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Cells, Volume 12, Issue 17 (September-1 2023) – 86 articles

Cover Story (view full-size image): Obesity-induced type 2 diabetes (T2D) has become a global epidemic with negative social and economic impacts. Moreover, there is an exponential growth of obesity-related health problems, such as dyslipidemia, fatty liver, high blood pressure, coronary artery diseases, and stroke. The growing body of evidence indicates that low-grade inflammation is mediated by classically activated macrophages in the pathogenesis of obesity-induced T2D. Despite the acknowledged importance of these macrophage subsets in the pathogenesis of metabolic syndrome (MS), the molecular events that govern metabolic tissue inflammation remain misunderstood. In this study, we provide the evidence that CITED2 restrains pro-inflammatory macrophage polarization and limits diet-induced obesity and insulin resistance. View this paper
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18 pages, 7044 KiB  
Article
Slow Interstitial Fluid Flow Activates TGF-β Signaling and Drives Fibrotic Responses in Human Tenon Fibroblasts
by Cornelius Jakob Wiedenmann, Charlotte Gottwald, Kosovare Zeqiri, Janne Frömmichen, Emma Bungert, Moritz Gläser, Jeanne Ströble, Robert Lohmüller, Thomas Reinhard, Jan Lübke and Günther Schlunck
Cells 2023, 12(17), 2205; https://doi.org/10.3390/cells12172205 - 4 Sep 2023
Viewed by 1387
Abstract
Background: Fibrosis limits the success of filtering glaucoma surgery. We employed 2D and 3D in vitro models to assess the effects of fluid flow on human tenon fibroblasts (HTF). Methods: HTF were exposed to continuous or pulsatile fluid flow for 48 or 72 [...] Read more.
Background: Fibrosis limits the success of filtering glaucoma surgery. We employed 2D and 3D in vitro models to assess the effects of fluid flow on human tenon fibroblasts (HTF). Methods: HTF were exposed to continuous or pulsatile fluid flow for 48 or 72 h, at rates expected at the transscleral outflow site after filtering surgery. In the 2D model, the F-actin cytoskeleton and fibronectin 1 (FN1) were visualized by confocal immunofluorescence microscopy. In the 3D model, mRNA and whole cell lysates were extracted to analyze the expression of fibrosis-associated genes by qPCR and Western blot. The effects of a small-molecule inhibitor of the TGF-β receptor ALK5 were studied. Results: Slow, continuous fluid flow induced fibrotic responses in the 2D and 3D models. It elicited changes in cell shape, the F-actin cytoskeleton, the deposition of FN1 and activated the intracellular TGF-β signaling pathway to induce expression of fibrosis-related genes, such as CTGF, FN1 and COL1A1. ALK5-inhibition reduced this effect. Intermittent fluid flow also induced fibrotic changes, which decreased with increasing pause duration. Conclusions: Slow interstitial fluid flow is sufficient to induce fibrosis, could underlie the intractable nature of fibrosis following filtering glaucoma surgery and might be a target for antifibrotic therapy. Full article
(This article belongs to the Special Issue Profibrotic Mediators in Hypertrophic Scarring)
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Figure 1

Figure 1
<p>Experimental setup. In the custom-made 3D flow chamber (3), an inlet in the chamber lid was connected to the influx syringe (1) that was mounted on a syringe pump (not shown). Flow is generated following a hydrostatic pressure gradient from the insert (4) through the cell-populated collagen gel (6) and the semipermeable insert membrane bottom (7) into the outer chamber (3). To maintain a hydrostatic gradient, the outer chamber (3) was drained close to the bottom by an efflux syringe (5) aspirating at the same flow rate as the influx syringe. To induce flow in the 2D µ-slide, the µ-slide inlet port was connected by a perfusor line (2) to an influx syringe (1). The outlet of the µ-slide (8) was connected to a second perfusor line that led into a collection tube (9) for the medium.</p>
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<p>F-actin cytoskeleton and 3D cell reconstructions of 3D HTF cultures exposed to slow fluid flow or control conditions for 72 h. HTF-populated collagen gels were exposed to either 0.2% FBS static medium (control), 666 µL/h flow (0.2% FBS), 10% FBS static medium or 5 ng/mL TGF-β1 (static, 0.2% FBS) for 72 h respectively. (<b>A</b>): Confocal image z-stack projections of F-actin stains are presented in the upper row. Three-dimensional reconstructions (lower row) of cell shapes were generated from z-stacks of F-actin stains using Imaris software. Image acquisition and representation are identical among different conditions. Three-dimensional reconstruction of 10% FBS was only performed for one experiment. (<b>B</b>). Relative change (RC) of cell count, surface/nucleus and volume/nucleus is displayed for flow and TGF-β1 stimulation. These data were calculated from the confocal image z-stack projections of <a href="#cells-12-02205-f002" class="html-fig">Figure 2</a>A. Asterisks indicate levels of significance in Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>F-actin and FN1 at different flow rates in the 2D model. (<b>A</b>): Confocal image z-stack projections of F-actin and FN1 in HTFs exposed to different flow rates or TGF-β1 (5 ng/mL) are displayed. Cells were preincubated in µ-slides for 24 h in starvation medium (0.2% FBS), then perfused for 72 h with the respective flow rate or stimulated with TGF-β1 (5 ng/mL) in starvation medium (0.2% FBS). Image acquisition and representation settings are identical for all conditions. The figure is representative of three independent experiments. (<b>B</b>): The relative change (RC) in mean signal intensities for F-actin and FN1 in the projected confocal image stacks of three independent experiments, as displayed in <a href="#cells-12-02205-f003" class="html-fig">Figure 3</a>A, is shown. Image acquisition and representation are identical under different conditions. Asterisks indicate levels of significance in Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>F-actin and FN1 at different pulsatile flow conditions in the 2D model. (<b>A</b>): Effects of different pulsatile flows on F-actin and FN1 after 72 h were compared to continuous flow and static conditions in the 2D model. Image acquisition and representation settings are identical for all conditions. The figure is representative of three independent experiments. (<b>B</b>): The relative change in the mean signal intensity of F-actin and FN1 in the confocal images of the three independent experiments as displayed in <a href="#cells-12-02205-f004" class="html-fig">Figure 4</a>A is shown. Image acquisition and representation are identical under different conditions. Asterisks indicate levels of significance in Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Flow-induced gene expression changes in the 3D model. Relative gene expression of fibrosis-associated genes in the 3D model compared to static controls is displayed for flow conditions (666 µL/h) and TGF-β1-stimulation (5 ng/mL) (72 h respectively, <span class="html-italic">n</span> = 4). Asterisks indicate levels of significance in Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>F-actin and LTBP1 under static and flow conditions in the 2D model. Effect of continuous flow (150 µL/h, 72 h) on LTBP1 in the 2D model. Image acquisition and representation settings are identical for all conditions. The figure is representative of three independent experiments.</p>
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<p>Effects of ALK5-inhibition on the flow-induced changes. (<b>A</b>): Effect of ALK5-I. (0.1 µM) on flow-exposed HTF in the 2D model. Cells were preincubated in µ-slides for 24 h in 0.2% FBS medium, then for additional two days incubated either in static or flow conditions in the absence or presence of an ALK5-I. F-actin and FN1 were detected by IF. Image acquisition and representation settings are identical for all conditions. The result is representative of seven independent experiments. (<b>B</b>): The relative change in the mean signal intensity of F-actin and FN1 in the confocal images of the seven independent experiments as displayed in <a href="#cells-12-02205-f007" class="html-fig">Figure 7</a>A is shown, with a region of interest automatically defined by ImageJ. Image acquisition and representation settings are identical for all conditions. Asterisks indicate levels of significance in Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>): Relative gene expression of fibrosis-associated genes in the 3D model under flow (666 µL/h), static conditions with ALK5-I. and flow conditions with ALK5-I. (0.1 µM, respectively) compared to static controls (72 h, <span class="html-italic">n</span> = 4). Asterisks indicate levels of significance according to Dunnett’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, *** <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>): Western blot analysis of protein levels in whole cell lysates of the 3D model under static conditions or flow (180 µL/h) in the absence or presence of an ALK5-inhibitor (0.1 µM, 72 h, <span class="html-italic">n</span> = 3).</p>
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21 pages, 1655 KiB  
Review
Combination of Genomic Landsscape and 3D Culture Functional Assays Bridges Sarcoma Phenotype to Target and Immunotherapy
by Filomena de Nigris, Concetta Meo and Wulf Palinski
Cells 2023, 12(17), 2204; https://doi.org/10.3390/cells12172204 - 4 Sep 2023
Cited by 2 | Viewed by 1858
Abstract
Genomic-based precision medicine has not only improved tumour therapy but has also shown its weaknesses. Genomic profiling and mutation analysis have identified alterations that play a major role in sarcoma pathogenesis and evolution. However, they have not been sufficient in predicting tumour vulnerability [...] Read more.
Genomic-based precision medicine has not only improved tumour therapy but has also shown its weaknesses. Genomic profiling and mutation analysis have identified alterations that play a major role in sarcoma pathogenesis and evolution. However, they have not been sufficient in predicting tumour vulnerability and advancing treatment. The relative rarity of sarcomas and the genetic heterogeneity between subtypes also stand in the way of gaining statistically significant results from clinical trials. Personalized three-dimensional tumour models that reflect the specific histologic subtype are emerging as functional assays to test anticancer drugs, complementing genomic screening. Here, we provide an overview of current target therapy for sarcomas and discuss functional assays based on 3D models that, by recapitulating the molecular pathways and tumour microenvironment, may predict patient response to treatments. This approach opens new avenues to improve precision medicine when genomic and pathway alterations are not sufficient to guide the choice of the most promising treatment. Furthermore, we discuss the aspects of the 3D culture assays that need to be improved, such as the standardisation of growth conditions and the definition of in vitro responses that can be used as a cut-off for clinical implementation. Full article
(This article belongs to the Special Issue Genomics and Novel Targeted Treatment of Soft-Tissue Sarcoma)
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Graphical abstract

Graphical abstract
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<p>Bridging genotype to phenotype. Genomic profiles can help to identify actionable mutations and vulnerable pathways targetable with multiple drugs. Patient-derived tumour models can be used for drug screening assays providing functional data with the potential to guide the selection of specific therapies, thus improving likely patient outcomes and avoiding overtreatment and toxicities.</p>
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<p>Targeted therapies for sarcomas. The illustration shows molecular pathways promoting sarcoma oncogenesis, including cell cycle progression, DNA repair, epigenetics, tumour microenvironment, and angiogenesis. Boxed in blue is a selection of approved drugs and their respective targets.</p>
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<p>Current limitations and advantages of functional assays based on ex vivo three-dimensional tumour models.</p>
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14 pages, 9359 KiB  
Article
Contribution of Oligodendrocytes, Microglia, and Astrocytes to Myelin Debris Uptake in an Explant Model of Inflammatory Demyelination in Rats
by Mariarosaria Cammarota and Francesca Boscia
Cells 2023, 12(17), 2203; https://doi.org/10.3390/cells12172203 - 3 Sep 2023
Cited by 1 | Viewed by 2213
Abstract
The internalization and degradation of myelin in glia contributes to the resolution of neuroinflammation and influences disease progression. The identification of a three-dimensional experimental model to study myelin processing under neuroinflammation will offer a novel approach for studying treatment strategies favoring inflammation resolution [...] Read more.
The internalization and degradation of myelin in glia contributes to the resolution of neuroinflammation and influences disease progression. The identification of a three-dimensional experimental model to study myelin processing under neuroinflammation will offer a novel approach for studying treatment strategies favoring inflammation resolution and neuroprotection. Here, by using a model of neuroinflammation in hippocampal explants, we show that myelin debris accumulated immediately after insult and declined at 3 days, a time point at which tentative repair processes were observed. Olig2+ oligodendrocytes upregulated the LRP1 receptor and progressively increased MBP immunoreactivity both at peri-membrane sites and within the cytosol. Oligodendrocyte NG2+ precursors increased in number and immunoreactivity one day after insult, and moderately internalized MBP particles. Three days after insult MBP was intensely coexpressed by microglia and, to a much lesser extent, by astrocytes. The engulfment of both MBP+ debris and whole MBP+ cells contributed to the greatest microglia response. In addition to improving our understanding of the spatial-temporal contribution of glial scarring to myelin uptake under neuroinflammation, our findings suggest that the exposure of hippocampal explants to LPS + IFN-γ-induced neuroinflammation may represent a valuable demyelination model for studying both the extrinsic and intrinsic myelin processing by glia under neuroinflammation. Full article
(This article belongs to the Special Issue Glial Scar: Formation and Regeneration)
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Figure 1

Figure 1
<p>LPS + IFN-γ-induced demyelination in the CA1 region of rat hippocampal organotypic slices. (<b>A</b>) Representative confocal images of MBP immunostaining in rat hippocampal slices under control conditions and after LPS + IFN-γ-treatment for 1, 2, and 3 days. The box diagram is displayed at higher magnification in the lower panels. Scale bars: 20 μm. (<b>B</b>) Confocal images showing higher magnification of MBP<sup>+</sup> cells observed after 3 days of LPS + IFN-γ. Scale bars: 10 μm. Nuclei are stained with DAPI (blue). C-D, Quantitative analysis of myelin debris accumulation (<b>C</b>) and intact myelin segments (<b>D</b>) under control conditions and after LPS + IFN-γ treatment for 1, 2, and 3 days. Data are expressed as the mean number of myelin debris or intact segments ± SEM per unit area (mm<sup>2</sup>) (n = 3). In (<b>C</b>), * <span class="html-italic">p</span> &lt; 0.05, 1 day, 2 days, and 3 days versus control; <sup>˄</sup> <span class="html-italic">p</span> &lt; 0.05, 3 days versus 1 day and 2 days. In (<b>D</b>), * <span class="html-italic">p</span> &lt; 0.05, 1 day, 2 days, and 3 days versus control; <sup>˄</sup> <span class="html-italic">p</span> &lt; 0.05, 3 days versus 2 days.</p>
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<p>Coexpression of Olig2 with LRP1 and MBP in the CA1 region during LPS + IFN-γ-induced demyelination. (<b>A</b>) Confocal images displaying coexpression of Olig2 (green) and LRP1 (red) in explants under control conditions and after LPS + IFN-γ treatment for 1 day. Nuclei are stained with DAPI (blue). Arrows point to intensely stained Olig2<sup>+</sup>/LRP1<sup>+</sup> cells at 1 day. Scale bars: 20 μm. B, left panels, Representative confocal images showing Olig2 (green) and MBP (red) coexpression under control conditions and after LPS + IFN-γ exposure for 1, 2, and 3 days. (<b>B</b>) right panels, Higher magnification images displaying representative double-labeled Olig2<sup>+</sup>/MBP<sup>+</sup> cells observed at day 1, day 2, and day 3. Note the accumulation of MBP immunoreactivity at peri-membrane soma sites of Olig2<sup>+</sup> cells at day 2 and within the cytosol at day 3. Scale bars: 20 μm. (<b>C</b>) Quantitative analysis of double-labeled LRP1<sup>+</sup>/Olig2<sup>+</sup> cells in slices under control conditions and after LPS + IFN-γ treatment for 1 day. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, LPS + IFN-γ versus control. (<b>D</b>) Quantitative analysis of the number of double-labeled Olig2<sup>+</sup>/MBP<sup>+</sup> scored according to somatic MBP immunoreactivity at day 1, day 2, and day 3. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, peri-MBP<sup>+</sup> versus peri-MBP<sup>++</sup> and cyto-MBP at 1 day; * <span class="html-italic">p</span> &lt; 0.05, peri-MBP<sup>++</sup> versus peri-MBP<sup>+</sup> and cyto-MBP at 2 days; * <span class="html-italic">p</span> &lt; 0.05, cyto-MBP versus peri-MBP<sup>+</sup> and peri-MBP<sup>++</sup> at 3 days.</p>
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<p>Coexpression of NG2 with MBP in the CA1 region during LPS + IFN-γ-induced demyelination. (<b>A</b>) Low magnification confocal images of NG2 immunostaining in explants under control conditions and after exposure to LPS + IFN-γ for 1, 2, and 3 days, respectively. Scale bars: 200 μm (<b>B</b>) MIP images displaying NG2 (green) and MBP (red) coexpression under control conditions and after LPS + IFN-γ exposure for 1, 2, and 3 days. Nuclei are stained with DAPI (blue). White pixels display the colocalized points. The box diagram in colocalized points is shown at higher magnification. Arrows point to colocalizing cells. Scale bars: 20 μm. (<b>C</b>,<b>D</b>) Densitometric analysis of NG2 fluorescence intensity (<b>C</b>) Quantitative analysis of the number of NG2+ cells (<b>D</b>) in hippocampal cultures under control conditions and after LPS + IFN-γ exposure for 1, 2, and 3 days. Data are expressed as percentage of control. * <span class="html-italic">p</span> &lt; 0.05, one day versus control. <sup>˄</sup> <span class="html-italic">p</span> &lt; 0.05, 2 days and 3 days versus 1 day. (<b>E</b>) Quantitative analysis of NG2 colocalization with MBP immunosignal in slices under control conditions and after LPS + IFN-γ treatment for 1, 2, and 3 days. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, 1 day versus control. <sup>˄</sup> <span class="html-italic">p</span> &lt; 0.05, 3 days versus 1 day. (<b>F</b>) Quantitative analysis of the number of NG2+ cells displaying MBP droplets in slices under control conditions and after LPS + IFN-γ treatment for 1, 2, and 3 days. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, 1 day versus control.</p>
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<p>Coexpression of GFAP with MBP in the CA1 region during LPS + IFN-γ-induced demyelination. (<b>A</b>) MIP confocal images showing GFAP (green) and MBP (red) coexpression under control conditions and after LPS + IFN-γ exposure for 1, 2, and 3 days. Nuclei are stained with DAPI (blue). Colocalization is shown by white pixels. The box diagram in colocalized points is shown at higher magnification. Arrows point to colocalizing cells. Scale bars: 20 μm. (<b>B</b>) Quantitative analysis of GFAP colocalization with MBP immunosignal under control conditions and following LPS + IFN-γ treatment for 1, 2, and 3 days. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, 3 days versus all groups. (<b>C</b>) Quantitative analysis of the number of GFAP<sup>+</sup> astrocytes with internalized MBP particles after LPS + IFN-γ exposure for 1, 2, and 3 days. Data are expressed as a percentage of the total number of GFAP<sup>+</sup> cells. * <span class="html-italic">p</span> &lt; 0.05, 3 days versus all groups.</p>
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<p>Coexpression of Iba1 with MBP in the CA1 region during LPS + IFN-γ-induced demyelination. (<b>A</b>) MIP images displaying Iba1 (green) and MBP (red) coexpression under control conditions and after LPS + IFN-γ exposure for 1, 2, and 3 days. Nuclei are stained with DAPI (blue). Colocalization is shown by white pixels. The box diagram in colocalized points is shown at higher magnification. Arrows point to colocalizing cells. Scale bars: 20 μm. (<b>B</b>) Quantitative analysis of Iba1 colocalization with MBP immunosignal in slices under control conditions and following LPS + IFN-γ treatment for 1, 2, and 3 days. The values represent the mean ± S.E.M. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, 3 days versus all groups. (<b>C</b>) Quantitative analysis of the number of Iba1<sup>+</sup> microglia with internalized MBP particles after LPS + IFN-γ exposure for 1, 2, and 3 days. Data are expressed as percentage of the total number of Iba1<sup>+</sup> cells. * <span class="html-italic">p</span> &lt; 0.05, 3 days versus all groups.</p>
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26 pages, 5238 KiB  
Article
MicroRNA-375 Is Induced during Astrocyte-to-Neuron Reprogramming and Promotes Survival of Reprogrammed Neurons when Overexpressed
by Xuanyu Chen, Ivan Sokirniy, Xin Wang, Mei Jiang, Natalie Mseis-Jackson, Christine Williams, Kristopher Mayes, Na Jiang, Brendan Puls, Quansheng Du, Yang Shi and Hedong Li
Cells 2023, 12(17), 2202; https://doi.org/10.3390/cells12172202 - 3 Sep 2023
Cited by 4 | Viewed by 1903
Abstract
While astrocyte-to-neuron (AtN) reprogramming holds great promise in regenerative medicine, the molecular mechanisms that govern this unique biological process remain elusive. To understand the function of miRNAs during the AtN reprogramming process, we performed RNA-seq of both mRNAs and miRNAs on human astrocyte [...] Read more.
While astrocyte-to-neuron (AtN) reprogramming holds great promise in regenerative medicine, the molecular mechanisms that govern this unique biological process remain elusive. To understand the function of miRNAs during the AtN reprogramming process, we performed RNA-seq of both mRNAs and miRNAs on human astrocyte (HA) cultures upon NeuroD1 overexpression. Bioinformatics analyses showed that NeuroD1 not only activated essential neuronal genes to initiate the reprogramming process but also induced miRNA changes in HA. Among the upregulated miRNAs, we identified miR-375 and its targets, neuronal ELAVL genes (nELAVLs), which encode a family of RNA-binding proteins and were also upregulated by NeuroD1. We further showed that manipulating the miR-375 level regulated nELAVLs’ expression during NeuroD1-mediated reprogramming. Interestingly, miR-375/nELAVLs were also induced by the reprogramming factors Neurog2 and ASCL1 in HA, suggesting a conserved function to neuronal reprogramming, and by NeuroD1 in the mouse astrocyte culture and spinal cord. Functionally, we showed that miR-375 overexpression improved NeuroD1-mediated reprogramming efficiency by promoting cell survival at early stages in HA and did not appear to compromise the maturation of the reprogrammed neurons. Lastly, overexpression of miR-375-refractory ELAVL4 induced apoptosis and reversed the cell survival-promoting effect of miR-375 during AtN reprogramming. Together, we demonstrated a neuroprotective role of miR-375 during NeuroD1-mediated AtN reprogramming. Full article
(This article belongs to the Special Issue Advances in Neurogenesis: 2nd Edition)
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Graphical abstract

Graphical abstract
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<p>NeuroD1 activates downstream neuronal genes to initiate AtN reprogramming. (<b>a</b>) Schematic and timeline of NeuroD1 (ND1)-mediated neuronal reprogramming in HA culture. (<b>b</b>) Representative live images of morphological changes of HA transduced with GFP or ND1-GFP retroviruses at 2 (D2), 6 (D6), and 14 (D14) DPI. Scale bar, 100 μm. (<b>c</b>) qRT-PCR results showing expression levels of ND1, DCX, and NeuN in infected HA cultures at 6 DPI. (<b>d</b>) Representative staining images of ND1, DCX, MAP2, and NeuN in ND1-GFP-infected cells in HA. Scale bar, 100 μm. (<b>e</b>) Quantitative analysis of (<b>d</b>). (<b>f</b>) Principal component analyses (PCA) on top 1000 detected mRNAs from RNA-seq data. (<b>g</b>) Volcano plot analyses between indicated samples showing upregulated (URGs) and downregulated (DRGs) genes by ND1 as well as GFP control groups at 2 and 6 DPI. (<b>h</b>) Ratios of URGs and DRGs from (<b>g</b>). (<b>i</b>) Gene ontology (GO) analysis of URGs and DRGs at different time points. The top 10 GO terms in Biological Processes were displayed. (<b>j</b>) Gene set enrichment analysis (GSEA) of cell type signature gene sets curated from cluster markers identified in single-cell sequencing studies of human tissue. Top 10 gene sets enriched in ND1-infected cells at 6 DPI were displayed. NES, normalized enrichment score; hnbm, human medial neuroblast; hrn, human red nucleus; hnbml, human mediolateral neuroblasts; ens, enteric nervous system; homtn, human oculomotor and trochlear nucleus; hda, human dopaminergic neurons; npcs, human neural progenitor cells; pfc, prefrontal cortex; opc, oligodendrocyte progenitor cells. Data represent mean ± SEM in (<b>c</b>,<b>e</b>) for n = 3. **, <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 (two-tailed Student <span class="html-italic">t</span>-test).</p>
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<p>Identification of 70 core genes induced by NeuroD1 during neuronal reprogramming and differentiation. (<b>a</b>) Venn diagram showing the overlap between our gene sets and published gene sets that were upregulated during ND1-induced embryonic stem (ES) cells-to-neuron differentiation (Pataskar A, et al. EMBO J. 2016) and microglia-to-neuron reprogramming (Matsuda T, et al. Neuron. 2019). (<b>b</b>) Table showing the selective functional annotation clusters of 70 overlapping genes by DAVID. (<b>c</b>) Most of the proteins encoded by 70 core genes were interconnected based on the STRING database.</p>
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<p>NeuroD1 induces expression changes of miRNAs that were predicted to target DEGs during AtN reprogramming. (<b>a</b>) Principal component analysis (PCA) on all detected miRNAs in HA. (<b>b</b>) Volcano plots of the upregulated (URmiRs) and downregulated (DRmiRs) miRNAs during ND1-mediated AtN reprogramming at 2 and 6 DPI. (<b>c</b>) Fold change ranking of URmiRs and DRmiRs in ND1-infected HA at 2 and 6 DPI as compared with GFP controls. The two most upregulated miRNAs, has-miR-375-3p and has-miR-124-3p, were highlighted in red. (<b>d</b>) Table showing top 15 highly expressed miRNAs in “ND1_D6” plus hsa-miR-375-3p and hsa-miR-124-3p with average read counts (RC) and ranking. (<b>e</b>) qRT-PCR analysis of miR-124-3p and miR-375-3p expression during AtN reprogramming process. Data represent mean ± SEM for n = 3. ***, <span class="html-italic">p</span> &lt; 0.001 (two-tailed Student <span class="html-italic">t</span>-test). (<b>f</b>) The overlap of DEGs and predicted targets from the TargetScan database (v7.2) of DEmiRs. (<b>g</b>) The overlap of DEGs and predicted targets of miR-375-3p and miR-124-3p. (<b>h</b>) GO analysis of miR-375-3p and miR-124-3p’s predicted targets overlapped with all DEGs at 2 and 6 DPI. The top 10 BPs were displayed. (<b>i</b>) The ratio of URGs/DRGs in the URmiRs of D2 and D6. <span class="html-italic">p</span>-value was calculated using the Student <span class="html-italic">t</span>-test.</p>
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<p>MiR-375 regulates protein expression level of nELAVLs during NeuroD1-mediated AtN reprogramming. (<b>a</b>) Heatmap representation of the 10 common predicted targets of miR-375-3p overlapped with DEGs at both 2 and 6 DPI. Normalized expression levels were also presented. (<b>b</b>) Computational prediction of miR-375-3p target sites in the 3′-UTRs of ELAVLs from TargetScan. (<b>c</b>) qRT-PCR analysis of ELAVLs mRNA expression during AtN reprogramming in HA (n = 3). (<b>d</b>) Immunoblot analysis and quantification of ELAVL2 and ELAVL4 protein in GFP- or ND1-infected HA cultures at 6 DPI (n = 3). (<b>e</b>) Venn diagrams of DEGs overlapped with AU-rich element (ARE) genes and the top 10 GO terms of ARE-containing DEGs. (<b>f</b>) Representative immunostaining images of GFP, RFP, ELAVL2, and ELAVL4 in HA coinfected with miR-375-GFP + ND1-RFP or miR-375decoy-GFP + ND1-RFP viral constructs. &gt;, ND1 alone-infected HA; *, coinfected HA. Scale bar, 20 μm. (<b>g</b>) Quantitation of ELAVL2 and ELAVL4 protein immunofluorescence intensity of coinfected HA in (<b>f</b>). The values of fluorescent intensity were from three experiments. <span class="html-italic">p</span>-values were calculated with ANOVA. Data represent 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; ns, not significant.</p>
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<p>MiR-375/nELAVLs were induced by neuronal reprogramming factors ASCL1 and Neurog2 in HA. (<b>a</b>) Representative live images of morphological changes in HA cultures transduced with GFP, ND1-GFP, ASCL1-GFP, and Neurog2-GFP retroviruses at 6 DPI. Arrow, the neurite of induced neuronal-like cells. Scale bar, 50 μm. (<b>b</b>) Quantification showing the percentage of neuron-like cells in infected HA cultures at 6 DPI (n = 4). (<b>c</b>–<b>e</b>) QRT-PCR analyses showing expression of the reprogramming factors (<b>c</b>), neuronal markers and ELAVLs (<b>d</b>), and miRNAs (<b>e</b>) in infected HA cultures at 6 DPI (n = 3). <span class="html-italic">p</span>-values were calculated with ANOVA. Data represent 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; ns, not significant.</p>
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<p>NeuroD1 induces miR-375/ELAVL4 expression in mouse astrocyte cultures and ELAVL4 expression in the injured spinal cord during AtN reprogramming. (<b>a</b>) Representative images of GFP, GFAP, and NeuN immunostaining in the injured mouse spinal cord at 2 weeks post injection (WPI) of a mixture of AAV5-GFAP-Cre with either AAV5-Flex-GFP (n = 6) or AAV5-Flex-ND1-GFP (n = 6) at a 1:9 ratio. *, stab injury sites. Scar bar, 100 μm. (<b>b</b>) Representative images of GFP and NeuroD1 immunostaining of the same experiment in (A). Scar bar, 20 μm. (<b>c</b>) Confocal images of (<b>a</b>) to show “transitional cells” that were GFAP+/NeuN+ infected by AAV5-ND1-GFP. &lt;, AAV-infected cells. Scar bar, 20 μm. (<b>d</b>) Confocal images of GFP, ND1, and ELAVL4 immunostaining of the same experiment in (A). &lt;, a ND1-GFP-infected astrocyte expresses ND1 and ELAVL4 protein. Scar bar, 10 μm. (<b>e</b>) Representative images of GFP, ND1, and ELAVL4 immunostaining in mouse astrocyte cultures infected with GFP or ND1-GFP retrovirus at 9 DPI. &gt;, infected cells. Scar bar, 50 μm. (<b>f</b>) QRT-PCR analysis of mouse astrocyte cultures infected with GFP or ND1-GFP retrovirus at 9 DPI (n = 3). The graph is presented as mean ± SEM. <span class="html-italic">p</span>-value was calculated using the two tailed un-paired Student <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.</p>
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<p>Overexpression of miR-375 increases the number of NeuroD1-reprogrammed neurons in HA. (<b>a</b>) Representative live images of HA coinfected by ND1-RFP with GFP, miR-375-GFP or miR-375decoy-GFP viruses at 2 DPI. Scale bar, 100 μm. (<b>b</b>) Quantification showing the percentage of coinfected HA with both GFP and RFP signals at 2 DPI (D2). (<b>c</b>) Representative immunostaining images of RFP showing ND1-RFP-infected HA, DCX, and NeuN in coinfection HA cultures at 14 DPI. Scale bar, 100 μm. (<b>d</b>,<b>e</b>) Quantitative analysis of the conversion efficiency (<b>d</b>) and the number of converted neurons with DCX+ or NeuN+ (<b>e</b>) in (<b>c</b>). (<b>f</b>) Representative RFP live images of coinfected HA cultures as indicated at 2 DPI (D2) and 28 DPI (D28). Scale bar, 100 μm. (<b>g</b>) Quantification showing the number of ND1-infected cells (RFP+) per field at different time points in (<b>f</b>). (<b>h</b>) qRT-PCR analysis of miR-375-3p expression in coinfected HA at early and late time points. Data represent mean ± SEM (n = 3). <span class="html-italic">p</span>-values were calculated with ANOVA. **, <span class="html-italic">p</span> &lt; 0.01; ****, <span class="html-italic">p</span> &lt; 0.0001; ns, not significant.</p>
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<p>Overexpression of miR-375 reduces apoptosis during AtN reprogramming. (<b>a</b>) Representative immunostaining images of Ki67 at 3 DPI (D3). &lt;, ND1-RFP-infected cells. Scale bar, 25 μm. (<b>b</b>) Graphs showing the proportion of Ki67+ HA in the ND1-infected and -uninfected cells at different time points post infection. (<b>c</b>) Representative immunostaining images of cleaved caspase-3 (CCasp3) in GFP+ND1- or miR-375+ND1-coinfected HA at 3 DPI. Scale bar, 25 μm. (<b>d</b>) Quantification showing the proportion of CCasp3+/RFP+ cells in the ND1-infected HA (RFP+) at different time points post infection. (<b>e</b>) Representative immunostaining images of CCasp3 in infected HA at 3 DPI treated with 250 μM of Cisplatin for 24 h. Scale bar, 25 μm. (<b>f</b>) The proportion of CCasp3+/RFP+ cells in ND1-infected HA (RFP+) at 3 DPI treated with 5 μM and 250 μM of Cisplatin for 24 h. (<b>g</b>) Quantitation of CCasp3 immunofluorescence intensity of CCasp3+ infected HA treated with vehicle, 5 μM and 250 μM of Cisplatin. Data represent mean ± SEM (n = 3 or 4). <span class="html-italic">p</span>-values were calculated with two-tailed Student <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; ***, <span class="html-italic">p</span> &lt; 0.0001; ns, not significant.</p>
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<p>Overexpression of miR-375 does not inhibit expression levels of ELAVL4 and other mature neuronal markers of reprogrammed neurons in long-term HA cultures. (<b>a</b>) FACS gating strategy on dissociated HA cultures co-infected with ND1-RFP and miR-375-GFP retroviruses at 3 DPI. (<b>b</b>) Representative live fluorescence images of sorted cells that were plated 1 day after FACS. n = 3. Scale bar, 100 μm. (<b>c</b>) Representative immunostaining images of ELAVL4, MAP2, synaptophysin (SYP), and NeuN in long-term cultures of FACS-sorted cells at 30 DPI. Scale bar, 100 μm. (<b>d</b>) Quantitation of immunofluorescence intensity in FACS-sorted cells in (<b>c</b>). ****, <span class="html-italic">p</span> &lt; 0.0001; ns, not significant.</p>
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<p>Overexpression of miR-375-refractory nELAVLs induces morphological change and apoptosis in HA and reverses miR-375-induced survival-promoting effect during NeuroD1-mediated AtN reprogramming. (<b>a</b>) Western blot analysis of ELAVL2 and ELAVL4 protein in retro-ELAVL2- or retro-ELAVL4-infected HA cells at 4 DPI. An antibody anti-ELAVL2/4 was used to recognize both proteins. (<b>b</b>) Representative live florescence images of HA infected with GFP, ELAVL2-GFP, or ELAVL4-GFP retroviruses at 5 DPI. Target cells were enlarged to show morphology. Scale bar, 100 μm. (<b>c</b>) Quantitation of the percentage of cells with neurite-like process in HA infected with GFP, ELAVL2-GFP, or ELAVL4-GFP retroviruses. (<b>d</b>) Quantitation of the proportion of CCasp3+ cells in HA infected with GFP, ELAVL2-GFP, or ELAVL4-GFP retroviruses at 3 DPI. (<b>e</b>) Quantitation of the proportion of CCasp3+ cells among RFP+ HA in different coinfection cultures as indicated at 3 DPI. (<b>f</b>) Schematic depicting the normal function of miR-375 during ND1-mediated AtN reprogramming as well as when overexpressed. Data represents mean ± SEM (n = 3). <span class="html-italic">p</span>-values were calculated with ANOVA. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ns, not significant.</p>
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23 pages, 3938 KiB  
Article
Calcineurin-Dependent Homeostatic Response of C. elegans Muscle Cells upon Prolonged Activation of Acetylcholine Receptors
by Franklin Florin, Benjamin Bonneau, Luis Briseño-Roa, Jean-Louis Bessereau and Maëlle Jospin
Cells 2023, 12(17), 2201; https://doi.org/10.3390/cells12172201 - 3 Sep 2023
Cited by 2 | Viewed by 1560
Abstract
Pharmacological adaptation is a common phenomenon observed during prolonged drug exposure and often leads to drug resistance. Understanding the cellular events involved in adaptation could provide new strategies to circumvent this resistance issue. We used the nematode Caenorhabditis elegans to analyze the adaptation [...] Read more.
Pharmacological adaptation is a common phenomenon observed during prolonged drug exposure and often leads to drug resistance. Understanding the cellular events involved in adaptation could provide new strategies to circumvent this resistance issue. We used the nematode Caenorhabditis elegans to analyze the adaptation to levamisole, an ionotropic acetylcholine receptor agonist, used for decades to treat nematode parasitic infections. Genetic screens in C. elegans identified “adapting mutants” that initially paralyze upon exposure to levamisole as the wild type (WT), but recover locomotion after a few hours whereas WT remain paralyzed. Here, we show that levamisole induces a sustained increase in cytosolic calcium concentration in the muscle cells of adapting mutants, lasting several hours and preceding a decrease in levamisole-sensitive acetylcholine receptors (L-AChR) at the muscle plasma membrane. This decrease correlated with a drop in calcium concentration, a relaxation of the animal’s body and a resumption of locomotion. The decrease in calcium and L-AChR content depends on calcineurin activation in muscle cells. We also showed that levamisole adaptation triggers homeostatic mechanisms in muscle cells including mitochondria remodeling, lysosomal tubulation and an increase in autophagic activity. Levamisole adaptation thus provides a new experimental paradigm for studying how cells cope with calcium stress. Full article
(This article belongs to the Special Issue Caenorhabditis elegans: Cell Biology and Physiology)
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Graphical abstract

Graphical abstract
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<p>Locomotion recovery during levamisole adaptation is correlated with a decrease in muscle contraction and cytosolic calcium concentration. (<b>a</b>) Paralysis of WT (n = 797), <span class="html-italic">lev-10(kr26)</span> (n = 831) and <span class="html-italic">molo-1(kr100)</span> (n = 257) animals after 18 h of 1 mM levamisole exposure. Data set is the same as that in <a href="#cells-12-02201-t001" class="html-table">Table 1</a>. (<b>b</b>) Mean speed of <span class="html-italic">lev-10(kr26)</span> and <span class="html-italic">molo-1(kr100)</span> animals placed for 10 h on control plates (n = 72 and n = 22, respectively) or 1 mM levamisole plates (n = 70 and n = 48, respectively). Speed was measured as mean centroid displacement for 50 s directly following mechanical stimulation. Kruskal–Wallis <span class="html-italic">p</span> &lt; 0.0001, Dunn’s post tests indicated on graph compare control condition to levamisole condition for each genotype; <span class="html-italic">p</span> &gt; 0.05 for <span class="html-italic">lev-10</span> control/<span class="html-italic">molo-1</span> control; <span class="html-italic">p</span> &lt; 0.05 for <span class="html-italic">lev-10</span> levamisole/<span class="html-italic">molo-1</span> levamisole. (<b>c</b>) Evolution of WT and <span class="html-italic">lev-10(kr26)</span> length during 1 mM levamisole exposure. Length was measured from pharynx to anus of WT and <span class="html-italic">lev-10</span> animals without levamisole treatment (-) (n = 115) or after 15 min (n = 129 and n = 132, respectively), 30 min (n = 121 and n = 114, respectively), 1 h (n = 108 and n = 111, respectively), 2 h (n = 113 and n = 116, respectively), 4 h (n = 101 and n = 103, respectively), 6 h (n = 89 and n = 102, respectively), 8 h (n = 83 and n = 117, respectively) and 10 h (n = 68 and n = 123, respectively) of levamisole treatment. Kruskal–Wallis test <span class="html-italic">p</span> &lt; 0.0001, Dunn’s post tests indicated on graph compare genotypes at equal time points. (<b>d</b>) Evolution of muscle calcium during 1 mM levamisole exposure. Ratio between green and red muscle fluorescence were quantified from freely moving WT, <span class="html-italic">lev-10(kr26)</span> and <span class="html-italic">molo-1(kr100)</span> animals expressing <span class="html-italic">Pmyo-3::GCaMP-6-mCherry</span>, before levamisole (n = 44, n = 21 and n = 25, respectively), after 2 h (n = 36, n = 14 and n = 27, respectively) or 10 h of levamisole exposure (n = 43, n = 29 and n = 27, respectively). Welch ANOVA <span class="html-italic">p</span> &lt; 0.0001; Dunnett’s T3 post tests indicated on the graph compare control condition to levamisole condition; <span class="html-italic">p</span> &gt; 0.05 for all tests between WT, <span class="html-italic">lev-10</span> and <span class="html-italic">molo-1</span> before levamisole exposure, for <span class="html-italic">lev-10</span>/<span class="html-italic">molo-1</span> at 2 h, for WT/<span class="html-italic">molo-1</span> at 10 h; <span class="html-italic">p</span> &lt; 0.005 for WT/<span class="html-italic">lev-10</span> and WT/<span class="html-italic">molo-1</span> at 2 h, for WT/<span class="html-italic">lev-10</span> and <span class="html-italic">lev-10</span>/<span class="html-italic">molo-1</span> at 10 h, for WT 2 h/10 h, for <span class="html-italic">lev-10</span> 2 h/10 h and for <span class="html-italic">molo-1</span> 2 h/10 h. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005, * <span class="html-italic">p</span> &lt; 0.05, ns (not significant) <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>L-AChR are downregulated at NMJs during adaptation. (<b>a</b>) Representative confocal pictures of fluorescent L-AChR present at the dorsal nerve cord of WT, <span class="html-italic">lev-10(kr26)</span>, <span class="html-italic">molo-1(kr100)</span> and <span class="html-italic">crld-1(kr407)</span> animals carrying <span class="html-italic">unc-29(kr208::TagRFP-T).</span> Images are displayed with head side oriented left and summed in depth. Images look-up-tables are fixed for each genotype but different between genotypes for better visualization. For all panels, worms were exposed to 1 mM levamisole on standard growth plates for 18 h; control condition corresponded to 18 h on standard growth plates. Scale bar: 20 μm. (<b>b</b>) Quantification of L-AChR content present at the dorsal nerve cord of WT, <span class="html-italic">lev-10(kr26)</span>, <span class="html-italic">molo-1(kr100)</span>, <span class="html-italic">crld-1(kr407)</span> carrying <span class="html-italic">unc-29(kr208::TagRFP-T)</span> without levamisole treatment (n = 34, n = 33, n = 82 and n = 33, respectively) or after 18 h on 1 mM levamisole plates (n = 38, n = 43, n = 87 and n = 36, respectively). The fluorescence value of the dorsal cord was normalized to the mean value of the corresponding control condition. Multiple Mann–Whitney tests compare levamisole to control condition within a same genotype. (<b>c</b>) Quantification of synaptic nicotine-sensitive AChR (N-AChR) from dorsal nerve cord of <span class="html-italic">lev-10(kr26)</span> mutants carrying <span class="html-italic">acr-16(kr440::wScarlet)</span> without levamisole treatment (n = 33) or after 18 h on 1 mM levamisole plates (n = 38). The fluorescence value of the dorsal cord after levamisole treatment was normalized to the mean value of the corresponding control condition. Mann–Whitney tests <span class="html-italic">p</span> = 0.3541. (<b>d</b>) Quantification of synaptic GABA receptor (GABAR) from dorsal nerve cord of <span class="html-italic">lev-10(kr26)</span> mutants carrying <span class="html-italic">unc-49(kr296::RFP)</span> without levamisole treatment (n = 33) or after 18 h on 1 mM levamisole plates (n = 38). The fluorescence value of the dorsal cord after levamisole treatment was normalized to the mean value of the corresponding control condition. Mann–Whitney tests <span class="html-italic">p</span> = 0.7614. (<b>e</b>) dTBC-sensitive currents of <span class="html-italic">lev-10(kr26)</span> muscle cells after 1 mM levamisole exposure during 2 h (n = 10) or 18 h (n = 9). Currents were recorded in the presence of 0.01 mM levamisole, with and without 0.1 mM dTBC to extract dTBC-sensitive currents. Mean currents were plotted. Standard deviations were plotted every 10 points. Mann–Whitney tests; <span class="html-italic">p</span> &lt; 0.05 at −10 mV, <span class="html-italic">p</span> &lt; 0.005 at -50, -20 and 0 mV, <span class="html-italic">p</span> &lt; 0.0005 at −60, −40, −30 mV. (<b>f</b>) Membrane resting potentials of <span class="html-italic">lev-10(kr26)</span> muscle cells without levamisole treatment (n = 7) or after 2 (n = 14) or 18 h (n = 9) on 1 mM levamisole. Kruskal–Wallis <span class="html-italic">p</span> = 0.0012, Dunn’s post tests indicated on graph tests compare levamisole and control condition; <span class="html-italic">p</span> &lt; 0.05 for 2 h/18 h of levamisole. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005, ** <span class="html-italic">p</span> &lt; 0.005, ns (not significant) <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>Muscle calcineurin is necessary for adaptation. (<b>a</b>) Paralysis of <span class="html-italic">lev-10(0)</span> with TAX-6/calcineurin tissue-specific degradation in different tissues after 18 h of 1 mM levamisole exposure, with or without 1 mM auxin applied from L4 stage. All animals carried <span class="html-italic">lev-10(kr26); tax-6(kr423[tax-6-aid-mNG])</span> and additionally either <span class="html-italic">Peft-3::TIR1-eBFP</span> (ubiquitous degradation, n = 277 without auxin, n = 189 with auxin), <span class="html-italic">Pmyo-3::TIR1-eBFP</span> (muscle degradation, n = 231 without auxin, n = 216 with auxin), <span class="html-italic">Prab-3::TIR1-eBFP</span> (neuron degradation, n = 194 without auxin, n = 173 with auxin), <span class="html-italic">Pdpy-7::TIR1-eBFP</span> (epidermis degradation, n = 168 without auxin, n = 181 with auxin). (<b>b</b>) Evolution of <span class="html-italic">lev-10(kr26)</span> length during levamisole exposure with or without muscle TAX-6/calcineurin degradation. <span class="html-italic">lev-10(kr26); tax-6(kr423[tax-6-aid-mNG]); Pmyo-3::TIR1-eBFP</span> animals were grown from the egg stage on plates without (no degradation) or with (muscle degradation) 1 mM auxin. Length was measured from pharynx to anus without levamisole treatment (-) (n = 122 without muscle TAX-6/calcineurin degradation, n = 126 with degradation) and after 15 min (n = 133 and n = 126 respectively), 30 min (n = 124 and n = 112, respectively), 1 h (n = 103 and n = 108, respectively), 2 h (n = 121 and n = 119, respectively), 4 h (n = 120 and n = 118, respectively), 6 h (n = 108 and n = 128, respectively), 8 h (n = 105 and n = 122, respectively) and 10 h (n = 104 and n = 140, respectively) of levamisole treatment. Kruskal–Wallis test <span class="html-italic">p</span> &lt; 0.0001, Dunn’s post tests indicated on graph compare genotypes at equal time points. (<b>c</b>) Evolution of muscle calcium from <span class="html-italic">lev-10(kr26); tax-6(kr540[tax-6-aid-eBFP); Pmyo-3::TIR1-eBFP</span> during 1 mM levamisole exposure. Ratio between green and red muscle fluorescence were quantified from freely moving <span class="html-italic">lev-10(kr26); tax-6(kr540[tax-6-aid-eBFP); Pmyo-3::TIR1-eBFP</span> grown from the egg stage on plates without (no. deg.) or with (muscle deg.) 1 mM auxin without levamisole treatment (-) (n = 21 and n = 23, respectively), after 2 h (n = 22 and n = 21, respectively) or 10 h of levamisole exposure (n = 17 and n = 25, respectively). Welch ANOVA <span class="html-italic">p</span> &lt; 0.0001; Dunnett’s T3 post tests indicated on the graph compare control condition to levamisole condition; <span class="html-italic">p</span> &gt; 0.05 at 2 h with or without TAX-6/calcineurin degradation, <span class="html-italic">p</span> &lt; 0.005 in control condition with or without TAX-6/calcineurin degradation, <span class="html-italic">p</span> &lt; 0.0005 for 2 h/10 h without or with calcineurin degradation and at 10 h with or without TAX-6/calcineurin degradation. (<b>d</b>) Quantification of L-AChR content present at the dorsal nerve cord of <span class="html-italic">lev-10(kr26); tax-6(kr423[tax-6-aid-mNG]); Pmyo-3::TIR1-eBFP</span> grown from the egg stage on plates without (no. deg.) or with (muscle deg.) 1 mM auxin without levamisole treatment (n = 21 and n = 24, respectively) or after 18 h on 1 mM levamisole plates (n = 24 and n = 22, respectively). The fluorescence value of the dorsal cord was normalized to the mean value of the corresponding control condition. Mann–Whitney tests compare levamisole to control condition within a same genotype. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005, ** <span class="html-italic">p</span> &lt; 0.005, * <span class="html-italic">p</span> &lt; 0.05, ns (not significant) <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>Levamisole induces muscle mitochondrial fragmentation that disappears during adaptation. (<b>a</b>) Representative confocal pictures of muscle mitochondrial morphology. Mitochondria were visualized in body-wall muscle cells from worms expressing a single copy of <span class="html-italic">Pmyo3::tom-20N-wScarlet</span>. Look-up-table is independent for each image and displays maximum pixel value in depth. Scale bar: 20 μm. (<b>b</b>) Evolution of muscle mitochondria fragmentation during adaptation. Mitochondria morphology was assayed in WT (n = 21) and <span class="html-italic">lev-10(kr26)</span> (n = 21) animals expressing <span class="html-italic">Pmyo3::tom-20N-wScarlet</span>. Worms were exposed to 1 mM levamisole for 18 h. For each worm, a minimum of 7 muscle cells between terminal bulb and anus were evaluated according to scale described in A. The most attributed grade for each worm was retained. (<b>c</b>) Quantification of muscle ATP during levamisole exposure. WT and <span class="html-italic">lev-10(kr26)</span> animals expressing <span class="html-italic">Pmyo-3::Queen-2m</span> were placed on control (-) (n = 18 for both genotypes) or 1 mM levamisole plates for 2 h (n = 19 and n = 16, respectively), 4 h (n = 23 and n = 24, respectively), 8 h (n = 18 for both) or 18 h (n = 28 and n = 21, respectively). Samples were excited at 405 nm and 488 nm and fluorescence ratio was plotted for each animal as an average of the three areas. Welch ANOVA test <span class="html-italic">p</span> &lt; 0.0001; Dunnett’s T3 post tests; <span class="html-italic">p</span> values indicated on the graph compare levamisole to control condition of the same genotype; <span class="html-italic">p</span> &gt; 0.05 for WT control/<span class="html-italic">lev-10</span> control, WT 2 h levamisole/<span class="html-italic">lev-10</span> 2 h levamisole, WT 4 h levamisole/<span class="html-italic">lev-10</span> 4 h levamisole; <span class="html-italic">p</span> &lt; 0.05 for WT 8 h levamisole/<span class="html-italic">lev-10</span> 8 h levamisole; <span class="html-italic">p</span> &lt; 0.0005 for WT 18 h levamisole/<span class="html-italic">lev-10</span> 18 h. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005, ns (not significant) <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>Muscle autophagic and lysosomal activities increase during adaptation. (<b>a</b>) Representative confocal images of muscle autophagosomes. Animals expressed <span class="html-italic">Pdyc-1::GFP-lgg-1</span>. Look-up-table is independent for each image. Maximum pixel value in depth was displayed. White arrowheads point to autophagosomes. Scale bar: 20 μm. (<b>b</b>) Quantification of muscle autophagic vesicles after levamisole exposure. Muscle autophagosomes were counted in WT and <span class="html-italic">lev-10(kr26)</span> animals expressing <span class="html-italic">Pdyc-1::GFP-lgg-1</span> without levamisole treatment (n = 35 and n = 37, respectively) and after 2 h (n = 21 and n = 22, respectively) or 18 h of levamisole exposure (n = 17 and n = 18, respectively). Vesicles were also counted from WT animals expressing <span class="html-italic">Pdyc-1::GFP-lgg-1G116A</span> (inac. <span class="html-italic">lgg-1</span>) to control for false positives (n = 15). Number of vesicles was determined in a minimum of 7 cells per worm and averaged by worm. Kruskal–Wallis <span class="html-italic">p</span> &lt; 0.0001; Dunn’s post tests; <span class="html-italic">p</span> values indicated on the graph compare levamisole to control condition of the same genotype; <span class="html-italic">p</span> &gt; 0.05 for WT/<span class="html-italic">lev-10</span> in control conditions and after 2 h of levamisole; <span class="html-italic">p</span> &lt; 0.05 for WT/<span class="html-italic">lev-10</span>; <span class="html-italic">p</span> &lt; 0.0001 for WT/inac. <span class="html-italic">lgg-1</span> after 18 h of levamisole. (<b>c</b>) Representative confocal images of lysosome morphology. Muscle lysosomes were visualized in worms expressing <span class="html-italic">Pmyo-3::lmp-1-eBFP</span>. Look-up-table is independent for each image and displays maximum pixel value in depth. Scale bar: 20 μm. (<b>d</b>) Evolution of muscle lysosomal morphology during levamisole exposure. Lysosome morphology was assessed in muscle cells of WT (n = 55 in control conditions, n = 45 and n = 16 after 2 or 18 h on levamisole) and <span class="html-italic">lev-10(kr26)</span> (n = 53 in control conditions, n = 48 and n = 15 after 2 or 18 h on levamisole) animals expressing <span class="html-italic">Pmyo-3::lmp-1-eBFP</span>. Lysosome tubulation was evaluated using the scale defined in c. Each worm was assessed with an image taken between the nerve ring and the vulva. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005, ns (not significant) <span class="html-italic">p</span> ≥ 0.05.</p>
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<p>Muscle degradation of TAX-6/calcineurin has an impact on mitochondrial homeostasis during adaptation but not on autophagy or lysosome tubulation. (<b>a</b>) Quantification of muscle autophagosomes counted in <span class="html-italic">lev-10(kr26); tax-6(kr540[tax-6-aid-eBFP]); krSi55[Pmyo-3::TIR1-eBFP]; kagIs1[Pdyc-1::GFP-lgg-1]</span> animals expressing <span class="html-italic">Pdyc-1::GFP-lgg-1</span> without or with TAX-6/calcineurin degradation before levamisole treatment (n = 17 and n = 16, respectively) or after 18 h of levamisole exposure (n = 16 for both). Number of vesicles was determined in a minimum of 7 cells per worm and averaged by worm. Kruskal–Wallis <span class="html-italic">p</span> &lt; 0.0001; Dunn’s post tests; <span class="html-italic">p</span> values indicated on the graph compare levamisole to control condition of the same genotype; <span class="html-italic">p</span> &gt; 0.05 for control without TAX-6/calcineurin degradation/control with degradation and for 18 h without TAX-6/calcineurin degradation/18 h with degradation. (<b>b</b>) Evaluation of muscle lysosomal morphology after levamisole exposure from muscle cells of WT (n ≥ 15) and <span class="html-italic">lev-10(kr26)</span> (n ≥ 13) worms expressing <span class="html-italic">Pmyo-3::lmp-1-eBFP</span>. Lysosome tubulation was evaluated using the scale defined in <a href="#cells-12-02201-f005" class="html-fig">Figure 5</a>c. Each worm was assessed with an image taken between the nerve ring and the vulva. (<b>c</b>) Evaluation of muscle mitochondria morphology during adaptation. Mitochondria fragmentation was evaluated from body-wall muscle cells of <span class="html-italic">lev-10(kr26)</span> (n = 21) and WT (n = 21) worms expressing <span class="html-italic">Pmyo3::tom-20N::wScarlet</span>. Worms were exposed to 1 mM levamisole for 18 h. For each worm, a minimum of 7 muscle cells between pharyngeal terminal bulb and anus were evaluated according to scale described in <a href="#cells-12-02201-f004" class="html-fig">Figure 4</a>a. The most attributed grade for each worm was plotted. For all <span class="html-italic">p</span> values: *** <span class="html-italic">p</span> &lt; 0.0005.</p>
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35 pages, 5714 KiB  
Review
Human iPSCs as Model Systems for BMP-Related Rare Diseases
by Gonzalo Sánchez-Duffhues and Christian Hiepen
Cells 2023, 12(17), 2200; https://doi.org/10.3390/cells12172200 - 2 Sep 2023
Viewed by 2749
Abstract
Disturbances in bone morphogenetic protein (BMP) signalling contribute to onset and development of a number of rare genetic diseases, including Fibrodysplasia ossificans progressiva (FOP), Pulmonary arterial hypertension (PAH), and Hereditary haemorrhagic telangiectasia (HHT). After decades of animal research to build a solid foundation [...] Read more.
Disturbances in bone morphogenetic protein (BMP) signalling contribute to onset and development of a number of rare genetic diseases, including Fibrodysplasia ossificans progressiva (FOP), Pulmonary arterial hypertension (PAH), and Hereditary haemorrhagic telangiectasia (HHT). After decades of animal research to build a solid foundation in understanding the underlying molecular mechanisms, the progressive implementation of iPSC-based patient-derived models will improve drug development by addressing drug efficacy, specificity, and toxicity in a complex humanized environment. We will review the current state of literature on iPSC-derived model systems in this field, with special emphasis on the access to patient source material and the complications that may come with it. Given the essential role of BMPs during embryonic development and stem cell differentiation, gain- or loss-of-function mutations in the BMP signalling pathway may compromise iPSC generation, maintenance, and differentiation procedures. This review highlights the need for careful optimization of the protocols used. Finally, we will discuss recent developments towards complex in vitro culture models aiming to resemble specific tissue microenvironments with multi-faceted cellular inputs, such as cell mechanics and ECM together with organoids, organ-on-chip, and microfluidic technologies. Full article
(This article belongs to the Special Issue iPS Cells (iPSCs) for Modelling and Treatment of Human Diseases 2022)
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Graphical abstract

Graphical abstract
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<p><b>BMP signal transduction</b>. The interaction between BMP ligands and BMP type I receptors induces the formation of membrane heterotetrameric complexes. This interaction is influenced by the relative availability of ligands and receptors, which is regulated by soluble antagonists, co-receptors, and extracellular matrix (ECM) components. The activated BMP receptors transmit signals intracellularly through their serine/threonine kinase domain, leading to the phosphorylation of the BMP canonical mediators, SMAD1/5/9, at the C-terminal domain. Moreover, BMP receptors regulate the activation of non-canonical signalling pathways, which serve as a signalling hub integrating tissue-specific environmental cues. Canonical and non-canonical signalling pathways intersect at various levels. Once activated, SMAD1/5/9 trimerize with SMAD4 and translocate into the nucleus to bind specific gene promoters in collaboration with transcription co-factors. A number of environmental cues co-influence signalling outcomes, including cellular mechanics such as mechanical shear forces or inflammatory or hypoxic microenvironments. Due to space limitations, details on the TGF-β-specific regulation of the signalling branch are not shown.</p>
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<p><b>iPSC generation and cell type differentiation in rare BMP diseases</b>. So far, iPSC lines have been successfully generated for three rare BMP diseases: Fibrodysplasia ossificans progressiva (FOP), Hereditary pulmonary arterial hypertension (HPAH), and Hereditary haemorrhagic telangiectasia (HHT). Various sources of somatic tissue have been explored for this purpose. Reprogramming efficiency may be affected by the BMP gene mutation, and small molecules, like LDN-193189, that aim to restore normal BMP signalling can be utilized. BMPs are potent inducers of mesoderm. Therefore, a combination of BMP agonists and Wnt antagonists (such as CHIR-99021) is employed to induce mesodermal differentiation. In order to generate vascular cells, ALK4/5/7 inhibition is often necessary in a subsequent step.</p>
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<p><b>Rare BMP diseases and iPSCs</b>. Fibrodysplasia ossificans progressiva (FOP) is a musculoskeletal disease caused by heterozygous gain-of-function mutations in <span class="html-italic">ACVR1</span>/ALK2, resulting in heightened activation of downstream ALK2 signalling in response to Activin-A. Increased ALK2 signalling leads to the formation of extraskeletal bone plaques. On the right is a photograph of the skeleton of a man with FOP, from the collections of the Anatomical Museum of the Leiden University Medical Center (LUMC). This photo has been reproduced with permission from LUMC (copyright belongs to LUMC). Hereditary pulmonary arterial hypertension (HPAH) is a cardiovascular disease characterized by progressive narrowing of the pulmonary arteries, resulting in elevated pulmonary arterial pressure (mPAP &gt; 20 mm Hg), followed by right ventricular dilatation and hypertrophy. Loss-of-function mutations in BMPR2 exacerbate disease severity and penetrance by disrupting the overall balance of TGF-β signalling in endothelial and smooth muscle cells. The right panel illustrates a cross-section of a healthy pulmonary artery (top) compared with a diseased occluded pulmonary artery (bottom). The formation of a pronounced neointima by aberrant cell growth into the lumen is shown. The PAH patient pulmonary arterial cross-section is part of a published dataset by C.H., recreated based on details mentioned in Hiepen C. et al. [<a href="#B168-cells-12-02200" class="html-bibr">168</a>]. Hereditary haemorrhagic telangiectasia (HHT) is associated with loss-of-function gene mutations in <span class="html-italic">ACVRL1</span> (ALK1) and <span class="html-italic">ENG</span> (endoglin), among others, which impair BMP signalling. It is characterized by varying degrees of lesions due to the instability of the microvascular networks. The right panel displays a typical microvascular subdermal lesion seen in HHT. The picture was contributed with friendly permission by Prof. Dr. Urban Geisthoff (Philipps University of Marburg; Germany).</p>
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<p><b>Current and Future Development of iPSC-based technology in BMP rare diseases</b>. Since the inception of the first iPSC-based cell models, significant progress has been made in developing more intricate and robust systems that accurately replicate diseased tissues and organs. Of particular interest in monogenic diseases is the utilization of gene editing tools such as CRISPR/Cas9 to create isogenic cell lines. While the first culturing approaches opted for 2 dimensional methods, more complex three dimensional culture conditions, such as embryoid bodies (EBs) and organoids, are emerging. This can be combined with co-culture of different cell types differentiated from the same parental iPSC line. Culturing those cells under conditions that expose them to microenvironmental cues, such as hypoxia, biomechanics, and inflammation, allows for effective and complex disease modelling. These systems have proven to be valuable platforms for drug screening and in-depth mechanistic investigations. Further advances, such as construction of complex cellular grafts or therapeutic extracellular vesicles (EVs) derived from iPSCs, hold the potential to revolutionize tissue engineering and iPSC-based cell therapies, presenting therapeutic alternatives for patients affected by rare BMP diseases.</p>
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18 pages, 14244 KiB  
Article
Morphology of Neutrophils during Their Activation and NETosis: Atomic Force Microscopy Study
by Viktoria Sergunova, Vladimir Inozemtsev, Nina Vorobjeva, Elena Kozlova, Ekaterina Sherstyukova, Snezhanna Lyapunova and Aleksandr Chernysh
Cells 2023, 12(17), 2199; https://doi.org/10.3390/cells12172199 - 2 Sep 2023
Cited by 4 | Viewed by 2214
Abstract
Confocal microscopy and fluorescence staining of cellular structures are commonly used to study neutrophil activation and NETosis. However, they do not reveal the specific characteristics of the neutrophil membrane surface, its nanostructure, and morphology. The aim of this study was to reveal the [...] Read more.
Confocal microscopy and fluorescence staining of cellular structures are commonly used to study neutrophil activation and NETosis. However, they do not reveal the specific characteristics of the neutrophil membrane surface, its nanostructure, and morphology. The aim of this study was to reveal the topography and nanosurface characteristics of neutrophils during activation and NETosis using atomic force microscopy (AFM). We showed the main stages of neutrophil activation and NETosis, which include control cell spreading, cell fragment formation, fusion of nuclear segments, membrane disruption, release of neutrophil extracellular traps (NETs), and final cell disintegration. Changes in neutrophil membrane nanosurface parameters during activation and NETosis were quantified. It was shown that with increasing activation time there was a decrease in the spectral intensity of the spatial periods. Exposure to the activator A23187 resulted in an increase in the number and average size of cell fragments over time. Exposure to the activators A23187 and PMA (phorbol 12-myristate 13-acetate) caused the same pattern of cell transformation from spherical cells with segmented nuclei to disrupted cells with NET release. A23187 induced NETosis earlier than PMA, but PMA resulted in more cells with NETosis at the end of the specified time interval (180 min). In our study, we used AFM as the main research tool. Confocal laser-scanning microscopy (CLSM) images are provided for identification and detailed analysis of the phenomena studied. In this way, we exploited the advantages of both techniques. Full article
(This article belongs to the Special Issue Advances in Scanning Probe Microscopy in Cell Biology)
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Figure 1

Figure 1
<p>Scheme of experiment using human donor neutrophils exposed to A23187 and PMA. Stage 1: Isolation of neutrophils. Stage 2: Activation of neutrophils by A23187/PMA and exposure to CO<sub>2</sub>. Colocalization of decondensed chromatin with myeloperoxidase after neutrophil stimulation with PMA and A23187. Blue, staining of chromatin with DAPI; green, staining of MPO with FITC-conjugated anti-MPO monoclonal antibodies. Scale bar: 25 µm. Stage 3: Acquisition of AFM and fluorescence images.</p>
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<p>AFM 2D and CLSM images and statistical distribution of typical neutrophil types upon exposure to activators. (<b>A</b>) type 1; (<b>B</b>) type 2; (<b>C</b>) type 3; (<b>D</b>) type 4. (<b>E</b>) AFM histograms of the distribution of typical neutrophil types after exposure to A23187 and PMA, (N = 70 ± 12); (<b>F</b>) widefield microscopy histogram of the distribution of typical neutrophil types after exposure to A23187 and PMA, (N = 150 ± 35). Data are expressed as mean ± SD. The color of the box corresponds to the statistical group. In CLSM images, red color (membrane) is WGA + Alexa Fluor 594 dye, blue color of nuclear chromatin is Hoechst 33,342 dye. The scale of the confocal images is 5 μm.</p>
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<p>Changes in neutrophil shape (type 1<tt>→</tt>type 4) and size (height and diameter) upon exposure to activator. (<b>A</b>) AFM 2D images of neutrophils and their profile after exposure to A23187; (<b>B</b>) AFM 2D images of neutrophils and their height profile after exposure to PMA. Box plots of neutrophil heights (<b>C</b>,<b>E</b>) and diameters (<b>D</b>,<b>F</b>) as a function of exposure time to A23187 and PMA. N = 30. The 75th percentile and whiskers indicate mean standard deviation. Horizontal line indicates median and square indicates mean. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 (Mann–Whitney test).</p>
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<p>Changes in the nanostructure of neutrophil membranes. (<b>A</b>) AFM 3D images of the original surface and images of the first and second order surfaces. (<b>B</b>) Profiles (green plots) of the heights of the first and second order nanosurfaces of neutrophil membranes (control); (<b>C</b>) spatial spectrum of the heights of the corresponding membrane surfaces (red plots). (<b>D</b>) Fourier transform of the original neutrophil membrane profiles (green plots): Profiles (green plots) and corresponding spatial spectra (red plots) for the original surface, for the first order surface (spectral window 600–1200 nm), for the second order surface (spectral window 50–300 nm) for the activators A23187 and PMA; (<b>E</b>) histograms of the maximum intensity spectra of the first order surfaces <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>I</mi> <mtext> </mtext> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for the two activators; (<b>F</b>) histograms of the maximum intensity spectra of the second order surfaces <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>I</mi> <mi>I</mi> <mtext> </mtext> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for the two activators; (<b>G</b>) plots of neutrophil profiles after exposure to A23187 and (<b>H</b>) after exposure to PMA.</p>
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<p>Changes in the nanostructure of neutrophil membranes. (<b>A</b>) AFM 3D images of the original surface and images of the first and second order surfaces. (<b>B</b>) Profiles (green plots) of the heights of the first and second order nanosurfaces of neutrophil membranes (control); (<b>C</b>) spatial spectrum of the heights of the corresponding membrane surfaces (red plots). (<b>D</b>) Fourier transform of the original neutrophil membrane profiles (green plots): Profiles (green plots) and corresponding spatial spectra (red plots) for the original surface, for the first order surface (spectral window 600–1200 nm), for the second order surface (spectral window 50–300 nm) for the activators A23187 and PMA; (<b>E</b>) histograms of the maximum intensity spectra of the first order surfaces <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>I</mi> <mtext> </mtext> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for the two activators; (<b>F</b>) histograms of the maximum intensity spectra of the second order surfaces <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>I</mi> <mi>I</mi> <mtext> </mtext> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for the two activators; (<b>G</b>) plots of neutrophil profiles after exposure to A23187 and (<b>H</b>) after exposure to PMA.</p>
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<p>Changes in the structural configuration of neutrophils after exposure to the activators A23187 and PMA at 30 min. (<b>A</b>) AFM 3D image of control neutrophil and its profile; (<b>B</b>) CLSM image of control neutrophil; (<b>C</b>) AFM 2D image of neutrophil after stimulation with activator A23187 and its profile (<b>D</b>); (<b>E</b>) AFM 2D image of neutrophil after stimulation with activator PMA and its profile at 30 min (<b>F</b>); (<b>G</b>) 3 D image of a 5 × 5 μm<sup>2</sup> area and its profile at 30 min after exposure to A23187; (<b>H</b>) 3D image of a 5 × 5 μm<sup>2</sup> area and its profile at 30 min after exposure to PMA; (<b>I</b>) CLSM image of neutrophil after stimulation with A23187 at 30 min; (<b>J</b>) CLSM image of neutrophil after stimulation with PMA at 30 min. In CLSM images, red is WGA + Alexa Fluor 594, green is phalloidin + Alexa Fluor 488 and blue is Hoechst33342. Scale bar: 5 μm. (<b>K</b>) AFM 3D image of a 6 × 5 μm<sup>2</sup> section after exposure to A23187 at 30 min, 60 min; 120 min, histograms for the average size of cell fragments “halo” after exposure to A23187 at 30, 60, 120 min.</p>
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<p>Characteristic changes in the structural configuration of neutrophils upon exposure to the activators A23187 and PMA at 60 and 120 min. (<b>A</b>) 3D AFM cell profile and (<b>B</b>) CLSM images of neutrophils at 60 min after exposure to A23187. (<b>C</b>) 3D AFM cell profile and (<b>D</b>) CLSM images of neutrophils 120 min after exposure to A23187. (<b>E</b>) 3D AFM cell profile and (<b>F</b>) CLSM images of neutrophils 60 min after exposure to PMA. (<b>G</b>) 3D AFM cell profile and (<b>H</b>) CLSM images of neutrophils 120 min after exposure to PMA. In CLSM images, red is WGA + Alexa Fluor 594, blue is Hoechst 33342. Scale bar: 5 µm.</p>
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<p>Characteristic 2D AFM and CLSM images of neutrophil NETosis at 240 min upon exposure to the activator A23187 (<b>A</b>) and at 180 min upon exposure to the activator PMA (<b>B</b>). (<b>1</b>) 3D AFM image of NETosis results in a 1.5 × 1.5 μm<sup>2</sup> area after exposure to activator A23187; (<b>2</b>) 3D AFM image of NETosis results in a 1.5 × 1.5 μm<sup>2</sup> area after exposure to activator PMA. (<b>C</b>) Histograms for the mean surface area of granules after exposure to A23187 purple and PMA blue. Total granules: A23187-468, PMA-820. Mean size, nm: A23187, 50 ± 20, PMA, 30 ± 10. Scale bar: 5 μm.</p>
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13 pages, 1629 KiB  
Article
Identification of Unique microRNA Profiles in Different Types of Idiopathic Inflammatory Myopathy
by Sandra Muñoz-Braceras, Iago Pinal-Fernandez, Maria Casal-Dominguez, Katherine Pak, José César Milisenda, Shajia Lu, Massimo Gadina, Faiza Naz, Gustavo Gutierrez-Cruz, Stefania Dell’Orso, Jiram Torres-Ruiz, Josep Maria Grau-Junyent, Albert Selva-O’Callaghan, Julie J. Paik, Jemima Albayda, Lisa Christopher-Stine, Thomas E. Lloyd, Andrea M. Corse and Andrew L. Mammen
Cells 2023, 12(17), 2198; https://doi.org/10.3390/cells12172198 - 2 Sep 2023
Cited by 5 | Viewed by 1922
Abstract
Dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotizing myopathy (IMNM), and inclusion body myositis (IBM) are four major types of idiopathic inflammatory myopathy (IIM). Muscle biopsies from each type of IIM have unique transcriptomic profiles. MicroRNAs (miRNAs) target messenger RNAs (mRNAs), thereby regulating their [...] Read more.
Dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotizing myopathy (IMNM), and inclusion body myositis (IBM) are four major types of idiopathic inflammatory myopathy (IIM). Muscle biopsies from each type of IIM have unique transcriptomic profiles. MicroRNAs (miRNAs) target messenger RNAs (mRNAs), thereby regulating their expression and modulating transcriptomic profiles. In this study, 18 DM, 12 IMNM, 6 AS, 6 IBM, and 6 histologically normal muscle biopsies underwent miRNA profiling using the NanoString nCounter system. Eleven miRNAs were exclusively differentially expressed in DM compared to controls, seven miRNAs were only differentially expressed in AS, and nine miRNAs were specifically upregulated in IBM. No differentially expressed miRNAs were identified in IMNM. We also analyzed miRNA-mRNA associations to identify putative targets of differentially expressed miRNAs. In DM and AS, these were predominantly related to inflammation and cell cycle progression. Moreover, our analysis showed an association between miR-30a-3p, miR-30e-3p, and miR-199b-5p downregulation in DM and the upregulation of target genes induced by type I interferon. In conclusion, we show that muscle biopsies from DM, AS, and IBM patients have unique miRNA signatures and that these miRNAs might play a role in regulating the expression of genes known to be involved in IIM pathogenesis. Full article
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Figure 1
<p>Expression profiles of differentially expressed miRNAs in each type of IIM. (<b>a</b>) Volcano plot representation of the differential expression analysis of each type of IIM (AS, DM, IBM, or IMNM) relative to histologically normal tissue (NT). Labels indicate the five differentially expressed miRNAs with the lowest <span class="html-italic">p</span>-adjusted value for each IIM type. (<b>b</b>) Heatmap representations including the fold change (<b>left</b>) and adjusted <span class="html-italic">p</span>-values (<b>right</b>) resulting from the differential expression analysis for each type of IIM relative to NT, selecting all the miRNAs identified as differentially expressed in at least one IIM type compared to NT. (<b>c</b>) Graphs represent log-scaled normalized expression levels of differentially expressed miRNAs that are representative of common IIM or IIM type-specific changes. The expression profile of the rest of the differentially expressed miRNAs identified can be found in <a href="#app1-cells-12-02198" class="html-app">Figure S2</a>. Note that, for the miRNAs whose expression was exceptionally high in a few samples, the y-axis has been broken for a better representation of the expression in the rest of the samples.</p>
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<p>Correlation of the expression of differentially expressed miRNAs. Heatmap representation of the Spearman’s rank correlation coefficients between the expression levels of the miRNAs that were differentially expressed in one or more types of IIM muscle compared to histologically normal muscle.</p>
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<p>Differentially expressed and negatively correlated miRNA-mRNA pairs in DM involve mRNAs enriched in the interferon pathways. (<b>a</b>) Dot plot shows the pathway enrichment of DM differentially expressed mRNAs that are negatively correlated with miRNAs identified also as DM differentially expressed. (<b>b</b>) Scatterplots represent the correlation of miR-30a-3p, miR-30e-3p, and miR-199b-5p with their most negatively correlated target in DM that is also differentially expressed in this IIM type and associated with the interferon-alpha response. The plots of the rest of the DM negatively correlated miRNA-mRNA pairs involving mRNAs included in the interferon-alpha response gene set can be visualized in <a href="#app1-cells-12-02198" class="html-app">Figure S5</a>. Spearman’s rank correlation (r<sub>s</sub>) and <span class="html-italic">p</span> values of the correlation across DM samples, all samples excluding DM samples (named nonDM for short), or the total of samples (all), are shown.</p>
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<p>Correlation between the expression of differentially expressed miRNAs and relevant genes in IIM. Heatmap representation of the Spearman’s rank correlation coefficients between the expression levels of miRNAs that were differentially expressed in one or more types of IIM and genes that are stimulated by interferon (<b>a</b>), genes that are abundant in mature or differentiating muscle (<b>b</b>), or genes whose expression is enriched in immune cells (<b>c</b>) across all samples.</p>
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18 pages, 7506 KiB  
Article
JNK Signalling Regulates Self-Renewal of Proliferative Urine-Derived Renal Progenitor Cells via Inhibition of Ferroptosis
by Lisa Nguyen, Leonie Thewes, Michelle Westerhoff, Wasco Wruck, Andreas S. Reichert, Carsten Berndt and James Adjaye
Cells 2023, 12(17), 2197; https://doi.org/10.3390/cells12172197 - 2 Sep 2023
Viewed by 1486
Abstract
With a global increase in chronic kidney disease patients, alternatives to dialysis and organ transplantation are needed. Stem cell-based therapies could be one possibility to treat chronic kidney disease. Here, we used multipotent urine-derived renal progenitor cells (UdRPCs) to study nephrogenesis. UdRPCs treated [...] Read more.
With a global increase in chronic kidney disease patients, alternatives to dialysis and organ transplantation are needed. Stem cell-based therapies could be one possibility to treat chronic kidney disease. Here, we used multipotent urine-derived renal progenitor cells (UdRPCs) to study nephrogenesis. UdRPCs treated with the JNK inhibitor—AEG3482 displayed decreased proliferation and downregulated transcription of cell cycle-associated genes as well as the kidney progenitor markers—SIX2, SALL1 and VCAM1. In addition, levels of activated SMAD2/3, which is associated with the maintenance of self-renewal in UdRPCs, were decreased. JNK inhibition resulted in less efficient oxidative phosphorylation and more lipid peroxidation via ferroptosis, an iron-dependent non-apoptotic cell death pathway linked to various forms of kidney disease. Our study is the first to describe the importance of JNK signalling as a link between maintenance of self-renewal and protection against ferroptosis in SIX2-positive renal progenitor cells. Full article
(This article belongs to the Section Stem Cells)
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<p>Inhibition of JNK reduces the proliferation of UdRPCs. (<b>A</b>) Morphology of the three UdRPCs UM51, UM27 and UF21 with or without AEG3482 treatment after 72 h. Scale bars represent 100 µm. (<b>B</b>) Ki67 expression in UdRPCs treated with or not treated with AEG3482 for 72 h. Scale bars represent 50 µm. (<b>C</b>) Ki67 proliferation assay for JNK-inhibited UdRPCs (n = 10; * <span class="html-italic">p</span>-value &lt; 0.05, *** <span class="html-italic">p</span>-value &lt; 0.001). (<b>D</b>) Bar graph of PI-measured cell death in JNK-inhibited UdRPCs compared to untreated controls (n = 5, * <span class="html-italic">p</span>-value &lt; 0.05, *** <span class="html-italic">p</span>-value &lt; 0.001). Error bars indicate STDEV. (<b>E</b>) mRNA expression of nephron progenitor markers SIX2, SALL1, VCAM1 and Ki67. Mean values were normalised to the housekeeping gene RPL37A. (<b>F</b>) Gene expression of cell cycle-related genes for the time points 24 h, 72 h and 120 h depicted in a Pearson’s heatmap.</p>
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<p>JNK signalling is associated with cell cycle processes in UdRPCs. (<b>A</b>) Representative enrichment clusters for control and JNK inhibition after 24 h depicted in a heatmap and cell cycle-related processes marked with an arrow. Venn diagram of control and JNK-inhibited UM51 cells for the time point 24 h. (<b>B</b>) Representative enrichment clusters for control and JNK inhibition after 72 h depicted in a heatmap and cell cycle-related processes marked with an arrow. Venn diagram of control and JNK-inhibited UM51 cells for the time point 72 h. (<b>C</b>) Representative enrichment clusters for control and JNK inhibition after 120 h depicted in a heatmap and cell cycle-related processes marked with an arrow. Venn diagram of control and JNK-inhibited UM51 cells for the time point 120 h.</p>
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<p>Inhibition of JNK signalling affects the downstream target cJUN and SMAD proteins. (<b>A</b>) Representative protein expression (%) of p- and t-JNK in UdRPCs with or without JNK inhibition. (<b>B</b>) Representative protein expression (%) of p- and t-cJUN in UdRPCs with or without JNK inhibition. (<b>C</b>) Immunofluorescence stainings of cJUN and p-cJUN in UdRPCs with or without JNK inhibition. Scale bars represent 50 µm. (<b>D</b>) Protein expression of p-/t-SMAD1/5 and p-/t-SMAD2/3 in UdRPCs with or without JNK inhibition.</p>
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<p><b>Inhibition of JNK signalling increases lipid peroxidation.</b> (<b>A</b>) Representative histograms of measured fluorescence intensities after BODIPY staining and the respective bar plots of mean fluorescence intensity of control or JNK-inhibited UdRPCs (n = 5; * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01). Error bars indicate SEM. (<b>B</b>) Representative histograms of measured fluorescence intensities after BODIPY staining and the respective bar plots of mean fluorescence intensity of control, AEG3482 and AEG3482 + Lip-1 (n = 5). Error bars indicate SEM. (<b>C</b>) Gene expression of iron metabolism-related genes in UM51 cells for the time points 24 h, 72 h and 120 h depicted in a Pearson’s heatmap. (<b>D</b>) Pearson’s heatmap depicting gene expression of glutathione metabolism-related genes in UM51 cells for the time points 24 h, 72 h and 120 h. (<b>E</b>) Representative protein expression (%) of GPX4, TFR1 and HO-1 in UdRPCs with or without JNK inhibition. (<b>F</b>) mRNA expression of glutathione metabolism-related markers GCLC, GCLM, GPX4. (<b>G</b>) mRNA expression of iron metabolism-related markers HMOX1, SLC11A2 and TFR1. Mean values were normalised to the housekeeping gene RPL37A.</p>
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<p>JNK signalling regulates mitochondrial respiration via membrane potential. (<b>A</b>) Measurement of OCR in real time in UdRPCs. Basal respiration, maximal respiration and spare respiratory capacity are depicted *** <span class="html-italic">p</span>-value &lt; 0.001). Error bars indicate SD. (<b>a</b>) OCR in UM51 (n = 1; N = 23). (<b>b</b>) OCR in UM27 (n = 1; N = 23). (<b>c</b>) OCR in UF21 (n = 1; N = 46). (<b>B</b>) Fluorescence images of MitoTracker Green and TMRM stainings in UdRPCs UM51, UM27 and UF21 with and without JNK inhibition. Scale bars depict 10 µm. (<b>C</b>) Measurement of mean TMRM fluorescence signal intensity in UdRPCs *** <span class="html-italic">p</span>-value &lt; 0.001). Outliers were removed. Error bars indicate SEM. (<b>a</b>) Mean TMRM signal in UM51 is depicted (n = 2; ctrl. N = 1; +JNK inhibitor N= 2). (<b>b</b>) Mean TMRM signal in UM27 is depicted (n = 2; ctrl. N = 1; +JNK inhibitor N = 2). Error bars indicate SEM. (<b>c</b>) Mean TMRM signal in UF21 is depicted (n = 2; ctrl. N = 1; +JNK inhibitor N = 2).</p>
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12 pages, 4389 KiB  
Article
Lysophosphatidic Acid Signalling Regulates Human Sperm Viability via the Phosphoinositide 3-Kinase/AKT Pathway
by Hao-Yu Liao and Cristian O’Flaherty
Cells 2023, 12(17), 2196; https://doi.org/10.3390/cells12172196 - 2 Sep 2023
Cited by 1 | Viewed by 1360
Abstract
Lysophosphatidic acid (LPA) signalling is essential for maintaining germ cell viability during mouse spermatogenesis; however, its role in human spermatozoa is unknown. We previously demonstrated that peroxiredoxin 6 (PRDX6) calcium-independent phospholipase A2 (iPLA2) releases lysophospholipids such as LPA or arachidonic [...] Read more.
Lysophosphatidic acid (LPA) signalling is essential for maintaining germ cell viability during mouse spermatogenesis; however, its role in human spermatozoa is unknown. We previously demonstrated that peroxiredoxin 6 (PRDX6) calcium-independent phospholipase A2 (iPLA2) releases lysophospholipids such as LPA or arachidonic acid (AA) and that inhibiting PRDX6 iPLA2 activity impairs sperm cell viability. The exogenous addition of LPA bypassed the inhibition of PRDX6 iPLA2 activity and maintained the active phosphoinositide 3-kinase (PI3K)/AKT pathway. Here, we aimed to study PI3K/AKT pathway regulation via LPA signalling and protein kinases in maintaining sperm viability. The localization of LPARs in human spermatozoa was determined using immunocytochemistry, and P-PI3K and P-AKT substrate phosphorylations via immunoblotting. Sperm viability was determined using the hypo-osmotic swelling test. LPAR1, 3, 5 and 6 were located on the sperm plasma membrane. The inhibition of LPAR1-3 with Ki16425 promoted the impairment of sperm viability and decreased the phosphorylation of PI3K AKT substrates. Inhibitors of PKC, receptor-type PTK and PLC impaired sperm viability and the PI3K/AKT pathway. Adding 1-oleoyl-2-acetyl-snglycerol (OAG), a cell-permeable analog of diacylglycerol (DAG), prevented the loss of sperm viability and maintained the phosphorylation of PI3K. In conclusion, human sperm viability is supported by LPAR signalling and regulated by PLC, PKC and RT-PTK by maintaining phosphorylation levels of PI3K and AKT substrates. Full article
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<p>Localization of LPARs in human spermatozoa. Human spermatozoa, non-permeabilized or permeabilized with 100% methanol (Perm), were incubated with anti-LPAR antibodies. Spermatozoa incubated with the second one alone did not show any signals. The green arrow indicates the sperm midpiece; the white arrowhead indicates the sperm equatorial segment; the light blue arrow indicates the sperm acrosome; and the red head arrow indicates the post-acrosomal region. Pictures were taken at 100× using a Carl Zeiss Axiophot microscope (Oberkochen, Germany), scale bar = 15 μm.</p>
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<p>The effect of Ki16425 causes the cell death of human spermatozoa. Spermatozoa were treated with Ki16425 for 3.5 h at 37 °C and a hypo-osmotic swelling buffer was added to distinguish whether the spermatozoa were non-viable or viable (<a href="#sec2-cells-12-02196" class="html-sec">Section 2</a>). The results are representative of sperm samples from different healthy donors (n = 4; *** <span class="html-italic">p</span> ≤ 0.001, ANOVA and Dunnett’s test).</p>
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<p>The effect of Ki16425 on PI3K and AKT substrate phosphorylation of human spermatozoa. Spermatozoa were treated with increasing concentrations of Ki16425 for 3.5 h at 37 °C. Sperm proteins were electrophoresed, electrotransferred and immunoblotted with (<b>A</b>) anti-P-PI3K or (<b>B</b>) anti-P-AKT-S antibodies. The membrane was stripped and reblotted with the anti-Tubulin antibody to confirm equal loading for each lane. The relative intensity of P-PI3K/P-AKT-S was obtained by normalizing each band’s intensity to the respective intensity of tubulin. The results are representative of sperm samples from different healthy donors (n = 4; * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, ANOVA and Dunnett’s test).</p>
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<p>The effect of kinase inhibitors caused the cell death of human spermatozoa. Spermatozoa were treated with kinase inhibitors for 3.5 h at 37 °C. Then, the hypo-osmotic swelling buffer was added to distinguish whether the sperm was non-viable or viable (<a href="#sec2-cells-12-02196" class="html-sec">Section 2</a>) (n = 3, which means a significant difference within the same strain). The results are representative of sperm samples from different healthy donors (ANOVA and Dunnett’s test; * <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>The different kinase inhibitors affected the PI3K phosphorylation of human spermatozoa. Levels of P-PI3K were determined in spermatozoa incubated with H89, chelerythrine, PD98059, PP2 and Tyrphostin A47 for 3.5 h at 37 °C. Sperm proteins were electrophoresed, electrotransferred and immunoblotted with the anti-P-PI3K antibody. The membrane was stripped and reblotted with the anti-Tubulin antibody to confirm equal loading for each lane. The relative intensity of P-PI3K was obtained by normalizing each band’s intensity to the respective intensity of tubulin. The results are representative of sperm samples from different healthy donors (n = 4; * <span class="html-italic">p</span> ≤ 0.05, *** <span class="html-italic">p</span> ≤ 0.001, ANOVA and Dunnett’s test).</p>
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<p>The different kinase inhibitors led to the effect on AKT substrates’ phosphorylation of human spermatozoa. Levels of P-AKT substrates were determined in spermatozoa incubated with H89, chelerythrine, PD98059, PP2 and Tyrphostin A47 for 3.5 h at 37 °C. Sperm proteins were electrophoresed, electrotransferred and immunoblotted with the anti-P-AKT-S antibody. The membrane was stripped and reblotted with the anti-Tubulin antibody to confirm equal loading for each lane. The relative intensity of P-AKT-S was obtained by normalizing each band’s intensity to the respective intensity of tubulin (n = 4; * <span class="html-italic">p</span> ≤ 0.05 ** <span class="html-italic">p</span> ≤ 0.01 *** <span class="html-italic">p</span> ≤ 0.001, ANOVA and Dunnett’s test).</p>
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<p>OAG prevents the loss of human sperm viability when U-73122 is present. Spermatozoa were incubated with U-73122 and OAG for 3.5 h at 37 °C and a hypo-osmotic swelling buffer was added to distinguish whether the spermatozoa were non-viable or viable (<a href="#sec2-cells-12-02196" class="html-sec">Section 2</a>). The results are representative of sperm samples from different healthy donors (n = 3 * means a significant difference within the same strain. ANOVA and Dunnett’s test; * <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>OAG prevents the loss of PI3K and AKT substrates’ phosphorylation when U−73122 is present. Levels of P-PI3K and P-AKT-S in spermatozoa incubated with U-73122 and OAG for 3.5 h at 37 °C. Sperm proteins were electrophoresed, electrotransferred and immunoblotted with the anti-PI3K/P-AKT-S antibody. The membrane was stripped and reblotted with the anti-Tubulin antibody to confirm equal loading for each lane. The relative intensity of (<b>A</b>) P-PI3K and (<b>B</b>) P-AKT-S was obtained by normalizing each band’s intensity to the respective intensity of tubulin. The results are representative of sperm samples from different healthy donors (n = 4, * means significant difference within the same strain. ANOVA and Dunnett’s test; * <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Lysophosphatidic signalling is essential to support viability in human spermatozoa. Lysophosphatidic acid (LPA) binds to LPAR (1, 3, 5 or 6) to activate the PI3K/AKT pathway in human spermatozoa to prevent apoptotic-like changes that could impair sperm viability. Ki6425 inhibits LPAR1 and LPAR3, thus preventing phosphorylations of PI3K and AKT substrates and leading to impairment of sperm viability. A similar fate occurs in spermatozoa treated with U−73122, TA47 or chelerythrine, inhibitors of PLC, receptor-type tyrosine-protein kinase (RT-PTK) and PKC, respectively.</p>
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19 pages, 4753 KiB  
Article
Lymphatic Endothelial-to-Myofibroblast Transition: A Potential New Mechanism Underlying Skin Fibrosis in Systemic Sclerosis
by Irene Rosa, Eloisa Romano, Bianca Saveria Fioretto, Khadija El Aoufy, Silvia Bellando-Randone, Marco Matucci-Cerinic and Mirko Manetti
Cells 2023, 12(17), 2195; https://doi.org/10.3390/cells12172195 - 1 Sep 2023
Cited by 8 | Viewed by 1663
Abstract
At present, only a few reports have addressed the possible contribution of the lymphatic vascular system to the pathogenesis of systemic sclerosis (SSc). Based on the evidence that blood vascular endothelial cells can undertake the endothelial-to-myofibroblast transition (EndMT) contributing to SSc-related skin fibrosis, [...] Read more.
At present, only a few reports have addressed the possible contribution of the lymphatic vascular system to the pathogenesis of systemic sclerosis (SSc). Based on the evidence that blood vascular endothelial cells can undertake the endothelial-to-myofibroblast transition (EndMT) contributing to SSc-related skin fibrosis, we herein investigated whether the lymphatic endothelium might represent an additional source of profibrotic myofibroblasts through a lymphatic EndMT (Ly-EndMT) process. Skin sections from patients with SSc and healthy donors were immunostained for the lymphatic endothelial cell-specific marker lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1) in combination with α-smooth muscle actin (α-SMA) as the main marker of myofibroblasts. Commercial human adult dermal lymphatic microvascular endothelial cells (HdLy-MVECs) were challenged with recombinant human transforming growth factor-β1 (TGFβ1) or serum from SSc patients and healthy donors. The expression of lymphatic endothelial cell/myofibroblast markers was measured by quantitative real-time PCR, Western blotting and immunofluorescence. Collagen gel contraction assay was performed to assess myofibroblast-like cell contractile ability. Lymphatic endothelial cells in intermediate stages of the Ly-EndMT process (i.e., coexpressing LYVE-1 and α-SMA) were found exclusively in the fibrotic skin of SSc patients. The culturing of HdLy-MVECs with SSc serum or profibrotic TGFβ1 led to the acquisition of a myofibroblast-like morphofunctional phenotype, as well as the downregulation of lymphatic endothelial cell-specific markers and the parallel upregulation of myofibroblast markers. In SSc, the Ly-EndMT might represent a previously overlooked pathogenetic process bridging peripheral microlymphatic dysfunction and skin fibrosis development. Full article
(This article belongs to the Special Issue The Role of Epithelial Cells in Scleroderma)
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<p>Presence of lymphatic endothelial-to-myofibroblast transition (Ly-EndMT) in dermal lymphatic capillaries of SSc patients. Representative fluorescence microphotographs of skin sections from healthy donors (<span class="html-italic">n</span> = 3) and patients with SSc (<span class="html-italic">n</span> = 5) double immunostained for the lymphatic endothelial cell-specific marker LYVE-1 (green) and the myofibroblast marker α-SMA (red). The nuclei are stained blue with 4′,6-diamidino-2-phenylindole (DAPI). In healthy skin, the expression of α-SMA is detectable exclusively in the pericytes and vascular smooth muscle cells of dermal blood microvessels and in the arrector pili muscles. In the fibrotic skin of SSc patients, colocalized LYVE-1 and α-SMA is evident as yellow staining in the transitional Ly-EndMT cells of dermal lymphatic capillaries (arrowheads). Arrows point to α-SMA<sup>+</sup> stromal myofibroblasts detected in SSc skin. Scale bar = 50 μm. Bars represent the mean ± SEM of the percentage of dermal lymphatic vessels displaying transitional endothelial cells per high-power field (40× original magnification). Unpaired Student’s <span class="html-italic">t</span>-test was used for statistical analysis. *** <span class="html-italic">p</span> &lt; 0.001 vs. healthy skin. LYVE-1: lymphatic vessel endothelial hyaluronan receptor-1; α-SMA: α-smooth muscle actin; SSc: systemic sclerosis; ECs, endothelial cells.</p>
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<p>Treatment with serum from SSc patients leads to the acquisition of myofibroblast-like morphological features in healthy dermal lymphatic microvascular endothelial cells (HdLy-MVECs). The representative phase-contrast microphotographs of HdLy-MVECs (<span class="html-italic">n</span> = 3 cell lines) at basal condition and after 72 h culture with serum from healthy controls (<span class="html-italic">n</span> = 6), serum from SSc patients (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1 are shown in the upper panels. Scale bar = 400 μm. The representative fluorescence microphotographs of HdLy-MVECs at the same experimental conditions and stained for filamentous actin (F-actin) with Alexa 488-conjugated phalloidin (green) are shown in the middle panels. The nuclei are stained blue with 4′,6-diamidino-2-phenylindole (DAPI). Scale bar = 50 μm. When compared to the basal condition, HdLy-MVEC morphology does not change in cultures challenged with serum from healthy donors. Upon treatment with SSc serum or TGFβ1, HdLy-MVECs lose their characteristic polygonal cobblestone-like morphology and exhibit an elongated spindle-shaped morphology. Bars in the lower panels represent the mean ± SEM of the percentage of cells with an elongated spindle-shaped morphology (<b>left</b>) or cells with stress fibers (<b>right</b>) per high-power field (40× original magnification). One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001 vs. healthy serum. TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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<p>Culture with serum from SSc patients induces changes in gene expression levels of lymphatic endothelial cell-specific markers and myofibroblast markers in healthy dermal lymphatic microvascular endothelial cells (HdLy-MVECs). HdLy-MVECs were subjected to 48 h treatment with serum from healthy controls (<span class="html-italic">n</span> = 6), serum from SSc patients (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1, and then examined for the expression levels of <span class="html-italic">PROX1</span>, <span class="html-italic">LYVE1</span>, <span class="html-italic">PDPN</span>, <span class="html-italic">S100A4</span>, <span class="html-italic">ACTA2</span>, <span class="html-italic">COL1A1</span>, <span class="html-italic">COL1A2</span>, and <span class="html-italic">SNAI1</span> genes by quantitative SYBR Green real-time PCR. For each gene, the basal expression level was set to 1 for the normalization of the other results. As a reference gene, 18S ribosomal RNA was used in all real-time PCR assays. Bars represent the mean ± SEM of triplicate gene expression determinations from three cell lines. One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span> <span class="html-italic">p</span> &lt; 0.05, <span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.01, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001 vs. healthy serum. TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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<p>Culture with SSc serum induces changes in protein expression of lymphatic endothelial cell markers and myofibroblast markers in healthy dermal microvascular endothelial cells (HdLy-MVECs). HdLy-MVECs were subjected to 72 h challenge with serum from healthy controls (<span class="html-italic">n</span> = 6), serum from patients with SSc (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1, and then analyzed by Western blotting for the protein expression of Prox1 (<b>A</b>), LYVE-1 (<b>B</b>), PDPN (<b>C</b>), S100A4 (<b>D</b>), α-SMA (<b>E</b>), and COL1A1 (<b>F</b>). Representative immunoblots are shown. The molecular weight in kDa is reported on the right of the bands. α-tubulin and GAPDH were used for normalization (loading controls). Bars represent the mean ± SEM of optical density in arbitrary units (a.u.). One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span> <span class="html-italic">p</span> &lt; 0.05, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001 vs. healthy serum. Prox1: prospero-related homeobox protein 1; LYVE-1: lymphatic vessel endothelial hyaluronan receptor-1; PDPN: podoplanin; α-SMA: α-smooth muscle actin; COL1A1: α-1 chain of type I collagen; TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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<p>Culture with SSc serum induces a myofibroblast-like immunophenotype in healthy dermal lymphatic microvascular endothelial cells (HdLy-MVECs). Representative fluorescence microphotographs of HdLy-MVECs at basal condition and after treatment for 72 h with healthy serum (<span class="html-italic">n</span> = 6), SSc serum (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1 immunostained for Prox1, LYVE-1, S100A4, α-SMA, and Snail1. The nuclei are stained blue with 4′,6-diamidino-2-phenylindole (DAPI). In HdLy-MVECs at basal condition and cultured in the presence of healthy serum, the expression of α-SMA, S100A4, and Snail1 is negligible. HdLy-MVECs challenged with SSc serum or TGFβ1 are characterized by the strong downregulation of nuclear Prox1 and cell surface LYVE-1, and by the parallel upregulation of α-SMA, partly assembled into stress fibers, as well as S100A4 and nuclear Snail1. Scale bar = 50 μm. Bars represent the mean ± SEM of the percentage of immunopositive cells or nuclei per high-power field (40× original magnification). One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001 vs. healthy serum. Prox1: prospero-related homeobox protein 1; LYVE-1: lymphatic vessel endothelial hyaluronan receptor-1; α-SMA: α-smooth muscle actin; TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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<p>Healthy dermal lymphatic microvascular endothelial cells (HdLy-MVECs) acquire a myofibroblast-like functional phenotype after culture with serum from SSc patients. The upper panel shows representative pictures of collagen gels with HdLy-MVECs at basal condition and after treatment for 72 h with serum from healthy donors (<span class="html-italic">n</span> = 6), serum from SSc patients (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1. Gel size in the presence of unstimulated HdLy-MVECs (basal) was assumed to be 100% for the normalization of the other results. Negative control consisted of collagen gel without embedded cells. Bars represent the mean ± SEM of triplicate determinations from three cell lines. One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span> <span class="html-italic">p</span> &lt; 0.05 vs. healthy serum. TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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<p>Culture with SSc serum activates Smad-dependent TGFβ signaling in healthy dermal lymphatic microvascular endothelial cells (HdLy-MVECs). HdLy-MVECs were subjected to 72 h challenge with serum from healthy controls (<span class="html-italic">n</span> = 6), serum from patients with SSc (<span class="html-italic">n</span> = 6), or recombinant human TGFβ1, and then analyzed by Western blotting for the protein expression of p-Smad3, Smad3 and α-tubulin (loading control). Representative immunoblots are shown. Molecular weight values (kDa) are indicated on the right of the bands. Bars represent the mean ± SEM of optical density in arbitrary units (a.u.). One-way ANOVA with post hoc Tukey’s test was used for statistical analysis. *** <span class="html-italic">p</span> &lt; 0.001 vs. basal condition; <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001 vs. healthy serum. p-Smad3: phosphorylated-Smad3; TGFβ1: transforming growth factor-β1; SSc: systemic sclerosis.</p>
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24 pages, 12072 KiB  
Article
ODF2 Negatively Regulates CP110 Levels at the Centrioles/Basal Bodies to Control the Biogenesis of Primary Cilia
by Madeline Otto and Sigrid Hoyer-Fender
Cells 2023, 12(17), 2194; https://doi.org/10.3390/cells12172194 - 1 Sep 2023
Cited by 3 | Viewed by 1209
Abstract
Primary cilia are essential sensory organelles that develop when an inhibitory cap consisting of CP110 and other proteins is eliminated. The degradation of CP110 by the ubiquitin-dependent proteasome pathway mediated by NEURL4 and HYLS1 removes the inhibitory cap. Here, we investigated the suitability [...] Read more.
Primary cilia are essential sensory organelles that develop when an inhibitory cap consisting of CP110 and other proteins is eliminated. The degradation of CP110 by the ubiquitin-dependent proteasome pathway mediated by NEURL4 and HYLS1 removes the inhibitory cap. Here, we investigated the suitability of rapamycin-mediated dimerization for centriolar recruitment and asked whether the induced recruitment of NEURL4 or HYLS1 to the centriole promotes primary cilia development and CP110 degradation. We used rapamycin-mediated dimerization with ODF2 to induce their targeted recruitment to the centriole. We found decreased CP110 levels in the transfected cells, but independent of rapamycin-mediated dimerization. By knocking down ODF2, we showed that ODF2 controls CP110 levels. The overexpression of ODF2 is not sufficient to promote the formation of primary cilia, but the overexpression of NEURL4 or HYLS1 is. The co-expression of ODF2 and HYLS1 resulted in the formation of tube-like structures, indicating an interaction. Thus, ODF2 controls primary cilia formation by negatively regulating the concentration of CP110 levels. Our data suggest that ODF2 most likely acts as a scaffold for the binding of proteins such as NEURL4 or HYLS1 to mediate CP110 degradation. Full article
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Figure 1

Figure 1
<p>Induced recruitment of NEURL4 to the centrosome by rapamycin-mediated dimerization with ODF2. NIH3T3 cells were transfected with plasmids encoding ODF2 (138NC; 138NC::mRFP::FKBP) and FRB::ECFP::NEURL4. (<b>A</b>) Centrosomal recruitment of FRB::ECFP::NEURL4 detected by autofluorescence of either mRFP ((<b>a</b>,<b>e</b>), in red) or ECFP ((<b>b</b>,<b>f</b>), in green). Nuclear staining with DAPI ((<b>c</b>,<b>g</b>), in blue) and merged images (<b>d</b>,<b>h</b>). Scale bars of 5 µm (<b>a</b>–<b>d</b>) or 10 µm (<b>e</b>–<b>h</b>). Incubation with rapamycin for either 24 h (1 nM rapamycin, (<b>a</b>–<b>d</b>)) or 20 min (1 nM rapamycin), followed by medium exchange and incubation without rapamycin for 24 h (<b>e</b>–<b>h</b>). (<b>B</b>) Decoration of FRB::ECFP::NEURL4 with anti-GFP antibodies. Autofluorescence of the mRFP-tag of ODF2 ((<b>a</b>,<b>e</b>,<b>i</b>); in red) at the centrosome (arrows). NEURL4 was immunologically detected with anti-GFP ((<b>b</b>,<b>f</b>,<b>j</b>), in green). Nuclear staining with DAPI ((<b>c</b>,<b>g</b>,<b>k</b>), in blue) and merged images (<b>d</b>,<b>h</b>,<b>l</b>). Scale bars are 5 µm. Incubation with 2 µL of DMSO for 24 h (<b>a</b>–<b>d</b>) as the control. Incubation with rapamycin for either 24 h (0.1 nM rapamycin, (<b>e</b>–<b>h</b>)) or 20 min (1 nM rapamycin) followed by medium exchange and incubation without rapamycin for 24 h (<b>i</b>–<b>l</b>).</p>
Full article ">Figure 1 Cont.
<p>Induced recruitment of NEURL4 to the centrosome by rapamycin-mediated dimerization with ODF2. NIH3T3 cells were transfected with plasmids encoding ODF2 (138NC; 138NC::mRFP::FKBP) and FRB::ECFP::NEURL4. (<b>A</b>) Centrosomal recruitment of FRB::ECFP::NEURL4 detected by autofluorescence of either mRFP ((<b>a</b>,<b>e</b>), in red) or ECFP ((<b>b</b>,<b>f</b>), in green). Nuclear staining with DAPI ((<b>c</b>,<b>g</b>), in blue) and merged images (<b>d</b>,<b>h</b>). Scale bars of 5 µm (<b>a</b>–<b>d</b>) or 10 µm (<b>e</b>–<b>h</b>). Incubation with rapamycin for either 24 h (1 nM rapamycin, (<b>a</b>–<b>d</b>)) or 20 min (1 nM rapamycin), followed by medium exchange and incubation without rapamycin for 24 h (<b>e</b>–<b>h</b>). (<b>B</b>) Decoration of FRB::ECFP::NEURL4 with anti-GFP antibodies. Autofluorescence of the mRFP-tag of ODF2 ((<b>a</b>,<b>e</b>,<b>i</b>); in red) at the centrosome (arrows). NEURL4 was immunologically detected with anti-GFP ((<b>b</b>,<b>f</b>,<b>j</b>), in green). Nuclear staining with DAPI ((<b>c</b>,<b>g</b>,<b>k</b>), in blue) and merged images (<b>d</b>,<b>h</b>,<b>l</b>). Scale bars are 5 µm. Incubation with 2 µL of DMSO for 24 h (<b>a</b>–<b>d</b>) as the control. Incubation with rapamycin for either 24 h (0.1 nM rapamycin, (<b>e</b>–<b>h</b>)) or 20 min (1 nM rapamycin) followed by medium exchange and incubation without rapamycin for 24 h (<b>i</b>–<b>l</b>).</p>
Full article ">Figure 2
<p>Increased amount of ODF2 at the basal body, but not the rapamycin-induced recruitment of NEURL4, caused a loss of CP110. NIH3T3 cells were transfected with <span class="html-italic">p138NC/Odf2::mRFP::FKBP</span> and <span class="html-italic">pFRB::ECFP:::Neurl4</span> (+T) and incubated, either with (+R) or without rapamycin (−R). Non-transfected cells (−T) served as controls. (<b>A</b>) Detection of the basal body in transfected cells by autofluorescence of the mRFP-tagged ODF2 ((<b>a</b>,<b>f</b>,<b>k</b>,<b>p</b>); in red). Anti-CP110 staining (<b>b</b>,<b>g</b>,<b>l</b>,<b>q</b>) (in magenta). Basal bodies were identified by their association with the ciliary axoneme decorated with acetylated tubulin ((<b>c</b>,<b>h</b>,<b>m</b>,<b>r</b>); in green). DAPI staining of nuclei ((<b>d</b>,<b>i</b>,<b>n</b>,<b>s</b>); in blue) and merged images (<b>e</b>,<b>j</b>,<b>o</b>,<b>t</b>). Scale bars are 5 µm. (<b>B</b>) CP110 was identified by antibody staining, and the CP110 area was captured by confocal imaging. CP110 intensities of individual basal bodies were quantified, followed by background subtraction, which was calculated as the average intensity of four different neighboring areas, and the corrected CP110 intensity was divided through the area. The average of the calculated CP110 intensities of the basal bodies of control cells (non-transfected and not incubated with rapamycin, −R−T) served as the reference to which all calculated CP110 intensities were related, giving the fold changes. In transfected cells, the average CP110 intensities were 0.658× (−R+T) and 0.6629× (+R+T) compared to the control (−R−T, 1×), whereas rapamycin-incubation in non-transfected cells (+R-T) had no effect, showing an average intensity of 0.9124×. A significant decrease in CP110 was found in the transfected cells compared to the non-transfected cells, both in the absence of rapamycin (−R+T compared to −R−T; <span class="html-italic">p</span> = 0.001472 **) and in the presence of rapamycin (+R+T compared to +R−T; <span class="html-italic">p</span> = 0.005132 <b><sup>++</sup></b>). (Student’s <span class="html-italic">t</span>-test, two-tailed, homoscedastic). <span class="html-italic">N</span> individual quantifications were performed: <span class="html-italic">n</span> = 92 (−R−T), <span class="html-italic">n</span> = 54 (−R+T), <span class="html-italic">n</span> = 88 (+R−T), <span class="html-italic">n</span> = 50 (+R+T). <span class="html-italic">p</span> &lt; 0.01 ** and <b><sup>++</sup></b>.</p>
Full article ">Figure 3
<p>ODF2 and CP110 levels are inversely correlated at centrosomes and basal bodies. NIH3T3 cells were forced to express ODF2 fused to mRFP (138NC/ODF2::mRFP::FKBP) ((<b>a</b>,<b>f</b>), red), which was detected by the autofluorescence of mRFP, whereas CP110 (anti-CP110, (<b>b</b>,<b>g</b>), pink; inset (<b>b</b>), white, and (<b>g</b>), yellow) and acetylated tubulin (anti-α-tubulin K40ac, (<b>c</b>,<b>h</b>), green) were detected by antibody staining. The centrosome ((<b>c</b>), arrow) and the primary cilium at the basal body (<b>h</b>) were decorated by acetylated tubulin. Nuclei were stained with DAPI ((<b>d</b>,<b>i</b>), blue). Merged images in (<b>e</b>,<b>j</b>). Scale bars are 5 µm. Arrows highlight centrosomes and basal bodies. The insets show the magnified areas of the centrosome and basal body, with CP110 stained from pink to white (<b>b</b>,<b>e</b>) or to yellow (<b>g</b>,<b>j</b>) to better distinguish between ODF2 and CP110 in the merged images.</p>
Full article ">Figure 4
<p>Inverse correlation between the amount of ODF2 and CP110 at the centrosome. ODF2 was depleted by the transfection of either the short hairpin plasmid <span class="html-italic">sh3</span> or siRNA. Depletion was rescued by co-transfection of <span class="html-italic">hCenexin</span> (<span class="html-italic">sh3</span> + <span class="html-italic">hCenexin</span> or <span class="html-italic">Odf2</span> siRNA + <span class="html-italic">hCenexin</span>). <span class="html-italic">K07</span> or control siRNA served as the controls. (<b>A</b>) Centrosomal quantification of ODF2 revealed significant ODF2-depletion by either <span class="html-italic">sh3</span> or <span class="html-italic">Odf2</span> siRNA transfection, and a successful rescue by co-transfection of <span class="html-italic">hCenexin</span>. Three biological replicates were used for analyses with <span class="html-italic">n</span> individual centrosomes: <span class="html-italic">K07 n</span> = 48, <span class="html-italic">sh3 n</span> = 19, <span class="html-italic">sh3</span> + <span class="html-italic">hCenexin n</span> = 35, control siRNA <span class="html-italic">n</span> = 35, <span class="html-italic">Odf2</span> siRNA <span class="html-italic">n</span> = 29, <span class="html-italic">Odf2</span> siRNA + <span class="html-italic">hCenexin n</span> = 31. One-way ANOVA with the Tukey HSD post-hoc test (<span class="html-italic">p</span> &lt; 0.01 **). (<b>B</b>) Quantification of CP110 at the centrosome in ODF2-depleted cells. Three biological replicates were used for analyses with <span class="html-italic">n</span> individual measurements: <span class="html-italic">K07 n</span> = 17, <span class="html-italic">sh3 n</span> = 44, <span class="html-italic">sh3</span> + <span class="html-italic">hCenexin n</span> = 22, control siRNA <span class="html-italic">n</span> = 33, <span class="html-italic">Odf2</span> siRNA <span class="html-italic">n</span> = 40, <span class="html-italic">Odf2</span> siRNA + <span class="html-italic">hCenexin n</span> = 28. One-way ANOVA with the Tukey HSD post-hoc test (<span class="html-italic">p</span> &lt; 0.01 **).</p>
Full article ">Figure 5
<p>Decrease in centrosomal ODF2 by both, either <span class="html-italic">sh3</span>- or <span class="html-italic">Odf2</span> siRNA-mediated knockdown. Transfected cells were identified by the autofluorescence of Centrin-Cherry-tagged centrosomes ((<b>a</b>,<b>e</b>,<b>i</b>,<b>m</b>,<b>q</b>,<b>u</b>), in red). ODF2 was detected by antibody staining ((<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>,<b>r</b>,<b>v</b>), in green). Nuclear stain with DAPI ((<b>c</b>,<b>g</b>,<b>k</b>,<b>o</b>,<b>s</b>,<b>w</b>), in blue). Merged images in (<b>d</b>,<b>h</b>,<b>l</b>,<b>p</b>,<b>t</b>,<b>x</b>). Cells were transfected with either the control plasmid <span class="html-italic">K07</span>, the short hairpin plasmid <span class="html-italic">sh3</span>, or both <span class="html-italic">sh3</span> + <span class="html-italic">hCenexin</span> for rescue. Additionally, the knockdown of ODF2 was also achieved by the transfection of <span class="html-italic">Odf2</span> siRNA and rescued by the co-transfection of <span class="html-italic">Odf2</span> siRNA and <span class="html-italic">hCenexin</span>. Control siRNA transfection served as the control experiment. Scale bars are 5 µm. Arrows highlight the centrosomes.</p>
Full article ">Figure 6
<p>ODF2 knockdown correlated with increased CP110 at the centrosome. Detection of transfected cells by the autofluorescence of Centrin-Cherry-tagged centrosomes ((<b>a</b>,<b>e</b>,<b>i</b>,<b>m</b>,<b>q</b>,<b>u</b>), in red). CP110 was detected by antibody staining ((<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>,<b>r</b>,<b>v</b>), in green). Nuclear staining with DAPI ((<b>c</b>,<b>g</b>,<b>k</b>,<b>o</b>,<b>s</b>,<b>w</b>), in blue). Merged images in (<b>d</b>,<b>h</b>,<b>l</b>,<b>p</b>,<b>t</b>,<b>x</b>). For ODF2 knockdown, the cells were transfected with the short hairpin plasmid <span class="html-italic">sh3</span>, and for rescue, with both <span class="html-italic">sh3</span> + <span class="html-italic">hCenexin</span>. Additionally, the knockdown of ODF2 was also achieved by transfection of <span class="html-italic">Odf2</span> siRNA and rescued by the co-transfection of <span class="html-italic">Odf2</span> siRNA + <span class="html-italic">hCenexin</span>. Transfection of the control plasmid <span class="html-italic">K07</span> or control siRNA served as the reference experiments. Scale bars are 5 µm. Arrows highlight the centrosomes.</p>
Full article ">Figure 7
<p>ODF2 is enriched in basal bodies. (<b>A</b>) Detection of endogenous ODF2 (in red) in centrosomes and basal bodies of NIH3T3 cells. Cells were cultivated in either normal medium (NM) or serum starvation medium (SSM), and centrosomes (<b>a</b>,<b>c</b>) and basal bodies (<b>b</b>,<b>d</b>) were detected by anti-ODF2 (in red) and anti-acetylated tubulin (in green) decoration. Basal bodies were identified by the presence of the primary cilium, which was decorated by anti-acetylated tubulin staining (in green). Nuclear staining with DAPI. All merged images. The insets show an overview of the whole cell, with the enlarged area highlighted by an arrow. Bars are 5 µm. (<b>B</b>) ODF2 was enriched in the basal bodies as compared to the centrosomes under both cultivation conditions, either in normal medium or serum starvation medium (<span class="html-italic">p</span> &lt; 0.001 ***, not significant ns). Quantification was performed by Fiji. For each quantification, the average intensity of the centrosomal/basal body background (always using four different areas) was subtracted from the intensity of ODF2 staining and the corrected intensity related to the measured area, thus yielding the relative quantity of ODF2. For the fold change calculation, the relative quantities of ODF2 were related to the average relative quantity in the centrosomes, either in NM or SSM, respectively. Three biological replicates with <span class="html-italic">n</span> measurements: centrosome in NM <span class="html-italic">n</span> = 52, basal body in NM: <span class="html-italic">n</span> = 48, centrosome in SSM <span class="html-italic">n</span> = 44, basal body in SSM <span class="html-italic">n</span> = 66.</p>
Full article ">Figure 8
<p>ODF2 overexpression did not promote primary cilia formation. Th indicated proteins were overexpressed, in cells incubated in either standard medium (NM) or serum-starvation medium (SSM), and the primary cilia counted. Bar chart of <a href="#cells-12-02194-t001" class="html-table">Table 1</a>.</p>
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<p>Increased ciliation by overexpression of either FRB::ECFP::NEURL4 or FRB::ECFP::HYLS1. (<b>A</b>) Exemplary demonstration of ciliated cells. Cilia were detected by decoration of ARL13b ((<b>c</b>), green) and the ECFP-tagged fusion proteins by anti GFP staining ((<b>b</b>), pink), in this case, FRB::ECFP::NEURL4. ODF2::mRFP::FKBP (when overexpressed) was detected by its RFP autofluorescence ((<b>a</b>), red). Merged image with DAPI staining ((<b>d</b>), blue). ODF2-mediated recruitment of NEURL4 was induced by rapamycin treatment. Scale bars: 5 µm. (<b>B</b>) Overexpression of ODF2 and NEURL4 or HYLS1 did not induce cilia formation. (<b>C</b>) Overexpression of either FRB::ECFP::NEURL4 or FRB::ECFP::HYLS1 caused an increase in ciliated cells in the total cell population compared to the untreated control cells (<span class="html-italic">p</span> &lt; 0.05 *, <span class="html-italic">p</span> &lt; 0.01 **). Cells were transfected with the indicated constructs, either without (−rapa) or with rapamycin treatment (+rapa), and the primary cilia were counted.</p>
Full article ">Figure 10
<p>Formation of tubes and rings by interaction of ODF2 and HYLS1. (<b>a</b>–<b>c</b>) Cytoplasmic expression of FRB::ECFP::HYLS1. Anti-GFP immunostaining for the detection of FRB::ECFP::HYLS1 (green) and ARL13b (red). (<b>d</b>–<b>g</b>,<b>h</b>–<b>k</b>,<b>l</b>–<b>o</b>) Formation of higher-order structures, i.e., tubes and rings, by co-expression and co-localization of 138NC::mRFP::FKBP and FRB::ECFP::HYLS1. Autofluorescence of mRFP-tagged 138NC (red), and anti-GFP immunodecoration of FRB::ECFP::HYLS1 (pink) and ARL13b (green). (<b>p</b>–<b>s</b>) Formation of fibers by 138NC::mRFP::FKBP (red autofluorescence) without co-localization of FRB::ECFP::HYLS1 (pink). Anti-GFP immunostaining of FRB::ECFP::HYLS1 (pink) and ARL13b (green). Localization of 138NC::mRFP::FKBP (<b>d</b>,<b>h</b>,<b>l</b>,<b>p</b>), FRB::ECFP::HYLS1 (<b>a</b>,<b>e</b>,<b>i</b>,<b>m</b>,<b>q</b>), ARL13b (<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>,<b>r</b>), and merged images including nuclear staining with DAPI (blue) (<b>c</b>,<b>g</b>,<b>k</b>,<b>o</b>,<b>s</b>). Bars are 10 µm (<b>a</b>–<b>c</b>), 2 µm (<b>d</b>–<b>g</b>), and 5 µm (<b>h</b>–<b>s</b>).</p>
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24 pages, 2552 KiB  
Review
Macrophage Implication in IPF: Updates on Immune, Epigenetic, and Metabolic Pathways
by Deepak Pokhreal, Bruno Crestani and Doumet Georges Helou
Cells 2023, 12(17), 2193; https://doi.org/10.3390/cells12172193 - 1 Sep 2023
Cited by 7 | Viewed by 4352
Abstract
Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease of unknown etiology with a poor prognosis. It is a chronic and progressive disease that has a distinct radiological and pathological pattern from common interstitial pneumonia. The use of immunosuppressive medication was shown [...] Read more.
Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease of unknown etiology with a poor prognosis. It is a chronic and progressive disease that has a distinct radiological and pathological pattern from common interstitial pneumonia. The use of immunosuppressive medication was shown to be completely ineffective in clinical trials, resulting in years of neglect of the immune component. However, recent developments in fundamental and translational science demonstrate that immune cells play a significant regulatory role in IPF, and macrophages appear to be among the most crucial. These highly plastic cells generate multiple growth factors and mediators that highly affect the initiation and progression of IPF. In this review, we will provide an update on the role of macrophages in IPF through a systemic discussion of various regulatory mechanisms involving immune receptors, cytokines, metabolism, and epigenetics. Full article
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Figure 1
<p>The pathogenic potential of pro-fibrotic macrophages that participate in the onset of pulmonary fibrosis. Repetitive epithelial cell injury caused by a variety of risk factors results in dysregulated epithelial function. AECs release coagulation factors and inflammatory mediators, leading to the activation/recruitment of various immune cells to the site of microinjury. Among these cells, pulmonary macrophages release profibrotic mediators such as TGF-β, IL-1β, PDGF, and CCL18, which lead to the activation of fibroblasts and their differentiation into myofibroblasts to heal the wounded area in the lung interstitium. Defective repair mechanisms and macrophage alternation cause excessive ECM production. Gaseous exchange is significantly decreased due to uncontrolled scarification, which eventually ends up causing respiratory distress. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 27 August 2023).</p>
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<p>Macrophage polarization. Classically activated macrophages (M1) are generally considered to be pro-inflammatory/anti-fibrotic, and alternatively activated macrophages (M2) are generally anti-inflammatory/pro-fibrotic. M2 macrophages are further classified as M2a, M2b, M2c, and M2d subtypes. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 27 August 2023).</p>
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<p>Schematic representation of macrophage-related mechanisms in IPF. Injurious and pro-fibrotic factors are shown in red. Protective /anti-fibrotic factors are shown in green. (<b>A</b>) Surface receptor-dependent mechanisms—Above-shown receptors were found to be upregulated in macrophages and implicated in lung fibrosis. (<b>B</b>) Metabolism-related mechanisms—Illustrating various fibrosis-related metabolites and metabolic regulators in macrophages. (<b>C</b>) Transcriptional and epigenetic mechanisms—Illustrating various transcriptional regulators and epigenetic modifications related to macrophages in lung fibrosis. (<b>D</b>) Subsets and macrophage-derived cytokines—Illustrating different cytokines and distinct subsets of lung macrophages in lung fibrosis. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 27 August 2023).</p>
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25 pages, 1413 KiB  
Review
Systemic Inflammatory Disorders, Immunosuppressive Treatment and Increase Risk of Head and Neck Cancers—A Narrative Review of Potential Physiopathological and Biological Mechanisms
by Nuno Vale, Mariana Pereira and Rui Amaral Mendes
Cells 2023, 12(17), 2192; https://doi.org/10.3390/cells12172192 - 1 Sep 2023
Cited by 1 | Viewed by 1612
Abstract
Head and neck cancers (HNCs) are known to present multiple factors likely to influence their development. This review aims to provide a comprehensive overview of the current scientific literature on the interplay between systemic inflammatory disorders, immunosuppressive treatments and their synergistic effect on [...] Read more.
Head and neck cancers (HNCs) are known to present multiple factors likely to influence their development. This review aims to provide a comprehensive overview of the current scientific literature on the interplay between systemic inflammatory disorders, immunosuppressive treatments and their synergistic effect on HNC risk. Both cell-mediated and humoral-mediated systemic inflammatory disorders involve dysregulated immune responses and chronic inflammation and these inflammatory conditions have been associated with an increased risk of HNC development, primarily in the head and neck region. Likewise, the interaction between systemic inflammatory disorders and immunosuppressive treatments appears to amplify the risk of HNC development, as chronic inflammation fosters a tumor-promoting microenvironment, while immunosuppressive therapies further compromise immune surveillance and anti-tumor immune responses. Understanding the molecular and cellular mechanisms underlying this interaction is crucial for developing targeted prevention strategies and therapeutic interventions. Additionally, the emerging field of immunotherapy provides potential avenues for managing HNCs associated with systemic inflammatory disorders, but further research is needed to determine its efficacy and safety in this specific context. Future studies are warranted to elucidate the underlying mechanisms and optimize preventive strategies and therapeutic interventions. Full article
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Figure 1

Figure 1
<p>Head and neck cancer regions. This image shows the relation between the different anatomical regions of the head and neck and viral-associated cancers. Adapted from [<a href="#B2-cells-12-02192" class="html-bibr">2</a>].</p>
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<p>Inflammatory bowel disease (IBD) is a term referring to two conditions: Crohn’s disease (CD) and ulcerative colitis (UC), both of which are characterized by chronic inflammation and damage of the gastrointestinal tract. CD (left) has inflammation in patches throughout the large bowel, while UC has uniform and continuous inflammation throughout. If not managed well, IBD flare-ups can be aggressive and debilitating to a person’s quality of life. Adapted from [<a href="#B102-cells-12-02192" class="html-bibr">102</a>]. Image made using BioRender software (2023).</p>
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<p>Structures of drugs azathioprine (<b>1</b>), cyclosporine (<b>2</b>), cyclophosphamide (<b>3</b>) and methotrexate (<b>4</b>). All structures were obtained using ChemDraw software (version 12.0, PerkinElmer, Inc., Waltham, MA, USA).</p>
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26 pages, 6636 KiB  
Article
Changes in the Proteome of Platelets from Patients with Critical Progression of COVID-19
by Monika Wolny, Svitlana Rozanova, Cornelius Knabbe, Kathy Pfeiffer, Katalin Barkovits, Katrin Marcus and Ingvild Birschmann
Cells 2023, 12(17), 2191; https://doi.org/10.3390/cells12172191 - 1 Sep 2023
Cited by 2 | Viewed by 1552
Abstract
Platelets, the smallest cells in human blood, known for their role in primary hemostasis, are also able to interact with pathogens and play a crucial role in the immune response. In severe coronavirus disease 2019 (COVID-19) cases, platelets become overactivated, resulting in the [...] Read more.
Platelets, the smallest cells in human blood, known for their role in primary hemostasis, are also able to interact with pathogens and play a crucial role in the immune response. In severe coronavirus disease 2019 (COVID-19) cases, platelets become overactivated, resulting in the release of granules, exacerbating inflammation and contributing to the cytokine storm. This study aims to further elucidate the role of platelets in COVID-19 progression and to identify predictive biomarkers for disease outcomes. A comparative proteome analysis of highly purified platelets from critically diseased COVID-19 patients with different outcomes (survivors and non-survivors) and age- and sex-matched controls was performed. Platelets from critically diseased COVID-19 patients exhibited significant changes in the levels of proteins associated with protein folding. In addition, a number of proteins with isomerase activity were found to be more highly abundant in patient samples, apparently exerting an influence on platelet activity via the non-genomic properties of the glucocorticoid receptor (GR) and the nuclear factor κ-light-chain-enhancer of activated B cells (NFκB). Moreover, carbonic anhydrase 1 (CA-1) was found to be a candidate biomarker in platelets, showing a significant increase in COVID-19 patients. Full article
(This article belongs to the Special Issue Platelet Biology and Functions)
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Graphical abstract

Graphical abstract
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<p>Studied patient cohorts (<b>a</b>) and comparison of their proteome profiles using principal component analysis (PCA) (<b>b</b>). (<b>a</b>) Between November and December 2021, COVID-19 patients (n = 19) admitted to the HDZ NRW in the ICU were included in this study. The highly purified platelets were prepared within two days after patient admission. The majority of patients died (ICU A, n = 11). Two patients had no identifiable outcome, as they were transferred to other hospitals (ICU N). Six patients survived and were transferred to the normal ward (ICU B). The platelets of five of these patients could be prepared at this time point (NW). The control group included age- and sex-equivalent healthy blood donors. (<b>b</b>) PCA, based on 721 PGs quantified with LFQs in all the samples, showed a distinct clustering of ICU patients (ICU A, non-survivors: red; ICU B, survivors: orange; ICU N, unknowns: gray), NW patients (green), and healthy controls (blue).</p>
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<p>Volcano plot of the different group comparisons. (<b>a</b>) ICU (A+B+N) vs. controls based on 723 PGs found in each sample. Of these, 38 were regulated significantly (FDR &lt; 0.01, S0 = 2). (<b>b</b>) Control vs. NW based on 771 PGs found in each sample. Among these, two (STAT1 and HBB) were significantly different (FDR &lt; 0.01, S0 = 2). (<b>c</b>) ICU (A+B+N) vs. NW based on 784 PGs found in each sample; one protein (FKBP5) was significantly different (FDR &lt; 0.04, S0 = 1). (<b>d</b>) ICU A vs. ICU B based on 784 PGs found in each sample; one protein (STAT1) was significantly varied (FDR &lt; 0.04, S0 = 1). ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Specific analysis of CRP. CRP was analyzed with PRM-MS (<b>top row</b>) and ELISA (<b>bottom row</b>) in both platelets (<b>a</b>,<b>d</b>) and plasma (<b>b</b>,<b>e</b>). A similar pattern was observed with both methods. ICU patients showed the highest CRP level, which was significantly increased compared with the control group. Survivors (ICU B) tended to have marginally lower CRP levels than non-survivors (ICU A). NW tended to present lower CRP levels than ICU. A similar pattern was observed in plasma samples. A correlation between platelet and plasma levels could be demonstrated by both PRM-MS (<span class="html-italic">p</span> &lt; 0.0001) (<b>c</b>) and ELISA (<span class="html-italic">p</span> &lt; 0.0001) (<b>f</b>). Platelet and plasma readings are presented as boxplots. Data were analyzed using the Kruskal–Wallis test followed by Dunn’s multiple comparison. The correlation was determined according to Spearman. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Specific analysis of CA-1. CA-1 was analyzed with PRM-MS and ELISA in both platelets (<b>a</b>,<b>b</b>) and plasma (<b>c</b>). With PRM-MS, CA-1 could only be detected in the platelet lysate. The abundance was significantly higher in the ICU group ((A+B+N) and ICU A), as well as the NW group, than in the control group. There was a tendency toward a decrease in ICU B (survivors) compared with ICU A (non-survivors) and NW. A similar pattern was found with ELISA. No changes were detected in plasma using ELISA. There was no correlation between platelet and plasma concentrations (<span class="html-italic">p</span> = 0.1336) (<b>d</b>). Platelet and plasma readings are presented as boxplots. Data were analyzed using the Kruskal–Wallis test followed by Dunn’s multiple comparison. The correlation was determined according to Spearman. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Comparison of the ICU patient group (A+B+N) with the healthy controls. (<b>a</b>) The top 20 enriched biological processes across 94 PGs exclusively found in ICU. Each bar represents a statistically ranked enrichment term. The proteins selectively present in the patient group showed associations with SARS-CoV-2 and COVID-19 and the further involvement of the immune response in pathway and process enrichment analysis. (<b>b</b>) Network of enriched terms for the 420 differentially abundant proteins in controls and ICU patients (<span class="html-italic">p</span> &lt; 0.05) colored by <span class="html-italic">p</span>-value. Terms containing more genes tend to have a more significant <span class="html-italic">p</span>-value; the darker the color, the more statistically significant the node (see legend for <span class="html-italic">p</span>-value ranges). Pathway and process analysis of regulated and exclusively expressed proteins showed correlation with various mechanisms, especially hemostasis, platelet aggregation and activation, and protein folding and localization. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>The top 20 enriched biological processes across 71 differently abundant PGs (<span class="html-italic">p</span> &lt; 0.05) from the comparison of ICU A vs. ICU B, colored by <span class="html-italic">p</span>-values (the darker the color, the more statistically significant). Each bar represents a statistically ranked enrichment term. Pathway and process analysis showed a clear correlation with hemostasis. In addition, changes in metabolism and cell adhesion were observed. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Specific analysis of Serpin A3 by PRM-MS (top row) and ELISA (bottom row) in both platelets (<b>a</b>,<b>d</b>) and plasma (<b>b</b>,<b>e</b>). Serpin A3 showed the same regulatory pattern in all samples, with the exception of the plasma levels determined with PRM-MS (<b>b</b>). ICU patients presented the highest level, which was significantly higher compared with the controls. NW tended to have a slightly higher abundance compared with controls. Plasma levels determined with PRM-MS revealed no significant differences. A high correlation between platelet and plasma concentration was found with ELISA (<span class="html-italic">p</span> &lt; 0.0001) (<b>f</b>) but not PRM-MS (<span class="html-italic">p</span> = 0.7371) (<b>c</b>). Platelet and plasma readings are presented as boxplots. Data were analyzed using the Kruskal–Wallis test followed by Dunn’s multiple comparison. The correlation was determined according to Spearman. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Specific analysis of SAA-2 by PRM-MS in both platelets (<b>a</b>) and plasma (<b>b</b>). The highest level was detected in the ICU samples for platelets and plasma. This was significantly increased compared with the control samples. In platelets, SAA-2 was not found in NW or controls. Correlation analysis revealed a high correlation between platelets and plasma (<span class="html-italic">p</span> &lt; 0.0001) (<b>c</b>). Platelet and plasma readings are presented as boxplots. Data were analyzed using the Kruskal–Wallis test followed by Dunn’s multiple comparison. The correlation was determined according to Spearman. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>The top 20 enriched biological processes across, colored by <span class="html-italic">p</span>-values (the darker the color, the more statistically significant). (<b>a</b>) 186 differently abundant PGs (<span class="html-italic">p</span> &lt; 0.05) from the comparison of controls vs. NW. Changes were mainly associated with cell adhesion and protein folding and platelet activation, signaling, and aggregation. (<b>b</b>) A total of 122 differently abundant PGs (<span class="html-italic">p</span> &lt; 0.05) from the comparison of ICU (A+B+N) vs. NW. This comparison showed the most significant association with protein folding and cell adhesion and platelet activation, signaling, and aggregation. (<b>c</b>) A total of 116 differently abundant PGs (<span class="html-italic">p</span> &lt; 0.05) from the comparison of ICU B vs. NW. Over time, the survivors showed changes associated with platelet degranulation and protein folding. ICU: intensive care unit (ICU A: non-survivors, ICU B: survivors, ICU N: unknown); NW: normal ward.</p>
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<p>Protein network associated with protein folding. Almost all proteins are upregulated in the patient group when comparing patients and controls. Only PDIA5 (blue arrow) shows downregulation. FKPB4 (red circle) is expressed exclusively in patient samples (ICU (A+B+N) and NW). FKBP5 (red circle) is among the proteins with the highest statistical significance and fold changes in the global proteomic analysis when comparing different groups.</p>
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17 pages, 6144 KiB  
Article
Attenuation of Oxidative Damage via Upregulating Nrf2/HO-1 Signaling Pathway by Protease SH21 with Exerting Anti-Inflammatory and Anticancer Properties In Vitro
by Hasan Tarek, Seung Sik Cho, Md. Selim Hossain and Jin Cheol Yoo
Cells 2023, 12(17), 2190; https://doi.org/10.3390/cells12172190 - 1 Sep 2023
Cited by 4 | Viewed by 1314
Abstract
Oxidative damage and inflammation are among the very significant aspects interrelated with cancer and other degenerative diseases. In this study, we investigated the biological activities of a 25 kDa protease (SH21) that was purified from Bacillus siamensis. SH21 exhibited very powerful antioxidant [...] Read more.
Oxidative damage and inflammation are among the very significant aspects interrelated with cancer and other degenerative diseases. In this study, we investigated the biological activities of a 25 kDa protease (SH21) that was purified from Bacillus siamensis. SH21 exhibited very powerful antioxidant and reactive oxygen species (ROS) generation inhibition activity in a dose-dependent approach. The mRNA and protein levels of antioxidant enzymes such as superoxide dismutase 1 (SOD1), catalase (CAT), and glutathione peroxidase 1 (GPx-1) were enhanced in the SH21-treated sample. SH21 also increased the transcriptional and translational activities of NF-E2-related factor 2 (Nrf2) with the subsequent development of detoxifying enzyme heme oxygenase-1 (HO-1). In addition, SH21 showed potential anti-inflammatory activity via inhibition of nitric oxide (NO) and proinflammatory cytokines, such as TNF-α, IL-6, and IL-1β, production in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. At concentrations of 60, 80, and 100 μg/mL, SH21 potentially suppressed nitric oxide synthase (iNOS) and cytokine gene expressions. Furthermore, SH21 significantly released lactate dehydrogenase (LDH) enzyme in cancer cell supernatant in a concentration-dependent manner and showed strong activity against three tested cancer cell lines, including HL-60, A549, and Hela. Our results suggest that SH21 has effective antioxidant, anti-inflammatory, and anticancer effects and could be an excellent therapeutic agent against inflammation-related diseases. Full article
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<p>Purification procedure of SH21. (<b>A</b>) The elution profiles of Sepharose CL-6B (80 cm × 1.8 cm) and (<b>B</b>) Sephadex G-75 column (1.5 × 20 cm). (<b>C</b>) SDS-PAGE analysis of purified SH21. Lane 1, protein marker (10–170 kDa). Lane 2, crude sample Lane 3, ammonium sulfate precipitation (40–80%) Lane 4, sample after Sepharose CL-6B. Lane 5, purified SH21 after Sephadex G-75 column.</p>
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<p>Antioxidant activity of protease SH21. The (<b>A</b>) DPPH assay, (<b>B</b>) ABTS assay, (<b>C</b>) Superoxide (SOD) radical scavenging assay, (<b>D</b>) Hydroxyl (HO) radical scavenging assay, (<b>E</b>) CUPRAC assay, and (<b>F</b>) FRAP assay were conducted with different concentrations of SH21, whereas ascorbic acid and gallic acid were used as standard antioxidant compounds. The reaction mixture without the sample was used as a negative control. All experiments were performed in triplicate. ** <span class="html-italic">p</span> &lt; 0.01, significantly different from the control, using the student’s <span class="html-italic">t</span>-test.</p>
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<p>Investigation of cell viability and ROS generation inhibition. (<b>A</b>) RAW 264.7 cells were seeded at a density of 2 × 10<sup>4</sup> cells per well and underwent an MTT assay. (<b>B</b>) Intracellular ROS generation. Each experiment was carried out three (n = 3) times (±) standard deviation. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.001, significantly different from normal control, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 significantly different from negative control, using the student’s <span class="html-italic">t</span>-test.</p>
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<p>Evaluation of primary and phase II antioxidant and detoxifying enzymes. RAW 264.7 cells were pretreated for 24 h with various concentrations of SH21. (<b>A</b>) The mRNA expressions of the primary antioxidant enzyme and phase II antioxidant (SOD1, CAT, GPx-1) were measured by RT-PCR and (<b>B</b>) western blot was carried out to estimate protein levels by using the same concentrations. (<b>C</b>) The mRNA levels of detoxifying enzyme HO-1 and nuclear factor Nrf2 were measured by RT-PCR in a dose-dependent manner. The protein expressions of Nrf2 and HO-1 were measured in (<b>D</b>,<b>E</b>) dose-dependent and (<b>F</b>,<b>G</b>) time-dependent manners by western blot analysis. Each result presents the mean of three separate experiments (±) standard deviation.</p>
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<p>Anti-inflammatory activity of protease SH21. Effect of SH21 on the production of (<b>A</b>) NO, (<b>B</b>) TNF-α, (<b>C</b>) IL-6, and (<b>D</b>) IL-1β in LPS-induced RAW 264.7 cells. The cells were treated with SH21 at various concentrations of (20–100 µg/mL) for 2 h and then stimulated with LPS (1 µg/mL) for 24 h. Vertical bars indicate the mean (n = 3) ± standard deviation. (<b>E</b>) Effects of SH21 on LPS-induced mRNA expression of iNOS, TNF-α, IL-6 and IL-1β. Data expressed the means ± SD from three separate experiments. Statistically, significances were expressed as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.001, significantly different from normal control.</p>
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<p>Anticancer activity of protease SH21 against three different cancer cell lines: (<b>A</b>) HL-60, (<b>B</b>) A549, and (<b>C</b>) Hela cells. Values were the means of triplicates (±) standard deviation (SD). Each experiment was perfomed three times (±) standard deviation. Significant differences were denoted as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt;0.01.</p>
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<p>Membrane disruption ability of SH21 was assessed by observing LDH release and confocal microscopy assay. LDH leakage was monitored in three cancer cells, namely, (<b>A</b>) HL-60, (<b>B</b>) A549, and (<b>C</b>) Hela cells. All cancer cells were treated with different concentrations (50–500 μg/mL) of SH21, and absorbance were taken at 490 nm. PBS and Triton X-100 (1%, <span class="html-italic">v</span>/<span class="html-italic">v</span>) employed negative and positive controls, respectively. Values were the means of triplicates (±) standard deviation (SD). Significant differences were denoted as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.001. (<b>D</b>) Live/dead staining assay of SH21 against cancer cells (HL-60, A549, and Hela). Membrane damage was evaluated by employing confocal laser scanning microscopy (CLSM). All tested cancer cells were incubated with 500 μg/mL of SH21 for 24 h. Green fluorescence (Calcein-AM) and red fluorescence (Ethidium homodimer-1) indicated live and dead cells, respectively. Scale Bars = 100 µm.</p>
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<p>Hypothetical antioxidant mechanism of SH21.</p>
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26 pages, 1146 KiB  
Review
Current Advances in Cellular Approaches for Pathophysiology and Treatment of Polycystic Ovary Syndrome
by Yi-Ru Tsai, Yen-Nung Liao and Hong-Yo Kang
Cells 2023, 12(17), 2189; https://doi.org/10.3390/cells12172189 - 31 Aug 2023
Cited by 2 | Viewed by 3605
Abstract
Polycystic ovary syndrome (PCOS) is a prevalent gynecological and endocrine disorder that results in irregular menstruation, incomplete follicular development, disrupted ovulation, and reduced fertility rates among affected women of reproductive age. While these symptoms can be managed through appropriate medication and lifestyle interventions, [...] Read more.
Polycystic ovary syndrome (PCOS) is a prevalent gynecological and endocrine disorder that results in irregular menstruation, incomplete follicular development, disrupted ovulation, and reduced fertility rates among affected women of reproductive age. While these symptoms can be managed through appropriate medication and lifestyle interventions, both etiology and treatment options remain limited. Here we provide a comprehensive overview of the latest advancements in cellular approaches utilized for investigating the pathophysiology of PCOS through in vitro cell models, to avoid the confounding systemic effects such as in vitro fertilization (IVF) therapy. The primary objective is to enhance the understanding of abnormalities in PCOS-associated folliculogenesis, particularly focusing on the aberrant roles of granulosa cells and other relevant cell types. Furthermore, this article encompasses analyses of the mechanisms and signaling pathways, microRNA expression and target genes altered in PCOS, and explores the pharmacological approaches considered as potential treatments. By summarizing the aforementioned key findings, this article not only allows us to appreciate the value of using in vitro cell models, but also provides guidance for selecting suitable research models to facilitate the identification of potential treatments and understand the pathophysiology of PCOS at the cellular level. Full article
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<p>The common in-vitro models used in PCOS studies are outlined. The primary GCs from humans and rodents, cell lines, and others related to the reproductive system or endocrine system are utilized in PCOS research.</p>
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<p>PCOS is a disorder characterized by polycystic ovaries, hyperandrogenism, and ovulatory dysfunction. The common concepts pertaining to PCOS in in-vitro studies are shown. In-vitro models can be used to study signal transduction, microRNA regulation, and subsequent biological processes.</p>
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13 pages, 3756 KiB  
Article
A Protocol for Organoids from the Urine of Bladder Cancer Patients
by Simon Walz, Paul Pollehne, Ruizhi Geng, Johannes Schneider, Moritz Maas, Wilhelm K. Aicher, Arnulf Stenzl, Bastian Amend and Niklas Harland
Cells 2023, 12(17), 2188; https://doi.org/10.3390/cells12172188 - 31 Aug 2023
Cited by 4 | Viewed by 1792
Abstract
This study investigates the feasibility of establishing urine-derived tumor organoids from bladder cancer (BC) patients as an alternative to tissue-derived organoids. BC is one of the most common cancers worldwide and current diagnostic methods involve invasive procedures. Here, we investigated the potential of [...] Read more.
This study investigates the feasibility of establishing urine-derived tumor organoids from bladder cancer (BC) patients as an alternative to tissue-derived organoids. BC is one of the most common cancers worldwide and current diagnostic methods involve invasive procedures. Here, we investigated the potential of using urine samples, which contain exfoliated tumor cells, to generate urine-derived BC organoids (uBCOs). Urine samples from 29 BC patients were collected and cells were isolated and cultured in a three-dimensional matrix. The establishment and primary expansion of uBCOs were successful in 83% of the specimens investigated. The culturing efficiency of uBCOs was comparable to cancer tissue-derived organoids. Immunohistochemistry and immunofluorescence to characterize the uBCOs exhibited similar expressions of BC markers compared to the parental tumor. These findings suggest that urine-derived BC organoids hold promise as a non-invasive tool for studying BC and evaluating therapeutic responses. This approach could potentially minimize the need for invasive procedures and provide a platform for personalized drug screening. Further research in this area may lead to improved diagnostic and treatment strategies for BC patients. Full article
(This article belongs to the Collection Advances in 3D Cell Culture)
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<p>Method and workflow of the culturing process of uBCOs. The materials and methods section contains a comprehensive and detailed step-by-step workflow, along with the materials and reagents used in the study. Abbreviations: BC, bladder cancer; uBCO, urine-derived bladder cancer organoid. Created with biorender.com (accessed 12 May 2023).</p>
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<p>Culturing efficiency of uBCOs (indicated in black) and tBCOs (indicated in grey hatched) (<b>A</b>) with respect to the individual passages (efficiency rate). Culturing efficiency according to patient characteristics and histopathological features with regard to the maximum achieved passage (<b>B</b>–<b>I</b>). Dots represent data from individual organoids. Boxes indicate median and 25th and 75th percentiles with min/max whiskers. <span class="html-italic">p</span>-values indicate differences between two groups (A: two-sided Fischer exact test; B: Mann–Whitney-U test). The ρ-values indicate linear correlations between two parameters (Spearman correlation). Abbreviations: uBCO, urine-derived bladder cancer organoid; tBCO, tissue-derived bladder cancer organoid; NMIBC, non-muscle- invasive bladder cancer; MIBC, muscle-invasive bladder cancer.</p>
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<p>Light microscopic images of uBCO #027 during cultivation in basal membrane extract in the upper row on the first, fourth, and thirteenth day of the first passage, in the middle and lower row on the first and seventh day of the third and fourth passage, respectively. Size bars indicate 100 mm. Abbreviations: uBCO, urine-derived bladder cancer organoid; P, passage; D, day.</p>
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<p>Immunohistochemical images from parental primary BC (left column) and autologous, uBCO #027 (middle column) in the fifth passage. The left column displays fluorescence staining of relevant antigens in urothelial cancer or tumor stem cell antigens in the corresponding uBCO #027 (passage 5). The tBCO only reached the first passage and could, therefore, not be implemented in the analysis. Abbreviations: BC, bladder cancer; uBCO, urine-derived bladder cancer organoid; tBCO, tissue-derived bladder cancer organoid HE, hematoxylin-eosin; GATA, glutamyl amino tranverase A; CK, cytoceratine; p, protein; CD, cluster of differentiation.</p>
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16 pages, 951 KiB  
Review
MT1-MMP as a Key Regulator of Metastasis
by Noritaka Tanaka and Takeharu Sakamoto
Cells 2023, 12(17), 2187; https://doi.org/10.3390/cells12172187 - 31 Aug 2023
Cited by 9 | Viewed by 2356
Abstract
Membrane type1-matrix metalloproteinase (MT1-MMP) is a member of metalloproteinases that is tethered to the transmembrane. Its major function in cancer progression is to directly degrade the extracellular matrix components, which are mainly type I–III collagen or indirectly type IV collagen through the activation [...] Read more.
Membrane type1-matrix metalloproteinase (MT1-MMP) is a member of metalloproteinases that is tethered to the transmembrane. Its major function in cancer progression is to directly degrade the extracellular matrix components, which are mainly type I–III collagen or indirectly type IV collagen through the activation of MMP-2 with a cooperative function of the tissue inhibitor of metalloproteinase-2 (TIMP-2). MT1-MMP is expressed as an inactive form (zymogen) within the endoplasmic reticulum (ER) and receives truncation processing via furin for its activation. Upon the appropriate trafficking of MT1-MMP from the ER, the Golgi apparatus to the cell surface membrane, MT1-MMP exhibits proteolytic activities to the surrounding molecules such as extracellular matrix components and cell surface molecules. MT1-MMP also retains a non-proteolytic ability to activate hypoxia-inducible factor 1 alpha (HIF-1A) via factors inhibiting the HIF-1 (FIH-1)-Mint3-HIF-1 axis, resulting in the upregulation of glucose metabolism and oxygen-independent ATP production. Through various functions of MT1-MMP, cancer cells gain motility on migration/invasion, thus causing metastasis. Despite the long-time efforts spent on the development of MT1-MMP interventions, none have been accomplished yet due to the side effects caused by off-target effects. Recently, MT1-MMP-specific small molecule inhibitors or an antibody have been reported and these inhibitors could potentially be novel agents for cancer treatment. Full article
(This article belongs to the Special Issue Cellular and Molecular Mechanisms of Cancer Invasion and Metastasis)
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<p>Exon mapping of MMP proteins. An exon mapping of MT1-MMP as a representative membrane type MMP is described compared with the MMP-1 as an original MMP and MMP-2 as an MMP that has a core relationship with MT1-MMP. SP: signal peptide, Pro: pro-domain, CAT: catalytic domain, HPX: HPX domain, TM: transmembrane domain, CPT: cytoplasmic tail, FN: fibronectin-like domain, RXKR: RXKR motif.</p>
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<p>Schematic non-proteolytic function of MT1-MMP. Non-proteolytic function of MT1-MMP is mainly regulated via its cytoplasmic region. The internalization and recycling circuit of MT1-MMP required for its intact proteinase activity is mediated via the palmitoylation at C574 residue. On the other hand, ECMs such as type I collagen induce the cell surface colocalization of MT1-MMP and integrin subunits, which is also bound with the cytoplasmic region of MT1-MMP. Under intercellular contacts, this complex impairs the internalization of MT1-MMP. Additionally, FIH-1 bound at the cytoplasmic region of MT1-MMP lacks its ability to bind with HIF-1; thus, it means MT1-MMP is indirectly involved in upregulating HIF-1 activity.</p>
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5 pages, 785 KiB  
Comment
βIV-Spectrin in Cardiac Fibroblasts: Implications for Fibrosis and Therapeutic Targeting in Cardiac Diseases. Comment on Nassal et al. Spectrin-Based Regulation of Cardiac Fibroblast Cell-Cell Communication. Cells 2023, 12, 748
by Wenjing Xiang, Ning Zhou, Lei Li, Faming Chen, Lei Li and Ying Wang
Cells 2023, 12(17), 2186; https://doi.org/10.3390/cells12172186 - 31 Aug 2023
Viewed by 872
Abstract
Fibroblasts in the heart, traditionally recognized as interstitial cells, have long been overlooked in the study of cardiac physiology and pathology [...] Full article
(This article belongs to the Special Issue Research Advances Related to Cardiovascular System)
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<p>βIV-spectrin/CaMKII/STAT3 signalosome regulates fibroblast–fibroblast communication and fibrosis. The reduction in βIV-spectrin caused by ablation or CaMKII-mediated degradation promotes STAT3 translation into the nucleus. βIV-spectrin forms a complex with STAT3 and CaMKII, which directly phosphorylate βIV-spectrin. CaMKII: Calcium/calmodulin-dependent protein kinase II. STAT3: signal transducer and activator of transcription 3 (STAT3).</p>
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<p>Dysregulation of βIV-spectrin in heart failure with preserved ejection fraction (HFpEF) (GSE3586).</p>
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12 pages, 1894 KiB  
Article
Aquaglyceroporins in Human Breast Cancer
by Teresa Kirkegaard, Andreas Riishede, Trine Tramm and Lene N. Nejsum
Cells 2023, 12(17), 2185; https://doi.org/10.3390/cells12172185 - 31 Aug 2023
Cited by 3 | Viewed by 1465
Abstract
Aquaporins are water channels that facilitate passive water transport across cellular membranes following an osmotic gradient and are essential in the regulation of body water homeostasis. Several aquaporins are overexpressed in breast cancer, and AQP1, AQP3 and AQP5 have been linked to spread [...] Read more.
Aquaporins are water channels that facilitate passive water transport across cellular membranes following an osmotic gradient and are essential in the regulation of body water homeostasis. Several aquaporins are overexpressed in breast cancer, and AQP1, AQP3 and AQP5 have been linked to spread to lymph nodes and poor prognosis. The subgroup aquaglyceroporins also facilitate the transport of glycerol and are thus involved in cellular metabolism. Transcriptomic analysis revealed that the three aquaglyceroporins, AQP3, AQP7 and AQP9, but not AQP10, are overexpressed in human breast cancer. It is, however, unknown if they are all expressed in the same cells or have a heterogeneous expression pattern. To investigate this, we employed immunohistochemical analysis of serial sections from human invasive ductal and lobular breast cancers. We found that AQP3, AQP7 and AQP9 are homogeneously expressed in almost all cells in both premalignant in situ lesions and invasive lesions. Thus, potential intervention strategies targeting cellular metabolism via the aquaglyceroporins should consider all three expressed aquaglyceroporins, namely AQP3, AQP7 and AQP9. Full article
(This article belongs to the Special Issue Advances in Aquaporins II)
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<p>Graphical illustration of workflow. Formalin-fixed, paraffin-embedded samples from invasive female breast cancer tumors were obtained in fully anonymized form from surgical specimens from the Department of Pathology, Aarhus University Hospital, Denmark. Following retrieval, the anonymized tissue samples were sectioned. For diagnostic assessment, hematoxylin and eosin (H&amp;E) stainings, as well as immunohistochemical stainings for E-cadherin, were performed. Samples representing invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) were further analyzed. Serial sections were processed for immunohistochemical stainings in the following order: #1 AQP7, #2 AQP9, #3 AQP3 and #4 SMMS-1. The SMMS-1 staining identified myoepithelial cells, allowing a distinction between in situ and invasion lesions. Images of all samples were captured by brightfield light microscopy, and select slides for publication figures were scanned on a NanoZoomer 2.0HT (Hamamatsu) with the 40× objective. Part of this figure was generated in BioRender.com; accessed on August 11th 2023.</p>
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<p>AQP7, AQP9 and AQP3 localize to epithelial cells of lobules and extralobular ducts in benign regions adjacent to tumor tissue. Formalin-fixed, paraffin-embedded samples from invasive female breast cancer tumors were obtained in fully anonymized form from surgical specimens and processed for immunohistochemistry with antibodies against AQP7, AQP9, AQP3 and SMMS-1. The figure depicts representative images from immunohistochemical stainings of serial sections from the normal/benign part of samples from invasive lobular carcinoma. (<b>A</b>) Lobule and (<b>B</b>) extralobular duct. Immune cells in the surrounding connective tissue also stain positive (red asterisks in B). Arrowheads in B point to myoepithelial cells, and the arrows in B point to the apical region of extralobular ducts. Slides were scanned in a NanoZoomer 2.0HT (Hamamatsu) using the 40× objective. White balance was adjusted in Adobe Photoshop CS4. Scale bars are 100 µm.</p>
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<p>AQP7, AQP9 and AQP3 localize to neoplastic cells of lobular carcinoma in situ and invasive lobular carcinoma. Formalin-fixed, paraffin-embedded samples from invasive female breast cancer tumors were obtained in fully anonymized form from surgical specimens and processed for immunohistochemistry with antibodies against AQP7, AQP9, AQP3 and SMMS-1. The figure depicts representative images from immunohistochemical stainings of serial sections from patients with invasive lobular carcinoma (ILC) and enlarged images (inserts) of the marked areas. (<b>A</b>) Images are from a region of lobular carcinoma in situ (LCIS) from an ILC patient, and (<b>B</b>) is ILC. Slides were scanned in a NanoZoomer 2.0HT (Hamamatsu) using the 40× objective. White balance was adjusted in Adobe Photoshop CS4. Scale bars are 100 µm (<b>A</b>,<b>B</b>) and 20 µm ((<b>A</b>,<b>B</b>), inserts).</p>
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<p>AQP7, AQP9 and AQP3 localize to neoplastic cells of ductal carcinoma in situ and invasive ductal carcinoma. Formalin-fixed, paraffin-embedded samples from invasive female breast cancer tumors were obtained in fully anonymized form from surgical specimens and processed for immunohistochemistry with antibodies against AQP7, AQP9, AQP3 and SMMS-1. The figure depicts representative images from immunohistochemical stainings of serial sections from patients with invasive ductal carcinoma (IDC) and enlarged images (inserts) of the marked areas. (<b>A</b>) Images are from a region of ductal carcinoma in situ (DCIS) from an IDC patient, and (<b>B</b>) is IDC. Slides were scanned in a NanoZoomer 2.0HT (Hamamatsu) using the 40x objective. White balance was adjusted in Adobe Photoshop CS4. Scale bars are 175 µm (<b>A</b>,<b>B</b>) and 50 µm ((<b>A</b>,<b>B</b>), inserts).</p>
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16 pages, 6947 KiB  
Article
miR-369-3p Modulates Intestinal Inflammatory Response via BRCC3/NLRP3 Inflammasome Axis
by Viviana Scalavino, Emanuele Piccinno, Anna Maria Valentini, Nicolò Schena, Raffaele Armentano, Gianluigi Giannelli and Grazia Serino
Cells 2023, 12(17), 2184; https://doi.org/10.3390/cells12172184 - 31 Aug 2023
Cited by 7 | Viewed by 1399
Abstract
Inflammasomes are multiprotein complexes expressed by immune cells in response to distinct stimuli that trigger inflammatory responses and the release of pro-inflammatory cytokines. Evidence suggests a different role of inflammasome NLRP3 in IBD. NLRP3 inflammasome activation can be controlled by post-translational modifications such [...] Read more.
Inflammasomes are multiprotein complexes expressed by immune cells in response to distinct stimuli that trigger inflammatory responses and the release of pro-inflammatory cytokines. Evidence suggests a different role of inflammasome NLRP3 in IBD. NLRP3 inflammasome activation can be controlled by post-translational modifications such as ubiquitination through BRCC3. The aim of this study was to investigate the effect of miR-369-3p on the expression and activation of NLRP3 inflammasomes via BRCC3 regulation. After bioinformatics prediction of Brcc3 as a gene target of miR-369-3p, in vitro, we validated its modulation in bone marrow-derived macrophages (BMDM). The increase in miR-369-3p significantly reduced BRCC3 gene and protein expression. This modulation, in turn, reduced the expression of NLRP3 and blocked the recruitment of ASC adaptor protein by NLRP3. As a result, miR-369-3p reduced the activity of Caspase-1 by the inflammasome, decreasing the cleavage of pro-IL-1β and pro-IL-18. These results support a novel mechanism that seems to act on post-translational modification of NLRP3 inflammasome activation by BRCC3. This may be an interesting new target in the personalized treatment of inflammatory disorders, including IBD. Full article
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<p>miR-369-3p targets Brrc3. (<b>A</b>) Sequence alignments of miR-369-3p and binding sites in 3′ UTR of Brcc3 mRNA. (<b>B</b>) The mRNA expression levels of Brcc3 evaluated by qRT-PCR in BMDM cells transfected with 30 nM and 50 nM of miR-369-3p mimic both in the basal condition and after inflammasome activation. (<b>C</b>) Western blot analysis of BRCC3 protein expression after miR-369-3p mimic transfection in unstimulated BMDM cells as well as following inflammasome activation of the BMDM cell line. To normalize data, GAPDH was used as housekeeping protein. Data are representative of four independent experiments. Raw data of the independent experiments of Western blot were reported in <a href="#app1-cells-12-02184" class="html-app">Supplementary File S1</a>. The histograms correspond to 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).</p>
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<p>miR-369-3p regulates Nlrp3 mRNA and protein expression. (<b>A</b>) The mRNA expression levels of Nlrp3 evaluated by qRT-PCR in BMDM cells transfected with 30 nM and 50 nM of miR-369-3p mimic both in the basal condition and after inflammasome-activating stimulations. (<b>B</b>) Western blot analysis of NLRP3 protein expression after miR-369-3p mimic transfection in unstimulated BMDM cells as well as following inflammasome activation in the BMDM cell line. To normalize data, GAPDH was used as housekeeping protein. Data are representative of four independent experiments. Raw data of the independent experiments of Western blot were reported in <a href="#app1-cells-12-02184" class="html-app">Supplementary File S1</a>. The histograms correspond to mean ± SEM. (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Immunofluorescence staining of ASC in BMDM cell cultures after miR-369-3p mimic transfection. Representative images of BMDM transfected with miR-369-3p mimic and stimulated with LPS 1 μg/mL for 4 h then Nigericin 20 μM for 30 min. Mock condition and transfected conditions were acquired by fluorescence microscopy. Red spots represent the expression of ASC, while DAPI (blue) correspond to cells’ nuclei. White arrows point to ASC expression. Original magnification, ×20. Scale bar presents 50 μm.</p>
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<p>miR-369-3p modulated the deubiquitination of NLRP3 by BRCC3. (<b>A</b>) Endogenous NLRP3 immunoprecipitates were analyzed for ubiquitination. miR-369-3p modulated the deubiquitination of NLRP3 by BRCC3 after miR-369-3p transient transfection and LPS and Nigericin stimulation. (<b>B</b>) Expression of NLRP3 and BRCC3 in BMDM lysates after miR-369-3p transient transfection and LPS and Nigericin stimulation (input samples). (<b>C</b>) Histograms of NLRP3 and BRCC3 expression from lysates of BMDM treated with miR-369-3p mimic and stimulated with LPS and Nigericin. To normalize data, GAPDH was used as housekeeping protein. Raw data of the independent experiments of Western blot were reported in <a href="#app1-cells-12-02184" class="html-app">Supplementary File S1</a>. The histograms correspond to mean ± SEM. Data are representative of four independent experiments. (* <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>miR-369-3p reduced the activation and release of caspase-1. (<b>A</b>) Western blot analysis of pro-CASP1 protein expression after miR-369-3p mimic transfection following LPS and Nigericin stimulations in the BMDM cell line. To normalize data, GAPDH was used as housekeeping protein. Raw data of the independent experiments of Western blot were reported in <a href="#app1-cells-12-02184" class="html-app">Supplementary File S1</a>. (<b>B</b>) Caspase-1 activity after LPS and Nigericin stimulations in the mimic transfected BMDM cell line. (<b>C</b>) In vitro cell viability assay of BMDM cells transiently transfected with miR-369-3p mimic and stimulated with LPS and Nigericin. (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>miR-369-3p modulated the release of pro-inflammatory cytokines, NLRP3 inflammasome-related. miR-369-3p induction in BMDM after mimic transfection led to a significant decrease in IL-1β (<b>A</b>), IL-18 (<b>B</b>) production in response to LPS and LPS and Nigericin stimulation. Data are representative of four independent experiments. (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>BRCC3 and NLRP3 and ASC expression in UC patients. (<b>A</b>) BRCC3, NLRP3, and ASC protein expression in formalin-fixed, paraffin-embedded tissues obtained from healthy controls and ulcerative colitis (UC) patients. Original magnification, ×4. (<b>B</b>) Inflammatory score representing the expression levels of BRCC3, NLRP3, and ASC proteins in the immune infiltrate (* <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001).</p>
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38 pages, 821 KiB  
Review
Mitochondrial Properties in Skeletal Muscle Fiber
by Han Dong and Shih-Yin Tsai
Cells 2023, 12(17), 2183; https://doi.org/10.3390/cells12172183 - 30 Aug 2023
Cited by 15 | Viewed by 10287
Abstract
Mitochondria are the primary source of energy production and are implicated in a wide range of biological processes in most eukaryotic cells. Skeletal muscle heavily relies on mitochondria for energy supplements. In addition to being a powerhouse, mitochondria evoke many functions in skeletal [...] Read more.
Mitochondria are the primary source of energy production and are implicated in a wide range of biological processes in most eukaryotic cells. Skeletal muscle heavily relies on mitochondria for energy supplements. In addition to being a powerhouse, mitochondria evoke many functions in skeletal muscle, including regulating calcium and reactive oxygen species levels. A healthy mitochondria population is necessary for the preservation of skeletal muscle homeostasis, while mitochondria dysregulation is linked to numerous myopathies. In this review, we summarize the recent studies on mitochondria function and quality control in skeletal muscle, focusing mainly on in vivo studies of rodents and human subjects. With an emphasis on the interplay between mitochondrial functions concerning the muscle fiber type-specific phenotypes, we also discuss the effect of aging and exercise on the remodeling of skeletal muscle and mitochondria properties. Full article
(This article belongs to the Special Issue Mitochondria: New Findings from Single Cells to Organs)
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<p>Molecular mechanism of mitochondrial dynamic and its relevant to age-related muscle atrophy. Mitochondria in skeletal muscle form a dynamic network and can be reshaped by fusion and fission process. Relationship between mitochondria dynamic and age-related muscle fiber atrophy is highlighted in this figure. Mitochondria in oxidative fiber is more filamentous which prevent the oxidative fiber from age-related loss of mitochondria and thus preserve the oxidative fiber with age. On the other hand, mitochondria in glycolytic fiber is more fragmented which cause glycolytic fiber to be affected more during aging.</p>
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13 pages, 634 KiB  
Perspective
Metformin: A New Inhibitor of the Wnt Signaling Pathway in Cancer
by Domenico Conza, Paola Mirra, Francesca Fiory, Luigi Insabato, Antonella Nicolò, Francesco Beguinot and Luca Ulianich
Cells 2023, 12(17), 2182; https://doi.org/10.3390/cells12172182 - 30 Aug 2023
Cited by 3 | Viewed by 1976
Abstract
The biguanide drug metformin is widely used in type 2 diabetes mellitus therapy, due to its ability to decrease serum glucose levels, mainly by reducing hepatic gluconeogenesis and glycogenolysis. A considerable number of studies have shown that metformin, besides its antidiabetic action, can [...] Read more.
The biguanide drug metformin is widely used in type 2 diabetes mellitus therapy, due to its ability to decrease serum glucose levels, mainly by reducing hepatic gluconeogenesis and glycogenolysis. A considerable number of studies have shown that metformin, besides its antidiabetic action, can improve other disease states, such as polycystic ovary disease, acute kidney injury, neurological disorders, cognitive impairment and renal damage. In addition, metformin is well known to suppress the growth and progression of different types of cancer cells both in vitro and in vivo. Accordingly, several epidemiological studies suggest that metformin is capable of lowering cancer risk and reducing the rate of cancer deaths among diabetic patients. The antitumoral effects of metformin have been proposed to be mainly mediated by the activation of the AMP-activated protein kinase (AMPK). However, a number of signaling pathways, both dependent and independent of AMPK activation, have been reported to be involved in metformin antitumoral action. Among these, the Wingless and Int signaling pathway have recently been included. Here, we will focus our attention on the main molecular mechanisms involved. Full article
(This article belongs to the Special Issue From Mechanisms to Therapeutics: Wnt Signaling in Cancer)
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<p>Metformin inhibits the Wnt pathway through multiple mechanisms. Wnt/β-catenin signaling can be initiated either by Wnt ligands, or by inactivating mutations of APC, or by stabilizing mutations of β-catenin, all resulting in β-catenin accumulation. β-catenin binds LEF/TCF transcription factors and induces target genes, regulating EMT, migration, stemness and chemoresistance of cancer cells. In the right section of the picture, metformin impairs ATP production and, thus, the activity of the ATP-dependent H+ pumps, leading to low intracellular pH, ER stress with UPR induction, an increase in DDIT3 and inhibition of the β-catenin/LEF/TCF complex formation. In the left section of the picture, metformin can upregulate Klotho, preventing Wnt ligand binding; inhibit directly Wnt3a; activate AMPK that can inhibit AKT, interfering with β-catenin accumulation; activate the LKB1/AMPK axis causing proteasomal destruction of Dvl3; inhibit, via AMPK, the HNF4α-dependent transcription of Wnt5; inhibit the METTL3 dependent m6A modification of PPARGC1A, causing BAMBI and β-catenin inhibition; increase tumor suppressor miR-145 and inhibit oncomiR miR-121, having inhibitory or stimulatory effects, respectively, on β-catenin activity, through still unidentified mediators.</p>
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21 pages, 1551 KiB  
Review
New Views of the DNA Repair Protein Ataxia–Telangiectasia Mutated in Central Neurons: Contribution in Synaptic Dysfunctions of Neurodevelopmental and Neurodegenerative Diseases
by Sabrina Briguglio, Clara Cambria, Elena Albizzati, Elena Marcello, Giovanni Provenzano, Angelisa Frasca and Flavia Antonucci
Cells 2023, 12(17), 2181; https://doi.org/10.3390/cells12172181 - 30 Aug 2023
Cited by 2 | Viewed by 2273
Abstract
Ataxia–Telangiectasia Mutated (ATM) is a serine/threonine protein kinase principally known to orchestrate DNA repair processes upon DNA double-strand breaks (DSBs). Mutations in the Atm gene lead to Ataxia–Telangiectasia (AT), a recessive disorder characterized by ataxic movements consequent to cerebellar atrophy or dysfunction, along [...] Read more.
Ataxia–Telangiectasia Mutated (ATM) is a serine/threonine protein kinase principally known to orchestrate DNA repair processes upon DNA double-strand breaks (DSBs). Mutations in the Atm gene lead to Ataxia–Telangiectasia (AT), a recessive disorder characterized by ataxic movements consequent to cerebellar atrophy or dysfunction, along with immune alterations, genomic instability, and predisposition to cancer. AT patients show variable phenotypes ranging from neurologic abnormalities and cognitive impairments to more recently described neuropsychiatric features pointing to symptoms hardly ascribable to the canonical functions of ATM in DNA damage response (DDR). Indeed, evidence suggests that cognitive abilities rely on the proper functioning of DSB machinery and specific synaptic changes in central neurons of ATM-deficient mice unveiled unexpected roles of ATM at the synapse. Thus, in the present review, upon a brief recall of DNA damage responses, we focus our attention on the role of ATM in neuronal physiology and pathology and we discuss recent findings showing structural and functional changes in hippocampal and cortical synapses of AT mouse models. Collectively, a deeper knowledge of ATM-dependent mechanisms in neurons is necessary not only for a better comprehension of AT neurological phenotypes, but also for a higher understanding of the pathological mechanisms in neurodevelopmental and degenerative disorders involving ATM dysfunctions. Full article
(This article belongs to the Topic Animal Models of Human Disease)
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<p>Structural and functional changes in ATM-depleted hippocampal neurons. On the left: excitatory and inhibitory WT neurons with normal (1) ATM expression, (2) Egr4 transcription factor activity, (3–4) KKC2 transcription and protein production, (5) kainate receptor (KAR) levels and (6) excitatory or inhibitory post-synaptic current (EPSC or IPSC) amplitude. On the right: (1) ATM is genetically reduced or pharmacologically inhibited by KU55933 (ΔATM). (2). The rapid Egr4-dependent activation of the <span class="html-italic">Kcc2b</span> promoter occurs, leading to (3) enhanced expression of KCC2. Higher KCC2 levels result in a premature development of GABAergic system (GABA-switch) and increased number of GABAergic synapses. (4) KCC2 coexists in a macromolecular complex with synaptic KARs and, at the presynapse, (5) KARs promote (6) clustering of glutamatergic and GABAergic synaptic vesicles in the Readily Releasable Pool (RRP), resulting in (7) higher evoked glutamatergic or GABAergic currents.</p>
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25 pages, 16017 KiB  
Systematic Review
Arginine, Transsulfuration, and Folic Acid Pathway Metabolomics in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis
by Angelo Zinellu and Arduino A. Mangoni
Cells 2023, 12(17), 2180; https://doi.org/10.3390/cells12172180 - 30 Aug 2023
Cited by 4 | Viewed by 1895
Abstract
There is an increasing interest in biomarkers of nitric oxide dysregulation and oxidative stress to guide management and identify new therapeutic targets in patients with chronic obstructive pulmonary disease (COPD). We conducted a systematic review and meta-analysis of the association between circulating metabolites [...] Read more.
There is an increasing interest in biomarkers of nitric oxide dysregulation and oxidative stress to guide management and identify new therapeutic targets in patients with chronic obstructive pulmonary disease (COPD). We conducted a systematic review and meta-analysis of the association between circulating metabolites within the arginine (arginine, citrulline, ornithine, asymmetric, ADMA, and symmetric, SDMA dimethylarginine), transsulfuration (methionine, homocysteine, and cysteine) and folic acid (folic acid, vitamin B6, and vitamin B12) metabolic pathways and COPD. We searched electronic databases from inception to 30 June 2023 and assessed the risk of bias and the certainty of evidence. In 21 eligible studies, compared to healthy controls, patients with stable COPD had significantly lower methionine (standardized mean difference, SMD = −0.50, 95% CI −0.95 to −0.05, p = 0.029) and folic acid (SMD = −0.37, 95% CI −0.65 to −0.09, p = 0.009), and higher homocysteine (SMD = 0.78, 95% CI 0.48 to 1.07, p < 0.001) and cysteine concentrations (SMD = 0.34, 95% CI 0.02 to 0.66, p = 0.038). Additionally, COPD was associated with significantly higher ADMA (SMD = 1.27, 95% CI 0.08 to 2.46, p = 0.037), SDMA (SMD = 3.94, 95% CI 0.79 to 7.08, p = 0.014), and ornithine concentrations (SMD = 0.67, 95% CI 0.13 to 1.22, p = 0.015). In subgroup analysis, the SMD of homocysteine was significantly associated with the biological matrix assessed and the forced expiratory volume in the first second to forced vital capacity ratio, but not with age, study location, or analytical method used. Our study suggests that the presence of significant alterations in metabolites within the arginine, transsulfuration, and folic acid pathways can be useful for assessing nitric oxide dysregulation and oxidative stress and identifying novel treatment targets in COPD. (PROSPERO registration number: CRD42023448036.) Full article
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<p>Schematic representation of the arginine, transsulfuration, and folic acid metabolic pathways. 5,10-MeTHF, 5,10-methylenetetrahydrofolate; CBS, cystathionine β-synthase; CGL, cystathionine γ-lyase; ADMA, asymmetric dimethylarginine; SDMA, symmetric dimethylarginine; DDAH1, dimethylarginine dimethylaminohydrolase 1; MAT, methionine adenosyltransferase; MHTFR, 5,10-methylenetetrahydrofolate reductase; MS, methionine synthase; NOS, nitric oxide synthase; PRMTs, protein arginine methyltransferases; SAH, S-adenosyl-homocysteine; SAM, S-adenosyl-methionine; SAHH, S-Adenosylhomocysteine hydrolase; SHMT, serine hydroxymethyltransferase. CBS and CGL are vitamin B<sub>6</sub> dependent; methionine synthase is vitamin B<sub>12</sub> dependent.</p>
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<p>PRISMA 2020 flow diagram.</p>
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<p>Forest plot of homocysteine concentrations in COPD patients and controls [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Sensitivity analysis of the association between homocysteine and COPD [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Funnel plot of studies investigating homocysteine in COPD after “trimming-and-filling”.</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to patient age (≤70 years or ˃70 years) [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to study continent [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to analytical method [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to the detection method used with liquid chromatography [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>].</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to measurement in serum or plasma [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B93-cells-12-02180" class="html-bibr">93</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating homocysteine concentrations in COPD patients and controls according to FEV<sub>1</sub> (≤55% or ˃55% years) [<a href="#B89-cells-12-02180" class="html-bibr">89</a>,<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B100-cells-12-02180" class="html-bibr">100</a>,<a href="#B104-cells-12-02180" class="html-bibr">104</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies examining homocysteine concentration in COPD patients and controls according to FEV<sub>1</sub>/FVC (≤60% vs. ˃60%) [<a href="#B90-cells-12-02180" class="html-bibr">90</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>,<a href="#B105-cells-12-02180" class="html-bibr">105</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating cysteine concentrations in COPD patients and controls [<a href="#B87-cells-12-02180" class="html-bibr">87</a>,<a href="#B106-cells-12-02180" class="html-bibr">106</a>].</p>
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<p>Forest plot of studies investigating methionine concentrations in COPD patients and controls [<a href="#B86-cells-12-02180" class="html-bibr">86</a>,<a href="#B88-cells-12-02180" class="html-bibr">88</a>].</p>
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<p>Forest plot of studies investigating vitamin B<sub>12</sub> concentrations in COPD patients and controls [<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>].</p>
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<p>Forest plot of studies investigating folic acid concentrations in COPD patients and controls [<a href="#B91-cells-12-02180" class="html-bibr">91</a>,<a href="#B92-cells-12-02180" class="html-bibr">92</a>,<a href="#B99-cells-12-02180" class="html-bibr">99</a>].</p>
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<p>Forest plot of studies investigating arginine concentrations in COPD patients and controls [<a href="#B86-cells-12-02180" class="html-bibr">86</a>,<a href="#B88-cells-12-02180" class="html-bibr">88</a>,<a href="#B94-cells-12-02180" class="html-bibr">94</a>,<a href="#B95-cells-12-02180" class="html-bibr">95</a>].</p>
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<p>Forest plot of studies investigating ADMA concentrations in COPD patients and controls [<a href="#B94-cells-12-02180" class="html-bibr">94</a>,<a href="#B95-cells-12-02180" class="html-bibr">95</a>,<a href="#B96-cells-12-02180" class="html-bibr">96</a>,<a href="#B97-cells-12-02180" class="html-bibr">97</a>,<a href="#B98-cells-12-02180" class="html-bibr">98</a>,<a href="#B101-cells-12-02180" class="html-bibr">101</a>].</p>
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<p>Forest plot of studies investigating SDMA concentrations in COPD patients and controls [<a href="#B94-cells-12-02180" class="html-bibr">94</a>,<a href="#B95-cells-12-02180" class="html-bibr">95</a>,<a href="#B102-cells-12-02180" class="html-bibr">102</a>].</p>
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<p>Forest plot of studies investigating ornithine concentrations in COPD patients and controls [<a href="#B86-cells-12-02180" class="html-bibr">86</a>,<a href="#B88-cells-12-02180" class="html-bibr">88</a>,<a href="#B103-cells-12-02180" class="html-bibr">103</a>].</p>
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13 pages, 3147 KiB  
Article
Comparative Shotgun Proteomics Reveals the Characteristic Protein Signature of Osteosarcoma Subtypes
by Maram Alaa, Nouran Al-Shehaby, Ali Mostafa Anwar, Nesma Farid, Mustafa Shaban Shawky, Manal Zamzam, Iman Zaky, Ahmed Elghounimy, Shahenda El-Naggar and Sameh Magdeldin
Cells 2023, 12(17), 2179; https://doi.org/10.3390/cells12172179 - 30 Aug 2023
Cited by 3 | Viewed by 1750
Abstract
Osteosarcoma is a primary malignant bone tumor affecting adolescents and young adults. This study aimed to identify proteomic signatures that distinguish between different osteosarcoma subtypes, providing insights into their molecular heterogeneity and potential implications for personalized treatment approaches. Using advanced proteomic techniques, we [...] Read more.
Osteosarcoma is a primary malignant bone tumor affecting adolescents and young adults. This study aimed to identify proteomic signatures that distinguish between different osteosarcoma subtypes, providing insights into their molecular heterogeneity and potential implications for personalized treatment approaches. Using advanced proteomic techniques, we analyzed FFPE tumor samples from a cohort of pediatric osteosarcoma patients representing four various subtypes. Differential expression analysis revealed a significant proteomic signature that discriminated between these subtypes, highlighting distinct molecular profiles associated with different tumor characteristics. In contrast, clinical determinants did not correlate with the proteome signature of pediatric osteosarcoma. The identified proteomics signature encompassed a diverse array of proteins involved in focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathways, and proteoglycans in cancer, among the top enriched pathways. These findings underscore the importance of considering the molecular heterogeneity of osteosarcoma during diagnosis or even when developing personalized treatment strategies. By identifying subtype-specific proteomics signatures, clinicians may be able to tailor therapy regimens to individual patients, optimizing treatment efficacy and minimizing adverse effects. Full article
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Graphical abstract

Graphical abstract
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<p>Histological response to chemotherapy and years-to-event predict the outcome of osteosarcoma patients. (<b>A</b>) Kaplan–Meier plot of overall (OS) and event-free survival (EFS) for 30 OS patients stratified according to histological response to chemotherapy (≥90% and &lt;90%). (<b>B</b>) Kaplan–Meier plot of OS for patients stratified according to years-to-event (≤1 year and &gt;1 year). Survival probability (<span class="html-italic">y</span>-axis) and time indicated in months (<span class="html-italic">x</span>-axis). <span class="html-italic">p</span>-values were calculated using the log-rank test.</p>
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<p>Clinical features do not affect the proteome signature of osteosarcoma patients. (<b>A</b>) Heat map and hierarchical clustering based on the total proteome identified. (<b>B</b>) Volcano plots of all proteins significantly altered by the (top) histological response to chemotherapy and (bottom) years-to-event (log2-fold-change threshold = 1, Benjamini–Hochberg corrected <span class="html-italic">p</span>-value threshold = 0.1). (<b>C</b>) Heat map and hierarchical clustering based on the top 20 genes differentiated between groups of (top) Histological response to chemotherapy and (bottom) years-to-event. Heat map colors are based on the <span class="html-italic">z</span>-scored (log2) intensity values. Grey and red correspond to decreased and increased expression levels, respectively.</p>
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<p>Proteome signatures of osteosarcoma samples segregate patients according to the pathological subtypes. (<b>A</b>) Venn diagram showing protein distribution among pathological subtypes. (<b>B</b>) Anova diagram showing differentially expressed proteins (DEPs) among pathological subtypes (<span class="html-italic">p</span>-value &lt; 0.05). (<b>C</b>) Partial least squares discrimination analysis (PLS-DA) based on DEPs showing segregation of pathological subtypes (left) and PLS permutation plot showing PLS-DA significance (right). (<b>D</b>) Heat map and hierarchical clustering based on DEPs differentiated between pathological subtypes. Heat map colors are based on the <span class="html-italic">z</span>-scored (log2) intensity values. Grey and red correspond to decreased and increased expression levels, respectively. (<b>E</b>) KEGG pathways according to DEPs among pathological subtypes. (<b>F</b>) PPI networks and molecular functions of DEPs unique to fibroblastic osteosarcoma. (<b>G</b>) PPI networks and molecular functions of DEPs unique to chondroblastic osteosarcoma, respectively.</p>
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18 pages, 4427 KiB  
Article
The Preventive Effect of Melatonin on Radiation-Induced Oral Mucositis
by Reiko Tokuyama-Toda, Hirochika Umeki, Mitsuru Okubo, Chika Terada-Ito, Toshio Yudo, Shinji Ide, Susumu Tadokoro, Masashi Shimozuma and Kazuhito Satomura
Cells 2023, 12(17), 2178; https://doi.org/10.3390/cells12172178 - 30 Aug 2023
Cited by 2 | Viewed by 1497
Abstract
Melatonin exerts various physiological effects through melatonin receptors and their ability to scavenge free radicals. Radiotherapy is a common treatment for head and neck tumors, but stomatitis, a side effect affecting irradiated oral mucosa, can impact treatment outcomes. This study investigated the preventive [...] Read more.
Melatonin exerts various physiological effects through melatonin receptors and their ability to scavenge free radicals. Radiotherapy is a common treatment for head and neck tumors, but stomatitis, a side effect affecting irradiated oral mucosa, can impact treatment outcomes. This study investigated the preventive effect of melatonin, a potent free radical scavenger, on radiation-induced oral mucositis. Mice were irradiated with 15 Gy of X-ray radiation to the head and neck, and the oral mucosa was histologically compared between a melatonin-administered group and a control group. The results showed that radiation-induced oral mucositis was suppressed in mice administered melatonin before and after irradiation. It was suggested that the mechanism involved the inhibition of apoptosis and the inhibition of DNA damage. From these findings, we confirmed that melatonin has a protective effect against radiation-induced oral mucositis. Full article
(This article belongs to the Special Issue Cellular and Molecular Biology of Melatonin)
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Figure 1
<p>Histology of the tongue in each group. (<b>a</b>,<b>b</b>) Control group (no irradiation). (<b>c</b>,<b>d</b>) IR group (vehicle intraperitoneal administration + 15 Gy irradiation + normal water feeding). (<b>e</b>,<b>f</b>) IR + Mel group (intraperitoneal administration of melatonin + 15 Gy irradiation + melatonin-containing water breeding). H–E staining. The basal layer exhibited cell enlargement, dense nuclei, an increased nucleus-to-cytoplasm ratio, and disordered arrangement. In addition, polygonal cells, degenerated cells, and keratohyalin-like granules were observed sporadically. The entire epithelium showed atrophy, dilation of capillaries in the lamina propria, and edematous space in the tongue muscle. In contrast, the tongues of mice in the IR + Mel group exhibited partial radiation damage. However, the thickness of the epithelial layer was maintained, and only slight disturbance was observed in the basal layer. Compared with the IR group, radiation damage was suppressed in the IR + Mel group, and the histological images resembled those of the nonirradiated control group. (<b>a</b>,<b>c</b>,<b>e</b>) Bars are 50 μm. (<b>b</b>,<b>d</b>,<b>f</b>) Bars are 20 μm. (<b>g</b>) Graph of the length of the epithelial leg in the tongue. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. In the IR group, the epithelial leg flattened significantly compared with the control group, whereas in the IR + Mel group, the flattening of the epithelial leg was suppressed, and the length was maintained.</p>
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<p>Histology of the buccal mucosa in each group. (<b>a</b>,<b>b</b>) Control group (no irradiation). (<b>c</b>,<b>d</b>) IR group (vehicle intraperitoneal administration + 15 Gy irradiation + normal water feeding). (<b>e</b>,<b>f</b>) IR + Mel group (intraperitoneal administration of melatonin + 15 Gy irradiation + melatonin-containing water breeding). H–E staining. As in the tongue, radiation damage was observed in both the epithelium and lamina propria in the IR group, whereas radiation damage was suppressed in the IR + Mel group. Histological images similar to those in the control group were observed. (<b>a</b>,<b>c</b>,<b>e</b>) Bars are 50 μm. (<b>b</b>,<b>d</b>,<b>f</b>) Bars are 20 μm. (<b>g</b>) Graph of the number of nucleated cells observed in the epithelium in the buccal mucosa. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. The IR group showed a significant decrease in cell number compared to the control group. In contrast, the IR + Mel group suppressed the decrease in cell number and maintained the number of nucleated cells.</p>
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<p>TUNEL staining image in the tongue. (<b>a</b>–<b>c</b>) Control group. (<b>d</b>–<b>f</b>) IR group. (<b>g</b>–<b>i</b>) IR + Mel group. (<b>a</b>,<b>d</b>,<b>g</b>) FITC. (<b>b</b>,<b>e</b>,<b>h</b>) DAPI. (<b>c</b>,<b>f</b>,<b>i</b>) merge. The scale bar is 50 μm. All images have the same magnification. The white dashed line represents the boundary between the epithelium and subepithelial tissue. (<b>j</b>) Graph of the number of TUNEL-positive cells in the epithelium, subepithelial tissue, and full thickness of the tongue mucosa. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Significantly, more positive cells were observed in the IR group than in the control group in all regions of the epithelium, subepithelial tissue, and mucosa. On the other hand, the number of positive cells in the RT + Mel group was higher than that in the control group, but it was significantly lower than that in the IR group.</p>
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<p>Immunohistochemical staining image of active caspase-3 in the tongue. (<b>a</b>–<b>c</b>) Control group. (<b>d</b>–<b>f</b>) IR group. (<b>g</b>–<b>i</b>) IR + Mel group. (<b>a</b>,<b>d</b>,<b>g</b>) Texas red for cleaved caspase-3. (<b>b</b>,<b>e</b>,<b>h</b>) DAPI. (<b>c</b>,<b>f</b>,<b>i</b>) merge. Scale bar is 50 μm. All images have the same magnification. The white dashed line represents the boundary between the epithelium and subepithelial tissue. (<b>j</b>) Graph of the number of cleaved caspase-3-positive cells in the epithelium, subepithelial tissue, and full thickness of the tongue mucosa. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Significantly more positive cells were observed in the IR group than in the control group in all regions of the epithelium, subepithelial tissue, and mucosa. On the other hand, although the RT + Mel group had more positive cells than the control group, it tended to decrease compared with the IR group.</p>
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<p>Immunohistochemical staining image of HMGB1 in the tongue. (<b>a</b>–<b>c</b>) Control group. (<b>d</b>–<b>f</b>) IR group. (<b>g</b>–<b>i</b>) IR + Mel group. (<b>a</b>,<b>d</b>,<b>g</b>) Texas red for HMGB1. (<b>b</b>,<b>e</b>,<b>h</b>) DAPI. (<b>c</b>,<b>f</b>,<b>i</b>) merge. The scale bar is 50 μm. All images have the same magnification. The white dashed line represents the boundary between the epithelium and subepithelial tissue. (<b>j</b>) Graph of the number of HMGB1-positive cells in the epithelium, subepithelial tissue, and full thickness of the tongue mucosa. * <span class="html-italic">p</span> &lt; 0.05. Although there was a significant difference between the control group and IR group in the whole mucosa, there was no significant difference between the other groups.</p>
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<p>Immunohistochemical staining image of 8-OHdG in the tongue. (<b>a</b>–<b>c</b>) Control group. (<b>d</b>–<b>f</b>) IR group. (<b>g</b>–<b>i</b>) IR + Mel group. (<b>a</b>,<b>d</b>,<b>g</b>) Texas red for 8-OHdG. (<b>b</b>,<b>e</b>,<b>h</b>) DAPI. (<b>c</b>,<b>f</b>,<b>i</b>) merge. Scale bar is 50 μm. All images have the same magnification. The white dashed line represents the boundary between the epithelium and subepithelial tissue. (<b>j</b>) Graph of the number of 8-OHdG-positive cells in the epithelium, subepithelial tissue, and full thickness of the tongue mucosa. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Significantly more positive cells were observed in the IR group than in the control group in all regions of the epithelium, subepithelial tissue, and mucosa. On the other hand, it was significantly decreased in the RT + Mel group compared with that in the IR group. The number of positive cells tended to be higher than in the control group, but the difference was not significant.</p>
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<p>Side effects due to melatonin administration. (<b>a</b>) Weight gain. (<b>b</b>–<b>e</b>) Peripheral blood biochemistry test results. (<b>b</b>) AST. (<b>c</b>) ALT. (<b>d</b>) BUN. (<b>e</b>) Creatinine. Changes in body weight (A) and serum levels of AST (B), ALT (C), BUN (D), and creatinine (E) in the experimental period. Generally, none of these laboratory parameters were significantly affected by melatonin application throughout the experimental period. Values are mean ± SE from 10 animals per group.</p>
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<p>Effect of melatonin on the proliferation of RT7 cells. (<b>a</b>) Effect of the addition of various melatonin concentrations on cell proliferation in a normal culture sample. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>b</b>) Effect of various melatonin concentrations on the proliferation of RT7 cells after irradiation with X-ray 10 Gy. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>c</b>) Effects of addition and non-addition of melatonin on cell proliferation with and without X-ray irradiation. The transition of cell proliferation is shown in the graph. It was revealed that melatonin has a cytostatic effect on RT7 cells under normal culture conditions. After irradiation, RT7 cells were influenced by irradiation, so the proliferation was slower than that in a normal culture sample, but cell proliferation was suppressed depending on the melatonin concentration. Among these results, the effect of melatonin was examined in nonX-ray irradiation and 10 Gy irradiation. In the absence of melatonin, cell proliferation was suppressed in the irradiation 10 Gy group compared with the control group, but when 1000 μM melatonin was added, there was no significant difference in proliferation between the control group and irradiation 10 Gy group.</p>
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24 pages, 12458 KiB  
Article
Human Mast Cells Upregulate Cathepsin B, a Novel Marker of Itch in Psoriasis
by Peter W. West, Chiara Tontini, Haris Atmoko, Orsolya Kiss, Terence Garner, Rajia Bahri, Richard B. Warren, Christopher E. M. Griffiths, Adam Stevens and Silvia Bulfone-Paus
Cells 2023, 12(17), 2177; https://doi.org/10.3390/cells12172177 - 30 Aug 2023
Viewed by 1834
Abstract
Mast cells (MCs) contribute to skin inflammation. In psoriasis, the activation of cutaneous neuroimmune networks commonly leads to itch. To dissect the unique contribution of MCs to the cutaneous neuroinflammatory response in psoriasis, we examined their density, distribution, relation to nerve fibres and [...] Read more.
Mast cells (MCs) contribute to skin inflammation. In psoriasis, the activation of cutaneous neuroimmune networks commonly leads to itch. To dissect the unique contribution of MCs to the cutaneous neuroinflammatory response in psoriasis, we examined their density, distribution, relation to nerve fibres and disease severity, and molecular signature by comparing RNA-seq analysis of MCs isolated from the skin of psoriasis patients and healthy volunteers. In involved psoriasis skin, MCs and Calcitonin Gene-Related Peptide (CGRP)-positive nerve fibres were spatially associated, and the increase of both MC and nerve fibre density correlated with disease severity. Gene set enrichment analysis of differentially expressed genes in involved psoriasis skin showed significant representation of neuron-related pathways (i.e., regulation of neuron projection along with dendrite and dendritic spine morphogenesis), indicating MC engagement in neuronal development and supporting the evidence of close MC–nerve fibre interaction. Furthermore, the analysis of 208 identified itch-associated genes revealed that CTSB, TLR4, and TACR1 were upregulated in MCs in involved skin. In both whole-skin published datasets and isolated MCs, CTSB was found to be a reliable indicator of the psoriasis condition. Furthermore, cathepsin B+ cells were increased in psoriasis skin and cathepsin B+ MC density correlated with disease severity. Therefore, our study provides evidence that cathepsin B could serve as a common indicator of the MC-dependent itch signature in psoriasis. Full article
(This article belongs to the Special Issue Mast Cells in Immunity and Inflammation)
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<p>Mast cell and nerve fiber density, spatial association, and correlation with psoriasis severity. Immunofluorescence microscopy was used to identify and measure the density of (<b>a</b>) dermal MC tryptase and (<b>b</b>) PGP 9.5+ nerve fibres in normal (<span class="html-italic">n</span> = 8) and involved psoriasis skin (<span class="html-italic">n</span> = 11). Each subject is represented by a different symbol. (<b>c</b>,<b>d</b>) The distance in µm between MCs and PGP9.5+ nerves and the normalised frequency distribution of MCs within a given distance of a PGP9.5+ nerve fibre. (<b>e</b>,<b>f</b>) Representative photomicrographs of normal (<b>e</b>) and involved (<b>f</b>) skin show MC tryptase (cyan) and PGP9.5 (magenta). Discrete areas of PGP9.5 immunofluorescence in the dermis are identified (arrowheads). Scale bar = 20 µm. The tissue density of tryptase-positive MCs (<b>g</b>) and PGP9.5-positive nerve fibres (<b>h</b>) was correlated with the psoriasis severity index (PASI). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, (unpaired <span class="html-italic">t</span>-test (<b>a</b>,<b>b</b>) Mann–Whitney U test (<b>c</b>), Pearson correlation (<b>g</b>,<b>h</b>)).</p>
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<p>Calcitonin gene-related peptide density, spatial association with MCs, and correlation with severity of psoriasis. Immunofluorescence microscopy was used to identify and measure the density of MCs (tryptase), nerve fibres (PGP 9.5), and CGRP in normal (<span class="html-italic">n</span> = 6) and involved psoriasis skin (<span class="html-italic">n</span> = 10). (<b>a</b>) CGRP nerve fibre density and (<b>b</b>) proportion of CGRP+ nerve fibres in skin sections. (<b>c</b>) Distance between MC and CGRP+ nerve fibres and (<b>d</b>) normalised frequency distribution of MCs within a given distance of a CGRP+ nerve fibres in skin. (<b>e</b>) Correlation between CGRP density and severity index (PASI). (<b>f</b>,<b>g</b>) Representative photomicrographs of normal (<b>f</b>) and involved (<b>g</b>) skin show MC tryptase (cyan), PGP9.5 (yellow), and CGRP (magenta). The inset panel shows a magnified area with colocalized fluorescence. Scale bar = 20µm. Data are median ± IQR of <span class="html-italic">n</span> = 6 and <span class="html-italic">n</span> = 10 donors. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant (Mann–Whitney U test (<b>a,c</b>), unpaired <span class="html-italic">t</span>-test (<b>b</b>), Pearson correlation (<b>e</b>)).</p>
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<p>RNA-seq analysis of mast cells isolated from normal and psoriasis skin. Mast cells (MCs) were isolated from normal (<span class="html-italic">n</span> = 7) and psoriasis-involved skin (<span class="html-italic">n</span> = 6, 2 donors pooled): (<b>a</b>) 3D PCA plot of log-transformed normalized matrix showing confidence ellipses (PCA1: 25.5%, PCA2: 12.42%, PCA3: 9.5% of explained variance) and (<b>b</b>) volcano plot of DEGs. Significantly different genes based on FDR-corrected <span class="html-italic">p</span> values and log2 fold change ± 2 are highlighted in red. (<b>c</b>) Z-score heatmap of the top 50 DEGs (q &lt; 0.05) ordered by log2 fold change.</p>
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<p>Canonical pathway (CP) analysis revealed functional enrichment of the CLEAR and Autophagy pathways in mast cells, with an upstream role for IL1B. Canonical pathway analysis was carried out on 203/249 differentially expressed genes (q &lt; 0.05) using IPA. (<b>a</b>) CP analysis of DEGs, showing a Fisher exact test <span class="html-italic">p</span>-value &lt; 0.05. Activation z-scores are displayed as orange for predicted activation. (<b>b</b>) Venn diagram of shared differentially-expressed genes identified in the CLEAR, Autophagy, and PI3K Signaling in B Lymphocytes (PI3K) canonical pathways. (<b>c</b>) Upstream regulator analysis of isolated MCs based on gene expression. Upregulated/downregulated genes (log2 fold change &gt; 0/&lt; 0) are respectively marked in magenta/cyan. Activation z-scores are displayed as orange arrows for predicted activation, blue for predicted inhibition, and grey for no prediction. Inconsistent findings are highlighted in yellow.</p>
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<p>Neuron-associated ontology terms in psoriasis mast cells in gene set enrichment analysis. Gene set enrichment analysis of RNA isolated from MCs from healthy (<span class="html-italic">n</span> = 7) and psoriasis lesional skin (<span class="html-italic">n</span> = 6, 2 donors pooled). (<b>a</b>) Scatterplot of significantly enriched ontology terms obtained through the gseGO, gseKEGG, and gsePathway functions of ClusterProfiler/ReactomePA R tools. The cut-off for significance was set to the false discovery rate-corrected <span class="html-italic">p</span> value of &lt; 0.05, and the analyses returned a total of 29 enriched ontology descriptions (23 GO, 2 KEGG, 4 REACTOME), as shown on the left of the plot. The colour of the dots represents the adjusted <span class="html-italic">p</span>-value, while the size of the dots represents the gene counts for each ontology term. The gene ratio is a measure of the total enrichment based on positive hits out of the total number of genes in that pathway. (<b>b</b>) Plot of enrichment scores and rank in the ordered dataset of the neuron-associated GO ontology terms GO:0048814, GO:0061001 and GO:0010975, with the highest-ranked genes showing the most enrichment.</p>
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<p>Psoriasis mast cells share half of their differentially expressed genes with whole skin, while mast cells and neuronal signatures are unequally represented across studies and conditions. (<b>a</b>) Venn diagram of the number of overlapping differentially expressed genes between healthy donors and psoriasis patients in isolated MCs and microarray datasets. (<b>b</b>) Log2 fold change compared to healthy controls of 15 overlapping genes across datasets. (<b>c</b>) Cell type enrichment analysis of publicly available datasets and of MCs isolated from normal skin and psoriasis lesions, performed using xCell. The proportion of samples in which significant (<span class="html-italic">p</span> &lt; 0.05) MC and neuron enrichment was detected is displayed per study. Isolated MCs were added as internal control for MC-specific signatures.</p>
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<p>Random forest analysis of itch-associated genes and cathepsin B expression in mast cells. Random forest plot of differentially expressed genes in (<b>a</b>) psoriasis whole skin and (<b>b</b>) isolated MCs. Attributes that were significant (green bars), tentative, (yellow bars), or rejected (red bars) as indicative of the psoriasis condition are listed in order of importance. Worst, average and best shadow in each iteration are shown (blue bars). Gene names of significantly indicative attributes are enlarged below each plot. (<b>c</b>) Cathepsin B measured in the cell-free supernatant from human MCs stimulated with SP (concentrations shown) for 1 h. (<b>d</b>,<b>e</b>) Representative photomicrographs of the dermis of healthy (<b>d</b>) and psoriasis skin (<b>e</b>), showing cathepsin B (magenta) and MC tryptase (cyan) with nuclei stained with DAPI. Scale bar = 20 µm. Cathepsin B+ MCs are indicated by arrows. Examples of cathepsin-stained mast cells have been enlarged in each panel. (<b>f</b>,<b>g</b>) Number of total cathepsin B+ cells (<b>f</b>) and cathepsin B+ MCs (<b>g</b>) in normal skin (blue dots) and psoriasis skin (red dots). Data are mean± SEM (<b>f</b>) and median± IQR (<b>g</b>) of <span class="html-italic">n</span> = 9 donors. **** = <span class="html-italic">p</span> &lt; 0.0001, ns: not significant (unpaired <span class="html-italic">t</span>-test). (<b>h</b>,<b>i</b>) total number of cathepsin B+ cells and cathepsin B+ MCs in psoriasis skin correlated with PASI. Significant positive correlation indicated by * <span class="html-italic">p</span> = 0.0225 (Pearson correlation).</p>
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23 pages, 8513 KiB  
Article
Adverse Crosstalk between Extracellular Matrix Remodeling and Ferroptosis in Basal Breast Cancer
by Christophe Desterke, Emma Cosialls, Yao Xiang, Rima Elhage, Clémence Duruel, Yunhua Chang and Ahmed Hamaï
Cells 2023, 12(17), 2176; https://doi.org/10.3390/cells12172176 - 30 Aug 2023
Viewed by 2013
Abstract
(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in women and causes a high number of mortality among this population due to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy, [...] Read more.
(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in women and causes a high number of mortality among this population due to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy, and inhibit distant metastases, potentially impacting the tumor microenvironment. (2) Methods: Through data mining, the ferroptosis/extracellular matrix remodeling literature text-mining results were integrated into the breast cancer transcriptome cohort, taking into account patients with distant relapse-free survival (DRFS) under adjuvant therapy (anthracyclin + taxanes) with validation in an independent METABRIC cohort, along with the MDA-MB-231 and HCC338 transcriptome functional experiments with ferroptosis activations (GSE173905). (3) Results: Ferroptosis/extracellular matrix remodeling text-mining identified 910 associated genes. Univariate Cox analyses focused on breast cancer (GSE25066) selected 252 individual significant genes, of which 170 were found to have an adverse expression. Functional enrichment of these 170 adverse genes predicted basal breast cancer signatures. Through text-mining, some ferroptosis-significant adverse-selected genes shared citations in the domain of ECM remodeling, such as TNF, IL6, SET, CDKN2A, EGFR, HMGB1, KRAS, MET, LCN2, HIF1A, and TLR4. A molecular score based on the expression of the eleven genes was found predictive of the worst prognosis breast cancer at the univariate level: basal subtype, short DRFS, high-grade values 3 and 4, and estrogen and progesterone receptor negative and nodal stages 2 and 3. This eleven-gene signature was validated as regulated by ferroptosis inductors (erastin and RSL3) in the triple-negative breast cancer cellular model MDA-MB-231. (4) Conclusions: The crosstalk between ECM remodeling-ferroptosis functionalities allowed for defining a molecular score, which has been characterized as an independent adverse parameter in the prognosis of breast cancer patients. The gene signature of this molecular score has been validated to be regulated by erastin/RSL3 ferroptosis activators. This molecular score could be promising to evaluate the ECM-related impact of ferroptosis target therapies in breast cancer. Full article
(This article belongs to the Special Issue Novel Mechanisms and Therapeutic Opportunities of Ferroptosis)
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<p>Gene expression profile related to ferroptosis functionality is associated with disease-free relapse survival in breast cancer. (<b>A</b>) Scatterplot of the text-mining normalized false discovery rate (FDR): negative log10 q-values versus number of positive articles in Pubmed for genes related to ferroptosis in breast cancer (green dots correspond to less significant selected genes); (<b>B</b>) Barplots of univariate Cox beta-coefficients and negative log10 <span class="html-italic">p</span>-values for the 50 best ferroptosis-related genes according disease-free relapse survival (DFRS) of breast cancer patients (transcriptome GSE25066, n = 508).</p>
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<p>Unfavorable ferroptosis-related genes were enriched in basal breast cancer. (<b>A</b>) Principal component analysis based on the expression of 170 adverse ferroptosis genes and stratified on dld-30 response prediction. (<b>B</b>) Principal component analysis based on expression of the 170 adverse ferroptosis genes and stratified by residual cancer burden response prediction. (<b>C</b>) Barplot of functional enrichment performed on the “MSIGDB CPG” database with the 170 unfavorable ferroptosis genes. (<b>D</b>) Functional enrichment network of 170 unfavorable ferroptosis genes enriched in advanced breast cancer signatures.</p>
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<p>Top fifteen genes prioritized by text mining and having adverse prognosis. (<b>A</b>) Circosplot of the 15 best ferroptosis genes most cited in literature associated with the following keywords: extracellular matrix (ECM) remodeling, cancer stem cell (CSC), breast cancer, lipid peroxidation, regulated cell death, and ferroptosis. (<b>B</b>) Barplot of Cox analyses of the top15 gene with DRFS (distant relapse-free survival) as the outcome.</p>
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<p>Eleven-gene signature shared between ferroptosis and extracellular matrix remodeling functionalities. (<b>A</b>) Alluvial plot of literature citation counts for the best 11 genes with co-occurrence in ferroptosis and extracellular matrix remodeling (ECM) functionalities. (<b>B</b>) Venn diagram testing overlap between 170 adverse gene signature and the ferroptosis databases. (<b>C</b>) Multi-ROC analysis of the expression (GSE25066) for the 11 genes against basal phenotype.</p>
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<p>Validation of the eleven-gene signature in the METBRIC breast cancer cohort. (<b>A</b>) Oncoprint of alterations affecting the eleven genes. (<b>B</b>) Barplot of the alteration frequencies by subtypes of breast cancer. (<b>C</b>) Relapse-free survival analysis stratified based on alterations. (<b>D</b>) Barplot of clinical parameters associated with the eleven gene alterations. (<b>E</b>) Example of association with tumor histologic grade. (<b>F</b>) Example of association with claudin-low/PAM50 classification.</p>
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<p>Immunohistochemistry of ductal breast carcinoma tissue section for testing the eleven markers of the ferroptosis/ECM signature. Representative expression image section was extracted from the Protein Atlas server, tumor cell expression was quantified according three levels of the cross (tumor cell staining intensities: +++, strong; ++, moderate; +, weak), and subcellular localization in the tumor cell was annotated as follows: NU: nuclear, CY: cytoplasm, MB: membrane.</p>
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<p>Regulation of the ferroptosis/extracellular matrix remodeling signature by ferroptosis inducers in triple-negative breast cancer cellular models. On principal component plots, the pink colored square symbols represent the barycenters of the groups. (<b>A</b>) Principal component correlation plot for the dataset GSE173905. (<b>B</b>) First principal map for the dataset GSE173905. (<b>C</b>) Principal component correlation plot for the dataset GSE162069. (<b>D</b>) First principal map for the dataset GSE154425 (in vitro and in vivo). (<b>E</b>) Principal component correlation plot for the dataset GSE162069. (<b>F</b>) First principal map for the dataset GSE154425.</p>
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<p>Ferroptosis/extracellular matrix remodeling molecular score is associated with a worse prognosis in breast cancer. For the transcriptome dataset (GSE25066), unsupervised principal component analysis was performed with the eleven-gene signature and stratified based on: (<b>A</b>) tumor grades (grades 3 and 4 were aggregated in one class), (<b>B</b>) pam50 molecular classification of breast tumors, (<b>C</b>) TNBC phenotype, and (<b>D</b>) dld-30 preoperative chemotherapy response. (<b>E</b>) Optimal threshold cutpoint determined for ferroptosis/ECM remodeling molecular score censored on the DRFS (distant relapse-free survival). (<b>F</b>) Kaplan–Meier and log-rank analyses censored on the DRFS and stratified based on the ferroptosis/ECM remodeling molecular score threshold.</p>
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<p>Ferroptosis/extracellular matrix remodeling molecular score is an independent adverse parameter in the prognosis of breast cancer patients. (<b>A</b>) Forestplot of the multivariable model censored based on the distant relapse-free survival, including the ferroptosis/extracellular matrix remodeling and clinico-biological relevant parameters, such as age, nodular status, grade, and molecule classes; significance: *: 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, **: 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) Bootstrap calibration plot of the DRFS multivariable model performed with 500 iterations using the Kaplan–Meier method at 10 months of follow-up: grey line (optimal model). (<b>C</b>) Nomogram of the DRFS multivariable model predicted at 10 months of follow-up.</p>
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