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23 pages, 6877 KiB  
Article
Hypoxic Human Microglia Promote Angiogenesis Through Extracellular Vesicle Release
by Alessandra Maria Testa, Livia Vignozzi, Diana Corallo, Sanja Aveic, Antonella Viola, Manuela Allegra and Roberta Angioni
Int. J. Mol. Sci. 2024, 25(23), 12508; https://doi.org/10.3390/ijms252312508 - 21 Nov 2024
Viewed by 222
Abstract
Microglia, the brain-resident immune cells, orchestrate neuroinflammatory responses and are crucial in the progression of neurological diseases, including ischemic stroke (IS), which accounts for approximately 85% of all strokes worldwide. Initially deemed detrimental, microglial activation has been shown to perform protective functions in [...] Read more.
Microglia, the brain-resident immune cells, orchestrate neuroinflammatory responses and are crucial in the progression of neurological diseases, including ischemic stroke (IS), which accounts for approximately 85% of all strokes worldwide. Initially deemed detrimental, microglial activation has been shown to perform protective functions in the ischemic brain. Besides their effects on neurons, microglia play a role in promoting post-ischemic angiogenesis, a pivotal step for restoring oxygen and nutrient supply. However, the molecular mechanisms underlying microglia–endothelial cell interactions remain largely unresolved, particularly in humans. Using both in vitro and in vivo models, we investigated the angiogenic signature and properties of extracellular vesicles (EVs) released by human microglia upon hypoxia–reperfusion stimulation. EVs were isolated and characterized in terms of their size, concentration, and protein content. Their angiogenic potential was evaluated using endothelial cell assays and a zebrafish xenograft model. The in vivo effects were further assessed in a mouse model of ischemic stroke. Our findings identified key proteins orchestrating the pro-angiogenic functions of human microglial EVs under hypoxic conditions. In vitro assays demonstrated that hypoxic EVs (hypEVs) promoted endothelial cell migration and tube formation. In vivo, hypEVs induced vessel sprouting in zebrafish and increased microvessel density in the perilesional area of mice following ischemic stroke. Full article
(This article belongs to the Special Issue Role of Extracellular Vesicles in Immunology)
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Figure 1

Figure 1
<p>Activation of human microglia in response to OGD/R stimulation. (<b>A</b>) Schematic representation of the experimental workflow relative to the stimulation of HMC-3 cells with the OGD/R in vitro protocol. Following exposure to OGD/R, cells were detached for viability and gene expression analyses, while the conditioned medium (CM) was collected to subsequently isolate microglia-derived EVs. (<b>B</b>) Flow cytometric analysis of Annexin V in HMC-3 cells. Histograms represent the percentage of apoptotic cells (Annexin V<sup>+</sup>) over the total cells, quantified after OGD (3 h) and OGD/R (24 h) stimulation. Normoxic cells were used as controls at each time point. Cells treated with staurosporine were used as a positive control. Data are reported as relative percentages of apoptotic cells normalised to normoxic controls. (<b>C</b>–<b>L</b>) qRT-PCR analysis of hypoxia-response genes and microglia activation markers in HMC-3 cells upon OGD/R, confirming microglia activation. Relative gene expression levels were normalised to the housekeeping gene <span class="html-italic">ACTB</span>. Bars represent mean ± SEM. N = 3. The Kruskal–Wallis test for multiple comparisons was applied. * <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. CM: conditioned medium; OGD/R: oxygen–glucose deprivation/reperfusion.</p>
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<p>Characterization of EVs isolated from HMC-3-CM. (<b>A</b>) Transmission electron microscopy (TEM) images of EVs isolated from contr-HMC-3-CM (<b>left</b>) or hyp-HMC-3-CM (<b>right</b>). White arrowheads indicate EVs. Scale bar: 50 nm. (<b>B</b>,<b>C</b>) Western blot of EV lysates, validating the expression of EV-associated proteins CD63 and CD9 in the purified samples. (<b>C</b>,<b>D</b>) Representative pictures of the nanoparticle tracking analysis (NTA) of EVs isolated by ultrafiltration. The plots represent the size distributions of EVs, displaying the estimated concentration (particles/mL) for each particle size (nm), in both control and hypoxic conditions. The highest peaks and indicated numbers represent the mode vesicle size (99 nm in contrEVs and 104 nm in hypEVs), highlighting an enrichment in vesicles in the small EV (sEV) range (&lt;200 nm).</p>
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<p>Functional characterisation of microglial EV effects on HBEC-5i brain microvascular cells. (<b>A</b>) Uptake of contrEVs and hypEVs by HBEC-5i cells. EVs isolated from contr-HMC-3-CM, hyp-HMC-3-CM, or the PBS vehicle were stained with Dil before treating HBEC-5i cells for 6 h. Representative panels of the gating strategy for cells exhibiting positive fluorescence in the PE (Dil) channel are shown. (<b>B</b>) The quantification represents the percentage of Dil<sup>+</sup> cells after incubation with EVs<sup>Dil</sup>, compared to cells incubated with PBS<sup>Dil</sup>. (<b>C</b>,<b>D</b>) Scratch wound healing assay performed on HBEC-5i cells. A confluent endothelial cell monolayer was scratched, and cells were cultured in the presence of contrEVs or hypEVs, or with rhVEGF, in serum-free medium. Images of the scratch borders were acquired using an inverted optical microscope with a 4× objective, at time 0 h and after 6 h of culture. Image analysis was performed using ImageJ 1.53k. Results are expressed as a migration index, calculated as the difference between the starting (time 0 h) and the final (time 6 h) distance between the migrating fronts, normalised to the unstimulated control. (<b>E</b>,<b>F</b>) Tube formation assay performed on HBEC-5i cells. Cells were seeded on Matrigel and cultured for 6 h in the presence of contrEVs, hypEVs, or in medium alone. Images were acquired using a 4× objective. Histograms represent the cumulative tube length of the networks, as quantified using the Angiogenesis Analyzer plugin in ImageJ 1.53k. Scale bar: 100 µm. Results of at least three independent experiments are presented for all assays. Bars represent mean ± SEM. The Kruskal–Wallis test for multiple comparisons with Dunn’s post hoc was applied. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant.</p>
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<p>Analysis of the angiogenic potential of microglial EVs in zebrafish. (<b>A</b>) Schematic of the experimental set-up. EVs were grafted with Matrigel in the proximity of the developing sub-intestinal vein (SIV), as shown by a black asterisk, and vascular sprouting from the SIV was assessed after 24 h. The area of vessel analysis is represented by a blue square. (<b>B</b>) Histograms represent the number of vessels sprouting towards the implant, counted for each embryo, divided into categories (no vessels, 1–2 vessels, &gt;3 vessels). Data are expressed as the percentage of embryos in each category (<span class="html-italic">n</span> = 36 embryos/group). (<b>C</b>) Representative confocal images showing whole-mount alkaline phosphatase (AP) staining of embryos at 1 dpi in lateral view. Injection of hypEVs stimulated the migration of SIV-derived vascular sprouts towards the implant, while injection with contrEVs resulted in a normal development of the vascular plexus. Chi-Square test was performed. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Analysis of the angiogenic protein cargo of hypEVs and contrEVs. (<b>A</b>) Proteome profiling analysis was performed on contrEVs and hypEVs. Protein content in EVs was quantified by micro-BCA for equal protein input (200 µg). The mean pixel density for each angiogenesis-related factor, expressed as fold to positive reference dots, is represented in a heat-map. Duplicate analyses of separate EV preparations for each EV type are represented. The non-parametric <span class="html-italic">t</span>-test was performed for each factor. Orange asterisks represent factors that are significantly down-regulated (* <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) in hypEVs. Green asterisks represent factors that are significantly enriched (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01) in hypEVs. (<b>B</b>) The volcano plot reports the −log(<span class="html-italic">p</span>-value) (Y axis), graphed against the mean pixel density differences (X axis). Orange dots represent factors that are significantly down-regulated (<span class="html-italic">p</span> &lt; 0.05) in hypEVs. Green dots represent factors that are significantly enriched (<span class="html-italic">p</span> &lt; 0.05) in hypEVs. (<b>C</b>–<b>F</b>) Primary mouse microglial cells were isolated from the brain of adult healthy mice and stimulated in vitro with the OGD/R protocol. The mRNA levels of the identified pro-angiogenic markers (<span class="html-italic">Artn</span>, <span class="html-italic">Eng</span>, <span class="html-italic">Hgf</span>, and <span class="html-italic">Prok1</span>) were quantified by qRT-PCR. Expression levels are normalised to the house-keeping gene <span class="html-italic">Actb.</span> N = 3. Bars represent mean ± SEM. The Kruskal–Wallis test was applied (<b>C</b>–<b>F</b>), * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Role of ENG, HGF, and ARTN in mediating hypEVs’ pro-angiogenic effect towards ECs. (<b>A</b>–<b>E</b>) Results of tube formation assays performed on HBEC-5i cells. Cells were seeded on Matrigel and cultured in a medium containing hypEVs (20 µg/mL), pre-incubated with blocking antibodies. Specifically, anti-ENG, anti-HGF, anti-ARTN antibodies, or antibody combinations were used, as indicated. Capillary-like networks were imaged after 6 h, using a 4× objective. Histograms represent the cumulative tube length of the networks, as quantified using the Angiogenesis Analyzer plugin in ImageJ. N = 3. Bars represent mean ± SEM. The Kruskal–Wallis test was applied. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant.</p>
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<p>Evaluation of the hypEV effects on brain angiogenesis in a mouse model for ischemic stroke. After MCAO, mice were injected intraperitoneally with PBS or hypEVs, immediately after the procedure and each 24 h, for a total of 5 consecutive days. Mice were perfused at day 7 post-MCAO (<span class="html-italic">n</span> = 5 PBS, <span class="html-italic">n</span> = 6 hypEVs). (<b>A</b>) Representative pictures of blood vessels in the perilesional area. Immunofluorescence on brain sections was performed using anti-CD31 antibodies (endothelial cells) and Hoechst (nuclei). Images were acquired at a confocal microscope, using a 25× objective. Scale bar: 50 µm. (<b>B</b>) Quantification of the CD31 signal in the perilesional region was performed using ImageJ, fixing a threshold to detect the percentage of the CD31<sup>+</sup> area, expressed as arbitrary units (a.u.), for each field of view analysed. (<b>C</b>) Analysis of proliferating nuclei in the perilesional area. The number of proliferating cells per field of view analysed is reported. Quantification of the Ki67<sup>+</sup> area was performed using ImageJ and is expressed as normalised to controls. Bars represent mean ± SEM. The non-parametric <span class="html-italic">t</span>-test was applied. * <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 9608 KiB  
Article
The Activation of p300 Enhances the Sensitivity of Pituitary Adenomas to Dopamine Agonist Treatment by Regulating the Transcription of DRD2
by Sihan Li, Xingbo Li, Quanji Wang, Qian Jiang, Zihan Wang, Linpeng Xu, Yimin Huang and Ting Lei
Int. J. Mol. Sci. 2024, 25(23), 12483; https://doi.org/10.3390/ijms252312483 - 21 Nov 2024
Viewed by 224
Abstract
Prolactinomas are commonly treated with dopamine receptor agonists (DAs), such as bromocriptine (BRC) and cabergoline (CAB). However, 10–30% of patients exhibit resistance to DA therapies. DA resistance is largely associated with reduced dopamine D2 receptor (DRD2) expression, potentially regulated by epigenetic modifications, though [...] Read more.
Prolactinomas are commonly treated with dopamine receptor agonists (DAs), such as bromocriptine (BRC) and cabergoline (CAB). However, 10–30% of patients exhibit resistance to DA therapies. DA resistance is largely associated with reduced dopamine D2 receptor (DRD2) expression, potentially regulated by epigenetic modifications, though the underlying mechanisms are still unclear. Clinical samples were assessed for p300 expression. MMQ and AtT-20 cells were engineered to overexpress either wild-type p300 or a histone acetyltransferase (HAT) domain-mutant form of p300. Mechanistic studies included cell proliferation assays, flow cytometry, immunohistochemistry, immunofluorescence, co-immunoprecipitation, chromatin immunoprecipitation followed by quantitative PCR, reverse transcription quantitative PCR, and Western blotting. Additionally, an in vivo nude mouse xenograft model was used to confirm the in vitro findings. DAs downregulated p300 through the cAMP-PKA-CREB pathway. Activation of the HAT domain of p300 increased H3K18/27 acetylation, promoted DRD2 transcription, and worked synergistically with DA to exert anti-tumor effects both in vitro and in vivo. Tanshinone IIA (Tan IIA) upregulated p300 and DRD2, enhancing the therapeutic efficacy of BRC. These findings highlight the role of p300 in regulating DRD2 transcription in DA-resistant prolactinomas. Combining Tan IIA with BRC may offer a promising strategy to overcome DA resistance. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1

Figure 1
<p>DA downregulates p300 expression in pituitary tumor cells. (<b>A</b>) A correlation analysis between the percentage reduction in the tumor maximum diameter and the percentage decrease in serum prolactin levels before and after BRC treatment in 50 patients with drug-resistant prolactinomas, classifying them into a relatively sensitive group (<span class="html-italic">n</span> = 25) and a relatively insensitive group (<span class="html-italic">n</span> = 25). (<b>B</b>) Immunohistochemistry (IHC) staining of p300 expression in the two patient groups, representative images (left), scale bar, 50 μm, quantification (right), <span class="html-italic">n</span> = 25. (<b>C</b>,<b>D</b>) q-PCR of p300 expression in MMQ and AtT-20 cells treated with BRC (0, 5, 10 μM) or CAB (0, 12.5, 25 μM) for 24 h, <span class="html-italic">n</span> = 3. (<b>E</b>,<b>F</b>) q-PCR of p300 expression in MMQ and AtT-20 cells treated with BRC (10 μM) or CAB (25 μM) for various periods (0, 12, 24 h), <span class="html-italic">n</span> = 3. (<b>G</b>,<b>H</b>) Western Blot (WB) analysis of p300 expression in MMQ and AtT-20 cells treated with BRC (0, 5, 10 μM) or CAB (0, 12.5, 25 μM) for 48 h, <span class="html-italic">n</span> = 3. (<b>I</b>,<b>J</b>) WB analysis of p300 expression in MMQ and AtT-20 cells treated with BRC (10 μM) or CAB (25 μM) for various periods (0, 24, 48 h), <span class="html-italic">n</span> = 3. (<b>K</b>–<b>M</b>) Subcutaneous tumor formation in nude mice using MMQ cells, followed by intraperitoneal injections of PBS, BRC (10 mg/kg/d), or CAB (15 mg/kg/d) for 2 weeks, q-PCR of p300 expression in MMQ (<b>L</b>) tumor tissues, immunofluorescence (IF) staining of Ki-67 expression in MMQ (<b>K</b>,<b>M</b>) tumor tissue sections, representative images (<b>K</b>), scale bar, 50 μm, quantification (<b>M</b>), <span class="html-italic">n</span> = 6. Data are shown as mean ± SD. Statistical analyses were conducted using Pearson’s correlation analysis, unpaired <span class="html-italic">t</span>-tests, and one-way ANOVA. Abbreviations: BRC, Bromocriptine; CAB, Cabergoline.</p>
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<p>DA downregulates p300 expression through inhibition of the cAMP−PKA−CREB pathway. (<b>A</b>–<b>E</b>) MMQ and AtT-20 cells were treated with BRC (10 μM), CAB (25 μM), FSK (50 μM), BRC (10 μM) + FSK (50 μM), or CAB (25 μM) + FSK (50 μM) for 48 h; the cAMP ELISA assay of Intracellular cAMP concentrations in MMQ (<b>A</b>) and AtT-20 (<b>B</b>) cells, PKA activity assay of intracellular PKA activity in MMQ (<b>C</b>) and AtT-20 (<b>D</b>) cells, (<b>E</b>) Western Blot (WB) analysis of total PKA-C, total CREB (t-CREB), phosphorylated CREB (p-CREB), CBP, and p300 expression in MMQ and AtT-20 cells, <span class="html-italic">n</span> = 3. (<b>F</b>) Co-immunoprecipitation (Co-IP) experiments using p-CREB antibodies were performed in MMQ and AtT-20 cells treated with BRC (10 μM) and CAB (25 μM) for 48 h, followed by WB analysis of CBP and p300 expression, <span class="html-italic">n</span> = 3. Data are shown as mean ± SD. Statistical analyses were conducted using one-way ANOVA. Abbreviations: BRC, Bromocriptine; CAB, Cabergoline; FSK, Forskolin.</p>
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<p>Activation of p300 HAT activity synergizes with DA to exert anti-proliferative effects in pituitary tumors both in vitro and in vivo. (<b>A</b>,<b>B</b>) CCK−8 assay of cell viability in MMQ (<b>A</b>) and AtT-20 (<b>B</b>) cells treated with BRC (10 μM), CTB (50 μM), or BRC (10 μM) + CTB (50 μM) for various periods (0, 3, 6, 9, 12, 15, 18, 21, 24, 30, 36, 42, 48 h), <span class="html-italic">n</span> = 3. (<b>C</b>–<b>E</b>) MMQ and AtT-20 cells were treated with BRC (10 μM), CAB (25 μM), CTB (50 μM), BRC (10 μM) + CTB (50 μM), or CAB (25 μM) + CTB (50 μM) for 48 h, (<b>C</b>) CCK-8 assay of cell viability, (<b>D</b>) PRL ELISA assay of supernatant prolactin concentration in the MMQ cells, and ACTH ELISA assay of supernatant ACTH concentration in the AtT-20 cells, (<b>E</b>) Annexin-V apoptosis flow cytometry analysis of cell apoptosis, quantification (right), <span class="html-italic">n</span> = 3. (<b>F</b>) Subcutaneous tumor formation in nude mice using MMQ cells, followed by intraperitoneal injections of PBS, BRC (10 mg/kg/d), CAB (15 mg/kg/d), CTB (20 mg/kg/d), BRC (10 mg/kg/d) + CTB (20 mg/kg/d), or CAB (15 mg/kg/d) + CTB (20 mg/kg/d) for 2 weeks, representative images of subcutaneous xenograft tumors (left), average volume of excised tumors (middle), average weight of excised tumors (right). (<b>G</b>) Immunofluorescence (IF) staining of Ki-67 expression in tumor tissue sections, scale bar, 50 μm, quantification (right), <span class="html-italic">n</span> = 6. Data were shown as mean ± SD. Statistical analyses were conducted using one-way ANOVA. Abbreviations: BRC, Bromocriptine; CAB, Cabergoline; CTB, N-(4-chloro-3-trifluoromethyl-phenyl)-2-ethoxy-benzamide); ns, not significant.</p>
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<p>p300 promotes DRD2 transcription by increasing histone H3K18/27 acetylation. (<b>A</b>) Western Blot (WB) analysis of p300 expression in MMQ and AtT-20 cells with p300 overexpression, <span class="html-italic">n</span> = 3. (<b>B</b>) For MMQ and AtT-20 cells, both the vector group and the p300 overexpression (OE) group were treated with BRC (10 μM) and CAB (25 μM) for 48 h, WB analysis of H3K18ac and H3K27ac expression, <span class="html-italic">n</span> = 3. (<b>C</b>) For MMQ and AtT-20 cells, the vector group, p300 overexpression (OE) group, and p300 HAT mutant overexpression (OE-mut) group were treated with BRC (10 μM) and CAB (25 μM) for 48 h, WB analysis of H3K18ac and H3K27ac expression, <span class="html-italic">n</span> = 3. (<b>D</b>) Co-immunoprecipitation (Co-IP) experiments using p300 antibodies were performed in MMQ and AtT-20 cells treated with BRC (10 μM) and CAB (25 μM) for 48 h, followed by WB analysis of H3K18ac and H3K27ac expression, <span class="html-italic">n</span> = 3. (<b>E</b>) Chromatin Immunoprecipitation (ChIP) experiments using H3K18ac and H3K27ac antibodies were performed in MMQ and AtT-20 cells treated with BRC (10 μM) and CAB (25 μM) for 48 h, followed by qPCR detection of the enrichment levels of H3K18ac and H3K27ac at the DRD2 promoter region, <span class="html-italic">n</span> = 3. (<b>F</b>) q-PCR of DRD2 expression in MMQ and AtT-20 cells treated with BRC (10 μM), CAB (25 μM), CTB (50 μM), BRC (10 μM) + CTB (50 μM), and CAB (25 μM) + CTB (50 μM) for 48 h, <span class="html-italic">n</span> = 3. (<b>G</b>) For MMQ and AtT-20 cells, both the vector group and the p300 overexpression (OE) group were treated with BRC (10 μM) and CAB (25 μM) for 48 h, q-PCR of DRD2 expression, <span class="html-italic">n</span> = 3. (<b>H</b>) For MMQ and AtT-20 cells, the vector group, p300 overexpression (OE) group, and p300 HAT mutant overexpression (OE-mut) group were treated with BRC (10 μM) and CAB (25 μM) for 48 h, q-PCR of DRD2 expression, <span class="html-italic">n</span> = 3. Data were shown as mean ± SD. Statistical analyses were conducted using one-way ANOVA. Abbreviations: BRC, Bromocriptine; CAB, Cabergoline; CTB, N-(4-chloro-3-trifluoromethyl-phenyl)-2-ethoxy-benzamide); ns, not significant.</p>
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<p>Tanshinone IIA upregulates p300 and synergizes with BRC to exert anti-tumor effects in pituitary tumors. (<b>A</b>–<b>C</b>) q-PCR of p300 expression in MMQ (<b>A</b>) and AtT-20 (<b>B</b>) cells treated with Tan IIA (0, 12.5, 25, 50 μM) for 24 h. (<b>C</b>) Western Blot (WB) analysis of p300 expression in MMQ and AtT-20 cells treated with Tan IIA (25 μM) for various periods (0, 24, and 48 h), <span class="html-italic">n</span> = 3. (<b>D</b>,<b>E</b>) Cell viability in MMQ (<b>D</b>) and AtT-20 (<b>E</b>) cells was measured using the CCK-8 assay after treatment with different concentrations of Tan IIA (0, 5, 10, 25, 50 μM) for 48 h, <span class="html-italic">n</span> = 3. (<b>F</b>) CCK-8 assay of cell viability in MMQ (<b>A</b>) and AtT-20 (<b>B</b>) cells treated with BRC (10 μM), CAB (25 μM), Tan IIA (25 μM), BRC (10 μM) + Tan IIA (25 μM), or CAB (25 μM) + Tan IIA (25 μM) for 48 h, <span class="html-italic">n</span> = 3. (<b>G</b>) Annexin-V apoptosis flow cytometry analysis of cell apoptosis in MMQ and AtT-20 cells treated with BRC (10 μM), Tan IIA (25 μM), or mix (BRC (10 μM) + Tan IIA (25 μM)) for 48 h, <span class="html-italic">n</span> = 3. (<b>H</b>,<b>I</b>) Subcutaneous tumor formation in nude mice using MMQ cells, followed by intraperitoneal injections of PBS, BRC (10 mg/kg/d), Tan IIA (20 mg/kg/d), or BRC (10 mg/kg/d) + Tan IIA (20 mg/kg/d) for 2 weeks, representative images of subcutaneous xenograft tumors (<b>H</b>), average volume of excised tumors ((<b>I</b>) left), average weight of excised tumors ((<b>I</b>) right). (<b>J</b>,<b>K</b>) MMQ cells were treated with BRC (10 μM), Tan IIA (25 μM), or mix (BRC (10 μM) + Tan IIA (25 μM)) for 48 h, q-PCR of p300 (<b>J</b>) and DRD2 (<b>K</b>) expression, <span class="html-italic">n</span> = 3. (<b>L</b>) A representative figure of the mechanism of action integrating the effects of DA, p300 and Tan IIA in the signaling pathway (figure created with Biorender.com). Data are shown as mean ± SD. Statistical analyses were conducted using one-way ANOVA. Abbreviations: DA, Dopamine agonist; BRC, Bromocriptine; CAB, Cabergoline; Tan IIA, Tanshinone IIA.</p>
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28 pages, 11739 KiB  
Article
Development and Characterization of an Oncolytic Human Adenovirus-Based Vector Co-Expressing the Adenovirus Death Protein and p14 Fusion-Associated Small Transmembrane Fusogenic Protein
by Kathy L. Poulin, Ryan G. Clarkin, Joshua Del Papa and Robin J. Parks
Int. J. Mol. Sci. 2024, 25(22), 12451; https://doi.org/10.3390/ijms252212451 - 20 Nov 2024
Viewed by 437
Abstract
Human adenovirus (HAdV)-based oncolytic vectors, which are designed to preferentially replicate in and kill cancer cells, have shown modest efficacy in human clinical trials in part due to poor viral distribution throughout the tumor mass. Previously, we showed that expression of the p14 [...] Read more.
Human adenovirus (HAdV)-based oncolytic vectors, which are designed to preferentially replicate in and kill cancer cells, have shown modest efficacy in human clinical trials in part due to poor viral distribution throughout the tumor mass. Previously, we showed that expression of the p14 fusion-associated small transmembrane (FAST) fusogenic protein could enhance oncolytic HAdV efficacy and reduce tumor growth rate in a human xenograft mouse model of cancer. We now explore whether co-expression of the adenovirus death protein (ADP) with p14 FAST protein could synergize to further enhance oncolytic vector efficacy. ADP is naturally encoded within the early region 3 (E3) of HAdV, a region which is frequently removed from HAdV-based vectors, and functions to enhance cell lysis and progeny release. We evaluated a variety of approaches to achieve optimal expression of the two proteins, the most efficient method being insertion of an expression cassette within the E3 deletion, consisting of the coding sequences for p14 FAST protein and ADP separated by a self-cleaving peptide derived from the porcine teschovirus-1 (P2A). However, the quantities of p14 FAST protein and ADP produced from this vector were reduced approximately 10-fold compared to a similar vector-expressing only p14 FAST protein and wildtype HAdV, respectively. Compared to our original oncolytic vector-expressing p14 FAST protein alone, reduced expression of p14 FAST protein and ADP from the P2A construct reduced cell-cell fusion, vector spread, and cell-killing activity in human A549 adenocarcinoma cells in culture. These studies show that a self-cleaving peptide can be used to express two different transgenes in an armed oncolytic HAdV vector, but also highlight the challenges in maintaining adequate transgene expression when modifying vector design. Full article
(This article belongs to the Special Issue Virus Engineering and Applications: 3rd Edition)
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Figure 1

Figure 1
<p>Viral constructs used in this study. A simplified transcription map for HAdV-5 is also shown, with the black arrows representing the indicated transcription unit. E—early transcription unit. L—late transcription unit. ITR—inverted terminal repeat. MLP—major late promoter. E1AΔ24—24-bp deletion in the conserved region 2 (CR2) of the E1A gene. 40SA—splice acceptor site derived from the HAdV-40 long fiber transcript. FAST—fusion-associated small transmembrane. HA—hemagglutinin epitope tag. RFP—red fluorescent protein. ins12.5K—transgene insertion within the E3 12.5K open reading frame. ADP—adenovirus death protein. FLAG—FLAG epitope tag. mutATG—mutation of the ADP start codon from ATG to TAG. P2A—self-cleaving peptide derived from porcine teschovirus-1. IRES—internal ribosome entry site derived from encephalomyocarditis virus (EMCV). Please note that the maps are not drawn to scale.</p>
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<p>Insertion of a splice acceptor-driven p14 FAST expression cassette in the intact E3 region inhibits efficient splicing of downstream E3 transcripts. Panel (<b>A</b>): A549 cells were infected at an MOI of 1 with AdRC129, CRAdFAST, AdRP3371, or AdRP3372, and crude cellular lysates were collected at 48 and 72 h post infection (hpi). The resulting proteins were analyzed by immunoblot for HAdV-5 hexon, p14 FAST protein (HA-tagged), ADP (FLAG-tagged), and tubulin. Panel (<b>B</b>): A549 cells were infected with CRAdFAST, AdRC129, or AdRP3371 at an MOI of 3 PFU/cell; total RNA was isolated 48 h later and was converted to cDNA. Samples were analyzed by conventional PCR for the total (<b>left panel</b>) fiber, p14 FAST or ADP, or using oligonucleotide primers designed to amplify these genes spliced to the HAdV-5 tripartite leader (<b>right panel</b>) and visualized by agarose gel electrophoresis. A schematic of the primer-binding sites (indicated with black arrows) is also shown. GOI = gene of interest (fiber, p14 FAST, or ADP).</p>
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<p>Insertion of an exogenous splice acceptor-driven expression cassette in the intact E3 region inhibits efficient expression of E3 proteins. Panel (<b>A</b>): A549 cells were infected at an MOI of 1 with CRAd or CRAdFAST in combination with either AdRC129 or AdRP3089, and crude cellular lysates were collected at 48 and 72 hpi. The resulting proteins were analyzed by immunoblot for HAdV-5 hexon, p14 FAST protein (HA-tagged), ADP (FLAG-tagged), RFP, and actin. Panel (<b>B</b>): A549 cells were infected at an MOI of 1 with AdRC129, AdRP3089, and AdRP3433, and crude cellular lysates were collected at 48 and 72 hpi. The resulting proteins were analyzed by immunoblot for HAdV-5 hexon, RFP, ADP (FLAG-tagged), and tubulin.</p>
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<p>Inclusion of a tandem 40SA-p14 FAST/40SA-ADP cassette replacing the E3 region does not permit expression of ADP. A549 cells infected at an MOI of 1 with AdRC129, CRAdFAST, or AdRP3442 were harvested at 48 and 72 hpi, and protein samples were analyzed by immunoblot for HAdV-5 hexon, p14 FAST protein (HA-tagged), ADP (FLAG-tagged), and tubulin.</p>
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<p>Removal of the upstream splice acceptor from the tandem 40SA-p14 FAST/40SA-ADP cassette does not lead to expression of ADP. Panel (<b>A</b>): A549 cells were transfected with pCI-neo-based plasmid containing 40SA-p14 FAST/40SA-ADP (pRP3454) or p14 FAST/40SA-ADP (pRP3455), and expression of p14 FAST (HA-tagged) and ADP (FLAG-tagged) was assessed 24 h later. Panel (<b>B</b>): A549 cells were transfected with pCI-neo-based plasmid deleted of the splice acceptor site located in the optimized chimeric intron normally contained in this plasmid, and containing 40SA-p14 FAST/40SA-ADP (pRP3460) or p14 FAST/40SA-ADP (pRP3462), and expression of p14 FAST (HA-tagged) and ADP (FLAG-tagged) was assessed 24 h later. Schematic representations of the various plasmids are also shown.</p>
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<p>Expression of p14 FAST protein and ADP from bicistronic expression constructs encoded within CRAd. Panel (<b>A</b>): A549 cells were infected at an MOI of 1 with CRAdFAST, AdRC116, AdRC125, or AdRC129. Crude cellular lysates were harvested at 48 and 72 hpi and analyzed by immunoblot for HAdV-5 hexon, p14 FAST protein (HA-tagged), ADP (FLAG-tagged), and tubulin. Panel (<b>B</b>): A549 cells were infected at varying MOI (10, 25, or 50) with AdRC125, and crude protein extracts were isolated at 48 and 72 hpi. In parallel, A549 cells were infected (MOI of 1) with CRAdFAST or AdRC129, and crude cell extracts were isolated at 72 hpi. The protein extracts were analyzed by immunoblot for HAdV-5 hexon, p14 FAST protein (HA-tagged), ADP (FLAG-tagged), and tubulin. Panel (<b>C</b>): A549 cells were infected with HAdV-5, AdRC129, or AdRC125 at an MOI of 10. Crude protein extracts were prepared in modified RIPA buffer and subjected to immunoprecipitation with antibody to FLAG or IgG. The resulting proteins and sample of input were subjected to immunoblot for FLAG.</p>
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<p>Immunoblot analysis of common loading control proteins in A549 cells infected with CRAdFAST. A549 cells were infected (MOI of 1) with CRAd or CRAdFAST. At varying times post-infection (24, 48, and 72 h), the cells were collected into the medium, cells and debris were pelleted by centrifugation at 10,000× <span class="html-italic">g</span>, and the resulting pellet was resuspended in 2× protein-loading buffer. The proteins were subsequently analyzed by immunoblot using antibodies to HAdV-5 hexon, p14 FAST (HA-tagged), vinculin, tubulin, actin, and histone H3. The immunoblots were processed using an Odyssey CLx imaging system (LI-COR), and the resulting quantified band intensities are provided below each panel of the figure.</p>
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<p>Analysis of viral plaque morphology and spread in tissue culture. Panel (<b>A</b>): A549 cells were infected with 10-fold serial dilutions of HAdV-5, CRAd, CRAdFAST, or AdRC116, and overlayed with medium supplemented with carboxymethylcellulose (CMC). Seven days later, the CMC medium was removed, and the monolayer was stained with crystal violet. Panel (<b>B</b>): High magnification bright-field microscopy images of plaques from Panel (<b>A</b>). Panel (<b>C</b>): A549 cells were infected with HAdV-5, CRAd, CRAdFAST, or AdRC116 at an MOI ranging from 0.01 to 10. Infected cells were fixed and stained with crystal violet 7 dpi. Three independent experiments were performed, and representative results are shown.</p>
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<p>Analysis of AdRC116-mediated cell killing in A549 cells in culture. Panel (<b>A</b>): A549 cells were infected at an MOI of 10 or 100 with CRAd, CRAdFAST, or AdRC116 (or mock-infected) and images were obtained 48 h later. Panel (<b>B</b>): A549 cells were infected at an MOI of 10 with CRAd, CRAdFAST, or AdRC116 (or mock-infected) and subjected to an MTS assay at 24-h intervals. Values were normalized to mock-infected cells. Panel (<b>C</b>): A549 cells were infected with varying MOI of CRAd, CRAdFAST, or AdRC116 and subjected to MTS assay at 48 hpi. Values were normalized to mock-infected cells. For Panels (<b>B</b>,<b>C</b>), the graphed values are the average with error bars representing standard deviation, and three independent experiments were performed in triplicate. **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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14 pages, 2475 KiB  
Article
Generation, Characterization, and Preclinical Studies of a Novel NKG2A-Targeted Antibody BRY805 for Cancer Immunotherapy
by Yaqiong Zhou, Yiru Wang, Jinfeng Liang, Jing Qian, Zhenhua Wu, Zhangzhao Gao, Jian Qi, Shanshan Zhu, Na Li, Yao Chen, Gang Chen, Lei Nie, Tingting Guo and Haibin Wang
Antibodies 2024, 13(4), 93; https://doi.org/10.3390/antib13040093 - 20 Nov 2024
Viewed by 262
Abstract
Immuno-oncology has revolutionized cancer treatment, with NKG2A emerging as a novel target for immunotherapy. The blockade of NKG2A using the immune checkpoint inhibitor (ICI) monalizumab has been shown to enhance the responses of both NK cells and CD8+ T cells. However, monalizumab has [...] Read more.
Immuno-oncology has revolutionized cancer treatment, with NKG2A emerging as a novel target for immunotherapy. The blockade of NKG2A using the immune checkpoint inhibitor (ICI) monalizumab has been shown to enhance the responses of both NK cells and CD8+ T cells. However, monalizumab has demonstrated limited efficacy in in vitro cytotoxic assays and clinical trials. In our study, we discovered and characterized a novel anti-NKG2A antibody, BRY805, which exhibits high specificity for the human CD94/NKG2A heterodimer complex and does not bind to the activating NKG2C receptor. In vitro cytotoxicity assays demonstrated that BRY805 effectively activated NK92 cells and primary NK cells, thereby enhancing the cytotoxic activity of effector cells against cancer cells overexpressing HLA-E, with significantly greater efficacy compared to monalizumab. Furthermore, BRY805 exhibited synergistic antitumor activity when combined with PD-L1 monoclonal antibodies. In a mouse xenograft model, BRY805 showed superior tumor control relative to monalizumab and demonstrated a favorable safety profile in non-human primate studies. Full article
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<p>Binding activity of BRY805 to proteins and cells was assessed through various methodologies. (<b>A</b>) The interaction of BRY805 with human NKG2A/CD94 extracellular domain (ECD) was quantified using enzyme-linked immunosorbent assay (ELISA). (<b>B</b>) The binding of BRY805 to cynomolgus NKG2A/CD94 ECD was evaluated via ELISA. (<b>C</b>) Flow cytometry analysis demonstrated the binding of BRY805 to CHOK1 cells overexpressing human NKG2A/CD94. (<b>D</b>) The binding of BRY805 to CHOK1 cells overexpressing cynomolgus NKG2A/CD94 was also analyzed using flow cytometry. (<b>E</b>) ELISA was employed to determine the binding affinity of BRY805 to human NKG2E. (<b>F</b>) Flow cytometry analysis further revealed the binding of BRY805 to CHOK1 cells overexpressing human NKG2C/CD94. (<b>G</b>) Analysis of the binding kinetics of BRY805 to the extracellular domain of hNKG2A/CD94 was conducted using Bio-Layer Interferometry.</p>
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<p>A competitive fluorescence-activated cell-sorting (FACS) experiment utilizing sHLA-E tetramers demonstrated that both BRY805 and monalizumab analog selectively blocked the binding of HLA-E to CD94/NKG2A expressing cells.</p>
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<p>BRY805 enhances the cytotoxicity of NK92 cells against tumor cells. Specifically, (<b>A</b>) the B-lymphoblastoid cell line LCL721.221 and (<b>B</b>) the myeloid leukemia cell line MOLM-13 were co-incubated with NK92 cells in the presence of serial dilutions of BRY805. The resulting cytotoxic effects were quantified using a time-resolved fluorometer to measure fluorescence.</p>
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<p>BRY805 augments the cytotoxic activity of primary natural killer (NK) cells against tumor cells. In this study, LCL721.221 cells were co-incubated with primary NK cells in the presence of serial dilutions of BRY805. The cytotoxic effects were quantitatively assessed using a time-resolved fluorometric assay to measure fluorescence.</p>
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<p>The combination of BRY805 and PD-L1 monoclonal antibody (MAb) significantly enhances the cytotoxic activity of NK92 cells against tumor cells. LCL721.221 cells were co-cultured with NK92 cells in the presence of BRY805 and serially diluted avelumab. Cytotoxicity was quantified by measuring fluorescence using a Tecan microplate reader. BRY805 effectively suppresses tumor growth in a Xenograft tumor model.</p>
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<p>In vivo efficacy of BRY805 evaluated in huHSC-NCG-hIL-5 mice bearing NCI-H1975 tumor xenografts. HuHSC-NCG-hIL-5 mice were subcutaneously injected with 2 × 10<sup>6</sup> NCI-H1975 tumor cells. Subsequently, molar equivalent doses of the specified antibodies were administered biweekly, and tumor size was measured biweekly.</p>
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14 pages, 2524 KiB  
Article
A Cancer-Specific Anti-Podoplanin Monoclonal Antibody, PMab-117-mG2a Exerts Antitumor Activities in Human Tumor Xenograft Models
by Tomohiro Tanaka, Hiroyuki Suzuki, Tomokazu Ohishi, Mika K. Kaneko and Yukinari Kato
Cells 2024, 13(22), 1833; https://doi.org/10.3390/cells13221833 - 6 Nov 2024
Viewed by 568
Abstract
Podoplanin (PDPN) overexpression is associated with poor clinical outcomes in various tumors. PDPN is involved in malignant tumor progression by promoting invasiveness and metastasis. Therefore, PDPN is considered a promising target of monoclonal antibody (mAb)-based therapy. Because PDPN also plays an essential role [...] Read more.
Podoplanin (PDPN) overexpression is associated with poor clinical outcomes in various tumors. PDPN is involved in malignant tumor progression by promoting invasiveness and metastasis. Therefore, PDPN is considered a promising target of monoclonal antibody (mAb)-based therapy. Because PDPN also plays an essential role in normal cells such as kidney podocytes, cancer specificity is required to reduce adverse effects on normal cells. We developed a cancer-specific mAb (CasMab) against PDPN, PMab-117 (rat IgM, kappa), by immunizing rats with PDPN-overexpressed glioblastoma cells. The recombinant mouse IgG2a-type PMab-117 (PMab-117-mG2a) reacted with the PDPN-positive tumor PC-10 and LN319 cells but not with PDPN-knockout LN319 cells in flow cytometry. PMab-117-mG2a did not react with normal kidney podocytes and normal epithelial cells from the lung bronchus, mammary gland, and corneal. In contrast, one of the non-CasMabs against PDPN, NZ-1, showed high reactivity to PDPN in both tumor and normal cells. Moreover, PMab-117-mG2a exerted antibody-dependent cellular cytotoxicity in the presence of effector splenocytes. In the human tumor xenograft models, PMab-117-mG2a exhibited potent antitumor effects. These results indicated that PMab-117-mG2a could be applied to antibody-based therapy against PDPN-expressing human tumors while reducing the adverse effects. Full article
(This article belongs to the Special Issue Recent Advances in Cancer Therapy—Second Edition)
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<p>Selection of anti-PDPN CasMab, PMab-117. (<b>A</b>) A scheme of CasMab selection from anti-PDPN mAb-producing hybridoma clones. (<b>B</b>) Flow cytometry using PMab-117 (10 μg/mL; Red line), NZ-1 (10 μg/mL; Red line), or buffer control (Black line) against LN229, LN229/PDPN, PC-10, LN319, and PDPN-knockout LN319 (BINDS-55). (<b>C</b>) Flow cytometry using PMab-117 (10 μg/mL; Red line), NZ-1 (10 μg/mL; Red line), or buffer control (Black line) against 293FT (human embryonic kidney) and PODO/TERT256 (TERT-expressed normal kidney podocyte).</p>
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<p>Production of PMab-117-mG<sub>2a</sub> and reactivity to cancer cells, normal kidney podocytes, and epithelial cells. (<b>A</b>) Class-switched mouse IgG<sub>2a</sub> mAb, PMab-117-mG<sub>2a,</sub> was generated from PMab-117 (rat IgM). (<b>B</b>) Flow cytometry using PMab-117-mG<sub>2a</sub> (1 μg/mL; Red line), NZ-1 (1 μg/mL; Red line), or buffer control (Black line) against LN229, LN229/PDPN, PC-10, LN319, and PDPN-knockout LN319 (BINDS-55). (<b>C</b>) Flow cytometry using PMab-117-mG<sub>2a</sub> (1 μg/mL; Red line), NZ-1 (1 μg/mL; Red line) or buffer control (Black line) against 293FT (human embryonic kidney), PODO/TERT256 (kidney podocyte), HBEC3-KT (lung bronchus epithelial cell), hTERT-HME1 (mammary gland epithelial cell), and hTCEpi (corneal epithelial cell).</p>
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<p>Determination of the binding affinity of PMab-117-mG<sub>2a</sub> and NZ-1 using flow cytometry. LN319 cells were suspended in PMab-117-mG<sub>2a</sub> (<b>A</b>) or NZ-1 (<b>B</b>) at indicated concentrations, followed by treatment with anti-mouse or rat IgG conjugated with Alexa Fluor 488. The SA3800 Cell Analyzer was used to analyze fluorescence data. The dissociation constant (<span class="html-italic">K</span><sub>D</sub>) values were determined using GraphPad Prism 6.</p>
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<p>ADCC activity by PMab-117-mG<sub>2a</sub> in PDPN-positive cells. ADCC induced by PMab-117-mG<sub>2a</sub> or control mouse IgG<sub>2a</sub> (PMab-231) against LN229/PDPN (<b>A</b>), PC-10 (<b>B</b>), and LN319 (<b>C</b>) cells. Values are shown as the mean ± SEM. Asterisks indicate statistical significance (** <span class="html-italic">p</span> &lt; 0.01; two-tailed unpaired <span class="html-italic">t</span>-test).</p>
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<p>Antitumor activity of PMab-117-mG<sub>2a</sub> against human tumor xenografts. (<b>A</b>–<b>C</b>) LN229/PDPN (<b>A</b>), PC-10 (<b>B</b>), and LN319 (<b>C</b>) cells were subcutaneously injected into BALB/c nude mice (day 0). PMab-117-mG<sub>2a</sub> (100 μg) or control mouse IgG (mIgG, 100 μg) were intraperitoneally injected into each mouse on days 1, 8, and 16 (LN229/PDPN and LN319, arrows) or days 1, 8, 14, and 22 (PC-10, arrows). The tumor volume is represented as the mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (two-way ANOVA and Sidak’s multiple comparisons test). (<b>D</b>–<b>F</b>) The mice were euthanized on day 30 (LN229/PDPN), day 28 (PC-10), or day 22 (LN319) after cell inoculation. The tumor weights of LN229/PDPN (<b>D</b>), PC-10 (<b>E</b>), and LN319 (<b>F</b>) xenografts were measured. Values are presented as the mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01, (two-tailed unpaired <span class="html-italic">t</span>-test). (<b>G</b>–<b>I</b>) LN229/PDPN (<b>G</b>), PC-10 (<b>H</b>), and LN319 (<b>I</b>) xenograft tumors (scale bar, 1 cm). (<b>J</b>–<b>L</b>) Body weights of LN229/PDPN (<b>J</b>), PC-10 (<b>K</b>), and LN319 (<b>L</b>) xenograft-bearing mice treated with control mIgG or PMab-117-mG<sub>2a</sub>.</p>
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15 pages, 3130 KiB  
Article
Role of Peroxisome Proliferator-Activated Receptor α-Dependent Mitochondrial Metabolism in Ovarian Cancer Stem Cells
by Seo Yul Lee, Min Joo Shin, Seong Min Choi, Dae Kyoung Kim, Mee Gyeon Choi, Jun Se Kim, Dong Soo Suh, Jae Ho Kim and Seong Jang Kim
Int. J. Mol. Sci. 2024, 25(21), 11760; https://doi.org/10.3390/ijms252111760 - 1 Nov 2024
Viewed by 610
Abstract
Peroxisome proliferator-activated receptors (PPARs), including PPAR-α, PPAR-β/δ, and PPAR-γ, are involved in various cellular responses, including metabolism and cell proliferation. Increasing evidence suggests that PPARs are closely associated with tumorigenesis and metastasis. However, the exact role of PPARs in energy metabolism and cancer [...] Read more.
Peroxisome proliferator-activated receptors (PPARs), including PPAR-α, PPAR-β/δ, and PPAR-γ, are involved in various cellular responses, including metabolism and cell proliferation. Increasing evidence suggests that PPARs are closely associated with tumorigenesis and metastasis. However, the exact role of PPARs in energy metabolism and cancer stem cell (CSC) proliferation remains unclear. This study investigated the role of PPARs in energy metabolism and tumorigenesis in ovarian CSCs. The expression of PPARs and fatty acid consumption as an energy source increased in spheroids derived from A2780 ovarian cancer cells (A2780-SP) compared with their parental cells. GW6471, a PPARα inhibitor, induced apoptosis in A2780-SP. PPARα silencing mediated by small hairpin RNA reduced A2780-SP cell proliferation. Treatment with GW6471 significantly inhibited the respiratory oxygen consumption of A2780-SP cells, with reduced dependency on fatty acids, glucose, and glutamine. In a xenograft tumor transplantation mouse model, intraperitoneal injection of GW6471 inhibited in vivo tumor growth of A2780-SP cells. These results suggest that PPARα plays a vital role in regulating the proliferation and energy metabolism of CSCs by altering mitochondrial activity and that it offers a promising therapeutic target to eradicate CSCs. Full article
(This article belongs to the Section Molecular Pharmacology)
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<p>Role of PPARs in energy metabolism of ovarian CSCs. (<b>A</b>) The dependence of each carbon source (fuel) dependency was calculated from the oxygen consumption rates measured by the Seahorse analyzer using the Mito Fuel Flex test kit. Cells were treated with 0.1% DMSO or 5 μM GW6471 for 24 h before performing the Mito Fuel Flex test. (<b>B</b>) The mRNA expression levels of PPARs in A2780 (non-CSC) and A2780-SP (CSC) cells. (<b>C</b>) PPAR transcriptional activity was measured using Dual-Luciferase Reporter assay. Cells were transfected with luciferase plasmid vectors 48 h before measuring luciferase signal activity. Data are presented as mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 (<span class="html-italic">n</span> = 3 for each group). PPAR, peroxisome proliferator-activated receptor; DMSO, dimethyl sulfoxide; CSC, cancer stem cell; mRNA, messenger ribonucleic acid; SEM, standard error of the mean.</p>
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<p>Effects of PPAR antagonists on cell viability of ovarian CSCs. (<b>A</b>) Cell viability was determined by MTT assay. CSCs were treated with the indicated concentrations of antagonists (GW6471, GW9662, GSK0660) for 48 h. (<b>B</b>) Representative images of spheroid formation assay of CSCs in the presence or absence of GW6471. Scale bar, 200 μm. (<b>C</b>) Quantification of spheroid number in spheroid formation assay. The number of spheroids with a diameter &gt; 100 μm was counted. Data are presented as mean ± SEM. *** <span class="html-italic">p</span> &lt; 0.001 (<span class="html-italic">n</span> = 3 for each group). (<b>D</b>) Representative immunocytochemistry images for detection of cleaved caspase-3. CSCs were treated with 0.1% DMSO (mock) or 10 μM GW6471 for 48 h before staining. Scale bar, 100 μm. PPAR, peroxisome proliferator-activated receptor; CSC, cancer stem cell; MTT, 3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide; DMSO, dimethyl sulfoxide; SEM, standard error of the mean.</p>
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<p>Effects of PPARα knockdown on cell proliferation and spheroid-forming abilities of CSCs. (<b>A</b>) Relative mRNA expression of PPARα in CSCs transduced with lentiviruses bearing sh-PPARα or sh-control was measured by qRT-PCR. (<b>B</b>) Cell proliferation assessed by WST assay at indicated time points. (<b>C</b>) Representative images of spheroid formation in CSCs transduced with lentiviruses bearing sh-PPARα or sh-control. Scale bar, 300 μm (<b>D</b>) Quantification of spheroid numbers in spheroid formation assay. The number of spheroids &gt; 150 μm in diameter. Data are presented as mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 (<span class="html-italic">n</span> = 3 for each group). PPAR: peroxisome proliferator-activated receptor; CSC: cancer stem cell; mRNA: messenger ribonucleic acid; sh: small hairpin; qRT-PCR: quantitative reverse transcription–polymerase chain reaction; WST: water-soluble tetrazolium salt; SEM: standard error of the mean.</p>
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<p>Effects of GW6471 on the mitochondrial metabolism of ovarian CSCs. (<b>A</b>) Oxygen consumption rates of the mock and GW6471-treated groups were measured with a Seahorse analyzer using the Mito Stress test kit. Oligo: oligomycin; FCCP: carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone; R/A: rotenone and antimycin A. (<b>B</b>) Mitochondrial metabolic parameters were calculated from the Mito Stress test results. Basal: basal respiration; maximal: maximal respiration; spare: spare respiratory capacity; non-mito: nonmitochondrial respiration. (<b>C</b>) Dependence on each carbon source (fuel) was calculated from oxygen consumption rates measured by a Seahorse analyzer using the Mito Fuel Flex test kit. CSCs were treated with 0.1% DMSO or 5 μM GW6471 for 24 h before performing the Mito Stress Test or Mito Fuel Flex test. Data are presented as mean ± SEM. *** <span class="html-italic">p</span> &lt; 0.001 (<span class="html-italic">n</span> = 3 for each group). CSC: cancer stem cell; DMSO: dimethyl sulfoxide; SEM: standard error of the mean.</p>
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<p>Effects of GW6471 on in vivo tumor growth of ovarian CSCs. (<b>A</b>) Experimental overview of ovarian cancer xenotransplantation model. Treatment with PBS or 20 μM GW6471 was started on day 22 after cell injection and continued twice a week until day 40. (<b>B</b>) Representative images of mice and resected tumors at day 43. (<b>C</b>) Tumor weight measured after resection on day 43. (<b>D</b>,<b>E</b>) Tumor volume (<b>D</b>) and mouse body weight (<b>E</b>) were measured twice a week from day 22 to day 43. Data are presented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 3 for each group). CSC: cancer stem cell; PBS: phosphate-buffered saline; SEM: standard error of the mean.</p>
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<p>Role of PPARs in the prognosis of patients with ovarian cancer. Kaplan–Meier survival curves for overall survival and disease-free survival stratified by the expression level of PPAR subtypes (<span class="html-italic">PPARA</span>, <span class="html-italic">PPARG</span>, <span class="html-italic">PPARD</span>). (<b>A</b>,<b>C</b>,<b>E</b>) Overall survival of patients with low (blue line) or high (red line) expression of <span class="html-italic">PPARA</span>, <span class="html-italic">PPARG</span>, or <span class="html-italic">PPARD</span>, respectively. (<b>B</b>,<b>D</b>,<b>F</b>) Disease-free survival for patients with low (blue line) or high (red line) expression of <span class="html-italic">PPARA</span>, <span class="html-italic">PPARG</span>, or <span class="html-italic">PPARD</span>, respectively. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001. PPAR: peroxisome proliferator-activated receptor; n: number of patients in each group; HR: hazard ratio; 95% CI: 95% confidence interval.</p>
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14 pages, 4183 KiB  
Article
Ubiquitin-Specific Protease 1 Promotes Bladder Cancer Progression by Stabilizing c-MYC
by Xia Zhang, Peng Peng, Li-Wei Bao, An-Qi Zhang, Bo Yu, Tao Li, Jing Lei, Hui-Hui Zhang and Shang-Ze Li
Cells 2024, 13(21), 1798; https://doi.org/10.3390/cells13211798 - 30 Oct 2024
Viewed by 675
Abstract
Background: Ubiquitination is an important post-transcriptional modification crucial for maintaining cell homeostasis. As a deubiquitination enzyme, ubiquitin-specific protease 1 (USP1) is associated with tumor progression; however, its role in bladder cancer is unknown. This study aimed to analyze USP1 expression and study its [...] Read more.
Background: Ubiquitination is an important post-transcriptional modification crucial for maintaining cell homeostasis. As a deubiquitination enzyme, ubiquitin-specific protease 1 (USP1) is associated with tumor progression; however, its role in bladder cancer is unknown. This study aimed to analyze USP1 expression and study its roles in bladder cancer. Methods: The web server GEPIA was used to analyze the USP1 expression. To explore USP1’s function in bladder cancer, we constructed USP1-knockout cell lines in UMUC3 cells. A FLAG-USP1 (WT USP1) plasmid and a plasmid FLAG-USP1 C90S (catalytic–inactive mutant) were used to overexpress USP1 in T24 cells. CCK8, colony formation, and Transwell assays were used to assess cell viability, proliferation, and migration. RNA-sequencing (RNA-seq) and dual-luciferase reporter assays were performed to screen the pathway. Co-immunoprecipitation and immunofluorescence were used to explore the interaction between USP1 and c-MYC. A xenograft mouse model was used to study the role of USP1 in bladder cancer. Results: USP1 expression was upregulated in human bladder cancer cells and correlated with poor patient prognosis. USP1 overexpression promoted cell proliferation, clone formation, and migration, and this was attenuated by genetic ablation of USP1. Furthermore, we observed that USP1 deficiency inhibited tumor formation in vivo. Mechanistically, the c-MYC pathway was remarkably activated compared with the other pathways. Furthermore, USP1 could interact with c-MYC and increase c-MYC’s stability depending on the catalytic activity of USP1. Conclusions: Our results suggested that high expression of USP1 promotes bladder cancer progression by stabilizing c-MYC; hence, USP1 may serve as a novel therapeutic target for treating bladder cancer. Full article
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<p>USP1 is overexpressed in bladder cancer. (<b>A</b>) USP1 expression in bladder urothelial carcinoma tissues compared with normal tissues in the GEPIA web server. (<b>B</b>) USP1 expression in bladder urothelial carcinoma tissues compared with normal tissues in the TNMplot web server (the upper from unpaired tumor and normal tissues, and the lower from paired tumor and normal tissues). (<b>C</b>) Analysis of overall survival for 94 patients with bladder cancer based on USP1 expression in the GEPIA web server. The high USP1 group showed significantly lower survival rates (<span class="html-italic">p</span> = 0.027). (<b>D</b>) Immunohistochemical staining of USP1 in tissue microarrays containing bladder cancer and adjacent tissues. (<b>E</b>) Tissue microarray data of USP1 expression in 66 pairs of tumor and adjacent tissues was analyzed by digital pathology image analysis software, and the number of positive cells per square millimeter was calculated (*** <span class="html-italic">p</span> &lt; 0.001). Data are presented as the mean ± SD. Statistical significance was analyzed by Student’s <span class="html-italic">t</span> test.</p>
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<p>USP1 overexpression promotes bladder cancer cell proliferation, migration, and invasion. (<b>A</b>) T24 cells were transfected with FLAG-USP1 or FLAG-USP1 C90S, as indicated, and protein levels were measured by Western blotting. GAPDH served as a control. (<b>B</b>) CCK-8 assays were used to analyze cell proliferation (<span class="html-italic">n</span> = 6). (<b>C</b>) Colony formation assays were performed to evaluate cell viability. The colonies were stained with crystal violet and photographed. The number of colonies was determined and plotted (<span class="html-italic">n</span> = 3). (<b>D</b>) Transwell experiments were used to evaluate the effects of USP1 overexpression on cell migration. The cells were imaged (left, ×20 magnification) and counted, and the results were plotted (right, <span class="html-italic">n</span> = 3). The data (mean ± SEM) are representative of 3 independent experiments. (<b>E</b>) Transwell chambers with Matrigel were used to evaluate the effects of USP1 overexpression on cell invasion. The cells were imaged (left, ×20 magnification) and counted, and the results were plotted (right, <span class="html-italic">n</span> = 3). The data (mean ± SEM) are representative of 3 independent experiments. Statistical significance was analyzed by ANOVA or Student’s <span class="html-italic">t</span> test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>USP1 deficiency inhibits cell proliferation, migration, and invasion. (<b>A</b>) USP1 protein levels in WT and USP1-deficient UMUC3 cells were measured by Western blotting, with GAPDH as a loading control. (<b>B</b>) Colony formation assays showed the viability of USP1-deficient UMUC3 bladder cancer cells. Colonies were stained with crystal violet and subsequently imaged (left). The number of colonies was determined and plotted (right, <span class="html-italic">n</span> = 3). (<b>C</b>) Cell proliferation was analyzed by CCK-8 assays with daily measurements for 5 days (<span class="html-italic">n</span> = 6). (<b>D</b>) Transwell experiments were used to evaluate the effects of USP1 deficiency on UMUC3 bladder cancer cell migration. The cells were imaged (left, ×20 magnification) and counted, and the results were plotted (right, <span class="html-italic">n</span> = 3). (<b>E</b>) Transwell chambers with Matrigel were used to evaluate the effects of USP1 deficiency on UMUC3 bladder cancer cell invasion. The cells were imaged (left, ×20 magnification) and counted, and the results were plotted (right, <span class="html-italic">n</span> = 3). The data (mean ± SD) are representative of 3 independent experiments. Statistical significance was analyzed by ANOVA or Student’s <span class="html-italic">t</span> test. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>USP1 regulates the c-MYC pathway. (<b>A</b>) KEGG pathway enrichment analysis on the basis of the most significantly differentially expressed genes between the WT and USP1-deficient groups (<span class="html-italic">p</span> &lt; 0.05 using Fisher’s exact test). (<b>B</b>) Bubble diagram showing the enrichment of differentially expressed genes in the biological process category. (<b>C</b>) Luciferase pathway screening revealed that USP1 significantly promoted c-MYC pathway activation in HEK293T cells (<span class="html-italic">n</span> = 3). (<b>D</b>) Luciferase assays showing c-MYC pathway activity in HEK293T cells transfected with increasing amounts (0, 400, and 800 ng) of the USP1 expression plasmid (<span class="html-italic">n</span> = 3). (<b>E</b>) The transcription of endogenous genes downstream of c-MYC was decreased in USP1-deficient UMUC3 cells, as examined by RT-qPCR (<span class="html-italic">n</span> = 3). The data (mean ± SD) are representative of 3 independent experiments. Statistical significance was analyzed by ANOVA or Student’s <span class="html-italic">t</span> test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>USP1 interacts with c-MYC and promotes c-MYC protein stability. (<b>A</b>) Immunofluorescence images of HA-USP1 (red) and FLAG-c-MYC (green) in HEK293T cells. DAPI was used as a nuclear stain (blue). (<b>B</b>,<b>C</b>) Immunoprecipitation experiments showed the interaction between USP1 and c-MYC in HEK293 cells. (<b>D</b>) Western blot analysis of c-MYC expression in USP1-overexpression T24 cells. (<b>E</b>) Western blot analysis of c-MYC expression in USP1-deficient UMUC3 cells. (<b>F</b>) Cells were transfected with increasing amounts of the HA-USP1 (0, 200, 400, and 800 ng), HA-USP1 C90S (400 and 800 ng), and FLAG-c-MYC plasmids, and Western blotting was performed to determine the effect of USP1 protein levels on c-MYC expression in HEK293T cells. (<b>G</b>) Cells were transfected with FLAG-c-MYC with HA-USP1 or HA-USP1 C90S, as indicated. Western blot analysis of c-MYC stability after treatment with CHX (50 μg/mL) for the indicated time. GAPDH served as a control. (<b>H</b>) Cells were co-transfected with FLAG-c-MYC and Myc-Ub with or without HA-USP1 or HA-USP1 C90S, treated with MG132 (10 μM) for 6 h, and then subjected to ubiquitination assays.</p>
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<p><span class="html-italic">USP1</span> deficiency represses tumor formation in vivo. (<b>A</b>,<b>B</b>) Tumors were harvested from euthanized mice and weighed, and representative images are shown. (<b>C</b>) Tumors were measured every 2 days, and the tumor volume was plotted. (<b>D</b>) Quantitative results of tumor weight. (<b>E</b>) Tumor tissues from each group of nude mice were prepared as paraffin sections and stained with HE, anti-USP1, anti-c-MYC, and anti-Ki67 antibodies. Statistical significance was analyzed by ANOVA or Student’s <span class="html-italic">t</span> test. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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16 pages, 1150 KiB  
Review
Familial Pancreatic Cancer Research: Bridging Gaps in Basic Research and Clinical Application
by Suyakarn Archasappawat, Fatimah Al-Musawi, Peiyi Liu, EunJung Lee and Chang-il Hwang
Biomolecules 2024, 14(11), 1381; https://doi.org/10.3390/biom14111381 - 30 Oct 2024
Viewed by 579
Abstract
Familial pancreatic cancer (FPC) represents a significant yet underexplored area in pancreatic cancer research. Basic research efforts are notably limited, and when present, they are predominantly centered on the BRCA1 and BRCA2 mutations due to the scarcity of other genetic variants associated with [...] Read more.
Familial pancreatic cancer (FPC) represents a significant yet underexplored area in pancreatic cancer research. Basic research efforts are notably limited, and when present, they are predominantly centered on the BRCA1 and BRCA2 mutations due to the scarcity of other genetic variants associated with FPC, leading to a limited understanding of the broader genetic landscape of FPC. This review examines the current state of FPC research, focusing on the molecular mechanisms driving pancreatic ductal adenocarcinoma (PDAC) progression. It highlights the role of homologous recombination (HR) and its therapeutic exploitation via synthetic lethality with PARP inhibitors in BRCA1/2-deficient tumors. The review discusses various pre-clinical models of FPC, including conventional two-dimensional (2D) cell lines, patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and genetically engineered mouse models (GEMMs), as well as new advancements in FPC research. Full article
(This article belongs to the Section Molecular Medicine)
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<p>Steps of homologous recombination. Homologous recombination (HR) repairs double-strand breaks (DSB) in three key steps. First, during pre-synapsis, the MRN complex processes the DNA ends to generate 3′ single-stranded DNA (ssDNA) overhang and recruits ATM kinase. During synapsis, RPA then binds the overhang, and BRCA1 is recruited to the site. BRCA1 then facilitates the recruitment of BRCA2, and PALB2, along with RAD51, mediates the invasion of the homologous DNA duplex to form a displacement loop (D-loop). Finally, DNA polymerases extend the invading strand using the homologous template, completing the repair process.</p>
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<p>Future directions for familial pancreatic cancer (FPC). This figure presents a roadmap for future advancements in FPC, highlighting key areas of focus to improve patient outcomes. It emphasizes a multi-pronged approach encompassing the establishment of robust FPC models, identification of new vulnerabilities, personalized medicine strategies, and the application of translational research.</p>
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14 pages, 10869 KiB  
Article
Novel 1,8-Naphthalimide Derivatives Inhibit Growth and Induce Apoptosis in Human Glioblastoma
by Cheng-Chi Lee, Chuan-Hsin Chang, Yin-Cheng Huang and Tzenge-Lien Shih
Int. J. Mol. Sci. 2024, 25(21), 11593; https://doi.org/10.3390/ijms252111593 - 29 Oct 2024
Viewed by 454
Abstract
Given the rapid advancement of functional 1,8-Naphthalimide derivatives in anticancer research, we synthesized these two novel naphthalimide derivatives with diverse substituents and investigated the effect on glioblastoma multiforme (GBM) cells. Cytotoxicity, apoptosis, cell cycle, topoisomerase II and Western blotting assays were evaluated for [...] Read more.
Given the rapid advancement of functional 1,8-Naphthalimide derivatives in anticancer research, we synthesized these two novel naphthalimide derivatives with diverse substituents and investigated the effect on glioblastoma multiforme (GBM) cells. Cytotoxicity, apoptosis, cell cycle, topoisomerase II and Western blotting assays were evaluated for these compounds against GBM in vitro. A human GBM xenograft mouse model established by subcutaneously injecting U87-MG cells and the treatment responses were assessed. Both compounds 3 and 4 exhibited significant antiproliferative activities, inducing apoptosis and cell death. Only compound 3 notably induced G2/M phase cell cycle arrest in the U87-MG GBM cells. Both compounds inhibited DNA topoisomerase II activity, resulting in DNA damage. The in vivo antiproliferative potential of compound 3 was further validated in a U87-MG GBM xenograft mouse model, without any discernible loss of body weight or kidney toxicity noted. This study presents novel findings demonstrating that 1,8-Naphthalimide derivatives exhibited significant GBM cell suppression in vitro and in vivo without causing adverse effects on body weight or kidney function. Further experiments, including investigations into mechanisms and pathways, as well as preclinical studies on the pharmacokinetics and pharmacodynamics, may be instrumental to the development of a new anti-GBM compound. Full article
(This article belongs to the Section Molecular Oncology)
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<p>Structures of (<b>a</b>) 1,8-Naphthanhydride, (<b>b</b>) 1,8-Naphthalimide, (<b>c</b>) Mitonafide, and (<b>d</b>) Amonafide.</p>
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<p>1,8-Naphthalimide derivatives <b>3</b> and <b>4</b>, used in the analyses in this study.</p>
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<p>Effects of compounds <b>3</b> and <b>4</b> on clonogenic survival of U87-MG GBM cells. U87-MG cells were seeded into culture plates and treated with compound <b>3</b> (<b>A</b>) or compound <b>4</b> (<b>B</b>) at a dose of 5, 10, or 25 μM, with TMZ used as a positive control (25 or 50 μM), for 8 days. Subsequently, the cells were fixed with 1% formalin containing 1% crystal violet, and colony formation was assessed using an inverted microscope. (<b>C</b>,<b>D</b>) Colony numbers were quantified in culture plates. Data are presented as the mean ± SE of three independent experiments, and statistical analysis revealed significant differences (*** <span class="html-italic">p</span> &lt; 0.001) compared with the control group.</p>
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<p>Induction of apoptosis and cell death in U87-MG GBM cells by compounds <b>3</b> and <b>4</b>. U87-MG cells were treated with either compound <b>3</b> (5 μM; <b>A</b>) or compound <b>4</b> (5 μM; <b>B</b>), with TMZ treatments at a dose of 50 μM used as a positive control. Data are presented as the mean ± SE of three independent experiments, and statistical analysis revealed significant differences (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01) compared with the control group.</p>
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<p>Effects of compounds <b>3</b> and <b>4</b> on the cell cycle distribution of U87-MG GBM cells in vitro. U87-MG GBM cells were treated with 5 μM of compound <b>3</b> (<b>A</b>), 5 μM of compound <b>4</b> (<b>B</b>), or 50 μM of TMZ as a positive control. After 24 h, the cells were harvested and stained with propidium iodide (PI). The percentage distribution of cells in the sub-G1, G0/G1, S, and G2/M phases was analyzed through flow cytometry (<b>C</b>,<b>D</b>). Data are presented as the mean ± SE of three independent experiments. Statistical analysis revealed significant differences (* <span class="html-italic">p</span> &lt; 0.05) compared with the control group.</p>
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<p>Inhibitory effect of compounds <b>3</b> and <b>4</b> on Top II. Supercoiled plasmid DNA (pHOT DNA) was incubated with Top II and various concentrations of compounds <b>3</b> and <b>4</b> (1, 10, and 100 μM) or etoposide (VP-16; 10, 100, and 500 μM). The reaction products were separated onto 1% agarose gel containing 0.5 μg/mL ethidium bromide. The experiment was independently replicated three times.</p>
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<p>Effects of compounds <b>3</b> and <b>4</b> on phosphorylation of H2AX (DNA damage marker) in U87-MG GBM cells. The expression of H2AX and its phosphorylation status at Ser 139 (γ-H2AX) were analyzed through immunoblotting by using antibodies against the phosphorylated and total protein, with GADPH serving as the loading control. Data are presented as means ± SEs (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with DMSO.</p>
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<p>In vivo antitumor activity of compounds <b>3</b> and <b>4</b> against U87 GBM xenografts. <span class="html-italic">Nonobese diabetic</span> (<span class="html-italic">NOD</span>)/<span class="html-italic">severe combined immunodeficient</span> (<span class="html-italic">SCID</span>) mice were subcutaneously implanted with U87 GBM cells and intraperitoneally injected with the vehicle (0.1% DMSO in saline), compound <b>3</b> (5 mg/kg), compound <b>4</b> (5 mg/kg), or TMZ (12.5 mg/kg) thrice weekly. Tumor growth (<b>A</b>), body weight (<b>B</b>), and tumor weight (<b>C</b>) were recorded after the mice were euthanized. Additionally, the serum levels of (<b>D</b>) BUN and (<b>E</b>) CRE were determined using an automated clinical chemistry analyzer. Data are presented as means ± standard errors of the mean. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 compared with the model group.</p>
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15 pages, 5747 KiB  
Article
Significance of LIF/LIFR Signaling in the Progression of Obesity-Driven Triple-Negative Breast Cancer
by Lois Randolph, Jaitri Joshi, Alondra Lee Rodriguez Sanchez, Uday P. Pratap, Rahul Gopalam, Yidong Chen, Zhao Lai, Bindu Santhamma, Edward R. Kost, Hareesh B. Nair, Ratna K. Vadlamudi, Panneerdoss Subbarayalu and Suryavathi Viswanadhapalli
Cancers 2024, 16(21), 3630; https://doi.org/10.3390/cancers16213630 - 28 Oct 2024
Viewed by 569
Abstract
American women with obesity have an increased incidence of triple-negative breast cancer (TNBC). The impact of obesity conditions on the tumor microenvironment is suspected to accelerate TNBC progression; however, the specific mechanism(s) remains elusive. This study explores the hypothesis that obesity upregulates leukemia [...] Read more.
American women with obesity have an increased incidence of triple-negative breast cancer (TNBC). The impact of obesity conditions on the tumor microenvironment is suspected to accelerate TNBC progression; however, the specific mechanism(s) remains elusive. This study explores the hypothesis that obesity upregulates leukemia inhibitory factor receptor (LIFR) oncogenic signaling in TNBC and assesses the efficacy of LIFR inhibition with EC359 in blocking TNBC progression. TNBC cell lines were co-cultured with human primary adipocytes, or adipocyte-conditioned medium, and treated with EC359. The effects of adiposity were measured using cell viability, colony formation, and invasion assays. Mechanistic studies utilized RNA-Seq, Western blotting, RT-qPCR, and reporter gene assays. The therapeutic potential of EC359 was tested using xenograft and patient-derived organoid (PDO) models. The results showed that adipose conditions increased TNBC cell proliferation and invasion, and these effects correlated with enhanced LIFR signaling. Accordingly, EC359 treatment reduced cell viability, colony formation, and invasion under adipose conditions and blocked adipose-mediated organoid growth and TNBC xenograft tumor growth. RNA-Seq analysis identified critical pathways modulated by LIF/LIFR signaling in diet-induced obesity mouse models. These findings suggest that adiposity contributes to TNBC progression via the activation of the LIF/LIFR pathway, and LIFR inhibition with EC359 represents a promising therapeutic approach for obesity-associated TNBC. Full article
(This article belongs to the Special Issue Pathology and Treatment of Triple-Negative Breast Cancer)
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<p>Obese-related adiposity conditions enhance LIFR downstream signaling. TNBC cells, BT-549 (<b>A</b>), and SUM-159 (<b>B</b>) were cultured with ADP-CM for 24 h, and the expression of LIFR target genes was analyzed by RT-qPCR. (<b>C</b>) TNBC cells cultured with ADP-CM for 24 h were analyzed using Western blot analysis to measure LIFR downstream signaling proteins. (<b>D</b>) The effects of EC359 treatment against adipose conditions on TNBC cell viability were determined using MTT assays. (<b>E</b>) The effects of ADP-CM and ADP-CM + EC359 (50 nM) on adiposity-induced cell survival of TNBC cells was measured using colony formation assays. (<b>F</b>) Representative images of colonies were shown. ns, not significant; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>ADP-CM or co-culture of adipocytes enhances LIFR downstream signaling in TNBC and addition of EC359 (100 nM) reduces its activity in vitro. TNBC cells that stably express the STAT3 reporter were used. ADP-CM (<b>A</b>) activates STAT3 reporter activity, with EC359 (100 nM) effectively reducing STAT3 activity in ADP-CM conditions. (<b>B</b>) BT-549 and MDA-MB-231 cells were cultured with ADP-CM in the presence or absence of EC359 (100 nM) for 24 h, and the expression of LIFR target genes was analyzed by RT-qPCR. (<b>C</b>) MDA-MB-231 cells were incubated with or without ADP-CM and EC359 (100 nM) and were analyzed using Western blot analysis to measure LIFR downstream signaling proteins. (<b>D</b>) Adipocytes were indirectly co-cultured with MDA-MB-231 cells using a transwell culture system in the presence or absence of EC359 (100 nM) for 24 h. Signaling was profiled by Western blotting. (<b>E</b>) Effect of adipose conditions on TNBC cell invasion in the presence or absence of EC359 (100 nM) was determined by Boyden chamber assay and invaded cells were quantified (<b>F</b>). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>EC359 reduces tumor volume and weight in adiposity and diet-induced TNBC CDX models. Primary human adipose cells were co-implanted with MDA-MB-231 cells via orthotopic injection (n = 7) and subsequently treated with or without EC359 (5 mg/kg, intraperitoneally, 5 days per week). Tumor volumes (<b>A</b>) and tumor weights (<b>B</b>) are shown in graph. (<b>C</b>) Body weights of Low-fat-diet- and high-fat-diet-induced TNBC xenograft models treated with or without EC359 (5 mg/kg/ip/5 days/week) are shown in graph. Tumor volume (<b>D</b>) and tumor weights (<b>E</b>) are shown. ns, not significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Low-fat diet and high-fat diet 4T1-TNBC syngeneic model. Diet-induced obesity syngeneic models (n = 8) were treated with vehicle or EC359 (5 mg/kg/ip/5 days/week). (<b>A</b>) Body weights shown in graph. Bar graphs of liver (<b>B</b>), fat tissue (<b>C</b>), spleen (<b>D</b>), and tumor volume (<b>E</b>) were shown. (<b>F</b>) Tumor weights of all the 3 groups are shown. (<b>G</b>) Representative images of excised tumors from diet-induced syngeneic xenografts treated with or without EC359. ns, not significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Differential gene expression analysis of TNBC syngeneic xenograft model. (<b>A</b>) GSEA reveals pathways with positive enrichment (upregulated pathways) in diet-induced obesity TNBC-4T1 syngeneic xenograft models after HFD feeding. (<b>B</b>) GSEA displays pathways with negative enrichment (downregulated pathways) in EC359-treated TNBC-4T1 syngeneic xenograft models after HFD feeding. Gene ontology analysis identifies biological processes with positive enrichment in HFD models (<b>C</b>) and negative enrichment in EC359-treated HFD models (<b>D</b>). Differentially expressed genes were filtered using log2FoldChange ≥ 2, and <span class="html-italic">p</span>adj &lt; 0.01.</p>
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12 pages, 2811 KiB  
Article
Targeting Oxidative Phosphorylation with a Novel Thiophene Carboxamide Increases the Efficacy of Imatinib against Leukemic Stem Cells in Chronic Myeloid Leukemia
by Kana Kusaba, Tatsuro Watanabe, Keisuke Kidoguchi, Yuta Yamamoto, Ayaka Tomoda, Toshimi Hoshiko, Naoto Kojima, Susumu Nakata and Shinya Kimura
Int. J. Mol. Sci. 2024, 25(20), 11093; https://doi.org/10.3390/ijms252011093 - 15 Oct 2024
Viewed by 861
Abstract
Patients with chronic myeloid leukemia (CML) respond to tyrosine kinase inhibitors (TKIs); however, CML leukemic stem cells (LSCs) exhibit BCR::ABL kinase-independent growth and are insensitive to TKIs, leading to disease relapse. To prevent this, new therapies targeting CML-LSCs are needed. Rates of mitochondria-mediated [...] Read more.
Patients with chronic myeloid leukemia (CML) respond to tyrosine kinase inhibitors (TKIs); however, CML leukemic stem cells (LSCs) exhibit BCR::ABL kinase-independent growth and are insensitive to TKIs, leading to disease relapse. To prevent this, new therapies targeting CML-LSCs are needed. Rates of mitochondria-mediated oxidative phosphorylation (OXPHOS) in CD34+CML cells within the primitive CML cell population are higher than those in normal undifferentiated hematopoietic cells; therefore, the inhibition of OXPHOS in CML-LSCs may be a potential cure for CML. NK-128 (C33H61NO5S) is a structurally simplified analog of JCI-20679, the design of which was based on annonaceous acetogenins. NK-128 exhibits antitumor activity against glioblastoma and human colon cancer cells by inhibiting OXPHOS and activating AMP-activated protein kinase (AMPK). Here, we demonstrate that NK-128 effectively suppresses the growth of CML cell lines and that the combination of imatinib and NK-128 is more potent than either alone in a CML xenograft mouse model. We also found that NK-128 inhibits colony formation by CD34+ CML cells isolated from the bone marrow of untreated CML patients. Taken together, these findings suggest that targeting OXPHOS is a beneficial approach to eliminating CML-LSCs, and may improve the treatment of CML. Full article
(This article belongs to the Collection Anticancer Drug Discovery and Development)
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<p>Chemical structure of thiophene carboxamide (NK-128).</p>
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<p>NK-128 inhibits the proliferation of CML and Philadelphia chromosome-positive acute lymphoblastic leukemia cell lines. (<b>A</b>) Cells were incubated with NK-128 and cell numbers were counted on Days 4 and 8. The number of viable cells is shown. NK-128 inhibited the proliferation of BV173 (<span class="html-italic">p</span> &lt; 0.01) and SUP-B15 (<span class="html-italic">p</span> &lt; 0.05) cells significantly, even at concentrations as low as 0.1 μM. (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). (<b>B</b>) Experimental scheme showing the generation of the K562 xenograft model using NOD/Shi-scid IL-2Rγ KO Jic mice. On Day 0, K562 cells (4.0 × 10<sup>6</sup> cells/mouse) were injected subcutaneously into NOD/Shi-scid IL-2Rγ KO Jic mice. Daily ip administration of the vehicle and NK-128 began on Day 8. (<b>C</b>) Tumor growth curves and (<b>D</b>) body weight for each group; vehicle (black; n = 10), 10 mg/kg NK-128 (blue; n = 10), and 20 mg/kg NK-128 (red; n = 10).</p>
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<p>Combination therapy with imatinib and NK-128 inhibits the proliferation of CML cell lines and Philadelphia chromosome-positive acute lymphoblastic leukemia cell lines to a greater extent than imatinib alone. (<b>A</b>) CML and Ph<sup>+</sup>ALL cell lines were treated with medium (black), imatinib (blue) or a combination of imatinib and 0.1 μM NK-128 (red). The concentration of imatinib was 0.25 μM for K562, 0.2 μM for MYL, 0.1 μM for BV173, 0.4 μM for SUP-B15, and 1 μM for MYL-R. The combination of NK-128 plus imatinib also inhibited the proliferation to a greater extent than imatinib alone. (** <span class="html-italic">p</span> &lt; 0.01) (<b>B</b>) Each cell line was treated with or without NK-128 and imatinib (IM) for 3 days. The number of cells stained with APC-annexin V was measured by flow cytometric analysis, as described in the <a href="#sec4-ijms-25-11093" class="html-sec">Section 4</a>. Results are the mean of three independent experiments with SD (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>ATP and lactate production by K562, BV173, MYL, and SUP-B15 cells. Oligomycin inhibits OXPHOS, and 2-DG inhibits glycolysis. ATP and lactate production levels were determined in K562 and MYL cell lines, which are less sensitive to NK-128 (<b>A</b>) or in the sensitive cell lines, BV173 and SUP-B15 (<b>B</b>), as described in the <a href="#sec4-ijms-25-11093" class="html-sec">Section 4</a>. The main metabolic pathways of K562 and MYL are glycolysis-dependent. Conversely, those of BV173 and SUP-B15 are OXPHOS-dependent. NK-128 inhibits the production of ATP in BV173 and SUP-B15 cells, which are mainly OXPHOS dependent.</p>
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<p>Combined treatment with NK-128 and TKI effectively suppresses tumor growth in xenograft model mice. (<b>A</b>) Experimental scheme showing the generation of the K562 xenograft model using NOD/Shi-scid IL-2Rγ KO Jic mice. On Day 0, K562 cells (5.0 × 10<sup>6</sup> cells/mouse) were injected subcutaneously into NOD/Shi-scid IL-2Rγ KO Jic mice. Vehicle, imatinib, and NK-128 were administered on Day 6 and continued daily for a total of 11 days (nothing was administered on Day 12). (<b>B</b>) The number of subjects in each group was determined by the following formula. (<b>B</b>) Tumor growth curves for each group. Vehicle (black; n = 10), imatinib (blue; n = 10), NK-128 (green; n = 10), and NK-128 + imatinib (red; n = 10). NK-128 + imatinib inhibited tumor growth significantly (versus vehicle: * <span class="html-italic">p</span> = 0.03). (<b>C</b>) Xenograft tumors were isolated on day 17 in each treatment group. They were arranged in descending order from the right side. (<b>D</b>) The weight of the isolated tumors was measured. NK-128 + imatinib inhibited xenograft tumor growth to a greater extent than vehicle (* <span class="html-italic">p</span> = 0.04) Each circle shows the sample value and the cross shows the median. (<b>E</b>) Bodyweight was measured from day 6 to day 17. (<b>F</b>) White blood cell count, (<b>G</b>) red blood cell count, (<b>H</b>) hemoglobin levels, and (<b>I</b>) platelet count after treatment. Differences between the vehicle and each treatment group were tested using Dunnett’s test. (* <span class="html-italic">p</span> &lt; 0.05. n.s. not significant).</p>
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<p>Colony formation by CML CD34+ cells in the presence of different concentrations of imatinib and NK-128. NK-128 monotherapy inhibited colony formation in a concentration-dependent manner (compared with no treatment). NK-128 (500 nM) combined with imatinib (200 nM) inhibited colony formation by CML CD34+ cells significantly compared with imatinib alone. (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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29 pages, 6809 KiB  
Article
Long-Term Human Immune Reconstitution, T-Cell Development, and Immune Reactivity in Mice Lacking the Murine Major Histocompatibility Complex: Validation with Cellular and Gene Expression Profiles
by Milita Darguzyte, Philipp Antczak, Daniel Bachurski, Patrick Hoelker, Nima Abedpour, Rahil Gholamipoorfard, Hans A. Schlößer, Kerstin Wennhold, Martin Thelen, Maria A. Garcia-Marquez, Johannes Koenig, Andreas Schneider, Tobias Braun, Frank Klawonn, Michael Damrat, Masudur Rahman, Jan-Malte Kleid, Sebastian J. Theobald, Eugen Bauer, Constantin von Kaisenberg, Steven R. Talbot, Leonard D. Shultz, Brian Soper and Renata Stripeckeadd Show full author list remove Hide full author list
Cells 2024, 13(20), 1686; https://doi.org/10.3390/cells13201686 - 12 Oct 2024
Viewed by 1188
Abstract
Background: Humanized mice transplanted with CD34+ hematopoietic cells (HPCs) are broadly used to study human immune responses and infections in vivo and for testing therapies pre-clinically. However, until now, it was not clear whether interactions between the mouse major histocompatibility complexes (MHCs) [...] Read more.
Background: Humanized mice transplanted with CD34+ hematopoietic cells (HPCs) are broadly used to study human immune responses and infections in vivo and for testing therapies pre-clinically. However, until now, it was not clear whether interactions between the mouse major histocompatibility complexes (MHCs) and/or the human leukocyte antigens (HLAs) were necessary for human T-cell development and immune reactivity. Methods: We evaluated the long-term (20-week) human hematopoiesis and human T-cell development in NOD Scid Gamma (NSG) mice lacking the expression of MHC class I and II (NSG-DKO). Triplicate experiments were performed with HPCs obtained from three donors, and humanization was confirmed in the reference strain NOD Rag Gamma (NRG). Further, we tested whether humanized NSG-DKO mice would respond to a lentiviral vector (LV) systemic delivery of HLA-A*02:01, HLA-DRB1*04:01, human GM-CSF/IFN-α, and the human cytomegalovirus gB antigen. Results: Human immune reconstitution was detectable in peripheral blood from 8 to 20 weeks after the transplantation of NSG-DKO. Human single positive CD4+ and CD8+ T-cells were detectable in lymphatic tissues (thymus, bone marrow, and spleen). LV delivery harnessed the detection of lymphocyte subsets in bone marrow (αβ and γδ T-cells and NK cells) and the expression of HLA-DR. Furthermore, RNA sequencing showed that LV delivery increased the expression of different human reactome pathways, such as defense responses to other organisms and viruses. Conclusions: Human T-cell development and reactivity are independent of the expression of murine MHCs in humanized mice. Therefore, humanized NSG-DKO is a promising new model for studying human immune responses, as it abrogates the xenograft mouse MHC interference. Full article
(This article belongs to the Section Stem Cells)
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Graphical abstract

Graphical abstract
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<p>Human hematopoietic engraftment and T-cell development in humanized NRG mice used as a reference strain. (<b>A</b>) Scheme of experiments: CD34<sup>+</sup> stem cell transplantation (HCT) i.v. after irradiation, blood collection (BL), and termination (X). The humanized mice were humanized with three different CB donors: #396 (depicted as a triangle), #395 (depicted as a square), and #376 (depicted as a circle). Both female and male mice were used in this experiment (details in <a href="#app1-cells-13-01686" class="html-app">Table S1</a>). Created in BioRender. Lab, S. (2024) BioRender.com/x52w302. (<b>B</b>) Blood analyses at weeks 8, 12, and 20 after HCT and longitudinal quantification of cells expressing huCD45, huCD34, huCD19, huCD3, huCD4, and huCD8 (in percentages). (<b>C</b>) Bone-marrow analyses showing the quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and double positive (DP) (in absolute cell counts, log scale). (<b>D</b>) Thymus analyses and the quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and DP (in absolute cell counts, log scale). (<b>E</b>) Spleen analyses and quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and DP (in absolute cell counts, log scale).</p>
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<p>Human hematopoietic engraftment and T-cell development in humanized NSG-DKO mice. (<b>A</b>) Scheme of experiments: CD34<sup>+</sup> stem-cell transplantation (HCT) i.v. after irradiation, lentivirus (LV) immunization i.v., bioluminescence imaging (BLI) analyses, blood collections (BL), and termination (X). Created in BioRender. Lab, S. (2024) BioRender.com/y83d419; (<b>B</b>) full-body BLI quantified as photons/second (p/s) at 8 or 12 weeks post-HCT of huNSG-DKO control (representative of one mouse) or after LV administration (representative of three mice). (<b>C</b>) Blood analyses at weeks 8, 12, and 20 after HCT and longitudinal quantification of cells expressing huCD45, huCD19, huCD3, huCD4, and huCD8 (in percentages). (<b>D</b>) Bone marrow analyses showing the quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and DP (in absolute cell counts, log scale). (<b>E</b>) Thymus analyses and quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and DP (in absolute cell counts, log scale). (<b>F</b>) Spleen analyses and quantification of cells expressing huCD45, huCD34, huCD3, huCD4, huCD8, and DP (in absolute cell counts, log scale).</p>
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<p>Human T-cell and B-cell maturation and activation in huNSG-DKO mice. (<b>A</b>) Analysis of central memory and effector memory T-cell subtypes within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in bone marrow (in percentages). (<b>B</b>) Analysis of central memory and effector memory T-cell subtypes within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in thymus (in percentages). (<b>C</b>) Analysis of central memory and effector memory T-cell subtypes within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in spleen (in percentages). (<b>D</b>) Analysis of T-cell activation markers PD-1 and CD69 within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in bone marrow (in absolute numbers, log scale). (<b>E</b>) Analysis of T-cell activation markers PD-1 and CD69 within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in thymus (in absolute numbers, log scale). (<b>F</b>) Analysis of T-cell activation markers PD-1 and CD69 within huCD4<sup>+</sup> and huCD8<sup>+</sup> T-cells in spleen (in absolute numbers, log scale). (<b>G</b>) Analysis of B-cell subtypes in spleens. B-cell subtypes: naïve, memory, regulatory, plasma cells, and plasmablasts (in absolute cell counts, log scale).</p>
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<p>CyTOF analysis of huNRG mice as a reference. (<b>A</b>) Scheme of sample preparation, staining, CyTOF measurement, and analysis of bone marrow samples. The mice were humanized using only one donor (CB #376). (<b>B</b>) Anti-human CD45-CD live cell barcoded analysis of immune cell types in bone marrow samples 20 weeks post-HCT. A total of 152,755 cells were analyzed for huNRG mice. t-SNE plots displaying different subtypes of human immune cells clustered using FlowSOM and annotated manually using the lineage markers presented in the dot plot below. (<b>C</b>) Dotplot of huNRG cell subtypes and their expression of different Maxpar Direct Immune Profiling lineage markers. The dot size corresponds to the fraction of cells expressing the indicated marker within each cell type, and the color indicates the median expression. (<b>D</b>) CyTOF analysis of monocytes, CD4<sup>+</sup>, CD8<sup>+</sup>, and γδ T-cells, and natural killer, myeloid, and plasmacytoid dendritic cell counts in bone marrow samples (in absolute cell numbers, log scale).</p>
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<p>CyTOF analysis of huNSG-DKO mice. LV delivery promotes T-cell reactivity in humanized NSG-DKO mice. (<b>A</b>) Anti-human CD45-CD live cell barcoded analysis of immune cell types in bone marrow samples 20 weeks post-HCT. A total of 185,888 cells and 160,343 cells were analyzed for huNSG-DKO and huNSG-DKO+LV, respectively. t-SNE plots displaying different subtypes of human immune cells clustered using FlowSOM and annotated manually using the lineage markers presented in the dot plot (panels below). (<b>B</b>) Dotplot of huNSG-DKO and huNSG-DKO+LV cell subtypes and their expression of different Maxpar Direct Immune Profiling lineage markers. The dot size corresponds to the fraction of cells expressing the indicated marker within each cell type, and the color indicates the median expression. (<b>C</b>) CyTOF analysis of CD4<sup>+</sup>, CD8<sup>+</sup>, and γδ T-cells and natural killer, monocytes, myeloid, and plasmacytoid dendritic cell counts in bone marrow samples (in absolute cell numbers, log scale). (<b>D</b>) On right side: overlay of HLA-DR expression on t-SNE embeddings of huNSG-DKO and huNSG-DKO+LV across various cell types, as depicted in panel A. On left side: HLA-DR expression in CD4<sup>+</sup>, CD8<sup>+</sup>, and γδ T-cells (in absolute numbers).</p>
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<p>Humanized NSG-DKO mice showed better humanization than huNRG mice that were used as a reference. (<b>A</b>) Scheme of sample preparation, RNA isolation, mRNA sequencing, and analysis of spleen samples. (<b>B</b>) Alignment of mouse (blue) versus human (yellow) genes. As expected, non-humanized NRG and NSG-DKO mice have higher frequencies of mouse upregulated transcripts than humanized mice and PBMCs. HuNRG mice still upregulate some mouse genes, while in comparison, huNSG-DKO mice do not. (<b>C</b>) Differentially expressed genes between humanized NRG and NSG-DKO mice. Higher frequencies of mouse transcripts are seen in huNRG, while huNSG-DKO upregulate more human genes.</p>
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<p>Humanized NSG-DKO mice show upregulation of several biomarkers of immune responses after LV delivery. (<b>A</b>) Genes responsible for defense response to viruses are upregulated in huNSG-DKO+LV. (<b>B</b>) Genes responsible for response to other organism are upregulated in huNSG-DKO+LV. (<b>C</b>) Recombinations in T-cell-receptor A-variable chain are polyclonal and more frequent in huNSG-DKO+LV. (<b>D</b>) Recombinations in T-cell-receptor B-variable chain are polyclonal and more frequent in huNSG-DKO+LV. (<b>E</b>) Recombinations in T-cell-receptor G- and D-variable chains are polyclonal and more frequent in huNSG-DKO+LV.</p>
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<p>Human genome pathways upregulated in huNSG-DKO + LV in comparison to huNSG-DKO mice. Humanized NSG-DKO mice show the activation of several immune (immune response, defense response, and myeloid leukocyte activation) pathways after LV delivery. The sizes of the bubbles represent their relevance.</p>
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<p>Mouse genome pathways upregulated in huNSG-DKO + LV in comparison to huNSG-DKO mice. LV delivery upregulates RNA-processing pathways within a mouse. The sizes of the bubbles represent their relevance.</p>
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19 pages, 13884 KiB  
Article
Development and Validation of a Comprehensive Prognostic and Depression Risk Index for Gastric Adenocarcinoma
by Sheng Tian, Yixin Liu, Pan Liu, Sachiyo Nomura, Yongchang Wei and Tianhe Huang
Int. J. Mol. Sci. 2024, 25(19), 10776; https://doi.org/10.3390/ijms251910776 - 7 Oct 2024
Viewed by 1050
Abstract
Depressive disorder contributes to the initiation and prognosis of patients with cancer, but the interaction between cancer and depressive disorder remains unclear. We generated a gastric adenocarcinoma patient-derived xenograft mice model, treated with chronic unpredictable mild stimulation. Based on the RNA-sequence from the [...] Read more.
Depressive disorder contributes to the initiation and prognosis of patients with cancer, but the interaction between cancer and depressive disorder remains unclear. We generated a gastric adenocarcinoma patient-derived xenograft mice model, treated with chronic unpredictable mild stimulation. Based on the RNA-sequence from the mouse model, patient data from TCGA, and MDD-related (major depressive disorder) genes from the GEO database, 56 hub genes were identified by the intersection of differential expression genes from the three datasets. Molecular subtypes and a prognostic signature were generated based on the 56 genes. A depressive mouse model was constructed to test the key changes in the signatures. The signature was constructed based on the NDUFA4L2, ANKRD45, and AQP3 genes. Patients with high risk-score had a worse overall survival than the patients with low scores, consistent with the results from the two GEO cohorts. The comprehensive results showed that a higher risk-score was correlated with higher levels of tumor immune exclusion, higher infiltration of M0 macrophages, M2 macrophages, and neutrophils, higher angiogenetic activities, and more enriched epithelial–mesenchymal transition signaling pathways. A higher risk score was correlated to a higher MDD score, elevated MDD-related cytokines, and the dysfunction of neurogenesis-related genes, and parts of these changes showed similar trends in the animal model. With the Genomics of Drug Sensitivity in Cancer database, we found that the gastric adenocarcinoma patients with high risk-score may be sensitive to Pazopanib, XMD8.85, Midostaurin, HG.6.64.1, Elesclomol, Linifanib, AP.24534, Roscovitine, Cytarabine, and Axitinib. The gene signature consisting of the NDUFA4L2, ANKRD45, and AQP3 genes is a promising biomarker to distinguish the prognosis, the molecular and immune characteristics, the depressive risk, and the therapy candidates for gastric adenocarcinoma patients. Full article
(This article belongs to the Section Molecular Informatics)
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Figure 1

Figure 1
<p>Major depressive disorder- and gastric adenocarcinoma-related hub genes (<b>A</b>) Workflow for the construction of the gastric adenocarcinoma PDX mice model treated with CUMS and gene profiling. (<b>B</b>) A visualization of the RNA-Seq results with Volcano Plot for PDX vs PDX with CUMS. (<b>C</b>) A heatmap of the differential genes’ expression between PDX vs PDX with CUMS. (<b>D</b>) A barplot of the enriched GO terms (including Biological Process, Cellular Component, and Molecular Function) for the differential genes between PDX vs PDX with CUMS. (<b>E</b>) The intersection of differential genes from MDD, PDX with CUMS, and the TCGA STAD cohort. (<b>F</b>) A survival analysis determined by K–M for the two clusters divided by the 56 genes in the STAD dataset. (<b>G</b>) The composition of infiltrating immune cells in the two clusters using CIBERSORT. (<b>H</b>,<b>I</b>) A violin plot of the ssGSEA score of EMT and tumorigenic cytokines in the two clusters. (<b>J</b>–<b>M</b>) A violin plot of the expression levels of PDCD1, CLDN18, KDR, and FGFR2 in the two clusters. (<b>N</b>) A violin plot of the MDD score in the two clusters. (<b>O</b>) A survival analysis determined by K–M for the group divided by MDD score in the STAD TCGA database; the cutoff MDD score was obtained by X-tile. (<b>P</b>) The relative expression levels of the neurogenesis markers in the two clusters. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, two-sided, Wilcoxon test.</p>
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<p>Major depressive disorder- and gastric adenocarcinoma-related hub genes (<b>A</b>) Workflow for the construction of the gastric adenocarcinoma PDX mice model treated with CUMS and gene profiling. (<b>B</b>) A visualization of the RNA-Seq results with Volcano Plot for PDX vs PDX with CUMS. (<b>C</b>) A heatmap of the differential genes’ expression between PDX vs PDX with CUMS. (<b>D</b>) A barplot of the enriched GO terms (including Biological Process, Cellular Component, and Molecular Function) for the differential genes between PDX vs PDX with CUMS. (<b>E</b>) The intersection of differential genes from MDD, PDX with CUMS, and the TCGA STAD cohort. (<b>F</b>) A survival analysis determined by K–M for the two clusters divided by the 56 genes in the STAD dataset. (<b>G</b>) The composition of infiltrating immune cells in the two clusters using CIBERSORT. (<b>H</b>,<b>I</b>) A violin plot of the ssGSEA score of EMT and tumorigenic cytokines in the two clusters. (<b>J</b>–<b>M</b>) A violin plot of the expression levels of PDCD1, CLDN18, KDR, and FGFR2 in the two clusters. (<b>N</b>) A violin plot of the MDD score in the two clusters. (<b>O</b>) A survival analysis determined by K–M for the group divided by MDD score in the STAD TCGA database; the cutoff MDD score was obtained by X-tile. (<b>P</b>) The relative expression levels of the neurogenesis markers in the two clusters. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, two-sided, Wilcoxon test.</p>
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<p>The gene signature based on the NDUFA4L2, ANKRD45, and AQP3 predicts the prognosis of gastric adenocarcinoma (<b>A</b>) Forest plot and risk-score coefficient for three independent prognostic genes (NDUFA4L2, ANKRD45, and AQP3) determined by multivariate Cox regression analysis. (<b>B</b>–<b>D</b>) A comparison of NDUFA4L2, ANKRD45, and AQP3 in normal tissue and gastric tumor tissue in the TCGA cohort. (<b>E</b>–<b>G</b>) A survival analysis determined by K–M for the NDUFA4L2, ANKRD45, and AQP3 genes in TCGA cohort, grouped according to the median of the expression value. (<b>H</b>,<b>I</b>) A survival analysis, heatmap, and survival status accompanied by the risk score in TCGA cohort, grouped according to the median of the risk score. (<b>J</b>,<b>K</b>) A survival analysis, heatmap, and survival status accompanied by the risk score in the GSE84437 cohort, grouped according to the best risk-score obtained by X-tile. (<b>L</b>) A heatmap of the association between the expression levels of the three genes and clinicopathological features. (<b>M</b>) The signature was an independent risk factor for STAD patients in TCGA cohort according to multivariate Cox analysis, and a nomogram based on risk score, age, and M stage. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, , two-sided, Wilcoxon test.</p>
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<p>The signature based on the NDUFA4L2, ANKRD45, and AQP3 defined the molecular characteristics and the tumor microenvironment of gastric adenocarcinoma (<b>A</b>–<b>C</b>) Immune and stromal scores using ESTIMATE between the high- and low-risk groups in TCGA STAD cohort. (<b>D</b>) TIDE score between the high- and low-risk groups. (<b>E</b>) The composition of infiltrating immune cells using CIBERSORT. (<b>F</b>–<b>H</b>) A survival analysis determined by K–M for the infiltration of M0 macrophages, M2 macrophage, and the neutrophils in the TCGA STAD database, grouped according to the CIBERSORT’s result. (<b>I</b>,<b>K</b>) A violin plot of the ssGSEA score of angiogenic and EMT between the high- and low-risk groups. (<b>J</b>) A violin plot of the KDR level. (<b>L</b>) A correlation analysis between the EMT score and the risk-score in the STAD dataset. (<b>M</b>,<b>N</b>) The top enriched terms in high-risk or low-risk groups using GSEA. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, two-sided, Wilcoxon test.</p>
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<p>The signature based on the NDUFA4L2, ANKRD45, and AQP3 defined the molecular characteristics and the tumor microenvironment of gastric adenocarcinoma (<b>A</b>–<b>C</b>) Immune and stromal scores using ESTIMATE between the high- and low-risk groups in TCGA STAD cohort. (<b>D</b>) TIDE score between the high- and low-risk groups. (<b>E</b>) The composition of infiltrating immune cells using CIBERSORT. (<b>F</b>–<b>H</b>) A survival analysis determined by K–M for the infiltration of M0 macrophages, M2 macrophage, and the neutrophils in the TCGA STAD database, grouped according to the CIBERSORT’s result. (<b>I</b>,<b>K</b>) A violin plot of the ssGSEA score of angiogenic and EMT between the high- and low-risk groups. (<b>J</b>) A violin plot of the KDR level. (<b>L</b>) A correlation analysis between the EMT score and the risk-score in the STAD dataset. (<b>M</b>,<b>N</b>) The top enriched terms in high-risk or low-risk groups using GSEA. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, two-sided, Wilcoxon test.</p>
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<p>The signature based on the NDUFA4L2, ANKRD45, and AQP3 estimated the depressive risk of gastric adenocarcinoma (<b>A</b>) A violin plot of the MDD score in the subgroups divided by risk-score in the STAD dataset. (<b>B</b>) The levels of neurogenesis markers in the subgroups in the STAD dataset. (<b>C</b>) A correlation analysis between the neurogenesis markers and the risk-score in the STAD dataset. (<b>D</b>) A correlation analysis between the neurogenesis markers and the MDD score in the STAD dataset. (<b>E</b>) The levels of MDD-related cytokines in the subgroups divided by the risk score in the STAD dataset. (<b>F</b>) The levels of neuron growth factors and receptors in the subgroups divided by the risk score in the STAD dataset. (<b>G</b>) A violin plot of MDD score in MDD patients in the GEO MDD dataset (GSE102556). (<b>H</b>) The ROC of the MDD score in the GEO MDD dataset (GSE102556). (<b>I</b>) The top enriched GO terms in the high-risk or low-risk group using GSEA. (<b>J</b>) A violin plot of the MDD score in MDD patients in the GEO MDD dataset (GSE80655). (<b>K</b>) The ROC of the MDD score in the GEO MDD dataset (GSE80655). (<b>L</b>) The top enriched GO terms in high-risk or low-risk group using GSEA. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, ns, not significant, two-sided, Wilcoxon test.</p>
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<p>The signature based on the NDUFA4L2, ANKRD45, and AQP3 estimated the depressive risk of gastric adenocarcinoma (<b>A</b>) A violin plot of the MDD score in the subgroups divided by risk-score in the STAD dataset. (<b>B</b>) The levels of neurogenesis markers in the subgroups in the STAD dataset. (<b>C</b>) A correlation analysis between the neurogenesis markers and the risk-score in the STAD dataset. (<b>D</b>) A correlation analysis between the neurogenesis markers and the MDD score in the STAD dataset. (<b>E</b>) The levels of MDD-related cytokines in the subgroups divided by the risk score in the STAD dataset. (<b>F</b>) The levels of neuron growth factors and receptors in the subgroups divided by the risk score in the STAD dataset. (<b>G</b>) A violin plot of MDD score in MDD patients in the GEO MDD dataset (GSE102556). (<b>H</b>) The ROC of the MDD score in the GEO MDD dataset (GSE102556). (<b>I</b>) The top enriched GO terms in the high-risk or low-risk group using GSEA. (<b>J</b>) A violin plot of the MDD score in MDD patients in the GEO MDD dataset (GSE80655). (<b>K</b>) The ROC of the MDD score in the GEO MDD dataset (GSE80655). (<b>L</b>) The top enriched GO terms in high-risk or low-risk group using GSEA. The results are presented as mean ± standard deviation (SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, ns, not significant, two-sided, Wilcoxon test.</p>
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<p>Signature genes’ expression and immune infiltration in gastric cancer following CUMS treatment (<b>A</b>) Representative signature genes’ IHC staining of tumor tissue from control and CUMS group (n = 3). (<b>B</b>,<b>C</b>) The quantification of IHC chromogen stain intensity for signature genes in the control and CUMS group tumor tissue (n = 3). (<b>D</b>) The mRNA levels of signature genes were assessed in tumor tissue from control and CUMS group using RT–PCR (n = 3). (<b>E</b>–<b>H</b>) The percentage of different subtypes of macrophages in tumor and blood determined by flow cytometry (n = 3). <span class="html-italic">p</span> values determined by two-tailed unpaired <span class="html-italic">t</span>-test. Data are represented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, ns, not significant.</p>
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<p>Potential treatment strategy for the signature defined gastric adenocarcinoma patients (<b>A</b>) Sensitive drugs screening for this subgroup with high risk score determined by the Genomics of Drug Sensitivity in Cancer database. (<b>B</b>) Sensitive drugs screening for this subgroup with low risk score determined by the Genomics of Drug Sensitivity in Cancer database. (<b>C</b>) Differentially expressed genes between the high- and low-risk groups in the TCGA cohort. (<b>D</b>) The structure of six potential target drugs screened from the cMap database.</p>
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20 pages, 5729 KiB  
Article
Combined PET Radiotracer Approach Reveals Insights into Stromal Cell-Induced Metabolic Changes in Pancreatic Cancer In Vitro and In Vivo
by Alina Doctor, Markus Laube, Sebastian Meister, Oliver C. Kiss, Klaus Kopka, Sandra Hauser and Jens Pietzsch
Cancers 2024, 16(19), 3393; https://doi.org/10.3390/cancers16193393 - 4 Oct 2024
Viewed by 971
Abstract
Background/Objective Pancreatic stellate cells (PSCs) in pancreatic adenocarcinoma (PDAC) are producing extracellular matrix, which promotes the formation of a dense fibrotic microenvironment. This makes PDAC a highly heterogeneous tumor-stroma-driven entity, associated with reduced perfusion, limited oxygen supply, high interstitial fluid pressure, and limited [...] Read more.
Background/Objective Pancreatic stellate cells (PSCs) in pancreatic adenocarcinoma (PDAC) are producing extracellular matrix, which promotes the formation of a dense fibrotic microenvironment. This makes PDAC a highly heterogeneous tumor-stroma-driven entity, associated with reduced perfusion, limited oxygen supply, high interstitial fluid pressure, and limited bioavailability of therapeutic agents. Methods In this study, spheroid and tumor xenograft models of human PSCs and PanC-1 cells were characterized radiopharmacologically using a combined positron emission tomography (PET) radiotracer approach. [18F]FDG, [18F]FMISO, and [18F]FAPI-74 were employed to monitor metabolic activity, hypoxic metabolic state, and functional expression of fibroblast activation protein alpha (FAPα), a marker of activated PSCs. Results In vitro, PanC-1 and multi-cellular tumor spheroids demonstrated comparable glucose uptake and hypoxia, whereas FAPα expression was significantly higher in PSC spheroids. In vivo, glucose uptake as well as the transition to hypoxia were comparable in PanC-1 and multi-cellular xenograft models. In mice injected with PSCs, FAPα expression decreased over a period of four weeks post-injection, which was attributed to the successive death of PSCs. In contrast, FAPα expression increased in both PanC-1 and multi-cellular xenograft models over time due to invasion of mouse fibroblasts. Conclusion The presented models are suitable for subsequently characterizing stromal cell-induced metabolic changes in tumors using noninvasive molecular imaging techniques. Full article
(This article belongs to the Special Issue Targeting the Tumor Microenvironment (Volume II))
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<p>Schematic illustration of the mode of action of the PET radiotracers [<sup>18</sup>F]FDG, [<sup>18</sup>F]FMISO (fluoromisonidazole), and [<sup>18</sup>F]FAPI-74. In addition to tumor cells, the tumor shown here also consists of PSCs and mouse fibroblasts (mF). The tumor consists of a metabolically active and normoxic marginal zone (pink), while the inner core represents a hypoxic region (brown). [<sup>18</sup>F]FDG (cyan circles) is taken up by the glucose transporter 1 (GLUT-1) transporter and metabolized by hexokinases (HK), then becoming trapped (red circle) in metabolically active regions of the tumor. [<sup>18</sup>F]FMISO binds to macromolecules (blue circle) in the hypoxic region. To target the tumor stroma, [<sup>18</sup>F]FAPI-74 can be used. The tracer binds to FAPα in cancer-associated fibroblasts. Created in BioRender. Doctor, A. (2024) BioRender.com/b29q105.</p>
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<p>Characterization of spheroid models. (<b>A</b>) Representative spheroid images of HPaSteC, PanC-1, and MCTS after 1, 4, and 7 days of incubation. Scale bar corresponds to 100 µm. (<b>B</b>,<b>C</b>) In vitro radiotracer uptake assay with HPaSteC, PanC-1, and MCTS spheroids. Percentage of injected dose per µg (mean + SD) and statistical difference *: HPaSteC vs. PanC-1, #: HPaSteC vs. MCTS (<span class="html-italic">p</span> &lt; 0.05, two-way ANOVA). (<b>B</b>) time-dependent [<sup>18</sup>F]FDG uptake. (<b>C</b>) [<sup>18</sup>F]FMISO uptake in normoxic and hypoxic conditions after 4 h of incubation. Statistical difference (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.0021 two-way ANOVA).</p>
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<p>Characteristics of PanC-1 and multi-cellular xenografts. (<b>A</b>) Growth curve of subcutaneous tumors in SCID beige mice after injection of PanC-1 cells and multi-cellular xenografts (mean ± SD, n = 15). (<b>B</b>) Representative histological images of H&amp;E staining. (<b>C</b>) Dynamic PET measurements of [<sup>18</sup>F]FDG. (<b>D</b>) PET measurements depicting the SUVmean of [<sup>18</sup>F]FDG and (<b>E</b>) [<sup>18</sup>F]FMISO in PanC-1 and multi-cellular tumor-bearing SCID mice versus tumor volume in cm<sup>3</sup> and Pearson’s correlation (r). (<b>F</b>) Illustration of size-dependent core formation. [<sup>18</sup>F]FDG PET showing metabolically active tumor regions (<b>top row</b>) and [<sup>18</sup>F]FMISO PET in the same mouse showing hypoxic tumor microenvironment (<b>bottom row</b>). The middle row illustrates the tumor size-dependent extent of metabolically active rim and hypoxic core. (<b>G</b>) Exemplary comparison between [<sup>18</sup>F]FDG PET imaging and fluorescent dye Hoechst 33342 for vascularization imaging. For better visibility, the blue channel was changed to white. (<b>H</b>) Sequential [<sup>18</sup>F]FDG and [<sup>18</sup>F]FMISO PET images of mice bearing xenograft tumors (white arrows). (<b>I</b>) Representative image of Hoechst 33342 fluorescence for blood vessel staining and pimonidazole staining for hypoxia. (<b>J</b>) Percentage of pimonidazole-positive tumor area and Hoechst fluorescence (<b>K</b>) in the tumor margin and core. Statistical analysis with one-way ANOVA (<span class="html-italic">p</span> * 0.0332, ** 0.0021, **** &lt; 0.0001).</p>
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<p>PET and MRI imaging reflect hypoxia and connective tissue. (<b>A</b>) Sequential [<sup>18</sup>F]FDG and [<sup>18</sup>F]FMISO PET images of mice bearing xenograft tumors in comparison of T2 weighted MRI (TRARE) and diffusion weighted MRI (DW) images. Arrows indicating the tumor lesion. (<b>B</b>) ADC values calculated for DW-MRI images with significant differences of **** <span class="html-italic">p</span> &lt; 0.0001 (One-way ANOVA).</p>
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<p>[<sup>18</sup>F]FAPI-74 as a PSC marker. (<b>A</b>) FAPα expression was determined via Western blot using cell lysates. (<b>B</b>) [<sup>18</sup>F]FAPI-74 uptake by spheroids after 30 min. Statistical analysis with one-way ANOVA (**** <span class="html-italic">p</span> &lt; 0.0001, n = 15). In vivo [<sup>18</sup>F]FAPI-74 uptake in HPaSteC (<b>C</b>), multi-cellular (<b>D</b>), and PanC-1 (<b>E</b>) xenograft tumor shown as Area under curve (AUC) in the course of 7 weeks and the corresponding PET images to the indicated time points after injection. The white arrow indicates the injection site. The original Western blot figures can be found in <a href="#app1-cancers-16-03393" class="html-app">Supplemental Materials Figure S6</a>.</p>
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<p>(<b>A</b>) Representative immunohistological images of markers for murine and human FAP, human nuclear mitotic antigen (NuMa), α-SMA (smooth muscle actin), human and murine Collagen I, and Cytokeratin 19 (KRT19). Staining control was performed using rabbit isotype antibody. Hematoxylin counterstains the cell nuclei in blue, and positive immunohistological staining is red. (<b>B</b>) Quantitative analysis of immunohistological positive staining with ImageJ (significant differences calculated with <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.05, * 0.0332, *** 0.0002, **** &lt;0.0001). (<b>C</b>) Sequential immunohistological staining of α-SMA and human cell nuclei in multi-cellular and PanC-1 xenograft tumors. Black arrows indicate blue nuclei corresponding to positive α-SMA staining. (<b>D</b>) Western blot shows α-SMA protein expression in monolayer cultured HPaSteC and in xenograft PanC-1 and multi-cellular tumors and none in PanC-1 cells. β-actin was used as loading control. The original Western blot figures can be found in <a href="#app1-cancers-16-03393" class="html-app">Supplemental Materials Figure S7</a>.</p>
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<p>Schematic representation of a subcutaneous PDAC+PSC tumor. Injected tumor cells attract mouse fibroblasts. As the tumor cells divide, more mouse fibroblasts invade the growing tumor mass, and the human pancreatic stellate cells become overgrown. Created in BioRender. Doctor, A. (2024) BioRender.com/k83f778.</p>
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22 pages, 21068 KiB  
Article
The Role of CENPK Splice Variant in Abiraterone Response in Metastatic Castration-Resistant Prostate Cancer
by Minhong Huang, Sisi Qin, Huanyao Gao, Wootae Kim, Fang Xie, Ping Yin, August John, Richard M. Weinshilboum and Liewei Wang
Cells 2024, 13(19), 1622; https://doi.org/10.3390/cells13191622 - 28 Sep 2024
Viewed by 898
Abstract
Most patients with metastatic prostate cancer eventually develop resistance to primary androgen deprivation therapy. To identify predictive biomarker for Abiraterone acetate/prednisone resistance, we screened alternative splice variants between responders and non-responders from the PROMOTE clinical study and pinned down the most significant variant, [...] Read more.
Most patients with metastatic prostate cancer eventually develop resistance to primary androgen deprivation therapy. To identify predictive biomarker for Abiraterone acetate/prednisone resistance, we screened alternative splice variants between responders and non-responders from the PROMOTE clinical study and pinned down the most significant variant, CENPK–delta8. Through preclinical patient-derived mouse xenograft (PDX) and 3D organoids obtained from responders and non-responders, as well as in vitro models, aberrant CENPK–delta8 expression was determined to link to drug resistance via enhanced migration and proliferation. The FLNA and FLOT1 were observed to specifically bind to CENK–delta8 rather than wild-type CENPK, underscoring the role of CENPK–delta8 in cytoskeleton organization and cell migration. Our study, leveraging data from the PROMOTE study, TCGA, and TCGA SpliceReq databases, highlights the important function of alternative splice variants in drug response and their potential to be prognostic biomarkers for improving individual therapeutic outcomes in precision medicine. Full article
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<p>Splice variants in prostate cancer cells and PDX 3D organoids in response to Abiraterone. Cytotoxicity assays of knockdown of splice variants in 22Rv1 (<b>A</b>) and LNCaP (<b>B</b>) cells treated with various doses of Abiraterone are shown. These genes were obtained from RNA-Seq data including <span class="html-italic">EMD</span>, <span class="html-italic">KIAA</span>, <span class="html-italic">TSC2</span>, <span class="html-italic">PKN1</span>, and <span class="html-italic">CENPK</span>. Cytotoxicity assays of splice variant overexpression in 22Rv1 (<b>C</b>) and LNCaP (<b>D</b>) cells treated with various doses of Abiraterone are shown. Cytotoxicity assays of PDX organoids (<b>E</b>–<b>H</b>) treated with indicated doses of Abiraterone or vehicle are shown. The 3D organoids were (<b>E</b>) knocked down with either wild-type EMD or wild-type CENPK, (<b>F</b>) overexpressed with wild-type EMD or wild-type CENPK, (<b>G</b>) knocked down with either EMD-cryptic 5′SS or CENPK–delta8, or (<b>H</b>) overexpressed with either EMD-cryptic 5′SS or CENPK–delta8. (<b>I</b>) Cytotoxicity assays of 3D organoids following Abiraterone treatment. The organoids were either with wild-type CENPK knockdown or with wild-type CENPK knockdown plus CENPK–delta8 overexpression. The PDX models come from two responder patients (PR06 and PR12) and two non-responder patients (PR03 and PR08). PR06: MC-PRX-04; PR12: MC-PRX-10; PR03: MC-PRX-01; PR08: MC-PRX-06. The <span class="html-italic">X</span>-axis indicates concentrations of Abiraterone. All data were presented as mean ± S.E.M. of at least five replicates and normalized to vehicle treatment (as shown on the right side). (<b>J</b>) Correlation plots of wild-type CENPK, CENPK–delta8, androgen receptor (AR), and androgen receptor splice variant-7 (AR–V7), as well as AR score plots with CENPK wild type and delta8. Patient data were from Mayo Clinic PROMOTE cohort. WT, wild-type CENPK; SPV, CENPK splice variant; OE, overexpression; KD, knockdown. Data were presented as mean ± S.E.M. of two replicates. Neg siRNA, nontarget siRNA control; EV, empty vector; WT, wild type; SPV, splice variant; OE, overexpression; ABI, Abiraterone. Statistically significant differences were denoted as *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ns, not significant.</p>
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<p>CENPK splice variant promoting cell migration and invasion. (<b>A</b>,<b>B</b>) Colony formation assays of CENPK–delta8 OE 22Rv1 and CENPK–delta8 OE LNCaP upon indicated Abiraterone treatment and their corresponding data analysis (<b>C</b>). The dashed/solid lines are the boundary between cells and empty space. All patient-derived organoids in this study have been examined for pathology validation before experiments. Data were presented as mean ± S.E.M. of different areas in two to three replicates. Representative images of wound healing assays in (<b>D</b>) LNCaP (upon 5 μM and 10 μM Abiraterone treatments) and (<b>E</b>) 22Rv1 (upon 5 μM Abiraterone treatment) with their corresponding quantification (<b>F</b>). Data were presented as mean ± S.E.M. of different areas in three replicates. Representative images of transwell migration assays in (<b>G</b>) CENPK–delta8–KD 22Rv1 and LNCaP and the corresponding data analysis (<b>H</b>). Representative images (<b>I</b>) of transwell migration assays in 22Rv1 and LNCaP with the overexpression of either empty vector, wild-type CENPK, or CENPK–delta8, and their corresponding data analysis (<b>J</b>). Representative images (<b>K</b>) of transwell invasion assays in CENPK–delta8–KD 22Rv1 and LNCaP, and the corresponding data analysis (<b>L</b>). Cells were treated with 5 μM Abiraterone. Migration and invasion imaging was completed using a 10× objective. EV, empty vector; SPV, CENPK splice variant or CENPK–delta8; WT, wild-type CENPK; OE, overexpression; KD, knockdown. Statistical significance indicated unpaired <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.</p>
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<p>Pathways and proteins specifically binding CENPK–delta8 in Abiraterone response. (<b>A</b>) Unique binding partners with CENPK–delta8 in CENPK SPV–OE LNCaP cells of IP–MS signals, compared to wild-type CENPK overexpression. (<b>B</b>) In comparison, unique pathways in CENPK WT–OE (in orange) and CENPK SPV–OE (in blue) LNCaP cells, respectively. (<b>C</b>) Predicted function analysis in CENPK SPV–OE through enriched pathways using the BioPlanet 2019 database set of Enrichr (see methods). CENPK SPV, CENPK–delta8; SPV, splice variant; WT, wild type. Representative confocal images of immunofluorescent staining with FLOT1 in CENPK SPV–KD LNCaP cells treated with various doses of Abiraterone (<b>D</b>). Scale bar, 50 μm. Representative 3D plots of FLOT1 intensity signal in nontarget/wildtype LNCaP with three different doses of Abiraterone (<b>E</b>) and 22Rv1 with 5 μM Abiraterone (<b>F</b>). (<b>G</b>) FLOT1 fluorescent intensity quantification in nontarget/wildtype LNCaP and 22Rv1. (<b>H</b>) Disease-free survival analysis in FLOT1 high expression vs. low expression (in TPM) prostate cancer patients based on TCGA data. The dashed lines show the confidence interval. (<b>I</b>) <span class="html-italic">FLOT1</span> relative expression in healthy subjects and prostate cancer patients based on GEO database. Representative confocal images (<b>J</b>) of immunofluorescent staining with FLNA in CENPK SPV–KD LNCaP cells treated with vehicle and 5 μM Abiraterone, 3D intensity signal (<b>K</b>), and FLNA intensity analysis (<b>L</b>). Scale bar, 50 μm. Data were presented as mean ± S.E.M. of four replicates. Scale bar: 100 μm. Statistical significance indicated unpaired <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; ns, not significant. The 3D plots were analyzed in the same setting. SPV, splice variant; WT, wild type. ABI, Abiraterone; KD, knockdown.</p>
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<p>The functional analysis of FLNA and FLOT1 knockdown in CENPK–SPV overexpressed cells. (<b>A</b>) Wound healing assays of FLNA KD and FLOT1 KD in CENPK–delta8–OE 22Rv1 cells treated with 5 µM Abiraterone and the corresponding data analysis (<b>B</b>). Migration and invasion imaging was completed using a 10× objective. The dashed/solid lines are the boundary between cells and empty space. Data were presented as mean ± S.E.M. of eight replicates and normalized to vehicle treatment (as shown on the right side ‘control_non-target’). Colony formation assays of FLNA KD and FLOT1 KD in CENPK–delta8–OE 22Rv1 cells treated with indicated doses of Abiraterone (<b>C</b>) and the corresponding data analysis (<b>D</b>). (<b>E</b>) Annexin V/PI staining flow cytometry results in 22Rv1 following Abiraterone acute treatment. Cells had either CENPK–delta8 KD or CENPK–delta8 and FLOT1 double KD. (<b>F</b>) The correlation analysis between FLOT1 and FLNA expression (in TPM) in prostate cancer patients is based on TCGA data. Data were presented as mean ± S.E.M. of two replicates. Statistical significance indicated unpaired <span class="html-italic">t</span>-test: ns, not significant, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns, not significant. SPV, CENPK splice variant or CENPK–delta8; OE, overexpression; KD, knockdown.</p>
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<p>Knockdown clinically relevant genes identified from Mayo PROMOTE patients reducing CENPK splice variant expression. (<b>A</b>–<b>C</b>) Bioinformatic analysis of CENPK–delta8 by mutation in relevant cancer genes, based on the sequencing data of the PROMOTE clinical study at Mayo Clinic. The qRT–PCR validation with Abiraterone treatment (<b>D</b>) in 22Rv1 after KD of the relevant genes. Statistically significant differences were denoted as *, <span class="html-italic">p</span> &lt; 0.05; ns, not significant.</p>
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<p>The schematic diagram for the functional study of splice variants in mCRPCs. Patients were recruited in the PROMOTE clinical trial to identify molecular signatures with response or lack of response to the standard treatment AA/P. Tumor tissues were employed to perform RNA–Seq (this is a cartoon, not data) for the analysis of disease-associated alternative splicing events and to generate PDX mouse models for the source of 3D organoids. The function of the CENPK splice variant in drug resistance was further analyzed in this study.</p>
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