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23 pages, 4579 KiB  
Review
Mechanistic Insights into Influenza A Virus-Induced Cell Death and Emerging Treatment Strategies
by Yuling Sun and Kaituo Liu
Vet. Sci. 2024, 11(11), 555; https://doi.org/10.3390/vetsci11110555 - 10 Nov 2024
Viewed by 897
Abstract
Influenza A virus (IAV) infection initiates a complex interplay of cell death modalities, including apoptosis, necroptosis, pyroptosis, and their integration, known as PANoptosis, which significantly impacts host immune responses and tissue integrity. These pathways are intricately regulated by viral proteins and host factors, [...] Read more.
Influenza A virus (IAV) infection initiates a complex interplay of cell death modalities, including apoptosis, necroptosis, pyroptosis, and their integration, known as PANoptosis, which significantly impacts host immune responses and tissue integrity. These pathways are intricately regulated by viral proteins and host factors, contributing to both viral clearance and pathogenesis-related tissue damage. This review comprehensively explores the molecular mechanisms underlying these cell death processes in influenza infection. We highlight the roles of key regulatory proteins, such as ZBP1 (Z-DNA binding protein 1) and RIPK3 (receptor-interacting protein kinase 3), in orchestrating these responses, emphasizing the dual roles of cell death in both antiviral defense and tissue injury. Furthermore, we discuss emerging therapeutic strategies targeting these pathways, aiming to enhance antiviral efficacy while minimizing collateral tissue damage. Future research should focus on targeted approaches to modulate cell death mechanisms, aiming to reduce tissue damage and improve clinical outcomes for patients with severe influenza. Full article
(This article belongs to the Special Issue Advances in Veterinary Clinical Microbiology)
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Figure 1
<p>Mechanisms of intrinsic and extrinsic apoptotic pathways. The intrinsic pathway is initiated by various intracellular stress signals, leading to the oligomerization of BAX and BAK, which permeabilizes the mitochondrial outer membrane and facilitates the release of cytochrome c. This triggers the formation of the apoptosome and subsequent activation of caspase-9. In contrast, the extrinsic pathway is initiated by death receptors such as FAS, which, upon ligand binding, recruit adaptor proteins like FADD and activate caspase-8. Caspase-9 and caspase-8 executes apoptosis through the activation of caspase-3 and caspase-7. Caspase-8 can process BID, thereby linking the intrinsic and extrinsic pathways, while XIAP inhibits active caspases, thereby regulating the execution of apoptosis.</p>
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<p>Regulation of cell death pathways by host factors and IAV proteins. During IAV infection, ZBP1 detects viral RNA, mediating apoptosis, necroptosis, and pyroptosis, with contributions from various host factors and viral proteins. PB1-F2, NP, and M2 promotes caspase-3/7-mediated apoptosis by modulating host proteins, while NS1 displays dual roles through distinct pathways. TAK1 downregulates extrinsic apoptosis by inhibiting RIPK1 activity. Activated caspase-3 promotes GSDME-mediated pyroptosis. Activated caspase-8 suppresses necroptosis by cleaving RIPK1. Host proteins caspase-6 and OPN, along with NS1, facilitate necroptosis, whereas TAK1 and the non-coding RNA miR-324-5p inhibit this process. Potassium efflux resulting from necroptosis activates the NLRP3 inflammasome, which promotes pyroptosis. M2, MxA and Galectin-3 enhance inflammasome activity, facilitating GSDMD-mediated pyroptosis, while PB1-F2 and NS1 inhibit pyroptosis through suppression of inflammasome activation. Additionally, viral RNA activates the RIG-I-MAVS pathway, leading to interferon production that upregulates ZBP1. ZBP1 orchestrates these three cell death modalities during influenza A virus infection, positively regulated by IRF1 and SPAG9. Overall, these pathways underscore the complex interplay between viral and host factors in regulating cell death during infection.</p>
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<p>Mechanisms of necroptosis pathways. TNF-α signals through the TNFR receptor to activate RIPK1, which subsequently recruits and activates RIPK3. Viral RNA is recognized by ZBP1, leading to the activation of RIPK1 and the subsequent recruitment and activation of RIPK3, or directly activating RIPK3 through ZBP1. TLR3 and TLR4 activate RIPK3 via the adaptor protein TRIF. Activated RIPK3 phosphorylates MLKL, resulting in MLKL oligomerization and translocation to the plasma membrane, where it forms pores that mediate calcium influx and release pro-inflammatory cytokines and DAMPs.</p>
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<p>Mechanisms of pyroptosis activation mediated by GSDMD and GSDME. External stimuli activate pattern recognition receptors, leading to NF-κB activation and promoting the transcription of pro-inflammatory cytokines (pro-IL-1β, pro-IL-18). This process facilitates the assembly of the NLRP3 inflammasome and activates caspase-1. Activated caspase-1 cleaves GSDMD, resulting in pore formation in the plasma membrane that disrupts osmotic balance and induces cell swelling. Additionally, caspases-8, -4, -5, and -11 can directly cleave GSDMD in response to various stimuli. Various stimuli activate caspase-9 and caspase-8, which subsequently activate caspase-3, leading to the cleavage of GSDME. Furthermore, granzyme B can also cleave GSDME directly, resulting in pore formation and the execution of pyroptosis.</p>
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<p>Mechanisms of PANoptosis activation during IAV infection. This diagram illustrates the PANoptosome complex during IAV infection, highlighting the role of ZBP1 in detecting viral RNA to activate pyroptosis, apoptosis, and necroptosis, thereby orchestrating a multifaceted immune response. ZBP1 detects viral RNA and initiates the PANoptotic response, leading to the activation of pyroptosis (via GSDMD and GSDME), apoptosis (via caspases-3 and -7), and necroptosis (via MLKL). The interaction between ZBP1 and SPAG9 enhances PANoptosome assembly, while ZBP1 expression is regulated by IRF1 and IFNAR, both of which modulate immune responses. Inhibition of TAK1 promotes RIPK1-dependent cell death. Additionally, ADAR1 prevents spontaneous activation of ZBP1, maintaining cellular homeostasis and mitigating the risk of excessive PANoptosis.</p>
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12 pages, 2995 KiB  
Article
Caspase-8-and Gasdermin D (GSDMD)-Dependent PANoptosis Participate in the Seasonal Atrophy of Scented Glands in Male Muskrats
by Xiaofeng Tong, Xuefei Zhao, Yue Ma, Haimeng Li, Jinpeng Zhang, Zuoyang Zhang, Sirui Hua, Bo Li, Wei Zhang, Yu Zhang and Suying Bai
Animals 2024, 14(22), 3194; https://doi.org/10.3390/ani14223194 - 7 Nov 2024
Viewed by 423
Abstract
The muskrat (Ondatra zibethicus) is an animal with special economic significance whose scented glands rapidly atrophy during the non-breeding season, but the mechanism of atrophy is not clear, with significant differences in apoptotic and pyroptotic signaling pathway expression according to transcriptome [...] Read more.
The muskrat (Ondatra zibethicus) is an animal with special economic significance whose scented glands rapidly atrophy during the non-breeding season, but the mechanism of atrophy is not clear, with significant differences in apoptotic and pyroptotic signaling pathway expression according to transcriptome sequencing. During the non-breeding season, key apoptosis-related genes such as Tnfr1 (TNF Receptor Superfamily Member 1A), TRADD (TNFRSF1A Associated via Death Domain), FADD (Fas Associated via Death Domain), Casp-8 (Cysteine-aspartic proteases-8), and Bax (Bcl-associated X protein) were upregulated in the scented glands, while Bcl2 (B-cell lymphoma-2) expression was downregulated. In the classical pyroptosis pathway, the mRNA expression levels of key genes including Nlrp3 (the Nod-like receptor family pyrin domain-containing 3), ASC (the apoptosis-associated speck-like protein), Casp-1 (Cysteine-aspartic proteases-1), Gsdmd (Gasdermin D), and IL-1β (Interleukin 1 Beta) were higher during the non-breeding season, similar to the transcription level of Ripk1 (Receptor Interacting Serine/Threonine Kinase 1) in the non-canonical pyroptosis pathway, while TAK1 (transforming growth factor kinase) expression was downregulated in this latter pathway. TUNEL assays and immunofluorescence analysis indicated increased apoptosis and GSDMD and Caspase-8 protein levels during the non-breeding season. Indeed, the protein levels of GSDMD-N, Caspase-8 p43, and Caspase-8 p18 were significantly higher during the non-breeding season, while the GSDMD levels were significantly lower compared to the secretion season. These results suggest that apoptosis and pyroptosis play regulatory roles in scented gland atrophy and that there is an interplay between them during this process. Full article
(This article belongs to the Section Mammals)
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<p>Transcriptomic analyses of scented glands during breeding and non-breeding seasons. (<b>a</b>) Venn diagram of shared and unique transcripts. (<b>b</b>,<b>c</b>) Clustering analysis heatmap and volcano plot of the differentially expressed genes. (<b>d</b>,<b>e</b>) GO and KEGG enrichment analyses.</p>
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<p>Real-time quantitative PCR was used to detect the mRNA expression levels of the following genes in muskrats’ scented glands during the breeding and non-breeding seasons. (<b>a</b>) TNFR1, TRADD, FADD, Caspase-8, BAX, and BCL2. (<b>b</b>) NLRP3, ASC, Caspase-1, GSDMD, and IL-1β. (<b>c</b>) TAK1, RIPK1, Caspase-8, GSDMD, and FADD. (<b>d</b>,<b>e</b>) Also shown are the protein expression results of GSDMD and Caspase-8. (<b>f</b>–<b>i</b>) The grayscale analysis of GSDMD, GSDMD-N, Caspase-8 p18, and Caspase-8 p43. B—breeding season; NB—non-breeding season. The error bars represent the means ± SEM (<span class="html-italic">n</span> = 3, each stage). * Statistical significance (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Immunofluorescence results in muskrats’ scented glands during the breeding (<b>a</b>–<b>d</b>) and non-breeding seasons (<b>e</b>–<b>h</b>). The green (<b>a</b>,<b>e</b>) and red (<b>b</b>,<b>f</b>) fluorescence signals represent GSDMD and Caspase-8, respectively. Scale bar = 100 μm.</p>
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<p>TUNEL results in scented glands during the breeding (<b>a</b>–<b>c</b>) and non-breeding seasons (<b>d</b>–<b>f</b>); scale bar = 100 μm. Apoptosis index during the breeding and non-breeding seasons is shown (<b>g</b>). The error bars represent the means ± SEM (<span class="html-italic">n</span> = 5, each stage). * Statistical significance (*** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Sketch of apoptosis and pyroptosis involved in muskrats’ scented glands.</p>
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14 pages, 2444 KiB  
Article
RIPK2 Is Crucial for the Microglial Inflammatory Response to Bacterial Muramyl Dipeptide but Not to Lipopolysaccharide
by Changjun Yang, Maria Carolina Machado da Silva, John Aaron Howell, Jonathan Larochelle, Lei Liu, Rachel E. Gunraj, Antônio Carlos Pinheiro de Oliveira and Eduardo Candelario-Jalil
Int. J. Mol. Sci. 2024, 25(21), 11754; https://doi.org/10.3390/ijms252111754 - 1 Nov 2024
Viewed by 580
Abstract
Receptor-interacting serine/threonine protein kinase 2 (RIPK2) is a kinase that is essential in modulating innate and adaptive immune responses. As a downstream signaling molecule for nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs), it is implicated in the signaling triggered by [...] Read more.
Receptor-interacting serine/threonine protein kinase 2 (RIPK2) is a kinase that is essential in modulating innate and adaptive immune responses. As a downstream signaling molecule for nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs), it is implicated in the signaling triggered by recognition of microbe-associated molecular patterns by NOD1/2 and TLRs. Upon activation of these innate immune receptors, RIPK2 mediates the release of pro-inflammatory factors by activating mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-κB). However, whether RIPK2 is essential for downstream inflammatory signaling following the activation of NOD1/2, TLRs, or both remains controversial. In this study, we examined the role of RIPK2 in NOD2- and TLR4-dependent signaling cascades following stimulation of microglial cells with bacterial muramyl dipeptide (MDP), a NOD2 agonist, or lipopolysaccharide (LPS), a TLR4 agonist. We utilized a highly specific proteolysis targeting chimera (PROTAC) molecule, GSK3728857A, and found dramatic degradation of RIPK2 in a concentration- and time-dependent manner. Importantly, the PROTAC completely abolished MDP-induced increases in iNOS and COX-2 protein levels and pro-inflammatory gene transcription of Nos2, Ptgs2, Il-1β, Tnfα, Il6, Ccl2, and Mmp9. However, increases in iNOS and COX-2 proteins and pro-inflammatory gene transcription induced by the TLR4 agonist, LPS, were only slightly attenuated with the GSK3728857A pretreatment. Further findings revealed that the RIPK2 PROTAC completely blocked the phosphorylation and activation of p65 NF-κB and p38 MAPK induced by MDP, but it had no effects on the phosphorylation of these two mediators triggered by LPS. Collectively, our findings strongly suggest that RIPK2 plays an essential role in the inflammatory responses of microglia to bacterial MDP but not to LPS. Full article
(This article belongs to the Special Issue Advances in Pro-inflammatory and Anti-inflammatory Cytokines)
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<p>Dose- and time-dependent degradation of RIPK2 by its proteolysis-targeting chimera in SIM-A9 cells. Model of RIPK2 degradation mediated by its proteolysis-targeting chimera (PROTAC) molecule GSK3728857A (<b>A</b>) and the molecular structure of the RIPK2 PROTAC (<b>B</b>). Representative immunoblots and graphs showing degradation of RIPK2 by RIPK2 PROTAC in microglial cells. (<b>C</b>,<b>D</b>) Incubation with various concentrations of RIPK2 PROTAC (0–10 μM) for 4 h degrades RIPK2 in a dose-dependent manner. (<b>E</b>,<b>F</b>) Similarly, time-dependent degradation of RIPK2 by RIPK2 PROTAC (1 μM) was also observed. One-way ANOVA with Bonferroni post-test; * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001 compared with control conditions. Data are normalized to β-actin and represented as mean ± SEM from three to four independent experiments.</p>
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<p>MDP dose-dependently induces pro-inflammatory gene expression in SIM-A9 microglial cells. Graphs show treatment with various concentrations (0 to 10,000 ng/mL) of muramyl dipeptide (MDP) for 24 h dose-dependently increases transcription of pro-inflammatory genes <span class="html-italic">Nos2</span> (<b>A</b>), <span class="html-italic">Il-1β</span> (<b>B</b>), <span class="html-italic">Tnfα</span> (<b>C</b>), <span class="html-italic">Il6</span> (<b>D</b>) and <span class="html-italic">Mmp9</span> (<b>E</b>) in microglial cells. qRT-PCR data are normalized to reference genes <span class="html-italic">Cyc1 and Rltr2aiap</span> and represented as fold increases compared to control cells. One-way ANOVA with Bonferroni post-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 compared with control conditions. Data are represented as mean ± SEM from four independent experiments.</p>
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<p>RIPK2 PROTAC reduces MDP-induced pro-inflammatory gene expression and iNOS protein levels in SIM-A9 cells. SIM-A9 cells were pretreated with 1 µM RIPK2 PROTAC for 4 h followed by 20 h incubation with 100 ng/mL MDP. After that, cells were harvested for RNA and protein extraction. (<b>A</b>) Graph shows RIPK2 degradation by RIPK2 PROTAC completely reduced MDP-induced transcription of pro-inflammatory genes <span class="html-italic">Nos2</span>, <span class="html-italic">Ptgs2</span>, <span class="html-italic">Il-1β</span>, <span class="html-italic">Tnfα</span>, <span class="html-italic">Il6</span>, <span class="html-italic">Ccl2,</span> and <span class="html-italic">Mmp9</span>. (<b>B</b>–<b>E</b>) Effects of RIPK2 PROTAC on MDP-induced iNOS, COX-2, and RIPK2 protein levels. qRT-PCR data are normalized to reference genes <span class="html-italic">Cyc1 and Rltr2aiap</span> and represented as fold changes compared to MDP treatment, and immunoblot data are normalized to β-actin. One-way ANOVA with Bonferroni post-test; *** <span class="html-italic">p</span> &lt; 0.001. Data are represented as mean ± SEM from three independent experiments.</p>
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<p>RIPK2 PROTAC suppresses activation of both NF-κB p65 and MAPK p38 induced by MDP in SIM-A9 cells. (<b>A</b>) SIM-A9 cells were stimulated with 100 ng/mL MDP for indicated periods (0 to 120 min). Immunoblots show MDP increased phosphorylation of NF-κB p65 and MAPK p38, and 60 min incubation of MDP induced maximal effects on both phosphorylated protein levels. Data are representative of three independent experiments with similar results. (<b>B</b>–<b>E</b>) SIM-A9 cells were pretreated with 1 µM RIPK2 PROTAC for 4 h followed by 60 min incubation of 100 ng/mL MDP. After that, cells were harvested for protein extraction and Western blots. Immunoblots and graphs show that RIPK2 PROTAC completely abolished effects of MDP on phosphorylation of both NF-κB p65 (<b>B</b>,<b>C</b>) and MAPK p38 (<b>B</b>,<b>D</b>), which was associated with marked degradation of RIPK2 by its PROTAC pretreatment (<b>B</b>,<b>E</b>). One-way ANOVA with Bonferroni post-test; *** <span class="html-italic">p</span> &lt; 0.001. Data are normalized to β-actin and represented as mean ± SEM from three independent experiments.</p>
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<p>Effects of RIPK2 PROTAC on LPS-induced pro-inflammatory gene expression, protein levels of iNOS and COX-2, and phosphorylated NF-κB p65 and MAPK p38 levels in microglia. SIM-A9 microglial cells were pretreated with 1 µM RIPK2 PROTAC for 4 h followed by 20 h incubation with 10 ng/mL lipopolysaccharide (LPS). After that, cells were harvested for RNA and protein extraction. (<b>A</b>) Graph shows that RIPK2 PROTAC partly reduced LPS-induced <span class="html-italic">Ptgs2</span>, <span class="html-italic">Il-1β</span>, <span class="html-italic">Il6</span>, <span class="html-italic">Ccl2,</span> and <span class="html-italic">Mmp9</span> gene transcription but did not affect gene expression of <span class="html-italic">Nos2</span> and <span class="html-italic">Tnfα</span>. (<b>B</b>–<b>E</b>) RIPK2 PROTAC had no effects on LPS-induced increase in iNOS protein levels (<b>B</b>,<b>C</b>), but it slightly attenuated LPS-induced upregulation of COX-2 levels (<b>B</b>,<b>D</b>). Treatment with RIPK2 PROTAC had no effects on increased levels of phosphorylated NF-κB p65 (<b>B</b>,<b>E</b>) or p38 MAPK (<b>B</b>,<b>F</b>) induced by LPS. At same time, LPS-triggered upregulation of RIPK2 was potently degraded by its PROTAC (<b>B</b>,<b>G</b>). qRT-PCR data are normalized to reference genes <span class="html-italic">Cyc1 and Rltr2aiap</span> and represented as fold changes compared to LPS treatment, and immunoblot data are normalized to β-actin. One-way ANOVA with Bonferroni post-test; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001. Data are represented as mean ± SEM from three independent experiments.</p>
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16 pages, 7981 KiB  
Review
Induced Necroptosis and Its Role in Cancer Immunotherapy
by Ziyao Zhang, Fangming Zhang, Wenjing Xie, Yubo Niu, Haonan Wang, Guofeng Li, Lingyun Zhao, Xing Wang and Wensheng Xie
Int. J. Mol. Sci. 2024, 25(19), 10760; https://doi.org/10.3390/ijms251910760 - 6 Oct 2024
Viewed by 1050
Abstract
Necroptosis is a type of regulated cell death (RCD) that is triggered by changes in the extracellular or intracellular milieu that are picked up by certain death receptors. Thanks to its potent capacity to induce immunological responses and overcome apoptotic resistance, it has [...] Read more.
Necroptosis is a type of regulated cell death (RCD) that is triggered by changes in the extracellular or intracellular milieu that are picked up by certain death receptors. Thanks to its potent capacity to induce immunological responses and overcome apoptotic resistance, it has garnered significant attention as a potential cancer treatment. Basic information for the creation of nano-biomedical treatments is provided by studies on the mechanisms underlying tumor necroptosis. Receptor-interacting protein kinase 1 (RIPK1)–RIPK3-mediated necroptosis, Toll-like receptor domain-containing adapter-inducing interferon (IFN)-β (TRIF)–RIPK3-mediated necroptosis, Z-DNA-binding protein 1 (ZBP1)–RIPK3-mediated necroptosis, and IFNR-mediated necroptosis are the four signaling pathways that collectively account for triggered necroptosis in this review. Necroptosis has garnered significant interest as a possible cancer treatment strategy because, in contrast to apoptosis, it elicits immunological responses that are relevant to therapy. Thus, a thorough discussion is held on the connections between tumor cell necroptosis and the immune environment, cancer immunosurveillance, and cells such as dendritic cells (DCs), cytotoxic T cells, natural killer (NK) cells, natural killer T (NKT) cells, and their respective cytokines. Lastly, a summary of the most recent nanomedicines that cause necroptosis in order to cause immunogenic cell death is provided in order to emphasize their promise for cancer immunotherapy. Full article
(This article belongs to the Section Molecular Immunology)
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<p>The timeline records the development of necroptosis for cancer treatment. RIPK1, receptor-interacting protein kinase 1; RIPK3, receptor-interacting protein kinase 3; MLKL, mixed-lineage kinase domain-like protein; ZBP1, Z-DNA binding protein 1; TRIF (also called TICAM-1), TIR domain-containing adapter-inducing interferon-β; TLR3, Toll-like receptor 3; TLR4, Toll-like receptor 4; ADAR1, adenosine deaminase acting on RNA enzyme-1.</p>
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<p>Signaling pathways to trigger cell necroptosis. The induced necroptosis is summarized into four signaling pathways: ① RIPK1/RIPK3/MLKL-, ② TRIF/RIPK3/MLKL-, ③ ZBP1/RPK3/LKKL-, and ④ IFNR/MLKL-mediated necroptosis. TNF, tumor necrosis factor; TNFR1, tumor necrosis factor receptor 1; LPS, lipopolysaccharide; TLR3/4, Toll-like receptors 3 and 4; IFNRs, type I/II interferon receptors; IFNR1, interferon alpha and beta receptor subunit 1.</p>
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<p>Signaling pathways to trigger cell necroptosis via RIPK1/RIPK3/MLKL axis. TRAIL, TNF-related apoptosis-inducing ligand; TNF, tumor necrosis factor; IFNα/β, interferon α/β; FasL, Fas ligand; TL1A, tumor necrosis factor-like cytokine 1A; APP, amyloid precursor protein.</p>
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<p>The immune microenvironment associated with tumor necroptosis. VEGF, vascular endothelial growth factor; FGFs, fibroblast growth factors; DAMPs, damage-associated molecular patterns.</p>
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<p>The relationship between necroptosis and cancer immunosurveillance. NF-κB, nuclear factor kappa-B; NKT cell, natural killer T cell.</p>
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14 pages, 2760 KiB  
Article
Genomic and Transcriptional Analysis of the Necroptosis Pathway Elements RIPK and MLKL in Sea Cucumber, Holothuria leucospilota
by Rong Chen, Qianying Huang, Yingzhu Rao, Junyan Wang, Ruiming Yu, Shuangxin Peng, Kaiyi Huang, Yihang Huang, Xiangxing Zhu, Dongsheng Tang, Xiaoli Zhang, Tiehao Lin, Ting Chen and Aifen Yan
Genes 2024, 15(10), 1297; https://doi.org/10.3390/genes15101297 - 3 Oct 2024
Viewed by 964
Abstract
Background: Receptor-interacting protein kinases (RIPKs) and mixed-lineage kinase domain-like protein (MLKL) are crucial in regulating innate immune responses and cell death signaling (necroptosis and apoptosis), and are potential candidates for genetic improvement in breeding programs. Knowledge about the RIPK family and MLKL in [...] Read more.
Background: Receptor-interacting protein kinases (RIPKs) and mixed-lineage kinase domain-like protein (MLKL) are crucial in regulating innate immune responses and cell death signaling (necroptosis and apoptosis), and are potential candidates for genetic improvement in breeding programs. Knowledge about the RIPK family and MLKL in sea cucumber remains limited. Methods: We searched the genomes of sea cucumber Holothuria leucospilota for genes encoding RIPKs and MLKL, performed phylogenetic tree, motif and functional domain analyses, and examined tissue distribution and embryonic development patterns using qPCR. Results: RIPK5 (Hl-RIPK5), RIPK7 (Hl-RIPK7) and MLKL (Hl-MLKL) were identified in sea cucumber H. leucospilota. Hl-RIPK5 and Hl-RIPK7 were mainly expressed in coelomocytes, suggesting that they play a role in innate immunity, whereas Hl-MLKL exhibited relatively low expression across tissues. During embryonic development, Hl-MLKL was highly expressed from the 2-cell stage to the morula stage, while Hl-RIPK5 and Hl-RIPK7 were primarily expressed after the morula stage, indicating different roles in embryonic development. In primary coelomocytes, Hl-RIPK5 transcriptional activity was significantly depressed by LPS, poly(I:C), or pathogen Vibrio harveyi. Hl-RIPK7 expression levels were unchanged following the same challenges. Hl-MLKL mRNA levels were significantly decreased with poly(I:C) or V. harveyi, but did not change with LPS. Conclusions: These findings provide valuable insights into the evolutionary tree and characterization of RIPK and MLKL genes in sea cucumber, contributing to the broader understanding of the RIPK gene family and MLKL in ancient echinoderms. Full article
(This article belongs to the Special Issue Genetics and Molecular Breeding in Fisheries and Aquaculture)
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<p>Numbers of the genes of the RIPK gene family and the MLKL gene among different species. In the evolutionary tree of 10 representative Deuterostomia species, the yellow branch represents Vertebrata, the green branch represents Urochordata and Cephalochordata, the purple branch represents Hemichordata and the red branch represents Echinodermata.</p>
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<p>The phylogenetic tree and functional domain analysis of the RIPK family (<b>A</b>) and MLKL (<b>B</b>).</p>
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<p>The protein motif patterns of RIPK5 (<b>A</b>), RIPK7 (<b>B</b>), and MLKL (<b>C</b>).</p>
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<p>The mRNA expression profiles of <span class="html-italic">Hl</span>-RIPK5 (<b>A</b>), <span class="html-italic">Hl</span>-RIPK7 (<b>B</b>), and <span class="html-italic">Hl</span>-MLKL (<b>C</b>) in various adult tissues of the sea cucumber <span class="html-italic">H. leucospilota</span>. Bars represent the mean ± SEM (<span class="html-italic">n</span> = 3).</p>
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<p>The mRNA expression profiles of <span class="html-italic">Hl-RIPK5</span> (<b>A</b>), <span class="html-italic">Hl-RIPK7</span> (<b>B</b>), and <span class="html-italic">MLKL</span> (<b>C</b>) in developing embryos and larvae of the sea cucumber <span class="html-italic">H. leucospilota</span>. (<b>D</b>) Typical embryonic and larval development of <span class="html-italic">H. leucospilota</span>. Numbers indicate time lapsed post-fertilization. Bars represent the mean ± SEM (<span class="html-italic">n</span> = 3).</p>
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<p>The transcriptional expression patterns of three <span class="html-italic">H. leucospilota</span> genes were examined following pathogenic and environmental challenges, including <span class="html-italic">V. harveyi</span> (10<sup>7</sup> cells/mL), LPS (10 mg/mL), and poly(I:C) (10 mg/mL). The genes analyzed were HOLleu05403 (<span class="html-italic">Hl</span>-RIPK5) (<b>A</b>), HOLleu04795 (<span class="html-italic">Hl</span>-RIPK7) (<b>B</b>), and HOLleu05122 (<span class="html-italic">Hl</span>-MLKL) (<b>C</b>). *: <span class="html-italic">p</span> &lt; 0.05 relative to Ctrl; **: <span class="html-italic">p</span> &lt; 0.01 relative to Ctrl. Bars represent the mean ± SEM (<span class="html-italic">n</span> = 3).</p>
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12 pages, 1148 KiB  
Article
Genomic and Socioeconomic Determinants of Racial Disparities in Breast Cancer Survival: Insights from the All of Us Program
by Nubaira Rizvi, Hui Lyu, Leah Vaidya, Xiao-Cheng Wu, Lucio Miele and Qingzhao Yu
Cancers 2024, 16(19), 3294; https://doi.org/10.3390/cancers16193294 - 27 Sep 2024
Viewed by 656
Abstract
Background: Breast cancer outcomes are worse among Black women in the U.S. compared to White women. While extensive research has focused on risk factors contributing to breast cancer; the role of genomic elements in health disparities between these racial groups remains unclear. [...] Read more.
Background: Breast cancer outcomes are worse among Black women in the U.S. compared to White women. While extensive research has focused on risk factors contributing to breast cancer; the role of genomic elements in health disparities between these racial groups remains unclear. This study aims to identify genomic variants and socioeconomic status (SES) determinants influencing racial disparities in breast cancer survival through multiple mediation analyses. Methods: Our investigation is based on the NIH-supported All of Us (AoU) program and analyzes 7452 female participants with malignant tumors of breast, including 5073 with genomic data. A log-rank test reveals significant racial differences in overall survival time between Black and White participants (p-value = 0.04). Multiple mediation analysis examines the effects of 9481 genetic variables across 23 chromosomes in explaining the racial disparity in survival, adjusting for SES variables. Results: 15 gene mutations, in addition to age, general health, and general quality of life, have significant effects (p-values < 0.001) in explaining the observed racial disparity. Mutations in TMEM132B, NARFL, SALL1, PAD12, RIPK1, ASB14, DCX, GNB1L, ARHGAP32, AL135787.1, WBP11, SLC16A12AS1, AP000345.1, IKBKB, and SUPT20H have significantly different distributions between Black and White participants. The disparity is completely explained by the included variables as the direct effect is insignificant (p-value = 0.73). Conclusions: The combined impact of SES determinants and genetic mutations can explain the observed differences in breast cancer survival among Black and White participants. Future studies will explore pathways and design in vivo and in vitro experiments to validate the functions of these genes Full article
(This article belongs to the Section Cancer Epidemiology and Prevention)
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<p>The quality control steps performed on the genomic data.</p>
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<p>The conceptual model exploring the relationship between race and survival rate among participants with breast cancer through different exploratory variables.</p>
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<p>Estimated exploratory variable effects with 95% confidence intervals on racial disparity in breast cancer survival.</p>
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<p>(<b>a</b>) The density of the number of mutations for the gene NARFL by race. (<b>b</b>) The breast cancer hazard rate by the number of mutations of gene NARFL from Chromosome 16.</p>
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11 pages, 4869 KiB  
Article
Long-Lasting, Fine-Tuned Anti-Tumor Activity of Recombinant Listeria monocytogenes Vaccine Is Controlled by Pyroptosis and Necroptosis Regulatory and Effector Molecules
by Abolaji S. Olagunju, Andrew V. D. Sardinha and Gustavo P. Amarante-Mendes
Pathogens 2024, 13(10), 828; https://doi.org/10.3390/pathogens13100828 - 25 Sep 2024
Cited by 1 | Viewed by 898
Abstract
One of the main objectives of developing new anti-cancer vaccine strategies is to effectively induce CD8+ T cell-mediated anti-tumor immunity. Live recombinant vectors, notably Listeria monocytogenes, have been shown to elicit a robust in vivo CD8+ T-cell response in preclinical settings. Significantly, [...] Read more.
One of the main objectives of developing new anti-cancer vaccine strategies is to effectively induce CD8+ T cell-mediated anti-tumor immunity. Live recombinant vectors, notably Listeria monocytogenes, have been shown to elicit a robust in vivo CD8+ T-cell response in preclinical settings. Significantly, it has been demonstrated that Listeria induces inflammatory/immunogenic cell death mechanisms such as pyroptosis and necroptosis in immune cells that favorably control immunological responses. Therefore, we postulated that the host’s response to Listeria-based vectors and the subsequent induction of CD8+ T cell-mediated immunity would be compromised by the lack of regulatory or effector molecules involved in pyroptosis or necroptosis. To test our hypothesis, we used recombinant L. monocytogenes carrying the ovalbumin gene (LM.OVA) to vaccinate wild-type (WT), caspase-1/11−/−, gsdmd−/−, ripk3−/−, and mlkl−/− C57Bl/6 mice. We performed an in vivo cytotoxicity assay to assess the efficacy of OVA-specific CD8+ T lymphocytes in eliminating target cells in wild-type and genetically deficient backgrounds. Furthermore, we evaluated the specific anti-tumor immune response in mice inoculated with the B16F0 and B16F0.OVA melanoma cell lines. Our findings demonstrated that while caspase-1/11 and GSDMD deficiencies interfere with the rapid control of LM.OVA infection, neither of the KOs seems to contribute to the early activation of OVA-specific CTL responses. In contrast, the individual deficiency of each one of these proteins positively impacts the generation of long-lasting effector CD8+ T cells. Full article
(This article belongs to the Special Issue Host Immune Responses to Intracellular Pathogens)
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<p>Bacterial burden in LM-OVA-infected mice. After 3 (<b>A</b>) and 7 (<b>B</b>) days post-LM-OVA (10<sup>3</sup> CFU) infection, mice were euthanized, and spleens were harvested and processed as described. Results are expressed as mean CFU ± standard deviation per group (n = 5) and represent three independent experiments. One-way ANOVA and Bonferroni post-tests were both used in statistical analysis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Early in vivo CTL activity triggered by LM-OVA vaccination in WT and necroptosis- or pyroptosis-deficient mice. (<b>A</b>) WT and knockout mice were vaccinated or not with LM-OVA and 6 days later injected with a single cell suspension containing OVA-pulsed target cells, as described in Material and Methods. On the next day, mice were euthanized, and spleens were processed for flow cytometry. (<b>B</b>) A bar chart showing the percentage of the LM.OVA-induced in vivo elimination of target cells in all mouse strains on day 7. (<b>C</b>) Representative flow cytometry density plots. The results are expressed as the mean ± standard deviation per group (n = 5) and represent three independent experiments. A one-way ANOVA and Bonferroni post-tests were both used in the statistical analysis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Early anti-tumor immune response induced by LM-OVA in WT and knockout mice. (<b>A</b>) Mice were vaccinated or not with LM-OVA and inoculated with the B16F0 and B16F0.OVA melanoma cell lines after 7 days. (<b>B</b>) Tumor growth was evaluated every two days. The results are expressed as the mean ± standard deviation per group (n = 5) and represent three independent experiments. A two-way ANOVA and Bonferroni post-tests were both used in the statistical analysis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Late in vivo CTL activity triggered by LM-OVA vaccination in WT and necroptosis- or pyroptosis-deficient mice. (<b>A</b>) WT and knockout mice were vaccinated or not with LM-OVA and 26 days later injected with a single cell suspension containing OVA-pulsed target cells, as described in M&amp;M. On the next day, mice were euthanized, and spleens were processed for flow cytometry. (<b>B</b>) A bar chart showing the percentage of the LM.OVA-induced in vivo elimination of target cells in all mouse strains on day 27. (<b>C</b>) Representative flow cytometry density plots. The results are expressed as the mean ± standard deviation per group (n = 5) and represent three independent experiments. A one-way ANOVA and Bonferroni post-tests were both used in the statistical analysis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Late anti-tumor immune response induced by LM-OVA in WT and knockout mice. (<b>A</b>) Mice were vaccinated or not with LM-OVA (1 × 10<sup>3</sup>) and inoculated with the B16F0 and B16F0.OVA melanoma cell lines after 27 days. Tumor growth was evaluated every two days. Partial protection was observed in all groups. (<b>B</b>) Full protection was observed in all groups of mice vaccinated with a high dose of LM-OVA (5 × 10<sup>3</sup>). The results are expressed as the mean ± standard deviation per group (n = 5) and represent three independent experiments. A two-way ANOVA and Bonferroni post-tests were both used in the statistical analysis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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20 pages, 2585 KiB  
Review
Significance of Necroptosis in Cartilage Degeneration
by Md Abdul Khaleque, Jea-Hoon Kim, Md Amit Hasan Tanvir, Jong-Beom Park and Young-Yul Kim
Biomolecules 2024, 14(9), 1192; https://doi.org/10.3390/biom14091192 - 21 Sep 2024
Viewed by 1445
Abstract
Cartilage, a critical tissue for joint function, often degenerates due to osteoarthritis (OA), rheumatoid arthritis (RA), and trauma. Recent research underscores necroptosis, a regulated form of necrosis, as a key player in cartilage degradation. Unlike apoptosis, necroptosis triggers robust inflammatory responses, exacerbating tissue [...] Read more.
Cartilage, a critical tissue for joint function, often degenerates due to osteoarthritis (OA), rheumatoid arthritis (RA), and trauma. Recent research underscores necroptosis, a regulated form of necrosis, as a key player in cartilage degradation. Unlike apoptosis, necroptosis triggers robust inflammatory responses, exacerbating tissue damage. Key mediators such as receptor-interacting serine/threonine-protein kinase-1 (RIPK1), receptor-interacting serine/threonine-protein kinase-3(RIPK3), and mixed lineage kinase domain-like (MLKL) are pivotal in this process. Studies reveal necroptosis contributes significantly to OA and RA pathophysiology, where elevated RIPK3 and associated proteins drive cartilage degradation. Targeting necroptotic pathways shows promise; inhibitors like Necrostatin-1 (Nec-1), GSK’872, and Necrosulfonamide (NSA) reduce necroptotic cell death, offering potential therapeutic avenues. Additionally, autophagy’s role in mitigating necroptosis-induced damage highlights the need for comprehensive strategies addressing multiple pathways. Despite these insights, further research is essential to fully understand necroptosis’ mechanisms and develop effective treatments. This review synthesizes current knowledge on necroptosis in cartilage degeneration, aiming to inform novel therapeutic approaches for OA, RA, and trauma. Full article
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<p>The graphical presentation of common initiation executes significantly different pathways of apoptosis and necroptosis.</p>
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<p>A comparison between healthy and degenerated cartilage in the knee joints.</p>
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<p>Typical molecular mechanisms of necroptosis: Necroptosis-mediated cell death is initiated by the activation of death receptors such as the TNF receptor, FAS, Toll-like receptor, or interferon receptor. These receptors activate RIPK1 or other RIP homologous interaction motif (RHIM) domain-containing proteins, which then interact with RIPK3 to form the necrosome complex. RIPK3 is activated through phosphorylation and subsequently phosphorylates MLKL. Phosphorylated MLKL oligomerizes and moves to the plasma membrane, triggering necroptosis. RIPK1, RIPK3, and MLKL are the core components of TNF-induced necroptosis. Additionally, RIPK3 can activate CaMK II, leading to the opening of the mPTP and necroptosis in cardiomyocytes. RIPK3 can also be activated by other RHIM domain-containing proteins like TRIF and DAI, expanding the mechanisms of RIPK3.</p>
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<p>Categorization of arthritis.</p>
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<p><b>Molecular mechanisms of necroptosis in OA and TMJOA</b>: In TMJOA, TNFα induces Syndecan 4 (SDC4), which amplifies TNFα signaling and triggers necroptosis, releasing cartilage-degrading enzymes and intensifying inflammation. Inhibiting RIPK3, pMLKL, and SDC4 protects cartilage and reduces inflammation. BMP7 induces necroptosis through RIP1, with BMP7 silencing reducing RIPK1-induced necroptosis and restoring ECM gene expression. High RIPK3 expression accelerates cartilage degradation, while RIPK3 inhibition by AZ-628 mitigates OA progression. TRADD inhibition with ICCB-19 blocks the RIPK1-TAK1 pathway, reducing inflammation and necroptosis. PLCγ1 inhibition, combined with apoptosis and necroptosis blockers, enhances cartilage matrix synthesis. RIPK1 knockdown disrupts the TRIF/MyD88-RIPK1-TRAF2 pathway, alleviating OA. AZD8330 activates cIAP1, inhibiting RIPK1-associated necrosis, and preserving cartilage.</p>
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<p><b>Molecular mechanisms of necroptosis in RA:</b> Nec-1 and amiloride inhibit necroptosis in RA chondrocytes by targeting the RIP1/RIP3/p-MLKL pathway, with ASIC1a-mediated upregulation reversible by PcTx-1 or Nec-1. IFN-γ mitigates necroptosis and inflammation by reducing MLKL and modulating inflammatory responses, despite its proinflammatory role. KW2449 ameliorates collagen-induced arthritis by inhibiting RIPK1-dependent necroptosis, reducing RIPK1 and MLKL levels. Irisin reduces necroptotic signaling and inflammation via the NF-kB and Nrf2/HO-1 pathways, downregulating TNF-α, MCP1, and HMGB1, promoting chondrocyte recovery.</p>
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<p><b>Molecular mechanisms of necroptosis in trauma:</b> Nec-1, a RIPK1 inhibitor, surpasses zVAD in protecting against trauma-induced necroptosis by reducing MLKL expression and PGE2 production. ROS are crucial in RIPK1-mediated necroptosis, which is more prominent in late-stage OA. Necroptosis markers RIPK3 and MLKL in OA cartilage are linked to PGE2 and NO release, and necrostatin-1 inhibits post-trauma necroptosis. In TMJOA, RIP1 inhibition reduces apoptosis and necroptosis. Mechanical stress induces necroptosis in chondrocytes, with Nec-1 and Z-VAD reducing TNF-α-induced ROS and necroptosis. D469del-COMP retention triggers necroptosis via ER stress, oxidative stress, and DNA damage.</p>
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21 pages, 5941 KiB  
Article
Bioactivated Glucoraphanin Modulates Genes Involved in Necroptosis on Motor-Neuron-like Nsc-34: A Transcriptomic Study
by Aurelio Minuti, Alessandra Trainito, Agnese Gugliandolo, Ivan Anchesi, Luigi Chiricosta, Renato Iori, Emanuela Mazzon and Marco Calabrò
Antioxidants 2024, 13(9), 1111; https://doi.org/10.3390/antiox13091111 - 14 Sep 2024
Viewed by 992
Abstract
Research on bioactive compounds has grown recently due to their health benefits and limited adverse effects, particularly in reducing the risk of chronic diseases, including neurodegenerative conditions. According to these observations, this study investigates the activity of sulforaphane (RS-GRA) on an in vitro [...] Read more.
Research on bioactive compounds has grown recently due to their health benefits and limited adverse effects, particularly in reducing the risk of chronic diseases, including neurodegenerative conditions. According to these observations, this study investigates the activity of sulforaphane (RS-GRA) on an in vitro model of differentiated NSC-34 cells. We performed a transcriptomic analysis at various time points (24 h, 48 h, and 72 h) and RS-GRA concentrations (1 µM, 5 µM, and 10 µM) to identify molecular pathways influenced by this compound and the effects of dosage and prolonged exposure. We found 39 differentially expressed genes consistently up- or downregulated across all conditions. Notably, Nfe2l2, Slc1a5, Slc7a11, Slc6a9, Slc6a5, Sod1, and Sod2 genes were consistently upregulated, while Ripk1, Glul, Ripk3, and Mlkl genes were downregulated. Pathway perturbation analysis showed that the overall dysregulation of these genes results in a significant increase in redox pathway activity (adjusted p-value 1.11 × 10−3) and a significant inhibition of the necroptosis pathway (adjusted p-value 4.64 × 10−3). These findings suggest RS-GRA’s potential as an adjuvant in neurodegenerative disease treatment, as both increased redox activity and necroptosis inhibition may be beneficial in this context. Furthermore, our data suggest two possible administration strategies, namely an acute approach with higher dosages and a chronic approach with lower dosages. Full article
(This article belongs to the Special Issue Role of Natural Antioxidants on Neuroprotection)
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<p>Hydrolysis reaction of GRA to RS-GRA exerted by Myr. The chemical structures of glucoraphanin and sulforaphane (RS-GRA) were obtained from the PubChem Compound Summary [<a href="#B14-antioxidants-13-01111" class="html-bibr">14</a>]. Details on the molecule’s properties can be found at <a href="https://pubchem.ncbi.nlm.nih.gov/compound/9548634" target="_blank">https://pubchem.ncbi.nlm.nih.gov/compound/9548634</a> (accessed on 25 July 2024); Sulforaphane|C6H11NOS2|CID 5350—PubChem (nih.gov).</p>
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<p>Cell viability tested with MTT assay in differentiated NSC-34 cells treated with RS-GRA at different concentrations (0.5–10 µM) after 24 h, 48 h, and 72 h. Results are normalized against CTRL and expressed as the mean ± SD. There were five biological replicates per condition. One-way ANOVA and a Bonferroni post hoc test showed no significant differences (<span class="html-italic">p</span>-value &lt; 0.05) between treated cells and CTRL. Darker and lighter hues represent Controls and Myrosinase-only wells, respectively.</p>
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<p>In the volcano plot, we report the log<sub>2</sub> fold-changes and <span class="html-italic">p</span>-values of all the genes explored in the DEA for each comparison. The line that intercepts the y axis is related to our threshold of significance of 0.05; all the genes above this line are considered to be differentially expressed. The x axis reports the log<sub>2</sub> fold-change that discriminates up- and downregulated DEGs defined by a red or green color, respectively. In the figures, we also report the top 10 genes that survived DEG selection.</p>
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<p>In the volcano plot, we report the log<sub>2</sub> fold-changes and <span class="html-italic">p</span>-values of all the genes explored in the DEA for each comparison. The line that intercepts the y axis is related to our threshold of significance of 0.05; all the genes above this line are considered to be differentially expressed. The x axis reports the log<sub>2</sub> fold-change that discriminates up- and downregulated DEGs defined by a red or green color, respectively. In the figures, we also report the top 10 genes that survived DEG selection.</p>
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<p>In the upset plot, we summarize all the DEGs that are consistently dysregulated in all investigated conditions. On the upper bar plot, we report the number of DEGs shared for each intersection considered (as indicated in the lower half of the plot). The bar plot on the left reports the DEGs resulting from each comparison. Only intersections with a size ≥ 9 are shown.</p>
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<p>In the plot, we report the pathways significantly enriched in DEGs that also showed a significant perturbation according to SPIA results. On the lefthand side, the pathways inhibited by RS-GRA treatment are shown (necroptosis, ubiquitin–proteasome pathway, and prostaglandin synthesis and regulation); of these, only necroptosis showed a perturbation score(tA) higher than |2| (necroptosis tA: −2.95). On the righthand side, the pathways activated by RS-GRA treatment are reported (one-carbon metabolism and related pathways, oxidative stress and redox pathway); of these, only the oxidative stress and redox pathway showed a tA higher than |2| (OS and redox pathway tA: 3.17). Bubbles hue indicates the adjusted <span class="html-italic">p</span>-values, while size is related to the ratio of DEGs/total genes within the pathway under investigation.</p>
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<p>Here, we report proteins levels at all concentrations and all time-steps (bar plots) and the bands from WB membranes. (<b>A</b>) Ripk1 concentration at the cytosolic level compared to the housekeeping protein Gapdh. A significant decrease in protein expression compared to controls was highlighted at the later time-steps (48 h at 5 µM and 10 µM dosages and 72 h at 1 µM). Interestingly, we detected a drastic increase in this protein expression at 72 h at the 10 µM dosage. (<b>B</b>) Mlkl concentration at the cytosolic level compared to the housekeeping protein Gapdh. A significant decrease in protein expression compared to controls was highlighted only at the 5 µM at 24 h. (<b>C</b>): Ripk3 concentration at the cytosolic level compared to the housekeeping protein Gapdh. For Ripk3, a significant decrease in protein expression compared to controls was highlighted at the last time-step (72 h) at 5 µM and 10 µM dosages. (<b>D</b>) Nrf2 concentration at the nuclear level compared to the housekeeping protein Lamin B. A significant increase in protein expression compared to controls was observable at multiple time-steps and multiple dosages. The original membranes are included as <a href="#app1-antioxidants-13-01111" class="html-app">Supplementary Materials (Supplementary Figures S2–S5)</a>. Asterisks (*) indicate <span class="html-italic">p</span>-value: * <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, respectively.</p>
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<p>Here, we report the overall trend of proteins’ levels at the three time-steps (24 h, 48 h, and 72 h). Each point corresponds to the mean protein expression (normalized by the housekeeping protein expression) of the three dosages for each time-step. In orange, the protein expression levels from treated cells are reported. In blue, the non-treated controls are shown.</p>
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<p>Here, we report the overall trend of proteins’ levels at the three tested concentrations (1 µM, 5 µM, and 10 µM) irrespective of the time-steps. Each point corresponds to the mean protein expression (normalized by the housekeeping protein expression) of the three time-steps for each dosage. In orange, the protein expression levels from treated cells are reported. In blue, the non-treated controls are shown.</p>
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<p>Here we report the oxidative stress and redox pathway from Wikipathways. DEGs from our data are reported in red (upregulated) or green (downregulated) according to their expression behavior. The figure was obtained from Wikipathways website and colored based on our data. The color intensity is based on a +9 to −9 scale that summarizes in how many conditions each transcript was significantly dysregulated (from upregulated at all time-steps and dosages to downregulated at all time-steps and dosages).</p>
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<p>Here we report the necroptosis pathway from KEGG. DEGs from our data are reported in red (upregulated) or green (downregulated) according to their expression behavior. The figure was obtained from KEGG website and colored based on our data. The color intensity is based on a +9 to −9 scale that summarizes in how many conditions each transcript was significantly dysregulated (from upregulated at all time-steps and dosages to downregulated at all time-steps and dosages).</p>
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<p>Here, we report the overall trend of proteins’ concentrations at the three tested concentrations (1 µM, 5 µM, and 10 µM) at the different time-steps (24 h, 48 h, and 72 h). Each point corresponds to the protein expression normalized by the housekeeping protein expression (GAPDH of necroptosis genes and Lamin B for Nrf2) of the three time-steps for each dosage. Protein expressions in untreated cells are reported in blue, while the different dosages (1 µM, 5 µM, and 10 µM) are reported in orange, gray, and yellow, respectively. Asterisks (*) indicate <span class="html-italic">p</span>-values: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001, respectively.</p>
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11 pages, 1182 KiB  
Article
Chromatin Regulator SMARCA4 Is Essential for MHV-Induced Inflammatory Cell Death, PANoptosis
by R. K. Subbarao Malireddi and Thirumala-Devi Kanneganti
Viruses 2024, 16(8), 1261; https://doi.org/10.3390/v16081261 - 6 Aug 2024
Viewed by 1044
Abstract
The innate immune system serves as the first line of defense against β-coronaviruses (β-CoVs), a family of viruses that includes SARS-CoV-2. Viral sensing via pattern recognition receptors triggers inflammation and cell death, which are essential components of the innate immune response that facilitate [...] Read more.
The innate immune system serves as the first line of defense against β-coronaviruses (β-CoVs), a family of viruses that includes SARS-CoV-2. Viral sensing via pattern recognition receptors triggers inflammation and cell death, which are essential components of the innate immune response that facilitate viral clearance. However, excessive activation of the innate immune system and inflammatory cell death can result in uncontrolled release of proinflammatory cytokines, resulting in cytokine storm and pathology. PANoptosis, innate immune, inflammatory cell death initiated by innate immune sensors and driven by caspases and RIPKs through PANoptosome complexes, has been implicated in the pathology of viral infections. Therefore, understanding the molecular mechanisms regulating PANoptosis in response to β-CoV infection is critical for identifying new therapeutic targets that can mitigate disease severity. In the current study, we analyzed findings from a cell death-based CRISPR screen with archetypal β-CoV mouse hepatitis virus (MHV) as the trigger to characterize host molecules required for inflammatory cell death. As a result, we identified SMARCA4, a chromatin regulator, as a putative host factor required for PANoptosis in response to MHV. Furthermore, we observed that gRNA-mediated deletion of Smarca4 inhibited MHV-induced PANoptotic cell death in macrophages. These findings have potential translational and clinical implications for the advancement of treatment strategies for β-CoVs and other infections. Full article
(This article belongs to the Special Issue PANoptosis in Viral Infection)
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<p><b>CRISPR screen identifies host factors that facilitate β-CoV-induced cell death.</b> (<b>A</b>) Volcano plot depicting the log2 mean fold change (FLC) for the gRNAs in the CRISPR screen based on an earlier genome-wide CRISPR screen in immortalized bone marrow-derived macrophages (iBMDMs) infected with mouse hepatitis virus (MHV; MOI 0.1) for 24 h. The newly identified enriched host gene <span class="html-italic">Smarca4</span> is labeled alongside the previously identified gene <span class="html-italic">Ceacam1</span>. (<b>B</b>) Scatter plot illustrating the enrichment of all four gRNAs targeting <span class="html-italic">Smarca4</span> in the pool of iBMDMs carrying gRNAs from the whole genome CRISPR screen following MHV infection. (<b>C</b>) Scatter plot illustrating the logarithmic distribution of the normalized gRNA counts in the control (uninfected) and MHV-infected pools of cells for each individual <span class="html-italic">Smarca4</span> gRNA, as shown in panel B.</p>
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<p><b>SMARCA4 is required for MHV-induced inflammatory cell death</b>. (<b>A</b>) Representative images of cell death in mouse hepatitis virus (MHV; MOI 0.1)-infected immortalized bone marrow-derived macrophages (iBMDMs) with and without <span class="html-italic">Smarca4</span> gRNA treatment at the indicated time points. The red mask denotes dead cells, and the scale bar represents 50 μm. (<b>B</b>) Quantification of the percentage of cells undergoing lytic cell death at specified time points following MHV infection in iBMDMs treated with or without <span class="html-italic">Smarca4</span> gRNA. (<b>C</b>) <span class="html-italic">Smarca4</span> expression in uninfected iBMDMs treated with and without <span class="html-italic">Smarca4</span> gRNA. <span class="html-italic">Actb</span> was used to normalize <span class="html-italic">Smarca4</span> expression. The reported data are representative of two independent experiments with 4–6 technical replicates (<b>A</b>–<b>C</b>). Similar results were obtained for each experiment. The data are shown as the mean ± SEM (<b>B</b>,<b>C</b>). The Student’s <span class="html-italic">t</span>-test was used to determine statistical significance. ** <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. Ctrl: Control with no gRNA; <span class="html-italic">Smarca4-g</span>: <span class="html-italic">Smarca4</span> gRNA-treated.</p>
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<p><b>SMARCA4 is required for MHV-induced PANoptosis</b>. (<b>A</b>) Immunoblot analysis of pro- (P45) and cleaved caspase-1 (P20; CASP1), pro- (P53) and activated (P30) gasdermin D (GSDMD), pro- (P55) and cleaved caspase-8 (P43, P18; CASP8), pro- (P35) and cleaved caspase-3 (P17; CASP3), and phospho-mixed lineage kinase domain-like pseudokinase (pMLKL) and total MLKL (tMLKL) from mouse hepatitis virus (MHV; MOI 0.1)-infected immortalized bone marrow-derived macrophages (iBMDMs) with and without <span class="html-italic">Smarca4</span> gRNA treatment at the indicated time points. Blots were reprobed using β-ACTIN antibody as an internal control. The reported data are representative of two independent experiments. Similar observations were obtained in each experiment. Ctrl: Control with no gRNA; <span class="html-italic">Smarca4-g</span>: <span class="html-italic">Smarca4</span> gRNA-treated. The red asterisk (*) denotes a non-specific band.</p>
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17 pages, 978 KiB  
Review
Regulation of RIPK1 Phosphorylation: Implications for Inflammation, Cell Death, and Therapeutic Interventions
by Jingchun Du and Zhigao Wang
Biomedicines 2024, 12(7), 1525; https://doi.org/10.3390/biomedicines12071525 - 9 Jul 2024
Cited by 1 | Viewed by 1579
Abstract
Receptor-interacting protein kinase 1 (RIPK1) plays a crucial role in controlling inflammation and cell death. Its function is tightly controlled through post-translational modifications, enabling its dynamic switch between promoting cell survival and triggering cell death. Phosphorylation of RIPK1 at various sites serves as [...] Read more.
Receptor-interacting protein kinase 1 (RIPK1) plays a crucial role in controlling inflammation and cell death. Its function is tightly controlled through post-translational modifications, enabling its dynamic switch between promoting cell survival and triggering cell death. Phosphorylation of RIPK1 at various sites serves as a critical mechanism for regulating its activity, exerting either activating or inhibitory effects. Perturbations in RIPK1 phosphorylation status have profound implications for the development of severe inflammatory diseases in humans. This review explores the intricate regulation of RIPK1 phosphorylation and dephosphorylation and highlights the potential of targeting RIPK1 phosphorylation as a promising therapeutic strategy for mitigating human diseases. Full article
(This article belongs to the Section Cell Biology and Pathology)
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<p>The domain structure of RIPK1 comprises a kinase domain (KD), an intermediate domain (ID), and a death domain (DD). Within the intermediate domain lies the RIP homotypic interaction motif domain (RHIM), which participates in polymerization and interacts with the RHIM domains of RIPK3, ZBP1, and TRIF. The death domain of RIPK1 facilitates homo-dimerization and interacts with the death domains of TNFR1, TRADD, and FADD. Abbreviations: RIPK1, receptor-interacting protein kinase 1; RIPK3, receptor-interacting protein kinase 3; ZBP1, Z-DNA-binding protein 1, also known as DAI (DNA-dependent activator of interferon regulatory factors) and DLM-1; TRIF, TIR-domain-containing adapter-inducing interferon β; TNFR1, tumor necrosis factor receptor 1; TRADD, TNFR1-associated death domain protein; FADD, Fas-associated death domain.</p>
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<p>Pleiotropic TNF signaling pathways. (1) Inflammation and cell survival. Engagement of TNF with its receptor TNFR1 leads to the recruitment of RIPK1 and TRADD through death domain interactions to initiate complex I formation. TRADD then recruits adaptor protein TRAF2/5, which binds E3 ligase cIAP1/2. cIAP1/2 catalyzes K63 ubiquitination of RIPK1, serving as a scaffold to recruit ubiquitin-binding proteins TAB2/3 and associated TAK1, activating the downstream MAPK pathway. Additionally, the K63 ubiquitin chain recruits another E3 complex, LUBAC, which catalyzes the M1 linear ubiquitin chains on RIPK1 and TNFR1. These linear ubiquitin chains recruit adaptor protein NEMO and associated IKKα/β, phosphorylating IκBα to promote its degradation and subsequent NF-κB activation. Both the MAPK pathway and the NF-κB pathway activate gene expression, which promotes cell survival and inflammation. (2) Apoptosis and/or pyroptosis. Under TNF treatment with protein synthesis inhibition by cycloheximide (CHX), complex I is converted to complex IIa, containing TRADD, FADD, and Caspase-8, leading to oligomerization and activation of Caspase-8 and subsequent apoptosis. Alternatively, co-treatment of TNF with a cIAP1/2 inhibitor, Smac-mimetic, converts complex I to complex IIb, containing RIPK1, FADD, and Caspase-8, which also activates Caspase-8 and apoptosis. Under some circumstances, such as during <span class="html-italic">Yersinia</span> infection, activated Caspase-8 cleaves gasdemin D or E to trigger pyroptosis. (3) Necroptosis. Inhibition of apoptosis with Z-VAD-FMK, along with the presence of RIPK3, leads to the conversion of complex II into the necrosome. The core components of the necrosome include RIPK1, RIPK3, and MLKL, resulting in polymerization and membrane translocation of MLKL and subsequent cell death. In general, the scaffold function of RIPK1 is important for inflammation and cell survival, while the kinase activity is important for complex IIb-dependent apoptosis as well as necroptosis. Under some circumstances, such as during pathogen infection, simultaneous activation of pyroptosis, apoptosis, and necroptosis occurs, which is defined as PANoptosis. Abbreviations: TNF, tumor necrosis factor; TRAF2/5, TNF receptor-associated factor protein 2/5; cIAP1/2, cellular inhibitor of apoptosis 1 and 2; TAB2/3, TAK1-binding protein 2/3; TAK1, transforming growth factor-β-activated kinase 1; LUBAC, the linear ubiquitin chain assembly complex; NEMO, NF-κB essential modulator; IKKα/β, IκB kinase α/β; IκBα, inhibitor of kB alpha; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; MAPK, mitogen-activated protein kinase; MLKL, mixed-lineage kinase-like protein; CHX, cycloheximide; Smac-mimetic, Second Mitochondria-derived Activator of Caspases-mimetic. The red stripes in the diagram of RIPK1 and RIPK3 indicate the RHIM domain.</p>
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<p>Phosphorylation events impacting RIPK1 kinase activity. Schematic representation of RIPK1, showing key phosphorylation sites. Green characters denote auto-activating phosphorylation events, while red characters denote inhibitory phosphorylation events. Multiple kinases catalyze inhibitory phosphorylation events, which serve as critical checkpoints for cell death activation. Notably, the kinase responsible for S89 phosphorylation has not yet been reported. Abbreviations: TBK1, TANK-binding kinase 1; IKKε, IκB kinase ε; MK2, MAPK-activated protein kinase 2; JAK1, Janus kinase 1; AMPK, AMP-activated protein kinase.</p>
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<p>Regulation of RIPK1 activity through phosphorylation and dephosphorylation. This schematic illustrates the dynamic regulation of RIPK1-dependent cell death pathways. Inhibitory phosphorylation events on RIPK1, catalyzed by multiple kinases, serve as important checkpoints for cell death activation. Following cell death induction, the PPP1R3G/PP1γ holoenzyme is recruited to complex I to remove the inhibitory phosphorylation on RIPK1. This process enables RIPK1 autophosphorylation to activate its kinase activity. Consequently, activated RIPK1 triggers downstream signaling cascades, leading to apoptosis and necroptosis. The balance between inhibitory phosphorylation and PPP1R3G/PP1γ-mediated dephosphorylation serves as a key regulatory mechanism for RIPK1-dependent cell death processes. The red stripes in the diagram of RIPK1 and RIPK3 indicate the RHIM domain.</p>
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18 pages, 5303 KiB  
Article
In Vitro and In Silico Anti-Glioblastoma Activity of Hydroalcoholic Extracts of Artemisia annua L. and Artemisia vulgaris L.
by Jurga Bernatoniene, Emilija Nemickaite, Daiva Majiene, Mindaugas Marksa and Dalia M. Kopustinskiene
Molecules 2024, 29(11), 2460; https://doi.org/10.3390/molecules29112460 - 23 May 2024
Cited by 1 | Viewed by 1594
Abstract
Glioblastoma, the most aggressive and challenging brain tumor, is a key focus in neuro-oncology due to its rapid growth and poor prognosis. The C6 glioma cell line is often used as a glioblastoma model due to its close simulation of human glioma characteristics, [...] Read more.
Glioblastoma, the most aggressive and challenging brain tumor, is a key focus in neuro-oncology due to its rapid growth and poor prognosis. The C6 glioma cell line is often used as a glioblastoma model due to its close simulation of human glioma characteristics, including rapid expansion and invasiveness. Alongside, herbal medicine, particularly Artemisia spp., is gaining attention for its anticancer potential, offering mechanisms like apoptosis induction, cell cycle arrest, and the inhibition of angiogenesis. In this study, we optimized extraction conditions of polyphenols from Artemisia annua L. and Artemisia vulgaris L. herbs and investigated their anticancer effects in silico and in vitro. Molecular docking of the main phenolic compounds of A. annua and A. vulgaris and potential target proteins, including programmed cell death (apoptosis) pathway proteins proapoptotic Bax (PDB ID 6EB6), anti-apoptotic Bcl-2 (PDB ID G5M), and the necroptosis pathway protein (PDB ID 7MON), mixed lineage kinase domain-like protein (MLKL), in complex with receptor-interacting serine/threonine-protein kinase 3 (RIPK3), revealed the high probability of their interactions, highlighting the possible influence of chlorogenic acid in modulating necroptosis processes. The cell viability of rat C6 glioma cell line was assessed using a nuclear fluorescent double-staining assay with Hoechst 33342 and propidium iodide. The extracts from A. annua and A. vulgaris have demonstrated anticancer activity in the glioblastoma model, with the synergistic effects of their combined compounds surpassing the efficacy of any single compound. Our results suggest the potential of these extracts as a basis for developing more effective glioblastoma treatments, emphasizing the importance of further research into their mechanisms of action and therapeutic applications. Full article
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Figure 1
<p>Plants of <span class="html-italic">Artemisia annua</span> L. (<b>a</b>) and <span class="html-italic">Artemisia vulgaris</span> L. (<b>b</b>).</p>
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<p>Chemical structures of main phenolic compounds detected in <span class="html-italic">Artemisia annua</span> L. and <span class="html-italic">Artemisia vulgaris</span> L. herbal hydroalcoholic extracts. <b>1</b>—apigenin, <b>2</b>—luteolin, <b>3</b>—neochlorogenic acid, <b>4</b>—chlorogenic acid, <b>5</b>—4-o-caffeoylquinic acid, <b>6</b>—caffeic acid, and <b>7</b>—isoquercitrin.</p>
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<p>Yields of main polyphenolic compounds (low values (<b>a</b>), high values (<b>b</b>)) from herbal hydroalcoholic extracts of <span class="html-italic">Artemisia annua</span> L. and <span class="html-italic">Artemisia vulgaris</span> L. Data are presented as mean ± standard error (SEM), n = 4. * <span class="html-italic">p</span> &lt; 0.05—statistically significant difference compared to corresponding <span class="html-italic">Artemisia annua</span> samples. The results were analyzed with a one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test.</p>
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<p>Total amount of phenolic compounds in herbal hydroalcoholic extracts of <span class="html-italic">Artemisia annua</span> L. and <span class="html-italic">Artemisia vulgaris</span> L. in the presence and absence of the excipient L-glutathione (1%). Data are presented as mean ± SEM, n = 4. * <span class="html-italic">p</span> &lt; 0.05—statistically significant difference compared to control without the excipient; # <span class="html-italic">p</span> &lt; 0.05—statistically significant difference of <span class="html-italic">A. vulgaris</span> samples compared to <span class="html-italic">A. annua</span> samples. The results were analyzed with a one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test.</p>
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<p>Docking of chlorogenic acid to MLKL/RIPK3 protein complex forms a necroptosis pathway. MLKL depicted in yellow, RIPK3 is depicted in red. Molecular docking studies were carried out using AutoDock Vina 4.05. All non-protein residues were removed, retaining pure protein structure for docking simulations. The structure presented has the lowest docking energy (−6.8 kcal/mol) and the highest number of hydrogen bonds (N18).</p>
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<p>Effects of different concentrations of <span class="html-italic">Artemisia annua</span> L. extract without (<b>a</b>) and with the excipient—1% of L-glutathione (<b>b</b>) on the viability of C6 cells. C6 cells were treated with different concentrations of extract (5–50 µg/mL of phenolic compounds) for 24 h. Data are presented as means of percentage of the untreated control cells ± SEM (n = 5). * <span class="html-italic">p</span> &lt; 0.05 versus control, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 versus extract without the excipient. The results were analyzed with one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test.</p>
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<p>Effects of different concentrations of <span class="html-italic">Artemisia annua</span> L. extract without (<b>a</b>) and with the excipient—1% of L-glutathione (<b>b</b>) on viability of C6 cells. C6 cells were treated with different concentrations of extract (5–70 µg/mL of phenolic compounds) for 24 h. Data are presented as means of percentage of the untreated control cells ± SE (n = 5). * <span class="html-italic">p</span> &lt; 0.05 versus control, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 versus extract without the excipient. The results were analyzed with one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test.</p>
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<p>Effects of different concentrations of chlorogenic acid on viability of C6 cells. C6 cells were treated with different concentrations (5–70 µg/mL) of chlorogenic acid for 24 h. Data are presented as means of percentage of the untreated control cells ± SE (n = 5). * <span class="html-italic">p</span> &lt; 0.05 versus control. The results were analyzed with one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test.</p>
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<p>Effects of different concentrations of <span class="html-italic">A. annua</span> L. hydroalcoholic extract on viability of C6 cells. Cells were double-stained with Hoechst 33342 and PI, and the viability was assessed under fluorescence microscope. Original magnification ×20. Typical photographs of control cells (<b>a</b>) and after treatment with (<b>b</b>) 10 µg/mL phenolic compounds, (<b>c</b>) 20 µg/mL phenolic compounds (<b>d</b>) 50 µg/mL phenolic compounds of investigated extract. Hoechst 33342-positive cells, exhibiting blue fluorescence, were considered viable cells. PI-stained cells exhibiting red fluorescence were considered necrotic.</p>
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14 pages, 3474 KiB  
Article
Oleanolic Acid Acetate Alleviates Cisplatin-Induced Nephrotoxicity via Inhibition of Apoptosis and Necroptosis In Vitro and In Vivo
by Bori Lee, Yeon-Yong Kim, Seungwon Jeong, Seung Woong Lee, Seung-Jae Lee, Mun-Chual Rho, Sang-Hyun Kim and Soyoung Lee
Toxics 2024, 12(4), 301; https://doi.org/10.3390/toxics12040301 - 18 Apr 2024
Cited by 1 | Viewed by 2146
Abstract
Cisplatin is a widely used anti-cancer drug for treating solid tumors, but it is associated with severe side effects, including nephrotoxicity. Various studies have suggested that the nephrotoxicity of cisplatin could be overcome; nonetheless, an effective adjuvant drug has not yet been established. [...] Read more.
Cisplatin is a widely used anti-cancer drug for treating solid tumors, but it is associated with severe side effects, including nephrotoxicity. Various studies have suggested that the nephrotoxicity of cisplatin could be overcome; nonetheless, an effective adjuvant drug has not yet been established. Oleanolic acid acetate (OAA), a triterpenoid isolated from Vigna angularis, is commonly used to treat inflammatory and allergic diseases. This study aimed to investigate the protective effects of OAA against cisplatin-induced apoptosis and necroptosis using TCMK-1 cells and a mouse model. In cisplatin-treated TCMK-1 cells, OAA treatment significantly reduced Bax and cleaved-caspase3 expression, whereas it increased Bcl-2 expression. Moreover, in a cisplatin-induced kidney injury mouse model, OAA treatment alleviated weight loss in the body and major organs and also relieved cisplatin-induced nephrotoxicity symptoms. RNA sequencing analysis of kidney tissues identified lipocalin-2 as the most upregulated gene by cisplatin. Additionally, necroptosis-related genes such as receptor-interacting protein kinase (RIPK) and mixed lineage kinase domain-like (MLKL) were identified. In an in vitro study, the phosphorylation of RIPKs and MLKL was reduced by OAA pretreatment in both cisplatin-treated cells and cells boosted via co-treatment with z-VAD-FMK. In conclusion, OAA could protect the kidney from cisplatin-induced nephrotoxicity and may serve as an anti-cancer adjuvant. Full article
(This article belongs to the Section Drugs Toxicity)
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<p>Effect of OAA on apoptotic responses in cisplatin-exposed TCMK-1 mouse kidney cells. Percentage of apoptotic cells determined using a fluorescence-activated cell sorting analysis using Annexin V and propidium iodide staining (<b>a</b>). Apoptosis-related protein expression was determined using a proteome profiler mouse apoptosis array. 1: reference; 2: Bcl-2; 3: c-caspase-3; 4: HIF-1 α; 5: HSP60 (<b>b</b>). Gene expression of Bcl-2 and Bax by qPCR (<b>c</b>,<b>d</b>). All data are presented as mean ± SD of three independent experiments. * <span class="html-italic">p</span> &lt; 0.05, significantly different from cisplatin-treated group.</p>
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<p>Effect of OAA on renal dysfunction in a mouse model of cisplatin-induced nephrotoxicity. Body weight change in C57BL/6 mice over 5 days in each group (<b>a</b>). Serum BUN and creatinine levels were determined (<b>b</b>). Liver, spleen, and kidney tissue weights were determined (<b>c</b>). Serum TNF-α, IL-1β, and IL-6 levels were measured using ELISA (<b>d</b>). All data are presented as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, significantly different from cisplatin-treated group.</p>
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<p>Effect of OAA on renal histology in a mouse model of cisplatin-induced nephrotoxicity. Representative histology of H&amp;E-stained renal tissues (×200). Scale bar = 500 µm. Arrows indicate glomerular capsule (<b>a</b>). Clinical score of renal toxicity in the kidneys. Tubular cell death, tubular dilation, and cast formation were scored from 1 to 5 in terms of the severity of the kidney slices (<b>b</b>). All data are presented as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, significantly different from cisplatin-treated group.</p>
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<p>Pattern of gene expression in renal tissues in a mouse model of cisplatin-induced nephrotoxicity. The volcano plot shows the correlation between DEGs. Cisplatin vs. control and cisplatin + 50 mg/kg OAA vs. cisplatin (<b>a</b>). Cluster heatmap shows the DEGs in each group (<b>b</b>). KEGG pathway showing the top 20 enriched pathways (<b>c</b>). Read count of <span class="html-italic">lcn2</span> and necroptosis-related genes, such as <span class="html-italic">ripk3</span> and <span class="html-italic">mlkl</span>, in RNA sequencing (<b>d</b>). All data are presented as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, significantly different from cisplatin-treated group.</p>
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<p>Effect of OAA on the necroptosis pathway in cisplatin-exposed TCMK-1 mouse kidney cells. The mRNA expression of necroptosis-related genes, such as <span class="html-italic">ripk1</span>, <span class="html-italic">ripk3</span>, and <span class="html-italic">mlkl</span>, measured by qPCR (<b>a</b>). Phosphorylation of necroptosis-associated proteins, including RIPK1, RIPK3, and MLKL, was measured by Western blot analysis (<b>b</b>). Phosphorylation of necroptosis-related proteins, such as RIPK1, RIPK3, and MLKL, was measured by Western blot analysis (<b>c</b>). All data are presented as mean ± SD of three independent experiments. * <span class="html-italic">p</span> &lt; 0.05, significantly different from cisplatin-treated group.</p>
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23 pages, 11243 KiB  
Article
The CaMK Family Differentially Promotes Necroptosis and Mouse Cardiac Graft Injury and Rejection
by Haitao Lu, Jifu Jiang, Jeffery Min, Xuyan Huang, Patrick McLeod, Weihua Liu, Aaron Haig, Lakshman Gunaratnam, Anthony M. Jevnikar and Zhu-Xu Zhang
Int. J. Mol. Sci. 2024, 25(8), 4428; https://doi.org/10.3390/ijms25084428 - 17 Apr 2024
Viewed by 1207
Abstract
Organ transplantation is associated with various forms of programmed cell death which can accelerate transplant injury and rejection. Targeting cell death in donor organs may represent a novel strategy for preventing allograft injury. We have previously demonstrated that necroptosis plays a key role [...] Read more.
Organ transplantation is associated with various forms of programmed cell death which can accelerate transplant injury and rejection. Targeting cell death in donor organs may represent a novel strategy for preventing allograft injury. We have previously demonstrated that necroptosis plays a key role in promoting transplant injury. Recently, we have found that mitochondria function is linked to necroptosis. However, it remains unknown how necroptosis signaling pathways regulate mitochondrial function during necroptosis. In this study, we investigated the receptor-interacting protein kinase 3 (RIPK3) mediated mitochondrial dysfunction and necroptosis. We demonstrate that the calmodulin-dependent protein kinase (CaMK) family members CaMK1, 2, and 4 form a complex with RIPK3 in mouse cardiac endothelial cells, to promote trans-phosphorylation during necroptosis. CaMK1 and 4 directly activated the dynamin-related protein-1 (Drp1), while CaMK2 indirectly activated Drp1 via the phosphoglycerate mutase 5 (PGAM5). The inhibition of CaMKs restored mitochondrial function and effectively prevented endothelial cell death. CaMKs inhibition inhibited activation of CaMKs and Drp1, and cell death and heart tissue injury (n = 6/group, p < 0.01) in a murine model of cardiac transplantation. Importantly, the inhibition of CaMKs greatly prolonged heart graft survival (n = 8/group, p < 0.01). In conclusion, CaMK family members orchestrate cell death in two different pathways and may be potential therapeutic targets in preventing cell death and transplant injury. Full article
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Figure 1
<p>The CaMK family participates in MVEC necroptosis. (<b>A</b>) Cells (20 × 10<sup>3</sup>/well) were seeded in quadruplicates in a 96-well plate. Cell death was induced by TNFα (T, 20 ng/mL) with Smac mimetic BV6 (S, 2 μM). Apoptosis was inhibited by caspase-8 inhibitor z-IETD (I, 30 μM). Necroptosis was inhibited by RIPK1 inhibitor Nec-1s (N, 10 μM). CaMK was inhibited by KN93 (K, 20 μg/mL). Cell death was detected by SYTOX Green uptake into the dead cell from 0 to 24 h by IncuCyte Image system (Essen Bioscience, Ann Arbor, MI, USA). (<b>B</b>) SYTOX uptake was quantified at 24 h. Data are shown as mean ± standard deviation (SD) of quadruplicates and representative of three independent experiments. **** <span class="html-italic">p</span> &lt; 0.0001. <span class="html-italic">t</span>-test. (<b>C</b>) Expression of CaMK1, CaMK2, and CaMK4 was quantified by real time PCR after cell death induction for 4 h. β-actin was used as endogenous control for mRNA expression. Data are shown as mean ± SD of three independent experiments. Western blot analysis of CaMK1 and p-CaMK1 (<b>D</b>,<b>E</b>), CaMK2 and p-CaMK2 (<b>F</b>,<b>G</b>), and CaMK4 and p-CaMK4 (<b>H</b>,<b>I</b>). Cells were collected for Western blot analysis 5 h after cell death induction. GAPDH was used as loading control. Images were quantified by densitometry (ImageJ 1.54g). Relative ratio = phosphorylated protein/total protein. Data are shown as the mean ± SD of three independent experiments. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>The CaMK family participates in MVEC necroptosis. (<b>A</b>) Cells (20 × 10<sup>3</sup>/well) were seeded in quadruplicates in a 96-well plate. Cell death was induced by TNFα (T, 20 ng/mL) with Smac mimetic BV6 (S, 2 μM). Apoptosis was inhibited by caspase-8 inhibitor z-IETD (I, 30 μM). Necroptosis was inhibited by RIPK1 inhibitor Nec-1s (N, 10 μM). CaMK was inhibited by KN93 (K, 20 μg/mL). Cell death was detected by SYTOX Green uptake into the dead cell from 0 to 24 h by IncuCyte Image system (Essen Bioscience, Ann Arbor, MI, USA). (<b>B</b>) SYTOX uptake was quantified at 24 h. Data are shown as mean ± standard deviation (SD) of quadruplicates and representative of three independent experiments. **** <span class="html-italic">p</span> &lt; 0.0001. <span class="html-italic">t</span>-test. (<b>C</b>) Expression of CaMK1, CaMK2, and CaMK4 was quantified by real time PCR after cell death induction for 4 h. β-actin was used as endogenous control for mRNA expression. Data are shown as mean ± SD of three independent experiments. Western blot analysis of CaMK1 and p-CaMK1 (<b>D</b>,<b>E</b>), CaMK2 and p-CaMK2 (<b>F</b>,<b>G</b>), and CaMK4 and p-CaMK4 (<b>H</b>,<b>I</b>). Cells were collected for Western blot analysis 5 h after cell death induction. GAPDH was used as loading control. Images were quantified by densitometry (ImageJ 1.54g). Relative ratio = phosphorylated protein/total protein. Data are shown as the mean ± SD of three independent experiments. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>CaMKs participate in necroptosis. CaMK2δ silencing in MVECs was confirmed by PCR 16 h after siRNA treatment (<b>A</b>) and Western blot (<b>B</b>) 24 h after siRNA treatment. Untreated cells (UT) or vehicle control (VC, transfection reagent) treated cells were used as controls. GAPDH from the same blot was used as the loading control. Data are pooled and represent three independent experiments. (<b>C</b>) CaMK2δ-siRNA or VC treated cells were harvested after 24 h and subjected to the cell death assay. SYTOX uptake/cell death was monitored by IncuCyte Image system. Data are shown as mean ± SD of quadruplicates and represent three independent experiments. siRNA-induced silencing of CaMK1 (<b>D</b>,<b>E</b>) and CaMK4 (<b>F</b>,<b>G</b>) in MVECs was confirmed by PCR and Western blot analysis. (<b>H</b>) CaMK1, CaMK4, or CaMK1+4 siRNAs or vehicle control (VC, EndoFectin) treated cells were harvested after 24 h and subjected to the cell death assay. SYTOX uptake was monitored for 24 h by IncuCyte Image system. Data are shown as mean ± SD of quadruplicates at 24 h and represent three independent experiments. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span>≤0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>CaMKs participate in necroptosis. CaMK2δ silencing in MVECs was confirmed by PCR 16 h after siRNA treatment (<b>A</b>) and Western blot (<b>B</b>) 24 h after siRNA treatment. Untreated cells (UT) or vehicle control (VC, transfection reagent) treated cells were used as controls. GAPDH from the same blot was used as the loading control. Data are pooled and represent three independent experiments. (<b>C</b>) CaMK2δ-siRNA or VC treated cells were harvested after 24 h and subjected to the cell death assay. SYTOX uptake/cell death was monitored by IncuCyte Image system. Data are shown as mean ± SD of quadruplicates and represent three independent experiments. siRNA-induced silencing of CaMK1 (<b>D</b>,<b>E</b>) and CaMK4 (<b>F</b>,<b>G</b>) in MVECs was confirmed by PCR and Western blot analysis. (<b>H</b>) CaMK1, CaMK4, or CaMK1+4 siRNAs or vehicle control (VC, EndoFectin) treated cells were harvested after 24 h and subjected to the cell death assay. SYTOX uptake was monitored for 24 h by IncuCyte Image system. Data are shown as mean ± SD of quadruplicates at 24 h and represent three independent experiments. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span>≤0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>RIPK3 and CaMKs form a complex during necroptosis. Cells were induced to undergo necroptosis, as shown in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>, and collected after 4 h. (<b>A</b>) Untreated and vehicle control treated cells were used as co-immunoprecipitation controls. Cell lysates were immunoprecipitated with CaMK1, CaMK4, and RIPK3 antibodies, respectively, and followed by Western blot analysis to detect CaMK2. (<b>B</b>) Cell lysates were immunoprecipitated with CaMK1, CaMK2, and CaMK4 antibodies, respectively. Rabbit IgG were used as control. The immunoprecipitants were used to detect RIPK3 in Western blot analysis. (<b>C</b>) Cell lysates were immunoprecipitated with CaMK1 and CaMK4 antibodies. The immunoprecipitants were used to detect CaMK2 in Western blot analysis. (<b>D</b>) CaMK1 siRNA or vehicle treated cells were harvested after 24 h for cell death induction and then collected after 4 h. Cell lysates were immunoprecipitated with CaMK2 antibody or rabbit IgG. The immunoprecipitants were used to detect CaMK4 in Western blot analysis. Data (<b>B</b>–<b>D</b>) represent three independent experiments. (<b>E</b>) Cell death was induced as described in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>. ATP level was quantified by the CellTiter-Glo<sup>®</sup> Luminescent Cell Viability kit. Data are shown as mean ± SD of three independent experiments. (<b>F</b>) Mitochondria were probed by MitoTracker. Fluorescent intensity was automatically quantified by the IncuCyte System. Data are shown as mean ± SD of three independent experiments. *** <span class="html-italic">p</span> ≤ 0.001; **** <span class="html-italic">p</span> ≤ 0.0001, 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>RIPK3 and CaMKs form a complex during necroptosis. Cells were induced to undergo necroptosis, as shown in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>, and collected after 4 h. (<b>A</b>) Untreated and vehicle control treated cells were used as co-immunoprecipitation controls. Cell lysates were immunoprecipitated with CaMK1, CaMK4, and RIPK3 antibodies, respectively, and followed by Western blot analysis to detect CaMK2. (<b>B</b>) Cell lysates were immunoprecipitated with CaMK1, CaMK2, and CaMK4 antibodies, respectively. Rabbit IgG were used as control. The immunoprecipitants were used to detect RIPK3 in Western blot analysis. (<b>C</b>) Cell lysates were immunoprecipitated with CaMK1 and CaMK4 antibodies. The immunoprecipitants were used to detect CaMK2 in Western blot analysis. (<b>D</b>) CaMK1 siRNA or vehicle treated cells were harvested after 24 h for cell death induction and then collected after 4 h. Cell lysates were immunoprecipitated with CaMK2 antibody or rabbit IgG. The immunoprecipitants were used to detect CaMK4 in Western blot analysis. Data (<b>B</b>–<b>D</b>) represent three independent experiments. (<b>E</b>) Cell death was induced as described in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>. ATP level was quantified by the CellTiter-Glo<sup>®</sup> Luminescent Cell Viability kit. Data are shown as mean ± SD of three independent experiments. (<b>F</b>) Mitochondria were probed by MitoTracker. Fluorescent intensity was automatically quantified by the IncuCyte System. Data are shown as mean ± SD of three independent experiments. *** <span class="html-italic">p</span> ≤ 0.001; **** <span class="html-italic">p</span> ≤ 0.0001, 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>CaMKs are responsible for Drp1 activation. (<b>A</b>) Cell death was induced as described in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>. Drp1 inhibitor Midivi-1 (50 μM) was added. Cell death was detected by IncuCyte Image system. SYTOX uptake was quantified at 24 h. Data are shown as mean ± SD of quadruplicates and represent three independent experiments. (<b>B</b>) Drp1siRNA or vehicle-treated cells were harvested after 24 h and subjected to the cell death assay. Data are shown as mean ± SD of quadruplicates and represent three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001, 1-way ANOVA; Tukey’s multiple comparisons. (<b>C</b>) p-Drp1 Western blot. Cell death was induced as described in <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a>. Drp1 inhibitor Midivi-1 or CaMKs inhibitor KN93 was added. Cells were collected for Western blot analysis 4 h after cell death induction. Untreated (UT) cells were used as controls. (<b>D</b>) Images were quantified by ImageJ. Relative Ratio of protein level = p-Drp1/Total Drp1. Data are shown as mean ± SD of three independent experiments. (<b>E</b>) CaMK1, CaMK2, CaMK4, CaMK1+4, and CaKM1+2+4 siRNAs-treated cells were harvested after 24 h for the cell death assay. Cells were collected after 4 h for Western blot analysis of p-Drp1 (S616). (<b>F</b>) Images were quantified by ImageJ. GAPDH was used to normalize protein levels. Data are shown as mean ± SD of three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>CaMK2 indirectly binds to Drp1 via PGAM5 while CaMK1 and CaMK4 directly bind to Drp1 without PGAM5. (<b>A</b>) Cells death was induced as <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a> and cells were collected 4 h after. Cell lysates were immunoprecipitated with CaMK1, CaMK2, and CaMK4 antibodies. Rabbit IgG was used as isotype control. The immunoprecipitants were used to detect PGAM5 in Western blot analysis. (<b>B</b>) Untreated and vehicle control treated cells were used as co-immunoprecipitation controls. Cell lysates were immunoprecipitated with CaMK1, CaMK2, CaMK4 and PGAM5 antibodies, respectively, and followed by Western blot analysis to detect Drp1. (<b>C</b>) PGAM5 siRNA- or vehicle control (VC)-treated cells were harvested after 24 h for the cell death assay. Four hours after, cell lysates were immunoprecipitated with anti-CaMK2 or control IgG. The immunoprecipitants were used to detect Drp1 in Western blot analysis. (<b>D</b>) CaMKs siRNAs- or vehicle control-treated cells were harvested after 24 h and used in the cell death assay. Cells were collected after 4 h and cell lysates were immunoprecipitated with PGAM5 antibody or rabbit IgG. Immunoprecipitants were used to detect Drp1 in Western blot. (<b>E</b>) Images were quantified by ImageJ. The relative level of Drp1 was calculated against the Drp1 level of necroptotic cells (TSI treated) in vehicle control (VC) group. Data are shown as mean ± SD of three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001; <span class="html-italic">t</span>-test. (<b>F</b>) PGAM5 siRNA- or VC-treated cells were harvested after 24 h and used in the cell death assay. Cells were collected after 4 h and cell lysates were immunoprecipitated with CaMK1 antibody or control IgG. The immunoprecipitants were used for anti-Drp1 detection in Western blot analysis. (<b>G</b>) CaMK1, CaMK2, or PGAM5 siRNA-treated cells were harvested after 24 h for the cell death assay. Cells were collected after 4 h and immunoprecipitated with CaMK4 antibody or control IgG. The immunoprecipitants were used for anti-Drp1 detection in Western blot analysis. (<b>H</b>) Images were quantified by ImageJ. The relative level of Drp1 was calculated against the Drp1 level of necroptotic cells (TSI treated) in vehicle control (VC) group. Data are shown as mean ± SD of three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
Full article ">Figure 5 Cont.
<p>CaMK2 indirectly binds to Drp1 via PGAM5 while CaMK1 and CaMK4 directly bind to Drp1 without PGAM5. (<b>A</b>) Cells death was induced as <a href="#ijms-25-04428-f001" class="html-fig">Figure 1</a> and cells were collected 4 h after. Cell lysates were immunoprecipitated with CaMK1, CaMK2, and CaMK4 antibodies. Rabbit IgG was used as isotype control. The immunoprecipitants were used to detect PGAM5 in Western blot analysis. (<b>B</b>) Untreated and vehicle control treated cells were used as co-immunoprecipitation controls. Cell lysates were immunoprecipitated with CaMK1, CaMK2, CaMK4 and PGAM5 antibodies, respectively, and followed by Western blot analysis to detect Drp1. (<b>C</b>) PGAM5 siRNA- or vehicle control (VC)-treated cells were harvested after 24 h for the cell death assay. Four hours after, cell lysates were immunoprecipitated with anti-CaMK2 or control IgG. The immunoprecipitants were used to detect Drp1 in Western blot analysis. (<b>D</b>) CaMKs siRNAs- or vehicle control-treated cells were harvested after 24 h and used in the cell death assay. Cells were collected after 4 h and cell lysates were immunoprecipitated with PGAM5 antibody or rabbit IgG. Immunoprecipitants were used to detect Drp1 in Western blot. (<b>E</b>) Images were quantified by ImageJ. The relative level of Drp1 was calculated against the Drp1 level of necroptotic cells (TSI treated) in vehicle control (VC) group. Data are shown as mean ± SD of three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001; <span class="html-italic">t</span>-test. (<b>F</b>) PGAM5 siRNA- or VC-treated cells were harvested after 24 h and used in the cell death assay. Cells were collected after 4 h and cell lysates were immunoprecipitated with CaMK1 antibody or control IgG. The immunoprecipitants were used for anti-Drp1 detection in Western blot analysis. (<b>G</b>) CaMK1, CaMK2, or PGAM5 siRNA-treated cells were harvested after 24 h for the cell death assay. Cells were collected after 4 h and immunoprecipitated with CaMK4 antibody or control IgG. The immunoprecipitants were used for anti-Drp1 detection in Western blot analysis. (<b>H</b>) Images were quantified by ImageJ. The relative level of Drp1 was calculated against the Drp1 level of necroptotic cells (TSI treated) in vehicle control (VC) group. Data are shown as mean ± SD of three independent experiments. **** <span class="html-italic">p</span> ≤ 0.0001; 1-way ANOVA; Tukey’s multiple comparisons.</p>
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<p>p-CaMK1, p-CaMK2, p-CaMK4, and p-Drp1 (S616) increases were inhibited by KN93 in the graft post heart transplantation. (<b>A</b>) B6-to-BALB/c heart transplantation and KN93 injection was performed as detailed in the Methods. The grafts (n = 3) were collected after 3 days for Western blot analysis by CaMK1, p-CaMK1, CaMK2, p-CaMK2, CaMK4, p-CaMK4, Drp1 and p-Drp1 (S616) antibodies, respectively. (<b>B</b>–<b>E</b>) Images were quantified by ImageJ. Relative ratio of protein = phosphorylated protein/total protein. Data are shown as mean ± SD of 3 transplants. **** <span class="html-italic">p</span> ≤ 0.0001; Student <span class="html-italic">t</span>-test.</p>
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<p>Inhibition of CaMKs attenuates heart transplant acute injury. (<b>A</b>) B6-to-BALB/c heart transplantation and KN93 or saline injection were performed as detailed in <a href="#sec4-ijms-25-04428" class="html-sec">Section 4</a>. Grafts (n = 6/group) were collected after 3 days for H&amp;E staining. Images were taken under 200 times magnification. (<b>B</b>) Graft injuries were scored for lymphocyte infiltration, infarction, and PMN infiltration from 0 to 5 in a blinded fashion. Scores were averaged as mean ± SD of 6 grafts. (<b>C</b>) Grafts (n = 6) were used for immunohistochemistry with anti-CD45, and positive areas (brown color) are indicated by red arrows. Images were taken under 200 times magnification. (<b>D</b>) Positive areas of each graft were automatically counted in six connected random areas under 200 times magnification by Image J and averaged in a double-blinded manner. (<b>E</b>) Grafts (n = 6) were assessed by TUNEL. B6 naive hearts were used as control. Brown color indicates TUNEL positive cells as indicated by red arrows. Images are at 200 times magnification. (<b>F</b>) Necroptosis in the graft was detected by p-MLKL immunohistochemistry. Images are at 200 times magnification. Positive cells are indicated by red arrows. (<b>G</b>) TUNEL positive areas were quantified as above. (<b>H</b>) p-MLKL positive areas were quantified as above. ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001. <span class="html-italic">t</span>-test.</p>
Full article ">Figure 7 Cont.
<p>Inhibition of CaMKs attenuates heart transplant acute injury. (<b>A</b>) B6-to-BALB/c heart transplantation and KN93 or saline injection were performed as detailed in <a href="#sec4-ijms-25-04428" class="html-sec">Section 4</a>. Grafts (n = 6/group) were collected after 3 days for H&amp;E staining. Images were taken under 200 times magnification. (<b>B</b>) Graft injuries were scored for lymphocyte infiltration, infarction, and PMN infiltration from 0 to 5 in a blinded fashion. Scores were averaged as mean ± SD of 6 grafts. (<b>C</b>) Grafts (n = 6) were used for immunohistochemistry with anti-CD45, and positive areas (brown color) are indicated by red arrows. Images were taken under 200 times magnification. (<b>D</b>) Positive areas of each graft were automatically counted in six connected random areas under 200 times magnification by Image J and averaged in a double-blinded manner. (<b>E</b>) Grafts (n = 6) were assessed by TUNEL. B6 naive hearts were used as control. Brown color indicates TUNEL positive cells as indicated by red arrows. Images are at 200 times magnification. (<b>F</b>) Necroptosis in the graft was detected by p-MLKL immunohistochemistry. Images are at 200 times magnification. Positive cells are indicated by red arrows. (<b>G</b>) TUNEL positive areas were quantified as above. (<b>H</b>) p-MLKL positive areas were quantified as above. ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001. <span class="html-italic">t</span>-test.</p>
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<p>Inhibition of CaMKs attenuates heart transplant chronic injury and rejection. (<b>A</b>) B6-to-BALB/c heart transplantation and anti-CD154 injection are detailed in <a href="#sec4-ijms-25-04428" class="html-sec">Section 4</a>. KN93 or saline was injected on day 1, 2, and 3 followed by every 48 h until 21 days post transplantation. Recipient mice (n = 4/group) were euthanized, and the grafts were collected for H&amp;E and elastin-trichrome staining. Images were taken under 200 times magnification. Representative images are shown. (<b>B</b>) Graft injuries were quantified blindly by a pathologist. Scores were averaged as mean ± SD of 4 grafts. ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001, <span class="html-italic">t</span>-test. Grafts were assessed by immunohistochemistry for anti-CD3 (<b>C</b>), anti-IgG (<b>E</b>) and anti-Foxp3 (<b>G</b>) and positive staining areas (brown color) are indicated by red arrows. Images were taken under 200 times magnification. Positive areas anti-CD3 (<b>D</b>), anti-IgG (<b>F</b>) and anti-FoxP3 (<b>H</b>) of each graft were automatically counted in six connected random areas under 200 times magnification by Image J and averaged in a double-blinded fashion. **** <span class="html-italic">p</span> ≤ 0.0001. <span class="html-italic">t</span>-test. (<b>I</b>) B6-to-BALB/c heart transplantation using KN93 or saline administration was performed as above. Graft survival was monitored daily. Cessation of beating is considered as rejection. n = 8 per group, ** <span class="html-italic">p</span> = 0.004. Log Rank test.</p>
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<p>Inhibition of CaMKs attenuates heart transplant chronic injury and rejection. (<b>A</b>) B6-to-BALB/c heart transplantation and anti-CD154 injection are detailed in <a href="#sec4-ijms-25-04428" class="html-sec">Section 4</a>. KN93 or saline was injected on day 1, 2, and 3 followed by every 48 h until 21 days post transplantation. Recipient mice (n = 4/group) were euthanized, and the grafts were collected for H&amp;E and elastin-trichrome staining. Images were taken under 200 times magnification. Representative images are shown. (<b>B</b>) Graft injuries were quantified blindly by a pathologist. Scores were averaged as mean ± SD of 4 grafts. ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001, <span class="html-italic">t</span>-test. Grafts were assessed by immunohistochemistry for anti-CD3 (<b>C</b>), anti-IgG (<b>E</b>) and anti-Foxp3 (<b>G</b>) and positive staining areas (brown color) are indicated by red arrows. Images were taken under 200 times magnification. Positive areas anti-CD3 (<b>D</b>), anti-IgG (<b>F</b>) and anti-FoxP3 (<b>H</b>) of each graft were automatically counted in six connected random areas under 200 times magnification by Image J and averaged in a double-blinded fashion. **** <span class="html-italic">p</span> ≤ 0.0001. <span class="html-italic">t</span>-test. (<b>I</b>) B6-to-BALB/c heart transplantation using KN93 or saline administration was performed as above. Graft survival was monitored daily. Cessation of beating is considered as rejection. n = 8 per group, ** <span class="html-italic">p</span> = 0.004. Log Rank test.</p>
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22 pages, 3201 KiB  
Article
IE1 of Human Cytomegalovirus Inhibits Necroptotic Cell Death via Direct and Indirect Modulation of the Necrosome Complex
by Anna Theresa Heusel, Sophie Rapp, Thomas Stamminger and Myriam Scherer
Viruses 2024, 16(2), 290; https://doi.org/10.3390/v16020290 - 13 Feb 2024
Cited by 3 | Viewed by 1725
Abstract
Programmed necrosis is an integral part of intrinsic immunity, serving to combat invading pathogens and restricting viral dissemination. The orchestration of necroptosis relies on a precise interplay within the necrosome complex, which consists of RIPK1, RIPK3 and MLKL. Human cytomegalovirus (HCMV) has been [...] Read more.
Programmed necrosis is an integral part of intrinsic immunity, serving to combat invading pathogens and restricting viral dissemination. The orchestration of necroptosis relies on a precise interplay within the necrosome complex, which consists of RIPK1, RIPK3 and MLKL. Human cytomegalovirus (HCMV) has been found to counteract the execution of necroptosis during infection. In this study, we identify the immediate-early 1 (IE1) protein as a key antagonist of necroptosis during HCMV infection. Infection data obtained in a necroptosis-sensitive cell culture system revealed a robust regulation of post-translational modifications (PTMs) of the necrosome complex as well as the importance of IE1 expression for an effective counteraction of necroptosis. Interaction analyses unveiled an association of IE1 and RIPK3, which occurs in an RHIM-domain independent manner. We propose that this interaction manipulates the PTMs of RIPK3 by promoting its ubiquitination. Furthermore, IE1 was found to exert an indirect activity by modulating the levels of MLKL via antagonizing its interferon-mediated upregulation. Overall, we claim that IE1 performs a broad modulation of innate immune signaling to impede the execution of necroptotic cell death, thereby generating a favorable environment for efficient viral replication. Full article
(This article belongs to the Special Issue Molecular Biology of Human Cytomegalovirus)
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Figure 1

Figure 1
<p>Generation of a necroptosis-sensitive cell culture model. (<b>A</b>) HFF and HEC-LTT cells were lentivirally transduced to stably express RIPK3. Successful integration of RIPK3 was monitored in Western blot analysis. (<b>B</b>) Expression of RIPK3 was analyzed by indirect immunofluorescence analysis. Cell nuclei were stained with DAPI. Scale bar, 100 µm. (<b>C</b>) The sensitivity of RIPK3 expressing cells to necroptosis was analyzed in a cell viability assay by monitoring intracellular ATP levels (CellTiter-Glo, Promega, Fitchburg, MA, USA). The cells were stimulated for 8 h with TBZ (TNFα (30 ng/mL), BV-6 (5 µM) and z-VAD-fmk (25 µM)). Green, DMSO-treated cells; grey, TBZ treated cells. Depicted values represent the means +/− SD derived from triplicates relative to control cells (%). (<b>D</b>) The modulation of the necrosome components after the indicated times of TBZ treatment was monitored in Western blot analysis. Β-actin served as internal loading control. Each experiment was performed three times in independent experiments and one representative experiment is shown. For statistical analysis a student’s <span class="html-italic">t</span>-test was performed (unpaired, two-tailed); *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Necrosome components are strongly modulated during HCMV infection. RIPK3 expressing HFF and HEC-LTT cells were infected for the indicated times and then harvested for Western blot analysis. HFF/RIPK3 cells were infected with AD169 (left panel) or TB40/E (middle panel) and HEC-LTT/RIPK3 with TB40/E (right panel). Infections were performed at an MOI of 5. Necrosome components and viral markers of infection were analyzed by Western blot experiments. Β-actin served as internal loading control. Three independent experiments were performed, and one representative experiment is shown.</p>
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<p>HCMV-mediated rescue from necroptotic cell death begins already at the early stages of infection. HFF/RIPK3 cells were infected for the indicated times and subsequently treated with TBZ for 8 h or with DMSO as control. Cells were infected with TB40/E WT (<b>A</b>), AD169 WT (<b>A</b>,<b>B</b>) and AD169 ΔIE1 (<b>B</b>) at an MOI of 3. AD169 UV (<b>B</b>) was used at increasing viral doses (calculated MOIs of 6, 12 and 18). Necroptotic cell death was monitored by analyzing intracellular ATP levels using a cell viability assay (CellTiter-Glo, Promega, Fitchburg, MA, USA). Red, mock infected cells; green, TB40/E WT infected cells; grey, AD169 WT infected cells; blue, AD169ΔIE1 infected cells; yellow, AD169 UV infected cells. Depicted values (%) represent the mean +/− SD derived from triplicates relative to the control (mock, DMSO). Each experiment was performed two times in independent experiments and one representative experiment is shown. For statistical analysis a student’s <span class="html-italic">t</span>-test was performed (unpaired, two-tailed); ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The immediate-early 1 (IE1) protein exhibits a strong anti-necroptotic activity by modulating the modification pattern of RIPK3. (<b>A</b>) HFF/RIPK3 cells with inducible IE1 expression (<b>A</b>,<b>B</b>) were treated with TBZ for the indicated times or with DMSO as control. Red, DMSO-treated cells; green, no IE1 expression; grey, IE1 expression. Depicted values represent the mean +/− SD derived from triplicates relative to the DMSO control (%). (<b>B</b>) Expression levels of p-RIPK3 S227, RIPK3 and MLKL were analyzed in presence or absence of IE1 after the indicated times of TBZ treatment by Western blot analysis. – IE1, no IE1 expression, + IE1, IE1 expression. (<b>C</b>) Expression plasmids of IE1 and RIPK3 were co-transfected in HEK293T cells, whereby increasing amounts of IE1 were used. The expression levels were monitored in Western blot analysis. (<b>D</b>) To confirm a ubiquitination of RIPK3 during necroptosis induction, HFF/RIPK3 cells were treated with DMSO or TBZ for 6 h +/− PYR-41 (inhibitor of ubiquitination, Selleck Chemicals LLC, Houston, TX, USA). (<b>E</b>) To confirm the ubiquitination of RIPK3 during HCMV infection, HFF/RIPK3 cells were infected with TB40/E at an MOI of 3 and at 24 hpi, PYR-41 was applied for 6 h. Protein levels of RIPK3 and IE1 were monitored in Western blot analysis. β-actin served as internal loading control. Each experiment was performed at least two times in independent experiments and always one representative experiment is shown. Fold changes of protein expression were determined according to signal intensities normalized to β-actin levels. For statistical analysis a student’s <span class="html-italic">t</span>-test was performed (unpaired, two-tailed); * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>IE1 associates with the necrosome complex via binding RIPK3 and this interaction is promoted by distinct domains of IE1 and RIPK3. (<b>A</b>,<b>C</b>) HEK293T cells were co-transfected with expression plasmids for the indicated proteins and co-immunoprecipitation (Co-IP) was performed. FEN1-IE1 interaction served as positive control [<a href="#B37-viruses-16-00290" class="html-bibr">37</a>]. The IP of FLAG-tagged RIPK3, MLKL and FEN1 constructs was performed by using protein A-sepharose beads with immobilized anti-FLAG antibody. (<b>B</b>) HFF/control and HFF/RIPK3 were infected with TB40/E at an MOI of 1 for 4–18 h and subsequently IE1 was precipitated using protein A-sepharose beads with immobilized anti-IE1. (<b>D</b>) Schematic illustration of truncated variants of IE1 (upper panel) and RIPK3 (lower panel) which were used for fine-mapping of the interaction interface (<b>E</b>,<b>F</b>). NLS, nuclear localization sequence; core, globular core domain; STAT, binding domain of STAT proteins; CTD, chromatin tethering domain; RHIM, RIP homotypic interaction motif. (<b>E</b>,<b>F</b>) Fine-mapping of the interaction interface of IE1 and RIPK3 in HEK293T by transfecting expression plasmids encoding FLAG-tagged IE1 and RIPK3 variants and subsequent Co-IP analysis by using magnetic beads with immobilized anti-FLAG antibody. In (<b>F</b>), purified protein of IE1 was utilized. Each experiment was performed at least three times and one representative experiment is shown.</p>
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<p>IE1 antagonizes the interferon-mediated upregulation of MLKL. HFF cells were treated with the indicated types of IFN (1000 U/mL) for 24 h. (<b>A</b>,<b>C</b>) Levels of necrosome components in the indicated HFF cells were analyzed in Western blot experiments. Β-actin served as internal loading control. Fold changes of protein expression were determined according to signal intensities normalized to β-actin levels. (<b>C</b>) − IE1, no IE1 expression; + IE1, IE1 expression; − IFN-β, no IFN-β stimulation; + IFN-β, IFN-β stimulation. (<b>B</b>,<b>D</b>) Modulation of MLKL transcription upon IFN-stimulation was analyzed by isolating total RNA and performing SYBR qPCR analysis in the indicated HFF cells. Depicted values represent the mean +/− SD derived from triplicates relative to untreated HFF/control cells and normalized to levels of the housekeeping gene <span class="html-italic">GAPDH</span>. Green, no IFN-β stimulation; grey, IFN-β stimulation. For statistical analysis a student’s <span class="html-italic">t</span>-test was performed with ΔCq-values (unpaired, two-tailed); ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. Each experiment was performed three times in independent experiments and one representative experiment is shown.</p>
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<p>IE1 modulates the activation of innate immune signaling to circumvent necroptosis. HFF/RIPK3 cells were infected with WT and ΔIE1 HCMV (both TB40/E) for 24 h at an MOI of 3. Subsequently, total RNA was isolated and necrosis/necroptosis-related profiling was performed by using the RT² Profiler Necrosis PCR array (Qiagen, Düsseldorf, Germany). Values above the dashed line (at Y = 1) indicate an upregulation and values below the dashed line indicate a downregulation during ΔIE1 infection compared to WT infection. Genes illustrated in pink are described as ISGs [<a href="#B48-viruses-16-00290" class="html-bibr">48</a>]. Depicted values represent the mean +/− SD derived from three repetitions, ΔIE1 relative to WT infected HFFs. For normalization the mean Cq-value of the housekeeping genes <span class="html-italic">ACTB</span>, <span class="html-italic">B2M</span>, <span class="html-italic">HPRT1</span> and <span class="html-italic">RPLP0</span> was utilized. For statistical analysis a multicomparison <span class="html-italic">t</span>-test was performed with ΔCq-values (unpaired, two-tailed); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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