b>) Pathway analysis was performed with the YAP/TAZ Up geneset using Metascape [<a href="#B38-cancers-16-00852" class="html-bibr">38</a>]. The top 20 enriched clusters are shown.</p>
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<p>YAP/TAZ signature genes are YAP/TAZ-dependent in metastatic melanoma cells. (<b>A</b>–<b>C</b>) A375-MA2 or A2058 cells were transfected with either a control siRNA or combined siRNA SMARTpools targeting YAP and TAZ for 24 h. Cells were then trypsinized and replated for an additional 48 h and then assayed by (<b>A</b>) Western blot (<b>B</b>) for TEAD transcriptional activity using a dual-luciferase reporter assay, or by (<b>C</b>) qPCR for the indicated genes. The plots (<b>B</b>,<b>C</b>) show the fold change in the siYAP/TAZ samples compared to the siControl samples, which were set to 1, and represented as a red dotted line. Each data point is an independent experiment and the mean ± SD is shown. Statistical significance was tested using a one-sample <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, **** <span class="html-italic">p</span> ≤ 0.0001, n.s. <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>YAP/TAZ signature genes are TEAD-dependent. (<b>A</b>,<b>B</b>) A375-MA2 or A2058 cells stably transduced with control vector, TEADi, or TEAD1<sup>Y421E</sup> (DN-TEAD1) were assayed for (<b>A)</b> TEAD transcriptional activity using a dual-luciferase reporter assay or (<b>B</b>) by qPCR for the indicated genes. The plots (<b>A</b>,<b>B</b>) show the fold change in the TEADi or DN-TEAD samples compared to the control samples, which were set to 1 and represented as a red dotted line. Each data point is an independent experiment and the mean ± SD is shown. Statistical significance was tested using a one-sample <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, **** <span class="html-italic">p</span> ≤ 0.0001, n.s. <span class="html-italic">p</span> &gt; 0.05. (<b>C</b>) Gene Set Enrichment Analysis (GSEA) was performed using our YAP/TAZ Up and YAP/TAZ Down genesets and a publicly available dataset (GSE60664, [<a href="#B46-cancers-16-00852" class="html-bibr">46</a>]) in which all 4 TEADs were knocked down in human melanoma cells (MM047). Shown are the enrichment plots with Normalized Enrichment Score (NES) and False Discovery Rate (FDR) for each geneset. (<b>D</b>) ChIP-seq datasets with the indicated TEADs were downloaded from ENCODE and analyzed for the presence of peaks associated with each YAP/TAZ signature gene. The heatmap indicates which genes had a TEAD peak for each dataset. Red indicates at least 1 TEAD peak mapped to that gene, gray indicates no TEAD peaks mapped to that gene.</p>
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<p>Upregulated YAP/TAZ signature genes are predictive of melanoma cell dependence upon YAP/TAZ-TEAD. RNA-seq data and dependency scores for 62 melanoma cell lines were downloaded from the DepMap Portal (<a href="#app1-cancers-16-00852" class="html-app">Table S4</a>). Cell lines were scored as dependent (Chronos Dependency Score of ≤−0.65) or independent (Chronos Dependency Score of ≥−0.65) for TEADs 1–4, YAP, or TAZ (WWTR1). (<b>A</b>) GSEA was performed on RNA-seq data to test for enrichment of the indicated genesets in cell lines dependent upon TEADs or either YAP or TAZ (YAP/TAZ). (<b>B</b>) The heatmap shows the relative expression (Z-Score of the log transformed TPM (log2(1 + TPM))) of each of the 80 YAP/TAZ Up genes in the DepMap melanoma cell lines. Chronos Dependency Scores for TEADs, YAP, or TAZ are shown in the pink and white heatmap. Blue dots indicate cell lines with Chronos Dependency Scores ≤−1.0, black dots indicate a score between −1.0 and −0.65. A red asterisk (*) indicates that the melanoma cell line was analyzed above for expression of the genes in our signature. (<b>C</b>) GSVA was used to score the enrichment of each indicated geneset in each of the 62 melanoma cell lines in (<b>B</b>) and then ROC curves were generated to test how well GSVA score could predict dependency upon YAP/TAZ or TEADs. The area under the curve (AUC), <span class="html-italic">p</span> Value, Youden Index J, Sensitivity, and Specificity values for each geneset are shown in the tables. The data used to generate this figure can be found in <a href="#app1-cancers-16-00852" class="html-app">Table S4</a>.</p>
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<p>Upregulated YAP/TAZ signature genes are correlated with increased YAP/TAZ activity in human melanoma. RNA-seq data from the TCGA human skin cutaneous melanoma (SKCM) project were downloaded and tumors with high (≥1 standard deviation from the mean) or low (≤−1 standard deviation from the mean) expression of <span class="html-italic">CTGF</span> or <span class="html-italic">CYR61</span> mRNA were analyzed. (<b>A</b>) GSEA analysis was performed to test for the enrichment of the YAP/TAZ Up geneset in <span class="html-italic">CTGF</span> or <span class="html-italic">CYR61</span> high vs. low tumors (NES and FDR are indicated). (<b>B</b>,<b>D</b>) The heatmaps show the relative expression (Z-Score of the log transformed TPM (log2(1 + TPM))) of each of the 80 YAP/TAZ Up genes in <span class="html-italic">CTGF</span> (<b>B</b>) or <span class="html-italic">CYR61</span> (<b>D</b>) high vs. low tumors. Tumors are sorted by <span class="html-italic">CTGF</span> or <span class="html-italic">CYR61</span> mRNA expression and genes are ranked from highest (<b>top</b>) to lowest (<b>bottom</b>) based on their Spearman Rank Correlation with either <span class="html-italic">CTGF</span> (<b>B</b>) or <span class="html-italic">CYR61</span> (<b>D</b>). (<b>C</b>,<b>E</b>) Spearman similarity matrix analysis was performed on the expression data shown in (<b>B</b>) and (<b>D</b>) and the Spearman Rank Correlation value for each pairwise comparison across all tumors analyzed in each set is shown. Genes are ranked the same as in (<b>B</b>,<b>D</b>). (<b>F</b>) The Spearman Rank Correlation values for each gene compared to <span class="html-italic">CTGF</span> or <span class="html-italic">CYR61</span> (same data as 1st rows in (<b>C</b>,<b>E</b>)). Genes with Correlation values ≥0.4 are indicated in blue, between 0.39 and 0.3 in yellow and &lt;0.3 in gray. (<b>G</b>) The Venn diagram shows the number of genes from each comparison in (<b>F</b>) with correlation values ≥0.4. The data used to generate this figure can be found in <a href="#app1-cancers-16-00852" class="html-app">Table S5</a>.</p>
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<p>YAP/TAZ signature genes are YAP/TAZ-dependent and predictive of dependence upon YAP/TAZ-TEAD in other cancer types. Publicly available datasets (<a href="#app1-cancers-16-00852" class="html-app">Table S1</a>, Tab 3) were downloaded from NCBI-GEO and GSEA was used to generate rank-ordered lists for the indicated cell lines and comparisons. (<b>A</b>) The heatmap shows the % Rank (the gene’s rank/total genes in rank-ordered list × 100) of each of the 80 YAP/TAZ Up and 52 YAP/TAZ Down genes. Green indicates that the gene’s rank is between 0 and 15% (i.e., enriched in YAP and/or TAZ high cells), red between 85 and 100% (i.e., enriched in the YAP and/or TAZ low cells), black between 15 and 85% (i.e., not enriched), and white indicates the gene was not included in the rank-ordered list for that dataset. (<b>B</b>) GSEA was performed on the same publicly available datasets using our YAP/TAZ Up and Down genesets and other published YAP Up genesets. The heatmaps show the Normalized Enrichment Score (NES), False Discovery Rate (FDR), and % of each gene set that was in the Leading Edge (% Leading Edge) for each comparison. (<b>C</b>) The Venn diagram shows overlap between our YAP/TAZ Up geneset and other published YAP Up genesets. Lists of each geneset are found in <a href="#app1-cancers-16-00852" class="html-app">Table S1</a>, Tab 4, GSEA results, and the % Rank values used to generate the heatmaps in this figure are found in <a href="#app1-cancers-16-00852" class="html-app">Table S5</a>. (<b>D</b>) RNA-seq data and dependency scores for all 1019 cell lines were downloaded from the DepMap Portal (see <a href="#app1-cancers-16-00852" class="html-app">Table S4</a>). Cell lines were scored as dependent (Chronos Dependency Score of ≤−0.65) or independent (Chronos Dependency Score of ≥−0.65) for TEADs 1–4, YAP, or TAZ. GSEA was performed on the RNA-seq data to test for enrichment of our YAP/TAZ Up genset. (<b>E</b>) GSVA was used to score the enrichment of each indicated geneset in each of the 1019 cell lines in the DepMap Portal and then ROC curves were generated to test if the GSVA score could predict dependency upon YAP/TAZ or TEADs. Area Under the Curve, <span class="html-italic">p</span> Value, Youden Index J, Sensitivity, and Specificity values for each geneset are shown in the tables.</p>
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14 pages, 2651 KiB  
Article
Overexpression of Growth Differentiation Factor 15 in Glioblastoma Stem Cells Promotes Their Radioresistance
by Alexandre Bentaberry-Rosa, Yvan Nicaise, Caroline Delmas, Valérie Gouazé-Andersson, Elizabeth Cohen-Jonathan-Moyal and Catherine Seva
Cancers 2024, 16(1), 27; https://doi.org/10.3390/cancers16010027 - 20 Dec 2023
Cited by 1 | Viewed by 1393
Abstract
GSCs play an important role in GBM recurrence. Understanding the resistance mechanisms in these cells is therefore crucial for radiation therapy optimization. In this study, using patient-derived GSCs, we demonstrate that GDF15, a cytokine belonging to the TGF-β superfamily, is regulated by irradiation [...] Read more.
GSCs play an important role in GBM recurrence. Understanding the resistance mechanisms in these cells is therefore crucial for radiation therapy optimization. In this study, using patient-derived GSCs, we demonstrate that GDF15, a cytokine belonging to the TGF-β superfamily, is regulated by irradiation (IR) and the transcription factor WWTR1/TAZ. Blocking WWTR1/TAZ using specific siRNAs significantly reduces GDF15 basal expression and reverses the upregulation of this cytokine induced by IR. Furthermore, we demonstrate that GDF15 plays an important role in GSC radioresistance. Targeting GDF15 expression by siRNA in GSCs expressing high levels of GDF15 sensitizes the cells to IR. In addition, we also found that GDF15 expression is critical for GSC spheroid formation, as GDF15 knockdown significantly reduces the number of GSC neurospheres. This study suggests that GDF15 targeting in combination with radiotherapy may be a feasible approach in patients with GBM. Full article
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Figure 1
<p>GDF15 expression in normal tissue, GBM, and GSCs. (<b>A</b>) The expression of GDF15 mRNA in normal tissue or GBM samples from the TCGA database was compared with the RNAseq data of GSCs derived from 13 human GBM biopsy specimens. The data were normalized with GAPDH expression. Pairwise t-tests were performed between the groups. Results are presented as means ± SD of log2 TPM. *** <span class="html-italic">p</span> &lt; 0.001; ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>,<b>C</b>) GDF15 secretion by GSCs (GSC02, GSC07) was analyzed using the R&amp;D Human XL Cytokine Array kit on 48 h conditioned media as described in “Methods”. EGF, which is added to the stem cell culture medium, can serve as a positive control. Images are representative of three independent experiments.</p>
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<p>Correlations between the expression of GDF15 and the Hippo pathway genes. (<b>A</b>) The correlations between GDF15 expression and, respectively, WWTR1/TAZ, YAP1, CYR61, and CTGF were obtained by the co-expression analysis in Gliovis as described in “Methods” using the TGCA database. Values correspond to the Spearman correlation coefficient (R) and its associated <span class="html-italic">p</span>-value. (<b>B</b>) The expression of WWTR1/TAZ, YAP1, Cyr61, and CTGF mRNA in normal tissue or GBM samples from the TCGA database was compared with the RNAseq data of GSCs derived from 13 human GBM biopsy specimens. The data were normalized with GAPDH expression. Pairwise t-tests were performed between the groups. Results are presented as means ± SD of log2 TPM. *** <span class="html-italic">p</span> &lt; 0.001; ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) The correlation curve between GDF15 and WWTR1/TAZ in GSCs was performed with Srplot as described in “Methods” using the RNAseq data. The values correspond to the Spearman correlation coefficient (R) and its associated <span class="html-italic">p</span>-value.</p>
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<p>Blocking WWTR1/TAZ decreases GDF15 expression in GSCs. Primary GSC neurospheres from two different patients (GSC02, GSC07) expressing for 48 h a WWTR1/TAZ-specific siRNA (si-WWTR1/TAZ) or a scramble control (si-Scr) were used. WWTR1/TAZ (<b>A</b>) and GDF15 (<b>C</b>) mRNA expression was analyzed using real-time PCR using GAPDH expression for normalization. (<b>B</b>) WWTR1/TAZ protein expression was analyzed using western blot. (<b>D</b>,<b>E</b>) GDF15 secretion by the GSCs was analyzed using the R&amp;D Human XL Cytokine Array kit on 48 h conditioned media as described in “methods”. (<b>E</b>) For the quantification, the average signal of the reference spots and the number of cells were used for normalization as described in “methods”. Images (<b>B</b>,<b>D</b>) are representative of three independent experiments. Quantifications of three experiments are presented as mean ± SD. *** <span class="html-italic">p</span> &lt; 0.001; ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Genes and pathways associated with GDF15 high expression in GSCs. (<b>A</b>) Volcano plot showing the genes that are differentially expressed between GSCs with a strong expression of GDF15 (GDF15 high) compared with GSCs expressing GDF15 weakly (GDF15 low), performed as described in “Methods”. (<b>B</b>) Gene ontology analysis (biological processes) was performed on the genes significantly up-regulated in the high expressing GDF15 GSCs as described in “Methods”.</p>
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<p>Down-regulation of GDF15 decreases the sphere-forming ability of GSCs. GSC02 expressing for 48 h specific siRNAs against GDF15 (si-GDF15(1), si-GDF15(2)) or a scramble control (si-Scr) were used for (<b>A</b>) GDF15 mRNA expression analyzed by real-time PCR, using GAPDH for normalization or (<b>B</b>,<b>C</b>) for neurosphere-forming analysis as described in “Methods”. Quantifications of three experiments are presented as mean ± SD. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>GDF15 mRNA expression is increased following IR in GSCs and down-regulation of GDF15 decreases radioresistance of GSCs. (<b>A</b>) Non-transfected GSCs or (<b>B</b>–<b>D</b>) GSC02 expressing for 48 h specific siRNAs against WWTR1/TAZ (si-WWTR1/TAZ), GDF15 (si-GDF15(1) or si-GDF15(2)), or a scramble control (si-Scr) were used. (<b>A</b>,<b>B</b>) GDF15 mRNA expression was analyzed under basal conditions (NIR) or 24 h after a single dose of IR (8 Gy) by real-time PCR, using GAPDH for normalization. (<b>C</b>) Neurosphere formation following increasing doses of x rays (0 to 6 Gy) was analyzed as described in “Methods”. (<b>D</b>) GSC02 expressing for 48 h a specific siRNA against GDF15 (si-GDF15(1)) or a scramble control (si-Scr) was used for the neurosphere-forming analysis following 6 Gy of IR as described in “Methods”. When indicated, rhGD15 (20 ng/mL) was added before IR. Quantifications of three experiments are presented as means ± SD. ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05.</p>
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22 pages, 8846 KiB  
Article
Identification of BRCC3 and BRCA1 as Regulators of TAZ Stability and Activity
by Silvia Sberna, Alejandro Lopez-Hernandez, Chiara Biancotto, Luca Motta, Adrian Andronache, Lisette G. G. C. Verhoef, Marieta Caganova and Stefano Campaner
Cells 2023, 12(20), 2431; https://doi.org/10.3390/cells12202431 - 11 Oct 2023
Cited by 1 | Viewed by 1758
Abstract
TAZ (WWTR1) is a transcriptional co-activator regulated by Hippo signaling, mechano-transduction, and G-protein couple receptors. Once activated, TAZ and its paralogue, YAP1, regulate gene expression programs promoting cell proliferation, survival, and differentiation, thus controlling embryonic development, tissue regeneration, and aging. YAP and TAZ [...] Read more.
TAZ (WWTR1) is a transcriptional co-activator regulated by Hippo signaling, mechano-transduction, and G-protein couple receptors. Once activated, TAZ and its paralogue, YAP1, regulate gene expression programs promoting cell proliferation, survival, and differentiation, thus controlling embryonic development, tissue regeneration, and aging. YAP and TAZ are also frequently activated in tumors, particularly in poorly differentiated and highly aggressive malignancies. Yet, mutations of YAP/TAZ or of their upstream regulators do not fully account for their activation in cancer, raising the possibility that other upstream regulatory pathways, still to be defined, are altered in tumors. In this work, we set out to identify novel regulators of TAZ by means of a siRNA-based screen. We identified 200 genes able to modulate the transcriptional activity of TAZ, with prominence for genes implicated in cell–cell contact, cytoskeletal tension, cell migration, WNT signaling, chromatin remodeling, and interleukins and NF–kappaB signaling. Among these genes we identified was BRCC3, a component of the BRCA1 complex that guards genome integrity and exerts tumor suppressive activity during cancer development. The loss of BRCC3 or BRCA1 leads to an increased level and activity of TAZ. Follow-up studies indicated that the cytoplasmic BRCA1 complex controls the ubiquitination and stability of TAZ. This may suggest that, in tumors, inactivating mutations of BRCA1 may unleash cell transformation by activating the TAZ oncogene. Full article
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Figure 1
<p>Identification of genes regulating TAZ activity by an siRNA screen. (<b>a</b>) Outline of the siRNA screen. (<b>b</b>,<b>c</b>) Dot plot of TEAD reporter activity and cell viability (Z-score normalized values). The different classes of control siRNA (<b>c</b>) and hits (<b>d</b>) are highlighted in colors. (<b>d</b>) Pie chart of the different classes of hits. (<b>e</b>) Gene Ontology network of the genes identified as regulators of TAZ activity.</p>
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<p>BRCC3, BRCA1, and other BRCA−1 complex components repress TAZ activity. (<b>a</b>,<b>b</b>) First, 150.000 pSLIK–TAZS89A–8xTEAD–LUC MCF10A cells were transfected with siRNA against BRCC3 or with a non−targeting sequence (siGFP) as control, in the presence or in absence of 2 µg/mL of doxycycline. After 48 h from transfection, cells were collected. (<b>a</b>) RT–qPCR analysis. The bar plot shows cDNA levels of two TAZ target genes, CTGF, and CYR61, normalized on GAPDH housekeeping gene and expressed as fold change. <span class="html-italic">T</span>–test was applied to evaluate the statistical significance: * <span class="html-italic">p</span> value &lt; 0.05 ** <span class="html-italic">p</span> value &lt; 0.01 *** <span class="html-italic">p</span> value &lt; 0.005. (<b>b</b>) WB analysis, vinculin was used as loading control. (<b>c</b>,<b>d</b>) Silencing of BARD1 and BRCA2 in MCF10A cells. RT–qPCR and WB analysis were performed after 48 h from the transfection in sub-confluent condition. (<b>c</b>) Bar plots showing the expression levels of BRCA2 and BARD1. <span class="html-italic">T</span>–test was applied to evaluate the statistical significance: *** <span class="html-italic">p</span> value &lt; 0.005 **** <span class="html-italic">p</span> value &lt; 0.001. (<b>d</b>) WB analysis, vinculin was used as loading control. (<b>e</b>–<b>g</b>) Analysis of TAZ and BRCA1 protein level by WB, following BRCA1−KD in the reported cell lines. (<b>b</b>,<b>d</b>–<b>g</b>) The normalized densitometric values of the WB bands are reported below the WB snapshots. (<b>h</b>,<b>i</b>) Analysis of TAZ target genes by RT–qPCR in (<b>h</b>) pSLIK–TAZS89A–8xTEAD–LUC MCF10A cells and (<b>i</b>) MDA–MB–231, KEK–293T, and HeLa cells.</p>
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<p>Silencing BRCA1 alters TAZ protein levels without affecting its intracellular distribution. For the evaluation of TAZ by Immunofluorescence analysis, HeLa cells were transfected with siBRCA1 (#458) or with a non−targeting siRNA, as control (siC). After 48 h, sub−confluent cells were fixed and stained with two TAZ antibodies. (<b>a</b>) Representative images at 100× magnification, scale bar = 30 µm, in blue Dapi stained nuclei, in red TAZ protein detected by the C22 antibody, in green TAZ protein stained with the C188 antibody. (<b>b</b>) Dot plot showing the quantification of the mean intensity of TAZ relative signal per cell detected with the C22 and the C188 antibody. Raw data were analyzed through the Fiji software (version 2.14.0/1.54f). Sample size: 80 cells. Mann−Whitney test was applied to run statistical analysis: **** <span class="html-italic">p</span> value &lt; 0.0001.</p>
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<p>Evidence of the intracellular proximity of TAZ and BRCA1. (<b>a</b>) HeLa cells were transfected with the pQCX–TAZwt–Myc–tag and the pMH–BRCA1–FLAG plasmids to overexpress TAZ and BRCA1. After 48 h from the transfection, sub-confluent cells were fixed and processed. Dot plot showing the quantification of the PLA–foci per cell. Cells were stained with only one primary antibody (TAZ or BRCA1) or with secondary probes only (no primary antibodies) as technical negative controls. Raw data were analyzed by ImageJ software. Cell area was measured by an ImageJ plugin that predicts polygonal shapes around the DAPI signal. Sample size: 590 cells per condition. Mann–Whitney test was applied to run statistical analysis: **** <span class="html-italic">p</span> value &lt;0.0001. (<b>b</b>,<b>c</b>) MCF10A–rtTA–Cas9–sgROSA and –sgTAZ (#2) cells were seeded in six wells on glass coverslip in presence of 1 µg/mL of doxycycline to induce Cas9 expression. After 72 h sub-confluent cells were fixed and processed. (<b>b</b>) Representative micrographs at 100× magnification of PLA–foci detected in MCF10A cells wild type for TAZ (sgROSA) or knock-out for TAZ (sgTAZ). In blue the nuclei stained with DAPI, in red the PLA signal showing TAZ–BRCA1 proximity. (<b>c</b>) Dot plot of the number of PLA foci per cell. Sample size: 635 cells per condition. Mann-Whitney test was applied to run statistical analysis: **** <span class="html-italic">p</span> value &lt; 0.0001. (<b>d</b>,<b>e</b>) HeLa and MCF10A cells were transfected with plasmids to overexpress TAZ and BRCA1. After 48 h, sub-confluent cells were fixed in PFA 4% and processed for IF and PLA analysis. (<b>d</b>) Representative images at 100× magnification: in blue Dapi stained nuclei, in red PLA-dots, in green TAZ overexpression detected by IF. (<b>e</b>) Bar plot showing the fraction of PLA dots per cell co-localizing with Dapi signal (nuclear proximity) or with TAZ signal but not with the Dapi (cytosolic proximity). Images were analysed with the ImageJ software. Sample size: 94 HeLa cells and 40 MCF10A cells overexpressing TAZ.</p>
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<p>Hippo signaling is epistatic over BRCA1 regulation. (<b>a</b>,<b>b</b>) WB analysis of Hippo pathway components (LATS1, MOB1, MST2, and LATS2) in (<b>a</b>) MCF10A–pSLIK–TAZS89A, and (<b>b</b>) MDA–MB–231, HEK–293T, and HeLa. Cells were transfected with siBRCA1 (#458) or with a non–targeting siRNA (siC) as control. After 48 h, cells were lysed for protein extraction. Vinculin was used as loading control. (<b>c</b>) WB analysis of HEK–293A cells wild-type (WT) or KO for LATS1, LATS2 or both (dKO). Cells were transfected with siBRCA1 (#458) or with a non-targeting siRNA (siC) as control for 48 h. Vinculin was used as loading control. (<b>d</b>) WB analysis of HEK–293A cells wild type (WT) or double knock-out for LATS1,2 (dKO). Cells were first transfected siRNA against BRCA1 (#458) or with a non-targeting siRNA (siC), and then, 24 h later, with pEGFP C3–LATS1 plasmid or an empty vector (pEGFP–EV). Cells were collected 48 h from the first transfection and processed for protein analysis. Vinculin was used as loading control. The normalized densitometric values of the WB bands are reported below the WB snapshots.</p>
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<p>Regulation of TAZ by BRCA1 is mainly by post–translational. (<b>a</b>–<b>c</b>) RT–qPCR analysis of (<b>a</b>) MCF10A–pSLIK–TAZS89A–8xTEAD–LUC cells, (<b>b</b>) MDA–MB–231, HEK–293T and HeLa cells, and (<b>c</b>) HEK-293A cells wild type (WT), single KO for LATS1 and LATS2 or dKO. cDNA levels were normalized to either GAPDH or RPLPO housekeeping genes and expressed as fold change. <span class="html-italic">T</span>–test was applied to evaluate the statistical significance: * <span class="html-italic">p</span> value &lt; 0.05 *** <span class="html-italic">p</span> value &lt; 0.005 **** <span class="html-italic">p</span> value &lt; 0.001. (<b>d</b>,<b>e</b>) WB analysis of confluent MCF10A cells treated with 5µM of the proteasome inhibitor MG132 for 6 h. (<b>e</b>) Densitometric analysis of the WB shown in (<b>d</b>). TAZ signal was normalized to Vinculin signal and expressed as fold change compared to the not-treated condition (-).</p>
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<p>BRCA1 regulates TAZ ubiquitylation (<b>a</b>,<b>b</b>) WB analysis of confluent MCF10A transfected with siBRCA1 (#458) or with a non-targeting sequence (siC), and treated with CHX for the indicated times. (<b>b</b>) Densitometric analysis of the WB shown in (<b>a</b>). (<b>c</b>,<b>d</b>) Analysis of TAZ ubiquitylation by immunoprecipitation (IP) and WB analysis. HEK–293A cells were transfected with siBRCA1 (#458) or a mock siRNA (siC). After 48 h, cells were transfected with plasmids encoding for TAZ (pMSCV-HA-TAZ) and Ubiquitin (pcDNA3–FLAG–Ub). At 72 h from the first transfection, cells were treated with 5 µM of proteasome inhibitor MG132 for 6 h. 2.5% of the immunoprecipitated lysate (Input) was loaded to assess the fold enrichment in TAZ IP. The normalized densitometric values of the WB bands are reported below the WB snapshots.</p>
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<p>Model of how BRCA1 and BRCC3 control TAZ. BRCA1/BRCC3 control TAZ activity by modulating (1) TAZ transcription (2) LATS1 protein level (3) TAZ ubiquitylation and proteasomal degradation. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 4 October 2023).</p>
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20 pages, 7203 KiB  
Article
A Matched Molecular and Clinical Analysis of the Epithelioid Haemangioendothelioma Cohort in the Stafford Fox Rare Cancer Program and Contextual Literature Review
by Arwa Abdelmogod, Lia Papadopoulos, Stephen Riordan, Melvin Wong, Martin Weltman, Ratana Lim, Christopher McEvoy, Andrew Fellowes, Stephen Fox, Justin Bedő, Jocelyn Penington, Kym Pham, Oliver Hofmann, Joseph H. A. Vissers, Sean Grimmond, Gayanie Ratnayake, Michael Christie, Catherine Mitchell, William K. Murray, Kelly McClymont, Peter Luk, Anthony T. Papenfuss, Damien Kee, Clare L. Scott, David Goldstein and Holly E. Barkeradd Show full author list remove Hide full author list
Cancers 2023, 15(17), 4378; https://doi.org/10.3390/cancers15174378 - 1 Sep 2023
Cited by 2 | Viewed by 2082
Abstract
Background: Epithelioid haemangioendothelioma (EHE) is an ultra-rare malignant vascular tumour with a prevalence of 1 per 1,000,000. It is typically molecularly characterised by a WWTR1::CAMTA1 gene fusion in approximately 90% of cases, or a YAP1::TFE3 gene fusion in approximately 10% of cases. EHE [...] Read more.
Background: Epithelioid haemangioendothelioma (EHE) is an ultra-rare malignant vascular tumour with a prevalence of 1 per 1,000,000. It is typically molecularly characterised by a WWTR1::CAMTA1 gene fusion in approximately 90% of cases, or a YAP1::TFE3 gene fusion in approximately 10% of cases. EHE cases are typically refractory to therapies, and no anticancer agents are reimbursed for EHE in Australia. Methods: We report a cohort of nine EHE cases with comprehensive histologic and molecular profiling from the Walter and Eliza Hall Institute of Medical Research Stafford Fox Rare Cancer Program (WEHI-SFRCP) collated via nation-wide referral to the Australian Rare Cancer (ARC) Portal. The diagnoses of EHE were confirmed by histopathological and immunohistochemical (IHC) examination. Molecular profiling was performed using the TruSight Oncology 500 assay, the TruSight RNA fusion panel, whole genome sequencing (WGS), or whole exome sequencing (WES). Results: Molecular analysis of RNA, DNA or both was possible in seven of nine cases. The WWTR1::CAMTA1 fusion was identified in five cases. The YAP1::TFE3 fusion was identified in one case, demonstrating unique morphology compared to cases with the more common WWTR1::CAMTA1 fusion. All tumours expressed typical endothelial markers CD31, ERG, and CD34 and were negative for pan-cytokeratin. Cases with a WWTR1::CAMTA1 fusion displayed high expression of CAMTA1 and the single case with a YAP1::TFE3 fusion displayed high expression of TFE3. Survival was highly variable and unrelated to molecular profile. Conclusions: This cohort of EHE cases provides molecular and histopathological characterisation and matching clinical information that emphasises the molecular patterns and variable clinical outcomes and adds to our knowledge of this ultra-rare cancer. Such information from multiple studies will advance our understanding, potentially improving treatment options. Full article
(This article belongs to the Collection Molecular Pathways in Cancers)
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<p>Analysis of case #368. (<b>a</b>) FFPE sections from tumour #368 were assessed by IHC. Representative images of H&amp;E, CD31, CD34, ERG and pan-CK staining are shown. Arrows indicate intracytoplasmic red blood cells. Scale bars = 100 μm. (<b>b</b>) RNA extracted from tumour sections was analysed using the TruSight Fusion panel. The exons contained within the <span class="html-italic">WWTR1::CAMTA1</span> fusion are shown above with the resulting fusion protein shown below. (<b>c</b>) CT performed at different time points confirming disease stability. Axial CT images of the liver demonstrating atrophy of the right hepatic lobe, hypertrophy of the left lobe, and multifocal hypodense lesions in 2009 (<b>A</b>,<b>D</b>), 2013 (<b>B</b>,<b>E</b>), and 2017 (<b>C</b>,<b>F</b>). Axial CT images of the lungs showing multiple bilateral small pulmonary nodules in 2009 (<b>G</b>) 2013 (<b>H</b>), and 2017 (<b>I</b>). IHC, immunohistochemistry; H&amp;E, haematoxylin and eosin; pan-CK, pan-cytokeratin; CT, computed tomography.</p>
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<p>Analysis of case #130. (<b>a</b>) FFPE sections from tumour #130 were assessed by IHC. Representative images of H&amp;E, CD31, CD34, ERG and pan-CK staining are shown. Arrows indicate intracytoplasmic red blood cells. Scale bars = 100 μm. (<b>b</b>) Whole body PET images in 2010 (<b>A</b>), 2015 (<b>B</b>), 2017 (<b>C</b>), and 2019 (<b>D</b>) showing overall stability of the metastatic disease. (<b>c</b>) DNA extracted from tumour sections was analysed by whole genome sequencing. CIRCOS plot indicates a tumour with low tumour mutational burden. (<b>d</b>) The exons contained within the <span class="html-italic">YAP1::TFE3</span> fusion are shown to the left, with the resulting fusion protein shown to the right. (<b>e</b>) The exons contained within the <span class="html-italic">CBX3::HECW1</span> fusion are shown to the left, with the resulting fusion protein shown to the right. IHC, immunohistochemistry; H&amp;E, haematoxylin and eosin; pan-CK, pan-cytokeratin; PET, positron emission tomography.</p>
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<p>Histological analysis of cases #104, #154, #162, #455, #503 and #521. (<b>a</b>) FFPE sections from tumours were stained with H&amp;E for pathological assessment. Scale bars = 100 μm. (<b>b</b>) FFPE sections were assessed by IHC. Representative images of H&amp;E, CD31, CD34, ERG and pan-CK staining are shown for five cases. For case #104, CD31, CD34, and CK7 staining was carried out. Scale bars = 100 μm. H&amp;E, haematoxylin and eosin; IHC, immunohistochemistry; pan-CK, pan-cytokeratin.</p>
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<p>Molecular analysis of cases #154, #455, #521 and #503. RNA extracted from tumour sections was analysed using the TruSight Fusion panel. (<b>a</b>) The exons contained within the <span class="html-italic">WWTR1::CAMTA1</span> fusions identified in cases #154, #455 and #521 are shown above with the resulting fusion protein shown below for each case. (<b>b</b>) The exons contained within the <span class="html-italic">WWTR1::CAMTA1</span> fusion identified in case #503 are shown above with the resulting fusion protein shown below. (<b>c</b>) The exons contained within the <span class="html-italic">FBN1::WWTR1</span> fusion identified in case #503 are shown above with the resulting fusion protein shown below.</p>
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<p>Histological analysis of CAMTA1 and TFE3 expression in cases #130, #154, #162, #368, #503 and #521. FFPE sections from tumours were stained with CAMTA1 and TFE3 antibodies. Representative images are shown. Scale bars = 100 μm.</p>
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11 pages, 6548 KiB  
Article
Dissecting the Methylomes of EGFR-Amplified Glioblastoma Reveals Altered DNA Replication and Packaging, and Chromatin and Gene Silencing Pathways
by Theo F. J. Kraus, Celina K. Langwieder, Dorothee Hölzl, Georg Hutarew, Hans U. Schlicker, Beate Alinger-Scharinger, Christoph Schwartz and Karl Sotlar
Cancers 2023, 15(13), 3525; https://doi.org/10.3390/cancers15133525 - 7 Jul 2023
Cited by 1 | Viewed by 1622
Abstract
Glioblastoma IDH wildtype is the most frequent brain tumor in adults. It shows a highly malignant behavior and devastating outcomes. To date, there is still no targeted therapy available; thus, patients’ median survival is limited to 12–15 months. Epithelial growth factor receptor ( [...] Read more.
Glioblastoma IDH wildtype is the most frequent brain tumor in adults. It shows a highly malignant behavior and devastating outcomes. To date, there is still no targeted therapy available; thus, patients’ median survival is limited to 12–15 months. Epithelial growth factor receptor (EGFR) is an interesting targetable candidate in advanced precision medicine for brain tumor patients. In this study, we performed integrated epigenome-wide DNA-methylation profiling of 866,895 methylation specific sites in 50 glioblastoma IDH wildtype samples, comparing EGFR amplified and non-amplified glioblastomas. We found 9849 significantly differentially methylated CpGs (DMCGs) with Δβ ≥ 0.1 and p-value < 0.05 in EGFR amplified, compared to EGFR non-amplified glioblastomas. Of these DMCGs, 2380 were annotated with tiling (2090), promoter (117), gene (69) and CpG islands (104); 7460 are located at other loci. Interestingly, the list of differentially methylated genes allocated eleven functionally relevant RNAs: five miRNAs (miR1180, miR1255B1, miR126, miR128-2, miR3125), two long non-coding RNAs (LINC00474, LINC01091), and four antisense RNAs (EPN2-AS1, MNX1-AS2, NKX2-2-AS1, WWTR1-AS1). Gene ontology (GO) analysis showed enrichment of “DNA replication-dependent nucleosome assembly”, “chromatin silencing at rDNA”, “regulation of gene silencing by miRNA”, “DNA packaging”, “posttranscriptional gene silencing”, “gene silencing by RNA”, “negative regulation of gene expression, epigenetic”, “regulation of gene silencing”, “protein-DNA complex subunit organization”, and “DNA replication-independent nucleosome organization” pathways being hypomethylated in EGFR amplified glioblastomas. In summary, dissecting the methylomes of EGFR amplified and non-amplified glioblastomas revealed altered DNA replication, DNA packaging, chromatin silencing and gene silencing pathways, opening potential novel targets for future precision medicine. Full article
(This article belongs to the Special Issue Pathology and Genetics of Glioblastoma)
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<p>Analysis of <span class="html-italic">EGFR</span> amplification. Analysis of <span class="html-italic">EGFR</span> amplification in glioblastoma IDH wildtype CNS WHO grade 4 was performed by evaluating relative probe intensities of copy number profiles generated during methylation analysis. Fifty glioblastoma samples were included, with twenty-five samples showing <span class="html-italic">EGFR</span> amplification (i.e., relative probe intensities of more than 0.6, indicated by red circle) (<b>a</b>,<b>b</b>), and twenty-five showing no <span class="html-italic">EGFR</span> amplification (i.e., relative probe intensities of less than 0.6, indicated by red circle) (<b>c</b>,<b>d</b>). Of all glioblastomas, (<b>e</b>) mean <span class="html-italic">EGFR</span> probe intensity level of amplified glioblastomas was 0.99, and of non-amplified was 0.04 (<b>f</b>). (<b>a</b>,<b>c</b>): scale bar: 50 µm; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Exploratory methylation analysis. After probe removal and filtering, exploratory methylation analysis was performed on 820,384 probes that remained: 245,013 were annotated with tiling regions, 43,306 were annotated with promoters, 33,801 were annotated with genes, and 25,763 were annotated with CpG islands (<b>a</b>). Analysis of beta value distributions and probe categories showed that CpG islands represent high densities of unmethylated beta values, while shelf and open sea regions showed higher densities of methylated values with an intermediate distribution of shores (<b>b</b>). Volcano plot showed distribution of differential methylation values (Δβ) and significance level (−log10); thereby, probes with Δβ ≥ 0.1 and <span class="html-italic">p</span>-value &lt; 0.05 are indicated in red color (<b>c</b>). Hierarchical clustering of top 100 DMCGs showed distinct clusters using Manhattan distance (<b>d</b>). Each pairwise comparison of probes resulted in 9849 significantly differentially methylated probes (Δβ ≥ 0.1, <span class="html-italic">p</span>-value &lt; 0.05) (<b>e</b>), with 5178 probes being hypermethylated and 4671 probes being hypomethylated (<b>f</b>).</p>
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<p>Differential methylation analysis. Differential methylation analysis revealed 2380 DMCGs that are annotated with tiling regions (2090, 21%), promoter regions (117, 1%), gene regions (69, 1%), and CpG island regions (104, 1%) (<b>a</b>), with 1287 being hypo- and 1093 being hypermethylated (<b>b</b>). Analysis of DMCG and genomic position showed distinct fractions of hypomethylated and hypermethylated DMCGs (<b>c</b>). Scatter plot of GO enrichment analysis indicated ranked regions with FDR adjusted <span class="html-italic">p</span>-values &lt; 0.05 in red color (<b>d</b>), FDR adjusted <span class="html-italic">p</span>-values ≥ 0.05 in blue dots, FDR adjusted <span class="html-italic">p</span>-values &lt; 0.05 in red dots). Results of GO analysis of top 1000 best ranking genes are demonstrated by word clouds of top terms being enriched in hypomethylated (<b>e</b>), and hypermethylated pathways in <span class="html-italic">EGFR</span> amplified glioblastomas (<b>f</b>).</p>
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23 pages, 8702 KiB  
Article
Skeletal Muscles of Sedentary and Physically Active Aged People Have Distinctive Genic Extrachromosomal Circular DNA Profiles
by Daniela Gerovska and Marcos J. Araúzo-Bravo
Int. J. Mol. Sci. 2023, 24(3), 2736; https://doi.org/10.3390/ijms24032736 - 1 Feb 2023
Cited by 8 | Viewed by 3265
Abstract
To bring new extrachromosomal circular DNA (eccDNA) enrichment technologies closer to the clinic, specifically for screening, early diagnosis, and monitoring of diseases or lifestyle conditions, it is paramount to identify the differential pattern of the genic eccDNA signal between two states. Current studies [...] Read more.
To bring new extrachromosomal circular DNA (eccDNA) enrichment technologies closer to the clinic, specifically for screening, early diagnosis, and monitoring of diseases or lifestyle conditions, it is paramount to identify the differential pattern of the genic eccDNA signal between two states. Current studies using short-read sequenced purified eccDNA data are based on absolute numbers of unique eccDNAs per sample or per gene, length distributions, or standard methods for RNA-seq differential analysis. Previous analyses of RNA-seq data found significant transcriptomics difference between sedentary and active life style skeletal muscle (SkM) in young people but very few in old. The first attempt using circulomics data from SkM and blood of aged lifelong sedentary and physically active males found no difference at eccDNA level. To improve the capability of finding differences between circulomics data groups, we designed a computational method to identify Differentially Produced per Gene Circles (DPpGCs) from short-read sequenced purified eccDNA data based on the circular junction, split-read signal, of the eccDNA, and implemented it into a software tool DifCir in Matlab. We employed DifCir to find to the distinctive features of the influence of the physical activity or inactivity in the aged SkM that would have remained undetected by transcriptomics methods. We mapped the data from tissue from SkM and blood from two groups of aged lifelong sedentary and physically active males using Circle_finder and subsequent merging and filtering, to find the number and length distribution of the unique eccDNA. Next, we used DifCir to find up-DPpGCs in the SkM of the sedentary and active groups. We assessed the functional enrichment of the DPpGCs using Disease Gene Network and Gene Set Enrichment Analysis. To find genes that produce eccDNA in a group without comparison with another group, we introduced a method to find Common PpGCs (CPpGCs) and used it to find CPpGCs in the SkM of the sedentary and active group. Finally, we found the eccDNA that carries whole genes. We discovered that the eccDNA in the SkM of the sedentary group is not statistically different from that of physically active aged men in terms of number and length distribution of eccDNA. In contrast, with DifCir we found distinctive gene-associated eccDNA fingerprints. We identified statistically significant up-DPpGCs in the two groups, with the top up-DPpGCs shed by the genes AGBL4, RNF213, DNAH7, MED13, and WWTR1 in the sedentary group, and ZBTB7C, TBCD, ITPR2, and DDX11-AS1 in the active group. The up-DPpGCs in both groups carry mostly gene fragments rather than whole genes. Though the subtle transcriptomics difference, we found RYR1 to be both transcriptionally up-regulated and up-DPpGCs gene in sedentary SkM. DifCir emphasizes the high sensitivity of the circulome compared to the transcriptome to detect the molecular fingerprints of exercise in aged SkM. It allows efficient identification of gene hotspots that excise more eccDNA in a health state or disease compared to a control condition. Full article
(This article belongs to the Special Issue Skeletal Muscle and Physical Exercise)
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<p>Experimental setup and computational analysis workflow. Isolation and purification of circular DNA from skeletal muscle (SkM) tissue (T) and blood (B) of sedentary (S) and active (A) individuals and subsequent assembly, annotation, quantification of eccDNA species, quantification of produced per gene circles (PpGCs), calculation of differentially PpGCs (DPpGCs), and identification of common PpGCs (CPpGCs) in the TS and TA groups using a democratic vote method. Functional enrichment analysis of the DPpGCs performed with GSEA and DisGeNET.</p>
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<p>Distributions of number of unique sequence and length of eccDNA in sedentary (S) and active (A) men in SkM tissue (T) and blood (B). (<b>A</b>) Number of unique eccDNAs in each sample of the S and A groups up to a size of 10<sup>4</sup> bp after merging and removal of eccDNA with less than 2 split reads. (<b>B</b>) Periodic enrichment of eccDNAs in the two groups in the size range from 0 to 10<sup>3</sup> bp. The vertical lines mark the local maxima of the more abundant lengths after smoothing. (<b>C</b>) Cumulative distribution of the lengths of the eccDNAs in the range from 0 to 10<sup>3</sup> bp. The S and A samples are depicted in blue and red, respectively. (<b>D</b>) Violin plots of the distribution of the length of the sequences of the eccDNAs in intergenic, and intron and exon genic regions. Data points are plotted with black dots, mean and median are shown as crosses and squares, respectively.</p>
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<p>Comparison of the sensitivity of circulomics and transcriptomics data from SkM to detect differences between sedentary and active individuals. (<b>A</b>) Pairwise scatter plot and (<b>B</b>) volcano plot of circulomics data from tissue sedentary (TS) and tissue active (TA). (<b>C</b>) Pairwise scatter plot and (<b>D</b>) volcano plot of RNA-seq data from TS and TA. In all plots the color bar indicates the scattering density. Darker blue color corresponds to higher scattering density. In the scatter plot the up-DPpGCs in the TA samples (ordinate) are shown with red dots, and up-DPpGCs and DEGs in the TS samples (abscissa), with green. Several gene positions are shown as orange circles. The levels are log<sub>2</sub>-scaled. The histograms visualize the eccDNA production and gene expression spectra.</p>
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<p>Heatmaps of the Differentially Up-Produced per Gene DNA Circles (up-DPpGCs) in the SkM of the sedentary lifestyle (TS) compared to the physically active (TA) group in decreasing order of significance. The color bar codifies the split read count of the eccDNA per gene in a log<sub>2</sub> scale. Higher count corresponds to a redder color. The –log<sub>10</sub> (<span class="html-italic">p</span>-value) and the absolute value of the log<sub>2</sub> of the fold change (FC) of the DPpGCs are presented in a table to the right of the heatmap. The PpGCs in the blood samples (leukocytes) from the physically active (BA) and sedentary (BS) group are presented in the heatmap for comparison.</p>
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<p>Heatmaps of the up-DPpGCs in the SkM of the physically active (TA) compared to the sedentary lifestyle (TS) group in decreasing order of significance. The color bar codifies the split read count of the eccDNA per gene in a log<sub>2</sub> scale. Higher count corresponds to a redder color. The –log<sub>10</sub> (<span class="html-italic">p</span>-value) and the absolute value of the log<sub>2</sub> of the fold change (FC) of the DPpGCs are presented in a table to the right of the heatmap. The PpGCs in the blood samples (leukocytes) from the physically active (BA) and sedentary (BS) group are presented in the heatmap for comparison.</p>
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<p>Track plots of the <span class="html-italic">loci</span> of the 8 top-ranked up-DPpGCs. (<b>A</b>) TS and (<b>B</b>) TA. Each horizontal line represents the length of a gene; each red line corresponds to an active (A) sample, each blue line to a sedentary (S) sample. The black bars represent the <span class="html-italic">loci</span> of the eccDNA.</p>
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<p>Enrichment analysis of DPpGCs. Chromosomal landscaping of the functional genomic <span class="html-italic">loci</span> giving rise to statistically significant up-DPpGCs in (<b>A</b>) TS and (<b>B</b>) TA. Bar plots of the –log<sub>10</sub>(<span class="html-italic">p</span>-value) of the significantly enriched DisGeNET up-DPpGCs in (<b>C</b>) TS and (<b>D</b>) TA.</p>
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<p>Analysis of the DPpGCs identified without (none) and with scaling for gene length (MaxDivL) methods. Venn diagram of the intersection of the up-DPpGCs identified without (red) and with (green) scaling for gene length for (<b>A</b>) TS and (<b>D</b>) TA. Comparison of the ranked up-DPpGCs without (left) and with (right) scaling for gene length (<b>B</b>) TS and (<b>E</b>) TA. The blue, red and green lines connect genes with equal, descending, and ascending ranks, respectively. The black gene names correspond to genes non-common between the two scaling methods. Histogram of the rank differences between the two scaling methods (<b>C</b>) TS (<b>F</b>) TA. (<b>G</b>) Relation between the –log<sub>10</sub>(<span class="html-italic">p</span>-value) of all the up-DPpGCs in TS and TA and the length of the underlying gene. Blue dots indicate PpGCs, red dots mark DPpGCs. The red line is the regression line of the –log<sub>10</sub>(<span class="html-italic">p</span>-value) of the statistically significant up-DPpGCs in function of the respective gene lengths.</p>
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<p>Democratic method results on the detection of common PpGCs (CPpGCs) in sedentary (TS) and active (TA) SkM. Distribution of the PpGCs for (<b>A</b>) TS and (<b>B</b>) TA. The vertical red line shows the position of the maximum of the distribution. Heatmaps of the CPpGCs in (<b>C</b>) TS and (<b>D</b>) TA. The color bars codify the split read counts of the eccDNAs per gene in a log<sub>2</sub> scale. Higher count corresponds to a redder color. The number of votes of the PpGCs is presented in tables to the right of the respective heatmaps.</p>
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<p>Boolean heatmap of whole genes embedded in eccDNAs in at least two samples. Numerical ‘0’ and ‘1’ correspond to the presence and absence of full genes, respectively. N: number of samples with eccDNA carrying the whole gene; L: length of the gene in bases.</p>
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16 pages, 3321 KiB  
Article
Neuroblastoma Tumor-Associated Mesenchymal Stromal Cells Regulate the Cytolytic Functions of NK Cells
by Sabina Di Matteo, Maria Antonietta Avanzini, Gloria Pelizzo, Valeria Calcaterra, Stefania Croce, Grazia Maria Spaggiari, Charles Theuer, Gianvincenzo Zuccotti, Lorenzo Moretta, Andrea Pelosi and Bruno Azzarone
Cancers 2023, 15(1), 19; https://doi.org/10.3390/cancers15010019 - 20 Dec 2022
Cited by 15 | Viewed by 2359
Abstract
Neuroblastoma tumor-associated mesenchymal stromal cells (NB-TA-MSC) have been extensively characterized for their pro-tumorigenic properties, while their immunosuppressive potential, especially against NK cells, has not been thoroughly investigated. Herein, we study the immune-regulatory potential of six primary young and senescent NB-TA-MSC on NK cell [...] Read more.
Neuroblastoma tumor-associated mesenchymal stromal cells (NB-TA-MSC) have been extensively characterized for their pro-tumorigenic properties, while their immunosuppressive potential, especially against NK cells, has not been thoroughly investigated. Herein, we study the immune-regulatory potential of six primary young and senescent NB-TA-MSC on NK cell function. Young cells display a phenotype (CD105+/CD90+/CD73+/CD29+/CD146+) typical of MSC cells and, in addition, express high levels of immunomodulatory molecules (MHC-I, PDL-1 and PDL-2 and transcriptional-co-activator WWTR1), able to hinder NK cell activity. Notably, four of them express the neuroblastoma marker GD2, the most common target for NB immunotherapy. From a functional point of view, young NB-TA-MSC, contrary to the senescent ones, are resistant to activated NK cell-mediated lysis, but this behavior is overcome using anti-CD105 antibody TRC105 that activates antibody-dependent cell-mediated cytotoxicity. In addition, proliferating NB-TA-MSC, but not the senescent ones, after six days of co-culture, inhibit proliferation, expression of activating receptors and cytolytic activity of freshly isolated NK. Inhibitors of the soluble immunosuppressive factors L-kynurenine and prostaglandin E2 efficiently counteract this latter effect. Our data highlight the presence of phenotypically heterogeneous NB-TA-MSC displaying potent immunoregulatory properties towards NK cells, whose inhibition could be mandatory to improve the antitumor efficacy of targeted immunotherapy. Full article
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<p>Evaluation of the expression of immunomodulatory molecules in primary NB-TA-MSC cultures. (<b>A</b>) Flow cytometry analysis of the indicated surface markers (PD-L1, PD-L2, CD47 and GD2) in primary NB-TA-MSC cultures. Light grey histograms represent unstained control; dark grey histograms represent stained samples. A representative experiment is shown of <span class="html-italic">n</span> = 5 experiments performed. (<b>B</b>) RT-qPCR analysis of TAZ transcript in different primary NB-TA-MSC cultures. The NB commercial cell line SK-N-AS was used as a reference control since these cells display a mesenchymal phenotype and high TAZ expression [<a href="#B27-cancers-15-00019" class="html-bibr">27</a>]. Histograms represent the fold change of gene transcript expression normalized for GAPDH expression compared to SK-N-AS expression, whose level is arbitrarily set as 1. Data are expressed as mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>Staining with the senescence-associated beta-galactosidase (SA-β-gal) assay of young/proliferative and growth-arrested NB-TA-MSC. (<b>A</b>) Representative images of young/proliferating NB-TA-MSC and (<b>B</b>) Growth-arrested NB-TA-MSC stained with SA-β-gal at the indicated culture passages. The senescent cells appear stained in blue. Images represent 10× with phase contrast optical microscope of <span class="html-italic">n</span> = 3 independent experiments. Yellow arrows indicate rare β-gal+ cells in young proliferating NB-TA-MSC cells.</p>
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<p>Susceptibility of primary NB-TA-MSC cultures to aNK cell-mediated lysis. (<b>A</b>) Allogeneic aNK cells were used as effector cells against CMFDA-labelled young/proliferating and (<b>B</b>) Senescent NB-TA-MSC-PGE primary cultures. Target cells were labelled with CMFDA, and allogeneic aNK cells were used as effectors at different E:T ratios as indicated. CMFDA-labelled K-562 cells were used as a positive control of lysis (K-562). An anti-CD105 IgG mAb, that induced ADCC (TRC 105) or irrelevant IgG mAb as a control (No Ab) was used. Data were expressed as mean ± SD (<span class="html-italic">n</span> = 4) of the percentage of cell lysis (CMFDA+ and PI+ cells). * <span class="html-italic">p</span> &lt; 0.05 No Ab vs. K-562.</p>
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<p>Evaluation of NK cell cytotoxic activity after co-culture with primary NB-TA-MSC. (<b>A</b>) Schematic representation of cells seeded in co-culture experiments. Freshly isolated NK cells were cultured in the upper chamber for 6 days with NB-TA-MSC cells in the lower chamber (Only Transwell) or a setting with NK cells in the upper chamber (NK Transwell) and NB-TA-MSC with NK cells in the lower chamber (Contact). NK cultured alone were used as the control (CTRL). (<b>B</b>) NK cell cytotoxicity assays against CMFDA-labelled K-562 cells after co-culture with young/proliferating NB-TA-2ZC, FA, DI and BU primary cultures under direct cell–cell contact (Contact) or under Transwell conditions (Transwell and Only Transwell). NK cultured alone were used as a control (CTRL). Percentages of cell lysis (CMFDA+ and PI+ cells) were expressed as mean ±SD (<span class="html-italic">n</span> = 4). * <span class="html-italic">p</span> &lt; 0.05 Contact vs. CTRL. ^ <span class="html-italic">p</span> &lt; 0.05 Transwell vs. CTRL ° <span class="html-italic">p</span> &lt; 0.05 Only Transwell vs. CTRL.</p>
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<p>Evaluation of NK cell proliferative potential and downregulation of NK activating receptors after co-culture with primary NB-TA-MSC cultures. (<b>A</b>) Live NK cell number (PI<sup>−</sup>) after co-culture with NB-TA–MSC primary cultures. Data were expressed as mean ±SD (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05 vs. CTRL. (<b>B</b>) Flow cytometry analysis of the activating receptors present on NK cells after co-culture with the indicated young/proliferating NB-T-MSC primary cultures under direct cell–cell contact or under Transwell conditions. Data were expressed as Fold change MFI compared with CTRL ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 vs. CTRL.</p>
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<p>Effect of IDO and PGE2 inhibitors on NK cytotoxicity and proliferation under cell-cell contact conditions. (<b>A</b>) Percentage of K-562 cells lysis in cytotoxicity assays using freshly isolated NK cells after co-culture for 6 days with young/proliferating TA–MSC 2ZC, FA, DI and BU primary cultures either in the presence or in the absence of IDO and PGE2 inhibitors (Contact and Contact + inhibitors). Values are expressed as mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 vs. NK Ctrl and vs. NK Ctrl + IDO and PGE2 inhibitors. (<b>B</b>) Live NK cell number (PI<sup>−</sup>) after co-culture with NB-TA–MSC primary cultures, either in the presence or in the absence of IDO and PGE2 inhibitors (Contact and Contact + inhibitors). Starting number of seeded NK cells and NK cultured alone (CTRL) were used as controls. Data were expressed as mean ± SD (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05 vs. CTRL.</p>
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<p>Evaluation of NK cell cytotoxic activity and proliferative potential after co-culture with senescent NB-TA-MSC cultures. NK-cell cytotoxicity assays against CMFDA-labelled K-562 cells after co-culture with (<b>A</b>) Senescent NB-TA–MSC-CO, PGE (<b>B</b>) Senescent NB-TA–MSC-2ZC culture, under direct cell-cell Contact or under Transwell conditions. Percentages of lysed cells were expressed as mean ± SD (<span class="html-italic">n</span> = 3). ns = not significant. (<b>C</b>) Live NK cell number (PI<sup>−</sup>) after co-culture with senescent NB-TA–MSC cultures. Co-culture of senescent NB-TA-MSC-PGE and 2ZC with NK cells did not affect cell number compared to CTRL, while the co-culture with senescent NB-TA-MSC-CO in direct-contact condition caused a slowdown of NK cells number. Data were expressed as mean ± SD (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05 vs. CTRL.</p>
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19 pages, 2174 KiB  
Review
Role of YAP as a Mechanosensing Molecule in Stem Cells and Stem Cell-Derived Hematopoietic Cells
by Nattaya Damkham, Surapol Issaragrisil and Chanchao Lorthongpanich
Int. J. Mol. Sci. 2022, 23(23), 14634; https://doi.org/10.3390/ijms232314634 - 23 Nov 2022
Cited by 8 | Viewed by 3271
Abstract
Yes-associated protein (YAP) and WW domain-containing transcription regulator protein 1 (WWTR1, also known as TAZ) are transcriptional coactivators in the Hippo signaling pathway. Both are well-known regulators of cell proliferation and organ size control, and they have significant roles in promoting cell proliferation [...] Read more.
Yes-associated protein (YAP) and WW domain-containing transcription regulator protein 1 (WWTR1, also known as TAZ) are transcriptional coactivators in the Hippo signaling pathway. Both are well-known regulators of cell proliferation and organ size control, and they have significant roles in promoting cell proliferation and differentiation. The roles of YAP and TAZ in stem cell pluripotency and differentiation have been extensively studied. However, the upstream mediators of YAP and TAZ are not well understood. Recently, a novel role of YAP in mechanosensing and mechanotransduction has been reported. The present review updates information on the regulation of YAP by mechanical cues such as extracellular matrix stiffness, fluid shear stress, and actin cytoskeleton tension in stem cell behaviors and differentiation. The review explores mesenchymal stem cell fate decisions, pluripotent stem cells (PSCs), self-renewal, pluripotency, and differentiation to blood products. Understanding how cells sense their microenvironment or niche and mimic those microenvironments in vitro could improve the efficiency of producing stem cell products and the efficacy of the products. Full article
(This article belongs to the Special Issue Emerging Research in Cell Death and Differentiation)
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<p>Preimplantation mouse embryo development. Inner cells with high adhesive forces acquire their inner cell mass fate, a source of embryonic stem cells. The outer cells have lower adhesive forces and become trophectoderm cells.</p>
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<p>Yes-associated protein (YAP) acts as a mechanosensing molecule in mesenchymal stem cells (MSCs) fate determination.</p>
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<p>Different types of flow and strain mediate YAP/TAZ activity in different cell types (<b>a</b>,<b>b</b>). Disturbed flow increased YAP activity in endothelial cells [<a href="#B86-ijms-23-14634" class="html-bibr">86</a>,<a href="#B89-ijms-23-14634" class="html-bibr">89</a>] and blood flow induced nuclear YAP in zebrafish vessels [<a href="#B87-ijms-23-14634" class="html-bibr">87</a>]. Circumferential strain induced YAP expression in human iPSCs, and blood flow induced YAP translocated into the nucleus for HSC formation in zebrafish [<a href="#B88-ijms-23-14634" class="html-bibr">88</a>]. ICF and CCF mediated YAP expression differently in human PDL [<a href="#B83-ijms-23-14634" class="html-bibr">83</a>].</p>
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<p>Role of YAP as a mechanosensor in PSC self-renewal [<a href="#B97-ijms-23-14634" class="html-bibr">97</a>] and differentiation into neurons [<a href="#B70-ijms-23-14634" class="html-bibr">70</a>,<a href="#B97-ijms-23-14634" class="html-bibr">97</a>,<a href="#B111-ijms-23-14634" class="html-bibr">111</a>,<a href="#B114-ijms-23-14634" class="html-bibr">114</a>], endothelial cells [<a href="#B114-ijms-23-14634" class="html-bibr">114</a>], and chondrocytes [<a href="#B112-ijms-23-14634" class="html-bibr">112</a>].</p>
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<p>Role of YAP/TAZ in HSC formation and blood cell development. (<b>a</b>) Blood flow-induced YAP activity promoted HSC formation in zebrafish embryos [<a href="#B88-ijms-23-14634" class="html-bibr">88</a>]. (<b>b</b>) Different flow types; CS (circumferential stretch) vs. WSS (wall shear stress)-induced YAP activity differently to control colony-forming unit (CFU) formation in hiPSCs [<a href="#B88-ijms-23-14634" class="html-bibr">88</a>]. (<b>c</b>) ECM stiffness mediated-YAP activity in macrophage inflammatory responses [<a href="#B139-ijms-23-14634" class="html-bibr">139</a>]. (<b>d</b>) Role of YAP and TAZ in CD4<sup>+</sup> T cell differentiation and functions [<a href="#B132-ijms-23-14634" class="html-bibr">132</a>,<a href="#B133-ijms-23-14634" class="html-bibr">133</a>,<a href="#B134-ijms-23-14634" class="html-bibr">134</a>]. (<b>e</b>) Role of YAP in megakaryocyte differentiation [<a href="#B95-ijms-23-14634" class="html-bibr">95</a>]. (<b>f</b>) YAP plays a critical role during erythrocyte maturation and enucleation from HSC [<a href="#B122-ijms-23-14634" class="html-bibr">122</a>]. Forward arrows define the promotion or requirement; blunt arrows refer to the inhibition. Upward arrows and downward arrows define the up-regulation and down-regulation, respectively.</p>
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19 pages, 1277 KiB  
Article
mRNA Capture Sequencing and RT-qPCR for the Detection of Pathognomonic, Novel, and Secondary Fusion Transcripts in FFPE Tissue: A Sarcoma Showcase
by Anneleen Decock, David Creytens, Steve Lefever, Joni Van der Meulen, Jasper Anckaert, Ariane De Ganck, Jill Deleu, Bram De Wilde, Carolina Fierro, Scott Kuersten, Manuel Luypaert, Isabelle Rottiers, Gary P. Schroth, Sandra Steyaert, Katrien Vanderheyden, Eveline Vanden Eynde, Kimberly Verniers, Joke Verreth, Jo Van Dorpe and Jo Vandesompele
Int. J. Mol. Sci. 2022, 23(19), 11007; https://doi.org/10.3390/ijms231911007 - 20 Sep 2022
Cited by 5 | Viewed by 3553
Abstract
We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer [...] Read more.
We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints. Next, whole-transcriptome TruSeq RNA Exome sequencing was applied to 17 fusion gene-negative alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS) tumors, for whom fluorescence in situ hybridization (FISH) did not identify the classical pathognomonic rearrangements. For six patients, a pathognomonic fusion transcript was readily detected, i.e., PAX3-FOXO1 in two ARMS patients, and EWSR1-FLI1, EWSR1-ERG, or EWSR1-NFATC2 in four URCS patients. For the 11 remaining patients, 11 newly identified fusion transcripts were confirmed by RT-qPCR, including COPS3-TOM1L2, NCOA1-DTNB, WWTR1-LINC01986, PLAA-MOB3B, AP1B1-CHEK2, and BRD4-LEUTX fusion transcripts in ARMS patients. Additionally, recurrently detected secondary fusion transcripts in patients diagnosed with EWSR1-NFATC2-positive sarcoma were confirmed (COPS4-TBC1D9, PICALM-SYTL2, SMG6-VPS53, and UBE2F-ALS2). In conclusion, this study shows that mRNA capture sequencing enhances the detection rate of pathognomonic fusions and enables the identification of novel and secondary fusion transcripts in sarcomas. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>mRNA capture sequencing and RT-qPCR for the detection of pathognomonic, novel, and secondary fusion transcripts in FFPE sarcoma tissue. Cohort I, comprising six patients with a known pathognomonic fusion, is profiled using the TruSight RNA Pan-Cancer Panel. These data validated the mRNA capture sequencing analysis workflow for the identification of fusion transcripts. Subsequently, a second cohort of sarcomas that were designated fusion gene-negative by FISH analysis was analyzed using TruSeq RNA Exome sequencing. Multiple pathognomonic, novel, and secondary fusion transcripts were picked up and confirmed by RT-qPCR.</p>
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16 pages, 840 KiB  
Review
Unraveling the Biology of Epithelioid Hemangioendothelioma, a TAZ–CAMTA1 Fusion Driven Sarcoma
by Caleb N. Seavey, Ajaybabu V. Pobbati and Brian P. Rubin
Cancers 2022, 14(12), 2980; https://doi.org/10.3390/cancers14122980 - 16 Jun 2022
Cited by 11 | Viewed by 3830
Abstract
The activities of YAP and TAZ, the end effectors of the Hippo pathway, are consistently altered in cancer, and this dysregulation drives aggressive tumor phenotypes. While the actions of these two proteins aid in tumorigenesis in the majority of cancers, the dysregulation of [...] Read more.
The activities of YAP and TAZ, the end effectors of the Hippo pathway, are consistently altered in cancer, and this dysregulation drives aggressive tumor phenotypes. While the actions of these two proteins aid in tumorigenesis in the majority of cancers, the dysregulation of these proteins is rarely sufficient for initial tumor development. Herein, we present a unique TAZ-driven cancer, epithelioid hemangioendothelioma (EHE), which harbors a WWTR1(TAZ)–CAMTA1 gene fusion in at least 90% of cases. Recent investigations have elucidated the mechanisms by which YAP/TAP-fusion oncoproteins function and drive tumorigenesis. This review presents a critical evaluation of this recent work, with a particular focus on how the oncoproteins alter the normal activity of TAZ and YAP, and, concurrently, we generate a framework for how we can target the gene fusions in patients. Since EHE represents a paradigm of YAP/TAZ dysregulation in cancer, targeted therapies for EHE may also be effective against other YAP/TAZ-dependent cancers. Full article
(This article belongs to the Special Issue Hippo Signaling Pathway in Cancers)
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<p>Schematic of the Hippo pathway. The top left represents the activation of the Hippo pathway with phosphorylated core Hippo kinases and YAP/TAZ. Phosphorylated YAP/TAZ leads to cytoplasmic retention via binding to 14-3-3 proteins and/or polyubiquitination and proteolytic degradation. Upon Hippo inactivation (top right), YAP/TAZ can migrate to the nucleus, where it can bind to its TEAD cofactors and activate transcription.</p>
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<p>Schematic of the proteins present in TAZ–CAMTA1 and YAP–TFE3 fusions, with the most common breakpoints within the proteins. TAZ and YAP are labeled above with the LATS1/2 phosphorylation sites. The markers below display the amino acid contributions of each exon, and the lines between sequences denote common fusion sites: WW: WW domain; TAD: transactivation domain; PDZ: PDZ-binding motif; CG-1: CG-1 DNA-binding domain; TIG: transcription-factor immunoglobulin domain; Ankyrin: ankyrin repeats; IQ: IQ calmodulin-binding motifs; NLS: nuclear-localization signal; SH3 BD: SH3-binding domain; bHLH: basic helix–loop–helix domain; LZ: leucine-zipper domain.</p>
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<p>Strategies for targeting the oncogenic effects of TAZ–CAMTA1. Top left demonstrates the normal oncogenic effect of TC. Strategy A demonstrates inhibition of TC via increasing the action of negative regulators leading to cytoplasmic retention and degradation. Strategy B demonstrates inhibition by protein–protein interaction to disrupt the interaction between TC and TEAD proteins. Strategy C demonstrates inhibition of the downstream targets of TC/TEAD transcription, which promote oncogenesis.</p>
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23 pages, 30736 KiB  
Article
YAP Inhibition by Verteporfin Causes Downregulation of Desmosomal Genes and Proteins Leading to the Disintegration of Intercellular Junctions
by Yunying Huang, Usama Sharif Ahmad, Ambreen Rehman, Jutamas Uttagomol and Hong Wan
Life 2022, 12(6), 792; https://doi.org/10.3390/life12060792 - 26 May 2022
Cited by 5 | Viewed by 3212
Abstract
The Hippo-YAP pathway serves as a central signalling hub in epithelial tissue generation and homeostasis. Yes-associated protein (YAP) is an essential downstream transcription cofactor of this pathway, with its activity being negatively regulated by Hippo kinase-mediated phosphorylation, leading to its cytoplasmic translocation or [...] Read more.
The Hippo-YAP pathway serves as a central signalling hub in epithelial tissue generation and homeostasis. Yes-associated protein (YAP) is an essential downstream transcription cofactor of this pathway, with its activity being negatively regulated by Hippo kinase-mediated phosphorylation, leading to its cytoplasmic translocation or degradation. Our recent study showed phospho-YAP complexes with Desmoglein-3 (Dsg3), the desmosomal cadherin known to be required for junction assembly and cell–cell adhesion. In this study, we show that YAP inhibition by Verteporfin (VP) caused a significant downregulation of desmosomal genes and a remarkable reduction in desmosomal proteins, including the Dsg3/phospho-YAP complex, resulting in attenuation of cell cohesion. We also found the desmosomal genes, along with E-cadherin, were the YAP-TEAD transcriptional targets and Dsg3 regulated key Hippo components, including WWTR1/TAZ, LATS2 and the key desmosomal molecules. Furthermore, Dsg3 and phospho-YAP exhibited coordinated regulation in response to varied cell densities and culture durations. Overexpression of Dsg3 could compensate for VP mediated loss of adhesion components and proper architecture of cell junctions. Thus, our findings suggest that Dsg3 plays a crucial role in the Hippo network and regulates junction configuration via complexing with phospho-YAP. Full article
(This article belongs to the Section Cell Biology and Tissue Engineering)
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<p>Dsg3 complexes with YAP/p-YAP, with its knockdown resulting in their significant reduction and disruption of intercellular adhesion in N/TERT cells. (<b>A</b>) Confocal super-resolution microscopy of N/TERT cells double-stained for Dsg3 and p-YAP showed their colocalisation, especially at cell borders. The enlarged dotted box for each channel is displayed on the right, respectively. (<b>B</b>) Co-immunoprecipitation (co-IP) in freshly confluent cell lysates with antibodies for p-YAP (IP: pYAP) or YAP (IP: YAP) that demonstrated Dsg3 physically interacted with the protein complexes purified with anti-p-YAP or YAP. The control lane was Beads alone, and input was lysates before IP (7~8%) (n = 2 independent experiments performed). (<b>C</b>) Western blotting for the indicated proteins in N/TERT cells pre-treated with Dsg3 specific or scrambled control siRNA for 2 days (n = 3 independent experiments). GAPDH was used as a loading control. (<b>D</b>) Densitometry for the indicated protein blots. (<b>E</b>) Image quantitation for the indicated proteins and their subcellular distribution in cells transfected with two hits of Dsg3 siRNA. A significant reduction in YAP/p-YAP was detected in cells with Dsg3 knockdown. The representative images for the indicated proteins in control and Dsg3 knockdown cells are displayed below the bar charts (n = 5 images/sample, representative of three independent experiments, Mean ± SD). (<b>F</b>) Dispase cell dissociation assay in N/TERTs with Dsg3 knockdown or control cells treated with scrambled siRNA. The siRNA pre-treated cells were pooled at confluent densities one day after siRNA transfection and were allowed to grow for 2 days before dispase treatment at 2.4 unit/mL until the epithelial cell sheets detached from the substrate, followed by mechanical stress to induce fragmentation. Images are displayed on the left and the quantitation of fragments is shown on the right (n = 4, Mean ± SD). (Student’s <span class="html-italic">t</span>-test for two-group comparison or one-way ANOVA for three group comparison, * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001). Scale bar in A, 10 µm and E, 20 µm.</p>
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<p>Coordinated regulation of Dsg3 and Hippo-YAP components in a cell density and a time-dependent manner. (<b>A</b>) Fluorescent images of N/TERT cells seeded at low, intermediate and high cell densities for 1 day before immunostaining for Dsg3 and p-YAP as well as Hippo kinase LATS1/2 showed a density-dependent increase in Dsg3 coupled with p-YAP/LATS1/2 nuclear exclusion, especially at the high cell density. (<b>B</b>) Quantification for the images shown in A. (<b>C</b>,<b>D</b>) Western blotting analysis of lysates extracted from cells grown at three different densities or in a time-course study that detected a cell density and time-dependent augmentation in both Dsg3 and p-YAP expression, with YAP levels appearing relatively stable, with the densitometry shown in C. For the time-course experiment, N/TERT cells were seeded in KSFM at low calcium (0.09 mM) before being replaced with KGM containing normal calcium concentration (1.8 mM). Cells were grown for various time frames before extraction or immunostaining for p-YAP and Dsg3 with the image quantitation shown in (<b>E</b>) and the representative images in <a href="#app1-life-12-00792" class="html-app">Figure S1</a>. A bell-shaped expression profile was detected for both p-YAP and Dsg3 (n = 5 images/coverslips, at least three experiments were performed, Mean ± SEM, <span class="html-italic">p</span> values were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001). Nuc: nucleus; Cyto: cytoplasm; Ca<sup>++</sup>: calcium ion. Scale bar, 20 µm.</p>
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<p>Coordinated regulation of Dsg3 and Hippo-YAP components in response to growing cell densities in T8 keratinocytes. (<b>A</b>) Fluorescent images of cutaneous carcinoma T8 (parental) cells seeded at low, intermediate and high cell densities for 1 day before immunostaining for Dsg3 and Hippo kinase LATS1/2 showed a density-dependent increase in Dsg3 coupled with LATS1/2 nuclear exclusion, especially at the high cell density. Image quantification for Dsg3 expression and LATS1/2 subcellular distribution was shown on the right. (<b>B</b>) Immunofluorescent staining for Dsg3, p-YAP and LATS1/2 in T8 stable lines with transduction of hDsg3.myc (D3) and the matched empty vector control line (Vect Ct) showed that the elevated Dsg3 expression was correlated with cytoplasmic translocation of p-YAP and LATS1/2, respectively. Image quantification for Dsg3 expression and p-YAP/LATS1/2 subcellular distribution was shown on the right. (n = 5 fields/coverslips, a representative from at least three experiments, Mean ± SEM, Student’s <span class="html-italic">t</span>-test or one-way ANOVA was used to determine the statistical significance for two groups or three groups comparison, respectively, * <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; 0.0001). Scale bar, 20 µm.</p>
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<p>Treatment of N/TERT keratinocytes with VP shows little or no effect on cell viability. (<b>A</b>,<b>B</b>) N/TERT cells were treated with VP at various time frames and dosages as indicated in the figure. Cells were seeded in a 24-well plate in triplicate overnight before being treated with VP at increasing concentrations, i.e., 1, 3, and 5 µg/mL for 2, 6 and 24 h before MTT assay (<b>A</b>) and Trypan blue assay (<b>B</b>). (<b>C</b>) Measurement of the ROS levels in cells treated with VP. Cells was seeded in a 96-well plate overnight before being treated with VP at various concentrations for 6 h. Then, cells were incubated with CellRox reagent (5 µm) for 30 min before brief washing with PBS followed by image acquisition with an INCA 2200 Analyzer system straightaway. Image quantitation indicated no significant increase in ROS in cells treated with VP compared to controls (n = 25 automated fields/well, Mean ± SEM).</p>
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<p>Desmosomal genes are the targets of YAP nuclear transcription activity and VP treatment causes drastic suppression of cell junctional proteins in N/TERT cells. (<b>A</b>) IMF analysis of YAP nuclear expression in cells treated with VP at different concentrations for various time points indicated a time and a dose-dependent reduction in nuclear YAP. (<b>B</b>) qPCR analysis for various cell anchoring junctional genes in N/TERTs treated in the presence and absence of VP at 2 µg/mL for 6 h (n = 4, Student’s <span class="html-italic">t</span>-test was used to determine the <span class="html-italic">p</span> values, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 and <span>$</span> <span class="html-italic">p</span> &lt; 0.0001). NS, no significance. Note that the <span class="html-italic">p</span>-value for <span class="html-italic">YAP1</span> was <span class="html-italic">p</span> = 0.08 based on the current test. (<b>C</b>) Western blotting analysis for the indicated cell junction assembly proteins showed a remarkable decrease, except for Dp and β-Catenin, in cells with treatment of VP in a dose-dependent manner, compared to the respective controls. Cells were treated with VP at various concentrations for 6 h before protein extraction. Dp: desmoplakin; E-Cad: E-cadherin; PKP1: plakophilin 1; PKP3: plakophilins 3; β-Cat: β-Catenin. (<b>D</b>) Dispase cell dissociation assay showed compromised cell–cell adhesion strength in cells treated with VP in a dose-dependent manner. N/TERT cells were seeded at confluent densities and grown for three days to allow the junctions to become established. Then, cells were treated with VP for the indicated concentrations alongside vehicle control for 6 h before dispase treatment at a 2.4 unit/mL concentration until the epithelial cell sheets detached from the substrate. This was followed by mechanical stress to induce fragmentation as displayed in the images on the right, and the quantitation of fragments in cells treated in the presence and absence of VP is shown on the left (Mean ± SEM, one-way ANOVA was used to determine the statistical significance, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Overexpression of Dsg3 can compensate VP-induced attenuation of junction protein expression and disruption of the anchoring junction architecture in T8 keratinocytes. (<b>A</b>) YAP/TAZ luciferase assay in skin-derived T8 carcinoma cell lines with transduction of hDsg3.myc (T8-D3) and matched vector control (Vect Ct) alongside parental cells (T8-P). Cells were transfected with the YAP/TAZ plasmid (8xGTIIC) for 24 h before the luciferase assay. Relatively higher luciferase activity of YAP/TAZ was detected in the T8-D3 cell line compared to controls (representative of three independent experiments, * <span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) Confocal images in Vect Ct and D3 cells that were treated with VP at 3 µg/mL for 2 and 6 h before formaldehyde fixation only without Triton and then immunostained for surface Dsg3 (5H10) and E-cadherin (HECD-1), both of which bind to the N-terminus of the extracellular domains of cadherins, as well as plakoglobin (Pg) in cells after being treated with Triton. Compensation for junction formation in both the E-cadherin and Pg staining was shown in D3 cells with overexpression of Dsg3 compared to Vect Ct cells treated with VP. Note that pronounced nuclear Pg was detected in Vect Ct with marked suppression in D3 cells (images were representative of three independent experiments). (<b>C</b>) Western blotting analysis for various cell junction assembly proteins in T8 Vect Ct and D3 lines exposed to VP at 3 µg/mL for 2 and 6 h. Again, compensation was detected for various proteins in D3 cells treated with VP. Scale bar, 20 µm.</p>
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<p>Effect of Dsg3 overexpression and VP treatment on the expression of various Hippo and junctional genes in T8 keratinocyte cell lines. qPCR data showed variations of individual genes in cells with Dsg3 overexpression and treated in the presence and absence of VP at 3 µg/mL for 6 h. Increased expression of <span class="html-italic">WWTR1</span> but a decrease in <span class="html-italic">DSC2</span>, <span class="html-italic">PKP1/3</span>, <span class="html-italic">JUP</span> and <span class="html-italic">DSP</span> were detected in D3 cells compared to the Vect Ct line. Increased expression of <span class="html-italic">WWTR1</span> and <span class="html-italic">CTNNA1</span> was also shown to be induced by VP treatment, but only <span class="html-italic">DSC2</span> and <span class="html-italic">JUP</span> displayed compensation in D3 cells with overexpression of Dsg3 relative to controls without VP exposure (n = 4, error bar: Mean ± SEM, * <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>Immunofluorescence for Dsg3 and p-YAP indicates their concurrent regulation in N/TERT cells in response to VP treatment. The epi-fluorescent microscopic images showed a clear trend of a dose and time-dependent reduction in Dsg3 and p-YAP in cells treated with VP at increasing concentrations, i.e., 1, 3, and 5 µg/mL for 2, 6 and 24 h. Image quantitation for both proteins was shown in the bar charts on the right. IMF intensities for each time point were normalised against DMSO vehicle control (n = 5 images/coverslips, data were the representative of at least three independent experiments, Mean ± SEM, two-way ANOVA was used to determine the statistical significance, * <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). Note that a concurrent sharp increase in both Dsg3 and p-YAP was shown in cells exposed to VP at the concentration of 1 µg/mL for 6 h, but in general, there was a trend of a time and dose-dependent loss of both proteins in the VP treated cells compared to DMSO controls. Scale bar, 20 µm.</p>
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<p>Immunofluorescent analysis of α-Catenin and keratin 14 also showed a dose-dependent reduction in VP treated cells. (<b>A</b>) Confocal images of N/TERTs were treated in the presence and absence of VP for 6 and 24 h, respectively, and double labelled for α-Catenin (α-Cat) and keratin 14 (K14). K14 perinuclear retraction was apparent in VP treated cells at 6 h time point compared to DMSO control. (<b>B</b>) Image quantitation for the protein staining shown in <b>A</b> (n = 5 images/coverslips, data were a representative of two independent experiments, Mean ± SEM, one-way ANOVA was used to determine statistical significance, * <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). A clear dose-dependent reduction in both proteins was shown in VP-treated cells at 6 h time point. At the 24 h, however, an increased expression of α-Catenin was detected in the VP treated cells at the concentration of 3 µg/mL but with a marked reduction at the concentration of 5 µg/mL accompanied by decreased K14. Scale bar, 20 µm.</p>
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<p>The schematic model illustrates that the desmosomal genes are the YAP transcriptional targets, and the Dsg3/p-YAP complex is required for epithelial cell junction formation. (<b>A</b>) Upon activation of YAP during the epithelial regeneration, the expression of Dsg3, along with other cell junction proteins, is induced which leads to YAP phosphorylation and cytoplasmic translocation and eventually sequestered by Dsg3 to the cell surface to facilitate junction assembly. (<b>B</b>) In contrast, YAP inhibition by Verteporfin causes suppression of desmosomal gene transcription including Dsg3 and therefore the attenuation of the Dsg3/p-YAP complex, leading to the defect in junction assembly and ultimately the disintegration of cell junctions.</p>
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15 pages, 4360 KiB  
Article
Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease
by Veronica Quarato, Salvatore D’Antona, Petronilla Battista, Roberta Zupo, Rodolfo Sardone, Isabella Castiglioni, Danilo Porro, Marco Frasca and Claudia Cava
Appl. Sci. 2022, 12(10), 5035; https://doi.org/10.3390/app12105035 - 16 May 2022
Cited by 2 | Viewed by 2857
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several [...] Read more.
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets. Full article
(This article belongs to the Special Issue Recent Advances in Bioinformatics and Health Informatics)
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<p>Computational workflow.</p>
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<p>Venn diagram analysis of differentially expressed genes: (<b>A</b>) down-regulated genes; (<b>B</b>) up-regulated genes.</p>
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<p>Hierarchical cluster dendrogram and gene module identified by weighted correlation network analysis (WGCNA). Gray genes are not included in any module.</p>
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<p>Co-expression network obtained with weighted correlation network analysis (WGCNA) and visualized with Cytoscape. Dark blue nodes correspond to the genes with a higher degree centrality. Grey colored edges indicate the gene interactions.</p>
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<p>Pathway analysis using ClueGo from 64 genes obtained with WGCNA. Nodes represent GO terms and the node size represents the term’s enrichment significance. (<b>A</b>) Clathrin coat assembly and synaptic vesicle endocytosis; (<b>B</b>) DNA damage response signal transduction by p53 class mediator; (<b>C</b>) positive regulation of transforming growth factor beta receptor signaling pathway and glutamine family amino acid catabolic process; (<b>D</b>) neurofilament cytoskeleton organization and neural nucleus development.</p>
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<p>Pathway analysis using ClueGo from 64 genes obtained with WGCNA. Nodes represent GO terms and the node size represents the term’s enrichment significance. (<b>A</b>) Clathrin coat assembly and synaptic vesicle endocytosis; (<b>B</b>) DNA damage response signal transduction by p53 class mediator; (<b>C</b>) positive regulation of transforming growth factor beta receptor signaling pathway and glutamine family amino acid catabolic process; (<b>D</b>) neurofilament cytoskeleton organization and neural nucleus development.</p>
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<p>Protein–protein interactions of 76 consensus differentially expressed genes in Alzheimer’s disease. Twelve genes identified with MCODE are highlighted.</p>
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<p>Interconnected regions using MCODE from the network obtained with STRING.</p>
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