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19 pages, 6886 KiB  
Article
GSK-3β in Dendritic Cells Exerts Opposite Functions in Regulating Cross-Priming and Memory CD8 T Cell Responses Independent of β-Catenin
by Chunmei Fu, Jie Wang, Tianle Ma, Congcong Yin, Li Zhou, Björn E. Clausen, Qing-Sheng Mi and Aimin Jiang
Vaccines 2024, 12(9), 1037; https://doi.org/10.3390/vaccines12091037 - 10 Sep 2024
Viewed by 509
Abstract
GSK-3β plays a critical role in regulating the Wnt/β-catenin signaling pathway, and manipulating GSK-3β in dendritic cells (DCs) has been shown to improve the antitumor efficacy of DC vaccines. Since the inhibition of GSK-3β leads to the activation of β-catenin, we hypothesize that [...] Read more.
GSK-3β plays a critical role in regulating the Wnt/β-catenin signaling pathway, and manipulating GSK-3β in dendritic cells (DCs) has been shown to improve the antitumor efficacy of DC vaccines. Since the inhibition of GSK-3β leads to the activation of β-catenin, we hypothesize that blocking GSK-3β in DCs negatively regulates DC-mediated CD8 T cell immunity and antitumor immunity. Using CD11c-GSK-3β−/− conditional knockout mice in which GSK-3β is genetically deleted in CD11c-expressing DCs, we surprisingly found that the deletion of GSK-3β in DCs resulted in increased antitumor immunity, which contradicted our initial expectation of reduced antitumor immunity due to the presumed upregulation of β-catenin in DCs. Indeed, we found by both Western blot and flow cytometry that the deletion of GSK-3β in DCs did not lead to augmented expression of β-catenin protein, suggesting that GSK-3β exerts its function independent of β-catenin. Supporting this notion, our single-cell RNA sequencing (scRNA-seq) analysis revealed that GSK-3β-deficient DCs exhibited distinct gene expression patterns with minimally overlapping differentially expressed genes (DEGs) compared to DCs with activated β-catenin. This suggests that the deletion of GSK-3β in DCs is unlikely to lead to upregulation of β-catenin at the transcriptional level. Consistent with enhanced antitumor immunity, we also found that CD11c-GSK-3β−/− mice exhibited significantly augmented cross-priming of antigen-specific CD8 T cells following DC-targeted vaccines. We further found that the deletion of GSK-3β in DCs completely abrogated memory CD8 T cell responses, suggesting that GSK-3β in DCs also plays a negative role in regulating the differentiation and/or maintenance of memory CD8 T cells. scRNA-seq analysis further revealed that although the deletion of GSK-3β in DCs positively regulated transcriptional programs for effector differentiation and function of primed antigen-specific CD8 T cells in CD11c-GSK-3β−/− mice during the priming phase, it resulted in significantly reduced antigen-specific memory CD8 T cells, consistent with diminished memory responses. Taken together, our data demonstrate that GSK-3β in DCs has opposite functions in regulating cross-priming and memory CD8 T cell responses, and GSK-3β exerts its functions independent of its regulation of β-catenin. These novel insights suggest that targeting GSK-3β in cancer immunotherapies must consider its dual role in CD8 T cell responses. Full article
(This article belongs to the Special Issue Vaccines Targeting Dendritic Cells)
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<p>Deletion of GSK-3β in DCs led to augmented antitumor immunity in CD11c-GSK-3β<sup>−/−</sup> mice. WT and CD11c-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 7–9) were inoculated with B16F10 melanoma cells, and tumor sizes were monitored. (<b>A</b>,<b>B</b>) CD11c-GSK-3β<sup>−/−</sup> mice exhibited reduced tumor growth compared to WT mice. Tumor sizes from the day of treatment are shown in (<b>A</b>) and tumor weights at the end of the experiment (day 20) are shown in (<b>B</b>). A linear mixed model (Lme4) was fitted to the data in (<b>A</b>), and ANOVA for the fitted linear mixed model was then performed to determine the difference between groups. Student’s <span class="html-italic">t</span>-tests were used for (<b>B</b>). *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Photo of the tumors at the day 20 after tumor inoculation. Data are representative of two experiments.</p>
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<p>GSK-3β<sup>−/−</sup> DCs exhibited different expression profiles from β-catenin<sup>active</sup> DCs by scRNA-seq. DCs sorted from spleens of WT (GSK-3β<sup>Flox/Flox</sup>) and CD11c-GSK-3β<sup>−/−</sup> mice, or from WT (β-catenin <sup>Exon3/Exon3</sup>) and CD11c-β-catenin<sup>active</sup> (CD11c-Cre β-catenin<sup>Exon3/Exon3</sup>), were subjected to scRNA-seq as described. (<b>A</b>) Uniform manifold approximation and projection (UMAP) dimensionality reduction mapping analysis of single-cell gene expression of integrated WT (GSK-3β<sup>Flox/Flox</sup>) and GSK-3β<sup>−/−</sup> DCs, and WT (β-catenin<sup>Exon3/Exon3</sup>) and β-catenin<sup>active</sup> DCs. Each dot represents one single cell. A total of 13 clusters were identified and color-coded as indicated. (<b>B</b>) Bubble plots showing the expression of key markers for pDC, cDC1, cDC2, and MoDCs cells among 13 UMAP clusters. The sizes of dots represent the percentages expressed; the color of dot represents the average expression. (<b>C</b>) Bubble plots depicting expression of top DEGs for UMAP clusters shown in (<b>A</b>). (<b>D</b>) Distribution of cells from WT/GSK-3β<sup>Flox/Flox</sup> and GSK-3β<sup>−/−</sup> (left), or WT/β-catenin<sup>Exon3/Exon3</sup> and β-catenin<sup>active</sup> DCs (right) within each of the 13 clusters as depicted in (<b>A</b>). (<b>E</b>) Venn plot showing the overlap of downregulated DEGs (left) and upregulated DEGs (right) in GSK-3β<sup>−/−</sup> DCs versus WT/GSK-3β<sup>Flox/Flox</sup> DCs (GSK-3β<sup>−/−</sup> vs. WT), and β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs (β-catenin<sup>active</sup> vs. WT). (<b>F</b>) Volcano plot visualizing expression of DEGs in GSK-3β<sup>−/−</sup> and WT/GSK-3β<sup>Flox/Flox</sup> DCs, and their expression pattern in β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs. DEGs in GSK-3β<sup>−/−</sup> DCs versus WT/GSK-3β<sup>Flox/Flox</sup> DCs are shown in volcano plot (upper), and expression of downregulated DEGs (lower left) and upregulated DEGs (lower right) in β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs are analyzed and shown in volcano plots. (<b>G</b>) GO enrichment analysis identifies top regulated biological process pathways in in GSK-3β<sup>−/−</sup> DCs vs. WT/GSK-3β<sup>Flox/Flox</sup> DCs (upper), and β-catenin<sup>active</sup> vs. WT/β-catenin<sup>Exon3/Exon3</sup> DCs (lower).</p>
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<p>Deletion of GSK-3β in DCs does not upregulate β-catenin. (<b>A</b>,<b>B</b>) GSK-3β<sup>−/−</sup> cDCs express similar levels of β-catenin to WT cDCs. WT and GSK-3β<sup>−/−</sup> splenic cDCs were isolated and subjected to Western blot. (<b>A</b>) Expression of GSK-3α/β, β-catenin, and β-actin by Western blotting is shown. One of three experiments is shown. (<b>B</b>) Statistical analysis of β-catenin expression is shown. The relative expression of β-catenin Western blot intensity relative to that of b-actin loading control was calculated, and the ratios for WT cDCs for each experiment were set at 1.0. (<b>C</b>,<b>D</b>) Deletion of GSK-3β in DCs does not upregulate β-catenin. Histogram overlay of β-catenin expression (<b>C</b>) and mean fluorescence intensity (MFI) of β-catenin expression (<b>D</b>) on gated CD11c<sup>+</sup>Bst2<sup>−</sup> cDCs are shown. Student’s <span class="html-italic">t</span>-test, and NS &gt; 0.05. Data shown are representative of at least three experiments.</p>
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<p>Deletion of GSK-3β in DCs abrogated memory CD8 T cell responses despite augmented cross-priming. (<b>A</b>,<b>B</b>) Deletion of GSK-3β in DCs led to significantly augmented cross-priming. WT and DC-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 4) were immunized with anti-DEC-205-OVA with CpG following adoptive transfer of naïve CFSE-labeled Thy1.1<sup>+</sup> OTI cells, and cross-priming was examined at day 4 after immunization. (<b>A</b>) The percentages of Thy1.1<sup>+</sup> OTI cells out of total CD8 T cells, and (<b>B</b>) the percentages of IFN-γ<sup>+</sup> cells out of total Thy1.1<sup>+</sup>CD8<sup>+</sup> OTI cells in both spleen and draining LN are shown. (<b>C</b>) CD8 memory responses were abrogated in CD11c-GSK-3β<sup>−/−</sup> mice upon recall. Immunized WT and CD11c-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 4–5) were recalled at day 21 and examined 5 days later. The percentages of Thy1.1<sup>+</sup> OTI cells out of total CD8 T cells are shown. Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001. Data shown are representative of at least two experiments.</p>
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<p>scRNA-seq of OVA-specific CD8 T cells identifies distinct populations and reveals differences between CD8 T cells primed in WT and CD11c-GSK-3β<sup>−/−</sup> mice. WT and CD11c-GSK-3β<sup>−/−</sup> mice adoptively transferred Thy1.1<sup>+</sup> OTI CD8 T cells were immunized with anti-DEC-205-OVA plus CpG. Spleen cells were harvested at day 4 or day 10 after immunization, and FACS-sorted OTI cells were subjected to scRNA-seq as described. (<b>A</b>,<b>B</b>) UMAP-dimensionality reduction mapping analysis of single-cell gene expression data of OTI cells isolated 4 or 10 days following vaccination with ant-DEC-205-OVA. Each dot represents one single cell. A total of 9 clusters were identified and color-coded as indicated. UMAP visualization of single cells from combined OTI cells (<b>A</b>), or OT1 cells from WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 and day 10 (<b>B</b>) are shown. (<b>C</b>) Bubble plots depicting expression of top DEGs for UMAP clusters shown in (<b>A</b>). (<b>D</b>) Distribution of OTI cells from either WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 or day 10 within each of the 9 clusters as depicted in (<b>A</b>). (<b>E</b>) Bubble plots showing the key signatures for CD8 T cells effector and memory phenotype. (<b>F</b>) Expression of effector markers among the UMAP clusters. Gradient expression levels are color-coded as indicated. (<b>G</b>) Violin plot depicting the module score of gene sets associated with effector on OTI cells from either WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 or day 10. *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001 (<b>H</b>) Signaling pathways that are significantly downregulated or upregulated in OTI cells primed in CD11c-GSK-3β<sup>−/−</sup> mice compared to OTI cells from WT mice at day 4 and day 10.</p>
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<p>Schematic representation of GSK-3β’s dual roles in regulating CD8 T cell responses. Inhibition of GSK-3β is generally believed to upregulate β-catenin, leading to increased IL-10 production, which suppresses cross-priming and reduces memory CD8 T cell responses. However, our studies demonstrate that genetic deletion of GSK-3β in CD11c<sup>+</sup> DCs does not result in β-catenin accumulation (activation). Instead, the deletion of GSK-3β in DCs enhances cross-priming of CD8 T cells, as indicated by an increase in effector cells and a higher effector index, based on scRNA-seq analysis. Despite this enhanced cross-priming, memory CD8 T cells are nearly abrogated in CD11c-GSK-3β<sup>−/−</sup> mice, likely due to a significant loss of both effector and memory CD8 T cell populations. Collectively, these findings reveal novel mechanisms by which GSK-3β exerts opposing effects on CD8 T cell responses.</p>
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18 pages, 10203 KiB  
Article
Single-Cell RNA Sequencing Reveals an Atlas of Hezuo Pig Testis Cells
by Zunqiang Yan, Pengfei Wang, Qiaoli Yang and Shuangbao Gun
Int. J. Mol. Sci. 2024, 25(18), 9786; https://doi.org/10.3390/ijms25189786 - 10 Sep 2024
Viewed by 226
Abstract
Spermatogenesis is a complex biological process crucial for male reproduction and is characterized by intricate interactions between testicular somatic cells and germ cells. Due to the cellular heterogeneity of the testes, investigating different cell types across developmental stages has been challenging. Single-cell RNA [...] Read more.
Spermatogenesis is a complex biological process crucial for male reproduction and is characterized by intricate interactions between testicular somatic cells and germ cells. Due to the cellular heterogeneity of the testes, investigating different cell types across developmental stages has been challenging. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable approach for addressing this limitation. Here, we conducted an unbiased transcriptomic study of spermatogenesis in sexually mature 4-month-old Hezuo pigs using 10× Genomics-based scRNA-seq. A total of 16,082 cells were collected from Hezuo pig testes, including germ cells (spermatogonia (SPG), spermatocytes (SPCs), spermatids (SPTs), and sperm (SP)) and somatic cells (Sertoli cells (SCs), Leydig cells (LCs), myoid cells (MCs), endothelial cells (ECs), and natural killer (NK) cells/macrophages). Pseudo-time analysis revealed that LCs and MCs originated from common progenitors in the Hezuo pig. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) in the different types of testicular germ cells were enriched in the PI3K–AKT, Wnt, HIF-1, and adherens junction signaling pathways, while the DEGs in testicular somatic cells were enriched in ECM–receptor interaction and antigen processing and presentation. Moreover, genes related to spermatogenesis, male gamete generation, sperm part, sperm flagellum, and peptide biosynthesis were expressed throughout spermatogenesis. Using immunohistochemistry, we verified several stage-specific marker genes (such as UCHL1, WT1, SOX9, and ACTA2) for SPG, SCs, and MCs. By exploring the changes in the transcription patterns of various cell types during spermatogenesis, our study provided novel insights into spermatogenesis and testicular cells in the Hezuo pig, thereby laying the foundation for the breeding and preservation of this breed. Full article
(This article belongs to the Section Molecular Biology)
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<p>Schedule of the scRNA-seq analysis of testicular cells of the Hezuo pig. (<b>A</b>) A schematic diagram of the experimental workflow for the scRNA-seq analysis of testicular cells. First, testicular samples were obtained from a healthy, sexually mature male Hezuo pig via surgical methods by a veterinarian. Secondly, the testis were cut into small fragments and cryopreserved in liquid nitrogen. Thirdly, following thawing, the testis fragments were subjected to enzymatic dissociation followed by scRNA-seq. Finally, cell cluster definition, differentially expressed gene (DEG) and cell marker identification, and functional enrichment analysis were conducted based on the sequencing data. Additionally, testicular cell markers were validated by immunohistochemistry on testicular sections. (<b>B</b>) Histological observation of testicular sections. spermatogonia (SPG), spermatocytes (SPCs), spermatids (SPTs), sperm (SP), Sertoli cells (SCs), Leydig cells (LCs). Left: ×100 magnification, scale bar = 100 μm; right: ×400 magnification, scale bar = 50 μm.</p>
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<p>Data quality. (<b>A</b>) A map of effective cell identification. The abscissa is the number of barcode sequences, and the ordinate is the number of unique molecular identifiers (UMIs). The green line of the barcode corresponds to the effective cell count, and the gray line represents the background noise. (<b>B</b>) t-distributed stochastic neighbor embedding (t-SNE) plot showing the cellular distribution. Each point in the figure represents a cell. (<b>C</b>) The map of sequencing saturation. (<b>D</b>) The median genes per cell as a function of downsampled sequencing depth in mean reads per cell, up to the observed sequencing depth. (<b>E</b>) The distribution of the number of genes detected. (<b>F</b>) The distribution of the total number of unique molecular identifiers (UMIs) detected. (<b>G</b>) The percentage distribution of mitochondrial genes expressed in individual cells.</p>
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<p>Single-cell transcriptome profiling and cluster identification in testicular cells. (<b>A</b>) t-distributed stochastic neighbor embedding (t-SNE) and (<b>B</b>) uniform manifold approximation and projection (UMAP) plots showing the 10× Genomics profile of unselected spermatogenic cells. Cell clusters are distinguished by color. (<b>C</b>) Classification stacking diagram showing the number of cells in each of the 14 clusters. (<b>D</b>) Histogram showing the proportion of cells in each of the 14 clusters. (<b>E</b>) t-SNE and (<b>F</b>) UMAP plots showing the transcript expression levels based on unique molecular identifiers (UMIs).</p>
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<p>Identification of differentially expressed genes (DEGs). (<b>A</b>) The number of DEGs in each cluster. (<b>B</b>) A heatmap of the top 50 DEGs among the clusters. In the upper panel, the cell clusters are differentiated by color. The colors from red to blue represent the expression level from high to low, respectively. (<b>C</b>–<b>E</b>) Violin plots illustrating the expression patterns of three selected DEGs in each cell cluster.</p>
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<p>Cell type identification in testicular tissue. (<b>A</b>) t-distributed stochastic neighbor embedding (t-SNE) plot of the results of cell type identification. (<b>B</b>–<b>O</b>) Violin plots of different cell type-specific gene expression patterns across different clusters. (<b>P</b>,<b>Q</b>) t-SNE plots of selected marker gene expression across all clusters.</p>
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<p>Pseudo-time analysis of myoid cells (MCs) and Leydig cells (LCs). Pseudo-time information (<b>A</b>) and differentiation status information (<b>B</b>) for the pseudo-time trajectory of clusters 9 and 11 predicted a common progenitor for the myoid and Leydig lineages. Pseudo-time information represents the developmental period, with the smaller the pseudo-time value, the earlier the developmental period. Different colors represent different differentiation states.</p>
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<p>Functional enrichment analysis of SCs and LCs. The top 20 GO terms associated with the DEGs in SCs (<b>A</b>) and LCs (<b>B</b>). The top 20 KEGG pathways related to the DEGs in SCs (<b>C</b>) and LCs (<b>D</b>).</p>
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<p>GO term enrichment analysis of the DEGs in (<b>A</b>) SPG, (<b>B</b>) SPCs, (<b>C</b>) SPTs, and (<b>D</b>) SP.</p>
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<p>Localization of the protein products of several marker genes using section immunofluorescence staining. The distribution of UCHL1 (<b>A</b>), WT1 (<b>B</b>), SOX9 (<b>C</b>), ACTA2 (<b>D</b>), and PCNA (<b>E</b>) expression in Hezuo pig testicular cells. As a control (<b>F</b>), IgG was used instead of the primary antibody. Scale bar = 50 μm.</p>
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10 pages, 3158 KiB  
Article
Role of Pulsed Electromagnetic Field on Alveolar Bone Remodeling during Orthodontic Retention Phase in Rat Models
by Hafiedz Maulana, Yuyun Yueniwati, Nur Permatasari and Hadi Suyono
Dent. J. 2024, 12(9), 287; https://doi.org/10.3390/dj12090287 - 9 Sep 2024
Viewed by 262
Abstract
Alveolar bone remodeling during the retention phase is essential for successful orthodontic treatment. Pulsed electromagnetic field (PEMF) therapy is an adjunctive therapy for bone-related diseases that induces osteogenesis and prevents bone loss. This study aimed to examine the role of PEMF exposure during [...] Read more.
Alveolar bone remodeling during the retention phase is essential for successful orthodontic treatment. Pulsed electromagnetic field (PEMF) therapy is an adjunctive therapy for bone-related diseases that induces osteogenesis and prevents bone loss. This study aimed to examine the role of PEMF exposure during the retention phase of orthodontic treatment in alveolar bone remodeling. A total of 36 male Wistar rats were divided into control, PEMF 7, and PEMF 14 groups; a 50 g force nickel–titanium closed-coil spring was inserted to create mesial movement in the first molar for 21 d. Furthermore, the spring was removed, and the interdental space was filled with glass ionomer cement. Concurrently, rats were exposed to a PEMF at 15 Hz with a maximum intensity of 2.0 mT 2 h daily, for 7 and 14 days. Afterwards, the cements were removed and the rats were euthanized on days 1, 3, 7, and 14 to evaluate the expression of Wnt5a mRNA and the levels of RANKL, OPG, ALP, and Runx2 on the tension side. The data were analyzed with ANOVA and post hoc tests, with p < 0.05 declared statistically significant. PEMF exposure significantly upregulated Wnt5a mRNA expression, OPG and ALP levels, and Runx2 expression, and decreased RANKL levels in the PEMF 7 and 14 groups compared to the control group (p < 0.05). This study showed that PEMF exposure promotes alveolar bone remodeling during the orthodontic retention phase on the tension side by increasing alveolar bone formation and inhibiting resorption. Full article
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<p>Research design. (<b>A</b>) PEMF stimulation phases, (<b>B</b>) orthodontic appliance installation, (<b>C</b>) post orthodontic tooth movement, (<b>D</b>) absorption of GCF sample with paper points, and (<b>E</b>) sampling region (white arrow) for RT-PCR. PEMF: pulsed electromagnetic field, GCF: gingival crevicular fluid, RT-PCR: real-time polymerase chain reaction.</p>
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<p>PEMF stimulator. (<b>A</b>) The PEMF device and rats were kept in a special fiber cage, placed between a Helmholtz coil and exposed 2 h/day. (<b>B</b>) The waveform was square with a burst width of 5 ms, burst wait of 60 ms, pulse width of 0.2 ms, pulse wait of 0.02 ms, pulse rise of 0.3 μs, and pulse fall of 2.0 μs.</p>
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<p>The histogram of Wnt5a mRNA expression. *: <span class="html-italic">p</span> &lt; 0.05, significant compared with control group. PEMF: pulsed electromagnetic field.</p>
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<p>The histogram of RANKL, OPG, and ALP levels. *: <span class="html-italic">p</span> &lt; 0.05, significant compared with control group; #: <span class="html-italic">p</span> &lt; 0.05, significant compared with PEMF 7 group. PEMF: pulsed electromagnetic field, RANKL: receptor activator of nuclear factor-kappa B ligand, OPG: osteoprotegerin, ALP: alkaline phosphatase.</p>
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<p>Histogram and immunohistochemical image of Runx2 expression. Runx2 positive-osteoblast (black arrow) and the direction of tooth movement (blue arrow). *: <span class="html-italic">p</span> &lt; 0.05, significant compared with control group; #: <span class="html-italic">p</span> &lt; 0.05, significant compared with PEMF 7 group. PEMF: pulsed electromagnetic field, T: tooth, PDL: periodontal ligament, AB: alveolar bone, Runx2: runt-related transcription factor 2.</p>
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17 pages, 3441 KiB  
Article
Functional Insights in PLS3-Mediated Osteogenic Regulation
by Wenchao Zhong, Janine Neugebauer, Janak L. Pathak, Xingyang Li, Gerard Pals, M. Carola Zillikens, Elisabeth M. W. Eekhoff, Nathalie Bravenboer, Qingbin Zhang, Matthias Hammerschmidt, Brunhilde Wirth and Dimitra Micha
Cells 2024, 13(17), 1507; https://doi.org/10.3390/cells13171507 - 9 Sep 2024
Viewed by 313
Abstract
Plastin-3 (PLS3) encodes T-plastin, an actin-bundling protein mediating the formation of actin filaments by which numerous cellular processes are regulated. Loss-of-function genetic defects in PLS3 are reported to cause X-linked osteoporosis and childhood-onset fractures. However, the molecular etiology of PLS3 remains elusive. Functional [...] Read more.
Plastin-3 (PLS3) encodes T-plastin, an actin-bundling protein mediating the formation of actin filaments by which numerous cellular processes are regulated. Loss-of-function genetic defects in PLS3 are reported to cause X-linked osteoporosis and childhood-onset fractures. However, the molecular etiology of PLS3 remains elusive. Functional compensation by actin-bundling proteins ACTN1, ACTN4, and FSCN1 was investigated in zebrafish following morpholino-mediated pls3 knockdown. Primary dermal fibroblasts from six patients with a PLS3 variant were also used to examine expression of these proteins during osteogenic differentiation. In addition, Pls3 knockdown in the murine MLO-Y4 cell line was employed to provide insights in global gene expression. Our results showed that ACTN1 and ACTN4 can rescue the skeletal deformities in zebrafish after pls3 knockdown, but this was inadequate for FSCN1. Patients’ fibroblasts showed the same osteogenic transdifferentiation ability as healthy donors. RNA-seq results showed differential expression in Wnt1, Nos1ap, and Myh3 after Pls3 knockdown in MLO-Y4 cells, which were also associated with the Wnt and Th17 cell differentiation pathways. Moreover, WNT2 was significantly increased in patient osteoblast-like cells compared to healthy donors. Altogether, our findings in different bone cell types indicate that the mechanism of PLS3-related pathology extends beyond actin-bundling proteins, implicating broader pathways of bone metabolism. Full article
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<p>Actinin-1 and Actinin-4 rescue malformation of craniofacial bone structure, body axis, and tail in 5-day-old col1a1:eGFP zebrafish with <span class="html-italic">pls3</span> morpholino (<span class="html-italic">pls3</span> MO). (<b>A</b>) Overview of different observed phenotypes of developing skeletal elements in col1α1:eGFP fish (cartilage green) with and without <span class="html-italic">pls3</span> MO knockdown. Phenotype is subdivided into three different types of severity: (<b>I</b>) normal: all structures are present and developed normally; (<b>II</b>) malformed: m, ch, and pq are shorter and partially malformed; (<b>III</b>) severely malformed: m, ch, and pq are shorter and partially malformed, some cb are absent. (m: Meckel’s cartilage; ch: ceratohyal; pq: palatoquadrate; cb: ceratobranchial). (<b>B</b>) Statistical analysis of phenotypes of control fish compared to knockdown and rescue fish. Control fish show 100% normal skull development, which is significantly reduced to 10.11% in 0.8 mM <span class="html-italic">pls3</span> MO-treated fish (<span class="html-italic">p</span> &lt; 0.0001). This effect is rescued by co-injection of 300 pg human <span class="html-italic">PLS3</span>, <span class="html-italic">ACTN1</span>, and <span class="html-italic">ACTN4</span> mRNA (61.43%, 45.90%, and 42.30% normal skull, respectively, compared to <span class="html-italic">pls3</span> MO: <span class="html-italic">p</span> &lt; 0.0001). Co-injection of <span class="html-italic">FSCN1</span> mRNA showed 22.72% normal skull development compared to <span class="html-italic">pls3</span> MO: <span class="html-italic">p</span> = 0.1105. (<b>C</b>) Analysis of body axis form with exemplary pictures for categorization. Control fish show 98.44% normal body axis, which is significantly reduced to 24.32% in 8 mM <span class="html-italic">pls3</span> MO-treated fish (<span class="html-italic">p</span> &lt; 0.0001). This effect is rescued by co-injection of 300 pg human <span class="html-italic">PLS3</span>, <span class="html-italic">ACTN1</span>, <span class="html-italic">ACTN4</span>, and <span class="html-italic">FSCN1</span> mRNA (66.28%, 52.87%, 58.47%, and 50.55% normal body axis, respectively, compared to <span class="html-italic">pls3</span> MO: <span class="html-italic">p</span> &lt; 0.0001). (<b>D</b>) Analysis of tail form with exemplary pictures for categorization. Control fish show 100% normal tail, which is significantly reduced to 52.43% in 0.8 mM <span class="html-italic">pls3</span> MO-treated fish (<span class="html-italic">p</span> &lt; 0.0001). This effect is rescued by co-injection of 300 pg human <span class="html-italic">PLS3, ACTN1</span>, and <span class="html-italic">ACTN4</span> mRNA (86.05%, 65.52%, and 72.68% normal tail, respectively, compared to <span class="html-italic">pls3</span> MO: <span class="html-italic">p</span> &lt; 0.0001, <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; 0.001). Co-injection of FSCN1 mRNA only showed 31.87% normal tail compared to <span class="html-italic">pls3</span> MO: <span class="html-italic">p</span> &lt; 0.001 (for every experimental set-up, n &gt; 50; scale bar = 100 μm).</p>
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<p>PLS3 expression in fibroblasts from healthy donors and PLS3 patients. (<b>A</b>) PLS3 is undetectable in fibroblast lysates of patients 1(P1), P2, P3, and P4 with the c.235del p.(Tyr79fs) frameshift mutation and P5 with the c.748+1G→A mutation. PLS3 expression of P6 with c.759_760insAAT insertion is stable. (<b>B</b>) Quantified Western blot results. (<b>C</b>) Relative gene expression of <span class="html-italic">PLS3</span> was measured by qPCR; GAPDH was used to normalize gene expression (error bars indicate standard deviation, ** <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>Actin-bundling protein expression in fibroblasts and osteoblast-like cells. The expression of PLS3, ACTN1, ACTN4, and FSCN1 was measured in cell lines from 5 healthy donors and 6 PLS3-variant patients on day 21. Protein expression was detected in whole-cell lysates of (<b>A</b>) primary fibroblasts (FB) and (<b>B</b>) osteoblast-like cells (OB) by Western blotting. (<b>C</b>,<b>D</b>) Quantification of Western blot results; the PLS3 expression of P6 is indicated separately. (<b>E</b>–<b>H</b>) Relative gene expression was measured by qPCR and results were normalized based on the expression of GAPDH (error bars indicate standard deviation per group, * <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>PLS3 knockdown in MLO-Y4 cells. (<b>A</b>) PLS3, ACTN1, and ACTN4 expression determined by Western blotting analysis from day 1 to 7 after the transfection. TUBA4A was used as a loading control. (<b>B</b>) <span class="html-italic">Pls3</span> mRNA expression after 24 h of transfection. <span class="html-italic">Tbp</span> was used to normalize gene expression. si-<span class="html-italic">Pls3</span>: small interfering <span class="html-italic">Pls3</span>. (Error bars indicate standard deviation, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Differential gene expression in MLO-Y4 cells following <span class="html-italic">Pls3</span> knockdown. (<b>A</b>) Expression of <span class="html-italic">Pls3</span> analyzed by RNA sequencing. Significant effect of the treatment: ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) The expression of actin-binding proteins was analyzed by RNA sequencing. (<b>C</b>) Heat map shows the expression pattern of mRNAs between the si-Con and si-<span class="html-italic">Pls3</span> groups: differentially upregulated mRNAs (total 259), and differentially downregulated mRNAs (total 368). (<b>D</b>) KEGG pathway enrichment analysis based on mRNA expression differences between the si-Con and si-Pls3 groups.</p>
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<p>Osteogenic transdifferentiation potential of PLS3 patient-variant fibroblasts. Primary fibroblasts from healthy donors and PLS3-variant patients were subjected to osteogenic transdifferentiation (OB); undifferentiated fibroblasts (FB) were grown in fibroblast medium. (<b>A</b>) Photos show representative results for stainings performed in 11 primary fibroblast cell lines and their transdifferentiated counterparts (C1–C5, P1–P6). Positive ALP staining (purple) on day 14 indicates ALP activity. (<b>B</b>) Quantification of the intensity of ALP staining. Bars indicate the mean of the staining and error bars the standard deviation. (<b>C</b>) ARS staining (red) on day 28 indicates calcium phosphate deposits. (<b>D</b>) Quantification of the intensity of ARS staining. (<b>E</b>–<b>G</b>) Relative gene expression was measured by qPCR for (<b>E</b>) <span class="html-italic">RUNX2</span>, (<b>F</b>) <span class="html-italic">ALP</span>, and (<b>G</b>) <span class="html-italic">OPN</span>. <span class="html-italic">GAPDH</span> was used to normalize gene expression (* <span class="html-italic">p</span>  &lt;  0.05, **** <span class="html-italic">p</span>  &lt;  0.0001 as measured by ANOVA).</p>
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<p>Relative gene expression of WNT pathway components in fibroblasts and osteoblast-like cells of PLS3-variant patients. Fibroblasts and osteoblast-like cells from six PLS3-variant patients and five controls. (<b>A</b>) The heat map shows the relative gene expression measured by qPCR. <span class="html-italic">GAPDH</span> was used to normalize gene expression. Relative gene expression of (<b>B</b>) <span class="html-italic">WNT1</span>, (<b>C</b>) <span class="html-italic">WNT2</span>, and (<b>D</b>) <span class="html-italic">SFRP1</span>, <span class="html-italic">FZD3</span>, <span class="html-italic">NFATC1</span>, <span class="html-italic">NFATC2</span>, and <span class="html-italic">CTNND2</span> (* <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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20 pages, 2673 KiB  
Article
Immune Cell Molecular Pharmacodynamics of Lanreotide in Relation to Treatment Response in Patients with Gastroenteropancreatic Neuroendocrine Tumors
by Sabah Alaklabi, Orla Maguire, Harsha Pattnaik, Yali Zhang, Jacky Chow, Jianmin Wang, Hans Minderman and Renuka Iyer
Cancers 2024, 16(17), 3104; https://doi.org/10.3390/cancers16173104 - 7 Sep 2024
Viewed by 439
Abstract
The CLARINET trial led to the approval of lanreotide for the treatment of patients with gastroenteropancreatic neuroendocrine tumors (NETs). It is hypothesized that lanreotide regulates proliferation, hormone synthesis, and other cellular functions via binding to somatostatin receptors (SSTR1–5) present in NETs. However, our [...] Read more.
The CLARINET trial led to the approval of lanreotide for the treatment of patients with gastroenteropancreatic neuroendocrine tumors (NETs). It is hypothesized that lanreotide regulates proliferation, hormone synthesis, and other cellular functions via binding to somatostatin receptors (SSTR1–5) present in NETs. However, our knowledge of how lanreotide affects the immune system is limited. In vitro studies have investigated functional immune response parameters with lanreotide treatment in healthy donor T cell subsets, encompassing the breadth of SSTR expression, apoptosis induction, cytokine production, and activity of transcription factor signaling pathways. In our study, we characterized in vitro immune mechanisms in healthy donor T cells in response to lanreotide. We also studied the in vivo effects by looking at differential gene expression pre- and post-lanreotide therapy in patients with NET. Immune-focused gene and protein expression profiling was performed on peripheral blood samples from 17 NET patients and correlated with clinical response. In vivo, lanreotide therapy showed reduced effects on wnt, T cell receptor (TCR), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) signaling in CD8+ T cells in responders compared to non-responders. Compared to non-responders, responders showed reduced effects on cytokine and chemokine signaling but greater effects on ubiquitination and proteasome degradation genes. Our results suggest significant lanreotide pharmacodynamic effects on immune function in vivo, which correlate with responses in NET patients. This is not evident from experimental in vitro settings. Full article
(This article belongs to the Special Issue Updates in Neuroendocrine Neoplasms)
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Figure 1
<p>SSTR expression and in vitro effects of lanreotide on healthy human T cell function and survival. (<b>A</b>) Expression of SSTR receptors 1–5 on Tc (<b>left</b> graph), Th (<b>middle</b> graph), and Treg (<b>right</b> graph) cell populations showing SSTR2 to be the most abundantly expressed SSTR in all T cell populations studied. (<b>B</b>) Effects of in vitro lanreotide exposure on T cell function and survival. Function measured as IFNg (<b>left</b> graph) and IL-2 (<b>middle</b> graph) production, and survival measured by apoptosis assay (<b>right</b> graph). (<b>C</b>) Effects of in vitro lanreotide exposure on transcription factor signaling measured by activity of NFAT (<b>left</b> graph), NFkB (<b>middle</b> graph) or ERK1/2 (<b>right</b> graph). N = 10 for all experiments.</p>
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<p>Proteomic and transcriptional analysis of in vivo lanreotide treatment in T cell subsets from NET patients. Paired samples obtained before and after treatment were analyzed for each patient. Volcano plots show a linear mixed model (LMM) analysis of Tc (<b>left</b> graphs), Th (<b>middle</b> graphs), and Treg (<b>right</b> graphs) cell populations fitted to test three different effects. The ‘Patient Effect’ (<b>upper</b> graphs) estimated if any gene was significantly different between Responder and Non-responder pre-treatment, where non-responders served as reference. The ‘Treatment effect’ (<b>middle</b> graphs) estimated if any genes were significantly changed between pre-treatment and post-treatment (regardless of response). For this analysis, the pre-treatment samples served as a reference. The ‘Interaction Effect’ estimated whether any genes were significantly changed before and after treatment in responders versus non-responders, where non-responders served as reference (lower graphs). Plots show the statistical significance (<span class="html-italic">p</span> value) versus the magnitude of change (fold change) for each of the 9 categories.</p>
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<p>Expression of genes of note from transcriptional analysis of in vivo lanreotide treatment in T cell subsets from NET patients. (<b>A</b>) Expression of TXNIP in non-responders (red bar) and responders (teal bar) in Tc (<b>left</b> graph), Th (<b>middle</b> graph), and Treg cells (<b>right</b> graph). (<b>B</b>) Most significantly altered genes between non-responders and responders pre-treatment shown for Th (<b>left</b> graph), Treg (<b>middle</b> graph), and Tc cells (<b>right</b> graph). (<b>C</b>) Expression of TXNIP in Tc cells pre- and post-treatment in non-responders and responders indicates upregulation with lanreotide treatment in responders only. (<b>D</b>) Interaction effect, i.e., significant change in gene expression between non-responders and responders following treatment shown for CCRL2 in TC cells (<b>right</b> graph).</p>
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<p>Gating strategy used in sorting T cell sub-populations. Isolating T cells for sorting followed a hierarchical gating strategy where, from left to right, upper to lower, singlets were selected based on forward scatter area against height. A lymphoid population was then selected based on forward scatter area against side scatter area. (Cells). Viable T cells were selected as CD3 positive and negative for LiveDead viability dye. CD4 negative, CD8 positive TC cells were selected. CD8 negative, CD4 positive were further gated as either CD25 negative, CD127 positive TH cells or CD127 dim/negative, CD25 positive Treg cells.</p>
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<p>T cell proportions in NET patients. The overall T cell percentages were assessed for all NET patient samples that had been sorted for NanoString analysis. The % CD3+ cells (<b>left</b> graph) is derived from the ‘cells’ population in the gating strategy. CD8+ and CD4+ cells are both derived from the CD3+ population (<b>middle</b> graphs). The Treg population is derived from the CD4+ cells (<b>right</b> graph).</p>
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16 pages, 3522 KiB  
Article
Repurposing Niclosamide to Modulate Renal RNA-Binding Protein HuR for the Treatment of Diabetic Nephropathy in db/db Mice
by Lili Zhuang, Wenjin Liu, Xiao-Qing Tsai, Connor Outtrim, Anna Tang, Zhou Wang and Yufeng Huang
Int. J. Mol. Sci. 2024, 25(17), 9651; https://doi.org/10.3390/ijms25179651 - 6 Sep 2024
Viewed by 246
Abstract
Hu antigen R (HuR) plays a key role in regulating genes critical to the pathogenesis of diabetic nephropathy (DN). This study investigates the therapeutic potential of niclosamide (NCS) as an HuR inhibitor in DN. Uninephrectomized mice were assigned to four groups: normal control; [...] Read more.
Hu antigen R (HuR) plays a key role in regulating genes critical to the pathogenesis of diabetic nephropathy (DN). This study investigates the therapeutic potential of niclosamide (NCS) as an HuR inhibitor in DN. Uninephrectomized mice were assigned to four groups: normal control; untreated db/db mice terminated at 14 and 22 weeks, respectively; and db/db mice treated with NCS (20 mg/kg daily via i.p.) from weeks 18 to 22. Increased HuR expression was observed in diabetic kidneys from db/db mice, which was mitigated by NCS treatment. Untreated db/db mice exhibited obesity, progressive hyperglycemia, albuminuria, kidney hypertrophy and glomerular mesangial matrix expansion, increased renal production of fibronectin and a-smooth muscle actin, and decreased glomerular WT-1+-podocytes and nephrin expression. NCS treatment did not affect mouse body weight, but reduced blood glucose and HbA1c levels and halted the DN progression observed in untreated db/db mice. Renal production of inflammatory and oxidative stress markers (NF-κBp65, TNF-a, MCP-1) and urine MDA levels increased during disease progression in db/db mice but were halted by NCS treatment. Additionally, the Wnt1-signaling-pathway downstream factor, Wisp1, was identified as a key downstream mediator of HuR-dependent action and found to be markedly increased in db/db mouse kidneys, which was normalized by NCS treatment. These findings suggest that inhibition of HuR with NCS is therapeutic for DN by improving hyperglycemia, renal inflammation, and oxidative stress. The reduction in renal Wisp1 expression also contributes to its renoprotective effects. This study supports the potential of repurposing HuR inhibitors as a novel therapy for DN. Full article
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Figure 1
<p>Increased renal HuR staining, and protein production were observed in the diabetic db/db mouse kidney. (<b>A</b>) Representative photomicrographs of renal immunofluorescent staining for HuR (red) at 400× magnification are shown from normal mice (NC), diabetic db/db mice at 14 weeks (db/db–14wk), diabetic db/db mice at 22 weeks (db/db–22wk) and diabetic db/db mice treated with NCS at 22 weeks (db/db + NCS–22wk). A few cells with cytoplasmic staining for HuR are indicated by arrows in the diabetic kidneys. (<b>B</b>) Representative Western blots illustrate the total cellular protein expression of HuR and ß-actin in the renal cortex tissue. (<b>C</b>) Quantification of the Western blot band density. Protein values are expressed as fold-changes relative to the normal control, which was set to unity. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS arrests the progression of albuminuria in diabetic db/db mice. Urine and urinary albumin excretion levels over 24 h (UAE/24 h) were collected and determined at the ages of 14, 18, and 22 weeks, as described in the <a href="#sec4-ijms-25-09651" class="html-sec">Section 4</a>. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS reduces glomerular hypertrophy, glomerulosclerosis, glomerular matrix protein deposition and expression in diabetic db/db mice. (<b>A</b>) The representative microscopic images illustrate PAS staining of kidney sections, which was used to detect glomerular size and extracellular matrix (ECM) deposition (stained pink). Magnification, ×400. (<b>B</b>) Representative photomicrographs of glomerular immunofluorescent staining for type IV collagen (Col-IV). Magnification, ×400. (<b>C</b>–<b>E</b>) The graphs summarize the results of average glomerular size (<b>C</b>), glomerular ECM deposition (<b>D</b>) and glomerular Col-IV staining score (<b>E</b>), quantified using image-J. (<b>F</b>) Western blots of FN, a-SMA, and ß-actin from normal mouse kidneys and diabetic kidneys of untreated and treated mice. Molecular weight is labelled on the right. (<b>G</b>,<b>H</b>) The graphs present the results of band density measurements for FN (<b>G</b>) and a-SMA (<b>H</b>) in the kidneys. The protein values are expressed relative to normal control, which was set to unity. (<b>I</b>–<b>K</b>) The graphs show the relative mRNA levels of FN (<b>I</b>), Collagen I-a1 (Col-I) (<b>J</b>), and Collagen IV-a1 (Col-IV) (<b>K</b>) in the kidneys, as determined by the real-time RT–PCR assay. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS reverses the glomerular podocyte number and nephrin expression in diabetic db/db mice. (<b>A</b>) Kidney sections from normal mice (NC), diabetic db/db mice at 14 weeks (db/db–14wk), diabetic db/db mice at 22 weeks (db/db–22wk) and diabetic db/db mice treated with NCS at 22 weeks (db/db + NCS–22wk) were stained with nephrin and WT-1-postive podocytes. Magnification, 400×. (<b>B</b>,<b>C</b>) The graphs summarize the results of glomerular nephrin staining (<b>B</b>) and WT-1<sup>+</sup> cells (<b>C</b>), quantified using image-J. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS reduces renal NF-kBp65 and Nox2 protein production in diabetic db/db mice. (<b>A</b>) Representative Western blots illustrate the protein expression of NF-kBp65, Nox2 and ß-actin in the kidney tissue from normal mice and diabetic untreated and treated mice. Molecular weight is labelled on the right. (<b>B</b>,<b>C</b>) The graphs present the results of band density measurements for NF-kBp65 (<b>B</b>) and Nox2 (<b>C</b>) in the kidneys. Protein values are expressed relative to normal control, which was set to unity. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS ameliorates renal angiopoietin (Angpt) 1 and 2 expression in diabetic db/db mice. (<b>A</b>) Representative Western blots illustrate the protein expression of Angpt1, Angpt2 and ß-actin in the kidney tissue from normal mice and diabetic untreated and treated mice. Molecular weight is labelled on the right. (<b>B</b>–<b>D</b>) The graphs present the results of band density measurements for Angpt1 (<b>B</b>), Angpt2 (<b>C</b>) and the ratio of Angpt1 to Angpt2 (<b>D</b>) in the kidneys. Protein values are expressed relative to normal control, which was set to unity. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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<p>Treatment with NCS reduces renal mRNA and protein expression of Wisp1 in diabetic db/db mice. (<b>A</b>) The graph shows the relative mRNA levels of Wisp1 in the kidneys, as determined by the real-time RT–PCR assay. (<b>B</b>) Representative Western blots illustrate the protein expression of Wisp1 and GAPDH in the kidney tissue from normal mice and diabetic untreated and treated mice. Molecular weight is labelled on the right. (<b>C</b>) The graph summarizes the results of band density measurements for Wisp1 and GAPDH in the kidneys. Protein values are expressed relative to normal control, which was set to unity. * <span class="html-italic">p</span> &lt; 0.05, vs. NC; # <span class="html-italic">p</span> &lt; 0.05, vs. db/db–14wk; £ <span class="html-italic">p</span> &lt; 0.05, vs. db/db–22wk.</p>
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22 pages, 3235 KiB  
Article
Advancing Human iPSC-Derived Cardiomyocyte Hypoxia Resistance for Cardiac Regenerative Therapies through a Systematic Assessment of In Vitro Conditioning
by Caroline A. Snyder, Kiera D. Dwyer and Kareen L. K. Coulombe
Int. J. Mol. Sci. 2024, 25(17), 9627; https://doi.org/10.3390/ijms25179627 - 5 Sep 2024
Viewed by 376
Abstract
Acute myocardial infarction (MI) is a sudden, severe cardiac ischemic event that results in the death of up to one billion cardiomyocytes (CMs) and subsequent decrease in cardiac function. Engineered cardiac tissues (ECTs) are a promising approach to deliver the necessary mass of [...] Read more.
Acute myocardial infarction (MI) is a sudden, severe cardiac ischemic event that results in the death of up to one billion cardiomyocytes (CMs) and subsequent decrease in cardiac function. Engineered cardiac tissues (ECTs) are a promising approach to deliver the necessary mass of CMs to remuscularize the heart. However, the hypoxic environment of the heart post-MI presents a critical challenge for CM engraftment. Here, we present a high-throughput, systematic study targeting several physiological features of human induced pluripotent stem cell-derived CMs (hiPSC-CMs), including metabolism, Wnt signaling, substrate, heat shock, apoptosis, and mitochondrial stabilization, to assess their efficacy in promoting ischemia resistance in hiPSC-CMs. The results of 2D experiments identify hypoxia preconditioning (HPC) and metabolic conditioning as having a significant influence on hiPSC-CM function in normoxia and hypoxia. Within 3D engineered cardiac tissues (ECTs), metabolic conditioning with maturation media (MM), featuring high fatty acid and calcium concentration, results in a 1.5-fold increase in active stress generation as compared to RPMI/B27 control ECTs in normoxic conditions. Yet, this functional improvement is lost after hypoxia treatment. Interestingly, HPC can partially rescue the function of MM-treated ECTs after hypoxia. Our systematic and iterative approach provides a strong foundation for assessing and leveraging in vitro culture conditions to enhance the hypoxia resistance, and thus the successful clinical translation, of hiPSC-CMs in cardiac regenerative therapies. Full article
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<p><b>Cellular dysfunction assessed through metabolic stress with 2 h of hypoxia is reduced in select treatment conditions.</b> MTT assay results before and after 2 h of hypoxia exposure (1% O<sub>2</sub>, 5% CO<sub>2</sub>) are displayed as absorbance (570 nm–690 nm) of 2D plated hiPSC-CMs treated with (<b>A</b>) varying culture media compositions for one week; (<b>B</b>) Wnt activation/inhibition overnight; (<b>C</b>) electrical pacing (EP) for one week; (<b>D</b>) PDMS or PCL substrate for one week; (<b>E</b>) heat shock (HS) for one hour 24 h prior to hypoxia, or hypoxia preconditioning (HPC) for 30 min 24 h prior to hypoxia; (<b>F</b>) apoptosis inhibitor (Cyclosporine A) or mitochondrial stabilizer (Mito-TEMPO) for 24 h. (<b>G</b>) Fold change in cell metabolic activity from normoxia to hypoxia shows less of an impact of hypoxia on viability (i.e., lower fold change) in select treatment groups. (<b>H</b>) Delta of fold change in cell metabolic activity is calculated as shown (right) and displayed (left) to compare across treatment conditions. <span class="html-italic">n =</span> 6–15 per group, with significance defined as * <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><b>Formation of ECTs with select conditions is comparable to standard (RPMI/B27) ECTs, with extent of 2D compaction correlating with CSA and elastic modulus.</b> (<b>A</b>) Conditions obtained from 2D screening results used for ECT testing. (<b>B</b>) Brightfield images illustrating tissue compaction from initial casting on day 0 (D0) to day 7 (D7) of in vitro culture (mold size, 3 × 9 mm). (<b>C</b>) Survival curve showing percentage of intact ECTs to assess structural survival. (<b>D</b>) Quantification of tissue compaction over 7-day culture; (<b>E</b>) Quantification of tissue cross sectional area at D7 of culture. (<b>F</b>) Elastic modulus for ECTs shows functional stiffness of tissues. <span class="html-italic">n</span> = 6–17 samples per group, with significance defined as ** <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. # represents significance between all groups except RPMI vs. FA; RPMI vs. RPMI-HPC; MM vs. MM-HPC; FA vs. RPMI-HPC; and FA vs. MM-HPC. <span>$</span> represents significance between RPMI vs. FA and FA vs. RPMI-HPC; &amp; represents significance between RPMI vs. MM; MM vs. FA; MM vs. RPMI-HPC; and RPMI-HPC vs. MM HPC. % represents significance between RPMI vs. RPMI-HPC and FA vs. RPMI-HPC. @ represents significance between RPMI vs. MM; RPMI vs. MM-HPC; MM vs. FA; and RPMI-HPC vs. MM HPC.</p>
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<p><b>MM-treated ECTs exhibit enhanced mechanical function assessed through active stress generation and kinetics.</b> (<b>A</b>) Active stress generation of normoxia ECTs from 0 to 30% stretch, measured at increments of 10% strain. (<b>B</b>) Active stress generation, (<b>C</b>) upstroke velocity (V<sub>up</sub>), and (<b>D</b>) time to 50% relaxation (T<sub>50</sub>) at 20% strain. <span class="html-italic">n</span> = 6–17 samples per group, with significance defined as * <span class="html-italic">p</span> &lt; 0.5; ** <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. Dashed box indicates 20% strain, which was the strain used to present the subsequent data.</p>
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<p><b>Hypoxia decreases ECT mechanical function assessed by stress generation across groups, with significant decrease in the MM-treated ECTs despite maintained cTnT area.</b> (<b>A</b>) Active stress generation of hypoxia ECTs from 0 to 30% stretch, measured at increments of 10%. (<b>B</b>) Active stress generation of hypoxia ECTs at 20% strain. (<b>C</b>) Comparison of active stress generation of normoxia (data reformatted from <a href="#ijms-25-09627-f003" class="html-fig">Figure 3</a>D to be used as comparison) and hypoxia ECTs at 20% strain. (<b>D</b>) Comparison of sarcomere area, measured by immunohistological staining of cTnT, normalized by number of nuclei in normoxia and hypoxia ECTs. <span class="html-italic">n</span> = 6–17 samples per group for mechanics quantification and <span class="html-italic">n</span> = 4–8 samples per group for histology quantification, with significance defined as * <span class="html-italic">p</span> &lt; 0.5; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001. Dashed box indicates 20% strain, which was the strain used to present the subsequent data. # indicates number.</p>
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<p><b>MM-treated ECTs undergo a metabolic functional shift during hypoxia which can be impacted by HPC.</b> (<b>A</b>) Colorimetric detection assay to identify L-lactate concentration, an indicator of anaerobic glucose metabolism and cell physiological stress. (<b>B</b>) L-lactate fold change from normoxia conditions, with normalization to the number of tissues and hours of culture. <span class="html-italic">n</span> = 3 per group. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p><b>Protocol for differentiation of human induced pluripotent stem cells (hiPSC) into cardiomyocytes (CMs) and subsequent culture.</b> </p>
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29 pages, 13064 KiB  
Review
Application of Nanotechnology and Phytochemicals in Anticancer Therapy
by Jin Hee Kim, Boluwatife Olamide Dareowolabi, Rekha Thiruvengadam and Eun-Yi Moon
Pharmaceutics 2024, 16(9), 1169; https://doi.org/10.3390/pharmaceutics16091169 - 5 Sep 2024
Viewed by 511
Abstract
Cancer is well recognized as a leading cause of mortality. Although surgery tends to be the primary treatment option for many solid cancers, cancer surgery is still a risk factor for metastatic diseases and recurrence. For this reason, a variety of medications has [...] Read more.
Cancer is well recognized as a leading cause of mortality. Although surgery tends to be the primary treatment option for many solid cancers, cancer surgery is still a risk factor for metastatic diseases and recurrence. For this reason, a variety of medications has been adopted for the postsurgical care of patients with cancer. However, conventional medicines have shown major challenges such as drug resistance, a high level of drug toxicity, and different drug responses, due to tumor heterogeneity. Nanotechnology-based therapeutic formulations could effectively overcome the challenges faced by conventional treatment methods. In particular, the combined use of nanomedicine with natural phytochemicals can enhance tumor targeting and increase the efficacy of anticancer agents with better solubility and bioavailability and reduced side effects. However, there is limited evidence in relation to the application of phytochemicals in cancer treatment, particularly focusing on nanotechnology. Therefore, in this review, first, we introduce the drug carriers used in advanced nanotechnology and their strengths and limitations. Second, we provide an update on well-studied nanotechnology-based anticancer therapies related to the carcinogenesis process, including signaling pathways related to transforming growth factor-β (TGF-β), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3 kinase (PI3K), Wnt, poly(ADP-ribose) polymerase (PARP), Notch, and Hedgehog (HH). Third, we introduce approved nanomedicines currently available for anticancer therapy. Fourth, we discuss the potential roles of natural phytochemicals as anticancer drugs. Fifth, we also discuss the synergistic effect of nanocarriers and phytochemicals in anticancer therapy. Full article
(This article belongs to the Special Issue Anti-Cancer Drug Delivery Systems)
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<p>Drug carriers used in advanced nanotechnology and their strengths and limitations [<a href="#B16-pharmaceutics-16-01169" class="html-bibr">16</a>,<a href="#B17-pharmaceutics-16-01169" class="html-bibr">17</a>,<a href="#B18-pharmaceutics-16-01169" class="html-bibr">18</a>,<a href="#B19-pharmaceutics-16-01169" class="html-bibr">19</a>,<a href="#B20-pharmaceutics-16-01169" class="html-bibr">20</a>,<a href="#B21-pharmaceutics-16-01169" class="html-bibr">21</a>,<a href="#B22-pharmaceutics-16-01169" class="html-bibr">22</a>,<a href="#B23-pharmaceutics-16-01169" class="html-bibr">23</a>,<a href="#B24-pharmaceutics-16-01169" class="html-bibr">24</a>,<a href="#B25-pharmaceutics-16-01169" class="html-bibr">25</a>,<a href="#B26-pharmaceutics-16-01169" class="html-bibr">26</a>,<a href="#B27-pharmaceutics-16-01169" class="html-bibr">27</a>,<a href="#B28-pharmaceutics-16-01169" class="html-bibr">28</a>,<a href="#B29-pharmaceutics-16-01169" class="html-bibr">29</a>,<a href="#B30-pharmaceutics-16-01169" class="html-bibr">30</a>,<a href="#B31-pharmaceutics-16-01169" class="html-bibr">31</a>,<a href="#B32-pharmaceutics-16-01169" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-16-01169" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-16-01169" class="html-bibr">34</a>,<a href="#B35-pharmaceutics-16-01169" class="html-bibr">35</a>,<a href="#B36-pharmaceutics-16-01169" class="html-bibr">36</a>,<a href="#B37-pharmaceutics-16-01169" class="html-bibr">37</a>,<a href="#B38-pharmaceutics-16-01169" class="html-bibr">38</a>,<a href="#B39-pharmaceutics-16-01169" class="html-bibr">39</a>,<a href="#B40-pharmaceutics-16-01169" class="html-bibr">40</a>,<a href="#B41-pharmaceutics-16-01169" class="html-bibr">41</a>,<a href="#B42-pharmaceutics-16-01169" class="html-bibr">42</a>,<a href="#B43-pharmaceutics-16-01169" class="html-bibr">43</a>,<a href="#B44-pharmaceutics-16-01169" class="html-bibr">44</a>,<a href="#B45-pharmaceutics-16-01169" class="html-bibr">45</a>,<a href="#B46-pharmaceutics-16-01169" class="html-bibr">46</a>,<a href="#B47-pharmaceutics-16-01169" class="html-bibr">47</a>]. NP, nanoparticle.</p>
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16 pages, 3183 KiB  
Article
HOTAIR Promotes the Hyperactivation of PI3K/Akt and Wnt/β-Catenin Signaling Pathways via PTEN Hypermethylation in Cervical Cancer
by Samuel Trujano-Camacho, David Cantú-de León, Eloy Pérez-Yepez, Carlos Contreras-Romero, Jossimar Coronel-Hernandez, Oliver Millan-Catalan, Mauricio Rodríguez-Dorantes, Cesar López-Camarillo, Concepción Gutiérrez-Ruiz, Nadia Jacobo-Herrera and Carlos Pérez-Plasencia
Cells 2024, 13(17), 1484; https://doi.org/10.3390/cells13171484 - 4 Sep 2024
Viewed by 375
Abstract
The mechanisms underlying the sustained activation of the PI3K/AKT and Wnt/β-catenin pathways mediated by HOTAIR in cervical cancer (CC) have not been extensively described. To address this knowledge gap in the literature, we explored the interactions between these pathways by driving HOTAIR expression [...] Read more.
The mechanisms underlying the sustained activation of the PI3K/AKT and Wnt/β-catenin pathways mediated by HOTAIR in cervical cancer (CC) have not been extensively described. To address this knowledge gap in the literature, we explored the interactions between these pathways by driving HOTAIR expression levels in HeLa cells. Our findings reveal that HOTAIR is a key regulator in sustaining the activation of both signaling pathways. Specifically, altering HOTAIR expression—either by knockdown or overexpression—significantly influenced the transcriptional activity of the PI3K/AKT and Wnt/β-catenin pathways. Additionally, we discovered that HIF1α directly induces HOTAIR transcription, which in turn leads to the epigenetic silencing of the PTEN promoter via DNMT1. This process leads to the sustained activation of both pathways, highlighting a novel regulatory axis involving HOTAIR and HIF1α in cervical cancer. Our results suggest a new model in which HOTAIR sustains reciprocal activation of the PI3K/AKT and Wnt/β-catenin pathways through the HOTAIR/HIF1α axis, thereby contributing to the oncogenic phenotype of cervical cancer. Full article
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<p>Enrichment of HOTAIR-associated transcription factors and pathways. (<b>A</b>) Enrichment of HOTAIR-associated signaling pathways from Webgestalt. (<b>B</b>) Enrichment of transcription factors associated with HOTAIR from Shinygo. (<b>C</b>) HOTAIR expression in CC cell lines in comparison with non-tumoral cell line. (<b>D</b>) Interaction probability of HOTAIR with β-catenin and HIF1α and its experimental corroboration by RIP assay. <span class="html-italic">p</span> &lt; 0.05 (*). <span class="html-italic">p</span> &lt; 0.01 (**). <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>PI3K/AKT regulates HOTAIR expression and Wnt/β-catenin activation in HeLa cell line. (<b>A</b>) IC50 and Western blot of PI3K/AKT pathway with BKM120 inhibitor. (<b>B</b>) BKM120 inhibits transcriptional activity of HIF1α, determined by measuring luciferase reporter and Glut1 and HK2 expression in HeLa cells. (<b>C</b>) HOTAIR levels detected in HeLa cells treated with anti-sense probe and BKM120 inhibitor. Bar = 30 and 10 μm. (<b>D</b>) Inhibition of HOTAIR expression with BKM inhibitor treatment. (<b>E</b>) Wnt/β-catenin transcriptional activity evaluated by TOP Flash activity and Cyclin D1, c-Myc expression with BKM120 inhibitor treatment. <span class="html-italic">p</span> &lt; 0.05 (*). <span class="html-italic">p</span> &lt; 0.01 (**). <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>HOTAIR regulates the Wnt/β-catenin and PI3K/AKT pathways in the HeLa cell line. (<b>A</b>) Expression levels of HOTAIR by different amounts of anti-sense probe. (<b>B</b>) HOTAIR overexpression in HeLa cell line. (<b>C</b>) Inhibition of HOTAIR affects transcriptional activity of HIF1α by luciferase activity, and HIF1α targets Glut1 and HK2 expression. (<b>D</b>) HOTAIR overexpression increases HIF1α transcriptional activity by luciferase activity, and HIF1α targets expression. (<b>E</b>) HOTAIR knockdown decreases Wnt/β-catenin transcriptional activity, as evaluated by TOP Flash activity and Cyclin D1, c-Myc expression. (<b>F</b>) HOTAIR overexpression increases Wnt/β-catenin transcriptional activity, as evaluated by TOP Flash activity and Cyclin D1, c-Myc. <span class="html-italic">p</span> &lt; 0.05 (*). <span class="html-italic">p</span> &lt; 0.01 (**). <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>HIF1α-mediated expression of HOTAIR in the HeLa cell line (<b>A</b>) HOTAIR promoter is enriched with Wnt response elements (WRE) and hypoxia response elements (HRE). (<b>B,C</b>) Stabilization of HIF1α by DMOG and assessment of its transcriptional activity in the HeLa cell line. (<b>D</b>) Over-activation of the transcriptional activity of Wnt/β- catenin by LiCl in HeLa cell line. (<b>E,F</b>) HOTAIR expression upon over-activation of HIF1α and Wnt/β-catenin pathway transcriptional activity by LiCl and DMOG in HeLa cell line. (<b>G</b>) HIF-1α is located in HOTAIR promoter by ChIP assay. <span class="html-italic">p</span> &lt; 0.05 (*). <span class="html-italic">p</span> &lt; 0.01 (**). <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>HOTAIR-mediated expression of PTEN. (<b>A</b>) PTEN expression levels in HeLa cell line. (<b>B</b>) PTEN expression upon HOTAIR inhibition and overexpression evaluated by qPCR and Western blot. (<b>C</b>) DNMT1 expression levels in HeLa cell line. (<b>D</b>) Interaction probability of HOTAIR with DNMT1 and its experimental corroboration by RIP assay. (<b>E</b>) 5-mC IP of PTEN promoter upon HOTAIR inhibition and overexpression. <span class="html-italic">p</span> &lt; 0.05 (*). <span class="html-italic">p</span> &lt; 0.01 (**). <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>HOTAIR-mediated over-activation of PI3K/AKT and Wnt/β-catenin signaling pathways through nuclear processes, methylation of PTEN promoter and feedback with HIF1α.</p>
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14 pages, 4547 KiB  
Article
Inhibition of KDM2/7 Promotes Notochordal Differentiation of hiPSCs
by Martha E. Diaz-Hernandez, Kimihide Murakami, Shizumasa Murata, Nazir M. Khan, Sreekala P. V. Shenoy, Katrin Henke, Hiroshi Yamada and Hicham Drissi
Cells 2024, 13(17), 1482; https://doi.org/10.3390/cells13171482 - 4 Sep 2024
Viewed by 355
Abstract
Intervertebral disc disease (IDD) is a debilitating spine condition that can be caused by intervertebral disc (IVD) damage which progresses towards IVD degeneration and dysfunction. Recently, human pluripotent stem cells (hPSCs) were recognized as a valuable resource for cell-based regenerative medicine in skeletal [...] Read more.
Intervertebral disc disease (IDD) is a debilitating spine condition that can be caused by intervertebral disc (IVD) damage which progresses towards IVD degeneration and dysfunction. Recently, human pluripotent stem cells (hPSCs) were recognized as a valuable resource for cell-based regenerative medicine in skeletal diseases. Therefore, adult somatic cells reprogrammed into human induced pluripotent stem cells (hiPSCs) represent an attractive cell source for the derivation of notochordal-like cells (NCs) as a first step towards the development of a regenerative therapy for IDD. Utilizing a differentiation method involving treatment with a four-factor cocktail targeting the BMP, FGF, retinoic acid, and Wnt signaling pathways, we differentiate CRISPR/Cas9-generated mCherry-reporter knock-in hiPSCs into notochordal-like cells. Comprehensive analysis of transcriptomic changes throughout the differentiation process identified regulation of histone methylation as a pivotal driver facilitating the differentiation of hiPSCs into notochordal-like cells. We further provide evidence that specific inhibition of histone demethylases KDM2A and KDM7A/B enhanced the lineage commitment of hiPSCs towards notochordal-like cells. Our results suggest that inhibition of KDMs could be leveraged to alter the epigenetic landscape of hiPSCs to control notochord-specific gene expression. Thus, our study highlights the importance of epigenetic regulators in stem cell-based regenerative approaches for the treatment of disc degeneration. Full article
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<p>Generation of Noto-2A-mCherry reporter human iPSC clones (hiPSCs). (<b>a</b>) Schematic representation of the targeting strategy. The top shows the genomic locus of the NOTO gene together with the CRISPR target site in exon 3. Below is the targeting vector containing exon2, a NEO cassette, exon 3 linked via 2A to mCherry, and the 3′ UTR of the NOTO gene. The two bottom rows show a schematic of the targeted locus before and after removal of the NEO cassette. (<b>b</b>) Gel image of confirmation PCRs showing multiple hiPSC clones with successful NOTO-2A-mcherry insertion; (<b>c</b>) Morphology of the Noto-2A-mCherry reporter colonies of YK27-hiPSCs in free feeder layer conditions, which did not show mCherry fluorescence under the pluripotency state.</p>
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<p>Noto-2A-mCherry iPSC clones maintain pluripotency. (<b>a</b>) RT- qPCR analysis of Noto-2A-mCherry-derived clones 14.3 and 10.1 showing expression of <span class="html-italic">NANOG, OCT4,</span> and <span class="html-italic">SOX2</span> stemness genes relative to expression in H9-MSCs. <span class="html-italic">GAPDH</span> was used as the housekeeping gene and internal control. * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01. (<b>b</b>) Immunofluorescence staining for the stemness markers SSEA-4 and OCT4 in Noto-2A-mCherry clones 14.3 and 10.1. Scale bar = 200 μm.</p>
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<p>Derivation of notochordal cells (NCs) from YK-27-Noto-2A-mCherry hiPSC clones. (<b>a</b>) Schematic representation of the timeline and treatment strategy for the derivation of NCs using four factors (LAFC: <b>L</b>DN193189, <b>A</b>GN193109, b<b>F</b>GF, and <b>C</b>HIR99021); (<b>b</b>) Representative brightfield and fluorescent images of NOTO-2A-mCherry hiPSC clones 14.3 and 10.1 after 5 days of notochordal differentiation without the addition of CHIR99021 on day 3 (LAF) and with the addition of CHIR99021 on day 3 of differentiation (LAFC). Images were taken at 20× magnification; Scale bar = 100 μm.</p>
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<p>Increased expression of notochordal marker genes in YK-27-Noto-2A-mCherry hiPSC clones during notochordal differentiation. Gene expression of early notochord markers <span class="html-italic">NOTO, SHH, FOXA2,</span> and BRACHYURY (<span class="html-italic">T</span>) in clones 14.3 (<b>left</b>) and 10.1 (<b>right</b>) during differentiation, as detected by RT-qPCR. <span class="html-italic">GAPDH</span> gene served as the internal control and data are represented as expression relative to undifferentiated hiPSCs at day 0 (D0). All data are from at least three independent experiments and represented as mean ± SD. * <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.</p>
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<p>Transcriptome analysis during notochordal differentiation. Bulk RNA sequencing was performed on days 0, 3, and 5 of notochordal differentiation of 3 different clones. Differential gene expression analysis revealed distinct transcriptomic signatures between the three groups. (<b>a</b>) Volcano plots of genes differentially expressed (expression &gt; 2-fold, false discovery rate [FDR] <span class="html-italic">p</span>-value &lt; 0.05) between the different stages of notochordal differentiation. Upregulated genes are shown as green dots, downregulated genes as red dots (<span class="html-italic">n</span>  =  3 of each clone); (<b>b</b>) Heat map showing gene expression values of the most differentially expressed genes during notochordal differentiation on days 0, 3, and day 5 from three different hiPSC clones (#10.1, #10.4, and #14.3). Expression values for each gene (row) were normalized across all samples (columns) by Z score. Color key indicates the intensity associated with normalized expression values; Green color indicates higher expression and red color indicates lower expression of genes.</p>
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<p>Gene ontology analysis of genes differentially expressed between hiPSCs and LAFC-treated cells at day 5 of notochordal differentiation. Dot plot of enriched genes categorized by biological processes. The horizontal axis represents the number of differentially expressed genes as the ratio of all genes within a GO term as “Gene Term Ratio”, while the vertical axis represents the enriched pathways. The color of the dots indicates the <span class="html-italic">p</span>-value and the size of the dots is relative to the number of differentially expressed genes within an identified biological process.</p>
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<p>Negative correlation between expression levels of <span class="html-italic">KDM7A</span> and notochordal genes. Gene expression values of <span class="html-italic">KDM7A</span> during the course of notochordal differentiation on days 0, 3, and 5. (<b>a</b>) Linear regression analysis using Pearson correlation showing negative correlation of <span class="html-italic">KDM7A</span> with <span class="html-italic">NOTO</span> during notochordal differentiation. Data are represented as RPKM values from three independent clones. Dark blue dots—day 0, yellow dots—day 3, and light blue dots—day 5 of differentiation. (<b>b</b>) Table summarizing linear regression analysis using Pearson correlation showing inverse correlation of <span class="html-italic">KDM7A</span> expression with the expression of notochordal marker genes <span class="html-italic">SHH, NOTO, FOXA2,</span> and <span class="html-italic">T</span> based on RPKM values.</p>
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<p>Inhibition of histone demethylases promotes the induction of hiPSC differentiation into notochordal-like cells. (<b>a</b>) Representative fluorescent images of NOTO-2A-mCherry-iPSCs during notochordal differentiation on day 3 of LAF or LAF + iKDM treatment and day 5 of LAFC or LAFC + iKDM treatment are shown. Scale bar 2 mm. (<b>b</b>) RT-qPCR analysis showing gene expression of <span class="html-italic">NOTO</span> in the 4 different treatment groups. <span class="html-italic">GAPDH</span> served as the internal control and data are represented as expression relative to undifferentiated hiPSCs at day 0 (D0). All data are represented as mean ± SD from four independent experiments. * <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.</p>
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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 1230
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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<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>
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<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>
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<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>
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<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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11 pages, 4240 KiB  
Article
Spatial Transcriptomics Analysis: Maternal Obesity Impairs Myogenic Cell Migration and Differentiation during Embryonic Limb Development
by Yao Gao, Md Nazmul Hossain, Liang Zhao, Jeanene Marie Deavila, Nathan C. Law, Mei-Jun Zhu, Gordon K. Murdoch and Min Du
Int. J. Mol. Sci. 2024, 25(17), 9488; https://doi.org/10.3390/ijms25179488 - 31 Aug 2024
Viewed by 363
Abstract
Limb muscle is responsible for physical activities and myogenic cell migration during embryogenesis is indispensable for limb muscle formation. Maternal obesity (MO) impairs prenatal skeletal muscle development, but the effects of MO on myogenic cell migration remain to be examined. C57BL/6 mice embryos [...] Read more.
Limb muscle is responsible for physical activities and myogenic cell migration during embryogenesis is indispensable for limb muscle formation. Maternal obesity (MO) impairs prenatal skeletal muscle development, but the effects of MO on myogenic cell migration remain to be examined. C57BL/6 mice embryos were collected at E13.5. The GeoMx DSP platform was used to customize five regions along myogenic cell migration routes (myotome, dorsal/ventral limb, limb stroma, limb tip), and data were analyzed by GeomxTools 3.6.0. A total of 2224 genes were down-regulated in the MO group. The GO enrichment analysis showed that MO inhibited migration-related biological processes. The signaling pathways guiding myogenic migration such as hepatocyte growth factor signaling, fibroblast growth factor signaling, Wnt signaling and GTPase signaling were down-regulated in the MO E13.5 limb tip. Correspondingly, the expression levels of genes involved in myogenic cell migration, such as Pax3, Gab1, Pxn, Tln2 and Arpc, were decreased in the MO group, especially in the dorsal and ventral sides of the limb. Additionally, myogenic differentiation-related genes were down-regulated in the MO limb. MO impedes myogenic cell migration and differentiation in the embryonic limb, providing an explanation for the impairment of fetal muscle development and offspring muscle function due to MO. Full article
(This article belongs to the Section Molecular Biology)
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<p>Schematic diagrams of GeoMx DSP and experiment design. (<b>a</b>) Workflow of GeoMx DSP platform. (<b>b</b>) Schematic diagram (<b>left</b>) and immunohistochemical staining (<b>right</b>) of the cross-section of E13.5 embryonic hind limb. Red fluorescence represents the MYF5 staining. ROI, region of interests.</p>
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<p>Maternal HFD feeding alters spatial transcriptomes of E13.5 embryonic limb. (<b>a</b>) UMAP plot of principal component analysis (PCA) based on treatments (CT and MO) and segments (ROIs). (<b>b</b>) Volcano plot of DEGs upregulated and downregulated in MO group compared with CT group. (<b>c</b>) The gene expression heatmap of CT and MO groups. CT, control; MO, maternal obesity; DEG, differentially expressed genes; A, myotome; B, dorsal limb; C, limb bud stroma; D, Ventral limb; E, limb tip; UMAP, Uniform Manifold Approximation and Projection.</p>
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<p>GO enrichment analysis of downregulated biological processes in MO compared with CT group. (<b>a</b>). Down-regulated myogenesis-related GO biological process terms in MO compared with CT group. (<b>b</b>). Down-regulated migration-related GO biological process terms in MO compared with CT group. CT, control; MO, maternal obesity; GO, gene ontology.</p>
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<p>Spatial transcriptome analysis of E13.5 limb tip (ROI_A). (<b>a</b>) Volcano plots of DEGs up-regulated and down-regulated in the MO embryonic limb tip compared with those in the CT group. (<b>b</b>) Down-regulated migration-related GO biological process terms in the limb tip of the MO embryo compared with the CT group. (<b>c</b>). Down-regulated myogenesis-related GO biological process terms in the limb tip of the MO embryo compared with the CT group. (<b>d</b>). Expression level of genes involved in HGF, Wnt and FGF signaling in the E13.5 limb tip. Asterisk (*) indicated a significant difference (<span class="html-italic">p</span> &lt; 0.05). CT, control; MO, maternal obesity; ROI, region-of-interests; GO, gene ontology.</p>
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<p>Maternal obesity inhibits the expression of genes involved in myogenesis in the E13.5 embryonic limb. (<b>a</b>) The expression heatmap of myogenesis-related genes between CT and MO treatments. (<b>b</b>) Comparison of gene expression among different ROIs. (<b>c</b>). The myogenic gene expression between CT and MO treatments among different ROIs. Asterisk (*) indicates significant difference (<span class="html-italic">p</span> &lt; 0.05). CT, control; MO, maternal obesity; ROI, region-of-interests; A, myotome; B, dorsal limb; C, limb bud stroma; D, Ventral limb; E, limb tip.</p>
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<p>Spatial expression of selected genes between treatments. (<b>a</b>) The expression heatmap of cell migration-related genes between CT and MO treatments. (<b>b</b>). The migration-related gene expression between CT and MO treatments at different ROIs. Asterisk (*) indicates significant difference (<span class="html-italic">p</span> &lt; 0.05). CT, control; MO, maternal obesity; ROI, region-of-interests; A, myotome; B, dorsal limb; C, limb bud stroma; D, Ventral limb; E, limb tip.</p>
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<p>Schematic diagram showing inhibition of myogenic cell migration due to maternal obesity. The limb muscle formation relies on embryonic myogenic cells migrating along the myotome to the limb axis. MO inhibited the expression of genes involved in myogenesis and migration. The genes involved in myogenesis such as <span class="html-italic">Pax7</span>, <span class="html-italic">Myf5</span>, <span class="html-italic">Myod1</span> and <span class="html-italic">Myl1/3</span> and genes involved in F-actin assembly and adhesion such as <span class="html-italic">Arpc1a</span>, <span class="html-italic">Pxn</span>, <span class="html-italic">Tln2</span> and <span class="html-italic">Rac1</span> were decreased in the MO limb, reducing the cell migratory capacity. Genes involved in migration attractive signaling such as <span class="html-italic">Wnt11</span>, <span class="html-italic">Gab1</span>, <span class="html-italic">Src</span> and <span class="html-italic">Fgf13</span> were decreased, disturbing cell migration.</p>
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19 pages, 23688 KiB  
Article
Analysis of the Long Non-Coding and Messenger RNA Expression Profiles in the Skin Tissue of Super Merino and Small-Tailed Han Sheep
by Jiaqi Fu, Xinyu Zhang, Dan Wang, Wenqing Liu, Caihong Zhang, Wei Wang, Wei Fan, Lichun Zhang and Fuliang Sun
Curr. Issues Mol. Biol. 2024, 46(9), 9588-9606; https://doi.org/10.3390/cimb46090570 - 31 Aug 2024
Viewed by 293
Abstract
Wool quality and yield are two important economic livestock traits. However, there are relatively few molecular studies on lncRNA for improving sheep wool, so these require further exploration. In this study, we examined skin tissue from the upper scapula of Super Merino (SM) [...] Read more.
Wool quality and yield are two important economic livestock traits. However, there are relatively few molecular studies on lncRNA for improving sheep wool, so these require further exploration. In this study, we examined skin tissue from the upper scapula of Super Merino (SM) and Small-Tailed Han (STH) sheep during the growing period. The apparent difference was verified via histological examination. High-throughput RNA sequencing identified differentially expressed (DE) long non-coding (lncRNAs) and messenger RNAs (mRNAs). The target gene of DE lncRNA and DE genes were enrichment analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify randomly selected DE lncRNAs and mRNAs. Finally, the DE, RAC2, WNT11, and FZD2 genes, which were enriched in the Wnt signaling pathway, were detected via immunohistochemistry. The results showed that a total of 20,888 lncRNAs and 31,579 mRNAs were identified in the skin tissues of the two sheep species. Among these, 56 lncRNAs and 616 mRNAs were differentially expressed. Through qRT-PCR, the trends in the randomly selected DE genes’ expression were confirmed to be aligned with the RNA-seq results. GO and KEGG enrichment analysis showed that DE lncRNA target genes were enriched in GO terms as represented by epidermal and skin development and keratin filature and in KEGG terms as represented by PI3K-Akt, Ras, MAPK, and Wnt signaling pathways, which were related to hair follicle growth and development. Finally, immunohistochemistry staining results indicated that RAC2, WNT11, and FZD2 were expressed in dermal papilla (DP). The lncRNAs MSTRG.9225.1 and MSTRG.98769.1 may indirectly participate in the regulation of hair follicle growth, development, and fiber traits by regulating their respective target genes, LOC114113396(KRTAP15-1), FGF1, and IGF1. In addition, MSTRG.84658.1 may regulate the Wnt signaling pathway involved in the development of sheep hair follicles by targeting RAC2. This study provides a theoretical reference for improving sheep breeding in the future and lays a foundation for further research on the effects of MSTRG.84658.1 and the target gene RAC2 on dermal papilla cells (DPC). Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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<p>A 40× magnification histological analysis of the skin tissues of Small-Tailed Han sheep and Super Merino sheep. (<b>a</b>): Cross-section of the STH tissues. (<b>a’</b>): Transverse section of the STH tissues. (<b>b</b>): Cross-section of the SM tissues. (<b>b’</b>): Transverse section of the SM tissues. “★”: Primary hair follicle. “▲”: Secondary hair follicle.</p>
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<p>(<b>a</b>) Statistical map for predicting long non-coding RNAs. The horizontal coordinate indicates four different types of lncRNAs. The ordinate is the number of each lncRNA and its percentage in the total. (<b>b</b>) Prediction method: Venn diagram. Each circle represents a method for predicting lncRNA, and the number in the circle represents the number of transcripts predicted to be positive. The intersection of the four circles is taken as the prediction result.</p>
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<p>(<b>a</b>) A comparison of the lncRNA and mRNA expression levels. The horizontal coordinate is the molecular type, and the vertical coordinate is log10 (FPKM + 1). The box chart statistics are the maximum, upper quartile, median, lower quartile, and minimum, respectively. (<b>b</b>) A comparison of the lncRNA and mRNA variable shear isomers. The horizontal coordinate is the distribution of the number of variable shear isomers per gene, and the vertical coordinate is log2 (number of gene).</p>
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<p>(<b>a</b>) Statistical diagram of the length distribution of mRNA and lncRNA. The horizontal coordinate is the length, and the vertical coordinate is the number of mRNAs or lncRNAs whose length is distributed within this range; the length unit is bp. (<b>b</b>) Statistical diagram of the number of exons corresponding to mRNAs and lncRNAs. The horizontal coordinate is the number of exons, and the vertical coordinate is the number of mRNAs or lncRNAs with exons distributed within this range. (<b>c</b>) Statistical map of ORF length corresponding to mRNAs and lncRNAs. The horizontal coordinate is the length, and the vertical coordinate is the mRNAs or the number of lncRNAs in this range.</p>
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<p>The top 20 enriched biological processes, cellular components, and molecular functions for differentially expressed lncRNA targets and mRNAs are listed. (<b>a</b>) Differentially expressed gene enrichment bar chart. (<b>b</b>) Differentially expressed lncRNA cis-target gene enrichment bar chart. (<b>c</b>) Differentially expressed lncRNA trans-target gene enrichment bar chart.</p>
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<p>The top 20 enriched biological processes, cellular components, and molecular functions for differentially expressed lncRNA targets and mRNAs are listed. (<b>a</b>) Differentially expressed gene enrichment bar chart. (<b>b</b>) Differentially expressed lncRNA cis-target gene enrichment bar chart. (<b>c</b>) Differentially expressed lncRNA trans-target gene enrichment bar chart.</p>
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<p>(<b>a</b>) The rich distribution diagram showing the differential expression of the mRNA KEGG channel. (<b>b</b>) The rich distribution diagram of the KEGG pathway of the lncRNA cis-target gene. (<b>c</b>) The rich distribution diagram showing the lncRNA trans-target gene KEGG pathway. The ordinate represents the name of the pathway, and the enrichment factor is the ratio of the proportion of differential genes that are annotated on a pathway to the proportion of all genes that are annotated to that pathway. The larger the enrichment factor, the more significant the enrichment level of differentially expressed genes or differentially expressed lncRNA cis–trans-target genes in this pathway. The color of the circle represents the qvalue, and the qvalue is the Pvalue after multiple hypothesis testing corrections. The smaller the qvalue, the more reliable the enrichment significance of differentially expressed genes in this pathway. The size of the circle indicates the number of genes enriched in the pathway, and the larger the circle, the more genes.</p>
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<p>PPI network of DE mRNAs associated with hair follicle growth and development. (<b>a</b>) Clustering heatmap of 61 potential DE mRNA expression. (<b>b</b>) Constructed PPI network based on the interaction scores between DE mRNAs. Nodes represent proteins and edges represent a cluster heatmap of DE mRNA expression in PPI networks interacting with each other. (<b>c</b>) DE mRNA expression heatmap in PPI network.</p>
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<p>The network of cis-target genes of DE lncRNAs are enriched in important GO terms and pathways that may be related to hair follicle growth and development. The diamond represents biological processes; the octagon represents cellular components; “v” stands for pathway. The circle represents the cis-target gene.</p>
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<p>The trans-target genes of DE lncRNAs are enriched in important GO terms and pathways that may be related to hair follicle growth and development. The diamond represents biological processes; the octagon represents cellular components; “v” stands for pathway. The circle represents the cis-target gene.</p>
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<p>Interaction network diagram of the differentially expressed lncRNA and cis-target genes. Triangles are the DE lncRNAs. Circles are the DE mRNAs. Red is used for genes that are highly expressed in SM, and green is used for the same in STH.</p>
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<p>Interaction network diagram of the DE lncRNA and DE trans-target genes. Triangles are the differentially expressed lncRNAs. Circles are the DE mRNAs. Genes highly expressed in SM are denoted in red, while the same in STH are indicated in green.</p>
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<p>The results of qRT-PCR were compared with those of RNA-seq. (<b>a</b>) Histogram of lncRNA and mRNA’s qRT-PCR validation results. (<b>b</b>) Histogram of lncRNA and mRNA’s RNA-seq results. The horizontal coordinate indicates the gene ID, and the vertical coordinate indicates the relative expression of the gene.</p>
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<p>The results of qRT-PCR were compared with those of RNA-seq. (<b>a</b>) Histogram of lncRNA and mRNA’s qRT-PCR validation results. (<b>b</b>) Histogram of lncRNA and mRNA’s RNA-seq results. The horizontal coordinate indicates the gene ID, and the vertical coordinate indicates the relative expression of the gene.</p>
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<p>Light image of immunohistochemical analysis of skin tissue. (<b>a</b>) Rabbit anti-RAC2 Polyclonal Antibody. (<b>b</b>) Rabbit anti-Wnt11. (<b>c</b>) Frizzled 2 Rabbit pAb. The nucleus of the hematoxylin stain is blue, and the positive expression of DAB is brownish yellow.</p>
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<p>Schematic representation of the effects of <span class="html-italic">MSTRG.84658.1</span> and <span class="html-italic">RAC2</span> on the expression of related proteins in the Wnt signaling pathway.</p>
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13 pages, 5484 KiB  
Article
Effect of High Glucose on Embryological Development of Zebrafish, Brachyodanio, Rerio through Wnt Pathway
by Ebony Thompson, Justin Hensley and Renfang Song Taylor
Int. J. Mol. Sci. 2024, 25(17), 9443; https://doi.org/10.3390/ijms25179443 - 30 Aug 2024
Viewed by 312
Abstract
Gestational diabetes mellitus (GDM) is a worldwide pregnancy complication. Gestational diabetes can significantly impact fetus development. However, the effects of high glucose on embryological development post-fertilization are yet to be researched. Danio rerio embryos are a great model for studying embryonic development. In [...] Read more.
Gestational diabetes mellitus (GDM) is a worldwide pregnancy complication. Gestational diabetes can significantly impact fetus development. However, the effects of high glucose on embryological development post-fertilization are yet to be researched. Danio rerio embryos are a great model for studying embryonic development. In this study, the effects on embryological (morphological and genetic) development were examined in the presence of a high-glucose environment that mimics the developing fetus in pregnant women with GDM. Fertilized zebrafish embryos were treated with normal media and high glucose for 5 days from 3 h post-fertilization (hpf) to 96 hpf, respectively, as control and experimental groups. Morphological changes are recorded with microscope images. Hatch rate and heart rate are compared between groups at set time points. RNA-Seq is performed to examine the gene changes in the experimental group. Glucose delayed the zebrafish embryo development by slowing the hatch rate by about 24 h. The brain, heart, and tail started showing smaller morphology in the glucose group compared to the control group at 24 hpf. Heart rate was faster in the glucose group compared to the control group on days 2 and 3 with a statistically significant difference. Among the zebrafish whole genome, the significantly changed genes were 556 upregulated genes and 1118 downregulated genes, respectively, in the high-glucose group. The metabolic and Wnt pathways are altered under high-glucose conditions. These conditions contribute to significant physiological differences that may provide insight into the functionality of post-embryological development. Full article
(This article belongs to the Section Molecular Biology)
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<p>Effect of glucose on zebrafish embryos. (<b>A</b>) The hatch rate of control and glucose groups. (<b>B</b>) The heart rate of control and glucose groups. (<b>C</b>) The morphology changes of control and glucose groups. Arrows indicate the changes and show a comparison of the two groups at different times of treatment.</p>
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<p>RNA seq data analysis with control and glucose treatment groups. (<b>A</b>) PCA analysis within treatment groups. (<b>B</b>) Volcano plots for gene changes. Upregulated genes are shown in red and downregulated genes are shown in green. (<b>C</b>) Gene differential expression in control and glucose groups. (<b>D</b>) Heatmap showing gene expression intensity. Red shows higher expression and green shows lower expression in RNA-seq levels.</p>
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<p>(A) GO enrichment pathway analysis of up- and downregulated DEGs. (<b>B</b>) The scatter plot of top 30 enriched KEGG pathways.</p>
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<p>The pathway of oxidative phosphorylation (the downregulated genes are shown in green).</p>
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<p>KEGG pathways from RNA seq data analysis (the downregulated genes are shown in green and upregulated genes are shown in red). (<b>A</b>) The glycolysis/gluconeogenesis pathway. (<b>B</b>) The Wnt signaling pathway.</p>
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<p>The schematic of experimental design on the effect of glucose on zebrafish embryos.</p>
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19 pages, 1262 KiB  
Review
Exploring the Role of CBX3 as a Potential Therapeutic Target in Lung Cancer
by Muhammad Aamir Wahab, Nunzio Del Gaudio, Biagio Gargiulo, Vincenzo Quagliariello, Nicola Maurea, Angela Nebbioso, Lucia Altucci and Mariarosaria Conte
Cancers 2024, 16(17), 3026; https://doi.org/10.3390/cancers16173026 - 30 Aug 2024
Viewed by 387
Abstract
Epigenetic changes regulate gene expression through histone modifications, chromatin remodeling, and protein translation of these modifications. The PRC1 and PRC2 complexes shape gene repression via histone modifications. Specifically, the CBX protein family aids PRC1 recruitment to chromatin, impacting the progressive multistep process driving [...] Read more.
Epigenetic changes regulate gene expression through histone modifications, chromatin remodeling, and protein translation of these modifications. The PRC1 and PRC2 complexes shape gene repression via histone modifications. Specifically, the CBX protein family aids PRC1 recruitment to chromatin, impacting the progressive multistep process driving chromatin silencing. Among family members, CBX3 is a complex protein involved in aberrant epigenetic mechanisms that drive lung cancer progression. CBX3 promotes lung tumorigenesis by interacting with key pathways such as PI3K/AKT, Ras/KRAS, Wnt/β-catenin, MAPK, Notch, and p53, leading to increased proliferation, inhibition of apoptosis, and enhanced resistance to therapy. Given our current lack of knowledge, additional research is required to uncover the intricate mechanisms underlying CBX3 activity, as well as its involvement in molecular pathways and its potential biomarker evaluation. Specifically, the dissimilar roles of CBX3 could be reexamined to gain a greater insight into lung cancer pathogenesis. This review aims to provide a clear overview of the context-related molecular profile of CBX3, which could be useful for addressing clinical challenges and developing novel targeted therapies based on personalized medicine. Full article
(This article belongs to the Special Issue The Genetic Analysis and Clinical Therapy in Lung Cancer)
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<p>(<b>A</b>). Ribbon diagram showing chromodomain of HP1 complexed with histone H3 tail containing monomethyl lysine 9. (<b>B</b>). Crystal structure of HP1α, HP1β, HP1γ chromoshadow domains (left); schematic representation of HP1 isoform proteins (right) [<a href="#B23-cancers-16-03026" class="html-bibr">23</a>]. LR = linker region; CD = chromodomain; CSD = chromoshadow domain.</p>
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<p>Cellular pathways directly or indirectly modulated by CBX3 in lung cancer.</p>
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